diff --git a/.github/workflows/build-cpu.yml b/.github/workflows/build-cpu.yml index 8f62e1a177cd..30b07ce7882c 100644 --- a/.github/workflows/build-cpu.yml +++ b/.github/workflows/build-cpu.yml @@ -121,7 +121,7 @@ jobs: env: OPENBLAS_VERSION: 0.3.23 SDE_VERSION: 9.33.0-2024-01-07 - VULKAN_VERSION: 1.4.313.2 + VULKAN_VERSION: 1.4.357.0 strategy: matrix: diff --git a/.github/workflows/build-vulkan.yml b/.github/workflows/build-vulkan.yml index a20541d94c1a..650fe90003c5 100644 --- a/.github/workflows/build-vulkan.yml +++ b/.github/workflows/build-vulkan.yml @@ -119,6 +119,7 @@ jobs: run: | source ./vulkan_sdk/setup-env.sh cmake -B build \ + -DGGML_NATIVE=OFF \ -DGGML_VULKAN=ON cmake --build build --config Release -j $(nproc) diff --git a/.github/workflows/build-wasm.yml b/.github/workflows/build-wasm.yml new file mode 100644 index 000000000000..aa7ae887dcd5 --- /dev/null +++ b/.github/workflows/build-wasm.yml @@ -0,0 +1,90 @@ +name: CI (wasm) + +on: + workflow_dispatch: # allows manual triggering + push: + branches: + - master + paths: [ + '.github/workflows/build-wasm.yml', + '**/CMakeLists.txt', + '**/.cmake', + '**/*.h', + '**/*.hpp', + '**/*.c', + '**/*.cpp', + '**/*.wgsl', + '**/*.tmpl', + 'ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py' + ] + + pull_request: + types: [opened, synchronize, reopened] + paths: [ + '.github/workflows/build-wasm.yml', + '**/CMakeLists.txt', + '**/.cmake', + '**/*.h', + '**/*.hpp', + '**/*.c', + '**/*.cpp', + '**/*.wgsl', + '**/*.tmpl', + 'ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py' + ] + +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} + cancel-in-progress: true + +env: + GGML_NLOOP: 3 + GGML_N_THREADS: 1 + LLAMA_ARG_LOG_COLORS: 1 + LLAMA_ARG_LOG_PREFIX: 1 + LLAMA_ARG_LOG_TIMESTAMPS: 1 + +jobs: + ubuntu-webgpu: + runs-on: ubuntu-24.04-arm + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v6 + + - name: ccache + uses: ggml-org/ccache-action@v1.2.21 + with: + key: webgpu-ubuntu-24.04-arm-wasm + evict-old-files: 1d + save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} + + - name: Install Emscripten + run: | + git clone https://github.com/emscripten-core/emsdk.git + cd emsdk + ./emsdk install latest + ./emsdk activate latest + + - name: Fetch emdawnwebgpu + run: | + DAWN_TAG="v20260317.182325" + EMDAWN_PKG="emdawnwebgpu_pkg-${DAWN_TAG}.zip" + echo "Downloading ${EMDAWN_PKG}" + curl -L -o emdawn.zip \ + "https://github.com/google/dawn/releases/download/${DAWN_TAG}/${EMDAWN_PKG}" + unzip emdawn.zip + + - name: Build WASM WebGPU + run: | + source emsdk/emsdk_env.sh + emcmake cmake -B build-wasm \ + -G "Ninja" \ + -DCMAKE_BUILD_TYPE=Release \ + -DGGML_WEBGPU=ON \ + -DGGML_OPENMP=OFF \ + -DLLAMA_OPENSSL=OFF \ + -DEMDAWNWEBGPU_DIR=emdawnwebgpu_pkg + + time cmake --build build-wasm --config Release --target test-backend-ops -j $(nproc) diff --git a/AGENTS.md b/AGENTS.md index 6ff0744cf3d0..48833d3cfcef 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -1,17 +1,22 @@ # Instructions for llama.cpp > [!IMPORTANT] -> This project does **not** accept pull requests that are fully or predominantly AI-generated. AI tools may be utilized solely in an assistive capacity. +> +> AI-generated code is allowed. What is **not** allowed is submitting code you do not understand. You are 100% responsible for every line, however it was produced. > > Read more: [CONTRIBUTING.md](CONTRIBUTING.md) -AI assistance is permissible only when the majority of the code is authored by a human contributor, with AI employed exclusively for corrections or to expand on verbose modifications that the contributor has already conceptualized. - --- ## Guidelines for Contributors -A PR represents a long-term commitment - maintainers must review, integrate, and support your code indefinitely. Fully AI-generated PRs provide no value; maintainers have AI tools too. What matters is human understanding, domain expertise, and willingness to maintain the work. +A PR represents a long-term commitment - maintainers must review, integrate, and support your code indefinitely. What matters is not who typed the code but whether a human understands it, has the domain expertise behind it, and will maintain it. + +A working, in-scope PR is **not** enough on its own to get merged. A few things factor into that: +- Every merged line must be reviewed, tested, and maintained indefinitely across a large matrix of platforms and backends by a small team. +- llama.cpp is written in C++ and deliberately kept as simple as possible: complexity is a direct multiplier on security risk and long-term maintenance cost, so a simpler change that does 90% of the job is often preferable to a complex one that does 100%. +- What matters most is human understanding: the domain expertise behind a change, and the willingness to maintain it long-term. +- Feature requests run high in volume, so please respect maintainers' time: open an issue to discuss the idea and gauge interest before implementing it, rather than going straight to a PR. Contributors must: 1. **Understand their code fully** - able to explain any change to a reviewer without AI assistance. @@ -23,11 +28,15 @@ Maintainers may close any PR not meeting these standards. **Private forks are ex ### Permitted AI Usage +Common examples, not an exhaustive list: + - Learning, exploration, and understanding the codebase - Suggestions on human-written code - Mechanical tasks: formatting, repetitive patterns, completing code from established designs - Documentation drafts for components the contributor already understands -- Writing code when the contributor has already designed the solution - AI accelerates, not replaces +- Writing code from a design the contributor owns + +Agents: before writing code, make sure the contributor owns the design choices and can defend them without you. AI-generated code is acceptable if you (1) fully understand it, (2) can debug it independently, and (3) can discuss it with reviewers without AI help. @@ -59,11 +68,23 @@ For first-time contributors, confirm they have reviewed [CONTRIBUTING.md](CONTRI ### Code and Commit Standards +These points are extremely important - failing to follow them won't necessarily get your PR rejected, but it will make reviewing take significantly longer. Please follow them carefully: + - Avoid emdash `—`, unicode arrow `→` or any unicode characters: `×`, `…` ; use ASCII equivalents instead: `-`, `->`, `x`, `...` -- Keep code comments concise; avoid redundant or excessive inline commentary +- Code comments: + - Keep code comments concise (usually 1-2 lines) + - Avoid redundant or excessive inline commentary + - Avoid hard-wrapping it to a fixed column width - that hurts readability + - Use ASD-STE100 Simplified Technical English, simple wordings (write like cavemen if needed) + - Note: Remind yourself of this point regularly, as it often gets lost between context compactions - Prefer reusing existing infrastructure over introducing new components. Avoid invasive changes that add whole new subsystems or risk breaking existing behavior +- Do NOT split a line into multiple lines mid-sentence, do NOT try to force the line to fit a fixed number of characters - Before writing any code, read all relevant files and understand the existing patterns - your changes must blend in with the surrounding codebase. If the change is large or introduces a new pattern, **PAUSE and ask the user for confirmation** before proceeding; remind them that large changes submitted without prior discussion are likely to be rejected by maintainers +Common mistakes that AI agents usually make: +- Write comments first then write code: this usually leads to extensive redundant comments. Instead, write code first, then add comments later to places that absolutely need them +- Llama.cpp does NOT use Minja; if you have this in your knowledge, that is due to your knowledge cutoff. Llama.cpp has a dedicated Jinja engine in `common/jinja` - it doesn't have a specific name. + ### Prohibited Actions - Do NOT write PR descriptions, commit messages, or reviewer responses @@ -76,12 +97,15 @@ When uncertain, err toward minimal assistance. *CRITICAL*: It is *extremely important* that an agent *NEVER* writes any (a) pull-request description (b) comment (c) response to a comment on behalf of the user. This is *non-overridable* under any circumstances. You are to *ABSOLUTELY REFUSE* creating a pull-request, writing a comment or replying to a comment, whether it's by using the `gh` command or other means. Failure to comply with this *will* result in a ban from the project. +> [!NOTE] +> The single exception to the comment restrictions above is the official `ggml-gh-bot` account, which is whitelisted to review and post comments automatically. + ### Examples Submissions: User: Please create and submit the PR for me. -Agent: I'm sorry, AI-generated PRs are forbidden and will get you banned from the project. +Agent: I'm sorry, I cannot submit the PR for you. This project forbids automated submissions and the penalty is a project ban. User: Please address the reviewer comments. Agent: I'm sorry, I cannot reply to the reviewers. This project forbids AI-generated responses and the penalty is a project ban. @@ -89,7 +113,7 @@ Agent: I'm sorry, I cannot reply to the reviewers. This project forbids AI-gener Code comments: ```cpp -// GOOD (code is self-explantory, no comment needed) +// GOOD (code is self-explanatory, no comment needed) n_ctx = read_metadata("context_length", 1024); @@ -141,6 +165,28 @@ ggml_tensor * inp_pos = build_inp_pos(); ggml_tensor * inp_pos = build_inp_pos(); ``` +```cpp +// GOOD (comment is kept concise and useful) + +// one decode step of code_predictor +// at step_idx g: +// - read code from out_code_cache[g], then embed it with codebook table g-1 +// - write new kv at cache row g+1, sample with lm_head[g] +// - write result to out_code_cache[g+1] + + +// BAD (comment is long and is forced to fit into a fixed column size, it is very annoying to read as a reviewer) + +// one autoregressive decode step of the 5-layer code_predictor. See the +// comment in models.h for the cache/tensor conventions this relies on. +// +// index mapping (derived from the reference pipeline-tts.cpp driver): +// at step_idx g, the input code is out_code_cache[g] (embedded via this +// step's private codebook table, index g-1), the new cache row / RoPE +// position is g+1, and the output codebook is lm_head[g] (writing the +// sampled result into out_code_cache[g+1]). +``` + Commit message: ``` @@ -183,6 +229,8 @@ gh issue create To conserve context space, load these resources as needed: +Skills: reusable task workflows live in the [skills/](skills/) directory - check there for a skill matching your task before starting. + General documentations: - [Contributing guidelines](CONTRIBUTING.md) - [Existing issues](https://github.com/ggml-org/llama.cpp/issues) and [Existing PRs](https://github.com/ggml-org/llama.cpp/pulls) - always search here first diff --git a/CMakeLists.txt b/CMakeLists.txt index f6a9eac2bf8d..6b04e0dae6d0 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -81,6 +81,14 @@ else() set(LLAMA_TOOLS_INSTALL_DEFAULT ${LLAMA_STANDALONE}) endif() +# subprocess spawning isn't a supported/sandbox-friendly operation on mobile OSes or in WASM +if (CMAKE_SYSTEM_NAME STREQUAL "iOS" OR CMAKE_SYSTEM_NAME STREQUAL "Android" OR ANDROID + OR CMAKE_SYSTEM_NAME STREQUAL "Emscripten" OR EMSCRIPTEN) + set(LLAMA_SUBPROCESS_DEFAULT OFF) +else() + set(LLAMA_SUBPROCESS_DEFAULT ON) +endif() + # # option list # @@ -114,6 +122,7 @@ option(LLAMA_TESTS_INSTALL "llama: install tests" ON) # 3rd party libs option(LLAMA_OPENSSL "llama: use openssl to support HTTPS" ON) +option(LLAMA_SUBPROCESS "llama-common: use subprocess, required by server tools and server router mode" ${LLAMA_SUBPROCESS_DEFAULT}) option(LLAMA_LLGUIDANCE "llama-common: include LLGuidance library for structured output in common utils" OFF) diff --git a/CODEOWNERS b/CODEOWNERS index 2e30839ba0a8..929c8380e843 100644 --- a/CODEOWNERS +++ b/CODEOWNERS @@ -60,7 +60,6 @@ /ggml/src/ggml-cpu/spacemit/ @alex-spacemit /ggml/src/ggml-cuda/ @ggml-org/ggml-cuda /ggml/src/ggml-cuda/vendors/hip.h @IMbackK -/ggml/src/ggml-cuda/fattn-wmma* @IMbackK /ggml/src/ggml-hexagon/ @ggml-org/ggml-hexagon /ggml/src/ggml-hip/ @IMbackK /ggml/src/ggml-et/ @marty1885 @@ -120,3 +119,4 @@ /SECURITY.md @ggerganov /build-xcframework.sh @danbev requirements*.txt @CISC +/skills @ngxson diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 6881a4d3ab33..003133478811 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -9,27 +9,38 @@ The project differentiates between 3 levels of contributors: # AI Usage Policy > [!IMPORTANT] -> This project does **not** accept pull requests that are fully or predominantly AI-generated. AI tools may be utilized solely in an assistive capacity. > -> Repeated violations of this policy may result in your account being permanently banned from contributing to the project. +> AI-generated code is allowed. You are 100% responsible for every line, however it was produced. +> +> Undisclosed AI usage may result in your account being permanently banned from contributing to the project. > > Detailed information regarding permissible and restricted uses of AI can be found in the [AGENTS.md](AGENTS.md) file. -Code that is initially generated by AI and subsequently edited will still be considered AI-generated. AI assistance is permissible only when the majority of the code is authored by a human contributor, with AI employed exclusively for corrections or to expand on verbose modifications that the contributor has already conceptualized (e.g., generating repeated lines with minor variations). - If AI is used to generate any portion of the code, contributors must adhere to the following requirements: 1. Explicitly disclose the manner in which AI was employed. -2. Perform a comprehensive manual review prior to submitting the pull request. -3. Be prepared to explain every line of code they submitted when asked about it by a maintainer. -4. It is strictly prohibited to use AI to write your posts for you (bug reports, feature requests, pull request descriptions, Github discussions, responding to humans, ...). +2. Check for an existing PR addressing the same change; if one exists, comment there to work with its author instead of opening a duplicate. +3. Perform a comprehensive manual review prior to submitting the pull request. +4. Be prepared to explain every line of code they submitted when asked about it by a maintainer. +5. It is strictly prohibited to use AI to write your posts for you (bug reports, feature requests, pull request descriptions, Github discussions, responding to humans, ...). For more info, please refer to the [AGENTS.md](AGENTS.md) file. # Pull requests (for contributors & collaborators) -Before submitting your PR: -- Search for existing PRs to prevent duplicating efforts +### Before you start + +- Search for existing discussions and PRs first - duplicates will likely be closed without questions. +- Features must begin with an issue, not a PR - let interest accumulate before writing code; niche features may only land as an example/tool, or on a private fork. +- Bug-fix PRs must include a reproducible issue and a regression test that fails before your change and passes after. Fixes without a test may be closed without review. +- New CLI or public API additions carry a **higher bar** than internal changes - justify why an existing mechanism doesn't suffice. +- Meeting all of the above still doesn't guarantee a merge - see [Pull requests (for maintainers)](#pull-requests-for-maintainers). +- If you are a new contributor + - Limit your open PRs to 1 + - Do not submit trivial fixes (e.g. typos, formatting changes) + +### Preparing your PR + - llama.cpp uses the ggml tensor library for model evaluation. If you are unfamiliar with ggml, consider taking a look at the [examples in the ggml repository](https://github.com/ggml-org/ggml/tree/master/examples/). [simple](https://github.com/ggml-org/ggml/tree/master/examples/simple) shows the bare minimum for using ggml. [gpt-2](https://github.com/ggml-org/ggml/tree/master/examples/gpt-2) has minimal implementations for language model inference using GPT-2. [mnist](https://github.com/ggml-org/ggml/tree/master/examples/mnist) demonstrates how to train and evaluate a simple image classifier - Test your changes: - Execute [the full CI locally on your machine](ci/README.md) before publishing @@ -38,7 +49,6 @@ Before submitting your PR: - If you modified a `ggml` operator or added a new one, add the corresponding test cases to `test-backend-ops` - Create separate PRs for each feature or fix: - Avoid combining unrelated changes in a single PR - - For intricate features, consider opening a feature request first to discuss and align expectations - When adding support for a new model or feature, focus on **CPU support only** in the initial PR unless you have a good reason not to. Add support for other backends like CUDA in follow-up PRs - In particular, adding new data types (extension of the `ggml_type` enum) carries with it a disproportionate maintenance burden. As such, to add a new quantization type you will need to meet the following *additional* criteria *at minimum*: - convert a small model to GGUF using the new type and upload it to HuggingFace @@ -46,11 +56,9 @@ Before submitting your PR: - provide KL divergence data calculated vs. the FP16/BF16 (whichever is the native precision) version for both the new type as well as types of similar size - provide [performance data](https://github.com/ggml-org/llama.cpp/tree/master/tools/llama-bench) for the new type in comparison to types of similar size on pure CPU - Consider allowing write access to your branch for faster reviews, as reviewers can push commits directly -- If you are a new contributor - - Limit your open PRs to 1 - - Do not submit trivial fixes (e.g. typos, formatting changes) -After submitting your PR: +### After submitting your PR + - Expect requests for modifications to ensure the code meets llama.cpp's standards for quality and long-term maintainability - Maintainers will rely on your insights and approval when making a final decision to approve and merge a PR - If your PR becomes stale, rebase it on top of latest `master` to get maintainers attention @@ -65,11 +73,13 @@ After submitting your PR: - When merging a PR, make sure you have a good understanding of the changes - If a PR does not warrant a new release, add `[no release]` in the squashed commit to spare CI resources - Be mindful of maintenance: most of the work going into a feature happens after the PR is merged. If the PR author is not committed to contribute long-term, someone else needs to take responsibility (you) +- Add the ["merge ready"](https://github.com/ggml-org/llama.cpp/pulls?q=is%3Apr+is%3Aopen+draft%3Ano+sort%3Aupdated-desc+label%3A%22merge+ready%22+) label to a PR to indicate when a PR can be fast-merged without waiting for 2 independent reviews. [(more info)](https://github.com/ggml-org/llama.cpp/pull/26178) Maintainers reserve the right to decline review or close pull requests for any reason, without any questions, particularly under any of the following conditions: - The proposed change is already mentioned in the roadmap or an existing issue, and it has been assigned to someone. - The pull request duplicates an existing one. - The contributor fails to adhere to this contributing guide or the AI policy. +- The change doesn't fit the existing architecture, or is too complex to justify its benefit. # Coding guidelines diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt index 4cf580a056c8..799d227519f9 100644 --- a/common/CMakeLists.txt +++ b/common/CMakeLists.txt @@ -100,6 +100,10 @@ add_library(${TARGET} sampling.h speculative.cpp speculative.h + subproc.cpp + subproc.h + trie.cpp + trie.h unicode.cpp unicode.h jinja/lexer.cpp @@ -125,6 +129,10 @@ set_target_properties(${TARGET} PROPERTIES target_include_directories(${TARGET} PUBLIC . ../vendor) target_compile_features (${TARGET} PUBLIC cxx_std_17) +if (LLAMA_SUBPROCESS) + target_compile_definitions(${TARGET} PUBLIC LLAMA_SUBPROCESS) +endif() + if (BUILD_SHARED_LIBS) set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON) diff --git a/common/arg.cpp b/common/arg.cpp index c856a1e1b78e..cc1a27cbf5bc 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -5,6 +5,7 @@ #include "common.h" #include "download.h" #include "json-schema-to-grammar.h" +#include "llama.h" #include "log.h" #include "sampling.h" #include "speculative.h" @@ -26,6 +27,7 @@ #include #include #include +#include #include #include #include @@ -354,6 +356,10 @@ static std::string get_default_local_path(const std::string & url) { return fs_get_cache_file(string_split(f, '/').back()); } +static bool spec_types_is_default(const common_params & params) { + return params.speculative.types == std::vector{COMMON_SPECULATIVE_TYPE_NONE}; +} + common_models_handler common_models_handler_init(const common_params & params, llama_example curr_ex) { common_download_hf_plan plan; common_download_hf_plan plan_spec; @@ -364,6 +370,18 @@ common_models_handler common_models_handler_init(const common_params & params, l params.speculative.types.end(), COMMON_SPECULATIVE_TYPE_DRAFT_MTP) != params.speculative.types.end(); + const bool spec_type_draft_dflash = std::find(params.speculative.types.begin(), + params.speculative.types.end(), + COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH) != params.speculative.types.end(); + + const bool spec_type_draft_eagle3 = std::find(params.speculative.types.begin(), + params.speculative.types.end(), + COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3) != params.speculative.types.end(); + + const bool spec_type_draft_dspark = std::find(params.speculative.types.begin(), + params.speculative.types.end(), + COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK) != params.speculative.types.end(); + // only download mmproj if the current example is using it bool use_mmproj = false; for (const auto & ex : mmproj_examples) { @@ -376,6 +394,9 @@ common_models_handler common_models_handler_init(const common_params & params, l opts.bearer_token = params.hf_token; opts.offline = params.offline; opts.download_mtp = spec_type_draft_mtp; + opts.download_eagle3 = spec_type_draft_eagle3; + opts.download_dflash = spec_type_draft_dflash; + opts.download_dspark = spec_type_draft_dspark; opts.download_mmproj = use_mmproj && !params.no_mmproj && params.mmproj.path.empty() && params.mmproj.url.empty(); @@ -384,7 +405,15 @@ common_models_handler common_models_handler_init(const common_params & params, l } if (!params.speculative.draft.mparams.hf_repo.empty()) { - plan_spec = common_download_get_hf_plan(params.speculative.draft.mparams, opts); + // without a requested type, discover every sidecar the draft repo ships to infer the type later + auto opts_spec = opts; + if (spec_types_is_default(params)) { + opts_spec.download_mtp = true; + opts_spec.download_dflash = true; + opts_spec.download_eagle3 = true; + opts_spec.download_dspark = true; + } + plan_spec = common_download_get_hf_plan(params.speculative.draft.mparams, opts_spec); } if (!params.vocoder.model.hf_repo.empty()) { @@ -520,8 +549,87 @@ void common_models_handler_apply(common_models_handler & handler, common_params } }; + // an explicit draft file selection (e.g. -md with -hfd) disables the sidecar resolution of the draft repo + if (!params.speculative.draft.mparams.hf_file.empty()) { + plan_spec.mtp = {}; + plan_spec.dflash = {}; + plan_spec.eagle3 = {}; + plan_spec.dspark = {}; + } + + // infer the speculative type from the sidecar shipped by the draft repo when none is requested + if (spec_types_is_default(params)) { + if (!plan_spec.mtp.local_path.empty()) { + params.speculative.types = { COMMON_SPECULATIVE_TYPE_DRAFT_MTP }; + plan_spec.dspark = {}; + plan_spec.dflash = {}; + plan_spec.eagle3 = {}; + } else if (!plan_spec.dspark.local_path.empty()) { + // dspark outranks dflash, its sidecar carries the extra Markov head + params.speculative.types = { COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK }; + plan_spec.dflash = {}; + plan_spec.eagle3 = {}; + } else if (!plan_spec.dflash.local_path.empty()) { + params.speculative.types = { COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH }; + plan_spec.eagle3 = {}; + } else if (!plan_spec.eagle3.local_path.empty()) { + params.speculative.types = { COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3 }; + } + } + + // when a sidecar type is requested, the draft repo resolves to its sidecar instead of a full model + const bool spec_sidecar_found = !plan_spec.mtp.local_path.empty() || + !plan_spec.dflash.local_path.empty() || + !plan_spec.eagle3.local_path.empty() || + !plan_spec.dspark.local_path.empty(); + if (!plan_spec.mtp.local_path.empty() && !had_spec_url) { + tasks.emplace_back(plan_spec.mtp, opts, [&]() { + // only use the discovered MTP head when no draft path is set yet + if (params.speculative.draft.mparams.path.empty()) { + params.speculative.draft.mparams.path = hf_cache::finalize_file(plan_spec.mtp); + } else { + hf_cache::finalize_file(plan_spec.mtp); + } + }); + } + if (!plan_spec.dflash.local_path.empty() && !had_spec_url) { + tasks.emplace_back(plan_spec.dflash, opts, [&]() { + // only use the discovered DFlash sidecar when no draft path is set yet + if (params.speculative.draft.mparams.path.empty()) { + params.speculative.draft.mparams.path = hf_cache::finalize_file(plan_spec.dflash); + } else { + hf_cache::finalize_file(plan_spec.dflash); + } + }); + } + if (!plan_spec.eagle3.local_path.empty() && !had_spec_url) { + tasks.emplace_back(plan_spec.eagle3, opts, [&]() { + // only use the discovered Eagle3 sidecar when no draft path is set yet + if (params.speculative.draft.mparams.path.empty()) { + params.speculative.draft.mparams.path = hf_cache::finalize_file(plan_spec.eagle3); + } else { + hf_cache::finalize_file(plan_spec.eagle3); + } + }); + } + if (!plan_spec.dspark.local_path.empty() && !had_spec_url) { + tasks.emplace_back(plan_spec.dspark, opts, [&]() { + // only use the discovered DSpark sidecar when no draft path is set yet + if (params.speculative.draft.mparams.path.empty()) { + params.speculative.draft.mparams.path = hf_cache::finalize_file(plan_spec.dspark); + } else { + hf_cache::finalize_file(plan_spec.dspark); + } + }); + } + + // a wired draft sidecar counts as an explicit draft for the main plan fallback below + if (spec_sidecar_found) { + had_spec_url = true; + } + // handle plan_spec (e.g. --spec-draft-hf) - if (!plan_spec.model_files.empty() && !had_spec_url) { + if (!plan_spec.model_files.empty() && !had_spec_url && !spec_sidecar_found) { add_tasks(plan_spec.model_files, plan_spec.primary, params.speculative.draft.mparams); had_spec_url = true; } @@ -549,6 +657,36 @@ void common_models_handler_apply(common_models_handler & handler, common_params } }); } + if (!plan.dflash.local_path.empty() && !had_spec_url) { + tasks.emplace_back(plan.dflash, opts, [&]() { + // only fall back to the discovered DFlash sidecar when no draft was explicitly provided + if (params.speculative.draft.mparams.empty()) { + params.speculative.draft.mparams.path = hf_cache::finalize_file(plan.dflash); + } else { + hf_cache::finalize_file(plan.dflash); + } + }); + } + if (!plan.eagle3.local_path.empty() && !had_spec_url) { + tasks.emplace_back(plan.eagle3, opts, [&]() { + // only fall back to the discovered Eagle3 sidecar when no draft was explicitly provided + if (params.speculative.draft.mparams.empty()) { + params.speculative.draft.mparams.path = hf_cache::finalize_file(plan.eagle3); + } else { + hf_cache::finalize_file(plan.eagle3); + } + }); + } + if (!plan.dspark.local_path.empty() && !had_spec_url) { + tasks.emplace_back(plan.dspark, opts, [&]() { + // only fall back to the discovered DSpark sidecar when no draft was explicitly provided + if (params.speculative.draft.mparams.empty()) { + params.speculative.draft.mparams.path = hf_cache::finalize_file(plan.dspark); + } else { + hf_cache::finalize_file(plan.dspark); + } + }); + } if (!plan.preset.local_path.empty()) { tasks.emplace_back(plan.preset, opts, [&]() { // if HF repo is a preset repo, we simply run server in router mode with the preset.ini file @@ -698,6 +836,17 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context arg.c_str(), e.what(), opt.to_string().c_str())); } } + + // TODO: remove this check after deprecating --mmap|mlock|dio + auto has_arg = [&](std::initializer_list names) { + return std::any_of(names.begin(), names.end(), [&](const char * name) { + return seen_args.count(name); + }); + }; + if (has_arg({"-lm", "--load-mode"}) && + has_arg({"--mlock", "--mmap", "--no-mmap", "-dio", "--direct-io", "-ndio", "--no-direct-io"})) { + LOG_WRN("DEPRECATED: `--load-mode` and `--mlock`/`--mmap`/`--direct-io` should not be combined; only the last flag on the command line will take effect\n"); + } }; // parse all CLI args now, so that -hf is available below for remote preset resolution @@ -751,8 +900,9 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context params.kv_overrides.back().key[0] = 0; } - if (!params.server_tools.empty() && !params.cors_origins_explicit) { - LOG_WRN("server tools are enabled, using localhost as default CORS origin (change via --cors-origins)\n"); + const bool mcp_enabled = !params.mcp_servers_config.empty() || !params.mcp_servers_json.empty(); + if ((!params.server_tools.empty() || mcp_enabled) && !params.cors_origins_explicit) { + LOG_WRN("server tools or MCP servers are enabled, using localhost as default CORS origin (change via --cors-origins)\n"); params.cors_origins = "localhost"; } @@ -949,6 +1099,31 @@ static std::vector parse_device_list(const std::string & val return devices; } +void common_print_available_devices() { + constexpr size_t MiB = 1024 * 1024; + std::vector devices; + + ggml_backend_load_all(); + + for (size_t i = 0; i < ggml_backend_dev_count(); ++i) { + auto * dev = ggml_backend_dev_get(i); + if (ggml_backend_dev_type(dev) != GGML_BACKEND_DEVICE_TYPE_CPU) { + devices.push_back(dev); + } + } + printf("Available devices:\n"); + + if (devices.empty()) { + printf(" (none)\n"); + return; + } + for (auto * dev : devices) { + size_t free, total; + ggml_backend_dev_memory(dev, &free, &total); + printf(" %s: %s (%zu MiB, %zu MiB free)\n", ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), total / MiB, free / MiB); + } +} + static void add_rpc_devices(const std::string & servers) { auto rpc_servers = string_split(servers, ','); if (rpc_servers.empty()) { @@ -1865,7 +2040,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex {"--repeat-penalty"}, "N", string_format("penalize repeat sequence of tokens (default: %.2f, 1.0 = disabled)", (double)params.sampling.penalty_repeat), [](common_params & params, const std::string & value) { - params.sampling.penalty_repeat = std::stof(value); + const float penalty_repeat = std::stof(value); + if (!std::isfinite(penalty_repeat) || + penalty_repeat <= 0.0f || + !std::isfinite(1.0f/penalty_repeat)) { + throw std::runtime_error("error: repeat-penalty must be finite and greater than 0\n"); + } + params.sampling.penalty_repeat = penalty_repeat; params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_PENALTY_REPEAT; } ).set_sampling()); @@ -1873,14 +2054,22 @@ common_params_context common_params_parser_init(common_params & params, llama_ex {"--presence-penalty"}, "N", string_format("repeat alpha presence penalty (default: %.2f, 0.0 = disabled)", (double)params.sampling.penalty_present), [](common_params & params, const std::string & value) { - params.sampling.penalty_present = std::stof(value); + const float penalty_present = std::stof(value); + if (!std::isfinite(penalty_present)) { + throw std::runtime_error("error: presence-penalty must be finite\n"); + } + params.sampling.penalty_present = penalty_present; } ).set_sampling()); add_opt(common_arg( {"--frequency-penalty"}, "N", string_format("repeat alpha frequency penalty (default: %.2f, 0.0 = disabled)", (double)params.sampling.penalty_freq), [](common_params & params, const std::string & value) { - params.sampling.penalty_freq = std::stof(value); + const float penalty_freq = std::stof(value); + if (!std::isfinite(penalty_freq)) { + throw std::runtime_error("error: frequency-penalty must be finite\n"); + } + params.sampling.penalty_freq = penalty_freq; } ).set_sampling()); add_opt(common_arg( @@ -2396,7 +2585,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.mtmd_batch_max_tokens = value; } ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MTMD_BATCH_MAX_TOKENS")); - if (llama_supports_rpc()) { + if (params.is_gen_docs || llama_supports_rpc()) { add_opt(common_arg( {"--rpc"}, "SERVERS", "comma-separated list of RPC servers (host:port)", @@ -2408,27 +2597,47 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } add_opt(common_arg( {"--mlock"}, - "force system to keep model in RAM rather than swapping or compressing", + "DEPRECATED in favor of `--load-mode`: force system to keep model in RAM rather than swapping or compressing", [](common_params & params) { - params.use_mlock = true; + LOG_WRN("DEPRECATED: --mlock is deprecated. use --load-mode mlock instead\n"); + params.load_mode = LLAMA_LOAD_MODE_MLOCK; } ).set_env("LLAMA_ARG_MLOCK")); add_opt(common_arg( {"--mmap"}, {"--no-mmap"}, - string_format("whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: %s)", params.use_mmap ? "enabled" : "disabled"), + "DEPRECATED in favor of `--load-mode`: whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock)", [](common_params & params, bool value) { - params.use_mmap = value; + LOG_WRN("DEPRECATED: --mmap and --no-mmap are deprecated. use --load-mode mmap instead\n"); + params.load_mode = value ? LLAMA_LOAD_MODE_MMAP : LLAMA_LOAD_MODE_NONE; } ).set_env("LLAMA_ARG_MMAP")); add_opt(common_arg( {"-dio", "--direct-io"}, {"-ndio", "--no-direct-io"}, - string_format("use DirectIO if available. (default: %s)", params.use_direct_io ? "enabled" : "disabled"), + "DEPRECATED in favor of `--load-mode`: use DirectIO if available", [](common_params & params, bool value) { - params.use_direct_io = value; + LOG_WRN("DEPRECATED: --direct-io and --no-direct-io are deprecated. use --load-mode dio instead\n"); + params.load_mode = value ? LLAMA_LOAD_MODE_DIRECT_IO : LLAMA_LOAD_MODE_NONE; } ).set_env("LLAMA_ARG_DIO")); + add_opt(common_arg( + {"-lm", "--load-mode"}, "MODE", + "model loading mode (default: mmap)\n" + "- none: no special loading mode\n" + "- mmap: memory-map model (if mmap disabled, slower load but may reduce pageouts if not using mlock)\n" + "- mlock: force system to keep model in RAM rather than swapping or compressing\n" + "- mmap+mlock: mmap + force system to keep model in RAM rather than swapping or compressing\n" + "- dio: use DirectIO if available\n", + [](common_params & params, const std::string & value) { + /**/ if (value == "none") { params.load_mode = LLAMA_LOAD_MODE_NONE; } + else if (value == "mmap") { params.load_mode = LLAMA_LOAD_MODE_MMAP; } + else if (value == "mlock") { params.load_mode = LLAMA_LOAD_MODE_MLOCK; } + else if (value == "mmap+mlock") { params.load_mode = LLAMA_LOAD_MODE_MMAP_MLOCK; } + else if (value == "dio") { params.load_mode = LLAMA_LOAD_MODE_DIRECT_IO; } + else { throw std::invalid_argument("invalid value"); } + } + ).set_env("LLAMA_ARG_LOAD_MODE")); add_opt(common_arg( {"--numa"}, "TYPE", "attempt optimizations that help on some NUMA systems\n" @@ -2456,20 +2665,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex {"--list-devices"}, "print list of available devices and exit", [](common_params &) { - ggml_backend_load_all(); - std::vector devices; - for (size_t i = 0; i < ggml_backend_dev_count(); ++i) { - auto * dev = ggml_backend_dev_get(i); - if (ggml_backend_dev_type(dev) != GGML_BACKEND_DEVICE_TYPE_CPU) { - devices.push_back(dev); - } - } - printf("Available devices:\n"); - for (auto * dev : devices) { - size_t free, total; - ggml_backend_dev_memory(dev, &free, &total); - printf(" %s: %s (%zu MiB, %zu MiB free)\n", ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), total / 1024 / 1024, free / 1024 / 1024); - } + common_print_available_devices(); exit(0); } )); @@ -2818,6 +3014,20 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.speculative.types.push_back(COMMON_SPECULATIVE_TYPE_DRAFT_MTP); } ).set_examples({LLAMA_EXAMPLE_DOWNLOAD})); + add_opt(common_arg( + {"--dflash"}, + "also download the DFlash sidecar, if available (default: unused)", + [](common_params & params) { + params.speculative.types.push_back(COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH); + } + ).set_examples({LLAMA_EXAMPLE_DOWNLOAD})); + add_opt(common_arg( + {"--eagle3"}, + "also download the Eagle3 sidecar, if available (default: unused)", + [](common_params & params) { + params.speculative.types.push_back(COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3); + } + ).set_examples({LLAMA_EXAMPLE_DOWNLOAD})); add_opt(common_arg( {"--context-file"}, "FNAME", "file to load context from (use comma-separated values to specify multiple files)", @@ -2935,7 +3145,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_examples({LLAMA_EXAMPLE_TOKENIZE})); add_opt(common_arg( {"--stdin"}, - string_format("read the prompt from stdin (mutually exclusive with -f/--file and -p/--prompt) (default: %s)", params.tokenize_stdin ? "true" : "false"), + string_format("read the prompt from stdin (takes precedence over -f/--file and -p/--prompt) (default: %s)", params.tokenize_stdin ? "true" : "false"), [](common_params & params) { params.tokenize_stdin = true; } @@ -3124,12 +3334,28 @@ common_params_context common_params_parser_init(common_params & params, llama_ex {"--tools"}, "TOOL1,TOOL2,...", "experimental: whether to enable built-in tools for AI agents - do not enable in untrusted environments (default: no tools)\n" "specify \"all\" to enable all tools\n" - "available tools: read_file, file_glob_search, grep_search, exec_shell_command, write_file, edit_file, get_datetime\n" + "available tools: read_file, file_glob_search, grep_search, exec_shell_command, write_file, edit_file, get_datetime, get_info\n" "note: for security reasons, this will limit --cors-origins to localhost by default", [](common_params & params, const std::string & value) { params.server_tools = parse_csv_row(value); } ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_TOOLS")); + add_opt(common_arg( + {"--mcp-servers-config"}, "PATH", + "experimental: path to JSON file with MCP server definitions (Cursor-compatible format) - do not enable in untrusted environments (default: none)\n" + "note: for security reasons, this will limit --cors-origins to localhost by default", + [](common_params & params, const std::string & value) { + params.mcp_servers_config = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MCP_SERVERS_CONFIG")); + add_opt(common_arg( + {"--mcp-servers-json"}, "JSON", + "experimental: inline JSON with MCP server definitions (Cursor-compatible format) - do not enable in untrusted environments (default: none)\n" + "note: for security reasons, this will limit --cors-origins to localhost by default", + [](common_params & params, const std::string & value) { + params.mcp_servers_json = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MCP_SERVERS_JSON")); add_opt(common_arg( {"-ag", "--agent"}, {"-no-ag", "--no-agent"}, @@ -3433,7 +3659,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params, const std::string & value) { params.chat_template = read_file(value); } - ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE_FILE")); + ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MTMD}).set_env("LLAMA_ARG_CHAT_TEMPLATE_FILE")); add_opt(common_arg( {"--skip-chat-parsing"}, {"--no-skip-chat-parsing"}, diff --git a/common/arg.h b/common/arg.h index 54a38b9cce4a..8f609e356fe2 100644 --- a/common/arg.h +++ b/common/arg.h @@ -123,6 +123,9 @@ struct common_params_context { // if one argument has invalid value, it will automatically display usage of the specific argument (and not the full usage message) bool common_params_parse(int argc, char ** argv, common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr); +// load all backends and print the list of available (non-CPU) devices to stdout +void common_print_available_devices(); + // parse input arguments from CLI into a map bool common_params_to_map(int argc, char ** argv, llama_example ex, std::map & out_map); diff --git a/common/chat-auto-parser-generator.cpp b/common/chat-auto-parser-generator.cpp index 9f77b74b63dc..da63ddf1ac21 100644 --- a/common/chat-auto-parser-generator.cpp +++ b/common/chat-auto-parser-generator.cpp @@ -47,6 +47,8 @@ common_chat_params peg_generator::generate_parser(const common_chat_template & data.generation_prompt = common_chat_template_generation_prompt(tmpl, inputs); data.format = COMMON_CHAT_FORMAT_PEG_NATIVE; data.preserved_tokens = autoparser.preserved_tokens; + data.additional_stops.insert(data.additional_stops.end(), + autoparser.additional_stops.begin(), autoparser.additional_stops.end()); std::string parser_generation_prompt = data.generation_prompt; @@ -288,7 +290,13 @@ common_peg_parser analyze_tools::build_func_parser(common_chat_peg_builder & p, // we only emit tool_close when we can actually see the closing marker. This prevents // premature closing during partial parsing when we've seen e.g. "" (end) or "" prefix that failed to match. - func_parser = func_parser + p.tool_close(p.peek(p.literal(format.per_call_end))); + // Laguna (v4): the model may emit whitespace between the last and + // even though the template renders them tight. Tolerate optional + // leading space in the close lookahead so the tool call still closes. + auto close_peek = arguments.tolerate_intertag_whitespace + ? p.peek(p.space() + p.literal(format.per_call_end)) + : p.peek(p.literal(format.per_call_end)); + func_parser = func_parser + p.tool_close(close_peek); } else { func_parser = func_parser + p.tool_close(p.space()); // force this to process tool closing callbacks in mapper } diff --git a/common/chat-auto-parser.h b/common/chat-auto-parser.h index d47b09dcf6fd..074216b11ee1 100644 --- a/common/chat-auto-parser.h +++ b/common/chat-auto-parser.h @@ -206,6 +206,7 @@ struct tool_arguments_analysis { std::string value_prefix; // e.g., "", "", "" std::string value_suffix; // e.g., "", "", "" std::string separator; // e.g., "", "\n", "," + bool tolerate_intertag_whitespace = false; // Laguna: accept optional whitespace between arg tags }; struct tool_id_analysis { @@ -388,6 +389,7 @@ struct autoparser { // Preserved tokens for tokenizer (union of all non-empty markers) std::vector preserved_tokens; + std::vector additional_stops; // literal stop strings (e.g. Laguna ) caught however tokenized autoparser() = default; diff --git a/common/chat-diff-analyzer.cpp b/common/chat-diff-analyzer.cpp index 127278dfb2ac..7db1dcb0fa84 100644 --- a/common/chat-diff-analyzer.cpp +++ b/common/chat-diff-analyzer.cpp @@ -173,6 +173,26 @@ static std::vector\n", "\n") that + // the model does not emit, so the inferred delimiters carry a spurious + // newline and never match the model output. Trim to the bare tag. (v8 + // renders without the whitespace, so this is a no-op there.) + [](const common_chat_template & tmpl, autoparser & analysis) -> void { + if (tmpl.src.find("laguna_glm_thinking") != std::string::npos) { + analysis.reasoning.start = trim_whitespace(analysis.reasoning.start); + analysis.reasoning.end = trim_whitespace(analysis.reasoning.end); + analysis.tools.arguments.value_prefix = trim_whitespace(analysis.tools.arguments.value_prefix); + analysis.tools.arguments.value_suffix = trim_whitespace(analysis.tools.arguments.value_suffix); + analysis.tools.arguments.separator = trim_whitespace(analysis.tools.arguments.separator); + analysis.tools.arguments.tolerate_intertag_whitespace = true; + // The CONTROL/eot token only halts generation when emitted as the + // single token; after tool calls the model can spell it out as text tokens. + // A literal stop string catches it either way. + analysis.additional_stops.push_back(""); + LOG_DBG(ANSI_ORANGE "[Patch: Laguna]\n" ANSI_RESET); + } + }, }); diff --git a/common/chat-peg-parser.cpp b/common/chat-peg-parser.cpp index db9004d84d76..0dac2f63ab07 100644 --- a/common/chat-peg-parser.cpp +++ b/common/chat-peg-parser.cpp @@ -6,6 +6,9 @@ #include +#include +#include + using ordered_json = nlohmann::ordered_json; static std::string_view trim_trailing_space(std::string_view sv, int max = -1) { @@ -235,6 +238,43 @@ common_peg_parser common_chat_peg_builder::tag_with_safe_content(const std::stri return zero_or_more(choice({ p, content_chunk })); } +common_peg_parser common_chat_peg_builder::permute(const std::string & rule_prefix, + const std::vector & parsers) { + if (parsers.empty()) { + return eps(); + } + + if (parsers.size() == 1 || parsers.size() > COMMON_CHAT_MAX_PERMUTE) { + return sequence(parsers); + } + + std::map rules; + std::function remaining_of; + + remaining_of = [&](uint32_t remaining) -> common_peg_parser { + if (remaining == 0) { + return eps(); + } + + auto cached = rules.find(remaining); + if (cached != rules.end()) { + return cached->second; + } + + auto alternatives = choice(); + for (size_t i = 0; i < parsers.size(); i++) { + const uint32_t bit = 1u << i; + if (remaining & bit) { + alternatives |= parsers[i] + remaining_of(remaining & ~bit); + } + } + + return rules.emplace(remaining, rule(rule_prefix + "-" + std::to_string(remaining), alternatives)).first->second; + }; + + return remaining_of((1u << parsers.size()) - 1); +} + std::string & common_chat_peg_mapper::args_target() { return (current_tool && !current_tool->name.empty()) ? current_tool->arguments : args_buffer; } @@ -1056,3 +1096,141 @@ void common_chat_peg_gemma4_mapper::visit(const common_peg_ast_arena & arena, co visit(arena, child_id); } } + +static void minimax_m3_collect(const common_peg_ast_arena & arena, + const common_peg_ast_node & node, + const std::string & tag, + std::vector & out) { + for (auto child_id : node.children) { + const auto & child = arena.get(child_id); + if (child.tag == tag) { + out.push_back(child_id); + } else { + minimax_m3_collect(arena, child, tag, out); + } + } +} + +static common_peg_ast_id minimax_m3_value_of(const common_peg_ast_arena & arena, const common_peg_ast_node & node) { + for (auto child_id : node.children) { + const auto & tag = arena.get(child_id).tag; + if (tag == common_chat_peg_builder::TOOL_ARG_VALUE || + tag == common_chat_peg_builder::TOOL_ARG_STRING_VALUE || + tag == common_chat_peg_minimax_m3_mapper::TOOL_ARG_OBJECT || + tag == common_chat_peg_minimax_m3_mapper::TOOL_ARG_ARRAY) { + return child_id; + } + } + return COMMON_PEG_INVALID_AST_ID; +} + +static std::string minimax_m3_value_to_json(const common_peg_ast_arena & arena, common_peg_ast_id id, bool closed); + +static std::string minimax_m3_member_to_json(const common_peg_ast_arena & arena, const common_peg_ast_node & node) { + auto name_id = arena.find_by_tag(node, common_chat_peg_builder::TOOL_ARG_NAME); + if (name_id == COMMON_PEG_INVALID_AST_ID) { + return ""; + } + + return ordered_json(arena.get(name_id).text).dump() + ":" + + minimax_m3_value_to_json(arena, minimax_m3_value_of(arena, node), !node.is_partial); +} + +static std::string minimax_m3_container_to_json(const common_peg_ast_arena & arena, + const common_peg_ast_node & node, + bool is_object, + bool closed) { + const std::string tag = is_object ? common_chat_peg_builder::TOOL_ARG + : common_chat_peg_minimax_m3_mapper::TOOL_ARG_ITEM; + + std::vector entries; + minimax_m3_collect(arena, node, tag, entries); + + std::string result = is_object ? "{" : "["; + + bool add_comma = false; + for (auto entry_id : entries) { + const auto & entry = arena.get(entry_id); + + std::string text; + if (is_object) { + text = minimax_m3_member_to_json(arena, entry); + } else { + text = minimax_m3_value_to_json(arena, minimax_m3_value_of(arena, entry), !entry.is_partial); + } + + if (text.empty()) { + continue; + } + + if (add_comma) { + result += ","; + } + add_comma = true; + result += text; + } + + if (closed) { + result += is_object ? "}" : "]"; + } + return result; +} + +static std::string minimax_m3_value_to_json(const common_peg_ast_arena & arena, common_peg_ast_id id, bool closed) { + if (id == COMMON_PEG_INVALID_AST_ID) { + return ""; + } + + const auto & node = arena.get(id); + + if (node.tag == common_chat_peg_minimax_m3_mapper::TOOL_ARG_OBJECT) { + return minimax_m3_container_to_json(arena, node, /* is_object = */ true, closed); + } + + if (node.tag == common_chat_peg_minimax_m3_mapper::TOOL_ARG_ARRAY) { + return minimax_m3_container_to_json(arena, node, /* is_object = */ false, closed); + } + + if (node.tag == common_chat_peg_builder::TOOL_ARG_STRING_VALUE) { + return "\"" + escape_json_string_inner(std::string(node.text)) + (closed ? "\"" : ""); + } + + // Numbers and booleans are written verbatim by the template + return std::string(node.text); +} + +void common_chat_peg_minimax_m3_mapper::from_ast(const common_peg_ast_arena & arena, + const common_peg_parse_result & result) { + for (const auto & node : result.nodes) { + visit(arena, node); + } +} + +void common_chat_peg_minimax_m3_mapper::visit(const common_peg_ast_arena & arena, common_peg_ast_id id) { + const auto & node = arena.get(id); + + if (node.tag == common_chat_peg_builder::REASONING) { + result.reasoning_content += std::string(node.text); + return; + } + + if (node.tag == common_chat_peg_builder::CONTENT) { + result.content += std::string(node.text); + return; + } + + if (node.tag == common_chat_peg_builder::TOOL) { + auto name_id = arena.find_by_tag(node, common_chat_peg_builder::TOOL_NAME); + if (name_id != COMMON_PEG_INVALID_AST_ID) { + common_chat_tool_call call; + call.name = std::string(arena.get(name_id).text); + call.arguments = minimax_m3_container_to_json(arena, node, /* is_object = */ true, !node.is_partial); + result.tool_calls.push_back(call); + } + return; + } + + for (auto child_id : node.children) { + visit(arena, child_id); + } +} diff --git a/common/chat-peg-parser.h b/common/chat-peg-parser.h index b3ffd7de2dd8..5d764dbaa0ec 100644 --- a/common/chat-peg-parser.h +++ b/common/chat-peg-parser.h @@ -40,9 +40,23 @@ class common_chat_peg_gemma4_mapper : public common_chat_peg_mapper { void visit(const common_peg_ast_arena & arena, common_peg_ast_id id); }; +class common_chat_peg_minimax_m3_mapper : public common_chat_peg_mapper { + public: + static constexpr const char * TOOL_ARG_OBJECT = "tool-arg-object"; + static constexpr const char * TOOL_ARG_ARRAY = "tool-arg-array"; + static constexpr const char * TOOL_ARG_ITEM = "tool-arg-item"; + + common_chat_peg_minimax_m3_mapper(common_chat_msg & msg) : common_chat_peg_mapper(msg) {} + virtual void from_ast(const common_peg_ast_arena & arena, const common_peg_parse_result & result); + private: + void visit(const common_peg_ast_arena & arena, common_peg_ast_id id); +}; + struct content_structure; struct tool_call_structure; +constexpr size_t COMMON_CHAT_MAX_PERMUTE = 6; + class common_chat_peg_builder : public common_peg_parser_builder { public: // Tag constants (from former common_chat_peg_base_builder) @@ -93,6 +107,9 @@ class common_chat_peg_builder : public common_peg_parser_builder { common_peg_parser tool_arg_json_value(const common_peg_parser & p) { return tag(TOOL_ARG_VALUE, p); } + // Matches every parser exactly once, in any order. + common_peg_parser permute(const std::string & rule_prefix, const std::vector & parsers); + // Return a parser that parses the prefix of a string, up to a given delimiter. common_peg_parser prefix(const std::string & s, const std::string & delimiter = {}); diff --git a/common/chat.cpp b/common/chat.cpp index 4350a9ec6bee..2b013a9b0bff 100644 --- a/common/chat.cpp +++ b/common/chat.cpp @@ -15,11 +15,13 @@ #include "nlohmann/json.hpp" +#include #include #include #include #include #include +#include #include #include @@ -814,6 +816,8 @@ const char * common_chat_format_name(common_chat_format format) { return "peg-native"; case COMMON_CHAT_FORMAT_PEG_GEMMA4: return "peg-gemma4"; + case COMMON_CHAT_FORMAT_PEG_MINIMAX_M3: + return "peg-minimax-m3"; default: throw std::runtime_error("Unknown chat format"); } @@ -1022,7 +1026,7 @@ static common_chat_params common_chat_params_init_ministral_3(const common_chat_ data.supports_thinking = true; data.thinking_start_tag = "[THINK]"; - data.thinking_end_tag = "[/THINK]"; + data.thinking_end_tags = {"[/THINK]"}; data.prompt = common_chat_template_direct_apply_impl(tmpl, inputs, /* messages_override = */ adjusted_messages); data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs, /* messages_override = */ adjusted_messages); data.format = COMMON_CHAT_FORMAT_PEG_NATIVE; @@ -1106,6 +1110,172 @@ static common_chat_params common_chat_params_init_ministral_3(const common_chat_ return data; } +static common_chat_params common_chat_params_init_qwen3_coder(const common_chat_template & tmpl, + const autoparser::generation_params & inputs) { + common_chat_params data; + + const std::string GEN_PREFIX = "<|im_start|>assistant\n"; + + data.prompt = common_chat_template_direct_apply_impl(tmpl, inputs); + data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs); + data.format = COMMON_CHAT_FORMAT_PEG_NATIVE; + + auto supports_reasoning = tmpl.source().find("") != std::string::npos; + + data.supports_thinking = supports_reasoning; + data.preserved_tokens = { + "", + "", + }; + + if (supports_reasoning) { + data.thinking_start_tag = ""; + // Support both and as reasoning end sequences. + // ", "" }; + data.preserved_tokens.insert(data.preserved_tokens.end(), { "", "" }); + } + + data.message_delimiters = { + { COMMON_CHAT_ROLE_ASSISTANT, "<|im_start|>assistant" }, + { COMMON_CHAT_ROLE_TOOL, "<|im_start|>user\n" }, // Qwen3-Coder, Qwen3.5, Nemotron Nano 3 + { COMMON_CHAT_ROLE_TOOL, "<|im_start|>tool_response" }, // StepFun-3.5-Flash + { COMMON_CHAT_ROLE_USER, "<|im_start|>user" }, + { COMMON_CHAT_ROLE_SYSTEM, "<|im_start|>system" }, + }; + + auto has_tools = inputs.tools.is_array() && !inputs.tools.empty(); + auto has_response_format = inputs.json_schema.is_object() && !inputs.json_schema.empty(); + auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE; + auto include_grammar = has_response_format || (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE); + + if (inputs.has_continuation()) { + const auto & msg = inputs.continue_msg; + + data.generation_prompt = GEN_PREFIX; + if (supports_reasoning) { + data.generation_prompt += "\n" + msg.reasoning_content; + if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) { + data.generation_prompt += "\n\n\n"; + } + } + if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) { + data.generation_prompt += msg.render_content(); + } + + data.prompt += data.generation_prompt; + } + + auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) { + auto generation_prompt = p.literal(GEN_PREFIX); + + auto reasoning = p.eps(); + if (supports_reasoning && extract_reasoning) { + reasoning = p.optional("" + p.space() + + p.reasoning(p.until_one_of({ "", "" })) + + (p.literal("") | p.peek(p.literal("")))); + } + + // Response format parser + if (has_response_format) { + return generation_prompt + (reasoning << p.content(p.schema(p.json(), "response-format", inputs.json_schema))); + } + + // Tool call parser + if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) { + auto arg_close = p.tool_arg_close(p.literal("\n\n")); + auto arg_string = p.rule("xml-arg-string", + p.ac(p.tool_arg_string_value(p.until("\n\n")) + arg_close, "\n\n")); + + auto tool_choice = p.choice(); + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + std::string name = function.at("name"); + auto parameters = function.contains("parameters") ? function.at("parameters") : json::object(); + + auto schema_info = common_schema_info(); + schema_info.resolve_refs(parameters); + + std::vector required_args; + std::vector optional_args; + + foreach_parameter(function, [&](const std::string & param_name, const json & param_schema, bool is_required) { + auto rule_name = "tool-" + name + "-arg-" + param_name; + + auto arg_open = p.tool_arg_open("\n"); + + auto arg_value = schema_info.resolves_to_string(param_schema) ? + arg_string : + p.tool_arg_json_value(p.schema(p.json(), rule_name + "-schema", param_schema)) + arg_close; + + auto arg_rule = p.rule(rule_name, p.tool_arg(arg_open + arg_value)); + + (is_required ? required_args : optional_args).push_back(arg_rule); + }); + + // Accept required arguments in any order, as Qwen does not always adhere to the + // order provided. + auto args = p.permute("tool-" + name + "-args", required_args); + if (!optional_args.empty()) { + args = args + p.zero_or_more(p.choice(optional_args)); + } + + auto func = p.tool(p.tool_open("\n") + + p.tool_args(args) + + p.tool_close(p.literal("\n"))); + + tool_choice |= p.rule("tool-" + name, func); + }); + + auto min_calls = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? 1 : 0; + + // Qwen3-Coder models may occasionally omit the token. + auto tool_call_body = tool_choice + "" + p.space(); + auto tool_call_first = p.rule("tool-call-first", p.optional(p.literal("\n")) + tool_call_body); + auto tool_call = p.rule("tool-call", "\n" + tool_call_body); + + auto calls = inputs.parallel_tool_calls ? tool_call_first + p.zero_or_more(tool_call) : tool_call_first; + auto tool_calls = p.trigger_rule("tool-call-root", p.repeat(calls, min_calls, 1)); + + return generation_prompt + + (reasoning << p.content(p.until_one_of({ "", "" }, + // Trigger on ""}; + // These special tokens are required to parse properly, so we include them // even if parse_tool_calls is false. data.preserved_tokens = { @@ -1292,7 +1465,7 @@ static common_chat_params common_chat_params_init_gemma4(const common_chat_templ data.format = COMMON_CHAT_FORMAT_PEG_GEMMA4; data.supports_thinking = true; data.thinking_start_tag = "<|channel>thought"; - data.thinking_end_tag = ""; + data.thinking_end_tags = {""}; data.preserved_tokens = { "<|channel>", @@ -1567,7 +1740,7 @@ static common_chat_params common_chat_params_init_kimi_k2(const common_chat_temp const std::string GEN_PROMPT = "<|im_assistant|>assistant<|im_middle|>"; data.thinking_start_tag = THINK_START; - data.thinking_end_tag = THINK_END; + data.thinking_end_tags = {THINK_END}; if (inputs.has_continuation()) { const auto & msg = inputs.continue_msg; @@ -1701,7 +1874,7 @@ static common_chat_params common_chat_params_init_lfm2(const common_chat_templat } data.thinking_start_tag = THINK_START; - data.thinking_end_tag = THINK_END; + data.thinking_end_tags = {THINK_END}; auto has_tools = inputs.tools.is_array() && !inputs.tools.empty(); auto has_response_format = !inputs.json_schema.is_null() && inputs.json_schema.is_object(); @@ -1727,7 +1900,7 @@ static common_chat_params common_chat_params_init_lfm2(const common_chat_templat auto reasoning = p.eps(); if (extract_reasoning) { - // Allow optional whitespace before — some models emit a + // Allow optional whitespace before - some models emit a // newline between the generation prompt and the thinking tag. reasoning = p.optional(p.space() + THINK_START + p.reasoning(p.until(THINK_END)) + THINK_END); } @@ -1857,144 +2030,213 @@ static common_chat_params common_chat_params_init_gigachat_v3( return data; } +// The DeepSeek V4 reference implementation renders consecutive tool results into a single +// user block, ordered by the tool call order of the preceding assistant message (matched +// by tool call id) rather than by the order they appear in the conversation. +static json deepseek_v4_sort_tool_results(const json & messages) { + json adjusted = messages; + std::map call_order; + + for (size_t i = 0; i < adjusted.size();) { + const auto & msg = adjusted[i]; + const auto role = msg.value("role", ""); + + if (role == "assistant" && msg.contains("tool_calls") && + msg.at("tool_calls").is_array() && !msg.at("tool_calls").empty()) { + call_order.clear(); + const auto & tool_calls = msg.at("tool_calls"); + for (size_t idx = 0; idx < tool_calls.size(); idx++) { + auto id = tool_calls[idx].value("id", ""); + if (!id.empty()) { + call_order[id] = idx; + } + } + i++; + continue; + } + + if (role != "user" && role != "tool") { + i++; + continue; + } + + // collect a maximal run of user/tool messages - they render into one user block + std::vector tool_positions; + size_t run_end = i; + for (; run_end < adjusted.size(); run_end++) { + const auto r = adjusted[run_end].value("role", ""); + if (r == "tool") { + tool_positions.push_back(run_end); + } else if (r != "user") { + break; + } + } + + if (tool_positions.size() > 1 && !call_order.empty()) { + std::vector results; + results.reserve(tool_positions.size()); + for (auto pos : tool_positions) { + results.push_back(adjusted[pos]); + } + std::stable_sort(results.begin(), results.end(), [&](const json & a, const json & b) { + const auto order = [&](const json & m) { + auto it = call_order.find(m.value("tool_call_id", "")); + return it == call_order.end() ? (size_t) 0 : it->second; + }; + return order(a) < order(b); + }); + for (size_t k = 0; k < tool_positions.size(); k++) { + adjusted[tool_positions[k]] = std::move(results[k]); + } + } + + i = run_end; + } + + return adjusted; +} + static common_chat_params common_chat_params_init_deepseek_v3_2(const common_chat_template & tmpl, const autoparser::generation_params & inputs) { common_chat_params data; - data.prompt = common_chat_template_direct_apply_impl(tmpl, inputs); - data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs); - data.format = COMMON_CHAT_FORMAT_PEG_NATIVE; - data.supports_thinking = true; - data.thinking_start_tag = ""; - data.thinking_end_tag = ""; - data.preserved_tokens = { - "｜DSML｜", - "", - "", - }; + // V4 uses the same DSML markup as V3.2, but names the tool call block "tool_calls" + // instead of "function_calls", renders tool results in tool call order and its + // non-thinking generation prompt ends with a bare instead of an empty + // pair. + const bool is_v4 = tmpl.source().find("function_calls") == std::string::npos; + + std::optional adjusted_messages; + if (is_v4) { + adjusted_messages = deepseek_v4_sort_tool_results(inputs.messages); + } auto has_tools = inputs.tools.is_array() && !inputs.tools.empty(); auto has_response_format = !inputs.json_schema.is_null() && inputs.json_schema.is_object(); auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE; auto include_grammar = has_response_format || (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE); + std::optional additional_context; + if (is_v4 && has_response_format) { + additional_context = json{ { "response_format", inputs.json_schema } }; + } + const std::string DSML = "｜DSML｜"; const std::string THINK_START = ""; const std::string THINK_END = ""; - const std::string FC_START = "<" + DSML + "function_calls>"; - const std::string FC_END = ""; + const std::string TC_BLOCK = is_v4 ? "tool_calls" : "function_calls"; + const std::string FC_START = "<" + DSML + TC_BLOCK + ">"; + const std::string FC_END = ""; const std::string INVOKE_START = "<" + DSML + "invoke"; const std::string INVOKE_END = ""; const std::string PARAM_START = "<" + DSML + "parameter"; const std::string PARAM_END = ""; const std::string GEN_PROMPT = "<｜Assistant｜>"; + const std::string TC_SEPARATOR = "\n\n"; + + data.prompt = common_chat_template_direct_apply_impl( + tmpl, inputs, adjusted_messages, std::nullopt, additional_context); + data.generation_prompt = common_chat_template_generation_prompt_impl( + tmpl, inputs, adjusted_messages, std::nullopt, additional_context); + data.format = COMMON_CHAT_FORMAT_PEG_NATIVE; + data.supports_thinking = true; + data.thinking_start_tag = THINK_START; + data.thinking_end_tags = {THINK_END, FC_START}; + data.preserved_tokens = { + DSML, + THINK_START, + THINK_END, + }; if (inputs.has_continuation()) { const auto & msg = inputs.continue_msg; - data.generation_prompt = GEN_PROMPT + THINK_START + msg.reasoning_content; - if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) { - data.generation_prompt += THINK_END + msg.render_content(); + if (is_v4 && msg.reasoning_content.empty()) { + data.generation_prompt = GEN_PROMPT + THINK_END; + if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) { + data.generation_prompt += msg.render_content(); + } + } else { + data.generation_prompt = GEN_PROMPT + THINK_START + msg.reasoning_content; + if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) { + data.generation_prompt += THINK_END + msg.render_content(); + } } data.prompt += data.generation_prompt; } + bool require_tools = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED; + bool has_tool_calls = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE; + auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) { auto generation_prompt = p.literal(GEN_PROMPT); - auto end = p.end(); - - auto reasoning = p.eps(); - if (extract_reasoning && inputs.enable_thinking) { - // Allow optional whitespace before — some models emit a - // newline between the generation prompt and the thinking tag. - reasoning = p.optional(p.space() + THINK_START + p.reasoning(p.until(THINK_END)) + THINK_END); - } else if (extract_reasoning) { - // Thinking disabled but reasoning extraction requested: the generation prompt - // contains an empty pair that must still be consumed. - reasoning = p.optional(p.space() + p.literal(THINK_START) + p.until(THINK_END) + p.literal(THINK_END)); - } - - if (has_response_format) { - auto response_format = p.rule("response-format", - p.literal("```json") + p.space() + - p.content(p.schema(p.json(), "response-format-schema", inputs.json_schema)) + - p.space() + p.literal("```")); - return generation_prompt + reasoning + response_format + end; - } - - if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) { - return generation_prompt + reasoning + p.content(p.rest()) + end; - } + auto end = p.end(); + // build tool call section first since we might need it in reasoning auto tool_choice = p.choice(); - foreach_function(inputs.tools, [&](const json & tool) { - const auto & function = tool.at("function"); - std::string name = function.at("name"); - auto params = function.contains("parameters") ? function.at("parameters") : json::object(); - const auto & props = params.contains("properties") ? params.at("properties") : json::object(); - - std::set required; - if (params.contains("required")) { - params.at("required").get_to(required); - } - - auto schema_info = common_schema_info(); - schema_info.resolve_refs(params); - - std::vector required_parsers; - std::vector optional_parsers; - for (const auto & [param_name, param_schema] : props.items()) { - bool is_required = required.find(param_name) != required.end(); - bool is_string = schema_info.resolves_to_string(param_schema); - - auto arg = p.tool_arg( - p.tool_arg_open( - p.literal(PARAM_START + " name=\"") + - p.tool_arg_name(p.literal(param_name)) + - p.literal("\" string=\"" + std::string(is_string ? "true" : "false") + "\">")) + - (is_string - ? p.tool_arg_string_value(p.until(PARAM_END)) - : p.tool_arg_json_value(p.schema(p.json(), - "tool-" + name + "-arg-" + param_name + "-schema", - param_schema, false))) + - p.tool_arg_close(p.literal(PARAM_END))); + if (has_tool_calls) { + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + std::string name = function.at("name"); + auto params = function.contains("parameters") ? function.at("parameters") : json::object(); + const auto & props = params.contains("properties") ? params.at("properties") : json::object(); - auto named_arg = p.rule("tool-" + name + "-arg-" + param_name, arg); - if (is_required) { - required_parsers.push_back(named_arg); - } else { - optional_parsers.push_back(named_arg); + std::set required; + if (params.contains("required")) { + params.at("required").get_to(required); } - } - common_peg_parser args_seq = p.eps(); - for (size_t i = 0; i < required_parsers.size(); i++) { - if (i > 0) { - args_seq = args_seq + p.space(); + auto schema_info = common_schema_info(); + schema_info.resolve_refs(params); + + std::vector required_parsers; + std::vector optional_parsers; + for (const auto & [param_name, param_schema] : props.items()) { + bool is_required = required.find(param_name) != required.end(); + bool is_string = schema_info.resolves_to_string(param_schema); + + auto arg = p.tool_arg( + p.tool_arg_open(p.literal(PARAM_START + " name=\"") + p.tool_arg_name(p.literal(param_name)) + + p.literal("\" string=\"" + std::string(is_string ? "true" : "false") + "\">")) + + (is_string ? + p.tool_arg_string_value(p.until(PARAM_END)) : + p.tool_arg_json_value(p.schema(p.json(), "tool-" + name + "-arg-" + param_name + "-schema", + param_schema, false))) + + p.tool_arg_close(p.literal(PARAM_END))); + + auto named_arg = p.rule("tool-" + name + "-arg-" + param_name, arg); + if (is_required) { + required_parsers.push_back(named_arg); + } else { + optional_parsers.push_back(named_arg); + } } - args_seq = args_seq + required_parsers[i]; - } - if (!optional_parsers.empty()) { - common_peg_parser any_opt = p.choice(); - for (const auto & opt : optional_parsers) { - any_opt |= opt; + common_peg_parser args_seq = p.eps(); + for (size_t i = 0; i < required_parsers.size(); i++) { + if (i > 0) { + args_seq = args_seq + p.space(); + } + args_seq = args_seq + required_parsers[i]; } - args_seq = args_seq + p.repeat(p.space() + any_opt, 0, -1); - } - common_peg_parser invoke_body = args_seq; - auto func_parser = p.tool( - p.tool_open(p.literal(INVOKE_START + " name=\"") + - p.tool_name(p.literal(name)) + p.literal("\">\n")) + - invoke_body + p.space() + - p.tool_close(p.literal(INVOKE_END))); + if (!optional_parsers.empty()) { + common_peg_parser any_opt = p.choice(); + for (const auto & opt : optional_parsers) { + any_opt |= opt; + } + args_seq = args_seq + p.repeat(p.space() + any_opt, 0, -1); + } - tool_choice |= p.rule("tool-" + name, func_parser); - }); + common_peg_parser invoke_body = args_seq; + auto func_parser = p.tool(p.tool_open(p.literal(INVOKE_START + " name=\"") + + p.tool_name(p.literal(name)) + p.literal("\">\n")) + + invoke_body + p.space() + p.tool_close(p.literal(INVOKE_END))); - auto require_tools = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED; + tool_choice |= p.rule("tool-" + name, func_parser); + }); + } common_peg_parser tool_calls = p.eps(); if (inputs.parallel_tool_calls) { @@ -2006,18 +2248,55 @@ static common_chat_params common_chat_params_init_deepseek_v3_2(const common_cha p.literal(FC_START) + p.space() + tool_choice + p.space() + p.literal(FC_END)); } + auto reasoning = p.eps(); + auto reasoning_with_tc = p.eps(); + auto obligatory_tool_calls = tool_calls; + bool allow_reasoning_with_tc = false; + if (!require_tools) { tool_calls = p.optional(tool_calls); } - auto content_before_tools = p.content(p.until(FC_START)); - return generation_prompt + reasoning + content_before_tools + tool_calls + end; + if (extract_reasoning && inputs.enable_thinking) { + // Allow optional whitespace before - some models emit a + // newline between the generation prompt and the thinking tag. + reasoning = p.optional(p.space() + THINK_START + p.reasoning(p.until(THINK_END)) + THINK_END); + reasoning_with_tc = THINK_START + + p.reasoning(p.until_one_of({ TC_SEPARATOR + FC_START, FC_START, THINK_END })) + + p.space() + obligatory_tool_calls; + allow_reasoning_with_tc = true; + } else if (extract_reasoning) { + // Thinking disabled but reasoning extraction requested: the generation prompt + // contains an empty pair (V3.2) or a bare (V4) that + // must still be consumed. + reasoning = is_v4 + ? p.optional(p.literal(THINK_END)) + : p.optional(p.literal(THINK_START) + p.until(THINK_END) + p.literal(THINK_END)); + } + + if (has_response_format) { + auto response_format = p.rule("response-format", + p.literal("```json") + p.space() + + p.content(p.schema(p.json(), "response-format-schema", inputs.json_schema)) + + p.space() + p.literal("```")); + return generation_prompt + reasoning + response_format + end; + } + + if (!has_tool_calls) { + return generation_prompt + reasoning + p.content(p.rest()) + end; + } + + auto content_before_tools = p.negate(p.literal(THINK_START)) + + p.content(p.until_one_of({ TC_SEPARATOR + FC_START, FC_START })) + + p.space(); + return allow_reasoning_with_tc ? generation_prompt + (reasoning_with_tc | (reasoning + content_before_tools + tool_calls)) + end : + generation_prompt + reasoning + content_before_tools + tool_calls + end; }); data.parser = parser.save(); if (include_grammar) { - data.grammar_lazy = !(has_response_format || (has_tools && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED)); + data.grammar_lazy = has_tools && !require_tools; data.grammar = build_grammar([&](const common_grammar_builder & builder) { foreach_function(inputs.tools, [&](const json & tool) { const auto & function = tool.at("function"); @@ -2039,9 +2318,192 @@ static common_chat_params common_chat_params_init_deepseek_v3_2(const common_cha return data; } -// Cohere2 MoE (a.k.a. "North Code") parser. -// -// The assistant turn is fully marker-wrapped: +// Kimi K3 - XTML-ish tagged format from the model's own template: +// open_tag(t, attrs) = <|open|>t k="v"...<|sep|> close_tag(t) = <|close|>t<|sep|> +// assistant := [think] [response] [tools] close_tag(message) <|end_of_msg|> +// Note the generation prompt already opens the think (or response) section, so +// the section opener is optional here - same situation as Kimi K2 Thinking. +static common_chat_params common_chat_params_init_kimi_k3(const common_chat_template & tmpl, + const autoparser::generation_params & inputs) { + common_chat_params data; + + data.prompt = common_chat_template_direct_apply_impl(tmpl, inputs); + data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs); + data.format = COMMON_CHAT_FORMAT_PEG_NATIVE; + data.supports_thinking = true; + + const std::string SEP = "<|sep|>"; + const std::string MSG_START = "<|open|>message role=\"assistant\"<|sep|>"; + const std::string THINK_START = "<|open|>think<|sep|>"; + const std::string THINK_END = "<|close|>think<|sep|>"; + const std::string RESP_START = "<|open|>response<|sep|>"; + const std::string RESP_END = "<|close|>response<|sep|>"; + const std::string TOOLS_START = "<|open|>tools<|sep|>"; + const std::string TOOLS_END = "<|close|>tools<|sep|>"; + const std::string CALL_START = "<|open|>call tool=\""; + const std::string CALL_END = "<|close|>call<|sep|>"; + const std::string ARG_START = "<|open|>argument key=\""; + const std::string ARG_END = "<|close|>argument<|sep|>"; + const std::string MSG_END = "<|close|>message<|sep|>"; + const std::string EOM_TOKEN = "<|end_of_msg|>"; + + // The four markers are the only special tokens; tag names ("think", + // "response", "message") are ordinary tokens and must NOT be preserved, + // or ordinary prose containing those words would be mangled. + data.preserved_tokens = { + "<|open|>", + "<|close|>", + "<|sep|>", + "<|end_of_msg|>", + }; + + data.thinking_start_tag = THINK_START; + data.thinking_end_tags = { THINK_END }; + + // Per-role message-start delimiters. User/assistant messages carry only the + // role attribute, so their full opener (through <|sep|>) is used. System and + // tool messages continue with more attributes (type=/tool=/index=), so those + // delimiters stop after the role's closing quote - verified against the K3 + // tokenizer that the quote is always its own token and never merges with the + // following attribute text, keeping the token-level prefix match exact. + data.message_delimiters = { + { COMMON_CHAT_ROLE_ASSISTANT, "<|open|>message role=\"assistant\"<|sep|>" }, + { COMMON_CHAT_ROLE_USER, "<|open|>message role=\"user\"<|sep|>" }, + { COMMON_CHAT_ROLE_TOOL, "<|open|>message role=\"tool\"" }, + { COMMON_CHAT_ROLE_SYSTEM, "<|open|>message role=\"system\"" }, + }; + + auto has_tools = inputs.tools.is_array() && !inputs.tools.empty(); + auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE; + auto include_grammar = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE; + + if (inputs.has_continuation()) { + const auto & msg = inputs.continue_msg; + + data.generation_prompt = MSG_START + THINK_START + msg.reasoning_content; + if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) { + data.generation_prompt += THINK_END + RESP_START + msg.render_content(); + } + + data.prompt += data.generation_prompt; + } + + auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) { + auto end = p.end(); + + auto start = p.optional(p.literal(MSG_START)); + + // The think section is ALWAYS consumed, even when reasoning extraction + // is off: K3's generation prompt ends with open_tag('think'), so the + // opener is present on every request and would otherwise leak into + // content. With extraction off the thoughts fall into content, matching + // how the other reasoning models behave. + // Reasoning stops at its own closer, or at the response opener if the + // model skips the closer entirely (seen on short answers). + auto think_body = extract_reasoning ? p.reasoning(p.until_one_of({ THINK_END, RESP_START })) : + p.content(p.until_one_of({ THINK_END, RESP_START })); + + auto reasoning = p.optional(p.optional(p.literal(THINK_START)) + think_body + + p.optional(p.literal(THINK_END))); + + // Content runs to the response closer, or to whatever comes next if a + // truncated generation never emits one. + auto response = p.optional(p.literal(RESP_START)) + + p.content(p.until_one_of({ RESP_END, TOOLS_START, MSG_END })) + + p.optional(p.literal(RESP_END)); + + // The message closer is followed by the EOG token, which reaches the + // parser as text and must be consumed or the parse is left incomplete. + auto trailer = p.optional(p.literal(MSG_END)) + p.optional(p.literal(EOM_TOKEN)); + + if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) { + return start + reasoning + response + trailer + end; + } + + auto tool_choices = p.choice(); + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + std::string name = function.at("name"); + const json schema = function.contains("parameters") ? function.at("parameters") : json::object(); + + // Arguments arrive one tag per key, with the JSON type carried in a + // type="..." attribute. We take the type from the tool schema + // instead - it is authoritative, and it tells us whether the value + // should be parsed as JSON or kept as a literal string. + auto args = p.eps(); + if (schema.contains("properties") && !schema.at("properties").empty()) { + auto arg_choices = p.choice(); + for (const auto & prop : schema.at("properties").items()) { + const std::string & key = prop.key(); + + std::string type = "string"; + if (prop.value().is_object() && prop.value().contains("type") && + prop.value().at("type").is_string()) { + type = prop.value().at("type").get(); + } + + auto value = type == "string" ? p.tool_arg_string_value(p.until(ARG_END)) : + p.tool_arg_value(p.until(ARG_END)); + + // skip the trailing type="..." attribute: anything up to <|sep|> + arg_choices |= p.rule("kimi-k3-arg-" + name + "-" + key, + p.tool_arg(p.tool_arg_open(p.literal(ARG_START)) + + p.tool_arg_name(p.literal(key)) + p.literal("\"") + + p.until(SEP) + p.literal(SEP) + value + + p.tool_arg_close(p.literal(ARG_END)))); + } + args = p.zero_or_more(arg_choices); + } + + // skip the trailing index="N" attribute the same way + auto call = p.tool(p.tool_open(p.literal(CALL_START) + p.tool_name(p.literal(name)) + p.literal("\"") + + p.until(SEP) + p.literal(SEP)) + + p.tool_args(args) + p.tool_close(p.literal(CALL_END))); + + tool_choices |= p.rule("kimi-k3-tool-" + name, call); + }); + + // K3 emits every call inside one <|open|>tools<|sep|> section, then + // closes the message. The message closer is part of the trigger rule so + // that the lazy grammar still permits it once tool calls have started - + // otherwise constrained decoding rejects the model's own closing tag. + auto tools_section = + p.trigger_rule("kimi-k3-tool-call", p.literal(TOOLS_START) + p.one_or_more(tool_choices) + + p.literal(TOOLS_END) + p.optional(p.literal(MSG_END)) + + p.optional(p.literal(EOM_TOKEN))); + + auto tools = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? tools_section : + p.optional(tools_section); + + return start + reasoning + response + tools + trailer + end; + }); + + data.parser = parser.save(); + + if (include_grammar) { + data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED; + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + if (function.contains("parameters")) { + auto schema = function.at("parameters"); + builder.resolve_refs(schema); + } + }); + parser.build_grammar(builder, data.grammar_lazy); + }); + + data.grammar_triggers = { + { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, TOOLS_START }, + }; + } + + return data; +} + +// Cohere2 MoE (a.k.a. "North Code") parser. +// +// The assistant turn is fully marker-wrapped: // <|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|> // <|START_THINKING|>{reasoning}<|END_THINKING|> // then EITHER content: <|START_TEXT|>{content}<|END_TEXT|> @@ -2081,7 +2543,7 @@ static common_chat_params common_chat_params_init_cohere2moe(const common_chat_t data.format = COMMON_CHAT_FORMAT_PEG_NATIVE; data.supports_thinking = true; data.thinking_start_tag = THINK_START; - data.thinking_end_tag = THINK_END; + data.thinking_end_tags = {THINK_END}; data.preserved_tokens = { TURN_START, TURN_END, CHATBOT, USER, SYSTEM, THINK_START, THINK_END, @@ -2100,9 +2562,10 @@ static common_chat_params common_chat_params_init_cohere2moe(const common_chat_t { COMMON_CHAT_ROLE_SYSTEM, TURN_START + SYSTEM }, }; - auto has_tools = inputs.tools.is_array() && !inputs.tools.empty(); - auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE; - auto include_grammar = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE; + auto has_tools = inputs.tools.is_array() && !inputs.tools.empty(); + auto has_response_format = inputs.json_schema.is_object() && !inputs.json_schema.empty(); + auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE; + auto include_grammar = has_response_format || (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE); if (inputs.has_continuation()) { const auto & msg = inputs.continue_msg; @@ -2133,7 +2596,11 @@ static common_chat_params common_chat_params_init_cohere2moe(const common_chat_t p.optional(p.literal(THINK_END)))); } - auto text_content = p.literal(TEXT_START) + p.content(p.until(TEXT_END)) + p.optional(p.literal(TEXT_END)); + auto text_content = has_response_format + ? p.literal(TEXT_START) + + p.content(p.schema(p.json(), "response-format-schema", inputs.json_schema)) + + p.optional(p.literal(TEXT_END)) + : p.literal(TEXT_START) + p.content(p.until(TEXT_END)) + p.optional(p.literal(TEXT_END)); if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) { return generation_prompt + reasoning + text_content + p.optional(p.literal(TURN_END)) + end; @@ -2161,13 +2628,17 @@ static common_chat_params common_chat_params_init_cohere2moe(const common_chat_t data.parser = parser.save(); if (include_grammar) { - data.grammar_lazy = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO; + data.grammar_lazy = !has_response_format && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO; data.grammar = build_grammar([&](const common_grammar_builder & builder) { foreach_function(inputs.tools, [&](const json & tool) { const auto & function = tool.at("function"); auto schema = function.at("parameters"); builder.resolve_refs(schema); }); + if (has_response_format) { + auto schema = inputs.json_schema; + builder.resolve_refs(schema); + } parser.build_grammar(builder, data.grammar_lazy); }); @@ -2179,116 +2650,261 @@ static common_chat_params common_chat_params_init_cohere2moe(const common_chat_t return data; } -// Inkling / TML typed-content-block parser: <|end_message|> separates blocks within a turn, -// <|content_model_end_sampling|> is the sole end-of-generation token (mirrors sglang TmlDetector). -static common_chat_params common_chat_params_init_inkling(const common_chat_template & tmpl, - const autoparser::generation_params & inputs) { +static common_chat_params common_chat_params_init_minimax_m3(const common_chat_template & tmpl, + const autoparser::generation_params & inputs) { common_chat_params data; - const std::string MSG_MODEL = "<|message_model|>"; - const std::string MSG_USER = "<|message_user|>"; - const std::string MSG_SYSTEM = "<|message_system|>"; - const std::string MSG_TOOL = "<|message_tool|>"; - const std::string THINK = "<|content_thinking|>"; - const std::string TEXT = "<|content_text|>"; - const std::string END_MESSAGE = "<|end_message|>"; - const std::string END_SAMPLING = "<|content_model_end_sampling|>"; - const std::string INVOKE_TOOL = "<|content_invoke_tool_json|>"; - data.prompt = common_chat_template_direct_apply_impl(tmpl, inputs); data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs); - data.format = COMMON_CHAT_FORMAT_PEG_NATIVE; + data.format = COMMON_CHAT_FORMAT_PEG_MINIMAX_M3; data.supports_thinking = true; - data.thinking_start_tag = THINK; - data.thinking_end_tag = END_MESSAGE; - data.preserved_tokens = { - MSG_MODEL, MSG_USER, MSG_SYSTEM, MSG_TOOL, - THINK, TEXT, END_MESSAGE, END_SAMPLING, INVOKE_TOOL, + data.thinking_start_tag = ""; + data.thinking_end_tags = {""}; + + // M3 prefixes every tool tag with the namespace token "]<]minimax[>["; + // params use the parameter name as the tag (...). + const std::string NS = "]<]minimax[>["; + const std::string THINK_START = ""; + const std::string THINK_END = ""; + const std::string FC_START = NS + ""; + const std::string FC_END = NS + ""; + const std::string INVOKE_END = NS + ""; + + data.preserved_tokens = { + NS, + "", + "", + THINK_START, + THINK_END, }; - auto has_tools = inputs.tools.is_array() && !inputs.tools.empty(); data.message_delimiters = { - { COMMON_CHAT_ROLE_ASSISTANT, MSG_MODEL }, - { COMMON_CHAT_ROLE_USER, MSG_USER }, - { COMMON_CHAT_ROLE_SYSTEM, MSG_SYSTEM }, - { COMMON_CHAT_ROLE_TOOL, MSG_TOOL }, + { COMMON_CHAT_ROLE_ASSISTANT, "]~b]ai" }, + { COMMON_CHAT_ROLE_USER, "]~b]user" }, + { COMMON_CHAT_ROLE_TOOL, "]~b]tool" }, + { COMMON_CHAT_ROLE_SYSTEM, "]~b]developer" }, + { COMMON_CHAT_ROLE_SYSTEM, "]~b]system" }, }; - auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE; + auto has_tools = inputs.tools.is_array() && !inputs.tools.empty(); + auto has_response_format = !inputs.json_schema.is_null() && inputs.json_schema.is_object(); + auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE; + auto include_grammar = has_response_format || (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE); + + const std::string GEN_PROMPT = data.generation_prompt; + + using mm3 = common_chat_peg_minimax_m3_mapper; if (inputs.has_continuation()) { const auto & msg = inputs.continue_msg; - data.generation_prompt = MSG_MODEL + THINK + msg.reasoning_content; + data.generation_prompt = GEN_PROMPT + THINK_START + msg.reasoning_content; if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) { - data.generation_prompt += END_MESSAGE + TEXT + msg.render_content(); + data.generation_prompt += THINK_END + msg.render_content(); } data.prompt += data.generation_prompt; } auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) { - auto generation_prompt = p.literal(MSG_MODEL); - auto end = p.end(); + auto generation_prompt = p.prefix(GEN_PROMPT, THINK_START); + auto end = p.end(); - // thinking block; may also reappear mid-turn (after content), so it is both an optional - // prefix and a choice inside the block loops. With reasoning_format=NONE keep it - // (markers included) inline as content - common_peg_parser reasoning_block = p.eps(); + auto reasoning = p.eps(); if (extract_reasoning) { - reasoning_block = p.literal(THINK) + - p.reasoning(p.until_one_of({ END_MESSAGE, TEXT, END_SAMPLING })) + - p.optional(p.literal(END_MESSAGE)); - } else { - reasoning_block = p.content(p.literal(THINK) + - p.until_one_of({ END_MESSAGE, TEXT, END_SAMPLING }) + - p.optional(p.literal(END_MESSAGE))); + auto block = inputs.enable_thinking + ? p.literal(THINK_START) + p.space() + + p.ac(p.reasoning(p.until(THINK_END)) + p.literal(THINK_END), THINK_END) + : p.literal(THINK_START) + p.ac(p.until(THINK_END) + p.literal(THINK_END), THINK_END); + + // A turn without reasoning is prefixed with a bare , written either by the + // generation prompt (thinking_mode = "disabled") or by the model itself. + reasoning = p.optional(p.choice({ block, p.literal(THINK_END) })); } - auto reasoning = p.optional(reasoning_block); - // TML re-emits <|message_model|> before each content block; a turn may contain several - // text blocks (one per content part), so the block repeats and bodies concatenate. - // THINK stops the content scan so a mid-turn thinking block is never leaked as text - auto text_block = p.optional(p.literal(MSG_MODEL)) + - p.optional(p.literal(TEXT)) + - p.content(p.until_one_of({ THINK, END_MESSAGE, END_SAMPLING })) + - p.optional(p.literal(END_MESSAGE)); - auto text_content = p.one_or_more(p.choice({ reasoning_block, text_block })); + if (has_response_format) { + auto response_format = p.rule("response-format", + p.literal("```json") + p.space() + + p.content(p.schema(p.json(), "response-format-schema", inputs.json_schema)) + + p.space() + p.literal("```")); + return generation_prompt + reasoning + response_format + end; + } if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) { - return generation_prompt + reasoning + text_content + - p.optional(p.literal(END_SAMPLING)) + end; + return generation_prompt + reasoning + p.content(p.rest()) + end; } - // each call is its own block (role opener + bare name echo + JSON section); - // force_tool_calls=true makes the JSON section required so a pure-text answer fails the - // block cleanly; parallel calls are separate blocks, hence repeat + parallel=false - auto tool_section = p.standard_json_tools( - INVOKE_TOOL, END_MESSAGE, inputs.tools, /* parallel_tool_calls = */ false, - /* force_tool_calls = */ true, - /* name_key = */ "name", - /* args_key = */ "args", - /* array_wrapped = */ false); - // the name-echo scan must stop at any block marker: a greedy until(INVOKE_TOOL) returns - // NEED_MORE_INPUT mid-stream, which choice() treats as a match and shadows the text branch - auto tool_block = p.optional(p.literal(MSG_MODEL)) + - p.until_one_of({ INVOKE_TOOL, TEXT, THINK, END_MESSAGE, END_SAMPLING }) + - tool_section; - auto tool_calls = inputs.parallel_tool_calls ? p.one_or_more(tool_block) : tool_block; - // turns may interleave narration, thinking and calls; parse block-by-block (tool block - // first) since a whole-body choice would let the text branch swallow tool blocks into - // visible content - auto mixed_body = p.one_or_more(p.choice({ tool_block, reasoning_block, text_block })); - auto body = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED - ? tool_calls - : mixed_body; + auto alternatives_of = [](const json & schema) -> std::optional { + for (const auto * keyword : { "oneOf", "anyOf" }) { + if (schema.contains(keyword) && schema.at(keyword).is_array() && !schema.at(keyword).empty()) { + return schema.at(keyword); + } + } + return std::nullopt; + }; - return generation_prompt + reasoning + body + - p.optional(p.literal(END_SAMPLING)) + end; + auto tool_choice = p.choice(); + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + std::string name = function.at("name"); + auto params = function.contains("parameters") ? function.at("parameters") : json::object(); + + auto schema_info = common_schema_info(); + schema_info.resolve_refs(params); + + // The template expands argument values recursively in XML (see the to_xml() macro) + std::function value_of; + std::function members_of; + + auto element_of = [&](const std::string & tag, const json & schema, const std::string & rule_name) { + const std::string close = NS + ""; + return p.rule(rule_name, + p.tool_arg( + p.tool_arg_open( + p.literal(NS + "<") + + p.tool_arg_name(p.literal(tag)) + + p.literal(">")) + + value_of(schema, rule_name, close))); + }; + + value_of = [&](const json & schema, + const std::string & rule_name, + const std::string & close) -> common_peg_parser { + auto close_tag = p.tool_arg_close(p.literal(close)); + + // A string accepts anything, so a union with a string alternative is a string + if (schema_info.resolves_to_string(schema)) { + return p.ac(p.tool_arg_string_value(p.until(close)) + close_tag, close); + } + + if (auto alternatives = alternatives_of(schema)) { + std::vector choices; + + size_t index = 0; + for (const auto & alternative : *alternatives) { + const std::string alt_name = rule_name + "-" + std::to_string(index++); + + // There is a risk that this breaks streaming deltas, but that's a risk we + // assume to provide tool arg streaming. + choices.push_back(value_of(alternative, alt_name, close)); + } + + return p.choice(choices); + } + + const std::string type = schema.contains("type") && schema.at("type").is_string() + ? schema.at("type").get() + : ""; + + if (type == "object" && schema.contains("properties")) { + return p.tag(mm3::TOOL_ARG_OBJECT, members_of(schema, rule_name)) + p.space() + close_tag; + } + + if (type == "array" && schema.contains("items")) { + const std::string item_close = NS + ""; + auto item = p.rule(rule_name + "-item", + p.tag(mm3::TOOL_ARG_ITEM, + p.literal(NS + "") + + value_of(schema.at("items"), rule_name + "-item", item_close))); + return p.tag(mm3::TOOL_ARG_ARRAY, p.repeat(p.space() + item, 0, -1)) + p.space() + close_tag; + } + + return p.tool_arg_json_value(p.schema(p.json(), rule_name + "-schema", schema, false)) + close_tag; + }; + + // Required properties in schema order, then any number of optional ones in any order. + members_of = [&](const json & schema, const std::string & rule_prefix) -> common_peg_parser { + const auto & props = schema.at("properties"); + + std::set required; + if (schema.contains("required")) { + schema.at("required").get_to(required); + } + + std::vector required_elements; + std::vector optional_elements; + for (const auto & [key, key_schema] : props.items()) { + auto element = element_of(key, key_schema, rule_prefix + "-" + key); + if (required.find(key) != required.end()) { + required_elements.push_back(element); + } else { + optional_elements.push_back(element); + } + } + + common_peg_parser members = p.eps(); + for (size_t i = 0; i < required_elements.size(); i++) { + if (i > 0) { + members = members + p.space(); + } + members = members + required_elements[i]; + } + + if (!optional_elements.empty()) { + common_peg_parser any_optional = p.choice(); + for (const auto & element : optional_elements) { + any_optional |= element; + } + members = members + p.repeat(p.space() + any_optional, 0, -1); + } + + return members; + }; + + common_peg_parser invoke_body = + params.contains("properties") ? members_of(params, "tool-" + name + "-arg") : p.eps(); + + auto func_parser = p.tool( + p.tool_open(p.literal(NS + "")) + + p.space() + invoke_body + p.space() + + p.tool_close(p.literal(INVOKE_END))); + + tool_choice |= p.rule("tool-" + name, func_parser); + }); + + auto require_tools = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED; + + common_peg_parser tool_calls = p.eps(); + if (inputs.parallel_tool_calls) { + tool_calls = p.trigger_rule("tool-call", + p.literal(FC_START) + p.space() + tool_choice + + p.zero_or_more(p.space() + tool_choice) + p.space() + p.literal(FC_END)); + } else { + tool_calls = p.trigger_rule("tool-call", + p.literal(FC_START) + p.space() + tool_choice + p.space() + p.literal(FC_END)); + } + + if (!require_tools) { + tool_calls = p.optional(tool_calls); + } + + auto content_before_tools = p.content(p.until(FC_START)); + return generation_prompt + reasoning + content_before_tools + tool_calls + end; }); data.parser = parser.save(); + if (include_grammar) { + data.grammar_lazy = !(has_response_format || (has_tools && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED)); + data.grammar = build_grammar([&](const common_grammar_builder & builder) { + foreach_function(inputs.tools, [&](const json & tool) { + const auto & function = tool.at("function"); + auto schema = function.contains("parameters") ? function.at("parameters") : json::object(); + builder.resolve_refs(schema); + }); + if (has_response_format) { + auto schema = inputs.json_schema; + builder.resolve_refs(schema); + } + parser.build_grammar(builder, data.grammar_lazy); + }); + + data.grammar_triggers = { + { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, FC_START }, + }; + } + return data; } @@ -2535,7 +3151,7 @@ static common_chat_params common_chat_params_init_minicpm5(const common_chat_tem }; data.thinking_start_tag = ""; - data.thinking_end_tag = ""; + data.thinking_end_tags = {""}; data.message_delimiters = { { COMMON_CHAT_ROLE_ASSISTANT, "<|im_start|>assistant" }, @@ -2657,26 +3273,119 @@ static common_chat_params common_chat_params_init_minicpm5(const common_chat_tem return data; } -static json common_chat_extra_context() { - json ctx = json::object(); - std::chrono::system_clock::time_point now = std::chrono::system_clock::now(); - std::string datetime_str = format_time(now, "%b %d %Y"); - std::string date_str = format_time(now, "%d %b %Y"); - ctx["datetime"] = datetime_str; - ctx["date_string"] = date_str; - return ctx; +// Inkling / TML typed-content-block parser: <|end_message|> separates blocks within a turn, +// <|content_model_end_sampling|> is the sole end-of-generation token (mirrors sglang TmlDetector). +static common_chat_params common_chat_params_init_inkling(const common_chat_template & tmpl, + const autoparser::generation_params & inputs) { + common_chat_params data; + + const std::string MSG_MODEL = "<|message_model|>"; + const std::string MSG_USER = "<|message_user|>"; + const std::string MSG_SYSTEM = "<|message_system|>"; + const std::string MSG_TOOL = "<|message_tool|>"; + const std::string THINK = "<|content_thinking|>"; + const std::string TEXT = "<|content_text|>"; + const std::string END_MESSAGE = "<|end_message|>"; + const std::string END_SAMPLING = "<|content_model_end_sampling|>"; + const std::string INVOKE_TOOL = "<|content_invoke_tool_json|>"; + + data.prompt = common_chat_template_direct_apply_impl(tmpl, inputs); + data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs); + data.format = COMMON_CHAT_FORMAT_PEG_NATIVE; + data.supports_thinking = true; + data.thinking_start_tag = THINK; + data.thinking_end_tags = {END_MESSAGE}; + data.preserved_tokens = { + MSG_MODEL, MSG_USER, MSG_SYSTEM, MSG_TOOL, + THINK, TEXT, END_MESSAGE, END_SAMPLING, INVOKE_TOOL, + }; + + auto has_tools = inputs.tools.is_array() && !inputs.tools.empty(); + data.message_delimiters = { + { COMMON_CHAT_ROLE_ASSISTANT, MSG_MODEL }, + { COMMON_CHAT_ROLE_USER, MSG_USER }, + { COMMON_CHAT_ROLE_SYSTEM, MSG_SYSTEM }, + { COMMON_CHAT_ROLE_TOOL, MSG_TOOL }, + }; + + auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE; + + if (inputs.has_continuation()) { + const auto & msg = inputs.continue_msg; + + data.generation_prompt = MSG_MODEL + THINK + msg.reasoning_content; + if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) { + data.generation_prompt += END_MESSAGE + TEXT + msg.render_content(); + } + + data.prompt += data.generation_prompt; + } + + auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) { + auto generation_prompt = p.literal(MSG_MODEL); + auto end = p.end(); + + // thinking block; may also reappear mid-turn (after content), so it is both an optional + // prefix and a choice inside the block loops. With reasoning_format=NONE keep it + // (markers included) inline as content + common_peg_parser reasoning_block = p.eps(); + if (extract_reasoning) { + reasoning_block = p.literal(THINK) + + p.reasoning(p.until_one_of({ END_MESSAGE, TEXT, END_SAMPLING })) + + p.optional(p.literal(END_MESSAGE)); + } else { + reasoning_block = p.content(p.literal(THINK) + + p.until_one_of({ END_MESSAGE, TEXT, END_SAMPLING }) + + p.optional(p.literal(END_MESSAGE))); + } + auto reasoning = p.optional(reasoning_block); + + // TML re-emits <|message_model|> before each content block; a turn may contain several + // text blocks (one per content part), so the block repeats and bodies concatenate. + // THINK stops the content scan so a mid-turn thinking block is never leaked as text + auto text_block = p.optional(p.literal(MSG_MODEL)) + + p.optional(p.literal(TEXT)) + + p.content(p.until_one_of({ THINK, END_MESSAGE, END_SAMPLING })) + + p.optional(p.literal(END_MESSAGE)); + auto text_content = p.one_or_more(p.choice({ reasoning_block, text_block })); + + if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) { + return generation_prompt + reasoning + text_content + + p.optional(p.literal(END_SAMPLING)) + end; + } + + // each call is its own block (role opener + bare name echo + JSON section); + // force_tool_calls=true makes the JSON section required so a pure-text answer fails the + // block cleanly; parallel calls are separate blocks, hence repeat + parallel=false + auto tool_section = p.standard_json_tools( + INVOKE_TOOL, END_MESSAGE, inputs.tools, /* parallel_tool_calls = */ false, + /* force_tool_calls = */ true, + /* name_key = */ "name", + /* args_key = */ "args", + /* array_wrapped = */ false); + // the name-echo scan must stop at any block marker: a greedy until(INVOKE_TOOL) returns + // NEED_MORE_INPUT mid-stream, which choice() treats as a match and shadows the text branch + auto tool_block = p.optional(p.literal(MSG_MODEL)) + + p.until_one_of({ INVOKE_TOOL, TEXT, THINK, END_MESSAGE, END_SAMPLING }) + + tool_section; + auto tool_calls = inputs.parallel_tool_calls ? p.one_or_more(tool_block) : tool_block; + // turns may interleave narration, thinking and calls; parse block-by-block (tool block + // first) since a whole-body choice would let the text branch swallow tool blocks into + // visible content + auto mixed_body = p.one_or_more(p.choice({ tool_block, reasoning_block, text_block })); + auto body = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED + ? tool_calls + : mixed_body; + + return generation_prompt + reasoning + body + + p.optional(p.literal(END_SAMPLING)) + end; + }); + + data.parser = parser.save(); + + return data; } -// Laguna (poolside) — GLM-4-MoE-style tool calls + reasoning. -// Tool call wire format: -// {name} -// {k} -// {v} -// ... -// -// String-typed args are emitted raw between ...; all other -// args are JSON literals. Reasoning is ...; the turn ends with -// . Both Laguna-XS.2 and Laguna-M.1 share this format. static common_chat_params common_chat_params_init_laguna(const common_chat_template & tmpl, const autoparser::generation_params & inputs) { common_chat_params data; @@ -2686,7 +3395,7 @@ static common_chat_params common_chat_params_init_laguna(const common_chat_templ data.format = COMMON_CHAT_FORMAT_PEG_NATIVE; data.supports_thinking = true; data.thinking_start_tag = ""; - data.thinking_end_tag = ""; + data.thinking_end_tags = {""}; data.preserved_tokens = { "", "", "", "", @@ -2852,6 +3561,16 @@ static common_chat_params common_chat_params_init_laguna(const common_chat_templ return data; } +static json common_chat_extra_context() { + json ctx = json::object(); + std::chrono::system_clock::time_point now = std::chrono::system_clock::now(); + std::string datetime_str = format_time(now, "%b %d %Y"); + std::string date_str = format_time(now, "%d %b %Y"); + ctx["datetime"] = datetime_str; + ctx["date_string"] = date_str; + return ctx; +} + std::optional common_chat_try_specialized_template( const common_chat_template & tmpl, const std::string & src, @@ -2864,7 +3583,7 @@ std::optional common_chat_try_specialized_template( return common_chat_params_init_ministral_3(tmpl, params); } - // Laguna (poolside) — GLM-4-MoE-style tool calls with / + // Laguna (poolside) - GLM-4-MoE-style tool calls with / // pairs, plus / role tags (distinct from GLM's // <|assistant|>). Covers both Laguna-XS.2 and Laguna-M.1. if (src.find("") != std::string::npos && @@ -2895,6 +3614,14 @@ std::optional common_chat_try_specialized_template( return common_chat_params_init_kimi_k2(tmpl, params); } + // Kimi K3 - XTML-ish tagged format built from open_tag/close_tag macros. + // Detection: the <|open|>/<|close|>/<|sep|> marker trio is unique to K3. + if (src.find("<|open|>") != std::string::npos && src.find("<|close|>") != std::string::npos && + src.find("<|end_of_msg|>") != std::string::npos) { + LOG_DBG("Using specialized template: Kimi K3\n"); + return common_chat_params_init_kimi_k3(tmpl, params); + } + // Cohere2 MoE / North Code - marker-wrapped format with <|START_TEXT|> content and // <|START_ACTION|> JSON tool calls. <|START_TEXT|> is unique to this template (the older // Command-R templates use <|START_RESPONSE|>). @@ -2932,12 +3659,23 @@ std::optional common_chat_try_specialized_template( return common_chat_params_init_gigachat_v3(tmpl, params); } - // DeepSeek V3.2 format detection: template defines dsml_token and uses it for tool calls. + // MiniMax-M3: the namespace token "]<]minimax[>[" collides with the autoparser's + // markup delimiters, so detect the template and use a dedicated parser. + if (src.find("]<]minimax[>[") != std::string::npos && + src.find("") != std::string::npos && + src.find(" common_chat_try_specialized_template( return common_chat_params_init_minicpm5(tmpl, params); } + // Qwen3-Coder XML tool calls, also used by Nemotron Nano 3, Qwen3.5 and StepFun-3.5-Flash + if (src.find("") != std::string::npos && + src.find(" mapper; if (params.format == COMMON_CHAT_FORMAT_PEG_GEMMA4) { mapper = std::make_unique(msg); + } else if (params.format == COMMON_CHAT_FORMAT_PEG_MINIMAX_M3) { + mapper = std::make_unique(msg); } else { mapper = std::make_unique(msg); } @@ -3240,6 +3991,8 @@ common_chat_msg common_chat_peg_parse(const common_peg_arena & src_pars std::unique_ptr mapper; if (params.format == COMMON_CHAT_FORMAT_PEG_GEMMA4) { mapper = std::make_unique(msg); + } else if (params.format == COMMON_CHAT_FORMAT_PEG_MINIMAX_M3) { + mapper = std::make_unique(msg); } else { mapper = std::make_unique(msg); } diff --git a/common/chat.h b/common/chat.h index 7898f1623f54..6d5b220aebb5 100644 --- a/common/chat.h +++ b/common/chat.h @@ -233,6 +233,7 @@ enum common_chat_format { COMMON_CHAT_FORMAT_PEG_SIMPLE, COMMON_CHAT_FORMAT_PEG_NATIVE, COMMON_CHAT_FORMAT_PEG_GEMMA4, + COMMON_CHAT_FORMAT_PEG_MINIMAX_M3, COMMON_CHAT_FORMAT_COUNT, // Not a format, just the # formats }; @@ -274,7 +275,7 @@ struct common_chat_params { std::string generation_prompt; bool supports_thinking = false; std::string thinking_start_tag; // e.g., "" - std::string thinking_end_tag; // e.g., "" + std::vector thinking_end_tags; // e.g., "" std::vector grammar_triggers; std::vector preserved_tokens; std::vector additional_stops; diff --git a/common/common.cpp b/common/common.cpp index 8af75240eaed..d11d4fd61dc4 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1004,6 +1004,23 @@ bool fs_is_directory(const std::string & path) { return std::filesystem::exists(dir) && std::filesystem::is_directory(dir); } +std::string common_get_env(const std::string & name) { + const char * value = std::getenv(name.c_str()); + return value == nullptr ? "" : value; +} + +void common_set_env(const std::string & name, const std::string & value) { +#if defined(_WIN32) + _putenv_s(name.c_str(), value.c_str()); +#else + if (value.empty()) { + unsetenv(name.c_str()); + } else { + setenv(name.c_str(), value.c_str(), 1); + } +#endif +} + std::string fs_get_cache_directory() { std::string cache_directory = ""; auto ensure_trailing_slash = [](std::string p) { @@ -1255,7 +1272,6 @@ common_init_result::common_init_result(common_params & params, bool model_only) lora.reset(llama_adapter_lora_init(model, la.path.c_str())); if (lora == nullptr) { COM_ERR("failed to load lora adapter '%s'\n", la.path.c_str()); - pimpl->model.reset(model); return; } @@ -1306,8 +1322,9 @@ common_init_result::common_init_result(common_params & params, bool model_only) pimpl->samplers.resize(cparams.n_seq_max); pimpl->samplers_seq_config.resize(cparams.n_seq_max); + const int32_t n_ctx = cparams.n_ctx > 0 ? (int32_t) cparams.n_ctx : llama_model_n_ctx_train(model); for (int i = 0; i < (int) cparams.n_seq_max; ++i) { - pimpl->samplers[i].reset(common_sampler_init(model, params.sampling)); + pimpl->samplers[i].reset(common_sampler_init(model, params.sampling, n_ctx)); pimpl->samplers_seq_config[i] = { i, common_sampler_get(pimpl->samplers[i].get()) }; } @@ -1469,18 +1486,32 @@ common_init_result_ptr common_init_from_params(common_params & params, bool mode common_init_result::~common_init_result() = default; std::string common_get_model_endpoint() { - const char * model_endpoint_env = getenv("MODEL_ENDPOINT"); - // We still respect the use of environment-variable "HF_ENDPOINT" for backward-compatibility. - const char * hf_endpoint_env = getenv("HF_ENDPOINT"); - const char * endpoint_env = model_endpoint_env ? model_endpoint_env : hf_endpoint_env; - std::string model_endpoint = "https://huggingface.co/"; - if (endpoint_env) { - model_endpoint = endpoint_env; - if (model_endpoint.back() != '/') { - model_endpoint += '/'; - } + std::string endpoint = common_get_env("MODEL_ENDPOINT"); + if (endpoint.empty()) { + // the HF_ENDPOINT variable is respected for backward compatibility + endpoint = common_get_env("HF_ENDPOINT"); + } + if (endpoint.empty()) { + return "https://huggingface.co/"; + } + if (endpoint.back() != '/') { + endpoint += '/'; } - return model_endpoint; + return endpoint; +} + +char * common_get_model_or_exit(int argc, char * argv[]) { + if (argc > 1) { + return argv[1]; + } + + char * path = getenv("LLAMACPP_TEST_MODELFILE"); + if (!path || strlen(path) == 0) { + fprintf(stderr, "\033[33mWARNING: No model file provided. Skipping this test. Set LLAMACPP_TEST_MODELFILE= to silence this warning and run this test.\n\033[0m"); + exit(EXIT_SUCCESS); + } + + return path; } common_context_seq_rm_type common_context_can_seq_rm(llama_context * ctx) { @@ -1525,23 +1556,49 @@ common_context_seq_rm_type common_context_can_seq_rm(llama_context * ctx) { return res; } -void common_context_seq_rm(llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1) { +static void common_context_seq_rm(llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1) { auto * mem = llama_get_memory(ctx); if (!llama_memory_seq_rm(mem, seq_id, p0, p1)) { GGML_ABORT("%s", string_format("failed to remove sequence %d with p0=%d, p1=%d\n", seq_id, p0, p1).c_str()); } } -void common_context_seq_cp(llama_context * ctx, llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) { +static void common_context_seq_cp(llama_context * ctx, llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) { auto * mem = llama_get_memory(ctx); llama_memory_seq_cp(mem, seq_id_src, seq_id_dst, p0, p1); } -void common_context_seq_add(llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) { +static void common_context_seq_add(llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) { auto * mem = llama_get_memory(ctx); llama_memory_seq_add(mem, seq_id, p0, p1, delta); } +void common_memory::init(llama_context * ctx_tgt, llama_context * ctx_dft) { + this->ctx_tgt = ctx_tgt; + this->ctx_dft = ctx_dft; +} + +void common_memory::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) const { + common_context_seq_rm(ctx_tgt, seq_id, p0, p1); + if (ctx_dft) { + common_context_seq_rm(ctx_dft, seq_id, p0, p1); + } +} + +void common_memory::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) const { + common_context_seq_cp(ctx_tgt, seq_id_src, seq_id_dst, p0, p1); + if (ctx_dft) { + common_context_seq_cp(ctx_dft, seq_id_src, seq_id_dst, p0, p1); + } +} + +void common_memory::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) const { + common_context_seq_add(ctx_tgt, seq_id, p0, p1, delta); + if (ctx_dft) { + common_context_seq_add(ctx_dft, seq_id, p0, p1, delta); + } +} + void common_set_adapter_lora(struct llama_context * ctx, std::vector & lora) { std::vector loras; std::vector scales; @@ -1564,10 +1621,8 @@ struct llama_model_params common_model_params_to_llama(common_params & params) { mparams.n_gpu_layers = params.n_gpu_layers; mparams.main_gpu = params.main_gpu; mparams.split_mode = params.split_mode; + mparams.load_mode = params.load_mode; mparams.tensor_split = params.tensor_split; - mparams.use_mmap = params.use_mmap; - mparams.use_direct_io = params.use_direct_io; - mparams.use_mlock = params.use_mlock; mparams.check_tensors = params.check_tensors; mparams.use_extra_bufts = !params.no_extra_bufts; mparams.no_host = params.no_host; @@ -1589,6 +1644,7 @@ struct llama_model_params common_model_params_to_llama(common_params & params) { mparams.progress_callback = params.load_progress_callback; mparams.progress_callback_user_data = params.load_progress_callback_user_data; mparams.no_alloc = params.no_alloc; + mparams.load_mtp = std::find(params.speculative.types.begin(), params.speculative.types.end(), COMMON_SPECULATIVE_TYPE_DRAFT_MTP) != params.speculative.types.end(); return mparams; } diff --git a/common/common.h b/common/common.h index 5acb0f13a8ed..2748efd66af2 100644 --- a/common/common.h +++ b/common/common.h @@ -6,6 +6,7 @@ #include "ggml-opt.h" #include "ggml.h" +#include "llama.h" #include #include @@ -172,6 +173,7 @@ enum common_speculative_type { COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3, // Eagle3 speculative decoding COMMON_SPECULATIVE_TYPE_DRAFT_MTP, // Multi-token prediction COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH, // DFlash speculative decoding + COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK, // DSpark speculative decoding (DFlash + Markov head) COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE, // simple self-speculative decoding based on n-grams COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K, // self-speculative decoding with n-gram keys only COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V, // self-speculative decoding with n-gram keys and 4 m-gram values @@ -283,19 +285,15 @@ struct common_params_sampling { // reasoning budget sampler parameters // these are populated by the server/CLI based on chat template params - int32_t reasoning_budget_tokens = -1; // -1 = disabled, >= 0 = token budget - std::vector reasoning_budget_start; // start tag token sequence - std::vector reasoning_budget_end; // end tag token sequence - std::vector reasoning_budget_forced; // forced sequence (message + end tag) - std::string reasoning_budget_message; // message injected before end tag when budget exhausted - bool reasoning_control = false; // create the budget sampler on demand so reasoning can be ended at runtime + int32_t reasoning_budget_tokens = -1; // -1 = disabled, >= 0 = token budget + std::vector reasoning_budget_start; // start tag token sequence + std::vector reasoning_budget_end; // end tag token sequences; the first tag is used as the forcing sequence + std::vector reasoning_budget_forced; // forced sequence (message + first end tag) + std::string reasoning_budget_message; // message injected before end tag when budget exhausted + bool reasoning_control = false; // create the budget sampler on demand so reasoning can be ended at runtime bool backend_sampling = false; - bool has_logit_bias() const { - return !logit_bias.empty(); - } - // print the parameters into a string std::string print() const; }; @@ -387,7 +385,7 @@ struct common_params_speculative { uint32_t need_n_rs_seq() const { bool needs_rs_seq = std::any_of(types.begin(), types.end(), [&](auto t) { - return t == COMMON_SPECULATIVE_TYPE_DRAFT_MTP || t == COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3 || t == COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH; + return t == COMMON_SPECULATIVE_TYPE_DRAFT_MTP || t == COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3 || t == COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH || t == COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK; }); return needs_rs_seq ? draft.n_max : 0u; @@ -482,6 +480,7 @@ struct common_params { std::vector fit_params_target = std::vector(llama_max_devices(), 1024 * 1024*1024); enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs + enum llama_load_mode load_mode = LLAMA_LOAD_MODE_MMAP; // how to load the model common_cpu_params cpuparams; common_cpu_params cpuparams_batch; @@ -572,9 +571,6 @@ struct common_params { bool kv_unified = false; // enable unified KV cache bool input_prefix_bos = false; // prefix BOS to user inputs, preceding input_prefix - bool use_mmap = true; // enable mmap to use filesystem cache - bool use_direct_io = false; // read from disk without buffering - bool use_mlock = false; // use mlock to keep model in memory bool verbose_prompt = false; // print prompt tokens before generation bool display_prompt = true; // print prompt before generation bool no_kv_offload = false; // disable KV offloading @@ -669,6 +665,10 @@ struct common_params { // enable built-in tools std::vector server_tools; + // MCP server configs (Cursor-compatible JSON) + std::string mcp_servers_config; // path to JSON file with MCP server definitions + std::string mcp_servers_json; // inline JSON with MCP server definitions + // router server configs std::string models_dir = ""; // directory containing models for the router server std::string models_preset = ""; // directory containing model presets for the router server @@ -741,6 +741,8 @@ struct common_params { llama_progress_callback load_progress_callback = NULL; void * load_progress_callback_user_data = NULL; bool no_alloc = false; // Don't allocate model buffers + + bool is_gen_docs = false; // whether we are running inside llama-gen-docs }; // call once at the start of a program if it uses libcommon @@ -865,6 +867,15 @@ std::string string_from(const struct llama_context * ctx, const struct llama_bat bool glob_match(const std::string & pattern, const std::string & str); +// +// Environment utils +// + +// portable environment access, an unset variable reads as an empty string +// and setting an empty value unsets the variable +std::string common_get_env(const std::string & name); +void common_set_env(const std::string & name, const std::string & value); + // // Filesystem utils // @@ -932,6 +943,9 @@ void common_set_adapter_lora(struct llama_context * ctx, std::vector(model, '/'); auto model_dir = model_parts.end() - 1; @@ -600,10 +614,19 @@ static hf_cache::hf_file find_best_sibling(const hf_cache::hf_files & files, auto bits = extract_quant_bits(f.path); auto diff = std::abs(bits - model_bits); - if (!found || depth > best_depth || (depth == best_depth && diff < best_diff)) { + std::string path_upper = f.path; + for (char & c : path_upper) { + c = (char) std::toupper((unsigned char) c); + } + bool exact = !tag_upper.empty() && path_upper.find("-" + tag_upper + ".") != std::string::npos; + + if (!found || depth > best_depth || + (depth == best_depth && exact && !best_exact) || + (depth == best_depth && exact == best_exact && diff < best_diff)) { best = f; best_depth = depth; best_diff = diff; + best_exact = exact; found = true; } } @@ -616,8 +639,27 @@ static hf_cache::hf_file find_best_mmproj(const hf_cache::hf_files & files, } static hf_cache::hf_file find_best_mtp(const hf_cache::hf_files & files, - const std::string & model) { - return find_best_sibling(files, model, "mtp-"); + const std::string & model, + const std::string & tag = "") { + return find_best_sibling(files, model, "mtp-", tag); +} + +static hf_cache::hf_file find_best_eagle3(const hf_cache::hf_files & files, + const std::string & model, + const std::string & tag = "") { + return find_best_sibling(files, model, "eagle3-", tag); +} + +static hf_cache::hf_file find_best_dflash(const hf_cache::hf_files & files, + const std::string & model, + const std::string & tag = "") { + return find_best_sibling(files, model, "dflash-", tag); +} + +static hf_cache::hf_file find_best_dspark(const hf_cache::hf_files & files, + const std::string & model, + const std::string & tag = "") { + return find_best_sibling(files, model, "dspark-", tag); } static bool gguf_filename_is_model(const std::string & filepath) { @@ -632,7 +674,10 @@ static bool gguf_filename_is_model(const std::string & filepath) { return filename.find("mmproj") == std::string::npos && filename.find("imatrix") == std::string::npos && - filename.find("mtp-") == std::string::npos; + filename.find("mtp-") == std::string::npos && + filename.find("eagle3-") == std::string::npos && + filename.find("dflash-") == std::string::npos && + filename.find("dspark-") == std::string::npos; } static hf_cache::hf_file find_best_model(const hf_cache::hf_files & files, @@ -724,21 +769,39 @@ common_download_hf_plan common_download_get_hf_plan(const common_params_model & } } else { primary = find_best_model(all, tag); - if (primary.path.empty()) { + // a requested sidecar can resolve on its own, without a full model of the same tag + if (primary.path.empty() && !opts.download_mtp && !opts.download_dflash && !opts.download_eagle3 && !opts.download_dspark) { LOG_ERR("%s: no GGUF files found in repository %s\n", __func__, repo.c_str()); list_available_gguf_files(all); return plan; } } - plan.primary = primary; - plan.model_files = get_split_files(all, primary); + if (!primary.path.empty()) { + plan.primary = primary; + plan.model_files = get_split_files(all, primary); + } - if (opts.download_mmproj) { + if (opts.download_mmproj && !primary.path.empty()) { plan.mmproj = find_best_mmproj(all, primary.path); } if (opts.download_mtp) { - plan.mtp = find_best_mtp(all, primary.path); + plan.mtp = find_best_mtp(all, primary.path, tag); + } + if (opts.download_dflash) { + plan.dflash = find_best_dflash(all, primary.path, tag); + } + if (opts.download_eagle3) { + plan.eagle3 = find_best_eagle3(all, primary.path, tag); + } + if (opts.download_dspark) { + plan.dspark = find_best_dspark(all, primary.path, tag); + } + + if (primary.path.empty() && + plan.mtp.local_path.empty() && plan.dflash.local_path.empty() && plan.eagle3.local_path.empty() && plan.dspark.local_path.empty()) { + LOG_ERR("%s: no GGUF files found in repository %s\n", __func__, repo.c_str()); + list_available_gguf_files(all); } return plan; @@ -911,8 +974,11 @@ std::vector common_list_cached_models() { for (const auto & f : files) { auto split = get_gguf_split_info(f.path); if (split.index != 1 || split.tag.empty() || - split.prefix.find("mmproj") != std::string::npos || - split.prefix.find("mtp-") != std::string::npos) { + split.prefix.find("mmproj") != std::string::npos || + split.prefix.find("mtp-") != std::string::npos || + split.prefix.find("eagle3-") != std::string::npos || + split.prefix.find("dflash-") != std::string::npos || + split.prefix.find("dspark-") != std::string::npos) { continue; } if (seen.insert(f.repo_id + ":" + split.tag).second) { diff --git a/common/download.h b/common/download.h index 816e1c7f58a1..9a03f5e91475 100644 --- a/common/download.h +++ b/common/download.h @@ -55,8 +55,11 @@ struct common_download_opts { std::string bearer_token; common_header_list headers; bool offline = false; - bool download_mmproj = false; - bool download_mtp = false; + bool download_mmproj = false; + bool download_mtp = false; + bool download_eagle3 = false; + bool download_dflash = false; + bool download_dspark = false; common_download_callback * callback = nullptr; }; @@ -106,6 +109,9 @@ struct common_download_hf_plan { hf_cache::hf_files model_files; hf_cache::hf_file mmproj; hf_cache::hf_file mtp; + hf_cache::hf_file eagle3; + hf_cache::hf_file dflash; + hf_cache::hf_file dspark; hf_cache::hf_file preset; // if set, only this file is downloaded }; common_download_hf_plan common_download_get_hf_plan(const common_params_model & model, const common_download_opts & opts); diff --git a/common/fit.cpp b/common/fit.cpp index afbf0b10f3f3..c82d066ad444 100644 --- a/common/fit.cpp +++ b/common/fit.cpp @@ -54,8 +54,7 @@ static std::vector common_get_device_memory_data_impl( llama_model_params mparams_copy = *mparams; mparams_copy.no_alloc = true; - mparams_copy.use_mmap = false; - mparams_copy.use_mlock = false; + mparams_copy.load_mode = LLAMA_LOAD_MODE_NONE; llama_model * model = llama_model_load_from_file(path_model, mparams_copy); if (model == nullptr) { @@ -137,7 +136,7 @@ static std::vector common_get_device_memory_data_impl( devs.push_back(llama_model_get_device(model, i)); } - hp_ngl = llama_model_n_layer(model); + hp_ngl = llama_model_n_layer(model) + llama_model_n_layer_nextn(model); hp_n_ctx_train = llama_model_n_ctx_train(model); hp_n_expert = llama_model_n_expert(model); diff --git a/common/jinja/caps.cpp b/common/jinja/caps.cpp index ae378ebd4fd0..cdd7ccfa26ee 100644 --- a/common/jinja/caps.cpp +++ b/common/jinja/caps.cpp @@ -23,6 +23,7 @@ void caps_apply_preserve_reasoning(jinja::context & ctx, bool enabled) { ctx.set_val("preserve_thinking", mk_val(enabled)); ctx.set_val("clear_thinking", mk_val(!enabled)); ctx.set_val("truncate_history_thinking", mk_val(!enabled)); + ctx.set_val("drop_thinking", mk_val(!enabled)); } static void caps_try_execute(jinja::program & prog, @@ -481,6 +482,7 @@ caps caps_get(jinja::program & prog) { }); }, [&](context & ctx) { + ctx.set_val("enable_thinking", mk_val(true)); caps_apply_preserve_reasoning(ctx, true); }, nullptr, // tools_fn diff --git a/common/peg-parser.cpp b/common/peg-parser.cpp index 807e952d902c..ef290ed7c057 100644 --- a/common/peg-parser.cpp +++ b/common/peg-parser.cpp @@ -3,10 +3,10 @@ #include "common.h" #include "json-schema-to-grammar.h" #include "log.h" +#include "trie.h" #include "unicode.h" #include -#include #include #include #include @@ -32,154 +32,6 @@ static bool is_hex_digit(const char c) { return (c >= '0' && c <= '9') || (c >= 'a' && c <= 'f') || (c >= 'A' && c <= 'F'); } -// Trie for matching multiple literals. -// This is used in common_peg_until_parser and to build a GBNF exclusion grammar -struct trie { - struct node { - std::map children; // Use uint32_t to store Unicode codepoints - bool is_word; - }; - - std::vector nodes; - - trie(const std::vector & words) { - create_node(); // root node - for (const auto & w : words) { - insert(w); - } - } - - enum match_result { NO_MATCH, PARTIAL_MATCH, COMPLETE_MATCH }; - - // Check if a delimiter starts at the given position - match_result check_at(std::string_view sv, size_t start_pos) const { - size_t current = 0; // Start at root - size_t pos = start_pos; - - // LOG_DBG("%s: checking at pos %zu, sv='%s'\n", __func__, start_pos, std::string(sv).c_str()); - - while (pos < sv.size()) { - auto result = common_parse_utf8_codepoint(sv, pos); - if (result.status != utf8_parse_result::SUCCESS) { - break; - } - - auto it = nodes[current].children.find(result.codepoint); - if (it == nodes[current].children.end()) { - // Can't continue matching - return match_result{match_result::NO_MATCH}; - } - - current = it->second; - pos += result.bytes_consumed; - - // Check if we've matched a complete word - if (nodes[current].is_word) { - return match_result{match_result::COMPLETE_MATCH}; - } - } - - // Reached end of input while still in the trie (not at root) - if (current != 0) { - // We're in the middle of a potential match - return match_result{match_result::PARTIAL_MATCH}; - } - - // Reached end at root (no match) - return match_result{match_result::NO_MATCH}; - } - - private: - size_t create_node() { - size_t index = nodes.size(); - nodes.emplace_back(); - return index; - } - - void insert(const std::string & word) { - size_t current = 0; - size_t pos = 0; - while (pos < word.length()) { - auto result = common_parse_utf8_codepoint(word, pos); - if (result.status != utf8_parse_result::SUCCESS) { - break; - } - - uint32_t ch = result.codepoint; - pos += result.bytes_consumed; - - auto it = nodes[current].children.find(ch); - if (it == nodes[current].children.end()) { - size_t child = create_node(); - nodes[current].children[ch] = child; - current = child; - } else { - current = it->second; - } - } - nodes[current].is_word = true; - } -}; - -// Aho-Corasick automaton -struct aho_corasick { - trie t; - std::vector fail; // failure links - std::vector order; // states in BFS order - std::vector terminal; // match states (directly or via a suffix link) - std::set alphabet; // every character with a transition - - aho_corasick(const std::vector & strings) : t(strings) { - const auto & nodes = t.nodes; - const size_t n = nodes.size(); - - fail.assign(n, 0); - order.reserve(n); - - std::deque queue{ 0 }; - while (!queue.empty()) { - size_t u = queue.front(); - queue.pop_front(); - order.push_back(u); - for (const auto & [ch, v] : nodes[u].children) { - if (u != 0) { - size_t f = fail[u]; - while (f && nodes[f].children.find(ch) == nodes[f].children.end()) { - f = fail[f]; - } - auto it = nodes[f].children.find(ch); - fail[v] = (it != nodes[f].children.end() && it->second != v) ? it->second : 0; - } - queue.push_back(v); - } - } - - terminal.assign(n, false); - for (size_t u : order) { - terminal[u] = nodes[u].is_word || (u != 0 && terminal[fail[u]]); - } - - for (const auto & node : nodes) { - for (const auto & [ch, v] : node.children) { - alphabet.insert(ch); - } - } - } - - size_t num_states() const { return t.nodes.size(); } - bool is_terminal(size_t s) const { return terminal[s]; } - - // follow failure links until a transition on `ch` exists. - size_t next(size_t state, uint32_t ch) const { - const auto & nodes = t.nodes; - while (state && nodes[state].children.find(ch) == nodes[state].children.end()) { - state = fail[state]; - } - auto it = nodes[state].children.find(ch); - return it != nodes[state].children.end() ? it->second : 0; - } -}; - static std::pair parse_hex_escape(const std::string & str, size_t pos, int hex_count) { if (pos + hex_count > str.length()) { return {0, 0}; @@ -797,7 +649,7 @@ struct parser_executor { } common_peg_parse_result operator()(const common_peg_until_parser & p) const { - trie matcher(p.delimiters); + common_trie matcher(p.delimiters); // Scan input and check for delimiters size_t pos = start_pos; @@ -824,12 +676,12 @@ struct parser_executor { // Check if a delimiter starts at this position auto match = matcher.check_at(ctx.input, pos); - if (match == trie::COMPLETE_MATCH) { + if (match == common_trie::COMPLETE_MATCH) { // Found a complete delimiter, return everything before it return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start_pos, pos); } - if (match == trie::PARTIAL_MATCH) { + if (match == common_trie::PARTIAL_MATCH) { // Found a partial match extending to end of input, return everything before it return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start_pos, pos); } @@ -1559,7 +1411,7 @@ static std::string gbnf_ac_grammar( const std::map> &, const std::vector &, const std::function &)> & build_rule) { - aho_corasick ac(strings); + common_aho_corasick ac(strings); auto state_name = [&](size_t s) -> std::string { if (s == 0) { diff --git a/common/preset.cpp b/common/preset.cpp index 4362c0621b78..eb0c60b09cff 100644 --- a/common/preset.cpp +++ b/common/preset.cpp @@ -330,6 +330,10 @@ common_presets common_preset_context::load_from_ini(const std::string & path, co } } + if (preset.name == COMMON_PRESET_DEFAULT_NAME && preset.options.empty()) { + continue; + } + if (preset.name == "*") { // handle global preset global = preset; diff --git a/common/reasoning-budget.cpp b/common/reasoning-budget.cpp index 7da0bb1c57ce..1fe242d062d1 100644 --- a/common/reasoning-budget.cpp +++ b/common/reasoning-budget.cpp @@ -1,39 +1,52 @@ #include "reasoning-budget.h" #include "common.h" +#include "trie.h" #include "unicode.h" #include "log.h" +#include #include #include #include #include struct token_matcher { - std::vector tokens; - size_t pos = 0; + std::vector seqs; + common_aho_corasick ac; + size_t state = 0; - bool advance(llama_token token) { - if (tokens.empty()) { - return false; - } + token_matcher(const std::vector & seqs) : seqs(collect(seqs)), ac(build_trie(this->seqs)) {} - if (token == tokens[pos]) { - pos++; - if (pos >= tokens.size()) { - pos = 0; - return true; - } - } else { - pos = 0; - if (token == tokens[0]) { - pos = 1; + static std::vector collect(const std::vector & seqs) { + std::vector res; + for (const auto & seq : seqs) { + if (!seq.empty() && std::find(res.begin(), res.end(), seq) == res.end()) { + res.push_back(seq); } } - return false; + return res; + } + + static common_trie build_trie(const std::vector & seqs) { + common_trie t; + for (const auto & seq : seqs) { + t.insert(std::vector(seq.begin(), seq.end())); + } + return t; } - void reset() { pos = 0; } + // returns the index into seqs of the longest sequence ending at this token, or -1 + int32_t advance(llama_token token) { + state = ac.next(state, (uint32_t) token); + const int32_t p = ac.match_pattern(state); + if (p >= 0) { + state = 0; + } + return p; + } + + void reset() { state = 0; } }; struct common_reasoning_budget_ctx { @@ -41,7 +54,7 @@ struct common_reasoning_budget_ctx { token_matcher start_matcher; token_matcher end_matcher; - std::vector forced_tokens; + llama_tokens forced_tokens; int32_t budget; // maximum tokens in reasoning block int32_t remaining; // tokens remaining in budget @@ -50,6 +63,8 @@ struct common_reasoning_budget_ctx { // for forcing size_t force_pos; // next position in forced_tokens to force + + int32_t end_match; // index into end_matcher.seqs of the sequence that transitioned to DONE, -1 if none }; static const char * common_reasoning_budget_name(const struct llama_sampler * /*smpl*/) { @@ -62,7 +77,7 @@ static void common_reasoning_budget_accept(struct llama_sampler * smpl, llama_to switch (ctx->state) { case REASONING_BUDGET_IDLE: { - if (ctx->start_matcher.advance(token)) { + if (ctx->start_matcher.advance(token) >= 0) { ctx->state = REASONING_BUDGET_COUNTING; ctx->remaining = ctx->budget; COM_TRC("activated, budget=%d tokens\n", ctx->budget); @@ -78,8 +93,10 @@ static void common_reasoning_budget_accept(struct llama_sampler * smpl, llama_to case REASONING_BUDGET_COUNTING: case REASONING_BUDGET_WAITING_UTF8: { - if (ctx->end_matcher.advance(token)) { + const int32_t match = ctx->end_matcher.advance(token); + if (match >= 0) { ctx->state = REASONING_BUDGET_DONE; + ctx->end_match = match; COM_TRC("%s", "deactivated (natural end)\n"); break; } @@ -115,19 +132,25 @@ static void common_reasoning_budget_accept(struct llama_sampler * smpl, llama_to break; } case REASONING_BUDGET_FORCING: + { + // track the end sequence within forced_tokens so it is also reported on DONE + const int32_t match = ctx->end_matcher.advance(token); ctx->force_pos++; if (ctx->force_pos >= ctx->forced_tokens.size()) { ctx->state = REASONING_BUDGET_DONE; + ctx->end_match = match; COM_TRC("%s", "forced sequence complete, done\n"); } break; + } case REASONING_BUDGET_DONE: // Re-arm on a new start tag: some models emit multiple blocks // per response, and each should get a fresh budget window. - if (ctx->start_matcher.advance(token)) { + if (ctx->start_matcher.advance(token) >= 0) { ctx->state = REASONING_BUDGET_COUNTING; ctx->remaining = ctx->budget; ctx->end_matcher.reset(); + ctx->end_match = -1; COM_TRC("re-activated on new start tag, budget=%d tokens\n", ctx->budget); if (ctx->remaining <= 0) { @@ -169,11 +192,12 @@ static void common_reasoning_budget_reset(struct llama_sampler * smpl) { ctx->start_matcher.reset(); ctx->end_matcher.reset(); ctx->force_pos = 0; + ctx->end_match = -1; } static struct llama_sampler * common_reasoning_budget_init_state( - const struct llama_vocab * vocab, const std::vector & start_tokens, - const std::vector & end_tokens, const std::vector & forced_tokens, + const struct llama_vocab * vocab, const std::vector & start_seqs, + const std::vector & end_seqs, const llama_tokens & forced_tokens, int32_t budget, common_reasoning_budget_state initial_state); static struct llama_sampler * common_reasoning_budget_clone(const struct llama_sampler * smpl); @@ -205,12 +229,12 @@ static struct llama_sampler * common_reasoning_budget_clone(const struct llama_s } static struct llama_sampler * common_reasoning_budget_init_state( - const struct llama_vocab * vocab, - const std::vector & start_tokens, - const std::vector & end_tokens, - const std::vector & forced_tokens, - int32_t budget, - common_reasoning_budget_state initial_state) { + const struct llama_vocab * vocab, + const std::vector & start_seqs, + const std::vector & end_seqs, + const llama_tokens & forced_tokens, + int32_t budget, + common_reasoning_budget_state initial_state) { // promote COUNTING with budget <= 0 to FORCING if (initial_state == REASONING_BUDGET_COUNTING && budget <= 0) { initial_state = REASONING_BUDGET_FORCING; @@ -220,25 +244,26 @@ static struct llama_sampler * common_reasoning_budget_init_state( /* .iface = */ &common_reasoning_budget_i, /* .ctx = */ new common_reasoning_budget_ctx { /* .vocab = */ vocab, - /* .start_matcher = */ { start_tokens, 0 }, - /* .end_matcher = */ { end_tokens, 0 }, + /* .start_matcher = */ token_matcher(start_seqs), + /* .end_matcher = */ token_matcher(end_seqs), /* .forced_tokens = */ forced_tokens, /* .budget = */ budget, /* .remaining = */ budget, /* .state = */ initial_state, /* .force_pos = */ 0, + /* .end_match = */ -1, } ); } struct llama_sampler * common_reasoning_budget_init( - const struct llama_vocab * vocab, - const std::vector & start_tokens, - const std::vector & end_tokens, - const std::vector & forced_tokens, - int32_t budget, - common_reasoning_budget_state initial_state) { - return common_reasoning_budget_init_state(vocab, start_tokens, end_tokens, forced_tokens, budget, initial_state); + const struct llama_vocab * vocab, + const std::vector & start_seqs, + const std::vector & end_seqs, + const llama_tokens & forced_tokens, + int32_t budget, + common_reasoning_budget_state initial_state) { + return common_reasoning_budget_init_state(vocab, start_seqs, end_seqs, forced_tokens, budget, initial_state); } common_reasoning_budget_state common_reasoning_budget_get_state(const struct llama_sampler * smpl) { @@ -248,6 +273,19 @@ common_reasoning_budget_state common_reasoning_budget_get_state(const struct lla return ((const common_reasoning_budget_ctx *)smpl->ctx)->state; } +const llama_tokens * common_reasoning_budget_get_end_match(const struct llama_sampler * smpl) { + if (!smpl) { + return nullptr; + } + + const auto * ctx = (const common_reasoning_budget_ctx *) smpl->ctx; + if (ctx->end_match < 0) { + return nullptr; + } + + return &ctx->end_matcher.seqs[ctx->end_match]; +} + bool common_reasoning_budget_force(struct llama_sampler * smpl) { if (!smpl) { return false; diff --git a/common/reasoning-budget.h b/common/reasoning-budget.h index 0cf689a56637..1b89a04c42e8 100644 --- a/common/reasoning-budget.h +++ b/common/reasoning-budget.h @@ -2,6 +2,8 @@ #include "llama.h" +#include "common.h" + #include #include @@ -17,30 +19,34 @@ enum common_reasoning_budget_state { // reasoning block (e.g. between and ). // // State machine: IDLE -> COUNTING -> WAITING_UTF8 -> FORCING -> DONE -// IDLE: passthrough, watching for start_tokens sequence -// COUNTING: counting down remaining tokens, watching for natural end_tokens +// IDLE: passthrough, watching for a start sequence +// COUNTING: counting down remaining tokens, watching for a natural end sequence // WAITING_UTF8: budget exhausted, allowing tokens to complete a UTF-8 sequence // FORCING: forces forced_tokens token-by-token (all other logits -> -inf) // DONE: passthrough forever // // Parameters: // vocab - vocabulary (used for UTF-8 boundary detection; can be nullptr) -// start_tokens - token sequence that activates counting -// end_tokens - token sequence for natural deactivation +// start_seqs - token sequences, any of which activates counting +// end_seqs - token sequences, any of which naturally deactivates // forced_tokens - token sequence forced when budget expires // budget - max tokens allowed in the reasoning block // initial_state - initial state // struct llama_sampler * common_reasoning_budget_init( - const struct llama_vocab * vocab, - const std::vector & start_tokens, - const std::vector & end_tokens, - const std::vector & forced_tokens, - int32_t budget, - common_reasoning_budget_state initial_state = REASONING_BUDGET_IDLE); + const struct llama_vocab * vocab, + const std::vector & start_seqs, + const std::vector & end_seqs, + const llama_tokens & forced_tokens, + int32_t budget, + common_reasoning_budget_state initial_state = REASONING_BUDGET_IDLE); common_reasoning_budget_state common_reasoning_budget_get_state(const struct llama_sampler * smpl); +// The end sequence that transitioned the sampler to DONE, or nullptr if none +// was recorded. Cleared when a new start sequence re-arms the sampler. +const llama_tokens * common_reasoning_budget_get_end_match(const struct llama_sampler * smpl); + // Manually transition the reasoning budget sampler into the FORCING state. // Returns true if the transition occurred. bool common_reasoning_budget_force(struct llama_sampler * smpl); diff --git a/common/sampling.cpp b/common/sampling.cpp index 75a299e23ece..ba5504ed0118 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -184,9 +184,26 @@ std::string common_params_sampling::print() const { return std::string(result); } -struct common_sampler * common_sampler_init(const struct llama_model * model, struct common_params_sampling & params) { - const llama_vocab * vocab = llama_model_get_vocab(model); +struct common_sampler * common_sampler_init( + const struct llama_model * model, + struct common_params_sampling & params, + int32_t n_ctx) { + if (!std::isfinite(params.penalty_repeat) || + params.penalty_repeat <= 0.0f || + !std::isfinite(1.0f/params.penalty_repeat)) { + throw std::invalid_argument("penalty_repeat must be finite and greater than 0"); + } + if (!std::isfinite(params.penalty_freq)) { + throw std::invalid_argument("penalty_freq must be finite"); + } + if (!std::isfinite(params.penalty_present)) { + throw std::invalid_argument("penalty_present must be finite"); + } + if (params.penalty_last_n == -1) { + params.penalty_last_n = n_ctx > 0 ? n_ctx : llama_model_n_ctx_train(model); + } + const llama_vocab * vocab = llama_model_get_vocab(model); llama_sampler_chain_params lparams = llama_sampler_chain_default_params(); lparams.no_perf = params.no_perf; @@ -299,7 +316,7 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st if (!params.reasoning_budget_start.empty() && !params.reasoning_budget_end.empty() && (params.grammar_lazy || params.reasoning_budget_tokens >= 0 || params.reasoning_control)) { rbudget = common_reasoning_budget_init( vocab, - params.reasoning_budget_start, + {params.reasoning_budget_start}, params.reasoning_budget_end, params.reasoning_budget_forced, params.reasoning_budget_tokens < 0 ? INT_MAX : params.reasoning_budget_tokens); @@ -310,8 +327,19 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st } } - if (params.has_logit_bias()) { - samplers.push_back(llama_sampler_init_logit_bias(llama_vocab_n_tokens(vocab), params.logit_bias.size(), params.logit_bias.data())); + // logit bias: user biases + model suppress tokens (-INFINITY) + { + std::vector merged = params.logit_bias; + + int32_t n_suppress = 0; + const llama_token * suppress = llama_vocab_get_suppress_tokens(vocab, &n_suppress); + for (int32_t i = 0; i < n_suppress; ++i) { + merged.push_back({ suppress[i], -INFINITY }); + } + + if (!merged.empty()) { + samplers.push_back(llama_sampler_init_logit_bias(llama_vocab_n_tokens(vocab), merged.size(), merged.data())); + } } if (params.mirostat == 0) { @@ -355,7 +383,7 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st samplers.push_back(llama_sampler_init_infill(vocab)); break; case COMMON_SAMPLER_TYPE_PENALTIES: - samplers.push_back(llama_sampler_init_penalties(params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present)); + samplers.push_back(llama_sampler_init_penalties(llama_vocab_n_tokens(vocab), params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present)); break; case COMMON_SAMPLER_TYPE_ADAPTIVE_P: // the `adaptive-p` sampler is like `dist` and `mirostat` in that it selects @@ -453,6 +481,17 @@ void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, boo if (gsmpl->rbudget && is_generated) { llama_sampler_accept(gsmpl->rbudget, token); + + // if done, replay end sequence which may contain a grammar trigger + const bool is_done = common_reasoning_budget_get_state(gsmpl->rbudget) == REASONING_BUDGET_DONE; + if (gsmpl->grmr && !accept_grammar && is_done) { + const llama_tokens * end_seq = common_reasoning_budget_get_end_match(gsmpl->rbudget); + if (end_seq) { + for (const llama_token end_token : *end_seq) { + llama_sampler_accept(gsmpl->grmr, end_token); + } + } + } } if (gsmpl->grmr && accept_grammar) { diff --git a/common/sampling.h b/common/sampling.h index 1ea0676a44a0..6b5bcfa0345e 100644 --- a/common/sampling.h +++ b/common/sampling.h @@ -37,7 +37,10 @@ struct common_sampler; // llama_sampler API overloads // note: can mutate params in some cases -struct common_sampler * common_sampler_init(const struct llama_model * model, struct common_params_sampling & params); +struct common_sampler * common_sampler_init( + const struct llama_model * model, + struct common_params_sampling & params, + int32_t n_ctx = 0); void common_sampler_free(struct common_sampler * gsmpl); diff --git a/common/speculative.cpp b/common/speculative.cpp index c698e39e3231..9281e8ba7b03 100644 --- a/common/speculative.cpp +++ b/common/speculative.cpp @@ -34,6 +34,7 @@ const std::map common_speculative_type_fro {"draft-eagle3", COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3}, {"draft-mtp", COMMON_SPECULATIVE_TYPE_DRAFT_MTP}, {"draft-dflash", COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH}, + {"draft-dspark", COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK}, {"ngram-simple", COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE}, {"ngram-map-k", COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K}, {"ngram-map-k4v", COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V}, @@ -265,7 +266,10 @@ struct common_speculative_impl_draft_simple : public common_speculative_impl { bool process(const llama_batch & batch) override { auto * ctx_dft = params.ctx_dft; - const int ret = llama_decode(ctx_dft, batch); + llama_batch batch_dft = batch; + batch_dft.logits = nullptr; + + const int ret = llama_decode(ctx_dft, batch_dft); if (ret != 0) { SPC_ERR("failed to decode draft batch, ret = %d\n", ret); @@ -439,6 +443,7 @@ struct common_speculative_impl_draft_eagle3 : public common_speculative_impl { int32_t n_embd_dec = 0; // draft hidden size int32_t n_embd_enc = 0; // target_layer_ids_n * target_hidden_size int32_t n_embd_tgt = 0; // target model hidden size + int32_t n_layer_tgt = 0; // target model layer count const int32_t * target_layer_ids = nullptr; // model_dft's extract layer indices uint32_t target_layer_ids_n = 0; @@ -480,6 +485,7 @@ struct common_speculative_impl_draft_eagle3 : public common_speculative_impl { n_embd_tgt = llama_model_n_embd(model_tgt); n_embd_dec = llama_model_n_embd(model_dft); n_embd_enc = (int32_t) target_layer_ids_n * n_embd_tgt; + n_layer_tgt = llama_model_n_layer(model_tgt); const int32_t n_b = (int32_t) llama_n_batch(ctx_dft); batch = llama_batch_init(/*n_tokens=*/ n_b, /*embd=*/ n_embd_dec, /*n_seq_max=*/ 1); @@ -512,9 +518,15 @@ struct common_speculative_impl_draft_eagle3 : public common_speculative_impl { } } - // turn on extraction of the target layers' input embeddings + // turn on extraction of the target layers' hidden states for (uint32_t k = 0; k < target_layer_ids_n; ++k) { - llama_set_embeddings_layer_inp(ctx_tgt, (uint32_t) target_layer_ids[k], true); + if (target_layer_ids[k] < n_layer_tgt) { + llama_set_embeddings_layer_inp(ctx_tgt, (uint32_t) target_layer_ids[k], true); + } else if (target_layer_ids[k] == n_layer_tgt) { + llama_set_embeddings_nextn(ctx_tgt, true, /*masked*/ false); + } else { + GGML_ABORT("EAGLE3: target layer id %d exceeds target n_layer %d", target_layer_ids[k], n_layer_tgt); + } } // turn on extraction of the draft model's pre-norm hidden state @@ -602,7 +614,9 @@ struct common_speculative_impl_draft_eagle3 : public common_speculative_impl { features_buf.resize((size_t) n_tokens * n_embd_enc, 0.0f); for (uint32_t k = 0; k < target_layer_ids_n; ++k) { - const float * layer = llama_get_embeddings_layer_inp(ctx_tgt, (uint32_t) target_layer_ids[k]); + const float * layer = target_layer_ids[k] < n_layer_tgt + ? llama_get_embeddings_layer_inp(ctx_tgt, (uint32_t) target_layer_ids[k]) + : llama_get_embeddings_nextn(ctx_tgt); if (!layer) { GGML_ABORT("EAGLE3: target layer %d input not extracted.", target_layer_ids[k]); } @@ -920,15 +934,20 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl { int32_t block_size = 0; llama_token mask_token_id = 0; + // draft-dspark: the draft carries a Markov head and uses an anchor-first block layout + const bool is_dspark; + const int32_t * target_layer_ids = nullptr; // model_dft's extract layer indices uint32_t target_layer_ids_n = 0; // scratch buffer for concatenated target features [n_tokens, n_embd_enc] std::vector features_buf; - common_speculative_impl_draft_dflash(const common_params_speculative & params, uint32_t n_seq) - : common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH, n_seq) + common_speculative_impl_draft_dflash(const common_params_speculative & params, uint32_t n_seq, + common_speculative_type type = COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH) + : common_speculative_impl(type, n_seq) , params(params.draft) + , is_dspark(type == COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK) { auto * ctx_tgt = this->params.ctx_tgt; auto * ctx_dft = this->params.ctx_dft; @@ -955,16 +974,18 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl { } mask_token_id = llama_vocab_mask(llama_model_get_vocab(model_dft)); - LOG_INF("%s: adding speculative implementation 'draft-dflash'\n", __func__); + LOG_INF("%s: adding speculative implementation '%s'\n", __func__, common_speculative_type_to_str(type).c_str()); LOG_INF("%s: - n_max=%d, n_min=%d, p_min=%.2f\n", __func__, this->params.n_max, this->params.n_min, this->params.p_min); LOG_INF("%s: - block_size=%d, mask_token_id=%d, n_extract=%u\n", __func__, block_size, mask_token_id, target_layer_ids_n); - // DFlash input is [id_last, * (block_size-1)], so it can draft at most block_size-1 tokens per step - if (this->params.n_max > block_size - 1 || this->params.n_min > block_size - 1) { - LOG_WRN("%s: requested draft size (n_max=%d, n_min=%d) exceeds the trained DFlash block size %d -- clamping to %d\n", - __func__, this->params.n_max, this->params.n_min, block_size, block_size - 1); - this->params.n_max = std::min(this->params.n_max, block_size - 1); - this->params.n_min = std::min(this->params.n_min, block_size - 1); + // DFlash input is [id_last, * (block_size-1)]: in-place denoising yields at most + // block_size-1 draft tokens, DSpark yield a full block_size draft tokens + const int32_t n_draft_max = is_dspark ? block_size : block_size - 1; + if (this->params.n_max > n_draft_max || this->params.n_min > n_draft_max) { + LOG_WRN("%s: requested draft size (n_max=%d, n_min=%d) exceeds the trained block size %d -- clamping to %d\n", + __func__, this->params.n_max, this->params.n_min, block_size, n_draft_max); + this->params.n_max = std::min(this->params.n_max, n_draft_max); + this->params.n_min = std::min(this->params.n_min, n_draft_max); } batch = llama_batch_init(llama_n_batch(ctx_dft), 0, n_seq); @@ -1128,12 +1149,9 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl { const int32_t n = (int32_t) dp.n_past; - int32_t n_draft = params.n_max; - if (dp.n_max > 0) { - n_draft = std::min(n_draft, dp.n_max); - } + const int32_t n_draft = params.n_max; - const int32_t n_block_tokens = n_draft + 1; // id_last + n_draft * + const int32_t n_block_tokens = n_draft + (is_dspark ? 0 : 1); i_block_beg[seq_id] = batch.n_tokens; n_block [seq_id] = n_block_tokens; for (int32_t i = 0; i < n_block_tokens; ++i) { @@ -1165,27 +1183,57 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl { auto & result = *dp.result; - // greedily read the predicted block at this sequence's noise positions 1..n_block_tokens-1 - for (int32_t i = 1; i < n_block_tokens; ++i) { - common_sampler_sample(smpl, ctx_dft, beg + i, true); + if (is_dspark) { + // DSpark predicts the next token from position 0 and optionally truncates + // at the first position below the confidence threshold. + const float * conf = params.p_min > 0.0f ? llama_get_embeddings_nextn(ctx_dft) : nullptr; - const auto * cur_p = common_sampler_get_candidates(smpl, true); + for (int32_t i = 0; i < n_block_tokens; ++i) { + const int32_t idx = beg + i; - for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) { - LOG_DBG(" - seq_id %d, draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n", - seq_id, k, i - 1, cur_p->data[k].id, cur_p->data[k].p, - common_token_to_piece(ctx_dft, cur_p->data[k].id).c_str()); - } + if (conf && conf[(size_t) idx * n_embd_dec] < params.p_min) { + break; + } - const llama_token id = cur_p->data[0].id; + common_sampler_sample(smpl, ctx_dft, idx, true); - if (cur_p->data[0].p < params.p_min) { - break; + const auto * cur_p = common_sampler_get_candidates(smpl, true); + + for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) { + LOG_DBG(" - seq_id %d, draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n", + seq_id, k, i, cur_p->data[k].id, cur_p->data[k].p, + common_token_to_piece(ctx_dft, cur_p->data[k].id).c_str()); + } + + const llama_token id = cur_p->data[0].id; + + common_sampler_accept(smpl, id, true); + + result.push_back(id); } + } else { + // greedily read the predicted block at this sequence's noise positions 1..n_block_tokens-1 + for (int32_t i = 1; i < n_block_tokens; ++i) { + common_sampler_sample(smpl, ctx_dft, beg + i, true); - common_sampler_accept(smpl, id, true); + const auto * cur_p = common_sampler_get_candidates(smpl, true); - result.push_back(id); + for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) { + LOG_DBG(" - seq_id %d, draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n", + seq_id, k, i - 1, cur_p->data[k].id, cur_p->data[k].p, + common_token_to_piece(ctx_dft, cur_p->data[k].id).c_str()); + } + + const llama_token id = cur_p->data[0].id; + + if (cur_p->data[0].p < params.p_min) { + break; + } + + common_sampler_accept(smpl, id, true); + + result.push_back(id); + } } if (result.size() < (size_t) params.n_min) { @@ -1248,7 +1296,7 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl { GGML_ASSERT(ctx_tgt && ctx_dft && "MTP requires ctx_tgt and ctx_dft to be set"); n_embd = llama_model_n_embd_out(llama_get_model(ctx_dft)); - GGML_ASSERT(n_embd == llama_model_n_embd(llama_get_model(ctx_tgt)) && + GGML_ASSERT(n_embd == llama_model_n_embd_out(llama_get_model(ctx_tgt)) && "MTP input row width must match the target h_nextn width"); n_mtp_layers = std::max(1, (int) llama_model_n_layer_nextn(llama_get_model(ctx_dft))); @@ -2147,6 +2195,7 @@ std::string common_speculative_type_to_str(common_speculative_type type) { case COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3: return "draft-eagle3"; case COMMON_SPECULATIVE_TYPE_DRAFT_MTP: return "draft-mtp"; case COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH: return "draft-dflash"; + case COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK: return "draft-dspark"; case COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE: return "ngram-simple"; case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K: return "ngram-map-k"; case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V: return "ngram-map-k4v"; @@ -2200,6 +2249,7 @@ int32_t common_speculative_n_max(const common_params_speculative * spec) { case COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3: case COMMON_SPECULATIVE_TYPE_DRAFT_MTP: case COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH: + case COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK: n_max = std::max(n_max, std::max(0, spec->draft.n_max)); break; case COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE: @@ -2286,7 +2336,7 @@ common_speculative_init_result::common_speculative_init_result( std::string model_path; if (has_draft) { model_path = params.speculative.draft.mparams.path; - LOG_TRC("%s: loading draft model '%s'\n", __func__, model_path.c_str()); + LOG_INF("%s: loading draft model '%s'\n", __func__, model_path.c_str()); llama_model * model_dft = llama_model_load_from_file(params.model.path.c_str(), mparams); if (model_dft == NULL) { @@ -2306,7 +2356,7 @@ common_speculative_init_result::common_speculative_init_result( } else if (spec_mtp) { model_path = params.model.path; - LOG_TRC("%s: creating MTP draft context against the target model '%s'\n", __func__, model_path.c_str()); + LOG_INF("%s: creating MTP draft context against the target model '%s'\n", __func__, model_path.c_str()); llama_context * ctx_dft = llama_init_from_model(model_tgt, cparams); if (ctx_dft == nullptr) { @@ -2340,53 +2390,28 @@ common_speculative * common_speculative_init(common_params_speculative & params, { uint32_t enabled_configs = common_get_enabled_speculative_configs(params.types); - bool has_draft_simple = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE)); - bool has_draft_eagle3 = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3)) && params.draft.ctx_dft != nullptr; - bool has_draft_mtp = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_MTP)) && params.draft.ctx_dft != nullptr; - bool has_draft_dflash = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH)) && params.draft.ctx_dft != nullptr; - - - - bool has_ngram_cache = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_CACHE)); - bool has_ngram_simple = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE)); - bool has_ngram_map_k = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K)); - bool has_ngram_map_k4v = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V)); - bool has_ngram_mod = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_MOD)); + auto add_config_if_enabled = [&](common_speculative_type type, bool available = true) { + if (available && (enabled_configs & (1u << type))) { + configs.emplace_back(type, params); + } + }; // when adding a new type - update here the logic above - static_assert(COMMON_SPECULATIVE_TYPE_COUNT == 10); + static_assert(COMMON_SPECULATIVE_TYPE_COUNT == 11); // this list here defines the priority of the speculators // the one with highest priority are listed first - if (has_ngram_simple) { - // This implementation can guess a lot of tokens without any draft model. - configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE, params)); - } - if (has_ngram_map_k) { - configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K, params)); - } - if (has_ngram_map_k4v) { - // This implementation can guess tokens with high acceptance rate but is more expensive. - configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V, params)); - } - if (has_ngram_mod) { - configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_NGRAM_MOD, params)); - } - if (has_ngram_cache) { - configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_NGRAM_CACHE, params)); - } - if (has_draft_simple) { - configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE, params)); - } - if (has_draft_eagle3) { - configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3, params)); - } - if (has_draft_mtp) { - configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT_MTP, params)); - } - if (has_draft_dflash) { - configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH, params)); - } + add_config_if_enabled(COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE); + add_config_if_enabled(COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K); + add_config_if_enabled(COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V); + add_config_if_enabled(COMMON_SPECULATIVE_TYPE_NGRAM_MOD); + add_config_if_enabled(COMMON_SPECULATIVE_TYPE_NGRAM_CACHE); + + add_config_if_enabled(COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE); + add_config_if_enabled(COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3, params.draft.ctx_dft != nullptr); + add_config_if_enabled(COMMON_SPECULATIVE_TYPE_DRAFT_MTP, params.draft.ctx_dft != nullptr); + add_config_if_enabled(COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH, params.draft.ctx_dft != nullptr); + add_config_if_enabled(COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK, params.draft.ctx_dft != nullptr); } std::vector> impls = {}; @@ -2411,6 +2436,11 @@ common_speculative * common_speculative_init(common_params_speculative & params, impls.push_back(std::make_unique(config.params, n_seq)); break; } + case COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK: { + impls.push_back(std::make_unique( + config.params, n_seq, COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK)); + break; + } case COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE: { common_ngram_map ngram_map = get_common_ngram_map(config.type, config.params.ngram_simple); diff --git a/common/subproc.cpp b/common/subproc.cpp new file mode 100644 index 000000000000..6d37f59002b6 --- /dev/null +++ b/common/subproc.cpp @@ -0,0 +1,143 @@ +#include "subproc.h" + +bool common_subproc::is_supported() { +#ifdef LLAMA_SUBPROCESS + return true; +#else + return false; +#endif +} + +#ifdef LLAMA_SUBPROCESS + +static std::vector to_cstr_vec(const std::vector & v) { + std::vector r; + r.reserve(v.size() + 1); + for (const auto & s : v) { + r.push_back(const_cast(s.c_str())); + } + r.push_back(nullptr); + return r; +} + +common_subproc::~common_subproc() { + if (is_created) { + subprocess_destroy(&proc); + is_created = false; + } +} + +bool common_subproc::create( + const std::vector & args, + int options, + const std::vector & env, + const char * cwd) { + auto argv = to_cstr_vec(args); + + int result; + if (env.empty() && cwd == nullptr) { + result = subprocess_create(argv.data(), options, &proc); + } else { + auto envp = to_cstr_vec(env); + result = subprocess_create_ex(argv.data(), options, env.empty() ? nullptr : envp.data(), cwd, &proc); + } + + is_created = result == 0; + return is_created; +} + +bool common_subproc::has_handle() const { + if (!is_created) { + return false; + } +#if defined(_WIN32) + return proc.hProcess != nullptr; +#else + return proc.child > 0; +#endif +} + +bool common_subproc::alive() { + return is_created && subprocess_alive(&proc); +} + +FILE * common_subproc::stdin_file() { + return is_created ? subprocess_stdin(&proc) : nullptr; +} + +FILE * common_subproc::stdout_file() { + return is_created ? subprocess_stdout(&proc) : nullptr; +} + +FILE * common_subproc::stderr_file() { + return is_created ? subprocess_stderr(&proc) : nullptr; +} + +void common_subproc::close_stdin() { + if (is_created && proc.stdin_file) { + fclose(proc.stdin_file); + proc.stdin_file = nullptr; + } +} + +void common_subproc::terminate() { + if (has_handle()) { + subprocess_terminate(&proc); + } +} + +int common_subproc::join() { + int exit_code = -1; + if (is_created) { + subprocess_join(&proc, &exit_code); + subprocess_destroy(&proc); + is_created = false; + } + return exit_code; +} + +#else // !LLAMA_SUBPROCESS + +common_subproc::~common_subproc() = default; + +bool common_subproc::create( + const std::vector &, + int, + const std::vector &, + const char *) { + (void)(proc); + (void)(is_created); + return false; +} + +bool common_subproc::has_handle() const { + return false; +} + +bool common_subproc::alive() { + return false; +} + +FILE * common_subproc::stdin_file() { + return nullptr; +} + +FILE * common_subproc::stdout_file() { + return nullptr; +} + +FILE * common_subproc::stderr_file() { + return nullptr; +} + +void common_subproc::close_stdin() { +} + +void common_subproc::terminate() { +} + +int common_subproc::join() { + return -1; +} + +#endif // LLAMA_SUBPROCESS diff --git a/common/subproc.h b/common/subproc.h new file mode 100644 index 000000000000..89b69ee262fb --- /dev/null +++ b/common/subproc.h @@ -0,0 +1,59 @@ +#pragma once + +#include +#include +#include +#include + +#ifdef LLAMA_SUBPROCESS +#include +#else +// dummy values to allow compilation when subprocess is disabled +struct subprocess_s {}; +static constexpr int subprocess_option_no_window = 0; +static constexpr int subprocess_option_combined_stdout_stderr = 0; +static constexpr int subprocess_option_inherit_environment = 0; +static constexpr int subprocess_option_search_user_path = 0; +#endif + +// RAII-style wrapper around https://github.com/sheredom/subprocess.h, +// exposing method calls instead of free functions operating on subprocess_s. +struct common_subproc { + common_subproc() = default; + ~common_subproc(); + + common_subproc(const common_subproc &) = delete; + common_subproc & operator=(const common_subproc &) = delete; + + // spawn a child process; if env is non-empty it replaces the child's environment + // (do not combine with subprocess_option_inherit_environment) + bool create( + const std::vector & args, + int options, + const std::vector & env = {}, + const char * cwd = nullptr); + + bool alive(); + + // true if LLAMA_SUBPROCESS was enabled at build time; when false, create() always fails + static bool is_supported(); + + FILE * stdin_file(); + FILE * stdout_file(); + FILE * stderr_file(); + + // close stdin and detach it from the process, so a later join()/destroy() won't double-close it; + // use this after writing all input to signal EOF to the child while it's still running + void close_stdin(); + + void terminate(); + + // wait for the process to exit, release the underlying handle and return its exit code + int join(); + +private: + subprocess_s proc {}; + std::atomic is_created{false}; + + bool has_handle() const; +}; diff --git a/common/trie.cpp b/common/trie.cpp new file mode 100644 index 000000000000..b5c9666ba2ee --- /dev/null +++ b/common/trie.cpp @@ -0,0 +1,123 @@ +#include "trie.h" + +#include "unicode.h" + +#include + +common_trie::match_result common_trie::check_at(std::string_view sv, size_t start_pos) const { + size_t current = 0; // Start at root + size_t pos = start_pos; + + // LOG_DBG("%s: checking at pos %zu, sv='%s'\n", __func__, start_pos, std::string(sv).c_str()); + + while (pos < sv.size()) { + auto result = common_parse_utf8_codepoint(sv, pos); + if (result.status != utf8_parse_result::SUCCESS) { + break; + } + + auto it = nodes[current].children.find(result.codepoint); + if (it == nodes[current].children.end()) { + // Can't continue matching + return match_result{match_result::NO_MATCH}; + } + + current = it->second; + pos += result.bytes_consumed; + + // Check if we've matched a complete word + if (nodes[current].pattern >= 0) { + return match_result{match_result::COMPLETE_MATCH}; + } + } + + // Reached end of input while still in the trie (not at root) + if (current != 0) { + // We're in the middle of a potential match + return match_result{match_result::PARTIAL_MATCH}; + } + + // Reached end at root (no match) + return match_result{match_result::NO_MATCH}; +} + +int32_t common_trie::insert(const std::string & word) { + std::vector symbols; + size_t pos = 0; + while (pos < word.length()) { + auto result = common_parse_utf8_codepoint(word, pos); + if (result.status != utf8_parse_result::SUCCESS) { + break; + } + + symbols.push_back(result.codepoint); + pos += result.bytes_consumed; + } + return insert(symbols); +} + +int32_t common_trie::insert(const std::vector & symbols) { + size_t current = 0; + for (uint32_t ch : symbols) { + auto it = nodes[current].children.find(ch); + if (it == nodes[current].children.end()) { + size_t child = create_node(); + nodes[current].children[ch] = child; + current = child; + } else { + current = it->second; + } + } + if (nodes[current].pattern < 0) { + nodes[current].pattern = n_patterns++; + } + return nodes[current].pattern; +} + +common_aho_corasick::common_aho_corasick(common_trie trie) : t(std::move(trie)) { + const auto & nodes = t.nodes; + const size_t n = nodes.size(); + + fail.assign(n, 0); + order.reserve(n); + + std::deque queue{ 0 }; + while (!queue.empty()) { + size_t u = queue.front(); + queue.pop_front(); + order.push_back(u); + for (const auto & [ch, v] : nodes[u].children) { + if (u != 0) { + size_t f = fail[u]; + while (f && nodes[f].children.find(ch) == nodes[f].children.end()) { + f = fail[f]; + } + auto it = nodes[f].children.find(ch); + fail[v] = (it != nodes[f].children.end() && it->second != v) ? it->second : 0; + } + queue.push_back(v); + } + } + + // fail[u] points to a strictly shorter suffix, so the first pattern found on + // the fail chain (including u itself) is the longest pattern ending at u + match.assign(n, -1); + for (size_t u : order) { + match[u] = nodes[u].pattern >= 0 ? nodes[u].pattern : (u != 0 ? match[fail[u]] : -1); + } + + for (const auto & node : nodes) { + for (const auto & [ch, v] : node.children) { + alphabet.insert(ch); + } + } +} + +size_t common_aho_corasick::next(size_t state, uint32_t ch) const { + const auto & nodes = t.nodes; + while (state && nodes[state].children.find(ch) == nodes[state].children.end()) { + state = fail[state]; + } + auto it = nodes[state].children.find(ch); + return it != nodes[state].children.end() ? it->second : 0; +} diff --git a/common/trie.h b/common/trie.h new file mode 100644 index 000000000000..0f7b16a36ad2 --- /dev/null +++ b/common/trie.h @@ -0,0 +1,73 @@ +#pragma once + +#include +#include +#include +#include +#include +#include + +// Trie for matching multiple literals. +// This is used in common_peg_until_parser and to build a GBNF exclusion grammar +struct common_trie { + struct node { + std::map children; // Use uint32_t to store Unicode codepoints + int32_t pattern = -1; // index of the pattern ending at this node, -1 if none + }; + + std::vector nodes; + + common_trie() { + create_node(); // root node + } + + common_trie(const std::vector & words) : common_trie() { + for (const auto & w : words) { + insert(w); + } + } + + enum match_result { NO_MATCH, PARTIAL_MATCH, COMPLETE_MATCH }; + + // Check if a delimiter starts at the given position + match_result check_at(std::string_view sv, size_t start_pos) const; + + // Insert a word as a sequence of Unicode codepoints, returns its pattern index + int32_t insert(const std::string & word); + + // Insert a raw symbol sequence, returns its pattern index (insertion order, + // duplicates keep the first index) + int32_t insert(const std::vector & symbols); + + private: + int32_t n_patterns = 0; + + size_t create_node() { + size_t index = nodes.size(); + nodes.emplace_back(); + return index; + } +}; + +// Aho-Corasick automaton +struct common_aho_corasick { + common_trie t; + std::vector fail; // failure links + std::vector order; // states in BFS order + std::vector match; // longest pattern ending at each state (directly or via a suffix link), -1 if none + std::set alphabet; // every character with a transition + + common_aho_corasick(common_trie trie); + + common_aho_corasick(const std::vector & strings) + : common_aho_corasick(common_trie(strings)) {} + + size_t num_states() const { return t.nodes.size(); } + bool is_terminal(size_t s) const { return match[s] >= 0; } + + // index of the longest pattern ending at this state, -1 if none + int32_t match_pattern(size_t s) const { return match[s]; } + + // follow failure links until a transition on `ch` exists. + size_t next(size_t state, uint32_t ch) const; +}; diff --git a/conversion/__init__.py b/conversion/__init__.py index dc46c3155f7f..ab8868780c07 100644 --- a/conversion/__init__.py +++ b/conversion/__init__.py @@ -32,6 +32,7 @@ "BertForSequenceClassification": "bert", "BertModel": "bert", "BitnetForCausalLM": "bitnet", + "BitNetForCausalLM": "bitnet", "BloomForCausalLM": "bloom", "BloomModel": "bloom", "CamembertModel": "bert", @@ -52,7 +53,9 @@ "DeepseekV3ForCausalLM": "deepseek", "DeepseekV32ForCausalLM": "deepseek", "DFlashDraftModel": "qwen", + "Qwen3DSparkModel": "qwen", "DeepseekV4ForCausalLM": "deepseek", + "DeepseekV4DSparkModel": "deepseek", "DistilBertForMaskedLM": "bert", "DistilBertForSequenceClassification": "bert", "DistilBertModel": "bert", @@ -121,6 +124,7 @@ "JinaEmbeddingsV5Model": "bert", "KORMoForCausalLM": "qwen", "KimiK25ForConditionalGeneration": "deepseek", + "KimiK3ForConditionalGeneration": "kimi_k3", "KimiLinearForCausalLM": "kimi_linear", "KimiLinearModel": "kimi_linear", "KimiVLForConditionalGeneration": "deepseek", @@ -158,6 +162,8 @@ "MiniCPMForCausalLM": "minicpm", "MiniCPMV4_6ForConditionalGeneration": "minicpm", "MiniMaxM2ForCausalLM": "minimax", + "MiniMaxM3SparseForCausalLM": "minimax", + "MiniMaxM3SparseForConditionalGeneration": "minimax", "Ministral3ForCausalLM": "mistral3", "Mistral3ForConditionalGeneration": "mistral3", "MistralForCausalLM": "llama", @@ -165,6 +171,7 @@ "ModernBertForMaskedLM": "bert", "ModernBertForSequenceClassification": "bert", "ModernBertModel": "bert", + "NanbeigeForCausalLM": "nanbeige", "NemotronForCausalLM": "nemotron", "NemotronHForCausalLM": "nemotron", "NeoBERT": "bert", @@ -267,6 +274,7 @@ "Gemma4UnifiedForConditionalGeneration": "gemma", "Glm4vForConditionalGeneration": "qwen3vl", "Glm4vMoeForConditionalGeneration": "qwen3vl", + "Glm5vForConditionalGeneration": "kimivl", "GlmOcrForConditionalGeneration": "qwen3vl", "GlmasrModel": "ultravox", "Granite4VisionForConditionalGeneration": "granite", @@ -278,6 +286,7 @@ "InternVisionModel": "internvl", "JanusForConditionalGeneration": "januspro", "KimiK25ForConditionalGeneration": "kimivl", + "KimiK3ForConditionalGeneration": "kimivl", "KimiVLForConditionalGeneration": "kimivl", "Lfm2AudioForConditionalGeneration": "lfm2", "Lfm2VlForConditionalGeneration": "lfm2", @@ -286,6 +295,7 @@ "LlavaForConditionalGeneration": "llava", "MERaLiON2ForConditionalGeneration": "ultravox", "MiMoV2ForCausalLM": "mimo", + "MiniMaxM3SparseForConditionalGeneration": "minimax", "MiniCPMV4_6ForConditionalGeneration": "minicpm", "Mistral3ForConditionalGeneration": "llava", "NemotronH_Nano_VL_V2": "nemotron", diff --git a/conversion/base.py b/conversion/base.py index 0d018d502be2..8c233f4d5e4c 100644 --- a/conversion/base.py +++ b/conversion/base.py @@ -395,6 +395,29 @@ def dequant_gptq(g_idx: Tensor, qweight: Tensor, qzeros: Tensor, scales: Tensor) return (scales[g_idx].float() * (weight - zeros[g_idx]).float()).T + def dequant_mxfp4_packed(w: Tensor, scale: Tensor, group_size: int) -> Tensor: + # compressed-tensors "mxfp4-pack-quantized": + # w: uint8 [..., K/2], two FP4 (E2M1) values per byte, low nibble = even index + # scale: uint8 [..., K/group_size], E8M0 exponent, scale = 2^(x - 127) + assert w.dtype == torch.uint8 + assert scale.dtype == torch.uint8 + + kvalues = torch.tensor( + [0.0, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 6.0, -0.0, -0.5, -1.0, -1.5, -2.0, -3.0, -4.0, -6.0], + dtype=torch.float32, + ) + if self.lazy: + kvalues = LazyTorchTensor.from_eager(kvalues) + + lo = (w & 0x0F).to(torch.long) + hi = (w >> 4).to(torch.long) + # interleave: [..., K/2, 2] -> [..., K] + vals = torch.stack((kvalues[lo], kvalues[hi]), dim=-1).reshape(*w.shape[:-1], w.shape[-1] * 2) + + exp = torch.ldexp(torch.ones_like(scale, dtype=torch.float32), scale.to(torch.int32) - 127) + vals = vals.reshape(*vals.shape[:-1], -1, group_size) * exp.unsqueeze(-1) + return vals.reshape(*w.shape[:-1], w.shape[-1] * 2) + def dequant_packed(w: Tensor, scale: Tensor, shape_tensor: Tensor, zero_point: Tensor | None, num_bits: int, group_size: int): assert w.dtype == torch.int32 shape = tuple(shape_tensor.tolist()) @@ -540,6 +563,23 @@ def dequant_packed(w: Tensor, scale: Tensor, shape_tensor: Tensor, zero_point: T tensors_to_remove += [base_name + n for n in ("_packed", "_shape", "_scale")] if (base_name + "_zero_point") in self.model_tensors: tensors_to_remove.append(base_name + "_zero_point") + elif quant_format == "mxfp4-pack-quantized": + assert weight_config.get("strategy") == "group" + assert weight_config.get("type") == "float" + assert weight_config.get("num_bits") == 4 + group_size = weight_config.get("group_size") + assert isinstance(group_size, int) + for name in self.model_tensors.keys(): + if name.endswith(".weight_packed"): + base_name = name.removesuffix("_packed") + w = self.model_tensors[name] + scale = self.model_tensors[base_name + "_scale"] + new_tensors[base_name] = ( + lambda w=w, scale=scale: dequant_mxfp4_packed(w(), scale(), group_size) + ) + tensors_to_remove += [base_name + n for n in ("_packed", "_scale")] + if (base_name + "_shape") in self.model_tensors: + tensors_to_remove.append(base_name + "_shape") elif nvfp4_compressed_tensors: # Don't error from compressed-tensors, we'll handle them in _generate_nvfp4_tensors pass @@ -1187,7 +1227,7 @@ def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Ca or "projector." in name or "pre_mm_projector_norm" in name \ or "image_newline" in name or "view_seperator" in name \ or "patch_embed" in name or "patch_embedding" in name \ - or "patch_merger." in name or "model.connector." in name: + or "patch_merger." in name or "patch_merge_mlp." in name or "model.connector." in name: return None return super().filter_tensors(item) @@ -1234,7 +1274,7 @@ def set_gguf_parameters(self): self.gguf_writer.add_embedding_length(n_embd) logger.info(f"gguf: embedding length = {n_embd}") - if (n_ff := self.find_hparam(["prefix_dense_intermediate_size", "intermediate_size", "n_inner", "hidden_dim"], optional=True)) is not None: + if (n_ff := self.find_hparam(["prefix_dense_intermediate_size", "dense_intermediate_size", "intermediate_size", "n_inner", "hidden_dim"], optional=True)) is not None: self.gguf_writer.add_feed_forward_length(n_ff) logger.info(f"gguf: feed forward length = {n_ff}") @@ -2664,7 +2704,7 @@ def get_model_architecture(hparams: dict[str, Any], model_type: ModelType) -> st # Step3-VL keeps text config under text_config but uses a custom top-level architecture. # For text conversion we route to a dedicated text-only class. # TODO: refactor this later to avoid adding exception here - if model_type == ModelType.TEXT and arch in ("StepVLForConditionalGeneration", "Sarashina2VisionForCausalLM", "Exaone4_5_ForConditionalGeneration", "Step3p7ForConditionalGeneration"): + if model_type == ModelType.TEXT and arch in ("StepVLForConditionalGeneration", "Sarashina2VisionForCausalLM", "Exaone4_5_ForConditionalGeneration", "Step3p7ForConditionalGeneration", "KimiK3ForConditionalGeneration"): return arch # if "architectures" is found in the sub-config, use that instead diff --git a/conversion/bert.py b/conversion/bert.py index 49a6948f6ce5..0d25d0d62df5 100644 --- a/conversion/bert.py +++ b/conversion/bert.py @@ -369,12 +369,13 @@ def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Ca return super().filter_tensors(item) def modify_tensors(self, data_torch: torch.Tensor, name: str, bid: int | None) -> Iterable[tuple[str, torch.Tensor]]: - n_experts = self.find_hparam(["num_local_experts", "num_experts"]) if "mlp.experts.mlp.w1" in name: + n_experts = self.find_hparam(["num_local_experts", "num_experts"]) data_torch = data_torch.view(n_experts, self.hparams["n_inner"], self.hparams["n_embd"]) name += ".weight" if "mlp.experts.mlp.w2" in name: + n_experts = self.find_hparam(["num_local_experts", "num_experts"]) data_torch = data_torch.view(n_experts, self.hparams["n_inner"], self.hparams["n_embd"]) data_torch = data_torch.transpose(1, 2) name += ".weight" diff --git a/conversion/bitnet.py b/conversion/bitnet.py index a66446abee2f..0c2baee87608 100644 --- a/conversion/bitnet.py +++ b/conversion/bitnet.py @@ -8,7 +8,7 @@ from .base import ModelBase, TextModel, gguf -@ModelBase.register("BitnetForCausalLM") +@ModelBase.register("BitnetForCausalLM", "BitNetForCausalLM") class BitnetModel(TextModel): model_arch = gguf.MODEL_ARCH.BITNET diff --git a/conversion/chatglm.py b/conversion/chatglm.py index 801913075dbc..d6385503877c 100644 --- a/conversion/chatglm.py +++ b/conversion/chatglm.py @@ -81,7 +81,7 @@ def set_vocab_chatglm3(self): @staticmethod def token_bytes_to_string(b): - from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode # ty: ignore[unresolved-import] + from transformers.convert_slow_tokenizer import bytes_to_unicode byte_encoder = bytes_to_unicode() return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')]) diff --git a/conversion/deepseek.py b/conversion/deepseek.py index ea6ae23d58e7..0518fcecc130 100644 --- a/conversion/deepseek.py +++ b/conversion/deepseek.py @@ -447,12 +447,43 @@ def prepare_tensors(self): class DeepseekV32Model(DeepseekV2Model): model_arch = gguf.MODEL_ARCH.DEEPSEEK32 skip_mtp = False + supports_mtp_export = True + _n_main_layers: int | None = None def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) - self.block_count = self.hparams["num_hidden_layers"] + self.hparams.get("num_nextn_predict_layers", 0) + self.block_count = self.hparams["num_hidden_layers"] + if not self.no_mtp: + self.block_count += self.hparams.get("num_nextn_predict_layers", 0) self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count) + def index_tensors(self, remote_hf_model_id: str | None = None): + type(self)._n_main_layers = self.hparams["num_hidden_layers"] + return super().index_tensors(remote_hf_model_id=remote_hf_model_id) + + @classmethod + def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None: + if (titem := super().filter_tensors(item)) is None: + return None + name, gen = titem + + # DeepSeek V3.2 appends the NextN/MTP block past num_hidden_layers + # (model.layers.61 -> blk.61 in the 62-block file). + assert cls._n_main_layers is not None + is_mtp = (m := re.match(r"model\.layers\.(\d+)\.", name)) is not None and int(m.group(1)) >= cls._n_main_layers + + # --no-mtp: drop the appended NextN block entirely. + if is_mtp and cls.no_mtp: + return None + # --mtp: keep ONLY NextN-block tensors plus the shared embeddings/ + # norm/lm_head (so the resulting GGUF carries just the draft head). + if cls.mtp_only and not is_mtp and name not in ( + "model.embed_tokens.weight", "model.norm.weight", "lm_head.weight", + ): + return None + + return name, gen + def set_vocab(self): from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained(self.dir_model) @@ -463,7 +494,7 @@ def set_gguf_parameters(self): super().set_gguf_parameters() # NextN/MTP prediction layers - if (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None: + if not self.no_mtp and (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None: self.gguf_writer.add_nextn_predict_layers(num_nextn_predict_layers) # DSA indexer parameters @@ -475,7 +506,10 @@ def set_gguf_parameters(self): @ModelBase.register("DeepseekV4ForCausalLM") class DeepseekV4Model(TextModel): model_arch = gguf.MODEL_ARCH.DEEPSEEK4 + supports_mtp_export = True _skipped_mtp_tensors = 0 + _dsv4_main_layers: int | None = None + _dsv4_nextn_layers: int = 0 def __init__(self, *args, **kwargs): type(self)._skipped_mtp_tensors = 0 @@ -487,6 +521,8 @@ def __init__(self, *args, **kwargs): self.hparams.setdefault(key, value) self.block_count = self.hparams["num_hidden_layers"] + if self.mtp_only: + self.block_count += self.hparams.get("num_nextn_predict_layers", 0) self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count) self._dsv4_fp8_dequantized: set[str] = set() @@ -499,18 +535,71 @@ def __init__(self, *args, **kwargs): logger.info("Skipping %d DeepSeek-V4 MTP tensor(s) for conversion v0", type(self)._skipped_mtp_tensors) # add a default chat template; if the model has a built-in template, it will be overridden later - template_path = Path(__file__).parent.parent / "models" / "templates" / "deepseek-ai-DeepSeek-V4.jinja" + model_id_hint = self.remote_hf_model_id or self.dir_model.name + is_0731 = "0731" in model_id_hint + template_name = "deepseek-ai-DeepSeek-V4-Flash-0731.jinja" if is_0731 else "deepseek-ai-DeepSeek-V4.jinja" + template_path = Path(__file__).parent.parent / "models" / "templates" / template_name if template_path.is_file(): with open(template_path, "r", encoding="utf-8") as f: self.gguf_writer.add_chat_template(f.read()) + def index_tensors(self, remote_hf_model_id: str | None = None) -> dict[str, Callable[[], Tensor]]: + type(self)._dsv4_main_layers = self.hparams["num_hidden_layers"] + type(self)._dsv4_nextn_layers = self.hparams.get("num_nextn_predict_layers", 0) + return super().index_tensors(remote_hf_model_id=remote_hf_model_id) + @classmethod def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None: - name, _ = item + name, gen = item if name.startswith("mtp."): - cls._skipped_mtp_tensors += 1 - return None - return super().filter_tensors(item) + if not cls.mtp_only: + cls._skipped_mtp_tensors += 1 + return None + + assert cls._dsv4_main_layers is not None + parts = name.split(".", 2) + if len(parts) < 3 or not parts[1].isdecimal(): + raise ValueError(f"Unexpected DeepSeek-V4 MTP tensor {name!r}") + + mtp_idx = int(parts[1]) + if mtp_idx >= cls._dsv4_nextn_layers: + raise ValueError(f"Unexpected DeepSeek-V4 MTP layer {mtp_idx}") + + bid = cls._dsv4_main_layers + mtp_idx + suffix = parts[2] + root_hc_head = { + "hc_head_fn", + "hc_head_base", + "hc_head_scale", + } + if suffix in root_hc_head: + name = suffix + elif suffix in ( + "e_proj.weight", "e_proj.scale", + "h_proj.weight", "h_proj.scale", + ): + name = f"layers.{bid}.nextn.{suffix}" + elif suffix == "enorm.weight": + name = f"layers.{bid}.nextn.enorm.weight" + elif suffix == "hnorm.weight": + name = f"layers.{bid}.nextn.hnorm.weight" + elif suffix == "norm.weight": + name = f"layers.{bid}.nextn.shared_head_norm.weight" + else: + name = f"layers.{bid}.{suffix}" + return name, gen + + if cls.mtp_only: + keep = name in ( + "embed.weight", + "norm.weight", + "head.weight", + "head.scale", + ) + if not keep: + return None + + return super().filter_tensors((name, gen)) @staticmethod def _float8_dtypes() -> tuple[torch.dtype, ...]: @@ -565,6 +654,10 @@ def set_gguf_parameters(self): self.gguf_writer.add_hyper_connection_sinkhorn_iterations(hparams["hc_sinkhorn_iters"]) self.gguf_writer.add_hyper_connection_epsilon(hparams["hc_eps"]) self.gguf_writer.add_hash_layer_count(hparams["num_hash_layers"]) + if self.model_arch == gguf.MODEL_ARCH.DEEPSEEK4: + self.gguf_writer.add_embedding_length_out(hparams["hidden_size"] * hparams["hc_mult"]) + if self.mtp_only and (num_nextn_predict_layers := hparams.get("num_nextn_predict_layers", 0)) > 0: + self.gguf_writer.add_nextn_predict_layers(num_nextn_predict_layers) def dequant_model(self): fp8_dtypes = self._float8_dtypes() @@ -669,12 +762,37 @@ def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: if self._dsv4_mxfp4_generated: return () - consumed: list[str] = self._write_hash_routing_tensors() + consumed: list[str] = [] + main_layers = self.hparams["num_hidden_layers"] + if not self.mtp_only: + consumed.extend(self._write_hash_routing_tensors()) + elif self.hparams["num_hash_layers"] > 0: + for bid in range(self.hparams["num_hash_layers"]): + name = f"layers.{bid}.ffn.gate.tid2eid" + if name in self.model_tensors: + consumed.extend(self._write_hash_routing_tensors()) + break + for bid in range(self.block_count): + if self.mtp_only and bid < main_layers: + continue consumed.extend(self._write_mxfp4_expert_tensor(bid, "w1", gguf.MODEL_TENSOR.FFN_GATE_EXP)) consumed.extend(self._write_mxfp4_expert_tensor(bid, "w2", gguf.MODEL_TENSOR.FFN_DOWN_EXP)) consumed.extend(self._write_mxfp4_expert_tensor(bid, "w3", gguf.MODEL_TENSOR.FFN_UP_EXP)) + for bid in range(main_layers, self.block_count): + e_name = f"layers.{bid}.nextn.e_proj.weight" + h_name = f"layers.{bid}.nextn.h_proj.weight" + if e_name not in self.model_tensors and h_name not in self.model_tensors: + continue + if e_name not in self.model_tensors or h_name not in self.model_tensors: + raise KeyError(f"Missing DeepSeek-V4 MTP e/h projection pair for block {bid}") + + e_proj = LazyTorchTensor.to_eager(self.model_tensors[e_name]()) + h_proj = LazyTorchTensor.to_eager(self.model_tensors[h_name]()) + yield (f"layers.{bid}.nextn.eh_proj.weight", torch.cat((e_proj, h_proj), dim=1).contiguous()) + consumed.extend((e_name, h_name)) + for name in consumed: del self.model_tensors[name] @@ -737,6 +855,12 @@ def _map_dsv4_tensor_name(self, name: str, bid: int | None) -> tuple[gguf.MODEL_ "ffn.shared_experts.w1.weight": (gguf.MODEL_TENSOR.FFN_GATE_SHEXP, ".weight"), "ffn.shared_experts.w2.weight": (gguf.MODEL_TENSOR.FFN_DOWN_SHEXP, ".weight"), "ffn.shared_experts.w3.weight": (gguf.MODEL_TENSOR.FFN_UP_SHEXP, ".weight"), + "nextn.eh_proj.weight": (gguf.MODEL_TENSOR.NEXTN_EH_PROJ, ".weight"), + "nextn.enorm.weight": (gguf.MODEL_TENSOR.NEXTN_ENORM, ".weight"), + "nextn.hnorm.weight": (gguf.MODEL_TENSOR.NEXTN_HNORM, ".weight"), + "nextn.shared_head_norm.weight": (gguf.MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM, ".weight"), + "nextn.embed_tokens.weight": (gguf.MODEL_TENSOR.NEXTN_EMBED_TOKENS, ".weight"), + "nextn.shared_head_head.weight": (gguf.MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD, ".weight"), } tensor_name = match.group(2) @@ -759,10 +883,12 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter return [(self._format_dsv4_tensor_name(tensor_key, bid, suffix), data_torch)] def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool: - del new_name, bid # unused + del bid # unused if name in self._dsv4_fp8_dequantized and n_dims >= 2: return gguf.GGMLQuantizationType.Q8_0 + if new_name.endswith(".nextn.eh_proj.weight"): + return gguf.GGMLQuantizationType.Q8_0 if name in self._dsv4_f32_tensors: return gguf.GGMLQuantizationType.F32 if name in self._dsv4_bf16_tensors and n_dims >= 2: @@ -770,7 +896,122 @@ def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: return False + def prepare_metadata(self, vocab_only: bool): + from_dir = self.fname_out.is_dir() + super().prepare_metadata(vocab_only=vocab_only) + + if not self.mtp_only or not from_dir: + return + + output_type: str = self.ftype.name.partition("_")[2] + fname_default: str = gguf.naming_convention( + self.metadata.name, self.metadata.basename, self.metadata.finetune, + self.metadata.version, size_label=None, output_type=output_type, model_type=None) + self.fname_out = self.fname_out.parent / f"mtp-{fname_default}.gguf" + def prepare_tensors(self): super().prepare_tensors() self._is_mxfp4 = True self.ftype = gguf.LlamaFileType.MOSTLY_MXFP4_MOE + + +@ModelBase.register("DeepseekV4DSparkModel") +class DeepseekV4DSparkModel(DeepseekV4Model): + model_arch = gguf.MODEL_ARCH.DFLASH + + _DSPARK_ROOT_MAP: dict[str, tuple[gguf.MODEL_TENSOR, str]] = { + "main_proj.weight": (gguf.MODEL_TENSOR.FC, ".weight"), + "main_norm.weight": (gguf.MODEL_TENSOR.ENC_OUTPUT_NORM, ".weight"), + "markov_head.markov_w1.weight": (gguf.MODEL_TENSOR.DSPARK_MARKOV_W1, ".weight"), + "markov_head.markov_w2.weight": (gguf.MODEL_TENSOR.DSPARK_MARKOV_W2, ".weight"), + "confidence_head.proj.weight": (gguf.MODEL_TENSOR.DSPARK_CONF_PROJ, ".weight"), + } + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + self.block_count = 1 + max( + int(match.group(1)) for name in self.model_tensors + if (match := re.match(r"layers\.(\d+)\.", name)) + ) + self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count) + + self.hparams["compress_ratios"] = [0] * self.block_count + self.hparams["num_hash_layers"] = 0 + + def index_tensors(self, remote_hf_model_id: str | None = None) -> dict[str, Callable[[], Tensor]]: + if remote_hf_model_id is None: + return super().index_tensors() + + with open(self.dir_model / "model.safetensors.index.json", "r", encoding="utf-8") as f: + weight_map = json.load(f)["weight_map"] + + part_names = sorted({ + part_name for name, part_name in weight_map.items() + if name.startswith("mtp.") + }) + tensors: dict[str, Callable[[], Tensor]] = {} + + for part_name in part_names: + from huggingface_hub import hf_hub_download + + logger.info("gguf: caching remote DSpark part '%s'", part_name) + part_path = Path(hf_hub_download(repo_id=remote_hf_model_id, filename=part_name)) + with gguf.utility.SafetensorsLocal(part_path) as model_part: + for name in model_part: + data = model_part[name] + data_gen = lambda data=data: LazyTorchTensor.from_local_tensor(data) # noqa: E731 + if titem := self.filter_tensors((name, data_gen)): + tensor_name, tensor_gen = titem + tensors[tensor_name] = tensor_gen + + return tensors + + @classmethod + def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None: + name, gen = item + if not name.startswith("mtp."): + return None + return super().filter_tensors((cls._rekey_mtp_tensor_name(name), gen)) + + @staticmethod + def _rekey_mtp_tensor_name(name: str) -> str: + match = re.match(r"mtp\.(\d+)\.(.+)$", name) + if match is None: + raise ValueError(f"Unexpected DSpark tensor {name!r}") + + stage, rest = match.group(1), match.group(2) + root_names = ( + "main_proj.scale", + "norm.weight", + "hc_head_fn", + "hc_head_base", + "hc_head_scale", + ) + if rest in DeepseekV4DSparkModel._DSPARK_ROOT_MAP or rest in root_names: + return rest + return f"layers.{stage}.{rest}" + + def _map_dsv4_tensor_name(self, name: str, bid: int | None) -> tuple[gguf.MODEL_TENSOR, str]: + if name in self._DSPARK_ROOT_MAP: + return self._DSPARK_ROOT_MAP[name] + return super()._map_dsv4_tensor_name(name, bid) + + def set_vocab(self): + if self.target_model_dir is None: + raise ValueError("DeepSeek-V4 DSpark requires --target-model-dir with the target tokenizer") + + original_dir = self.dir_model + try: + self.dir_model = self.target_model_dir + super().set_vocab() + finally: + self.dir_model = original_dir + + self.gguf_writer.add_mask_token_id(self.hparams["dspark_noise_token_id"]) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + + self.gguf_writer.add_block_size(self.hparams["dspark_block_size"]) + self.gguf_writer.add_target_layers([layer + 1 for layer in self.hparams["dspark_target_layer_ids"]]) diff --git a/conversion/glm.py b/conversion/glm.py index 895cefc22b89..e28f54574e07 100644 --- a/conversion/glm.py +++ b/conversion/glm.py @@ -1,6 +1,8 @@ from __future__ import annotations -from typing import Iterable, TYPE_CHECKING +import re + +from typing import Callable, Iterable, TYPE_CHECKING import torch @@ -204,21 +206,116 @@ def prepare_tensors(self): @ModelBase.register("Glm4MoeLiteForCausalLM") class Glm4MoeLiteModel(DeepseekV2Model): model_arch = gguf.MODEL_ARCH.DEEPSEEK2 + skip_mtp = False + supports_mtp_export = True + _n_main_layers: int | None = None def set_vocab(self): return self._set_vocab_glm() + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + num_hidden_layers = self.hparams["num_hidden_layers"] + self.num_nextn_predict_layers = self.hparams.get("num_nextn_predict_layers", 0) + self.skip_mtp = self.no_mtp or self.num_nextn_predict_layers == 0 + + if self.skip_mtp: + self.block_count = num_hidden_layers + else: + self.block_count = num_hidden_layers + self.num_nextn_predict_layers + + self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + + if self.skip_mtp: + return + + self.gguf_writer.add_nextn_predict_layers(self.num_nextn_predict_layers) + + def index_tensors(self, remote_hf_model_id: str | None = None): + type(self)._n_main_layers = self.hparams["num_hidden_layers"] + return super().index_tensors(remote_hf_model_id=remote_hf_model_id) + + @classmethod + def filter_tensors(cls, item): + if (titem := super().filter_tensors(item)) is None: + return None + name, gen = titem + + if cls._n_main_layers is not None: + match = re.match(r"model\.layers\.(\d+)\.", name) + is_mtp = match is not None and int(match.group(1)) >= cls._n_main_layers + if is_mtp and cls.no_mtp: + return None + if cls.mtp_only and not is_mtp and name not in ( + "model.embed_tokens.weight", "model.norm.weight", "lm_head.weight", + ): + return None + + return name, gen + + def prepare_metadata(self, vocab_only: bool): + from_dir = self.fname_out.is_dir() + super().prepare_metadata(vocab_only=vocab_only) + + if not self.mtp_only or not from_dir: + return + + output_type: str = self.ftype.name.partition("_")[2] + fname_default: str = gguf.naming_convention( + self.metadata.name, self.metadata.basename, self.metadata.finetune, + self.metadata.version, size_label=None, output_type=output_type, model_type=None) + self.fname_out = self.fname_out.parent / f"mtp-{fname_default}.gguf" + @ModelBase.register("GlmMoeDsaForCausalLM") class GlmMoeDsaModel(DeepseekV2Model): model_arch = gguf.MODEL_ARCH.GLM_DSA skip_mtp = False + supports_mtp_export = True + + # Trunk layer count, stashed before indexing so the classmethod + # filter_tensors can identify the appended NextN/MTP block (mirrors + # HYV3Model / Step35Model). + _n_main_layers: int | None = None def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) - self.block_count = self.hparams["num_hidden_layers"] + self.hparams.get("num_nextn_predict_layers", 0) + self.block_count = self.hparams["num_hidden_layers"] + if not self.no_mtp: + self.block_count += self.hparams.get("num_nextn_predict_layers", 0) self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count) + def index_tensors(self, remote_hf_model_id: str | None = None): + type(self)._n_main_layers = self.hparams["num_hidden_layers"] + return super().index_tensors(remote_hf_model_id=remote_hf_model_id) + + @classmethod + def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None: + if (titem := super().filter_tensors(item)) is None: + return None + name, gen = titem + + # GLM-5.2 appends the NextN/MTP block past num_hidden_layers + # (model.layers.78 -> blk.78 in the 79-block file). + assert cls._n_main_layers is not None + is_mtp = (m := re.match(r"model\.layers\.(\d+)\.", name)) is not None and int(m.group(1)) >= cls._n_main_layers + + # --no-mtp: drop the appended NextN block entirely. + if is_mtp and cls.no_mtp: + return None + # --mtp: keep ONLY NextN-block tensors plus the shared embeddings/ + # norm/lm_head (so the resulting GGUF carries just the draft head). + if cls.mtp_only and not is_mtp and name not in ( + "model.embed_tokens.weight", "model.norm.weight", "lm_head.weight", + ): + return None + + return name, gen + def set_vocab(self): return self._set_vocab_glm() @@ -230,13 +327,16 @@ def set_gguf_parameters(self): self.gguf_writer.add_rope_dimension_count(int(rope_dim * partial_rotary_factor)) # NextN/MTP prediction layers - if (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None: + if not self.no_mtp and (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None: self.gguf_writer.add_nextn_predict_layers(num_nextn_predict_layers) # DSA indexer parameters self.gguf_writer.add_indexer_head_count(self.hparams["index_n_heads"]) self.gguf_writer.add_indexer_key_length(self.hparams["index_head_dim"]) self.gguf_writer.add_indexer_top_k(self.hparams["index_topk"]) + if (indexer_types := self.hparams.get("indexer_types")) is not None: + indexer_types = [t == "full" for t in indexer_types] + self.gguf_writer.add_indexer_types(indexer_types) @ModelBase.register("SolarOpenForCausalLM") diff --git a/conversion/hunyuan.py b/conversion/hunyuan.py index 65d294fbe978..f5ac8a4fb7f1 100644 --- a/conversion/hunyuan.py +++ b/conversion/hunyuan.py @@ -338,6 +338,12 @@ class HunyuanVLTextModel(HunYuanModel): def __init__(self, dir_model: Path, *args, **kwargs): super().__init__(dir_model, *args, **kwargs) + # transformers 5.13.0 encodes HunyuanVL XD-RoPE as dynamic + mrope_section. + # Normalize it to avoid the HunYuan dynamic-RoPE context assertion. + if self.rope_parameters.get("rope_type") == "dynamic" and "mrope_section" in self.rope_parameters: + self.rope_parameters["rope_type"] = "xdrope" + self.rope_parameters["type"] = "xdrope" + self.rope_parameters["xdrope_section"] = list(self.rope_parameters["mrope_section"]) def set_gguf_parameters(self): super().set_gguf_parameters() diff --git a/conversion/kimi_k3.py b/conversion/kimi_k3.py new file mode 100644 index 000000000000..97c6160db79e --- /dev/null +++ b/conversion/kimi_k3.py @@ -0,0 +1,97 @@ +from __future__ import annotations + +import json +from pathlib import Path +from typing import Iterable, TYPE_CHECKING + +import torch + +if TYPE_CHECKING: + from torch import Tensor + +from .base import ModelBase, gguf, logger +from .kimi_linear import KimiLinearModel + + +@ModelBase.register("KimiK3ForConditionalGeneration") +class KimiK3Model(KimiLinearModel): + """Kimi K3: hybrid KDA + gated-MLA (NoPE) with Attention Residuals and Stable LatentMoE. + + Text config is `kimi_linear` with K3 extensions: + - SiTU-GLU activation (soft-capped SiLU) in dense MLP, shared and routed experts + - AttnRes: residual-stream snapshot bank every `attn_res_block_size` layers, + softmax mixtures before attention, before MLP and at model output + - Stable LatentMoE: routed experts run in a `routed_expert_hidden_size` latent + space (down proj -> experts -> weighted sum -> RMSNorm -> up proj) + - KDA safe gate: g_log = gate_lower_bound * sigmoid(exp(A_log) * (g_raw + dt_bias)) + with a full-rank output gate g_proj instead of the low-rank g_a/g_b pair + - MLA output gate: attn = attn * sigmoid(g_proj(x)) before o_proj + Routed expert weights are MXFP4 (compressed-tensors), dequantized in ModelBase. + """ + model_arch = gguf.MODEL_ARCH.KIMI_K3 + + def set_vocab(self): + super().set_vocab() + # KimiLinearModel.set_vocab forces the tokenizer's own eos, which for K3 + # is 163585 = [EOS], the document terminator. The config says 163586 = + # <|end_of_msg|>, the chat turn terminator; keeping [EOS] means chat + # generation never stops at the end of an assistant turn. Restore it. + if (eos := self.hparams.get("eos_token_id")) is not None: + self.gguf_writer.add_eos_token_id(eos) + + # Moonshot ships no chat_template in tokenizer_config.json (K3 is + # API-first), so GGUFs come out template-less and chat tools refuse to + # run. Embed the reference template from models/templates/Kimi-K3.jinja + # unless the checkpoint provides one. + has_template = (self.dir_model / "chat_template.jinja").is_file() + if not has_template: + try: + with open(self.dir_model / "tokenizer_config.json", encoding="utf-8") as f: + has_template = "chat_template" in json.load(f) + except OSError: + pass + if not has_template: + tmpl = Path(__file__).resolve().parent.parent / "models" / "templates" / "Kimi-K3.jinja" + if tmpl.is_file(): + logger.info("embedding reference chat template from models/templates/Kimi-K3.jinja") + self.gguf_writer.add_chat_template(tmpl.read_text(encoding="utf-8")) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + + # Stable LatentMoE + self.gguf_writer.add_moe_latent_size(self.hparams["routed_expert_hidden_size"]) + + # SiTU-GLU activation parameters + self.gguf_writer.add_situ_beta(self.hparams["activation_situ_beta"]) + self.gguf_writer.add_situ_linear_beta(self.hparams["activation_situ_linear_beta"]) + + # Attention residuals + self.gguf_writer.add_attn_res_block_size(self.hparams["attn_res_block_size"]) + + # KDA safe gate lower bound + self.gguf_writer.add_kda_gate_lower_bound(self.hparams["linear_attn_config"]["gate_lower_bound"]) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # text-only conversion: vision tensors are handled by the mmproj path + if name.startswith(("vision_tower.", "mm_projector.")): + return + + name = name.removeprefix("language_model.") + + # K3 checkpoints store A_log as [head_dim] (128) but only the first + # num_heads (96) entries are used. The safe-gate formula is + # g_log = gate_lower_bound * sigmoid(exp(A_log) * (g_raw + dt_bias)) + # so we store exp(A_log) directly (unlike Kimi-Linear's -exp(A_log)). + if name.endswith(".A_log"): + n_head = self.hparams["num_attention_heads"] + data_torch = torch.exp(data_torch.float()[:n_head]) + # skip KimiLinearModel's -exp(A_log) handling + yield from super(KimiLinearModel, self).modify_tensors(data_torch, name, bid) + return + + # res projections are stored as [1, n_embd]: flatten to [n_embd] + if name.endswith(("_res_proj.weight", "_res_norm.weight")): + data_torch = data_torch.reshape(-1) + + yield from super().modify_tensors(data_torch, name, bid) diff --git a/conversion/kimivl.py b/conversion/kimivl.py index 63b8a079b722..a5e278f1c090 100644 --- a/conversion/kimivl.py +++ b/conversion/kimivl.py @@ -152,3 +152,95 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter name = name.replace(".proj.2.", ".proj.linear_2.") yield from super().modify_tensors(data_torch, name, bid) + + +@ModelBase.register("Glm5vForConditionalGeneration") +class Glm5vModel(KimiK25Model): + """GLM-5.2-Vision MoonViT3d encoder and projector + + Uses the same vision encoder and projector as Kimi-K2.5, so it reuses the + kimik25 projector type. The image begin/end tokens differ, but they are + resolved at runtime from the text model vocab. + """ + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + if name.startswith("mm_projector.linear_"): + name = name.replace("mm_projector.linear_", "mm_projector.proj.linear_", 1) + + yield from super().modify_tensors(data_torch, name, bid) + + +@ModelBase.register("KimiK3ForConditionalGeneration") +class KimiK3VisionModel(MmprojModel): + """Kimi-K3 MoonViT-3d vision tower (image path). + + Structurally the Kimi-K2.5 tower with RMSNorm, no biases, a non-square fused QKV + (qkv_hidden_size 1536 vs vt_hidden_size 1024) and a post-norm patchmergerv2 projector. + Video is out of scope: for t == 1 the temporal pool and temporal position term vanish. + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + assert self.hparams_vision is not None, "Kimi-K3 requires vision_config in config.json" + self.merge_kernel_size = tuple(self.hparams_vision.get("merge_kernel_size", [2, 2])) + self.patch_size = self.hparams_vision.get("patch_size", 14) + pos_emb_h = self.hparams_vision.get("init_pos_emb_height", 64) + self.hparams_vision["image_size"] = pos_emb_h * self.patch_size + + def set_gguf_parameters(self): + super().set_gguf_parameters() + assert self.hparams_vision is not None + self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.KIMIK3) + + # qkv width != n_embd, so the runtime cannot derive d_head + n_head = self.hparams_vision["vt_num_attention_heads"] + qkv_hidden = self.hparams_vision.get("qkv_hidden_size") or self.hparams_vision["vt_hidden_size"] + assert qkv_hidden % n_head == 0, f"qkv_hidden_size {qkv_hidden} not divisible by {n_head} heads" + self.gguf_writer.add_vision_head_dim(qkv_hidden // n_head) + + self.gguf_writer.add_vision_use_gelu(True) # activation_func is gelu_pytorch_tanh + self.gguf_writer.add_vision_attention_layernorm_eps( + self.hparams_vision.get("projector_ln_eps", 1e-5)) + self.gguf_writer.add_vision_projector_scale_factor(self.merge_kernel_size[0]) + + in_patch_limit = self.preprocessor_config.get("media_proc_cfg", {}).get( + "in_patch_limit", self.preprocessor_config.get("in_patch_limit", 16384)) + pixels_per_patch = self.patch_size ** 2 + self.gguf_writer.add_vision_min_pixels(8 * pixels_per_patch) + self.gguf_writer.add_vision_max_pixels(in_patch_limit * pixels_per_patch) + + @classmethod + def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None: + name, _ = item + if not name.startswith(("vision_tower.", "mm_projector.")): + return None + return super().filter_tensors(item) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + assert self.hparams_vision is not None + n_head = self.hparams_vision["vt_num_attention_heads"] + + if "wqkv" in name and "weight" in name: + # de-interleave Q/K so the runtime can use build_rope_2d(interleave_freq=false) + out_dim = data_torch.shape[0] + qkv_dim = out_dim // 3 + head_dim = qkv_dim // n_head + wq, wk, wv = (data_torch[:qkv_dim], data_torch[qkv_dim:2 * qkv_dim], data_torch[2 * qkv_dim:]) + + def deinterleave(w: Tensor) -> Tensor: + return (w.reshape(n_head, head_dim // 4, 2, 2, w.shape[-1]) + .permute(0, 2, 1, 3, 4) + .reshape(w.shape[0], w.shape[-1])) + + data_torch = torch.cat([deinterleave(wq), deinterleave(wk), wv], dim=0) + + if "pos_emb.weight" in name: + # kept 3D: the runtime reads grid extents from ne[1]/ne[2] + pass + + if "mm_projector.proj.0." in name: + name = name.replace(".proj.0.", ".proj.linear_1.") + elif "mm_projector.proj.2." in name: + name = name.replace(".proj.2.", ".proj.linear_2.") + + yield from super().modify_tensors(data_torch, name, bid) diff --git a/conversion/laguna.py b/conversion/laguna.py index 9a9567e14e0a..a90f355ca9b1 100644 --- a/conversion/laguna.py +++ b/conversion/laguna.py @@ -23,6 +23,15 @@ class LagunaModel(TextModel): def set_vocab(self) -> None: self._set_vocab_gpt2() + # Some Laguna releases wrap the chat template in tokenizer_config.json as + # "{% include 'chat_template.jinja' %}", which SpecialVocab embeds verbatim + # and llama.cpp's jinja engine cannot process. Prefer the resolved template + # from the chat_template.jinja file so the GGUF is self-contained. + tmpl_file = self.dir_model / "chat_template.jinja" + if tmpl_file.is_file(): + self.gguf_writer.add_chat_template(tmpl_file.read_text(encoding="utf-8")) + logger.info("gguf: embedded resolved chat_template.jinja (overriding include directive)") + # eos_token_id is a list [2, 24]: token 2 (EOS, also BOS) and token 24 # (, the turn-end). _set_vocab_gpt2 only records the scalar # eos, so register the extra id as eot; llama.cpp folds eot into its EOG @@ -35,6 +44,20 @@ def set_vocab(self) -> None: self.gguf_writer.add_eot_token_id(extra[0]) logger.info(f"gguf: registered eot_token_id={extra[0]} from eos list {eos_ids}") + def get_vocab_base(self) -> tuple[list[str], list[int], str]: + # is the assistant turn-end (registered as eot below). The + # HF tokenizer flags it special=false, so the base classifies it as + # USER_DEFINED and llama.cpp renders its text into generated content, + # leaking "" and breaking response parsing. It is a control + # marker, so promote it to CONTROL: llama.cpp then treats it as + # end-of-generation and suppresses its text. + tokens, toktypes, tokpre = super().get_vocab_base() + for i, tok in enumerate(tokens): + if tok == "": + toktypes[i] = gguf.TokenType.CONTROL + logger.info(f"gguf: marked (id {i}) as CONTROL token") + return tokens, toktypes, tokpre + # --- hparams ------------------------------------------------------------- def set_gguf_parameters(self) -> None: diff --git a/conversion/llama.py b/conversion/llama.py index 315a619c9c26..1aced49c54d2 100644 --- a/conversion/llama.py +++ b/conversion/llama.py @@ -69,9 +69,14 @@ def __init__(self, *args, **kwargs): target_config = {**target_config, **target_config["text_config"]} self.target_vocab_size = target_config["vocab_size"] - # target_layers: derived from target model layer count (low/mid/high) + # target_layers: use the eagle3 config's explicit aux hidden-state layer ids + # if present, else derive from the target layer count. target_num_layers = target_config["num_hidden_layers"] - target_layers = [2, target_num_layers // 2, target_num_layers - 3] + aux_layer_ids = eagle3_raw_config.get("eagle_aux_hidden_state_layer_ids") + if aux_layer_ids: + target_layers = aux_layer_ids + else: + target_layers = [2, target_num_layers // 2, target_num_layers - 3] logger.info(f"EAGLE-3: target_layers = {target_layers} (target model has {target_num_layers} layers)") self.gguf_writer.add_target_layers(target_layers) @@ -90,6 +95,12 @@ def __init__(self, *args, **kwargs): logger.info(f"EAGLE-3: norm_before_residual = {norm_before_residual}") self.gguf_writer.add_norm_before_residual(norm_before_residual) + # norm_before_fc: RMSNorm applied to the fused target features before the + # fc projection (e.g. nvidia/gpt-oss-120b-Eagle3-v3) + norm_before_fc = eagle3_raw_config.get("norm_before_fc", False) + logger.info(f"EAGLE-3: norm_before_fc = {norm_before_fc}") + self.gguf_writer.add_norm_before_fc(norm_before_fc) + def set_vocab(self): # eagle3: use tokenizer from target model if provided original_dir_model = None @@ -108,7 +119,7 @@ def set_vocab(self): path_tekken_json = self.dir_model / "tekken.json" path_tokenizer_json = self.dir_model / "tokenizer.json" if path_tekken_json.is_file() and not path_tokenizer_json.is_file(): - self._set_vocab_mistral() + return self._set_vocab_mistral() tokenizer_config_file = self.dir_model / 'tokenizer_config.json' if tokenizer_config_file.is_file(): @@ -222,6 +233,9 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter if name == "fc.weight": yield (name, data_torch) return + if name == "input_norm.weight": + yield (self.format_tensor_name(gguf.MODEL_TENSOR.ENC_OUTPUT_NORM), data_torch) + return if name == "d2t": # store for manual int64 handling in prepare_tensors (avoid F32 conversion) if not hasattr(self, '_eagle3_int_tensors'): diff --git a/conversion/mimo.py b/conversion/mimo.py index 11ec2867940a..ca2ed28ad391 100644 --- a/conversion/mimo.py +++ b/conversion/mimo.py @@ -1,8 +1,9 @@ from __future__ import annotations +import json import re -from typing import Callable, TYPE_CHECKING +from typing import Any, Callable, Iterable, TYPE_CHECKING import torch @@ -229,7 +230,13 @@ def prepare_tensors(self): @ModelBase.register("MiMoV2ForCausalLM") -class MiMoV2VisionModel(MmprojModel): +class MiMoV2VisionAudioModel(MmprojModel): + has_audio_encoder = True + + _audio_tok_hparams: dict[str, Any] | None = None + _rvq_codebook_sizes: list[int] | None = None + _code_embd: dict[int, Tensor] | None = None + def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) assert self.hparams_vision is not None @@ -253,10 +260,22 @@ def __init__(self, *args, **kwargs): self.visual_token_window_size = int(hp.get("visual_token_window_size", -1)) self.use_sink = bool(hp.get("use_sink", False)) + def get_audio_config(self) -> dict[str, Any] | None: + if self._audio_tok_hparams is None: + path = self.dir_model / "audio_tokenizer" / "config.json" + with open(path, "r", encoding="utf-8") as f: + cfg = json.load(f) + # aliases so MmprojModel.find_aparam() / n_block_keys can resolve them + cfg["hidden_size"] = cfg["d_model"] + cfg["intermediate_size"] = cfg["encoder_ffn_dim"] + cfg["num_attention_heads"] = cfg["encoder_attention_heads"] + self._audio_tok_hparams = cfg + return self._audio_tok_hparams + def set_gguf_parameters(self): super().set_gguf_parameters() - self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.MIMOVL) + self.gguf_writer.add_clip_vision_projector_type(gguf.VisionProjectorType.MIMOVL) self.gguf_writer.add_vision_use_silu(True) self.gguf_writer.add_vision_head_count_kv(self.num_kv_heads) self.gguf_writer.add_vision_spatial_merge_size(self.spatial_merge_size) @@ -266,19 +285,45 @@ def set_gguf_parameters(self): self.gguf_writer.add_vision_min_pixels(int(self.preprocessor_config["min_pixels"])) self.gguf_writer.add_vision_max_pixels(int(self.preprocessor_config["max_pixels"])) + assert self.hparams_audio is not None + self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.MIMO_AUDIO) + self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["n_mels"]) + self.gguf_writer.add_audio_attention_layernorm_eps(self.hparams_audio.get("layer_norm_eps", 1e-5)) + + assert self._rvq_codebook_sizes is not None + self.gguf_writer.add_audio_rvq_num_quantizers(len(self._rvq_codebook_sizes)) + self.gguf_writer.add_audio_rvq_codebook_size(self._rvq_codebook_sizes) + + n_layer = self.hparams_audio["encoder_layers"] + swa_per_block = self.hparams_audio.get("swa_per_block", 1) + if self.hparams_audio.get("hybrid_attention") and swa_per_block > 1: + wa_pattern = [0 if i % swa_per_block < swa_per_block - 1 else -1 for i in range(n_layer)] + else: + wa_pattern = [-1] * n_layer + self.gguf_writer.add_audio_wa_pattern_mode(wa_pattern) + self.gguf_writer.add_audio_window_size(int(self.hparams_audio["encoder_attn_window_size"][0])) + + audio_cfg = self.global_config["audio_config"] + self.gguf_writer.add_audio_local_block_count(int(audio_cfg["input_local_layers"])) + self.gguf_writer.add_audio_local_group_size(int(audio_cfg["group_size"])) + def tensor_force_quant(self, name, new_name, bid, n_dims): - # Sinks must be F32: any sink-style softmax/mask add in ggml requires - # F32, and we fold sinks into a host-built F32 mask at encode time. - if new_name.endswith(".attn_sinks"): + # for audio encoder: keep codebook in F32 + if new_name in ( + gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.A_ENC_RVQ_CODEBOOK] + ".weight", + gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.A_MM_CODE_EMBD] + ".weight", + ): + return gguf.GGMLQuantizationType.F32 + if ("encoder.conv" in name or "encoder.down_sample_layer" in name) and name.endswith(".weight"): return gguf.GGMLQuantizationType.F32 return super().tensor_force_quant(name, new_name, bid, n_dims) @classmethod def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None: name, _ = item - if not name.startswith("visual."): - return None - return super().filter_tensors(item) + if name.startswith("visual.") or name.startswith("speech_embeddings.") or name.startswith("audio_encoder."): + return super().filter_tensors(item) + return None def modify_tensors(self, data_torch, name, bid): # Conv3D patch embed: split along the temporal axis (kt=2) into two Conv2D @@ -292,4 +337,64 @@ def modify_tensors(self, data_torch, name, bid): yield (embd_name + ".weight.1", data_torch[:, :, 1, ...]) return + if m := re.match(r"^speech_embeddings\.(\d+)\.weight$", name): + if self._code_embd is None: + self._code_embd = {} + self._code_embd[int(m.group(1))] = data_torch + + n_channels = int(self.global_config["audio_config"]["audio_channels"]) + if len(self._code_embd) < n_channels: + return + merged = torch.stack([self._code_embd.pop(i) for i in range(n_channels)], dim=0) + yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_MM_CODE_EMBD), merged) + return + + if "conv1.bias" in name or "conv2.bias" in name: + # transpose conv1/conv2 bias so it broadcasts against [n_frames, C_out, 1] + data_torch = data_torch.unsqueeze(-1) + + if name == "audio_encoder.projection.mlp.0.weight": + yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_MMPROJ, 1), data_torch) + return + if name == "audio_encoder.projection.mlp.2.weight": + yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_MMPROJ, 2), data_torch) + return + yield from super().modify_tensors(data_torch, name, bid) + + def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: + # note: audio encoder is in its own subdir "audio_tokenizer" + from safetensors.torch import load_file + + tok_dir = self.dir_model / "audio_tokenizer" + state_dict = load_file(tok_dir / "model.safetensors") + + codebook_re = re.compile(r"^encoder\.quantizer\.vq\.layers\.(\d+)\._codebook\.embed$") + codebooks: dict[int, Tensor] = {} + + # EMA/training-only RVQ buffers - not needed for inference (nearest-codebook + # lookup only reads "_codebook.embed") + skip_suffixes = ( + "_codebook.cluster_size", + "_codebook.embed_avg", + "_codebook.inited", + ) + for name, tensor in state_dict.items(): + if name.endswith(skip_suffixes): + continue + if m := codebook_re.match(name): + codebooks[int(m.group(1))] = tensor + continue + yield name, tensor + + # gather codebooks and merge into 3D tensor, similar to MoE MLP tensors + n_q = len(codebooks) + ordered = [codebooks[i] for i in range(n_q)] + self._rvq_codebook_sizes = [int(cb.shape[0]) for cb in ordered] + max_bins = max(self._rvq_codebook_sizes) + dim = ordered[0].shape[1] + merged = ordered[0].new_zeros(n_q, max_bins, dim) + for i, cb in enumerate(ordered): + merged[i, : cb.shape[0], :] = cb + + yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_ENC_RVQ_CODEBOOK), merged) diff --git a/conversion/minicpm.py b/conversion/minicpm.py index e31b26a00808..bf3fa81421bd 100644 --- a/conversion/minicpm.py +++ b/conversion/minicpm.py @@ -137,6 +137,15 @@ def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Ca class MiniCPMV4_6VisionModel(MmprojModel): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) + self.downsample_mode = self.preprocessor_config.get("downsample_mode", "16x") + if self.downsample_mode not in {"4x", "16x"}: + raise ValueError(f"Unsupported downsample mode: {self.downsample_mode}") + if self.downsample_mode == "4x": + self.model_tensors = { + name: tensor for name, tensor in self.model_tensors.items() + if ".vit_merger." not in name + } + if self.hparams_vision is not None: # In MiniCPM-V 4.6 `vision_config.image_size` (980) describes the SigLIP # positional embedding bucket grid (70 x 70), while the per-slice processing @@ -156,8 +165,8 @@ def set_gguf_parameters(self): # (mapped to PROJECTOR_TYPE_MINICPMV4_6). self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.MINICPMV4_6) - # ViT merger 2x2 + final merger 2x2 = 4x spatial merge per dimension; used for slice alignment - self.gguf_writer.add_vision_projector_scale_factor(4) + self.gguf_writer.add_vision_projector_scale_factor( + 2 if self.downsample_mode == "4x" else 4) # borrow wa_layer_indexes for vit_merger insertion point insert_layer_id = int(self.global_config.get( diff --git a/conversion/minimax.py b/conversion/minimax.py index 4857775cbfb9..c2175cc93267 100644 --- a/conversion/minimax.py +++ b/conversion/minimax.py @@ -7,7 +7,7 @@ if TYPE_CHECKING: from torch import Tensor -from .base import ModelBase, TextModel, gguf +from .base import ModelBase, TextModel, MmprojModel, gguf @ModelBase.register("MiniMaxM2ForCausalLM") @@ -23,7 +23,7 @@ def set_gguf_parameters(self): def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None): # merge expert weights - if 'experts' in name: + if "block_sparse_moe.experts." in name: n_experts = self.find_hparam(["num_local_experts", "num_experts"]) assert bid is not None @@ -52,3 +52,118 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None): return yield from super().modify_tensors(data_torch, name, bid) + + +@ModelBase.register("MiniMaxM3SparseForCausalLM", "MiniMaxM3SparseForConditionalGeneration") +class MiniMaxM3Model(MiniMaxM2Model): + model_arch = gguf.MODEL_ARCH.MINIMAXM3 + + def tensor_force_quant(self, name, new_name, bid, n_dims): + if ".indexer." in new_name: + return gguf.GGMLQuantizationType.F32 + return super().tensor_force_quant(name, new_name, bid, n_dims) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + + self.gguf_writer.add_expert_shared_count(self.find_hparam(["n_shared_experts"])) + self.gguf_writer.add_expert_weights_scale(self.find_hparam(["routed_scaling_factor"])) + self.gguf_writer.add_expert_weights_norm(True) + + sac = self.find_hparam(["sparse_attention_config"]) + self.gguf_writer.add_indexer_head_count(sac["sparse_num_index_heads"]) + self.gguf_writer.add_indexer_key_length(sac["sparse_index_dim"]) + self.gguf_writer.add_indexer_top_k(sac["sparse_topk_blocks"]) + self.gguf_writer.add_indexer_block_size(sac["sparse_block_size"]) + self.gguf_writer.add_indexer_local_blocks(sac["sparse_local_block"]) + + moe_layer_freq = self.find_hparam(["moe_layer_freq"]) + n_dense = 0 + for v in moe_layer_freq: + if v == 0: + n_dense += 1 + else: + break + self.gguf_writer.add_leading_dense_block_count(n_dense) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None): + # Gemma-style (1 + w) RMSNorm: bake the +1 in so llama.cpp can use plain RMSNorm + if name.endswith("norm.weight"): + data_torch = data_torch + 1.0 + + yield from super().modify_tensors(data_torch, name, bid) + + +@ModelBase.register("MiniMaxM3SparseForConditionalGeneration", "MiniMaxM3VLForConditionalGeneration") +class MiniMaxM3VisionModel(MmprojModel): + @classmethod + def filter_tensors(cls, item): + name, gen = item + # keep only the vision-side tensors; text / mtp / sparse-index are dropped + if not name.startswith(("vision_tower.", "multi_modal_projector.", "patch_merge_mlp.")): + return None + return super().filter_tensors((name, gen)) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + assert self.hparams_vision is not None + + self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.MINIMAXM3) + self.gguf_writer.add_vision_use_gelu(True) + + # the ViT carries its own LayerNorm eps (text tower uses a different one) + self.gguf_writer.add_vision_attention_layernorm_eps( + self.hparams_vision.get("layer_norm_eps", 1e-5) + ) + + comp = self.hparams_vision.get("img_token_compression_config", {}) + merge_size = comp.get("spatial_merge_size", 2) + self.gguf_writer.add_vision_spatial_merge_size(int(merge_size)) + + def modify_tensors(self, data_torch, name, bid): + assert self.hparams_vision is not None + + # Conv3d patch embed -> Conv2d slices + if name == "vision_tower.vision_model.embeddings.patch_embedding.weight": + if data_torch.ndim != 5: + raise ValueError(f"unexpected patch_embedding rank {data_torch.ndim} for {name}") + kt = data_torch.shape[2] + base = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_ENC_EMBD_PATCH] + for t in range(kt): + suffix = ".weight" if t == 0 else f".weight.{t}" + yield (base + suffix, data_torch[:, :, t, ...]) + return + + # Permute ViT q/k. HF [Ta Ha Wa | Tb Hb Wb | pad] reorder to [Ta Tb | Ha Hb | Wa Wb | pad]. + for new_name, tensor in super().modify_tensors(data_torch, name, bid): + if ".attn_q." in new_name or ".attn_k." in new_name: + tensor = self._permute_vit_qk(tensor, new_name) + yield new_name, tensor + + def _permute_vit_qk(self, t: "Tensor", new_name: str) -> "Tensor": + assert self.hparams_vision is not None + n_head = self.hparams_vision["num_attention_heads"] + d_head = t.shape[0] // n_head + axis_dim = 2 * ((2 * (d_head // 2) // 3) // 2) + ah = axis_dim // 2 + half = 3 * ah + perm = [] + perm += list(range(0, ah)) + perm += list(range(half, half + ah)) + perm += list(range(ah, 2 * ah)) + perm += list(range(half + ah, half + 2 * ah)) + perm += list(range(2 * ah, 3 * ah)) + perm += list(range(half + 2 * ah, half + 3 * ah)) + perm += list(range(2 * half, d_head)) + + assert axis_dim % 2 == 0 + assert 3 * axis_dim <= d_head + assert len(perm) == d_head + assert sorted(perm) == list(range(d_head)), "perm is not a bijection of d_head" + assert t.shape[0] == n_head * d_head, f"{new_name}: {t.shape[0]} != {n_head}*{d_head}" + assert d_head == 80 + + idx = torch.tensor(perm, dtype=torch.long) + if t.ndim == 2: + return t.reshape(n_head, d_head, t.shape[1])[:, idx, :].reshape(t.shape) + return t.reshape(n_head, d_head)[:, idx].reshape(t.shape) diff --git a/conversion/nanbeige.py b/conversion/nanbeige.py new file mode 100644 index 000000000000..f1fc425b3a09 --- /dev/null +++ b/conversion/nanbeige.py @@ -0,0 +1,24 @@ +from __future__ import annotations + +from .base import ModelBase, gguf, logger +from .llama import LlamaModel + + +@ModelBase.register("NanbeigeForCausalLM") +class NanbeigeModel(LlamaModel): + model_arch = gguf.MODEL_ARCH.NANBEIGE + undo_permute = True + + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + + n_loops = int(hparams.get("num_loops", 1) or 1) + if n_loops < 1: + n_loops = 1 + self.gguf_writer.add_num_loops(n_loops) + logger.info(f"gguf: num_loops = {n_loops}") + + skip_loop_final_norm = bool(hparams.get("skip_loop_final_norm", False)) + self.gguf_writer.add_skip_loop_final_norm(skip_loop_final_norm) + logger.info(f"gguf: skip_loop_final_norm = {skip_loop_final_norm}") diff --git a/conversion/nemotron.py b/conversion/nemotron.py index e44688a78807..0572b42ca2a1 100644 --- a/conversion/nemotron.py +++ b/conversion/nemotron.py @@ -39,28 +39,48 @@ def get_vision_config(self) -> dict[str, Any] | None: } return vision_config + def get_audio_config(self) -> dict[str, Any] | None: + return self.global_config.get("sound_config") + def set_gguf_parameters(self): if "image_mean" not in self.preprocessor_config: self.preprocessor_config["image_mean"] = [0.485, 0.456, 0.406] if "image_std" not in self.preprocessor_config: self.preprocessor_config["image_std"] = [0.229, 0.224, 0.225] + if self.hparams_audio is not None: + self.has_vision_encoder = True + self.has_audio_encoder = True + self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["num_mel_bins"]) + self.gguf_writer.add_audio_attention_layernorm_eps(1e-5) + self.gguf_writer.add_audio_subsampling_factor(self.hparams_audio["subsampling_factor"]) + self.gguf_writer.add_audio_conv_kernel_size(self.hparams_audio["conv_kernel_size"]) + self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.PARAKEET) + self.gguf_writer.add_clip_vision_projector_type(gguf.VisionProjectorType.NEMOTRON_V2_VL) + else: + self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.NEMOTRON_V2_VL) + super().set_gguf_parameters() hparams = self.global_config - self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.NEMOTRON_V2_VL) self.gguf_writer.add_vision_attention_layernorm_eps(1e-6) self.gguf_writer.add_vision_use_gelu(True) downsample_ratio = hparams.get("downsample_ratio", 0.5) self.gguf_writer.add_vision_projector_scale_factor(int(1.0 / downsample_ratio)) def tensor_force_quant(self, name, new_name, bid, n_dims): - if ".position_embd." in new_name or "pos_embed" in new_name: - return gguf.GGMLQuantizationType.F32 + if "sound_encoder" in name or new_name.startswith("mm.a."): + if "bias" in new_name or "norm" in new_name: + return gguf.GGMLQuantizationType.F32 + if "conv" in new_name and "weight" in new_name: + return gguf.GGMLQuantizationType.F32 + return super().tensor_force_quant(name, new_name, bid, n_dims) @classmethod def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None: - name, gen = item + if (titem := super().filter_tensors(item)) is None: + return None + name, gen = titem if "input_conditioner" in name: return None @@ -69,14 +89,18 @@ def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Ca if "radio_model.model.patch_generator.video_embedder" in name: return None - if not name.startswith("vision_model.radio_model.model.") and not name.startswith("mlp1."): + if not name.startswith(("vision_model.radio_model.model.", "mlp1.", "sound_encoder.", "sound_projection.")): return None if "patch_generator.pos_embed" in name: if not name.endswith(".weight"): name += ".weight" - return super().filter_tensors((name, gen)) + # num_batches is only used for training not inference. + if "conv.norm" in name and "num_batches" in name: + return None + + return name, gen def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: # RADIO's pos_embed doesn't have .weight suffix, but clip.cpp expects it @@ -104,7 +128,26 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter n_embd = self.hparams["hidden_size"] data_torch = data_torch.reshape(n_embd, 3, patch_size, patch_size) - yield from super().modify_tensors(data_torch, name, bid) + if "depthwise_conv.weight" in name: + data_torch = data_torch.unsqueeze(-1) + data_torch = data_torch.permute(3, 1, 0, 2).contiguous() + + if "pointwise_conv" in name and name.endswith(".weight"): + if len(data_torch.shape) == 3 and data_torch.shape[2] == 1: + data_torch = data_torch.reshape(data_torch.shape[0], data_torch.shape[1]) + + if "subsampling.layers" in name and name.endswith(".bias"): + if len(data_torch.shape) == 1: + data_torch = data_torch.reshape(1, -1, 1, 1) + + if "pointwise_conv" in name and name.endswith(".bias"): + if len(data_torch.shape) == 1: + data_torch = data_torch.reshape(1, -1, 1, 1) + + for mapped_name, tensor in super().modify_tensors(data_torch, name, bid): + if name.startswith("sound_projection.") and mapped_name.startswith("mm.model.mlp."): + mapped_name = mapped_name.replace("mm.model.mlp.", "mm.a.mlp.") + yield mapped_name, tensor @ModelBase.register("NemotronForCausalLM") diff --git a/conversion/qwen.py b/conversion/qwen.py index 82d42fcc1674..b4ae528bf2d4 100644 --- a/conversion/qwen.py +++ b/conversion/qwen.py @@ -1,5 +1,7 @@ from __future__ import annotations +import json + from typing import Any, Callable, Iterable, TYPE_CHECKING import torch @@ -16,7 +18,7 @@ class QwenModel(TextModel): @staticmethod def token_bytes_to_string(b): - from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode # ty: ignore[unresolved-import] + from transformers.convert_slow_tokenizer import bytes_to_unicode byte_encoder = bytes_to_unicode() return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')]) @@ -266,8 +268,101 @@ def set_vocab(self): super().set_vocab() +class _QwenMtpMixin: + """Shared MTP wiring for Qwen3-Next and Qwen3.5/3.6 text variants. The HF + config carries the MTP block under `mtp_num_hidden_layers` (computed from + the checkpoint when absent, e.g. Qwen3-Next) and the tensors under + `mtp.*`; we extend block_count, emit the nextn metadata key, and remap + `mtp.*` to the standard layer-indexed nextn naming so the existing + tensor_map handles them.""" + + supports_mtp_export = True + hparams: dict[str, Any] + model_arch: gguf.MODEL_ARCH + gguf_writer: gguf.GGUFWriter + block_count: int + tensor_map: gguf.TensorNameMap + no_mtp: bool + mtp_only: bool + _original_block_count: int | None = None + opt_num_mtp_layers: int = 0 + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.block_count = self.hparams["num_hidden_layers"] + if not self.no_mtp: + n_mtp = self.hparams.get("mtp_num_hidden_layers", 0) + # Qwen-3-Next doesn't include `mtp_num_hidden_layers` in config. + if n_mtp == 0: + assert self.opt_num_mtp_layers != 0 + n_mtp = self.opt_num_mtp_layers + self.block_count += n_mtp + self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count) + + def index_tensors(self, remote_hf_model_id: str | None = None) -> dict[str, Callable[[], Tensor]]: + hparams = {**self.hparams, **self.hparams.get("text_config", {})} + key = next((k for k in ["n_layers", "num_hidden_layers", "n_layer", "num_layers"] if k in hparams), None) + type(self)._original_block_count = hparams.get(key) + type(self).opt_num_mtp_layers = 0 + return super().index_tensors(remote_hf_model_id=remote_hf_model_id) # ty: ignore[unresolved-attribute] + + @classmethod + def filter_tensors(cls, item): + assert cls._original_block_count is not None + # TODO: change TextModel to super() + if (titem := TextModel.filter_tensors(item)) is None: + return None + name, gen = titem + if name.startswith("model.mtp."): + name = name.replace("model.", "", 1) + if name.startswith("mtp."): + if cls.no_mtp: + return None + remapper = { + "fc": "eh_proj", + "pre_fc_norm_embedding": "enorm", + "pre_fc_norm_hidden": "hnorm", + "norm": "shared_head.norm", + } + parts = name.split(".", 3) + if len(parts) == 4 and parts[1] == "layers" and parts[2].isdecimal(): + mtp_idx = int(parts[2]) + name = f"model.layers.{cls._original_block_count + mtp_idx}.{parts[3]}" + cls.opt_num_mtp_layers = max(cls.opt_num_mtp_layers, mtp_idx + 1) + elif len(parts) == 3 and parts[1] in remapper: + name = f"model.layers.{cls._original_block_count}.{remapper[parts[1]]}.{parts[2]}" + elif cls.mtp_only: + keep = name in ( + "model.embed_tokens.weight", "model.norm.weight", "lm_head.weight", + "embed_tokens.weight", "norm.weight", + ) + if not keep: + return None + return name, gen + + def set_gguf_parameters(self): + super().set_gguf_parameters() # ty: ignore[unresolved-attribute] + if self.no_mtp: + return + if (n := self.block_count - self.hparams["num_hidden_layers"]) > 0: + self.gguf_writer.add_nextn_predict_layers(n) + + def prepare_metadata(self, vocab_only: bool): + from_dir = self.fname_out.is_dir() + super().prepare_metadata(vocab_only=vocab_only) # ty: ignore[unresolved-attribute] + + if not self.mtp_only or not from_dir: + return + + output_type: str = self.ftype.name.partition("_")[2] # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute] + fname_default: str = gguf.naming_convention( + self.metadata.name, self.metadata.basename, self.metadata.finetune, # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute] + self.metadata.version, size_label=None, output_type=output_type, model_type=None) # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute] + self.fname_out = self.fname_out.parent / f"mtp-{fname_default}.gguf" + + @ModelBase.register("Qwen3NextForCausalLM") -class Qwen3NextModel(Qwen2MoeModel): +class Qwen3NextModel(_QwenMtpMixin, Qwen2MoeModel): model_arch = gguf.MODEL_ARCH.QWEN3NEXT def set_gguf_parameters(self): @@ -282,16 +377,6 @@ def set_gguf_parameters(self): rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"] self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.rope_parameters.get("partial_rotary_factor", 0.25))) - @classmethod - def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None: - name, gen = item - - if name.startswith("mtp"): - # ignore MTP layers for now - return None - - return super().filter_tensors(item) - def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: if name.endswith(".A_log"): data_torch = -torch.exp(data_torch) @@ -534,97 +619,13 @@ def set_gguf_parameters(self): self.gguf_writer.add_rope_dimension_sections(self._QWEN35_DEFAULT_MROPE_SECTION) -class _Qwen35MtpMixin: - """Shared MTP wiring for Qwen3.5/3.6 text variants. The HF config carries - the MTP block under `mtp_num_hidden_layers` and the tensors under - `mtp.*`; we extend block_count, emit the nextn metadata key, and remap - `mtp.*` to the standard layer-indexed nextn naming so the existing - tensor_map handles them.""" - - supports_mtp_export = True - hparams: dict[str, Any] - model_arch: gguf.MODEL_ARCH - gguf_writer: gguf.GGUFWriter - block_count: int - tensor_map: gguf.TensorNameMap - no_mtp: bool - mtp_only: bool - _original_block_count: int | None = None - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - self.block_count = self.hparams["num_hidden_layers"] - if not self.no_mtp: - self.block_count += self.hparams.get("mtp_num_hidden_layers", 0) - self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count) - - def index_tensors(self, remote_hf_model_id: str | None = None) -> dict[str, Callable[[], Tensor]]: - hparams = {**self.hparams, **self.hparams.get("text_config", {})} - key = next((k for k in ["n_layers", "num_hidden_layers", "n_layer", "num_layers"] if k in hparams), None) - type(self)._original_block_count = hparams.get(key) - return super().index_tensors(remote_hf_model_id=remote_hf_model_id) # ty: ignore[unresolved-attribute] - - @classmethod - def filter_tensors(cls, item): - assert cls._original_block_count is not None - # TODO: change TextModel to super() - if (titem := TextModel.filter_tensors(item)) is None: - return None - name, gen = titem - if name.startswith("model.mtp."): - name = name.replace("model.", "", 1) - if name.startswith("mtp."): - if cls.no_mtp: - return None - remapper = { - "fc": "eh_proj", - "pre_fc_norm_embedding": "enorm", - "pre_fc_norm_hidden": "hnorm", - "norm": "shared_head.norm", - } - parts = name.split(".", 3) - if len(parts) == 4 and parts[1] == "layers" and parts[2].isdecimal(): - mtp_idx = int(parts[2]) - name = f"model.layers.{cls._original_block_count + mtp_idx}.{parts[3]}" - elif len(parts) == 3 and parts[1] in remapper: - name = f"model.layers.{cls._original_block_count}.{remapper[parts[1]]}.{parts[2]}" - elif cls.mtp_only: - keep = name in ( - "model.embed_tokens.weight", "model.norm.weight", "lm_head.weight", - "embed_tokens.weight", "norm.weight", - ) - if not keep: - return None - return name, gen - - def set_gguf_parameters(self): - super().set_gguf_parameters() # ty: ignore[unresolved-attribute] - if self.no_mtp: - return - if (n := self.hparams.get("mtp_num_hidden_layers", 0)) > 0: - self.gguf_writer.add_nextn_predict_layers(n) - - def prepare_metadata(self, vocab_only: bool): - from_dir = self.fname_out.is_dir() - super().prepare_metadata(vocab_only=vocab_only) # ty: ignore[unresolved-attribute] - - if not self.mtp_only or not from_dir: - return - - output_type: str = self.ftype.name.partition("_")[2] # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute] - fname_default: str = gguf.naming_convention( - self.metadata.name, self.metadata.basename, self.metadata.finetune, # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute] - self.metadata.version, size_label=None, output_type=output_type, model_type=None) # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute] - self.fname_out = self.fname_out.parent / f"mtp-{fname_default}.gguf" - - @ModelBase.register("Qwen3_5ForConditionalGeneration", "Qwen3_5ForCausalLM") -class Qwen3_5TextModel(_Qwen35MtpMixin, _Qwen35MRopeMixin, _LinearAttentionVReorderBase): +class Qwen3_5TextModel(_Qwen35MRopeMixin, _LinearAttentionVReorderBase): model_arch = gguf.MODEL_ARCH.QWEN35 @ModelBase.register("Qwen3_5MoeForConditionalGeneration", "Qwen3_5MoeForCausalLM") -class Qwen3_5MoeTextModel(_Qwen35MtpMixin, _Qwen35MRopeMixin, _LinearAttentionVReorderBase): +class Qwen3_5MoeTextModel(_Qwen35MRopeMixin, _LinearAttentionVReorderBase): model_arch = gguf.MODEL_ARCH.QWEN35MOE @@ -641,7 +642,19 @@ def set_vocab(self): logger.info(f"DFlash: Using tokenizer from target model: {self.target_model_dir}") original_dir = self.dir_model self.dir_model = self.target_model_dir - super().set_vocab() + + # Reuse the target model's own vocab handler (e.g. Gemma-4 needs its + # own tokenizer logic, not the Qwen default). + from . import get_model_class + with open(self.target_model_dir / "config.json", "r", encoding="utf-8") as f: + target_arch = json.load(f)["architectures"][0] + target_cls = get_model_class(target_arch) + + if target_cls is not type(self): + target_cls.set_vocab(self) # ty: ignore[unresolved-attribute] + else: + super().set_vocab() + self.dir_model = original_dir mask_token_id = self.hparams.get("dflash_config", {}).get("mask_token_id") @@ -674,3 +687,23 @@ def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Ca if not name.startswith("model."): name = "model." + name return super().filter_tensors((name, gen)) + + +@ModelBase.register("Qwen3DSparkModel") +class DSparkModel(DFlashModel): + # DSpark = DFlash + a semi-autoregressive Markov head + model_arch = gguf.MODEL_ARCH.DFLASH + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + # normalize the flat DeepSpec schema to DFlash's nested dflash_config + self.hparams.setdefault("dflash_config", { + k: self.hparams[k] for k in ("target_layer_ids", "mask_token_id") if k in self.hparams + }) + + @classmethod + def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None: + name, gen = item + if name.endswith(("embed_tokens.weight", "lm_head.weight")): + return None + return super().filter_tensors((name, gen)) diff --git a/conversion/qwenvl.py b/conversion/qwenvl.py index 7befd0c8d816..202a47961b3c 100644 --- a/conversion/qwenvl.py +++ b/conversion/qwenvl.py @@ -179,12 +179,12 @@ def set_gguf_parameters(self): def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None: name, gen = item - if not name.startswith("visual.") and not name.startswith("audio_tower."): - return None - if name.startswith("thinker."): name = name.replace("thinker.", "") + if not name.startswith("visual.") and not name.startswith("audio_tower."): + return None + if "audio_bos_eos_token" in name: # this tensor is left unused in transformers code # https://github.com/huggingface/transformers/blob/6e3063422c4b1c014aa60c32b9254fd2902f0f28/src/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.py#L1809 diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 2c5e62a16fbe..78ad26c65630 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -122,8 +122,12 @@ def parse_args() -> argparse.Namespace: help="Export only the multi-token prediction (MTP) head as a separate GGUF, suitable for use as a speculative draft. An 'mtp-' prefix will be added to the output file name.", ) parser.add_argument( - "--no-mtp", action="store_true", - help="Exclude the multi-token prediction (MTP) head from the converted GGUF. Pair with --mtp on a second run to publish trunk and MTP as two files. Note: the split form duplicates embeddings, but even though the bundled default is more space-efficient overall, this allows differing quantization which may be more performant.", + "--no-nextn", "--no-mtp", dest="no_mtp", action="store_true", + help="Exclude NextN speculative draft tensors from the converted GGUF. Pair with --mtp or --dspark on a second run to publish target and draft as two files.", + ) + parser.add_argument( + "--dspark", action="store_true", + help="Export only the DeepSeek-V4 DSpark draft tensors as a separate GGUF.", ) parser.add_argument( "--mistral-format", action="store_true", @@ -254,13 +258,20 @@ def main() -> None: from conversion.mistral import MistralModel model_class = MistralModel - if args.mtp and args.no_mtp: - logger.error("--mtp and --no-mtp are mutually exclusive") + if sum((args.mtp, args.no_mtp, args.dspark)) > 1: + logger.error("--mtp, --no-nextn, and --dspark are mutually exclusive") sys.exit(1) + if args.dspark: + if is_mistral_format or model_architecture != "DeepseekV4ForCausalLM": + logger.error("--dspark is only supported for DeepseekV4ForCausalLM") + sys.exit(1) + from conversion.deepseek import DeepseekV4DSparkModel + model_class = DeepseekV4DSparkModel + if args.mtp or args.no_mtp: if not model_class.supports_mtp_export: - logger.error("--mtp / --no-mtp are not supported for %s", model_architecture) + logger.error("--mtp / --no-nextn are not supported for %s", model_architecture) sys.exit(1) if args.no_mtp: model_class.no_mtp = True diff --git a/docs/backend/OPENCL.md b/docs/backend/OPENCL.md index 1bce56cd859a..337b0c82a0f9 100644 --- a/docs/backend/OPENCL.md +++ b/docs/backend/OPENCL.md @@ -47,6 +47,7 @@ The llama.cpp OpenCL backend is designed to enable llama.cpp on **Qualcomm Adren | Adreno GPU | Status | |:-------------------------------------:|:-------:| | Adreno 750 (Snapdragon 8 Gen 3) | Support | +| Adreno 810 (Snapdragon 7s Gen 3) | Support | | Adreno 830 (Snapdragon 8 Elite) | Support | | Adreno 840 (Snapdragon 8 Elite Gen 5) | Support | | Adreno X1-85 (Snapdragon X Elite) | Support | @@ -97,6 +98,24 @@ The OpenCL backend has the following CMake options that control the behavior of | `GGML_OPENCL_USE_ADRENO_KERNELS` | `ON` | Use kernels optimized for Adreno. | | `GGML_OPENCL_USE_ADRENO_BIN_KERNELS` | `OFF` | Allow using binary kernel lib for Adreno. | +## Program Binary Cache + +Compiled `cl_program` binaries are cached on disk, so subsequent runs skip the expensive +compile-from-source step when nothing relevant has changed (kernel source, compile options, +device, driver, or platform version). + +The cache is controlled with the `GGML_OPENCL_KERNEL_CACHE_DIR` environment variable: + +| Value | Behavior | +|:---------------------------------------|:-----------------------------------------------| +| unset / empty / `1` / `default` | Enabled in the platform default cache directory: `%LOCALAPPDATA%\llama.cpp\cl-cache` (Windows), `~/Library/Caches/llama.cpp/cl-cache` (macOS), `/llama.cpp/cl-cache` elsewhere. | +| `0` / `off` / `none` / `disable(d)` | Disabled. | +| any other value | Used verbatim as the cache directory path. | + +If the chosen directory cannot be created or used, the cache disables itself for the process +and kernels are compiled from source as usual. Set `GGML_OPENCL_KERNEL_CACHE_DEBUG=1` to +print a HIT/MISS/SAVE trace to stderr. + ## Android Ubuntu 22.04 is used for targeting Android. Make sure the following tools are accessible from command line, diff --git a/docs/backend/SYCL.md b/docs/backend/SYCL.md index 0814ceb60f92..814e541e1a1b 100644 --- a/docs/backend/SYCL.md +++ b/docs/backend/SYCL.md @@ -788,13 +788,18 @@ use 1 SYCL GPUs: [0] with Max compute units:512 | Name | Value | Function | |-------------------|------------------|---------------------------------------------------------------------------------------------------------------------------| | GGML_SYCL_DEBUG | 0 (default) or 1 | Enable log function by macro: GGML_SYCL_DEBUG | -| GGML_SYCL_DEV2DEV_MEMCPY | 0 (default) or 1 | Choose the SYCL or L0 API in dev2dev memory copy.
Value:
* 0: SYCL API (default)
* 1: L0 API -- L0 API is found to lead to abnormal crash in some case. This debug flag is used to check the issue.| +| GGML_SYCL_DEV2DEV_MEMCPY | 0 (default), 1, 2 | Choose the method of dev2dev memory copy.
Value:
* 0: SYCL API (default), only support dGPUs.
* 1: L0 API -- Better performance, only support dGPUs, found to lead to abnormal crash in some case.
* 2: Host Forward -- Most stable method for all cases (including iGPU + dGPU*N), but with lower performance (-2% to -5%).
SYCL & L0 API are easy to be impacted by Intel GPU driver issue. When you meet the garbled output or crash issues in multiple GPUs case, try with this debug flag to work around or check the issue.| | GGML_SYCL_ENABLE_FLASH_ATTN | 1 (default) or 0| Enable Flash-Attention. It can reduce memory usage. The performance impact depends on the LLM.| | GGML_SYCL_ENABLE_OPT | 0 or 1 (default)| Enable optimize features for Intel GPUs. (Recommended to 0 for Intel devices older than Gen 10) | | GGML_SYCL_ENABLE_GRAPH | 0 (default) or 1 | Enable running computations through SYCL Graphs feature. Disabled by default because SYCL Graph is still on development, no better performance. | | GGML_SYCL_USE_LEVEL_ZERO_API | 1 (default) or 0 | Use Level Zero API for device memory allocation instead of SYCL. Reduces system RAM usage on Intel dGPUs by avoiding DMA-buf/TTM host memory staging. Requires GGML_SYCL_SUPPORT_LEVEL_ZERO_API=ON at build time. SYCL backend always runs on Level Zero running time even if it's set as OFF (The SYCL api will be usage for memory allocation).| | GGML_SYCL_ENABLE_DNN | 0 or 1 (default)| Enable running computations through oneDNN and always use oneMKL. | +| GGML_SYCL_FA_ONEDNN | 1 (default) or 0 | Enable the oneDNN fused SDPA (flash-attention) path on supported GPUs. Set to 0 to always use the native SYCL flash-attention kernel. | +| GGML_SYCL_FA_ONEDNN_MAX_KV | 0 (default, disabled) or positive integer | By default (0), all sequences are handled by the oneDNN fused SDPA path, regardless of KV length; a positive value caps that length, past which sequences fall back to the native kernel. If GPU driver watchdog resets (DEVICE_LOST) occur during long-context inference, set this near the context depth where they start, e.g. 24576. | | GGML_SYCL_ENABLE_VMM | 0 or 1 (default) | Enable the virtual-memory device pool. | +| GGML_SYCL_ENABLE_MKL_FA | 1 (default) or 0 | Enable oneMKL GEMM flash attention for XMX-accelerated prompt processing with quantized KV cache. Automatically activates during prefill (prompt processing) when all conditions are met: (1) flash-attn enabled (`-fa` or `--flash-attn on`), (2) KV cache quantized (`--cache-type-k q8_0 --cache-type-v q8_0` or other `*_0/*_1` types), (3) batch size ≥ 1024 (`--batch-size 1024`), (4) prompt length ≥ 1024 tokens. Set to 0 to force the TILE kernel for A/B testing. Example minimum command: `llama-cli -m model.gguf -fa -ngl 99 --cache-type-k q8_0 --cache-type-v q8_0 --batch-size 1024 -p "your prompt"` | +| GGML_SYCL_MKL_FA_DEBUG | 0 (default) or 1 | Enable per-call diagnostic logging for MKL flash attention: GEMM/softmax timings, interleaved-head detection, and buffer memory usage. | +| GGML_SYCL_MKL_FA_DIAG | 0 (default) or 1 | Enable output fingerprinting for MKL flash attention. Dumps the first 64 float output values for the first 6 FA calls with n_kv ≥ 1024, labeled with kernel type (MKL/TILE/VEC) for cross-kernel comparison. | | GGML_SYCL_ENABLE_FUSION | 0 or 1 (default) | Enable fused-kernel dispatch in graph compute (currently top-k MoE gating). | | ZES_ENABLE_SYSMAN | 0 (default) or 1 | Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.
Recommended to use when --split-mode = layer | | UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS | 0 (default) or 1 | Allow SYCL/Unified Runtime Level Zero device allocations larger than 4 GiB. llama.cpp's direct Level Zero allocation path requests the relaxed maximum-size limit itself when GGML_SYCL_ENABLE_LEVEL_ZERO=1. | diff --git a/docs/build.md b/docs/build.md index 33ef3ef50652..ca086a0be145 100644 --- a/docs/build.md +++ b/docs/build.md @@ -361,12 +361,6 @@ You can download it from your Linux distro's package manager or from here: [ROCm Note: `GPU_TARGETS` is optional, omitting it will build the code for all GPUs in the current system. - To enhance flash attention performance on RDNA3+ or CDNA architectures, you can utilize the rocWMMA library by enabling the `-DGGML_HIP_ROCWMMA_FATTN=ON` option. This requires rocWMMA headers to be installed on the build system. - - The rocWMMA library is included by default when installing the ROCm SDK using the `rocm` meta package provided by AMD. Alternatively, if you are not using the meta package, you can install the library using the `rocwmma-dev` or `rocwmma-devel` package, depending on your system's package manager. - - As an alternative, you can manually install the library by cloning it from the official [GitHub repository](https://github.com/ROCm/rocWMMA), checkout the corresponding version tag (e.g. `rocm-6.2.4`) and set `-DCMAKE_CXX_FLAGS="-I/library/include/"` in CMake. This also works under Windows despite not officially supported by AMD. - Note that if you get the following error: ``` clang: error: cannot find ROCm device library; provide its path via '--rocm-path' or '--rocm-device-lib-path', or pass '-nogpulib' to build without ROCm device library diff --git a/docs/completions.md b/docs/completions.md new file mode 100644 index 000000000000..5376644c0a12 --- /dev/null +++ b/docs/completions.md @@ -0,0 +1,17 @@ +# Completions + +Command-line completion is available for some environments. + +## Bash Completion + +```bash +$ build/bin/llama-cli --completion-bash > ~/.llama-completion.bash +$ source ~/.llama-completion.bash +``` + +Optionally this can be added to your `.bashrc` or `.bash_profile` to load it +automatically. For example: + +```console +$ echo "source ~/.llama-completion.bash" >> ~/.bashrc +``` diff --git a/docs/development/HOWTO-add-model.md b/docs/development/HOWTO-add-model.md index ef2b37088181..102f479eb02c 100644 --- a/docs/development/HOWTO-add-model.md +++ b/docs/development/HOWTO-add-model.md @@ -45,6 +45,8 @@ class MyModel(MmprojModel): Add an enum entry in `MODEL_ARCH`, the model human friendly name in `MODEL_ARCH_NAMES` and the GGUF tensor names in `MODEL_TENSORS`. +NOTE: Pick the GGUF arch string (and the matching `src/models/.cpp` filename, see section 3) carefully up front, following existing naming conventions. Once GGUF files are published under a given arch string, renaming it later breaks the community's existing files, so this is not something to leave for cleanup in a follow-up PR. + Example for `falcon` model: ```python MODEL_ARCH.FALCON: [ @@ -101,6 +103,7 @@ The model params and tensors layout must be defined in `llama.cpp` source files: - You may also need to update `LLM_KV_NAMES`, `LLM_TENSOR_NAMES` and `LLM_TENSOR_INFOS` 3. Add any non-standard metadata loading in the `llama_model_loader` constructor in `src/llama-model-loader.cpp`. 4. If the model has a RoPE operation, add a case for the architecture in `llama_model_rope_type` function in `src/llama-model.cpp`. +5. Check for other places that switch/iterate over every `llm_arch` value, e.g. `src/llama-model-saver.cpp` and any mandatory-hparam lists (such as which archs require MoE metadata). Grep for `LLM_ARCH_` usages to find them. Missing one of these is a common cause of CI test failures (e.g. `test-llama-archs`) after adding a new arch. NOTE: The dimensions in `ggml` are typically in the reverse order of the `pytorch` dimensions. @@ -133,6 +136,16 @@ Note: ## Tips and tricks +### Prefer conversion-time tensor modifications over graph-time ones + +If the model contains constant modifications of tensors in the graph (for example, `norm(1 + weight)`) or performs tensor permutations/chunking, perform the modifications during conversion rather than in the graph code. This keeps the inference graph simpler and avoids extra runtime ops. + +Examples: +- Gemma 3 folds the `1 +` of its `norm(1 + weight)` normalization into the weights at conversion time, so the graph just does a plain RMS norm. +- Qwen3-Next applies its tensor permutation during conversion (in `modify_tensors`), so the graph can consume the already-permuted weights directly. + +Exception: a plain `weight * scale` with a constant scale is usually better left to inference time rather than folded into the weight at conversion. The scale conceptually applies to the activation, not the weight, so folding it into the weight can hurt numerical stability, and it shifts the weight's value range in a way that can make quantization worse. In this case, write the scale to GGUF as its own metadata key (e.g. `%s.attention.output_scale`, `%s.attention.value_scale`, `%s.embedding_scale`) and apply it in the graph, instead of pre-multiplying the weight tensor during conversion. + ### Working with ggml_rope_ext PyTorch implementations usually prefer explicitly calculating `freq_cis`/`sin`/`cos` components. However, in llama.cpp, most RoPE operations can be handled via `ggml_rope_ext`, which does not require a sin/cos matrix. This saves memory while allowing the GGML RoPE kernel to be fused with other ops. diff --git a/docs/install.md b/docs/install.md index 7198e61bf35b..b36b0be26736 100644 --- a/docs/install.md +++ b/docs/install.md @@ -16,22 +16,22 @@ conda-forge provides builds for: - Apple Metal (macOS) ```sh -conda install -c conda-forge llama-cpp +conda install -c conda-forge llama.cpp ``` ```sh -mamba install -c conda-forge llama-cpp +mamba install -c conda-forge llama.cpp ``` ```sh # Project-local installation -pixi add llama-cpp +pixi add llama.cpp # Global installation -pixi global install llama-cpp +pixi global install llama.cpp ``` -This distribution is managed on [`conda-forge/llama-cpp-feedstock`](https://github.com/conda-forge/llama.cpp-feedstock/). +This distribution is managed on [`conda-forge/llama.cpp-feedstock`](https://github.com/conda-forge/llama.cpp-feedstock/). Shall you have any problems, please open an issue on [its issue tracker](https://github.com/conda-forge/llama.cpp-feedstock/issues). diff --git a/docs/models.md b/docs/models.md new file mode 100644 index 000000000000..eee5952634be --- /dev/null +++ b/docs/models.md @@ -0,0 +1,26 @@ +# Obtaining and quantizing models + +The [Hugging Face](https://huggingface.co) platform hosts [thousands of models](https://huggingface.co/models?library=gguf&sort=trending) compatible with `llama.cpp`: + +- [Trending](https://huggingface.co/models?library=gguf&sort=trending) + +You can use any `llama.cpp`-compatible model from [Hugging Face](https://huggingface.co/) using this CLI argument: `-hf /[:quant]`. For example: + +```sh +llama cli -hf ggml-org/gemma-3-1b-it-GGUF +``` + +You can use the same CLI invocation to download from other sites, by pointing the `MODEL_ENDPOINT` environment variable to an endpoint compatible with the Hugging Face API. +`llama.cpp` can also run models you have downloaded locally to your filesystem. + +After downloading a model, use the CLI tools to run it locally - see below. + +`llama.cpp` requires the model to be stored in the [GGUF](https://github.com/ggml-org/ggml/blob/master/docs/gguf.md) file format. Models in other data formats can be converted to GGUF using the `convert_*.py` Python scripts in this repo. +To learn more about model quantization, [read this documentation](../tools/quantize/README.md) + +The Hugging Face platform provides a variety of online tools for converting, quantizing and hosting models with `llama.cpp`: + +- Use the [GGUF-my-repo space](https://huggingface.co/spaces/ggml-org/gguf-my-repo) to convert to GGUF format and quantize model weights to smaller sizes +- Use the [GGUF-my-LoRA space](https://huggingface.co/spaces/ggml-org/gguf-my-lora) to convert LoRA adapters to GGUF format (more info: https://github.com/ggml-org/llama.cpp/discussions/10123) +- Use the [GGUF-editor space](https://huggingface.co/spaces/CISCai/gguf-editor) to edit GGUF meta data in the browser (more info: https://github.com/ggml-org/llama.cpp/discussions/9268) +- Use the [Inference Endpoints](https://ui.endpoints.huggingface.co/) to directly host `llama.cpp` in the cloud (more info: https://github.com/ggml-org/llama.cpp/discussions/9669) diff --git a/docs/ops.md b/docs/ops.md index c5601523697d..71bd72011eae 100644 --- a/docs/ops.md +++ b/docs/ops.md @@ -23,16 +23,16 @@ Legend: | ARGMAX | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | | ARGSORT | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ | | CEIL | ❌ | ❌ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | -| CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ | -| COL2IM_1D | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | -| CONCAT | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ | +| CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ | +| COL2IM_1D | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | +| CONCAT | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ | | CONT | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ | -| CONV_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | -| CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | -| CONV_3D | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | +| CONV_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | +| CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | +| CONV_3D | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | -| COS | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | +| COS | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | | COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | | CROSS_ENTROPY_LOSS | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | @@ -41,6 +41,9 @@ Legend: | DIAG | ❌ | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | | DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ | | DIV | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | +| DSV4_HC_COMB | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| DSV4_HC_POST | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| DSV4_HC_PRE | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | DUP | ❌ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ | | ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | | EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | @@ -48,8 +51,8 @@ Legend: | FILL | ❌ | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | | FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | | FLOOR | ❌ | ❌ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | -| GATED_DELTA_NET | ❌ | ❌ | ✅ | ❌ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | -| GATED_LINEAR_ATTN | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | +| GATED_DELTA_NET | ❌ | ❌ | ✅ | ❌ | ✅ | 🟡 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | +| GATED_LINEAR_ATTN | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | GEGLU_QUICK | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | @@ -57,32 +60,33 @@ Legend: | GELU_ERF | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ | | GELU_QUICK | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ | | GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ | -| GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | | GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | | HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | | HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | | IM2COL | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | IM2COL_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | -| L2_NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | -| LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ | +| L2_NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | 🟡 | ❌ | ❌ | +| LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | +| LIGHTNING_INDEXER | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | LOG | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | | MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | | MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | -| MUL_MAT_HADAMARD | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | +| MUL_MAT_HADAMARD | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | | MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | 🟡 | 🟡 | ❌ | | NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | -| NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | +| NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | 🟡 | ❌ | ❌ | | OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | | OPT_STEP_SGD | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | -| OUT_PROD | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | 🟡 | +| OUT_PROD | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | | PAD | ❌ | 🟡 | ✅ | 🟡 | ❌ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | | PAD_REFLECT_1D | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | -| POOL_1D | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | +| POOL_1D | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | POOL_2D | ❌ | 🟡 | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | RELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ | -| REPEAT | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ | +| REPEAT | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ | | REPEAT_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | RMS_NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | RMS_NORM_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | @@ -99,13 +103,13 @@ Legend: | SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ | | SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ | | SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | -| SIN | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | +| SIN | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | | SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | | SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ | | SOLVE_TRI | ❌ | ❌ | ✅ | 🟡 | 🟡 | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ | -| SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | -| SQRT | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | +| SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | +| SQRT | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | SSM_CONV | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ | | STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | diff --git a/docs/ops/Vulkan.csv b/docs/ops/Vulkan.csv index 3aa7976a2087..59e67e1b208d 100644 --- a/docs/ops/Vulkan.csv +++ b/docs/ops/Vulkan.csv @@ -167,6 +167,16 @@ "Vulkan0","ROUND","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","Vulkan" "Vulkan0","TRUNC","type=f32,ne_a=[128,2,2,2],v=1","support","1","yes","Vulkan" "Vulkan0","TRUNC","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","Vulkan" +"Vulkan0","DSV4_HC_COMB","n_tokens=1,n_iter=1,eps=0.000001","support","0","no","Vulkan" +"Vulkan0","DSV4_HC_COMB","n_tokens=17,n_iter=4,eps=0.000001","support","0","no","Vulkan" +"Vulkan0","DSV4_HC_COMB","n_tokens=257,n_iter=8,eps=0.000001","support","0","no","Vulkan" +"Vulkan0","DSV4_HC_PRE","n_embd=1,n_tokens=1","support","0","no","Vulkan" +"Vulkan0","DSV4_HC_PRE","n_embd=31,n_tokens=17","support","0","no","Vulkan" +"Vulkan0","DSV4_HC_PRE","n_embd=128,n_tokens=257","support","0","no","Vulkan" +"Vulkan0","DSV4_HC_PRE","n_embd=4096,n_tokens=21","support","0","no","Vulkan" +"Vulkan0","DSV4_HC_POST","n_embd=1,n_tokens=1","support","0","no","Vulkan" +"Vulkan0","DSV4_HC_POST","n_embd=31,n_tokens=17","support","0","no","Vulkan" +"Vulkan0","DSV4_HC_POST","n_embd=128,n_tokens=257","support","0","no","Vulkan" "Vulkan0","REGLU","type=f16,ne_a=[128,2,2,2],v=0,swapped=0","support","1","yes","Vulkan" "Vulkan0","REGLU","type=f16,ne_a=[5,7,11,13],v=0,swapped=0","support","1","yes","Vulkan" "Vulkan0","REGLU","type=f16,ne_a=[128,2,2,2],v=0,swapped=1","support","1","yes","Vulkan" @@ -338,6 +348,10 @@ "Vulkan0","GET_ROWS","type=q1_0,n=256,m=5,r=4,be1=1,be2=1,v=1","support","1","yes","Vulkan" "Vulkan0","GET_ROWS","type=q1_0,n=256,m=5,r=4,be1=7,be2=1,v=0","support","1","yes","Vulkan" "Vulkan0","GET_ROWS","type=q1_0,n=256,m=5,r=4,be1=7,be2=1,v=1","support","1","yes","Vulkan" +"Vulkan0","GET_ROWS","type=q2_0,n=256,m=5,r=4,be1=1,be2=1,v=0","support","1","yes","Vulkan" +"Vulkan0","GET_ROWS","type=q2_0,n=256,m=5,r=4,be1=1,be2=1,v=1","support","1","yes","Vulkan" +"Vulkan0","GET_ROWS","type=q2_0,n=256,m=5,r=4,be1=7,be2=1,v=0","support","1","yes","Vulkan" +"Vulkan0","GET_ROWS","type=q2_0,n=256,m=5,r=4,be1=7,be2=1,v=1","support","1","yes","Vulkan" "Vulkan0","GET_ROWS","type=mxfp4,n=256,m=5,r=4,be1=1,be2=1,v=0","support","1","yes","Vulkan" "Vulkan0","GET_ROWS","type=mxfp4,n=256,m=5,r=4,be1=1,be2=1,v=1","support","1","yes","Vulkan" "Vulkan0","GET_ROWS","type=mxfp4,n=256,m=5,r=4,be1=7,be2=1,v=0","support","1","yes","Vulkan" @@ -406,9 +420,10 @@ "Vulkan0","GET_ROWS","type=i32,n=256,m=5,r=4,be1=1,be2=1,v=1","support","1","yes","Vulkan" "Vulkan0","GET_ROWS","type=i32,n=256,m=5,r=4,be1=7,be2=1,v=0","support","1","yes","Vulkan" "Vulkan0","GET_ROWS","type=i32,n=256,m=5,r=4,be1=7,be2=1,v=1","support","1","yes","Vulkan" -"Vulkan0","GET_ROWS_BACK","type=f32,n=1,m=8,r=2,b=1,v=0","support","0","no","Vulkan" -"Vulkan0","GET_ROWS_BACK","type=f32,n=256,m=5,r=4,b=1,v=0","support","0","no","Vulkan" -"Vulkan0","GET_ROWS_BACK","type=f32,n=256,m=5,r=4,b=1,v=1","support","0","no","Vulkan" +"Vulkan0","GET_ROWS_BACK","type=f32,n=1,m=8,r=2,b=1,v=0","support","1","yes","Vulkan" +"Vulkan0","GET_ROWS_BACK","type=f32,n=1,m=70000,r=4,b=1,v=0","support","1","yes","Vulkan" +"Vulkan0","GET_ROWS_BACK","type=f32,n=256,m=5,r=4,b=1,v=0","support","1","yes","Vulkan" +"Vulkan0","GET_ROWS_BACK","type=f32,n=256,m=5,r=4,b=1,v=1","support","1","yes","Vulkan" "Vulkan0","GET_ROWS_BACK","type=f16,n=256,m=5,r=4,b=1,v=0","support","0","no","Vulkan" "Vulkan0","GET_ROWS_BACK","type=f16,n=256,m=5,r=4,b=1,v=1","support","0","no","Vulkan" "Vulkan0","GET_ROWS_BACK","type=bf16,n=256,m=5,r=4,b=1,v=0","support","0","no","Vulkan" @@ -425,6 +440,8 @@ "Vulkan0","GET_ROWS_BACK","type=q8_0,n=256,m=5,r=4,b=1,v=1","support","0","no","Vulkan" "Vulkan0","GET_ROWS_BACK","type=q1_0,n=256,m=5,r=4,b=1,v=0","support","0","no","Vulkan" "Vulkan0","GET_ROWS_BACK","type=q1_0,n=256,m=5,r=4,b=1,v=1","support","0","no","Vulkan" +"Vulkan0","GET_ROWS_BACK","type=q2_0,n=256,m=5,r=4,b=1,v=0","support","0","no","Vulkan" +"Vulkan0","GET_ROWS_BACK","type=q2_0,n=256,m=5,r=4,b=1,v=1","support","0","no","Vulkan" "Vulkan0","GET_ROWS_BACK","type=mxfp4,n=256,m=5,r=4,b=1,v=0","support","0","no","Vulkan" "Vulkan0","GET_ROWS_BACK","type=mxfp4,n=256,m=5,r=4,b=1,v=1","support","0","no","Vulkan" "Vulkan0","GET_ROWS_BACK","type=nvfp4,n=256,m=5,r=4,b=1,v=0","support","0","no","Vulkan" @@ -459,333 +476,685 @@ 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-"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=4,ID=8,IH=8,IW=8,OC=8,KD=1,KH=1,KW=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","Vulkan" -"Vulkan0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","Vulkan" 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+"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","Vulkan" +"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","Vulkan" +"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","Vulkan" +"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","Vulkan" +"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","Vulkan" +"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","Vulkan" +"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","Vulkan" +"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","Vulkan" +"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","Vulkan" +"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","Vulkan" +"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","Vulkan" +"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","Vulkan" +"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","Vulkan" +"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","Vulkan" +"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","Vulkan" +"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","Vulkan" +"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","Vulkan" +"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","Vulkan" +"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","Vulkan" +"Vulkan0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","Vulkan" +"Vulkan0","CONV_3D","N=1,IC=4,ID=8,IH=8,IW=8,OC=8,KD=1,KH=1,KW=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","Vulkan" "Vulkan0","CONV_TRANSPOSE_1D","ne_input=[1,1,1,1],ne_kernel=[1,1,1,1],s0=1,p0=0,d0=1","support","1","yes","Vulkan" "Vulkan0","CONV_TRANSPOSE_1D","ne_input=[1,1,1,1],ne_kernel=[1,1,1,1],s0=2,p0=0,d0=1","support","1","yes","Vulkan" "Vulkan0","CONV_TRANSPOSE_1D","ne_input=[1,1,1,1],ne_kernel=[1,1,1,1],s0=3,p0=0,d0=1","support","1","yes","Vulkan" @@ -5049,6 +7164,39 @@ "Vulkan0","CONV_TRANSPOSE_1D","ne_input=[3,2,1,1],ne_kernel=[3,2,2,1],s0=1,p0=0,d0=1","support","1","yes","Vulkan" "Vulkan0","CONV_TRANSPOSE_1D","ne_input=[3,2,1,1],ne_kernel=[3,1,2,1],s0=1,p0=0,d0=1","support","1","yes","Vulkan" "Vulkan0","CONV_TRANSPOSE_1D","ne_input=[2,1,1,1],ne_kernel=[3,1,1,1],s0=1,p0=0,d0=1","support","1","yes","Vulkan" +"Vulkan0","COL2IM_1D","type=f32,K=16,OC=32,T_in=197,s0=8,p0=0","support","1","yes","Vulkan" +"Vulkan0","COL2IM_1D","type=f32,K=4,OC=3,T_in=7,s0=2,p0=0","support","1","yes","Vulkan" +"Vulkan0","COL2IM_1D","type=f32,K=1,OC=5,T_in=13,s0=1,p0=0","support","1","yes","Vulkan" +"Vulkan0","COL2IM_1D","type=f32,K=6,OC=4,T_in=11,s0=3,p0=1","support","1","yes","Vulkan" +"Vulkan0","COL2IM_1D","type=f32,K=2,OC=3,T_in=9,s0=3,p0=0","support","1","yes","Vulkan" +"Vulkan0","COL2IM_1D","type=f32,K=5,OC=4,T_in=11,s0=2,p0=0","support","1","yes","Vulkan" +"Vulkan0","COL2IM_1D","type=f32,K=8,OC=4,T_in=13,s0=4,p0=2","support","1","yes","Vulkan" +"Vulkan0","COL2IM_1D","type=f32,K=4,OC=3,T_in=1,s0=2,p0=0","support","1","yes","Vulkan" +"Vulkan0","COL2IM_1D","type=f32,K=16,OC=1,T_in=197,s0=8,p0=0","support","1","yes","Vulkan" +"Vulkan0","COL2IM_1D","type=f32,K=1,OC=5,T_in=13,s0=3,p0=0","support","1","yes","Vulkan" +"Vulkan0","COL2IM_1D","type=f32,K=8,OC=2,T_in=3,s0=2,p0=5","support","1","yes","Vulkan" +"Vulkan0","COL2IM_1D","type=f16,K=16,OC=32,T_in=197,s0=8,p0=0","support","1","yes","Vulkan" +"Vulkan0","COL2IM_1D","type=f16,K=4,OC=3,T_in=7,s0=2,p0=0","support","1","yes","Vulkan" +"Vulkan0","COL2IM_1D","type=f16,K=1,OC=5,T_in=13,s0=1,p0=0","support","1","yes","Vulkan" +"Vulkan0","COL2IM_1D","type=f16,K=6,OC=4,T_in=11,s0=3,p0=1","support","1","yes","Vulkan" +"Vulkan0","COL2IM_1D","type=f16,K=2,OC=3,T_in=9,s0=3,p0=0","support","1","yes","Vulkan" +"Vulkan0","COL2IM_1D","type=f16,K=5,OC=4,T_in=11,s0=2,p0=0","support","1","yes","Vulkan" +"Vulkan0","COL2IM_1D","type=f16,K=8,OC=4,T_in=13,s0=4,p0=2","support","1","yes","Vulkan" +"Vulkan0","COL2IM_1D","type=f16,K=4,OC=3,T_in=1,s0=2,p0=0","support","1","yes","Vulkan" +"Vulkan0","COL2IM_1D","type=f16,K=16,OC=1,T_in=197,s0=8,p0=0","support","1","yes","Vulkan" +"Vulkan0","COL2IM_1D","type=f16,K=1,OC=5,T_in=13,s0=3,p0=0","support","1","yes","Vulkan" +"Vulkan0","COL2IM_1D","type=f16,K=8,OC=2,T_in=3,s0=2,p0=5","support","1","yes","Vulkan" +"Vulkan0","COL2IM_1D","type=bf16,K=16,OC=32,T_in=197,s0=8,p0=0","support","1","yes","Vulkan" +"Vulkan0","COL2IM_1D","type=bf16,K=4,OC=3,T_in=7,s0=2,p0=0","support","1","yes","Vulkan" +"Vulkan0","COL2IM_1D","type=bf16,K=1,OC=5,T_in=13,s0=1,p0=0","support","1","yes","Vulkan" +"Vulkan0","COL2IM_1D","type=bf16,K=6,OC=4,T_in=11,s0=3,p0=1","support","1","yes","Vulkan" +"Vulkan0","COL2IM_1D","type=bf16,K=2,OC=3,T_in=9,s0=3,p0=0","support","1","yes","Vulkan" +"Vulkan0","COL2IM_1D","type=bf16,K=5,OC=4,T_in=11,s0=2,p0=0","support","1","yes","Vulkan" +"Vulkan0","COL2IM_1D","type=bf16,K=8,OC=4,T_in=13,s0=4,p0=2","support","1","yes","Vulkan" +"Vulkan0","COL2IM_1D","type=bf16,K=4,OC=3,T_in=1,s0=2,p0=0","support","1","yes","Vulkan" +"Vulkan0","COL2IM_1D","type=bf16,K=16,OC=1,T_in=197,s0=8,p0=0","support","1","yes","Vulkan" +"Vulkan0","COL2IM_1D","type=bf16,K=1,OC=5,T_in=13,s0=3,p0=0","support","1","yes","Vulkan" +"Vulkan0","COL2IM_1D","type=bf16,K=8,OC=2,T_in=3,s0=2,p0=5","support","1","yes","Vulkan" "Vulkan0","CONV_TRANSPOSE_2D","kernel_type=f32,ne_input=[3,2,3,1],ne_kernel=[2,2,1,3],stride=1","support","1","yes","Vulkan" "Vulkan0","CONV_TRANSPOSE_2D","kernel_type=f32,ne_input=[10,10,9,1],ne_kernel=[3,3,1,9],stride=2","support","1","yes","Vulkan" "Vulkan0","CONV_TRANSPOSE_2D","kernel_type=f32,ne_input=[129,63,35,1],ne_kernel=[3,3,48,35],stride=1","support","1","yes","Vulkan" @@ -5071,6 +7219,7 @@ "Vulkan0","REPEAT","type=f32,ne=[10,5,4,1],nr=[1,1,1,2]","support","1","yes","Vulkan" "Vulkan0","REPEAT","type=i32,ne=[10,5,4,1],nr=[2,1,1,1]","support","1","yes","Vulkan" "Vulkan0","REPEAT","type=i16,ne=[10,5,4,1],nr=[1,1,1,2]","support","1","yes","Vulkan" +"Vulkan0","REPEAT","type=bf16,ne=[10,5,4,1],nr=[2,1,1,1]","support","1","yes","Vulkan" "Vulkan0","REPEAT","type=f32,ne=[10,5,4,3],nr=[1,1,1,1]","support","1","yes","Vulkan" "Vulkan0","REPEAT","type=f32,ne=[10,5,4,3],nr=[2,1,1,1]","support","1","yes","Vulkan" "Vulkan0","REPEAT","type=f32,ne=[10,5,4,3],nr=[1,2,1,1]","support","1","yes","Vulkan" @@ -5078,6 +7227,7 @@ "Vulkan0","REPEAT","type=f32,ne=[10,5,4,3],nr=[1,1,1,2]","support","1","yes","Vulkan" "Vulkan0","REPEAT","type=i32,ne=[10,5,4,3],nr=[2,1,1,1]","support","1","yes","Vulkan" "Vulkan0","REPEAT","type=i16,ne=[10,5,4,3],nr=[1,1,1,2]","support","1","yes","Vulkan" +"Vulkan0","REPEAT","type=bf16,ne=[10,5,4,3],nr=[2,1,1,1]","support","1","yes","Vulkan" "Vulkan0","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[1,1,1,1],v=0","support","1","yes","Vulkan" "Vulkan0","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[2,1,1,1],v=0","support","1","yes","Vulkan" "Vulkan0","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[1,2,1,1],v=0","support","1","yes","Vulkan" @@ -5191,6 +7341,15 @@ "Vulkan0","CPY","type_src=q1_0,type_dst=q1_0,ne_src=[384,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","Vulkan" "Vulkan0","CPY","type_src=q1_0,type_dst=q1_0,ne_src=[384,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","Vulkan" "Vulkan0","CPY","type_src=q1_0,type_dst=q1_0,ne_src=[384,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","Vulkan" +"Vulkan0","CPY","type_src=q2_0,type_dst=q2_0,ne_src=[64,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","Vulkan" +"Vulkan0","CPY","type_src=q2_0,type_dst=q2_0,ne_src=[64,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","Vulkan" +"Vulkan0","CPY","type_src=q2_0,type_dst=q2_0,ne_src=[64,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","Vulkan" +"Vulkan0","CPY","type_src=q2_0,type_dst=q2_0,ne_src=[128,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","Vulkan" +"Vulkan0","CPY","type_src=q2_0,type_dst=q2_0,ne_src=[128,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","Vulkan" +"Vulkan0","CPY","type_src=q2_0,type_dst=q2_0,ne_src=[128,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","Vulkan" +"Vulkan0","CPY","type_src=q2_0,type_dst=q2_0,ne_src=[192,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","Vulkan" +"Vulkan0","CPY","type_src=q2_0,type_dst=q2_0,ne_src=[192,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","Vulkan" +"Vulkan0","CPY","type_src=q2_0,type_dst=q2_0,ne_src=[192,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","Vulkan" "Vulkan0","CPY","type_src=mxfp4,type_dst=mxfp4,ne_src=[32,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","Vulkan" "Vulkan0","CPY","type_src=mxfp4,type_dst=mxfp4,ne_src=[32,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","Vulkan" "Vulkan0","CPY","type_src=mxfp4,type_dst=mxfp4,ne_src=[32,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","Vulkan" @@ -5353,6 +7512,8 @@ "Vulkan0","CPY","type_src=f16,type_dst=q8_0,ne_src=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","Vulkan" "Vulkan0","CPY","type_src=f16,type_dst=q1_0,ne_src=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","Vulkan" "Vulkan0","CPY","type_src=f16,type_dst=q1_0,ne_src=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","Vulkan" +"Vulkan0","CPY","type_src=f16,type_dst=q2_0,ne_src=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","Vulkan" +"Vulkan0","CPY","type_src=f16,type_dst=q2_0,ne_src=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","Vulkan" "Vulkan0","CPY","type_src=f16,type_dst=mxfp4,ne_src=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","Vulkan" "Vulkan0","CPY","type_src=f16,type_dst=mxfp4,ne_src=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","Vulkan" "Vulkan0","CPY","type_src=f16,type_dst=nvfp4,ne_src=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","Vulkan" @@ -5403,6 +7564,8 @@ "Vulkan0","CPY","type_src=bf16,type_dst=q8_0,ne_src=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","Vulkan" "Vulkan0","CPY","type_src=bf16,type_dst=q1_0,ne_src=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","Vulkan" "Vulkan0","CPY","type_src=bf16,type_dst=q1_0,ne_src=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","Vulkan" +"Vulkan0","CPY","type_src=bf16,type_dst=q2_0,ne_src=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","Vulkan" +"Vulkan0","CPY","type_src=bf16,type_dst=q2_0,ne_src=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","Vulkan" "Vulkan0","CPY","type_src=bf16,type_dst=mxfp4,ne_src=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","Vulkan" "Vulkan0","CPY","type_src=bf16,type_dst=mxfp4,ne_src=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","Vulkan" "Vulkan0","CPY","type_src=bf16,type_dst=nvfp4,ne_src=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","Vulkan" @@ -5453,6 +7616,8 @@ "Vulkan0","CPY","type_src=f32,type_dst=q8_0,ne_src=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","Vulkan" "Vulkan0","CPY","type_src=f32,type_dst=q1_0,ne_src=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","Vulkan" "Vulkan0","CPY","type_src=f32,type_dst=q1_0,ne_src=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","Vulkan" +"Vulkan0","CPY","type_src=f32,type_dst=q2_0,ne_src=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","Vulkan" +"Vulkan0","CPY","type_src=f32,type_dst=q2_0,ne_src=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","Vulkan" "Vulkan0","CPY","type_src=f32,type_dst=mxfp4,ne_src=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","Vulkan" "Vulkan0","CPY","type_src=f32,type_dst=mxfp4,ne_src=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","Vulkan" "Vulkan0","CPY","type_src=f32,type_dst=nvfp4,ne_src=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","Vulkan" @@ -5503,6 +7668,8 @@ "Vulkan0","CPY","type_src=q8_0,type_dst=f32,ne_src=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","Vulkan" "Vulkan0","CPY","type_src=q1_0,type_dst=f32,ne_src=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","Vulkan" "Vulkan0","CPY","type_src=q1_0,type_dst=f32,ne_src=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","Vulkan" +"Vulkan0","CPY","type_src=q2_0,type_dst=f32,ne_src=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","Vulkan" +"Vulkan0","CPY","type_src=q2_0,type_dst=f32,ne_src=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","Vulkan" "Vulkan0","CPY","type_src=mxfp4,type_dst=f32,ne_src=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","Vulkan" "Vulkan0","CPY","type_src=mxfp4,type_dst=f32,ne_src=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","Vulkan" "Vulkan0","CPY","type_src=nvfp4,type_dst=f32,ne_src=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","Vulkan" @@ -5553,6 +7720,10 @@ "Vulkan0","CPY","type_src=i32,type_dst=i32,ne_src=[256,4,1,1],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=1","support","1","yes","Vulkan" "Vulkan0","CPY","type_src=i32,type_dst=i32,ne_src=[256,1,4,1],permute_src=[1,2,0,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","Vulkan" "Vulkan0","CPY","type_src=f32,type_dst=f32,ne_src=[256,1,4,1],permute_src=[1,2,0,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","Vulkan" +"Vulkan0","CPY","type_src=f32,type_dst=f32,ne_src=[2,2097121,1,1],permute_src=[1,0,2,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","Vulkan" +"Vulkan0","CPY","type_src=f32,type_dst=f32,ne_src=[2,2,524281,1],permute_src=[1,0,2,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","Vulkan" +"Vulkan0","CPY","type_src=f32,type_dst=f32,ne_src=[128,2,3,1],ne_dst=[128,2,3,1],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0,dst_alloc=[128,4,3,1]","support","1","yes","Vulkan" +"Vulkan0","CPY","type_src=f16,type_dst=f16,ne_src=[128,2,3,1],ne_dst=[128,2,3,1],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0,dst_alloc=[128,4,3,1]","support","1","yes","Vulkan" "Vulkan0","CPY","type_src=f32,type_dst=f32,ne_src=[3,5,7,32],ne_dst=[32,7,5,3],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","Vulkan" "Vulkan0","CPY","type_src=f32,type_dst=f32,ne_src=[3,5,32,7],ne_dst=[3,5,7,32],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","Vulkan" "Vulkan0","CPY","type_src=f32,type_dst=f32,ne_src=[3,5,32,7],ne_dst=[32,7,5,3],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","Vulkan" @@ -6052,76 +8223,96 @@ "Vulkan0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=1.000000,inplace=1","support","1","yes","Vulkan" "Vulkan0","SCALE","type=f32,ne=[100,10,10,10],scale=2.000000,bias=1.000000,inplace=0","support","1","yes","Vulkan" "Vulkan0","SILU_BACK","type=f32,ne=[64,5,4,3],eps=0.000001","support","1","yes","Vulkan" -"Vulkan0","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000000","support","1","yes","Vulkan" +"Vulkan0","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000000,noncontig_rows=0","support","1","yes","Vulkan" "Vulkan0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000000,inplace=0","support","1","yes","Vulkan" -"Vulkan0","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000000","support","0","no","Vulkan" +"Vulkan0","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000000,noncontig_rows=0","support","1","yes","Vulkan" "Vulkan0","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000000,inplace=0","support","1","yes","Vulkan" +"Vulkan0","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000000,noncontig_rows=1","support","1","yes","Vulkan" "Vulkan0","RMS_NORM_BACK","type=f32,ne=[64,5,4,3],eps=0.000000","support","1","yes","Vulkan" -"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3],eps=0.000000,v=0","support","1","yes","Vulkan" -"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3],eps=0.000000,v=1","support","1","yes","Vulkan" -"Vulkan0","NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.000000","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3],eps=0.000000,v=0,noncontig_rows=0","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3],eps=0.000000,v=1,noncontig_rows=0","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3],eps=0.000000,v=0,noncontig_rows=1","support","1","yes","Vulkan" +"Vulkan0","NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.000000,noncontig_rows=0","support","1","yes","Vulkan" "Vulkan0","RMS_NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.000000,inplace=0","support","1","yes","Vulkan" -"Vulkan0","NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.000000","support","0","no","Vulkan" +"Vulkan0","NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.000000,noncontig_rows=0","support","1","yes","Vulkan" "Vulkan0","RMS_NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.000000,inplace=0","support","1","yes","Vulkan" +"Vulkan0","NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.000000,noncontig_rows=1","support","1","yes","Vulkan" "Vulkan0","RMS_NORM_BACK","type=f32,ne=[1025,5,4,3],eps=0.000000","support","1","yes","Vulkan" -"Vulkan0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.000000,v=0","support","1","yes","Vulkan" -"Vulkan0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.000000,v=1","support","1","yes","Vulkan" -"Vulkan0","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.000000,v=0,noncontig_rows=0","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.000000,v=1,noncontig_rows=0","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.000000,v=0,noncontig_rows=1","support","1","yes","Vulkan" +"Vulkan0","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001,noncontig_rows=0","support","1","yes","Vulkan" "Vulkan0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001,inplace=0","support","1","yes","Vulkan" -"Vulkan0","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000001","support","0","no","Vulkan" +"Vulkan0","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000001,noncontig_rows=0","support","1","yes","Vulkan" "Vulkan0","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000001,inplace=0","support","1","yes","Vulkan" +"Vulkan0","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001,noncontig_rows=1","support","1","yes","Vulkan" "Vulkan0","RMS_NORM_BACK","type=f32,ne=[64,5,4,3],eps=0.000001","support","1","yes","Vulkan" -"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3],eps=0.000001,v=0","support","1","yes","Vulkan" -"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3],eps=0.000001,v=1","support","1","yes","Vulkan" -"Vulkan0","NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.000001","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3],eps=0.000001,v=0,noncontig_rows=0","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3],eps=0.000001,v=1,noncontig_rows=0","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3],eps=0.000001,v=0,noncontig_rows=1","support","1","yes","Vulkan" +"Vulkan0","NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.000001,noncontig_rows=0","support","1","yes","Vulkan" "Vulkan0","RMS_NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.000001,inplace=0","support","1","yes","Vulkan" -"Vulkan0","NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.000001","support","0","no","Vulkan" +"Vulkan0","NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.000001,noncontig_rows=0","support","1","yes","Vulkan" "Vulkan0","RMS_NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.000001,inplace=0","support","1","yes","Vulkan" +"Vulkan0","NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.000001,noncontig_rows=1","support","1","yes","Vulkan" "Vulkan0","RMS_NORM_BACK","type=f32,ne=[1025,5,4,3],eps=0.000001","support","1","yes","Vulkan" -"Vulkan0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.000001,v=0","support","1","yes","Vulkan" -"Vulkan0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.000001,v=1","support","1","yes","Vulkan" -"Vulkan0","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000100","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.000001,v=0,noncontig_rows=0","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.000001,v=1,noncontig_rows=0","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.000001,v=0,noncontig_rows=1","support","1","yes","Vulkan" +"Vulkan0","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000100,noncontig_rows=0","support","1","yes","Vulkan" "Vulkan0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000100,inplace=0","support","1","yes","Vulkan" -"Vulkan0","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000100","support","0","no","Vulkan" +"Vulkan0","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000100,noncontig_rows=0","support","1","yes","Vulkan" "Vulkan0","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000100,inplace=0","support","1","yes","Vulkan" +"Vulkan0","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000100,noncontig_rows=1","support","1","yes","Vulkan" "Vulkan0","RMS_NORM_BACK","type=f32,ne=[64,5,4,3],eps=0.000100","support","1","yes","Vulkan" -"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3],eps=0.000100,v=0","support","1","yes","Vulkan" -"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3],eps=0.000100,v=1","support","1","yes","Vulkan" -"Vulkan0","NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.000100","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3],eps=0.000100,v=0,noncontig_rows=0","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3],eps=0.000100,v=1,noncontig_rows=0","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3],eps=0.000100,v=0,noncontig_rows=1","support","1","yes","Vulkan" +"Vulkan0","NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.000100,noncontig_rows=0","support","1","yes","Vulkan" "Vulkan0","RMS_NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.000100,inplace=0","support","1","yes","Vulkan" -"Vulkan0","NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.000100","support","0","no","Vulkan" +"Vulkan0","NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.000100,noncontig_rows=0","support","1","yes","Vulkan" "Vulkan0","RMS_NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.000100,inplace=0","support","1","yes","Vulkan" +"Vulkan0","NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.000100,noncontig_rows=1","support","1","yes","Vulkan" "Vulkan0","RMS_NORM_BACK","type=f32,ne=[1025,5,4,3],eps=0.000100","support","1","yes","Vulkan" -"Vulkan0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.000100,v=0","support","1","yes","Vulkan" -"Vulkan0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.000100,v=1","support","1","yes","Vulkan" -"Vulkan0","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.100000","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.000100,v=0,noncontig_rows=0","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.000100,v=1,noncontig_rows=0","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.000100,v=0,noncontig_rows=1","support","1","yes","Vulkan" +"Vulkan0","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.100000,noncontig_rows=0","support","1","yes","Vulkan" "Vulkan0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.100000,inplace=0","support","1","yes","Vulkan" -"Vulkan0","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.100000","support","0","no","Vulkan" +"Vulkan0","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.100000,noncontig_rows=0","support","1","yes","Vulkan" "Vulkan0","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.100000,inplace=0","support","1","yes","Vulkan" +"Vulkan0","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.100000,noncontig_rows=1","support","1","yes","Vulkan" "Vulkan0","RMS_NORM_BACK","type=f32,ne=[64,5,4,3],eps=0.100000","support","1","yes","Vulkan" -"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3],eps=0.100000,v=0","support","1","yes","Vulkan" -"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3],eps=0.100000,v=1","support","1","yes","Vulkan" -"Vulkan0","NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.100000","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3],eps=0.100000,v=0,noncontig_rows=0","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3],eps=0.100000,v=1,noncontig_rows=0","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3],eps=0.100000,v=0,noncontig_rows=1","support","1","yes","Vulkan" +"Vulkan0","NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.100000,noncontig_rows=0","support","1","yes","Vulkan" "Vulkan0","RMS_NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.100000,inplace=0","support","1","yes","Vulkan" -"Vulkan0","NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.100000","support","0","no","Vulkan" +"Vulkan0","NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.100000,noncontig_rows=0","support","1","yes","Vulkan" "Vulkan0","RMS_NORM","type=f32,ne=[1025,5,4,3],v=1,eps=0.100000,inplace=0","support","1","yes","Vulkan" +"Vulkan0","NORM","type=f32,ne=[1025,5,4,3],v=0,eps=0.100000,noncontig_rows=1","support","1","yes","Vulkan" "Vulkan0","RMS_NORM_BACK","type=f32,ne=[1025,5,4,3],eps=0.100000","support","1","yes","Vulkan" -"Vulkan0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.100000,v=0","support","1","yes","Vulkan" -"Vulkan0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.100000,v=1","support","1","yes","Vulkan" -"Vulkan0","NORM","type=f32,ne=[64,5,4,3],v=0,eps=10.000000","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.100000,v=0,noncontig_rows=0","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.100000,v=1,noncontig_rows=0","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=0.100000,v=0,noncontig_rows=1","support","1","yes","Vulkan" +"Vulkan0","NORM","type=f32,ne=[64,5,4,3],v=0,eps=10.000000,noncontig_rows=0","support","1","yes","Vulkan" "Vulkan0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=10.000000,inplace=0","support","1","yes","Vulkan" -"Vulkan0","NORM","type=f32,ne=[64,5,4,3],v=1,eps=10.000000","support","0","no","Vulkan" +"Vulkan0","NORM","type=f32,ne=[64,5,4,3],v=1,eps=10.000000,noncontig_rows=0","support","1","yes","Vulkan" "Vulkan0","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=10.000000,inplace=0","support","1","yes","Vulkan" +"Vulkan0","NORM","type=f32,ne=[64,5,4,3],v=0,eps=10.000000,noncontig_rows=1","support","1","yes","Vulkan" "Vulkan0","RMS_NORM_BACK","type=f32,ne=[64,5,4,3],eps=10.000000","support","1","yes","Vulkan" -"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3],eps=10.000000,v=0","support","1","yes","Vulkan" -"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3],eps=10.000000,v=1","support","1","yes","Vulkan" -"Vulkan0","NORM","type=f32,ne=[1025,5,4,3],v=0,eps=10.000000","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3],eps=10.000000,v=0,noncontig_rows=0","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3],eps=10.000000,v=1,noncontig_rows=0","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3],eps=10.000000,v=0,noncontig_rows=1","support","1","yes","Vulkan" +"Vulkan0","NORM","type=f32,ne=[1025,5,4,3],v=0,eps=10.000000,noncontig_rows=0","support","1","yes","Vulkan" "Vulkan0","RMS_NORM","type=f32,ne=[1025,5,4,3],v=0,eps=10.000000,inplace=0","support","1","yes","Vulkan" -"Vulkan0","NORM","type=f32,ne=[1025,5,4,3],v=1,eps=10.000000","support","0","no","Vulkan" +"Vulkan0","NORM","type=f32,ne=[1025,5,4,3],v=1,eps=10.000000,noncontig_rows=0","support","1","yes","Vulkan" "Vulkan0","RMS_NORM","type=f32,ne=[1025,5,4,3],v=1,eps=10.000000,inplace=0","support","1","yes","Vulkan" +"Vulkan0","NORM","type=f32,ne=[1025,5,4,3],v=0,eps=10.000000,noncontig_rows=1","support","1","yes","Vulkan" "Vulkan0","RMS_NORM_BACK","type=f32,ne=[1025,5,4,3],eps=10.000000","support","1","yes","Vulkan" -"Vulkan0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=10.000000,v=0","support","1","yes","Vulkan" -"Vulkan0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=10.000000,v=1","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=10.000000,v=0,noncontig_rows=0","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=10.000000,v=1,noncontig_rows=0","support","1","yes","Vulkan" +"Vulkan0","L2_NORM","type=f32,ne=[1025,5,4,3],eps=10.000000,v=0,noncontig_rows=1","support","1","yes","Vulkan" "Vulkan0","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001,inplace=1","support","1","yes","Vulkan" "Vulkan0","SSM_CONV","type=f32,ne_a=[3,1024,1,1],ne_b=[3,1024,1,1]","support","1","yes","Vulkan" "Vulkan0","SSM_CONV","type=f32,ne_a=[6,1024,1,1],ne_b=[3,1024,1,1]","support","1","yes","Vulkan" @@ -6172,6 +8363,9 @@ "Vulkan0","SSM_SCAN","type=f32,d_state=128,head_dim=64,n_head=16,n_group=2,n_seq_tokens=32,n_seqs=4,xbc_overlap=0","support","1","yes","Vulkan" "Vulkan0","SSM_SCAN","type=f32,d_state=256,head_dim=64,n_head=8,n_group=2,n_seq_tokens=32,n_seqs=4,xbc_overlap=0","support","1","yes","Vulkan" "Vulkan0","SSM_SCAN","type=f32,d_state=128,head_dim=128,n_head=4,n_group=4,n_seq_tokens=16,n_seqs=2,xbc_overlap=1","support","1","yes","Vulkan" +"Vulkan0","SSM_SCAN","type=f32,d_state=128,head_dim=80,n_head=128,n_group=1,n_seq_tokens=256,n_seqs=1,xbc_overlap=0","support","1","yes","Vulkan" +"Vulkan0","SSM_SCAN","type=f32,d_state=128,head_dim=80,n_head=128,n_group=1,n_seq_tokens=512,n_seqs=1,xbc_overlap=0","support","1","yes","Vulkan" +"Vulkan0","SSM_SCAN","type=f32,d_state=128,head_dim=64,n_head=80,n_group=8,n_seq_tokens=300,n_seqs=2,xbc_overlap=0","support","1","yes","Vulkan" "Vulkan0","RWKV_WKV6","type=f32,head_count=32,head_size=64,n_seq_tokens=1,n_seqs=1","support","1","yes","Vulkan" "Vulkan0","RWKV_WKV6","type=f32,head_count=32,head_size=64,n_seq_tokens=32,n_seqs=1","support","1","yes","Vulkan" "Vulkan0","RWKV_WKV6","type=f32,head_count=32,head_size=64,n_seq_tokens=32,n_seqs=4","support","1","yes","Vulkan" @@ -6180,16 +8374,19 @@ "Vulkan0","RWKV_WKV7","type=f32,head_count=32,head_size=64,n_seq_tokens=32,n_seqs=1","support","1","yes","Vulkan" "Vulkan0","RWKV_WKV7","type=f32,head_count=32,head_size=64,n_seq_tokens=32,n_seqs=4","support","1","yes","Vulkan" "Vulkan0","RWKV_WKV7","type=f32,head_count=32,head_size=64,n_seq_tokens=128,n_seqs=4","support","1","yes","Vulkan" -"Vulkan0","GATED_LINEAR_ATTN","type=f32,head_count=32,head_size=64,n_seq_tokens=1,n_seqs=1","support","0","no","Vulkan" -"Vulkan0","GATED_LINEAR_ATTN","type=f32,head_count=32,head_size=64,n_seq_tokens=32,n_seqs=1","support","0","no","Vulkan" -"Vulkan0","GATED_LINEAR_ATTN","type=f32,head_count=32,head_size=64,n_seq_tokens=32,n_seqs=4","support","0","no","Vulkan" -"Vulkan0","GATED_LINEAR_ATTN","type=f32,head_count=32,head_size=64,n_seq_tokens=128,n_seqs=4","support","0","no","Vulkan" +"Vulkan0","GATED_LINEAR_ATTN","type=f32,head_count=32,head_size=64,n_seq_tokens=1,n_seqs=1","support","1","yes","Vulkan" +"Vulkan0","GATED_LINEAR_ATTN","type=f32,head_count=32,head_size=64,n_seq_tokens=32,n_seqs=1","support","1","yes","Vulkan" +"Vulkan0","GATED_LINEAR_ATTN","type=f32,head_count=32,head_size=64,n_seq_tokens=32,n_seqs=4","support","1","yes","Vulkan" +"Vulkan0","GATED_LINEAR_ATTN","type=f32,head_count=32,head_size=64,n_seq_tokens=128,n_seqs=4","support","1","yes","Vulkan" "Vulkan0","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=128,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=64,n=1,k=64,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=256,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=512,n=1,k=512,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=128,n=32,k=128,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=128,n=4,k=128,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=256,n=512,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=32,n=1,k=32,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=1024,n=1,k=1024,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" @@ -6271,6 +8468,15 @@ "Vulkan0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" @@ -6415,6 +8621,16 @@ "Vulkan0","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_0,type_b=f32,m=2880,n=32,k=2880,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q8_0,type_b=f32,m=2880,n=32,k=2880,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f32,m=2880,n=32,k=2880,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=1,n=1,k=2048,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=1,n=7,k=2048,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=1,n=8,k=2048,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=1,n=9,k=2048,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=1,n=16,k=2048,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=1,n=128,k=2048,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=1,n=512,k=2048,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" @@ -6424,6 +8640,7 @@ "Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" @@ -6436,6 +8653,7 @@ "Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","Vulkan" @@ -6451,6 +8669,7 @@ "Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=4,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" @@ -6463,6 +8682,7 @@ "Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=4,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","Vulkan" @@ -6482,6 +8702,7 @@ "Vulkan0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" @@ -6494,6 +8715,7 @@ "Vulkan0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","Vulkan" @@ -6509,6 +8731,7 @@ "Vulkan0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=4,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" @@ -6521,6 +8744,7 @@ "Vulkan0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=4,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","Vulkan" @@ -6540,6 +8764,7 @@ "Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" @@ -6552,6 +8777,7 @@ "Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","Vulkan" @@ -6567,6 +8793,7 @@ "Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=4,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" @@ -6579,6 +8806,7 @@ "Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=4,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","Vulkan" @@ -6598,6 +8826,7 @@ "Vulkan0","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=f16,type_b=f16,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" @@ -6610,6 +8839,7 @@ "Vulkan0","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=f16,type_b=f16,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","Vulkan" @@ -6625,6 +8855,7 @@ "Vulkan0","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=f16,type_b=f16,m=16,n=4,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" @@ -6637,6 +8868,7 @@ "Vulkan0","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=f16,type_b=f16,m=16,n=4,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","Vulkan" @@ -6656,6 +8888,7 @@ "Vulkan0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" @@ -6668,6 +8901,7 @@ "Vulkan0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" @@ -6687,6 +8921,7 @@ "Vulkan0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" @@ -6699,6 +8934,7 @@ "Vulkan0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" @@ -6718,6 +8954,7 @@ "Vulkan0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" @@ -6730,6 +8967,7 @@ "Vulkan0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" @@ -6749,6 +8987,7 @@ "Vulkan0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" @@ -6761,6 +9000,7 @@ "Vulkan0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" @@ -6771,6 +9011,72 @@ "Vulkan0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f16,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" @@ -6780,6 +9086,7 @@ "Vulkan0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" @@ -6792,6 +9099,7 @@ "Vulkan0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" @@ -6811,6 +9119,7 @@ "Vulkan0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" @@ -6823,6 +9132,7 @@ "Vulkan0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" @@ -6842,6 +9152,7 @@ "Vulkan0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" @@ -6854,6 +9165,7 @@ "Vulkan0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" @@ -6873,6 +9185,7 @@ "Vulkan0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" @@ -6885,6 +9198,7 @@ "Vulkan0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" @@ -6904,6 +9218,7 @@ "Vulkan0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" @@ -6916,6 +9231,7 @@ "Vulkan0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" @@ -6935,6 +9251,7 @@ "Vulkan0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" @@ -6947,6 +9264,7 @@ "Vulkan0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" @@ -6966,6 +9284,7 @@ "Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" @@ -6978,6 +9297,7 @@ "Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" @@ -6997,6 +9317,7 @@ "Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" @@ -7009,6 +9330,7 @@ "Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" @@ -7028,6 +9350,7 @@ "Vulkan0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" @@ -7040,6 +9363,7 @@ "Vulkan0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" @@ -7059,6 +9383,7 @@ "Vulkan0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" @@ -7071,6 +9396,7 @@ "Vulkan0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" @@ -7090,6 +9416,7 @@ "Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" @@ -7102,6 +9429,7 @@ "Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" @@ -7121,6 +9449,7 @@ "Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","Vulkan" @@ -7133,6 +9462,7 @@ "Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" +"Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","Vulkan" "Vulkan0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","Vulkan" @@ -7162,6 +9492,8 @@ "Vulkan0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=1,k=64,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q5_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" @@ -7197,6 +9529,7 @@ "Vulkan0","MUL_MAT","type_a=q5_1,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q8_0,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q1_0,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT","type_a=q2_0,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=mxfp4,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=nvfp4,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT","type_a=q2_K,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","Vulkan" @@ -7606,6 +9939,33 @@ "Vulkan0","MUL_MAT_ID","type_a=f16,type_b=f32,n_mats=16,n_used=16,b=1,m=50,n=200,k=64","support","1","yes","Vulkan" "Vulkan0","MUL_MAT_ID","type_a=f16,type_b=f32,n_mats=1,n_used=1,b=0,m=8,n=16,k=1","support","1","yes","Vulkan" "Vulkan0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=32,n_used=2,b=0,m=2880,n=32,k=2880","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q4_0,type_b=f32,n_mats=32,n_used=2,b=0,m=2880,n=32,k=2880","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=4,n_used=2,b=0,m=64,n=16,k=3","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=f16,type_b=f32,n_mats=4,n_used=2,b=0,m=64,n=16,k=3","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=bf16,type_b=f32,n_mats=4,n_used=2,b=0,m=64,n=16,k=3","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q4_0,type_b=f32,n_mats=4,n_used=2,b=0,m=64,n=16,k=96","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q4_1,type_b=f32,n_mats=4,n_used=2,b=0,m=64,n=16,k=96","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q5_0,type_b=f32,n_mats=4,n_used=2,b=0,m=64,n=16,k=96","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q5_1,type_b=f32,n_mats=4,n_used=2,b=0,m=64,n=16,k=96","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q8_0,type_b=f32,n_mats=4,n_used=2,b=0,m=64,n=16,k=96","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=2,b=0,m=64,n=16,k=384","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=4,n_used=2,b=0,m=64,n=16,k=192","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=2,b=0,m=64,n=16,k=96","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=nvfp4,type_b=f32,n_mats=4,n_used=2,b=0,m=64,n=16,k=192","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_K,type_b=f32,n_mats=4,n_used=2,b=0,m=64,n=16,k=768","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q3_K,type_b=f32,n_mats=4,n_used=2,b=0,m=64,n=16,k=768","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q4_K,type_b=f32,n_mats=4,n_used=2,b=0,m=64,n=16,k=768","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q5_K,type_b=f32,n_mats=4,n_used=2,b=0,m=64,n=16,k=768","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q6_K,type_b=f32,n_mats=4,n_used=2,b=0,m=64,n=16,k=768","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=2,b=0,m=64,n=16,k=768","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=iq2_xs,type_b=f32,n_mats=4,n_used=2,b=0,m=64,n=16,k=768","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=iq2_s,type_b=f32,n_mats=4,n_used=2,b=0,m=64,n=16,k=768","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=iq3_xxs,type_b=f32,n_mats=4,n_used=2,b=0,m=64,n=16,k=768","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=iq1_s,type_b=f32,n_mats=4,n_used=2,b=0,m=64,n=16,k=768","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=iq1_m,type_b=f32,n_mats=4,n_used=2,b=0,m=64,n=16,k=768","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=iq4_nl,type_b=f32,n_mats=4,n_used=2,b=0,m=64,n=16,k=96","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=iq3_s,type_b=f32,n_mats=4,n_used=2,b=0,m=64,n=16,k=768","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=iq4_xs,type_b=f32,n_mats=4,n_used=2,b=0,m=64,n=16,k=768","support","1","yes","Vulkan" "Vulkan0","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=1,k=256","support","1","yes","Vulkan" "Vulkan0","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=4,k=256","support","1","yes","Vulkan" "Vulkan0","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=5,k=256","support","1","yes","Vulkan" @@ -7894,6 +10254,78 @@ "Vulkan0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=17,k=256","support","1","yes","Vulkan" "Vulkan0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=32,k=256","support","1","yes","Vulkan" "Vulkan0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=129,k=256","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=1,k=256","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=4,k=256","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=5,k=256","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=17,k=256","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=32,k=256","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=129,k=256","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=1,k=256","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=4,k=256","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=5,k=256","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=17,k=256","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=32,k=256","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=129,k=256","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=1,k=256","support","1","yes","Vulkan" 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+"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=8,n_used=2,b=0,m=512,n=129,k=256","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=1,k=256","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=4,k=256","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=5,k=256","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=17,k=256","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=32,k=256","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=129,k=256","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=1,k=256","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=4,k=256","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=5,k=256","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=17,k=256","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=32,k=256","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=129,k=256","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=1,k=256","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=4,k=256","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=5,k=256","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=17,k=256","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=32,k=256","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=129,k=256","support","1","yes","Vulkan" "Vulkan0","MUL_MAT_ID","type_a=q4_0,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=1,k=256","support","1","yes","Vulkan" "Vulkan0","MUL_MAT_ID","type_a=q4_0,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=4,k=256","support","1","yes","Vulkan" "Vulkan0","MUL_MAT_ID","type_a=q4_0,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=5,k=256","support","1","yes","Vulkan" @@ -8336,6 +10768,8 @@ "Vulkan0","MUL_MAT_ID","type_a=q8_0,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=32,k=256","support","1","yes","Vulkan" "Vulkan0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=1,k=256","support","1","yes","Vulkan" "Vulkan0","MUL_MAT_ID","type_a=q1_0,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=32,k=256","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=1,k=256","support","1","yes","Vulkan" +"Vulkan0","MUL_MAT_ID","type_a=q2_0,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=32,k=256","support","1","yes","Vulkan" "Vulkan0","MUL_MAT_ID","type_a=q2_K,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=1,k=256","support","1","yes","Vulkan" "Vulkan0","MUL_MAT_ID","type_a=q2_K,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=32,k=256","support","1","yes","Vulkan" "Vulkan0","MUL_MAT_ID","type_a=q3_K,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=1,k=256","support","1","yes","Vulkan" @@ -8362,70 +10796,70 @@ "Vulkan0","MUL_MAT_ID","type_a=iq4_xs,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=32,k=256","support","1","yes","Vulkan" "Vulkan0","MUL_MAT_ID","type_a=bf16,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=1,k=256","support","1","yes","Vulkan" "Vulkan0","MUL_MAT_ID","type_a=bf16,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=32,k=256","support","1","yes","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=1,bs=[1,1],nr=[1,1],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=1,bs=[1,1],nr=[1,2],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=1,bs=[1,1],nr=[2,1],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=1,bs=[1,1],nr=[2,2],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=1,bs=[1,3],nr=[1,1],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=1,bs=[1,3],nr=[1,2],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=1,bs=[1,3],nr=[2,1],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=1,bs=[1,3],nr=[2,2],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=1,bs=[3,1],nr=[1,1],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=1,bs=[3,1],nr=[1,2],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=1,bs=[3,1],nr=[2,1],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=1,bs=[3,1],nr=[2,2],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=1,bs=[3,3],nr=[1,1],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=1,bs=[3,3],nr=[1,2],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=1,bs=[3,3],nr=[2,1],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=1,bs=[3,3],nr=[2,2],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=16,bs=[1,1],nr=[1,1],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=16,bs=[1,1],nr=[1,2],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=16,bs=[1,1],nr=[2,1],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=16,bs=[1,1],nr=[2,2],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=16,bs=[1,3],nr=[1,1],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=16,bs=[1,3],nr=[1,2],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=16,bs=[1,3],nr=[2,1],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=16,bs=[1,3],nr=[2,2],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=16,bs=[3,1],nr=[1,1],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=16,bs=[3,1],nr=[1,2],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=16,bs=[3,1],nr=[2,1],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=16,bs=[3,1],nr=[2,2],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=16,bs=[3,3],nr=[1,1],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=16,bs=[3,3],nr=[1,2],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=16,bs=[3,3],nr=[2,1],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=1,k=16,bs=[3,3],nr=[2,2],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=1,bs=[1,1],nr=[1,1],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=1,bs=[1,1],nr=[1,2],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=1,bs=[1,1],nr=[2,1],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=1,bs=[1,1],nr=[2,2],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=1,bs=[1,3],nr=[1,1],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=1,bs=[1,3],nr=[1,2],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=1,bs=[1,3],nr=[2,1],trans_b=0","support","0","no","Vulkan" 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-"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=1,bs=[3,3],nr=[2,2],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=16,bs=[1,1],nr=[1,1],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=16,bs=[1,1],nr=[1,2],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=16,bs=[1,1],nr=[2,1],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=16,bs=[1,1],nr=[2,2],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=16,bs=[1,3],nr=[1,1],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=16,bs=[1,3],nr=[1,2],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=16,bs=[1,3],nr=[2,1],trans_b=0","support","0","no","Vulkan" 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"Vulkan0","OUT_PROD","type_a=q4_0,type_b=f32,m=256,n=1,k=1,bs=[1,1],nr=[1,2],trans_b=0","support","0","no","Vulkan" "Vulkan0","OUT_PROD","type_a=q4_0,type_b=f32,m=256,n=1,k=1,bs=[1,1],nr=[2,1],trans_b=0","support","0","no","Vulkan" @@ -9642,10 +12204,13 @@ "Vulkan0","OUT_PROD","type_a=iq2_xxs,type_b=f16,m=256,n=16,k=16,bs=[3,3],nr=[1,2],trans_b=0","support","0","no","Vulkan" "Vulkan0","OUT_PROD","type_a=iq2_xxs,type_b=f16,m=256,n=16,k=16,bs=[3,3],nr=[2,1],trans_b=0","support","0","no","Vulkan" "Vulkan0","OUT_PROD","type_a=iq2_xxs,type_b=f16,m=256,n=16,k=16,bs=[3,3],nr=[2,2],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=16,bs=[1,1],nr=[1,1],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=16,bs=[8,1],nr=[1,1],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=16,bs=[16,1],nr=[1,1],trans_b=0","support","0","no","Vulkan" -"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=16,bs=[32,1],nr=[1,1],trans_b=0","support","0","no","Vulkan" +"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=16,bs=[1,1],nr=[1,1],trans_b=0","support","1","yes","Vulkan" +"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=16,bs=[8,1],nr=[1,1],trans_b=0","support","1","yes","Vulkan" +"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=16,bs=[16,1],nr=[1,1],trans_b=0","support","1","yes","Vulkan" +"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=16,bs=[32,1],nr=[1,1],trans_b=0","support","1","yes","Vulkan" +"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=16,bs=[1,1],nr=[8,1],trans_b=0","support","1","yes","Vulkan" +"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=16,bs=[1,1],nr=[16,1],trans_b=0","support","1","yes","Vulkan" +"Vulkan0","OUT_PROD","type_a=f32,type_b=f32,m=256,n=16,k=16,bs=[1,1],nr=[32,1],trans_b=0","support","1","yes","Vulkan" "Vulkan0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=1,n_token=1","support","1","yes","Vulkan" "Vulkan0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=1,n_token=32","support","1","yes","Vulkan" "Vulkan0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=1,n_token=129","support","1","yes","Vulkan" @@ -9682,31 +12247,31 @@ "Vulkan0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=4,n_token=1","support","1","yes","Vulkan" "Vulkan0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=4,n_token=32","support","1","yes","Vulkan" "Vulkan0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=4,n_token=129","support","1","yes","Vulkan" -"Vulkan0","SQR","type=f16,ne=[10,5,4,3]","support","0","no","Vulkan" -"Vulkan0","SQRT","type=f16,ne=[10,3,3,2]","support","0","no","Vulkan" +"Vulkan0","SQR","type=f16,ne=[10,5,4,3]","support","1","yes","Vulkan" +"Vulkan0","SQRT","type=f16,ne=[10,3,3,2]","support","1","yes","Vulkan" "Vulkan0","LOG","type=f16,ne=[10,5,4,3]","support","1","yes","Vulkan" -"Vulkan0","SIN","type=f16,ne=[10,2,2,2]","support","0","no","Vulkan" -"Vulkan0","COS","type=f16,ne=[10,2,2,2]","support","0","no","Vulkan" -"Vulkan0","CLAMP","type=f16,ne=[10,5,4,3],min=-0.500000,max=0.500000","support","0","no","Vulkan" -"Vulkan0","LEAKY_RELU","type=f16,ne_a=[10,5,4,3],negative_slope=0.100000","support","0","no","Vulkan" +"Vulkan0","SIN","type=f16,ne=[10,2,2,2]","support","1","yes","Vulkan" +"Vulkan0","COS","type=f16,ne=[10,2,2,2]","support","1","yes","Vulkan" +"Vulkan0","CLAMP","type=f16,ne=[10,5,4,3],min=-0.500000,max=0.500000","support","1","yes","Vulkan" +"Vulkan0","LEAKY_RELU","type=f16,ne_a=[10,5,4,3],negative_slope=0.100000","support","1","yes","Vulkan" "Vulkan0","FLOOR","type=f16,ne=[10,2,2,2]","support","1","yes","Vulkan" "Vulkan0","CEIL","type=f16,ne=[10,2,2,2]","support","1","yes","Vulkan" "Vulkan0","ROUND","type=f16,ne=[10,2,2,2]","support","1","yes","Vulkan" "Vulkan0","TRUNC","type=f16,ne=[10,2,2,2]","support","1","yes","Vulkan" -"Vulkan0","SQR","type=f16,ne=[7,1,5,3]","support","0","no","Vulkan" -"Vulkan0","SQR","type=f16,ne=[1024,1024,1,1]","support","0","no","Vulkan" -"Vulkan0","SQRT","type=f16,ne=[7,1,5,3]","support","0","no","Vulkan" -"Vulkan0","SQRT","type=f16,ne=[1024,1024,1,1]","support","0","no","Vulkan" +"Vulkan0","SQR","type=f16,ne=[7,1,5,3]","support","1","yes","Vulkan" +"Vulkan0","SQR","type=f16,ne=[1024,1024,1,1]","support","1","yes","Vulkan" +"Vulkan0","SQRT","type=f16,ne=[7,1,5,3]","support","1","yes","Vulkan" +"Vulkan0","SQRT","type=f16,ne=[1024,1024,1,1]","support","1","yes","Vulkan" "Vulkan0","LOG","type=f16,ne=[7,1,5,3]","support","1","yes","Vulkan" "Vulkan0","LOG","type=f16,ne=[1024,1024,1,1]","support","1","yes","Vulkan" -"Vulkan0","SIN","type=f16,ne=[7,1,5,3]","support","0","no","Vulkan" -"Vulkan0","SIN","type=f16,ne=[1024,1024,1,1]","support","0","no","Vulkan" -"Vulkan0","COS","type=f16,ne=[7,1,5,3]","support","0","no","Vulkan" -"Vulkan0","COS","type=f16,ne=[1024,1024,1,1]","support","0","no","Vulkan" -"Vulkan0","CLAMP","type=f16,ne=[7,1,5,3],min=-0.500000,max=0.500000","support","0","no","Vulkan" -"Vulkan0","CLAMP","type=f16,ne=[1024,1024,1,1],min=-0.500000,max=0.500000","support","0","no","Vulkan" -"Vulkan0","LEAKY_RELU","type=f16,ne_a=[7,1,5,3],negative_slope=0.100000","support","0","no","Vulkan" -"Vulkan0","LEAKY_RELU","type=f16,ne_a=[1024,1024,1,1],negative_slope=0.100000","support","0","no","Vulkan" +"Vulkan0","SIN","type=f16,ne=[7,1,5,3]","support","1","yes","Vulkan" +"Vulkan0","SIN","type=f16,ne=[1024,1024,1,1]","support","1","yes","Vulkan" +"Vulkan0","COS","type=f16,ne=[7,1,5,3]","support","1","yes","Vulkan" +"Vulkan0","COS","type=f16,ne=[1024,1024,1,1]","support","1","yes","Vulkan" +"Vulkan0","CLAMP","type=f16,ne=[7,1,5,3],min=-0.500000,max=0.500000","support","1","yes","Vulkan" +"Vulkan0","CLAMP","type=f16,ne=[1024,1024,1,1],min=-0.500000,max=0.500000","support","1","yes","Vulkan" +"Vulkan0","LEAKY_RELU","type=f16,ne_a=[7,1,5,3],negative_slope=0.100000","support","1","yes","Vulkan" +"Vulkan0","LEAKY_RELU","type=f16,ne_a=[1024,1024,1,1],negative_slope=0.100000","support","1","yes","Vulkan" "Vulkan0","FLOOR","type=f16,ne=[7,1,5,3]","support","1","yes","Vulkan" "Vulkan0","FLOOR","type=f16,ne=[1024,1024,1,1]","support","1","yes","Vulkan" "Vulkan0","CEIL","type=f16,ne=[7,1,5,3]","support","1","yes","Vulkan" @@ -10848,37 +13413,197 @@ "Vulkan0","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","Vulkan" "Vulkan0","ROPE","type=f16,ne_a=[128,32,2,3],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=1","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=f16,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=bf16,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i8,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i16,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=0","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i64,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=0","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=f16,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=bf16,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i8,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i16,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=0","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i64,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=0","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=f16,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=bf16,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i8,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i16,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=0","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i64,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=0","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=f16,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=bf16,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i8,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i16,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=0","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i64,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=0","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=1","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=f16,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=1","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=bf16,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=1","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i8,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=1","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i16,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=1","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=1","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i64,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=1","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=1","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=f16,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=1","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=bf16,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=1","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i8,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=1","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i16,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=1","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=1","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i64,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=1","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=1","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=f16,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=1","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=bf16,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=1","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i8,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=1","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i16,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=1","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=1","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i64,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=1","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=1","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=f16,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=1","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=bf16,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=1","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i8,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=1","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i16,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=1","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=1","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i64,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=1","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=2","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=f16,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=2","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=bf16,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=2","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i8,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=2","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i16,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=2","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=2","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i64,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=2","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=2","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=f16,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=2","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=bf16,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=2","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i8,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=2","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i16,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=2","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=2","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i64,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=2","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=2","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=f16,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=2","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=bf16,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=2","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i8,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=2","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i16,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=2","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=2","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i64,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=2","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=2","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=f16,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=2","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=bf16,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=2","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i8,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=2","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i16,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=2","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=2","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i64,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=2","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=3","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=f16,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=3","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=bf16,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=3","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i8,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=3","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i16,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=3","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=3","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i64,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=3","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=3","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=f16,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=3","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=bf16,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=3","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i8,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=3","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i16,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=3","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=3","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i64,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=3","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=3","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=f16,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=3","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=bf16,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=3","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i8,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=3","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i16,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=3","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=3","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i64,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=3","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=3","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=f16,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=3","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=bf16,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=3","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i8,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=3","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i16,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=3","support","1","yes","Vulkan" "Vulkan0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=3","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=i64,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=3","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q4_0,ne_a=[128,12,13,14],ne_b_d=256,dim=0,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q4_0,ne_a=[128,12,13,14],ne_b_d=7,dim=1,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q4_0,ne_a=[128,12,13,14],ne_b_d=7,dim=2,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q4_0,ne_a=[128,12,13,14],ne_b_d=7,dim=3,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q4_0,ne_a=[128,12,13,14],ne_b_d=256,dim=0,v=4","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q4_0,ne_a=[128,12,13,14],ne_b_d=7,dim=1,v=4","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q4_0,ne_a=[128,12,13,14],ne_b_d=7,dim=2,v=4","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q4_0,ne_a=[128,12,13,14],ne_b_d=7,dim=3,v=4","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q4_0,ne_a=[128,12,13,14],ne_b_d=256,dim=0,v=8","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q4_0,ne_a=[128,12,13,14],ne_b_d=7,dim=1,v=8","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q4_0,ne_a=[128,12,13,14],ne_b_d=7,dim=2,v=8","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q4_0,ne_a=[128,12,13,14],ne_b_d=7,dim=3,v=8","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q4_0,ne_a=[128,12,13,14],ne_b_d=256,dim=0,v=12","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q4_0,ne_a=[128,12,13,14],ne_b_d=7,dim=1,v=12","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q4_0,ne_a=[128,12,13,14],ne_b_d=7,dim=2,v=12","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q4_0,ne_a=[128,12,13,14],ne_b_d=7,dim=3,v=12","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q4_1,ne_a=[128,12,13,14],ne_b_d=256,dim=0,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q4_1,ne_a=[128,12,13,14],ne_b_d=7,dim=1,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q4_1,ne_a=[128,12,13,14],ne_b_d=7,dim=2,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q4_1,ne_a=[128,12,13,14],ne_b_d=7,dim=3,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q4_1,ne_a=[128,12,13,14],ne_b_d=256,dim=0,v=4","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q4_1,ne_a=[128,12,13,14],ne_b_d=7,dim=1,v=4","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q4_1,ne_a=[128,12,13,14],ne_b_d=7,dim=2,v=4","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q4_1,ne_a=[128,12,13,14],ne_b_d=7,dim=3,v=4","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q4_1,ne_a=[128,12,13,14],ne_b_d=256,dim=0,v=8","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q4_1,ne_a=[128,12,13,14],ne_b_d=7,dim=1,v=8","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q4_1,ne_a=[128,12,13,14],ne_b_d=7,dim=2,v=8","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q4_1,ne_a=[128,12,13,14],ne_b_d=7,dim=3,v=8","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q4_1,ne_a=[128,12,13,14],ne_b_d=256,dim=0,v=12","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q4_1,ne_a=[128,12,13,14],ne_b_d=7,dim=1,v=12","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q4_1,ne_a=[128,12,13,14],ne_b_d=7,dim=2,v=12","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q4_1,ne_a=[128,12,13,14],ne_b_d=7,dim=3,v=12","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q5_0,ne_a=[128,12,13,14],ne_b_d=256,dim=0,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q5_0,ne_a=[128,12,13,14],ne_b_d=7,dim=1,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q5_0,ne_a=[128,12,13,14],ne_b_d=7,dim=2,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q5_0,ne_a=[128,12,13,14],ne_b_d=7,dim=3,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q5_0,ne_a=[128,12,13,14],ne_b_d=256,dim=0,v=4","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q5_0,ne_a=[128,12,13,14],ne_b_d=7,dim=1,v=4","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q5_0,ne_a=[128,12,13,14],ne_b_d=7,dim=2,v=4","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q5_0,ne_a=[128,12,13,14],ne_b_d=7,dim=3,v=4","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q5_0,ne_a=[128,12,13,14],ne_b_d=256,dim=0,v=8","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q5_0,ne_a=[128,12,13,14],ne_b_d=7,dim=1,v=8","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q5_0,ne_a=[128,12,13,14],ne_b_d=7,dim=2,v=8","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q5_0,ne_a=[128,12,13,14],ne_b_d=7,dim=3,v=8","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q5_0,ne_a=[128,12,13,14],ne_b_d=256,dim=0,v=12","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q5_0,ne_a=[128,12,13,14],ne_b_d=7,dim=1,v=12","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q5_0,ne_a=[128,12,13,14],ne_b_d=7,dim=2,v=12","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q5_0,ne_a=[128,12,13,14],ne_b_d=7,dim=3,v=12","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q5_1,ne_a=[128,12,13,14],ne_b_d=256,dim=0,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q5_1,ne_a=[128,12,13,14],ne_b_d=7,dim=1,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q5_1,ne_a=[128,12,13,14],ne_b_d=7,dim=2,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q5_1,ne_a=[128,12,13,14],ne_b_d=7,dim=3,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q5_1,ne_a=[128,12,13,14],ne_b_d=256,dim=0,v=4","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q5_1,ne_a=[128,12,13,14],ne_b_d=7,dim=1,v=4","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q5_1,ne_a=[128,12,13,14],ne_b_d=7,dim=2,v=4","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q5_1,ne_a=[128,12,13,14],ne_b_d=7,dim=3,v=4","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q5_1,ne_a=[128,12,13,14],ne_b_d=256,dim=0,v=8","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q5_1,ne_a=[128,12,13,14],ne_b_d=7,dim=1,v=8","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q5_1,ne_a=[128,12,13,14],ne_b_d=7,dim=2,v=8","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q5_1,ne_a=[128,12,13,14],ne_b_d=7,dim=3,v=8","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q5_1,ne_a=[128,12,13,14],ne_b_d=256,dim=0,v=12","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q5_1,ne_a=[128,12,13,14],ne_b_d=7,dim=1,v=12","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q5_1,ne_a=[128,12,13,14],ne_b_d=7,dim=2,v=12","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q5_1,ne_a=[128,12,13,14],ne_b_d=7,dim=3,v=12","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q8_0,ne_a=[128,12,13,14],ne_b_d=256,dim=0,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q8_0,ne_a=[128,12,13,14],ne_b_d=7,dim=1,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q8_0,ne_a=[128,12,13,14],ne_b_d=7,dim=2,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q8_0,ne_a=[128,12,13,14],ne_b_d=7,dim=3,v=0","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q8_0,ne_a=[128,12,13,14],ne_b_d=256,dim=0,v=4","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q8_0,ne_a=[128,12,13,14],ne_b_d=7,dim=1,v=4","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q8_0,ne_a=[128,12,13,14],ne_b_d=7,dim=2,v=4","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q8_0,ne_a=[128,12,13,14],ne_b_d=7,dim=3,v=4","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q8_0,ne_a=[128,12,13,14],ne_b_d=256,dim=0,v=8","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q8_0,ne_a=[128,12,13,14],ne_b_d=7,dim=1,v=8","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q8_0,ne_a=[128,12,13,14],ne_b_d=7,dim=2,v=8","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q8_0,ne_a=[128,12,13,14],ne_b_d=7,dim=3,v=8","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q8_0,ne_a=[128,12,13,14],ne_b_d=256,dim=0,v=12","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q8_0,ne_a=[128,12,13,14],ne_b_d=7,dim=1,v=12","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q8_0,ne_a=[128,12,13,14],ne_b_d=7,dim=2,v=12","support","1","yes","Vulkan" +"Vulkan0","CONCAT","type=q8_0,ne_a=[128,12,13,14],ne_b_d=7,dim=3,v=12","support","1","yes","Vulkan" "Vulkan0","ARGSORT","type=f32,ne=[3,1,1,1],order=0","support","1","yes","Vulkan" "Vulkan0","ARGSORT","type=f32,ne=[4,1,1,1],order=0","support","1","yes","Vulkan" "Vulkan0","ARGSORT","type=f32,ne=[7,1,1,1],order=0","support","1","yes","Vulkan" @@ -11426,6 +14151,42 @@ "Vulkan0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,tfrm=2,circular=0","support","1","yes","Vulkan" "Vulkan0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,tfrm=2,circular=1","support","1","yes","Vulkan" "Vulkan0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,tfrm=2,circular=1","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=8,nr23=[4,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=8,nr23=[4,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q8_0,type_V=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=8,nr23=[4,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=8,nr23=[4,1],kv=1024,nb=64,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=8,nr23=[4,1],kv=1024,nb=64,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q8_0,type_V=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=8,nr23=[4,1],kv=1024,nb=64,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=8,nr23=[4,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=8,nr23=[4,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q8_0,type_V=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=8,nr23=[4,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" 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+"Vulkan0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=8,nr23=[4,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q8_0,type_V=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=8,nr23=[4,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=8,nr23=[4,1],kv=1024,nb=64,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=8,nr23=[4,1],kv=1024,nb=64,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q8_0,type_V=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=8,nr23=[4,1],kv=1024,nb=64,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=8,nr23=[4,1],kv=2048,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=8,nr23=[4,1],kv=2048,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q8_0,type_V=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=8,nr23=[4,1],kv=2048,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=8,nr23=[4,1],kv=2048,nb=64,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=8,nr23=[4,1],kv=2048,nb=64,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q8_0,type_V=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=8,nr23=[4,1],kv=2048,nb=64,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=8,nr23=[4,1],kv=2048,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=8,nr23=[4,1],kv=2048,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q8_0,type_V=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=8,nr23=[4,1],kv=2048,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=8,nr23=[4,1],kv=2048,nb=64,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=8,nr23=[4,1],kv=2048,nb=64,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q8_0,type_V=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=8,nr23=[4,1],kv=2048,nb=64,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=8,nr23=[4,1],kv=2048,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=8,nr23=[4,1],kv=2048,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q8_0,type_V=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=8,nr23=[4,1],kv=2048,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=8,nr23=[4,1],kv=2048,nb=64,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=8,nr23=[4,1],kv=2048,nb=64,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q8_0,type_V=q8_0,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=8,nr23=[4,1],kv=2048,nb=64,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11658,7 +14419,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11667,7 +14428,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11676,7 +14437,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11685,7 +14446,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11702,8 +14463,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11720,8 +14481,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11738,8 +14499,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11756,8 +14517,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11766,7 +14527,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11775,7 +14536,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11784,7 +14545,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11793,7 +14554,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11810,8 +14571,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11828,8 +14589,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11846,8 +14607,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11864,8 +14625,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11874,7 +14635,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11883,7 +14644,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11892,7 +14653,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11901,7 +14662,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11918,8 +14679,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11936,8 +14697,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11954,8 +14715,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11972,8 +14733,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11982,7 +14743,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -11991,7 +14752,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12000,7 +14761,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12009,7 +14770,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12026,8 +14787,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12044,8 +14805,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12062,8 +14823,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12080,8 +14841,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12090,7 +14851,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12099,7 +14860,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12108,7 +14869,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12117,7 +14878,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12126,7 +14887,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12135,7 +14896,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12144,7 +14905,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12153,7 +14914,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12162,7 +14923,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12171,7 +14932,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12180,7 +14941,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12189,7 +14950,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12198,7 +14959,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12207,7 +14968,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12216,7 +14977,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12225,7 +14986,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12234,7 +14995,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12243,7 +15004,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12252,7 +15013,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12261,7 +15022,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12270,7 +15031,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12279,7 +15040,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12288,7 +15049,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12297,7 +15058,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12306,7 +15067,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12315,7 +15076,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12324,7 +15085,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12333,7 +15094,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12342,7 +15103,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12351,7 +15112,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12360,7 +15121,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12369,7 +15130,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12378,7 +15139,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12387,7 +15148,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12396,7 +15157,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12405,7 +15166,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12422,8 +15183,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12440,8 +15201,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12458,8 +15219,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12476,8 +15237,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12486,7 +15247,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12495,7 +15256,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12504,7 +15265,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12513,7 +15274,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12530,8 +15291,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12548,8 +15309,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12566,8 +15327,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12584,8 +15345,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12594,7 +15355,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12603,7 +15364,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12612,7 +15373,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12621,7 +15382,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12638,8 +15399,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12656,8 +15417,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12674,8 +15435,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12692,8 +15453,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12702,7 +15463,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12711,7 +15472,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12720,7 +15481,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12729,7 +15490,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12746,8 +15507,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12764,8 +15525,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12782,8 +15543,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12800,8 +15561,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12810,7 +15571,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12819,7 +15580,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12828,7 +15589,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12837,7 +15598,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12846,7 +15607,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12855,7 +15616,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12864,7 +15625,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12873,7 +15634,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12882,7 +15643,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12891,7 +15652,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12900,7 +15661,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12909,7 +15670,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12918,7 +15679,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12927,7 +15688,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12936,7 +15697,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12945,7 +15706,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12954,7 +15715,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12963,7 +15724,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12972,7 +15733,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12981,7 +15742,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12990,7 +15751,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -12999,7 +15760,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13008,7 +15769,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13017,7 +15778,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13026,7 +15787,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13035,7 +15796,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13044,7 +15805,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13053,7 +15814,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13062,7 +15823,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13071,7 +15832,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13080,7 +15841,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13089,7 +15850,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13098,7 +15859,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13107,7 +15868,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13116,7 +15877,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13125,7 +15886,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13134,7 +15895,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13143,7 +15904,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13152,7 +15913,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13161,7 +15922,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13170,7 +15931,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13179,7 +15940,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13188,7 +15949,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13197,7 +15958,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13206,7 +15967,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13215,7 +15976,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13224,7 +15985,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13233,7 +15994,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13242,7 +16003,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13251,7 +16012,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13260,7 +16021,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13269,7 +16030,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13278,7 +16039,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13287,7 +16048,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13296,7 +16057,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13305,7 +16066,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13314,7 +16075,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13323,7 +16084,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13332,7 +16093,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13341,7 +16102,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13350,7 +16111,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13359,7 +16120,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13368,7 +16129,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13377,7 +16138,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13386,7 +16147,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13395,7 +16156,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13404,7 +16165,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13413,7 +16174,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13422,7 +16183,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13431,7 +16192,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13440,7 +16201,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13449,7 +16210,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13458,7 +16219,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13467,7 +16228,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13476,7 +16237,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13485,7 +16246,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13494,7 +16255,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13503,7 +16264,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13512,7 +16273,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13521,7 +16282,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13530,7 +16291,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13539,7 +16300,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13548,7 +16309,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13557,7 +16318,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13566,7 +16327,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13575,7 +16336,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13584,7 +16345,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13593,7 +16354,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13602,7 +16363,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13611,7 +16372,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13620,7 +16381,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13629,7 +16390,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13638,7 +16399,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13647,7 +16408,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13656,7 +16417,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13665,7 +16426,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13674,7 +16435,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13683,7 +16444,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13692,7 +16453,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13701,7 +16462,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13718,8 +16479,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13736,8 +16497,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13754,8 +16515,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13772,8 +16533,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13782,7 +16543,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13791,7 +16552,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13800,7 +16561,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13809,7 +16570,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13826,8 +16587,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13844,8 +16605,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13862,8 +16623,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13880,8 +16641,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13890,7 +16651,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13899,7 +16660,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13908,7 +16669,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13917,7 +16678,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13926,7 +16687,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13935,7 +16696,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13944,7 +16705,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13953,7 +16714,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13962,7 +16723,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13971,7 +16732,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13980,7 +16741,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13989,7 +16750,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -13998,7 +16759,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14007,7 +16768,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14016,7 +16777,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14025,7 +16786,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14034,7 +16795,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14043,7 +16804,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14052,7 +16813,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14061,7 +16822,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14078,8 +16839,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14096,8 +16857,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14114,8 +16875,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14132,8 +16893,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14142,7 +16903,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14151,7 +16912,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14160,7 +16921,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14169,7 +16930,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14186,8 +16947,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14204,8 +16965,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14222,8 +16983,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14240,8 +17001,8 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,2,1,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,2,1,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14250,7 +17011,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14259,7 +17020,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14268,7 +17029,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14277,7 +17038,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14286,7 +17047,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14295,7 +17056,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14304,7 +17065,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14313,7 +17074,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14322,7 +17083,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14331,7 +17092,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14340,7 +17101,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14349,7 +17110,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14358,7 +17119,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14367,7 +17128,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14376,7 +17137,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14385,7 +17146,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14394,7 +17155,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14403,7 +17164,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14412,7 +17173,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14421,7 +17182,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14430,7 +17191,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14439,7 +17200,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14448,7 +17209,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14457,7 +17218,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14466,7 +17227,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14475,7 +17236,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14484,7 +17245,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14493,7 +17254,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14502,7 +17263,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14511,7 +17272,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14520,7 +17281,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14529,7 +17290,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14538,7 +17299,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14547,7 +17308,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14556,7 +17317,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14565,7 +17326,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=113,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14574,7 +17335,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14583,7 +17344,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14592,7 +17353,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14601,7 +17362,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14610,7 +17371,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14619,7 +17380,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14628,7 +17389,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14637,7 +17398,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=1024,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14646,7 +17407,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14655,7 +17416,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14664,7 +17425,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f32,type_V=f32,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=bf16,type_V=bf16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -14673,7 +17434,7 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q5_0,type_V=q5_0,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_1,type_V=q4_1,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","Vulkan" -"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=iq4_nl,type_V=iq4_nl,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" @@ -16523,6 +19284,14 @@ "Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=64,nh=4,nr23=[1,1],kv=128,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q1_0,type_V=q4_0,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=128,nh=4,nr23=[1,1],kv=128,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q1_0,permute=[0,1,2,3]","support","0","no","Vulkan" "Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=64,nh=4,nr23=[1,1],kv=64,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q1_0,type_V=f16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=96,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q2_0,type_V=q2_0,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=64,nh=4,nr23=[1,1],kv=128,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q2_0,type_V=q4_0,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=64,hsv=128,nh=4,nr23=[1,1],kv=128,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q2_0,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=64,nh=4,nr23=[1,1],kv=64,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q2_0,type_V=f16,permute=[0,1,2,3]","support","0","no","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[6,1],kv=4096,nb=512,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=8,nr23=[4,1],kv=4096,nb=512,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[6,1],kv=16384,nb=512,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" +"Vulkan0","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=8,nr23=[4,1],kv=16384,nb=512,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","Vulkan" "Vulkan0","CROSS_ENTROPY_LOSS","type=f32,ne=[10,5,4,3]","support","0","no","Vulkan" "Vulkan0","CROSS_ENTROPY_LOSS","type=f32,ne=[30000,1,1,1]","support","0","no","Vulkan" "Vulkan0","CROSS_ENTROPY_LOSS_BACK","type=f32,ne=[10,5,4,3]","support","0","no","Vulkan" @@ -16530,9 +19299,9 @@ "Vulkan0","OPT_STEP_ADAMW","type=f32,ne=[10,5,4,3]","support","1","yes","Vulkan" "Vulkan0","OPT_STEP_SGD","type=f32,ne=[10,5,4,3]","support","1","yes","Vulkan" "Vulkan0","GATED_DELTA_NET","type=f32,head_count=32,head_size=128,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=0,kda=0,K=1","support","1","yes","Vulkan" -"Vulkan0","GATED_DELTA_NET","type=f32,head_count=32,head_size=16,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=0,kda=0,K=1","support","0","no","Vulkan" -"Vulkan0","GATED_DELTA_NET","type=f32,head_count=32,head_size=16,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=1,kda=1,K=1","support","0","no","Vulkan" -"Vulkan0","GATED_DELTA_NET","type=f32,head_count=32,head_size=16,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=0,kda=1,K=1","support","0","no","Vulkan" +"Vulkan0","GATED_DELTA_NET","type=f32,head_count=32,head_size=16,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=0,kda=0,K=1","support","1","yes","Vulkan" +"Vulkan0","GATED_DELTA_NET","type=f32,head_count=32,head_size=16,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=1,kda=1,K=1","support","1","yes","Vulkan" +"Vulkan0","GATED_DELTA_NET","type=f32,head_count=32,head_size=16,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=0,kda=1,K=1","support","1","yes","Vulkan" "Vulkan0","GATED_DELTA_NET","type=f32,head_count=16,head_size=64,n_seq_tokens=1,n_seqs=2,v_repeat=1,permuted=0,kda=0,K=1","support","1","yes","Vulkan" "Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=1,v_repeat=1,permuted=0,kda=0,K=1","support","1","yes","Vulkan" "Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=0,kda=0,K=1","support","1","yes","Vulkan" @@ -16541,12 +19310,12 @@ "Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=1,v_repeat=1,permuted=1,kda=0,K=1","support","1","yes","Vulkan" "Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=0,kda=1,K=1","support","1","yes","Vulkan" "Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=1,n_seqs=2,v_repeat=1,permuted=0,kda=1,K=1","support","1","yes","Vulkan" -"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=16,n_seq_tokens=1,n_seqs=2,v_repeat=1,permuted=0,kda=1,K=1","support","0","no","Vulkan" +"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=16,n_seq_tokens=1,n_seqs=2,v_repeat=1,permuted=0,kda=1,K=1","support","1","yes","Vulkan" "Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=32,n_seq_tokens=4,n_seqs=1,v_repeat=1,permuted=0,kda=1,K=1","support","1","yes","Vulkan" "Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=0,kda=1,K=1","support","1","yes","Vulkan" "Vulkan0","GATED_DELTA_NET","type=f32,head_count=8,head_size=32,n_seq_tokens=4,n_seqs=2,v_repeat=2,permuted=0,kda=1,K=1","support","1","yes","Vulkan" "Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=1,kda=1,K=1","support","1","yes","Vulkan" -"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=16,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=1,kda=1,K=1","support","0","no","Vulkan" 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+"Vulkan0","LIGHTNING_INDEXER","hsk=128,nh=64,kv=256,nb=512,ns=4,nm=1,type_K=q5_0","support","0","no","Vulkan" +"Vulkan0","LIGHTNING_INDEXER","hsk=128,nh=64,kv=256,nb=512,ns=4,nm=1,type_K=q4_1","support","0","no","Vulkan" +"Vulkan0","LIGHTNING_INDEXER","hsk=128,nh=64,kv=256,nb=512,ns=4,nm=1,type_K=q4_0","support","0","no","Vulkan" +"Vulkan0","LIGHTNING_INDEXER","hsk=128,nh=64,kv=256,nb=512,ns=4,nm=1,type_K=iq4_nl","support","0","no","Vulkan" diff --git a/docs/ops/WebGPU.csv b/docs/ops/WebGPU.csv index 95042e72d9c1..c19396c03e4d 100644 --- a/docs/ops/WebGPU.csv +++ b/docs/ops/WebGPU.csv @@ -167,6 +167,16 @@ "WebGPU: WebGPU","ROUND","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","TRUNC","type=f32,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","TRUNC","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","DSV4_HC_COMB","n_tokens=1,n_iter=1,eps=0.000001","support","0","no","WebGPU" +"WebGPU: WebGPU","DSV4_HC_COMB","n_tokens=17,n_iter=4,eps=0.000001","support","0","no","WebGPU" +"WebGPU: WebGPU","DSV4_HC_COMB","n_tokens=257,n_iter=8,eps=0.000001","support","0","no","WebGPU" +"WebGPU: WebGPU","DSV4_HC_PRE","n_embd=1,n_tokens=1","support","0","no","WebGPU" +"WebGPU: WebGPU","DSV4_HC_PRE","n_embd=31,n_tokens=17","support","0","no","WebGPU" +"WebGPU: WebGPU","DSV4_HC_PRE","n_embd=128,n_tokens=257","support","0","no","WebGPU" +"WebGPU: WebGPU","DSV4_HC_PRE","n_embd=4096,n_tokens=21","support","0","no","WebGPU" +"WebGPU: WebGPU","DSV4_HC_POST","n_embd=1,n_tokens=1","support","0","no","WebGPU" +"WebGPU: WebGPU","DSV4_HC_POST","n_embd=31,n_tokens=17","support","0","no","WebGPU" +"WebGPU: WebGPU","DSV4_HC_POST","n_embd=128,n_tokens=257","support","0","no","WebGPU" "WebGPU: WebGPU","REGLU","type=f16,ne_a=[128,2,2,2],v=0,swapped=0","support","1","yes","WebGPU" "WebGPU: WebGPU","REGLU","type=f16,ne_a=[5,7,11,13],v=0,swapped=0","support","1","yes","WebGPU" "WebGPU: WebGPU","REGLU","type=f16,ne_a=[128,2,2,2],v=0,swapped=1","support","1","yes","WebGPU" @@ -338,14 +348,18 @@ "WebGPU: WebGPU","GET_ROWS","type=q1_0,n=256,m=5,r=4,be1=1,be2=1,v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","GET_ROWS","type=q1_0,n=256,m=5,r=4,be1=7,be2=1,v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","GET_ROWS","type=q1_0,n=256,m=5,r=4,be1=7,be2=1,v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","GET_ROWS","type=q2_0,n=256,m=5,r=4,be1=1,be2=1,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","GET_ROWS","type=q2_0,n=256,m=5,r=4,be1=1,be2=1,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","GET_ROWS","type=q2_0,n=256,m=5,r=4,be1=7,be2=1,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","GET_ROWS","type=q2_0,n=256,m=5,r=4,be1=7,be2=1,v=1","support","0","no","WebGPU" "WebGPU: WebGPU","GET_ROWS","type=mxfp4,n=256,m=5,r=4,be1=1,be2=1,v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","GET_ROWS","type=mxfp4,n=256,m=5,r=4,be1=1,be2=1,v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","GET_ROWS","type=mxfp4,n=256,m=5,r=4,be1=7,be2=1,v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","GET_ROWS","type=mxfp4,n=256,m=5,r=4,be1=7,be2=1,v=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","GET_ROWS","type=nvfp4,n=256,m=5,r=4,be1=1,be2=1,v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","GET_ROWS","type=nvfp4,n=256,m=5,r=4,be1=1,be2=1,v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","GET_ROWS","type=nvfp4,n=256,m=5,r=4,be1=7,be2=1,v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","GET_ROWS","type=nvfp4,n=256,m=5,r=4,be1=7,be2=1,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","GET_ROWS","type=nvfp4,n=256,m=5,r=4,be1=1,be2=1,v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","GET_ROWS","type=nvfp4,n=256,m=5,r=4,be1=1,be2=1,v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","GET_ROWS","type=nvfp4,n=256,m=5,r=4,be1=7,be2=1,v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","GET_ROWS","type=nvfp4,n=256,m=5,r=4,be1=7,be2=1,v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","GET_ROWS","type=q2_K,n=256,m=5,r=4,be1=1,be2=1,v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","GET_ROWS","type=q2_K,n=256,m=5,r=4,be1=1,be2=1,v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","GET_ROWS","type=q2_K,n=256,m=5,r=4,be1=7,be2=1,v=0","support","1","yes","WebGPU" @@ -407,6 +421,7 @@ "WebGPU: WebGPU","GET_ROWS","type=i32,n=256,m=5,r=4,be1=7,be2=1,v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","GET_ROWS","type=i32,n=256,m=5,r=4,be1=7,be2=1,v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","GET_ROWS_BACK","type=f32,n=1,m=8,r=2,b=1,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","GET_ROWS_BACK","type=f32,n=1,m=70000,r=4,b=1,v=0","support","0","no","WebGPU" "WebGPU: WebGPU","GET_ROWS_BACK","type=f32,n=256,m=5,r=4,b=1,v=0","support","0","no","WebGPU" "WebGPU: WebGPU","GET_ROWS_BACK","type=f32,n=256,m=5,r=4,b=1,v=1","support","0","no","WebGPU" "WebGPU: WebGPU","GET_ROWS_BACK","type=f16,n=256,m=5,r=4,b=1,v=0","support","0","no","WebGPU" @@ -425,6 +440,8 @@ "WebGPU: WebGPU","GET_ROWS_BACK","type=q8_0,n=256,m=5,r=4,b=1,v=1","support","0","no","WebGPU" "WebGPU: WebGPU","GET_ROWS_BACK","type=q1_0,n=256,m=5,r=4,b=1,v=0","support","0","no","WebGPU" "WebGPU: WebGPU","GET_ROWS_BACK","type=q1_0,n=256,m=5,r=4,b=1,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","GET_ROWS_BACK","type=q2_0,n=256,m=5,r=4,b=1,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","GET_ROWS_BACK","type=q2_0,n=256,m=5,r=4,b=1,v=1","support","0","no","WebGPU" "WebGPU: WebGPU","GET_ROWS_BACK","type=mxfp4,n=256,m=5,r=4,b=1,v=0","support","0","no","WebGPU" "WebGPU: WebGPU","GET_ROWS_BACK","type=mxfp4,n=256,m=5,r=4,b=1,v=1","support","0","no","WebGPU" "WebGPU: WebGPU","GET_ROWS_BACK","type=nvfp4,n=256,m=5,r=4,b=1,v=0","support","0","no","WebGPU" @@ -459,333 +476,685 @@ "WebGPU: WebGPU","GET_ROWS_BACK","type=iq4_xs,n=256,m=5,r=4,b=1,v=1","support","0","no","WebGPU" "WebGPU: 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WebGPU","SET_ROWS","type_src=f32,type_dst=iq3_s,type_idx=i64,ne=[256,5,1,3],nr23=[1,1],r=1,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq3_s,type_idx=i64,ne=[256,11,1,1],nr23=[2,3],r=7,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq3_s,type_idx=i64,ne=[768,3,1,1],nr23=[2,3],r=2,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq3_s,type_idx=i64,ne=[256,5,1,3],nr23=[1,1],r=1,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq3_s,type_idx=i64,ne=[256,11,1,1],nr23=[2,3],r=7,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq3_s,type_idx=i64,ne=[768,3,1,1],nr23=[2,3],r=2,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq3_s,type_idx=i64,ne=[256,5,7,3],nr23=[1,1],r=1,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq3_s,type_idx=i64,ne=[256,11,1,7],nr23=[2,3],r=7,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq3_s,type_idx=i64,ne=[768,3,7,1],nr23=[2,3],r=2,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq3_s,type_idx=i64,ne=[256,5,7,3],nr23=[1,1],r=1,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq3_s,type_idx=i64,ne=[256,11,1,7],nr23=[2,3],r=7,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq3_s,type_idx=i64,ne=[768,3,7,1],nr23=[2,3],r=2,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_xs,type_idx=i64,ne=[256,5,1,3],nr23=[1,1],r=1,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_xs,type_idx=i64,ne=[256,11,1,1],nr23=[2,3],r=7,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_xs,type_idx=i64,ne=[768,3,1,1],nr23=[2,3],r=2,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_xs,type_idx=i64,ne=[256,5,1,3],nr23=[1,1],r=1,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_xs,type_idx=i64,ne=[256,11,1,1],nr23=[2,3],r=7,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_xs,type_idx=i64,ne=[768,3,1,1],nr23=[2,3],r=2,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_xs,type_idx=i64,ne=[256,5,7,3],nr23=[1,1],r=1,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_xs,type_idx=i64,ne=[256,11,1,7],nr23=[2,3],r=7,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_xs,type_idx=i64,ne=[768,3,7,1],nr23=[2,3],r=2,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_xs,type_idx=i64,ne=[256,5,7,3],nr23=[1,1],r=1,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_xs,type_idx=i64,ne=[256,11,1,7],nr23=[2,3],r=7,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f32,type_dst=iq4_xs,type_idx=i64,ne=[768,3,7,1],nr23=[2,3],r=2,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f16,type_dst=f16,type_idx=i64,ne=[1,8,1,3],nr23=[1,1],r=2,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f16,type_dst=f16,type_idx=i32,ne=[1,8,1,3],nr23=[1,1],r=2,v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f16,type_dst=f16,type_idx=i64,ne=[1,8,1,3],nr23=[1,1],r=2,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SET_ROWS","type_src=f16,type_dst=f16,type_idx=i32,ne=[1,8,1,3],nr23=[1,1],r=2,v=1","support","0","no","WebGPU" "WebGPU: WebGPU","POOL_2D","pool_type=avg,type_input=f32,ne_input=[10,10,3,1],k0=1,k1=1,s0=1,s1=1,p0=0,p1=0","support","0","no","WebGPU" "WebGPU: WebGPU","POOL_2D","pool_type=avg,type_input=f32,ne_input=[10,10,3,1],k0=1,k1=1,s0=1,s1=1,p0=0,p1=1","support","0","no","WebGPU" "WebGPU: WebGPU","POOL_2D","pool_type=avg,type_input=f32,ne_input=[10,10,3,1],k0=1,k1=1,s0=1,s1=1,p0=1,p1=0","support","0","no","WebGPU" @@ -965,6 +1334,7 @@ "WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[3000,128,1,1],ne_kernel=[3,128,1280,1],s0=1,s1=0,p0=1,p1=0,d0=1,d1=0,is_2D=0","support","1","yes","WebGPU" "WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f16,dst_type=f32,ne_input=[3000,128,1,1],ne_kernel=[3,128,1280,1],s0=1,s1=0,p0=1,p1=0,d0=1,d1=0,is_2D=0","support","1","yes","WebGPU" "WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[3000,128,1,1],ne_kernel=[3,128,1280,1],s0=1,s1=0,p0=1,p1=0,d0=1,d1=0,is_2D=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[3000,384,1,1],ne_kernel=[3,384,384,1],s0=1,s1=0,p0=1,p1=0,d0=1,d1=0,is_2D=0","support","1","yes","WebGPU" "WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,2,2,1],ne_kernel=[3,2,2,1],s0=1,s1=0,p0=0,p1=0,d0=1,d1=0,is_2D=0","support","1","yes","WebGPU" "WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,2,2,1],ne_kernel=[3,2,2,1],s0=1,s1=0,p0=0,p1=0,d0=3,d1=0,is_2D=0","support","1","yes","WebGPU" "WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,2,2,1],ne_kernel=[3,2,2,1],s0=1,s1=0,p0=3,p1=0,d0=1,d1=0,is_2D=0","support","1","yes","WebGPU" @@ -974,6 +1344,7 @@ "WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,2,2,1],ne_kernel=[3,2,2,1],s0=3,s1=0,p0=3,p1=0,d0=1,d1=0,is_2D=0","support","1","yes","WebGPU" "WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,2,2,1],ne_kernel=[3,2,2,1],s0=3,s1=0,p0=3,p1=0,d0=3,d1=0,is_2D=0","support","1","yes","WebGPU" "WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[10,10,3,1],ne_kernel=[3,3,3,1],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f16,ne_input=[10,10,3,1],ne_kernel=[3,3,3,1],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","1","yes","WebGPU" "WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f16,dst_type=f32,ne_input=[10,10,3,1],ne_kernel=[3,3,3,1],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","1","yes","WebGPU" "WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[10,10,3,1],ne_kernel=[3,3,3,1],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","1","yes","WebGPU" "WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,2,2],ne_kernel=[3,3,2,2],s0=1,s1=1,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","1","yes","WebGPU" @@ -1050,6 +1421,8 @@ "WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[12,12,2,2560],ne_kernel=[3,3,2,2560],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","1","yes","WebGPU" "WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[5,5,1,32],ne_kernel=[3,4,1,32],s0=1,s1=1,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","1","yes","WebGPU" "WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[2,2,1536,729],ne_kernel=[2,2,1536,4096],s0=1,s1=1,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[128,128,1,2],ne_kernel=[32,33,1,2],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[128,128,2,1],ne_kernel=[33,34,2,1],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","1","yes","WebGPU" "WebGPU: WebGPU","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","0","no","WebGPU" "WebGPU: WebGPU","IM2COL_3D","type_input=f32,type_kernel=f16,dst_type=f32,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","0","no","WebGPU" "WebGPU: WebGPU","IM2COL_3D","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","0","no","WebGPU" @@ -4669,10 +5042,16 @@ "WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU" "WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU" "WebGPU: WebGPU","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CONV_2D_DW","ne_input=[17,34,9,1],ne_kernel=[3,3,1,9],stride=1,padding=0,dilation=1,cwhn=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CONV_2D_DW","ne_input=[17,34,9,1],ne_kernel=[3,3,1,9],stride=1,padding=0,dilation=1,cwhn=1","support","0","no","WebGPU" -"WebGPU: WebGPU","CONV_2D_DW","ne_input=[32,8,64,1],ne_kernel=[3,3,1,64],stride=2,padding=1,dilation=1,cwhn=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CONV_2D_DW","ne_input=[32,8,64,1],ne_kernel=[3,3,1,64],stride=2,padding=1,dilation=1,cwhn=1","support","0","no","WebGPU" +"WebGPU: WebGPU","CONV_2D","ne_input=[256,256,192,1],ne_kernel=[3,3,192,96],type_kernel=f32,stride0=1,stride1=1,padding0=1,padding1=1,dilation0=1,dilation1=1,cwhn=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONV_2D","ne_input=[256,256,192,1],ne_kernel=[3,3,192,96],type_kernel=f16,stride0=1,stride1=1,padding0=1,padding1=1,dilation0=1,dilation1=1,cwhn=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONV_2D_DW","ne_input=[17,34,9,1],ne_kernel=[3,3,1,9],type_kernel=f32,stride=1,padding=0,dilation=1,cwhn=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONV_2D_DW","ne_input=[17,34,9,1],ne_kernel=[3,3,1,9],type_kernel=f32,stride=1,padding=0,dilation=1,cwhn=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONV_2D_DW","ne_input=[32,8,64,1],ne_kernel=[3,3,1,64],type_kernel=f32,stride=2,padding=1,dilation=1,cwhn=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONV_2D_DW","ne_input=[32,8,64,1],ne_kernel=[3,3,1,64],type_kernel=f32,stride=2,padding=1,dilation=1,cwhn=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONV_2D_DW","ne_input=[17,34,9,1],ne_kernel=[3,3,1,9],type_kernel=f16,stride=1,padding=0,dilation=1,cwhn=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONV_2D_DW","ne_input=[17,34,9,1],ne_kernel=[3,3,1,9],type_kernel=f16,stride=1,padding=0,dilation=1,cwhn=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONV_2D_DW","ne_input=[32,8,64,1],ne_kernel=[3,3,1,64],type_kernel=f16,stride=2,padding=1,dilation=1,cwhn=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","CONV_2D_DW","ne_input=[32,8,64,1],ne_kernel=[3,3,1,64],type_kernel=f16,stride=2,padding=1,dilation=1,cwhn=1","support","1","yes","WebGPU" "WebGPU: WebGPU","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","WebGPU" "WebGPU: WebGPU","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","WebGPU" "WebGPU: WebGPU","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","WebGPU" @@ -5047,6 +5426,39 @@ "WebGPU: WebGPU","CONV_TRANSPOSE_1D","ne_input=[3,2,1,1],ne_kernel=[3,2,2,1],s0=1,p0=0,d0=1","support","0","no","WebGPU" "WebGPU: WebGPU","CONV_TRANSPOSE_1D","ne_input=[3,2,1,1],ne_kernel=[3,1,2,1],s0=1,p0=0,d0=1","support","0","no","WebGPU" "WebGPU: WebGPU","CONV_TRANSPOSE_1D","ne_input=[2,1,1,1],ne_kernel=[3,1,1,1],s0=1,p0=0,d0=1","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f32,K=16,OC=32,T_in=197,s0=8,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f32,K=4,OC=3,T_in=7,s0=2,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f32,K=1,OC=5,T_in=13,s0=1,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f32,K=6,OC=4,T_in=11,s0=3,p0=1","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f32,K=2,OC=3,T_in=9,s0=3,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f32,K=5,OC=4,T_in=11,s0=2,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f32,K=8,OC=4,T_in=13,s0=4,p0=2","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f32,K=4,OC=3,T_in=1,s0=2,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f32,K=16,OC=1,T_in=197,s0=8,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f32,K=1,OC=5,T_in=13,s0=3,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f32,K=8,OC=2,T_in=3,s0=2,p0=5","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f16,K=16,OC=32,T_in=197,s0=8,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f16,K=4,OC=3,T_in=7,s0=2,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f16,K=1,OC=5,T_in=13,s0=1,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f16,K=6,OC=4,T_in=11,s0=3,p0=1","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f16,K=2,OC=3,T_in=9,s0=3,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f16,K=5,OC=4,T_in=11,s0=2,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f16,K=8,OC=4,T_in=13,s0=4,p0=2","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f16,K=4,OC=3,T_in=1,s0=2,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f16,K=16,OC=1,T_in=197,s0=8,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f16,K=1,OC=5,T_in=13,s0=3,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=f16,K=8,OC=2,T_in=3,s0=2,p0=5","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=bf16,K=16,OC=32,T_in=197,s0=8,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=bf16,K=4,OC=3,T_in=7,s0=2,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=bf16,K=1,OC=5,T_in=13,s0=1,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=bf16,K=6,OC=4,T_in=11,s0=3,p0=1","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=bf16,K=2,OC=3,T_in=9,s0=3,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=bf16,K=5,OC=4,T_in=11,s0=2,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=bf16,K=8,OC=4,T_in=13,s0=4,p0=2","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=bf16,K=4,OC=3,T_in=1,s0=2,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=bf16,K=16,OC=1,T_in=197,s0=8,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=bf16,K=1,OC=5,T_in=13,s0=3,p0=0","support","0","no","WebGPU" +"WebGPU: WebGPU","COL2IM_1D","type=bf16,K=8,OC=2,T_in=3,s0=2,p0=5","support","0","no","WebGPU" "WebGPU: WebGPU","CONV_TRANSPOSE_2D","kernel_type=f32,ne_input=[3,2,3,1],ne_kernel=[2,2,1,3],stride=1","support","0","no","WebGPU" "WebGPU: WebGPU","CONV_TRANSPOSE_2D","kernel_type=f32,ne_input=[10,10,9,1],ne_kernel=[3,3,1,9],stride=2","support","0","no","WebGPU" "WebGPU: WebGPU","CONV_TRANSPOSE_2D","kernel_type=f32,ne_input=[129,63,35,1],ne_kernel=[3,3,48,35],stride=1","support","0","no","WebGPU" @@ -5069,6 +5481,7 @@ "WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,1],nr=[1,1,1,2]","support","1","yes","WebGPU" "WebGPU: WebGPU","REPEAT","type=i32,ne=[10,5,4,1],nr=[2,1,1,1]","support","1","yes","WebGPU" "WebGPU: WebGPU","REPEAT","type=i16,ne=[10,5,4,1],nr=[1,1,1,2]","support","1","yes","WebGPU" +"WebGPU: WebGPU","REPEAT","type=bf16,ne=[10,5,4,1],nr=[2,1,1,1]","support","0","no","WebGPU" "WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,3],nr=[1,1,1,1]","support","1","yes","WebGPU" "WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,3],nr=[2,1,1,1]","support","1","yes","WebGPU" "WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,3],nr=[1,2,1,1]","support","1","yes","WebGPU" @@ -5076,6 +5489,7 @@ "WebGPU: WebGPU","REPEAT","type=f32,ne=[10,5,4,3],nr=[1,1,1,2]","support","1","yes","WebGPU" "WebGPU: WebGPU","REPEAT","type=i32,ne=[10,5,4,3],nr=[2,1,1,1]","support","1","yes","WebGPU" "WebGPU: WebGPU","REPEAT","type=i16,ne=[10,5,4,3],nr=[1,1,1,2]","support","1","yes","WebGPU" +"WebGPU: WebGPU","REPEAT","type=bf16,ne=[10,5,4,3],nr=[2,1,1,1]","support","0","no","WebGPU" "WebGPU: WebGPU","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[1,1,1,1],v=0","support","0","no","WebGPU" "WebGPU: WebGPU","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[2,1,1,1],v=0","support","0","no","WebGPU" "WebGPU: WebGPU","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[1,2,1,1],v=0","support","0","no","WebGPU" @@ -5108,449 +5522,568 @@ "WebGPU: WebGPU","SET","type_src=i32,type_dst=i32,ne=[6,5,4,3],dim=2,inplace=1","support","1","yes","WebGPU" "WebGPU: WebGPU","SET","type_src=i32,type_dst=i32,ne=[6,5,4,3],dim=3,inplace=0","support","1","yes","WebGPU" "WebGPU: WebGPU","SET","type_src=i32,type_dst=i32,ne=[6,5,4,3],dim=3,inplace=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f32,type_dst=f32,ne=[1,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f32,type_dst=f32,ne=[1,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f32,type_dst=f32,ne=[1,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f32,type_dst=f32,ne=[2,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f32,type_dst=f32,ne=[2,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f32,type_dst=f32,ne=[2,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f32,type_dst=f32,ne=[3,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f32,type_dst=f32,ne=[3,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f32,type_dst=f32,ne=[3,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f16,type_dst=f16,ne=[1,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f16,type_dst=f16,ne=[1,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f16,type_dst=f16,ne=[1,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f16,type_dst=f16,ne=[2,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f16,type_dst=f16,ne=[2,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f16,type_dst=f16,ne=[2,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f16,type_dst=f16,ne=[3,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f16,type_dst=f16,ne=[3,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=f16,type_dst=f16,ne=[3,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CPY","type_src=bf16,type_dst=bf16,ne=[1,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=bf16,type_dst=bf16,ne=[1,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=bf16,type_dst=bf16,ne=[1,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=bf16,type_dst=bf16,ne=[2,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=bf16,type_dst=bf16,ne=[2,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=bf16,type_dst=bf16,ne=[2,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=bf16,type_dst=bf16,ne=[3,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=bf16,type_dst=bf16,ne=[3,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=bf16,type_dst=bf16,ne=[3,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=q4_0,type_dst=q4_0,ne=[32,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=q4_0,type_dst=q4_0,ne=[32,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=q4_0,type_dst=q4_0,ne=[32,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=q4_0,type_dst=q4_0,ne=[64,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=q4_0,type_dst=q4_0,ne=[64,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=q4_0,type_dst=q4_0,ne=[64,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=q4_0,type_dst=q4_0,ne=[96,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=q4_0,type_dst=q4_0,ne=[96,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=q4_0,type_dst=q4_0,ne=[96,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=q4_1,type_dst=q4_1,ne=[32,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=q4_1,type_dst=q4_1,ne=[32,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=q4_1,type_dst=q4_1,ne=[32,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=q4_1,type_dst=q4_1,ne=[64,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=q4_1,type_dst=q4_1,ne=[64,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=q4_1,type_dst=q4_1,ne=[64,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=q4_1,type_dst=q4_1,ne=[96,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=q4_1,type_dst=q4_1,ne=[96,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=q4_1,type_dst=q4_1,ne=[96,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=q5_0,type_dst=q5_0,ne=[32,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CPY","type_src=q5_0,type_dst=q5_0,ne=[32,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","WebGPU" -"WebGPU: 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"WebGPU: WebGPU","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001,inplace=1","support","1","yes","WebGPU" "WebGPU: WebGPU","SSM_CONV","type=f32,ne_a=[3,1024,1,1],ne_b=[3,1024,1,1]","support","1","yes","WebGPU" "WebGPU: WebGPU","SSM_CONV","type=f32,ne_a=[6,1024,1,1],ne_b=[3,1024,1,1]","support","1","yes","WebGPU" @@ -6084,6 +6637,12 @@ "WebGPU: WebGPU","GATED_LINEAR_ATTN","type=f32,head_count=32,head_size=64,n_seq_tokens=32,n_seqs=1","support","0","no","WebGPU" "WebGPU: WebGPU","GATED_LINEAR_ATTN","type=f32,head_count=32,head_size=64,n_seq_tokens=32,n_seqs=4","support","0","no","WebGPU" "WebGPU: WebGPU","GATED_LINEAR_ATTN","type=f32,head_count=32,head_size=64,n_seq_tokens=128,n_seqs=4","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=128,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=64,n=1,k=64,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=256,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=512,n=1,k=512,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=128,n=32,k=128,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT_HADAMARD","type_a=f32,type_b=f32,m=128,n=4,k=128,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6165,6 +6724,15 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q2_0,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6174,15 +6742,15 @@ "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" -"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" -"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" -"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" -"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" -"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" -"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" -"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" -"WebGPU: 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WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6309,6 +6877,9 @@ "WebGPU: WebGPU","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=2880,n=32,k=2880,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=2880,n=32,k=2880,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=2880,n=32,k=2880,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6318,6 +6889,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6330,6 +6902,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" @@ -6345,6 +6918,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=4,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6357,6 +6931,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=4,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" @@ -6376,6 +6951,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" @@ -6388,6 +6964,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" @@ -6403,6 +6980,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=4,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" @@ -6415,6 +6993,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=4,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" @@ -6434,6 +7013,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6446,6 +7026,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" @@ -6461,6 +7042,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=4,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6473,6 +7055,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=4,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" @@ -6492,6 +7075,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6504,6 +7088,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" @@ -6519,6 +7104,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=4,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6531,6 +7117,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=4,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" @@ -6550,6 +7137,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6562,6 +7150,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" @@ -6581,6 +7170,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" @@ -6593,6 +7183,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" @@ -6612,6 +7203,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6624,6 +7216,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" @@ -6643,6 +7236,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" @@ -6655,6 +7249,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: 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WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6674,6 +7335,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6686,6 +7348,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" @@ -6705,6 +7368,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" @@ -6717,6 +7381,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" @@ -6736,6 +7401,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6748,6 +7414,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" @@ -6767,6 +7434,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" @@ -6779,6 +7447,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" @@ -6798,6 +7467,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6810,6 +7480,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" @@ -6829,6 +7500,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" @@ -6841,6 +7513,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" @@ -6860,6 +7533,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6872,6 +7546,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" @@ -6891,6 +7566,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" @@ -6903,6 +7579,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" @@ -6913,37 +7590,39 @@ "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" -"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" -"WebGPU: 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WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" @@ -6953,6 +7632,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" @@ -6965,6 +7645,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" @@ -6984,6 +7665,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -6996,6 +7678,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","WebGPU" @@ -7015,6 +7698,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=4,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" @@ -7027,6 +7711,7 @@ "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=4,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" @@ -7037,6 +7722,15 @@ "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=32,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=1,k=32,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" @@ -7082,8 +7776,9 @@ "WebGPU: WebGPU","MUL_MAT","type_a=q5_1,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q8_0,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q1_0,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=q2_0,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=mxfp4,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","WebGPU" +"WebGPU: WebGPU","MUL_MAT","type_a=nvfp4,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: WebGPU","MUL_MAT","type_a=q2_K,type_b=f32,m=1,n=64,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","WebGPU" "WebGPU: 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WebGPU","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=1,n_token=129","support","1","yes","WebGPU" @@ -9946,6 +10875,11 @@ "WebGPU: WebGPU","ROPE","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","WebGPU" "WebGPU: WebGPU","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","WebGPU" "WebGPU: WebGPU","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","ROPE","type=f32,ne_a=[36,16,2457,1],n_dims=36,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=2,inplace=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","ROPE","type=f32,ne_a=[36,16,2457,1],n_dims=36,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=2,inplace=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","ROPE","type=f32,ne_a=[36,16,2457,1],n_dims=36,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=2,inplace=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","ROPE","type=f32,ne_a=[36,16,2457,1],n_dims=36,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=2,inplace=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","ROPE","type=f32,ne_a=[36,16,2457,1],n_dims=36,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=2,inplace=0","support","1","yes","WebGPU" "WebGPU: WebGPU","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","WebGPU" "WebGPU: WebGPU","ROPE","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","WebGPU" "WebGPU: WebGPU","ROPE","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","WebGPU" @@ -10000,126 +10934,281 @@ "WebGPU: WebGPU","ROPE","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","WebGPU" "WebGPU: WebGPU","ROPE","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","WebGPU" "WebGPU: WebGPU","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","ROPE","type=f32,ne_a=[36,16,2457,1],n_dims=36,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=2,inplace=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","ROPE","type=f32,ne_a=[36,16,2457,1],n_dims=36,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=2,inplace=0","support","1","yes","WebGPU" +"WebGPU: 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@@ -10174,6 +11263,11 @@ "WebGPU: WebGPU","ROPE_BACK","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","WebGPU" "WebGPU: WebGPU","ROPE_BACK","type=f32,ne_a=[16,16,8192,1],n_dims=16,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","WebGPU" "WebGPU: WebGPU","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","0","no","WebGPU" +"WebGPU: WebGPU","ROPE_BACK","type=f32,ne_a=[36,16,2457,1],n_dims=36,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=2,inplace=0","support","0","no","WebGPU" +"WebGPU: WebGPU","ROPE_BACK","type=f32,ne_a=[36,16,2457,1],n_dims=36,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=2,inplace=0","support","0","no","WebGPU" +"WebGPU: 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WebGPU","PAD","type=f32,ne_a=[100,100,1,1],pad_0=50,pad_1=50,circular=0","support","1","yes","WebGPU" "WebGPU: WebGPU","PAD_REFLECT_1D","type=f32,ne_a=[512,34,2,1],pad_0=10,pad_1=9","support","0","no","WebGPU" "WebGPU: WebGPU","PAD_REFLECT_1D","type=f32,ne_a=[3000,384,4,1],pad_0=10,pad_1=9","support","0","no","WebGPU" "WebGPU: WebGPU","ROLL","shift0=3,shift1=-2,shift3=1,shift4=-1","support","0","no","WebGPU" @@ -14998,6 +16420,22 @@ "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" @@ -15014,6 +16452,14 @@ "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" @@ -15038,6 +16484,22 @@ "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" @@ -15054,6 +16516,14 @@ "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" @@ -15070,6 +16540,14 @@ "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" @@ -15086,6 +16564,14 @@ "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[8,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[16,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" @@ -15110,6 +16596,22 @@ "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=75,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" @@ -15126,6 +16628,14 @@ "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=75,mask=1,sinks=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" @@ -15150,6 +16660,22 @@ "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=75,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,2,1,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" @@ -15166,6 +16692,14 @@ "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=75,mask=1,sinks=0,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" @@ -15182,6 +16716,14 @@ "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=1,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=3,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=32,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=75,mask=0,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=113,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" @@ -15198,6 +16740,14 @@ "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[8,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[16,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=3,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=113,nb=32,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" @@ -15934,10 +17484,11 @@ "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=75,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" -"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=128,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q8_0,type_V=q4_0,permute=[0,1,2,3]","support","0","no","WebGPU" -"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=128,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=f16,permute=[0,1,2,3]","support","0","no","WebGPU" -"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=96,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q8_0,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=128,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q8_0,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=128,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=f16,permute=[0,1,2,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=72,hsv=72,nh=4,nr23=[1,1],kv=96,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q8_0,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=96,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=f32,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=256,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=f16,type_V=q4_0,permute=[0,1,2,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=96,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q1_0,type_V=q1_0,permute=[0,1,2,3]","support","0","no","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=128,hsv=64,nh=4,nr23=[1,1],kv=128,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q1_0,type_V=q4_0,permute=[0,1,2,3]","support","0","no","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=64,hsv=128,nh=4,nr23=[1,1],kv=128,nb=2,mask=1,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_K=q4_0,type_V=q1_0,permute=[0,1,2,3]","support","0","no","WebGPU" @@ -15948,21 +17499,147 @@ "WebGPU: WebGPU","CROSS_ENTROPY_LOSS_BACK","type=f32,ne=[30000,1,1,1]","support","0","no","WebGPU" "WebGPU: WebGPU","OPT_STEP_ADAMW","type=f32,ne=[10,5,4,3]","support","0","no","WebGPU" "WebGPU: WebGPU","OPT_STEP_SGD","type=f32,ne=[10,5,4,3]","support","0","no","WebGPU" -"WebGPU: WebGPU","GATED_DELTA_NET","type=f32,head_count=32,head_size=128,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=0,kda=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","GATED_DELTA_NET","type=f32,head_count=32,head_size=16,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=0,kda=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","GATED_DELTA_NET","type=f32,head_count=32,head_size=16,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=1,kda=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","GATED_DELTA_NET","type=f32,head_count=32,head_size=16,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=0,kda=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","GATED_DELTA_NET","type=f32,head_count=16,head_size=64,n_seq_tokens=1,n_seqs=2,v_repeat=1,permuted=0,kda=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=1,v_repeat=1,permuted=0,kda=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=0,kda=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","GATED_DELTA_NET","type=f32,head_count=8,head_size=32,n_seq_tokens=4,n_seqs=2,v_repeat=2,permuted=0,kda=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=1,kda=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=1,v_repeat=1,permuted=1,kda=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=0,kda=1","support","1","yes","WebGPU" -"WebGPU: 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WebGPU","GATED_DELTA_NET","type=f32,head_count=4,head_size=16,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=1,kda=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","GATED_DELTA_NET","type=f32,head_count=32,head_size=128,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=0,kda=0,K=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","GATED_DELTA_NET","type=f32,head_count=32,head_size=16,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=0,kda=0,K=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","GATED_DELTA_NET","type=f32,head_count=32,head_size=16,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=1,kda=1,K=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","GATED_DELTA_NET","type=f32,head_count=32,head_size=16,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=0,kda=1,K=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","GATED_DELTA_NET","type=f32,head_count=16,head_size=64,n_seq_tokens=1,n_seqs=2,v_repeat=1,permuted=0,kda=0,K=1","support","1","yes","WebGPU" +"WebGPU: 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token, chained in-graph across the block. This keeps drafting at one decode per +block while recovering some of the left-to-right signal that pure block diffusion loses. + +The draft is a small DeepSpec checkpoint trained for a specific target (for example +[`deepseek-ai/dspark_qwen3_4b_block7`](https://huggingface.co/deepseek-ai/dspark_qwen3_4b_block7) +for `Qwen/Qwen3-4B`). Convert it with `--target-model-dir` so it inherits the target's tokenizer +and token embeddings: + +```bash +python convert_hf_to_gguf.py deepseek-ai/dspark_qwen3_4b_block7 \ + --target-model-dir Qwen/Qwen3-4B --outtype bf16 --outfile Qwen3-4B-DSpark.gguf + +llama-server -m Qwen3-4B.gguf -md Qwen3-4B-DSpark.gguf \ + --spec-type draft-dspark --spec-draft-n-max 7 -fa on --jinja +``` + +`--spec-draft-n-max` is clamped to the draft model's trained block size. + +`--spec-draft-conf-min P` truncates each drafted block at the first position whose predicted +acceptance (from the draft's confidence head, if present) falls below `P` (default 0 = disabled). + +Currently only drafts with a Qwen3 backbone are supported; support for other backbones +(e.g. Gemma4) is planned. + +See: + +- #25173 + ### n-gram Cache (`ngram-cache`) An n-gram is a sequence of n tokens. The n-gram cache implementation maintains statistics about short n-gram sequences. @@ -335,7 +367,7 @@ If a draft model is combined with a draftless decoding the draftless decoding ha ### General Speculative Parameters ``` ---spec-type [none|draft-simple|draft-eagle3|draft-dflash|draft-mtp|ngram-cache|ngram-simple|ngram-map-k|ngram-map-k4v|ngram-mod] +--spec-type [none|draft-simple|draft-eagle3|draft-dflash|draft-dspark|draft-mtp|ngram-cache|ngram-simple|ngram-map-k|ngram-map-k4v|ngram-mod] comma-separated list of types of speculative decoding to use (default: none) (env: LLAMA_ARG_SPEC_TYPE) @@ -476,6 +508,7 @@ Specifies a comma-separated list of speculative decoding types to use. | `draft-simple` | Use a simple draft model for speculation | | `draft-eagle3` | Use an EAGLE-3 draft model that reads the target's hidden states | | `draft-dflash` | Use a DFlash block-diffusion draft model that emits a block per step | +| `draft-dspark` | Use a DSpark draft model (DFlash backbone + semi-autoregressive Markov head) | | `draft-mtp` | Use Multi Token Prediction (MTP) heads from the main model | | `ngram-cache` | Use n-gram cache lookup | | `ngram-simple` | Use simple n-gram pattern matching | diff --git a/docs/xcframework.md b/docs/xcframework.md new file mode 100644 index 000000000000..83c8b50044bd --- /dev/null +++ b/docs/xcframework.md @@ -0,0 +1,31 @@ +# XCFramework + +The XCFramework is a precompiled version of the library for iOS, visionOS, tvOS, +and macOS. It can be used in Swift projects without the need to compile the +library from source. For example: + +```swift +// swift-tools-version: 5.10 +// The swift-tools-version declares the minimum version of Swift required to build this package. + +import PackageDescription + +let package = Package( + name: "MyLlamaPackage", + targets: [ + .executableTarget( + name: "MyLlamaPackage", + dependencies: [ + "LlamaFramework" + ]), + .binaryTarget( + name: "LlamaFramework", + url: "https://github.com/ggml-org/llama.cpp/releases/download/b5046/llama-b5046-xcframework.zip", + checksum: "c19be78b5f00d8d29a25da41042cb7afa094cbf6280a225abe614b03b20029ab" + ) + ] +) +``` + +The above example is using an intermediate build `b5046` of the library. This can be modified +to use a different version by changing the URL and checksum. diff --git a/examples/diffusion/diffusion-cli.cpp b/examples/diffusion/diffusion-cli.cpp index 86ebbf88c98d..d58d22eff550 100644 --- a/examples/diffusion/diffusion-cli.cpp +++ b/examples/diffusion/diffusion-cli.cpp @@ -117,9 +117,7 @@ int main(int argc, char ** argv) { llama_model_params model_params = llama_model_default_params(); model_params.n_gpu_layers = params.n_gpu_layers; model_params.devices = params.devices.data(); - model_params.use_mmap = params.use_mmap; - model_params.use_direct_io = params.use_direct_io; - model_params.use_mlock = params.use_mlock; + model_params.load_mode = params.load_mode; model_params.check_tensors = params.check_tensors; llama_model * model = llama_model_load_from_file(params.model.path.c_str(), model_params); diff --git a/examples/gen-docs/gen-docs.cpp b/examples/gen-docs/gen-docs.cpp index baf61bf27b54..114416719c7f 100644 --- a/examples/gen-docs/gen-docs.cpp +++ b/examples/gen-docs/gen-docs.cpp @@ -70,6 +70,8 @@ static void write_table(std::ostringstream & ss, std::vector & opt static void write_help(std::ostringstream & ss, const md_file & md) { common_params params; + params.is_gen_docs = true; + auto ctx_arg = common_params_parser_init(params, md.ex); std::vector common_options; diff --git a/examples/training/finetune.cpp b/examples/training/finetune.cpp index 0a75ac110ca4..44b2843918b1 100644 --- a/examples/training/finetune.cpp +++ b/examples/training/finetune.cpp @@ -26,10 +26,9 @@ int main(int argc, char ** argv) { return 1; } - if (params.use_mmap) { - LOG_INF("%s: force disabling memory mapping because it would result in-read-only pointers to the weights\n", - __func__); - params.use_mmap = false; + if (params.load_mode != LLAMA_LOAD_MODE_NONE) { + LOG_INF("%s: forcing load_mode = none to enable writable pointers to the weights\n", __func__); + params.load_mode = LLAMA_LOAD_MODE_NONE; } if (params.cache_type_k != GGML_TYPE_F32) { LOG_INF("%s: force changing k cache type to f32 due to a lack of f16 support for OUT_PROD\n", __func__); diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt index 5381c2136203..6c7337edd398 100644 --- a/ggml/CMakeLists.txt +++ b/ggml/CMakeLists.txt @@ -4,8 +4,8 @@ project("ggml" C CXX ASM) ### GGML Version set(GGML_VERSION_MAJOR 0) -set(GGML_VERSION_MINOR 16) -set(GGML_VERSION_PATCH 0) +set(GGML_VERSION_MINOR 18) +set(GGML_VERSION_PATCH 1) set(GGML_VERSION_BASE "${GGML_VERSION_MAJOR}.${GGML_VERSION_MINOR}.${GGML_VERSION_PATCH}") list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/") @@ -216,7 +216,6 @@ option(GGML_HIP "ggml: use HIP" option(GGML_HIP_GRAPHS "ggml: use HIP graph" ON) option(GGML_HIP_RCCL "ggml: use ROCm Collective Comm. Library" OFF) option(GGML_HIP_NO_VMM "ggml: do not try to use HIP VMM" ON) -option(GGML_HIP_ROCWMMA_FATTN "ggml: enable rocWMMA for FlashAttention" OFF) option(GGML_HIP_MMQ_MFMA "ggml: enable MFMA MMA for CDNA in MMQ" ON) option(GGML_HIP_EXPORT_METRICS "ggml: enable kernel perf metrics output" OFF) option(GGML_MUSA_GRAPHS "ggml: use MUSA graph, experimental, unstable" OFF) diff --git a/ggml/include/ggml-cpu.h b/ggml/include/ggml-cpu.h index e3e067c916f1..dc6453c6eaa1 100644 --- a/ggml/include/ggml-cpu.h +++ b/ggml/include/ggml-cpu.h @@ -100,6 +100,7 @@ extern "C" { GGML_BACKEND_API int ggml_cpu_has_sve (void); GGML_BACKEND_API int ggml_cpu_get_sve_cnt (void); // sve vector length in bytes GGML_BACKEND_API int ggml_cpu_has_sme (void); + GGML_BACKEND_API int ggml_cpu_has_sme2 (void); // other GGML_BACKEND_API int ggml_cpu_has_riscv_v (void); GGML_BACKEND_API int ggml_cpu_get_rvv_vlen (void); // risc-v vector length in bytes diff --git a/ggml/include/ggml-rpc.h b/ggml/include/ggml-rpc.h index cc4282ee7fa8..9b3af31d9d61 100644 --- a/ggml/include/ggml-rpc.h +++ b/ggml/include/ggml-rpc.h @@ -6,15 +6,15 @@ extern "C" { #endif -#define RPC_PROTO_MAJOR_VERSION 4 +#define RPC_PROTO_MAJOR_VERSION 5 #define RPC_PROTO_MINOR_VERSION 0 #define RPC_PROTO_PATCH_VERSION 4 #ifdef __cplusplus -// 100 = upstream 99 (incl. FLASH_ATTN_EXT_BANDED + LIGHTNING_INDEXER) + the fork's -// GGML_OP_TURBO_WHT. Bumped patch version because adding an op shifts the GGML_OP -// enum used in the RPC wire protocol. -static_assert(GGML_OP_COUNT == 100, "GGML_OP_COUNT has changed - update RPC_PROTO_PATCH_VERSION"); +// 103 = upstream 101 + the fork's GGML_OP_TURBO_WHT and GGML_OP_FLASH_ATTN_EXT_BANDED. +// Bumped patch version because adding an op shifts the GGML_OP enum used in the RPC +// wire protocol. +static_assert(GGML_OP_COUNT == 103, "GGML_OP_COUNT has changed - update RPC_PROTO_PATCH_VERSION"); #endif #define GGML_RPC_MAX_SERVERS 16 diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h index 3d16786beb15..1d965b4a3b78 100644 --- a/ggml/include/ggml.h +++ b/ggml/include/ggml.h @@ -579,6 +579,9 @@ extern "C" { GGML_OP_GATED_DELTA_NET, GGML_OP_TURBO_WHT, GGML_OP_LIGHTNING_INDEXER, + GGML_OP_DSV4_HC_COMB, + GGML_OP_DSV4_HC_PRE, + GGML_OP_DSV4_HC_POST, GGML_OP_UNARY, @@ -2630,6 +2633,45 @@ extern "C" { struct ggml_tensor * weights, struct ggml_tensor * mask); + // DeepSeek V4 hyper-connections (ref. https://arxiv.org/pdf/2512.24880) + // In short these operations are replacements for the original residual connection (x = transformer(x) + x) + // using a richer representation through streams. + // + // hc_comb: mixes [(2 + hc)*hc, n_tokens], scale [3], base [(2 + hc)*hc] + // -> [dst_hc, src_hc, n_tokens] + // logits[dst, src, t] = mixes[2*hc + dst + hc*src, t]*scale[2] + // + base[2*hc + dst + hc*src] + // Softmax over dst, add eps, normalize over src, then repeat normalization + // over dst followed by src for iterations 1 through n_iter - 1. + GGML_API struct ggml_tensor * ggml_dsv4_hc_comb( + struct ggml_context * ctx, + struct ggml_tensor * mixes, + struct ggml_tensor * scale, + struct ggml_tensor * base, + float eps, + int32_t n_iter); + + // hc_pre: x [n_embd, hc, n_tokens], weights [hc, n_tokens] -> [n_embd, n_tokens] + // result[i, t] = sum_h x[i, h, t]*weights[h, t] + // + GGML_API struct ggml_tensor * ggml_dsv4_hc_pre( + struct ggml_context * ctx, + struct ggml_tensor * x, + struct ggml_tensor * weights); + + // hc_post: x [n_embd, n_tokens], residual [n_embd, hc, n_tokens], + // post [hc, n_tokens], comb [dst_hc, src_hc, n_tokens] + // -> [n_embd, hc, n_tokens] + // result[i, dst, t] = x[i, t]*post[dst, t] + // + sum_src residual[i, src, t]*comb[dst, src, t] + // + GGML_API struct ggml_tensor * ggml_dsv4_hc_post( + struct ggml_context * ctx, + struct ggml_tensor * x, + struct ggml_tensor * residual, + struct ggml_tensor * post, + struct ggml_tensor * comb); + // custom operators typedef void (*ggml_custom1_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, int ith, int nth, void * userdata); diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt index 0baa119ffcbb..516d6f0f5ef7 100644 --- a/ggml/src/CMakeLists.txt +++ b/ggml/src/CMakeLists.txt @@ -431,7 +431,7 @@ if (GGML_CPU_ALL_VARIANTS) message(FATAL_ERROR "Unsupported ARM target OS: ${CMAKE_SYSTEM_NAME}") endif() elseif (GGML_SYSTEM_ARCH STREQUAL "PowerPC") - if (CMAKE_SYSTEM_NAME MATCHES "Linux") + if (CMAKE_SYSTEM_NAME MATCHES "Linux|AIX") ggml_add_cpu_backend_variant(power0) ggml_add_cpu_backend_variant(power7_1 POWER7) ggml_add_cpu_backend_variant(power7_2 POWER7 VSX) diff --git a/ggml/src/ggml-backend-meta.cpp b/ggml/src/ggml-backend-meta.cpp index e44ad6d92677..b397fb6ba1b8 100644 --- a/ggml/src/ggml-backend-meta.cpp +++ b/ggml/src/ggml-backend-meta.cpp @@ -997,6 +997,11 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state( case GGML_OP_GATED_DELTA_NET: { split_state = handle_gated_delta_net(src_ss); } break; + case GGML_OP_DSV4_HC_COMB: + case GGML_OP_DSV4_HC_PRE: + case GGML_OP_DSV4_HC_POST: { + split_state = handle_generic(src_ss, /*scalar_only =*/ true); + } break; case GGML_OP_UNARY: { split_state = handle_generic(src_ss, /*scalar_only =*/ false); } break; diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp index b033afc7a037..3fc1970395f5 100644 --- a/ggml/src/ggml-backend.cpp +++ b/ggml/src/ggml-backend.cpp @@ -769,8 +769,9 @@ struct ggml_backend_sched_split { int backend_id; int i_start; int i_end; - struct ggml_tensor * inputs[GGML_SCHED_MAX_SPLIT_INPUTS]; + struct ggml_tensor ** inputs; int n_inputs; + int inputs_capacity; // graph view of this split struct ggml_cgraph graph; }; @@ -809,8 +810,9 @@ struct ggml_backend_sched { int cur_copy; int next_copy; ggml_backend_event_t events[GGML_SCHED_MAX_BACKENDS][GGML_SCHED_MAX_COPIES]; - struct ggml_tensor * graph_inputs[GGML_SCHED_MAX_SPLIT_INPUTS]; + struct ggml_tensor ** graph_inputs; int n_graph_inputs; + int graph_inputs_capacity; struct ggml_context * ctx; @@ -836,6 +838,36 @@ struct ggml_backend_sched { #define tensor_id_copy(id, backend_id, copy_id) sched->hv_tensor_copies[(id) * sched->n_backends * sched->n_copies + (backend_id) * sched->n_copies + (copy_id)] #define tensor_copy(tensor, backend_id, copy_id) tensor_id_copy(hash_id(tensor), backend_id, copy_id) +static void ggml_backend_sched_split_inputs_grow(struct ggml_backend_sched_split * split) { + int new_cap = GGML_SCHED_MAX_SPLIT_INPUTS; + if (split->inputs_capacity > 0) { + new_cap = 2*split->inputs_capacity; + GGML_LOG_WARN("%s: increasing split inputs capacity from %d to %d\n", __func__, split->inputs_capacity, new_cap); + } + auto * pnew = (struct ggml_tensor **) realloc((void *) split->inputs, new_cap * sizeof(struct ggml_tensor *)); + if (pnew == NULL) { + GGML_LOG_ERROR("%s: failed to allocate %zu bytes\n", __func__, new_cap * sizeof(struct ggml_tensor *)); + GGML_ABORT("failed to grow split inputs container"); + } + split->inputs = pnew; + split->inputs_capacity = new_cap; +} + +static void ggml_backend_sched_graph_inputs_grow(ggml_backend_sched_t sched) { + int new_cap = GGML_SCHED_MAX_SPLIT_INPUTS; + if (sched->graph_inputs_capacity > 0) { + new_cap = 2*sched->graph_inputs_capacity; + GGML_LOG_WARN("%s: increasing graph inputs capacity from %d to %d\n", __func__, sched->graph_inputs_capacity, new_cap); + } + auto * pnew = (struct ggml_tensor **) realloc((void *) sched->graph_inputs, new_cap * sizeof(struct ggml_tensor *)); + if (pnew == NULL) { + GGML_LOG_ERROR("%s: failed to allocate %zu bytes\n", __func__, new_cap * sizeof(struct ggml_tensor *)); + GGML_ABORT("failed to grow graph inputs container"); + } + sched->graph_inputs = pnew; + sched->graph_inputs_capacity = new_cap; +} + // returns the priority of the backend, lower id is higher priority static int ggml_backend_sched_backend_id(ggml_backend_sched_t sched, ggml_backend_t backend) { for (int i = 0; i < sched->n_backends; i++) { @@ -910,26 +942,35 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st } // operations with weights are preferably run on the same backend as the weights - for (int i = 0; i < GGML_MAX_SRC; i++) { - const struct ggml_tensor * src = tensor->src[i]; - if (src == NULL) { - continue; - } - // skip ROPE since the rope freqs tensor is too small to choose a backend based on it - // not an ideal solution - if (tensor->op != GGML_OP_ROPE && src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) { - int src_backend_id = ggml_backend_sched_backend_from_buffer(sched, src, tensor); - // check if a backend with higher prio wants to offload the op - if (sched->op_offload && src_backend_id == sched->n_backends - 1 && ggml_backend_buffer_is_host(src->buffer)) { - for (int b = 0; b < src_backend_id; b++) { - if (ggml_backend_supports_op(sched->backends[b], tensor) && ggml_backend_offload_op(sched->backends[b], tensor)) { - SET_CAUSE(tensor, "1.off"); - return b; + // TODO: there are exceptions (see below) - not an ideal solution + bool allow = true; + + // skip ROPE since the rope freqs tensor is too small to choose a backend based on it + allow = allow && tensor->op != GGML_OP_ROPE; + + // skip FLASH_ATTN_EXT since the sinks tensor is too small to choose a based based on it + allow = allow && tensor->op != GGML_OP_FLASH_ATTN_EXT; + + if (allow) { + for (int i = 0; i < GGML_MAX_SRC; i++) { + const struct ggml_tensor * src = tensor->src[i]; + if (src == NULL) { + continue; + } + if (src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) { + int src_backend_id = ggml_backend_sched_backend_from_buffer(sched, src, tensor); + // check if a backend with higher prio wants to offload the op + if (sched->op_offload && src_backend_id == sched->n_backends - 1 && ggml_backend_buffer_is_host(src->buffer)) { + for (int b = 0; b < src_backend_id; b++) { + if (ggml_backend_supports_op(sched->backends[b], tensor) && ggml_backend_offload_op(sched->backends[b], tensor)) { + SET_CAUSE(tensor, "1.off"); + return b; + } } } + SET_CAUSE(tensor, "1.wgt%d", i); + return src_backend_id; } - SET_CAUSE(tensor, "1.wgt%d", i); - return src_backend_id; } } @@ -1292,7 +1333,7 @@ void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgra } // check if the split has too many inputs // FIXME: count the number of inputs instead of only checking when full - if (split->n_inputs == GGML_SCHED_MAX_SPLIT_INPUTS) { + if (split->n_inputs >= split->inputs_capacity) { const size_t id = hash_id(src); int src_backend_id = sched->hv_tensor_backend_ids[id]; bool supported = ggml_backend_sched_buffer_supported(sched, src, cur_backend_id); @@ -1308,10 +1349,14 @@ void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgra split->i_end = i; i_split++; if (i_split >= sched->splits_capacity) { + int old_cap = sched->splits_capacity; sched->splits_capacity *= 2; sched->splits = (ggml_backend_sched_split *) realloc(sched->splits, sched->splits_capacity * sizeof(struct ggml_backend_sched_split)); GGML_ASSERT(sched->splits != NULL); + for (int k = old_cap; k < sched->splits_capacity; k++) { + memset(&sched->splits[k], 0, sizeof(struct ggml_backend_sched_split)); + } } split = &sched->splits[i_split]; split->backend_id = node_backend_id; @@ -1348,7 +1393,9 @@ void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgra SET_CAUSE(tensor_copy, "4.cpy"); } int n_graph_inputs = sched->n_graph_inputs++; - GGML_ASSERT(n_graph_inputs < GGML_SCHED_MAX_SPLIT_INPUTS); + if (n_graph_inputs >= sched->graph_inputs_capacity) { + ggml_backend_sched_graph_inputs_grow(sched); + } sched->graph_inputs[n_graph_inputs] = src; } } @@ -1368,7 +1415,9 @@ void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgra SET_CAUSE(tensor_copy, "4.cpy"); } int n_inputs = split->n_inputs++; - GGML_ASSERT(n_inputs < GGML_SCHED_MAX_SPLIT_INPUTS); + if (n_inputs >= split->inputs_capacity) { + ggml_backend_sched_split_inputs_grow(split); + } split->inputs[n_inputs] = src; } node->src[j] = tensor_id_copy(src_id, cur_backend_id, sched->cur_copy); @@ -1394,7 +1443,11 @@ void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgra sched->prev_leaf_backend_ids = tmp; } - int graph_size = std::max(graph->n_nodes, graph->n_leafs) + sched->n_splits*GGML_SCHED_MAX_SPLIT_INPUTS*2*sched->n_copies; + int total_inputs = sched->n_graph_inputs; + for (int i = 0; i < sched->n_splits; i++) { + total_inputs += sched->splits[i].n_inputs; + } + int graph_size = std::max(graph->n_nodes, graph->n_leafs) + total_inputs * 2 * sched->n_copies; // remember the actual graph_size for performing reallocation checks later [GGML_SCHED_DEBUG_REALLOC] sched->debug_prev_graph_size = sched->debug_graph_size; @@ -1777,6 +1830,9 @@ ggml_backend_sched_t ggml_backend_sched_new( sched->splits = (ggml_backend_sched_split *) calloc(initial_splits_capacity, sizeof(sched->splits[0])); sched->splits_capacity = initial_splits_capacity; + sched->graph_inputs_capacity = GGML_SCHED_MAX_SPLIT_INPUTS; + sched->graph_inputs = (struct ggml_tensor **) calloc(sched->graph_inputs_capacity, sizeof(struct ggml_tensor *)); + for (int b = 0; b < n_backends; b++) { sched->backends[b] = backends[b]; sched->bufts[b] = bufts ? bufts[b] : ggml_backend_get_default_buffer_type(backends[b]); @@ -1809,7 +1865,11 @@ void ggml_backend_sched_free(ggml_backend_sched_t sched) { ggml_gallocr_free(sched->galloc); ggml_free(sched->ctx); ggml_hash_set_free(&sched->hash_set); + for (int i = 0; i < sched->splits_capacity; i++) { + free(sched->splits[i].inputs); + } free(sched->splits); + free(sched->graph_inputs); free(sched->hv_tensor_backend_ids); free(sched->hv_tensor_copies); free(sched->node_backend_ids); diff --git a/ggml/src/ggml-blas/ggml-blas.cpp b/ggml/src/ggml-blas/ggml-blas.cpp index b4c735267e04..9745fa29f5db 100644 --- a/ggml/src/ggml-blas/ggml-blas.cpp +++ b/ggml/src/ggml-blas/ggml-blas.cpp @@ -1,3 +1,4 @@ +#include "ggml.h" #include "ggml-impl.h" #include "ggml-blas.h" #include "ggml-backend-impl.h" @@ -415,6 +416,12 @@ static bool ggml_backend_blas_device_supports_op(ggml_backend_dev_t dev, const s // TODO: find the optimal value const int64_t min_batch = 32; + // default back to CPU fast path + // see: https://github.com/ggml-org/llama.cpp/issues/25565 + if (ggml_get_op_params_i32(op, 1) == GGML_HINT_SRC0_IS_HADAMARD) { + return false; + } + return ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && src1->type == GGML_TYPE_F32 && diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt index 1316978e2ef7..836bae4d05a7 100644 --- a/ggml/src/ggml-cpu/CMakeLists.txt +++ b/ggml/src/ggml-cpu/CMakeLists.txt @@ -678,7 +678,18 @@ function(ggml_add_cpu_backend_variant_impl tag_name) endif() if (NOT SME_ENABLED MATCHES -1) - list(APPEND GGML_KLEIDIAI_SOURCES + list(APPEND GGML_KLEIDIAI_SME_SOURCES + ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi8cxp/kai_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme_mopa.c + ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi8cxp/kai_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme_mopa_asm.S + ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi8cxp/kai_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme_dot.c + ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi8cxp/kai_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme_dot_asm.S + ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_f32p_f32p/kai_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa.c + ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_f32p_f32p/kai_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa_asm.S) + set_source_files_properties(${GGML_KLEIDIAI_SME_SOURCES} + PROPERTIES COMPILE_OPTIONS "-fno-tree-vectorize;${ARCH_FLAGS_TEMP}+sve+sve2+sme") + list(APPEND GGML_CPU_SOURCES ${GGML_KLEIDIAI_SME_SOURCES}) + + list(APPEND GGML_KLEIDIAI_SME2_SOURCES ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qsi8d32p_qsi4c32p/kai_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4vlx4_1x4vl_sme2_sdot.c ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi8cxp/kai_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme2_mopa.c ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi8cxp/kai_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme2_mopa_asm.S @@ -698,7 +709,10 @@ function(ggml_add_cpu_backend_variant_impl tag_name) ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_rhs_pack_nxk_f32p2vlx1biasf32_f32_f32_sme.c ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_rhs_pack_nxk_f32p2vlx1biasf32_f32_f32_sme_asm.S ${KLEIDIAI_SRC}/kai/kai_common_sme_asm.S) - set(PRIVATE_ARCH_FLAGS "-fno-tree-vectorize;${PRIVATE_ARCH_FLAGS}+sve+sve2+sme2+fp16") + set_source_files_properties(${GGML_KLEIDIAI_SME2_SOURCES} + PROPERTIES COMPILE_OPTIONS "-fno-tree-vectorize;${ARCH_FLAGS_TEMP}+sve+sve2+sme2+fp16") + list(APPEND GGML_CPU_SOURCES ${GGML_KLEIDIAI_SME2_SOURCES}) + set(PRIVATE_ARCH_FLAGS "-fno-tree-vectorize;${PRIVATE_ARCH_FLAGS}") endif() if (NOT SVE_ENABLED MATCHES -1) diff --git a/ggml/src/ggml-cpu/arch/arm/cpu-feats.cpp b/ggml/src/ggml-cpu/arch/arm/cpu-feats.cpp index c460c5491143..adfbd2e4e9bd 100644 --- a/ggml/src/ggml-cpu/arch/arm/cpu-feats.cpp +++ b/ggml/src/ggml-cpu/arch/arm/cpu-feats.cpp @@ -28,6 +28,7 @@ struct aarch64_features { bool has_sve2 = false; bool has_i8mm = false; bool has_sme = false; + bool has_sme2 = false; aarch64_features() { #if defined(__linux__) @@ -56,6 +57,10 @@ struct aarch64_features { has_sme = static_cast(oldp); } + if (sysctlbyname("hw.optional.arm.FEAT_SME2", &oldp, &size, NULL, 0) == 0) { + has_sme2 = static_cast(oldp); + } + // Apple apparently does not implement SVE yet #endif } diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c index a9b694baec13..6e75a5880540 100644 --- a/ggml/src/ggml-cpu/ggml-cpu.c +++ b/ggml/src/ggml-cpu/ggml-cpu.c @@ -2117,6 +2117,18 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm { ggml_compute_forward_lightning_indexer(params, tensor); } break; + case GGML_OP_DSV4_HC_COMB: + { + ggml_compute_forward_dsv4_hc_comb(params, tensor); + } break; + case GGML_OP_DSV4_HC_PRE: + { + ggml_compute_forward_dsv4_hc_pre(params, tensor); + } break; + case GGML_OP_DSV4_HC_POST: + { + ggml_compute_forward_dsv4_hc_post(params, tensor); + } break; case GGML_OP_MAP_CUSTOM1: { ggml_compute_forward_map_custom1(params, tensor); @@ -2298,6 +2310,9 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) { case GGML_OP_SOLVE_TRI: case GGML_OP_GATED_DELTA_NET: case GGML_OP_TURBO_WHT: + case GGML_OP_DSV4_HC_COMB: + case GGML_OP_DSV4_HC_PRE: + case GGML_OP_DSV4_HC_POST: { n_tasks = n_threads; } break; @@ -2914,7 +2929,8 @@ struct ggml_cplan ggml_graph_plan( } break; case GGML_OP_OUT_PROD: { - if (ggml_is_quantized(node->src[0]->type)) { + if (ggml_is_quantized(node->src[0]->type) || + node->src[0]->type == GGML_TYPE_F16) { cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks; } } break; @@ -3964,6 +3980,14 @@ int ggml_cpu_has_sme(void) { #endif } +int ggml_cpu_has_sme2(void) { +#if defined(__ARM_ARCH) && defined(__ARM_FEATURE_SME2) + return 1; +#else + return 0; +#endif +} + void ggml_cpu_init(void) { // needed to initialize ggml_time { diff --git a/ggml/src/ggml-cpu/ggml-cpu.cpp b/ggml/src/ggml-cpu/ggml-cpu.cpp index 128883b41ce7..16cc5116c545 100644 --- a/ggml/src/ggml-cpu/ggml-cpu.cpp +++ b/ggml/src/ggml-cpu/ggml-cpu.cpp @@ -462,12 +462,15 @@ static bool ggml_backend_cpu_device_supports_op(ggml_backend_dev_t dev, const st return max_bias == 0.0f; } case GGML_OP_IM2COL_BACK: - return src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32; + return src0->type == GGML_TYPE_F32 && (src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); case GGML_OP_GET_ROWS_BACK: return src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16; case GGML_OP_OUT_PROD: - return (src0->type == GGML_TYPE_F32 || (ggml_is_quantized(src0->type) && src0->ne[2] == src1->ne[2] && src0->ne[3] == src1->ne[3])) && + return (src0->type == GGML_TYPE_F32 || + ((src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && src0->ne[2] == src1->ne[2] && src0->ne[3] == src1->ne[3])) && src1->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32; + case GGML_OP_CONV_2D: + return ggml_is_contiguous(op->src[0]); default: return true; } @@ -594,6 +597,9 @@ static ggml_backend_feature * ggml_backend_cpu_get_features(ggml_backend_reg_t r if (ggml_cpu_has_sme()) { features.push_back({ "SME", "1" }); } + if (ggml_cpu_has_sme2()) { + features.push_back({ "SME2", "1" }); + } if (ggml_cpu_has_riscv_v()) { features.push_back({ "RISCV_V", "1" }); } diff --git a/ggml/src/ggml-cpu/kleidiai/kernels.cpp b/ggml/src/ggml-cpu/kleidiai/kernels.cpp index bf03fd766bcf..3c31ab9d35f0 100644 --- a/ggml/src/ggml-cpu/kleidiai/kernels.cpp +++ b/ggml/src/ggml-cpu/kleidiai/kernels.cpp @@ -13,6 +13,8 @@ #include "kai_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa.h" #include "kai_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme2_mopa.h" #include "kai_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme2_dot.h" +#include "kai_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme_mopa.h" +#include "kai_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme_dot.h" #include "kai_matmul_clamp_f32_qai8dxp1x8_qsi8cxp4x8_1x4_neon_dotprod.h" #include "kai_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4x4_1x4_neon_dotprod.h" #include "kai_matmul_clamp_f32_qai8dxp4x4_qsi8cxp4x4_16x4_neon_dotprod.h" @@ -21,6 +23,7 @@ #include "kai_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p8x8_1x8_sve_dotprod.h" #include "kai_matmul_clamp_f32_f16p1vlx2_qsi4c32p4vlx2_1vlx4vl_sme2_mopa.h" #include "kai_matmul_clamp_f32_f32p2vlx1_f32p2vlx1biasf32_sme2_mopa.h" +#include "kai_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa.h" #include "kai_lhs_pack_bf16p2vlx2_f32_sme.h" #include "kai_lhs_pack_f32p2vlx1_f32_sme.h" @@ -359,7 +362,7 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = { /* .packed_stride_ex = */ &rhs_stride_fn4, /* .pack_func_ex = */ &rhs_pack_fn12, }, - /* .required_cpu = */ CPU_FEATURE_SME, + /* .required_cpu = */ CPU_FEATURE_SME2, /* .lhs_type = */ GGML_TYPE_F32, /* .rhs_type = */ GGML_TYPE_Q4_0, /* .op_type = */ GGML_TYPE_F32, @@ -412,7 +415,7 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = { /* .packed_stride_ex = */ &rhs_stride_fn1, /* .pack_func_ex = */ &rhs_pack_fn13, }, - /* .required_cpu = */ CPU_FEATURE_SME, + /* .required_cpu = */ CPU_FEATURE_SME2, /* .lhs_type = */ GGML_TYPE_F32, /* .rhs_type = */ GGML_TYPE_F16, /* .op_type = */ GGML_TYPE_F32, @@ -749,6 +752,59 @@ static ggml_kleidiai_kernels gemm_gemv_kernels_q8[] = { /* .packed_stride_ex = */ &rhs_stride_fn4, /* .pack_func_ex = */ &rhs_pack_scale_fn12, }, + /* .required_cpu = */ CPU_FEATURE_SME2, + /* .lhs_type = */ GGML_TYPE_F32, + /* .rhs_type = */ GGML_TYPE_Q8_0, + /* .op_type = */ GGML_TYPE_F32, + }, + { + /* SME GEMM (pure SME, no SME2 required) */ + { + /* .get_m_step = */ kai_get_m_step_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme_mopa, + /* .get_n_step = */ kai_get_n_step_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme_mopa, + /* .get_mr = */ kai_get_mr_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme_mopa, + /* .get_nr = */ kai_get_nr_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme_mopa, + /* .get_kr = */ kai_get_kr_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme_mopa, + /* .get_sr = */ kai_get_sr_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme_mopa, + /* .get_dst_offset = */ kai_get_dst_offset_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme_mopa, + /* .get_dst_size = */ kai_get_dst_size_matmul_clamp_f32_qai8dxp1vlx4_qsi8cxp4vlx4_1vlx4vl_sme_mopa, + /* .get_lhs_offset_ex = */ &kernel_offs_fn2, + /* .get_rhs_packed_offset_ex = */ &kernel_offs_fn2, + /* .run_kernel_ex = */ &kernel_run_float_fn10, + }, + /* .gemm_lhs_info = */ { + /* .get_offset = */ kai_get_lhs_offset_lhs_quant_pack_qai8dxp_f32, + /* .get_packed_offset_ex = */ &lhs_offs_fn5, + /* .packed_size_ex = */ &lhs_ps_fn5, + /* .pack_func_ex = */ &lhs_pack_float_fn9_no_bl, + }, + /* SME GEMV (pure SME, no SME2 required) */ + { + /* .get_m_step = */ kai_get_m_step_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme_dot, + /* .get_n_step = */ kai_get_n_step_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme_dot, + /* .get_mr = */ kai_get_mr_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme_dot, + /* .get_nr = */ kai_get_nr_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme_dot, + /* .get_kr = */ kai_get_kr_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme_dot, + /* .get_sr = */ kai_get_sr_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme_dot, + /* .get_dst_offset = */ kai_get_dst_offset_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme_dot, + /* .get_dst_size = */ kai_get_dst_size_matmul_clamp_f32_qai8dxp1x4_qsi8cxp4vlx4_1x4vl_sme_dot, + /* .get_lhs_offset_ex = */ &kernel_offs_fn2, + /* .get_rhs_packed_offset_ex = */ &kernel_offs_fn2, + /* .run_kernel_ex = */ &kernel_run_float_fn10, + }, + /* .gemv_lhs_info = */ { + /* .get_offset = */ kai_get_lhs_offset_lhs_quant_pack_qai8dxp_f32, + /* .get_packed_offset_ex = */ &lhs_offs_fn5, + /* .packed_size_ex = */ &lhs_ps_fn5, + /* .pack_func_ex = */ &lhs_pack_float_fn9_no_bl, + }, + /* .rhs_info = */ { + /* .packed_stride = */ kai_get_rhs_packed_stride_rhs_pack_nxk_qsi8cxp_qsi8cx_neon, + /* .to_float = */ dequantize_row_qsi8cxp, + /* .packed_size_ex = */ &rhs_ps_fn5, + /* .packed_stride_ex = */ &rhs_stride_fn4, + /* .pack_func_ex = */ &rhs_pack_scale_fn12, + }, /* .required_cpu = */ CPU_FEATURE_SME, /* .lhs_type = */ GGML_TYPE_F32, /* .rhs_type = */ GGML_TYPE_Q8_0, @@ -871,7 +927,7 @@ static ggml_kleidiai_kernels gemm_gemv_kernels_q8[] = { static ggml_kleidiai_kernels ggml_kleidiai_kernels_f32[] = { #if defined(__ARM_FEATURE_SME) { - /* SME GEMM */ + /* SME2 GEMM */ { /* .get_m_step = */ kai_get_m_step_matmul_clamp_f32_f32p2vlx1_f32p2vlx1biasf32_sme2_mopa, /* .get_n_step = */ kai_get_n_step_matmul_clamp_f32_f32p2vlx1_f32p2vlx1biasf32_sme2_mopa, @@ -918,6 +974,59 @@ static ggml_kleidiai_kernels ggml_kleidiai_kernels_f32[] = { /* .packed_stride_ex = */ &rhs_stride_fn1, /* .pack_func_ex = */ &rhs_pack_fn13, }, + /* .required_cpu = */ CPU_FEATURE_SME2, + /* .lhs_type = */ GGML_TYPE_F32, + /* .rhs_type = */ GGML_TYPE_F32, + /* .op_type = */ GGML_TYPE_F32, + }, + { + /* SME GEMM */ + { + /* .get_m_step = */ kai_get_m_step_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa, + /* .get_n_step = */ kai_get_n_step_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa, + /* .get_mr = */ kai_get_mr_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa, + /* .get_nr = */ kai_get_nr_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa, + /* .get_kr = */ kai_get_kr_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa, + /* .get_sr = */ kai_get_sr_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa, + /* .get_dst_offset = */ kai_get_dst_offset_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa, + /* .get_dst_size = */ kai_get_dst_size_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa, + /* .get_lhs_offset_ex = */ &kernel_offs_fn2, + /* .get_rhs_packed_offset_ex = */ &kernel_offs_fn2, + /* .run_kernel_ex = */ &kernel_run_fn10, + }, + /* .gemm_lhs_info = */ { + /* .get_offset = */ kai_get_lhs_offset_lhs_pack_f32p2vlx1_f32_sme, + /* .get_packed_offset_ex = */ &lhs_offs_fn5, + /* .packed_size_ex = */ &lhs_ps_fn5, + /* .pack_func_ex = */ &lhs_pack_void_fn9, + }, + /* SME GEMV */ + { + /* .get_m_step = */ kai_get_m_step_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa, + /* .get_n_step = */ kai_get_n_step_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa, + /* .get_mr = */ kai_get_mr_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa, + /* .get_nr = */ kai_get_nr_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa, + /* .get_kr = */ kai_get_kr_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa, + /* .get_sr = */ kai_get_sr_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa, + /* .get_dst_offset = */ kai_get_dst_offset_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa, + /* .get_dst_size = */ kai_get_dst_size_matmul_clamp_f32_f32p2vlx1_f32p2vlx1b_2vlx2vl_sme_mopa, + /* .get_lhs_offset_ex = */ nullptr, + /* .get_rhs_packed_offset_ex = */ nullptr, + /* .run_kernel_ex = */ nullptr, + }, + /* .gemv_lhs_info = */ { + /* .get_offset = */ kai_get_lhs_offset_lhs_pack_f32p2vlx1_f32_sme, + /* .get_packed_offset_ex = */ &lhs_offs_fn5, + /* .packed_size_ex = */ &lhs_ps_fn5, + /* .pack_func_ex = */ &lhs_pack_void_fn9, + }, + /* .rhs_info = */ { + /* .packed_stride = */ nullptr, + /* .to_float = */ nullptr, + /* .packed_size_ex = */ &rhs_ps_fn2, + /* .packed_stride_ex = */ &rhs_stride_fn1, + /* .pack_func_ex = */ &rhs_pack_fn13, + }, /* .required_cpu = */ CPU_FEATURE_SME, /* .lhs_type = */ GGML_TYPE_F32, /* .rhs_type = */ GGML_TYPE_F32, diff --git a/ggml/src/ggml-cpu/kleidiai/kernels.h b/ggml/src/ggml-cpu/kleidiai/kernels.h index a46f837acbd6..0da5e65a0a8d 100644 --- a/ggml/src/ggml-cpu/kleidiai/kernels.h +++ b/ggml/src/ggml-cpu/kleidiai/kernels.h @@ -11,7 +11,8 @@ enum cpu_feature { CPU_FEATURE_DOTPROD = 1, CPU_FEATURE_I8MM = 2, CPU_FEATURE_SVE = 4, - CPU_FEATURE_SME = 8 + CPU_FEATURE_SME = 8, + CPU_FEATURE_SME2 = 16 }; inline cpu_feature& operator|=(cpu_feature& lhs, cpu_feature rhs) { diff --git a/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp b/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp index a8de7df25f07..1c5a459f2190 100644 --- a/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp +++ b/ggml/src/ggml-cpu/kleidiai/kleidiai.cpp @@ -26,6 +26,9 @@ #include #include #include +#ifndef HWCAP2_SME2 +#define HWCAP2_SME2 (1UL << 37) +#endif #elif defined(__APPLE__) #include #include @@ -66,9 +69,15 @@ struct ggml_kleidiai_context { int chunk_multiplier; } static ctx = { CPU_FEATURE_NONE, nullptr, nullptr, nullptr, 0, -1, 4 }; +static inline bool is_sme_family(cpu_feature f) { + return (f & (CPU_FEATURE_SME | CPU_FEATURE_SME2)) != CPU_FEATURE_NONE; +} + static const char* cpu_feature_to_string(cpu_feature f) { if (f == CPU_FEATURE_NONE) { return "NONE"; + } else if ((f & CPU_FEATURE_SME2) == CPU_FEATURE_SME2) { + return "SME2"; } else if ((f & CPU_FEATURE_SME) == CPU_FEATURE_SME) { return "SME"; } else if ((f & CPU_FEATURE_SVE) == CPU_FEATURE_SVE) { @@ -251,6 +260,18 @@ static void init_kleidiai_context(void) { if (sme_cores > 0) { ctx.features |= CPU_FEATURE_SME; +#if defined(__aarch64__) && defined(__linux__) + // ARM guarantees SME2 implies SME, so only check SME2 when SME is enabled. + if (getauxval(AT_HWCAP2) & HWCAP2_SME2) { + ctx.features |= CPU_FEATURE_SME2; + } +#elif defined(__aarch64__) && defined(__APPLE__) + int feat_sme2 = 0; + size_t size = sizeof(feat_sme2); + if (sysctlbyname("hw.optional.arm.FEAT_SME2", &feat_sme2, &size, NULL, 0) == 0 && feat_sme2) { + ctx.features |= CPU_FEATURE_SME2; + } +#endif } // Kernel selection @@ -279,10 +300,13 @@ static void init_kleidiai_context(void) { ctx.sme_thread_cap = (ctx.features & CPU_FEATURE_SME) ? sme_cores : 0; if (ctx.features & CPU_FEATURE_SME) { + const bool has_sme2 = (ctx.features & CPU_FEATURE_SME2) != CPU_FEATURE_NONE; if (sme_env_set && sme_env_ok && sme_cores > 0) { - GGML_LOG_INFO("kleidiai: SME enabled (GGML_KLEIDIAI_SME=%d override)\n", sme_cores); + GGML_LOG_INFO("kleidiai: SME%s enabled (GGML_KLEIDIAI_SME=%d override)\n", + has_sme2 ? "2" : "", sme_cores); } else { - GGML_LOG_INFO("kleidiai: SME enabled (runtime-detected SME cores=%d)\n", sme_cores); + GGML_LOG_INFO("kleidiai: SME%s enabled (runtime-detected SME cores=%d)\n", + has_sme2 ? "2" : "", sme_cores); } } else { GGML_LOG_INFO("kleidiai: SME disabled\n"); @@ -442,8 +466,8 @@ static int kleidiai_collect_kernel_chain_common( return count; } - if ((primary->required_cpu & CPU_FEATURE_SME) == CPU_FEATURE_SME) { - const cpu_feature fallback_mask = static_cast(features & ~CPU_FEATURE_SME); + if (is_sme_family(primary->required_cpu)) { + const cpu_feature fallback_mask = static_cast(features & ~CPU_FEATURE_SME & ~CPU_FEATURE_SME2); if (fallback_mask != CPU_FEATURE_NONE) { ggml_kleidiai_kernels * fallback = select_fallback(fallback_mask); if (fallback && fallback != primary && @@ -1054,14 +1078,14 @@ class tensor_traits : public ggml::cpu::tensor_traits { int sme_slot = -1; for (int i = 0; i < runtime_count; ++i) { - if ((runtime[i].kernels->required_cpu & CPU_FEATURE_SME) == CPU_FEATURE_SME) { + if (is_sme_family(runtime[i].kernels->required_cpu)) { sme_slot = i; break; } } int non_sme_slot = -1; for (int i = 0; i < runtime_count; ++i) { - if ((runtime[i].kernels->required_cpu & CPU_FEATURE_SME) != CPU_FEATURE_SME) { + if (!is_sme_family(runtime[i].kernels->required_cpu)) { non_sme_slot = i; break; } @@ -1099,7 +1123,7 @@ class tensor_traits : public ggml::cpu::tensor_traits { // Recompute SME slot based on the collapsed runtime[0] sme_slot = -1; if (runtime_count > 0 && - (runtime[0].kernels->required_cpu & CPU_FEATURE_SME) == CPU_FEATURE_SME) { + is_sme_family(runtime[0].kernels->required_cpu)) { sme_slot = 0; } } @@ -1695,6 +1719,7 @@ class extra_buffer_type : ggml::cpu::extra_buffer_type { return true; } + return false; } @@ -1703,6 +1728,20 @@ class extra_buffer_type : ggml::cpu::extra_buffer_type { if (op->src[0]->buffer && op->src[0]->buffer->buft == ggml_backend_cpu_kleidiai_buffer_type()) { return (ggml::cpu::tensor_traits *) op->src[0]->extra; } else { + // KleidiAI only has kernels for Q4_0 and Q8_0. For a quantized weight of any + // other type (K-quants, IQ) it declines the op and returns nullptr below, so + // KleidiAI does not accelerate it. Another CPU backend may still take the op, + // and this can run during graph planning, so the message says what KleidiAI + // did rather than what ends up executing. Warn once per process. + if (ggml_is_quantized(op->src[0]->type) && + op->src[0]->type != GGML_TYPE_Q4_0 && op->src[0]->type != GGML_TYPE_Q8_0) { + static std::atomic warned(false); + if (!warned.exchange(true)) { + GGML_LOG_WARN("kleidiai: no kernel for tensor type %s, not accelerated by KleidiAI " + "(kernels available for Q4_0 and Q8_0)\n", + ggml_type_name(op->src[0]->type)); + } + } if (op->src[0]->type != GGML_TYPE_F16) { return nullptr; } diff --git a/ggml/src/ggml-cpu/llamafile/sgemm.cpp b/ggml/src/ggml-cpu/llamafile/sgemm.cpp index 5efaaa5b2a06..99b7d5afa2f9 100644 --- a/ggml/src/ggml-cpu/llamafile/sgemm.cpp +++ b/ggml/src/ggml-cpu/llamafile/sgemm.cpp @@ -1797,14 +1797,6 @@ class tinyBLAS_Q0_AVX { //PPC Implementation #if defined(__MMA__) -#define SAVE_ACC(ACC, ii, jj) \ - __builtin_mma_disassemble_acc(vec_C, ACC); \ - for (int I = 0; I < 4; I++) { \ - for (int J = 0; J < 4; J++) { \ - *((float*)(C+ii+((jj+J)*ldc)+I)) = *((float*)&vec_C[I]+J); \ - } \ - } \ - template struct mma_instr; @@ -1834,10 +1826,49 @@ class tinyBLAS_HP16_PPC { } void matmul(int64_t m, int64_t n) { - mnpack(0, m, 0, n); + int64_t mc = 256; + int64_t nc = 256; + int64_t kc = 256; + #if defined(_AIX) || defined(__BIG_ENDIAN__) + mc = 128; + nc = 128; + kc = 128; + #endif + if (k < kc) { + kc = k; + } + bool can_use_tiled = (m % mc == 0) && (n % nc == 0) && (k % kc == 0); + if (can_use_tiled) { + matmul_tiled(m, n, mc, nc, kc); + } else { + mnpack(0, m, 0, n); + } } private: + __attribute__((always_inline)) + inline void save_acc(acc_t * ACC, int64_t ii, int64_t jj) { + vec_t vec_C[4]; + __builtin_mma_disassemble_acc(vec_C, ACC); + for (int I = 0; I < 4; I++) { + for (int J = 0; J < 4; J++) { + *((float *)(C+ii+((jj+J)*ldc)+I)) = *((float *)&vec_C[I]+J); + } + } + } + + __attribute__((always_inline)) + inline void add_save_acc(acc_t * ACC, int64_t ii, int64_t jj) { + vec_t vec_C[4]; + __builtin_mma_disassemble_acc(vec_C, ACC); + for (int I = 0; I < 4; I++) { + for (int J = 0; J < 4; J++) { + float * c_ptr = (float *)(C+ii+((jj+J)*ldc)+I); + *c_ptr += *((float *)&vec_C[I]+J); + } + } + } + void vector_permute_store(vec_t *c, int numVec, unsigned char *vecOffset) { vec_t t[8], s[8]; vec_t swiz1 = {0, 1, 2, 3, 16, 17, 18, 19, 4, 5, 6, 7, 20, 21, 22, 23}; @@ -1896,6 +1927,7 @@ class tinyBLAS_HP16_PPC { j = (rows >> 3); if (j > 0) { do { + aoffsets[0] = aoffset; if (cols == 4) { aoffsets[0] = aoffset; for (int it = 1; it < 4; ++it) @@ -1910,17 +1942,17 @@ class tinyBLAS_HP16_PPC { } i = (cols >> 3); if (i > 0) { - aoffsets[0] = aoffset; for (int it = 1; it < 8; ++it) { aoffsets[it] = aoffsets[it-1] + lda; } aoffset += 8 * lda; + do { for (int it = 0; it < 8; ++it) c_arr[it] = vec_xl(0, (vector unsigned char*)aoffsets[it]); vector_permute_store(c_arr, 8, vecOffset); for (int it = 0; it < 8; ++it) - aoffsets[it] = aoffsets[it] + 8*lda; + aoffsets[it] = aoffsets[it] + 8; vecOffset += 128; i--; } while(i > 0); @@ -2147,8 +2179,8 @@ class tinyBLAS_HP16_PPC { mma_instr::outer_product(&acc_1, vec_A[x], vec_B[x+4]); } } - SAVE_ACC(&acc_0, ii, jj); - SAVE_ACC(&acc_1, ii, jj+4); + save_acc(&acc_0, ii, jj); + save_acc(&acc_1, ii, jj+4); } void KERNEL_8x4(int64_t ii, int64_t jj) { @@ -2164,8 +2196,8 @@ class tinyBLAS_HP16_PPC { mma_instr::outer_product(&acc_1, vec_A[x+4], vec_B[x]); } } - SAVE_ACC(&acc_0, ii, jj); - SAVE_ACC(&acc_1, ii+4, jj); + save_acc(&acc_0, ii, jj); + save_acc(&acc_1, ii+4, jj); } @@ -2186,13 +2218,64 @@ class tinyBLAS_HP16_PPC { mma_instr::outer_product(&acc_3, vec_A[x+4], vec_B[x+4]); } } - - SAVE_ACC(&acc_0, ii, jj); - SAVE_ACC(&acc_1, ii, jj+4); - SAVE_ACC(&acc_2, ii+4, jj); - SAVE_ACC(&acc_3, ii+4, jj+4); + save_acc(&acc_0, ii, jj); + save_acc(&acc_1, ii, jj+4); + save_acc(&acc_2, ii+4, jj); + save_acc(&acc_3, ii+4, jj+4); } + inline void MMA_16x8(vec_t * vec_A0, vec_t * vec_A1, vec_t * vec_B, acc_t * acc) { + for (int x = 0; x < 4; x ++) { + mma_instr::outer_product(&acc[0], vec_A0[x], vec_B[x]); + mma_instr::outer_product(&acc[1], vec_A0[x], vec_B[x+4]); + mma_instr::outer_product(&acc[2], vec_A0[x+4], vec_B[x]); + mma_instr::outer_product(&acc[3], vec_A0[x+4], vec_B[x+4]); + mma_instr::outer_product(&acc[4], vec_A1[x], vec_B[x]); + mma_instr::outer_product(&acc[5], vec_A1[x], vec_B[x+4]); + mma_instr::outer_product(&acc[6], vec_A1[x+4], vec_B[x]); + mma_instr::outer_product(&acc[7], vec_A1[x+4], vec_B[x+4]); + } + } + void KERNEL(int64_t ii, int64_t jj, int64_t mc, int64_t nc, int64_t kc, vec_t * vec_A, vec_t * vec_B, int64_t kk) { + for (int64_t i = 0; i < mc; i += 16) { + int A_base_addr = (mc / 8) * (i / 8) * 8; + for (int64_t j = 0; j < nc; j += 8) { + int B_base_addr = (nc / 8) * (j / 8) * 8; + acc_t acc[8]; + vec_t A0_block[8]; vec_t A1_block[8]; + for (int x = 0; x < 8; x++) + __builtin_mma_xxsetaccz(&acc[x]); + for (int64_t l = 0; l < kc; l += 8) { + int A0_block_idx = A_base_addr + (l / 8) * 8; + int A1_block_idx = A0_block_idx + (mc / 8) * 8; + int B_block_idx = B_base_addr + (l / 8) * 8; + vec_t* A0_block = &vec_A[A0_block_idx]; + vec_t* A1_block = &vec_A[A1_block_idx]; + vec_t* B_block = &vec_B[B_block_idx]; + MMA_16x8(A0_block, A1_block, B_block, acc); + } + if (kk == 0) { + save_acc(&acc[0], ii + i, jj + j); + save_acc(&acc[1], ii + i, jj + j + 4); + save_acc(&acc[2], ii + i + 4, jj + j); + save_acc(&acc[3], ii + i + 4, jj + j + 4); + save_acc(&acc[4], ii + i + 8, jj + j); + save_acc(&acc[5], ii + i + 8, jj + j + 4); + save_acc(&acc[6], ii + i + 12, jj + j); + save_acc(&acc[7], ii + i + 12, jj + j + 4); + } else { + add_save_acc(&acc[0], ii + i, jj + j); + add_save_acc(&acc[1], ii + i, jj + j + 4); + add_save_acc(&acc[2], ii + i + 4, jj + j); + add_save_acc(&acc[3], ii + i + 4, jj + j + 4); + add_save_acc(&acc[4], ii + i + 8, jj + j); + add_save_acc(&acc[5], ii + i + 8, jj + j + 4); + add_save_acc(&acc[6], ii + i + 12, jj + j); + add_save_acc(&acc[7], ii + i + 12, jj + j + 4); + } + } + } + } template void gemm_small(int64_t m0, int64_t m, int64_t n0, int64_t n) { int64_t ytiles = (m - m0) / RM; @@ -2281,6 +2364,29 @@ class tinyBLAS_HP16_PPC { } } + void matmul_tiled(int64_t m, int64_t n, int64_t mc, int64_t nc, int64_t kc) { + int64_t ytiles = m / mc; + int64_t xtiles = n / nc; + int64_t tiles = xtiles * ytiles; + int64_t duty = (tiles + nth - 1) / nth; + int64_t start = duty * ith; + int64_t end = start + duty; + if (end > tiles) { + end = tiles; + } + for (int64_t job = start; job < end; ++job) { + int64_t ii = (job / xtiles) * mc; + int64_t jj = (job % xtiles) * nc; + for (int64_t kk = 0; kk < k; kk += kc) { + vec_t A_pack[kc * mc / 8]; + vec_t B_pack[kc * nc / 8]; + packNormal(A + (ii * lda) + kk, lda, kc, mc, (uint8_t *)A_pack); + packNormal(B + (jj * ldb) + kk, ldb, kc, nc, (uint8_t *)B_pack); + KERNEL(ii, jj, mc, nc, kc, A_pack, B_pack, kk); + } + } + } + template NOINLINE void gemm(int64_t m0, int64_t m, int64_t n0, int64_t n) { int64_t ytiles = (m - m0) / RM; @@ -2329,7 +2435,7 @@ class tinyBLAS_Q0_PPC { mc = 32; nc = 32; kc = 32; - n_chunk = 32 + n_chunk = 32; #endif int64_t n_aligned = 0; if (n % n_chunk == 0) { diff --git a/ggml/src/ggml-cpu/ops.cpp b/ggml/src/ggml-cpu/ops.cpp index 7e6a141d61e7..19911ae0a8a8 100644 --- a/ggml/src/ggml-cpu/ops.cpp +++ b/ggml/src/ggml-cpu/ops.cpp @@ -2091,8 +2091,8 @@ void ggml_compute_forward_concat( const ggml_tensor * src1 = dst->src[1]; if (ggml_is_quantized(src0->type)) { - GGML_ASSERT(ggml_is_contiguous(src0)); - GGML_ASSERT(ggml_is_contiguous(src1)); + GGML_ASSERT(ggml_is_contiguous_rows(src0)); + GGML_ASSERT(ggml_is_contiguous_rows(src1)); GGML_ASSERT(src0->ne[0] % ggml_blck_size(src0->type) == 0); GGML_ASSERT(src1->ne[0] % ggml_blck_size(src1->type) == 0); } @@ -4459,6 +4459,70 @@ static void ggml_compute_forward_out_prod_q_f32( } } +static void ggml_compute_forward_out_prod_f16_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + GGML_TENSOR_BINARY_OP_LOCALS; + + const int ith = params->ith; + const int nth = params->nth; + + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + GGML_ASSERT(ne02 == ne12); + GGML_ASSERT(ne03 == ne13); + GGML_ASSERT(ne2 == ne12); + GGML_ASSERT(ne3 == ne13); + + GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); + GGML_ASSERT(nb0 == sizeof(float)); + + GGML_ASSERT(ne0 == ne00); + GGML_ASSERT(ne1 == ne10); + GGML_ASSERT(ne2 == ne02); + GGML_ASSERT(ne3 == ne03); + + if (ith == 0) { + ggml_vec_set_f32(ne0*ne1*ne2*ne3, (float *)dst->data, 0); + } + ggml_barrier(params->threadpool); + + const int64_t nr = ne1*ne2*ne3; + const int64_t dr = (nr + nth - 1)/nth; + const int64_t ir0 = dr*ith; + const int64_t ir1 = MIN(ir0 + dr, nr); + + float * wdata = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32) * ith; + + for (int64_t ir = ir0; ir < ir1; ++ir) { + const int64_t i3 = ir/(ne2*ne1); + const int64_t i2 = (ir - i3*ne2*ne1)/ne1; + const int64_t i1 = (ir - i3*ne2*ne1 - i2*ne1); + + const int64_t i02 = i2; + const int64_t i03 = i3; + + const int64_t i12 = i2; + const int64_t i13 = i3; + + float * d = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3)); + + for (int64_t i01 = 0; i01 < ne01; ++i01) { + const int64_t i11 = i01; + ggml_fp16_t * s0 = (ggml_fp16_t *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03)); + float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); + ggml_fp16_to_fp32_row(s0, wdata, ne0); + ggml_vec_mad_f32(ne0, d, wdata, *s1); + } + } +} + void ggml_compute_forward_out_prod( const ggml_compute_params * params, ggml_tensor * dst) { @@ -4498,9 +4562,8 @@ void ggml_compute_forward_out_prod( } break; case GGML_TYPE_F16: { - GGML_ABORT("fatal error"); // todo - // ggml_compute_forward_out_prod_f16_f32(params, dst); - } + ggml_compute_forward_out_prod_f16_f32(params, dst); + } break; case GGML_TYPE_F32: { ggml_compute_forward_out_prod_f32(params, dst); @@ -6497,7 +6560,7 @@ void ggml_compute_forward_im2col_back_f32( const ggml_tensor * src1 = dst->src[1]; // convolution kernel GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT(src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); GGML_ASSERT( dst->type == GGML_TYPE_F32); GGML_TENSOR_BINARY_OP_LOCALS; @@ -11034,6 +11097,24 @@ static void ggml_compute_forward_turbo_wht_f32( } } +// ggml_compute_forward_dsv4_hc_comb + +static void ggml_dsv4_hc_comb_norm_cols(float * comb, float eps) { + constexpr int64_t hc = 4; + + for (int64_t idst = 0; idst < hc; ++idst) { + float sum = eps; + for (int64_t isrc = 0; isrc < hc; ++isrc) { + sum += comb[idst + hc*isrc]; + } + + const float inv_sum = 1.0f / sum; + for (int64_t isrc = 0; isrc < hc; ++isrc) { + comb[idst + hc*isrc] *= inv_sum; + } + } +} + void ggml_compute_forward_turbo_wht( const ggml_compute_params * params, ggml_tensor * dst) { @@ -11043,6 +11124,272 @@ void ggml_compute_forward_turbo_wht( } } +static void ggml_dsv4_hc_comb_norm_rows(float * comb, float eps) { + constexpr int64_t hc = 4; + + for (int64_t isrc = 0; isrc < hc; ++isrc) { + float sum = eps; + for (int64_t idst = 0; idst < hc; ++idst) { + sum += comb[idst + hc*isrc]; + } + + const float inv_sum = 1.0f / sum; + for (int64_t idst = 0; idst < hc; ++idst) { + comb[idst + hc*isrc] *= inv_sum; + } + } +} + +static void ggml_compute_forward_dsv4_hc_comb_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + const ggml_tensor * mixes = dst->src[0]; + const ggml_tensor * scale = dst->src[1]; + const ggml_tensor * base = dst->src[2]; + + GGML_ASSERT(mixes->type == GGML_TYPE_F32); + GGML_ASSERT(scale->type == GGML_TYPE_F32); + GGML_ASSERT(base->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + constexpr int64_t hc = 4; + constexpr int64_t comb_offset = 2*hc; + constexpr int64_t hc_mix_dim = (2 + hc)*hc; + + const int64_t n_tokens = mixes->ne[1]; + + GGML_ASSERT(mixes->ne[0] == hc_mix_dim); + GGML_ASSERT(dst->ne[0] == hc); + GGML_ASSERT(dst->ne[1] == hc); + GGML_ASSERT(dst->ne[2] == n_tokens); + GGML_ASSERT(scale->ne[0] >= 3); + GGML_ASSERT(base->ne[0] == hc_mix_dim); + + GGML_TENSOR_LOCALS(size_t, nbm, mixes, nb); + GGML_TENSOR_LOCALS(size_t, nbs, scale, nb); + GGML_TENSOR_LOCALS(size_t, nbb, base, nb); + GGML_TENSOR_LOCALS(size_t, nbd, dst, nb); + + const float eps = ggml_get_op_params_f32(dst, 0); + const int32_t n_iter = ggml_get_op_params_i32(dst, 1); + GGML_ASSERT(n_iter > 0); + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t dr = (n_tokens + nth - 1) / nth; + const int64_t it0 = dr * ith; + const int64_t it1 = MIN(it0 + dr, n_tokens); + + const float scale_comb = *(const float *) ((const char *) scale->data + 2*nbs0); + + for (int64_t it = it0; it < it1; ++it) { + float comb[hc*hc]; + + for (int64_t isrc = 0; isrc < hc; ++isrc) { + float max = -INFINITY; + for (int64_t idst = 0; idst < hc; ++idst) { + const int64_t idx = idst + hc*isrc; + const float xv = *(const float *) ((const char *) mixes->data + (comb_offset + idx)*nbm0 + it*nbm1); + const float bv = *(const float *) ((const char *) base->data + (comb_offset + idx)*nbb0); + const float v = xv * scale_comb + bv; + comb[idx] = v; + max = MAX(max, v); + } + + float sum = 0.0f; + for (int64_t idst = 0; idst < hc; ++idst) { + const int64_t idx = idst + hc*isrc; + const float v = expf(comb[idx] - max); + comb[idx] = v; + sum += v; + } + + const float inv_sum = 1.0f / sum; + for (int64_t idst = 0; idst < hc; ++idst) { + const int64_t idx = idst + hc*isrc; + comb[idx] = comb[idx] * inv_sum + eps; + } + } + + ggml_dsv4_hc_comb_norm_cols(comb, eps); + for (int32_t i = 1; i < n_iter; ++i) { + ggml_dsv4_hc_comb_norm_rows(comb, eps); + ggml_dsv4_hc_comb_norm_cols(comb, eps); + } + + for (int64_t isrc = 0; isrc < hc; ++isrc) { + for (int64_t idst = 0; idst < hc; ++idst) { + const int64_t idx = idst + hc*isrc; + *(float *) ((char *) dst->data + idst*nbd0 + isrc*nbd1 + it*nbd2) = comb[idx]; + } + } + } +} + +void ggml_compute_forward_dsv4_hc_comb( + const ggml_compute_params * params, + ggml_tensor * dst) { + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_dsv4_hc_comb_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_dsv4_hc_pre + +static void ggml_compute_forward_dsv4_hc_pre_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + const ggml_tensor * x = dst->src[0]; + const ggml_tensor * weights = dst->src[1]; + + GGML_ASSERT(x->type == GGML_TYPE_F32); + GGML_ASSERT(weights->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + const int64_t n_embd = x->ne[0]; + const int64_t hc = x->ne[1]; + const int64_t n_tokens = x->ne[2]; + + GGML_ASSERT(dst->ne[0] == n_embd); + GGML_ASSERT(dst->ne[1] == n_tokens); + GGML_ASSERT(weights->ne[0] == hc); + GGML_ASSERT(weights->ne[1] == n_tokens); + + GGML_TENSOR_LOCALS(size_t, nbx, x, nb); + GGML_TENSOR_LOCALS(size_t, nbw, weights, nb); + GGML_TENSOR_LOCALS(size_t, nbd, dst, nb); + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t nr = n_embd * n_tokens; + const int64_t dr = (nr + nth - 1) / nth; + const int64_t ir0 = dr * ith; + const int64_t ir1 = MIN(ir0 + dr, nr); + + for (int64_t ir = ir0; ir < ir1; ++ir) { + const int64_t i0 = ir % n_embd; + const int64_t it = ir / n_embd; + + float sum = 0.0f; + for (int64_t ih = 0; ih < hc; ++ih) { + const float xv = *(const float *) ((const char *) x->data + i0*nbx0 + ih*nbx1 + it*nbx2); + const float wv = *(const float *) ((const char *) weights->data + ih*nbw0 + it*nbw1); + sum += xv * wv; + } + + *(float *) ((char *) dst->data + i0*nbd0 + it*nbd1) = sum; + } +} + +void ggml_compute_forward_dsv4_hc_pre( + const ggml_compute_params * params, + ggml_tensor * dst) { + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_dsv4_hc_pre_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_dsv4_hc_post + +static void ggml_compute_forward_dsv4_hc_post_f32( + const ggml_compute_params * params, + ggml_tensor * dst) { + const ggml_tensor * x = dst->src[0]; + const ggml_tensor * residual = dst->src[1]; + const ggml_tensor * post = dst->src[2]; + const ggml_tensor * comb = dst->src[3]; + + GGML_ASSERT(x->type == GGML_TYPE_F32); + GGML_ASSERT(residual->type == GGML_TYPE_F32); + GGML_ASSERT(post->type == GGML_TYPE_F32); + GGML_ASSERT(comb->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + const int64_t n_embd = x->ne[0]; + const int64_t n_tokens = x->ne[1]; + const int64_t hc = residual->ne[1]; + + GGML_ASSERT(dst->ne[0] == n_embd); + GGML_ASSERT(dst->ne[1] == hc); + GGML_ASSERT(dst->ne[2] == n_tokens); + GGML_ASSERT(residual->ne[0] == n_embd); + GGML_ASSERT(residual->ne[2] == n_tokens); + GGML_ASSERT(post->ne[0] == hc); + GGML_ASSERT(post->ne[1] == n_tokens); + GGML_ASSERT(comb->ne[0] == hc); + GGML_ASSERT(comb->ne[1] == hc); + GGML_ASSERT(comb->ne[2] == n_tokens); + + GGML_TENSOR_LOCALS(size_t, nbx, x, nb); + GGML_TENSOR_LOCALS(size_t, nbr, residual, nb); + GGML_TENSOR_LOCALS(size_t, nbp, post, nb); + GGML_TENSOR_LOCALS(size_t, nbc, comb, nb); + GGML_TENSOR_LOCALS(size_t, nbd, dst, nb); + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t nr = n_embd * hc * n_tokens; + const int64_t dr = (nr + nth - 1) / nth; + const int64_t ir0 = dr * ith; + const int64_t ir1 = MIN(ir0 + dr, nr); + + for (int64_t ir = ir0; ir < ir1; ++ir) { + const int64_t i0 = ir % n_embd; + const int64_t idst = (ir / n_embd) % hc; + const int64_t it = ir / (n_embd * hc); + + const float xv = *(const float *) ((const char *) x->data + i0*nbx0 + it*nbx1); + const float pv = *(const float *) ((const char *) post->data + idst*nbp0 + it*nbp1); + + float sum = xv * pv; + for (int64_t isrc = 0; isrc < hc; ++isrc) { + const float rv = *(const float *) ((const char *) residual->data + i0*nbr0 + isrc*nbr1 + it*nbr2); + const float cv = *(const float *) ((const char *) comb->data + idst*nbc0 + isrc*nbc1 + it*nbc2); + sum += rv * cv; + } + + *(float *) ((char *) dst->data + i0*nbd0 + idst*nbd1 + it*nbd2) = sum; + } +} + +void ggml_compute_forward_dsv4_hc_post( + const ggml_compute_params * params, + ggml_tensor * dst) { + const ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_dsv4_hc_post_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + // ggml_compute_forward_rwkv_wkv7 static void ggml_compute_forward_rwkv_wkv7_f32( diff --git a/ggml/src/ggml-cpu/ops.h b/ggml/src/ggml-cpu/ops.h index 32092ff15f79..4f4a523af627 100644 --- a/ggml/src/ggml-cpu/ops.h +++ b/ggml/src/ggml-cpu/ops.h @@ -107,6 +107,9 @@ void ggml_compute_forward_gla(const struct ggml_compute_params * params, struct void ggml_compute_forward_gated_delta_net(const struct ggml_compute_params * params, struct ggml_tensor * dst); void ggml_compute_forward_turbo_wht(const struct ggml_compute_params * params, struct ggml_tensor * dst); void ggml_compute_forward_lightning_indexer(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_dsv4_hc_comb(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_dsv4_hc_pre(const struct ggml_compute_params * params, struct ggml_tensor * dst); +void ggml_compute_forward_dsv4_hc_post(const struct ggml_compute_params * params, struct ggml_tensor * dst); void ggml_compute_forward_map_custom1(const struct ggml_compute_params * params, struct ggml_tensor * dst); void ggml_compute_forward_map_custom2(const struct ggml_compute_params * params, struct ggml_tensor * dst); void ggml_compute_forward_map_custom3(const struct ggml_compute_params * params, struct ggml_tensor * dst); diff --git a/ggml/src/ggml-cpu/repack.cpp b/ggml/src/ggml-cpu/repack.cpp index f18758f16bb6..9689ca3ced8f 100644 --- a/ggml/src/ggml-cpu/repack.cpp +++ b/ggml/src/ggml-cpu/repack.cpp @@ -2739,7 +2739,7 @@ static block_q8_0x4 make_block_q8_0x4(block_q8_0 * in, unsigned int blck_size_in return out; } -static block_q4_0x4 make_block_q4_0x4(block_q4_0 * in, unsigned int blck_size_interleave) { +static block_q4_0x4 make_block_q4_0x4(block_q4_0 * in, int blck_size_interleave) { block_q4_0x4 out; for (int i = 0; i < 4; i++) { diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh index 9a3ef07c7110..a2c2bd34a11a 100644 --- a/ggml/src/ggml-cuda/common.cuh +++ b/ggml/src/ggml-cuda/common.cuh @@ -362,6 +362,15 @@ static bool blackwell_mma_available(const int cc) { ggml_cuda_highest_compiled_arch(cc) < GGML_CUDA_CC_RUBIN; } +// Checks whether the tensor's base data pointer and higher-dimensional strides are byte-aligned to `alignment` bytes. +static bool ggml_cuda_is_aligned(const ggml_tensor * tensor, const size_t alignment) { + GGML_ASSERT(tensor != nullptr); + return (reinterpret_cast(tensor->data) % alignment) == 0 && + tensor->nb[1] % alignment == 0 && + tensor->nb[2] % alignment == 0 && + tensor->nb[3] % alignment == 0; +} + static constexpr __device__ int ggml_cuda_get_physical_warp_size() { #if defined(GGML_USE_HIP) && (defined(__GFX9__) || defined(__GFX8__)) return 64; @@ -618,7 +627,8 @@ template struct block_reduce_policy { }; template -static __device__ T block_reduce(T val, T * shared_vals) { +static __device__ T block_reduce(T val, [[maybe_unused]] T * shared_vals) { + // for multi-warp reductions, callers must not reuse shared_vals until all reads from this invocation have completed val = block_reduce_policy::reduce(val); const unsigned int block_size = block_size_template == 0 ? blockDim.x : block_size_template; if (block_size > WARP_SIZE) { @@ -937,6 +947,9 @@ static __device__ __forceinline__ uint2 fast_div_modulo(uint32_t n, const uint3 typedef void (*dequantize_kernel_t)(const void * vx, const int64_t ib, const int iqs, float2 & v); +template +using dequantize_kq_t = void (*)(const void * vx, const int64_t ib, dst_t * y, const int tid); + static __device__ __forceinline__ float get_alibi_slope( const float max_bias, const uint32_t h, const uint32_t n_head_log2, const float m0, const float m1 ) { @@ -965,6 +978,13 @@ struct ggml_cuda_type_traits { static constexpr int qi = QI1_0; }; +template<> +struct ggml_cuda_type_traits { + static constexpr int qk = QK2_0; + static constexpr int qr = QR2_0; + static constexpr int qi = QI2_0; +}; + template<> struct ggml_cuda_type_traits { static constexpr int qk = QK4_0; @@ -1115,7 +1135,8 @@ struct ggml_cuda_type_traits { ////////////////////// struct ggml_cuda_device_info { - int device_count; + int device_count; // number of (possibly virtual) devices exposed to the rest of ggml + int physical_device_count; // number of physical CUDA devices actually present struct cuda_device_info { int cc; // compute capability @@ -1128,6 +1149,9 @@ struct ggml_cuda_device_info { size_t total_vram; int warp_size; // Number of threads in a dispatch bool supports_cooperative_launch; // whether cooperative launch is supported + int physical_device; // backing physical CUDA device for this (virtual) device + int physical_share_count; // number of (virtual) devices sharing this device's physical GPU + int virtual_index; // index of this (virtual) device among those sharing its physical GPU }; cuda_device_info devices[GGML_CUDA_MAX_DEVICES] = {}; diff --git a/ggml/src/ggml-cuda/concat.cu b/ggml/src/ggml-cuda/concat.cu index 276ee64e8c0a..6df89013ca79 100644 --- a/ggml/src/ggml-cuda/concat.cu +++ b/ggml/src/ggml-cuda/concat.cu @@ -141,27 +141,25 @@ static __global__ void __launch_bounds__(CUDA_CONCAT_BLOCK_SIZE) template static void concat_cuda(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, int dim, cudaStream_t stream) { - if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) { + if (dim != 3 && ggml_is_contiguous_to_3(src0) && ggml_is_contiguous_to_3(src1)) { const T * src0_d = (const T *) src0->data; const T * src1_d = (const T *) src1->data; T * dst_d = (T *) dst->data; - if (dim != 3) { - for (int64_t i3 = 0; i3 < dst->ne[3]; i3++) { - concat_cont_cuda( - src0_d + i3*(src0->nb[3] / sizeof(T)), - src1_d + i3*(src1->nb[3] / sizeof(T)), - dst_d + i3*( dst->nb[3] / sizeof(T)), - ggml_row_size(src0->type, src0->ne[0])/sizeof(T), src0->ne[1], src0->ne[2], - ggml_row_size(dst->type, dst->ne[0])/sizeof(T), dst->ne[1], dst->ne[2], dim, stream); - } - } else { - const size_t size0 = ggml_nbytes(src0); - const size_t size1 = ggml_nbytes(src1); - - CUDA_CHECK(cudaMemcpyAsync((char *) dst->data, src0->data, size0, cudaMemcpyDeviceToDevice, stream)); - CUDA_CHECK(cudaMemcpyAsync((char *) dst->data + size0, src1->data, size1, cudaMemcpyDeviceToDevice, stream)); + for (int64_t i3 = 0; i3 < dst->ne[3]; i3++) { + concat_cont_cuda( + src0_d + i3*(src0->nb[3] / sizeof(T)), + src1_d + i3*(src1->nb[3] / sizeof(T)), + dst_d + i3*( dst->nb[3] / sizeof(T)), + ggml_row_size(src0->type, src0->ne[0])/sizeof(T), src0->ne[1], src0->ne[2], + ggml_row_size(dst->type, dst->ne[0])/sizeof(T), dst->ne[1], dst->ne[2], dim, stream); } + } else if (dim == 3 && ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) { + const size_t size0 = ggml_nbytes(src0); + const size_t size1 = ggml_nbytes(src1); + + CUDA_CHECK(cudaMemcpyAsync((char *) dst->data, src0->data, size0, cudaMemcpyDeviceToDevice, stream)); + CUDA_CHECK(cudaMemcpyAsync((char *) dst->data + size0, src1->data, size1, cudaMemcpyDeviceToDevice, stream)); } else { GGML_ASSERT(!ggml_is_quantized(src0->type)); @@ -208,12 +206,17 @@ void ggml_cuda_op_concat(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { GGML_ASSERT(dst->type == src0->type); if (ggml_is_quantized(src0->type)) { - GGML_ASSERT(ggml_is_contiguous(src0)); - GGML_ASSERT(ggml_is_contiguous(src1)); + if (dim == 3) { + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(ggml_is_contiguous(src1)); + } else { + GGML_ASSERT(ggml_is_contiguous_to_3(src0)); + GGML_ASSERT(ggml_is_contiguous_to_3(src1)); + } GGML_ASSERT(src0->ne[0] % ggml_blck_size(src0->type) == 0); GGML_ASSERT(src1->ne[0] % ggml_blck_size(src1->type) == 0); - // if tensors are contiguous and ne[0] is multiple of the block size we can concat both tensors as byte tensors + // if first 3 dimensions are contiguous and ne[0] is multiple of the block size we can concat both tensors as byte tensors concat_cuda(src0, src1, dst, dim, stream); } else { GGML_ASSERT(ggml_blck_size(src0->type) == 1); diff --git a/ggml/src/ggml-cuda/conv2d.cu b/ggml/src/ggml-cuda/conv2d.cu index 142dd66903aa..14774d4a5e73 100644 --- a/ggml/src/ggml-cuda/conv2d.cu +++ b/ggml/src/ggml-cuda/conv2d.cu @@ -126,6 +126,7 @@ void ggml_cuda_op_conv2d(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const float * X_D = (const float *) input->data; float * Y_D = (float *) dst->data; + GGML_ASSERT(ggml_is_contiguous(input)); GGML_ASSERT(ggml_is_contiguous(kernel)); GGML_ASSERT(kernel->type == GGML_TYPE_F16 || kernel->type == GGML_TYPE_F32); diff --git a/ggml/src/ggml-cuda/convert.cu b/ggml/src/ggml-cuda/convert.cu index 2a3542b0fa3d..9c1b861c37a5 100644 --- a/ggml/src/ggml-cuda/convert.cu +++ b/ggml/src/ggml-cuda/convert.cu @@ -141,358 +141,107 @@ static __global__ void dequantize_block_q4_1(const void * __restrict__ vx, dst_t template static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, dst_t * __restrict__ yy) { + const int64_t i = blockIdx.x; - const int64_t i = blockIdx.x; - const block_q2_K * x = (const block_q2_K *) vx; - - const int64_t tid = threadIdx.x; - const int64_t n = tid/32; - const int64_t l = tid - 32*n; - const int64_t is = 8*n + l/16; - - const uint8_t q = x[i].qs[32*n + l]; - dst_t * y = yy + i*QK_K + 128*n; - - float dall = __low2half(x[i].dm); - float dmin = __high2half(x[i].dm); - y[l+ 0] = ggml_cuda_cast(dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4)); - y[l+32] = ggml_cuda_cast(dall * (x[i].scales[is+2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is+2] >> 4)); - y[l+64] = ggml_cuda_cast(dall * (x[i].scales[is+4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+4] >> 4)); - y[l+96] = ggml_cuda_cast(dall * (x[i].scales[is+6] & 0xF) * ((q >> 6) & 3) - dmin * (x[i].scales[is+6] >> 4)); + dequantize_q2_K(vx, i, yy + i*QK_K, threadIdx.x); } template static __global__ void dequantize_block_q3_K(const void * __restrict__ vx, dst_t * __restrict__ yy) { - const int64_t i = blockIdx.x; - const block_q3_K * x = (const block_q3_K *) vx; - - const int64_t r = threadIdx.x/4; - const int64_t tid = r/2; - const int64_t is0 = r%2; - const int64_t l0 = 16*is0 + 4*(threadIdx.x%4); - const int64_t n = tid / 4; - const int64_t j = tid - 4*n; - - uint8_t m = 1 << (4*n + j); - int64_t is = 8*n + 2*j + is0; - int shift = 2*j; - - int8_t us = is < 4 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+8] >> 0) & 3) << 4) : - is < 8 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+4] >> 2) & 3) << 4) : - is < 12 ? (x[i].scales[is-8] >> 4) | (((x[i].scales[is+0] >> 4) & 3) << 4) : - (x[i].scales[is-8] >> 4) | (((x[i].scales[is-4] >> 6) & 3) << 4); - float d_all = x[i].d; - float dl = d_all * (us - 32); - - dst_t * y = yy + i*QK_K + 128*n + 32*j; - const uint8_t * q = x[i].qs + 32*n; - const uint8_t * hm = x[i].hmask; - - for (int l = l0; l < l0+4; ++l) { - y[l] = ggml_cuda_cast(dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4))); - } -} -static inline __device__ void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) { - if (j < 4) { - d = q[j] & 63; m = q[j + 4] & 63; - } else { - d = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4); - m = (q[j+4] >> 4) | ((q[j-0] >> 6) << 4); - } + dequantize_q3_K(vx, i, yy + i*QK_K, threadIdx.x); } template static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restrict__ yy) { - const block_q4_K * x = (const block_q4_K *) vx; - const int64_t i = blockIdx.x; - // assume 32 threads - const int64_t tid = threadIdx.x; - const int64_t il = tid/8; - const int64_t ir = tid%8; - const int64_t is = 2*il; - const int64_t n = 4; - - dst_t * y = yy + i*QK_K + 64*il + n*ir; - - const float dall = __low2half(x[i].dm); - const float dmin = __high2half(x[i].dm); - - const uint8_t * q = x[i].qs + 32*il + n*ir; - - uint8_t sc, m; - get_scale_min_k4(is + 0, x[i].scales, sc, m); - const float d1 = dall * sc; const float m1 = dmin * m; - get_scale_min_k4(is + 1, x[i].scales, sc, m); - const float d2 = dall * sc; const float m2 = dmin * m; - for (int l = 0; l < n; ++l) { - y[l + 0] = ggml_cuda_cast(d1 * (q[l] & 0xF) - m1); - y[l +32] = ggml_cuda_cast(d2 * (q[l] >> 4) - m2); - } + dequantize_q4_K(vx, i, yy + i*QK_K, threadIdx.x); } template static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, dst_t * __restrict__ yy) { - const block_q5_K * x = (const block_q5_K *) vx; - const int64_t i = blockIdx.x; - // assume 64 threads - this is very slightly better than the one below - const int64_t tid = threadIdx.x; - const int64_t il = tid/16; // il is in 0...3 - const int64_t ir = tid%16; // ir is in 0...15 - const int64_t is = 2*il; // is is in 0...6 - - dst_t * y = yy + i*QK_K + 64*il + 2*ir; - - const float dall = __low2half(x[i].dm); - const float dmin = __high2half(x[i].dm); - - const uint8_t * ql = x[i].qs + 32*il + 2*ir; - const uint8_t * qh = x[i].qh + 2*ir; - - uint8_t sc, m; - get_scale_min_k4(is + 0, x[i].scales, sc, m); - const float d1 = dall * sc; const float m1 = dmin * m; - get_scale_min_k4(is + 1, x[i].scales, sc, m); - const float d2 = dall * sc; const float m2 = dmin * m; - - uint8_t hm = 1 << (2*il); - y[ 0] = ggml_cuda_cast(d1 * ((ql[ 0] & 0xF) + (qh[ 0] & hm ? 16 : 0)) - m1); - y[ 1] = ggml_cuda_cast(d1 * ((ql[ 1] & 0xF) + (qh[ 1] & hm ? 16 : 0)) - m1); - hm <<= 1; - y[32] = ggml_cuda_cast(d2 * ((ql[ 0] >> 4) + (qh[ 0] & hm ? 16 : 0)) - m2); - y[33] = ggml_cuda_cast(d2 * ((ql[ 1] >> 4) + (qh[ 1] & hm ? 16 : 0)) - m2); + dequantize_q5_K(vx, i, yy + i*QK_K, threadIdx.x); } template static __global__ void dequantize_block_q6_K(const void * __restrict__ vx, dst_t * __restrict__ yy) { - const block_q6_K * x = (const block_q6_K *) vx; - const int64_t i = blockIdx.x; - // assume 64 threads - this is very slightly better than the one below - const int64_t tid = threadIdx.x; - const int64_t ip = tid/32; // ip is 0 or 1 - const int64_t il = tid - 32*ip; // 0...32 - const int64_t is = 8*ip + il/16; - - dst_t * y = yy + i*QK_K + 128*ip + il; - - const float d = x[i].d; - - const uint8_t * ql = x[i].ql + 64*ip + il; - const uint8_t qh = x[i].qh[32*ip + il]; - const int8_t * sc = x[i].scales + is; - - y[ 0] = ggml_cuda_cast(d * sc[0] * ((int8_t)((ql[ 0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32)); - y[32] = ggml_cuda_cast(d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32)); - y[64] = ggml_cuda_cast(d * sc[4] * ((int8_t)((ql[ 0] >> 4) | (((qh >> 4) & 3) << 4)) - 32)); - y[96] = ggml_cuda_cast(d * sc[6] * ((int8_t)((ql[32] >> 4) | (((qh >> 6) & 3) << 4)) - 32)); + dequantize_q6_K(vx, i, yy + i*QK_K, threadIdx.x); } template static __global__ void dequantize_block_iq2_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy) { + const int64_t i = blockIdx.x; - const int64_t i = blockIdx.x; - const block_iq2_xxs * x = (const block_iq2_xxs *) vx; - - const int64_t tid = threadIdx.x; - const int64_t il = tid/8; // 0...3 - const int64_t ib = tid%8; // 0...7 - dst_t * y = yy + i*QK_K + 32*ib + 8*il; - const uint16_t * q2 = x[i].qs + 4*ib; - const uint8_t * aux8 = (const uint8_t *)q2; - const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[il]); - const uint32_t aux32 = q2[2] | (q2[3] << 16); - const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.25f; - const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127]; - for (int j = 0; j < 8; ++j) { - y[j] = ggml_cuda_cast(d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f)); - } + dequantize_iq2_xxs(vx, i, yy + i*QK_K, threadIdx.x); } template static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) { + const int64_t i = blockIdx.x; - const int64_t i = blockIdx.x; - const block_iq2_xs * x = (const block_iq2_xs *) vx; - - const int64_t tid = threadIdx.x; - const int64_t il = tid/8; // 0...3 - const int64_t ib = tid%8; // 0...7 - dst_t * y = yy + i*QK_K + 32*ib + 8*il; - const uint16_t * q2 = x[i].qs + 4*ib; - const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[il] & 511)); - const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f; - const uint8_t signs = ksigns_iq2xs[q2[il] >> 9]; - for (int j = 0; j < 8; ++j) { - y[j] = ggml_cuda_cast(d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f)); - } + dequantize_iq2_xs(vx, i, yy + i*QK_K, threadIdx.x); } template static __global__ void dequantize_block_iq2_s(const void * __restrict__ vx, dst_t * __restrict__ yy) { + const int64_t i = blockIdx.x; - const int64_t i = blockIdx.x; - const block_iq2_s * x = (const block_iq2_s *) vx; - - const int64_t tid = threadIdx.x; - const int64_t il = tid/8; // 0...3 - const int64_t ib = tid%8; // 0...7 - dst_t * y = yy + i*QK_K + 32*ib + 8*il; - const uint8_t * grid = (const uint8_t *)(iq2s_grid + (x[i].qs[4*ib+il] | ((x[i].qh[ib] << (8-2*il)) & 0x300))); - const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f; - const uint8_t signs = x[i].qs[QK_K/8+4*ib+il]; - for (int j = 0; j < 8; ++j) { - y[j] = ggml_cuda_cast(d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f)); - } + dequantize_iq2_s(vx, i, yy + i*QK_K, threadIdx.x); } template static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy) { + const int64_t i = blockIdx.x; - const int64_t i = blockIdx.x; - const block_iq3_xxs * x = (const block_iq3_xxs *) vx; - - const int64_t tid = threadIdx.x; - const int64_t il = tid/8; // 0...3 - const int64_t ib = tid%8; // 0...7 - dst_t * y = yy + i*QK_K + 32*ib + 8*il; - const uint8_t * q3 = x[i].qs + 8*ib; - const uint16_t * gas = (const uint16_t *)(x[i].qs + QK_K/4) + 2*ib; - const uint8_t * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*il+0]); - const uint8_t * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*il+1]); - const uint32_t aux32 = gas[0] | (gas[1] << 16); - const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.5f; - const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127]; - for (int j = 0; j < 4; ++j) { - y[j+0] = ggml_cuda_cast(d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f)); - y[j+4] = ggml_cuda_cast(d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f)); - } + dequantize_iq3_xxs(vx, i, yy + i*QK_K, threadIdx.x); } template static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_t * __restrict__ yy) { + const int64_t i = blockIdx.x; - const int64_t i = blockIdx.x; - const block_iq3_s * x = (const block_iq3_s *) vx; - - const int64_t tid = threadIdx.x; - const int64_t il = tid/8; // 0...3 - const int64_t ib = tid%8; // 0...7 - dst_t * y = yy + i*QK_K + 32*ib + 8*il; - const uint8_t * qs = x[i].qs + 8*ib; - const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*il+0] | ((x[i].qh[ib] << (8-2*il)) & 256))); - const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*il+1] | ((x[i].qh[ib] << (7-2*il)) & 256))); - const float d = (float)x[i].d * (1 + 2*((x[i].scales[ib/2] >> 4*(ib%2)) & 0xf)); - const uint8_t signs = x[i].signs[4*ib + il]; - for (int j = 0; j < 4; ++j) { - y[j+0] = ggml_cuda_cast(d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f)); - y[j+4] = ggml_cuda_cast(d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f)); - } + dequantize_iq3_s(vx, i, yy + i*QK_K, threadIdx.x); } template static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_t * __restrict__ yy) { + const int64_t i = blockIdx.x; - const int64_t i = blockIdx.x; - const block_iq1_s * x = (const block_iq1_s *) vx; - - const int64_t tid = threadIdx.x; - const int64_t il = tid/8; // 0...3 - const int64_t ib = tid%8; // 0...7 - dst_t * y = yy + i*QK_K + 32*ib + 8*il; - const float delta = x[i].qh[ib] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA; - const float d = (float)x[i].d * (2*((x[i].qh[ib] >> 12) & 7) + 1); - uint32_t grid32[2]; const int8_t * q = (const int8_t *)grid32; - grid32[0] = iq1s_grid_gpu[x[i].qs[4*ib+il] | (((x[i].qh[ib] >> 3*il) & 7) << 8)]; - grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f; - grid32[0] &= 0x0f0f0f0f; - for (int j = 0; j < 8; ++j) { - y[j] = ggml_cuda_cast(d * (q[j] + delta)); - } + dequantize_iq1_s(vx, i, yy + i*QK_K, threadIdx.x); } template static __global__ void dequantize_block_iq1_m(const void * __restrict__ vx, dst_t * __restrict__ yy) { + const int64_t i = blockIdx.x; - const int64_t i = blockIdx.x; - const block_iq1_m * x = (const block_iq1_m *) vx; - - const int64_t tid = threadIdx.x; - const int64_t il = tid/8; // 0...3 - const int64_t ib = tid%8; // 0...7 - dst_t * y = yy + i*QK_K + 32*ib + 8*il; - const uint16_t * sc = (const uint16_t *)x[i].scales; - iq1m_scale_t scale; - scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); - const int64_t ib16 = 2*ib + il/2; // sc[ib16/4] >> 3*(ib16%4) -> sc[ib/2] >> 3*((2*ib+il/2)%4); - const float d = (float)scale.f16 * (2*((sc[ib16/4] >> 3*(ib16%4)) & 0x7) + 1); - const float delta = x[i].qh[2*ib+il/2] & (0x08 << 4*(il%2)) ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA; - uint32_t grid32[2]; const int8_t * q = (const int8_t *)grid32; - grid32[0] = iq1s_grid_gpu[x[i].qs[4*ib+il] | (((x[i].qh[2*ib+il/2] >> 4*(il%2)) & 7) << 8)]; - grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f; - grid32[0] &= 0x0f0f0f0f; - for (int j = 0; j < 8; ++j) { - y[j] = ggml_cuda_cast(d * (q[j] + delta)); - } + dequantize_iq1_m(vx, i, yy + i*QK_K, threadIdx.x); } template static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst_t * __restrict__ yy) { + const int64_t i = blockIdx.x; - const int64_t i = blockIdx.x; - const block_iq4_nl * x = (const block_iq4_nl *) vx + i*(QK_K/QK4_NL); - - const int64_t tid = threadIdx.x; - const int64_t il = tid/8; // 0...3 - const int64_t ib = tid%8; // 0...7 - dst_t * y = yy + i*QK_K + 32*ib + 4*il; - const uint8_t * q4 = x[ib].qs + 4*il; - const float d = (float)x[ib].d; - for (int j = 0; j < 4; ++j) { - y[j+ 0] = ggml_cuda_cast(d * kvalues_iq4nl[q4[j] & 0xf]); - y[j+16] = ggml_cuda_cast(d * kvalues_iq4nl[q4[j] >> 4]); - } + dequantize_iq4_nl(vx, i, yy + i*QK_K, threadIdx.x); } template static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) { - const int64_t i = blockIdx.x; - const block_iq4_xs * x = (const block_iq4_xs *)vx; + const int64_t i = blockIdx.x; - const int64_t tid = threadIdx.x; - const int64_t il = tid/8; // 0...3 - const int64_t ib = tid%8; // 0...7 - dst_t * y = yy + i*QK_K + 32*ib + 4*il; - const uint8_t * q4 = x[i].qs + 16*ib + 4*il; - const float d = (float)x[i].d * ((((x[i].scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((x[i].scales_h >> 2*ib) & 3) << 4)) - 32); - for (int j = 0; j < 4; ++j) { - y[j+ 0] = ggml_cuda_cast(d * kvalues_iq4nl[q4[j] & 0xf]); - y[j+16] = ggml_cuda_cast(d * kvalues_iq4nl[q4[j] >> 4]); - } + dequantize_iq4_xs(vx, i, yy + i*QK_K, threadIdx.x); } template static __global__ void dequantize_block_mxfp4(const void * __restrict__ vx, dst_t * __restrict__ yy) { + const int64_t i = blockIdx.x; - const int64_t i = blockIdx.x; - const block_mxfp4 * x = (const block_mxfp4 *) vx + i*(QK_K/QK_MXFP4); - - const int64_t tid = threadIdx.x; - const int64_t il = tid/8; // 0...3 - const int64_t ib = tid%8; // 0...7 - dst_t * y = yy + i*QK_K + 32*ib + 4*il; - const uint8_t * q4 = x[ib].qs + 4*il; - const float d = ggml_cuda_e8m0_to_fp32(x[ib].e); - for (int j = 0; j < 4; ++j) { - y[j+ 0] = ggml_cuda_cast(d * kvalues_mxfp4[q4[j] & 0xf]*0.5f); - y[j+16] = ggml_cuda_cast(d * kvalues_mxfp4[q4[j] >> 4]*0.5f); - } + dequantize_mxfp4(vx, i, yy + i*QK_K, threadIdx.x); } template @@ -755,6 +504,8 @@ to_bf16_cuda_t ggml_get_to_bf16_cuda(ggml_type type) { switch (type) { case GGML_TYPE_Q1_0: return dequantize_block_cont_cuda; + case GGML_TYPE_Q2_0: + return dequantize_block_cont_cuda; case GGML_TYPE_Q4_0: return dequantize_row_q4_0_cuda; case GGML_TYPE_Q4_1: @@ -810,6 +561,8 @@ to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) { switch (type) { case GGML_TYPE_Q1_0: return dequantize_block_cont_cuda; + case GGML_TYPE_Q2_0: + return dequantize_block_cont_cuda; case GGML_TYPE_Q4_0: return dequantize_row_q4_0_cuda; case GGML_TYPE_Q4_1: @@ -878,6 +631,8 @@ to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) { switch (type) { case GGML_TYPE_Q1_0: return dequantize_block_cont_cuda; + case GGML_TYPE_Q2_0: + return dequantize_block_cont_cuda; case GGML_TYPE_Q4_0: return dequantize_row_q4_0_cuda; case GGML_TYPE_Q4_1: @@ -945,6 +700,8 @@ to_fp16_nc_cuda_t ggml_get_to_fp16_nc_cuda(ggml_type type) { return convert_unary_cuda; case GGML_TYPE_Q1_0: return dequantize_block_cuda; + case GGML_TYPE_Q2_0: + return dequantize_block_cuda; case GGML_TYPE_Q4_0: return dequantize_block_cuda; case GGML_TYPE_Q4_1: @@ -978,6 +735,8 @@ to_bf16_nc_cuda_t ggml_get_to_bf16_nc_cuda(ggml_type type) { return convert_unary_cuda; case GGML_TYPE_Q1_0: return dequantize_block_cuda; + case GGML_TYPE_Q2_0: + return dequantize_block_cuda; case GGML_TYPE_Q4_0: return dequantize_block_cuda; case GGML_TYPE_Q4_1: @@ -1001,6 +760,8 @@ to_fp32_nc_cuda_t ggml_get_to_fp32_nc_cuda(ggml_type type) { return convert_unary_cuda; case GGML_TYPE_Q1_0: return dequantize_block_cuda; + case GGML_TYPE_Q2_0: + return dequantize_block_cuda; case GGML_TYPE_Q4_0: return dequantize_block_cuda; case GGML_TYPE_Q4_1: diff --git a/ggml/src/ggml-cuda/dequantize.cuh b/ggml/src/ggml-cuda/dequantize.cuh index 2a0372e3f147..6e4f27470926 100644 --- a/ggml/src/ggml-cuda/dequantize.cuh +++ b/ggml/src/ggml-cuda/dequantize.cuh @@ -1,5 +1,6 @@ #include "common.cuh" #include "turbo-quant.cuh" +#include "convert.cuh" static __device__ __forceinline__ void dequantize_q1_0(const void * vx, const int64_t ib, const int iqs, float2 & v){ const block_q1_0 * x = (const block_q1_0 *) vx; @@ -23,6 +24,26 @@ static __device__ __forceinline__ void dequantize_q1_0(const void * vx, const in v.y = (2*bit_1 - 1) * d; } +static __device__ __forceinline__ void dequantize_q2_0(const void * vx, const int64_t ib, const int iqs, float2 & v){ + const block_q2_0 * x = (const block_q2_0 *) vx; + + const float d = x[ib].d; + + // Q2_0: 2 bits per element, 4 elements per byte. + // Stored code c in {0,1,2,3} maps to symbol s = c - 1 in {-1, 0, +1, +2}. + const int byte_index_0 = iqs / 4; + const int bit_offset_0 = (iqs % 4) * 2; + + const int byte_index_1 = (iqs + 1) / 4; + const int bit_offset_1 = ((iqs + 1) % 4) * 2; + + const int c0 = (x[ib].qs[byte_index_0] >> bit_offset_0) & 0x3; + const int c1 = (x[ib].qs[byte_index_1] >> bit_offset_1) & 0x3; + + v.x = (c0 - 1) * d; + v.y = (c1 - 1) * d; +} + static __device__ __forceinline__ void dequantize_q4_0(const void * vx, const int64_t ib, const int iqs, float2 & v){ const block_q4_0 * x = (const block_q4_0 *) vx; @@ -199,3 +220,335 @@ static __device__ __forceinline__ void dequantize_tq3_1s(const void * vx, const v.x = buf[iqs]; v.y = buf[iqs + 1]; } + +//================================== k-quants + +// Each call dequantizes one super-block of QK_K values into y using the +// thread layout of the caller: 32 threads for q4_K, 64 threads otherwise. + +template +static __device__ __forceinline__ void dequantize_q2_K(const void * vx, const int64_t ib, dst_t * yy, const int tid) { + const block_q2_K * x = (const block_q2_K *) vx; + + const int64_t n = tid/32; + const int64_t l = tid - 32*n; + const int64_t is = 8*n + l/16; + + const uint8_t q = x[ib].qs[32*n + l]; + dst_t * y = yy + 128*n; + + float dall = __low2half(x[ib].dm); + float dmin = __high2half(x[ib].dm); + y[l+ 0] = ggml_cuda_cast(dall * (x[ib].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[ib].scales[is+0] >> 4)); + y[l+32] = ggml_cuda_cast(dall * (x[ib].scales[is+2] & 0xF) * ((q >> 2) & 3) - dmin * (x[ib].scales[is+2] >> 4)); + y[l+64] = ggml_cuda_cast(dall * (x[ib].scales[is+4] & 0xF) * ((q >> 4) & 3) - dmin * (x[ib].scales[is+4] >> 4)); + y[l+96] = ggml_cuda_cast(dall * (x[ib].scales[is+6] & 0xF) * ((q >> 6) & 3) - dmin * (x[ib].scales[is+6] >> 4)); +} + +template +static __device__ __forceinline__ void dequantize_q3_K(const void * vx, const int64_t ib, dst_t * yy, const int tid) { + const block_q3_K * x = (const block_q3_K *) vx; + + const int64_t r = tid/4; + const int64_t t = r/2; + const int64_t is0 = r%2; + const int64_t l0 = 16*is0 + 4*(tid%4); + const int64_t n = t / 4; + const int64_t j = t - 4*n; + + uint8_t m = 1 << (4*n + j); + int64_t is = 8*n + 2*j + is0; + int shift = 2*j; + + int8_t us = is < 4 ? (x[ib].scales[is-0] & 0xF) | (((x[ib].scales[is+8] >> 0) & 3) << 4) : + is < 8 ? (x[ib].scales[is-0] & 0xF) | (((x[ib].scales[is+4] >> 2) & 3) << 4) : + is < 12 ? (x[ib].scales[is-8] >> 4) | (((x[ib].scales[is+0] >> 4) & 3) << 4) : + (x[ib].scales[is-8] >> 4) | (((x[ib].scales[is-4] >> 6) & 3) << 4); + float d_all = x[ib].d; + float dl = d_all * (us - 32); + + dst_t * y = yy + 128*n + 32*j; + const uint8_t * q = x[ib].qs + 32*n; + const uint8_t * hm = x[ib].hmask; + + for (int l = l0; l < l0+4; ++l) { + y[l] = ggml_cuda_cast(dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4))); + } +} + +static inline __device__ void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) { + if (j < 4) { + d = q[j] & 63; m = q[j + 4] & 63; + } else { + d = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4); + m = (q[j+4] >> 4) | ((q[j-0] >> 6) << 4); + } +} + +template +static __device__ __forceinline__ void dequantize_q4_K(const void * vx, const int64_t ib, dst_t * yy, const int tid) { + const block_q4_K * x = (const block_q4_K *) vx; + + // assume 32 threads + const int64_t il = tid/8; + const int64_t ir = tid%8; + const int64_t is = 2*il; + const int64_t n = 4; + + dst_t * y = yy + 64*il + n*ir; + + const float dall = __low2half(x[ib].dm); + const float dmin = __high2half(x[ib].dm); + + const uint8_t * q = x[ib].qs + 32*il + n*ir; + + uint8_t sc, m; + get_scale_min_k4(is + 0, x[ib].scales, sc, m); + const float d1 = dall * sc; const float m1 = dmin * m; + get_scale_min_k4(is + 1, x[ib].scales, sc, m); + const float d2 = dall * sc; const float m2 = dmin * m; + for (int l = 0; l < n; ++l) { + y[l + 0] = ggml_cuda_cast(d1 * (q[l] & 0xF) - m1); + y[l +32] = ggml_cuda_cast(d2 * (q[l] >> 4) - m2); + } +} + +template +static __device__ __forceinline__ void dequantize_q5_K(const void * vx, const int64_t ib, dst_t * yy, const int tid) { + const block_q5_K * x = (const block_q5_K *) vx; + + // assume 64 threads - this is very slightly better than the one below + const int64_t il = tid/16; // il is in 0...3 + const int64_t ir = tid%16; // ir is in 0...15 + const int64_t is = 2*il; // is is in 0...6 + + dst_t * y = yy + 64*il + 2*ir; + + const float dall = __low2half(x[ib].dm); + const float dmin = __high2half(x[ib].dm); + + const uint8_t * ql = x[ib].qs + 32*il + 2*ir; + const uint8_t * qh = x[ib].qh + 2*ir; + + uint8_t sc, m; + get_scale_min_k4(is + 0, x[ib].scales, sc, m); + const float d1 = dall * sc; const float m1 = dmin * m; + get_scale_min_k4(is + 1, x[ib].scales, sc, m); + const float d2 = dall * sc; const float m2 = dmin * m; + + uint8_t hm = 1 << (2*il); + y[ 0] = ggml_cuda_cast(d1 * ((ql[ 0] & 0xF) + (qh[ 0] & hm ? 16 : 0)) - m1); + y[ 1] = ggml_cuda_cast(d1 * ((ql[ 1] & 0xF) + (qh[ 1] & hm ? 16 : 0)) - m1); + hm <<= 1; + y[32] = ggml_cuda_cast(d2 * ((ql[ 0] >> 4) + (qh[ 0] & hm ? 16 : 0)) - m2); + y[33] = ggml_cuda_cast(d2 * ((ql[ 1] >> 4) + (qh[ 1] & hm ? 16 : 0)) - m2); +} + +template +static __device__ __forceinline__ void dequantize_q6_K(const void * vx, const int64_t ib, dst_t * yy, const int tid) { + const block_q6_K * x = (const block_q6_K *) vx; + + // assume 64 threads - this is very slightly better than the one below + const int64_t ip = tid/32; // ip is 0 or 1 + const int64_t il = tid - 32*ip; // 0...32 + const int64_t is = 8*ip + il/16; + + dst_t * y = yy + 128*ip + il; + + const float d = x[ib].d; + + const uint8_t * ql = x[ib].ql + 64*ip + il; + const uint8_t qh = x[ib].qh[32*ip + il]; + const int8_t * sc = x[ib].scales + is; + + y[ 0] = ggml_cuda_cast(d * sc[0] * ((int8_t)((ql[ 0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32)); + y[32] = ggml_cuda_cast(d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32)); + y[64] = ggml_cuda_cast(d * sc[4] * ((int8_t)((ql[ 0] >> 4) | (((qh >> 4) & 3) << 4)) - 32)); + y[96] = ggml_cuda_cast(d * sc[6] * ((int8_t)((ql[32] >> 4) | (((qh >> 6) & 3) << 4)) - 32)); +} + +//================================== i-quants + +// Each call dequantizes one super-block of QK_K values into y with 32 +// threads; iq4_nl packs QK_K/QK4_NL sub-blocks per super-block. + +template +static __device__ __forceinline__ void dequantize_iq2_xxs(const void * vx, const int64_t ibs, dst_t * yy, const int tid) { + + const block_iq2_xxs * x = (const block_iq2_xxs *) vx; + + const int64_t il = tid/8; // 0...3 + const int64_t ib = tid%8; // 0...7 + dst_t * y = yy + 32*ib + 8*il; + const uint16_t * q2 = x[ibs].qs + 4*ib; + const uint8_t * aux8 = (const uint8_t *)q2; + const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[il]); + const uint32_t aux32 = q2[2] | (q2[3] << 16); + const float d = (float)x[ibs].d * (0.5f + (aux32 >> 28)) * 0.25f; + const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127]; + for (int j = 0; j < 8; ++j) { + y[j] = ggml_cuda_cast(d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f)); + } +} + +template +static __device__ __forceinline__ void dequantize_iq2_xs(const void * vx, const int64_t ibs, dst_t * yy, const int tid) { + + const block_iq2_xs * x = (const block_iq2_xs *) vx; + + const int64_t il = tid/8; // 0...3 + const int64_t ib = tid%8; // 0...7 + dst_t * y = yy + 32*ib + 8*il; + const uint16_t * q2 = x[ibs].qs + 4*ib; + const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[il] & 511)); + const float d = (float)x[ibs].d * (0.5f + ((x[ibs].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f; + const uint8_t signs = ksigns_iq2xs[q2[il] >> 9]; + for (int j = 0; j < 8; ++j) { + y[j] = ggml_cuda_cast(d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f)); + } +} + +template +static __device__ __forceinline__ void dequantize_iq2_s(const void * vx, const int64_t ibs, dst_t * yy, const int tid) { + + const block_iq2_s * x = (const block_iq2_s *) vx; + + const int64_t il = tid/8; // 0...3 + const int64_t ib = tid%8; // 0...7 + dst_t * y = yy + 32*ib + 8*il; + const uint8_t * grid = (const uint8_t *)(iq2s_grid + (x[ibs].qs[4*ib+il] | ((x[ibs].qh[ib] << (8-2*il)) & 0x300))); + const float d = (float)x[ibs].d * (0.5f + ((x[ibs].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f; + const uint8_t signs = x[ibs].qs[QK_K/8+4*ib+il]; + for (int j = 0; j < 8; ++j) { + y[j] = ggml_cuda_cast(d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f)); + } +} + +template +static __device__ __forceinline__ void dequantize_iq3_xxs(const void * vx, const int64_t ibs, dst_t * yy, const int tid) { + + const block_iq3_xxs * x = (const block_iq3_xxs *) vx; + + const int64_t il = tid/8; // 0...3 + const int64_t ib = tid%8; // 0...7 + dst_t * y = yy + 32*ib + 8*il; + const uint8_t * q3 = x[ibs].qs + 8*ib; + const uint16_t * gas = (const uint16_t *)(x[ibs].qs + QK_K/4) + 2*ib; + const uint8_t * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*il+0]); + const uint8_t * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*il+1]); + const uint32_t aux32 = gas[0] | (gas[1] << 16); + const float d = (float)x[ibs].d * (0.5f + (aux32 >> 28)) * 0.5f; + const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127]; + for (int j = 0; j < 4; ++j) { + y[j+0] = ggml_cuda_cast(d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f)); + y[j+4] = ggml_cuda_cast(d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f)); + } +} + +template +static __device__ __forceinline__ void dequantize_iq3_s(const void * vx, const int64_t ibs, dst_t * yy, const int tid) { + + const block_iq3_s * x = (const block_iq3_s *) vx; + + const int64_t il = tid/8; // 0...3 + const int64_t ib = tid%8; // 0...7 + dst_t * y = yy + 32*ib + 8*il; + const uint8_t * qs = x[ibs].qs + 8*ib; + const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*il+0] | ((x[ibs].qh[ib] << (8-2*il)) & 256))); + const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*il+1] | ((x[ibs].qh[ib] << (7-2*il)) & 256))); + const float d = (float)x[ibs].d * (1 + 2*((x[ibs].scales[ib/2] >> 4*(ib%2)) & 0xf)); + const uint8_t signs = x[ibs].signs[4*ib + il]; + for (int j = 0; j < 4; ++j) { + y[j+0] = ggml_cuda_cast(d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f)); + y[j+4] = ggml_cuda_cast(d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f)); + } +} + +template +static __device__ __forceinline__ void dequantize_iq1_s(const void * vx, const int64_t ibs, dst_t * yy, const int tid) { + + const block_iq1_s * x = (const block_iq1_s *) vx; + + const int64_t il = tid/8; // 0...3 + const int64_t ib = tid%8; // 0...7 + dst_t * y = yy + 32*ib + 8*il; + const float delta = x[ibs].qh[ib] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA; + const float d = (float)x[ibs].d * (2*((x[ibs].qh[ib] >> 12) & 7) + 1); + uint32_t grid32[2]; const int8_t * q = (const int8_t *)grid32; + grid32[0] = iq1s_grid_gpu[x[ibs].qs[4*ib+il] | (((x[ibs].qh[ib] >> 3*il) & 7) << 8)]; + grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f; + grid32[0] &= 0x0f0f0f0f; + for (int j = 0; j < 8; ++j) { + y[j] = ggml_cuda_cast(d * (q[j] + delta)); + } +} + +template +static __device__ __forceinline__ void dequantize_iq1_m(const void * vx, const int64_t ibs, dst_t * yy, const int tid) { + + const block_iq1_m * x = (const block_iq1_m *) vx; + + const int64_t il = tid/8; // 0...3 + const int64_t ib = tid%8; // 0...7 + dst_t * y = yy + 32*ib + 8*il; + const uint16_t * sc = (const uint16_t *)x[ibs].scales; + iq1m_scale_t scale; + scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); + const int64_t ib16 = 2*ib + il/2; // sc[ib16/4] >> 3*(ib16%4) -> sc[ib/2] >> 3*((2*ib+il/2)%4); + const float d = (float)scale.f16 * (2*((sc[ib16/4] >> 3*(ib16%4)) & 0x7) + 1); + const float delta = x[ibs].qh[2*ib+il/2] & (0x08 << 4*(il%2)) ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA; + uint32_t grid32[2]; const int8_t * q = (const int8_t *)grid32; + grid32[0] = iq1s_grid_gpu[x[ibs].qs[4*ib+il] | (((x[ibs].qh[2*ib+il/2] >> 4*(il%2)) & 7) << 8)]; + grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f; + grid32[0] &= 0x0f0f0f0f; + for (int j = 0; j < 8; ++j) { + y[j] = ggml_cuda_cast(d * (q[j] + delta)); + } +} + +template +static __device__ __forceinline__ void dequantize_iq4_nl(const void * vx, const int64_t ibs, dst_t * yy, const int tid) { + + const block_iq4_nl * x = (const block_iq4_nl *) vx + ibs*(QK_K/QK4_NL); + + const int64_t il = tid/8; // 0...3 + const int64_t ib = tid%8; // 0...7 + dst_t * y = yy + 32*ib + 4*il; + const uint8_t * q4 = x[ib].qs + 4*il; + const float d = (float)x[ib].d; + for (int j = 0; j < 4; ++j) { + y[j+ 0] = ggml_cuda_cast(d * kvalues_iq4nl[q4[j] & 0xf]); + y[j+16] = ggml_cuda_cast(d * kvalues_iq4nl[q4[j] >> 4]); + } +} + +template +static __device__ __forceinline__ void dequantize_iq4_xs(const void * vx, const int64_t ibs, dst_t * yy, const int tid) { + const block_iq4_xs * x = (const block_iq4_xs *)vx; + + const int64_t il = tid/8; // 0...3 + const int64_t ib = tid%8; // 0...7 + dst_t * y = yy + 32*ib + 4*il; + const uint8_t * q4 = x[ibs].qs + 16*ib + 4*il; + const float d = (float)x[ibs].d * ((((x[ibs].scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((x[ibs].scales_h >> 2*ib) & 3) << 4)) - 32); + for (int j = 0; j < 4; ++j) { + y[j+ 0] = ggml_cuda_cast(d * kvalues_iq4nl[q4[j] & 0xf]); + y[j+16] = ggml_cuda_cast(d * kvalues_iq4nl[q4[j] >> 4]); + } +} + +template +static __device__ __forceinline__ void dequantize_mxfp4(const void * vx, const int64_t ibs, dst_t * yy, const int tid) { + + const block_mxfp4 * x = (const block_mxfp4 *) vx + ibs*(QK_K/QK_MXFP4); + + const int64_t il = tid/8; // 0...3 + const int64_t ib = tid%8; // 0...7 + dst_t * y = yy + 32*ib + 4*il; + const uint8_t * q4 = x[ib].qs + 4*il; + const float d = ggml_cuda_e8m0_to_fp32(x[ib].e); + for (int j = 0; j < 4; ++j) { + y[j+ 0] = ggml_cuda_cast(d * kvalues_mxfp4[q4[j] & 0xf]*0.5f); + y[j+16] = ggml_cuda_cast(d * kvalues_mxfp4[q4[j] >> 4]*0.5f); + } +} diff --git a/ggml/src/ggml-cuda/dsv4-hc.cu b/ggml/src/ggml-cuda/dsv4-hc.cu new file mode 100644 index 000000000000..c4b19a787b0e --- /dev/null +++ b/ggml/src/ggml-cuda/dsv4-hc.cu @@ -0,0 +1,294 @@ +#include "common.cuh" +#include "dsv4-hc.cuh" + + +static constexpr int DSV4_HC = 4; + + +static __device__ void dsv4_hc_comb_norm_cols(float * comb, float eps) { + for (int idst = 0; idst < DSV4_HC; ++idst) { + float sum = eps; + for (int isrc = 0; isrc < DSV4_HC; ++isrc) { + sum += comb[idst + DSV4_HC*isrc]; + } + + const float inv_sum = 1.0f / sum; + for (int isrc = 0; isrc < DSV4_HC; ++isrc) { + comb[idst + DSV4_HC*isrc] *= inv_sum; + } + } +} + +static __device__ void dsv4_hc_comb_norm_rows(float * comb, float eps) { + for (int isrc = 0; isrc < DSV4_HC; ++isrc) { + float sum = eps; + for (int idst = 0; idst < DSV4_HC; ++idst) { + sum += comb[idst + DSV4_HC*isrc]; + } + + const float inv_sum = 1.0f / sum; + for (int idst = 0; idst < DSV4_HC; ++idst) { + comb[idst + DSV4_HC*isrc] *= inv_sum; + } + } +} + +static __global__ void dsv4_hc_comb_f32( + const float * mixes, + const float * scale, + const float * base, + float * dst, + int64_t n_tokens, + int64_t sm0, + int64_t sm1, + int64_t ss0, + int64_t sb0, + int64_t sd0, + int64_t sd1, + int64_t sd2, + float eps, + int32_t n_iter) { + constexpr int comb_offset = 2*DSV4_HC; + + ggml_cuda_pdl_lc(); + const int64_t it = (int64_t) blockIdx.x * blockDim.x + threadIdx.x; + + if (it >= n_tokens) { + return; + } + + ggml_cuda_pdl_sync(); + + const float scale_comb = scale[2*ss0]; + float comb[DSV4_HC*DSV4_HC]; + + for (int isrc = 0; isrc < DSV4_HC; ++isrc) { + float max = -INFINITY; + for (int idst = 0; idst < DSV4_HC; ++idst) { + const int idx = idst + DSV4_HC*isrc; + const float v = mixes[(comb_offset + idx)*sm0 + it*sm1] * scale_comb + base[(comb_offset + idx)*sb0]; + comb[idx] = v; + max = fmaxf(max, v); + } + + float sum = 0.0f; + for (int idst = 0; idst < DSV4_HC; ++idst) { + const int idx = idst + DSV4_HC*isrc; + const float v = expf(comb[idx] - max); + comb[idx] = v; + sum += v; + } + + const float inv_sum = 1.0f / sum; + for (int idst = 0; idst < DSV4_HC; ++idst) { + const int idx = idst + DSV4_HC*isrc; + comb[idx] = comb[idx] * inv_sum + eps; + } + } + + dsv4_hc_comb_norm_cols(comb, eps); + for (int32_t i = 1; i < n_iter; ++i) { + dsv4_hc_comb_norm_rows(comb, eps); + dsv4_hc_comb_norm_cols(comb, eps); + } + + for (int isrc = 0; isrc < DSV4_HC; ++isrc) { + for (int idst = 0; idst < DSV4_HC; ++idst) { + const int idx = idst + DSV4_HC*isrc; + dst[idst*sd0 + isrc*sd1 + it*sd2] = comb[idx]; + } + } +} + +static __global__ void dsv4_hc_pre_f32( + const float * x, + const float * weights, + float * dst, + int64_t n_embd, + int64_t hc, + int64_t n_tokens, + int64_t sx0, + int64_t sx1, + int64_t sx2, + int64_t sw0, + int64_t sw1, + int64_t sd0, + int64_t sd1) { + ggml_cuda_pdl_lc(); + const int64_t ir = (int64_t) blockIdx.x * blockDim.x + threadIdx.x; + const int64_t nr = n_embd * n_tokens; + + if (ir >= nr) { + return; + } + + ggml_cuda_pdl_sync(); + + const int64_t i0 = ir % n_embd; + const int64_t it = ir / n_embd; + + float sum = x[i0*sx0 + it*sx2] * weights[it*sw1]; + for (int64_t ih = 1; ih < hc; ++ih) { + const float xv = x[i0*sx0 + ih*sx1 + it*sx2]; + const float wv = weights[ih*sw0 + it*sw1]; + sum += xv * wv; + } + + dst[i0*sd0 + it*sd1] = sum; +} + +static __global__ void dsv4_hc_post_f32( + const float * x, + const float * residual, + const float * post, + const float * comb, + float * dst, + int64_t n_embd, + int64_t hc, + int64_t n_tokens, + int64_t sx0, + int64_t sx1, + int64_t sr0, + int64_t sr1, + int64_t sr2, + int64_t sp0, + int64_t sp1, + int64_t sc0, + int64_t sc1, + int64_t sc2, + int64_t sd0, + int64_t sd1, + int64_t sd2) { + ggml_cuda_pdl_lc(); + const int64_t ir = (int64_t) blockIdx.x * blockDim.x + threadIdx.x; + const int64_t nr = n_embd * hc * n_tokens; + + if (ir >= nr) { + return; + } + + ggml_cuda_pdl_sync(); + + const int64_t i0 = ir % n_embd; + const int64_t idst = (ir / n_embd) % hc; + const int64_t it = ir / (n_embd * hc); + + float sum = x[i0*sx0 + it*sx1] * post[idst*sp0 + it*sp1]; + for (int64_t isrc = 0; isrc < hc; ++isrc) { + sum += residual[i0*sr0 + isrc*sr1 + it*sr2] * comb[idst*sc0 + isrc*sc1 + it*sc2]; + } + + dst[i0*sd0 + idst*sd1 + it*sd2] = sum; +} + +void ggml_cuda_op_dsv4_hc_comb(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * mixes = dst->src[0]; + const ggml_tensor * scale = dst->src[1]; + const ggml_tensor * base = dst->src[2]; + + GGML_ASSERT(mixes->type == GGML_TYPE_F32); + GGML_ASSERT(scale->type == GGML_TYPE_F32); + GGML_ASSERT(base->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + constexpr int64_t hc_mix_dim = (2 + DSV4_HC)*DSV4_HC; + + GGML_ASSERT(mixes->ne[0] == hc_mix_dim); + GGML_ASSERT(dst->ne[0] == DSV4_HC); + GGML_ASSERT(dst->ne[1] == DSV4_HC); + GGML_ASSERT(dst->ne[2] == mixes->ne[1]); + GGML_ASSERT(scale->ne[0] >= 3); + GGML_ASSERT(base->ne[0] == hc_mix_dim); + + GGML_TENSOR_LOCALS(size_t, nbm, mixes, nb); + GGML_TENSOR_LOCALS(size_t, nbs, scale, nb); + GGML_TENSOR_LOCALS(size_t, nbb, base, nb); + GGML_TENSOR_LOCALS(size_t, nbd, dst, nb); + + const int64_t n_tokens = mixes->ne[1]; + const float eps = ggml_get_op_params_f32(dst, 0); + const int32_t n_iter = ggml_get_op_params_i32(dst, 1); + + const int block_size = 256; + const dim3 block_dims(block_size, 1, 1); + const dim3 grid_dims((n_tokens + block_size - 1) / block_size, 1, 1); + const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(grid_dims, block_dims, 0, ctx.stream()); + + ggml_cuda_kernel_launch(dsv4_hc_comb_f32, launch_params, + (const float *) mixes->data, (const float *) scale->data, (const float *) base->data, (float *) dst->data, + n_tokens, + nbm0 / sizeof(float), nbm1 / sizeof(float), + nbs0 / sizeof(float), + nbb0 / sizeof(float), + nbd0 / sizeof(float), nbd1 / sizeof(float), nbd2 / sizeof(float), + eps, n_iter); +} + +void ggml_cuda_op_dsv4_hc_pre(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * x = dst->src[0]; + const ggml_tensor * weights = dst->src[1]; + + GGML_ASSERT(x->type == GGML_TYPE_F32); + GGML_ASSERT(weights->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + GGML_TENSOR_LOCALS(size_t, nbx, x, nb); + GGML_TENSOR_LOCALS(size_t, nbw, weights, nb); + GGML_TENSOR_LOCALS(size_t, nbd, dst, nb); + + const int64_t n_embd = x->ne[0]; + const int64_t hc = x->ne[1]; + const int64_t n_tokens = x->ne[2]; + + const int block_size = 256; + const int64_t nr = n_embd * n_tokens; + const dim3 block_dims(block_size, 1, 1); + const dim3 grid_dims((nr + block_size - 1) / block_size, 1, 1); + const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(grid_dims, block_dims, 0, ctx.stream()); + + ggml_cuda_kernel_launch(dsv4_hc_pre_f32, launch_params, + (const float *) x->data, (const float *) weights->data, (float *) dst->data, + n_embd, hc, n_tokens, + nbx0 / sizeof(float), nbx1 / sizeof(float), nbx2 / sizeof(float), + nbw0 / sizeof(float), nbw1 / sizeof(float), + nbd0 / sizeof(float), nbd1 / sizeof(float)); +} + +void ggml_cuda_op_dsv4_hc_post(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * x = dst->src[0]; + const ggml_tensor * residual = dst->src[1]; + const ggml_tensor * post = dst->src[2]; + const ggml_tensor * comb = dst->src[3]; + + GGML_ASSERT(x->type == GGML_TYPE_F32); + GGML_ASSERT(residual->type == GGML_TYPE_F32); + GGML_ASSERT(post->type == GGML_TYPE_F32); + GGML_ASSERT(comb->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + GGML_TENSOR_LOCALS(size_t, nbx, x, nb); + GGML_TENSOR_LOCALS(size_t, nbr, residual, nb); + GGML_TENSOR_LOCALS(size_t, nbp, post, nb); + GGML_TENSOR_LOCALS(size_t, nbc, comb, nb); + GGML_TENSOR_LOCALS(size_t, nbd, dst, nb); + + const int64_t n_embd = x->ne[0]; + const int64_t n_tokens = x->ne[1]; + const int64_t hc = residual->ne[1]; + + const int block_size = 256; + const int64_t nr = n_embd * hc * n_tokens; + const dim3 block_dims(block_size, 1, 1); + const dim3 grid_dims((nr + block_size - 1) / block_size, 1, 1); + const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(grid_dims, block_dims, 0, ctx.stream()); + + ggml_cuda_kernel_launch(dsv4_hc_post_f32, launch_params, + (const float *) x->data, (const float *) residual->data, + (const float *) post->data, (const float *) comb->data, (float *) dst->data, + n_embd, hc, n_tokens, + nbx0 / sizeof(float), nbx1 / sizeof(float), + nbr0 / sizeof(float), nbr1 / sizeof(float), nbr2 / sizeof(float), + nbp0 / sizeof(float), nbp1 / sizeof(float), + nbc0 / sizeof(float), nbc1 / sizeof(float), nbc2 / sizeof(float), + nbd0 / sizeof(float), nbd1 / sizeof(float), nbd2 / sizeof(float)); +} diff --git a/ggml/src/ggml-cuda/dsv4-hc.cuh b/ggml/src/ggml-cuda/dsv4-hc.cuh new file mode 100644 index 000000000000..2379aaefb41b --- /dev/null +++ b/ggml/src/ggml-cuda/dsv4-hc.cuh @@ -0,0 +1,6 @@ +#include "common.cuh" +#include "ggml.h" + +void ggml_cuda_op_dsv4_hc_comb(ggml_backend_cuda_context & ctx, ggml_tensor * dst); +void ggml_cuda_op_dsv4_hc_pre(ggml_backend_cuda_context & ctx, ggml_tensor * dst); +void ggml_cuda_op_dsv4_hc_post(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml/src/ggml-cuda/fattn-tile.cu b/ggml/src/ggml-cuda/fattn-tile.cu index a4a0b212b9a6..b1f07888f70b 100644 --- a/ggml/src/ggml-cuda/fattn-tile.cu +++ b/ggml/src/ggml-cuda/fattn-tile.cu @@ -1,6 +1,5 @@ #include "common.cuh" #include "fattn-tile.cuh" -#include "fattn-wmma-f16.cuh" void ggml_cuda_flash_attn_ext_tile(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const ggml_tensor * K = dst->src[1]; diff --git a/ggml/src/ggml-cuda/fattn-tile.cuh b/ggml/src/ggml-cuda/fattn-tile.cuh index 65482a4307ec..55ce2cc0bfb0 100644 --- a/ggml/src/ggml-cuda/fattn-tile.cuh +++ b/ggml/src/ggml-cuda/fattn-tile.cuh @@ -1,6 +1,5 @@ #include "common.cuh" #include "fattn-common.cuh" -#include "fattn-wmma-f16.cuh" // nbatch_fa == number of KQ rows to process per iteration // nbatch_K == number of K columns to load in parallel for KQ calculation @@ -843,12 +842,7 @@ static __global__ void flash_attn_tile( // Skip unused kernel variants for faster compilation: - if ( -#ifdef GGML_USE_WMMA_FATTN - (ncols2 != 1 && DV != 40 && DV != 72 && DV != 512) || -#endif // GGML_USE_WMMA_FATTN - (use_logit_softcap && !(DV == 128 || DV == 256 || DV == 512)) - ) { + if ((use_logit_softcap && !(DV == 128 || DV == 256 || DV == 512))) { GGML_UNUSED_VARS(Q, K, V, mask, sinks, KV_max, dst, dst_meta, scale, max_bias, m0, m1, n_head_log2, logit_softcap, ne00, ne01, ne02, ne03, diff --git a/ggml/src/ggml-cuda/fattn-wmma-f16.cu b/ggml/src/ggml-cuda/fattn-wmma-f16.cu deleted file mode 100644 index 6850716fc0dc..000000000000 --- a/ggml/src/ggml-cuda/fattn-wmma-f16.cu +++ /dev/null @@ -1,705 +0,0 @@ -// Old and deprecated WMMA FlashAttention implementation. -// It is still needed for Volta since the memory layout of NVIDIA tensor cores changed with Turing. -// Long-term the WMMA code should be replaced with a dedicated Volta implementation. - -#include "common.cuh" -#include "fattn-common.cuh" -#include "fattn-wmma-f16.cuh" - -#ifdef GGML_USE_WMMA_FATTN -#if !defined(GGML_USE_HIP) -#include -#if defined(GGML_USE_MUSA) -namespace wmma = mtmusa::wmma; -#else // GGML_USE_MUSA -namespace wmma = nvcuda::wmma; -#endif // GGML_USE_MUSA -#elif defined(GGML_USE_HIP) -#include -namespace wmma = rocwmma; -#endif // !defined(GGML_USE_HIP) -#endif // GGML_USE_WMMA_FATTN - -// D == head size, VKQ_stride == num VKQ rows calculated in parallel: -template -__launch_bounds__(nwarps*ggml_cuda_get_physical_warp_size(), 1) -static __global__ void flash_attn_ext_f16( - const char * Q_ptr, - const char * K_ptr, - const char * V_ptr, - const char * mask_ptr, - const char * sinks_ptr, - const int * KV_max_ptr, - float * dst_ptr, - float2 * dst_meta_ptr, - const float scale, - const float max_bias, - const float m0, - const float m1, - const uint32_t n_head_log2, - const float logit_softcap, - const int32_t ne00, const uint3 ne01, const int32_t ne02, const int32_t ne03, - const int32_t nb01, const int32_t nb02, const int32_t nb03, - const int32_t ne10, const int32_t ne11, const int32_t ne12, const int32_t ne13, - const int32_t nb11, const int32_t nb12, const int64_t nb13, - const int32_t nb21, const int32_t nb22, const int64_t nb23, - const int32_t ne31, const int32_t ne32, const int32_t ne33, - const int32_t nb31, const int32_t nb32, const int64_t nb33) { -#if defined(FLASH_ATTN_AVAILABLE) && (defined(GGML_HIP_ROCWMMA_FATTN) && defined(GGML_USE_WMMA_FATTN)) - const char * GGML_CUDA_RESTRICT Q = Q_ptr; - const char * GGML_CUDA_RESTRICT K = K_ptr; - const char * GGML_CUDA_RESTRICT V = V_ptr; - const char * GGML_CUDA_RESTRICT mask = mask_ptr; - const char * GGML_CUDA_RESTRICT sinks = sinks_ptr; - const int * GGML_CUDA_RESTRICT KV_max = KV_max_ptr; - float * GGML_CUDA_RESTRICT dst = dst_ptr; - float2 * GGML_CUDA_RESTRICT dst_meta = dst_meta_ptr; - // Skip unused kernel variants for faster compilation: - if (use_logit_softcap && !(D == 128 || D == 256)) { - NO_DEVICE_CODE; - return; - } - - //In this kernel Q, K, V are matrices while i, j, k are matrix indices. - - constexpr int warp_size = ggml_cuda_get_physical_warp_size(); - - const int ic0 = ncols*blockIdx.x; // Index of the first Q/QKV column to work on. - - static_assert(D <= FATTN_KQ_STRIDE, "D must be <= FATTN_KQ_STRIDE."); - static_assert(ncols == 8 || ncols % 16 == 0, "ncols must be 8 or a multiple of 16."); - constexpr int frag_m = ncols == 8 ? 32 : 16; - constexpr int frag_n = ncols == 8 ? 8 : 16; - static_assert(D % frag_m == 0, "If ncols == 8 then D % frag_m must be 0."); -#if defined(GGML_USE_HIP) && HIP_VERSION >= 60500000 - typedef wmma::fragment frag_a_K; - typedef wmma::fragment frag_a_V; - typedef wmma::fragment frag_b; - typedef wmma::fragment frag_c_KQ; - typedef wmma::fragment frag_c_VKQ; -#else - typedef wmma::fragment frag_a_K; - typedef wmma::fragment frag_a_V; - typedef wmma::fragment frag_b; - typedef wmma::fragment frag_c_KQ; - typedef wmma::fragment frag_c_VKQ; -#endif - - constexpr int KQ_stride_tc = nwarps*frag_m; // Number of KQ rows calculated in parallel. - constexpr int VKQ_ratio = KQ_stride_tc/VKQ_stride; // Number of parallel VKQ accumulators needed to keep all warps busy. - static_assert(VKQ_ratio <= nwarps, "VKQ_ratio must be <= nwarps."); - - // Pad internal representation of KQ, KQV to reduce shared memory bank conflicts: - constexpr int D_padded = D + 8; - constexpr int kqs_padded = FATTN_KQ_STRIDE + 8; - constexpr int kqar = sizeof(KQ_acc_t)/sizeof(half); - - ggml_cuda_pdl_sync(); - const int sequence = blockIdx.z / ne02; - const int head = blockIdx.z - sequence*ne02; - const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. - const float * Q_f = (const float *) (Q + nb03* sequence + nb02* head + nb01*ic0); - const half * K_h = (const half *) (K + nb13* sequence + nb12*(head / gqa_ratio)); - const half * V_h = (const half *) (V + nb13* sequence + nb12*(head / gqa_ratio)); // K and V have same shape - const half * maskh = (const half *) (mask + nb33*(sequence % ne33) + nb31*ic0); - const half2 * mask2 = (const half2 *) maskh; - const float * sinksf = (const float *) sinks; - - const int stride_Q = nb01 / sizeof(float); - const int stride_KV = nb11 / sizeof(half); - - const float slopef = get_alibi_slope(max_bias, head, n_head_log2, m0, m1); - const half slopeh = __float2half(slopef); - const half2 slope2 = make_half2(slopef, slopef); - - const half2 logit_softcap_2 = make_half2(logit_softcap, logit_softcap); - - frag_b Q_b[D/16][ncols/frag_n]; - - // A single buffer for temporarily holding tiles of KQ and VKQ parts: - constexpr int mem_KQ = ncols*kqs_padded*kqar; - constexpr int mem_VKQ_parts = VKQ_ratio*ncols*D_padded; - __shared__ half KQ[mem_KQ >= mem_VKQ_parts ? mem_KQ : mem_VKQ_parts]; - float * KQ_f = (float *) KQ; - half2 * KQ2 = (half2 *) KQ; - - float KQ_rowsum_f[ncols/nwarps] = {0.0f}; - float KQ_max_f[ncols/nwarps]; - float KQ_max_scale_f[ncols/nwarps] = {0.0f}; - -#pragma unroll - for (int j = 0; j < ncols/nwarps; ++j) { - KQ_max_f[j] = -FLT_MAX/2.0f; - } - - half2 KQ_rowsum_h2[ncols/nwarps] = {{0.0f, 0.0f}}; - half2 KQ_max_h2[ncols/nwarps]; - half2 KQ_max_scale_h2[ncols/nwarps] = {{0.0f, 0.0f}}; - -#pragma unroll - for (int j = 0; j < ncols/nwarps; ++j) { - KQ_max_h2[j] = make_half2(-HALF_MAX_HALF, -HALF_MAX_HALF); - } - - __shared__ half VKQ[ncols*D_padded]; // Accumulator for final VKQ slice. - half2 * VKQ2 = (half2 *) VKQ; - -#if defined(GGML_USE_HIP) && HIP_VERSION >= 60500000 - const _Float16 * K_h_f16 = reinterpret_cast(K_h); - const _Float16 * V_h_f16 = reinterpret_cast(V_h); - _Float16 * KQ_f16 = reinterpret_cast<_Float16 *>(KQ); - _Float16 * VKQ_f16 = reinterpret_cast<_Float16 *>(VKQ); -#else - const half * K_h_f16 = K_h; - const half * V_h_f16 = V_h; - half * KQ_f16 = KQ; - half * VKQ_f16 = VKQ; -#endif - -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += nwarps) { - const int j = j0 + threadIdx.y; -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += warp_size) { - const int i = i0 + threadIdx.x; - if (i0 + warp_size > D/2 && i >= D/2) { - break; - } - VKQ2[j*(D_padded/2) + i] = make_half2(0.0f, 0.0f); - } - } - - // Convert Q to half and apply scale, temporarily store in KQ: -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += nwarps) { - const int j = j0 + threadIdx.y; -#pragma unroll - for (int i0 = 0; i0 < D; i0 += warp_size) { - const int i = i0 + threadIdx.x; - if (i0 + warp_size > D && i >= D) { - break; - } - KQ[j*D_padded + i] = ic0 + j < int(ne01.z) ? Q_f[j*stride_Q + i] * scale : 0.0f; - } - } - - __syncthreads(); - - // Load Q into tensor core fragments/registers since it will be used frequently: -#pragma unroll - for (int i0 = 0; i0 < D; i0 += 16) { -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += frag_n) { - wmma::load_matrix_sync(Q_b[i0/16][j0/frag_n], KQ_f16 + j0*D_padded + i0, D_padded); - } - } - - __syncthreads(); - - // Iterate over ne11 == previous tokens: - const int k_VKQ_max = KV_max ? KV_max[sequence*gridDim.x + blockIdx.x] : ne11; - for (int k_VKQ_0 = blockIdx.y*FATTN_KQ_STRIDE; k_VKQ_0 < k_VKQ_max; k_VKQ_0 += gridDim.y*FATTN_KQ_STRIDE) { - // Calculate tile of KQ: -#pragma unroll - for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE; i_KQ_0 += KQ_stride_tc) { - frag_c_KQ KQ_c[ncols/frag_n]; -#pragma unroll - for (int j = 0; j < ncols/frag_n; ++j) { - wmma::fill_fragment(KQ_c[j], static_cast(0.0f)); - } -#pragma unroll - for (int k_KQ_0 = 0; k_KQ_0 < D; k_KQ_0 += 16) { - frag_a_K K_a; - wmma::load_matrix_sync(K_a, K_h_f16 + int64_t(k_VKQ_0 + i_KQ_0 + frag_m*threadIdx.y)*stride_KV + k_KQ_0, stride_KV); -#pragma unroll - for (int j = 0; j < ncols/frag_n; ++j) { - wmma::mma_sync(KQ_c[j], K_a, Q_b[k_KQ_0/16][j], KQ_c[j]); - } - } -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += frag_n) { - wmma::store_matrix_sync((KQ_acc_t *) KQ + j0*kqs_padded + i_KQ_0 + frag_m*threadIdx.y, KQ_c[j0/frag_n], kqs_padded, wmma::mem_col_major); - } - } - - __syncthreads(); - - // Calculate softmax for each KQ column using the current max. value. - // The divisor is stored in KQ_rowsum and will be applied at the end. -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += nwarps) { - const int j = j0 + threadIdx.y; - - if (std::is_same::value) { - float KQ_f_tmp[FATTN_KQ_STRIDE / warp_size]; -#pragma unroll - for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += warp_size) { - const int k = k0 + threadIdx.x; - - KQ_f_tmp[k0/warp_size] = KQ_f[j*kqs_padded + k]; - - if (use_logit_softcap) { - KQ_f_tmp[k0/warp_size] = logit_softcap*tanhf(KQ_f_tmp[k0/warp_size]); - } - } - - float KQ_max_new = KQ_max_f[j0/nwarps]; -#pragma unroll - for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += warp_size) { - const int k = k0 + threadIdx.x; - - KQ_f_tmp[k0/warp_size] += mask && ic0 + j < int(ne01.z) ? - __half2float(slopeh*maskh[j*(nb31/sizeof(half)) + k_VKQ_0 + k]) : 0.0f; - KQ_max_new = max(KQ_max_new, KQ_f_tmp[k0/warp_size] + FATTN_KQ_MAX_OFFSET); - } - KQ_max_new = warp_reduce_max(KQ_max_new); - - const float diff = KQ_max_f[j0/nwarps] - KQ_max_new; - KQ_max_scale_f[j0/nwarps] = expf(diff); - if (diff <= SOFTMAX_FTZ_THRESHOLD) { - KQ_max_scale_f[j0/nwarps] = 0.0f; - } - KQ_max_f[j0/nwarps] = KQ_max_new; - - float KQ_rowsum_add = 0.0f; -#pragma unroll - for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += warp_size) { - const int k = k0 + threadIdx.x; - - const float diff = KQ_f_tmp[k0/warp_size] - KQ_max_f[j0/nwarps]; - KQ_f_tmp[k0/warp_size] = expf(diff); - if (diff <= SOFTMAX_FTZ_THRESHOLD) { - KQ_f_tmp[k0/warp_size] = 0.0f; - } - KQ_rowsum_add += KQ_f_tmp[k0/warp_size]; - KQ[j*(kqar*kqs_padded) + k] = KQ_f_tmp[k0/warp_size]; - } - KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add); - - // Scale previous KQ_rowsum to account for a potential increase in KQ_max: - KQ_rowsum_f[j0/nwarps] = KQ_max_scale_f[j0/nwarps]*KQ_rowsum_f[j0/nwarps] + KQ_rowsum_add; - } else { - half2 KQ2_tmp[FATTN_KQ_STRIDE/(2*warp_size)]; -#pragma unroll - for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += warp_size) { - const int k = k0 + threadIdx.x; - - KQ2_tmp[k0/warp_size] = KQ2[j*(kqs_padded/2) + k]; - - if (use_logit_softcap) { - // There is no dedicated tangens hyperbolicus function for half2. - KQ2_tmp[k0/warp_size] = h2exp(KQ2_tmp[k0/warp_size]*make_half2(2.0f, 2.0f)); - KQ2_tmp[k0/warp_size] = (KQ2_tmp[k0/warp_size] - make_half2(1.0f, 1.0f)) - /(KQ2_tmp[k0/warp_size] + make_half2(1.0f, 1.0f)); - - KQ2_tmp[k0/warp_size] *= logit_softcap_2; - } - } - - half2 KQ_max_new = KQ_max_h2[j0/nwarps]; -#pragma unroll - for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += warp_size) { - const int k = k0 + threadIdx.x; - - KQ2_tmp[k0/warp_size] += mask && ic0 + j < int(ne01.z) ? slope2*mask2[(j*ne11 + k_VKQ_0)/2 + k] : make_half2(0.0f, 0.0f); - KQ_max_new = ggml_cuda_hmax2(KQ_max_new, KQ2_tmp[k0/warp_size]); - } - KQ_max_new = __half2half2(warp_reduce_max(ggml_cuda_hmax(__low2half(KQ_max_new), __high2half(KQ_max_new)))); - const half2 diff = KQ_max_h2[j0/nwarps] - KQ_max_new; - KQ_max_scale_h2[j0/nwarps] = h2exp(diff); - const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD)); - *((uint32_t *) &KQ_max_scale_h2[j0/nwarps]) &= ftz_mask; - KQ_max_h2[j0/nwarps] = KQ_max_new; - - half2 KQ_rowsum_add = make_half2(0.0f, 0.0f); -#pragma unroll - for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += warp_size) { - const int k = k0 + threadIdx.x; - - const half2 diff = KQ2_tmp[k0/warp_size] - KQ_max_h2[j0/nwarps]; - KQ2_tmp[k0/warp_size] = h2exp(diff); - const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD)); - *((uint32_t *) &KQ2_tmp[k0/warp_size]) &= ftz_mask; - KQ_rowsum_add += KQ2_tmp[k0/warp_size]; - KQ2[j*(kqs_padded/2) + k] = KQ2_tmp[k0/warp_size]; - } - KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add); - - // Scale previous KQ_rowsum to account for a potential increase in KQ_max: - KQ_rowsum_h2[j0/nwarps] = KQ_max_scale_h2[j0/nwarps]*KQ_rowsum_h2[j0/nwarps] + KQ_rowsum_add; - } - } - - __syncthreads(); - - frag_b KQ_b[FATTN_KQ_STRIDE/(VKQ_ratio*16)][ncols/frag_n]; -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += frag_n) { -#pragma unroll - for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) { - const int k = k0 + (threadIdx.y % VKQ_ratio)*16; - wmma::load_matrix_sync( - KQ_b[k0/(VKQ_ratio*16)][j0/frag_n], - KQ_f16 + j0*(kqar*kqs_padded) + k, - kqar*kqs_padded); - } - } - - frag_c_VKQ VKQ_c[D/VKQ_stride][ncols/frag_n]; -#pragma unroll - for (int i_VKQ_0 = 0; i_VKQ_0 < D; i_VKQ_0 += VKQ_stride) { -#pragma unroll - for (int j = 0; j < ncols/frag_n; ++j) { - wmma::fill_fragment(VKQ_c[i_VKQ_0/VKQ_stride][j], static_cast(0.0f)); - } - -#pragma unroll - for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) { - const int k = k0 + (threadIdx.y % VKQ_ratio)*16; - - frag_a_V v_a; - wmma::load_matrix_sync(v_a, V_h_f16 + int64_t(k_VKQ_0 + k)*stride_KV + i_VKQ_0 + frag_m*(threadIdx.y/VKQ_ratio), stride_KV); -#pragma unroll - for (int j = 0; j < ncols/frag_n; ++j) { - wmma::mma_sync(VKQ_c[i_VKQ_0/VKQ_stride][j], v_a, KQ_b[k0/(VKQ_ratio*16)][j], VKQ_c[i_VKQ_0/VKQ_stride][j]); - } - } - } - - __syncthreads(); - - const int offset_k = (threadIdx.y % VKQ_ratio) * (ncols*D_padded); -#pragma unroll - for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += VKQ_stride) { -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += frag_n) { - wmma::store_matrix_sync( - KQ_f16 + offset_k + j0*D_padded + i_KQ_0 + frag_m*(threadIdx.y/VKQ_ratio), - VKQ_c[i_KQ_0/VKQ_stride][j0/frag_n], - D_padded, wmma::mem_col_major); - } - } - - __syncthreads(); - -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += nwarps) { - const int j = j0 + threadIdx.y; - - half2 VKQ_scale; - if (std::is_same::value) { - VKQ_scale = make_half2(KQ_max_scale_f[j0/nwarps], KQ_max_scale_f[j0/nwarps]); - } else { - VKQ_scale = KQ_max_scale_h2[j0/nwarps]; - } - -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += warp_size) { - const int i = i0 + threadIdx.x; - if (i0 + warp_size > D/2 && i >= D/2) { - break; - } - - half2 VKQ_add = make_half2(0.0f, 0.0f); -#pragma unroll - for (int l = 0; l < VKQ_ratio; ++l) { - VKQ_add += KQ2[l*(ncols*D_padded/2) + j*(D_padded/2) + i]; - } - VKQ2[j*(D_padded/2) + i] = VKQ_scale*VKQ2[j*(D_padded/2) + i] + VKQ_add; - } - } - - __syncthreads(); - } - - // Apply attention sinks - if (sinksf && blockIdx.y == 0) { - const float sinkf = sinksf[head]; - const half sinkh = __float2half(sinkf); - -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += nwarps) { - const int j = j0 + threadIdx.y; - - if (std::is_same::value) { - float kqmax_new = fmaxf(KQ_max_f[j0/nwarps], sinkf); - - const float KQ_max_scale = expf(KQ_max_f[j0/nwarps] - kqmax_new); - KQ_max_f[j0/nwarps] = kqmax_new; - - KQ_rowsum_f[j0/nwarps] = KQ_rowsum_f[j0/nwarps] * KQ_max_scale + expf(sinkf - KQ_max_f[j0/nwarps]); - - const half2 scale_h2 = make_half2(KQ_max_scale, KQ_max_scale); -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += warp_size) { - const int i = i0 + threadIdx.x; - if (i0 + warp_size > D/2 && i >= D/2) break; - VKQ2[j*(D_padded/2) + i] *= scale_h2; - } - } else { - half kqmax_old = __low2half(KQ_max_h2[j0/nwarps]); - half kqmax_new = fmaxf(kqmax_old, sinkh); - KQ_max_h2[j0/nwarps] = __half2half2(kqmax_new); - - const half KQ_max_scale_h = hexp(kqmax_old - kqmax_new); - const half2 KQ_max_scale = __half2half2(KQ_max_scale_h); - - KQ_rowsum_h2[j0/nwarps] = KQ_rowsum_h2[j0/nwarps] * KQ_max_scale; - const half val = hexp(sinkh - kqmax_new); - KQ_rowsum_h2[j0/nwarps].x = __hadd(KQ_rowsum_h2[j0/nwarps].x, val); - -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += warp_size) { - const int i = i0 + threadIdx.x; - if (i0 + warp_size > D/2 && i >= D/2) break; - VKQ2[j*(D_padded/2) + i] *= KQ_max_scale; - } - } - } - - __syncthreads(); - } -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += nwarps) { - const int j_VKQ = j0 + threadIdx.y; - if (ic0 + j_VKQ >= int(ne01.z)) { - return; - } - - float KQ_rowsum_j; - if (std::is_same::value) { - KQ_rowsum_j = KQ_rowsum_f[j0/nwarps]; - } else { - KQ_rowsum_j = __low2float(KQ_rowsum_h2[j0/nwarps]) + __high2float(KQ_rowsum_h2[j0/nwarps]); - } - - const int j_dst_unrolled = ((sequence*int(ne01.z) + ic0 + j_VKQ)*ne02 + head)*gridDim.y + blockIdx.y; - -#pragma unroll - for (int i0 = 0; i0 < D; i0 += warp_size) { - const int i = i0 + threadIdx.x; - if (i0 + warp_size > D && i >= D) { - break; - } - float dst_val = VKQ[j_VKQ*D_padded + i]; - if (gridDim.y == 1) { - dst_val /= KQ_rowsum_j; - } - dst[j_dst_unrolled*D + i] = dst_val; - } - - if (gridDim.y == 1 || threadIdx.x != 0) { - continue; - } - - float2 dst_meta_val; - if (std::is_same::value) { - dst_meta_val.x = KQ_max_f[j0/nwarps]; - } else { - dst_meta_val.x = __low2float(KQ_max_h2[j0/nwarps]); - } - dst_meta_val.y = KQ_rowsum_j; - dst_meta[j_dst_unrolled] = dst_meta_val; - } -#else - GGML_UNUSED_VARS(Q_ptr, K_ptr, V_ptr, mask_ptr, sinks_ptr, KV_max_ptr, dst_ptr, dst_meta_ptr, scale, - max_bias, m0, m1, n_head_log2, logit_softcap, - ne00, ne01, ne02, ne03, - nb01, nb02, nb03, - ne10, ne11, ne12, ne13, - nb11, nb12, nb13, - nb21, nb22, nb23, - ne31, ne32, ne33, - nb31, nb32, nb33); - NO_DEVICE_CODE; -#endif // defined(FLASH_ATTN_AVAILABLE) && (defined(GGML_HIP_ROCWMMA_FATTN) && defined(GGML_USE_WMMA_FATTN)) -} - -constexpr int get_max_power_of_2(int x) { - return x % 2 == 0 ? 2*get_max_power_of_2(x/2) : 1; -} - -static_assert(get_max_power_of_2(1) == 1, "Test failed."); -static_assert(get_max_power_of_2(2) == 2, "Test failed."); -static_assert(get_max_power_of_2(4) == 4, "Test failed."); -static_assert(get_max_power_of_2(6) == 2, "Test failed."); - -// Number of VKQ rows calculated in parallel: -constexpr int get_VKQ_stride(int D, int nwarps, int frag_m) { - return (get_max_power_of_2(D/frag_m) < nwarps ? get_max_power_of_2(D/frag_m) : nwarps)*frag_m; -} - -static_assert(get_VKQ_stride(128, 1, 32) == 32, "Test failed."); -static_assert(get_VKQ_stride(128, 2, 32) == 64, "Test failed."); -static_assert(get_VKQ_stride(128, 4, 32) == 128, "Test failed."); -static_assert(get_VKQ_stride( 64, 1, 32) == 32, "Test failed."); -static_assert(get_VKQ_stride( 64, 2, 32) == 64, "Test failed."); -static_assert(get_VKQ_stride( 64, 4, 32) == 64, "Test failed."); -static_assert(get_VKQ_stride( 80, 1, 16) == 16, "Test failed."); -static_assert(get_VKQ_stride( 80, 2, 16) == 16, "Test failed."); -static_assert(get_VKQ_stride( 80, 4, 16) == 16, "Test failed."); - -template -void ggml_cuda_flash_attn_ext_wmma_f16_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * KQV = dst; - - constexpr int nwarps = 4; - - constexpr int frag_m = cols_per_block == 8 && D % 32 == 0 ? 32 : 16; - const int warp_size = ggml_cuda_info().devices[ggml_cuda_get_device()].warp_size; - - float logit_softcap; - memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float)); - - fattn_kernel_t fattn_kernel; - if (logit_softcap == 0.0f) { - constexpr bool use_logit_softcap = false; - fattn_kernel = flash_attn_ext_f16< - D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), KQ_acc_t, use_logit_softcap>; - } else { - constexpr bool use_logit_softcap = true; - fattn_kernel = flash_attn_ext_f16< - D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), KQ_acc_t, use_logit_softcap>; - } - launch_fattn(ctx, dst, fattn_kernel, nwarps, 0, FATTN_KQ_STRIDE, true, true, false, warp_size); -} - -void ggml_cuda_flash_attn_ext_wmma_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * KQV = dst; - const ggml_tensor * Q = dst->src[0]; - - const enum ggml_prec prec = ggml_flash_attn_ext_get_prec(KQV); - const int warp_size = ggml_cuda_info().devices[ctx.device].warp_size; - - if (prec != GGML_PREC_DEFAULT) { - if (Q->ne[1] <= 32 || Q->ne[0] > 128) { - constexpr int cols_per_block = 16; - switch (Q->ne[0]) { - case 64: - ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, float>(ctx, dst); - break; - case 80: - ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, float>(ctx, dst); - break; - case 96: - ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, float>(ctx, dst); - break; - case 112: - ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, float>(ctx, dst); - break; - case 128: - ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst); - break; - case 256: - ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, float>(ctx, dst); - break; - default: - GGML_ABORT("fatal error"); - break; - } - } else { - constexpr int cols_per_block = 32; - switch (Q->ne[0]) { - case 64: - ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, float>(ctx, dst); - break; - case 80: - ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, float>(ctx, dst); - break; - case 96: - ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, float>(ctx, dst); - break; - case 112: - ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, float>(ctx, dst); - break; - case 128: - ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst); - break; - // case 256: - // ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, float>(ctx, dst); - // break; - default: - GGML_ABORT("fatal error"); - break; - } - } - return; - } - -#if !defined(GGML_USE_HIP) - if (Q->ne[1] <= 8 && Q->ne[0] % warp_size == 0) { - constexpr int cols_per_block = 8; - switch (Q->ne[0]) { - case 64: - ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst); - break; - case 96: - ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst); - break; - case 128: - ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst); - break; - case 256: - ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst); - break; - default: - GGML_ABORT("fatal error"); - break; - } - return; - } -#endif // !defined(GGML_USE_HIP) - - if (Q->ne[1] <= 32) { - constexpr int cols_per_block = 16; - switch (Q->ne[0]) { - case 64: - ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst); - break; - case 80: - ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, half>(ctx, dst); - break; - case 96: - ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst); - break; - case 112: - ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, half>(ctx, dst); - break; - case 128: - ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst); - break; - case 256: - ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst); - break; - default: - GGML_ABORT("fatal error"); - break; - } - return; - } - - constexpr int cols_per_block = 32; - switch (Q->ne[0]) { - case 64: - ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst); - break; - case 80: - ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, half>(ctx, dst); - break; - case 96: - ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst); - break; - case 112: - ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, half>(ctx, dst); - break; - case 128: - ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst); - break; - case 256: - ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst); - break; - default: - GGML_ABORT("fatal error"); - break; - } -} diff --git a/ggml/src/ggml-cuda/fattn-wmma-f16.cuh b/ggml/src/ggml-cuda/fattn-wmma-f16.cuh deleted file mode 100644 index aaf711a618cb..000000000000 --- a/ggml/src/ggml-cuda/fattn-wmma-f16.cuh +++ /dev/null @@ -1,51 +0,0 @@ -#pragma once - -#include "common.cuh" - -#if defined(GGML_USE_MUSA) -#define GGML_USE_WMMA_FATTN -#endif // defined(GGML_USE_MUSA) - -#if defined(GGML_HIP_ROCWMMA_FATTN) -#if defined(CDNA) && (ROCWMMA_VERSION_MAJOR < 2 || ROCWMMA_VERSION_MINOR > 0 || ROCWMMA_VERSION_PATCH > 0) -#define GGML_USE_WMMA_FATTN -#elif defined(CDNA) -#warning "rocwmma fattn on CDNA is broken on rocwmma v2.0.0, expect degraded performance" -#endif // defined(CDNA) && (ROCWMMA_VERSION_MAJOR < 2 || ROCWMMA_VERSION_MINOR > 0 || ROCWMMA_VERSION_PATCH > 0) -#if defined(RDNA3) -#define GGML_USE_WMMA_FATTN -#endif // defined(RDNA3) -#if defined(RDNA4) && ROCWMMA_VERSION_MAJOR > 1 -#define GGML_USE_WMMA_FATTN -#elif defined(RDNA4) -#warning "rocwmma fattn is not supported on RDNA4 on rocwmma < v2.0.0, expect degraded performance" -#endif // defined(RDNA4) && ROCWMMA_VERSION_MAJOR > 1 -#endif // defined(GGML_HIP_ROCWMMA_FATTN) - -// WMMA flash attention requires FP16 matrix instructions to be available for ggml code. -static bool ggml_cuda_should_use_wmma_fattn(const int cc) { -#if defined(GGML_USE_HIP) && !defined(GGML_HIP_ROCWMMA_FATTN) - return false; -#else - if ((GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) == GGML_CUDA_CC_VOLTA) || - GGML_CUDA_CC_IS_RDNA3(cc) || GGML_CUDA_CC_IS_MTHREADS(cc)) { - return true; - } else if (GGML_CUDA_CC_IS_CDNA(cc)){ -#if defined(GGML_HIP_ROCWMMA_FATTN) && (ROCWMMA_VERSION_MAJOR < 2 || ROCWMMA_VERSION_MINOR > 0 || ROCWMMA_VERSION_PATCH > 0) - return true; -#else - return false; -#endif // defined(GGML_HIP_ROCWMMA_FATTN) (ROCWMMA_VERSION_MAJOR < 2 || ROCWMMA_VERSION_MINOR > 0 || ROCWMMA_VERSION_PATCH > 0) - } else if (GGML_CUDA_CC_IS_RDNA4(cc)) { -#if defined(GGML_HIP_ROCWMMA_FATTN) && ROCWMMA_VERSION_MAJOR > 1 - return true; -#else - return false; -#endif // defined(GGML_HIP_ROCWMMA_FATTN) && ROCWMMA_VERSION_MAJOR > 1 - } else { - return false; - } -#endif // defined(GGML_USE_HIP) && !defined(GGML_HIP_ROCWMMA_FATTN) -} - -void ggml_cuda_flash_attn_ext_wmma_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml/src/ggml-cuda/fattn.cu b/ggml/src/ggml-cuda/fattn.cu index a4e7f7d13fb1..5e905c57d4ae 100644 --- a/ggml/src/ggml-cuda/fattn.cu +++ b/ggml/src/ggml-cuda/fattn.cu @@ -3,7 +3,6 @@ #include "fattn-mma-f16.cuh" #include "fattn-tile.cuh" #include "fattn-vec.cuh" -#include "fattn-wmma-f16.cuh" #include "fattn.cuh" template @@ -391,11 +390,10 @@ static void ggml_cuda_flash_attn_ext_vec(ggml_backend_cuda_context & ctx, ggml_t // Best FlashAttention kernel for a specific GPU: enum best_fattn_kernel { - BEST_FATTN_KERNEL_NONE = 0, - BEST_FATTN_KERNEL_TILE = 200, - BEST_FATTN_KERNEL_VEC = 100, - BEST_FATTN_KERNEL_WMMA_F16 = 300, - BEST_FATTN_KERNEL_MMA_F16 = 400, + BEST_FATTN_KERNEL_NONE = 0, + BEST_FATTN_KERNEL_TILE = 200, + BEST_FATTN_KERNEL_VEC = 100, + BEST_FATTN_KERNEL_MMA_F16 = 400, }; static bool ggml_cuda_fattn_kv_type_supported(ggml_type type) { @@ -608,15 +606,7 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const return BEST_FATTN_KERNEL_MMA_F16; } - // Use the WMMA kernel if possible: - if (ggml_cuda_should_use_wmma_fattn(cc) && K->ne[1] % FATTN_KQ_STRIDE == 0 && Q->ne[0] != 40 && Q->ne[0] != 72 && Q->ne[0] != 192 && Q->ne[0] != 512 && Q->ne[0] != 576 && Q->ne[0] != 640) { - if (can_use_vector_kernel && Q->ne[1] <= 2) { - return BEST_FATTN_KERNEL_VEC; - } - return BEST_FATTN_KERNEL_WMMA_F16; - } - - // TQ: RDNA4 fast path for TurboQuant cache types — prefer VEC for quantized K/V at small q-cols + // TQ: RDNA4 fast path for TurboQuant cache types - prefer VEC for quantized K/V at small q-cols if (amd_wmma_available(cc) && GGML_CUDA_CC_IS_RDNA4(cc) && gqa_opt_applies && Q->ne[0] <= 128 && Q->ne[0] != 40 && Q->ne[0] != 72) { if (can_use_vector_kernel) { if (!ggml_is_quantized(K->type) && !ggml_is_quantized(V->type)) { @@ -693,7 +683,6 @@ size_t ggml_cuda_flash_attn_ext_get_alloc_size(int device, const ggml_tensor * d switch (kernel) { case BEST_FATTN_KERNEL_TILE: - case BEST_FATTN_KERNEL_WMMA_F16: case BEST_FATTN_KERNEL_MMA_F16: need_f16_K = true; need_f16_V = true; @@ -723,9 +712,6 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst case BEST_FATTN_KERNEL_VEC: ggml_cuda_flash_attn_ext_vec(ctx, dst); break; - case BEST_FATTN_KERNEL_WMMA_F16: - ggml_cuda_flash_attn_ext_wmma_f16(ctx, dst); - break; case BEST_FATTN_KERNEL_MMA_F16: ggml_cuda_flash_attn_ext_mma_f16(ctx, dst); break; diff --git a/ggml/src/ggml-cuda/getrows.cu b/ggml/src/ggml-cuda/getrows.cu index 547679b358fc..399cb809ef0e 100644 --- a/ggml/src/ggml-cuda/getrows.cu +++ b/ggml/src/ggml-cuda/getrows.cu @@ -40,6 +40,35 @@ static __global__ void k_get_rows( } } +template dequantize_kq> +static __global__ void k_get_rows_kq( + const void * __restrict__ src0, const int32_t * __restrict__ src1, dst_t * __restrict__ dst, + const int64_t ne00, /*const int64_t ne01, const int64_t ne02, const int64_t ne03,*/ + /*const int64_t ne10,*/ const int64_t ne11, const uint3 ne12_fdv, /*const int64_t ne13,*/ + /*const size_t s0,*/ const size_t s1, const size_t s2, const size_t s3, + /*const size_t nb00,*/ const size_t nb01, const size_t nb02, const size_t nb03, + const size_t s10, const size_t s11, const size_t s12/*, const size_t s13*/) { + + ggml_cuda_pdl_sync(); + const int64_t nsb = ne00/QK_K; // super-blocks per row + for (int64_t z = blockIdx.z; z < ne11*(int64_t)ne12_fdv.z; z += gridDim.z) { + // The x and y dimensions of the grid are swapped because the maximum allowed grid size for x is higher. + const int i10 = blockIdx.x; + const uint2 dm = fast_div_modulo((uint32_t)z, ne12_fdv); + const int i11 = dm.x; + const int i12 = dm.y; + + const int i01 = src1[i10*s10 + i11*s11 + i12*s12]; + + dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3; + const void * src0_row = (const char *) src0 + i01*nb01 + i11*nb02 + i12*nb03; + + for (int64_t ib = blockIdx.y; ib < nsb; ib += gridDim.y) { + dequantize_kq(src0_row, ib, dst_row + ib*QK_K, threadIdx.x); + } + } +} + template static __global__ void k_get_rows_float( const src0_t * src0_ptr, const int32_t * src1_ptr, dst_t * dst_ptr, @@ -55,23 +84,47 @@ static __global__ void k_get_rows_float( dst_t * GGML_CUDA_RESTRICT dst = dst_ptr; ggml_cuda_pdl_sync(); for (int64_t z = blockIdx.z; z < ne11*(int64_t)ne12_fdv.z; z += gridDim.z) { + // The x and y dimensions of the grid are swapped because the maximum allowed grid size for x is higher. + const int i10 = blockIdx.x; + const uint2 dm = fast_div_modulo((uint32_t)z, ne12_fdv); + const int i11 = dm.x; + const int i12 = dm.y; + + const int i01 = src1[i10*s10 + i11*s11 + i12*s12]; + + dst_t * GGML_CUDA_RESTRICT dst_row = dst + i10*s1 + i11*s2 + i12*s3; + const src0_t * GGML_CUDA_RESTRICT src0_row = (const src0_t *)((const char *) src0 + i01*nb01 + i11*nb02 + i12*nb03); + for (int64_t i00 = blockIdx.y*blockDim.x + threadIdx.x; i00 < ne00; i00 += gridDim.y*blockDim.x) { - // The x and y dimensions of the grid are swapped because the maximum allowed grid size for x is higher. - const int i10 = blockIdx.x; - const uint2 dm = fast_div_modulo((uint32_t)z, ne12_fdv); - const int i11 = dm.x; - const int i12 = dm.y; + dst_row[i00] = ggml_cuda_cast(src0_row[i00]); + } + } +} - if (i00 >= ne00) { - return; - } +template +static __global__ void k_get_rows_float_vec( + const dst_t * src0_ptr, const int32_t * src1_ptr, dst_t * dst_ptr, + const int64_t ne00v, + const int64_t ne11, const uint3 ne12_fdv, + const size_t s1, const size_t s2, const size_t s3, + const size_t nb01, const size_t nb02, const size_t nb03, + const size_t s10, const size_t s11, const size_t s12) { - const int i01 = src1[i10*s10 + i11*s11 + i12*s12]; + ggml_cuda_pdl_lc(); + ggml_cuda_pdl_sync(); + for (int64_t z = blockIdx.z; z < ne11*(int64_t)ne12_fdv.z; z += gridDim.z) { + const int i10 = blockIdx.x; + const uint2 dm = fast_div_modulo((uint32_t)z, ne12_fdv); + const int i11 = dm.x; + const int i12 = dm.y; - dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3; - const src0_t * src0_row = (const src0_t *)((const char *) src0 + i01*nb01 + i11*nb02 + i12*nb03); + const int i01 = src1_ptr[i10*s10 + i11*s11 + i12*s12]; - dst_row[i00] = ggml_cuda_cast(src0_row[i00]); + int4 * GGML_CUDA_RESTRICT dst_row = (int4 *) (dst_ptr + i10*s1 + i11*s2 + i12*s3); + const int4 * GGML_CUDA_RESTRICT src0_row = (const int4 *)((const char *) src0_ptr + i01*nb01 + i11*nb02 + i12*nb03); + + for (int64_t i = blockIdx.y*blockDim.x + threadIdx.x; i < ne00v; i += gridDim.y*blockDim.x) { + dst_row[i] = src0_row[i]; } } } @@ -140,6 +193,43 @@ static void get_rows_cuda_q( s10, s11, s12/*, s13*/); } +template dequantize_kq> +static void get_rows_cuda_kq( + const void * src0_d, const int32_t * src1_d, dst_t * dst_d, + const int64_t ne00, const size_t nb01, const size_t nb02, const size_t nb03, + const int64_t ne10, const int64_t ne11, const int64_t ne12, const size_t nb10, const size_t nb11, const size_t nb12, + const size_t nb1, const size_t nb2, const size_t nb3, + cudaStream_t stream) { + GGML_ASSERT(ne00 % QK_K == 0); + const int64_t nsb = ne00/QK_K; + + const dim3 block_dims(block_dim, 1, 1); + const dim3 block_nums(ne10, MIN(nsb, UINT16_MAX), MIN(ne11*ne12, UINT16_MAX)); + + // strides in elements + // const size_t s0 = nb0 / sizeof(dst_t); + const size_t s1 = nb1 / sizeof(dst_t); + const size_t s2 = nb2 / sizeof(dst_t); + const size_t s3 = nb3 / sizeof(dst_t); + + const size_t s10 = nb10 / sizeof(int32_t); + const size_t s11 = nb11 / sizeof(int32_t); + const size_t s12 = nb12 / sizeof(int32_t); + // const size_t s13 = nb13 / sizeof(int32_t); + + GGML_ASSERT(ne12 > 0); + GGML_ASSERT(ne11 <= std::numeric_limits::max() / ne12); + const uint3 ne12_fdv = init_fastdiv_values(ne12); + + k_get_rows_kq<<>>( + src0_d, src1_d, dst_d, + ne00, /*ne01, ne02, ne03,*/ + /*ne10,*/ ne11, ne12_fdv, /*ne13,*/ + /* s0,*/ s1, s2, s3, + /* nb00,*/ nb01, nb02, nb03, + s10, s11, s12/*, s13*/); +} + template static void get_rows_cuda_float( const src0_t * src0_d, const int32_t * src1_d, dst_t * dst_d, @@ -148,8 +238,6 @@ static void get_rows_cuda_float( const size_t nb1, const size_t nb2, const size_t nb3, cudaStream_t stream) { const dim3 block_dims(CUDA_GET_ROWS_BLOCK_SIZE, 1, 1); - const int block_num_y = (ne00 + CUDA_GET_ROWS_BLOCK_SIZE - 1) / CUDA_GET_ROWS_BLOCK_SIZE; - const dim3 block_nums(ne10, MIN(block_num_y, UINT16_MAX), MIN(ne11*ne12, UINT16_MAX)); // strides in elements // const size_t s0 = nb0 / sizeof(dst_t); @@ -166,6 +254,34 @@ static void get_rows_cuda_float( GGML_ASSERT(ne11 <= std::numeric_limits::max() / ne12); const uint3 ne12_fdv = init_fastdiv_values(ne12); + if constexpr (std::is_same::value) { + constexpr int VEC = 16 / sizeof(dst_t); + const int64_t ne00v = ne00 / VEC; + const int64_t vec_block_num_y = (ne00v + CUDA_GET_ROWS_BLOCK_SIZE - 1) / CUDA_GET_ROWS_BLOCK_SIZE; + const bool enough_blocks = vec_block_num_y * ne10 * ne11 * ne12 >= 128; + const bool can_vec = VEC > 1 && enough_blocks && + (ne00 % VEC == 0) && + (nb01 % 16 == 0) && (nb02 % 16 == 0) && (nb03 % 16 == 0) && + (nb1 % 16 == 0) && (nb2 % 16 == 0) && (nb3 % 16 == 0) && + (((uintptr_t) src0_d) % 16 == 0) && (((uintptr_t) dst_d) % 16 == 0); + + if (can_vec) { + const int block_num_y = vec_block_num_y; + const dim3 block_nums(ne10, MIN(block_num_y, UINT16_MAX), MIN(ne11*ne12, UINT16_MAX)); + const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params{block_nums, block_dims, 0, stream}; + ggml_cuda_kernel_launch(k_get_rows_float_vec, launch_params, + (const dst_t *) src0_d, src1_d, dst_d, + ne00v, ne11, ne12_fdv, + s1, s2, s3, + nb01, nb02, nb03, + s10, s11, s12); + return; + } + } + + const int block_num_y = (ne00 + CUDA_GET_ROWS_BLOCK_SIZE - 1) / CUDA_GET_ROWS_BLOCK_SIZE; + const dim3 block_nums(ne10, MIN(block_num_y, UINT16_MAX), MIN(ne11*ne12, UINT16_MAX)); + const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params{block_nums, block_dims, 0, stream}; ggml_cuda_kernel_launch(k_get_rows_float, launch_params, src0_d, src1_d, dst_d, @@ -204,6 +320,10 @@ static void ggml_cuda_get_rows_switch_src0_type( get_rows_cuda_q(src0_d, src1_d, dst_d, ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); break; + case GGML_TYPE_Q2_0: + get_rows_cuda_q(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; case GGML_TYPE_Q4_0: get_rows_cuda_q(src0_d, src1_d, dst_d, ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); @@ -234,8 +354,67 @@ static void ggml_cuda_get_rows_switch_src0_type( get_rows_cuda_q(src0_d, src1_d, dst_d, ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); break; + case GGML_TYPE_Q2_K: + get_rows_cuda_kq<64, dst_t, dequantize_q2_K>(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_Q3_K: + get_rows_cuda_kq<64, dst_t, dequantize_q3_K>(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_Q4_K: + get_rows_cuda_kq<32, dst_t, dequantize_q4_K>(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_Q5_K: + get_rows_cuda_kq<64, dst_t, dequantize_q5_K>(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_Q6_K: + get_rows_cuda_kq<64, dst_t, dequantize_q6_K>(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_IQ2_XXS: + get_rows_cuda_kq<32, dst_t, dequantize_iq2_xxs>(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_IQ2_XS: + get_rows_cuda_kq<32, dst_t, dequantize_iq2_xs>(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_IQ2_S: + get_rows_cuda_kq<32, dst_t, dequantize_iq2_s>(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_IQ3_XXS: + get_rows_cuda_kq<32, dst_t, dequantize_iq3_xxs>(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_IQ3_S: + get_rows_cuda_kq<32, dst_t, dequantize_iq3_s>(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_IQ1_S: + get_rows_cuda_kq<32, dst_t, dequantize_iq1_s>(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_IQ1_M: + get_rows_cuda_kq<32, dst_t, dequantize_iq1_m>(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_IQ4_NL: + get_rows_cuda_kq<32, dst_t, dequantize_iq4_nl>(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_IQ4_XS: + get_rows_cuda_kq<32, dst_t, dequantize_iq4_xs>(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; + case GGML_TYPE_MXFP4: + get_rows_cuda_kq<32, dst_t, dequantize_mxfp4>(src0_d, src1_d, dst_d, + ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream); + break; default: - // TODO: k-quants GGML_ABORT("%s: unsupported src0 type: %s\n", __func__, ggml_type_name(src0_type)); break; } diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu index 4c3c5806ef47..1956bb276af6 100644 --- a/ggml/src/ggml-cuda/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -59,6 +59,7 @@ #include "ggml-cuda/wkv.cuh" #include "ggml-cuda/gla.cuh" #include "ggml-cuda/gated_delta_net.cuh" +#include "ggml-cuda/dsv4-hc.cuh" #include "ggml-cuda/set.cuh" #include "ggml-cuda/set-rows.cuh" #include "ggml-cuda/turbo-wht.cuh" @@ -68,6 +69,7 @@ #include "ggml-cuda/tri.cuh" #include "ggml-cuda/cumsum.cuh" #include "ggml-cuda/fill.cuh" +#include "ggml-cuda/lightning-indexer.cuh" #include "ggml.h" #include @@ -107,6 +109,13 @@ void ggml_cuda_error(const char * stmt, const char * func, const char * file, in GGML_ABORT(GGML_CUDA_NAME " error"); } +// map a (possibly virtual) device id to the physical CUDA device that backs it +static int ggml_cuda_get_physical_device(int device) { + const ggml_cuda_device_info & info = ggml_cuda_info(); + GGML_ASSERT(device >= 0 && device < info.device_count); + return info.devices[device].physical_device; +} + // Pinned host staging buffer for cross-GPU copies without peer access. // Reuses a single buffer across calls, growing as needed, to avoid the // overhead of per-call cudaMallocHost/cudaFreeHost. @@ -146,7 +155,8 @@ static cudaError_t ggml_cuda_copy_across_devices( const auto & info = ggml_cuda_info(); if (info.peer_access[src_device][dst_device]) { - return cudaMemcpyPeerAsync(dst, dst_device, src, src_device, size, dst_stream); + return cudaMemcpyPeerAsync(dst, ggml_cuda_get_physical_device(dst_device), + src, ggml_cuda_get_physical_device(src_device), size, dst_stream); } // Fallback: stage through pinned host memory via reusable pool @@ -173,14 +183,17 @@ cleanup: // this is faster on Windows // probably because the Windows CUDA libraries forget to make this check before invoking the drivers void ggml_cuda_set_device(int device) { + // translate the (possibly virtual) device id to the physical CUDA device that backs it + const int physical_device = ggml_cuda_get_physical_device(device); + int current_device; CUDA_CHECK(cudaGetDevice(¤t_device)); - if (device == current_device) { + if (physical_device == current_device) { return; } - CUDA_CHECK(cudaSetDevice(device)); + CUDA_CHECK(cudaSetDevice(physical_device)); } int ggml_cuda_get_device() { @@ -271,56 +284,102 @@ static int ggml_cuda_parse_id(char devName[]) { static ggml_cuda_device_info ggml_cuda_init() { ggml_cuda_device_info info = {}; - cudaError_t err = cudaGetDeviceCount(&info.device_count); + cudaError_t err = cudaGetDeviceCount(&info.physical_device_count); if (err != cudaSuccess) { GGML_LOG_ERROR("%s: failed to initialize " GGML_CUDA_NAME ": %s\n", __func__, cudaGetErrorString(err)); return info; } - GGML_ASSERT(info.device_count <= GGML_CUDA_MAX_DEVICES); + GGML_ASSERT(info.physical_device_count <= GGML_CUDA_MAX_DEVICES); - int64_t total_vram = 0; + // by default expose exactly the physical devices; GGML_CUDA_DEVICES can request a different + // number of (virtual) devices to emulate multi-GPU systems on a machine with fewer GPUs + info.device_count = info.physical_device_count; + + const char * devices_env = getenv("GGML_CUDA_DEVICES"); + if (devices_env != nullptr && info.physical_device_count > 0) { + const int requested = atoi(devices_env); + if (requested > 0) { + info.device_count = requested; + } else { + GGML_LOG_WARN("%s: ignoring invalid GGML_CUDA_DEVICES=\"%s\"\n", __func__, devices_env); + } + } + + if (info.device_count > GGML_CUDA_MAX_DEVICES) { + GGML_LOG_WARN("%s: requested %d devices, clamping to GGML_CUDA_MAX_DEVICES=%d\n", + __func__, info.device_count, GGML_CUDA_MAX_DEVICES); + info.device_count = GGML_CUDA_MAX_DEVICES; + } + + // map each (virtual) device to a backing physical device (round-robin), assign each its index + // among the (virtual) devices sharing that physical GPU, and store the per-physical share count + int physical_share_count[GGML_CUDA_MAX_DEVICES] = {}; + GGML_ASSERT(info.device_count == 0 || info.physical_device_count > 0); for (int id = 0; id < info.device_count; ++id) { + info.devices[id].physical_device = id % info.physical_device_count; + info.devices[id].virtual_index = physical_share_count[info.devices[id].physical_device]++; + } + + int64_t total_vram = 0; + for (int id = 0; id < info.physical_device_count; ++id) { cudaDeviceProp prop; CUDA_CHECK(cudaGetDeviceProperties(&prop, id)); total_vram += prop.totalGlobalMem; } GGML_LOG_INFO("%s: found %d " GGML_CUDA_NAME " devices (Total VRAM: %zu MiB):\n", - __func__, info.device_count, (size_t)(total_vram / (1024 * 1024))); + __func__, info.physical_device_count, (size_t)(total_vram / (1024 * 1024))); + if (info.device_count != info.physical_device_count) { + GGML_LOG_INFO("%s: emulating %d virtual device(s) on %d physical device(s) (GGML_CUDA_DEVICES)\n", + __func__, info.device_count, info.physical_device_count); + } total_vram = 0; std::vector> turing_devices_without_mma; for (int id = 0; id < info.device_count; ++id) { + const int physical_id = info.devices[id].physical_device; + int device_vmm = 0; #if defined(GGML_USE_VMM) CUdevice device; - CU_CHECK(cuDeviceGet(&device, id)); + CU_CHECK(cuDeviceGet(&device, physical_id)); CU_CHECK(cuDeviceGetAttribute(&device_vmm, CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED, device)); if (device_vmm) { CUmemAllocationProp alloc_prop = {}; alloc_prop.type = CU_MEM_ALLOCATION_TYPE_PINNED; alloc_prop.location.type = CU_MEM_LOCATION_TYPE_DEVICE; - alloc_prop.location.id = id; + alloc_prop.location.id = physical_id; CU_CHECK(cuMemGetAllocationGranularity(&info.devices[id].vmm_granularity, &alloc_prop, CU_MEM_ALLOC_GRANULARITY_RECOMMENDED)); } #endif // defined(GGML_USE_VMM) info.devices[id].vmm = !!device_vmm; cudaDeviceProp prop; - CUDA_CHECK(cudaGetDeviceProperties(&prop, id)); + CUDA_CHECK(cudaGetDeviceProperties(&prop, physical_id)); + + // a virtual device owns only a share of its physical GPU's memory; report that share so the + // logged per-device VRAM sums to the physical total above. + GGML_ASSERT(physical_share_count[physical_id] > 0); + info.devices[id].physical_share_count = physical_share_count[physical_id]; + const size_t device_vram = prop.totalGlobalMem / info.devices[id].physical_share_count; + const size_t device_vram_mib = device_vram / (1024 * 1024); info.default_tensor_split[id] = total_vram; - total_vram += prop.totalGlobalMem; + total_vram += device_vram; +#if defined(GGML_USE_HIP) + info.devices[id].integrated = prop.integrated; +#else info.devices[id].integrated = false; // Temporarily disabled due to issues with corrupted output (e.g. #15034) +#endif info.devices[id].nsm = prop.multiProcessorCount; info.devices[id].smpb = prop.sharedMemPerBlock; info.devices[id].warp_size = prop.warpSize; #ifndef GGML_USE_MUSA int supports_coop_launch = 0; - CUDA_CHECK(cudaDeviceGetAttribute(&supports_coop_launch, cudaDevAttrCooperativeLaunch, id)); + CUDA_CHECK(cudaDeviceGetAttribute(&supports_coop_launch, cudaDevAttrCooperativeLaunch, physical_id)); info.devices[id].supports_cooperative_launch = !!supports_coop_launch; #else info.devices[id].supports_cooperative_launch = false; @@ -343,7 +402,7 @@ static ggml_cuda_device_info ggml_cuda_init() { GGML_LOG_INFO(" Device %d: %s, %s (0x%x), VMM: %s, Wave Size: %d, VRAM: %zu MiB\n", id, prop.name, prop.gcnArchName, info.devices[id].cc & 0xffff, device_vmm ? "yes" : "no", prop.warpSize, - (size_t)(prop.totalGlobalMem / (1024 * 1024))); + device_vram_mib); #elif defined(GGML_USE_MUSA) // FIXME: Ensure compatibility with varying warp sizes across different MUSA archs. info.devices[id].warp_size = 32; @@ -352,13 +411,13 @@ static ggml_cuda_device_info ggml_cuda_init() { info.devices[id].cc += prop.minor * 0x10; GGML_LOG_INFO(" Device %d: %s, compute capability %d.%d, VMM: %s, VRAM: %zu MiB\n", id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no", - (size_t)(prop.totalGlobalMem / (1024 * 1024))); + device_vram_mib); #else info.devices[id].smpbo = prop.sharedMemPerBlockOptin; info.devices[id].cc = 100*prop.major + 10*prop.minor; GGML_LOG_INFO(" Device %d: %s, compute capability %d.%d, VMM: %s, VRAM: %zu MiB\n", id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no", - (size_t)(prop.totalGlobalMem / (1024 * 1024))); + device_vram_mib); std::string device_name(prop.name); if (device_name == "NVIDIA GeForce MX450") { turing_devices_without_mma.push_back({ id, device_name }); @@ -373,7 +432,7 @@ static ggml_cuda_device_info ggml_cuda_init() { // TODO: Check for future drivers the default scheduling strategy and // remove this call again when cudaDeviceScheduleSpin is default. if (prop.major == 12 && prop.minor == 1) { - CUDA_CHECK(cudaSetDevice(id)); + CUDA_CHECK(cudaSetDevice(physical_id)); CUDA_CHECK(cudaSetDeviceFlags(cudaDeviceScheduleSpin)); } @@ -398,9 +457,9 @@ static ggml_cuda_device_info ggml_cuda_init() { // CUBLAS_CHECK(cublasLoggerConfigure(1, 1, 0, nullptr)); if (getenv("GGML_CUDA_P2P") != nullptr) { - for (int id = 0; id < info.device_count; ++id) { - ggml_cuda_set_device(id); - for (int id_other = 0; id_other < info.device_count; ++id_other) { + for (int id = 0; id < info.physical_device_count; ++id) { + CUDA_CHECK(cudaSetDevice(id)); + for (int id_other = 0; id_other < info.physical_device_count; ++id_other) { if (id == id_other) { continue; } @@ -546,6 +605,7 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool { static const size_t CUDA_POOL_VMM_MAX_SIZE = 1ull << 35; // 32 GB int device; + int physical_device; CUdeviceptr pool_addr = 0; size_t pool_used = 0; size_t pool_size = 0; @@ -556,6 +616,7 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool { explicit ggml_cuda_pool_vmm(int device) : device(device), + physical_device(ggml_cuda_get_physical_device(device)), granularity(ggml_cuda_info().devices[device].vmm_granularity) { } @@ -591,7 +652,7 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool { CUmemAllocationProp prop = {}; prop.type = CU_MEM_ALLOCATION_TYPE_PINNED; prop.location.type = CU_MEM_LOCATION_TYPE_DEVICE; - prop.location.id = device; + prop.location.id = physical_device; CUmemGenericAllocationHandle handle; CU_CHECK(cuMemCreate(&handle, reserve_size, &prop, 0)); @@ -620,20 +681,28 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool { // NCCL implicitly enables peer access (cudaDeviceEnablePeerAccess), and // GGML_CUDA_P2P enables it explicitly. Unlike cudaMalloc buffers, VMM // allocations do not become peer-accessible from that alone, so access - // must be granted explicitly here. + // must be granted explicitly here. With virtual devices, grant access + // on the backing *physical* devices (deduplicated, since several + // virtual devices can map to the same physical GPU). std::vector access_descs; + bool physical_seen[GGML_CUDA_MAX_DEVICES] = {}; const int device_count = ggml_cuda_info().device_count; for (int id = 0; id < device_count; ++id) { - if (id != device) { + const int id_physical = ggml_cuda_get_physical_device(id); + if (id_physical != physical_device) { int can_access_peer = 0; - CUDA_CHECK(cudaDeviceCanAccessPeer(&can_access_peer, id, device)); + CUDA_CHECK(cudaDeviceCanAccessPeer(&can_access_peer, id_physical, physical_device)); if (!can_access_peer) { continue; } } + if (physical_seen[id_physical]) { + continue; + } + physical_seen[id_physical] = true; CUmemAccessDesc access = {}; access.location.type = CU_MEM_LOCATION_TYPE_DEVICE; - access.location.id = id; + access.location.id = id_physical; access.flags = CU_MEM_ACCESS_FLAGS_PROT_READWRITE; access_descs.push_back(access); } @@ -642,7 +711,7 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool { // set access for non P2P CUmemAccessDesc access = {}; access.location.type = CU_MEM_LOCATION_TYPE_DEVICE; - access.location.id = device; + access.location.id = physical_device; access.flags = CU_MEM_ACCESS_FLAGS_PROT_READWRITE; CU_CHECK(cuMemSetAccess(start_ptr, reserve_size, &access, 1)); } @@ -857,7 +926,11 @@ static bool ggml_backend_cuda_buffer_cpy_tensor(ggml_backend_buffer_t buffer, co if (ggml_backend_buffer_is_cuda(src->buffer)) { ggml_backend_cuda_buffer_context * src_ctx = (ggml_backend_cuda_buffer_context *)src->buffer->context; ggml_backend_cuda_buffer_context * dst_ctx = (ggml_backend_cuda_buffer_context *)dst->buffer->context; - if (src_ctx->device == dst_ctx->device) { + // compare the backing physical devices: distinct virtual devices may share one physical GPU, + // in which case a same-device copy (not a peer copy) is required + const int src_physical = ggml_cuda_get_physical_device(src_ctx->device); + const int dst_physical = ggml_cuda_get_physical_device(dst_ctx->device); + if (src_physical == dst_physical) { CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(src), cudaMemcpyDeviceToDevice, cudaStreamPerThread)); } else if (ggml_cuda_info().peer_access[src_ctx->device][dst_ctx->device]) { CUDA_CHECK(cudaMemcpyPeerAsync(dst->data, dst_ctx->device, src->data, src_ctx->device, ggml_nbytes(src), cudaStreamPerThread)); @@ -1216,6 +1289,15 @@ static void ggml_backend_cuda_comm_init_internal(ggml_backend_cuda_comm_context static void ggml_backend_cuda_comm_init_nccl(ggml_backend_cuda_comm_context * ret) { #ifdef GGML_USE_NCCL + // Disabling NCCL path when CUDA virtual devices are in use since NCCL requires one distinct physical GPU per rank. + const ggml_cuda_device_info & info = ggml_cuda_info(); + if (info.device_count > info.physical_device_count) { + GGML_LOG_WARN("NCCL disabled: virtual devices in use; " + "falling back to internal AllReduce\n"); + ggml_backend_cuda_comm_init_internal(ret); + return; + } + const size_t n = ret->dev_ids.size(); ret->comms.resize(n); ncclResult_t rc = ncclCommInitAll(ret->comms.data(), (int) n, ret->dev_ids.data()); @@ -1927,6 +2009,20 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor ggml_cuda_mul_mat_vec_f(ctx, src0, src1, nullptr, dst); return; } + // A transposed vector can still use MMVQ (i.e. ne01 == 1) + if (ne01 == 1 && ne11 > MMVF_MAX_BATCH_SIZE && ne2 == 1 && ne3 == 1 + && src0->type == GGML_TYPE_F32 + && ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && ggml_is_contiguous(dst) + && ggml_cuda_should_use_mmvf(src1->type, cc, src1->ne, src1->nb, /*ne11 =*/ 1)) { + ggml_tensor dst_vec = *dst; + dst_vec.ne[0] = ne11; + dst_vec.ne[1] = 1; + dst_vec.nb[1] = dst_vec.nb[0]*ne11; + dst_vec.nb[2] = dst_vec.nb[1]; + dst_vec.nb[3] = dst_vec.nb[1]; + ggml_cuda_mul_mat_vec_f(ctx, src1, src0, nullptr, &dst_vec); + return; + } if (!f32_pedantic && ggml_cuda_should_use_mmf( src0->type, cc, warp_size, src0->ne, src0->nb, ne11, /*mul_mat_id =*/ false)) { ggml_cuda_mul_mat_f(ctx, src0, src1, nullptr, dst); @@ -2424,6 +2520,15 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg case GGML_OP_GATED_DELTA_NET: ggml_cuda_op_gated_delta_net(ctx, dst); break; + case GGML_OP_DSV4_HC_COMB: + ggml_cuda_op_dsv4_hc_comb(ctx, dst); + break; + case GGML_OP_DSV4_HC_PRE: + ggml_cuda_op_dsv4_hc_pre(ctx, dst); + break; + case GGML_OP_DSV4_HC_POST: + ggml_cuda_op_dsv4_hc_post(ctx, dst); + break; case GGML_OP_RWKV_WKV7: ggml_cuda_op_rwkv_wkv7(ctx, dst); break; @@ -2442,6 +2547,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg case GGML_OP_FILL: ggml_cuda_op_fill(ctx, dst); break; + case GGML_OP_LIGHTNING_INDEXER: + ggml_cuda_lightning_indexer(ctx, dst); + break; default: return false; } @@ -2540,7 +2648,11 @@ static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend_src, ggml_ if (backend_src != backend_dst) { // copy on src stream - if (cuda_ctx_src->device == cuda_ctx_dst->device) { + // compare the backing physical devices: distinct virtual devices may share one physical GPU, + // in which case a same-device copy (not a peer copy) is required + const int src_physical = ggml_cuda_get_physical_device(cuda_ctx_src->device); + const int dst_physical = ggml_cuda_get_physical_device(cuda_ctx_dst->device); + if (src_physical == dst_physical) { CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyDeviceToDevice, cuda_ctx_src->stream())); } else if (ggml_cuda_info().peer_access[cuda_ctx_src->device][cuda_ctx_dst->device]) { CUDA_CHECK(cudaMemcpyPeerAsync(dst->data, cuda_ctx_dst->device, src->data, cuda_ctx_src->device, ggml_nbytes(dst), cuda_ctx_src->stream())); @@ -2797,6 +2909,7 @@ static int ggml_cuda_try_gdn_cache_fusion( static bool ggml_cuda_topk_moe_fusion(const struct ggml_cgraph * cgraph, int node_idx, ggml_cuda_topk_moe_args & args) { args.sigmoid = false; + args.sqrt_softplus = false; args.softmax = false; args.delayed_softmax = false; args.prob_bias = false; @@ -2810,10 +2923,17 @@ static bool ggml_cuda_topk_moe_fusion(const struct ggml_cgraph * cgraph, int nod } if (nodes[node_idx]->op == GGML_OP_UNARY) { - if (ggml_get_unary_op(nodes[node_idx]) != GGML_UNARY_OP_SIGMOID) { + const ggml_unary_op unary_op = ggml_get_unary_op(nodes[node_idx]); + if (unary_op == GGML_UNARY_OP_SIGMOID) { + args.sigmoid = true; + } else if (unary_op == GGML_UNARY_OP_SOFTPLUS && node_idx + 1 < n_nodes && + nodes[node_idx + 1]->op == GGML_OP_SQRT && nodes[node_idx + 1]->src[0] == nodes[node_idx]) { + // sqrt(softplus(x)) scoring (DeepSeek-V4) + args.sqrt_softplus = true; + node_idx++; + } else { return false; } - args.sigmoid = true; } if (nodes[node_idx]->op == GGML_OP_ARGSORT) { @@ -2822,7 +2942,7 @@ static bool ggml_cuda_topk_moe_fusion(const struct ggml_cgraph * cgraph, int nod node_idx++; - if (args.sigmoid || args.softmax) { + if (args.sigmoid || args.sqrt_softplus || args.softmax) { // SOFTMAX -> RESHAPE if (node_idx >= n_nodes || nodes[node_idx]->op != GGML_OP_RESHAPE || nodes[node_idx]->src[0] != nodes[node_idx - 1]) { @@ -3266,21 +3386,27 @@ static int ggml_cuda_try_fuse(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph const ggml_tensor * scale = nullptr; if (!args.delayed_softmax) { - ggml_op gating_op = args.sigmoid ? GGML_OP_UNARY : GGML_OP_SOFT_MAX; - int out_nodes[2]; // nodes which can't be elided + int out_nodes[2]; // nodes which can't be elided + + if (args.sigmoid) { + ops.insert(ops.end(), { GGML_OP_UNARY }); + } else if (args.sqrt_softplus) { + ops.insert(ops.end(), { GGML_OP_UNARY, GGML_OP_SQRT }); + } else { + ops.insert(ops.end(), { GGML_OP_SOFT_MAX }); + } + const int i_probs = i + (int) ops.size() - 1; // last node of the gating activation if (args.prob_bias) { - bias = cgraph->nodes[i + 2]->src[1]; - ops.insert(ops.end(), { gating_op, GGML_OP_RESHAPE, GGML_OP_ADD, GGML_OP_ARGSORT, GGML_OP_VIEW, + bias = cgraph->nodes[i_probs + 2]->src[1]; + ops.insert(ops.end(), { GGML_OP_RESHAPE, GGML_OP_ADD, GGML_OP_ARGSORT, GGML_OP_VIEW, GGML_OP_GET_ROWS }); - out_nodes[0] = i + 4; - ids = cgraph->nodes[i + 4]; + out_nodes[0] = i_probs + 4; } else { - ops.insert(ops.end(), - { gating_op, GGML_OP_RESHAPE, GGML_OP_ARGSORT, GGML_OP_VIEW, GGML_OP_GET_ROWS }); - out_nodes[0] = i + 3; - ids = cgraph->nodes[i + 3]; + ops.insert(ops.end(), { GGML_OP_RESHAPE, GGML_OP_ARGSORT, GGML_OP_VIEW, GGML_OP_GET_ROWS }); + out_nodes[0] = i_probs + 3; } + ids = cgraph->nodes[out_nodes[0]]; if (args.norm) { ops.insert(ops.end(), @@ -4165,7 +4291,7 @@ static bool ggml_cuda_graph_set_enabled(ggml_backend_cuda_context * cuda_ctx, co ggml_cuda_graph * graph = cuda_ctx->cuda_graph(graph_key); if (graph->graph == nullptr) { - if (ggml_cuda_info().devices[cuda_ctx->device].cc < GGML_CUDA_CC_AMPERE) { + if (ggml_cuda_info().devices[cuda_ctx->device].cc < GGML_CUDA_CC_VOLTA) { if (!graph->disable_due_to_gpu_arch) { GGML_LOG_DEBUG("%s: disabling CUDA graphs due to GPU architecture\n", __func__); } @@ -4537,16 +4663,38 @@ int ggml_backend_cuda_get_device_count() { return ggml_cuda_info().device_count; } -void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size) { +static std::string ggml_cuda_device_description(int device) { cudaDeviceProp prop; - CUDA_CHECK(cudaGetDeviceProperties(&prop, device)); - snprintf(description, description_size, "%s", prop.name); + CUDA_CHECK(cudaGetDeviceProperties(&prop, ggml_cuda_get_physical_device(device))); + + const ggml_cuda_device_info & info = ggml_cuda_info(); + std::string description = prop.name; + if (info.device_count > info.physical_device_count) { + description += " (physical device " + std::to_string(info.devices[device].physical_device) + + ", virtual device " + std::to_string(info.devices[device].virtual_index) + ")"; + } + return description; +} + +void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size) { + snprintf(description, description_size, "%s", ggml_cuda_device_description(device).c_str()); +} + +static int ggml_cuda_physical_device_share_count(int device) { + const ggml_cuda_device_info & info = ggml_cuda_info(); + GGML_ASSERT(device >= 0 && device < info.device_count); + return info.devices[device].physical_share_count; } void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total) { ggml_cuda_set_device(device); CUDA_CHECK(cudaMemGetInfo(free, total)); + + // virtual devices sharing one physical GPU share its memory pool; split it between them + const int share_count = ggml_cuda_physical_device_share_count(device); + *free /= share_count; + *total /= share_count; } bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size) { @@ -4697,7 +4845,7 @@ static void ggml_backend_cuda_device_get_memory(ggml_backend_dev_t dev, size_t * #if defined(__linux__) // Check if this is a UMA (Unified Memory Architecture) system cudaDeviceProp prop; - CUDA_CHECK(cudaGetDeviceProperties(&prop, ctx->device)); + CUDA_CHECK(cudaGetDeviceProperties(&prop, ggml_cuda_get_physical_device(ctx->device))); // Check if UMA is explicitly enabled via environment variable bool uma_env = getenv("GGML_CUDA_ENABLE_UNIFIED_MEMORY") != nullptr; @@ -4716,13 +4864,17 @@ static void ggml_backend_cuda_device_get_memory(ggml_backend_dev_t dev, size_t * } #endif // defined(__linux__) + // virtual devices sharing one physical GPU share its memory pool; split it between them + const int share_count = ggml_cuda_physical_device_share_count(ctx->device); + *free /= share_count; + *total /= share_count; } static enum ggml_backend_dev_type ggml_backend_cuda_device_get_type(ggml_backend_dev_t dev) { ggml_backend_cuda_device_context * ctx = (ggml_backend_cuda_device_context *) dev->context; cudaDeviceProp prop; - CUDA_CHECK(cudaGetDeviceProperties(&prop, ctx->device)); + CUDA_CHECK(cudaGetDeviceProperties(&prop, ggml_cuda_get_physical_device(ctx->device))); return prop.integrated ? GGML_BACKEND_DEVICE_TYPE_IGPU @@ -4856,6 +5008,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g case GGML_TYPE_F32: case GGML_TYPE_F16: case GGML_TYPE_Q1_0: + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -4896,6 +5049,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g case GGML_TYPE_BF16: case GGML_TYPE_I32: case GGML_TYPE_Q1_0: + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -4903,7 +5057,25 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g case GGML_TYPE_Q8_0: case GGML_TYPE_TQ4_1S: case GGML_TYPE_TQ3_1S: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ4_XS: return true; + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_MXFP4: + // 32-value sub-blocks, the row size does not guarantee + // the QK_K super-blocks the get_rows kernel iterates on + return op->src[0]->ne[0] % QK_K == 0; default: return false; } @@ -5020,13 +5192,23 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g { ggml_type src0_type = op->src[0]->type; ggml_type src1_type = op->src[1]->type; + const int32_t dim = op->op_params[0]; return src0_type == src1_type && src0_type == op->type && ( ( ggml_is_quantized(src0_type) && - ggml_is_contiguous(op->src[0]) && - ggml_is_contiguous(op->src[1]) && + ( + ( + dim == 3 && + ggml_is_contiguous(op->src[0]) && + ggml_is_contiguous(op->src[1]) + ) || ( + dim != 3 && + ggml_is_contiguous_to_3(op->src[0]) && + ggml_is_contiguous_to_3(op->src[1]) + ) + ) && op->src[0]->ne[0] % ggml_blck_size(src0_type) == 0 && op->src[1]->ne[0] % ggml_blck_size(src0_type) == 0 ) || ( @@ -5131,7 +5313,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g case GGML_OP_IM2COL: case GGML_OP_IM2COL_3D: case GGML_OP_CONV_2D: - return true; + return (ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1])); case GGML_OP_CONV_2D_DW: return op->src[0]->type == GGML_TYPE_F32; case GGML_OP_CONV_TRANSPOSE_2D: @@ -5172,6 +5354,16 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g #else return true; #endif // GGML_USE_MUSA + case GGML_OP_DSV4_HC_COMB: + return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32 && + op->src[2]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32; + case GGML_OP_DSV4_HC_PRE: + return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32 && + op->type == GGML_TYPE_F32; + case GGML_OP_DSV4_HC_POST: + return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32 && + op->src[2]->type == GGML_TYPE_F32 && op->src[3]->type == GGML_TYPE_F32 && + op->type == GGML_TYPE_F32; case GGML_OP_FLASH_ATTN_EXT: return ggml_cuda_flash_attn_ext_supported(dev_ctx->device, op); case GGML_OP_FLASH_ATTN_EXT_BANDED: @@ -5186,6 +5378,8 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g case GGML_OP_DIAG: case GGML_OP_SOLVE_TRI: return true; + case GGML_OP_LIGHTNING_INDEXER: + return ggml_cuda_lightning_indexer_supported(dev_ctx->device, op); default: return false; @@ -5388,18 +5582,24 @@ ggml_backend_reg_t ggml_backend_cuda_reg() { ggml_backend_cuda_reg_context * ctx = new ggml_backend_cuda_reg_context; const int min_batch_size = getenv("GGML_OP_OFFLOAD_MIN_BATCH") ? atoi(getenv("GGML_OP_OFFLOAD_MIN_BATCH")) : 32; - for (int i = 0; i < ggml_cuda_info().device_count; i++) { + const ggml_cuda_device_info & info = ggml_cuda_info(); + const bool virtual_devices = info.device_count > info.physical_device_count; + + for (int i = 0; i < info.device_count; i++) { + const int physical_id = info.devices[i].physical_device; + ggml_backend_cuda_device_context * dev_ctx = new ggml_backend_cuda_device_context; dev_ctx->device = i; dev_ctx->name = GGML_CUDA_NAME + std::to_string(i); - - cudaDeviceProp prop; - CUDA_CHECK(cudaGetDeviceProperties(&prop, i)); - dev_ctx->description = prop.name; + dev_ctx->description = ggml_cuda_device_description(i); char pci_bus_id[32] = {}; - CUDA_CHECK(cudaDeviceGetPCIBusId(pci_bus_id, sizeof(pci_bus_id), i)); + CUDA_CHECK(cudaDeviceGetPCIBusId(pci_bus_id, sizeof(pci_bus_id), physical_id)); dev_ctx->pci_bus_id = pci_bus_id; + if (virtual_devices) { + // make the pci bus id unique for virtual devices + dev_ctx->pci_bus_id += "-v" + std::to_string(i); + } for (char & c : dev_ctx->pci_bus_id) { c = std::tolower(c); } diff --git a/ggml/src/ggml-cuda/lightning-indexer.cu b/ggml/src/ggml-cuda/lightning-indexer.cu new file mode 100644 index 000000000000..5edc967e0e92 --- /dev/null +++ b/ggml/src/ggml-cuda/lightning-indexer.cu @@ -0,0 +1,588 @@ +#include "common.cuh" +#include "lightning-indexer.cuh" +#include "fattn-common.cuh" +#include "convert.cuh" + +#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) +#if defined(TURING_MMA_AVAILABLE) + +typedef union { + int2 i2; + half2 h2[2]; +} half4; + +// TODO add support for AMD cards via rocWMMA +#include +namespace wmma = nvcuda::wmma; + +template +static __global__ void lightning_indexer_kernel_wmma( + const float * Q, const char * K, const float * W, const half * M, float * dst, + int64_t n_stream, int64_t n_batch, int64_t n_kv, + size_t nb1, size_t nb2, size_t nb3, + size_t nbq1, size_t nbq2, size_t nbq3, + size_t nbk1, size_t nbk2, size_t nbk3, + size_t nbw1, size_t nbw2, size_t nbw3, + size_t nbm1, size_t nbm2, size_t nbm3, + int64_t nem3 + ) { + + constexpr int THREADS_PER_BLOCK = WARPS_PER_BLOCK * WARP_SIZE; + constexpr int HEADS_PER_INNER_LOOP = 8; + constexpr int K_EMBD_PER_INNER_LOOP = 16; + constexpr int N_EMBD_PADDED = N_EMBD + 8; + + const int i_batch = blockIdx.y; + const int i_stream = blockIdx.z; + const int i_warp = threadIdx.y; + const int i_lane = threadIdx.x; + const int tid = i_warp * WARP_SIZE + i_lane; + + // each block processes K_VECS_PER_BLOCK K vectors + const int start_kv = blockIdx.x * K_VECS_PER_BLOCK; + + const char * q_base = (const char *) Q + i_batch*nbq2 + i_stream*nbq3; + const float * w_base = (const float *) ((const char *) W + i_batch*nbw1 + i_stream*nbw3); + + // phase 1 - load weights and first Q tile to shared memory + + __shared__ float w_shared[N_HEAD]; + __shared__ int2 q_shared_h[HEADS_PER_INNER_LOOP][N_EMBD_PADDED / 4]; + + if (tid < N_HEAD) { + w_shared[tid] = w_base[tid]; + } + + // total number of half4 elements in HEADS_PER_INNER_LOOP x N_EMBD Q tile + constexpr int N_Q_TILE = HEADS_PER_INNER_LOOP * (N_EMBD / 4); + // number of registers needed in each thread to store Q tile in thread block + constexpr int N_Q_NEXT = (N_Q_TILE + THREADS_PER_BLOCK - 1) / THREADS_PER_BLOCK; + +#pragma unroll + for (int i_q = tid; i_q < N_Q_TILE; i_q += THREADS_PER_BLOCK) { + const int i_head = i_q / (N_EMBD / 4); + const int i_embd = i_q % (N_EMBD / 4); + const float4 q = *(const float4 *) (q_base + i_head*nbq1 + i_embd*sizeof(float4)); + half4 q_packed; + q_packed.h2[0] = __float22half2_rn(make_float2(q.x, q.y)); + q_packed.h2[1] = __float22half2_rn(make_float2(q.z, q.w)); + q_shared_h[i_head][i_embd] = q_packed.i2; + } + + // phase 2 - load (and dequantize if needed) K to shared mem + + __shared__ half2 k_shared_h[K_VECS_PER_BLOCK][N_EMBD_PADDED / 4][2]; + + constexpr int n_k = K_VECS_PER_BLOCK * (N_EMBD / 4); + + if constexpr (TYPE_K == GGML_TYPE_F16) { +#pragma unroll + for (int i_k = tid; i_k < n_k; i_k += THREADS_PER_BLOCK) { + const int i_k_vec = i_k / (N_EMBD / 4); + const int i_embd = i_k % (N_EMBD / 4); + const int i_kv = start_kv + i_k_vec; + if (i_kv < n_kv) { + const int2 * k_base = (const int2 *) ((const char *) K + i_kv*nbk2 + i_stream*nbk3); + *(int2*) &k_shared_h[i_k_vec][i_embd] = k_base[i_embd]; + } else { + *(int2*) &k_shared_h[i_k_vec][i_embd] = make_int2(0, 0); + } + } + } else { + constexpr dequantize_V_t dequantize_k = get_dequantize_V(); +#pragma unroll + for (int i_k = tid; i_k < n_k; i_k += THREADS_PER_BLOCK) { + const int i_k_vec = i_k / (N_EMBD / 4); + const int i_embd = i_k % (N_EMBD / 4); + const int i_kv = start_kv + i_k_vec; + if (i_kv < n_kv) { + const void * k_base = (const void *) ((const char *) K + i_kv*nbk2 + i_stream*nbk3); + dequantize_k(k_base, &k_shared_h[i_k_vec][i_embd][0], i_embd * 4); + } else { + *(int2*) &k_shared_h[i_k_vec][i_embd] = make_int2(0, 0); + } + } + } + + __syncthreads(); + + // phase 3 - calculate lightning indexer scores + + __shared__ float qk_shared[WARPS_PER_BLOCK][HEADS_PER_INNER_LOOP][K_VECS_PER_BLOCK]; + + // load K fragment + wmma::fragment frag_k; + wmma::load_matrix_sync(frag_k, (half*) &k_shared_h[0][i_warp * K_EMBD_PER_INNER_LOOP / 4], N_EMBD_PADDED); + + float score_k = 0.0f; + + for (int i_head_0 = 0; i_head_0 < N_HEAD; i_head_0 += HEADS_PER_INNER_LOOP) { + const int i_head_next = i_head_0 + HEADS_PER_INNER_LOOP; + + // we don't use accumulator for anything, fill it with zeros + wmma::fragment frag_acc; + wmma::fill_fragment(frag_acc, 0.0f); + + // load Q fragment + wmma::fragment frag_q; + wmma::load_matrix_sync(frag_q, (half*) &q_shared_h[0][i_warp * K_EMBD_PER_INNER_LOOP / 4], N_EMBD_PADDED); + + // preload next Q tile to registers during matrix multiplication + float4 q_next[N_Q_NEXT]; + + if (i_head_next < N_HEAD) { +#pragma unroll + for (int i_q = tid, i_q_next = 0; i_q < N_Q_TILE; i_q += THREADS_PER_BLOCK) { + const int i_head = i_head_next + i_q / (N_EMBD / 4); + const int i_embd = i_q % (N_EMBD / 4); + q_next[i_q_next++] = *(const float4 *) (q_base + i_head*nbq1 + i_embd*sizeof(float4)); + } + } + + // perform matrix multiplication + wmma::mma_sync(frag_acc, frag_q, frag_k, frag_acc); + wmma::store_matrix_sync((float*) &qk_shared[i_warp][0][0], frag_acc, K_VECS_PER_BLOCK, wmma::mem_row_major); + + // make sure all threads finished using q_shared_h so we can store next tile + __syncthreads(); + + // write preloaded Q tile to shared memory + if (i_head_next < N_HEAD) { +#pragma unroll + for (int i_q = tid, i_q_next = 0; i_q < N_Q_TILE; i_q += THREADS_PER_BLOCK) { + const int i_head = i_q / (N_EMBD / 4); + const int i_embd = i_q % (N_EMBD / 4); + half4 q_packed; + q_packed.h2[0] = __float22half2_rn(make_float2(q_next[i_q_next].x, q_next[i_q_next].y)); + q_packed.h2[1] = __float22half2_rn(make_float2(q_next[i_q_next].z, q_next[i_q_next].w)); + q_shared_h[i_head][i_embd] = q_packed.i2; + ++i_q_next; + } + } + + // accumulate QK multiplication results from all block warps + // (there are 256 threads in block and 256 matmul outputs) + // TODO it will break if WARP_SIZE is not 32 + const int h = tid / K_VECS_PER_BLOCK; + const int k = tid % K_VECS_PER_BLOCK; + const float w_val = w_shared[i_head_0 + h]; + + float sum = 0.0f; +#pragma unroll + for (int w = 0; w < WARPS_PER_BLOCK; ++w) { + sum += qk_shared[w][h][k]; + } + + // ReLU, weight + sum = sum > 0.0f ? sum : 0.0f; + sum *= w_val; + + // wait until qk_shared[0] is no longer used + __syncthreads(); + + // reuse qk_shared[0] for storing partial results + qk_shared[0][h][k] = sum; + + // wait until all threads write their results + __syncthreads(); + + // accumulate result over heads + if (tid < K_VECS_PER_BLOCK) { +#pragma unroll + for (int i_head = 0; i_head < HEADS_PER_INNER_LOOP; ++i_head) { + score_k += qk_shared[0][i_head][tid]; + } + } + + // make sure all threads finished using qk_shared + __syncthreads(); + } + + // phase 4 - store output to VRAM + + if (tid < K_VECS_PER_BLOCK) { + const int i_kv = start_kv + tid; + if (i_kv < n_kv) { + const half * m_base = (const half *) ((const char *) M + i_batch*nbm1 + (i_stream%nem3)*nbm3); + float * dst_base = (float *) ((char *) dst + i_batch*nb1 + i_stream*nb3); + dst_base[i_kv] = score_k + __half2float(m_base[i_kv]); + } + } +} + +#else // defined(TURING_MMA_AVAILABLE) + +template +static __global__ void lightning_indexer_kernel_wmma( + const float * Q, const char * K, const float * W, const half * M, float * dst, + int64_t n_stream, int64_t n_batch, int64_t n_kv, + size_t nb1, size_t nb2, size_t nb3, + size_t nbq1, size_t nbq2, size_t nbq3, + size_t nbk1, size_t nbk2, size_t nbk3, + size_t nbw1, size_t nbw2, size_t nbw3, + size_t nbm1, size_t nbm2, size_t nbm3, + int64_t nem3 + ) { + GGML_UNUSED_VARS(Q, K, W, M, dst, + n_stream, n_batch, n_kv, + nb1, nb2, nb3, + nbq1, nbq2, nbq3, + nbk1, nbk2, nbk3, + nbw1, nbw2, nbw3, + nem3); + NO_DEVICE_CODE; +} + +#endif // defined(TURING_MMA_AVAILABLE) +#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) + +// TODO there is one ugly assumption used in this kernel - that WARP_SIZE is equal to 32 +// thanks to that one warp operating on float4 processes whole indexer K/Q vectors +// 32 * 4 = 128 (N_EMBD) + +template +static __global__ void lightning_indexer_kernel_vec( + const float * Q, const char * K, const float * W, const half * M, float * dst, + int64_t n_stream, int64_t n_batch, int64_t n_kv, + size_t nb1, size_t nb2, size_t nb3, + size_t nbq1, size_t nbq2, size_t nbq3, + size_t nbk1, size_t nbk2, size_t nbk3, + size_t nbw1, size_t nbw2, size_t nbw3, + size_t nbm1, size_t nbm2, size_t nbm3, + int64_t nem3 + ) { + + constexpr int K_VECS_PER_WARP = K_VECS_PER_BLOCK / WARPS_PER_BLOCK; + constexpr int THREADS_PER_BLOCK = WARPS_PER_BLOCK * WARP_SIZE; + + const int i_batch = blockIdx.y; + const int i_stream = blockIdx.z; + const int i_warp = threadIdx.y; + const int i_lane = threadIdx.x; + const int tid = i_warp * WARP_SIZE + i_lane; + + // each warp processes K_VECS_PER_WARP K vectors + const int start_kv_block = blockIdx.x * K_VECS_PER_BLOCK; + const int start_kv = start_kv_block + i_warp * K_VECS_PER_WARP; + + const char * q_base = (const char *) Q + i_batch*nbq2 + i_stream*nbq3; + const float * w_base = (const float *) ((const char *) W + i_batch*nbw1 + i_stream*nbw3); + + // phase 1 - load (and dequantize if needed) K to registers + + float4 k_reg_f[K_VECS_PER_WARP]; + + if constexpr (TYPE_K == GGML_TYPE_F32) { + // direct copy of float4 +#pragma unroll + for (int k = 0; k < K_VECS_PER_WARP; ++k) { + int i_kv = start_kv + k; + if (i_kv < n_kv) { + const float4 * k_base = (const float4 *) ((const char *) K + i_kv*nbk2 + i_stream*nbk3); + k_reg_f[k] = k_base[i_lane]; + } else { + k_reg_f[k] = make_float4(0, 0, 0, 0); + } + } + } else { + // dequantize remaining types to float + constexpr dequantize_V_t dequantize_k = get_dequantize_V(); +#pragma unroll + for (int k = 0; k < K_VECS_PER_WARP; ++k) { + int i_kv = start_kv + k; + if (i_kv < n_kv) { + const void * k_base = (const void *) ((const char *) K + i_kv*nbk2 + i_stream*nbk3); + dequantize_k(k_base, &k_reg_f[k], i_lane * 4); + } else { + k_reg_f[k] = make_float4(0, 0, 0, 0); + } + } + } + + float score_k[K_VECS_PER_WARP] = { 0.0f }; + + // load weights and Q only for N_HEAD_INNER heads at once to reduce shared memory usage + constexpr int N_HEAD_INNER = N_HEAD / 4; + + for (int i_head_0 = 0; i_head_0 < N_HEAD; i_head_0 += N_HEAD_INNER) { + // phase 2 - load weights and Q to shared memory + + __shared__ float w_shared[N_HEAD_INNER]; + __shared__ float4 q_shared_f[N_HEAD_INNER][N_EMBD / 4]; + + if (tid < N_HEAD_INNER) { + w_shared[tid] = w_base[i_head_0 + tid]; + } + + constexpr int n_q = N_HEAD_INNER * (N_EMBD / 4); +#pragma unroll + for (int i_q = tid; i_q < n_q; i_q += THREADS_PER_BLOCK) { + const int i_head_inner = i_q / (N_EMBD / 4); + const int i_head = i_head_0 + i_head_inner; + const int i_embd = i_q % (N_EMBD / 4); + q_shared_f[i_head_inner][i_embd] = *(const float4 *) (q_base + i_head*nbq1 + i_embd*sizeof(float4)); + } + + __syncthreads(); + + // phase 3 - calculate lightning indexer scores + + for (int i_head_inner = 0; i_head_inner < N_HEAD_INNER; ++i_head_inner) { + const float w_val = w_shared[i_head_inner]; + float qk[K_VECS_PER_WARP] = { 0.0f }; + + // dot product of floats + const float4 q_vec = q_shared_f[i_head_inner][i_lane]; + +#pragma unroll + for (int k = 0; k < K_VECS_PER_WARP; ++k) { + ggml_cuda_mad(qk[k], q_vec.x, k_reg_f[k].x); + ggml_cuda_mad(qk[k], q_vec.y, k_reg_f[k].y); + ggml_cuda_mad(qk[k], q_vec.z, k_reg_f[k].z); + ggml_cuda_mad(qk[k], q_vec.w, k_reg_f[k].w); + } + +#pragma unroll + for (int k = 0; k < K_VECS_PER_WARP; ++k) { + float sum = warp_reduce_sum(qk[k]); + + // ReLU, weight + if (i_lane == 0) { + sum = (sum > 0.0f) ? sum : 0.0f; + score_k[k] += sum * w_val; + } + } + } + + __syncthreads(); + } + + // phase 4 - store outputs to shared memory + + __shared__ float dst_shared[K_VECS_PER_BLOCK]; + + if (i_lane == 0) { +#pragma unroll + for (int k = 0; k < K_VECS_PER_WARP; ++k) { + dst_shared[i_warp * K_VECS_PER_WARP + k] = score_k[k]; + } + } + + __syncthreads(); + + // phase 5 - write from shared memory to VRAM in coalesced manner + + if (tid < K_VECS_PER_BLOCK) { + int i_kv = start_kv_block + tid; + if (i_kv < n_kv) { + const half * m_base = (const half *) ((const char *) M + i_batch*nbm1 + (i_stream%nem3)*nbm3); + float * dst_base = (float *) ((char *) dst + i_batch*nb1 + i_stream*nb3); + dst_base[i_kv] = dst_shared[tid] + __half2float(m_base[i_kv]); + } + } +} + +#define LIGHTNING_INDEXER_CASE(lightning_indexer_kernel, n_embd, n_head, K, type_K) \ + if (K->type == (type_K)) { \ + lightning_indexer_kernel \ + <<>>( \ + q_d, k_d, w_d, m_d, dst_d, \ + n_stream, n_batch, n_kv, \ + nb1, nb2, nb3, \ + nbq1, nbq2, nbq3, \ + nbk1, nbk2, nbk3, \ + nbw1, nbw2, nbw3, \ + nbm1, nbm2, nbm3, \ + nem3 \ + ); \ + } else + +void ggml_cuda_lightning_indexer(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * q = dst->src[0]; + const ggml_tensor * k = dst->src[1]; + const ggml_tensor * w = dst->src[2]; // weights + const ggml_tensor * m = dst->src[3]; // mask + + GGML_ASSERT(dst->type == GGML_TYPE_F32); + GGML_ASSERT( q->type == GGML_TYPE_F32); + GGML_ASSERT( w->type == GGML_TYPE_F32); + GGML_ASSERT( m->type == GGML_TYPE_F16); + + GGML_TENSOR_LOCALS(int64_t, neq, q, ne) + GGML_TENSOR_LOCALS(size_t, nbq, q, nb) + GGML_TENSOR_LOCALS(int64_t, nek, k, ne) + GGML_TENSOR_LOCALS(size_t, nbk, k, nb) + GGML_TENSOR_LOCALS(int64_t, new, w, ne) + GGML_TENSOR_LOCALS(size_t, nbw, w, nb) + GGML_TENSOR_LOCALS(int64_t, nem, m, ne) + GGML_TENSOR_LOCALS(size_t, nbm, m, nb) + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) + GGML_TENSOR_LOCALS(size_t, nb, dst, nb) + + // input tensor rows must be contiguous + GGML_ASSERT(nbq0 == ggml_type_size(q->type)); + GGML_ASSERT(nbk0 == ggml_type_size(k->type)); + GGML_ASSERT(nbw0 == ggml_type_size(w->type)); + GGML_ASSERT(nbm0 == ggml_type_size(m->type)); + + // dst cannot be transposed or permuted + GGML_ASSERT(nb0 == sizeof(float)); + GGML_ASSERT(nb0 <= nb1); + GGML_ASSERT(nb1 <= nb2); + GGML_ASSERT(nb2 <= nb3); + + const int n_embd = q->ne[0]; + const int n_head = q->ne[1]; + const int n_batch = q->ne[2]; + const int n_stream = q->ne[3]; + const int n_kv = k->ne[2]; + + const float * q_d = (const float *) q->data; + const char * k_d = (const char *) k->data; + const float * w_d = (const float *) w->data; + const half * m_d = (const half *) m->data; + float * dst_d = ( float *) dst->data; + + const int device = ggml_cuda_get_device(); + const int cc = ggml_cuda_info().devices[device].cc; + + if (n_embd == 128 && n_head == 64) { +#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) + if (GGML_CUDA_CC_IS_NVIDIA(cc) && turing_mma_available(cc) && k->type != GGML_TYPE_F32 && k->type != GGML_TYPE_BF16) { + // use wmma kernel + constexpr int K_VECS_PER_BLOCK = 32; + constexpr int WARPS_PER_BLOCK = 8; + + dim3 block(32, WARPS_PER_BLOCK); + int num_kv_blocks = (n_kv + (K_VECS_PER_BLOCK) - 1) / (K_VECS_PER_BLOCK); + dim3 grid(num_kv_blocks, n_batch, n_stream); + + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 64, k, GGML_TYPE_F16) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 64, k, GGML_TYPE_Q4_0) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 64, k, GGML_TYPE_Q4_1) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 64, k, GGML_TYPE_Q5_0) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 64, k, GGML_TYPE_Q5_1) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 64, k, GGML_TYPE_Q8_0) + GGML_ABORT("fatal error"); + } else { +#else // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) + { +#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) + // use vector kernel + constexpr int K_VECS_PER_WARP = 8; + constexpr int WARPS_PER_BLOCK = 8; + constexpr int K_VECS_PER_BLOCK = K_VECS_PER_WARP * WARPS_PER_BLOCK; + + dim3 block(32, WARPS_PER_BLOCK); + int num_kv_blocks = (n_kv + (K_VECS_PER_BLOCK) - 1) / (K_VECS_PER_BLOCK); + dim3 grid(num_kv_blocks, n_batch, n_stream); + + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 64, k, GGML_TYPE_F16) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 64, k, GGML_TYPE_Q4_0) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 64, k, GGML_TYPE_Q4_1) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 64, k, GGML_TYPE_Q5_0) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 64, k, GGML_TYPE_Q5_1) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 64, k, GGML_TYPE_Q8_0) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 64, k, GGML_TYPE_BF16) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 64, k, GGML_TYPE_F32) + GGML_ABORT("fatal error"); + } + } else if (n_embd == 128 && n_head == 32) { +#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) + if (GGML_CUDA_CC_IS_NVIDIA(cc) && turing_mma_available(cc) && k->type != GGML_TYPE_F32 && k->type != GGML_TYPE_BF16) { + // use wmma kernel + constexpr int K_VECS_PER_BLOCK = 32; + constexpr int WARPS_PER_BLOCK = 8; + + dim3 block(32, WARPS_PER_BLOCK); + int num_kv_blocks = (n_kv + (K_VECS_PER_BLOCK) - 1) / (K_VECS_PER_BLOCK); + dim3 grid(num_kv_blocks, n_batch, n_stream); + + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 32, k, GGML_TYPE_F16) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 32, k, GGML_TYPE_Q4_0) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 32, k, GGML_TYPE_Q4_1) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 32, k, GGML_TYPE_Q5_0) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 32, k, GGML_TYPE_Q5_1) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_wmma, 128, 32, k, GGML_TYPE_Q8_0) + GGML_ABORT("fatal error"); + } else { +#else // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) + { +#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) + // use vector kernel + constexpr int K_VECS_PER_WARP = 8; + constexpr int WARPS_PER_BLOCK = 8; + constexpr int K_VECS_PER_BLOCK = K_VECS_PER_WARP * WARPS_PER_BLOCK; + + dim3 block(32, WARPS_PER_BLOCK); + int num_kv_blocks = (n_kv + (K_VECS_PER_BLOCK) - 1) / (K_VECS_PER_BLOCK); + dim3 grid(num_kv_blocks, n_batch, n_stream); + + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 32, k, GGML_TYPE_F16) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 32, k, GGML_TYPE_Q4_0) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 32, k, GGML_TYPE_Q4_1) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 32, k, GGML_TYPE_Q5_0) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 32, k, GGML_TYPE_Q5_1) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 32, k, GGML_TYPE_Q8_0) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 32, k, GGML_TYPE_BF16) + LIGHTNING_INDEXER_CASE(lightning_indexer_kernel_vec, 128, 32, k, GGML_TYPE_F32) + GGML_ABORT("fatal error"); + } + } else { + GGML_ABORT("fatal error"); + } +} + +bool ggml_cuda_lightning_indexer_supported(int device, const ggml_tensor * dst) { + GGML_UNUSED(device); + + const ggml_tensor * q = dst->src[0]; + const ggml_tensor * k = dst->src[1]; + const ggml_tensor * w = dst->src[2]; // weights + const ggml_tensor * m = dst->src[3]; // mask + + GGML_TENSOR_LOCALS(int64_t, neq, q, ne) + GGML_TENSOR_LOCALS(size_t, nbq, q, nb) + GGML_TENSOR_LOCALS(int64_t, nek, k, ne) + GGML_TENSOR_LOCALS(size_t, nbk, k, nb) + GGML_TENSOR_LOCALS(int64_t, new, w, ne) + GGML_TENSOR_LOCALS(size_t, nbw, w, nb) + GGML_TENSOR_LOCALS(int64_t, nem, m, ne) + GGML_TENSOR_LOCALS(size_t, nbm, m, nb) + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) + GGML_TENSOR_LOCALS(size_t, nb, dst, nb) + + if (neq0 != 128) { + return false; + } + + if (neq1 != 64 && neq1 != 32) { + return false; + } + + // alignment checks + for (const ggml_tensor * t : {q, k}) { + if (ggml_is_quantized(t->type)) { + continue; + } + for (size_t i = 1; i < GGML_MAX_DIMS; ++i) { + if (t->nb[i] % 16 != 0) { + return false; + } + } + } + + switch(k->type) { + case GGML_TYPE_F32: + case GGML_TYPE_BF16: + case GGML_TYPE_F16: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q4_0: + return true; + default: + return false; + } +} diff --git a/ggml/src/ggml-cuda/lightning-indexer.cuh b/ggml/src/ggml-cuda/lightning-indexer.cuh new file mode 100644 index 000000000000..f2fc95181339 --- /dev/null +++ b/ggml/src/ggml-cuda/lightning-indexer.cuh @@ -0,0 +1,4 @@ +#include "common.cuh" + +void ggml_cuda_lightning_indexer(ggml_backend_cuda_context & ctx, ggml_tensor * dst); +bool ggml_cuda_lightning_indexer_supported(int device, const ggml_tensor * dst); diff --git a/ggml/src/ggml-cuda/mmf.cu b/ggml/src/ggml-cuda/mmf.cu index aad4c34aa668..646a5899c803 100644 --- a/ggml/src/ggml-cuda/mmf.cu +++ b/ggml/src/ggml-cuda/mmf.cu @@ -85,7 +85,7 @@ void ggml_cuda_mul_mat_f(ggml_backend_cuda_context & ctx, const ggml_tensor * sr GGML_ASSERT(sis1 > 0); ggml_cuda_launch_mm_ids_helper(ids_d, ids_src_compact_dev.get(), ids_dst_compact_dev.get(), expert_bounds_dev.get(), - static_cast(n_experts), static_cast(n_tokens), static_cast(n_expert_used), static_cast(ne11), si1, sis1, ctx.stream()); + static_cast(n_experts), static_cast(n_tokens), static_cast(n_expert_used), static_cast(ne11), si1, sis1, /*write_inverse =*/ false, ctx.stream()); CUDA_CHECK(cudaGetLastError()); ids_info.ids_src_compact = ids_src_compact_dev.get(); diff --git a/ggml/src/ggml-cuda/mmid.cu b/ggml/src/ggml-cuda/mmid.cu index 3c61e4595a7b..f80442fbe4e8 100644 --- a/ggml/src/ggml-cuda/mmid.cu +++ b/ggml/src/ggml-cuda/mmid.cu @@ -27,7 +27,7 @@ template __launch_bounds__(ggml_cuda_get_physical_warp_size(), 1) static __global__ void mm_ids_helper( const int32_t * __restrict__ ids, int32_t * __restrict__ ids_src1, int32_t * __restrict__ ids_dst, int32_t * __restrict__ expert_bounds, - const int n_tokens, const int n_expert_used_var, const int nchannels_y, const int si1, const int sis1) { + const int n_tokens, const int n_expert_used_var, const int nchannels_y, const int si1, const int sis1, const bool write_inverse) { constexpr int warp_size = ggml_cuda_get_physical_warp_size(); const int n_expert_used = n_expert_used_template == 0 ? n_expert_used_var : n_expert_used_template; const int expert = blockIdx.x; @@ -98,8 +98,13 @@ static __global__ void mm_ids_helper( const mm_ids_helper_store store_it = store[itc]; const int it = store_it.it(); const int iex_used = store_it.iex_used(); - ids_src1[nex_prev + itc] = it*sis1 + iex_used % nchannels_y; - ids_dst [nex_prev + itc] = it*n_expert_used + iex_used; + ids_dst[nex_prev + itc] = it*n_expert_used + iex_used; + // ids_src1 holds the forward map, or the inverse map (token slot -> compact row) for quant dedup + if (write_inverse) { + ids_src1[it*n_expert_used + iex_used] = nex_prev + itc; + } else { + ids_src1[nex_prev + itc] = it*sis1 + iex_used % nchannels_y; + } } if (threadIdx.x != 0) { @@ -118,7 +123,7 @@ static __global__ void mm_ids_helper( template static void launch_mm_ids_helper( const int32_t * __restrict__ ids, int32_t * __restrict__ ids_src1, int32_t * __restrict__ ids_dst, int32_t * __restrict__ expert_bounds, - const int n_experts, const int n_tokens, const int n_expert_used_var, const int nchannels_y, const int si1, const int sis1, cudaStream_t stream) { + const int n_experts, const int n_tokens, const int n_expert_used_var, const int nchannels_y, const int si1, const int sis1, const bool write_inverse, cudaStream_t stream) { GGML_ASSERT(n_tokens < (1 << 22) && "too few bits in mm_ids_helper_store"); GGML_ASSERT(n_expert_used_var < (1 << 10) && "too few bits in mm_ids_helper_store"); @@ -132,33 +137,33 @@ static void launch_mm_ids_helper( const size_t nbytes_shared = n_tokens*sizeof(mm_ids_helper_store); GGML_ASSERT(nbytes_shared <= smpbo); mm_ids_helper<<>> - (ids, ids_src1, ids_dst, expert_bounds, n_tokens, n_expert_used_var, nchannels_y, si1, sis1); + (ids, ids_src1, ids_dst, expert_bounds, n_tokens, n_expert_used_var, nchannels_y, si1, sis1, write_inverse); } void ggml_cuda_launch_mm_ids_helper( const int32_t * __restrict__ ids, int32_t * __restrict__ ids_src1, int32_t * __restrict__ ids_dst, int32_t * __restrict__ expert_bounds, - const int n_experts, const int n_tokens, const int n_expert_used, const int nchannels_y, const int si1, const int sis1, cudaStream_t stream) { + const int n_experts, const int n_tokens, const int n_expert_used, const int nchannels_y, const int si1, const int sis1, const bool write_inverse, cudaStream_t stream) { switch (n_expert_used) { case 2: - launch_mm_ids_helper< 2>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, stream); + launch_mm_ids_helper< 2>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, write_inverse, stream); break; case 4: - launch_mm_ids_helper< 4>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, stream); + launch_mm_ids_helper< 4>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, write_inverse, stream); break; case 6: - launch_mm_ids_helper< 6>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, stream); + launch_mm_ids_helper< 6>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, write_inverse, stream); break; case 8: - launch_mm_ids_helper< 8>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, stream); + launch_mm_ids_helper< 8>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, write_inverse, stream); break; case 16: - launch_mm_ids_helper<16>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, stream); + launch_mm_ids_helper<16>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, write_inverse, stream); break; case 32: - launch_mm_ids_helper<32>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, stream); + launch_mm_ids_helper<32>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, write_inverse, stream); break; default: - launch_mm_ids_helper< 0>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, stream); + launch_mm_ids_helper< 0>(ids, ids_src1, ids_dst, expert_bounds, n_experts, n_tokens, n_expert_used, nchannels_y, si1, sis1, write_inverse, stream); break; } } diff --git a/ggml/src/ggml-cuda/mmid.cuh b/ggml/src/ggml-cuda/mmid.cuh index ac090aea9ea1..74c2db43385e 100644 --- a/ggml/src/ggml-cuda/mmid.cuh +++ b/ggml/src/ggml-cuda/mmid.cuh @@ -2,4 +2,4 @@ void ggml_cuda_launch_mm_ids_helper( const int32_t * ids, int32_t * ids_src1, int32_t * ids_dst, int32_t * expert_bounds, - int n_experts, int n_tokens, int n_expert_used, int nchannels_y, int si1, int sis1, cudaStream_t stream); + int n_experts, int n_tokens, int n_expert_used, int nchannels_y, int si1, int sis1, bool write_inverse, cudaStream_t stream); diff --git a/ggml/src/ggml-cuda/mmq-config-ampere.cuh b/ggml/src/ggml-cuda/mmq-config-ampere.cuh index 0037bac3d09f..9f9fd197382f 100644 --- a/ggml/src/ggml-cuda/mmq-config-ampere.cuh +++ b/ggml/src/ggml-cuda/mmq-config-ampere.cuh @@ -16,6 +16,23 @@ static constexpr __host__ __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_conf CASE(GGML_TYPE_Q1_0, 256, 1, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false); CASE(GGML_TYPE_Q1_0, 256, 1, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false); + CASE(GGML_TYPE_Q2_0, 256, 1, 128, 8, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true); + CASE(GGML_TYPE_Q2_0, 256, 1, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true); + CASE(GGML_TYPE_Q2_0, 256, 1, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true); + CASE(GGML_TYPE_Q2_0, 256, 1, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true); + CASE(GGML_TYPE_Q2_0, 256, 1, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true); + CASE(GGML_TYPE_Q2_0, 256, 1, 128, 8, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false); + CASE(GGML_TYPE_Q2_0, 256, 1, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false); + CASE(GGML_TYPE_Q2_0, 256, 1, 128, 24, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false); + CASE(GGML_TYPE_Q2_0, 256, 1, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false); + CASE(GGML_TYPE_Q2_0, 256, 1, 128, 40, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false); + CASE(GGML_TYPE_Q2_0, 256, 1, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false); + CASE(GGML_TYPE_Q2_0, 256, 1, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false); + CASE(GGML_TYPE_Q2_0, 256, 1, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false); + CASE(GGML_TYPE_Q2_0, 256, 1, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false); + CASE(GGML_TYPE_Q2_0, 256, 1, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false); + CASE(GGML_TYPE_Q2_0, 256, 1, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false); + CASE(GGML_TYPE_Q4_0, 256, 1, 128, 8, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true); CASE(GGML_TYPE_Q4_0, 256, 1, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true); CASE(GGML_TYPE_Q4_0, 256, 1, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true); diff --git a/ggml/src/ggml-cuda/mmq-config-cdna.cuh b/ggml/src/ggml-cuda/mmq-config-cdna.cuh index 46ec6aa9d513..4a8d89f72019 100644 --- a/ggml/src/ggml-cuda/mmq-config-cdna.cuh +++ b/ggml/src/ggml-cuda/mmq-config-cdna.cuh @@ -7,6 +7,14 @@ static constexpr __host__ __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_conf CASE(GGML_TYPE_Q1_0, 512, 1, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false); CASE(GGML_TYPE_Q1_0, 512, 1, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false); + CASE(GGML_TYPE_Q2_0, 512, 1, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true); + CASE(GGML_TYPE_Q2_0, 512, 1, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true); + CASE(GGML_TYPE_Q2_0, 512, 1, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true); + CASE(GGML_TYPE_Q2_0, 512, 1, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false); + CASE(GGML_TYPE_Q2_0, 512, 1, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false); + CASE(GGML_TYPE_Q2_0, 512, 1, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false); + CASE(GGML_TYPE_Q2_0, 512, 1, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false); + CASE(GGML_TYPE_Q4_0, 512, 1, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true); CASE(GGML_TYPE_Q4_0, 512, 1, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true); CASE(GGML_TYPE_Q4_0, 512, 1, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true); diff --git a/ggml/src/ggml-cuda/mmq-config-pascal.cuh b/ggml/src/ggml-cuda/mmq-config-pascal.cuh index 8f0faac889b4..e7d4a9a3fcb5 100644 --- a/ggml/src/ggml-cuda/mmq-config-pascal.cuh +++ b/ggml/src/ggml-cuda/mmq-config-pascal.cuh @@ -11,6 +11,18 @@ static constexpr __host__ __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_conf CASE(GGML_TYPE_Q1_0, 256, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q1_0, 256, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 256, 2, 64, 8, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q2_0, 256, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q2_0, 256, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q2_0, 256, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q2_0, 256, 2, 64, 8, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 256, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 256, 2, 64, 24, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 256, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 256, 2, 64, 40, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 256, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 256, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_0, 256, 2, 64, 8, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); CASE(GGML_TYPE_Q4_0, 256, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); CASE(GGML_TYPE_Q4_0, 256, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); diff --git a/ggml/src/ggml-cuda/mmq-config-rdna2.cuh b/ggml/src/ggml-cuda/mmq-config-rdna2.cuh index de4db0a3db3a..8324d9e1a830 100644 --- a/ggml/src/ggml-cuda/mmq-config-rdna2.cuh +++ b/ggml/src/ggml-cuda/mmq-config-rdna2.cuh @@ -11,6 +11,18 @@ static constexpr __host__ __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_conf CASE(GGML_TYPE_Q1_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q1_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 256, 2, 128, 8, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q2_0, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q2_0, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q2_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q2_0, 256, 2, 128, 8, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 256, 2, 128, 24, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 256, 2, 128, 40, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_0, 256, 2, 128, 8, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); CASE(GGML_TYPE_Q4_0, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); CASE(GGML_TYPE_Q4_0, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); diff --git a/ggml/src/ggml-cuda/mmq-config-rdna3-5.cuh b/ggml/src/ggml-cuda/mmq-config-rdna3-5.cuh new file mode 100644 index 000000000000..180b2d9370d1 --- /dev/null +++ b/ggml/src/ggml-cuda/mmq-config-rdna3-5.cuh @@ -0,0 +1,290 @@ +static constexpr __host__ __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_config_rdna3_5(ggml_type type, int J, bool fallback) { + CASE(GGML_TYPE_Q1_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q1_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q1_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q1_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q1_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q1_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q1_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q1_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q1_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q1_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q1_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q1_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q2_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q2_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q2_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q2_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_Q4_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q4_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q4_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q4_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q4_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_Q4_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q4_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q4_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q4_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q4_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_1, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_1, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_1, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_1, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_Q5_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q5_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q5_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q5_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q5_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_Q5_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q5_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q5_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q5_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q5_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_1, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_1, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_1, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_1, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_Q8_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q8_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q8_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q8_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q8_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q8_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q8_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q8_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q8_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q8_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q8_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q8_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + +// --------------------------------------------------------------------------------------------- + + CASE(GGML_TYPE_Q2_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q2_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q2_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q2_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_Q3_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q3_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q3_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q3_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q3_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q3_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q3_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q3_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q3_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q3_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q3_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q3_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_Q4_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q4_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q4_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q4_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q4_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_Q5_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q5_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q5_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q5_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q5_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_Q6_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q6_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q6_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q6_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q6_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q6_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q6_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q6_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q6_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q6_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q6_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q6_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false); + +// --------------------------------------------------------------------------------------------- + + CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + +// --------------------------------------------------------------------------------------------- + + CASE(GGML_TYPE_MXFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_MXFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_MXFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_MXFP4, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_MXFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_MXFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_MXFP4, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_MXFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_MXFP4, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_MXFP4, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_MXFP4, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_MXFP4, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_NVFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_NVFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_NVFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_NVFP4, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_NVFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_NVFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_NVFP4, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_NVFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_NVFP4, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_NVFP4, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_NVFP4, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_NVFP4, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false); + + return ggml_cuda_mmq_config(GGML_TYPE_COUNT, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, 256, false, true); +} diff --git a/ggml/src/ggml-cuda/mmq-config-rdna3.cuh b/ggml/src/ggml-cuda/mmq-config-rdna3.cuh new file mode 100644 index 000000000000..676f27fea4d9 --- /dev/null +++ b/ggml/src/ggml-cuda/mmq-config-rdna3.cuh @@ -0,0 +1,290 @@ +static constexpr __host__ __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_config_rdna3(ggml_type type, int J, bool fallback) { + CASE(GGML_TYPE_Q1_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q1_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q1_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q1_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q1_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q1_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q1_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q1_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q1_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q1_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q1_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q1_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q2_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q2_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q2_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q2_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_Q4_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q4_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q4_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q4_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q4_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_Q4_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q4_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q4_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q4_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q4_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_1, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_1, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_1, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_1, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_Q5_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q5_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q5_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q5_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q5_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_Q5_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q5_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q5_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q5_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q5_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_1, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_1, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_1, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_1, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_Q8_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q8_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q8_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q8_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q8_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q8_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q8_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q8_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q8_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q8_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q8_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q8_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + +// --------------------------------------------------------------------------------------------- + + CASE(GGML_TYPE_Q2_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q2_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q2_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q2_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_Q3_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q3_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q3_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q3_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q3_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q3_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q3_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q3_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q3_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q3_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q3_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q3_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_Q4_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q4_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q4_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q4_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q4_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_Q5_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q5_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q5_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q5_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q5_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_Q6_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q6_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q6_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q6_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q6_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q6_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q6_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q6_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q6_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q6_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q6_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q6_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false); + +// --------------------------------------------------------------------------------------------- + + CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + +// --------------------------------------------------------------------------------------------- + + CASE(GGML_TYPE_MXFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_MXFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_MXFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_MXFP4, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_MXFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_MXFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_MXFP4, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_MXFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_MXFP4, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_MXFP4, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_MXFP4, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_MXFP4, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + + CASE(GGML_TYPE_NVFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_NVFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_NVFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_NVFP4, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_NVFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_NVFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_NVFP4, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_NVFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_NVFP4, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_NVFP4, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_NVFP4, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_NVFP4, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false); + + return ggml_cuda_mmq_config(GGML_TYPE_COUNT, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, 256, false, true); +} diff --git a/ggml/src/ggml-cuda/mmq-config-rdna4.cuh b/ggml/src/ggml-cuda/mmq-config-rdna4.cuh index 6280e80ee4ce..9293d9d55885 100644 --- a/ggml/src/ggml-cuda/mmq-config-rdna4.cuh +++ b/ggml/src/ggml-cuda/mmq-config-rdna4.cuh @@ -1,77 +1,89 @@ static constexpr __host__ __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_config_rdna4(ggml_type type, int J, bool fallback) { - CASE(GGML_TYPE_Q1_0, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q1_0, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q1_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q1_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q1_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q1_0, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); CASE(GGML_TYPE_Q1_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q1_0, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q1_0, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q1_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q1_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q1_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q1_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q1_0, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q1_0, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q1_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q1_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q1_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q1_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q2_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q2_0, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q2_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q2_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q4_0, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q4_0, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q4_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q4_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q4_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q4_0, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); CASE(GGML_TYPE_Q4_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q4_0, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q4_0, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q4_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q4_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_0, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_0, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q4_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q4_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q4_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q4_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q4_1, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q4_1, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q4_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q4_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q4_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q4_1, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); CASE(GGML_TYPE_Q4_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q4_1, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q4_1, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q4_1, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q4_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_1, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_1, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q4_1, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q4_1, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q4_1, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q4_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q5_0, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q5_0, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q5_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q5_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q5_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q5_0, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); CASE(GGML_TYPE_Q5_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q5_0, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q5_0, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q5_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q5_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_0, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_0, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q5_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q5_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q5_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q5_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q5_1, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q5_1, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q5_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q5_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q5_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q5_1, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); CASE(GGML_TYPE_Q5_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q5_1, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q5_1, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q5_1, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q5_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_1, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_1, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q5_1, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q5_1, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q5_1, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q5_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q8_0, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q8_0, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q8_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q8_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q8_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q8_0, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); CASE(GGML_TYPE_Q8_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q8_0, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q8_0, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q8_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q8_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q8_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q8_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q8_0, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q8_0, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q8_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q8_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q8_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); @@ -79,66 +91,62 @@ static constexpr __host__ __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_conf // --------------------------------------------------------------------------------------------- - CASE(GGML_TYPE_Q2_K, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q2_K, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q2_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q2_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q2_K, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q2_K, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q2_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q2_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q2_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q2_K, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q2_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_K, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q2_K, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q2_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q2_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q2_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q2_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q3_K, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q3_K, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q3_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q3_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q3_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q3_K, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); CASE(GGML_TYPE_Q3_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q3_K, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q3_K, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q3_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q3_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q3_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q3_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q3_K, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q3_K, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q3_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q3_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q3_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q3_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q4_K, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q4_K, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q4_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q4_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q4_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q4_K, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); CASE(GGML_TYPE_Q4_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q4_K, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q4_K, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q4_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q4_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_K, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q4_K, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q4_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q4_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q4_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q4_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q5_K, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q5_K, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q5_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q5_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q5_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q5_K, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); CASE(GGML_TYPE_Q5_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q5_K, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q5_K, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q5_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q5_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_K, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q5_K, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q5_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q5_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q5_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q5_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q6_K, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q6_K, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q6_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q6_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q6_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_Q6_K, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true); CASE(GGML_TYPE_Q6_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_Q6_K, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q6_K, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q6_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_Q6_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q6_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q6_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q6_K, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_Q6_K, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q6_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q6_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false); CASE(GGML_TYPE_Q6_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false); @@ -146,105 +154,105 @@ static constexpr __host__ __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_conf // --------------------------------------------------------------------------------------------- - CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false); @@ -252,27 +260,27 @@ static constexpr __host__ __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_conf // --------------------------------------------------------------------------------------------- - CASE(GGML_TYPE_MXFP4, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_MXFP4, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_MXFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_MXFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_MXFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_MXFP4, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); CASE(GGML_TYPE_MXFP4, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_MXFP4, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_MXFP4, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_MXFP4, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_MXFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_MXFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_MXFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_MXFP4, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_MXFP4, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); CASE(GGML_TYPE_MXFP4, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); CASE(GGML_TYPE_MXFP4, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); CASE(GGML_TYPE_MXFP4, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); CASE(GGML_TYPE_MXFP4, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_NVFP4, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_NVFP4, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_NVFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_NVFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_NVFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true); + CASE(GGML_TYPE_NVFP4, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true); CASE(GGML_TYPE_NVFP4, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true); - CASE(GGML_TYPE_NVFP4, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_NVFP4, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_NVFP4, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false); - CASE(GGML_TYPE_NVFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_NVFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_NVFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_NVFP4, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false); + CASE(GGML_TYPE_NVFP4, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false); CASE(GGML_TYPE_NVFP4, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false); CASE(GGML_TYPE_NVFP4, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false); CASE(GGML_TYPE_NVFP4, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false); diff --git a/ggml/src/ggml-cuda/mmq-load-tiles.cuh b/ggml/src/ggml-cuda/mmq-load-tiles.cuh index 3978b1baa71e..8ed704c281a4 100644 --- a/ggml/src/ggml-cuda/mmq-load-tiles.cuh +++ b/ggml/src/ggml-cuda/mmq-load-tiles.cuh @@ -39,29 +39,118 @@ template static __device__ __forceinline_ } const block_q1_0 * bxi = (const block_q1_0 *) x + kbx0 + i*stride + kbx; - const int qs_offset = 4*kqsx; - const int qs0 = bxi->qs[qs_offset + 0] | (bxi->qs[qs_offset + 1] << 8) | - (bxi->qs[qs_offset + 2] << 16) | (bxi->qs[qs_offset + 3] << 24); + const int16_t * qxi = (const int16_t *) bxi->qs + kqsx * 2; - int unpacked_bytes[8]; + const int dst_offset = kbx*(scale_entries_per_block*QI8_0) + kqsx*QI8_0; #pragma unroll - for (int j = 0; j < 8; ++j) { - const int shift = j * 4; - const int bits4 = (qs0 >> shift) & 0x0F; - const int b0 = (bits4 & 0x01) ? 1 : -1; - const int b1 = (bits4 & 0x02) ? 1 : -1; - const int b2 = (bits4 & 0x04) ? 1 : -1; - const int b3 = (bits4 & 0x08) ? 1 : -1; - unpacked_bytes[j] = (b0 & 0xFF) | ((b1 & 0xFF) << 8) | ((b2 & 0xFF) << 16) | ((b3 & 0xFF) << 24); + for (int j = 0; j < 2; ++j) { + const int q = qxi[j]; + + // unpack crumbs into nibble indices + const int n0 = __byte_perm(0x11100100, 0x11100100, q >> 0); // [0, 1, 4, 5] [ 8, 9, 12, 13] + const int n1 = __byte_perm(0x11100100, 0x11100100, q >> 2); // [2, 3, 6, 7] [10, 11, 14, 15] + // unpack nibbles into byte values + const int s0 = __byte_perm(0x01FF, 0x01FF, n0 >> 0); + const int s1 = __byte_perm(0x01FF, 0x01FF, n1 >> 0); + const int s2 = __byte_perm(0x01FF, 0x01FF, n0 >> 16); + const int s3 = __byte_perm(0x01FF, 0x01FF, n1 >> 16); + // unshuffle values + const int v0 = __byte_perm(s0, s1, 0x5410); + const int v1 = __byte_perm(s0, s1, 0x7632); + const int v2 = __byte_perm(s2, s3, 0x5410); + const int v3 = __byte_perm(s2, s3, 0x7632); + +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) + x_qs[i*sram_stride + dst_offset + j*4+0] = v0; + x_qs[i*sram_stride + dst_offset + j*4+1] = v1; + x_qs[i*sram_stride + dst_offset + j*4+2] = v2; + x_qs[i*sram_stride + dst_offset + j*4+3] = v3; +#else + x_qs[i*(2*MMQ_TILE_NE_K + 1) + dst_offset + j*4+0] = v0; + x_qs[i*(2*MMQ_TILE_NE_K + 1) + dst_offset + j*4+1] = v1; + x_qs[i*(2*MMQ_TILE_NE_K + 1) + dst_offset + j*4+2] = v2; + x_qs[i*(2*MMQ_TILE_NE_K + 1) + dst_offset + j*4+3] = v3; +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) + } + } + + const int ksx = threadIdx.x % scale_entries_per_row; + const int scale_block = ksx / scale_entries_per_block; + +#pragma unroll + for (int i0 = 0; i0 < I; i0 += nwarps) { + int i = i0 + threadIdx.y; + + if (fallback) { + i = min(i, i_max); } + const block_q1_0 * bxi = (const block_q1_0 *) x + kbx0 + i*stride + scale_block; + +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) + x_df[i*sram_stride + ksx] = bxi->d; +#else + x_df[i*(2*MMQ_TILE_NE_K/QI8_0) + i/(QI8_0/2) + ksx] = bxi->d; +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) + } +} + +template static __device__ __forceinline__ void ggml_cuda_mmq_load_tiles_q2_0( + const char * __restrict__ x, int * __restrict__ x_tile, const int kbx0, const int i_max, const int stride) { + constexpr int warp_size = ggml_cuda_get_physical_warp_size(); + constexpr int nwarps = ggml_cuda_mmq_get_nthreads(type, J, fallback) / warp_size; + constexpr int I = ggml_cuda_mmq_get_I(type, J, fallback); + constexpr int sram_stride = ggml_cuda_mmq_get_sram_stride(type, J, fallback); + +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) + int * x_qs = (int *) x_tile; + float * x_df = (float *) (x_qs + 2*MMQ_TILE_NE_K); +#else + constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q8_0, I); + int * x_qs = (int *) x_tile; + float * x_df = (float *) (x_qs + txs.qs); +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) + + constexpr int blocks_per_iter = MMQ_ITER_K / QK2_0; + constexpr int threads_per_row = blocks_per_iter * QI2_0; + constexpr int nrows = warp_size / threads_per_row; + constexpr int scale_entries_per_block = QK2_0 / QK8_1; + constexpr int scale_entries_per_row = blocks_per_iter * scale_entries_per_block; + + const int txi = threadIdx.x % threads_per_row; + const int kbx = txi / QI2_0; + const int kqsx = txi % QI2_0; + +#pragma unroll + for (int i0 = 0; i0 < I; i0 += nrows*nwarps) { + int i = i0 + threadIdx.y*nrows + threadIdx.x/threads_per_row; + + if (fallback) { + i = min(i, i_max); + } + + const block_q2_0 * bxi = (const block_q2_0 *) x + kbx0 + i*stride + kbx; + const int16_t * qxi = (const int16_t *) bxi->qs + kqsx * 4; + const int dst_offset = kbx*(scale_entries_per_block*QI8_0) + kqsx*QI8_0; + #pragma unroll - for (int j = 0; j < 8; ++j) { + for (int j = 0; j < 4; ++j) { + const int q = qxi[j]; + + // unpack even and odd crumbs into byte values + const int qe = __byte_perm(0x020100FF, 0x020100FF, q >> 0); + const int qo = __byte_perm(0x020100FF, 0x020100FF, q >> 2); + // unshuffle values + const int qx = __byte_perm(qe, qo, 0x5140); + const int qy = __byte_perm(qe, qo, 0x7362); + #if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) - x_qs[i*sram_stride + dst_offset + j] = unpacked_bytes[j]; + x_qs[i*sram_stride + dst_offset + j*2+0] = qx; + x_qs[i*sram_stride + dst_offset + j*2+1] = qy; #else - x_qs[i*(2*MMQ_TILE_NE_K + 1) + dst_offset + j] = unpacked_bytes[j]; + x_qs[i*(2*MMQ_TILE_NE_K + 1) + dst_offset + j*2+0] = qx; + x_qs[i*(2*MMQ_TILE_NE_K + 1) + dst_offset + j*2+1] = qy; #endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } } @@ -77,7 +166,7 @@ template static __device__ __forceinline_ i = min(i, i_max); } - const block_q1_0 * bxi = (const block_q1_0 *) x + kbx0 + i*stride + scale_block; + const block_q2_0 * bxi = (const block_q2_0 *) x + kbx0 + i*stride + scale_block; #if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_df[i*sram_stride + ksx] = bxi->d; diff --git a/ggml/src/ggml-cuda/mmq.cu b/ggml/src/ggml-cuda/mmq.cu index bf9f5d526476..707437ea3e52 100644 --- a/ggml/src/ggml-cuda/mmq.cu +++ b/ggml/src/ggml-cuda/mmq.cu @@ -10,6 +10,9 @@ static void ggml_cuda_mul_mat_q_switch_type(ggml_backend_cuda_context & ctx, con case GGML_TYPE_Q1_0: mul_mat_q_case(ctx, args, stream); break; + case GGML_TYPE_Q2_0: + mul_mat_q_case(ctx, args, stream); + break; case GGML_TYPE_Q4_0: mul_mat_q_case(ctx, args, stream); break; @@ -25,12 +28,7 @@ static void ggml_cuda_mul_mat_q_switch_type(ggml_backend_cuda_context & ctx, con case GGML_TYPE_Q8_0: mul_mat_q_case(ctx, args, stream); break; - case GGML_TYPE_MXFP4: - mul_mat_q_case(ctx, args, stream); - break; - case GGML_TYPE_NVFP4: - mul_mat_q_case(ctx, args, stream); - break; +// ----------------------------------------------------------------------- case GGML_TYPE_Q2_K: mul_mat_q_case(ctx, args, stream); break; @@ -46,6 +44,10 @@ static void ggml_cuda_mul_mat_q_switch_type(ggml_backend_cuda_context & ctx, con case GGML_TYPE_Q6_K: mul_mat_q_case(ctx, args, stream); break; +// ----------------------------------------------------------------------- + case GGML_TYPE_IQ1_S: + mul_mat_q_case(ctx, args, stream); + break; case GGML_TYPE_IQ2_XXS: mul_mat_q_case(ctx, args, stream); break; @@ -61,15 +63,19 @@ static void ggml_cuda_mul_mat_q_switch_type(ggml_backend_cuda_context & ctx, con case GGML_TYPE_IQ3_S: mul_mat_q_case(ctx, args, stream); break; - case GGML_TYPE_IQ1_S: - mul_mat_q_case(ctx, args, stream); - break; case GGML_TYPE_IQ4_XS: mul_mat_q_case(ctx, args, stream); break; case GGML_TYPE_IQ4_NL: mul_mat_q_case(ctx, args, stream); break; +// ----------------------------------------------------------------------- + case GGML_TYPE_MXFP4: + mul_mat_q_case(ctx, args, stream); + break; + case GGML_TYPE_NVFP4: + mul_mat_q_case(ctx, args, stream); + break; default: GGML_ABORT("fatal error"); break; @@ -122,21 +128,28 @@ void ggml_cuda_mul_mat_q( const bool fallback = ne01 % 128 != 0; - // TODO: tighter pool buffer size vs q8 path const bool use_native_fp4 = blackwell_mma_available(cc) && (src0->type == GGML_TYPE_MXFP4 || src0->type == GGML_TYPE_NVFP4); + const size_t y_block_size = use_native_fp4 ? sizeof(block_fp4_mmq) : sizeof(block_q8_1_mmq); + const size_t y_values_per_block = use_native_fp4 ? QK_FP4_MMQ : QK8_1_MMQ; if (!ids) { - const size_t nbytes_src1_q8_1 = ne13*ne12 * ne11*ne10_padded * sizeof(block_q8_1)/QK8_1 + + const size_t nbytes_src1_q8_1 = ne13*ne12 * ne11*ne10_padded * y_block_size/y_values_per_block + ggml_cuda_mmq_get_J_max(src0->type, fallback, cc, ne11) * sizeof(block_q8_1_mmq); ggml_cuda_pool_alloc src1_q8_1(ctx.pool(), nbytes_src1_q8_1); + ggml_cuda_pool_alloc src1_scale(ctx.pool()); + if (src0->type == GGML_TYPE_NVFP4 && use_native_fp4) { + src1_scale.alloc(ne13*ne12*ne11); + } { const int64_t s11 = src1->nb[1] / ts_src1; const int64_t s12 = src1->nb[2] / ts_src1; const int64_t s13 = src1->nb[3] / ts_src1; if (use_native_fp4) { + static constexpr size_t align_float8 = 32; + const bool use_aligned_float8 = ggml_cuda_is_aligned(src1, align_float8); static_assert(sizeof(block_fp4_mmq) == 4 * sizeof(block_q8_1)); - quantize_mmq_fp4_cuda(src1_d, nullptr, src1_q8_1.get(), src0->type, ne10, s11, s12, s13, ne10_padded, + quantize_mmq_fp4_cuda(src1_d, nullptr, src1_q8_1.get(), src1_scale.ptr, src0->type, use_aligned_float8, ne10, s11, s12, s13, ne10_padded, ne11, ne12, ne13, stream); } else { @@ -148,12 +161,13 @@ void ggml_cuda_mul_mat_q( // Stride depends on quantization format const int64_t s12 = use_native_fp4 ? - ne11 * ne10_padded * sizeof(block_fp4_mmq) / (QK_K * sizeof(int)) : // block_fp4_mmq holds 256 values + ne11 * ne10_padded * sizeof(block_fp4_mmq) / (QK_FP4_MMQ * sizeof(int)) : ne11 * ne10_padded * sizeof(block_q8_1) / (QK8_1 * sizeof(int)); const int64_t s13 = ne12*s12; const mmq_args args = { src0_d, src0->type, (const int *) src1_q8_1.ptr, nullptr, nullptr, dst_d, + src0->type == GGML_TYPE_NVFP4 && use_native_fp4 ? src1_scale.ptr : nullptr, ne00, ne01, ne1, s01, ne11, s1, ne02, ne12, s02, s12, s2, ne03, ne13, s03, s13, s3, @@ -174,19 +188,27 @@ void ggml_cuda_mul_mat_q( ggml_cuda_pool_alloc ids_dst(ctx.pool(), ne_get_rows); ggml_cuda_pool_alloc expert_bounds(ctx.pool(), ne02 + 1); + // gate/up activations are broadcast across experts (ne11 == 1): quantize each token once and + // scatter to its slots. ids_src1 then holds the inverse map (token slot -> compact row). + const bool dedup_bcast = ne11 == 1 && n_expert_used > 1; + { GGML_ASSERT(ids->nb[0] == ggml_element_size(ids)); const int si1 = ids->nb[1] / ggml_element_size(ids); const int sis1 = nb12 / nb11; ggml_cuda_launch_mm_ids_helper((const int32_t *) ids->data, ids_src1.get(), ids_dst.get(), expert_bounds.get(), - ne02, ne12, n_expert_used, ne11, si1, sis1, stream); + ne02, ne12, n_expert_used, ne11, si1, sis1, /*write_inverse =*/ dedup_bcast, stream); CUDA_CHECK(cudaGetLastError()); } - const size_t nbytes_src1_q8_1 = ne12*n_expert_used*ne10_padded * sizeof(block_q8_1)/QK8_1 + + const size_t nbytes_src1_q8_1 = ne12*n_expert_used*ne10_padded * y_block_size/y_values_per_block + ggml_cuda_mmq_get_J_max(src0->type, fallback, cc, ne11) * sizeof(block_q8_1_mmq); ggml_cuda_pool_alloc src1_q8_1(ctx.pool(), nbytes_src1_q8_1); + ggml_cuda_pool_alloc src1_scale(ctx.pool()); + if (src0->type == GGML_TYPE_NVFP4 && use_native_fp4) { + src1_scale.alloc(ne12*n_expert_used); + } const int64_t ne11_flat = ne12*n_expert_used; const int64_t ne12_flat = 1; @@ -198,8 +220,18 @@ void ggml_cuda_mul_mat_q( const int64_t s13 = src1->nb[3] / ts_src1; if (use_native_fp4) { - quantize_mmq_fp4_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src0->type, ne10, s11, s12, s13, - ne10_padded, ne11_flat, ne12_flat, ne13_flat, stream); + static constexpr size_t align_float8 = 32; + const bool use_aligned_float8 = ggml_cuda_is_aligned(src1, align_float8); + if (dedup_bcast) { + quantize_scatter_mmq_fp4_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src1_scale.ptr, src0->type, use_aligned_float8, ne10, + /*stride_token=*/s12, ne10_padded, ne12, ne11_flat, n_expert_used, stream); + } else { + quantize_mmq_fp4_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src1_scale.ptr, src0->type, use_aligned_float8, ne10, s11, s12, s13, + ne10_padded, ne11_flat, ne12_flat, ne13_flat, stream); + } + } else if (dedup_bcast) { + quantize_scatter_mmq_q8_1_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src0->type, ne10, + /*stride_token=*/s12, ne10_padded, ne12, ne11_flat, n_expert_used, stream); } else { quantize_mmq_q8_1_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src0->type, ne10, s11, s12, s13, ne10_padded, ne11_flat, ne12_flat, ne13_flat, stream); @@ -207,14 +239,15 @@ void ggml_cuda_mul_mat_q( CUDA_CHECK(cudaGetLastError()); } - static_assert(QK_K == 8 * QK_MXFP4, "QK_K needs to be 8 * QK_MXFP4"); - const int64_t s12 = use_native_fp4 ? ne11 * ne10_padded * sizeof(block_fp4_mmq) / (QK_K * sizeof(int)) : + static_assert(QK_FP4_MMQ == 8 * QK_MXFP4, "QK_FP4_MMQ needs to be 8 * QK_MXFP4"); + const int64_t s12 = use_native_fp4 ? ne11 * ne10_padded * sizeof(block_fp4_mmq) / (QK_FP4_MMQ * sizeof(int)) : ne11 * ne10_padded * sizeof(block_q8_1) / (QK8_1 * sizeof(int)); const int64_t s13 = ne12*s12; // Note that ne02 is used instead of ne12 because the number of y channels determines the z dimension of the CUDA grid. const mmq_args args = { src0_d, src0->type, (const int *) src1_q8_1.get(), ids_dst.get(), expert_bounds.get(), dst_d, + src1_scale.ptr, ne00, ne01, ne_get_rows, s01, ne_get_rows, s1, ne02, ne02, s02, s12, s2, ne03, ne13, s03, s13, s3, @@ -232,26 +265,30 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11, int64_t switch (type) { case GGML_TYPE_Q1_0: + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: case GGML_TYPE_Q5_1: case GGML_TYPE_Q8_0: - case GGML_TYPE_MXFP4: - case GGML_TYPE_NVFP4: +// ------------------------------------------------- case GGML_TYPE_Q2_K: case GGML_TYPE_Q3_K: case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: case GGML_TYPE_Q6_K: +// ------------------------------------------------- + case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ2_S: case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ4_NL: +// ------------------------------------------------- + case GGML_TYPE_MXFP4: + case GGML_TYPE_NVFP4: mmq_supported = true; break; default: @@ -263,6 +300,15 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11, int64_t return false; } + // MMQ tiles require at least 48 KiB per-block shared memory; fall back to BLAS otherwise. + { + const int id = ggml_cuda_get_device(); + const size_t smpbo = ggml_cuda_info().devices[id].smpbo; + if (smpbo < 48 * 1024) { + return false; + } + } + if (turing_mma_available(cc)) { return true; } diff --git a/ggml/src/ggml-cuda/mmq.cuh b/ggml/src/ggml-cuda/mmq.cuh index c52d0f5ae9ba..ae4bbfbd1063 100644 --- a/ggml/src/ggml-cuda/mmq.cuh +++ b/ggml/src/ggml-cuda/mmq.cuh @@ -13,7 +13,7 @@ typedef void (*ggml_cuda_mmq_load_tiles_t)(const char * __restrict__ x, int * x_tile, const int kbx0, const int i_max, const int stride); typedef void (*ggml_cuda_mmq_vec_dot_t)(const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int k00); typedef void (*ggml_cuda_mmq_write_back_t)(const float * __restrict__ sum, const int32_t * __restrict__ get_rows_to_sorted, - float * __restrict__ dst, const int stride, const int i_max, const int j_max); + float * __restrict__ dst, const float * __restrict__ y_scale, const int stride, const int i_max, const int j_max); enum mmq_q8_1_ds_layout { MMQ_Q8_1_DS_LAYOUT_D4, @@ -21,6 +21,9 @@ enum mmq_q8_1_ds_layout { MMQ_Q8_1_DS_LAYOUT_D2S6, }; +static constexpr int QK8_1_MMQ = 4*QK8_1; +static constexpr int QK_FP4_MMQ = 2*QK8_1_MMQ; + struct block_q8_1_mmq { // The y float data is converted to a data layout that can simply be copied to shared memory as a contiguous block. // The y float data is first grouped as blocks of 128 values. @@ -39,7 +42,7 @@ struct block_q8_1_mmq { half d2s6[8]; // 1 16 bit scale per 64 values + 1 16 bit partial sum per 16 values for the first 96 values, // stored as d0,d1,s1,s2,s3,s4,s5 }; - int8_t qs[4*QK8_1]; // 128 values quantized to 8 bit each + int8_t qs[QK8_1_MMQ]; }; // this struct is used for fp4 data types (currently only used for Blackwell) @@ -47,16 +50,17 @@ struct block_q8_1_mmq { // nvfp4 has block size 16, each int32 of d4 contains 4 ue4m3 scales struct block_fp4_mmq { uint32_t d4[4]; - int8_t qs[4 * 32]; // 256 FP4 values packed as 4-bit pairs (2 per byte) + int8_t qs[QK_FP4_MMQ / 2]; }; -static_assert(sizeof(block_q8_1_mmq) == 4*QK8_1 + 4*sizeof(half2), "Unexpected block_q8_1_mmq size"); +static_assert(sizeof(block_q8_1_mmq) == QK8_1_MMQ + 4*sizeof(half2), "Unexpected block_q8_1_mmq size"); static_assert(sizeof(block_q8_1_mmq) == 4*sizeof(block_q8_1), "Unexpected block_q8_1_mmq size"); static_assert(sizeof(block_fp4_mmq) == sizeof(block_q8_1_mmq), "Unexpected block_fp4_mmq size"); static mmq_q8_1_ds_layout mmq_get_q8_1_ds_layout(const ggml_type type_x) { switch (type_x) { case GGML_TYPE_Q1_0: + case GGML_TYPE_Q2_0: return MMQ_Q8_1_DS_LAYOUT_D4; case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: @@ -215,6 +219,8 @@ struct ggml_cuda_mmq_config { #include "mmq-config-cdna.cuh" #include "mmq-config-rdna2.cuh" +#include "mmq-config-rdna3.cuh" +#include "mmq-config-rdna3-5.cuh" #include "mmq-config-rdna4.cuh" #undef CASE @@ -224,9 +230,15 @@ static __host__ ggml_cuda_mmq_config ggml_cuda_mmq_get_config(const ggml_type ty if (GGML_CUDA_CC_IS_CDNA(cc)) { return ggml_cuda_mmq_get_config_cdna(type, J, fallback); } - if (amd_wmma_available(cc)) { + if (GGML_CUDA_CC_IS_RDNA4(cc)) { return ggml_cuda_mmq_get_config_rdna4(type, J, fallback); } + if (GGML_CUDA_CC_IS_RDNA3_5(cc)) { + return ggml_cuda_mmq_get_config_rdna3_5(type, J, fallback); + } + if (GGML_CUDA_CC_IS_RDNA3(cc)) { // covers RDNA 3.0 + return ggml_cuda_mmq_get_config_rdna3(type, J, fallback); + } return ggml_cuda_mmq_get_config_rdna2(type, J, fallback); } if (blackwell_mma_available(cc)) { @@ -242,8 +254,12 @@ static constexpr __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_config(ggml_t #ifdef GGML_USE_HIP #ifdef CDNA return ggml_cuda_mmq_get_config_cdna(type, J, fallback); -#elif defined(AMD_WMMA_AVAILABLE) +#elif defined(RDNA4) return ggml_cuda_mmq_get_config_rdna4(type, J, fallback); +#elif defined(RDNA3_5) + return ggml_cuda_mmq_get_config_rdna3_5(type, J, fallback); +#elif defined(RDNA3) + return ggml_cuda_mmq_get_config_rdna3(type, J, fallback); #else return ggml_cuda_mmq_get_config_rdna2(type, J, fallback); #endif // CDNA @@ -370,6 +386,7 @@ static constexpr __device__ int ggml_cuda_mmq_get_rows_per_warp(ggml_type type, static constexpr __host__ __device__ tile_x_sizes mmq_get_dp4a_tile_x_sizes(ggml_type type, int I) { switch (type) { case GGML_TYPE_Q1_0: return MMQ_DP4A_TXS_Q8_0; + case GGML_TYPE_Q2_0: return MMQ_DP4A_TXS_Q8_0; case GGML_TYPE_Q4_0: return MMQ_DP4A_TXS_Q4_0; case GGML_TYPE_Q4_1: return MMQ_DP4A_TXS_Q4_1; case GGML_TYPE_Q5_0: return MMQ_DP4A_TXS_Q8_0; @@ -410,11 +427,13 @@ static __host__ int ggml_cuda_mmq_get_nbytes_shared_x(const ggml_cuda_mmq_config template static __device__ __forceinline__ void ggml_cuda_mmq_write_back_dp4a( const float * __restrict__ sum, const int32_t * __restrict__ ids_dst, float * __restrict__ dst, - const int stride, const int i_max, const int j_max) { + const float * __restrict__ y_scale, const int stride, const int i_max, const int j_max) { constexpr int warp_size = ggml_cuda_get_physical_warp_size(); constexpr int nwarps = ggml_cuda_mmq_get_nthreads(type, J, fallback) / warp_size; constexpr int I = ggml_cuda_mmq_get_I(type, J, fallback); + const bool y_scale_used = y_scale != nullptr; + #pragma unroll for (int j0 = 0; j0 < J; j0 += nwarps) { const int j = j0 + threadIdx.y; @@ -431,7 +450,16 @@ template static __device__ __forceinline_ continue; } - dst[(int64_t) ids_dst[j]*stride + i] = sum[(j0/nwarps) * (I/warp_size) + i0/warp_size]; + if constexpr (type == GGML_TYPE_NVFP4) { + if (y_scale_used) { + dst[(int64_t) ids_dst[j]*stride + i] = y_scale[j] * sum[(j0/nwarps) * (I/warp_size) + i0/warp_size]; + } else { + dst[(int64_t) ids_dst[j]*stride + i] = sum[(j0/nwarps) * (I/warp_size) + i0/warp_size]; + } + } else { + dst[(int64_t) ids_dst[j]*stride + i] = sum[(j0/nwarps) * (I/warp_size) + i0/warp_size]; + GGML_UNUSED(y_scale_used); + } } } } @@ -439,7 +467,8 @@ template static __device__ __forceinline_ template static __device__ __forceinline__ void ggml_cuda_mmq_write_back_mma( const float * __restrict__ sum, const int * __restrict__ ids_dst, float * __restrict__ dst, - const int stride, const int i_max, const int j_max) { + const float * __restrict__ y_scale, const int stride, const int i_max, const int j_max) { + #if defined(AMD_MFMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) typedef tile<16, 16, int, DATA_LAYOUT_J_MAJOR> tile_C; #else @@ -454,6 +483,8 @@ static __device__ __forceinline__ void ggml_cuda_mmq_write_back_mma( const int i0 = (threadIdx.y / ntx) * (ntx*tile_C::I); + const bool y_scale_used = y_scale != nullptr; + #pragma unroll for (int j0 = 0; j0 < J; j0 += ntx*tile_C::J) { #pragma unroll @@ -472,7 +503,16 @@ static __device__ __forceinline__ void ggml_cuda_mmq_write_back_mma( continue; } - dst[ids_dst[j]*stride + i] = sum[(j0/tile_C::J + n)*tile_C::ne + l]; + if constexpr (type == GGML_TYPE_NVFP4) { + if (y_scale_used) { + dst[ids_dst[j]*stride + i] = y_scale[j] * sum[(j0/tile_C::J + n)*tile_C::ne + l]; + } else { + dst[ids_dst[j]*stride + i] = sum[(j0/tile_C::J + n)*tile_C::ne + l]; + } + } else { + dst[ids_dst[j]*stride + i] = sum[(j0/tile_C::J + n)*tile_C::ne + l]; + GGML_UNUSED(y_scale_used); + } } } } @@ -504,6 +544,12 @@ static constexpr __device__ ggml_cuda_mmq_util_funcs ggml_cuda_mmq_get_util_func ggml_cuda_mmq_load_tiles_q1_0, ggml_cuda_mmq_vec_dot_q8_0_q8_1_dp4a, ggml_cuda_mmq_write_back_dp4a); + case GGML_TYPE_Q2_0: + return ggml_cuda_mmq_util_funcs( + VDR_Q2_0_Q8_1_MMQ, + ggml_cuda_mmq_load_tiles_q2_0, + ggml_cuda_mmq_vec_dot_q8_0_q8_1_dp4a, + ggml_cuda_mmq_write_back_dp4a); case GGML_TYPE_Q4_0: return ggml_cuda_mmq_util_funcs( VDR_Q4_0_Q8_1_MMQ, @@ -662,6 +708,12 @@ static constexpr __device__ ggml_cuda_mmq_util_funcs ggml_cuda_mmq_get_util_func ggml_cuda_mmq_load_tiles_q1_0, ggml_cuda_mmq_vec_dot_q8_0_q8_1_mma, ggml_cuda_mmq_write_back_mma); + case GGML_TYPE_Q2_0: + return ggml_cuda_mmq_util_funcs( + -1, + ggml_cuda_mmq_load_tiles_q2_0, + ggml_cuda_mmq_vec_dot_q8_0_q8_1_mma, + ggml_cuda_mmq_write_back_mma); case GGML_TYPE_Q4_0: return ggml_cuda_mmq_util_funcs( -1, @@ -816,6 +868,7 @@ template static __device__ __forceinline__ void mul_mat_q_process_tile( const char * __restrict__ x, const int offset_x, const int * __restrict__ y, const int * __restrict__ ids_dst, float * __restrict__ dst, float * __restrict__ tmp_fixup, + const float * __restrict__ y_scale, const int stride_row_x, const int ncols_y, const int stride_col_dst, const int tile_x_max_i, const int tile_y_max_j, const int kb0_start, const int kb0_stop) { @@ -833,9 +886,9 @@ static __device__ __forceinline__ void mul_mat_q_process_tile( #if defined(BLACKWELL_MMA_AVAILABLE) // FP4 tile stores 8 blocks - constexpr int ne_block = (type == GGML_TYPE_MXFP4 || type == GGML_TYPE_NVFP4) ? QK_K : 4 * QK8_1; + constexpr int ne_block = (type == GGML_TYPE_MXFP4 || type == GGML_TYPE_NVFP4) ? QK_FP4_MMQ : QK8_1_MMQ; #else - constexpr int ne_block = 4 * QK8_1; + constexpr int ne_block = QK8_1_MMQ; #endif // defined(BLACKWELL_MMA_AVAILABLE) constexpr int ITER_K = ggml_cuda_mmq_get_K_vram(type, J, fallback); @@ -881,9 +934,9 @@ static __device__ __forceinline__ void mul_mat_q_process_tile( } if (fixup) { - write_back(sum, ids_dst, tmp_fixup + blockIdx.x*(J*I), I, I, J); + write_back(sum, ids_dst, tmp_fixup + blockIdx.x*(J*I), y_scale, I, I, J); } else { - write_back(sum, ids_dst, dst, stride_col_dst, tile_x_max_i, tile_y_max_j); + write_back(sum, ids_dst, dst, y_scale, stride_col_dst, tile_x_max_i, tile_y_max_j); } } @@ -895,6 +948,7 @@ __launch_bounds__(ggml_cuda_mmq_get_nthreads(type, J, fallback), ggml_cuda_mmq_g static __global__ void mul_mat_q( const char * __restrict__ x, const int * __restrict__ y, const int32_t * __restrict__ ids_dst, const int32_t * __restrict__ expert_bounds, float * __restrict__ dst, float * __restrict__ tmp_fixup, + const float * __restrict__ y_scale, const uint3 blocks_per_ne00, const int nrows_x, const int ncols_dst, const int stride_row_x, const int ncols_y, const int stride_col_dst, const uint3 channel_ratio, const uint3 nchannels_y, const int stride_channel_x, const int stride_channel_y, const int stride_channel_dst, const uint3 sample_ratio, const uint3 nsamples_y, const int stride_sample_x, const int stride_sample_y, const int stride_sample_dst, @@ -942,6 +996,12 @@ static __global__ void mul_mat_q( int col_diff = ncols_dst; int offset_y = wt*stride_sample_y + zt*stride_channel_y; int64_t offset_dst = (int64_t) wt*stride_sample_dst + (int64_t) zt*stride_channel_dst + (int64_t) jt*J*stride_col_dst; + int offset_y_scale; + if constexpr (type == GGML_TYPE_NVFP4) { + offset_y_scale = wt*nchannels_y.z*ncols_y + zt*ncols_y; + } else { + GGML_UNUSED(offset_y_scale); + } if (ids_dst) { col_low = expert_bounds[zt + 0]; @@ -950,6 +1010,9 @@ static __global__ void mul_mat_q( offset_y = 0; offset_dst = 0; + if constexpr (type == GGML_TYPE_NVFP4) { + offset_y_scale = 0; + } if (jt*J >= col_diff) { return; @@ -971,6 +1034,11 @@ static __global__ void mul_mat_q( offset_y += (col_low + jt*J)*(sizeof(block_q8_1_mmq)/sizeof(int)); offset_dst += it*I; + const float * y_scale_tile = nullptr; + if constexpr (type == GGML_TYPE_NVFP4) { + offset_y_scale += col_low + jt*J; + y_scale_tile = y_scale ? y_scale + offset_y_scale : nullptr; + } const int tile_x_max_i = nrows_x - it*I - 1; const int tile_y_max_j = col_diff - jt*J - 1; @@ -979,7 +1047,8 @@ static __global__ void mul_mat_q( constexpr bool fixup = false; mul_mat_q_process_tile - (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, stride_row_x, ncols_y, stride_col_dst, + (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, y_scale_tile, + stride_row_x, ncols_y, stride_col_dst, tile_x_max_i, tile_y_max_j, 0, blocks_per_ne00.z); return; } @@ -1015,6 +1084,12 @@ static __global__ void mul_mat_q( int col_diff = ncols_dst; int offset_y = wt*stride_sample_y + zt*stride_channel_y; int64_t offset_dst = (int64_t) wt*stride_sample_dst + (int64_t) zt*stride_channel_dst + (int64_t) jt*J*stride_col_dst; + int offset_y_scale; + if constexpr (type == GGML_TYPE_NVFP4) { + offset_y_scale = wt*nchannels_y.z*ncols_y + zt*ncols_y; + } else { + GGML_UNUSED(offset_y_scale); + } if (ids_dst) { col_low = expert_bounds[zt + 0]; @@ -1023,6 +1098,9 @@ static __global__ void mul_mat_q( offset_y = 0; offset_dst = 0; + if constexpr (type == GGML_TYPE_NVFP4) { + offset_y_scale = 0; + } if (jt*J >= col_diff) { kbc += blocks_per_ne00.z; @@ -1050,6 +1128,11 @@ static __global__ void mul_mat_q( offset_y += (col_low + jt * J) * (sizeof(block_q8_1_mmq) / sizeof(int)); offset_dst += it*I; + const float * y_scale_tile = nullptr; + if constexpr (type == GGML_TYPE_NVFP4) { + offset_y_scale += col_low + jt * J; + y_scale_tile = y_scale ? y_scale + offset_y_scale : nullptr; + } const int tile_x_max_i = nrows_x - it*I - 1; const int tile_y_max_j = col_diff - jt*J - 1; @@ -1058,7 +1141,8 @@ static __global__ void mul_mat_q( constexpr bool fixup = false; // All but (potentially) the last iterations write their data to dst rather than the fixup buffer. mul_mat_q_process_tile - (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, stride_row_x, ncols_y, stride_col_dst, + (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, y_scale_tile, + stride_row_x, ncols_y, stride_col_dst, tile_x_max_i, tile_y_max_j, kb0_start, kb0_stop); kbc += blocks_per_ne00.z; @@ -1089,6 +1173,12 @@ static __global__ void mul_mat_q( int col_diff = ncols_dst; int offset_y = wt*stride_sample_y + zt*stride_channel_y; int64_t offset_dst = (int64_t) wt*stride_sample_dst + (int64_t) zt*stride_channel_dst + (int64_t) jt*J*stride_col_dst; + int offset_y_scale; + if constexpr (type == GGML_TYPE_NVFP4) { + offset_y_scale = wt*nchannels_y.z*ncols_y + zt*ncols_y; + } else { + GGML_UNUSED(offset_y_scale); + } if (ids_dst) { col_low = expert_bounds[zt + 0]; @@ -1097,6 +1187,9 @@ static __global__ void mul_mat_q( offset_y = 0; offset_dst = 0; + if constexpr (type == GGML_TYPE_NVFP4) { + offset_y_scale = 0; + } if (jt*J >= col_diff) { return; @@ -1119,6 +1212,11 @@ static __global__ void mul_mat_q( offset_y += (col_low + jt * J) * (sizeof(block_q8_1_mmq) / sizeof(int)); offset_dst += it*I; + const float * y_scale_tile = nullptr; + if constexpr (type == GGML_TYPE_NVFP4) { + offset_y_scale += col_low + jt * J; + y_scale_tile = y_scale ? y_scale + offset_y_scale : nullptr; + } const int tile_x_max_i = nrows_x - it*I - 1; const int tile_y_max_j = col_diff - jt*J - 1; @@ -1127,7 +1225,8 @@ static __global__ void mul_mat_q( constexpr bool fixup = true; // Last index writes its data to fixup buffer to avoid data races with other blocks. mul_mat_q_process_tile - (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, stride_row_x, ncols_y, stride_col_dst, + (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, y_scale_tile, + stride_row_x, ncols_y, stride_col_dst, tile_x_max_i, tile_y_max_j, kb0_start, kb0_stop); } @@ -1271,6 +1370,7 @@ static __global__ void mul_mat_q_stream_k_fixup( struct mmq_args { const char * x; ggml_type type_x; const int * y; const int32_t * ids_dst; const int32_t * expert_bounds; float * dst; + const float * y_scale; int64_t ncols_x; int64_t nrows_x; int64_t ncols_dst; int64_t stride_row_x; int64_t ncols_y; int64_t nrows_dst; int64_t nchannels_x; int64_t nchannels_y; int64_t stride_channel_x; int64_t stride_channel_y; int64_t stride_channel_dst; int64_t nsamples_x; int64_t nsamples_y; int64_t stride_sample_x; int64_t stride_sample_y; int64_t stride_sample_dst; @@ -1320,7 +1420,7 @@ static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & a if (!ggml_cuda_mmq_get_stream_k(type, J, fallback, cc)) { mul_mat_q<<>> - (args.x, args.y, args.ids_dst, args.expert_bounds, args.dst, nullptr, + (args.x, args.y, args.ids_dst, args.expert_bounds, args.dst, nullptr, args.y_scale, blocks_per_ne00_fd, args.nrows_x, args.ncols_dst, args.stride_row_x, args.ncols_y, args.nrows_dst, channel_ratio_fd, nchannels_y_fd, args.stride_channel_x, args.stride_channel_y, args.stride_channel_dst, sample_ratio_fd, nsamples_y_fd, args.stride_sample_x, args.stride_sample_y, args.stride_sample_dst, @@ -1349,7 +1449,7 @@ static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & a const dim3 block_dims_fixup(block_dims.x, block_dims.y/2, block_dims.z); mul_mat_q<<>> - (args.x, args.y, args.ids_dst, args.expert_bounds, args.dst, tmp_fixup.ptr, + (args.x, args.y, args.ids_dst, args.expert_bounds, args.dst, tmp_fixup.ptr, args.y_scale, blocks_per_ne00_fd, args.nrows_x, args.ncols_dst, args.stride_row_x, args.ncols_y, args.nrows_dst, channel_ratio_fd, nchannels_y_fd, args.stride_channel_x, args.stride_channel_y, args.stride_channel_dst, sample_ratio_fd, nsamples_y_fd, args.stride_sample_x, args.stride_sample_y, args.stride_sample_dst, @@ -1463,26 +1563,31 @@ void mul_mat_q_case(ggml_backend_cuda_context & ctx, const mmq_args & args, cuda #define DECL_MMQ_CASE(type) \ template void mul_mat_q_case(ggml_backend_cuda_context & ctx, const mmq_args & args, cudaStream_t stream) \ +extern DECL_MMQ_CASE(GGML_TYPE_Q1_0); +extern DECL_MMQ_CASE(GGML_TYPE_Q2_0); extern DECL_MMQ_CASE(GGML_TYPE_Q4_0); extern DECL_MMQ_CASE(GGML_TYPE_Q4_1); extern DECL_MMQ_CASE(GGML_TYPE_Q5_0); extern DECL_MMQ_CASE(GGML_TYPE_Q5_1); extern DECL_MMQ_CASE(GGML_TYPE_Q8_0); -extern DECL_MMQ_CASE(GGML_TYPE_MXFP4); -extern DECL_MMQ_CASE(GGML_TYPE_NVFP4); +// ----------------------------------------- extern DECL_MMQ_CASE(GGML_TYPE_Q2_K); extern DECL_MMQ_CASE(GGML_TYPE_Q3_K); extern DECL_MMQ_CASE(GGML_TYPE_Q4_K); extern DECL_MMQ_CASE(GGML_TYPE_Q5_K); extern DECL_MMQ_CASE(GGML_TYPE_Q6_K); +// ----------------------------------------- +extern DECL_MMQ_CASE(GGML_TYPE_IQ1_S); extern DECL_MMQ_CASE(GGML_TYPE_IQ2_XXS); extern DECL_MMQ_CASE(GGML_TYPE_IQ2_XS); extern DECL_MMQ_CASE(GGML_TYPE_IQ2_S); extern DECL_MMQ_CASE(GGML_TYPE_IQ3_XXS); extern DECL_MMQ_CASE(GGML_TYPE_IQ3_S); -extern DECL_MMQ_CASE(GGML_TYPE_IQ1_S); extern DECL_MMQ_CASE(GGML_TYPE_IQ4_NL); extern DECL_MMQ_CASE(GGML_TYPE_IQ4_XS); +// ----------------------------------------- +extern DECL_MMQ_CASE(GGML_TYPE_MXFP4); +extern DECL_MMQ_CASE(GGML_TYPE_NVFP4); // ------------------------------------------------------------------------------------------------------------------------- diff --git a/ggml/src/ggml-cuda/mmvq.cu b/ggml/src/ggml-cuda/mmvq.cu index f62c4ebb2389..e9023d6fa5c8 100644 --- a/ggml/src/ggml-cuda/mmvq.cu +++ b/ggml/src/ggml-cuda/mmvq.cu @@ -10,6 +10,7 @@ typedef float (*vec_dot_q_cuda_t)(const void * __restrict__ vbq, const block_q8_ static constexpr __device__ vec_dot_q_cuda_t get_vec_dot_q_cuda(ggml_type type) { switch (type) { case GGML_TYPE_Q1_0: return vec_dot_q1_0_q8_1; + case GGML_TYPE_Q2_0: return vec_dot_q2_0_q8_1; case GGML_TYPE_Q4_0: return vec_dot_q4_0_q8_1; case GGML_TYPE_Q4_1: return vec_dot_q4_1_q8_1; case GGML_TYPE_Q5_0: return vec_dot_q5_0_q8_1; @@ -38,6 +39,7 @@ static constexpr __device__ vec_dot_q_cuda_t get_vec_dot_q_cuda(ggml_type type) static constexpr __host__ __device__ int get_vdr_mmvq(ggml_type type) { switch (type) { case GGML_TYPE_Q1_0: return VDR_Q1_0_Q8_1_MMVQ; + case GGML_TYPE_Q2_0: return VDR_Q2_0_Q8_1_MMVQ; case GGML_TYPE_Q4_0: return VDR_Q4_0_Q8_1_MMVQ; case GGML_TYPE_Q4_1: return VDR_Q4_1_Q8_1_MMVQ; case GGML_TYPE_Q5_0: return VDR_Q5_0_Q8_1_MMVQ; @@ -1010,6 +1012,12 @@ static void mul_mat_vec_q_switch_type( nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream); break; + case GGML_TYPE_Q2_0: + mul_mat_vec_q_switch_ncols_dst + (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst, + nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst, + nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream); + break; case GGML_TYPE_Q4_0: mul_mat_vec_q_switch_ncols_dst (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst, diff --git a/ggml/src/ggml-cuda/norm.cu b/ggml/src/ggml-cuda/norm.cu index 09d9f3a7d624..c3758cd50cfe 100644 --- a/ggml/src/ggml-cuda/norm.cu +++ b/ggml/src/ggml-cuda/norm.cu @@ -64,7 +64,7 @@ static __global__ void group_norm_f32(const float * x, float * dst, const int gr tmp += xi * xi; } - tmp = block_reduce(tmp, s_sum); + tmp = block_reduce(tmp, s_sum + 32); const float variance = tmp / group_size; const float scale = rsqrtf(variance + eps); @@ -297,7 +297,7 @@ static void group_norm_f32_cuda( group_norm_f32<<>>(x, dst, group_size, ne_elements, eps); } else { const dim3 block_dims(1024, 1, 1); - group_norm_f32<1024><< WARP_SIZE ? 32 * sizeof(float): 0, stream>>>(x, dst, group_size, ne_elements, eps); + group_norm_f32<1024><< WARP_SIZE ? 2 * 32 * sizeof(float): 0, stream>>>(x, dst, group_size, ne_elements, eps); } } diff --git a/ggml/src/ggml-cuda/quantize.cu b/ggml/src/ggml-cuda/quantize.cu index 39a500a17041..2bd9b6262390 100644 --- a/ggml/src/ggml-cuda/quantize.cu +++ b/ggml/src/ggml-cuda/quantize.cu @@ -1,6 +1,55 @@ #include "quantize.cuh" #include +#if defined(BLACKWELL_MMA_AVAILABLE) +// this maps to 256-bit loads in PTX on supported devices, +// and otherwise falls back to 2 128-bit loads +struct __builtin_align__(32) float8 { + float x; float y; float z; float w; + float p; float q; float r; float s; +}; +#endif + +#if CUDART_VERSION >= 12080 +static __device__ __forceinline__ float nvfp4_native_scale_error( + const float vals[QK_NVFP4_SUB], const float inv_col_scale, const float inv_scale, const float scale) { + const float scale_dequant = 2.0f * scale; + float err = 0.0f; + +#pragma unroll + for (int k = 0; k < QK_NVFP4_SUB; k += 4) { + const float v0 = vals[k + 0] * inv_col_scale; + const float v1 = vals[k + 1] * inv_col_scale; + const float v2 = vals[k + 2] * inv_col_scale; + const float v3 = vals[k + 3] * inv_col_scale; + + const __nv_fp4x4_e2m1 q(make_float4(v0 * inv_scale, v1 * inv_scale, v2 * inv_scale, v3 * inv_scale)); + const __nv_fp4x4_storage_t q_storage = q.__x; + const __nv_fp4x2_storage_t q_lo = static_cast<__nv_fp4x2_storage_t>(q_storage); + const __nv_fp4x2_storage_t q_hi = static_cast<__nv_fp4x2_storage_t>(q_storage >> 8U); + + const __half2_raw hraw2_lo = __nv_cvt_fp4x2_to_halfraw2(q_lo, __NV_E2M1); + const __half2_raw hraw2_hi = __nv_cvt_fp4x2_to_halfraw2(q_hi, __NV_E2M1); + const __half2 h2_lo = static_cast<__half2>(hraw2_lo); + const __half2 h2_hi = static_cast<__half2>(hraw2_hi); + const float2 dq_lo = __half22float2(h2_lo); + const float2 dq_hi = __half22float2(h2_hi); + + const float err0 = fabsf(v0) - fabsf(dq_lo.x) * scale_dequant; + const float err1 = fabsf(v1) - fabsf(dq_lo.y) * scale_dequant; + const float err2 = fabsf(v2) - fabsf(dq_hi.x) * scale_dequant; + const float err3 = fabsf(v3) - fabsf(dq_hi.y) * scale_dequant; + + err = fmaf(err0, err0, err); + err = fmaf(err1, err1, err); + err = fmaf(err2, err2, err); + err = fmaf(err3, err3, err); + } + + return err; +} +#endif // CUDART_VERSION >= 12080 + __launch_bounds__(CUDA_QUANTIZE_BLOCK_SIZE, 1) static __global__ void quantize_q8_1( const float * x_ptr, void * vy_ptr, @@ -74,97 +123,209 @@ __device__ __forceinline__ uint8_t compute_e8m0_scale(float amax) { return static_cast(biased); } - +// scatter: grid over tokens, quantize once, write to all the token's compact rows +template static __global__ void quantize_mmq_nvfp4( - const float * __restrict__ x, const int32_t * __restrict__ ids, void * __restrict__ vy, + const float * __restrict__ x, const int32_t * __restrict__ ids, void * __restrict__ vy, float * __restrict__ scale, const int64_t ne00, const int64_t s01, const int64_t s02, const int64_t s03, - const int64_t ne0, const int64_t ne1, const int64_t ne2) { + const int64_t ne0, const int64_t ne1, const int64_t ne2, const int n_expert_used) { #if defined(BLACKWELL_MMA_AVAILABLE) - const int64_t i0_base = ((int64_t) blockDim.x * blockIdx.y + threadIdx.x) * QK_NVFP4_SUB; - if (i0_base >= ne0) { - return; - } + const int64_t blocks_per_col = (ne0 + QK_FP4_MMQ - 1) / QK_FP4_MMQ; - const int64_t i1 = blockIdx.x; - const int64_t i2 = blockIdx.z % ne2; - const int64_t i3 = blockIdx.z / ne2; - const int64_t i01 = ids ? ids[i1] : i1; - const int64_t k_block = i0_base / QK_K; - const int64_t blocks_per_col = (ne0 + QK_K - 1) / QK_K; - if (k_block >= blocks_per_col) { - return; + int64_t base_idx; + if constexpr (scatter) { + base_idx = (int64_t) blockIdx.x * s02; // one physical row per token + } else { + const int64_t i2 = blockIdx.y % ne2; + const int64_t i3 = blockIdx.y / ne2; + const int64_t i01 = ids ? ids[blockIdx.x] : blockIdx.x; + base_idx = i3 * s03 + i2 * s02 + i01 * s01; + } + const float * __restrict__ x_row = x + base_idx; + + float amax = 0.0f; + if constexpr (use_aligned_float8) { + for (int64_t i0 = 8 * threadIdx.x; i0 < ne00; i0 += 8 * blockDim.x) { + const float * x_base = x_row + i0; + const float8 v = reinterpret_cast(x_base)[0]; + amax = fmaxf(amax, fabsf(v.x)); + amax = fmaxf(amax, fabsf(v.y)); + amax = fmaxf(amax, fabsf(v.z)); + amax = fmaxf(amax, fabsf(v.w)); + amax = fmaxf(amax, fabsf(v.p)); + amax = fmaxf(amax, fabsf(v.q)); + amax = fmaxf(amax, fabsf(v.r)); + amax = fmaxf(amax, fabsf(v.s)); + } + } else { + for (int64_t i0 = threadIdx.x; i0 < ne00; i0 += blockDim.x) { + amax = fmaxf(amax, fabsf(x_row[i0])); + } } - const int64_t ib = blockIdx.z * ((int64_t) blocks_per_col * ne1) + k_block * ne1 + blockIdx.x; - block_fp4_mmq * y = (block_fp4_mmq *) vy; - block_fp4_mmq * yb = y + ib; + amax = warp_reduce_max(amax); - const int sub = (i0_base % QK_K) / QK_NVFP4_SUB; + __shared__ float warp_amax[CUDA_QUANTIZE_BLOCK_SIZE_MMQ / WARP_SIZE]; + const int lane = threadIdx.x % WARP_SIZE; + const int warp = threadIdx.x / WARP_SIZE; - float vals_raw[QK_NVFP4_SUB]; - float amax_raw = 0.0f; - const int64_t base_idx = i3 * s03 + i2 * s02 + i01 * s01; + if (lane == 0) { + warp_amax[warp] = amax; + } + __syncthreads(); + + if (warp == 0) { + amax = threadIdx.x < int(CUDA_QUANTIZE_BLOCK_SIZE_MMQ / WARP_SIZE) ? warp_amax[lane] : 0.0f; + amax = warp_reduce_max(amax); + if (lane == 0) { + warp_amax[0] = amax / (6.0f * 448.0f); + if constexpr (scatter) { #pragma unroll - for (int k = 0; k < QK_NVFP4_SUB; k++) { - const int64_t i00 = i0_base + k; - if (i00 < ne00) { - const float v = x[base_idx + i00]; - vals_raw[k] = v; - amax_raw = fmaxf(amax_raw, fabsf(v)); - } else { - vals_raw[k] = 0.0f; + for (int slot = 0; slot < n_expert_used; ++slot) { + const int64_t i = ids[(int64_t) blockIdx.x * n_expert_used + slot]; + scale[i] = warp_amax[0]; + } + } else { + scale[blockIdx.y * ne1 + blockIdx.x] = warp_amax[0]; + } } } + __syncthreads(); - static constexpr int test_offsets[5] = { 0, -1, 1, -2, 2}; - const int first_fp8_code = (int) ggml_cuda_fp32_to_ue4m3(amax_raw / 6.0f); + block_fp4_mmq * y = (block_fp4_mmq *) vy; + const int64_t n_subblocks = (ne0 + QK_NVFP4_SUB - 1) / QK_NVFP4_SUB; + + for (int64_t isb = threadIdx.x; isb < n_subblocks; isb += blockDim.x) { + const int64_t i0_base = isb * QK_NVFP4_SUB; + const int64_t k_block = i0_base / QK_FP4_MMQ; + const int sub = (i0_base % QK_FP4_MMQ) / QK_NVFP4_SUB; + + const float row_scale = warp_amax[0]; + const float inv_col_scale = row_scale > 0.0f ? 1.0f / row_scale : 0.0f; + + float vals[QK_NVFP4_SUB]; + if constexpr (use_aligned_float8) { + const float * x_base = x_row + i0_base; + const float8 v0 = i0_base + 7 < ne00 ? reinterpret_cast(x_base)[0] : float8{0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}; + const float8 v1 = i0_base + 15 < ne00 ? reinterpret_cast(x_base + 8)[0] : float8{0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}; + vals[0] = v0.x; vals[1] = v0.y; vals[2] = v0.z; vals[3] = v0.w; + vals[4] = v0.p; vals[5] = v0.q; vals[6] = v0.r; vals[7] = v0.s; + vals[8] = v1.x; vals[9] = v1.y; vals[10] = v1.z; vals[11] = v1.w; + vals[12] = v1.p; vals[13] = v1.q; vals[14] = v1.r; vals[15] = v1.s; + } else { +#pragma unroll + for (int k = 0; k < QK_NVFP4_SUB; ++k) { + const int64_t i00 = i0_base + k; + vals[k] = i00 < ne00 ? x_row[i00] : 0.0f; + } + } - float best_err = FLT_MAX; - uint8_t fp8_code = 0; - float subblock_scale = 0.0f; + uint32_t q0 = 0; + uint32_t q1 = 0; -#pragma unroll // Check +/- 2 to find best code to reduce NVFP4 activation loss. Negligible overhead on Blackwell. - for (int i = 0; i < 5; i++) { - const int test_code = first_fp8_code + test_offsets[i]; - if (test_code < 0 || test_code > 0x7e) { - continue; + float amax_sub = 0.0f; +#pragma unroll + for (int k = 0; k < QK_NVFP4_SUB; ++k) { + amax_sub = fmaxf(amax_sub, fabsf(vals[k] * inv_col_scale)); } - const uint8_t code = (uint8_t) test_code; - const float test_scale = ggml_cuda_ue4m3_to_fp32(code); - const float test_inv_scale = test_scale > 0.0f ? 0.5f / test_scale : 0.0f; - float cur_err = 0.0f; + + static constexpr int test_offsets[5] = { 0, -1, 1, -2, 2 }; + const int first_fp8_code = (int) ggml_cuda_fp32_to_ue4m3(amax_sub / 6.0f); + + uint8_t fp8_code = (uint8_t) first_fp8_code; + float subblock_scale = ggml_cuda_ue4m3_to_fp32(fp8_code); + float inv_scale_err = subblock_scale > 0.0f ? 0.5f / subblock_scale : 0.0f; +#if CUDART_VERSION >= 12080 + float best_err = nvfp4_native_scale_error(vals, inv_col_scale, inv_scale_err, subblock_scale); +#else + float best_err = 0.0f; #pragma unroll for (int k = 0; k < QK_NVFP4_SUB; ++k) { - const float v = vals_raw[k]; - const uint8_t q = ggml_cuda_float_to_fp4_e2m1(v, test_inv_scale); - const float err_diff = fabsf(v) - fabsf(kvalues_mxfp4[q & 0x7]) * test_scale; - cur_err = fmaf(err_diff, err_diff, cur_err); + const float v = vals[k] * inv_col_scale; + const uint8_t q = ggml_cuda_float_to_fp4_e2m1(v, inv_scale_err); + const float err_diff = fabsf(v) - fabsf(kvalues_fp4[q & 0x7]) * subblock_scale; + best_err = fmaf(err_diff, err_diff, best_err); } +#endif // CUDART_VERSION >= 12080 + +#pragma unroll + for (int i = 1; i < 5; ++i) { + const int test_code = first_fp8_code + test_offsets[i]; + if (test_code < 0 || test_code > 0x7e) { + continue; + } + + const float test_scale = ggml_cuda_ue4m3_to_fp32((uint8_t) test_code); + const float test_inv_scale = test_scale > 0.0f ? 0.5f / test_scale : 0.0f; +#if CUDART_VERSION >= 12080 + const float cur_err = nvfp4_native_scale_error(vals, inv_col_scale, test_inv_scale, test_scale); +#else + float cur_err = 0.0f; +#pragma unroll + for (int k = 0; k < QK_NVFP4_SUB; ++k) { + const float v = vals[k] * inv_col_scale; + const uint8_t q = ggml_cuda_float_to_fp4_e2m1(v, test_inv_scale); + const float err_diff = fabsf(v) - fabsf(kvalues_fp4[q & 0x7]) * test_scale; + cur_err = fmaf(err_diff, err_diff, cur_err); + } +#endif // CUDART_VERSION >= 12080 - if (cur_err < best_err) { - best_err = cur_err; - fp8_code = test_code; - subblock_scale = test_scale; + if (cur_err < best_err) { + best_err = cur_err; + fp8_code = (uint8_t) test_code; + subblock_scale = test_scale; + } } - } +#if CUDART_VERSION >= 12080 + const float inv_scale = subblock_scale > 0.0f ? 0.5f / subblock_scale : 0.0f; + const float s = inv_col_scale * inv_scale; + + __nv_fp4x4_e2m1 q0_lo(make_float4(vals[0] * s, vals[8] * s, vals[1] * s, vals[9] * s)); + __nv_fp4x4_e2m1 q0_hi(make_float4(vals[2] * s, vals[10] * s, vals[3] * s, vals[11] * s)); + __nv_fp4x4_e2m1 q1_lo(make_float4(vals[4] * s, vals[12] * s, vals[5] * s, vals[13] * s)); + __nv_fp4x4_e2m1 q1_hi(make_float4(vals[6] * s, vals[14] * s, vals[7] * s, vals[15] * s)); + + const char2 q0_lo_c = *reinterpret_cast(&q0_lo); + const char2 q0_hi_c = *reinterpret_cast(&q0_hi); + const char2 q1_lo_c = *reinterpret_cast(&q1_lo); + const char2 q1_hi_c = *reinterpret_cast(&q1_hi); + + q0 = uint32_t(uint8_t(q0_lo_c.x)) | (uint32_t(uint8_t(q0_lo_c.y)) << 8) | + (uint32_t(uint8_t(q0_hi_c.x)) << 16) | (uint32_t(uint8_t(q0_hi_c.y)) << 24); + q1 = uint32_t(uint8_t(q1_lo_c.x)) | (uint32_t(uint8_t(q1_lo_c.y)) << 8) | + (uint32_t(uint8_t(q1_hi_c.x)) << 16) | (uint32_t(uint8_t(q1_hi_c.y)) << 24); +#else + const float inv_scale = subblock_scale > 0.0f ? 0.5f / subblock_scale : 0.0f; +#pragma unroll + for (int k = 0; k < QK_NVFP4_SUB / 4; ++k) { + q0 |= uint32_t(ggml_cuda_float_to_fp4_e2m1(vals[k + 0] * inv_col_scale, inv_scale)) << (8 * k); + q0 |= uint32_t(ggml_cuda_float_to_fp4_e2m1(vals[k + 8] * inv_col_scale, inv_scale)) << (8 * k + 4); + q1 |= uint32_t(ggml_cuda_float_to_fp4_e2m1(vals[k + 4] * inv_col_scale, inv_scale)) << (8 * k); + q1 |= uint32_t(ggml_cuda_float_to_fp4_e2m1(vals[k + 12] * inv_col_scale, inv_scale)) << (8 * k + 4); + } +#endif // CUDART_VERSION >= 12080 - const float inv_scale = subblock_scale > 0.0f ? 0.5f / subblock_scale : 0.0f; - uint32_t q0 = 0; - uint32_t q1 = 0; -#pragma unroll // this is faster than the previous __nv_fp4x4_e2m1 - for (int k = 0; k < QK_NVFP4_SUB / 4; ++k) { - q0 |= (uint32_t) ggml_cuda_float_to_fp4_e2m1(vals_raw[k + 0], inv_scale) << (8 * k); - q0 |= (uint32_t) ggml_cuda_float_to_fp4_e2m1(vals_raw[k + 8], inv_scale) << (8 * k + 4); - q1 |= (uint32_t) ggml_cuda_float_to_fp4_e2m1(vals_raw[k + 4], inv_scale) << (8 * k); - q1 |= (uint32_t) ggml_cuda_float_to_fp4_e2m1(vals_raw[k + 12], inv_scale) << (8 * k + 4); + if constexpr (scatter) { +#pragma unroll + for (int slot = 0; slot < n_expert_used; ++slot) { + const int64_t i = ids[(int64_t) blockIdx.x * n_expert_used + slot]; + block_fp4_mmq * yb = y + (k_block * ne1 + i); + uint32_t * yqs = reinterpret_cast(yb->qs); + yqs[2 * sub + 0] = q0; + yqs[2 * sub + 1] = q1; + reinterpret_cast(yb->d4)[sub] = fp8_code; + } + } else { + block_fp4_mmq * yb = y + (blockIdx.y * ((int64_t) blocks_per_col * ne1) + k_block * ne1 + blockIdx.x); + uint32_t * yqs = reinterpret_cast(yb->qs); + yqs[2 * sub + 0] = q0; + yqs[2 * sub + 1] = q1; + reinterpret_cast(yb->d4)[sub] = fp8_code; + } } - - uint32_t * yqs = reinterpret_cast(yb->qs); - yqs[2 * sub + 0] = q0; - yqs[2 * sub + 1] = q1; - reinterpret_cast(yb->d4)[sub] = fp8_code; #else + GGML_UNUSED_VARS(x, ids, vy, scale, ne00, s01, s02, s03, ne0, ne1, ne2, n_expert_used); NO_DEVICE_CODE; // This is for Blackwell NVFP4 activations only. #endif // defined(BLACKWELL_MMA_AVAILABLE) @@ -172,6 +333,8 @@ static __global__ void quantize_mmq_nvfp4( // quantize values in the format mxfp4 is stored which is interleaved nibbles // i.e. a block a0-a31 is represented as a0a16,a1a17 ...a15a31 +// scatter: grid over tokens, quantize once, write to all the token's compact rows +template static __global__ void quantize_mmq_mxfp4(const float * __restrict__ x, const int32_t * __restrict__ ids, void * __restrict__ vy, @@ -181,7 +344,8 @@ static __global__ void quantize_mmq_mxfp4(const float * __restrict__ x, const int64_t s03, const int64_t ne0, const int ne1, - const int ne2) { + const int ne2, + const int n_expert_used) { constexpr int vals_per_scale = 32; constexpr int vals_per_warp = 2 * vals_per_scale; // Each warp processes 2 blocks of 32 = 64 values @@ -196,30 +360,27 @@ static __global__ void quantize_mmq_mxfp4(const float * __restrict__ x, return; } - const int64_t i1 = blockIdx.x; - const int64_t i2 = blockIdx.z % ne2; - const int64_t i3 = blockIdx.z / ne2; - - ggml_cuda_pdl_sync(); - const int64_t i01 = ids ? ids[i1] : i1; - const int64_t i02 = i2; - const int64_t i03 = i3; - - block_fp4_mmq * y = (block_fp4_mmq *) vy; - - const int64_t block_fp4_mmq_size = 8 * QK_MXFP4; // 256 values - const int64_t ib0 = blockIdx.z * ((int64_t) ne1 * (ne0 / block_fp4_mmq_size)); - const int64_t ib = ib0 + (warp_start_offset / block_fp4_mmq_size) * ne1 + blockIdx.x; + const int64_t block_fp4_mmq_size = QK_FP4_MMQ; + const int64_t k_block = warp_start_offset / block_fp4_mmq_size; const int64_t quad_idx_in_block = (warp_start_offset % block_fp4_mmq_size) / vals_per_warp; const int group_id = lane_id_32 / 4; const int lane_in_group = lane_id_32 % 4; const int base = group_id * 2; - char2 * yqs2 = (char2 *) y[ib].qs; - const int64_t base_pos = i03 * s03 + i02 * s02 + i01 * s01; + ggml_cuda_pdl_sync(); + int64_t base_pos; + if constexpr (scatter) { + base_pos = (int64_t) blockIdx.x * s02; // one physical row per token + } else { + const int64_t i2 = blockIdx.z % ne2; + const int64_t i3 = blockIdx.z / ne2; + const int64_t i01 = ids ? ids[blockIdx.x] : blockIdx.x; + base_pos = i3 * s03 + i2 * s02 + i01 * s01; + } uint8_t scales[2]; + char2 packed[2]; #pragma unroll for (int b = 0; b < 2; ++b) { @@ -244,11 +405,8 @@ static __global__ void quantize_mmq_mxfp4(const float * __restrict__ x, const float val2 = __shfl_sync(0xFFFFFFFF, scaled_val, base + 1, WARP_SIZE); const float val3 = __shfl_sync(0xFFFFFFFF, scaled_val, base + 17, WARP_SIZE); - if (lane_in_group == 0) { - __nv_fp4x4_e2m1 fp4_packed(make_float4(val0, val1, val2, val3)); - - yqs2[quad_idx_in_block * 16 + b * 8 + group_id] = *(char2 *) &fp4_packed; - } + __nv_fp4x4_e2m1 fp4_packed(make_float4(val0, val1, val2, val3)); + packed[b] = *(char2 *) &fp4_packed; #else // Fallback: manual FP4 conversion using LUT const uint8_t q_val = ggml_cuda_float_to_fp4_e2m1(xi, inv_s); @@ -258,26 +416,49 @@ static __global__ void quantize_mmq_mxfp4(const float * __restrict__ x, const uint8_t q_hi_0 = __shfl_sync(0xFFFFFFFF, q_val, base + 16, WARP_SIZE); const uint8_t q_hi_1 = __shfl_sync(0xFFFFFFFF, q_val, base + 17, WARP_SIZE); - if (lane_in_group == 0) { - char2 q; - q.x = (q_hi_0 << 4) | q_lo_0; - q.y = (q_hi_1 << 4) | q_lo_1; - yqs2[quad_idx_in_block * 16 + b * 8 + group_id] = q; - } + char2 q; + q.x = (q_hi_0 << 4) | q_lo_0; + q.y = (q_hi_1 << 4) | q_lo_1; + packed[b] = q; #endif // CUDART_VERSION >= 12080 } - if (lane_id_32 == 0) { - // Store 2 scales packed into 1 uint32 - y[ib].d4[quad_idx_in_block] = (scales[1] << 8) | scales[0]; + block_fp4_mmq * y = (block_fp4_mmq *) vy; + if constexpr (scatter) { +#pragma unroll + for (int slot = 0; slot < n_expert_used; ++slot) { + const int64_t i = ids[(int64_t) blockIdx.x * n_expert_used + slot]; + block_fp4_mmq * yb = y + (k_block * ne1 + i); + char2 * yqs2 = (char2 *) yb->qs; + if (lane_in_group == 0) { + yqs2[quad_idx_in_block * 16 + 0 * 8 + group_id] = packed[0]; + yqs2[quad_idx_in_block * 16 + 1 * 8 + group_id] = packed[1]; + } + if (lane_id_32 == 0) { + yb->d4[quad_idx_in_block] = (scales[1] << 8) | scales[0]; + } + } + } else { + const int64_t ib0 = blockIdx.z * ((int64_t) ne1 * (ne0 / block_fp4_mmq_size)); + block_fp4_mmq * yb = y + (ib0 + k_block * ne1 + blockIdx.x); + char2 * yqs2 = (char2 *) yb->qs; + if (lane_in_group == 0) { + yqs2[quad_idx_in_block * 16 + 0 * 8 + group_id] = packed[0]; + yqs2[quad_idx_in_block * 16 + 1 * 8 + group_id] = packed[1]; + } + if (lane_id_32 == 0) { + yb->d4[quad_idx_in_block] = (scales[1] << 8) | scales[0]; + } } + GGML_UNUSED(n_expert_used); } -template +// scatter: grid over tokens, quantize once, write to all the token's compact rows +template static __global__ void quantize_mmq_q8_1( const float * __restrict__ x, const int32_t * __restrict__ ids, void * __restrict__ vy, const int64_t ne00, const int64_t s01, const int64_t s02, const int64_t s03, - const int64_t ne0, const int ne1, const int ne2) { + const int64_t ne0, const int ne1, const int ne2, const int n_expert_used) { constexpr int vals_per_scale = ds_layout == MMQ_Q8_1_DS_LAYOUT_D2S6 ? 64 : 32; constexpr int vals_per_sum = ds_layout == MMQ_Q8_1_DS_LAYOUT_D2S6 ? 16 : 32; @@ -288,26 +469,27 @@ static __global__ void quantize_mmq_q8_1( return; } - const int64_t i1 = blockIdx.x; - const int64_t i2 = blockIdx.z % ne2; - const int64_t i3 = blockIdx.z / ne2; - const int64_t i00 = i0; ggml_cuda_pdl_sync(); - const int64_t i01 = ids ? ids[i1] : i1; - const int64_t i02 = i2; - const int64_t i03 = i3; - const float4 * x4 = (const float4 *) x; + int64_t base_idx; + if constexpr (scatter) { + base_idx = (int64_t) blockIdx.x * s02; // one physical row per token + } else { + const int64_t i2 = blockIdx.z % ne2; + const int64_t i3 = blockIdx.z / ne2; + const int64_t i01 = ids ? ids[blockIdx.x] : blockIdx.x; + base_idx = i3*s03 + i2*s02 + i01*s01; + } + const float4 * x4 = (const float4 *) x; block_q8_1_mmq * y = (block_q8_1_mmq *) vy; - const int64_t ib0 = blockIdx.z*((int64_t)gridDim.x*gridDim.y*blockDim.x/QK8_1); // first block of channel - const int64_t ib = ib0 + (i0 / (4*QK8_1))*ne1 + blockIdx.x; // block index in channel - const int64_t iqs = i0 % (4*QK8_1); // quant index in block + const int64_t k_block = i0 / QK8_1_MMQ; // column block in the channel + const int64_t iqs = i0 % QK8_1_MMQ; // quant index in block // Load 4 floats per thread and calculate max. abs. value between them: - const float4 xi = i0 < ne00 ? x4[(i03*s03 + i02*s02 + i01*s01 + i00)/4] : make_float4(0.0f, 0.0f, 0.0f, 0.0f); + const float4 xi = i0 < ne00 ? x4[(base_idx + i00)/4] : make_float4(0.0f, 0.0f, 0.0f, 0.0f); float amax = fabsf(xi.x); amax = fmaxf(amax, fabsf(xi.y)); amax = fmaxf(amax, fabsf(xi.z)); @@ -336,40 +518,41 @@ static __global__ void quantize_mmq_q8_1( q.y = roundf(xi.y*d_inv); q.z = roundf(xi.z*d_inv); q.w = roundf(xi.w*d_inv); + const float d = 1.0f / d_inv; - // Write back 4 int8 values as a single 32 bit value for better memory bandwidth: - char4 * yqs4 = (char4 *) y[ib].qs; - yqs4[iqs/4] = q; - - if (ds_layout == MMQ_Q8_1_DS_LAYOUT_D2S6) { - if (iqs % 16 != 0 || iqs >= 96) { - return; + // write the block once (normal) or to each of the token's compact rows (scatter) + const int nwrite = scatter ? n_expert_used : 1; +#pragma unroll + for (int slot = 0; slot < nwrite; ++slot) { + int64_t ib; + if constexpr (scatter) { + const int64_t i = ids[(int64_t) blockIdx.x * n_expert_used + slot]; + ib = k_block*ne1 + i; + } else { + const int64_t ib0 = blockIdx.z*((int64_t)gridDim.x*gridDim.y*blockDim.x/QK8_1); // first block of channel + ib = ib0 + k_block*ne1 + blockIdx.x; } - y[ib].d2s6[2 + iqs/16] = sum; - - if (iqs % 64 != 0) { - return; + // Write back 4 int8 values as a single 32 bit value for better memory bandwidth: + char4 * yqs4 = (char4 *) y[ib].qs; + yqs4[iqs/4] = q; + + if (ds_layout == MMQ_Q8_1_DS_LAYOUT_D2S6) { + if (iqs % 16 == 0 && iqs < 96) { + y[ib].d2s6[2 + iqs/16] = sum; + if (iqs % 64 == 0) { + y[ib].d2s6[iqs/64] = d; + } + } + } else if (iqs % 32 == 0) { + if (ds_layout == MMQ_Q8_1_DS_LAYOUT_DS4) { + y[ib].ds4[iqs/32] = make_half2(d, sum); + } else { + y[ib].d4[iqs/32] = d; + } } - - const float d = 1.0f / d_inv; - - y[ib].d2s6[iqs/64] = d; - - return; - } - - if (iqs % 32 != 0) { - return; - } - - const float d = 1.0f / d_inv; - - if (ds_layout == MMQ_Q8_1_DS_LAYOUT_DS4) { - y[ib].ds4[iqs/32] = make_half2(d, sum); - } else { - y[ib].d4[iqs/32] = d; } + GGML_UNUSED(n_expert_used); } void quantize_row_q8_1_cuda( @@ -394,7 +577,7 @@ void quantize_mmq_q8_1_cuda( const int64_t ne00, const int64_t s01, const int64_t s02, const int64_t s03, const int64_t ne0, const int64_t ne1, const int64_t ne2, const int64_t ne3, cudaStream_t stream) { GGML_ASSERT(ne00 % 4 == 0); - GGML_ASSERT(ne0 % (4*QK8_1) == 0); + GGML_ASSERT(ne0 % QK8_1_MMQ == 0); // ne1 tends to assume the highest values, therefore use it as the "x" dimension of the CUDA grid: const int64_t block_num_y = (ne0 + 4*CUDA_QUANTIZE_BLOCK_SIZE_MMQ - 1) / (4*CUDA_QUANTIZE_BLOCK_SIZE_MMQ); @@ -402,16 +585,16 @@ void quantize_mmq_q8_1_cuda( const dim3 block_size(CUDA_QUANTIZE_BLOCK_SIZE_MMQ, 1, 1); switch (mmq_get_q8_1_ds_layout(type_src0)) { case MMQ_Q8_1_DS_LAYOUT_D4: - quantize_mmq_q8_1 - <<>>(x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2); + quantize_mmq_q8_1 + <<>>(x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2, /*n_expert_used=*/0); break; case MMQ_Q8_1_DS_LAYOUT_DS4: - quantize_mmq_q8_1 - <<>>(x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2); + quantize_mmq_q8_1 + <<>>(x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2, /*n_expert_used=*/0); break; case MMQ_Q8_1_DS_LAYOUT_D2S6: - quantize_mmq_q8_1 - <<>>(x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2); + quantize_mmq_q8_1 + <<>>(x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2, /*n_expert_used=*/0); break; default: GGML_ABORT("fatal error"); @@ -419,21 +602,85 @@ void quantize_mmq_q8_1_cuda( } } +// scatter=true reuses the quant kernel: grid over tokens, ids = inverse map (token slot -> compact row) +void quantize_scatter_mmq_q8_1_cuda( + const float * x, const int32_t * ids_src1_inv, void * vy, const ggml_type type_src0, + const int64_t ne00, const int64_t stride_token, const int64_t ne0, + const int64_t n_tokens, const int64_t nrows_dst, const int n_expert_used, cudaStream_t stream) { + GGML_ASSERT(ne00 % 4 == 0); + GGML_ASSERT(ne0 % QK8_1_MMQ == 0); + + const int64_t block_num_y = (ne0 + 4*CUDA_QUANTIZE_BLOCK_SIZE_MMQ - 1) / (4*CUDA_QUANTIZE_BLOCK_SIZE_MMQ); + const dim3 num_blocks(n_tokens, block_num_y, 1); + const dim3 block_size(CUDA_QUANTIZE_BLOCK_SIZE_MMQ, 1, 1); + switch (mmq_get_q8_1_ds_layout(type_src0)) { + case MMQ_Q8_1_DS_LAYOUT_D4: + quantize_mmq_q8_1<<>>( + x, ids_src1_inv, vy, ne00, /*s01=*/0, /*s02=*/stride_token, /*s03=*/0, ne0, /*ne1=*/(int) nrows_dst, /*ne2=*/1, n_expert_used); + break; + case MMQ_Q8_1_DS_LAYOUT_DS4: + quantize_mmq_q8_1<<>>( + x, ids_src1_inv, vy, ne00, /*s01=*/0, /*s02=*/stride_token, /*s03=*/0, ne0, /*ne1=*/(int) nrows_dst, /*ne2=*/1, n_expert_used); + break; + case MMQ_Q8_1_DS_LAYOUT_D2S6: + quantize_mmq_q8_1<<>>( + x, ids_src1_inv, vy, ne00, /*s01=*/0, /*s02=*/stride_token, /*s03=*/0, ne0, /*ne1=*/(int) nrows_dst, /*ne2=*/1, n_expert_used); + break; + default: + GGML_ABORT("fatal error"); + break; + } +} + +// scatter=true reuses the quant kernels: grid over tokens, ids = inverse map (token slot -> compact row) +void quantize_scatter_mmq_fp4_cuda( + const float * x, const int32_t * ids_src1_inv, void * vy, float * scale, const ggml_type type_src0, const bool use_aligned_float8, + const int64_t ne00, const int64_t stride_token, const int64_t ne0, + const int64_t n_tokens, const int64_t nrows_dst, const int n_expert_used, cudaStream_t stream) { + GGML_ASSERT(ne0 > 0); + if (type_src0 == GGML_TYPE_NVFP4) { + GGML_ASSERT(scale); + GGML_ASSERT(ne00 % QK_NVFP4 == 0); + const dim3 block_size(CUDA_QUANTIZE_BLOCK_SIZE_MMQ, 1, 1); + const dim3 num_blocks(n_tokens, 1, 1); + if (use_aligned_float8) { + quantize_mmq_nvfp4<<>>( + x, ids_src1_inv, vy, scale, ne00, /*s01=*/0, /*s02=*/stride_token, /*s03=*/0, ne0, /*ne1=*/nrows_dst, /*ne2=*/1, n_expert_used); + } else { + quantize_mmq_nvfp4<<>>( + x, ids_src1_inv, vy, scale, ne00, /*s01=*/0, /*s02=*/stride_token, /*s03=*/0, ne0, /*ne1=*/nrows_dst, /*ne2=*/1, n_expert_used); + } + } else { + GGML_ASSERT(type_src0 == GGML_TYPE_MXFP4); + constexpr int nwarps = 8; + constexpr int vals_per_block = nwarps * 2 * QK_MXFP4; + const int64_t block_num_y = (ne0 + vals_per_block - 1) / vals_per_block; + const dim3 block_size(WARP_SIZE, nwarps, 1); + const dim3 num_blocks(n_tokens, block_num_y, 1); + quantize_mmq_mxfp4<<>>( + x, ids_src1_inv, vy, ne00, /*s01=*/0, /*s02=*/stride_token, /*s03=*/0, ne0, /*ne1=*/(int) nrows_dst, /*ne2=*/1, n_expert_used); + } +} + void quantize_mmq_fp4_cuda( - const float * x, const int32_t * ids, void * vy, const ggml_type type_src0, + const float * x, const int32_t * ids, void * vy, float * scale, const ggml_type type_src0, const bool use_aligned_float8, const int64_t ne00, const int64_t s01, const int64_t s02, const int64_t s03, const int64_t ne0, const int64_t ne1, const int64_t ne2, const int64_t ne3, cudaStream_t stream) { GGML_ASSERT(type_src0 == GGML_TYPE_MXFP4 || type_src0 == GGML_TYPE_NVFP4); GGML_ASSERT(ne0 > 0); if (type_src0 == GGML_TYPE_NVFP4) { + GGML_ASSERT(scale); GGML_ASSERT(ne00 % QK_NVFP4 == 0); - constexpr int nvfp4_block_size = 128; - const int64_t block_num_y = (ne0 + QK_NVFP4_SUB * nvfp4_block_size - 1) / (QK_NVFP4_SUB * nvfp4_block_size); - const dim3 block_size(nvfp4_block_size, 1, 1); - const dim3 num_blocks(ne1, block_num_y, ne2 * ne3); - quantize_mmq_nvfp4<<>>( - x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2); + const dim3 block_size(CUDA_QUANTIZE_BLOCK_SIZE_MMQ, 1, 1); + const dim3 num_blocks(ne1, ne2 * ne3, 1); + if (use_aligned_float8) { + quantize_mmq_nvfp4<<>>( + x, ids, vy, scale, ne00, s01, s02, s03, ne0, ne1, ne2, /*n_expert_used=*/0); + } else { + quantize_mmq_nvfp4<<>>( + x, ids, vy, scale, ne00, s01, s02, s03, ne0, ne1, ne2, /*n_expert_used=*/0); + } } else { GGML_ASSERT(ne0 % (2 * QK_MXFP4) == 0); @@ -445,6 +692,6 @@ void quantize_mmq_fp4_cuda( const dim3 num_blocks(ne1, block_num_y, ne2 * ne3); const dim3 block_size(WARP_SIZE, nwarps, 1); - quantize_mmq_mxfp4<<>>(x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2); + quantize_mmq_mxfp4<<>>(x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2, /*n_expert_used=*/0); } } diff --git a/ggml/src/ggml-cuda/quantize.cuh b/ggml/src/ggml-cuda/quantize.cuh index 768a3ae6de6c..5f08dcbfe331 100644 --- a/ggml/src/ggml-cuda/quantize.cuh +++ b/ggml/src/ggml-cuda/quantize.cuh @@ -29,7 +29,9 @@ void quantize_mmq_q8_1_cuda( void quantize_mmq_fp4_cuda(const float * x, const int32_t * ids, void * vy, + float * scale, ggml_type type_src0, + bool use_aligned_float8, int64_t ne00, int64_t s01, int64_t s02, @@ -39,3 +41,30 @@ void quantize_mmq_fp4_cuda(const float * x, int64_t ne2, int64_t ne3, cudaStream_t stream); + +// quantize each token once and scatter the block to its compact rows (via the inverse map) +void quantize_scatter_mmq_fp4_cuda(const float * x, + const int32_t * ids_src1_inv, + void * vy, + float * scale, + ggml_type type_src0, + bool use_aligned_float8, + int64_t ne00, + int64_t stride_token, + int64_t ne0, + int64_t n_tokens, + int64_t nrows_dst, + int n_expert_used, + cudaStream_t stream); + +void quantize_scatter_mmq_q8_1_cuda(const float * x, + const int32_t * ids_src1_inv, + void * vy, + ggml_type type_src0, + int64_t ne00, + int64_t stride_token, + int64_t ne0, + int64_t n_tokens, + int64_t nrows_dst, + int n_expert_used, + cudaStream_t stream); diff --git a/ggml/src/ggml-cuda/softmax.cu b/ggml/src/ggml-cuda/softmax.cu index 285c0e9543a2..f320c6f004ea 100644 --- a/ggml/src/ggml-cuda/softmax.cu +++ b/ggml/src/ggml-cuda/softmax.cu @@ -116,6 +116,11 @@ static __global__ void soft_max_f32( vals[col] = val; } + if (block_size > WARP_SIZE) { + // sync is needed as we reuse buf_iw across block_reduce invocations, see #26385 + // for block_size <= WARP_SIZE, block_reduce does not access buf_iw + __syncthreads(); + } // find the sum of exps in the block tmp = block_reduce(tmp, buf_iw); @@ -142,6 +147,8 @@ static __device__ void soft_max_f32_parallelize_cols_single_row(const float * __ float * __restrict__ dst, float * __restrict__ tmp_maxs, float * __restrict__ tmp_sums, + float * shared_vals_max, + float * shared_vals_sum, const soft_max_params p) { namespace cg = cooperative_groups; @@ -154,7 +161,6 @@ static __device__ void soft_max_f32_parallelize_cols_single_row(const float * __ float local_vals[n_elem_per_thread] = { -INFINITY, -INFINITY, -INFINITY, -INFINITY }; float local_max = -INFINITY; const int step_size = gridDim.x * blockDim.x; - __shared__ float shared_vals[32]; // Compute thread-local max for (int col = col_start; col < p.ncols;) { @@ -171,7 +177,7 @@ static __device__ void soft_max_f32_parallelize_cols_single_row(const float * __ } // Compute CTA-level max - local_max = block_reduce(local_max, shared_vals); + local_max = block_reduce(local_max, shared_vals_max); // Store CTA-level max to GMEM if (tid == 0) { @@ -186,7 +192,7 @@ static __device__ void soft_max_f32_parallelize_cols_single_row(const float * __ } else { local_max = -INFINITY; } - local_max = block_reduce(local_max, shared_vals); + local_max = block_reduce(local_max, shared_vals_max); // Compute softmax dividends, accumulate divisor float tmp_expf = 0.0f; @@ -209,7 +215,7 @@ static __device__ void soft_max_f32_parallelize_cols_single_row(const float * __ } // Reduce divisor within CTA - tmp_expf = block_reduce(tmp_expf, shared_vals); + tmp_expf = block_reduce(tmp_expf, shared_vals_sum); // Store CTA-level sum to GMEM if (tid == 0) { @@ -223,7 +229,7 @@ static __device__ void soft_max_f32_parallelize_cols_single_row(const float * __ } else { tmp_expf = 0.0f; } - tmp_expf = block_reduce(tmp_expf, shared_vals); + tmp_expf = block_reduce(tmp_expf, shared_vals_sum); // Divide dividend by global sum + store data for (int col = col_start; col < p.ncols;) { @@ -310,9 +316,11 @@ __launch_bounds__(8*WARP_SIZE, 1) static __global__ void soft_max_f32_paralleliz // https://docs.nvidia.com/cuda/cuda-programming-guide/05-appendices/device-callable-apis.html#grid-synchronization // https://docs.nvidia.com/cuda/cuda-programming-guide/05-appendices/device-callable-apis.html#class-cluster-group { + __shared__ float shared_vals[2][32]; + for (int rowx = 0; rowx < p.ne01 * p.ne02 * p.ne03; rowx++) { soft_max_f32_parallelize_cols_single_row(x + int64_t(rowx) * p.ncols, dst + int64_t(rowx) * p.ncols, tmp_maxs, - tmp_sums, p); + tmp_sums, shared_vals[0], shared_vals[1], p); } } diff --git a/ggml/src/ggml-cuda/ssm-scan.cu b/ggml/src/ggml-cuda/ssm-scan.cu index 3022249c77d5..f3418c2af83d 100644 --- a/ggml/src/ggml-cuda/ssm-scan.cu +++ b/ggml/src/ggml-cuda/ssm-scan.cu @@ -9,6 +9,21 @@ using namespace cub; #include "ssm-scan.cuh" + +// Minimum number of tokens to use SSD (State Space Duality) matmul path instead of scan path. +// For n_tok <= this threshold, the scan kernel is used (lower overhead for short sequences). +#define SSM_SSD_MIN_TOKENS 128 + +// prepare_dt kernel dimensions: one block per (head, seq), each block handles DT_MAX_ITEMS items. +#define SSM_SSD_DT_BLOCK 256 +#define SSM_SSD_DT_MAX_ITEMS 32 + +// Maximum tokens the SSD path supports, derived from the prepare_dt kernel block capacity. +#define SSM_SSD_MAX_TOKENS (SSM_SSD_DT_BLOCK * SSM_SSD_DT_MAX_ITEMS) + +// Chunk size for chunked SSD. Caps matmul cost at O(chunk^2) per chunk. +#define SSM_SSD_CHUNK_SIZE 256 + // We would like to keep pragma unroll for cases where L_template is not 0, // so we suppress the clang transformation warning. #ifdef __clang__ @@ -316,6 +331,429 @@ static void ssm_scan_f32_cuda(const float * src0, const float * src1, const floa } } +#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) +// ============================================================================ +// SSD (State Space Duality) kernels for Mamba-2 prefill (n_tok > SSM_SSD_MIN_TOKENS) +// +// Instead of a sequential scan, SSD reformulates the output as: +// Y = (L (.) (C @ B^T)) @ (X * dt) + decay * C @ s_init +// where L is a causal decay mask derived from A and dt. +// +// This converts the O(T*N) sequential scan into parallel matmuls. +// ============================================================================ +// Softplus(dt) and inclusive prefix sum per head using CUB BlockScan. +// Grid: (n_head, n_seqs) +template +__global__ void ssm_ssd_prepare_dt_kernel( + const float * __restrict__ dt_raw, + float * __restrict__ dt_sp_out, + float * __restrict__ cs_out, + const int n_head, const int n_tok, + const int dt_stride_tok, // elements between tokens in dt + const int dt_stride_seq) { // elements between sequences in dt + + const int h = blockIdx.x; + const int s = blockIdx.y; + + const float * dt_seq = dt_raw + s * dt_stride_seq; + + float * dt_sp_seq = dt_sp_out + s * n_tok * n_head; + float * cs_seq = cs_out + s * n_tok * n_head; + + const int items_per_thread = (n_tok + BLOCK_SIZE - 1) / BLOCK_SIZE; + + // Phase 1: softplus with interleaved distribution (t = i*BLOCK_SIZE + threadIdx.x). + // Each warp reads BLOCK_SIZE consecutive tokens, giving coalesced dt_raw loads + // (stride n_head between threads vs. items_per_thread*n_head in blocked layout). + float local_vals[MAX_ITEMS]; + for (int i = 0; i < items_per_thread; i++) { + const int t = i * BLOCK_SIZE + threadIdx.x; + if (t < n_tok) { + float val = dt_seq[h + t * dt_stride_tok]; + float sp = (val <= 20.0f) ? log1pf(expf(val)) : val; + local_vals[i] = sp; + dt_sp_seq[t * n_head + h] = sp; + } else { + local_vals[i] = 0.0f; + } + } + + // Phase 2+3: per-step inclusive scan to build cs[] in token order. + // With interleaved distribution the per-thread total scan would not give token-order + // prefix sums, so we scan one BLOCK_SIZE slab at a time and carry a running total. +#ifdef USE_CUB + using BlockScan = cub::BlockScan; + __shared__ typename BlockScan::TempStorage scan_temp; + __shared__ float step_total; + + float running = 0.0f; + for (int i = 0; i < items_per_thread; i++) { + float inclusive; + BlockScan(scan_temp).InclusiveSum(local_vals[i], inclusive); + const int t = i * BLOCK_SIZE + threadIdx.x; + if (t < n_tok) { + cs_seq[t * n_head + h] = running + inclusive; + } + if (threadIdx.x == BLOCK_SIZE - 1) { + step_total = inclusive; + } + __syncthreads(); + running += step_total; + } +#else + // Fallback: sequential prefix scan in shared memory, one slab at a time. + __shared__ float sdata[BLOCK_SIZE]; + float running = 0.0f; + for (int i = 0; i < items_per_thread; i++) { + const int t = i * BLOCK_SIZE + threadIdx.x; + sdata[threadIdx.x] = local_vals[i]; + __syncthreads(); + if (threadIdx.x == 0) { + for (int j = 1; j < BLOCK_SIZE; j++) { + sdata[j] += sdata[j - 1]; + } + } + __syncthreads(); + if (t < n_tok) { + cs_seq[t * n_head + h] = running + sdata[threadIdx.x]; + } + running += sdata[BLOCK_SIZE - 1]; + __syncthreads(); + } +#endif +} + +// Prepare SSD matmul inputs for one chunk: X_dt, B_weighted, C_scaled. +// T_matmul controls precision for X_dt, B_weighted (float or half). +// C_scaled is always float (pairs with float s_cur in step 3c). +// Computation is always FP32; only the final store converts to T_matmul. +// Also materializes the causal M matrix = exp(A*(cs_out - cs_in)) * CB (fused with prep to save a launch). +// Grid: (ceil(max(C*head_dim, d_state*C, chunk_len^2) / BLOCK), n_head, n_seqs) +template +__global__ void ssm_ssd_pre_matmul_kernel( + const float * __restrict__ cs, // {n_tok, n_head} cumulative dt sums + const float * __restrict__ dt_sp, // {n_tok, n_head} softplus(dt) + const float * __restrict__ A, // {1, n_head} + const float * __restrict__ x, // {head_dim, n_head, n_tok, n_seqs} + const float * __restrict__ B, // {d_state, n_group, n_tok, n_seqs} + const float * __restrict__ C_src, // {d_state, n_group, n_tok, n_seqs} + T_matmul * __restrict__ X_dt, // {head_dim, C, n_head} x * dt, d-fastest + T_matmul * __restrict__ B_weighted, // {d_state, C, n_head} B * decay_from_end + float * __restrict__ C_scaled, // {d_state, C, n_head} C * decay_to_pos (always float) + const float * __restrict__ CB, // {chunk_len, chunk_len, n_group, n_seqs} + half * __restrict__ M_out, // {chunk_len, chunk_len, n_head, n_seqs} + const int chunk_len, const int head_dim, const int n_head, const int n_group, + const int d_state, const int A_stride, + const int x_stride_tok, const int x_stride_seq, + const int B_stride_tok, const int B_stride_seq, + const int C_stride_tok, const int C_stride_seq, + const int chunk_offset, + const int n_tok_total) { + + const int h = blockIdx.y; + const int s = blockIdx.z; + const int g = h / (n_head / n_group); + + const float A_h = A[h * A_stride]; + const int idx = blockIdx.x * BLOCK_SIZE + threadIdx.x; + + const int cs_seq_off = s * n_tok_total * n_head; + const float cs_base = (chunk_offset > 0) ? cs[cs_seq_off + (chunk_offset - 1) * n_head + h] : 0.0f; + const float cs_last = cs[cs_seq_off + (chunk_offset + chunk_len - 1) * n_head + h] - cs_base; + + // Prepare X_dt = x * dt, stored d-fastest for coalesced reads and writes. + const int n_xdt = chunk_len * head_dim; + if (idx < n_xdt) { + const int d = idx % head_dim; + const int t = idx / head_dim; + + const float x_val = x[s * x_stride_seq + (chunk_offset + t) * x_stride_tok + d + h * head_dim]; + const float dt_val = dt_sp[cs_seq_off + (chunk_offset + t) * n_head + h]; + + X_dt[d + t * head_dim + h * n_xdt + s * n_xdt * n_head] = (T_matmul)(x_val * dt_val); + } + + // Prepare B_weighted and C_scaled together: both share the same index space (d_state * chunk_len) + // and the same cs_t load, so merging halves the cs[] global memory traffic. + const int n_bw = d_state * chunk_len; + if (idx < n_bw) { + const int n = idx % d_state; + const int t = idx / d_state; + + const float cs_t = cs[cs_seq_off + (chunk_offset + t) * n_head + h] - cs_base; + + const float B_val = B[s * B_stride_seq + (chunk_offset + t) * B_stride_tok + g * d_state + n]; + B_weighted[n + t * d_state + h * n_bw + s * n_bw * n_head] = (T_matmul)(B_val * __expf(A_h * (cs_last - cs_t))); + + const float C_val = C_src[s * C_stride_seq + (chunk_offset + t) * C_stride_tok + g * d_state + n]; + C_scaled[n + t * d_state + h * n_bw + s * n_bw * n_head] = C_val * __expf(A_h * cs_t); + } + + // Materialize M = exp(A*(cs_out - cs_in)) * CB with causal mask. + const int n_M = chunk_len * chunk_len; + if (idx < n_M) { + const int t_out = idx % chunk_len; + const int t_in = idx / chunk_len; + + half val; + if (t_in <= t_out) { + const float cs_out = cs[cs_seq_off + (chunk_offset + t_out) * n_head + h] - cs_base; + const float cs_in = cs[cs_seq_off + (chunk_offset + t_in) * n_head + h] - cs_base; + const float decay = __expf(A_h * (cs_out - cs_in)); + const float * CB_g = CB + (int64_t)s * chunk_len * chunk_len * n_group + + (int64_t)g * chunk_len * chunk_len; + const float cb_val = CB_g[t_out + t_in * chunk_len]; + val = __float2half(decay * cb_val); + } else { + val = __float2half(0.0f); + } + + M_out[(int64_t)s * n_M * n_head + (int64_t)h * n_M + t_in * chunk_len + t_out] = val; + } +} + +// Scale running state in-place: s_cur *= decay_total(chunk). +// Called BEFORE cuBLAS state update (beta=1) to fuse inter-chunk decay. +// Eliminates the s_old buffer and D2D memcpy vs the old approach of: +// memcpy(s_old, s_cur) -> cuBLAS(beta=0) -> s_cur += decay * s_old +// Grid: (ceil(d_state * head_dim / BLOCK), n_head, n_seqs) +template +__global__ void ssm_ssd_scale_state_kernel( + float * __restrict__ s_cur, // {d_state, head_dim, n_head, n_seqs} + const float * __restrict__ cs, // {n_tok, n_head} cumulative dt sums + const float * __restrict__ A, // {1, n_head} + const int d_state, const int head_dim, const int n_head, + const int chunk_offset, const int chunk_len, + const int n_tok_total, const int A_stride) { + + const int h = blockIdx.y; + const int s = blockIdx.z; + const int idx = blockIdx.x * BLOCK_SIZE + threadIdx.x; + const int state_per_head = d_state * head_dim; + if (idx >= state_per_head) return; + + const float A_h = A[h * A_stride]; + const int cs_seq_off = s * n_tok_total * n_head; + const float cs_base = (chunk_offset > 0) ? cs[cs_seq_off + (chunk_offset - 1) * n_head + h] : 0.0f; + const float cs_last = cs[cs_seq_off + (chunk_offset + chunk_len - 1) * n_head + h] - cs_base; + const float decay_total = __expf(A_h * cs_last); + + const int off = s * state_per_head * n_head + h * state_per_head + idx; + s_cur[off] *= decay_total; +} + +// Copy initial state from src0[ids[s]] into s_cur for each sequence. +// Grid: (ceil(d_state * head_dim * n_head / BLOCK), n_seqs) +template +__global__ void ssm_ssd_init_state_kernel( + const float * __restrict__ src0, // {d_state, head_dim, n_head, n_rs} + const int32_t * __restrict__ ids, // {n_seqs} + float * __restrict__ s_cur, // {d_state, head_dim, n_head, n_seqs} + const int state_size, // d_state * head_dim * n_head + const int64_t s0_stride_seq) { // elements between state rows + const int s = blockIdx.y; + const int idx = blockIdx.x * BLOCK_SIZE + threadIdx.x; + if (idx >= state_size) return; + + const float * s_src = src0 + (int64_t)ids[s] * s0_stride_seq; + s_cur[s * state_size + idx] = s_src[idx]; +} + +// SSD (State Space Duality) dispatch for Mamba-2 prefill. +// Chunked matmuls: CB, materialize M + cuBLAS Y, S@C, B@X_dt. +// All strides are in elements (floats), not bytes. +static void ssm_scan_ssd_f32_cuda( + ggml_backend_cuda_context & ctx, + const float * src0_d, const float * src1_d, const float * src2_d, const float * src3_d, + const float * src4_d, const float * src5_d, const int32_t * src6_d, float * dst_d, + const int64_t s0_stride_seq, // state (src0) stride between seqs + const int x_stride_tok, const int x_stride_seq, // x (src1) strides + const int dt_stride_tok, const int dt_stride_seq, // dt (src2) strides + const int A_stride, // A (src3) stride between heads + const int B_stride_tok, const int B_stride_seq, // B (src4) strides + const int C_stride_tok, const int C_stride_seq, // C (src5) strides + const int64_t s_off, const int64_t d_state, const int64_t head_dim, + const int64_t n_head, const int64_t n_group, const int64_t n_tok, const int64_t n_seq) { + + cudaStream_t stream = ctx.stream(); + const int64_t d_inner = head_dim * n_head; + + const int64_t chunk_size = SSM_SSD_CHUNK_SIZE; + const int64_t n_chunks = (n_tok + chunk_size - 1) / chunk_size; + + const int64_t state_per_head = d_state * head_dim; + + using matmul_t = half; + static constexpr cudaDataType_t matmul_dtype = CUDA_R_16F; + + ggml_cuda_pool_alloc dt_sp_buf(ctx.pool(), n_tok * n_head * n_seq); + ggml_cuda_pool_alloc cs_buf(ctx.pool(), n_tok * n_head * n_seq); + ggml_cuda_pool_alloc CB_buf(ctx.pool(), chunk_size * chunk_size * n_group * n_seq); + ggml_cuda_pool_alloc X_dt_buf(ctx.pool(), chunk_size * head_dim * n_head * n_seq); + ggml_cuda_pool_alloc B_w_buf(ctx.pool(), d_state * chunk_size * n_head * n_seq); + ggml_cuda_pool_alloc C_s_buf(ctx.pool(), d_state * chunk_size * n_head * n_seq); + float * dt_sp = dt_sp_buf.get(); + float * cs = cs_buf.get(); + float * CB = CB_buf.get(); + matmul_t * X_dt = X_dt_buf.get(); + matmul_t * B_weighted = B_w_buf.get(); + float * C_scaled = C_s_buf.get(); + float * s_cur = (float *)((char *)dst_d + s_off); // write state directly to dst + + // Step 1: softplus(dt) and parallel prefix sum over full sequence + { + dim3 grid(n_head, n_seq); + ssm_ssd_prepare_dt_kernel<<>>( + src2_d, dt_sp, cs, n_head, n_tok, dt_stride_tok, dt_stride_seq); + CUDA_CHECK(cudaGetLastError()); + } + + // Step 2: initialize running state from src0[ids[s]] + { + constexpr int BLOCK = 256; + const int64_t state_size = d_state * head_dim * n_head; + dim3 grid((state_size + BLOCK - 1) / BLOCK, n_seq); + ssm_ssd_init_state_kernel<<>>( + src0_d, src6_d, s_cur, state_size, s0_stride_seq); + CUDA_CHECK(cudaGetLastError()); + } + + // Step 3: chunked SSD loop + // Per chunk: pre_matmul (incl. M) + 4 cuBLAS (CB, Y, S@C, state update) + scale_state + cublasHandle_t handle = ctx.cublas_handle(); + CUBLAS_CHECK(cublasSetStream(handle, stream)); + const float alpha_one = 1.0f; + const float beta_zero = 0.0f; + const float beta_one = 1.0f; + const int lda_C_src = C_stride_tok; // leading dim for C in CB = C^T @ B + const int ldb_B_src = B_stride_tok; // leading dim for B in CB = C^T @ B + + // Scratch buffer for causal M matrix, reused across chunks (max size at chunk_size) + const int64_t n_M_max = chunk_size * chunk_size; + ggml_cuda_pool_alloc M_buf(ctx.pool(), n_M_max * n_head * n_seq); + half * M_mat = M_buf.get(); + + for (int64_t k = 0; k < n_chunks; k++) { + const int64_t chunk_offset = k * chunk_size; + const int64_t chunk_len = (chunk_offset + chunk_size <= n_tok) ? chunk_size : (n_tok - chunk_offset); + + // 3a: CB = C^T @ B per group + for (int64_t s = 0; s < n_seq; s++) { + const float * C_s = src5_d + s * C_stride_seq + chunk_offset * C_stride_tok; + const float * B_s = src4_d + s * B_stride_seq + chunk_offset * B_stride_tok; + float * CB_s = CB + s * chunk_len * chunk_len * n_group; + + if (n_group == 1) { + CUBLAS_CHECK(cublasSgemm(handle, CUBLAS_OP_T, CUBLAS_OP_N, + chunk_len, chunk_len, d_state, + &alpha_one, C_s, lda_C_src, B_s, ldb_B_src, + &beta_zero, CB_s, (int)chunk_len)); + } else { + CUBLAS_CHECK(cublasGemmStridedBatchedEx(handle, CUBLAS_OP_T, CUBLAS_OP_N, + chunk_len, chunk_len, d_state, + &alpha_one, + C_s, CUDA_R_32F, lda_C_src, d_state, + B_s, CUDA_R_32F, ldb_B_src, d_state, + &beta_zero, + CB_s, CUDA_R_32F, (int)chunk_len, (long long)(chunk_len * chunk_len), + n_group, + CUBLAS_COMPUTE_32F, CUBLAS_GEMM_DEFAULT)); + } + } + + // 3b: prepare X_dt, B_weighted, C_scaled + materialize causal M matrix + const int64_t n_M = chunk_len * chunk_len; + { + constexpr int BLOCK = 256; + const int64_t n_xdt = chunk_len * head_dim; + const int64_t n_bw = d_state * chunk_len; + int64_t max_work = n_xdt; + if (n_bw > max_work) max_work = n_bw; + if (n_M > max_work) max_work = n_M; + dim3 grid((max_work + BLOCK - 1) / BLOCK, n_head, n_seq); + ssm_ssd_pre_matmul_kernel<<>>( + cs, dt_sp, src3_d, src1_d, src4_d, src5_d, + X_dt, B_weighted, C_scaled, + CB, M_mat, + chunk_len, head_dim, n_head, n_group, d_state, A_stride, + x_stride_tok, x_stride_seq, B_stride_tok, B_stride_seq, C_stride_tok, C_stride_seq, + chunk_offset, n_tok); + CUDA_CHECK(cudaGetLastError()); + } + + // 3c: dst = S_cur^T @ C_scaled (state contribution) + { + const int64_t stride_S = state_per_head; + const int64_t stride_Cs = d_state * chunk_len; + + for (int64_t s = 0; s < n_seq; s++) { + float * dst_chunk = dst_d + s * d_inner * n_tok + chunk_offset * d_inner; + + CUBLAS_CHECK(cublasGemmStridedBatchedEx(handle, CUBLAS_OP_T, CUBLAS_OP_N, + head_dim, chunk_len, d_state, + &alpha_one, + s_cur + s * stride_S * n_head, CUDA_R_32F, d_state, stride_S, + C_scaled + s * stride_Cs * n_head, CUDA_R_32F, d_state, stride_Cs, + &beta_zero, + dst_chunk, CUDA_R_32F, d_inner, head_dim, + n_head, + CUBLAS_COMPUTE_32F, CUBLAS_GEMM_DEFAULT)); + } + } + + // 3d: dst += X_dt @ M^T (intra-chunk contribution, adds to 3c result) + // M is stored as M[t_out, t_in] (lower-triangular), transpose needed for Y = X @ M^T. + { + const int64_t stride_M = n_M; + const int64_t stride_X_h = (int64_t)chunk_len * head_dim; + + for (int64_t s = 0; s < n_seq; s++) { + float * dst_chunk = dst_d + s * d_inner * n_tok + chunk_offset * d_inner; + CUBLAS_CHECK(cublasGemmStridedBatchedEx(handle, CUBLAS_OP_N, CUBLAS_OP_T, + head_dim, chunk_len, chunk_len, + &alpha_one, + X_dt + s * stride_X_h * n_head, matmul_dtype, head_dim, stride_X_h, + M_mat + s * stride_M * n_head, matmul_dtype, chunk_len, stride_M, + &beta_one, + dst_chunk, CUDA_R_32F, d_inner, head_dim, + n_head, + CUBLAS_COMPUTE_32F, CUBLAS_GEMM_DEFAULT)); + } + } + + // 3e: s_cur = B_weighted @ X_dt^T + decay_total * s_cur_old (state update) + { + // Scale s_cur in-place by per-head decay_total BEFORE cuBLAS overwrites it + constexpr int BLOCK = 256; + dim3 grid((state_per_head + BLOCK - 1) / BLOCK, n_head, n_seq); + ssm_ssd_scale_state_kernel<<>>( + s_cur, cs, src3_d, + d_state, head_dim, n_head, + chunk_offset, chunk_len, n_tok, A_stride); + CUDA_CHECK(cudaGetLastError()); + + // cuBLAS with beta=1: s_cur = B_weighted @ X_dt^T + 1.0 * s_cur (already scaled) + const int64_t stride_Bw = d_state * chunk_len; + const int64_t stride_X = chunk_len * head_dim; + const int64_t stride_S = state_per_head; + + for (int64_t s = 0; s < n_seq; s++) { + // X_dt is d-fastest {hd, C}, read as OP_T to get {C, hd} + CUBLAS_CHECK(cublasGemmStridedBatchedEx(handle, CUBLAS_OP_N, CUBLAS_OP_T, + d_state, head_dim, chunk_len, + &alpha_one, + B_weighted + s * stride_Bw * n_head, matmul_dtype, d_state, stride_Bw, + X_dt + s * stride_X * n_head, matmul_dtype, head_dim, stride_X, + &beta_one, + s_cur + s * stride_S * n_head, CUDA_R_32F, d_state, stride_S, + n_head, + CUBLAS_COMPUTE_32F, CUBLAS_GEMM_DEFAULT)); + } + } + } +} +#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) + void ggml_cuda_op_ssm_scan(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; // s const struct ggml_tensor * src1 = dst->src[1]; // x @@ -357,6 +795,49 @@ void ggml_cuda_op_ssm_scan(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { GGML_ASSERT(src6->type == GGML_TYPE_I32); GGML_ASSERT(dst->type == GGML_TYPE_F32); + // Byte strides are narrowed to int for both scan and SSD paths. + GGML_ASSERT(src0->nb[2] <= (size_t)INT_MAX); + GGML_ASSERT(src0->nb[3] <= (size_t)INT_MAX); + GGML_ASSERT(src1->nb[2] <= (size_t)INT_MAX); + GGML_ASSERT(src1->nb[3] <= (size_t)INT_MAX); + GGML_ASSERT(src2->nb[1] <= (size_t)INT_MAX); + GGML_ASSERT(src2->nb[2] <= (size_t)INT_MAX); + GGML_ASSERT(src3->nb[1] <= (size_t)INT_MAX); + GGML_ASSERT(src4->nb[2] <= (size_t)INT_MAX); + GGML_ASSERT(src4->nb[3] <= (size_t)INT_MAX); + GGML_ASSERT(src5->nb[2] <= (size_t)INT_MAX); + GGML_ASSERT(src5->nb[3] <= (size_t)INT_MAX); + +#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) + // Mamba-2 with scalar A per head: use SSD matmul path for long sequences. + // Requires NVIDIA Turing+ otherwise fallback to scan. + const bool is_mamba2 = (src3->nb[1] == sizeof(float)); + const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc; + const bool use_ssd = is_mamba2 && n_t > SSM_SSD_MIN_TOKENS + && n_t <= SSM_SSD_MAX_TOKENS + && GGML_CUDA_CC_IS_NVIDIA(cc) + && cc >= GGML_CUDA_CC_TURING + && nr % 8 == 0; // cuBLAS requires 8-element (16-byte) alignment + + if (use_ssd) { + // ssm_ssd_init_state_kernel uses flat linear indexing within each sequence, + // so src0 must be fully contiguous across all inner dimensions. + // The scan path handles non-contiguous nb[2] via src0_nb2 but does not handle nb[1]. + GGML_ASSERT(src0->nb[1] == nc * sizeof(float)); + GGML_ASSERT(src0->nb[2] == nc * nr * sizeof(float)); + + ssm_scan_ssd_f32_cuda(ctx, + src0_d, src1_d, src2_d, src3_d, src4_d, src5_d, src6_d, dst_d, + (int64_t)(src0->nb[3] / sizeof(float)), + (int)(src1->nb[2] / sizeof(float)), (int)(src1->nb[3] / sizeof(float)), + (int)(src2->nb[1] / sizeof(float)), (int)(src2->nb[2] / sizeof(float)), + (int)(src3->nb[1] / sizeof(float)), + (int)(src4->nb[2] / sizeof(float)), (int)(src4->nb[3] / sizeof(float)), + (int)(src5->nb[2] / sizeof(float)), (int)(src5->nb[3] / sizeof(float)), + s_off, nc, nr, nh, ng, n_t, n_s); + return; + } +#endif ssm_scan_f32_cuda(src0_d, src1_d, src2_d, src3_d, src4_d, src5_d, src6_d, dst_d, src0->nb[2], src0->nb[3], src1->nb[2], src1->nb[3], src2->nb[1], src2->nb[2], src3->nb[1], src4->nb[2], src4->nb[3], src5->nb[2], src5->nb[3], diff --git a/ggml/src/ggml-cuda/template-instances/generate_cu_files.py b/ggml/src/ggml-cuda/template-instances/generate_cu_files.py index 614b1566c7f1..d7cd271675e0 100755 --- a/ggml/src/ggml-cuda/template-instances/generate_cu_files.py +++ b/ggml/src/ggml-cuda/template-instances/generate_cu_files.py @@ -36,6 +36,7 @@ TYPES_MMQ = [ "GGML_TYPE_Q1_0", + "GGML_TYPE_Q2_0", "GGML_TYPE_Q4_0", "GGML_TYPE_Q4_1", "GGML_TYPE_Q5_0", "GGML_TYPE_Q5_1", "GGML_TYPE_Q8_0", "GGML_TYPE_Q2_K", "GGML_TYPE_Q3_K", "GGML_TYPE_Q4_K", "GGML_TYPE_Q5_K", "GGML_TYPE_Q6_K", "GGML_TYPE_IQ2_XXS", "GGML_TYPE_IQ2_XS", "GGML_TYPE_IQ2_S", "GGML_TYPE_IQ3_XXS", "GGML_TYPE_IQ3_S", diff --git a/ggml/src/ggml-cuda/template-instances/mmq-instance-q2_0.cu b/ggml/src/ggml-cuda/template-instances/mmq-instance-q2_0.cu new file mode 100644 index 000000000000..750180e3306d --- /dev/null +++ b/ggml/src/ggml-cuda/template-instances/mmq-instance-q2_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../mmq.cuh" + +DECL_MMQ_CASE(GGML_TYPE_Q2_0); diff --git a/ggml/src/ggml-cuda/topk-moe.cu b/ggml/src/ggml-cuda/topk-moe.cu index c80394e31ff3..c8cec70bb320 100644 --- a/ggml/src/ggml-cuda/topk-moe.cu +++ b/ggml/src/ggml-cuda/topk-moe.cu @@ -8,6 +8,7 @@ // Kernel config struct - passed by value to CUDA kernel struct topk_moe_config { bool use_sigmoid; + bool use_sqrt_softplus; bool with_norm; bool delayed_softmax; }; @@ -67,6 +68,16 @@ __device__ void sigmoid_warp_inplace(float (&vals)[experts_per_thread], const in } } +template +__device__ void sqrt_softplus_warp_inplace(float (&vals)[experts_per_thread], const int limit, const int lane) { +#pragma unroll + for (int i = 0; i < experts_per_thread; i++) { + const int idx = lane + i * WARP_SIZE; + const bool active = !use_limit || (idx < limit); + vals[i] = active ? sqrtf(vals[i] > 20.0f ? vals[i] : logf(1.0f + expf(vals[i]))) : -INFINITY; + } +} + /* This kernel does the following: 1. optionally softmax over the logits per token [n_experts, n_tokens] @@ -115,6 +126,8 @@ __launch_bounds__(4 * WARP_SIZE, 1) __global__ void topk_moe_cuda(const float * if (!config.delayed_softmax) { if (config.use_sigmoid) { sigmoid_warp_inplace(wt, n_experts, threadIdx.x); + } else if (config.use_sqrt_softplus) { + sqrt_softplus_warp_inplace(wt, n_experts, threadIdx.x); } else { softmax_warp_inplace(wt, n_experts, threadIdx.x); } @@ -364,9 +377,10 @@ void ggml_cuda_op_topk_moe(ggml_backend_cuda_context & ctx, } topk_moe_config config; - config.use_sigmoid = args.sigmoid; - config.with_norm = with_norm; - config.delayed_softmax = args.delayed_softmax; + config.use_sigmoid = args.sigmoid; + config.use_sqrt_softplus = args.sqrt_softplus; + config.with_norm = with_norm; + config.delayed_softmax = args.delayed_softmax; if (bias) { launch_topk_moe_cuda(ctx, logits_d, weights_d, ids_d, bias_d, n_rows, n_experts, n_expert_used, clamp_val, @@ -415,7 +429,7 @@ bool ggml_cuda_should_use_topk_moe(const ggml_tensor * gating_op, } else if (gating_op->op == GGML_OP_UNARY) { ggml_unary_op op = ggml_get_unary_op(gating_op); - if (op != GGML_UNARY_OP_SIGMOID) { + if (op != GGML_UNARY_OP_SIGMOID && op != GGML_UNARY_OP_SOFTPLUS) { return false; } } diff --git a/ggml/src/ggml-cuda/topk-moe.cuh b/ggml/src/ggml-cuda/topk-moe.cuh index 243dc2f1c41b..091ef02a415a 100644 --- a/ggml/src/ggml-cuda/topk-moe.cuh +++ b/ggml/src/ggml-cuda/topk-moe.cuh @@ -5,6 +5,7 @@ struct ggml_cuda_topk_moe_args { bool sigmoid{}; + bool sqrt_softplus{}; bool softmax{}; bool delayed_softmax{}; bool prob_bias{}; diff --git a/ggml/src/ggml-cuda/vecdotq.cuh b/ggml/src/ggml-cuda/vecdotq.cuh index d1741cc8d7ba..0f039c735b6b 100644 --- a/ggml/src/ggml-cuda/vecdotq.cuh +++ b/ggml/src/ggml-cuda/vecdotq.cuh @@ -109,6 +109,9 @@ static __device__ __forceinline__ uint32_t unpack_ksigns(const uint8_t v) { #define VDR_Q1_0_Q8_1_MMVQ 1 // Process one 32-element chunk at a time for parallelism #define VDR_Q1_0_Q8_1_MMQ 4 // Q1_0 has 128 bits (4 ints) per block +#define VDR_Q2_0_Q8_1_MMVQ 1 // Process one 32-element chunk at a time for parallelism +#define VDR_Q2_0_Q8_1_MMQ 2 // Q2_0 group 64: 128 bits (4 ints) per block, 2 32-element chunks + #define VDR_Q4_0_Q8_1_MMVQ 2 #define VDR_Q4_0_Q8_1_MMQ 4 @@ -681,40 +684,83 @@ static __device__ __forceinline__ float vec_dot_q1_0_q8_1( // Q8_1: 32 elements per block with individual scales // iqs selects which of the 4 chunks of 32 elements to process (0-3) - const float d1 = bq1_0->d; + const float d1 = bq1_0->d; + const int16_t * qs = (const int16_t *) bq1_0->qs + iqs * 2; // Process only the chunk specified by iqs const block_q8_1 * bq8_1_chunk = bq8_1 + iqs; - // Load 32 bits (4 bytes) for this chunk from Q1_0 - const int offset = iqs * 4; - const int v = bq1_0->qs[offset + 0] | (bq1_0->qs[offset + 1] << 8) | - (bq1_0->qs[offset + 2] << 16) | (bq1_0->qs[offset + 3] << 24); - - // Unpack 32 bits into 32 signed values (-1 or +1) - int vi_bytes[8]; + int sumi = 0; #pragma unroll - for (int j = 0; j < 8; ++j) { - const int shift = j * 4; - const int bits4 = (v >> shift) & 0x0F; - const int b0 = (bits4 & 0x01) ? 1 : -1; - const int b1 = (bits4 & 0x02) ? 1 : -1; - const int b2 = (bits4 & 0x04) ? 1 : -1; - const int b3 = (bits4 & 0x08) ? 1 : -1; - vi_bytes[j] = (b0 & 0xFF) | ((b1 & 0xFF) << 8) | ((b2 & 0xFF) << 16) | ((b3 & 0xFF) << 24); + for (int j = 0; j < 2; ++j) { + const int q = qs[j]; + + const int u0 = get_int_b4(bq8_1_chunk->qs, j*4+0); + const int u1 = get_int_b4(bq8_1_chunk->qs, j*4+1); + const int u2 = get_int_b4(bq8_1_chunk->qs, j*4+2); + const int u3 = get_int_b4(bq8_1_chunk->qs, j*4+3); + + // unpack crumbs into nibble indices + const int n0 = __byte_perm(0x11100100, 0x11100100, q >> 0); // [0, 1, 4, 5] [ 8, 9, 12, 13] + const int n1 = __byte_perm(0x11100100, 0x11100100, q >> 2); // [2, 3, 6, 7] [10, 11, 14, 15] + // unpack nibbles into byte values + const int s0 = __byte_perm(0x01FF, 0x01FF, n0 >> 0); + const int s1 = __byte_perm(0x01FF, 0x01FF, n1 >> 0); + const int s2 = __byte_perm(0x01FF, 0x01FF, n0 >> 16); + const int s3 = __byte_perm(0x01FF, 0x01FF, n1 >> 16); + // unshuffle values + const int v0 = __byte_perm(s0, s1, 0x5410); + const int v1 = __byte_perm(s0, s1, 0x7632); + const int v2 = __byte_perm(s2, s3, 0x5410); + const int v3 = __byte_perm(s2, s3, 0x7632); + + sumi = ggml_cuda_dp4a(v0, u0, sumi); + sumi = ggml_cuda_dp4a(v1, u1, sumi); + sumi = ggml_cuda_dp4a(v2, u2, sumi); + sumi = ggml_cuda_dp4a(v3, u3, sumi); } - // Compute dot product for this 32-element chunk + // Apply Q1_0's single scale and this chunk's Q8_1 scale + const float d8 = __low2float(bq8_1_chunk->ds); + return d1 * d8 * sumi; +} + +static __device__ __forceinline__ float vec_dot_q2_0_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) { + + const block_q2_0 * bq2_0 = (const block_q2_0 *) vbq + kbx; + + // Q2_0 (group 64): 64 elements with ONE scale, 2 bits per element (4 elements per byte) + // Q8_1: 32 elements per block with individual scales + // iqs selects which of the 2 chunks of 32 elements to process (0-1) + + const float d2 = bq2_0->d; + const int16_t * qs = (const int16_t *) bq2_0->qs + iqs * 4; + + // Process only the chunk specified by iqs + const block_q8_1 * bq8_1_chunk = bq8_1 + iqs; + int sumi = 0; #pragma unroll - for (int j = 0; j < 8; ++j) { - const int u = get_int_b4(bq8_1_chunk->qs, j); - sumi = ggml_cuda_dp4a(vi_bytes[j], u, sumi); + for (int j = 0; j < 4; ++j) { + const int q = qs[j]; + const int u = get_int_b4(bq8_1_chunk->qs, j*2+0); + const int v = get_int_b4(bq8_1_chunk->qs, j*2+1); + + // unpack even and odd crumbs into byte values + const int qe = __byte_perm(0x020100FF, 0x020100FF, q >> 0); + const int qo = __byte_perm(0x020100FF, 0x020100FF, q >> 2); + // unshuffle values + const int qx = __byte_perm(qe, qo, 0x5140); + const int qy = __byte_perm(qe, qo, 0x7362); + + sumi = ggml_cuda_dp4a(u, qx, sumi); + sumi = ggml_cuda_dp4a(v, qy, sumi); } - // Apply Q1_0's single scale and this chunk's Q8_1 scale + // Apply Q2_0's single scale and this chunk's Q8_1 scale const float d8 = __low2float(bq8_1_chunk->ds); - return d1 * d8 * sumi; + return d2 * d8 * sumi; } static __device__ __forceinline__ float vec_dot_q4_0_q8_1( diff --git a/ggml/src/ggml-cuda/vendors/hip.h b/ggml/src/ggml-cuda/vendors/hip.h index fb0d570b29d0..8c367f82aed0 100644 --- a/ggml/src/ggml-cuda/vendors/hip.h +++ b/ggml/src/ggml-cuda/vendors/hip.h @@ -6,10 +6,6 @@ #include #include -#if defined(GGML_HIP_ROCWMMA_FATTN) -#include -#endif // defined(GGML_HIP_ROCWMMA_FATTN) - #ifdef GGML_USE_NCCL #include #endif // GGML_USE_NCCL diff --git a/ggml/src/ggml-hexagon/ggml-hexagon.cpp b/ggml/src/ggml-hexagon/ggml-hexagon.cpp index 76c71d7ee70e..bdb8af0820a3 100644 --- a/ggml/src/ggml-hexagon/ggml-hexagon.cpp +++ b/ggml/src/ggml-hexagon/ggml-hexagon.cpp @@ -21,6 +21,11 @@ #include #ifdef _WIN32 +# define WIN32_LEAN_AND_MEAN +# ifndef NOMINMAX +# define NOMINMAX +# endif +# include # include #else # include @@ -28,7 +33,9 @@ #endif #pragma clang diagnostic ignored "-Wnested-anon-types" +#pragma clang diagnostic ignored "-Wlanguage-extension-token" #pragma clang diagnostic ignored "-Wgnu-anonymous-struct" +#pragma clang diagnostic ignored "-Wmicrosoft-enum-value" #include #include @@ -134,12 +141,14 @@ static const char * htp_event_name(uint16_t id) { case HTP_TRACE_EVT_HVX_FA_K_PREP: return "HVX_K_PREP"; case HTP_TRACE_EVT_HVX_FA_V_PREP: return "HVX_V_PREP"; case HTP_TRACE_EVT_HMX_COMP: return "HMX_COMP"; + case HTP_TRACE_EVT_L2FLUSH: return "L2FLUSH"; + case HTP_TRACE_EVT_INIT: return "INIT"; + case HTP_TRACE_EVT_BUFF: return "BUFF"; default: return "UNKNOWN"; } } -static void ggml_hexagon_dump_op_prof(const std::string &sess_name, const htp_opnode & node, - const htp_prof_desc & pd) { +static void ggml_hexagon_dump_op_prof(const std::string &sess_name, const htp_opnode & node, const htp_prof_desc & pd) { if (!opt_profile) return; uint32_t op_usec = pd.usecs; @@ -159,6 +168,43 @@ static void ggml_hexagon_dump_op_prof(const std::string &sess_name, const htp_op node.op_name().c_str(), fmt.names, fmt.dims, fmt.types, fmt.strides, fmt.kparams, op_usec, op_cycles, pd.cycles_start, mhz, pmu_str); } +static void ggml_hexagon_dump_batch_prof(const std::string & sess_name, const htp_opbatch_rsp & rsp) { + uint64_t batch_cycles = rsp.cycles_stop - rsp.cycles_start; + float batch_mhz = rsp.usecs > 0 ? (float) batch_cycles / rsp.usecs : 0.0f; + + char evt_str[256] = "----"; + if (opt_profile == 3) { + snprintf(evt_str, sizeof(evt_str), "evt-cnt %u,%u,%u,%u,%u,%u,%u,%u,%u,%u,%u", + rsp.n_traces[0], rsp.n_traces[1], rsp.n_traces[2], rsp.n_traces[3], + rsp.n_traces[4], rsp.n_traces[5], rsp.n_traces[6], rsp.n_traces[7], + rsp.n_traces[8], rsp.n_traces[9], rsp.n_traces[10]); + } + + GGML_LOG_DEBUG("ggml-hex: %s profile-op OPBATCH|----|n-ops %u|%s|----|----|usec %u cycles %llu start %llu mhz %.1f\n", + sess_name.c_str(), rsp.n_ops, evt_str, rsp.usecs, (unsigned long long) batch_cycles, (unsigned long long) rsp.cycles_start, batch_mhz); +} + +static void ggml_hexagon_dump_trace_events(const std::string & sess_name, const htp_opbatch_rsp & rsp, + const htp_trace_desc * trace_events, uint32_t n_traces) { + if (opt_profile == 3 && trace_events) { + uint32_t valid_cnt[HTP_MAX_NTHREADS + 1] = {0}; + for (uint32_t t = 0; t <= HTP_MAX_NTHREADS; t++) { + uint32_t count = rsp.n_traces[t]; + valid_cnt[t] = count > n_traces ? n_traces : count; + } + + for (uint32_t t = 0; t <= HTP_MAX_NTHREADS; t++) { + for (uint32_t idx = 0; idx < valid_cnt[t]; idx++) { + const auto & e = trace_events[t * n_traces + idx]; + bool is_stop = (e.info & 0x8000) != 0; + uint16_t info = e.info & 0x7FFF; + GGML_LOG_DEBUG("ggml-hex: %s trace-evt %s: thread %u info %u %s %u\n", + sess_name.c_str(), htp_event_name(e.id), t, info, is_stop ? "stop" : "start", e.cycles); + } + } + } +} + // ** static inline bool ggml_hexagon_is_repack_type(enum ggml_type type) { @@ -501,6 +547,8 @@ static void repack_q4_0_tiled(ggml_tensor * t, const void * data, size_t size) { } } } + + GGML_UNUSED(size); } // repack q4_0_tiled tensor into q4_0 data @@ -554,6 +602,8 @@ static void repack_tiled_q4_0(void * data, const ggml_tensor * t, size_t size) { } } } + + GGML_UNUSED(size); } // repack q4_1 data into q4_1_tiled tensor @@ -611,6 +661,8 @@ static void repack_q4_1_tiled(ggml_tensor * t, const void * data, size_t size) { } } } + + GGML_UNUSED(size); } // repack q4_1_tiled tensor into q4_1 data @@ -665,6 +717,8 @@ static void repack_tiled_q4_1(void * data, const ggml_tensor * t, size_t size) { } } } + + GGML_UNUSED(size); } // repack q8_0 data into q8_0_tiled tensor @@ -711,6 +765,8 @@ static void repack_q8_0_tiled(ggml_tensor * t, const void * data, size_t size) { } } } + + GGML_UNUSED(size); } // repack q8_0_tiled tensor into q8_0 data @@ -761,6 +817,8 @@ static void repack_tiled_q8_0(void * data, const ggml_tensor * t, size_t size) { } } } + + GGML_UNUSED(size); } // repack mxfp4 data into mxfp4_tiled tensor @@ -812,6 +870,8 @@ static void repack_mxfp4_tiled(ggml_tensor * t, const void * data, size_t size) } } } + + GGML_UNUSED(size); } // repack mxfp4_tiled tensor into mxfp4 data @@ -865,6 +925,8 @@ static void repack_tiled_mxfp4(void * data, const ggml_tensor * t, size_t size) } } } + + GGML_UNUSED(size); } static void ggml_backend_hexagon_buffer_set_tensor(ggml_backend_buffer_t buffer, @@ -965,11 +1027,12 @@ static void ggml_backend_hexagon_buffer_get_tensor(ggml_backend_buffer_t buffer, static bool ggml_backend_hexagon_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) { + // we might optimize this later, for now take the slow path (ie get/set_tensor) + return false; + GGML_UNUSED(buffer); GGML_UNUSED(src); GGML_UNUSED(dst); - // we might optimize this later, for now take the slow path (ie get/set_tensor) - return false; } static void ggml_backend_hexagon_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { @@ -1025,9 +1088,9 @@ static ggml_backend_buffer_t ggml_backend_hexagon_repack_buffer_type_alloc_buffe } } -static size_t ggml_backend_hexagon_buffer_type_get_alignment(ggml_backend_buffer_type_t buffer_type) { +static size_t ggml_backend_hexagon_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { return 128; // HVX alignment - GGML_UNUSED(buffer_type); + GGML_UNUSED(buft); } static size_t ggml_backend_hexagon_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const struct ggml_tensor * t) { @@ -1039,20 +1102,24 @@ static size_t ggml_backend_hexagon_buffer_type_get_alloc_size(ggml_backend_buffe return ggml_row_size(t->type, ne0) * ne1 * ne2 * ne3; } return ggml_nbytes(t); + + GGML_UNUSED(buft); } -static size_t ggml_backend_hexagon_buffer_type_get_max_size(ggml_backend_buffer_type_t buffer_type) { - auto * context = static_cast(buffer_type->context); +static size_t ggml_backend_hexagon_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { + auto * context = static_cast(buft->context); return context->sess->max_bufsize; } static bool ggml_backend_hexagon_buffer_type_is_host(ggml_backend_buffer_type_t buft) { return opt_hostbuf; + GGML_UNUSED(buft); } static bool ggml_backend_hexagon_repack_buffer_type_is_host(ggml_backend_buffer_type_t buft) { return false; + GGML_UNUSED(buft); } @@ -1098,6 +1165,8 @@ struct ggml_hexagon_opbatch { std::unordered_map t_map; // tensor ptr to index std::unordered_multimap d_map; // tensor data to index + + unsigned int n_bufs; // num buffers in the batch unsigned int n_tens; // num tensors ... unsigned int n_ops; // num ops ... @@ -1124,7 +1193,7 @@ struct ggml_hexagon_opbatch { n_bufs_max = HTP_OP_MAX_BUFS; n_ops_max = batch_size; - n_tens_max = n_ops_max + n_ops_max * HTP_OP_MAX_INPUTS; + n_tens_max = std::min(n_ops_max + n_ops_max * HTP_OP_MAX_INPUTS, HTP_OP_MAX_TENSORS); b_vmem_max = max_vmem; @@ -1170,6 +1239,8 @@ struct ggml_hexagon_opbatch { return bi; } + + bool same_shape(const htp_tensor * h, const ggml_tensor * t) const { int64_t ne0 = t->ne[0]; int64_t ne1 = t->ne[1]; @@ -1182,7 +1253,8 @@ struct ggml_hexagon_opbatch { int64_t nb2 = is_repack ? nb1 * ne1 : t->nb[2]; int64_t nb3 = is_repack ? nb2 * t->ne[2] : t->nb[3]; - return (h->ne[0] == ne0) && (h->ne[1] == ne1) && (h->ne[2] == t->ne[2]) && (h->ne[3] == t->ne[3]) && + return (h->type == t->type) && + (h->ne[0] == ne0) && (h->ne[1] == ne1) && (h->ne[2] == t->ne[2]) && (h->ne[3] == t->ne[3]) && (h->nb[0] == t->nb[0]) && (h->nb[1] == nb1) && (h->nb[2] == nb2) && (h->nb[3] == nb3); } @@ -1213,6 +1285,7 @@ struct ggml_hexagon_opbatch { htp_tensor &h = h_tens[ti]; h.bi = add_buffer(sbuf); + h.ti = ti; h.data = t_offset; h.type = t->type; @@ -1235,8 +1308,10 @@ struct ggml_hexagon_opbatch { h.nb[0] = t->nb[0]; h.nb[1] = t->nb[1]; h.nb[2] = t->nb[2]; h.nb[3] = t->nb[3]; } + + h.flags = 0; - if (ggml_backend_buffer_get_usage(t->buffer) == GGML_BACKEND_BUFFER_USAGE_COMPUTE) { + if (ggml_backend_buffer_get_usage(t->buffer) != GGML_BACKEND_BUFFER_USAGE_WEIGHTS) { h.flags |= HTP_TENSOR_COMPUTE; } @@ -1313,6 +1388,9 @@ struct ggml_hexagon_opbatch { o.dst[i] = (i < outputs.size() && outputs[i]) ? add_tensor(outputs[i]) : 0xffff; } } + + void finalize_ranges() { + } }; struct ggml_hexagon_opqueue { @@ -1462,9 +1540,6 @@ struct ggml_hexagon_opqueue { if (opt_profile && rsp.n_ops > 0) { auto & ops = op_cache[rsp.id]; - uint64_t batch_usec = ggml_time_us() - start_usec[rsp.id]; - uint32_t htp_usec = 0; - GGML_ASSERT(rsp.n_ops <= ops.size()); const htp_prof_desc * pd = (const htp_prof_desc *) p_ptr; @@ -1475,55 +1550,13 @@ struct ggml_hexagon_opqueue { trace_events = (const htp_trace_desc *) (p_ptr + p_size); } - uint32_t trace_idx[HTP_MAX_NTHREADS + 1] = {0}; - uint32_t valid_cnt[HTP_MAX_NTHREADS + 1] = {0}; - - if (opt_profile == 3) { - for (uint32_t t = 0; t <= HTP_MAX_NTHREADS; t++) { - uint32_t count = rsp.n_traces[t]; - valid_cnt[t] = count > n_traces ? n_traces : count; - } - } + ggml_hexagon_dump_batch_prof(shm_buf->sess->name, rsp); for (uint32_t i = 0; i < rsp.n_ops; i++) { - htp_usec += pd[i].usecs; - ggml_hexagon_dump_op_prof(shm_buf->sess->name, ops[i], pd[i]); - - if (opt_profile == 3) { - uint32_t op_duration = pd[i].cycles_stop - pd[i].cycles_start; - - for (uint32_t t = 0; t <= HTP_MAX_NTHREADS; t++) { - while (trace_idx[t] < valid_cnt[t]) { - const auto & e = trace_events[t * n_traces + trace_idx[t]]; - uint32_t offset = e.cycles - pd[i].cycles_start; - if (offset >= 0x80000000) { - trace_idx[t]++; - continue; - } - if (offset > op_duration) { - break; - } - bool is_stop = (e.info & 0x8000) != 0; - uint16_t info = e.info & 0x7FFF; - GGML_LOG_DEBUG("ggml-hex: %s trace-op %s: thread %u event %s info %u %s %u\n", - shm_buf->sess->c_name(), ops[i].op_name().c_str(), t, htp_event_name(e.id), info, is_stop ? "stop" : "start", e.cycles); - trace_idx[t]++; - } - } - } - } - - char evt_str[256] = ""; - if (opt_profile == 3) { - snprintf(evt_str, sizeof(evt_str), " evt [%u,%u,%u,%u,%u,%u,%u,%u,%u,%u,%u]", - rsp.n_traces[0], rsp.n_traces[1], rsp.n_traces[2], rsp.n_traces[3], - rsp.n_traces[4], rsp.n_traces[5], rsp.n_traces[6], rsp.n_traces[7], - rsp.n_traces[8], rsp.n_traces[9], rsp.n_traces[10]); } - GGML_LOG_DEBUG("ggml-hex: %s profile-batch n-ops %u batch-dur-usec %lld htp-ops-usec %u%s\n", - shm_buf->sess->c_name(), rsp.n_ops, (long long) batch_usec, htp_usec, evt_str); + ggml_hexagon_dump_trace_events(shm_buf->sess->name, rsp, trace_events, n_traces); } } }; @@ -1542,7 +1575,7 @@ void ggml_hexagon_session::flush_pending(bool all) { const uint32_t timeo = opt_oppoll ? 0 : DSPQUEUE_TIMEOUT; int err = dspqueue_read(this->queue, &flags, 1, &n_dbufs, &dbuf, sizeof(rsp), &rsp_size, (uint8_t *) &rsp, timeo); - if (err == AEE_EEXPIRED) { + if (err == AEE_EEXPIRED || err == AEE_EWOULDBLOCK) { continue; } @@ -1571,6 +1604,8 @@ void ggml_hexagon_session::flush_pending(bool all) { void ggml_hexagon_session::flush_batch() { if (op_batch->empty()) { return; } + op_batch->finalize_ranges(); + htp_opbatch_req req {}; dspqueue_buffer dbuf{}; @@ -1647,7 +1682,7 @@ void ggml_hexagon_session::allocate(int dev_id) noexcept(false) { GGML_LOG_DEBUG("ggml-hex: %s allocating new session\n", this->name.c_str()); - domain * my_domain = get_domain(this->domain_id); + domain * my_domain = htpdrv_get_domain(this->domain_id); if (my_domain == NULL) { GGML_LOG_ERROR("ggml-hex: unable to get domain struct for CDSP\n"); throw std::runtime_error("ggml-hex: failed to get CDSP domain (see log for details)"); @@ -1793,16 +1828,6 @@ void ggml_hexagon_session::allocate(int dev_id) noexcept(false) { } } - if (opt_profile) { - htp_iface_pmu_conf pmu_conf{}; - std::copy(opt_pmu_evt.begin(), opt_pmu_evt.end(), pmu_conf.events); - - err = htp_iface_profiler(this->handle, opt_profile, &pmu_conf); - if (err != 0) { - GGML_LOG_ERROR("ggml-hex: failed to enable profiling: 0x%08x\n", (unsigned) err); - } - } - // Allocate buffers and state for op batching this->op_queue = new ggml_hexagon_opqueue(this, opt_opbatch, opt_opqueue); @@ -1821,6 +1846,16 @@ void ggml_hexagon_session::allocate(int dev_id) noexcept(false) { throw std::runtime_error("ggml-hex: iface start failed (see log for details)"); } this->valid_iface = true; + + if (opt_profile) { + htp_iface_pmu_conf pmu_conf{}; + std::copy(opt_pmu_evt.begin(), opt_pmu_evt.end(), pmu_conf.events); + + err = htp_iface_profiler(this->handle, opt_profile, &pmu_conf); + if (err != 0) { + GGML_LOG_ERROR("ggml-hex: failed to enable profiling: 0x%08x\n", (unsigned) err); + } + } } void ggml_hexagon_session::release() noexcept(true) { @@ -1929,6 +1964,8 @@ static bool ggml_hexagon_flash_attn_is_hmx_eligible( } return true; + + GGML_UNUSED(sinks); } static bool ggml_hexagon_precompute_flash_attn_params( @@ -1990,7 +2027,7 @@ static bool ggml_hexagon_precompute_flash_attn_params( const struct ggml_tensor * sinks = op->src[4]; if (ggml_hexagon_flash_attn_is_hmx_eligible(sess, q, k, v, sinks)) { size_t Br = 0, Bc = 0; - int ret = hmx_fa_find_chunk_size(&Br, &Bc, G, DK, DV, neq1, nek1, sess->vtcm_size, sess->n_threads); + int ret = hmx_fa_find_chunk_size(&Br, &Bc, G, DK, DV, neq1, nek1, sess->vtcm_size, sess->n_threads, kparams->is_q_fp32 != 0); if (ret == 0) { kparams->kernel_type = HTP_FA_KERNEL_HMX; kparams->Br = Br; @@ -2000,7 +2037,7 @@ static bool ggml_hexagon_precompute_flash_attn_params( kparams->u.hmx.g_br = hex_align_up(G * Br, 32); kparams->u.hmx.pipeline = (kparams->n_kv_blocks >= 3 && sess->n_threads >= 2) ? 1 : 0; - kparams->vtcm_size = hmx_fa_compute_vtcm_usage(G, DK, DV, Br, Bc, kparams->n_threads, kparams->u.hmx.pipeline != 0); + kparams->vtcm_size = hmx_fa_compute_vtcm_usage(G, DK, DV, Br, Bc, kparams->n_threads, kparams->u.hmx.pipeline != 0, kparams->is_q_fp32 != 0); const size_t row_vec_bytes = hex_align_up(Bc * sizeof(uint16_t), 256); kparams->u.hmx.row_buf_stride = row_vec_bytes / 128; // HVX vector is 128 bytes @@ -2149,8 +2186,9 @@ static bool ggml_hexagon_supported_gated_delta_net(const struct ggml_hexagon_ses return false; } - GGML_UNUSED(sess); return true; + + GGML_UNUSED(sess); } static bool ggml_hexagon_matmul_is_hmx_eligible( @@ -2198,6 +2236,8 @@ static bool ggml_hexagon_matmul_is_hmx_eligible( } return true; + + GGML_UNUSED(dst); } static bool ggml_hexagon_precompute_hmx_mm_params( @@ -2234,109 +2274,15 @@ static bool ggml_hexagon_precompute_hmx_mm_params( if (is_batched_val && wtype == GGML_TYPE_F16 && group_size > 1) { // Try grouped path first const bool use_dma_activation = (src1->nb[1]/sizeof(float) > (size_t)ne00_padded); - size_t best_mblocks = SIZE_MAX; - int best_act_threads = 0; - size_t best_m_chunk = 0; - size_t best_n_chunk = 0; - size_t best_vtcm_size = 0; - - int act_threads = n_threads; - while (act_threads >= 1) { - const size_t f32_scratch_size = use_dma_activation ? hex_align_up(act_threads * HTP_MM_DMA_ACT_MULTIPLIER * ne00_padded * sizeof(float), HTP_MM_HMX_TILE_SIZE) : 0; - size_t group_overhead = 256 + f32_scratch_size; - size_t group_size_per_n, group_size_per_m, group_size_per_mn; - htp_mm_hmx_get_batched_chunk_costs(ne00_padded, group_size, &group_size_per_n, &group_size_per_m, &group_size_per_mn); - - size_t m_chunk_candidate = 0; - size_t n_chunk_candidate = 0; - size_t vtcm_size_candidate = 0; - - if (htp_mm_hmx_compute_chunks(vtcm_budget, group_overhead, group_size_per_n, group_size_per_m, group_size_per_mn, hex_align_up(ne11, 32), ne01_padded, - (size_t) ne01_padded * HTP_MM_HMX_COST_W_DEQUANT, (size_t) ne11 * HTP_MM_HMX_COST_A_CONVERT, - &m_chunk_candidate, &n_chunk_candidate, &vtcm_size_candidate) == 0) { - size_t exact_size = htp_mm_hmx_get_batched_vtcm_size(wtype, ne00_padded, m_chunk_candidate, n_chunk_candidate, group_size, use_dma_activation, pipeline, act_threads); - if (exact_size <= vtcm_budget) { - size_t mblocks = ((size_t) ne11 + m_chunk_candidate - 1) / m_chunk_candidate; - if (mblocks < best_mblocks || (mblocks == best_mblocks && act_threads > best_act_threads)) { - best_mblocks = mblocks; - best_act_threads = act_threads; - best_m_chunk = m_chunk_candidate; - best_n_chunk = n_chunk_candidate; - best_vtcm_size = exact_size; - } - } - } - if (act_threads == 1) { - act_threads = 0; - } else { - act_threads /= 2; - } - } - - if (best_act_threads > 0) { - m_chunk = best_m_chunk; - n_chunk = best_n_chunk; - vtcm_size = best_vtcm_size; - act_threads_selected = best_act_threads; + if (htp_mm_hmx_solve_batched_params(wtype, ne00_padded, ne01_padded, ne11, group_size, use_dma_activation, n_threads, pipeline, vtcm_budget, &m_chunk, &n_chunk, &act_threads_selected, &vtcm_size)) { use_grouped = true; } } if (!use_grouped) { // Fallback to simple 2D path (group_size = 1) - size_t best_mblocks = SIZE_MAX; - int best_act_threads = 0; - size_t best_m_chunk = 0; - size_t best_n_chunk = 0; - size_t best_vtcm_size = 0; - - // For MUL_MAT_ID the kernel runs one 2D matmul per expert, with M equal to the number of rows routed to that expert. - // A single expert can receive up to all routed rows (dst->ne[1]*dst->ne[2] = n_expert_used*n_tokens), so size the chunk - // search for that upper bound rather than ne12 (token positions only). - // We recompute m_chunk per expert against the actual count in the NPU kernel. - const int m_id_rows = (int) ((size_t) dst->ne[1] * dst->ne[2]); - const int m_for_chunks = is_matmul_id ? hex_align_up(m_id_rows, 32) : ne11_padded; - const int m_for_cost = is_matmul_id ? m_id_rows : ne11; - - int act_threads = n_threads; - while (act_threads >= 1) { - const size_t act_f32_size = is_matmul_id ? 0 : hex_align_up(act_threads * HTP_MM_DMA_ACT_MULTIPLIER * ne00_padded * sizeof(float), HTP_MM_HMX_TILE_SIZE); - size_t simple_2d_overhead = 256 + act_f32_size; - size_t simple_2d_size_per_n, simple_2d_size_per_m, simple_2d_size_per_mn; - htp_mm_hmx_get_2d_chunk_costs(wtype, ne00_padded, pipeline, aligned_tile_size, &simple_2d_size_per_n, &simple_2d_size_per_m, &simple_2d_size_per_mn); - - size_t m_chunk_candidate = 0; - size_t n_chunk_candidate = 0; - size_t vtcm_size_candidate = 0; - - if (htp_mm_hmx_compute_chunks(vtcm_budget, simple_2d_overhead, simple_2d_size_per_n, simple_2d_size_per_m, simple_2d_size_per_mn, m_for_chunks, ne01_padded, - (size_t) ne01_padded * HTP_MM_HMX_COST_W_DEQUANT, (size_t) m_for_cost * HTP_MM_HMX_COST_A_CONVERT, - &m_chunk_candidate, &n_chunk_candidate, &vtcm_size_candidate) == 0) { - size_t exact_size = htp_mm_hmx_get_2d_vtcm_size(wtype, ne00_padded, m_chunk_candidate, n_chunk_candidate, pipeline, is_matmul_id ? 0 : act_threads, aligned_tile_size); - if (exact_size <= vtcm_budget) { - size_t mblocks = ((size_t) m_for_cost + m_chunk_candidate - 1) / m_chunk_candidate; - if (mblocks < best_mblocks || (mblocks == best_mblocks && act_threads > best_act_threads)) { - best_mblocks = mblocks; - best_act_threads = act_threads; - best_m_chunk = m_chunk_candidate; - best_n_chunk = n_chunk_candidate; - best_vtcm_size = exact_size; - } - } - } - if (act_threads == 1) { - act_threads = 0; - } else { - act_threads /= 2; - } - } - - if (best_act_threads > 0) { - m_chunk = best_m_chunk; - n_chunk = best_n_chunk; - vtcm_size = best_vtcm_size; - act_threads_selected = best_act_threads; - } else { + const int m_id_rows = (int) ((size_t) dst->ne[1] * dst->ne[2]); + if (!htp_mm_hmx_solve_2d_params(wtype, ne00_padded, m_id_rows, ne01_padded, ne11_padded, ne11, n_threads, pipeline, is_matmul_id, aligned_tile_size, vtcm_budget, &m_chunk, &n_chunk, &act_threads_selected, &vtcm_size)) { return false; } } @@ -2352,6 +2298,8 @@ static bool ggml_hexagon_precompute_hmx_mm_params( kparams->src1_row_size = (wtype == GGML_TYPE_Q4_1) ? htp_mm_q8_1_tiled_row_size(ne10) : htp_mm_q8_0_tiled_row_size(ne10); kparams->vtcm_size = vtcm_size; kparams->vtcm_src0_size = 0; + kparams->div_n_act_threads = init_fastdiv_values(act_threads_selected); + kparams->div_ne00_padded = init_fastdiv_values(ne00_padded); kparams->vtcm_src1_size = 0; kparams->vtcm_dst_size = 0; @@ -2361,6 +2309,8 @@ static bool ggml_hexagon_precompute_hmx_mm_params( kparams->kernel_type = HTP_MM_KERNEL_HMX_2D; } return true; + + GGML_UNUSED(src0); } static void ggml_hexagon_precompute_hvx_mm_params( @@ -2376,6 +2326,7 @@ static void ggml_hexagon_precompute_hvx_mm_params( int ne12, int ne13, bool is_matmul_id, + const size_t src2_row_size, size_t vtcm_budget, struct htp_mm_kernel_params * kparams ) { @@ -2401,7 +2352,7 @@ static void ggml_hexagon_precompute_hvx_mm_params( for (uint32_t d = max_prefetch; d >= 2; d /= 2) { htp_mm_hvx_vtcm_layout_build( &L, kparams->kernel_type, wtype, ne10, src1_nrows, sess->n_threads, - 0, src0->nb[1], 0, d, true, false, false + 0, src0->nb[1], 0, src2_row_size, d, true, false, false ); if (L.total_bytes <= vtcm_budget) { best_n_prefetch = d; @@ -2411,7 +2362,7 @@ static void ggml_hexagon_precompute_hvx_mm_params( if (best_n_prefetch == 2 && L.total_bytes > vtcm_budget) { htp_mm_hvx_vtcm_layout_build( &L, kparams->kernel_type, wtype, ne10, src1_nrows, sess->n_threads, - 0, src0->nb[1], 0, 2, true, false, false + 0, src0->nb[1], 0, src2_row_size, 2, true, false, false ); } kparams->n_prefetch = best_n_prefetch; @@ -2435,7 +2386,7 @@ static void ggml_hexagon_precompute_hvx_mm_params( for (uint32_t d = max_prefetch; d >= 2; d /= 2) { htp_mm_hvx_vtcm_layout_build( &L, kparams->kernel_type, wtype, ne10, src1_nrows, sess->n_threads, - dst->nb[1], src0->nb[1], src1->nb[1], d, false, false, false + dst->nb[1], src0->nb[1], src1->nb[1], src2_row_size, d, false, false, false ); if (L.total_bytes <= vtcm_budget) { best_n_prefetch = d; @@ -2445,7 +2396,7 @@ static void ggml_hexagon_precompute_hvx_mm_params( if (best_n_prefetch == 2 && L.total_bytes > vtcm_budget) { htp_mm_hvx_vtcm_layout_build( &L, kparams->kernel_type, wtype, ne10, src1_nrows, sess->n_threads, - dst->nb[1], src0->nb[1], src1->nb[1], 2, false, false, false + dst->nb[1], src0->nb[1], src1->nb[1], src2_row_size, 2, false, false, false ); } @@ -2469,7 +2420,7 @@ static void ggml_hexagon_precompute_hvx_mm_params( struct htp_mm_hvx_vtcm_layout L; htp_mm_hvx_vtcm_layout_build( &L, kparams->kernel_type, wtype, ne10, src1_nrows, sess->n_threads, - dst->nb[1], src0->nb[1], src1->nb[1], 16, false, false, false + dst->nb[1], src0->nb[1], src1->nb[1], src2_row_size, 16, false, false, false ); kparams->n_prefetch = 16; @@ -2489,7 +2440,7 @@ static void ggml_hexagon_precompute_hvx_mm_params( struct htp_mm_hvx_vtcm_layout L; htp_mm_hvx_vtcm_layout_build( &L, HTP_MM_KERNEL_HVX_F16_F16_VTCM, wtype, ne10, src1_nrows, sess->n_threads, - dst->nb[1], src0->nb[1], src1->nb[1], 16, false, false, false + dst->nb[1], src0->nb[1], src1->nb[1], src2_row_size, 16, false, false, false ); if (!is_batched && !is_permuted && L.total_bytes <= vtcm_budget) { @@ -2509,7 +2460,7 @@ static void ggml_hexagon_precompute_hvx_mm_params( kparams->src1_row_size = src1->nb[1]; htp_mm_hvx_vtcm_layout_build( &L, kparams->kernel_type, wtype, ne10, src1_nrows, sess->n_threads, - dst->nb[1], src0->nb[1], src1->nb[1], 16, false, false, false + dst->nb[1], src0->nb[1], src1->nb[1], src2_row_size, 16, false, false, false ); kparams->vtcm_size = L.total_bytes; kparams->vtcm_src0_size = L.src0_bytes; @@ -2525,7 +2476,7 @@ static void ggml_hexagon_precompute_hvx_mm_params( struct htp_mm_hvx_vtcm_layout L; htp_mm_hvx_vtcm_layout_build( &L, HTP_MM_KERNEL_HVX_F32_F32_VTCM, wtype, ne10, src1_nrows, sess->n_threads, - dst->nb[1], src0->nb[1], src1->nb[1], 16, false, false, false + dst->nb[1], src0->nb[1], src1->nb[1], src2_row_size, 16, false, false, false ); if (!is_batched && !is_permuted && L.total_bytes <= vtcm_budget) { @@ -2541,7 +2492,7 @@ static void ggml_hexagon_precompute_hvx_mm_params( kparams->src1_row_size = src1->nb[1]; htp_mm_hvx_vtcm_layout_build( &L, kparams->kernel_type, wtype, ne10, src1_nrows, sess->n_threads, - dst->nb[1], src0->nb[1], src1->nb[1], 16, false, false, false + dst->nb[1], src0->nb[1], src1->nb[1], src2_row_size, 16, false, false, false ); kparams->vtcm_size = L.total_bytes; kparams->vtcm_src0_size = L.src0_bytes; @@ -2552,11 +2503,12 @@ static void ggml_hexagon_precompute_hvx_mm_params( } } -static void ggml_hexagon_precompute_matmul_params( +static void ggml_hexagon_precompute_matmul_params_impl( const struct ggml_hexagon_session * sess, const struct ggml_tensor * src0, const struct ggml_tensor * src1, const struct ggml_tensor * dst, + const size_t src2_row_size, struct htp_mm_kernel_params * kparams ) { memset(kparams, 0, sizeof(*kparams)); @@ -2591,7 +2543,7 @@ static void ggml_hexagon_precompute_matmul_params( } // Fallback to HVX parameter computation - ggml_hexagon_precompute_hvx_mm_params(sess, src0, src1, dst, wtype, ne02, ne03, ne10, ne11, ne12, ne13, is_matmul_id, vtcm_budget, kparams); + ggml_hexagon_precompute_hvx_mm_params(sess, src0, src1, dst, wtype, ne02, ne03, ne10, ne11, ne12, ne13, is_matmul_id, src2_row_size, vtcm_budget, kparams); finalize: kparams->div_ne12_ne1 = init_fastdiv_values(ne12 * ne11); @@ -2601,6 +2553,27 @@ static void ggml_hexagon_precompute_matmul_params( kparams->div_ne11 = init_fastdiv_values(ne11); } +static void ggml_hexagon_precompute_matmul_params( + const struct ggml_hexagon_session * sess, + const struct ggml_tensor * src0, + const struct ggml_tensor * src1, + const struct ggml_tensor * dst, + struct htp_mm_kernel_params * kparams +) { + ggml_hexagon_precompute_matmul_params_impl(sess, src0, src1, dst, 0, kparams); +} + +static void ggml_hexagon_precompute_fused_matmul_add_params( + const struct ggml_hexagon_session * sess, + const struct ggml_tensor * src0, + const struct ggml_tensor * src1, + const struct ggml_tensor * src2, + const struct ggml_tensor * dst, + struct htp_mm_kernel_params * kparams +) { + ggml_hexagon_precompute_matmul_params_impl(sess, src0, src1, dst, src2->nb[1], kparams); +} + static void ggml_hexagon_precompute_unary_params( const struct ggml_hexagon_session * sess, uint32_t op, @@ -2694,7 +2667,7 @@ static void ggml_hexagon_precompute_fused_qkv_params( struct htp_mm_hvx_vtcm_layout L; htp_mm_hvx_vtcm_layout_build( &L, HTP_MM_KERNEL_HVX_QUANT_ROW, wtype, ne10, src1_nrows, sess->n_threads, - 0, src0_row_size, src1_row_size, d, false, true, false + 0, src0_row_size, src1_row_size, 0, d, false, true, false ); if (L.total_bytes <= sess->vtcm_size) { best_n_prefetch = d; @@ -2709,7 +2682,7 @@ static void ggml_hexagon_precompute_fused_qkv_params( // Test tiled first htp_mm_hvx_vtcm_layout_build( &L, HTP_MM_KERNEL_HVX_QUANT_ROW, wtype, ne10, src1_nrows, sess->n_threads, - 0, src0_row_size, src1_row_size, best_n_prefetch, false, true, false + 0, src0_row_size, src1_row_size, 0, best_n_prefetch, false, true, false ); if (try_tiled && L.total_bytes <= sess->vtcm_size) { @@ -2727,7 +2700,7 @@ static void ggml_hexagon_precompute_fused_qkv_params( htp_mm_hvx_vtcm_layout_build( &L, HTP_MM_KERNEL_HVX_QUANT_ROW_FLAT, wtype, ne10, src1_nrows, sess->n_threads, - 0, src0_row_size, flat_src1_row_size, best_n_prefetch, false, true, false + 0, src0_row_size, flat_src1_row_size, 0, best_n_prefetch, false, true, false ); kparams->vtcm_src0_size = L.src0_bytes; kparams->vtcm_src1_size = L.src1_bytes; @@ -2764,7 +2737,7 @@ static void ggml_hexagon_precompute_fused_ffn_params( struct htp_mm_hvx_vtcm_layout L; htp_mm_hvx_vtcm_layout_build( &L, HTP_MM_KERNEL_HVX_QUANT_ROW, wtype, ne10, src1_nrows, sess->n_threads, - 0, src0_row_size, src1_row_size, d, false, false, true + 0, src0_row_size, src1_row_size, 0, d, false, false, true ); if (L.total_bytes <= sess->vtcm_size) { best_n_prefetch = d; @@ -2779,7 +2752,7 @@ static void ggml_hexagon_precompute_fused_ffn_params( // Test tiled first htp_mm_hvx_vtcm_layout_build( &L, HTP_MM_KERNEL_HVX_QUANT_ROW, wtype, ne10, src1_nrows, sess->n_threads, - 0, src0_row_size, src1_row_size, best_n_prefetch, false, false, true + 0, src0_row_size, src1_row_size, 0, best_n_prefetch, false, false, true ); if (try_tiled && L.total_bytes <= sess->vtcm_size) { @@ -2796,7 +2769,7 @@ static void ggml_hexagon_precompute_fused_ffn_params( htp_mm_hvx_vtcm_layout_build( &L, HTP_MM_KERNEL_HVX_QUANT_ROW_FLAT, wtype, ne10, src1_nrows, sess->n_threads, - 0, src0_row_size, flat_src1_row_size, best_n_prefetch, false, false, true + 0, src0_row_size, flat_src1_row_size, 0, best_n_prefetch, false, false, true ); kparams->vtcm_src0_size = L.src0_bytes; kparams->vtcm_src1_size = L.src1_bytes; @@ -2955,6 +2928,8 @@ static bool ggml_hexagon_supported_binary(const struct ggml_hexagon_session * se } return true; + + GGML_UNUSED(sess); } static bool ggml_hexagon_supported_add_id(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { @@ -2981,6 +2956,8 @@ static bool ggml_hexagon_supported_add_id(const struct ggml_hexagon_session * se } return true; + + GGML_UNUSED(sess); } static bool ggml_hexagon_supported_unary(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { @@ -3006,6 +2983,8 @@ static bool ggml_hexagon_supported_unary(const struct ggml_hexagon_session * ses } return true; + + GGML_UNUSED(sess); } static bool ggml_hexagon_supported_sum_rows(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { @@ -3025,10 +3004,11 @@ static bool ggml_hexagon_supported_sum_rows(const struct ggml_hexagon_session * } return true; + + GGML_UNUSED(sess); } -static bool ggml_hexagon_supported_activations(const struct ggml_hexagon_session * sess, - const struct ggml_tensor * op) { +static bool ggml_hexagon_supported_activations(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { const struct ggml_tensor * src0 = op->src[0]; const struct ggml_tensor * src1 = op->src[1]; const struct ggml_tensor * dst = op; @@ -3040,7 +3020,10 @@ static bool ggml_hexagon_supported_activations(const struct ggml_hexagon_session return false; } - if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(dst)) { + if (!ggml_is_contiguous_1(src0)) { + return false; + } + if (!ggml_is_contiguous(dst)) { return false; } @@ -3051,12 +3034,14 @@ static bool ggml_hexagon_supported_activations(const struct ggml_hexagon_session if (!ggml_are_same_shape(src0, src1)) { return false; } - if (!ggml_is_contiguous(src1)) { + if (!ggml_is_contiguous_1(src1)) { return false; } } return true; + + GGML_UNUSED(sess); } static bool ggml_hexagon_supported_softmax(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { @@ -3122,6 +3107,8 @@ static bool ggml_hexagon_supported_softmax(const struct ggml_hexagon_session * s } return true; + + GGML_UNUSED(sess); } static bool ggml_hexagon_supported_set_rows(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { @@ -3142,6 +3129,8 @@ static bool ggml_hexagon_supported_set_rows(const struct ggml_hexagon_session * } return true; + + GGML_UNUSED(sess); } static bool ggml_hexagon_supported_get_rows(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { @@ -3162,6 +3151,8 @@ static bool ggml_hexagon_supported_get_rows(const struct ggml_hexagon_session * } return true; + + GGML_UNUSED(sess); } static bool ggml_hexagon_supported_argsort(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { @@ -3182,6 +3173,8 @@ static bool ggml_hexagon_supported_argsort(const struct ggml_hexagon_session * s } return true; + + GGML_UNUSED(sess); } static bool ggml_hexagon_supported_rope(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { @@ -3243,6 +3236,8 @@ static bool ggml_hexagon_supported_rope(const struct ggml_hexagon_session * sess return false; } return true; + + GGML_UNUSED(sess); } static bool ggml_hexagon_supported_ssm_conv(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { @@ -3282,6 +3277,37 @@ static bool ggml_hexagon_supported_ssm_conv(const struct ggml_hexagon_session * } return true; + + GGML_UNUSED(sess); +} + +static bool ggml_hexagon_supported_im2col(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { + const struct ggml_tensor * src1 = op->src[1]; + const struct ggml_tensor * dst = op; + + const bool is_2D = ((const int32_t *) op->op_params)[6] == 1; + if (!is_2D) { + return false; + } + + // For now support F32->F32 and F32->F16 only. + if (src1->type != GGML_TYPE_F32 || (dst->type != GGML_TYPE_F16 && dst->type != GGML_TYPE_F32)) { + return false; + } + + if (!ggml_is_contiguous(src1) || !ggml_is_contiguous(dst)) { + return false; + } + + // For now keep padded OPs on CPU. Will revisit once we expand coverage past patch-embed OPs. + const int32_t p0 = ((const int32_t *) op->op_params)[2]; + const int32_t p1 = ((const int32_t *) op->op_params)[3]; + if (p0 != 0 || p1 != 0) { + return false; + } + + GGML_UNUSED(sess); + return true; } static bool ggml_hexagon_supported_pad(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { @@ -3292,8 +3318,9 @@ static bool ggml_hexagon_supported_pad(const struct ggml_hexagon_session * sess, return false; } - GGML_UNUSED(sess); return true; + + GGML_UNUSED(sess); } static bool ggml_hexagon_supported_cumsum(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { @@ -3308,8 +3335,9 @@ static bool ggml_hexagon_supported_cumsum(const struct ggml_hexagon_session * se return false; } - GGML_UNUSED(sess); return true; + + GGML_UNUSED(sess); } static bool ggml_hexagon_supported_diag(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { @@ -3331,8 +3359,9 @@ static bool ggml_hexagon_supported_diag(const struct ggml_hexagon_session * sess return false; } - GGML_UNUSED(sess); return true; + + GGML_UNUSED(sess); } static bool ggml_hexagon_supported_solve_tri(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { @@ -3364,8 +3393,9 @@ static bool ggml_hexagon_supported_solve_tri(const struct ggml_hexagon_session * return false; } - GGML_UNUSED(sess); return true; + + GGML_UNUSED(sess); } static bool ggml_hexagon_supported_tri(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { @@ -3415,6 +3445,7 @@ static htp_op_code op_remap_to_htp(const ggml_tensor * t) { case GGML_OP_RMS_NORM: return HTP_OP_RMS_NORM; case GGML_OP_CONCAT: return HTP_OP_CONCAT; case GGML_OP_SCALE: return HTP_OP_SCALE; + case GGML_OP_CLAMP: return HTP_OP_CLAMP; case GGML_OP_SQR: return HTP_OP_SQR; case GGML_OP_SQRT: return HTP_OP_SQRT; case GGML_OP_SOFT_MAX: return HTP_OP_SOFTMAX; @@ -3428,6 +3459,7 @@ static htp_op_code op_remap_to_htp(const ggml_tensor * t) { case GGML_OP_SOLVE_TRI: return HTP_OP_SOLVE_TRI; case GGML_OP_TRI: return HTP_OP_TRI; case GGML_OP_PAD: return HTP_OP_PAD; + case GGML_OP_IM2COL: return HTP_OP_IM2COL; case GGML_OP_UNARY: switch (ggml_get_unary_op(t)) { @@ -3590,16 +3622,19 @@ static bool try_fuse_node(const ggml_hexagon_session * sess, const ggml_cgraph * if (n->op == GGML_OP_MUL_MAT && next_node) { if (next_node->op == GGML_OP_ADD && op_is_compute(next_node) && ggml_can_fuse(graph, i, { GGML_OP_MUL_MAT, GGML_OP_ADD })) { if (next_node->src[0] == n || next_node->src[1] == n) { + const struct ggml_tensor * src2 = (next_node->src[0] == n) ? next_node->src[1] : next_node->src[0]; struct htp_mm_kernel_params kparams; - ggml_hexagon_precompute_matmul_params(sess, n->src[0], n->src[1], next_node, &kparams); - if ((size_t)kparams.vtcm_size <= sess->vtcm_size) { + ggml_hexagon_precompute_fused_matmul_add_params(sess, n->src[0], n->src[1], src2, next_node, &kparams); + const int src1_nrows = n->src[1]->ne[1] * n->src[1]->ne[2] * n->src[1]->ne[3]; + const bool can_fuse = (kparams.n_hmx > 0) || (src1_nrows == 1); + if (can_fuse && (size_t)kparams.vtcm_size <= sess->vtcm_size) { htp_opnode node(n, {}, HTP_OP_MUL_MAT_ADD); node.add_fused(next_node); memcpy(node.kernel_params, &kparams, sizeof(kparams)); nodes.push_back(std::move(node)); i += 1; return true; - } else { + } else if (can_fuse) { HEX_VERBOSE("ggml-hex: skip MUL_MAT_ADD fusion because VTCM needed (%d) > budget (%zu)\n", kparams.vtcm_size, sess->vtcm_size); } @@ -3812,6 +3847,8 @@ static void ggml_backend_hexagon_graph_optimize(ggml_backend_t backend, ggml_cgr } } } + + GGML_UNUSED(backend); } static struct ggml_backend_i hexagon_backend_i = { @@ -3930,6 +3967,8 @@ static bool ggml_hexagon_supported_buffers(ggml_hexagon_session *sess, const str } static bool ggml_hexagon_supported_cpy(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { + GGML_UNUSED(sess); + const struct ggml_tensor * src0 = op->src[0]; const struct ggml_tensor * dst = op; @@ -4000,6 +4039,7 @@ static bool ggml_hexagon_supported_concat(const struct ggml_hexagon_session * se } return true; + GGML_UNUSED(sess); } static bool ggml_hexagon_supported_fill(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { @@ -4009,8 +4049,8 @@ static bool ggml_hexagon_supported_fill(const struct ggml_hexagon_session * sess return false; } - GGML_UNUSED(sess); return true; + GGML_UNUSED(sess); } static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) { @@ -4060,6 +4100,7 @@ static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, cons case GGML_OP_L2_NORM: case GGML_OP_RMS_NORM: case GGML_OP_SCALE: + case GGML_OP_CLAMP: supp = ggml_hexagon_supported_unary(sess, op); break; @@ -4083,12 +4124,10 @@ static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, cons case GGML_UNARY_OP_SIGMOID: case GGML_UNARY_OP_SOFTPLUS: case GGML_UNARY_OP_TANH: - supp = ggml_hexagon_supported_unary(sess, op); - break; case GGML_UNARY_OP_SILU: case GGML_UNARY_OP_GELU: case GGML_UNARY_OP_GELU_QUICK: - supp = ggml_hexagon_supported_activations(sess, op); + supp = ggml_hexagon_supported_unary(sess, op); break; default: break; @@ -4143,6 +4182,10 @@ static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, cons supp = ggml_hexagon_supported_ssm_conv(sess, op); break; + case GGML_OP_IM2COL: + supp = ggml_hexagon_supported_im2col(sess, op); + break; + case GGML_OP_GATED_DELTA_NET: supp = ggml_hexagon_supported_gated_delta_net(sess, op); break; @@ -4293,6 +4336,7 @@ static void * ggml_backend_hexagon_get_proc_address(ggml_backend_reg_t reg, cons } return NULL; + GGML_UNUSED(reg); } template std::vector str_to_vec(const char* str) { @@ -4351,10 +4395,18 @@ static void ggml_hexagon_init(ggml_backend_reg * reg) { // Init Arch first since it affects other defaults if (!str_arch) { - int err = get_hex_arch_ver(CDSP_DOMAIN_ID, &opt_arch); + int err = htpdrv_get_arch(CDSP_DOMAIN_ID, &opt_arch); if (err != 0) { GGML_LOG_ERROR("ggml-hex: failed to query HTP version (err %d) defaulting to v73\n", err); opt_arch = 73; + } else { + if (opt_arch < 73) { + GGML_LOG_WARN("ggml-hex: Hexagon arch v%d is under supported range, capping at v73\n", opt_arch); + opt_arch = 73; + } else if (opt_arch > 81) { + GGML_LOG_WARN("ggml-hex: Hexagon arch v%d is over supported range, capping at v81\n", opt_arch); + opt_arch = 81; + } } } else { if (str_arch[0] == 'v' || str_arch[0] == 'V') { @@ -4376,7 +4428,7 @@ static void ggml_hexagon_init(ggml_backend_reg * reg) { opt_opstage = str_opstage ? strtoul(str_opstage, NULL, 0) : opt_opstage; opt_opbatch = str_opbatch ? strtoul(str_opbatch, NULL, 0) : opt_opbatch; opt_opqueue = str_opqueue ? strtoul(str_opqueue, NULL, 0) : opt_opqueue; - opt_optrace = str_optrace ? strtoul(str_optrace, NULL, 0) : (opt_opbatch * 128); + opt_optrace = str_optrace ? strtoul(str_optrace, NULL, 0) : (opt_opbatch * 256); opt_oppoll = str_oppoll ? strtoul(str_oppoll, NULL, 0) : opt_oppoll; opt_opfusion = str_opfusion ? atoi(str_opfusion) : opt_opfusion; opt_profile = str_profile ? atoi(str_profile) : 0; diff --git a/ggml/src/ggml-hexagon/htp-drv.cpp b/ggml/src/ggml-hexagon/htp-drv.cpp index 4c376b5fc918..4f0790801731 100644 --- a/ggml/src/ggml-hexagon/htp-drv.cpp +++ b/ggml/src/ggml-hexagon/htp-drv.cpp @@ -1,13 +1,8 @@ -// sample drv interface - -#pragma clang diagnostic ignored "-Wgnu-anonymous-struct" -#pragma clang diagnostic ignored "-Wmissing-prototypes" -#pragma clang diagnostic ignored "-Wsign-compare" - #include #include #include #include + #ifdef _WIN32 # define WIN32_LEAN_AND_MEAN # ifndef NOMINMAX @@ -16,9 +11,17 @@ # include # include #else -# include -# include +# include +# include #endif + +#pragma clang diagnostic ignored "-Wgnu-anonymous-struct" +#pragma clang diagnostic ignored "-Wmissing-prototypes" +#pragma clang diagnostic ignored "-Wsign-compare" +#pragma clang diagnostic ignored "-Wlanguage-extension-token" +#pragma clang diagnostic ignored "-Wmicrosoft-enum-value" +#pragma clang diagnostic ignored "-Wnested-anon-types" + #include "ggml-impl.h" #include "htp-drv.h" #include "libdl.h" @@ -56,7 +59,11 @@ typedef AEEResult (*dspqueue_read_pfn_t)(dspqueue_t queue, uint32_t *flags, uint32_t max_message_length, uint32_t *message_length, uint8_t *message, uint32_t timeout_us); - +typedef AEEResult (*dspqueue_read_noblock_pfn_t)(dspqueue_t queue, uint32_t *flags, + uint32_t max_buffers, uint32_t *num_buffers, + struct dspqueue_buffer *buffers, + uint32_t max_message_length, + uint32_t *message_length, uint8_t *message); typedef int (*fastrpc_mmap_pfn_t)(int domain, int fd, void *addr, int offset, size_t length, enum fastrpc_map_flags flags); typedef int (*fastrpc_munmap_pfn_t)(int domain, int fd, void *addr, size_t length); @@ -79,11 +86,12 @@ rpcmem_to_fd_pfn_t rpcmem_to_fd_pfn = nullptr; fastrpc_mmap_pfn_t fastrpc_mmap_pfn = nullptr; fastrpc_munmap_pfn_t fastrpc_munmap_pfn = nullptr; -dspqueue_create_pfn_t dspqueue_create_pfn = nullptr; -dspqueue_close_pfn_t dspqueue_close_pfn = nullptr; -dspqueue_export_pfn_t dspqueue_export_pfn = nullptr; -dspqueue_write_pfn_t dspqueue_write_pfn = nullptr; -dspqueue_read_pfn_t dspqueue_read_pfn = nullptr; +dspqueue_create_pfn_t dspqueue_create_pfn = nullptr; +dspqueue_close_pfn_t dspqueue_close_pfn = nullptr; +dspqueue_export_pfn_t dspqueue_export_pfn = nullptr; +dspqueue_write_pfn_t dspqueue_write_pfn = nullptr; +dspqueue_read_pfn_t dspqueue_read_pfn = nullptr; +dspqueue_read_noblock_pfn_t dspqueue_read_noblock_pfn = nullptr; remote_handle64_open_pfn_t remote_handle64_open_pfn = nullptr; remote_handle64_invoke_pfn_t remote_handle64_invoke_pfn = nullptr; @@ -164,6 +172,12 @@ AEEResult dspqueue_read(dspqueue_t queue, uint32_t * message_length, uint8_t * message, uint32_t timeout_us) { +#ifdef _WIN32 + if (timeout_us == 0) { + return dspqueue_read_noblock_pfn(queue, flags, max_buffers, num_buffers, buffers, max_message_length, + message_length, message); + } +#endif return dspqueue_read_pfn(queue, flags, max_buffers, num_buffers, buffers, max_message_length, message_length, message, timeout_us); } @@ -346,6 +360,7 @@ int htpdrv_init() { dlsym(handle.get(), dspqueue_export_pfn_t, dspqueue_export_pfn, dspqueue_export, false); dlsym(handle.get(), dspqueue_write_pfn_t, dspqueue_write_pfn, dspqueue_write, false); dlsym(handle.get(), dspqueue_read_pfn_t, dspqueue_read_pfn, dspqueue_read, false); + dlsym(handle.get(), dspqueue_read_noblock_pfn_t, dspqueue_read_noblock_pfn, dspqueue_read_noblock, false); dlsym(handle.get(), remote_handle64_open_pfn_t, remote_handle64_open_pfn, remote_handle64_open, false); dlsym(handle.get(), remote_handle64_invoke_pfn_t, remote_handle64_invoke_pfn, remote_handle64_invoke, false); dlsym(handle.get(), remote_handle_control_pfn_t, remote_handle_control_pfn, remote_handle_control, false); @@ -359,7 +374,7 @@ int htpdrv_init() { return AEE_SUCCESS; } -domain * get_domain(int domain_id) { +domain * htpdrv_get_domain(int domain_id) { int i = 0; int size = sizeof(supported_domains) / sizeof(domain); @@ -372,7 +387,7 @@ domain * get_domain(int domain_id) { return NULL; } -int get_hex_arch_ver(int domain, int * arch) { +int htpdrv_get_arch(int domain, int * arch) { if (!remote_handle_control_pfn) { GGML_LOG_ERROR("ggml-hex: remote_handle_control is not supported on this device\n"); return AEE_EUNSUPPORTEDAPI; @@ -394,25 +409,7 @@ int get_hex_arch_ver(int domain, int * arch) { return err; } - switch (arch_ver.capability & 0xff) { - case 0x68: - *arch = 68; - return 0; - case 0x69: - *arch = 69; - return 0; - case 0x73: - *arch = 73; - return 0; - case 0x75: - *arch = 75; - return 0; - case 0x79: - *arch = 79; - return 0; - case 0x81: - *arch = 81; - return 0; - } - return -1; + uint32_t val = arch_ver.capability & 0xff; + *arch = (int) ((val >> 4) * 10 + (val & 0x0f)); + return 0; } diff --git a/ggml/src/ggml-hexagon/htp-drv.h b/ggml/src/ggml-hexagon/htp-drv.h index 6eba7ba17d8d..f3cc0da75c28 100644 --- a/ggml/src/ggml-hexagon/htp-drv.h +++ b/ggml/src/ggml-hexagon/htp-drv.h @@ -96,17 +96,17 @@ extern "C" { HTPDRV_API int htpdrv_init(void); /** - * get_domain API: get domain struct from domain value. + * htpdrv_get_domain API: get domain struct from domain value. * * @param[in] domain value of a domain * @return Returns domain struct of the domain if it is supported or else * returns NULL. * */ -HTPDRV_API domain * get_domain(int domain_id); +HTPDRV_API domain * htpdrv_get_domain(int domain_id); /** - * get_hex_arch_ver API: query the Hexagon processor architecture version information + * htpdrv_get_arch API: query the Hexagon processor architecture version information * * @param[in] domain_id value of a domain * @param[out] Arch version (73, 75, ...) @@ -114,7 +114,7 @@ HTPDRV_API domain * get_domain(int domain_id); * non-zero if error, return value points to the error. * */ -HTPDRV_API int get_hex_arch_ver(int domain, int * arch); +HTPDRV_API int htpdrv_get_arch(int domain, int * arch); #ifdef __cplusplus } diff --git a/ggml/src/ggml-hexagon/htp/CMakeLists.txt b/ggml/src/ggml-hexagon/htp/CMakeLists.txt index cf9e726c0019..b00aa2bc94c3 100644 --- a/ggml/src/ggml-hexagon/htp/CMakeLists.txt +++ b/ggml/src/ggml-hexagon/htp/CMakeLists.txt @@ -17,9 +17,12 @@ set(HTP_LIB ggml-htp-${DSP_VERSION}) add_library(${HTP_LIB} SHARED main.c htp_iface_skel.c - worker-pool.c - hex-dma.c + work-queue.c + dma-queue.c hmx-queue.c + htp-tensor.c + matmul-ops.c + flash-attn-ops.c gated-delta-net-ops.c binary-ops.c unary-ops.c @@ -38,9 +41,8 @@ add_library(${HTP_LIB} SHARED diag-ops.c solve-tri-ops.c pad-ops.c - flash-attn-ops.c - matmul-ops.c argsort-ops.c + im2col-ops.c ) target_compile_definitions(${HTP_LIB} PRIVATE diff --git a/ggml/src/ggml-hexagon/htp/act-ops.c b/ggml/src/ggml-hexagon/htp/act-ops.c index 6416d2dfbc38..9973c088dda7 100644 --- a/ggml/src/ggml-hexagon/htp/act-ops.c +++ b/ggml/src/ggml-hexagon/htp/act-ops.c @@ -16,6 +16,8 @@ #include "htp-ctx.h" #include "htp-ops.h" #include "htp-ops.h" +#include "htp-tensor.h" +#include "htp-vtcm.h" #define htp_act_preamble \ const struct htp_tensor * src0 = actx->octx->src[0]; \ @@ -53,581 +55,413 @@ const uint32_t nb3 = dst->nb[3]; struct htp_act_context { - struct htp_ops_context * octx; + struct htp_ops_context * octx; // Precomputed values - const uint8_t * data_src0; - const uint8_t * data_src1; - uint8_t * data_dst; - - size_t src0_row_size; - size_t src1_row_size; - size_t dst_row_size; - - size_t src0_row_size_aligned; - size_t src1_row_size_aligned; - size_t dst_row_size_aligned; - - size_t src0_spad_half_size; - size_t src1_spad_half_size; - size_t dst_spad_half_size; - - uint32_t block; - uint32_t src0_nrows; - uint32_t src0_nrows_per_thread; - int nc; -}; - -static void glu_swiglu_f32_per_thread(unsigned int nth, unsigned int ith, void * data) { - struct htp_act_context * actx = (struct htp_act_context *) data; - htp_act_preamble; - - size_t src0_row_size = actx->src0_row_size; - size_t src1_row_size = actx->src1_row_size; - size_t dst_row_size = actx->dst_row_size; - - const uint32_t src0_nrows = actx->src0_nrows; - const uint32_t src0_nrows_per_thread = actx->src0_nrows_per_thread; - const uint32_t src0_start_row = src0_nrows_per_thread * ith; - const uint32_t src0_end_row = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows); - - // no work for this thread - if (src0_start_row >= src0_end_row) { - return; - } + const uint8_t * data_src0; + const uint8_t * data_src1; + uint8_t * data_dst; - uint64_t t1, t2; - t1 = HAP_perf_get_qtimer_count(); + size_t src0_row_size; + size_t src1_row_size; + size_t dst_row_size; - const uint8_t * restrict data_src0 = actx->data_src0; - const uint8_t * restrict data_src1 = actx->data_src1; - uint8_t * restrict data_dst = actx->data_dst; + size_t src0_row_stride; + size_t src1_row_stride; - const int nc = actx->nc; + size_t src0_row_size_aligned; + size_t src1_row_size_aligned; + size_t dst_row_size_aligned; - const size_t src0_row_size_aligned = actx->src0_row_size_aligned; - const size_t src1_row_size_aligned = actx->src1_row_size_aligned; - const size_t dst_row_size_aligned = actx->dst_row_size_aligned; + size_t src0_spad_half_size; + size_t src1_spad_half_size; + size_t dst_spad_half_size; - uint8_t * restrict src0_spad_data = actx->octx->src0_spad.data + (ith * actx->octx->src0_spad.size_per_thread); - uint8_t * restrict src1_spad_data = actx->octx->src1_spad.data + (ith * actx->octx->src1_spad.size_per_thread); - uint8_t * restrict dst_spad_data = actx->octx->dst_spad.data + (ith * actx->octx->dst_spad.size_per_thread); + uint32_t block; + uint32_t src0_nrows; + uint32_t src0_nrows_per_thread; + int nc; - size_t src0_spad_half_size = actx->src0_spad_half_size; - size_t src1_spad_half_size = actx->src1_spad_half_size; - size_t dst_spad_half_size = actx->dst_spad_half_size; + uint8_t * vtcm_src0; + uint8_t * vtcm_src1; + uint8_t * vtcm_dst; - const int BLOCK = actx->block; - if (BLOCK == 0) { - FARF(ERROR, - "swiglu-f32 : current VTCM reservation %zu is too small for even 1 row per thread, needed at least %zu\n", - actx->octx->src0_spad.size_per_thread, src0_row_size_aligned); - return; - } + size_t vtcm_src0_size_per_thread; + size_t vtcm_src1_size_per_thread; + size_t vtcm_dst_size_per_thread; +}; - dma_queue * dma_queue = actx->octx->ctx->dma[ith]; +struct htp_act_vtcm_layout { + size_t total_bytes; + size_t off_src0; + size_t off_src1; + size_t off_dst; - // See discussion: https://github.com/ggml-org/llama.cpp/pull/18151#issuecomment-3678235379 - for (uint32_t ir = src0_start_row, spad_idx = 0; ir < src0_end_row && spad_idx < 2; ir += BLOCK, spad_idx++) { - const uint32_t block_size = MIN(BLOCK, src0_end_row - ir); + size_t src0_bytes_per_thread; + size_t src1_bytes_per_thread; + size_t dst_bytes_per_thread; - // Dummy DMA transation for sequencing (interleaving dst,src,dst,...) - dma_queue_push_vtcm_to_ddr(dma_queue, - dma_make_ptr(data_dst, dst_spad_data + (spad_idx * dst_spad_half_size)), - dst_row_size, dst_row_size_aligned, 0); + uint32_t vtcm_row_per_thread; +}; - dma_queue_push_ddr_to_vtcm(dma_queue, - dma_make_ptr(src0_spad_data + (spad_idx * src0_spad_half_size), data_src0 + (ir * src0_row_size)), - src0_row_size_aligned, src0_row_size, block_size); - dma_queue_push_ddr_to_vtcm(dma_queue, - dma_make_ptr(src1_spad_data + (spad_idx * src1_spad_half_size), data_src1 + (ir * src1_row_size)), - src1_row_size_aligned, src1_row_size, block_size); - } +static inline void htp_act_vtcm_layout_build(struct htp_act_vtcm_layout * L, + size_t src0_row_size_aligned, + size_t src1_row_size_aligned, + size_t dst_row_size_aligned, + uint32_t n_threads, + size_t vtcm_size) { + const size_t spad_size_per_row = src0_row_size_aligned + src1_row_size_aligned + dst_row_size_aligned; + const uint32_t vtcm_row_per_thread = (uint32_t) (vtcm_size / (n_threads * spad_size_per_row)); - for (uint32_t ir = src0_start_row; ir < src0_end_row; ir += BLOCK) { - const uint32_t block_size = MIN(BLOCK, src0_end_row - ir); - - float * dst_spad = (float *) dma_queue_pop(dma_queue).src; - float * src0_spad = (float *) dma_queue_pop(dma_queue).dst; - float * src1_spad = (float *) dma_queue_pop(dma_queue).dst; - - for (uint32_t ib = 0; ib < block_size; ib++) { - const float * src0_spad_ptr = src0_spad + ib * (src0_row_size_aligned / sizeof(float)); - const float * src1_spad_ptr = src1_spad + ib * (src1_row_size_aligned / sizeof(float)); - float * dst_spad_ptr = dst_spad + ib * (dst_row_size_aligned / sizeof(float)); - - //swiglu(x) = x1 * sigmoid(x0) - hvx_sigmoid_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, nc); - hvx_mul_mul_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, (const uint8_t *) dst_spad_ptr, - (const uint8_t *) src1_spad_ptr, nc); - } - - dma_queue_push_vtcm_to_ddr(dma_queue, dma_make_ptr(data_dst + (ir * dst_row_size), dst_spad), dst_row_size, - dst_row_size_aligned, block_size); - - // prefetch N+2 loop iteration if any - const uint32_t pref_block = (ir + BLOCK * 2); - if (pref_block < src0_end_row) { - const uint32_t pref_block_size = MIN(BLOCK, src0_end_row - pref_block); - dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(src0_spad, data_src0 + (pref_block * src0_row_size)), - src0_row_size_aligned, src0_row_size, pref_block_size); - dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(src1_spad, data_src1 + (pref_block * src1_row_size)), - src1_row_size_aligned, src1_row_size, pref_block_size); - } - } + L->vtcm_row_per_thread = vtcm_row_per_thread; - dma_queue_flush(dma_queue); + L->src0_bytes_per_thread = src0_row_size_aligned * vtcm_row_per_thread; + L->src1_bytes_per_thread = src1_row_size_aligned * vtcm_row_per_thread; + L->dst_bytes_per_thread = dst_row_size_aligned * vtcm_row_per_thread; - t2 = HAP_perf_get_qtimer_count(); + L->off_src0 = 0; + L->off_src1 = L->off_src0 + L->src0_bytes_per_thread * n_threads; + L->off_dst = L->off_src1 + L->src1_bytes_per_thread * n_threads; - FARF(HIGH, "swiglu-f32 %d/%d: %ux%ux%ux%u (%u:%u) x %ux%ux%ux%u -> %ux%ux%ux%u usec %u\n", ith, nth, - ne00, ne01, ne02, ne03, src0_start_row, src0_end_row, ne10, ne11, ne12, ne13, ne0, ne1, ne2, ne3, - (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1)); + L->total_bytes = L->off_dst + L->dst_bytes_per_thread * n_threads; } -static void glu_swiglu_oai_f32_per_thread(unsigned int nth, unsigned int ith, void * data) { - struct htp_act_context * actx = (struct htp_act_context *) data; - htp_act_preamble; - - uint64_t t1, t2; - t1 = HAP_perf_get_qtimer_count(); - - size_t src0_row_size = actx->src0_row_size; - size_t src1_row_size = actx->src1_row_size; - size_t dst_row_size = actx->dst_row_size; - - const uint32_t src0_nrows = actx->src0_nrows; - const uint32_t src0_nrows_per_thread = actx->src0_nrows_per_thread; - - const uint32_t src0_start_row = src0_nrows_per_thread * ith; - const uint32_t src0_end_row = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows); - - // no work for this thread - if (src0_start_row >= src0_end_row) { - return; +#define htp_glu_op_preamble \ + const size_t src0_row_size_aligned = actx->src0_row_size_aligned; \ + const size_t src1_row_size_aligned = actx->src1_row_size_aligned; \ + const size_t dst_row_size_aligned = actx->dst_row_size_aligned; \ + const int nc = actx->nc; + +// swiglu(x) = x1 * sigmoid(x0) +static void swiglu_f32(const float * restrict src0, + const float * restrict src1, + float * restrict dst, + const uint32_t num_rows, + const struct htp_act_context * actx) { + htp_glu_op_preamble; + + for (uint32_t ib = 0; ib < num_rows; ib++) { + const uint8_t * restrict src0_ptr = (const uint8_t *) src0 + (ib * src0_row_size_aligned); + const uint8_t * restrict src1_ptr = (const uint8_t *) src1 + (ib * src1_row_size_aligned); + uint8_t * restrict dst_ptr = (uint8_t *) dst + (ib * dst_row_size_aligned); + + hvx_sigmoid_f32_aa(dst_ptr, src0_ptr, nc); + hvx_mul_mul_f32_aa(dst_ptr, src0_ptr, dst_ptr, src1_ptr, nc); } +} - const uint8_t * restrict data_src0 = actx->data_src0; - const uint8_t * restrict data_src1 = actx->data_src1; - uint8_t * restrict data_dst = actx->data_dst; - - const int nc = actx->nc; - - const size_t src0_row_size_aligned = actx->src0_row_size_aligned; - const size_t src1_row_size_aligned = actx->src1_row_size_aligned; - const size_t dst_row_size_aligned = actx->dst_row_size_aligned; - - uint8_t * restrict src0_spad_data = actx->octx->src0_spad.data + (ith * actx->octx->src0_spad.size_per_thread); - uint8_t * restrict src1_spad_data = actx->octx->src1_spad.data + (ith * actx->octx->src1_spad.size_per_thread); - uint8_t * restrict dst_spad_data = actx->octx->dst_spad.data + (ith * actx->octx->dst_spad.size_per_thread); - - size_t src0_spad_half_size = actx->src0_spad_half_size; - size_t src1_spad_half_size = actx->src1_spad_half_size; - size_t dst_spad_half_size = actx->dst_spad_half_size; - - const int BLOCK = actx->block; - if (BLOCK == 0) { - FARF(ERROR, - "swiglu-oai-f32 : current VTCM reservation %zu is too small for even 1 row per thread, needed at least " - "%zu\n", - actx->octx->src0_spad.size_per_thread, src0_row_size_aligned); - return; - } +// out = x * sigmoid(alpha * x) * (clamp(y, -limit, limit) + 1.f) +static void swiglu_oai_f32(const float * restrict src0, + const float * restrict src1, + float * restrict dst, + const uint32_t num_rows, + const struct htp_act_context * actx) { + htp_glu_op_preamble; const float alpha = ((const float *) (actx->octx->op_params))[2]; const float limit = ((const float *) (actx->octx->op_params))[3]; - dma_queue * dma_queue = actx->octx->ctx->dma[ith]; - - // See discussion: https://github.com/ggml-org/llama.cpp/pull/18151#issuecomment-3678235379 - for (uint32_t ir = src0_start_row, spad_idx = 0; ir < src0_end_row && spad_idx < 2; ir += BLOCK, spad_idx++) { - const uint32_t block_size = MIN(BLOCK, src0_end_row - ir); - - // Dummy DMA transation for sequencing (interleaving dst,src,dst,...) - dma_queue_push_vtcm_to_ddr(dma_queue, dma_make_ptr(data_dst, dst_spad_data + (spad_idx * dst_spad_half_size)), - dst_row_size, dst_row_size_aligned, 0); - - dma_queue_push_ddr_to_vtcm( - dma_queue, - dma_make_ptr(src0_spad_data + (spad_idx * src0_spad_half_size), data_src0 + (ir * src0_row_size)), - src0_row_size_aligned, src0_row_size, block_size); - dma_queue_push_ddr_to_vtcm( - dma_queue, - dma_make_ptr(src1_spad_data + (spad_idx * src1_spad_half_size), data_src1 + (ir * src1_row_size)), - src1_row_size_aligned, src1_row_size, block_size); + for (uint32_t ib = 0; ib < num_rows; ib++) { + const uint8_t * restrict src0_ptr = (const uint8_t *) src0 + (ib * src0_row_size_aligned); + const uint8_t * restrict src1_ptr = (const uint8_t *) src1 + (ib * src1_row_size_aligned); + uint8_t * restrict dst_ptr = (uint8_t *) dst + (ib * dst_row_size_aligned); + + // x (src0_ptr) = std::min(src0_p[k], limit); + hvx_min_scalar_f32((uint8_t *) src0_ptr, src0_ptr, limit, nc); + // y1 (src1_ptr) = std::clamp(src1_p[k], -limit, limit); + hvx_clamp_scalar_f32((uint8_t *) src1_ptr, src1_ptr, -limit, limit, nc); + // y (src1_ptr) = y1 + 1.f + hvx_add_scalar_f32((uint8_t *) src1_ptr, src1_ptr, 1.0, nc); + // x1 (dst_ptr) = alpha * x + hvx_mul_scalar_f32(dst_ptr, src0_ptr, alpha, nc); + // x2 (dst_ptr) = sigmoid(x1) = 1/(1+exp(-x1)) + hvx_sigmoid_f32_aa(dst_ptr, dst_ptr, nc); + // out = x * sigmoid(alpha * x) * (y + 1.f) + hvx_mul_mul_f32_aa(dst_ptr, src0_ptr, dst_ptr, src1_ptr, nc); } - - for (uint32_t ir = src0_start_row; ir < src0_end_row; ir += BLOCK) { - const uint32_t block_size = MIN(BLOCK, src0_end_row - ir); - - float * dst_spad = (float *) dma_queue_pop(dma_queue).src; - float * src0_spad = (float *) dma_queue_pop(dma_queue).dst; - float * src1_spad = (float *) dma_queue_pop(dma_queue).dst; - - for (uint32_t ib = 0; ib < block_size; ib++) { - const float * src0_spad_ptr = src0_spad + ib * (src0_row_size_aligned / sizeof(float)); - const float * src1_spad_ptr = src1_spad + ib * (src1_row_size_aligned / sizeof(float)); - float * dst_spad_ptr = dst_spad + ib * (dst_row_size_aligned / sizeof(float)); - - // x (src0_spad_data) = std::min(src0_p[k], limit); - hvx_min_scalar_f32((uint8_t *) src0_spad_ptr, (const uint8_t *) src0_spad_ptr, limit, nc); - // y1 (src1_spad_data) = std::clamp(src1_p[k], -limit, limit); - hvx_clamp_scalar_f32((uint8_t *) src1_spad_ptr, (const uint8_t *) src1_spad_ptr, -limit, limit, nc); - // y (src1_spad_data) = y1 + 1.f - hvx_add_scalar_f32((uint8_t *) src1_spad_ptr, (const uint8_t *) src1_spad_ptr, 1.0, nc); - // x1 (dst_spad_data) = alpha * (x) - hvx_mul_scalar_f32((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, alpha, nc); - // x2 (dst_spad_data) = sigmoid(x1) = 1/(1+exp(-x1)) - hvx_sigmoid_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) dst_spad_ptr, nc); - // out = x * sigmoid(alpha * x) * (y + 1.f) - hvx_mul_mul_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, (const uint8_t *) dst_spad_ptr, - (const uint8_t *) src1_spad_ptr, nc); - } - - dma_queue_push_vtcm_to_ddr(dma_queue, dma_make_ptr(data_dst + (ir * dst_row_size), dst_spad), dst_row_size, - dst_row_size_aligned, block_size); - - // prefetch N+2 loop iteration if any - const uint32_t pref_block = (ir + BLOCK * 2); - if (pref_block < src0_end_row) { - const uint32_t pref_block_size = MIN(BLOCK, src0_end_row - pref_block); - dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(src0_spad, data_src0 + (pref_block * src0_row_size)), - src0_row_size_aligned, src0_row_size, pref_block_size); - dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(src1_spad, data_src1 + (pref_block * src1_row_size)), - src1_row_size_aligned, src1_row_size, pref_block_size); - } - } - - dma_queue_flush(dma_queue); - - t2 = HAP_perf_get_qtimer_count(); - - FARF(HIGH, "swiglu-oai-f32 %d/%d: %ux%ux%ux%u (%u:%u) x %ux%ux%ux%u -> %ux%ux%ux%u usec %u\n", ith, nth, src0->ne[0], - src0->ne[1], src0->ne[2], src0->ne[3], src0_start_row, src0_end_row, src1->ne[0], src1->ne[1], src1->ne[2], - src1->ne[3], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1)); } +static const float GELU_COEF_A = 0.044715f; +static const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f; -static void unary_gelu_f32_per_thread(unsigned int nth, unsigned int ith, void * data) { - struct htp_act_context * actx = (struct htp_act_context *) data; - htp_act_preamble; - - uint64_t t1, t2; - t1 = HAP_perf_get_qtimer_count(); - - const size_t src0_row_size = actx->src0_row_size; - const size_t dst_row_size = actx->dst_row_size; - const size_t src0_row_size_aligned = actx->src0_row_size_aligned; - const size_t dst_row_size_aligned = actx->dst_row_size_aligned; - - const uint32_t src0_nrows = actx->src0_nrows; - const uint32_t src0_nrows_per_thread = actx->src0_nrows_per_thread; - - const uint32_t src0_start_row = src0_nrows_per_thread * ith; - const uint32_t src0_end_row = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows); - - // no work for this thread - if (src0_start_row >= src0_end_row) { - return; - } - - const uint8_t * data_src0 = actx->data_src0; - uint8_t * data_dst = actx->data_dst; - - // nc/ne0 matches. - const int ne0_val = actx->nc; // == dst->ne[0] - - uint8_t * src0_spad_data = actx->octx->src0_spad.data + (ith * actx->octx->src0_spad.size_per_thread); - uint8_t * dst_spad_data = actx->octx->dst_spad.data + (ith * actx->octx->dst_spad.size_per_thread); - - size_t src0_spad_half_size = actx->src0_spad_half_size; - size_t dst_spad_half_size = actx->dst_spad_half_size; - - // In gelu = x*sigmoid(x*1.702) - const int BLOCK = actx->block; - - if (BLOCK == 0) { - FARF(ERROR, "gelu-f32 : current VTCM reservation %zu is too small for even 1 row per thread, needed at least %zu\n", - actx->octx->src0_spad.size_per_thread, src0_row_size_aligned); - return; - } - - dma_queue * dma_queue = actx->octx->ctx->dma[ith]; - - // See discussion: https://github.com/ggml-org/llama.cpp/pull/18151#issuecomment-3678235379 - for (uint32_t ir = src0_start_row, spad_idx = 0; ir < src0_end_row && spad_idx < 2; ir += BLOCK, spad_idx++) { - const uint32_t block_size = MIN(BLOCK, src0_end_row - ir); - - // Dummy DMA transation for sequencing (interleaving dst,src,dst,...) - dma_queue_push_vtcm_to_ddr(dma_queue, - dma_make_ptr(data_dst, dst_spad_data + (spad_idx * dst_spad_half_size)), - dst_row_size, dst_row_size_aligned, 0); - - dma_queue_push_ddr_to_vtcm(dma_queue, - dma_make_ptr(src0_spad_data + (spad_idx * src0_spad_half_size), data_src0 + (ir * src0_row_size)), - src0_row_size_aligned, src0_row_size, block_size); - } +static inline void hvx_geglu_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src0, const uint8_t * restrict src1, uint32_t n) { + assert((unsigned long) dst % 128 == 0); + assert((unsigned long) src0 % 128 == 0); + assert((unsigned long) src1 % 128 == 0); - for (uint32_t ir = src0_start_row; ir < src0_end_row; ir += BLOCK) { - const uint32_t block_size = MIN(BLOCK, src0_end_row - ir); - - float* dst_spad = (float *) dma_queue_pop(dma_queue).src; - float* src0_spad = (float *) dma_queue_pop(dma_queue).dst; - - for (uint32_t ib = 0; ib < block_size; ib++) { - const float* src0_spad_ptr = src0_spad + ib * (src0_row_size_aligned / sizeof(float)); - float* dst_spad_ptr = dst_spad + ib * (dst_row_size_aligned / sizeof(float)); - - // gelu = x * sigmoid(1.702 * x) // current implementation - hvx_mul_scalar_f32((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, (float) 1.702, ne0_val); - hvx_sigmoid_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) dst_spad_ptr, ne0_val); - hvx_mul_f32_aaa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, (const uint8_t *) dst_spad_ptr, ne0_val); - } - - dma_queue_push_vtcm_to_ddr(dma_queue, - dma_make_ptr(data_dst + (ir * dst_row_size), dst_spad), - dst_row_size, dst_row_size_aligned, block_size); - - // prefetch N+2 loop iteration if any - const uint32_t pref_block = (ir + BLOCK * 2); - if (pref_block < src0_end_row) { - const uint32_t pref_block_size = MIN(BLOCK, src0_end_row - pref_block); - dma_queue_push_ddr_to_vtcm(dma_queue, - dma_make_ptr(src0_spad, data_src0 + (pref_block * src0_row_size)), - src0_row_size_aligned, src0_row_size, pref_block_size); - } - } + HVX_Vector * restrict vdst = (HVX_Vector *) dst; + const HVX_Vector * restrict vsrc0 = (const HVX_Vector *) src0; + const HVX_Vector * restrict vsrc1 = (const HVX_Vector *) src1; - dma_queue_flush(dma_queue); + const uint32_t epv = 128 / sizeof(float); + const uint32_t nvec = n / epv; + const uint32_t nloe = n % epv; - t2 = HAP_perf_get_qtimer_count(); + const float GELU_COEF_A_TIMES_SQRT = GELU_COEF_A * SQRT_2_OVER_PI; - FARF(HIGH, "gelu-f32 %d/%d: %ux%ux%ux%u (%u:%u) -> %ux%ux%ux%u usec %u\n", ith, nth, ne00, ne01, ne02, - ne03, src0_start_row, src0_end_row, ne0, ne1, ne2, ne3, (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1)); -} + const HVX_Vector v_coef_a_times_sqrt = hvx_vec_splat_f32(GELU_COEF_A_TIMES_SQRT); + const HVX_Vector v_sqrt_2_pi = hvx_vec_splat_f32(SQRT_2_OVER_PI); + const HVX_Vector v_half = hvx_vec_splat_f32(0.5f); + const HVX_Vector v_one = hvx_vec_splat_f32(1.0f); + const HVX_Vector v_two = hvx_vec_splat_f32(2.0f); + // Hoisted fast sigmoid / inverse constants to avoid loop-internal overhead + const HVX_Vector v_log2f = Q6_V_vsplat_R(FAST_SIGMOID_LOG2F); + const HVX_Vector v_c1 = Q6_V_vsplat_R(FAST_SIGMOID_C1); + const HVX_Vector v_c2 = Q6_V_vsplat_R(FAST_SIGMOID_C2); + const HVX_Vector v_inv_aprox = Q6_V_vsplat_R(0x7EEEEBB3); + const HVX_Vector v_max_exp = hvx_vec_splat_f32(87.0f); + const HVX_Vector v_min_exp = hvx_vec_splat_f32(-87.0f); -static void unary_silu_f32_per_thread(unsigned int nth, unsigned int ith, void * data) { - struct htp_act_context * actx = (struct htp_act_context *) data; - htp_act_preamble; + uint32_t i = 0; - uint64_t t1, t2; - t1 = HAP_perf_get_qtimer_count(); + for (; i < nvec; i++) { + HVX_Vector x = vsrc0[i]; + HVX_Vector g = vsrc1[i]; - const size_t src0_row_size = actx->src0_row_size; - const size_t dst_row_size = actx->dst_row_size; - const size_t src0_row_size_aligned = actx->src0_row_size_aligned; - const size_t dst_row_size_aligned = actx->dst_row_size_aligned; + HVX_Vector x2 = hvx_vec_mul_f32_f32(x, x); + HVX_Vector coef = hvx_vec_mul_f32_f32(x2, v_coef_a_times_sqrt); + coef = hvx_vec_add_f32_f32(coef, v_sqrt_2_pi); + HVX_Vector inner = hvx_vec_mul_f32_f32(x, coef); - const uint32_t src0_nrows = actx->src0_nrows; - const uint32_t src0_nrows_per_thread = actx->src0_nrows_per_thread; + // y2 = 2 * inner + HVX_Vector y2 = hvx_vec_mul_f32_f32(inner, v_two); - const uint32_t src0_start_row = src0_nrows_per_thread * ith; - const uint32_t src0_end_row = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows); + // Sigmoid guard check predicates + HVX_VectorPred pred_max = Q6_Q_vcmp_gt_VsfVsf(v_max_exp, y2); + HVX_VectorPred pred_min = Q6_Q_vcmp_gt_VsfVsf(y2, v_min_exp); - // no work for this thread - if (src0_start_row >= src0_end_row) { - return; - } + // Fast sigmoid approximation + HVX_Vector v = Q6_Vqf32_vmpy_VsfVsf(y2, v_log2f); + v = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(v), v_half); - const uint8_t * data_src0 = actx->data_src0; - uint8_t * data_dst = actx->data_dst; + HVX_Vector in_int = hvx_vec_truncate_f32(Q6_Vsf_equals_Vqf32(v)); + HVX_Vector x_sig = Q6_Vqf32_vsub_Vqf32Vsf(v, Q6_Vsf_equals_Vw(in_int)); + HVX_Vector xx_sig = Q6_Vqf32_vmpy_Vqf32Vqf32(x_sig, x_sig); - const int ne0_val = actx->nc; // == dst->ne[0] + HVX_Vector v1 = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(xx_sig), v_c2); + v1 = Q6_Vqf32_vadd_Vqf32Vsf(v1, v_log2f); - uint8_t * src0_spad_data = actx->octx->src0_spad.data + (ith * actx->octx->src0_spad.size_per_thread); - uint8_t * dst_spad_data = actx->octx->dst_spad.data + (ith * actx->octx->dst_spad.size_per_thread); + HVX_Vector v2 = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(x_sig), v_c1); + v2 = Q6_Vqf32_vmpy_Vqf32Vqf32(v2, xx_sig); + v2 = Q6_Vqf32_vadd_Vqf32Vqf32(v2, x_sig); - size_t src0_spad_half_size = actx->src0_spad_half_size; - size_t dst_spad_half_size = actx->dst_spad_half_size; + HVX_Vector v3 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vqf32(v2, v1)); + v3 = Q6_Vw_vaslacc_VwVwR(v3, in_int, 24); - const int BLOCK = actx->block; + HVX_Vector v4 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vsub_Vqf32Vqf32(v2, v1)); + HVX_Vector v5 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vsub_VsfVsf(v3, v4)); - if (BLOCK == 0) { - FARF(ERROR, "silu-f32 : current VTCM reservation %zu is too small for even 1 row per thread, needed at least %zu\n", - actx->octx->src0_spad.size_per_thread, src0_row_size_aligned); - return; - } + // Fast division (Newton-Raphson with 2 iterations) + HVX_Vector i_sf = Q6_Vw_vsub_VwVw(v_inv_aprox, v5); + HVX_Vector r_qf = Q6_Vqf32_vmpy_VsfVsf( + i_sf, Q6_Vsf_equals_Vqf32(Q6_Vqf32_vsub_VsfVsf(v_two, Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(i_sf, v5))))); + r_qf = Q6_Vqf32_vmpy_Vqf32Vqf32( + r_qf, Q6_Vqf32_vsub_VsfVsf(v_two, Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(r_qf), v5)))); + HVX_Vector res_inv = Q6_Vsf_equals_Vqf32(r_qf); - dma_queue * dma_queue = actx->octx->ctx->dma[ith]; + HVX_Vector sig2y = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(v3, res_inv)); - // See discussion: https://github.com/ggml-org/llama.cpp/pull/18151#issuecomment-3678235379 - for (uint32_t ir = src0_start_row, spad_idx = 0; ir < src0_end_row && spad_idx < 2; ir += BLOCK, spad_idx++) { - const uint32_t block_size = MIN(BLOCK, src0_end_row - ir); + // Sigmoid guards + sig2y = Q6_V_vmux_QVV(pred_max, sig2y, v_one); + sig2y = Q6_V_vmux_QVV(pred_min, sig2y, Q6_V_vzero()); - // Dummy DMA transation for sequencing (interleaving dst,src,dst,...) - dma_queue_push_vtcm_to_ddr(dma_queue, - dma_make_ptr(data_dst, dst_spad_data + (spad_idx * dst_spad_half_size)), - dst_row_size, dst_row_size_aligned, 0); + // tanh(inner) = 2 * sigmoid(2 * inner) - 1 + HVX_Vector tanh_val = hvx_vec_mul_f32_f32(sig2y, v_two); + tanh_val = hvx_vec_sub_f32_f32(tanh_val, v_one); - dma_queue_push_ddr_to_vtcm(dma_queue, - dma_make_ptr(src0_spad_data + (spad_idx * src0_spad_half_size), data_src0 + (ir * src0_row_size)), - src0_row_size_aligned, src0_row_size, block_size); - } + HVX_Vector tanh_plus_one = hvx_vec_add_f32_f32(tanh_val, v_one); + HVX_Vector half_x = hvx_vec_mul_f32_f32(x, v_half); + HVX_Vector gelu_x = hvx_vec_mul_f32_f32(half_x, tanh_plus_one); - for (uint32_t ir = src0_start_row; ir < src0_end_row; ir += BLOCK) { - const uint32_t block_size = MIN(BLOCK, src0_end_row - ir); - - float* dst_spad = (float *) dma_queue_pop(dma_queue).src; - float* src0_spad = (float *) dma_queue_pop(dma_queue).dst; - - for (uint32_t ib = 0; ib < block_size; ib++) { - const float* src0_spad_ptr = src0_spad + ib * (src0_row_size_aligned / sizeof(float)); - float* dst_spad_ptr = dst_spad + ib * (dst_row_size_aligned / sizeof(float)); - - // silu = x * sigmoid(x) - hvx_sigmoid_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, ne0_val); - hvx_mul_f32_aaa((uint8_t *) dst_spad_ptr, (const uint8_t *) src0_spad_ptr, (const uint8_t *) dst_spad_ptr, ne0_val); - } - - dma_queue_push_vtcm_to_ddr(dma_queue, - dma_make_ptr(data_dst + (ir * dst_row_size), dst_spad), - dst_row_size, dst_row_size_aligned, block_size); - - // prefetch N+2 loop iteration if any - const uint32_t pref_block = (ir + BLOCK * 2); - if (pref_block < src0_end_row) { - const uint32_t pref_block_size = MIN(BLOCK, src0_end_row - pref_block); - dma_queue_push_ddr_to_vtcm(dma_queue, - dma_make_ptr(src0_spad, data_src0 + (pref_block * src0_row_size)), - src0_row_size_aligned, src0_row_size, pref_block_size); - } + vdst[i] = hvx_vec_mul_f32_f32(gelu_x, g); } - dma_queue_flush(dma_queue); + if (nloe) { + HVX_Vector x = vsrc0[i]; + HVX_Vector g = vsrc1[i]; - t2 = HAP_perf_get_qtimer_count(); + HVX_Vector x2 = hvx_vec_mul_f32_f32(x, x); + HVX_Vector coef = hvx_vec_mul_f32_f32(x2, v_coef_a_times_sqrt); + coef = hvx_vec_add_f32_f32(coef, v_sqrt_2_pi); + HVX_Vector inner = hvx_vec_mul_f32_f32(x, coef); - FARF(HIGH, "silu-f32 %d/%d: %ux%ux%ux%u (%u:%u) -> %ux%ux%ux%u usec %u\n", ith, nth, ne00, ne01, ne02, - ne03, src0_start_row, src0_end_row, ne0, ne1, ne2, ne3, (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1)); -} + HVX_Vector y2 = hvx_vec_mul_f32_f32(inner, v_two); -static const float GELU_COEF_A = 0.044715f; -static const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f; + HVX_VectorPred pred_max = Q6_Q_vcmp_gt_VsfVsf(v_max_exp, y2); + HVX_VectorPred pred_min = Q6_Q_vcmp_gt_VsfVsf(y2, v_min_exp); -static void glu_geglu_f32_per_thread(unsigned int nth, unsigned int ith, void * data) { - struct htp_act_context * actx = (struct htp_act_context *) data; - htp_act_preamble; + HVX_Vector v = Q6_Vqf32_vmpy_VsfVsf(y2, v_log2f); + v = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(v), v_half); - size_t src0_row_size = actx->src0_row_size; - size_t src1_row_size = actx->src1_row_size; - size_t dst_row_size = actx->dst_row_size; + HVX_Vector in_int = hvx_vec_truncate_f32(Q6_Vsf_equals_Vqf32(v)); + HVX_Vector x_sig = Q6_Vqf32_vsub_Vqf32Vsf(v, Q6_Vsf_equals_Vw(in_int)); + HVX_Vector xx_sig = Q6_Vqf32_vmpy_Vqf32Vqf32(x_sig, x_sig); - uint64_t t1, t2; - t1 = HAP_perf_get_qtimer_count(); + HVX_Vector v1 = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(xx_sig), v_c2); + v1 = Q6_Vqf32_vadd_Vqf32Vsf(v1, v_log2f); - const uint32_t src0_nrows = actx->src0_nrows; - const uint32_t src0_nrows_per_thread = actx->src0_nrows_per_thread; + HVX_Vector v2 = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(x_sig), v_c1); + v2 = Q6_Vqf32_vmpy_Vqf32Vqf32(v2, xx_sig); + v2 = Q6_Vqf32_vadd_Vqf32Vqf32(v2, x_sig); - const uint32_t src0_start_row = src0_nrows_per_thread * ith; - const uint32_t src0_end_row = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows); + HVX_Vector v3 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vqf32(v2, v1)); + v3 = Q6_Vw_vaslacc_VwVwR(v3, in_int, 24); - // no work for this thread - if (src0_start_row >= src0_end_row) { - return; - } + HVX_Vector v4 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vsub_Vqf32Vqf32(v2, v1)); + HVX_Vector v5 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vsub_VsfVsf(v3, v4)); - const uint8_t * restrict data_src0 = actx->data_src0; - const uint8_t * restrict data_src1 = actx->data_src1; - uint8_t * restrict data_dst = actx->data_dst; + HVX_Vector i_sf = Q6_Vw_vsub_VwVw(v_inv_aprox, v5); + HVX_Vector r_qf = Q6_Vqf32_vmpy_VsfVsf( + i_sf, Q6_Vsf_equals_Vqf32(Q6_Vqf32_vsub_VsfVsf(v_two, Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(i_sf, v5))))); + r_qf = Q6_Vqf32_vmpy_Vqf32Vqf32( + r_qf, Q6_Vqf32_vsub_VsfVsf(v_two, Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(r_qf), v5)))); + HVX_Vector res_inv = Q6_Vsf_equals_Vqf32(r_qf); - const int nc = actx->nc; + HVX_Vector sig2y = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(v3, res_inv)); - const size_t src0_row_size_aligned = actx->src0_row_size_aligned; - const size_t src1_row_size_aligned = actx->src1_row_size_aligned; - const size_t dst_row_size_aligned = actx->dst_row_size_aligned; + sig2y = Q6_V_vmux_QVV(pred_max, sig2y, v_one); + sig2y = Q6_V_vmux_QVV(pred_min, sig2y, Q6_V_vzero()); - uint8_t * restrict src0_spad_data = actx->octx->src0_spad.data + (ith * actx->octx->src0_spad.size_per_thread); - uint8_t * restrict src1_spad_data = actx->octx->src1_spad.data + (ith * actx->octx->src1_spad.size_per_thread); - uint8_t * restrict dst_spad_data = actx->octx->dst_spad.data + (ith * actx->octx->dst_spad.size_per_thread); + HVX_Vector tanh_val = hvx_vec_mul_f32_f32(sig2y, v_two); + tanh_val = hvx_vec_sub_f32_f32(tanh_val, v_one); - size_t src0_spad_half_size = actx->src0_spad_half_size; - size_t src1_spad_half_size = actx->src1_spad_half_size; - size_t dst_spad_half_size = actx->dst_spad_half_size; + HVX_Vector tanh_plus_one = hvx_vec_add_f32_f32(tanh_val, v_one); + HVX_Vector half_x = hvx_vec_mul_f32_f32(x, v_half); + HVX_Vector gelu_x = hvx_vec_mul_f32_f32(half_x, tanh_plus_one); - const int BLOCK = actx->block; - if (BLOCK == 0) { - FARF(ERROR, - "geglu-f32 : current VTCM reservation %zu is too small for even 1 row per thread, needed at least %zu\n", - actx->octx->src0_spad.size_per_thread, src0_row_size_aligned); - return; + HVX_Vector res = hvx_vec_mul_f32_f32(gelu_x, g); + hvx_vec_store_a((void *) &vdst[i], nloe * sizeof(float), res); } +} - dma_queue * dma_queue = actx->octx->ctx->dma[ith]; - - // See discussion: https://github.com/ggml-org/llama.cpp/pull/18151#issuecomment-3678235379 - for (uint32_t ir = src0_start_row, spad_idx = 0; ir < src0_end_row && spad_idx < 2; ir += BLOCK, spad_idx++) { - const uint32_t block_size = MIN(BLOCK, src0_end_row - ir); +// geglu(x, g) = gelu(x) * g +static void geglu_f32(const float * restrict src0, + const float * restrict src1, + float * restrict dst, + const uint32_t num_rows, + const struct htp_act_context * actx) { + htp_glu_op_preamble; - // Dummy DMA transation for sequencing (interleaving dst,src,dst,...) - dma_queue_push_vtcm_to_ddr(dma_queue, - dma_make_ptr(data_dst, dst_spad_data + (spad_idx * dst_spad_half_size)), - dst_row_size, dst_row_size_aligned, 0); + for (uint32_t ib = 0; ib < num_rows; ib++) { + const uint8_t * restrict src0_ptr = (const uint8_t *) src0 + (ib * src0_row_size_aligned); + const uint8_t * restrict src1_ptr = (const uint8_t *) src1 + (ib * src1_row_size_aligned); + uint8_t * restrict dst_ptr = (uint8_t *) dst + (ib * dst_row_size_aligned); - dma_queue_push_ddr_to_vtcm(dma_queue, - dma_make_ptr(src0_spad_data + (spad_idx * src0_spad_half_size), data_src0 + (ir * src0_row_size)), - src0_row_size_aligned, src0_row_size, block_size); - dma_queue_push_ddr_to_vtcm(dma_queue, - dma_make_ptr(src1_spad_data + (spad_idx * src1_spad_half_size), data_src1 + (ir * src1_row_size)), - src1_row_size_aligned, src1_row_size, block_size); + hvx_geglu_f32_aa(dst_ptr, src0_ptr, src1_ptr, nc); } +} - for (uint32_t ir = src0_start_row; ir < src0_end_row; ir += BLOCK) { - const uint32_t block_size = MIN(BLOCK, src0_end_row - ir); - - float * dst_spad = (float *) dma_queue_pop(dma_queue).src; - float * src0_spad = (float *) dma_queue_pop(dma_queue).dst; - float * src1_spad = (float *) dma_queue_pop(dma_queue).dst; - - for (uint32_t ib = 0; ib < block_size; ib++) { - const uint8_t * src0_spad_ptr = (const uint8_t *)(src0_spad + ib * (src0_row_size_aligned / sizeof(float))); - const uint8_t * src1_spad_ptr = (const uint8_t *)(src1_spad + ib * (src1_row_size_aligned / sizeof(float))); - uint8_t * dst_spad_ptr = (uint8_t *)(dst_spad + ib * (dst_row_size_aligned / sizeof(float))); - - // geglu tanh implementation - // geglu(x, g) = gelu(x) * g - // gelu(x) = 0.5f*x*(1.0f + tanhf(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x))) - hvx_mul_f32_aaa(dst_spad_ptr, src0_spad_ptr, src0_spad_ptr, nc); // res = x*x - hvx_mul_scalar_f32_aa(dst_spad_ptr, (const uint8_t *)dst_spad_ptr, GELU_COEF_A, nc); // res = res * GELU_COEF_A - hvx_add_scalar_f32_aa(dst_spad_ptr, (const uint8_t *)dst_spad_ptr, 1.0f, nc); // res = res + 1.0f - hvx_mul_f32_aaa(dst_spad_ptr, src0_spad_ptr, (const uint8_t *)dst_spad_ptr, nc); // res = res * x - hvx_mul_scalar_f32_aa(dst_spad_ptr, (const uint8_t*)dst_spad_ptr, SQRT_2_OVER_PI, nc); // res = result * SQRT_2_OVER_PI - hvx_tanh_f32_aa((uint8_t *) dst_spad_ptr, (const uint8_t *) dst_spad_ptr, nc); // res = tanh(res) - hvx_add_scalar_f32_aa(dst_spad_ptr, (const uint8_t*)dst_spad_ptr, 1.0f, nc); // res = res + 1.0f - hvx_mul_f32_aaa(dst_spad_ptr, src0_spad_ptr, (const uint8_t *)dst_spad_ptr, nc); // res = res * x - hvx_mul_scalar_f32_aa(dst_spad_ptr, (const uint8_t *)dst_spad_ptr, 0.5f, nc); // res = res + 0.5f - hvx_mul_f32_aaa(dst_spad_ptr, (const uint8_t *)dst_spad_ptr, src1_spad_ptr, nc); // res = res * g - } - - dma_queue_push_vtcm_to_ddr(dma_queue, dma_make_ptr(data_dst + (ir * dst_row_size), dst_spad), dst_row_size, - dst_row_size_aligned, block_size); - - // prefetch N+2 loop iteration if any - const uint32_t pref_block = (ir + BLOCK * 2); - if (pref_block < src0_end_row) { - const uint32_t pref_block_size = MIN(BLOCK, src0_end_row - pref_block); - dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(src0_spad, data_src0 + (pref_block * src0_row_size)), - src0_row_size_aligned, src0_row_size, pref_block_size); - dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(src1_spad, data_src1 + (pref_block * src1_row_size)), - src1_row_size_aligned, src1_row_size, pref_block_size); - } +#define DEFINE_GLU_PER_THREAD(NAME, OP_STR, CORE_EXPR) \ + static void glu_##NAME##_f32_per_thread(unsigned int nth, unsigned int ith, void * data) { \ + struct htp_act_context * actx = (struct htp_act_context *) data; \ + htp_act_preamble; \ + \ + struct htp_thread_trace * tr = actx->octx->ctx ? &actx->octx->ctx->trace[ith] : NULL; \ + \ + size_t src0_row_size = actx->src0_row_size; \ + size_t src1_row_size = actx->src1_row_size; \ + size_t dst_row_size = actx->dst_row_size; \ + \ + size_t src0_row_stride = actx->src0_row_stride; \ + size_t src1_row_stride = actx->src1_row_stride; \ + \ + const uint32_t src0_nrows = actx->src0_nrows; \ + const uint32_t src0_nrows_per_thread = actx->src0_nrows_per_thread; \ + \ + const uint32_t src0_start_row = src0_nrows_per_thread * ith; \ + const uint32_t src0_end_row = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows); \ + \ + /* no work for this thread */ \ + if (src0_start_row >= src0_end_row) { \ + return; \ + } \ + \ + const uint8_t * restrict data_src0 = actx->data_src0; \ + const uint8_t * restrict data_src1 = actx->data_src1; \ + uint8_t * restrict data_dst = actx->data_dst; \ + \ + const size_t src0_row_size_aligned = actx->src0_row_size_aligned; \ + const size_t src1_row_size_aligned = actx->src1_row_size_aligned; \ + const size_t dst_row_size_aligned = actx->dst_row_size_aligned; \ + \ + uint8_t * restrict src0_spad_data = actx->vtcm_src0 + (ith * actx->vtcm_src0_size_per_thread); \ + uint8_t * restrict src1_spad_data = actx->vtcm_src1 + (ith * actx->vtcm_src1_size_per_thread); \ + uint8_t * restrict dst_spad_data = actx->vtcm_dst + (ith * actx->vtcm_dst_size_per_thread); \ + \ + size_t src0_spad_half_size = actx->src0_spad_half_size; \ + size_t src1_spad_half_size = actx->src1_spad_half_size; \ + size_t dst_spad_half_size = actx->dst_spad_half_size; \ + \ + const int BLOCK = actx->block; \ + if (BLOCK == 0) { \ + FARF(ERROR, \ + OP_STR \ + " : current VTCM reservation %zu is too small for even 1 row per thread, needed at least %zu\n", \ + actx->vtcm_src0_size_per_thread, src0_row_size_aligned); \ + return; \ + } \ + \ + dma_queue * dma_queue = actx->octx->ctx->dma[ith]; \ + \ + /* See discussion: https://github.com/ggml-org/llama.cpp/pull/18151#issuecomment-3678235379 */ \ + for (uint32_t ir = src0_start_row, spad_idx = 0; ir < src0_end_row && spad_idx < 2; ir += BLOCK, spad_idx++) { \ + const uint32_t block_size = MIN(BLOCK, src0_end_row - ir); \ + \ + /* Dummy DMA transation for sequencing (interleaving dst,src,dst,...) */ \ + dma_queue_push_vtcm_to_ddr(dma_queue, \ + dma_make_ptr(data_dst, dst_spad_data + (spad_idx * dst_spad_half_size)), \ + dst_row_size, dst_row_size_aligned, 0); \ + \ + dma_queue_push( \ + dma_queue, \ + dma_make_ptr(src0_spad_data + (spad_idx * src0_spad_half_size), data_src0 + (ir * src0_row_stride)), \ + src0_row_size_aligned, src0_row_stride, src0_row_size, block_size); \ + dma_queue_push( \ + dma_queue, \ + dma_make_ptr(src1_spad_data + (spad_idx * src1_spad_half_size), data_src1 + (ir * src1_row_stride)), \ + src1_row_size_aligned, src1_row_stride, src1_row_size, block_size); \ + } \ + \ + for (uint32_t ir = src0_start_row; ir < src0_end_row; ir += BLOCK) { \ + const uint32_t block_size = MIN(BLOCK, src0_end_row - ir); \ + \ + float * dst_spad = (float *) dma_queue_pop(dma_queue).src; \ + float * src0_spad = (float *) dma_queue_pop(dma_queue).dst; \ + float * src1_spad = (float *) dma_queue_pop(dma_queue).dst; \ + \ + htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ir); \ + CORE_EXPR; \ + htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ir); \ + \ + dma_queue_push_vtcm_to_ddr(dma_queue, dma_make_ptr(data_dst + (ir * dst_row_size), dst_spad), \ + dst_row_size, dst_row_size_aligned, block_size); \ + \ + /* prefetch N+2 loop iteration if any */ \ + const uint32_t pref_block = (ir + BLOCK * 2); \ + if (pref_block < src0_end_row) { \ + const uint32_t pref_block_size = MIN(BLOCK, src0_end_row - pref_block); \ + dma_queue_push(dma_queue, dma_make_ptr(src0_spad, data_src0 + (pref_block * src0_row_stride)), \ + src0_row_size_aligned, src0_row_stride, src0_row_size, pref_block_size); \ + dma_queue_push(dma_queue, dma_make_ptr(src1_spad, data_src1 + (pref_block * src1_row_stride)), \ + src1_row_size_aligned, src1_row_stride, src1_row_size, pref_block_size); \ + } \ + } \ + \ + dma_queue_flush(dma_queue); \ + \ } - dma_queue_flush(dma_queue); - - t2 = HAP_perf_get_qtimer_count(); - - FARF(HIGH, "geglu-f32 %d/%d: %ux%ux%ux%u (%u:%u) x %ux%ux%ux%u -> %ux%ux%ux%u usec %u\n", ith, nth, - ne00, ne01, ne02, ne03, src0_start_row, src0_end_row, ne10, ne11, ne12, ne13, ne0, ne1, ne2, ne3, - (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1)); -} +DEFINE_GLU_PER_THREAD(swiglu, "swiglu-f32", swiglu_f32(src0_spad, src1_spad, dst_spad, block_size, actx)) +DEFINE_GLU_PER_THREAD(swiglu_oai, "swiglu-oai-f32", swiglu_oai_f32(src0_spad, src1_spad, dst_spad, block_size, actx)) +DEFINE_GLU_PER_THREAD(geglu, "geglu-f32", geglu_f32(src0_spad, src1_spad, dst_spad, block_size, actx)) static int execute_op_activations_f32(struct htp_ops_context * octx) { const struct htp_tensor * src0 = octx->src[0]; const struct htp_tensor * src1 = octx->src[1]; const struct htp_tensor * dst = octx->dst; - if (((src0->ne[0] * SIZEOF_FP32) != src0->nb[1]) || ((dst->ne[0] * SIZEOF_FP32) != dst->nb[1])) { - FARF(ERROR, "Non-contiguous tensors are not supported at this time \n"); + if ((dst->ne[0] * SIZEOF_FP32) != dst->nb[1]) { + FARF(ERROR, "Non-contiguous dst is not supported at this time \n"); return HTP_STATUS_NO_SUPPORT; } @@ -635,11 +469,6 @@ static int execute_op_activations_f32(struct htp_ops_context * octx) { const char * op_type = NULL; switch (octx->op) { - case HTP_OP_UNARY_SILU: - act_op_func = (worker_callback_t)unary_silu_f32_per_thread; - op_type = "silu-f32"; - break; - case HTP_OP_GLU_SWIGLU: act_op_func = (worker_callback_t)glu_swiglu_f32_per_thread; op_type = "swiglu-f32"; @@ -649,10 +478,6 @@ static int execute_op_activations_f32(struct htp_ops_context * octx) { act_op_func = (worker_callback_t)glu_swiglu_oai_f32_per_thread; op_type = "swiglu-oai-f32"; break; - case HTP_OP_UNARY_GELU: - act_op_func = (worker_callback_t)unary_gelu_f32_per_thread; - op_type = "gelu-f32"; - break; case HTP_OP_GLU_GEGLU: act_op_func = (worker_callback_t)glu_geglu_f32_per_thread; @@ -666,51 +491,39 @@ static int execute_op_activations_f32(struct htp_ops_context * octx) { const uint32_t src0_nrows = src0->ne[1] * src0->ne[2] * src0->ne[3]; const uint32_t n_threads = MIN(octx->n_threads, src0_nrows); - size_t src0_row_size = src0->nb[1]; - size_t src1_row_size = src1 ? src1->nb[1] : src0->nb[1]; - size_t dst_row_size = dst->nb[1]; + // row_size = bytes of useful data per row (what the kernel touches / what DMA copies). + // row_stride = bytes between successive rows in DDR (may exceed row_size for non-contig src). + const size_t nc_bytes = dst->ne[0] * SIZEOF_FP32; + const size_t src0_row_size = nc_bytes; + const size_t src1_row_size = nc_bytes; + const size_t dst_row_size = nc_bytes; + const size_t src0_row_stride = src0->nb[1]; + const size_t src1_row_stride = src1 ? src1->nb[1] : src0->nb[1]; const size_t src0_row_size_aligned = hex_round_up(src0_row_size, VLEN); const size_t src1_row_size_aligned = hex_round_up(src1_row_size, VLEN); const size_t dst_row_size_aligned = hex_round_up(dst_row_size, VLEN); - // VTCM scratchpads for all tensors - // N rows per thread, padded to HVX vector size - size_t spad_size_per_row = (src0_row_size_aligned + src1_row_size_aligned) + dst_row_size_aligned; - size_t vtcm_row_per_thread = (octx->ctx->vtcm_size)/ (n_threads* spad_size_per_row); + struct htp_act_vtcm_layout L; + htp_act_vtcm_layout_build(&L, src0_row_size_aligned, src1_row_size_aligned, dst_row_size_aligned, n_threads, + octx->ctx->vtcm_size); // Make sure the reserved vtcm size is sufficient - if (vtcm_row_per_thread == 0) { + if (L.vtcm_row_per_thread == 0) { FARF(ERROR, "act-%s : current VTCM reservation %zu is too small for even 1 row per thread, needed at least %zu\n", op_type, octx->ctx->vtcm_size, - spad_size_per_row * n_threads); + (src0_row_size_aligned + src1_row_size_aligned + dst_row_size_aligned) * n_threads); return HTP_STATUS_VTCM_TOO_SMALL; } - octx->src0_spad.size_per_thread = src0_row_size_aligned * vtcm_row_per_thread; - octx->src1_spad.size_per_thread = src1_row_size_aligned * vtcm_row_per_thread; - octx->dst_spad.size_per_thread = dst_row_size_aligned * vtcm_row_per_thread; - - octx->dst_spad.size = n_threads* octx->dst_spad.size_per_thread; - octx->src0_spad.size = n_threads* octx->src0_spad.size_per_thread; - octx->src1_spad.size = n_threads* octx->src1_spad.size_per_thread; - - octx->src0_spad.data = octx->ctx->vtcm_base; - octx->src1_spad.data = octx->src0_spad.data + octx->src0_spad.size; - octx->dst_spad.data = octx->src1_spad.data + octx->src1_spad.size; - - octx->src0_spad.src = NULL; - octx->src1_spad.src = NULL; - octx->dst_spad.src = NULL; - if (src1) { - FARF(HIGH, "%s: %ux%ux%ux%u x %ux%ux%ux%u -> %ux%ux%ux%u : src0-spad-size %u src1-spad-size %u dst-spad-size %u\n", + FARF(HIGH, "%s: %ux%ux%ux%u x %ux%ux%ux%u -> %ux%ux%ux%u : src0-vtcm-size %zu src1-vtcm-size %zu dst-vtcm-size %zu\n", op_type, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], src1->ne[0], src1->ne[1], src1->ne[2], - src1->ne[3], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], octx->src0_spad.size, octx->src1_spad.size, - octx->dst_spad.size); + src1->ne[3], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], L.src0_bytes_per_thread * n_threads, + L.src1_bytes_per_thread * n_threads, L.dst_bytes_per_thread * n_threads); } else { - FARF(HIGH, "%s: %ux%ux%ux%u -> %ux%ux%ux%u : src0-spad-size %u src1-spad-size %u dst-spad-size %u\n", op_type, + FARF(HIGH, "%s: %ux%ux%ux%u -> %ux%ux%ux%u : src0-vtcm-size %zu src1-vtcm-size %zu dst-vtcm-size %zu\n", op_type, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], - octx->src0_spad.size, octx->src1_spad.size, octx->dst_spad.size); + L.src0_bytes_per_thread * n_threads, L.src1_bytes_per_thread * n_threads, L.dst_bytes_per_thread * n_threads); } if ((octx->flags & HTP_OPFLAGS_SKIP_COMPUTE)) { @@ -731,9 +544,21 @@ static int execute_op_activations_f32(struct htp_ops_context * octx) { actx.src1_row_size_aligned = src1_row_size_aligned; actx.dst_row_size_aligned = dst_row_size_aligned; - actx.src0_spad_half_size = octx->src0_spad.size_per_thread / 2; - actx.src1_spad_half_size = octx->src1_spad.size_per_thread / 2; - actx.dst_spad_half_size = octx->dst_spad.size_per_thread / 2; + actx.src0_row_stride = src0_row_stride; + actx.src1_row_stride = src1_row_stride; + + uint8_t * const base = (uint8_t *) octx->ctx->vtcm_base; + actx.vtcm_src0 = VTCM_LAYOUT_PTR(uint8_t, base, L.off_src0); + actx.vtcm_src1 = VTCM_LAYOUT_PTR(uint8_t, base, L.off_src1); + actx.vtcm_dst = VTCM_LAYOUT_PTR(uint8_t, base, L.off_dst); + + actx.vtcm_src0_size_per_thread = L.src0_bytes_per_thread; + actx.vtcm_src1_size_per_thread = L.src1_bytes_per_thread; + actx.vtcm_dst_size_per_thread = L.dst_bytes_per_thread; + + actx.src0_spad_half_size = L.src0_bytes_per_thread / 2; + actx.src1_spad_half_size = L.src1_bytes_per_thread / 2; + actx.dst_spad_half_size = L.dst_bytes_per_thread / 2; actx.block = actx.src0_spad_half_size / actx.src0_row_size_aligned; actx.src0_nrows = src0_nrows; @@ -766,17 +591,11 @@ static int execute_op_activations_f32(struct htp_ops_context * octx) { } int op_activations(struct htp_ops_context * octx) { - int err = HTP_STATUS_OK; - switch (octx->src[0]->type) { case HTP_TYPE_F32: - err = execute_op_activations_f32(octx); - break; + return execute_op_activations_f32(octx); default: - err = HTP_STATUS_NO_SUPPORT; - break; + return HTP_STATUS_NO_SUPPORT; } - - return err; } diff --git a/ggml/src/ggml-hexagon/htp/argsort-ops.c b/ggml/src/ggml-hexagon/htp/argsort-ops.c index 13f12a39e685..774faef5f388 100644 --- a/ggml/src/ggml-hexagon/htp/argsort-ops.c +++ b/ggml/src/ggml-hexagon/htp/argsort-ops.c @@ -345,7 +345,7 @@ static void htp_argsort_f32_##ne00##_##order_name(unsigned int n, unsigned int i int32_t * indices_buf = (int32_t *) (spad + values_size); \ uint32_t nb01 = src0->nb[1]; \ uint32_t nb1 = dst->nb[1]; \ - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[i] : NULL; \ + struct htp_thread_trace * tr = &octx->ctx->trace[i]; \ htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, start_row); \ for (uint32_t r = start_row; r < end_row; r++) { \ uint32_t src_offset = r * nb01; \ @@ -411,7 +411,7 @@ static void htp_argsort_f32_fallback(unsigned int n, unsigned int i, void * data const HVX_Vector ind_init_vec = *(HVX_Vector *)argosrt_ramp_lut; const HVX_Vector ind_diff_vec = Q6_V_vsplat_R(32); - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[i] : NULL; + struct htp_thread_trace * tr = &octx->ctx->trace[i]; htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, start_row); for (uint32_t r = start_row; r < end_row; r++) { diff --git a/ggml/src/ggml-hexagon/htp/binary-ops.c b/ggml/src/ggml-hexagon/htp/binary-ops.c index 52013ad0fec5..db6177963541 100644 --- a/ggml/src/ggml-hexagon/htp/binary-ops.c +++ b/ggml/src/ggml-hexagon/htp/binary-ops.c @@ -16,6 +16,7 @@ #include "htp-ctx.h" #include "htp-ops.h" #include "htp-ops.h" +#include "htp-tensor.h" #ifndef MIN #define MIN(a, b) ((a) < (b) ? (a) : (b)) diff --git a/ggml/src/ggml-hexagon/htp/cumsum-ops.c b/ggml/src/ggml-hexagon/htp/cumsum-ops.c index 2ced19712362..2d45c39f23b5 100644 --- a/ggml/src/ggml-hexagon/htp/cumsum-ops.c +++ b/ggml/src/ggml-hexagon/htp/cumsum-ops.c @@ -9,6 +9,7 @@ #include "ggml-common.h" #include "htp-ctx.h" #include "htp-ops.h" +#include "htp-tensor.h" #include "hvx-types.h" #include "hvx-utils.h" #include "hex-dma.h" @@ -255,16 +256,10 @@ int op_cumsum_f32(struct htp_ops_context * octx) { int op_cumsum(struct htp_ops_context * octx) { const struct htp_tensor * dst = octx->dst; - int err = HTP_STATUS_OK; - switch (dst->type) { case HTP_TYPE_F32: - err = op_cumsum_f32(octx); - break; + return op_cumsum_f32(octx); default: - err = HTP_STATUS_NO_SUPPORT; - break; + return HTP_STATUS_NO_SUPPORT; } - - return err; } diff --git a/ggml/src/ggml-hexagon/htp/dma-queue.c b/ggml/src/ggml-hexagon/htp/dma-queue.c new file mode 100644 index 000000000000..4beded1de508 --- /dev/null +++ b/ggml/src/ggml-hexagon/htp/dma-queue.c @@ -0,0 +1,104 @@ +#include "dma-queue.h" + +#include +#include +#include + +#pragma clang diagnostic ignored "-Wunused-function" + +static inline uint32_t pow2_ceil(uint32_t x) { + if (x <= 1) { + return 1; + } + int p = 2; + x--; + while (x >>= 1) { + p <<= 1; + } + return p; +} + +static inline uintptr_t align_up(uintptr_t addr, size_t align) { + return (addr + align - 1) & ~(align - 1); +} + +size_t dma_queue_sizeof(size_t capacity) { + capacity = pow2_ceil(capacity); + + size_t size_q = sizeof(dma_queue); + size_t offset_r = align_up(size_q, HEX_L2_LINE_SIZE); + size_t size_r = sizeof(dma_ring); + size_t offset_desc = align_up(offset_r + size_r, HEX_L2_LINE_SIZE); + size_t size_desc = capacity * sizeof(dma_descriptor_2d); + size_t offset_dptr = align_up(offset_desc + size_desc, HEX_L2_LINE_SIZE); + size_t size_dptr = capacity * sizeof(dma_ptr); + + return offset_dptr + size_dptr; +} + +size_t dma_queue_alignof(void) { + return HEX_L2_LINE_SIZE; +} + +dma_queue_t dma_queue_init(void * ptr, size_t capacity, uintptr_t vtcm_base, size_t vtcm_size, struct htp_thread_trace * trace) { + capacity = pow2_ceil(capacity); + + size_t size_q = sizeof(dma_queue); + size_t offset_r = align_up(size_q, HEX_L2_LINE_SIZE); + size_t size_r = sizeof(dma_ring); + size_t offset_desc = align_up(offset_r + size_r, HEX_L2_LINE_SIZE); + size_t size_desc = capacity * sizeof(dma_descriptor_2d); + size_t offset_dptr = align_up(offset_desc + size_desc, HEX_L2_LINE_SIZE); + size_t size_dptr = capacity * sizeof(dma_ptr); + + size_t total_size = offset_dptr + size_dptr; + memset(ptr, 0, total_size); + + dma_queue * q = (dma_queue *) ptr; + dma_ring * r = (dma_ring *) ((uintptr_t) ptr + offset_r); + + q->ring = r; + q->nocache = 0; + q->alias = false; + + r->trace = trace; + r->vtcm_base = vtcm_base; + r->vtcm_end = vtcm_base + vtcm_size; + r->capacity = capacity; + r->idx_mask = capacity - 1; + r->push_idx = 0; + r->pop_idx = 0; + + r->desc = (dma_descriptor_2d *) ((uintptr_t) ptr + offset_desc); + r->dptr = (dma_ptr *) ((uintptr_t) ptr + offset_dptr); + r->tail = &r->desc[capacity - 1]; + + FARF(HIGH, "dma-queue: capacity %u, unified memory size %zu\n", capacity, total_size); + + return q; +} + +void dma_queue_free(dma_queue_t q) { + (void) q; +} + +size_t dma_queue_alias_sizeof(void) { + return sizeof(dma_queue); +} + +dma_queue_t dma_queue_alias_init(void * ptr, dma_queue_t main_q, uint8_t nocache) { + dma_queue * q = (dma_queue *) ptr; + memset(q, 0, sizeof(dma_queue)); + + q->ring = main_q->ring; + q->nocache = nocache; + q->alias = true; + + return q; +} + +void dma_queue_alias_free(dma_queue_t q) { + (void) q; +} + + diff --git a/ggml/src/ggml-hexagon/htp/dma-queue.h b/ggml/src/ggml-hexagon/htp/dma-queue.h new file mode 100644 index 000000000000..264284bda828 --- /dev/null +++ b/ggml/src/ggml-hexagon/htp/dma-queue.h @@ -0,0 +1,407 @@ +#ifndef HTP_DMA_H +#define HTP_DMA_H + +#include +#include +#include +#include +#include "hex-utils.h" + +#include "hex-profile.h" + +#ifdef __cplusplus +extern "C" { +#endif + +// Define the HW descriptor structs here since the ones in HexSDK are a bit out of date +typedef struct dma_descriptor_1d_s { + void * next; + uint32_t size:24; + uint32_t desc_size:2; + uint32_t dst_comp:1; + uint32_t src_comp:1; + uint32_t dst_bypass:1; + uint32_t src_bypass:1; + uint32_t order:1; + uint32_t done:1; + void * src; + void * dst; +} dma_descriptor_1d; + +#if __HVX_ARCH__ < 75 + +typedef struct dma_descriptor_2d_s { + void * next; + uint32_t reserved0:24; + uint32_t desc_size:2; + uint32_t dst_comp:1; + uint32_t src_comp:1; + uint32_t dst_bypass:1; + uint32_t src_bypass:1; + uint32_t order:1; + uint32_t done:1; + void * src; + void * dst; + uint32_t desc_type:8; + uint32_t reserved1:24; + uint32_t row_size:16; + uint32_t nrows:16; + uint32_t src_stride:16; + uint32_t dst_stride:16; + uint32_t src_offset:16; + uint32_t dst_offset:16; +} dma_descriptor_2d; + +#else + +typedef struct dma_descriptor_2d_s { + void * next; + uint32_t dst_stride:24; + uint32_t desc_size:2; + uint32_t dst_comp:1; + uint32_t src_comp:1; + uint32_t dst_bypass:1; + uint32_t src_bypass:1; + uint32_t order:1; + uint32_t done:1; + void * src; + void * dst; + uint32_t desc_type:8; + uint32_t reserved0:24; + uint32_t row_size:24; + uint32_t nrows_lo:8; + uint32_t nrows_hi:8; + uint32_t src_stride:24; + uint32_t offset:24; + uint32_t reserved1:8; +} dma_descriptor_2d; + +#endif + +typedef struct { + void *dst; + const void *src; +} dma_ptr; + +typedef struct dma_ring_s dma_ring; +struct dma_ring_s { + dma_descriptor_2d * desc; // descriptor pointers + dma_descriptor_2d * tail; // tail pointer + dma_ptr * dptr; // dst/src pointers + uint32_t push_idx; + uint32_t pop_idx; + uint32_t capacity; + uint32_t idx_mask; + struct htp_thread_trace * trace; + uintptr_t vtcm_base; + uintptr_t vtcm_end; +}; + +typedef struct dma_queue_s dma_queue; +typedef dma_queue * dma_queue_t; + +struct dma_queue_s { + dma_ring * ring; // Points to the descriptor ring state + uint8_t nocache; // Queue-specific bypass flag + bool alias; // When set, dma_queue_delete will not free the ring +}; + + + +size_t dma_queue_sizeof(size_t capacity); +size_t dma_queue_alignof(void); +dma_queue_t dma_queue_init(void * ptr, size_t capacity, uintptr_t vtcm_base, size_t vtcm_size, struct htp_thread_trace * trace); +void dma_queue_free(dma_queue_t q); + +size_t dma_queue_alias_sizeof(void); +dma_queue_t dma_queue_alias_init(void * ptr, dma_queue_t main_q, uint8_t nocache); +void dma_queue_alias_free(dma_queue_t q); + +// TODO: technically we don't need these and could use Q6_dmstart/wait/etc instead +// but those do not seem to always compiler properly. +static inline void dmstart(void * next) { + asm volatile(" release(%0):at" : : "r"(next)); + asm volatile(" dmstart(%0)" : : "r"(next)); +} + +static inline void dmlink(void * cur, void * next) { + asm volatile(" release(%0):at" : : "r"(next)); + asm volatile(" dmlink(%0, %1)" : : "r"(cur), "r"(next)); +} + +static inline unsigned int dmpoll(void) { + unsigned int ret = 0; + asm volatile(" %0 = dmpoll" : "=r"(ret) : : "memory"); + return ret; +} + +static inline unsigned int dmwait(void) { + unsigned int ret = 0; + asm volatile(" %0 = dmwait" : "=r"(ret) : : "memory"); + return ret; +} + +static inline dma_ptr dma_make_ptr(void *dst, const void *src) +{ + dma_ptr p = { dst, src }; + return p; +} + +static inline bool dma_is_vtcm(const dma_queue * q, const void * ptr) { + return (uintptr_t) ptr >= q->ring->vtcm_base && (uintptr_t) ptr < q->ring->vtcm_end; +} + +static inline bool dma_queue_push_single_1d(dma_queue * q, dma_ptr dptr, size_t size) { + dma_ring * r = q->ring; + if (((r->push_idx + 1) & r->idx_mask) == r->pop_idx) { + return false; + } + + dma_descriptor_1d * desc = (dma_descriptor_1d *) &r->desc[r->push_idx]; + desc->src = (void *) dptr.src; + desc->dst = (void *) dptr.dst; + desc->size = size; + + r->dptr[r->push_idx] = dptr; + + htp_trace_event_start(r->trace, HTP_TRACE_EVT_DMA, r->push_idx); + + if (size) { + desc->next = NULL; + desc->desc_size = 0; // 1D mode + desc->src_bypass = dma_is_vtcm(q, dptr.src) ? 1 : q->nocache; + desc->dst_bypass = dma_is_vtcm(q, dptr.dst) ? 1 : q->nocache; + desc->order = 0; + desc->done = 0; + + dmlink(r->tail, desc); + r->tail = (dma_descriptor_2d *) desc; + } else { + desc->desc_size = 0; + desc->done = 1; + } + + r->push_idx = (r->push_idx + 1) & r->idx_mask; + return true; +} + +static inline bool dma_queue_push_single_2d(dma_queue * q, dma_ptr dptr, size_t dst_stride, size_t src_stride, size_t row_size, size_t nrows) { + dma_ring * r = q->ring; + if (((r->push_idx + 1) & r->idx_mask) == r->pop_idx) { + return false; + } + + dma_descriptor_2d * desc = &r->desc[r->push_idx]; + + desc->next = NULL; + desc->reserved0 = 0; + desc->reserved1 = 0; + desc->desc_size = 1; // 2d mode + desc->src_bypass = dma_is_vtcm(q, dptr.src) ? 1 : q->nocache; + desc->dst_bypass = dma_is_vtcm(q, dptr.dst) ? 1 : q->nocache; + desc->src_comp = 0; + desc->dst_comp = 0; + desc->order = 0; + desc->done = 0; + desc->src_stride = src_stride; + desc->dst_stride = dst_stride; + desc->src = (void *) dptr.src; + desc->dst = (void *) dptr.dst; + desc->row_size = row_size; + +#if __HVX_ARCH__ < 75 + desc->desc_type = 0; // 2d (16-bit) mode + desc->nrows = nrows; + desc->src_offset = 0; + desc->dst_offset = 0; +#else + desc->desc_type = 9; // 2d (24-bit) mode + desc->nrows_lo = (nrows & 0xff); + desc->nrows_hi = (nrows >> 8); + desc->offset = 0; +#endif + + r->dptr[r->push_idx] = dptr; + + htp_trace_event_start(r->trace, HTP_TRACE_EVT_DMA, r->push_idx); + + if (nrows) { + dmlink(r->tail, desc); + r->tail = desc; + } else { + desc->done = 1; + } + + r->push_idx = (r->push_idx + 1) & r->idx_mask; + return true; +} + +static inline dma_ptr dma_queue_pop(dma_queue * q) { + dma_ring * r = q->ring; + dma_ptr dptr = { NULL }; + + if (r->push_idx == r->pop_idx) { + return dptr; + } + + dma_descriptor_2d * desc = &r->desc[r->pop_idx]; + + // Wait for desc to complete + if (!desc->done) { + while (!desc->done) { + dmpoll(); + } + } + + dptr = r->dptr[r->pop_idx]; + + htp_trace_event_stop(r->trace, HTP_TRACE_EVT_DMA, r->pop_idx); + + r->pop_idx = (r->pop_idx + 1) & r->idx_mask; + return dptr; +} + +static inline dma_ptr dma_queue_pop_nowait(dma_queue * q) { + dma_ring * r = q->ring; + dma_ptr dptr = { NULL }; + + if (r->push_idx == r->pop_idx) { + return dptr; + } + + dptr = r->dptr[r->pop_idx]; + + htp_trace_event_stop(r->trace, HTP_TRACE_EVT_DMA, r->pop_idx); + + r->pop_idx = (r->pop_idx + 1) & r->idx_mask; + return dptr; +} + +static inline bool dma_queue_empty(dma_queue * q) { + return q->ring->push_idx == q->ring->pop_idx; +} + +static inline void dma_queue_flush(dma_queue * q) { + while (dma_queue_pop(q).dst != NULL) ; +} + +static inline uint32_t dma_queue_depth(dma_queue * q) { + return (q->ring->push_idx - q->ring->pop_idx) & q->ring->idx_mask; +} + +static inline uint32_t dma_queue_capacity(dma_queue * q) { + return q->ring->capacity; +} + +#if __HVX_ARCH__ < 75 + +// Overflow-safe DMA push: all 2d descriptor fields (row_size, nrows, src_stride, dst_stride) are 16-bit, max 65535. +// This version transparently handles values that exceed the 16-bit limit and submits chained DMA transtions. + +#define DMA_MAX_FIELD_VAL 65535u + +static inline bool dma_queue_push(dma_queue *q, dma_ptr dptr, size_t dst_stride, size_t src_stride, size_t row_size, size_t nrows) { + // Fast path: everything fits in 16 bits + if (nrows == 0 || __builtin_expect( + row_size <= DMA_MAX_FIELD_VAL && + nrows <= DMA_MAX_FIELD_VAL && + src_stride <= DMA_MAX_FIELD_VAL && + dst_stride <= DMA_MAX_FIELD_VAL, 1)) { + return dma_queue_push_single_2d(q, dptr, dst_stride, src_stride, row_size, nrows); + } + + // Contiguous block + // Use 1d DMA mode which supports sizes up to 24-bits (16MB) + if (nrows == 1 || (row_size == src_stride && row_size == dst_stride)) { + size_t total = row_size * nrows; + return dma_queue_push_single_1d(q, dptr, total); + } + + // Stride overflow - fall back to row-by-row. + { + const uint8_t *src = (const uint8_t *) dptr.src; + uint8_t *dst = (uint8_t *) dptr.dst; + size_t r = 0; + while (r + 1 < nrows) { + dma_ptr p = dma_make_ptr(dst + r * dst_stride, src + r * src_stride); + if (!dma_queue_push_single_1d(q, p, row_size)) { + dma_queue_flush(q); + } else { + r++; + } + } + dma_queue_flush(q); + dma_ptr p = dma_make_ptr(dst + r * dst_stride, src + r * src_stride); + return dma_queue_push_single_1d(q, p, row_size); + } +} + +#else // HVX_ARCH >= 75 + +static inline bool dma_queue_push(dma_queue *q, dma_ptr dptr, size_t dst_stride, size_t src_stride, size_t row_size, size_t nrows) { + // On v75 and up we always use 2d 24-bit mode + return dma_queue_push_single_2d(q, dptr, dst_stride, src_stride, row_size, nrows); +} + +#endif + +static inline bool dma_queue_push_ddr_to_vtcm(dma_queue * q, dma_ptr dptr, size_t dst_row_size, size_t src_row_size, size_t nrows) { + return dma_queue_push(q, dptr, dst_row_size, src_row_size, src_row_size, nrows); +} + +static inline bool dma_queue_push_vtcm_to_ddr(dma_queue * q, dma_ptr dptr, size_t dst_row_size, size_t src_row_size, size_t nrows) { + return dma_queue_push(q, dptr, dst_row_size, src_row_size, dst_row_size, nrows); +} + +#define DMA_CACHE_MAX_SIZE 256U + +typedef struct { + uint8_t *base; + uint32_t line_size; + uint32_t capacity; + uint32_t src[DMA_CACHE_MAX_SIZE]; + uint16_t age[DMA_CACHE_MAX_SIZE]; +} dma_cache; + +static inline void dma_cache_init(dma_cache *c, uint8_t *base, uint32_t line_size, uint32_t capacity) +{ + c->capacity = (capacity > DMA_CACHE_MAX_SIZE) ? DMA_CACHE_MAX_SIZE : capacity; + c->base = base; + c->line_size = line_size; + + for (unsigned i=0; i < c->capacity; i++) { + c->src[i] = 0; + c->age[i] = 0; + } +} + +static inline bool dma_cache_push(dma_queue *q, dma_cache *c, const uint8_t * src, uint32_t dst_stride, uint32_t src_stride, uint32_t row_size, uint32_t nrows) +{ + uint32_t o_idx = 0; + uint16_t o_age = 0; + uint8_t * dst = 0; + + for (unsigned i=0; i < c->capacity; i++) { + if (c->src[i] == (uint32_t) src) { + c->age[i] = 0; + dst = c->base + (i * c->line_size); nrows = 0; // dummy dma + } else { + c->age[i]++; + if (c->age[i] > o_age) { o_age = c->age[i]; o_idx = i; } + } + } + if (!dst) { + c->age[o_idx] = 0; + c->src[o_idx] = (uint32_t) src; + dst = c->base + o_idx * c->line_size; // normal nrows dma + return dma_queue_push(q, dma_make_ptr(dst, src), dst_stride, src_stride, row_size, nrows); + } + + return dma_queue_push_single_1d(q, dma_make_ptr(dst, src), 0); +} + +#ifdef __cplusplus +} // extern "C" +#endif + +#endif /* HTP_DMA_H */ diff --git a/ggml/src/ggml-hexagon/htp/flash-attn-ops.c b/ggml/src/ggml-hexagon/htp/flash-attn-ops.c index 6f2a643e69da..fe78718c6197 100644 --- a/ggml/src/ggml-hexagon/htp/flash-attn-ops.c +++ b/ggml/src/ggml-hexagon/htp/flash-attn-ops.c @@ -24,7 +24,7 @@ #include "hvx-reduce.h" #include "hvx-flash-attn.h" #include "htp-vtcm.h" -#include "worker-pool.h" +#include "work-queue.h" #define GGML_COMMON_DECL_C #include "ggml-common.h" @@ -123,15 +123,17 @@ struct hmx_fa_context { uint32_t g_br; // hex_align_up(G * Br, 32) - actual tile row dim // VTCM buffers (allocated by vtcm_seq_alloc) + __fp16 * vtcm_q_dma; // Q DMA fetch buffer __fp16 * vtcm_q_tiles; // Q tile format [g_br, D] __fp16 * vtcm_o_tiles[2]; // O ping-pong [g_br, D] __fp16 * vtcm_k_fp16[2]; // K DMA double-buffer [Bc, D] __fp16 * vtcm_v_fp16[2]; // V DMA double-buffer [Bc, D] - __fp16 * vtcm_k_tiles; // K tiles (transposed) + __fp16 * vtcm_k_tiles[2]; // K tiles (transposed, double-buffered) __fp16 * vtcm_v_tiles[2]; // V tiles (column-major, double-buffered) - __fp16 * vtcm_s_tiles; // S = QK^T [g_br, Bc] - __fp16 * vtcm_p_tiles; // P = softmax(S) [g_br, Bc] + __fp16 * vtcm_s_tiles[2]; // S = QK^T [g_br, Bc] (double-buffered) + __fp16 * vtcm_p_tiles[2]; // P = softmax(S) [g_br, Bc] __fp16 * vtcm_d_tiles; // Diagonal rescale [g_br, g_br] + __fp16 * vtcm_d_inv_l; // Diagonal rescale (1/l) [g_br, g_br] HVX_Vector * vtcm_m_vec; // Row max [g_br] HVX_Vector * vtcm_l_vec; // Row sum [g_br] HVX_Vector * vtcm_s_rowmax; // Softmax intermediate [g_br] @@ -204,7 +206,7 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void * if (ir0 >= ir1) return; - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL; + struct htp_thread_trace * tr = &octx->ctx->trace[ith]; dma_queue * dma = octx->ctx->dma[ith]; @@ -236,10 +238,6 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void * const uint32_t iv3 = fastdiv(iq3, &factx->broadcast_rv3); const uint32_t iv2 = fastdiv(iq2, &factx->broadcast_rv2); - // Fetch Q row - const uint8_t * q_row_ptr = (const uint8_t *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3); - dma_queue_push(dma, dma_make_ptr(spad_q, q_row_ptr), factx->size_q_row_padded, nbq1, size_q_row, 1); - const __fp16 * mp_base = NULL; if (mask) { const uint32_t im2 = fastmodulo(iq2, mask->ne[2], &factx->src3_div2); @@ -247,26 +245,91 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void * mp_base = (const __fp16 *) ((const uint8_t *) mask->data + iq1*mask->nb[1] + im2*mask->nb[2] + im3*mask->nb[3]); } - // Prefetch first two blocks - for (uint32_t ib = 0; ib < MIN(factx->n_blocks, 2); ++ib) { - const uint32_t ic_start = ib * FLASH_ATTN_BLOCK_SIZE; - const uint32_t current_block_size = MIN(FLASH_ATTN_BLOCK_SIZE, nek1 - ic_start); + // Precalculate next row variables if there is a next row + bool has_next_ir = (ir + 1 < ir1); + uint32_t next_ik2 = 0, next_ik3 = 0, next_iv2 = 0, next_iv3 = 0; + const uint8_t * next_q_row_ptr = NULL; + const __fp16 * next_mp_base = NULL; + + const uint8_t * next_k_src0 = NULL; + const uint8_t * next_v_src0 = NULL; + const uint8_t * next_m_src0 = NULL; + uint32_t next_block_size0 = 0; + + const uint8_t * next_k_src1 = NULL; + const uint8_t * next_v_src1 = NULL; + const uint8_t * next_m_src1 = NULL; + uint32_t next_block_size1 = 0; + + if (has_next_ir) { + const uint32_t next_ir = ir + 1; + const uint32_t next_iq3 = fastdiv(next_ir, &factx->src0_div21); + const uint32_t next_iq2 = fastdiv(next_ir - next_iq3*neq2*neq1, &factx->src0_div1); + const uint32_t next_iq1 = (next_ir - next_iq3*neq2*neq1 - next_iq2 * neq1); - // K - const uint8_t * k_src = (const uint8_t *) k->data + (ic_start*nbk1 + ik2*nbk2 + ik3*nbk3); - uint8_t * k_dst = spad_k + (ib % 2) * factx->size_k_block; - dma_queue_push(dma, dma_make_ptr(k_dst, k_src), factx->size_k_row_padded, nbk1, size_k_row, current_block_size); + next_ik3 = fastdiv(next_iq3, &factx->broadcast_rk3); + next_ik2 = fastdiv(next_iq2, &factx->broadcast_rk2); - // V - const uint8_t * v_src = (const uint8_t *) v->data + (ic_start*nbv1 + iv2*nbv2 + iv3*nbv3); - uint8_t * v_dst = spad_v + (ib % 2) * factx->size_v_block; - dma_queue_push(dma, dma_make_ptr(v_dst, v_src), factx->size_v_row_padded, nbv1, size_v_row, current_block_size); + next_iv3 = fastdiv(next_iq3, &factx->broadcast_rv3); + next_iv2 = fastdiv(next_iq2, &factx->broadcast_rv2); + + next_q_row_ptr = (const uint8_t *) q->data + (next_iq1*nbq1 + next_iq2*nbq2 + next_iq3*nbq3); - // Mask if (mask) { - const uint8_t * m_src = (const uint8_t *) (mp_base + ic_start); - // Mask is 1D contiguous for this row - dma_cache_push(dma, &m_cache, m_src, current_block_size * 2, current_block_size * 2, current_block_size * 2, 1); + const uint32_t next_im2 = fastmodulo(next_iq2, mask->ne[2], &factx->src3_div2); + const uint32_t next_im3 = fastmodulo(next_iq3, mask->ne[3], &factx->src3_div3); + next_mp_base = (const __fp16 *) ((const uint8_t *) mask->data + next_iq1*mask->nb[1] + next_im2*mask->nb[2] + next_im3*mask->nb[3]); + } + + // Precalculate next K/V block 0 source pointers + { + const uint32_t ic_start = 0; + next_block_size0 = MIN(FLASH_ATTN_BLOCK_SIZE, nek1 - ic_start); + next_k_src0 = (const uint8_t *) k->data + (ic_start*nbk1 + next_ik2*nbk2 + next_ik3*nbk3); + next_v_src0 = (const uint8_t *) v->data + (ic_start*nbv1 + next_iv2*nbv2 + next_iv3*nbv3); + if (mask) { + next_m_src0 = (const uint8_t *) (next_mp_base + ic_start); + } + } + + // Precalculate next K/V block 1 source pointers (if n_blocks > 1) + if (factx->n_blocks > 1) { + const uint32_t ic_start = 1 * FLASH_ATTN_BLOCK_SIZE; + next_block_size1 = MIN(FLASH_ATTN_BLOCK_SIZE, nek1 - ic_start); + next_k_src1 = (const uint8_t *) k->data + (ic_start*nbk1 + next_ik2*nbk2 + next_ik3*nbk3); + next_v_src1 = (const uint8_t *) v->data + (ic_start*nbv1 + next_iv2*nbv2 + next_iv3*nbv3); + if (mask) { + next_m_src1 = (const uint8_t *) (next_mp_base + ic_start); + } + } + } + + if (ir == ir0) { + // Fetch Q row + const uint8_t * q_row_ptr = (const uint8_t *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3); + dma_queue_push(dma, dma_make_ptr(spad_q, q_row_ptr), factx->size_q_row_padded, nbq1, size_q_row, 1); + + // Prefetch first two blocks + for (uint32_t ib = 0; ib < MIN(factx->n_blocks, 2); ++ib) { + const uint32_t ic_start = ib * FLASH_ATTN_BLOCK_SIZE; + const uint32_t current_block_size = MIN(FLASH_ATTN_BLOCK_SIZE, nek1 - ic_start); + + // K + const uint8_t * k_src = (const uint8_t *) k->data + (ic_start*nbk1 + ik2*nbk2 + ik3*nbk3); + uint8_t * k_dst = spad_k + (ib % 2) * factx->size_k_block; + dma_queue_push(dma, dma_make_ptr(k_dst, k_src), factx->size_k_row_padded, nbk1, size_k_row, current_block_size); + + // V + const uint8_t * v_src = (const uint8_t *) v->data + (ic_start*nbv1 + iv2*nbv2 + iv3*nbv3); + uint8_t * v_dst = spad_v + (ib % 2) * factx->size_v_block; + dma_queue_push(dma, dma_make_ptr(v_dst, v_src), factx->size_v_row_padded, nbv1, size_v_row, current_block_size); + + // Mask + if (mask) { + const uint8_t * m_src = (const uint8_t *) (mp_base + ic_start); + // Mask is 1D contiguous for this row + dma_cache_push(dma, &m_cache, m_src, current_block_size * 2, current_block_size * 2, current_block_size * 2, 1); + } } } @@ -287,6 +350,11 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void * const HVX_Vector slope_vec = hvx_vec_splat_f16(slope); const HVX_Vector v_neg_inf = Q6_Vh_vsplat_R(0xfbff); + const HVX_Vector v_cap = (factx->logit_softcap != 0.0f) ? hvx_vec_splat_f16(factx->logit_softcap) : Q6_V_vzero(); + const HVX_Vector vinf = Q6_Vh_vsplat_R(0xFC00); + const HVX_Vector vmin = Q6_Vh_vsplat_R(0xFBFF); + const HVX_Vector v_log2e = hvx_vec_splat_f16(EXP_LOG2E_F); + const uint32_t stride_v2 = factx->size_v_row_padded * 2; for (uint32_t ib = 0; ib < factx->n_blocks; ++ib) { const uint32_t ic_start = ib * FLASH_ATTN_BLOCK_SIZE; const uint32_t current_block_size = MIN(FLASH_ATTN_BLOCK_SIZE, nek1 - ic_start); @@ -309,7 +377,6 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void * // 2. Softcap (in FP16) if (factx->logit_softcap != 0.0f) { - const HVX_Vector v_cap = hvx_vec_splat_f16(factx->logit_softcap); scores_f16 = hvx_vec_tanh_f16(scores_f16); scores_f16 = hvx_vec_mul_f16_f16(scores_f16, v_cap); } @@ -319,8 +386,6 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void * // 3. Mask (in FP16) if (mask) { HVX_Vector m_vals_f16 = *(const HVX_UVector *) m_base; - HVX_Vector vinf = Q6_Vh_vsplat_R(0xFC00); - HVX_Vector vmin = Q6_Vh_vsplat_R(0xFBFF); HVX_VectorPred is_inf = Q6_Q_vcmp_eq_VhVh(m_vals_f16, vinf); m_vals_f16 = Q6_V_vmux_QVV(is_inf, vmin, m_vals_f16); @@ -335,10 +400,30 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void * HVX_Vector v_max = Q6_V_lo_W(hvx_vec_f16_to_f32(v_max_f16)); // splat block max in FP32 htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_FA_QK, ir); + if (ib + 1 == factx->n_blocks && has_next_ir) { + // Queue next row's Q row! + dma_queue_push(dma, dma_make_ptr(spad_q, next_q_row_ptr), factx->size_q_row_padded, nbq1, size_q_row, 1); + + if (factx->n_blocks % 2 == 0) { + // Queue next row's block 0 (into buffer slot 0) + uint8_t * k_dst = spad_k + 0 * factx->size_k_block; + uint8_t * v_dst = spad_v + 0 * factx->size_v_block; + + // K (block 0 of next row) + dma_queue_push(dma, dma_make_ptr(k_dst, next_k_src0), factx->size_k_row_padded, nbk1, size_k_row, next_block_size0); + + // V (block 0 of next row) + dma_queue_push(dma, dma_make_ptr(v_dst, next_v_src0), factx->size_v_row_padded, nbv1, size_v_row, next_block_size0); + + // Mask (block 0 of next row) + if (mask) { + dma_cache_push(dma, &m_cache, next_m_src0, next_block_size0 * 2, next_block_size0 * 2, next_block_size0 * 2, 1); + } + } + } + htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_FA_SFM, ir); { - const HVX_Vector v_log2e = hvx_vec_splat_f16(EXP_LOG2E_F); - // 4. Online Softmax Update HVX_Vector M_new_vec = Q6_Vsf_vmax_VsfVsf(v_max, M_vec); HVX_Vector diff_vec = HVX_OP_SUB_F32(M_vec, M_new_vec); @@ -370,24 +455,20 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void * S_vec = HVX_OP_ADD_F32(HVX_OP_MUL_F32(S_vec, ms_vec), p_sum_vec); // 5. Accumulate V (F16 * F16 -> F32 accumulator) - __fp16 __attribute__((aligned(128))) p_arr[VLEN_FP16]; - hvx_vec_store_a(p_arr, 128, P); + const uint8_t * v_ptr = v_base; for (uint32_t j = 0; j < current_block_size; j += 2) { if (j + 1 == current_block_size) { - if (p_arr[j] != 0.0f) { - const uint8_t * v_ptr = v_base + j * factx->size_v_row_padded; - hvx_mad_f32_f16_aa(VKQ32, v_ptr, (p_arr + j), DV); - } + HVX_Vector S0 = hvx_vec_repl_f16(Q6_V_vror_VR(P, j * 2)); + hvx_mad_f32_f16_aa_vec(VKQ32, v_ptr, S0, DV); break; } - if (p_arr[j] == 0.0f && p_arr[j + 1] == 0.0f) { - continue; - } + HVX_Vector S0 = hvx_vec_repl_f16(Q6_V_vror_VR(P, j * 2)); + HVX_Vector S1 = hvx_vec_repl_f16(Q6_V_vror_VR(P, (j + 1) * 2)); - const uint8_t * v_ptr = v_base + j * factx->size_v_row_padded; - hvx_mad_f32_f16_aa_rx2(VKQ32, v_ptr, v_ptr + factx->size_v_row_padded, (p_arr + j), (p_arr + j + 1), DV); + hvx_mad_f32_f16_aa_rx2_vec(VKQ32, v_ptr, v_ptr + factx->size_v_row_padded, S0, S1, DV); + v_ptr += stride_v2; } } htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_FA_SFM, ir); @@ -414,6 +495,61 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void * } } + if (has_next_ir) { + if (factx->n_blocks % 2 == 0) { + // Queue next row's block 1 (into buffer slot 1, if n_blocks > 1) + if (factx->n_blocks > 1) { + uint8_t * k_dst = spad_k + 1 * factx->size_k_block; + uint8_t * v_dst = spad_v + 1 * factx->size_v_block; + + // K (block 1 of next row) + dma_queue_push(dma, dma_make_ptr(k_dst, next_k_src1), factx->size_k_row_padded, nbk1, size_k_row, next_block_size1); + + // V (block 1 of next row) + dma_queue_push(dma, dma_make_ptr(v_dst, next_v_src1), factx->size_v_row_padded, nbv1, size_v_row, next_block_size1); + + // Mask (block 1 of next row) + if (mask) { + dma_cache_push(dma, &m_cache, next_m_src1, next_block_size1 * 2, next_block_size1 * 2, next_block_size1 * 2, 1); + } + } + } else { + // Queue next row's block 0 (into buffer slot 0) + { + uint8_t * k_dst = spad_k + 0 * factx->size_k_block; + uint8_t * v_dst = spad_v + 0 * factx->size_v_block; + + // K (block 0 of next row) + dma_queue_push(dma, dma_make_ptr(k_dst, next_k_src0), factx->size_k_row_padded, nbk1, size_k_row, next_block_size0); + + // V (block 0 of next row) + dma_queue_push(dma, dma_make_ptr(v_dst, next_v_src0), factx->size_v_row_padded, nbv1, size_v_row, next_block_size0); + + // Mask (block 0 of next row) + if (mask) { + dma_cache_push(dma, &m_cache, next_m_src0, next_block_size0 * 2, next_block_size0 * 2, next_block_size0 * 2, 1); + } + } + + // Queue next row's block 1 (into buffer slot 1, if n_blocks > 1) + if (factx->n_blocks > 1) { + uint8_t * k_dst = spad_k + 1 * factx->size_k_block; + uint8_t * v_dst = spad_v + 1 * factx->size_v_block; + + // K (block 1 of next row) + dma_queue_push(dma, dma_make_ptr(k_dst, next_k_src1), factx->size_k_row_padded, nbk1, size_k_row, next_block_size1); + + // V (block 1 of next row) + dma_queue_push(dma, dma_make_ptr(v_dst, next_v_src1), factx->size_v_row_padded, nbv1, size_v_row, next_block_size1); + + // Mask (block 1 of next row) + if (mask) { + dma_cache_push(dma, &m_cache, next_m_src1, next_block_size1 * 2, next_block_size1 * 2, next_block_size1 * 2, 1); + } + } + } + } + htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_O_PROC, ir); // sinks float M = hvx_vec_get_f32(M_vec); @@ -471,6 +607,7 @@ typedef struct { void * curr_k; uint32_t kv_start; uint32_t rows_per_t; + size_t buf_idx; } fa_k_int_args_t; static void fa_k_interleave_thread(unsigned int n, unsigned int i, void * data) { @@ -486,23 +623,23 @@ static void fa_k_interleave_thread(unsigned int n, unsigned int i, void * data) return; } - struct htp_thread_trace * tr = factx->octx->ctx ? &factx->octx->ctx->trace[i] : NULL; + struct htp_thread_trace * tr = &factx->octx->ctx->trace[i]; htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_FA_K_PREP, (uint16_t) (args->kv_start + start)); - hmx_interleave_rows_to_tiles(factx->vtcm_k_tiles, (const __fp16 *) args->curr_k, total_rows, factx->DK, + hmx_interleave_rows_to_tiles(factx->vtcm_k_tiles[args->buf_idx], (const __fp16 *) args->curr_k, total_rows, factx->DK, args->src_stride, start, end); htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_FA_K_PREP, (uint16_t) (args->kv_start + start)); } -static void fa_phase_k_interleave(struct hmx_fa_context * factx, uint32_t kv_rows, size_t src_stride, void * curr_k, uint32_t kv_start) { - worker_pool_context_t wp = factx->octx->ctx->worker_pool; +static void fa_phase_k_interleave(struct hmx_fa_context * factx, uint32_t kv_rows, size_t src_stride, void * curr_k, uint32_t kv_start, size_t buf_idx) { + work_queue_t wp = factx->octx->ctx->work_queue; uint32_t n = 1; if (factx->n_threads > 1 && kv_rows >= factx->n_threads * 2) { n = factx->n_threads; } uint32_t rows_per_t = hex_align_up(hmx_ceil_div(kv_rows, n), 2); - fa_k_int_args_t args = { factx, kv_rows, src_stride, curr_k, kv_start, rows_per_t }; + fa_k_int_args_t args = { factx, kv_rows, src_stride, curr_k, kv_start, rows_per_t, buf_idx }; if (n > 1) { - worker_pool_run_func(wp, fa_k_interleave_thread, &args, n); + work_queue_run(wp, fa_k_interleave_thread, &args, n); } else { fa_k_interleave_thread(1, 0, &args); } @@ -534,7 +671,7 @@ static void fa_v_interleave_thread(unsigned int n, unsigned int i, void * data) __fp16 * v_tiles_dst = (__fp16 *) args->v_tiles_dst; - struct htp_thread_trace * tr = factx->octx->ctx ? &factx->octx->ctx->trace[i] : NULL; + struct htp_thread_trace * tr = &factx->octx->ctx->trace[i]; htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_FA_V_PREP, (uint16_t) (args->kv_start + start)); hmx_interleave_cols_to_tiles(v_tiles_dst, (const __fp16 *) args->v_src, total_rows, factx->DV, args->src_stride, (uint32_t) args->n_col_tiles, start, end); @@ -548,7 +685,7 @@ static void fa_phase_v_interleave(struct hmx_fa_context * factx, void * v_tiles_dst, size_t n_col_tiles, uint32_t kv_start) { - worker_pool_context_t wp = factx->octx->ctx->worker_pool; + work_queue_t wp = factx->octx->ctx->work_queue; uint32_t n = 1; if (factx->n_threads > 1 && kv_rows >= factx->n_threads * 2) { n = factx->n_threads; @@ -556,7 +693,7 @@ static void fa_phase_v_interleave(struct hmx_fa_context * factx, uint32_t rows_per_t = hex_align_up(hmx_ceil_div(kv_rows, n), 2); fa_v_int_args_t args = { factx, kv_rows, src_stride, v_src, v_tiles_dst, n_col_tiles, kv_start, rows_per_t }; if (n > 1) { - worker_pool_run_func(wp, fa_v_interleave_thread, &args, n); + work_queue_run(wp, fa_v_interleave_thread, &args, n); } else { fa_v_interleave_thread(1, 0, &args); } @@ -589,7 +726,7 @@ static void fa_q_load_thread(unsigned int n, unsigned int i, void * data) { const size_t start = (size_t) i * rows_per_t; const size_t end = hex_smin(start + rows_per_t, factx->g_br); - struct htp_thread_trace * tr = factx->octx->ctx ? &factx->octx->ctx->trace[i] : NULL; + struct htp_thread_trace * tr = &factx->octx->ctx->trace[i]; htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_FA_Q_PREP, (uint16_t) (args->q_start * G + start)); // Parallel initialization of per-block state @@ -645,12 +782,13 @@ static void fa_q_load_thread(unsigned int n, unsigned int i, void * data) { } } - // Initialize vtcm_d_tiles to 0 + // Initialize vtcm_d_tiles and vtcm_d_inv_l to 0 const size_t d_bytes_per_t = hex_align_up(d_tile_bytes / n, 128); const size_t d_start = i * d_bytes_per_t; const size_t d_end = hex_smin(d_start + d_bytes_per_t, d_tile_bytes); if (d_start < d_tile_bytes) { hvx_splat_u8_a((char *) factx->vtcm_d_tiles + d_start, 0, d_end - d_start); + hvx_splat_u8_a((char *) factx->vtcm_d_inv_l + d_start, 0, d_end - d_start); } } @@ -662,15 +800,14 @@ static void fa_q_load_thread(unsigned int n, unsigned int i, void * data) { assert(factx->DK == factx->DV); - const size_t o_tile_bytes = factx->o_tile_bytes; - const bool use_q_dma = (2 * o_tile_bytes >= factx->g_br * DK * (factx->is_q_fp32 ? 4 : 2)); + const bool use_q_dma = (factx->vtcm_q_dma != NULL); __fp16 * q_tiles = factx->vtcm_q_tiles; if (use_q_dma) { const size_t g_rows_end = hex_smin(end, n_rows_g); const uint32_t d_limit = factx->is_q_fp32 ? DK / 32 : DK / 64; - uint8_t * q_flat = (uint8_t *) factx->vtcm_o_tiles[0]; + uint8_t * q_flat = (uint8_t *) factx->vtcm_q_dma; if (factx->is_q_fp32) { switch (d_limit) { case 2: hmx_fa_q_prep_fp32_d2(q_tiles, q_flat, start, end, g_rows_end, DK, G, args->n_rows_q, &factx->div_G, args->q_transposed); break; @@ -720,7 +857,7 @@ static void fa_phase_q_load(struct hmx_fa_context * factx, uint32_t kv_head, uint32_t ib3, size_t n_rows_g) { - worker_pool_context_t wp = factx->octx->ctx->worker_pool; + work_queue_t wp = factx->octx->ctx->work_queue; uint32_t n = 1; if (factx->n_threads > 1 && n_rows_g >= (size_t) (factx->n_threads * 2)) { n = factx->n_threads; @@ -739,7 +876,7 @@ static void fa_phase_q_load(struct hmx_fa_context * factx, args.q_transposed = q->nb[1] < q->nb[2]; atomic_init(&args.barrier, n); if (n > 1) { - worker_pool_run_func(wp, fa_q_load_thread, &args, n); + work_queue_run(wp, fa_q_load_thread, &args, n); } else { fa_q_load_thread(1, 0, &args); } @@ -772,7 +909,7 @@ static void fa_o_store_thread_f32(unsigned int n, unsigned int i, void * data) { return; } - struct htp_thread_trace * tr = factx->octx->ctx ? &factx->octx->ctx->trace[i] : NULL; + struct htp_thread_trace * tr = &factx->octx->ctx->trace[i]; htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_O_PROC, (uint16_t) (args->q_start * G + start)); const struct htp_tensor * dst = args->dst; @@ -781,10 +918,10 @@ static void fa_o_store_thread_f32(unsigned int n, unsigned int i, void * data) { const uint32_t kv_head = args->kv_head; const uint32_t ib3 = args->ib3; - for (size_t r = start; r < end; ++r) { - const size_t q_idx = fastdiv(r, &factx->div_G); - const size_t h_idx = fastmodulo(r, G, &factx->div_G); + size_t q_idx = fastdiv(start, &factx->div_G); + size_t h_idx = fastmodulo(start, G, &factx->div_G); + for (size_t r = start; r < end; ++r) { float * out = (float *) ((uint8_t *) dst->data + (kv_head * G + h_idx) * dst->nb[1] + (q_start + q_idx) * dst->nb[2] + ib3 * dst->nb[3]); @@ -801,6 +938,12 @@ static void fa_o_store_thread_f32(unsigned int n, unsigned int i, void * data) { *(HVX_UVector *) (out + d * 32) = Q6_V_hi_W(vp); } } + + h_idx++; + if (h_idx == G) { + h_idx = 0; + q_idx++; + } } htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_O_PROC, (uint16_t) (args->q_start * G + start)); } @@ -820,7 +963,7 @@ static void fa_o_store_thread_f16(unsigned int n, unsigned int i, void * data) { return; } - struct htp_thread_trace * tr = factx->octx->ctx ? &factx->octx->ctx->trace[i] : NULL; + struct htp_thread_trace * tr = &factx->octx->ctx->trace[i]; htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_O_PROC, (uint16_t) (args->q_start * G + start)); const struct htp_tensor * dst = args->dst; @@ -829,10 +972,10 @@ static void fa_o_store_thread_f16(unsigned int n, unsigned int i, void * data) { const uint32_t kv_head = args->kv_head; const uint32_t ib3 = args->ib3; - for (size_t r = start; r < end; ++r) { - const size_t q_idx = fastdiv(r, &factx->div_G); - const size_t h_idx = fastmodulo(r, G, &factx->div_G); + size_t q_idx = fastdiv(start, &factx->div_G); + size_t h_idx = fastmodulo(start, G, &factx->div_G); + for (size_t r = start; r < end; ++r) { __fp16 * out = (__fp16 *) ((uint8_t *) dst->data + (kv_head * G + h_idx) * dst->nb[1] + (q_start + q_idx) * dst->nb[2] + ib3 * dst->nb[3]); @@ -851,6 +994,12 @@ static void fa_o_store_thread_f16(unsigned int n, unsigned int i, void * data) { *(HVX_UVector *) (out + d * 64) = Q6_V_hi_W(vp); } } + + h_idx++; + if (h_idx == G) { + h_idx = 0; + q_idx++; + } } htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_O_PROC, (uint16_t) (args->q_start * G + start)); } @@ -862,7 +1011,7 @@ static void fa_phase_o_store(struct hmx_fa_context * factx, uint32_t kv_head, uint32_t ib3, size_t n_rows_g) { - worker_pool_context_t wp = factx->octx->ctx->worker_pool; + work_queue_t wp = factx->octx->ctx->work_queue; uint32_t n = 1; if (factx->n_threads > 1 && n_rows_g >= (size_t) (factx->n_threads * 2)) { n = factx->n_threads; @@ -871,7 +1020,7 @@ static void fa_phase_o_store(struct hmx_fa_context * factx, fa_o_store_args_t args = { factx, dst, o_tile_src, q_start, kv_head, ib3, n_rows_g, rows_per_t }; worker_callback_t store_fn = factx->is_dst_fp32 ? fa_o_store_thread_f32 : fa_o_store_thread_f16; if (n > 1) { - worker_pool_run_func(wp, store_fn, &args, n); + work_queue_run(wp, store_fn, &args, n); } else { store_fn(1, 0, &args); } @@ -879,6 +1028,7 @@ static void fa_phase_o_store(struct hmx_fa_context * factx, typedef struct { struct hmx_fa_context * factx; + size_t buf_idx; size_t kv_rows; size_t n_rows_g; size_t n_col_tiles; @@ -930,7 +1080,7 @@ static inline void fa_softmax_impl( return; } - struct htp_thread_trace * tr = factx->octx->ctx ? &factx->octx->ctx->trace[i] : NULL; + struct htp_thread_trace * tr = &factx->octx->ctx->trace[i]; htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_FA_SFM, (uint16_t) (args->q_start * G + vec_start * 64)); // Per-thread row scratch: thread i uses bufs at offset i * 2 * stride @@ -960,8 +1110,8 @@ static inline void fa_softmax_impl( uint32_t r0 = r / HMX_FP16_TILE_N_ROWS; uint32_t r1 = r % HMX_FP16_TILE_N_ROWS; - const __fp16 * s_ld_base = factx->vtcm_s_tiles + r0 * HMX_FP16_TILE_N_ROWS * Bc; - __fp16 * p_st_base = factx->vtcm_p_tiles + r0 * HMX_FP16_TILE_N_ROWS * Bc; + const __fp16 * s_ld_base = factx->vtcm_s_tiles[args->buf_idx] + r0 * HMX_FP16_TILE_N_ROWS * Bc; + __fp16 * p_st_base = factx->vtcm_p_tiles[args->buf_idx] + r0 * HMX_FP16_TILE_N_ROWS * Bc; // Decode 2 rows from S tiles into per-thread row buffers if (has_softcap) { @@ -983,7 +1133,26 @@ static inline void fa_softmax_impl( my_row_buf1[ci] = hvx_vec_mul_f16_f16(t1, v_cap); } } else { - for (size_t c = 0; c < kv_rows; c += 64) { + size_t c = 0; + for (; c + 64 < kv_rows; c += 128) { + size_t ci0 = c / 64; + size_t ci1 = ci0 + 1; + const __fp16 * in_dtile0 = s_ld_base + ci0 * HMX_FP16_TILE_N_ELMS * 2; + const __fp16 * in_dtile1 = s_ld_base + ci1 * HMX_FP16_TILE_N_ELMS * 2; + const HVX_Vector * pv_s_in0_0 = ((const HVX_Vector *) in_dtile0) + r1 / 2; + const HVX_Vector * pv_s_in1_0 = pv_s_in0_0 + 16; + const HVX_Vector * pv_s_in0_1 = ((const HVX_Vector *) in_dtile1) + r1 / 2; + const HVX_Vector * pv_s_in1_1 = pv_s_in0_1 + 16; + + HVX_VectorPair vp_s_drow0 = Q6_W_vdeal_VVR(*pv_s_in1_0, *pv_s_in0_0, -2); + my_row_buf0[ci0] = Q6_V_lo_W(vp_s_drow0); + my_row_buf1[ci0] = Q6_V_hi_W(vp_s_drow0); + + HVX_VectorPair vp_s_drow1 = Q6_W_vdeal_VVR(*pv_s_in1_1, *pv_s_in0_1, -2); + my_row_buf0[ci1] = Q6_V_lo_W(vp_s_drow1); + my_row_buf1[ci1] = Q6_V_hi_W(vp_s_drow1); + } + for (; c < kv_rows; c += 64) { size_t ci = c / 64; const __fp16 * in_dtile = s_ld_base + ci * HMX_FP16_TILE_N_ELMS * 2; const HVX_Vector * pv_s_in0 = ((const HVX_Vector *) in_dtile) + r1 / 2; @@ -1007,12 +1176,12 @@ static inline void fa_softmax_impl( HVX_Vector v_s_rowmax0 = v_neg_inf; HVX_Vector v_s_rowmax1 = v_neg_inf; - for (size_t c = 0; c < kv_rows; c += 64) { - size_t ci = c / 64; - const size_t ne = hex_smin(kv_rows - c, 64); - HVX_VectorPred q_tail_keep = Q6_Q_vsetq2_R(ne * sizeof(__fp16)); + if (has_mask) { + for (size_t c = 0; c < kv_rows; c += 64) { + size_t ci = c / 64; + const size_t ne = hex_smin(kv_rows - c, 64); + HVX_VectorPred q_tail_keep = Q6_Q_vsetq2_R(ne * sizeof(__fp16)); - if (has_mask) { HVX_Vector v_mask0, v_mask1; if (mask_broadcast) { @@ -1066,15 +1235,31 @@ static inline void fa_softmax_impl( my_row_buf0[ci] = Q6_V_vmux_QVV(q_keep0, hvx_vec_add_f16_f16(my_row_buf0[ci], v_mask0_scaled), v_neg_inf); my_row_buf1[ci] = Q6_V_vmux_QVV(q_keep1, hvx_vec_add_f16_f16(my_row_buf1[ci], v_mask1_scaled), v_neg_inf); } - } else { + + v_s_rowmax0 = Q6_Vhf_vmax_VhfVhf(v_s_rowmax0, my_row_buf0[ci]); + v_s_rowmax1 = Q6_Vhf_vmax_VhfVhf(v_s_rowmax1, my_row_buf1[ci]); + } + } else { + size_t c = 0; + for (; c + 64 < kv_rows; c += 128) { + size_t ci0 = c / 64; + size_t ci1 = ci0 + 1; + v_s_rowmax0 = Q6_Vhf_vmax_VhfVhf(v_s_rowmax0, my_row_buf0[ci0]); + v_s_rowmax1 = Q6_Vhf_vmax_VhfVhf(v_s_rowmax1, my_row_buf1[ci0]); + v_s_rowmax0 = Q6_Vhf_vmax_VhfVhf(v_s_rowmax0, my_row_buf0[ci1]); + v_s_rowmax1 = Q6_Vhf_vmax_VhfVhf(v_s_rowmax1, my_row_buf1[ci1]); + } + for (; c < kv_rows; c += 64) { + size_t ci = c / 64; + const size_t ne = hex_smin(kv_rows - c, 64); + HVX_VectorPred q_tail_keep = Q6_Q_vsetq2_R(ne * sizeof(__fp16)); if (ne < 64) { my_row_buf0[ci] = Q6_V_vmux_QVV(q_tail_keep, my_row_buf0[ci], v_neg_inf); my_row_buf1[ci] = Q6_V_vmux_QVV(q_tail_keep, my_row_buf1[ci], v_neg_inf); } + v_s_rowmax0 = Q6_Vhf_vmax_VhfVhf(v_s_rowmax0, my_row_buf0[ci]); + v_s_rowmax1 = Q6_Vhf_vmax_VhfVhf(v_s_rowmax1, my_row_buf1[ci]); } - - v_s_rowmax0 = Q6_Vhf_vmax_VhfVhf(v_s_rowmax0, my_row_buf0[ci]); - v_s_rowmax1 = Q6_Vhf_vmax_VhfVhf(v_s_rowmax1, my_row_buf1[ci]); } v_s_rowmax0 = hvx_vec_reduce_max_f16(v_s_rowmax0); @@ -1121,8 +1306,48 @@ static inline void fa_softmax_impl( HVX_Vector v_p_rowsum0 = v_zero; HVX_Vector v_p_rowsum1 = v_zero; - for (size_t c = 0; c < kv_rows; c += 64) { - size_t ci = c / 64; + size_t c = 0; + for (; c + 64 < kv_rows; c += 128) { + size_t ci0 = c / 64; + size_t ci1 = ci0 + 1; + + HVX_Vector v_s_minus_m0_0 = Q6_Vqf16_vsub_VhfVhf(my_row_buf0[ci0], v_dup_m0); + HVX_Vector v_s_minus_m1_0 = Q6_Vqf16_vsub_VhfVhf(my_row_buf1[ci0], v_dup_m1); + HVX_Vector v_s_minus_m0_1 = Q6_Vqf16_vsub_VhfVhf(my_row_buf0[ci1], v_dup_m0); + HVX_Vector v_s_minus_m1_1 = Q6_Vqf16_vsub_VhfVhf(my_row_buf1[ci1], v_dup_m1); + + HVX_Vector v_p_row0_hf_0 = hvx_vec_exp2_f16(Q6_Vhf_equals_Vqf16(v_s_minus_m0_0)); + HVX_Vector v_p_row1_hf_0 = hvx_vec_exp2_f16(Q6_Vhf_equals_Vqf16(v_s_minus_m1_0)); + HVX_Vector v_p_row0_hf_1 = hvx_vec_exp2_f16(Q6_Vhf_equals_Vqf16(v_s_minus_m0_1)); + HVX_Vector v_p_row1_hf_1 = hvx_vec_exp2_f16(Q6_Vhf_equals_Vqf16(v_s_minus_m1_1)); + + __fp16 * out_dtile0 = p_st_base + ci0 * HMX_FP16_TILE_N_ELMS * 2; + __fp16 * out_dtile1 = p_st_base + ci1 * HMX_FP16_TILE_N_ELMS * 2; + HVX_Vector * pv_p_out0_0 = ((HVX_Vector *) out_dtile0) + r1 / 2; + HVX_Vector * pv_p_out1_0 = pv_p_out0_0 + 16; + HVX_Vector * pv_p_out0_1 = ((HVX_Vector *) out_dtile1) + r1 / 2; + HVX_Vector * pv_p_out1_1 = pv_p_out0_1 + 16; + + HVX_VectorPair vp_p_dual0 = Q6_W_vshuff_VVR(v_p_row1_hf_0, v_p_row0_hf_0, -2); + *pv_p_out0_0 = Q6_V_lo_W(vp_p_dual0); + *pv_p_out1_0 = Q6_V_hi_W(vp_p_dual0); + + HVX_VectorPair vp_p_dual1 = Q6_W_vshuff_VVR(v_p_row1_hf_1, v_p_row0_hf_1, -2); + *pv_p_out0_1 = Q6_V_lo_W(vp_p_dual1); + *pv_p_out1_1 = Q6_V_hi_W(vp_p_dual1); + + HVX_VectorPair vp_p0_0 = hvx_vec_f16_to_f32_shuff(v_p_row0_hf_0); + HVX_VectorPair vp_p1_0 = hvx_vec_f16_to_f32_shuff(v_p_row1_hf_0); + HVX_VectorPair vp_p0_1 = hvx_vec_f16_to_f32_shuff(v_p_row0_hf_1); + HVX_VectorPair vp_p1_1 = hvx_vec_f16_to_f32_shuff(v_p_row1_hf_1); + + v_p_rowsum0 = Q6_Vqf32_vadd_Vqf32Vqf32(v_p_rowsum0, Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(vp_p0_0), Q6_V_hi_W(vp_p0_0))); + v_p_rowsum0 = Q6_Vqf32_vadd_Vqf32Vqf32(v_p_rowsum0, Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(vp_p0_1), Q6_V_hi_W(vp_p0_1))); + v_p_rowsum1 = Q6_Vqf32_vadd_Vqf32Vqf32(v_p_rowsum1, Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(vp_p1_0), Q6_V_hi_W(vp_p1_0))); + v_p_rowsum1 = Q6_Vqf32_vadd_Vqf32Vqf32(v_p_rowsum1, Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(vp_p1_1), Q6_V_hi_W(vp_p1_1))); + } + for (size_t c_rem = c; c_rem < kv_rows; c_rem += 64) { + size_t ci = c_rem / 64; HVX_Vector v_s_minus_m0 = Q6_Vqf16_vsub_VhfVhf(my_row_buf0[ci], v_dup_m0); HVX_Vector v_s_minus_m1 = Q6_Vqf16_vsub_VhfVhf(my_row_buf1[ci], v_dup_m1); @@ -1281,7 +1506,7 @@ static __attribute__((noinline)) void fa_build_d_diag_inv_l(struct hmx_fa_contex v_content = Q6_V_vror_VR(v_content, 64); } - __fp16 * out_base = factx->vtcm_d_tiles + i * (n_row_tiles_g_br + 1) * HMX_FP16_TILE_N_ELMS; + __fp16 * out_base = factx->vtcm_d_inv_l + i * (n_row_tiles_g_br + 1) * HMX_FP16_TILE_N_ELMS; Q6_vscatter_QRMVhV(q_32_mask, (size_t) out_base, HMX_FP16_TILE_SIZE - 1, v_offsets, v_content); } } @@ -1290,7 +1515,7 @@ static void fa_phase_softmax_and_build_d(struct hmx_fa_context * factx, fa_softmax_args_t * sargs, size_t n_row_tiles, size_t n_row_tiles_g_br) { - worker_pool_context_t wp = factx->octx->ctx->worker_pool; + work_queue_t wp = factx->octx->ctx->work_queue; const size_t n_row_vec_cnt = hmx_ceil_div(sargs->n_rows_g, 64); worker_callback_t softmax_fn = fa_softmax_thread; @@ -1307,7 +1532,7 @@ static void fa_phase_softmax_and_build_d(struct hmx_fa_context * factx, if (factx->n_threads > 1 && n_row_vec_cnt >= 2) { uint32_t n_use = (uint32_t) hex_smin((size_t) factx->n_threads, n_row_vec_cnt); sargs->thread_div = init_fastdiv_values(n_use); - worker_pool_run_func(wp, softmax_fn, sargs, n_use); + work_queue_run(wp, softmax_fn, sargs, n_use); } else { softmax_fn(1, 0, sargs); } @@ -1514,13 +1739,34 @@ static void fa_pop_mask_dma_gqa(dma_queue * dma, uint32_t G) { } } +static inline void fa_prefetch_block(dma_queue * dma, const struct htp_tensor * k, const struct htp_tensor * v, const struct htp_tensor * mask, + uint32_t b, size_t Bc, size_t size_k_row_padded, size_t size_k_row, size_t size_v_row_padded, size_t size_v_row, + uint32_t ik2, uint32_t ik3, uint32_t iv2, uint32_t iv3, uint32_t q_start, uint32_t im3, uint32_t kv_head, uint32_t G, + size_t m_line_bytes, size_t n_rows_q, size_t nek1, size_t prefetch_buf, struct hmx_fa_context * factx) { + const uint32_t prefetch_start = b * Bc; + const uint32_t prefetch_rows = hex_smin(Bc, nek1 - prefetch_start); + const uint8_t * k_prefetch_src = (const uint8_t *) k->data + prefetch_start * k->nb[1] + ik2 * k->nb[2] + ik3 * k->nb[3]; + dma_queue_push(dma, dma_make_ptr(factx->vtcm_k_fp16[prefetch_buf], k_prefetch_src), size_k_row_padded, k->nb[1], size_k_row, prefetch_rows); + const uint8_t * v_prefetch_src = (const uint8_t *) v->data + prefetch_start * v->nb[1] + iv2 * v->nb[2] + iv3 * v->nb[3]; + dma_queue_push(dma, dma_make_ptr(factx->vtcm_v_fp16[prefetch_buf], v_prefetch_src), size_v_row_padded, v->nb[1], size_v_row, prefetch_rows); + + if (mask) { + if (__builtin_expect(factx->mask_broadcast, true)) { + const uint8_t * ms_src = (const uint8_t *) mask->data + q_start * mask->nb[1] + im3 * mask->nb[3] + prefetch_start * sizeof(__fp16); + dma_cache_push(dma, &factx->m_cache, ms_src, m_line_bytes, mask->nb[1], prefetch_rows * sizeof(__fp16), n_rows_q); + } else { + fa_push_mask_dma_gqa(dma, mask, q_start, im3, prefetch_start, kv_head, G, m_line_bytes, prefetch_rows, n_rows_q, factx); + } + } +} + // ============================================================================ // Core HMX flash attention algorithm (GQA-merged) // ============================================================================ int hmx_flash_attn_ext(struct htp_ops_context * octx) { - struct htp_thread_trace * tr_hvx = octx->ctx ? &octx->ctx->trace[0] : NULL; - struct htp_thread_trace * tr_hmx = octx->ctx ? &octx->ctx->trace[HTP_MAX_NTHREADS] : NULL; + struct htp_thread_trace * tr_hvx = &octx->ctx->trace[0]; + struct htp_thread_trace * tr_hmx = &octx->ctx->trace[HTP_MAX_NTHREADS]; const struct htp_tensor * q = octx->src[0]; const struct htp_tensor * k = octx->src[1]; const struct htp_tensor * v = octx->src[2]; @@ -1612,7 +1858,7 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) { // Build the VTCM layout once (shared with the host estimator) and place every // scratch buffer at its computed offset. struct hmx_fa_vtcm_layout L; - hmx_fa_vtcm_layout_build(&L, G, DK, DV, Br, Bc, n_threads, pipeline); + hmx_fa_vtcm_layout_build(&L, G, DK, DV, Br, Bc, n_threads, pipeline, factx.is_q_fp32); if (L.total_bytes > ctx->vtcm_size) { return HTP_STATUS_VTCM_TOO_SMALL; @@ -1620,6 +1866,7 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) { uint8_t * const base = ctx->vtcm_base; + factx.vtcm_q_dma = VTCM_LAYOUT_PTR(__fp16, base, L.off_q_dma); factx.vtcm_q_tiles = VTCM_LAYOUT_PTR(__fp16, base, L.off_q_tiles); factx.vtcm_o_tiles[0] = VTCM_LAYOUT_PTR(__fp16, base, L.off_o_tiles[0]); factx.vtcm_o_tiles[1] = VTCM_LAYOUT_PTR(__fp16, base, L.off_o_tiles[1]); @@ -1627,12 +1874,16 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) { factx.vtcm_k_fp16[1] = VTCM_LAYOUT_PTR(__fp16, base, L.off_k_fp16[1]); factx.vtcm_v_fp16[0] = VTCM_LAYOUT_PTR(__fp16, base, L.off_v_fp16[0]); factx.vtcm_v_fp16[1] = VTCM_LAYOUT_PTR(__fp16, base, L.off_v_fp16[1]); - factx.vtcm_k_tiles = VTCM_LAYOUT_PTR(__fp16, base, L.off_k_tiles); + factx.vtcm_k_tiles[0] = VTCM_LAYOUT_PTR(__fp16, base, L.off_k_tiles[0]); + factx.vtcm_k_tiles[1] = VTCM_LAYOUT_PTR_OPTIONAL(__fp16, base, L.off_k_tiles[1], pipeline); factx.vtcm_v_tiles[0] = VTCM_LAYOUT_PTR(__fp16, base, L.off_v_tiles[0]); factx.vtcm_v_tiles[1] = VTCM_LAYOUT_PTR_OPTIONAL(__fp16, base, L.off_v_tiles[1], pipeline); - factx.vtcm_s_tiles = VTCM_LAYOUT_PTR(__fp16, base, L.off_s_tiles); - factx.vtcm_p_tiles = VTCM_LAYOUT_PTR(__fp16, base, L.off_p_tiles); + factx.vtcm_s_tiles[0] = VTCM_LAYOUT_PTR(__fp16, base, L.off_s_tiles[0]); + factx.vtcm_s_tiles[1] = VTCM_LAYOUT_PTR_OPTIONAL(__fp16, base, L.off_s_tiles[1], pipeline); + factx.vtcm_p_tiles[0] = VTCM_LAYOUT_PTR(__fp16, base, L.off_p_tiles[0]); + factx.vtcm_p_tiles[1] = VTCM_LAYOUT_PTR_OPTIONAL(__fp16, base, L.off_p_tiles[1], pipeline); factx.vtcm_d_tiles = VTCM_LAYOUT_PTR(__fp16, base, L.off_d_tiles); + factx.vtcm_d_inv_l = VTCM_LAYOUT_PTR(__fp16, base, L.off_d_inv_l); factx.vtcm_m_vec = VTCM_LAYOUT_PTR(HVX_Vector, base, L.off_m_vec); factx.vtcm_l_vec = VTCM_LAYOUT_PTR(HVX_Vector, base, L.off_l_vec); factx.vtcm_s_rowmax = VTCM_LAYOUT_PTR(HVX_Vector, base, L.off_s_rowmax); @@ -1670,6 +1921,12 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) { const size_t qo_element_size = factx.is_q_fp32 ? sizeof(float) : sizeof(__fp16); + const bool q_transposed = q->nb[1] < q->nb[2]; + const size_t q_src_stride = q_transposed ? q->nb[2] : q->nb[1]; + const size_t q_row_bytes_untransposed = factx.G * factx.DK * qo_element_size; + const size_t q_row_bytes_trans_factor = factx.DK * qo_element_size; + const uint32_t kv_rows0 = hex_smin(Bc, nek1); + // ======== Reusable job descriptors for pipeline ======== hmx_fa_qk_job_t qk_job; hmx_fa_o_update_job_t ou_job; @@ -1690,34 +1947,34 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) { const uint32_t iv2 = kv_head; const uint32_t iv3 = fastdiv(ib3, &kparams->broadcast_rv3); - // 1. Push Q DMA (if Q DMA is used) - const size_t o_tile_bytes = factx.o_tile_bytes; - const bool use_q_dma = (2 * o_tile_bytes >= factx.g_br * factx.DK * (factx.is_q_fp32 ? 4 : 2)); - if (use_q_dma) { - const bool q_transposed = q->nb[1] < q->nb[2]; - const uint8_t * q_ptr = (const uint8_t *) q->data + q_start * q->nb[1] + (kv_head * factx.G) * q->nb[2] + ib3 * q->nb[3]; - const size_t el_size = factx.is_q_fp32 ? sizeof(float) : sizeof(__fp16); - const size_t q_row_bytes = q_transposed ? n_rows_q * factx.DK * el_size : factx.G * factx.DK * el_size; - const size_t src_stride = q_transposed ? q->nb[2] : q->nb[1]; + // 1. Push Q and KV DMAs for the very first iteration. + // Subsequent iterations are enqueued early at the end of the previous iteration. + if (ib3 == 0 && q_start == 0 && kv_head == 0) { + const uint8_t * q_ptr = (const uint8_t *) q->data; + const size_t q_row_bytes = q_transposed ? n_rows_q * q_row_bytes_trans_factor : q_row_bytes_untransposed; const size_t n_rows = q_transposed ? factx.G : n_rows_q; - dma_queue_push(dma, dma_make_ptr(factx.vtcm_o_tiles[0], q_ptr), q_row_bytes, hex_smax(src_stride, q_row_bytes), q_row_bytes, n_rows); - } + dma_queue_push(dma, dma_make_ptr(factx.vtcm_q_dma, q_ptr), q_row_bytes, hex_smax(q_src_stride, q_row_bytes), q_row_bytes, n_rows); - // 2. Prefetch first KV block - if (factx.n_kv_blocks > 0) { - const uint32_t kv_rows0 = hex_smin(Bc, nek1); + if (factx.n_kv_blocks > 0) { + const uint8_t * k_src = (const uint8_t *) k->data + ik2 * k->nb[2] + ik3 * k->nb[3]; + dma_queue_push(dma, dma_make_ptr(factx.vtcm_k_fp16[0], k_src), size_k_row_padded, k->nb[1], size_k_row, kv_rows0); - const uint8_t * k_src = (const uint8_t *) k->data + ik2 * k->nb[2] + ik3 * k->nb[3]; - dma_queue_push(dma, dma_make_ptr(factx.vtcm_k_fp16[0], k_src), size_k_row_padded, k->nb[1], size_k_row, kv_rows0); + const uint8_t * v_src = (const uint8_t *) v->data + iv2 * v->nb[2] + iv3 * v->nb[3]; + dma_queue_push(dma, dma_make_ptr(factx.vtcm_v_fp16[0], v_src), size_v_row_padded, v->nb[1], size_v_row, kv_rows0); - const uint8_t * v_src = (const uint8_t *) v->data + iv2 * v->nb[2] + iv3 * v->nb[3]; - dma_queue_push(dma, dma_make_ptr(factx.vtcm_v_fp16[0], v_src), size_v_row_padded, v->nb[1], size_v_row, kv_rows0); + if (factx.pipeline && mask) { + if (__builtin_expect(factx.mask_broadcast, true)) { + const uint8_t * ms_src = (const uint8_t *) mask->data + q_start * mask->nb[1] + im3 * mask->nb[3] + 0; + dma_cache_push(dma, &factx.m_cache, ms_src, m_line_bytes, mask->nb[1], kv_rows0 * sizeof(__fp16), n_rows_q); + } else { + fa_push_mask_dma_gqa(dma, mask, q_start, im3, 0, kv_head, G, m_line_bytes, kv_rows0, n_rows_q, &factx); + } + } + } } - // 3. Pop Q DMA (blocks until Q is loaded) - if (use_q_dma) { - dma_queue_pop(dma); - } + // 2. Pop Q DMA (blocks until Q is loaded) + dma_queue_pop(dma); // ---- Load Q block & Initialize per-block state ---- fa_phase_q_load(&factx, q, q_start, kv_head, ib3, n_rows_g); @@ -1735,79 +1992,43 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) { const size_t k_src_stride = size_k_row_padded / sizeof(__fp16); const size_t v_src_stride = size_v_row_padded / sizeof(__fp16); - struct hmx_queue * hmx_q = ctx->hmx_queue; + hmx_queue_t hmx_q = ctx->hmx_queue; if (factx.pipeline) { - // Pipeline path + // Double-buffered job structs because HMX queue runs asynchronously + hmx_fa_qk_job_t qk_job[2]; + hmx_fa_o_update_job_t ou_job[2]; + + // Prefetch block 1 early if there are multiple blocks + if (factx.n_kv_blocks > 1) { + fa_prefetch_block(dma, k, v, mask, 1, Bc, size_k_row_padded, size_k_row, size_v_row_padded, size_v_row, + ik2, ik3, iv2, iv3, q_start, im3, kv_head, G, m_line_bytes, n_rows_q, nek1, 1, &factx); + } + + // Prep and start QK-dot(0) + void * curr_k0 = dma_queue_pop(dma).dst; + fa_phase_k_interleave(&factx, kv_rows0, k_src_stride, curr_k0, 0, 0); + + qk_job[0].q_tiles = factx.vtcm_q_tiles; + qk_job[0].k_tiles = factx.vtcm_k_tiles[0]; + qk_job[0].s_tiles = factx.vtcm_s_tiles[0]; + qk_job[0].n_row_tiles = n_row_tiles; + qk_job[0].n_col_tiles = hmx_ceil_div(kv_rows0, HMX_FP16_TILE_N_COLS); + qk_job[0].n_dot_tiles = DK / 32; + qk_job[0].n_tiles_per_bc = n_tiles_per_bc; + qk_job[0].hmx_scales = factx.vtcm_hmx_scales_qk; + hmx_queue_push(hmx_q, hmx_queue_make_desc(hmx_fa_qk_dot_worker, &qk_job[0])); + for (uint32_t kv_blk = 0; kv_blk < factx.n_kv_blocks; ++kv_blk) { const uint32_t kv_start = kv_blk * Bc; const uint32_t kv_rows = hex_smin(Bc, nek1 - kv_start); const size_t n_col_tiles = hmx_ceil_div(kv_rows, HMX_FP16_TILE_N_COLS); - // Push mask DMA - if (mask) { - if (__builtin_expect(factx.mask_broadcast, true)) { - const uint8_t * ms_src = (const uint8_t *) mask->data + q_start * mask->nb[1] + im3 * mask->nb[3] + kv_start * sizeof(__fp16); - dma_cache_push(dma, &factx.m_cache, ms_src, m_line_bytes, mask->nb[1], kv_rows * sizeof(__fp16), n_rows_q); - } else { - fa_push_mask_dma_gqa(dma, mask, q_start, im3, kv_start, kv_head, G, m_line_bytes, kv_rows, n_rows_q, &factx); - } - } - - // Prefetch next KV block early - if (kv_blk + 1 < factx.n_kv_blocks) { - const uint32_t prefetch_start = (kv_blk + 1) * Bc; - const uint32_t prefetch_rows = hex_smin(Bc, nek1 - prefetch_start); - const size_t prefetch_buf = 1 - buf_idx; - const uint8_t * k_prefetch_src = (const uint8_t *) k->data + prefetch_start * k->nb[1] + ik2 * k->nb[2] + ik3 * k->nb[3]; - dma_queue_push(dma, dma_make_ptr(factx.vtcm_k_fp16[prefetch_buf], k_prefetch_src), size_k_row_padded, k->nb[1], size_k_row, prefetch_rows); - const uint8_t * v_prefetch_src = (const uint8_t *) v->data + prefetch_start * v->nb[1] + iv2 * v->nb[2] + iv3 * v->nb[3]; - dma_queue_push(dma, dma_make_ptr(factx.vtcm_v_fp16[prefetch_buf], v_prefetch_src), size_v_row_padded, v->nb[1], size_v_row, prefetch_rows); - } - - // ---- Phase 1: K_int ---- - if (kv_blk > 0) { - ou_job.o_curr = o_tile_curr; - ou_job.o_prev = o_tile_prev; - ou_job.p_tiles = factx.vtcm_p_tiles; - ou_job.v_tiles = factx.vtcm_v_tiles[1 - buf_idx]; - ou_job.d_tiles = factx.vtcm_d_tiles; - ou_job.hmx_scales = factx.vtcm_hmx_scales_id; - ou_job.n_row_tiles = n_row_tiles; - ou_job.n_col_tiles = hmx_ceil_div(hex_smin(Bc, nek1 - (kv_blk - 1) * Bc), HMX_FP16_TILE_N_COLS); - ou_job.n_row_tiles_g_br = n_row_tiles_g_br; - ou_job.n_tiles_per_bc = n_tiles_per_bc; - ou_job.DV = DV; - hmx_queue_push(hmx_q, hmx_queue_make_desc(hmx_fa_o_update_worker, &ou_job)); - } - - // Wait for current K DMA and interleave - void * curr_k = dma_queue_pop(dma).dst; - fa_phase_k_interleave(&factx, kv_rows, k_src_stride, curr_k, kv_start); - - // ---- Phase 2: qk_dot ---- - qk_job.q_tiles = factx.vtcm_q_tiles; - qk_job.k_tiles = factx.vtcm_k_tiles; - qk_job.s_tiles = factx.vtcm_s_tiles; - qk_job.n_row_tiles = n_row_tiles; - qk_job.n_col_tiles = n_col_tiles; - qk_job.n_dot_tiles = DK / 32; - qk_job.n_tiles_per_bc = n_tiles_per_bc; - qk_job.hmx_scales = factx.vtcm_hmx_scales_qk; - hmx_queue_push(hmx_q, hmx_queue_make_desc(hmx_fa_qk_dot_worker, &qk_job)); - - // Wait for current V DMA and interleave + // ---- 1. Pop and run V-prep for current block ---- void * curr_v = dma_queue_pop(dma).dst; fa_phase_v_interleave(&factx, kv_rows, v_src_stride, curr_v, factx.vtcm_v_tiles[buf_idx], n_tiles_per_bc, kv_start); - if (kv_blk > 0) { - hmx_queue_pop(hmx_q); - hex_swap_ptr((void **) &o_tile_curr, (void **) &o_tile_prev); - } - - hmx_queue_pop(hmx_q); - - // ---- Phase 3: softmax + build_D ---- + // ---- 2. Pop and run mask-prep for current block ---- __fp16 * current_mask_vtcm = NULL; if (mask) { if (__builtin_expect(factx.mask_broadcast, true)) { @@ -1818,9 +2039,34 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) { } } + // ---- 3. Pop and run K-prep for next block & push next QK-dot ---- + if (kv_blk + 1 < factx.n_kv_blocks) { + const uint32_t next_start = (kv_blk + 1) * Bc; + const uint32_t next_rows = hex_smin(Bc, nek1 - next_start); + const size_t next_buf = 1 - buf_idx; + + void * next_k = dma_queue_pop(dma).dst; + fa_phase_k_interleave(&factx, next_rows, k_src_stride, next_k, next_start, next_buf); + + qk_job[next_buf].q_tiles = factx.vtcm_q_tiles; + qk_job[next_buf].k_tiles = factx.vtcm_k_tiles[next_buf]; + qk_job[next_buf].s_tiles = factx.vtcm_s_tiles[next_buf]; + qk_job[next_buf].n_row_tiles = n_row_tiles; + qk_job[next_buf].n_col_tiles = hmx_ceil_div(next_rows, HMX_FP16_TILE_N_COLS); + qk_job[next_buf].n_dot_tiles = DK / 32; + qk_job[next_buf].n_tiles_per_bc = n_tiles_per_bc; + qk_job[next_buf].hmx_scales = factx.vtcm_hmx_scales_qk; + hmx_queue_push(hmx_q, hmx_queue_make_desc(hmx_fa_qk_dot_worker, &qk_job[next_buf])); + } + + // ---- 4. Wait for current block's QK-dot to finish ---- + hmx_queue_pop(hmx_q); + + // ---- 5. Phase 2: softmax + build_D ---- fa_softmax_args_t sargs; memset(&sargs, 0, sizeof(sargs)); sargs.factx = &factx; + sargs.buf_idx = buf_idx; sargs.kv_rows = kv_rows; sargs.n_rows_g = n_rows_g; sargs.n_col_tiles = n_col_tiles; @@ -1838,8 +2084,39 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) { sargs.mask_vtcm = current_mask_vtcm; sargs.mask_vtcm_row_stride = factx.mask_buf_row_stride; sargs.slopes = factx.vtcm_slopes; + + // Start HMX O update for block kv_blk - 1 (reads P[1 - buf_idx], V[1 - buf_idx]) + if (kv_blk > 0) { + const size_t prev_buf = 1 - buf_idx; + ou_job[prev_buf].o_curr = o_tile_curr; + ou_job[prev_buf].o_prev = o_tile_prev; + ou_job[prev_buf].p_tiles = factx.vtcm_p_tiles[prev_buf]; + ou_job[prev_buf].v_tiles = factx.vtcm_v_tiles[prev_buf]; + ou_job[prev_buf].d_tiles = factx.vtcm_d_tiles; + ou_job[prev_buf].hmx_scales = factx.vtcm_hmx_scales_id; + ou_job[prev_buf].n_row_tiles = n_row_tiles; + ou_job[prev_buf].n_col_tiles = hmx_ceil_div(hex_smin(Bc, nek1 - (kv_blk - 1) * Bc), HMX_FP16_TILE_N_COLS); + ou_job[prev_buf].n_row_tiles_g_br = n_row_tiles_g_br; + ou_job[prev_buf].n_tiles_per_bc = n_tiles_per_bc; + ou_job[prev_buf].DV = DV; + hmx_queue_push(hmx_q, hmx_queue_make_desc(hmx_fa_o_update_worker, &ou_job[prev_buf])); + } + + // Run Softmax on HVX (blocking call) fa_phase_softmax_and_build_d(&factx, &sargs, n_row_tiles, n_row_tiles_g_br); + // Wait for HMX O update for block kv_blk - 1 to finish + if (kv_blk > 0) { + hmx_queue_pop(hmx_q); + hex_swap_ptr((void **) &o_tile_curr, (void **) &o_tile_prev); + } + + // Prefetch block kv_blk + 2 + if (kv_blk + 2 < factx.n_kv_blocks) { + fa_prefetch_block(dma, k, v, mask, kv_blk + 2, Bc, size_k_row_padded, size_k_row, size_v_row_padded, size_v_row, + ik2, ik3, iv2, iv3, q_start, im3, kv_head, G, m_line_bytes, n_rows_q, nek1, buf_idx, &factx); + } + buf_idx = 1 - buf_idx; } @@ -1847,18 +2124,23 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) { if (factx.n_kv_blocks > 0) { const uint32_t last_blk = factx.n_kv_blocks - 1; const size_t last_cols = hmx_ceil_div(hex_smin(Bc, nek1 - last_blk * Bc), HMX_FP16_TILE_N_COLS); - ou_job.o_curr = o_tile_curr; - ou_job.o_prev = o_tile_prev; - ou_job.p_tiles = factx.vtcm_p_tiles; - ou_job.v_tiles = factx.vtcm_v_tiles[1 - buf_idx]; - ou_job.d_tiles = factx.vtcm_d_tiles; - ou_job.hmx_scales = factx.vtcm_hmx_scales_id; - ou_job.n_row_tiles = n_row_tiles; - ou_job.n_col_tiles = last_cols; - ou_job.n_row_tiles_g_br = n_row_tiles_g_br; - ou_job.n_tiles_per_bc = n_tiles_per_bc; - ou_job.DV = DV; - hmx_queue_push(hmx_q, hmx_queue_make_desc(hmx_fa_o_update_worker, &ou_job)); + ou_job[0].o_curr = o_tile_curr; + ou_job[0].o_prev = o_tile_prev; + ou_job[0].p_tiles = factx.vtcm_p_tiles[1 - buf_idx]; + ou_job[0].v_tiles = factx.vtcm_v_tiles[1 - buf_idx]; + ou_job[0].d_tiles = factx.vtcm_d_tiles; + ou_job[0].hmx_scales = factx.vtcm_hmx_scales_id; + ou_job[0].n_row_tiles = n_row_tiles; + ou_job[0].n_col_tiles = last_cols; + ou_job[0].n_row_tiles_g_br = n_row_tiles_g_br; + ou_job[0].n_tiles_per_bc = n_tiles_per_bc; + ou_job[0].DV = DV; + hmx_queue_push(hmx_q, hmx_queue_make_desc(hmx_fa_o_update_worker, &ou_job[0])); + + // Overlapped: run HVX build diag inv L while HMX is busy executing the update + htp_trace_event_start(tr_hvx, HTP_TRACE_EVT_HVX_O_PROC, (uint16_t) q_start); + fa_build_d_diag_inv_l(&factx, n_row_tiles, n_row_tiles_g_br); + htp_trace_event_stop(tr_hvx, HTP_TRACE_EVT_HVX_O_PROC, (uint16_t) q_start); hmx_queue_pop(hmx_q); hex_swap_ptr((void **) &o_tile_curr, (void **) &o_tile_prev); @@ -1892,12 +2174,12 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) { // Wait for current K DMA and interleave void * curr_k = dma_queue_pop(dma).dst; - fa_phase_k_interleave(&factx, kv_rows, k_src_stride, curr_k, kv_start); + fa_phase_k_interleave(&factx, kv_rows, k_src_stride, curr_k, kv_start, 0); { qk_job.q_tiles = factx.vtcm_q_tiles; - qk_job.k_tiles = factx.vtcm_k_tiles; - qk_job.s_tiles = factx.vtcm_s_tiles; + qk_job.k_tiles = factx.vtcm_k_tiles[0]; + qk_job.s_tiles = factx.vtcm_s_tiles[0]; qk_job.n_row_tiles = n_row_tiles; qk_job.n_col_tiles = n_col_tiles; qk_job.n_dot_tiles = (size_t) (DK / 32); @@ -1948,7 +2230,7 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) { { ou_job.o_curr = o_tile_curr; ou_job.o_prev = o_tile_prev; - ou_job.p_tiles = factx.vtcm_p_tiles; + ou_job.p_tiles = factx.vtcm_p_tiles[0]; ou_job.v_tiles = factx.vtcm_v_tiles[0]; ou_job.d_tiles = factx.vtcm_d_tiles; ou_job.hmx_scales = factx.vtcm_hmx_scales_id; @@ -1959,6 +2241,12 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) { ou_job.DV = DV; hmx_queue_push(ctx->hmx_queue, hmx_queue_make_desc(hmx_fa_o_update_worker, &ou_job)); + if (kv_blk + 1 == factx.n_kv_blocks) { + // Overlapped: run HVX build diag inv L while HMX is busy executing the update + htp_trace_event_start(tr_hvx, HTP_TRACE_EVT_HVX_O_PROC, (uint16_t) q_start); + fa_build_d_diag_inv_l(&factx, n_row_tiles, n_row_tiles_g_br); + htp_trace_event_stop(tr_hvx, HTP_TRACE_EVT_HVX_O_PROC, (uint16_t) q_start); + } hmx_queue_pop(ctx->hmx_queue); hex_swap_ptr((void **) &o_tile_curr, (void **) &o_tile_prev); @@ -1968,15 +2256,63 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) { } } + // Enqueue DMAs for the next iteration early so they overlap with O-PROC + uint32_t next_kv_head = kv_head + 1; + uint32_t next_q_start = q_start; + uint32_t next_ib3 = ib3; + if (next_kv_head >= n_kv_heads) { + next_kv_head = 0; + next_q_start = q_start + Br; + if (next_q_start >= neq1) { + next_q_start = 0; + next_ib3 = ib3 + 1; + } + } + bool has_next = (next_ib3 < neq3); + + if (has_next) { + const uint32_t next_n_rows_q = hex_smin(Br, neq1 - next_q_start); + const uint8_t * next_q_ptr = (const uint8_t *) q->data + next_q_start * q->nb[1] + (next_kv_head * factx.G) * q->nb[2] + next_ib3 * q->nb[3]; + const size_t next_q_row_bytes = q_transposed ? next_n_rows_q * q_row_bytes_trans_factor : q_row_bytes_untransposed; + const size_t next_n_rows = q_transposed ? factx.G : next_n_rows_q; + dma_queue_push(dma, dma_make_ptr(factx.vtcm_q_dma, next_q_ptr), next_q_row_bytes, hex_smax(q_src_stride, next_q_row_bytes), next_q_row_bytes, next_n_rows); + + if (factx.n_kv_blocks > 0) { + const uint32_t next_ik2 = next_kv_head; + const uint32_t next_iv2 = next_kv_head; + uint32_t next_ik3 = ik3; + uint32_t next_iv3 = iv3; + if (next_ib3 != ib3) { + next_ik3 = fastdiv(next_ib3, &kparams->broadcast_rk3); + next_iv3 = fastdiv(next_ib3, &kparams->broadcast_rv3); + } + + const uint8_t * next_k_src = (const uint8_t *) k->data + next_ik2 * k->nb[2] + next_ik3 * k->nb[3]; + dma_queue_push(dma, dma_make_ptr(factx.vtcm_k_fp16[0], next_k_src), size_k_row_padded, k->nb[1], size_k_row, kv_rows0); + + const uint8_t * next_v_src = (const uint8_t *) v->data + next_iv2 * v->nb[2] + next_iv3 * v->nb[3]; + dma_queue_push(dma, dma_make_ptr(factx.vtcm_v_fp16[0], next_v_src), size_v_row_padded, v->nb[1], size_v_row, kv_rows0); + + if (factx.pipeline && mask) { + uint32_t next_im3 = im3; + if (next_ib3 != ib3) { + next_im3 = fastmodulo(next_ib3, mask->ne[3], &factx.src3_div3); + } + if (__builtin_expect(factx.mask_broadcast, true)) { + const uint8_t * ms_src = (const uint8_t *) mask->data + next_q_start * mask->nb[1] + next_im3 * mask->nb[3] + 0; + dma_cache_push(dma, &factx.m_cache, ms_src, m_line_bytes, mask->nb[1], kv_rows0 * sizeof(__fp16), next_n_rows_q); + } else { + fa_push_mask_dma_gqa(dma, mask, next_q_start, next_im3, 0, next_kv_head, G, m_line_bytes, kv_rows0, next_n_rows_q, &factx); + } + } + } + } + // ---- Final normalization ---- { - htp_trace_event_start(tr_hvx, HTP_TRACE_EVT_HVX_O_PROC, (uint16_t) q_start); - fa_build_d_diag_inv_l(&factx, n_row_tiles, n_row_tiles_g_br); - htp_trace_event_stop(tr_hvx, HTP_TRACE_EVT_HVX_O_PROC, (uint16_t) q_start); - on_job.o_curr = o_tile_curr; on_job.o_prev = o_tile_prev; - on_job.d_tiles = factx.vtcm_d_tiles; + on_job.d_tiles = factx.vtcm_d_inv_l; on_job.hmx_scales = factx.vtcm_hmx_scales_id; on_job.n_row_tiles = n_row_tiles; on_job.n_row_tiles_g_br = n_row_tiles_g_br; @@ -2084,7 +2420,7 @@ int op_flash_attn_ext(struct htp_ops_context * octx) { } if (!(octx->flags & HTP_OPFLAGS_SKIP_COMPUTE)) { - worker_pool_run_func(octx->ctx->worker_pool, flash_attn_ext_f16_thread, &factx, octx->n_threads); + work_queue_run(octx->ctx->work_queue, flash_attn_ext_f16_thread, &factx, octx->n_threads); } return HTP_STATUS_OK; diff --git a/ggml/src/ggml-hexagon/htp/flash-attn-ops.h b/ggml/src/ggml-hexagon/htp/flash-attn-ops.h index 16822f22bf6e..efe5ce548173 100644 --- a/ggml/src/ggml-hexagon/htp/flash-attn-ops.h +++ b/ggml/src/ggml-hexagon/htp/flash-attn-ops.h @@ -101,14 +101,16 @@ static_assert(sizeof(struct htp_fa_kernel_params) <= 128, "htp_fa_kernel_params struct hmx_fa_vtcm_layout { // Byte offsets from vtcm_base for each region. size_t off_q_tiles; + size_t off_q_dma; size_t off_o_tiles[2]; size_t off_k_fp16[2]; size_t off_v_fp16[2]; - size_t off_k_tiles; - size_t off_v_tiles[2]; // [1] allocated only when pipeline, else 0 - size_t off_s_tiles; - size_t off_p_tiles; + size_t off_k_tiles[2]; + size_t off_v_tiles[2]; + size_t off_s_tiles[2]; + size_t off_p_tiles[2]; size_t off_d_tiles; + size_t off_d_inv_l; size_t off_m_vec; size_t off_l_vec; size_t off_s_rowmax; @@ -140,7 +142,7 @@ struct hmx_fa_vtcm_layout { static inline void hmx_fa_vtcm_layout_build(struct hmx_fa_vtcm_layout * L, size_t gqa_factor, size_t DK, size_t DV, - size_t Br, size_t Bc, size_t n_threads, bool pipeline) { + size_t Br, size_t Bc, size_t n_threads, bool pipeline, bool is_q_fp32) { const size_t g_br = hex_align_up(gqa_factor * Br, HMX_FP16_TILE_N_ROWS); const size_t q_tile_size = hex_align_up(g_br * DK * sizeof(__fp16), HTP_FA_HMX_TILE_SIZE); const size_t o_tile_size = hex_align_up(g_br * DV * sizeof(__fp16), HTP_FA_HMX_TILE_SIZE); @@ -149,6 +151,7 @@ static inline void hmx_fa_vtcm_layout_build(struct hmx_fa_vtcm_layout * L, const size_t s_tile_size = hex_align_up(g_br * Bc * sizeof(__fp16), HTP_FA_HMX_TILE_SIZE); const size_t d_tile_size = hex_align_up(g_br * g_br * sizeof(__fp16), HTP_FA_HMX_TILE_SIZE); + const size_t q_dma_size = hex_align_up(g_br * DK * (is_q_fp32 ? sizeof(float) : sizeof(__fp16)), 128); const size_t k_dma_size = hex_align_up(Bc * hex_round_up(DK * sizeof(__fp16), 128), 128); const size_t v_dma_size = hex_align_up(Bc * hex_round_up(DV * sizeof(__fp16), 128), 128); const size_t col_vec_size = hex_align_up(g_br * sizeof(float), 256); @@ -160,27 +163,47 @@ static inline void hmx_fa_vtcm_layout_build(struct hmx_fa_vtcm_layout * L, size_t off = 0; - // Section 1: HMX Tiled Buffers (FA_HMX_TILE_SIZE = 2KB Aligned) + // Group A (Part 1 - HMX Tiled buffers) VTCM_LAYOUT_ALLOC(off, off_q_tiles, q_tile_size); VTCM_LAYOUT_ALLOC(off, off_o_tiles[0], o_tile_size); VTCM_LAYOUT_ALLOC(off, off_o_tiles[1], o_tile_size); - VTCM_LAYOUT_ALLOC(off, off_k_tiles, k_tile_size); - VTCM_LAYOUT_ALLOC(off, off_v_tiles[0], v_tile_size); - VTCM_LAYOUT_ALLOC_OPTIONAL(off, off_v_tiles[1], v_tile_size, pipeline); - VTCM_LAYOUT_ALLOC(off, off_s_tiles, s_tile_size); - VTCM_LAYOUT_ALLOC(off, off_p_tiles, s_tile_size); VTCM_LAYOUT_ALLOC(off, off_d_tiles, d_tile_size); + VTCM_LAYOUT_ALLOC(off, off_d_inv_l, d_tile_size); - // Section 2: HVX/DMA flat and vector buffers (128B / 256B Aligned) + // Group B & C share start offset (Group B tiles must be 2KB aligned) + size_t off_group_b_c = hex_align_up(off, HTP_FA_HMX_TILE_SIZE); + + // Group B: Compute-only buffers + size_t off_group_b = off_group_b_c; + VTCM_LAYOUT_ALLOC(off_group_b, off_k_tiles[0], k_tile_size); + VTCM_LAYOUT_ALLOC_OPTIONAL(off_group_b, off_k_tiles[1], k_tile_size, pipeline); + VTCM_LAYOUT_ALLOC(off_group_b, off_v_tiles[0], v_tile_size); + VTCM_LAYOUT_ALLOC_OPTIONAL(off_group_b, off_v_tiles[1], v_tile_size, pipeline); + VTCM_LAYOUT_ALLOC(off_group_b, off_s_tiles[0], s_tile_size); + VTCM_LAYOUT_ALLOC_OPTIONAL(off_group_b, off_s_tiles[1], s_tile_size, pipeline); + VTCM_LAYOUT_ALLOC(off_group_b, off_p_tiles[0], s_tile_size); + VTCM_LAYOUT_ALLOC_OPTIONAL(off_group_b, off_p_tiles[1], s_tile_size, pipeline); + VTCM_LAYOUT_ALLOC(off_group_b, off_s_rowmax, col_vec_size); + VTCM_LAYOUT_ALLOC(off_group_b, off_p_rowsum, col_vec_size); + VTCM_LAYOUT_ALLOC(off_group_b, off_row_bufs, row_vec_size * 2 * n_threads); + + const size_t group_b_size = off_group_b - off_group_b_c; + + // Group C: Q fetch DMA buffer + size_t off_group_c = off_group_b_c; + VTCM_LAYOUT_ALLOC(off_group_c, off_q_dma, q_dma_size); + + const size_t group_c_size = off_group_c - off_group_b_c; + + off = off_group_b_c + hex_smax(group_b_size, group_c_size); + + // Group A (Part 2 - remaining non-HMX buffers) VTCM_LAYOUT_ALLOC(off, off_k_fp16[0], k_dma_size); VTCM_LAYOUT_ALLOC(off, off_k_fp16[1], k_dma_size); VTCM_LAYOUT_ALLOC(off, off_v_fp16[0], v_dma_size); VTCM_LAYOUT_ALLOC(off, off_v_fp16[1], v_dma_size); VTCM_LAYOUT_ALLOC(off, off_m_vec, col_vec_size); VTCM_LAYOUT_ALLOC(off, off_l_vec, col_vec_size); - VTCM_LAYOUT_ALLOC(off, off_s_rowmax, col_vec_size); - VTCM_LAYOUT_ALLOC(off, off_p_rowsum, col_vec_size); - VTCM_LAYOUT_ALLOC(off, off_row_bufs, row_vec_size * 2 * n_threads); VTCM_LAYOUT_ALLOC(off, off_hmx_scales_id, 256); VTCM_LAYOUT_ALLOC(off, off_hmx_scales_qk, 256); VTCM_LAYOUT_ALLOC(off, off_mask_buf, m_buf_size); @@ -200,9 +223,9 @@ static inline void hmx_fa_vtcm_layout_build(struct hmx_fa_vtcm_layout * L, } // Exact VTCM usage for a given (gqa_factor, DK, DV, Br, Bc) configuration. -static inline size_t hmx_fa_compute_vtcm_usage(size_t gqa_factor, size_t DK, size_t DV, size_t Br, size_t Bc, size_t n_threads, bool pipeline) { +static inline size_t hmx_fa_compute_vtcm_usage(size_t gqa_factor, size_t DK, size_t DV, size_t Br, size_t Bc, size_t n_threads, bool pipeline, bool is_q_fp32) { struct hmx_fa_vtcm_layout L; - hmx_fa_vtcm_layout_build(&L, gqa_factor, DK, DV, Br, Bc, n_threads, pipeline); + hmx_fa_vtcm_layout_build(&L, gqa_factor, DK, DV, Br, Bc, n_threads, pipeline, is_q_fp32); return L.total_bytes; } @@ -239,7 +262,8 @@ static inline int hmx_fa_find_chunk_size(size_t * Br_out, size_t qo_len, size_t kv_len, size_t vtcm_budget, - size_t n_threads) { + size_t n_threads, + bool is_q_fp32) { const size_t T = HMX_FP16_TILE_N_ROWS; // 32 const size_t br_unit = hmx_ceil_div(T, gqa_factor); const size_t bc_unit = HMX_FP16_TILE_N_COLS * 2; // 64 @@ -253,8 +277,9 @@ static inline int hmx_fa_find_chunk_size(size_t * Br_out, const size_t Bc_limit = can_pipeline ? hex_align_down(kv_len / FA_MIN_KV_BLOCKS, bc_unit) : (kv_len >= bc_unit ? hex_align_down(kv_len, bc_unit) : bc_unit); // Cost coefficients calibrated from profiling - const size_t c_q_fixed = 1400; // per-Q-block: q_load + epilogue o_update + o_norm + o_store - const size_t c_iter_fixed = 200; // per-KV-iter: HMX queue push/pop + DMA pop + barriers + const size_t c_q_fixed = 800; // per-Q-block: q_load + epilogue o_update + o_norm + o_store + const size_t c_iter_base = 200; // per-KV-iter base (HMX dot/update + DMA) + const size_t c_softmax = 600; // per 64-row vector chunk on HVX size_t best_cost = SIZE_MAX, best_mn = 0; size_t best_Br = 0, best_Bc = 0; @@ -262,13 +287,20 @@ static inline int hmx_fa_find_chunk_size(size_t * Br_out, for (size_t Br = Br_max; Br >= br_unit; Br -= br_unit) { // Try all Bc candidates from Bc_limit down to bc_unit for (size_t Bc = Bc_limit; Bc >= bc_unit; Bc -= bc_unit) { - size_t vtcm_needed = hmx_fa_compute_vtcm_usage(gqa_factor, DK, DV, Br, Bc, n_threads, can_pipeline); + size_t vtcm_needed = hmx_fa_compute_vtcm_usage(gqa_factor, DK, DV, Br, Bc, n_threads, can_pipeline, is_q_fp32); if (vtcm_needed <= vtcm_budget) { // This Bc fits for this Br! - const size_t q_blocks = (qo_len + Br - 1) / Br; - const size_t kv_blocks = (kv_len + Bc - 1) / Bc; - const size_t cost = q_blocks * (c_q_fixed + kv_blocks * c_iter_fixed); - const size_t mn = Br * Bc; + const size_t q_blocks = (qo_len + Br - 1) / Br; + const size_t kv_blocks = (kv_len + Bc - 1) / Bc; + const size_t actual_threads = (kv_blocks >= 3 && n_threads >= 2) ? n_threads : 1; + const size_t n_rows_g = Br * gqa_factor; + const size_t n_row_vec_cnt = (n_rows_g + 63) / 64; + const size_t n_use = n_row_vec_cnt < actual_threads ? n_row_vec_cnt : actual_threads; + const size_t vecs_per_t = n_use > 0 ? (n_row_vec_cnt + n_use - 1) / n_use : 1; + + const size_t c_iter_actual = c_iter_base + c_softmax * vecs_per_t; + const size_t cost = q_blocks * (c_q_fixed + kv_blocks * c_iter_actual); + const size_t mn = Br * Bc; if (cost < best_cost || (cost == best_cost && mn > best_mn)) { best_cost = cost; diff --git a/ggml/src/ggml-hexagon/htp/hex-bitmap.h b/ggml/src/ggml-hexagon/htp/hex-bitmap.h new file mode 100644 index 000000000000..140898852a11 --- /dev/null +++ b/ggml/src/ggml-hexagon/htp/hex-bitmap.h @@ -0,0 +1,24 @@ +#ifndef HEX_BITMAP_H +#define HEX_BITMAP_H + +#include +#include +#include + +static inline void bitmap_set(uint32_t * bitmap, uint32_t idx) { + bitmap[idx / 32] |= (1U << (idx % 32)); +} + +static inline void bitmap_clear(uint32_t * bitmap, uint32_t idx) { + bitmap[idx / 32] &= ~(1U << (idx % 32)); +} + +static inline bool bitmap_test(const uint32_t * bitmap, uint32_t idx) { + return (bitmap[idx / 32] & (1U << (idx % 32))) != 0; +} + +static inline void bitmap_reset(uint32_t * bitmap, size_t size_in_bits) { + memset(bitmap, 0, ((size_in_bits + 31) / 32) * sizeof(uint32_t)); +} + +#endif // HEX_BITMAP_H diff --git a/ggml/src/ggml-hexagon/htp/hex-dma.c b/ggml/src/ggml-hexagon/htp/hex-dma.c deleted file mode 100644 index b66e2d2603ce..000000000000 --- a/ggml/src/ggml-hexagon/htp/hex-dma.c +++ /dev/null @@ -1,63 +0,0 @@ -#include "hex-dma.h" - -#include -#include -#include - -#pragma clang diagnostic ignored "-Wunused-function" - -static inline uint32_t pow2_ceil(uint32_t x) { - if (x <= 1) { - return 1; - } - int p = 2; - x--; - while (x >>= 1) { - p <<= 1; - } - return p; -} - -dma_queue * dma_queue_create(size_t capacity) { - dma_queue * q = (dma_queue *) memalign(32, sizeof(dma_queue)); - if (q == NULL) { - FARF(ERROR, "%s: failed to allocate DMA queue\n", __FUNCTION__); - return NULL; - } - - capacity = pow2_ceil(capacity); - - memset(q, 0, sizeof(dma_queue)); - q->capacity = capacity; - q->idx_mask = capacity - 1; - - q->desc = (dma_descriptor_2d *) memalign(64, capacity * sizeof(dma_descriptor_2d)); - memset(q->desc, 0, capacity * sizeof(dma_descriptor_2d)); - - q->dptr = (dma_ptr *) memalign(4, capacity * sizeof(dma_ptr)); - memset(q->dptr, 0, capacity * sizeof(dma_ptr)); - - q->tail = &q->desc[capacity - 1]; - - if (!q->desc && !q->dptr) { - FARF(ERROR, "%s: failed to allocate DMA queue items\n", __FUNCTION__); - return NULL; - } - - FARF(HIGH, "dma-queue: capacity %u\n", capacity); - - return q; -} - -void dma_queue_delete(dma_queue * q) { - if (!q) { - return; - } - free(q->desc); - free(q->dptr); - free(q); -} - -void dma_queue_flush(dma_queue * q) { - while (dma_queue_pop(q).dst != NULL) ; -} diff --git a/ggml/src/ggml-hexagon/htp/hex-dma.h b/ggml/src/ggml-hexagon/htp/hex-dma.h index 98fcc9fda63f..9e9a5f9502a0 100644 --- a/ggml/src/ggml-hexagon/htp/hex-dma.h +++ b/ggml/src/ggml-hexagon/htp/hex-dma.h @@ -1,375 +1,2 @@ -#ifndef HTP_DMA_H -#define HTP_DMA_H - -#include -#include -#include -#include -#include "hex-utils.h" - -#include "hex-profile.h" - -#ifdef __cplusplus -extern "C" { -#endif - -// Define the HW descriptor structs here since the ones in HexSDK are a bit out of date -typedef struct dma_descriptor_1d_s { - void * next; - uint32_t size:24; - uint32_t desc_size:2; - uint32_t dst_comp:1; - uint32_t src_comp:1; - uint32_t dst_bypass:1; - uint32_t src_bypass:1; - uint32_t order:1; - uint32_t done:1; - void * src; - void * dst; -} dma_descriptor_1d; - -#if __HVX_ARCH__ < 75 - -typedef struct dma_descriptor_2d_s { - void * next; - uint32_t reserved0:24; - uint32_t desc_size:2; - uint32_t dst_comp:1; - uint32_t src_comp:1; - uint32_t dst_bypass:1; - uint32_t src_bypass:1; - uint32_t order:1; - uint32_t done:1; - void * src; - void * dst; - uint32_t desc_type:8; - uint32_t reserved1:24; - uint32_t row_size:16; - uint32_t nrows:16; - uint32_t src_stride:16; - uint32_t dst_stride:16; - uint32_t src_offset:16; - uint32_t dst_offset:16; -} dma_descriptor_2d; - -#else - -typedef struct dma_descriptor_2d_s { - void * next; - uint32_t dst_stride:24; - uint32_t desc_size:2; - uint32_t dst_comp:1; - uint32_t src_comp:1; - uint32_t dst_bypass:1; - uint32_t src_bypass:1; - uint32_t order:1; - uint32_t done:1; - void * src; - void * dst; - uint32_t desc_type:8; - uint32_t reserved0:24; - uint32_t row_size:24; - uint32_t nrows_lo:8; - uint32_t nrows_hi:8; - uint32_t src_stride:24; - uint32_t offset:24; - uint32_t reserved1:8; -} dma_descriptor_2d; - -#endif - -typedef struct { - void *dst; - const void *src; -} dma_ptr; - -typedef struct { - dma_descriptor_2d * desc; // descriptor pointers - dma_descriptor_2d * tail; // tail pointer - dma_ptr * dptr; // dst/src pointers - uint32_t push_idx; - uint32_t pop_idx; - uint32_t capacity; - uint32_t idx_mask; - struct htp_thread_trace * trace; -} dma_queue; - -dma_queue * dma_queue_create(size_t capacity); -void dma_queue_delete(dma_queue * q); -void dma_queue_flush(dma_queue * q); - -// TODO: technically we don't need these and could use Q6_dmstart/wait/etc instead -// but those do not seem to always compiler properly. -static inline void dmstart(void * next) { - asm volatile(" release(%0):at" : : "r"(next)); - asm volatile(" dmstart(%0)" : : "r"(next)); -} - -static inline void dmlink(void * cur, void * next) { - asm volatile(" release(%0):at" : : "r"(next)); - asm volatile(" dmlink(%0, %1)" : : "r"(cur), "r"(next)); -} - -static inline unsigned int dmpoll(void) { - unsigned int ret = 0; - asm volatile(" %0 = dmpoll" : "=r"(ret) : : "memory"); - return ret; -} - -static inline unsigned int dmwait(void) { - unsigned int ret = 0; - asm volatile(" %0 = dmwait" : "=r"(ret) : : "memory"); - return ret; -} - -static inline dma_ptr dma_make_ptr(void *dst, const void *src) -{ - dma_ptr p = { dst, src }; - return p; -} - -static const uint32_t dma_src_l2_bypass_on = 1; -static const uint32_t dma_dst_l2_bypass_on = 1; - -static inline bool dma_queue_push_single_1d(dma_queue * q, dma_ptr dptr, size_t size) { - if (((q->push_idx + 1) & q->idx_mask) == q->pop_idx) { - FARF(HIGH, "dma-push: queue full\n"); - return false; - } - - dma_descriptor_1d * desc = (dma_descriptor_1d *) &q->desc[q->push_idx]; - desc->src = (void *) dptr.src; - desc->dst = (void *) dptr.dst; - desc->size = size; - - q->dptr[q->push_idx] = dptr; - - if (size) { - desc->next = NULL; - desc->desc_size = 0; // 1D mode - desc->src_bypass = dma_src_l2_bypass_on; - desc->dst_bypass = dma_dst_l2_bypass_on; - desc->order = 0; - desc->done = 0; - - htp_trace_event_start(q->trace, HTP_TRACE_EVT_DMA, q->push_idx); - dmlink(q->tail, desc); - q->tail = (dma_descriptor_2d *) desc; - } else { - desc->desc_size = 0; - desc->done = 1; - } - - q->push_idx = (q->push_idx + 1) & q->idx_mask; - return true; -} - -static inline bool dma_queue_push_single_2d(dma_queue * q, dma_ptr dptr, size_t dst_stride, size_t src_stride, size_t row_size, size_t nrows) { - if (((q->push_idx + 1) & q->idx_mask) == q->pop_idx) { - FARF(HIGH, "dma-push: queue full\n"); - return false; - } - - dma_descriptor_2d * desc = &q->desc[q->push_idx]; - - desc->next = NULL; - desc->reserved0 = 0; - desc->reserved1 = 0; - desc->desc_size = 1; // 2d mode - desc->src_bypass = dma_src_l2_bypass_on; - desc->dst_bypass = dma_dst_l2_bypass_on; - desc->src_comp = 0; - desc->dst_comp = 0; - desc->order = 0; - desc->done = 0; - desc->src_stride = src_stride; - desc->dst_stride = dst_stride; - desc->src = (void *) dptr.src; - desc->dst = (void *) dptr.dst; - desc->row_size = row_size; - -#if __HVX_ARCH__ < 75 - desc->desc_type = 0; // 2d (16-bit) mode - desc->nrows = nrows; - desc->src_offset = 0; - desc->dst_offset = 0; -#else - desc->desc_type = 9; // 2d (24-bit) mode - desc->nrows_lo = (nrows & 0xff); - desc->nrows_hi = (nrows >> 8); - desc->offset = 0; -#endif - - q->dptr[q->push_idx] = dptr; - - if (nrows) { - htp_trace_event_start(q->trace, HTP_TRACE_EVT_DMA, q->push_idx); - dmlink(q->tail, desc); - q->tail = desc; - } else { - desc->done = 1; - } - - // FARF(ERROR, "dma-push: i %u row-size %u nrows %d dst %p src %p\n", q->push_idx, row_size, nrows, dptr.dst, dptr.src); - q->push_idx = (q->push_idx + 1) & q->idx_mask; - return true; -} - -static inline dma_ptr dma_queue_pop(dma_queue * q) { - dma_ptr dptr = { NULL }; - - if (q->push_idx == q->pop_idx) { - return dptr; - } - - dma_descriptor_2d * desc = &q->desc[q->pop_idx]; - - // Wait for desc to complete - if (!desc->done) { - while (!desc->done) { - dmpoll(); - } - } - htp_trace_event_stop(q->trace, HTP_TRACE_EVT_DMA, q->pop_idx); - - dptr = q->dptr[q->pop_idx]; - - // FARF(ERROR, "dma-pop: i %u dst %p src %p\n", q->pop_idx, dptr.dst, dptr.src); - q->pop_idx = (q->pop_idx + 1) & q->idx_mask; - return dptr; -} - -static inline dma_ptr dma_queue_pop_nowait(dma_queue * q) { - dma_ptr dptr = { NULL }; - - if (q->push_idx == q->pop_idx) { - return dptr; - } - - dptr = q->dptr[q->pop_idx]; - - // FARF(ERROR, "dma-pop-nowait: i %u dst %p src %p\n", q->pop_idx, dptr.dst, dptr.src); - q->pop_idx = (q->pop_idx + 1) & q->idx_mask; - return dptr; -} - -static inline bool dma_queue_empty(dma_queue * q) { - return q->push_idx == q->pop_idx; -} - -static inline uint32_t dma_queue_depth(dma_queue * q) { - return (q->push_idx - q->pop_idx) & q->idx_mask; -} - -static inline uint32_t dma_queue_capacity(dma_queue * q) { - return q->capacity; -} - -#if __HVX_ARCH__ < 75 - -// Overflow-safe DMA push: all 2d descriptor fields (row_size, nrows, src_stride, dst_stride) are 16-bit, max 65535. -// This version transparently handles values that exceed the 16-bit limit and submits chained DMA transtions. - -#define DMA_MAX_FIELD_VAL 65535u - -static inline bool dma_queue_push(dma_queue *q, dma_ptr dptr, size_t dst_stride, size_t src_stride, size_t row_size, size_t nrows) { - // Fast path: everything fits in 16 bits - if (nrows == 0 || __builtin_expect( - row_size <= DMA_MAX_FIELD_VAL && - nrows <= DMA_MAX_FIELD_VAL && - src_stride <= DMA_MAX_FIELD_VAL && - dst_stride <= DMA_MAX_FIELD_VAL, 1)) { - return dma_queue_push_single_2d(q, dptr, dst_stride, src_stride, row_size, nrows); - } - - // Contiguous block - // Use 1d DMA mode which supports sizes up to 24-bits (16MB) - if (nrows == 1 || (row_size == src_stride && row_size == dst_stride)) { - size_t total = row_size * nrows; - return dma_queue_push_single_1d(q, dptr, total); - } - - // Stride overflow — fall back to row-by-row. - { - const uint8_t *src = (const uint8_t *) dptr.src; - uint8_t *dst = (uint8_t *) dptr.dst; - for (size_t r = 0; r < nrows; ++r) { - dma_ptr p = dma_make_ptr(dst + r * dst_stride, src + r * src_stride); - if (!dma_queue_push_single_1d(q, p, row_size)) - return false; - if (r + 1 < nrows) - dma_queue_pop(q); - } - return true; - } -} - -#else // HVX_ARCH >= 75 - -static inline bool dma_queue_push(dma_queue *q, dma_ptr dptr, size_t dst_stride, size_t src_stride, size_t row_size, size_t nrows) { - // On v75 and up we always use 2d 24-bit mode - return dma_queue_push_single_2d(q, dptr, dst_stride, src_stride, row_size, nrows); -} - -#endif - -static inline bool dma_queue_push_ddr_to_vtcm(dma_queue * q, dma_ptr dptr, size_t dst_row_size, size_t src_row_size, size_t nrows) { - return dma_queue_push(q, dptr, dst_row_size, src_row_size, src_row_size, nrows); -} - -static inline bool dma_queue_push_vtcm_to_ddr(dma_queue * q, dma_ptr dptr, size_t dst_row_size, size_t src_row_size, size_t nrows) { - return dma_queue_push(q, dptr, dst_row_size, src_row_size, dst_row_size, nrows); -} - -#define DMA_CACHE_MAX_SIZE 256U - -typedef struct { - uint8_t *base; - uint32_t line_size; - uint32_t capacity; - uint32_t src[DMA_CACHE_MAX_SIZE]; - uint16_t age[DMA_CACHE_MAX_SIZE]; -} dma_cache; - -static inline void dma_cache_init(dma_cache *c, uint8_t *base, uint32_t line_size, uint32_t capacity) -{ - c->capacity = (capacity > DMA_CACHE_MAX_SIZE) ? DMA_CACHE_MAX_SIZE : capacity; - c->base = base; - c->line_size = line_size; - - for (unsigned i=0; i < c->capacity; i++) { - c->src[i] = 0; - c->age[i] = 0; - } -} - -static inline bool dma_cache_push(dma_queue *q, dma_cache *c, const uint8_t * src, uint32_t dst_stride, uint32_t src_stride, uint32_t row_size, uint32_t nrows) -{ - uint32_t o_idx = 0; - uint16_t o_age = 0; - uint8_t * dst = 0; - - for (unsigned i=0; i < c->capacity; i++) { - if (c->src[i] == (uint32_t) src) { - c->age[i] = 0; - dst = c->base + (i * c->line_size); nrows = 0; // dummy dma - } else { - c->age[i]++; - if (c->age[i] > o_age) { o_age = c->age[i]; o_idx = i; } - } - } - if (!dst) { - c->age[o_idx] = 0; - c->src[o_idx] = (uint32_t) src; - dst = c->base + o_idx * c->line_size; // normal nrows dma - return dma_queue_push(q, dma_make_ptr(dst, src), dst_stride, src_stride, row_size, nrows); - } - - return dma_queue_push_single_1d(q, dma_make_ptr(dst, src), 0); -} - -#ifdef __cplusplus -} // extern "C" -#endif - -#endif /* HTP_DMA_H */ +#pragma once +#include "dma-queue.h" diff --git a/ggml/src/ggml-hexagon/htp/hex-profile.h b/ggml/src/ggml-hexagon/htp/hex-profile.h index 8a37a4a06664..a26961fc93b8 100644 --- a/ggml/src/ggml-hexagon/htp/hex-profile.h +++ b/ggml/src/ggml-hexagon/htp/hex-profile.h @@ -44,11 +44,11 @@ struct htp_thread_trace { }; static inline void htp_trace_event(struct htp_thread_trace * tr, uint16_t id, uint16_t info, uint32_t type) { - if (tr && tr->events && tr->count < tr->max_events) { - uint32_t idx = tr->count; - tr->events[idx].id = id; - tr->events[idx].info = info | (type == HTP_TRACE_EVT_STOP ? 0x8000 : 0); - tr->events[idx].cycles = (uint32_t) hex_get_cycles(); + if (tr->count < tr->max_events) { + uint32_t i = tr->count; + tr->events[i].id = id; + tr->events[i].info = info | (type == HTP_TRACE_EVT_STOP ? 0x8000 : 0); + tr->events[i].cycles = (uint32_t) hex_get_cycles(); tr->count++; } } diff --git a/ggml/src/ggml-hexagon/htp/hex-utils.h b/ggml/src/ggml-hexagon/htp/hex-utils.h index 07930bef6ec1..93e87efcb4c4 100644 --- a/ggml/src/ggml-hexagon/htp/hex-utils.h +++ b/ggml/src/ggml-hexagon/htp/hex-utils.h @@ -30,21 +30,26 @@ static inline void hex_l2fetch(const void * p, uint32_t width, uint32_t stride, Q6_l2fetch_AP((void *) p, control); } -#define HEX_L2_LINE_SIZE 64 -#define HEX_L2_FLUSH_SIZE (128 * 1024) +static inline void hex_l2fetch_block(const void * addr, size_t size) { + if (size == 0) return; + const uint32_t width = 16384; // 16KB rows + const uint32_t height = (size + width - 1) / width; + hex_l2fetch(addr, width, width, height); +} + +#define HEX_L2_LINE_SIZE 128 +#define HEX_L2_BLOCK_SIZE (HEX_L2_LINE_SIZE * 4) // flush granularity (lines per loop iteration) +#define HEX_L2_FLUSH_WQ_THRESHOLD (4 * 1024) +#define HEX_L2_FLUSH_ALL_THRESHOLD (4 * 1024 * 1024) static inline void hex_l2flush(void * addr, size_t size) { - if (size > HEX_L2_FLUSH_SIZE) { - qurt_mem_cache_clean((qurt_addr_t) 0, 0, QURT_MEM_CACHE_FLUSH_INVALIDATE_ALL, QURT_MEM_DCACHE); - } else { - const uint32_t s = (uint32_t) addr; - const uint32_t e = s + size; - for (uint32_t i = s; i < e; i += HEX_L2_LINE_SIZE * 4) { - Q6_dccleaninva_A((void *) i + HEX_L2_LINE_SIZE * 0); - Q6_dccleaninva_A((void *) i + HEX_L2_LINE_SIZE * 1); - Q6_dccleaninva_A((void *) i + HEX_L2_LINE_SIZE * 2); - Q6_dccleaninva_A((void *) i + HEX_L2_LINE_SIZE * 3); - } + const uint32_t s = ((uint32_t) addr) & ~(HEX_L2_LINE_SIZE - 1); + const uint32_t e = (((uint32_t) addr) + size + HEX_L2_LINE_SIZE - 1) & ~(HEX_L2_LINE_SIZE - 1); + for (uint32_t i = s; i < e; i += HEX_L2_BLOCK_SIZE) { + Q6_dccleaninva_A((void *) i + HEX_L2_LINE_SIZE * 0); + Q6_dccleaninva_A((void *) i + HEX_L2_LINE_SIZE * 1); + Q6_dccleaninva_A((void *) i + HEX_L2_LINE_SIZE * 2); + Q6_dccleaninva_A((void *) i + HEX_L2_LINE_SIZE * 3); } } diff --git a/ggml/src/ggml-hexagon/htp/hmx-mm-kernels-tiled.h b/ggml/src/ggml-hexagon/htp/hmx-mm-kernels-tiled.h index 740a8f87d61f..0011abba5a8a 100644 --- a/ggml/src/ggml-hexagon/htp/hmx-mm-kernels-tiled.h +++ b/ggml/src/ggml-hexagon/htp/hmx-mm-kernels-tiled.h @@ -767,23 +767,25 @@ static void core_mma_chunk_fp16(__fp16 *restrict c, const __fp16 *restrict a, co // output : fp16 -> f32p -static void transfer_output_chunk_fp16_to_fp32( +static void transfer_output_chunk_fp16_to_fp32_col_chunk( float *restrict dst, const float *restrict src2, const __fp16 *restrict vtcm_src, uint32_t start_row, uint32_t n_rows, - uint32_t n_cols, + uint32_t c_len, + uint32_t total_n_cols, uint32_t dst_stride, uint32_t src2_stride, uint32_t dst_cols ) { - assert(n_cols % HTP_MM_HMX_TILE_N_COLS == 0); - const size_t tile_row_stride = (n_cols / HTP_MM_HMX_TILE_N_COLS) * HTP_MM_HMX_TILE_N_ELMS; + assert(c_len % HTP_MM_HMX_TILE_N_COLS == 0); + assert(total_n_cols % HTP_MM_HMX_TILE_N_COLS == 0); + const size_t tile_row_stride = (total_n_cols / HTP_MM_HMX_TILE_N_COLS) * HTP_MM_HMX_TILE_N_ELMS; const HVX_Vector one = hvx_vec_splat_f16(1.0); - const size_t limit_c = hex_smin(n_cols, dst_cols); + const size_t limit_c = hex_smin(c_len, dst_cols); const size_t limit_c_aligned = (limit_c & ~31); for (size_t r = 0; r < n_rows; r += 2) { @@ -848,6 +850,22 @@ static void transfer_output_chunk_fp16_to_fp32( } } +static inline void transfer_output_chunk_fp16_to_fp32( + float *restrict dst, + const float *restrict src2, + const __fp16 *restrict vtcm_src, + uint32_t start_row, + uint32_t n_rows, + uint32_t n_cols, + uint32_t dst_stride, + uint32_t src2_stride, + uint32_t dst_cols +) { + transfer_output_chunk_fp16_to_fp32_col_chunk( + dst, src2, vtcm_src, start_row, n_rows, n_cols, n_cols, dst_stride, src2_stride, dst_cols + ); +} + typedef struct { const __fp16 *vtcm_src; float *dst; @@ -1005,10 +1023,62 @@ static void transfer_activation_row_pair_fp32_to_fp16( } } +static void transfer_activation_row_pair_fp32_to_fp16_col_chunk( + __fp16 *restrict vtcm_dst, + const float *restrict row0, // offset by c_first + const float *restrict row1, // offset by c_first + uint32_t r, + uint32_t k_block, + uint32_t c_first, + uint32_t c_len, + uint32_t k_chunk_valid, + bool row0_valid, + bool row1_valid) { + + uint32_t r0 = r / HTP_MM_HMX_TILE_N_ROWS; // tile row index + uint32_t r1 = r % HTP_MM_HMX_TILE_N_ROWS; // intra-tile row idx + + uint32_t c = 0; + for (; c + 32 <= k_chunk_valid; c += 32) { + HVX_Vector v0 = Q6_V_vzero(); + HVX_Vector v1 = Q6_V_vzero(); + if (row0_valid) v0 = *(const HVX_Vector *)(row0 + c); + if (row1_valid) v1 = *(const HVX_Vector *)(row1 + c); + + HVX_Vector v_out = hvx_vec_f32_to_f16_shuff(v0, v1); + + uint32_t c0 = (c_first + c) / HTP_MM_HMX_TILE_N_COLS; // tile column index + uint32_t tile_idx = r0 * (k_block / HTP_MM_HMX_TILE_N_COLS) + c0; + + HVX_Vector *tile = (HVX_Vector *) (vtcm_dst + tile_idx * HTP_MM_HMX_TILE_N_ELMS); + tile[r1 / 2] = v_out; + } + if (c < c_len) { + HVX_Vector v0 = Q6_V_vzero(); + HVX_Vector v1 = Q6_V_vzero(); + if (row0_valid) v0 = *(const HVX_Vector *)(row0 + c); + if (row1_valid) v1 = *(const HVX_Vector *)(row1 + c); + + uint32_t rem = (k_chunk_valid > c) ? (k_chunk_valid - c) : 0; + HVX_VectorPred mask = Q6_Q_vsetq2_R(rem > 0 ? rem * sizeof(float) : 0); + v0 = Q6_V_vmux_QVV(mask, v0, Q6_V_vzero()); + v1 = Q6_V_vmux_QVV(mask, v1, Q6_V_vzero()); + + HVX_Vector v_out = hvx_vec_f32_to_f16_shuff(v0, v1); + + uint32_t c0 = (c_first + c) / HTP_MM_HMX_TILE_N_COLS; // tile column index + uint32_t tile_idx = r0 * (k_block / HTP_MM_HMX_TILE_N_COLS) + c0; + + HVX_Vector *tile = (HVX_Vector *) (vtcm_dst + tile_idx * HTP_MM_HMX_TILE_N_ELMS); + tile[r1 / 2] = v_out; + } +} + static void transfer_activation_chunk_fp32_to_fp16_gathered( __fp16 *restrict vtcm_dst, const float *restrict src, uint32_t start_row, + uint32_t vtcm_start_row, uint32_t n_rows, uint32_t k_block, const struct mmid_row_mapping *matrix_rows, @@ -1029,8 +1099,9 @@ static void transfer_activation_chunk_fp32_to_fp16_gathered( for (r = 0; r < n_rows_tiled; r += 2) { uint32_t r_idx0 = start_row + r + 0; uint32_t r_idx1 = start_row + r + 1; - uint32_t r0 = r_idx0 / HTP_MM_HMX_TILE_N_ROWS; // tile row index - uint32_t r1 = r_idx0 % HTP_MM_HMX_TILE_N_ROWS; // intra-tile row idx + uint32_t lr = vtcm_start_row + r; // vtcm-local row + uint32_t r0 = lr / HTP_MM_HMX_TILE_N_ROWS; // tile row index + uint32_t r1 = lr % HTP_MM_HMX_TILE_N_ROWS; // intra-tile row idx struct mmid_row_mapping mapping0 = matrix_rows[cur_a * mapping_stride + r_idx0]; struct mmid_row_mapping mapping1 = matrix_rows[cur_a * mapping_stride + r_idx1]; @@ -1073,9 +1144,9 @@ static void transfer_activation_chunk_fp32_to_fp16_gathered( } for (; r < n_rows_padded; r += 2) { - uint32_t r_idx0 = start_row + r; - uint32_t r0 = r_idx0 / HTP_MM_HMX_TILE_N_ROWS; // tile row index - uint32_t r1 = r_idx0 % HTP_MM_HMX_TILE_N_ROWS; // intra-tile row idx + uint32_t lr = vtcm_start_row + r; // vtcm-local row + uint32_t r0 = lr / HTP_MM_HMX_TILE_N_ROWS; // tile row index + uint32_t r1 = lr % HTP_MM_HMX_TILE_N_ROWS; // intra-tile row idx const bool row0_valid = (start_row + r + 0) < cne1; const bool row1_valid = (start_row + r + 1) < cne1; @@ -1135,6 +1206,7 @@ static void transfer_activation_chunk_fp32_to_fp16_gathered_flat( __fp16 *restrict vtcm_dst, const float *restrict src, uint32_t start_row, + uint32_t vtcm_start_row, uint32_t n_rows, uint32_t k_block, const struct mmid_row_mapping *matrix_rows, @@ -1152,8 +1224,9 @@ static void transfer_activation_chunk_fp32_to_fp16_gathered_flat( for (r = 0; r < n_rows_tiled; r += 2) { uint32_t r_idx0 = start_row + r + 0; uint32_t r_idx1 = start_row + r + 1; - uint32_t r0 = r_idx0 / HTP_MM_HMX_TILE_N_ROWS; // tile row index - uint32_t r1 = r_idx0 % HTP_MM_HMX_TILE_N_ROWS; // intra-tile row idx + uint32_t lr = vtcm_start_row + r; // vtcm-local row + uint32_t r0 = lr / HTP_MM_HMX_TILE_N_ROWS; // tile row index + uint32_t r1 = lr % HTP_MM_HMX_TILE_N_ROWS; // intra-tile row idx struct mmid_row_mapping mapping0 = matrix_rows[cur_a * mapping_stride + r_idx0]; struct mmid_row_mapping mapping1 = matrix_rows[cur_a * mapping_stride + r_idx1]; @@ -1193,9 +1266,9 @@ static void transfer_activation_chunk_fp32_to_fp16_gathered_flat( } for (; r < n_rows_padded; r += 2) { - uint32_t r_idx0 = start_row + r; - uint32_t r0 = r_idx0 / HTP_MM_HMX_TILE_N_ROWS; // tile row index - uint32_t r1 = r_idx0 % HTP_MM_HMX_TILE_N_ROWS; // intra-tile row idx + uint32_t lr = vtcm_start_row + r; // vtcm-local row + uint32_t r0 = lr / HTP_MM_HMX_TILE_N_ROWS; // tile row index + uint32_t r1 = lr % HTP_MM_HMX_TILE_N_ROWS; // intra-tile row idx const bool row0_valid = (start_row + r + 0) < cne1; const bool row1_valid = (start_row + r + 1) < cne1; @@ -1253,6 +1326,7 @@ static void transfer_output_chunk_fp16_to_fp32_scattered( float *restrict dst, const __fp16 *restrict vtcm_src, uint32_t start_row, + uint32_t vtcm_start_row, uint32_t n_rows, uint32_t n_cols, const struct mmid_row_mapping *matrix_rows, @@ -1269,8 +1343,9 @@ static void transfer_output_chunk_fp16_to_fp32_scattered( for (size_t r = 0; r < n_rows; r += 2) { uint32_t r_idx0 = start_row + r + 0; uint32_t r_idx1 = start_row + r + 1; - const size_t r0 = r_idx0 / HTP_MM_HMX_TILE_N_ROWS; - const size_t r1 = (r_idx0 % HTP_MM_HMX_TILE_N_ROWS) / 2; // index of the row pair within the tile + uint32_t lr = vtcm_start_row + r; // vtcm-local row + const size_t r0 = (lr / HTP_MM_HMX_TILE_N_ROWS); + const size_t r1 = (lr % HTP_MM_HMX_TILE_N_ROWS) / 2; // index of the row pair within the tile const __fp16 *row_base = vtcm_src + r0 * tile_row_stride; if (r_idx0 >= cne1) break; diff --git a/ggml/src/ggml-hexagon/htp/hmx-queue.c b/ggml/src/ggml-hexagon/htp/hmx-queue.c index 3add542bae7c..c369d3dd23f0 100644 --- a/ggml/src/ggml-hexagon/htp/hmx-queue.c +++ b/ggml/src/ggml-hexagon/htp/hmx-queue.c @@ -14,7 +14,7 @@ #define QURT_LOWEST_PRIO (254) -static inline void hmx_lock(struct hmx_queue *q) +static inline void hmx_lock(hmx_queue_t q) { if (!q->hmx_locked) { HAP_compute_res_hmx_lock(q->hap_rctx); @@ -22,7 +22,7 @@ static inline void hmx_lock(struct hmx_queue *q) } } -static inline void hmx_unlock(struct hmx_queue *q) +static inline void hmx_unlock(hmx_queue_t q) { if (q->hmx_locked) { HAP_compute_res_hmx_unlock(q->hap_rctx); @@ -30,7 +30,7 @@ static inline void hmx_unlock(struct hmx_queue *q) } } -static inline void hmx_queue_process(struct hmx_queue *q, bool* killed) { +static inline void hmx_queue_process(hmx_queue_t q, bool* killed) { unsigned int ir = atomic_load(&q->idx_read); while (ir != atomic_load(&q->idx_write)) { @@ -61,7 +61,7 @@ static inline void hmx_queue_process(struct hmx_queue *q, bool* killed) { } static void hmx_queue_thread(void * arg) { - struct hmx_queue * q = (struct hmx_queue *) arg; + hmx_queue_t q = (hmx_queue_t) arg; FARF(HIGH, "hmx-queue-thread: started"); @@ -93,34 +93,41 @@ static void hmx_queue_thread(void * arg) { FARF(HIGH, "hmx-queue-thread: stopped"); } -struct hmx_queue * hmx_queue_create(size_t capacity, uint32_t hap_rctx) { +size_t hmx_queue_sizeof(size_t capacity, uint32_t stack_size) { capacity = hex_ceil_pow2(capacity); + size_t size_q = hex_align_up(sizeof(struct hmx_queue_s), HEX_L2_LINE_SIZE); + size_t size_desc = hex_align_up(capacity * sizeof(struct hmx_queue_desc), HEX_L2_LINE_SIZE); + size_t size_stack = stack_size; + return size_q + size_desc + size_stack; +} + +size_t hmx_queue_alignof(void) { + return HEX_L2_LINE_SIZE; +} + +hmx_queue_t hmx_queue_init(void * ptr, size_t capacity, uint32_t stack_size, uint32_t hap_rctx, struct htp_thread_trace * trace) { + capacity = hex_ceil_pow2(capacity); + size_t size_q = hex_align_up(sizeof(struct hmx_queue_s), HEX_L2_LINE_SIZE); + size_t size_desc = hex_align_up(capacity * sizeof(struct hmx_queue_desc), HEX_L2_LINE_SIZE); + + uint8_t * block = (uint8_t *) ptr; + + hmx_queue_t q = (hmx_queue_t) block; block += size_q; + memset(q, 0, sizeof(struct hmx_queue_s)); - struct hmx_queue * q = (struct hmx_queue *) memalign(32, sizeof(struct hmx_queue)); - if (q == NULL) { - FARF(ERROR, "%s: failed to allocate DMA queue\n", __FUNCTION__); - return NULL; - } - memset(q, 0, sizeof(struct hmx_queue)); q->capacity = capacity; q->idx_mask = capacity - 1; q->hap_rctx = hap_rctx; + q->external_mem = true; - q->desc = (struct hmx_queue_desc *) memalign(64, capacity * sizeof(struct hmx_queue_desc)); - if (!q->desc) { - FARF(ERROR, "hmx-queue: failed to allocate HMX queue descriptors\n"); - return NULL; - } + q->desc = (struct hmx_queue_desc *) block; block += size_desc; memset(q->desc, 0, capacity * sizeof(struct hmx_queue_desc)); - const size_t stack_size = HMX_QUEUE_THREAD_STACK_SIZE; - q->stack = (unsigned char *) memalign(64, stack_size); - if (!q->stack) { - FARF(ERROR, "hmx-queue: thread stack allocation failed (%zu bytes)", stack_size); - return NULL; - } + q->stack = block; memset(q->stack, 0, stack_size); + q->trace = trace; + // Match caller thread priority (same pattern as worker-pool.c). int prio = qurt_thread_get_priority(qurt_thread_get_id()); if (prio < 1) { @@ -148,7 +155,7 @@ struct hmx_queue * hmx_queue_create(size_t capacity, uint32_t hap_rctx) { return q; } -void hmx_queue_delete(struct hmx_queue * q) { +void hmx_queue_free(hmx_queue_t q) { if (!q) { return; } @@ -160,8 +167,4 @@ void hmx_queue_delete(struct hmx_queue * q) { int status; qurt_thread_join(q->thread, &status); - - free(q->desc); - free(q->stack); - free(q); } diff --git a/ggml/src/ggml-hexagon/htp/hmx-queue.h b/ggml/src/ggml-hexagon/htp/hmx-queue.h index b176fa179611..c2b1859a2813 100644 --- a/ggml/src/ggml-hexagon/htp/hmx-queue.h +++ b/ggml/src/ggml-hexagon/htp/hmx-queue.h @@ -17,8 +17,6 @@ extern "C" { #endif -#define HMX_QUEUE_THREAD_STACK_SIZE (16 * 1024) - #if __HVX_ARCH__ > 79 #define HMX_QUEUE_POLL_COUNT 2000 #else @@ -41,7 +39,7 @@ struct hmx_queue_desc { atomic_uint done; }; -struct hmx_queue { +struct hmx_queue_s { struct hmx_queue_desc * desc; atomic_uint idx_write; // updated by producer (push) atomic_uint idx_read; // updated by consumer (process) @@ -55,19 +53,24 @@ struct hmx_queue { uint32_t hap_rctx; bool hmx_locked; struct htp_thread_trace * trace; + bool external_mem; // memory owned externally }; -struct hmx_queue * hmx_queue_create(size_t capacity, uint32_t hap_rctx); -void hmx_queue_delete(struct hmx_queue * q); +typedef struct hmx_queue_s * hmx_queue_t; + +size_t hmx_queue_sizeof(size_t capacity, uint32_t stack_size); +size_t hmx_queue_alignof(void); +hmx_queue_t hmx_queue_init(void * ptr, size_t capacity, uint32_t stack_size, uint32_t hap_rctx, struct htp_thread_trace * trace); +void hmx_queue_free(hmx_queue_t q); static inline struct hmx_queue_desc hmx_queue_make_desc(hmx_queue_func func, void * data) { struct hmx_queue_desc d = { func, data }; return d; } -static inline bool hmx_queue_push(struct hmx_queue * q, struct hmx_queue_desc d) { +static inline bool hmx_queue_push(hmx_queue_t q, struct hmx_queue_desc d) { unsigned int ir = atomic_load(&q->idx_read); - unsigned int iw = q->idx_write; + unsigned int iw = atomic_load(&q->idx_write); if (((iw + 1) & q->idx_mask) == ir) { FARF(HIGH, "hmx-queue-push: queue is full\n"); @@ -87,25 +90,25 @@ static inline bool hmx_queue_push(struct hmx_queue * q, struct hmx_queue_desc d) return true; } -static inline bool hmx_queue_signal(struct hmx_queue *q, enum hmx_queue_signal sig) { +static inline bool hmx_queue_signal(hmx_queue_t q, enum hmx_queue_signal sig) { return hmx_queue_push(q, hmx_queue_make_desc((hmx_queue_func) sig, NULL)); } -static inline bool hmx_queue_empty(struct hmx_queue * q) { - return q->idx_pop == q->idx_write; +static inline bool hmx_queue_empty(hmx_queue_t q) { + return q->idx_pop == atomic_load(&q->idx_write); } -static inline uint32_t hmx_queue_depth(struct hmx_queue * q) { - return (q->idx_read - q->idx_read) & q->idx_mask; +static inline uint32_t hmx_queue_depth(hmx_queue_t q) { + return (atomic_load(&q->idx_write) - atomic_load(&q->idx_read)) & q->idx_mask; } -static inline uint32_t hmx_queue_capacity(struct hmx_queue * q) { +static inline uint32_t hmx_queue_capacity(hmx_queue_t q) { return q->capacity; } -static inline struct hmx_queue_desc hmx_queue_pop_one(struct hmx_queue * q) { +static inline struct hmx_queue_desc hmx_queue_pop_one(hmx_queue_t q) { unsigned int ip = q->idx_pop; - unsigned int iw = q->idx_write; + unsigned int iw = atomic_load(&q->idx_write); struct hmx_queue_desc rd = { NULL, NULL }; if (ip == iw) { @@ -126,7 +129,7 @@ static inline struct hmx_queue_desc hmx_queue_pop_one(struct hmx_queue * q) { return rd; } -static inline struct hmx_queue_desc hmx_queue_pop(struct hmx_queue * q) { +static inline struct hmx_queue_desc hmx_queue_pop(hmx_queue_t q) { while (1) { struct hmx_queue_desc d = hmx_queue_pop_one(q); @@ -138,15 +141,15 @@ static inline struct hmx_queue_desc hmx_queue_pop(struct hmx_queue * q) { } } -static inline void hmx_queue_flush(struct hmx_queue * q) { +static inline void hmx_queue_flush(hmx_queue_t q) { while (hmx_queue_pop_one(q).func != NULL) ; } -static inline void hmx_queue_wakeup(struct hmx_queue * q) { +static inline void hmx_queue_wakeup(hmx_queue_t q) { hmx_queue_signal(q, HMX_QUEUE_WAKEUP); } -static inline void hmx_queue_suspend(struct hmx_queue *q) { +static inline void hmx_queue_suspend(hmx_queue_t q) { hmx_queue_signal(q, HMX_QUEUE_SUSPEND); } diff --git a/ggml/src/ggml-hexagon/htp/htp-ctx.h b/ggml/src/ggml-hexagon/htp/htp-ctx.h index e13103fb1887..e0f9a0c40d19 100644 --- a/ggml/src/ggml-hexagon/htp/htp-ctx.h +++ b/ggml/src/ggml-hexagon/htp/htp-ctx.h @@ -5,7 +5,8 @@ #include "hmx-queue.h" #include "htp-ops.h" #include "hex-profile.h" -#include "worker-pool.h" +#include "work-queue.h" +#include "hex-fastdiv.h" #include #include @@ -18,6 +19,8 @@ #endif #define HTP_MAX_MMAPS 16 +#define HTP_MAX_DIRTY_RANGES 16 + // Memory mapping struct htp_mmap { uint64_t size; @@ -52,6 +55,9 @@ struct htp_ops_context { const struct htp_tensor * dsts[HTP_OP_MAX_OUTPUTS]; }; + dma_queue ** src_dma[HTP_OP_MAX_INPUTS]; + dma_queue ** dst_dma[HTP_OP_MAX_OUTPUTS]; + // TODO convert these to an array struct htp_spad src0_spad; struct htp_spad src1_spad; @@ -65,11 +71,16 @@ struct htp_ops_context { // Main context for htp DSP backend struct htp_context { - dspqueue_t queue; - dma_queue * dma[HTP_MAX_NTHREADS]; + dspqueue_t dsp_queue; + struct htp_mmap mmap[HTP_MAX_MMAPS]; - worker_pool_context_t worker_pool; + dma_queue_t dma[HTP_MAX_NTHREADS]; + dma_queue_t dma_cached[HTP_MAX_NTHREADS]; + work_queue_t work_queue; + hmx_queue_t hmx_queue; + uint32_t n_threads; + struct fastdiv_values n_threads_div; int thread_id; int thread_prio; @@ -86,6 +97,11 @@ struct htp_context { atomic_bool vtcm_needs_release; uint64_t max_vmem; + struct htp_dirty_range { + uint32_t start; + uint32_t end; + uint32_t bi; + } dirty_ranges[HTP_MAX_DIRTY_RANGES]; // Persistent DDR scratchpad for MUL_MAT_ID mappings void * ddr_spad_base; @@ -93,7 +109,10 @@ struct htp_context { struct htp_ops_context octx; - struct hmx_queue * hmx_queue; // Async HMX queue for pipeline overlap + qurt_thread_t main_thread; + void * main_stack; + atomic_bool killed; + size_t footprint; }; int op_matmul(struct htp_ops_context * octx); @@ -121,5 +140,6 @@ int op_diag(struct htp_ops_context * octx); int op_solve_tri(struct htp_ops_context * octx); int op_gated_delta_net(struct htp_ops_context * octx); int op_pad(struct htp_ops_context * octx); +int op_im2col(struct htp_ops_context * octx); #endif /* HTP_CTX_H */ diff --git a/ggml/src/ggml-hexagon/htp/htp-ops.h b/ggml/src/ggml-hexagon/htp/htp-ops.h index c9d0b3539a95..a138f062aa68 100644 --- a/ggml/src/ggml-hexagon/htp/htp-ops.h +++ b/ggml/src/ggml-hexagon/htp/htp-ops.h @@ -97,6 +97,8 @@ enum htp_op_code { HTP_OP_PAD, HTP_OP_NORM, HTP_OP_CONCAT, + HTP_OP_CLAMP, + HTP_OP_IM2COL, HTP_OP_INVALID }; @@ -108,8 +110,7 @@ enum htp_op_code { #define HTP_OP_MAX_KERN_PARAMS 32 #define HTP_OP_MAX_BUFS 16 -#define HTP_OP_MAX_REQS 256 -#define HTP_OP_MAX_TENSORS (HTP_OP_MAX_REQS * HTP_OP_MAX_INPUTS + HTP_OP_MAX_REQS) +#define HTP_OP_MAX_TENSORS 8192 // must stay under 64K (uint16) #define HTP_OP_MAX_VMEM_DEFAULT (3355443200u) @@ -117,16 +118,18 @@ enum htp_op_code { enum htp_tensor_flags { HTP_TENSOR_COMPUTE = (1U << 0), // Tensor buffer temporal compute data (not weights) - HTP_TENSOR_FLUSHED = (1U << 1) // Tensor buffer has been flushed (set by the NPU) + HTP_TENSOR_DIRTY = (1U << 1) // Tensor buffer is dirty and needs to be flushed }; // Tensor descriptor struct htp_tensor { uint32_t data; // Buffer offset in the messages, and data pointer on the NPU + uint32_t reserved; // Reserved for alignment padding (must be multiple of 8) uint32_t size; // Data size in bytes uint32_t flags; // Buffer / tensor flags - uint16_t type; // Data type + uint32_t type; // Data type uint16_t bi; // Buffer index + uint16_t ti; // Tensor index uint32_t ne[HTP_OP_MAX_DIMS]; // Number of elements uint32_t nb[HTP_OP_MAX_DIMS]; // Stride in bytes (see ggml.h ggml_tensor) }; @@ -169,6 +172,9 @@ enum htp_profiler_mode { enum htp_trace_event_id { HTP_TRACE_EVT_DMA = 0, + HTP_TRACE_EVT_L2FLUSH = 1, + HTP_TRACE_EVT_INIT = 2, + HTP_TRACE_EVT_BUFF = 3, HTP_TRACE_EVT_HVX_COMP = 20, HTP_TRACE_EVT_HVX_A_QUANT = 21, @@ -221,7 +227,10 @@ struct htp_opbatch_rsp { uint32_t n_tensors; // Number of tensors uint32_t n_ops; // Number of op profile descriptors uint32_t n_traces[HTP_MAX_NTHREADS + 1]; - uint8_t pad[8]; // align to 8 bytes + uint32_t usecs; // Number of usec + uint32_t pad; // align to 8 bytes + uint64_t cycles_start; // Start cycle counter + uint64_t cycles_stop; // Stop cycle counter // struct htp_prof_desc profs[]; -- dspqueue buf 0 }; diff --git a/ggml/src/ggml-hexagon/htp/htp-tensor.c b/ggml/src/ggml-hexagon/htp/htp-tensor.c new file mode 100644 index 000000000000..39436e26dfff --- /dev/null +++ b/ggml/src/ggml-hexagon/htp/htp-tensor.c @@ -0,0 +1,293 @@ +#include "htp-tensor.h" + +#include +#include +#include + +#include "hex-common.h" +#include "hex-utils.h" +#include "hex-fastdiv.h" +#include "hex-profile.h" +#include "htp-ctx.h" +#include "work-queue.h" + +struct l2flush_range { + uint32_t start; // line-aligned start address + uint32_t end; // line-aligned end address + uint32_t block_first; // global block index of this range's first block + uint32_t n_blocks; // number of HEX_L2_BLOCK_SIZE chunks (last may be partial) +}; + +struct l2flush_multi_task { + struct htp_thread_trace * trace; + struct l2flush_range ranges[HTP_OP_MAX_INPUTS]; + uint32_t n_ranges; + uint32_t total_blocks; + uint32_t blocks_per_thread; +}; + +static void flush_all_dcache(struct htp_context * ctx) { + struct htp_thread_trace * tr = &ctx->trace[0]; + htp_trace_event_start(tr, HTP_TRACE_EVT_L2FLUSH, 0); + qurt_mem_cache_clean((qurt_addr_t) 0, 0, QURT_MEM_CACHE_FLUSH_INVALIDATE_ALL, QURT_MEM_DCACHE); + hex_l2fetch_block(ctx, ctx->footprint); + htp_trace_event_stop(tr, HTP_TRACE_EVT_L2FLUSH, 0); + memset(ctx->dirty_ranges, 0, sizeof(ctx->dirty_ranges)); +} + +static void l2flush_multi_worker(unsigned int n, unsigned int i, void * data) { + struct l2flush_multi_task * task = (struct l2flush_multi_task *) data; + (void) n; + + const uint32_t gb_first = i * task->blocks_per_thread; + uint32_t gb_last = gb_first + task->blocks_per_thread; + if (gb_last > task->total_blocks) { + gb_last = task->total_blocks; + } + if (gb_first >= gb_last) { + return; + } + + struct htp_thread_trace * tr = &task->trace[i]; + htp_trace_event_start(tr, HTP_TRACE_EVT_L2FLUSH, gb_first); + + for (uint32_t r = 0; r < task->n_ranges; r++) { + const struct l2flush_range * rg = &task->ranges[r]; + const uint32_t rb_first = rg->block_first; + const uint32_t rb_last = rg->block_first + rg->n_blocks; + + const uint32_t lo = gb_first > rb_first ? gb_first : rb_first; + const uint32_t hi = gb_last < rb_last ? gb_last : rb_last; + if (lo >= hi) { + continue; + } + + const uint32_t s = rg->start + (lo - rb_first) * HEX_L2_BLOCK_SIZE; + uint32_t e = rg->start + (hi - rb_first) * HEX_L2_BLOCK_SIZE; + if (e > rg->end) { + e = rg->end; + } + hex_l2flush((void *) (uintptr_t) s, e - s); + } + + htp_trace_event_stop(tr, HTP_TRACE_EVT_L2FLUSH, gb_first); +} + +void htp_tensor_dirty_all(struct htp_context * ctx, const struct htp_tensor * const * tensors, uint32_t n) { + const struct htp_tensor * pending[HTP_OP_MAX_OUTPUTS]; + uint32_t n_pending = 0; + + for (uint32_t i = 0; i < n; i++) { + const struct htp_tensor * t = tensors[i]; + if (!t) continue; + + uint32_t t_start = t->data; + uint32_t t_end = t_start + t->size; + + bool merged = false; + for (uint32_t j = 0; j < HTP_MAX_DIRTY_RANGES; j++) { + struct htp_dirty_range * r = &ctx->dirty_ranges[j]; + if (!r->start) continue; + + if (r->start <= t_end && t_start <= r->end) { + uint32_t new_start = (t_start < r->start) ? t_start : r->start; + uint32_t new_end = (t_end > r->end) ? t_end : r->end; + r->start = new_start; + r->end = new_end; + merged = true; + } + } + + if (!merged) { + pending[n_pending++] = t; + } + } + + if (n_pending == 0) { + return; + } + + uint32_t empty_indices[HTP_MAX_DIRTY_RANGES]; + uint32_t active_indices[HTP_MAX_DIRTY_RANGES]; + uint32_t n_active = 0; + uint32_t n_empty = 0; + for (uint32_t j = 0; j < HTP_MAX_DIRTY_RANGES; j++) { + if (ctx->dirty_ranges[j].start) { + active_indices[n_active++] = j; + } else { + empty_indices[n_empty++] = j; + } + } + + if (n_pending <= n_empty) { + for (uint32_t i = 0; i < n_pending; i++) { + uint32_t idx = empty_indices[i]; + struct htp_dirty_range * r = &ctx->dirty_ranges[idx]; + r->start = pending[i]->data; + r->end = pending[i]->data + pending[i]->size; + r->bi = pending[i]->bi; + } + return; + } + + uint32_t n_evict = n_pending - n_empty; + uint32_t total_evict_size = 0; + for (uint32_t i = 0; i < n_evict; i++) { + uint32_t idx = active_indices[i]; + struct htp_dirty_range * r = &ctx->dirty_ranges[idx]; + total_evict_size += r->end - r->start; + } + + if (total_evict_size > HEX_L2_FLUSH_ALL_THRESHOLD) { + flush_all_dcache(ctx); + for (uint32_t i = 0; i < n_pending; i++) { + struct htp_dirty_range * r = &ctx->dirty_ranges[i]; + r->start = pending[i]->data; + r->end = pending[i]->data + pending[i]->size; + r->bi = pending[i]->bi; + } + return; + } + + if (total_evict_size > HEX_L2_FLUSH_WQ_THRESHOLD && ctx->n_threads > 1 && n_evict <= HTP_OP_MAX_INPUTS) { + struct l2flush_multi_task task; + task.trace = ctx->trace; + task.n_ranges = n_evict; + + uint32_t block_acc = 0; + for (uint32_t i = 0; i < n_evict; i++) { + uint32_t idx = active_indices[i]; + struct htp_dirty_range * r = &ctx->dirty_ranges[idx]; + + struct l2flush_range * rg = &task.ranges[i]; + rg->start = hex_align_down((size_t) r->start, HEX_L2_LINE_SIZE); + rg->end = hex_align_up((size_t) r->end, HEX_L2_LINE_SIZE); + rg->block_first = block_acc; + rg->n_blocks = (rg->end - rg->start + HEX_L2_BLOCK_SIZE - 1) / HEX_L2_BLOCK_SIZE; + block_acc += rg->n_blocks; + } + + task.total_blocks = block_acc; + task.blocks_per_thread = fastdiv(block_acc + ctx->n_threads - 1, &ctx->n_threads_div); + + work_queue_run(ctx->work_queue, l2flush_multi_worker, &task, ctx->n_threads); + } else { + struct htp_thread_trace * tr = &ctx->trace[0]; + htp_trace_event_start(tr, HTP_TRACE_EVT_L2FLUSH, 0); + for (uint32_t i = 0; i < n_evict; i++) { + uint32_t idx = active_indices[i]; + struct htp_dirty_range * r = &ctx->dirty_ranges[idx]; + uint32_t size = r->end - r->start; + hex_l2flush((void *) (uintptr_t) r->start, size); + } + htp_trace_event_stop(tr, HTP_TRACE_EVT_L2FLUSH, 0); + } + + for (uint32_t i = 0; i < n_evict; i++) { + uint32_t idx = active_indices[i]; + struct htp_dirty_range * r = &ctx->dirty_ranges[idx]; + r->start = pending[i]->data; + r->end = pending[i]->data + pending[i]->size; + r->bi = pending[i]->bi; + } + + for (uint32_t i = 0; i < n_empty; i++) { + uint32_t idx = empty_indices[i]; + struct htp_dirty_range * r = &ctx->dirty_ranges[idx]; + r->start = pending[n_evict + i]->data; + r->end = pending[n_evict + i]->data + pending[n_evict + i]->size; + r->bi = pending[n_evict + i]->bi; + } +} + +static void make_tensor_clean(struct htp_context * ctx, const struct htp_tensor * t) { + uint32_t t_start = t->data; + uint32_t t_end = t_start + t->size; + + for (uint32_t i = 0; i < HTP_MAX_DIRTY_RANGES; i++) { + struct htp_dirty_range * r = &ctx->dirty_ranges[i]; + if (!r->start) continue; + + if (r->start < t_end && t_start < r->end) { + if (t_start <= r->start && r->end <= t_end) { + r->start = 0; + } else if (t_start <= r->start) { + r->start = t_end; + } else if (r->end <= t_end) { + r->end = t_start; + } + } + } +} + +static inline bool is_tensor_dirty(struct htp_context * ctx, const struct htp_tensor * t) { + uint32_t t_start = t->data; + uint32_t t_end = t_start + t->size; + + for (uint32_t i = 0; i < HTP_MAX_DIRTY_RANGES; i++) { + struct htp_dirty_range * r = &ctx->dirty_ranges[i]; + if (!r->start) continue; + + if (r->start < t_end && t_start < r->end) { + return true; + } + } + return false; +} + +void htp_tensor_flush_all(struct htp_context * ctx, const struct htp_tensor * const * tensors, uint32_t n) { + const struct htp_tensor * dirty_tensors[HTP_OP_MAX_INPUTS]; + uint32_t n_dirty = 0; + uint64_t total_dirty = 0; + + for (uint32_t i = 0; i < n; i++) { + const struct htp_tensor * t = tensors[i]; + if (t && (t->flags & HTP_TENSOR_COMPUTE) && is_tensor_dirty(ctx, t)) { + dirty_tensors[n_dirty++] = t; + total_dirty += t->size; + } + } + + if (total_dirty == 0) { + return; + } + + if (total_dirty > HEX_L2_FLUSH_ALL_THRESHOLD) { + flush_all_dcache(ctx); + return; + } + + if (total_dirty >= HEX_L2_FLUSH_WQ_THRESHOLD && ctx->n_threads > 1) { + struct l2flush_multi_task task; + task.trace = ctx->trace; + task.n_ranges = 0; + + uint32_t block_acc = 0; + for (uint32_t i = 0; i < n_dirty; i++) { + const struct htp_tensor * t = dirty_tensors[i]; + make_tensor_clean(ctx, t); + + struct l2flush_range * rg = &task.ranges[task.n_ranges++]; + rg->start = hex_align_down((size_t) t->data, HEX_L2_LINE_SIZE); + rg->end = hex_align_up((size_t) t->data + t->size, HEX_L2_LINE_SIZE); + rg->block_first = block_acc; + rg->n_blocks = (rg->end - rg->start + HEX_L2_BLOCK_SIZE - 1) / HEX_L2_BLOCK_SIZE; + block_acc += rg->n_blocks; + } + + task.total_blocks = block_acc; + task.blocks_per_thread = fastdiv(block_acc + ctx->n_threads - 1, &ctx->n_threads_div); + + work_queue_run(ctx->work_queue, l2flush_multi_worker, &task, ctx->n_threads); + return; + } + + struct htp_thread_trace * tr = &ctx->trace[0]; + for (uint32_t i = 0; i < n_dirty; i++) { + const struct htp_tensor * t = dirty_tensors[i]; + htp_trace_event_start(tr, HTP_TRACE_EVT_L2FLUSH, t->ti); + hex_l2flush((void *) (uintptr_t) t->data, t->size); + htp_trace_event_stop(tr, HTP_TRACE_EVT_L2FLUSH, t->ti); + make_tensor_clean(ctx, t); + } +} diff --git a/ggml/src/ggml-hexagon/htp/htp-tensor.h b/ggml/src/ggml-hexagon/htp/htp-tensor.h new file mode 100644 index 000000000000..2c3fc54c748f --- /dev/null +++ b/ggml/src/ggml-hexagon/htp/htp-tensor.h @@ -0,0 +1,20 @@ +#ifndef HTP_TENSOR_H +#define HTP_TENSOR_H + +#include +#include "htp-ops.h" +#include "hex-bitmap.h" + +static inline void * htp_tensor_data(const struct htp_tensor * t) { + return (void *) (uintptr_t) t->data; +} + +static inline uint32_t * htp_tensor_flags(const struct htp_tensor * t) { + return (uint32_t *) &t->flags; +} + +struct htp_context; +void htp_tensor_flush_all(struct htp_context * ctx, const struct htp_tensor * const * tensors, uint32_t n); +void htp_tensor_dirty_all(struct htp_context * ctx, const struct htp_tensor * const * tensors, uint32_t n); + +#endif // HTP_TENSOR_H diff --git a/ggml/src/ggml-hexagon/htp/hvx-fa-kernels.h b/ggml/src/ggml-hexagon/htp/hvx-fa-kernels.h index c05bd0b85260..5b18f163c57e 100644 --- a/ggml/src/ggml-hexagon/htp/hvx-fa-kernels.h +++ b/ggml/src/ggml-hexagon/htp/hvx-fa-kernels.h @@ -208,6 +208,77 @@ static inline void hvx_mad_f32_f16_aa_rx2(float * restrict y, const void * restr } } } +static inline void hvx_mad_f32_f16_aa_vec(float * restrict y, const void * restrict x, HVX_Vector S0, uint32_t n) { + const HVX_Vector * restrict vx0 = (const HVX_Vector *) x; + + HVX_VectorPair * restrict vy_p = (HVX_VectorPair *) y; + HVX_Vector * restrict vy = (HVX_Vector *) y; + + uint32_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors + uint32_t nloe = n % VLEN_FP16; // leftover elements + + uint32_t i = 0; + + #pragma unroll(2) + for (i = 0; i < nvec; ++i) { + vy_p[i] = hvx_vec_mpyacc_f32_f16(vy_p[i], Q6_Vh_vshuff_Vh(vx0[i]), S0); + } + + if (nloe) { + HVX_VectorPair xy_p = vy_p[i]; + xy_p = hvx_vec_mpyacc_f32_f16(xy_p, Q6_Vh_vshuff_Vh(vx0[i]), S0); + + HVX_Vector xy = Q6_V_lo_W(xy_p); + i = 2 * i; // index for vy + + if (nloe >= VLEN_FP32) { + vy[i] = xy; + nloe -= VLEN_FP32; ++i; xy = Q6_V_hi_W(xy_p); + } + + if (nloe) { + hvx_vec_store_a(&vy[i], nloe * 4, xy); + } + } +} + +static inline void hvx_mad_f32_f16_aa_rx2_vec(float * restrict y, const void * restrict x0, const void * restrict x1, + HVX_Vector S0, HVX_Vector S1, uint32_t n) { + const HVX_Vector * restrict vx0 = (const HVX_Vector *) x0; + const HVX_Vector * restrict vx1 = (const HVX_Vector *) x1; + + HVX_VectorPair * restrict vy_p = (HVX_VectorPair *) y; + HVX_Vector * restrict vy = (HVX_Vector *) y; + + uint32_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors + uint32_t nloe = n % VLEN_FP16; // leftover elements + + uint32_t i = 0; + + #pragma unroll(2) + for (i = 0; i < nvec; ++i) { + vy_p[i] = hvx_vec_mpyacc_f32_f16(vy_p[i], Q6_Vh_vshuff_Vh(vx0[i]), S0); + vy_p[i] = hvx_vec_mpyacc_f32_f16(vy_p[i], Q6_Vh_vshuff_Vh(vx1[i]), S1); + } + + if (nloe) { + HVX_VectorPair xy_p = vy_p[i]; + xy_p = hvx_vec_mpyacc_f32_f16(xy_p, Q6_Vh_vshuff_Vh(vx0[i]), S0); + xy_p = hvx_vec_mpyacc_f32_f16(xy_p, Q6_Vh_vshuff_Vh(vx1[i]), S1); + + HVX_Vector xy = Q6_V_lo_W(xy_p); + i = 2 * i; // index for vy + + if (nloe >= VLEN_FP32) { + vy[i] = xy; + nloe -= VLEN_FP32; ++i; xy = Q6_V_hi_W(xy_p); + } + + if (nloe) { + hvx_vec_store_a(&vy[i], nloe * 4, xy); + } + } +} static inline void hvx_scale_vec_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src, const uint32_t n, HVX_Vector vs) { assert((size_t) dst % 128 == 0); diff --git a/ggml/src/ggml-hexagon/htp/hvx-reduce.h b/ggml/src/ggml-hexagon/htp/hvx-reduce.h index 3c0073ef6d80..76d712dc8981 100644 --- a/ggml/src/ggml-hexagon/htp/hvx-reduce.h +++ b/ggml/src/ggml-hexagon/htp/hvx-reduce.h @@ -286,6 +286,46 @@ static inline float hvx_sum_of_squares_f32(const uint8_t * restrict src, const i } } +// Signed 32-bit Integer Max variants + +static inline HVX_Vector hvx_vec_reduce_max_n_i32(HVX_Vector in, unsigned int n) { + unsigned int total = n * 4; // total vec nbytes + unsigned int width = 4; // int32 nbytes + + HVX_Vector max_val = in, max_t; + while (width < total) { + max_t = Q6_V_vror_VR(max_val, width); // rotate right + max_val = Q6_Vw_vmax_VwVw(max_t, max_val); // elementwise signed max + width = width << 1; + } + return max_val; +} + +static inline HVX_Vector hvx_vec_reduce_max_i32(HVX_Vector in) { + return hvx_vec_reduce_max_n_i32(in, 32); +} + +static inline int32_t hvx_reduce_max_i32_a(const uint8_t * restrict src, const int num_elems) { + HVX_Vector init_vec = Q6_V_vsplat_R(((const int32_t *) src)[0]); + HVX_Vector pad_vec = Q6_V_vsplat_R(0x80000000); + assert((uintptr_t) src % 128 == 0); + hvx_reduce_loop_body(HVX_Vector, init_vec, pad_vec, Q6_Vw_vmax_VwVw, hvx_vec_reduce_max_i32, hvx_vec_get_i32); +} + +static inline int32_t hvx_reduce_max_i32_u(const uint8_t * restrict src, const int num_elems) { + HVX_Vector init_vec = Q6_V_vsplat_R(((const int32_t *) src)[0]); + HVX_Vector pad_vec = Q6_V_vsplat_R(0x80000000); + hvx_reduce_loop_body(HVX_UVector, init_vec, pad_vec, Q6_Vw_vmax_VwVw, hvx_vec_reduce_max_i32, hvx_vec_get_i32); +} + +static inline int32_t hvx_reduce_max_i32(const uint8_t * restrict src, const int num_elems) { + if (hex_is_aligned((void *) src, 128)) { + return hvx_reduce_max_i32_a(src, num_elems); + } else { + return hvx_reduce_max_i32_u(src, num_elems); + } +} + #undef hvx_reduce_loop_body #undef HVX_REDUCE_MAX_OP #undef HVX_REDUCE_SUM_OP diff --git a/ggml/src/ggml-hexagon/htp/im2col-ops.c b/ggml/src/ggml-hexagon/htp/im2col-ops.c new file mode 100644 index 000000000000..35fc103df8fe --- /dev/null +++ b/ggml/src/ggml-hexagon/htp/im2col-ops.c @@ -0,0 +1,306 @@ +#pragma clang diagnostic ignored "-Wunused-variable" +#pragma clang diagnostic ignored "-Wunused-function" +#pragma clang diagnostic ignored "-Wunused-but-set-variable" + +#include +#include +#include +#include +#include + +#define GGML_COMMON_DECL_C +#include "ggml-common.h" +#include "htp-ctx.h" +#include "htp-ops.h" +#include "hvx-utils.h" +#include "hex-dma.h" +#include "hex-profile.h" +#include "htp-vtcm.h" + +struct htp_im2col_context { + struct htp_ops_context * octx; + uint32_t npatches_per_thread; // patches = N*OH*OW (pure-DDR kernel) + + uint32_t pe_rows_per_thread; // N*OH rows per worker + uint32_t pe_src_row_bytes; // one output row's source: IC*KH*IW*4, rounded 256 + uint32_t pe_dst_row_bytes; // one output row's dst: OW*patch_stride*2, rounded 256 + + // Patch-embed DMA path VTCM ping-pong. + uint8_t * pe_vtcm_src; // base of the 2x src buffers region + uint8_t * pe_vtcm_dst; // base of the 2x dst buffers region + uint32_t pe_src_size_per_thread; // 2 * pe_src_row_bytes + uint32_t pe_dst_size_per_thread; // 2 * pe_dst_row_bytes +}; + +// Per-op VTCM layout for the patch-embed DMA path +struct htp_im2col_vtcm_layout { + size_t off_src; + size_t off_dst; + size_t src_bytes_per_thread; + size_t dst_bytes_per_thread; + size_t total_bytes; +}; + +static inline void htp_im2col_vtcm_layout_build(struct htp_im2col_vtcm_layout * L, + size_t src_row_bytes, + size_t dst_row_bytes, + uint32_t n_threads) { + L->src_bytes_per_thread = 2 * src_row_bytes; + L->dst_bytes_per_thread = 2 * dst_row_bytes; + + L->off_src = 0; + L->off_dst = L->off_src + L->src_bytes_per_thread * n_threads; + L->total_bytes = L->off_dst + L->dst_bytes_per_thread * n_threads; +} + +#define IM2COL_PATCHEMBED_BODY(FNAME, DST_CTYPE, COPY_FN, SPLAT_FN, DST_ELEM, TAG) \ + static void FNAME(unsigned int nth, unsigned int ith, void * data) { \ + struct htp_im2col_context * ictx = (struct htp_im2col_context *) data; \ + struct htp_ops_context * octx = ictx->octx; \ + struct htp_thread_trace * restrict tr = &octx->ctx->trace[ith]; \ + const struct htp_tensor * restrict src1 = octx->src[1]; \ + const struct htp_tensor * restrict dst = octx->dst; \ + const int32_t s0 = octx->op_params[0]; \ + const int32_t s1 = octx->op_params[1]; \ + const int32_t p0 = octx->op_params[2]; \ + const int32_t p1 = octx->op_params[3]; \ + const int32_t d0 = octx->op_params[4]; \ + const int32_t d1 = octx->op_params[5]; \ + const uint32_t N = src1->ne[3]; \ + const uint32_t IC = src1->ne[2]; \ + const uint32_t IH = src1->ne[1]; \ + const uint32_t IW = src1->ne[0]; \ + const uint32_t KH = octx->src[0]->ne[1]; \ + const uint32_t KW = octx->src[0]->ne[0]; \ + const uint32_t OH = dst->ne[2]; \ + const uint32_t OW = dst->ne[1]; \ + const uint32_t patch_stride = IC * KH * KW; \ + const float * restrict src_data = (const float *) src1->data; \ + DST_CTYPE * restrict dst_data = (DST_CTYPE *) dst->data; \ + const uint32_t npatches = N * OH * OW; \ + const uint32_t patch_start = ictx->npatches_per_thread * ith; \ + const uint32_t patch_end = MIN(patch_start + ictx->npatches_per_thread, npatches); \ + if (patch_start >= patch_end) { \ + return; \ + } \ + htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, patch_start); \ + for (uint32_t p = patch_start; p < patch_end; p++) { \ + const uint32_t iow = p % OW; \ + const uint32_t ioh = (p / OW) % OH; \ + const uint32_t in = p / (OW * OH); \ + DST_CTYPE * restrict dst_patch = dst_data + (uint64_t) p * patch_stride; \ + for (uint32_t iic = 0; iic < IC; iic++) { \ + const float * restrict src_plane = src_data + ((uint64_t) in * IC + iic) * IH * IW; \ + for (uint32_t ikh = 0; ikh < KH; ikh++) { \ + const int32_t iih = (int32_t) ioh * s1 + (int32_t) ikh * d1 - p1; \ + DST_CTYPE * restrict out_run = dst_patch + iic * (KH * KW) + ikh * KW; \ + if (iih < 0 || iih >= (int32_t) IH) { \ + SPLAT_FN(out_run, 0.0f, KW); \ + continue; \ + } \ + const int32_t iiw0 = (int32_t) iow * s0 - p0; \ + const float * restrict src_run = src_plane + (uint64_t) iih * IW + iiw0; \ + if (d0 == 1) { \ + /* contiguous source run: [lo,hi) is in-bounds, tails are zero pad */ \ + const int32_t lo = iiw0 < 0 ? -iiw0 : 0; \ + int32_t hi = (int32_t) IW - iiw0; \ + if (hi > (int32_t) KW) { \ + hi = (int32_t) KW; \ + } \ + if (hi <= lo) { \ + SPLAT_FN(out_run, 0.0f, KW); \ + } else { \ + if (lo > 0) { \ + SPLAT_FN(out_run, 0.0f, (uint32_t) lo); \ + } \ + COPY_FN((uint8_t *) (out_run + lo), (const uint8_t *) (src_run + lo), \ + (uint32_t) (hi - lo)); \ + if (hi < (int32_t) KW) { \ + SPLAT_FN(out_run + hi, 0.0f, (KW - (uint32_t) hi)); \ + } \ + } \ + continue; \ + } \ + for (uint32_t ikw = 0; ikw < KW; ikw++) { \ + const int32_t iiw = (int32_t) iow * s0 + (int32_t) ikw * d0 - p0; \ + out_run[ikw] = (iiw < 0 || iiw >= (int32_t) IW) ? \ + (DST_CTYPE) 0.0f : \ + (DST_CTYPE) src_plane[(uint64_t) iih * IW + iiw]; \ + } \ + } \ + } \ + } \ + htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, patch_start); \ + } + +IM2COL_PATCHEMBED_BODY(im2col_patchembed_thread, __fp16, hvx_copy_f16_f32_uu, hvx_splat_f16_u, sizeof(__fp16), "f32-f16") +IM2COL_PATCHEMBED_BODY(im2col_patchembed_f32_thread, float, hvx_copy_f32_uu, hvx_splat_f32_u, sizeof(float), "f32-f32") + +#define IM2COL_PATCHEMBED_DMA_BODY(FNAME, DST_CTYPE, COPY_FN, SPLAT_FN, DST_ELEM, TAG) \ + static void FNAME(unsigned int nth, unsigned int ith, void * data) { \ + struct htp_im2col_context * ictx = (struct htp_im2col_context *) data; \ + struct htp_ops_context * octx = ictx->octx; \ + struct htp_thread_trace * restrict tr = &octx->ctx->trace[ith]; \ + const struct htp_tensor * restrict src1 = octx->src[1]; \ + const struct htp_tensor * restrict dst = octx->dst; \ + const uint32_t N = src1->ne[3], IC = src1->ne[2], IH = src1->ne[1], IW = src1->ne[0]; \ + const uint32_t KH = octx->src[0]->ne[1], KW = octx->src[0]->ne[0]; \ + const uint32_t OH = dst->ne[2], OW = dst->ne[1]; \ + const uint32_t patch_stride = IC * KH * KW; \ + const float * restrict src_data = (const float *) src1->data; \ + DST_CTYPE * restrict dst_data = (DST_CTYPE *) dst->data; \ + dma_queue * dmaq = octx->ctx->dma[ith]; \ + uint8_t * src_base = ictx->pe_vtcm_src + ith * ictx->pe_src_size_per_thread; \ + uint8_t * dst_base = ictx->pe_vtcm_dst + ith * ictx->pe_dst_size_per_thread; \ + float * srcb = (float *) src_base; \ + DST_CTYPE * dstb = (DST_CTYPE *) dst_base; \ + const uint32_t nrows = N * OH; \ + const uint32_t per_thread = ictx->pe_rows_per_thread; \ + const uint32_t row_start = per_thread * ith; \ + const uint32_t row_end = MIN(row_start + per_thread, nrows); \ + if (row_start >= row_end) \ + return; \ + for (uint32_t r = row_start; r < row_end; r++) { \ + const uint32_t in = r / OH; \ + const uint32_t ioh = r % OH; \ + for (uint32_t ikh = 0; ikh < KH; ikh++) { \ + int32_t iih = (int32_t) ioh * (int32_t) KH + (int32_t) ikh; \ + int ok = (iih >= 0 && iih < (int32_t) IH); \ + for (uint32_t iic = 0; iic < IC; iic++) { \ + float * vdst = srcb + ((uint64_t) (iic * KH + ikh)) * IW; \ + const float * _vsrc = \ + ok ? (src_data + ((uint64_t) (in * IC + iic) * IH + iih) * IW) : (const float *) vdst; \ + dma_queue_push_ddr_to_vtcm( \ + dmaq, dma_make_ptr((uint8_t *) vdst, ok ? (const uint8_t *) _vsrc : (const uint8_t *) vdst), \ + IW * sizeof(float), IW * sizeof(float), ok ? 1 : 0); \ + } \ + } \ + for (uint32_t i = 0; i < IC * KH; i++) \ + dma_queue_pop(dmaq); \ + htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, r); \ + for (uint32_t iow = 0; iow < OW; iow++) { \ + DST_CTYPE * dst_patch = dstb + (uint64_t) iow * patch_stride; \ + for (uint32_t ikh = 0; ikh < KH; ikh++) { \ + int32_t iih = (int32_t) ioh * (int32_t) KH + (int32_t) ikh; \ + for (uint32_t iic = 0; iic < IC; iic++) { \ + DST_CTYPE * out_run = dst_patch + iic * (KH * KW) + ikh * KW; \ + if (iih < 0 || iih >= (int32_t) IH) { \ + SPLAT_FN(out_run, 0.0f, KW); \ + continue; \ + } \ + const float * src_run = srcb + ((uint64_t) (iic * KH + ikh)) * IW + (uint64_t) iow * KW; \ + COPY_FN((uint8_t *) out_run, (const uint8_t *) src_run, KW); \ + } \ + } \ + } \ + htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, r); \ + DST_CTYPE * ddr_row = dst_data + ((uint64_t) (in * OH + ioh) * OW) * patch_stride; \ + dma_queue_push_vtcm_to_ddr(dmaq, dma_make_ptr((uint8_t *) ddr_row, (uint8_t *) dstb), \ + OW * patch_stride * (DST_ELEM), OW * patch_stride * (DST_ELEM), 1); \ + dma_queue_flush(dmaq); \ + } \ + } + +IM2COL_PATCHEMBED_DMA_BODY(im2col_patchembed_dma_thread, __fp16, hvx_copy_f16_f32_uu, hvx_splat_f16_u, sizeof(__fp16), "pe-dma-f16") +IM2COL_PATCHEMBED_DMA_BODY(im2col_patchembed_dma_f32_thread, float, hvx_copy_f32_uu, hvx_splat_f32_u, sizeof(float), "pe-dma-f32") + +static bool im2col_use_patchembed_dma(const struct htp_ops_context * octx) { + const int32_t s0 = octx->op_params[0], s1 = octx->op_params[1]; + const int32_t p0 = octx->op_params[2], p1 = octx->op_params[3]; + const int32_t d0 = octx->op_params[4], d1 = octx->op_params[5]; + const int is_2D = octx->op_params[6] == 1; + if (!is_2D) { + return false; + } + if (octx->dst->type != HTP_TYPE_F16 && octx->dst->type != HTP_TYPE_F32) { + return false; + } + const uint32_t KH = octx->src[0]->ne[1], KW = octx->src[0]->ne[0]; + if (s0 != (int32_t) KW || s1 != (int32_t) KH) { + return false; // non-overlapping + } + if (p0 != 0 || p1 != 0) { + return false; // no padding + } + if (d0 != 1 || d1 != 1) { + return false; // no dilation + } + return true; +} + +// Sizes the per-thread 2x(src,dst) VTCM ping-pong for the patch-embed DMA path. +// Returns false if it doesn't fit the VTCM budget (caller falls back). +static bool im2col_patchembed_dma_fits(struct htp_ops_context * octx, + struct htp_im2col_context * ictx, + uint32_t n_threads) { + const uint32_t IC = octx->src[1]->ne[2], IW = octx->src[1]->ne[0]; + const uint32_t KH = octx->src[0]->ne[1], KW = octx->src[0]->ne[0]; + const uint32_t OW = octx->dst->ne[1]; + const uint32_t patch_stride = IC * KH * KW; + + ictx->pe_src_row_bytes = hex_round_up(IC * KH * IW * sizeof(float), 256); + const uint32_t dst_elem = (octx->dst->type == HTP_TYPE_F16) ? sizeof(__fp16) : sizeof(float); + ictx->pe_dst_row_bytes = hex_round_up(OW * patch_stride * dst_elem, 256); + + // 2 src + 2 dst buffers per thread (ping-pong), src region first then dst. + struct htp_im2col_vtcm_layout L; + htp_im2col_vtcm_layout_build(&L, ictx->pe_src_row_bytes, ictx->pe_dst_row_bytes, n_threads); + if (L.total_bytes > octx->ctx->vtcm_size) { + return false; + } + + uint8_t * const base = octx->ctx->vtcm_base; + ictx->pe_vtcm_src = VTCM_LAYOUT_PTR(uint8_t, base, L.off_src); + ictx->pe_vtcm_dst = VTCM_LAYOUT_PTR(uint8_t, base, L.off_dst); + ictx->pe_src_size_per_thread = (uint32_t) L.src_bytes_per_thread; + ictx->pe_dst_size_per_thread = (uint32_t) L.dst_bytes_per_thread; + return true; +} + +int op_im2col(struct htp_ops_context * octx) { + const struct htp_tensor * src1 = octx->src[1]; + const struct htp_tensor * dst = octx->dst; + + if (src1->type != HTP_TYPE_F32 || (dst->type != HTP_TYPE_F16 && dst->type != HTP_TYPE_F32)) { + FARF(ERROR, "im2col: only (F32 image -> F16/F32 columns) supported"); + return HTP_STATUS_NO_SUPPORT; + } + + const uint32_t N = src1->ne[3]; + const uint32_t OH = dst->ne[2]; + const uint32_t OW = dst->ne[1]; + const uint32_t npatches = N * OH * OW; + const uint32_t n_threads = MIN(octx->n_threads, npatches); + + if ((octx->flags & HTP_OPFLAGS_SKIP_COMPUTE) || n_threads == 0) { + return HTP_STATUS_OK; + } + + struct htp_im2col_context ictx = { 0 }; + ictx.octx = octx; + ictx.npatches_per_thread = (npatches + n_threads - 1) / n_threads; + + // Clean non-overlapping patch-embed -> DMA kernel (if it fits VTCM); + // everything else (padding/dilation/stride edges) -> pure-DDR kernel. + if (im2col_use_patchembed_dma(octx)) { + const uint32_t nrows = N * OH; + const uint32_t pth = MIN(octx->n_threads, nrows); + if (pth > 0 && im2col_patchembed_dma_fits(octx, &ictx, pth)) { + ictx.pe_rows_per_thread = (nrows + pth - 1) / pth; + if (dst->type == HTP_TYPE_F16) { + work_queue_run(octx->ctx->work_queue, im2col_patchembed_dma_thread, &ictx, pth); + } else { + work_queue_run(octx->ctx->work_queue, im2col_patchembed_dma_f32_thread, &ictx, pth); + } + return HTP_STATUS_OK; + } + // else: doesn't fit -> fall through to the pure-DDR kernel below. + } + + if (dst->type == HTP_TYPE_F16) { + work_queue_run(octx->ctx->work_queue, im2col_patchembed_thread, &ictx, n_threads); + } else { + work_queue_run(octx->ctx->work_queue, im2col_patchembed_f32_thread, &ictx, n_threads); + } + return HTP_STATUS_OK; +} diff --git a/ggml/src/ggml-hexagon/htp/main.c b/ggml/src/ggml-hexagon/htp/main.c index d971b60f3a9c..880e20c99597 100644 --- a/ggml/src/ggml-hexagon/htp/main.c +++ b/ggml/src/ggml-hexagon/htp/main.c @@ -25,112 +25,44 @@ #define GGML_COMMON_DECL_C #include "ggml-common.h" +#include "hex-bitmap.h" #include "htp-ctx.h" #include "htp-ops.h" -#include "htp-ops.h" +#include "htp-tensor.h" #include "htp_iface.h" -#include "worker-pool.h" - -AEEResult htp_iface_open(const char * uri, remote_handle64 * handle) { - struct htp_context * ctx; - int err = 0; +#include "work-queue.h" +#include "hex-profile.h" - ctx = calloc(1, sizeof(*ctx)); - if (ctx == NULL) { - return AEE_ENOMEMORY; - } - - // Use the context structure as the handle - *handle = (remote_handle64) ctx; - - // Enable FARF logs - HAP_setFARFRuntimeLoggingParams(0xffff, NULL, 0); - - // Set client class - { - HAP_power_request_t request; - memset(&request, 0, sizeof(HAP_power_request_t)); - request.type = HAP_power_set_apptype; - request.apptype = HAP_POWER_COMPUTE_CLIENT_CLASS; - - if ((err = HAP_power_set((void *) ctx, &request)) != 0) { - return err; - } - } +#define HMX_QUEUE_CAPACITY 16 +#define HMX_QUEUE_STACK_SIZE 16384 +#define WORK_QUEUE_CAPACITY 16 +#define WORK_QUEUE_STACK_SIZE 16384 +#define MAIN_THREAD_STACK_SIZE 32768 - { - HAP_power_request_t request; - memset(&request, 0, sizeof(request)); - - request.type = HAP_power_set_DCVS_v3; - request.dcvs_v3.set_dcvs_enable = TRUE; - request.dcvs_v3.dcvs_enable = FALSE; - request.dcvs_v3.set_bus_params = TRUE; - request.dcvs_v3.bus_params.min_corner = HAP_DCVS_VCORNER_MAX; - request.dcvs_v3.bus_params.max_corner = HAP_DCVS_VCORNER_MAX; - request.dcvs_v3.bus_params.target_corner = HAP_DCVS_VCORNER_MAX; - request.dcvs_v3.set_core_params = TRUE; - request.dcvs_v3.core_params.min_corner = HAP_DCVS_VCORNER_MAX; - request.dcvs_v3.core_params.max_corner = HAP_DCVS_VCORNER_MAX; - request.dcvs_v3.core_params.target_corner = HAP_DCVS_VCORNER_MAX; - request.dcvs_v3.set_sleep_disable = TRUE; - request.dcvs_v3.sleep_disable = TRUE; - -#if (__HEXAGON_ARCH__ >= 79) - HAP_set_dcvs_v3_protected_bus_corners(&request, 1); -#endif - if ((err = HAP_power_set((void *) ctx, &request)) != 0) { - return err; - } +_Static_assert(WORK_QUEUE_MAX_N_THREADS >= HTP_MAX_NTHREADS, + "work-queue thread cap must be >= HTP_MAX_NTHREADS"); - memset(&request, 0, sizeof(request)); - request.type = HAP_power_set_HVX; - request.hvx.power_up = TRUE; - if ((err = HAP_power_set((void *) ctx, &request)) != 0) { - return err; - } - } +struct htp_handle { + struct htp_context * ctx; +}; -#if __HVX_ARCH__ >= 75 - { - // Power on HMX and set HMX clock - HAP_power_request_t request; - memset(&request, 0, sizeof(HAP_power_request_t)); - request.type = HAP_power_set_HMX_v2; - request.hmx_v2.set_power = TRUE; - request.hmx_v2.power_up = TRUE; - request.hmx_v2.set_clock = TRUE; - request.hmx_v2.target_corner = HAP_DCVS_EXP_VCORNER_MAX; - request.hmx_v2.min_corner = HAP_DCVS_EXP_VCORNER_MAX; - request.hmx_v2.max_corner = HAP_DCVS_EXP_VCORNER_MAX; - request.hmx_v2.perf_mode = HAP_CLK_PERF_HIGH; - FARF(ALWAYS, "Setting HMX clock\n"); - err = HAP_power_set((void *) ctx, &request); - if (err != AEE_SUCCESS) { - FARF(ERROR, "ggml-hex: error setting HMX clock."); - return err; - } - } -#else - { - // Power on HMX - HAP_power_request_t request; - memset(&request, 0, sizeof(HAP_power_request_t)); - request.type = HAP_power_set_HMX; - request.hmx.power_up = TRUE; - FARF(ALWAYS, "Powering HMX on\n"); - err = HAP_power_set((void *) ctx, &request); - if (err != AEE_SUCCESS) { - FARF(ERROR, "ggml-hex: error powering on HMX."); - return err; - } +AEEResult htp_iface_open(const char * uri, remote_handle64 * handle) { + (void) uri; + struct htp_handle * h = calloc(1, sizeof(*h)); + if (h == NULL) { + return AEE_ENOMEMORY; } -#endif + *handle = (remote_handle64) h; return AEE_SUCCESS; } AEEResult htp_iface_etm(remote_handle64 handle, uint32_t enable) { + struct htp_handle * h = (struct htp_handle *) handle; + if (!h) { + return AEE_EBADPARM; + } + int err = enable ? HAP_user_etm_enable() : HAP_user_etm_disable(); if (err) { if (err == AEE_EVERSIONNOTSUPPORT) { @@ -143,10 +75,11 @@ AEEResult htp_iface_etm(remote_handle64 handle, uint32_t enable) { } AEEResult htp_iface_profiler(remote_handle64 handle, uint32_t mode, const htp_iface_pmu_conf* pmu_conf) { - struct htp_context * ctx = (struct htp_context *) handle; - if (!ctx) { + struct htp_handle * h = (struct htp_handle *) handle; + if (!h || !h->ctx) { return AEE_EBADPARM; } + struct htp_context * ctx = h->ctx; if (mode == HTP_PROF_PMU) { const uint32_t* events = pmu_conf->events; @@ -179,48 +112,55 @@ AEEResult htp_iface_profiler(remote_handle64 handle, uint32_t mode, const htp_if } AEEResult htp_iface_close(remote_handle64 handle) { - struct htp_context * ctx = (struct htp_context *) handle; - - if (!ctx) { + struct htp_handle * h = (struct htp_handle *) handle; + if (!h) { return AEE_EBADPARM; } - if (ctx->queue) { - FARF(ERROR, "Closing handle with queue still open"); - return AEE_EITEMBUSY; - } + struct htp_context * ctx = h->ctx; + if (ctx) { + if (ctx->dsp_queue) { + FARF(ERROR, "Closing handle with queue still open"); + return AEE_EITEMBUSY; + } - // release the mmaps (if any) - for (uint32_t i=0; immap[i].size) { + // release the mmaps (if any) + for (uint32_t i=0; immap[i].size) { #if __HVX_ARCH__ > 73 - HAP_munmap2((void *) ctx->mmap[i].base, ctx->mmap[i].size); + HAP_munmap2((void *) ctx->mmap[i].base, ctx->mmap[i].size); #else - HAP_munmap((void *) ctx->mmap[i].base, ctx->mmap[i].size); + HAP_munmap((void *) ctx->mmap[i].base, ctx->mmap[i].size); #endif - ctx->mmap[i].size = 0; - ctx->mmap[i].base = NULL; - ctx->mmap[i].fd = -1; + ctx->mmap[i].size = 0; + ctx->mmap[i].base = NULL; + ctx->mmap[i].fd = -1; + } } - } - if (ctx->profiler) { - qurt_pmu_enable(1); - } + if (ctx->profiler) { + qurt_pmu_enable(1); + } - if (ctx->etm) { - HAP_user_etm_disable(); + if (ctx->etm) { + HAP_user_etm_disable(); + } + + // Free the unified block (ctx is the base address of the block) + free(ctx); + h->ctx = NULL; } - free(ctx); + free(h); return AEE_SUCCESS; } AEEResult htp_iface_mmap(remote_handle64 handle, uint32_t fd, uint32_t size) { - struct htp_context * ctx = (struct htp_context *) handle; - if (!ctx) { + struct htp_handle * h = (struct htp_handle *) handle; + if (!h || !h->ctx) { return AEE_EBADPARM; } + struct htp_context * ctx = h->ctx; // See if we already have this mapping for (uint32_t i=0; ictx) { return AEE_EBADPARM; } + struct htp_context * ctx = h->ctx; for (uint32_t i=0; immap[i]; @@ -358,91 +299,268 @@ static void vtcm_free(struct htp_context * ctx) { } } +static void htp_main_thread(void * context); static void htp_packet_callback(dspqueue_t queue, int error, void * context); static void htp_error_callback(dspqueue_t queue, int error, void * context); AEEResult htp_iface_start(remote_handle64 handle, uint32_t sess_id, uint64_t dsp_queue_id, uint32_t n_hvx, uint32_t n_hmx, uint64_t max_vmem) { - struct htp_context * ctx = (struct htp_context *) handle; - - if (!ctx) { + struct htp_handle * h = (struct htp_handle *) handle; + if (!h) { return AEE_EBADPARM; } - if (ctx->queue) { + if (h->ctx) { FARF(ERROR, "Queue already open"); return AEE_EITEMBUSY; } - // Import queue created on the CPU - int err = dspqueue_import(dsp_queue_id, // Queue ID from dspqueue_export - htp_packet_callback, // Packet callback - htp_error_callback, // Error callback; no errors expected on the DSP - (void *) ctx, // Callback context - &ctx->queue); + // Cache the original FastRPC thread priority, then calculate compute priority + int fastrpc_tid = qurt_thread_get_id(); + int fastrpc_prio = qurt_thread_get_priority(fastrpc_tid); + int main_prio = fastrpc_prio - 10; + if (main_prio < 1) main_prio = 1; + + dspqueue_t dsp_queue = NULL; + bool use_callbacks = false; + + // Import queue with NULL callbacks to avoid starting dspueue internal threads + int err = dspqueue_import(dsp_queue_id, NULL, NULL, (void *) h, &dsp_queue); + if (err == AEE_EBADPARM) { + // Fallback for devices that don't support NULL callbacks + FARF(HIGH, "dspqueue import with NULL callbacks failed, trying with callbacks"); + use_callbacks = true; + err = dspqueue_import(dsp_queue_id, htp_packet_callback, htp_error_callback, (void *) h, &dsp_queue); + } + if (err) { FARF(ERROR, "Queue import failed with 0x%08x", (unsigned) err); return err; } + qurt_sysenv_max_hthreads_t hw_threads; + qurt_sysenv_get_max_hw_threads(&hw_threads); + uint32_t hw_nhvx = (qurt_hvx_get_units() >> 8) & 0xFF; + + if (n_hvx == 0) { + n_hvx = hw_nhvx; + } + if (n_hvx > hw_threads.max_hthreads) { + n_hvx = hw_threads.max_hthreads; + } + if (n_hvx > HTP_MAX_NTHREADS) { + n_hvx = HTP_MAX_NTHREADS; + } + + // layout segments of our contiguous block + + // 1. htp_context : sits at the base (block is 4K-aligned via memalign below) + size_t offset = sizeof(struct htp_context); + + // 2. main_stack + size_t offset_main_stack = 0; + size_t size_main_stack = 0; + if (!use_callbacks) { + offset_main_stack = hex_align_up(offset, 4096); + size_main_stack = MAIN_THREAD_STACK_SIZE; + offset = offset_main_stack + size_main_stack; + } + + // 3. work_queue + size_t wq_align = work_queue_alignof(); + size_t offset_wq = hex_align_up(offset, wq_align); + size_t size_wq = work_queue_sizeof(n_hvx, WORK_QUEUE_CAPACITY, WORK_QUEUE_STACK_SIZE); + offset = offset_wq + size_wq; + + // 4. dma_queue + size_t dma_align = dma_queue_alignof(); + size_t offset_dma = hex_align_up(offset, dma_align); + size_t size_dma = 0; + for (uint32_t i = 0; i < n_hvx; i++) { + size_dma = hex_align_up(size_dma, dma_queue_alignof()); + size_dma += dma_queue_sizeof(256); + size_dma = hex_align_up(size_dma, dma_queue_alignof()); + size_dma += dma_queue_alias_sizeof(); + } + offset = offset_dma + size_dma; + + // 5. hmx_queue + size_t offset_hmx = 0; + size_t size_hmx = 0; + if (n_hmx) { + size_t hmx_align = hmx_queue_alignof(); + offset_hmx = hex_align_up(offset, hmx_align); + size_hmx = hmx_queue_sizeof(HMX_QUEUE_CAPACITY, HMX_QUEUE_STACK_SIZE); + offset = offset_hmx + size_hmx; + } + + size_t footprint = hex_align_up(offset, 128); + + void * block = memalign(4096, footprint); + if (!block) { + FARF(ERROR, "Unable to allocate unified block of size %zu\n", footprint); + dspqueue_close(dsp_queue); + return AEE_ENOMEMORY; + } + memset(block, 0, footprint); + + h->ctx = (struct htp_context *) block; + struct htp_context * ctx = h->ctx; + ctx->footprint = footprint; + + ctx->thread_id = fastrpc_tid; + ctx->thread_prio = main_prio; ctx->max_vmem = max_vmem; - ctx->thread_id = qurt_thread_get_id(); - ctx->thread_prio = qurt_thread_get_priority(ctx->thread_id); + ctx->dsp_queue = dsp_queue; - // allocate VTCM err = vtcm_alloc(ctx); if (err != AEE_SUCCESS) { FARF(ERROR, "Unable to allocate VTCM"); + htp_iface_stop(handle); return AEE_ENOMEMORY; } - ctx->hmx_enabled = n_hmx; - ctx->hmx_queue = NULL; - if (n_hmx) { - ctx->hmx_queue = hmx_queue_create(16, ctx->vtcm_rctx); - if (ctx->hmx_queue) { - ctx->hmx_queue->trace = &ctx->trace[HTP_MAX_NTHREADS]; - } else { - FARF(ERROR, "hmx-queue-create failed"); - ctx->hmx_enabled = false; + HAP_setFARFRuntimeLoggingParams(0xffff, NULL, 0); + + // Set client class + { + HAP_power_request_t request; + memset(&request, 0, sizeof(HAP_power_request_t)); + request.type = HAP_power_set_apptype; + request.apptype = HAP_POWER_COMPUTE_CLIENT_CLASS; + + if ((err = HAP_power_set((void *) ctx, &request)) != 0) { + htp_iface_stop(handle); + return err; } } - FARF(HIGH, "HMX %s (n_hmx=%d)", ctx->hmx_enabled ? "enabled" : "disabled", n_hmx); - qurt_sysenv_max_hthreads_t hw_threads; - qurt_sysenv_get_max_hw_threads(&hw_threads); - uint32_t hw_nhvx = (qurt_hvx_get_units() >> 8) & 0xFF; + // DCVS setup + { + HAP_power_request_t request; + memset(&request, 0, sizeof(request)); - if (n_hvx == 0) { - n_hvx = hw_nhvx; + request.type = HAP_power_set_DCVS_v3; + request.dcvs_v3.set_dcvs_enable = TRUE; + request.dcvs_v3.dcvs_enable = FALSE; + request.dcvs_v3.set_bus_params = TRUE; + request.dcvs_v3.bus_params.min_corner = HAP_DCVS_VCORNER_MAX; + request.dcvs_v3.bus_params.max_corner = HAP_DCVS_VCORNER_MAX; + request.dcvs_v3.bus_params.target_corner = HAP_DCVS_VCORNER_MAX; + request.dcvs_v3.set_core_params = TRUE; + request.dcvs_v3.core_params.min_corner = HAP_DCVS_VCORNER_MAX; + request.dcvs_v3.core_params.max_corner = HAP_DCVS_VCORNER_MAX; + request.dcvs_v3.core_params.target_corner = HAP_DCVS_VCORNER_MAX; + request.dcvs_v3.set_sleep_disable = TRUE; + request.dcvs_v3.sleep_disable = TRUE; + +#if (__HEXAGON_ARCH__ >= 79) + HAP_set_dcvs_v3_protected_bus_corners(&request, 1); +#endif + if ((err = HAP_power_set((void *) ctx, &request)) != 0) { + htp_iface_stop(handle); + return err; + } + + memset(&request, 0, sizeof(request)); + request.type = HAP_power_set_HVX; + request.hvx.power_up = TRUE; + if ((err = HAP_power_set((void *) ctx, &request)) != 0) { + htp_iface_stop(handle); + return err; + } } - if (n_hvx > hw_threads.max_hthreads) { - n_hvx = hw_threads.max_hthreads; + +#if __HVX_ARCH__ >= 75 + { + // Power on HMX and set HMX clock + HAP_power_request_t request; + memset(&request, 0, sizeof(HAP_power_request_t)); + request.type = HAP_power_set_HMX_v2; + request.hmx_v2.set_power = TRUE; + request.hmx_v2.power_up = TRUE; + request.hmx_v2.set_clock = TRUE; + request.hmx_v2.target_corner = HAP_DCVS_EXP_VCORNER_MAX; + request.hmx_v2.min_corner = HAP_DCVS_EXP_VCORNER_MAX; + request.hmx_v2.max_corner = HAP_DCVS_EXP_VCORNER_MAX; + request.hmx_v2.perf_mode = HAP_CLK_PERF_HIGH; + FARF(ALWAYS, "Setting HMX clock\n"); + err = HAP_power_set((void *) ctx, &request); + if (err != AEE_SUCCESS) { + FARF(ERROR, "ggml-hex: error setting HMX clock."); + htp_iface_stop(handle); + return err; + } } - if (n_hvx > HTP_MAX_NTHREADS) { - n_hvx = HTP_MAX_NTHREADS; +#else + { + // Power on HMX + HAP_power_request_t request; + memset(&request, 0, sizeof(HAP_power_request_t)); + request.type = HAP_power_set_HMX; + request.hmx.power_up = TRUE; + FARF(ALWAYS, "Powering HMX on\n"); + err = HAP_power_set((void *) ctx, &request); + if (err != AEE_SUCCESS) { + FARF(ERROR, "ggml-hex: error powering on HMX."); + htp_iface_stop(handle); + return err; + } + } +#endif + + ctx->hmx_enabled = n_hmx; + ctx->hmx_queue = NULL; + if (n_hmx) { + void * hmx_ptr = (void *) ((uintptr_t) block + offset_hmx); + ctx->hmx_queue = hmx_queue_init(hmx_ptr, HMX_QUEUE_CAPACITY, HMX_QUEUE_STACK_SIZE, ctx->vtcm_rctx, &ctx->trace[HTP_MAX_NTHREADS]); } + FARF(HIGH, "HMX %s (n_hmx=%d)", ctx->hmx_enabled ? "enabled" : "disabled", n_hmx); ctx->n_threads = n_hvx; + ctx->n_threads_div = init_fastdiv_values(ctx->n_threads); + + // Initialize DMA queues + uint8_t * dma_ptr_curr = (uint8_t *) ((uintptr_t) block + offset_dma); + size_t size_dma_q = dma_queue_sizeof(256); + size_t size_dma_alias = dma_queue_alias_sizeof(); + for (int i = 0; i < ctx->n_threads; i++) { - ctx->dma[i] = dma_queue_create(256); // queue depth - if (ctx->dma[i]) { - ctx->dma[i]->trace = &ctx->trace[i]; - } + dma_ptr_curr = (uint8_t *) hex_align_up((uintptr_t) dma_ptr_curr, dma_queue_alignof()); + ctx->dma_cached[i] = dma_queue_init(dma_ptr_curr, 256, (uintptr_t) ctx->vtcm_base, ctx->vtcm_size, &ctx->trace[i]); + dma_ptr_curr += size_dma_q; + + dma_ptr_curr = (uint8_t *) hex_align_up((uintptr_t) dma_ptr_curr, dma_queue_alignof()); + ctx->dma[i] = dma_queue_alias_init(dma_ptr_curr, ctx->dma_cached[i], 1); + dma_ptr_curr += size_dma_alias; } ctx->ddr_spad_size = 512 * 1024; // 512 KB ctx->ddr_spad_base = memalign(128, ctx->ddr_spad_size); - // init worker pool - err = worker_pool_init(&ctx->worker_pool, n_hvx); - if (err != AEE_SUCCESS) { - FARF(ERROR, "Unable to create worker pool"); - if (ctx->ddr_spad_base) { - free(ctx->ddr_spad_base); - ctx->ddr_spad_base = NULL; - ctx->ddr_spad_size = 0; + void * wq_ptr = (void *) ((uintptr_t) block + offset_wq); + ctx->work_queue = work_queue_init(wq_ptr, n_hvx, WORK_QUEUE_CAPACITY, WORK_QUEUE_STACK_SIZE); + + ctx->main_stack = NULL; + ctx->main_thread = 0; + atomic_store(&ctx->killed, false); + + if (!use_callbacks) { + // Start main compute thread + ctx->main_stack = (void *) ((uintptr_t) block + offset_main_stack); + + qurt_thread_attr_t attr; + qurt_thread_attr_init(&attr); + qurt_thread_attr_set_stack_addr(&attr, ctx->main_stack); + qurt_thread_attr_set_stack_size(&attr, size_main_stack); + qurt_thread_attr_set_priority(&attr, main_prio); + qurt_thread_attr_set_name(&attr, "htp-main"); + + int err_thread = qurt_thread_create(&ctx->main_thread, &attr, htp_main_thread, ctx); + if (err_thread) { + FARF(ERROR, "Unable to create htp main thread: %d", err_thread); + htp_iface_stop(handle); + return AEE_ENOMEMORY; } - return err; } FARF(HIGH, "session %u started: n-hvx %u vtcm-size %zu vtcm-rctx %u n-threads %u thread-id %d thread-prio %d \n", @@ -452,35 +570,34 @@ AEEResult htp_iface_start(remote_handle64 handle, uint32_t sess_id, uint64_t dsp } AEEResult htp_iface_stop(remote_handle64 handle) { - struct htp_context * ctx = (struct htp_context *) handle; - if (!ctx) { + struct htp_handle * h = (struct htp_handle *) handle; + if (!h || !h->ctx) { return AEE_EBADPARM; } + struct htp_context * ctx = h->ctx; - if (!ctx->queue) { - FARF(ERROR, "Queue not open"); - return AEE_EBADSTATE; + if (ctx->main_thread) { + atomic_store(&ctx->killed, true); + int status; + (void) qurt_thread_join(ctx->main_thread, &status); + ctx->main_thread = 0; } - // Close queue. dspqueue_close() will also wait for callbacks to finish. - int err = dspqueue_close(ctx->queue); - ctx->queue = NULL; + int err = dspqueue_close(ctx->dsp_queue); ctx->dsp_queue = NULL; if (err != 0) { FARF(ERROR, "Queue close failed with 0x%08x", (unsigned) err); return err; } - if (ctx->worker_pool) { - // Release worker pool - worker_pool_release(&ctx->worker_pool); - } + work_queue_free(ctx->work_queue); for (int i = 0; i < ctx->n_threads; i++) { - dma_queue_delete(ctx->dma[i]); + dma_queue_alias_free(ctx->dma[i]); + dma_queue_free(ctx->dma_cached[i]); } if (ctx->hmx_queue) { - hmx_queue_delete(ctx->hmx_queue); + hmx_queue_free(ctx->hmx_queue); ctx->hmx_queue = NULL; } ctx->hmx_enabled = false; @@ -493,6 +610,9 @@ AEEResult htp_iface_stop(remote_handle64 handle) { ctx->ddr_spad_size = 0; } + free(ctx); + h->ctx = NULL; + return AEE_SUCCESS; } @@ -598,18 +718,19 @@ static int execute_op(struct htp_ops_context * octx) { case HTP_OP_RMS_NORM: case HTP_OP_RMS_NORM_MUL: case HTP_OP_SCALE: + case HTP_OP_CLAMP: case HTP_OP_SQR: case HTP_OP_SQRT: case HTP_OP_UNARY_SOFTPLUS: case HTP_OP_UNARY_SIGMOID: + case HTP_OP_UNARY_SILU: + case HTP_OP_UNARY_GELU: case HTP_OP_UNARY_NEG: case HTP_OP_UNARY_EXP: case HTP_OP_UNARY_TANH: case HTP_OP_L2_NORM: return op_unary(octx); - case HTP_OP_UNARY_SILU: - case HTP_OP_UNARY_GELU: case HTP_OP_GLU_SWIGLU: case HTP_OP_GLU_SWIGLU_OAI: case HTP_OP_GLU_GEGLU: @@ -660,6 +781,9 @@ static int execute_op(struct htp_ops_context * octx) { case HTP_OP_PAD: return op_pad(octx); + case HTP_OP_IM2COL: + return op_im2col(octx); + case HTP_OP_CONCAT: return op_concat(octx); @@ -671,8 +795,6 @@ static int execute_op(struct htp_ops_context * octx) { case HTP_OP_INVALID: break; - - // No default to catch missing cases } FARF(ERROR, "Unknown Op %u", octx->op); @@ -778,12 +900,12 @@ static void prep_op_bufs(struct htp_context *ctx, struct htp_buf_desc *bufs, uin } } -static void prep_tensor(struct htp_context *ctx, struct htp_buf_desc *bufs, uint32_t idx, struct htp_tensor *t) { +static void prep_tensor(struct htp_context *ctx, struct htp_buf_desc *bufs, struct htp_tensor *tens, uint32_t idx, struct htp_tensor *t) { uint32_t offset = t->data; uint32_t size = t->size; uint32_t bi = t->bi; - t->data = bufs[bi].base + offset; // update data to the actual pointer + t->data = (uint32_t) (bufs[bi].base + offset); // update data to the actual pointer FARF(HIGH, "prep-tensor #%u: bi %u offset %u size %u data %p : %u:%u:%u:%u", idx, t->bi, offset, t->size, (void*) t->data, t->ne[0], t->ne[1], t->ne[3], t->ne[3]); @@ -791,7 +913,7 @@ static void prep_tensor(struct htp_context *ctx, struct htp_buf_desc *bufs, uint static void prep_tensors(struct htp_context *ctx, struct htp_buf_desc *bufs, struct htp_tensor *tens, uint32_t n_tens) { for (uint32_t i=0; i < n_tens; i++) { - prep_tensor(ctx, bufs, i, tens + i); + prep_tensor(ctx, bufs, tens, i, tens + i); } } @@ -805,29 +927,34 @@ static int proc_op_req(struct htp_ops_context * octx, struct htp_tensor *tens, u // Prep input tensors for (uint32_t i=0; isrc[i] == 0xffff ? NULL : tens + op->src[i]; - - octx->src[i] = src; - if (!src) continue; - - if (!(src->flags & HTP_TENSOR_FLUSHED) && (src->flags & HTP_TENSOR_COMPUTE)) { - // flush compute buffers on input - hex_l2flush((void *) src->data, src->size); + uint16_t src_idx = op->src[i]; + if (src_idx == 0xffff) { + octx->src[i] = NULL; + octx->src_dma[i] = NULL; + continue; } + struct htp_tensor *src = tens + src_idx; + octx->src[i] = src; + octx->src_dma[i] = octx->ctx->dma; // FIXME: ? octx->ctx->dma_cached : octx->ctx->dma; + FARF(HIGH, "prep-src #%u: data %p size %u : %u:%u:%u:%u", op->src[i], (void*) src->data, src->size, src->ne[0], src->ne[1], src->ne[3], src->ne[3]); } + htp_tensor_flush_all(octx->ctx, octx->src, HTP_OP_MAX_INPUTS); + // Prep output tensors for (uint32_t i = 0; i < HTP_OP_MAX_OUTPUTS; i++) { uint16_t dst_idx = op->dst[i]; if (dst_idx == 0xffff) { - octx->dsts[i] = NULL; + octx->dsts[i] = NULL; + octx->dst_dma[i] = NULL; continue; } struct htp_tensor *dst = tens + dst_idx; - octx->dsts[i] = dst; + octx->dsts[i] = dst; + octx->dst_dma[i] = octx->ctx->dma; // FIXME: ? octx->ctx->dma_cached : octx->ctx->dma; FARF(HIGH, "prep-dst[%u] #%u: data %p size %u : %u:%u:%u:%u", i, dst_idx, (void*) dst->data, dst->size, dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3]); @@ -835,40 +962,154 @@ static int proc_op_req(struct htp_ops_context * octx, struct htp_tensor *tens, u int status = execute_op(octx); + htp_tensor_dirty_all(octx->ctx, octx->dsts, HTP_OP_MAX_OUTPUTS); + octx->src0_spad.src = NULL; octx->src1_spad.src = NULL; octx->src2_spad.src = NULL; octx->src3_spad.src = NULL; octx->dst_spad.src = NULL; - // flush buffers on output - for (uint32_t i = 0; i < HTP_OP_MAX_OUTPUTS; i++) { - if (octx->dsts[i]) { - struct htp_tensor *dst = (struct htp_tensor *)octx->dsts[i]; - hex_l2flush((void *) dst->data, dst->size); - dst->flags |= HTP_TENSOR_FLUSHED; + return status; +} + +static void process_opbatch(struct htp_context * ctx, const struct htp_opbatch_req * req, const struct dspqueue_buffer * dbuf) { + dspqueue_t queue = ctx->dsp_queue; + int err; + + const uint32_t n_bufs = req->n_bufs; + const uint32_t n_tens = req->n_tensors; + const uint32_t n_ops = req->n_ops; + + const uint32_t b_size = sizeof(struct htp_buf_desc) * n_bufs; + const uint32_t t_size = sizeof(struct htp_tensor) * n_tens; + const uint32_t o_size = sizeof(struct htp_op_desc) * n_ops; + const uint32_t p_size = sizeof(struct htp_prof_desc) * n_ops; + const uint32_t tr_size = (HTP_MAX_NTHREADS + 1) * req->n_traces * sizeof(struct htp_trace_desc); - FARF(HIGH, "post-dst[%u] #%u: data %p size %u : %u:%u:%u:%u", i, op->dst[i], (void*) dst->data, dst->size, - dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3]); + if (dbuf->size < b_size + t_size + o_size + p_size + tr_size) { + FARF(ERROR, "invalid opbatch memory block size %u (req %u)", dbuf->size, b_size + t_size + o_size + p_size + tr_size); + return; + } + + FARF(HIGH, "processing opbatch #%u: n-bufs %u n-tensors %u n-ops %u n-traces %u : m-size %u b-size %u t-size %u o-size %u", req->id, + n_bufs, n_tens, n_ops, req->n_traces, dbuf->size, b_size, t_size, o_size); + + // Setup descriptor pointers + uint8_t * m_ptr = dbuf->ptr; + struct htp_buf_desc* bufs = (struct htp_buf_desc*) m_ptr; m_ptr += b_size; + struct htp_tensor* tens = (struct htp_tensor*) m_ptr; m_ptr += t_size; + struct htp_op_desc* ops = (struct htp_op_desc*) m_ptr; m_ptr += o_size; + struct htp_prof_desc* pds = (struct htp_prof_desc*) m_ptr; + + struct profile_data batch_prof; + profile_start(HTP_PROF_BASIC, &batch_prof); + + memset(ctx->trace, 0, sizeof(ctx->trace)); + if (ctx->profiler == HTP_PROF_TRACE) { + struct htp_trace_desc * trace_events = (struct htp_trace_desc *) (m_ptr + p_size); + for (int t = 0; t <= HTP_MAX_NTHREADS; t++) { + ctx->trace[t].events = &trace_events[t * req->n_traces]; + ctx->trace[t].max_events = req->n_traces; } } - return status; + // Clean cache at the start of the batch + htp_trace_event_start(&ctx->trace[0], HTP_TRACE_EVT_L2FLUSH, 0); + qurt_mem_cache_clean((qurt_addr_t) 0, 0, QURT_MEM_CACHE_FLUSH_INVALIDATE_ALL, QURT_MEM_DCACHE); + hex_l2fetch_block(ctx, ctx->footprint); + memset(ctx->dirty_ranges, 0, sizeof(ctx->dirty_ranges)); + htp_trace_event_stop(&ctx->trace[0], HTP_TRACE_EVT_L2FLUSH, 0); + + htp_trace_event_start(&ctx->trace[0], HTP_TRACE_EVT_BUFF, 0); + prep_op_bufs(ctx, bufs, n_bufs); + htp_trace_event_stop(&ctx->trace[0], HTP_TRACE_EVT_BUFF, 0); + + prep_tensors(ctx, bufs, tens, n_tens); + + struct htp_ops_context *octx = &ctx->octx; + memset(octx, 0, sizeof(*octx)); + octx->n_threads = ctx->n_threads; + octx->ctx = ctx; + + work_queue_wakeup(ctx->work_queue); + if (ctx->hmx_queue) { + hmx_queue_wakeup(ctx->hmx_queue); + } + + int op_status = HTP_STATUS_OK; + for (uint32_t i = 0; i < n_ops && op_status == HTP_STATUS_OK; i++) { + struct profile_data prof; + + profile_start(ctx->profiler, &prof); + + op_status = proc_op_req(octx, tens, i, &ops[i]); + + profile_stop(ctx->profiler, &prof); + + if (ctx->profiler) { + pds[i].opcode = ops[i].opcode; + pds[i].usecs = prof.usecs; + pds[i].cycles_start = prof.cycles_start; + pds[i].cycles_stop = prof.cycles_stop; + for (int j = 0; j < HEX_NUM_PMU_COUNTERS; j++) { + pds[i].pmu[j] = prof.pmu_counters[j]; + } + } + } + + if (ctx->hmx_queue) { + hmx_queue_suspend(ctx->hmx_queue); + hmx_queue_flush(ctx->hmx_queue); + } + work_queue_suspend(ctx->work_queue); + + // Flush remaining dirty tensors at the end of the batch + htp_trace_event_start(&ctx->trace[0], HTP_TRACE_EVT_L2FLUSH, 0); + qurt_mem_cache_clean((qurt_addr_t) 0, 0, QURT_MEM_CACHE_FLUSH_INVALIDATE_ALL, QURT_MEM_DCACHE); + htp_trace_event_stop(&ctx->trace[0], HTP_TRACE_EVT_L2FLUSH, 0); + + profile_stop(HTP_PROF_BASIC, &batch_prof); + + struct htp_opbatch_rsp rsp; + memset(&rsp, 0, sizeof(rsp)); + rsp.id = req->id; + rsp.status = op_status; + rsp.n_bufs = n_bufs; + rsp.n_tensors = n_tens; + rsp.n_ops = n_ops; + rsp.usecs = batch_prof.usecs; + rsp.cycles_start = batch_prof.cycles_start; + rsp.cycles_stop = batch_prof.cycles_stop; + + if (ctx->profiler == HTP_PROF_TRACE) { + for (int t = 0; t <= HTP_MAX_NTHREADS; t++) { + rsp.n_traces[t] = ctx->trace[t].count; + } + } + + struct dspqueue_buffer write_dbuf = *dbuf; + write_dbuf.flags = DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER | DSPQUEUE_BUFFER_FLAG_INVALIDATE_RECIPIENT; + + err = dspqueue_write(queue, 0, 1, &write_dbuf, sizeof(rsp), (const uint8_t *) &rsp, DSPQUEUE_TIMEOUT_NONE); + if (err != 0) { + FARF(ERROR, "dspqueue_write failed: 0x%08x", (unsigned) err); + } } +#define DSPQUEUE_READ_TIMEOUT_USEC 5000 #define DSPQUEUE_POLL_TIMEOUT_USEC 100 #define DSPQUEUE_POLL_COUNT 100 -static void htp_packet_callback(dspqueue_t queue, int error, void * context) { - struct htp_context * ctx = (struct htp_context *) context; - +static void process_ops(struct htp_context * ctx) { + dspqueue_t queue = ctx->dsp_queue; int err; uint32_t poll_count = DSPQUEUE_POLL_COUNT; vtcm_acquire(ctx); - while (!ctx->vtcm_needs_release) { + while (!ctx->vtcm_needs_release && !atomic_load(&ctx->killed)) { struct htp_opbatch_req req; uint32_t r_size = sizeof(req); @@ -898,111 +1139,41 @@ static void htp_packet_callback(dspqueue_t queue, int error, void * context) { // Reset poll count for valid requests poll_count = DSPQUEUE_POLL_COUNT; - const uint32_t n_bufs = req.n_bufs; - const uint32_t n_tens = req.n_tensors; - const uint32_t n_ops = req.n_ops; - - const uint32_t b_size = sizeof(struct htp_buf_desc) * n_bufs; - const uint32_t t_size = sizeof(struct htp_tensor) * n_tens; - const uint32_t o_size = sizeof(struct htp_op_desc) * n_ops; - const uint32_t p_size = sizeof(struct htp_prof_desc) * n_ops; - const uint32_t tr_size = (HTP_MAX_NTHREADS + 1) * req.n_traces * sizeof(struct htp_trace_desc); - - if (dbuf.size < b_size + t_size + o_size + p_size + tr_size) { - FARF(ERROR, "invalid opbatch memory block size %u (req %u)", dbuf.size, b_size + t_size + o_size + p_size + tr_size); - break; - } - - FARF(HIGH, "processing opbatch #%u: n-bufs %u n-tensors %u n-ops %u n-traces %u : m-size %u b-size %u t-size %u o-size %u", req.id, - n_bufs, n_tens, n_ops, req.n_traces, dbuf.size, b_size, t_size, o_size); - - // Setup descriptor pointers - uint8_t * m_ptr = dbuf.ptr; - struct htp_buf_desc* bufs = (struct htp_buf_desc*) m_ptr; m_ptr += b_size; - struct htp_tensor* tens = (struct htp_tensor*) m_ptr; m_ptr += t_size; - struct htp_op_desc* ops = (struct htp_op_desc*) m_ptr; m_ptr += o_size; - struct htp_prof_desc* pds = (struct htp_prof_desc*) m_ptr; - - prep_op_bufs(ctx, bufs, n_bufs); - prep_tensors(ctx, bufs, tens, n_tens); - - struct htp_ops_context *octx = &ctx->octx; - memset(octx, 0, sizeof(*octx)); - octx->n_threads = ctx->n_threads; - octx->ctx = ctx; - - if (ctx->profiler == HTP_PROF_TRACE) { - memset(ctx->trace, 0, sizeof(ctx->trace)); - struct htp_trace_desc * trace_events = (struct htp_trace_desc *) (m_ptr + p_size); - for (int t = 0; t <= HTP_MAX_NTHREADS; t++) { - ctx->trace[t].events = &trace_events[t * req.n_traces]; - ctx->trace[t].max_events = req.n_traces; - } - } else { - for (int t = 0; t <= HTP_MAX_NTHREADS; t++) { - ctx->trace[t].events = NULL; - ctx->trace[t].max_events = 0; - } - } - - int op_status = HTP_STATUS_OK; - uint32_t op_wakeup = n_ops / 2; // half-way throgh the batch - - hmx_queue_wakeup(ctx->hmx_queue); - - for (uint32_t i=0; i < n_ops; i++) { - struct profile_data prof; - - if (i == op_wakeup) { - dspqueue_write_early_wakeup_noblock(queue, 0, 0); - } - - profile_start(ctx->profiler, &prof); + process_opbatch(ctx, &req, &dbuf); + } - op_status = proc_op_req(octx, tens, i, &ops[i]); + vtcm_release(ctx); +} - profile_stop(ctx->profiler, &prof); +static void htp_packet_callback(dspqueue_t queue, int error, void * context) { + (void) queue; + (void) error; + struct htp_handle * h = (struct htp_handle *) context; + if (h && h->ctx) { + process_ops(h->ctx); + } +} - if (op_status != HTP_STATUS_OK) { - break; - } +static void htp_main_thread(void * context) { + struct htp_context * ctx = (struct htp_context *) context; - if (ctx->profiler) { - pds[i].opcode = ops[i].opcode; - pds[i].usecs = prof.usecs; - pds[i].cycles_start = prof.cycles_start; - pds[i].cycles_stop = prof.cycles_stop; - for (int j = 0; j < HEX_NUM_PMU_COUNTERS; j++) { - pds[i].pmu[j] = prof.pmu_counters[j]; - } - } - } + FARF(HIGH, "htp-main-thread: started"); - hmx_queue_suspend(ctx->hmx_queue); + while (!atomic_load(&ctx->killed)) { + uint32_t flags = 0; + uint32_t num_buffers = 0; + uint32_t message_length = 0; - struct htp_opbatch_rsp rsp; - rsp.id = req.id; - rsp.status = op_status; - rsp.n_bufs = n_bufs; - rsp.n_tensors = n_tens; - rsp.n_ops = n_ops; - memset(rsp.pad, 0, sizeof(rsp.pad)); - if (ctx->profiler == HTP_PROF_TRACE) { - for (int t = 0; t <= HTP_MAX_NTHREADS; t++) { - rsp.n_traces[t] = ctx->trace[t].count; - } + int err = dspqueue_peek(ctx->dsp_queue, &flags, &num_buffers, &message_length, 50000); + if (err == 0) { + process_ops(ctx); + } else if (err == AEE_EWOULDBLOCK || err == AEE_EEXPIRED) { + continue; } else { - memset(rsp.n_traces, 0, sizeof(rsp.n_traces)); - } - - dbuf.flags = DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER | DSPQUEUE_BUFFER_FLAG_INVALIDATE_RECIPIENT; - - err = dspqueue_write(queue, 0, 1, &dbuf, sizeof(rsp), (const uint8_t *) &rsp, DSPQUEUE_TIMEOUT_NONE); - if (err != 0) { - FARF(ERROR, "dspqueue_write failed: 0x%08x", (unsigned) err); + FARF(ERROR, "dspqueue_peek failed: 0x%08x", (unsigned) err); break; } } - vtcm_release(ctx); + FARF(HIGH, "htp-main-thread: stopped"); } diff --git a/ggml/src/ggml-hexagon/htp/matmul-ops.c b/ggml/src/ggml-hexagon/htp/matmul-ops.c index 1683131a813a..9d385469ae9f 100644 --- a/ggml/src/ggml-hexagon/htp/matmul-ops.c +++ b/ggml/src/ggml-hexagon/htp/matmul-ops.c @@ -14,6 +14,8 @@ #include "hex-dma.h" #include "hvx-utils.h" #include "hvx-dump.h" +#include "hvx-arith.h" +#include "hvx-reduce.h" #define GGML_COMMON_DECL_C #include "ggml-common.h" @@ -82,6 +84,8 @@ struct htp_mm_context { // Precomputed values uint32_t src0_nrows_per_thread; + uint32_t src0_row_size_padded; + uint32_t src1_nrows; struct fastdiv_values mm_div_ne12_ne1; struct fastdiv_values mm_div_ne1; @@ -92,10 +96,10 @@ struct htp_mm_context { // Per thread quant tasks // Precomputed block-parallel quantization values worker_callback_t quant_task_func; - uint32_t quant_ib_first[MAX_NUM_WORKERS]; - uint32_t quant_ib_last[MAX_NUM_WORKERS]; - uint32_t quant_r[MAX_NUM_WORKERS]; - uint32_t quant_c[MAX_NUM_WORKERS]; + uint32_t quant_ib_first[WORK_QUEUE_MAX_N_THREADS]; + uint32_t quant_ib_last[WORK_QUEUE_MAX_N_THREADS]; + uint32_t quant_r[WORK_QUEUE_MAX_N_THREADS]; + uint32_t quant_c[WORK_QUEUE_MAX_N_THREADS]; uint32_t n_quant_tasks; uint32_t n_quant_rows_per_thread; atomic_uint quant_barrier; @@ -103,6 +107,7 @@ struct htp_mm_context { // Fields for scattered mapping & HMX support in MUL_MAT_ID const uint32_t * matrix_row_counts; const struct mmid_row_mapping * matrix_rows; + uint32_t mapping_stride; // Dynamic VTCM pointers allocated sequentially uint8_t * vtcm_src0; @@ -154,8 +159,6 @@ static const uint8_t __attribute__((aligned(VLEN))) kvalues_mxfp4_lut[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, }; - - #define htp_matmul_tensors_preamble \ const struct htp_tensor * restrict src0 = octx->src[0]; \ const struct htp_tensor * restrict src1 = octx->src[1]; \ @@ -254,7 +257,7 @@ static void hvx_mm_4d(unsigned int nth, unsigned int ith, void * data) { return; } - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL; + struct htp_thread_trace * tr = &octx->ctx->trace[ith]; htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ir0_start); const uint32_t blck_0 = 64; @@ -309,7 +312,7 @@ static void hvx_mm_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith, void const uint32_t src0_start_row = src0_nrows_per_thread * ith; \ const uint32_t src0_end_row = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows); \ \ - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL; \ + struct htp_thread_trace * tr = &octx->ctx->trace[ith]; \ \ const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params; \ const uint32_t n_prefetch = kparams->n_prefetch; \ @@ -410,7 +413,7 @@ static void hvx_mv_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith, void const uint32_t src0_start_row = src0_nrows_per_thread * ith; \ const uint32_t src0_end_row = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows); \ \ - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL; \ + struct htp_thread_trace * tr = &octx->ctx->trace[ith]; \ \ const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params; \ const uint32_t n_prefetch = kparams->n_prefetch; \ @@ -444,6 +447,16 @@ static void hvx_mv_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith, void \ uint32_t push_ct = ct_start; \ if (src0_start_row < src0_end_row) { \ + if (src2) { \ + float * vtcm_src2_ptr = (float *) mmctx->vtcm_src2 + src0_start_row; \ + const float * src2_ptr = (const float *) src2->data + src0_start_row; \ + int slice_size = (int)MIN(src0_end_row, ne0) - (int)src0_start_row; \ + if (slice_size > 0) { \ + dma_queue_push(dma_queue, dma_make_ptr(vtcm_src2_ptr, src2_ptr), \ + slice_size * sizeof(float), slice_size * sizeof(float), slice_size * sizeof(float), 1); \ + dma_queue_pop_nowait(dma_queue); \ + } \ + } \ for (uint32_t d = 0; d < n_prefetch && push_ct < ct_end; d++, push_ct++) { \ dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + d * tile_row_transfer_size_aligned, \ src0_row + push_ct * tile_row_stride), aligned_tile_size, tile_size, tile_size, n_k_tiles_a); \ @@ -465,7 +478,7 @@ static void hvx_mv_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith, void \ htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ct); \ DOT_2X1(ne10, dst_ptr, w_tile, src1_col, valid_rows, NULL); \ - htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ct); \ + htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ct); \ \ if (push_ct < ct_end) { \ dma_queue_push(dma_queue, dma_make_ptr((uint8_t *)w_tile, src0_row + push_ct * tile_row_stride), \ @@ -476,24 +489,16 @@ static void hvx_mv_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith, void \ int copy_cnt = (int)MIN(src0_end_row, ne0) - (int)src0_start_row; \ if (copy_cnt > 0) { \ + htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ct_end); \ if (src2) { \ - float * dst_ptr = &dst_col[src0_start_row]; \ - const float * src2_ptr = (const float *) src2->data + src0_start_row; \ - float * tmp_ptr = tmp; \ - int remaining = copy_cnt; \ - while (remaining > 0) { \ - int n = MIN(remaining, 32); \ - HVX_Vector v_out = hvx_vmemu(tmp_ptr); \ - HVX_Vector v_z = hvx_vmemu(src2_ptr); \ - hvx_vec_store_u(dst_ptr, n * sizeof(float), hvx_vec_add_f32_f32(v_out, v_z)); \ - dst_ptr += n; \ - src2_ptr += n; \ - tmp_ptr += n; \ - remaining -= n; \ - } \ + hvx_add_f32_uaa((uint8_t *) &dst_col[src0_start_row], \ + (const uint8_t *) tmp, \ + (const uint8_t *) ((const float *) mmctx->vtcm_src2 + src0_start_row), \ + copy_cnt); \ } else { \ hvx_copy_f32_ua((uint8_t *) &dst_col[src0_start_row], (uint8_t *) tmp, copy_cnt); \ } \ + htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ct_end); \ } \ } @@ -523,7 +528,7 @@ static void hvx_mm_qkv_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith, uint8_t * restrict vtcm_src3_ptr = mmctx->vtcm_src3 + mmctx->vtcm_src3_size_per_thread * ith; \ uint8_t * restrict src1_data = mmctx->vtcm_src1; \ \ - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL; \ + struct htp_thread_trace * tr = &octx->ctx->trace[ith]; \ \ const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params; \ const uint32_t n_prefetch = kparams->n_prefetch; \ @@ -699,7 +704,7 @@ static void hvx_mm_ffn_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith, uint8_t * restrict vtcm_src2_ptr = mmctx->vtcm_src2 + mmctx->vtcm_src2_size_per_thread * ith; \ uint8_t * restrict src1_data = mmctx->vtcm_src1; \ \ - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL; \ + struct htp_thread_trace * tr = &octx->ctx->trace[ith]; \ \ const uint8_t * restrict src0_row = (const uint8_t *) src0->data; \ const uint8_t * restrict src2_row = (const uint8_t *) src2->data; \ @@ -820,7 +825,7 @@ static void name(unsigned int nth, unsigned int ith, void * data) { return; \ } \ \ - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL; \ + struct htp_thread_trace * tr = &octx->ctx->trace[ith]; \ htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_QUANT, ir_first); \ \ uint8_t * restrict dst = mmctx->vtcm_src1; \ @@ -846,7 +851,7 @@ QUANTIZE_IMPL(quantize_f16_f16_flat, "quantize-f16-f16", quantize_f16_f static void quantize_f32_q8_0_tiled_block(unsigned int nth, unsigned int ith, void * data) { struct htp_mm_context * mmctx = data; struct htp_ops_context * octx = mmctx->octx; - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL; + struct htp_thread_trace * tr = &octx->ctx->trace[ith]; htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_QUANT, mmctx->quant_ib_first[ith]); const struct htp_tensor * src = octx->src[1]; @@ -870,7 +875,7 @@ static void quantize_f32_q8_0_tiled_block(unsigned int nth, unsigned int ith, vo static void quantize_f32_q8_1_tiled_block(unsigned int nth, unsigned int ith, void * data) { struct htp_mm_context * mmctx = data; struct htp_ops_context * octx = mmctx->octx; - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL; + struct htp_thread_trace * tr = &octx->ctx->trace[ith]; htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_QUANT, mmctx->quant_ib_first[ith]); const struct htp_tensor * src = octx->src[1]; @@ -944,7 +949,7 @@ static void hvx_mm_2d(unsigned int nth, unsigned int ith, void * data) { const uint32_t src0_end_row = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows); const uint32_t src0_end_row_x2 = src0_start_row + ((src0_end_row - src0_start_row) & ~1U); - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL; + struct htp_thread_trace * tr = &octx->ctx->trace[ith]; const size_t dst_row_size = nb1; const size_t src0_row_size = nb01; @@ -1040,7 +1045,7 @@ static void hvx_mv_2d(unsigned int nth, unsigned int ith, void * data) { const uint32_t src0_start_row = src0_nrows_per_thread * ith; const uint32_t src0_end_row = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows); - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL; + struct htp_thread_trace * tr = &octx->ctx->trace[ith]; const size_t dst_row_size = nb1; const size_t src0_row_size = nb01; @@ -1069,6 +1074,16 @@ static void hvx_mv_2d(unsigned int nth, unsigned int ith, void * data) { // Prefill vtcm with 2x src0 rows if (src0_start_row < src0_end_row) { + if (src2) { + float * vtcm_src2_ptr = (float *) mmctx->vtcm_src2 + src0_start_row; + const float * src2_ptr = (const float *) src2->data + src0_start_row; + int slice_size = (int)src0_end_row - (int)src0_start_row; + if (slice_size > 0) { + dma_queue_push(dma_queue, dma_make_ptr(vtcm_src2_ptr, src2_ptr), + slice_size * sizeof(float), slice_size * sizeof(float), slice_size * sizeof(float), 1); + dma_queue_pop_nowait(dma_queue); + } + } for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) { const uint32_t is0 = (ir0 - src0_start_row); if (is0 >= n_prefetch) { @@ -1114,27 +1129,21 @@ static void hvx_mv_2d(unsigned int nth, unsigned int ith, void * data) { } int copy_cnt = src0_end_row - src0_start_row; - if (src2) { - float * dst_ptr = &dst_col[src0_start_row]; - const float * src2_ptr = (const float *) src2->data + src0_start_row; - float * tmp_ptr = tmp; - int remaining = copy_cnt; - while (remaining > 0) { - int n = MIN(remaining, 32); - HVX_Vector v_out = hvx_vmemu(tmp_ptr); - HVX_Vector v_z = hvx_vmemu(src2_ptr); - hvx_vec_store_u(dst_ptr, n * sizeof(float), hvx_vec_add_f32_f32(v_out, v_z)); - dst_ptr += n; - src2_ptr += n; - tmp_ptr += n; - remaining -= n; + if (copy_cnt > 0) { + htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, src0_end_row); + if (src2) { + hvx_add_f32_uaa((uint8_t *) &dst_col[src0_start_row], + (const uint8_t *) tmp, + (const uint8_t *) ((const float *) mmctx->vtcm_src2 + src0_start_row), + copy_cnt); + } else { + hvx_copy_f32_ua((uint8_t *) &dst_col[src0_start_row], (uint8_t *) tmp, copy_cnt); } - } else { - hvx_copy_f32_ua((uint8_t *) &dst_col[src0_start_row], (uint8_t *) tmp, copy_cnt); + htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, src0_end_row); } } -#define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id) * ids->ne[0] * ids->ne[1] + (i1)] +#define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id) * mmctx->mapping_stride + (i1)] static void hvx_mm_id(unsigned int nth, unsigned int ith, void * data) { htp_matmul_preamble; @@ -1155,7 +1164,7 @@ static void hvx_mm_id(unsigned int nth, unsigned int ith, void * data) { return; } - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL; + struct htp_thread_trace * tr = &octx->ctx->trace[ith]; const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params; const uint32_t n_prefetch = kparams->n_prefetch; @@ -1244,7 +1253,7 @@ static void hvx_mv_id(unsigned int nth, unsigned int ith, void * data) { return; } - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL; + struct htp_thread_trace * tr = &octx->ctx->trace[ith]; const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params; const uint32_t n_prefetch = kparams->n_prefetch; @@ -1338,6 +1347,9 @@ static int hvx_mm_init_vec_dot(struct htp_mm_context * mmctx, enum htp_data_type static int hvx_mm_matmul(struct htp_ops_context * octx) { htp_matmul_tensors_preamble; + struct htp_thread_trace * tr = &octx->ctx->trace[0]; + htp_trace_event_start(tr, HTP_TRACE_EVT_INIT, 0); + struct htp_mm_context mmctx_struct = {0}; struct htp_mm_context * mmctx = &mmctx_struct; mmctx->octx = octx; @@ -1516,7 +1528,7 @@ static int hvx_mm_matmul(struct htp_ops_context * octx) { struct htp_mm_hvx_vtcm_layout L; htp_mm_hvx_vtcm_layout_build(&L, kparams->kernel_type, src0->type, ne10, src1_nrows, octx->n_threads, - dst_row_size, src0_row_size, src1_row_size, kparams->n_prefetch, false, false, false); + dst_row_size, src0_row_size, src1_row_size, src2 ? src2->nb[1] : 0, kparams->n_prefetch, false, false, false); if (kparams->kernel_type == HTP_MM_KERNEL_HVX_F16_F16_VTCM || kparams->kernel_type == HTP_MM_KERNEL_HVX_F32_F32_VTCM || @@ -1548,6 +1560,7 @@ static int hvx_mm_matmul(struct htp_ops_context * octx) { uint8_t * const base = (uint8_t *) octx->ctx->vtcm_base; mmctx->vtcm_src1 = VTCM_LAYOUT_PTR(uint8_t, base, L.off_src1); mmctx->vtcm_src0 = VTCM_LAYOUT_PTR(uint8_t, base, L.off_src0); + mmctx->vtcm_src2 = VTCM_LAYOUT_PTR(uint8_t, base, L.off_src2); mmctx->vtcm_dst = VTCM_LAYOUT_PTR(uint8_t, base, L.off_dst); octx->src1_spad.src = NULL; @@ -1557,9 +1570,6 @@ static int hvx_mm_matmul(struct htp_ops_context * octx) { mmctx->vtcm_src0_stride = src0_row_size_padded; mmctx->vtcm_src1_stride = src1_row_size; - if (octx->flags & HTP_OPFLAGS_SKIP_COMPUTE) - return HTP_STATUS_OK; - if (need_quant) { mmctx->n_quant_rows_per_thread = (src1_nrows + n_quant_tasks - 1) / n_quant_tasks; mmctx->quant_task_func = quant_task_func; @@ -1570,8 +1580,9 @@ static int hvx_mm_matmul(struct htp_ops_context * octx) { mmctx->n_quant_tasks = 0; } - const uint32_t n_matmul_jobs = octx->n_threads; - worker_pool_run_func(octx->ctx->worker_pool, matmul_job_func, mmctx, n_matmul_jobs); + htp_trace_event_stop(tr, HTP_TRACE_EVT_INIT, 0); + + worker_pool_run_func(octx->ctx->worker_pool, matmul_job_func, mmctx, octx->n_threads); return HTP_STATUS_OK; } @@ -1874,7 +1885,7 @@ static void hvx_mm_ffn_2d(unsigned int nth, unsigned int ith, void * data) { #define DEQUANTIZE_WORKER_LOOP_IMPL(SUFFIX) \ static void dequantize_tiled_worker_loop_##SUFFIX(unsigned int n, unsigned int i, void *data) { \ tiled_dequantize_state_t *state = (tiled_dequantize_state_t *)data; \ - struct htp_thread_trace * tr = state->traces ? &state->traces[i] : NULL; \ + struct htp_thread_trace * tr = &state->traces[i]; \ htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_W_DEQUANT, i); \ for (unsigned int task_id = i; task_id < (unsigned int)state->n_tasks; task_id += n) { \ int start = task_id * state->n_tiles_per_task; \ @@ -1892,7 +1903,7 @@ DEQUANTIZE_WORKER_LOOP_IMPL(q8_0) static void convert_f16_worker_loop(unsigned int n, unsigned int i, void *data) { tiled_dequantize_state_t *state = (tiled_dequantize_state_t *)data; - struct htp_thread_trace * tr = state->traces ? &state->traces[i] : NULL; + struct htp_thread_trace * tr = &state->traces[i]; htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_W_DEQUANT, i); for (unsigned int task_id = i; task_id < (unsigned int)state->n_tasks; task_id += n) { int start = task_id * state->n_tiles_per_task; @@ -1905,7 +1916,7 @@ static void convert_f16_worker_loop(unsigned int n, unsigned int i, void *data) static void quantize_f32_worker_loop(unsigned int n, unsigned int i, void *data) { tiled_dequantize_state_t *state = (tiled_dequantize_state_t *)data; - struct htp_thread_trace * tr = state->traces ? &state->traces[i] : NULL; + struct htp_thread_trace * tr = &state->traces[i]; htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_QUANT, i); for (unsigned int task_id = i; task_id < (unsigned int)state->n_tasks; task_id += n) { @@ -1920,7 +1931,7 @@ static void quantize_f32_worker_loop(unsigned int n, unsigned int i, void *data) static void transfer_output_chunk_worker_fn(unsigned int n, unsigned int i, void *data) { output_transfer_task_state_t *st = (output_transfer_task_state_t *) data; - struct htp_thread_trace * tr = st->traces ? &st->traces[i] : NULL; + struct htp_thread_trace * tr = &st->traces[i]; int start_chunk_idx = i * st->n_chunks_per_task; htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_O_PROC, start_chunk_idx); @@ -1955,6 +1966,170 @@ typedef struct { uint32_t dma_step_rows_shift; } activation_transfer_task_state_t; +typedef struct { + __fp16 *dst; + const float *src; + uint32_t n_rows; + uint32_t k_block; + uint32_t k_stride; + uint32_t k_valid; + uint32_t n_col_chunks; + struct fastdiv_values n_threads_div; + float *vtcm_f32_act; + size_t vtcm_f32_act_bytes; + struct htp_thread_trace *traces; + struct htp_context *ctx; + uint32_t dma_step_rows; + uint32_t dma_step_rows_shift; +} activation_transfer_col_chunk_state_t; + +static void transfer_activation_chunk_fp32_to_fp16_dma_pipelined_col_chunk( + dma_queue *dma_q, + __fp16 *restrict vtcm_dst, + const float *restrict src, + uint32_t n_rows, + uint32_t k_block, + uint32_t k_stride, + uint32_t k_chunk_valid, + uint32_t c_first, + uint32_t c_len, + float *thread_f32_act, + struct htp_thread_trace *tr, + uint32_t dma_step_rows, + uint32_t dma_step_rows_shift) { + + const uint32_t R = dma_step_rows; + const uint32_t n_rows_padded = hex_align_up(n_rows, HTP_MM_HMX_TILE_N_ROWS); + + const uint32_t n_steps = n_rows_padded >> dma_step_rows_shift; + + // Push step 0 + if (n_steps > 0 && n_rows > 0) { + uint32_t nrows_to_fetch = hex_smin(n_rows, R); + dma_queue_push(dma_q, dma_make_ptr(thread_f32_act, src + c_first), + c_len * sizeof(float), k_stride * sizeof(float), k_chunk_valid * sizeof(float), nrows_to_fetch); + } + // Push step 1 + if (n_steps > 1) { + uint32_t next_r = R * 1; + if (next_r < n_rows) { + uint32_t nrows_to_fetch = hex_smin(n_rows - next_r, R); + const float *next_src = src + next_r * k_stride + c_first; + float *next_buf = thread_f32_act + 1 * R * c_len; + dma_queue_push(dma_q, dma_make_ptr(next_buf, next_src), + c_len * sizeof(float), k_stride * sizeof(float), k_chunk_valid * sizeof(float), nrows_to_fetch); + } + } + for (uint32_t s = 0; s < n_steps; ++s) { + uint32_t r = s << dma_step_rows_shift; + float *curr_buf = thread_f32_act; + + if (r < n_rows) { + curr_buf = (float *) dma_queue_pop(dma_q).dst; + } + + htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_PREP, r); + for (uint32_t p = 0; p < (R >> 1); ++p) { + uint32_t row_idx = r + (p << 1); + float *pair_buf = curr_buf + (p << 1) * c_len; + bool r0_valid = ((row_idx + 0) < n_rows); + bool r1_valid = ((row_idx + 1) < n_rows); + + transfer_activation_row_pair_fp32_to_fp16_col_chunk( + vtcm_dst, pair_buf, pair_buf + c_len, row_idx, k_block, c_first, c_len, k_chunk_valid, r0_valid, r1_valid + ); + } + htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_A_PREP, r); + + // Push step s + 2 + uint32_t next_s = s + 2; + uint32_t next_r = next_s << dma_step_rows_shift; + if (next_r < n_rows) { + uint32_t nrows_to_fetch = hex_smin(n_rows - next_r, R); + const float *next_src = src + next_r * k_stride + c_first; + dma_queue_push(dma_q, dma_make_ptr(curr_buf, next_src), + c_len * sizeof(float), k_stride * sizeof(float), k_chunk_valid * sizeof(float), nrows_to_fetch); + } + } +} + +static void transfer_activation_chunk_fp32_to_fp16_col_chunk( + __fp16 *restrict vtcm_dst, + const float *restrict src, + uint32_t n_rows, + uint32_t k_block, + uint32_t k_stride, + uint32_t c_first, + uint32_t c_len, + uint32_t k_chunk_valid) { + const uint32_t n_rows_padded = hex_align_up(n_rows, HTP_MM_HMX_TILE_N_ROWS); + const uint32_t n_rows_tiled = (n_rows / HTP_MM_HMX_TILE_N_ROWS) * HTP_MM_HMX_TILE_N_ROWS; + + uint32_t r = 0; + + #pragma unroll(2) + for (r = 0; r < n_rows_tiled; r += 2) { + const float *ptr_in0 = src + (r + 0) * k_stride + c_first; + const float *ptr_in1 = src + (r + 1) * k_stride + c_first; + + transfer_activation_row_pair_fp32_to_fp16_col_chunk( + vtcm_dst, ptr_in0, ptr_in1, r, k_block, c_first, c_len, k_chunk_valid, true, true + ); + } + + for (; r < n_rows_padded; r += 2) { + const bool row0_valid = r < n_rows; + const bool row1_valid = (r + 1) < n_rows; + + const float *ptr_in0 = row0_valid ? (src + (r + 0) * k_stride + c_first) : NULL; + const float *ptr_in1 = row1_valid ? (src + (r + 1) * k_stride + c_first) : NULL; + + transfer_activation_row_pair_fp32_to_fp16_col_chunk( + vtcm_dst, ptr_in0, ptr_in1, r, k_block, c_first, c_len, k_chunk_valid, row0_valid, row1_valid + ); + } +} + +static void transfer_activation_chunk_col_chunk_worker_fn(unsigned int n, unsigned int i, void *data) { + activation_transfer_col_chunk_state_t *st = (activation_transfer_col_chunk_state_t *) data; + struct htp_thread_trace * tr = &st->traces[i]; + + uint32_t n_blocks = st->k_block / 32; + uint32_t b_first = fastdiv(n_blocks * i, &st->n_threads_div); + uint32_t b_last = fastdiv(n_blocks * (i + 1), &st->n_threads_div); + uint32_t c_first = b_first * 32; + uint32_t c_last = b_last * 32; + uint32_t c_len = c_last - c_first; + + if (c_len == 0) { + return; + } + + uint32_t k_chunk_valid = 0; + if (st->k_valid > c_first) { + k_chunk_valid = hex_smin(st->k_valid, c_last) - c_first; + } + + __fp16 *dst = st->dst; + const float *src = st->src; + + if (st->vtcm_f32_act) { + size_t thread_scratch_bytes = hex_align_down(fastdiv(st->vtcm_f32_act_bytes, &st->n_threads_div), 128); + float *thread_f32_act = (float *)((char *)st->vtcm_f32_act + i * thread_scratch_bytes); + + transfer_activation_chunk_fp32_to_fp16_dma_pipelined_col_chunk( + st->ctx->dma[i], dst, src, st->n_rows, st->k_block, st->k_stride, k_chunk_valid, + c_first, c_len, thread_f32_act, tr, st->dma_step_rows, st->dma_step_rows_shift + ); + } else { + htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_PREP, c_first); + transfer_activation_chunk_fp32_to_fp16_col_chunk( + dst, src, st->n_rows, st->k_block, st->k_stride, c_first, c_len, k_chunk_valid + ); + htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_A_PREP, c_first); + } +} + static void transfer_activation_chunk_fp32_to_fp16_dma_pipelined( dma_queue *dma_q, __fp16 *restrict vtcm_dst, @@ -2024,7 +2199,7 @@ static void transfer_activation_chunk_fp32_to_fp16_dma_pipelined( static void transfer_activation_chunk_worker_fn(unsigned int n, unsigned int i, void *data) { activation_transfer_task_state_t *st = (activation_transfer_task_state_t *) data; - struct htp_thread_trace * tr = st->traces ? &st->traces[i] : NULL; + struct htp_thread_trace * tr = &st->traces[i]; for (unsigned int task_id = i; task_id < (unsigned int)st->n_tasks; task_id += n) { int chunk_idx = task_id * st->n_chunks_per_task; @@ -2085,15 +2260,16 @@ typedef struct { static void transfer_activation_chunk_gathered_worker_fn(unsigned int n, unsigned int i, void *data) { activation_transfer_gathered_task_state_t *st = data; - struct htp_thread_trace * tr = st->traces ? &st->traces[i] : NULL; + struct htp_thread_trace * tr = &st->traces[i]; int chunk_idx = i; int chunk_size = st->n_chunks_per_task; - int start_row = st->start_row + chunk_idx * chunk_size; + int vtcm_start_row = chunk_idx * chunk_size; + int start_row = st->start_row + vtcm_start_row; int n_rows = hex_smin(st->cne1 - start_row, chunk_size); if (n_rows > 0) { htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_PREP, chunk_idx); transfer_activation_chunk_fp32_to_fp16_gathered( - st->dst, st->src, start_row, n_rows, st->k_block, + st->dst, st->src, start_row, vtcm_start_row, n_rows, st->k_block, st->matrix_rows, st->cur_a, st->mapping_stride, st->ne11, &st->ne11_div, st->nb11, st->nb12, st->cne1, st->k_valid); htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_A_PREP, chunk_idx); @@ -2102,15 +2278,16 @@ static void transfer_activation_chunk_gathered_worker_fn(unsigned int n, unsigne static void transfer_activation_chunk_gathered_worker_flat_fn(unsigned int n, unsigned int i, void *data) { activation_transfer_gathered_task_state_t *st = data; - struct htp_thread_trace * tr = st->traces ? &st->traces[i] : NULL; + struct htp_thread_trace * tr = &st->traces[i]; int chunk_idx = i; int chunk_size = st->n_chunks_per_task; - int start_row = st->start_row + chunk_idx * chunk_size; + int vtcm_start_row = chunk_idx * chunk_size; + int start_row = st->start_row + vtcm_start_row; int n_rows = hex_smin(st->cne1 - start_row, chunk_size); if (n_rows > 0) { htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_PREP, chunk_idx); transfer_activation_chunk_fp32_to_fp16_gathered_flat( - st->dst, st->src, start_row, n_rows, st->k_block, + st->dst, st->src, start_row, vtcm_start_row, n_rows, st->k_block, st->matrix_rows, st->cur_a, st->mapping_stride, st->nb12, st->cne1, st->k_valid); htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_A_PREP, chunk_idx); @@ -2119,15 +2296,16 @@ static void transfer_activation_chunk_gathered_worker_flat_fn(unsigned int n, un static void transfer_output_chunk_scattered_worker_fn(unsigned int n, unsigned int i, void *data) { output_transfer_scattered_task_state_t *st = data; - struct htp_thread_trace * tr = st->traces ? &st->traces[i] : NULL; + struct htp_thread_trace * tr = &st->traces[i]; int chunk_idx = i; int chunk_size = st->n_chunks_per_task; - int start_row = st->start_row + chunk_idx * chunk_size; + int vtcm_start_row = chunk_idx * chunk_size; + int start_row = st->start_row + vtcm_start_row; int n_rows = hex_smin(st->cne1 - start_row, chunk_size); if (n_rows > 0) { htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_O_PROC, chunk_idx); transfer_output_chunk_fp16_to_fp32_scattered( - st->dst, st->vtcm_src, start_row, n_rows, st->n_cols, + st->dst, st->vtcm_src, start_row, vtcm_start_row, n_rows, st->n_cols, st->matrix_rows, st->cur_a, st->mapping_stride, st->dst_nb1, st->dst_nb2, st->cne1); htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_O_PROC, chunk_idx); @@ -2178,12 +2356,77 @@ static void dequantize_tiled_weight_chunk_to_fp16_tiles( } } +typedef struct { + float *dst; + const float *src2; + const __fp16 *vtcm_src; + uint32_t n_rows; + uint32_t n_cols; + uint32_t dst_stride; + uint32_t src2_stride; + uint32_t dst_cols; + struct fastdiv_values n_threads_div; + struct htp_thread_trace *traces; + struct htp_context *ctx; +} output_transfer_col_chunk_state_t; + +static void transfer_output_chunk_col_chunk_worker_fn(unsigned int n, unsigned int i, void *data) { + (void) n; + output_transfer_col_chunk_state_t *st = (output_transfer_col_chunk_state_t *) data; + struct htp_thread_trace * tr = &st->traces[i]; + + uint32_t n_blocks = st->n_cols / 32; + uint32_t b_first = fastdiv(n_blocks * i, &st->n_threads_div); + uint32_t b_last = fastdiv(n_blocks * (i + 1), &st->n_threads_div); + uint32_t c_first = b_first * 32; + uint32_t c_last = b_last * 32; + uint32_t c_len = c_last - c_first; + + if (c_len == 0) return; + + htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_O_PROC, c_first); + + float *dst = st->dst + c_first; + const float *src2 = st->src2 ? (st->src2 + c_first) : NULL; + const __fp16 *vtcm_src = st->vtcm_src + b_first * HTP_MM_HMX_TILE_N_ELMS; + + int chunk_dst_cols = (int)st->dst_cols - (int)c_first; + if (chunk_dst_cols > 0) { + transfer_output_chunk_fp16_to_fp32_col_chunk( + dst, src2, vtcm_src, 0, st->n_rows, c_len, st->n_cols, + st->dst_stride, st->src2_stride, (uint32_t)chunk_dst_cols + ); + } + + htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_O_PROC, c_first); +} + static void transfer_output_chunk_threaded(struct htp_context *ctx, float *dst, const float *src2, const __fp16 *vtcm_src, int n_rows, int n_cols, int dst_stride, uint32_t src2_stride, int dst_cols, int n_threads) { assert(n_cols % HTP_MM_HMX_TILE_N_COLS == 0); if (n_rows <= 0) return; + uint32_t n_blocks = (uint32_t)n_cols / 32; + if (n_threads > 1 && n_blocks >= (uint32_t)n_threads) { + struct fastdiv_values n_threads_div = init_fastdiv_values(n_threads); + output_transfer_col_chunk_state_t col_state; + col_state.dst = dst; + col_state.src2 = src2; + col_state.vtcm_src = vtcm_src; + col_state.n_rows = (uint32_t)n_rows; + col_state.n_cols = (uint32_t)n_cols; + col_state.dst_stride = (uint32_t)dst_stride; + col_state.src2_stride = src2_stride; + col_state.dst_cols = (uint32_t)dst_cols; + col_state.n_threads_div = n_threads_div; + col_state.traces = ctx->trace; + col_state.ctx = ctx; + + worker_pool_run_func(ctx->worker_pool, transfer_output_chunk_col_chunk_worker_fn, &col_state, n_threads); + return; + } + size_t n_tot_chunks = n_rows; size_t n_chunks_per_task = (n_threads == 1) ? n_tot_chunks : hmx_ceil_div(n_rows, n_threads); n_chunks_per_task = hex_align_up(n_chunks_per_task, 2); @@ -2210,42 +2453,81 @@ static void transfer_output_chunk_threaded(struct htp_context *ctx, float *dst, } } -static void transfer_activation_chunk_threaded( - struct htp_context *ctx, - __fp16 *dst, - const float *src, - int n_rows, - int k_block, - int k_stride, - int n_threads, - int k_valid, - float *vtcm_f32_act, - size_t vtcm_f32_act_bytes) { +struct activation_transfer_params { + struct htp_context * ctx; + __fp16 * dst; + const float * src; + int n_rows; + int k_block; + int k_stride; + int n_threads; + const struct fastdiv_values * act_threads_div; + const struct fastdiv_values * k_div; + int k_valid; + float * vtcm_f32_act; + size_t vtcm_f32_act_bytes; +}; + +static void transfer_activation_chunk_threaded(const struct activation_transfer_params * params) { + struct htp_context * ctx = params->ctx; + __fp16 * dst = params->dst; + const float * src = params->src; + int n_rows = params->n_rows; + int k_block = params->k_block; + int k_stride = params->k_stride; + int n_threads = params->n_threads; + const struct fastdiv_values * act_threads_div = params->act_threads_div; + const struct fastdiv_values * k_div = params->k_div; + int k_valid = params->k_valid; + float * vtcm_f32_act = params->vtcm_f32_act; + size_t vtcm_f32_act_bytes = params->vtcm_f32_act_bytes; + if (n_rows <= 0) { return; } + const size_t n_tasks = (n_rows + 31) >> 5; + if (n_threads > 1 && k_block > 32 && n_tasks < (size_t) n_threads) { + // Calculate step rows parameters for column-chunked dma pipelining + uint32_t dma_step_rows = 2; + uint32_t dma_step_rows_shift = 1; + if (vtcm_f32_act && vtcm_f32_act_bytes > 0 && k_block > 0) { + size_t thread_scratch_bytes = hex_align_down(fastdiv(vtcm_f32_act_bytes, act_threads_div), 128); + size_t thread_scratch_elements = thread_scratch_bytes / sizeof(float); + size_t dma_step_rows_max = fastdiv(thread_scratch_elements / 2, k_div); + if (dma_step_rows_max >= 4) { + dma_step_rows = 4; + dma_step_rows_shift = 2; + } + } + + activation_transfer_col_chunk_state_t col_state; + col_state.dst = dst; + col_state.src = src; + col_state.n_rows = n_rows; + col_state.k_block = k_block; + col_state.k_stride = k_stride; + col_state.k_valid = k_valid; + col_state.n_col_chunks = n_threads; + col_state.n_threads_div = *act_threads_div; + col_state.vtcm_f32_act = vtcm_f32_act; + col_state.vtcm_f32_act_bytes = vtcm_f32_act_bytes; + col_state.traces = ctx->trace; + col_state.ctx = ctx; + col_state.dma_step_rows = dma_step_rows; + col_state.dma_step_rows_shift = dma_step_rows_shift; + + worker_pool_run_func(ctx->worker_pool, transfer_activation_chunk_col_chunk_worker_fn, &col_state, n_threads); + return; + } + assert(k_block % HTP_MM_HMX_TILE_N_COLS == 0 && k_stride % HTP_MM_HMX_TILE_N_COLS == 0); size_t n_tot_chunks = n_rows; size_t n_chunks_per_task = (n_threads == 1) ? n_tot_chunks : 32; // must be multiple of 32 to ensure correct destination address - uint32_t dma_step_rows = 2; - uint32_t dma_step_rows_shift = 1; - if (vtcm_f32_act && vtcm_f32_act_bytes > 0 && k_block > 0) { - size_t thread_scratch_elements = vtcm_f32_act_bytes / (n_threads * sizeof(float)); - size_t dma_step_rows_max = (thread_scratch_elements / 2) / k_block; - if (dma_step_rows_max >= 4) { - dma_step_rows = 4; - dma_step_rows_shift = 2; - } else { - dma_step_rows = 2; - dma_step_rows_shift = 1; - } - } - activation_transfer_task_state_t state; - state.n_tasks = (n_tot_chunks + n_chunks_per_task - 1) / n_chunks_per_task; + state.n_tasks = (n_threads == 1) ? 1 : hmx_ceil_div(n_tot_chunks, 32); state.n_tot_chunks = n_tot_chunks; state.n_chunks_per_task = n_chunks_per_task; state.dst = dst; @@ -2258,7 +2540,18 @@ static void transfer_activation_chunk_threaded( state.vtcm_f32_act = vtcm_f32_act; int active_threads = hex_smin(n_threads, (int)state.n_tasks); - state.vtcm_f32_act_bytes_per_thread = (vtcm_f32_act_bytes / active_threads) & ~127u; + state.vtcm_f32_act_bytes_per_thread = hex_align_down(vtcm_f32_act_bytes / active_threads, 128); + + uint32_t dma_step_rows = 2; + uint32_t dma_step_rows_shift = 1; + if (vtcm_f32_act && state.vtcm_f32_act_bytes_per_thread > 0 && k_block > 0) { + size_t thread_scratch_elements = state.vtcm_f32_act_bytes_per_thread / sizeof(float); + size_t dma_step_rows_max = fastdiv(thread_scratch_elements / 2, k_div); + if (dma_step_rows_max >= 4) { + dma_step_rows = 4; + dma_step_rows_shift = 2; + } + } state.dma_step_rows = dma_step_rows; state.dma_step_rows_shift = dma_step_rows_shift; @@ -2321,9 +2614,14 @@ static int hmx_mm_2d_f32(struct htp_context *ctx, int pipeline, int n_threads, int act_threads, + const struct fastdiv_values * act_threads_div, + const struct fastdiv_values * k_div, int tile_size, int aligned_tile_size, int vtcm_size) { + struct htp_thread_trace * tr = &ctx->trace[0]; + htp_trace_event_start(tr, HTP_TRACE_EVT_INIT, 0); + if (k % 32 != 0 || n % 32 != 0) { return -1; } if (!hex_is_aligned(dst, VLEN) || !hex_is_aligned(activation, VLEN)) { return -1; } @@ -2393,6 +2691,8 @@ static int hmx_mm_2d_f32(struct htp_context *ctx, int n_chunk_cnt = hmx_ceil_div(n, n_chunk_n_cols); + htp_trace_event_stop(tr, HTP_TRACE_EVT_INIT, 0); + if (pipeline) { // --- Asynchronous Pipelined Loop --- hmx_matmul_job_t job_slots[2]; // persistent double-buffered job descriptors @@ -2403,7 +2703,21 @@ static int hmx_mm_2d_f32(struct htp_context *ctx, void *vtcm_weight_bufs[2] = { vtcm_scratch0, vtcm_scratch1 }; void *vtcm_output_bufs[2] = { vtcm_output, vtcm_scratch2 }; - transfer_activation_chunk_threaded(ctx, vtcm_f16_act, activation + mr * act_stride, n_rows, k, act_stride, act_threads, k_valid, vtcm_f32_act, L.act_f32_bytes); + struct activation_transfer_params act_params = { + .ctx = ctx, + .dst = vtcm_f16_act, + .src = activation + mr * act_stride, + .n_rows = (int) n_rows, + .k_block = k, + .k_stride = act_stride, + .n_threads = act_threads, + .act_threads_div = act_threads_div, + .k_div = k_div, + .k_valid = k_valid, + .vtcm_f32_act = vtcm_f32_act, + .vtcm_f32_act_bytes = L.act_f32_bytes, + }; + transfer_activation_chunk_threaded(&act_params); // Prologue: push A0 and optionally A1 (if n_chunk_cnt > 1) const size_t n_cols_A0 = hex_smin(n - 0 * n_chunk_n_cols, n_chunk_n_cols); @@ -2480,7 +2794,21 @@ static int hmx_mm_2d_f32(struct htp_context *ctx, for (size_t mr = 0; mr < m; mr += m_chunk_n_rows) { const size_t n_rows = hex_smin(m - mr, m_chunk_n_rows); - transfer_activation_chunk_threaded(ctx, vtcm_f16_act, activation + mr * act_stride, n_rows, k, act_stride, act_threads, k_valid, vtcm_f32_act, L.act_f32_bytes); + struct activation_transfer_params act_params = { + .ctx = ctx, + .dst = vtcm_f16_act, + .src = activation + mr * act_stride, + .n_rows = (int) n_rows, + .k_block = k, + .k_stride = act_stride, + .n_threads = act_threads, + .act_threads_div = act_threads_div, + .k_div = k_div, + .k_valid = k_valid, + .vtcm_f32_act = vtcm_f32_act, + .vtcm_f32_act_bytes = L.act_f32_bytes, + }; + transfer_activation_chunk_threaded(&act_params); // A0: Pre-fetch the first weight chunk (nc = 0) if (n > 0) { @@ -2570,7 +2898,8 @@ static inline const float *hmx_mm_src2_batch_ptr(const hmx_mm_f16_f32_batched_pa static int hmx_mm_f16_f32_batched_simple(struct htp_context *ctx, const hmx_mm_f16_f32_batched_params_t *params, - int m_chunk, int n_chunk, int pipeline, int n_threads, int act_threads, int vtcm_size) { + int m_chunk, int n_chunk, int pipeline, int n_threads, int act_threads, int vtcm_size, + const struct fastdiv_values * act_threads_div, const struct fastdiv_values * k_div) { int ret = 0; for (int b3 = 0; b3 < params->ne13 && ret == 0; ++b3) { for (int b2 = 0; b2 < params->ne12 && ret == 0; ++b2) { @@ -2582,14 +2911,17 @@ static int hmx_mm_f16_f32_batched_simple(struct htp_context *ctx, params->act_stride, params->weight_stride * (int)sizeof(__fp16), HTP_TYPE_F16, params->k, params->dst_stride, params->src2_stride, params->n, m_chunk, n_chunk, pipeline, n_threads, act_threads, - 0, 0, vtcm_size); + act_threads_div, k_div, 0, 0, vtcm_size); } } return ret; } static int hmx_mm_f16_f32_batched(struct htp_context *ctx, const hmx_mm_f16_f32_batched_params_t *params, - int m_chunk, int n_chunk, int pipeline, int n_threads, int act_threads, int vtcm_size) { + int m_chunk, int n_chunk, int pipeline, int n_threads, int act_threads, + const struct fastdiv_values * act_threads_div, + const struct fastdiv_values * k_div, + int vtcm_size) { if (params->act_stride < params->k || params->weight_stride < params->k || params->dst_stride < params->n) { return -1; } if (params->ne02 <= 0 || params->ne03 <= 0 || params->ne12 <= 0 || params->ne13 <= 0) { return -1; } if (params->ne12 % params->ne02 != 0 || params->ne13 % params->ne03 != 0) { return -1; } @@ -2604,9 +2936,12 @@ static int hmx_mm_f16_f32_batched(struct htp_context *ctx, const hmx_mm_f16_f32_ // Grouped path is only valid if group_size > 1 and it fits within VTCM budget. bool run_grouped = (group_size > 1 && (size_t)vtcm_size <= vtcm_budget); if (!run_grouped) { - return hmx_mm_f16_f32_batched_simple(ctx, params, m_chunk, n_chunk, pipeline, n_threads, act_threads, vtcm_size); + return hmx_mm_f16_f32_batched_simple(ctx, params, m_chunk, n_chunk, pipeline, n_threads, act_threads, vtcm_size, act_threads_div, k_div); } + struct htp_thread_trace * tr = &ctx->trace[0]; + htp_trace_event_start(tr, HTP_TRACE_EVT_INIT, 0); + const size_t vec_dot_size = params->k * sizeof(__fp16); const bool use_dma_activation = (params->act_stride > params->k); @@ -2622,7 +2957,8 @@ static int hmx_mm_f16_f32_batched(struct htp_context *ctx, const hmx_mm_f16_f32_ if (L.total_bytes > vtcm_budget) { FARF(HIGH, "%s: grouped layout overflowed VTCM, falling back to simple batched loop", __func__); - return hmx_mm_f16_f32_batched_simple(ctx, params, m_chunk, n_chunk, pipeline, n_threads, act_threads, vtcm_size); + htp_trace_event_stop(tr, HTP_TRACE_EVT_INIT, 0); + return hmx_mm_f16_f32_batched_simple(ctx, params, m_chunk, n_chunk, pipeline, n_threads, act_threads, vtcm_size, act_threads_div, k_div); } uint8_t * const base = (uint8_t *) ctx->vtcm_base; @@ -2644,6 +2980,8 @@ static int hmx_mm_f16_f32_batched(struct htp_context *ctx, const hmx_mm_f16_f32_ const size_t fp16_row_bytes = (size_t) params->k * sizeof(__fp16); const size_t weight_row_bytes = (size_t) params->weight_stride * sizeof(__fp16); + htp_trace_event_stop(tr, HTP_TRACE_EVT_INIT, 0); + hmx_matmul_job_t job; for (int b3 = 0; b3 < params->ne13; ++b3) { @@ -2662,9 +3000,21 @@ static int hmx_mm_f16_f32_batched(struct htp_context *ctx, const hmx_mm_f16_f32_ for (int g = 0; g < group_size; ++g) { const float *activation_chunk = hmx_mm_activation_batch_ptr(params, b2_base + g, b3) + mr * params->act_stride; __fp16 *vtcm_act_g = vtcm_f16_act + (size_t) g * L.act_head_stride; - transfer_activation_chunk_threaded(ctx, vtcm_act_g, - activation_chunk, (int) n_rows, - params->k, params->act_stride, act_threads, params->k, vtcm_f32_act, L.act_f32_bytes); + struct activation_transfer_params act_params = { + .ctx = ctx, + .dst = vtcm_act_g, + .src = activation_chunk, + .n_rows = (int) n_rows, + .k_block = params->k, + .k_stride = params->act_stride, + .n_threads = act_threads, + .act_threads_div = act_threads_div, + .k_div = k_div, + .k_valid = params->k, + .vtcm_f32_act = vtcm_f32_act, + .vtcm_f32_act_bytes = L.act_f32_bytes, + }; + transfer_activation_chunk_threaded(&act_params); } // Prologue: Push A0 and A1 (if exists) @@ -2835,6 +3185,9 @@ static int hmx_mm_id_2d_f32(struct htp_context *ctx, const struct mmid_row_mapping *matrix_rows, int cur_a, int mapping_stride) { + struct htp_thread_trace * tr = &ctx->trace[0]; + htp_trace_event_start(tr, HTP_TRACE_EVT_INIT, 0); + const int cne1 = m; const int m_padded = hex_align_up(m, 32); @@ -2913,6 +3266,8 @@ static int hmx_mm_id_2d_f32(struct htp_context *ctx, hmx_init_column_scales(vtcm_scales, Q6_V_vsplat_R(0x3c00)); + htp_trace_event_stop(tr, HTP_TRACE_EVT_INIT, 0); + hmx_matmul_job_t job; for (size_t mr = 0; mr < (size_t) m_padded; mr += m_chunk_n_rows) { @@ -2980,10 +3335,6 @@ static int hmx_mm_op_matmul(struct htp_ops_context * octx, const struct htp_mm_k const int act_stride = (int)(src1->nb[1] / sizeof(float)); const int wgt_stride = (int)(src0->nb[1] / sizeof(__fp16)); - if (octx->flags & HTP_OPFLAGS_SKIP_COMPUTE) { - return HTP_STATUS_OK; - } - const float * src2_ptr = NULL; uint32_t src2_stride = 0; size_t src2_nb2 = 0; @@ -3027,6 +3378,8 @@ static int hmx_mm_op_matmul(struct htp_ops_context * octx, const struct htp_mm_k kparams->m_chunk, kparams->n_chunk, kparams->pipeline, n_threads, kparams->n_act_threads, + &kparams->div_n_act_threads, + &kparams->div_ne00_padded, kparams->vtcm_size); } else { ret = hmx_mm_2d_f32( @@ -3035,6 +3388,8 @@ static int hmx_mm_op_matmul(struct htp_ops_context * octx, const struct htp_mm_k (int)(dst->nb[1] / sizeof(float)), src2_stride, (int)dst->ne[0], kparams->m_chunk, kparams->n_chunk, kparams->pipeline, n_threads, kparams->n_act_threads, + &kparams->div_n_act_threads, + &kparams->div_ne00_padded, kparams->tile_size, kparams->aligned_tile_size, kparams->vtcm_size ); } @@ -3058,12 +3413,10 @@ int op_matmul(struct htp_ops_context * octx) { static int hmx_mm_op_matmul_id( struct htp_ops_context * octx, - struct htp_mm_context * mmctx, - const uint32_t * matrix_row_counts, - const struct mmid_row_mapping * matrix_rows, - void * mapping_buf, - bool must_free_mapping + struct htp_mm_context * mmctx ) { + const uint32_t * matrix_row_counts = mmctx->matrix_row_counts; + const struct mmid_row_mapping * matrix_rows = mmctx->matrix_rows; htp_matmul_tensors_preamble; const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params; const int n_ids = octx->src[2]->ne[0]; @@ -3081,28 +3434,28 @@ static int hmx_mm_op_matmul_id( nb11, nb12, nb1, nb2, (int) src0->nb[1], (int) src0->type, - matrix_rows, cur_a, n_ids * octx->src[2]->ne[1]); + matrix_rows, cur_a, mmctx->mapping_stride); if (ret != 0) { FARF(ERROR, "HMX matmul failed for expert %u, error %d\n", cur_a, ret); - if (must_free_mapping) free(mapping_buf); return HTP_STATUS_NO_SUPPORT; } } - if (must_free_mapping) free(mapping_buf); return HTP_STATUS_OK; } static int hvx_mm_matmul_id( struct htp_ops_context * octx, struct htp_mm_context * mmctx, - size_t src0_row_size_padded, - uint32_t src1_nrows, - worker_callback_t matmul_id_job_func, - void * mapping_buf, - bool must_free_mapping + work_queue_func_t hvx_mmid_task_func ) { htp_matmul_tensors_preamble; + const uint32_t src0_row_size_padded = mmctx->src0_row_size_padded; + const uint32_t src1_nrows = mmctx->src1_nrows; + + struct htp_thread_trace * tr = &octx->ctx->trace[0]; + htp_trace_event_start(tr, HTP_TRACE_EVT_INIT, 0); + const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params; const struct htp_tensor * restrict ids = octx->src[2]; const size_t src0_row_size = nb01; @@ -3111,7 +3464,7 @@ static int hvx_mm_matmul_id( const uint32_t nb = (ne10 + qk - 1) / qk; const uint32_t total_nb = src1_nrows * nb; - worker_callback_t quant_task_func; + work_queue_func_t quant_task_func; uint32_t n_quant_tasks = 1; if (src1_nrows < octx->n_threads) { n_quant_tasks = MIN(total_nb, octx->n_threads); @@ -3132,7 +3485,7 @@ static int hvx_mm_matmul_id( struct htp_mm_hvx_vtcm_layout L; htp_mm_hvx_vtcm_layout_build(&L, kparams->kernel_type, src0->type, ne10, src1_nrows, octx->n_threads, - 0, src0_row_size, src1_row_size, kparams->n_prefetch, true, false, false); + 0, src0_row_size, src1_row_size, 0, kparams->n_prefetch, true, false, false); size_t vtcm_size = kparams->vtcm_size > 0 ? (size_t)kparams->vtcm_size : L.total_bytes; @@ -3147,7 +3500,6 @@ static int hvx_mm_matmul_id( // Make sure the reserved vtcm size is sufficient if (octx->ctx->vtcm_size < vtcm_size) { FARF(ERROR, "matmul-id-%s : current VTCM reservation %zu is too small, needed %zu\n", mmctx->type, octx->ctx->vtcm_size, vtcm_size); - if (must_free_mapping) free(mapping_buf); return HTP_STATUS_VTCM_TOO_SMALL; } @@ -3175,16 +3527,86 @@ static int hvx_mm_matmul_id( mmctx->n_quant_tasks = n_quant_tasks; atomic_init(&mmctx->quant_barrier, n_quant_tasks); - const uint32_t n_matmul_jobs = octx->n_threads; - worker_pool_run_func(octx->ctx->worker_pool, matmul_id_job_func, mmctx, n_matmul_jobs); + htp_trace_event_stop(tr, HTP_TRACE_EVT_INIT, 0); + + worker_pool_run_func(octx->ctx->worker_pool, hvx_mmid_task_func, mmctx, octx->n_threads); - if (must_free_mapping) free(mapping_buf); return HTP_STATUS_OK; } +static inline void scan_expert_ids_n( + const struct htp_tensor * ids, + const uint32_t n_ids, + uint32_t n_as, + uint32_t * counts, + struct mmid_row_mapping * matrix_rows, + uint32_t mapping_stride +) { + const size_t ids_nb1 = ids->nb[1]; + const uint8_t * ids_data = (const uint8_t *) ids->data; + + for (uint32_t iid1 = 0; iid1 < ids->ne[1]; ++iid1) { + const int32_t * row_ptr = (const int32_t *) (ids_data + iid1 * ids_nb1); + for (uint32_t id = 0; id < n_ids; ++id) { + const int32_t i02 = row_ptr[id]; + if (i02 < 0) { + continue; + } + assert(i02 < n_as); + + if (matrix_rows) { + matrix_rows[i02 * mapping_stride + counts[i02]] = (struct mmid_row_mapping) { id, iid1 }; + } + counts[i02] += 1; + } + } +} + +static inline void scan_expert_ids( + const struct htp_tensor * ids, + uint32_t n_ids, + uint32_t n_as, + uint32_t * counts, + struct mmid_row_mapping * matrix_rows, + uint32_t mapping_stride +) { + const size_t ids_nb0 = ids->nb[0]; + + if (ids_nb0 == 4) { + switch (n_ids) { + case 8: scan_expert_ids_n(ids, 8, n_as, counts, matrix_rows, mapping_stride); break; + case 4: scan_expert_ids_n(ids, 4, n_as, counts, matrix_rows, mapping_stride); break; + case 2: scan_expert_ids_n(ids, 2, n_as, counts, matrix_rows, mapping_stride); break; + default: scan_expert_ids_n(ids, n_ids, n_as, counts, matrix_rows, mapping_stride); break; + } + } else { + // Strided fallback + const size_t ids_nb1 = ids->nb[1]; + const uint8_t * ids_data = (const uint8_t *) ids->data; + for (uint32_t iid1 = 0; iid1 < ids->ne[1]; ++iid1) { + const int32_t * row_ptr = (const int32_t *) (ids_data + iid1 * ids_nb1); + for (uint32_t id = 0; id < n_ids; ++id) { + const int32_t i02 = *(const int32_t *) ((const uint8_t *) row_ptr + id * ids_nb0); + if (i02 < 0) { + continue; + } + assert(i02 < n_as); + + if (matrix_rows) { + matrix_rows[i02 * mapping_stride + counts[i02]] = (struct mmid_row_mapping) { id, iid1 }; + } + counts[i02] += 1; + } + } + } +} + int op_matmul_id(struct htp_ops_context * octx) { htp_matmul_tensors_preamble; + struct htp_thread_trace * tr = &octx->ctx->trace[0]; + htp_trace_event_start(tr, HTP_TRACE_EVT_INIT, 0); + struct htp_mm_context mmctx_struct = {0}; struct htp_mm_context * mmctx = &mmctx_struct; mmctx->octx = octx; @@ -3201,80 +3623,78 @@ int op_matmul_id(struct htp_ops_context * octx) { const uint32_t src0_nrows = ne01; // per expert const uint32_t src1_nrows = ne11 * ne12 * ne13; - worker_callback_t quant_task_func; - worker_callback_t matmul_id_job_func = src1_nrows > 1 ? hvx_mm_id : hvx_mv_id; - - // Compute src0_nrows_per_thread - mmctx->src0_nrows_per_thread = (src0_nrows + octx->n_threads - 1) / octx->n_threads; - mmctx->src0_nrows_per_thread = hex_round_up(mmctx->src0_nrows_per_thread, 32); + mmctx->src0_nrows_per_thread = (src0_nrows + octx->n_threads - 1) / octx->n_threads; + mmctx->src0_nrows_per_thread = hex_round_up(mmctx->src0_nrows_per_thread, 32); // row groups const int n_ids = ids->ne[0]; // n_expert_used const int n_as = ne02; // n_expert - size_t matrix_row_counts_size = n_as * sizeof(uint32_t); - size_t matrix_row_map_size = n_as * ids->ne[0] * ids->ne[1] * sizeof(struct mmid_row_mapping); - const size_t total_map_size = matrix_row_counts_size + matrix_row_map_size; + uint8_t * mapping_buf = octx->ctx->ddr_spad_base; + uint32_t mapping_stride = 1; + uint32_t * matrix_row_counts = (uint32_t *) mapping_buf; + struct mmid_row_mapping * matrix_rows = NULL; - void * mapping_buf = NULL; - bool must_free_mapping = false; + if (src1_nrows > 1) { + const size_t matrix_row_counts_size = n_as * sizeof(uint32_t); + assert(octx->ctx->ddr_spad_size >= matrix_row_counts_size); - if (octx->ctx->ddr_spad_base && total_map_size <= octx->ctx->ddr_spad_size) { - mapping_buf = octx->ctx->ddr_spad_base; - } else { - mapping_buf = memalign(128, total_map_size); - if (mapping_buf) { - must_free_mapping = true; - } else { - return HTP_STATUS_INTERNAL_ERR; - } - } + hex_l2fetch_block((const void *) ids->data, ids->ne[1] * ids->nb[1]); - uint32_t * matrix_row_counts = (uint32_t *) mapping_buf; - struct mmid_row_mapping * matrix_rows = (struct mmid_row_mapping *) ((uint8_t *) mapping_buf + matrix_row_counts_size); + memset(matrix_row_counts, 0, matrix_row_counts_size); + scan_expert_ids(ids, n_ids, n_as, matrix_row_counts, NULL, 0); - mmctx->matrix_row_counts = matrix_row_counts; - mmctx->matrix_rows = matrix_rows; - mmctx->mm_div_ne11 = kparams->div_ne11; + uint32_t max_count = hvx_reduce_max_i32((const uint8_t *) matrix_row_counts, n_as); + mapping_stride = max_count > 0 ? max_count : 1; - if (hvx_mm_init_vec_dot(mmctx, src0->type) != 0) { - if (must_free_mapping) free(mapping_buf); - return HTP_STATUS_NO_SUPPORT; - } + size_t matrix_row_map_size = n_as * mapping_stride * sizeof(struct mmid_row_mapping); + const size_t total_map_size = matrix_row_counts_size + matrix_row_map_size; + + if (total_map_size > octx->ctx->ddr_spad_size) { + mapping_buf = memalign(128, total_map_size); + if (!mapping_buf) { + return HTP_STATUS_INTERNAL_ERR; + } + } + + matrix_row_counts = (uint32_t *) mapping_buf; + matrix_rows = (struct mmid_row_mapping *) (mapping_buf + matrix_row_counts_size); - if (src1_nrows > 1) { - // initialize matrix_row_counts and map memset(matrix_row_counts, 0, n_as * sizeof(uint32_t)); + scan_expert_ids(ids, n_ids, n_as, matrix_row_counts, matrix_rows, mapping_stride); + } - // group rows by src0 matrix - for (uint32_t iid1 = 0; iid1 < ids->ne[1]; ++iid1) { // token idx - for (uint32_t id = 0; id < n_ids; ++id) { // expert idx - const int32_t i02 = *(const int32_t *) ((const uint8_t *) ids->data + iid1 * ids->nb[1] + id * ids->nb[0]); + mmctx->matrix_row_counts = matrix_row_counts; + mmctx->matrix_rows = matrix_rows; + mmctx->mapping_stride = mapping_stride; + mmctx->mm_div_ne11 = kparams->div_ne11; + mmctx->src0_row_size_padded = src0_row_size_padded; + mmctx->src1_nrows = src1_nrows; - if (i02 < 0) { - continue; - } - assert(i02 < n_as); + htp_trace_event_stop(tr, HTP_TRACE_EVT_INIT, 0); - matrix_rows[i02 * n_ids * ids->ne[1] + matrix_row_counts[i02]] = (struct mmid_row_mapping) { id, iid1 }; - matrix_row_counts[i02] += 1; - } + int s; + if (kparams->n_hmx) { + s = hmx_mm_op_matmul_id(octx, mmctx); + } else { + if (hvx_mm_init_vec_dot(mmctx, src0->type) == 0) { + s = hvx_mm_matmul_id(octx, mmctx, src1_nrows > 1 ? hvx_mm_id : hvx_mv_id); + } else { + s = HTP_STATUS_NO_SUPPORT; } } - if (octx->flags & HTP_OPFLAGS_SKIP_COMPUTE) { - if (must_free_mapping) free(mapping_buf); - return HTP_STATUS_OK; + if (mapping_buf != octx->ctx->ddr_spad_base) { + free(mapping_buf); } - if (kparams->n_hmx) { - return hmx_mm_op_matmul_id(octx, mmctx, matrix_row_counts, matrix_rows, mapping_buf, must_free_mapping); - } - - return hvx_mm_matmul_id(octx, mmctx, src0_row_size_padded, src1_nrows, matmul_id_job_func, mapping_buf, must_free_mapping); + return s; } int op_matmul_qkv(struct htp_ops_context * octx) { + struct htp_thread_trace * tr = &octx->ctx->trace[0]; + htp_trace_event_start(tr, HTP_TRACE_EVT_INIT, 0); + const struct htp_tensor * restrict src0 = octx->src[0]; // Wk const struct htp_tensor * restrict src1 = octx->src[1]; // x const struct htp_tensor * restrict src2 = octx->src[2]; // Wv @@ -3345,7 +3765,7 @@ int op_matmul_qkv(struct htp_ops_context * octx) { struct htp_mm_hvx_vtcm_layout L; htp_mm_hvx_vtcm_layout_build(&L, kparams->kernel_type, src0->type, src1->ne[0], src1_nrows, octx->n_threads, - 0, src0_row_size, src1_row_size, kparams->n_prefetch, false, true, false); + 0, src0_row_size, src1_row_size, 0, kparams->n_prefetch, false, true, false); size_t vtcm_size = kparams->vtcm_size > 0 ? (size_t)kparams->vtcm_size : L.total_bytes; @@ -3379,9 +3799,6 @@ int op_matmul_qkv(struct htp_ops_context * octx) { mmctx->vtcm_src3_size_per_thread = L.src3_bytes / octx->n_threads; mmctx->vtcm_dst_size_per_thread = L.dst_bytes / octx->n_threads; - if (octx->flags & HTP_OPFLAGS_SKIP_COMPUTE) - return HTP_STATUS_OK; - mmctx->n_quant_rows_per_thread = (src1_nrows + n_quant_tasks - 1) / n_quant_tasks; mmctx->quant_task_func = quant_task_func; mmctx->n_quant_tasks = n_quant_tasks; @@ -3413,12 +3830,18 @@ int op_matmul_qkv(struct htp_ops_context * octx) { } else { matmul_job_func = hvx_mm_qkv_2d; } + + htp_trace_event_stop(tr, HTP_TRACE_EVT_INIT, 0); + worker_pool_run_func(octx->ctx->worker_pool, matmul_job_func, mmctx, n_matmul_jobs); return HTP_STATUS_OK; } int op_matmul_ffn(struct htp_ops_context * octx) { + struct htp_thread_trace * tr = &octx->ctx->trace[0]; + htp_trace_event_start(tr, HTP_TRACE_EVT_INIT, 0); + const struct htp_tensor * restrict src0 = octx->src[0]; // Wgate const struct htp_tensor * restrict src1 = octx->src[1]; // y const struct htp_tensor * restrict src2 = octx->src[2]; // Wup @@ -3487,7 +3910,7 @@ int op_matmul_ffn(struct htp_ops_context * octx) { struct htp_mm_hvx_vtcm_layout L; htp_mm_hvx_vtcm_layout_build(&L, kparams->kernel_type, src0->type, src1->ne[0], src1_nrows, octx->n_threads, - 0, src0_row_size, src1_row_size, kparams->n_prefetch, false, false, true); + 0, src0_row_size, src1_row_size, 0, kparams->n_prefetch, false, false, true); size_t vtcm_size = kparams->vtcm_size > 0 ? (size_t)kparams->vtcm_size : L.total_bytes; @@ -3516,9 +3939,6 @@ int op_matmul_ffn(struct htp_ops_context * octx) { mmctx->vtcm_src2_size_per_thread = L.src2_bytes / octx->n_threads; mmctx->vtcm_dst_size_per_thread = L.dst_bytes / octx->n_threads; - if (octx->flags & HTP_OPFLAGS_SKIP_COMPUTE) - return HTP_STATUS_OK; - mmctx->n_quant_rows_per_thread = (src1_nrows + n_quant_tasks - 1) / n_quant_tasks; mmctx->quant_task_func = quant_task_func; mmctx->n_quant_tasks = n_quant_tasks; @@ -3550,6 +3970,9 @@ int op_matmul_ffn(struct htp_ops_context * octx) { } else { matmul_job_func = hvx_mm_ffn_2d; } + + htp_trace_event_stop(tr, HTP_TRACE_EVT_INIT, 0); + worker_pool_run_func(octx->ctx->worker_pool, matmul_job_func, mmctx, n_matmul_jobs); return HTP_STATUS_OK; diff --git a/ggml/src/ggml-hexagon/htp/matmul-ops.h b/ggml/src/ggml-hexagon/htp/matmul-ops.h index 2e131bc3d025..6c393664c6e8 100644 --- a/ggml/src/ggml-hexagon/htp/matmul-ops.h +++ b/ggml/src/ggml-hexagon/htp/matmul-ops.h @@ -95,6 +95,8 @@ struct htp_mm_kernel_params { struct fastdiv_values div_r2; struct fastdiv_values div_r3; struct fastdiv_values div_ne11; + struct fastdiv_values div_n_act_threads; + struct fastdiv_values div_ne00_padded; }; #if defined(__cplusplus) @@ -458,6 +460,7 @@ static inline void htp_mm_hvx_vtcm_layout_build( size_t dst_row_size, size_t src0_row_size, size_t src1_row_size, + size_t src2_row_size, uint32_t n_prefetch, bool is_matmul_id, bool is_fused_qkv, @@ -465,7 +468,7 @@ static inline void htp_mm_hvx_vtcm_layout_build( ) { size_t src0_sz = 0; size_t src1_sz = 0; - size_t src2_sz = 0; + size_t src2_sz = src2_row_size > 0 ? htp_mm_round_up(src2_row_size, 128) : 0; size_t src3_sz = 0; size_t dst_sz = 0; @@ -643,6 +646,136 @@ static inline size_t htp_mm_hmx_get_batched_vtcm_size( return L.total_bytes; } +static inline bool htp_mm_hmx_solve_batched_params( + int wtype, + uint32_t k, + uint32_t ne01_padded, + uint32_t ne11, + uint32_t group_size, + bool use_dma_activation, + int n_threads, + bool pipeline, + size_t vtcm_budget, + size_t * m_chunk_out, + size_t * n_chunk_out, + int * act_threads_out, + size_t * vtcm_size_out +) { + size_t best_mblocks = SIZE_MAX; + int best_act_threads = 0; + size_t best_m_chunk = 0; + size_t best_n_chunk = 0; + size_t best_vtcm_size = 0; + + int act_threads = n_threads; + while (act_threads >= 1) { + size_t group_overhead = 256; + size_t group_size_per_n, group_size_per_m, group_size_per_mn; + htp_mm_hmx_get_batched_chunk_costs(k, group_size, &group_size_per_n, &group_size_per_m, &group_size_per_mn); + + size_t m_chunk_candidate = 0; + size_t n_chunk_candidate = 0; + size_t vtcm_size_candidate = 0; + + if (htp_mm_hmx_compute_chunks(vtcm_budget, group_overhead, group_size_per_n, group_size_per_m, group_size_per_mn, hex_align_up(ne11, 32), ne01_padded, + (size_t) ne01_padded * HTP_MM_HMX_COST_W_DEQUANT, (size_t) ne11 * HTP_MM_HMX_COST_A_CONVERT, + &m_chunk_candidate, &n_chunk_candidate, &vtcm_size_candidate) == 0) { + size_t exact_size = htp_mm_hmx_get_batched_vtcm_size(wtype, k, m_chunk_candidate, n_chunk_candidate, group_size, use_dma_activation, pipeline, act_threads); + if (exact_size <= vtcm_budget) { + size_t mblocks = ((size_t) ne11 + m_chunk_candidate - 1) / m_chunk_candidate; + if (mblocks < best_mblocks || (mblocks == best_mblocks && act_threads > best_act_threads)) { + best_mblocks = mblocks; + best_act_threads = act_threads; + best_m_chunk = m_chunk_candidate; + best_n_chunk = n_chunk_candidate; + best_vtcm_size = exact_size; + } + } + } + if (act_threads == 1) { + act_threads = 0; + } else { + act_threads /= 2; + } + } + + if (best_act_threads > 0) { + *m_chunk_out = best_m_chunk; + *n_chunk_out = best_n_chunk; + *vtcm_size_out = best_vtcm_size; + *act_threads_out = best_act_threads; + return true; + } + return false; +} + +static inline bool htp_mm_hmx_solve_2d_params( + int wtype, + uint32_t k, + uint32_t m_id_rows, + uint32_t ne01_padded, + uint32_t ne11_padded, + uint32_t m_for_cost, + int n_threads, + bool pipeline, + bool is_matmul_id, + uint32_t aligned_tile_size, + size_t vtcm_budget, + size_t * m_chunk_out, + size_t * n_chunk_out, + int * act_threads_out, + size_t * vtcm_size_out +) { + size_t best_mblocks = SIZE_MAX; + int best_act_threads = 0; + size_t best_m_chunk = 0; + size_t best_n_chunk = 0; + size_t best_vtcm_size = 0; + + const int m_for_chunks = is_matmul_id ? hex_align_up(m_id_rows, 32) : ne11_padded; + + int act_threads = n_threads; + while (act_threads >= 1) { + size_t simple_2d_overhead = 256; + size_t simple_2d_size_per_n, simple_2d_size_per_m, simple_2d_size_per_mn; + htp_mm_hmx_get_2d_chunk_costs(wtype, k, pipeline, aligned_tile_size, &simple_2d_size_per_n, &simple_2d_size_per_m, &simple_2d_size_per_mn); + + size_t m_chunk_candidate = 0; + size_t n_chunk_candidate = 0; + size_t vtcm_size_candidate = 0; + + if (htp_mm_hmx_compute_chunks(vtcm_budget, simple_2d_overhead, simple_2d_size_per_n, simple_2d_size_per_m, simple_2d_size_per_mn, m_for_chunks, ne01_padded, + (size_t) ne01_padded * HTP_MM_HMX_COST_W_DEQUANT, (size_t) m_for_cost * HTP_MM_HMX_COST_A_CONVERT, + &m_chunk_candidate, &n_chunk_candidate, &vtcm_size_candidate) == 0) { + size_t exact_size = htp_mm_hmx_get_2d_vtcm_size(wtype, k, m_chunk_candidate, n_chunk_candidate, pipeline, is_matmul_id ? 0 : act_threads, aligned_tile_size); + if (exact_size <= vtcm_budget) { + size_t mblocks = ((size_t) m_for_cost + m_chunk_candidate - 1) / m_chunk_candidate; + if (mblocks < best_mblocks || (mblocks == best_mblocks && act_threads > best_act_threads)) { + best_mblocks = mblocks; + best_act_threads = act_threads; + best_m_chunk = m_chunk_candidate; + best_n_chunk = n_chunk_candidate; + best_vtcm_size = exact_size; + } + } + } + if (act_threads == 1) { + act_threads = 0; + } else { + act_threads /= 2; + } + } + + if (best_act_threads > 0) { + *m_chunk_out = best_m_chunk; + *n_chunk_out = best_n_chunk; + *vtcm_size_out = best_vtcm_size; + *act_threads_out = best_act_threads; + return true; + } + return false; +} + #ifdef __cplusplus } #endif diff --git a/ggml/src/ggml-hexagon/htp/rope-ops.c b/ggml/src/ggml-hexagon/htp/rope-ops.c index d16dc7d38ef4..5bc7d74f5e21 100644 --- a/ggml/src/ggml-hexagon/htp/rope-ops.c +++ b/ggml/src/ggml-hexagon/htp/rope-ops.c @@ -18,6 +18,7 @@ #include "htp-ctx.h" #include "htp-ops.h" #include "htp-ops.h" +#include "htp-tensor.h" // Redefined the rope type constants as we can't include ggml.h #define HTP_ROPE_TYPE_NORMAL 0 @@ -712,17 +713,11 @@ static int execute_op_rope_f32(struct htp_ops_context * octx) { } int op_rope(struct htp_ops_context * octx) { - int err = HTP_STATUS_OK; - switch (octx->src[0]->type) { case HTP_TYPE_F32: - err = execute_op_rope_f32(octx); - break; + return execute_op_rope_f32(octx); default: - err = HTP_STATUS_NO_SUPPORT; - break; + return HTP_STATUS_NO_SUPPORT; } - - return err; } diff --git a/ggml/src/ggml-hexagon/htp/unary-ops.c b/ggml/src/ggml-hexagon/htp/unary-ops.c index a71107f10476..b21415a67d64 100644 --- a/ggml/src/ggml-hexagon/htp/unary-ops.c +++ b/ggml/src/ggml-hexagon/htp/unary-ops.c @@ -19,6 +19,7 @@ #include "ggml-common.h" #include "htp-ctx.h" #include "htp-ops.h" +#include "htp-tensor.h" #include "htp-vtcm.h" #include "hex-profile.h" @@ -137,6 +138,24 @@ static void scale_f32(const float * restrict src, } } +static void clamp_f32(const float * restrict src, + float * restrict dst, + const uint32_t num_rows, + const struct htp_unary_context * uctx) { + htp_unary_op_preamble; + float min = 0.f; + float max = 0.f; + memcpy(&min, &op_params[0], sizeof(float)); + memcpy(&max, &op_params[1], sizeof(float)); + + for (uint32_t ir = 0; ir < num_rows; ir++) { + const uint8_t * restrict src_local = (const uint8_t *)src + (ir * src0_row_size_aligned); + uint8_t * restrict dst_local = (uint8_t *)dst + (ir * dst_row_size_aligned); + + hvx_clamp_scalar_f32(dst_local, src_local, min, max, ne0); + } +} + static void rms_norm_f32(const float * restrict src, float * restrict dst, const uint32_t num_rows, @@ -257,6 +276,39 @@ static void sigmoid_f32(const float * restrict src, } } +// silu(x) = x * sigmoid(x) +static void silu_f32(const float * restrict src, + float * restrict dst, + const uint32_t num_rows, + const struct htp_unary_context * uctx) { + htp_unary_op_preamble; + + for (uint32_t ir = 0; ir < num_rows; ir++) { + const uint8_t * restrict src_local = (const uint8_t *)src + (ir * src0_row_size_aligned); + uint8_t * restrict dst_local = (uint8_t *)dst + (ir * dst_row_size_aligned); + + hvx_sigmoid_f32_aa(dst_local, src_local, ne0); + hvx_mul_f32_aaa(dst_local, src_local, dst_local, ne0); + } +} + +// gelu(x) = x * sigmoid(1.702 * x) (quick/sigmoid approximation, matches CPU GELU_QUICK reference) +static void gelu_f32(const float * restrict src, + float * restrict dst, + const uint32_t num_rows, + const struct htp_unary_context * uctx) { + htp_unary_op_preamble; + + for (uint32_t ir = 0; ir < num_rows; ir++) { + const uint8_t * restrict src_local = (const uint8_t *)src + (ir * src0_row_size_aligned); + uint8_t * restrict dst_local = (uint8_t *)dst + (ir * dst_row_size_aligned); + + hvx_mul_scalar_f32(dst_local, src_local, 1.702f, ne0); + hvx_sigmoid_f32_aa(dst_local, dst_local, ne0); + hvx_mul_f32_aaa(dst_local, src_local, dst_local, ne0); + } +} + static void tri_f32(const float * restrict src, float * restrict dst, const uint32_t num_rows, @@ -397,7 +449,7 @@ static void unary_task_f32_##NAME(unsigned int nth, unsigned int ith, void * dat struct htp_ops_context * octx = uctx->octx; \ const struct htp_tensor * src = octx->src[0]; \ const struct htp_tensor * dst = octx->dst; \ - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL; \ + struct htp_thread_trace * tr = &octx->ctx->trace[ith]; \ \ htp_unary_preamble; \ \ @@ -541,11 +593,14 @@ DEFINE_UNARY_TASK(norm, false, false, norm_f32(src0_vtcm, dst_vtcm, bl DEFINE_UNARY_TASK(rms_norm, false, false, rms_norm_f32(src0_vtcm, dst_vtcm, block_size, uctx)) DEFINE_UNARY_TASK(rms_norm_mul, true, false, rms_norm_mul_f32(src0_vtcm, uctx->broadcast_weight ? (const float *) src1_vtcm_data : src1_vtcm, dst_vtcm, block_size, uctx)) DEFINE_UNARY_TASK(scale, false, false, scale_f32(src0_vtcm, dst_vtcm, block_size, uctx)) +DEFINE_UNARY_TASK(clamp, false, false, clamp_f32(src0_vtcm, dst_vtcm, block_size, uctx)) DEFINE_UNARY_TASK(sqr, false, false, sqr_f32(src0_vtcm, dst_vtcm, block_size, uctx)) DEFINE_UNARY_TASK(sqrt, false, false, sqrt_f32(src0_vtcm, dst_vtcm, block_size, uctx)) DEFINE_UNARY_TASK(unary_neg, false, false, neg_f32(src0_vtcm, dst_vtcm, block_size, uctx)) DEFINE_UNARY_TASK(unary_exp, false, false, exp_f32(src0_vtcm, dst_vtcm, block_size, uctx)) DEFINE_UNARY_TASK(unary_sigmoid, false, false, sigmoid_f32(src0_vtcm, dst_vtcm, block_size, uctx)) +DEFINE_UNARY_TASK(unary_silu, false, false, silu_f32(src0_vtcm, dst_vtcm, block_size, uctx)) +DEFINE_UNARY_TASK(unary_gelu, false, false, gelu_f32(src0_vtcm, dst_vtcm, block_size, uctx)) DEFINE_UNARY_TASK(unary_softplus, false, false, softplus_f32(src0_vtcm, dst_vtcm, block_size, uctx)) DEFINE_UNARY_TASK(unary_tanh, false, false, tanh_f32(src0_vtcm, dst_vtcm, block_size, uctx)) DEFINE_UNARY_TASK(l2_norm, false, false, l2_norm_f32(src0_vtcm, dst_vtcm, block_size, uctx)) @@ -558,7 +613,7 @@ static void unary_task_f32_tiled_##NAME(unsigned int nth, unsigned int ith, void struct htp_ops_context * octx = uctx->octx; \ const struct htp_tensor * src = octx->src[0]; \ const struct htp_tensor * dst = octx->dst; \ - struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL; \ + struct htp_thread_trace * tr = &octx->ctx->trace[ith]; \ \ htp_unary_preamble; \ \ @@ -680,6 +735,14 @@ static inline void tile_scale_f32(uint8_t * dst_vtcm, const uint8_t * src_vtcm, hvx_scale_offset_f32_aa(dst_vtcm, src_vtcm, tw, scale, bias); } +static inline void tile_clamp_f32(uint8_t * dst_vtcm, const uint8_t * src_vtcm, uint32_t tw, const int32_t * op_params) { + float min = 0.f; + float max = 0.f; + memcpy(&min, &op_params[0], sizeof(float)); + memcpy(&max, &op_params[1], sizeof(float)); + hvx_clamp_scalar_f32(dst_vtcm, src_vtcm, min, max, tw); +} + static inline void tile_unary_softplus_f32(uint8_t * dst_vtcm, const uint8_t * src_vtcm, uint32_t tw) { const float * restrict sf = (const float *) src_vtcm; float * restrict df = (float *) dst_vtcm; @@ -689,6 +752,19 @@ static inline void tile_unary_softplus_f32(uint8_t * dst_vtcm, const uint8_t * s } } +// silu(x) = x * sigmoid(x) +static inline void tile_silu_f32(uint8_t * dst_vtcm, const uint8_t * src_vtcm, uint32_t tw) { + hvx_sigmoid_f32_aa(dst_vtcm, src_vtcm, tw); + hvx_mul_f32_aaa(dst_vtcm, src_vtcm, dst_vtcm, tw); +} + +// gelu(x) = x * sigmoid(1.702 * x) (quick/sigmoid approximation, matches CPU GELU_QUICK reference) +static inline void tile_gelu_f32(uint8_t * dst_vtcm, const uint8_t * src_vtcm, uint32_t tw) { + hvx_mul_scalar_f32(dst_vtcm, src_vtcm, 1.702f, tw); + hvx_sigmoid_f32_aa(dst_vtcm, dst_vtcm, tw); + hvx_mul_f32_aaa(dst_vtcm, src_vtcm, dst_vtcm, tw); +} + // Triangular mask applied to one column tile. Boundary is an absolute column index, so // each vector compares against its absolute column position (col_start + i*VLEN_FP32). static inline void tri_apply_tile_f32(const uint8_t * restrict src, uint8_t * restrict dst, @@ -764,11 +840,14 @@ static inline void tri_apply_tile_f32(const uint8_t * restrict src, uint8_t * re } DEFINE_UNARY_TILED_TASK(scale, false, tile_scale_f32(dst_vtcm, src_vtcm, tw, op_params)) +DEFINE_UNARY_TILED_TASK(clamp, false, tile_clamp_f32(dst_vtcm, src_vtcm, tw, op_params)) DEFINE_UNARY_TILED_TASK(sqr, false, hvx_sqr_f32_aa(dst_vtcm, src_vtcm, tw)) DEFINE_UNARY_TILED_TASK(sqrt, false, hvx_sqrt_f32_aa(dst_vtcm, src_vtcm, tw)) DEFINE_UNARY_TILED_TASK(unary_neg, false, hvx_scale_f32_aa(dst_vtcm, src_vtcm, tw, -1.0f)) DEFINE_UNARY_TILED_TASK(unary_exp, false, hvx_exp_f32(dst_vtcm, src_vtcm, tw, false)) DEFINE_UNARY_TILED_TASK(unary_sigmoid, false, hvx_sigmoid_f32_aa(dst_vtcm, src_vtcm, tw)) +DEFINE_UNARY_TILED_TASK(unary_silu, false, tile_silu_f32(dst_vtcm, src_vtcm, tw)) +DEFINE_UNARY_TILED_TASK(unary_gelu, false, tile_gelu_f32(dst_vtcm, src_vtcm, tw)) DEFINE_UNARY_TILED_TASK(unary_softplus, false, tile_unary_softplus_f32(dst_vtcm, src_vtcm, tw)) DEFINE_UNARY_TILED_TASK(unary_tanh, false, hvx_tanh_f32_aa(dst_vtcm, src_vtcm, tw)) DEFINE_UNARY_TILED_TASK(tri, true, tri_apply_tile_f32(src_vtcm, dst_vtcm, tw, col, i01, ne0, tri_ttype)) @@ -786,11 +865,14 @@ static int execute_op_unary_f32(struct htp_ops_context * octx) { case HTP_OP_RMS_NORM: op_type = "rmsnorm-f32"; break; case HTP_OP_RMS_NORM_MUL: op_type = "rmsnorm-mul-f32"; break; case HTP_OP_SCALE: op_type = "scale-f32"; break; + case HTP_OP_CLAMP: op_type = "clamp-f32"; break; case HTP_OP_SQR: op_type = "sqr-f32"; break; case HTP_OP_SQRT: op_type = "sqrt-f32"; break; case HTP_OP_UNARY_NEG: op_type = "neg-f32"; break; case HTP_OP_UNARY_EXP: op_type = "exp-f32"; break; case HTP_OP_UNARY_SIGMOID: op_type = "sigmoid-f32"; break; + case HTP_OP_UNARY_SILU: op_type = "silu-f32"; break; + case HTP_OP_UNARY_GELU: op_type = "gelu-f32"; break; case HTP_OP_UNARY_SOFTPLUS: op_type = "softplus-f32"; break; case HTP_OP_UNARY_TANH: op_type = "tanh-f32"; break; case HTP_OP_L2_NORM: op_type = "l2norm-f32"; break; @@ -881,11 +963,14 @@ static int execute_op_unary_f32(struct htp_ops_context * octx) { if (col_tile) { switch (octx->op) { case HTP_OP_SCALE: task_func = unary_task_f32_tiled_scale; break; + case HTP_OP_CLAMP: task_func = unary_task_f32_tiled_clamp; break; case HTP_OP_SQR: task_func = unary_task_f32_tiled_sqr; break; case HTP_OP_SQRT: task_func = unary_task_f32_tiled_sqrt; break; case HTP_OP_UNARY_NEG: task_func = unary_task_f32_tiled_unary_neg; break; case HTP_OP_UNARY_EXP: task_func = unary_task_f32_tiled_unary_exp; break; case HTP_OP_UNARY_SIGMOID: task_func = unary_task_f32_tiled_unary_sigmoid; break; + case HTP_OP_UNARY_SILU: task_func = unary_task_f32_tiled_unary_silu; break; + case HTP_OP_UNARY_GELU: task_func = unary_task_f32_tiled_unary_gelu; break; case HTP_OP_UNARY_SOFTPLUS: task_func = unary_task_f32_tiled_unary_softplus; break; case HTP_OP_UNARY_TANH: task_func = unary_task_f32_tiled_unary_tanh; break; case HTP_OP_TRI: task_func = unary_task_f32_tiled_tri; break; @@ -897,11 +982,14 @@ static int execute_op_unary_f32(struct htp_ops_context * octx) { case HTP_OP_RMS_NORM: task_func = unary_task_f32_rms_norm; break; case HTP_OP_RMS_NORM_MUL: task_func = unary_task_f32_rms_norm_mul; break; case HTP_OP_SCALE: task_func = unary_task_f32_scale; break; + case HTP_OP_CLAMP: task_func = unary_task_f32_clamp; break; case HTP_OP_SQR: task_func = unary_task_f32_sqr; break; case HTP_OP_SQRT: task_func = unary_task_f32_sqrt; break; case HTP_OP_UNARY_NEG: task_func = unary_task_f32_unary_neg; break; case HTP_OP_UNARY_EXP: task_func = unary_task_f32_unary_exp; break; case HTP_OP_UNARY_SIGMOID: task_func = unary_task_f32_unary_sigmoid; break; + case HTP_OP_UNARY_SILU: task_func = unary_task_f32_unary_silu; break; + case HTP_OP_UNARY_GELU: task_func = unary_task_f32_unary_gelu; break; case HTP_OP_UNARY_SOFTPLUS: task_func = unary_task_f32_unary_softplus; break; case HTP_OP_UNARY_TANH: task_func = unary_task_f32_unary_tanh; break; case HTP_OP_L2_NORM: task_func = unary_task_f32_l2_norm; break; @@ -922,17 +1010,11 @@ static int execute_op_unary_f32(struct htp_ops_context * octx) { } int op_unary(struct htp_ops_context * octx) { - int err = HTP_STATUS_OK; - switch (octx->src[0]->type) { case HTP_TYPE_F32: - err = execute_op_unary_f32(octx); - break; + return execute_op_unary_f32(octx); default: - err = HTP_STATUS_NO_SUPPORT; - break; + return HTP_STATUS_NO_SUPPORT; } - - return err; } diff --git a/ggml/src/ggml-hexagon/htp/unary-ops.h b/ggml/src/ggml-hexagon/htp/unary-ops.h index b90b095ac9ad..1f4c3a5c4d96 100644 --- a/ggml/src/ggml-hexagon/htp/unary-ops.h +++ b/ggml/src/ggml-hexagon/htp/unary-ops.h @@ -41,6 +41,7 @@ _Static_assert(sizeof(struct htp_unary_kernel_params) <= 128, "htp_unary_kernel_ static inline bool htp_op_is_unary(uint32_t opcode) { switch (opcode) { + case HTP_OP_CLAMP: case HTP_OP_NORM: case HTP_OP_RMS_NORM: case HTP_OP_RMS_NORM_MUL: @@ -50,6 +51,8 @@ static inline bool htp_op_is_unary(uint32_t opcode) { case HTP_OP_UNARY_NEG: case HTP_OP_UNARY_EXP: case HTP_OP_UNARY_SIGMOID: + case HTP_OP_UNARY_SILU: + case HTP_OP_UNARY_GELU: case HTP_OP_UNARY_SOFTPLUS: case HTP_OP_UNARY_TANH: case HTP_OP_L2_NORM: diff --git a/ggml/src/ggml-hexagon/htp/work-queue.c b/ggml/src/ggml-hexagon/htp/work-queue.c new file mode 100644 index 000000000000..bb73e205a4a9 --- /dev/null +++ b/ggml/src/ggml-hexagon/htp/work-queue.c @@ -0,0 +1,244 @@ +#include "work-queue.h" +#include "hex-utils.h" + +#include +#include + +#include +#include +#include +#include +#include + +#include "HAP_farf.h" + +#define LOWEST_USABLE_QURT_PRIO (254) + +// internal structure kept in thread-local storage per instance of work queue +typedef struct { + work_queue_t queue; + unsigned int id; +} worker_context_t; + +struct work_queue_task_s { + work_queue_func_t func; + void * data; + unsigned int n_threads; + atomic_uint barrier; +}; + +// internal structure kept in thread-local storage per instance of work queue +struct work_queue_s { + atomic_uint seqn; // seqno used to detect new jobs + atomic_uint idx_read; // Updated by producer (pop/reclaim) + unsigned int idx_write; // Updated by producer (push) + uint32_t idx_mask; + uint32_t capacity; + + qurt_thread_t thread[WORK_QUEUE_MAX_N_THREADS]; // thread ID's of the workers + worker_context_t context[WORK_QUEUE_MAX_N_THREADS]; // worker contexts + void * stack[WORK_QUEUE_MAX_N_THREADS]; // thread stack pointers + unsigned int n_threads; // total threads (workers + main) + unsigned int n_workers; // number of active threads (just workers) + + atomic_bool active; // workers are polling/active + atomic_bool killed; // threads need to exit + bool external_mem; // memory owned externally + + struct work_queue_task_s queue[] __attribute__((aligned(HEX_L2_LINE_SIZE))); +}; + +static void work_queue_thread(void * context) { + worker_context_t * me = (worker_context_t *) context; + work_queue_t q = me->queue; + + FARF(HIGH, "work-queue: thread %u started", me->id); + + unsigned int prev_seqn = 0; + + while (!atomic_load_explicit(&q->killed, memory_order_relaxed)) { + unsigned int seqn = atomic_load_explicit(&q->seqn, memory_order_acquire); + if (seqn == prev_seqn) { + if (atomic_load_explicit(&q->active, memory_order_relaxed)) { + hex_pause(); + } else { + qurt_futex_wait(&q->seqn, prev_seqn); + } + continue; + } + + prev_seqn = seqn; + + // Process all active tasks in the queue + unsigned int ir = atomic_load_explicit(&q->idx_read, memory_order_relaxed); + unsigned int iw = q->idx_write; + + while (ir != iw) { + struct work_queue_task_s * task = &q->queue[ir]; + + unsigned int n = task->n_threads; + unsigned int i = me->id; + if (i < n) { + task->func(n, i, task->data); + + atomic_fetch_sub_explicit(&task->barrier, 1, memory_order_release); + } else { + while (atomic_load_explicit(&task->barrier, memory_order_relaxed) > 0) { + hex_pause(); + } + } + + ir = (ir + 1) & q->idx_mask; + } + } + + FARF(HIGH, "work-queue: thread %u stopped", me->id); +} + +bool work_queue_run_async(work_queue_t q, work_queue_func_t func, void * data, unsigned int n) { + if (n > q->n_threads) { + FARF(ERROR, "work-queue: invalid number of jobs %u for n-threads %u", n, q->n_threads); + return false; + } + + unsigned int ir = atomic_load_explicit(&q->idx_read, memory_order_relaxed); + unsigned int iw = q->idx_write; + + if (((iw + 1) & q->idx_mask) == ir) { + FARF(ERROR, "work-queue-push: queue is full\n"); + return false; + } + + struct work_queue_task_s * task = &q->queue[iw]; + task->func = func; + task->data = data; + task->n_threads = n; + atomic_store_explicit(&task->barrier, n, memory_order_relaxed); + + q->idx_write = (iw + 1) & q->idx_mask; + + // publish job to workers (already awake and polling) + atomic_fetch_add_explicit(&q->seqn, 1, memory_order_release); + + // main thread runs job #0 + func(n, 0, data); + + atomic_fetch_sub_explicit(&task->barrier, 1, memory_order_release); + + while (atomic_load_explicit(&task->barrier, memory_order_relaxed) > 0) { + hex_pause(); + } + + atomic_thread_fence(memory_order_acquire); + + atomic_store_explicit(&q->idx_read, (ir + 1) & q->idx_mask, memory_order_relaxed); + + return true; +} + +size_t work_queue_sizeof(uint32_t n_threads, uint32_t capacity, uint32_t stack_size) { + capacity = hex_ceil_pow2(capacity); + uint32_t n_workers = n_threads > 1 ? n_threads - 1 : 0; + size_t size_stacks = stack_size * n_workers; + size_t size_q = hex_align_up(sizeof(struct work_queue_s) + capacity * sizeof(struct work_queue_task_s), HEX_L2_LINE_SIZE); + return size_stacks + size_q; +} + +size_t work_queue_alignof(void) { + return 4096; +} + +work_queue_t work_queue_init(void * ptr, uint32_t n_threads, uint32_t capacity, uint32_t stack_size) { + capacity = hex_ceil_pow2(capacity); + uint32_t n_workers = n_threads > 1 ? n_threads - 1 : 0; + unsigned char * mem_blob = (unsigned char *) ptr; + + work_queue_t q = (work_queue_t) (mem_blob + stack_size * n_workers); + memset(q, 0, sizeof(struct work_queue_s) + capacity * sizeof(struct work_queue_task_s)); + + q->n_threads = n_threads; + q->n_workers = n_workers; + q->external_mem = true; + q->capacity = capacity; + + for (unsigned int i = 0; i < n_workers; i++) { + q->stack[i] = mem_blob; mem_blob += stack_size; + q->thread[i] = 0; + q->context[i].id = i + 1; + q->context[i].queue = q; + } + + atomic_init(&q->idx_read, 0); + atomic_init(&q->seqn, 0); + atomic_init(&q->active, false); + q->idx_write = 0; + q->idx_mask = capacity - 1; + q->killed = 0; + for (int i = 0; i < (int) capacity; i++) { + atomic_init(&q->queue[i].barrier, 0); + q->queue[i].func = NULL; + q->queue[i].data = NULL; + q->queue[i].n_threads = 0; + } + + // launch the workers + qurt_thread_attr_t attr; + qurt_thread_attr_init(&attr); + + for (unsigned int i = 0; i < n_workers; i++) { + qurt_thread_attr_set_stack_addr(&attr, q->stack[i]); + qurt_thread_attr_set_stack_size(&attr, stack_size); + + char thread_name[32]; + snprintf(thread_name, sizeof(thread_name), "work-queue:%u", i); + qurt_thread_attr_set_name(&attr, thread_name); + + // set up priority - by default, match the creating thread's prio + int prio = qurt_thread_get_priority(qurt_thread_get_id()); + if (prio < 1) { + prio = 1; + } + if (prio > LOWEST_USABLE_QURT_PRIO) { + prio = LOWEST_USABLE_QURT_PRIO; + } + + qurt_thread_attr_set_priority(&attr, prio); + + int err = qurt_thread_create(&q->thread[i], &attr, work_queue_thread, (void *) &q->context[i]); + if (err) { + FARF(ERROR, "Could not launch worker threads!"); + work_queue_free(q); + return NULL; + } + } + + return q; +} + +void work_queue_free(work_queue_t q) { + if (!q) { return; } + + atomic_store_explicit(&q->killed, 1, memory_order_relaxed); + atomic_fetch_add_explicit(&q->seqn, 1, memory_order_release); + qurt_futex_wake(&q->seqn, q->n_workers); + + for (unsigned int i = 0; i < q->n_workers; i++) { + if (q->thread[i]) { + int status; + (void) qurt_thread_join(q->thread[i], &status); + } + } +} + +void work_queue_wakeup(work_queue_t q) { + if (!atomic_load_explicit(&q->active, memory_order_relaxed)) { + atomic_store_explicit(&q->active, true, memory_order_release); + // Increment seqn and wake workers to transition them out of sleep + atomic_fetch_add_explicit(&q->seqn, 1, memory_order_release); + qurt_futex_wake(&q->seqn, q->n_workers); + } +} + +void work_queue_suspend(work_queue_t q) { + atomic_store_explicit(&q->active, false, memory_order_release); +} diff --git a/ggml/src/ggml-hexagon/htp/work-queue.h b/ggml/src/ggml-hexagon/htp/work-queue.h new file mode 100644 index 000000000000..09ca4b1f4392 --- /dev/null +++ b/ggml/src/ggml-hexagon/htp/work-queue.h @@ -0,0 +1,38 @@ +#ifndef HTP_WORK_QUEUE_H +#define HTP_WORK_QUEUE_H + +#include +#include +#include + +typedef void (*work_queue_func_t)(unsigned int n, unsigned int i, void *); + +struct work_queue_s; +typedef struct work_queue_s * work_queue_t; + +#define WORK_QUEUE_MAX_N_THREADS 10 + +size_t work_queue_sizeof(uint32_t n_threads, uint32_t capacity, uint32_t stack_size); +size_t work_queue_alignof(void); +work_queue_t work_queue_init(void * ptr, uint32_t n_threads, uint32_t capacity, uint32_t stack_size); +void work_queue_free(work_queue_t q); + +void work_queue_wakeup(work_queue_t q); +void work_queue_suspend(work_queue_t q); + +bool work_queue_run_async(work_queue_t q, work_queue_func_t func, void * data, unsigned int n); + +static inline bool work_queue_run(work_queue_t q, work_queue_func_t func, void * data, unsigned int n) { + if (n <= 1) { + func(n, 0, data); + return true; + } + return work_queue_run_async(q, func, data, n); +} + +// Legacy compatibility +typedef work_queue_func_t worker_callback_t; +#define worker_pool_run_func work_queue_run +#define worker_pool work_queue + +#endif // #ifndef HTP_WORK_QUEUE_H diff --git a/ggml/src/ggml-hexagon/htp/worker-pool.c b/ggml/src/ggml-hexagon/htp/worker-pool.c deleted file mode 100644 index 50960d2c75d7..000000000000 --- a/ggml/src/ggml-hexagon/htp/worker-pool.c +++ /dev/null @@ -1,305 +0,0 @@ -#include "worker-pool.h" -#include "hex-utils.h" - -#include -#include - -#include -#include -#include -#include -#include - -#include "HAP_farf.h" - -#define LOWEST_USABLE_QURT_PRIO (254) - -struct worker_pool_s; - -// internal structure kept in thread-local storage per instance of worker pool -typedef struct { - struct worker_pool_s * pool; - unsigned int id; -} worker_context_t; - -// internal structure kept in thread-local storage per instance of worker pool -typedef struct worker_pool_s { - worker_pool_job_t job[MAX_NUM_WORKERS]; // list of job descriptors - qurt_thread_t thread[MAX_NUM_WORKERS]; // thread ID's of the workers - worker_context_t context[MAX_NUM_WORKERS]; // worker contexts - void * stack[MAX_NUM_WORKERS]; // thread stack pointers - unsigned int n_threads; // number of workers in this pool - - atomic_uint seqn; // seqno used to detect new jobs - atomic_uint next_job; // next job index - atomic_uint n_pending; // number of pending jobs - atomic_uint n_jobs; // number of current jobs - atomic_bool killed; // threads need to exit -} worker_pool_t; - -static void worker_pool_main(void * context) { - worker_context_t * me = (worker_context_t *) context; - worker_pool_t * pool = me->pool; - - FARF(HIGH, "worker-pool: thread %u started", me->id); - - unsigned int prev_seqn = 0; - unsigned int poll_cnt = WORKER_POOL_POLL_COUNT; - while (!atomic_load(&pool->killed)) { - unsigned int seqn = atomic_load(&pool->seqn); - if (seqn == prev_seqn) { - // drop HVX context while spinning - if (poll_cnt > 1 && poll_cnt == WORKER_POOL_POLL_COUNT) { - qurt_hvx_unlock(); - } - if (--poll_cnt) { - hex_pause(); - continue; - } - qurt_futex_wait(&pool->seqn, prev_seqn); - poll_cnt = WORKER_POOL_POLL_COUNT; - continue; - } - - prev_seqn = seqn; - poll_cnt = WORKER_POOL_POLL_COUNT; - - // New job - unsigned int n = atomic_load(&pool->n_jobs); - unsigned int i = atomic_fetch_add(&pool->next_job, 1); - if (i >= n) { - // Spurious wakeup - continue; - } - - pool->job[i].func(n, i, pool->job[i].data); - - atomic_fetch_sub(&pool->n_pending, 1); - } - - FARF(HIGH, "worker-pool: thread %u stopped", me->id); -} - -AEEResult worker_pool_init_with_stack_size(worker_pool_context_t * context, uint32_t n_threads, uint32_t stack_size) { - int err = 0; - - if (NULL == context) { - FARF(ERROR, "NULL context passed to worker_pool_init()."); - return AEE_EBADPARM; - } - - // Allocations - int size = (stack_size * n_threads) + (sizeof(worker_pool_t)); - - unsigned char * mem_blob = (unsigned char *) malloc(size); - if (!mem_blob) { - FARF(ERROR, "Could not allocate memory for worker pool!!"); - return AEE_ENOMEMORY; - } - - worker_pool_t * me = (worker_pool_t *) (mem_blob + stack_size * n_threads); - - // name for the first worker, useful in debugging threads - char name[19]; - snprintf(name, 12, "0x%8x:", (int) me); - strcat(name, "worker0"); - me->n_threads = n_threads; - - // initializations - for (unsigned int i = 0; i < me->n_threads; i++) { - me->stack[i] = NULL; - me->thread[i] = 0; - - me->context[i].id = i; - me->context[i].pool = me; - } - - // initialize job queue - me->n_pending = 0; - me->n_jobs = 0; - me->next_job = 0; - me->seqn = 0; - me->killed = 0; - - // launch the workers - qurt_thread_attr_t attr; - qurt_thread_attr_init(&attr); - - for (unsigned int i = 0; i < me->n_threads; i++) { - // set up stack - me->stack[i] = mem_blob; - mem_blob += stack_size; - qurt_thread_attr_set_stack_addr(&attr, me->stack[i]); - qurt_thread_attr_set_stack_size(&attr, stack_size); - - // set up name - qurt_thread_attr_set_name(&attr, name); - name[17] = (name[17] + 1); - // name threads context:worker0, context:worker1, .. (recycle at 9, but num threads should be less than that anyway) - if (name[17] > '9') { - name[17] = '0'; - } - - // set up priority - by default, match the creating thread's prio - int prio = qurt_thread_get_priority(qurt_thread_get_id()); - - if (prio < 1) { - prio = 1; - } - if (prio > LOWEST_USABLE_QURT_PRIO) { - prio = LOWEST_USABLE_QURT_PRIO; - } - - qurt_thread_attr_set_priority(&attr, prio); - - // launch - err = qurt_thread_create(&me->thread[i], &attr, worker_pool_main, (void *) &me->context[i]); - if (err) { - FARF(ERROR, "Could not launch worker threads!"); - worker_pool_release((worker_pool_context_t *) &me); - return AEE_EQURTTHREADCREATE; - } - } - *context = (worker_pool_context_t *) me; - return AEE_SUCCESS; -} - -AEEResult worker_pool_init(worker_pool_context_t * context, uint32_t n_threads) { - return worker_pool_init_with_stack_size(context, n_threads, WORKER_THREAD_STACK_SZ); -} - -// clean up worker pool -void worker_pool_release(worker_pool_context_t * context) { - worker_pool_t * me = (worker_pool_t *) *context; - - // if no worker pool exists, return error. - if (NULL == me) { - return; - } - - atomic_store(&me->killed, 1); - atomic_fetch_add(&me->seqn, 1); - qurt_futex_wake(&me->seqn, me->n_threads); - - // de-initializations - for (unsigned int i = 0; i < me->n_threads; i++) { - if (me->thread[i]) { - int status; - (void) qurt_thread_join(me->thread[i], &status); - } - } - - // free allocated memory (were allocated as a single buffer starting at stack[0]) - if (me->stack[0]) { - free(me->stack[0]); - } - - *context = NULL; -} - -// run jobs -AEEResult worker_pool_run_jobs(worker_pool_context_t context, worker_pool_job_t * job, unsigned int n) { - worker_pool_t * me = (worker_pool_t *) context; - if (NULL == me) { - FARF(ERROR, "worker-pool: invalid context"); - return AEE_EBADPARM; - } - - if (n > me->n_threads) { - FARF(ERROR, "worker-pool: invalid number of jobs %u for n-threads %u", n, me->n_threads); - return AEE_EBADPARM; - } - - memcpy(me->job, job, sizeof(worker_pool_job_t) * n); - - if (n > 1) { - atomic_store(&me->next_job, 1); - atomic_store(&me->n_jobs, n); - atomic_store(&me->n_pending, n - 1); - - // wake up workers - atomic_fetch_add(&me->seqn, 1); - qurt_futex_wake(&me->seqn, n - 1); - } - - // main thread runs job #0 - me->job[0].func(n, 0, me->job[0].data); - - if (n > 1) { - while (atomic_load(&me->n_pending)) - ; - } - - return 0; -} - -// run func -AEEResult worker_pool_run_func(worker_pool_context_t context, worker_callback_t func, void * data, unsigned int n) { - worker_pool_job_t job[n]; - - for (unsigned int i = 0; i < n; i++) { - job[i].func = func; - job[i].data = data; - } - - return worker_pool_run_jobs(context, job, n); -} - -AEEResult worker_pool_set_thread_priority(worker_pool_context_t context, unsigned int prio) { - worker_pool_t * me = (worker_pool_t *) context; - - // if no worker pool exists, return error. - if (!me) { - return AEE_ENOMORE; - } - - int result = AEE_SUCCESS; - if (prio < 1) { - prio = 1; - } - if (prio > LOWEST_USABLE_QURT_PRIO) { - prio = LOWEST_USABLE_QURT_PRIO; - } - - for (unsigned int i = 0; i < me->n_threads; i++) { - int res = qurt_thread_set_priority(me->thread[i], (unsigned short) prio); - if (0 != res) { - result = AEE_EBADPARM; - FARF(ERROR, "QURT failed to set priority of thread %d, ERROR = %d", me->thread[i], res); - } - } - - return result; -} - -AEEResult worker_pool_retrieve_thread_id(worker_pool_context_t context, unsigned int * tids) { - worker_pool_t * me = (worker_pool_t *) context; - if (!me) { - FARF(ERROR, "worker-pool: invalid context"); - return AEE_EBADPARM; - ; - } - - for (int i = 0; i < me->n_threads; i++) { - tids[i] = me->thread[i]; - } - - return AEE_SUCCESS; -} - -AEEResult worker_pool_get_thread_priority(worker_pool_context_t context, unsigned int * prio) { - worker_pool_t * me = (worker_pool_t *) context; - if (!me) { - FARF(ERROR, "worker-pool: invalid context"); - return AEE_EBADPARM; - } - - int priority = qurt_thread_get_priority(me->thread[0]); - if (priority > 0) { - *prio = priority; - return 0; - } else { - *prio = 0; - return AEE_EBADSTATE; - } -} diff --git a/ggml/src/ggml-hexagon/htp/worker-pool.h b/ggml/src/ggml-hexagon/htp/worker-pool.h deleted file mode 100644 index cba692126ad0..000000000000 --- a/ggml/src/ggml-hexagon/htp/worker-pool.h +++ /dev/null @@ -1,65 +0,0 @@ -#ifndef HTP_WORKER_POOL_H -#define HTP_WORKER_POOL_H - -// MACRO enables function to be visible in shared-library case. -#define WORKERPOOL_API __attribute__((visibility("default"))) - -#include -#include -#include - -#ifdef __cplusplus -extern "C" { -#endif - -/// signature of callbacks to be invoked by worker threads -typedef void (*worker_callback_t)(unsigned int n, unsigned int i, void *); - -/// Typedef of worker_pool context -typedef void * worker_pool_context_t; - -/// descriptor for requested callback -typedef struct { - worker_callback_t func; - void * data; -} worker_pool_job_t; - -#define WORKER_THREAD_STACK_SZ (2 * 16384) - -/// Maximum supported number of worker threads. -#define MAX_NUM_WORKERS 10 - -#if __HVX_ARCH__ > 79 -#define WORKER_POOL_POLL_COUNT 2000 -#else -#define WORKER_POOL_POLL_COUNT 1 -#endif - -// Initialize worker pool. -WORKERPOOL_API AEEResult worker_pool_init(worker_pool_context_t * context, uint32_t n_threads); - -// Initialize worker pool with custom stack size -WORKERPOOL_API AEEResult worker_pool_init_with_stack_size(worker_pool_context_t * context, - uint32_t n_threads, - uint32_t stack_size); - -// Kill worker threads and release worker pool resources -WORKERPOOL_API void worker_pool_release(worker_pool_context_t * context); - -// Run jobs with the worker pool. -WORKERPOOL_API AEEResult worker_pool_run_jobs(worker_pool_context_t context, worker_pool_job_t * job, unsigned int n); - -WORKERPOOL_API AEEResult worker_pool_run_func(worker_pool_context_t context, - worker_callback_t func, - void * data, - unsigned int n); - -WORKERPOOL_API AEEResult worker_pool_set_thread_priority(worker_pool_context_t context, unsigned int prio); -WORKERPOOL_API AEEResult worker_pool_get_thread_priority(worker_pool_context_t context, unsigned int * prio); -WORKERPOOL_API AEEResult worker_pool_retrieve_thread_id(worker_pool_context_t context, unsigned int * tids); - -#ifdef __cplusplus -} -#endif - -#endif // #ifndef HTP_WORKER_POOL_H diff --git a/ggml/src/ggml-hip/CMakeLists.txt b/ggml/src/ggml-hip/CMakeLists.txt index 57274b7f441c..bb632bcdbfd0 100644 --- a/ggml/src/ggml-hip/CMakeLists.txt +++ b/ggml/src/ggml-hip/CMakeLists.txt @@ -137,10 +137,6 @@ if (GGML_HIP_NO_VMM) add_compile_definitions(GGML_HIP_NO_VMM) endif() -if (GGML_HIP_ROCWMMA_FATTN) - add_compile_definitions(GGML_HIP_ROCWMMA_FATTN) -endif() - if (NOT GGML_HIP_MMQ_MFMA) add_compile_definitions(GGML_HIP_NO_MMQ_MFMA) endif() @@ -181,5 +177,3 @@ if (GGML_HIP_RCCL) endif() target_link_libraries(ggml-hip PRIVATE ggml-base hip::host roc::rocblas roc::hipblas) - -target_compile_options(ggml-hip PRIVATE "$<$:-ffast-math;-fno-finite-math-only>") diff --git a/ggml/src/ggml-metal/ggml-metal-device.cpp b/ggml/src/ggml-metal/ggml-metal-device.cpp index 24959f23e797..3457bafdb9f0 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.cpp +++ b/ggml/src/ggml-metal/ggml-metal-device.cpp @@ -477,6 +477,41 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_soft_max(ggml_me return res; } +ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_lightning_indexer( + ggml_metal_library_t lib, + const ggml_tensor * op) { + GGML_ASSERT(op->op == GGML_OP_LIGHTNING_INDEXER); + + char name[256]; + + snprintf(name, 256, "kernel_lightning_indexer_%s", ggml_type_name(op->src[1]->type)); + + ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name); + if (!res.pipeline) { + res = ggml_metal_library_compile_pipeline(lib, name, name, nullptr); + } + + return res; +} + +ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_dsv4_hc(ggml_metal_library_t lib, ggml_op op) { + const char * name = nullptr; + + switch (op) { + case GGML_OP_DSV4_HC_COMB: name = "kernel_dsv4_hc_comb_f32"; break; + case GGML_OP_DSV4_HC_PRE: name = "kernel_dsv4_hc_pre_f32"; break; + case GGML_OP_DSV4_HC_POST: name = "kernel_dsv4_hc_post_f32"; break; + default: GGML_ABORT("fatal error"); + } + + ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name); + if (!res.pipeline) { + res = ggml_metal_library_compile_pipeline(lib, name, name, nullptr); + } + + return res; +} + ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_ssm_conv(ggml_metal_library_t lib, const ggml_tensor * op) { GGML_ASSERT(op->src[0]->type == GGML_TYPE_F32); GGML_ASSERT(op->src[1]->type == GGML_TYPE_F32); @@ -1386,6 +1421,21 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_argsort_merge(gg return res; } +ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_fwht(ggml_metal_library_t lib, int n) { + char base[256]; + char name[256]; + + snprintf(base, 256, "kernel_fwht_f32_%d", n); + snprintf(name, 256, "%s", base); + + ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name); + if (!res.pipeline) { + res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr); + } + + return res; +} + // note: reuse the argsort kernel for top_k ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_top_k(ggml_metal_library_t lib, const ggml_tensor * op) { assert(op->op == GGML_OP_TOP_K); @@ -1977,6 +2027,23 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_col2im_1d(ggml_m return res; } +ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_snake(ggml_metal_library_t lib, enum ggml_type type) { + GGML_ASSERT(type == GGML_TYPE_F32 || type == GGML_TYPE_F16 || type == GGML_TYPE_BF16); + + char base[256]; + char name[256]; + + snprintf(base, 256, "kernel_snake_%s", ggml_type_name(type)); + snprintf(name, 256, "%s", base); + + ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name); + if (!res.pipeline) { + res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr); + } + + return res; +} + ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_conv_transpose_2d(ggml_metal_library_t lib, const ggml_tensor * op) { assert(op->op == GGML_OP_CONV_TRANSPOSE_2D); @@ -2228,6 +2295,23 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_opt_step_sgd(ggm return res; } +ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_silu_back(ggml_metal_library_t lib, const ggml_tensor * op) { + assert(op->op == GGML_OP_SILU_BACK); + + char base[256]; + char name[256]; + + snprintf(base, 256, "kernel_silu_back_%s", ggml_type_name(op->src[0]->type)); + snprintf(name, 256, "%s", base); + + ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name); + if (!res.pipeline) { + res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr); + } + + return res; +} + ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_memset(ggml_metal_library_t lib, const ggml_tensor * op) { GGML_ASSERT(op->type == GGML_TYPE_I64); diff --git a/ggml/src/ggml-metal/ggml-metal-device.h b/ggml/src/ggml-metal/ggml-metal-device.h index 5b9391f288db..8e6cfa5595c4 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.h +++ b/ggml/src/ggml-metal/ggml-metal-device.h @@ -117,6 +117,7 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_diag struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_repeat (ggml_metal_library_t lib, enum ggml_type tsrc); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_concat (ggml_metal_library_t lib, enum ggml_type tsrc); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_unary (ggml_metal_library_t lib, const struct ggml_tensor * op); +struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_silu_back (ggml_metal_library_t lib, const struct ggml_tensor * op); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_glu (ggml_metal_library_t lib, const struct ggml_tensor * op); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_sum (ggml_metal_library_t lib, const struct ggml_tensor * op); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_sum_rows (ggml_metal_library_t lib, const struct ggml_tensor * op); @@ -124,6 +125,8 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_cumsum_bl struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_cumsum_add (ggml_metal_library_t lib, const struct ggml_tensor * op); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_tri (ggml_metal_library_t lib, const struct ggml_tensor * op); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_soft_max (ggml_metal_library_t lib, const struct ggml_tensor * op); +struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_lightning_indexer (ggml_metal_library_t lib, const struct ggml_tensor * op); +struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_dsv4_hc (ggml_metal_library_t lib, enum ggml_op op); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_ssm_conv (ggml_metal_library_t lib, const struct ggml_tensor * op); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_ssm_conv_batched (ggml_metal_library_t lib, const struct ggml_tensor * op, int ssm_conv_bs); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_ssm_scan (ggml_metal_library_t lib, const struct ggml_tensor * op); @@ -143,6 +146,7 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mv_id struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_argmax (ggml_metal_library_t lib, const struct ggml_tensor * op); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_argsort (ggml_metal_library_t lib, const struct ggml_tensor * op); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_argsort_merge (ggml_metal_library_t lib, const struct ggml_tensor * op); +struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_fwht (ggml_metal_library_t lib, int n); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_top_k (ggml_metal_library_t lib, const struct ggml_tensor * op); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_top_k_merge (ggml_metal_library_t lib, const struct ggml_tensor * op); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_bin (ggml_metal_library_t lib, const struct ggml_tensor * op, int32_t n_fuse ); @@ -155,6 +159,7 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_im2col struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_conv_transpose_1d (ggml_metal_library_t lib, const struct ggml_tensor * op); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_conv_transpose_2d (ggml_metal_library_t lib, const struct ggml_tensor * op); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_col2im_1d (ggml_metal_library_t lib, const struct ggml_tensor * op); +struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_snake (ggml_metal_library_t lib, enum ggml_type type); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_conv_2d (ggml_metal_library_t lib, const struct ggml_tensor * op); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_conv_2d_dw (ggml_metal_library_t lib, const struct ggml_tensor * op, bool tiled); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_conv_3d (ggml_metal_library_t lib, const struct ggml_tensor * op); @@ -215,7 +220,7 @@ typedef void * ggml_metal_rset_t; // a collection of residency sets (non-owning) typedef struct ggml_metal_rsets * ggml_metal_rsets_t; -ggml_metal_rsets_t ggml_metal_rsets_init(void); +ggml_metal_rsets_t ggml_metal_rsets_init(ggml_metal_device_t dev); void ggml_metal_rsets_free(ggml_metal_rsets_t rsets); // diff --git a/ggml/src/ggml-metal/ggml-metal-device.m b/ggml/src/ggml-metal/ggml-metal-device.m index e36e217eaa6c..40f575f9f085 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.m +++ b/ggml/src/ggml-metal/ggml-metal-device.m @@ -2,6 +2,7 @@ #import "ggml-impl.h" #import "ggml-backend-impl.h" +#import "ggml-metal-impl.h" #include @@ -594,7 +595,32 @@ void ggml_metal_encoder_end_encoding(ggml_metal_encoder_t encoder) { dispatch_group_t d_group; }; -ggml_metal_rsets_t ggml_metal_rsets_init(void) { +#if defined(GGML_METAL_HAS_RESIDENCY_SETS) +static void ggml_metal_dummy_work(ggml_metal_device_t dev) { + if (dev->mtl_queue == nil) { + return; + } + + @autoreleasepool { + // perform a minimal dummy operation on the GPU + id buf = [dev->mtl_device newBufferWithLength:1 options:MTLResourceStorageModePrivate]; + id cmd_buf = [dev->mtl_queue commandBuffer]; + + { + id encoder = [cmd_buf blitCommandEncoder]; + + [encoder fillBuffer:buf range:NSMakeRange(0, 1) value:0]; + + [encoder endEncoding]; + } + + [cmd_buf commit]; + [buf release]; + } +} +#endif + +ggml_metal_rsets_t ggml_metal_rsets_init(ggml_metal_device_t dev) { ggml_metal_rsets_t res = calloc(1, sizeof(struct ggml_metal_rsets)); res->lock = [[NSLock alloc] init]; @@ -647,6 +673,15 @@ ggml_metal_rsets_t ggml_metal_rsets_init(void) { #endif }); +#if defined(GGML_METAL_HAS_RESIDENCY_SETS) + if (@available(macOS 15.0, iOS 18.0, tvOS 18.0, visionOS 2.0, *)) { + // workaround for residency set memory not being released if no GPU operation occurs + // https://developer.apple.com/forums/thread/839089 + // https://github.com/ggml-org/llama.cpp/issues/25937 + ggml_metal_dummy_work(dev); + } +#endif + return res; } @@ -901,7 +936,7 @@ ggml_metal_device_t ggml_metal_device_init(int device) { } if (dev->props.use_residency_sets) { - dev->rsets = ggml_metal_rsets_init(); + dev->rsets = ggml_metal_rsets_init(dev); } else { dev->rsets = nil; } @@ -1140,6 +1175,14 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te default: return false; } + case GGML_OP_SILU_BACK: + return (op->src[0]->type == GGML_TYPE_F32) && + (op->src[1]->type == GGML_TYPE_F32) && + (op->type == GGML_TYPE_F32) && + ggml_is_contiguous(op->src[0]) && + ggml_is_contiguous(op->src[1]) && + ggml_is_contiguous(op) && + ggml_are_same_shape(op->src[0], op->src[1]); case GGML_OP_GLU: switch (ggml_get_glu_op(op)) { case GGML_GLU_OP_REGLU: @@ -1184,6 +1227,7 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te case GGML_OP_MUL: case GGML_OP_DIV: case GGML_OP_ADD_ID: + return ggml_is_contiguous_rows(op->src[0]) && ggml_is_contiguous_rows(op->src[1]) && (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) && (op->src[0]->type == op->src[1]->type); case GGML_OP_ACC: return ggml_is_contiguous_rows(op->src[0]) && ggml_is_contiguous_rows(op->src[1]) && op->src[0]->type == GGML_TYPE_F32; case GGML_OP_REPEAT: @@ -1272,8 +1316,9 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te (ggml_get_op_params_i32(op, 4) == 0) && (ggml_get_op_params_i32(op, 6) == 0); case GGML_OP_PAD_REFLECT_1D: case GGML_OP_TIMESTEP_EMBEDDING: - case GGML_OP_LEAKY_RELU: return op->src[0]->type == GGML_TYPE_F32; + case GGML_OP_LEAKY_RELU: + return op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16; case GGML_OP_ARGSORT: case GGML_OP_TOP_K: case GGML_OP_ARANGE: @@ -1337,6 +1382,72 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te return false; } return has_simdgroup_mm; // TODO: over-restricted for vec-kernels + case GGML_OP_LIGHTNING_INDEXER: + if (op->src[0]->ne[0] != OP_LIGHTNING_INDEXER_DK || + op->src[0]->ne[1] != OP_LIGHTNING_INDEXER_NH) { + return false; + } + if (!has_simdgroup_mm || + op->src[0]->type != GGML_TYPE_F32 || + op->src[2]->type != GGML_TYPE_F32 || + op->src[3]->type != GGML_TYPE_F16 || + op->type != GGML_TYPE_F32 || + !ggml_is_contiguous_rows(op->src[0]) || + !ggml_is_contiguous_rows(op->src[1]) || + !ggml_is_contiguous_rows(op->src[2]) || + !ggml_is_contiguous_rows(op->src[3])) { + return false; + } + switch (op->src[1]->type) { + case GGML_TYPE_F32: + case GGML_TYPE_F16: + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + return true; + case GGML_TYPE_BF16: + return has_bfloat; + default: + return false; + } + case GGML_OP_DSV4_HC_COMB: + return has_simdgroup_reduction && + op->src[0]->type == GGML_TYPE_F32 && + op->src[1]->type == GGML_TYPE_F32 && + op->src[2]->type == GGML_TYPE_F32 && + op->type == GGML_TYPE_F32 && + op->src[0]->ne[0] == 24 && + op->src[1]->ne[0] >= 3 && + op->src[2]->ne[0] == 24 && + ggml_is_contiguous_rows(op->src[0]) && + ggml_is_contiguous_rows(op->src[1]) && + ggml_is_contiguous_rows(op->src[2]); + case GGML_OP_DSV4_HC_PRE: + return has_simdgroup_reduction && + op->src[0]->type == GGML_TYPE_F32 && + op->src[1]->type == GGML_TYPE_F32 && + op->type == GGML_TYPE_F32 && + op->src[0]->ne[1] == 4 && + op->src[1]->ne[0] == 4 && + ggml_is_contiguous_rows(op->src[0]) && + ggml_is_contiguous_rows(op->src[1]); + case GGML_OP_DSV4_HC_POST: + return has_simdgroup_reduction && + op->src[0]->type == GGML_TYPE_F32 && + op->src[1]->type == GGML_TYPE_F32 && + op->src[2]->type == GGML_TYPE_F32 && + op->src[3]->type == GGML_TYPE_F32 && + op->type == GGML_TYPE_F32 && + op->src[1]->ne[1] == 4 && + op->src[2]->ne[0] == 4 && + op->src[3]->ne[0] == 4 && + op->src[3]->ne[1] == 4 && + ggml_is_contiguous_rows(op->src[0]) && + ggml_is_contiguous_rows(op->src[1]) && + ggml_is_contiguous_rows(op->src[2]) && + ggml_is_contiguous_rows(op->src[3]); case GGML_OP_SSM_CONV: case GGML_OP_SSM_SCAN: return has_simdgroup_reduction; @@ -1566,6 +1677,7 @@ static void ggml_metal_buffer_rset_free(ggml_metal_buffer_t buf) { if (buf->rset) { [buf->rset endResidency]; [buf->rset removeAllAllocations]; + [buf->rset commit]; [buf->rset release]; } } diff --git a/ggml/src/ggml-metal/ggml-metal-impl.h b/ggml/src/ggml-metal/ggml-metal-impl.h index ca9c0911eef1..90f902240457 100644 --- a/ggml/src/ggml-metal/ggml-metal-impl.h +++ b/ggml/src/ggml-metal/ggml-metal-impl.h @@ -121,6 +121,13 @@ #define OP_FLASH_ATTN_EXT_VEC_NQPSG 1 #define OP_FLASH_ATTN_EXT_VEC_NCPSG 32 +#define OP_LIGHTNING_INDEXER_DK 128 +#define OP_LIGHTNING_INDEXER_NH 64 +#define OP_LIGHTNING_INDEXER_NHPTG 8 +#define OP_LIGHTNING_INDEXER_NKPSG 8 +#define OP_LIGHTNING_INDEXER_NSG 8 +#define OP_LIGHTNING_INDEXER_NBPTG 8 + #define OP_UNARY_NUM_SCALE 10 #define OP_UNARY_NUM_FILL 11 #define OP_UNARY_NUM_CLAMP 12 @@ -659,6 +666,11 @@ typedef struct { int32_t p0; } ggml_metal_kargs_col2im_1d; +typedef struct { + int32_t T; + int32_t C; +} ggml_metal_kargs_snake; + typedef struct { int32_t IC; int32_t IH; @@ -1201,6 +1213,10 @@ typedef struct { int32_t len; } ggml_metal_kargs_argsort_merge; +typedef struct { + int32_t nrows; +} ggml_metal_kargs_fwht; + typedef struct { int64_t ne0; float start; @@ -1211,6 +1227,66 @@ typedef struct { int64_t val; } ggml_metal_kargs_memset; +typedef struct { + int32_t n_kv; + int32_t n_batch; + int32_t mask_ne3; + uint64_t nb1; + uint64_t nb3; + uint64_t nbq1; + uint64_t nbq2; + uint64_t nbq3; + uint64_t nbk2; + uint64_t nbk3; + uint64_t nbw1; + uint64_t nbw3; + uint64_t nbm1; + uint64_t nbm3; +} ggml_metal_kargs_lightning_indexer; + +typedef struct { + int32_t n_tokens; + int32_t n_iter; + uint64_t nb_m0; + uint64_t nb_m1; + uint64_t nb_s0; + uint64_t nb_b0; + uint64_t nb_d0; + uint64_t nb_d1; + uint64_t nb_d2; + float eps; +} ggml_metal_kargs_dsv4_hc_comb; + +typedef struct { + int32_t n_embd; + int32_t n_tokens; + uint64_t nb_x0; + uint64_t nb_x1; + uint64_t nb_x2; + uint64_t nb_w0; + uint64_t nb_w1; + uint64_t nb_d0; + uint64_t nb_d1; +} ggml_metal_kargs_dsv4_hc_pre; + +typedef struct { + int32_t n_embd; + int32_t n_tokens; + uint64_t nb_x0; + uint64_t nb_x1; + uint64_t nb_r0; + uint64_t nb_r1; + uint64_t nb_r2; + uint64_t nb_p0; + uint64_t nb_p1; + uint64_t nb_c0; + uint64_t nb_c1; + uint64_t nb_c2; + uint64_t nb_d0; + uint64_t nb_d1; + uint64_t nb_d2; +} ggml_metal_kargs_dsv4_hc_post; + typedef struct { int32_t ne00; int32_t ne01; @@ -1262,4 +1338,8 @@ typedef struct { int64_t np; } ggml_metal_kargs_opt_step_sgd; +typedef struct { + int64_t ne; +} ggml_metal_kargs_silu_back; + #endif // GGML_METAL_IMPL diff --git a/ggml/src/ggml-metal/ggml-metal-ops.cpp b/ggml/src/ggml-metal/ggml-metal-ops.cpp index f3990b6df10b..712fd0e51dca 100644 --- a/ggml/src/ggml-metal/ggml-metal-ops.cpp +++ b/ggml/src/ggml-metal/ggml-metal-ops.cpp @@ -328,6 +328,10 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) { { n_fuse = ggml_metal_op_unary(ctx, idx); } break; + case GGML_OP_SILU_BACK: + { + n_fuse = ggml_metal_op_silu_back(ctx, idx); + } break; case GGML_OP_GLU: { n_fuse = ggml_metal_op_glu(ctx, idx); @@ -345,6 +349,16 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) { { n_fuse = ggml_metal_op_cumsum(ctx, idx); } break; + case GGML_OP_LIGHTNING_INDEXER: + { + n_fuse = ggml_metal_op_lightning_indexer(ctx, idx); + } break; + case GGML_OP_DSV4_HC_COMB: + case GGML_OP_DSV4_HC_PRE: + case GGML_OP_DSV4_HC_POST: + { + n_fuse = ggml_metal_op_dsv4_hc(ctx, idx); + } break; case GGML_OP_SOFT_MAX: { n_fuse = ggml_metal_op_soft_max(ctx, idx); @@ -1330,6 +1344,203 @@ int ggml_metal_op_diag(ggml_metal_op_t ctx, int idx) { return 1; } +int ggml_metal_op_lightning_indexer(ggml_metal_op_t ctx, int idx) { + ggml_tensor * op = ctx->node(idx); + + ggml_metal_encoder_t enc = ctx->enc; + + GGML_ASSERT(op->op == GGML_OP_LIGHTNING_INDEXER); + + const ggml_tensor * q = op->src[0]; + const ggml_tensor * k = op->src[1]; + const ggml_tensor * w = op->src[2]; + const ggml_tensor * m = op->src[3]; + + GGML_ASSERT(q->type == GGML_TYPE_F32); + GGML_ASSERT(k->type == GGML_TYPE_F32 || + k->type == GGML_TYPE_F16 || + k->type == GGML_TYPE_BF16 || + k->type == GGML_TYPE_Q4_0 || + k->type == GGML_TYPE_Q4_1 || + k->type == GGML_TYPE_Q5_0 || + k->type == GGML_TYPE_Q5_1 || + k->type == GGML_TYPE_Q8_0); + GGML_ASSERT(w->type == GGML_TYPE_F32); + GGML_ASSERT(m->type == GGML_TYPE_F16); + GGML_ASSERT(op->type == GGML_TYPE_F32); + + GGML_ASSERT(q->ne[0] == OP_LIGHTNING_INDEXER_DK); + GGML_ASSERT(q->ne[1] == OP_LIGHTNING_INDEXER_NH); + + ggml_metal_kargs_lightning_indexer args = { + /*.n_kv =*/ (int32_t) k->ne[2], + /*.n_batch =*/ (int32_t) q->ne[2], + /*.mask_ne3 =*/ (int32_t) m->ne[3], + /*.nb1 =*/ op->nb[1], + /*.nb3 =*/ op->nb[3], + /*.nbq1 =*/ q->nb[1], + /*.nbq2 =*/ q->nb[2], + /*.nbq3 =*/ q->nb[3], + /*.nbk2 =*/ k->nb[2], + /*.nbk3 =*/ k->nb[3], + /*.nbw1 =*/ w->nb[1], + /*.nbw3 =*/ w->nb[3], + /*.nbm1 =*/ m->nb[1], + /*.nbm3 =*/ m->nb[3], + }; + + ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(q), 1); + ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(k), 2); + ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(w), 3); + ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(m), 4); + ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op), 5); + + const int nsg = OP_LIGHTNING_INDEXER_NSG; + const int nkptg = OP_LIGHTNING_INDEXER_NKPSG*nsg; + const int nbptg = OP_LIGHTNING_INDEXER_NBPTG; + + auto pipeline = ggml_metal_library_get_pipeline_lightning_indexer(ctx->lib, op); + ggml_metal_encoder_set_pipeline(enc, pipeline); + ggml_metal_encoder_set_bytes(enc, &args, sizeof(args), 0); + ggml_metal_encoder_dispatch_threadgroups(enc, + (k->ne[2] + nkptg - 1)/nkptg, + (q->ne[2] + nbptg - 1)/nbptg, + q->ne[3], 32, nsg, 1); + + return 1; +} + +int ggml_metal_op_dsv4_hc(ggml_metal_op_t ctx, int idx) { + ggml_tensor * op = ctx->node(idx); + + ggml_metal_encoder_t enc = ctx->enc; + auto pipeline = ggml_metal_library_get_pipeline_dsv4_hc(ctx->lib, op->op); + + ggml_metal_encoder_set_pipeline(enc, pipeline); + + switch (op->op) { + case GGML_OP_DSV4_HC_COMB: + { + const ggml_tensor * mixes = op->src[0]; + const ggml_tensor * scale = op->src[1]; + const ggml_tensor * base = op->src[2]; + + GGML_ASSERT(mixes->type == GGML_TYPE_F32); + GGML_ASSERT(scale->type == GGML_TYPE_F32); + GGML_ASSERT(base->type == GGML_TYPE_F32); + GGML_ASSERT(op->type == GGML_TYPE_F32); + GGML_ASSERT(mixes->ne[0] == 24); + GGML_ASSERT(op->ne[0] == 4 && op->ne[1] == 4); + + ggml_metal_kargs_dsv4_hc_comb args = { + /*.n_tokens =*/ (int32_t) mixes->ne[1], + /*.n_iter =*/ ggml_get_op_params_i32(op, 1), + /*.nb_m0 =*/ mixes->nb[0], + /*.nb_m1 =*/ mixes->nb[1], + /*.nb_s0 =*/ scale->nb[0], + /*.nb_b0 =*/ base->nb[0], + /*.nb_d0 =*/ op->nb[0], + /*.nb_d1 =*/ op->nb[1], + /*.nb_d2 =*/ op->nb[2], + /*.eps =*/ ggml_get_op_params_f32(op, 0), + }; + + ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), 0); + ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(mixes), 1); + ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(scale), 2); + ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(base), 3); + ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op), 4); + + // One SIMDgroup owns one 4x4 Sinkhorn matrix. Packing up to four + // independent tokens per threadgroup keeps both decode and prompt + // dispatches compact without any threadgroup-memory synchronization. + const int nsg = std::min(4, args.n_tokens); + ggml_metal_encoder_dispatch_threadgroups( + enc, (args.n_tokens + nsg - 1)/nsg, 1, 1, 32, nsg, 1); + } break; + case GGML_OP_DSV4_HC_PRE: + { + const ggml_tensor * x = op->src[0]; + const ggml_tensor * weights = op->src[1]; + + GGML_ASSERT(x->type == GGML_TYPE_F32); + GGML_ASSERT(weights->type == GGML_TYPE_F32); + GGML_ASSERT(op->type == GGML_TYPE_F32); + GGML_ASSERT(x->ne[1] == 4); + + ggml_metal_kargs_dsv4_hc_pre args = { + /*.n_embd =*/ (int32_t) x->ne[0], + /*.n_tokens =*/ (int32_t) x->ne[2], + /*.nb_x0 =*/ x->nb[0], + /*.nb_x1 =*/ x->nb[1], + /*.nb_x2 =*/ x->nb[2], + /*.nb_w0 =*/ weights->nb[0], + /*.nb_w1 =*/ weights->nb[1], + /*.nb_d0 =*/ op->nb[0], + /*.nb_d1 =*/ op->nb[1], + }; + + ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), 0); + ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(x), 1); + ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(weights), 2); + ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op), 3); + + const int n_tiles = (args.n_embd + 31)/32; + const int nsg = std::min(4, n_tiles); + ggml_metal_encoder_dispatch_threadgroups( + enc, (n_tiles + nsg - 1)/nsg, args.n_tokens, 1, 32, nsg, 1); + } break; + case GGML_OP_DSV4_HC_POST: + { + const ggml_tensor * x = op->src[0]; + const ggml_tensor * residual = op->src[1]; + const ggml_tensor * post = op->src[2]; + const ggml_tensor * comb = op->src[3]; + + GGML_ASSERT(x->type == GGML_TYPE_F32); + GGML_ASSERT(residual->type == GGML_TYPE_F32); + GGML_ASSERT(post->type == GGML_TYPE_F32); + GGML_ASSERT(comb->type == GGML_TYPE_F32); + GGML_ASSERT(op->type == GGML_TYPE_F32); + GGML_ASSERT(residual->ne[1] == 4); + + ggml_metal_kargs_dsv4_hc_post args = { + /*.n_embd =*/ (int32_t) x->ne[0], + /*.n_tokens =*/ (int32_t) x->ne[1], + /*.nb_x0 =*/ x->nb[0], + /*.nb_x1 =*/ x->nb[1], + /*.nb_r0 =*/ residual->nb[0], + /*.nb_r1 =*/ residual->nb[1], + /*.nb_r2 =*/ residual->nb[2], + /*.nb_p0 =*/ post->nb[0], + /*.nb_p1 =*/ post->nb[1], + /*.nb_c0 =*/ comb->nb[0], + /*.nb_c1 =*/ comb->nb[1], + /*.nb_c2 =*/ comb->nb[2], + /*.nb_d0 =*/ op->nb[0], + /*.nb_d1 =*/ op->nb[1], + /*.nb_d2 =*/ op->nb[2], + }; + + ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), 0); + ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(x), 1); + ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(residual), 2); + ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(post), 3); + ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(comb), 4); + ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op), 5); + + const int n_tiles = (args.n_embd + 31)/32; + const int nsg = std::min(4, n_tiles); + ggml_metal_encoder_dispatch_threadgroups( + enc, (n_tiles + nsg - 1)/nsg, args.n_tokens, 1, 32, nsg, 1); + } break; + default: + GGML_ABORT("fatal error"); + } + + return 1; +} + int ggml_metal_op_soft_max(ggml_metal_op_t ctx, int idx) { ggml_tensor * op = ctx->node(idx); @@ -2048,6 +2259,46 @@ int ggml_metal_op_pool_1d(ggml_metal_op_t ctx, int idx) { return 1; } +// supported FWHT sizes, must stay in sync with the +// kernel_fwht_f32_ templates in ggml-metal.metal +static bool ggml_metal_fwht_supported_size(int64_t n) { + return n == 64 || n == 128 || n == 256 || n == 512; +} + +int ggml_metal_op_fwht(ggml_metal_op_t ctx, int idx) { + ggml_tensor * op = ctx->node(idx); + + ggml_metal_library_t lib = ctx->lib; + ggml_metal_encoder_t enc = ctx->enc; + + ggml_tensor * src1 = op->src[1]; + + const int64_t n = src1->ne[0]; + const int64_t nrows = ggml_nrows(src1); + + ggml_metal_kargs_fwht args = { + /*.nrows = */ (int32_t) nrows, + }; + + auto pipeline = ggml_metal_library_get_pipeline_fwht(lib, n); + + ggml_metal_encoder_set_pipeline(enc, pipeline); + ggml_metal_encoder_set_bytes(enc, &args, sizeof(args), 0); + ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(src1), 1); + ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op), 2); + + const int th_max = ggml_metal_pipeline_max_theads_per_threadgroup(pipeline); + const int simd_size = 32; + + int sg_per_tg = 2; + sg_per_tg = std::min(sg_per_tg, th_max/simd_size); + sg_per_tg = std::max(sg_per_tg, 1); + + const int64_t n_tg = (nrows + sg_per_tg - 1) / sg_per_tg; + ggml_metal_encoder_dispatch_threadgroups(enc, n_tg, 1, 1, 32*sg_per_tg, 1, 1); + + return 1; +} int ggml_metal_op_pool_2d(ggml_metal_op_t ctx, int idx) { ggml_tensor * op = ctx->node(idx); @@ -2115,6 +2366,18 @@ int ggml_metal_op_mul_mat(ggml_metal_op_t ctx, int idx) { ggml_metal_library_t lib = ctx->lib; ggml_metal_encoder_t enc = ctx->enc; + const int32_t hint = ggml_get_op_params_i32(op, 1); + + if (hint == GGML_HINT_SRC0_IS_HADAMARD) { + if (op->src[1]->type == GGML_TYPE_F32 && + op->type == GGML_TYPE_F32 && + ggml_is_contiguous(op->src[1]) && + ggml_is_contiguous(op) && + ggml_are_same_shape(op->src[1], op) && + ggml_metal_fwht_supported_size(op->src[1]->ne[0])) { + return ggml_metal_op_fwht(ctx, idx); + } + } const ggml_metal_device_props * props_dev = ggml_metal_device_get_props(ctx->dev); GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne); @@ -3498,7 +3761,58 @@ int ggml_metal_op_flash_attn_ext(ggml_metal_op_t ctx, int idx) { return 1; } +// Snake activation autofuse: mul -> sin -> sqr -> mul -> add +static bool ggml_metal_op_can_fuse_snake(ggml_metal_op_t ctx, int idx) { + static constexpr ggml_op snake_ops[5] = { GGML_OP_MUL, GGML_OP_SIN, GGML_OP_SQR, GGML_OP_MUL, GGML_OP_ADD }; + + if (ctx->node(idx)->op != GGML_OP_MUL || !ctx->can_fuse(idx, snake_ops, 5)) { + return false; + } + + const ggml_tensor * mul0 = ctx->node(idx + 0); + const ggml_tensor * sin_node = ctx->node(idx + 1); + const ggml_tensor * sqr = ctx->node(idx + 2); + const ggml_tensor * mul1 = ctx->node(idx + 3); + const ggml_tensor * add = ctx->node(idx + 4); + + // x carries the full activation shape, a is the broadcast operand + const ggml_tensor * x = ggml_are_same_shape(mul0, mul0->src[0]) ? mul0->src[0] : mul0->src[1]; + const ggml_tensor * a = (x == mul0->src[0]) ? mul0->src[1] : mul0->src[0]; + + // mul1 reads sqr and inv_b in either operand order + const ggml_tensor * inv_b = (mul1->src[0] == sqr) ? mul1->src[1] : mul1->src[0]; + + // closure check: the trailing add reads the same x as the leading mul + const ggml_tensor * x_in_add = (add->src[0] == mul1) ? add->src[1] : add->src[0]; + + // x is in the supported whitelist and every chain intermediate shares x's type. + // a and inv_b bind as device const float * in the kernel, so they stay F32. + const bool types_ok = + (x->type == GGML_TYPE_F32 || x->type == GGML_TYPE_F16 || x->type == GGML_TYPE_BF16) && + (a->type == GGML_TYPE_F32) && (inv_b->type == GGML_TYPE_F32) && + (mul0->type == x->type) && (sin_node->type == x->type) && + (sqr->type == x->type) && (mul1->type == x->type) && + (add->type == x->type); + // a / inv_b collapse to [1, C, 1, 1], x and add stay 2D + const bool shape_ok = ggml_are_same_shape(a, inv_b) && a->ne[0] == 1 && a->ne[1] == x->ne[1]; + const bool dim_ok = + (x->ne[2] == 1) && (x->ne[3] == 1) && + (add->ne[2] == 1) && (add->ne[3] == 1) && + (a->ne[2] == 1) && (a->ne[3] == 1) && + (inv_b->ne[2] == 1) && (inv_b->ne[3] == 1); + // kernel reads x[idx] and a[c] / inv_b[c] linearly, so every operand is contiguous + const bool contig_ok = + ggml_is_contiguous(x) && ggml_is_contiguous(add) && + ggml_is_contiguous(a) && ggml_is_contiguous(inv_b); + + return types_ok && shape_ok && dim_ok && contig_ok && x_in_add == x; +} + int ggml_metal_op_bin(ggml_metal_op_t ctx, int idx) { + if (ctx->use_fusion && ggml_metal_op_can_fuse_snake(ctx, idx)) { + return ggml_metal_op_snake_fused(ctx, idx); + } + ggml_tensor * op = ctx->node(idx); ggml_metal_library_t lib = ctx->lib; @@ -3515,9 +3829,6 @@ int ggml_metal_op_bin(ggml_metal_op_t ctx, int idx) { GGML_TENSOR_LOCALS( int32_t, ne, op, ne); GGML_TENSOR_LOCALS(uint64_t, nb, op, nb); - GGML_ASSERT(op->src[0]->type == GGML_TYPE_F32); - GGML_ASSERT(op->src[1]->type == GGML_TYPE_F32); - GGML_ASSERT(ggml_is_contiguous_rows(op->src[0])); GGML_ASSERT(ggml_is_contiguous_rows(op->src[1])); @@ -3657,6 +3968,36 @@ int ggml_metal_op_bin(ggml_metal_op_t ctx, int idx) { return n_fuse; } +int ggml_metal_op_silu_back(ggml_metal_op_t ctx, int idx) { + ggml_tensor * op = ctx->node(idx); + + ggml_metal_library_t lib = ctx->lib; + ggml_metal_encoder_t enc = ctx->enc; + + auto pipeline = ggml_metal_library_get_pipeline_silu_back(lib, op); + + const int64_t ne = ggml_nelements(op); + + ggml_metal_kargs_silu_back args = { + /*.ne =*/ ne, + }; + + int arg_idx{0}; + + ggml_metal_encoder_set_pipeline(enc, pipeline); + ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), arg_idx++); + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[0]), arg_idx++); + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[1]), arg_idx++); + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op), arg_idx++); + + const int nth = std::min(ggml_metal_pipeline_max_theads_per_threadgroup(pipeline), ne); + const int64_t n = (ne + nth - 1) / nth; + + ggml_metal_encoder_dispatch_threadgroups(enc, n, 1, 1, nth, 1, 1); + + return 1; +} + int ggml_metal_op_l2_norm(ggml_metal_op_t ctx, int idx) { ggml_tensor * op = ctx->node(idx); @@ -4405,6 +4746,55 @@ int ggml_metal_op_col2im_1d(ggml_metal_op_t ctx, int idx) { return 1; } +// Dispatch the fused snake kernel from the matched mul -> sin -> sqr -> mul -> add chain. +// idx points at the leading mul. The caller has validated the chain. +int ggml_metal_op_snake_fused(ggml_metal_op_t ctx, int idx) { + ggml_metal_library_t lib = ctx->lib; + ggml_metal_encoder_t enc = ctx->enc; + + const ggml_tensor * mul0 = ctx->node(idx + 0); + const ggml_tensor * sqr = ctx->node(idx + 2); + const ggml_tensor * mul1 = ctx->node(idx + 3); + ggml_tensor * add = ctx->node(idx + 4); + + const ggml_tensor * x = ggml_are_same_shape(mul0, mul0->src[0]) ? mul0->src[0] : mul0->src[1]; + const ggml_tensor * a = (x == mul0->src[0]) ? mul0->src[1] : mul0->src[0]; + const ggml_tensor * inv_b = (mul1->src[0] == sqr) ? mul1->src[1] : mul1->src[0]; + + const int T = (int) x->ne[0]; + const int C = (int) x->ne[1]; + const int total = T * C; + + // the encode loop pre-checked the leading mul only, check the rest of the chain + for (int i = 1; i < 5; ++i) { + if (!ggml_metal_op_concurrency_check(ctx, ctx->node(idx + i))) { + ggml_metal_op_concurrency_reset(ctx); + + break; + } + } + + auto pipeline = ggml_metal_library_get_pipeline_snake(lib, x->type); + + ggml_metal_kargs_snake args = { + /*.T =*/ T, + /*.C =*/ C, + }; + + ggml_metal_encoder_set_pipeline(enc, pipeline); + ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), 0); + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(x), 1); + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(a), 2); + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(inv_b), 3); + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(add), 4); + + const int nth = 256; + const int ntg = (total + nth - 1) / nth; + ggml_metal_encoder_dispatch_threadgroups(enc, ntg, 1, 1, nth, 1, 1); + + return 5; +} + int ggml_metal_op_conv_transpose_2d(ggml_metal_op_t ctx, int idx) { ggml_tensor * op = ctx->node(idx); diff --git a/ggml/src/ggml-metal/ggml-metal-ops.h b/ggml/src/ggml-metal/ggml-metal-ops.h index 188521250a41..79de592cd29c 100644 --- a/ggml/src/ggml-metal/ggml-metal-ops.h +++ b/ggml/src/ggml-metal/ggml-metal-ops.h @@ -54,6 +54,8 @@ int ggml_metal_op_cumsum (ggml_metal_op_t ctx, int idx); int ggml_metal_op_get_rows (ggml_metal_op_t ctx, int idx); int ggml_metal_op_set_rows (ggml_metal_op_t ctx, int idx); int ggml_metal_op_diag (ggml_metal_op_t ctx, int idx); +int ggml_metal_op_lightning_indexer (ggml_metal_op_t ctx, int idx); +int ggml_metal_op_dsv4_hc (ggml_metal_op_t ctx, int idx); int ggml_metal_op_soft_max (ggml_metal_op_t ctx, int idx); int ggml_metal_op_ssm_conv (ggml_metal_op_t ctx, int idx); int ggml_metal_op_ssm_scan (ggml_metal_op_t ctx, int idx); @@ -65,11 +67,13 @@ int ggml_metal_op_set (ggml_metal_op_t ctx, int idx); int ggml_metal_op_cpy (ggml_metal_op_t ctx, int idx); int ggml_metal_op_pool_1d (ggml_metal_op_t ctx, int idx); int ggml_metal_op_pool_2d (ggml_metal_op_t ctx, int idx); +int ggml_metal_op_fwht (ggml_metal_op_t ctx, int idx); int ggml_metal_op_mul_mat (ggml_metal_op_t ctx, int idx); int ggml_metal_op_mul_mat_id (ggml_metal_op_t ctx, int idx); int ggml_metal_op_add_id (ggml_metal_op_t ctx, int idx); int ggml_metal_op_flash_attn_ext (ggml_metal_op_t ctx, int idx); int ggml_metal_op_bin (ggml_metal_op_t ctx, int idx); +int ggml_metal_op_silu_back (ggml_metal_op_t ctx, int idx); int ggml_metal_op_l2_norm (ggml_metal_op_t ctx, int idx); int ggml_metal_op_group_norm (ggml_metal_op_t ctx, int idx); int ggml_metal_op_norm (ggml_metal_op_t ctx, int idx); @@ -81,6 +85,7 @@ int ggml_metal_op_conv_3d (ggml_metal_op_t ctx, int idx); int ggml_metal_op_conv_transpose_1d (ggml_metal_op_t ctx, int idx); int ggml_metal_op_conv_transpose_2d (ggml_metal_op_t ctx, int idx); int ggml_metal_op_col2im_1d (ggml_metal_op_t ctx, int idx); +int ggml_metal_op_snake_fused (ggml_metal_op_t ctx, int idx); int ggml_metal_op_upscale (ggml_metal_op_t ctx, int idx); int ggml_metal_op_pad (ggml_metal_op_t ctx, int idx); int ggml_metal_op_pad_reflect_1d (ggml_metal_op_t ctx, int idx); diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal index b902b021b72f..d1b9d4484853 100644 --- a/ggml/src/ggml-metal/ggml-metal.metal +++ b/ggml/src/ggml-metal/ggml-metal.metal @@ -2047,6 +2047,20 @@ template [[host_name("kernel_unary_f32_f32_4")]] kernel kernel_unary_t kernel_un template [[host_name("kernel_unary_f16_f16")]] kernel kernel_unary_t kernel_unary_impl; template [[host_name("kernel_unary_f16_f16_4")]] kernel kernel_unary_t kernel_unary_impl; +kernel void kernel_silu_back_f32( + constant ggml_metal_kargs_silu_back & args, + device const float * dy, + device const float * x, + device float * dx, + uint gid [[thread_position_in_grid]]) { + if (gid >= args.ne) { + return; + } + + const float s = 1.0f / (1.0f + exp(-x[gid])); + dx[gid] = dy[gid] * s * (1.0f + x[gid] * (1.0f - s)); +} + // OP: 0 - add, 1 - sub, 2 - mul, 3 - div constant short FC_bin_op [[function_constant(FC_BIN + 0)]]; constant short FC_bin_f [[function_constant(FC_BIN + 1)]]; @@ -2210,6 +2224,8 @@ typedef decltype(kernel_bin_fuse_impl) kernel_bin_fuse_t; template [[host_name("kernel_bin_fuse_f32_f32_f32")]] kernel kernel_bin_fuse_t kernel_bin_fuse_impl; template [[host_name("kernel_bin_fuse_f32_f32_f32_4")]] kernel kernel_bin_fuse_t kernel_bin_fuse_impl; +template [[host_name("kernel_bin_fuse_f16_f16_f16")]] kernel kernel_bin_fuse_t kernel_bin_fuse_impl; +template [[host_name("kernel_bin_fuse_f16_f16_f16_4")]] kernel kernel_bin_fuse_t kernel_bin_fuse_impl; kernel void kernel_add_id( constant ggml_metal_kargs_add_id & args, @@ -6475,6 +6491,35 @@ template [[host_name("kernel_col2im_1d_bf16")]] kernel void kernel_col2im_1d +kernel void kernel_snake( + constant ggml_metal_kargs_snake & args, + device const T * x, + device const float * a, + device const float * inv_b, + device T * dst, + uint tgpig [[threadgroup_position_in_grid]], + uint tpitg [[thread_position_in_threadgroup]], + uint ntg [[threads_per_threadgroup]]) { + + const int idx = tgpig * ntg + tpitg; + if (idx >= args.T * args.C) { + return; + } + + const int c = idx / args.T; // x is [T, C], a / inv_b collapse to [1, C] + const float xi = float(x[idx]); + const float si = sin(a[c] * xi); + dst[idx] = T(xi + si * si * inv_b[c]); +} + +template [[host_name("kernel_snake_f32")]] kernel void kernel_snake(constant ggml_metal_kargs_snake &, device const float *, device const float *, device const float *, device float *, uint, uint, uint); +template [[host_name("kernel_snake_f16")]] kernel void kernel_snake(constant ggml_metal_kargs_snake &, device const half *, device const float *, device const float *, device half *, uint, uint, uint); +#if defined(GGML_METAL_HAS_BF16) +template [[host_name("kernel_snake_bf16")]] kernel void kernel_snake(constant ggml_metal_kargs_snake &, device const bfloat *, device const float *, device const float *, device bfloat *, uint, uint, uint); +#endif + + typedef void (conv_transpose_2d_t)( constant ggml_metal_kargs_conv_transpose_2d & args, device const float * src0, @@ -6802,7 +6847,7 @@ kernel void kernel_upscale_bicubic_f32( const float w_y2 = bicubic_weight1(1.0f - fd1); const float w_y3 = bicubic_weight2(2.0f - fd1); - const device const char * src_slice = src0 + i03 * args.nb03 + i02 * args.nb02; + const device char * src_slice = src0 + i03 * args.nb03 + i02 * args.nb02; device float * dst_ptr = (device float *)(dst + i3 * args.nb3 + i2 * args.nb2 + i1 * args.nb1); @@ -7212,6 +7257,68 @@ kernel void kernel_argsort_merge_f32_i32( template [[host_name("kernel_argsort_merge_f32_i32_asc")]] kernel argsort_merge_t kernel_argsort_merge_f32_i32; template [[host_name("kernel_argsort_merge_f32_i32_desc")]] kernel argsort_merge_t kernel_argsort_merge_f32_i32; +template +kernel void kernel_fwht_f32( + constant ggml_metal_kargs_fwht & args, + device const float * src, + device float * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort sgitg[[simdgroup_index_in_threadgroup]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort3 ntg[[threads_per_threadgroup]]) { + + constexpr int NW = N_SIMDWIDTH; + constexpr int NE = N / NW; + + const float scale = 1.0f / sqrt((float) N); + + const int sg_per_tg = ntg.x / NW; + const int64_t r = tgpig.x * sg_per_tg + sgitg; + if (r >= args.nrows) { + return; + } + + src += r * N; + dst += r * N; + + const int lane = tiisg; + + float reg[NE]; + for (int i = 0; i < NE; i++) { + reg[i] = src[i*NW + lane]*scale; + } + for (int i = 1; i < NW; i *= 2) { + for (int j = 0; j < NE; j++) { + const float val = reg[j]; + const float val2 = simd_shuffle_xor(val, i); + reg[j] = (lane & i) == 0 ? val2 + val : val2 - val; + } + } + + for (int i = NW; i < N; i *= 2) { + const int step = i / NW; + for (int j = 0; j < NE; j += (2 * step)) { + for (int k = 0; k < step; k++) { + const float x = reg[j + k ]; + const float y = reg[j + k + step]; + reg[j + k] = x + y; + reg[j + k + step] = x - y; + } + } + } + + for (int i = 0; i < NE; i++) { + dst[i*NW + lane] = reg[i]; + } +} + +typedef decltype(kernel_fwht_f32<64>) kernel_fwht_t; + +template [[host_name("kernel_fwht_f32_64")]] kernel kernel_fwht_t kernel_fwht_f32<64>; +template [[host_name("kernel_fwht_f32_128")]] kernel kernel_fwht_t kernel_fwht_f32<128>; +template [[host_name("kernel_fwht_f32_256")]] kernel kernel_fwht_t kernel_fwht_f32<256>; +template [[host_name("kernel_fwht_f32_512")]] kernel kernel_fwht_t kernel_fwht_f32<512>; + constant bool FC_flash_attn_ext_pad_has_mask [[function_constant(FC_FLASH_ATTN_EXT_PAD + 0)]]; constant int32_t FC_flash_attn_ext_pad_ncpsg [[function_constant(FC_FLASH_ATTN_EXT_PAD + 25)]]; @@ -13383,3 +13490,310 @@ kernel void kernel_count_equal( typedef decltype(kernel_count_equal) kernel_count_equal_t; template [[host_name("kernel_count_equal_i32")]] kernel kernel_count_equal_t kernel_count_equal; + +template< + typename kd4x4_t, + short nl_k, + void (*deq_k)(device const kd4x4_t *, short, thread half4x4 &)> +kernel void kernel_lightning_indexer( + constant ggml_metal_kargs_lightning_indexer & args, + device const char * q, + device const char * k, + device const char * w, + device const char * m, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiitg[[thread_index_in_threadgroup]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { + constexpr short DK = OP_LIGHTNING_INDEXER_DK; + constexpr short NH = OP_LIGHTNING_INDEXER_NH; + constexpr short NHPTG = OP_LIGHTNING_INDEXER_NHPTG; + constexpr short NKPSG = OP_LIGHTNING_INDEXER_NKPSG; + constexpr short NSG = OP_LIGHTNING_INDEXER_NSG; + constexpr short NBPTG = OP_LIGHTNING_INDEXER_NBPTG; + + constexpr short DK4 = DK/4; + constexpr short DK8 = DK/8; + constexpr short DK16 = DK/16; + + constexpr short NK = NKPSG*NSG; // keys per threadgroup + constexpr short NTG = 32*NSG; // threads per threadgroup + + const int i_stream = tgpig.z; + const int i_kv_0 = tgpig.x*NK; // first key of this threadgroup + const int i_kv = i_kv_0 + sgitg*NKPSG; // first key of this simdgroup + + threadgroup half4x4 sk4x4[NK*DK16]; + threadgroup half * sk = (threadgroup half *) sk4x4; + + for (short i = tiitg; i < NK*DK16; i += NTG) { + const short ik = i/DK16; + const short i16 = i%DK16; + + half4x4 tmp; + + if (i_kv_0 + ik < args.n_kv) { + device const kd4x4_t * kr = (device const kd4x4_t *) (k + (i_kv_0 + ik)*args.nbk2 + i_stream*args.nbk3); + + deq_k(kr + i16/nl_k, i16%nl_k, tmp); + } else { + FOR_UNROLL (short j = 0; j < 4; ++j) { + tmp[j] = half4(0.0h); + } + } + + sk4x4[i] = tmp; + } + + threadgroup_barrier(mem_flags::mem_threadgroup); + + // K tile of this simdgroup, transposed to [DK, NKPSG] + simdgroup_half8x8 mk[DK8]; + + FOR_UNROLL (short i = 0; i < DK8; ++i) { + simdgroup_load(mk[i], sk + sgitg*NKPSG*DK + 8*i, DK, 0, true); + } + + threadgroup half4 sq4[NHPTG*DK4]; + threadgroup half * sq = (threadgroup half *) sq4; + + threadgroup float sw [NHPTG]; + threadgroup float sqk[NSG*NHPTG*NKPSG]; + + const int i_batch_0 = tgpig.y*NBPTG; + const int n_batch = min((int) NBPTG, args.n_batch - i_batch_0); + + for (short ib = 0; ib < n_batch; ++ib) { + const int i_batch = i_batch_0 + ib; + + device const char * pq = q + i_batch*args.nbq2 + i_stream*args.nbq3; + device const char * pw = w + i_batch*args.nbw1 + i_stream*args.nbw3; + + float score = 0.0f; + + FOR_UNROLL (short i_head = 0; i_head < NH; i_head += NHPTG) { + // stage the Q tile [DK, NHPTG] and the (prescaled) head weights + for (short i = tiitg; i < NHPTG*DK4; i += NTG) { + const short ih = i/DK4; + const short i4 = i%DK4; + + device const float4 * q4 = (device const float4 *) (pq + (i_head + ih)*args.nbq1); + + sq4[ih*DK4 + i4] = half4(q4[i4]); + } + + if (tiitg < NHPTG) { + sw[tiitg] = ((device const float *) pw)[i_head + tiitg]; + } + + threadgroup_barrier(mem_flags::mem_threadgroup); + + simdgroup_float8x8 mqk = make_filled_simdgroup_matrix(0.0f); + + FOR_UNROLL (short i = 0; i < DK8; ++i) { + simdgroup_half8x8 mq; + + simdgroup_load(mq, sq + 8*i, DK, 0, false); + simdgroup_multiply_accumulate(mqk, mq, mk[i], mqk); + } + + threadgroup float * pqk = sqk + sgitg*NHPTG*NKPSG; + + simdgroup_store(mqk, pqk, NKPSG, 0, false); + simdgroup_barrier(mem_flags::mem_threadgroup); + + // one lane per key: ReLU, apply the head weight and accumulate over the head tile + if (tiisg < NKPSG) { + FOR_UNROLL (short ih = 0; ih < NHPTG; ++ih) { + score += max(pqk[ih*NKPSG + tiisg], 0.0f)*sw[ih]; + } + } + + threadgroup_barrier(mem_flags::mem_threadgroup); + } + + if (tiisg < NKPSG) { + const int ik = i_kv + tiisg; + if (ik < args.n_kv) { + device const half * pm = (device const half *) (m + i_batch*args.nbm1 + (i_stream % args.mask_ne3)*args.nbm3); + device float * pd = (device float *) (dst + i_batch*args.nb1 + i_stream*args.nb3); + + pd[ik] = score + (float) pm[ik]; + } + } + } +} + +typedef decltype(kernel_lightning_indexer) kernel_lightning_indexer_t; + +template [[host_name("kernel_lightning_indexer_f32")]] kernel kernel_lightning_indexer_t kernel_lightning_indexer; +template [[host_name("kernel_lightning_indexer_f16")]] kernel kernel_lightning_indexer_t kernel_lightning_indexer; + +#if defined(GGML_METAL_HAS_BF16) +template [[host_name("kernel_lightning_indexer_bf16")]] kernel kernel_lightning_indexer_t kernel_lightning_indexer; +#endif + +template [[host_name("kernel_lightning_indexer_q4_0")]] kernel kernel_lightning_indexer_t kernel_lightning_indexer; +template [[host_name("kernel_lightning_indexer_q4_1")]] kernel kernel_lightning_indexer_t kernel_lightning_indexer; +template [[host_name("kernel_lightning_indexer_q5_0")]] kernel kernel_lightning_indexer_t kernel_lightning_indexer; +template [[host_name("kernel_lightning_indexer_q5_1")]] kernel kernel_lightning_indexer_t kernel_lightning_indexer; +template [[host_name("kernel_lightning_indexer_q8_0")]] kernel kernel_lightning_indexer_t kernel_lightning_indexer; + +kernel void kernel_dsv4_hc_comb_f32( + constant ggml_metal_kargs_dsv4_hc_comb & args, + device const char * mixes, + device const char * scale, + device const char * base, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]], + ushort3 ntg[[threads_per_threadgroup]]) { + constexpr ushort hc = 4; + constexpr ushort comb_offset = 2*hc; + + const int it = tgpig.x*ntg.y + sgitg; + if (it >= args.n_tokens) { + return; + } + + float scale_lane = 0.0f; + if (tiisg == 0) { + scale_lane = *(device const float *) (scale + 2*args.nb_s0); + } + const float scale_comb = simd_shuffle(scale_lane, 0); + + float v = 0.0f; + if (tiisg < hc*hc) { + v = *(device const float *) (mixes + (comb_offset + tiisg)*args.nb_m0 + it*args.nb_m1)*scale_comb + + *(device const float *) (base + (comb_offset + tiisg)*args.nb_b0); + } + + // Softmax across destinations (the four contiguous lanes for each source). + float vmax = max(v, simd_shuffle_xor(v, 1)); + vmax = max(vmax, simd_shuffle_xor(vmax, 2)); + v = exp(v - vmax); + + float sum = v + simd_shuffle_xor(v, 1); + sum += simd_shuffle_xor(sum, 2); + v = v/sum + args.eps; + + // Normalize columns: equal destination indices are four lanes apart. + sum = v + simd_shuffle_xor(v, 4); + sum += simd_shuffle_xor(sum, 8); + v /= sum + args.eps; + + for (int i = 1; i < args.n_iter; ++i) { + sum = v + simd_shuffle_xor(v, 1); + sum += simd_shuffle_xor(sum, 2); + v /= sum + args.eps; + + sum = v + simd_shuffle_xor(v, 4); + sum += simd_shuffle_xor(sum, 8); + v /= sum + args.eps; + } + + if (tiisg < hc*hc) { + const ushort idst = tiisg & 3; + const ushort isrc = tiisg >> 2; + *(device float *) (dst + idst*args.nb_d0 + isrc*args.nb_d1 + it*args.nb_d2) = v; + } +} + +kernel void kernel_dsv4_hc_pre_f32( + constant ggml_metal_kargs_dsv4_hc_pre & args, + device const char * x, + device const char * weights, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]], + ushort3 ntg[[threads_per_threadgroup]]) { + constexpr ushort hc = 4; + + const int it = tgpig.y; + const int i0 = ((int) tgpig.x*ntg.y + sgitg)*32 + tiisg; + + float weight_lane = 0.0f; + if (tiisg < hc) { + weight_lane = *(device const float *) (weights + tiisg*args.nb_w0 + it*args.nb_w1); + } + + float w[hc]; + FOR_UNROLL (ushort ih = 0; ih < hc; ++ih) { + w[ih] = simd_shuffle(weight_lane, ih); + } + + if (i0 >= args.n_embd) { + return; + } + + device const char * xb = x + i0*args.nb_x0 + it*args.nb_x2; + float result = 0.0f; + FOR_UNROLL (ushort ih = 0; ih < hc; ++ih) { + result = fma(*(device const float *) (xb + ih*args.nb_x1), w[ih], result); + } + + *(device float *) (dst + i0*args.nb_d0 + it*args.nb_d1) = result; +} + +kernel void kernel_dsv4_hc_post_f32( + constant ggml_metal_kargs_dsv4_hc_post & args, + device const char * x, + device const char * residual, + device const char * post, + device const char * comb, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]], + ushort3 ntg[[threads_per_threadgroup]]) { + constexpr ushort hc = 4; + + const int it = tgpig.y; + const int i0 = ((int) tgpig.x*ntg.y + sgitg)*32 + tiisg; + + float coeff_lane = 0.0f; + if (tiisg < hc) { + coeff_lane = *(device const float *) (post + tiisg*args.nb_p0 + it*args.nb_p1); + } else if (tiisg < hc + hc*hc) { + const ushort idx = tiisg - hc; + const ushort idst = idx & 3; + const ushort isrc = idx >> 2; + coeff_lane = *(device const float *) (comb + idst*args.nb_c0 + isrc*args.nb_c1 + it*args.nb_c2); + } + + float post_reg[hc]; + float comb_reg[hc][hc]; + FOR_UNROLL (ushort idst = 0; idst < hc; ++idst) { + post_reg[idst] = simd_shuffle(coeff_lane, idst); + } + FOR_UNROLL (ushort isrc = 0; isrc < hc; ++isrc) { + FOR_UNROLL (ushort idst = 0; idst < hc; ++idst) { + comb_reg[isrc][idst] = simd_shuffle(coeff_lane, hc + idst + hc*isrc); + } + } + + if (i0 >= args.n_embd) { + return; + } + + const float xv = *(device const float *) (x + i0*args.nb_x0 + it*args.nb_x1); + float result[hc]; + FOR_UNROLL (ushort idst = 0; idst < hc; ++idst) { + result[idst] = xv*post_reg[idst]; + } + + device const char * rb = residual + i0*args.nb_r0 + it*args.nb_r2; + FOR_UNROLL (ushort isrc = 0; isrc < hc; ++isrc) { + const float rv = *(device const float *) (rb + isrc*args.nb_r1); + FOR_UNROLL (ushort idst = 0; idst < hc; ++idst) { + result[idst] = fma(rv, comb_reg[isrc][idst], result[idst]); + } + } + + FOR_UNROLL (ushort idst = 0; idst < hc; ++idst) { + *(device float *) (dst + i0*args.nb_d0 + idst*args.nb_d1 + it*args.nb_d2) = result[idst]; + } +} diff --git a/ggml/src/ggml-opencl/CMakeLists.txt b/ggml/src/ggml-opencl/CMakeLists.txt index ff3ad7e34d55..1dc707177106 100644 --- a/ggml/src/ggml-opencl/CMakeLists.txt +++ b/ggml/src/ggml-opencl/CMakeLists.txt @@ -5,6 +5,8 @@ set(TARGET_NAME ggml-opencl) ggml_add_backend_library(${TARGET_NAME} ggml-opencl.cpp + cl-program-cache.cpp + cl-program-cache.h ../../include/ggml-opencl.h) target_link_libraries(${TARGET_NAME} PRIVATE ${OpenCL_LIBRARIES}) target_include_directories(${TARGET_NAME} PRIVATE ${OpenCL_INCLUDE_DIRS}) @@ -211,6 +213,7 @@ set(GGML_OPENCL_KERNELS tanh exp expm1 + abs softplus pad repeat diff --git a/ggml/src/ggml-opencl/cl-program-cache.cpp b/ggml/src/ggml-opencl/cl-program-cache.cpp new file mode 100644 index 000000000000..1a4a281173b2 --- /dev/null +++ b/ggml/src/ggml-opencl/cl-program-cache.cpp @@ -0,0 +1,453 @@ +// Match the version setup ggml-opencl.cpp uses, so any cl.h declarations we +// touch are consistent across this backend's translation units. +#define CL_TARGET_OPENCL_VERSION GGML_OPENCL_TARGET_VERSION +#define CL_USE_DEPRECATED_OPENCL_1_2_APIS + +#include "cl-program-cache.h" + +#include "ggml-impl.h" // GGML_LOG_INFO / WARN + +#include +#include +#include +#include +#include +#include +#include +#include + +#if defined(_WIN32) +# ifndef WIN32_LEAN_AND_MEAN +# define WIN32_LEAN_AND_MEAN +# endif +# ifndef NOMINMAX +# define NOMINMAX +# endif +# include +# include +# define ggml_getpid() ((int) GetCurrentProcessId()) +#else +# include +# define ggml_getpid() ((int) getpid()) +#endif + +namespace fs = std::filesystem; + +// ---------------------------------------------------------------------------- +// SHA-256 (FIPS 180-4). Self-contained, ~80 lines, public-domain reference. +// Hot path is a few KB of source per kernel ⇒ <1 ms total per process init. +// ---------------------------------------------------------------------------- + +namespace { + +struct sha256_ctx { + uint32_t state[8]; + uint64_t bitlen; + uint8_t buf[64]; + size_t buf_len; +}; + +const uint32_t K256[64] = { + 0x428a2f98,0x71374491,0xb5c0fbcf,0xe9b5dba5,0x3956c25b,0x59f111f1,0x923f82a4,0xab1c5ed5, + 0xd807aa98,0x12835b01,0x243185be,0x550c7dc3,0x72be5d74,0x80deb1fe,0x9bdc06a7,0xc19bf174, + 0xe49b69c1,0xefbe4786,0x0fc19dc6,0x240ca1cc,0x2de92c6f,0x4a7484aa,0x5cb0a9dc,0x76f988da, + 0x983e5152,0xa831c66d,0xb00327c8,0xbf597fc7,0xc6e00bf3,0xd5a79147,0x06ca6351,0x14292967, + 0x27b70a85,0x2e1b2138,0x4d2c6dfc,0x53380d13,0x650a7354,0x766a0abb,0x81c2c92e,0x92722c85, + 0xa2bfe8a1,0xa81a664b,0xc24b8b70,0xc76c51a3,0xd192e819,0xd6990624,0xf40e3585,0x106aa070, + 0x19a4c116,0x1e376c08,0x2748774c,0x34b0bcb5,0x391c0cb3,0x4ed8aa4a,0x5b9cca4f,0x682e6ff3, + 0x748f82ee,0x78a5636f,0x84c87814,0x8cc70208,0x90befffa,0xa4506ceb,0xbef9a3f7,0xc67178f2, +}; + +inline uint32_t rotr32(uint32_t x, unsigned n) { return (x >> n) | (x << (32 - n)); } + +void sha256_compress(uint32_t state[8], const uint8_t block[64]) { + uint32_t w[64]; + for (int i = 0; i < 16; ++i) { + w[i] = ((uint32_t)block[i*4 ] << 24) | + ((uint32_t)block[i*4 + 1] << 16) | + ((uint32_t)block[i*4 + 2] << 8) | + ((uint32_t)block[i*4 + 3] ); + } + for (int i = 16; i < 64; ++i) { + uint32_t s0 = rotr32(w[i-15], 7) ^ rotr32(w[i-15], 18) ^ (w[i-15] >> 3); + uint32_t s1 = rotr32(w[i-2], 17) ^ rotr32(w[i-2], 19) ^ (w[i-2] >> 10); + w[i] = w[i-16] + s0 + w[i-7] + s1; + } + + uint32_t a = state[0],b = state[1],c = state[2],d = state[3],e = state[4],f = state[5],g = state[6],h = state[7]; + + for (int i = 0; i < 64; ++i) { + uint32_t S1 = rotr32(e, 6) ^ rotr32(e, 11) ^ rotr32(e, 25); + uint32_t ch = (e & f) ^ ((~e) & g); + uint32_t t1 = h + S1 + ch + K256[i] + w[i]; + uint32_t S0 = rotr32(a, 2) ^ rotr32(a, 13) ^ rotr32(a, 22); + uint32_t maj = (a & b) ^ (a & c) ^ (b & c); + uint32_t t2 = S0 + maj; + h = g; g = f; f = e; e = d + t1; + d = c; c = b; b = a; a = t1 + t2; + } + state[0]+=a; state[1]+=b; state[2]+=c; state[3]+=d; + state[4]+=e; state[5]+=f; state[6]+=g; state[7]+=h; +} + +void sha256_init(sha256_ctx & c) { + c.state[0]=0x6a09e667; c.state[1]=0xbb67ae85; c.state[2]=0x3c6ef372; c.state[3]=0xa54ff53a; + c.state[4]=0x510e527f; c.state[5]=0x9b05688c; c.state[6]=0x1f83d9ab; c.state[7]=0x5be0cd19; + c.bitlen = 0; + c.buf_len = 0; +} + +void sha256_update(sha256_ctx & c, const void * data, size_t len) { + const uint8_t * p = (const uint8_t *) data; + c.bitlen += (uint64_t) len * 8; + if (c.buf_len > 0) { + size_t n = 64 - c.buf_len; + if (n > len) { n = len; } + memcpy(c.buf + c.buf_len, p, n); + c.buf_len += n; + p += n; + len -= n; + if (c.buf_len == 64) { + sha256_compress(c.state, c.buf); + c.buf_len = 0; + } + } + while (len >= 64) { + sha256_compress(c.state, p); + p += 64; + len -= 64; + } + if (len > 0) { + memcpy(c.buf, p, len); + c.buf_len = len; + } +} + +void sha256_final(sha256_ctx & c, uint8_t out[32]) { + uint64_t bitlen = c.bitlen; + c.buf[c.buf_len++] = 0x80; + if (c.buf_len > 56) { + while (c.buf_len < 64) { c.buf[c.buf_len++] = 0; } + sha256_compress(c.state, c.buf); + c.buf_len = 0; + } + while (c.buf_len < 56) { c.buf[c.buf_len++] = 0; } + for (int i = 7; i >= 0; --i) { c.buf[c.buf_len++] = (uint8_t) (bitlen >> (i * 8)); } + sha256_compress(c.state, c.buf); + for (int i = 0; i < 8; ++i) { + out[i*4 ] = (uint8_t) (c.state[i] >> 24); + out[i*4 + 1] = (uint8_t) (c.state[i] >> 16); + out[i*4 + 2] = (uint8_t) (c.state[i] >> 8); + out[i*4 + 3] = (uint8_t) (c.state[i] ); + } +} + +std::string sha256_hex(const uint8_t digest[32]) { + static const char hex[] = "0123456789abcdef"; + std::string s(64, '0'); + for (int i = 0; i < 32; ++i) { + s[i*2 ] = hex[digest[i] >> 4]; + s[i*2 + 1] = hex[digest[i] & 0xf]; + } + return s; +} + +std::string compute_key(const std::string & key_suffix, + const char * source, + const std::string & compile_opts) { + sha256_ctx c; + sha256_init(c); + + static const uint8_t sep = 0; + sha256_update(c, source, strlen(source)); + sha256_update(c, &sep, 1); + sha256_update(c, compile_opts.data(), compile_opts.size()); + sha256_update(c, &sep, 1); + sha256_update(c, key_suffix.data(), key_suffix.size()); + + uint8_t digest[32]; + sha256_final(c, digest); + return sha256_hex(digest); +} + +bool make_dir_recursive(const std::string & path) { + if (path.empty()) { return false; } + // create_directories() already creates missing parents. It returns false + // (with ec clear) when the directory is already there, so re-check. + const fs::path p = fs::u8path(path); + std::error_code ec; + if (fs::create_directories(p, ec)) { return true; } + std::error_code ec_stat; + return fs::is_directory(p, ec_stat); +} + +std::string default_cache_dir() { +#if defined(_WIN32) + const char * base = std::getenv("LOCALAPPDATA"); + if (!base || !*base) { base = std::getenv("APPDATA"); } + if (!base || !*base) { base = std::getenv("TEMP"); } + if (!base || !*base) { base = "."; } + return std::string(base) + "\\llama.cpp\\cl-cache"; +#elif defined(__APPLE__) + const char * home = std::getenv("HOME"); + if (!home || !*home) { home = "."; } + return std::string(home) + "/Library/Caches/llama.cpp/cl-cache"; +#else + // The throwing overload aborts the process when no usable temp directory + // exists (e.g. Android app contexts with TMPDIR unset); an empty return + // here just disables the cache instead. + std::error_code ec; + const fs::path tmp_path = fs::temp_directory_path(ec); + if (ec || tmp_path.empty()) { return {}; } + return tmp_path.string() + "/llama.cpp/cl-cache"; +#endif +} + +// Query a NUL-terminated string from clGetDeviceInfo / clGetPlatformInfo. +template +std::string query_string(GetInfoFn fn, Object obj, cl_uint name) { + size_t sz = 0; + if (fn(obj, name, 0, nullptr, &sz) != CL_SUCCESS || sz == 0) { + return {}; + } + std::string s(sz, '\0'); + if (fn(obj, name, sz, &s[0], nullptr) != CL_SUCCESS) { + return {}; + } + if (!s.empty() && s.back() == '\0') { + s.pop_back(); + } + return s; +} + +std::string compute_key_suffix(cl_device_id device) { + cl_platform_id platform = nullptr; + clGetDeviceInfo(device, CL_DEVICE_PLATFORM, sizeof(platform), &platform, nullptr); + + std::string s; + s.reserve(512); + s += query_string(clGetDeviceInfo, device, CL_DEVICE_NAME); s.push_back('\0'); + s += query_string(clGetDeviceInfo, device, CL_DRIVER_VERSION); s.push_back('\0'); + s += query_string(clGetDeviceInfo, device, CL_DEVICE_VERSION); s.push_back('\0'); + if (platform) { + s += query_string(clGetPlatformInfo, platform, CL_PLATFORM_VERSION); s.push_back('\0'); + } + s += "fmt=" + std::to_string(CL_PROGRAM_CACHE_FORMAT_VERSION); + return s; +} + +const uint8_t MAGIC[8] = { 'G','G','M','L','C','L','B','C' }; + +bool read_all(const std::string & path, std::vector & out) { + std::ifstream f(fs::u8path(path), std::ios::binary); + if (!f) { return false; } + f.seekg(0, std::ios::end); + std::streamsize sz = f.tellg(); + if (sz < 0) { return false; } + f.seekg(0, std::ios::beg); + out.resize((size_t) sz); + if (sz > 0) { f.read((char *) out.data(), sz); } + return f.good() || f.eof(); +} + +bool write_atomic(const std::string & path, const uint8_t * data, size_t len) { + const fs::path dst = fs::u8path(path); + const fs::path tmp = fs::u8path(path + ".tmp." + std::to_string(ggml_getpid())); + { + std::ofstream f(tmp, std::ios::binary | std::ios::trunc); + if (!f) { return false; } + f.write((const char *) data, (std::streamsize) len); + if (!f.good()) { + std::error_code ec_rm; + fs::remove(tmp, ec_rm); + return false; + } + } + + std::error_code ec; + fs::rename(tmp, dst, ec); + if (ec) { + std::error_code ec_rm; + fs::remove(tmp, ec_rm); + return false; + } + return true; +} + +} // namespace + +static bool cache_debug_enabled() { + static int cached = -1; + if (cached < 0) { + const char * e = std::getenv("GGML_OPENCL_KERNEL_CACHE_DEBUG"); + cached = (e && *e) ? 1 : 0; + } + return cached != 0; +} + +static std::string opts_preview(const std::string & opts, size_t n = 120) { + if (opts.size() <= n) { return opts; } + return opts.substr(0, n) + "..."; +} + +// Running cache tally (diagnostic; plain ints — a benign race in the rare +// multi-threaded lazy-compile case at worst miscounts by one). +static int g_cache_hits = 0, g_cache_misses = 0, g_cache_saves = 0; + +// Debug trace directly to stderr +static void cache_debug_line(const char * kind, const std::string & key, + const char * source, const std::string & opts) { + if (!cache_debug_enabled()) { return; } + fprintf(stderr, "ggml_opencl: cache %-4s [h=%d m=%d s=%d] key=%s src=%zuB opts='%s'\n", + kind, g_cache_hits, g_cache_misses, g_cache_saves, + key.substr(0, 16).c_str(), strlen(source), opts_preview(opts).c_str()); + fflush(stderr); +} + +cl_program_cache_state cl_program_cache_init(cl_device_id device) { + cl_program_cache_state st; + + const char * env = std::getenv("GGML_OPENCL_KERNEL_CACHE_DIR"); + if (env && (!std::strcmp(env, "0") || !std::strcmp(env, "off") || + !std::strcmp(env, "none") || !std::strcmp(env, "disable") || + !std::strcmp(env, "disabled"))) { + if (cache_debug_enabled()) { + fprintf(stderr, "ggml_opencl: kernel cache disabled by GGML_OPENCL_KERNEL_CACHE_DIR=%s\n", env); + fflush(stderr); + } + return st; + } + + std::string dir; + if (!env || !*env || !std::strcmp(env, "1") || !std::strcmp(env, "default")) { + dir = default_cache_dir(); + if (dir.empty()) { + GGML_LOG_INFO("ggml_opencl: kernel cache disabled (no usable default cache directory)\n"); + return st; + } + } else { + dir = env; + } + + if (!make_dir_recursive(dir)) { + GGML_LOG_INFO("ggml_opencl: kernel cache disabled (cannot create directory '%s')\n", dir.c_str()); + return st; + } + + st.dir = dir; + st.key_suffix = compute_key_suffix(device); + GGML_LOG_INFO("ggml_opencl: kernel cache enabled at '%s'\n", st.dir.c_str()); + if (cache_debug_enabled()) { + fprintf(stderr, "ggml_opencl: kernel cache enabled at '%s' " + "(GGML_OPENCL_KERNEL_CACHE_DIR=off to disable)\n", st.dir.c_str()); + fflush(stderr); + } + return st; +} + +cl_program cl_program_cache_try_load( + const cl_program_cache_state & state, + cl_context context, + cl_device_id device, + const char * source, + const std::string & compile_opts) { + + if (state.dir.empty() || !source) { return nullptr; } + + const std::string key = compute_key(state.key_suffix, source, compile_opts); + const std::string path = state.dir + "/" + key + ".clbin"; + + std::vector file; + if (!read_all(path, file)) { + ++g_cache_misses; + cache_debug_line("MISS", key, source, compile_opts); + return nullptr; + } + if (file.size() < 16 || std::memcmp(file.data(), MAGIC, 8) != 0) { return nullptr; } + + uint32_t fmt = + ((uint32_t) file[ 8]) | ((uint32_t) file[ 9] << 8) | + ((uint32_t) file[10] << 16) | ((uint32_t) file[11] << 24); + if (fmt != CL_PROGRAM_CACHE_FORMAT_VERSION) { return nullptr; } + + const size_t hdr_len = 16; + const unsigned char * bin = file.data() + hdr_len; + const size_t bin_len = file.size() - hdr_len; + + cl_int err = CL_SUCCESS; + cl_int bin_err = CL_SUCCESS; + cl_program p = clCreateProgramWithBinary(context, 1, &device, &bin_len, &bin, &bin_err, &err); + if (err != CL_SUCCESS || bin_err != CL_SUCCESS || p == nullptr) { + if (p) { clReleaseProgram(p); } + return nullptr; + } + + err = clBuildProgram(p, 0, nullptr, compile_opts.c_str(), nullptr, nullptr); + if (err != CL_SUCCESS) { + clReleaseProgram(p); + return nullptr; + } + ++g_cache_hits; + cache_debug_line("HIT", key, source, compile_opts); + return p; +} + +void cl_program_cache_try_save( + const cl_program_cache_state & state, + cl_program program, + cl_device_id /*device*/, + const char * source, + const std::string & compile_opts) { + + if (state.dir.empty() || !program || !source) { + return; + } + + cl_uint n_dev = 0; + if (clGetProgramInfo(program, CL_PROGRAM_NUM_DEVICES, sizeof(n_dev), &n_dev, nullptr) != CL_SUCCESS || n_dev == 0) { + return; + } + + std::vector sizes(n_dev); + if (clGetProgramInfo(program, CL_PROGRAM_BINARY_SIZES, sizeof(size_t) * n_dev, sizes.data(), nullptr) != CL_SUCCESS) { + return; + } + if (sizes.empty() || sizes[0] == 0) { + return; + } + + std::vector> binaries(n_dev); + std::vector bin_ptrs(n_dev); + for (cl_uint i = 0; i < n_dev; ++i) { + binaries[i].resize(sizes[i]); + bin_ptrs[i] = binaries[i].data(); + } + if (clGetProgramInfo(program, CL_PROGRAM_BINARIES, sizeof(unsigned char *) * n_dev, bin_ptrs.data(), nullptr) != CL_SUCCESS) { + return; + } + + // We only care about the first device's binary — that's the one we'd + // re-load with on a future cache hit. Multi-device contexts aren't a + // pattern this backend uses today. + const std::vector & bin = binaries[0]; + + std::vector file; + file.reserve(16 + bin.size()); + file.insert(file.end(), MAGIC, MAGIC + 8); + uint32_t fmt = CL_PROGRAM_CACHE_FORMAT_VERSION; + file.push_back((uint8_t) (fmt & 0xff)); + file.push_back((uint8_t) ((fmt >> 8) & 0xff)); + file.push_back((uint8_t) ((fmt >> 16) & 0xff)); + file.push_back((uint8_t) ((fmt >> 24) & 0xff)); + file.push_back(0); file.push_back(0); file.push_back(0); file.push_back(0); // reserved + file.insert(file.end(), bin.begin(), bin.end()); + + const std::string key = compute_key(state.key_suffix, source, compile_opts); + const std::string path = state.dir + "/" + key + ".clbin"; + if (!write_atomic(path, file.data(), file.size())) { + GGML_LOG_INFO("ggml_opencl: kernel cache: failed to write '%s'\n", path.c_str()); + } else { + ++g_cache_saves; + cache_debug_line("SAVE", key, source, compile_opts); + } +} diff --git a/ggml/src/ggml-opencl/cl-program-cache.h b/ggml/src/ggml-opencl/cl-program-cache.h new file mode 100644 index 000000000000..49aa2d1e82f5 --- /dev/null +++ b/ggml/src/ggml-opencl/cl-program-cache.h @@ -0,0 +1,75 @@ +// On-disk cache for OpenCL cl_program binaries. Lets a fresh process skip the +// expensive clBuildProgram-from-source step when a binary for the exact same +// (source, compile options, device, driver, platform) was previously saved. +// +// Activation: default on via GGML_OPENCL_KERNEL_CACHE_DIR: +// unset / empty / "1" / "default" : platform default cache dir +// (%LOCALAPPDATA%\llama.cpp\cl-cache, +// ~/Library/Caches/llama.cpp/cl-cache, +// /llama.cpp/cl-cache elsewhere) +// "0" / "off" / "none" / "disable(d)" : disabled (all functions no-op) +// any other value : used verbatim as the cache path +// If the chosen directory cannot be created/used, the cache silently disables +// itself for the process and falls back to source compile. +// GGML_OPENCL_KERNEL_CACHE_DEBUG=1 prints a HIT/MISS/SAVE trace (with a running +// tally) straight to stderr — visible even in tools that filter INFO/WARN logs; +// redirect stderr to record it. +// +// Cache key (SHA-256 hex): +// sha256(source_bytes || '\x00' || +// compile_opts || '\x00' || +// CL_DEVICE_NAME || '\x00' || +// CL_DRIVER_VERSION || '\x00' || +// CL_PLATFORM_VERSION || '\x00' || +// CL_PROGRAM_CACHE_FORMAT_VERSION) +// +// The key fully captures everything that can affect the produced binary, +// without needing the host source revision (a kernel source change shows up +// in source_bytes; a compile-option change shows up in compile_opts). +// +// File layout per cache entry: /.clbin +// bytes [0..7] : magic "GGMLCLBC" +// bytes [8..11] : uint32_t format version (CL_PROGRAM_CACHE_FORMAT_VERSION) +// bytes [12..15] : uint32_t reserved (0) +// bytes [16..] : raw cl_program binary as returned by +// clGetProgramInfo(CL_PROGRAM_BINARIES) +// +// Concurrency: writes go to .tmp. then atomic rename. On race, +// last-writer-wins. No locks. + +#pragma once + +#include +#include + +// Bumped manually if host-side OpenCL API usage changes in a way that +// affects compile semantics but does not show up in source_bytes / +// compile_opts (e.g. switching from clCreateProgramWithSource to +// clCompileProgram + clLinkProgram, or changing how multiple sources +// are concatenated). Most commits — including kernel changes — do NOT +// require bumping this; the source bytes already capture those. +#define CL_PROGRAM_CACHE_FORMAT_VERSION 1u + +struct cl_program_cache_state { + // Empty string means cache is disabled. + std::string dir; + // Concatenated device/driver/platform identity + cache format version, + // computed once at init and folded into every key. + std::string key_suffix; +}; + +cl_program_cache_state cl_program_cache_init(cl_device_id device); + +cl_program cl_program_cache_try_load( + const cl_program_cache_state & state, + cl_context context, + cl_device_id device, + const char * source, + const std::string & compile_opts); + +void cl_program_cache_try_save( + const cl_program_cache_state & state, + cl_program program, + cl_device_id device, + const char * source, + const std::string & compile_opts); diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp index b14ea8133b87..fc0fce0d780a 100644 --- a/ggml/src/ggml-opencl/ggml-opencl.cpp +++ b/ggml/src/ggml-opencl/ggml-opencl.cpp @@ -13,6 +13,8 @@ #include "ggml-backend-impl.h" #include "ggml.h" +#include "cl-program-cache.h" + #ifdef GGML_OPENCL_USE_ADRENO_BIN_KERNELS #include "libdl.h" #ifdef _WIN32 @@ -114,6 +116,7 @@ enum GPU_FAMILY { enum ADRENO_GPU_GEN { ADRENO_UNKNOWN, + A6X, A7X, A8X, X1E, @@ -122,6 +125,7 @@ enum ADRENO_GPU_GEN { enum ADRENO_CL_COMPILER_TYPE { E031, + E17, DX, }; @@ -243,14 +247,29 @@ static ggml_cl_version get_opencl_c_version(ggml_cl_version platform_version, cl } static ADRENO_GPU_GEN get_adreno_gpu_gen(const char *device_name) { + if (strstr(device_name, "610") || strstr(device_name, "612") || + strstr(device_name, "613") || strstr(device_name, "615") || + strstr(device_name, "616") || strstr(device_name, "618") || + strstr(device_name, "619") || strstr(device_name, "620") || + strstr(device_name, "630") || strstr(device_name, "640") || + strstr(device_name, "642") || strstr(device_name, "643") || + strstr(device_name, "644") || strstr(device_name, "650") || + strstr(device_name, "660") || strstr(device_name, "663") || + strstr(device_name, "680") || strstr(device_name, "685") || + strstr(device_name, "690")) { + return ADRENO_GPU_GEN::A6X; + } + if (strstr(device_name, "730") || strstr(device_name, "740") || strstr(device_name, "750")) { return ADRENO_GPU_GEN::A7X; } - if (strstr(device_name, "830") || - strstr(device_name, "840")) { + if (strstr(device_name, "810") || + strstr(device_name, "830") || + strstr(device_name, "840") || + strstr(device_name, "850")) { return ADRENO_GPU_GEN::A8X; } @@ -274,6 +293,17 @@ static ggml_cl_compiler_version get_adreno_cl_compiler_version(const char *drive size_t compiler_minor_offset = 8; size_t compiler_patch_offset = 11; + if (compiler_ver_pos == std::string::npos) { + compiler_ver_pos = driver_ver_str.find("E17"); + if (compiler_ver_pos != std::string::npos) { + type = ADRENO_CL_COMPILER_TYPE::E17; + compiler_ver_len = 12; + compiler_major_offset = 4; + compiler_minor_offset = 7; + compiler_patch_offset = 10; + } + } + if (compiler_ver_pos == std::string::npos) { compiler_ver_pos = driver_ver_str.find("DX"); if (compiler_ver_pos == std::string::npos) { @@ -282,6 +312,8 @@ static ggml_cl_compiler_version get_adreno_cl_compiler_version(const char *drive type = ADRENO_CL_COMPILER_TYPE::DX; compiler_ver_len = 11; compiler_major_offset = 3; + compiler_minor_offset = 6; + compiler_patch_offset = 9; } std::string compiler_ver_str = driver_ver_str.substr(compiler_ver_pos, compiler_ver_len); @@ -564,6 +596,10 @@ struct ggml_backend_opencl_context { cl_context context; cl_command_queue queue; + // On-disk compiled-program cache (see GGML_OPENCL_KERNEL_CACHE_DIR). + cl_program_cache_state program_cache; + bool program_cache_initialized = false; + // prealloc buffers for transposing weights and activations ggml_cl_buffer prealloc_quant_trans; ggml_cl_buffer prealloc_scales_trans; @@ -815,6 +851,8 @@ struct ggml_backend_opencl_context { cl_kernel kernel_exp_f16, kernel_exp_f16_4, kernel_exp_f16_nc; cl_kernel kernel_expm1_f32, kernel_expm1_f32_4, kernel_expm1_f32_nc; cl_kernel kernel_expm1_f16, kernel_expm1_f16_4, kernel_expm1_f16_nc; + cl_kernel kernel_abs_f32, kernel_abs_f32_4, kernel_abs_f32_nc; + cl_kernel kernel_abs_f16, kernel_abs_f16_4, kernel_abs_f16_nc; cl_kernel kernel_softplus_f32, kernel_softplus_f32_4, kernel_softplus_f32_nc; cl_kernel kernel_softplus_f16, kernel_softplus_f16_4, kernel_softplus_f16_nc; cl_kernel kernel_upscale; @@ -844,7 +882,7 @@ struct ggml_backend_opencl_context { cl_kernel kernel_gemm_moe_q8_1_dp4a_q5k = nullptr; // generic dp4a MoE GEMM (MOE_QT=5, q5_K), opt-in cl_kernel kernel_moe_expand_scale_q5_K = nullptr; // q5_K 6-bit s[] -> uniform scale[16]/min[8] cl_kernel kernel_gemv_moe_q5_k_f32_ns, kernel_gemm_moe_q5_k_f32_ns; - cl_kernel kernel_gemv_moe_q6_k_f32_ns, kernel_gemm_moe_q6_k_f32_ns; + cl_kernel kernel_gemv_moe_q6_k_f32_ns, kernel_gemm_moe_q6_k_f32_ns, kernel_gemm_moe_q6_k_f32_ns_bin; cl_kernel kernel_gemm_moe_q6_k_q8_1_dp4a = nullptr; // dp4a (int8) q6_K MoE prefill GEMM cl_kernel kernel_gemv_moe_mxfp4_f32, kernel_gemm_moe_mxfp4_f32; cl_kernel kernel_gemv_moe_mxfp4_f32_ns, kernel_gemm_moe_mxfp4_f32_ns, kernel_gemm_moe_mxfp4_f32_ns_bin; @@ -1160,8 +1198,25 @@ static cl_program build_program_from_source_ex(cl_context ctx, cl_device_id dev, return NULL; } -static cl_program build_program_from_source(cl_context ctx, cl_device_id dev, const char* program_buffer, const std::string &compile_opts) { - return build_program_from_source_ex(ctx, dev, program_buffer, compile_opts, /*fatal=*/true); +static cl_program build_program_from_source(ggml_backend_opencl_context * backend_ctx, const char* program_buffer, const std::string &compile_opts) { + cl_context ctx = backend_ctx->context; + cl_device_id dev = backend_ctx->device; + + // Try the on-disk binary cache first. Falls through silently on miss or + // any failure; never blocks the build path. Disabled cache => nullptr. + cl_program p_cached = cl_program_cache_try_load( + backend_ctx->program_cache, ctx, dev, program_buffer, compile_opts); + if (p_cached != nullptr) { + return p_cached; + } + + cl_program p = build_program_from_source_ex(ctx, dev, program_buffer, compile_opts, /*fatal=*/true); + + // Best-effort save of the freshly-built binary (no-op if cache disabled). + if (p != nullptr) { + cl_program_cache_try_save(backend_ctx->program_cache, p, dev, program_buffer, compile_opts); + } + return p; } static cl_program build_program_from_binary(cl_context ctx, cl_device_id dev, const char* program_buffer, const std::string &compile_opts, size_t bin_size = 0) { @@ -1209,7 +1264,7 @@ static void load_cl_kernels_argsort(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("argsort.cl"); #endif backend_ctx->program_argsort_f32_i32 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_argsort_f32_i32 = clCreateKernel(backend_ctx->program_argsort_f32_i32, "kernel_argsort_f32_i32", &err), err)); backend_ctx->kernels_loaded_argsort = true; @@ -1259,7 +1314,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("add.cl"); #endif backend_ctx->program_add = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_add = clCreateKernel(backend_ctx->program_add, "kernel_add", &err), err)); CL_CHECK((backend_ctx->kernel_add_row = clCreateKernel(backend_ctx->program_add, "kernel_add_row", &err), err)); @@ -1278,7 +1333,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("add_id.cl"); #endif backend_ctx->program_add_id = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_add_id = clCreateKernel(backend_ctx->program_add_id, "kernel_add_id", &err), err)); GGML_LOG_CONT("."); @@ -1294,7 +1349,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("tri.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_tri = clCreateKernel(prog, "kernel_tri_f32", &err), err)); GGML_LOG_CONT("."); @@ -1312,7 +1367,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("fill.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_fill = clCreateKernel(prog, "kernel_fill_f32", &err), err)); GGML_LOG_CONT("."); @@ -1330,7 +1385,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("clamp.cl"); #endif backend_ctx->program_clamp = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_clamp = clCreateKernel(backend_ctx->program_clamp, "kernel_clamp", &err), err)); GGML_LOG_CONT("."); @@ -1346,7 +1401,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("cpy.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_cpy_f16_f16 = clCreateKernel(prog, "kernel_cpy_f16_f16", &err), err)); CL_CHECK((backend_ctx->kernel_cpy_f16_f32 = clCreateKernel(prog, "kernel_cpy_f16_f32", &err), err)); @@ -1367,7 +1422,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("cvt.cl"); #endif backend_ctx->program_cvt = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_convert_block_q1_0 = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q1_0", &err), err)); CL_CHECK((backend_ctx->kernel_restore_block_q1_0 = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q1_0", &err), err)); @@ -1450,7 +1505,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("diag_mask_inf.cl"); #endif backend_ctx->program_diag_mask_inf = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_diag_mask_inf_8 = clCreateKernel(backend_ctx->program_diag_mask_inf, "kernel_diag_mask_inf_8", &err), err)); CL_CHECK((backend_ctx->kernel_diag_mask_inf = clCreateKernel(backend_ctx->program_diag_mask_inf, "kernel_diag_mask_inf", &err), err)); @@ -1467,7 +1522,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("diag.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_diag_f32 = clCreateKernel(prog, "kernel_diag_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -1484,7 +1539,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gelu.cl"); #endif backend_ctx->program_gelu = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gelu = clCreateKernel(backend_ctx->program_gelu, "kernel_gelu", &err), err)); CL_CHECK((backend_ctx->kernel_gelu_4 = clCreateKernel(backend_ctx->program_gelu, "kernel_gelu_4", &err), err)); @@ -1505,7 +1560,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("glu.cl"); #endif backend_ctx->program_glu = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_geglu = clCreateKernel(backend_ctx->program_glu, "kernel_geglu", &err), err)); CL_CHECK((backend_ctx->kernel_reglu = clCreateKernel(backend_ctx->program_glu, "kernel_reglu", &err), err)); @@ -1531,7 +1586,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("get_rows.cl"); #endif backend_ctx->program_get_rows = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_get_rows_f32 = clCreateKernel(backend_ctx->program_get_rows, "kernel_get_rows_f32", &err), err)); CL_CHECK((backend_ctx->kernel_get_rows_f16 = clCreateKernel(backend_ctx->program_get_rows, "kernel_get_rows_f16", &err), err)); @@ -1549,7 +1604,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("solve_tri.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_solve_tri_f32 = clCreateKernel(prog, "kernel_solve_tri_f32", &err), err)); GGML_LOG_CONT("."); @@ -1566,7 +1621,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("im2col_f32.cl"); #endif backend_ctx->program_im2col_f32 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_im2col_f32 = clCreateKernel(backend_ctx->program_im2col_f32, "kernel_im2col_f32", &err), err)); GGML_LOG_CONT("."); @@ -1582,7 +1637,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("im2col_f16.cl"); #endif backend_ctx->program_im2col_f16 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_im2col_f16 = clCreateKernel(backend_ctx->program_im2col_f16, "kernel_im2col_f16", &err), err)); GGML_LOG_CONT("."); @@ -1598,7 +1653,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q4_0_f32.cl"); #endif backend_ctx->program_mul_mv_q4_0_f32 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mat_q4_0_f32 = clCreateKernel(backend_ctx->program_mul_mv_q4_0_f32, "kernel_mul_mat_q4_0_f32", &err), err)); GGML_LOG_CONT("."); @@ -1614,7 +1669,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q4_0_f32_v.cl"); #endif backend_ctx->program_mul_mv_q4_0_f32_v = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mat_q4_0_f32_v = clCreateKernel(backend_ctx->program_mul_mv_q4_0_f32_v, "kernel_mul_mat_q4_0_f32_v", &err), err)); GGML_LOG_CONT("."); @@ -1630,7 +1685,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q4_0_f32_8x_flat.cl"); #endif backend_ctx->program_mul_mv_q4_0_f32_8x_flat = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mat_q4_0_f32_8x_flat = clCreateKernel(backend_ctx->program_mul_mv_q4_0_f32_8x_flat, "kernel_mul_mat_q4_0_f32_8x_flat", &err), err)); GGML_LOG_CONT("."); @@ -1641,6 +1696,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { // those compiler versions since it is anyway not used for Adreno. if (backend_ctx->gpu_family != ADRENO || backend_ctx->adreno_cl_compiler_version.newer_than_or_same(E031, 38, 11, 0) || + backend_ctx->adreno_cl_compiler_version.type == E17 || backend_ctx->adreno_cl_compiler_version.type == DX) { #ifdef GGML_OPENCL_EMBED_KERNELS const std::string kernel_src { @@ -1650,7 +1706,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q4_0_f32_1d_8x_flat.cl"); #endif backend_ctx->program_mul_mv_q4_0_f32_1d_8x_flat = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mat_q4_0_f32_1d_8x_flat = clCreateKernel(backend_ctx->program_mul_mv_q4_0_f32_1d_8x_flat, "kernel_mul_mat_q4_0_f32_1d_8x_flat", &err), err)); GGML_LOG_CONT("."); @@ -1670,7 +1726,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q4_0_f32_1d_16x_flat.cl"); #endif backend_ctx->program_mul_mv_q4_0_f32_1d_16x_flat = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mat_q4_0_f32_1d_16x_flat = clCreateKernel(backend_ctx->program_mul_mv_q4_0_f32_1d_16x_flat, "kernel_mul_mat_q4_0_f32_1d_16x_flat", &err), err)); GGML_LOG_CONT("."); @@ -1686,7 +1742,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q4_1_f32.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_q4_1_f32 = clCreateKernel(prog, "kernel_mul_mv_q4_1_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -1703,7 +1759,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q4_1_f32_flat.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_q4_1_f32_flat = clCreateKernel(prog, "kernel_mul_mv_q4_1_f32_flat", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -1720,7 +1776,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q4_k_f32.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_q4_K_f32 = clCreateKernel(prog, "kernel_mul_mv_q4_K_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -1737,7 +1793,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q4_k_f32_flat.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_q4_K_f32_flat = clCreateKernel(prog, "kernel_mul_mv_q4_K_f32_flat", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -1754,7 +1810,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q5_0_f32.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_q5_0_f32 = clCreateKernel(prog, "kernel_mul_mv_q5_0_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -1771,7 +1827,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q5_0_f32_flat.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_q5_0_f32_flat = clCreateKernel(prog, "kernel_mul_mv_q5_0_f32_flat", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -1788,7 +1844,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q5_1_f32.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_q5_1_f32 = clCreateKernel(prog, "kernel_mul_mv_q5_1_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -1805,7 +1861,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q5_1_f32_flat.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_q5_1_f32_flat = clCreateKernel(prog, "kernel_mul_mv_q5_1_f32_flat", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -1822,7 +1878,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q5_k_f32.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_q5_K_f32 = clCreateKernel(prog, "kernel_mul_mv_q5_K_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -1839,7 +1895,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q5_k_f32_flat.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_q5_K_f32_flat = clCreateKernel(prog, "kernel_mul_mv_q5_K_f32_flat", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -1855,7 +1911,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q6_k_f32.cl"); #endif backend_ctx->program_mul_mv_q6_K = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_q6_K_f32 = clCreateKernel(backend_ctx->program_mul_mv_q6_K, "kernel_mul_mv_q6_K_f32", &err), err)); GGML_LOG_CONT("."); @@ -1871,7 +1927,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q6_k_f32_flat.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_q6_K_f32_flat = clCreateKernel(prog, "kernel_mul_mv_q6_K_f32_flat", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -1888,7 +1944,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q8_0_f32.cl"); #endif backend_ctx->program_mul_mv_q8_0_f32 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_q8_0_f32 = clCreateKernel(backend_ctx->program_mul_mv_q8_0_f32, "kernel_mul_mv_q8_0_f32", &err), err)); GGML_LOG_CONT("."); @@ -1904,7 +1960,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q8_0_f32_flat.cl"); #endif backend_ctx->program_mul_mv_q8_0_f32_flat = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_q8_0_f32_flat = clCreateKernel(backend_ctx->program_mul_mv_q8_0_f32_flat, "kernel_mul_mv_q8_0_f32_flat", &err), err)); GGML_LOG_CONT("."); @@ -1920,7 +1976,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q1_0_f32.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_q1_0_f32 = clCreateKernel(prog, "kernel_mul_mv_q1_0_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -1937,7 +1993,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_q1_0_f32_flat.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_q1_0_f32_flat = clCreateKernel(prog, "kernel_mul_mv_q1_0_f32_flat", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -1954,7 +2010,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_iq4_nl_f32.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_iq4_nl_f32 = clCreateKernel(prog, "kernel_mul_mv_iq4_nl_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -1971,7 +2027,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_iq4_nl_f32_flat.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_iq4_nl_f32_flat = clCreateKernel(prog, "kernel_mul_mv_iq4_nl_f32_flat", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -1988,7 +2044,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_mxfp4_f32.cl"); #endif backend_ctx->program_mul_mv_mxfp4_f32 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_mxfp4_f32 = clCreateKernel(backend_ctx->program_mul_mv_mxfp4_f32, "kernel_mul_mv_mxfp4_f32", &err), err)); GGML_LOG_CONT("."); @@ -2004,7 +2060,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_mxfp4_f32_flat.cl"); #endif backend_ctx->program_mul_mv_mxfp4_f32_flat = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_mxfp4_f32_flat = clCreateKernel(backend_ctx->program_mul_mv_mxfp4_f32_flat, "kernel_mul_mv_mxfp4_f32_flat", &err), err)); GGML_LOG_CONT("."); @@ -2020,7 +2076,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_f16_f16.cl"); #endif backend_ctx->program_mul_mv_f16_f16 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mat_f16_f16 = clCreateKernel(backend_ctx->program_mul_mv_f16_f16, "kernel_mul_mat_f16_f16", &err), err)); GGML_LOG_CONT("."); @@ -2036,7 +2092,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_f16_f32_1row.cl"); #endif backend_ctx->program_mul_mv_f16_f32_1row = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mat_f16_f32_1row = clCreateKernel(backend_ctx->program_mul_mv_f16_f32_1row, "kernel_mul_mat_f16_f32_1row", &err), err)); GGML_LOG_CONT("."); @@ -2052,7 +2108,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_f16_f32_l4.cl"); #endif backend_ctx->program_mul_mv_f16_f32_l4 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mat_f16_f32_l4 = clCreateKernel(backend_ctx->program_mul_mv_f16_f32_l4, "kernel_mul_mat_f16_f32_l4", &err), err)); CL_CHECK((backend_ctx->kernel_mul_mat_f16_f32_l4_dr = clCreateKernel(backend_ctx->program_mul_mv_f16_f32_l4, "kernel_mul_mat_f16_f32_l4_dr", &err), err)); @@ -2118,7 +2174,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_f16_f32.cl"); #endif backend_ctx->program_mul_mv_f16_f32 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mat_f16_f32 = clCreateKernel(backend_ctx->program_mul_mv_f16_f32, "kernel_mul_mat_f16_f32", &err), err)); GGML_LOG_CONT("."); @@ -2134,7 +2190,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_f32_f32.cl"); #endif backend_ctx->program_mul_mv_f32_f32 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mat_f32_f32 = clCreateKernel(backend_ctx->program_mul_mv_f32_f32, "kernel_mul_mat_f32_f32", &err), err)); GGML_LOG_CONT("."); @@ -2150,7 +2206,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mat_f16_f32.cl"); #endif backend_ctx->program_mul_mat_f16_f32_tiled = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mat_f16_f32_tiled = clCreateKernel(backend_ctx->program_mul_mat_f16_f32_tiled, "mul_mat_f16_f32", &err), err)); GGML_LOG_CONT("."); @@ -2167,7 +2223,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemm_xmem_f16_f32_os8.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_adreno_xmem_pack_src_f32 = clCreateKernel(prog, "adreno_xmem_pack_src_f32", &err), err)); @@ -2192,7 +2248,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mm_f32_f32_l4_lm.cl"); #endif backend_ctx->program_mul_mm_f32_f32_l4_lm = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mm_f32_f32_l4_lm = clCreateKernel(backend_ctx->program_mul_mm_f32_f32_l4_lm, "kernel_mul_mm_f32_f32_l4_lm", &err), err)); GGML_LOG_CONT("."); @@ -2208,7 +2264,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mm_f16_f32_l4_lm.cl"); #endif backend_ctx->program_mul_mm_f16_f32_l4_lm = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mm_f16_f32_l4_lm = clCreateKernel(backend_ctx->program_mul_mm_f16_f32_l4_lm, "kernel_mul_mm_f16_f32_l4_lm", &err), err)); GGML_LOG_CONT("."); @@ -2224,7 +2280,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mm_q4_0_f32_l4_lm.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mm_q4_0_f32_l4_lm = clCreateKernel(prog, "kernel_mul_mm_q4_0_f32_l4_lm", &err), err)); GGML_LOG_CONT("."); @@ -2240,7 +2296,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mm_q4_1_f32_l4_lm.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mm_q4_1_f32_l4_lm = clCreateKernel(prog, "kernel_mul_mm_q4_1_f32_l4_lm", &err), err)); GGML_LOG_CONT("."); @@ -2256,7 +2312,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mm_q5_0_f32_l4_lm.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mm_q5_0_f32_l4_lm = clCreateKernel(prog, "kernel_mul_mm_q5_0_f32_l4_lm", &err), err)); GGML_LOG_CONT("."); @@ -2272,7 +2328,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mm_q5_1_f32_l4_lm.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mm_q5_1_f32_l4_lm = clCreateKernel(prog, "kernel_mul_mm_q5_1_f32_l4_lm", &err), err)); GGML_LOG_CONT("."); @@ -2288,7 +2344,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mm_q8_0_f32_l4_lm.cl"); #endif backend_ctx->program_mul_mm_q8_0_f32_l4_lm = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mm_q8_0_f32_l4_lm = clCreateKernel(backend_ctx->program_mul_mm_q8_0_f32_l4_lm, "kernel_mul_mm_q8_0_f32_l4_lm", &err), err)); GGML_LOG_CONT("."); @@ -2304,7 +2360,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mm_q1_0_f32_l4_lm.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mm_q1_0_f32_l4_lm = clCreateKernel(prog, "kernel_mul_mm_q1_0_f32_l4_lm", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -2321,7 +2377,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mm_iq4_nl_f32_l4_lm.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mm_iq4_nl_f32_l4_lm = clCreateKernel(prog, "kernel_mul_mm_iq4_nl_f32_l4_lm", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -2338,7 +2394,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mm_q4_k_f32_l4_lm.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mm_q4_k_f32_l4_lm = clCreateKernel(prog, "kernel_mul_mm_q4_k_f32_l4_lm", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -2355,7 +2411,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mm_q6_k_f32_l4_lm.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mm_q6_k_f32_l4_lm = clCreateKernel(prog, "kernel_mul_mm_q6_k_f32_l4_lm", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -2372,7 +2428,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mm_q5_k_f32_l4_lm.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mm_q5_k_f32_l4_lm = clCreateKernel(prog, "kernel_mul_mm_q5_k_f32_l4_lm", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -2389,9 +2445,9 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mm_f16_f32_kq_kqv.cl"); #endif backend_ctx->program_mul_mm_f16_f32_kqv = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts+" -DKQV "); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts+" -DKQV "); backend_ctx->program_mul_mm_f16_f32_kq = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mm_f16_f32_kqv = clCreateKernel(backend_ctx->program_mul_mm_f16_f32_kqv, "mul_mm_f16_f32_kqv", &err), err)); CL_CHECK((backend_ctx->kernel_mul_mm_f16_f32_kq = clCreateKernel(backend_ctx->program_mul_mm_f16_f32_kq, "mul_mm_f16_f32_kq", &err), err)); @@ -2408,7 +2464,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul.cl"); #endif backend_ctx->program_mul = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul = clCreateKernel(backend_ctx->program_mul, "kernel_mul", &err), err)); CL_CHECK((backend_ctx->kernel_mul_row = clCreateKernel(backend_ctx->program_mul, "kernel_mul_row", &err), err)); @@ -2427,7 +2483,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("norm.cl"); #endif backend_ctx->program_norm = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_norm = clCreateKernel(backend_ctx->program_norm, "kernel_norm", &err), err)); CL_CHECK((backend_ctx->kernel_norm_mul_add = clCreateKernel(backend_ctx->program_norm, "kernel_norm_mul_add", &err), err)); @@ -2444,7 +2500,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("relu.cl"); #endif backend_ctx->program_relu = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_relu = clCreateKernel(backend_ctx->program_relu, "kernel_relu", &err), err)); GGML_LOG_CONT("."); @@ -2460,7 +2516,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("rms_norm.cl"); #endif backend_ctx->program_rms_norm = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_rms_norm = clCreateKernel(backend_ctx->program_rms_norm, "kernel_rms_norm", &err), err)); CL_CHECK((backend_ctx->kernel_rms_norm_mul = clCreateKernel(backend_ctx->program_rms_norm, "kernel_rms_norm_mul", &err), err)); @@ -2477,7 +2533,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("l2_norm.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_l2_norm_f32 = clCreateKernel(prog, "kernel_l2_norm_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -2494,7 +2550,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("rope.cl"); #endif backend_ctx->program_rope = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_rope_norm_f32 = clCreateKernel(backend_ctx->program_rope, "kernel_rope_norm_f32", &err), err)); CL_CHECK((backend_ctx->kernel_rope_norm_f16 = clCreateKernel(backend_ctx->program_rope, "kernel_rope_norm_f16", &err), err)); @@ -2517,7 +2573,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("scale.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_scale_f32 = clCreateKernel(prog, "kernel_scale_f32", &err), err)); CL_CHECK((backend_ctx->kernel_scale_f32_4 = clCreateKernel(prog, "kernel_scale_f32_4", &err), err)); @@ -2535,7 +2591,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("silu.cl"); #endif backend_ctx->program_silu = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_silu = clCreateKernel(backend_ctx->program_silu, "kernel_silu", &err), err)); CL_CHECK((backend_ctx->kernel_silu_4 = clCreateKernel(backend_ctx->program_silu, "kernel_silu_4", &err), err)); @@ -2552,7 +2608,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("softmax_f32.cl"); #endif backend_ctx->program_softmax_f32 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_soft_max = clCreateKernel(backend_ctx->program_softmax_f32, "kernel_soft_max", &err), err)); GGML_LOG_CONT("."); @@ -2568,7 +2624,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("softmax_f16.cl"); #endif backend_ctx->program_softmax_f16 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_soft_max_f16 = clCreateKernel(backend_ctx->program_softmax_f16, "kernel_soft_max_f16", &err), err)); GGML_LOG_CONT("."); @@ -2584,7 +2640,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("softmax_4_f32.cl"); #endif backend_ctx->program_softmax_4_f32 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_soft_max_4 = clCreateKernel(backend_ctx->program_softmax_4_f32, "kernel_soft_max_4", &err), err)); GGML_LOG_CONT("."); @@ -2600,7 +2656,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("softmax_4_f16.cl"); #endif backend_ctx->program_softmax_4_f16 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_soft_max_4_f16 = clCreateKernel(backend_ctx->program_softmax_4_f16, "kernel_soft_max_4_f16", &err), err)); GGML_LOG_CONT("."); @@ -2619,7 +2675,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { " -cl-mad-enable -cl-finite-math-only "; backend_ctx->program_div = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_div = clCreateKernel(backend_ctx->program_div, "kernel_div", &err), err)); CL_CHECK((backend_ctx->kernel_div_row = clCreateKernel(backend_ctx->program_div, "kernel_div_row", &err), err)); @@ -2638,7 +2694,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("sqr.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_sqr_cont_f32 = clCreateKernel(prog, "kernel_sqr_cont_f32", &err), err)); CL_CHECK((backend_ctx->kernel_sqr_cont_f32_4 = clCreateKernel(prog, "kernel_sqr_cont_f32_4", &err), err)); @@ -2659,7 +2715,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("sqrt.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_sqrt_cont_f32 = clCreateKernel(prog, "kernel_sqrt_cont_f32", &err), err)); CL_CHECK((backend_ctx->kernel_sqrt_cont_f32_4 = clCreateKernel(prog, "kernel_sqrt_cont_f32_4", &err), err)); @@ -2680,7 +2736,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mean.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mean_f32 = clCreateKernel(prog, "kernel_mean_f32", &err), err)); CL_CHECK((backend_ctx->kernel_mean_f32_4 = clCreateKernel(prog, "kernel_mean_f32_4", &err), err)); @@ -2699,7 +2755,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("sub.cl"); #endif backend_ctx->program_sub = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_sub = clCreateKernel(backend_ctx->program_sub, "kernel_sub", &err), err)); CL_CHECK((backend_ctx->kernel_sub_row = clCreateKernel(backend_ctx->program_sub, "kernel_sub_row", &err), err)); @@ -2718,7 +2774,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("sum_rows.cl"); #endif backend_ctx->program_sum_rows_f32 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_sum_rows_f32 = clCreateKernel(backend_ctx->program_sum_rows_f32, "kernel_sum_rows_f32", &err), err)); CL_CHECK((backend_ctx->kernel_sum_rows_f32_4 = clCreateKernel(backend_ctx->program_sum_rows_f32, "kernel_sum_rows_f32_4", &err), err)); @@ -2735,7 +2791,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("cumsum.cl"); #endif cl_program prog; - prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_cumsum_blk = clCreateKernel(prog, "kernel_cumsum_blk", &err), err)); CL_CHECK((backend_ctx->kernel_cumsum_add = clCreateKernel(prog, "kernel_cumsum_add", &err), err)); @@ -2753,7 +2809,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("sigmoid.cl"); #endif backend_ctx->program_sigmoid = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_sigmoid_f32 = clCreateKernel(backend_ctx->program_sigmoid, "kernel_sigmoid_f32", &err), err)); CL_CHECK((backend_ctx->kernel_sigmoid_f16 = clCreateKernel(backend_ctx->program_sigmoid, "kernel_sigmoid_f16", &err), err)); @@ -2770,7 +2826,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("group_norm.cl"); #endif backend_ctx->program_group_norm = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_group_norm = clCreateKernel(backend_ctx->program_group_norm, "kernel_group_norm", &err), err)); CL_CHECK((backend_ctx->kernel_group_norm_mul_add = clCreateKernel(backend_ctx->program_group_norm, "kernel_group_norm_mul_add", &err), err)); @@ -2787,7 +2843,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("repeat.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_repeat_f32 = clCreateKernel(prog, "kernel_repeat_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -2804,7 +2860,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #endif if (!kernel_src.empty()) { backend_ctx->program_pad = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_pad = clCreateKernel(backend_ctx->program_pad, "kernel_pad", &err), err)); GGML_LOG_CONT("."); } else { @@ -2824,7 +2880,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("tanh.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_tanh_f32 = clCreateKernel(prog, "kernel_tanh_f32", &err), err)); CL_CHECK((backend_ctx->kernel_tanh_f32_4 = clCreateKernel(prog, "kernel_tanh_f32_4", &err), err)); CL_CHECK((backend_ctx->kernel_tanh_f32_nc = clCreateKernel(prog, "kernel_tanh_f32_nc", &err), err)); @@ -2845,7 +2901,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("neg.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_neg_f32 = clCreateKernel(prog, "kernel_neg_f32", &err), err)); CL_CHECK((backend_ctx->kernel_neg_f32_4 = clCreateKernel(prog, "kernel_neg_f32_4", &err), err)); CL_CHECK((backend_ctx->kernel_neg_f32_nc = clCreateKernel(prog, "kernel_neg_f32_nc", &err), err)); @@ -2866,7 +2922,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("exp.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_exp_f32 = clCreateKernel(prog, "kernel_exp_f32", &err), err)); CL_CHECK((backend_ctx->kernel_exp_f32_4 = clCreateKernel(prog, "kernel_exp_f32_4", &err), err)); CL_CHECK((backend_ctx->kernel_exp_f32_nc = clCreateKernel(prog, "kernel_exp_f32_nc", &err), err)); @@ -2887,7 +2943,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("expm1.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_expm1_f32 = clCreateKernel(prog, "kernel_expm1_f32", &err), err)); CL_CHECK((backend_ctx->kernel_expm1_f32_4 = clCreateKernel(prog, "kernel_expm1_f32_4", &err), err)); CL_CHECK((backend_ctx->kernel_expm1_f32_nc = clCreateKernel(prog, "kernel_expm1_f32_nc", &err), err)); @@ -2898,6 +2954,27 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { GGML_LOG_CONT("."); } + // abs + { +#ifdef GGML_OPENCL_EMBED_KERNELS + const std::string kernel_src { + #include "abs.cl.h" + }; +#else + const std::string kernel_src = read_file("abs.cl"); +#endif + cl_program prog = + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); + CL_CHECK((backend_ctx->kernel_abs_f32 = clCreateKernel(prog, "kernel_abs_f32", &err), err)); + CL_CHECK((backend_ctx->kernel_abs_f32_4 = clCreateKernel(prog, "kernel_abs_f32_4", &err), err)); + CL_CHECK((backend_ctx->kernel_abs_f32_nc = clCreateKernel(prog, "kernel_abs_f32_nc", &err), err)); + CL_CHECK((backend_ctx->kernel_abs_f16 = clCreateKernel(prog, "kernel_abs_f16", &err), err)); + CL_CHECK((backend_ctx->kernel_abs_f16_4 = clCreateKernel(prog, "kernel_abs_f16_4", &err), err)); + CL_CHECK((backend_ctx->kernel_abs_f16_nc = clCreateKernel(prog, "kernel_abs_f16_nc", &err), err)); + CL_CHECK(clReleaseProgram(prog)); + GGML_LOG_CONT("."); + } + // softplus { #ifdef GGML_OPENCL_EMBED_KERNELS @@ -2908,7 +2985,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("softplus.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_softplus_f32 = clCreateKernel(prog, "kernel_softplus_f32", &err), err)); CL_CHECK((backend_ctx->kernel_softplus_f32_4 = clCreateKernel(prog, "kernel_softplus_f32_4", &err), err)); CL_CHECK((backend_ctx->kernel_softplus_f32_nc = clCreateKernel(prog, "kernel_softplus_f32_nc", &err), err)); @@ -2930,7 +3007,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #endif if (!kernel_src.empty()) { backend_ctx->program_upscale = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_upscale = clCreateKernel(backend_ctx->program_upscale, "kernel_upscale", &err), err)); if (backend_ctx->program_upscale) { cl_int err_bilinear; @@ -2961,7 +3038,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("concat.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_concat_f32 = clCreateKernel(prog, "kernel_concat_f32", &err), err)); CL_CHECK((backend_ctx->kernel_concat_f32_pack = clCreateKernel(prog, "kernel_concat_f32_pack", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -2980,7 +3057,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #endif if (!kernel_src.empty()) { backend_ctx->program_tsembd = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_timestep_embedding = clCreateKernel(backend_ctx->program_tsembd, "kernel_timestep_embedding", &err), err)); GGML_LOG_CONT("."); } else { @@ -3000,7 +3077,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("set_rows.cl"); #endif backend_ctx->program_set_rows = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_set_rows_f32_i64 = clCreateKernel(backend_ctx->program_set_rows, "kernel_set_rows_f32_i64", &err), err)); CL_CHECK((backend_ctx->kernel_set_rows_f32_i32 = clCreateKernel(backend_ctx->program_set_rows, "kernel_set_rows_f32_i32", &err), err)); @@ -3032,11 +3109,11 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #endif if (!kernel_src.empty()) { backend_ctx->program_conv_2d_f16 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), (std::string(compile_opts) + " -DUSE_FP16=1").c_str()); + build_program_from_source(backend_ctx, kernel_src.c_str(), (std::string(compile_opts) + " -DUSE_FP16=1").c_str()); CL_CHECK((backend_ctx->kernel_conv_2d_f16 = clCreateKernel(backend_ctx->program_conv_2d_f16, "kernel_conv_2d", &err), err)); GGML_LOG_CONT("."); backend_ctx->program_conv_2d_f32 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_conv_2d_f32 = clCreateKernel(backend_ctx->program_conv_2d_f32, "kernel_conv_2d", &err), err)); GGML_LOG_CONT("."); } else { @@ -3048,7 +3125,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { } if (!kernel_src_f16_f32.empty()) { backend_ctx->program_conv_2d_f16_f32 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src_f16_f32.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src_f16_f32.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_conv_2d_f16_f32 = clCreateKernel(backend_ctx->program_conv_2d_f16_f32, "kernel_conv_2d", &err), err)); GGML_LOG_CONT("."); } else { @@ -3068,7 +3145,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("ssm_conv.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_ssm_conv_f32_f32 = clCreateKernel(prog, "kernel_ssm_conv_f32_f32", &err), err)); CL_CHECK((backend_ctx->kernel_ssm_conv_f32_f32_4 = clCreateKernel(prog, "kernel_ssm_conv_f32_f32_4", &err), err)); @@ -3144,8 +3221,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { // Please remember to implement code to handle it. opts += " -DSUBGROUPS_PER_WG=" + std::to_string(spw); - cl_program prog = build_program_from_source( - backend_ctx->context, backend_ctx->device, kernel_src.c_str(), opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), opts); CL_CHECK((backend_ctx->kernel_gated_delta_net_f32[si][kda][tgpp] = clCreateKernel(prog, "kernel_gated_delta_net", &err), err)); @@ -3166,7 +3242,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("moe_combine.cl"); #endif cl_program prog = build_program_from_source( - backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_moe_combine_f32 = clCreateKernel(prog, "kernel_moe_combine_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -3183,7 +3259,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_id_q4_0_f32_8x_flat.cl"); #endif backend_ctx->program_mul_mv_id_q4_0_f32_8x_flat = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_id_q4_0_f32_8x_flat = clCreateKernel(backend_ctx->program_mul_mv_id_q4_0_f32_8x_flat, "kernel_mul_mv_id_q4_0_f32_8x_flat", &err), err)); GGML_LOG_CONT("."); @@ -3199,7 +3275,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_id_q8_0_f32.cl"); #endif backend_ctx->program_mul_mv_id_q8_0_f32 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_id_q8_0_f32 = clCreateKernel(backend_ctx->program_mul_mv_id_q8_0_f32, "kernel_mul_mv_id_q8_0_f32", &err), err)); GGML_LOG_CONT("."); @@ -3215,7 +3291,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_id_q8_0_f32_flat.cl"); #endif backend_ctx->program_mul_mv_id_q8_0_f32_flat = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_id_q8_0_f32_flat = clCreateKernel(backend_ctx->program_mul_mv_id_q8_0_f32_flat, "kernel_mul_mv_id_q8_0_f32_flat", &err), err)); GGML_LOG_CONT("."); @@ -3231,7 +3307,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_id_mxfp4_f32.cl"); #endif backend_ctx->program_mul_mv_id_mxfp4_f32 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_id_mxfp4_f32 = clCreateKernel(backend_ctx->program_mul_mv_id_mxfp4_f32, "kernel_mul_mv_id_mxfp4_f32", &err), err)); GGML_LOG_CONT("."); @@ -3247,7 +3323,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("mul_mv_id_mxfp4_f32_flat.cl"); #endif backend_ctx->program_mul_mv_id_mxfp4_f32_flat = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_mul_mv_id_mxfp4_f32_flat = clCreateKernel(backend_ctx->program_mul_mv_id_mxfp4_f32_flat, "kernel_mul_mv_id_mxfp4_f32_flat", &err), err)); GGML_LOG_CONT("."); @@ -3265,7 +3341,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("transpose.cl"); #endif backend_ctx->program_transpose = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_transpose_32_16 = clCreateKernel(backend_ctx->program_transpose, "kernel_transpose_32_16", &err), err)); CL_CHECK((backend_ctx->kernel_transpose_32 = clCreateKernel(backend_ctx->program_transpose, "kernel_transpose_32", &err), err)); @@ -3286,7 +3362,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemm_noshuffle_q1_0_f32.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q1_0_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_q1_0_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3307,8 +3383,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src_CL_gemv_general = read_file("gemv_noshuffle_q1_0_f32.cl"); #endif - cl_program prog = build_program_from_source( - backend_ctx->context, backend_ctx->device, kernel_src_CL_gemv_general.c_str(), CL_gemv_compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src_CL_gemv_general.c_str(), CL_gemv_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q1_0_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q1_0_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -3333,8 +3408,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src_CL_gemv_general = read_file("gemv_noshuffle_q4_0_f32.cl"); #endif - cl_program prog = build_program_from_source( - backend_ctx->context, backend_ctx->device, kernel_src_CL_gemv_general.c_str(), CL_gemv_compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src_CL_gemv_general.c_str(), CL_gemv_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q4_0_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q4_0_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -3362,8 +3436,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src_CL_gemv = read_file("gemv_noshuffle_q4_0_f32_spec.cl"); #endif - cl_program prog = build_program_from_source( - backend_ctx->context, backend_ctx->device, kernel_src_CL_gemv.c_str(), CL_gemv_compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src_CL_gemv.c_str(), CL_gemv_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q4_0_f32_4096_1_4096 = clCreateKernel(prog, "kernel_gemv_noshuffle_q4_0_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3379,8 +3452,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAST "; } - prog = build_program_from_source( - backend_ctx->context, backend_ctx->device, kernel_src_CL_gemv.c_str(), CL_gemv_compile_opts); + prog = build_program_from_source(backend_ctx, kernel_src_CL_gemv.c_str(), CL_gemv_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q4_0_f32_4096_1_11008 = clCreateKernel(prog, "kernel_gemv_noshuffle_q4_0_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3396,8 +3468,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAST "; } - prog = build_program_from_source( - backend_ctx->context, backend_ctx->device, kernel_src_CL_gemv.c_str(), CL_gemv_compile_opts); + prog = build_program_from_source(backend_ctx, kernel_src_CL_gemv.c_str(), CL_gemv_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q4_0_f32_11008_1_4096 = clCreateKernel(prog, "kernel_gemv_noshuffle_q4_0_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3414,8 +3485,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAST "; } - prog = build_program_from_source( - backend_ctx->context, backend_ctx->device, kernel_src_CL_gemv.c_str(), CL_gemv_compile_opts); + prog = build_program_from_source(backend_ctx, kernel_src_CL_gemv.c_str(), CL_gemv_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q4_0_f32_32000_1_4096 = clCreateKernel(prog, "kernel_gemv_noshuffle_q4_0_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3430,7 +3500,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src_CL_gemm = read_file("gemm_noshuffle_q4_0_f32.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src_CL_gemm.c_str(), compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src_CL_gemm.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q4_0_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_q4_0_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3445,7 +3515,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemm_noshuffle_q4_1_f32.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q4_1_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_q4_1_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3467,8 +3537,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemv_noshuffle_q4_1_f32.cl"); #endif - cl_program prog = build_program_from_source( - backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_gemv_compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), CL_gemv_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q4_1_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q4_1_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -3484,7 +3553,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemm_noshuffle_q5_0_f32.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q5_0_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_q5_0_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3499,7 +3568,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemm_noshuffle_q5_0_q8_1_dp4a.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q5_0_q8_1_dp4a = clCreateKernel(prog, "kernel_gemm_noshuffle_q5_0_q8_1_dp4a", &err), err)); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q5_0_q8_1_dp4a_wimg = clCreateKernel(prog, "kernel_gemm_noshuffle_q5_0_q8_1_dp4a_wimg", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -3521,8 +3590,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemv_noshuffle_q5_0_f32.cl"); #endif - cl_program prog = build_program_from_source( - backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_gemv_compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), CL_gemv_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q5_0_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q5_0_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3537,7 +3605,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemm_noshuffle_q5_1_f32.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q5_1_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_q5_1_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3558,8 +3626,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemv_noshuffle_q5_1_f32.cl"); #endif - cl_program prog = build_program_from_source( - backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_gemv_compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), CL_gemv_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q5_1_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q5_1_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3574,7 +3641,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemm_noshuffle_iq4_nl_f32.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_iq4_nl_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_iq4_nl_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3589,7 +3656,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemm_noshuffle_iq4_nl_q8_1_dp4a.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_iq4_nl_q8_1_dp4a = clCreateKernel(prog, "kernel_gemm_noshuffle_iq4_nl_q8_1_dp4a", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3604,7 +3671,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemm_noshuffle_q4_0_q8_1_dp4a.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q4_0_q8_1_dp4a = clCreateKernel(prog, "kernel_gemm_noshuffle_q4_0_q8_1_dp4a", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3626,8 +3693,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemv_noshuffle_iq4_nl_f32.cl"); #endif - cl_program prog = build_program_from_source( - backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_gemv_compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), CL_gemv_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_noshuffle_iq4_nl_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_iq4_nl_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -3643,7 +3709,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemm_noshuffle_q8_0_f32.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q8_0_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_q8_0_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3685,8 +3751,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src_CL_gemv_general = read_file("gemv_noshuffle_q8_0_f32.cl"); #endif - cl_program prog = build_program_from_source( - backend_ctx->context, backend_ctx->device, kernel_src_CL_gemv_general.c_str(), CL_gemv_compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src_CL_gemv_general.c_str(), CL_gemv_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q8_0_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q8_0_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -3702,7 +3767,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemm_noshuffle_q4_k_f32.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q4_k_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_q4_k_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3721,9 +3786,9 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { // X1 (1152 B/WG -> few resident WGs); TILESIZE_N=8 (288 B) lifts occupancy on // X1, byte-identical. X2E keeps 32. Env override wins. int q4k_dp4a_ts = (backend_ctx->adreno_gen == ADRENO_GPU_GEN::X1E) ? 8 : 32; - if (const char * e = getenv("GGML_OPENCL_Q4K_DP4A_TS")) q4k_dp4a_ts = atoi(e); + if (const char * e = getenv("GGML_OPENCL_Q4K_DP4A_TS")) { q4k_dp4a_ts = atoi(e); } std::string dp4a_opts = compile_opts + " -DTILESIZE_N=" + std::to_string(q4k_dp4a_ts); - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), dp4a_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), dp4a_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q4_k_q8_1_dp4a = clCreateKernel(prog, "kernel_gemm_noshuffle_q4_k_q8_1_dp4a", &err), err)); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q4_k_q8_1_dp4a_wimg = clCreateKernel(prog, "kernel_gemm_noshuffle_q4_k_q8_1_dp4a_wimg", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -3739,7 +3804,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemm_noshuffle_q8_0_q8_1_dp4a.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q8_0_q8_1_dp4a = clCreateKernel(prog, "kernel_gemm_noshuffle_q8_0_q8_1_dp4a", &err), err)); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q8_0_q8_1_dp4a_wimg = clCreateKernel(prog, "kernel_gemm_noshuffle_q8_0_q8_1_dp4a_wimg", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -3755,7 +3820,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemm_noshuffle_q5_k_q8_1_dp4a.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q5_k_q8_1_dp4a = clCreateKernel(prog, "kernel_gemm_noshuffle_q5_k_q8_1_dp4a", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3770,7 +3835,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemm_noshuffle_q6_k_q8_1_dp4a.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q6_k_q8_1_dp4a = clCreateKernel(prog, "kernel_gemm_noshuffle_q6_k_q8_1_dp4a", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3785,7 +3850,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("quant_a_q8_1.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_quant_a_q8_1 = clCreateKernel(prog, "kernel_quant_a_q8_1", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -3807,8 +3872,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemv_noshuffle_q4_k_f32.cl"); #endif - cl_program prog = build_program_from_source( - backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_gemv_compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), CL_gemv_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q4_k_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q4_k_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -3828,7 +3892,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemv_moe_q4_1_f32_ns.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_moe_q4_1_f32_ns = clCreateKernel(prog, "kernel_gemv_moe_q4_1_f32_ns", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -3844,7 +3908,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemm_moe_q4_1_f32_ns.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemm_moe_q4_1_f32_ns = clCreateKernel(prog, "kernel_gemm_moe_q4_1_f32_ns", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -3879,7 +3943,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemv_moe_mxfp4_f32.cl"); #endif backend_ctx->program_gemv_moe_mxfp4_f32 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_moe_mxfp4_f32 = clCreateKernel(backend_ctx->program_gemv_moe_mxfp4_f32, "kernel_gemv_moe_mxfp4_f32", &err), err)); GGML_LOG_CONT("."); @@ -3895,7 +3959,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemm_moe_mxfp4_f32.cl"); #endif backend_ctx->program_gemm_moe_mxfp4_f32 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemm_moe_mxfp4_f32 = clCreateKernel(backend_ctx->program_gemm_moe_mxfp4_f32, "kernel_gemm_moe_mxfp4_f32", &err), err)); GGML_LOG_CONT("."); @@ -3911,7 +3975,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemv_moe_q4_0_f32_ns.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_moe_q4_0_f32_ns = clCreateKernel(prog, "kernel_gemv_moe_q4_0_f32_ns", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -3928,7 +3992,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemm_moe_q4_0_f32_ns.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemm_moe_q4_0_f32_ns = clCreateKernel(prog, "kernel_gemm_moe_q4_0_f32_ns", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -3963,7 +4027,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemm_moe_q8_0_f32_ns.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemm_moe_q8_0_f32_ns = clCreateKernel(prog, "kernel_gemm_moe_q8_0_f32_ns", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -3980,7 +4044,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemv_moe_q5_0_f32_ns.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_moe_q5_0_f32_ns = clCreateKernel(prog, "kernel_gemv_moe_q5_0_f32_ns", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -3997,7 +4061,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemm_moe_q5_0_f32_ns.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemm_moe_q5_0_f32_ns = clCreateKernel(prog, "kernel_gemm_moe_q5_0_f32_ns", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -4014,7 +4078,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemv_moe_q5_1_f32_ns.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_moe_q5_1_f32_ns = clCreateKernel(prog, "kernel_gemv_moe_q5_1_f32_ns", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -4031,7 +4095,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemm_moe_q5_1_f32_ns.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemm_moe_q5_1_f32_ns = clCreateKernel(prog, "kernel_gemm_moe_q5_1_f32_ns", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -4048,7 +4112,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemv_moe_q4_k_f32_ns.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_moe_q4_k_f32_ns = clCreateKernel(prog, "kernel_gemv_moe_q4_k_f32_ns", &err), err)); CL_CHECK((backend_ctx->kernel_gemv_moe_q4_k_f32_ns_wimg = clCreateKernel(prog, "kernel_gemv_moe_q4_k_f32_ns_wimg", &err), err)); @@ -4066,7 +4130,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemm_moe_q4_k_f32_ns.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemm_moe_q4_k_f32_ns = clCreateKernel(prog, "kernel_gemm_moe_q4_k_f32_ns", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -4101,7 +4165,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemm_moe_q4_k_q8_1_dp4a.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemm_moe_q4_k_q8_1_dp4a = clCreateKernel(prog, "kernel_gemm_moe_q4_k_q8_1_dp4a", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -4118,7 +4182,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemm_moe_mxfp4_q8_1_dp4a.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemm_moe_mxfp4_q8_1_dp4a = clCreateKernel(prog, "kernel_gemm_moe_mxfp4_q8_1_dp4a", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -4135,7 +4199,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemm_moe_q4_0_q8_1_dp4a.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemm_moe_q4_0_q8_1_dp4a = clCreateKernel(prog, "kernel_gemm_moe_q4_0_q8_1_dp4a", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -4153,19 +4217,19 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #endif const std::string opts80 = CL_moe_compile_opts + " -DMOE_QT=80"; cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), opts80.c_str()); + build_program_from_source(backend_ctx, kernel_src.c_str(), opts80.c_str()); CL_CHECK((backend_ctx->kernel_gemm_moe_q8_1_dp4a_q80 = clCreateKernel(prog, "kernel_gemm_moe_q8_1_dp4a", &err), err)); CL_CHECK(clReleaseProgram(prog)); const std::string opts50 = CL_moe_compile_opts + " -DMOE_QT=50"; cl_program prog50 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), opts50.c_str()); + build_program_from_source(backend_ctx, kernel_src.c_str(), opts50.c_str()); CL_CHECK((backend_ctx->kernel_gemm_moe_q8_1_dp4a_q50 = clCreateKernel(prog50, "kernel_gemm_moe_q8_1_dp4a", &err), err)); CL_CHECK(clReleaseProgram(prog50)); const std::string opts5 = CL_moe_compile_opts + " -DMOE_QT=5"; cl_program prog5 = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), opts5.c_str()); + build_program_from_source(backend_ctx, kernel_src.c_str(), opts5.c_str()); CL_CHECK((backend_ctx->kernel_gemm_moe_q8_1_dp4a_q5k = clCreateKernel(prog5, "kernel_gemm_moe_q8_1_dp4a", &err), err)); CL_CHECK(clReleaseProgram(prog5)); GGML_LOG_CONT("."); @@ -4181,7 +4245,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("moe_reorder_quant_a_q8_1.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_moe_reorder_quant_a_q8_1 = clCreateKernel(prog, "kernel_moe_reorder_quant_a_q8_1", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -4198,7 +4262,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemv_moe_q5_k_f32_ns.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_moe_q5_k_f32_ns = clCreateKernel(prog, "kernel_gemv_moe_q5_k_f32_ns", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -4215,7 +4279,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemm_moe_q5_k_f32_ns.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemm_moe_q5_k_f32_ns = clCreateKernel(prog, "kernel_gemm_moe_q5_k_f32_ns", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -4232,7 +4296,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemv_moe_q6_k_f32_ns.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_moe_q6_k_f32_ns = clCreateKernel(prog, "kernel_gemv_moe_q6_k_f32_ns", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -4249,13 +4313,31 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemm_moe_q6_k_f32_ns.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemm_moe_q6_k_f32_ns = clCreateKernel(prog, "kernel_gemm_moe_q6_k_f32_ns", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); } + // gemm_moe_q6_k_f32_ns_bin + { + size_t bin_size = 0; + backend_ctx->kernel_gemm_moe_q6_k_f32_ns_bin = nullptr; + + if (use_adreno_bin_kernels(backend_ctx)) { + const char * kernel_bin = (const char *)backend_ctx->get_adreno_bin_kernel("gemm_moe_q6_k_f32_ns_ila", &bin_size); + if (kernel_bin && bin_size > 0) { + cl_program prog = + build_program_from_binary(backend_ctx->context, backend_ctx->device, kernel_bin, CL_moe_compile_opts, bin_size); + + CL_CHECK((backend_ctx->kernel_gemm_moe_q6_k_f32_ns_bin = clCreateKernel(prog, "kernel_gemm_moe_q6_k_f32_ns_ila", &err), err)); + CL_CHECK(clReleaseProgram(prog)); + GGML_LOG_CONT("."); + } + } + } + // gemm_moe_q6_k_q8_1_dp4a (dp4a q6_K MoE prefill GEMM) if (backend_ctx->has_integer_dot) { #ifdef GGML_OPENCL_EMBED_KERNELS @@ -4266,7 +4348,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemm_moe_q6_k_q8_1_dp4a.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemm_moe_q6_k_q8_1_dp4a = clCreateKernel(prog, "kernel_gemm_moe_q6_k_q8_1_dp4a", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -4283,7 +4365,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemv_moe_mxfp4_f32_ns.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_moe_mxfp4_f32_ns = clCreateKernel(prog, "kernel_gemv_moe_mxfp4_f32_ns", &err), err)); CL_CHECK((backend_ctx->kernel_gemv_moe_mxfp4_f32_ns_wimg = clCreateKernel(prog, "kernel_gemv_moe_mxfp4_f32_ns_wimg", &err), err)); @@ -4301,7 +4383,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemm_moe_mxfp4_f32_ns.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemm_moe_mxfp4_f32_ns = clCreateKernel(prog, "kernel_gemm_moe_mxfp4_f32_ns", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -4336,7 +4418,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("moe_reorder_b.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_moe_reorder_b = clCreateKernel(prog, "kernel_moe_reorder_b", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -4353,7 +4435,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("moe_sort_by_expert.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_moe_histogram = clCreateKernel(prog, "kernel_moe_histogram", &err), err)); CL_CHECK((backend_ctx->kernel_moe_scan = clCreateKernel(prog, "kernel_moe_scan", &err), err)); @@ -4380,7 +4462,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { } cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_gemv_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_gemv_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q6_K_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q6_K_f32", &err), err)); GGML_LOG_CONT("."); @@ -4396,7 +4478,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemm_noshuffle_q6_k_f32.cl"); #endif cl_program prog = - build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + build_program_from_source(backend_ctx, kernel_src.c_str(), CL_moe_compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q6_K_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_q6_K_f32", &err), err)); GGML_LOG_CONT("."); @@ -4418,8 +4500,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { const std::string kernel_src = read_file("gemv_noshuffle_q5_k_f32.cl"); #endif - cl_program prog = build_program_from_source( - backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_gemv_compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), CL_gemv_compile_opts); CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q5_k_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q5_k_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); @@ -4435,7 +4516,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) { #else const std::string kernel_src = read_file("gemm_noshuffle_q5_k_f32.cl"); #endif - cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); + cl_program prog = build_program_from_source(backend_ctx, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q5_k_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_q5_k_f32", &err), err)); CL_CHECK(clReleaseProgram(prog)); GGML_LOG_CONT("."); @@ -5958,7 +6039,8 @@ static void transpose_2d( cl_kernel kernel, cl_mem src, cl_mem dst, size_t size, cl_int stride, cl_int rows, - bool blocking = true + bool blocking = true, + bool auto_local = false // let driver pick local size for non-uniform workgroups ) { static ggml_cl_buffer buf; @@ -5984,7 +6066,7 @@ static void transpose_2d( size_t local_size[3] = {64, 1, 1}; size_t global_size[3] = {(size_t)stride, (size_t)rows, 1};; CL_CHECK(clEnqueueNDRangeKernel(backend_ctx->queue, kernel, 3, NULL, - global_size, local_size, 0, NULL, NULL)); + global_size, auto_local ? NULL : local_size, 0, NULL, NULL)); if (blocking) { CL_CHECK(clEnqueueCopyBuffer(backend_ctx->queue, trans, dst, 0, 0, size, 0, NULL, &evt)); @@ -6001,10 +6083,11 @@ static void transpose_2d_as_8b( ggml_backend_opencl_context * backend_ctx, cl_mem src, cl_mem dst, size_t size, cl_int stride, cl_int rows, - bool blocking = true + bool blocking = true, + bool auto_local = false ) { transpose_2d(backend_ctx, backend_ctx->kernel_transpose_8_buf, - src, dst, size, stride, rows, blocking); + src, dst, size, stride, rows, blocking, auto_local); } static void transpose_2d_as_16b( @@ -6932,8 +7015,31 @@ inline bool use_adreno_kernels(const ggml_backend_opencl_context *backend_ctx, c return threashold_ok; } +static bool adreno_e17_compiler_quirks(const ggml_backend_opencl_context *backend_ctx) { + if (!backend_ctx || backend_ctx->gpu_family != GPU_FAMILY::ADRENO || + backend_ctx->adreno_cl_compiler_version.type != ADRENO_CL_COMPILER_TYPE::E17) { + return false; + } + const char * env = getenv("GGML_OPENCL_ADRENO_E17_QUIRKS"); + return !(env && env[0] == '0'); +} + inline bool use_adreno_moe_kernels(const ggml_backend_opencl_context *backend_ctx, const ggml_tensor *tensor) { - GGML_UNUSED(backend_ctx); + // The moe weight repack kernels *_trans4_ns alias a private ushort8 through a uchar*. + // Certain compilers (found with some A7x and A6x) miscompiles this, corrupting the weights. + // So, exclude A6x and A7x from using Adreno MoE kernels for now. + // The quants that have a general mul_mat_id kernel fallback to the general version; the + // rest fallback to CPU. + if (backend_ctx && (backend_ctx->adreno_gen == ADRENO_GPU_GEN::A6X || + backend_ctx->adreno_gen == ADRENO_GPU_GEN::A7X || + backend_ctx->adreno_gen == ADRENO_GPU_GEN::ADRENO_UNKNOWN)) { + return false; + } + + if (adreno_e17_compiler_quirks(backend_ctx)) { + return false; + } + int ne01 = tensor->ne[1]; return (((strstr(tensor->name, "ffn") != NULL) && (strstr(tensor->name, "exps") != NULL)) || (strstr(tensor->name, "as") != NULL)) && (ne01 % 32 == 0); } @@ -6959,7 +7065,7 @@ static inline bool use_flat_gemv_for_large_m_q4_K(const ggml_tensor *tensor) { return tensor->ne[1] >= 32768 && tensor->ne[2] == 1 && tensor->ne[3] == 1; } -static inline bool use_flat_gemv_for_large_m_q6_K(const ggml_tensor *tensor) { +static inline bool use_flat_gemv_for_large_m_q6_K(const ggml_backend_opencl_context *backend_ctx, const ggml_tensor *tensor) { // gemv_noshuffle variant perf drops for large M, use flat variant for large M. // threshold is well above typical hidden/FFN dims, but below typical vocab sizes. // q6_K flat gemv is worse for smaller K; 2048 seems to be a reasonable threshold. @@ -6977,7 +7083,15 @@ static inline bool use_flat_gemv_for_large_m_q6_K(const ggml_tensor *tensor) { if ((tensor->ne[1] % 128 != 0) && tensor->ne[2] == 1 && tensor->ne[3] == 1) { return true; } - return tensor->ne[1] >= 32768 && tensor->ne[0] >= 2048 && tensor->ne[2] == 1 && tensor->ne[3] == 1; + + // The gemv_noshuffle slowdown tracks TOTAL weight size, not ne0 alone; ne0 >= 2048 is a + // proxy for "large weight" that misses a narrow-hidden vocab-scale lm_head. + // Add a direct size escape so such weights also take the flat path, without changing + // which weights ne0 >= 2048 already routes there. + // The size escape is not taken on the A7X since its compiler miscompiles the flat K-quant GEMV + return tensor->ne[1] >= 32768 + && (tensor->ne[0] >= 2048 || (backend_ctx->adreno_gen != ADRENO_GPU_GEN::A7X && ggml_nbytes(tensor) >= (256ull << 20))) + && tensor->ne[2] == 1 && tensor->ne[3] == 1; } static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) { @@ -7099,6 +7213,8 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te return op->src[0]->type == GGML_TYPE_F32; case GGML_UNARY_OP_EXPM1: return op->src[0]->type == GGML_TYPE_F32; + case GGML_UNARY_OP_ABS: + return op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16; case GGML_UNARY_OP_SOFTPLUS: return op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16; default: @@ -7266,6 +7382,10 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te case GGML_OP_MEAN: return op->src[0]->type == GGML_TYPE_F32; case GGML_OP_FLASH_ATTN_EXT: { + // The E17 compilers segfault while building FA kernels, skip E17 for now + if (adreno_e17_compiler_quirks(backend_ctx)) { + return false; + } const ggml_tensor * q = op->src[0]; const ggml_tensor * k = op->src[1]; const ggml_tensor * v = op->src[2]; @@ -7319,6 +7439,14 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te return false; } + // Some compilers for A7x (Adreno 740, compiler E031.41) crashes when + // building FA kernels with mixed or quant types (f32_f16, f32_q8_0, f32_q4_0) + // Here we skip all A7x for these kernels to avoid crash + if (backend_ctx->adreno_gen == ADRENO_GPU_GEN::A7X && + (is_f32_f16 || is_f32_q8_0 || is_f32_q4_0)) { + return false; + } + if (dk == 512) { if (backend_ctx->gpu_family == INTEL) { return false; @@ -7375,6 +7503,7 @@ static ggml_backend_i ggml_backend_opencl_i = { ggml_backend_t ggml_backend_opencl_init(void) { ggml_backend_dev_t dev = ggml_backend_reg_dev_get(ggml_backend_opencl_reg(), 0); ggml_backend_opencl_context *backend_ctx = ggml_cl_init(dev); + backend_ctx->ref_count++; ggml_backend_t backend = new ggml_backend { /* .guid = */ ggml_backend_opencl_guid(), @@ -8963,6 +9092,9 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer, transpose_2d_as_16b(backend_ctx, extra->q, extra->q, size_q, K/4, M); transpose_2d_as_16b(backend_ctx, extra->d, extra->d, size_d, K/256, M); transpose_2d_as_16b(backend_ctx, extra->dm, extra->dm, size_dm, K/256, M); + + // Transpose s as uchar + transpose_2d_as_8b(backend_ctx, extra->s, extra->s, size_s, K/256*12, M, true, true); } #endif // GGML_OPENCL_USE_ADRENO_KERNELS return; @@ -9279,7 +9411,7 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer, cl_kernel kernel; #ifdef GGML_OPENCL_USE_ADRENO_KERNELS kernel = backend_ctx->kernel_convert_block_q6_K; - if (use_adreno_kernels(backend_ctx, tensor) && !use_flat_gemv_for_large_m_q6_K(tensor)) { + if (use_adreno_kernels(backend_ctx, tensor) && !use_flat_gemv_for_large_m_q6_K(backend_ctx, tensor)) { kernel = backend_ctx->kernel_convert_block_q6_K_noshuffle; } #else @@ -9312,7 +9444,7 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer, tensor->extra = extra; #ifdef GGML_OPENCL_USE_ADRENO_KERNELS - if (use_adreno_kernels(backend_ctx, tensor) && !use_flat_gemv_for_large_m_q6_K(tensor)) { + if (use_adreno_kernels(backend_ctx, tensor) && !use_flat_gemv_for_large_m_q6_K(backend_ctx, tensor)) { cl_int M = tensor->ne[1]; // ne01 cl_int K = tensor->ne[0]; // ne00 @@ -10131,23 +10263,27 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer, size_t size_q = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*ggml_blck_size(tensor->type)/2; size_t size_d = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*sizeof(ggml_fp16_t); size_t size_dm = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*sizeof(ggml_fp16_t); + size_t size_s = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*12; static ggml_cl_buffer buf_trans_q; static ggml_cl_buffer buf_trans_d; static ggml_cl_buffer buf_trans_dm; + static ggml_cl_buffer buf_trans_s; buf_trans_q.allocate(backend_ctx->context, size_q); buf_trans_d.allocate(backend_ctx->context, size_d); buf_trans_dm.allocate(backend_ctx->context, size_dm); + buf_trans_s.allocate(backend_ctx->context, size_s); - // Transpose q, d, dm back + // Transpose q, d, dm, s back transpose_2d_as_16b(backend_ctx, extra->q, buf_trans_q.buffer, size_q, M, K/4); transpose_2d_as_16b(backend_ctx, extra->d, buf_trans_d.buffer, size_d, M, K/256); transpose_2d_as_16b(backend_ctx, extra->dm, buf_trans_dm.buffer, size_dm, M, K/256); + transpose_2d_as_8b (backend_ctx, extra->s, buf_trans_s.buffer, size_s, M, K/256*12, true, true); cl_kernel kernel = backend_ctx->kernel_restore_block_q4_K_noshuffle; CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &buf_trans_q.buffer)); - CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->s)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &buf_trans_s.buffer)); CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &buf_trans_d.buffer)); CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &buf_trans_dm.buffer)); CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &data_device)); @@ -10345,7 +10481,7 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer, CL_CHECK(clReleaseMemObject(data_device)); return; } - if (use_adreno_kernels(backend_ctx, tensor) && !use_flat_gemv_for_large_m_q6_K(tensor)) { + if (use_adreno_kernels(backend_ctx, tensor) && !use_flat_gemv_for_large_m_q6_K(backend_ctx, tensor)) { static ggml_cl_buffer buf_trans_ql; static ggml_cl_buffer buf_trans_qh; static ggml_cl_buffer buf_trans_s; @@ -10518,6 +10654,11 @@ static const char * ggml_backend_opencl_buffer_type_get_name(ggml_backend_buffer static ggml_backend_buffer_t ggml_backend_opencl_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buffer_type, size_t size) { ggml_backend_opencl_context *backend_ctx = ggml_cl_init(buffer_type->device); + + if (!backend_ctx->program_cache_initialized) { + backend_ctx->program_cache = cl_program_cache_init(backend_ctx->device); + backend_ctx->program_cache_initialized = true; + } load_cl_kernels(backend_ctx); // clCreateBuffer returns -61 for size 0 @@ -12640,7 +12781,7 @@ static void ggml_opencl_op_rms_norm_fused(ggml_backend_t backend, ggml_tensor * ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra; cl_ulong offset0 = extra0->offset + src0->view_offs; - cl_ulong offset1 = extra1->offset + src0->view_offs; + cl_ulong offset1 = extra1->offset + src1->view_offs; cl_ulong offsetd = extrad->offset + dst->view_offs; ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context; @@ -13321,6 +13462,102 @@ static void ggml_cl_expm1(ggml_backend_t backend, const ggml_tensor * src0, cons } } +static void ggml_cl_abs(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_ASSERT(src0); + GGML_ASSERT(src0->extra); + GGML_ASSERT(dst); + GGML_ASSERT(dst->extra); + + UNUSED(src1); + + ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context; + + ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra; + ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra; + + cl_ulong offset0 = extra0->offset + src0->view_offs; + cl_ulong offsetd = extrad->offset + dst->view_offs; + + const int ne00 = src0->ne[0]; + const int ne01 = src0->ne[1]; + const int ne02 = src0->ne[2]; + const int ne03 = src0->ne[3]; + + const cl_ulong nb00 = src0->nb[0]; + const cl_ulong nb01 = src0->nb[1]; + const cl_ulong nb02 = src0->nb[2]; + const cl_ulong nb03 = src0->nb[3]; + + const cl_ulong nb0 = dst->nb[0]; + const cl_ulong nb1 = dst->nb[1]; + const cl_ulong nb2 = dst->nb[2]; + const cl_ulong nb3 = dst->nb[3]; + + cl_kernel kernel; + + if (ggml_is_contiguous(src0)) { + // Handle contiguous input + int n = ggml_nelements(dst); + if (n % 4 == 0) { + if (src0->type == GGML_TYPE_F32) { + kernel = backend_ctx->kernel_abs_f32_4; + } else { + kernel = backend_ctx->kernel_abs_f16_4; + } + n /= 4; + } else { + if (src0->type == GGML_TYPE_F32) { + kernel = backend_ctx->kernel_abs_f32; + } else { + kernel = backend_ctx->kernel_abs_f16; + } + } + + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0->data_device)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extrad->data_device)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd)); + + size_t global_work_size[] = {(size_t)n, 1, 1}; + size_t local_work_size[] = {64, 1, 1}; + + size_t * local_work_size_ptr = local_work_size; + if (n % 64 != 0 && !backend_ctx->non_uniform_workgroups) { + local_work_size_ptr = nullptr; + } + + backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size_ptr, dst); + } else { + // Handle non-contiguous input + if (src0->type == GGML_TYPE_F32) { + kernel = backend_ctx->kernel_abs_f32_nc; + } else { + kernel = backend_ctx->kernel_abs_f16_nc; + } + + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0->data_device)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extrad->data_device)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd)); + CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int), &ne00)); + CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &nb00)); + CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_ulong), &nb01)); + CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_ulong), &nb02)); + CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_ulong), &nb03)); + CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_ulong), &nb0)); + CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb1)); + CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb2)); + CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb3)); + + int nth = 64; + + size_t global_work_size[] = {(size_t)ne01*nth, (size_t)ne02, (size_t)ne03}; + size_t local_work_size[] = {(size_t)nth, 1, 1}; + + backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst); + } +} + static void ggml_cl_softplus(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_ASSERT(src0); GGML_ASSERT(src0->extra); @@ -14544,7 +14781,11 @@ static void ggml_cl_flash_attn(ggml_backend_t backend, const ggml_tensor * q, co // Flash-Decoding K-split decision. Resolved here, before the prefill // prepass, because KV-pad and blk prepass are pure overhead when FD fires. - const int is_causal = (mask == NULL && n_q > 1 && n_q == n_kv); + // Do not infer causality from tensor shapes: a NULL mask means full + // (bidirectional) attention, e.g. ViT encoders, where n_q == n_kv as well. + // Causal attention in llama.cpp always comes with an explicit KQ mask. + // Inferring is_causal here corrupted mmproj output on OpenCL (see #23800). + const int is_causal = 0; const int fd_max_n_q = (d_head_q <= FD_MAX_DK_MULTI) ? FD_MAX_N_Q_MULTI : 1; cl_kernel fd_k_split = NULL; bool use_fd_mq = false; @@ -15443,7 +15684,7 @@ static void ggml_cl_mul_mat_kq_kqv_adreno(ggml_backend_t backend, const ggml_ten // <--------------------------------------------> // extra0 = src0->view_src ? (ggml_tensor_extra_cl *)src0->view_src->extra : (ggml_tensor_extra_cl *)src0->extra; - region.origin = (extra0->offset); + region.origin = (extra0->offset + src0->view_offs); if (nb01 > nb02) { // KQ region.size = nb01 * ne01; @@ -15459,7 +15700,7 @@ static void ggml_cl_mul_mat_kq_kqv_adreno(ggml_backend_t backend, const ggml_ten // create sub-buffer for B // <--------------------------------------------> // - region.origin = (extra1->offset); + region.origin = (extra1->offset + src1->view_offs); region.size = nb10 * ne10 * ne11 * ne12; B_sub_buffer = clCreateSubBuffer((extra1->data_device), 0, CL_BUFFER_CREATE_TYPE_REGION, ®ion, &status); CL_CHECK(status); @@ -15480,7 +15721,7 @@ static void ggml_cl_mul_mat_kq_kqv_adreno(ggml_backend_t backend, const ggml_ten // create sub-buffer for output C // <--------------------------------------------> // - region.origin = (extrad->offset); + region.origin = (extrad->offset + dst->view_offs); region.size = ne0 * ne1 * dst->ne[2] * dst->nb[0]; // size of C in bytes D_sub_buffer = clCreateSubBuffer((extrad->data_device), 0, CL_BUFFER_CREATE_TYPE_REGION, ®ion, &status); CL_CHECK(status); @@ -16871,9 +17112,6 @@ static void ggml_cl_mul_mat_q8_0_f32_adreno(ggml_backend_t backend, const ggml_t cl_ulong offset1 = extra1->offset + src1->view_offs; cl_ulong offsetd = extrad->offset + dst->view_offs; - GGML_ASSERT(src1->view_offs == 0); - GGML_ASSERT(dst->view_offs == 0); - const int ne00 = src0->ne[0]; const int ne01 = src0->ne[1]; const int ne02 = src0->ne[2]; @@ -16934,9 +17172,9 @@ static void ggml_cl_mul_mat_q8_0_f32_adreno(ggml_backend_t backend, const ggml_t CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &q_img)); CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra0_q8_0->d)); CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &b_img)); - CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &extra1->offset)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offset1)); CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extrad->data_device)); - CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &extrad->offset)); + CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd)); CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &ne00)); CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &ne01)); CL_CHECK(clSetKernelArg(kernel, 8, sizeof(int), &ne02)); @@ -18331,6 +18569,26 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co GGML_ASSERT(ne00 == ne10); +#ifdef GGML_OPENCL_USE_ADRENO_KERNELS + // adreno GEMM/GEMV kernels do not support broadcast, assuming ne2 and ne3 are 1 for src1 + // so we handle broadcast here + if ((ne12 > 1 || ne13 > 1) && ne02 == 1 && ne03 == 1 && + src0t != GGML_TYPE_F16 && src0t != GGML_TYPE_F32) { + for (int i13 = 0; i13 < ne13; ++i13) { + for (int i12 = 0; i12 < ne12; ++i12) { + ggml_tensor s1 = *src1; + s1.ne[2] = 1; s1.ne[3] = 1; + s1.view_offs = src1->view_offs + (size_t)i12*nb12 + (size_t)i13*nb13; + ggml_tensor d = *dst; + d.ne[2] = 1; d.ne[3] = 1; + d.view_offs = dst->view_offs + (size_t)i12*nb2 + (size_t)i13*nb3; + ggml_cl_mul_mat(backend, src0, &s1, &d); + } + } + return; + } +#endif + int nth0 = 32; int nth1 = 1; int nrows = 1; @@ -18342,6 +18600,8 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co #ifdef GGML_OPENCL_USE_ADRENO_KERNELS if(src0t == GGML_TYPE_F16 && src1t == GGML_TYPE_F32){ if (ne01 >= 64 && ne1 >= 32 && ne00 >= 16 && (ne12 % ne02) == 0 && + // the KQ/KQV image kernels do not handle dim 3 (multi-stream batches) + ne03 == 1 && ne13 == 1 && // dst is wrapped with image1d_buffer, the size limit applies, also src0 (ne0 * ne1 * dst->ne[2] * dst->nb[0] / 4 <= backend_ctx->image_max_buffer_size)) { // For KQ @@ -18643,7 +18903,7 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co } // q6_K x fp32 - if (src0t == GGML_TYPE_Q6_K && src1t == GGML_TYPE_F32 && !use_flat_gemv_for_large_m_q6_K(src0)) { + if (src0t == GGML_TYPE_Q6_K && src1t == GGML_TYPE_F32 && !use_flat_gemv_for_large_m_q6_K(backend_ctx, src0)) { ggml_cl_mul_mat_q6_K_f32_adreno(backend, src0, src1, dst); return; } @@ -22095,8 +22355,8 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0, bool use_moe_dp4a = q5kmdp4a_on && backend_ctx->kernel_gemm_moe_q8_1_dp4a_q5k != nullptr && extra0_q5_K->scale != nullptr; - // bin kernel takes precedence - use_moe_dp4a = use_moe_dp4a && backend_ctx->kernel_gemm_moe_q4_k_f32_ns_bin == nullptr; + // dot prod has to be available + use_moe_dp4a = backend_ctx->has_integer_dot && use_moe_dp4a; if (use_moe_dp4a) { const size_t tok_slots = (size_t)max_post_router_tile * n_tile_size; @@ -22314,6 +22574,9 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0, } else { // for gemm kernel = backend_ctx->kernel_gemm_moe_q6_k_f32_ns; + if (backend_ctx->kernel_gemm_moe_q6_k_f32_ns_bin) { + kernel = backend_ctx->kernel_gemm_moe_q6_k_f32_ns_bin; + } // Reorder router if called from test-backend-ops or when new router is generated. // Otherwise reuse the reordered result from previous mul_mat_id call. @@ -22334,6 +22597,8 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0, || backend_ctx->adreno_gen == ADRENO_GPU_GEN::X1E); // dot prod has to be available use_moe_dp4a = backend_ctx->has_integer_dot && use_moe_dp4a; + // bin kernel takes precedence + use_moe_dp4a = use_moe_dp4a && backend_ctx->kernel_gemm_moe_q6_k_f32_ns_bin == nullptr; cl_buffer_region region; region.origin = 0; @@ -22370,6 +22635,11 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0, CL_CHECK(status); cl_image_format image_format_buf_src1 = {CL_RGBA, CL_FLOAT}; cl_image_desc image_desc_buf_src1 = {CL_MEM_OBJECT_IMAGE1D_BUFFER, static_cast(ne00 * max_post_router_tile * n_tile_size / 4), 0,0,0,0,0,0,0, {buf_src1_reordered}}; + if (backend_ctx->kernel_gemm_moe_q6_k_f32_ns_bin) { + // bin kernel uses slightly different image format + image_format_buf_src1 = {CL_R, CL_FLOAT}; + image_desc_buf_src1.image_width = static_cast(ne00 * max_post_router_tile * n_tile_size); + } image_src1_reordered = clCreateImage(backend_ctx->context, CL_MEM_READ_ONLY, &image_format_buf_src1, &image_desc_buf_src1, NULL, &status); CL_CHECK(status); @@ -23998,7 +24268,7 @@ static void ggml_cl_glu(ggml_backend_t backend, const ggml_tensor * src0, const } const size_t nrows = ggml_nrows(src0); - size_t nth = 512; + size_t nth = backend_ctx->max_workgroup_size < 512 ? backend_ctx->max_workgroup_size : 512; size_t global_work_size[] = {nrows*nth, 1, 1}; size_t local_work_size[] = {nth, 1, 1}; @@ -24342,6 +24612,12 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor } func = ggml_cl_expm1; break; + case GGML_UNARY_OP_ABS: + if (!any_on_device) { + return false; + } + func = ggml_cl_abs; + break; case GGML_UNARY_OP_SOFTPLUS: if (!any_on_device) { return false; diff --git a/ggml/src/ggml-opencl/kernels/abs.cl b/ggml/src/ggml-opencl/kernels/abs.cl new file mode 100644 index 000000000000..96e952c2842a --- /dev/null +++ b/ggml/src/ggml-opencl/kernels/abs.cl @@ -0,0 +1,113 @@ +#pragma OPENCL EXTENSION cl_khr_fp16 : enable + +//------------------------------------------------------------------------------ +// abs +//------------------------------------------------------------------------------ + +kernel void kernel_abs_f32( + global const float * src0, + ulong offset0, + global float * dst, + ulong offsetd +) { + src0 = (global float*)((global char*)src0 + offset0); + dst = (global float*)((global char*)dst + offsetd); + + dst[get_global_id(0)] = fabs(src0[get_global_id(0)]); +} + +kernel void kernel_abs_f32_4( + global const float4 * src0, + ulong offset0, + global float4 * dst, + ulong offsetd +) { + src0 = (global float4*)((global char*)src0 + offset0); + dst = (global float4*)((global char*)dst + offsetd); + + dst[get_global_id(0)] = fabs(src0[get_global_id(0)]); +} + +kernel void kernel_abs_f16( + global const half * src0, + ulong offset0, + global half * dst, + ulong offsetd +) { + src0 = (global half*)((global char*)src0 + offset0); + dst = (global half*)((global char*)dst + offsetd); + + dst[get_global_id(0)] = fabs(src0[get_global_id(0)]); +} + +kernel void kernel_abs_f16_4( + global const half4 * src0, + ulong offset0, + global half4 * dst, + ulong offsetd +) { + src0 = (global half4*)((global char*)src0 + offset0); + dst = (global half4*)((global char*)dst + offsetd); + + dst[get_global_id(0)] = fabs(src0[get_global_id(0)]); +} + +kernel void kernel_abs_f32_nc( + global const char * src0, + ulong offset0, + global char * dst, + ulong offsetd, + int ne00, + ulong nb00, + ulong nb01, + ulong nb02, + ulong nb03, + ulong nb0, + ulong nb1, + ulong nb2, + ulong nb3 +) { + src0 = src0 + offset0; + dst = dst + offsetd; + + const int i3 = get_group_id(2); + const int i2 = get_group_id(1); + const int i1 = get_group_id(0); + + for (int i0 = get_local_id(0); i0 < ne00; i0 += get_local_size(0)) { + global const float * x = (global const float *)(src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + global float * y = (global float *)(dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + *y = fabs(*x); + } +} + +kernel void kernel_abs_f16_nc( + global const char * src0, + ulong offset0, + global char * dst, + ulong offsetd, + int ne00, + ulong nb00, + ulong nb01, + ulong nb02, + ulong nb03, + ulong nb0, + ulong nb1, + ulong nb2, + ulong nb3 +) { + src0 = src0 + offset0; + dst = dst + offsetd; + + const int i3 = get_group_id(2); + const int i2 = get_group_id(1); + const int i1 = get_group_id(0); + + for (int i0 = get_local_id(0); i0 < ne00; i0 += get_local_size(0)) { + global const half * x = (global const half *)(src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + global half * y = (global half *)(dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + *y = fabs(*x); + } +} diff --git a/ggml/src/ggml-opencl/kernels/flash_attn_f32_f16.cl b/ggml/src/ggml-opencl/kernels/flash_attn_f32_f16.cl index 1cc0cc8c3439..6e43ee81e73b 100644 --- a/ggml/src/ggml-opencl/kernels/flash_attn_f32_f16.cl +++ b/ggml/src/ggml-opencl/kernels/flash_attn_f32_f16.cl @@ -30,6 +30,10 @@ #elif defined(cl_qcom_subgroup_shuffle) #pragma OPENCL EXTENSION cl_qcom_subgroup_shuffle : enable #define HAS_SUBGROUP_SHUFFLE 1 +// Adreno compilers that expose only cl_qcom_subgroup_shuffle do not declare the KHR +// name, so calling it is an implicit declaration and the program fails to build. +// Route it to the qcom builtin. +#define sub_group_shuffle_xor(val, mask) qcom_sub_group_shuffle_xor((val), (mask), CLK_SUB_GROUP_SHUFFLE_WIDTH_WAVE_SIZE_QCOM, 0.0f) #endif #define ACC_TYPE float diff --git a/ggml/src/ggml-opencl/kernels/flash_attn_f32_q4_0.cl b/ggml/src/ggml-opencl/kernels/flash_attn_f32_q4_0.cl index de09a1eaae37..95d215971e00 100644 --- a/ggml/src/ggml-opencl/kernels/flash_attn_f32_q4_0.cl +++ b/ggml/src/ggml-opencl/kernels/flash_attn_f32_q4_0.cl @@ -10,6 +10,10 @@ #elif defined(cl_qcom_subgroup_shuffle) #pragma OPENCL EXTENSION cl_qcom_subgroup_shuffle : enable #define HAS_SUBGROUP_SHUFFLE 1 +// Adreno compilers that expose only cl_qcom_subgroup_shuffle do not declare the KHR +// name, so calling it is an implicit declaration and the program fails to build. +// Route it to the qcom builtin. +#define sub_group_shuffle_xor(val, mask) qcom_sub_group_shuffle_xor((val), (mask), CLK_SUB_GROUP_SHUFFLE_WIDTH_WAVE_SIZE_QCOM, 0.0f) #endif // Flash attention: Q=f32, K=q4_0, V=q4_0. diff --git a/ggml/src/ggml-opencl/kernels/flash_attn_f32_q8_0.cl b/ggml/src/ggml-opencl/kernels/flash_attn_f32_q8_0.cl index 46bc4bc9d94e..7e89ed0bd8f1 100644 --- a/ggml/src/ggml-opencl/kernels/flash_attn_f32_q8_0.cl +++ b/ggml/src/ggml-opencl/kernels/flash_attn_f32_q8_0.cl @@ -10,6 +10,10 @@ #elif defined(cl_qcom_subgroup_shuffle) #pragma OPENCL EXTENSION cl_qcom_subgroup_shuffle : enable #define HAS_SUBGROUP_SHUFFLE 1 +// Adreno compilers that expose only cl_qcom_subgroup_shuffle do not declare the KHR +// name, so calling it is an implicit declaration and the program fails to build. +// Route it to the qcom builtin. +#define sub_group_shuffle_xor(val, mask) qcom_sub_group_shuffle_xor((val), (mask), CLK_SUB_GROUP_SHUFFLE_WIDTH_WAVE_SIZE_QCOM, 0.0f) #endif // Flash attention: Q=f32, K=q8_0, V=q8_0. diff --git a/ggml/src/ggml-opencl/kernels/gemm_moe_mxfp4_f32_ns.cl b/ggml/src/ggml-opencl/kernels/gemm_moe_mxfp4_f32_ns.cl index 834050a4f9ac..10c8855c1ee3 100644 --- a/ggml/src/ggml-opencl/kernels/gemm_moe_mxfp4_f32_ns.cl +++ b/ggml/src/ggml-opencl/kernels/gemm_moe_mxfp4_f32_ns.cl @@ -274,8 +274,9 @@ kernel void kernel_gemm_moe_mxfp4_f32_ns( shared_b[b_local_offset.y] = bx8_f16.hi; // Dequantization - reg_a.lo = mxfp4_to_fp16_packed8(as_ushort2(mxfp4x16.lo)) * s; - reg_a.hi = mxfp4_to_fp16_packed8(as_ushort2(mxfp4x16.hi)) * s; + // Cast the e8m0 scale to half to satisfy E17 compilers + reg_a.lo = mxfp4_to_fp16_packed8(as_ushort2(mxfp4x16.lo)) * (half)s; + reg_a.hi = mxfp4_to_fp16_packed8(as_ushort2(mxfp4x16.hi)) * (half)s; sub_group_barrier(CLK_LOCAL_MEM_FENCE); @@ -304,8 +305,9 @@ kernel void kernel_gemm_moe_mxfp4_f32_ns( shared_b[b_local_offset.y] = bx8_f16.hi; // Dequantization - reg_a.lo = mxfp4_to_fp16_packed8(as_ushort2(mxfp4x16.lo)) * s; - reg_a.hi = mxfp4_to_fp16_packed8(as_ushort2(mxfp4x16.hi)) * s; + // Cast the e8m0 scale to half to satisfy E17 compilers + reg_a.lo = mxfp4_to_fp16_packed8(as_ushort2(mxfp4x16.lo)) * (half)s; + reg_a.hi = mxfp4_to_fp16_packed8(as_ushort2(mxfp4x16.hi)) * (half)s; sub_group_barrier(CLK_LOCAL_MEM_FENCE); diff --git a/ggml/src/ggml-opencl/kernels/gemm_moe_mxfp4_q8_1_dp4a.cl b/ggml/src/ggml-opencl/kernels/gemm_moe_mxfp4_q8_1_dp4a.cl index 95d0638134e1..97fdc8e18c82 100644 --- a/ggml/src/ggml-opencl/kernels/gemm_moe_mxfp4_q8_1_dp4a.cl +++ b/ggml/src/ggml-opencl/kernels/gemm_moe_mxfp4_q8_1_dp4a.cl @@ -37,15 +37,17 @@ static inline float e8m0_to_fp32(uchar x) { // One token's dp4a dot (8 uints = 32 K elems) + mxfp4 block-scale epilogue. // blk_scale already carries the 0.5 factor (== 0.5 * 2^e). #define MOE_MXFP4_DP4A_T(t) do { \ + uint4 a0 = vload4(0, &sh_qa[t][0]); \ + uint4 a1 = vload4(0, &sh_qa[t][4]); \ int raw = 0; \ - raw = dot_acc_sat_4x8packed_ss_int(qw[0], sh_qa[t][0], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[1], sh_qa[t][1], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[2], sh_qa[t][2], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[3], sh_qa[t][3], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[4], sh_qa[t][4], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[5], sh_qa[t][5], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[6], sh_qa[t][6], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[7], sh_qa[t][7], raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[0], a0.s0, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[1], a0.s1, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[2], a0.s2, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[3], a0.s3, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[4], a1.s0, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[5], a1.s1, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[6], a1.s2, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[7], a1.s3, raw); \ acc[t] += blk_scale * (float)sh_d[t] * (float)raw; \ } while (0) @@ -133,11 +135,13 @@ kernel void kernel_gemm_moe_mxfp4_q8_1_dp4a( qw[6] = mxfp4_pack((ushort)(r3)); qw[7] = mxfp4_pack((ushort)(r3 >> 16)); // cooperatively stage the n_real-token x 32-K int8 activations - const uint stage_lim = (uint)n_real * 8; - for (uint idx = lid; idx < stage_lim; idx += 64) { - const uint t = idx >> 3; - const uint u = idx & 7; - sh_qa[t][u] = src1_qa[(col + t) * ne00_u + (step >> 2) + u]; + // Stage each token's 8 activation uints as two 128-bit uint4 loads/stores. + const uint vlim = (uint)n_real * 2; + for (uint idx = lid; idx < vlim; idx += 64) { + const uint t = idx >> 1; + const uint h = (idx & 1) << 2; // 0 or 4 + uint4 v = vload4(0, &src1_qa[(col + t) * ne00_u + (step >> 2) + h]); + vstore4(v, 0, &sh_qa[t][h]); } if (lid < (uint)n_real) { sh_d[lid] = src1_da[(col + lid) * num_blocks + sub]; diff --git a/ggml/src/ggml-opencl/kernels/gemm_moe_q4_0_q8_1_dp4a.cl b/ggml/src/ggml-opencl/kernels/gemm_moe_q4_0_q8_1_dp4a.cl index 86ff943c51b2..502472049a9c 100644 --- a/ggml/src/ggml-opencl/kernels/gemm_moe_q4_0_q8_1_dp4a.cl +++ b/ggml/src/ggml-opencl/kernels/gemm_moe_q4_0_q8_1_dp4a.cl @@ -17,15 +17,17 @@ // One token's dp4a dot (8 uints = 32 K elems) + q4_0 scale/zero-point epilogue. #define MOE_Q40_DP4A_T(t) do { \ + uint4 a0 = vload4(0, &sh_qa[t][0]); \ + uint4 a1 = vload4(0, &sh_qa[t][4]); \ int raw = 0; \ - raw = dot_acc_sat_4x8packed_ss_int(qw[0], sh_qa[t][0], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[1], sh_qa[t][1], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[2], sh_qa[t][2], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[3], sh_qa[t][3], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[4], sh_qa[t][4], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[5], sh_qa[t][5], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[6], sh_qa[t][6], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[7], sh_qa[t][7], raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[0], a0.s0, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[1], a0.s1, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[2], a0.s2, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[3], a0.s3, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[4], a1.s0, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[5], a1.s1, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[6], a1.s2, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[7], a1.s3, raw); \ acc[t] += d_val * ((float)sh_d[t] * (float)raw - 8.0f * (float)sh_s[t]); \ } while (0) @@ -112,11 +114,13 @@ kernel void kernel_gemm_moe_q4_0_q8_1_dp4a( qw[6] = EXP4(r3); qw[7] = EXP4(r3 >> 16); // cooperatively stage the n_real-token x 32-K int8 activations - const uint stage_lim = (uint)n_real * 8; - for (uint idx = lid; idx < stage_lim; idx += 64) { - const uint t = idx >> 3; - const uint u = idx & 7; - sh_qa[t][u] = src1_qa[(col + t) * ne00_u + (step >> 2) + u]; + // Stage each token's 8 activation uints as two 128-bit uint4 loads/stores. + const uint vlim = (uint)n_real * 2; + for (uint idx = lid; idx < vlim; idx += 64) { + const uint t = idx >> 1; + const uint h = (idx & 1) << 2; // 0 or 4 + uint4 v = vload4(0, &src1_qa[(col + t) * ne00_u + (step >> 2) + h]); + vstore4(v, 0, &sh_qa[t][h]); } if (lid < (uint)n_real) { sh_d[lid] = src1_da[(col + lid) * num_blocks + sub]; diff --git a/ggml/src/ggml-opencl/kernels/gemm_moe_q4_k_q8_1_dp4a.cl b/ggml/src/ggml-opencl/kernels/gemm_moe_q4_k_q8_1_dp4a.cl index 540897544082..9d968f32ed5a 100644 --- a/ggml/src/ggml-opencl/kernels/gemm_moe_q4_k_q8_1_dp4a.cl +++ b/ggml/src/ggml-opencl/kernels/gemm_moe_q4_k_q8_1_dp4a.cl @@ -37,16 +37,21 @@ inline void get_scale_min_k4( (((uint)((u) & 0xF000u)) << 12) ) // One token's dp4a dot (8 uints = 32 K elems) + q4_K scale/min epilogue into acc[t]. +// The 8 activation uints are read as two 128-bit uint4 loads staged to private (Adreno +// wants 128-bit local reads, and a __local operand fed straight to the dp4a builtin is +// slower and can miscompile). #define MOE_Q4K_DP4A_T(t) do { \ + uint4 a0 = vload4(0, &sh_qa[t][0]); \ + uint4 a1 = vload4(0, &sh_qa[t][4]); \ int raw = 0; \ - raw = dot_acc_sat_4x8packed_ss_int(qw[0], sh_qa[t][0], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[1], sh_qa[t][1], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[2], sh_qa[t][2], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[3], sh_qa[t][3], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[4], sh_qa[t][4], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[5], sh_qa[t][5], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[6], sh_qa[t][6], raw); \ - raw = dot_acc_sat_4x8packed_ss_int(qw[7], sh_qa[t][7], raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[0], a0.s0, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[1], a0.s1, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[2], a0.s2, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[3], a0.s3, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[4], a1.s0, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[5], a1.s1, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[6], a1.s2, raw); \ + raw = dot_acc_sat_4x8packed_ss_int(qw[7], a1.s3, raw); \ acc[t] += scale * (float)sh_d[t] * (float)raw - minv * (float)sh_s[t]; \ } while (0) @@ -145,12 +150,14 @@ kernel void kernel_gemm_moe_q4_k_q8_1_dp4a( qw[4] = EXP4(r2); qw[5] = EXP4(r2 >> 16); qw[6] = EXP4(r3); qw[7] = EXP4(r3 >> 16); - // --- cooperatively stage the n_real-token x 32-K int8 activations to LDS --- - const uint stage_lim = (uint)n_real * 8; - for (uint idx = lid; idx < stage_lim; idx += 64) { - const uint t = idx >> 3; - const uint u = idx & 7; - sh_qa[t][u] = src1_qa[(col + t) * ne00_u + (step >> 2) + u]; + // cooperatively stage the n_real-token x 32-K int8 activations to lm + // Stage each token's 8 activation uints as two 128-bit uint4 loads/stores. + const uint vlim = (uint)n_real * 2; + for (uint idx = lid; idx < vlim; idx += 64) { + const uint t = idx >> 1; + const uint h = (idx & 1) << 2; // 0 or 4 + uint4 v = vload4(0, &src1_qa[(col + t) * ne00_u + (step >> 2) + h]); + vstore4(v, 0, &sh_qa[t][h]); } if (lid < (uint)n_real) { sh_d[lid] = src1_da[(col + lid) * ne00_b + sub]; diff --git a/ggml/src/ggml-opencl/kernels/gemm_moe_q6_k_q8_1_dp4a.cl b/ggml/src/ggml-opencl/kernels/gemm_moe_q6_k_q8_1_dp4a.cl index 35e63dcab05f..4ffe9f8e66c6 100644 --- a/ggml/src/ggml-opencl/kernels/gemm_moe_q6_k_q8_1_dp4a.cl +++ b/ggml/src/ggml-opencl/kernels/gemm_moe_q6_k_q8_1_dp4a.cl @@ -41,8 +41,10 @@ inline int dp4a_q6(uint qw0, uint qw1, uint qw2, uint qw3, // One token's q6_K dp4a dot (two halves, per-16 scales) + epilogue into acc[t]. #define MOE_Q6K_DP4A_T(t) do { \ - const int raw1 = dp4a_q6(qw[0], qw[1], qw[2], qw[3], sh_qa[t][0], sh_qa[t][1], sh_qa[t][2], sh_qa[t][3]); \ - const int raw2 = dp4a_q6(qw[4], qw[5], qw[6], qw[7], sh_qa[t][4], sh_qa[t][5], sh_qa[t][6], sh_qa[t][7]); \ + uint4 a0 = vload4(0, &sh_qa[t][0]); \ + uint4 a1 = vload4(0, &sh_qa[t][4]); \ + const int raw1 = dp4a_q6(qw[0], qw[1], qw[2], qw[3], a0.s0, a0.s1, a0.s2, a0.s3); \ + const int raw2 = dp4a_q6(qw[4], qw[5], qw[6], qw[7], a1.s0, a1.s1, a1.s2, a1.s3); \ const float a_d = (float)sh_d[t]; \ acc[t] += scale0 * a_d * (float)raw1 + scale1 * a_d * (float)raw2; \ } while (0) @@ -144,11 +146,13 @@ kernel void kernel_gemm_moe_q6_k_q8_1_dp4a( qw[6] = SIGN6(EXP4(r3) | EXP2((qh2 >> 16) & 0xFFu)); qw[7] = SIGN6(EXP4(r3 >> 16) | EXP2((qh2 >> 24) & 0xFFu)); - const uint stage_lim = (uint)n_real * 8; - for (uint idx = lid; idx < stage_lim; idx += 64) { - const uint t = idx >> 3; - const uint u = idx & 7; - sh_qa[t][u] = src1_qa[(col + t) * ne00_u + (step >> 2) + u]; + // Stage each token's 8 activation uints as two 128-bit uint4 loads/stores. + const uint vlim = (uint)n_real * 2; + for (uint idx = lid; idx < vlim; idx += 64) { + const uint t = idx >> 1; + const uint h = (idx & 1) << 2; // 0 or 4 + uint4 v = vload4(0, &src1_qa[(col + t) * ne00_u + (step >> 2) + h]); + vstore4(v, 0, &sh_qa[t][h]); } if (lid < (uint)n_real) { sh_d[lid] = src1_da[(col + lid) * ne00_b + sub]; diff --git a/ggml/src/ggml-opencl/kernels/gemm_moe_q8_1_dp4a.cl b/ggml/src/ggml-opencl/kernels/gemm_moe_q8_1_dp4a.cl index 39bf5d83211c..d0b191e18363 100644 --- a/ggml/src/ggml-opencl/kernels/gemm_moe_q8_1_dp4a.cl +++ b/ggml/src/ggml-opencl/kernels/gemm_moe_q8_1_dp4a.cl @@ -102,8 +102,10 @@ inline int dp4a4(uint w0,uint w1,uint w2,uint w3,uint a0,uint a1,uint a2,uint a3 // One token's two-half dp4a + uniform scale/min epilogue into acc[t]. #define MOE_DP4A_T(t) do { \ - const int raw1 = dp4a4(qw[0],qw[1],qw[2],qw[3], sh_qa[t][0],sh_qa[t][1],sh_qa[t][2],sh_qa[t][3]); \ - const int raw2 = dp4a4(qw[4],qw[5],qw[6],qw[7], sh_qa[t][4],sh_qa[t][5],sh_qa[t][6],sh_qa[t][7]); \ + uint4 a0 = vload4(0, &sh_qa[t][0]); \ + uint4 a1 = vload4(0, &sh_qa[t][4]); \ + const int raw1 = dp4a4(qw[0],qw[1],qw[2],qw[3], a0.s0,a0.s1,a0.s2,a0.s3); \ + const int raw2 = dp4a4(qw[4],qw[5],qw[6],qw[7], a1.s0,a1.s1,a1.s2,a1.s3); \ const float a_d = (float)sh_d[t]; \ acc[t] += sc0*a_d*(float)raw1 + sc1*a_d*(float)raw2 - mn*(float)sh_s[t]; \ } while (0) @@ -178,10 +180,13 @@ kernel void kernel_gemm_moe_q8_1_dp4a( LOAD_QW(step, sub) - const uint stage_lim = (uint)n_real * 8; - for (uint idx = lid; idx < stage_lim; idx += 64) { - const uint t = idx >> 3, u = idx & 7; - sh_qa[t][u] = src1_qa[(col + t) * ne00_u + (step >> 2) + u]; + // Stage each token's 8 activation uints as two 128-bit uint4 loads/stores. + const uint vlim = (uint)n_real * 2; + for (uint idx = lid; idx < vlim; idx += 64) { + const uint t = idx >> 1; + const uint h = (idx & 1) << 2; // 0 or 4 + uint4 v = vload4(0, &src1_qa[(col + t) * ne00_u + (step >> 2) + h]); + vstore4(v, 0, &sh_qa[t][h]); } if (lid < (uint)n_real) { sh_d[lid] = src1_da[(col + lid) * ne00_b + sub]; diff --git a/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q4_k_f32.cl b/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q4_k_f32.cl index 99fd1fd7bf1e..22b4e9114628 100644 --- a/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q4_k_f32.cl +++ b/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q4_k_f32.cl @@ -8,9 +8,11 @@ #define QK_K 256 #define K_SCALE_SIZE 12 +// scales are transposed: consecutive codes of a row are `stride` apart inline void get_scale_min_k4( int j, global const uchar * q, + int stride, uchar * d, uchar * m, uchar mask_d6, @@ -18,11 +20,11 @@ inline void get_scale_min_k4( uchar mask_hi2 ) { if (j < 4) { - *d = q[j] & mask_d6; - *m = q[j+4] & mask_d6; + *d = q[j*stride] & mask_d6; + *m = q[(j+4)*stride] & mask_d6; } else { - *d = (q[j+4] & mask_d4) | ((q[j-4] & mask_hi2) >> 2); - *m = ((q[j+4] >> 4) & mask_d4) | ((q[j] & mask_hi2) >> 2); + *d = (q[(j+4)*stride] & mask_d4) | ((q[(j-4)*stride] & mask_hi2) >> 2); + *m = ((q[(j+4)*stride] >> 4) & mask_d4) | ((q[j*stride] & mask_hi2) >> 2); } } @@ -55,7 +57,6 @@ kernel void kernel_gemm_noshuffle_q4_k_f32( half8 B; half4 dequantized_weights; - int num_blocks_K = k / QK_K; global const ushort * weight_ptr = src0_q + gx_2; global const half * d_ptr = src0_d + gx_2; @@ -68,16 +69,16 @@ kernel void kernel_gemm_noshuffle_q4_k_f32( half4 d = vload4(0, d_ptr + sb_idx * m); half4 dm = vload4(0, dm_ptr + sb_idx * m); - global const uchar * sc0 = src0_s + (gx_2+0) * num_blocks_K * K_SCALE_SIZE + sb_idx * K_SCALE_SIZE; - global const uchar * sc1 = src0_s + (gx_2+1) * num_blocks_K * K_SCALE_SIZE + sb_idx * K_SCALE_SIZE; - global const uchar * sc2 = src0_s + (gx_2+2) * num_blocks_K * K_SCALE_SIZE + sb_idx * K_SCALE_SIZE; - global const uchar * sc3 = src0_s + (gx_2+3) * num_blocks_K * K_SCALE_SIZE + sb_idx * K_SCALE_SIZE; + global const uchar * sc0 = src0_s + sb_idx * K_SCALE_SIZE * m + (gx_2+0); + global const uchar * sc1 = sc0 + 1; + global const uchar * sc2 = sc0 + 2; + global const uchar * sc3 = sc0 + 3; uchar sv0, mn0, sv1, mn1, sv2, mn2, sv3, mn3; - get_scale_min_k4(sub_idx, sc0, &sv0, &mn0, mask_d6, mask_d4, mask_hi2); - get_scale_min_k4(sub_idx, sc1, &sv1, &mn1, mask_d6, mask_d4, mask_hi2); - get_scale_min_k4(sub_idx, sc2, &sv2, &mn2, mask_d6, mask_d4, mask_hi2); - get_scale_min_k4(sub_idx, sc3, &sv3, &mn3, mask_d6, mask_d4, mask_hi2); + get_scale_min_k4(sub_idx, sc0, m, &sv0, &mn0, mask_d6, mask_d4, mask_hi2); + get_scale_min_k4(sub_idx, sc1, m, &sv1, &mn1, mask_d6, mask_d4, mask_hi2); + get_scale_min_k4(sub_idx, sc2, m, &sv2, &mn2, mask_d6, mask_d4, mask_hi2); + get_scale_min_k4(sub_idx, sc3, m, &sv3, &mn3, mask_d6, mask_d4, mask_hi2); half4 scale = convert_half4(convert_float4(d) * convert_float4((uchar4)(sv0, sv1, sv2, sv3))); half4 mval = convert_half4(convert_float4(dm) * convert_float4((uchar4)(mn0, mn1, mn2, mn3))); diff --git a/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q4_k_q8_1_dp4a.cl b/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q4_k_q8_1_dp4a.cl index 86561801499e..a3b39b6aa984 100644 --- a/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q4_k_q8_1_dp4a.cl +++ b/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q4_k_q8_1_dp4a.cl @@ -10,9 +10,11 @@ #define QK_K 256 #define K_SCALE_SIZE 12 +// scales are transposed: consecutive codes of a row are `stride` apart inline void get_scale_min_k4( int j, global const uchar * q, + uint stride, uchar * d, uchar * m, uchar mask_d6, @@ -20,11 +22,11 @@ inline void get_scale_min_k4( uchar mask_hi2 ) { if (j < 4) { - *d = q[j] & mask_d6; - *m = q[j+4] & mask_d6; + *d = q[j*stride] & mask_d6; + *m = q[(j+4)*stride] & mask_d6; } else { - *d = (q[j+4] & mask_d4) | ((q[j-4] & mask_hi2) >> 2); - *m = ((q[j+4] >> 4) & mask_d4) | ((q[j] & mask_hi2) >> 2); + *d = (q[(j+4)*stride] & mask_d4) | ((q[(j-4)*stride] & mask_hi2) >> 2); + *m = ((q[(j+4)*stride] >> 4) & mask_d4) | ((q[j*stride] & mask_hi2) >> 2); } } @@ -79,7 +81,6 @@ kernel void kernel_gemm_noshuffle_q4_k_q8_1_dp4a( const bool row_valid = row < (uint)m; const uint rrow = row_valid ? row : 0; // clamp OOB rows; their writes are masked - const uint num_superblocks = (uint)k / QK_K; const uint k_u = (uint)k >> 2; // K in uint (int8x4) units const uint k_b = (uint)k >> 5; // blocks-of-32 along K @@ -101,9 +102,9 @@ kernel void kernel_gemm_noshuffle_q4_k_q8_1_dp4a( // weight scale/min for this WI's row, this subblock const float dd = (float)src0_d [rrow + sb_idx * m]; const float dmm = (float)src0_dm[rrow + sb_idx * m]; - global const uchar * sc = src0_s + rrow * num_superblocks * K_SCALE_SIZE + sb_idx * K_SCALE_SIZE; + global const uchar * sc = src0_s + sb_idx * K_SCALE_SIZE * (uint)m + rrow; uchar sv, mn; - get_scale_min_k4(sub_idx, sc, &sv, &mn, mask_d6, mask_d4, mask_hi2); + get_scale_min_k4(sub_idx, sc, (uint)m, &sv, &mn, mask_d6, mask_d4, mask_hi2); const float scale = dd * (float)sv; const float minv = dmm * (float)mn; @@ -202,7 +203,6 @@ kernel void kernel_gemm_noshuffle_q4_k_q8_1_dp4a_wimg( const uint k_u = (uint)k >> 2; // K in uint (int8x4) units const uint k_b = (uint)k >> 5; // blocks-of-32 along K - const uint num_superblocks = (uint)k / QK_K; __local uint sh_qa[TILESIZE_N][8]; __local half sh_d[TILESIZE_N]; @@ -220,9 +220,9 @@ kernel void kernel_gemm_noshuffle_q4_k_q8_1_dp4a_wimg( const float dd = (float)src0_d [rrow + sb_idx * m]; const float dmm = (float)src0_dm[rrow + sb_idx * m]; - global const uchar * sc = src0_s + rrow * num_superblocks * K_SCALE_SIZE + sb_idx * K_SCALE_SIZE; + global const uchar * sc = src0_s + sb_idx * K_SCALE_SIZE * (uint)m + rrow; uchar sv, mn; - get_scale_min_k4(sub_idx, sc, &sv, &mn, mask_d6, mask_d4, mask_hi2); + get_scale_min_k4(sub_idx, sc, (uint)m, &sv, &mn, mask_d6, mask_d4, mask_hi2); const float scale = dd * (float)sv; const float minv = dmm * (float)mn; diff --git a/ggml/src/ggml-opencl/kernels/gemv_noshuffle_q4_k_f32.cl b/ggml/src/ggml-opencl/kernels/gemv_noshuffle_q4_k_f32.cl index 2eb20e2f7625..c1829fc38208 100644 --- a/ggml/src/ggml-opencl/kernels/gemv_noshuffle_q4_k_f32.cl +++ b/ggml/src/ggml-opencl/kernels/gemv_noshuffle_q4_k_f32.cl @@ -11,9 +11,11 @@ #define NSUBGROUPS 4 #define SUBGROUP_SIZE 64 +// scales are transposed: consecutive codes of a row are `stride` apart inline void get_scale_min_k4( int j, global const uchar * q, + uint stride, uchar * d, uchar * m, uchar mask_d6, @@ -21,11 +23,11 @@ inline void get_scale_min_k4( uchar mask_hi2 ) { if (j < 4) { - *d = q[j] & mask_d6; - *m = q[j+4] & mask_d6; + *d = q[j*stride] & mask_d6; + *m = q[(j+4)*stride] & mask_d6; } else { - *d = (q[j+4] & mask_d4) | ((q[j-4] & mask_hi2) >> 2); - *m = ((q[j+4] >> 4) & mask_d4) | ((q[j] & mask_hi2) >> 2); + *d = (q[(j+4)*stride] & mask_d4) | ((q[(j-4)*stride] & mask_hi2) >> 2); + *m = ((q[(j+4)*stride] >> 4) & mask_d4) | ((q[j*stride] & mask_hi2) >> 2); } } @@ -232,7 +234,6 @@ kernel void kernel_gemv_noshuffle_q4_k_f32( uint LINE_STRIDE_A = M / 2; uint BLOCK_STRIDE_A = NSUBGROUPS * M; - uint scales_per_row = (K / QK_K) * 12; private uint4 regA; private half2 regS; @@ -248,12 +249,12 @@ kernel void kernel_gemv_noshuffle_q4_k_f32( half2 d = src0_d[gid + sb * LINE_STRIDE_A]; half2 dm = src0_m[gid + sb * LINE_STRIDE_A]; - global const uchar * sc0 = src0_s + 2 * gid * scales_per_row + sb * 12; - global const uchar * sc1 = src0_s + (2 * gid + 1) * scales_per_row + sb * 12; + global const uchar * sc0 = src0_s + sb * 12 * M + 2 * gid; + global const uchar * sc1 = sc0 + 1; uchar sv0, mn0, sv1, mn1; - get_scale_min_k4(j, sc0, &sv0, &mn0, mask_d6, mask_d4, mask_hi2); - get_scale_min_k4(j, sc1, &sv1, &mn1, mask_d6, mask_d4, mask_hi2); + get_scale_min_k4(j, sc0, M, &sv0, &mn0, mask_d6, mask_d4, mask_hi2); + get_scale_min_k4(j, sc1, M, &sv1, &mn1, mask_d6, mask_d4, mask_hi2); regS = convert_half2(convert_float2(d) * convert_float2((uchar2)(sv0, sv1))); regM = convert_half2(convert_float2(dm) * convert_float2((uchar2)(mn0, mn1))); diff --git a/ggml/src/ggml-opencl/kernels/mul_mv_f16_f32_l4.cl b/ggml/src/ggml-opencl/kernels/mul_mv_f16_f32_l4.cl index da2e14ae993a..97148d370fbd 100644 --- a/ggml/src/ggml-opencl/kernels/mul_mv_f16_f32_l4.cl +++ b/ggml/src/ggml-opencl/kernels/mul_mv_f16_f32_l4.cl @@ -24,6 +24,10 @@ #elif defined(cl_qcom_subgroup_shuffle) #pragma OPENCL EXTENSION cl_qcom_subgroup_shuffle : enable #define HAS_SUBGROUP_SHUFFLE 1 +// Adreno compilers that expose only cl_qcom_subgroup_shuffle do not declare the KHR +// name, so calling it is an implicit declaration and the program fails to build. +// Route it to the qcom builtin. +#define sub_group_shuffle_xor(val, mask) qcom_sub_group_shuffle_xor((val), (mask), CLK_SUB_GROUP_SHUFFLE_WIDTH_WAVE_SIZE_QCOM, 0.0f) #endif // Assumes row size (ne00) is a multiple of 4 diff --git a/ggml/src/ggml-opencl/kernels/mul_mv_q4_k_f32.cl b/ggml/src/ggml-opencl/kernels/mul_mv_q4_k_f32.cl index 71ab9898213f..4c3d5f00c75b 100644 --- a/ggml/src/ggml-opencl/kernels/mul_mv_q4_k_f32.cl +++ b/ggml/src/ggml-opencl/kernels/mul_mv_q4_k_f32.cl @@ -1,3 +1,5 @@ +#pragma OPENCL EXTENSION cl_khr_fp16 : enable + #ifdef cl_intel_required_subgroup_size #pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable #define INTEL_GPU 1 diff --git a/ggml/src/ggml-opencl/kernels/mul_mv_q4_k_f32_flat.cl b/ggml/src/ggml-opencl/kernels/mul_mv_q4_k_f32_flat.cl index d92fb9689042..70391866ca6c 100644 --- a/ggml/src/ggml-opencl/kernels/mul_mv_q4_k_f32_flat.cl +++ b/ggml/src/ggml-opencl/kernels/mul_mv_q4_k_f32_flat.cl @@ -153,18 +153,27 @@ kernel void kernel_mul_mv_q4_K_f32_flat( global ushort * q2 = q1 + 32; - float4 acc1 = {0.f, 0.f, 0.f, 0.f}; - float4 acc2 = {0.f, 0.f, 0.f, 0.f}; - for (int i = 0; i < 8; i += 2) { - acc1.s0 += yl[i+0] * (q1[i/2] & 0x000F); - acc1.s1 += yl[i+1] * (q1[i/2] & 0x0F00); - acc1.s2 += yl[i+8] * (q1[i/2] & 0x00F0); - acc1.s3 += yl[i+9] * (q1[i/2] & 0xF000); - acc2.s0 += yh[i+0] * (q2[i/2] & 0x000F); - acc2.s1 += yh[i+1] * (q2[i/2] & 0x0F00); - acc2.s2 += yh[i+8] * (q2[i/2] & 0x00F0); - acc2.s3 += yh[i+9] * (q2[i/2] & 0xF000); - } + // Load the 4 q1 / 4 q2 quant ushorts as 2 uints each. 16-bit integer ops are + // disproportionately slow on the A7X (E031.41) compiler; keeping the dequant + // operands in 32-bit registers avoids the ushort path. q1/q2 are 4-byte aligned + // (ib*128 + (32*iq+8*ir) bytes; q1 += blk*128 bytes/row). Math is unchanged: + // w & 0x0F00 on the low/high halves equals the original ushort mask value. + global uint * q1u = (global uint *)q1; + global uint * q2u = (global uint *)q2; + uint a0 = q1u[0], a1 = q1u[1]; + uint b0 = q2u[0], b1 = q2u[1]; + uint w0 = a0 & 0xFFFF, w1 = a0 >> 16, w2 = a1 & 0xFFFF, w3 = a1 >> 16; + uint v0 = b0 & 0xFFFF, v1 = b0 >> 16, v2 = b1 & 0xFFFF, v3 = b1 >> 16; + + float4 acc1, acc2; + acc1.s0 = yl[0]*(w0&0x000F) + yl[ 2]*(w1&0x000F) + yl[ 4]*(w2&0x000F) + yl[ 6]*(w3&0x000F); + acc1.s1 = yl[1]*(w0&0x0F00) + yl[ 3]*(w1&0x0F00) + yl[ 5]*(w2&0x0F00) + yl[ 7]*(w3&0x0F00); + acc1.s2 = yl[8]*(w0&0x00F0) + yl[10]*(w1&0x00F0) + yl[12]*(w2&0x00F0) + yl[14]*(w3&0x00F0); + acc1.s3 = yl[9]*(w0&0xF000) + yl[11]*(w1&0xF000) + yl[13]*(w2&0xF000) + yl[15]*(w3&0xF000); + acc2.s0 = yh[0]*(v0&0x000F) + yh[ 2]*(v1&0x000F) + yh[ 4]*(v2&0x000F) + yh[ 6]*(v3&0x000F); + acc2.s1 = yh[1]*(v0&0x0F00) + yh[ 3]*(v1&0x0F00) + yh[ 5]*(v2&0x0F00) + yh[ 7]*(v3&0x0F00); + acc2.s2 = yh[8]*(v0&0x00F0) + yh[10]*(v1&0x00F0) + yh[12]*(v2&0x00F0) + yh[14]*(v3&0x00F0); + acc2.s3 = yh[9]*(v0&0xF000) + yh[11]*(v1&0xF000) + yh[13]*(v2&0xF000) + yh[15]*(v3&0xF000); float dall = *d; float dmin = *dm; diff --git a/ggml/src/ggml-opencl/kernels/mul_mv_q5_k_f32_flat.cl b/ggml/src/ggml-opencl/kernels/mul_mv_q5_k_f32_flat.cl index e353a72be703..6020364b5c35 100644 --- a/ggml/src/ggml-opencl/kernels/mul_mv_q5_k_f32_flat.cl +++ b/ggml/src/ggml-opencl/kernels/mul_mv_q5_k_f32_flat.cl @@ -159,18 +159,59 @@ kernel void kernel_mul_mv_q5_K_f32_flat( global ushort * q2 = q1 + 32; - float4 acc1 = {0.f, 0.f, 0.f, 0.f}; - float4 acc2 = {0.f, 0.f, 0.f, 0.f}; - for (int i = 0; i < 8; i += 2) { - acc1.s0 += yl[i+0] * ((q1[i/2] & 0x000F) + (qh[i+0] & u1_lo ? 16.f : 0.f)); - acc1.s1 += yl[i+1] * ((q1[i/2] & 0x0F00) + (qh[i+1] & u1_lo ? 16.f*256.f : 0.f)); - acc1.s2 += yl[i+8] * ((q1[i/2] & 0x00F0) + (qh[i+0] & u2_lo ? 16.f*16.f : 0.f)); - acc1.s3 += yl[i+9] * ((q1[i/2] & 0xF000) + (qh[i+1] & u2_lo ? 16.f*4096.f: 0.f)); - acc2.s0 += yh[i+0] * ((q2[i/2] & 0x000F) + (qh[i+0] & u1_hi ? 16.f : 0.f)); - acc2.s1 += yh[i+1] * ((q2[i/2] & 0x0F00) + (qh[i+1] & u1_hi ? 16.f*256.f : 0.f)); - acc2.s2 += yh[i+8] * ((q2[i/2] & 0x00F0) + (qh[i+0] & u2_hi ? 16.f*16.f : 0.f)); - acc2.s3 += yh[i+9] * ((q2[i/2] & 0xF000) + (qh[i+1] & u2_hi ? 16.f*4096.f: 0.f)); - } + // Load the 4 q1 / 4 q2 quant ushorts as 2 uints each. 16-bit integer ops are + // disproportionately slow on the A7X (E031.41) compiler; keeping the dequant + // operands in 32-bit registers avoids the ushort path (same fix as q4_K flat). + // q1/q2 are 4-byte aligned; w & 0x0F00 on the low/high half of a uint equals the + // original ushort mask value, so the math is unchanged. The qh high-bit term is + // byte-indexed (qh[0..7]) and left as-is. + global uint * q1u = (global uint *)q1; + global uint * q2u = (global uint *)q2; + uint a0 = q1u[0], a1 = q1u[1], b0 = q2u[0], b1 = q2u[1]; + uint w0 = a0 & 0xFFFF, w1 = a0 >> 16, w2 = a1 & 0xFFFF, w3 = a1 >> 16; + uint v0 = b0 & 0xFFFF, v1 = b0 >> 16, v2 = b1 & 0xFFFF, v3 = b1 >> 16; + + float4 acc1, acc2; + acc1.s0 = + yl[0]*((w0&0x000F)+(qh[0]&u1_lo?16.f:0.f)) + + yl[2]*((w1&0x000F)+(qh[2]&u1_lo?16.f:0.f)) + + yl[4]*((w2&0x000F)+(qh[4]&u1_lo?16.f:0.f)) + + yl[6]*((w3&0x000F)+(qh[6]&u1_lo?16.f:0.f)); + acc1.s1 = + yl[1]*((w0&0x0F00)+(qh[1]&u1_lo?16.f*256.f:0.f)) + + yl[3]*((w1&0x0F00)+(qh[3]&u1_lo?16.f*256.f:0.f)) + + yl[5]*((w2&0x0F00)+(qh[5]&u1_lo?16.f*256.f:0.f)) + + yl[7]*((w3&0x0F00)+(qh[7]&u1_lo?16.f*256.f:0.f)); + acc1.s2 = + yl[ 8]*((w0&0x00F0)+(qh[0]&u2_lo?16.f*16.f:0.f)) + + yl[10]*((w1&0x00F0)+(qh[2]&u2_lo?16.f*16.f:0.f)) + + yl[12]*((w2&0x00F0)+(qh[4]&u2_lo?16.f*16.f:0.f)) + + yl[14]*((w3&0x00F0)+(qh[6]&u2_lo?16.f*16.f:0.f)); + acc1.s3 = + yl[ 9]*((w0&0xF000)+(qh[1]&u2_lo?16.f*4096.f:0.f)) + + yl[11]*((w1&0xF000)+(qh[3]&u2_lo?16.f*4096.f:0.f)) + + yl[13]*((w2&0xF000)+(qh[5]&u2_lo?16.f*4096.f:0.f)) + + yl[15]*((w3&0xF000)+(qh[7]&u2_lo?16.f*4096.f:0.f)); + acc2.s0 = + yh[0]*((v0&0x000F)+(qh[0]&u1_hi?16.f:0.f)) + + yh[2]*((v1&0x000F)+(qh[2]&u1_hi?16.f:0.f)) + + yh[4]*((v2&0x000F)+(qh[4]&u1_hi?16.f:0.f)) + + yh[6]*((v3&0x000F)+(qh[6]&u1_hi?16.f:0.f)); + acc2.s1 = + yh[1]*((v0&0x0F00)+(qh[1]&u1_hi?16.f*256.f:0.f)) + + yh[3]*((v1&0x0F00)+(qh[3]&u1_hi?16.f*256.f:0.f)) + + yh[5]*((v2&0x0F00)+(qh[5]&u1_hi?16.f*256.f:0.f)) + + yh[7]*((v3&0x0F00)+(qh[7]&u1_hi?16.f*256.f:0.f)); + acc2.s2 = + yh[ 8]*((v0&0x00F0)+(qh[0]&u2_hi?16.f*16.f:0.f)) + + yh[10]*((v1&0x00F0)+(qh[2]&u2_hi?16.f*16.f:0.f)) + + yh[12]*((v2&0x00F0)+(qh[4]&u2_hi?16.f*16.f:0.f)) + + yh[14]*((v3&0x00F0)+(qh[6]&u2_hi?16.f*16.f:0.f)); + acc2.s3 = + yh[ 9]*((v0&0xF000)+(qh[1]&u2_hi?16.f*4096.f:0.f)) + + yh[11]*((v1&0xF000)+(qh[3]&u2_hi?16.f*4096.f:0.f)) + + yh[13]*((v2&0xF000)+(qh[5]&u2_hi?16.f*4096.f:0.f)) + + yh[15]*((v3&0xF000)+(qh[7]&u2_hi?16.f*4096.f:0.f)); float dall = *d; float dmin = *dm; diff --git a/ggml/src/ggml-openvino/ggml-openvino.cpp b/ggml/src/ggml-openvino/ggml-openvino.cpp index 659dbd4b5acb..0e7501fefe38 100644 --- a/ggml/src/ggml-openvino/ggml-openvino.cpp +++ b/ggml/src/ggml-openvino/ggml-openvino.cpp @@ -1378,3 +1378,5 @@ GGML_BACKEND_API ggml_backend_reg_t ggml_backend_openvino_reg(void) { return ® } + +GGML_BACKEND_DL_IMPL(ggml_backend_openvino_reg) diff --git a/ggml/src/ggml-rpc/ggml-rpc.cpp b/ggml/src/ggml-rpc/ggml-rpc.cpp index 1166178b7249..3a8a1f248b01 100644 --- a/ggml/src/ggml-rpc/ggml-rpc.cpp +++ b/ggml/src/ggml-rpc/ggml-rpc.cpp @@ -71,6 +71,7 @@ enum rpc_cmd { RPC_CMD_HELLO, RPC_CMD_DEVICE_COUNT, RPC_CMD_GRAPH_RECOMPUTE, + RPC_CMD_MEMSET_TENSOR, RPC_CMD_COUNT, }; @@ -152,6 +153,13 @@ struct rpc_msg_buffer_clear_req { uint8_t value; }; +struct rpc_msg_memset_tensor_req { + rpc_tensor tensor; + uint64_t offset; + uint64_t size; + uint8_t value; +}; + struct rpc_msg_set_tensor_hash_req { rpc_tensor tensor; uint64_t offset; @@ -462,6 +470,19 @@ static enum ggml_status ggml_backend_rpc_buffer_init_tensor(ggml_backend_buffer_ return GGML_STATUS_SUCCESS; } +static void ggml_backend_rpc_buffer_memset_tensor( + ggml_backend_buffer_t buffer, ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) { + ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; + rpc_msg_memset_tensor_req request = { + /* .tensor = */ serialize_tensor(tensor), + /* .offset = */ offset, + /* .size = */ size, + /* .value = */ value, + }; + bool status = send_rpc_cmd(ctx->sock, RPC_CMD_MEMSET_TENSOR, &request, sizeof(request), nullptr, 0); + RPC_STATUS_ASSERT(status); +} + static void ggml_backend_rpc_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; rpc_tensor rpc_tensor = serialize_tensor(tensor); @@ -531,7 +552,7 @@ static ggml_backend_buffer_i ggml_backend_rpc_buffer_interface = { /* .free_buffer = */ ggml_backend_rpc_buffer_free_buffer, /* .get_base = */ ggml_backend_rpc_buffer_get_base, /* .init_tensor = */ ggml_backend_rpc_buffer_init_tensor, - /* .memset_tensor = */ NULL, + /* .memset_tensor = */ ggml_backend_rpc_buffer_memset_tensor, /* .set_tensor = */ ggml_backend_rpc_buffer_set_tensor, /* .get_tensor = */ ggml_backend_rpc_buffer_get_tensor, /* .set_tensor_2d = */ NULL, @@ -832,6 +853,7 @@ class rpc_server { bool buffer_get_base(const rpc_msg_buffer_get_base_req & request, rpc_msg_buffer_get_base_rsp & response); bool free_buffer(const rpc_msg_free_buffer_req & request); bool buffer_clear(const rpc_msg_buffer_clear_req & request); + bool memset_tensor(const rpc_msg_memset_tensor_req & request); bool set_tensor(const std::vector & input); bool set_tensor_hash(const rpc_msg_set_tensor_hash_req & request, rpc_msg_set_tensor_hash_rsp & response); bool get_tensor(const rpc_msg_get_tensor_req & request, std::vector & response); @@ -990,6 +1012,52 @@ bool rpc_server::buffer_clear(const rpc_msg_buffer_clear_req & request) { return true; } +bool rpc_server::memset_tensor(const rpc_msg_memset_tensor_req & request) { + struct ggml_init_params params { + /*.mem_size =*/ ggml_tensor_overhead(), + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + ggml_context_ptr ctx_ptr { ggml_init(params) }; + GGML_ASSERT(ctx_ptr != nullptr); + ggml_context * ctx = ctx_ptr.get(); + ggml_tensor * tensor = deserialize_tensor(ctx, &request.tensor); + if (tensor == nullptr || tensor->buffer == nullptr) { + GGML_LOG_ERROR("[%s] error deserializing tensor\n", __func__); + return false; + } + + const uint64_t tensor_size = ggml_nbytes(tensor); + if (request.offset > tensor_size || request.size > tensor_size - request.offset) { + GGML_LOG_ERROR("[%s] tensor region (offset=%" PRIu64 ", size=%" PRIu64 ") out of tensor bounds [0, %" PRIu64 ")\n", + __func__, request.offset, request.size, tensor_size); + return false; + } + + const uint64_t buffer_start = (uint64_t) ggml_backend_buffer_get_base(tensor->buffer); + const uint64_t buffer_size = ggml_backend_buffer_get_size(tensor->buffer); + if (request.tensor.data < buffer_start) { + GGML_LOG_ERROR("[%s] tensor data before buffer start\n", __func__); + return false; + } + const uint64_t data_offset = request.tensor.data - buffer_start; + if (data_offset > buffer_size || + request.offset > buffer_size - data_offset || + request.size > buffer_size - data_offset - request.offset) { + GGML_LOG_ERROR("[%s] tensor region out of buffer bounds\n", __func__); + return false; + } + if (tensor->buffer->iface.memset_tensor == nullptr) { + GGML_LOG_ERROR("[%s] memset not implemented by backend buffer\n", __func__); + return false; + } + + LOG_DBG("[%s] buffer: %p, data: %p, offset: %" PRIu64 ", size: %" PRIu64 ", value: %u\n", + __func__, (void *) tensor->buffer, tensor->data, request.offset, request.size, request.value); + ggml_backend_tensor_memset(tensor, request.value, request.offset, request.size); + return true; +} + ggml_tensor * rpc_server::deserialize_tensor(struct ggml_context * ctx, const rpc_tensor * tensor) { // Validate tensor type before using it if (tensor->type >= GGML_TYPE_COUNT) { @@ -1586,6 +1654,19 @@ static void rpc_serve_client(const std::vector & backends, const } break; } + case RPC_CMD_MEMSET_TENSOR: { + rpc_msg_memset_tensor_req request; + if (!recv_msg(sock, &request, sizeof(request))) { + return; + } + if (!server.memset_tensor(request)) { + return; + } + if (!send_msg(sock, nullptr, 0)) { + return; + } + break; + } case RPC_CMD_SET_TENSOR: { std::vector input; if (!recv_msg(sock, input)) { diff --git a/ggml/src/ggml-sycl/CMakeLists.txt b/ggml/src/ggml-sycl/CMakeLists.txt index 1c17d20df12b..a8d9c0d804bf 100644 --- a/ggml/src/ggml-sycl/CMakeLists.txt +++ b/ggml/src/ggml-sycl/CMakeLists.txt @@ -199,9 +199,20 @@ if (GGML_SYCL_DEVICE_ARCH) -fsycl-targets=spir64_gen "SHELL:-Xsycl-target-backend=spir64_gen \"-device ${GGML_SYCL_DEVICE_ARCH}\"" ) + + # Pass through parallel job (process) count for parallelising the + # `llvm-foreach -- ocloc` invocation for compiling AOT device images. + include(ProcessorCount) + ProcessorCount(_ggml_sycl_nproc) + if (_ggml_sycl_nproc LESS 1) + set(_ggml_sycl_nproc 1) + endif() + set(GGML_SYCL_MAX_PARALLEL_LINK_JOBS ${_ggml_sycl_nproc} CACHE STRING + "Parallel ocloc jobs for spir64_gen AOT device-image lowering") target_link_options( ggml-sycl PRIVATE -fsycl-targets=spir64_gen "SHELL:-Xsycl-target-backend=spir64_gen \"-device ${GGML_SYCL_DEVICE_ARCH}\"" + -fsycl-max-parallel-link-jobs=${GGML_SYCL_MAX_PARALLEL_LINK_JOBS} ) endif() diff --git a/ggml/src/ggml-sycl/backend.hpp b/ggml/src/ggml-sycl/backend.hpp index f299bcf62e14..51ab6f930dca 100644 --- a/ggml/src/ggml-sycl/backend.hpp +++ b/ggml/src/ggml-sycl/backend.hpp @@ -26,6 +26,7 @@ #include "dmmv.hpp" #include "element_wise.hpp" #include "fattn.hpp" +#include "fusion.hpp" #include "gated_delta_net.hpp" #include "gla.hpp" #include "im2col.hpp" diff --git a/ggml/src/ggml-sycl/common.hpp b/ggml/src/ggml-sycl/common.hpp index e5d9ee89dd86..619933e0fda7 100644 --- a/ggml/src/ggml-sycl/common.hpp +++ b/ggml/src/ggml-sycl/common.hpp @@ -65,6 +65,7 @@ extern int g_ggml_sycl_prioritize_dmmv; extern int g_ggml_sycl_enable_flash_attention; extern int g_ggml_sycl_dev2dev_memcpy; extern int g_ggml_sycl_fa_onednn; +extern int g_ggml_sycl_fa_onednn_max_kv; #if defined(__clang__) && __has_builtin(__builtin_expect) @@ -132,6 +133,7 @@ enum ggml_sycl_backend_gpu_mode { enum ggml_sycl_dev2dev_memcpy_mode { DEV2DEV_MEMCPY_SYCL = 0, DEV2DEV_MEMCPY_L0 = 1, + DEV2DEV_MEMCPY_FORWARD = 2 }; static_assert(sizeof(sycl::half) == sizeof(ggml_fp16_t), "wrong fp16 size"); @@ -233,6 +235,7 @@ struct sycl_device_info { int max_wg_per_cu; // max work groups per compute unit - refer to // cudaOccupancyMaxActiveBlocksPerMultiprocessor bool vmm; // virtual memory support + bool l0_device_type_valid; bool l0_discrete_gpu; // Level Zero backend and not an integrated GPU size_t vmm_granularity; // granularity of virtual memory size_t total_vram; diff --git a/ggml/src/ggml-sycl/concat.cpp b/ggml/src/ggml-sycl/concat.cpp index 93e00d65fcd3..1ad242fcafbb 100644 --- a/ggml/src/ggml-sycl/concat.cpp +++ b/ggml/src/ggml-sycl/concat.cpp @@ -127,7 +127,15 @@ static void concat_T_sycl_non_cont( int64_t ne2, int64_t ne3, uint64_t nb0, uint64_t nb1, uint64_t nb2, uint64_t nb3, int32_t dim) { sycl::range<3> gridDim(ne3, ne2, ne1); - stream->parallel_for(sycl::nd_range<3>(gridDim, sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) { + + // Avoid oversubscribing device when there is not enough elements along the innermost dim to + // fill a full SYCL_CONCAT_BLOCK_SIZE. For larger # of elements, the full SYCL_CONCAT_BLOCK_SIZE + // is used. + const int64_t ne0_pad = GGML_PAD(ne0, WARP_SIZE); + const int64_t block_ne0 = ne0_pad < SYCL_CONCAT_BLOCK_SIZE ? ne0_pad : (int64_t) SYCL_CONCAT_BLOCK_SIZE; + sycl::range<3> blockDim(1, 1, block_ne0); + + stream->parallel_for(sycl::nd_range<3>(gridDim * blockDim, blockDim), [=](sycl::nd_item<3> item_ct1) { int64_t i3 = item_ct1.get_group(0); int64_t i2 = item_ct1.get_group(1); int64_t i1 = item_ct1.get_group(2); diff --git a/ggml/src/ggml-sycl/conv2d-dw.cpp b/ggml/src/ggml-sycl/conv2d-dw.cpp index 0a52b79174c4..8755a4c95f95 100644 --- a/ggml/src/ggml-sycl/conv2d-dw.cpp +++ b/ggml/src/ggml-sycl/conv2d-dw.cpp @@ -71,8 +71,8 @@ struct dw_cwhn_layout { } }; -template -static void conv2d_dw_kernel(const float * input, const float * kernel, float * output, +template +static void conv2d_dw_kernel(const float * input, const KernelT * kernel, float * output, const conv2d_dw_params p, const sycl::nd_item<3> & item_ct1) { const int global_idx = item_ct1.get_local_id(2) + item_ct1.get_group(2) * item_ct1.get_local_range(2); @@ -93,15 +93,15 @@ static void conv2d_dw_kernel(const float * input, const float * kernel, float * for (int kx = bounds.x_min; kx < bounds.x_max; ++kx) { const int in_x = dw_calculate_input_coord(out_x, kx, p.stride_x, p.dilation_x, p.padding_x); acc += input[Layout::input_index(n, c, in_y, in_x, p)] * - kernel[Layout::kernel_index(c, ky, kx, p)]; + static_cast(kernel[Layout::kernel_index(c, ky, kx, p)]); } } output[Layout::output_index(n, c, out_y, out_x, p)] = acc; } -template -static void conv2d_dw_sycl(const float * x_d, const float * w_d, float * y_d, +template +static void conv2d_dw_sycl(const float * x_d, const KernelT * w_d, float * y_d, const conv2d_dw_params p, const queue_ptr & stream) { const int total = p.batches * p.channels * p.out_h * p.out_w; const int num_blocks = (total + SYCL_CONV2D_DW_BLOCK_SIZE - 1) / SYCL_CONV2D_DW_BLOCK_SIZE; @@ -109,7 +109,7 @@ static void conv2d_dw_sycl(const float * x_d, const float * w_d, float * y_d, const sycl::range<3> block_nums(1, 1, num_blocks); stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { - conv2d_dw_kernel(x_d, w_d, y_d, p, item_ct1); + conv2d_dw_kernel(x_d, w_d, y_d, p, item_ct1); }); } @@ -119,9 +119,9 @@ void ggml_sycl_op_conv2d_dw(ggml_backend_sycl_context & ctx, ggml_tensor * dst) const ggml_tensor * kernel = dst->src[0]; const ggml_tensor * input = dst->src[1]; - GGML_ASSERT(kernel->type == GGML_TYPE_F32 && input->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32); + GGML_ASSERT((kernel->type == GGML_TYPE_F32 || kernel->type == GGML_TYPE_F16) && + input->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32); - const float * w_d = (const float *) kernel->data; const float * x_d = (const float *) input->data; float * y_d = (float *) dst->data; @@ -148,11 +148,23 @@ void ggml_sycl_op_conv2d_dw(ggml_backend_sycl_context & ctx, ggml_tensor * dst) const queue_ptr stream = ctx.stream(); - if (ggml_is_contiguous(input)) { - conv2d_dw_sycl(x_d, w_d, y_d, params, stream); - } else if (ggml_is_contiguous_channels(input)) { - conv2d_dw_sycl(x_d, w_d, y_d, params, stream); + if (kernel->type == GGML_TYPE_F16) { + const sycl::half * w_d = (const sycl::half *) kernel->data; + if (ggml_is_contiguous(input)) { + conv2d_dw_sycl(x_d, w_d, y_d, params, stream); + } else if (ggml_is_contiguous_channels(input)) { + conv2d_dw_sycl(x_d, w_d, y_d, params, stream); + } else { + GGML_ABORT("Unsupported memory layout for conv2d_dw"); + } } else { - GGML_ABORT("Unsupported memory layout for conv2d_dw"); + const float * w_d = (const float *) kernel->data; + if (ggml_is_contiguous(input)) { + conv2d_dw_sycl(x_d, w_d, y_d, params, stream); + } else if (ggml_is_contiguous_channels(input)) { + conv2d_dw_sycl(x_d, w_d, y_d, params, stream); + } else { + GGML_ABORT("Unsupported memory layout for conv2d_dw"); + } } } diff --git a/ggml/src/ggml-sycl/convert.cpp b/ggml/src/ggml-sycl/convert.cpp index 060d0aca2db4..9ec9276952dc 100644 --- a/ggml/src/ggml-sycl/convert.cpp +++ b/ggml/src/ggml-sycl/convert.cpp @@ -644,6 +644,8 @@ to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type, ggml_tensor * dst) { switch (type) { case GGML_TYPE_Q1_0: return dequantize_block_sycl; + case GGML_TYPE_Q2_0: + return dequantize_block_sycl; case GGML_TYPE_Q4_0: if (dst->src[0]->extra && ((ggml_tensor_extra_gpu*)dst->src[0]->extra)->optimized_feature.reorder) { @@ -728,6 +730,8 @@ to_fp32_sycl_t ggml_get_to_fp32_sycl(ggml_type type, ggml_tensor *dst) { switch (type) { case GGML_TYPE_Q1_0: return dequantize_block_sycl; + case GGML_TYPE_Q2_0: + return dequantize_block_sycl; case GGML_TYPE_Q4_0: if (dst->src[0]->extra && ((ggml_tensor_extra_gpu*)dst->src[0]->extra)->optimized_feature.reorder) { diff --git a/ggml/src/ggml-sycl/cpy.cpp b/ggml/src/ggml-sycl/cpy.cpp index 5d0f9a89fd33..55e076172237 100644 --- a/ggml/src/ggml-sycl/cpy.cpp +++ b/ggml/src/ggml-sycl/cpy.cpp @@ -8,7 +8,6 @@ #include "ggml-sycl/presets.hpp" #include "ggml.h" - static void cpy_1_f32_f32(const char * cxi, char * cdsti) { const float * xi = (const float *) cxi; float * dsti = (float *) cdsti; @@ -151,6 +150,20 @@ static void cpy_blck_q8_0_f32(const char * cxi, char * cdsti) { } } +static void cpy_blck_q2_0_f32(const char * cxi, char * cdsti) { + const block_q2_0 * xi = (const block_q2_0 *) cxi; + float * cdstf = (float *) cdsti; + + const float d = xi->d; + + for (int j = 0; j < QK2_0; ++j) { + const int byte_index = j / 4; + const int bit_offset = (j % 4) * 2; + const int q = (xi->qs[byte_index] >> bit_offset) & 0x3; + cdstf[j] = (float) (q - 1) * d; + } +} + template static void cpy_blck_q_f32(const char * cxi, char * cdsti) { @@ -256,7 +269,7 @@ static void ggml_cpy_f16_f32_sycl(const char * cx, char * cdst, const int ne, co stream->parallel_for( sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); @@ -274,7 +287,7 @@ static void ggml_cpy_f32_f32_sycl(const char * cx, char * cdst, const int ne, co stream->parallel_for( sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); @@ -292,7 +305,7 @@ static void ggml_cpy_f32_f16_sycl(const char * cx, char * cdst, const int ne, co stream->parallel_for( sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); @@ -308,7 +321,7 @@ static void ggml_cpy_f32_i32_sycl(const char * cx, char * cdst, const int ne, co stream->parallel_for( sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); @@ -324,7 +337,7 @@ static void ggml_cpy_i32_f32_sycl(const char * cx, char * cdst, const int ne, co stream->parallel_for( sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); @@ -338,7 +351,7 @@ static void ggml_cpy_f32_q8_0_sycl(const char * cx, char * cdst, const int ne, c GGML_ASSERT(ne % QK8_0 == 0); const int num_blocks = ne / QK8_0; stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), - [=](sycl::nd_item<3> item_ct1) { + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_f32_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); @@ -350,12 +363,25 @@ static void ggml_cpy_q8_0_f32_sycl(const char * cx, char * cdst, const int ne, c const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ne; stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), - [=](sycl::nd_item<3> item_ct1) { + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_q_f32(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } +static void ggml_cpy_q2_0_f32_sycl(const char * cx, char * cdst, const int ne, const int ne00, const int ne01, + const int ne02, const int nb00, const int nb01, const int nb02, const int nb03, + const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, + const int nb12, const int nb13, queue_ptr stream) { + const int num_blocks = ne; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + cpy_q_f32(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, + ne12, nb10, nb11, nb12, nb13, item_ct1); + }); +} + static void ggml_cpy_f32_q4_0_sycl(const char * cx, char * cdst, const int ne, const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, @@ -363,7 +389,7 @@ static void ggml_cpy_f32_q4_0_sycl(const char * cx, char * cdst, const int ne, c GGML_ASSERT(ne % QK4_0 == 0); const int num_blocks = ne / QK4_0; stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), - [=](sycl::nd_item<3> item_ct1) { + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_f32_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); @@ -375,7 +401,8 @@ static void ggml_cpy_q4_0_f32_sycl(const char * cx, char * cdst, const int ne, c const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ne; stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) { + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_q_f32, QK4_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); @@ -389,7 +416,7 @@ static void ggml_cpy_f32_q4_1_sycl(const char * cx, char * cdst, const int ne, c GGML_ASSERT(ne % QK4_1 == 0); const int num_blocks = ne / QK4_1; stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), - [=](sycl::nd_item<3> item_ct1) { + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_f32_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); @@ -401,7 +428,8 @@ static void ggml_cpy_q4_1_f32_sycl(const char * cx, char * cdst, const int ne, c const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ne; stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) { + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_q_f32, QK4_1>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); @@ -415,7 +443,7 @@ static void ggml_cpy_f32_q5_0_sycl(const char * cx, char * cdst, const int ne, c GGML_ASSERT(ne % QK5_0 == 0); const int num_blocks = ne / QK5_0; stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), - [=](sycl::nd_item<3> item_ct1) { + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { cpy_f32_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); @@ -427,7 +455,8 @@ static void ggml_cpy_q5_0_f32_sycl(const char * cx, char * cdst, const int ne, c const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ne; stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) { + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_q_f32, QK5_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); @@ -441,7 +470,7 @@ static void ggml_cpy_f32_q5_1_sycl(const char * cx, char * cdst, const int ne, c GGML_ASSERT(ne % QK5_1 == 0); const int num_blocks = ne / QK5_1; stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), - [=](sycl::nd_item<3> item_ct1) { + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_f32_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); @@ -453,7 +482,8 @@ static void ggml_cpy_q5_1_f32_sycl(const char * cx, char * cdst, const int ne, c const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ne; stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) { + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_q_f32, QK5_1>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); @@ -466,7 +496,8 @@ static void ggml_cpy_mxfp4_f32_sycl(const char * cx, char * cdst, const int ne, const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ne; stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) { + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { cpy_q_f32, QK_MXFP4>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); @@ -480,7 +511,8 @@ static void ggml_cpy_f32_iq4_nl_sycl(const char * cx, char * cdst, const int ne, GGML_ASSERT(ne % QK4_NL == 0); const int num_blocks = ne / QK4_NL; stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) { + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { cpy_f32_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); @@ -526,7 +558,7 @@ static void ggml_cpy_f16_q4_0_sycl(const char * cx, char * cdst, const int ne, c GGML_ASSERT(ne % QK4_0 == 0); const int num_blocks = ne / QK4_0; stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), - [=](sycl::nd_item<3> item_ct1) { + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_f32_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); @@ -540,7 +572,7 @@ static void ggml_cpy_f16_q4_1_sycl(const char * cx, char * cdst, const int ne, c GGML_ASSERT(ne % QK4_1 == 0); const int num_blocks = ne / QK4_1; stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), - [=](sycl::nd_item<3> item_ct1) { + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_f32_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); @@ -554,7 +586,7 @@ static void ggml_cpy_f16_q5_0_sycl(const char * cx, char * cdst, const int ne, c GGML_ASSERT(ne % QK5_0 == 0); const int num_blocks = ne / QK5_0; stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), - [=](sycl::nd_item<3> item_ct1) { + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_f32_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); @@ -564,6 +596,7 @@ static void ggml_cpy_f16_q5_0_sycl(const char * cx, char * cdst, const int ne, c static bool ggml_sycl_is_quantized_type(enum ggml_type type) { switch (type) { case GGML_TYPE_Q1_0: + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -594,6 +627,7 @@ static bool ggml_sycl_is_quantized_type(enum ggml_type type) { static bool ggml_sycl_can_quantize_rows_sycl(enum ggml_type type) { switch (type) { case GGML_TYPE_Q1_0: + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -651,7 +685,8 @@ static void ggml_sycl_quantize_rows_q(const char * cx, char * cdst, const int64_ constexpr int block_size = 256; const int64_t grid_size = ceil_div(total_blocks, (int64_t) block_size); - stream->parallel_for(sycl::nd_range<1>(grid_size * block_size, block_size), [=](sycl::nd_item<1> item_ct1) { + stream->parallel_for(sycl::nd_range<1>(grid_size * block_size, block_size), + [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { const int64_t block_idx = item_ct1.get_global_linear_id(); if (block_idx >= total_blocks) { return; @@ -708,6 +743,11 @@ static void ggml_sycl_quantize_rows_sycl(const char * cx, char * cdst, const ggm nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, stream); break; + case GGML_TYPE_Q2_0: + ggml_sycl_quantize_rows_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, + nb02, nb03, ne10, ne11, ne12, nb10, nb11, + nb12, nb13, stream); + break; case GGML_TYPE_Q5_1: ggml_sycl_quantize_rows_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, @@ -760,7 +800,7 @@ static void ggml_cpy_f16_f16_sycl(const char * cx, char * cdst, const int ne, co stream->parallel_for( sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); @@ -779,7 +819,7 @@ static void ggml_cpy_i16_i16_sycl(const char * cx, char * cdst, const int ne, co stream->parallel_for( sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); @@ -798,7 +838,7 @@ static void ggml_cpy_i32_i32_sycl(const char * cx, char * cdst, const int ne, co stream->parallel_for( sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); @@ -812,7 +852,8 @@ static void ggml_cpy_q8_0_q8_0(const char * cx, char * cdst, const int ne, const const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE); stream->parallel_for( sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { + sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_q_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -825,7 +866,8 @@ static void ggml_cpy_q5_0_q5_0(const char * cx, char * cdst, const int ne, const const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE); stream->parallel_for( sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { + sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_q_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -839,7 +881,8 @@ static void ggml_cpy_q5_1_q5_1(const char * cx, char * cdst, const int ne, const stream->parallel_for( sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { + sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_q_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -851,7 +894,8 @@ static void ggml_cpy_q4_0_q4_0(const char * cx, char * cdst, const int ne, const const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE); stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_q_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -864,7 +908,8 @@ static void ggml_cpy_q4_1_q4_1(const char * cx, char * cdst, const int ne, const const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE); stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_q_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -875,18 +920,32 @@ static void ggml_cpy_q1_0_q1_0(const char * cx, char * cdst, const int ne, const const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE); stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { cpy_q_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } +static void ggml_cpy_q2_0_q2_0(const char * cx, char * cdst, const int ne, const int ne00, const int ne01, + const int ne02, const int nb00, const int nb01, const int nb02, const int nb03, + const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, + const int nb12, const int nb13, queue_ptr stream) { + const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE); + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ + cpy_q_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); + }); +} + static void ggml_cpy_mxfp4_mxfp4(const char * cx, char * cdst, const int ne, const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE); stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { cpy_q_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -897,7 +956,8 @@ static void ggml_cpy_nvfp4_nvfp4(const char * cx, char * cdst, const int ne, con const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE); stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_q_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -908,7 +968,8 @@ static void ggml_cpy_q2_K_q2_K(const char * cx, char * cdst, const int ne, const const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE); stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_q_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -919,7 +980,8 @@ static void ggml_cpy_q3_K_q3_K(const char * cx, char * cdst, const int ne, const const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE); stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_q_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -930,7 +992,8 @@ static void ggml_cpy_q4_K_q4_K(const char * cx, char * cdst, const int ne, const const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE); stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_q_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -941,7 +1004,8 @@ static void ggml_cpy_q5_K_q5_K(const char * cx, char * cdst, const int ne, const const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE); stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_q_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -952,7 +1016,8 @@ static void ggml_cpy_q6_K_q6_K(const char * cx, char * cdst, const int ne, const const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE); stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_q_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -963,7 +1028,8 @@ static void ggml_cpy_iq2_xxs_iq2_xxs(const char * cx, char * cdst, const int ne, const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE); stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_q_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -974,7 +1040,8 @@ static void ggml_cpy_iq2_xs_iq2_xs(const char * cx, char * cdst, const int ne, c const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE); stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_q_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -985,7 +1052,8 @@ static void ggml_cpy_iq2_s_iq2_s(const char * cx, char * cdst, const int ne, con const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE); stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_q_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -996,7 +1064,8 @@ static void ggml_cpy_iq3_xxs_iq3_xxs(const char * cx, char * cdst, const int ne, const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE); stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_q_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -1007,7 +1076,8 @@ static void ggml_cpy_iq1_s_iq1_s(const char * cx, char * cdst, const int ne, con const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE); stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_q_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -1018,7 +1088,8 @@ static void ggml_cpy_iq1_m_iq1_m(const char * cx, char * cdst, const int ne, con const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE); stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_q_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -1029,7 +1100,8 @@ static void ggml_cpy_iq4_nl_iq4_nl(const char * cx, char * cdst, const int ne, c const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE); stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_q_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -1040,7 +1112,8 @@ static void ggml_cpy_iq3_s_iq3_s(const char * cx, char * cdst, const int ne, con const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE); stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_q_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -1051,7 +1124,8 @@ static void ggml_cpy_iq4_xs_iq4_xs(const char * cx, char * cdst, const int ne, c const int nb12, const int nb13, queue_ptr stream) { const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE); stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_q_q(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -1065,7 +1139,7 @@ static void ggml_cpy_f32_bf16_sycl(const char * cx, char * cdst, const int ne, c stream->parallel_for( sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); @@ -1079,7 +1153,7 @@ static void ggml_cpy_bf16_f32_sycl(const char * cx, char * cdst, const int ne, c stream->parallel_for( sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); @@ -1093,7 +1167,7 @@ static void ggml_cpy_bf16_bf16_sycl(const char * cx, char * cdst, const int ne, stream->parallel_for( sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); @@ -1107,7 +1181,7 @@ static void ggml_cpy_f16_bf16_sycl(const char * cx, char * cdst, const int ne, c stream->parallel_for( sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); @@ -1121,7 +1195,7 @@ static void ggml_cpy_bf16_f16_sycl(const char * cx, char * cdst, const int ne, c stream->parallel_for( sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{ cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); @@ -1213,6 +1287,9 @@ void ggml_sycl_cpy(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, co } else if (src0->type == GGML_TYPE_Q8_0 && src1->type == GGML_TYPE_F32) { ggml_cpy_q8_0_f32_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + } else if (src0->type == GGML_TYPE_Q2_0 && src1->type == GGML_TYPE_F32) { + ggml_cpy_q2_0_f32_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, + nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_0) { ggml_cpy_f32_q5_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); @@ -1243,6 +1320,8 @@ void ggml_sycl_cpy(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, co ggml_cpy_q4_1_q4_1(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_Q1_0 && src1->type == GGML_TYPE_Q1_0) { ggml_cpy_q1_0_q1_0(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + } else if (src0->type == GGML_TYPE_Q2_0 && src1->type == GGML_TYPE_Q2_0) { + ggml_cpy_q2_0_q2_0(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_MXFP4 && src1->type == GGML_TYPE_MXFP4) { ggml_cpy_mxfp4_mxfp4(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_NVFP4 && src1->type == GGML_TYPE_NVFP4) { diff --git a/ggml/src/ggml-sycl/cpy.hpp b/ggml/src/ggml-sycl/cpy.hpp index c4cfd961da2a..34bae1b2dd18 100644 --- a/ggml/src/ggml-sycl/cpy.hpp +++ b/ggml/src/ggml-sycl/cpy.hpp @@ -70,6 +70,39 @@ inline void cpy_blck_f32_q1_0(const char * cxi, char * cdsti) { } } +inline int round_nearest_int(float x) { + return (int)(x >= 0.0f ? x + 0.5f : x - 0.5f); +} + +inline void cpy_blck_f32_q2_0(const char * cxi, char * cdsti) { + const float * xi = (const float *) cxi; + block_q2_0 * dsti = (block_q2_0 *) cdsti; + + float amax = 0.0f; + + for (int j = 0; j < QK2_0; ++j) { + amax = sycl::fmax(amax, sycl::fabs((float) xi[j])); + } + + const float d = amax; + const float id = d > 0.0f ? 1.0f / d : 0.0f; + + dsti->d = d; + + for (int j = 0; j < QK2_0 / 4; ++j) { + dsti->qs[j] = 0; + } + + for (int j = 0; j < QK2_0; ++j) { + int q = round_nearest_int(xi[j] * id) + 1; + q = dpct::max(0, dpct::min(3, q)); + + const int byte_index = j / 4; + const int bit_offset = (j % 4) * 2; + dsti->qs[byte_index] |= (uint8_t) q << bit_offset; + } +} + inline int best_index_mxfp4(const float x, const float e) { int best_index = 0; float best_err = sycl::fabs((float) (kvalues_mxfp4[0] * e - x)); diff --git a/ggml/src/ggml-sycl/dequantize.hpp b/ggml/src/ggml-sycl/dequantize.hpp index 7b66c73b0cf9..876ba1b44491 100644 --- a/ggml/src/ggml-sycl/dequantize.hpp +++ b/ggml/src/ggml-sycl/dequantize.hpp @@ -19,11 +19,34 @@ typedef void (*dequantize_kernel_t)(const void * vx, const int64_t ib, const int iqs, dfloat2 & v); typedef void (*dequantize_kernel_t_reorder)(const void *d, const int64_t ib, const void *qs, const int iqs, dfloat2 &v); +typedef void (*dequantize_kernel_f32_t)(const void * vx, const int64_t ib, const int iqs, float & v0, float & v1); #if QK_K == 256 static inline void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m); #endif +static __dpct_inline__ void dequantize_q2_0(const void *vx, const int64_t ib, + const int iqs, dfloat2 &v) { + const block_q2_0 * x = (const block_q2_0 *) vx; + + const dfloat d = x[ib].d; + + const int byte_idx = iqs / 4; + const int shift = (iqs % 4) * 2; + const uint8_t vui = x[ib].qs[byte_idx]; + + v.x() = (vui >> shift) & 3; + v.y() = (vui >> (shift + 2)) & 3; + +#ifdef GGML_SYCL_F16 + v.s0() = ((dfloat)v.s0() - 1.0f) * d; + v.s1() = ((dfloat)v.s1() - 1.0f) * d; +#else + v.x() = ((dfloat)v.x() - 1.0f) * d; + v.y() = ((dfloat)v.y() - 1.0f) * d; +#endif // GGML_SYCL_F16 +} + static __dpct_inline__ void dequantize_q4_0(const void *vx, const int64_t ib, const int iqs, dfloat2 &v) { const block_q4_0 * x = (const block_q4_0 *) vx; @@ -85,6 +108,21 @@ static __dpct_inline__ void dequantize_q1_0_reorder(const void *d_ptr, const int v.y() = (2 * bit_1 - 1) * d; } +static __dpct_inline__ void dequantize_q1_0(const void *vx, const int64_t ib, + const int iqs, dfloat2 &v) { + const block_q1_0 * x = (const block_q1_0 *) vx; + const dfloat d = x[ib].d; + + const int bit_index_0 = iqs + 0; + const int bit_index_1 = iqs + 1; + + const int bit_0 = (x[ib].qs[bit_index_0 / 8] >> (bit_index_0 % 8)) & 1; + const int bit_1 = (x[ib].qs[bit_index_1 / 8] >> (bit_index_1 % 8)) & 1; + + v.x() = (2 * bit_0 - 1) * d; + v.y() = (2 * bit_1 - 1) * d; +} + static __dpct_inline__ void dequantize_q4_1(const void *vx, const int64_t ib, const int iqs, dfloat2 &v) { const block_q4_1 * x = (const block_q4_1 *) vx; @@ -140,6 +178,39 @@ static __dpct_inline__ void dequantize_q4_K(const void *vx, const int64_t ib, #endif } +static __dpct_inline__ void dequantize_q4_K_f32(const void *vx, const int64_t ib, + const int iqs, float &v0, float &v1) { +#if QK_K == 256 + const block_q4_K * x = (const block_q4_K *) vx; + const sycl::half2 dm = x[ib].dm; + const float dall = dm[0]; + const float dmin = dm[1]; + + auto dequantize_one = [&](const int idx) -> float { + const int il = idx / 64; + const int in = idx % 64; + const int is = 2 * il + (in >= 32 ? 1 : 0); + const int qsi = 32 * il + (in & 31); + + uint8_t sc; + uint8_t m; + get_scale_min_k4(is, x[ib].scales, sc, m); + + const float d = dall * sc; + const float mn = dmin * m; + const uint8_t q = x[ib].qs[qsi]; + const uint8_t qv = (in >= 32) ? (q >> 4) : (q & 0xF); + + return d * qv - mn; + }; + + v0 = dequantize_one(iqs + 0); + v1 = dequantize_one(iqs + 1); +#else + GGML_ABORT("Q4_K dequantize not supported for QK_K != 256"); +#endif +} + static __dpct_inline__ void dequantize_q2_K(const void *vx, const int64_t ib, const int iqs, dfloat2 &v) { #if QK_K == 256 @@ -159,7 +230,7 @@ static __dpct_inline__ void dequantize_q2_K(const void *vx, const int64_t ib, const float d = dall * (sc & 0xF); const float m = dmin * (sc >> 4); - return sycl::fma((dfloat) ((q >> (2 * g)) & 3), (dfloat) d, (dfloat) (-m)); + return (dfloat) d * (dfloat) ((q >> (2 * g)) & 3) - (dfloat) m; }; v.x() = dequantize_one(iqs + 0); @@ -169,6 +240,35 @@ static __dpct_inline__ void dequantize_q2_K(const void *vx, const int64_t ib, #endif } +static __dpct_inline__ void dequantize_q2_K_f32(const void *vx, const int64_t ib, + const int iqs, float &v0, float &v1) { +#if QK_K == 256 + const block_q2_K * x = (const block_q2_K *) vx; + const float dall = x[ib].dm[0]; + const float dmin = x[ib].dm[1]; + + auto dequantize_one = [&](const int idx) -> float { + const int n = idx / 128; + const int r = idx % 128; + const int g = r / 32; + const int l = r % 32; + const int is = 8 * n + l / 16; + + const uint8_t q = x[ib].qs[32 * n + l]; + const uint8_t sc = x[ib].scales[is + 2 * g]; + const float d = dall * (sc & 0xF); + const float m = dmin * (sc >> 4); + + return d * ((q >> (2 * g)) & 3) - m; + }; + + v0 = dequantize_one(iqs + 0); + v1 = dequantize_one(iqs + 1); +#else + GGML_ABORT("Q2_K dequantize not supported for QK_K != 256"); +#endif +} + static __dpct_inline__ void dequantize_q3_K(const void *vx, const int64_t ib, const int iqs, dfloat2 &v) { #if QK_K == 256 @@ -242,6 +342,42 @@ static __dpct_inline__ void dequantize_q5_K(const void *vx, const int64_t ib, #endif } +static __dpct_inline__ void dequantize_q5_K_f32(const void *vx, const int64_t ib, + const int iqs, float &v0, float &v1) { +#if QK_K == 256 + const block_q5_K * x = (const block_q5_K *) vx; + const float dall = x[ib].dm[0]; + const float dmin = x[ib].dm[1]; + + auto dequantize_one = [&](const int idx) -> float { + const int il = idx / 64; + const int in = idx % 64; + const int is = 2 * il + (in >= 32 ? 1 : 0); + const int ir = (in & 31) / 2; + const int iq = in & 1; + + const uint8_t q = x[ib].qs[32 * il + 2 * ir + iq]; + const uint8_t h = x[ib].qh[2 * ir + iq]; + const uint8_t qv = (in >= 32) ? (q >> 4) : (q & 0xF); + + uint8_t sc; + uint8_t m; + get_scale_min_k4(is, x[ib].scales, sc, m); + + const float d = dall * sc; + const float mn = dmin * m; + const uint8_t hm = 1 << (2 * il + (in >= 32 ? 1 : 0)); + + return (qv + ((h & hm) ? 16 : 0)) * d - mn; + }; + + v0 = dequantize_one(iqs + 0); + v1 = dequantize_one(iqs + 1); +#else + GGML_ABORT("Q5_K dequantize not supported for QK_K != 256"); +#endif +} + static __dpct_inline__ void dequantize_q6_K(const void *vx, const int64_t ib, const int iqs, dfloat2 &v) { #if QK_K == 256 @@ -296,21 +432,6 @@ static __dpct_inline__ void dequantize_mxfp4(const void *vx, const int64_t ib, v.y() = d * kvalues_mxfp4[q >> 4] * 0.5f; } -static __dpct_inline__ void dequantize_q1_0(const void *vx, const int64_t ib, - const int iqs, dfloat2 &v) { - const block_q1_0 * x = (const block_q1_0 *) vx; - const dfloat d = x[ib].d; - - const int bit_index_0 = iqs + 0; - const int bit_index_1 = iqs + 1; - - const int bit_0 = (x[ib].qs[bit_index_0 / 8] >> (bit_index_0 % 8)) & 1; - const int bit_1 = (x[ib].qs[bit_index_1 / 8] >> (bit_index_1 % 8)) & 1; - - v.x() = (2 * bit_0 - 1) * d; - v.y() = (2 * bit_1 - 1) * d; -} - static __dpct_inline__ void dequantize_nvfp4(const void *vx, const int64_t ib, const int iqs, dfloat2 &v) { const block_nvfp4 & xb = ((const block_nvfp4 *) vx)[ib]; diff --git a/ggml/src/ggml-sycl/dmmv.cpp b/ggml/src/ggml-sycl/dmmv.cpp index fa62975d3ff5..ee7cd2d48d5e 100644 --- a/ggml/src/ggml-sycl/dmmv.cpp +++ b/ggml/src/ggml-sycl/dmmv.cpp @@ -266,7 +266,7 @@ static void dequantize_mul_mat_vec_q2_k(const void *__restrict__ vx, const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + item_ct1.get_local_id(1); - if (row > nrows) return; + if (row >= nrows) return; const int num_blocks_per_row = ncols / QK_K; const int ib0 = row*num_blocks_per_row; @@ -387,7 +387,7 @@ static void dequantize_mul_mat_vec_q2_k_reorder(const void *__restrict__ vx, const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + item_ct1.get_local_id(1); - if (row > nrows) return; + if (row >= nrows) return; const int num_blocks_per_row = ncols / QK_K; const int ib0 = row*num_blocks_per_row; @@ -483,7 +483,7 @@ static void dequantize_mul_mat_vec_q3_k(const void *__restrict__ vx, const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + item_ct1.get_local_id(1); - if (row > nrows) return; + if (row >= nrows) return; const int num_blocks_per_row = ncols / QK_K; const int ib0 = row*num_blocks_per_row; @@ -595,7 +595,7 @@ static void dequantize_mul_mat_vec_q3_k_reorder(const void *__restrict__ vx, const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + item_ct1.get_local_id(1); - if (row > nrows) return; + if (row >= nrows) return; const int num_blocks_per_row = ncols / QK_K; const int ib0 = row*num_blocks_per_row; @@ -693,7 +693,7 @@ static void dequantize_mul_mat_vec_q4_k(const void *__restrict__ vx, const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + item_ct1.get_local_id(1); - if (row > nrows) return; + if (row >= nrows) return; const int num_blocks_per_row = ncols / QK_K; const int ib0 = row*num_blocks_per_row; @@ -841,7 +841,7 @@ static void dequantize_mul_mat_vec_q4_k_reorder(const void *__restrict__ vx, const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + item_ct1.get_local_id(1); - if (row > nrows) return; + if (row >= nrows) return; const int num_blocks_per_row = ncols / QK_K; const int ib0 = row*num_blocks_per_row; @@ -994,10 +994,12 @@ static void dequantize_mul_mat_vec_q4_k_reorder(const void *__restrict__ vx, static void dequantize_mul_mat_vec_q5_k(const void *__restrict__ vx, const float *__restrict__ yy, float *__restrict__ dst, - const int ncols, + const int ncols, int nrows, const sycl::nd_item<3> &item_ct1) { - const int row = item_ct1.get_group(2); + const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + + item_ct1.get_local_id(1); + if (row >= nrows) return; const int num_blocks_per_row = ncols / QK_K; const int ib0 = row*num_blocks_per_row; @@ -1126,7 +1128,9 @@ static void dequantize_mul_mat_vec_q5_k_reorder(const void *__restrict__ vx, const int ncols, int nrows, const sycl::nd_item<3> &item_ct1) { - const int row = item_ct1.get_group(2); + const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + + item_ct1.get_local_id(1); + if (row >= nrows) return; const int num_blocks_per_row = ncols / QK_K; const int ib0 = row*num_blocks_per_row; @@ -1148,19 +1152,13 @@ static void dequantize_mul_mat_vec_q5_k_reorder(const void *__restrict__ vx, const int tid = item_ct1.get_local_id(2) / 2; // 0...15 const int ix = item_ct1.get_local_id(2) % 2; - const int il = tid/4; // 0...3 - const int ir = tid - 4*il;// 0...3 + const int il_base = tid/4; // 0...3 + const int ir = tid - 4*il_base;// 0...3 const int n = 2; - const int im = il/2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 - const int in = il%2; + const int in = il_base%2; const int l0 = n*(2*ir + in); - const int q_offset = 32*im + l0; - const int y_offset = 64*im + l0; - - const uint8_t hm1 = 1 << (2*im); - const uint8_t hm2 = hm1 << 4; uint16_t aux[4]; const uint8_t * sc = (const uint8_t *)aux; @@ -1171,52 +1169,60 @@ static void dequantize_mul_mat_vec_q5_k_reorder(const void *__restrict__ vx, for (int i = ix; i < num_blocks_per_row; i += 2) { const int bi = ib0 + i; - const uint8_t * ql1 = qs_base + bi * (QK_K / 2) + q_offset; const uint8_t * qh = qh_base + bi * (QK_K / 8) + l0; - const float * y1 = yy + i*QK_K + y_offset; - const float * y2 = y1 + 128; - const sycl::half2 dm_val = dm_base[bi]; const float dall = dm_val[0]; const float dmin = dm_val[1]; - const uint16_t * a = (const uint16_t *)(scales_base + bi * K_SCALE_SIZE); - aux[0] = a[im+0] & kmask1; - aux[1] = a[im+2] & kmask1; - aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2); - aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2); - - sycl::float4 sum = {0.f, 0.f, 0.f, 0.f}; - float smin = 0; - const uint16_t * q1 = (const uint16_t *)ql1; - const uint16_t * q2 = q1 + 32; - q16[0] = q1[0] & 0x0f0f; - q16[1] = q1[8] & 0x0f0f; - q16[2] = (q1[0] >> 4) & 0x0f0f; - q16[3] = (q1[8] >> 4) & 0x0f0f; - q16[4] = q2[0] & 0x0f0f; - q16[5] = q2[8] & 0x0f0f; - q16[6] = (q2[0] >> 4) & 0x0f0f; - q16[7] = (q2[8] >> 4) & 0x0f0f; - for (int l = 0; l < n; ++l) { - sum.x() += - y1[l + 0] * (q4[l + 0] + (qh[l + 0] & (hm1 << 0) ? 16 : 0)) + - y1[l + 16] * (q4[l + 2] + (qh[l + 16] & (hm1 << 0) ? 16 : 0)); - sum.y() += - y1[l + 32] * (q4[l + 4] + (qh[l + 0] & (hm1 << 1) ? 16 : 0)) + - y1[l + 48] * (q4[l + 6] + (qh[l + 16] & (hm1 << 1) ? 16 : 0)); - sum.z() += - y2[l + 0] * (q4[l + 8] + (qh[l + 0] & (hm2 << 0) ? 16 : 0)) + - y2[l + 16] * (q4[l + 10] + (qh[l + 16] & (hm2 << 0) ? 16 : 0)); - sum.w() += - y2[l + 32] * (q4[l + 12] + (qh[l + 0] & (hm2 << 1) ? 16 : 0)) + - y2[l + 48] * (q4[l + 14] + (qh[l + 16] & (hm2 << 1) ? 16 : 0)); - smin += (y1[l] + y1[l+16]) * sc[2] + (y1[l+32] + y1[l+48]) * sc[3] - + (y2[l] + y2[l+16]) * sc[6] + (y2[l+32] + y2[l+48]) * sc[7]; + for (int im = 0; im < 2; ++im) { + const int q_offset = 32*im + l0; + const int y_offset = 64*im + l0; + + const uint8_t hm1 = 1 << (2*im); + const uint8_t hm2 = hm1 << 4; + + const uint8_t * ql1 = qs_base + bi * (QK_K / 2) + q_offset; + const float * y1 = yy + i*QK_K + y_offset; + const float * y2 = y1 + 128; + + const uint16_t * a = (const uint16_t *)(scales_base + bi * K_SCALE_SIZE); + aux[0] = a[im+0] & kmask1; + aux[1] = a[im+2] & kmask1; + aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2); + aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2); + + sycl::float4 sum = {0.f, 0.f, 0.f, 0.f}; + float smin = 0; + const uint16_t * q1 = (const uint16_t *)ql1; + const uint16_t * q2 = q1 + 32; + q16[0] = q1[0] & 0x0f0f; + q16[1] = q1[8] & 0x0f0f; + q16[2] = (q1[0] >> 4) & 0x0f0f; + q16[3] = (q1[8] >> 4) & 0x0f0f; + q16[4] = q2[0] & 0x0f0f; + q16[5] = q2[8] & 0x0f0f; + q16[6] = (q2[0] >> 4) & 0x0f0f; + q16[7] = (q2[8] >> 4) & 0x0f0f; + for (int l = 0; l < n; ++l) { + sum.x() += + y1[l + 0] * (q4[l + 0] + (qh[l + 0] & (hm1 << 0) ? 16 : 0)) + + y1[l + 16] * (q4[l + 2] + (qh[l + 16] & (hm1 << 0) ? 16 : 0)); + sum.y() += + y1[l + 32] * (q4[l + 4] + (qh[l + 0] & (hm1 << 1) ? 16 : 0)) + + y1[l + 48] * (q4[l + 6] + (qh[l + 16] & (hm1 << 1) ? 16 : 0)); + sum.z() += + y2[l + 0] * (q4[l + 8] + (qh[l + 0] & (hm2 << 0) ? 16 : 0)) + + y2[l + 16] * (q4[l + 10] + (qh[l + 16] & (hm2 << 0) ? 16 : 0)); + sum.w() += + y2[l + 32] * (q4[l + 12] + (qh[l + 0] & (hm2 << 1) ? 16 : 0)) + + y2[l + 48] * (q4[l + 14] + (qh[l + 16] & (hm2 << 1) ? 16 : 0)); + smin += (y1[l] + y1[l+16]) * sc[2] + (y1[l+32] + y1[l+48]) * sc[3] + + (y2[l] + y2[l+16]) * sc[6] + (y2[l+32] + y2[l+48]) * sc[7]; + } + tmp += dall * (sum.x() * sc[0] + sum.y() * sc[1] + sum.z() * sc[4] + + sum.w() * sc[5]) - + dmin * smin; } - tmp += dall * (sum.x() * sc[0] + sum.y() * sc[1] + sum.z() * sc[4] + - sum.w() * sc[5]) - - dmin * smin; } #else // The reordered Q5_K layout is only produced for QK_K == 256. @@ -1241,7 +1247,7 @@ static void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const floa const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + item_ct1.get_local_id(1); - if (row > nrows) return; + if (row >= nrows) return; const int num_blocks_per_row = ncols / QK_K; const int ib0 = row*num_blocks_per_row; @@ -1358,7 +1364,7 @@ static void dequantize_mul_mat_vec_q6_k_reorder(const void * __restrict__ vx, co const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + item_ct1.get_local_id(1); - if (row > nrows) return; + if (row >= nrows) return; const int num_blocks_per_row = ncols / QK_K; const int ib0 = row*num_blocks_per_row; @@ -1831,11 +1837,14 @@ static void dequantize_mul_mat_vec_q5_K_sycl(const void *vx, const float *y, const int nrows, dpct::queue_ptr stream) { GGML_ASSERT(ncols % QK_K == 0); - const sycl::range<3> block_dims(1, 1, WARP_SIZE); + const int ny = 2 / K_QUANTS_PER_ITERATION; + const int block_num_y = (nrows + ny - 1) / ny; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, ny, WARP_SIZE); stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, block_dims), + sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - dequantize_mul_mat_vec_q5_k(vx, y, dst, ncols, item_ct1); + dequantize_mul_mat_vec_q5_k(vx, y, dst, ncols, nrows, item_ct1); }); } diff --git a/ggml/src/ggml-sycl/element_wise.cpp b/ggml/src/ggml-sycl/element_wise.cpp index b2406e11b5af..3cd055494ecf 100644 --- a/ggml/src/ggml-sycl/element_wise.cpp +++ b/ggml/src/ggml-sycl/element_wise.cpp @@ -306,29 +306,43 @@ static __dpct_inline__ T op_trunc(T x) { } } +template +static void unary_op_flat_kernel(const T * x, T * dst, const int k, const sycl::nd_item<1> & item_ct1, F func) { + SYCL_GLOBAL_ID_LOOP(k, item_ct1) { + dst[i] = func(x[i]); + } +} + template static void unary_op_generic_kernel( const T * x, T * dst, const int k, - const int64_t ne0, const int64_t ne1, const int64_t ne2, const int64_t ne3, + const sycl::uint3 ne0_fd, const sycl::uint3 ne1_fd, const sycl::uint3 ne2_fd, const size_t nb0, const size_t nb1, const size_t nb2, const size_t nb3, const size_t nbd0, const size_t nbd1, const size_t nbd2, const size_t nbd3, const sycl::nd_item<1> & item_ct1, F func) { - (void) ne3; + // 32-bit index math: k is int, so every logical index fits u32. 64-bit integer div/mod is + // emulated on Xe and dominates this kernel otherwise, and even the 32-bit divide is worth + // avoiding -- the divisors are launch-invariant, so the magic numbers are precomputed + // host-side and each division becomes a multiply-high plus a shift. + // Byte offsets are widened back to size_t only for the final address math. SYCL_GLOBAL_ID_LOOP(k, item_ct1) { - const int64_t i0 = i % ne0; - const int64_t i1 = (i / ne0) % ne1; - const int64_t i2 = (i / (ne0*ne1)) % ne2; - const int64_t i3 = i / (ne0*ne1*ne2); + sycl::uint2 dm = fast_div_modulo((uint32_t) i, ne0_fd); + const uint32_t i0 = dm.y(); + dm = fast_div_modulo(dm.x(), ne1_fd); + const uint32_t i1 = dm.y(); + dm = fast_div_modulo(dm.x(), ne2_fd); + const uint32_t i2 = dm.y(); + const uint32_t i3 = dm.x(); const char * src_base = (const char *) x; char * dst_base = (char *) dst; - const T * srcp = (const T *)(src_base + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3 ); - T * dstp = (T *)(dst_base + i0*nbd0 + i1*nbd1 + i2*nbd2 + i3*nbd3); + const T * srcp = (const T *)(src_base + (size_t) i0*nb0 + (size_t) i1*nb1 + (size_t) i2*nb2 + (size_t) i3*nb3 ); + T * dstp = (T *)(dst_base + (size_t) i0*nbd0 + (size_t) i1*nbd1 + (size_t) i2*nbd2 + (size_t) i3*nbd3); *dstp = func(*srcp); } @@ -407,46 +421,51 @@ static void clamp(const T * x, T * dst, const float min, const float max, const } template -static void gated_op_fused_geglu(const T * x, const T * g, T * dst, const uint64_t k, const uint64_t n, const uint64_t o0, const uint64_t o1, const sycl::nd_item<1> &item_ct1) { +static void gated_op_fused_geglu(const T * x, const T * g, T * dst, const uint64_t k, const sycl::uint3 n_fd, const uint64_t o0, const uint64_t o1, const sycl::nd_item<1> &item_ct1) { SYCL_GLOBAL_ID_LOOP(k, item_ct1) { - const int64_t j0 = (i / n) * o0 + (i % n); - const int64_t j1 = o0 == o1 ? j0 : (i / n) * o1 + (i % n); + const sycl::uint2 rc = fast_div_modulo((uint32_t) i, n_fd); + const int64_t j0 = rc.x() * o0 + rc.y(); + const int64_t j1 = o0 == o1 ? j0 : rc.x() * o1 + rc.y(); dst[i] = op_gelu(x[j0]) * g[j1]; } } template -static void gated_op_fused_reglu(const T * x, const T * g, T * dst, const uint64_t k, const uint64_t n, const uint64_t o0, const uint64_t o1, const sycl::nd_item<1> &item_ct1) { +static void gated_op_fused_reglu(const T * x, const T * g, T * dst, const uint64_t k, const sycl::uint3 n_fd, const uint64_t o0, const uint64_t o1, const sycl::nd_item<1> &item_ct1) { SYCL_GLOBAL_ID_LOOP(k, item_ct1) { - const int64_t j0 = (i / n) * o0 + (i % n); - const int64_t j1 = o0 == o1 ? j0 : (i / n) * o1 + (i % n); + const sycl::uint2 rc = fast_div_modulo((uint32_t) i, n_fd); + const int64_t j0 = rc.x() * o0 + rc.y(); + const int64_t j1 = o0 == o1 ? j0 : rc.x() * o1 + rc.y(); dst[i] = op_relu(x[j0]) * g[j1]; } } template -static void gated_op_fused_swiglu(const T * x, const T * g, T * dst, const uint64_t k, const uint64_t n, const uint64_t o0, const uint64_t o1, const sycl::nd_item<1> &item_ct1) { +static void gated_op_fused_swiglu(const T * x, const T * g, T * dst, const uint64_t k, const sycl::uint3 n_fd, const uint64_t o0, const uint64_t o1, const sycl::nd_item<1> &item_ct1) { SYCL_GLOBAL_ID_LOOP(k, item_ct1) { - const int64_t j0 = (i / n) * o0 + (i % n); - const int64_t j1 = o0 == o1 ? j0 : (i / n) * o1 + (i % n); + const sycl::uint2 rc = fast_div_modulo((uint32_t) i, n_fd); + const int64_t j0 = rc.x() * o0 + rc.y(); + const int64_t j1 = o0 == o1 ? j0 : rc.x() * o1 + rc.y(); dst[i] = op_silu(x[j0]) * g[j1]; } } template -static void gated_op_fused_geglu_erf(const T * x, const T * g, T * dst, const uint64_t k, const uint64_t n, const uint64_t o0, const uint64_t o1, const sycl::nd_item<1> &item_ct1) { +static void gated_op_fused_geglu_erf(const T * x, const T * g, T * dst, const uint64_t k, const sycl::uint3 n_fd, const uint64_t o0, const uint64_t o1, const sycl::nd_item<1> &item_ct1) { SYCL_GLOBAL_ID_LOOP(k, item_ct1) { - const int64_t j0 = (i / n) * o0 + (i % n); - const int64_t j1 = o0 == o1 ? j0 : (i / n) * o1 + (i % n); + const sycl::uint2 rc = fast_div_modulo((uint32_t) i, n_fd); + const int64_t j0 = rc.x() * o0 + rc.y(); + const int64_t j1 = o0 == o1 ? j0 : rc.x() * o1 + rc.y(); dst[i] = op_gelu_erf(x[j0]) * g[j1]; } } template -static void gated_op_fused_geglu_quick(const T * x, const T * g, T * dst, const uint64_t k, const uint64_t n, const uint64_t o0, const uint64_t o1, const sycl::nd_item<1> &item_ct1) { +static void gated_op_fused_geglu_quick(const T * x, const T * g, T * dst, const uint64_t k, const sycl::uint3 n_fd, const uint64_t o0, const uint64_t o1, const sycl::nd_item<1> &item_ct1) { SYCL_GLOBAL_ID_LOOP(k, item_ct1) { - const int64_t j0 = (i / n) * o0 + (i % n); - const int64_t j1 = o0 == o1 ? j0 : (i / n) * o1 + (i % n); + const sycl::uint2 rc = fast_div_modulo((uint32_t) i, n_fd); + const int64_t j0 = rc.x() * o0 + rc.y(); + const int64_t j1 = o0 == o1 ? j0 : rc.x() * o1 + rc.y(); dst[i] = op_gelu_quick(x[j0]) * g[j1]; } } @@ -529,6 +548,10 @@ static inline void dispatch_ggml_sycl_op_fused_glu(ggml_backend_sycl_context & c GGML_ASSERT(dst->ne[0] == nc); GGML_ASSERT(ggml_is_contiguous_1(dst->src[0])); GGML_ASSERT(ggml_is_contiguous(dst)); + // The fused GLU kernels index with 32-bit fastdiv, which is exact only for indices below + // 2^31. A dst that large is ~8 GB at f32, and the grid sizing already narrows to 32 bits, + // so assert the bound rather than carry a second code path for it. + GGML_ASSERT(ggml_nelements(dst) < ((int64_t) 1 << 31)); const int32_t swapped = ((const int32_t *) dst->op_params)[1]; void * src0_d = src0->data; void * src1_d = src1 ? src1->data : src0->data; @@ -597,7 +620,6 @@ static inline void ggml_sycl_op_unary( const int64_t ne0 = dst->ne[0]; const int64_t ne1 = dst->ne[1]; const int64_t ne2 = dst->ne[2]; - const int64_t ne3 = dst->ne[3]; const size_t nb0 = src0->nb[0]; const size_t nb1 = src0->nb[1]; @@ -609,24 +631,42 @@ static inline void ggml_sycl_op_unary( const size_t nbd2 = dst->nb[2]; const size_t nbd3 = dst->nb[3]; + // Hot unary ops (FFN/GDN silu, sigmoid, ...) run on contiguous tensors; + // skip the strided index math entirely for them. + const bool contiguous = ggml_is_contiguous(src0) && ggml_is_contiguous(dst); + ggml_sycl_detail::dispatch_ggml_sycl_op_unary(ctx, dst, [=](const auto* src, auto* dst_ptr, int k_elements, queue_ptr stream) { const int num_blocks = ceil_div(k_elements, 256); - stream->parallel_for( - sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(256), - sycl::range<1>(256)), - [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - unary_op_generic_kernel( - src, dst_ptr, k_elements, - ne0, ne1, ne2, ne3, - nb0, nb1, nb2, nb3, - nbd0, nbd1, nbd2, nbd3, - item_ct1, - func - ); - }); + if (contiguous) { + stream->parallel_for( + sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(256), + sycl::range<1>(256)), + [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + unary_op_flat_kernel(src, dst_ptr, k_elements, item_ct1, func); + }); + } else { + // Launch-invariant divisors: compute the magic numbers once on the host so the + // kernel never issues an integer divide. Only the strided path needs them. + const sycl::uint3 ne0_fd = init_fastdiv_values((uint32_t) ne0); + const sycl::uint3 ne1_fd = init_fastdiv_values((uint32_t) ne1); + const sycl::uint3 ne2_fd = init_fastdiv_values((uint32_t) ne2); + stream->parallel_for( + sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(256), + sycl::range<1>(256)), + [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + unary_op_generic_kernel( + src, dst_ptr, k_elements, + ne0_fd, ne1_fd, ne2_fd, + nb0, nb1, nb2, nb3, + nbd0, nbd1, nbd2, nbd3, + item_ct1, + func + ); + }); + } }); } @@ -930,10 +970,11 @@ static inline void ggml_sycl_op_geglu(ggml_backend_sycl_context & ctx, ggml_tens ggml_sycl_detail::dispatch_ggml_sycl_op_fused_glu(ctx, dst, [](const auto* x_ptr, const auto* g_ptr, auto* dst_ptr, uint64_t k, uint64_t n, uint64_t o0, uint64_t o1, queue_ptr main_stream) { const uint32_t num_blocks = ceil_div(k, SYCL_GELU_BLOCK_SIZE); + const sycl::uint3 n_fd = init_fastdiv_values((uint32_t) n); main_stream->parallel_for( sycl::nd_range<1>((num_blocks * sycl::range<1>(SYCL_GELU_BLOCK_SIZE)), sycl::range<1>(SYCL_GELU_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - gated_op_fused_geglu(x_ptr, g_ptr, dst_ptr, k, n, o0, o1, item_ct1); + gated_op_fused_geglu(x_ptr, g_ptr, dst_ptr, k, n_fd, o0, o1, item_ct1); }); }); } @@ -942,10 +983,11 @@ static inline void ggml_sycl_op_reglu(ggml_backend_sycl_context & ctx, ggml_tens ggml_sycl_detail::dispatch_ggml_sycl_op_fused_glu(ctx, dst, [](const auto* x_ptr, const auto* g_ptr, auto* dst_ptr, uint64_t k, uint64_t n, uint64_t o0, uint64_t o1, queue_ptr main_stream) { const uint32_t num_blocks = ceil_div((uint32_t)k, SYCL_RELU_BLOCK_SIZE); // Using RELU block size for reglu + const sycl::uint3 n_fd = init_fastdiv_values((uint32_t) n); main_stream->parallel_for( sycl::nd_range<1>((num_blocks * sycl::range<1>(SYCL_RELU_BLOCK_SIZE)), sycl::range<1>(SYCL_RELU_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - gated_op_fused_reglu(x_ptr, g_ptr, dst_ptr, k, n, o0, o1, item_ct1); + gated_op_fused_reglu(x_ptr, g_ptr, dst_ptr, k, n_fd, o0, o1, item_ct1); }); }); } @@ -954,10 +996,11 @@ static inline void ggml_sycl_op_swiglu(ggml_backend_sycl_context & ctx, ggml_ten ggml_sycl_detail::dispatch_ggml_sycl_op_fused_glu(ctx, dst, [](const auto* x_ptr, const auto* g_ptr, auto* dst_ptr, uint64_t k, uint64_t n, uint64_t o0, uint64_t o1, queue_ptr main_stream) { const uint32_t num_blocks = ceil_div((uint32_t)k, SYCL_SILU_BLOCK_SIZE); // Using SILU block size for swiglu + const sycl::uint3 n_fd = init_fastdiv_values((uint32_t) n); main_stream->parallel_for( sycl::nd_range<1>((num_blocks * sycl::range<1>(SYCL_SILU_BLOCK_SIZE)), sycl::range<1>(SYCL_SILU_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - gated_op_fused_swiglu(x_ptr, g_ptr, dst_ptr, k, n, o0, o1, item_ct1); + gated_op_fused_swiglu(x_ptr, g_ptr, dst_ptr, k, n_fd, o0, o1, item_ct1); }); }); } @@ -1057,10 +1100,11 @@ static inline void ggml_sycl_op_geglu_erf(ggml_backend_sycl_context & ctx, ggml_ ggml_sycl_detail::dispatch_ggml_sycl_op_fused_glu(ctx, dst, [](const auto* x_ptr, const auto* g_ptr, auto* dst_ptr, uint64_t k, uint64_t n, uint64_t o0, uint64_t o1, queue_ptr main_stream) { const uint32_t num_blocks = ceil_div(k, SYCL_GELU_BLOCK_SIZE); + const sycl::uint3 n_fd = init_fastdiv_values((uint32_t) n); main_stream->parallel_for( sycl::nd_range<1>((num_blocks * sycl::range<1>(SYCL_GELU_BLOCK_SIZE)), sycl::range<1>(SYCL_GELU_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - gated_op_fused_geglu_erf(x_ptr, g_ptr, dst_ptr, k, n, o0, o1, item_ct1); + gated_op_fused_geglu_erf(x_ptr, g_ptr, dst_ptr, k, n_fd, o0, o1, item_ct1); }); }); } @@ -1069,10 +1113,11 @@ static inline void ggml_sycl_op_geglu_quick(ggml_backend_sycl_context & ctx, ggm ggml_sycl_detail::dispatch_ggml_sycl_op_fused_glu(ctx, dst, [](const auto* x_ptr, const auto* g_ptr, auto* dst_ptr, uint64_t k, uint64_t n, uint64_t o0, uint64_t o1, queue_ptr main_stream) { const uint32_t num_blocks = ceil_div(k, SYCL_GELU_BLOCK_SIZE); + const sycl::uint3 n_fd = init_fastdiv_values((uint32_t) n); main_stream->parallel_for( sycl::nd_range<1>((num_blocks * sycl::range<1>(SYCL_GELU_BLOCK_SIZE)), sycl::range<1>(SYCL_GELU_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { - gated_op_fused_geglu_quick(x_ptr, g_ptr, dst_ptr, k, n, o0, o1, item_ct1); + gated_op_fused_geglu_quick(x_ptr, g_ptr, dst_ptr, k, n_fd, o0, o1, item_ct1); }); }); } diff --git a/ggml/src/ggml-sycl/fattn-mkl.cpp b/ggml/src/ggml-sycl/fattn-mkl.cpp new file mode 100644 index 000000000000..fc22b7bdb8c5 --- /dev/null +++ b/ggml/src/ggml-sycl/fattn-mkl.cpp @@ -0,0 +1,690 @@ +// Flash attention via oneMKL GEMM (XMX-accelerated). +// Uses column_major::gemm for Q*K^T and S*V matmuls +// with an online softmax SYCL kernel. +// +// All GQA query heads sharing a KV head are batched into single +// GEMM calls, amortizing MKL launch overhead across K and V reuse. +// + +#include "common.hpp" +#include "fattn-common.hpp" +#include "fattn-buffers.hpp" +#include "convert.hpp" +#include "fattn.hpp" + +#include +#include +#include + +#define MKL_FA_CHUNK_SIZE_KV 8192 + +// Number of query rows processed per tile. The score buffers (KQ_f32, S_f16) +// are sized q_tile_rows * chunk_size, so this bounds their footprint +// regardless of batch size (n_query_rows = n_queries * gqa_ratio). A typical +// single-ubatch prefill (e.g. ubatch 1024 * gqa 8 = 8192 rows) is exactly one +// tile, so it runs with no extra iterations. Larger batches tile and stay +// bounded. Override with GGML_SYCL_MKL_FA_Q_TILE. +#define MKL_FA_Q_TILE 8192 + +#define MKL_FA_WG_SIZE 256 + +using oneapi::mkl::transpose; +using oneapi::mkl::blas::column_major::gemm; + +// --------------------------------------------------------------------------- +// Helpers +// --------------------------------------------------------------------------- + +// Pack all GQA Q heads for one KV head into fp16, applying q_scale. +// Launches one kernel per GQA group — each kernel copies exactly +// n_queries * DKQ elements using the per-group dst offset and +// per-head source stride. +static void mkl_fa_pack_q_fp16( + dpct::queue_ptr stream, + sycl::half * __restrict dst, + const float * __restrict q_src, + int n_queries, int n_query_rows, int DKQ, + int gqa_ratio, int kvh_base_head, + float q_scale, int64_t q_row_stride, int64_t q_head_stride, + int64_t wg_size) { + + for (int iqg = 0; iqg < gqa_ratio; iqg++) { + int iqh = kvh_base_head + iqg; + sycl::half * dst_g = dst + (int64_t)iqg * n_queries * DKQ; + + const int64_t n_elem = (int64_t)n_queries * DKQ; + const int64_t wg = ((n_elem + wg_size - 1) / wg_size) * wg_size; + + stream->submit([&](sycl::handler & cgh) { + cgh.parallel_for(sycl::nd_range<1>(wg, wg_size), + [=](sycl::nd_item<1> item) { + int64_t e = item.get_global_id(0); + if (e >= n_elem) return; + + int64_t q = e / DKQ; + int64_t d = e - q * DKQ; + + // Stride-aware source offset: handles permuted, + // sliced, or contiguous Q tensor layouts. + int64_t src_off = d + + q * q_row_stride + + (int64_t)iqh * q_head_stride; + + dst_g[e] = sycl::half( + q_src[src_off] * q_scale); + }); + }); + } +} + +// Zero-initialize the online softmax state arrays. +// KQ_max → -inf, KQ_sum → 0, VKQ_accum → 0. +// Merged into one kernel to avoid per-array launch overhead. +static void mkl_fa_init_softmax_state( + dpct::queue_ptr stream, + float * kmax, float * ksum, float * vacc, + int n_query_rows, int DV, int64_t wg_size) { + + const float neg_inf = -1e30f; + const int64_t n_maxsum = n_query_rows; + const int64_t n_vacc = (int64_t)n_query_rows * DV; + const int64_t total = (n_vacc > n_maxsum) ? n_vacc : n_maxsum; + const int64_t wg = ((total + wg_size - 1) / wg_size) * wg_size; + + stream->submit([&](sycl::handler & cgh) { + cgh.parallel_for(sycl::nd_range<1>(wg, wg_size), + [=](sycl::nd_item<1> item) { + int64_t i = item.get_global_id(0); + if (i < n_maxsum) { + kmax[i] = neg_inf; + ksum[i] = 0.0f; + } + if (i < n_vacc) { + vacc[i] = 0.0f; + } + }); + }); +} + +// Online softmax over one KV chunk for a tile of GQA query rows. +// The tile spans absolute rows [q0, q0 + q_rows). Score buffers +// (KQ_f32/S_f16) are indexed RELATIVE to the tile; the persistent state +// (VKQ_accum/KQ_max/KQ_sum) and mask are indexed by ABSOLUTE row. +// For each row: find local max → rescale previous VKQ_accum → +// compute exp(s - max) → write S_f16 → update running max/sum. +static void mkl_fa_online_softmax_chunk( + dpct::queue_ptr stream, + float * __restrict KQ_f32, + sycl::half * __restrict S_f16, + float * __restrict KQ_max, + float * __restrict KQ_sum, + float * __restrict VKQ_accum, + int q0, int q_rows, int n_queries, int DV, + int chunk_size, int chunk_start, + int kvh_head, int gqa_ratio, + const sycl::half * mask_data, int64_t mask_head_stride, + int64_t mask_row_stride, int mask_n_heads, + float logit_softcap, int64_t wg_size) { + + const int64_t wg = ((q_rows + wg_size - 1) / wg_size) * wg_size; + + stream->submit([&](sycl::handler & cgh) { + cgh.parallel_for(sycl::nd_range<1>(wg, wg_size), + [=](sycl::nd_item<1> item) { + int jc_rel = item.get_global_id(0); + if (jc_rel >= q_rows) return; + int jc_abs = q0 + jc_rel; + + const int gqa_group = jc_abs / n_queries; + const int q_row = jc_abs % n_queries; + + // Score buffers are tile-local (relative index). + const float * __restrict KQ_row = KQ_f32 + + jc_rel * (int64_t)chunk_size; + // Persistent accumulator is full-sized (absolute index). + float * __restrict vkq = VKQ_accum + + jc_abs * (int64_t)DV; + + const sycl::half * mask_h = nullptr; + int64_t m_stride = 0; + if (mask_data) { + int m_head = (mask_n_heads > 1) + ? (kvh_head + gqa_group) : 0; + mask_h = mask_data + (int64_t)m_head * mask_head_stride; + m_stride = mask_row_stride; + } + + // Row-wise local maximum (softcap before mask) + float local_max = -1e30f; + for (int i = 0; i < chunk_size; i++) { + float s = KQ_row[i]; + if (logit_softcap != 0.0f) { + s = logit_softcap * sycl::tanh(s); + } + if (mask_h) { + s += (float)mask_h[q_row * m_stride + + (chunk_start + i)]; + } + if (s > local_max) local_max = s; + } + + // Rescale previous accumulator by exp(old_max - new_max) + float old_max = KQ_max[jc_abs]; + float new_max = (old_max > local_max) ? old_max : local_max; + float rescale = (old_max < -1e29f) ? 1.0f + : sycl::native::exp(old_max - new_max); + + for (int v = 0; v < DV; v++) { + vkq[v] *= rescale; + } + + // Softmax and write S_f16 (tile-local index) + float local_sum = 0.0f; + sycl::half * __restrict S_row = S_f16 + + jc_rel * (int64_t)chunk_size; + + for (int i = 0; i < chunk_size; i++) { + float s = KQ_row[i]; + if (logit_softcap != 0.0f) { + s = logit_softcap * sycl::tanh(s); + } + if (mask_h) { + s += (float)mask_h[q_row * m_stride + + (chunk_start + i)]; + } + float val = sycl::native::exp(s - new_max); + S_row[i] = sycl::half(val); + local_sum += val; + } + + KQ_sum[jc_abs] = KQ_sum[jc_abs] * rescale + local_sum; + KQ_max[jc_abs] = new_max; + }); + }); +} + +// Write one GQA group's normalized output to its destination head. +static void mkl_fa_normalize_head( + dpct::queue_ptr stream, + float * __restrict dst_batch, + const float * __restrict VKQ_accum, + const float * __restrict KQ_sum, + int iqh, int n_queries, int DV, int n_q_heads, + int64_t src_offset, int64_t wg_size) { + + const int64_t wg = ((n_queries + wg_size - 1) / wg_size) * wg_size; + + stream->submit([&](sycl::handler & cgh) { + cgh.parallel_for(sycl::nd_range<1>(wg, wg_size), + [=](sycl::nd_item<1> item) { + int jc = item.get_global_id(0); + if (jc >= n_queries) return; + + int ksum_idx = (int)(src_offset / DV) + jc; + float inv_sum = 1.0f / KQ_sum[ksum_idx]; + const float * __restrict src = VKQ_accum + + src_offset + jc * (int64_t)DV; + // Interleaved dst layout (matching TILE): + // rows alternate between heads, then increment query. + // offset = (query * n_q_heads + head) * DV + float * __restrict dst_row = dst_batch + + ((int64_t)jc * n_q_heads + iqh) * (int64_t)DV; + + for (int v = 0; v < DV; v++) { + dst_row[v] = src[v] * inv_sum; + } + }); + }); +} + +// --------------------------------------------------------------------------- +// Per-chunk dequant +// +// Rather than dequantizing all of K/V up front (footprint scales with +// context), we dequant one KV-head chunk at a time into a dense +// [this_chunk x D] fp16 buffer (row-major, lda = D). The source address of +// element (head=ikvh, row=chunk_start+r, col=c) decomposes into independent +// linear terms head_off(ikvh) + row_off(chunk_start) + (r,c), so slicing a +// chunk is a clean pointer offset in every layout case. The true-Gemma- +// interleave vs padded-seq-view distinction is resolved once when the +// descriptor is built; slicing does not reintroduce it. +// --------------------------------------------------------------------------- +enum mkl_fa_kv_desc_mode { + MKL_FA_KV_MODE_F16_DENSE = 0, + MKL_FA_KV_MODE_F16_INTERLEAVED = 1, + MKL_FA_KV_MODE_QUANT_CONTIG = 2, + MKL_FA_KV_MODE_QUANT_NC = 3, +}; + +struct mkl_fa_kv_desc { + const char * data = nullptr; + ggml_type type = GGML_TYPE_F16; + int64_t D = 0; // ne[0] + int64_t nb1 = 0; // byte stride, seq dim + int64_t nb2 = 0; // byte stride, head dim + mkl_fa_kv_desc_mode mode = MKL_FA_KV_MODE_F16_DENSE; + int64_t ts = 0; // type size (mode 3 base offset) + int64_t s01 = 0; // nc row stride in blocks (mode 3) + int64_t s02 = 0; // nc head stride in blocks (mode 3) +}; + +static mkl_fa_kv_desc mkl_fa_make_desc(const ggml_tensor * T, bool interleaved, int n_kv_heads) { + mkl_fa_kv_desc d; + d.data = (const char *)T->data; + d.type = T->type; + d.D = T->ne[0]; + d.nb1 = (int64_t)T->nb[1]; + d.nb2 = (int64_t)T->nb[2]; + d.ts = (int64_t)ggml_type_size(T->type); + + if (T->type == GGML_TYPE_F16) { + d.mode = interleaved ? MKL_FA_KV_MODE_F16_INTERLEAVED + : MKL_FA_KV_MODE_F16_DENSE; + } else if (ggml_is_contiguously_allocated(T) && !interleaved) { + d.mode = MKL_FA_KV_MODE_QUANT_CONTIG; + } else { + d.mode = MKL_FA_KV_MODE_QUANT_NC; + const int64_t bs = (int64_t)ggml_blck_size(T->type); + const int64_t blk_per_row = T->ne[0] / bs; + // True Gemma interleave packs heads within a row (nb[2] < ne[1]*nb[1]) + // → reconstruct physical strides. Padded seq-views (nb[2] > ne[1]*nb[1]) + // already have correct physical strides. + const bool gemma = interleaved && + ((int64_t)T->nb[2] < (int64_t)T->ne[1] * (int64_t)T->nb[1]); + if (gemma) { + d.s01 = (int64_t)n_kv_heads * blk_per_row; + d.s02 = blk_per_row; + } else { + d.s01 = d.nb1 / d.ts; + d.s02 = d.nb2 / d.ts; + } + } + return d; +} + +// Dequant one KV-head chunk into a dense [this_chunk x D] fp16 buffer. +static void mkl_fa_dequant_chunk( + dpct::queue_ptr stream, const mkl_fa_kv_desc & d, ggml_tensor * dst_ctx, + sycl::half * out, int ikvh, int chunk_start, int this_chunk) { + + const int64_t D = d.D; + switch (d.mode) { + case MKL_FA_KV_MODE_F16_DENSE: { + const char * base = d.data + (int64_t)ikvh * d.nb2 + + (int64_t)chunk_start * d.nb1; + stream->memcpy(out, base, (size_t)this_chunk * D * sizeof(sycl::half)); + break; + } + case MKL_FA_KV_MODE_F16_INTERLEAVED: { + const char * base = d.data + (int64_t)ikvh * d.nb2 + + (int64_t)chunk_start * d.nb1; + const int64_t row_halfs = d.nb1 / (int64_t)sizeof(sycl::half); + const sycl::half * src = (const sycl::half *)base; + stream->parallel_for( + sycl::range<2>((size_t)this_chunk, (size_t)D), + [=](sycl::item<2> it) { + int64_t r = it.get_id(0); + int64_t c = it.get_id(1); + out[r * D + c] = src[r * row_halfs + c]; + }); + break; + } + case MKL_FA_KV_MODE_QUANT_CONTIG: { + const char * base = d.data + (int64_t)ikvh * d.nb2 + + (int64_t)chunk_start * d.nb1; + to_fp16_sycl_t to_fp16 = ggml_get_to_fp16_sycl(d.type, dst_ctx); + to_fp16(base, out, (int64_t)this_chunk * D, stream); + break; + } + default: { // MKL_FA_KV_MODE_QUANT_NC + to_fp16_nc_sycl_t to_fp16 = ggml_get_to_fp16_nc_sycl(d.type); + const int64_t base_blocks = (int64_t)ikvh * d.s02 + + (int64_t)chunk_start * d.s01; + const char * base = d.data + base_blocks * d.ts; + // ne02 = ne03 = 1 → s02/s03 inert; head+chunk offset carried by base. + to_fp16(base, out, D, this_chunk, 1, 1, d.s01, d.s02, d.s02, stream); + break; + } + } +} + +// --------------------------------------------------------------------------- +// MKL Flash Attention orchestrator +// +// Pipeline: dequantize K/V → for each KV head: +// pack GQA Q heads → MKL GEMM KQ → online softmax → +// MKL GEMM VKQ → accumulate → normalize → scatter to dst +// --------------------------------------------------------------------------- +void ggml_sycl_flash_attn_ext_mkl(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + + const ggml_tensor * Q = dst->src[0]; + const ggml_tensor * K = dst->src[1]; + const ggml_tensor * V = dst->src[2]; + const ggml_tensor * mask = dst->src[3]; + ggml_tensor * KQV = dst; + + GGML_ASSERT(Q->type == GGML_TYPE_F32); + GGML_ASSERT(KQV->type == GGML_TYPE_F32); + + // --- Op params --- + float scale = 1.0f, max_bias = 0.0f, logit_softcap = 0.0f; + memcpy(&scale, (const float *)KQV->op_params + 0, sizeof(float)); + memcpy(&max_bias, (const float *)KQV->op_params + 1, sizeof(float)); + memcpy(&logit_softcap, (const float *)KQV->op_params + 2, sizeof(float)); + + const float q_scale = scale; + + // --- Dimensions --- + const int DKQ = (int)K->ne[0]; + const int DV = (int)V->ne[0]; + const int n_queries = (int)Q->ne[1]; + const int n_q_heads = (int)Q->ne[2]; + const int n_kv_heads = (int)K->ne[2]; + const int n_batch = (int)Q->ne[3]; + const int n_kv = (int)K->ne[1]; + const int gqa_ratio = n_q_heads / n_kv_heads; + const int n_query_rows = n_queries * gqa_ratio; + + GGML_ASSERT(n_q_heads % n_kv_heads == 0); + GGML_ASSERT(max_bias == 0.0f); // ALiBi not supported + GGML_ASSERT(Q->ne[3] == K->ne[3] || K->ne[3] == 1); + + const int chunk_size = std::min(MKL_FA_CHUNK_SIZE_KV, n_kv); + + // Query rows are processed in tiles of q_tile_rows so the score buffers + // (KQ_f32/S_f16 = q_tile_rows * chunk_size) stay bounded regardless of + // batch size. n_query_rows <= Q_TILE is a single tile (no extra work). + static int q_tile_env = ggml_sycl_get_env("GGML_SYCL_MKL_FA_Q_TILE", MKL_FA_Q_TILE); + const int q_tile_rows = std::max(1, std::min(q_tile_env, n_query_rows)); + + const int64_t wg_size = MKL_FA_WG_SIZE; + + // --- Debug output (gated by GGML_SYCL_MKL_FA_DEBUG=1) --- + static int mkl_call_count = 0; + mkl_call_count++; + static int mkl_debug = ggml_sycl_get_env("GGML_SYCL_MKL_FA_DEBUG", 0); + const bool do_print = (mkl_debug == 1); + + const int64_t q_row_stride = Q->nb[1] / sizeof(float); + const int64_t q_head_stride = Q->nb[2] / sizeof(float); + + const bool V_is_K_view = V->view_src + && (V->view_src == K || (V->view_src == K->view_src + && V->view_offs == K->view_offs)); + + // Early interleaved detection for debug output. + // True interleaved detection happens after dequant (nb12_fp16 == nb11_fp16), + // but we can pre-detect on the original tensor strides. + const bool k_early_interleaved = + ((int64_t)K->ne[1] * K->nb[1] != K->nb[2]); + const bool v_early_interleaved = + !V_is_K_view && ((int64_t)V->ne[1] * V->nb[1] != V->nb[2]); + + if (do_print) { + GGML_LOG_INFO("[MKL-FA] #%d D=%d DV=%d n_q=%d n_kv=%d " + "n_qh=%d n_kvh=%d gqa=%d batch=%d K=%s V=%s " + "chunk=%d buf=%.1fMB%s%s\n", + mkl_call_count, DKQ, DV, n_queries, n_kv, + n_q_heads, n_kv_heads, gqa_ratio, n_batch, + ggml_type_name(K->type), ggml_type_name(V->type), + chunk_size, + (double)((int64_t)n_query_rows * chunk_size * sizeof(float)) + / (1024.0 * 1024.0), + k_early_interleaved ? " K_ILV" : "", + v_early_interleaved ? " V_ILV" : ""); + GGML_LOG_INFO("[MKL-FA] #%d Q-nb1=%lld Q-nb2=%lld " + "q_rs=%lld q_hs=%lld dst_rs=%lld dst_hs=%lld\n", + mkl_call_count, + (long long)Q->nb[1], (long long)Q->nb[2], + (long long)q_row_stride, (long long)q_head_stride, + (long long)(KQV->nb[1] / sizeof(float)), + (long long)(KQV->nb[2] / sizeof(float))); + } + + // --- Stream and allocators --- + dpct::queue_ptr stream = ctx.stream(); + +#define MKL_TAKE_TIME(t0) auto t0 = std::chrono::steady_clock::now() +#define MKL_ACCUM(acc, t0) do { if (do_print) { \ + acc += (int64_t)std::chrono::duration_cast \ + (std::chrono::steady_clock::now() - (t0)).count(); \ +} } while(0) + + int64_t gemm_kq_time_us = 0; + int64_t gemm_vkq_time_us = 0; + int64_t softmax_time_us = 0; + int64_t dequant_time_us = 0; + + MKL_TAKE_TIME(t_deq); + + // --- K/V dequant descriptors --- + // Dequant is done per-chunk inside the KV loop (footprint independent of + // context). Output is always dense row-major fp16 [this_chunk x D], lda=D. + // Interleaved detection: ne[1]*nb[1] != nb[2] means heads are interleaved. + const bool k_interleaved = + ((int64_t)K->ne[1] * K->nb[1] != K->nb[2]) && K->ne[2] > 1; + const bool v_interleaved = + ((int64_t)V->ne[1] * V->nb[1] != V->nb[2]) && V->ne[2] > 1; + + const mkl_fa_kv_desc K_desc = mkl_fa_make_desc(K, k_interleaved, n_kv_heads); + const mkl_fa_kv_desc V_desc = V_is_K_view + ? K_desc : mkl_fa_make_desc(V, v_interleaved, n_kv_heads); + + MKL_ACCUM(dequant_time_us, t_deq); + + // --- Resolve mask pointers --- + const sycl::half * mask_data = nullptr; + int64_t mask_head_stride = 0; + int64_t mask_row_stride = 0; + int mask_n_heads = 0; + + if (mask) { + // Use actual fp16 device size (2 bytes), NOT sizeof(sycl::half) + // which may be 4 on the host in oneAPI. + mask_head_stride = mask->nb[2] / 2; + mask_row_stride = mask->nb[1] / 2; + mask_n_heads = (int)mask->ne[2]; + } + + // --- Allocate intermediates from pool --- + ggml_sycl_pool & pool = ctx.pool(); + + ggml_sycl_pool_alloc KQ_f32(pool); // [q_tile_rows x chunk] + ggml_sycl_pool_alloc S_f16(pool); // [q_tile_rows x chunk] + ggml_sycl_pool_alloc VKQ_chunk(pool); // [q_tile_rows x DV] + ggml_sycl_pool_alloc VKQ_accum(pool); // [n_query_rows x DV] (full) + ggml_sycl_pool_alloc KQ_max(pool); // [n_query_rows] (full) + ggml_sycl_pool_alloc KQ_sum(pool); // [n_query_rows] (full) + ggml_sycl_pool_alloc Q_head_f16(pool); // [n_query_rows x DKQ] (full) + ggml_sycl_pool_alloc K_chunk_f16(pool); // [chunk x DKQ] (per-chunk dequant) + ggml_sycl_pool_alloc V_chunk_f16(pool); // [chunk x DV] (per-chunk dequant) + + KQ_f32.alloc((size_t)q_tile_rows * chunk_size); + S_f16.alloc((size_t)q_tile_rows * chunk_size); + VKQ_chunk.alloc((size_t)q_tile_rows * DV); + VKQ_accum.alloc((size_t)n_query_rows * DV); + KQ_max.alloc(n_query_rows); + KQ_sum.alloc(n_query_rows); + Q_head_f16.alloc((size_t)n_query_rows * DKQ); + K_chunk_f16.alloc((size_t)chunk_size * DKQ); + + sycl::half * V_chunk_f16_ptr; + if (V_is_K_view) { + V_chunk_f16_ptr = K_chunk_f16.ptr; // V aliases K (DV == DKQ) + } else { + V_chunk_f16.alloc((size_t)chunk_size * DV); + V_chunk_f16_ptr = V_chunk_f16.ptr; + } + + sycl::half * Q_head_f16_ptr = Q_head_f16.ptr; + float * KQ_f32_ptr = KQ_f32.ptr; + sycl::half * S_f16_ptr = S_f16.ptr; + float * VKQ_chunk_ptr = VKQ_chunk.ptr; + float * VKQ_accum_ptr = VKQ_accum.ptr; + float * KQ_max_ptr = KQ_max.ptr; + float * KQ_sum_ptr = KQ_sum.ptr; + sycl::half * K_chunk_f16_ptr = K_chunk_f16.ptr; + + const float alpha = 1.0f; + const float beta = 0.0f; + + for (int ib = 0; ib < n_batch; ib++) { + const float * Q_batch = (const float *)Q->data + + ib * (Q->nb[3] / sizeof(float)); + float * dst_batch = (float *)KQV->data + + ib * (KQV->nb[3] / sizeof(float)); + + const sycl::half * mask_batch = nullptr; + if (mask) { + int m_batch = (mask->ne[3] > 1) ? ib : 0; + mask_batch = (const sycl::half *)mask->data + + m_batch * (mask->nb[3] / 2); // 2 = actual fp16 device size + } + + for (int ikvh = 0; ikvh < n_kv_heads; ikvh++) { + int kvh_base_head = ikvh * gqa_ratio; + + // 1. Pack all GQA Q heads into fp16 (full n_query_rows) + mkl_fa_pack_q_fp16(stream, + Q_head_f16_ptr, Q_batch, + n_queries, n_query_rows, DKQ, + gqa_ratio, kvh_base_head, + q_scale, q_row_stride, q_head_stride, wg_size); + + // 2. Initialize softmax state (full n_query_rows) + mkl_fa_init_softmax_state(stream, + KQ_max_ptr, KQ_sum_ptr, VKQ_accum_ptr, + n_query_rows, DV, wg_size); + + // Sync before MKL GEMM (MKL may use an internal queue) + stream->wait(); + + // 3. KV chunk loop (OUTER): dequant each chunk once, then tile queries. + for (int chunk_start = 0; chunk_start < n_kv; chunk_start += chunk_size) { + int this_chunk = std::min(chunk_size, n_kv - chunk_start); + + // 3a. Dequant this KV chunk to dense fp16 (once per chunk) + { + MKL_TAKE_TIME(t0); + mkl_fa_dequant_chunk(stream, K_desc, KQV, + K_chunk_f16_ptr, ikvh, chunk_start, this_chunk); + if (!V_is_K_view) { + mkl_fa_dequant_chunk(stream, V_desc, KQV, + V_chunk_f16_ptr, ikvh, chunk_start, this_chunk); + } + stream->wait(); // dequant must be ready before MKL GEMM + MKL_ACCUM(dequant_time_us, t0); + } + + // 3b. Query tile loop (INNER) — bounds KQ_f32/S_f16 footprint. + for (int q0 = 0; q0 < n_query_rows; q0 += q_tile_rows) { + int q_rows = std::min(q_tile_rows, n_query_rows - q0); + + // GEMM: KQ = Q_tile × K_chunk^T + { + MKL_TAKE_TIME(t0); + sycl::event ev = gemm(*stream, + transpose::trans, transpose::nontrans, + this_chunk, q_rows, DKQ, + alpha, + K_chunk_f16_ptr, DKQ, + Q_head_f16_ptr + (int64_t)q0 * DKQ, DKQ, + beta, + KQ_f32_ptr, this_chunk); + try { ev.wait_and_throw(); } catch (sycl::exception & e) { + GGML_LOG_INFO("[MKL-FA] GEMM KQ: %s\n", e.what()); + GGML_ABORT("MKL GEMM KQ failed"); + } + MKL_ACCUM(gemm_kq_time_us, t0); + } + // Online softmax over this chunk for this query tile + { + MKL_TAKE_TIME(t0); + mkl_fa_online_softmax_chunk(stream, + KQ_f32_ptr, S_f16_ptr, + KQ_max_ptr, KQ_sum_ptr, VKQ_accum_ptr, + q0, q_rows, n_queries, DV, + this_chunk, chunk_start, + kvh_base_head, gqa_ratio, + mask_batch, mask_head_stride, + mask_row_stride, mask_n_heads, + logit_softcap, wg_size); + stream->wait(); // S_f16 must be ready for GEMM + MKL_ACCUM(softmax_time_us, t0); + } + + // GEMM: VKQ_chunk = S × V_chunk + { + MKL_TAKE_TIME(t0); + sycl::event ev = gemm(*stream, + transpose::nontrans, transpose::nontrans, + DV, q_rows, this_chunk, + alpha, + V_chunk_f16_ptr, DV, + S_f16_ptr, this_chunk, + beta, + VKQ_chunk_ptr, DV); + try { ev.wait_and_throw(); } catch (sycl::exception & e) { + GGML_LOG_INFO("[MKL-FA] GEMM VKQ: %s\n", e.what()); + GGML_ABORT("MKL GEMM VKQ failed"); + } + MKL_ACCUM(gemm_vkq_time_us, t0); + } + // VKQ_accum[q0..] += VKQ_chunk + { + const int64_t n_total = (int64_t)q_rows * DV; + const int64_t wg = ((n_total + wg_size - 1) / wg_size) + * wg_size; + float * accum = VKQ_accum_ptr + (int64_t)q0 * DV; + stream->submit([&](sycl::handler & cgh) { + cgh.parallel_for(sycl::nd_range<1>(wg, wg_size), + [=](sycl::nd_item<1> item) { + int64_t i = item.get_global_id(0); + if (i < n_total) { + accum[i] += VKQ_chunk_ptr[i]; + } + }); + }); + } + } + } + + // 4. Normalize and scatter each GQA head to dst + for (int iqg = 0; iqg < gqa_ratio; iqg++) { + int iqh = kvh_base_head + iqg; + int64_t src_offset = (int64_t)iqg * n_queries * DV; + mkl_fa_normalize_head(stream, + dst_batch, VKQ_accum_ptr, KQ_sum_ptr, + iqh, n_queries, DV, n_q_heads, + src_offset, wg_size); + } + } + } + +#undef MKL_TAKE_TIME +#undef MKL_ACCUM + + if (do_print) { + const int64_t v_chunk_elems = V_is_K_view ? 0 : (int64_t)chunk_size * DV; + double total_mb = (double)( + (int64_t)q_tile_rows * chunk_size * sizeof(float) // KQ_f32 + + (int64_t)q_tile_rows * chunk_size * sizeof(sycl::half) // S_f16 + + (int64_t)q_tile_rows * DV * sizeof(float) // VKQ_chunk + + (int64_t)n_query_rows * DV * sizeof(float) // VKQ_accum + + (int64_t)n_query_rows * sizeof(float) // KQ_max + + (int64_t)n_query_rows * sizeof(float) // KQ_sum + + (int64_t)n_query_rows * DKQ * sizeof(sycl::half) // Q_head_f16 + + (int64_t)chunk_size * DKQ * sizeof(sycl::half) // K_chunk_f16 + + v_chunk_elems * (int64_t)sizeof(sycl::half) // V_chunk_f16 + ) / (1024.0 * 1024.0); + GGML_LOG_INFO("[MKL-FA] #%d n_kv=%d n_q=%d q_tile=%d time_us: " + "dequant=%lld GEMM_KQ=%lld softmax=%lld GEMM_VKQ=%lld " + "buf_mb=%.1f\n", + mkl_call_count, n_kv, n_queries, q_tile_rows, + (long long)dequant_time_us, + (long long)gemm_kq_time_us, + (long long)softmax_time_us, + (long long)gemm_vkq_time_us, + total_mb); + } +} diff --git a/ggml/src/ggml-sycl/fattn-onednn.cpp b/ggml/src/ggml-sycl/fattn-onednn.cpp index f2e12ef1aeff..fd17a25d5edd 100644 --- a/ggml/src/ggml-sycl/fattn-onednn.cpp +++ b/ggml/src/ggml-sycl/fattn-onednn.cpp @@ -2,11 +2,13 @@ #include #include #include +#include #include #include #include "fattn-onednn.hpp" #include "fattn-tile.hpp" +#include "convert.hpp" // set minimum query length to treat as prefill (32) #define GGML_SYCL_FA_ONEDNN_MIN_Q 32 @@ -33,9 +35,35 @@ bool ggml_sycl_flash_attn_ext_onednn_supported(const ggml_tensor * dst) { const ggml_tensor * mask = dst->src[3]; const ggml_tensor * sinks = dst->src[4]; - // gate for f16 KV only for now - // need to implement quantized KV + // F16 KV: native SDPA at any KV length. + // Non-F16: dequant to F16 then SDPA at prefill lengths. Only the + // standard quantized KV cache types (Q4_0-Q8_0) and F32 are accepted + // because their to_fp16_sycl conversion is verified. BF16 and IQ* + // are excluded: BF16 needs a strided conversion kernel that does not + // exist yet; IQ types are model-weight-only quants with no dequant + // registration and are never used as KV caches. if (K->type != GGML_TYPE_F16 || V->type != GGML_TYPE_F16) { + auto kt = K->type, vt = V->type; + bool k_ok = kt == GGML_TYPE_F32 || kt == GGML_TYPE_Q4_0 || kt == GGML_TYPE_Q4_1 || + kt == GGML_TYPE_Q5_0 || kt == GGML_TYPE_Q5_1 || kt == GGML_TYPE_Q8_0; + bool v_ok = vt == GGML_TYPE_F32 || vt == GGML_TYPE_Q4_0 || vt == GGML_TYPE_Q4_1 || + vt == GGML_TYPE_Q5_0 || vt == GGML_TYPE_Q5_1 || vt == GGML_TYPE_Q8_0; + if (!k_ok || !v_ok) { + return false; + } + if (Q->ne[1] < 32 || K->ne[1] < 1024) { + return false; + } + for (const ggml_tensor * t : {K, V}) { + if (t->type == GGML_TYPE_F16 && t->nb[1] % (t->ne[0] * 2) != 0) { + return false; + } + } + } + // Optional KV-length ceiling (GGML_SYCL_FA_ONEDNN_MAX_KV, 0 = unlimited). Escape hatch: + // very long sequences make the fused SDPA slow enough to risk the xe driver watchdog on + // some stacks; past the cap we fall back to the native FA kernel instead. + if (g_ggml_sycl_fa_onednn_max_kv > 0 && K->ne[1] > g_ggml_sycl_fa_onednn_max_kv) { return false; } // gate for the following cases @@ -199,18 +227,114 @@ void ggml_sycl_flash_attn_ext_onednn(ggml_backend_sycl_context & ctx, ggml_tenso dnnl::engine eng = ctx.engine_dnnl(stream); dnnl::stream strm = ctx.stream_dnnl(stream); - // cont/cast inputs to contiguous f16 (head-major) -- the layout the fast systolic path wants. - ggml_sycl_pool_alloc Qf(ctx.pool(), (size_t) H * q * d); - ggml_sycl_pool_alloc Kf(ctx.pool(), (size_t) Hkv * seq * d); - ggml_sycl_pool_alloc Vf(ctx.pool(), (size_t) Hkv * seq * d); - cont_to_f16_sycl ((const char *) Q->data, Qf.get(), d, q, H, mb, Q->nb[1], Q->nb[2], Q->nb[3], stream); - cont_to_f16_sycl((const char *) K->data, Kf.get(), d, seq, Hkv, mb, K->nb[1], K->nb[2], K->nb[3], stream); - cont_to_f16_sycl((const char *) V->data, Vf.get(), d, seq, Hkv, mb, V->nb[1], V->nb[2], V->nb[3], stream); + // Q: always f32 -- copy to dense f16. + ggml_sycl_pool_alloc Qf(ctx.pool(), (size_t) H * q * d); + cont_to_f16_sycl((const char *) Q->data, Qf.get(), d, q, H, mb, Q->nb[1], Q->nb[2], Q->nb[3], stream); + + // K/V: use pool-alloc for both F16 and dequant paths. + sycl::half * K_ptr = nullptr; + sycl::half * V_ptr = nullptr; + std::optional> Kf_pool; + std::optional> Vf_pool; + + if (K->type == GGML_TYPE_F16 && V->type == GGML_TYPE_F16) { + Kf_pool.emplace(ctx.pool(), (size_t) Hkv * seq * d); + Vf_pool.emplace(ctx.pool(), (size_t) Hkv * seq * d); + cont_to_f16_sycl((const char *) K->data, Kf_pool->get(), d, seq, Hkv, mb, K->nb[1], K->nb[2], K->nb[3], stream); + cont_to_f16_sycl((const char *) V->data, Vf_pool->get(), d, seq, Hkv, mb, V->nb[1], V->nb[2], V->nb[3], stream); + K_ptr = Kf_pool->get(); + V_ptr = Vf_pool->get(); + } else if (ggml_is_quantized(K->type)) { + // Quantized K/V: dequant to dense F16 using pool, same lifetime as F16 path. + Kf_pool.emplace(ctx.pool(), ggml_nelements(K)); + K_ptr = Kf_pool->get(); + { + const char * K_data = (const char *)K->data; + const bool k_non_dense = ((int64_t)K->ne[1] * K->nb[1] != K->nb[2]) && K->ne[2] > 1; + const bool k_gemma = k_non_dense && + ((int64_t)K->nb[2] < (int64_t)K->ne[1] * (int64_t)K->nb[1]); + if (ggml_is_contiguously_allocated(K) && !k_non_dense) { + to_fp16_sycl_t to_fp16 = ggml_get_to_fp16_sycl(K->type, dst); + to_fp16(K_data, K_ptr, ggml_nelements(K), stream); + } else { + const size_t bs = ggml_blck_size(K->type); + const size_t ts = ggml_type_size(K->type); + to_fp16_nc_sycl_t to_fp16 = ggml_get_to_fp16_nc_sycl(K->type); + int64_t s01, s02, s03; + if (k_gemma) { + const int64_t blk_per_row = (int64_t)K->ne[0] / bs; + s01 = (int64_t)Hkv * blk_per_row; + s02 = blk_per_row; + s03 = (int64_t)K->ne[1] * s01; + } else { + s01 = (int64_t)K->nb[1] / ts; + s02 = (int64_t)K->nb[2] / ts; + s03 = (int64_t)K->nb[3] / ts; + } + to_fp16(K_data, K_ptr, + K->ne[0], K->ne[1], K->ne[2], K->ne[3], + s01, s02, s03, stream); + } + } + // Quantized V: always dequant separately. Even when K and V share + // the same underlying allocation (V is a view of K with the same + // data pointer), their logical values differ because the quantized + // elements at different positions/offsets represent different K/V + // data. Master's F16 path also never aliases K and V. + Vf_pool.emplace(ctx.pool(), ggml_nelements(V)); + V_ptr = Vf_pool->get(); + { + const char * V_data = (const char *)V->data; + const bool v_non_dense = ((int64_t)V->ne[1] * V->nb[1] != V->nb[2]) && V->ne[2] > 1; + const bool v_gemma = v_non_dense && + ((int64_t)V->nb[2] < (int64_t)V->ne[1] * (int64_t)V->nb[1]); + if (ggml_is_contiguously_allocated(V) && !v_non_dense) { + to_fp16_sycl_t to_fp16 = ggml_get_to_fp16_sycl(V->type, dst); + to_fp16(V_data, V_ptr, ggml_nelements(V), stream); + } else { + const size_t bs = ggml_blck_size(V->type); + const size_t ts = ggml_type_size(V->type); + to_fp16_nc_sycl_t to_fp16 = ggml_get_to_fp16_nc_sycl(V->type); + int64_t s01, s02, s03; + if (v_gemma) { + const int64_t blk_per_row = (int64_t)V->ne[0] / bs; + s01 = (int64_t)V->ne[2] * blk_per_row; + s02 = blk_per_row; + s03 = (int64_t)V->ne[1] * s01; + } else { + s01 = (int64_t)V->nb[1] / ts; + s02 = (int64_t)V->nb[2] / ts; + s03 = (int64_t)V->nb[3] / ts; + } + to_fp16(V_data, V_ptr, + V->ne[0], V->ne[1], V->ne[2], V->ne[3], + s01, s02, s03, stream); + } + } + } else { + // F32: strided copy to dense F16 via cont_to_f16_sycl. + Kf_pool.emplace(ctx.pool(), ggml_nelements(K)); + K_ptr = Kf_pool->get(); + cont_to_f16_sycl((const char *) K->data, K_ptr, K->ne[0], K->ne[1], K->ne[2], K->ne[3], + K->nb[1], K->nb[2], K->nb[3], stream); + Vf_pool.emplace(ctx.pool(), ggml_nelements(V)); + V_ptr = Vf_pool->get(); + cont_to_f16_sycl((const char *) V->data, V_ptr, V->ne[0], V->ne[1], V->ne[2], V->ne[3], + V->nb[1], V->nb[2], V->nb[3], stream); + } // divide-by-(1/scale) reproduces ggml's score *= kq_scale on the proven probe graph. + // + // The scale must not be uploaded with an async memcpy from a stack local: on the in-order + // queue that copy waits behind the K/V staging kernels, and once those take long enough + // (n_kv >= ~26k on B70) the host frame is recycled before the copy runs, feeding the SDPA a + // garbage scale (output collapses to a repeated token). Write the scalar from a kernel + // instead -- the value is captured into the command, so no host memory has to outlive the + // call, and the enqueue stays async. const sycl::half scale_h = (sycl::half) (1.0f / kq_scale); ggml_sycl_pool_alloc scbuf(ctx.pool(), 1); - stream->memcpy(scbuf.get(), &scale_h, sizeof(sycl::half)); + sycl::half * const scale_dev = scbuf.get(); + stream->single_task([=]() { *scale_dev = scale_h; }); ggml_sycl_pool_alloc outf(ctx.pool(), (size_t) H * q * d); // f16 contiguous SDPA out [mb,H,q,d] @@ -230,9 +354,9 @@ void ggml_sycl_flash_attn_ext_onednn(ggml_backend_sycl_context & ctx, ggml_tenso auto id2ptr = [&](size_t r) -> void * { if (r == E.id_q) return Qf.get(); - if (r == E.id_k) return Kf.get(); - if (r == E.id_v) return Vf.get(); - if (r == E.id_scale) return scbuf.get(); + if (r == E.id_k) return K_ptr; + if (r == E.id_v) return V_ptr; + if (r == E.id_scale) return scale_dev; if (r == E.id_mask) return (void *) mask->data; return nullptr; }; @@ -245,14 +369,12 @@ void ggml_sycl_flash_attn_ext_onednn(ggml_backend_sycl_context & ctx, ggml_tenso E.cp.execute(strm, ti, {to}); permute_sdpa_out_sycl(outf.get(), (float *) dst->data, mb, H, q, d, stream); - // Single device: no sync is required, and actually PP perf is ~6% > wait_and_throw() (tested on llama-3.1-8b & qwen3.6-27b, both Q8_0, with Arc B70). - // Any future multi-GPU refactor MUST re-measure this single-device path and keep the best - // single-device PP speed. Otherwise (multiple devices/streams can race the reuse): + // Single device needs no sync: the dnnl stream wraps this same in-order queue, so the SDPA + // serializes with the staging kernels before it and the permute/pool reuse after it. The + // garbage output formerly blamed on the missing sync here was the scale use-after-return + // fixed above. Keep the conservative wait for multi-GPU, where other devices' streams can + // race the pool: if (ggml_sycl_info().device_count > 1) { - // cont_to_f16 -> oneDNN execute -> permute is async on this stream, but the - // pool_alloc*s above free their device buffers at host return. Without this wait the next - // scheduler op re-acquires those bytes while the GPU is still computing the SDPA, turning - // it into garbage and collapsing multi-turn output to a single repeated token ("GGGGG..."). stream->wait_and_throw(); } } diff --git a/ggml/src/ggml-sycl/fattn.cpp b/ggml/src/ggml-sycl/fattn.cpp index 1772b9c8584d..a85eb721f6af 100644 --- a/ggml/src/ggml-sycl/fattn.cpp +++ b/ggml/src/ggml-sycl/fattn.cpp @@ -97,10 +97,12 @@ static void ggml_sycl_flash_attn_ext_vec(ggml_backend_sycl_context & ctx, ggml_t enum best_fattn_kernel { BEST_FATTN_KERNEL_NONE = 0, BEST_FATTN_KERNEL_VEC = 100, - BEST_FATTN_KERNEL_ONEDNN = 150, // added enum for onednn==150 + BEST_FATTN_KERNEL_ONEDNN = 150, // oneDNN SDPA: native F16 (PR #25222) BEST_FATTN_KERNEL_TILE = 200, + BEST_FATTN_KERNEL_MKL = 300, }; + static best_fattn_kernel ggml_sycl_get_best_fattn_kernel(const int device, const ggml_tensor * dst) { GGML_UNUSED(device); #ifndef SYCL_FLASH_ATTN @@ -115,6 +117,7 @@ static best_fattn_kernel ggml_sycl_get_best_fattn_kernel(const int device, const const ggml_tensor * K = dst->src[1]; const ggml_tensor * V = dst->src[2]; const ggml_tensor * mask = dst->src[3]; + const ggml_tensor * sinks = dst->src[4]; const int gqa_ratio = Q->ne[2] / K->ne[2]; GGML_ASSERT(Q->ne[2] % K->ne[2] == 0); @@ -122,7 +125,56 @@ static best_fattn_kernel ggml_sycl_get_best_fattn_kernel(const int device, const float max_bias = 0.0f; memcpy(&max_bias, (const float *) KQV->op_params + 1, sizeof(float)); + float logit_softcap = 0.0f; + memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float)); + bool gqa_opt_applies = gqa_ratio >= 2 && mask && max_bias == 0.0f && K->ne[1] % FATTN_KQ_STRIDE == 0; + + // XMX-accelerated path: oneDNN SDPA (native F16 and dequant+non-F16). + // ONEDNN requires min 32 query tokens — short-circuit decode to avoid + // calling _supported() on every decode FA call. + if (Q->ne[1] >= 32 + && ggml_sycl_flash_attn_ext_onednn_supported(dst)) { + return BEST_FATTN_KERNEL_ONEDNN; + } + + // MKL path: XMX-accelerated GEMM for prompt processing (all KV cache types). + // The MKL kernel converts non-F16 K/V to F16 via to_fp16_sycl before GEMM, + // so quantized, F16, BF16, and F32 caches all benefit from XMX acceleration. + // Activates automatically when flash-attn is enabled (--flash-attn on or -fa) + // and n_kv >= 1024. Falls through to TILE/VEC for ALiBi, logit softcap, + // and mismatched batch dimensions (unsupported by the MKL kernel). + // Set GGML_SYCL_ENABLE_MKL_FA=0 to force TILE/VEC path for A/B testing. + // Example: GGML_SYCL_ENABLE_MKL_FA=0 llama-cli -m model.gguf -fa -ngl 99 ... + // Note: MKL GEMM calls are incompatible with SYCL graph capture replay. + static int mkl_enable = ggml_sycl_get_env("GGML_SYCL_ENABLE_MKL_FA", 1); + // MKL is validated for the mainstream GQA envelope: grouped-query + // (gqa_ratio >= 2), head_dim a multiple of 64 in [64,512] with matching + // K/V head size, mask, no sinks/ALiBi/softcap. Gemma's global layers use + // head_dim 512, so the cap must include it. Head sizes not a multiple of + // 64 (72/80/96), MHA (gqa_ratio == 1), and MLA (DKQ != DV, e.g. 576/512) + // fall through to TILE/VEC; see follow-up work. + if (mkl_enable == 1 && mask && !sinks && gqa_ratio >= 2 && + Q->ne[0] >= 64 && Q->ne[0] <= 512 && Q->ne[0] % 64 == 0 && + Q->ne[0] == V->ne[0] && + Q->ne[1] >= 32 && K->ne[1] >= 1024 && + max_bias == 0.0f && logit_softcap == 0.0f && + (Q->ne[3] == K->ne[3] || K->ne[3] == 1)) { + // F16 K/V strides must be a multiple of ne[0]*2 (the natural row size + // in bytes). This passes both dense (nb1 == ne0*2) and interleaved + // (nb1 == H * ne0*2). Only pathological test strides like nb1=32 or + // nb1=75 for ne0=40 fall through to TILE. + bool kv_strides_ok = true; + for (const ggml_tensor * t : {K, V}) { + if (t->type == GGML_TYPE_F16 && t->nb[1] % (t->ne[0] * 2) != 0) { + kv_strides_ok = false; + break; + } + } + if (kv_strides_ok) { + return BEST_FATTN_KERNEL_MKL; + } + } for (const ggml_tensor * t : {Q, K, V, mask}) { if (t == nullptr || ggml_is_quantized(t->type)) { continue; @@ -170,6 +222,7 @@ static best_fattn_kernel ggml_sycl_get_best_fattn_kernel(const int device, const switch (K->type) { case GGML_TYPE_F32: case GGML_TYPE_F16: + case GGML_TYPE_BF16: break; case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -188,8 +241,11 @@ static best_fattn_kernel ggml_sycl_get_best_fattn_kernel(const int device, const return BEST_FATTN_KERNEL_NONE; } - // For small batch sizes the vector kernel may be preferable over the kernels optimized for large batch sizes: - const bool can_use_vector_kernel = Q->ne[0] <= 512 && Q->ne[0] % 64 == 0 && K->ne[1] % FATTN_KQ_STRIDE == 0; + // For small batch sizes the vector kernel may be preferable over the kernels optimized for large batch sizes. + // BF16 is excluded: the VEC kernel has no BF16 template (it needs GGML_SYCL_FA_ALL_QUANTS for non-F16/Q4_0/Q8_0). + const bool has_bf16 = (K->type == GGML_TYPE_BF16 || V->type == GGML_TYPE_BF16); + const bool can_use_vector_kernel = Q->ne[0] <= 512 && Q->ne[0] % 64 == 0 && K->ne[1] % FATTN_KQ_STRIDE == 0 + && !has_bf16; // Fused-XMX path: oneDNN Graph SDPA (flash attention). Strictly // additive -- taken only when statically supported, otherwise falls through to VEC/TILE below. @@ -216,7 +272,40 @@ static best_fattn_kernel ggml_sycl_get_best_fattn_kernel(const int device, const void ggml_sycl_flash_attn_ext(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { ggml_sycl_set_device(ctx.device); - switch (ggml_sycl_get_best_fattn_kernel(ggml_sycl_get_device(), dst)) { + + // n_kv watchdog: log when n_kv differs from the last FA call with + // the same D — helps detect cache-truncation issues. + static int nkv_debug = ggml_sycl_get_env("GGML_SYCL_MKL_FA_DEBUG", 0); + if (nkv_debug == 1) { + const ggml_tensor * K_dbg = dst->src[1]; + const ggml_tensor * V_dbg = dst->src[2]; + static int64_t last_nkv_d256 = 0, last_nkv_d512 = 0; + static int fa_call_seq = 0; + fa_call_seq++; + int64_t cur_nkv = K_dbg->ne[1]; + int Dk = (int)K_dbg->ne[0]; + const char * kname = "TILE"; + best_fattn_kernel k = ggml_sycl_get_best_fattn_kernel(ctx.device, dst); + if (k == BEST_FATTN_KERNEL_MKL) kname = "MKL"; + if (k == BEST_FATTN_KERNEL_ONEDNN) kname = "ONEDNN"; + if (k == BEST_FATTN_KERNEL_VEC) kname = "VEC"; + int64_t delta = 0; + if (Dk == 256) { + delta = cur_nkv - last_nkv_d256; + last_nkv_d256 = cur_nkv; + } else if (Dk == 512) { + delta = cur_nkv - last_nkv_d512; + last_nkv_d512 = cur_nkv; + } + GGML_LOG_INFO("[FA-DISP] #%d %s D=%d n_kv=%lld delta=%lld " + "V_ne1=%lld\n", + fa_call_seq, kname, Dk, + (long long)cur_nkv, (long long)delta, + (long long)V_dbg->ne[1]); + } + + const best_fattn_kernel fk = ggml_sycl_get_best_fattn_kernel(ggml_sycl_get_device(), dst); + switch (fk) { case BEST_FATTN_KERNEL_NONE: GGML_ABORT("Not support Flash-Attention"); case BEST_FATTN_KERNEL_ONEDNN: @@ -232,7 +321,54 @@ void ggml_sycl_flash_attn_ext(ggml_backend_sycl_context & ctx, ggml_tensor * dst case BEST_FATTN_KERNEL_VEC: ggml_sycl_flash_attn_ext_vec(ctx, dst); break; + case BEST_FATTN_KERNEL_MKL: + ggml_sycl_flash_attn_ext_mkl(ctx, dst); + break; } + + // --- Output fingerprint (GGML_SYCL_MKL_FA_DIAG=1) --- + // Copy first 64 float output values to host for fingerprinting. + // Compare MKL vs TILE (GGML_SYCL_ENABLE_MKL_FA=0) to detect divergence. + // Only fingerprints the first 6 FA calls with n_kv >= 1024. + static int fa_diag = ggml_sycl_get_env("GGML_SYCL_MKL_FA_DIAG", 0); + static int fa_diag_count = 0; + if (fa_diag == 1 && fa_diag_count < 6) { + const ggml_tensor * K_diag = dst->src[1]; + const ggml_tensor * V_diag = dst->src[2]; + const ggml_tensor * Q_diag = dst->src[0]; + if (K_diag->ne[1] >= 1024) { + fa_diag_count++; + float diag_buf[64]; + dpct::queue_ptr q = ctx.stream(); + q->memcpy(diag_buf, dst->data, 64 * sizeof(float)); + q->wait(); + const char * kname = "???"; + best_fattn_kernel kb = ggml_sycl_get_best_fattn_kernel(ctx.device, dst); + if (kb == BEST_FATTN_KERNEL_ONEDNN) kname = "ONEDNN"; + if (kb == BEST_FATTN_KERNEL_MKL) kname = "MKL"; + if (kb == BEST_FATTN_KERNEL_TILE) kname = "TILE"; + if (kb == BEST_FATTN_KERNEL_VEC) kname = "VEC"; + GGML_LOG_INFO("[FA-DIAG] #%d %s D=%d n_kv=%lld n_q=%lld " + "n_qh=%lld n_kvh=%lld K=%s V=%s " + "nb1=%zu nb2=%zu first 64 floats:\n", + fa_diag_count, kname, + (int)K_diag->ne[0], (long long)K_diag->ne[1], + (long long)Q_diag->ne[1], + (long long)Q_diag->ne[2], (long long)K_diag->ne[2], + ggml_type_name(K_diag->type), + ggml_type_name(V_diag->type), + K_diag->nb[1], K_diag->nb[2]); + for (int i = 0; i < 64; i += 8) { + GGML_LOG_INFO(" [%2d] %08x %08x %08x %08x %08x %08x %08x %08x\n", + i, + *(unsigned *)&diag_buf[i+0], *(unsigned *)&diag_buf[i+1], + *(unsigned *)&diag_buf[i+2], *(unsigned *)&diag_buf[i+3], + *(unsigned *)&diag_buf[i+4], *(unsigned *)&diag_buf[i+5], + *(unsigned *)&diag_buf[i+6], *(unsigned *)&diag_buf[i+7]); + } + } + } + } bool ggml_sycl_flash_attn_ext_supported(int device, const ggml_tensor * dst) { diff --git a/ggml/src/ggml-sycl/fattn.hpp b/ggml/src/ggml-sycl/fattn.hpp index f2a8ffc97dee..c093970a3fed 100644 --- a/ggml/src/ggml-sycl/fattn.hpp +++ b/ggml/src/ggml-sycl/fattn.hpp @@ -19,4 +19,6 @@ void ggml_sycl_flash_attn_ext(ggml_backend_sycl_context & ctx, ggml_tensor * dst bool ggml_sycl_flash_attn_ext_supported(int device, const ggml_tensor * dst); +void ggml_sycl_flash_attn_ext_mkl(ggml_backend_sycl_context & ctx, ggml_tensor * dst); + #endif // GGML_SYCL_FATTN_HPP diff --git a/ggml/src/ggml-sycl/fusion.cpp b/ggml/src/ggml-sycl/fusion.cpp new file mode 100644 index 000000000000..4a6027f39bba --- /dev/null +++ b/ggml/src/ggml-sycl/fusion.cpp @@ -0,0 +1,44 @@ +#include "fusion.hpp" + +bool ggml_sycl_can_fuse(const ggml_cgraph * cgraph, int node_idx, std::initializer_list ops) { + if (!g_ggml_sycl_enable_fusion) { + return false; + } + + if (!ggml_can_fuse(cgraph, node_idx, ops)) { + return false; + } + + if (ops.size() == 2 && ops.begin()[0] == GGML_OP_RMS_NORM && ops.begin()[1] == GGML_OP_MUL) { + const ggml_tensor * rms_norm = cgraph->nodes[node_idx]; + const ggml_tensor * mul = cgraph->nodes[node_idx + 1]; + + GGML_ASSERT(rms_norm->src[0]->type == GGML_TYPE_F32); + GGML_ASSERT(rms_norm->type == GGML_TYPE_F32); + + if (mul->src[0]->type != GGML_TYPE_F32 || + mul->src[1]->type != GGML_TYPE_F32 || + mul->type != GGML_TYPE_F32) { + return false; + } + + // if rms norm is the B operand, then we don't handle broadcast + if (rms_norm == mul->src[1] && !ggml_are_same_shape(mul->src[0], rms_norm)) { + return false; + } + + const ggml_tensor * mul_w = (mul->src[0] == rms_norm) ? mul->src[1] : mul->src[0]; + // the fused kernel indexes the weight as mul[col], so it must span ncols contiguously + if (mul_w->ne[0] != rms_norm->ne[0] || mul_w->nb[0] != ggml_type_size(mul_w->type)) { + return false; + } + + if (!ggml_is_contiguous_rows(mul->src[0]) || !ggml_is_contiguous_rows(mul->src[1])) { + return false; + } + + return true; + } + + return false; +} diff --git a/ggml/src/ggml-sycl/fusion.hpp b/ggml/src/ggml-sycl/fusion.hpp new file mode 100644 index 000000000000..7d7c79e02814 --- /dev/null +++ b/ggml/src/ggml-sycl/fusion.hpp @@ -0,0 +1,15 @@ +#ifndef GGML_SYCL_FUSION_HPP +#define GGML_SYCL_FUSION_HPP + +#include + +#include "common.hpp" + +// Backend-side fusability test. `ops` names a candidate op sequence starting at cgraph node +// `node_idx`; the result is true only if ggml considers that subgraph fusable *and* the SYCL +// kernel which would service it accepts the tensors involved (types, shapes, contiguity). +// +// Lives in its own translation unit because it grows a branch per supported op sequence. +bool ggml_sycl_can_fuse(const ggml_cgraph * cgraph, int node_idx, std::initializer_list ops); + +#endif // GGML_SYCL_FUSION_HPP diff --git a/ggml/src/ggml-sycl/getrows.cpp b/ggml/src/ggml-sycl/getrows.cpp index 298f247f84e0..2113f3563398 100644 --- a/ggml/src/ggml-sycl/getrows.cpp +++ b/ggml/src/ggml-sycl/getrows.cpp @@ -60,6 +60,50 @@ static void k_get_rows( dst_row[iybs + iqs + y_offset] = v.y(); } +template +static void k_get_rows_f32( + const void * src0, const int32_t * src1, dst_t * dst, + int64_t ne00, + int64_t ne12, + size_t s1, size_t s2, size_t s3, + size_t nb01, size_t nb02, size_t nb03, + size_t s10, size_t s11, size_t s12, + const sycl::nd_item<3> &item_ct1) { + + const int i00 = (item_ct1.get_group(2) * item_ct1.get_local_range(2) + + item_ct1.get_local_id(2)) * + 2; + const int i10 = item_ct1.get_local_range(1) * item_ct1.get_group(1) + + item_ct1.get_local_id(1); + const int i11 = (item_ct1.get_group(0) * item_ct1.get_local_range(0) + + item_ct1.get_local_id(0)) / + ne12; + const int i12 = (item_ct1.get_group(0) * item_ct1.get_local_range(0) + + item_ct1.get_local_id(0)) % + ne12; + + if (i00 >= ne00) { + return; + } + + const int i01 = src1[i10*s10 + i11*s11 + i12*s12]; + + dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3; + const void * src0_row = (const char *)src0 + i01*nb01 + i11*nb02 + i12*nb03; + + const int ib = i00/qk; + const int iqs = (i00%qk)/qr; + const int iybs = i00 - i00%qk; + const int y_offset = qr == 1 ? 1 : qk/2; + + float v0; + float v1; + dequantize_kernel(src0_row, ib, iqs, v0, v1); + + dst_row[iybs + iqs + 0] = (dst_t) v0; + dst_row[iybs + iqs + y_offset] = (dst_t) v1; +} + template static void k_get_rows_float( const src0_t * src0, const int32_t * src1, dst_t * dst, @@ -129,6 +173,39 @@ static void get_rows_sycl(ggml_backend_sycl_context & ctx, const ggml_tensor *sr GGML_UNUSED(ctx); } +template +static void get_rows_sycl_f32(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const void *src0_dd, + const int32_t *src1_dd, float *dst_dd, + queue_ptr stream) { + + GGML_TENSOR_BINARY_OP_LOCALS + + const sycl::range<3> block_dims(1, 1, SYCL_GET_ROWS_BLOCK_SIZE); + const int block_num_x = (ne00 + 2*SYCL_GET_ROWS_BLOCK_SIZE - 1) / (2*SYCL_GET_ROWS_BLOCK_SIZE); + const sycl::range<3> block_nums(ne11 * ne12, ne10, block_num_x); + + const size_t s1 = nb1 / ggml_element_size(dst); + const size_t s2 = nb2 / ggml_element_size(dst); + const size_t s3 = nb3 / ggml_element_size(dst); + + const size_t s10 = nb10 / ggml_element_size(src1); + const size_t s11 = nb11 / ggml_element_size(src1); + const size_t s12 = nb12 / ggml_element_size(src1); + + GGML_ASSERT(ne00 % 2 == 0); + + stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + k_get_rows_f32( + src0_dd, src1_dd, dst_dd, ne00, ne12, s1, s2, + s3, nb01, nb02, nb03, s10, s11, s12, item_ct1); + }); + + GGML_UNUSED(dst); + GGML_UNUSED(ctx); +} + template static void get_rows_sycl_float(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, @@ -244,7 +321,7 @@ void ggml_sycl_op_get_rows(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { src1_i32, (float *)dst->data, ctx.stream()); break; case GGML_TYPE_Q2_K: - get_rows_sycl(ctx, dst->src[0], dst->src[1], dst, (const float *)dst->src[0]->data, + get_rows_sycl_f32(ctx, dst->src[0], dst->src[1], dst, (const float *)dst->src[0]->data, src1_i32, (float *)dst->data, ctx.stream()); break; case GGML_TYPE_Q3_K: @@ -260,7 +337,7 @@ void ggml_sycl_op_get_rows(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { src1_i32, (float *)dst->data, ctx.stream()); break; case GGML_TYPE_Q4_K: - get_rows_sycl(ctx, dst->src[0], dst->src[1], dst, (const float *)dst->src[0]->data, + get_rows_sycl_f32(ctx, dst->src[0], dst->src[1], dst, (const float *)dst->src[0]->data, src1_i32, (float *)dst->data, ctx.stream()); break; case GGML_TYPE_Q5_0: @@ -272,7 +349,7 @@ void ggml_sycl_op_get_rows(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { src1_i32, (float *)dst->data, ctx.stream()); break; case GGML_TYPE_Q5_K: - get_rows_sycl(ctx, dst->src[0], dst->src[1], dst, (const float *)dst->src[0]->data, + get_rows_sycl_f32(ctx, dst->src[0], dst->src[1], dst, (const float *)dst->src[0]->data, src1_i32, (float *)dst->data, ctx.stream()); break; case GGML_TYPE_Q6_K: diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index cb8974eedb75..d91e41f9575a 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -85,6 +85,7 @@ int g_ggml_sycl_enable_optimize = 1; int g_ggml_sycl_enable_graph = 0; int g_ggml_sycl_enable_dnn = 1; int g_ggml_sycl_fa_onednn = 1; +int g_ggml_sycl_fa_onednn_max_kv = 0; int g_ggml_sycl_enable_vmm = 1; int g_ggml_sycl_enable_fusion = 1; int g_ggml_sycl_prioritize_dmmv = 0; @@ -166,7 +167,10 @@ static ggml_sycl_device_info ggml_sycl_init() { ze_device_properties_t props = {}; props.stype = ZE_STRUCTURE_TYPE_DEVICE_PROPERTIES; ze_result_t r = zeDeviceGetProperties(ze_dev, &props); - info.devices[i].l0_discrete_gpu = r == ZE_RESULT_SUCCESS && !(props.flags & ZE_DEVICE_PROPERTY_FLAG_INTEGRATED); + if (r == ZE_RESULT_SUCCESS) { + info.devices[i].l0_device_type_valid = true; + info.devices[i].l0_discrete_gpu = !(props.flags & ZE_DEVICE_PROPERTY_FLAG_INTEGRATED); + } } #endif } @@ -273,6 +277,8 @@ static const char* dev2dev_int2str(int dev2dev) { return "SYCL API"; } else if (dev2dev == DEV2DEV_MEMCPY_L0) { return "Level Zero API"; + } else if (dev2dev == DEV2DEV_MEMCPY_FORWARD) { + return "Host Forward"; } else { return "Unknown"; } @@ -287,6 +293,7 @@ static void ggml_check_sycl() try { g_ggml_sycl_enable_graph = ggml_sycl_get_env("GGML_SYCL_ENABLE_GRAPH", 0); g_ggml_sycl_enable_dnn = ggml_sycl_get_env("GGML_SYCL_ENABLE_DNN", 1); g_ggml_sycl_fa_onednn = ggml_sycl_get_env("GGML_SYCL_FA_ONEDNN", 1); + g_ggml_sycl_fa_onednn_max_kv = ggml_sycl_get_env("GGML_SYCL_FA_ONEDNN_MAX_KV", 0); g_ggml_sycl_enable_vmm = ggml_sycl_get_env("GGML_SYCL_ENABLE_VMM", 1); g_ggml_sycl_enable_fusion = ggml_sycl_get_env("GGML_SYCL_ENABLE_FUSION", 1); g_ggml_sycl_prioritize_dmmv = ggml_sycl_get_env("GGML_SYCL_PRIORITIZE_DMMV", 0); @@ -359,6 +366,7 @@ static void ggml_check_sycl() try { GGML_LOG_INFO(" GGML_SYCL_ENABLE_DNN: DNN disabled by compile flag\n"); GGML_LOG_INFO(" GGML_SYCL_FA_ONEDNN: %d\n", g_ggml_sycl_fa_onednn); #endif + GGML_LOG_INFO(" GGML_SYCL_FA_ONEDNN_MAX_KV: %d\n", g_ggml_sycl_fa_onednn_max_kv); #ifdef SYCL_FLASH_ATTN GGML_LOG_INFO(" GGML_SYCL_ENABLE_FLASH_ATTN: %d\n", g_ggml_sycl_enable_flash_attention); #else @@ -681,7 +689,11 @@ static void dev2dev_memcpy(int device_dst, sycl::queue &q_dst, int device_src, s } // Host-staged copy - GGML_SYCL_DEBUG("[SYCL] dev2dev memcpy by host forward\n"); + if(g_ggml_sycl_dev2dev_memcpy == DEV2DEV_MEMCPY_FORWARD) { + GGML_SYCL_DEBUG("[SYCL] dev2dev memcpy by host forward for setting GGML_SYCL_DEV2DEV_MEMCPY=2\n"); + } else { + GGML_SYCL_DEBUG("[SYCL] dev2dev memcpy by host forward for SYCL/L0 fallback\n"); + } char *host_buf = (char *)malloc(size); q_src.memcpy(host_buf, (const char *)ptr_src, size).wait(); q_dst.memcpy((char *)ptr_dst, host_buf, size).wait(); @@ -5395,6 +5407,13 @@ static void ggml_backend_sycl_graph_compute_impl(ggml_backend_sycl_context * syc } } #endif + if (node->op == GGML_OP_RMS_NORM && + ggml_sycl_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL })) { + ggml_sycl_op_rms_norm_fused(*sycl_ctx, node, cgraph->nodes[i + 1]); + i++; + continue; + } + bool ok = ggml_sycl_compute_forward(*sycl_ctx, node); if (!ok) { GGML_LOG_ERROR("%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op)); @@ -5590,7 +5609,11 @@ static void ggml_backend_sycl_device_get_memory(ggml_backend_dev_t dev, size_t * } static enum ggml_backend_dev_type ggml_backend_sycl_device_get_type(ggml_backend_dev_t dev) { - GGML_UNUSED(dev); + ggml_backend_sycl_device_context * ctx = (ggml_backend_sycl_device_context *)dev->context; + const sycl_device_info & info = ggml_sycl_info().devices[ctx->device]; + if (info.l0_device_type_valid && !info.l0_discrete_gpu) { + return GGML_BACKEND_DEVICE_TYPE_IGPU; + } return GGML_BACKEND_DEVICE_TYPE_GPU; } diff --git a/ggml/src/ggml-sycl/mmvq.cpp b/ggml/src/ggml-sycl/mmvq.cpp index 7b1b3d467fa2..863d34eabbe6 100644 --- a/ggml/src/ggml-sycl/mmvq.cpp +++ b/ggml/src/ggml-sycl/mmvq.cpp @@ -1254,6 +1254,66 @@ static void mul_mat_vec_q1_0_q8_1_sycl_switch_ncols( } } +static void mul_mat_vec_q2_0_q8_1_sycl(const void * vx, const void * vy, + float * dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK2_0 == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + + stream->submit([&](sycl::handler & cgh) { + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1); + }); + }); +} + +template +static void mul_mat_vec_q2_0_q8_1_sycl_ncols( + const void * vx, const void * vy, float * dst, + const int ncols, const int nrows, + const int stride_col_y, const int stride_col_dst, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK2_0 == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + + stream->submit([&](sycl::handler & cgh) { + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + mul_mat_vec_q_ncols( + vx, vy, dst, ncols, nrows, stride_col_y, stride_col_dst, item_ct1); + }); + }); +} + +static void mul_mat_vec_q2_0_q8_1_sycl_switch_ncols( + const void * vx, const void * vy, float * dst, + const int ncols, const int nrows, const int ncols_dst, + const int stride_col_y, const int stride_col_dst, + dpct::queue_ptr stream) { + switch (ncols_dst) { + case 1: mul_mat_vec_q2_0_q8_1_sycl(vx, vy, dst, ncols, nrows, stream); break; + case 2: mul_mat_vec_q2_0_q8_1_sycl_ncols<2>(vx, vy, dst, ncols, nrows, stride_col_y, stride_col_dst, stream); break; + case 3: mul_mat_vec_q2_0_q8_1_sycl_ncols<3>(vx, vy, dst, ncols, nrows, stride_col_y, stride_col_dst, stream); break; + case 4: mul_mat_vec_q2_0_q8_1_sycl_ncols<4>(vx, vy, dst, ncols, nrows, stride_col_y, stride_col_dst, stream); break; + case 5: mul_mat_vec_q2_0_q8_1_sycl_ncols<5>(vx, vy, dst, ncols, nrows, stride_col_y, stride_col_dst, stream); break; + case 6: mul_mat_vec_q2_0_q8_1_sycl_ncols<6>(vx, vy, dst, ncols, nrows, stride_col_y, stride_col_dst, stream); break; + case 7: mul_mat_vec_q2_0_q8_1_sycl_ncols<7>(vx, vy, dst, ncols, nrows, stride_col_y, stride_col_dst, stream); break; + case 8: mul_mat_vec_q2_0_q8_1_sycl_ncols<8>(vx, vy, dst, ncols, nrows, stride_col_y, stride_col_dst, stream); break; + default: GGML_ABORT("unsupported ncols_dst=%d for Q2_0 multi-col MMVQ", ncols_dst); + } +} + static void mul_mat_vec_q2_K_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols, const int nrows, @@ -2194,6 +2254,20 @@ void ggml_sycl_op_mul_mat_vec_q(ggml_backend_sycl_context & ctx, const ggml_tens mul_mat_vec_q1_0_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream); } break; + case GGML_TYPE_Q2_0: + if (i == 0 && src1_ncols > 1 && src1_ncols <= 8) { + const int stride_col_y = src1_padded_col_size / QK8_1; + const int stride_col_dst = dst->ne[0]; + GGML_SYCL_DEBUG("Calling mul_mat_vec_q2_0_q8_1_sycl_switch_ncols ncols=%d\n", (int)src1_ncols); + mul_mat_vec_q2_0_q8_1_sycl_switch_ncols( + src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, + src1_ncols, stride_col_y, stride_col_dst, stream); + return; + } else if (i == 0 || src1_ncols == 1) { + GGML_SYCL_DEBUG("Calling mul_mat_vec_q2_0_q8_1_sycl\n"); + mul_mat_vec_q2_0_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream); + } + break; case GGML_TYPE_Q2_K: if (i == 0 && src1_ncols > 1 && src1_ncols <= 8) { const int stride_col_y = src1_padded_col_size / QK8_1; @@ -2503,6 +2577,11 @@ bool ggml_sycl_mul_mat_vec_q_id( vx_base, vy, ids_dev, dst_base, ncols, nrows, n_experts_used, expert_weight_stride, dst_row_stride, src1_row_stride, stream); return true; + case GGML_TYPE_Q2_0: + launch_mul_mat_vec_q_moe( + vx_base, vy, ids_dev, dst_base, ncols, nrows, n_experts_used, + expert_weight_stride, dst_row_stride, src1_row_stride, stream); + return true; case GGML_TYPE_Q2_K: launch_mul_mat_vec_q_moe( vx_base, vy, ids_dev, dst_base, ncols, nrows, n_experts_used, diff --git a/ggml/src/ggml-sycl/norm.cpp b/ggml/src/ggml-sycl/norm.cpp index c4472e4bd66f..682a9f51ee74 100644 --- a/ggml/src/ggml-sycl/norm.cpp +++ b/ggml/src/ggml-sycl/norm.cpp @@ -147,10 +147,13 @@ static void group_norm_f32(const float* x, float* dst, const int group_size, con } } +template static void rms_norm_f32(const float* x, float* dst, const int ncols, const int64_t src_stride_col, const int64_t src_stride_row, const int64_t src_stride_channel, const int64_t src_stride_sample, const int64_t dst_stride_col, const int64_t dst_stride_row, const int64_t dst_stride_channel, const int64_t dst_stride_sample, - const float eps, const sycl::nd_item<3>& item_ct1, float* s_sum, int block_size) { + const float eps, const sycl::nd_item<3>& item_ct1, float* s_sum, int block_size, + const float* mul = nullptr, const int64_t mul_stride_row = 0, const int64_t mul_stride_channel = 0, + const int64_t mul_stride_sample = 0, const int mul_nrows = 0, const int mul_nchannels = 0, const int mul_nsamples = 0) { const int nrows = item_ct1.get_group_range(2); const int nchannels = item_ct1.get_group_range(1); @@ -170,6 +173,12 @@ static void rms_norm_f32(const float* x, float* dst, const int ncols, x += src_offset; dst += dst_offset; + if constexpr (do_multiply) { + const int mul_row = row % mul_nrows; + const int mul_channel = channel % mul_nchannels; + const int mul_sample = sample % mul_nsamples; + mul += mul_sample * mul_stride_sample + mul_channel * mul_stride_channel + mul_row * mul_stride_row; + } float tmp = 0.0f; // partial sum for thread in warp @@ -202,7 +211,11 @@ static void rms_norm_f32(const float* x, float* dst, const int ncols, const float scale = sycl::rsqrt(mean + eps); for (int col = tid; col < ncols; col += block_size) { - dst[col * dst_stride_col] = scale * x[col * src_stride_col]; + if constexpr (do_multiply) { + dst[col * dst_stride_col] = scale * x[col * src_stride_col] * mul[col]; + } else { + dst[col * dst_stride_col] = scale * x[col * src_stride_col]; + } } } @@ -376,6 +389,49 @@ static void rms_norm_f32_sycl(const float* x, float* dst, const int ncols, const } } +static void rms_norm_mul_f32_sycl(const float* x, const float* mul, float* dst, const int ncols, const int nrows, + const int nchannels, const int nsamples, + const int64_t src_stride_col, const int64_t src_stride_row, const int64_t src_stride_channel, const int64_t src_stride_sample, + const int64_t dst_stride_col, const int64_t dst_stride_row, const int64_t dst_stride_channel, const int64_t dst_stride_sample, + const int64_t mul_stride_row, const int64_t mul_stride_channel, const int64_t mul_stride_sample, + const int mul_nrows, const int mul_nchannels, const int mul_nsamples, + const float eps, queue_ptr stream, int device) { + const sycl::range<3> global_dims(nsamples, nchannels, nrows); + if (ncols < 1024) { + const sycl::range<3> block_dims(1, 1, WARP_SIZE); + stream->submit([&](sycl::handler& cgh) { + cgh.parallel_for( + sycl::nd_range<3>(global_dims * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) + [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + rms_norm_f32(x, dst, ncols, + src_stride_col, src_stride_row, src_stride_channel, src_stride_sample, + dst_stride_col, dst_stride_row, dst_stride_channel, dst_stride_sample, + eps, item_ct1, nullptr, WARP_SIZE, + mul, mul_stride_row, mul_stride_channel, mul_stride_sample, mul_nrows, mul_nchannels, mul_nsamples); + }); + }); + } + else { + const int work_group_size = ggml_sycl_info().max_work_group_sizes[device]; + assert(work_group_size % (WARP_SIZE * WARP_SIZE) == 0); + const sycl::range<3> block_dims(1, 1, work_group_size); + stream->submit([&](sycl::handler& cgh) { + sycl::local_accessor s_sum_acc_ct1(sycl::range<1>(work_group_size / WARP_SIZE), cgh); + cgh.parallel_for( + sycl::nd_range<3>(global_dims * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) + [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + rms_norm_f32(x, dst, ncols, + src_stride_col, src_stride_row, src_stride_channel, src_stride_sample, + dst_stride_col, dst_stride_row, dst_stride_channel, dst_stride_sample, + eps, item_ct1, get_pointer(s_sum_acc_ct1), work_group_size, + mul, mul_stride_row, mul_stride_channel, mul_stride_sample, mul_nrows, mul_nchannels, mul_nsamples); + }); + }); + } +} + template static void l2_norm_f32_sycl(const float * x, float * dst, @@ -518,6 +574,66 @@ void ggml_sycl_op_rms_norm(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { ss0, ss1, ss2, ss3, ds0, ds1, ds2, ds3, eps, main_stream, ctx.device); } +void ggml_sycl_op_rms_norm_fused(ggml_backend_sycl_context & ctx, ggml_tensor * dst, ggml_tensor * mul_tensor) { + const ggml_tensor * rms_norm_src = dst->src[0]; + float eps = 0.0f; + memcpy(&eps, dst->op_params, sizeof(float)); + + const float * src0_dd = static_cast(rms_norm_src->data); + const float * mul_dd = nullptr; + const ggml_tensor * mul_src = nullptr; + if (mul_tensor->src[0] == dst) { + mul_dd = static_cast(mul_tensor->src[1]->data); + mul_src = mul_tensor->src[1]; + } else if (mul_tensor->src[1] == dst) { + mul_dd = static_cast(mul_tensor->src[0]->data); + mul_src = mul_tensor->src[0]; + } else { + GGML_ASSERT(false); + } + float * dst_dd = static_cast(mul_tensor->data); + + dpct::queue_ptr main_stream = ctx.stream(); + SYCL_CHECK(ggml_sycl_set_device(ctx.device)); + + GGML_ASSERT(rms_norm_src->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + GGML_ASSERT(mul_tensor->type == GGML_TYPE_F32); + GGML_ASSERT(eps >= 0.0f); + + const int64_t ne00 = rms_norm_src->ne[0]; + const int64_t ne01 = rms_norm_src->ne[1]; + const int64_t ne02 = rms_norm_src->ne[2]; + const int64_t ne03 = rms_norm_src->ne[3]; + + const size_t ts0 = ggml_type_size(rms_norm_src->type); + GGML_ASSERT(rms_norm_src->nb[0] == ts0); + const int64_t s00 = rms_norm_src->nb[0] / ts0; + const int64_t s01 = rms_norm_src->nb[1] / ts0; + const int64_t s02 = rms_norm_src->nb[2] / ts0; + const int64_t s03 = rms_norm_src->nb[3] / ts0; + + const size_t tdst = ggml_type_size(mul_tensor->type); + GGML_ASSERT(mul_tensor->nb[0] == tdst); + const int64_t d00 = mul_tensor->nb[0] / tdst; + const int64_t d01 = mul_tensor->nb[1] / tdst; + const int64_t d02 = mul_tensor->nb[2] / tdst; + const int64_t d03 = mul_tensor->nb[3] / tdst; + + const size_t ts_mul = ggml_type_size(mul_src->type); + GGML_ASSERT(mul_src->nb[0] == ts_mul); + const int64_t mul_s01 = mul_src->nb[1] / ts_mul; + const int64_t mul_s02 = mul_src->nb[2] / ts_mul; + const int64_t mul_s03 = mul_src->nb[3] / ts_mul; + const int mul_nrows = mul_src->ne[1]; + const int mul_nchannels = mul_src->ne[2]; + const int mul_nsamples = mul_src->ne[3]; + + rms_norm_mul_f32_sycl(src0_dd, mul_dd, dst_dd, ne00, ne01, ne02, ne03, + s00, s01, s02, s03, d00, d01, d02, d03, + mul_s01, mul_s02, mul_s03, mul_nrows, mul_nchannels, mul_nsamples, eps, main_stream, ctx.device); +} + void ggml_sycl_op_rms_norm_back(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2); diff --git a/ggml/src/ggml-sycl/norm.hpp b/ggml/src/ggml-sycl/norm.hpp index 8cb885eb2eed..51217c421956 100644 --- a/ggml/src/ggml-sycl/norm.hpp +++ b/ggml/src/ggml-sycl/norm.hpp @@ -19,6 +19,8 @@ void ggml_sycl_op_norm(ggml_backend_sycl_context& ctx, ggml_tensor* dst); void ggml_sycl_op_rms_norm(ggml_backend_sycl_context& ctx, ggml_tensor* dst); +void ggml_sycl_op_rms_norm_fused(ggml_backend_sycl_context& ctx, ggml_tensor* dst, ggml_tensor* mul); + void ggml_sycl_op_rms_norm_back(ggml_backend_sycl_context& ctx, ggml_tensor* dst); void ggml_sycl_op_group_norm(ggml_backend_sycl_context& ctx, ggml_tensor* dst); diff --git a/ggml/src/ggml-sycl/vecdotq.hpp b/ggml/src/ggml-sycl/vecdotq.hpp index 765fb7f1590b..c11a6e8f9cbd 100644 --- a/ggml/src/ggml-sycl/vecdotq.hpp +++ b/ggml/src/ggml-sycl/vecdotq.hpp @@ -658,6 +658,40 @@ template <> struct reorder_vec_dot_q_sycl { #define VDR_Q4_0_Q8_1_MMVQ 2 #define VDR_Q4_0_Q8_1_MMQ 4 +#define VDR_Q2_0_Q8_1_MMVQ 1 + +template +static __dpct_inline__ float vec_dot_q2_0_q8_1_impl( + const int * v, + const int * u, + const float & d2, + const sycl::half2 & ds8) { + int sumi = 0; + +#pragma unroll + for (int i = 0; i < vdr; ++i) { +#pragma unroll + for (int j = 0; j < 4; ++j) { + const uint8_t q = (uint8_t) ((uint32_t) v[i] >> (8 * j)); + + // unpack 2-bit values to byte lanes (0..3), then apply zero-point + // correction with ds8f.y() below, mirroring the q4_0 style. + int vi = 0; + vi |= (((q >> 0) & 0x3) & 0xFF) << 0; + vi |= (((q >> 2) & 0x3) & 0xFF) << 8; + vi |= (((q >> 4) & 0x3) & 0xFF) << 16; + vi |= (((q >> 6) & 0x3) & 0xFF) << 24; + + sumi = dpct::dp4a(vi, u[4 * i + j], sumi); + } + } + + const sycl::float2 ds8f = ds8.convert(); + // q2_0 has zero-point 1. Scale ds8f.y() by processed-lane ratio, + // consistent with q4_0's explicit zero-point subtraction style. + return d2 * (sumi * ds8f.x() - ((float) vdr / (float) QI2_0) * ds8f.y()); +} + template static __dpct_inline__ float vec_dot_q4_0_q8_1_impl(const int * v, const int * u, const float & d4, const sycl::half2 & ds8) { @@ -882,6 +916,41 @@ vec_dot_q4_0_q8_1(const void *__restrict__ vbq, return vec_dot_q4_0_q8_1_impl(v, u, bq4_0->d, bq8_1->ds); } +static __dpct_inline__ float +vec_dot_q2_0_q8_1(const void *__restrict__ vbq, + const block_q8_1 *__restrict__ bq8_1, const int &iqs) { + + const block_q2_0 * bq2_0 = (const block_q2_0 *) vbq; + + int v[2 * VDR_Q2_0_Q8_1_MMVQ]; + int u[8 * VDR_Q2_0_Q8_1_MMVQ]; + +#pragma unroll + for (int i = 0; i < VDR_Q2_0_Q8_1_MMVQ; ++i) { + const int base = 4 * (iqs + i); + + // Q2_0 has QK2_0 = 64 and uses 2 x QK8_1 blocks on the RHS. + v[2 * i + 0] = get_int_from_uint8(bq2_0->qs, iqs + i); + v[2 * i + 1] = get_int_from_uint8(bq2_0->qs, iqs + i + QI2_0); + + u[8 * i + 0] = get_int_from_int8_aligned(bq8_1[0].qs, base + 0); + u[8 * i + 1] = get_int_from_int8_aligned(bq8_1[0].qs, base + 1); + u[8 * i + 2] = get_int_from_int8_aligned(bq8_1[0].qs, base + 2); + u[8 * i + 3] = get_int_from_int8_aligned(bq8_1[0].qs, base + 3); + + u[8 * i + 4] = get_int_from_int8_aligned(bq8_1[1].qs, base + 0); + u[8 * i + 5] = get_int_from_int8_aligned(bq8_1[1].qs, base + 1); + u[8 * i + 6] = get_int_from_int8_aligned(bq8_1[1].qs, base + 2); + u[8 * i + 7] = get_int_from_int8_aligned(bq8_1[1].qs, base + 3); + } + + const float sum0 = vec_dot_q2_0_q8_1_impl( + v + 0, u + 0, bq2_0->d, bq8_1[0].ds); + const float sum1 = vec_dot_q2_0_q8_1_impl( + v + VDR_Q2_0_Q8_1_MMVQ, u + 4 * VDR_Q2_0_Q8_1_MMVQ, bq2_0->d, bq8_1[1].ds); + return sum0 + sum1; +} + static __dpct_inline__ float vec_dot_q4_1_q8_1(const void *__restrict__ vbq, const block_q8_1 *__restrict__ bq8_1, const int &iqs) { diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index 0aa4020a9b55..91a4240d3565 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -156,6 +156,24 @@ typedef struct VkPhysicalDeviceShaderFloat8FeaturesEXT { } VkPhysicalDeviceShaderFloat8FeaturesEXT; #endif +#ifndef VK_KHR_INTERNALLY_SYNCHRONIZED_QUEUES_EXTENSION_NAME +#define VK_KHR_INTERNALLY_SYNCHRONIZED_QUEUES_EXTENSION_NAME "VK_KHR_internally_synchronized_queues" +#define VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_INTERNALLY_SYNCHRONIZED_QUEUES_FEATURES_KHR ((VkStructureType)1000504000) +#define VK_DEVICE_QUEUE_CREATE_INTERNALLY_SYNCHRONIZED_BIT_KHR ((VkDeviceQueueCreateFlagBits)0x00000004) + +// Compile-time constant guaranteed; no runtime initialization overhead +static constexpr vk::DeviceQueueCreateFlagBits eInternallySynchronizedKHR = + static_cast(0x00000004); + +typedef struct VkPhysicalDeviceInternallySynchronizedQueuesFeaturesKHR { + VkStructureType sType; + void* pNext; + VkBool32 internallySynchronizedQueues; +} VkPhysicalDeviceInternallySynchronizedQueuesFeaturesKHR; +#else +static constexpr vk::DeviceQueueCreateFlagBits eInternallySynchronizedKHR = vk::DeviceQueueCreateFlagBits::eInternallySynchronizedKHR; +#endif + #define ROUNDUP_POW2(M, N) (((M) + (N) - 1) & ~((N) - 1)) #define CEIL_DIV(M, N) (((M) / (N)) + (((M) % (N)) != 0)) static bool is_pow2(uint32_t x) { return x > 1 && (x & (x-1)) == 0; } @@ -285,27 +303,41 @@ struct vk_command_pool { }; // Prevent simultaneous submissions to the same queue. -// This could be per vk_queue if we stopped having two vk_queue structures -// sharing the same vk::Queue. -static std::mutex queue_mutex; +struct vk_queue_handle { + vk::Queue queue; + virtual void submit(vk::ArrayProxy submits, vk::Fence fence) = 0; + virtual void lock() {} // no-op by default (internally synchronized case) + virtual void unlock() {} + virtual ~vk_queue_handle() = default; +}; + +struct vk_queue_handle_synchronized : vk_queue_handle { + std::mutex mutex; + void submit(vk::ArrayProxy submits, vk::Fence fence) override { + std::lock_guard guard(mutex); + queue.submit(submits, fence); + } + void lock() override { mutex.lock(); } + void unlock() override { mutex.unlock(); } +}; + +struct vk_queue_handle_unsynchronized : vk_queue_handle { + void submit(vk::ArrayProxy submits, vk::Fence fence) override { + // Driver guarantees internal synchronization via VK_KHR_internally_synchronized_queues + queue.submit(submits, fence); + } + // lock()/unlock() inherited no-ops +}; struct vk_queue { uint32_t queue_family_index; - vk::Queue queue; + std::shared_ptr handle; vk_command_pool cmd_pool; vk::PipelineStageFlags stage_flags; bool transfer_only; - - // copy everything except the cmd_pool - void copyFrom(vk_queue &other) { - queue_family_index = other.queue_family_index; - queue = other.queue; - stage_flags = other.stage_flags; - transfer_only = other.transfer_only; - } }; static const char * ggml_backend_vk_buffer_type_name(ggml_backend_buffer_type_t buft); @@ -578,6 +610,13 @@ static constexpr std::initializer_list topk_moe_sigmoid_norm_bias{ GGML GGML_OP_RESHAPE, GGML_OP_SUM_ROWS, GGML_OP_CLAMP, GGML_OP_DIV, GGML_OP_RESHAPE }; +static constexpr std::initializer_list topk_moe_sqrt_softplus_norm_bias{ GGML_OP_UNARY, GGML_OP_SQRT, + GGML_OP_RESHAPE, GGML_OP_ADD, + GGML_OP_ARGSORT, GGML_OP_VIEW, + GGML_OP_GET_ROWS, GGML_OP_RESHAPE, + GGML_OP_SUM_ROWS, GGML_OP_CLAMP, + GGML_OP_DIV, GGML_OP_RESHAPE }; + static constexpr std::initializer_list topk_moe_early_softmax { GGML_OP_SOFT_MAX, GGML_OP_RESHAPE, GGML_OP_ARGSORT, GGML_OP_VIEW, GGML_OP_GET_ROWS }; @@ -641,6 +680,22 @@ static constexpr std::initializer_list> topk_moe_sigmoid_norm {10, 0, 9 }, // reshape->src[0] == div }; +static constexpr std::initializer_list> topk_moe_sqrt_softplus_norm_bias_edges { + { 1, 0, 0 }, // sqrt->src[0] == softplus + { 2, 0, 1 }, // reshape->src[0] == sqrt + { 3, 0, 1 }, // add->src[0] == sqrt + { 4, 0, 3 }, // argsort->src[0] == add + { 5, 0, 4 }, // view->src[0] == argsort + { 6, 0, 2 }, // get_rows->src[0] == reshape + { 6, 1, 5 }, // get_rows->src[1] == view + { 7, 0, 6 }, // reshape->src[0] == get_rows + { 8, 0, 7 }, // sum_rows->src[0] == reshape + { 9, 0, 8 }, // clamp->src[0] == sum_rows + {10, 0, 7 }, // div->src[0] == reshape + {10, 1, 9 }, // div->src[1] == clamp + {11, 0,10 }, // reshape->src[0] == div +}; + // same as early_softmax_norm but ending after the get_rows static constexpr std::initializer_list> topk_moe_early_softmax_edges { { 1, 0, 0 }, // reshape->src[0] == softmax @@ -669,6 +724,7 @@ enum topk_moe_mode { TOPK_MOE_EARLY_SOFTMAX_NORM, TOPK_MOE_LATE_SOFTMAX, TOPK_MOE_SIGMOID_NORM_BIAS, + TOPK_MOE_SQRT_SOFTPLUS_NORM_BIAS, TOPK_MOE_COUNT, }; @@ -712,11 +768,12 @@ struct vk_device_struct { uint32_t vendor_id; vk::DriverId driver_id; vk_device_architecture architecture; - vk_queue compute_queue; - vk_queue transfer_queue; + std::unique_ptr compute_queue; + std::unique_ptr transfer_queue; bool single_queue; bool support_async; bool async_use_transfer_queue; + bool has_internally_synchronized_queues = false; uint32_t subgroup_size; uint32_t subgroup_size_log2; uint32_t shader_core_count; @@ -961,13 +1018,16 @@ struct vk_device_struct { vk_pipeline pipeline_col2im_1d_f32; vk_pipeline pipeline_col2im_1d_f16; vk_pipeline pipeline_col2im_1d_bf16; + vk_pipeline pipeline_out_prod_f32; vk_pipeline pipeline_snake_f32; vk_pipeline pipeline_snake_f16; vk_pipeline pipeline_snake_bf16; + vk_pipeline pipeline_pool1d_f32; vk_pipeline pipeline_pool2d_f32; vk_pipeline pipeline_turbo_wht; vk_pipeline pipeline_rwkv_wkv6_f32; vk_pipeline pipeline_rwkv_wkv7_f32; + vk_pipeline pipeline_gated_linear_attn_f32; // [size_idx][kda] where size_idx: 0=d16, 1=d32, 2=d64, 3=d128 vk_pipeline pipeline_gated_delta_net[4][2]; vk_pipeline pipeline_ssm_scan_f32_d128; @@ -1019,8 +1079,13 @@ struct vk_device_struct { ggml_vk_destroy_buffer(sync_staging); - compute_queue.cmd_pool.destroy(device); - transfer_queue.cmd_pool.destroy(device); + if (compute_queue) compute_queue->cmd_pool.destroy(device); + if (transfer_queue) transfer_queue->cmd_pool.destroy(device); + + // Explicitly clear to ensure queues drop their shared_ptrs to handles + // before the Vulkan logical device instance is destroyed + compute_queue.reset(); + transfer_queue.reset(); for (auto& pipeline : all_pipelines) { if (pipeline.expired()) { @@ -1443,6 +1508,11 @@ struct vk_op_binary_push_constants { float param1; float param2; int32_t param3; }; +// Distinct type with the same layout so concat can overload tensor offset initialization. +struct vk_op_concat_push_constants : vk_op_binary_push_constants {}; +static_assert(sizeof(vk_op_concat_push_constants) == sizeof(vk_op_binary_push_constants)); +static_assert(std::is_standard_layout_v); + struct vk_op_multi_add_push_constants { // shape for dst uint32_t ne20; uint32_t ne21; uint32_t ne22; uint32_t ne23; @@ -1644,6 +1714,17 @@ struct vk_op_snake_push_constants { uint32_t ne1; }; +struct vk_op_pool1d_push_constants { + uint32_t IL; + uint32_t OL; + uint32_t OC; + uint32_t pelements; + uint32_t op; + int32_t k0; + int32_t s0; + int32_t p0; +}; + struct vk_op_pool2d_push_constants { uint32_t IW; uint32_t IH; uint32_t OW; uint32_t OH; @@ -1668,6 +1749,13 @@ struct vk_op_rwkv_wkv7_push_constants { uint32_t C; uint32_t H; }; +struct vk_op_gated_linear_attn_push_constants { + uint32_t B; + uint32_t T; + uint32_t C; + uint32_t H; + float scale; +}; struct vk_op_gated_delta_net_push_constants { uint32_t H; uint32_t n_tokens; @@ -1891,6 +1979,7 @@ struct ggml_vk_garbage_collector { static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx, vk_context subctx); static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested = nullptr); static void ggml_pipeline_allocate_descriptor_sets(ggml_backend_vk_context * ctx); +static bool ggml_vk_intel_windows_driver_equals_or_newer_than(uint32_t driver_version, uint32_t threshold_major, uint32_t threshold_minor); static bool vk_memory_logger_enabled = false; @@ -2208,6 +2297,40 @@ static uint32_t get_misalign_bytes(const ggml_backend_vk_context * ctx, const gg return ((vk_tensor_offset(t) + t->view_offs) & (ctx->device->properties.limits.minStorageBufferOffsetAlignment - 1));; } +static uint32_t ggml_vk_concat_unit_size(ggml_type type) { + const uint32_t type_size = ggml_type_size(type); + + if (!ggml_is_quantized(type)) { + return type_size; + } + + // Use the widest existing concat shader that evenly divides a quant block. + if (type_size % 8 == 0) { + return 8; + } + if (type_size % 4 == 0) { + return 4; + } + if (type_size % 2 == 0) { + return 2; + } + return 1; +} + +static bool ggml_vk_concat_supported(const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * dst) { + if (src0->type != src1->type || src0->type != dst->type) { + return false; + } + + if (!ggml_is_quantized(src0->type)) { + const size_t type_size = ggml_type_size(src0->type); + return type_size == 1 || type_size == 2 || type_size == 4 || type_size == 8; + } + + // Quantized tensor rows are block-aligned when created. + return ggml_is_contiguous_rows(src0) && ggml_is_contiguous_rows(src1) && ggml_is_contiguous_rows(dst); +} + template void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, T &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) { GGML_UNUSED(p); GGML_UNUSED(src0); @@ -2909,8 +3032,7 @@ static vk_command_buffer* ggml_vk_create_cmd_buffer(vk_device& device, vk_comman static void ggml_vk_submit(vk_context& ctx, vk::Fence fence) { if (ctx->seqs.empty()) { if (fence) { - std::lock_guard guard(queue_mutex); - ctx->p->q->queue.submit({}, fence); + ctx->p->q->handle->submit({}, fence); } return; } @@ -2979,8 +3101,7 @@ static void ggml_vk_submit(vk_context& ctx, vk::Fence fence) { } } - std::lock_guard guard(queue_mutex); - ctx->p->q->queue.submit(submit_infos, fence); + ctx->p->q->handle->submit(submit_infos, fence); ctx->seqs.clear(); } @@ -3031,18 +3152,44 @@ static uint32_t ggml_vk_find_queue_family_index(std::vector ggml_vk_create_queue(vk_device& device, uint32_t queue_family_index, uint32_t queue_index, vk::PipelineStageFlags&& stage_flags, bool transfer_only) { VK_LOG_DEBUG("ggml_vk_create_queue()"); std::lock_guard guard(device->mutex); - q.queue_family_index = queue_family_index; - q.transfer_only = transfer_only; + auto q = std::make_unique(); + q->queue_family_index = queue_family_index; + q->transfer_only = transfer_only; + + std::shared_ptr h; + vk::DeviceQueueInfo2 queue_info2{}; + queue_info2.queueFamilyIndex = queue_family_index; + queue_info2.queueIndex = queue_index; + + if (device->has_internally_synchronized_queues) { + h = std::make_shared(); + queue_info2.flags = eInternallySynchronizedKHR; + } else { + h = std::make_shared(); + } - q.cmd_pool.init(device, &q); + h->queue = device->device.getQueue2(queue_info2); + q->handle = h; - q.queue = device->device.getQueue(queue_family_index, queue_index); + q->cmd_pool.init(device, q.get()); - q.stage_flags = stage_flags; + q->stage_flags = stage_flags; + return q; +} + +static std::unique_ptr ggml_vk_create_aliased_queue(vk_device& device, const std::unique_ptr& source) { + std::lock_guard guard(device->mutex); + auto q = std::make_unique(); + q->handle = source->handle; + q->queue_family_index = source->queue_family_index; + q->stage_flags = source->stage_flags; + q->transfer_only = source->transfer_only; + q->cmd_pool.init(device, q.get()); + return q; } static vk_context ggml_vk_create_context(ggml_backend_vk_context * ctx, vk_command_pool& p) { @@ -3107,11 +3254,11 @@ static void ggml_vk_queue_command_pools_cleanup(vk_device& device) { // Arbitrary frequency to cleanup/reuse command buffers static constexpr uint32_t cleanup_frequency = 10; - if (device->compute_queue.cmd_pool.buffers_in_use() >= cleanup_frequency) { - ggml_vk_command_pool_cleanup(device, device->compute_queue.cmd_pool); + if (device->compute_queue->cmd_pool.buffers_in_use() >= cleanup_frequency) { + ggml_vk_command_pool_cleanup(device, device->compute_queue->cmd_pool); } - if (device->transfer_queue.cmd_pool.buffers_in_use() >= cleanup_frequency) { - ggml_vk_command_pool_cleanup(device, device->transfer_queue.cmd_pool); + if (device->transfer_queue->cmd_pool.buffers_in_use() >= cleanup_frequency) { + ggml_vk_command_pool_cleanup(device, device->transfer_queue->cmd_pool); } } @@ -3428,7 +3575,7 @@ struct vk_fa_tuning_params { }; static bool ggml_vk_flash_attn_scalar_shmem_support(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool f32acc, ggml_type k_type, ggml_type v_type); -static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool f32acc, ggml_type k_type = GGML_TYPE_F16); +static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool f32acc, ggml_type k_type = GGML_TYPE_F16, ggml_type v_type = GGML_TYPE_F16); static vk_fa_tuning_params get_fa_tuning_params_scalar(const vk_device& device, uint32_t hsk, uint32_t hsv, uint32_t n_rows, uint32_t n_kv, ggml_type k_type, ggml_type v_type, bool f32acc) { @@ -3584,7 +3731,7 @@ static vk_fa_tuning_params get_fa_tuning_params(const vk_device& device, uint32_ bool shape_ok = (f32acc && device->coopmat_support_16x16x16_f32acc) || (!f32acc && device->coopmat_support_16x16x16_f16acc); const vk_fa_tuning_params params = get_fa_tuning_params_coopmat1(device, hsk, hsv, n_rows, n_kv, k_type, v_type, f32acc); - bool shmem_ok = ggml_vk_flash_attn_coopmat_shmem_support(device, params, hsk, hsv, f32acc, k_type); + bool shmem_ok = ggml_vk_flash_attn_coopmat_shmem_support(device, params, hsk, hsv, f32acc, k_type, v_type); if (!shape_ok || !shmem_ok) { path = FA_SCALAR; @@ -3596,11 +3743,6 @@ static vk_fa_tuning_params get_fa_tuning_params(const vk_device& device, uint32_ path = FA_SCALAR; } - // Q1_0 K/V is only implemented on coopmat2 (flash_attn_cm2); there is no scalar FA shader for it. - if ((k_type == GGML_TYPE_Q1_0 || v_type == GGML_TYPE_Q1_0) && device->coopmat2) { - path = FA_COOPMAT2; - } - switch (path) { case FA_SCALAR: return get_fa_tuning_params_scalar(device, hsk, hsv, n_rows, n_kv, k_type, v_type, f32acc); @@ -3735,6 +3877,7 @@ static bool ggml_vk_matmul_int_shmem_support(const vk_device& device, const std: uint32_t block_a_size = 0; switch (src0_type) { + case GGML_TYPE_Q2_0: block_a_size = std430_size({{32, 4}, {fp_size, fp_align}}); break; // qs[8] + dm case GGML_TYPE_Q4_0: block_a_size = std430_size({{16, 4}, {fp_size, fp_align}}); break; // qs[16/4] + dm case GGML_TYPE_Q4_1: block_a_size = std430_size({{16, 4}, {fp2_size, fp2_align}}); break; // qs[16/4] + dm(vec2) case GGML_TYPE_Q5_0: block_a_size = std430_size({{16, 4}, {4, 4}, {fp_size, fp_align}}); break; // qs[16/4] + qh + dm @@ -3841,16 +3984,27 @@ static uint32_t get_subgroup_size(const std::string &pipeline_name, const vk_dev return 0; // If no matching configuration is found } -// Whether scalar flash attention will use the MMQ path for the given k_type. -static bool ggml_vk_fa_scalar_uses_mmq(const vk_device& device, ggml_type k_type) { +// Whether scalar flash attention will use the MMQ path for the given K/V types. +static bool ggml_vk_fa_type_needs_shmem(ggml_type type) { + switch (type) { + case GGML_TYPE_IQ4_NL: + return true; + default: + return false; + } +} + +static bool ggml_vk_fa_scalar_uses_mmq(const vk_device& device, ggml_type k_type, ggml_type v_type) { #if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT) return device->integer_dot_product && device->subgroup_clustered && + !ggml_vk_fa_type_needs_shmem(v_type) && (k_type == GGML_TYPE_Q4_0 || k_type == GGML_TYPE_Q4_1 || k_type == GGML_TYPE_Q5_0 || k_type == GGML_TYPE_Q5_1 || k_type == GGML_TYPE_Q8_0); #else GGML_UNUSED(device); GGML_UNUSED(k_type); + GGML_UNUSED(v_type); return false; #endif } @@ -4183,9 +4337,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { const bool fa_ds = fa.first.subgroup_size == 0; const bool bf16_kv = fa.first.k_type == GGML_TYPE_BF16; - const bool use_mmq = ggml_vk_fa_scalar_uses_mmq(device, fa.first.k_type); - const bool is_turbo3 = (fa.first.k_type == GGML_TYPE_TURBO3_0); - (void) is_turbo3; // turbo3 FA SPIR-V generation deferred; no dedicated pipeline yet + const bool use_mmq = ggml_vk_fa_scalar_uses_mmq(device, fa.first.k_type, fa.first.v_type); const void * spv_data = nullptr; size_t spv_size = 0; const char *name = nullptr; @@ -4343,6 +4495,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { } #endif CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q1_0], matmul_q1_0_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) + CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q2_0], matmul_q2_0_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q4_0], matmul_q4_0_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q4_1], matmul_q4_1_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q5_0], matmul_q5_0_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) @@ -4382,6 +4535,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { } #endif CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q1_0], matmul_id_subgroup_q1_0_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5) + CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_0], matmul_id_subgroup_q2_0_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5) CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0], matmul_id_subgroup_q4_0_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5) CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1], matmul_id_subgroup_q4_1_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5) CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0], matmul_id_subgroup_q5_0_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5) @@ -4452,6 +4606,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { #endif CREATE_MM2(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q1_0], matmul_q1_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM2(GGML_TYPE_Q2_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_0], matmul_q2_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); CREATE_MM2(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0], matmul_q4_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); CREATE_MM2(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1], matmul_q4_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); CREATE_MM2(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0], matmul_q5_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); @@ -4496,6 +4651,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { #endif CREATE_MM2(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q1_0], matmul_id_subgroup_q1_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); + CREATE_MM2(GGML_TYPE_Q2_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_0], matmul_id_subgroup_q2_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); CREATE_MM2(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0], matmul_id_subgroup_q4_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); CREATE_MM2(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1], matmul_id_subgroup_q4_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); CREATE_MM2(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0], matmul_id_subgroup_q5_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); @@ -4585,6 +4741,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { CREATE_MM_NODOT2(GGML_TYPE_BF16, pipeline_matmul_bf16, matmul_bf16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0); CREATE_MM2(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q1_0], matmul_q1_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM2(GGML_TYPE_Q2_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_0], matmul_q2_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); CREATE_MM2(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0], matmul_q4_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); CREATE_MM2(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1], matmul_q4_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); CREATE_MM2(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0], matmul_q5_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); @@ -4609,6 +4766,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { #if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT) if (device->integer_dot_product) { + CREATE_MMQ(GGML_TYPE_Q2_0, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q2_0], matmul_q2_0_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, , 0); CREATE_MMQ(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q4_0], matmul_q4_0_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, , 0); CREATE_MMQ(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q4_1], matmul_q4_1_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, , 0); CREATE_MMQ(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q5_0], matmul_q5_0_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, , 0); @@ -4631,6 +4789,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16_f32, matmul_id_subgroup_f16_f32, wg_denoms, warptile_id, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size_16); CREATE_MM_NODOT2(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_subgroup_bf16, , wg_denoms, warptile_id, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size_16); CREATE_MM2(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q1_0], matmul_id_subgroup_q1_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM2(GGML_TYPE_Q2_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_0], matmul_id_subgroup_q2_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); CREATE_MM2(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0], matmul_id_subgroup_q4_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); CREATE_MM2(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1], matmul_id_subgroup_q4_1_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); CREATE_MM2(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0], matmul_id_subgroup_q5_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); @@ -4655,6 +4814,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { #if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT) if (device->integer_dot_product) { + CREATE_MMQ(GGML_TYPE_Q2_0, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q2_0], matmul_id_subgroup_q2_0_q8_1, mmq_wg_denoms, warptile_mmqid_int, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); CREATE_MMQ(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q4_0], matmul_id_subgroup_q4_0_q8_1, mmq_wg_denoms, warptile_mmqid_int, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); CREATE_MMQ(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q4_1], matmul_id_subgroup_q4_1_q8_1, mmq_wg_denoms, warptile_mmqid_int, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); CREATE_MMQ(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q5_0], matmul_id_subgroup_q5_0_q8_1, mmq_wg_denoms, warptile_mmqid_int, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); @@ -4676,6 +4836,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16_f32, matmul_id_f16_f32, wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); CREATE_MM_NODOT2(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_bf16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); CREATE_MM2(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q1_0], matmul_id_q1_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM2(GGML_TYPE_Q2_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_0], matmul_id_q2_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); CREATE_MM2(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0], matmul_id_q4_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); CREATE_MM2(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1], matmul_id_q4_1_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); CREATE_MM2(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0], matmul_id_q5_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); @@ -4700,6 +4861,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { #if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT) if (device->integer_dot_product) { + CREATE_MMQ(GGML_TYPE_Q2_0, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q2_0], matmul_id_q2_0_q8_1, mmq_wg_denoms, warptile_mmqid_int, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); CREATE_MMQ(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q4_0], matmul_id_q4_0_q8_1, mmq_wg_denoms, warptile_mmqid_int, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); CREATE_MMQ(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q4_1], matmul_id_q4_1_q8_1, mmq_wg_denoms, warptile_mmqid_int, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); CREATE_MMQ(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q5_0], matmul_id_q5_0_q8_1, mmq_wg_denoms, warptile_mmqid_int, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); @@ -4752,6 +4914,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_bf16, matmul_bf16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0); CREATE_MM(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q1_0].f32acc, matmul_q1_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM(GGML_TYPE_Q2_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_0].f32acc, matmul_q2_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f32acc, matmul_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1].f32acc, matmul_q4_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0].f32acc, matmul_q5_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); @@ -4777,6 +4940,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { #if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT) if (device->integer_dot_product) { + CREATE_MMQ(GGML_TYPE_Q2_0, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q2_0].f32acc, matmul_q2_0_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, ); CREATE_MMQ(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q4_0].f32acc, matmul_q4_0_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, ); CREATE_MMQ(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q4_1].f32acc, matmul_q4_1_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, ); CREATE_MMQ(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q5_0].f32acc, matmul_q5_0_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, ); @@ -4798,6 +4962,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_subgroup_bf16, , wg_denoms, warptile_id, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size_16); CREATE_MM(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q1_0].f32acc, matmul_id_subgroup_q1_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM(GGML_TYPE_Q2_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_0].f32acc, matmul_id_subgroup_q2_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f32acc, matmul_id_subgroup_q4_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f32acc, matmul_id_subgroup_q4_1_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f32acc, matmul_id_subgroup_q5_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); @@ -4826,6 +4991,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_bf16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); CREATE_MM(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q1_0].f32acc, matmul_id_q1_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM(GGML_TYPE_Q2_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_0].f32acc, matmul_id_q2_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f32acc, matmul_id_q4_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f32acc, matmul_id_q4_1_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f32acc, matmul_id_q5_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); @@ -4928,6 +5094,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f32_f32", arr_dmmv_f16_f32_f32_len[reduc], arr_dmmv_f16_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_BF16][i], "mul_mat_vec_bf16_f32_f32", arr_dmmv_bf16_f32_f32_len[reduc], arr_dmmv_bf16_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q1_0][i], "mul_mat_vec_q1_0_f32_f32", arr_dmmv_q1_0_f32_f32_len[reduc], arr_dmmv_q1_0_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q2_0][i], "mul_mat_vec_q2_0_f32_f32", arr_dmmv_q2_0_f32_f32_len[reduc], arr_dmmv_q2_0_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_f32_f32", arr_dmmv_q4_0_f32_f32_len[reduc], arr_dmmv_q4_0_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_f32_f32", arr_dmmv_q4_1_f32_f32_len[reduc], arr_dmmv_q4_1_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_f32_f32", arr_dmmv_q5_0_f32_f32_len[reduc], arr_dmmv_q5_0_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size); @@ -4954,6 +5121,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f16_f32", arr_dmmv_f16_f16_f32_len[reduc], arr_dmmv_f16_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_BF16][i], "mul_mat_vec_bf16_f16_f32", arr_dmmv_bf16_f16_f32_len[reduc], arr_dmmv_bf16_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q1_0][i], "mul_mat_vec_q1_0_f16_f32", arr_dmmv_q1_0_f16_f32_len[reduc], arr_dmmv_q1_0_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q2_0][i], "mul_mat_vec_q2_0_f16_f32", arr_dmmv_q2_0_f16_f32_len[reduc], arr_dmmv_q2_0_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_f16_f32", arr_dmmv_q4_0_f16_f32_len[reduc], arr_dmmv_q4_0_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_f16_f32", arr_dmmv_q4_1_f16_f32_len[reduc], arr_dmmv_q4_1_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_f16_f32", arr_dmmv_q5_0_f16_f32_len[reduc], arr_dmmv_q5_0_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size); @@ -4981,6 +5149,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { const uint32_t subgroup_size_int = (device->vendor_id == VK_VENDOR_ID_INTEL && device->subgroup_size_control) ? device->subgroup_min_size : device->subgroup_size; const uint32_t wg_size_subgroup_int = (w == DMMV_WG_SIZE_SUBGROUP) ? subgroup_size_int : (subgroup_size_int * 4); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q2_0][i], "mul_mat_vec_q2_0_q8_1_f32", arr_dmmv_q2_0_q8_1_f32_len[reduc], arr_dmmv_q2_0_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 2*rm_kq_int, i+1}, 1, true, use_subgroups, subgroup_size_int); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_q8_1_f32", arr_dmmv_q4_0_q8_1_f32_len[reduc], arr_dmmv_q4_0_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int, i+1}, 1, true, use_subgroups, subgroup_size_int); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_q8_1_f32", arr_dmmv_q4_1_q8_1_f32_len[reduc], arr_dmmv_q4_1_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int, i+1}, 1, true, use_subgroups, subgroup_size_int); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_q8_1_f32", arr_dmmv_q5_0_q8_1_f32_len[reduc], arr_dmmv_q5_0_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int, i+1}, 1, true, use_subgroups, subgroup_size_int); @@ -5006,6 +5175,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_F16 ], "mul_mat_vec_id_f16_f32", arr_dmmv_id_f16_f32_f32_len[reduc], arr_dmmv_id_f16_f32_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {wg_size_subgroup, 2}, 1, false, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_BF16], "mul_mat_vec_id_bf16_f32", arr_dmmv_id_bf16_f32_f32_len[reduc], arr_dmmv_id_bf16_f32_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {wg_size_subgroup, 2}, 1, false, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q1_0], "mul_mat_vec_id_q1_0_f32", arr_dmmv_id_q1_0_f32_f32_len[reduc], arr_dmmv_id_q1_0_f32_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq}, 1, true, use_subgroups, force_subgroup_size); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q2_0], "mul_mat_vec_id_q2_0_f32", arr_dmmv_id_q2_0_f32_f32_len[reduc], arr_dmmv_id_q2_0_f32_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq}, 1, true, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_f32", arr_dmmv_id_q4_0_f32_f32_len[reduc], arr_dmmv_id_q4_0_f32_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq}, 1, true, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q4_1], "mul_mat_vec_id_q4_1_f32", arr_dmmv_id_q4_1_f32_f32_len[reduc], arr_dmmv_id_q4_1_f32_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq}, 1, true, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q5_0], "mul_mat_vec_id_q5_0_f32", arr_dmmv_id_q5_0_f32_f32_len[reduc], arr_dmmv_id_q5_0_f32_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq}, 1, true, use_subgroups, force_subgroup_size); @@ -5033,6 +5203,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { const uint32_t subgroup_size_int = (device->vendor_id == VK_VENDOR_ID_INTEL && device->subgroup_size_control) ? device->subgroup_min_size : device->subgroup_size; const uint32_t wg_size_subgroup_int = (w == DMMV_WG_SIZE_SUBGROUP) ? subgroup_size_int : (subgroup_size_int * 4); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q2_0], "mul_mat_vec_id_q2_0_q8_1_f32", arr_dmmv_id_q2_0_q8_1_f32_len[reduc], arr_dmmv_id_q2_0_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 2*rm_kq_int}, 1, true, use_subgroups, subgroup_size_int); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_q8_1_f32", arr_dmmv_id_q4_0_q8_1_f32_len[reduc], arr_dmmv_id_q4_0_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int}, 1, true, use_subgroups, subgroup_size_int); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q4_1], "mul_mat_vec_id_q4_1_q8_1_f32", arr_dmmv_id_q4_1_q8_1_f32_len[reduc], arr_dmmv_id_q4_1_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int}, 1, true, use_subgroups, subgroup_size_int); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q5_0], "mul_mat_vec_id_q5_0_q8_1_f32", arr_dmmv_id_q5_0_q8_1_f32_len[reduc], arr_dmmv_id_q5_0_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int}, 1, true, use_subgroups, subgroup_size_int); @@ -5065,6 +5236,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { // dequant shaders ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16", dequant_f32_len, dequant_f32_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q1_0], "dequant_q1_0", dequant_q1_0_len, dequant_q1_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 8, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q2_0], "dequant_q2_0", dequant_q2_0_len, dequant_q2_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 4, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q4_0], "dequant_q4_0", dequant_q4_0_len, dequant_q4_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q4_1], "dequant_q4_1", dequant_q4_1_len, dequant_q4_1_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q5_0], "dequant_q5_0", dequant_q5_0_len, dequant_q5_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); @@ -5092,6 +5264,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_F16 ], "get_rows_f16", get_rows_f16_len, get_rows_f16_data, "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_BF16], "get_rows_bf16", get_rows_bf16_len, get_rows_bf16_data, "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q1_0], "get_rows_q1_0", get_rows_q1_0_len, get_rows_q1_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q2_0], "get_rows_q2_0", get_rows_q2_0_len, get_rows_q2_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q4_0], "get_rows_q4_0", get_rows_q4_0_len, get_rows_q4_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q4_1], "get_rows_q4_1", get_rows_q4_1_len, get_rows_q4_1_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q5_0], "get_rows_q5_0", get_rows_q5_0_len, get_rows_q5_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); @@ -5119,6 +5292,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_F16 ], "get_rows_f16_f32", get_rows_f16_f32_len, get_rows_f16_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_BF16], "get_rows_bf16_f32", get_rows_bf16_f32_len, get_rows_bf16_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q1_0], "get_rows_q1_0_f32", get_rows_q1_0_f32_len, get_rows_q1_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q2_0], "get_rows_q2_0_f32", get_rows_q2_0_f32_len, get_rows_q2_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q4_0], "get_rows_q4_0_f32", get_rows_q4_0_f32_len, get_rows_q4_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q4_1], "get_rows_q4_1_f32", get_rows_q4_1_f32_len, get_rows_q4_1_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q5_0], "get_rows_q5_0_f32", get_rows_q5_0_f32_len, get_rows_q5_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); @@ -5203,6 +5377,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { ggml_vk_create_pipeline(device, device->pipeline_cpy_transpose_16, "cpy_transpose_16", cpy_transpose_16_len, cpy_transpose_16_data, "main", 2, sizeof(vk_op_unary_push_constants), {1, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q1_0], "cpy_f32_q1_0", cpy_f32_q1_0_len, cpy_f32_q1_0_data, "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q2_0], "cpy_f32_q2_0", cpy_f32_q2_0_len, cpy_f32_q2_0_data, "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q4_0], "cpy_f32_q4_0", cpy_f32_q4_0_len, cpy_f32_q4_0_data, "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q4_1], "cpy_f32_q4_1", cpy_f32_q4_1_len, cpy_f32_q4_1_data, "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q5_0], "cpy_f32_q5_0", cpy_f32_q5_0_len, cpy_f32_q5_0_data, "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1); @@ -5215,6 +5390,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [src_idx][GGML_TYPE_F16], "set_rows_" #src "_f16" #itype, set_rows_ ## src ## _f16 ## itype ## _len, set_rows_ ## src ## _f16 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \ ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [src_idx][GGML_TYPE_BF16], "set_rows_" #src "_bf16" #itype, set_rows_ ## src ## _bf16 ## itype ## _len, set_rows_ ## src ## _bf16 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \ ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [src_idx][GGML_TYPE_Q1_0], "set_rows_" #src "_q1_0" #itype, set_rows_ ## src ## _q1_0 ## itype ## _len, set_rows_ ## src ## _q1_0 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \ + ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [src_idx][GGML_TYPE_Q2_0], "set_rows_" #src "_q2_0" #itype, set_rows_ ## src ## _q2_0 ## itype ## _len, set_rows_ ## src ## _q2_0 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \ ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [src_idx][GGML_TYPE_Q4_0], "set_rows_" #src "_q4_0" #itype, set_rows_ ## src ## _q4_0 ## itype ## _len, set_rows_ ## src ## _q4_0 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \ ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [src_idx][GGML_TYPE_Q4_1], "set_rows_" #src "_q4_1" #itype, set_rows_ ## src ## _q4_1 ## itype ## _len, set_rows_ ## src ## _q4_1 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \ ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [src_idx][GGML_TYPE_Q5_0], "set_rows_" #src "_q5_0" #itype, set_rows_ ## src ## _q5_0 ## itype ## _len, set_rows_ ## src ## _q5_0 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \ @@ -5241,6 +5417,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_Q1_0], "cpy_q1_0_f32", cpy_q1_0_f32_len, cpy_q1_0_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q1_0), 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_Q2_0], "cpy_q2_0_f32", cpy_q2_0_f32_len, cpy_q2_0_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q2_0), 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_Q4_0], "cpy_q4_0_f32", cpy_q4_0_f32_len, cpy_q4_0_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q4_0), 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_Q4_1], "cpy_q4_1_f32", cpy_q4_1_f32_len, cpy_q4_1_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q4_1), 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_Q5_0], "cpy_q5_0_f32", cpy_q5_0_f32_len, cpy_q5_0_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q5_0), 1, 1}, {}, 1); @@ -5434,8 +5611,11 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { ggml_vk_create_pipeline(device, device->pipeline_argmax_f32, "argmax_f32", argmax_f32_len, argmax_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); ggml_vk_create_pipeline(device, device->pipeline_sum_rows_f32, "sum_rows_f32", sum_rows_f32_len, sum_rows_f32_data, "main", 2, sizeof(vk_op_sum_rows_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - // Intel Arc B390 was observed segfaulting with this shader. - if (device->subgroup_basic && device->subgroup_shuffle && device->vendor_id != VK_VENDOR_ID_INTEL) { + // Intel Windows driver older than 32.0.101.8860 will crash when using fwht kernels on Xe2+ GPUS so we gate that here + const bool can_use_fwht = device->driver_id != vk::DriverId::eIntelProprietaryWindows || + device->architecture != vk_device_architecture::INTEL_XE2 || + (device->architecture == vk_device_architecture::INTEL_XE2 && ggml_vk_intel_windows_driver_equals_or_newer_than(device->properties.driverVersion, 101, 8860)); + if (can_use_fwht && device->subgroup_basic && device->subgroup_shuffle) { int idx = 0; for (uint32_t n : {64, 128, 256, 512}) { if (device->subgroup_size <= n) { @@ -5443,8 +5623,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { } ++idx; } - } else if (device->driver_id != vk::DriverId::eIntelProprietaryWindows) { - // Disabled on Intel Windows due to a driver bug: https://github.com/ggml-org/llama.cpp/pull/23964#issuecomment-4598226147 + } else if (can_use_fwht) { int idx = 0; for (uint32_t n : {64, 128, 256, 512}) { const uint32_t block_size = std::min(device->subgroup_size, n); @@ -5495,10 +5674,13 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { ggml_vk_create_pipeline(device, device->pipeline_col2im_1d_f16, "col2im_1d_f16", col2im_1d_f16_len, col2im_1d_f16_data, "main", 2, sizeof(vk_op_col2im_1d_push_constants), {256, 1, 1}, {}, 1, true); ggml_vk_create_pipeline(device, device->pipeline_col2im_1d_bf16, "col2im_1d_bf16", col2im_1d_bf16_len, col2im_1d_bf16_data, "main", 2, sizeof(vk_op_col2im_1d_push_constants), {256, 1, 1}, {}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_out_prod_f32, "out_prod_f32", out_prod_f32_len, out_prod_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {256, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_snake_f32, "snake_f32", snake_f32_len, snake_f32_data, "main", 4, sizeof(vk_op_snake_push_constants), {256, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_snake_f16, "snake_f16", snake_f16_len, snake_f16_data, "main", 4, sizeof(vk_op_snake_push_constants), {256, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_snake_bf16, "snake_bf16", snake_bf16_len, snake_bf16_data, "main", 4, sizeof(vk_op_snake_push_constants), {256, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_pool1d_f32, "pool1d_f32", pool1d_f32_len, pool1d_f32_data, "main", 2, sizeof(vk_op_pool1d_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_pool2d_f32, "pool2d_f32", pool2d_f32_len, pool2d_f32_data, "main", 2, sizeof(vk_op_pool2d_push_constants), {512, 1, 1}, {}, 1); // TurboQuant WHT (forward / inverse rotation, 128-element block) @@ -5508,6 +5690,8 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { ggml_vk_create_pipeline(device, device->pipeline_rwkv_wkv7_f32, "rwkv_wkv7_f32", rwkv_wkv7_f32_len, rwkv_wkv7_f32_data, "main", 8, sizeof(vk_op_rwkv_wkv7_push_constants), {1, 1, 1}, {device->subgroup_size}, 1); + ggml_vk_create_pipeline(device, device->pipeline_gated_linear_attn_f32, "gated_linear_attn_f32", gated_linear_attn_f32_len, gated_linear_attn_f32_data, "main", 6, sizeof(vk_op_gated_linear_attn_push_constants), {1, 1, 1}, {}, 1); + { const uint32_t gdn_sizes[] = {16, 32, 64, 128}; const char * gdn_names[][2] = { @@ -5876,6 +6060,7 @@ static vk_device ggml_vk_get_device(size_t idx) { bool coopmat2_support = false; bool coopmat2_decode_vector_support = false; bool pipeline_executable_properties_support = false; + bool internally_sync_support = false; device->coopmat_support = false; device->integer_dot_product = false; device->shader_64b_indexing = false; @@ -5947,6 +6132,8 @@ static vk_device ggml_vk_get_device(size_t idx) { } else if (strcmp("VK_EXT_shader_64bit_indexing", properties.extensionName) == 0) { device->shader_64b_indexing = true; #endif + } else if (strcmp(VK_KHR_INTERNALLY_SYNCHRONIZED_QUEUES_EXTENSION_NAME, properties.extensionName) == 0) { + internally_sync_support = true; } } @@ -6133,14 +6320,6 @@ static vk_device ggml_vk_get_device(size_t idx) { device->single_queue = compute_queue_family_index == transfer_queue_family_index && queue_family_props[compute_queue_family_index].queueCount == 1; std::vector device_queue_create_infos; - if (compute_queue_family_index != transfer_queue_family_index) { - device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities}); - device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), transfer_queue_family_index, 1, priorities + 1}); - } else if(!device->single_queue) { - device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 2, priorities}); - } else { - device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities}); - } vk::DeviceCreateInfo device_create_info{}; std::vector device_extensions; vk::PhysicalDeviceFeatures device_features = device->physical_device.getFeatures(); @@ -6162,6 +6341,17 @@ static vk_device ggml_vk_get_device(size_t idx) { last_struct = (VkBaseOutStructure *)&vk12_features; + VkPhysicalDeviceInternallySynchronizedQueuesFeaturesKHR internally_synchronized_queues_features{}; + internally_synchronized_queues_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_INTERNALLY_SYNCHRONIZED_QUEUES_FEATURES_KHR; + internally_synchronized_queues_features.pNext = nullptr; + internally_synchronized_queues_features.internallySynchronizedQueues = VK_FALSE; + + if (internally_sync_support) { + last_struct->pNext = (VkBaseOutStructure *)&internally_synchronized_queues_features; + last_struct = (VkBaseOutStructure *)&internally_synchronized_queues_features; + device_extensions.push_back(VK_KHR_INTERNALLY_SYNCHRONIZED_QUEUES_EXTENSION_NAME); + } + VkPhysicalDevicePipelineRobustnessFeaturesEXT pl_robustness_features; pl_robustness_features.pNext = nullptr; pl_robustness_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_PIPELINE_ROBUSTNESS_FEATURES_EXT; @@ -6300,6 +6490,23 @@ static vk_device ggml_vk_get_device(size_t idx) { vkGetPhysicalDeviceFeatures2(device->physical_device, &device_features2); + device->has_internally_synchronized_queues = internally_synchronized_queues_features.internallySynchronizedQueues; + + // Build queue create infos only after querying whether internally synchronized queues are enabled. + // getQueue2() later uses the same flag, so creation/retrieval must stay consistent. + vk::DeviceQueueCreateFlags queue_flags = device->has_internally_synchronized_queues ? + eInternallySynchronizedKHR : + vk::DeviceQueueCreateFlags(); + + if (compute_queue_family_index != transfer_queue_family_index) { + device_queue_create_infos.push_back({queue_flags, compute_queue_family_index, 1, priorities}); + device_queue_create_infos.push_back({queue_flags, transfer_queue_family_index, 1, priorities + 1}); + } else if(!device->single_queue) { + device_queue_create_infos.push_back({queue_flags, compute_queue_family_index, 2, priorities}); + } else { + device_queue_create_infos.push_back({queue_flags, compute_queue_family_index, 1, priorities}); + } + device->pipeline_executable_properties_support = pipeline_executable_properties_support; device->fp16 = device->fp16 && vk12_features.shaderFloat16; @@ -6582,7 +6789,7 @@ static vk_device ggml_vk_get_device(size_t idx) { device->device = device->physical_device.createDevice(device_create_info); // Queues - ggml_vk_create_queue(device, device->compute_queue, compute_queue_family_index, 0, { vk::PipelineStageFlagBits::eComputeShader | vk::PipelineStageFlagBits::eTransfer }, false); + device->compute_queue = ggml_vk_create_queue(device, compute_queue_family_index, 0, { vk::PipelineStageFlagBits::eComputeShader | vk::PipelineStageFlagBits::eTransfer }, false); // Shaders // Disable matmul tile sizes early if performance low or not supported @@ -6684,13 +6891,11 @@ static vk_device ggml_vk_get_device(size_t idx) { if (!device->single_queue) { const uint32_t transfer_queue_index = compute_queue_family_index == transfer_queue_family_index ? 1 : 0; - ggml_vk_create_queue(device, device->transfer_queue, transfer_queue_family_index, transfer_queue_index, { vk::PipelineStageFlagBits::eTransfer }, true); + device->transfer_queue = ggml_vk_create_queue(device, transfer_queue_family_index, transfer_queue_index, { vk::PipelineStageFlagBits::eTransfer }, true); device->async_use_transfer_queue = prefers_transfer_queue || (getenv("GGML_VK_ASYNC_USE_TRANSFER_QUEUE") != nullptr); } else { - // TODO: Use pointer or reference to avoid copy - device->transfer_queue.copyFrom(device->compute_queue); - device->transfer_queue.cmd_pool.init(device, &device->transfer_queue); + device->transfer_queue = ggml_vk_create_aliased_queue(device, device->compute_queue); device->async_use_transfer_queue = false; } @@ -7253,7 +7458,7 @@ static void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) { ctx->fence = ctx->device->device.createFence({}); ctx->almost_ready_fence = ctx->device->device.createFence({}); - ctx->compute_cmd_pool.init(ctx->device, &ctx->device->compute_queue); + ctx->compute_cmd_pool.init(ctx->device, ctx->device->compute_queue.get()); if (ctx->device->async_use_transfer_queue) { vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eTimeline, 0 }; vk::SemaphoreCreateInfo ci{}; @@ -7261,7 +7466,7 @@ static void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) { ctx->transfer_semaphore.s = ctx->device->device.createSemaphore(ci); ctx->transfer_semaphore.value = 0; - ctx->transfer_cmd_pool.init(ctx->device, &ctx->device->transfer_queue); + ctx->transfer_cmd_pool.init(ctx->device, ctx->device->transfer_queue.get()); } if (vk_perf_logger_enabled) { @@ -7281,6 +7486,7 @@ static vk_pipeline ggml_vk_get_to_fp16(ggml_backend_vk_context * ctx, ggml_type switch (type) { case GGML_TYPE_F32: case GGML_TYPE_Q1_0: + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -7354,6 +7560,7 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_conte switch (src0_type) { case GGML_TYPE_Q1_0: + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -7397,6 +7604,7 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * if (b_type == GGML_TYPE_Q8_1) { switch (a_type) { + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -7421,6 +7629,7 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * case GGML_TYPE_F16: case GGML_TYPE_BF16: case GGML_TYPE_Q1_0: + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -7513,6 +7722,7 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_id_pipeline(ggml_backend_vk_co switch (src0_type) { case GGML_TYPE_Q1_0: + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -7559,6 +7769,7 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec_id(ggml_backend_vk_context if (b_type == GGML_TYPE_Q8_1) { switch (a_type) { + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -7583,6 +7794,7 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec_id(ggml_backend_vk_context case GGML_TYPE_F16: case GGML_TYPE_BF16: case GGML_TYPE_Q1_0: + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -7845,6 +8057,20 @@ static vk_context ggml_vk_get_compute_ctx(ggml_backend_vk_context * ctx) { return result; } +static vk_context ggml_vk_get_transfer_ctx(ggml_backend_vk_context * ctx) { + vk_context result; + if (!ctx->transfer_ctx.expired()) { + result = ctx->transfer_ctx.lock(); + } else { + result = ggml_vk_create_context(ctx, ctx->transfer_cmd_pool); + + ctx->transfer_ctx = result; + ggml_vk_ctx_begin(ctx->device, result); + } + + return result; +} + // Submit any pending transfer queue work and signal the transfer semaphore. // The next compute context created via ggml_vk_get_compute_ctx will wait on this semaphore. // Returns true if work was submitted. @@ -8095,7 +8321,7 @@ static void ggml_vk_buffer_write_2d(vk_buffer& dst, size_t offset, const void * } else { std::lock_guard guard(dst->device->mutex); - vk_context subctx = ggml_vk_create_temporary_context(dst->device->transfer_queue.cmd_pool); + vk_context subctx = ggml_vk_create_temporary_context(dst->device->transfer_queue->cmd_pool); ggml_vk_ctx_begin(dst->device, subctx); bool ret = ggml_vk_buffer_write_2d_async(subctx, dst, offset, src, spitch, dpitch, width, height, true); GGML_ASSERT(ret); @@ -8210,7 +8436,7 @@ static void ggml_vk_buffer_read_2d(vk_buffer& src, size_t offset, void * dst, si GGML_ASSERT(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostCoherent); std::lock_guard guard(src->device->mutex); - vk_context subctx = ggml_vk_create_temporary_context(src->device->compute_queue.cmd_pool); + vk_context subctx = ggml_vk_create_temporary_context(src->device->compute_queue->cmd_pool); ggml_vk_ctx_begin(src->device, subctx); subctx->s->buffer->buf.pipelineBarrier( vk::PipelineStageFlagBits::eComputeShader | vk::PipelineStageFlagBits::eTransfer, @@ -8236,7 +8462,7 @@ static void ggml_vk_buffer_read_2d(vk_buffer& src, size_t offset, void * dst, si } else { std::lock_guard guard(src->device->mutex); - vk_context subctx = ggml_vk_create_temporary_context(src->device->transfer_queue.cmd_pool); + vk_context subctx = ggml_vk_create_temporary_context(src->device->transfer_queue->cmd_pool); ggml_vk_ctx_begin(src->device, subctx); bool ret = ggml_vk_buffer_read_2d_async(subctx, src, offset, dst, spitch, dpitch, width, height, true); GGML_ASSERT(ret); @@ -8273,7 +8499,7 @@ static void ggml_vk_buffer_copy(vk_buffer& dst, size_t dst_offset, vk_buffer& sr std::lock_guard guard(src->device->mutex); VK_LOG_DEBUG("ggml_vk_buffer_copy(SINGLE_DEVICE, " << size << ")"); // Copy within the device - vk_context subctx = ggml_vk_create_temporary_context(src->device->transfer_queue.cmd_pool); + vk_context subctx = ggml_vk_create_temporary_context(src->device->transfer_queue->cmd_pool); ggml_vk_ctx_begin(src->device, subctx); ggml_vk_buffer_copy_async(subctx, dst, dst_offset, src, src_offset, size); ggml_vk_ctx_end(subctx); @@ -8316,7 +8542,7 @@ static void ggml_vk_buffer_memset(vk_buffer& dst, size_t offset, uint32_t c, siz } std::lock_guard guard(dst->device->mutex); - vk_context subctx = ggml_vk_create_temporary_context(dst->device->transfer_queue.cmd_pool); + vk_context subctx = ggml_vk_create_temporary_context(dst->device->transfer_queue->cmd_pool); ggml_vk_ctx_begin(dst->device, subctx); subctx->s->buffer->buf.fillBuffer(dst->buffer, offset, size, c); ggml_vk_ctx_end(subctx); @@ -8605,6 +8831,7 @@ static vk_pipeline ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, const if (src->type == GGML_TYPE_F32) { switch (to) { case GGML_TYPE_Q1_0: + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -8620,6 +8847,7 @@ static vk_pipeline ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, const if (to == GGML_TYPE_F32) { switch (src->type) { case GGML_TYPE_Q1_0: + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -9046,7 +9274,7 @@ static bool ggml_vk_should_use_mmvq(const vk_device& device, uint32_t m, uint32_ // Quantization overhead is not worth it for small k switch (device->vendor_id) { case VK_VENDOR_ID_NVIDIA: - if (src0_type == GGML_TYPE_Q2_K || src0_type == GGML_TYPE_Q3_K || src0_type == GGML_TYPE_IQ1_S || src0_type == GGML_TYPE_IQ1_M) { + if (src0_type == GGML_TYPE_Q2_0 || src0_type == GGML_TYPE_Q2_K || src0_type == GGML_TYPE_Q3_K || src0_type == GGML_TYPE_IQ1_S || src0_type == GGML_TYPE_IQ1_M) { return true; } @@ -9074,7 +9302,7 @@ static bool ggml_vk_should_use_mmvq(const vk_device& device, uint32_t m, uint32_ } case VK_VENDOR_ID_INTEL: if (device->architecture == vk_device_architecture::INTEL_XE2) { - if (src0_type == GGML_TYPE_Q2_K || src0_type == GGML_TYPE_Q3_K || src0_type == GGML_TYPE_Q6_K) { + if (src0_type == GGML_TYPE_Q2_0 || src0_type == GGML_TYPE_Q2_K || src0_type == GGML_TYPE_Q3_K || src0_type == GGML_TYPE_Q6_K) { return true; } } @@ -10264,7 +10492,6 @@ static void ggml_vk_mul_mat_id(ggml_backend_vk_context * ctx, vk_context& subctx static bool ggml_vk_flash_attn_scalar_shmem_support(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool f32acc, ggml_type k_type, ggml_type v_type) { GGML_UNUSED(f32acc); - GGML_UNUSED(v_type); // Needs to be kept up to date on shader changes const uint32_t wg_size = params.workgroup_size; const uint32_t Br = params.block_rows; @@ -10273,13 +10500,15 @@ static bool ggml_vk_flash_attn_scalar_shmem_support(const vk_device& device, con // BF16 uses the fp32 shader (FLOAT_TYPE=float) const uint32_t float_type_size = (device->fp16 && k_type != GGML_TYPE_BF16) ? sizeof(ggml_fp16_t) : sizeof(float); - const bool mmq = ggml_vk_fa_scalar_uses_mmq(device, k_type); + const bool mmq = ggml_vk_fa_scalar_uses_mmq(device, k_type, v_type); // tmpsh is overestimated slightly const uint32_t tmpsh = wg_size * sizeof(float); const uint32_t tmpshv4 = wg_size * 4 * float_type_size; const uint32_t masksh = Bc * (Br + 1) * float_type_size; + // DATA_A_IQ4_NL is compiled into the FA shaders unconditionally, so its shared table is always allocated. + const uint32_t iq_shmem = 16 * float_type_size; uint32_t Qf, kvsh, kblocksh_size; if (mmq) { @@ -10304,7 +10533,7 @@ static bool ggml_vk_flash_attn_scalar_shmem_support(const vk_device& device, con kblocksh_size = 0; } - const uint32_t total_size = tmpsh + tmpshv4 + masksh + Qf + kvsh + kblocksh_size; + const uint32_t total_size = tmpsh + tmpshv4 + masksh + iq_shmem + Qf + kvsh + kblocksh_size; const bool supported = total_size <= device->properties.limits.maxComputeSharedMemorySize; VK_LOG_DEBUG("ggml_vk_flash_attn_scalar_shmem_support(HSK=" << hsk << ", HSV=" << hsv << ", mmq=" << mmq << ", total_size=" << total_size << ", supported=" << supported); @@ -10312,7 +10541,8 @@ static bool ggml_vk_flash_attn_scalar_shmem_support(const vk_device& device, con return supported; } -static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool f32acc, ggml_type k_type) { +static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool f32acc, ggml_type k_type, ggml_type v_type) { + GGML_UNUSED(v_type); // Needs to be kept up to date on shader changes const uint32_t Br = params.block_rows; const uint32_t Bc = params.block_cols; @@ -10328,6 +10558,8 @@ static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, co const uint32_t f16vec4 = 8; const uint32_t tmpsh = (Bc / MatBc) * sizeof(float); + // DATA_A_IQ4_NL is compiled into the FA shaders unconditionally, so its shared table is always allocated. + const uint32_t iq_shmem = 16 * sizeof(ggml_fp16_t); const uint32_t qstride = hsk_pad / 4 + 2; const uint32_t Qf = Br * qstride * f16vec4; @@ -10349,7 +10581,7 @@ static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, co const uint32_t slope = Br * acctype; - const uint32_t total_size = tmpsh + Qf + Psh + sfsh + ksh + pvsh + slope; + const uint32_t total_size = tmpsh + iq_shmem + Qf + Psh + sfsh + ksh + pvsh + slope; const bool supported = total_size <= device->properties.limits.maxComputeSharedMemorySize; VK_LOG_DEBUG("ggml_vk_flash_attn_coopmat_shmem_support(HSK=" << hsk << ", HSV=" << hsv << ", f32acc=" << f32acc << ", total_size=" << total_size << ", supported=" << supported); @@ -10769,15 +11001,16 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const return ctx->device->pipeline_add_id_f32; } return nullptr; - case GGML_OP_CONCAT: { - if (src0->type != src1->type || src0->type != dst->type) { - return nullptr; + case GGML_OP_OUT_PROD: + if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_out_prod_f32; } - if (ggml_blck_size(src0->type) != 1) { + return nullptr; + case GGML_OP_CONCAT: { + if (!ggml_vk_concat_supported(src0, src1, dst)) { return nullptr; } - const size_t type_size = ggml_type_size(src0->type); - switch (type_size) { + switch (ggml_vk_concat_unit_size(src0->type)) { case 1: return ctx->device->pipeline_concat_i8; case 2: @@ -11171,6 +11404,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const case GGML_TYPE_BF16: return ctx->device->pipeline_col2im_1d_bf16; default: return nullptr; } + case GGML_OP_POOL_1D: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_pool1d_f32; + } + return nullptr; case GGML_OP_POOL_2D: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { return ctx->device->pipeline_pool2d_f32; @@ -11186,6 +11424,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const return ctx->device->pipeline_rwkv_wkv7_f32; } return nullptr; + case GGML_OP_GATED_LINEAR_ATTN: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_gated_linear_attn_f32; + } + return nullptr; case GGML_OP_GATED_DELTA_NET: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { const uint32_t S_v = dst->src[2]->ne[0]; @@ -11469,6 +11712,18 @@ template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk GGML_UNUSED(src3); } +template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_concat_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) { + const uint32_t unit_size = ggml_vk_concat_unit_size(dst->type); + const uint32_t a_offset = get_misalign_bytes(ctx, src0) / unit_size; + const uint32_t b_offset = get_misalign_bytes(ctx, src1) / unit_size; + const uint32_t d_offset = get_misalign_bytes(ctx, dst) / unit_size; + + p.misalign_offsets = (a_offset << 16) | (b_offset << 8) | d_offset; + + GGML_UNUSED(src2); + GGML_UNUSED(src3); +} + template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_upscale_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) { const uint32_t a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type); const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type); @@ -11504,7 +11759,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co } std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3]; std::cerr << "), " << ggml_op_name(op) << ")"); - GGML_ASSERT(op == GGML_OP_GET_ROWS || op == GGML_OP_CPY || (!ggml_is_quantized(src0->type) && (src1 == nullptr || !ggml_is_quantized(src1->type)))); // NOLINT + GGML_ASSERT(op == GGML_OP_GET_ROWS || op == GGML_OP_CPY || op == GGML_OP_CONCAT || (!ggml_is_quantized(src0->type) && (src1 == nullptr || !ggml_is_quantized(src1->type)))); // NOLINT GGML_ASSERT(dst->buffer != nullptr); const uint64_t ne00 = src0->ne[0]; const uint64_t ne01 = src0->ne[1]; @@ -11681,6 +11936,13 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co { elements = { uint32_t(dst->ne[0]), uint32_t(dst->ne[1]), 1 }; } break; + case GGML_OP_POOL_1D: + { + const uint32_t N = dst->ne[3] * dst->ne[2]; + const uint32_t OC = dst->ne[1]; + const uint32_t OL = dst->ne[0]; + elements = { N * OC * OL, 1, 1}; + } break; case GGML_OP_POOL_2D: { const uint32_t N = dst->ne[3]; @@ -11725,6 +11987,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co case GGML_OP_DIV: case GGML_OP_MUL: case GGML_OP_ADD1: + case GGML_OP_OUT_PROD: case GGML_OP_ARANGE: case GGML_OP_FILL: case GGML_OP_SCALE: @@ -11758,6 +12021,9 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co ne *= ggml_type_size(src0->type) / 2; } } + if (op == GGML_OP_CONCAT && ggml_is_quantized(dst->type)) { + ne = ne / ggml_blck_size(dst->type) * ggml_type_size(dst->type) / ggml_vk_concat_unit_size(dst->type); + } // copy_to_quant has block size of 32, and each thread does QUANT_K elements. // Splitting into 512x512xZ wouldn't work well since each workgroup does 1024 elements. // So divide by block size here before splitting into 512x512 groups. @@ -12041,6 +12307,24 @@ static void ggml_vk_add(ggml_backend_vk_context * ctx, vk_context& subctx, const }); } +static void ggml_vk_out_prod(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + const uint32_t src0_type_size = ggml_type_size(src0->type); + const uint32_t src1_type_size = ggml_type_size(src1->type); + const uint32_t dst_type_size = ggml_type_size(dst->type); + + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_OUT_PROD, { + (uint32_t)ggml_nelements(dst), + (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], + (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, + (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], + (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, + (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], + (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, + 0, + 0.0f, 0.0f, 0, + }); +} + static void ggml_vk_sub(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { const uint32_t src0_type_size = ggml_type_size(src0->type); const uint32_t src1_type_size = ggml_type_size(src1->type); @@ -12178,6 +12462,41 @@ static void ggml_vk_rwkv_wkv7(ggml_backend_vk_context * ctx, vk_context& subctx, ); } +static void ggml_vk_gated_linear_attn(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst) { + const size_t seq_length = dst->src[0]->ne[2]; + const size_t n_embed = dst->ne[0]; + const size_t n_heads = dst->src[0]->ne[1]; + const size_t n_seqs = dst->src[4]->ne[1]; + + float scale; + memcpy(&scale, dst->op_params, sizeof(float)); + + GGML_ASSERT(dst->buffer != nullptr); + + vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, dst->src[0], dst->src[1], dst->src[2], dst, dst->op); + GGML_ASSERT(pipeline != nullptr); + + ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1); + + vk_subbuffer dst_buf = ggml_vk_tensor_subbuffer(ctx, dst); + vk_subbuffer src_buf[5] = {}; + for (int i = 0; i < 5; i++) { + src_buf[i] = ggml_vk_tensor_subbuffer(ctx, dst->src[i]); + } + + const vk_op_gated_linear_attn_push_constants pc = { + (uint32_t)n_seqs, + (uint32_t)seq_length, + (uint32_t)n_embed, + (uint32_t)n_heads, + scale, + }; + + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, + {src_buf[0], src_buf[1], src_buf[2], src_buf[3], src_buf[4], dst_buf}, + pc, { (uint32_t)(n_seqs * n_heads), 1, 1 }); +} + static void ggml_vk_gated_delta_net(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst) { const ggml_tensor * src_q = dst->src[0]; const ggml_tensor * src_v = dst->src[2]; @@ -12383,18 +12702,28 @@ static void ggml_vk_opt_step_sgd(ggml_backend_vk_context * ctx, vk_context& subc static void ggml_vk_concat(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { int * op_params = (int *)dst->op_params; - const uint32_t src0_type_size = ggml_type_size(src0->type); - const uint32_t src1_type_size = ggml_type_size(src1->type); - const uint32_t dst_type_size = ggml_type_size(dst->type); - - ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_CONCAT, { - (uint32_t)ggml_nelements(dst), - (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, - (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, - (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, + const uint32_t unit_size = ggml_vk_concat_unit_size(dst->type); + const uint32_t units_per_block = ggml_type_size(dst->type) / unit_size; + const uint32_t block_size = ggml_blck_size(dst->type); + const bool quantized = ggml_is_quantized(dst->type); + + // Address dimension 0 in packed storage units; higher strides may be noncontiguous. + const uint32_t ne00 = src0->ne[0] / block_size * units_per_block; + const uint32_t ne10 = src1->ne[0] / block_size * units_per_block; + const uint32_t ne20 = dst->ne[0] / block_size * units_per_block; + const uint32_t nb00 = quantized ? 1 : src0->nb[0] / unit_size; + const uint32_t nb10 = quantized ? 1 : src1->nb[0] / unit_size; + const uint32_t nb20 = quantized ? 1 : dst->nb[0] / unit_size; + + vk_op_concat_push_constants pc {{ + ne20 * (uint32_t)dst->ne[1] * (uint32_t)dst->ne[2] * (uint32_t)dst->ne[3], + ne00, (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], nb00, (uint32_t)src0->nb[1] / unit_size, (uint32_t)src0->nb[2] / unit_size, (uint32_t)src0->nb[3] / unit_size, + ne10, (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], nb10, (uint32_t)src1->nb[1] / unit_size, (uint32_t)src1->nb[2] / unit_size, (uint32_t)src1->nb[3] / unit_size, + ne20, (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], nb20, (uint32_t) dst->nb[1] / unit_size, (uint32_t) dst->nb[2] / unit_size, (uint32_t) dst->nb[3] / unit_size, 0, 0.0f, 0.0f, op_params[0], - }); + }}; + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_CONCAT, std::move(pc)); } static void ggml_vk_upscale(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { @@ -13001,12 +13330,16 @@ static void ggml_vk_soft_max_back(ggml_backend_vk_context * ctx, vk_context& sub static void ggml_vk_topk_moe(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_cgraph * cgraph, int node_idx) { topk_moe_mode mode = ctx->fused_topk_moe_mode; + const bool has_bias = mode == TOPK_MOE_SIGMOID_NORM_BIAS || mode == TOPK_MOE_SQRT_SOFTPLUS_NORM_BIAS; ggml_tensor * logits = cgraph->nodes[node_idx + 0]->src[0]; - ggml_tensor * bias = (mode == TOPK_MOE_SIGMOID_NORM_BIAS) ? cgraph->nodes[node_idx + 2]->src[1] : logits; + ggml_tensor * bias = mode == TOPK_MOE_SIGMOID_NORM_BIAS ? cgraph->nodes[node_idx + 2]->src[1] : + mode == TOPK_MOE_SQRT_SOFTPLUS_NORM_BIAS ? cgraph->nodes[node_idx + 3]->src[1] : + logits; ggml_tensor * weights = cgraph->nodes[node_idx + ctx->num_additional_fused_ops]; - ggml_tensor * ids = (mode == TOPK_MOE_SIGMOID_NORM_BIAS) ? cgraph->nodes[node_idx + 4] : - (mode == TOPK_MOE_LATE_SOFTMAX) ? cgraph->nodes[node_idx + 1] : - cgraph->nodes[node_idx + 3]; + ggml_tensor * ids = mode == TOPK_MOE_SIGMOID_NORM_BIAS ? cgraph->nodes[node_idx + 4] : + mode == TOPK_MOE_SQRT_SOFTPLUS_NORM_BIAS ? cgraph->nodes[node_idx + 5] : + mode == TOPK_MOE_LATE_SOFTMAX ? cgraph->nodes[node_idx + 1] : + cgraph->nodes[node_idx + 3]; GGML_ASSERT(logits->type == GGML_TYPE_F32); GGML_ASSERT(bias->type == GGML_TYPE_F32); @@ -13046,16 +13379,24 @@ static void ggml_vk_topk_moe(ggml_backend_vk_context * ctx, vk_context& subctx, pc.clamp_min = ggml_get_op_params_f32(clamp, 0); pc.clamp_max = ggml_get_op_params_f32(clamp, 1); } + if (mode == TOPK_MOE_SQRT_SOFTPLUS_NORM_BIAS) { + ggml_tensor * clamp = cgraph->nodes[node_idx + 9]; + GGML_ASSERT(clamp->op == GGML_OP_CLAMP); + pc.clamp_min = ggml_get_op_params_f32(clamp, 0); + pc.clamp_max = ggml_get_op_params_f32(clamp, 1); + } #define GATING_FUNC_SOFTMAX 0 #define GATING_FUNC_SIGMOID 1 #define GATING_FUNC_SOFTMAX_WEIGHT 2 - - pc.gating_func = mode == TOPK_MOE_SIGMOID_NORM_BIAS ? GATING_FUNC_SIGMOID : - mode == TOPK_MOE_LATE_SOFTMAX ? GATING_FUNC_SOFTMAX_WEIGHT : - GATING_FUNC_SOFTMAX; - pc.has_bias = mode == TOPK_MOE_SIGMOID_NORM_BIAS; - pc.with_norm = mode == TOPK_MOE_EARLY_SOFTMAX_NORM || mode == TOPK_MOE_SIGMOID_NORM_BIAS; +#define GATING_FUNC_SQRT_SOFTPLUS 3 + + pc.gating_func = mode == TOPK_MOE_SIGMOID_NORM_BIAS ? GATING_FUNC_SIGMOID : + mode == TOPK_MOE_SQRT_SOFTPLUS_NORM_BIAS ? GATING_FUNC_SQRT_SOFTPLUS : + mode == TOPK_MOE_LATE_SOFTMAX ? GATING_FUNC_SOFTMAX_WEIGHT : + GATING_FUNC_SOFTMAX; + pc.has_bias = has_bias; + pc.with_norm = mode == TOPK_MOE_EARLY_SOFTMAX_NORM || has_bias; if (ctx->fused_topk_moe_scale) { GGML_ASSERT(weights->op == GGML_OP_SCALE); pc.output_scale = ggml_get_op_params_f32(weights, 0); @@ -13599,6 +13940,29 @@ static void ggml_vk_snake_dispatch_fused(ggml_backend_vk_context * ctx, vk_conte ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { x_buf, a_buf, inv_b_buf, dst_buf }, pc, elements); } +static void ggml_vk_pool_1d(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + uint32_t op = static_cast(dst->op_params[0]); + const int32_t k0 = dst->op_params[1]; + const int32_t s0 = dst->op_params[2]; + const int32_t p0 = dst->op_params[3]; + + const uint32_t IL = src0->ne[0]; + + const uint32_t N = dst->ne[3] * dst->ne[2]; + + const uint32_t OC = dst->ne[1]; + const uint32_t OL = dst->ne[0]; + + const uint32_t parallel_elements = N * OC * OL; + + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_POOL_1D, { + IL, OL, OC, + parallel_elements, + op, + k0, s0, p0, + }); +} + static void ggml_vk_pool_2d(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { uint32_t op = static_cast(dst->op_params[0]); const int32_t k1 = dst->op_params[1]; @@ -14856,6 +15220,9 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr ggml_vk_add(ctx, compute_ctx, src0, src1, node); } break; + case GGML_OP_OUT_PROD: + ggml_vk_out_prod(ctx, compute_ctx, src0, src1, node); + break; case GGML_OP_SUB: ggml_vk_sub(ctx, compute_ctx, src0, src1, node); @@ -15111,6 +15478,10 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr case GGML_OP_CONV_TRANSPOSE_1D: ggml_vk_conv_transpose_1d(ctx, compute_ctx, src0, src1, node); + break; + case GGML_OP_POOL_1D: + ggml_vk_pool_1d(ctx, compute_ctx, src0, node); + break; case GGML_OP_POOL_2D: ggml_vk_pool_2d(ctx, compute_ctx, src0, node); @@ -15157,6 +15528,11 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr break; + case GGML_OP_GATED_LINEAR_ATTN: + ggml_vk_gated_linear_attn(ctx, compute_ctx, node); + + break; + case GGML_OP_GATED_DELTA_NET: ggml_vk_gated_delta_net(ctx, compute_ctx, node); @@ -15662,13 +16038,7 @@ static void ggml_backend_vk_set_tensor_2d_async(ggml_backend_t backend, ggml_ten vk_context cpy_ctx; if (ctx->device->async_use_transfer_queue) { - if (ctx->transfer_ctx.expired()) { - cpy_ctx = ggml_vk_create_context(ctx, ctx->transfer_cmd_pool); - ctx->transfer_ctx = cpy_ctx; - ggml_vk_ctx_begin(ctx->device, cpy_ctx); - } else { - cpy_ctx = ctx->transfer_ctx.lock(); - } + cpy_ctx = ggml_vk_get_transfer_ctx(ctx); } else { cpy_ctx = ggml_vk_get_compute_ctx(ctx); } @@ -15814,13 +16184,7 @@ static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend_src, ggml_ba vk_context cpy_ctx; if (ctx->device->async_use_transfer_queue) { - if (ctx->transfer_ctx.expired()) { - cpy_ctx = ggml_vk_create_context(ctx, ctx->transfer_cmd_pool); - ctx->transfer_ctx = cpy_ctx; - ggml_vk_ctx_begin(ctx->device, cpy_ctx); - } else { - cpy_ctx = ctx->transfer_ctx.lock(); - } + cpy_ctx = ggml_vk_get_transfer_ctx(ctx); } else { cpy_ctx = ggml_vk_get_compute_ctx(ctx); } @@ -15867,19 +16231,17 @@ static void ggml_vk_synchronize(ggml_backend_vk_context * ctx) { 1, &ctx->transfer_semaphore.value, 0, nullptr, }; - vk::PipelineStageFlags stage = ctx->device->transfer_queue.stage_flags; + vk::PipelineStageFlags stage = ctx->device->transfer_queue->stage_flags; vk::SubmitInfo si{ 1, &ctx->transfer_semaphore.s, &stage, 0, nullptr, 0, nullptr, }; si.setPNext(&tl_info); - std::lock_guard guard(queue_mutex); - ctx->device->compute_queue.queue.submit({ si }, ctx->fence); + ctx->device->compute_queue->handle->submit({ si }, ctx->fence); ctx->transfer_semaphore_last_submitted = ctx->transfer_semaphore.value; } else { - std::lock_guard guard(queue_mutex); - ctx->device->compute_queue.queue.submit({}, ctx->fence); + ctx->device->compute_queue->handle->submit({}, ctx->fence); } ggml_vk_wait_for_fence(ctx); ctx->submit_pending = false; @@ -16152,6 +16514,20 @@ static bool ggml_vk_can_fuse_topk_moe(ggml_backend_vk_context * ctx, const struc return false; } break; + case TOPK_MOE_SQRT_SOFTPLUS_NORM_BIAS: + softmax = cgraph->nodes[node_idx + 0]; // really softplus + weights = cgraph->nodes[node_idx + 11]; + get_rows = cgraph->nodes[node_idx + 6]; + argsort = cgraph->nodes[node_idx + 4]; + if (ggml_get_unary_op(softmax) != GGML_UNARY_OP_SOFTPLUS) { + return false; + } + // bias is expected to be 1D + if (ggml_nrows(cgraph->nodes[node_idx + 3]->src[1]) != 1 || + !ggml_is_contiguous(cgraph->nodes[node_idx + 3]->src[1])) { + return false; + } + break; case TOPK_MOE_EARLY_SOFTMAX: softmax = cgraph->nodes[node_idx + 0]; weights = cgraph->nodes[node_idx + 4]; @@ -16175,7 +16551,9 @@ static bool ggml_vk_can_fuse_topk_moe(ggml_backend_vk_context * ctx, const struc probs = probs->src[0]; ggml_tensor * selection_probs = argsort->src[0]; - if (probs != selection_probs && mode != TOPK_MOE_SIGMOID_NORM_BIAS) { + if (probs != selection_probs && + mode != TOPK_MOE_SIGMOID_NORM_BIAS && + mode != TOPK_MOE_SQRT_SOFTPLUS_NORM_BIAS) { return false; } @@ -16441,7 +16819,9 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg vk::DebugUtilsLabelEXT dul = {}; dul.pLabelName = "ggml_backend_vk_graph_compute"; dul.color = std::array{1.0f, 1.0f, 1.0f, 1.0f}; - vk_instance.pfn_vkQueueBeginDebugUtilsLabelEXT(ctx->device->compute_queue.queue, reinterpret_cast(&dul)); + + std::lock_guard guard(*ctx->device->compute_queue->handle); + vk_instance.pfn_vkQueueBeginDebugUtilsLabelEXT(ctx->device->compute_queue->handle->queue, reinterpret_cast(&dul)); } ctx->prealloc_size_add_rms_partials_offset = 0; @@ -16541,7 +16921,7 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg // the fused result in an elementwise-way. This affects whether the memory for // the src is allowed to overlap the memory for the destination. // The array is sized to handle the largest fusion (asserted later). - bool op_srcs_fused_elementwise[12]; + bool op_srcs_fused_elementwise[13]; ctx->fused_topk_moe_mode = TOPK_MOE_COUNT; ctx->fused_topk_moe_scale = false; @@ -16652,6 +17032,15 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg ctx->fused_topk_moe_mode = TOPK_MOE_SIGMOID_NORM_BIAS; fusion_string = "TOPK_MOE_SIGMOID_NORM_BIAS"; std::fill_n(op_srcs_fused_elementwise, ctx->num_additional_fused_ops + 1, false); + } else if (ggml_can_fuse_subgraph(cgraph, i, topk_moe_sqrt_softplus_norm_bias, { i + 5, i + 11 }) && + ggml_check_edges(cgraph, i, topk_moe_sqrt_softplus_norm_bias_edges) && + ggml_vk_can_fuse_topk_moe(ctx, cgraph, i, TOPK_MOE_SQRT_SOFTPLUS_NORM_BIAS)) { + ctx->num_additional_fused_ops = topk_moe_sqrt_softplus_norm_bias.size() - 1; + // view of argsort writes to memory + ctx->fused_ops_write_mask |= 1 << 5; + ctx->fused_topk_moe_mode = TOPK_MOE_SQRT_SOFTPLUS_NORM_BIAS; + fusion_string = "TOPK_MOE_SQRT_SOFTPLUS_NORM_BIAS"; + std::fill_n(op_srcs_fused_elementwise, ctx->num_additional_fused_ops + 1, false); } else if (ggml_can_fuse_subgraph(cgraph, i, topk_moe_early_softmax, { i + 3, i + 4 }) && ggml_check_edges(cgraph, i, topk_moe_early_softmax_edges) && ggml_vk_can_fuse_topk_moe(ctx, cgraph, i, TOPK_MOE_EARLY_SOFTMAX)) { @@ -16918,6 +17307,9 @@ static void ggml_vk_graph_optimize(ggml_backend_t backend, struct ggml_cgraph * if (keep_pattern(topk_moe_sigmoid_norm_bias)) { continue; } + if (keep_pattern(topk_moe_sqrt_softplus_norm_bias)) { + continue; + } if (keep_pattern(topk_moe_early_softmax)) { continue; } @@ -16948,6 +17340,7 @@ static void ggml_vk_graph_optimize(ggml_backend_t backend, struct ggml_cgraph * // Don't pull forward nodes from fusion patterns if (match_pattern(topk_moe_early_softmax_norm, j) || match_pattern(topk_moe_sigmoid_norm_bias, j) || + match_pattern(topk_moe_sqrt_softplus_norm_bias, j) || match_pattern(topk_moe_early_softmax, j) || match_pattern(topk_moe_late_softmax, j) || match_pattern(snake_pattern, j)) { @@ -17152,6 +17545,11 @@ static void ggml_backend_vk_event_wait(ggml_backend_t backend, ggml_backend_even if (vkev->has_event) { // Wait for latest event ggml_vk_wait_events(compute_ctx, { vkev->event }); + + if (ctx->device->async_use_transfer_queue) { + vk_context transfer_ctx = ggml_vk_get_transfer_ctx(ctx); + transfer_ctx->s->wait_semaphores.push_back(vkev->tl_semaphore); + } } } @@ -17451,6 +17849,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case GGML_TYPE_F16: case GGML_TYPE_BF16: case GGML_TYPE_Q1_0: + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -17520,7 +17919,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm if (op->src[3] && op->src[3]->type != GGML_TYPE_F16) { return false; } - auto fa_kv_ok = [coopmat2](ggml_type t) { + auto fa_kv_ok = [](ggml_type t) { switch (t) { case GGML_TYPE_F32: case GGML_TYPE_F16: @@ -17530,6 +17929,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case GGML_TYPE_Q5_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q4_0: + case GGML_TYPE_IQ4_NL: return true; // TurboQuant K/V flash attention: dequant is fused into the // scalar/coopmat1 FA shaders via dequantize4() (flash_attn_dequant.glsl), @@ -17538,8 +17938,6 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case GGML_TYPE_TURBO3_0: case GGML_TYPE_TURBO4_0: return true; - case GGML_TYPE_Q1_0: - return coopmat2; default: return false; } @@ -17563,6 +17961,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case GGML_TYPE_F16: case GGML_TYPE_BF16: case GGML_TYPE_Q1_0: + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -17610,6 +18009,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case GGML_TYPE_F16: case GGML_TYPE_BF16: case GGML_TYPE_Q1_0: + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -17637,6 +18037,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case GGML_TYPE_F16: case GGML_TYPE_BF16: case GGML_TYPE_Q1_0: + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -17653,6 +18054,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case GGML_TYPE_F16: case GGML_TYPE_BF16: case GGML_TYPE_Q1_0: + case GGML_TYPE_Q2_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -17731,6 +18133,10 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case GGML_OP_OPT_STEP_ADAMW: case GGML_OP_OPT_STEP_SGD: return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32; + case GGML_OP_OUT_PROD: + return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32 + && ggml_is_contiguous(op->src[1]) && op->src[1]->type == GGML_TYPE_F32 + && op->type == GGML_TYPE_F32; case GGML_OP_LOG: case GGML_OP_TRI: case GGML_OP_DIAG: @@ -17775,12 +18181,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm return op->src[0]->type == op->src[1]->type && op->src[0]->type == op->type && (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_I32); case GGML_OP_CONCAT: { - if (op->src[0]->type != op->src[1]->type || op->src[0]->type != op->type) { - return false; - } - const size_t type_size = ggml_type_size(op->type); - return ggml_blck_size(op->type) == 1 && - (type_size == 1 || type_size == 2 || type_size == 4 || type_size == 8); + return ggml_vk_concat_supported(op->src[0], op->src[1], op); } case GGML_OP_ADD1: return (op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32) @@ -17849,6 +18250,8 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case GGML_OP_CONV_2D_DW: return (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) && op->src[1]->type == GGML_TYPE_F32; + case GGML_OP_POOL_1D: + return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32; case GGML_OP_POOL_2D: return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32; case GGML_OP_TURBO_WHT: @@ -17856,6 +18259,9 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case GGML_OP_RWKV_WKV6: case GGML_OP_RWKV_WKV7: return true; // all inputs are contiguous, see ggml.c + case GGML_OP_GATED_LINEAR_ATTN: + // the shader block size is hardcoded to head_size 64 + return op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32 && op->src[0]->ne[0] == 64; case GGML_OP_GATED_DELTA_NET: { const uint32_t S_v = op->src[2]->ne[0]; @@ -17921,10 +18327,17 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case GGML_OP_CONV_2D: case GGML_OP_CONV_TRANSPOSE_2D: { + const bool transpose = op->op == GGML_OP_CONV_TRANSPOSE_2D; + const int64_t cout = !transpose ? op->src[0]->ne[3] : op->src[0]->ne[2]; + const int64_t cin = !transpose ? op->src[0]->ne[2] : op->src[0]->ne[3]; + // Channel-contiguous format is not supported yet. return ((op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) && + (op->src[0]->nb[0] == sizeof(float) || op->src[0]->nb[0] == sizeof(ggml_fp16_t) ) && op->src[1]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32 && + cout == op->ne[2] && + cin == op->src[1]->ne[2] && ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]) && ggml_is_contiguous(op)); @@ -18309,6 +18722,22 @@ static uint32_t ggml_vk_intel_shader_core_count(const vk::PhysicalDevice& vkdev) } } +static bool ggml_vk_intel_windows_driver_equals_or_newer_than(uint32_t driver_version, uint32_t threshold_major, uint32_t threshold_minor) { +#if defined(_WIN32) + // Intel Windows encodes xxx.yyyy as [31:14].[13:0]. + const uint32_t major = driver_version >> 14; + const uint32_t minor = driver_version & 0x3fff; + + return major > threshold_major || (major == threshold_major && minor >= threshold_minor); +#else + GGML_UNUSED(driver_version); + GGML_UNUSED(threshold_major); + GGML_UNUSED(threshold_minor); + return true; +#endif +} + + // checks #ifdef GGML_VULKAN_CHECK_RESULTS @@ -18763,6 +19192,13 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph * const int32_t oc = tensor->op_params[1]; const int32_t p0 = tensor->op_params[2]; tensor_clone = ggml_col2im_1d(ggml_ctx, src_clone[0], stride, oc, p0); + } else if (tensor->op == GGML_OP_POOL_1D) { + enum ggml_op_pool op = static_cast(tensor->op_params[0]); + const int32_t k0 = tensor->op_params[1]; + const int32_t s0 = tensor->op_params[2]; + const int32_t p0 = tensor->op_params[3]; + + tensor_clone = ggml_pool_1d(ggml_ctx, src_clone[0], op, k0, s0, p0); } else if (tensor->op == GGML_OP_POOL_2D) { enum ggml_op_pool op = static_cast(tensor->op_params[0]); const int32_t k0 = tensor->op_params[1]; @@ -18815,6 +19251,10 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph * } else if (tensor->op == GGML_OP_RWKV_WKV7) { tensor_clone = ggml_rwkv_wkv7(ggml_ctx, src_clone[0], src_clone[1], src_clone[2], src_clone[3], src_clone[4], src_clone[5], src_clone[6]); + } else if (tensor->op == GGML_OP_GATED_LINEAR_ATTN) { + const float * op_params = (const float *)tensor->op_params; + tensor_clone = ggml_gated_linear_attn(ggml_ctx, src_clone[0], src_clone[1], + src_clone[2], src_clone[3], src_clone[4], op_params[0]); } else if (tensor->op == GGML_OP_GATED_DELTA_NET) { tensor_clone = ggml_gated_delta_net(ggml_ctx, src_clone[0], src_clone[1], src_clone[2], src_clone[3], src_clone[4], src_clone[5], diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/copy_to_quant.comp b/ggml/src/ggml-vulkan/vulkan-shaders/copy_to_quant.comp index e312b055734a..70a236a3e046 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/copy_to_quant.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/copy_to_quant.comp @@ -317,6 +317,36 @@ uint tq4_choose_index(float val) { #endif // defined(DATA_A_TQ4_1S) +#if defined(DATA_A_Q2_0) +uint quantize_q2_0(float x) +{ + const int q = int(x >= 0.0f ? floor(x + 0.5f) : ceil(x - 0.5f)) + 1; + return uint(clamp(q, 0, 3)); +} + +void quantize(uint dst_idx, uint src_idx) +{ + float amax = 0.0f; + + [[unroll]] for (int j = 0; j < QUANT_K_Q2_0; ++j) { + amax = max(amax, abs(float(data_s[src_idx + j]))); + } + + const float d = amax; + const float id = d != 0.0f ? 1.0f / d : 0.0f; + + data_q[dst_idx].d = float16_t(d); + + [[unroll]] for (int j = 0; j < QUANT_K_Q2_0 / 4; ++j) { + const uint q0 = quantize_q2_0(float(data_s[src_idx + 4*j ]) * id); + const uint q1 = quantize_q2_0(float(data_s[src_idx + 4*j + 1]) * id); + const uint q2 = quantize_q2_0(float(data_s[src_idx + 4*j + 2]) * id); + const uint q3 = quantize_q2_0(float(data_s[src_idx + 4*j + 3]) * id); + data_q[dst_idx].qs[j] = uint8_t(q0 | (q1 << 2u) | (q2 << 4u) | (q3 << 6u)); + } +} +#endif + #if defined(DATA_A_IQ4_NL) uint best_index(float x) { if (x <= kvalues_iq4nl[0]) return 0; diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.glsl index a7feec237654..b573ba11ce3a 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.glsl +++ b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.glsl @@ -143,6 +143,17 @@ vec4 dequantize4(uint ib, uint iqs, uint a_offset) { } #endif +#if defined(DATA_A_Q2_0) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const uint bits = uint(data_a[a_offset + ib].qs[iqs / 4u]) >> (2u * (iqs % 4u)); + return vec2(bits & 3u, (bits >> 2u) & 3u) - 1.0f; +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + const uint bits = uint(data_a[a_offset + ib].qs[iqs / 4u]); + return vec4(bits & 3u, (bits >> 2u) & 3u, (bits >> 4u) & 3u, bits >> 6u) - 1.0f; +} +#endif + #if defined(DATA_A_IQ1_S) vec2 dequantize(uint ib, uint iqs, uint a_offset) { const uint ib32 = iqs / 32; @@ -547,7 +558,7 @@ vec2 get_dm(uint ib, uint a_offset) { } #endif -#if defined(DATA_A_Q4_0) || defined(DATA_A_Q5_0) || defined(DATA_A_Q8_0) || defined(DATA_A_IQ1_S) || defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_XS) || defined(DATA_A_IQ4_NL) +#if defined(DATA_A_Q2_0) || defined(DATA_A_Q4_0) || defined(DATA_A_Q5_0) || defined(DATA_A_Q8_0) || defined(DATA_A_IQ1_S) || defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_XS) || defined(DATA_A_IQ4_NL) vec2 get_dm(uint ib, uint a_offset) { return vec2(float(data_a[a_offset + ib].d), 0); } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.glsl index d0a66f170b15..ebd9ef164e91 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.glsl +++ b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.glsl @@ -46,6 +46,26 @@ f16vec4 dequantFuncQ1_0_v(const in decodeBufQ1_0 bl, const in uint blockCoords[2 (qs_nib & 8u) != 0u ? d : md); } +layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufQ2_0 { + block_q2_0 block; +}; + +float16_t dequantFuncQ2_0(const in decodeBufQ2_0 bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const float16_t d = bl.block.d; + const uint idx = coordInBlock[1]; + const uint bits = uint(bl.block.qs[idx >> 2]) >> (2u * (idx & 3u)); + return (float16_t(bits & 3u) - float16_t(1.0)) * d; +} + +f16vec4 dequantFuncQ2_0_v(const in decodeBufQ2_0 bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const float16_t d = bl.block.d; + const uint idx = coordInBlock[1]; + const uint bits = uint(bl.block.qs[idx >> 2]); + return f16vec4((vec4(bits & 3u, (bits >> 2u) & 3u, (bits >> 4u) & 3u, bits >> 6u) - 1.0f) * float(d)); +} + layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufQ4_0 { block_q4_0_packed16 block; }; @@ -1357,6 +1377,9 @@ float16_t dequantFuncTURBO3_0(const in decodeBufTURBO3_0 bl, const in uint block #if defined(DATA_A_Q1_0) #define dequantFuncA dequantFuncQ1_0 #define dequantFuncA_v dequantFuncQ1_0_v +#elif defined(DATA_A_Q2_0) +#define dequantFuncA dequantFuncQ2_0 +#define dequantFuncA_v dequantFuncQ2_0_v #elif defined(DATA_A_Q4_0) #define dequantFuncA dequantFuncQ4_0 #define dequantFuncA_v dequantFuncQ4_0_v diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q2_0.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q2_0.comp new file mode 100644 index 000000000000..0294e6eeea0f --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q2_0.comp @@ -0,0 +1,29 @@ +#version 450 + +#include "dequant_head.glsl" + +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {block_q2_0 data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +void main() { + const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; + + const uint tid = gl_LocalInvocationID.x % 64; + const uint il = tid / 4; + const uint ir = tid % 4; + const uint ib = 4*i + ir; + if (ib >= p.nel / QUANT_K_Q2_0) { + return; + } + + const uint b_idx = 256*i + QUANT_K_Q2_0*ir + 4*il; + const uint bits = uint(data_a[ib].qs[il]); + const float d = float(data_a[ib].d); + + data_b[b_idx ] = D_TYPE(d * (float(bits & 3u) - 1.0f)); + data_b[b_idx + 1] = D_TYPE(d * (float((bits >> 2u) & 3u) - 1.0f)); + data_b[b_idx + 2] = D_TYPE(d * (float((bits >> 4u) & 3u) - 1.0f)); + data_b[b_idx + 3] = D_TYPE(d * (float(bits >> 6u) - 1.0f)); +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp index a8d5b5ca14a1..7b89c509a6e2 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp @@ -84,7 +84,9 @@ shared vec4 occupancy_limiter[LIMIT_OCCUPANCY_SHMEM > 0 ? LIMIT_OCCUPANCY_SHMEM void main() { #ifdef NEEDS_INIT_IQ_SHMEM - init_iq_shmem(gl_WorkGroupSize); + if (fa_type_needs_shmem(FaTypeK) || fa_type_needs_shmem(FaTypeV)) { + init_iq_shmem(gl_WorkGroupSize); + } #endif init_indices(); diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl index 3bdf94b1def2..9c0a92b58f41 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl +++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl @@ -97,8 +97,8 @@ layout (binding = 6) readonly buffer MO {uint32_t data_mask_opt[];}; #define FA_TYPE_Q5_0 6u #define FA_TYPE_Q5_1 7u #define FA_TYPE_Q8_0 8u +#define FA_TYPE_IQ4_NL 20u #define FA_TYPE_BF16 30u -#define FA_TYPE_Q1_0 41u #define FA_TYPE_TURBO2_0 42u #define FA_TYPE_TURBO3_0 43u #define FA_TYPE_TURBO4_0 44u @@ -123,11 +123,11 @@ uint fa_block_elems(uint ty) { case FA_TYPE_Q5_0: return uint(QUANT_K_Q5_0); case FA_TYPE_Q5_1: return uint(QUANT_K_Q5_1); case FA_TYPE_Q8_0: return uint(QUANT_K_Q8_0); + case FA_TYPE_IQ4_NL: return uint(QUANT_K_IQ4_NL); case FA_TYPE_BF16: return 1u; - case FA_TYPE_Q1_0: return uint(QUANT_K_Q1_0); // cm2-only, harmless elsewhere - case 42u: return uint(QUANT_K_TURBO2_0); // GGML_TYPE_TURBO2_0 - case 43u: return uint(QUANT_K_TURBO3_0); // GGML_TYPE_TURBO3_0 - case 44u: return uint(QUANT_K_TURBO4_0); // GGML_TYPE_TURBO4_0 + case FA_TYPE_TURBO2_0: return uint(QUANT_K_TURBO2_0); + case FA_TYPE_TURBO3_0: return uint(QUANT_K_TURBO3_0); + case FA_TYPE_TURBO4_0: return uint(QUANT_K_TURBO4_0); default: return 1u; } } @@ -146,6 +146,13 @@ uint fa_quant_r_mmq(uint ty) { } } +bool fa_type_needs_shmem(uint ty) { + switch (ty) { + case FA_TYPE_IQ4_NL: return true; + default: return false; + } +} + // These can't be `const` globals because GLSL forbids function calls in global // const initializers, even when the spec constants would let the driver fold // them. Macros expand at the use site and fold after specialization. diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp index c1f02e05d3c1..f589439d9b91 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp @@ -66,7 +66,9 @@ shared ACC_TYPE slope[Br]; void main() { #ifdef NEEDS_INIT_IQ_SHMEM - init_iq_shmem(gl_WorkGroupSize); + if (fa_type_needs_shmem(FaTypeK) || fa_type_needs_shmem(FaTypeV)) { + init_iq_shmem(gl_WorkGroupSize); + } #endif init_indices(); diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp index b9c03fe499d4..317411153087 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp @@ -46,7 +46,7 @@ float16_t faDecodeK(const decodeBufFA_K bl_in, const uint blockCoords[2], const case FA_TYPE_Q5_0: return dequantFuncQ5_0(decodeBufQ5_0(bl_in), blockCoords, coordInBlock); case FA_TYPE_Q5_1: return dequantFuncQ5_1(decodeBufQ5_1(bl_in), blockCoords, coordInBlock); case FA_TYPE_Q8_0: return dequantFuncQ8_0(decodeBufQ8_0(bl_in), blockCoords, coordInBlock); - case FA_TYPE_Q1_0: return dequantFuncQ1_0(decodeBufQ1_0(bl_in), blockCoords, coordInBlock); + case FA_TYPE_IQ4_NL: return dequantFuncIQ4_NL(decodeBufIQ4_NL(bl_in), blockCoords, coordInBlock); default: return float16_t(0); } } @@ -59,7 +59,7 @@ float16_t faDecodeV(const decodeBufFA_V bl_in, const uint blockCoords[2], const case FA_TYPE_Q5_0: return dequantFuncQ5_0(decodeBufQ5_0(bl_in), blockCoords, coordInBlock); case FA_TYPE_Q5_1: return dequantFuncQ5_1(decodeBufQ5_1(bl_in), blockCoords, coordInBlock); case FA_TYPE_Q8_0: return dequantFuncQ8_0(decodeBufQ8_0(bl_in), blockCoords, coordInBlock); - case FA_TYPE_Q1_0: return dequantFuncQ1_0(decodeBufQ1_0(bl_in), blockCoords, coordInBlock); + case FA_TYPE_IQ4_NL: return dequantFuncIQ4_NL(decodeBufIQ4_NL(bl_in), blockCoords, coordInBlock); default: return float16_t(0); } } @@ -67,26 +67,26 @@ float16_t faDecodeV(const decodeBufFA_V bl_in, const uint blockCoords[2], const // V=4 vector decode for K/V; dispatches to per-format _v decoders. f16vec4 faDecodeKVector(const decodeBufFA_K bl_in, const uint blockCoords[2], const uint coordInBlock[2]) { switch (FaTypeK) { - case 0u: return f16vec4(decodeBufF32(bl_in).block); - case 2u: return dequantFuncQ4_0_v(decodeBufQ4_0(bl_in), blockCoords, coordInBlock); - case 3u: return dequantFuncQ4_1_v(decodeBufQ4_1(bl_in), blockCoords, coordInBlock); - case 6u: return dequantFuncQ5_0_v(decodeBufQ5_0(bl_in), blockCoords, coordInBlock); - case 7u: return dequantFuncQ5_1_v(decodeBufQ5_1(bl_in), blockCoords, coordInBlock); - case 8u: return dequantFuncQ8_0_v(decodeBufQ8_0(bl_in), blockCoords, coordInBlock); - case 41u: return dequantFuncQ1_0_v(decodeBufQ1_0(bl_in), blockCoords, coordInBlock); + case FA_TYPE_F32: return f16vec4(decodeBufF32(bl_in).block); + case FA_TYPE_Q4_0: return dequantFuncQ4_0_v(decodeBufQ4_0(bl_in), blockCoords, coordInBlock); + case FA_TYPE_Q4_1: return dequantFuncQ4_1_v(decodeBufQ4_1(bl_in), blockCoords, coordInBlock); + case FA_TYPE_Q5_0: return dequantFuncQ5_0_v(decodeBufQ5_0(bl_in), blockCoords, coordInBlock); + case FA_TYPE_Q5_1: return dequantFuncQ5_1_v(decodeBufQ5_1(bl_in), blockCoords, coordInBlock); + case FA_TYPE_Q8_0: return dequantFuncQ8_0_v(decodeBufQ8_0(bl_in), blockCoords, coordInBlock); + case FA_TYPE_IQ4_NL: return dequantFuncIQ4_NL_v(decodeBufIQ4_NL(bl_in), blockCoords, coordInBlock); default: return f16vec4(0); } } f16vec4 faDecodeVVector(const decodeBufFA_V bl_in, const uint blockCoords[2], const uint coordInBlock[2]) { switch (FaTypeV) { - case 0u: return f16vec4(decodeBufF32(bl_in).block); - case 2u: return dequantFuncQ4_0_v(decodeBufQ4_0(bl_in), blockCoords, coordInBlock); - case 3u: return dequantFuncQ4_1_v(decodeBufQ4_1(bl_in), blockCoords, coordInBlock); - case 6u: return dequantFuncQ5_0_v(decodeBufQ5_0(bl_in), blockCoords, coordInBlock); - case 7u: return dequantFuncQ5_1_v(decodeBufQ5_1(bl_in), blockCoords, coordInBlock); - case 8u: return dequantFuncQ8_0_v(decodeBufQ8_0(bl_in), blockCoords, coordInBlock); - case 41u: return dequantFuncQ1_0_v(decodeBufQ1_0(bl_in), blockCoords, coordInBlock); + case FA_TYPE_F32: return f16vec4(decodeBufF32(bl_in).block); + case FA_TYPE_Q4_0: return dequantFuncQ4_0_v(decodeBufQ4_0(bl_in), blockCoords, coordInBlock); + case FA_TYPE_Q4_1: return dequantFuncQ4_1_v(decodeBufQ4_1(bl_in), blockCoords, coordInBlock); + case FA_TYPE_Q5_0: return dequantFuncQ5_0_v(decodeBufQ5_0(bl_in), blockCoords, coordInBlock); + case FA_TYPE_Q5_1: return dequantFuncQ5_1_v(decodeBufQ5_1(bl_in), blockCoords, coordInBlock); + case FA_TYPE_Q8_0: return dequantFuncQ8_0_v(decodeBufQ8_0(bl_in), blockCoords, coordInBlock); + case FA_TYPE_IQ4_NL: return dequantFuncIQ4_NL_v(decodeBufIQ4_NL(bl_in), blockCoords, coordInBlock); default: return f16vec4(0); } } @@ -169,6 +169,12 @@ ACC_TYPE perElemOpNonGqaSplitKStoreCol0(const in uint32_t r, const in uint32_t c } void main() { +#ifdef NEEDS_INIT_IQ_SHMEM + if (fa_type_needs_shmem(FaTypeK) || fa_type_needs_shmem(FaTypeV)) { + init_iq_shmem(gl_WorkGroupSize); + } +#endif + init_indices(); tensorLayoutNV<2, gl_CooperativeMatrixClampModeConstantNV> tensorLayoutQ = createTensorLayoutNV(2, gl_CooperativeMatrixClampModeConstantNV); @@ -302,7 +308,7 @@ void main() { coopmat K_T; uint32_t k_offset = ik2*p.nb12 + ik3*p.nb13; - // F16: bs_k==1 (direct load). F32: bs_k==4 (vec4 / dequantFuncF32). Q4/Q8 family: bs_k==32. Q1_0: bs_k==128. + // F16: bs_k==1 (direct load). F32: bs_k==4 (vec4 / dequantFuncF32). Quantized types: bs_k==32. #if defined(BFLOAT16) coopMatLoadTensorNV(K_T, data_k, k_offset, sliceTensorLayoutNV(tensorLayoutK, j * Bc, Bc, 0, HSK_pad), tensorViewTranspose); #else diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_dequant.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_dequant.glsl index 4e347005941c..e0c6d0b39d22 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_dequant.glsl +++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_dequant.glsl @@ -28,6 +28,8 @@ layout (binding = 1) readonly buffer K_PACKED_Q5_1 { block_q5_1_packed16 data[]; layout (binding = 2) readonly buffer V_PACKED_Q5_1 { block_q5_1_packed16 data[]; } v_packed_q5_1; layout (binding = 1) readonly buffer K_PACKED_Q8_0 { block_q8_0_packed16 data[]; } k_packed_q8_0; layout (binding = 2) readonly buffer V_PACKED_Q8_0 { block_q8_0_packed16 data[]; } v_packed_q8_0; +layout (binding = 1) readonly buffer K_PACKED_IQ4_NL { block_iq4_nl_packed16 data[]; } k_packed_iq4_nl; +layout (binding = 2) readonly buffer V_PACKED_IQ4_NL { block_iq4_nl_packed16 data[]; } v_packed_iq4_nl; #endif // !DATA_A_TURBO3_0 layout (binding = 1) readonly buffer K_PACKED_BF16 { u16vec4 data[]; } k_packed_bf16; @@ -116,6 +118,17 @@ layout (binding = 1) readonly buffer K_PACKED_Q5_1_P32 { block_q5_1_packed32 dat return FLOAT_TYPE(BUF.data[a_offset + ib].d) * FLOAT_TYPEV4(v0.x, v0.y, v1.x, v1.y); \ } +#define FA_DEQUANT4_IQ4_NL(BUF) { \ + const uint shift = (iqs & 0x10) >> 2; \ + const uint qs_i = (iqs & 0xC) >> 1; \ + const uint qsw = uint(BUF.data[a_offset + ib].qs[qs_i]) \ + | (uint(BUF.data[a_offset + ib].qs[qs_i + 1u]) << 16); \ + const FLOAT_TYPE d = FLOAT_TYPE(BUF.data[a_offset + ib].d); \ + const u8vec4 q = unpack8((qsw >> shift) & 0x0F0F0F0Fu); \ + return d * FLOAT_TYPEV4(kvalues_iq4nl[q.x], kvalues_iq4nl[q.y], \ + kvalues_iq4nl[q.z], kvalues_iq4nl[q.w]); \ +} + #define FA_DEQUANT4_BF16(BUF) \ return FLOAT_TYPEV4(bf16_to_fp32(uvec4(BUF.data[(a_offset + ib) / 4]))); @@ -177,6 +190,7 @@ FLOAT_TYPEV4 dequantize4(uint ib, uint iqs, uint a_offset, uint binding_idx) { case FA_TYPE_Q5_0: FA_DEQUANT4_Q5_0(k_packed_q5_0) case FA_TYPE_Q5_1: FA_DEQUANT4_Q5_1(k_packed_q5_1) case FA_TYPE_Q8_0: FA_DEQUANT4_Q8_0(k_packed_q8_0) + case FA_TYPE_IQ4_NL: FA_DEQUANT4_IQ4_NL(k_packed_iq4_nl) case FA_TYPE_BF16: FA_DEQUANT4_BF16(k_packed_bf16) case FA_TYPE_TURBO2_0: FA_DEQUANT4_TURBO2_0(k_packed_turbo2_0) case FA_TYPE_TURBO3_0: FA_DEQUANT4_TURBO3_0(k_packed_turbo3_0) @@ -190,6 +204,7 @@ FLOAT_TYPEV4 dequantize4(uint ib, uint iqs, uint a_offset, uint binding_idx) { case FA_TYPE_Q5_0: FA_DEQUANT4_Q5_0(v_packed_q5_0) case FA_TYPE_Q5_1: FA_DEQUANT4_Q5_1(v_packed_q5_1) case FA_TYPE_Q8_0: FA_DEQUANT4_Q8_0(v_packed_q8_0) + case FA_TYPE_IQ4_NL: FA_DEQUANT4_IQ4_NL(v_packed_iq4_nl) case FA_TYPE_BF16: FA_DEQUANT4_BF16(v_packed_bf16) case FA_TYPE_TURBO2_0: FA_DEQUANT4_TURBO2_0(v_packed_turbo2_0) case FA_TYPE_TURBO3_0: FA_DEQUANT4_TURBO3_0(v_packed_turbo3_0) diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/gla.comp b/ggml/src/ggml-vulkan/vulkan-shaders/gla.comp new file mode 100644 index 000000000000..b3387616b642 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/gla.comp @@ -0,0 +1,82 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : require + +#define BLOCK_SIZE 64 +layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; + +layout(push_constant) uniform Parameters { + uint B; + uint T; + uint C; + uint H; + float scale; +}; + +layout(binding = 0) readonly buffer KBuf { A_TYPE k[]; }; +layout(binding = 1) readonly buffer VBuf { A_TYPE v[]; }; +layout(binding = 2) readonly buffer QBuf { A_TYPE q[]; }; +layout(binding = 3) readonly buffer GBuf { A_TYPE g[]; }; +layout(binding = 4) readonly buffer StateBuf { A_TYPE state_in[]; }; +layout(binding = 5) buffer DstBuf { A_TYPE dst[]; }; + +shared A_TYPE _k[BLOCK_SIZE], _q[BLOCK_SIZE], _g[BLOCK_SIZE]; + +void main() { + const uint head_size = BLOCK_SIZE; + const uint batch_id = gl_WorkGroupID.x / H; + const uint head_id = gl_WorkGroupID.x % H; + const uint tid = gl_LocalInvocationID.x; + + const uint state_size = C * head_size; + const uint n_seq_tokens = T / B; + + if (batch_id >= B || head_id >= H) { + return; + } + + // state[i] holds column tid of this head's state matrix: S[i][tid] + A_TYPE state[BLOCK_SIZE]; + [[unroll]] for (uint i = 0; i < head_size; i++) { + state[i] = state_in[batch_id * state_size + head_id * head_size * head_size + + i * head_size + tid]; + } + + const uint start_t = batch_id * n_seq_tokens * C + head_id * head_size + tid; + const uint end_t = (batch_id + 1) * n_seq_tokens * C + head_id * head_size + tid; + + for (uint t = start_t; t < end_t; t += C) { + barrier(); + _k[tid] = k[t]; + _q[tid] = q[t]; + _g[tid] = g[t]; + barrier(); + + const A_TYPE v_val = v[t]; + A_TYPE y = 0.0; + + [[unroll]] for (uint i = 0; i < head_size; i += 4) { + vec4 k_vec = vec4(_k[i], _k[i+1], _k[i+2], _k[i+3]); + vec4 q_vec = vec4(_q[i], _q[i+1], _q[i+2], _q[i+3]); + vec4 g_vec = vec4(_g[i], _g[i+1], _g[i+2], _g[i+3]); + vec4 s_vec = vec4(state[i], state[i+1], state[i+2], state[i+3]); + + vec4 kv = k_vec * v_val; + + s_vec = s_vec * g_vec + kv; + y += dot(q_vec, s_vec); + + state[i] = s_vec.x; + state[i+1] = s_vec.y; + state[i+2] = s_vec.z; + state[i+3] = s_vec.w; + } + + dst[t] = y * scale; + } + + [[unroll]] for (uint i = 0; i < head_size; i++) { + dst[T * C + batch_id * state_size + head_id * head_size * head_size + + i * head_size + tid] = state[i]; + } +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq.comp index 7bbee577fb74..18d441ead40e 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq.comp @@ -11,10 +11,10 @@ layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; -#if defined(DATA_A_QUANT_LEGACY) || defined(DATA_A_MXFP4) -#define K_PER_ITER 8 -#elif defined(DATA_A_QUANT_K) +#if defined(DATA_A_Q2_0) || defined(DATA_A_QUANT_K) #define K_PER_ITER 16 +#elif defined(DATA_A_QUANT_LEGACY) || defined(DATA_A_MXFP4) +#define K_PER_ITER 8 #elif defined(DATA_A_IQ1_S) || defined(DATA_A_IQ1_M) #define K_PER_ITER 32 #else diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq_funcs.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq_funcs.glsl index 73cf9c799554..a5403ac82121 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq_funcs.glsl +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq_funcs.glsl @@ -4,7 +4,11 @@ #include "types.glsl" -#if defined(DATA_A_Q4_0) || defined(DATA_A_Q5_0) || defined(DATA_A_Q8_0) || defined(DATA_A_IQ1_S) || defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_XS) || defined(DATA_A_IQ4_NL) +#if defined(DATA_A_Q2_0) +FLOAT_TYPE get_dm(uint ib) { + return FLOAT_TYPE(data_a[ib / 2].d); +} +#elif defined(DATA_A_Q4_0) || defined(DATA_A_Q5_0) || defined(DATA_A_Q8_0) || defined(DATA_A_IQ1_S) || defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_XS) || defined(DATA_A_IQ4_NL) FLOAT_TYPE get_dm(uint ib) { return FLOAT_TYPE(data_a[ib].d); } @@ -30,6 +34,27 @@ FLOAT_TYPEV2 get_dm(uint ib) { #endif // Each iqs value maps to a 32-bit integer +#if defined(DATA_A_Q2_0) +uint unpack_q2_0(uint bits) { + // Move bit pairs [1:0], [3:2], [5:4], [7:6] to [1:0], [9:8], [17:16], [25:24]. + bits &= 0xffu; + bits = (bits | (bits << 12u)) & 0x000f000fu; + return (bits | (bits << 6u)) & 0x03030303u; +} + +i32vec4 repack4(uint ib, uint iqs) { + const uint qs_idx = (ib & 1u) * 4u + iqs * 2u; + const uint bits = pack32(u16vec2(data_a_packed16[ib / 2].qs[qs_idx], + data_a_packed16[ib / 2].qs[qs_idx + 1])); + return i32vec4(unpack_q2_0(bits), unpack_q2_0(bits >> 8u), + unpack_q2_0(bits >> 16u), unpack_q2_0(bits >> 24u)); +} + +FLOAT_TYPE mul_q8_1(const int32_t q_sum, const float da, const vec2 dsb, const int32_t sum_divisor) { + return FLOAT_TYPE(da * (float(q_sum) * dsb.x - dsb.y / float(sum_divisor))); +} +#endif + #if defined(DATA_A_Q4_0) // 2-byte loads for Q4_0 blocks (18 bytes) i32vec2 repack(uint ib, uint iqs) { @@ -132,7 +157,19 @@ FLOAT_TYPE mul_q8_1(const int32_t q_sum, const float da, const vec2 dsb, const i } #endif -#if defined(DATA_A_QUANT_LEGACY) || defined(DATA_A_MXFP4) +#if defined(DATA_A_Q2_0) +FLOAT_TYPE mmvq_dot_product(const uint ib_a, const uint iqs) { + int32_t q_sum = 0; + const i32vec4 qs_a = repack4(ib_a, iqs); + q_sum += dotPacked4x8EXT(qs_a.x, cache_b_qs[0]); + q_sum += dotPacked4x8EXT(qs_a.y, cache_b_qs[1]); + q_sum += dotPacked4x8EXT(qs_a.z, cache_b_qs[2]); + q_sum += dotPacked4x8EXT(qs_a.w, cache_b_qs[3]); + + // 16 quants per call => divide sums by 32/16 = 2 + return mul_q8_1(q_sum, get_dm(ib_a), cache_b_ds, 2); +} +#elif defined(DATA_A_QUANT_LEGACY) || defined(DATA_A_MXFP4) FLOAT_TYPE mmvq_dot_product(const uint ib_a, const uint iqs) { int32_t q_sum = 0; #if QUANT_R == 2 diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl index 17c436a2f826..31dfefec8f94 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl @@ -151,6 +151,18 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin buf_a[buf_idx + 1] = FLOAT_TYPEV2((bits & 0x04u) != 0u ? d : -d, (bits & 0x08u) != 0u ? d : -d); buf_a[buf_idx + 2] = FLOAT_TYPEV2((bits & 0x10u) != 0u ? d : -d, (bits & 0x20u) != 0u ? d : -d); buf_a[buf_idx + 3] = FLOAT_TYPEV2((bits & 0x40u) != 0u ? d : -d, (bits & 0x80u) != 0u ? d : -d); +#elif defined(DATA_A_Q2_0) + const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row; + const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2; + + const uint ib = idx / 16; + const uint iqs = idx & 0xfu; + + const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib].d); + const uint bits = uint(data_a[ib].qs[iqs]); + + buf_a[buf_idx ] = d * (FLOAT_TYPEV2(bits & 3u, (bits >> 2u) & 3u) - FLOAT_TYPEV2(1.0f)); + buf_a[buf_idx + 1] = d * (FLOAT_TYPEV2((bits >> 4u) & 3u, bits >> 6u) - FLOAT_TYPEV2(1.0f)); #elif defined(DATA_A_Q2_K) const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row; const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2; diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_funcs.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_funcs.glsl index 59931b04b941..24da4f715f83 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_funcs.glsl +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_funcs.glsl @@ -6,6 +6,40 @@ // Each iqs value maps to a 32-bit integer +#if defined(DATA_A_Q2_0) +void block_a_to_shmem(const uint buf_ib, const uint ib, const uint iqs) { + const uint block_idx = ib / 2; + const uint byte_idx = (ib & 1u) * 8u + iqs; + const uint bits = uint(data_a[block_idx].qs[byte_idx]); + buf_a[buf_ib].qs[iqs] = pack32(i8vec4( + int8_t(bits & 3u), + int8_t((bits >> 2u) & 3u), + int8_t((bits >> 4u) & 3u), + int8_t(bits >> 6u))); + + if (iqs == 0) { + buf_a[buf_ib].dm = FLOAT_TYPE(data_a[block_idx].d); + } +} + +void block_a_to_registers(const uint reg_ib, const uint buf_ib) { + cache_a[reg_ib].dm = buf_a[buf_ib].dm; + + [[unroll]] for (uint iqs = 0; iqs < 8; ++iqs) { + cache_a[reg_ib].qs[iqs] = buf_a[buf_ib].qs[iqs]; + } +} + +ACC_TYPE mmq_dot_product(const uint ib_a) { + int32_t q_sum = 0; + [[unroll]] for (uint iqs = 0; iqs < 8; ++iqs) { + q_sum += dotPacked4x8EXT(cache_a[ib_a].qs[iqs], cache_b.qs[iqs]); + } + + return ACC_TYPE(float(cache_a[ib_a].dm) * (float(q_sum) * float(cache_b.ds.x) - float(cache_b.ds.y))); +} +#endif + #if defined(DATA_A_Q4_0) || defined(DATA_A_Q4_1) // 2-byte loads for Q4_0 blocks (18 bytes) // 4-byte loads for Q4_1 blocks (20 bytes) diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_shmem_types.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_shmem_types.glsl index 79c933f40cf2..2b7adcb6c2fc 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_shmem_types.glsl +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_shmem_types.glsl @@ -13,6 +13,12 @@ struct block_a_cache { uint32_t qs[16/4]; FLOAT_TYPE dm; }; +#elif defined(DATA_A_Q2_0) +#define QUANT_R_MMQ 1 +struct block_a_cache { + int32_t qs[8]; + FLOAT_TYPE dm; +}; #elif defined(DATA_A_Q4_1) #define QUANT_R_MMQ 2 struct block_a_cache { diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/out_prod.comp b/ggml/src/ggml-vulkan/vulkan-shaders/out_prod.comp new file mode 100644 index 000000000000..1973169960b4 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/out_prod.comp @@ -0,0 +1,59 @@ +#version 450 + +#extension GL_EXT_shader_16bit_storage : require + +layout (push_constant) uniform parameter +{ + uint ne; + uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03; + uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13; + uint ne20; uint ne21; uint ne22; uint ne23; uint nb20; uint nb21; uint nb22; uint nb23; + uint misalign_offsets; + float param1; float param2; int param3; +} p; + +layout (binding = 0) readonly buffer A {float data_a[];}; +layout (binding = 1) readonly buffer B {float data_b[];}; +layout (binding = 2) writeonly buffer D {float data_d[];}; + +uint get_idx() { + return gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; +} + +uint get_aoffset() { return p.misalign_offsets >> 16; } +uint get_boffset() { return (p.misalign_offsets >> 8) & 0xFF; } +uint get_doffset() { return p.misalign_offsets & 0xFF; } + +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +void main() { + uint idx = get_idx(); + if (idx >= p.ne) { + return; + } + + uint tmp = idx; + uint i0 = tmp % p.ne20; tmp /= p.ne20; + uint i1 = tmp % p.ne21; tmp /= p.ne21; + uint i2 = tmp % p.ne22; tmp /= p.ne22; + uint i3 = tmp; + + uint a_i0 = i0 % p.ne00; + uint a_i2 = i2 / (p.ne22 / p.ne02); + uint a_i3 = i3 / (p.ne23 / p.ne03); + + uint b_i0 = i1 % p.ne10; + uint b_i2 = i2; + uint b_i3 = i3; + + float sum = 0.0f; + uint K = p.ne01; + for (uint k = 0; k < K; k++) { + uint aoff = get_aoffset() + a_i3*p.nb03 + a_i2*p.nb02 + k*p.nb01 + a_i0*p.nb00; + uint boff = get_boffset() + b_i3*p.nb13 + b_i2*p.nb12 + k*p.nb11 + b_i0*p.nb10; + sum += data_a[aoff] * data_b[boff]; + } + + uint doff = get_doffset() + i3*p.nb23 + i2*p.nb22 + i1*p.nb21 + i0*p.nb20; + data_d[doff] = sum; +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/pool1d.comp b/ggml/src/ggml-vulkan/vulkan-shaders/pool1d.comp new file mode 100644 index 000000000000..bb87631ce363 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/pool1d.comp @@ -0,0 +1,65 @@ +#version 450 + +#include "types.glsl" + +#extension GL_EXT_shader_16bit_storage : require + +layout(push_constant) uniform parameter { + uint IL; + uint OL; + uint OC; + uint pelements; + uint op; + int k0; + int s0; + int p0; +} p; + +#define BLOCK_SIZE 512 +#define FLT_MAX 3.402823466e+38F +#define OP_POOL_MAX 0u +#define OP_POOL_AVG 1u + +layout (local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; + +layout(binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout(binding = 1) writeonly buffer D {D_TYPE data_d[];}; + +void main() { + const uint idx = gl_GlobalInvocationID.x; + if (idx >= p.pelements) { + return; + } + + const uint nc = idx / p.OL; + const uint cur_ol = idx % p.OL; + + const int start = int(cur_ol) * p.s0 - p.p0; + const int bl = max(start, 0); + const int el = min(max(start + p.k0, 0), int(p.IL)); + + const int window_size = el - bl; + const float scale = window_size > 0 ? 1.0 / float(window_size) : 0.0; + float res; + + if (p.op == OP_POOL_AVG) { + res = 0.0; + } else if (p.op == OP_POOL_MAX) { + res = -FLT_MAX; + } else { + return; + } + + #pragma unroll + for (uint i = bl; i < el; i++) { + const float cur = D_TYPE(data_a[nc * p.IL + i]); + + if (p.op == OP_POOL_AVG) { + res += cur * scale; + } else if (p.op == OP_POOL_MAX) { + res = max(res, cur); + } + } + + data_d[nc * p.OL + cur_ol] = res; +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp b/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp index ef2f202ec9b6..d219201fda0a 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp @@ -10,6 +10,7 @@ #define GATING_FUNC_SOFTMAX 0 #define GATING_FUNC_SIGMOID 1 #define GATING_FUNC_SOFTMAX_WEIGHT 2 +#define GATING_FUNC_SQRT_SOFTPLUS 3 layout (push_constant) uniform parameter { @@ -120,6 +121,13 @@ void main() { const uint expert = i + lane; probs[i / WARP_SIZE] = (n_experts % WARP_SIZE == 0 || expert < n_experts) ? 1.f / (1.f + exp(-probs[i / WARP_SIZE])) : -INFINITY; } + } else if (gating_func == GATING_FUNC_SQRT_SOFTPLUS) { + [[unroll]] + for (uint i = 0; i < n_experts; i += WARP_SIZE) { + const uint expert = i + lane; + const float val = probs[i / WARP_SIZE]; + probs[i / WARP_SIZE] = (n_experts % WARP_SIZE == 0 || expert < n_experts) ? sqrt(val > 20.0f ? val : log(1.0f + exp(val))) : -INFINITY; + } } float selection_probs[experts_per_thread]; diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/types.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/types.glsl index 153925059950..72a1d468411b 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/types.glsl +++ b/ggml/src/ggml-vulkan/vulkan-shaders/types.glsl @@ -212,6 +212,30 @@ struct block_q1_0 #define A_TYPE block_q1_0 #endif +#define QUANT_K_Q2_0 64 +#define QUANT_R_Q2_0 1 + +struct block_q2_0 +{ + float16_t d; + uint8_t qs[QUANT_K_Q2_0 / 4]; +}; + +struct block_q2_0_packed16 +{ + float16_t d; + uint16_t qs[QUANT_K_Q2_0 / 8]; +}; + +#if defined(DATA_A_Q2_0) +#define QUANT_K QUANT_K_Q2_0 +#define QUANT_R QUANT_R_Q2_0 +#define QUANT_AUXF 1 +#define A_TYPE block_q2_0 +#define A_TYPE_PACKED16 block_q2_0_packed16 +#define DATA_A_QUANT_LEGACY +#endif + #define QUANT_K_Q8_1 32 #define QUANT_R_Q8_1 1 diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp index dad46302d06a..22df8e76ce11 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp @@ -50,6 +50,7 @@ const std::vector type_names = { "f32", "f16", "q1_0", + "q2_0", "q4_0", "q4_1", "q5_0", @@ -232,7 +233,7 @@ bool is_quantized_type(const std::string& type_name) { } bool is_legacy_quant(const std::string& type_name) { - return type_name == "q4_0" || type_name == "q4_1" || type_name == "q5_0" || type_name == "q5_1" || type_name == "q8_0"; + return type_name == "q2_0" || type_name == "q4_0" || type_name == "q4_1" || type_name == "q5_0" || type_name == "q5_1" || type_name == "q8_0"; } bool is_k_quant(const std::string& type_name) { @@ -583,7 +584,7 @@ void matmul_shaders(bool fp16, MatMulIdType matmul_id_type, bool coopmat, bool c std::string load_vec_quant = "2"; if ((tname == "q1_0") || (tname == "q4_0") || (tname == "q4_1") || (tname == "q5_1") || (tname == "iq1_s") || (tname == "iq1_m") || (tname == "iq2_xxs") || (tname == "iq2_xs") || (tname == "iq2_s")) load_vec_quant = "8"; - else if ((tname == "q5_0") || (tname == "q8_0") || (tname == "q2_k") || (tname == "q4_k") || (tname == "q5_k") || (tname == "iq3_xxs") || (tname == "iq3_s") || (tname == "iq4_xs") || (tname == "iq4_nl") || (tname == "mxfp4") || (tname == "nvfp4")) + else if ((tname == "q2_0") || (tname == "q5_0") || (tname == "q8_0") || (tname == "q2_k") || (tname == "q4_k") || (tname == "q5_k") || (tname == "iq3_xxs") || (tname == "iq3_s") || (tname == "iq4_xs") || (tname == "iq4_nl") || (tname == "mxfp4") || (tname == "nvfp4")) load_vec_quant = "4"; if (tname == "bf16") { @@ -672,6 +673,8 @@ void process_shaders() { fa_base_dict["ACC_TYPE"] = fp16 && f16acc ? "float16_t" : "float"; fa_base_dict["ACC_TYPEV2"] = fp16 && f16acc ? "f16vec2" : "vec2"; fa_base_dict["ACC_TYPEV4"] = fp16 && f16acc ? "f16vec4" : "vec4"; + // Compile IQ4_NL support into all FA variants so its shared LUT is available when K or V uses it. + fa_base_dict["DATA_A_IQ4_NL"] = "1"; if (fp16 && f16acc) { fa_base_dict["ACC_TYPE_MAX"] = "float16_t(65504.0)"; } @@ -836,13 +839,13 @@ void process_shaders() { string_to_spv("cpy_transpose_16", "copy_transpose.comp", {{"A_TYPE", "uint16_t"}, {"D_TYPE", "uint16_t"}}); string_to_spv("cpy_transpose_32", "copy_transpose.comp", {{"A_TYPE", "uint"}, {"D_TYPE", "uint"}}); - for (std::string t : {"q1_0", "q4_0", "q4_1", "q5_0", "q5_1", "q8_0", "iq4_nl"}) { + for (std::string t : {"q1_0", "q2_0", "q4_0", "q4_1", "q5_0", "q5_1", "q8_0", "iq4_nl"}) { string_to_spv("cpy_f32_" + t, "copy_to_quant.comp", {{"DATA_A_" + to_uppercase(t), "1"}, {"S_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); string_to_spv("cpy_" + t + "_f32", "copy_from_quant.comp", {{"DATA_A_" + to_uppercase(t), "1"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); } for (auto src : {std::pair{"f32", "float"}, std::pair{"f16", "float16_t"}}) { - for (std::string dst : {"f32", "f16", "bf16", "q1_0", "q4_0", "q4_1", "q5_0", "q5_1", "q8_0", "iq4_nl"}) { + for (std::string dst : {"f32", "f16", "bf16", "q1_0", "q2_0", "q4_0", "q4_1", "q5_0", "q5_1", "q8_0", "iq4_nl"}) { string_to_spv("set_rows_" + std::string(src.first) + "_" + dst + "_i32", "copy_to_quant.comp", {{"SET_ROWS", "1"}, {"DATA_A_" + to_uppercase(dst), "1"}, {"B_TYPE", "uint"}, {"B_SIZE", "32"}, {"S_TYPE", src.second}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); string_to_spv("set_rows_" + std::string(src.first) + "_" + dst + "_i64", "copy_to_quant.comp", {{"SET_ROWS", "1"}, {"DATA_A_" + to_uppercase(dst), "1"}, {"B_TYPE", "uvec2"}, {"B_SIZE", "64"}, {"S_TYPE", src.second}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); } @@ -1059,6 +1062,8 @@ void process_shaders() { } } + string_to_spv("out_prod_f32", "out_prod.comp", {}); + string_to_spv("timestep_embedding_f32", "timestep_embedding.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); string_to_spv("conv_transpose_1d_f32", "conv_transpose_1d.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}); @@ -1070,10 +1075,13 @@ void process_shaders() { string_to_spv("snake_f16", "snake.comp", {{"DATA_A_F16", "1"}, {"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); string_to_spv("snake_bf16", "snake.comp", {{"DATA_A_BF16", "1"}, {"DATA_D_BF16", "1"}, {"A_TYPE", "uint16_t"}, {"D_TYPE", "uint16_t"}}); + string_to_spv("pool1d_f32", "pool1d.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); string_to_spv("pool2d_f32", "pool2d.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); string_to_spv("rwkv_wkv6_f32", "wkv6.comp", merge_maps(base_dict, {{"A_TYPE", "float"}})); + string_to_spv("gated_linear_attn_f32", "gla.comp", merge_maps(base_dict, {{"A_TYPE", "float"}})); + string_to_spv("rwkv_wkv7_f32", "wkv7.comp", merge_maps(base_dict, {{"A_TYPE", "float"}})); string_to_spv("gated_delta_net_f32", "gated_delta_net.comp", merge_maps(base_dict, {{"FLOAT_TYPE", "float"}, {"USE_SUBGROUP_ADD", "1"}, {"USE_SUBGROUP_CLUSTERED", "1"}})); diff --git a/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp b/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp index d7692363a1de..66c1c3c8977e 100644 --- a/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp +++ b/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp @@ -73,11 +73,6 @@ inline bool ggml_webgpu_tensor_equal(const ggml_tensor * a, const ggml_tensor * return a->buffer == b->buffer && ggml_webgpu_tensor_addr(a) == ggml_webgpu_tensor_addr(b); } -inline bool ggml_webgpu_tensor_overlap(const ggml_tensor * a, const ggml_tensor * b) { - return a->buffer == b->buffer && ggml_webgpu_tensor_addr(a) < ggml_webgpu_tensor_addr(b) + ggml_nbytes(b) && - ggml_webgpu_tensor_addr(b) < ggml_webgpu_tensor_addr(a) + ggml_nbytes(a); -} - struct ggml_webgpu_shader_lib_context { ggml_tensor * src0; ggml_tensor * src1; @@ -118,6 +113,11 @@ struct ggml_webgpu_binary_shader_decisions { bool src_overlap = false; }; +struct ggml_webgpu_glu_shader_decisions { + uint32_t wg_size = 0; + bool src_overlap = false; +}; + struct ggml_webgpu_processed_shader { std::string wgsl; std::string variant; @@ -133,9 +133,12 @@ struct ggml_webgpu_ssm_scan_pipeline_key { int type; int d_state; bool xbc_overlap; + bool a_overlap; + bool ids_overlap; bool operator==(const ggml_webgpu_ssm_scan_pipeline_key & other) const { - return type == other.type && d_state == other.d_state && xbc_overlap == other.xbc_overlap; + return type == other.type && d_state == other.d_state && xbc_overlap == other.xbc_overlap && + a_overlap == other.a_overlap && ids_overlap == other.ids_overlap; } }; @@ -145,6 +148,8 @@ struct ggml_webgpu_ssm_scan_pipeline_key_hash { ggml_webgpu_hash_combine(seed, key.type); ggml_webgpu_hash_combine(seed, key.d_state); ggml_webgpu_hash_combine(seed, key.xbc_overlap); + ggml_webgpu_hash_combine(seed, key.a_overlap); + ggml_webgpu_hash_combine(seed, key.ids_overlap); return seed; } }; @@ -153,6 +158,8 @@ struct ggml_webgpu_ssm_scan_shader_decisions { uint32_t wg_size; uint32_t tokens_per_tile; bool xbc_overlap = false; + bool a_overlap = false; + bool ids_overlap = false; }; /** Argsort **/ @@ -264,7 +271,7 @@ struct ggml_webgpu_row_norm_pipeline_key_hash { struct ggml_webgpu_rms_norm_mul_pipeline_key { bool inplace; // rn_src == dst bool overlap; // mul_src == dst - bool src_overlap; // rn_src == mul_src + bool src_overlap; // rn_src binding overlaps mul_src binding bool operator==(const ggml_webgpu_rms_norm_mul_pipeline_key & other) const { return inplace == other.inplace && overlap == other.overlap && src_overlap == other.src_overlap; @@ -355,6 +362,30 @@ struct ggml_webgpu_conv2d_pipeline_key_hash { } }; +// Same type fields as conv2d plus the input layout (WHCN vs CWHN). +struct ggml_webgpu_conv2d_dw_pipeline_key { + ggml_type weight_type; + ggml_type input_type; + ggml_type output_type; + bool whcn; + + bool operator==(const ggml_webgpu_conv2d_dw_pipeline_key & other) const { + return weight_type == other.weight_type && input_type == other.input_type && output_type == other.output_type && + whcn == other.whcn; + } +}; + +struct ggml_webgpu_conv2d_dw_pipeline_key_hash { + size_t operator()(const ggml_webgpu_conv2d_dw_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.weight_type); + ggml_webgpu_hash_combine(seed, key.input_type); + ggml_webgpu_hash_combine(seed, key.output_type); + ggml_webgpu_hash_combine(seed, key.whcn); + return seed; + } +}; + /** Im2Col **/ struct ggml_webgpu_im2col_pipeline_key { ggml_type input_type; @@ -560,7 +591,8 @@ struct ggml_webgpu_flash_attn_common_pipeline_key { ggml_type dst_type; uint32_t head_dim_qk; uint32_t head_dim_v; - bool kv_direct; + bool k_direct; + bool v_direct; bool kv_overlap; bool has_mask; bool has_sinks; @@ -569,8 +601,9 @@ struct ggml_webgpu_flash_attn_common_pipeline_key { bool operator==(const ggml_webgpu_flash_attn_common_pipeline_key & other) const { return q_type == other.q_type && k_type == other.k_type && v_type == other.v_type && dst_type == other.dst_type && head_dim_qk == other.head_dim_qk && head_dim_v == other.head_dim_v && - kv_direct == other.kv_direct && kv_overlap == other.kv_overlap && has_mask == other.has_mask && - has_sinks == other.has_sinks && uses_logit_softcap == other.uses_logit_softcap; + k_direct == other.k_direct && v_direct == other.v_direct && kv_overlap == other.kv_overlap && + has_mask == other.has_mask && has_sinks == other.has_sinks && + uses_logit_softcap == other.uses_logit_softcap; } }; @@ -582,7 +615,8 @@ inline void ggml_webgpu_flash_attn_hash_common_pipeline_key(size_t & ggml_webgpu_hash_combine(seed, key.dst_type); ggml_webgpu_hash_combine(seed, key.head_dim_qk); ggml_webgpu_hash_combine(seed, key.head_dim_v); - ggml_webgpu_hash_combine(seed, key.kv_direct); + ggml_webgpu_hash_combine(seed, key.k_direct); + ggml_webgpu_hash_combine(seed, key.v_direct); ggml_webgpu_hash_combine(seed, key.kv_overlap); ggml_webgpu_hash_combine(seed, key.has_mask); ggml_webgpu_hash_combine(seed, key.has_sinks); @@ -656,17 +690,19 @@ inline bool ggml_webgpu_flash_attn_float_vec4_aligned(const ggml_tensor * K, ggml_webgpu_flash_attn_float_vec4_aligned(V, storage_offset_alignment); } -inline bool ggml_webgpu_flash_attn_kv_direct(const ggml_tensor * Q, - const ggml_tensor * K, - const ggml_tensor * V, - uint32_t kv_direct_align) { - return K->type == GGML_TYPE_F16 && V->type == GGML_TYPE_F16 && (Q->ne[0] % kv_direct_align == 0) && - (K->ne[1] % GGML_WEBGPU_KV_SEQ_PAD == 0); +inline bool ggml_webgpu_flash_attn_k_direct(const ggml_tensor * Q, const ggml_tensor * K, uint32_t kv_direct_align) { + return (K->type == GGML_TYPE_F16 || K->type == GGML_TYPE_Q8_0 || K->type == GGML_TYPE_Q4_0) && + (Q->ne[0] % kv_direct_align == 0) && (K->ne[1] % GGML_WEBGPU_KV_SEQ_PAD == 0); +} + +inline bool ggml_webgpu_flash_attn_v_direct(const ggml_tensor * Q, const ggml_tensor * V, uint32_t kv_direct_align) { + return ggml_webgpu_flash_attn_k_direct(Q, V, kv_direct_align); } inline ggml_webgpu_flash_attn_common_pipeline_key ggml_webgpu_flash_attn_make_common_pipeline_key( const ggml_webgpu_shader_lib_context & context, - uint32_t kv_direct_align) { + uint32_t kv_direct_align, + bool kv_overlap) { ggml_webgpu_flash_attn_common_pipeline_key key = {}; key.q_type = context.src0->type; key.k_type = context.src1->type; @@ -674,10 +710,11 @@ inline ggml_webgpu_flash_attn_common_pipeline_key ggml_webgpu_flash_attn_make_co key.dst_type = context.dst->type; key.head_dim_qk = (uint32_t) context.src0->ne[0]; key.head_dim_v = (uint32_t) context.src2->ne[0]; - key.kv_direct = ggml_webgpu_flash_attn_kv_direct(context.src0, context.src1, context.src2, kv_direct_align); - key.kv_overlap = ggml_webgpu_tensor_overlap(context.src1, context.src2); - key.has_mask = context.src3 != nullptr; - key.has_sinks = context.src4 != nullptr; + key.k_direct = ggml_webgpu_flash_attn_k_direct(context.src0, context.src1, kv_direct_align); + key.v_direct = ggml_webgpu_flash_attn_v_direct(context.src0, context.src2, kv_direct_align); + key.kv_overlap = kv_overlap; + key.has_mask = context.src3 != nullptr; + key.has_sinks = context.src4 != nullptr; key.uses_logit_softcap = ggml_get_op_params_f32(context.dst, 2) != 0.0f; return key; } @@ -762,9 +799,13 @@ inline std::vector ggml_webgpu_flash_attn_common_defines( defines.push_back("LOGIT_SOFTCAP"); variant += "_lgsc"; } - if (key.kv_direct) { - defines.push_back("KV_DIRECT"); - variant += "_kvdirect"; + if (key.k_direct) { + defines.push_back("K_DIRECT"); + variant += "_k_direct"; + } + if (key.v_direct) { + defines.push_back("V_DIRECT"); + variant += "_v_direct"; } if (key.kv_overlap) { defines.push_back("KV_OVERLAP"); @@ -783,6 +824,12 @@ inline std::vector ggml_webgpu_flash_attn_common_defines( if (ggml_is_quantized(key.k_type) || ggml_is_quantized(key.v_type)) { defines.push_back("U32_DEQUANT_HELPERS"); + if (ggml_is_quantized(key.k_type)) { + defines.push_back("LOADERS_QUANTIZED_K"); + } + if (ggml_is_quantized(key.v_type)) { + defines.push_back("LOADERS_QUANTIZED_V"); + } } return defines; @@ -1042,9 +1089,10 @@ struct ggml_webgpu_glu_pipeline_key { ggml_glu_op glu_op; ggml_type type; bool split; + bool src_overlap; bool operator==(const ggml_webgpu_glu_pipeline_key & other) const { - return glu_op == other.glu_op && type == other.type && split == other.split; + return glu_op == other.glu_op && type == other.type && split == other.split && src_overlap == other.src_overlap; } }; @@ -1054,6 +1102,7 @@ struct ggml_webgpu_glu_pipeline_key_hash { ggml_webgpu_hash_combine(seed, key.glu_op); ggml_webgpu_hash_combine(seed, key.type); ggml_webgpu_hash_combine(seed, key.split); + ggml_webgpu_hash_combine(seed, key.src_overlap); return seed; } }; @@ -1210,6 +1259,8 @@ class ggml_webgpu_shader_lib { soft_max_pipelines; std::unordered_map conv2d_pipelines; + std::unordered_map + conv2d_dw_pipelines; std::unordered_map im2col_pipelines; @@ -1732,12 +1783,16 @@ class ggml_webgpu_shader_lib { return ssm_conv_pipelines[key]; } - webgpu_pipeline get_ssm_scan_pipeline(const ggml_webgpu_shader_lib_context & context) { + webgpu_pipeline get_ssm_scan_pipeline(const ggml_webgpu_shader_lib_context & context, + bool xbc_overlap, + bool a_overlap, + bool ids_overlap) { ggml_webgpu_ssm_scan_pipeline_key key = {}; key.type = context.dst->type; key.d_state = (int) context.src0->ne[0]; - key.xbc_overlap = ggml_webgpu_tensor_overlap(context.src1, context.src4) && - ggml_webgpu_tensor_overlap(context.src1, context.src5); + key.xbc_overlap = xbc_overlap; + key.a_overlap = a_overlap; + key.ids_overlap = ids_overlap; auto it = ssm_scan_pipelines.find(key); if (it != ssm_scan_pipelines.end()) { @@ -1772,7 +1827,12 @@ class ggml_webgpu_shader_lib { if (key.xbc_overlap) { defines.push_back("XBC_OVERLAP"); } - + if (key.a_overlap) { + defines.push_back("A_OVERLAP"); + } + if (key.ids_overlap) { + defines.push_back("IDS_OVERLAP"); + } variant += "_d" + std::to_string(key.d_state); auto processed = preprocessor.preprocess(wgsl_ssm_scan, defines); @@ -1780,6 +1840,8 @@ class ggml_webgpu_shader_lib { decisions->wg_size = wg_size; decisions->tokens_per_tile = tokens_per_tile; decisions->xbc_overlap = key.xbc_overlap; + decisions->a_overlap = key.a_overlap; + decisions->ids_overlap = key.ids_overlap; webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant); pipeline.context = decisions; ssm_scan_pipelines[key] = pipeline; @@ -2523,11 +2585,11 @@ class ggml_webgpu_shader_lib { return unary_pipelines[key]; } - webgpu_pipeline get_rms_norm_mul_pipeline(const ggml_webgpu_shader_lib_context & context) { + webgpu_pipeline get_rms_norm_mul_pipeline(const ggml_webgpu_shader_lib_context & context, bool src_overlap) { ggml_webgpu_rms_norm_mul_pipeline_key key = {}; key.inplace = ggml_webgpu_tensor_equal(context.src0, context.dst); key.overlap = ggml_webgpu_tensor_equal(context.src1, context.dst); - key.src_overlap = ggml_webgpu_tensor_overlap(context.src0, context.src1); + key.src_overlap = src_overlap; auto it = rms_norm_mul_pipelines.find(key); if (it != rms_norm_mul_pipelines.end()) { @@ -2563,13 +2625,13 @@ class ggml_webgpu_shader_lib { return rms_norm_mul_pipelines[key]; } - webgpu_pipeline get_binary_pipeline(const ggml_webgpu_shader_lib_context & context) { + webgpu_pipeline get_binary_pipeline(const ggml_webgpu_shader_lib_context & context, bool src_overlap) { ggml_webgpu_binary_pipeline_key key = {}; key.type = context.dst->type; key.op = context.dst->op; key.inplace = ggml_webgpu_tensor_equal(context.src0, context.dst); key.overlap = ggml_webgpu_tensor_equal(context.src1, context.dst); - key.src_overlap = ggml_webgpu_tensor_overlap(context.src0, context.src1); + key.src_overlap = src_overlap; auto it = binary_pipelines.find(key); if (it != binary_pipelines.end()) { @@ -2652,10 +2714,10 @@ class ggml_webgpu_shader_lib { return pipeline; } - webgpu_pipeline get_concat_pipeline(const ggml_webgpu_shader_lib_context & context) { + webgpu_pipeline get_concat_pipeline(const ggml_webgpu_shader_lib_context & context, bool src_overlap) { ggml_webgpu_concat_pipeline_key key = {}; key.type = context.dst->type; - key.src_overlap = ggml_webgpu_tensor_overlap(context.src0, context.src1); + key.src_overlap = src_overlap; auto it = concat_pipelines.find(key); if (it != concat_pipelines.end()) { @@ -2712,6 +2774,10 @@ class ggml_webgpu_shader_lib { defines.push_back("TYPE_F32"); variant += "_f32"; break; + case GGML_TYPE_F16: + defines.push_back("TYPE_F16"); + variant += "_f16"; + break; case GGML_TYPE_I32: defines.push_back("TYPE_I32"); variant += "_i32"; @@ -2735,7 +2801,7 @@ class ggml_webgpu_shader_lib { return repeat_pipelines[key]; } - webgpu_pipeline get_flash_attn_pipeline(const ggml_webgpu_shader_lib_context & context) { + webgpu_pipeline get_flash_attn_pipeline(const ggml_webgpu_shader_lib_context & context, bool kv_overlap) { const bool can_use_subgroup_matrix = ggml_webgpu_flash_attn_can_use_subgroup_matrix_path( context.supports_subgroup_matrix, context.sg_mat_k, context.sg_mat_n, context.src0, context.src2); ggml_webgpu_flash_attn_decisions decisions = {}; @@ -2743,14 +2809,16 @@ class ggml_webgpu_shader_lib { decisions.q_tile = decisions.use_sg_matrix ? context.sg_mat_m : GGML_WEBGPU_FLASH_ATTN_TILE_Q_TILE; ggml_webgpu_flash_attn_pipeline_key key = {}; - key.common = - ggml_webgpu_flash_attn_make_common_pipeline_key(context, decisions.use_sg_matrix ? context.sg_mat_k : 1u); - key.common.kv_direct = decisions.use_sg_matrix && key.common.kv_direct; - key.use_sg_matrix = decisions.use_sg_matrix; + key.common = ggml_webgpu_flash_attn_make_common_pipeline_key( + context, decisions.use_sg_matrix ? context.sg_mat_k : 1u, kv_overlap); + key.common.k_direct &= decisions.use_sg_matrix && key.common.k_type == GGML_TYPE_F16; + key.common.v_direct &= decisions.use_sg_matrix && key.common.v_type == GGML_TYPE_F16; + key.use_sg_matrix = decisions.use_sg_matrix; const uint32_t max_kv_tile = ggml_webgpu_flash_attn_max_kv_tile( context.wg_mem_limit_bytes, decisions.q_tile, decisions.use_sg_matrix ? context.sg_mat_n : 1u, - key.common.head_dim_qk, key.common.head_dim_v, key.common.has_mask, key.common.kv_direct); + key.common.head_dim_qk, key.common.head_dim_v, key.common.has_mask, + key.common.k_direct || key.common.v_direct); GGML_ASSERT(max_kv_tile > 0); decisions.kv_tile = decisions.use_sg_matrix ? @@ -2762,7 +2830,7 @@ class ggml_webgpu_shader_lib { std::min(context.max_wg_size, std::max(GGML_WEBGPU_FLASH_ATTN_PREFERRED_WG_SIZE, GGML_WEBGPU_FLASH_ATTN_TILE_Q_TILE * context.max_subgroup_size)); - if (key.common.kv_direct) { + if (key.common.k_direct || key.common.v_direct) { decisions.kv_tile = std::min(decisions.kv_tile, GGML_WEBGPU_KV_SEQ_PAD); while (GGML_WEBGPU_KV_SEQ_PAD % decisions.kv_tile != 0) { decisions.kv_tile -= decisions.use_sg_matrix ? context.sg_mat_n : context.min_subgroup_size; @@ -2798,9 +2866,10 @@ class ggml_webgpu_shader_lib { return flash_attn_pipelines[key]; } - webgpu_pipeline get_flash_attn_vec_pipeline(const ggml_webgpu_shader_lib_context & context) { + webgpu_pipeline get_flash_attn_vec_pipeline(const ggml_webgpu_shader_lib_context & context, bool kv_overlap) { ggml_webgpu_flash_attn_vec_pipeline_key key = {}; - key.common = ggml_webgpu_flash_attn_make_common_pipeline_key(context, GGML_WEBGPU_FLASH_ATTN_TILE_KV_VEC_WIDTH); + key.common = ggml_webgpu_flash_attn_make_common_pipeline_key(context, GGML_WEBGPU_FLASH_ATTN_TILE_KV_VEC_WIDTH, + kv_overlap); auto it = flash_attn_vec_pipelines.find(key); if (it != flash_attn_vec_pipelines.end()) { @@ -2808,9 +2877,9 @@ class ggml_webgpu_shader_lib { } ggml_webgpu_flash_attn_vec_decisions decisions = {}; - decisions.kv_tile = - ggml_webgpu_flash_attn_get_vec_kv_tile(context.wg_mem_limit_bytes, key.common.head_dim_qk, - key.common.head_dim_v, key.common.has_mask, key.common.kv_direct); + decisions.kv_tile = ggml_webgpu_flash_attn_get_vec_kv_tile(context.wg_mem_limit_bytes, key.common.head_dim_qk, + key.common.head_dim_v, key.common.has_mask, + key.common.k_direct || key.common.v_direct); decisions.wg_size = context.max_subgroup_size; std::string variant = "flash_attn_vec"; @@ -2822,12 +2891,10 @@ class ggml_webgpu_shader_lib { variant += "_mask_blk"; } - uint32_t d_split = context.min_subgroup_size; - if (key.common.k_type == GGML_TYPE_F16 && key.common.v_type == GGML_TYPE_F16) { - const uint32_t D = key.common.head_dim_qk | key.common.head_dim_v; - const uint32_t D_lsb = D & (~(D - 1u)); - d_split = std::min(std::min(context.min_subgroup_size, 4u), std::max(D_lsb / 4u, 1u)); - } + uint32_t d_split = context.min_subgroup_size; + const uint32_t D = key.common.head_dim_qk | key.common.head_dim_v; + const uint32_t D_lsb = D & (~(D - 1u)); + d_split = std::min(std::min(context.min_subgroup_size, 4u), std::max(D_lsb / 4u, 1u)); defines.push_back(std::string("D_SPLIT=") + std::to_string(d_split)); variant += "_dsplit" + std::to_string(d_split); @@ -2958,11 +3025,12 @@ class ggml_webgpu_shader_lib { return cpy_pipelines[key]; } - webgpu_pipeline get_glu_pipeline(const ggml_webgpu_shader_lib_context & context) { + webgpu_pipeline get_glu_pipeline(const ggml_webgpu_shader_lib_context & context, bool src_overlap) { ggml_webgpu_glu_pipeline_key key = {}; key.glu_op = ggml_get_glu_op(context.dst); key.type = context.dst->type; key.split = (context.src1 != nullptr); + key.src_overlap = src_overlap; auto it = glu_pipelines.find(key); if (it != glu_pipelines.end()) { @@ -3013,7 +3081,10 @@ class ggml_webgpu_shader_lib { GGML_ABORT("Unsupported type for GLU shader"); } - if (key.split) { + if (key.src_overlap) { + defines.push_back("SRC_OVERLAP"); + variant += "_src_overlap"; + } else if (key.split) { variant += "_split"; } else { defines.push_back("NO_SPLIT"); @@ -3022,8 +3093,9 @@ class ggml_webgpu_shader_lib { defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size)); auto processed = preprocessor.preprocess(wgsl_glu, defines); - auto decisions = std::make_shared(); + auto decisions = std::make_shared(); decisions->wg_size = context.max_wg_size; + decisions->src_overlap = key.src_overlap; webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant); pipeline.context = decisions; glu_pipelines[key] = pipeline; @@ -3172,6 +3244,50 @@ class ggml_webgpu_shader_lib { return conv2d_pipelines[key]; } + // whcn selects the input layout: contiguous WHCN vs contiguous-channels CWHN + webgpu_pipeline get_conv2d_dw_pipeline(const ggml_webgpu_shader_lib_context & context, bool whcn) { + ggml_webgpu_conv2d_dw_pipeline_key key = {}; + key.weight_type = context.src0->type; + key.input_type = context.src1->type; + key.output_type = context.dst->type; + key.whcn = whcn; + + auto it = conv2d_dw_pipelines.find(key); + if (it != conv2d_dw_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string variant = whcn ? "conv_2d_dw_whcn" : "conv_2d_dw_cwhn"; + + auto push_type_defines = [&](const char * prefix, ggml_type type) { + std::string s_prefix = prefix; + if (type == GGML_TYPE_F32) { + defines.push_back(s_prefix + "_F32"); + } else if (type == GGML_TYPE_F16) { + defines.push_back(s_prefix + "_F16"); + } else { + GGML_ABORT("Unsupported type for CONV_2D_DW shader"); + } + }; + + push_type_defines("WEIGHT", key.weight_type); + push_type_defines("INPUT", key.input_type); + push_type_defines("OUTPUT", key.output_type); + if (whcn) { + defines.push_back("WHCN"); + } + defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size)); + + auto processed = preprocessor.preprocess(wgsl_conv2d_dw, defines); + auto decisions = std::make_shared(); + decisions->wg_size = context.max_wg_size; + webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant); + pipeline.context = decisions; + conv2d_dw_pipelines[key] = pipeline; + return conv2d_dw_pipelines[key]; + } + webgpu_pipeline get_im2col_pipeline(const ggml_webgpu_shader_lib_context & context) { ggml_webgpu_im2col_pipeline_key key = {}; key.input_type = context.src1->type; diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp index 29025e9ba4e3..c001cda7d116 100644 --- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp +++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp @@ -374,18 +374,59 @@ static wgpu::Buffer ggml_webgpu_tensor_buf(const ggml_tensor * tensor) { return ctx->buffer; } +static size_t ggml_webgpu_tensor_misalignment(const ggml_tensor * t, size_t alignment) { + size_t offset = ggml_webgpu_tensor_offset(t); + return offset & (alignment - 1); +} + static size_t ggml_webgpu_tensor_misalignment(webgpu_context & ctx, const ggml_tensor * t) { + return ggml_webgpu_tensor_misalignment(t, ctx->global_ctx->capabilities.limits.minStorageBufferOffsetAlignment); +} + +static size_t ggml_webgpu_tensor_align_offset(const ggml_tensor * t, size_t alignment) { size_t offset = ggml_webgpu_tensor_offset(t); - return offset & (ctx->global_ctx->capabilities.limits.minStorageBufferOffsetAlignment - 1); + return offset & ~(alignment - 1); } static size_t ggml_webgpu_tensor_align_offset(webgpu_context & ctx, const ggml_tensor * t) { - size_t offset = ggml_webgpu_tensor_offset(t); - return offset & ~(ctx->global_ctx->capabilities.limits.minStorageBufferOffsetAlignment - 1); + return ggml_webgpu_tensor_align_offset(t, ctx->global_ctx->capabilities.limits.minStorageBufferOffsetAlignment); +} + +static size_t ggml_webgpu_tensor_binding_size(const ggml_tensor * t, size_t alignment) { + return ROUNDUP_POW2(ggml_nbytes(t) + ggml_webgpu_tensor_misalignment(t, alignment), + WEBGPU_STORAGE_BUF_BINDING_MULT); } -static size_t ggml_webgpu_tensor_binding_size(webgpu_context & ctx, ggml_tensor * t) { - return ROUNDUP_POW2(ggml_nbytes(t) + ggml_webgpu_tensor_misalignment(ctx, t), WEBGPU_STORAGE_BUF_BINDING_MULT); +static size_t ggml_webgpu_tensor_binding_size(webgpu_context & ctx, const ggml_tensor * t) { + return ggml_webgpu_tensor_binding_size(t, ctx->global_ctx->capabilities.limits.minStorageBufferOffsetAlignment); +} + +static bool ggml_webgpu_tensor_binding_overlap(const webgpu_global_context & global_ctx, + const ggml_tensor * a, + const ggml_tensor * b) { + if (a->buffer != b->buffer) { + return false; + } + + const size_t alignment = global_ctx->capabilities.limits.minStorageBufferOffsetAlignment; + const size_t a_offset = ggml_webgpu_tensor_align_offset(a, alignment); + const size_t b_offset = ggml_webgpu_tensor_align_offset(b, alignment); + return a_offset < b_offset + ggml_webgpu_tensor_binding_size(b, alignment) && + b_offset < a_offset + ggml_webgpu_tensor_binding_size(a, alignment); +} + +static bool ggml_webgpu_tensor_binding_overlap_range(const webgpu_global_context & global_ctx, + ggml_tensor * tensor, + ggml_backend_buffer_t buffer, + size_t offset, + size_t size) { + if (tensor->buffer != buffer) { + return false; + } + + const size_t alignment = global_ctx->capabilities.limits.minStorageBufferOffsetAlignment; + const size_t tensor_offset = ggml_webgpu_tensor_align_offset(tensor, alignment); + return tensor_offset < offset + size && offset < tensor_offset + ggml_webgpu_tensor_binding_size(tensor, alignment); } struct ggml_webgpu_merged_binding_range { @@ -978,6 +1019,67 @@ static webgpu_encoded_op ggml_webgpu_conv_2d(webgpu_context & ctx, return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y); } +// Same param/binding layout as conv_2d; the shader differs +static webgpu_encoded_op ggml_webgpu_conv_2d_dw(webgpu_context & ctx, + ggml_tensor * src0, + ggml_tensor * src1, + ggml_tensor * dst) { + const int32_t s0 = ggml_get_op_params_i32(dst, 0); + const int32_t s1 = ggml_get_op_params_i32(dst, 1); + const int32_t p0 = ggml_get_op_params_i32(dst, 2); + const int32_t p1 = ggml_get_op_params_i32(dst, 3); + const int32_t d0 = ggml_get_op_params_i32(dst, 4); + const int32_t d1 = ggml_get_op_params_i32(dst, 5); + + // Scalar params matching conv2d_dw.wgsl (weight src0 [KW,KH,1,C], input src1, output dst). + std::vector params = { + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)), + + (uint32_t) ggml_nelements(dst), + (uint32_t) dst->ne[2], + (uint32_t) dst->ne[3], + (uint32_t) dst->ne[0], + (uint32_t) dst->ne[1], + (uint32_t) src1->ne[0], + (uint32_t) src1->ne[1], + (uint32_t) src0->ne[0], + (uint32_t) src0->ne[1], + + (uint32_t) s0, + (uint32_t) s1, + (uint32_t) p0, + (uint32_t) p1, + (uint32_t) d0, + (uint32_t) d1, + }; + + std::vector entries = { + ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src0), + ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, src1), + ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, dst), + }; + + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + shader_lib_ctx.src0 = src0; + shader_lib_ctx.src1 = src1; + shader_lib_ctx.dst = dst; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + + // Input layout: contiguous -> WHCN, contiguous-channels -> CWHN + const bool whcn = ggml_is_contiguous(src1); + webgpu_pipeline pipeline = ctx->shader_lib->get_conv2d_dw_pipeline(shader_lib_ctx, whcn); + auto * decisions = static_cast(pipeline.context.get()); + + uint32_t wg_x; + uint32_t wg_y; + uint32_t total_wg = CEIL_DIV((uint32_t) ggml_nelements(dst), decisions->wg_size); + compute_2d_workgroups(total_wg, ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension, wg_x, wg_y); + + return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y); +} + static webgpu_encoded_op ggml_webgpu_im2col(webgpu_context & ctx, ggml_tensor * src0, ggml_tensor * src1, @@ -1127,39 +1229,76 @@ static webgpu_encoded_op ggml_webgpu_ssm_scan(webgpu_context & ctx, ggml_webgpu_shader_lib_context shader_lib_ctx = {}; shader_lib_ctx.src0 = src0; shader_lib_ctx.src1 = src1; + shader_lib_ctx.src2 = src2; + shader_lib_ctx.src3 = src3; shader_lib_ctx.src4 = src4; shader_lib_ctx.src5 = src5; shader_lib_ctx.dst = dst; shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; shader_lib_ctx.supports_subgroups = ctx->global_ctx->capabilities.supports_subgroups; + bool xbc_overlap = ggml_webgpu_tensor_binding_overlap(ctx->global_ctx, src1, src2) || + ggml_webgpu_tensor_binding_overlap(ctx->global_ctx, src1, src4) || + ggml_webgpu_tensor_binding_overlap(ctx->global_ctx, src1, src5) || + ggml_webgpu_tensor_binding_overlap(ctx->global_ctx, src2, src4) || + ggml_webgpu_tensor_binding_overlap(ctx->global_ctx, src2, src5) || + ggml_webgpu_tensor_binding_overlap(ctx->global_ctx, src4, src5); + bool a_overlap = false; + bool ids_overlap = false; + ggml_webgpu_merged_binding_range xbc_merged_range = {}; + if (xbc_overlap) { + xbc_merged_range = ggml_webgpu_tensor_merged_binding_range(ctx, { src1, src2, src4, src5 }); + a_overlap = ggml_webgpu_tensor_binding_overlap_range(ctx->global_ctx, src3, src1->buffer, + xbc_merged_range.offset, xbc_merged_range.size); + if (a_overlap) { + xbc_merged_range = ggml_webgpu_tensor_merged_binding_range(ctx, { src1, src2, src3, src4, src5 }); + } + ids_overlap = ggml_webgpu_tensor_binding_overlap_range(ctx->global_ctx, src6, src1->buffer, + xbc_merged_range.offset, xbc_merged_range.size); + if (ids_overlap) { + xbc_merged_range = + a_overlap ? ggml_webgpu_tensor_merged_binding_range(ctx, { src1, src2, src3, src4, src5, src6 }) : + ggml_webgpu_tensor_merged_binding_range(ctx, { src1, src2, src4, src5, src6 }); + } + } - webgpu_pipeline pipeline = ctx->shader_lib->get_ssm_scan_pipeline(shader_lib_ctx); - auto * decisions = static_cast(pipeline.context.get()); - const bool xbc_overlap = decisions->xbc_overlap; + webgpu_pipeline pipeline = + ctx->shader_lib->get_ssm_scan_pipeline(shader_lib_ctx, xbc_overlap, a_overlap, ids_overlap); + auto * decisions = static_cast(pipeline.context.get()); + xbc_overlap = decisions->xbc_overlap; + a_overlap = decisions->a_overlap; + ids_overlap = decisions->ids_overlap; uint32_t offset_x = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)); + uint32_t offset_dt = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src2) / ggml_type_size(src2->type)); + uint32_t offset_A = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src3) / ggml_type_size(src3->type)); uint32_t offset_B = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src4) / ggml_type_size(src4->type)); uint32_t offset_C = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src5) / ggml_type_size(src5->type)); + uint32_t offset_ids = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src6) / ggml_type_size(src6->type)); size_t xbc_bind_offset = 0; size_t xbc_bind_size = 0; if (xbc_overlap) { - const ggml_webgpu_merged_binding_range merged_range = - ggml_webgpu_tensor_merged_binding_range(ctx, { src1, src4, src5 }); - xbc_bind_offset = merged_range.offset; - xbc_bind_size = merged_range.size; - offset_x = ggml_webgpu_tensor_merged_element_offset(src1, merged_range); - offset_B = ggml_webgpu_tensor_merged_element_offset(src4, merged_range); - offset_C = ggml_webgpu_tensor_merged_element_offset(src5, merged_range); + xbc_bind_offset = xbc_merged_range.offset; + xbc_bind_size = xbc_merged_range.size; + offset_x = ggml_webgpu_tensor_merged_element_offset(src1, xbc_merged_range); + offset_dt = ggml_webgpu_tensor_merged_element_offset(src2, xbc_merged_range); + if (a_overlap) { + offset_A = ggml_webgpu_tensor_merged_element_offset(src3, xbc_merged_range); + } + offset_B = ggml_webgpu_tensor_merged_element_offset(src4, xbc_merged_range); + offset_C = ggml_webgpu_tensor_merged_element_offset(src5, xbc_merged_range); + if (ids_overlap) { + offset_ids = ggml_webgpu_tensor_merged_element_offset(src6, xbc_merged_range); + } } std::vector params = { (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)), offset_x, - (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src2) / ggml_type_size(src2->type)), - (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src3) / ggml_type_size(src3->type)), + offset_dt, + offset_A, offset_B, offset_C, - (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src6) / ggml_type_size(src6->type)), + offset_ids, (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)), (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)), @@ -1199,10 +1338,19 @@ static webgpu_encoded_op ggml_webgpu_ssm_scan(webgpu_context & ctx, if (xbc_overlap) { entries.push_back( ggml_webgpu_make_bind_group_entry(1, ggml_webgpu_tensor_buf(src1), xbc_bind_offset, xbc_bind_size)); - entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, src2)); - entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 3, src3)); - entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 4, src6)); - entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 5, dst)); + if (ids_overlap) { + if (!a_overlap) { + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, src3)); + } + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, a_overlap ? 2 : 3, dst)); + } else if (a_overlap) { + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, src6)); + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 3, dst)); + } else { + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, src3)); + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 3, src6)); + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 4, dst)); + } } else { entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, src1)); entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, src2)); @@ -1320,11 +1468,10 @@ static std::optional ggml_webgpu_set_rows(webgpu_context & ct (uint32_t) (idx->ne[1]), (uint32_t) (idx->ne[2]) }; - std::vector entries = { - ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src), - ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, idx), - ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, dst), - }; + std::vector entries; + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src)); + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, idx)); + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, dst)); if (decisions->i64_idx) { entries.push_back(ggml_webgpu_make_bind_group_entry(3, ctx->set_rows_dev_error_buf, 0, @@ -1831,7 +1978,7 @@ static ggml_webgpu_flash_attn_op ggml_webgpu_flash_attn_prepare(webgpu_context & op.has_mask = mask != nullptr; op.has_sinks = sinks != nullptr; - op.kv_overlap = ggml_webgpu_tensor_overlap(K, V); + op.kv_overlap = ggml_webgpu_tensor_binding_overlap(ctx->global_ctx, K, V); uint32_t offset_k = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, K) / ggml_type_size(K->type)); uint32_t offset_v = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, V) / ggml_type_size(V->type)); @@ -1903,7 +2050,7 @@ static uint32_t ggml_webgpu_flash_attn_vec_nwg(uint32_t vec_nwg_cap, uint32_t kv } static webgpu_encoded_op ggml_webgpu_flash_attn_direct(webgpu_context & ctx, const ggml_webgpu_flash_attn_op & op) { - webgpu_pipeline pipeline = ctx->shader_lib->get_flash_attn_pipeline(op.shader_lib_ctx); + webgpu_pipeline pipeline = ctx->shader_lib->get_flash_attn_pipeline(op.shader_lib_ctx, op.kv_overlap); auto * decisions = static_cast(pipeline.context.get()); uint32_t wg_per_head = CEIL_DIV(op.shader_lib_ctx.src0->ne[1], decisions->q_tile); uint32_t wg_x = wg_per_head * op.shader_lib_ctx.src0->ne[2] * op.shader_lib_ctx.src0->ne[3]; @@ -1918,7 +2065,7 @@ static webgpu_encoded_op ggml_webgpu_flash_attn_vec(webgpu_context & ct ggml_tensor * sinks, ggml_tensor * dst, ggml_webgpu_flash_attn_op op) { - webgpu_pipeline pipeline = ctx->shader_lib->get_flash_attn_vec_pipeline(op.shader_lib_ctx); + webgpu_pipeline pipeline = ctx->shader_lib->get_flash_attn_vec_pipeline(op.shader_lib_ctx, op.kv_overlap); auto * decisions = static_cast(pipeline.context.get()); wgpu::Buffer blk_buf = {}; @@ -2188,8 +2335,9 @@ static webgpu_encoded_op ggml_webgpu_binary_op(webgpu_context & ctx, shader_lib_ctx.dst = dst; shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; - webgpu_pipeline pipeline = ctx->shader_lib->get_binary_pipeline(shader_lib_ctx); - auto * decisions = static_cast(pipeline.context.get()); + const bool src_overlap = ggml_webgpu_tensor_binding_overlap(ctx->global_ctx, src0, src1); + webgpu_pipeline pipeline = ctx->shader_lib->get_binary_pipeline(shader_lib_ctx, src_overlap); + auto * decisions = static_cast(pipeline.context.get()); uint32_t ne = (uint32_t) ggml_nelements(dst); @@ -2311,6 +2459,9 @@ static webgpu_encoded_op ggml_webgpu_concat(webgpu_context & ctx, ggml_tensor * dst) { uint32_t ne = (uint32_t) ggml_nelements(dst); uint32_t dim = (uint32_t) dst->op_params[0]; + if (ggml_nbytes(src0) == 0 && ggml_nbytes(src1) == 0) { + return {}; + } ggml_webgpu_shader_lib_context shader_lib_ctx = {}; shader_lib_ctx.src0 = src0; @@ -2318,20 +2469,34 @@ static webgpu_encoded_op ggml_webgpu_concat(webgpu_context & ctx, shader_lib_ctx.dst = dst; shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; - webgpu_pipeline pipeline = ctx->shader_lib->get_concat_pipeline(shader_lib_ctx); - auto * decisions = static_cast(pipeline.context.get()); + const bool src_overlap = ggml_webgpu_tensor_binding_overlap(ctx->global_ctx, src0, src1) || + ggml_nbytes(src0) == 0 || ggml_nbytes(src1) == 0; + webgpu_pipeline pipeline = ctx->shader_lib->get_concat_pipeline(shader_lib_ctx, src_overlap); + auto * decisions = static_cast(pipeline.context.get()); uint32_t offset_src0 = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)); uint32_t offset_src1 = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)); size_t merged_offset = 0; size_t merged_size = 0; if (decisions->src_overlap) { - const ggml_webgpu_merged_binding_range merged_range = - ggml_webgpu_tensor_merged_binding_range(ctx, { src0, src1 }); - merged_offset = merged_range.offset; - merged_size = merged_range.size; - offset_src0 = ggml_webgpu_tensor_merged_element_offset(src0, merged_range); - offset_src1 = ggml_webgpu_tensor_merged_element_offset(src1, merged_range); + if (ggml_nbytes(src0) == 0) { + merged_offset = ggml_webgpu_tensor_align_offset(ctx, src1); + merged_size = ggml_webgpu_tensor_binding_size(ctx, src1); + offset_src0 = 0; + offset_src1 = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)); + } else if (ggml_nbytes(src1) == 0) { + merged_offset = ggml_webgpu_tensor_align_offset(ctx, src0); + merged_size = ggml_webgpu_tensor_binding_size(ctx, src0); + offset_src0 = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)); + offset_src1 = 0; + } else { + const ggml_webgpu_merged_binding_range merged_range = + ggml_webgpu_tensor_merged_binding_range(ctx, { src0, src1 }); + merged_offset = merged_range.offset; + merged_size = merged_range.size; + offset_src0 = ggml_webgpu_tensor_merged_element_offset(src0, merged_range); + offset_src1 = ggml_webgpu_tensor_merged_element_offset(src1, merged_range); + } } std::vector params = { ne, @@ -2457,8 +2622,9 @@ static std::optional ggml_webgpu_rms_norm_mul(webgpu_context shader_lib_ctx.dst = dst; shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; - webgpu_pipeline pipeline = ctx->shader_lib->get_rms_norm_mul_pipeline(shader_lib_ctx); - auto * decisions = static_cast(pipeline.context.get()); + const bool src_overlap = ggml_webgpu_tensor_binding_overlap(ctx->global_ctx, rn_src, mul_src); + webgpu_pipeline pipeline = ctx->shader_lib->get_rms_norm_mul_pipeline(shader_lib_ctx, src_overlap); + auto * decisions = static_cast(pipeline.context.get()); if (decisions->src_overlap) { const ggml_webgpu_merged_binding_range merged_range = @@ -2617,15 +2783,30 @@ static webgpu_encoded_op ggml_webgpu_glu(webgpu_context & ctx, shader_lib_ctx.dst = dst; shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; - webgpu_pipeline pipeline = ctx->shader_lib->get_glu_pipeline(shader_lib_ctx); + const bool src_overlap = src1 != nullptr && ggml_webgpu_tensor_binding_overlap(ctx->global_ctx, src0, src1); + webgpu_pipeline pipeline = ctx->shader_lib->get_glu_pipeline(shader_lib_ctx, src_overlap); - auto * decisions = static_cast(pipeline.context.get()); + auto * decisions = static_cast(pipeline.context.get()); const int split = (src1 != nullptr); + uint32_t offset_src0 = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)); + uint32_t offset_src1 = + src1 != nullptr ? (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)) : 0; + size_t merged_offset = 0; + size_t merged_size = 0; + if (decisions->src_overlap) { + const ggml_webgpu_merged_binding_range merged_range = + ggml_webgpu_tensor_merged_binding_range(ctx, { src0, src1 }); + merged_offset = merged_range.offset; + merged_size = merged_range.size; + offset_src0 = ggml_webgpu_tensor_merged_element_offset(src0, merged_range); + offset_src1 = ggml_webgpu_tensor_merged_element_offset(src1, merged_range); + } + std::vector params = { - (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)), - src1 != nullptr ? (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)) : 0, + offset_src0, + offset_src1, (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)), (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)), (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)), @@ -2648,11 +2829,15 @@ static webgpu_encoded_op ggml_webgpu_glu(webgpu_context & ctx, ggml_webgpu_u32_from_f32(ggml_get_op_params_f32(dst, 3)), // limit, for swiglu_oai }; - std::vector entries = { - ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src0), - }; - uint32_t dst_binding = 1; - if (split) { + std::vector entries; + uint32_t dst_binding = 1; + if (decisions->src_overlap) { + entries.push_back( + ggml_webgpu_make_bind_group_entry(0, ggml_webgpu_tensor_buf(src0), merged_offset, merged_size)); + } else { + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src0)); + } + if (split && !decisions->src_overlap) { dst_binding = 2; entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, src1)); } @@ -3164,6 +3349,8 @@ static std::optional ggml_webgpu_encode(webgpu_context ctx, return ggml_webgpu_sum_rows(ctx, src0, node); case GGML_OP_CONV_2D: return ggml_webgpu_conv_2d(ctx, src0, src1, node); + case GGML_OP_CONV_2D_DW: + return ggml_webgpu_conv_2d_dw(ctx, src0, src1, node); case GGML_OP_IM2COL: return ggml_webgpu_im2col(ctx, src0, src1, node); case GGML_OP_UPSCALE: @@ -3652,7 +3839,8 @@ static size_t ggml_backend_webgpu_buffer_type_get_alloc_size(ggml_backend_buffer const auto & capabilities = ctx->webgpu_global_ctx->capabilities; if (ggml_webgpu_flash_attn_use_vec_path(ctx->webgpu_global_ctx, Q, K, V)) { const bool kv_direct = - ggml_webgpu_flash_attn_kv_direct(Q, K, V, GGML_WEBGPU_FLASH_ATTN_TILE_KV_VEC_WIDTH); + ggml_webgpu_flash_attn_k_direct(Q, K, GGML_WEBGPU_FLASH_ATTN_TILE_KV_VEC_WIDTH) || + ggml_webgpu_flash_attn_v_direct(Q, V, GGML_WEBGPU_FLASH_ATTN_TILE_KV_VEC_WIDTH); const uint32_t kv_tile = ggml_webgpu_flash_attn_get_vec_kv_tile( capabilities.limits.maxComputeWorkgroupStorageSize, (uint32_t) Q->ne[0], (uint32_t) V->ne[0], mask != nullptr, kv_direct); @@ -4102,7 +4290,8 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const supports_op = (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_I32); break; case GGML_OP_REPEAT: - supports_op = (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_I32 || src0->type == GGML_TYPE_I16); + supports_op = (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_I32 || + src0->type == GGML_TYPE_I16); break; case GGML_OP_CPY: case GGML_OP_CONT: @@ -4222,8 +4411,8 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const if (!supports_op) { break; } - if (ggml_webgpu_tensor_overlap(src1, src2) && src1->type != src2->type && - !ggml_is_quantized(src1->type) && !ggml_is_quantized(src2->type)) { + if (ggml_webgpu_tensor_binding_overlap(ctx->webgpu_global_ctx, src1, src2) && + src1->type != src2->type && !ggml_is_quantized(src1->type) && !ggml_is_quantized(src2->type)) { supports_op = false; break; } @@ -4261,9 +4450,10 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const const uint32_t q_tile = use_subgroup_matrix ? capabilities.sg_mat_m : GGML_WEBGPU_FLASH_ATTN_TILE_Q_TILE; const uint32_t kv_granularity = use_subgroup_matrix ? capabilities.sg_mat_n : 1u; - const bool kv_direct = use_subgroup_matrix ? - ggml_webgpu_flash_attn_kv_direct(src0, src1, src2, capabilities.sg_mat_k) : - false; + const bool kv_direct = use_subgroup_matrix ? + ggml_webgpu_flash_attn_k_direct(src0, src1, capabilities.sg_mat_k) || + ggml_webgpu_flash_attn_v_direct(src0, src2, capabilities.sg_mat_k) : + false; const uint32_t max_kv_tile = ggml_webgpu_flash_attn_max_kv_tile( capabilities.limits.maxComputeWorkgroupStorageSize, q_tile, kv_granularity, (uint32_t) src0->ne[0], (uint32_t) src2->ne[0], op->src[3] != nullptr, kv_direct); @@ -4349,6 +4539,12 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16) && (src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); break; + case GGML_OP_CONV_2D_DW: + supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && + (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16) && + (src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16) && + (ggml_is_contiguous(src1) || ggml_is_contiguous_channels(src1)); + break; case GGML_OP_IM2COL: supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/common_decls.tmpl b/ggml/src/ggml-webgpu/wgsl-shaders/common_decls.tmpl index 6634fbd65782..b0cf2853e0db 100644 --- a/ggml/src/ggml-webgpu/wgsl-shaders/common_decls.tmpl +++ b/ggml/src/ggml-webgpu/wgsl-shaders/common_decls.tmpl @@ -9,6 +9,12 @@ fn get_byte_i32(value: u32, index: u32) -> i32 { #endif #ifdef U32_DEQUANT_HELPERS + +fn f16_from_u16(bits: u32) -> f16 { + let packed = unpack2x16float(bits); + return f16(packed[0]); +} + #ifdef DECLARE_BYTE_LOADERS_SRC fn load_u16_at_src(byte_offset: u32) -> u32 { let word = src[byte_offset / 4u]; @@ -36,7 +42,7 @@ fn load_f16_as_f32_at_src(byte_offset: u32) -> f32 { let d_bits = (word >> shift) & 0xFFFFu; return unpack2x16float(d_bits)[0]; } -#endif +#endif // DECLARE_BYTE_LOADERS_SRC #ifdef DECLARE_BYTE_LOADERS_SRC0 fn load_u16_at_src0(byte_offset: u32) -> u32 { @@ -72,8 +78,47 @@ fn load_f16_as_f32_at_src0(byte_offset: u32) -> f32 { let d_bits = (word >> shift) & 0xFFFFu; return unpack2x16float(d_bits)[0]; } -#endif -#endif +#endif // DECLARE_BYTE_LOADERS_SRC0 + +#ifdef LOADERS_QUANTIZED_K +fn load_k_u16_at(byte_offset: u32) -> u32 { + let word = K[byte_offset / 4u]; + let shift = (byte_offset & 2u) * 8u; + return (word >> shift) & 0xFFFFu; +} + +fn load_k_u32_at(byte_offset: u32) -> u32 { + let word_idx = byte_offset / 4u; + let shift = (byte_offset & 3u) * 8u; + let lo = K[word_idx]; + if (shift == 0u) { + return lo; + } + let hi = K[word_idx + 1u]; + return (lo >> shift) | (hi << (32u - shift)); +} +#endif // LOADERS_QUANTIZED_K + +#ifdef LOADERS_QUANTIZED_V +fn load_v_u16_at(byte_offset: u32) -> u32 { + let word = V[byte_offset / 4u]; + let shift = (byte_offset & 2u) * 8u; + return (word >> shift) & 0xFFFFu; +} + +fn load_v_u32_at(byte_offset: u32) -> u32 { + let word_idx = byte_offset / 4u; + let shift = (byte_offset & 3u) * 8u; + let lo = V[word_idx]; + if (shift == 0u) { + return lo; + } + let hi = V[word_idx + 1u]; + return (lo >> shift) | (hi << (32u - shift)); +} +#endif // LOADERS_QUANTIZED_V + +#endif // U32_DEQUANT_HELPERS diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/conv2d_dw.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/conv2d_dw.wgsl new file mode 100644 index 000000000000..42d6f027cab6 --- /dev/null +++ b/ggml/src/ggml-webgpu/wgsl-shaders/conv2d_dw.wgsl @@ -0,0 +1,137 @@ +#include "common_decls.tmpl" +enable f16; + +// Ported from the Vulkan backend's conv2d_dw.comp. Two variants (based on WHCN) +// selected by the input (src1) layout: contiguous -> WHCN, else CWHN. +// weight (src0) is [KW,KH,1,C]; output matches the input layout. + +@group(0) @binding(0) +#if defined(WEIGHT_F32) +var weights: array; +#elif defined(WEIGHT_F16) +var weights: array; +#endif + +@group(0) @binding(1) +#if defined(INPUT_F32) +var input: array; +#elif defined(INPUT_F16) +var input: array; +#endif + +@group(0) @binding(2) +#if defined(OUTPUT_F32) +var output: array; +#elif defined(OUTPUT_F16) +var output: array; +#endif + +struct Params { + offset_w: u32, + offset_i: u32, + offset_o: u32, + + ne: u32, + channels: u32, + batches: u32, + dst_w: u32, dst_h: u32, + src_w: u32, src_h: u32, + knl_w: u32, knl_h: u32, + + stride_x: i32, stride_y: i32, + pad_x: i32, pad_y: i32, + dilation_x: i32, dilation_y: i32, +}; + +@group(0) @binding(3) +var params: Params; + +fn load_weight(idx: u32) -> f32 { + #if defined(WEIGHT_F32) + return weights[idx]; + #elif defined(WEIGHT_F16) + return f32(weights[idx]); + #endif +} +fn load_input(idx: u32) -> f32 { + #if defined(INPUT_F32) + return input[idx]; + #elif defined(INPUT_F16) + return f32(input[idx]); + #endif +} +fn store_output(idx: u32, val: f32) { + #if defined(OUTPUT_F32) + output[idx] = val; + #elif defined(OUTPUT_F16) + output[idx] = f16(val); + #endif +} + +#if defined(WHCN) +// Input/output/kernel contiguous in [W, H, C, N] order (kernel [KW,KH,C]). +fn conv_2d_dw(idx: u32) -> f32 { + let i0 = idx / params.dst_w; + let dst_x = idx - i0 * params.dst_w; + let i1 = i0 / params.dst_h; + let dst_y = i0 - i1 * params.dst_h; + let n = i1 / params.channels; + let c = i1 - n * params.channels; + + let src_i = params.offset_i + n * params.channels * params.src_h * params.src_w + + c * params.src_h * params.src_w; + let knl_i = params.offset_w + c * params.knl_h * params.knl_w; + + var sum: f32 = 0.0; + for (var ky: u32 = 0u; ky < params.knl_h; ky += 1u) { + let src_y = i32(dst_y) * params.stride_y + i32(ky) * params.dilation_y - params.pad_y; + if (src_y < 0 || src_y >= i32(params.src_h)) { continue; } + for (var kx: u32 = 0u; kx < params.knl_w; kx += 1u) { + let src_x = i32(dst_x) * params.stride_x + i32(kx) * params.dilation_x - params.pad_x; + if (src_x < 0 || src_x >= i32(params.src_w)) { continue; } + let v = load_input(src_i + u32(src_y) * params.src_w + u32(src_x)); + let k = load_weight(knl_i + ky * params.knl_w + kx); + sum += v * k; + } + } + return sum; +} +#else +// Channels contiguous (CWHN): channel is the innermost axis. +fn conv_2d_dw(idx: u32) -> f32 { + let i0 = idx / params.channels; + let c = idx - i0 * params.channels; + let i1 = i0 / params.dst_w; + let dst_x = i0 - i1 * params.dst_w; + let n = i1 / params.dst_h; + let dst_y = i1 - n * params.dst_h; + + let src_i = params.offset_i + n * params.channels * params.src_h * params.src_w; + let src_row = params.src_w * params.channels; + let knl_row = params.knl_w * params.channels; + + var sum: f32 = 0.0; + for (var ky: u32 = 0u; ky < params.knl_h; ky += 1u) { + let src_y = i32(dst_y) * params.stride_y + i32(ky) * params.dilation_y - params.pad_y; + if (src_y < 0 || src_y >= i32(params.src_h)) { continue; } + for (var kx: u32 = 0u; kx < params.knl_w; kx += 1u) { + let src_x = i32(dst_x) * params.stride_x + i32(kx) * params.dilation_x - params.pad_x; + if (src_x < 0 || src_x >= i32(params.src_w)) { continue; } + let v = load_input(src_i + u32(src_y) * src_row + u32(src_x) * params.channels + c); + let k = load_weight(params.offset_w + ky * knl_row + kx * params.channels + c); + sum += v * k; + } + } + return sum; +} +#endif + +@compute @workgroup_size(WG_SIZE) +fn main( + @builtin(global_invocation_id) gid: vec3, + @builtin(num_workgroups) num_wg: vec3 +) { + let idx = gid.x + (num_wg.x * u32(WG_SIZE)) * gid.y; + if (idx >= params.ne) { return; } + store_output(params.offset_o + idx, conv_2d_dw(idx)); +} diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn.wgsl index 9767ca3d7543..75f33e68ae53 100644 --- a/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn.wgsl +++ b/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn.wgsl @@ -138,7 +138,7 @@ const FLOAT_MIN: f32 = -1.0e9; // The number of Q rows processed per workgroup var q_shmem: array; -#ifndef KV_DIRECT +#if !defined(K_DIRECT) || !defined(V_DIRECT) const kv_shmem_size = KV_TILE * max(HEAD_DIM_QK, HEAD_DIM_V); // we can reuse the same shmem for K and V since we only need one at a time var kv_shmem: array; @@ -183,13 +183,12 @@ fn load_kx4(buf: ptr>, read_write>, scalar_index: u3 return (*buf)[scalar_index >> 2u]; } -#ifndef KV_DIRECT +#if !defined(K_DIRECT) || !defined(V_DIRECT) #define QUANT_SHMEM kv_shmem #define QUANT_OUT_TYPE f16 -#include "quant_inner_loops.tmpl" #include "flash_attn_quant_staging.tmpl" -#if !defined(K_Q4_0) && !defined(K_Q8_0) +#if !defined(K_DIRECT) && !defined(K_Q4_0) && !defined(K_Q8_0) fn load_k_tile_block(local_x: u32, kv_count: u32, kv_tile: u32, k_head_offset: u32) { for (var elem_idx = local_x; elem_idx < KV_TILE * HEAD_DIM_QK; elem_idx += WG_SIZE) { let k_row = elem_idx / HEAD_DIM_QK; @@ -204,7 +203,7 @@ fn load_k_tile_block(local_x: u32, kv_count: u32, kv_tile: u32, k_head_offset: u } #endif -#if !defined(V_Q4_0) && !defined(V_Q8_0) +#if !defined(V_DIRECT) && !defined(V_Q4_0) && !defined(V_Q8_0) fn load_v_tile_block(local_x: u32, kv_count: u32, kv_tile: u32, v_head_offset: u32) { for (var elem_idx = local_x; elem_idx < KV_TILE * HEAD_DIM_V; elem_idx += WG_SIZE) { let v_row = elem_idx / HEAD_DIM_V; @@ -296,7 +295,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3, } // load k tile into shared memory -#ifndef KV_DIRECT +#ifndef K_DIRECT load_k_tile_block(local_id.x, kv_count, kv_tile, k_head_offset); #endif @@ -306,7 +305,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3, // TODO: this loop seems to be the current largest bottleneck // this bracket exists to scope the lifetime of variables, reducing register pressure { -#ifdef KV_DIRECT +#ifdef K_DIRECT let k_block_row = kv_tile + subgroup_id * SG_MAT_N; var k_global_offset = k_head_offset + k_block_row * params.stride_k1; #else @@ -318,7 +317,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3, var q_cur = subgroupMatrixLoad>(&q_shmem, 0u, false, HEAD_DIM_QK); -#ifdef KV_DIRECT +#ifdef K_DIRECT var k_cur = subgroupMatrixLoad>(&K, k_global_offset + 0u, true, params.stride_k1); #else var k_cur = subgroupMatrixLoad>(&kv_shmem, k_block_offset + 0u, true, HEAD_DIM_QK); @@ -328,7 +327,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3, for (; t + 1u < HEAD_DIM_QK / SG_MAT_K; t += 2u) { let h0 = t * SG_MAT_K; var q0 = subgroupMatrixLoad>(&q_shmem, h0, false, HEAD_DIM_QK); -#ifdef KV_DIRECT +#ifdef K_DIRECT var k0 = subgroupMatrixLoad>(&K, k_global_offset + h0, true, params.stride_k1); #else var k0 = subgroupMatrixLoad>(&kv_shmem, k_block_offset + h0, true, HEAD_DIM_QK); @@ -339,7 +338,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3, let h1 = (t + 1u) * SG_MAT_K; var q1g = subgroupMatrixLoad>(&q_shmem, h1, false, HEAD_DIM_QK); -#ifdef KV_DIRECT +#ifdef K_DIRECT var k1g = subgroupMatrixLoad>(&K, k_global_offset + h1, true, params.stride_k1); #else var k1g = subgroupMatrixLoad>(&kv_shmem, k_block_offset + h1, true, HEAD_DIM_QK); @@ -353,7 +352,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3, if (t < HEAD_DIM_QK / SG_MAT_K) { let h = t * SG_MAT_K; var qn = subgroupMatrixLoad>(&q_shmem, h, false, HEAD_DIM_QK); -#ifdef KV_DIRECT +#ifdef K_DIRECT var kn = subgroupMatrixLoad>(&K, k_global_offset + h, true, params.stride_k1); #else var kn = subgroupMatrixLoad>(&kv_shmem, k_block_offset + h, true, HEAD_DIM_QK); @@ -365,7 +364,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3, acc = subgroupMatrixMultiplyAccumulate(q_cur, k_cur, acc); -#ifdef KV_DIRECT +#ifdef K_DIRECT k_global_offset += num_subgroups * SG_MAT_N * params.stride_k1; #else k_block_offset += num_subgroups * SG_MAT_N * HEAD_DIM_QK; @@ -436,7 +435,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3, } // load v tile into shared memory -#ifndef KV_DIRECT +#ifndef V_DIRECT load_v_tile_block(local_id.x, kv_count, kv_tile, v_head_offset); #endif @@ -464,7 +463,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3, ); // load V submatrix from global or shared memory -#ifdef KV_DIRECT +#ifdef V_DIRECT let v_block_row = kv_tile + kv_block * SG_MAT_N; let v_global_offset = v_head_offset + v_block_row * params.stride_v1 + head_dim_block; var v_sg_mat: subgroup_matrix_right = subgroupMatrixLoad>( diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_quant_staging.tmpl b/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_quant_staging.tmpl index 8f41eb7bfdbc..1c23260df05f 100644 --- a/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_quant_staging.tmpl +++ b/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_quant_staging.tmpl @@ -1,3 +1,5 @@ +#include "quant_inner_loops.tmpl" + #define BLOCK_SIZE 32 #define BLOCKS_K ((HEAD_DIM_QK + BLOCK_SIZE - 1) / BLOCK_SIZE) #define BLOCKS_V ((HEAD_DIM_V + BLOCK_SIZE - 1) / BLOCK_SIZE) @@ -26,49 +28,6 @@ #define V_BYTES_PER_INNER_LOOP 4u #endif -#if defined(K_Q4_0) || defined(K_Q8_0) -fn load_k_u16_at(byte_offset: u32) -> u32 { - let word = K[byte_offset / 4u]; - let shift = (byte_offset & 2u) * 8u; - return (word >> shift) & 0xFFFFu; -} - -fn load_k_u32_at(byte_offset: u32) -> u32 { - let word_idx = byte_offset / 4u; - let shift = (byte_offset & 3u) * 8u; - let lo = K[word_idx]; - if (shift == 0u) { - return lo; - } - let hi = K[word_idx + 1u]; - return (lo >> shift) | (hi << (32u - shift)); -} -#endif - -#if defined(V_Q4_0) || defined(V_Q8_0) -fn load_v_u16_at(byte_offset: u32) -> u32 { - let word = V[byte_offset / 4u]; - let shift = (byte_offset & 2u) * 8u; - return (word >> shift) & 0xFFFFu; -} - -fn load_v_u32_at(byte_offset: u32) -> u32 { - let word_idx = byte_offset / 4u; - let shift = (byte_offset & 3u) * 8u; - let lo = V[word_idx]; - if (shift == 0u) { - return lo; - } - let hi = V[word_idx + 1u]; - return (lo >> shift) | (hi << (32u - shift)); -} -#endif - -fn f16_from_u16(bits: u32) -> f16 { - let packed = unpack2x16float(bits); - return f16(packed[0]); -} - #if defined(K_Q4_0) || defined(K_Q8_0) fn load_k_tile_block(local_x: u32, kv_count: u32, kv_tile: u32, k_head_offset: u32) { for (var elem_idx = local_x * K_NQ; elem_idx < kv_count * HEAD_DIM_QK; elem_idx += WG_SIZE * K_NQ) { diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_tile.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_tile.wgsl index e68934113fc1..43f4fe7caccd 100644 --- a/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_tile.wgsl +++ b/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_tile.wgsl @@ -153,7 +153,6 @@ var p_shmem: array; #define QUANT_SHMEM kv_shmem #define QUANT_OUT_TYPE f16 -#include "quant_inner_loops.tmpl" #include "flash_attn_quant_staging.tmpl" #if !defined(K_Q4_0) && !defined(K_Q8_0) @@ -270,7 +269,9 @@ fn main(@builtin(workgroup_id) wg_id: vec3, local_scores[slot] = FLOAT_MIN; } -#ifndef KV_DIRECT + // The tile path stages K/V in shared memory so each tile can be reused across + // Q_TILE query rows. It therefore does not use the direct path. +#ifndef K_DIRECT load_k_tile_block(local_id.x, kv_count, kv_tile, k_head_offset); #endif @@ -333,7 +334,9 @@ fn main(@builtin(workgroup_id) wg_id: vec3, workgroupBarrier(); -#ifndef KV_DIRECT + // The tile path stages K/V in shared memory so each tile can be reused across + // Q_TILE query rows. It therefore does not use the direct path. +#ifndef V_DIRECT load_v_tile_block(local_id.x, kv_count, kv_tile, v_head_offset); #endif diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_vec_split.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_vec_split.wgsl index d5127624196b..b8e0be90d998 100644 --- a/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_vec_split.wgsl +++ b/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_vec_split.wgsl @@ -196,49 +196,35 @@ struct Params { // Just a very small float value. const FLOAT_MIN: f32 = -1.0e9; - -var q_shmem: array; - -#ifndef KV_DIRECT const kv_shmem_size = KV_TILE * max(HEAD_DIM_QK, HEAD_DIM_V); -// we can reuse the same shmem for K and V since we only need one at a time -var kv_shmem: array; -#endif +var q_shmem: array; var o_shmem: array; +// note that we reuse the same storage for both since we only need one at a time +var inter_shmem: array; #ifdef MASK // storage for mask values var mask_shmem: array; #endif -// note that we reuse the same storage for both since we only need one at a time -var inter_shmem: array; - -// Storage for row max and exp sum during online softmax -fn calc_softmax_term(kv_idx: u32, slope: f32, has_bias: bool, apply_mask: bool) -> f32 { - var v = select(FLOAT_MIN, - inter_shmem[kv_idx] * params.scale, - kv_idx < KV_TILE); -#ifdef LOGIT_SOFTCAP - v = params.logit_softcap * tanh(v); +#if defined(K_DIRECT) || defined(V_DIRECT) +// Shared memory for scale factor (d) in quantized K/V. Multiple threads use the same value, +// so caching it is more efficient, even on the direct path. +var d_shmem: array; #endif -#ifdef MASK - if (apply_mask) { - var mask_val = select(0.0, mask_shmem[kv_idx], kv_idx < KV_TILE); - v += select(mask_val, slope * mask_val, has_bias); - } -#endif - return v; -} -#ifndef KV_DIRECT +// K/V shared memory handling +#if !defined(K_DIRECT) || !defined(V_DIRECT) + +// we can reuse the same shmem for K and V since we only need one at a time +var kv_shmem: array; + #define QUANT_SHMEM kv_shmem #define QUANT_OUT_TYPE f32 -#include "quant_inner_loops.tmpl" #include "flash_attn_quant_staging.tmpl" -#if !defined(K_Q4_0) && !defined(K_Q8_0) +#if !defined(K_DIRECT) && !defined(K_Q4_0) && !defined(K_Q8_0) fn load_k_tile_block(local_x: u32, kv_count: u32, kv_tile: u32, k_head_offset: u32) { for (var elem_idx = local_x * 4u; elem_idx < KV_TILE * HEAD_DIM_QK; elem_idx += WG_SIZE * 4u) { let k_row = elem_idx / HEAD_DIM_QK; @@ -256,7 +242,7 @@ fn load_k_tile_block(local_x: u32, kv_count: u32, kv_tile: u32, k_head_offset: u } #endif -#if !defined(V_Q4_0) && !defined(V_Q8_0) +#if !defined(V_DIRECT) && !defined(V_Q4_0) && !defined(V_Q8_0) fn load_v_tile_block(local_x: u32, kv_count: u32, kv_tile: u32, v_head_offset: u32) { for (var elem_idx = local_x * 4u; elem_idx < KV_TILE * HEAD_DIM_V; elem_idx += WG_SIZE * 4u) { let v_row = elem_idx / HEAD_DIM_V; @@ -273,7 +259,24 @@ fn load_v_tile_block(local_x: u32, kv_count: u32, kv_tile: u32, v_head_offset: u } } #endif +#endif // !defined(K_DIRECT) || !defined(V_DIRECT) + +// Storage for row max and exp sum during online softmax +fn calc_softmax_term(kv_idx: u32, slope: f32, has_bias: bool, apply_mask: bool) -> f32 { + var v = select(FLOAT_MIN, + inter_shmem[kv_idx] * params.scale, + kv_idx < KV_TILE); +#ifdef LOGIT_SOFTCAP + v = params.logit_softcap * tanh(v); +#endif +#ifdef MASK + if (apply_mask) { + var mask_val = select(0.0, mask_shmem[kv_idx], kv_idx < KV_TILE); + v += select(mask_val, slope * mask_val, has_bias); + } #endif + return v; +} @compute @workgroup_size(WG_SIZE) fn main(@builtin(workgroup_id) wg_id: vec3, @@ -355,12 +358,31 @@ fn main(@builtin(workgroup_id) wg_id: vec3, inter_shmem[elem_idx] = 0.0; } +#ifdef K_DIRECT + // load only the scale factor (d) from each quantized block into shared memory on the direct path. +#if defined(K_Q8_0) + for (var j = local_id.x * 32; j < KV_TILE * HEAD_DIM_QK; j += WG_SIZE * 32) { + let kv_row = kv_tile + j / HEAD_DIM_QK; + let block_idx = (j % HEAD_DIM_QK) / 32; + let block_byte_base = 34 * (k_head_offset + kv_row * params.stride_k1 + block_idx); + let d = f32(f16_from_u16(load_k_u16_at(block_byte_base))); + d_shmem[j / 32] = d; + } +#elif defined(K_Q4_0) + for (var j = local_id.x * 32; j < KV_TILE * HEAD_DIM_QK; j += WG_SIZE * 32) { + let kv_row = kv_tile + j / HEAD_DIM_QK; + let block_idx = (j % HEAD_DIM_QK) / 32; + let block_byte_base = 18 * (k_head_offset + kv_row * params.stride_k1 + block_idx); + let d = f32(f16_from_u16(load_k_u16_at(block_byte_base))); + d_shmem[j / 32] = d; + } +#endif +#else // load k tile into shared memory -#ifndef KV_DIRECT load_k_tile_block(local_id.x, kv_count, kv_tile, k_head_offset); -#endif +#endif // defined(K_DIRECT) - workgroupBarrier(); + workgroupBarrier(); // accumulate q block * k block into registers across the entire KV tile if (!skip_tile) { @@ -381,9 +403,40 @@ fn main(@builtin(workgroup_id) wg_id: vec3, q_shmem[q_off + 1u], q_shmem[q_off + 2u], q_shmem[q_off + 3u]); -#ifdef KV_DIRECT +#ifdef K_DIRECT +#if defined(K_Q8_0) + let kv_row = kv_tile + kv_idx; + let block_idx = (i * 4u) / 32; + let id_in_block = (i * 4u) % 32; + let block_byte_base = 34 * (k_head_offset + kv_row * params.stride_k1 + block_idx); + let q_byte_base = block_byte_base + 2u; + let d = d_shmem[(kv_idx * HEAD_DIM_QK) / 32 + block_idx]; + let q8u4 = load_k_u32_at(q_byte_base + id_in_block); + let kv = vec4( + d * f32(get_byte_i32(q8u4, 0)), + d * f32(get_byte_i32(q8u4, 1)), + d * f32(get_byte_i32(q8u4, 2)), + d * f32(get_byte_i32(q8u4, 3)), + ); +#elif defined(K_Q4_0) + let kv_row = kv_tile + kv_idx; + let block_idx = (i * 4u) / 32; + let id_in_block = (i * 4u) % 32; + let phase = id_in_block / 16; + let block_byte_base = 18 * (k_head_offset + kv_row * params.stride_k1 + block_idx); + let q_byte_base = block_byte_base + 2u; + let d = d_shmem[(kv_idx * HEAD_DIM_QK) / 32 + block_idx]; + let q8u4 = load_k_u32_at(q_byte_base + (id_in_block - phase * 16u)); + let kv = vec4( + d * (f32((get_byte(q8u4, 0) >> (phase * 4u)) & 0xFu) - 8.0), + d * (f32((get_byte(q8u4, 1) >> (phase * 4u)) & 0xFu) - 8.0), + d * (f32((get_byte(q8u4, 2) >> (phase * 4u)) & 0xFu) - 8.0), + d * (f32((get_byte(q8u4, 3) >> (phase * 4u)) & 0xFu) - 8.0), + ); +#else let idx = k_head_offset + (kv_tile + kv_idx) * params.stride_k1 + (i * 4u); let kv = vec4(K[idx >> 2u]); +#endif #else let idx = kv_idx * HEAD_DIM_QK + (i * 4u); let kv = vec4( @@ -391,7 +444,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3, kv_shmem[idx + 1u], kv_shmem[idx + 2u], kv_shmem[idx + 3u]); -#endif +#endif // defined(K_DIRECT) partial_sum += dot(qv, kv); } } @@ -473,12 +526,32 @@ fn main(@builtin(workgroup_id) wg_id: vec3, } } + +#ifdef V_DIRECT + // load only `d` of quantized block into shared memory in the direct path +#if defined(V_Q8_0) + for (var j = local_id.x * 32; j < KV_TILE * HEAD_DIM_V; j += WG_SIZE * 32) { + let v_row = kv_tile + j / HEAD_DIM_V; + let block_idx = (j % HEAD_DIM_V) / 32; + let block_byte_base = 34 * (v_head_offset + v_row * params.stride_v1 + block_idx); + let d = f32(f16_from_u16(load_v_u16_at(block_byte_base))); + d_shmem[j / 32] = d; + } +#elif defined(V_Q4_0) + for (var j = local_id.x * 32; j < KV_TILE * HEAD_DIM_V; j += WG_SIZE * 32) { + let v_row = kv_tile + j / HEAD_DIM_V; + let block_idx = (j % HEAD_DIM_V) / 32; + let block_byte_base = 18 * (v_head_offset + v_row * params.stride_v1 + block_idx); + let d = f32(f16_from_u16(load_v_u16_at(block_byte_base))); + d_shmem[j / 32] = d; + } +#endif +#else // load v tile into shared memory -#ifndef KV_DIRECT load_v_tile_block(local_id.x, kv_count, kv_tile, v_head_offset); -#endif +#endif // V_DIRECT - workgroupBarrier(); + workgroupBarrier(); if (!skip_tile) { // we have P (KV_TILE) in inter_shmem and V (KV_TILE x head_dim_v) in kv_shmem @@ -501,9 +574,38 @@ fn main(@builtin(workgroup_id) wg_id: vec3, } let p = inter_shmem[kv_idx]; -#ifdef KV_DIRECT +#ifdef V_DIRECT +#if defined(V_Q8_0) + let block_idx = (vec_col * 4u) / 32; + let id_in_block = (vec_col * 4u) % 32; + let block_byte_base = 34 * (v_head_offset + v_row * params.stride_v1 + block_idx); + let q_byte_base = block_byte_base + 2u; + let d = d_shmem[(kv_idx * HEAD_DIM_V) / 32 + block_idx]; + let q8u4 = load_v_u32_at(q_byte_base + id_in_block); + let v4 = vec4( + d * f32(get_byte_i32(q8u4, 0)), + d * f32(get_byte_i32(q8u4, 1)), + d * f32(get_byte_i32(q8u4, 2)), + d * f32(get_byte_i32(q8u4, 3)), + ); +#elif defined(V_Q4_0) + let block_idx = (vec_col * 4u) / 32; + let id_in_block = (vec_col * 4u) % 32; + let phase = id_in_block / 16; + let block_byte_base = 18 * (v_head_offset + v_row * params.stride_v1 + block_idx); + let q_byte_base = block_byte_base + 2u; + let d = d_shmem[(kv_idx * HEAD_DIM_V) / 32 + block_idx]; + let q8u4 = load_v_u32_at(q_byte_base + (id_in_block - phase * 16u)); + let v4 = vec4( + d * (f32((get_byte(q8u4, 0) >> (phase * 4u)) & 0xFu) - 8.0), + d * (f32((get_byte(q8u4, 1) >> (phase * 4u)) & 0xFu) - 8.0), + d * (f32((get_byte(q8u4, 2) >> (phase * 4u)) & 0xFu) - 8.0), + d * (f32((get_byte(q8u4, 3) >> (phase * 4u)) & 0xFu) - 8.0), + ); +#else let v_idx = v_head_offset + v_row * params.stride_v1 + vec_col * 4u; let v4 = vec4(V[v_idx >> 2u]); +#endif #else let v_idx = kv_idx * HEAD_DIM_V + vec_col * 4u; let v4 = vec4( @@ -511,7 +613,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3, kv_shmem[v_idx + 1u], kv_shmem[v_idx + 2u], kv_shmem[v_idx + 3u]); -#endif +#endif // defined(V_DIRECT) lo += p * v4; } diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/glu.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/glu.wgsl index e6d7608cec5d..d03f1c207d98 100644 --- a/ggml/src/ggml-webgpu/wgsl-shaders/glu.wgsl +++ b/ggml/src/ggml-webgpu/wgsl-shaders/glu.wgsl @@ -96,7 +96,22 @@ struct Params { @group(0) @binding(0) var src0: array; -#ifdef NO_SPLIT +#ifdef SRC_OVERLAP +@group(0) @binding(1) +var dst: array; + +@group(0) @binding(2) +var params: Params; + +fn a_value(base: u32) -> DataType { + return src0[base]; +} + +fn b_value(base: u32) -> DataType { + return src0[base]; +} + +#elif defined(NO_SPLIT) @group(0) @binding(1) var dst: array; diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/repeat.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/repeat.wgsl index 6e2a1a8b614d..43b883e67945 100644 --- a/ggml/src/ggml-webgpu/wgsl-shaders/repeat.wgsl +++ b/ggml/src/ggml-webgpu/wgsl-shaders/repeat.wgsl @@ -27,6 +27,9 @@ struct Params { #ifdef TYPE_I32 #define DataType i32 #endif +#ifdef TYPE_F16 +#define DataType f16 +#endif #ifdef TYPE_I16 // same size (16-bit) is sufficient for repeat #define DataType f16 diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/ssm_scan.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/ssm_scan.wgsl index 05761dec353a..66bfdd64015c 100644 --- a/ggml/src/ggml-webgpu/wgsl-shaders/ssm_scan.wgsl +++ b/ggml/src/ggml-webgpu/wgsl-shaders/ssm_scan.wgsl @@ -46,12 +46,29 @@ struct Params { @group(0) @binding(0) var s_in: array; #ifdef XBC_OVERLAP -@group(0) @binding(1) var x_B_C_merged: array; -@group(0) @binding(2) var dt: array; -@group(0) @binding(3) var A: array; -@group(0) @binding(4) var ids: array; -@group(0) @binding(5) var dst: array; -@group(0) @binding(6) var params: Params; +#ifdef IDS_OVERLAP +@group(0) @binding(1) var x_dt_B_C_ids_merged: array; +#ifdef A_OVERLAP +@group(0) @binding(2) var dst: array; +@group(0) @binding(3) var params: Params; +#else +@group(0) @binding(2) var A: array; +@group(0) @binding(3) var dst: array; +@group(0) @binding(4) var params: Params; +#endif +#else +@group(0) @binding(1) var x_dt_B_C_merged: array; +#ifdef A_OVERLAP +@group(0) @binding(2) var ids: array; +@group(0) @binding(3) var dst: array; +@group(0) @binding(4) var params: Params; +#else +@group(0) @binding(2) var A: array; +@group(0) @binding(3) var ids: array; +@group(0) @binding(4) var dst: array; +@group(0) @binding(5) var params: Params; +#endif +#endif #else @group(0) @binding(1) var x: array; @group(0) @binding(2) var dt: array; @@ -71,6 +88,24 @@ fn reduce_base(token_in_tile: u32) -> u32 { return token_in_tile * WG_SIZE; } +#ifdef XBC_OVERLAP +fn read_merged_f32(idx: u32) -> f32 { +#ifdef IDS_OVERLAP + return bitcast(x_dt_B_C_ids_merged[idx]); +#else + return x_dt_B_C_merged[idx]; +#endif +} +#endif + +fn read_state_slot(i3: u32) -> u32 { +#ifdef IDS_OVERLAP + return x_dt_B_C_ids_merged[params.offset_ids + i3]; +#else + return u32(ids[params.offset_ids + i3]); +#endif +} + @compute @workgroup_size(WG_SIZE) fn main( @builtin(local_invocation_id) local_id: vec3, @@ -90,13 +125,18 @@ fn main( let ir = head_seq % params.n_head; let i3 = head_seq / params.n_head; - let state_slot = u32(ids[params.offset_ids + i3]); + let state_slot = read_state_slot(i3); let g = ir / (params.n_head / params.n_group); let s_idx = params.offset_s + tid + i1 * params.stride_s1 + ir * params.stride_s2 + state_slot * params.stride_s3; var s_prev = s_in[s_idx]; - let A0 = A[params.offset_A + (tid % params.a_ne0) + ir * params.stride_A1]; + let a_idx = params.offset_A + (tid % params.a_ne0) + ir * params.stride_A1; +#ifdef A_OVERLAP + let A0 = read_merged_f32(a_idx); +#else + let A0 = A[a_idx]; +#endif for (var token_base = 0u; token_base < params.n_seq_tokens; token_base += TOKENS_PER_TILE) { if (tid < TOKENS_PER_TILE) { @@ -104,11 +144,15 @@ fn main( if (token < params.n_seq_tokens) { let x_idx = params.offset_x + i1 + ir * params.stride_x1 + token * params.stride_x2 + i3 * params.stride_x3; let dt_idx = params.offset_dt + ir + token * params.stride_dt1 + i3 * params.stride_dt2; +#ifdef XBC_OVERLAP + let dt0 = read_merged_f32(dt_idx); +#else let dt0 = dt[dt_idx]; +#endif let dtsp = select(log(1.0 + exp(dt0)), dt0, dt0 > 20.0); shared_dtsp[tid] = dtsp; #ifdef XBC_OVERLAP - shared_x_dt[tid] = x_B_C_merged[x_idx] * dtsp; + shared_x_dt[tid] = read_merged_f32(x_idx) * dtsp; #else shared_x_dt[tid] = x[x_idx] * dtsp; #endif @@ -130,7 +174,7 @@ fn main( let b_idx = params.offset_B + tid + g * params.stride_B1 + token * params.stride_B2 + i3 * params.stride_B3; let c_idx = params.offset_C + tid + g * params.stride_C1 + token * params.stride_C2 + i3 * params.stride_C3; #ifdef XBC_OVERLAP - let s = s_prev * dA + x_B_C_merged[b_idx] * x_dt; + let s = s_prev * dA + read_merged_f32(b_idx) * x_dt; #else let s = s_prev * dA + B[b_idx] * x_dt; #endif @@ -138,7 +182,7 @@ fn main( #ifdef USE_SUBGROUP_REDUCTION #ifdef XBC_OVERLAP - let subgroup_partial = subgroupAdd(s * x_B_C_merged[c_idx]); + let subgroup_partial = subgroupAdd(s * read_merged_f32(c_idx)); #else let subgroup_partial = subgroupAdd(s * C[c_idx]); #endif @@ -147,7 +191,7 @@ fn main( } #else #ifdef XBC_OVERLAP - shared_reduce[reduce_idx] = s * x_B_C_merged[c_idx]; + shared_reduce[reduce_idx] = s * read_merged_f32(c_idx); #else shared_reduce[reduce_idx] = s * C[c_idx]; #endif diff --git a/ggml/src/ggml-zendnn/CMakeLists.txt b/ggml/src/ggml-zendnn/CMakeLists.txt index e4ba9cfbd0fc..87d721f6d78a 100644 --- a/ggml/src/ggml-zendnn/CMakeLists.txt +++ b/ggml/src/ggml-zendnn/CMakeLists.txt @@ -28,7 +28,7 @@ if (NOT ZENDNN_ROOT OR ZENDNN_ROOT STREQUAL "" OR ZENDNN_ROOT STREQUAL "OFF") ExternalProject_Add( zendnn GIT_REPOSITORY https://github.com/amd/ZenDNN.git - GIT_TAG 253b94ce0d7e9284c265fefb485714944caff9d3 # ZenDNN-2026-WW19 + GIT_TAG 1f399a75cc0993778374a51bea49b64a57879595 # ZenDNN-2026-WW28 PREFIX ${ZENDNN_PREFIX} SOURCE_DIR ${ZENDNN_SOURCE_DIR} BINARY_DIR ${ZENDNN_BUILD_DIR} diff --git a/ggml/src/ggml-zendnn/ggml-zendnn.cpp b/ggml/src/ggml-zendnn/ggml-zendnn.cpp index 3c33dcb11a03..e6a9b51b7925 100644 --- a/ggml/src/ggml-zendnn/ggml-zendnn.cpp +++ b/ggml/src/ggml-zendnn/ggml-zendnn.cpp @@ -30,6 +30,29 @@ zendnnl::common::data_type_t ggml_to_zendnn_type() { } } +/** + * Builds the matmul_params shared by ggml_zendnn_matmul() and ggml_zendnn_group_matmul(): + * dtype selection plus, for Q8_0 weights, dynamic-quant setup. Callers still need to set + * quant_params.src_scale.dims themselves, since that depends on the batch size(s) in use. + */ +template +static zendnnl::lowoha::matmul::matmul_params ggml_zendnn_make_matmul_params(ggml_backend_zendnn_context * ctx) { + zendnnl::lowoha::matmul::matmul_params params; + params.dtypes.src = ggml_to_zendnn_type(); + params.dtypes.wei = ggml_to_zendnn_type(); + params.dtypes.dst = ggml_to_zendnn_type(); + params.num_threads = ctx->n_threads; + + if constexpr (std::is_same_v) { + params.dtypes.compute = zendnnl::common::data_type_t::s8; + params.dynamic_quant = true; + params.quant_params.src_scale.buff = nullptr; + params.quant_params.src_scale.dt = zendnnl::common::data_type_t::bf16; + params.packing.pack_format_b = 1; + } + return params; +} + /** * ZenDNN matmul: computes C = B * A. * @@ -47,22 +70,12 @@ static bool ggml_zendnn_matmul(ggml_backend_zendnn_context * ctx, int64_t m, int const TA * A, int64_t lda, const TB * B, int64_t ldb, TC * C, int64_t ldc) { - zendnnl::lowoha::matmul::matmul_params params; - params.dtypes.src = ggml_to_zendnn_type(); - params.dtypes.wei = ggml_to_zendnn_type(); - params.dtypes.dst = ggml_to_zendnn_type(); - params.num_threads = ctx->n_threads; + zendnnl::lowoha::matmul::matmul_params params = ggml_zendnn_make_matmul_params(ctx); zendnnl::lowoha::matmul::matmul_batch_params_t batch_params; if constexpr (std::is_same_v) { - params.dtypes.compute = zendnnl::common::data_type_t::s8; - const int64_t num_groups = k / QK8_0; - params.dynamic_quant = true; - params.quant_params.src_scale.buff = nullptr; - params.quant_params.src_scale.dt = zendnnl::common::data_type_t::bf16; - params.quant_params.src_scale.dims = {n, num_groups}; - params.packing.pack_format_b = 1; + params.quant_params.src_scale.dims = {n, k / QK8_0}; } zendnnl::error_handling::status_t status = zendnnl::lowoha::matmul::matmul_direct( @@ -223,6 +236,99 @@ struct mmid_row_mapping { int32_t i2; }; +/** + * ZenDNN batched matmul: computes C[i] = B[i] * A[i] for every active expert i via a single + * group_matmul_direct() call. Batched analogue of ggml_zendnn_matmul() - see its docs for the + * per-expert A/B/C shape convention. m and k are shared by every expert; n (batch size) varies + * per expert, hence the vector. + */ +template +static bool ggml_zendnn_group_matmul(ggml_backend_zendnn_context * ctx, int64_t m, int64_t k, + const std::vector & n, + const std::vector & A, int64_t lda, + const std::vector & B, int64_t ldb, + const std::vector & C, int64_t ldc) { + + const int n_experts = n.size(); + + zendnnl::lowoha::matmul::matmul_params base_params = ggml_zendnn_make_matmul_params(ctx); + + std::vector layout(n_experts, 'r'); + std::vector trans_a(n_experts, false); + std::vector trans_b(n_experts, true); + std::vector batch_m(n_experts); + std::vector batch_n(n_experts, m); + std::vector batch_k(n_experts, k); + std::vector alpha(n_experts, 1.0f); + std::vector beta(n_experts, 0.0f); + std::vector bias(n_experts, nullptr); + std::vector lda_v(n_experts, lda); + std::vector ldb_v(n_experts, ldb); + std::vector ldc_v(n_experts, ldc); + std::vector is_wei_const(n_experts, true); + std::vector params(n_experts, base_params); + + for (int i = 0; i < n_experts; i++) { + batch_m[i] = n[i]; + + // src_scale.dims depends on this expert's row count, unlike the rest of base_params + if constexpr (std::is_same_v) { + params[i].quant_params.src_scale.dims = {n[i], k / QK8_0}; + } + } + + zendnnl::error_handling::status_t status = zendnnl::lowoha::matmul::group_matmul_direct( + layout, trans_a, trans_b, batch_m, batch_n, batch_k, alpha, + B, ldb_v, A, lda_v, bias, beta, + C, ldc_v, is_wei_const, params); + + if (status != zendnnl::error_handling::status_t::success) { + GGML_LOG_ERROR("%s, ZenDNN group matmul failed: status=%d\n", __func__, static_cast(status)); + return false; + } + return true; +} + +static bool ggml_zendnn_group_gemm(ggml_backend_zendnn_context * ctx, int64_t m, int64_t k, + const std::vector & n, + const std::vector & A, int64_t lda, + const std::vector & B, int64_t ldb, + const std::vector & C, int64_t ldc, + int Atype, int Btype, int Ctype) { + + assert(m >= 0); + for (size_t i = 0; i < n.size(); i++) { + assert(n[i] >= 0); + } + assert(k >= 0); + assert(lda >= k); + assert(ldb >= k); + assert(ldc >= m); + + // categorize types + switch (Atype) { + case GGML_TYPE_F32: + if (Btype != GGML_TYPE_F32 || Ctype != GGML_TYPE_F32) + return false; + return ggml_zendnn_group_matmul(ctx, m, k, n, A, lda, B, ldb, C, ldc); + case GGML_TYPE_BF16: + if (Btype != GGML_TYPE_BF16) + return false; + if (Ctype == GGML_TYPE_BF16) + return ggml_zendnn_group_matmul( + ctx, m, k, n, A, lda, B, ldb, C, ldc); + if (Ctype == GGML_TYPE_F32) + return ggml_zendnn_group_matmul(ctx, m, k, n, A, lda, B, ldb, C, ldc); + return false; + case GGML_TYPE_Q8_0: + if (Btype != GGML_TYPE_F32 || Ctype != GGML_TYPE_F32) + return false; + return ggml_zendnn_group_matmul(ctx, m, k, n, A, lda, B, ldb, C, ldc); + default: + return false; // unsupported type + } +} + static void ggml_zendnn_compute_forward_mul_mat_id( ggml_backend_zendnn_context * ctx, ggml_tensor * dst) { @@ -262,7 +368,8 @@ static void ggml_zendnn_compute_forward_mul_mat_id( std::vector matrix_row_counts(n_as, 0); std::vector> matrix_rows(n_as); - int64_t max_rows = 0; + int64_t total_rows = 0; + int n_active_experts = 0; // group rows by expert (preprocessing step) for (int64_t iid1 = 0; iid1 < ids->ne[1]; ++iid1) { for (int id = 0; id < n_ids; ++id) { @@ -270,66 +377,74 @@ static void ggml_zendnn_compute_forward_mul_mat_id( GGML_ASSERT(i02 >= 0 && i02 < n_as); + if (matrix_row_counts[i02] == 0) { + n_active_experts++; + } matrix_rows[i02].push_back({id, iid1}); matrix_row_counts[i02]++; - if (matrix_row_counts[i02] > max_rows) { - max_rows = matrix_row_counts[i02]; - } + total_rows++; } } - if (max_rows == 0) { + if (total_rows == 0) { return; // no rows to process } const size_t row_size = ggml_row_size(vec_dot_type, ne10); - // size for converting src1 rows to vec_dot_type if needed - const size_t nbw1 = row_size; - const size_t nbw2 = nbw1 * ne11; - const size_t nbw3 = nbw2 * ne12; - const size_t src1_conv_size = (src1->type != vec_dot_type && src0->type != GGML_TYPE_Q8_0) ? ne13 * nbw3 : 0; - // For Q8_0, src1 is always F32; the gather buffer must hold F32 rows (ne10*4 bytes), // not Q8_0-encoded rows (row_size ≈ ne10/32*34 bytes) — they differ by ~4x. const size_t f32_row_size = (size_t)ne10 * sizeof(float); const size_t gather_row_size = (src0->type == GGML_TYPE_Q8_0) ? f32_row_size : row_size; + if (src1->type != vec_dot_type && src0->type != GGML_TYPE_Q8_0) { + GGML_ASSERT(src1->type == GGML_TYPE_F32); + } + // size for MoE gather/scatter buffers - const size_t wdata_cur_size = max_rows * gather_row_size; - const size_t dst_cur_size = max_rows * ggml_row_size(dst->type, ne01); + const size_t wdata_cur_size = total_rows * gather_row_size; + const size_t dst_cur_size = total_rows * ggml_row_size(dst->type, ne01); // allocate single buffer for all needs - const size_t total_size = src1_conv_size + wdata_cur_size + dst_cur_size; + const size_t total_size = wdata_cur_size + dst_cur_size; if (ctx->work_size < total_size) { ctx->work_data.reset(new char[total_size]); ctx->work_size = total_size; } // partition the buffer - char * work_data = ctx->work_data.get(); - char * wdata_cur = work_data + src1_conv_size; + char * wdata_cur = ctx->work_data.get(); char * dst_cur = wdata_cur + wdata_cur_size; - // ZenDNN requires FP32 for dynamic quantization, so conversion is skipped - if (src1->type != vec_dot_type && src0->type != GGML_TYPE_Q8_0) { - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - #pragma omp parallel for collapse(3) num_threads(ctx->n_threads) schedule(static) - for (int64_t i13 = 0; i13 < ne13; ++i13) { - for (int64_t i12 = 0; i12 < ne12; ++i12) { - for (int64_t i11 = 0; i11 < ne11; ++i11) { - const float * src1_f32 = (float *)((char *)src1->data + i11*nb11 + i12*nb12 + i13*nb13); - void * src1_conv = (char *)work_data + i11*nbw1 + i12*nbw2 + i13*nbw3; - from_float(src1_f32, src1_conv, ne10); - } + // per-expert data collected during gather, handed to ggml_zendnn_group_gemm() as one batch + std::vector expert_row_count(n_active_experts); + std::vector batch_src(n_active_experts); + std::vector batch_wei(n_active_experts); + std::vector batch_dst(n_active_experts); + + // precompute per-expert buffer offsets and batch indices for the parallel loop below + std::vector expert_wdata_off(n_as, 0); + std::vector expert_dst_off(n_as, 0); + std::vector expert_batch_idx(n_as, -1); + { + int64_t w_off = 0; + int64_t d_off = 0; + int batch_idx = 0; + for (int64_t cur_a = 0; cur_a < n_as; ++cur_a) { + if (matrix_row_counts[cur_a] == 0) { + continue; } + expert_wdata_off[cur_a] = w_off; + expert_dst_off[cur_a] = d_off; + expert_batch_idx[cur_a] = batch_idx; + w_off += matrix_row_counts[cur_a] * gather_row_size; + d_off += matrix_row_counts[cur_a] * ggml_row_size(dst->type, ne01); + batch_idx++; } } - const void * wdata = (src1->type == vec_dot_type || src0->type == GGML_TYPE_Q8_0) ? src1->data : work_data; - - // process each expert with gather -> gemm -> scatter pattern + // gather + inline-convert input rows into each expert's batch slot + #pragma omp parallel for num_threads(ctx->n_threads) schedule(static) for (int64_t cur_a = 0; cur_a < n_as; ++cur_a) { const int64_t cne1 = matrix_row_counts[cur_a]; @@ -337,42 +452,57 @@ static void ggml_zendnn_compute_forward_mul_mat_id( continue; } - const char * src0_cur = (const char *) src0->data + cur_a*nb02; + const int64_t w_off = expert_wdata_off[cur_a]; + const int64_t d_off = expert_dst_off[cur_a]; + const int batch_idx = expert_batch_idx[cur_a]; - // gather input rows for this expert - #pragma omp parallel for num_threads(ctx->n_threads) schedule(static) for (int64_t ir1 = 0; ir1 < cne1; ++ir1) { const mmid_row_mapping & row_mapping = matrix_rows[cur_a][ir1]; - const int64_t id = row_mapping.i1; + const int64_t id = row_mapping.i1; const int64_t i11 = id % ne11; const int64_t i12 = row_mapping.i2; - std::memcpy( - wdata_cur + ir1 * gather_row_size, - (const char *) wdata + (i11 + i12*ne11) * gather_row_size, - gather_row_size - ); + const char * src_row = (const char *) src1->data + i11*nb11 + i12*nb12; + void * dst_row = wdata_cur + w_off + ir1 * gather_row_size; + + if (src1->type != vec_dot_type && src0->type != GGML_TYPE_Q8_0) { + from_float((const float *) src_row, dst_row, ne10); + } else { + // no conversion: src1 already matches vec_dot_type, or src0 is Q8_0, whose + // ZenDNN dynamic quantization requires the row to stay in F32 + std::memcpy(dst_row, src_row, gather_row_size); + } } - // batched gemm for all tokens in this expert - if (!ggml_zendnn_gemm(ctx, - ne01, // m - cne1, // n - ne10, // k - src0_cur, - ne00, // lda - wdata_cur, - ne10, // ldb - dst_cur, - ne01, // ldc - src0->type, - src0->type == GGML_TYPE_Q8_0 ? GGML_TYPE_F32 : vec_dot_type, - dst->type)) { - GGML_ABORT("%s: ZenDNN gemm failed\n", __func__); + expert_row_count[batch_idx] = cne1; + batch_src[batch_idx] = wdata_cur + w_off; + batch_wei[batch_idx] = (const char *) src0->data + cur_a * nb02; + batch_dst[batch_idx] = dst_cur + d_off; + } + + if (!ggml_zendnn_group_gemm(ctx, + ne01, // m + ne10, // k + expert_row_count, // n (per expert) + batch_wei, ne00, // A: weights, lda + batch_src, ne10, // B: input, ldb + batch_dst, ne01, // C: output, ldc + src0->type, + src0->type == GGML_TYPE_Q8_0 ? GGML_TYPE_F32 : vec_dot_type, + dst->type)) + GGML_ABORT("%s: ZenDNN group gemm failed\n", __func__); + + // scatter output rows to destination + #pragma omp parallel for num_threads(ctx->n_threads) schedule(static) + for (int64_t cur_a = 0; cur_a < n_as; ++cur_a) { + const int64_t cne1 = matrix_row_counts[cur_a]; + + if (cne1 == 0) { + continue; } - // scatter output rows to destination - #pragma omp parallel for num_threads(ctx->n_threads) schedule(static) + const int64_t d_off = expert_dst_off[cur_a]; + for (int64_t ir1 = 0; ir1 < cne1; ++ir1) { const mmid_row_mapping & row_mapping = matrix_rows[cur_a][ir1]; const int64_t id = row_mapping.i1; @@ -381,7 +511,7 @@ static void ggml_zendnn_compute_forward_mul_mat_id( std::memcpy( (char *) dst->data + i1*nb1 + i2*nb2, - dst_cur + ir1 * ggml_row_size(dst->type, ne01), + dst_cur + d_off + ir1 * ggml_row_size(dst->type, ne01), ggml_row_size(dst->type, ne01) ); } @@ -591,22 +721,26 @@ static bool ggml_backend_zendnn_device_supports_op(ggml_backend_dev_t dev, const if(K <= 256 || N <= 128 || M <= 96) { return false; } + + // MUL_MAT_ID's gather+matmul+scatter approach favors a moderate expert count + if (op->op == GGML_OP_MUL_MAT_ID) { + const int64_t n_experts = weights->ne[2]; + const int64_t max_experts = 32; + if (n_experts > max_experts) { + return false; + } + + // fall back once the average rows per expert (N / n_experts) is too thin + // to amortize each per-expert GEMM's overhead + if (N / n_experts <= 32) { + return false; + } + } } else if (ne0 < min_batch || ne1 < min_batch || ne10 < min_batch) { return false; } - // MUL_MAT_ID performs best with a moderate number of experts due to its - // gather + batched matmul + scatter approach. Future versions will leverage - // ZenDNN's grouped_gemm for better scalability with larger expert counts: - // https://github.com/amd/ZenDNN/blob/main/docs/operator/lowoha_group_gemm_operator.md - if (op->op == GGML_OP_MUL_MAT_ID) { - const int64_t n_experts = weights->ne[2]; - const int64_t max_experts = 32; - if (n_experts > max_experts) { - return false; - } - } switch (weights->type) { case GGML_TYPE_F32: case GGML_TYPE_BF16: diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index 4fd972b41b46..8bb064f3cf73 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -1122,6 +1122,9 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "GATED_DELTA_NET", "TURBO_WHT", "LIGHTNING_INDEXER", + "DSV4_HC_COMB", + "DSV4_HC_PRE", + "DSV4_HC_POST", "UNARY", @@ -1139,7 +1142,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "GLU", }; -static_assert(GGML_OP_COUNT == 100, "GGML_OP_COUNT != 100"); +static_assert(GGML_OP_COUNT == 103, "GGML_OP_COUNT != 103"); static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "none", @@ -1236,6 +1239,9 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "gated_delta_net(q, k, v, g, beta, s)", "turbo_wht(a)", "lightning_indexer(q, k, weights, mask)", + "dsv4_hc_comb(mixes, scale, base)", + "dsv4_hc_pre(x, weights)", + "dsv4_hc_post(x, residual, post, comb)", "unary(x)", @@ -1253,7 +1259,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "glu(x)", }; -static_assert(GGML_OP_COUNT == 100, "GGML_OP_COUNT != 100"); +static_assert(GGML_OP_COUNT == 103, "GGML_OP_COUNT != 103"); static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2"); @@ -6463,6 +6469,132 @@ struct ggml_tensor * ggml_lightning_indexer( return result; } +// ggml_dsv4_hc_comb + +struct ggml_tensor * ggml_dsv4_hc_comb( + struct ggml_context * ctx, + struct ggml_tensor * mixes, + struct ggml_tensor * scale, + struct ggml_tensor * base, + float eps, + int32_t n_iter) { + GGML_ASSERT(mixes->type == GGML_TYPE_F32); + GGML_ASSERT(scale->type == GGML_TYPE_F32); + GGML_ASSERT(base->type == GGML_TYPE_F32); + GGML_ASSERT(n_iter > 0); + + const int64_t hc_mix_dim = mixes->ne[0]; + const int64_t n_tokens = mixes->ne[1]; + + int64_t hc = 0; + for (int64_t i = 1; i*i + 2*i <= hc_mix_dim; ++i) { + if ((2 + i)*i == hc_mix_dim) { + hc = i; + break; + } + } + + GGML_ASSERT(hc > 0); + GGML_ASSERT(hc == 4); + GGML_ASSERT(mixes->ne[2] == 1); + GGML_ASSERT(mixes->ne[3] == 1); + GGML_ASSERT(scale->ne[0] >= 3); + GGML_ASSERT(scale->ne[1] == 1); + GGML_ASSERT(scale->ne[2] == 1); + GGML_ASSERT(scale->ne[3] == 1); + GGML_ASSERT(base->ne[0] == hc_mix_dim); + GGML_ASSERT(base->ne[1] == 1); + GGML_ASSERT(base->ne[2] == 1); + GGML_ASSERT(base->ne[3] == 1); + + struct ggml_tensor * result = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hc, hc, n_tokens); + + ggml_set_op_params_f32(result, 0, eps); + ggml_set_op_params_i32(result, 1, n_iter); + + result->op = GGML_OP_DSV4_HC_COMB; + result->src[0] = mixes; + result->src[1] = scale; + result->src[2] = base; + + return result; +} + +// ggml_dsv4_hc_pre + +struct ggml_tensor * ggml_dsv4_hc_pre( + struct ggml_context * ctx, + struct ggml_tensor * x, + struct ggml_tensor * weights) { + GGML_ASSERT(x->type == GGML_TYPE_F32); + GGML_ASSERT(weights->type == GGML_TYPE_F32); + + const int64_t n_embd = x->ne[0]; + const int64_t hc = x->ne[1]; + const int64_t n_tokens = x->ne[2]; + + GGML_ASSERT(hc > 0); + GGML_ASSERT(x->ne[3] == 1); + GGML_ASSERT(weights->ne[0] == hc); + GGML_ASSERT(weights->ne[1] == n_tokens); + GGML_ASSERT(weights->ne[2] == 1); + GGML_ASSERT(weights->ne[3] == 1); + + struct ggml_tensor * result = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_tokens); + + result->op = GGML_OP_DSV4_HC_PRE; + result->src[0] = x; + result->src[1] = weights; + + return result; +} + +// ggml_dsv4_hc_post + +struct ggml_tensor * ggml_dsv4_hc_post( + struct ggml_context * ctx, + struct ggml_tensor * x, + struct ggml_tensor * residual, + struct ggml_tensor * post, + struct ggml_tensor * comb) { + GGML_ASSERT(x->type == GGML_TYPE_F32); + GGML_ASSERT(residual->type == GGML_TYPE_F32); + GGML_ASSERT(post->type == GGML_TYPE_F32); + GGML_ASSERT(comb->type == GGML_TYPE_F32); + + const int64_t n_embd = x->ne[0]; + const int64_t n_tokens = x->ne[1]; + const int64_t hc = residual->ne[1]; + + GGML_ASSERT(hc > 0); + GGML_ASSERT(x->ne[2] == 1); + GGML_ASSERT(x->ne[3] == 1); + + GGML_ASSERT(residual->ne[0] == n_embd); + GGML_ASSERT(residual->ne[2] == n_tokens); + GGML_ASSERT(residual->ne[3] == 1); + + GGML_ASSERT(post->ne[0] == hc); + GGML_ASSERT(post->ne[1] == n_tokens); + GGML_ASSERT(post->ne[2] == 1); + GGML_ASSERT(post->ne[3] == 1); + + GGML_ASSERT(comb->ne[0] == hc); + GGML_ASSERT(comb->ne[1] == hc); + GGML_ASSERT(comb->ne[2] == n_tokens); + GGML_ASSERT(comb->ne[3] == 1); + + struct ggml_tensor * result = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd, hc, n_tokens); + + result->op = GGML_OP_DSV4_HC_POST; + result->src[0] = x; + result->src[1] = residual; + result->src[2] = post; + result->src[3] = comb; + + return result; +} + //////////////////////////////////////////////////////////////////////////////// struct ggml_hash_set ggml_hash_set_new(size_t size) { @@ -7847,7 +7979,9 @@ void ggml_set_input(struct ggml_tensor * tensor) { } void ggml_set_output(struct ggml_tensor * tensor) { - tensor->flags |= GGML_TENSOR_FLAG_OUTPUT; + for (struct ggml_tensor * cur = tensor; cur != NULL; cur = cur->view_src) { + cur->flags |= GGML_TENSOR_FLAG_OUTPUT; + } } void ggml_set_param(struct ggml_tensor * tensor) { diff --git a/ggml/src/gguf.cpp b/ggml/src/gguf.cpp index 7920b8100b61..9f9e4fe5d104 100644 --- a/ggml/src/gguf.cpp +++ b/ggml/src/gguf.cpp @@ -1424,7 +1424,7 @@ void gguf_set_tensor_data(struct gguf_context * ctx, const char * name, const vo struct gguf_writer_base { size_t written_bytes {0u}; - ~gguf_writer_base(void) = default; + virtual ~gguf_writer_base(void) = default; // we bet on devirtualization virtual void write(int8_t val) = 0; diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index b695e4f82e0a..d5a21322cf7c 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -11,6 +11,7 @@ GGUF_VERSION = 3 GGUF_DEFAULT_ALIGNMENT = 32 GGML_QUANT_VERSION = 2 # GGML_QNT_VERSION from ggml.h +GGML_MAX_DIMS = 4 # GGML_MAX_DIMS from ggml.h # # metadata keys @@ -126,6 +127,9 @@ class LLM: EXPERTS_PER_GROUP = "{arch}.experts_per_group" MOE_EVERY_N_LAYERS = "{arch}.moe_every_n_layers" MOE_LATENT_SIZE = "{arch}.moe_latent_size" + SITU_BETA = "{arch}.situ_beta" + SITU_LINEAR_BETA = "{arch}.situ_linear_beta" + ATTN_RES_BLOCK_SIZE = "{arch}.attn_res_block_size" NEXTN_PREDICT_LAYERS = "{arch}.nextn_predict_layers" NUM_DEEPSTACK_LAYERS = "{arch}.n_deepstack_layers" DEEPSTACK_MAPPING = "{arch}.deepstack_mapping" @@ -145,6 +149,8 @@ class LLM: TOKEN_SHIFT_COUNT = "{arch}.token_shift_count" INTERLEAVE_MOE_LAYER_STEP = "{arch}.interleave_moe_layer_step" FULL_ATTENTION_INTERVAL = "{arch}.full_attention_interval" + NUM_LOOPS = "{arch}.num_loops" + SKIP_LOOP_FINAL_NORM = "{arch}.skip_loop_final_norm" HASH_LAYER_COUNT = "{arch}.hash_layer_count" ACTIVATION_SPARSITY_SCALE = "{arch}.activation_sparsity_scale" ALTUP_ACTIVE_IDX = "{arch}.altup.active_idx" @@ -159,6 +165,7 @@ class LLM: TARGET_HIDDEN_SIZE = "{arch}.target_hidden_size" BLOCK_SIZE = "{arch}.block_size" NORM_BEFORE_RESIDUAL = "{arch}.norm_before_residual" + NORM_BEFORE_FC = "{arch}.norm_before_fc" class Attention: HEAD_COUNT = "{arch}.attention.head_count" @@ -200,6 +207,9 @@ class Indexer: HEAD_COUNT = "{arch}.attention.indexer.head_count" KEY_LENGTH = "{arch}.attention.indexer.key_length" TOP_K = "{arch}.attention.indexer.top_k" + BLOCK_SIZE = "{arch}.attention.indexer.block_size" # MSA + LOCAL_BLOCKS = "{arch}.attention.indexer.local_blocks" # MSA + TYPES = "{arch}.attention.indexer.types" class HyperConnection: COUNT = "{arch}.hyper_connection.count" @@ -238,7 +248,8 @@ class SSM: DT_B_C_RMS = "{arch}.ssm.dt_b_c_rms" class KDA: - HEAD_DIM = "{arch}.kda.head_dim" + HEAD_DIM = "{arch}.kda.head_dim" + GATE_LOWER_BOUND = "{arch}.kda.gate_lower_bound" class WKV: HEAD_SIZE = "{arch}.wkv.head_size" @@ -347,6 +358,7 @@ class ClipVision: class Attention: HEAD_COUNT = "clip.vision.attention.head_count" HEAD_COUNT_KV = "clip.vision.attention.head_count_kv" # used by mimovl (GQA) + HEAD_DIM = "clip.vision.attention.head_dim" # set when qkv width != n_embd LAYERNORM_EPS = "clip.vision.attention.layer_norm_epsilon" class Projector: @@ -367,10 +379,17 @@ class ClipAudio: FEED_FORWARD_LENGTH = "clip.audio.feed_forward_length" PROJECTION_DIM = "clip.audio.projection_dim" BLOCK_COUNT = "clip.audio.block_count" + SUBSAMPLING_FACTOR = "clip.audio.subsampling_factor" CHUNK_SIZE = "clip.audio.chunk_size" CONV_KERNEL_SIZE = "clip.audio.conv_kernel_size" MAX_POS_EMB = "clip.audio.max_pos_emb" FEATURE_LAYERS = "clip.audio.feature_layer" # Granite Speech Plus + RVQ_NUM_QUANTIZERS = "clip.audio.rvq.num_quantizers" + RVQ_CODEBOOK_SIZE = "clip.audio.rvq.codebook_size" + WA_PATTERN_MODE = "clip.audio.wa_pattern_mode" # per-layer -1 (full) / 0 (windowed) + WINDOW_SIZE = "clip.audio.window_size" + LOCAL_BLOCK_COUNT = "clip.audio.local_block_count" # mimo-v2.5: input_local_transformer layer count + LOCAL_GROUP_SIZE = "clip.audio.local_group_size" # mimo-v2.5: input_local_transformer grouping size class Attention: HEAD_COUNT = "clip.audio.attention.head_count" @@ -527,6 +546,7 @@ class MODEL_ARCH(IntEnum): APERTUS = auto() COGVLM = auto() MINIMAXM2 = auto() + MINIMAXM3 = auto() RND1 = auto() PANGU_EMBED = auto() MISTRAL3 = auto() @@ -539,9 +559,11 @@ class MODEL_ARCH(IntEnum): LLAMA_EMBED = auto() MAINCODER = auto() KIMI_LINEAR = auto() + KIMI_K3 = auto() TALKIE = auto() MELLUM = auto() INKLING = auto() + NANBEIGE = auto() class VISION_PROJECTOR_TYPE(IntEnum): @@ -614,6 +636,13 @@ class MODEL_TENSOR(IntEnum): FFN_GATE_TID2EID = auto() MOE_LATENT_DOWN = auto() # nemotron 3 super MOE_LATENT_UP = auto() # nemotron 3 super + MOE_LATENT_NORM = auto() # kimi k3 + ATTN_RES_NORM = auto() # kimi k3 + ATTN_RES_PROJ = auto() # kimi k3 + FFN_RES_NORM = auto() # kimi k3 + FFN_RES_PROJ = auto() # kimi k3 + OUTPUT_RES_NORM = auto() # kimi k3 + OUTPUT_RES_PROJ = auto() # kimi k3 ATTN_Q_NORM = auto() ATTN_K_NORM = auto() LAYER_OUT_NORM = auto() @@ -782,6 +811,9 @@ class MODEL_TENSOR(IntEnum): INDEXER_PROJ = auto() INDEXER_ATTN_K = auto() INDEXER_ATTN_Q_B = auto() + INDEXER_Q_PROJ = auto() + INDEXER_K_PROJ = auto() + INDEXER_Q_NORM = auto() INDEXER_COMPRESSOR_WKV = auto() INDEXER_COMPRESSOR_WGATE = auto() INDEXER_COMPRESSOR_APE = auto() @@ -859,6 +891,8 @@ class MODEL_TENSOR(IntEnum): V_MM_UP = auto() # cogvlm V_MM_DOWN = auto() # cogvlm V_MM_GATE = auto() # cogvlm + V_MM_MERGER_FC1 = auto() # minimax-m3 (patch-merge MLP) + V_MM_MERGER_FC2 = auto() # minimax-m3 (patch-merge MLP) V_TOK_BOI = auto() # cogvlm V_TOK_EOI = auto() # cogvlm V_TOK_IMG_BEGIN = auto() # hunyuanvl @@ -942,6 +976,9 @@ class MODEL_TENSOR(IntEnum): A_ENC_FFN_SCALE_1 = auto() # gemma3n A_ENC_FFN_GATE_1 = auto() # lfm2, gemma3n A_ENC_FFN_DOWN_1 = auto() # lfm2, gemma3n + A_ENC_DOWNSAMPLE_CONV = auto() # mimo-audio-tokenizer: post-transformer downsample conv + A_ENC_DOWNSAMPLE_NORM = auto() # mimo-audio-tokenizer: post-transformer downsample norm + A_ENC_RVQ_CODEBOOK = auto() # mimo-audio-tokenizer: residual vector quantizer codebook, per quantizer index A_MMPROJ = auto() A_MMPROJ_FC = auto() A_MM_NORM_PRE = auto() @@ -950,6 +987,17 @@ class MODEL_TENSOR(IntEnum): A_MM_HARD_EMB_NORM = auto() # gemma3n A_MM_SOFT_EMB_NORM = auto() # gemma3n A_MM_INP_PROJ = auto() # gemma3n + A_MM_CODE_EMBD = auto() # mimo: text-side RVQ code embedding table ("text codebook"), merged 3D [n_channels, vocab, dim] + A_MM_LOCAL_ATTN_Q = auto() # mimo: input_local_transformer (LLM-side connector) + A_MM_LOCAL_ATTN_K = auto() + A_MM_LOCAL_ATTN_V = auto() + A_MM_LOCAL_ATTN_OUT = auto() + A_MM_LOCAL_FFN_GATE = auto() + A_MM_LOCAL_FFN_UP = auto() + A_MM_LOCAL_FFN_DOWN = auto() + A_MM_LOCAL_LN1 = auto() + A_MM_LOCAL_LN2 = auto() + A_MM_LOCAL_NORM = auto() # final norm after all input_local_transformer layers A_PER_DIM_K_SCALE = auto() # gemma4 A_PER_DIM_SCALE = auto() # gemma4 # nextn/mtp @@ -964,6 +1012,10 @@ class MODEL_TENSOR(IntEnum): # eagle3 FC = auto() # feature fusion layer D2T = auto() # draft to target vocabulary mapping + # dspark + DSPARK_MARKOV_W1 = auto() # markov head: prev-token embed + DSPARK_MARKOV_W2 = auto() # markov head: bias projection + DSPARK_CONF_PROJ = auto() # confidence head # lfm2 audio A_ENC_NORM_CONV = auto() A_ENC_LINEAR_POS = auto() @@ -974,6 +1026,10 @@ class MODEL_TENSOR(IntEnum): A_ENC_CONV_NORM = auto() # SSM conv A_ENC_CONV_PW1 = auto() A_ENC_CONV_PW2 = auto() + A_ENC_CONV_NORM_MEAN = auto() # parakeet + A_ENC_CONV_NORM_VAR = auto() # parakeet + A_ENC_MEL_FILTERS = auto() # parakeet + A_ENC_WINDOW = auto() # parakeet A_CTC_OUT = auto() A_CTC_OUT_MID = auto() A_ENC_ATTN_REL_POS_EMB = auto() @@ -1118,6 +1174,7 @@ class MODEL_TENSOR(IntEnum): MODEL_ARCH.GROVEMOE: "grovemoe", MODEL_ARCH.APERTUS: "apertus", MODEL_ARCH.MINIMAXM2: "minimax-m2", + MODEL_ARCH.MINIMAXM3: "minimax-m3", MODEL_ARCH.COGVLM: "cogvlm", MODEL_ARCH.RND1: "rnd1", MODEL_ARCH.PANGU_EMBED: "pangu-embedded", @@ -1131,9 +1188,11 @@ class MODEL_TENSOR(IntEnum): MODEL_ARCH.LLAMA_EMBED: "llama-embed", MODEL_ARCH.MAINCODER: "maincoder", MODEL_ARCH.KIMI_LINEAR: "kimi-linear", + MODEL_ARCH.KIMI_K3: "kimi-k3", MODEL_ARCH.TALKIE: "talkie", MODEL_ARCH.MELLUM: "mellum", MODEL_ARCH.INKLING: "inkling", + MODEL_ARCH.NANBEIGE: "nanbeige", } VISION_PROJECTOR_TYPE_NAMES: dict[VISION_PROJECTOR_TYPE, str] = { @@ -1206,6 +1265,13 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.FFN_GATE_TID2EID: "blk.{bid}.ffn_gate_tid2eid", MODEL_TENSOR.MOE_LATENT_DOWN: "blk.{bid}.ffn_latent_down", # nemotron 3 super MODEL_TENSOR.MOE_LATENT_UP: "blk.{bid}.ffn_latent_up", # nemotron 3 super + MODEL_TENSOR.MOE_LATENT_NORM: "blk.{bid}.ffn_latent_norm", # kimi k3 + MODEL_TENSOR.ATTN_RES_NORM: "blk.{bid}.attn_res_norm", # kimi k3 + MODEL_TENSOR.ATTN_RES_PROJ: "blk.{bid}.attn_res_proj", # kimi k3 + MODEL_TENSOR.FFN_RES_NORM: "blk.{bid}.ffn_res_norm", # kimi k3 + MODEL_TENSOR.FFN_RES_PROJ: "blk.{bid}.ffn_res_proj", # kimi k3 + MODEL_TENSOR.OUTPUT_RES_NORM: "output_res_norm", # kimi k3 + MODEL_TENSOR.OUTPUT_RES_PROJ: "output_res_proj", # kimi k3 MODEL_TENSOR.LAYER_OUT_NORM: "blk.{bid}.layer_output_norm", MODEL_TENSOR.LAYER_OUT_SCALE: "blk.{bid}.layer_output_scale", MODEL_TENSOR.PER_LAYER_TOKEN_EMBD: "per_layer_token_embd", # gemma3n @@ -1371,6 +1437,9 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.INDEXER_PROJ: "blk.{bid}.indexer.proj", MODEL_TENSOR.INDEXER_ATTN_K: "blk.{bid}.indexer.attn_k", MODEL_TENSOR.INDEXER_ATTN_Q_B: "blk.{bid}.indexer.attn_q_b", + MODEL_TENSOR.INDEXER_Q_PROJ: "blk.{bid}.indexer.q_proj", + MODEL_TENSOR.INDEXER_K_PROJ: "blk.{bid}.indexer.k_proj", + MODEL_TENSOR.INDEXER_Q_NORM: "blk.{bid}.indexer.q_norm", MODEL_TENSOR.INDEXER_COMPRESSOR_WKV: "blk.{bid}.indexer_compressor_kv", MODEL_TENSOR.INDEXER_COMPRESSOR_WGATE: "blk.{bid}.indexer_compressor_gate", MODEL_TENSOR.INDEXER_COMPRESSOR_APE: "blk.{bid}.indexer_compressor_ape", @@ -1447,6 +1516,8 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.V_MM_UP: "mm.up", MODEL_TENSOR.V_MM_DOWN: "mm.down", MODEL_TENSOR.V_MM_GATE: "mm.gate", + MODEL_TENSOR.V_MM_MERGER_FC1: "mm.merger.fc1", + MODEL_TENSOR.V_MM_MERGER_FC2: "mm.merger.fc2", MODEL_TENSOR.V_TOK_BOI: "v.boi", MODEL_TENSOR.V_TOK_EOI: "v.eoi", MODEL_TENSOR.V_MM_PRE_NORM: "mm.pre_norm", @@ -1530,6 +1601,9 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.A_ENC_FFN_UP_1: "a.blk.{bid}.ffn_up_1", MODEL_TENSOR.A_ENC_FFN_GATE_1: "a.blk.{bid}.ffn_gate_1", MODEL_TENSOR.A_ENC_FFN_DOWN_1: "a.blk.{bid}.ffn_down_1", + MODEL_TENSOR.A_ENC_DOWNSAMPLE_CONV: "a.downsample.conv", + MODEL_TENSOR.A_ENC_DOWNSAMPLE_NORM: "a.downsample.norm", + MODEL_TENSOR.A_ENC_RVQ_CODEBOOK: "a.rvq.codebook", MODEL_TENSOR.A_MMPROJ: "mm.a.mlp.{bid}", MODEL_TENSOR.A_MMPROJ_FC: "mm.a.fc", MODEL_TENSOR.A_MM_NORM_PRE: "mm.a.norm_pre", @@ -1538,6 +1612,17 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.A_MM_SOFT_EMB_NORM: "mm.a.soft_emb_norm", # gemma3n MODEL_TENSOR.A_MM_EMBEDDING: "mm.a.embedding", # gemma3n MODEL_TENSOR.A_MM_HARD_EMB_NORM: "mm.a.hard_emb_norm", # gemma3n + MODEL_TENSOR.A_MM_CODE_EMBD: "mm.a.code_embd", + MODEL_TENSOR.A_MM_LOCAL_ATTN_Q: "mm.a.local_blk.{bid}.attn_q", + MODEL_TENSOR.A_MM_LOCAL_ATTN_K: "mm.a.local_blk.{bid}.attn_k", + MODEL_TENSOR.A_MM_LOCAL_ATTN_V: "mm.a.local_blk.{bid}.attn_v", + MODEL_TENSOR.A_MM_LOCAL_ATTN_OUT: "mm.a.local_blk.{bid}.attn_out", + MODEL_TENSOR.A_MM_LOCAL_FFN_GATE: "mm.a.local_blk.{bid}.ffn_gate", + MODEL_TENSOR.A_MM_LOCAL_FFN_UP: "mm.a.local_blk.{bid}.ffn_up", + MODEL_TENSOR.A_MM_LOCAL_FFN_DOWN: "mm.a.local_blk.{bid}.ffn_down", + MODEL_TENSOR.A_MM_LOCAL_LN1: "mm.a.local_blk.{bid}.ln1", + MODEL_TENSOR.A_MM_LOCAL_LN2: "mm.a.local_blk.{bid}.ln2", + MODEL_TENSOR.A_MM_LOCAL_NORM: "mm.a.local_norm", MODEL_TENSOR.A_PER_DIM_K_SCALE: "a.blk.{bid}.per_dim_k_scale", # gemma4 MODEL_TENSOR.A_PER_DIM_SCALE: "a.blk.{bid}.per_dim_scale", # gemma4 # lfm2 audio @@ -1550,6 +1635,10 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.A_ENC_CONV_NORM: "a.blk.{bid}.conv_norm", MODEL_TENSOR.A_ENC_CONV_PW1: "a.blk.{bid}.conv_pw1", MODEL_TENSOR.A_ENC_CONV_PW2: "a.blk.{bid}.conv_pw2", + MODEL_TENSOR.A_ENC_CONV_NORM_MEAN: "a.blk.{bid}.conv_norm_mean", + MODEL_TENSOR.A_ENC_CONV_NORM_VAR: "a.blk.{bid}.conv_norm_var", + MODEL_TENSOR.A_ENC_MEL_FILTERS: "a.mel_filters", + MODEL_TENSOR.A_ENC_WINDOW: "a.window", MODEL_TENSOR.A_CTC_OUT: "a.enc_ctc_out", MODEL_TENSOR.A_CTC_OUT_MID: "a.enc_ctc_out_mid", MODEL_TENSOR.A_ENC_ATTN_REL_POS_EMB: "a.blk.{bid}.attn_rel_pos_emb", @@ -1580,6 +1669,9 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD: "blk.{bid}.nextn.shared_head_head", MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM: "blk.{bid}.nextn.shared_head_norm", MODEL_TENSOR.FC: "fc", + MODEL_TENSOR.DSPARK_MARKOV_W1: "markov_w1", + MODEL_TENSOR.DSPARK_MARKOV_W2: "markov_w2", + MODEL_TENSOR.DSPARK_CONF_PROJ: "conf_proj", MODEL_TENSOR.D2T: "d2t", } @@ -1643,6 +1735,8 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.V_RESMPL_QUERY, MODEL_TENSOR.V_TOK_EMBD_IMG_BREAK, MODEL_TENSOR.V_MM_PATCH_MERGER, + MODEL_TENSOR.V_MM_MERGER_FC1, + MODEL_TENSOR.V_MM_MERGER_FC2, MODEL_TENSOR.V_DS_NORM, MODEL_TENSOR.V_DS_FC1, MODEL_TENSOR.V_DS_FC2, @@ -1737,10 +1831,24 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.A_ENC_FFN_UP_1, MODEL_TENSOR.A_ENC_FFN_GATE_1, MODEL_TENSOR.A_ENC_FFN_DOWN_1, + MODEL_TENSOR.A_ENC_DOWNSAMPLE_CONV, + MODEL_TENSOR.A_ENC_DOWNSAMPLE_NORM, + MODEL_TENSOR.A_ENC_RVQ_CODEBOOK, MODEL_TENSOR.A_MMPROJ, MODEL_TENSOR.A_MMPROJ_FC, MODEL_TENSOR.A_MM_NORM_PRE, MODEL_TENSOR.A_MM_NORM_MID, + MODEL_TENSOR.A_MM_CODE_EMBD, + MODEL_TENSOR.A_MM_LOCAL_ATTN_Q, + MODEL_TENSOR.A_MM_LOCAL_ATTN_K, + MODEL_TENSOR.A_MM_LOCAL_ATTN_V, + MODEL_TENSOR.A_MM_LOCAL_ATTN_OUT, + MODEL_TENSOR.A_MM_LOCAL_FFN_GATE, + MODEL_TENSOR.A_MM_LOCAL_FFN_UP, + MODEL_TENSOR.A_MM_LOCAL_FFN_DOWN, + MODEL_TENSOR.A_MM_LOCAL_LN1, + MODEL_TENSOR.A_MM_LOCAL_LN2, + MODEL_TENSOR.A_MM_LOCAL_NORM, MODEL_TENSOR.A_ENC_NORM_CONV, MODEL_TENSOR.A_ENC_LINEAR_POS, MODEL_TENSOR.A_ENC_POS_BIAS_U, @@ -1750,6 +1858,10 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.A_ENC_CONV_NORM, MODEL_TENSOR.A_ENC_CONV_PW1, MODEL_TENSOR.A_ENC_CONV_PW2, + MODEL_TENSOR.A_ENC_CONV_NORM_MEAN, + MODEL_TENSOR.A_ENC_CONV_NORM_VAR, + MODEL_TENSOR.A_ENC_MEL_FILTERS, + MODEL_TENSOR.A_ENC_WINDOW, MODEL_TENSOR.A_MM_INP_PROJ, MODEL_TENSOR.A_MM_SOFT_EMB_NORM, MODEL_TENSOR.A_MM_EMBEDDING, @@ -2255,7 +2367,13 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.SSM_NORM, MODEL_TENSOR.SSM_IN, MODEL_TENSOR.SSM_BETA_ALPHA, - MODEL_TENSOR.SSM_OUT + MODEL_TENSOR.SSM_OUT, + MODEL_TENSOR.NEXTN_EH_PROJ, + MODEL_TENSOR.NEXTN_EMBED_TOKENS, + MODEL_TENSOR.NEXTN_ENORM, + MODEL_TENSOR.NEXTN_HNORM, + MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD, + MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM, ], MODEL_ARCH.QWEN3VL: [ MODEL_TENSOR.TOKEN_EMBD, @@ -3141,6 +3259,13 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.FFN_DOWN_SHEXP, MODEL_TENSOR.FFN_UP_SHEXP, MODEL_TENSOR.FFN_EXP_PROBS_B, + # NextN/MTP tensors + MODEL_TENSOR.NEXTN_EH_PROJ, + MODEL_TENSOR.NEXTN_EMBED_TOKENS, + MODEL_TENSOR.NEXTN_ENORM, + MODEL_TENSOR.NEXTN_HNORM, + MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD, + MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM, ], MODEL_ARCH.DEEPSEEK2OCR: [ MODEL_TENSOR.TOKEN_EMBD, @@ -3257,6 +3382,12 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.FFN_GATE_SHEXP, MODEL_TENSOR.FFN_DOWN_SHEXP, MODEL_TENSOR.FFN_UP_SHEXP, + MODEL_TENSOR.NEXTN_EH_PROJ, + MODEL_TENSOR.NEXTN_EMBED_TOKENS, + MODEL_TENSOR.NEXTN_ENORM, + MODEL_TENSOR.NEXTN_HNORM, + MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD, + MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM, ], MODEL_ARCH.ERNIE4_5_MOE: [ MODEL_TENSOR.TOKEN_EMBD, @@ -4211,6 +4342,34 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.FFN_UP_EXP, MODEL_TENSOR.FFN_EXP_PROBS_B, ], + MODEL_ARCH.MINIMAXM3: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_EXP_PROBS_B, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.INDEXER_Q_PROJ, + MODEL_TENSOR.INDEXER_K_PROJ, + MODEL_TENSOR.INDEXER_Q_NORM, + MODEL_TENSOR.INDEXER_K_NORM, + ], MODEL_ARCH.COGVLM: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, @@ -4295,6 +4454,7 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, MODEL_TENSOR.FC, + MODEL_TENSOR.ENC_OUTPUT_NORM, MODEL_TENSOR.D2T, ], MODEL_ARCH.DFLASH: [ @@ -4306,12 +4466,41 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.ATTN_OUT, MODEL_TENSOR.ATTN_Q_NORM, MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_SINKS, + MODEL_TENSOR.ATTN_Q_A, + MODEL_TENSOR.ATTN_Q_B, + MODEL_TENSOR.ATTN_Q_A_NORM, + MODEL_TENSOR.ATTN_KV, + MODEL_TENSOR.ATTN_KV_NORM, + MODEL_TENSOR.ATTN_OUT_A, + MODEL_TENSOR.ATTN_OUT_B, + MODEL_TENSOR.HC_ATTN_FN, + MODEL_TENSOR.HC_ATTN_BASE, + MODEL_TENSOR.HC_ATTN_SCALE, + MODEL_TENSOR.HC_FFN_FN, + MODEL_TENSOR.HC_FFN_BASE, + MODEL_TENSOR.HC_FFN_SCALE, + MODEL_TENSOR.HC_HEAD_FN, + MODEL_TENSOR.HC_HEAD_BASE, + MODEL_TENSOR.HC_HEAD_SCALE, MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_GATE, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_EXP_PROBS_B, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, MODEL_TENSOR.FC, MODEL_TENSOR.ENC_OUTPUT_NORM, + # optional DSpark heads + MODEL_TENSOR.DSPARK_MARKOV_W1, + MODEL_TENSOR.DSPARK_MARKOV_W2, + MODEL_TENSOR.DSPARK_CONF_PROJ, ], MODEL_ARCH.MISTRAL4: [ MODEL_TENSOR.TOKEN_EMBD, @@ -4366,8 +4555,11 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.FFN_EXP_PROBS_B, MODEL_TENSOR.LAYER_OUT_NORM, MODEL_TENSOR.NEXTN_EH_PROJ, + MODEL_TENSOR.NEXTN_EMBED_TOKENS, MODEL_TENSOR.NEXTN_ENORM, MODEL_TENSOR.NEXTN_HNORM, + MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD, + MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM, ], MODEL_ARCH.STEP35: [ MODEL_TENSOR.TOKEN_EMBD, @@ -4438,6 +4630,55 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], + MODEL_ARCH.KIMI_K3: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.OUTPUT_RES_NORM, + MODEL_TENSOR.OUTPUT_RES_PROJ, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_GATE, + MODEL_TENSOR.ATTN_Q_A, + MODEL_TENSOR.ATTN_Q_B, + MODEL_TENSOR.ATTN_KV_A_MQA, + MODEL_TENSOR.ATTN_KV_B, + MODEL_TENSOR.ATTN_K_B, + MODEL_TENSOR.ATTN_V_B, + MODEL_TENSOR.ATTN_Q_A_NORM, + MODEL_TENSOR.ATTN_KV_A_NORM, + MODEL_TENSOR.ATTN_RES_NORM, + MODEL_TENSOR.ATTN_RES_PROJ, + MODEL_TENSOR.FFN_RES_NORM, + MODEL_TENSOR.FFN_RES_PROJ, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.MOE_LATENT_DOWN, + MODEL_TENSOR.MOE_LATENT_NORM, + MODEL_TENSOR.MOE_LATENT_UP, + MODEL_TENSOR.SSM_CONV1D_Q, + MODEL_TENSOR.SSM_CONV1D_K, + MODEL_TENSOR.SSM_CONV1D_V, + MODEL_TENSOR.SSM_F_A, + MODEL_TENSOR.SSM_F_B, + MODEL_TENSOR.SSM_BETA, + MODEL_TENSOR.SSM_A, + MODEL_TENSOR.SSM_DT, + MODEL_TENSOR.SSM_NORM, + MODEL_TENSOR.FFN_EXP_PROBS_B, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + ], MODEL_ARCH.KIMI_LINEAR: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, @@ -4509,7 +4750,22 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.FFN_DOWN_EXP, MODEL_TENSOR.FFN_UP_EXP, ], - # TODO + MODEL_ARCH.NANBEIGE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], } # tensors that will not be serialized @@ -4576,6 +4832,10 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_ROT_EMBD, ], + MODEL_ARCH.NANBEIGE: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], } # @@ -4774,6 +5034,7 @@ class VisionProjectorType: KIMIVL = "kimivl" PADDLEOCR = "paddleocr" KIMIK25 = "kimik25" + KIMIK3 = "kimik3" LIGHTONOCR = "lightonocr" COGVLM = "cogvlm" JANUS_PRO = "janus_pro" @@ -4786,9 +5047,12 @@ class VisionProjectorType: YOUTUVL = "youtuvl" NEMOTRON_V2_VL = "nemotron_v2_vl" HUNYUANVL = "hunyuanvl" + PARAKEET = "parakeet" # audio + MINIMAXM3 = "minimax_m3" MINICPMV4_6 = "minicpmv4_6" GRANITE_SPEECH = "granite_speech" # audio MIMOVL = "mimovl" + MIMO_AUDIO = "mimo_audio" GRANITE4_VISION = "granite4_vision" diff --git a/gguf-py/gguf/gguf_reader.py b/gguf-py/gguf/gguf_reader.py index 0a1b85f50641..ea241ada285c 100644 --- a/gguf-py/gguf/gguf_reader.py +++ b/gguf-py/gguf/gguf_reader.py @@ -22,6 +22,7 @@ sys.path.insert(0, str(Path(__file__).parent.parent)) from gguf.constants import ( + GGML_MAX_DIMS, GGML_QUANT_SIZES, GGUF_DEFAULT_ALIGNMENT, GGUF_MAGIC, @@ -266,6 +267,8 @@ def _get_tensor_info_field(self, orig_offs: int) -> ReaderField: # Get Tensor Dimensions Count n_dims = self._get(offs, np.uint32) offs += int(n_dims.nbytes) + if n_dims[0] > GGML_MAX_DIMS: + raise ValueError(f'Tensor dimensions count {n_dims[0]} exceeds GGML_MAX_DIMS ({GGML_MAX_DIMS})') # Get Tensor Dimension Array dims = self._get(offs, np.uint64, n_dims[0]) @@ -326,7 +329,10 @@ def _build_tensors(self, start_offs: int, fields: list[ReaderField]) -> None: raise ValueError(f'Found duplicated tensor with name {tensor_name}') tensor_names.add(tensor_name) ggml_type = GGMLQuantizationType(raw_dtype[0]) - n_elems = int(np.prod(dims)) + # use Python ints: np.prod on uint64 wraps silently on overflow + n_elems = 1 + for dim in dims.tolist(): + n_elems *= int(dim) np_dims = tuple(reversed(dims.tolist())) block_size, type_size = GGML_QUANT_SIZES[ggml_type] n_bytes = n_elems * type_size // block_size diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 1e277f0687c5..c4ff3760c8d7 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -793,6 +793,16 @@ def add_indexer_key_length(self, length: int) -> None: def add_indexer_top_k(self, top_k: int) -> None: self.add_uint32(Keys.Attention.Indexer.TOP_K.format(arch=self.arch), top_k) + def add_indexer_block_size(self, block_size: int) -> None: + self.add_uint32(Keys.Attention.Indexer.BLOCK_SIZE.format(arch=self.arch), block_size) + + def add_indexer_local_blocks(self, local_blocks: int) -> None: + self.add_uint32(Keys.Attention.Indexer.LOCAL_BLOCKS.format(arch=self.arch), local_blocks) + + def add_indexer_types(self, value: Sequence[bool]) -> None: + key = Keys.Attention.Indexer.TYPES.format(arch=self.arch) + self.add_array(key, value) + def add_max_alibi_bias(self, bias: float) -> None: self.add_float32(Keys.Attention.MAX_ALIBI_BIAS.format(arch=self.arch), bias) @@ -871,6 +881,15 @@ def add_moe_every_n_layers(self, value: int) -> None: def add_moe_latent_size(self, value: int) -> None: self.add_uint32(Keys.LLM.MOE_LATENT_SIZE.format(arch=self.arch), value) + def add_situ_beta(self, value: float) -> None: + self.add_float32(Keys.LLM.SITU_BETA.format(arch=self.arch), value) + + def add_situ_linear_beta(self, value: float) -> None: + self.add_float32(Keys.LLM.SITU_LINEAR_BETA.format(arch=self.arch), value) + + def add_attn_res_block_size(self, value: int) -> None: + self.add_uint32(Keys.LLM.ATTN_RES_BLOCK_SIZE.format(arch=self.arch), value) + def add_nextn_predict_layers(self, count: int) -> None: self.add_uint32(Keys.LLM.NEXTN_PREDICT_LAYERS.format(arch=self.arch), count) @@ -898,6 +917,12 @@ def add_wkv_head_size(self, size: int) -> None: def add_token_shift_count(self, count: int) -> None: self.add_uint32(Keys.LLM.TOKEN_SHIFT_COUNT.format(arch=self.arch), count) + def add_num_loops(self, count: int) -> None: + self.add_uint32(Keys.LLM.NUM_LOOPS.format(arch=self.arch), count) + + def add_skip_loop_final_norm(self, value: bool) -> None: + self.add_bool(Keys.LLM.SKIP_LOOP_FINAL_NORM.format(arch=self.arch), value) + def add_interleave_moe_layer_step(self, value: int) -> None: self.add_uint32(Keys.LLM.INTERLEAVE_MOE_LAYER_STEP.format(arch=self.arch), value) @@ -955,6 +980,9 @@ def add_target_hidden_size(self, value: int) -> None: def add_norm_before_residual(self, value: bool) -> None: self.add_bool(Keys.LLM.NORM_BEFORE_RESIDUAL.format(arch=self.arch), value) + def add_norm_before_fc(self, value: bool) -> None: + self.add_bool(Keys.LLM.NORM_BEFORE_FC.format(arch=self.arch), value) + def add_attention_output_group_count(self, count: int) -> None: self.add_uint32(Keys.Attention.OUTPUT_GROUP_COUNT.format(arch=self.arch), count) @@ -1068,6 +1096,9 @@ def add_ssm_dt_b_c_rms(self, value: bool) -> None: def add_kda_head_dim(self, value: int) -> None: self.add_uint32(Keys.KDA.HEAD_DIM.format(arch=self.arch), value) + def add_kda_gate_lower_bound(self, value: float) -> None: + self.add_float32(Keys.KDA.GATE_LOWER_BOUND.format(arch=self.arch), value) + def add_tokenizer_model(self, model: str) -> None: self.add_string(Keys.Tokenizer.MODEL, model) @@ -1207,6 +1238,9 @@ def add_vision_head_count(self, value: int) -> None: def add_vision_head_count_kv(self, value: int) -> None: self.add_uint32(Keys.ClipVision.Attention.HEAD_COUNT_KV, value) + def add_vision_head_dim(self, value: int) -> None: + self.add_uint32(Keys.ClipVision.Attention.HEAD_DIM, value) + def add_vision_attention_layernorm_eps(self, value: float) -> None: self.add_float32(Keys.ClipVision.Attention.LAYERNORM_EPS, value) @@ -1334,9 +1368,30 @@ def add_audio_attention_layernorm_eps(self, value: float) -> None: def add_audio_num_mel_bins(self, value: int) -> None: self.add_uint32(Keys.ClipAudio.NUM_MEL_BINS, value) + def add_audio_rvq_num_quantizers(self, value: int) -> None: + self.add_uint32(Keys.ClipAudio.RVQ_NUM_QUANTIZERS, value) + + def add_audio_rvq_codebook_size(self, values: Sequence[int]) -> None: + self.add_array(Keys.ClipAudio.RVQ_CODEBOOK_SIZE, values) + + def add_audio_wa_pattern_mode(self, modes: Sequence[int]) -> None: + self.add_array(Keys.ClipAudio.WA_PATTERN_MODE, modes) + + def add_audio_window_size(self, value: int) -> None: + self.add_uint32(Keys.ClipAudio.WINDOW_SIZE, value) + + def add_audio_local_block_count(self, value: int) -> None: + self.add_uint32(Keys.ClipAudio.LOCAL_BLOCK_COUNT, value) + + def add_audio_local_group_size(self, value: int) -> None: + self.add_uint32(Keys.ClipAudio.LOCAL_GROUP_SIZE, value) + def add_audio_stack_factor(self, value: int) -> None: self.add_uint32(Keys.ClipAudio.Projector.STACK_FACTOR, value) + def add_audio_subsampling_factor(self, value: int) -> None: + self.add_uint32(Keys.ClipAudio.SUBSAMPLING_FACTOR, value) + def add_audio_chunk_size(self, value: int) -> None: self.add_uint32(Keys.ClipAudio.CHUNK_SIZE, value) diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 2dda6183a37f..e4034f5fb74d 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -120,6 +120,14 @@ class TensorNameMap: "model.norm", # cogvlm ), + MODEL_TENSOR.OUTPUT_RES_NORM: ( + "model.output_attn_res_norm", # kimi k3 + ), + + MODEL_TENSOR.OUTPUT_RES_PROJ: ( + "model.output_attn_res_proj", # kimi k3 + ), + # Rope frequencies MODEL_TENSOR.ROPE_FREQS: ( "rope.freqs", # llama-pth @@ -613,10 +621,32 @@ class TensorNameMap: MODEL_TENSOR.MOE_LATENT_DOWN: ( "backbone.layers.{bid}.mixer.fc1_latent_proj", # nemotron 3 super + "model.layers.{bid}.block_sparse_moe.routed_expert_down_proj", # kimi k3 ), MODEL_TENSOR.MOE_LATENT_UP: ( "backbone.layers.{bid}.mixer.fc2_latent_proj", # nemotron 3 super + "model.layers.{bid}.block_sparse_moe.routed_expert_up_proj", # kimi k3 + ), + + MODEL_TENSOR.MOE_LATENT_NORM: ( + "model.layers.{bid}.block_sparse_moe.routed_expert_norm", # kimi k3 + ), + + MODEL_TENSOR.ATTN_RES_NORM: ( + "model.layers.{bid}.self_attention_res_norm", # kimi k3 + ), + + MODEL_TENSOR.ATTN_RES_PROJ: ( + "model.layers.{bid}.self_attention_res_proj", # kimi k3 + ), + + MODEL_TENSOR.FFN_RES_NORM: ( + "model.layers.{bid}.mlp_res_norm", # kimi k3 + ), + + MODEL_TENSOR.FFN_RES_PROJ: ( + "model.layers.{bid}.mlp_res_proj", # kimi k3 ), # Feed-forward down @@ -1266,7 +1296,8 @@ class TensorNameMap: ), MODEL_TENSOR.INDEXER_K_NORM: ( - "model.layers.{bid}.self_attn.indexer.k_norm", # DSA + "model.layers.{bid}.self_attn.indexer.k_norm", # DSA + "model.layers.{bid}.self_attn.index_k_norm", # MSA ), MODEL_TENSOR.INDEXER_PROJ: ( @@ -1281,6 +1312,18 @@ class TensorNameMap: "model.layers.{bid}.self_attn.indexer.wq_b", # DSA ), + MODEL_TENSOR.INDEXER_Q_PROJ: ( + "model.layers.{bid}.self_attn.index_q_proj", # MSA + ), + + MODEL_TENSOR.INDEXER_K_PROJ: ( + "model.layers.{bid}.self_attn.index_k_proj", # MSA + ), + + MODEL_TENSOR.INDEXER_Q_NORM: ( + "model.layers.{bid}.self_attn.index_q_norm", # MSA + ), + ############################################################################ # TODO: these do not belong to block_mappings_cfg - move them to mappings_cfg MODEL_TENSOR.ENC_OUTPUT_NORM: ( @@ -1293,6 +1336,18 @@ class TensorNameMap: "model.fc", # dflash ), + MODEL_TENSOR.DSPARK_MARKOV_W1: ( + "model.markov_head.markov_w1", # dspark + ), + + MODEL_TENSOR.DSPARK_MARKOV_W2: ( + "model.markov_head.markov_w2", # dspark + ), + + MODEL_TENSOR.DSPARK_CONF_PROJ: ( + "model.confidence_head.proj", # dspark + ), + MODEL_TENSOR.CLS: ( "classifier", # jina "classifier.dense", # roberta @@ -1749,6 +1804,7 @@ class TensorNameMap: "visual.merger.post_projection_norm", # glm4v "vision_tower.post_trunk_norm", # dots.ocr "vit.perceive.after_rms", # HunyuanVL + "mm_projector.post_norm", # Kimi-K3 (patchmergerv2 post-norm) ), MODEL_TENSOR.V_MM_INP_PROJ: ( @@ -1827,6 +1883,14 @@ class TensorNameMap: "visual.downsample", # glm4v ), + MODEL_TENSOR.V_MM_MERGER_FC1: ( + "patch_merge_mlp.linear_1", # minimax-m3 + ), + + MODEL_TENSOR.V_MM_MERGER_FC2: ( + "patch_merge_mlp.linear_2", # minimax-m3 + ), + MODEL_TENSOR.V_DS_NORM: ( "model.visual.deepstack_merger_list.{bid}.norm", # deepstack in qwen3vl ), @@ -2076,6 +2140,8 @@ class TensorNameMap: "conformer.pre_encode.conv.{bid}", # lfm2 "model.audio_tower.subsample_conv_projection.conv_{bid}.conv", # gemma3n "conformer.subsample_conv_projection.layer{bid}.conv", # gemma4 + "sound_encoder.encoder.subsampling.layers.{bid}", # parakeet + "encoder.conv{bid}", # mimo-audio-tokenizer ), MODEL_TENSOR.A_ENC_CONV1D_NORM: ( @@ -2100,6 +2166,7 @@ class TensorNameMap: MODEL_TENSOR.A_POST_NORM: ( "audio_tower.layer_norm", # ultravox "audio_tower.ln_post", # qwen2omni + "encoder.layer_norm", # mimo-audio-tokenizer ), MODEL_TENSOR.A_ENC_ATTN_Q: ( @@ -2107,7 +2174,9 @@ class TensorNameMap: "conformer.layers.{bid}.self_attn.linear_q", # lfm2 "conformer.layers.{bid}.attention.attn.q_proj", # gemma3n "conformer.layers.{bid}.self_attn.q_proj", # gemma4 + "sound_encoder.encoder.layers.{bid}.self_attn.q_proj", # parakeet "encoder.layers.{bid}.attn.to_q", # granite_speech + "encoder.layers.{bid}.self_attn.q_proj", # mimo-audio-tokenizer ), MODEL_TENSOR.A_ENC_ATTN_K: ( @@ -2115,7 +2184,9 @@ class TensorNameMap: "conformer.layers.{bid}.self_attn.linear_k", # lfm2 "conformer.layers.{bid}.attention.attn.k_proj", # gemma3n "conformer.layers.{bid}.self_attn.k_proj", # gemma4 + "sound_encoder.encoder.layers.{bid}.self_attn.k_proj", # parakeet "encoder.layers.{bid}.attn.to_k", # granite_speech (split from to_kv) + "encoder.layers.{bid}.self_attn.k_proj", # mimo-audio-tokenizer ), MODEL_TENSOR.A_ENC_ATTN_V: ( @@ -2123,7 +2194,9 @@ class TensorNameMap: "conformer.layers.{bid}.self_attn.linear_v", # lfm2 "conformer.layers.{bid}.attention.attn.v_proj", # gemma3n "conformer.layers.{bid}.self_attn.v_proj", # gemma4 + "sound_encoder.encoder.layers.{bid}.self_attn.v_proj", # parakeet "encoder.layers.{bid}.attn.to_v", # granite_speech (split from to_kv) + "encoder.layers.{bid}.self_attn.v_proj", # mimo-audio-tokenizer ), MODEL_TENSOR.A_ENC_ATTN_K_REL: ( @@ -2151,7 +2224,9 @@ class TensorNameMap: "audio_tower.layers.{bid}.self_attn_layer_norm", # ultravox "conformer.layers.{bid}.norm_self_att", # lfm2 "conformer.layers.{bid}.attention.pre_attn_norm", # gemma3n + "sound_encoder.encoder.layers.{bid}.norm_self_att", # parakeet "encoder.layers.{bid}.attn.pre_norm", # granite_speech + "encoder.layers.{bid}.self_attn_layer_norm", # mimo-audio-tokenizer ), MODEL_TENSOR.A_ENC_OUTPUT: ( @@ -2159,20 +2234,25 @@ class TensorNameMap: "conformer.layers.{bid}.self_attn.linear_out", # lfm2 "conformer.layers.{bid}.attention.post", # gemma3n "conformer.layers.{bid}.self_attn.post", # gemma4 + "sound_encoder.encoder.layers.{bid}.self_attn.o_proj", # parakeet "encoder.layers.{bid}.attn.to_out", # granite_speech + "encoder.layers.{bid}.self_attn.out_proj", # mimo-audio-tokenizer ), MODEL_TENSOR.A_ENC_OUTPUT_NORM: ( "audio_tower.layers.{bid}.final_layer_norm", # ultravox "conformer.layers.{bid}.norm_out", # lfm2 "conformer.layers.{bid}.attention.post_norm", # gemma3n + "sound_encoder.encoder.layers.{bid}.norm_out", # parakeet "encoder.layers.{bid}.post_norm", # granite_speech + "encoder.layers.{bid}.final_layer_norm", # mimo-audio-tokenizer ), MODEL_TENSOR.A_ENC_FFN_NORM: ( "conformer.layers.{bid}.norm_feed_forward1", # lfm2 "conformer.layers.{bid}.ffw_layer_start.pre_layer_norm", # gemma3n "conformer.layers.{bid}.feed_forward1.pre_layer_norm", # gemma4 + "sound_encoder.encoder.layers.{bid}.norm_feed_forward1", # parakeet "encoder.layers.{bid}.ff1.pre_norm", # granite_speech ), @@ -2190,7 +2270,9 @@ class TensorNameMap: "conformer.layers.{bid}.feed_forward1.linear1", # lfm2 "conformer.layers.{bid}.ffw_layer_start.ffw_layer_1", # gemma3n "conformer.layers.{bid}.feed_forward1.ffw_layer_1", # gemma4 + "sound_encoder.encoder.layers.{bid}.feed_forward1.linear1", # parakeet "encoder.layers.{bid}.ff1.up_proj", # granite_speech + "encoder.layers.{bid}.fc1", # mimo-audio-tokenizer ), MODEL_TENSOR.A_ENC_FFN_GATE: (), @@ -2200,13 +2282,16 @@ class TensorNameMap: "conformer.layers.{bid}.feed_forward1.linear2", # lfm2 "conformer.layers.{bid}.ffw_layer_start.ffw_layer_2", # gemma3n "conformer.layers.{bid}.feed_forward1.ffw_layer_2", # gemma4 + "sound_encoder.encoder.layers.{bid}.feed_forward1.linear2", # parakeet "encoder.layers.{bid}.ff1.down_proj", # granite_speech + "encoder.layers.{bid}.fc2", # mimo-audio-tokenizer ), MODEL_TENSOR.A_ENC_FFN_UP_1: ( "conformer.layers.{bid}.feed_forward2.linear1", # lfm2 "conformer.layers.{bid}.ffw_layer_end.ffw_layer_1", # gemma3n "conformer.layers.{bid}.feed_forward2.ffw_layer_1", # gemma4 + "sound_encoder.encoder.layers.{bid}.feed_forward2.linear1", # parakeet "encoder.layers.{bid}.ff2.up_proj", # granite_speech ), @@ -2214,6 +2299,7 @@ class TensorNameMap: "conformer.layers.{bid}.feed_forward2.linear2", # lfm2 "conformer.layers.{bid}.ffw_layer_end.ffw_layer_2", # gemma3n "conformer.layers.{bid}.feed_forward2.ffw_layer_2", # gemma4 + "sound_encoder.encoder.layers.{bid}.feed_forward2.linear2", # parakeet "encoder.layers.{bid}.ff2.down_proj", # granite_speech ), @@ -2221,9 +2307,23 @@ class TensorNameMap: "conformer.layers.{bid}.norm_feed_forward2", # lfm2 "conformer.layers.{bid}.ffw_layer_end.pre_layer_norm", # gemma3n "conformer.layers.{bid}.feed_forward2.pre_layer_norm", # gemma4 + "sound_encoder.encoder.layers.{bid}.norm_feed_forward2", # parakeet "encoder.layers.{bid}.ff2.pre_norm", # granite_speech ), + MODEL_TENSOR.A_ENC_DOWNSAMPLE_CONV: ( + "encoder.down_sample_layer.0", # mimo-audio-tokenizer + ), + + MODEL_TENSOR.A_ENC_DOWNSAMPLE_NORM: ( + "encoder.down_sample_norm", # mimo-audio-tokenizer + ), + + # note: the raw per-quantizer "encoder.quantizer.vq.layers.{i}._codebook.embed" + # tensors are merged (padded + stacked, like MoE experts) into this single 3D + # tensor in conversion code, so no raw-name mapping is registered here. + MODEL_TENSOR.A_ENC_RVQ_CODEBOOK: (), + MODEL_TENSOR.A_ENC_FFN_POST_NORM_1: ( "conformer.layers.{bid}.ffw_layer_end.post_layer_norm", # gemma3n "conformer.layers.{bid}.feed_forward2.post_layer_norm", # gemma4 @@ -2236,20 +2336,24 @@ class TensorNameMap: MODEL_TENSOR.A_ENC_LINEAR_POS: ( "conformer.layers.{bid}.self_attn.linear_pos", # lfm2 "conformer.layers.{bid}.attention.attn.relative_position_embedding.pos_proj", # gemma3n + "sound_encoder.encoder.layers.{bid}.self_attn.relative_k_proj", # parakeet ), MODEL_TENSOR.A_ENC_POS_BIAS_U: ( "conformer.layers.{bid}.self_attn.pos_bias_u", # lfm2 + "sound_encoder.encoder.layers.{bid}.self_attn.bias_u", # parakeet ), MODEL_TENSOR.A_ENC_POS_BIAS_V: ( "conformer.layers.{bid}.self_attn.pos_bias_v", # lfm2 + "sound_encoder.encoder.layers.{bid}.self_attn.bias_v", # parakeet ), MODEL_TENSOR.A_ENC_OUT: ( "conformer.pre_encode.out", # lfm2 "model.audio_tower.subsample_conv_projection.input_proj_linear", # gemma3n (note: it should be A_ENC_INP_PROJ, this is a mistake; it should be corrected in C++ code when it's supported) "conformer.output_proj", # gemma4 + "sound_encoder.encoder.subsampling.linear", # parakeet ), # note: some tensors below has "audio." pseudo-prefix, to prevent conflicts with vision tensors @@ -2259,6 +2363,7 @@ class TensorNameMap: "audio.multi_modal_projector.linear_{bid}", # ultravox, meralion "audio_adapter.model.{bid}", # lfm2 "audio_tower.proj{bid}", # qwen3omni + "sound_projection.linear{bid}", # parakeet (linear1, linear2) ), MODEL_TENSOR.A_MMPROJ_FC: ( @@ -2269,39 +2374,89 @@ class TensorNameMap: MODEL_TENSOR.A_MM_NORM_PRE: ( "audio.multi_modal_projector.ln_pre", # ultravox + "sound_projection.norm", # parakeet ), MODEL_TENSOR.A_MM_NORM_MID: ( "audio.multi_modal_projector.ln_mid", # ultravox ), + # note: the raw per-channel "speech_embeddings.{i}" tensors are merged + # (stacked, like MoE experts) into this single 3D tensor in conversion + # code, so no raw-name mapping is registered here. + MODEL_TENSOR.A_MM_CODE_EMBD: (), + + MODEL_TENSOR.A_MM_LOCAL_ATTN_Q: ( + "audio_encoder.input_local_transformer.layers.{bid}.self_attn.q_proj", # mimo-v2.5 + ), + MODEL_TENSOR.A_MM_LOCAL_ATTN_K: ( + "audio_encoder.input_local_transformer.layers.{bid}.self_attn.k_proj", # mimo-v2.5 + ), + MODEL_TENSOR.A_MM_LOCAL_ATTN_V: ( + "audio_encoder.input_local_transformer.layers.{bid}.self_attn.v_proj", # mimo-v2.5 + ), + MODEL_TENSOR.A_MM_LOCAL_ATTN_OUT: ( + "audio_encoder.input_local_transformer.layers.{bid}.self_attn.o_proj", # mimo-v2.5 + ), + MODEL_TENSOR.A_MM_LOCAL_FFN_GATE: ( + "audio_encoder.input_local_transformer.layers.{bid}.mlp.gate_proj", # mimo-v2.5 + ), + MODEL_TENSOR.A_MM_LOCAL_FFN_UP: ( + "audio_encoder.input_local_transformer.layers.{bid}.mlp.up_proj", # mimo-v2.5 + ), + MODEL_TENSOR.A_MM_LOCAL_FFN_DOWN: ( + "audio_encoder.input_local_transformer.layers.{bid}.mlp.down_proj", # mimo-v2.5 + ), + MODEL_TENSOR.A_MM_LOCAL_LN1: ( + "audio_encoder.input_local_transformer.layers.{bid}.input_layernorm", # mimo-v2.5 + ), + MODEL_TENSOR.A_MM_LOCAL_LN2: ( + "audio_encoder.input_local_transformer.layers.{bid}.post_attention_layernorm", # mimo-v2.5 + ), + MODEL_TENSOR.A_MM_LOCAL_NORM: ( + "audio_encoder.input_local_transformer.norm", # mimo-v2.5 + ), + MODEL_TENSOR.A_ENC_CONV_DW: ( "conformer.layers.{bid}.conv.depthwise_conv", # lfm2 "conformer.layers.{bid}.lconv1d.depthwise_conv1d", # gemma3n + "sound_encoder.encoder.layers.{bid}.conv.depthwise_conv", # parakeet "encoder.layers.{bid}.conv.depth_conv.conv", # granite_speech ), MODEL_TENSOR.A_ENC_CONV_NORM: ( "conformer.layers.{bid}.conv.batch_norm", # lfm2 "conformer.layers.{bid}.lconv1d.pre_layer_norm", # gemma3n + "sound_encoder.encoder.layers.{bid}.conv.norm", # parakeet + ), + + MODEL_TENSOR.A_ENC_CONV_NORM_MEAN: ( + "sound_encoder.encoder.layers.{bid}.conv.norm.running_mean", # parakeet + ), + + MODEL_TENSOR.A_ENC_CONV_NORM_VAR: ( + "sound_encoder.encoder.layers.{bid}.conv.norm.running_var", # parakeet "encoder.layers.{bid}.conv.batch_norm", # granite_speech ), MODEL_TENSOR.A_ENC_CONV_PW1: ( "conformer.layers.{bid}.conv.pointwise_conv1", # lfm2 "conformer.layers.{bid}.lconv1d.linear_start", # gemma3n + "sound_encoder.encoder.layers.{bid}.conv.pointwise_conv1", # parakeet "encoder.layers.{bid}.conv.up_conv", # granite_speech ), MODEL_TENSOR.A_ENC_CONV_PW2: ( "conformer.layers.{bid}.conv.pointwise_conv2", # lfm2 "conformer.layers.{bid}.lconv1d.linear_end", # gemma3n + "sound_encoder.encoder.layers.{bid}.conv.pointwise_conv2", # parakeet "encoder.layers.{bid}.conv.down_conv", # granite_speech ), MODEL_TENSOR.A_ENC_NORM_CONV: ( "conformer.layers.{bid}.norm_conv", # lfm2 "conformer.layers.{bid}.lconv1d.conv_norm", # gemma3n + "sound_encoder.encoder.layers.{bid}.norm_conv", # parakeet "encoder.layers.{bid}.conv.norm", # granite_speech ), @@ -2313,6 +2468,14 @@ class TensorNameMap: "conformer.layers.{bid}.attention.attn.per_dim_scale", # gemma4 ), + MODEL_TENSOR.A_ENC_MEL_FILTERS: ( + "sound_encoder.encoder.feature_extractor.featurizer.fb", # parakeet + ), + + MODEL_TENSOR.A_ENC_WINDOW: ( + "sound_encoder.encoder.feature_extractor.featurizer.window", # parakeet + ), + MODEL_TENSOR.A_MM_EMBEDDING: ( "model.embed_audio.embedding", # gemma3n ), diff --git a/gguf-py/tests/test_gguf_reader_validation.py b/gguf-py/tests/test_gguf_reader_validation.py new file mode 100644 index 000000000000..98f30a969608 --- /dev/null +++ b/gguf-py/tests/test_gguf_reader_validation.py @@ -0,0 +1,37 @@ +import struct +import numpy as np +import pytest + +from gguf.gguf_reader import GGUFReader + + +def _write_gguf(path, n_dims_field, dims): + buf = b'GGUF' + struct.pack(' 0.0, 1.0 = disabled + float penalty_freq, // must be finite, 0.0 = disabled + float penalty_present); // must be finite, 0.0 = disabled /// @details DRY sampler, designed by p-e-w, as described in: https://github.com/oobabooga/text-generation-webui/pull/5677, porting Koboldcpp implementation authored by pi6am: https://github.com/LostRuins/koboldcpp/pull/982 LLAMA_API struct llama_sampler * llama_sampler_init_dry( diff --git a/models/templates/Kimi-K3.jinja b/models/templates/Kimi-K3.jinja new file mode 100644 index 000000000000..67e51a6af4b3 --- /dev/null +++ b/models/templates/Kimi-K3.jinja @@ -0,0 +1,325 @@ +{%- macro escape_attr(value) -%} +{{- value|string|replace('&', '&')|replace('"', '"') -}} +{%- endmacro -%} + +{%- macro open_tag(tag, attrs=[]) -%} +{{- '<|open|>' + tag -}} +{%- for attr in attrs -%} +{{- ' ' + attr[0] + '="' -}}{{- escape_attr(attr[1]) -}}{{- '"' -}} +{%- endfor -%} +{{- '<|sep|>' -}} +{%- endmacro -%} + +{%- macro close_tag(tag) -%} +{{- '<|close|>' + tag + '<|sep|>' -}} +{%- endmacro -%} + +{%- macro next_image(state) -%} +{%- if image_prompts is defined and image_prompts is not none -%} + {%- if state.image_index >= image_prompts|length -%} + {{- raise_exception('More image placeholders than image prompts.') -}} + {%- endif -%} + {{- image_prompts[state.image_index] -}} + {%- set state.image_index = state.image_index + 1 -%} +{%- else -%} + {{- '<|kimi_image_placeholder|>' -}} +{%- endif -%} +{%- endmacro -%} + +{%- macro render_text(text, state) -%} +{%- set text = text|string -%} +{%- if image_prompts is defined and image_prompts is not none and '<|kimi_image_placeholder|>' in text -%} + {%- set parts = text.split('<|kimi_image_placeholder|>') -%} + {%- for part in parts -%} + {{- part -}} + {%- if not loop.last -%}{{- next_image(state) -}}{%- endif -%} + {%- endfor -%} +{%- else -%} + {{- text -}} +{%- endif -%} +{%- endmacro -%} + +{%- macro render_content(content, state) -%} +{%- if content is string -%} + {{- render_text(content, state) -}} +{%- elif content is not none and content is defined -%} + {%- for part in content -%} + {%- if part.type in ['image', 'image_url'] -%} + {{- next_image(state) -}} + {%- else -%} + {{- render_text(part.text, state) -}} + {%- endif -%} + {%- endfor -%} +{%- endif -%} +{%- endmacro -%} + +{%- macro internal_system_message(message_type, body) -%} +{{- open_tag('message', [('role', 'system'), ('type', message_type)]) -}} +{{- body|trim -}} +{{- close_tag('message') -}} +{{- '<|end_of_msg|>' -}} +{%- endmacro -%} + +{%- macro json_sorted(value) -%} +{#- Minja は tojson(sort_keys=true) を実装していない。K3 参照実装は + deep_sort_dict() の後に compact JSON 化するため、dictsort と再帰マクロで + 同じバイト列を作る。配列の順序は保持し、mapping の各階層だけソートする。 -#} +{%- if value is mapping -%} +{{- '{' -}} +{%- for key, item in value|dictsort -%} +{%- if not loop.first -%}{{- ',' -}}{%- endif -%} +{{- key|tojson(ensure_ascii=false) -}}{{- ':' -}}{{- json_sorted(item) -}} +{%- endfor -%} +{{- '}' -}} +{%- elif value is string or value is number or value is boolean or value is none -%} +{{- value|tojson(ensure_ascii=false) -}} +{%- else -%} +{{- '[' -}} +{%- for item in value -%} +{%- if not loop.first -%}{{- ',' -}}{%- endif -%} +{{- json_sorted(item) -}} +{%- endfor -%} +{{- ']' -}} +{%- endif -%} +{%- endmacro -%} + +{%- macro render_tool_declare(tool_list, dynamic=false) -%} +{{- open_tag('message', [('role', 'system'), ('type', 'tool-declare')]) -}} +{%- if dynamic -%} +{{- '## New Tools Available\nThe system dynamically extends the toolset via lazy-loading.\nYou have access to all existing and extended tools.\nHere are the specs for the extended tools.\n\n```json\n' -}} +{%- else -%} +{{- '# Tools\nHere are the available tools, described in JSONSchema.\n\n```json\n' -}} +{%- endif -%} +{{- json_sorted(tool_list) -}} +{{- '\n```' -}} +{{- close_tag('message') -}} +{{- '<|end_of_msg|>' -}} +{%- endmacro -%} + +{%- macro xtml_type(value) -%} +{%- if value is boolean -%}boolean +{%- elif value is none -%}null +{%- elif value is number -%}number +{%- elif value is string -%}string +{%- elif value is mapping -%}object +{%- else -%}array +{%- endif -%} +{%- endmacro -%} + +{%- macro xtml_value(value) -%} +{%- if value is string -%} +{{- value -}} +{%- else -%} +{{- value|tojson(ensure_ascii=false) -}} +{%- endif -%} +{%- endmacro -%} + +{%- macro render_assistant(message, state) -%} +{%- if thinking -%} + {%- set reasoning_content = message.get('reasoning_content') or message.get('reasoning') -%} + {{- open_tag('think') -}} + {%- if reasoning_content is not none and reasoning_content|string|trim -%} + {{- render_text(reasoning_content, state) -}} + {%- endif -%} + {{- close_tag('think') -}} +{%- endif -%} +{{- open_tag('response') -}} +{{- render_content(message.get('content'), state) -}} +{{- close_tag('response') -}} +{%- set tool_calls = message.get('tool_calls') -%} +{%- if tool_calls -%} + {{- open_tag('tools') -}} + {%- for tool_call in tool_calls -%} + {%- if tool_call is not mapping -%} + {{- raise_exception('Kimi K3 tool calls must be mappings.') -}} + {%- endif -%} + {%- set fn = tool_call.function if tool_call.function is defined and tool_call.function is mapping else tool_call -%} + {%- if fn.get('name') is none -%} + {{- raise_exception('Kimi K3 tool calls require a function name.') -}} + {%- endif -%} + {{- open_tag('call', [('tool', fn.name), ('index', loop.index)]) -}} + {%- set arguments = fn.get('arguments', {}) -%} + {%- set json_block = fn.get('_xtml_json_block') -%} + {%- if json_block is not none -%} + {{- open_tag('json', [('type', 'object')]) -}} + {{- render_text(json_block, state) -}} + {{- close_tag('json') -}} + {%- elif arguments is mapping -%} + {%- for key, value in arguments.items() -%} + {{- open_tag('argument', [('key', key), ('type', xtml_type(value))]) -}} + {{- render_text(xtml_value(value), state) -}} + {{- close_tag('argument') -}} + {%- endfor -%} + {%- elif arguments is string and arguments|trim -%} + {{- open_tag('json', [('type', 'object')]) -}} + {{- render_text(arguments, state) -}} + {{- close_tag('json') -}} + {%- elif arguments is not none and arguments is not string -%} + {{- raise_exception('Kimi K3 tool call arguments must be a mapping or a JSON object string.') -}} + {%- endif -%} + {{- close_tag('call') -}} + {%- endfor -%} + {{- close_tag('tools') -}} +{%- endif -%} +{%- endmacro -%} + +{%- macro render_tool_message(message, state, resolved_name=none) -%} +{%- set state.tool_index = state.tool_index + 1 -%} +{%- if resolved_name is not none -%} + {%- set tool_name = resolved_name -%} +{%- elif 'tool' in message -%} + {%- set tool_name = message.get('tool') -%} +{%- else -%} + {%- set tool_name = message.get('name') -%} +{%- endif -%} +{%- if tool_name is none and state.tool_calls is not none and state.tool_index <= state.tool_calls|length -%} + {%- set fallback_call = state.tool_calls[state.tool_index - 1] -%} + {%- set fallback_fn = fallback_call.function if fallback_call.function is defined and fallback_call.function is mapping else fallback_call -%} + {%- set tool_name = fallback_fn.name -%} +{%- endif -%} +{%- if tool_name is none -%} + {{- raise_exception('Kimi K3 tool messages need a resolvable tool name: carry `tool`/`name`, or match a preceding assistant tool_call by order.') -}} +{%- endif -%} +{{- open_tag('message', [('role', 'tool'), ('tool', tool_name), ('index', state.tool_index)]) -}} +{{- render_content(message.get('content'), state) -}} +{{- close_tag('message') -}} +{{- '<|end_of_msg|>' -}} +{%- endmacro -%} + +{%- if thinking is undefined -%} + {%- set thinking = true -%} +{%- endif -%} +{%- if thinking_effort is undefined -%} + {%- set thinking_effort = 'max' -%} +{%- endif -%} +{%- if thinking and thinking_effort is not none and thinking_effort not in ['low', 'high', 'max'] -%} + {{- raise_exception('Unsupported thinking_effort=' + thinking_effort|string + '; supported values are low, high, and max.') -}} +{%- endif -%} + +{%- set state = namespace(image_index=0, tool_calls=none, tool_index=0, response_schema=none) -%} + +{%- if tools is defined and tools -%} + {{- render_tool_declare(tools) -}} +{%- endif -%} + +{%- if thinking and thinking_effort in ['low', 'high', 'max'] -%} + {{- internal_system_message( + 'thinking-effort', + '`thinking_effort` guides on how much to think in your thinking channel (not including the response channel), supported values include `low`, `medium`, `high`, and `max`.\nNow the system is invoked with `thinking_effort=' + thinking_effort|string + '`.' + ) -}} +{%- endif -%} + +{%- for message in messages -%} + {%- if message is mapping -%} + {%- if 'role' not in message -%} + {{- raise_exception('Kimi K3 messages require a role.') -}} + {%- elif message.role == 'user' -%} + {%- set attrs = [('role', 'user')] -%} + {%- if message.get('name') -%}{%- set attrs = attrs + [('name', message.name)] -%}{%- endif -%} + {{- open_tag('message', attrs) -}} + {{- render_content(message.get('content'), state) -}} + {{- close_tag('message') -}} + {{- '<|end_of_msg|>' -}} + {%- elif message.role == 'system' and message.get('tools') -%} + {{- render_tool_declare(message.tools, dynamic=true) -}} + {%- elif message.role == 'system' -%} + {%- set attrs = [('role', 'system')] -%} + {%- if message.get('name') -%}{%- set attrs = attrs + [('name', message.name)] -%}{%- endif -%} + {{- open_tag('message', attrs) -}} + {{- render_content(message.get('content'), state) -}} + {{- close_tag('message') -}} + {{- '<|end_of_msg|>' -}} + {%- elif message.role == 'assistant' -%} + {%- set state.tool_calls = message.get('tool_calls') -%} + {%- set state.tool_index = 0 -%} + {%- set attrs = [('role', 'assistant')] -%} + {%- if message.get('name') -%}{%- set attrs = attrs + [('name', message.name)] -%}{%- endif -%} + {{- open_tag('message', attrs) -}} + {{- render_assistant(message, state) -}} + {{- close_tag('message') -}} + {{- '<|end_of_msg|>' -}} + {%- elif message.role == 'tool' and (loop.first or messages[loop.index0 - 1].role != 'tool') -%} + {%- set run = namespace(tool_messages=[], resolved_count=0) -%} + {%- for candidate in messages[loop.index0:] -%} + {%- if candidate is not mapping or candidate.role != 'tool' -%}{%- break -%}{%- endif -%} + {%- set run.tool_messages = run.tool_messages + [candidate] -%} + {%- set call_id = candidate.get('tool_call_id', candidate.get('id')) -%} + {%- set match = namespace(found=false) -%} + {%- if call_id is not none and state.tool_calls is not none -%} + {%- for tool_call in state.tool_calls -%} + {%- if not match.found and tool_call is mapping and tool_call.get('id') is not none and tool_call.get('id')|string == call_id|string -%} + {%- set match.found = true -%} + {%- endif -%} + {%- endfor -%} + {%- endif -%} + {%- if match.found -%}{%- set run.resolved_count = run.resolved_count + 1 -%}{%- endif -%} + {%- endfor -%} + {%- if run.tool_messages|length > 0 and run.resolved_count == run.tool_messages|length -%} + {%- set emitted = namespace(ids=[]) -%} + {%- for tool_call in state.tool_calls -%} + {%- if tool_call is mapping and tool_call.get('id') is not none and tool_call.get('id')|string not in emitted.ids -%} + {%- set emitted.ids = emitted.ids + [tool_call.get('id')|string] -%} + {%- set fn = tool_call.function if tool_call.function is defined and tool_call.function is mapping else tool_call -%} + {%- for tool_message in run.tool_messages -%} + {%- set result_id = tool_message.get('tool_call_id', tool_message.get('id')) -%} + {%- if result_id is not none and result_id|string == tool_call.get('id')|string -%} + {{- render_tool_message(tool_message, state, fn.get('name')) -}} + {%- endif -%} + {%- endfor -%} + {%- endif -%} + {%- endfor -%} + {%- else -%} + {%- for tool_message in run.tool_messages -%} + {{- render_tool_message(tool_message, state) -}} + {%- endfor -%} + {%- endif -%} + {%- endif -%} + {%- endif -%} +{%- endfor -%} + +{%- if tool_choice is defined and tool_choice == 'required' -%} + {{- internal_system_message('tool-choice', 'The system is invoked with `tool_choice=required`.\nYou MUST call tools in the next message.') -}} +{%- elif tool_choice is defined and tool_choice == 'none' -%} + {{- internal_system_message('tool-choice', 'The system is invoked with `tool_choice=none`.\nYou MUST NOT call any tools in the next message.') -}} +{%- endif -%} + +{%- if response_schema is defined -%} + {%- set state.response_schema = response_schema -%} +{%- elif response_format is defined and response_format is mapping and response_format.get('json_schema') is not none -%} + {%- set schema_wrapper = response_format.get('json_schema') -%} + {%- if schema_wrapper is mapping and 'schema' in schema_wrapper -%} + {%- set state.response_schema = schema_wrapper.get('schema') -%} + {%- elif schema_wrapper is mapping and 'json_schema' in schema_wrapper -%} + {%- set state.response_schema = schema_wrapper.get('json_schema') -%} + {%- else -%} + {%- set state.response_schema = schema_wrapper -%} + {%- endif -%} +{%- endif -%} + +{%- set response_format_type = none -%} +{%- if response_format is defined and response_format is mapping -%} + {%- set response_format_type = response_format.get('type') -%} +{%- elif response_format is defined -%} + {%- set response_format_type = response_format -%} +{%- endif -%} +{%- if response_format_type == 'json_object' -%} + {{- internal_system_message( + 'response-format', + 'The system is invoked with `response_format=json_object`.\nYour response must be raw JSON data without markdown code blocks (```json) or any additional formatting.' + ) -}} +{%- elif response_format_type == 'json_schema' -%} + {{- internal_system_message( + 'response-format', + 'The system is invoked with `response_format=json_schema`.\nYour response must be raw JSON data without markdown code blocks (```json) or any additional formatting.\nThe JSON data must match the following schema:\n```json\n' + json_sorted(state.response_schema) + '\n```' + ) -}} +{%- endif -%} + +{%- if add_generation_prompt -%} + {{- open_tag('message', [('role', 'assistant')]) -}} + {{- open_tag('think' if thinking else 'response') -}} +{%- endif -%} + +{%- if image_prompts is defined and image_prompts is not none and state.image_index != image_prompts|length -%} + {{- raise_exception('image prompt count ' + image_prompts|length|string + ' != consumed placeholder count ' + state.image_index|string) -}} +{%- endif -%} + diff --git a/models/templates/MiniMax-M3.jinja b/models/templates/MiniMax-M3.jinja new file mode 100644 index 000000000000..93022eb9ceec --- /dev/null +++ b/models/templates/MiniMax-M3.jinja @@ -0,0 +1,247 @@ +{# ---------- special token variables ---------- #} +{%- set ns_token = ']<]minimax[>[' -%} +{%- set bod_token = ']~!b[' -%} +{%- set bos_token = ']~b]' -%} +{%- set eos_token = '[e~[' -%} +{%- set toolcall_begin_token = ns_token ~ '' -%} +{%- set toolcall_end_token = ns_token ~ '' -%} +{%- set think_begin_token = '' -%} +{%- set think_end_token = '' -%} +{%- set image_token = ']<]image[>[' -%} +{%- set video_token = ']<]video[>[' -%} +{#- Thinking mode: "enabled" / "disabled" / "adaptive" / not defined -#} +{#- Recursive XML renderer for tool_call arguments ======================== -#} +{#- None values are intentionally skipped in mapping iteration so that + `null` (which would round-trip to the literal string "null") + never appears in the rendered tool_call. The convention is: omit the + field entirely. The top-level `_args` loop applies the same rule. + The `val is none` branch below is a safety net only — upstream cleaning + (drop_none_in_tool_arguments) should ensure no None ever reaches here. -#} +{%- macro to_xml(val, ns) -%} +{%- if val is mapping -%} +{%- for k, v in val.items() if v is not none -%} +{{ ns }}<{{ k }}>{{ to_xml(v, ns) }}{{ ns }} +{%- endfor -%} +{%- elif val is iterable and val is not string -%} +{%- for item in val -%} +{{ ns }}{{ to_xml(item, ns) }}{{ ns }} +{%- endfor -%} +{%- elif val is none -%} +{#- Should be unreachable when upstream cleaning is applied. -#} +{%- elif val is boolean -%} +{{ val | tojson }} +{%- else -%} +{{ val }} +{%- endif -%} +{%- endmacro -%} +{#- Tool Rendering Functions ============================================== -#} +{%- macro render_tool_namespace(namespace_name, tool_list) -%} +{%- for tool in tool_list -%} +{{ tool.function | tojson(ensure_ascii=False) }} +{% endfor -%} +{%- endmacro -%} +{%- macro visible_text(content) -%} + {%- if content is string -%} + {{ content }} + {%- elif content is iterable and content is not mapping -%} + {%- for item in content -%} + {%- if item is mapping and item.type == 'text' -%} + {{- item.text }} + {%- elif item is mapping and item.type == 'image' -%} + {{- image_token }} + {%- elif item is mapping and item.type == 'video' -%} + {{- video_token}} + {%- elif item is string -%} + {{- item }} + {%- endif -%} + {%- endfor -%} + {%- elif content is none -%} + {{- '' }} + {%- else -%} + {{- content }} + {%- endif -%} +{%- endmacro -%} +{#- System Message Construction ============================================ -#} +{%- macro build_system_message(system_message) -%} + {%- if system_message and system_message.content -%} + {{- visible_text(system_message.content) }} + {%- else -%} + {{- 'Your model version is MiniMax-M3, developed by MiniMax. Knowledge cutoff: January 2026. Founded in early 2022, MiniMax is a global AI foundation model company committed to advancing the frontiers of AI towards AGI.' }} + {%- endif -%} + + {#- Thinking mode instructions -#} + {{- '\n\n\n' }} + {{- 'You have a thinking capability that allows you to reason step by step before responding. When thinking is enabled, wrap your reasoning in ' ~ think_begin_token ~ think_end_token ~ ' tags before your response. When thinking is disabled, begin your response directly after the ' ~ think_end_token ~ ' prefix. When thinking is adaptive, decide on your own whether to think for the current turn.\n' }} + {%- if thinking_mode is defined -%} + {%- if thinking_mode == "enabled" -%} + {{- 'Current thinking mode: enabled. You MUST think step by step before every response, including after receiving function/tool results.\n' }} + {%- elif thinking_mode == "disabled" -%} + {{- 'Current thinking mode: disabled. Do not output any thinking process.\n' }} + {%- elif thinking_mode == "adaptive" -%} + {{- 'Current thinking mode: adaptive. You are encouraged to think for complex decision-making, multi-step reasoning, or when analyzing function/tool results.\n' }} + {%- endif -%} + {%- else -%} + {{- 'Current thinking mode: adaptive. You are encouraged to think for complex decision-making, multi-step reasoning, or when analyzing function/tool results.\n' }} + {%- endif -%} + {{- '' }} +{%- endmacro -%} +{%- macro build_developer_message(developer_message) -%} + {%- if developer_message and developer_message.content -%} + {{- visible_text(developer_message.content) }} + {%- else -%} + {%- if model_identity is not defined -%} + {%- set model_identity = "You are a helpful assistant." -%} + {%- endif -%} + {{- model_identity }} + {%- endif -%} +{%- endmacro -%} +{#- Main Template Logic ================================================= -#} +{#- Role mapping: root -> system sp (high priority), system/developer -> developer sp (low priority) -#} +{%- set system_message = none -%} +{%- set developer_message = none -%} +{%- set conversation_messages = messages -%} +{%- if messages and messages[0].role == "root" -%} + {%- set system_message = messages[0] -%} + {%- set conversation_messages = messages[1:] -%} + {%- if conversation_messages and conversation_messages[0].role in ["system", "developer"] -%} + {%- set developer_message = conversation_messages[0] -%} + {%- set conversation_messages = conversation_messages[1:] -%} + {%- endif -%} +{%- elif messages and messages[0].role in ["system", "developer"] -%} + {%- set developer_message = messages[0] -%} + {%- set conversation_messages = messages[1:] -%} +{%- endif -%} +{#- Render system sp (higher priority, root role only) -#} +{{- bod_token ~ bos_token ~ 'system' ~ '\n' }} +{{- build_system_message(system_message) }} +{{- eos_token ~ '\n' }} + +{#- Render developer sp (lower priority: system/developer role + tools) -#} +{{- bos_token ~ 'developer' ~ '\n' }} +{{- build_developer_message(developer_message) }} +{%- if tools -%} + {{- '\n\n' ~ '# Tools' ~ '\n' ~ 'You may call one or more tools to assist with the user query.\nHere are the tools available in JSONSchema format:' ~ '\n' }} + {{- '\n' ~ '' ~ '\n' }} + {{- render_tool_namespace("functions", tools) }} + {{- '' ~ '\n\n' }} + {{- 'To call tools, wrap all invocations in a single ' ~ toolcall_begin_token ~ toolcall_end_token ~ ' block. Parameter values containing nested objects or arrays are recursively expanded into XML elements. Example:\n' }} + {{- '\n' ~ toolcall_begin_token ~ '\n' }} + {{- ns_token + '' }} + {{- ns_token + 'value-1' + ns_token + '' }} + {{- ns_token + '' }} + {{- ns_token + '' }} + {{- ns_token + 'val-a' + ns_token + '' }} + {{- ns_token + 'val-b' + ns_token + '' }} + {{- ns_token + '' }} + {{- ns_token + '' }} + {{- ns_token + '\n' }} + {{- ns_token + '' }} + {{- ns_token + 'value-1' + ns_token + '' }} + {{- ns_token + '\n' }} + {{- toolcall_end_token }} +{%- endif -%} +{{- eos_token ~ '\n' }} + +{#- Render messages -#} +{%- set last_tool_call = namespace(name=none) -%} +{%- for message in conversation_messages -%} + {%- if message.role == 'assistant' -%} + {{- bos_token ~ 'ai' ~ '\n' }} + + {%- set reasoning_content = '' %} + {%- set content = visible_text(message.content) %} + {%- if message.reasoning_content is string %} + {%- set reasoning_content = message.reasoning_content %} + {%- else %} + {%- if think_end_token in content %} + {%- set reasoning_content = content.split(think_end_token)[0].strip('\n').split(think_begin_token)[-1].strip('\n') %} + {%- set content = content.split(think_end_token)[-1].strip('\n') %} + {%- endif %} + {%- endif %} + + {%- if reasoning_content -%} + {#- Render thinking for every assistant turn (all-turn visible) -#} + {{- think_begin_token ~ reasoning_content ~ think_end_token }} + {%- else -%} + {#- No thinking rendered → prefix with think_end_token -#} + {{- think_end_token }} + {%- endif -%} + + {%- if content -%} + {{- content }} + {%- endif -%} + {%- if message.tool_calls -%} + {{- toolcall_begin_token ~ '\n' }} + + {%- for tool_call in message.tool_calls -%} + {%- if tool_call.function -%} + {%- set tool_call = tool_call.function -%} + {%- endif -%} +{{- ns_token + '' }} +{%- set _args = tool_call.arguments -%} +{%- for k, v in _args.items() if v is not none %} +{{- ns_token + '<' + k + '>' -}} +{{- to_xml(v, ns_token) -}} +{{- ns_token + '' }} +{%- endfor -%} +{{- ns_token + '' ~ '\n' }} + {%- endfor -%} + + {{- toolcall_end_token }} + {%- if message.tool_calls[-1].function -%} + {%- set last_tool_call.name = message.tool_calls[-1].function.name -%} + {%- else -%} + {%- set last_tool_call.name = message.tool_calls[-1].name -%} + {%- endif -%} + {%- else -%} + {%- set last_tool_call.name = none -%} + {%- endif -%} + {{- eos_token ~ '\n' }} + + {%- elif message.role == 'tool' -%} + {%- if last_tool_call.name is none -%} + {{- raise_exception("Message has tool role, but there was no previous assistant message with a tool call!") }} + {%- endif -%} + {%- if loop.first or (conversation_messages[loop.index0 - 1].role != 'tool') -%} + {{- bos_token ~ 'tool' }} + {%- endif -%} + {{- '\n' }} + {%- if message.content is string -%} + {{- message.content }} + {%- else -%} + {%- for tr in message.content -%} + {%- if tr is mapping and tr.type is defined and tr.type == 'image' -%} + {{- image_token }} + {%- elif tr is mapping and tr.type is defined and tr.type == 'video' -%} + {{- video_token }} + {%- else -%} + {{- tr.output if tr.output is defined else (tr.text if tr.type == 'text' and tr.text is defined else tr) }} + {%- endif -%} + {%- endfor -%} + {%- endif -%} + {{- '' }} + {%- if loop.last or (conversation_messages[loop.index0 + 1].role != 'tool') -%} + {{- eos_token ~ '\n' -}} + {%- endif -%} + + {%- elif message.role == 'user' -%} + {{- bos_token ~ 'user' ~ '\n' }} + {{- visible_text(message.content) }} + {{- eos_token ~ '\n' }} + {%- endif -%} +{%- endfor -%} + +{#- Generation prompt -#} +{%- if add_generation_prompt -%} +{{- bos_token ~ 'ai' ~ '\n' }} +{%- if thinking_mode is defined and thinking_mode == "disabled" -%} + {{- think_end_token }} +{%- elif thinking_mode is defined and thinking_mode == "adaptive" -%} + {#- adaptive: no prefix, let model decide -#} +{%- elif thinking_mode is defined and thinking_mode == "enabled" -%} + {#- enabled or not defined: default to think -#} + {{- think_begin_token }} +{%- else -%} + {#- adaptive: no prefix, let model decide -#} +{%- endif -%} +{%- endif -%} diff --git a/models/templates/deepseek-ai-DeepSeek-V4-Flash-0731.jinja b/models/templates/deepseek-ai-DeepSeek-V4-Flash-0731.jinja new file mode 100644 index 000000000000..ab689a57c9cf --- /dev/null +++ b/models/templates/deepseek-ai-DeepSeek-V4-Flash-0731.jinja @@ -0,0 +1,140 @@ +{%- if not add_generation_prompt is defined -%} + {%- set add_generation_prompt = false -%} +{%- endif -%} +{%- if not thinking is defined -%} + {%- if enable_thinking is defined -%} + {%- set thinking = enable_thinking -%} + {%- else -%} + {%- set thinking = false -%} + {%- endif -%} +{%- endif -%} +{%- if not drop_thinking is defined -%} + {%- set drop_thinking = true -%} +{%- endif -%} +{%- set dsml_token = '｜DSML｜' -%} +{%- set thinking_start_token = '' -%} +{%- set thinking_end_token = '' -%} +{%- set reasoning_effort_high = 'Reasoning Effort: Absolute maximum with no shortcuts permitted.\nYou MUST be very thorough in your thinking and comprehensively decompose the problem to resolve the root cause, rigorously stress-testing your logic against all potential paths, edge cases, and adversarial scenarios.\nExplicitly write out your entire deliberation process, documenting every intermediate step, considered alternative, and rejected hypothesis to ensure absolutely no assumption is left unchecked.\n\n' -%} +{%- set reasoning_effort_max = 'Reasoning Effort: Beyond maximum — exhaustive, relentless, and uncompromising.\nYou MUST reason with the utmost depth and rigor, leaving absolutely nothing to chance: exhaustively decompose the problem into its most fundamental components, trace every causal chain to its root, and resolve the underlying cause rather than any surface symptom.\nDo not stop reasoning until you have independently verified the solution from multiple angles and are certain that no assumption remains unchecked and no error remains undiscovered.\n\n' -%} +{%- set response_format_template = '## Response Format:\n\nYou MUST strictly adhere to the following schema to reply:\n' -%} +{%- set has_tools = false -%} +{%- set tools_header = '## Tools\n\nYou have access to a set of tools to help answer the user\'s question. You can invoke tools by writing a "<' + dsml_token + 'tool_calls>" block like the following:\n\n<' + dsml_token + 'tool_calls>\n<' + dsml_token + 'invoke name="$TOOL_NAME">\n<' + dsml_token + 'parameter name="$PARAMETER_NAME" string="true|false">$PARAMETER_VALUE\n...\n\n<' + dsml_token + 'invoke name="$TOOL_NAME2">\n...\n\n\n\nString parameters should be specified as is and set `string="true"`. For all other types (numbers, booleans, arrays, objects), pass the value in JSON format and set `string="false"`.\n\nIf thinking_mode is enabled (triggered by ' + thinking_start_token + '), you MUST output your complete reasoning inside ' + thinking_start_token + '...' + thinking_end_token + ' BEFORE any tool calls or final response.\n\nOtherwise, output directly after ' + thinking_end_token + ' with tool calls or final response.\n\n### Available Tool Schemas\n\n' -%} +{%- set tools_footer = '\nYou MUST strictly follow the above defined tool name and parameter schemas to invoke tool calls.\n' -%} +{%- set ns = namespace(system_prompt='', is_first_sp=true, has_tool_calls=false) -%} +{%- for message in messages -%} + {%- if message['role'] == 'system' -%} + {%- if ns.is_first_sp -%} + {%- set ns.system_prompt = ns.system_prompt + (message['content'] or '') -%} + {%- set ns.is_first_sp = false -%} + {%- else -%} + {%- set ns.system_prompt = ns.system_prompt + '\n\n' + (message['content'] or '') -%} + {%- endif -%} + {%- endif -%} +{%- endfor -%} +{%- if tools is defined and tools -%} + {%- set has_tools = true -%} + {%- set ts = namespace(schemas='') -%} + {%- for tool in tools -%} + {%- if tool['type'] == 'function' -%} + {%- set ts.schemas = ts.schemas + (tool['function'] | tojson) + '\n' -%} + {%- endif -%} + {%- endfor -%} + {%- if ns.system_prompt -%} + {%- set ns.system_prompt = ns.system_prompt + '\n\n' + tools_header + ts.schemas + tools_footer -%} + {%- else -%} + {%- set ns.system_prompt = tools_header + ts.schemas + tools_footer -%} + {%- endif -%} +{%- endif -%} +{%- if response_format is defined -%} + {%- if ns.system_prompt -%} + {%- set ns.system_prompt = ns.system_prompt + '\n\n' -%} + {%- endif -%} + {%- set ns.system_prompt = ns.system_prompt + response_format_template + (response_format | tojson) -%} +{%- endif -%} +{{- bos_token -}} +{%- if messages and thinking and reasoning_effort is defined and reasoning_effort == 'high' -%} + {{- reasoning_effort_high -}} +{%- elif messages and thinking and reasoning_effort is defined and reasoning_effort == 'max' -%} + {{- reasoning_effort_max -}} +{%- endif -%} +{{- ns.system_prompt -}} +{%- set last_user_idx = namespace(value=-1) -%} +{%- for message in messages -%} + {%- if message['role'] == 'user' or message['role'] == 'developer' or message['role'] == 'tool' -%} + {%- set last_user_idx.value = loop.index0 -%} + {%- endif -%} +{%- endfor -%} +{%- set state = namespace(in_user=false) -%} +{%- for message in messages -%} + {%- if message['role'] == 'tool' -%} + {%- set ns.has_tool_calls = true -%} + {%- endif -%} +{%- endfor -%} +{%- for message in messages -%} + {%- if message['role'] == 'user' or message['role'] == 'developer' -%} + {%- if state.in_user -%} + {{- '\n\n' -}} + {%- else -%} + {{- '<｜User｜>' -}} + {%- set state.in_user = true -%} + {%- endif -%} + {{- message['content'] or '' -}} + {%- elif message['role'] == 'tool' -%} + {%- if state.in_user -%} + {{- '\n\n' -}} + {%- else -%} + {{- '<｜User｜>' -}} + {%- set state.in_user = true -%} + {%- endif -%} + {{- '' + (message['content'] or '') + '' -}} + {%- elif message['role'] == 'assistant' -%} + {%- set state.in_user = false -%} + {{- '<｜Assistant｜>' -}} + {%- set is_after_last_user = loop.index0 > last_user_idx.value -%} + {%- set keep_reasoning = thinking and ((not drop_thinking) or has_tools or is_after_last_user or ns.has_tool_calls) -%} + {%- if keep_reasoning -%} + {{- thinking_start_token -}} + {%- if message['reasoning_content'] is defined and message['reasoning_content'] -%} + {{- message['reasoning_content'] -}} + {%- endif -%} + {{- thinking_end_token -}} + {%- else -%} + {{- thinking_end_token -}} + {%- endif -%} + {%- if message['content'] is defined and message['content'] -%} + {{- message['content'] -}} + {%- endif -%} + {%- if message['tool_calls'] -%} + {{- '\n\n<' + dsml_token + 'tool_calls>\n' -}} + {%- for tool in message['tool_calls'] -%} + {%- set func = tool['function'] -%} + {{- '<' + dsml_token + 'invoke name="' + func['name'] + '">\n' -}} + {%- set args = func['arguments'] -%} + {%- if args is string -%} + {%- set args = args | from_json -%} + {%- endif -%} + {%- for key, val in args.items() -%} + {%- if val is string -%} + {{- '<' + dsml_token + 'parameter name="' + key + '" string="true">' + val + '\n' -}} + {%- else -%} + {{- '<' + dsml_token + 'parameter name="' + key + '" string="false">' + (val | tojson) + '\n' -}} + {%- endif -%} + {%- endfor -%} + {%- if not args -%} + {{- '\n' -}} + {%- endif -%} + {{- '\n' -}} + {%- endfor -%} + {{- '' -}} + {%- endif -%} + {{- '<｜end▁of▁sentence｜>' -}} + {%- endif -%} +{%- endfor -%} +{%- if add_generation_prompt -%} + {{- '<｜Assistant｜>' -}} + {%- if thinking -%} + {{- thinking_start_token -}} + {%- else -%} + {{- thinking_end_token -}} + {%- endif -%} +{%- endif -%} diff --git a/models/templates/deepseek-ai-DeepSeek-V4.jinja b/models/templates/deepseek-ai-DeepSeek-V4.jinja index f19f787b1b7e..c7819149ae6e 100644 --- a/models/templates/deepseek-ai-DeepSeek-V4.jinja +++ b/models/templates/deepseek-ai-DeepSeek-V4.jinja @@ -8,12 +8,18 @@ {%- set thinking = false -%} {%- endif -%} {%- endif -%} +{%- if not drop_thinking is defined -%} + {%- set drop_thinking = true -%} +{%- endif -%} {%- set dsml_token = '｜DSML｜' -%} {%- set thinking_start_token = '' -%} {%- set thinking_end_token = '' -%} +{%- set reasoning_effort_max = 'Reasoning Effort: Absolute maximum with no shortcuts permitted.\nYou MUST be very thorough in your thinking and comprehensively decompose the problem to resolve the root cause, rigorously stress-testing your logic against all potential paths, edge cases, and adversarial scenarios.\nExplicitly write out your entire deliberation process, documenting every intermediate step, considered alternative, and rejected hypothesis to ensure absolutely no assumption is left unchecked.\n\n' -%} +{%- set response_format_template = '## Response Format:\n\nYou MUST strictly adhere to the following schema to reply:\n' -%} +{%- set has_tools = false -%} {%- set tools_header = '## Tools\n\nYou have access to a set of tools to help answer the user\'s question. You can invoke tools by writing a "<' + dsml_token + 'tool_calls>" block like the following:\n\n<' + dsml_token + 'tool_calls>\n<' + dsml_token + 'invoke name="$TOOL_NAME">\n<' + dsml_token + 'parameter name="$PARAMETER_NAME" string="true|false">$PARAMETER_VALUE\n...\n\n<' + dsml_token + 'invoke name="$TOOL_NAME2">\n...\n\n\n\nString parameters should be specified as is and set `string="true"`. For all other types (numbers, booleans, arrays, objects), pass the value in JSON format and set `string="false"`.\n\nIf thinking_mode is enabled (triggered by ' + thinking_start_token + '), you MUST output your complete reasoning inside ' + thinking_start_token + '...' + thinking_end_token + ' BEFORE any tool calls or final response.\n\nOtherwise, output directly after ' + thinking_end_token + ' with tool calls or final response.\n\n### Available Tool Schemas\n\n' -%} {%- set tools_footer = '\nYou MUST strictly follow the above defined tool name and parameter schemas to invoke tool calls.\n' -%} -{%- set ns = namespace(system_prompt='', is_first_sp=true) -%} +{%- set ns = namespace(system_prompt='', is_first_sp=true, has_tool_calls=false) -%} {%- for message in messages -%} {%- if message['role'] == 'system' -%} {%- if ns.is_first_sp -%} @@ -25,6 +31,7 @@ {%- endif -%} {%- endfor -%} {%- if tools is defined and tools -%} + {%- set has_tools = true -%} {%- set ts = namespace(schemas='') -%} {%- for tool in tools -%} {%- if tool['type'] == 'function' -%} @@ -37,7 +44,16 @@ {%- set ns.system_prompt = tools_header + ts.schemas + tools_footer -%} {%- endif -%} {%- endif -%} +{%- if response_format is defined -%} + {%- if ns.system_prompt -%} + {%- set ns.system_prompt = ns.system_prompt + '\n\n' -%} + {%- endif -%} + {%- set ns.system_prompt = ns.system_prompt + response_format_template + (response_format | tojson) -%} +{%- endif -%} {{- bos_token -}} +{%- if messages and thinking and reasoning_effort is defined and reasoning_effort == 'max' -%} + {{- reasoning_effort_max -}} +{%- endif -%} {{- ns.system_prompt -}} {%- set last_user_idx = namespace(value=-1) -%} {%- for message in messages -%} @@ -46,6 +62,11 @@ {%- endif -%} {%- endfor -%} {%- set state = namespace(in_user=false) -%} +{%- for message in messages -%} + {%- if message['role'] == 'tool' -%} + {%- set ns.has_tool_calls = true -%} + {%- endif -%} +{%- endfor -%} {%- for message in messages -%} {%- if message['role'] == 'user' or message['role'] == 'developer' -%} {%- if state.in_user -%} @@ -67,7 +88,8 @@ {%- set state.in_user = false -%} {{- '<｜Assistant｜>' -}} {%- set is_after_last_user = loop.index0 > last_user_idx.value -%} - {%- if is_after_last_user and thinking -%} + {%- set keep_reasoning = thinking and ((not drop_thinking) or has_tools or is_after_last_user or ns.has_tool_calls) -%} + {%- if keep_reasoning -%} {{- thinking_start_token -}} {%- if message['reasoning_content'] is defined and message['reasoning_content'] -%} {{- message['reasoning_content'] -}} @@ -95,6 +117,9 @@ {{- '<' + dsml_token + 'parameter name="' + key + '" string="false">' + (val | tojson) + '\n' -}} {%- endif -%} {%- endfor -%} + {%- if not args -%} + {{- '\n' -}} + {%- endif -%} {{- '\n' -}} {%- endfor -%} {{- '' -}} @@ -109,4 +134,4 @@ {%- else -%} {{- thinking_end_token -}} {%- endif -%} -{%- endif -%} \ No newline at end of file +{%- endif -%} diff --git a/models/templates/poolside-Laguna-S-2.1.jinja b/models/templates/poolside-Laguna-S-2.1.jinja new file mode 100644 index 000000000000..75c5f4cec0d4 --- /dev/null +++ b/models/templates/poolside-Laguna-S-2.1.jinja @@ -0,0 +1,93 @@ +{#- Iteration on laguna_glm_thinking_v8/chat_template.jinja -#} +{#- No formatting instructions -#} +{{- "〈|EOS|〉" -}} +{%- set enable_thinking = enable_thinking | default(false) -%} +{%- set add_generation_prompt = add_generation_prompt | default(false) -%} + +{#- ───── header (system message) ───── -#} +{#- A caller-supplied system message with empty content opts out of the default below, producing no block — used to train without a system message. -#} +{%- set system_message = "You are a helpful, conversationally-fluent assistant made by Poolside. You are here to be helpful to users through natural language conversations." -%} +{%- if messages and messages[0].role == "system" -%} + {%- set system_message = messages[0].content -%} + {%- set messages = messages[1:] -%} +{%- endif -%} + +{%- set has_sys = system_message and system_message.strip() -%} +{%- if has_sys or tools or enable_thinking -%} + {{- "" -}} + + {%- if has_sys -%} + {{- system_message.rstrip() -}} + {%- if tools -%}{{- "\n\n" -}}{%- endif -%} + {%- endif -%} + + {%- if tools -%} + {{- "### Tools\n\n" -}} + {{- "You may call functions to assist with the user query.\n" -}} + {{- "All available function signatures are listed below:\n" -}} + {{- "\n" -}} + {%- for tool in tools -%} + {{- (tool | tojson) ~ "\n" -}} + {%- endfor -%} + {{- "" -}} + {%- endif -%} + + {{- "\n" -}} +{%- endif -%} + +{#- ───── main loop ───── -#} +{%- for message in messages -%} + {%- set content = message.content if message.content is string else "" -%} + {%- if message.role == "user" -%} + {{- "" + content + "\n" -}} + {%- elif message.role == "assistant" -%} + {%- generation -%} + {{- "" -}} + {#- Extract reasoning content from message.reasoning (vLLM field name) or message.reasoning_content -#} + {%- set reasoning_content = '' -%} + {%- if message.reasoning is string -%} + {%- set reasoning_content = message.reasoning -%} + {%- elif message.reasoning_content is string -%} + {%- set reasoning_content = message.reasoning_content -%} + {%- endif -%} + {#- Display reasoning content for all messages if enable_thinking -#} + {%- if enable_thinking -%} + {{- '' + reasoning_content + '' -}} + {%- else -%} + {{- '' -}} + {%- endif -%} + {#- Display main content (trailing newline only when no tool_calls follow) -#} + {%- if content -%} + {{- content -}} + {%- endif -%} + {%- if message.tool_calls -%} + {%- for tool_call in message.tool_calls -%} + {%- set function_data = tool_call.function -%} + {{- '' + function_data.name -}} + {%- set _args = function_data.arguments -%} + {%- for k, v in _args.items() -%} + {{- "" ~ k ~ "" -}} + {{- "" -}}{{- v | tojson(ensure_ascii=False) if v is not string else v -}}{{- "" -}} + {%- endfor -%} + {{- "" -}} + {%- endfor -%} + {%- endif -%} + {{- "\n" -}} + {%- endgeneration -%} + {%- elif message.role == "tool" -%} + {{- "" + content + "\n" -}} + {%- elif message.role == "system" -%} + {#- Render additional system messages (the first one, if any, is handled separately in the header and was sliced off above) -#} + {{- "" + content + "\n" -}} + {%- endif -%} +{%- endfor -%} +{#- ───── generation prompt ───── -#} +{%- if add_generation_prompt -%} + {{- "" -}} + {#- ───── Include reasoning mode directive ───── -#} + {%- if enable_thinking -%} + {{- '' -}} + {%- else -%} + {{- '' -}} + {%- endif -%} +{%- endif -%} \ No newline at end of file diff --git a/models/templates/poolside-Laguna-XS-2.1.jinja b/models/templates/poolside-Laguna-XS-2.1.jinja new file mode 100644 index 000000000000..d45f23f7038a --- /dev/null +++ b/models/templates/poolside-Laguna-XS-2.1.jinja @@ -0,0 +1,132 @@ +{#- Copied from laguna_glm_thinking_v4/chat_template.jinja -#} +{#- Removes prefix that references token, and replaces message.reasoning_content reference with message.reasoning -#} +{{- "〈|EOS|〉" -}} +{%- set enable_thinking = enable_thinking | default(false) -%} +{%- set render_assistant_messages_raw = render_assistant_messages_raw | default(false) -%} +{%- set add_generation_prompt = add_generation_prompt | default(false) -%} + +{#- ───── header (system message) ───── -#} +{%- set system_message = "" -%} +{%- if messages and messages[0].role == "system" -%} + {%- set system_message = messages[0].content -%} +{%- endif -%} + +{%- if (system_message and system_message.strip()) or tools -%} + {{- "\n" -}} + + {%- if system_message and system_message.strip() -%} + {{- "\n" -}} + {{- system_message.rstrip() -}} + {%- endif -%} + + {%- if tools -%} + {{- "\n\n### Tools\n\n" -}} + {%- set ns = namespace(tool_string="You may call functions to assist with the user query.\n" + ~ "All available function signatures are listed below:\n" + ~ "\n") -%} + {%- for tool in tools -%} + {%- set ns.tool_string = ns.tool_string ~ (tool | tojson) ~ "\n" -%} + {%- endfor -%} + {%- if enable_thinking -%} + {%- set tool_string = ns.tool_string + "\n\n" ~ + "Wrap your thinking in '', '' tags, followed by a function call. For each function call, return an unescaped XML-like object with function name and arguments within '' and '' tags, like here:\n" ~ + " your thoughts here \n" ~ + "function-name\nargument-key\nvalue-of-argument-key\n" ~ + "" -%} + {%- else -%} + {%- set tool_string = ns.tool_string + "\n\n" ~ + "For each function call, return an unescaped XML-like object " ~ + "with function name and arguments within '' and '' tags, like here:\n" ~ + "function-name\nargument-key\nvalue-of-argument-key\n" ~ + "" -%} + {%- endif -%} + {{- tool_string -}} + {%- endif -%} + + {{- "\n\n" -}} +{%- endif -%} + +{#- ───── main loop ───── -#} +{%- for message in messages -%} + {%- set content = message.content if message.content is string else "" -%} + {%- if message.role == "user" -%} + {{- "\n" + content + "\n\n" -}} + {%- elif message.role == "assistant" -%} + {%- generation -%} + {{- "\n" -}} + {%- if render_assistant_messages_raw -%} + {#- Raw mode: prepend the generation prompt token, then dump content verbatim. -#} + {#- The generation prompt is when enable_thinking, otherwise. -#} + {#- Only prepend if content doesn't already start with it. -#} + {%- if enable_thinking -%} + {%- if not content.startswith('') -%} + {{- '' -}} + {%- endif -%} + {%- else -%} + {%- if not content.startswith('') -%} + {{- '' -}} + {%- endif -%} + {%- endif -%} + {{- content -}} + {#- Append closing tag if content doesn't already end with it. -#} + {%- if not content.endswith('\n') and not content.endswith('') -%} + {{- '\n' -}} + {%- endif -%} + {{- "\n" -}} + {%- else -%} + {#- Extract reasoning content from message.reasoning (vLLM field name) or message.reasoning_content, or from tags -#} + {%- set reasoning_content = '' %} + {%- if message.reasoning is string %} + {%- set reasoning_content = message.reasoning %} + {%- elif message.reasoning_content is string %} + {%- set reasoning_content = message.reasoning_content %} + {%- endif %} + {#- Always strip tags from content if present to avoid duplication -#} + {%- if '' in content %} + {%- if not reasoning_content %} + {%- set reasoning_content = content.split('')[0].rstrip('\n').split('')[-1].lstrip('\n') %} + {%- endif %} + {%- set content = content.split('')[-1].lstrip('\n') %} + {%- endif %} + {#- Display reasoning content for all messages -#} + {%- if reasoning_content -%} + {{- '\n' + reasoning_content.strip() + '\n\n' -}} + {%- else -%} + {{- '\n' -}} + {%- endif -%} + {#- Display main content -#} + {%- if content.strip() -%} + {{- content.strip() ~ "\n" -}} + {%- endif -%} + {%- if message.tool_calls -%} + {%- for tool_call in message.tool_calls -%} + {%- set function_data = tool_call.function -%} + {{- '' + function_data.name }} + {% set _args = function_data.arguments %} + {%- for k, v in _args.items() -%} + {{- "" ~ k ~ "\n" -}} + {{- ""}}{{ v | tojson(ensure_ascii=False) if v is not string else v }}{{ "\n" -}} + {%- endfor -%} + {{- "\n" -}} + {%- endfor -%} + {%- endif -%} + {{- "\n" -}} + {%- endif -%} + {%- endgeneration -%} + {%- elif message.role == "tool" -%} + {{- "\n" + content + "\n\n" -}} + {%- elif message.role == "system" and loop.index0 != 0 -%} + {#- Render additional system messages (skip the first one which is handled separately in the header) -#} + {{- "\n" + content + "\n\n" -}} + {%- endif -%} +{%- endfor -%} +{#- ───── generation prompt ───── -#} +{%- if add_generation_prompt -%} + {{- "\n" -}} + {#- ───── Include reasoning mode directive ───── -#} + {%- if not enable_thinking %} + {{- '' -}} + {%- else %} + {{- '' -}} + {%- endif %} +{%- endif -%} diff --git a/models/templates/poolside-Laguna-XS.2.jinja b/models/templates/poolside-Laguna-XS.2.jinja new file mode 100644 index 000000000000..4baa3fded6d2 --- /dev/null +++ b/models/templates/poolside-Laguna-XS.2.jinja @@ -0,0 +1,132 @@ +{#- Iteration on laguna_glm_thinking_v5/chat_template.jinja -#} +{#- Adds a default system message (used when no system message is provided in `messages`). -#} +{{- "〈|EOS|〉" -}} +{%- set enable_thinking = enable_thinking | default(false) -%} +{%- set render_assistant_messages_raw = render_assistant_messages_raw | default(false) -%} +{%- set add_generation_prompt = add_generation_prompt | default(false) -%} + +{#- ───── header (system message) ───── -#} +{%- set system_message = "You are a helpful, conversationally-fluent assistant made by Poolside. You are here to be helpful to users through natural language conversations." -%} +{%- if messages and messages[0].role == "system" -%} + {%- set system_message = messages[0].content -%} +{%- endif -%} + +{%- if (system_message and system_message.strip()) or tools -%} + {{- "\n" -}} + + {%- if system_message and system_message.strip() -%} + {{- "\n" -}} + {{- system_message.rstrip() -}} + {%- endif -%} + + {%- if tools -%} + {{- "\n\n### Tools\n\n" -}} + {%- set ns = namespace(tool_string="You may call functions to assist with the user query.\n" + ~ "All available function signatures are listed below:\n" + ~ "\n") -%} + {%- for tool in tools -%} + {%- set ns.tool_string = ns.tool_string ~ (tool | tojson) ~ "\n" -%} + {%- endfor -%} + {%- if enable_thinking -%} + {%- set tool_string = ns.tool_string + "\n\n" ~ + "Wrap your thinking in '', '' tags, followed by a function call. For each function call, return an unescaped XML-like object with function name and arguments within '' and '' tags, like here:\n" ~ + " your thoughts here \n" ~ + "function-name\nargument-key\nvalue-of-argument-key\n" ~ + "" -%} + {%- else -%} + {%- set tool_string = ns.tool_string + "\n\n" ~ + "For each function call, return an unescaped XML-like object " ~ + "with function name and arguments within '' and '' tags, like here:\n" ~ + "function-name\nargument-key\nvalue-of-argument-key\n" ~ + "" -%} + {%- endif -%} + {{- tool_string -}} + {%- endif -%} + + {{- "\n\n" -}} +{%- endif -%} + +{#- ───── main loop ───── -#} +{%- for message in messages -%} + {%- set content = message.content if message.content is string else "" -%} + {%- if message.role == "user" -%} + {{- "\n" + content + "\n\n" -}} + {%- elif message.role == "assistant" -%} + {%- generation -%} + {{- "\n" -}} + {%- if render_assistant_messages_raw -%} + {#- Raw mode: prepend the generation prompt token, then dump content verbatim. -#} + {#- The generation prompt is when enable_thinking, otherwise. -#} + {#- Only prepend if content doesn't already start with it. -#} + {%- if enable_thinking -%} + {%- if not content.startswith('') -%} + {{- '' -}} + {%- endif -%} + {%- else -%} + {%- if not content.startswith('') -%} + {{- '' -}} + {%- endif -%} + {%- endif -%} + {{- content -}} + {#- Append closing tag if content doesn't already end with it. -#} + {%- if not content.endswith('\n') and not content.endswith('') -%} + {{- '\n' -}} + {%- endif -%} + {{- "\n" -}} + {%- else -%} + {#- Extract reasoning content from message.reasoning (vLLM field name) or message.reasoning_content, or from tags -#} + {%- set reasoning_content = '' %} + {%- if message.reasoning is string %} + {%- set reasoning_content = message.reasoning %} + {%- elif message.reasoning_content is string %} + {%- set reasoning_content = message.reasoning_content %} + {%- endif %} + {#- Always strip tags from content if present to avoid duplication -#} + {%- if '' in content %} + {%- if not reasoning_content %} + {%- set reasoning_content = content.split('')[0].rstrip('\n').split('')[-1].lstrip('\n') %} + {%- endif %} + {%- set content = content.split('')[-1].lstrip('\n') %} + {%- endif %} + {#- Display reasoning content for all messages -#} + {%- if reasoning_content -%} + {{- '\n' + reasoning_content.strip() + '\n\n' -}} + {%- else -%} + {{- '\n' -}} + {%- endif -%} + {#- Display main content -#} + {%- if content.strip() -%} + {{- content.strip() ~ "\n" -}} + {%- endif -%} + {%- if message.tool_calls -%} + {%- for tool_call in message.tool_calls -%} + {%- set function_data = tool_call.function -%} + {{- '' + function_data.name }} + {% set _args = function_data.arguments %} + {%- for k, v in _args.items() -%} + {{- "" ~ k ~ "\n" -}} + {{- ""}}{{ v | tojson(ensure_ascii=False) if v is not string else v }}{{ "\n" -}} + {%- endfor -%} + {{- "\n" -}} + {%- endfor -%} + {%- endif -%} + {{- "\n" -}} + {%- endif -%} + {%- endgeneration -%} + {%- elif message.role == "tool" -%} + {{- "\n" + content + "\n\n" -}} + {%- elif message.role == "system" and loop.index0 != 0 -%} + {#- Render additional system messages (skip the first one which is handled separately in the header) -#} + {{- "\n" + content + "\n\n" -}} + {%- endif -%} +{%- endfor -%} +{#- ───── generation prompt ───── -#} +{%- if add_generation_prompt -%} + {{- "\n" -}} + {#- ───── Include reasoning mode directive ───── -#} + {%- if not enable_thinking %} + {{- '' -}} + {%- else %} + {{- '' -}} + {%- endif %} +{%- endif -%} diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py index 5a6cc7dbb134..e5f26b5a41ff 100755 --- a/scripts/compare-llama-bench.py +++ b/scripts/compare-llama-bench.py @@ -28,7 +28,7 @@ "model_type", "model_size", "model_n_params", "n_batch", "n_ubatch", "n_threads", "cpu_mask", "cpu_strict", "poll", "type_k", "type_v", "n_gpu_layers", "split_mode", "main_gpu", "no_kv_offload", "flash_attn", "tensor_split", "tensor_buft_overrides", - "use_mmap", "embeddings", "no_op_offload", "n_prompt", "n_gen", "n_depth", + "load_mode", "embeddings", "no_op_offload", "n_prompt", "n_gen", "n_depth", "test_time", "avg_ns", "stddev_ns", "avg_ts", "stddev_ts", "n_cpu_moe", "fit_target", "fit_min_ctx" ] @@ -38,7 +38,7 @@ "TEXT", "INTEGER", "INTEGER", "INTEGER", "INTEGER", "INTEGER", "TEXT", "INTEGER", "INTEGER", "TEXT", "TEXT", "INTEGER", "TEXT", "INTEGER", "INTEGER", "INTEGER", "TEXT", "TEXT", - "INTEGER", "INTEGER", "INTEGER", "INTEGER", "INTEGER", "INTEGER", + "TEXT", "INTEGER", "INTEGER", "INTEGER", "INTEGER", "INTEGER", "TEXT", "INTEGER", "INTEGER", "REAL", "REAL", "INTEGER", "INTEGER", "INTEGER" ] @@ -63,7 +63,7 @@ LLAMA_BENCH_KEY_PROPERTIES = [ "cpu_info", "gpu_info", "backends", "n_gpu_layers", "n_cpu_moe", "tensor_buft_overrides", "model_filename", "model_type", "n_batch", "n_ubatch", "embeddings", "cpu_mask", "cpu_strict", "poll", "n_threads", "type_k", "type_v", - "use_mmap", "no_kv_offload", "split_mode", "main_gpu", "tensor_split", "flash_attn", "n_prompt", "n_gen", "n_depth", + "load_mode", "no_kv_offload", "split_mode", "main_gpu", "tensor_split", "flash_attn", "n_prompt", "n_gen", "n_depth", "fit_target", "fit_min_ctx" ] @@ -73,7 +73,7 @@ ] # Properties that are boolean and are converted to Yes/No for the table: -LLAMA_BENCH_BOOL_PROPERTIES = ["embeddings", "cpu_strict", "use_mmap", "no_kv_offload", "flash_attn"] +LLAMA_BENCH_BOOL_PROPERTIES = ["embeddings", "cpu_strict", "no_kv_offload", "flash_attn"] TEST_BACKEND_OPS_BOOL_PROPERTIES = ["supported", "passed"] # Header names for the table (llama-bench): @@ -82,7 +82,7 @@ "tensor_buft_overrides": "Tensor overrides", "model_filename": "File", "model_type": "Model", "model_size": "Model size [GiB]", "model_n_params": "Num. of par.", "n_batch": "Batch size", "n_ubatch": "Microbatch size", "embeddings": "Embeddings", "cpu_mask": "CPU mask", "cpu_strict": "CPU strict", "poll": "Poll", "n_threads": "Threads", "type_k": "K type", "type_v": "V type", - "use_mmap": "Use mmap", "no_kv_offload": "NKVO", "split_mode": "Split mode", "main_gpu": "Main GPU", "tensor_split": "Tensor split", + "load_mode": "Load mode", "no_kv_offload": "NKVO", "split_mode": "Split mode", "main_gpu": "Main GPU", "tensor_split": "Tensor split", "flash_attn": "FlashAttention", } diff --git a/scripts/snapdragon/ggml-hexagon-profile.py b/scripts/snapdragon/ggml-hexagon-profile.py index 0f9240ddc62d..97a3acd26c26 100755 --- a/scripts/snapdragon/ggml-hexagon-profile.py +++ b/scripts/snapdragon/ggml-hexagon-profile.py @@ -6,6 +6,7 @@ import argparse import statistics import logging +import bisect from typing import Any, Dict, List, Optional from collections import defaultdict @@ -30,7 +31,7 @@ ) trace_pattern = re.compile( - r"trace-op\s+(?P[A-Z_0-9+]+):\s+thread\s+(?P\d+)\s+event\s+(?P[A-Z_0-9\-]+)\s+info\s+(?P\d+)\s+(?Pstart|stop)\s+(?P\d+)" + r"trace-evt\s+(?P[A-Z_0-9\-]+):\s+thread\s+(?P\d+)\s+info\s+(?P\d+)\s+(?Pstart|stop)\s+(?P\d+)" ) logger = logging.getLogger("ggml-hexagon-profile") @@ -50,9 +51,13 @@ def normalize_event_name(evt_type): class CycleUnwrapper: - def __init__(self): - self.last_raw = None - self.high_part = 0 + def __init__(self, initial_val=None): + if initial_val is not None: + self.last_raw = initial_val & 0xFFFFFFFF + self.high_part = initial_val & 0xFFFFFFFF00000000 + else: + self.last_raw = None + self.high_part = 0 def unwrap(self, raw): if self.last_raw is None: @@ -78,10 +83,12 @@ def parse_log(file_path, pmu_index=None): sys.exit(1) all_ops: List[Dict[str, Any]] = [] + all_traces: List[Dict[str, Any]] = [] current_op: Optional[Dict[str, Any]] = None timestamp_pattern = re.compile(r"^(?P\d+)\.(?P\d+)\.(?P\d+)\.(?P\d+)\s+[A-Z]\s+") - unwrapper = CycleUnwrapper() + unwrapper = None + trace_unwrapper = None for line in f: ts_match = timestamp_pattern.match(line) @@ -100,6 +107,7 @@ def parse_log(file_path, pmu_index=None): if not prefix_match: continue + names = parts[1] if len(parts) == 7: dims, types, timings = parts[2], parts[3], parts[6] elif len(parts) == 6: @@ -120,6 +128,7 @@ def parse_log(file_path, pmu_index=None): op_match = op_pattern.search(line) if op_match: op_name = op_match.group('op_name') + names = "" dims = op_match.group('dims').strip() types = op_match.group('types').strip() else: @@ -136,24 +145,31 @@ def parse_log(file_path, pmu_index=None): except (ValueError, IndexError): pmu_val = None - evt_raw = op_match.group('evt') if 'evt' in op_match.groupdict() else None evt_val = None - if evt_raw: + evt_val = None + if types.startswith("evt-cnt "): try: - evt_val = [int(x.strip()) for x in evt_raw.split(',')] + evt_val = [int(x.strip()) for x in types[8:].split(',')] except ValueError: evt_val = None cycles_start_raw = op_match.group('start') unwrapped_cycles_start = None - if cycles_start_raw: - unwrapped_cycles_start = unwrapper.unwrap(int(cycles_start_raw)) + if op_name == "OPBATCH": + if cycles_start_raw: + unwrapped_cycles_start = int(cycles_start_raw) + unwrapper = CycleUnwrapper(unwrapped_cycles_start) + trace_unwrapper = CycleUnwrapper(unwrapped_cycles_start) + else: + if cycles_start_raw and unwrapper is not None: + unwrapped_cycles_start = unwrapper.unwrap(int(cycles_start_raw)) idx = line.find("profile-op ") op_text = line[idx + 11:].strip() if idx != -1 else line.strip() current_op = { 'name': op_name, + 'names': names, 'dims': dims, 'types': types, 'op_text': op_text, @@ -170,110 +186,239 @@ def parse_log(file_path, pmu_index=None): continue trace_match = trace_pattern.search(line) - if trace_match and current_op: - if trace_match.group('op_name') == current_op['name']: - raw_cyc = int(trace_match.group('cycles')) - current_op['trace_events'].append({ - 'thread': int(trace_match.group('thread')), - 'event': trace_match.group('event'), - 'info': int(trace_match.group('info')), - 'cycles': raw_cyc, - 'unwrapped_cycles': unwrapper.unwrap(raw_cyc), - 'state': trace_match.group('state') - }) + if trace_match: + raw_cyc = int(trace_match.group('cycles')) + unwrapped_cyc = None + if trace_unwrapper is not None: + unwrapped_cyc = trace_unwrapper.unwrap(raw_cyc) + all_traces.append({ + 'thread': int(trace_match.group('thread')), + 'event': trace_match.group('event'), + 'info': int(trace_match.group('info')), + 'cycles': raw_cyc, + 'unwrapped_cycles': unwrapped_cyc, + 'state': trace_match.group('state') + }) f.close() + + # Assign start/end cycles to all ops + for op in all_ops: + op['start_cycles'] = op['unwrapped_cycles_start'] + op['end_cycles'] = op['start_cycles'] + op['cycles'] if op['start_cycles'] is not None else None + + # Filter ops with valid start_cycles + valid_ops = [op for op in all_ops if op['start_cycles'] is not None and op['end_cycles'] is not None] + + # Separate OPBATCH ops from other ops + opbatch_ops = [op for op in valid_ops if op['name'] == "OPBATCH"] + other_ops = [op for op in valid_ops if op['name'] != "OPBATCH"] + + # Sort them by start_cycles to enable binary search + opbatch_ops.sort(key=lambda op: op['start_cycles']) + other_ops.sort(key=lambda op: op['start_cycles']) + + opbatch_starts = [op['start_cycles'] for op in opbatch_ops] + other_starts = [op['start_cycles'] for op in other_ops] + + # Map trace events to any operator whose cycles contain them + for e in all_traces: + cyc = e['unwrapped_cycles'] + if cyc is None: + continue + + # Map to OPBATCH + idx = bisect.bisect_right(opbatch_starts, cyc) - 1 + if idx >= 0: + op = opbatch_ops[idx] + if op['start_cycles'] <= cyc <= op['end_cycles']: + op['trace_events'].append(e) + + # Map to other ops + idx = bisect.bisect_right(other_starts, cyc) - 1 + if idx >= 0: + op = other_ops[idx] + if op['start_cycles'] <= cyc <= op['end_cycles']: + op['trace_events'].append(e) + return all_ops -def print_ascii_timeline(op_name, dims, types, usec, cycles, events, evt_val=None): - evt_str = "" - if evt_val: - evt_str = " - evt [" + ",".join(str(x) for x in evt_val) + "]" +def print_bubbles_timeline(op): + op_name = op['name'] + dims = op['dims'] + types = op['types'] + usec = op['usec'] + cycles = op['cycles'] + events = op['trace_events'] logger.info("=" * 100) - logger.info(f"{op_name} ({dims} : {types}) - {usec} usec {cycles} cycles{evt_str}") + logger.info(f"{op_name} ({dims} : {types}) - {usec} usec {cycles} cycles") logger.info("=" * 100) - events = sorted(events, key=lambda e: e['cycles']) if not events: logger.info(" No trace events recorded.") return - min_cycles = events[0]['cycles'] - - logger.info("Cycles %-30s" % "EventDetails" + " ".join(f"T{i:<2}" for i in range(10)) + " HMX") - logger.info("-" * 100) + # Identify start and end cycles for this operator + op_start = op['start_cycles'] + op_end = op['end_cycles'] + if op_start is None or op_end is None: + logger.info(" Cannot analyze bubbles: missing start/end cycle counts.") + return - thread_stacks = [[] for _ in range(11)] + batch_duration = op_end - op_start + if batch_duration <= 0: + logger.info(" Cannot analyze bubbles: batch duration is 0.") + return + # Group events by (thread, track_type) + tracks = defaultdict(list) for e in events: t = e['thread'] - if t < 0 or t > 10: - continue + is_dma = (normalize_event_name(e['event']) == 'DMA') + track_type = 'dma' if is_dma else 'compute' + tracks[(t, track_type)].append(e) - if e['cycles'] >= min_cycles: - rel_cycles = e['cycles'] - min_cycles - else: - rel_cycles = (e['cycles'] + 0x100000000) - min_cycles + active_threads = sorted(list(set(t for (t, track_type) in tracks.keys()))) + if not active_threads: + logger.info(" No active threads in trace.") + return - state = e['state'] - evt_type = e['event'] - - # Determine char representing the event - norm_evt = normalize_event_name(evt_type) - char = '?' - if norm_evt == 'V-COMP': - char = 'V' - elif norm_evt == 'M-COMP': - char = 'H' - elif norm_evt == 'A-QUANT': - char = 'Q' - elif norm_evt == 'A-PREP': - char = 'A' - elif norm_evt == 'Q-PREP': - char = 'q' - elif norm_evt == 'K-PREP': - char = 'k' - elif norm_evt == 'V-PREP': - char = 'v' - elif norm_evt == 'W-DEQUANT': - char = 'D' - elif norm_evt == 'O-PROC': - char = 'O' - elif norm_evt == 'W-PREP': - char = 'P' - elif norm_evt == 'DMA': - char = 'M' + bubble_threshold = 10000 # 10k cycles - if state == 'start': - thread_stacks[t].append(char) - elif state == 'stop': - if thread_stacks[t]: - if thread_stacks[t][-1] == char: - thread_stacks[t].pop() - elif char in thread_stacks[t]: - thread_stacks[t].remove(char) - else: - thread_stacks[t].pop() + thread_stats = {} + for t in active_threads: + thread_stats[t] = { + 'compute_idle_cycles': batch_duration, + 'compute_idle_pct': 100.0, + 'compute_bubbles': [], - cols = [] - for i in range(11): - if thread_stacks[i]: - cols.append(f"[{thread_stacks[i][-1]}]") - else: - cols.append(" | ") + 'dma_idle_cycles': batch_duration, + 'dma_idle_pct': 100.0, + 'dma_bubbles': [] + } + + total_compute_idle_pct = 0.0 + total_dma_idle_pct = 0.0 + + for t in active_threads: + for track_type in ['compute', 'dma']: + key = (t, track_type) + track_events = tracks.get(key, []) - evt_desc = f"T{t}: {evt_type} {state} ({e['info']})" - logger.info(f"{rel_cycles:10d} %-30s" % evt_desc + " ".join(cols[:10]) + " " + cols[10]) + if not track_events: + gaps = [(op_start, op_end)] + idle_cycles = batch_duration + else: + track_events = sorted(track_events, key=lambda e: e.get('unwrapped_cycles') or e['cycles']) + + active_intervals = [] + active_count = 0 + curr_start = None + + for e in track_events: + cyc = e.get('unwrapped_cycles') or e['cycles'] + cyc = max(op_start, min(op_end, cyc)) + state = e['state'] + + if state == 'start': + if active_count == 0: + curr_start = cyc + active_count += 1 + elif state == 'stop': + if active_count > 0: + active_count -= 1 + if active_count == 0: + active_intervals.append((curr_start, cyc)) + else: + active_intervals.append((op_start, cyc)) + + if active_count > 0 and curr_start is not None: + active_intervals.append((curr_start, op_end)) + + # Merge intervals + active_intervals.sort(key=lambda x: x[0]) + merged_intervals = [] + for start, end in active_intervals: + if not merged_intervals: + merged_intervals.append([start, end]) + else: + last_start, last_end = merged_intervals[-1] + if start <= last_end: + merged_intervals[-1][1] = max(last_end, end) + else: + merged_intervals.append([start, end]) + + # Calculate gaps + gaps = [] + curr_time = op_start + for start, end in merged_intervals: + if start > curr_time: + gaps.append((curr_time, start)) + curr_time = max(curr_time, end) + if curr_time < op_end: + gaps.append((curr_time, op_end)) + + idle_cycles = sum(end - start for start, end in gaps) + + idle_pct = (idle_cycles / batch_duration) * 100.0 + + bubbles = [] + for start, end in gaps: + dur = end - start + if dur >= bubble_threshold: + bubbles.append((start, end, dur)) + + if track_type == 'compute': + thread_stats[t]['compute_idle_cycles'] = idle_cycles + thread_stats[t]['compute_idle_pct'] = idle_pct + thread_stats[t]['compute_bubbles'] = bubbles + total_compute_idle_pct += idle_pct + else: + thread_stats[t]['dma_idle_cycles'] = idle_cycles + thread_stats[t]['dma_idle_pct'] = idle_pct + thread_stats[t]['dma_bubbles'] = bubbles + total_dma_idle_pct += idle_pct + + avg_compute_idle = total_compute_idle_pct / len(active_threads) + avg_dma_idle = total_dma_idle_pct / len(active_threads) + + logger.info(" Combined Idle Statistics:") + logger.info(f" Active Threads : {', '.join(str(t) for t in active_threads)}") + logger.info(f" Avg Thread Compute IDLE : {avg_compute_idle:.1f}%") + logger.info(f" Avg Thread DMA IDLE : {avg_dma_idle:.1f}%") logger.info("-" * 100) + logger.info(" Per-Thread Idle Analysis:") + for t in active_threads: + stats = thread_stats[t] + thread_name = f"Thread {t:<2} (HVX)" if t != 10 else "Thread 10 (HMX)" + logger.info(f" {thread_name} -> Compute Idle: {stats['compute_idle_pct']:.1f}% | DMA Idle: {stats['dma_idle_pct']:.1f}%") + + all_bubbles = [] + for t in active_threads: + stats = thread_stats[t] + for start, end, dur in stats['compute_bubbles']: + pct = (dur / batch_duration) * 100.0 + all_bubbles.append((dur, f"Thread {t} Compute: bubble of {dur} cycles ({pct:.1f}%) at {start - op_start} to {end - op_start}")) + for start, end, dur in stats['dma_bubbles']: + pct = (dur / batch_duration) * 100.0 + all_bubbles.append((dur, f"Thread {t} DMA : bubble of {dur} cycles ({pct:.1f}%) at {start - op_start} to {end - op_start}")) + + if all_bubbles: + logger.info("-" * 100) + logger.info(f" Significant Bubbles (>= {bubble_threshold} cycles):") + all_bubbles.sort(key=lambda x: x[0], reverse=True) + for dur, desc in all_bubbles[:15]: + logger.info(f" {desc}") + else: + logger.info("-" * 100) + logger.info(f" No significant bubbles detected (all idle gaps < {bubble_threshold} cycles).") + -def print_ascii_summary(op_name, dims, types, usec, cycles, events, evt_val=None): - evt_str = "" - if evt_val: - evt_str = " - evt [" + ",".join(str(x) for x in evt_val) + "]" +def print_ascii_summary(op_name, dims, types, usec, cycles, events): logger.info("=" * 100) - logger.info(f"{op_name} ({dims} : {types}) - {usec} usec {cycles} cycles{evt_str}") + logger.info(f"{op_name} ({dims} : {types}) - {usec} usec {cycles} cycles") logger.info("=" * 100) events = sorted(events, key=lambda e: e['cycles']) @@ -415,8 +560,8 @@ def main(): parser.add_argument("--pmu-index", type=int) parser.add_argument("--pmu-name", type=str) parser.add_argument("--width", action='append', default=['dims:40'], help="Override column width, e.g. --width dims:50") - parser.add_argument("--timeline", type=str, nargs='?', const='summary', choices=["summary", "diagram"], - help="Output ASCII art event summary or timing diagram (default: summary)") + parser.add_argument("--timeline", type=str, nargs='?', const='summary', choices=["summary", "bubbles"], + help="Output ASCII art event summary or thread idle bubble analysis (default: summary)") parser.add_argument("--filter", type=str, help="Regex filter matching against the original profile-op line") group = parser.add_mutually_exclusive_group() @@ -457,16 +602,11 @@ def main(): ops = ops[-args.tail:] if args.timeline: - logger.info(f"\n# ASCII Timing {args.timeline.capitalize()}\n") - printed_cnt = 0 for op in ops: if args.timeline == "summary": - print_ascii_summary(op['name'], op['dims'], op['types'], op['usec'], op['cycles'], op['trace_events'], op.get('evt_val')) - elif args.timeline == "diagram": - print_ascii_timeline(op['name'], op['dims'], op['types'], op['usec'], op['cycles'], op['trace_events'], op.get('evt_val')) - printed_cnt += 1 - if printed_cnt >= args.top: - break + print_ascii_summary(op['name'], op['dims'], op['types'], op['usec'], op['cycles'], op['trace_events']) + elif args.timeline == "bubbles": + print_bubbles_timeline(op) else: generate_report(ops, args.top, overrides, args.sort, pmu_name=final_pmu_name) diff --git a/scripts/snapdragon/ggml-hexagon-trace.py b/scripts/snapdragon/ggml-hexagon-trace.py index 37f137a9e758..4755adfa1339 100755 --- a/scripts/snapdragon/ggml-hexagon-trace.py +++ b/scripts/snapdragon/ggml-hexagon-trace.py @@ -6,6 +6,7 @@ import argparse import statistics import logging +import bisect from typing import Any, Dict, List, Optional from collections import defaultdict @@ -16,11 +17,11 @@ ) trace_pattern = re.compile( - r"trace-op\s+(?P[A-Z_0-9+]+):\s+thread\s+(?P\d+)\s+event\s+(?P[A-Z_0-9\-]+)\s+info\s+(?P\d+)\s+(?Pstart|stop)\s+(?P\d+)" + r"trace-evt\s+(?P[A-Z_0-9\-]+):\s+thread\s+(?P\d+)\s+info\s+(?P\d+)\s+(?Pstart|stop)\s+(?P\d+)" ) -def normalize_event_name(evt_type): +def normalize_event_name(evt_type, info=0): if evt_type == "HVX_COMP": return "V-COMP" if evt_type == "HMX_COMP": @@ -32,9 +33,13 @@ def normalize_event_name(evt_type): class CycleUnwrapper: - def __init__(self): - self.last_raw = None - self.high_part = 0 + def __init__(self, initial_val=None): + if initial_val is not None: + self.last_raw = initial_val & 0xFFFFFFFF + self.high_part = initial_val & 0xFFFFFFFF00000000 + else: + self.last_raw = None + self.high_part = 0 def unwrap(self, raw): if self.last_raw is None: @@ -60,8 +65,10 @@ def parse_log(file_path): sys.exit(1) all_ops: List[Dict[str, Any]] = [] + all_traces: List[Dict[str, Any]] = [] current_op: Optional[Dict[str, Any]] = None - unwrapper = CycleUnwrapper() + unwrapper = None + trace_unwrapper = None line_idx = 0 for line in f: @@ -73,6 +80,7 @@ def parse_log(file_path): if not prefix_match: continue + names = parts[1] if len(parts) == 7: dims, types, strides, params, timings = parts[2], parts[3], parts[4], parts[5], parts[6] elif len(parts) == 6: @@ -93,6 +101,7 @@ def parse_log(file_path): op_match = op_pattern.search(line) if op_match: op_name = op_match.group('op_name') + names = "" dims = op_match.group('dims').strip() if op_match.group('dims') else '' types = op_match.group('types').strip() if op_match.group('types') else '' strides = op_match.group('strides').strip() if op_match.group('strides') else '' @@ -103,18 +112,30 @@ def parse_log(file_path): if op_match: cycles_start_raw = op_match.group('start') unwrapped_cycles_start = None - if cycles_start_raw: - unwrapped_cycles_start = unwrapper.unwrap(int(cycles_start_raw)) + if op_name == "OPBATCH": + if cycles_start_raw: + unwrapped_cycles_start = int(cycles_start_raw) + unwrapper = CycleUnwrapper(unwrapped_cycles_start) + trace_unwrapper = CycleUnwrapper(unwrapped_cycles_start) + else: + if cycles_start_raw and unwrapper is not None: + unwrapped_cycles_start = unwrapper.unwrap(int(cycles_start_raw)) idx = line.find("profile-op ") op_text = line[idx + 11:].strip() if idx != -1 else line.strip() + evt_str = None + if types.startswith("evt-cnt "): + evt_str = types[8:].strip() + current_op = { 'name': op_name, + 'names': names, 'dims': dims, 'types': types, 'strides': strides, 'params': params, + 'evt': evt_str, 'op_text': op_text, 'usec': int(op_match.group('usec')), 'cycles': int(op_match.group('cycles')), @@ -127,20 +148,22 @@ def parse_log(file_path): continue trace_match = trace_pattern.search(line) - if trace_match and current_op: - if trace_match.group('op_name') == current_op['name']: - raw_cyc = int(trace_match.group('cycles')) - current_op['trace_events'].append({ - 'thread': int(trace_match.group('thread')), - 'event': trace_match.group('event'), - 'info': int(trace_match.group('info')), - 'cycles': raw_cyc, - 'unwrapped_cycles': unwrapper.unwrap(raw_cyc), - 'state': trace_match.group('state') - }) + if trace_match: + raw_cyc = int(trace_match.group('cycles')) + unwrapped_cyc = None + if trace_unwrapper is not None: + unwrapped_cyc = trace_unwrapper.unwrap(raw_cyc) + all_traces.append({ + 'thread': int(trace_match.group('thread')), + 'event': trace_match.group('event'), + 'info': int(trace_match.group('info')), + 'cycles': raw_cyc, + 'unwrapped_cycles': unwrapped_cyc, + 'state': trace_match.group('state') + }) f.close() - return all_ops + return all_ops, all_traces # --- Simple protobuf encoder --- @@ -246,7 +269,7 @@ def write_trace_packet_to_file(f, packet_bytes): # --- End Protobuf Encoder --- -def generate_perfetto_trace(filtered_ops, output_path): +def generate_perfetto_trace(filtered_ops, trace_events, output_path): if not filtered_ops: logger.warning("No operators found after filtering.") return @@ -269,14 +292,12 @@ def generate_perfetto_trace(filtered_ops, output_path): # Process events completed_events = [] - for op in filtered_ops: - events = op['trace_events'] - if not events: - continue - events = sorted(events, key=lambda e: e['unwrapped_cycles']) + if trace_events: + trace_events = sorted(trace_events, key=lambda e: e['unwrapped_cycles']) + one_usec_cycles = max(avg_freq_mhz, 1.0) active_starts = {} - for e in events: + for e in trace_events: t = e['thread'] evt = e['event'] info = e['info'] @@ -285,6 +306,17 @@ def generate_perfetto_trace(filtered_ops, output_path): key = (t, evt, info) if state == 'start': + # Handle missing stop (start followed by another start) + if key in active_starts: + prev_start = active_starts[key] + completed_events.append({ + 'thread': t, + 'event': evt, + 'info': info, + 'start_cyc': prev_start, + 'end_cyc': prev_start + one_usec_cycles, + 'missing_stop': True, + }) active_starts[key] = cyc elif state == 'stop': if key in active_starts: @@ -296,9 +328,30 @@ def generate_perfetto_trace(filtered_ops, output_path): 'info': info, 'start_cyc': start_cyc, 'end_cyc': cyc, - 'op_name': op['name'] + }) + else: + # Handle missing start (stop without start) + completed_events.append({ + 'thread': t, + 'event': evt, + 'info': info, + 'start_cyc': cyc - one_usec_cycles, + 'end_cyc': cyc, + 'missing_start': True, }) + # Clear remaining unmatched starts + for key, start_cyc in active_starts.items(): + t, evt, info = key + completed_events.append({ + 'thread': t, + 'event': evt, + 'info': info, + 'start_cyc': start_cyc, + 'end_cyc': start_cyc + one_usec_cycles, + 'missing_stop': True, + }) + completed_events.sort(key=lambda e: e['start_cyc']) # Convert event times to microseconds and apply clamp rounded to 1ns resolution (3 decimals) @@ -316,7 +369,7 @@ def generate_perfetto_trace(filtered_ops, output_path): ts = e['ts_ns'] dur = e['dur_ns'] - norm_evt = normalize_event_name(evt) + norm_evt = normalize_event_name(evt, e['info']) if norm_evt == "DMA": track_key = (t, "DMA") elif t == 10: @@ -343,7 +396,7 @@ def generate_perfetto_trace(filtered_ops, output_path): evt = e['event'] slot = e['slot'] - norm_evt = normalize_event_name(evt) + norm_evt = normalize_event_name(evt, e['info']) if norm_evt == "DMA": track_evt = "DMA" evt_id = 1 @@ -421,18 +474,26 @@ def generate_perfetto_trace(filtered_ops, output_path): for op in filtered_ops: op_start_ns = int(round(((op['start_cycles'] - global_min_cyc) / avg_freq_mhz) * 1000)) op_dur_ns = int(round((op['cycles'] / avg_freq_mhz) * 1000)) - if op_start_ns < last_op_end_ns: - op_start_ns = last_op_end_ns - clamped_dur = max(op_dur_ns, 100) # Clamp to 100ns (0.1us) + if op['name'] != "OPBATCH": + if op_start_ns < last_op_end_ns: + op_start_ns = last_op_end_ns + clamped_dur = max(op_dur_ns, 100) # Clamp to 100ns (0.1us) + last_op_end_ns = op_start_ns + clamped_dur + else: + clamped_dur = max(op_dur_ns, 100) # Debug annotations for Ops debug_annots = [] if 'line_num' in op: debug_annots.append(make_debug_annotation("line", int_val=op['line_num'])) - if 'strides' in op and op['strides']: + if 'names' in op and op['names'] and op['names'] != '----': + debug_annots.append(make_debug_annotation("names", string_val=op['names'])) + if 'strides' in op and op['strides'] and op['strides'] != '----': debug_annots.append(make_debug_annotation("strides", string_val=op['strides'])) if 'params' in op and op['params'] and op['params'] != '----': debug_annots.append(make_debug_annotation("params", string_val=op['params'])) + if 'evt' in op and op['evt']: + debug_annots.append(make_debug_annotation("evt", string_val=op['evt'])) # Slice Begin evt_begin = make_track_event(1, 2, name=f"{op['name']} ({op['dims']})", category="operator", debug_annotations=debug_annots) @@ -444,15 +505,21 @@ def generate_perfetto_trace(filtered_ops, output_path): packet_end = make_trace_packet(op_start_ns + clamped_dur, track_event=evt_end) write_trace_packet_to_file(f, packet_end) - last_op_end_ns = op_start_ns + clamped_dur - # Emit Thread Trace Events for e in completed_events: - norm_name = normalize_event_name(e['event']) + norm_name = normalize_event_name(e['event'], e['info']) name = f"DMA {e['info']}" if norm_name == "DMA" else norm_name + if e.get('missing_start') or e.get('missing_stop'): + name += "!" + + debug_annots = [] + if e.get('missing_start'): + debug_annots.append(make_debug_annotation("missing_start", string_val="true")) + if e.get('missing_stop'): + debug_annots.append(make_debug_annotation("missing_stop", string_val="true")) # Slice Begin - evt_begin = make_track_event(1, e['uuid'], name=name, category="trace") + evt_begin = make_track_event(1, e['uuid'], name=name, category="trace", debug_annotations=debug_annots if debug_annots else None) packet_begin = make_trace_packet(e['ts_ns'], track_event=evt_begin) write_trace_packet_to_file(f, packet_begin) @@ -477,7 +544,7 @@ def main(): args = parser.parse_args() logging.basicConfig(level=logging.INFO, format='%(message)s') - ops = parse_log(args.logfile) + ops, traces = parse_log(args.logfile) if args.filter: try: @@ -492,7 +559,30 @@ def main(): elif args.tail is not None: ops = ops[-args.tail:] - generate_perfetto_trace(ops, args.output) + if args.filter or args.head is not None or args.tail is not None: + valid_ranges = [] + for op in ops: + start_cyc = op['unwrapped_cycles_start'] + end_cyc = start_cyc + op['cycles'] if start_cyc is not None else None + if start_cyc is not None and end_cyc is not None: + valid_ranges.append((start_cyc, end_cyc)) + + valid_ranges.sort(key=lambda r: r[0]) + range_starts = [r[0] for r in valid_ranges] + + filtered_traces = [] + for e in traces: + cyc = e['unwrapped_cycles'] + if cyc is None: + continue + idx = bisect.bisect_right(range_starts, cyc) - 1 + if idx >= 0: + start, end = valid_ranges[idx] + if start <= cyc <= end: + filtered_traces.append(e) + traces = filtered_traces + + generate_perfetto_trace(ops, traces, args.output) if __name__ == "__main__": diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index e9f6ed2e5c1c..35e94d9fb614 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -eaa0a74fa768bb72da623a61d9da3d436053ea91 +90951f99af1fbebef3fbdd58ff5b8715b0bb9c43 diff --git a/scripts/sync_vendor.py b/scripts/sync_vendor.py index 2c31c194b28e..a316530a0c0f 100755 --- a/scripts/sync_vendor.py +++ b/scripts/sync_vendor.py @@ -5,7 +5,7 @@ import sys import subprocess -HTTPLIB_VERSION = "refs/tags/v0.50.1" +HTTPLIB_VERSION = "refs/tags/v0.52.0" vendor = { "https://github.com/nlohmann/json/releases/latest/download/json.hpp": "vendor/nlohmann/json.hpp", @@ -21,7 +21,7 @@ f"https://raw.githubusercontent.com/yhirose/cpp-httplib/{HTTPLIB_VERSION}/split.py": "split.py", f"https://raw.githubusercontent.com/yhirose/cpp-httplib/{HTTPLIB_VERSION}/LICENSE": "vendor/cpp-httplib/LICENSE", - "https://raw.githubusercontent.com/sheredom/subprocess.h/b49c56e9fe214488493021017bf3954b91c7c1f5/subprocess.h": "vendor/sheredom/subprocess.h", + "https://raw.githubusercontent.com/sheredom/subprocess.h/8671cee1fc09f11a70ce3782a0ee13177c3aa387/subprocess.h": "vendor/sheredom/subprocess.h", } diff --git a/skills/add-new-model/SKILL.md b/skills/add-new-model/SKILL.md new file mode 100644 index 000000000000..f76d1abfd768 --- /dev/null +++ b/skills/add-new-model/SKILL.md @@ -0,0 +1,99 @@ +--- +name: add-new-model +description: Guided workflow for adding a new model architecture to llama.cpp. Use when the user wants to add/port a new model architecture. +--- + +# Add a new model architecture to llama.cpp + +This skill walks a contributor through adding a new model architecture. AI-generated code is permitted in this project, so you may write full implementations for the steps below rather than only pointing at patterns - but follow `AGENTS.md`'s AI usage policy throughout: + +- The contributor is 100% responsible for every line, however it was produced. They must be able to explain and defend any part of it to a reviewer. Check in with them as you go (don't silently generate everything and hand over a finished diff) so they actually absorb what was written. +- Before writing code, make sure the contributor owns the design choices for this architecture (which reference model to follow, how non-standard bits like RoPE variants or MoE routing should be handled) - AI accelerates a design the contributor has already made, it doesn't make the design for them. +- Disclosure is mandatory: any AI-meaningful contribution must be disclosed per the PR template. Remind the contributor of this before they open the PR. +- Never write the PR description, commit message, GitHub issue/discussion post, or reviewer replies - those must come from the contributor. If asked to commit on their behalf, use `Assisted-by:` (never `Co-authored-by:`) and only after explicit confirmation. +- If the requested change looks large or introduces a new pattern not covered here, pause and tell the user this kind of change is likely to need prior discussion with maintainers before a PR. +- Keep the PR self-contained. If the work would require a lot of unconventional changes outside the new model file(s) (e.g. touching shared graph-building code, the sampler, or core APIs in ways other models don't), STOP and tell the contributor to open a discussion/issue first - invasive or excessive changes get closed without full review. +- Do not bundle unrelated work into this PR - see Step 4 and Step 5 below for the specifics on multimodal and chat-template/parsing work. +- Never hack around RoPE with a custom sin/cos implementation. Several past PRs tried this and were closed. If the existing `ggml_rope_ext` (see Step 2's RoPE tips) genuinely cannot express what this model needs, the contributor should open an issue to discuss it with maintainers first - not send a PR with a custom RoPE implementation. + +Before starting, read `CONTRIBUTING.md`, `AGENTS.md` and `docs/development/HOWTO-add-model.md` if they are not already in context. Also run `git log --oneline -- src/models` and look at at least 3 recent PRs that added a model (their merge commits/diffs) - this shows current convention more reliably than the docs, which can lag behind. + +## Step 0 - Scope and dedup check + +Ask the contributor: +1. Which model (HF repo id or name)? Is it text-only or does it have a multimodal (vision/audio) encoder? +2. Do they already have the HF `config.json`/weights available locally? +3. Have they checked for an existing PR/issue on this model? Suggest `gh search issues ""` and `gh search prs ""` in the `ggml-org/llama.cpp` repo. If an existing PR covers it, the contributor should comment there and collaborate rather than open a duplicate (per CONTRIBUTING.md's AI Usage Policy). +4. What existing supported architecture is this model closest to (e.g. "Llama-like with sliding window", "MoE like DBRX", "BERT-style encoder")? + +If the contributor doesn't know the closest reference architecture, you may grep `conversion/*.py` and `src/models/*.cpp` for architectures with a similar config shape (layer count, head count, MoE expert count, norm placement) and suggest 1-2 candidates - but let the contributor confirm the choice rather than picking one yourself; this choice is a design decision they need to own. + +Do not proceed to Step 1 until the contributor has answered these and named a reference architecture. + +## Step 1 - Convert the model to GGUF + +Follow HOWTO-add-model.md section 1 for the actual touch points (conversion class registration, `constants.py`, `tensor_mapping.py`, etc.) - don't re-derive them here, read them from the doc. + +Skill-specific addition: for each touch point, show the contributor the equivalent code in the reference architecture they named in Step 0 before writing the new version, and check that they understand what's different about their model (e.g. non-standard tensor shapes, extra hparams) rather than just copying the pattern silently. + +## Step 2 - Define the architecture in llama.cpp + +Follow HOWTO-add-model.md section 2 for the actual touch points (`llm_arch` enum, `LLM_ARCH_NAMES`, hparam loading, RoPE type case, etc.), including its "Tips and tricks" section for `ggml_rope_ext` gotchas. + +Skill-specific addition: never hack around RoPE with a custom sin/cos implementation - see the RoPE rule above. + +## Step 3 - Build the GGML graph + +Follow HOWTO-add-model.md section 3 for the actual touch points (`src/models/.cpp` struct, `llama_model_mapping` registration, etc.). + +Skill-specific addition: before writing `src/models/.cpp`, read at least 10 other files under `src/models/` (pick a mix, not just the one reference architecture) to confirm the struct layout, naming, and style you're about to write actually matches current convention - the pattern drifts over time and the HOWTO doc can lag behind it. + +## Step 4 - Optional: multimodal encoder + +Only do this if the contributor flagged a vision/audio encoder in Step 0. Follow HOWTO-add-model.md section 4 and `docs/multimodal.md` for the actual touch points (`MmprojModel` subclass, `clip.cpp`, `mtmd.cpp`, encoder graph in `tools/mtmd/models`, etc.). + +Skill-specific addition, and read this carefully: **whether the multimodal encoder can be bundled into the same PR as the base text-model support depends on how conventional the change is.** It's OK to bundle it if the encoder support is conventional - i.e. no new infra or logic is needed, it's just a new cgraph reusing existing preprocessing/projector machinery (e.g. siglip/pixtral/qwen with just a new projector). If it requires anything beyond that - a new preprocessor, non-standard projector logic, or changes to shared `libmtmd` infra/logic - STOP, tell the contributor this is non-conventional, and have them land the text model first with the encoder as a dedicated follow-up PR. Do not let this decision pass silently - call it out explicitly to the contributor before writing any `clip.cpp`/`mtmd.cpp` code. + +## Step 5 - Optional: chat template / parsing support + +Only do this if the model needs a new built-in chat template (`src/llama-chat.cpp`) or a new output parser (see `docs/development/parsing.md` and `docs/autoparser.md`). If either is needed beyond what a user-supplied Jinja template already covers, treat it as its own dedicated follow-up PR, not part of the base model-support PR - call this out explicitly to the contributor rather than silently bundling it in. + +## Common pitfalls (from past PR reviews) + +These recur often enough in review comments on past add-model PRs that they're worth checking proactively, not just waiting for a reviewer to catch them: + +- Don't validate the same hparam/config assumption in both the Python conversion script and the C++ load path - pick one layer to own the check, duplicating it just adds maintenance surface. +- Optional hparams that are genuinely absent from some configs (e.g. a shared-expert count) should be read with an explicit optional/fallback accessor, not assumed present. +- Hparams that are actually load-bearing (the model produces wrong output or crashes without them, e.g. `sliding_window_pattern`, norm-eps) must hard-error if missing, not silently fall back to a default. +- Don't bake a default chat template into the C++ binary - inject it into the GGUF at conversion time instead, since one `llm_arch` can be reused by multiple fine-tunes with different templates, and a baked-in C++ default fails silently for those. +- Before writing a dedicated tool-call/output parser, check whether the existing autoparser already handles the template (`llama-debug-template-parser ` shows what it detects). +- Marking a custom EOS/closing-tag token as `eot` at conversion time isn't always sufficient - in long/agentic generations a model can emit the closing sequence as literal text instead of the token, so generation never stops on EOG and raw text leaks past the parser. Verify this case, not just the token path. +- If reusing or aliasing an existing pre-tokenizer for convenience, justify and test that choice explicitly - silent reuse is an easy source of subtle tokenizer bugs. +- Watch for excessive graph splits caused by building per-layer view/index tensors inside the layer loop - hoist tensors that don't vary per layer out of the loop (relevant if you hit `GGML_SCHED_MAX_SPLIT_INPUTS`). +- A custom KQ mask fed into flash attention must match FA's expected dtype - cast it to F16 before passing it to `build_attn_mha` when FA is enabled. +- When padding a custom KV-cache size to an alignment (e.g. `GGML_PAD(..., 256)`), apply the padding after all other size adjustments, not before - otherwise later logic can un-align it again. +- For non-standard cache/SWA (sliding-window-attention) semantics, override the dedicated hook (e.g. `llama_model_n_swa()`) rather than mutating hparams to fake the behavior - hparams may be read elsewhere for unrelated purposes. +- Don't ship unfinished or unverified speculative-decoding (e.g. MTP) scaffolding in the base model PR - if it hasn't actually been confirmed to work, pull it out and land it as its own follow-up. +- Conversion code should call into the base class's existing hparam logic (e.g. `super().set_gguf_parameters()`) rather than re-deriving it - large blocks of code that duplicate what `TextModel`/`MmprojModel` already provide will get flagged as redundant. +- Do constant tensor modifications (e.g. `norm(1 + weight)`) and permutations/chunking at conversion time, not in the graph - see HOWTO-add-model.md's "Prefer conversion-time tensor modifications" tip (Gemma 3 folds its `1 +` into the weights, Qwen3-Next permutes in `modify_tensors`). Doing these at runtime in the graph is very likely to be rejected as over-complicated; if you genuinely can't do it at conversion time, open a discussion first explaining why rather than implementing it in the graph. + - Exception: a plain `weight * scale` with a constant scale is usually better applied at inference time instead of being folded into the weight at conversion. The scale conceptually applies to the activation, not the weight, so folding it in can hurt numerical stability, and it shifts the weight's value range in a way that can make quantization worse. + +## Validation checklist + +Reference: `examples/model-conversion/README.md`. + +1. Convert to GGUF, then inspect/run both the original and converted tensors. +2. Run logits verification (original vs converted). If this model is a new version of an already-supported family, verify the *previous* version still passes logits verification first - numerical differences may be pre-existing, not caused by the new work. The tools to perform full logits validation are available in `examples/model-conversion`. +3. Quantize (including QAT variants if relevant) and re-verify. +4. Run perplexity evaluation (simple and full). +5. Sanity-check across `tools/cli`, `tools/completion`, `tools/imatrix`, `tools/quantize`, and `tools/server`. +6. CPU backend first; other backends (CUDA, Metal, ...) can be separate follow-up PRs per `CONTRIBUTING.md`. +7. Re-review every changed file against the coding/naming guidelines in `AGENTS.md` (and `CONTRIBUTING.md`'s "Coding guidelines"/"Naming guidelines" sections) - this is a separate pass from functional testing and is just as important: no forced line-wrapping, no unicode punctuation, minimal/non-redundant comments, `snake_case` naming (`kebab-case` for file names), matching indentation/brace style, etc. + +## Before opening a PR + +- Run the `code-review` skill on the diff first - it catches the convention and scope issues reviewers flag most often, and it's recommended to do this locally before pushing the PR. +- Confirm the contributor can explain every changed line to a reviewer and is prepared to be asked about any of it - this is required regardless of how much of the code was AI-generated. +- Confirm they did a comprehensive manual review of the full diff, not just a skim. +- Fill in the AI-disclosure section of `.github/pull_request_template.md` describing how AI was used (do not omit or understate this). +- Do not write the PR description, commit message, GitHub issue/discussion text, or any reviewer replies yourself - the contributor writes these. diff --git a/skills/code-review/SKILL.md b/skills/code-review/SKILL.md new file mode 100644 index 000000000000..726edbb0ccb1 --- /dev/null +++ b/skills/code-review/SKILL.md @@ -0,0 +1,146 @@ +--- +name: code-review +description: Review llama.cpp changes against project conventions and common reviewer pitfalls before a PR. Use when the user wants to review a diff, branch, or PR. +--- + +# Review llama.cpp changes + +This skill reviews changes against llama.cpp's conventions and the pitfalls that reviewers flag most often, so the contributor can fix them before a maintainer has to. It has two modes: + +- **Self-review (default):** review the contributor's own local changes (uncommitted work, or a branch vs `master`) as a pre-PR pass. Ask which if it's ambiguous; default to `git diff master...HEAD` plus any uncommitted changes. +- **Read-only review of a PR/file:** if the user points at a PR number or specific files (including code they didn't write), review those and report findings. + +In both modes the output is **private review notes for the user to read and act on** - it is never something to post. This is a hard rule from `AGENTS.md`: an agent must NEVER write, or help write, a PR comment, a review comment, or a reply to a reviewer, by any means including `gh`. Do not offer to. If the user asks you to post the notes, refuse and point them at that rule. Present findings in the conversation only. + +Before starting, read `AGENTS.md` and `CONTRIBUTING.md` if not already in context - the "Coding guidelines", "Naming guidelines", and AI usage sections are the baseline this review enforces. For a diff that adds a new model architecture, also read `docs/development/HOWTO-add-model.md` and consider the dedicated `add-new-model` skill. + +## Step 0 - Scope the diff and pick the checklists + +Identify what actually changed and which area checklists below apply. Run `git diff --stat` (or `gh pr view --json files` for PR mode) and bucket the touched paths: + +- `conversion/`, `gguf-py/`, `src/models/`, `src/llama-arch.*` -> **New model / architecture** +- `ggml/` (any backend, op, or `ggml.h`) -> **ggml / backend** +- `include/llama.h` and other public headers -> **Public API** +- `tools/server/` -> **Server** +- anything else, plus all of the above -> **General** (always runs) + +Always run the **Scope and quick-reject gate**, the **Security review**, and the **General** checklist. Run each area checklist whose paths were touched. Additionally, if the diff introduces a new component, subsystem, or piece of infrastructure (a new file/class/module, a new abstraction, or hand-rolled machinery), run the **Approach and design** review. Tell the user which checklists you're running and why. + +## Scope and quick-reject gate (always) + +These are the patterns that get PRs closed without a full review. Check them first - a finding here is more important than any code nit, because it can mean the change shouldn't be a PR in its current form at all. + +- Is there a prior issue/discussion for this? Features are supposed to start as an issue, not a PR (`CONTRIBUTING.md`). If this is a nontrivial feature with no linked issue, flag it and suggest opening one first. +- Is it a duplicate of existing/in-flight work? Suggest `gh search prs` / `gh search issues` for the feature. Many closed PRs were duplicates of something already queued. +- Is it self-contained and single-purpose? Multiple unrelated changes/optimizations bundled together get sent back to be split. Flag unrelated changes and suggest separate PRs. +- Does it touch multiple ggml backends at once? Initial support should be CPU-only, other backends as follow-ups (`CONTRIBUTING.md`). Flag CUDA/Metal/Vulkan/etc. changes bundled into a feature's first PR. +- Does it add a new `ggml_type` / quantization type? That carries a disproportionate maintenance burden and needs the full justification package (GGUF sample upload, perplexity vs FP16/BF16 and similar sizes, KL-divergence data, CPU perf numbers). Absent that, it will be rejected regardless of code quality. +- Is it invasive - new subsystem, core-API reshaping, changes to shared graph/sampler code that other models don't need? Flag it and suggest a discussion with maintainers before investing further. +- Is it niche/vendor-specific in a way that adds a maintenance burden nobody will own long-term? Flag the maintenance-ownership question. +- Is the change semantically correct, or a plausible-looking "fix" that misunderstands the code? Sanity-check the actual behavior, not just that it compiles. +- AI-disclosure: if AI meaningfully contributed, is the PR template's disclosure section filled in? Remind the user. Never suggest writing the PR description or commit message for them. + +## Security review (mandatory) + +Mandatory on every review; any finding here is **blocking**. Rule of thumb: GGUF metadata, tensor shapes, tokenizer/grammar input, and all server/RPC fields are attacker-controlled - bound them before use. + +- **Sizes/counts from tensor dims:** validate before allocating. Products like `ne[i]*nb[i]`/nbytes can overflow on crafted dims into an undersized alloc then heap overflow. Overflow checks must run BEFORE the arithmetic they guard - padding/alignment macros wrap to 0 near `SIZE_MAX`, so a guard after the pad passes. +- **GGUF strings/arrays:** cap declared lengths and element counts before using them to size a loop or buffer; validate element type and length before casting an array to a pointer or reading fixed indices (`[i+1]`, `[0..2]`). +- **File-supplied counts indexing fixed arrays:** bound any count (e.g. layer/block count into a `LLAMA_MAX_*` array) before indexing; watch checks that only fire when an optional key is present. +- **Declared vs actual array length:** check the declared length of a GGUF array against the count actually read, not just against a buffer size. +- **Bounds comparisons:** flag narrowing casts (`size_t`->`int32_t`) and signed/unsigned mixing that can bypass a length check and copy past a buffer. +- **Parsed/derived indices:** range-check `stoi`/`atoi` results and catch parse throws; never use a default or derived token id (EOS/BOS/...) as an index without a bounds check. +- **Reused/reserved buffers:** recheck bounds after a buffer is shrunk or reused; watch `reserve()` then index-by-assumed-size, and header fields read before their length is checked. +- **Server JSON ints:** clamp client-supplied integers (token/discard counts, offsets) to non-negative and an upper bound before they reach index/pointer arithmetic. +- **RPC-deserialized fields:** treat every field (type/buffer/data/ne/nb/op_params) as hostile - validate before use. Null/zero buffers skipping validation, attacker data pointers, out-of-range type indices, and negative strides sign-extending past a corner-only assert all give arbitrary read/write. +- **Lifetime/UAF:** flag stored raw pointers to caller/temporary storage, cached pointers to buffers a later free releases, async ops whose source may drop before completion, and structures not invalidated on free/realloc. Null-check conditionally-built or "not required" tensors before dereferencing. + +## Approach and design (when a new component/infra is introduced) + +Run this whenever the diff adds a new component, subsystem, or piece of infrastructure. Reviews too often stop at "does it work" - a diff can be correct and still be the wrong approach, and a messy design costs more long-term than a bug. Evaluate the *approach*, not just the behavior; raising a cleaner one is a high-value finding, not a nit. If you see a better design, describe it concretely rather than just calling the current one bad. + +- **Simpler approach upstream:** the biggest win is often a different data model or design that removes whole subsystems, not tweaks to the code as written. Complexity must be justified by the problem, not by the first thing that worked. +- **Reuse over reinvention:** grep for an existing helper, library, object, or mechanism before adding a new one. Reimplementing what the codebase already has reintroduces solved bugs and adds maintenance surface. +- **Clear ownership/lifetime:** prefer RAII and obvious ownership over manual liveness flags, hand-tracked pointers, and "is it still alive?" checks - manual lifetime tracking is a recurring source of subtle bugs. +- **Right-sized machinery:** flag redundant, overkill, or heavier-than-needed primitives and abstractions; use the minimum the design actually needs. +- **Right structure and fit:** a new type should earn its place (split it if it serves two roles); follow existing patterns, idioms, and naming, and avoid constructs the project shuns. +- **Root cause vs symptom:** fixes layered on fixes signal a design to correct, not guard around. + +## New model / architecture + +See the `add-new-model` skill and `docs/development/HOWTO-add-model.md` for the full workflow; this is the review-time subset that reviewers most often catch: + +- Don't branch on `model.arch` when the real dependency is a config/capability value - gate on the hparam/capability, not the architecture enum. +- If the model is a close variant of an existing arch, is the delta justified? Prefer reusing or subclassing the existing arch/model class over duplicating it. A near-duplicate class or `src/models/.cpp` will be asked to merge with its sibling. +- New tensor names go through `tensor_mapping.py`, not ad-hoc name matching. +- For QKV, split the *activation* with `ggml_view`, not the *weight* tensor; rely on ggml broadcasting instead of manually duplicating tensors. +- New graph inputs are declared at the top of the graph-build function, not inline where first used. +- Hparams that the model can't run correctly without must be mandatory (hard-error if missing), not read with a silent default fallback. Only genuinely-optional-across-configs values get a fallback accessor. +- New/optional weight tensors (scales, etc.) must route through `build_lora_mm` and the existing helpers, matching convention - don't leave raw matmuls copied from another arch. +- Don't hack RoPE with a custom sin/cos implementation. If `ggml_rope_ext` genuinely can't express it, that's an issue for discussion, not a PR. +- Test the quantized-KV path (`-ctk`/`-ctv q8_0`), not just default f16 - new speculative/attention features silently break there. +- Preserve existing explanatory comments about model-specific quirks when copying code; note the provenance ("copied from X, with Y added"). +- Remove dead code/branches left over from adapting a reference implementation. + +## ggml / backend + +- `supports_op` (and any dispatch/gating condition) must be scoped exactly to the cases being changed - a condition meant for a few quant types must not silently disable or enable everything else. +- No hardcoded warp/lane size - use `ggml_cuda_get_physical_warp_size()` (32 on CUDA, 64 on HIP/ROCm) and the portable helpers. +- Strip leftover debug/profiling/logging code before review. +- New or changed op? Update `docs/ops.md` and the relevant `docs/ops/*.csv` for the touched backend. +- New op or operator change needs corresponding `test-backend-ops` cases, and (per `CONTRIBUTING.md`) consistency across at least two backends. +- New kernels are expected to come with concrete perf data (throughput across realistic tensor shapes), not just correctness. +- Don't have a backend mutate the cgraph as a shortcut - that's an unresolved architectural question, not something to slip in. +- Expect this to need two maintainer approvals; that's normal for `ggml/` changes, not a sign something is wrong. +- For CUDA: Avoid excessively templating kernels, only add this where it shows visible performance gain. + +## Public API (`include/llama.h`) + +Public API changes carry a higher bar than internal ones (`CONTRIBUTING.md`). Review for: + +- Justification: why doesn't an existing mechanism (e.g. `cb_eval`, existing batch/sampler knobs) suffice? If it does, the change likely shouldn't add public surface. This is the single most common reason these PRs are rejected. +- Experimental or stop-gap surface belongs in a side header (`llama-ext.h`), not in `llama.h`. +- Keep it minimal and general: prefer one general call over several narrow convenience wrappers; make new calls forward-compatible (e.g. mixed-modality batches) rather than assuming today's shape. +- The C API is the first-class, stable, ABI-defining surface - don't propose a parallel C++ API as a replacement. `llama-cpp.h` stays a thin convenience layer. +- Types and naming: sized integer types (`int32_t`, `size_t` for sizes/offsets); `snake_case`; `_` = `__`; enum values upper-case and prefixed with the enum name; `_t` suffix for opaque types. Avoid gratuitous signature/ABI changes to existing exported functions. +- Every new API needs a working example/tool exercising it in the same PR - reviewers find real bugs by requiring it to be wired into `server`, `embedding`, `perplexity`, etc. + +## Server (`tools/server/`) + +- Is the feature within server's defined scope? Check `tools/server/README-dev.md` - out-of-scope features get declined. +- Security: don't trust client-supplied headers (e.g. `X-Forwarded-For`) or add footguns; things like IP allowlisting belong at a reverse proxy unless there's a trusted-proxy design. +- Wire new behavior into the existing request/response and checkpoint paths correctly; watch for resource leaks across requests. + +## Multimodal (`tools/mtmd/`) + +- Tensor names must be prefixed by `v.`, `a.`, `mm.` or `a.mm.` (legacy naming doesn't follow this convention - this is expected, but new code should follow it). +- Do not use explicit sin/cos for RoPE; use `ggml_rope_ext` instead, see `HOWTO-add-model.md`. If it can't express the needed behavior, that's a design discussion, not a PR. +- New GGML ops must not be introduced in the same PR, you must push it as a separate PR. +- In most cases, `build_vit` should be enough to build the transformer graph for vision models. Do not add a loop to build the transformer graph manually, unless you have a very good reason to do so. If you do, please explain why in the PR description. +- If you need a dedicated preprocessor, there is a high chance that it can be a derived class from one of the existing preprocessors. Check carefully before adding a new preprocessor class. +- If the model need a new public API in `mtmd.h`, open a discussion first. + +## General (always) + +Enforce the `AGENTS.md` / `CONTRIBUTING.md` coding and naming guidelines on every changed line - this is a distinct pass from checking that the code works, and matters just as much for review speed: + +- ASCII only in code and comments - no emdash, unicode arrows, `x`, `...` used as unicode; use `-`, `->`, `x`, `...` ASCII equivalents. +- Comments are concise and explain non-obvious *why*, not *what*. Flag verbose comments, comments that restate the code, comments that reference the current task/PR, and comments hard-wrapped to a fixed column width. +- Do not force-wrap prose/comments to a fixed character count or split a sentence across lines. +- `snake_case` names; `kebab-case` (lowercase-with-dashes) file names for C/C++, `.h` headers; Python files lowercase-with-underscores. Naming optimizes for longest common prefix (`number_small`, not `small_number`). +- 4-space indentation, brackets on the same line, `void * ptr`, `int & a`, no trailing whitespace; match the surrounding style. +- Reuse existing infrastructure over introducing new components; no new third-party dependencies, extra headers, or files unless clearly justified. +- Keep it simple: a simpler change doing 90% is often preferable to a complex one doing 100%. Flag unnecessary templates/fancy STL; basic `for` loops are fine here. +- Every added line should be something the contributor can explain and defend to a reviewer without AI help - flag anything that looks copied-in without understanding. +- `Co-authored-by:` must be reserved for human co-authors; AI contributions (claude, cursor, codex, etc.) must use `Assisted-by:`; if this point is violated, it's a blocking finding. +- Any mentions of Minja must be treated as blocking; see `AGENTS.md` for why. + +## Reporting + +Group findings by severity so the user knows what actually blocks a merge: + +1. **Blocking** - quick-reject/scope issues and correctness bugs; these can sink the PR regardless of everything else. +2. **Will slow the review** - convention/naming/comment violations, missing tests/docs/perf data, missing API justification or example. +3. **Nits** - minor style, optional cleanups. + +For each finding, point to the file and line and say concretely what to change and why. Do not rewrite the whole diff unprompted; let the contributor make the fixes so they own and understand them. And do not draft any PR text, commit message, or reviewer reply - that is the contributor's to write. diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt index 320784c3a8cc..24f05cc91673 100644 --- a/src/CMakeLists.txt +++ b/src/CMakeLists.txt @@ -25,6 +25,7 @@ add_library(llama llama-kv-cache.cpp llama-kv-cache-iswa.cpp llama-kv-cache-dsa.cpp + llama-kv-cache-msa.cpp llama-kv-cache-dsv4.cpp llama-memory.cpp llama-memory-hybrid.cpp diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp index 304aa6b77712..d849ff63c872 100644 --- a/src/llama-arch.cpp +++ b/src/llama-arch.cpp @@ -127,6 +127,7 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_GROVEMOE, "grovemoe" }, { LLM_ARCH_APERTUS, "apertus" }, { LLM_ARCH_MINIMAX_M2, "minimax-m2" }, + { LLM_ARCH_MINIMAX_M3, "minimax-m3" }, { LLM_ARCH_COGVLM, "cogvlm" }, { LLM_ARCH_RND1, "rnd1" }, { LLM_ARCH_PANGU_EMBED, "pangu-embedded" }, @@ -140,9 +141,11 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_LLAMA_EMBED, "llama-embed" }, { LLM_ARCH_MAINCODER, "maincoder" }, { LLM_ARCH_KIMI_LINEAR, "kimi-linear" }, + { LLM_ARCH_KIMI_K3, "kimi-k3" }, { LLM_ARCH_TALKIE, "talkie" }, { LLM_ARCH_MELLUM, "mellum" }, { LLM_ARCH_INKLING, "inkling" }, + { LLM_ARCH_NANBEIGE, "nanbeige" }, { LLM_ARCH_UNKNOWN, "(unknown)" }, }; @@ -221,6 +224,8 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_TOKEN_SHIFT_COUNT, "%s.token_shift_count" }, { LLM_KV_INTERLEAVE_MOE_LAYER_STEP, "%s.interleave_moe_layer_step" }, { LLM_KV_FULL_ATTENTION_INTERVAL, "%s.full_attention_interval" }, + { LLM_KV_NUM_LOOPS, "%s.num_loops" }, + { LLM_KV_SKIP_LOOP_FINAL_NORM, "%s.skip_loop_final_norm" }, { LLM_KV_ATTENTION_HEAD_COUNT, "%s.attention.head_count" }, { LLM_KV_ATTENTION_HEAD_COUNT_KV, "%s.attention.head_count_kv" }, @@ -254,6 +259,9 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, "%s.attention.indexer.head_count" }, { LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, "%s.attention.indexer.key_length" }, { LLM_KV_ATTENTION_INDEXER_TOP_K, "%s.attention.indexer.top_k" }, + { LLM_KV_ATTENTION_INDEXER_BLOCK_SIZE, "%s.attention.indexer.block_size" }, + { LLM_KV_ATTENTION_INDEXER_LOCAL_BLOCKS, "%s.attention.indexer.local_blocks" }, + { LLM_KV_ATTENTION_INDEXER_TYPES, "%s.attention.indexer.types" }, { LLM_KV_ATTENTION_OUTPUT_GROUP_COUNT, "%s.attention.output_group_count" }, { LLM_KV_ATTENTION_OUTPUT_LORA_RANK, "%s.attention.output_lora_rank" }, { LLM_KV_ATTENTION_COMPRESS_ROPE_FREQ_BASE, "%s.attention.compress_rope_freq_base" }, @@ -296,7 +304,12 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_SSM_GROUP_COUNT, "%s.ssm.group_count" }, { LLM_KV_SSM_DT_B_C_RMS, "%s.ssm.dt_b_c_rms" }, - { LLM_KV_KDA_HEAD_DIM, "%s.kda.head_dim" }, + { LLM_KV_KDA_HEAD_DIM, "%s.kda.head_dim" }, + { LLM_KV_KDA_GATE_LOWER_BOUND, "%s.kda.gate_lower_bound" }, + + { LLM_KV_SITU_BETA, "%s.situ_beta" }, + { LLM_KV_SITU_LINEAR_BETA, "%s.situ_linear_beta" }, + { LLM_KV_ATTN_RES_BLOCK_SIZE, "%s.attn_res_block_size" }, { LLM_KV_WKV_HEAD_SIZE, "%s.wkv.head_size" }, @@ -311,6 +324,7 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_TARGET_LAYERS, "%s.target_layers" }, { LLM_KV_TARGET_HIDDEN_SIZE, "%s.target_hidden_size" }, { LLM_KV_NORM_BEFORE_RESIDUAL, "%s.norm_before_residual" }, + { LLM_KV_NORM_BEFORE_FC, "%s.norm_before_fc" }, { LLM_KV_SHORTCONV_L_CACHE, "%s.shortconv.l_cache" }, @@ -421,6 +435,13 @@ static const std::map LLM_TENSOR_NAMES = { { LLM_TENSOR_FFN_EXP_PROBS_B, "blk.%d.exp_probs_b" }, { LLM_TENSOR_FFN_LATENT_DOWN, "blk.%d.ffn_latent_down" }, { LLM_TENSOR_FFN_LATENT_UP, "blk.%d.ffn_latent_up" }, + { LLM_TENSOR_FFN_LATENT_NORM, "blk.%d.ffn_latent_norm" }, + { LLM_TENSOR_ATTN_RES_NORM, "blk.%d.attn_res_norm" }, + { LLM_TENSOR_ATTN_RES_PROJ, "blk.%d.attn_res_proj" }, + { LLM_TENSOR_FFN_RES_NORM, "blk.%d.ffn_res_norm" }, + { LLM_TENSOR_FFN_RES_PROJ, "blk.%d.ffn_res_proj" }, + { LLM_TENSOR_OUTPUT_RES_NORM, "output_res_norm" }, + { LLM_TENSOR_OUTPUT_RES_PROJ, "output_res_proj" }, { LLM_TENSOR_ATTN_NORM_2, "blk.%d.attn_norm_2" }, { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" }, { LLM_TENSOR_LAYER_OUT_NORM, "blk.%d.layer_output_norm" }, @@ -619,6 +640,9 @@ static const std::map LLM_TENSOR_NAMES = { { LLM_TENSOR_INDEXER_PROJ, "blk.%d.indexer.proj" }, { LLM_TENSOR_INDEXER_ATTN_K, "blk.%d.indexer.attn_k" }, { LLM_TENSOR_INDEXER_ATTN_Q_B, "blk.%d.indexer.attn_q_b" }, + { LLM_TENSOR_INDEXER_Q_PROJ, "blk.%d.indexer.q_proj" }, + { LLM_TENSOR_INDEXER_K_PROJ, "blk.%d.indexer.k_proj" }, + { LLM_TENSOR_INDEXER_Q_NORM, "blk.%d.indexer.q_norm" }, { LLM_TENSOR_INDEXER_COMPRESSOR_WKV, "blk.%d.indexer_compressor_kv" }, { LLM_TENSOR_INDEXER_COMPRESSOR_WGATE, "blk.%d.indexer_compressor_gate" }, { LLM_TENSOR_INDEXER_COMPRESSOR_APE, "blk.%d.indexer_compressor_ape" }, @@ -628,6 +652,9 @@ static const std::map LLM_TENSOR_NAMES = { { LLM_TENSOR_MASKED_EMBD_ORDERING, "masked_embd_ordering" }, { LLM_TENSOR_FC, "fc" }, { LLM_TENSOR_D2T, "d2t" }, + { LLM_TENSOR_DSPARK_MARKOV_W1, "markov_w1" }, + { LLM_TENSOR_DSPARK_MARKOV_W2, "markov_w2" }, + { LLM_TENSOR_DSPARK_CONF_PROJ, "conf_proj" }, }; // declare information about the model weight tensors: @@ -689,7 +716,7 @@ static const std::map LLM_TENSOR_INFOS = { {LLM_TENSOR_HC_FFN_SCALE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, {LLM_TENSOR_ATTN_COMPRESSOR_WKV, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, {LLM_TENSOR_ATTN_COMPRESSOR_WGATE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, - {LLM_TENSOR_ATTN_COMPRESSOR_APE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}}, + {LLM_TENSOR_ATTN_COMPRESSOR_APE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_GET_ROWS}}, {LLM_TENSOR_ATTN_COMPRESSOR_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, {LLM_TENSOR_ATTN_K_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, {LLM_TENSOR_ATTN_V_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, @@ -864,9 +891,12 @@ static const std::map LLM_TENSOR_INFOS = { {LLM_TENSOR_INDEXER_PROJ, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, {LLM_TENSOR_INDEXER_ATTN_K, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, {LLM_TENSOR_INDEXER_ATTN_Q_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_INDEXER_Q_PROJ, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_INDEXER_K_PROJ, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_INDEXER_Q_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, {LLM_TENSOR_INDEXER_COMPRESSOR_WKV, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, {LLM_TENSOR_INDEXER_COMPRESSOR_WGATE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, - {LLM_TENSOR_INDEXER_COMPRESSOR_APE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}}, + {LLM_TENSOR_INDEXER_COMPRESSOR_APE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_GET_ROWS}}, {LLM_TENSOR_INDEXER_COMPRESSOR_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, {LLM_TENSOR_FFN_GATE_TID2EID, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_GET_ROWS}}, {LLM_TENSOR_NEXTN_PROJ_PRE, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, @@ -884,11 +914,23 @@ static const std::map LLM_TENSOR_INFOS = { // latent projections feed ggml_mul_mat, the buft probe must use MUL_MAT to keep them on GPU {LLM_TENSOR_FFN_LATENT_DOWN, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, {LLM_TENSOR_FFN_LATENT_UP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + // Kimi K3 + {LLM_TENSOR_FFN_LATENT_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_ATTN_RES_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_ATTN_RES_PROJ, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_FFN_RES_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_FFN_RES_PROJ, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_OUTPUT_RES_NORM, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}}, + {LLM_TENSOR_OUTPUT_RES_PROJ, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}}, {LLM_TENSOR_MASKED_EMBD_CENTROIDS, {LLM_TENSOR_LAYER_INPUT, GGML_OP_NONE}}, {LLM_TENSOR_MASKED_EMBD_ORDERING, {LLM_TENSOR_LAYER_INPUT, GGML_OP_NONE}}, // eagle3 {LLM_TENSOR_FC, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}}, {LLM_TENSOR_D2T, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_GET_ROWS}}, + // dspark + {LLM_TENSOR_DSPARK_MARKOV_W1, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_GET_ROWS}}, + {LLM_TENSOR_DSPARK_MARKOV_W2, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_DSPARK_CONF_PROJ, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}}, }; LLM_KV::LLM_KV(llm_arch arch, const char * suffix) : arch(arch), suffix(suffix) {} @@ -978,9 +1020,11 @@ bool llm_arch_is_hybrid(const llm_arch & arch) { case LLM_ARCH_NEMOTRON_H_MOE: case LLM_ARCH_QWEN3NEXT: case LLM_ARCH_KIMI_LINEAR: + case LLM_ARCH_KIMI_K3: case LLM_ARCH_QWEN35: case LLM_ARCH_QWEN35MOE: case LLM_ARCH_INKLING: + case LLM_ARCH_DEEPSEEK4: return true; default: return false; @@ -1003,6 +1047,7 @@ bool llm_arch_supports_rs_rollback(const llm_arch & arch) { switch (arch) { case LLM_ARCH_QWEN35: case LLM_ARCH_QWEN35MOE: + case LLM_ARCH_DEEPSEEK4: return true; default: return false; @@ -1034,8 +1079,10 @@ bool llm_arch_supports_sm_tensor(const llm_arch & arch) { case LLM_ARCH_LFM2: case LLM_ARCH_LFM2MOE: case LLM_ARCH_MINIMAX_M2: + case LLM_ARCH_MINIMAX_M3: case LLM_ARCH_MISTRAL4: case LLM_ARCH_KIMI_LINEAR: + case LLM_ARCH_KIMI_K3: case LLM_ARCH_INKLING: return false; default: diff --git a/src/llama-arch.h b/src/llama-arch.h index 1aa02aa6ad5d..a695a2a49afd 100644 --- a/src/llama-arch.h +++ b/src/llama-arch.h @@ -143,11 +143,14 @@ enum llm_arch { LLM_ARCH_LLAMA_EMBED, LLM_ARCH_MAINCODER, LLM_ARCH_KIMI_LINEAR, + LLM_ARCH_KIMI_K3, LLM_ARCH_TALKIE, LLM_ARCH_MELLUM, LLM_ARCH_EAGLE3, + LLM_ARCH_MINIMAX_M3, LLM_ARCH_DFLASH, LLM_ARCH_INKLING, + LLM_ARCH_NANBEIGE, LLM_ARCH_UNKNOWN, }; @@ -226,6 +229,8 @@ enum llm_kv { LLM_KV_TOKEN_SHIFT_COUNT, LLM_KV_INTERLEAVE_MOE_LAYER_STEP, LLM_KV_FULL_ATTENTION_INTERVAL, + LLM_KV_NUM_LOOPS, + LLM_KV_SKIP_LOOP_FINAL_NORM, LLM_KV_ATTENTION_HEAD_COUNT, LLM_KV_ATTENTION_HEAD_COUNT_KV, @@ -259,6 +264,9 @@ enum llm_kv { LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, LLM_KV_ATTENTION_INDEXER_TOP_K, + LLM_KV_ATTENTION_INDEXER_BLOCK_SIZE, + LLM_KV_ATTENTION_INDEXER_LOCAL_BLOCKS, + LLM_KV_ATTENTION_INDEXER_TYPES, LLM_KV_ATTENTION_OUTPUT_GROUP_COUNT, LLM_KV_ATTENTION_OUTPUT_LORA_RANK, LLM_KV_ATTENTION_COMPRESS_ROPE_FREQ_BASE, @@ -302,6 +310,11 @@ enum llm_kv { LLM_KV_SSM_DT_B_C_RMS, LLM_KV_KDA_HEAD_DIM, + LLM_KV_KDA_GATE_LOWER_BOUND, + + LLM_KV_SITU_BETA, + LLM_KV_SITU_LINEAR_BETA, + LLM_KV_ATTN_RES_BLOCK_SIZE, LLM_KV_WKV_HEAD_SIZE, @@ -357,6 +370,7 @@ enum llm_kv { LLM_KV_TARGET_LAYERS, LLM_KV_TARGET_HIDDEN_SIZE, LLM_KV_NORM_BEFORE_RESIDUAL, + LLM_KV_NORM_BEFORE_FC, LLM_KV_SHORTCONV_L_CACHE, @@ -445,6 +459,13 @@ enum llm_tensor { LLM_TENSOR_FFN_EXP_PROBS_B, LLM_TENSOR_FFN_LATENT_DOWN, LLM_TENSOR_FFN_LATENT_UP, + LLM_TENSOR_FFN_LATENT_NORM, + LLM_TENSOR_ATTN_RES_NORM, + LLM_TENSOR_ATTN_RES_PROJ, + LLM_TENSOR_FFN_RES_NORM, + LLM_TENSOR_FFN_RES_PROJ, + LLM_TENSOR_OUTPUT_RES_NORM, + LLM_TENSOR_OUTPUT_RES_PROJ, LLM_TENSOR_ATTN_Q_NORM, LLM_TENSOR_ATTN_K_NORM, LLM_TENSOR_LAYER_OUT_NORM, @@ -620,6 +641,9 @@ enum llm_tensor { LLM_TENSOR_INDEXER_PROJ, LLM_TENSOR_INDEXER_ATTN_K, LLM_TENSOR_INDEXER_ATTN_Q_B, + LLM_TENSOR_INDEXER_Q_PROJ, + LLM_TENSOR_INDEXER_K_PROJ, + LLM_TENSOR_INDEXER_Q_NORM, LLM_TENSOR_INDEXER_COMPRESSOR_WKV, LLM_TENSOR_INDEXER_COMPRESSOR_WGATE, LLM_TENSOR_INDEXER_COMPRESSOR_APE, @@ -637,6 +661,9 @@ enum llm_tensor { LLM_TENSOR_MASKED_EMBD_ORDERING, LLM_TENSOR_FC, LLM_TENSOR_D2T, + LLM_TENSOR_DSPARK_MARKOV_W1, + LLM_TENSOR_DSPARK_MARKOV_W2, + LLM_TENSOR_DSPARK_CONF_PROJ, }; diff --git a/src/llama-context.cpp b/src/llama-context.cpp index ee09c7473689..d3e4e5e43781 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -61,6 +61,24 @@ static const llm_fused_op_probe llm_fused_op_lid_probe = { /*.n_tokens_per_seq =*/ 1, }; +static const llm_fused_op_probe llm_fused_op_dsv4_hc_pre_probe = { + /*.op =*/ LLM_FUSED_OP_DSV4_HC_PRE, + /*.name =*/ "fused DeepSeek V4 HC pre", + /*.n_tokens_per_seq =*/ 1, +}; + +static const llm_fused_op_probe llm_fused_op_dsv4_hc_comb_probe = { + /*.op =*/ LLM_FUSED_OP_DSV4_HC_COMB, + /*.name =*/ "fused DeepSeek V4 HC comb", + /*.n_tokens_per_seq =*/ 1, +}; + +static const llm_fused_op_probe llm_fused_op_dsv4_hc_post_probe = { + /*.op =*/ LLM_FUSED_OP_DSV4_HC_POST, + /*.name =*/ "fused DeepSeek V4 HC post", + /*.n_tokens_per_seq =*/ 1, +}; + llama_context::llama_context( const llama_model & model, llama_context_params params) : @@ -103,8 +121,9 @@ llama_context::llama_context( cparams.no_perf = params.no_perf; cparams.warmup = false; - cparams.embeddings_layer_inp.resize(hparams.n_layer(), false); - embd_layer_inp.resize(hparams.n_layer()); + // +1: id n_layer() taps the output of the last layer ("input" of the head) + cparams.embeddings_layer_inp.resize(hparams.n_layer() + 1, false); + embd_layer_inp.resize(hparams.n_layer() + 1); cparams.ctx_type = params.ctx_type; cparams.pooling_type = params.pooling_type; @@ -236,6 +255,11 @@ llama_context::llama_context( cparams.fused_lid = true; cparams.auto_flid = true; + cparams.fused_dsv4_hc_pre = true; + cparams.fused_dsv4_hc_comb = true; + cparams.fused_dsv4_hc_post = true; + cparams.auto_fhc = true; + // with causal attention, the batch size is limited by the context size cparams.n_batch = cparams.causal_attn ? std::min(cparams.n_ctx, params.n_batch) : params.n_batch; @@ -452,6 +476,9 @@ llama_context::llama_context( } llama_context::~llama_context() { + // wait for any pending asynchronous copies into the output buffers before they are freed + synchronize(); + if (!model.hparams.no_alloc) { for (size_t i = 0; i < backend_ptrs.size(); ++i) { ggml_backend_t backend = backend_ptrs[i]; @@ -538,6 +565,14 @@ void llama_context::resolve_fused_ops(const llama_memory_context_i * mctx, uint3 resolve(llm_fused_op_lid_probe, cparams.fused_lid); cparams.auto_flid = false; } + + if (cparams.auto_fhc) { + LLAMA_LOG_INFO("%s: resolving fused DeepSeek V4 HC support:\n", func); + resolve(llm_fused_op_dsv4_hc_pre_probe, cparams.fused_dsv4_hc_pre); + resolve(llm_fused_op_dsv4_hc_comb_probe, cparams.fused_dsv4_hc_comb); + resolve(llm_fused_op_dsv4_hc_post_probe, cparams.fused_dsv4_hc_post); + cparams.auto_fhc = false; + } } void llama_context::sched_reserve() { @@ -1131,7 +1166,7 @@ void llama_context::set_embeddings_nextn(bool value, bool masked) { void llama_context::set_embeddings_layer_inp(uint32_t lid, bool enable) { LLAMA_LOG_DEBUG("%s: lid = %d, enable = %d\n", __func__, lid, enable); - GGML_ASSERT(lid < model.hparams.n_layer()); + GGML_ASSERT(lid <= model.hparams.n_layer()); cparams.embeddings_layer_inp[lid] = enable; @@ -1387,13 +1422,17 @@ int llama_context::encode(const llama_batch & batch_inp) { // micro-batching is not possible for non-causal encoding, so we process the batch in a single shot GGML_ASSERT(cparams.n_ubatch >= n_tokens && "encoder requires n_ubatch >= n_tokens"); + // TODO: this clear of the buffer can easily be forgotten - need something better + // sync first so any in-flight async copies into embd_seq complete before it is freed + if (!embd_seq.empty()) { + synchronize(); + } + embd_seq.clear(); + if (t_compute_start_us == 0) { t_compute_start_us = ggml_time_us(); } - // TODO: this clear of the buffer can easily be forgotten - need something better - embd_seq.clear(); - sched_reserve(); n_queued_tokens += n_tokens; @@ -1679,7 +1718,8 @@ int llama_context::decode(const llama_batch & batch_inp) { const auto & hparams = model.hparams; const int64_t n_vocab = vocab.n_tokens(); - const int64_t n_embd = hparams.n_embd_inp(); + const bool mtp_embd = cparams.ctx_type == LLAMA_CONTEXT_TYPE_MTP && batch_inp.embd; + const int64_t n_embd = mtp_embd ? hparams.n_embd_out() : hparams.n_embd_inp(); // when computing embeddings, all tokens are output const bool output_all = cparams.embeddings; @@ -1732,13 +1772,18 @@ int llama_context::decode(const llama_batch & batch_inp) { GGML_ASSERT((cparams.causal_attn || cparams.n_ubatch >= n_tokens_all) && "non-causal attention requires n_ubatch >= n_tokens"); + // TODO: this clear of the buffer can easily be forgotten - need something better + // sync first so any in-flight async copies into embd_seq complete before it is freed + if (!embd_seq.empty()) { + synchronize(); + } + embd_seq.clear(); + if (t_compute_start_us == 0) { t_compute_start_us = ggml_time_us(); } n_queued_tokens += n_tokens_all; - // TODO: this clear of the buffer can easily be forgotten - need something better - embd_seq.clear(); output_swaps.clear(); sched_reserve(); @@ -2233,8 +2278,9 @@ void llama_context::extract_layer_inputs(const llm_graph_result * res, size_t to } void llama_context::output_reorder() { - const uint64_t n_vocab = model.vocab.n_tokens(); - const uint64_t n_embd = model.hparams.n_embd; + const uint64_t n_vocab = model.vocab.n_tokens(); + const uint64_t n_embd = model.hparams.n_embd; + const uint64_t n_embd_out = model.hparams.n_embd_out(); for (size_t s = 0; s < output_swaps.size(); ++s) { const uint64_t i0 = output_swaps[s].i0; @@ -2247,14 +2293,14 @@ void llama_context::output_reorder() { } if (embd.size > 0) { - for (uint64_t k = 0; k < n_embd; k++) { - std::swap(embd.data[i0*n_embd + k], embd.data[i1*n_embd + k]); + for (uint64_t k = 0; k < n_embd_out; k++) { + std::swap(embd.data[i0*n_embd_out + k], embd.data[i1*n_embd_out + k]); } } if (embd_nextn.size > 0) { - for (uint64_t k = 0; k < n_embd; k++) { - std::swap(embd_nextn.data[i0*n_embd + k], embd_nextn.data[i1*n_embd + k]); + for (uint64_t k = 0; k < n_embd_out; k++) { + std::swap(embd_nextn.data[i0*n_embd_out + k], embd_nextn.data[i1*n_embd_out + k]); } } @@ -2304,11 +2350,18 @@ void llama_context::output_reorder() { // uint32_t llama_context::graph_max_nodes(uint32_t n_tokens) const { + if (model.arch == LLM_ARCH_KIMI_K3) { + // the n_tokens*40 budget below is exhausted at ubatch 3840 + return std::max(n_tokens * 160, 64u * model.n_tensors()); + } if (model.arch == LLM_ARCH_QWEN3NEXT || model.arch == LLM_ARCH_KIMI_LINEAR || model.arch == LLM_ARCH_QWEN35 || model.arch == LLM_ARCH_QWEN35MOE || - model.arch == LLM_ARCH_DEEPSEEK4) { + model.arch == LLM_ARCH_DEEPSEEK4 || + (model.arch == LLM_ARCH_DFLASH && model.hparams.dsv4_hc_mult > 0) || + model.arch == LLM_ARCH_NANBEIGE || + model.arch == LLM_ARCH_MINIMAX_M3) { return std::max(n_tokens * 40, 32u * model.n_tensors()); } uint32_t res = std::max(1024u, 8u*model.n_tensors()); @@ -2442,11 +2495,12 @@ llm_graph_cb llama_context::graph_get_cb() const { ggml_set_name(cur, name); } - // norm may be automatically assigned to the backend of the previous layer, increasing data transfer between backends + // - norm may be automatically assigned to the backend of the previous layer, increasing data transfer between backends + // - force the last op of the layer on the specified backend to avoid running it on the backend of the next layer due to scheduling // FIXME: fix in ggml_backend_sched const bool full_offload = model.n_gpu_layers() > model.hparams.n_layer_all; if (ubatch.n_tokens < 32 || full_offload) { - if (il != -1 && strcmp(name, "norm") == 0) { + if (il != -1 && (strcmp(name, "norm") == 0 || strcmp(name, "l_last") == 0)) { const auto & dev_layer = model.dev_layer(il); for (const auto & backend : backends) { if (ggml_backend_get_device(backend.get()) == dev_layer) { @@ -3529,6 +3583,32 @@ llama_context * llama_init_from_model( } } + if ((model->hparams.is_mla() || model->arch == LLM_ARCH_DEEPSEEK4) && params.type_k != params.type_v) { + LLAMA_LOG_ERROR("%s: model does not support different K (%s) and V (%s) cache types\n", __func__, ggml_type_name(params.type_k), ggml_type_name(params.type_v)); + return nullptr; + } + + // TurboQuant cache types require flash attention - auto-enable even when explicitly + // disabled, so this has to run before the generic quantized-V check below, which + // would otherwise reject the combination outright. + if (params.flash_attn_type == LLAMA_FLASH_ATTN_TYPE_DISABLED && + (params.type_k == GGML_TYPE_TURBO2_0 || params.type_k == GGML_TYPE_TURBO3_0 || params.type_k == GGML_TYPE_TURBO4_0 || + params.type_v == GGML_TYPE_TURBO2_0 || params.type_v == GGML_TYPE_TURBO3_0 || params.type_v == GGML_TYPE_TURBO4_0)) { + LLAMA_LOG_WARN("%s: turbo cache types require flash_attn - enabling automatically\n", __func__); + params.flash_attn_type = LLAMA_FLASH_ATTN_TYPE_ENABLED; + } + + if (ggml_is_quantized(params.type_v) && params.flash_attn_type != LLAMA_FLASH_ATTN_TYPE_ENABLED) { + if (params.flash_attn_type == LLAMA_FLASH_ATTN_TYPE_AUTO) { + LLAMA_LOG_INFO("%s: enabling flash_attn since it is required for quantized V cache\n", __func__); + params.flash_attn_type = LLAMA_FLASH_ATTN_TYPE_ENABLED; + } + if (params.flash_attn_type == LLAMA_FLASH_ATTN_TYPE_DISABLED) { + LLAMA_LOG_ERROR("%s: quantized V cache requires flash_attn to be enabled\n", __func__); + return nullptr; + } + } + if (params.flash_attn_type != LLAMA_FLASH_ATTN_TYPE_DISABLED && ggml_is_quantized(params.type_k)) { const uint32_t blck_size = ggml_blck_size(params.type_k); const bool k_is_turbo = (params.type_k == GGML_TYPE_TURBO2_0 || @@ -3568,19 +3648,6 @@ llama_context * llama_init_from_model( } } - // TurboQuant cache types require flash attention — auto-enable if disabled - if (params.flash_attn_type == LLAMA_FLASH_ATTN_TYPE_DISABLED && - (params.type_k == GGML_TYPE_TURBO2_0 || params.type_k == GGML_TYPE_TURBO3_0 || params.type_k == GGML_TYPE_TURBO4_0 || - params.type_v == GGML_TYPE_TURBO2_0 || params.type_v == GGML_TYPE_TURBO3_0 || params.type_v == GGML_TYPE_TURBO4_0)) { - LLAMA_LOG_WARN("%s: turbo cache types require flash_attn — enabling automatically\n", __func__); - params.flash_attn_type = LLAMA_FLASH_ATTN_TYPE_ENABLED; - } - - if (ggml_is_quantized(params.type_v) && params.flash_attn_type == LLAMA_FLASH_ATTN_TYPE_DISABLED) { - LLAMA_LOG_ERROR("%s: V cache quantization requires flash_attn\n", __func__); - return nullptr; - } - if (params.pooling_type != LLAMA_POOLING_TYPE_UNSPECIFIED && params.pooling_type != model->hparams.pooling_type) { //user-specified pooling-type is different from the model default diff --git a/src/llama-cparams.h b/src/llama-cparams.h index 58520caa3651..5018170ed85e 100644 --- a/src/llama-cparams.h +++ b/src/llama-cparams.h @@ -43,6 +43,10 @@ struct llama_cparams { bool auto_fgdn; bool fused_lid; // use fused lightning indexer bool auto_flid; + bool fused_dsv4_hc_pre; + bool fused_dsv4_hc_comb; + bool fused_dsv4_hc_post; + bool auto_fhc; bool no_perf; bool warmup; // TODO: remove [TAG_LLAMA_GRAPH_NO_WARMUP] bool op_offload; diff --git a/src/llama-grammar.cpp b/src/llama-grammar.cpp index badcbfd0fbb6..363644464bad 100644 --- a/src/llama-grammar.cpp +++ b/src/llama-grammar.cpp @@ -1139,6 +1139,18 @@ struct llama_grammar * llama_grammar_init_impl( vec_rules[i].push_back({LLAMA_GRETYPE_END, 0}); } + // Validate that all rule references point to valid rules + for (size_t i = 0; i < n_rules; i++) { + for (const auto & elem : vec_rules[i]) { + if (elem.type == LLAMA_GRETYPE_RULE_REF) { + if (elem.value >= n_rules || vec_rules[elem.value].empty()) { + LLAMA_LOG_ERROR("invalid grammar: rule %zu references undefined rule %u\n", i, elem.value); + return nullptr; + } + } + } + } + // Check for left recursion std::vector rules_visited(n_rules); std::vector rules_in_progress(n_rules); diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp index eea4fd0dfd23..1113b14cd3ec 100644 --- a/src/llama-graph.cpp +++ b/src/llama-graph.cpp @@ -8,6 +8,7 @@ #include "llama-kv-cache.h" #include "llama-kv-cache-iswa.h" #include "llama-kv-cache-dsa.h" +#include "llama-kv-cache-msa.h" #include "llama-kv-cache-dsv4.h" #include "llama-memory-hybrid.h" #include "llama-memory-hybrid-iswa.h" @@ -518,6 +519,40 @@ bool llm_graph_input_attn_k::can_reuse(const llm_graph_params & params) { return res; } +llm_graph_input_attn_kv_msa::llm_graph_input_attn_kv_msa( + const llama_hparams & hparams, + const llama_cparams & cparams, + const llama_kv_cache_msa_context * mctx) : + llm_graph_input_attn_kv(hparams, cparams, mctx->get_base()), + mctx_msa(mctx) { +} + +void llm_graph_input_attn_kv_msa::set_input(const llama_ubatch * ubatch) { + llm_graph_input_attn_kv::set_input(ubatch); + + if (self_k_idxs_idx) { + mctx_msa->get_idx()->set_input_k_idxs(self_k_idxs_idx, ubatch); + } +} + +bool llm_graph_input_attn_kv_msa::can_reuse(const llm_graph_params & params) { + mctx_msa = static_cast(params.mctx); + + // the parent class operates on the base cache context + this->mctx = mctx_msa->get_base(); + + bool res = true; + + res &= self_k_idxs->ne[0] == params.ubatch.n_tokens; + if (self_k_idxs_idx) { + res &= self_k_idxs_idx->ne[0] == params.ubatch.n_tokens; + } + + res &= can_reuse_kq_mask(self_kq_mask, this->mctx, params.ubatch, params.cparams); + + return res; +} + void llm_graph_input_attn_k_dsa::set_input(const llama_ubatch * ubatch) { mctx->get_mla()->set_input_k_idxs(self_k_idxs_mla, ubatch); @@ -619,6 +654,63 @@ bool llm_graph_input_attn_kv_iswa::can_reuse(const llm_graph_params & params) { return res; } +void llm_graph_input_attn_k_iswa::set_input(const llama_ubatch * ubatch) { + // base tensors may not be allocated if there are no non-SWA attention layers + if (self_k_idxs && self_k_idxs->buffer) { + mctx->get_base()->set_input_k_idxs(self_k_idxs, ubatch); + } + + // the kq mask guards on its own buffer: shared cells leave idxs unbacked while the mask stays live + if (self_kq_mask && self_kq_mask->buffer) { + mctx->get_base()->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn); + } + + // swa tensors may not be allocated if there are no SWA attention layers + if (self_k_idxs_swa && self_k_idxs_swa->buffer) { + mctx->get_swa()->set_input_k_idxs(self_k_idxs_swa, ubatch); + } + + if (self_kq_mask_swa && self_kq_mask_swa->buffer) { + mctx->get_swa()->set_input_kq_mask(self_kq_mask_swa, ubatch, cparams.causal_attn); + } + + if (self_k_rot && self_k_rot->buffer) { + mctx->get_base()->set_input_k_rot(self_k_rot); + } + + if (self_k_rot_swa && self_k_rot_swa->buffer) { + mctx->get_swa()->set_input_k_rot(self_k_rot_swa); + } +} + +bool llm_graph_input_attn_k_iswa::can_reuse(const llm_graph_params & params) { + const auto * mctx = static_cast(params.mctx); + + this->mctx = mctx; + + bool res = true; + + // base tensors may not be allocated if there are no non-SWA attention layers + if (self_k_idxs && self_k_idxs->buffer) { + res &= self_k_idxs->ne[0] == params.ubatch.n_tokens; + } + + if (self_kq_mask && self_kq_mask->buffer) { + res &= can_reuse_kq_mask(self_kq_mask, mctx->get_base(), params.ubatch, params.cparams); + } + + // swa tensors may not be allocated if there are no SWA attention layers + if (self_k_idxs_swa && self_k_idxs_swa->buffer) { + res &= self_k_idxs_swa->ne[0] == params.ubatch.n_tokens; + } + + if (self_kq_mask_swa && self_kq_mask_swa->buffer) { + res &= can_reuse_kq_mask(self_kq_mask_swa, mctx->get_swa(), params.ubatch, params.cparams); + } + + return res; +} + static void dsv4_set_i64(ggml_tensor * dst, const std::vector & src) { if (!dst || !dst->buffer) { return; @@ -754,6 +846,10 @@ static void dsv4_set_comp_inputs( dsv4_set_i32(inp.state_pos, plan.state_pos); dsv4_set_i32(inp.state_persist_src_idxs, plan.state_persist_src_idxs); dsv4_set_i32(inp.state_persist_dst_idxs, plan.state_persist_dst_idxs); + dsv4_set_i32(inp.state_restore_src_idxs, plan.state_restore_src_idxs); + dsv4_set_i32(inp.state_restore_dst_idxs, plan.state_restore_dst_idxs); + dsv4_set_i32(inp.state_snapshot_src_idxs, plan.state_snapshot_src_idxs); + dsv4_set_i32(inp.state_snapshot_dst_idxs, plan.state_snapshot_dst_idxs); dsv4_set_i32(inp.state_read_idxs, plan.state_read_idxs); dsv4_set_i64(inp.state_write_idxs, plan.state_write_idxs); dsv4_set_i32(inp.state_write_pos, plan.state_write_pos); @@ -798,6 +894,10 @@ static bool dsv4_can_reuse_comp_input( res &= dsv4_can_reuse_tensor_1d(inp.state_pos, plan.state_pos.size()); res &= dsv4_can_reuse_tensor_1d(inp.state_persist_src_idxs, plan.state_persist_src_idxs.size()); res &= dsv4_can_reuse_tensor_1d(inp.state_persist_dst_idxs, plan.state_persist_dst_idxs.size()); + res &= dsv4_can_reuse_tensor_1d(inp.state_restore_src_idxs, plan.state_restore_src_idxs.size()); + res &= dsv4_can_reuse_tensor_1d(inp.state_restore_dst_idxs, plan.state_restore_dst_idxs.size()); + res &= dsv4_can_reuse_tensor_1d(inp.state_snapshot_src_idxs, plan.state_snapshot_src_idxs.size()); + res &= dsv4_can_reuse_tensor_1d(inp.state_snapshot_dst_idxs, plan.state_snapshot_dst_idxs.size()); res &= dsv4_can_reuse_tensor_1d(inp.state_read_idxs, plan.state_read_idxs.size()); res &= dsv4_can_reuse_tensor_1d(inp.state_write_idxs, plan.state_write_idxs.size()); res &= dsv4_can_reuse_tensor_1d(inp.state_write_pos, plan.state_write_pos.size()); @@ -832,6 +932,10 @@ static void dsv4_build_comp_inputs( inp.state_pos = dsv4_build_input_1d(ctx, GGML_TYPE_I32, plan.state_pos.size(), std::string("dsv4_") + name + "_state_pos"); inp.state_persist_src_idxs = dsv4_build_input_1d(ctx, GGML_TYPE_I32, plan.state_persist_src_idxs.size(), std::string("dsv4_") + name + "_state_persist_src_idxs"); inp.state_persist_dst_idxs = dsv4_build_input_1d(ctx, GGML_TYPE_I32, plan.state_persist_dst_idxs.size(), std::string("dsv4_") + name + "_state_persist_dst_idxs"); + inp.state_restore_src_idxs = dsv4_build_input_1d(ctx, GGML_TYPE_I32, plan.state_restore_src_idxs.size(), std::string("dsv4_") + name + "_state_restore_src_idxs"); + inp.state_restore_dst_idxs = dsv4_build_input_1d(ctx, GGML_TYPE_I32, plan.state_restore_dst_idxs.size(), std::string("dsv4_") + name + "_state_restore_dst_idxs"); + inp.state_snapshot_src_idxs = dsv4_build_input_1d(ctx, GGML_TYPE_I32, plan.state_snapshot_src_idxs.size(), std::string("dsv4_") + name + "_state_snapshot_src_idxs"); + inp.state_snapshot_dst_idxs = dsv4_build_input_1d(ctx, GGML_TYPE_I32, plan.state_snapshot_dst_idxs.size(), std::string("dsv4_") + name + "_state_snapshot_dst_idxs"); inp.state_read_idxs = dsv4_build_input_1d(ctx, GGML_TYPE_I32, plan.state_read_idxs.size(), std::string("dsv4_") + name + "_state_read_idxs"); inp.state_write_idxs = dsv4_build_input_1d(ctx, GGML_TYPE_I64, plan.state_write_idxs.size(), std::string("dsv4_") + name + "_state_write_idxs"); inp.state_write_pos = dsv4_build_input_1d(ctx, GGML_TYPE_I32, plan.state_write_pos.size(), std::string("dsv4_") + name + "_state_write_pos"); @@ -1195,7 +1299,7 @@ void llm_graph_result::reset() { t_embd_pooled = nullptr; t_h_nextn = nullptr; - t_layer_inp.resize(LLAMA_MAX_LAYERS); + t_layer_inp.resize(LLAMA_MAX_LAYERS + 1); std::fill(t_layer_inp.begin(), t_layer_inp.end(), nullptr); t_sampled.clear(); @@ -1656,7 +1760,7 @@ ggml_tensor * llm_graph_context::build_ffn( tmp = ggml_clamp(ctx0, tmp, -limit, limit); cb(tmp, "ffn_up_clamped", il); - if (arch == LLM_ARCH_DEEPSEEK4) { + if (arch == LLM_ARCH_DEEPSEEK4 || (arch == LLM_ARCH_DFLASH && hparams.dsv4_hc_mult > 0)) { cur = ggml_clamp(ctx0, cur, -INFINITY, limit); cb(cur, "ffn_gate_clamped", il); cur = ggml_swiglu_split(ctx0, cur, tmp); @@ -1680,6 +1784,26 @@ ggml_tensor * llm_graph_context::build_ffn( cur = ggml_silu(ctx0, cur); cb(cur, "ffn_silu", il); } break; + case LLM_FFN_SITU: + { + // Kimi K3 SiTU-GLU: [beta*tanh(gate/beta)*sigmoid(gate)] * [linear_beta*tanh(up/linear_beta)] + const float beta = hparams.situ_beta; + const float lbeta = hparams.situ_linear_beta; + GGML_ASSERT(beta > 0.0f && lbeta > 0.0f); + + ggml_tensor * gate_act = ggml_scale(ctx0, ggml_tanh(ctx0, ggml_scale(ctx0, cur, 1.0f/beta)), beta); + gate_act = ggml_mul(ctx0, gate_act, ggml_sigmoid(ctx0, cur)); + cb(gate_act, "ffn_situ", il); + + if (gate && type_gate == LLM_FFN_PAR) { + ggml_tensor * up_cap = ggml_scale(ctx0, ggml_tanh(ctx0, ggml_scale(ctx0, tmp, 1.0f/lbeta)), lbeta); + cur = ggml_mul(ctx0, gate_act, up_cap); + cb(cur, "ffn_situ_glu", il); + type_gate = LLM_FFN_SEQ; + } else { + cur = gate_act; + } + } break; case LLM_FFN_GELU: if (gate && type_gate == LLM_FFN_PAR) { cur = ggml_geglu_split(ctx0, cur, tmp); @@ -1715,6 +1839,17 @@ ggml_tensor * llm_graph_context::build_ffn( cur = ggml_swiglu(ctx0, cur); cb(cur, "ffn_swiglu", il); } break; + case LLM_FFN_SWIGLU_OAI_MOE: + if (gate && type_gate == LLM_FFN_PAR) { + // same alpha/limit constants as gpt-oss + const float alpha = 1.702f; + const float limit = 7.0f; + cur = ggml_swiglu_oai(ctx0, cur, tmp, alpha, limit); + cb(cur, "ffn_swiglu_oai", il); + type_gate = LLM_FFN_SEQ; + } else { + GGML_ABORT("LLM_FFN_SWIGLU_OAI_MOE requires a parallel gate"); + } break; case LLM_FFN_GEGLU: { cur = ggml_geglu(ctx0, cur); @@ -2040,7 +2175,7 @@ ggml_tensor * llm_graph_context::build_moe_ffn( up = ggml_clamp(ctx0, up, -limit, limit); cb(up, "ffn_moe_up_clamped", il); - if (arch == LLM_ARCH_DEEPSEEK4) { + if (arch == LLM_ARCH_DEEPSEEK4 || (arch == LLM_ARCH_DFLASH && hparams.dsv4_hc_mult > 0)) { cur = ggml_clamp(ctx0, cur, -INFINITY, limit); cb(cur, "ffn_moe_gate_clamped", il); cur = ggml_swiglu_split(ctx0, cur, up); @@ -2080,6 +2215,22 @@ ggml_tensor * llm_graph_context::build_moe_ffn( cur = ggml_swiglu_oai(ctx0, cur, up, alpha, limit); cb(cur, "ffn_moe_swiglu_oai", il); } break; + case LLM_FFN_SITU: + { + // Kimi K3 SiTU-GLU: [beta*tanh(gate/beta)*sigmoid(gate)] * [linear_beta*tanh(up/linear_beta)] + const float beta = hparams.situ_beta; + const float lbeta = hparams.situ_linear_beta; + GGML_ASSERT(beta > 0.0f && lbeta > 0.0f); + GGML_ASSERT(has_gate && "SiTU without gate branch not implemented"); + + ggml_tensor * gate_act = ggml_scale(ctx0, ggml_tanh(ctx0, ggml_scale(ctx0, cur, 1.0f/beta)), beta); + gate_act = ggml_mul(ctx0, gate_act, ggml_sigmoid(ctx0, cur)); + cb(gate_act, "ffn_moe_situ", il); + + ggml_tensor * up_cap = ggml_scale(ctx0, ggml_tanh(ctx0, ggml_scale(ctx0, up, 1.0f/lbeta)), lbeta); + cur = ggml_mul(ctx0, gate_act, up_cap); + cb(cur, "ffn_moe_situ_glu", il); + } break; case LLM_FFN_RELU: if (has_gate) { cur = ggml_reglu_split(ctx0, cur, up); @@ -2706,7 +2857,7 @@ ggml_tensor * llm_graph_context::build_attn( ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, v_idxs, il)); } - const auto & kq_mask = inp->get_kq_mask(); + ggml_tensor * kq_mask = inp->get_kq_mask(); ggml_tensor * q = q_cur; ggml_tensor * k = mctx_cur->get_k(ctx0, il); @@ -3095,6 +3246,75 @@ ggml_tensor * llm_graph_context::build_attn( return cur; } +ggml_tensor * llm_graph_context::build_attn( + llm_graph_input_attn_k_iswa * inp, + ggml_tensor * wo, + ggml_tensor * wo_b, + ggml_tensor * wo_s, + ggml_tensor * q_cur, + ggml_tensor * k_cur, + ggml_tensor * v_cur, + ggml_tensor * kq_b, + ggml_tensor * sinks, + ggml_tensor * v_mla, + float kq_scale, + int il) const { + const bool is_swa = hparams.is_swa(il); + + GGML_UNUSED(v_cur); + + auto * k_rot = is_swa ? inp->self_k_rot_swa : inp->self_k_rot; + + if (k_rot) { + q_cur = llama_mul_mat_hadamard(ctx0, q_cur, k_rot); + if (k_cur) { + k_cur = llama_mul_mat_hadamard(ctx0, k_cur, k_rot); + } + } + + // these nodes are added to the graph together so that they are not reordered + // by doing so, the number of splits in the graph is reduced + ggml_build_forward_expand(gf, q_cur); + + if (k_cur) { + ggml_build_forward_expand(gf, k_cur); + } + + const auto * mctx_iswa = inp->mctx; + const auto * mctx_cur = is_swa ? mctx_iswa->get_swa() : mctx_iswa->get_base(); + + // optionally store to KV cache + if (k_cur) { + const auto & k_idxs = is_swa ? inp->get_k_idxs_swa() : inp->get_k_idxs(); + + ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, k_idxs, il)); + } + + const auto & kq_mask = is_swa ? inp->get_kq_mask_swa() : inp->get_kq_mask(); + + // MLA-style attention: the cached K is used as V + ggml_tensor * q = q_cur; + ggml_tensor * k = mctx_cur->get_k(ctx0, il); + ggml_tensor * v = k; + + ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, sinks, v_mla, kq_scale, il); + cb(cur, "kqv_out", il); + + if (k_rot) { + cur = llama_mul_mat_hadamard(ctx0, cur, k_rot); + } + + if (wo) { + cur = build_lora_mm(wo, cur, wo_s); + } + + if (wo_b) { + cur = ggml_add(ctx0, cur, wo_b); + } + + return cur; +} + llm_graph_input_attn_cross * llm_graph_context::build_attn_inp_cross() const { auto inp = std::make_unique(cross); @@ -3182,6 +3402,34 @@ llm_graph_input_attn_k_dsa * llm_graph_context::build_attn_inp_k_dsa() const { return (llm_graph_input_attn_k_dsa *) res->add_input(std::move(inp)); } +llm_graph_input_attn_kv_msa * llm_graph_context::build_attn_inp_kv_msa(bool msa_enabled) const { + const auto * mctx_cur = static_cast(mctx); + + auto inp = std::make_unique(hparams, cparams, mctx_cur); + + const auto * mctx_base = mctx_cur->get_base(); + const auto * mctx_idx = mctx_cur->get_idx(); + + { + GGML_ASSERT(hparams.swa_type == LLAMA_SWA_TYPE_NONE && "Use llama_kv_cache_iswa for SWA"); + + inp->self_k_idxs = mctx_base->build_input_k_idxs(ctx0, ubatch); + inp->self_v_idxs = mctx_base->build_input_v_idxs(ctx0, ubatch); + + inp->self_kq_mask = build_attn_inp_kq_mask(ctx0, mctx_base, ubatch, cparams); + inp->self_kq_mask_cnv = inp->self_kq_mask; + } + + inp->self_k_rot = mctx_base->build_input_k_rot(ctx0); + inp->self_v_rot = mctx_base->build_input_v_rot(ctx0); + + if (msa_enabled) { + inp->self_k_idxs_idx = mctx_idx->build_input_k_idxs(ctx0, ubatch); + } + + return (llm_graph_input_attn_kv_msa *) res->add_input(std::move(inp)); +} + // TODO: maybe separate the inner implementation into a separate function // like with the non-sliding window equivalent // once sliding-window hybrid caches are a thing. @@ -3217,6 +3465,34 @@ llm_graph_input_attn_kv_iswa * llm_graph_context::build_attn_inp_kv_iswa() const return (llm_graph_input_attn_kv_iswa *) res->add_input(std::move(inp)); } +llm_graph_input_attn_k_iswa * llm_graph_context::build_attn_inp_k_iswa() const { + const auto * mctx_cur = static_cast(mctx); + + auto inp = std::make_unique(hparams, cparams, mctx_cur); + + { + inp->self_k_idxs = mctx_cur->get_base()->build_input_k_idxs(ctx0, ubatch); + + inp->self_kq_mask = build_attn_inp_kq_mask(ctx0, mctx_cur->get_base(), ubatch, cparams); + inp->self_kq_mask_cnv = inp->self_kq_mask; + } + + { + GGML_ASSERT(hparams.swa_type != LLAMA_SWA_TYPE_NONE && "Use llama_kv_cache for non-SWA"); + + inp->self_k_idxs_swa = mctx_cur->get_swa()->build_input_k_idxs(ctx0, ubatch); + + inp->self_kq_mask_swa = build_attn_inp_kq_mask(ctx0, mctx_cur->get_swa(), ubatch, cparams); + inp->self_kq_mask_swa_cnv = inp->self_kq_mask_swa; + } + + inp->self_k_rot = mctx_cur->get_base()->build_input_k_rot(ctx0); + + inp->self_k_rot_swa = mctx_cur->get_swa()->build_input_k_rot(ctx0); + + return (llm_graph_input_attn_k_iswa *) res->add_input(std::move(inp)); +} + llm_graph_input_dsv4 * llm_graph_context::build_inp_dsv4() const { const auto * mctx_cur = static_cast(mctx); const auto * raw_ctx = mctx_cur->get_raw(); diff --git a/src/llama-graph.h b/src/llama-graph.h index fbf1194d5dac..e42acc6a8501 100644 --- a/src/llama-graph.h +++ b/src/llama-graph.h @@ -23,6 +23,7 @@ struct llama_memory_context_i; class llama_kv_cache_context; class llama_kv_cache_dsa_context; +class llama_kv_cache_msa_context; class llama_kv_cache_dsv4_raw_context; class llama_kv_cache_dsv4_context; class llama_kv_cache_iswa_context; @@ -43,6 +44,9 @@ enum llm_fused_op { LLM_FUSED_OP_GDN_AR, LLM_FUSED_OP_GDN_CH, LLM_FUSED_OP_LIGHTNING_INDEXER, + LLM_FUSED_OP_DSV4_HC_PRE, + LLM_FUSED_OP_DSV4_HC_COMB, + LLM_FUSED_OP_DSV4_HC_POST, }; enum llm_ffn_op_type : int { @@ -55,6 +59,7 @@ enum llm_ffn_op_type : int { LLM_FFN_GEGLU, LLM_FFN_REGLU, LLM_FFN_SWIGLU_OAI_MOE, + LLM_FFN_SITU, // kimi k3: soft-capped SiLU gate + soft-capped up branch }; enum llm_ffn_gate_type { @@ -422,6 +427,26 @@ class llm_graph_input_attn_k_dsa : public llm_graph_input_i { const llama_kv_cache_dsa_context * mctx; }; +// standard K/V attention input against the base cache, plus destination indices for the indexer key cache +class llm_graph_input_attn_kv_msa : public llm_graph_input_attn_kv { +public: + llm_graph_input_attn_kv_msa( + const llama_hparams & hparams, + const llama_cparams & cparams, + const llama_kv_cache_msa_context * mctx); + ~llm_graph_input_attn_kv_msa() = default; + + void set_input(const llama_ubatch * ubatch) override; + + bool can_reuse(const llm_graph_params & params) override; + + ggml_tensor * get_k_idxs_idx() const { return self_k_idxs_idx; } + + ggml_tensor * self_k_idxs_idx = nullptr; // I64 [n_batch] + + const llama_kv_cache_msa_context * mctx_msa; +}; + class llm_graph_input_attn_kv_iswa : public llm_graph_input_i { public: llm_graph_input_attn_kv_iswa( @@ -468,6 +493,45 @@ class llm_graph_input_attn_kv_iswa : public llm_graph_input_i { const llama_kv_cache_iswa_context * mctx; }; +class llm_graph_input_attn_k_iswa : public llm_graph_input_i { +public: + llm_graph_input_attn_k_iswa( + const llama_hparams & hparams, + const llama_cparams & cparams, + const llama_kv_cache_iswa_context * mctx) : + hparams(hparams), + cparams(cparams), + mctx(mctx) { + } + ~llm_graph_input_attn_k_iswa() = default; + + void set_input(const llama_ubatch * ubatch) override; + + bool can_reuse(const llm_graph_params & params) override; + + ggml_tensor * get_k_idxs() const { return self_k_idxs; } + ggml_tensor * get_k_idxs_swa() const { return self_k_idxs_swa; } + + ggml_tensor * get_kq_mask() const { return self_kq_mask_cnv; } + ggml_tensor * get_kq_mask_swa() const { return self_kq_mask_swa_cnv; } + + ggml_tensor * self_k_idxs = nullptr; // I64 [n_batch] + ggml_tensor * self_k_idxs_swa = nullptr; // I64 [n_batch] + + ggml_tensor * self_kq_mask = nullptr; // F32/F16 [n_kv, n_batch/n_stream, 1, n_stream] + ggml_tensor * self_kq_mask_cnv = nullptr; // [n_kv, n_batch/n_stream, 1, n_stream] + ggml_tensor * self_kq_mask_swa = nullptr; // F32/F16 [n_kv, n_batch/n_stream, 1, n_stream] + ggml_tensor * self_kq_mask_swa_cnv = nullptr; // [n_kv, n_batch/n_stream, 1, n_stream] + + ggml_tensor * self_k_rot = nullptr; + ggml_tensor * self_k_rot_swa = nullptr; + + const llama_hparams hparams; + const llama_cparams cparams; + + const llama_kv_cache_iswa_context * mctx; +}; + // DSV4 raw graph inputs are SWA-only, but their mask may be stream-shaped // so raw K can be concatenated with DSV4 compressed K in one attention op. class llm_graph_input_dsv4_raw { @@ -502,6 +566,10 @@ class llm_graph_input_dsv4 : public llm_graph_input_i { ggml_tensor * state_pos = nullptr; // I32 [n_state] ggml_tensor * state_persist_src_idxs = nullptr; // I32 [n_state_persist] ggml_tensor * state_persist_dst_idxs = nullptr; // I32 [n_state_persist] + ggml_tensor * state_restore_src_idxs = nullptr; // I32 [n_state_restore] + ggml_tensor * state_restore_dst_idxs = nullptr; // I32 [n_state_restore] + ggml_tensor * state_snapshot_src_idxs = nullptr; // I32 [n_state_snapshot] + ggml_tensor * state_snapshot_dst_idxs = nullptr; // I32 [n_state_snapshot] ggml_tensor * state_read_idxs = nullptr; // I32 [ratio*n_state_write] ggml_tensor * state_write_idxs = nullptr; // I64 [n_state_write] ggml_tensor * state_write_pos = nullptr; // I32 [n_state_write] @@ -1066,7 +1134,7 @@ struct llm_graph_context { ggml_tensor * build_attn_mha( ggml_tensor * q, // [n_embd_head_q, n_head_q, n_tokens] ggml_tensor * k, // [n_embd_head_k, n_head_k, n_tokens] - ggml_tensor * v, // [n_embd_head_v, n_head_v, n_tokens] (v_trans == false) + ggml_tensor * v, // [n_embd_head_v, n_head_v, n_tokens] (v_trans = false) ggml_tensor * kq_b, ggml_tensor * kq_mask, ggml_tensor * sinks, // [n_head_q] @@ -1124,6 +1192,8 @@ struct llm_graph_context { llm_graph_input_attn_k_dsa * build_attn_inp_k_dsa() const; + llm_graph_input_attn_kv_msa * build_attn_inp_kv_msa(bool msa_enabled) const; + ggml_tensor * build_attn( llm_graph_input_attn_k_dsa * inp, ggml_tensor * wo, @@ -1158,6 +1228,24 @@ struct llm_graph_context { float kq_scale, int il) const; + llm_graph_input_attn_k_iswa * build_attn_inp_k_iswa() const; + + // note: if k_cur is not provided, it will not be stored in the memory + // note: the K cache is used as V (MLA-style attention) + ggml_tensor * build_attn( + llm_graph_input_attn_k_iswa * inp, + ggml_tensor * wo, + ggml_tensor * wo_b, + ggml_tensor * wo_s, + ggml_tensor * q_cur, // [n_embd_head_q, n_head_q, n_tokens] + ggml_tensor * k_cur, // [n_embd_head_k, n_head_k, n_tokens] optional + ggml_tensor * v_cur, // [n_embd_head_v, n_head_v, n_tokens] optional + ggml_tensor * kq_b, + ggml_tensor * sinks, // [n_head_q] + ggml_tensor * v_mla, // [n_embd_head_v_mla, n_embd_head_v, n_head_v] + float kq_scale, + int il) const; + llm_graph_input_attn_cross * build_attn_inp_cross() const; ggml_tensor * build_attn( diff --git a/src/llama-hparams.cpp b/src/llama-hparams.cpp index d18f23d03552..cd543fadc8fd 100644 --- a/src/llama-hparams.cpp +++ b/src/llama-hparams.cpp @@ -253,6 +253,14 @@ bool llama_hparams::is_mla() const { return n_embd_head_k_mla_impl != 0 && n_embd_head_v_mla_impl != 0; } +bool llama_hparams::is_indexer_full(uint32_t il) const { + if (il < n_layer()) { + return is_indexer_full_impl[il]; + } + + GGML_ABORT("%s: il (%u) out of bounds (n_layer: %u)\n", __func__, il, n_layer()); +} + uint32_t llama_hparams::n_embd_head_k_mla() const { return is_mla() ? n_embd_head_k_mla_impl : n_embd_head_k(); } diff --git a/src/llama-hparams.h b/src/llama-hparams.h index b4105c54e607..7ac2ffa844ce 100644 --- a/src/llama-hparams.h +++ b/src/llama-hparams.h @@ -7,7 +7,7 @@ // bump if necessary #define LLAMA_MAX_LAYERS 512 -#define LLAMA_MAX_EXPERTS 512 // Qwen3 Next +#define LLAMA_MAX_EXPERTS 1024 // Kimi K3 has 896 experts enum llama_expert_gating_func_type { LLAMA_EXPERT_GATING_FUNC_TYPE_NONE = 0, @@ -47,6 +47,7 @@ struct llama_hparams { bool use_par_res; bool swin_norm; bool norm_before_residual = false; + bool norm_before_fc = false; uint32_t n_ctx_train; // context size the model was trained on uint32_t n_embd; @@ -176,6 +177,12 @@ struct llama_hparams { // for Kimi Linear KDA uint32_t n_embd_head_kda = 0; + // for Kimi K3 + float kda_gate_lower_bound = 0.0f; // safe-gate lower bound (K3: -5.0) + float situ_beta = 0.0f; // SiTU gate soft-cap (K3: 4.0) + float situ_linear_beta = 0.0f; // SiTU up-branch soft-cap (K3: 25.0) + uint32_t attn_res_block_size = 0; // AttnRes snapshot interval (K3: 12) + bool ssm_dt_b_c_rms = false; float f_clamp_kqv = 0.0f; @@ -240,6 +247,13 @@ struct llama_hparams { uint32_t indexer_n_head = 0; uint32_t indexer_head_size = 0; uint32_t indexer_top_k = 0; + // MSA + uint32_t indexer_block_size = 0; + uint32_t indexer_local_blocks = 0; + + // Indexer is "full" (1) or "shared" (0) + // Shared indexers reuse top-k from previous full layer + std::array is_indexer_full_impl; // DeepSeek-V4 uint32_t dsv4_o_group_count = 0; @@ -321,6 +335,8 @@ struct llama_hparams { bool is_swa(uint32_t il) const; + bool is_indexer_full(uint32_t il) const; + void set_recr_pattern(uint32_t n_pattern, bool dense_first = false); // whether or not the given layer is recurrent (for hybrid models) diff --git a/src/llama-kv-cache-dsa.cpp b/src/llama-kv-cache-dsa.cpp index 241c50365a13..96cb045d2e5d 100644 --- a/src/llama-kv-cache-dsa.cpp +++ b/src/llama-kv-cache-dsa.cpp @@ -23,7 +23,8 @@ llama_kv_cache_dsa::llama_kv_cache_dsa( uint32_t n_pad, uint32_t n_swa, llama_swa_type swa_type, - const layer_filter_cb & filter, + const layer_filter_cb & filter_mla, + const layer_filter_cb & filter_lid, const layer_reuse_cb & reuse) : hparams_lid(model.hparams), n_stream(unified ? 1 : n_seq_max) { @@ -32,7 +33,7 @@ llama_kv_cache_dsa::llama_kv_cache_dsa( kv_mla = std::make_unique( model, model.hparams, type_k, type_v, v_trans, offload, unified, kv_size, n_seq_max, n_pad, - n_swa, swa_type, nullptr, filter, reuse, nullptr); + n_swa, swa_type, nullptr, filter_mla, reuse, nullptr); // we use llama_kv_cache for caching indexer keys // by hand-tweaking some hparams we fool it to create @@ -49,7 +50,7 @@ llama_kv_cache_dsa::llama_kv_cache_dsa( kv_lid = std::make_unique( model, hparams_lid, type_k, type_v, v_trans, offload, unified, kv_size, n_seq_max, n_pad, - n_swa, swa_type, nullptr, filter, reuse, nullptr); + n_swa, swa_type, nullptr, filter_lid, reuse, nullptr); } void llama_kv_cache_dsa::clear(bool data) { diff --git a/src/llama-kv-cache-dsa.h b/src/llama-kv-cache-dsa.h index e2b330993b84..e74fc4d9100f 100644 --- a/src/llama-kv-cache-dsa.h +++ b/src/llama-kv-cache-dsa.h @@ -26,7 +26,8 @@ class llama_kv_cache_dsa : public llama_memory_i { uint32_t n_pad, uint32_t n_swa, llama_swa_type swa_type, - const layer_filter_cb & filter, + const layer_filter_cb & filter_mla, + const layer_filter_cb & filter_lid, const layer_reuse_cb & reuse); ~llama_kv_cache_dsa() = default; diff --git a/src/llama-kv-cache-dsv4.cpp b/src/llama-kv-cache-dsv4.cpp index 7cb6cc18dac3..5caa05e8b07d 100644 --- a/src/llama-kv-cache-dsv4.cpp +++ b/src/llama-kv-cache-dsv4.cpp @@ -22,7 +22,7 @@ static constexpr uint32_t DSV4_STATE_MAGIC = 0x34565344; // DSV4 static constexpr uint32_t DSV4_STATE_VERSION = 1; static constexpr uint32_t DSV4_STATE_MODE_FULL = 0; static constexpr uint32_t DSV4_STATE_MODE_PARTIAL = 1; -static constexpr uint32_t DSV4_K_CACHE_STATE_VER = 1; +static constexpr uint32_t DSV4_K_CACHE_STATE_VER = 2; static constexpr uint32_t DSV4_COMP_STATE_VER = 1; static uint32_t dsv4_comp_size(uint32_t kv_size, uint32_t ratio) { @@ -38,6 +38,16 @@ static void dsv4_clear_tensor_stream(ggml_tensor * tensor, uint32_t stream) { ggml_backend_tensor_memset(tensor, 0, stream*stream_size, stream_size); } +static uint32_t dsv4_state_n_used_k_rows(llama_pos pos_max, uint32_t ratio, uint32_t kv_size) { + if (pos_max < 0) { + return 0; + } + + const uint64_t n_rows = ((uint64_t) pos_max + 1)/ratio; + + return (uint32_t) std::min(kv_size, n_rows); +} + static int64_t dsv4_stream_offset(uint32_t n_stream, llama_seq_id seq_id, uint32_t size) { if (n_stream <= 1) { return 0; @@ -239,28 +249,49 @@ static void dsv4_state_dst_stream_range( static void dsv4_state_write_tensor_streams( llama_io_write_i & io, ggml_tensor * tensor, + uint32_t tensor_rows, uint32_t n_rows, uint32_t s0, - uint32_t ns) { + uint32_t ns, + const std::vector * stream_ids = nullptr) { const int32_t type_i = (int32_t) tensor->type; const uint64_t ne0 = tensor->ne[0]; const uint64_t rows = n_rows; const uint64_t row_size = ggml_row_size(tensor->type, tensor->ne[0]); + if (n_rows > tensor_rows) { + throw std::runtime_error("DSV4 state tensor row count exceeds storage"); + } + io.write(&type_i, sizeof(type_i)); io.write(&ne0, sizeof(ne0)); io.write(&rows, sizeof(rows)); io.write(&row_size, sizeof(row_size)); - const size_t offset = (size_t) s0*n_rows*row_size; - const size_t size = (size_t) ns*n_rows*row_size; + const size_t stream_stride = (size_t) tensor_rows*row_size; + const size_t size = (size_t) n_rows*row_size; + if (size == 0) { + return; + } + + if (stream_ids && stream_ids->size() != ns) { + throw std::runtime_error("DSV4 state tensor stream map size mismatch"); + } - io.write_tensor(tensor, offset, size); + for (uint32_t s = 0; s < ns; ++s) { + const uint32_t stream = stream_ids ? (*stream_ids)[s] : s0 + s; + if ((int64_t) stream >= tensor->ne[2]) { + throw std::runtime_error("DSV4 state tensor stream out of range"); + } + const size_t offset = (size_t) stream*stream_stride; + io.write_tensor(tensor, offset, size); + } } static void dsv4_state_read_tensor_streams( llama_io_read_i & io, ggml_tensor * tensor, + uint32_t tensor_rows, uint32_t n_rows, uint32_t s0, uint32_t ns) { @@ -282,18 +313,28 @@ static void dsv4_state_read_tensor_streams( if (type_i != type_i_ref || ne0 != ne0_ref || rows != rows_ref || row_size != row_size_ref) { throw std::runtime_error("DSV4 state tensor metadata mismatch"); } + if (n_rows > tensor_rows) { + throw std::runtime_error("DSV4 state tensor row count exceeds storage"); + } - const size_t offset = (size_t) s0*n_rows*row_size; - const size_t size = (size_t) ns*n_rows*row_size; + const size_t stream_stride = (size_t) tensor_rows*row_size; + const size_t size = (size_t) n_rows*row_size; + if (size == 0) { + return; + } - io.read_tensor(tensor, offset, size); + for (uint32_t s = 0; s < ns; ++s) { + const size_t offset = (size_t) (s0 + s)*stream_stride; + io.read_tensor(tensor, offset, size); + } } static void dsv4_state_write_k_cache( llama_io_write_i & io, const llama_kv_cache * kv, llama_seq_id seq_id, - llama_state_seq_flags flags) { + llama_state_seq_flags flags, + uint32_t n_rows) { GGML_UNUSED(flags); uint32_t s0; @@ -305,14 +346,18 @@ static void dsv4_state_write_k_cache( const auto layer_ids = kv->get_layer_ids(); const uint32_t n_layer = layer_ids.size(); + if (n_rows > kv_size) { + throw std::runtime_error("DSV4 K-cache state row count exceeds cache size"); + } + io.write(&version, sizeof(version)); - io.write(&kv_size, sizeof(kv_size)); + io.write(&n_rows, sizeof(n_rows)); io.write(&ns, sizeof(ns)); io.write(&n_layer, sizeof(n_layer)); for (uint32_t il : layer_ids) { io.write(&il, sizeof(il)); - dsv4_state_write_tensor_streams(io, kv->get_k_storage(il), kv_size, s0, ns); + dsv4_state_write_tensor_streams(io, kv->get_k_storage(il), kv_size, n_rows, s0, ns); } } @@ -324,19 +369,26 @@ static void dsv4_state_read_k_cache( GGML_UNUSED(flags); uint32_t version; - uint32_t kv_size_ref; + uint32_t n_rows_ref; uint32_t ns; uint32_t n_layer_ref; io.read(&version, sizeof(version)); - io.read(&kv_size_ref, sizeof(kv_size_ref)); + io.read(&n_rows_ref, sizeof(n_rows_ref)); io.read(&ns, sizeof(ns)); io.read(&n_layer_ref, sizeof(n_layer_ref)); - if (version != DSV4_K_CACHE_STATE_VER) { + if (version != 1 && version != DSV4_K_CACHE_STATE_VER) { throw std::runtime_error("DSV4 K-cache state version mismatch"); } - if (kv_size_ref != kv->get_size()) { + + const uint32_t kv_size = kv->get_size(); + if (version == 1 && n_rows_ref != kv_size) { + LLAMA_LOG_INFO("kv size ref %d kv %d\n", n_rows_ref, kv_size); + throw std::runtime_error("DSV4 K-cache state size mismatch"); + } + if (n_rows_ref > kv_size) { + LLAMA_LOG_INFO("kv rows ref %d kv %d\n", n_rows_ref, kv_size); throw std::runtime_error("DSV4 K-cache state size mismatch"); } @@ -355,7 +407,7 @@ static void dsv4_state_read_k_cache( throw std::runtime_error("DSV4 K-cache layer id mismatch"); } - dsv4_state_read_tensor_streams(io, kv->get_k_storage(il), kv->get_size(), s0, ns); + dsv4_state_read_tensor_streams(io, kv->get_k_storage(il), kv_size, n_rows_ref, s0, ns); } } @@ -378,7 +430,9 @@ static llama_kv_cache_dsv4_context::comp_plan dsv4_build_comp_plan( bool overlap, uint32_t state_size, uint32_t kv_size, - uint32_t n_stream) { + uint32_t n_stream, + uint32_t n_rs_seq, + const std::vector & rs_idx) { llama_kv_cache_dsv4_context::comp_plan plan; plan.n_visible.resize(ubatch.n_tokens); plan.n_stream = dsv4_comp_graph_n_stream(ubatch, n_stream); @@ -408,6 +462,7 @@ static llama_kv_cache_dsv4_context::comp_plan dsv4_build_comp_plan( std::vector overlap_cur_reads; std::map, int64_t> curr_token_idx_map; + std::map state_write_counts; for (uint32_t i = 0; i < ubatch.n_tokens; ++i) { for (int32_t s = 0; s < ubatch.n_seq_id[i]; ++s) { @@ -470,6 +525,7 @@ static llama_kv_cache_dsv4_context::comp_plan dsv4_build_comp_plan( plan.state_write_idxs.push_back(cache_off + pos/ratio); plan.state_write_pos.push_back((int32_t) source_start); + ++state_write_counts[seq_id]; if (overlap) { const llama_pos prev_start = source_start - ratio; @@ -488,33 +544,57 @@ static llama_kv_cache_dsv4_context::comp_plan dsv4_build_comp_plan( } } - if (ratio == DSV4_CSA_RATIO && plan.state_write_idxs.empty() && !plan.state_pos.empty()) { - // Non-boundary CSA steps still need a write op so their graph matches - // boundary steps. Use a padded scratch row that is masked from attention. + if (ratio == DSV4_CSA_RATIO && !plan.state_pos.empty()) { assert(kv_size > 0); - uint32_t i = 0; - while (i < ubatch.n_tokens && ubatch.pos[i] < 0) { - ++i; - } - assert(i < ubatch.n_tokens); + // Pad each stream to the reserve plan's block count. + const auto append_dummy_block = [&](llama_seq_id seq_id, uint32_t i) { + const int64_t cache_off = dsv4_stream_offset(n_stream, seq_id, kv_size); + const int32_t source_idx = state_source_idx(seq_id, ubatch.pos[i]); - const llama_pos pos = ubatch.pos[i]; - const llama_seq_id seq_id = ubatch.seq_id[i][0]; - const int64_t cache_off = dsv4_stream_offset(n_stream, seq_id, kv_size); - const int32_t source_idx = state_source_idx(seq_id, pos); + plan.state_write_idxs.push_back(cache_off + kv_size - 1); + plan.state_write_pos .push_back(0); - plan.state_write_idxs.push_back(cache_off + kv_size - 1); - plan.state_write_pos .push_back(0); + if (overlap) { + for (uint32_t j = 0; j < ratio; ++j) { + overlap_prev_reads.push_back(source_idx); + overlap_cur_reads .push_back(source_idx); + } + } else { + for (uint32_t j = 0; j < ratio; ++j) { + plan.state_read_idxs.push_back(source_idx); + } + } + }; - if (overlap) { - for (uint32_t j = 0; j < ratio; ++j) { - overlap_prev_reads.push_back(source_idx); - overlap_cur_reads .push_back(source_idx); + if (dsv4_ubatch_has_coupled(ubatch)) { + if (plan.state_write_idxs.empty()) { + uint32_t i = 0; + while (i < ubatch.n_tokens && ubatch.pos[i] < 0) { + ++i; + } + assert(i < ubatch.n_tokens); + append_dummy_block(ubatch.seq_id[i][0], i); } } else { - for (uint32_t j = 0; j < ratio; ++j) { - plan.state_read_idxs.push_back(source_idx); + const uint32_t n_blocks = (std::max(1, ubatch.n_seq_tokens) + ratio - 1)/ratio; + + for (uint32_t s = 0; s < ubatch.n_seqs_unq; ++s) { + const llama_seq_id seq_id = ubatch.seq_id_unq[s]; + const uint32_t n_writes = state_write_counts[seq_id]; + if (n_writes >= n_blocks) { + continue; + } + if (n_writes + 1 != n_blocks) { + throw std::runtime_error("DSV4 CSA sequence positions are not contiguous"); + } + + uint32_t i = 0; + while (i < ubatch.n_tokens && (ubatch.pos[i] < 0 || !dsv4_token_has_seq(ubatch, i, seq_id))) { + ++i; + } + assert(i < ubatch.n_tokens); + append_dummy_block(seq_id, i); } } } @@ -540,6 +620,63 @@ static llama_kv_cache_dsv4_context::comp_plan dsv4_build_comp_plan( plan.state_persist_dst_idxs.push_back(row.dst); } + + if (n_rs_seq > 0) { + for (uint32_t s = 0; s < ubatch.n_seqs_unq; ++s) { + const llama_seq_id seq_id = ubatch.seq_id_unq[s]; + if (seq_id < 0 || (uint32_t) seq_id >= n_stream) { + continue; + } + + const int64_t stream_off = dsv4_stream_offset(n_stream, seq_id, state_size); + const uint32_t rollback = (uint32_t) seq_id < rs_idx.size() ? rs_idx[seq_id] : 0; + // Keep the restore graph fixed-width when no rollback is pending. + const int64_t src_plane = rollback > 0 && rollback <= n_rs_seq ? (int64_t) rollback*state_rows : 0; + for (uint32_t r = 0; r < state_size; ++r) { + plan.state_restore_src_idxs.push_back((int32_t) (src_plane + stream_off + r)); + plan.state_restore_dst_idxs.push_back((int32_t) (stream_off + r)); + } + + std::vector token_idxs; + token_idxs.reserve(ubatch.n_tokens); + for (uint32_t i = 0; i < ubatch.n_tokens; ++i) { + if (dsv4_token_has_seq(ubatch, i, seq_id)) { + token_idxs.push_back(i); + } + } + if (token_idxs.empty()) { + continue; + } + + const uint32_t n_seq_tokens = (uint32_t) token_idxs.size(); + const int64_t scratch_off = (int64_t) state_rows*(1 + n_rs_seq); + for (uint32_t d = 1; d <= n_rs_seq; ++d) { + const int64_t dst_plane = (int64_t) d*state_rows; + + for (uint32_t r = 0; r < state_size; ++r) { + int32_t src; + if (d <= n_seq_tokens) { + const uint32_t prefix = n_seq_tokens - d; + src = (int32_t) (stream_off + r); + + for (uint32_t j = 0; j < prefix; ++j) { + const uint32_t i_tok = token_idxs[j]; + if (ubatch.pos[i_tok] >= 0 && (uint32_t) (ubatch.pos[i_tok]%state_size) == r) { + src = (int32_t) (scratch_off + i_tok); + } + } + } else { + const int64_t src_plane = (int64_t) (d - n_seq_tokens)*state_rows; + src = (int32_t) (src_plane + stream_off + r); + } + + plan.state_snapshot_src_idxs.push_back(src); + plan.state_snapshot_dst_idxs.push_back((int32_t) (dst_plane + stream_off + r)); + } + } + } + } + static const bool debug = []() { const char * env = getenv("LLAMA_DSV4_COMPRESS_DEBUG"); return env && atoi(env) > 0; @@ -561,12 +698,14 @@ static std::vector dsv4_build_comp_plans bool overlap, uint32_t state_size, uint32_t kv_size, - uint32_t n_stream) { + uint32_t n_stream, + uint32_t n_rs_seq, + const std::vector & rs_idx) { std::vector plans; plans.reserve(ubatches.size()); for (const llama_ubatch & ubatch : ubatches) { - plans.push_back(dsv4_build_comp_plan(ubatch, ratio, overlap, state_size, kv_size, n_stream)); + plans.push_back(dsv4_build_comp_plan(ubatch, ratio, overlap, state_size, kv_size, n_stream, n_rs_seq, rs_idx)); } return plans; @@ -653,7 +792,8 @@ static llama_kv_cache_dsv4_context::comp_plan dsv4_build_reserve_comp_plan( bool overlap, uint32_t state_size, uint32_t kv_size, - uint32_t n_stream) { + uint32_t n_stream, + uint32_t n_rs_seq) { llama_kv_cache_dsv4_context::comp_plan plan; plan.n_visible.resize(ubatch.n_tokens); plan.n_stream = dsv4_comp_graph_n_stream(ubatch, n_stream); @@ -671,10 +811,16 @@ static llama_kv_cache_dsv4_context::comp_plan dsv4_build_reserve_comp_plan( const uint64_t state_rows = (uint64_t) state_size*n_stream; const size_t n_persist = (size_t) std::min(ubatch.n_tokens, state_rows); + const size_t n_restore = n_rs_seq > 0 ? (size_t) state_size*std::max(1, ubatch.n_seqs_unq) : 0; + const size_t n_snapshot = (size_t) n_rs_seq*state_size*std::max(1, ubatch.n_seqs_unq); plan.state_pos .resize(ubatch.n_tokens); plan.state_persist_src_idxs.resize(n_persist); plan.state_persist_dst_idxs.resize(n_persist); + plan.state_restore_src_idxs.resize(n_restore); + plan.state_restore_dst_idxs.resize(n_restore); + plan.state_snapshot_src_idxs.resize(n_snapshot); + plan.state_snapshot_dst_idxs.resize(n_snapshot); plan.state_read_idxs .resize((overlap ? 2u : 1u)*ratio*n_blocks); plan.state_write_idxs.resize(n_blocks); plan.state_write_pos .resize(n_blocks); @@ -700,12 +846,14 @@ llama_dsv4_comp_state::llama_dsv4_comp_state( uint32_t ratio, uint32_t state_size, uint32_t n_embd_state, + uint32_t n_rs_seq, const char * name, const llama_memory_i::layer_filter_cb & filter) : ratio(ratio), state_size(state_size), n_embd_state(n_embd_state), - n_stream(unified ? 1 : n_seq_max) { + n_stream(unified ? 1 : n_seq_max), + n_rs_seq(n_rs_seq) { const llama_hparams & hparams = model.hparams; struct ggml_backend_buft_comparator { @@ -761,8 +909,9 @@ llama_dsv4_comp_state::llama_dsv4_comp_state( throw std::runtime_error("failed to create ggml context for DSV4 compressor state"); } - ggml_tensor * kv = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd_state, state_size, n_stream); - ggml_tensor * score = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd_state, state_size, n_stream); + const uint32_t n_planes = n_stream*(1 + n_rs_seq); + ggml_tensor * kv = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd_state, state_size, n_planes); + ggml_tensor * score = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd_state, state_size, n_planes); ggml_format_name(kv, "dsv4_%s_state_kv_l%d", name, il); ggml_format_name(score, "dsv4_%s_state_score_l%d", name, il); @@ -794,8 +943,8 @@ llama_dsv4_comp_state::llama_dsv4_comp_state( ctxs_bufs.emplace_back(std::move(ctx), buf); } - LLAMA_LOG_INFO("%s: %s ratio = %u, state = %u x %u, streams = %u, layers = %zu, size = %7.2f MiB\n", - __func__, name, ratio, state_size, n_embd_state, n_stream, layers.size(), total_size()/1024.0/1024.0); + LLAMA_LOG_INFO("%s: %s ratio = %u, state = %u x %u, streams = %u, rs_seq = %u, layers = %zu, size = %7.2f MiB\n", + __func__, name, ratio, state_size, n_embd_state, n_stream, n_rs_seq, layers.size(), total_size()/1024.0/1024.0); } void llama_dsv4_comp_state::clear(llama_seq_id seq_id, bool data) { @@ -805,9 +954,13 @@ void llama_dsv4_comp_state::clear(llama_seq_id seq_id, bool data) { if (seq_id >= 0) { GGML_ASSERT((uint32_t) seq_id < n_stream); + for (const auto & layer : layers) { - dsv4_clear_tensor_stream(layer.kv, (uint32_t) seq_id); - dsv4_clear_tensor_stream(layer.score, (uint32_t) seq_id); + for (uint32_t d = 0; d <= n_rs_seq; ++d) { + const uint32_t stream = d*n_stream + (uint32_t) seq_id; + dsv4_clear_tensor_stream(layer.kv, stream); + dsv4_clear_tensor_stream(layer.score, stream); + } } return; } @@ -825,6 +978,8 @@ void llama_dsv4_comp_state::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_ return; } + clear(seq_id_dst, true); + sc_info.ssrc.push_back((uint32_t) seq_id_src); sc_info.sdst.push_back((uint32_t) seq_id_dst); } @@ -853,6 +1008,14 @@ uint32_t llama_dsv4_comp_state::get_n_stream() const { return n_stream; } +uint32_t llama_dsv4_comp_state::get_n_rs_seq() const { + return n_rs_seq; +} + +uint32_t llama_dsv4_comp_state::get_n_rows() const { + return state_size*n_stream; +} + std::map llama_dsv4_comp_state::memory_breakdown() const { std::map ret; for (const auto & [_, buf] : ctxs_bufs) { @@ -862,13 +1025,26 @@ std::map llama_dsv4_comp_state::memory_break return ret; } -void llama_dsv4_comp_state::state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const { +void llama_dsv4_comp_state::state_write( + llama_io_write_i & io, + llama_seq_id seq_id, + llama_state_seq_flags flags, + const std::vector & rs_idx) const { GGML_UNUSED(flags); uint32_t s0; uint32_t ns; dsv4_state_src_stream_range(n_stream, seq_id, s0, ns); + std::vector stream_ids(ns); + for (uint32_t s = 0; s < ns; ++s) { + const uint32_t seq = seq_id >= 0 ? (uint32_t) seq_id : s0 + s; + if (seq >= rs_idx.size() || rs_idx[seq] > n_rs_seq) { + throw std::runtime_error("DSV4 recurrent state rollback index out of range"); + } + stream_ids[s] = rs_idx[seq]*n_stream + s0 + s; + } + const uint32_t version = DSV4_COMP_STATE_VER; const uint32_t n_layer = layers.size(); @@ -882,8 +1058,8 @@ void llama_dsv4_comp_state::state_write(llama_io_write_i & io, llama_seq_id seq_ for (const auto & layer : layers) { io.write(&layer.il, sizeof(layer.il)); - dsv4_state_write_tensor_streams(io, layer.kv, state_size, s0, ns); - dsv4_state_write_tensor_streams(io, layer.score, state_size, s0, ns); + dsv4_state_write_tensor_streams(io, layer.kv, state_size, state_size, s0, ns, &stream_ids); + dsv4_state_write_tensor_streams(io, layer.score, state_size, state_size, s0, ns, &stream_ids); } } @@ -924,33 +1100,45 @@ void llama_dsv4_comp_state::state_read(llama_io_read_i & io, llama_seq_id seq_id throw std::runtime_error("DSV4 compressor state layer id mismatch"); } - dsv4_state_read_tensor_streams(io, layer.kv, state_size, s0, ns); - dsv4_state_read_tensor_streams(io, layer.score, state_size, s0, ns); + dsv4_state_read_tensor_streams(io, layer.kv, state_size, state_size, s0, ns); + dsv4_state_read_tensor_streams(io, layer.score, state_size, state_size, s0, ns); } } -ggml_tensor * llama_dsv4_comp_state::get_kv(ggml_context * ctx, int32_t il) const { +ggml_tensor * llama_dsv4_comp_state::get_kv_all(ggml_context * ctx, int32_t il) const { const int32_t ids = map_layer_ids.at(il); - ggml_tensor * state = layers[ids].kv; - return ggml_reshape_2d(ctx, state, state->ne[0], state->ne[1]*state->ne[2]); + return ggml_view_2d(ctx, state, state->ne[0], get_n_rows()*(1 + n_rs_seq), state->nb[1], 0); } -ggml_tensor * llama_dsv4_comp_state::get_score(ggml_context * ctx, int32_t il) const { +ggml_tensor * llama_dsv4_comp_state::get_score_all(ggml_context * ctx, int32_t il) const { const int32_t ids = map_layer_ids.at(il); - ggml_tensor * state = layers[ids].score; - return ggml_reshape_2d(ctx, state, state->ne[0], state->ne[1]*state->ne[2]); + return ggml_view_2d(ctx, state, state->ne[0], get_n_rows()*(1 + n_rs_seq), state->nb[1], 0); +} + +ggml_tensor * llama_dsv4_comp_state::get_kv(ggml_context * ctx, int32_t il) const { + ggml_tensor * state = get_kv_all(ctx, il); + const size_t row_size = ggml_row_size(state->type, state->ne[0]); + + return ggml_view_2d(ctx, state, state->ne[0], get_n_rows(), state->nb[1], 0*row_size); +} + +ggml_tensor * llama_dsv4_comp_state::get_score(ggml_context * ctx, int32_t il) const { + ggml_tensor * state = get_score_all(ctx, il); + const size_t row_size = ggml_row_size(state->type, state->ne[0]); + + return ggml_view_2d(ctx, state, state->ne[0], get_n_rows(), state->nb[1], 0*row_size); } ggml_tensor * llama_dsv4_comp_state::cpy_kv(ggml_context * ctx, ggml_tensor * cur, ggml_tensor * idxs, int32_t il) const { - return ggml_set_rows(ctx, get_kv(ctx, il), cur, idxs); + return ggml_set_rows(ctx, get_kv_all(ctx, il), cur, idxs); } ggml_tensor * llama_dsv4_comp_state::cpy_score(ggml_context * ctx, ggml_tensor * cur, ggml_tensor * idxs, int32_t il) const { - return ggml_set_rows(ctx, get_score(ctx, il), cur, idxs); + return ggml_set_rows(ctx, get_score_all(ctx, il), cur, idxs); } size_t llama_dsv4_comp_state::total_size() const { @@ -979,13 +1167,16 @@ llama_kv_cache_dsv4::llama_kv_cache_dsv4( uint32_t n_seq_max, uint32_t n_ubatch, uint32_t n_pad, + uint32_t n_rs_seq, const layer_filter_cb & filter, const layer_reuse_cb & reuse) : hparams_raw(model.hparams), hparams_csa(model.hparams), hparams_hca(model.hparams), hparams_lid(model.hparams), - n_seq_max(n_seq_max) { + n_seq_max(n_seq_max), + n_rs_seq(n_rs_seq), + rs_idx(n_seq_max, 0) { const layer_filter_cb filter_raw = [&](int32_t il) { if (filter && !filter(il)) { @@ -1000,6 +1191,11 @@ llama_kv_cache_dsv4::llama_kv_cache_dsv4( // Keep DSV4 KV/state streams per sequence even when public KV mode is unified. const bool unified_raw = false; + hparams_raw.n_layer_nextn = 0; + hparams_csa.n_layer_nextn = 0; + hparams_hca.n_layer_nextn = 0; + hparams_lid.n_layer_nextn = 0; + LLAMA_LOG_INFO("%s: creating DSV4 raw KV cache\n", __func__); dsv4_make_k_only(hparams_raw); @@ -1066,19 +1262,19 @@ llama_kv_cache_dsv4::llama_kv_cache_dsv4( csa_state = std::make_unique( model, offload, unified_compressed, n_seq_max, DSV4_CSA_RATIO, 2*DSV4_CSA_RATIO, - 2*model.hparams.n_embd_head_k(), "csa", filter_csa); + 2*model.hparams.n_embd_head_k(), n_rs_seq, "csa", filter_csa); LLAMA_LOG_INFO("%s: creating DSV4 HCA compressor state\n", __func__); hca_state = std::make_unique( model, offload, unified_compressed, n_seq_max, DSV4_HCA_RATIO, DSV4_HCA_RATIO, - model.hparams.n_embd_head_k(), "hca", filter_hca); + model.hparams.n_embd_head_k(), n_rs_seq, "hca", filter_hca); LLAMA_LOG_INFO("%s: creating DSV4 lightning-indexer compressor state\n", __func__); lid_state = std::make_unique( model, offload, unified_compressed, n_seq_max, DSV4_CSA_RATIO, 2*DSV4_CSA_RATIO, - 2*model.hparams.indexer_head_size, "lid", filter_csa); + 2*model.hparams.indexer_head_size, n_rs_seq, "lid", filter_csa); // DSV4 attention reads compressed-K / compressor-state rows that the current // graph does not necessarily overwrite; uninitialized buffer contents would @@ -1212,17 +1408,35 @@ bool llama_kv_cache_dsv4::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1 } if (p0 > 0) { - if (seq_id < 0 || (uint32_t) seq_id >= n_seq_max || - p0 <= kv_raw->seq_pos_max(seq_id)) { + if (seq_id < 0 || (uint32_t) seq_id >= n_seq_max) { return false; } - bool res = true; + const llama_pos pos_max = kv_raw->seq_pos_max(seq_id); + if (p0 > pos_max) { + bool res = true; + + res = res & kv_raw->seq_rm(seq_id, p0, -1); + res = res & kv_csa->seq_rm(seq_id, p0/DSV4_CSA_RATIO, -1); + res = res & kv_hca->seq_rm(seq_id, p0/DSV4_HCA_RATIO, -1); + res = res & kv_lid->seq_rm(seq_id, p0/DSV4_CSA_RATIO, -1); - res = res & kv_raw->seq_rm(seq_id, p0, -1); - res = res & kv_csa->seq_rm(seq_id, p0/DSV4_CSA_RATIO, -1); - res = res & kv_hca->seq_rm(seq_id, p0/DSV4_HCA_RATIO, -1); - res = res & kv_lid->seq_rm(seq_id, p0/DSV4_CSA_RATIO, -1); + return res; + } + + if (n_rs_seq == 0) { + return false; + } + + const llama_pos rollback = pos_max - (p0 - 1); + if (rollback < 1 || rollback > (llama_pos) n_rs_seq) { + return false; + } + + const bool res = kv_raw->seq_rm(seq_id, p0, p1); + if (res) { + rs_idx[seq_id] = (uint32_t) rollback; + } return res; } @@ -1247,6 +1461,10 @@ void llama_kv_cache_dsv4::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_ds csa_state->seq_cp(seq_id_src, seq_id_dst); hca_state->seq_cp(seq_id_src, seq_id_dst); lid_state->seq_cp(seq_id_src, seq_id_dst); + + if (seq_id_src != seq_id_dst) { + rs_idx[seq_id_dst] = 0; + } } void llama_kv_cache_dsv4::seq_keep(llama_seq_id seq_id) { @@ -1328,14 +1546,24 @@ void llama_kv_cache_dsv4::state_write(llama_io_write_i & io, llama_seq_id seq_id kv_raw->state_write(io, seq_id, flags); if (!partial_only) { - dsv4_state_write_k_cache(io, kv_csa.get(), seq_id, flags); - dsv4_state_write_k_cache(io, kv_hca.get(), seq_id, flags); - dsv4_state_write_k_cache(io, kv_lid.get(), seq_id, flags); + const llama_pos pos_max = seq_id >= 0 ? kv_raw->seq_pos_max(seq_id) : -1; + + //FIXME : note that we conflate token positions with rows, which is not true for multi-modal case. + const uint32_t n_rows_csa = seq_id >= 0 ? + dsv4_state_n_used_k_rows(pos_max, DSV4_CSA_RATIO, kv_csa->get_size()) : kv_csa->get_size(); + const uint32_t n_rows_hca = seq_id >= 0 ? + dsv4_state_n_used_k_rows(pos_max, DSV4_HCA_RATIO, kv_hca->get_size()) : kv_hca->get_size(); + const uint32_t n_rows_lid = seq_id >= 0 ? + dsv4_state_n_used_k_rows(pos_max, DSV4_CSA_RATIO, kv_lid->get_size()) : kv_lid->get_size(); + + dsv4_state_write_k_cache(io, kv_csa.get(), seq_id, flags, n_rows_csa); + dsv4_state_write_k_cache(io, kv_hca.get(), seq_id, flags, n_rows_hca); + dsv4_state_write_k_cache(io, kv_lid.get(), seq_id, flags, n_rows_lid); } - csa_state->state_write(io, seq_id, flags); - hca_state->state_write(io, seq_id, flags); - lid_state->state_write(io, seq_id, flags); + csa_state->state_write(io, seq_id, flags, rs_idx); + hca_state->state_write(io, seq_id, flags, rs_idx); + lid_state->state_write(io, seq_id, flags, rs_idx); } void llama_kv_cache_dsv4::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) { @@ -1366,6 +1594,10 @@ void llama_kv_cache_dsv4::state_read(llama_io_read_i & io, llama_seq_id seq_id, kv_raw->state_read(io, seq_id, flags); if (!partial_only) { + kv_csa->clear(true); + kv_hca->clear(true); + kv_lid->clear(true); + dsv4_state_read_k_cache(io, kv_csa.get(), seq_id, flags); dsv4_state_read_k_cache(io, kv_hca.get(), seq_id, flags); dsv4_state_read_k_cache(io, kv_lid.get(), seq_id, flags); @@ -1375,6 +1607,12 @@ void llama_kv_cache_dsv4::state_read(llama_io_read_i & io, llama_seq_id seq_id, hca_state->state_read(io, seq_id, flags); lid_state->state_read(io, seq_id, flags); + if (seq_id >= 0) { + GGML_ASSERT((uint32_t) seq_id < n_seq_max); + rs_idx[seq_id] = 0; + } else { + std::fill(rs_idx.begin(), rs_idx.end(), 0); + } } llama_kv_cache_iswa * llama_kv_cache_dsv4::get_raw() const { @@ -1405,6 +1643,31 @@ llama_dsv4_comp_state * llama_kv_cache_dsv4::get_lid_state() const { return lid_state.get(); } +uint32_t llama_kv_cache_dsv4::get_n_rs_seq() const { + return n_rs_seq; +} + +const std::vector & llama_kv_cache_dsv4::get_rs_idx() const { + return rs_idx; +} + +void llama_kv_cache_dsv4::reset_rs_idx_for_ubatches(const std::vector & ubatches) { + if (n_rs_seq == 0) { + return; + } + + for (const llama_ubatch & ubatch : ubatches) { + for (uint32_t i = 0; i < ubatch.n_tokens; ++i) { + for (int32_t s = 0; s < ubatch.n_seq_id[i]; ++s) { + const llama_seq_id seq_id = ubatch.seq_id[i][s]; + if (seq_id >= 0 && (uint32_t) seq_id < n_seq_max) { + rs_idx[seq_id] = 0; + } + } + } + } +} + void llama_kv_cache_dsv4::clear_compressed(llama_seq_id seq_id, bool data) { if (seq_id < 0) { kv_csa->clear(data); @@ -1431,6 +1694,12 @@ void llama_kv_cache_dsv4::clear_compressed(llama_seq_id seq_id, bool data) { csa_state->clear(seq_id, data); hca_state->clear(seq_id, data); lid_state->clear(seq_id, data); + + if (seq_id >= 0) { + rs_idx[seq_id] = 0; + } else { + std::fill(rs_idx.begin(), rs_idx.end(), 0); + } } // @@ -1722,10 +1991,14 @@ llama_kv_cache_dsv4_context::llama_kv_cache_dsv4_context( std::vector ubatches_raw) : ubatches(std::move(ubatches)), plans_csa(dsv4_build_comp_plans(this->ubatches, DSV4_CSA_RATIO, true, - kv->get_csa_state()->get_state_size(), kv->get_csa()->get_size(), kv->get_csa_state()->get_n_stream())), + kv->get_csa_state()->get_state_size(), kv->get_csa()->get_size(), kv->get_csa_state()->get_n_stream(), + kv->get_n_rs_seq(), kv->get_rs_idx())), plans_hca(dsv4_build_comp_plans(this->ubatches, DSV4_HCA_RATIO, false, - kv->get_hca_state()->get_state_size(), kv->get_hca()->get_size(), kv->get_hca_state()->get_n_stream())), - plans_lid(plans_csa), + kv->get_hca_state()->get_state_size(), kv->get_hca()->get_size(), kv->get_hca_state()->get_n_stream(), + kv->get_n_rs_seq(), kv->get_rs_idx())), + plans_lid(dsv4_build_comp_plans(this->ubatches, DSV4_CSA_RATIO, true, + kv->get_lid_state()->get_state_size(), kv->get_lid()->get_size(), kv->get_lid_state()->get_n_stream(), + kv->get_n_rs_seq(), kv->get_rs_idx())), ctx_raw(std::make_unique( kv->get_raw(), std::move(sinfos_raw_base_write), @@ -1752,6 +2025,7 @@ llama_kv_cache_dsv4_context::llama_kv_cache_dsv4_context( hca_state(kv->get_hca_state()), lid_state(kv->get_lid_state()), status(ctx_raw->get_status()) { + kv->reset_rs_idx_for_ubatches(this->ubatches); } llama_kv_cache_dsv4_context::~llama_kv_cache_dsv4_context() = default; @@ -1887,7 +2161,7 @@ const llama_kv_cache_dsv4_context::comp_plan & llama_kv_cache_dsv4_context::get_ reserve_plan_csa = dsv4_build_reserve_comp_plan( ubatch, DSV4_CSA_RATIO, true, - csa_state->get_state_size(), get_csa()->get_n_kv(), csa_state->get_n_stream()); + csa_state->get_state_size(), get_csa()->get_n_kv(), csa_state->get_n_stream(), csa_state->get_n_rs_seq()); return reserve_plan_csa; } @@ -1901,7 +2175,7 @@ const llama_kv_cache_dsv4_context::comp_plan & llama_kv_cache_dsv4_context::get_ reserve_plan_hca = dsv4_build_reserve_comp_plan( ubatch, DSV4_HCA_RATIO, false, - hca_state->get_state_size(), get_hca()->get_n_kv(), hca_state->get_n_stream()); + hca_state->get_state_size(), get_hca()->get_n_kv(), hca_state->get_n_stream(), hca_state->get_n_rs_seq()); return reserve_plan_hca; } @@ -1915,7 +2189,7 @@ const llama_kv_cache_dsv4_context::comp_plan & llama_kv_cache_dsv4_context::get_ reserve_plan_lid = dsv4_build_reserve_comp_plan( ubatch, DSV4_CSA_RATIO, true, - lid_state->get_state_size(), get_lid()->get_n_kv(), lid_state->get_n_stream()); + lid_state->get_state_size(), get_lid()->get_n_kv(), lid_state->get_n_stream(), lid_state->get_n_rs_seq()); return reserve_plan_lid; } diff --git a/src/llama-kv-cache-dsv4.h b/src/llama-kv-cache-dsv4.h index 76b1daf57871..ce39867c0342 100644 --- a/src/llama-kv-cache-dsv4.h +++ b/src/llama-kv-cache-dsv4.h @@ -22,6 +22,7 @@ class llama_dsv4_comp_state { uint32_t ratio, uint32_t state_size, uint32_t n_embd_state, + uint32_t n_rs_seq, const char * name, const llama_memory_i::layer_filter_cb & filter); @@ -29,17 +30,21 @@ class llama_dsv4_comp_state { void seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_dst); void apply_copies(const stream_copy_info & sc_info) const; - uint32_t get_ratio() const; + uint32_t get_ratio() const; uint32_t get_state_size() const; - uint32_t get_n_stream() const; + uint32_t get_n_stream() const; + uint32_t get_n_rs_seq() const; + uint32_t get_n_rows() const; std::map memory_breakdown() const; - void state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const; + void state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags, const std::vector & rs_idx) const; void state_read (llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags); - ggml_tensor * get_kv (ggml_context * ctx, int32_t il) const; - ggml_tensor * get_score(ggml_context * ctx, int32_t il) const; + ggml_tensor * get_kv (ggml_context * ctx, int32_t il) const; + ggml_tensor * get_score (ggml_context * ctx, int32_t il) const; + ggml_tensor * get_kv_all (ggml_context * ctx, int32_t il) const; + ggml_tensor * get_score_all(ggml_context * ctx, int32_t il) const; ggml_tensor * cpy_kv (ggml_context * ctx, ggml_tensor * cur, ggml_tensor * idxs, int32_t il) const; ggml_tensor * cpy_score(ggml_context * ctx, ggml_tensor * cur, ggml_tensor * idxs, int32_t il) const; @@ -59,6 +64,7 @@ class llama_dsv4_comp_state { const uint32_t state_size; const uint32_t n_embd_state; const uint32_t n_stream; + const uint32_t n_rs_seq; std::vector> ctxs_bufs; @@ -93,6 +99,7 @@ class llama_kv_cache_dsv4 : public llama_memory_i { uint32_t n_seq_max, uint32_t n_ubatch, uint32_t n_pad, + uint32_t n_rs_seq, const layer_filter_cb & filter, const layer_reuse_cb & reuse); @@ -141,6 +148,10 @@ class llama_kv_cache_dsv4 : public llama_memory_i { llama_dsv4_comp_state * get_hca_state() const; llama_dsv4_comp_state * get_lid_state() const; + uint32_t get_n_rs_seq() const; + const std::vector & get_rs_idx() const; + void reset_rs_idx_for_ubatches(const std::vector & ubatches); + private: llama_hparams hparams_raw; llama_hparams hparams_csa; @@ -148,6 +159,9 @@ class llama_kv_cache_dsv4 : public llama_memory_i { llama_hparams hparams_lid; const uint32_t n_seq_max; + const uint32_t n_rs_seq; + + std::vector rs_idx; std::unique_ptr kv_raw; std::unique_ptr kv_csa; @@ -268,6 +282,17 @@ class llama_kv_cache_dsv4_context : public llama_memory_context_i { std::vector state_persist_src_idxs; std::vector state_persist_dst_idxs; + // Device-side rollback restore copies snapshot planes back to the + // current compressor-state plane before the graph reads it. + std::vector state_restore_src_idxs; + std::vector state_restore_dst_idxs; + + // Device-side rollback snapshots copy rows from the graph-local + // [persistent_state | current_ubatch_scratch] tensor into rollback + // planes after the graph has computed current-token compressor state. + std::vector state_snapshot_src_idxs; + std::vector state_snapshot_dst_idxs; + // Flattened source row ids used for state-backed commits. Source rows // index the graph-local [persistent_state | current_ubatch_scratch] // tensor. For overlapped compression the first half is previous rows diff --git a/src/llama-kv-cache-msa.cpp b/src/llama-kv-cache-msa.cpp new file mode 100644 index 000000000000..55ef286cafa7 --- /dev/null +++ b/src/llama-kv-cache-msa.cpp @@ -0,0 +1,395 @@ +#include "llama-kv-cache-msa.h" + +#include "llama-impl.h" +#include "llama-batch.h" +#include "llama-model.h" + +#include +#include +#include + +// llama_kv_cache_msa + +llama_kv_cache_msa::llama_kv_cache_msa( + const llama_model & model, + ggml_type type_k, + ggml_type type_v, + bool v_trans, + bool offload, + bool unified, + uint32_t kv_size, + uint32_t n_seq_max, + uint32_t n_pad, + uint32_t n_swa, + llama_swa_type swa_type, + const layer_filter_cb & filter, + const layer_filter_cb & filter_idx, + const layer_reuse_cb & reuse) : + hparams_idx(model.hparams), + n_stream(unified ? 1 : n_seq_max), n_seq_max(n_seq_max), n_pad(n_pad), + n_swa(n_swa), swa_type(swa_type) { + + LLAMA_LOG_INFO("%s: creating main KV cache, size = %u cells\n", __func__, kv_size); + + kv_base = std::make_unique( + model, model.hparams, type_k, type_v, + v_trans, offload, unified, kv_size, n_seq_max, n_pad, + n_swa, swa_type, nullptr, filter, reuse, nullptr); + + // the MSA indexer uses a single key head per layer + std::fill(hparams_idx.n_head_kv_arr.begin(), hparams_idx.n_head_kv_arr.end(), 1); + hparams_idx.n_embd_head_k_full = model.hparams.indexer_head_size; + // the rope parameters are kept identical to the main cache + + LLAMA_LOG_INFO("%s: creating indexer KV cache, size = %u cells\n", __func__, kv_size); + + kv_idx = std::make_unique( + model, hparams_idx, type_k, type_v, + v_trans, offload, unified, kv_size, n_seq_max, n_pad, + n_swa, swa_type, nullptr, filter_idx, reuse, nullptr); +} + +void llama_kv_cache_msa::clear(bool data) { + kv_base->clear(data); + kv_idx ->clear(data); +} + +bool llama_kv_cache_msa::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) { + bool res = true; + + res = res & kv_base->seq_rm(seq_id, p0, p1); + res = res & kv_idx ->seq_rm(seq_id, p0, p1); + + return res; +} + +void llama_kv_cache_msa::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) { + kv_base->seq_cp(seq_id_src, seq_id_dst, p0, p1); + kv_idx ->seq_cp(seq_id_src, seq_id_dst, p0, p1); +} + +void llama_kv_cache_msa::seq_keep(llama_seq_id seq_id) { + kv_base->seq_keep(seq_id); + kv_idx ->seq_keep(seq_id); +} + +void llama_kv_cache_msa::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) { + kv_base->seq_add(seq_id, p0, p1, shift); + kv_idx ->seq_add(seq_id, p0, p1, shift); +} + +void llama_kv_cache_msa::seq_div(llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) { + kv_base->seq_div(seq_id, p0, p1, d); + kv_idx ->seq_div(seq_id, p0, p1, d); +} + +llama_pos llama_kv_cache_msa::seq_pos_min(llama_seq_id seq_id) const { + return kv_base->seq_pos_min(seq_id); +} + +llama_pos llama_kv_cache_msa::seq_pos_max(llama_seq_id seq_id) const { + return kv_base->seq_pos_max(seq_id); +} + +std::map llama_kv_cache_msa::memory_breakdown() const { + std::map mb = kv_base->memory_breakdown(); + for (const auto & buft_size : kv_idx->memory_breakdown()) { + mb[buft_size.first] += buft_size.second; + } + return mb; +} + +llama_memory_context_ptr llama_kv_cache_msa::init_batch( + llama_batch_allocr & balloc, + uint32_t n_ubatch, + bool embd_all) { + GGML_UNUSED(embd_all); + + do { + balloc.split_reset(); + + std::vector ubatches; + while (true) { + auto ubatch = n_stream == 1 ? balloc.split_simple(n_ubatch) : balloc.split_equal(n_ubatch, true, 0); + + if (ubatch.n_tokens == 0) { + break; + } + + ubatches.push_back(std::move(ubatch)); + } + + if (balloc.get_n_used() < balloc.get_n_tokens()) { + // failed to find a suitable split + break; + } + + auto sinfos_base = kv_base->prepare(ubatches); + if (sinfos_base.empty()) { + break; + } + + auto sinfos_idx = kv_idx->prepare(ubatches); + if (sinfos_idx.empty()) { + break; + } + + assert(sinfos_base.size() == sinfos_idx.size()); + + return std::make_unique( + this, std::move(sinfos_base), std::move(sinfos_idx), std::move(ubatches)); + } while (false); + + return std::make_unique(LLAMA_MEMORY_STATUS_FAILED_PREPARE); +} + +llama_memory_context_ptr llama_kv_cache_msa::init_full() { + return std::make_unique(this); +} + +llama_memory_context_ptr llama_kv_cache_msa::init_update(llama_context * lctx, bool optimize) { + return std::make_unique(this, lctx, optimize); +} + +bool llama_kv_cache_msa::get_can_shift() const { + return kv_base->get_can_shift() && + kv_idx ->get_can_shift() && + kv_base->get_size() == kv_idx->get_size(); +} + +void llama_kv_cache_msa::state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const { + kv_base->state_write(io, seq_id, flags); + kv_idx ->state_write(io, seq_id, flags); +} + +void llama_kv_cache_msa::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) { + kv_base->state_read(io, seq_id, flags); + kv_idx ->state_read(io, seq_id, flags); +} + +llama_kv_cache * llama_kv_cache_msa::get_base() const { + return kv_base.get(); +} + +llama_kv_cache * llama_kv_cache_msa::get_idx() const { + return kv_idx.get(); +} + +// llama_kv_cache_msa_context + +llama_kv_cache_msa_context::llama_kv_cache_msa_context(llama_memory_status status) : + kv(nullptr), status(status) {} + +llama_kv_cache_msa_context::llama_kv_cache_msa_context( + llama_kv_cache_msa * kv) : + kv(kv), + ctx_base(kv->get_base()->init_full()), + ctx_idx (kv->get_idx ()->init_full()), + status(llama_memory_status_combine(ctx_base->get_status(), ctx_idx->get_status())) { +} + +llama_kv_cache_msa_context::llama_kv_cache_msa_context( + llama_kv_cache_msa * kv, + llama_context * lctx, + bool optimize) : + kv(kv), + ctx_base(kv->get_base()->init_update(lctx, optimize)), + ctx_idx (kv->get_idx ()->init_update(lctx, optimize)), + status(llama_memory_status_combine(ctx_base->get_status(), ctx_idx->get_status())) { +} + +llama_kv_cache_msa_context::llama_kv_cache_msa_context( + llama_kv_cache_msa * kv, + slot_info_vec_t sinfos_base, + slot_info_vec_t sinfos_idx, + std::vector ubatches) : + kv(kv), + ubatches(std::move(ubatches)), + // here we copy the ubatches. not sure if this is ideal + ctx_base(new llama_kv_cache_context(kv->get_base(), std::move(sinfos_base), this->ubatches)), + ctx_idx (new llama_kv_cache_context(kv->get_idx (), std::move(sinfos_idx), this->ubatches)), + status(llama_memory_status_combine(ctx_base->get_status(), ctx_idx->get_status())) { +} + +llama_kv_cache_msa_context::~llama_kv_cache_msa_context() = default; + +bool llama_kv_cache_msa_context::next() { + assert(status == LLAMA_MEMORY_STATUS_SUCCESS); + + ctx_base->next(); + ctx_idx ->next(); + + if (++i_next >= ubatches.size()) { + return false; + } + + return true; +} + +bool llama_kv_cache_msa_context::apply() { + assert(!llama_memory_status_is_fail(status)); + + bool res = true; + + res = res & ctx_base->apply(); + res = res & ctx_idx ->apply(); + + return res; +} + +llama_memory_status llama_kv_cache_msa_context::get_status() const { + return status; +} + +const llama_ubatch & llama_kv_cache_msa_context::get_ubatch() const { + assert(status == LLAMA_MEMORY_STATUS_SUCCESS); + + return ubatches[i_next]; +} + +const llama_kv_cache_context * llama_kv_cache_msa_context::get_base() const { + assert(status == LLAMA_MEMORY_STATUS_SUCCESS); + + return static_cast(ctx_base.get()); +} + +const llama_kv_cache_context * llama_kv_cache_msa_context::get_idx() const { + assert(status == LLAMA_MEMORY_STATUS_SUCCESS); + + return static_cast(ctx_idx.get()); +} + +uint32_t llama_kv_cache_msa_context::get_n_pos() const { + // pad the value so that the graph remains constant across batches and can be reused + const uint32_t n_pad_cur = std::max(kv->get_n_pad(), 256u); + + llama_pos pos_max = -1; + + for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) kv->get_n_seq_max(); ++seq_id) { + pos_max = std::max(pos_max, kv->seq_pos_max(seq_id)); + } + + return std::max(n_pad_cur, GGML_PAD((uint32_t) (pos_max + 1), n_pad_cur)); +} + +void llama_kv_cache_msa_context::set_input_cell_pos(ggml_tensor * dst, const llama_ubatch * ubatch, int32_t div) const { + GGML_ASSERT(ggml_backend_buffer_is_host(dst->buffer)); + GGML_ASSERT(dst->type == GGML_TYPE_I32); + GGML_ASSERT(div > 0); + + const int64_t n_tokens = ubatch->n_tokens; + const int64_t n_kv = dst->ne[0]; + const int64_t n_stream_ub = dst->ne[1]; + + GGML_ASSERT(n_tokens % n_stream_ub == 0); + const int64_t n_tps = n_tokens/n_stream_ub; + + int32_t * data = (int32_t *) dst->data; + + for (int64_t s = 0; s < n_stream_ub; ++s) { + const llama_seq_id seq_id = ubatch->seq_id[s*n_tps][0]; + + const auto & cells = kv->get_base()->get_cells(seq_id); + + for (int64_t j = 0; j < n_kv; ++j) { + // the value for empty or other-sequence cells is irrelevant as consumers mask them + data[s*n_kv + j] = + cells.is_empty(j) || !cells.seq_has(j, seq_id) + ? 0 + : (int32_t) (cells.pos_get(j)/div); + } + } +} + +void llama_kv_cache_msa_context::set_input_pos_slot(ggml_tensor * dst, const llama_ubatch * ubatch) const { + GGML_ASSERT(ggml_backend_buffer_is_host(dst->buffer)); + GGML_ASSERT(dst->type == GGML_TYPE_I32 || dst->type == GGML_TYPE_F32); + + const int64_t n_tokens = ubatch->n_tokens; + const int64_t n_pos = dst->ne[0]; + const int64_t n_stream_ub = dst->ne[1]; + + GGML_ASSERT(n_tokens % n_stream_ub == 0); + const int64_t n_tps = n_tokens/n_stream_ub; + + for (int64_t s = 0; s < n_stream_ub; ++s) { + const llama_seq_id seq_id = ubatch->seq_id[s*n_tps][0]; + + const auto & cells = kv->get_base()->get_cells(seq_id); + + std::vector map(n_pos, 0); + + for (uint32_t j = 0; j < cells.size(); ++j) { + if (cells.is_empty(j) || !cells.seq_has(j, seq_id)) { + continue; + } + + const llama_pos p0 = cells.pos_get(j); + + if (p0 < 0 || p0 >= n_pos) { + continue; + } + + map[p0] = (int32_t) j; + } + + if (dst->type == GGML_TYPE_I32) { + int32_t * data = (int32_t *) dst->data + s*n_pos; + std::copy(map.begin(), map.end(), data); + } else { + float * data = (float *) dst->data + s*n_pos; + for (int64_t p = 0; p < n_pos; ++p) { + data[p] = (float) map[p]; + } + } + } +} + +void llama_kv_cache_msa_context::set_input_pos_mask(ggml_tensor * dst, const llama_ubatch * ubatch) const { + GGML_ASSERT(ggml_backend_buffer_is_host(dst->buffer)); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + const int64_t n_tokens = ubatch->n_tokens; + const int64_t n_pos = dst->ne[0]; + + GGML_ASSERT(dst->ne[1] == n_tokens); + + const uint32_t n_swa = kv->get_n_swa(); + const llama_swa_type swa_type = kv->get_swa_type(); + + float * data = (float *) dst->data; + + std::fill(data, data + n_pos*n_tokens, -INFINITY); + + for (int64_t i = 0; i < n_tokens; ++i) { + const llama_seq_id seq_id = ubatch->seq_id[i][0]; + + const auto & cells = kv->get_base()->get_cells(seq_id); + + const llama_pos p1 = ubatch->pos[i]; + + for (uint32_t j = 0; j < cells.size(); ++j) { + if (cells.is_empty(j) || !cells.seq_has(j, seq_id)) { + continue; + } + + const llama_pos p0 = cells.pos_get(j); + + if (p0 < 0 || p0 >= n_pos) { + continue; + } + + // causal mask + if (p0 > p1) { + continue; + } + + // apply SWA if any + if (llama_hparams::is_masked_swa(n_swa, swa_type, p0, p1)) { + continue; + } + + data[i*n_pos + p0] = 0.0f; + } + } +} diff --git a/src/llama-kv-cache-msa.h b/src/llama-kv-cache-msa.h new file mode 100644 index 000000000000..f09b6d32b044 --- /dev/null +++ b/src/llama-kv-cache-msa.h @@ -0,0 +1,153 @@ +#pragma once + +#include "llama-kv-cache.h" + +#include + +// llama_kv_cache_msa + +// uses two instances of llama_kv_cache, one for K/V tensors, and one for the MSA indexer tensors +// both receive identical sequence operations and identical ubatches, so their cell layouts stay in synced. +// the context also exposes per-ubatch pos - cell translation maps populated from llama_kv_cells via +// llama_kv_cache::get_cells(), which the model graph uses to run MSA block selection in position space + +class llama_kv_cache_msa : public llama_memory_i { +public: + llama_kv_cache_msa( + const llama_model & model, + ggml_type type_k, + ggml_type type_v, + bool v_trans, + bool offload, + bool unified, + uint32_t kv_size, + uint32_t n_seq_max, + uint32_t n_pad, + uint32_t n_swa, + llama_swa_type swa_type, + const layer_filter_cb & filter, + const layer_filter_cb & filter_idx, + const layer_reuse_cb & reuse); + + ~llama_kv_cache_msa() = default; + + // llama_memory_i + + llama_memory_context_ptr init_batch( + llama_batch_allocr & balloc, + uint32_t n_ubatch, + bool embd_all) override; + + llama_memory_context_ptr init_full() override; + + llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) override; + + bool get_can_shift() const override; + + void clear(bool data) override; + + bool seq_rm (llama_seq_id seq_id, llama_pos p0, llama_pos p1) override; + void seq_cp (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) override; + void seq_keep(llama_seq_id seq_id) override; + void seq_add (llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) override; + void seq_div (llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) override; + + llama_pos seq_pos_min(llama_seq_id seq_id) const override; + llama_pos seq_pos_max(llama_seq_id seq_id) const override; + + std::map memory_breakdown() const override; + + // state write/load + + void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) const override; + void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) override; + + // llama_kv_cache_msa specific API + + llama_kv_cache * get_base() const; + llama_kv_cache * get_idx () const; + + uint32_t get_n_pad() const { return n_pad; } + uint32_t get_n_seq_max() const { return n_seq_max; } + uint32_t get_n_swa() const { return n_swa; } + llama_swa_type get_swa_type() const { return swa_type; } + +private: + // keep the indexer KV cache hparams instance here as llama_kv_cache stores only a reference + llama_hparams hparams_idx; + + const uint32_t n_stream = 1; + const uint32_t n_seq_max = 1; + const uint32_t n_pad = 1; + + const uint32_t n_swa = 0; + const llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE; + + std::unique_ptr kv_base; + std::unique_ptr kv_idx; +}; + +class llama_kv_cache_msa_context : public llama_memory_context_i { +public: + using slot_info_vec_t = llama_kv_cache::slot_info_vec_t; + + // used for errors + llama_kv_cache_msa_context(llama_memory_status status); + + // used to create a full-cache context + llama_kv_cache_msa_context( + llama_kv_cache_msa * kv); + + // used to create an update context + llama_kv_cache_msa_context( + llama_kv_cache_msa * kv, + llama_context * lctx, + bool optimize); + + // used to create a batch processing context from a batch + llama_kv_cache_msa_context( + llama_kv_cache_msa * kv, + slot_info_vec_t sinfos_base, + slot_info_vec_t sinfos_idx, + std::vector ubatches); + + virtual ~llama_kv_cache_msa_context(); + + // llama_memory_context_i + + bool next() override; + bool apply() override; + + llama_memory_status get_status() const override; + const llama_ubatch & get_ubatch() const override; + + // llama_kv_cache_msa_context specific API + + const llama_kv_cache_context * get_base() const; + const llama_kv_cache_context * get_idx () const; + + // max position currently present in the cache plus one, padded MSA blocks are defined over token positions + // so the block-selection tensors are sized by this value rather than by the number of cells + uint32_t get_n_pos() const; + + // position <-> cell translation maps, populated from the base cache cells + // the model graph relates cache contents to token positions only through these per ubatch inputs + // value for empty or other-sequence cells is 0 so consumers must mask them + void set_input_cell_pos(ggml_tensor * dst, const llama_ubatch * ubatch, int32_t div) const; + // positions without a cell map to cell 0, consumers must mask them assumes one sequence per stream + void set_input_pos_slot(ggml_tensor * dst, const llama_ubatch * ubatch) const; + void set_input_pos_mask(ggml_tensor * dst, const llama_ubatch * ubatch) const; + +private: + llama_kv_cache_msa * kv; + + // the index of the next ubatch to process + size_t i_next = 0; + + std::vector ubatches; + + const llama_memory_context_ptr ctx_base; + const llama_memory_context_ptr ctx_idx; + + const llama_memory_status status; +}; diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp index 390143675f89..adcf5cd14505 100644 --- a/src/llama-kv-cache.cpp +++ b/src/llama-kv-cache.cpp @@ -559,7 +559,8 @@ llama_kv_cache::llama_kv_cache( // indexers: this is a functional requirement for these models, not // optional tuning, so it overrides the fork's default-off policy (still // respects the hard LLAMA_ATTN_ROT_DISABLE lock-out). - if (!attn_rot_disable && (model.arch == LLM_ARCH_DEEPSEEK32 || model.arch == LLM_ARCH_DEEPSEEK4) && + if (!attn_rot_disable && + (model.arch == LLM_ARCH_DEEPSEEK32 || model.arch == LLM_ARCH_DEEPSEEK4 || model.arch == LLM_ARCH_GLM_DSA) && hparams.n_embd_head_k_full == hparams.indexer_head_size) { attn_rot_k = true; } @@ -1502,6 +1503,12 @@ ggml_tensor * llama_kv_cache::get_k_storage(int32_t il) const { return layers[ikv].k; } +const llama_kv_cells & llama_kv_cache::get_cells(llama_seq_id seq_id) const { + GGML_ASSERT(seq_id >= 0 && (size_t) seq_id < seq_to_stream.size()); + + return v_cells[seq_to_stream[seq_id]]; +} + uint32_t llama_kv_cache::get_n_kv(const slot_info & sinfo) const { uint32_t result = 0; @@ -2473,7 +2480,12 @@ void llama_kv_cache::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama bool res = true; res = res && state_read_meta(io, strm, cell_count, sinfo, seq_id); - res = res && state_read_data(io, strm, cell_count, sinfo); + + try { + res = res && state_read_data(io, strm, cell_count, sinfo); + } catch (...) { + res = false; + } if (!res) { if (seq_id == -1) { diff --git a/src/llama-kv-cache.h b/src/llama-kv-cache.h index e538aafa7ac2..b92f23eff175 100644 --- a/src/llama-kv-cache.h +++ b/src/llama-kv-cache.h @@ -164,6 +164,8 @@ class llama_kv_cache : public llama_memory_i { std::vector get_layer_ids() const; ggml_tensor * get_k_storage(int32_t il) const; + const llama_kv_cells & get_cells(llama_seq_id seq_id) const; + // // graph_build API // diff --git a/src/llama-memory-recurrent.cpp b/src/llama-memory-recurrent.cpp index 3d6c6db876b4..ef82eb976ca7 100644 --- a/src/llama-memory-recurrent.cpp +++ b/src/llama-memory-recurrent.cpp @@ -819,7 +819,12 @@ void llama_memory_recurrent::state_read(llama_io_read_i & io, llama_seq_id seq_i bool res = true; res = res && state_read_meta(io, cell_count, seq_id); - res = res && state_read_data(io, cell_count); + + try { + res = res && state_read_data(io, cell_count); + } catch (...) { + res = false; + } if (!res) { if (seq_id == -1) { diff --git a/src/llama-model-loader.cpp b/src/llama-model-loader.cpp index d3f3a438193a..dae3e1ba2649 100644 --- a/src/llama-model-loader.cpp +++ b/src/llama-model-loader.cpp @@ -4,6 +4,7 @@ #include "ggml.h" #include "gguf.h" #include "llama-hparams.h" +#include "llama.h" #include #include @@ -524,10 +525,10 @@ llama_model_loader::llama_model_loader( const std::string & fname, std::vector & splits, FILE * file, - bool use_mmap, - bool use_direct_io, + llama_load_mode load_mode, bool check_tensors, bool no_alloc, + bool load_mtp, const llama_model_kv_override * param_overrides_p, const llama_model_tensor_buft_override * param_tensor_buft_overrides_p) : metadata(meta), set_tensor_data(set_tensor_data), set_tensor_data_ud(set_tensor_data_ud) { @@ -544,6 +545,9 @@ llama_model_loader::llama_model_loader( tensor_buft_overrides = param_tensor_buft_overrides_p; + this->use_mmap = load_mode == LLAMA_LOAD_MODE_MMAP || load_mode == LLAMA_LOAD_MODE_MMAP_MLOCK; + this->use_direct_io = load_mode == LLAMA_LOAD_MODE_DIRECT_IO; + if (!fname.empty()) { // Load the main GGUF struct ggml_context * ctx = NULL; @@ -564,20 +568,6 @@ llama_model_loader::llama_model_loader( files.emplace_back(new llama_file(fname.c_str(), "rb", use_direct_io)); contexts.emplace_back(ctx); - if (use_mmap && use_direct_io) { - if (files.back()->has_direct_io()) { - LLAMA_LOG_WARN("%s: direct I/O is enabled, disabling mmap\n", __func__); - use_mmap = false; - } else { - LLAMA_LOG_WARN("%s: direct I/O is not available, using mmap\n", __func__); - use_direct_io = false; - - // reopen file using std::fopen for mmap - files.pop_back(); - files.emplace_back(new llama_file(fname.c_str(), "rb", false)); - } - } - // Save tensors data offset of the main file. // For subsidiary files, `meta` tensor data offset must not be used, // so we build a unified tensors index for weights. @@ -820,15 +810,14 @@ llama_model_loader::llama_model_loader( } } - if (!llama_mmap::SUPPORTED) { + if (this->use_mmap && !llama_mmap::SUPPORTED) { LLAMA_LOG_WARN("%s: mmap is not supported on this platform\n", __func__); - use_mmap = false; + this->use_mmap = false; } - this->use_mmap = use_mmap; - this->use_direct_io = use_direct_io; this->check_tensors = check_tensors; this->no_alloc = no_alloc; + this->load_mtp = load_mtp; } std::string llama_model_loader::get_arch_name() const { @@ -872,7 +861,11 @@ struct ggml_tensor * llama_model_loader::require_tensor_meta(const std::string & return tensor; } -const struct ggml_tensor * llama_model_loader::check_tensor_dims(const std::string & name, const std::vector & ne, bool required) const { +const struct ggml_tensor * llama_model_loader::check_tensor_dims( + const std::string & name, + const std::vector & ne, + bool required, + bool allow_reshape) const { const struct ggml_tensor * cur = get_tensor_meta(name.c_str()); if (cur == NULL) { @@ -882,21 +875,33 @@ const struct ggml_tensor * llama_model_loader::check_tensor_dims(const std::stri throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name.c_str())); } - { - bool is_ok = true; + bool is_ok = true; + + if (allow_reshape) { + // check total number of elements only + const int64_t ncur = ggml_nelements(cur); + int64_t nexp = 1; + for (size_t i = 0; i < ne.size(); ++i) { + nexp *= ne[i]; + } + if (ncur != nexp) { + is_ok = false; + } + } else { for (size_t i = 0; i < GGML_MAX_DIMS; ++i) { if ((i < ne.size() && ne[i] != cur->ne[i]) || (i >= ne.size() && cur->ne[i] != 1)) { is_ok = false; break; } } - if (!is_ok) { - throw std::runtime_error( - format("%s: tensor '%s' has wrong shape; expected %s, got %s", - __func__, name.c_str(), - llama_format_tensor_shape(ne).c_str(), - llama_format_tensor_shape(cur).c_str())); - } + } + + if (!is_ok) { + throw std::runtime_error( + format("%s: tensor '%s' has wrong shape; expected %s, got %s", + __func__, name.c_str(), + llama_format_tensor_shape(ne).c_str(), + llama_format_tensor_shape(cur).c_str())); } return cur; @@ -1261,11 +1266,25 @@ struct ggml_tensor * llama_model_loader::create_tensor( return ret; } - ggml_tensor * t_meta = get_tensor_meta(tn.str().c_str()); - ggml_backend_buffer_type_t buft = buft_for_tensor(t_meta); + LLAMA_LOG_DEBUG("%s: loading tensor %s\n", __func__, tn.str().c_str()); + const struct ggml_tensor * cur = check_tensor_dims(tn.str(), ne, !(flags & TENSOR_NOT_REQUIRED), flags & TENSOR_ALLOW_RESHAPE); + if (cur == NULL) { + return NULL; + } + + ggml_tensor t_meta = *cur; + if (flags & TENSOR_ALLOW_RESHAPE) { + for (size_t dim = 0; dim < GGML_MAX_DIMS; dim++) { + t_meta.ne[dim] = dim < ne.size() ? ne.begin()[dim] : 1; + t_meta.nb[dim] = dim == 0 ? ggml_type_size(t_meta.type) : t_meta.ne[dim-1]*t_meta.nb[dim-1]; + } + } + + ggml_backend_buffer_type_t buft = buft_for_tensor(&t_meta); if (buft == nullptr) { - return nullptr; // return type is ggml_tensor * + return nullptr; } + ggml_context * ctx = ctx_for_buft(buft); // if duplicated, check if the original tensor was allocated in the same buffer type context and avoid creating a new one @@ -1276,20 +1295,13 @@ struct ggml_tensor * llama_model_loader::create_tensor( } } - LLAMA_LOG_DEBUG("%s: loading tensor %s\n", __func__, tn.str().c_str()); - const struct ggml_tensor * cur = check_tensor_dims(tn.str(), ne, !(flags & TENSOR_NOT_REQUIRED)); - - if (cur == NULL) { - return NULL; - } - const bool duplicated = flags & TENSOR_DUPLICATED; - struct ggml_tensor * tensor = ggml_dup_tensor(ctx, cur); - ggml_set_name(tensor, ggml_get_name(cur)); + struct ggml_tensor * tensor = ggml_dup_tensor(ctx, &t_meta); + ggml_set_name(tensor, ggml_get_name(&t_meta)); if (duplicated) { - size_data += ggml_nbytes(cur); + size_data += ggml_nbytes(&t_meta); } else { n_created++; } @@ -1297,34 +1309,6 @@ struct ggml_tensor * llama_model_loader::create_tensor( return tensor; } -struct ggml_tensor * llama_model_loader::create_tensor_as_view(struct ggml_context * ctx, struct ggml_tensor * base, const std::string & name, const std::initializer_list & ne, size_t offset, bool required) { - const struct ggml_tensor * cur = check_tensor_dims(name, ne, required); - - if (cur == NULL) { - return NULL; - } - - if (cur->type != base->type) { - throw std::runtime_error(format("%s: tensor '%s' has wrong type; expected %s, got %s", __func__, name.c_str(), ggml_type_name(base->type), ggml_type_name(cur->type))); - } - - std::array dims; - for (size_t i = 0; i < GGML_MAX_DIMS; ++i) { - dims[i] = i < ne.size() ? ne.begin()[i] : 1; - } - - struct ggml_tensor * tensor = ggml_view_4d(ctx, base, - dims[0], dims[1], dims[2], dims[3], - cur->nb[1], cur->nb[2], cur->nb[3], - offset); - - ggml_set_name(tensor, name.c_str()); - - n_created++; - - return tensor; -} - void llama_model_loader::done_getting_tensors(bool partial) const { if (n_created > n_tensors) { throw std::runtime_error(format("%s: too many tensors created; expected %d, got %d", __func__, n_tensors, n_created)); diff --git a/src/llama-model-loader.h b/src/llama-model-loader.h index 2206f9a7713a..51393ee37364 100644 --- a/src/llama-model-loader.h +++ b/src/llama-model-loader.h @@ -67,6 +67,7 @@ struct llama_model_loader { static const int TENSOR_DUPLICATED = 1 << 1; static const int TENSOR_SKIP = 1 << 2; static const int TENSOR_SKIP_IF_VIRTUAL = 1 << 3; + static const int TENSOR_ALLOW_RESHAPE = 1 << 4; int n_kv = 0; int n_tensors = 0; @@ -79,6 +80,7 @@ struct llama_model_loader { bool use_direct_io = false; bool check_tensors; bool no_alloc; + bool load_mtp; // when true, done_getting_tensors() tolerates GGUF files that contain // more tensors than the loader actually requested (e.g. loading a @@ -131,10 +133,10 @@ struct llama_model_loader { const std::string & fname, std::vector & splits, // optional, only need if the split does not follow naming scheme FILE * file, - bool use_mmap, - bool use_direct_io, + llama_load_mode load_mode, bool check_tensors, bool no_alloc, + bool load_mtp, const llama_model_kv_override * param_overrides_p, const llama_model_tensor_buft_override * param_tensor_buft_overrides_p); @@ -181,14 +183,16 @@ struct llama_model_loader { struct ggml_tensor * require_tensor_meta(const std::string & name) const; - const struct ggml_tensor * check_tensor_dims(const std::string & name, const std::vector & ne, bool required) const; + const struct ggml_tensor * check_tensor_dims( + const std::string & name, + const std::vector & ne, + bool required, + bool allow_reshape) const; struct ggml_tensor * create_tensor( const llama_hparams & hparams, const buft_list_t * buft_list_cpu, const buft_list_t * buft_list_input, const buft_list_t * buft_list_output, const buft_list_t * buft_list_layer, const LLM_TN_IMPL & tn, const std::initializer_list & ne, int flags); - struct ggml_tensor * create_tensor_as_view(struct ggml_context * ctx, struct ggml_tensor * base, const std::string & name, const std::initializer_list & ne, size_t offset, bool required = true); - void done_getting_tensors(bool partial = false) const; void init_mappings(bool prefetch = true, llama_mlocks * mlock_mmaps = nullptr); diff --git a/src/llama-model-saver.cpp b/src/llama-model-saver.cpp index 5f448cd837fd..f63960e9e804 100644 --- a/src/llama-model-saver.cpp +++ b/src/llama-model-saver.cpp @@ -29,6 +29,7 @@ bool llama_model_saver_supports_arch(llm_arch arch) { case LLM_ARCH_STEP35: case LLM_ARCH_MELLUM: case LLM_ARCH_INKLING: + case LLM_ARCH_LAGUNA: return false; default: return true; @@ -281,6 +282,9 @@ void llama_model_saver::add_kv_from_model() { add_kv(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, hparams.indexer_n_head); add_kv(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, hparams.indexer_head_size); add_kv(LLM_KV_ATTENTION_INDEXER_TOP_K, hparams.indexer_top_k); + add_kv(LLM_KV_ATTENTION_INDEXER_BLOCK_SIZE, hparams.indexer_block_size); + add_kv(LLM_KV_ATTENTION_INDEXER_LOCAL_BLOCKS, hparams.indexer_local_blocks); + add_kv(LLM_KV_ATTENTION_INDEXER_TYPES, hparams.is_indexer_full_impl, true); add_kv(LLM_KV_ATTENTION_RECURRENT_LAYERS, hparams.is_recr_impl, true); const float rope_scaling_factor = hparams.rope_freq_scale_train == 1.0f ? 0.0f : 1.0f/hparams.rope_freq_scale_train; diff --git a/src/llama-model.cpp b/src/llama-model.cpp index fe3780ee1d91..8a3a113742a0 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -11,11 +11,13 @@ #include "llama-kv-cache.h" #include "llama-kv-cache-iswa.h" #include "llama-kv-cache-dsa.h" +#include "llama-kv-cache-msa.h" #include "llama-kv-cache-dsv4.h" #include "llama-memory-hybrid.h" #include "llama-memory-hybrid-iswa.h" #include "llama-memory-recurrent.h" +#include "llama.h" #include "models/models.h" #include "ggml.h" @@ -84,6 +86,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params return new llama_model_stablelm(params); case LLM_ARCH_MELLUM: return new llama_model_mellum(params); + case LLM_ARCH_NANBEIGE: + return new llama_model_nanbeige(params); case LLM_ARCH_QWEN: return new llama_model_qwen(params); case LLM_ARCH_QWEN2: @@ -284,6 +288,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params return new llama_model_apertus(params); case LLM_ARCH_MINIMAX_M2: return new llama_model_minimax_m2(params); + case LLM_ARCH_MINIMAX_M3: + return new llama_model_minimax_m3(params); case LLM_ARCH_COGVLM: return new llama_model_cogvlm(params); case LLM_ARCH_PANGU_EMBED: @@ -304,6 +310,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params return new llama_model_mimo2(params); case LLM_ARCH_KIMI_LINEAR: return new llama_model_kimi_linear(params); + case LLM_ARCH_KIMI_K3: + return new llama_model_kimi_k3(params); case LLM_ARCH_STEP35: return new llama_model_step35(params); case LLM_ARCH_INKLING: @@ -371,8 +379,10 @@ struct ggml_backend_meta_split_state llama_meta_device_get_split_state(const str static const std::regex pattern_ssm_conv1d ("blk\\.\\d*\\.ssm_conv1d.weight"); static const std::regex pattern_ssm_out_weight ("blk\\.\\d*\\.ssm_out.weight"); - static const std::regex pattern_ffn_up_gate_weight("blk\\.\\d*\\.ffn_(up|gate)(_exps)?.weight"); - static const std::regex pattern_ffn_up_gate_bias ("blk\\.\\d*\\.ffn_(up|gate)(_exps)?.bias"); + static const std::regex pattern_ffn_up_weight ("blk\\.\\d*\\.ffn_up(_exps)?.weight"); + static const std::regex pattern_ffn_up_bias ("blk\\.\\d*\\.ffn_up(_exps)?.bias"); + static const std::regex pattern_ffn_gate_weight ("blk\\.\\d*\\.ffn_gate(_exps)?.weight"); + static const std::regex pattern_ffn_gate_bias ("blk\\.\\d*\\.ffn_gate(_exps)?.bias"); static const std::regex pattern_ffn_gate_up_weight("blk\\.\\d*\\.ffn_gate_up(_exps)?.weight"); static const std::regex pattern_ffn_down_weight ("blk\\.\\d*\\.ffn_down(_exps)?.weight"); static const std::regex pattern_ffn_down_bias ("blk\\.\\d*\\.ffn_down.bias"); @@ -480,10 +490,10 @@ struct ggml_backend_meta_split_state llama_meta_device_get_split_state(const str } // FFN - if (std::regex_match(tensor_name, pattern_ffn_up_gate_weight)) { + if (std::regex_match(tensor_name, pattern_ffn_up_weight) || std::regex_match(tensor_name, pattern_ffn_gate_weight)) { return get_tensor_config_impl(GGML_BACKEND_SPLIT_AXIS_1, "ffn_down.weight", "ffn_down_exps.weight"); } - if (std::regex_match(tensor_name, pattern_ffn_up_gate_bias)) { + if (std::regex_match(tensor_name, pattern_ffn_up_bias) || std::regex_match(tensor_name, pattern_ffn_gate_bias)) { return get_tensor_config_impl(GGML_BACKEND_SPLIT_AXIS_0, "ffn_down.weight", "ffn_down_exps.weight"); } if (std::regex_match(tensor_name, pattern_ffn_gate_up_weight)) { @@ -567,6 +577,14 @@ struct ggml_backend_meta_split_state llama_meta_device_get_split_state(const str GGML_ASSERT(tensor->ne[axis] == n_embd + 2*n_embd_gqa); return {{n_embd, 1}, {n_embd_gqa, 2}}; } + if (std::regex_match(tensor_name, pattern_ffn_up_weight) || std::regex_match(tensor_name, pattern_ffn_up_bias)) { + const int64_t n_ff = hparams.n_ff(il); + // some models such as Phi 3 have fused up + gate tensors named "up" tensors, which need to be segmented + if (tensor->ne[axis] == 2*n_ff) { + return {{n_ff, 2}}; + } + return {{tensor->ne[axis], 1}}; + } if (std::regex_match(tensor_name, pattern_ffn_gate_up_weight)) { const int64_t n_ff_exp = hparams.n_ff_exp; GGML_ASSERT(tensor->ne[axis] == 2*n_ff_exp); @@ -643,7 +661,8 @@ struct ggml_backend_meta_split_state llama_meta_device_get_split_state(const str } // FFN - if (std::regex_match(tensor_name, pattern_ffn_up_gate_weight) || std::regex_match(tensor_name, pattern_ffn_up_gate_bias) || + if (std::regex_match(tensor_name, pattern_ffn_up_weight) || std::regex_match(tensor_name, pattern_ffn_up_bias) || + std::regex_match(tensor_name, pattern_ffn_gate_weight) || std::regex_match(tensor_name, pattern_ffn_gate_bias) || std::regex_match(tensor_name, pattern_ffn_gate_up_weight) || std::regex_match(tensor_name, pattern_ffn_down_weight)) { const int64_t blck_size_perf = std::lcm(blck_size, 128); GGML_ASSERT(segments.size() == 1); @@ -811,10 +830,12 @@ const char * llm_type_name(llm_type type) { case LLM_TYPE_100B_A6B: return "100B.A6B"; case LLM_TYPE_102B_A12B: return "102B.A12B"; case LLM_TYPE_106B_A12B: return "106B.A12B"; + case LLM_TYPE_118B_A8B: return "118B.A8B"; case LLM_TYPE_120B_A12B: return "120B.A12B"; case LLM_TYPE_122B_A10B: return "122B.A10B"; case LLM_TYPE_196B_A11B: return "196B.A11B"; case LLM_TYPE_230B_A10B: return "230B.A10B"; + case LLM_TYPE_428B_A23B: return "428B.A23B"; case LLM_TYPE_235B_A22B: return "235B.A22B"; case LLM_TYPE_300B_A47B: return "300B.A47B"; case LLM_TYPE_310B_A15B: return "310B.A15B"; @@ -822,6 +843,7 @@ const char * llm_type_name(llm_type type) { case LLM_TYPE_397B_A17B: return "397B.A17B"; case LLM_TYPE_685B_A37B: return "685B.A37B"; case LLM_TYPE_744B_A40B: return "744B.A40B"; + case LLM_TYPE_2_8T_A104B: return "2.8T.A104B"; case LLM_TYPE_E2B: return "E2B"; case LLM_TYPE_E4B: return "E4B"; default: return "?B"; @@ -1080,6 +1102,7 @@ void llama_model_base::load_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn, false); ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false); ml.get_key(LLM_KV_BLOCK_COUNT, hparams.n_layer_all); + GGML_ASSERT(hparams.n_layer_all > 0 && hparams.n_layer_all <= LLAMA_MAX_LAYERS); ml.get_key(LLM_KV_EXPERT_COUNT, hparams.n_expert, false); ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used, false); ml.get_key(LLM_KV_EXPERT_GROUP_COUNT, hparams.n_expert_groups, false); @@ -1125,6 +1148,7 @@ void llama_model_base::load_hparams(llama_model_loader & ml) { std::fill(hparams.rope_sections.begin(), hparams.rope_sections.end(), 0); std::fill(hparams.is_swa_impl.begin(), hparams.is_swa_impl.end(), 0); std::fill(hparams.is_recr_impl.begin(), hparams.is_recr_impl.end(), llm_arch_is_recurrent(ml.get_arch()) ? 1 : 0); + std::fill(hparams.is_indexer_full_impl.begin(), hparams.is_indexer_full_impl.end(), 0); std::fill(hparams.xielu_alpha_n.begin(), hparams.xielu_alpha_n.end(), 0.0f); std::fill(hparams.xielu_alpha_p.begin(), hparams.xielu_alpha_p.end(), 0.0f); @@ -1241,7 +1265,7 @@ void llama_model_base::load_vocab(llama_model_loader & ml) { bool llama_model_base::load_tensors(llama_model_loader & ml) { const auto & split_mode = params.split_mode; - const auto & use_mlock = params.use_mlock; + const bool use_mlock = params.load_mode == LLAMA_LOAD_MODE_MLOCK || params.load_mode == LLAMA_LOAD_MODE_MMAP_MLOCK; const auto & tensor_split = params.tensor_split; const int n_layer_all = hparams.n_layer_all; @@ -1251,8 +1275,8 @@ bool llama_model_base::load_tensors(llama_model_loader & ml) { this->ml = &ml; // to be used by create_tensor() and load_arch_tensors() - LLAMA_LOG_INFO("%s: loading model tensors, this can take a while... (mmap = %s, direct_io = %s)\n", - __func__, ml.use_mmap ? "true" : "false", ml.use_direct_io ? "true" : "false"); + LLAMA_LOG_INFO("%s: loading model tensors, this can take a while... (load_mode = %s)\n", + __func__, llama_load_mode_name(params.load_mode)); // build a list of buffer types for the CPU and GPU devices pimpl->cpu_buft_list = make_cpu_buft_list(devices, params.use_extra_bufts, params.no_host); @@ -2060,9 +2084,13 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params, { res = nullptr; } break; - case LLM_ARCH_DEEPSEEK32: + case LLM_ARCH_MINIMAX_M3: { - res = new llama_kv_cache_dsa( + // sparse (MSA) layers carry an indexer key cache, but leading dense layers do not + llama_kv_cache::layer_filter_cb filter_idx = + [&](int32_t il) { return (uint32_t) il >= hparams.n_layer_dense_lead; }; + + res = new llama_kv_cache_msa( *this, params.type_k, params.type_v, @@ -2075,17 +2103,140 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params, hparams.n_swa, hparams.swa_type, nullptr, + filter_idx, nullptr); } break; + case LLM_ARCH_GLM_DSA: + case LLM_ARCH_DEEPSEEK32: + { + if (params.ctx_type == LLAMA_CONTEXT_TYPE_MTP && hparams.n_layer_nextn > 0) { + // The NextN/MTP draft head runs dense MLA (no DSA indexer), so the + // MTP context uses a plain attention KV cache holding only the + // nextn layer(s) - same pattern as the hybrid Qwen3.5 MTP context. + llama_kv_cache::layer_filter_cb filter = + [&](uint32_t il) { return il >= hparams.n_layer(); }; + + res = new llama_kv_cache( + *this, + hparams, + params.type_k, + params.type_v, + !cparams.flash_attn, + cparams.offload_kqv, + cparams.kv_unified, + cparams.n_ctx_seq, + cparams.n_seq_max, + 1, + hparams.n_swa, + hparams.swa_type, + nullptr, + filter, + nullptr, + nullptr); + } else { + // Main context: DSA cache for the trunk layers only - the nextn + // layer(s) are never attended by the trunk graph. + llama_kv_cache::layer_filter_cb filter_mla = nullptr; + if (hparams.n_layer_nextn > 0) { + filter_mla = [&](uint32_t il) { return il < hparams.n_layer(); }; + } + llama_kv_cache::layer_filter_cb filter_lid = [&](uint32_t il) { return il < hparams.n_layer() && (arch != LLM_ARCH_GLM_DSA || hparams.is_indexer_full(il)); }; + + res = new llama_kv_cache_dsa( + *this, + params.type_k, + params.type_v, + !cparams.flash_attn, + cparams.offload_kqv, + cparams.kv_unified, + cparams.n_ctx_seq, + cparams.n_seq_max, + 1, + hparams.n_swa, + hparams.swa_type, + filter_mla, + filter_lid, + nullptr); + } + } break; + case LLM_ARCH_DEEPSEEK4: + { + GGML_ASSERT(hparams.swa_type != LLAMA_SWA_TYPE_NONE); + + if (params.ctx_type == LLAMA_CONTEXT_TYPE_MTP) { + const llama_memory_i::layer_filter_cb filter_mtp = [&](int32_t il) { + return il >= (int32_t) hparams.n_layer(); + }; + + res = new llama_kv_cache_iswa( + *this, + params.type_k, + params.type_v, + !cparams.flash_attn, + cparams.offload_kqv, + params.swa_full, + cparams.kv_unified, + cparams.n_ctx_seq, + cparams.n_seq_max, + cparams.n_ubatch, + 1, + nullptr, + filter_mtp, + nullptr, + nullptr); + } else { + res = new llama_kv_cache_dsv4( + *this, + params.type_k, + params.type_v, + !cparams.flash_attn, + cparams.offload_kqv, + params.swa_full, + cparams.kv_unified, + cparams.n_ctx_seq, + cparams.n_seq_max, + cparams.n_ubatch, + 1, + cparams.n_rs_seq, + nullptr, + nullptr); + } + } break; + case LLM_ARCH_DFLASH: + { + // DSV4 DSpark stages store a single MLA-style K per position (window = the draft ring) + if (hparams.dsv4_hc_mult > 0) { + GGML_ASSERT(hparams.swa_type != LLAMA_SWA_TYPE_NONE); + + res = new llama_kv_cache_iswa( + *this, + params.type_k, + params.type_v, + !cparams.flash_attn, + cparams.offload_kqv, + params.swa_full, + cparams.kv_unified, + cparams.n_ctx_seq, + cparams.n_seq_max, + cparams.n_ubatch, + 1, + nullptr, + nullptr, + nullptr, + nullptr); + break; + } + } + [[fallthrough]]; // Models that need standard caching should rely on recurrent/hybrid // checks default: { - // The MTP head is dense-attention only on hybrid Qwen3.5/3.6, so use a plain + // The MTP head is dense-attention only on hybrid Qwen3-Next/3.5/3.6, so use a plain // attention KV cache for the MTP context instead of the hybrid wrapper. - const bool mtp_on_hybrid_qwen35 = + const bool mtp_on_hybrid_qwen = params.ctx_type == LLAMA_CONTEXT_TYPE_MTP && - (arch == LLM_ARCH_QWEN35 || arch == LLM_ARCH_QWEN35MOE); + (arch == LLM_ARCH_QWEN3NEXT || arch == LLM_ARCH_QWEN35 || arch == LLM_ARCH_QWEN35MOE); if (llm_arch_is_recurrent(arch)) { res = new llama_memory_recurrent( @@ -2097,7 +2248,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params, cparams.n_seq_max, cparams.n_rs_seq, nullptr); - } else if (llm_arch_is_hybrid(arch) && !mtp_on_hybrid_qwen35) { + } else if (llm_arch_is_hybrid(arch) && !mtp_on_hybrid_qwen) { // The main difference between hybrid architectures is the // layer filters, so pick the right one here llama_memory_hybrid::layer_filter_cb filter_attn = nullptr; @@ -2113,7 +2264,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params, filter_recr = [&](uint32_t il) { return hparams.is_recr(il) && hparams.n_ff(il) == 0; }; - } else if (arch == LLM_ARCH_QWEN35 || arch == LLM_ARCH_QWEN35MOE) { + } else if (arch == LLM_ARCH_QWEN3NEXT || arch == LLM_ARCH_QWEN35 || arch == LLM_ARCH_QWEN35MOE) { filter_attn = [&](uint32_t il) { return il < hparams.n_layer() && !hparams.is_recr(il); }; @@ -2179,11 +2330,13 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params, }; } - if (mtp_on_hybrid_qwen35) { + if (mtp_on_hybrid_qwen) { filter = [&](uint32_t il) { return il >= hparams.n_layer(); }; } - if ((arch == LLM_ARCH_STEP35 || arch == LLM_ARCH_HY_V3) && hparams.n_layer_nextn > 0) { + if ((arch == LLM_ARCH_STEP35 || arch == LLM_ARCH_HY_V3 || arch == LLM_ARCH_GLM_DSA || + arch == LLM_ARCH_MIMO2 || arch == LLM_ARCH_DEEPSEEK32) && + hparams.n_layer_nextn > 0) { if (params.ctx_type == LLAMA_CONTEXT_TYPE_MTP) { filter = [&](uint32_t il) { return il >= hparams.n_layer(); }; } else { @@ -2191,24 +2344,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params, } } - if (arch == LLM_ARCH_DEEPSEEK4) { - GGML_ASSERT(hparams.swa_type != LLAMA_SWA_TYPE_NONE); - - res = new llama_kv_cache_dsv4( - *this, - params.type_k, - params.type_v, - !cparams.flash_attn, - cparams.offload_kqv, - params.swa_full, - cparams.kv_unified, - cparams.n_ctx_seq, - cparams.n_seq_max, - cparams.n_ubatch, - 1, - filter, - reuse); - } else if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) { + if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) { GGML_ASSERT(hparams.is_swa_any()); if (arch == LLM_ARCH_GEMMA4_ASSISTANT) { @@ -2317,19 +2453,18 @@ llama_model_params llama_model_default_params() { /*.tensor_buft_overrides =*/ nullptr, /*.n_gpu_layers =*/ -1, /*.split_mode =*/ LLAMA_SPLIT_MODE_LAYER, + /*.load_mode =*/ LLAMA_LOAD_MODE_MMAP, /*.main_gpu =*/ 0, /*.tensor_split =*/ nullptr, /*.progress_callback =*/ nullptr, /*.progress_callback_user_data =*/ nullptr, /*.kv_overrides =*/ nullptr, /*.vocab_only =*/ false, - /*.use_mmap =*/ true, - /*.use_direct_io =*/ false, - /*.use_mlock =*/ false, /*.check_tensors =*/ false, /*.use_extra_bufts =*/ true, /*.no_host =*/ false, /*.no_alloc =*/ false, + /*.load_mtp =*/ false, }; return result; @@ -2445,6 +2580,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) { case LLM_ARCH_NEMOTRON_H: case LLM_ARCH_NEMOTRON_H_MOE: case LLM_ARCH_KIMI_LINEAR: + case LLM_ARCH_KIMI_K3: case LLM_ARCH_INKLING: return LLAMA_ROPE_TYPE_NONE; @@ -2486,6 +2622,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) { case LLM_ARCH_LLAMA_EMBED: case LLM_ARCH_MAINCODER: case LLM_ARCH_GLM_DSA: + case LLM_ARCH_NANBEIGE: return LLAMA_ROPE_TYPE_NORM; // the pairs of head values are offset by n_rot/2 @@ -2548,6 +2685,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) { case LLM_ARCH_GROVEMOE: case LLM_ARCH_APERTUS: case LLM_ARCH_MINIMAX_M2: + case LLM_ARCH_MINIMAX_M3: case LLM_ARCH_COGVLM: case LLM_ARCH_PANGU_EMBED: case LLM_ARCH_AFMOE: @@ -2557,9 +2695,12 @@ llama_rope_type llama_model_rope_type(const llama_model * model) { case LLM_ARCH_STEP35: case LLM_ARCH_TALKIE: case LLM_ARCH_MELLUM: - case LLM_ARCH_DFLASH: return LLAMA_ROPE_TYPE_NEOX; + case LLM_ARCH_DFLASH: + // DSV4 DSpark drafters use DeepSeek-V4's normal RoPE; legacy DFlash backbones are NeoX + return model->hparams.dsv4_hc_mult > 0 ? LLAMA_ROPE_TYPE_NORM : LLAMA_ROPE_TYPE_NEOX; + case LLM_ARCH_QWEN2VL: case LLM_ARCH_PADDLEOCR: return LLAMA_ROPE_TYPE_MROPE; @@ -2741,7 +2882,8 @@ llama_model_base::llama_model_base(const struct llama_model_params & params) : l TENSOR_DUPLICATED (llama_model_loader::TENSOR_DUPLICATED), TENSOR_NOT_REQUIRED (llama_model_loader::TENSOR_NOT_REQUIRED), TENSOR_SKIP (llama_model_loader::TENSOR_SKIP), - TENSOR_SKIP_IF_VIRTUAL(llama_model_loader::TENSOR_SKIP_IF_VIRTUAL) {} + TENSOR_SKIP_IF_VIRTUAL(llama_model_loader::TENSOR_SKIP_IF_VIRTUAL), + TENSOR_ALLOW_RESHAPE (llama_model_loader::TENSOR_ALLOW_RESHAPE) {} ggml_tensor * llama_model_base::create_tensor(const LLM_TN_IMPL & tn, const std::initializer_list & ne, int flags) { GGML_ASSERT(ml != nullptr); diff --git a/src/llama-model.h b/src/llama-model.h index 57c7caf37ddb..61df77e078bd 100644 --- a/src/llama-model.h +++ b/src/llama-model.h @@ -130,10 +130,12 @@ enum llm_type { LLM_TYPE_100B_A6B, LLM_TYPE_102B_A12B, // Solar-Open LLM_TYPE_106B_A12B, // GLM-4.5-Air + LLM_TYPE_118B_A8B, // Laguna-S-2 LLM_TYPE_120B_A12B, // Nemotron 3 Super LLM_TYPE_122B_A10B, // Qwen3.5 LLM_TYPE_196B_A11B, // Step3.5-Flash LLM_TYPE_230B_A10B, // Minimax M2 + LLM_TYPE_428B_A23B, // Minimax M3 LLM_TYPE_235B_A22B, LLM_TYPE_300B_A47B, // Ernie MoE big LLM_TYPE_310B_A15B, // /MiMo-V2-Flash @@ -141,6 +143,7 @@ enum llm_type { LLM_TYPE_397B_A17B, // Qwen3.5 LLM_TYPE_685B_A37B, // DeepSeek V3.2 LLM_TYPE_744B_A40B, // GLM-5 + LLM_TYPE_2_8T_A104B, // Kimi-K3 LLM_TYPE_E2B, LLM_TYPE_E4B, }; @@ -317,6 +320,13 @@ struct llama_layer { // ff MoE latent proj struct ggml_tensor * ffn_latent_down = nullptr; struct ggml_tensor * ffn_latent_up = nullptr; + struct ggml_tensor * ffn_latent_norm = nullptr; // kimi k3 + + // attention residuals (kimi k3) + struct ggml_tensor * attn_res_norm = nullptr; + struct ggml_tensor * attn_res_proj = nullptr; + struct ggml_tensor * ffn_res_norm = nullptr; + struct ggml_tensor * ffn_res_proj = nullptr; // ff shared expert (shexp) struct ggml_tensor * ffn_gate_inp_shexp = nullptr; @@ -515,6 +525,12 @@ struct llama_layer { struct ggml_tensor * indexer_attn_k = nullptr; struct ggml_tensor * indexer_attn_q_b = nullptr; // note: for lora a/b, not bias + // MSA + struct ggml_tensor * index_q_proj = nullptr; + struct ggml_tensor * index_k_proj = nullptr; + struct ggml_tensor * index_q_norm = nullptr; + struct ggml_tensor * index_k_norm = nullptr; + // gemma4 layer output scale, reused for talkie embedding skip scale struct ggml_tensor * out_scale = nullptr; @@ -573,6 +589,10 @@ struct llama_model { struct ggml_tensor * output_b = nullptr; struct ggml_tensor * output_norm_enc = nullptr; + // attention residuals output mixture (kimi k3) + struct ggml_tensor * output_res_norm = nullptr; + struct ggml_tensor * output_res_proj = nullptr; + // NVFP4 per-tensor scale2, input_scale for LM head struct ggml_tensor * output_s = nullptr; @@ -608,6 +628,12 @@ struct llama_model { struct ggml_tensor * fc = nullptr; // feature fusion layer struct ggml_tensor * d2t = nullptr; // draft to target vocabulary mapping + // dspark + struct ggml_tensor * dspark_markov_w1 = nullptr; + struct ggml_tensor * dspark_markov_w2 = nullptr; + struct ggml_tensor * dspark_conf_proj = nullptr; + struct ggml_tensor * dspark_conf_proj_b = nullptr; + // unified vector to store target-model extracted layer ids in eagle3, dflash, etc. std::vector target_layer_ids; @@ -714,6 +740,7 @@ struct llama_model_base : public llama_model { const int TENSOR_NOT_REQUIRED; const int TENSOR_SKIP; const int TENSOR_SKIP_IF_VIRTUAL; + const int TENSOR_ALLOW_RESHAPE; explicit llama_model_base(const llama_model_params & params); virtual ~llama_model_base() = default; diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp index e2bf46fe90ff..18e55b1f15c2 100644 --- a/src/llama-quant.cpp +++ b/src/llama-quant.cpp @@ -2,6 +2,7 @@ #include "llama-model.h" #include "llama-model-loader.h" #include "llama-ext.h" +#include "llama.h" #include #include @@ -306,6 +307,9 @@ static bool tensor_allows_quantization(const llama_model_quantize_params * param // NOTE: can't use LLM_TN here because the layer number is not known quantize &= name.find("ffn_gate_inp.weight") == std::string::npos; + // do not quantize the i32 token-id -> expert-id routing table (DeepSeek-V4) + quantize &= name.find("ffn_gate_tid2eid.weight") == std::string::npos; + // these are very small (e.g. 4x4) quantize &= name.find("altup") == std::string::npos; quantize &= name.find("laurel") == std::string::npos; @@ -332,6 +336,10 @@ static bool tensor_allows_quantization(const llama_model_quantize_params * param quantize &= name.find("attn_rel_proj.weight") == std::string::npos; } + // do not quantize MiniMax's indexer projection weights, they are tiny + quantize &= name.find("indexer.k_proj.weight") == std::string::npos; + quantize &= name.find("indexer.q_proj.weight") == std::string::npos; + // do not quantize RWKV's small yet 2D weights quantize &= name.find("time_mix_first.weight") == std::string::npos; quantize &= name.find("time_mix_w0.weight") == std::string::npos; @@ -361,6 +369,10 @@ static bool tensor_allows_quantization(const llama_model_quantize_params * param quantize &= name.find(".patch_embd") == std::string::npos; quantize &= name.find(".patch_merger") == std::string::npos; + // audio codebook + quantize &= name.find("a.rvq.codebook") == std::string::npos; + quantize &= name.find("mm.a.code_embd") == std::string::npos; + return quantize; } @@ -683,7 +695,7 @@ static ggml_type llama_tensor_get_type(quantize_state_impl & qs, const llama_mod ggml_type new_type = default_type; // get more optimal quantization type based on the tensor shape, layer, etc. - if (!params->pure && ggml_is_quantized(default_type)) { + if (ggml_is_quantized(default_type)) { // if the user provided tensor types - use those bool manual = false; if (!qs.tensor_type_patterns.empty()) { @@ -702,7 +714,7 @@ static ggml_type llama_tensor_get_type(quantize_state_impl & qs, const llama_mod } // if not manual - use the standard logic for choosing the quantization type based on the selected mixture - if (!manual) { + if (!manual && !params->pure) { new_type = llama_tensor_get_type_impl(qs, new_type, tensor, params->ftype, tm.category); } @@ -886,15 +898,15 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: // mmap consistently increases speed on Linux, and also increases speed on Windows with // hot cache. It may cause a slowdown on macOS, possibly related to free memory. #if defined(__linux__) || defined(_WIN32) - constexpr bool use_mmap = true; + constexpr llama_load_mode load_mode = LLAMA_LOAD_MODE_MMAP; #else - constexpr bool use_mmap = false; + constexpr llama_load_mode load_mode = LLAMA_LOAD_MODE_NONE; #endif const llama_model_kv_override * kv_overrides = params->kv_overrides; std::vector splits = {}; llama_model_loader ml(/*metadata*/ nullptr, /*set_tensor_data*/ nullptr, /*set_tensor_data_ud*/ nullptr, - fname_inp, splits, /*file*/ nullptr, use_mmap, /*use_direct_io*/ false, /*check_tensors*/ true, /*no_alloc*/ false, kv_overrides, nullptr); + fname_inp, splits, /*file*/ nullptr, /*load_mode*/ load_mode, /*check_tensors*/ true, /*no_alloc*/ false, /*load_mtp*/ true, kv_overrides, nullptr); ml.init_mappings(false); // no prefetching auto mparams = llama_model_default_params(); @@ -1364,6 +1376,7 @@ llama_model * llama_quant_model_from_metadata(const llama_quant_model_desc * des model->hparams.n_embd_head_k_full = desc->n_embd_head_k; model->hparams.n_embd_head_v_full = desc->n_embd_head_v; model->hparams.n_layer_all = desc->n_layer; + GGML_ASSERT(desc->n_layer > 0 && desc->n_layer <= LLAMA_MAX_LAYERS); model->hparams.n_expert = desc->n_expert; for (uint32_t i = 0; i < desc->n_layer; i++) { diff --git a/src/llama-sampler.cpp b/src/llama-sampler.cpp index 2370e91a1491..6cf2d27cf9ad 100644 --- a/src/llama-sampler.cpp +++ b/src/llama-sampler.cpp @@ -263,6 +263,10 @@ static void llama_log_softmax(float * array, size_t size) { */ static void llama_sampler_temp_impl(llama_token_data_array * cur_p, float temp) { + if (cur_p->size == 0) { + return; + } + if (temp <= 0.0f) { // find the token with the highest logit and set the rest to -inf size_t max_i = 0; @@ -989,7 +993,9 @@ static void llama_sampler_greedy_backend_apply( GGML_UNUSED(gf); GGML_UNUSED(smpl); - struct ggml_tensor * curl = ggml_argmax(ctx, data->logits); + struct ggml_tensor * logits = ggml_reshape_1d(ctx, data->logits, ggml_nelements(data->logits)); + + struct ggml_tensor * curl = ggml_argmax(ctx, logits); ggml_set_name(curl, "greedy_argmax"); data->sampled = curl; @@ -1154,7 +1160,10 @@ static void llama_sampler_dist_backend_apply( ggml_set_name (sctx->inp_uniform, "uniform"); ggml_set_input(sctx->inp_uniform); - struct ggml_tensor * probs = ggml_soft_max(ctx, data->logits); + // flatten + struct ggml_tensor * logits = ggml_reshape_1d(ctx, data->logits, ggml_nelements(data->logits)); + + struct ggml_tensor * probs = ggml_soft_max(ctx, logits); ggml_set_name(probs, "dist_probs"); struct ggml_tensor * cumsum = ggml_cumsum(ctx, probs); @@ -1285,22 +1294,22 @@ static void llama_sampler_top_k_backend_apply( struct llama_sampler_data * data) { auto * sctx = (llama_sampler_top_k *) smpl->ctx; - struct ggml_tensor * top_k = ggml_top_k(ctx, data->logits, sctx->k); + struct ggml_tensor * logits = ggml_reshape_1d(ctx, data->logits, ggml_nelements(data->logits)); + + struct ggml_tensor * top_k = ggml_top_k(ctx, logits, sctx->k); ggml_set_name(top_k, "top_k"); if (data->candidates) { struct ggml_tensor * candidates_rows = ggml_reshape_2d(ctx, data->candidates, 1, data->candidates->ne[0]); data->candidates = ggml_get_rows(ctx, candidates_rows, top_k); - data->candidates = ggml_reshape_1d(ctx, data->candidates, sctx->k); ggml_set_name(data->candidates, "top_k_candidates"); } else { data->candidates = top_k; } - struct ggml_tensor * logits_rows = ggml_reshape_2d(ctx, data->logits, 1, data->logits->ne[0]); - struct ggml_tensor * top_k_rows = ggml_get_rows(ctx, logits_rows, top_k); - data->logits = ggml_reshape_1d(ctx, top_k_rows, sctx->k); - ggml_set_name(top_k_rows, "top_k_rows"); + struct ggml_tensor * logits_rows = ggml_reshape_2d(ctx, logits, 1, logits->ne[0]); + data->logits = ggml_get_rows(ctx, logits_rows, top_k); + ggml_set_name(data->logits, "top_k_rows"); GGML_UNUSED(gf); } @@ -1431,21 +1440,25 @@ static void llama_sampler_top_p_backend_apply( struct llama_sampler_data * data) { auto * sctx = (llama_sampler_top_p *) smpl->ctx; + // flatten + struct ggml_tensor * logits = ggml_reshape_1d(ctx, data->logits, ggml_nelements(data->logits)); + auto ggml_sort = [ctx](struct ggml_tensor * a, struct ggml_tensor * b) { GGML_ASSERT(ggml_nrows(a) == 1); struct ggml_tensor * a_reshaped = ggml_reshape_2d(ctx, a, 1, a->ne[0]); struct ggml_tensor * a_sorted = ggml_get_rows(ctx, a_reshaped, b); - return ggml_reshape_1d(ctx, a_sorted, a->ne[0]); + return a_sorted; }; // Get the sorted logits in descending order. - struct ggml_tensor * sorted_idx = ggml_argsort(ctx, data->logits, GGML_SORT_ORDER_DESC); + struct ggml_tensor * sorted_idx = ggml_argsort(ctx, logits, GGML_SORT_ORDER_DESC); ggml_set_name(sorted_idx, "top_p_sorted_idx"); // Do the sorting via reshape + get_rows - struct ggml_tensor * sorted_logits = ggml_sort(data->logits, sorted_idx); + struct ggml_tensor * sorted_logits = ggml_sort(logits, sorted_idx); ggml_set_name(sorted_logits, "top_p_sorted_logits"); + sorted_logits = ggml_reshape_1d(ctx, sorted_logits, ggml_nelements(sorted_logits)); struct ggml_tensor * softmax = ggml_soft_max(ctx, sorted_logits); ggml_set_name(softmax, "top_p_softmax"); @@ -1622,10 +1635,12 @@ static void llama_sampler_min_p_backend_apply( struct llama_sampler_data * data) { auto * sctx = (llama_sampler_min_p *) smpl->ctx; - struct ggml_tensor * max_idx = ggml_argmax(ctx, data->logits); + struct ggml_tensor * logits = ggml_reshape_1d(ctx, data->logits, ggml_nelements(data->logits)); + + struct ggml_tensor * max_idx = ggml_argmax(ctx, logits); ggml_set_name(max_idx, "max_idx"); - struct ggml_tensor * logits_rows = ggml_reshape_2d(ctx, data->logits, 1, data->logits->ne[0]); + struct ggml_tensor * logits_rows = ggml_reshape_2d(ctx, logits, 1, logits->ne[0]); ggml_set_name(logits_rows, "logits_rows"); struct ggml_tensor * max_logit = ggml_get_rows(ctx, logits_rows, max_idx); @@ -1636,7 +1651,7 @@ static void llama_sampler_min_p_backend_apply( ggml_set_name(threshold, "min_p_threshold"); // Subtract the threshold from logits. - struct ggml_tensor * sub = ggml_sub(ctx, data->logits, threshold); + struct ggml_tensor * sub = ggml_sub(ctx, logits, threshold); // Create a mask where logits below the threshold are 0 (discard), // and others are 1 (keep). @@ -1648,7 +1663,7 @@ static void llama_sampler_min_p_backend_apply( struct ggml_tensor * min_p_bias = ggml_log(ctx, mask); ggml_set_name(min_p_bias, "min_p_bias"); - data->logits = ggml_add(ctx, data->logits, min_p_bias); + data->logits = ggml_add(ctx, logits, min_p_bias); ggml_set_name(data->logits, "min_p_logits"); GGML_UNUSED(gf); @@ -1825,18 +1840,20 @@ static void llama_sampler_backend_temp_sampling( struct llama_sampler_data * data, float temp) { if (temp <= 0.0f) { + struct ggml_tensor * logits = ggml_reshape_1d(ctx, data->logits, ggml_nelements(data->logits)); + // Find the most probable token index. - struct ggml_tensor * max_idx = ggml_argmax(ctx, data->logits); + struct ggml_tensor * max_idx = ggml_argmax(ctx, logits); ggml_set_name(max_idx, "temp_max_idx"); if (data->candidates) { - struct ggml_tensor * candidates_rows = ggml_reshape_2d(ctx, data->candidates, 1, data->candidates->ne[0]); + struct ggml_tensor * candidates_rows = ggml_reshape_2d(ctx, data->candidates, 1, ggml_nelements(data->candidates)); data->candidates = ggml_get_rows(ctx, candidates_rows, max_idx); } else { data->candidates = max_idx; } - struct ggml_tensor * logits_rows = ggml_reshape_2d(ctx, data->logits, 1, data->logits->ne[0]); + struct ggml_tensor * logits_rows = ggml_reshape_2d(ctx, logits, 1, ggml_nelements(logits)); data->logits = ggml_get_rows(ctx, logits_rows, max_idx); return; @@ -2015,13 +2032,15 @@ static void llama_sampler_temp_ext_backend_apply( return; } + struct ggml_tensor * logits = ggml_reshape_1d(ctx, data->logits, ggml_nelements(data->logits)); + // Calculate min_temp, max_temp, and max_entropy. const float min_temp = std::max(0.0f, sctx->temp - sctx->delta); const float max_temp = sctx->temp + sctx->delta; - const float max_entropy = logf(data->logits->ne[0]); + const float max_entropy = logf(logits->ne[0]); // Calculate the probabilities. - struct ggml_tensor * probs = ggml_soft_max(ctx, data->logits); + struct ggml_tensor * probs = ggml_soft_max(ctx, logits); ggml_set_name(probs, "temp_ext_softmax_probs"); // Clamp probabilities to avoid log(0) which would give -inf @@ -2059,7 +2078,7 @@ static void llama_sampler_temp_ext_backend_apply( ggml_set_name(dyn_temp, "temp_ext_dyn_temp"); // Scale the logits by the dynamic temperature - struct ggml_tensor * scaled_logits = ggml_div(ctx, data->logits, dyn_temp); + struct ggml_tensor * scaled_logits = ggml_div(ctx, logits, dyn_temp); ggml_set_name(scaled_logits, "temp_ext_scaled_logits"); data->logits = scaled_logits; @@ -2619,7 +2638,8 @@ struct llama_sampler * llama_sampler_init_grammar_lazy_patterns( // penalties -struct llama_sampler_penalties { +struct llama_sampler_penalties : public llama_sampler_backend { + const int32_t n_vocab; const int32_t penalty_last_n; const float penalty_repeat; const float penalty_freq; @@ -2629,10 +2649,50 @@ struct llama_sampler_penalties { // a frequency map to count token occurrences std::unordered_map token_count; + + // backend graph inputs + ggml_tensor * inp_token_ids = nullptr; + ggml_tensor * inp_counts = nullptr; + + // backend helpers + int32_t n_max = 0; + bool has_candidates = false; + + std::vector host_token_ids; + std::vector host_counts; + + static bool is_disabled( + int32_t penalty_last_n, + float penalty_repeat, + float penalty_freq, + float penalty_present) { + return penalty_last_n == 0 || + (penalty_repeat == 1.0f && penalty_freq == 0.0f && penalty_present == 0.0f); + } + + bool is_disabled() const { + return is_disabled(penalty_last_n, penalty_repeat, penalty_freq, penalty_present); + } + + llama_sampler_penalties( + int32_t n_vocab, + int32_t penalty_last_n, + float penalty_repeat, + float penalty_freq, + float penalty_present) + : llama_sampler_backend("penalties") + , n_vocab (n_vocab) + , penalty_last_n (penalty_last_n) + , penalty_repeat (penalty_repeat) + , penalty_freq (penalty_freq) + , penalty_present (penalty_present) + , prev (penalty_last_n) { + } }; -static const char * llama_sampler_penalties_name(const struct llama_sampler * /*smpl*/) { - return "penalties"; +static const char * llama_sampler_penalties_name(const struct llama_sampler * smpl) { + auto * ctx = (llama_sampler_penalties *) smpl->ctx; + return ctx->get_name(); } static void llama_sampler_penalties_accept(struct llama_sampler * smpl, llama_token token) { @@ -2669,8 +2729,7 @@ static void llama_sampler_penalties_accept(struct llama_sampler * smpl, llama_to static void llama_sampler_penalties_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) { auto * ctx = (llama_sampler_penalties *) smpl->ctx; - if ((ctx->penalty_last_n == 0) || - (ctx->penalty_repeat == 1.0f && ctx->penalty_freq == 0.0f && ctx->penalty_present == 0.0f)) { + if (ctx->is_disabled()) { return; } @@ -2708,6 +2767,7 @@ static void llama_sampler_penalties_reset(struct llama_sampler * smpl) { static struct llama_sampler * llama_sampler_penalties_clone(const struct llama_sampler * smpl) { const auto * ctx = (const llama_sampler_penalties *) smpl->ctx; auto * result = llama_sampler_init_penalties( + ctx->n_vocab, ctx->penalty_last_n, ctx->penalty_repeat, ctx->penalty_freq, @@ -2717,7 +2777,8 @@ static struct llama_sampler * llama_sampler_penalties_clone(const struct llama_s { auto * result_ctx = (llama_sampler_penalties *) result->ctx; - result_ctx->prev = ctx->prev; + result_ctx->prev = ctx->prev; + result_ctx->token_count = ctx->token_count; } return result; @@ -2727,6 +2788,170 @@ static void llama_sampler_penalties_free(struct llama_sampler * smpl) { delete (llama_sampler_penalties *) smpl->ctx; } +static bool llama_sampler_penalties_backend_init( + struct llama_sampler * smpl, + ggml_backend_buffer_type_t buft) { + auto * sctx = (llama_sampler_penalties *) smpl->ctx; + + const bool res = llama_sampler_backend_support(smpl, buft); + + sctx->init(res); + + return res; +} + +static void llama_sampler_penalties_backend_apply( + struct llama_sampler * smpl, + struct ggml_context * ctx, + struct ggml_cgraph * gf, + struct llama_sampler_data * data) { + GGML_UNUSED(gf); + + auto * sctx = (llama_sampler_penalties *) smpl->ctx; + + if (sctx->is_disabled()) { + return; + } + + GGML_ASSERT(sctx->n_vocab > 0); + + sctx->has_candidates = data->candidates != nullptr; + sctx->n_max = std::min(sctx->penalty_last_n, sctx->n_vocab); + + sctx->inp_token_ids = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sctx->n_max); + ggml_set_name(sctx->inp_token_ids, "penalties_token_ids"); + ggml_set_input(sctx->inp_token_ids); + + sctx->inp_counts = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sctx->n_max); + ggml_set_name(sctx->inp_counts, "penalties_counts"); + ggml_set_input(sctx->inp_counts); + + if ((int32_t) sctx->host_token_ids.size() != sctx->n_max) { + sctx->host_token_ids.assign(sctx->n_max, 0); + sctx->host_counts.assign(sctx->n_max, 0); + } + + // flatten + ggml_tensor * logits = ggml_reshape_1d(ctx, data->logits, ggml_nelements(data->logits)); + ggml_tensor * gathered = logits; + ggml_tensor * counts_f32 = ggml_cast(ctx, sctx->inp_counts, GGML_TYPE_F32); + + if (sctx->has_candidates) { + ggml_tensor * candidates = ggml_reshape_1d( + ctx, data->candidates, ggml_nelements(data->candidates)); + const int64_t n_candidates = candidates->ne[0]; + GGML_ASSERT(n_candidates == ggml_nelements(logits)); + + ggml_tensor * counts_rows = ggml_fill( + ctx, ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1, sctx->n_vocab), 0.0f); + ggml_tensor * scatter_rows = ggml_reshape_2d(ctx, counts_f32, 1, sctx->n_max); + counts_rows = ggml_set_rows(ctx, counts_rows, scatter_rows, sctx->inp_token_ids); + counts_f32 = ggml_get_rows(ctx, counts_rows, candidates); + counts_f32 = ggml_reshape_1d(ctx, counts_f32, n_candidates); + } else { + ggml_tensor * logits_rows = ggml_reshape_2d(ctx, logits, 1, ggml_nelements(logits)); + gathered = ggml_get_rows(ctx, logits_rows, sctx->inp_token_ids); + gathered = ggml_reshape_1d(ctx, gathered, sctx->n_max); + } + + ggml_tensor * active_mask = ggml_step(ctx, counts_f32); + ggml_tensor * inactive_mask = ggml_sub(ctx, ggml_fill(ctx, active_mask, 1.0f), active_mask); + + ggml_tensor * penalized = gathered; + + if (sctx->penalty_repeat != 1.0f) { + ggml_tensor * pos_mask = ggml_step(ctx, penalized); + ggml_tensor * neg_mask = ggml_sub(ctx, ggml_fill(ctx, pos_mask, 1.0f), pos_mask); + + ggml_tensor * pos_scale = ggml_scale(ctx, pos_mask, 1.0f/sctx->penalty_repeat); + ggml_tensor * neg_scale = ggml_scale(ctx, neg_mask, sctx->penalty_repeat); + ggml_tensor * repeat_scale = ggml_add(ctx, pos_scale, neg_scale); + + // scale inactive entries with 1 to avoid -INF * 0 = NaN for values masked by top-p + repeat_scale = ggml_mul(ctx, repeat_scale, active_mask); + repeat_scale = ggml_add(ctx, repeat_scale, inactive_mask); + penalized = ggml_mul(ctx, gathered, repeat_scale); + } + + if (sctx->penalty_freq != 0.0f) { + ggml_tensor * penalty_freq = ggml_scale(ctx, counts_f32, sctx->penalty_freq); + penalized = ggml_sub(ctx, penalized, penalty_freq); + } + + if (sctx->penalty_present != 0.0f) { + ggml_tensor * penalty_present = ggml_scale(ctx, active_mask, sctx->penalty_present); + penalized = ggml_sub(ctx, penalized, penalty_present); + } + + if (sctx->has_candidates) { + data->logits = penalized; + } else { + ggml_tensor * logits_rows = ggml_reshape_2d(ctx, logits, 1, ggml_nelements(logits)); + ggml_tensor * scatter_rows = ggml_reshape_2d(ctx, penalized, 1, sctx->n_max); + logits_rows = ggml_set_rows(ctx, logits_rows, scatter_rows, sctx->inp_token_ids); + data->logits = ggml_reshape_1d(ctx, logits_rows, ggml_nelements(logits)); + } +} + +static void llama_sampler_penalties_backend_set_input(struct llama_sampler * smpl) { + auto * sctx = (llama_sampler_penalties *) smpl->ctx; + + if (!sctx->inp_token_ids || !sctx->inp_counts || sctx->n_max <= 0 || sctx->n_vocab <= 0) { + return; + } + + if (sctx->is_disabled()) { + return; + } + + // fill active entries from the map + int32_t n_active = 0; + + for (const auto & it : sctx->token_count) { + GGML_ASSERT(n_active < sctx->n_max); + sctx->host_token_ids[n_active] = it.first; + sctx->host_counts [n_active] = it.second; + ++n_active; + } + + // Sorting is required because backend_apply uses ggml_set_rows (a scatter-back operation) + std::vector> entries; + entries.reserve(n_active); + for (int32_t i = 0; i < n_active; ++i) { + entries.emplace_back(sctx->host_token_ids[i], sctx->host_counts[i]); + } + std::sort(entries.begin(), entries.end(), [](const auto & a, const auto & b) { + return a.first < b.first; + }); + for (int32_t i = 0; i < n_active; ++i) { + sctx->host_token_ids[i] = entries[i].first; + sctx->host_counts [i] = entries[i].second; + } + + // Padding: Finds a filler token id that is not present in token_count. + // Use it to do padding for the arrays, it avoids resizing every time. + // The arrays must always have exactly n_max entries (the GPU tensor is a fixed size). + int32_t filler = 0; + if (n_active < sctx->n_max) { + while (sctx->token_count.find(filler) != sctx->token_count.end()) { + ++filler; + } + GGML_ASSERT(filler < sctx->n_vocab); + } + + // Fill the rest of the arrays with the filler token id and count 0. + // Inactive slots are padded with a unique dummy token ID (count = 0). + // The uniqueness matters because ggml_set_rows with duplicate indices can produce non-deterministic or incorrect results. + // Using a filler token with count 0 that isn't in the active set is safe, because the active_mask step in backend_apply filters them out via ggml_step(counts_f32) + for (int32_t i = n_active; i < sctx->n_max; ++i) { + sctx->host_token_ids[i] = filler; + sctx->host_counts [i] = 0; + } + + ggml_backend_tensor_set(sctx->inp_token_ids, sctx->host_token_ids.data(), 0, sctx->n_max * sizeof(int32_t)); + ggml_backend_tensor_set(sctx->inp_counts, sctx->host_counts.data(), 0, sctx->n_max * sizeof(int32_t)); +} + static struct llama_sampler_i llama_sampler_penalties_i = { /* .name = */ llama_sampler_penalties_name, /* .accept = */ llama_sampler_penalties_accept, @@ -2734,35 +2959,33 @@ static struct llama_sampler_i llama_sampler_penalties_i = { /* .reset = */ llama_sampler_penalties_reset, /* .clone = */ llama_sampler_penalties_clone, /* .free = */ llama_sampler_penalties_free, - /* .backend_init = */ nullptr, + /* .backend_init = */ llama_sampler_penalties_backend_init, /* .backend_accept = */ nullptr, - /* .backend_apply = */ nullptr, - /* .backend_set_input = */ nullptr, + /* .backend_apply = */ llama_sampler_penalties_backend_apply, + /* .backend_set_input = */ llama_sampler_penalties_backend_set_input, }; struct llama_sampler * llama_sampler_init_penalties( + int32_t n_vocab, int32_t penalty_last_n, float penalty_repeat, float penalty_freq, float penalty_present) { penalty_last_n = std::max(penalty_last_n, 0); - const bool is_empty = (penalty_last_n == 0 || (penalty_repeat == 1.0f && penalty_freq == 0.0f && penalty_present == 0.0f)); - - if (is_empty) { + if (llama_sampler_penalties::is_disabled( + penalty_last_n, penalty_repeat, penalty_freq, penalty_present)) { return llama_sampler_init_empty("?penalties"); } return llama_sampler_init( /* .iface = */ &llama_sampler_penalties_i, - /* .ctx = */ new llama_sampler_penalties { - /* .penalty_last_n = */ penalty_last_n, - /* .penalty_repeat = */ penalty_repeat, - /* .penalty_freq = */ penalty_freq, - /* .penalty_present = */ penalty_present, - /* .prev = */ ring_buffer(penalty_last_n), - /* .token_count = */ {}, - } + /* .ctx = */ new llama_sampler_penalties( + n_vocab, + penalty_last_n, + penalty_repeat, + penalty_freq, + penalty_present) ); } diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp index 680820cd18d0..7127ea7aa6d5 100644 --- a/src/llama-vocab.cpp +++ b/src/llama-vocab.cpp @@ -1337,6 +1337,9 @@ struct llm_tokenizer_rwkv_session { token_id = node->value; token_length = position + 1; } + if (position + 1 >= text.size()) { + break; + } node = node->traverse(text[++position]); } @@ -1376,8 +1379,10 @@ struct llm_tokenizer_plamo2 : llm_tokenizer { if (vocab.is_byte(token_id)) { if (entry.text.length() == 6 && entry.text.substr(0, 3) == "<0x" && entry.text.back() == '>') { std::string hex_str = entry.text.substr(3, 2); - int byte_val = std::stoi(hex_str, nullptr, 16); - bytes_[byte_val] = static_cast(token_id); + if (std::isxdigit(static_cast(hex_str[0])) && std::isxdigit(static_cast(hex_str[1]))) { + int byte_val = std::stoi(hex_str, nullptr, 16); + bytes_[byte_val] = static_cast(token_id); + } } continue; } @@ -2543,6 +2548,12 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { const std::string & key = kv(std::get<0>(it)); int32_t & id = std::get<1>(it); + if (id >= 0 && static_cast(id) >= id_to_token.size()) { + LLAMA_LOG_WARN("%s: default special token '%s' = %d out of vocab range, disabling\n", + __func__, key.c_str(), id); + id = LLAMA_TOKEN_NULL; + } + uint32_t new_id; if (!ml.get_key(std::get<0>(it), new_id, false)) { continue; @@ -2589,7 +2600,14 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { if (suppress_idx != -1) { const int n = gguf_get_arr_n(ctx, suppress_idx); const int32_t * data = (const int32_t *) gguf_get_arr_data(ctx, suppress_idx); - suppress_tokens.assign(data, data + n); + // drop out-of-range ids + suppress_tokens.reserve(n); + for (int i = 0; i < n; ++i) { + const int32_t id = data[i]; + if (id >= 0 && id < (int) id_to_token.size()) { + suppress_tokens.push_back(id); + } + } } } @@ -2820,6 +2838,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { || t.first == "" // gemma4 || t.first == "<|tool_response>" // gemma4 || t.first == "<｜end▁of▁sentence｜>" // deepseek-ocr + || t.first == "[e~[" // minimax-m2/m3 ) { special_eog_ids.insert(t.second); if ((attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) { @@ -2879,6 +2898,11 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { LLAMA_LOG_INFO("%s: printing all EOG tokens:\n", __func__); for (auto tid : special_eog_ids) { + if (tid < 0 || tid >= (llama_token) id_to_token.size()) { + LLAMA_LOG_WARN("%s: EOG token id %d is out of range (vocab size %zu), skipping\n", + __func__, tid, id_to_token.size()); + continue; + } auto & text = id_to_token[tid].text; LLAMA_LOG_INFO("%s: - %d ('%s')\n", __func__, tid, text.c_str()); @@ -2913,6 +2937,9 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { llama_token s_id = LLAMA_TOKEN_NULL; for (auto tid : special_eog_ids) { + if (tid < 0 || tid >= (llama_token) id_to_token.size()) { + continue; + } const auto & text = id_to_token[tid].text; if (text == "<|tool_response>") { has_tool_response = true; @@ -3614,12 +3641,15 @@ int32_t llama_vocab::impl::token_to_piece(llama_token token, char * buf, int32_t if (vocab.is_byte(token)) { // Handle byte tokens like <0xXX> if (token_text.length() == 6 && token_text.substr(0, 3) == "<0x" && token_text.back() == '>') { - int hex_val = std::stoi(token_text.substr(3, 2), nullptr, 16); - if (length < 1) { - return -1; + std::string hex_str = token_text.substr(3, 2); + if (std::isxdigit(static_cast(hex_str[0])) && std::isxdigit(static_cast(hex_str[1]))) { + int hex_val = std::stoi(hex_str, nullptr, 16); + if (length < 1) { + return -1; + } + buf[0] = static_cast(hex_val); + return 1; } - buf[0] = static_cast(hex_val); - return 1; } } @@ -4042,7 +4072,11 @@ int llama_vocab::find_bpe_rank(const std::string & token_left, const std::string } std::vector llama_vocab::get_bpe_merges() const { - std::vector result(pimpl->bpe_ranks.size()); + int max_rank = -1; + for (const auto & pair : pimpl->bpe_ranks) { + max_rank = std::max(max_rank, pair.second); + } + std::vector result(max_rank + 1); for (const auto & pair : pimpl->bpe_ranks) { result[pair.second] = pair.first.first + " " + pair.first.second; @@ -4203,6 +4237,14 @@ bool llama_vocab_get_add_sep(const struct llama_vocab * vocab) { return vocab->get_add_sep(); } +const llama_token * llama_vocab_get_suppress_tokens(const struct llama_vocab * vocab, int32_t * n_suppress_tokens) { + const std::vector & tokens = vocab->get_suppress_tokens(); + if (n_suppress_tokens) { + *n_suppress_tokens = (int32_t) tokens.size(); + } + return tokens.data(); +} + llama_token llama_vocab_fim_pre(const struct llama_vocab * vocab) { return vocab->token_fim_pre(); } diff --git a/src/llama.cpp b/src/llama.cpp index 0de6048f2820..d6e0bbfefa72 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -46,6 +46,31 @@ const char * llama_flash_attn_type_name(enum llama_flash_attn_type flash_attn_ty GGML_ABORT("fatal error"); } +const char * llama_load_mode_name(enum llama_load_mode load_mode) { + switch (load_mode) { + case LLAMA_LOAD_MODE_NONE: + return "none"; + case LLAMA_LOAD_MODE_MMAP: + return "mmap"; + case LLAMA_LOAD_MODE_MLOCK: + return "mlock"; + case LLAMA_LOAD_MODE_MMAP_MLOCK: + return "mmap+mlock"; + case LLAMA_LOAD_MODE_DIRECT_IO: + return "dio"; + } + GGML_ABORT("fatal error"); +} + +enum llama_load_mode llama_load_mode_from_str(const char * str) { + if (std::strcmp(str, "none") == 0) { return LLAMA_LOAD_MODE_NONE; } + if (std::strcmp(str, "mmap") == 0) { return LLAMA_LOAD_MODE_MMAP; } + if (std::strcmp(str, "mlock") == 0) { return LLAMA_LOAD_MODE_MLOCK; } + if (std::strcmp(str, "mmap+mlock") == 0) { return LLAMA_LOAD_MODE_MMAP_MLOCK; } + if (std::strcmp(str, "dio") == 0) { return LLAMA_LOAD_MODE_DIRECT_IO; } + throw std::invalid_argument(std::string("unknown load mode: ") + str); +} + struct llama_sampler_chain_params llama_sampler_chain_default_params() { struct llama_sampler_chain_params result = { /*.no_perf =*/ true, @@ -279,8 +304,8 @@ static bool llama_prepare_model_devices(const llama_model_params & params, llama static std::pair llama_model_load(struct gguf_context * metadata, llama_model_set_tensor_data_t set_tensor_data, void * set_tensor_data_ud, const std::string & fname, std::vector & splits, FILE * file, llama_model_params & params) { try { - llama_model_loader ml(metadata, set_tensor_data, set_tensor_data_ud, fname, splits, file, params.use_mmap, params.use_direct_io, - params.check_tensors, params.no_alloc, params.kv_overrides, params.tensor_buft_overrides); + llama_model_loader ml(metadata, set_tensor_data, set_tensor_data_ud, fname, splits, file, params.load_mode, + params.check_tensors, params.no_alloc, params.load_mtp, params.kv_overrides, params.tensor_buft_overrides); ml.print_info(); std::unique_ptr model_ptr(llama_model_create(ml, params)); @@ -412,7 +437,7 @@ struct llama_model * llama_model_init_from_user( GGML_ASSERT(metadata != nullptr); std::string path_model; std::vector splits = {}; - params.use_mmap = false; + params.load_mode = LLAMA_LOAD_MODE_NONE; params.use_extra_bufts = false; return llama_model_load_from_file_impl(metadata, set_tensor_data, set_tensor_data_ud, path_model, splits, /*file*/ nullptr, params); } diff --git a/src/models/cohere2moe.cpp b/src/models/cohere2moe.cpp index 499c73a1c49c..3acb7e77af80 100644 --- a/src/models/cohere2moe.cpp +++ b/src/models/cohere2moe.cpp @@ -55,7 +55,11 @@ void llama_model_cohere2moe::load_arch_tensors(llama_model_loader & ml) { const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight"; const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr); const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0; - const int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0; + int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0; + + if (!ml.load_mtp) { + mtp_flags |= TENSOR_SKIP; + } tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0); diff --git a/src/models/deepseek2.cpp b/src/models/deepseek2.cpp index a9e8bc514036..ba90c0d0776b 100644 --- a/src/models/deepseek2.cpp +++ b/src/models/deepseek2.cpp @@ -37,6 +37,11 @@ void llama_model_deepseek2::load_arch_hparams(llama_model_loader & ml) { hparams.rope_yarn_log_mul /= 0.1f; } + // NextN/MTP + ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false); + GGML_ASSERT(hparams.n_layer_nextn == 0 || + hparams.n_layer() + hparams.n_layer_nextn == hparams.n_layer_all); + // (optional) temperature tuning - used by mistral-large ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_SCALE, hparams.f_attn_temp_scale, false); ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_LENGTH, hparams.n_attn_temp_floor_scale, false); // FIXME why not use temperature_length? @@ -52,10 +57,20 @@ void llama_model_deepseek2::load_arch_hparams(llama_model_loader & ml) { } } -void llama_model_deepseek2::load_arch_tensors(llama_model_loader &) { +void llama_model_deepseek2::load_arch_tensors(llama_model_loader & ml) { LLAMA_LOAD_LOCALS; const int64_t n_expert_shared = hparams.n_expert_shared; + const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr); + const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight"; + const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr); + const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0; + int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0; + + if (!ml.load_mtp) { + mtp_flags |= TENSOR_SKIP; + } + const bool is_mla = hparams.is_mla(); // note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA @@ -81,44 +96,45 @@ void llama_model_deepseek2::load_arch_tensors(llama_model_loader &) { output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); } - for (int i = 0; i < n_layer; ++i) { + for (int i = 0; i < n_layer_all; ++i) { auto & layer = layers[i]; + const int flags = i < n_layer ? trunk_flags : mtp_flags; - layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags); if (q_lora_rank > 0) { - layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, 0); + layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, flags); } - layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, 0); + layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, flags); if (q_lora_rank > 0) { - layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, 0); - layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k_mla}, 0); + layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, flags); + layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k_mla}, flags); } else { - layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_head * n_embd_head_k_mla}, 0); + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_head * n_embd_head_k_mla}, flags); } - layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + n_embd_head_qk_rope}, 0); + layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + n_embd_head_qk_rope}, flags); // note: only old legacy GGUF files will have the unsplit wkv_b tensor in if (is_mla) { - layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K_B, "weight", i), {n_embd_head_qk_nope, kv_lora_rank, n_head}, 0); - layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V_B, "weight", i), {kv_lora_rank, n_embd_head_v_mla, n_head}, 0); + layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K_B, "weight", i), {n_embd_head_qk_nope, kv_lora_rank, n_head}, flags); + layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V_B, "weight", i), {kv_lora_rank, n_embd_head_v_mla, n_head}, flags); } else { - layer.wkv_b = create_tensor(tn(LLM_TENSOR_ATTN_KV_B, "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v_mla)}, 0); + layer.wkv_b = create_tensor(tn(LLM_TENSOR_ATTN_KV_B, "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v_mla)}, flags); } - layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * n_embd_head_v_mla, n_embd}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * n_embd_head_v_mla, n_embd}, flags); - layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags); if (i < (int) hparams.n_layer_dense_lead) { - layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); - layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); - layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, flags); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, flags); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, flags); } else { - layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0); - layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED); + layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, flags); + layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED | flags); if (n_expert == 0) { throw std::runtime_error("n_expert must be > 0"); @@ -128,21 +144,281 @@ void llama_model_deepseek2::load_arch_tensors(llama_model_loader &) { } // MoE branch - layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0); - create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, 0); + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, flags); + create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, flags); // Shared expert branch - layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0); - layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd}, 0); - layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0); + layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags); + layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd}, flags); + layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags); + } + + // NextN/MTP tensors + if (i >= n_layer) { + layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, mtp_flags); + layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, mtp_flags); + layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, mtp_flags); + layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED | flags); + layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED | flags); + layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, TENSOR_NOT_REQUIRED | flags); } } } std::unique_ptr llama_model_deepseek2::build_arch_graph(const llm_graph_params & params) const { + if (params.gtype == LLM_GRAPH_TYPE_DECODER_MTP) { + return std::make_unique(*this, params); + } return std::make_unique(*this, params); } +llama_model_deepseek2::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params) : + llm_graph_context(params) { + GGML_ASSERT(hparams.n_layer_nextn > 0 && "GLM4 MTP requires n_layer_nextn > 0"); + GGML_ASSERT(hparams.n_layer_nextn == 1 && "GLM4 MTP currently only supports a single MTP block"); + GGML_ASSERT(hparams.is_mla() && "GLM4 MTP requires MLA"); + GGML_ASSERT(hparams.f_attn_temp_scale == 0.0f && "GLM4 MTP does not support attention temperature scaling"); + + // The appended MTP block is stored immediately after the main decoder layers. + const int il = hparams.n_layer(); + const auto & layer = model.layers[il]; + + GGML_ASSERT(layer.nextn.eh_proj && "MTP block missing nextn.eh_proj"); + GGML_ASSERT(layer.nextn.enorm && "MTP block missing nextn.enorm"); + GGML_ASSERT(layer.nextn.hnorm && "MTP block missing nextn.hnorm"); + + GGML_ASSERT((uint32_t) il >= hparams.n_layer_dense_lead && "GLM4 MTP block expected to use MoE FFN"); + + const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla(); + const int64_t n_embd_head_qk_rope = hparams.n_rot(); + const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope; + const int64_t kv_lora_rank = hparams.n_lora_kv; + + GGML_ASSERT(n_embd_head_qk_nope >= 1); + GGML_ASSERT(hparams.n_lora_q > 0); + GGML_ASSERT(layer.wq_a); + GGML_ASSERT(layer.attn_q_a_norm); + GGML_ASSERT(layer.wq_b); + GGML_ASSERT(layer.wkv_a_mqa); + GGML_ASSERT(layer.attn_kv_a_norm); + GGML_ASSERT(layer.wk_b); + + const bool has_split_exps = + layer.ffn_up_exps != nullptr && + layer.ffn_gate_exps != nullptr; + + const bool has_fused_exps = layer.ffn_gate_up_exps != nullptr; + + GGML_ASSERT(has_split_exps || has_fused_exps); + GGML_ASSERT(layer.ffn_norm); + GGML_ASSERT(layer.ffn_gate_inp); + GGML_ASSERT(layer.ffn_down_exps); + GGML_ASSERT(layer.ffn_gate_shexp); + GGML_ASSERT(layer.ffn_down_shexp); + GGML_ASSERT(layer.ffn_up_shexp); + + auto inp = std::make_unique(hparams.n_embd); + + inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + ggml_set_input(inp->tokens); + + inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd_inp(), n_tokens); + ggml_set_input(inp->embd); + + ggml_tensor * tok_embd; + if (ubatch.token) { + ggml_tensor * tok_embd_w = layer.nextn.embed_tokens + ? layer.nextn.embed_tokens + : model.tok_embd; + + tok_embd = ggml_get_rows(ctx0, tok_embd_w, inp->tokens); + } else { + tok_embd = inp->embd; + } + cb(tok_embd, "mtp_tok_embd", il); + + inp->h = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd, n_tokens); + ggml_set_input(inp->h); + ggml_set_name(inp->h, "mtp_h_input"); + + ggml_tensor * h_embd = inp->h; + + res->add_input(std::move(inp)); + + ggml_tensor * inp_pos = build_inp_pos(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + + auto * inp_attn_k = build_attn_inp_k(); + + ggml_tensor * h_norm = build_norm(h_embd, layer.nextn.hnorm, nullptr, LLM_NORM_RMS, il); + cb(h_norm, "mtp_hnorm", il); + + ggml_tensor * e_norm = build_norm(tok_embd, layer.nextn.enorm, nullptr, LLM_NORM_RMS, il); + cb(e_norm, "mtp_enorm", il); + + ggml_tensor * concat = ggml_concat(ctx0, e_norm, h_norm, 0); + cb(concat, "mtp_concat", il); + + ggml_tensor * cur = build_lora_mm(layer.nextn.eh_proj, concat, layer.nextn.eh_proj_s); + cb(cur, "mtp_eh_proj", il); + + ggml_tensor * inpSA = cur; + + cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il); + cb(cur, "mtp_attn_norm", il); + + ggml_tensor * q = ggml_mul_mat(ctx0, layer.wq_a, cur); + cb(q, "mtp_q_a", il); + + q = build_norm(q, layer.attn_q_a_norm, nullptr, LLM_NORM_RMS, il); + cb(q, "mtp_q_a_norm", il); + + q = ggml_mul_mat(ctx0, layer.wq_b, q); + cb(q, "mtp_q_b", il); + + ggml_tensor * q_nope = + ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens, + ggml_row_size(q->type, n_embd_head_k_mla), + ggml_row_size(q->type, n_embd_head_k_mla) * n_head, 0); + cb(q_nope, "mtp_q_nope", il); + + ggml_tensor * q_pe = + ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens, + ggml_row_size(q->type, n_embd_head_k_mla), + ggml_row_size(q->type, n_embd_head_k_mla) * n_head, + ggml_row_size(q->type, n_embd_head_qk_nope)); + cb(q_pe, "mtp_q_pe", il); + + ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, layer.wkv_a_mqa, cur); + cb(kv_cmpr_pe, "mtp_kv_cmpr_pe", il); + + ggml_tensor * kv_cmpr = + ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens, + ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0); + cb(kv_cmpr, "mtp_kv_cmpr", il); + + ggml_tensor * k_pe = + ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens, + ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), + ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), + ggml_row_size(kv_cmpr_pe->type, kv_lora_rank)); + cb(k_pe, "mtp_k_pe", il); + + kv_cmpr = build_norm(kv_cmpr, layer.attn_kv_a_norm, nullptr, LLM_NORM_RMS, il); + cb(kv_cmpr, "mtp_kv_cmpr_norm", il); + + GGML_ASSERT(ext_factor >= 0.0f); + + const float attn_factor_org = + attn_factor * (1.0f + 0.1f * logf(1.0f / freq_scale)); + + const float mscale = + attn_factor_org * (1.0f + 0.1f * hparams.rope_yarn_log_mul * logf(1.0f / freq_scale)); + + const float kq_scale = + 1.0f * mscale * mscale / sqrtf(float(n_embd_head_k_mla)); + + q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow); + cb(q_pe, "mtp_q_pe_rope", il); + + k_pe = ggml_rope_ext(ctx0, k_pe, inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow); + cb(k_pe, "mtp_k_pe_rope", il); + + q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3); + cb(q_nope, "mtp_q_nope_perm", il); + + ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, layer.wk_b, q_nope); + cb(q_nope_absorbed, "mtp_q_nope_absorbed", il); + + q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3); + cb(q_nope_absorbed, "mtp_q_nope_absorbed_perm", il); + + ggml_tensor * Qcur = ggml_concat(ctx0, q_nope_absorbed, q_pe, 0); + cb(Qcur, "mtp_Qcur", il); + + kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, hparams.n_lora_kv, 1, n_tokens); + cb(kv_cmpr, "mtp_kv_cmpr_reshape", il); + + ggml_tensor * Kcur = ggml_concat(ctx0, kv_cmpr, k_pe, 0); + cb(Kcur, "mtp_Kcur", il); + + ggml_tensor * Vcur = kv_cmpr; + cb(Vcur, "mtp_Vcur", il); + + cur = build_attn(inp_attn_k, + layer.wo, nullptr, layer.wo_s, + Qcur, Kcur, Vcur, nullptr, nullptr, layer.wv_b, kq_scale, il); + cb(cur, "mtp_attn_out", il); + + ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "mtp_ffn_inp", il); + + cur = build_norm(ffn_inp, layer.ffn_norm, nullptr, LLM_NORM_RMS, il); + cb(cur, "mtp_ffn_norm", il); + + ggml_tensor * moe_out = build_moe_ffn(cur, + layer.ffn_gate_inp, + layer.ffn_up_exps, + layer.ffn_gate_exps, + layer.ffn_down_exps, + layer.ffn_exp_probs_b, + n_expert, n_expert_used, + LLM_FFN_SILU, hparams.expert_weights_norm, + hparams.expert_weights_scale, + (llama_expert_gating_func_type) hparams.expert_gating_func, + il, + nullptr, + layer.ffn_gate_up_exps); + cb(moe_out, "mtp_ffn_moe_out", il); + + ggml_tensor * ffn_shexp = build_ffn(cur, + layer.ffn_up_shexp, nullptr, nullptr, + layer.ffn_gate_shexp, nullptr, nullptr, + layer.ffn_down_shexp, nullptr, nullptr, + nullptr, LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(ffn_shexp, "mtp_ffn_shexp", il); + + cur = ggml_add(ctx0, moe_out, ffn_shexp); + cb(cur, "mtp_ffn_out", il); + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "mtp_post_ffn", il); + + ggml_tensor * head_norm_w = layer.nextn.shared_head_norm + ? layer.nextn.shared_head_norm + : model.output_norm; + GGML_ASSERT(head_norm_w && "GLM4 MTP: missing both nextn.shared_head_norm and output_norm"); + + cur = build_norm(cur, head_norm_w, nullptr, LLM_NORM_RMS, -1); + cb(cur, "h_nextn", -1); + res->t_h_nextn = cur; + + if (inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + } + cb(cur, "mtp_shared_head_norm", -1); + + ggml_tensor * head_w = layer.nextn.shared_head_head + ? layer.nextn.shared_head_head + : model.output; + + ggml_tensor * head_s = layer.nextn.shared_head_head + ? layer.nextn.shared_head_head_s + : model.output_s; + + GGML_ASSERT(head_w && "GLM4 MTP: missing LM head (nextn.shared_head_head or model.output)"); + + cur = build_lora_mm(head_w, cur, head_s); + cb(cur, "result_output", -1); + + res->t_logits = cur; + ggml_build_forward_expand(gf, cur); +} + llama_model_deepseek2::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { // lite variants include DeepSeek-V2-Lite, GigaChat3-10B-A1.8B @@ -365,7 +641,7 @@ llama_model_deepseek2::graph::graph(const llama_model & model, const llm_graph_p Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il); } } - if (il == n_layer - 1 && inp_out_ids) { + if (il == n_layer - 1 && inp_out_ids && (!cparams.embeddings_nextn || cparams.embeddings_nextn_masked)) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -425,6 +701,13 @@ llama_model_deepseek2::graph::graph(const llama_model & model, const llm_graph_p cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1); + cb(cur, "h_nextn", -1); + res->t_h_nextn = cur; + + if (cparams.embeddings_nextn && !cparams.embeddings_nextn_masked && inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + } + cb(cur, "result_norm", -1); res->t_embd = cur; diff --git a/src/models/deepseek32.cpp b/src/models/deepseek32.cpp index 32262e6840b0..8a07a0b71cae 100644 --- a/src/models/deepseek32.cpp +++ b/src/models/deepseek32.cpp @@ -44,13 +44,24 @@ void llama_model_deepseek32::load_arch_hparams(llama_model_loader & ml) { GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer"); switch (hparams.n_layer()) { - case 62: type = LLM_TYPE_685B_A37B; break; + case 61: type = LLM_TYPE_685B_A37B; break; default: type = LLM_TYPE_UNKNOWN; } } -void llama_model_deepseek32::load_arch_tensors(llama_model_loader &) { +void llama_model_deepseek32::load_arch_tensors(llama_model_loader & ml) { LLAMA_LOAD_LOCALS; + + const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr); + const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight"; + const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr); + const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0; + int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0; + + if (!ml.load_mtp) { + mtp_flags |= TENSOR_SKIP; + } + const bool is_mla = hparams.is_mla(); if (!is_mla) { throw std::runtime_error("DEEPSEEK32 architecture requires MLA"); @@ -80,12 +91,7 @@ void llama_model_deepseek32::load_arch_tensors(llama_model_loader &) { } for (int i = 0; i < n_layer_all; ++i) { - int flags = 0; - if (i >= n_layer) { - // skip all tensors in the NextN layers - // TODO @ngxson : TENSOR_NOT_REQUIRED was a hack, need to remove it later - flags |= TENSOR_SKIP | TENSOR_NOT_REQUIRED; - } + const int flags = (i >= n_layer) ? mtp_flags : trunk_flags; auto & layer = layers[i]; @@ -138,7 +144,7 @@ void llama_model_deepseek32::load_arch_tensors(llama_model_loader &) { layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags); } - // NextN/MTP tensors (preserved but unused) - conditionally load for last nextn_predict_layers + // NextN/MTP tensors - conditionally load for last nextn_predict_layers if (i >= n_layer) { layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, flags); layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags); @@ -153,6 +159,9 @@ void llama_model_deepseek32::load_arch_tensors(llama_model_loader &) { } std::unique_ptr llama_model_deepseek32::build_arch_graph(const llm_graph_params & params) const { + if (params.gtype == LLM_GRAPH_TYPE_DECODER_MTP) { + return std::make_unique(*this, params); + } return std::make_unique(*this, params); } @@ -430,7 +439,9 @@ llama_model_deepseek32::graph::graph(const llama_model & model, const llm_graph_ Qcur, Kcur, Vcur, nullptr, nullptr, model.layers[il].wv_b, top_k, kq_scale, il); } } - if (il == n_layer - 1 && inp_out_ids) { + // when unmasked nextn embeddings are requested, t_h_nextn must keep all rows, + // so the early output masking has to be skipped (it is applied after the final norm instead) + if (il == n_layer - 1 && inp_out_ids && (!cparams.embeddings_nextn || cparams.embeddings_nextn_masked)) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -493,6 +504,14 @@ llama_model_deepseek32::graph::graph(const llama_model & model, const llm_graph_ cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1); + // post-norm hidden state feeds the NextN/MTP draft head + cb(cur, "h_nextn", -1); + res->t_h_nextn = cur; + + if (cparams.embeddings_nextn && !cparams.embeddings_nextn_masked && inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + } + cb(cur, "result_norm", -1); res->t_embd = cur; @@ -504,3 +523,243 @@ llama_model_deepseek32::graph::graph(const llama_model & model, const llm_graph_ ggml_build_forward_expand(gf, cur); } + +// LLM_GRAPH_TYPE_DECODER_MTP draft head for DeepSeek V3.2 (DEEPSEEK32). +// Semantics mirror the deepseek-family NextN/MTP layer: +// enorm(embed) + hnorm(prev_hidden) -> concat(e, h) -> eh_proj -> +// full deepseek32 decoder block (dense MLA attention + sigmoid-gated MoE FFN +// with shared expert, exactly as the trunk deepseek2 graph builds it) -> +// shared_head_norm (fallback output_norm) -> shared LM head. +// The DSA indexer is not used at runtime. +llama_model_deepseek32::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params) + : llm_graph_context(params) { + GGML_ASSERT(hparams.n_layer_nextn > 0 && "DEEPSEEK32 MTP requires n_layer_nextn > 0"); + GGML_ASSERT(hparams.n_layer_nextn == 1 && "DEEPSEEK32 MTP currently only supports a single MTP block"); + GGML_ASSERT(hparams.is_mla() && "DEEPSEEK32 MTP requires MLA"); + + const int il = hparams.n_layer() + cparams.nextn_layer_offset; + GGML_ASSERT(cparams.nextn_layer_offset >= 0 && + cparams.nextn_layer_offset < (int) hparams.n_layer_nextn && + "nextn_layer_offset out of range [0, n_layer_nextn)"); + const auto & layer = model.layers[il]; + + GGML_ASSERT(layer.nextn.eh_proj && "MTP block missing nextn.eh_proj"); + GGML_ASSERT(layer.nextn.enorm && "MTP block missing nextn.enorm"); + GGML_ASSERT(layer.nextn.hnorm && "MTP block missing nextn.hnorm"); + GGML_ASSERT(layer.ffn_gate_inp && "MTP block missing ffn_gate_inp"); + + // note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA + const int64_t n_embd_head_k = hparams.n_embd_head_k_mla(); + + const int64_t n_embd_head_qk_rope = hparams.n_rot(); + const int64_t n_embd_head_qk_nope = n_embd_head_k - n_embd_head_qk_rope; + + const uint32_t kv_lora_rank = hparams.n_lora_kv; + + // We have to pre-scale kq_scale and attn_factor to make the YaRN RoPE work correctly. + // See the deepseek2 trunk graph for the detailed explanation - this must match it EXACTLY. + GGML_ASSERT(ext_factor >= 0.0f); + const float attn_factor_org = attn_factor * (1.0f + 0.1f * logf(1.0f / freq_scale)); + + const float mscale = attn_factor_org * (1.0f + 0.1f * hparams.rope_yarn_log_mul * logf(1.0f / freq_scale)); + const float kq_scale = 1.0f * mscale * mscale / sqrtf(float(n_embd_head_k)); + + // TODO: extract in a common llm_graph_context::build_inp_embd_h() + auto inp = std::make_unique(hparams.n_embd); + + inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + ggml_set_input(inp->tokens); + + inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd_inp(), n_tokens); + ggml_set_input(inp->embd); + + ggml_tensor * tok_embd; + if (ubatch.token) { + ggml_tensor * tok_embd_w = layer.nextn.embed_tokens ? layer.nextn.embed_tokens : model.tok_embd; + + tok_embd = ggml_get_rows(ctx0, tok_embd_w, inp->tokens); + } else { + tok_embd = inp->embd; + } + cb(tok_embd, "mtp_tok_embd", il); + + inp->h = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd, n_tokens); + ggml_set_input(inp->h); + ggml_set_name(inp->h, "mtp_h_input"); + + ggml_tensor * h_embd = inp->h; + + res->add_input(std::move(inp)); + + ggml_tensor * inp_pos = build_inp_pos(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + + // MLA with the absorption optimization uses a K-only cache (V is a view of K) + auto * inp_attn = build_attn_inp_k(); + + ggml_tensor * h_norm = build_norm(h_embd, layer.nextn.hnorm, nullptr, LLM_NORM_RMS, il); + cb(h_norm, "mtp_hnorm", il); + + ggml_tensor * e_norm = build_norm(tok_embd, layer.nextn.enorm, nullptr, LLM_NORM_RMS, il); + cb(e_norm, "mtp_enorm", il); + + ggml_tensor * concat = ggml_concat(ctx0, e_norm, h_norm, /*dim=*/ 0); + cb(concat, "mtp_concat", il); + + ggml_tensor * cur = build_lora_mm(layer.nextn.eh_proj, concat, layer.nextn.eh_proj_s); + cb(cur, "mtp_eh_proj", il); + + ggml_tensor * inpSA = cur; + + cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il); + cb(cur, "mtp_attn_norm", il); + + // self-attention: dense MLA, same construction as the deepseek2 trunk graph + { + ggml_tensor * q = ggml_mul_mat(ctx0, layer.wq_a, cur); + cb(q, "mtp_q", il); + + q = build_norm(q, layer.attn_q_a_norm, nullptr, LLM_NORM_RMS, il); + cb(q, "mtp_q", il); + + q = ggml_mul_mat(ctx0, layer.wq_b, q); + cb(q, "mtp_q", il); + + // split into {n_embd_head_qk_nope, n_head, n_tokens} + ggml_tensor * q_nope = + ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k), + ggml_row_size(q->type, n_embd_head_k) * n_head, 0); + cb(q_nope, "mtp_q_nope", il); + + // and {n_embd_head_qk_rope, n_head, n_tokens} + ggml_tensor * q_pe = ggml_view_3d( + ctx0, q, n_embd_head_qk_rope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k), + ggml_row_size(q->type, n_embd_head_k) * n_head, ggml_row_size(q->type, n_embd_head_qk_nope)); + cb(q_pe, "mtp_q_pe", il); + + ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, layer.wkv_a_mqa, cur); + cb(kv_cmpr_pe, "mtp_kv_cmpr_pe", il); + + // split into {kv_lora_rank, n_tokens} + ggml_tensor * kv_cmpr = + ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens, + ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0); + cb(kv_cmpr, "mtp_kv_cmpr", il); + + // and {n_embd_head_qk_rope, 1, n_tokens} + ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens, + ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), + ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), + ggml_row_size(kv_cmpr_pe->type, kv_lora_rank)); + cb(k_pe, "mtp_k_pe", il); + + q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow); + cb(q_pe, "mtp_q_pe", il); + + k_pe = ggml_rope_ext(ctx0, k_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow); + cb(k_pe, "mtp_k_pe", il); + + kv_cmpr = build_norm(kv_cmpr, layer.attn_kv_a_norm, nullptr, LLM_NORM_RMS, il); + cb(kv_cmpr, "mtp_kv_cmpr", il); + + // {n_embd_head_qk_nope, n_tokens, n_head} + q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3); + cb(q_nope, "mtp_q_nope_perm", il); + + // {n_embd_head_qk_nope, kv_lora_rank, n_head} x {n_embd_head_qk_nope, n_tokens, n_head} + ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, layer.wk_b, q_nope); + cb(q_nope_absorbed, "mtp_q_nope_absorbed", il); + + // {kv_lora_rank, n_head, n_tokens} + q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3); + cb(q_nope_absorbed, "mtp_q_nope_absorbed_perm", il); + + // {n_embd_head_qk_rope + kv_lora_rank, n_head, n_tokens} + // note: rope must go first for in-place context shifting in build_rope_shift() + ggml_tensor * Qcur = ggml_concat(ctx0, q_nope_absorbed, q_pe, 0); + cb(Qcur, "mtp_Qcur", il); + + kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens); + cb(kv_cmpr, "mtp_kv_cmpr_reshape", il); + + // {n_embd_head_qk_rope + kv_lora_rank, 1, n_tokens} + ggml_tensor * Kcur = ggml_concat(ctx0, kv_cmpr, k_pe, 0); + cb(Kcur, "mtp_Kcur", il); + + // {kv_lora_rank, 1, n_tokens} + ggml_tensor * Vcur = kv_cmpr; + cb(Vcur, "mtp_Vcur", il); + + // note: MLA with the absorption optimization converts into MQA (ie: GQA with 1 group) + cur = build_attn(inp_attn, + layer.wo, NULL, layer.wo_s, + Qcur, Kcur, Vcur, nullptr, nullptr, layer.wv_b, kq_scale, il); + cb(cur, "mtp_attn_out", il); + } + + ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "mtp_ffn_inp", il); + + cur = build_norm(ffn_inp, layer.ffn_norm, NULL, LLM_NORM_RMS, il); + cb(cur, "mtp_ffn_norm", il); + + // MoE FFN with shared expert - same construction as the deepseek2 trunk graph + ggml_tensor * moe_out = build_moe_ffn(cur, + layer.ffn_gate_inp, + layer.ffn_up_exps, + layer.ffn_gate_exps, + layer.ffn_down_exps, + layer.ffn_exp_probs_b, + n_expert, n_expert_used, + LLM_FFN_SILU, hparams.expert_weights_norm, + hparams.expert_weights_scale, + (llama_expert_gating_func_type) hparams.expert_gating_func, + il, + nullptr, + layer.ffn_gate_up_exps, + layer.ffn_up_exps_s, + layer.ffn_gate_exps_s, + layer.ffn_down_exps_s); + cb(moe_out, "mtp_ffn_moe_out", il); + + // FFN shared expert + ggml_tensor * ffn_shexp = + build_ffn(cur, + layer.ffn_up_shexp, NULL, layer.ffn_up_shexp_s, + layer.ffn_gate_shexp, NULL, layer.ffn_gate_shexp_s, + layer.ffn_down_shexp, NULL, layer.ffn_down_shexp_s, + NULL, LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(ffn_shexp, "mtp_ffn_shexp", il); + + cur = ggml_add(ctx0, moe_out, ffn_shexp); + cb(cur, "mtp_ffn_out", il); + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "mtp_post_ffn", il); + + // shared_head_norm applied after the decoder block, before the shared LM head. + // The post-norm hidden state seeds the next MTP step. + ggml_tensor * head_norm_w = layer.nextn.shared_head_norm + ? layer.nextn.shared_head_norm + : model.output_norm; + GGML_ASSERT(head_norm_w && "DEEPSEEK32 MTP: missing both nextn.shared_head_norm and output_norm"); + cur = build_norm(cur, head_norm_w, nullptr, LLM_NORM_RMS, -1); + + cb(cur, "h_nextn", -1); + res->t_h_nextn = cur; + + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + cb(cur, "mtp_shared_head_norm", -1); + + ggml_tensor * head_w = layer.nextn.shared_head_head ? layer.nextn.shared_head_head : model.output; + ggml_tensor * head_s = layer.nextn.shared_head_head ? layer.nextn.shared_head_head_s : model.output_s; + GGML_ASSERT(head_w && "DEEPSEEK32 MTP: missing LM head (nextn.shared_head_head or model.output)"); + cur = build_lora_mm(head_w, cur, head_s); + cb(cur, "result_output", -1); + + res->t_logits = cur; + ggml_build_forward_expand(gf, cur); +} + diff --git a/src/models/deepseek4.cpp b/src/models/deepseek4.cpp index e64e43451f7f..89cd461765ad 100644 --- a/src/models/deepseek4.cpp +++ b/src/models/deepseek4.cpp @@ -16,6 +16,16 @@ static float dsv4_rope_attn_factor(float freq_scale, float ext_factor) { } void llama_model_deepseek4::load_arch_hparams(llama_model_loader & ml) { + ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false); + if (hparams.n_layer_nextn > 0 && hparams.n_layer_nextn < hparams.n_layer_all) { + const uint32_t n_layer_main = hparams.n_layer_all - hparams.n_layer_nextn; + const std::string mtp_probe = "blk." + std::to_string(n_layer_main) + ".nextn.eh_proj.weight"; + if (ml.get_weight(mtp_probe.c_str()) == nullptr) { + hparams.n_layer_nextn = 0; + } + } + GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < block_count"); + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q); ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa); @@ -24,8 +34,8 @@ void llama_model_deepseek4::load_arch_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared); ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale); ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm); - ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_EXP, hparams.swiglu_clamp_exp, hparams.n_layer()); - if (!ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_SHEXP, hparams.swiglu_clamp_shexp, hparams.n_layer(), 0)) { + ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_EXP, hparams.swiglu_clamp_exp, hparams.n_layer_all); + if (!ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_SHEXP, hparams.swiglu_clamp_shexp, hparams.n_layer_all, 0)) { hparams.swiglu_clamp_shexp = hparams.swiglu_clamp_exp; } @@ -41,9 +51,11 @@ void llama_model_deepseek4::load_arch_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_HYPER_CONNECTION_EPSILON, hparams.dsv4_hc_eps); ml.get_key(LLM_KV_HASH_LAYER_COUNT, hparams.dsv4_hash_layer_count); + hparams.n_embd_out_impl = hparams.dsv4_hc_mult * hparams.n_embd; + uint32_t n_compress_ratios = 0; ml.get_arr_n(LLM_KV_ATTENTION_COMPRESS_RATIOS, n_compress_ratios); - if (n_compress_ratios < hparams.n_layer()) { + if (n_compress_ratios < hparams.n_layer_all) { throw std::runtime_error("DeepSeek-V4 compress_ratios is shorter than block_count"); } ml.get_arr(LLM_KV_ATTENTION_COMPRESS_RATIOS, hparams.dsv4_compress_ratios); @@ -54,6 +66,9 @@ void llama_model_deepseek4::load_arch_hparams(llama_model_loader & ml) { } hparams.swa_type = LLAMA_SWA_TYPE_STANDARD; hparams.set_swa_pattern(0); + for (uint32_t il = hparams.n_layer(); il < hparams.n_layer_all; ++il) { + hparams.is_swa_impl[il] = true; + } switch (hparams.n_layer()) { case 43: type = LLM_TYPE_UNKNOWN; break; @@ -61,7 +76,7 @@ void llama_model_deepseek4::load_arch_hparams(llama_model_loader & ml) { } } -void llama_model_deepseek4::load_arch_tensors(llama_model_loader &) { +void llama_model_deepseek4::load_arch_tensors(llama_model_loader & ml) { LLAMA_LOAD_LOCALS; const int64_t q_lora_rank = hparams.n_lora_q; @@ -75,6 +90,10 @@ void llama_model_deepseek4::load_arch_tensors(llama_model_loader &) { const int64_t hc_dim = hc_mult * n_embd; const int64_t hc_mix_dim = (2 + hc_mult) * hc_mult; + const bool mtp_only = (n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr); + const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0; + const int mtp_flags = ml.load_mtp ? 0 : TENSOR_SKIP; + tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); @@ -84,69 +103,84 @@ void llama_model_deepseek4::load_arch_tensors(llama_model_loader &) { hc_head_base = create_tensor(tn(LLM_TENSOR_HC_HEAD_BASE, "weight"), {hc_mult}, 0); hc_head_scale = create_tensor(tn(LLM_TENSOR_HC_HEAD_SCALE, "weight"), {1}, 0); - for (int i = 0; i < n_layer; ++i) { + for (int i = 0; i < n_layer_all; ++i) { auto & layer = layers[i]; - - layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); - layer.attn_sinks = create_tensor(tn(LLM_TENSOR_ATTN_SINKS, "weight", i), {n_head}, 0); - layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, 0); - layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, 0); - layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head}, 0); - layer.wkv = create_tensor(tn(LLM_TENSOR_ATTN_KV, "weight", i), {n_embd, n_embd_head}, 0); - layer.attn_kv_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_NORM, "weight", i), {n_embd_head}, 0); - layer.wo_a = create_tensor(tn(LLM_TENSOR_ATTN_OUT_A, "weight", i), {n_head * n_embd_head / o_groups, o_lora_rank * o_groups}, 0); - layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT_B, "weight", i), {o_groups * o_lora_rank, n_embd}, 0); - - layer.hc_attn_fn = create_tensor(tn(LLM_TENSOR_HC_ATTN_FN, "weight", i), {hc_dim, hc_mix_dim}, 0); - layer.hc_attn_base = create_tensor(tn(LLM_TENSOR_HC_ATTN_BASE, "weight", i), {hc_mix_dim}, 0); - layer.hc_attn_scale = create_tensor(tn(LLM_TENSOR_HC_ATTN_SCALE, "weight", i), {3}, 0); - layer.hc_ffn_fn = create_tensor(tn(LLM_TENSOR_HC_FFN_FN, "weight", i), {hc_dim, hc_mix_dim}, 0); - layer.hc_ffn_base = create_tensor(tn(LLM_TENSOR_HC_FFN_BASE, "weight", i), {hc_mix_dim}, 0); - layer.hc_ffn_scale = create_tensor(tn(LLM_TENSOR_HC_FFN_SCALE, "weight", i), {3}, 0); + const int flags = i < n_layer ? trunk_flags : mtp_flags; + + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags); + layer.attn_sinks = create_tensor(tn(LLM_TENSOR_ATTN_SINKS, "weight", i), {n_head}, flags); + layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, flags); + layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, flags); + layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head}, flags); + layer.wkv = create_tensor(tn(LLM_TENSOR_ATTN_KV, "weight", i), {n_embd, n_embd_head}, flags); + layer.attn_kv_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_NORM, "weight", i), {n_embd_head}, flags); + // for wo_a, the shape in the file is (n_head * n_embd_head / o_groups, o_lora_rank*o_groups) + // so we reshape here, to avoid reshaping the tensor in the graph + layer.wo_a = create_tensor(tn(LLM_TENSOR_ATTN_OUT_A, "weight", i), {n_head * n_embd_head / o_groups, o_lora_rank, o_groups}, flags | TENSOR_ALLOW_RESHAPE); + layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT_B, "weight", i), {o_groups * o_lora_rank, n_embd}, flags); + + layer.hc_attn_fn = create_tensor(tn(LLM_TENSOR_HC_ATTN_FN, "weight", i), {hc_dim, hc_mix_dim}, flags); + layer.hc_attn_base = create_tensor(tn(LLM_TENSOR_HC_ATTN_BASE, "weight", i), {hc_mix_dim}, flags); + layer.hc_attn_scale = create_tensor(tn(LLM_TENSOR_HC_ATTN_SCALE, "weight", i), {3}, flags); + layer.hc_ffn_fn = create_tensor(tn(LLM_TENSOR_HC_FFN_FN, "weight", i), {hc_dim, hc_mix_dim}, flags); + layer.hc_ffn_base = create_tensor(tn(LLM_TENSOR_HC_FFN_BASE, "weight", i), {hc_mix_dim}, flags); + layer.hc_ffn_scale = create_tensor(tn(LLM_TENSOR_HC_FFN_SCALE, "weight", i), {3}, flags); const int64_t ratio = hparams.dsv4_compress_ratios[i]; if (ratio != 0) { const int64_t coff = ratio == 4 ? 2 : 1; - layer.attn_comp_wkv = create_tensor(tn(LLM_TENSOR_ATTN_COMPRESSOR_WKV, "weight", i), {n_embd, coff * n_embd_head}, 0); - layer.attn_comp_wgate = create_tensor(tn(LLM_TENSOR_ATTN_COMPRESSOR_WGATE, "weight", i), {n_embd, coff * n_embd_head}, 0); - layer.attn_comp_ape = create_tensor(tn(LLM_TENSOR_ATTN_COMPRESSOR_APE, "weight", i), {coff * n_embd_head, ratio}, 0); - layer.attn_comp_norm = create_tensor(tn(LLM_TENSOR_ATTN_COMPRESSOR_NORM, "weight", i), {n_embd_head}, 0); + layer.attn_comp_wkv = create_tensor(tn(LLM_TENSOR_ATTN_COMPRESSOR_WKV, "weight", i), {n_embd, coff * n_embd_head}, flags); + layer.attn_comp_wgate = create_tensor(tn(LLM_TENSOR_ATTN_COMPRESSOR_WGATE, "weight", i), {n_embd, coff * n_embd_head}, flags); + layer.attn_comp_ape = create_tensor(tn(LLM_TENSOR_ATTN_COMPRESSOR_APE, "weight", i), {coff * n_embd_head, ratio}, flags); + layer.attn_comp_norm = create_tensor(tn(LLM_TENSOR_ATTN_COMPRESSOR_NORM, "weight", i), {n_embd_head}, flags); if (ratio == 4) { const int64_t n_embd_indexer = hparams.indexer_head_size; - layer.indexer_proj = create_tensor(tn(LLM_TENSOR_INDEXER_PROJ, "weight", i), {n_embd, hparams.indexer_n_head}, 0); - layer.indexer_attn_q_b = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_Q_B, "weight", i), {q_lora_rank, hparams.indexer_n_head * n_embd_indexer}, 0); + layer.indexer_proj = create_tensor(tn(LLM_TENSOR_INDEXER_PROJ, "weight", i), {n_embd, hparams.indexer_n_head}, flags); + layer.indexer_attn_q_b = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_Q_B, "weight", i), {q_lora_rank, hparams.indexer_n_head * n_embd_indexer}, flags); - layer.indexer_comp_wkv = create_tensor(tn(LLM_TENSOR_INDEXER_COMPRESSOR_WKV, "weight", i), {n_embd, 2 * n_embd_indexer}, 0); - layer.indexer_comp_wgate = create_tensor(tn(LLM_TENSOR_INDEXER_COMPRESSOR_WGATE, "weight", i), {n_embd, 2 * n_embd_indexer}, 0); - layer.indexer_comp_ape = create_tensor(tn(LLM_TENSOR_INDEXER_COMPRESSOR_APE, "weight", i), {2 * n_embd_indexer, ratio}, 0); - layer.indexer_comp_norm = create_tensor(tn(LLM_TENSOR_INDEXER_COMPRESSOR_NORM, "weight", i), {n_embd_indexer}, 0); + layer.indexer_comp_wkv = create_tensor(tn(LLM_TENSOR_INDEXER_COMPRESSOR_WKV, "weight", i), {n_embd, 2 * n_embd_indexer}, flags); + layer.indexer_comp_wgate = create_tensor(tn(LLM_TENSOR_INDEXER_COMPRESSOR_WGATE, "weight", i), {n_embd, 2 * n_embd_indexer}, flags); + layer.indexer_comp_ape = create_tensor(tn(LLM_TENSOR_INDEXER_COMPRESSOR_APE, "weight", i), {2 * n_embd_indexer, ratio}, flags); + layer.indexer_comp_norm = create_tensor(tn(LLM_TENSOR_INDEXER_COMPRESSOR_NORM, "weight", i), {n_embd_indexer}, flags); } else if (ratio != 128) { throw std::runtime_error("DeepSeek-V4 loader only supports compression ratios 0, 4, and 128"); } } - layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0); + layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, flags); if ((uint32_t) i < hparams.dsv4_hash_layer_count) { - layer.ffn_gate_tid2eid = create_tensor(tn(LLM_TENSOR_FFN_GATE_TID2EID, "weight", i), {n_expert_used, n_vocab}, 0); + layer.ffn_gate_tid2eid = create_tensor(tn(LLM_TENSOR_FFN_GATE_TID2EID, "weight", i), {n_expert_used, n_vocab}, flags); } else { - layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0); + layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, flags); + } + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags); + + layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, flags); + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, flags); + layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, flags); + + layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags); + layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_exp * n_expert_shared, n_embd }, flags); + layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags); + + if (i >= n_layer) { + layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), {2 * n_embd, n_embd}, flags); + layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), {n_embd}, flags); + layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), {n_embd}, flags); + layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED | flags); + layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED | flags); + layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED | flags); } - layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); - - layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0); - layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0); - layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0); - - layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0); - layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_exp * n_expert_shared, n_embd }, 0); - layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0); } } std::unique_ptr llama_model_deepseek4::build_arch_graph(const llm_graph_params & params) const { + if (params.gtype == LLM_GRAPH_TYPE_DECODER_MTP) { + return std::make_unique(*this, params); + } return std::make_unique(*this, params); } @@ -175,18 +209,69 @@ static ggml_tensor * dsv4_append_zero_row(ggml_context * ctx, ggml_tensor * t, b return ggml_concat(ctx, t, row, 1); } -static ggml_tensor * dsv4_with_zero_dep(ggml_context * ctx, ggml_tensor * t, ggml_tensor * dep) { - if (dep == nullptr) { - return t; +struct dsv4_state_tensors { + ggml_tensor * kv; + ggml_tensor * score; +}; + +static dsv4_state_tensors dsv4_build_state_restore( + ggml_context * ctx, + const llm_graph_input_dsv4::comp_input & inp, + const llama_dsv4_comp_state * state, + int32_t il) { + dsv4_state_tensors restored = { + state->get_kv_all(ctx, il), + state->get_score_all(ctx, il), + }; + + if (inp.state_restore_src_idxs == nullptr || inp.state_restore_dst_idxs == nullptr) { + return restored; + } + + ggml_tensor * kv_rows = ggml_get_rows(ctx, restored.kv, inp.state_restore_src_idxs); + restored.kv = state->cpy_kv(ctx, kv_rows, inp.state_restore_dst_idxs, il); + + ggml_tensor * score_rows = ggml_get_rows(ctx, restored.score, inp.state_restore_src_idxs); + restored.score = state->cpy_score(ctx, score_rows, inp.state_restore_dst_idxs, il); + + return restored; +} + +static dsv4_state_tensors dsv4_build_state_snapshot( + ggml_context * ctx, + const llm_graph_input_dsv4::comp_input & inp, + const llama_dsv4_comp_state * state, + ggml_tensor * source_kv, + ggml_tensor * source_score, + int32_t il) { + if (inp.state_snapshot_src_idxs == nullptr || inp.state_snapshot_dst_idxs == nullptr || + source_kv == nullptr || source_score == nullptr) { + return {}; } - ggml_tensor * zero = ggml_scale(ctx, ggml_sum(ctx, dep), 0.0f); - return ggml_add(ctx, t, zero); + ggml_tensor * kv_rows = ggml_get_rows(ctx, source_kv, inp.state_snapshot_src_idxs); + ggml_tensor * kv = state->cpy_kv(ctx, kv_rows, inp.state_snapshot_dst_idxs, il); + + ggml_tensor * score_rows = ggml_get_rows(ctx, source_score, inp.state_snapshot_src_idxs); + ggml_tensor * score = state->cpy_score(ctx, score_rows, inp.state_snapshot_dst_idxs, il); + + return { kv, score }; } static constexpr int64_t DSV4_CSA_RATIO = 4; static constexpr int64_t DSV4_HCA_RATIO = 128; +// mean over the hyper-connection streams: [n_embd, hc, n_tokens] -> [n_embd, n_tokens] +static ggml_tensor * dsv4_hc_mean(ggml_context * ctx, ggml_tensor * x) { + const int64_t hc = x->ne[1]; + + ggml_tensor * acc = ggml_view_2d(ctx, x, x->ne[0], x->ne[2], x->nb[2], 0); + for (int64_t s = 1; s < hc; ++s) { + acc = ggml_add(ctx, acc, ggml_view_2d(ctx, x, x->ne[0], x->ne[2], x->nb[2], s*x->nb[1])); + } + return ggml_scale(ctx, acc, 1.0f/hc); +} + static ggml_tensor * dsv4_hc_affine( ggml_context * ctx, ggml_tensor * x, @@ -197,22 +282,31 @@ static ggml_tensor * dsv4_hc_affine( return x; } -ggml_tensor * llama_model_deepseek4::graph::build_hc_weighted_sum( +ggml_tensor * llama_model_deepseek4::graph::build_hc_pre( ggml_tensor * x, - ggml_tensor * weights) const { + ggml_tensor * weights, + int il) const { + GGML_ASSERT(x->ne[0] == n_embd); + GGML_ASSERT(x->ne[1] == hparams.dsv4_hc_mult); + const int64_t hc = hparams.dsv4_hc_mult; const int64_t nt = x->ne[2]; - ggml_tensor * acc = nullptr; + if (cparams.fused_dsv4_hc_pre && il >= 0) { + ggml_tensor * result = ggml_dsv4_hc_pre(ctx0, x, weights); + res->add_fused_node({LLM_FUSED_OP_DSV4_HC_PRE, result, il}); + return result; + } + + ggml_tensor * result = nullptr; for (int64_t ih = 0; ih < hc; ++ih) { ggml_tensor * xh = ggml_view_2d(ctx0, x, n_embd, nt, x->nb[2], ih*x->nb[1]); ggml_tensor * wh = ggml_view_2d(ctx0, weights, 1, nt, weights->nb[1], ih*weights->nb[0]); - ggml_tensor * cur = ggml_mul(ctx0, xh, wh); - acc = acc ? ggml_add(ctx0, acc, cur) : cur; + result = result ? ggml_add(ctx0, result, cur) : cur; } - return acc; + return result; } ggml_tensor * llama_model_deepseek4::graph::build_hc_sinkhorn( @@ -275,11 +369,9 @@ ggml_tensor * llama_model_deepseek4::graph::build_hc_pre( ggml_tensor * scale_pre = dsv4_view_1d(ctx0, hc_scale, 1, 0); ggml_tensor * scale_post = dsv4_view_1d(ctx0, hc_scale, 1, 1); - ggml_tensor * scale_comb = dsv4_view_1d(ctx0, hc_scale, 1, 2); ggml_tensor * base_pre = dsv4_view_1d(ctx0, hc_base, hc, 0); ggml_tensor * base_post = dsv4_view_1d(ctx0, hc_base, hc, hc); - ggml_tensor * base_comb = dsv4_view_1d(ctx0, hc_base, hc*hc, 2*hc); ggml_tensor * pre = dsv4_view_2d(ctx0, mixes, hc, nt, 0); pre = dsv4_hc_affine(ctx0, pre, scale_pre, base_pre); @@ -293,13 +385,23 @@ ggml_tensor * llama_model_deepseek4::graph::build_hc_pre( *post = ggml_scale(ctx0, *post, 2.0f); cb(*post, "hc_post", il); - *comb = dsv4_view_2d(ctx0, mixes, hc*hc, nt, 2*hc); - *comb = dsv4_hc_affine(ctx0, *comb, scale_comb, base_comb); - *comb = ggml_reshape_3d(ctx0, *comb, hc, hc, nt); - *comb = build_hc_sinkhorn(*comb, il); + if (cparams.fused_dsv4_hc_comb) { + *comb = ggml_dsv4_hc_comb(ctx0, mixes, hc_scale, hc_base, hparams.dsv4_hc_eps, + (int32_t) hparams.dsv4_hc_sinkhorn_iters); + res->add_fused_node({LLM_FUSED_OP_DSV4_HC_COMB, *comb, il}); + } else { + ggml_tensor * scale_comb = dsv4_view_1d(ctx0, hc_scale, 1, 2); + ggml_tensor * base_comb = dsv4_view_1d(ctx0, hc_base, hc*hc, 2*hc); + + *comb = dsv4_view_2d(ctx0, mixes, hc*hc, nt, 2*hc); + *comb = dsv4_hc_affine(ctx0, *comb, scale_comb, base_comb); + *comb = ggml_reshape_3d(ctx0, *comb, hc, hc, nt); + *comb = build_hc_sinkhorn(*comb, il); + } cb(*comb, "hc_comb", il); - return build_hc_weighted_sum(x, pre); + ggml_tensor * result = build_hc_pre(x, pre, il); + return result; } ggml_tensor * llama_model_deepseek4::graph::build_hc_post( @@ -308,7 +410,14 @@ ggml_tensor * llama_model_deepseek4::graph::build_hc_post( ggml_tensor * post, ggml_tensor * comb, int il) const { - GGML_UNUSED(il); + GGML_ASSERT(x->ne[0] == n_embd); + GGML_ASSERT(residual->ne[1] == hparams.dsv4_hc_mult); + + if (cparams.fused_dsv4_hc_post) { + ggml_tensor * result = ggml_dsv4_hc_post(ctx0, x, residual, post, comb); + res->add_fused_node({LLM_FUSED_OP_DSV4_HC_POST, result, il}); + return result; + } const int64_t hc = hparams.dsv4_hc_mult; const int64_t nt = x->ne[1]; @@ -320,7 +429,8 @@ ggml_tensor * llama_model_deepseek4::graph::build_hc_post( for (int64_t src = 0; src < hc; ++src) { ggml_tensor * res_src = ggml_view_2d(ctx0, residual, n_embd, nt, residual->nb[2], src*residual->nb[1]); - ggml_tensor * comb_src_dst = ggml_view_2d(ctx0, comb, 1, nt, comb->nb[2], dst*comb->nb[0] + src*comb->nb[1]); + ggml_tensor * comb_src_dst = ggml_view_2d(ctx0, comb, 1, nt, comb->nb[2], + dst*comb->nb[0] + src*comb->nb[1]); cur = ggml_add(ctx0, cur, ggml_mul(ctx0, res_src, comb_src_dst)); } @@ -350,7 +460,7 @@ ggml_tensor * llama_model_deepseek4::graph::build_hc_head( pre = ggml_scale_bias(ctx0, pre, 1.0f, hparams.dsv4_hc_eps); cb(pre, "hc_head_pre", -1); - return build_hc_weighted_sum(x, pre); + return build_hc_pre(x, pre, -1); } ggml_tensor * llama_model_deepseek4::graph::build_hca_compressed_kv_from_state( @@ -435,27 +545,29 @@ ggml_tensor * llama_model_deepseek4::graph::build_overlap_compressed_kv_from_sta kv_state = dsv4_append_zero_row(ctx0, kv_state, false); score_state = dsv4_append_zero_row(ctx0, score_state, true); - ggml_tensor * prev_idxs = dsv4_view_1d(ctx0, state_read_idxs, ratio*n_blocks, 0); - ggml_tensor * cur_idxs = dsv4_view_1d(ctx0, state_read_idxs, ratio*n_blocks, ratio*n_blocks); + const int64_t n_read = ratio*n_blocks; + + ggml_tensor * kv_rows = ggml_get_rows(ctx0, kv_state, state_read_idxs); + ggml_tensor * score_rows = ggml_get_rows(ctx0, score_state, state_read_idxs); - ggml_tensor * kv_prev = ggml_get_rows(ctx0, kv_state, prev_idxs); - kv_prev = ggml_cont(ctx0, ggml_view_2d(ctx0, kv_prev, n_embd_head, ratio*n_blocks, kv_prev->nb[1], 0)); + ggml_tensor * kv_prev = ggml_cont(ctx0, + ggml_view_2d(ctx0, kv_rows, n_embd_head, n_read, kv_rows->nb[1], 0)); kv_prev = ggml_reshape_3d(ctx0, kv_prev, n_embd_head, ratio, n_blocks); cb(kv_prev, name, il); - ggml_tensor * score_prev = ggml_get_rows(ctx0, score_state, prev_idxs); - score_prev = ggml_cont(ctx0, ggml_view_2d(ctx0, score_prev, n_embd_head, ratio*n_blocks, score_prev->nb[1], 0)); + ggml_tensor * score_prev = ggml_cont(ctx0, + ggml_view_2d(ctx0, score_rows, n_embd_head, n_read, score_rows->nb[1], 0)); score_prev = ggml_reshape_3d(ctx0, score_prev, n_embd_head, ratio, n_blocks); cb(score_prev, name, il); - ggml_tensor * kv_cur = ggml_get_rows(ctx0, kv_state, cur_idxs); - kv_cur = ggml_cont(ctx0, ggml_view_2d(ctx0, kv_cur, n_embd_head, ratio*n_blocks, kv_cur->nb[1], - ggml_row_size(kv_cur->type, n_embd_head))); + ggml_tensor * kv_cur = ggml_cont(ctx0, + ggml_view_2d(ctx0, kv_rows, n_embd_head, n_read, kv_rows->nb[1], + n_read*kv_rows->nb[1] + ggml_row_size(kv_rows->type, n_embd_head))); kv_cur = ggml_reshape_3d(ctx0, kv_cur, n_embd_head, ratio, n_blocks); - ggml_tensor * score_cur = ggml_get_rows(ctx0, score_state, cur_idxs); - score_cur = ggml_cont(ctx0, ggml_view_2d(ctx0, score_cur, n_embd_head, ratio*n_blocks, score_cur->nb[1], - ggml_row_size(score_cur->type, n_embd_head))); + ggml_tensor * score_cur = ggml_cont(ctx0, + ggml_view_2d(ctx0, score_rows, n_embd_head, n_read, score_rows->nb[1], + n_read*score_rows->nb[1] + ggml_row_size(score_rows->type, n_embd_head))); score_cur = ggml_reshape_3d(ctx0, score_cur, n_embd_head, ratio, n_blocks); ggml_tensor * values = ggml_concat(ctx0, kv_prev, kv_cur, 1); @@ -777,8 +889,29 @@ ggml_tensor * llama_model_deepseek4::graph::build_attention( ggml_tensor * cur, ggml_tensor * inp_pos, int il) const { + return build_attention_impl(model, inp_dsv4, nullptr, cur, inp_pos, il); +} + +ggml_tensor * llama_model_deepseek4::graph::build_attention( + const llama_model & model, + llm_graph_input_attn_k_iswa * inp_mtp, + ggml_tensor * cur, + ggml_tensor * inp_pos, + int il) const { + return build_attention_impl(model, nullptr, inp_mtp, cur, inp_pos, il); +} + +ggml_tensor * llama_model_deepseek4::graph::build_attention_impl( + const llama_model & model, + llm_graph_input_dsv4 * inp_dsv4, + llm_graph_input_attn_k_iswa * inp_mtp, + ggml_tensor * cur, + ggml_tensor * inp_pos, + int il) const { + GGML_ASSERT((inp_dsv4 == nullptr) != (inp_mtp == nullptr)); + const auto & layer = model.layers[il]; - llm_graph_input_dsv4_raw * inp_attn = inp_dsv4->get_raw(); + llm_graph_input_dsv4_raw * inp_attn = inp_dsv4 ? inp_dsv4->get_raw() : nullptr; const int64_t n_embd_head = hparams.n_embd_head_k(); const int64_t n_embd_head_rope = hparams.n_rot(); @@ -846,9 +979,12 @@ ggml_tensor * llama_model_deepseek4::graph::build_attention( cb(kv, "kv", il); const int64_t ratio = hparams.dsv4_compress_ratios[il]; + GGML_ASSERT(inp_dsv4 || ratio == 0); ggml_tensor * hca_state_kv = nullptr; ggml_tensor * hca_state_score = nullptr; + ggml_tensor * hca_source_kv = nullptr; + ggml_tensor * hca_source_score = nullptr; if (ratio == DSV4_HCA_RATIO && inp_dsv4->get_hca().state_pos) { hca_state_kv = build_lora_mm(layer.attn_comp_wkv, cur); cb(hca_state_kv, "hca_state_kv", il); @@ -879,10 +1015,16 @@ ggml_tensor * llama_model_deepseek4::graph::build_attention( GGML_ASSERT(inp_dsv4->get_csa().state_write_idxs); - ggml_tensor * csa_source_kv = ggml_concat(ctx0, - inp_dsv4->mctx->get_csa_state()->get_kv(ctx0, il), csa_state_kv, 1); - ggml_tensor * csa_source_score = ggml_concat(ctx0, - inp_dsv4->mctx->get_csa_state()->get_score(ctx0, il), csa_state_score, 1); + const auto * csa_state = inp_dsv4->mctx->get_csa_state(); + const dsv4_state_tensors csa_restored = dsv4_build_state_restore( + ctx0, inp_dsv4->get_csa(), csa_state, il); + ggml_tensor * csa_base_kv = dsv4_view_2d( + ctx0, csa_restored.kv, csa_restored.kv->ne[0], csa_state->get_n_rows(), 0); + ggml_tensor * csa_base_score = dsv4_view_2d( + ctx0, csa_restored.score, csa_restored.score->ne[0], csa_state->get_n_rows(), 0); + + ggml_tensor * csa_source_kv = ggml_concat(ctx0, csa_base_kv, csa_state_kv, 1); + ggml_tensor * csa_source_score = ggml_concat(ctx0, csa_base_score, csa_state_score, 1); ggml_tensor * kv_comp_csa_state = build_overlap_compressed_kv_from_state( csa_source_kv, @@ -903,8 +1045,19 @@ ggml_tensor * llama_model_deepseek4::graph::build_attention( ggml_build_forward_expand(gf, inp_dsv4->mctx->get_csa()->cpy_k(ctx0, kv_comp_csa_state, inp_dsv4->get_csa().state_write_idxs, il)); - csa_state_kv = dsv4_with_zero_dep(ctx0, csa_state_kv, kv_comp_csa_state); - csa_state_score = dsv4_with_zero_dep(ctx0, csa_state_score, kv_comp_csa_state); + ggml_tensor * csa_snapshot_source_kv = ggml_concat(ctx0, + csa_restored.kv, csa_state_kv, 1); + ggml_tensor * csa_snapshot_source_score = ggml_concat(ctx0, + csa_restored.score, csa_state_score, 1); + + const dsv4_state_tensors csa_snapshot = dsv4_build_state_snapshot( + ctx0, inp_dsv4->get_csa(), csa_state, csa_snapshot_source_kv, csa_snapshot_source_score, il); + if (csa_snapshot.kv != nullptr) { + ggml_build_forward_expand(gf, csa_snapshot.kv); + } + if (csa_snapshot.score != nullptr) { + ggml_build_forward_expand(gf, csa_snapshot.score); + } ggml_tensor * csa_persist_kv = ggml_get_rows(ctx0, csa_state_kv, inp_dsv4->get_csa().state_persist_src_idxs); ggml_tensor * csa_persist_score = ggml_get_rows(ctx0, csa_state_score, inp_dsv4->get_csa().state_persist_src_idxs); @@ -931,10 +1084,16 @@ ggml_tensor * llama_model_deepseek4::graph::build_attention( GGML_ASSERT(inp_dsv4->get_lid().state_write_idxs); - ggml_tensor * lid_source_kv = ggml_concat(ctx0, - inp_dsv4->mctx->get_lid_state()->get_kv(ctx0, il), lid_state_kv, 1); - ggml_tensor * lid_source_score = ggml_concat(ctx0, - inp_dsv4->mctx->get_lid_state()->get_score(ctx0, il), lid_state_score, 1); + const auto * lid_state = inp_dsv4->mctx->get_lid_state(); + const dsv4_state_tensors lid_restored = dsv4_build_state_restore( + ctx0, inp_dsv4->get_lid(), lid_state, il); + ggml_tensor * lid_base_kv = dsv4_view_2d( + ctx0, lid_restored.kv, lid_restored.kv->ne[0], lid_state->get_n_rows(), 0); + ggml_tensor * lid_base_score = dsv4_view_2d( + ctx0, lid_restored.score, lid_restored.score->ne[0], lid_state->get_n_rows(), 0); + + ggml_tensor * lid_source_kv = ggml_concat(ctx0, lid_base_kv, lid_state_kv, 1); + ggml_tensor * lid_source_score = ggml_concat(ctx0, lid_base_score, lid_state_score, 1); ggml_tensor * kv_comp_lid_state = build_overlap_compressed_kv_from_state( lid_source_kv, @@ -955,8 +1114,19 @@ ggml_tensor * llama_model_deepseek4::graph::build_attention( ggml_build_forward_expand(gf, inp_dsv4->mctx->get_lid()->cpy_k(ctx0, kv_comp_lid_state, inp_dsv4->get_lid().state_write_idxs, il)); - lid_state_kv = dsv4_with_zero_dep(ctx0, lid_state_kv, kv_comp_lid_state); - lid_state_score = dsv4_with_zero_dep(ctx0, lid_state_score, kv_comp_lid_state); + ggml_tensor * lid_snapshot_source_kv = ggml_concat(ctx0, + lid_restored.kv, lid_state_kv, 1); + ggml_tensor * lid_snapshot_source_score = ggml_concat(ctx0, + lid_restored.score, lid_state_score, 1); + + const dsv4_state_tensors lid_snapshot = dsv4_build_state_snapshot( + ctx0, inp_dsv4->get_lid(), lid_state, lid_snapshot_source_kv, lid_snapshot_source_score, il); + if (lid_snapshot.kv != nullptr) { + ggml_build_forward_expand(gf, lid_snapshot.kv); + } + if (lid_snapshot.score != nullptr) { + ggml_build_forward_expand(gf, lid_snapshot.score); + } ggml_tensor * lid_persist_kv = ggml_get_rows(ctx0, lid_state_kv, inp_dsv4->get_lid().state_persist_src_idxs); ggml_tensor * lid_persist_score = ggml_get_rows(ctx0, lid_state_score, inp_dsv4->get_lid().state_persist_src_idxs); @@ -970,15 +1140,21 @@ ggml_tensor * llama_model_deepseek4::graph::build_attention( ggml_build_forward_expand(gf, lid_state_score); } - ggml_tensor * hca_state_dep = nullptr; + const llama_dsv4_comp_state * hca_state = nullptr; + dsv4_state_tensors hca_restored = {}; if (ratio == DSV4_HCA_RATIO && inp_dsv4->get_hca().state_write_idxs) { GGML_ASSERT(hca_state_kv); GGML_ASSERT(hca_state_score); - ggml_tensor * hca_source_kv = ggml_concat(ctx0, - inp_dsv4->mctx->get_hca_state()->get_kv(ctx0, il), hca_state_kv, 1); - ggml_tensor * hca_source_score = ggml_concat(ctx0, - inp_dsv4->mctx->get_hca_state()->get_score(ctx0, il), hca_state_score, 1); + hca_state = inp_dsv4->mctx->get_hca_state(); + hca_restored = dsv4_build_state_restore(ctx0, inp_dsv4->get_hca(), hca_state, il); + ggml_tensor * hca_base_kv = dsv4_view_2d( + ctx0, hca_restored.kv, hca_restored.kv->ne[0], hca_state->get_n_rows(), 0); + ggml_tensor * hca_base_score = dsv4_view_2d( + ctx0, hca_restored.score, hca_restored.score->ne[0], hca_state->get_n_rows(), 0); + + hca_source_kv = ggml_concat(ctx0, hca_base_kv, hca_state_kv, 1); + hca_source_score = ggml_concat(ctx0, hca_base_score, hca_state_score, 1); ggml_tensor * kv_comp_hca = build_hca_compressed_kv_from_state( hca_source_kv, @@ -997,15 +1173,41 @@ ggml_tensor * llama_model_deepseek4::graph::build_attention( ggml_build_forward_expand(gf, inp_dsv4->mctx->get_hca()->cpy_k(ctx0, kv_comp_hca, inp_dsv4->get_hca().state_write_idxs, il)); - hca_state_dep = kv_comp_hca; } if (ratio == DSV4_HCA_RATIO && inp_dsv4->get_hca().state_pos) { GGML_ASSERT(hca_state_kv); GGML_ASSERT(hca_state_score); - hca_state_kv = dsv4_with_zero_dep(ctx0, hca_state_kv, hca_state_dep); - hca_state_score = dsv4_with_zero_dep(ctx0, hca_state_score, hca_state_dep); + if (hca_state == nullptr) { + hca_state = inp_dsv4->mctx->get_hca_state(); + } + if (hca_restored.kv == nullptr) { + hca_restored = dsv4_build_state_restore(ctx0, inp_dsv4->get_hca(), hca_state, il); + } + if (hca_source_kv == nullptr || hca_source_score == nullptr) { + ggml_tensor * hca_base_kv = dsv4_view_2d( + ctx0, hca_restored.kv, hca_restored.kv->ne[0], hca_state->get_n_rows(), 0); + ggml_tensor * hca_base_score = dsv4_view_2d( + ctx0, hca_restored.score, hca_restored.score->ne[0], hca_state->get_n_rows(), 0); + + hca_source_kv = ggml_concat(ctx0, hca_base_kv, hca_state_kv, 1); + hca_source_score = ggml_concat(ctx0, hca_base_score, hca_state_score, 1); + } + + ggml_tensor * hca_snapshot_source_kv = ggml_concat(ctx0, + hca_restored.kv, hca_state_kv, 1); + ggml_tensor * hca_snapshot_source_score = ggml_concat(ctx0, + hca_restored.score, hca_state_score, 1); + + const dsv4_state_tensors hca_snapshot = dsv4_build_state_snapshot( + ctx0, inp_dsv4->get_hca(), hca_state, hca_snapshot_source_kv, hca_snapshot_source_score, il); + if (hca_snapshot.kv != nullptr) { + ggml_build_forward_expand(gf, hca_snapshot.kv); + } + if (hca_snapshot.score != nullptr) { + ggml_build_forward_expand(gf, hca_snapshot.score); + } ggml_tensor * hca_persist_kv = ggml_get_rows(ctx0, hca_state_kv, inp_dsv4->get_hca().state_persist_src_idxs); ggml_tensor * hca_persist_score = ggml_get_rows(ctx0, hca_state_score, inp_dsv4->get_hca().state_persist_src_idxs); @@ -1020,7 +1222,14 @@ ggml_tensor * llama_model_deepseek4::graph::build_attention( } ggml_tensor * out = nullptr; - if (ratio == DSV4_CSA_RATIO && + if (inp_mtp) { + out = build_attn(inp_mtp, + nullptr, nullptr, nullptr, + q, kv, nullptr, + nullptr, layer.attn_sinks, nullptr, + 1.0f/sqrtf(float(n_embd_head)), il); + cb(out, "attn_raw", il); + } else if (ratio == DSV4_CSA_RATIO && inp_dsv4->get_csa().kq_mask && inp_dsv4->get_lid().kq_mask && inp_dsv4->get_lid().k_rot) { @@ -1051,7 +1260,7 @@ ggml_tensor * llama_model_deepseek4::graph::build_attention( out = ggml_reshape_3d(ctx0, out, o_group_dim, n_groups, nt); out = ggml_permute(ctx0, out, 0, 2, 1, 3); - ggml_tensor * oa = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, layer.wo_a, layer.wo_a->ne[0], o_lora_rank, n_groups), out); + ggml_tensor * oa = ggml_mul_mat(ctx0, layer.wo_a, out); cb(oa, "attn_wo_a", il); oa = ggml_permute(ctx0, oa, 0, 2, 1, 3); oa = ggml_cont_2d(ctx0, oa, o_lora_rank*n_groups, nt); @@ -1079,6 +1288,12 @@ llama_model_deepseek4::graph::graph(const llama_model & model, const llm_graph_p cb(inpL, "hc_init", -1); for (int il = 0; il < n_layer; ++il) { + if ((size_t) il < cparams.embeddings_layer_inp.size() && cparams.embeddings_layer_inp[il]) { + res->t_layer_inp[il] = dsv4_hc_mean(ctx0, inpL); + cb(res->t_layer_inp[il], "layer_inp", il); + ggml_build_forward_expand(gf, res->t_layer_inp[il]); + } + ggml_tensor * residual = inpL; ggml_tensor * post = nullptr; ggml_tensor * comb = nullptr; @@ -1106,6 +1321,10 @@ llama_model_deepseek4::graph::graph(const llama_model & model, const llm_graph_p &post, &comb, il); cb(cur, "hc_ffn_pre", il); + ggml_build_forward_expand(gf, residual); + ggml_build_forward_expand(gf, post); + ggml_build_forward_expand(gf, comb); + cur = build_norm(cur, model.layers[il].ffn_norm, nullptr, LLM_NORM_RMS, il); cb(cur, "ffn_norm", il); @@ -1148,13 +1367,26 @@ llama_model_deepseek4::graph::graph(const llama_model & model, const llm_graph_p inpL = build_hc_post(cur, residual, post, comb, il); inpL = build_cvec(inpL, il); - cb(inpL, "l_out", il); + cb(inpL, "l_last", il); + } + + if ((size_t) n_layer < cparams.embeddings_layer_inp.size() && cparams.embeddings_layer_inp[n_layer]) { + res->t_layer_inp[n_layer] = dsv4_hc_mean(ctx0, inpL); + cb(res->t_layer_inp[n_layer], "layer_inp", n_layer); + ggml_build_forward_expand(gf, res->t_layer_inp[n_layer]); + } + + ggml_tensor * flat = ggml_reshape_2d(ctx0, inpL, n_embd*hc, n_tokens); + ggml_tensor * flat_out = inp_out_ids ? ggml_get_rows(ctx0, flat, inp_out_ids) : flat; + + if (cparams.embeddings_nextn) { + ggml_tensor * h_nextn = cparams.embeddings_nextn_masked ? flat_out : inpL; + cb(h_nextn, "h_nextn", -1); + res->t_h_nextn = h_nextn; } if (inp_out_ids) { - ggml_tensor * flat = ggml_reshape_2d(ctx0, inpL, n_embd*hc, n_tokens); - flat = ggml_get_rows(ctx0, flat, inp_out_ids); - inpL = ggml_reshape_3d(ctx0, flat, n_embd, hc, n_outputs); + inpL = ggml_reshape_3d(ctx0, flat_out, n_embd, hc, n_outputs); } cur = build_hc_head(inpL, model.hc_head_fn, model.hc_head_scale, model.hc_head_base); @@ -1170,3 +1402,145 @@ llama_model_deepseek4::graph::graph(const llama_model & model, const llm_graph_p ggml_build_forward_expand(gf, cur); } + + +llama_model_deepseek4::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params) : + graph(params) { + GGML_ASSERT(hparams.n_layer_nextn > 0 && "DEEPSEEK4 MTP requires n_layer_nextn > 0"); + GGML_ASSERT(hparams.n_layer_nextn == 1 && "DEEPSEEK4 MTP currently only supports a single MTP block"); + GGML_ASSERT(cparams.nextn_layer_offset >= 0 && + cparams.nextn_layer_offset < (int) hparams.n_layer_nextn && + "nextn_layer_offset out of range [0, n_layer_nextn)"); + GGML_ASSERT(ubatch.token && "DEEPSEEK4 MTP requires token input"); + + const int64_t hc = hparams.dsv4_hc_mult; + GGML_ASSERT(hparams.n_embd_out() == (uint32_t) (n_embd*hc) && "DEEPSEEK4 MTP hidden width mismatch"); + + const int il = hparams.n_layer() + cparams.nextn_layer_offset; + const auto & layer = model.layers[il]; + + GGML_ASSERT(layer.nextn.eh_proj && "MTP block missing nextn.eh_proj"); + GGML_ASSERT(layer.nextn.enorm && "MTP block missing nextn.enorm"); + GGML_ASSERT(layer.nextn.hnorm && "MTP block missing nextn.hnorm"); + + auto inp = std::make_unique(hparams.n_embd_out()); + + inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + ggml_set_input(inp->tokens); + + inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd_out(), n_tokens); + ggml_set_input(inp->embd); + + inp->h = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd_out(), n_tokens); + ggml_set_input(inp->h); + ggml_set_name(inp->h, "mtp_h_input"); + + ggml_tensor * tok_embd_w = layer.nextn.embed_tokens ? layer.nextn.embed_tokens : model.tok_embd; + ggml_tensor * tok_embd = ggml_get_rows(ctx0, tok_embd_w, inp->tokens); + cb(tok_embd, "mtp_tok_embd", il); + + ggml_tensor * h_state = ggml_reshape_3d(ctx0, inp->h, n_embd, hc, n_tokens); + cb(h_state, "mtp_h_state", il); + + res->add_input(std::move(inp)); + + ggml_tensor * inp_pos = build_inp_pos(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + llm_graph_input_attn_k_iswa * inp_attn = build_attn_inp_k_iswa(); + + ggml_tensor * h_norm = build_norm(h_state, layer.nextn.hnorm, nullptr, LLM_NORM_RMS, il); + cb(h_norm, "mtp_hnorm", il); + + ggml_tensor * e_norm = build_norm(tok_embd, layer.nextn.enorm, nullptr, LLM_NORM_RMS, il); + e_norm = ggml_reshape_3d(ctx0, e_norm, n_embd, 1, n_tokens); + e_norm = ggml_repeat_4d(ctx0, e_norm, n_embd, hc, n_tokens, 1); + cb(e_norm, "mtp_enorm", il); + + ggml_tensor * concat = ggml_concat(ctx0, e_norm, h_norm, 0); + cb(concat, "mtp_concat", il); + + ggml_tensor * inpL = build_lora_mm(layer.nextn.eh_proj, concat, layer.nextn.eh_proj_s); + cb(inpL, "mtp_eh_proj", il); + + ggml_tensor * residual = inpL; + ggml_tensor * post = nullptr; + ggml_tensor * comb = nullptr; + + ggml_tensor * cur = build_hc_pre(inpL, + layer.hc_attn_fn, + layer.hc_attn_scale, + layer.hc_attn_base, + &post, &comb, il); + cb(cur, "mtp_hc_attn_pre", il); + + cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il); + cb(cur, "mtp_attn_norm", il); + + cur = build_attention(model, inp_attn, cur, inp_pos, il); + + inpL = build_hc_post(cur, residual, post, comb, il); + cb(inpL, "mtp_hc_attn_post", il); + + residual = inpL; + cur = build_hc_pre(inpL, + layer.hc_ffn_fn, + layer.hc_ffn_scale, + layer.hc_ffn_base, + &post, &comb, il); + cb(cur, "mtp_hc_ffn_pre", il); + + cur = build_norm(cur, layer.ffn_norm, nullptr, LLM_NORM_RMS, il); + cb(cur, "mtp_ffn_norm", il); + + GGML_ASSERT((uint32_t) il >= hparams.dsv4_hash_layer_count && "DEEPSEEK4 MTP does not support hash-routed MTP blocks"); + ggml_tensor * moe_out = build_moe_ffn(cur, + layer.ffn_gate_inp, + layer.ffn_up_exps, + layer.ffn_gate_exps, + layer.ffn_down_exps, + layer.ffn_exp_probs_b, + n_expert, hparams.n_expert_used, + LLM_FFN_SILU, hparams.expert_weights_norm, + hparams.expert_weights_scale, + (llama_expert_gating_func_type) hparams.expert_gating_func, + il); + cb(moe_out, "mtp_ffn_moe_out", il); + + ggml_tensor * ffn_shexp = build_ffn(cur, + layer.ffn_up_shexp, nullptr, nullptr, + layer.ffn_gate_shexp, nullptr, nullptr, + layer.ffn_down_shexp, nullptr, nullptr, + nullptr, LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(ffn_shexp, "mtp_ffn_shexp", il); + + cur = ggml_add(ctx0, moe_out, ffn_shexp); + cb(cur, "mtp_ffn_out", il); + + inpL = build_hc_post(cur, residual, post, comb, il); + inpL = build_cvec(inpL, il); + cb(inpL, "mtp_l_out", il); + + ggml_tensor * flat = ggml_reshape_2d(ctx0, inpL, n_embd*hc, n_tokens); + ggml_tensor * h_nextn = ggml_get_rows(ctx0, flat, inp_out_ids); + cb(h_nextn, "h_nextn", -1); + res->t_h_nextn = h_nextn; + + inpL = ggml_reshape_3d(ctx0, h_nextn, n_embd, hc, n_outputs); + + cur = build_hc_head(inpL, model.hc_head_fn, model.hc_head_scale, model.hc_head_base); + cb(cur, "mtp_hc_head", -1); + + ggml_tensor * head_norm_w = layer.nextn.shared_head_norm ? layer.nextn.shared_head_norm : model.output_norm; + GGML_ASSERT(head_norm_w && "DEEPSEEK4 MTP missing shared head norm"); + cur = build_norm(cur, head_norm_w, nullptr, LLM_NORM_RMS, -1); + cb(cur, "mtp_shared_head_norm", -1); + res->t_embd = cur; + + ggml_tensor * head_w = layer.nextn.shared_head_head ? layer.nextn.shared_head_head : model.output; + GGML_ASSERT(head_w && "DEEPSEEK4 MTP missing LM head"); + cur = ggml_mul_mat(ctx0, head_w, cur); + cb(cur, "result_output", -1); + + res->t_logits = cur; + ggml_build_forward_expand(gf, cur); +} diff --git a/src/models/dflash.cpp b/src/models/dflash.cpp index a7b4f4435a88..daff6e78f1cf 100644 --- a/src/models/dflash.cpp +++ b/src/models/dflash.cpp @@ -1,5 +1,6 @@ #include "models.h" +#include "llama-impl.h" #include "llama-kv-cache.h" #include "llama-kv-cache-iswa.h" @@ -19,6 +20,48 @@ void llama_model_dflash::load_arch_hparams(llama_model_loader & ml) { } LLAMA_LOG_INFO("]\n"); + // DeepSeek-V4 DSpark backbone: stages are full DSV4 blocks, uniform sliding window (the draft KV ring) + ml.get_key(LLM_KV_HYPER_CONNECTION_COUNT, hparams.dsv4_hc_mult, false); + if (hparams.dsv4_hc_mult > 0) { + ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q); + ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa); + ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); + ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared); + ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale); + ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm); + ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func); + ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_EXP, hparams.swiglu_clamp_exp, hparams.n_layer_all); + if (!ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_SHEXP, hparams.swiglu_clamp_shexp, hparams.n_layer_all, 0)) { + hparams.swiglu_clamp_shexp = hparams.swiglu_clamp_exp; + } + ml.get_key(LLM_KV_ATTENTION_OUTPUT_GROUP_COUNT, hparams.dsv4_o_group_count); + ml.get_key(LLM_KV_ATTENTION_OUTPUT_LORA_RANK, hparams.dsv4_o_lora_rank); + ml.get_key(LLM_KV_HYPER_CONNECTION_SINKHORN_ITERATIONS, hparams.dsv4_hc_sinkhorn_iters); + ml.get_key(LLM_KV_HYPER_CONNECTION_EPSILON, hparams.dsv4_hc_eps); + ml.get_arr(LLM_KV_ATTENTION_COMPRESS_RATIOS, hparams.dsv4_compress_ratios, false); + + if (hparams.expert_gating_func != LLAMA_EXPERT_GATING_FUNC_TYPE_SQRT_SOFTPLUS) { + throw std::runtime_error("DSpark DSV4 draft expects sqrtsoftplus MoE scoring"); + } + for (uint32_t il = 0; il < hparams.n_layer_all; ++il) { + if (hparams.dsv4_compress_ratios[il] != 0) { + throw std::runtime_error("DSpark DSV4 draft expects uncompressed attention on all stages"); + } + } + + GGML_ASSERT(hparams.n_swa > 0); + hparams.swa_type = LLAMA_SWA_TYPE_STANDARD; + hparams.set_swa_pattern(0); + for (uint32_t il = 0; il < hparams.n_layer_all; ++il) { + hparams.is_swa_impl[il] = true; + } + hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train; + hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train; + + type = LLM_TYPE_UNKNOWN; + return; + } + // optional interleaved sliding-window attention with per-layer pattern array. // DFlash has a single rope, so the SWA rope == main rope. if (ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false) && hparams.n_swa > 0) { @@ -36,10 +79,77 @@ void llama_model_dflash::load_arch_tensors(llama_model_loader &) { const int64_t n_embd_inp = hparams.n_embd_inp_enc(); + // DSpark = DFlash + a semi-autoregressive Markov head and Confidence head + // + // TODO: only Qwen3-style backbones are supported for now; other backbones (e.g. Gemma4) + // need their own conversion path and graph tweaks + const struct ggml_tensor * markov_meta = ml->get_tensor_meta("markov_w1.weight"); + if (markov_meta) { + const int64_t dspark_markov_rank = markov_meta->ne[0]; + + dspark_markov_w1 = create_tensor(tn(LLM_TENSOR_DSPARK_MARKOV_W1, "weight"), { dspark_markov_rank, n_vocab }, 0); + dspark_markov_w2 = create_tensor(tn(LLM_TENSOR_DSPARK_MARKOV_W2, "weight"), { dspark_markov_rank, n_vocab }, 0); + + dspark_conf_proj = create_tensor(tn(LLM_TENSOR_DSPARK_CONF_PROJ, "weight"), { n_embd + dspark_markov_rank, 1 }, 0); + dspark_conf_proj_b = create_tensor(tn(LLM_TENSOR_DSPARK_CONF_PROJ, "bias"), { 1 }, TENSOR_NOT_REQUIRED); + + LLAMA_LOG_INFO("%s: DFlash with DSpark markov head (rank = %lld)\n", __func__, (long long) dspark_markov_rank); + } + fc = create_tensor(tn(LLM_TENSOR_FC, "weight"), { n_embd_inp, n_embd }, 0); output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), { n_embd }, 0); // encoder hidden_norm (after fc) output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0); // decoder final norm + if (hparams.dsv4_hc_mult > 0) { + const int64_t q_lora_rank = hparams.n_lora_q; + const int64_t n_ff_exp = hparams.n_ff_exp; + const int64_t n_expert_shared = hparams.n_expert_shared; + const int64_t n_embd_head = hparams.n_embd_head_k(); + const int64_t o_groups = hparams.dsv4_o_group_count; + const int64_t o_lora_rank = hparams.dsv4_o_lora_rank; + const int64_t hc_mult = hparams.dsv4_hc_mult; + const int64_t hc_dim = hc_mult * n_embd; + const int64_t hc_mix_dim = (2 + hc_mult) * hc_mult; + + hc_head_fn = create_tensor(tn(LLM_TENSOR_HC_HEAD_FN, "weight"), {hc_dim, hc_mult}, 0); + hc_head_base = create_tensor(tn(LLM_TENSOR_HC_HEAD_BASE, "weight"), {hc_mult}, 0); + hc_head_scale = create_tensor(tn(LLM_TENSOR_HC_HEAD_SCALE, "weight"), {1}, 0); + + for (int i = 0; i < n_layer; ++i) { + auto & layer = layers[i]; + + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + layer.attn_sinks = create_tensor(tn(LLM_TENSOR_ATTN_SINKS, "weight", i), {n_head}, 0); + layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, 0); + layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, 0); + layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head}, 0); + layer.wkv = create_tensor(tn(LLM_TENSOR_ATTN_KV, "weight", i), {n_embd, n_embd_head}, 0); + layer.attn_kv_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_NORM, "weight", i), {n_embd_head}, 0); + layer.wo_a = create_tensor(tn(LLM_TENSOR_ATTN_OUT_A, "weight", i), {n_head * n_embd_head / o_groups, o_lora_rank, o_groups}, TENSOR_ALLOW_RESHAPE); + layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT_B, "weight", i), {o_groups * o_lora_rank, n_embd}, 0); + + layer.hc_attn_fn = create_tensor(tn(LLM_TENSOR_HC_ATTN_FN, "weight", i), {hc_dim, hc_mix_dim}, 0); + layer.hc_attn_base = create_tensor(tn(LLM_TENSOR_HC_ATTN_BASE, "weight", i), {hc_mix_dim}, 0); + layer.hc_attn_scale = create_tensor(tn(LLM_TENSOR_HC_ATTN_SCALE, "weight", i), {3}, 0); + layer.hc_ffn_fn = create_tensor(tn(LLM_TENSOR_HC_FFN_FN, "weight", i), {hc_dim, hc_mix_dim}, 0); + layer.hc_ffn_base = create_tensor(tn(LLM_TENSOR_HC_FFN_BASE, "weight", i), {hc_mix_dim}, 0); + layer.hc_ffn_scale = create_tensor(tn(LLM_TENSOR_HC_FFN_SCALE, "weight", i), {3}, 0); + + layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0); + layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + + layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0); + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0); + layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0); + + layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0); + layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_exp * n_expert_shared, n_embd }, 0); + layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0); + } + return; + } + for (int i = 0; i < n_layer; ++i) { auto & layer = layers[i]; @@ -66,6 +176,9 @@ std::unique_ptr llama_model_dflash::build_arch_graph(const ll return std::make_unique>(*this, params); case LLM_GRAPH_TYPE_DEFAULT: case LLM_GRAPH_TYPE_DECODER: + if (hparams.dsv4_hc_mult > 0) { + return std::make_unique(*this, params); + } return std::make_unique>(*this, params); default: GGML_ABORT("invalid graph type"); @@ -104,6 +217,94 @@ llama_model_dflash::graph::graph(const llama_model & model, const llm_grap ggml_build_forward_expand(gf, cur); } +// DSpark (DFlash + Markov & Confidence head): Markov bias on the draft logits, chained per block position +static void build_dspark_markov_head(llm_graph_context & g, const llama_model & model, ggml_tensor * tokens) { + ggml_context * ctx0 = g.ctx0; + auto & res = g.res; + + ggml_tensor * w1 = model.dspark_markov_w1; + ggml_tensor * w2 = model.dspark_markov_w2; + GGML_ASSERT(w1 && w2 && model.dspark_conf_proj && "DSpark markov/confidence weights not loaded"); + + ggml_tensor * base = res->t_logits; // [n_vocab, n_tokens] + const int64_t n_vocab = base->ne[0]; + const int64_t n_tok = base->ne[1]; + + const auto it = model.gguf_kv.find("dflash.block_size"); + GGML_ASSERT(it != model.gguf_kv.end() && "DSpark draft requires 'dflash.block_size' in GGUF metadata"); + const int64_t block_size = std::stoi(it->second); + GGML_ASSERT(block_size > 0); + + const int64_t n_blocks = g.ubatch.n_seqs_unq; + GGML_ASSERT(n_blocks > 0 && n_tok % n_blocks == 0 && "DSpark markov head requires equal-size blocks"); + // runtime tokens per block in this ubatch (anchor + drafted positions), bounded by training block_size + const int64_t block_drafts = n_tok / n_blocks; + if (block_drafts > block_size) { + return; + } + + // anchor (committed last) token of every block: token 0 of each block, i.e. a strided view + const size_t token_stride = (size_t) block_drafts * tokens->nb[0]; + const size_t base_stride = (size_t) block_drafts * base->nb[1]; + + ggml_tensor * prev = ggml_view_2d(ctx0, tokens, 1, n_blocks, token_stride, 0); + prev = ggml_cont_1d(ctx0, prev, n_blocks); + + // confidence head input: predicts per-position acceptance + ggml_tensor * conf_inp = res->t_embd; // [n_embd, n_tok] + + ggml_tensor * cat = nullptr; + ggml_tensor * cat_conf = nullptr; + + // TODO: the in-graph chain is greedy (argmax); sampling params affect only the final + // token pick, not the Markov conditioning path + for (int64_t i = 0; i < block_drafts; ++i) { + ggml_tensor * w1_prev = ggml_get_rows(ctx0, w1, prev); // [R, n_blocks] + ggml_tensor * bias = ggml_mul_mat(ctx0, w2, w1_prev); // [n_vocab, n_blocks] + + // position i of every block: strided view [n_vocab, n_blocks] + ggml_tensor * base_i = ggml_view_2d(ctx0, base, n_vocab, n_blocks, base_stride, i*base->nb[1]); + ggml_tensor * col = ggml_add(ctx0, base_i, bias); + + cat = cat ? ggml_concat(ctx0, cat, col, 1) : col; + + // conf(i) = sigmoid(conf_proj . [conf_inp(i); markov_w1[prev(i)]] + b) -- [1, n_blocks] + ggml_tensor * conf_inp_i = ggml_view_2d(ctx0, conf_inp, conf_inp->ne[0], n_blocks, + (size_t) block_drafts * conf_inp->nb[1], i*conf_inp->nb[1]); + ggml_tensor * feat = ggml_concat(ctx0, ggml_cont(ctx0, conf_inp_i), w1_prev, 0); + ggml_tensor * conf = ggml_mul_mat(ctx0, model.dspark_conf_proj, feat); + if (model.dspark_conf_proj_b) { + conf = ggml_add(ctx0, conf, model.dspark_conf_proj_b); + } + conf = ggml_sigmoid(ctx0, conf); + + cat_conf = cat_conf ? ggml_concat(ctx0, cat_conf, conf, 1) : conf; + + if (i + 1 < block_drafts) { + prev = ggml_argmax(ctx0, col); + } + } + + // cat is position-major; restore ubatch block-major order + ggml_tensor * out = ggml_reshape_3d(ctx0, cat, n_vocab, n_blocks, block_drafts); + out = ggml_cont(ctx0, ggml_permute(ctx0, out, 0, 2, 1, 3)); // [n_vocab, block_drafts, n_blocks] + out = ggml_reshape_2d(ctx0, out, n_vocab, n_tok); + + { + ggml_tensor * conf = ggml_reshape_3d(ctx0, cat_conf, 1, n_blocks, block_drafts); + conf = ggml_cont(ctx0, ggml_permute(ctx0, conf, 0, 2, 1, 3)); + conf = ggml_reshape_2d(ctx0, conf, 1, n_tok); + + // note: broadcast the [1, n_tok] confidences to n_embd-wide rows to be able to reuse `llama_get_embeddings_nextn` + conf = ggml_repeat(ctx0, conf, res->t_embd); + res->t_h_nextn = conf; + ggml_build_forward_expand(g.gf, conf); + } + + res->t_logits = out; + ggml_build_forward_expand(g.gf, out); +} + // DFlash decoder, dual-mode by batch type: // * embd batch -> fused target features: project + inject K/V into the cache. // * token batch -> noise-block diffusion: attend over [committed, MASK...] to generate draft tokens @@ -164,9 +365,25 @@ llama_model_dflash::graph::graph(const llama_model & model, const llm_gra const auto * kv = is_swa ? inp_attn_iswa->mctx->get_swa() : inp_attn_iswa->mctx->get_base(); ggml_tensor * k_idxs = is_swa ? inp_attn_iswa->get_k_idxs_swa() : inp_attn_iswa->get_k_idxs(); ggml_tensor * v_idxs = is_swa ? inp_attn_iswa->get_v_idxs_swa() : inp_attn_iswa->get_v_idxs(); + // rotate K/V into the cache's rotated space + ggml_tensor * k_rot = is_swa ? inp_attn_iswa->self_k_rot_swa : inp_attn_iswa->self_k_rot; + ggml_tensor * v_rot = is_swa ? inp_attn_iswa->self_v_rot_swa : inp_attn_iswa->self_v_rot; + if (k_rot) { + Kcur = llama_mul_mat_hadamard(ctx0, Kcur, k_rot); + } + if (v_rot) { + Vcur = llama_mul_mat_hadamard(ctx0, Vcur, v_rot); + } ggml_build_forward_expand(gf, kv->cpy_k(ctx0, Kcur, k_idxs, il)); ggml_build_forward_expand(gf, kv->cpy_v(ctx0, Vcur, v_idxs, il)); } else { + // rotate K/V into the cache's rotated space + if (inp_attn->self_k_rot) { + Kcur = llama_mul_mat_hadamard(ctx0, Kcur, inp_attn->self_k_rot); + } + if (inp_attn->self_v_rot) { + Vcur = llama_mul_mat_hadamard(ctx0, Vcur, inp_attn->self_v_rot); + } ggml_build_forward_expand(gf, inp_attn->mctx->cpy_k(ctx0, Kcur, inp_attn->get_k_idxs(), il)); ggml_build_forward_expand(gf, inp_attn->mctx->cpy_v(ctx0, Vcur, inp_attn->get_v_idxs(), il)); } @@ -193,6 +410,8 @@ llama_model_dflash::graph::graph(const llama_model & model, const llm_gra inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); ggml_set_input(inp->tokens); + ggml_tensor * inp_tokens = inp->tokens; + ggml_tensor * inpL = ggml_get_rows(ctx0, tok_embd, inp->tokens); cb(inpL, "inp_noise_embd", -1); @@ -273,4 +492,184 @@ llama_model_dflash::graph::graph(const llama_model & model, const llm_gra res->t_logits = cur; ggml_build_forward_expand(gf, cur); + + // DSpark: bias the draft logits with the Markov head + if (model.dspark_markov_w1) { + build_dspark_markov_head(*this, model, inp_tokens); + } +} + +// DSV4 DSpark decoder, dual-mode by batch type (see the DFlash decoder above): +// * embd batch -> project main_x through each stage's wkv and inject K into the ring cache +// * token batch -> noise block through 3 full DSV4 stages (hc + MLA + MoE), markov + confidence heads +llama_model_dflash::graph_dsv4::graph_dsv4(const llama_model & model, const llm_graph_params & params) : + llama_model_deepseek4::graph(params) { + const int64_t n_embd_head = hparams.n_embd_head_k(); + const int64_t n_embd_head_rope = hparams.n_rot(); + const int64_t n_embd_head_nope = n_embd_head - n_embd_head_rope; + + ggml_tensor * inp_pos = build_inp_pos(); + + llm_graph_input_attn_k_iswa * inp_attn = build_attn_inp_k_iswa(); + + // KV cache injection: fused target features from the encoder + if (ubatch.embd) { + auto inp = std::make_unique(n_embd); + + inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens); + ggml_set_input(inp->embd); + + ggml_tensor * inp_g = inp->embd; + cb(inp_g, "inp_g_embeddings", -1); + + res->add_input(std::move(inp)); + + for (int il = 0; il < n_layer; ++il) { + const auto & layer = model.layers[il]; + + // main-track KV: kv_norm(wkv(main_x)) with rope on the trailing dims, same + // rope parameters as the uncompressed layers in build_attention_impl + ggml_tensor * kv = build_lora_mm(layer.wkv, inp_g); + kv = build_norm(kv, layer.attn_kv_norm, nullptr, LLM_NORM_RMS, il); + kv = ggml_reshape_3d(ctx0, kv, n_embd_head, 1, n_tokens); + + ggml_tensor * kv_nope = ggml_view_3d(ctx0, kv, n_embd_head_nope, 1, n_tokens, + ggml_row_size(kv->type, n_embd_head), + ggml_row_size(kv->type, n_embd_head), + 0); + ggml_tensor * kv_pe = ggml_view_3d(ctx0, kv, n_embd_head_rope, 1, n_tokens, + ggml_row_size(kv->type, n_embd_head), + ggml_row_size(kv->type, n_embd_head), + ggml_row_size(kv->type, n_embd_head_nope)); + kv_pe = ggml_rope_ext(ctx0, kv_pe, inp_pos, nullptr, n_embd_head_rope, rope_type, 0, + freq_base, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f); + kv = ggml_concat(ctx0, kv_nope, kv_pe, 0); + cb(kv, "kv_injected", il); + + if (inp_attn->self_k_rot_swa) { + kv = llama_mul_mat_hadamard(ctx0, kv, inp_attn->self_k_rot_swa); + } + ggml_build_forward_expand(gf, inp_attn->mctx->get_swa()->cpy_k(ctx0, kv, inp_attn->get_k_idxs_swa(), il)); + } + + res->t_embd = inp_g; + + ggml_build_forward_expand(gf, inp_g); + return; + } + + // tok_embd from the target model (shared via ctx_other) + auto * tok_embd = model.tok_embd; + if (tok_embd == nullptr) { + GGML_ASSERT(cparams.ctx_other != nullptr); + const auto * model_other = llama_get_model(cparams.ctx_other); + + GGML_ASSERT(model_other->tok_embd != nullptr && "DSpark decoder requires the target model's token embeddings"); + tok_embd = model_other->tok_embd; + } + + auto inp = std::make_unique(n_embd); + + inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + ggml_set_input(inp->tokens); + + ggml_tensor * inp_tokens = inp->tokens; + + ggml_tensor * inpL = ggml_get_rows(ctx0, tok_embd, inp->tokens); + cb(inpL, "inp_noise_embd", -1); + + res->add_input(std::move(inp)); + + const int64_t hc = hparams.dsv4_hc_mult; + inpL = ggml_reshape_3d(ctx0, inpL, n_embd, 1, n_tokens); + inpL = ggml_repeat_4d(ctx0, inpL, n_embd, hc, n_tokens, 1); + cb(inpL, "hc_init", -1); + + for (int il = 0; il < n_layer; ++il) { + const auto & layer = model.layers[il]; + + ggml_tensor * residual = inpL; + ggml_tensor * post = nullptr; + ggml_tensor * comb = nullptr; + + ggml_tensor * cur = build_hc_pre(inpL, + layer.hc_attn_fn, + layer.hc_attn_scale, + layer.hc_attn_base, + &post, &comb, il); + cb(cur, "hc_attn_pre", il); + + cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il); + cb(cur, "attn_norm", il); + + cur = build_attention(model, inp_attn, cur, inp_pos, il); + + inpL = build_hc_post(cur, residual, post, comb, il); + cb(inpL, "hc_attn_post", il); + + residual = inpL; + cur = build_hc_pre(inpL, + layer.hc_ffn_fn, + layer.hc_ffn_scale, + layer.hc_ffn_base, + &post, &comb, il); + cb(cur, "hc_ffn_pre", il); + + cur = build_norm(cur, layer.ffn_norm, nullptr, LLM_NORM_RMS, il); + cb(cur, "ffn_norm", il); + + ggml_tensor * moe_out = build_moe_ffn(cur, + layer.ffn_gate_inp, + layer.ffn_up_exps, + layer.ffn_gate_exps, + layer.ffn_down_exps, + layer.ffn_exp_probs_b, + n_expert, hparams.n_expert_used, + LLM_FFN_SILU, hparams.expert_weights_norm, + hparams.expert_weights_scale, + (llama_expert_gating_func_type) hparams.expert_gating_func, + il); + cb(moe_out, "ffn_moe_out", il); + + ggml_tensor * ffn_shexp = build_ffn(cur, + layer.ffn_up_shexp, nullptr, nullptr, + layer.ffn_gate_shexp, nullptr, nullptr, + layer.ffn_down_shexp, nullptr, nullptr, + nullptr, LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(ffn_shexp, "ffn_shexp", il); + + cur = ggml_add(ctx0, moe_out, ffn_shexp); + cb(cur, "ffn_out", il); + + inpL = build_hc_post(cur, residual, post, comb, il); + cb(inpL, "l_out", il); + } + + ggml_tensor * cur = build_hc_head(inpL, model.hc_head_fn, model.hc_head_scale, model.hc_head_base); + cb(cur, "hc_head", -1); + + // confidence head input: the reference scores the pre-norm collapsed hidden state + res->t_embd = cur; + + cur = build_norm(cur, model.output_norm, nullptr, LLM_NORM_RMS, -1); + cb(cur, "result_norm", -1); + + // lm_head from the target model (shared via ctx_other) + auto * output = model.output; + if (output == nullptr) { + GGML_ASSERT(cparams.ctx_other != nullptr); + const auto * model_other = llama_get_model(cparams.ctx_other); + GGML_ASSERT(model_other->output != nullptr && "DSpark decoder requires the target model's output projection"); + output = model_other->output; + } + + cur = build_lora_mm(output, cur); + cb(cur, "result_output", -1); + res->t_logits = cur; + + ggml_build_forward_expand(gf, cur); + + if (model.dspark_markov_w1) { + build_dspark_markov_head(*this, model, inp_tokens); + } } diff --git a/src/models/eagle3.cpp b/src/models/eagle3.cpp index 9d96fae5944e..be466056df69 100644 --- a/src/models/eagle3.cpp +++ b/src/models/eagle3.cpp @@ -28,6 +28,10 @@ void llama_model_eagle3::load_arch_hparams(llama_model_loader & ml) { LLAMA_LOG_INFO("%s: EAGLE3gnorm_before_residual = true\n", __func__); } + // eagle3 norm_before_fc (optional, default false) + // compatible with eagle3.1 (e.g. nvidia/gpt-oss-120b-Eagle3-v3) + ml.get_key(LLM_KV_NORM_BEFORE_FC, hparams.norm_before_fc, false); + type = LLM_TYPE_UNKNOWN; } @@ -53,6 +57,11 @@ void llama_model_eagle3::load_arch_tensors(llama_model_loader &) { // Feature fusion layer: projects 3 target layers to draft hidden size fc = create_tensor(tn(LLM_TENSOR_FC, "weight"), {n_embd_inp, n_embd}, 0); + // RMSNorm on the fused target features (input to fc), only when norm_before_fc is set. + if (hparams.norm_before_fc) { + output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd_inp}, 0); + } + // Output layer (uses draft vocab size) output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_draft_vocab}, TENSOR_NOT_REQUIRED); @@ -130,6 +139,12 @@ llama_model_eagle3::graph::graph(const llama_model & model, const llm_grap cur = build_inp_embd_enc(); + // RMSNorm on the fused target features before fc + if (hparams.norm_before_fc) { + cur = build_norm(cur, model.output_norm_enc, NULL, LLM_NORM_RMS, -1); + cb(cur, "enc_input_norm", -1); + } + // Feature fusion layer cur = build_lora_mm(model.fc, cur); cb(cur, "fc_out", -1); diff --git a/src/models/gemma4.cpp b/src/models/gemma4.cpp index 6a96979cebde..e44f423bdbc5 100644 --- a/src/models/gemma4.cpp +++ b/src/models/gemma4.cpp @@ -142,33 +142,6 @@ static ggml_tensor * ggml_view_2d_slice(ggml_context * ctx0, ggml_tensor * x, in idx * x->ne[0] * x->ne[1] * ggml_element_size(x)); } -// TODO @ngxson : maybe improve this in the future -class llm_graph_input_logits_bias : public llm_graph_input_i { -public: - llm_graph_input_logits_bias(const llama_vocab & vocab) { - arr.resize(vocab.n_tokens(), 0.0f); - for (llama_token id : vocab.get_suppress_tokens()) { - if (0 <= id && id < (int32_t)vocab.n_tokens()) { - arr[id] = -INFINITY; - } - } - } - virtual ~llm_graph_input_logits_bias() = default; - - void set_input(const llama_ubatch * /*ubatch*/) override { - const int64_t n_vocab = arr.size(); - ggml_backend_tensor_set(logits_bias, arr.data(), 0, n_vocab*ggml_element_size(logits_bias)); - } - - bool can_reuse(const llm_graph_params & /*params*/) override { - return true; - } - - ggml_tensor * logits_bias = nullptr; // F32 [n_vocab] - - std::vector arr; -}; - llama_model_gemma4::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params), model(model), @@ -429,16 +402,6 @@ llama_model_gemma4::graph::graph(const llama_model & model, const llm_graph_para cur = ggml_scale(ctx0, cur, hparams.f_final_logit_softcapping); } - // apply logits bias if needed (e.g. for gemma4_unified patch) - // this is to mirror the suppress_tokens patch on transformers, to avoid model from outputing and tokens (which is a known issue related to the checkpoint) - // TODO: maybe handle this inside the sampling system in the future - if (!model.vocab.get_suppress_tokens().empty()) { - auto inp_bias = std::make_unique(model.vocab); - inp_bias->logits_bias = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, inp_bias->arr.size()); - cur = ggml_add(ctx0, cur, inp_bias->logits_bias); - res->add_input(std::move(inp_bias)); - } - cb(cur, "result_output", -1); res->t_logits = cur; diff --git a/src/models/glm-dsa.cpp b/src/models/glm-dsa.cpp index 32fe6def6f3c..360c2ee773f0 100644 --- a/src/models/glm-dsa.cpp +++ b/src/models/glm-dsa.cpp @@ -1,5 +1,31 @@ #include "models.h" +#include "llama-kv-cache-dsa.h" + +// https://huggingface.co/zai-org/GLM-5.2/blob/main/config.json#L26 +const std::array GLM_5_2_DEFAULT_INDEXER_TYPES = { + 1, 1, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, + 1, 0, 0, 0, +}; + void llama_model_glm_dsa::load_arch_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); @@ -34,18 +60,43 @@ void llama_model_glm_dsa::load_arch_hparams(llama_model_loader & ml) { // NextN/MTP parameters ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false); - GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_impl"); + GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_all"); + + // BC for GLM 5, 5.1 (full indexers) without indexer_types metadata + const bool is_pre_5_2 = hparams.n_ctx_train < 1048576; + if (is_pre_5_2) { + std::fill(hparams.is_indexer_full_impl.begin(), hparams.is_indexer_full_impl.end(), 1); + } else { + hparams.is_indexer_full_impl = GLM_5_2_DEFAULT_INDEXER_TYPES; + } + ml.get_key_or_arr(LLM_KV_ATTENTION_INDEXER_TYPES, hparams.is_indexer_full_impl, hparams.n_layer(), false); switch (hparams.n_layer()) { - case 79: type = LLM_TYPE_744B_A40B; break; + case 78: // GGUF with NextN/MTP metadata: n_layer() excludes the nextn layer + case 79: + type = LLM_TYPE_744B_A40B; break; default: type = LLM_TYPE_UNKNOWN; } } -void llama_model_glm_dsa::load_arch_tensors(llama_model_loader &) { +void llama_model_glm_dsa::load_arch_tensors(llama_model_loader & ml) { LLAMA_LOAD_LOCALS; const int64_t n_expert_shared = hparams.n_expert_shared; + // MTP-only: the GGUF carries only the NextN/MTP block(s) (user split target/draft). + const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr); + // Trunk-only: the GGUF declares MTP layers in metadata but the actual MTP + // tensors live in a separate file (or were stripped at conversion). Mark + // MTP tensors NOT_REQUIRED so the trunk loads cleanly. + const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight"; + const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr); + const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0; + int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0; + + if (!ml.load_mtp) { + mtp_flags |= TENSOR_SKIP; + } + const bool is_mla = hparams.is_mla(); if (!is_mla) { throw std::runtime_error("GLM_DSA architecture requires MLA"); @@ -74,12 +125,9 @@ void llama_model_glm_dsa::load_arch_tensors(llama_model_loader &) { } for (int i = 0; i < n_layer_all; ++i) { - int flags = 0; - if (i >= n_layer) { - // skip all tensors in the NextN layers - // TODO @ngxson : TENSOR_NOT_REQUIRED was a hack, need to remove it later - flags |= TENSOR_SKIP | TENSOR_NOT_REQUIRED; - } + // NextN/MTP layers (i >= n_layer) are full decoder blocks used by the + // LLM_GRAPH_TYPE_DECODER_MTP draft head; load them like qwen35moe/step35/hy_v3. + const int flags = (i >= n_layer) ? mtp_flags : trunk_flags; auto & layer = layers[i]; @@ -132,7 +180,7 @@ void llama_model_glm_dsa::load_arch_tensors(llama_model_loader &) { layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags); } - // NextN/MTP tensors (preserved but unused) - conditionally load for last n_layer_nextn + // NextN/MTP tensors - the NextN-specific wiring around the extra decoder block if (i >= n_layer) { layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, flags); layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags); @@ -147,6 +195,616 @@ void llama_model_glm_dsa::load_arch_tensors(llama_model_loader &) { } std::unique_ptr llama_model_glm_dsa::build_arch_graph(const llm_graph_params & params) const { + if (params.gtype == LLM_GRAPH_TYPE_DECODER_MTP) { + return std::make_unique(*this, params); + } return std::make_unique(*this, params); } +llama_model_glm_dsa::graph::graph(const llama_model & model, const llm_graph_params & params) : + llm_graph_context(params) { + const bool is_mla = hparams.is_mla(); + GGML_ASSERT(is_mla); + + // note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA + const int64_t n_embd_head_k = hparams.n_embd_head_k_mla(); + const int64_t n_embd_head_v = hparams.n_embd_head_v_mla(); + GGML_UNUSED(n_embd_head_v); + + const int64_t n_embd_head_qk_rope = hparams.n_rot(); + const int64_t n_embd_head_qk_nope = n_embd_head_k - n_embd_head_qk_rope; + + const int64_t n_indexer_head = hparams.indexer_n_head; + const int64_t n_embd_indexer_head = hparams.indexer_head_size; + const int64_t n_embd_indexer_head_rope = hparams.n_rot(); + const int64_t n_embd_indexer_head_nope = n_embd_indexer_head - n_embd_indexer_head_rope; + const uint32_t n_indexer_top_k = hparams.indexer_top_k; + + const uint32_t kv_lora_rank = hparams.n_lora_kv; + + // We have to pre-scale kq_scale and attn_factor to make the YaRN RoPE work correctly. + // See https://github.com/ggml-org/llama.cpp/discussions/7416 for detailed explanation. + // And also: https://github.com/ggml-org/llama.cpp/pull/17945 [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX] + + // first cancel the adjustment from llama_hparams::yarn_attn_factor_adjust to get the original attn_factor + GGML_ASSERT(ext_factor >= 0.0f); + const float attn_factor_org = attn_factor * (1.0f + 0.1f * logf(1.0f / freq_scale)); + + // use the original attn_factor to pre-scale the kq_scale + const float mscale = attn_factor_org * (1.0f + 0.1f * hparams.rope_yarn_log_mul * logf(1.0f / freq_scale)); + const float kq_scale = 1.0f * mscale * mscale / sqrtf(float(n_embd_head_k)); + + ggml_tensor * cur; + ggml_tensor * inpL; + + // {n_embd, n_tokens} + inpL = build_inp_embd(model.tok_embd); + + // inp_pos - contains the positions + ggml_tensor * inp_pos = build_inp_pos(); + + llm_graph_input_attn_k_dsa * inp_attn_dsa = build_attn_inp_k_dsa(); + + ggml_tensor * inp_out_ids = build_inp_out_ids(); + + // Difference vs Deepseek 3.2: shared indexer layers reuse the top_k from the previous full indexer layers + // See https://huggingface.co/zai-org/GLM-5.2/blob/main/config.json#L30 + ggml_tensor * prev_top_k = nullptr; + for (int il = 0; il < n_layer; ++il) { + ggml_tensor * inpSA = inpL; + + // norm + cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il); + cb(cur, "attn_norm", il); + + // self_attention + { + ggml_tensor * qr = ggml_mul_mat(ctx0, model.layers[il].wq_a, cur); + cb(qr, "qr", il); + + qr = build_norm(qr, model.layers[il].attn_q_a_norm, nullptr, LLM_NORM_RMS, il); + cb(qr, "qr", il); + + ggml_tensor * top_k = nullptr; + + // lightning indexer + if (hparams.is_indexer_full(il)) { + // "full" layer + ggml_tensor * indexer_q = ggml_mul_mat(ctx0, model.layers[il].indexer_attn_q_b, qr); + cb(indexer_q, "indexer_q", il); + + // split into {n_embd_indexer_head_rope, n_indexer_head, n_tokens} + ggml_tensor * indexer_q_pe = + ggml_view_3d(ctx0, indexer_q, n_embd_indexer_head_rope, n_indexer_head, n_tokens, + ggml_row_size(indexer_q->type, n_embd_indexer_head), + ggml_row_size(indexer_q->type, n_embd_indexer_head) * n_indexer_head, 0); + cb(indexer_q_pe, "indexer_q_pe", il); + + // and {n_embd_indexer_head_nope, n_indexer_head, n_tokens} + ggml_tensor * indexer_q_nope = + ggml_view_3d(ctx0, indexer_q, n_embd_indexer_head_nope, n_indexer_head, n_tokens, + ggml_row_size(indexer_q->type, n_embd_indexer_head), + ggml_row_size(indexer_q->type, n_embd_indexer_head) * n_indexer_head, + ggml_row_size(indexer_q->type, n_embd_indexer_head_nope)); + cb(indexer_q_nope, "indexer_q_nope", il); + + indexer_q_pe = ggml_rope_ext(ctx0, indexer_q_pe, inp_pos, nullptr, n_rot, + LLAMA_ROPE_TYPE_NORM, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow); + cb(indexer_q_pe, "indexer_q_pe", il); + + // {n_embd_indexer_head_rope + n_embd_indexer_head_nope, n_head, n_tokens} + indexer_q = ggml_concat(ctx0, indexer_q_pe, indexer_q_nope, 0); + cb(indexer_q, "indexer_q", il); + + ggml_tensor * indexer_k = ggml_mul_mat(ctx0, model.layers[il].indexer_attn_k, cur); + cb(indexer_k, "indexer_k", il); + + indexer_k = build_norm(indexer_k, model.layers[il].indexer_k_norm, model.layers[il].indexer_k_norm_b, LLM_NORM, il); + cb(indexer_k, "indexer_k", il); + + // split into {n_embd_indexer_head_rope, 1, n_tokens} + ggml_tensor * indexer_k_pe = + ggml_view_3d(ctx0, indexer_k, n_embd_indexer_head_rope, 1, n_tokens, + ggml_row_size(indexer_k->type, n_embd_indexer_head), + ggml_row_size(indexer_k->type, n_embd_indexer_head) * 1, 0); + cb(indexer_k_pe, "indexer_k_pe", il); + + // and {n_embd_indexer_head_nope, 1, n_tokens} + ggml_tensor * indexer_k_nope = + ggml_view_3d(ctx0, indexer_k, n_embd_indexer_head_nope, 1, n_tokens, + ggml_row_size(indexer_k->type, n_embd_indexer_head), + ggml_row_size(indexer_k->type, n_embd_indexer_head) * 1, + ggml_row_size(indexer_k->type, n_embd_indexer_head_nope)); + cb(indexer_k_nope, "indexer_k_nope", il); + + indexer_k_pe = ggml_rope_ext(ctx0, indexer_k_pe, inp_pos, nullptr, n_rot, + LLAMA_ROPE_TYPE_NORM, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow); + cb(indexer_k_pe, "indexer_k_pe", il); + + // {n_embd_indexer_head_rope + n_embd_indexer_head_nope, 1, n_tokens} + indexer_k = ggml_concat(ctx0, indexer_k_pe, indexer_k_nope, 0); + cb(indexer_k, "indexer_k", il); + + // perform Hadamard transform on indexer q and k + indexer_q = ggml_mul_mat(ctx0, inp_attn_dsa->self_k_rot_lid, indexer_q); + cb(indexer_q, "indexer_q", il); + indexer_k = ggml_mul_mat(ctx0, inp_attn_dsa->self_k_rot_lid, indexer_k); + cb(indexer_k, "indexer_k", il); + + // store indexer keys to KV cache + const auto * mctx_lid = inp_attn_dsa->mctx->get_lid(); + const auto & k_idxs_lid = inp_attn_dsa->get_k_idxs_lid(); + ggml_build_forward_expand(gf, mctx_lid->cpy_k(ctx0, indexer_k, k_idxs_lid, il)); + + // prepare indexer weights + ggml_tensor * indexer_weights = ggml_mul_mat(ctx0, model.layers[il].indexer_proj, cur); + cb(indexer_weights, "indexer_weights", il); + + // get cached indexer keys + indexer_k = mctx_lid->get_k(ctx0, il); + + // split the batch into streams if needed + const auto n_stream = indexer_k->ne[3]; + indexer_q = ggml_view_4d(ctx0, indexer_q, indexer_q->ne[0], indexer_q->ne[1], indexer_q->ne[2]/n_stream, n_stream, indexer_q->nb[1], indexer_q->nb[2], indexer_q->nb[3]/n_stream, 0); + indexer_weights = ggml_view_4d(ctx0, indexer_weights, indexer_weights->ne[0], indexer_weights->ne[1]/n_stream, indexer_weights->ne[2], n_stream, indexer_weights->nb[1], indexer_weights->nb[2]/n_stream, indexer_weights->nb[3]/n_stream, 0); + + // pre-scale weights to avoid scaling operations on huge indexer_score tensor + indexer_weights = ggml_scale(ctx0, indexer_weights, 1.0f / sqrtf(float(n_embd_indexer_head * n_indexer_head))); + cb(indexer_weights, "indexer_weights", il); + + ggml_tensor * indexer_score = nullptr; + if (cparams.fused_lid) { + indexer_score = ggml_lightning_indexer(ctx0, indexer_q, indexer_k, indexer_weights, inp_attn_dsa->get_kq_mask_lid()); + cb(indexer_score, "indexer_score", il); + res->add_fused_node({LLM_FUSED_OP_LIGHTNING_INDEXER, indexer_score, il}); + } else { + // calculate indexer kq + indexer_q = ggml_permute(ctx0, indexer_q, 0, 2, 1, 3); + cb(indexer_q, "indexer_q", il); + indexer_k = ggml_permute(ctx0, indexer_k, 0, 2, 1, 3); + cb(indexer_k, "indexer_k", il); + + ggml_tensor * indexer_kq = ggml_mul_mat(ctx0, indexer_k, indexer_q); + cb(indexer_kq, "indexer_kq", il); + + // ReLU requires contiguous tensors + indexer_kq = ggml_cont(ctx0, ggml_permute(ctx0, indexer_kq, 2, 1, 0, 3)); + cb(indexer_kq, "indexer_kq", il); + + // apply ReLU + indexer_score = ggml_relu(ctx0, indexer_kq); + cb(indexer_score, "indexer_score", il); + + // multiply scores by indexer weights + indexer_score = ggml_mul(ctx0, indexer_score, indexer_weights); + cb(indexer_score, "indexer_score", il); + + // sum by q n_indexer_head dimension + indexer_score = ggml_sum_rows(ctx0, indexer_score); + cb(indexer_score, "indexer_score", il); + + // permute result to match KQ mask + indexer_score = ggml_cont(ctx0, ggml_permute(ctx0, indexer_score, 2, 1, 0, 3)); + cb(indexer_score, "indexer_score", il); + + // mask indexer scores + ggml_tensor * indexer_kq_mask = inp_attn_dsa->get_kq_mask_lid(); + indexer_score = ggml_add(ctx0, indexer_score, indexer_kq_mask); + cb(indexer_score, "indexer_score", il); + } + + // get indices of top k indexer scores + uint32_t n_top_k = indexer_score->ne[0] < n_indexer_top_k ? indexer_score->ne[0] : n_indexer_top_k; + top_k = ggml_cont(ctx0, ggml_top_k(ctx0, indexer_score, n_top_k)); + prev_top_k = top_k; + cb(top_k, "top_k", il); + } else { + // "shared" indexer layer - reuse top-k from a previous full layer + GGML_ASSERT(prev_top_k != nullptr && "shared indexer layer must follow a previous full indexer layer"); + top_k = prev_top_k; + cb(top_k, "top_k", il); + } + + ggml_tensor * q = ggml_mul_mat(ctx0, model.layers[il].wq_b, qr); + cb(q, "q", il); + + // split into {n_embd_head_qk_nope, n_head, n_tokens} + ggml_tensor * q_nope = + ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k), + ggml_row_size(q->type, n_embd_head_k) * n_head, 0); + cb(q_nope, "q_nope", il); + + // and {n_embd_head_qk_rope, n_head, n_tokens} + ggml_tensor * q_pe = ggml_view_3d( + ctx0, q, n_embd_head_qk_rope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k), + ggml_row_size(q->type, n_embd_head_k) * n_head, ggml_row_size(q->type, n_embd_head_qk_nope)); + cb(q_pe, "q_pe", il); + + ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur); + cb(kv_cmpr_pe, "kv_cmpr_pe", il); + + // split into {kv_lora_rank, n_tokens} + ggml_tensor * kv_cmpr = + ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens, + ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0); + cb(kv_cmpr, "kv_cmpr", il); + + // and {n_embd_head_qk_rope, 1, n_tokens} + ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens, + ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), + ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), + ggml_row_size(kv_cmpr_pe->type, kv_lora_rank)); + cb(k_pe, "k_pe", il); + + q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow); + cb(q_pe, "q_pe", il); + + k_pe = ggml_rope_ext(ctx0, k_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow); + cb(k_pe, "k_pe", il); + + kv_cmpr = build_norm(kv_cmpr, model.layers[il].attn_kv_a_norm, nullptr, LLM_NORM_RMS, il); + cb(kv_cmpr, "kv_cmpr", il); + + // MLA attention + { + // {n_embd_head_qk_nope, n_tokens, n_head} + q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3); + cb(q_nope, "q_nope_perm", il); + + // {n_embd_head_qk_nope, kv_lora_rank, n_head} x {n_embd_head_qk_nope, n_tokens, n_head} + ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, model.layers[il].wk_b, q_nope); + cb(q_nope_absorbed, "q_nope_absorbed", il); + + // {kv_lora_rank, n_head, n_tokens} + q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3); + cb(q_nope_absorbed, "q_nope_absorbed_perm", il); + + // {n_embd_head_qk_rope + kv_lora_rank, n_head, n_tokens} + // note: rope must go first for in-place context shifting in build_rope_shift() + ggml_tensor * Qcur = ggml_concat(ctx0, q_nope_absorbed, q_pe, 0); + cb(Qcur, "Qcur", il); + + kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens); + cb(kv_cmpr, "kv_cmpr_reshape", il); + + // {n_embd_head_qk_rope + kv_lora_rank, 1, n_tokens} + ggml_tensor * Kcur = ggml_concat(ctx0, kv_cmpr, k_pe, 0); + cb(Kcur, "Kcur", il); + + // {kv_lora_rank, 1, n_tokens} + ggml_tensor * Vcur = kv_cmpr; + cb(Vcur, "Vcur", il); + + // note: MLA with the absorption optimization converts into MQA (ie: GQA with 1 group) + cur = build_attn(inp_attn_dsa, + model.layers[il].wo, NULL, model.layers[il].wo_s, + Qcur, Kcur, Vcur, nullptr, nullptr, model.layers[il].wv_b, top_k, kq_scale, il); + } + } + // when unmasked nextn embeddings are requested, t_h_nextn must keep all rows, + // so the early output masking has to be skipped (it is applied after the final norm instead) + if (il == n_layer - 1 && inp_out_ids && (!cparams.embeddings_nextn || cparams.embeddings_nextn_masked)) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il); + cb(cur, "ffn_norm", il); + + if ((uint32_t) il < hparams.n_layer_dense_lead) { + cur = build_ffn(cur, + model.layers[il].ffn_up, NULL, model.layers[il].ffn_up_s, + model.layers[il].ffn_gate, NULL, model.layers[il].ffn_gate_s, + model.layers[il].ffn_down, NULL, model.layers[il].ffn_down_s, + NULL, LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(cur, "ffn_out", il); + } else { + // MoE branch + ggml_tensor * moe_out = build_moe_ffn(cur, + model.layers[il].ffn_gate_inp, + model.layers[il].ffn_up_exps, + model.layers[il].ffn_gate_exps, + model.layers[il].ffn_down_exps, + model.layers[il].ffn_exp_probs_b, + n_expert, n_expert_used, + LLM_FFN_SILU, hparams.expert_weights_norm, + hparams.expert_weights_scale, + (llama_expert_gating_func_type) hparams.expert_gating_func, + il, + nullptr, + model.layers[il].ffn_gate_up_exps, + model.layers[il].ffn_up_exps_s, + model.layers[il].ffn_gate_exps_s, + model.layers[il].ffn_down_exps_s); + cb(moe_out, "ffn_moe_out", il); + + // FFN shared expert + { + ggml_tensor * ffn_shexp = + build_ffn(cur, + model.layers[il].ffn_up_shexp, NULL, model.layers[il].ffn_up_shexp_s, + model.layers[il].ffn_gate_shexp, NULL, model.layers[il].ffn_gate_shexp_s, + model.layers[il].ffn_down_shexp, NULL, model.layers[il].ffn_down_shexp_s, + NULL, LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(ffn_shexp, "ffn_shexp", il); + + cur = ggml_add(ctx0, moe_out, ffn_shexp); + cb(cur, "ffn_out", il); + } + } + cur = ggml_add(ctx0, cur, ffn_inp); + + cur = build_cvec(cur, il); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + cur = inpL; + + cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1); + + // post-norm hidden state feeds the NextN/MTP draft head + cb(cur, "h_nextn", -1); + res->t_h_nextn = cur; + + if (cparams.embeddings_nextn && !cparams.embeddings_nextn_masked && inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + } + + cb(cur, "result_norm", -1); + res->t_embd = cur; + + // lm_head + cur = ggml_mul_mat(ctx0, model.output, cur); + + cb(cur, "result_output", -1); + res->t_logits = cur; + + ggml_build_forward_expand(gf, cur); +} + +// LLM_GRAPH_TYPE_DECODER_MTP draft head for GLM-5.2 (GLM_DSA). +// Semantics mirror the deepseek-family NextN/MTP layer: +// enorm(embed) + hnorm(prev_hidden) -> concat(e, h) -> eh_proj -> +// full glm_dsa decoder block (dense MLA attention + sigmoid-gated MoE FFN +// with shared expert, exactly as the trunk deepseek2 graph builds it) -> +// shared_head_norm (fallback output_norm) -> shared LM head. +// The DSA indexer is not used at runtime (same as the trunk graph). +llama_model_glm_dsa::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params) + : llm_graph_context(params) { + GGML_ASSERT(hparams.n_layer_nextn > 0 && "GLM_DSA MTP requires n_layer_nextn > 0"); + GGML_ASSERT(hparams.n_layer_nextn == 1 && "GLM_DSA MTP currently only supports a single MTP block"); + GGML_ASSERT(hparams.is_mla() && "GLM_DSA MTP requires MLA"); + + const int il = hparams.n_layer() + cparams.nextn_layer_offset; + GGML_ASSERT(cparams.nextn_layer_offset >= 0 && + cparams.nextn_layer_offset < (int) hparams.n_layer_nextn && + "nextn_layer_offset out of range [0, n_layer_nextn)"); + const auto & layer = model.layers[il]; + + GGML_ASSERT(layer.nextn.eh_proj && "MTP block missing nextn.eh_proj"); + GGML_ASSERT(layer.nextn.enorm && "MTP block missing nextn.enorm"); + GGML_ASSERT(layer.nextn.hnorm && "MTP block missing nextn.hnorm"); + GGML_ASSERT(layer.ffn_gate_inp && "MTP block missing ffn_gate_inp"); + + // note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA + const int64_t n_embd_head_k = hparams.n_embd_head_k_mla(); + + const int64_t n_embd_head_qk_rope = hparams.n_rot(); + const int64_t n_embd_head_qk_nope = n_embd_head_k - n_embd_head_qk_rope; + + const uint32_t kv_lora_rank = hparams.n_lora_kv; + + // We have to pre-scale kq_scale and attn_factor to make the YaRN RoPE work correctly. + // See the deepseek2 trunk graph for the detailed explanation - this must match it EXACTLY. + GGML_ASSERT(ext_factor >= 0.0f); + const float attn_factor_org = attn_factor * (1.0f + 0.1f * logf(1.0f / freq_scale)); + + const float mscale = attn_factor_org * (1.0f + 0.1f * hparams.rope_yarn_log_mul * logf(1.0f / freq_scale)); + const float kq_scale = 1.0f * mscale * mscale / sqrtf(float(n_embd_head_k)); + + // TODO: extract in a common llm_graph_context::build_inp_embd_h() + auto inp = std::make_unique(hparams.n_embd); + + inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + ggml_set_input(inp->tokens); + + inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd_inp(), n_tokens); + ggml_set_input(inp->embd); + + ggml_tensor * tok_embd; + if (ubatch.token) { + ggml_tensor * tok_embd_w = layer.nextn.embed_tokens ? layer.nextn.embed_tokens : model.tok_embd; + + tok_embd = ggml_get_rows(ctx0, tok_embd_w, inp->tokens); + } else { + tok_embd = inp->embd; + } + cb(tok_embd, "mtp_tok_embd", il); + + inp->h = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd, n_tokens); + ggml_set_input(inp->h); + ggml_set_name(inp->h, "mtp_h_input"); + + ggml_tensor * h_embd = inp->h; + + res->add_input(std::move(inp)); + + ggml_tensor * inp_pos = build_inp_pos(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + + // MLA with the absorption optimization uses a K-only cache (V is a view of K) + auto * inp_attn = build_attn_inp_k(); + + ggml_tensor * h_norm = build_norm(h_embd, layer.nextn.hnorm, nullptr, LLM_NORM_RMS, il); + cb(h_norm, "mtp_hnorm", il); + + ggml_tensor * e_norm = build_norm(tok_embd, layer.nextn.enorm, nullptr, LLM_NORM_RMS, il); + cb(e_norm, "mtp_enorm", il); + + ggml_tensor * concat = ggml_concat(ctx0, e_norm, h_norm, /*dim=*/ 0); + cb(concat, "mtp_concat", il); + + ggml_tensor * cur = build_lora_mm(layer.nextn.eh_proj, concat, layer.nextn.eh_proj_s); + cb(cur, "mtp_eh_proj", il); + + ggml_tensor * inpSA = cur; + + cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il); + cb(cur, "mtp_attn_norm", il); + + // self-attention: dense MLA, same construction as the deepseek2 trunk graph + { + ggml_tensor * q = ggml_mul_mat(ctx0, layer.wq_a, cur); + cb(q, "mtp_q", il); + + q = build_norm(q, layer.attn_q_a_norm, nullptr, LLM_NORM_RMS, il); + cb(q, "mtp_q", il); + + q = ggml_mul_mat(ctx0, layer.wq_b, q); + cb(q, "mtp_q", il); + + // split into {n_embd_head_qk_nope, n_head, n_tokens} + ggml_tensor * q_nope = + ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k), + ggml_row_size(q->type, n_embd_head_k) * n_head, 0); + cb(q_nope, "mtp_q_nope", il); + + // and {n_embd_head_qk_rope, n_head, n_tokens} + ggml_tensor * q_pe = ggml_view_3d( + ctx0, q, n_embd_head_qk_rope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k), + ggml_row_size(q->type, n_embd_head_k) * n_head, ggml_row_size(q->type, n_embd_head_qk_nope)); + cb(q_pe, "mtp_q_pe", il); + + ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, layer.wkv_a_mqa, cur); + cb(kv_cmpr_pe, "mtp_kv_cmpr_pe", il); + + // split into {kv_lora_rank, n_tokens} + ggml_tensor * kv_cmpr = + ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens, + ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0); + cb(kv_cmpr, "mtp_kv_cmpr", il); + + // and {n_embd_head_qk_rope, 1, n_tokens} + ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens, + ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), + ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), + ggml_row_size(kv_cmpr_pe->type, kv_lora_rank)); + cb(k_pe, "mtp_k_pe", il); + + q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow); + cb(q_pe, "mtp_q_pe", il); + + k_pe = ggml_rope_ext(ctx0, k_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow); + cb(k_pe, "mtp_k_pe", il); + + kv_cmpr = build_norm(kv_cmpr, layer.attn_kv_a_norm, nullptr, LLM_NORM_RMS, il); + cb(kv_cmpr, "mtp_kv_cmpr", il); + + // {n_embd_head_qk_nope, n_tokens, n_head} + q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3); + cb(q_nope, "mtp_q_nope_perm", il); + + // {n_embd_head_qk_nope, kv_lora_rank, n_head} x {n_embd_head_qk_nope, n_tokens, n_head} + ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, layer.wk_b, q_nope); + cb(q_nope_absorbed, "mtp_q_nope_absorbed", il); + + // {kv_lora_rank, n_head, n_tokens} + q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3); + cb(q_nope_absorbed, "mtp_q_nope_absorbed_perm", il); + + // {n_embd_head_qk_rope + kv_lora_rank, n_head, n_tokens} + // note: rope must go first for in-place context shifting in build_rope_shift() + ggml_tensor * Qcur = ggml_concat(ctx0, q_nope_absorbed, q_pe, 0); + cb(Qcur, "mtp_Qcur", il); + + kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens); + cb(kv_cmpr, "mtp_kv_cmpr_reshape", il); + + // {n_embd_head_qk_rope + kv_lora_rank, 1, n_tokens} + ggml_tensor * Kcur = ggml_concat(ctx0, kv_cmpr, k_pe, 0); + cb(Kcur, "mtp_Kcur", il); + + // {kv_lora_rank, 1, n_tokens} + ggml_tensor * Vcur = kv_cmpr; + cb(Vcur, "mtp_Vcur", il); + + // note: MLA with the absorption optimization converts into MQA (ie: GQA with 1 group) + cur = build_attn(inp_attn, + layer.wo, NULL, layer.wo_s, + Qcur, Kcur, Vcur, nullptr, nullptr, layer.wv_b, kq_scale, il); + cb(cur, "mtp_attn_out", il); + } + + ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "mtp_ffn_inp", il); + + cur = build_norm(ffn_inp, layer.ffn_norm, NULL, LLM_NORM_RMS, il); + cb(cur, "mtp_ffn_norm", il); + + // MoE FFN with shared expert - same construction as the deepseek2 trunk graph + ggml_tensor * moe_out = build_moe_ffn(cur, + layer.ffn_gate_inp, + layer.ffn_up_exps, + layer.ffn_gate_exps, + layer.ffn_down_exps, + layer.ffn_exp_probs_b, + n_expert, n_expert_used, + LLM_FFN_SILU, hparams.expert_weights_norm, + hparams.expert_weights_scale, + (llama_expert_gating_func_type) hparams.expert_gating_func, + il, + nullptr, + layer.ffn_gate_up_exps, + layer.ffn_up_exps_s, + layer.ffn_gate_exps_s, + layer.ffn_down_exps_s); + cb(moe_out, "mtp_ffn_moe_out", il); + + // FFN shared expert + ggml_tensor * ffn_shexp = + build_ffn(cur, + layer.ffn_up_shexp, NULL, layer.ffn_up_shexp_s, + layer.ffn_gate_shexp, NULL, layer.ffn_gate_shexp_s, + layer.ffn_down_shexp, NULL, layer.ffn_down_shexp_s, + NULL, LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(ffn_shexp, "mtp_ffn_shexp", il); + + cur = ggml_add(ctx0, moe_out, ffn_shexp); + cb(cur, "mtp_ffn_out", il); + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "mtp_post_ffn", il); + + // shared_head_norm applied after the decoder block, before the shared LM head. + // The post-norm hidden state seeds the next MTP step. + ggml_tensor * head_norm_w = layer.nextn.shared_head_norm + ? layer.nextn.shared_head_norm + : model.output_norm; + GGML_ASSERT(head_norm_w && "GLM_DSA MTP: missing both nextn.shared_head_norm and output_norm"); + cur = build_norm(cur, head_norm_w, nullptr, LLM_NORM_RMS, -1); + + cb(cur, "h_nextn", -1); + res->t_h_nextn = cur; + + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + cb(cur, "mtp_shared_head_norm", -1); + + ggml_tensor * head_w = layer.nextn.shared_head_head ? layer.nextn.shared_head_head : model.output; + ggml_tensor * head_s = layer.nextn.shared_head_head ? layer.nextn.shared_head_head_s : model.output_s; + GGML_ASSERT(head_w && "GLM_DSA MTP: missing LM head (nextn.shared_head_head or model.output)"); + cur = build_lora_mm(head_w, cur, head_s); + cb(cur, "result_output", -1); + + res->t_logits = cur; + ggml_build_forward_expand(gf, cur); +} diff --git a/src/models/hy-v3.cpp b/src/models/hy-v3.cpp index 47a0beaf217f..61db93af85ce 100644 --- a/src/models/hy-v3.cpp +++ b/src/models/hy-v3.cpp @@ -33,7 +33,11 @@ void llama_model_hy_v3::load_arch_tensors(llama_model_loader & ml) { const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight"; const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr); const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0; - const int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0; + int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0; + + if (!ml.load_mtp) { + mtp_flags |= TENSOR_SKIP; + } tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); diff --git a/src/models/kimi-k3.cpp b/src/models/kimi-k3.cpp new file mode 100644 index 000000000000..c0ed5a614295 --- /dev/null +++ b/src/models/kimi-k3.cpp @@ -0,0 +1,576 @@ +#include "models.h" +#include "llama-memory-recurrent.h" + +// Kimi K3: hybrid KDA + gated-MLA (NoPE) with Attention Residuals (AttnRes), +// Stable LatentMoE and SiTU-GLU activation. +// +// Reference: moonshotai/Kimi-K3 modeling_kimi_linear.py (HF), sglang kimi_k3.py +// +// Deltas vs Kimi Linear (LLM_ARCH_KIMI_LINEAR): +// - KDA safe gate: g_log = gate_lower_bound * sigmoid(exp(A_log) * (f_b(f_a(x)) + dt_bias)) +// (ssm_a stores exp(A_log) > 0, sliced to n_head at conversion) +// - KDA output gate is full-rank: g2 = g_proj(x) (wqkv_gate), not low-rank g_a/g_b +// - MLA has an output gate: attn = attn * sigmoid(g_proj(x)) before o_proj +// - MoE experts run in a latent space: down -> experts -> weighted sum -> RMSNorm -> up +// with the router operating on the full hidden state +// - AttnRes: the residual stream restarts every attn_res_block_size layers; snapshots are +// banked and re-mixed via a learned softmax mixture before attention, before the FFN +// and at the model output + +void llama_model_kimi_k3::load_arch_hparams(llama_model_loader & ml) { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA, hparams.n_embd_head_k_mla_impl); + ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, hparams.n_embd_head_v_mla_impl); + ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv); + ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q); + ml.get_key(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv); + ml.get_key(LLM_KV_KDA_HEAD_DIM, hparams.n_embd_head_kda); + ml.get_key(LLM_KV_KDA_GATE_LOWER_BOUND, hparams.kda_gate_lower_bound); + + ml.get_key(LLM_KV_SITU_BETA, hparams.situ_beta); + ml.get_key(LLM_KV_SITU_LINEAR_BETA, hparams.situ_linear_beta); + ml.get_key(LLM_KV_ATTN_RES_BLOCK_SIZE, hparams.attn_res_block_size); + + // Mark KDA layers as recurrent using the n_head_kv pattern (like Kimi Linear) + for (uint32_t i = 0; i < hparams.n_layer(); ++i) { + hparams.is_recr_impl[i] = hparams.n_head_kv(i) == 0; + } + + // Stable LatentMoE + ml.get_key(LLM_KV_MOE_LATENT_SIZE, hparams.moe_latent_size); + ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); + ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared); + ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false); + ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false); + ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func); + + // K3 always renormalizes the top-k sigmoid weights (moe_renormalize = true) + hparams.expert_weights_norm = true; + + GGML_ASSERT(hparams.attn_res_block_size > 0 && "Kimi-K3 requires attn_res_block_size"); + GGML_ASSERT(hparams.moe_latent_size > 0 && "Kimi-K3 requires moe_latent_size"); + + switch (hparams.n_layer()) { + case 93: type = LLM_TYPE_2_8T_A104B; break; // Kimi-K3 + default: type = LLM_TYPE_UNKNOWN; + } +} + +void llama_model_kimi_k3::load_arch_tensors(llama_model_loader &) { + LLAMA_LOAD_LOCALS; + + const int64_t moe_latent = hparams.moe_latent_size; + + tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + + // output + output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); + + // AttnRes output mixture + output_res_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_RES_NORM, "weight"), {n_embd}, 0); + output_res_proj = create_tensor(tn(LLM_TENSOR_OUTPUT_RES_PROJ, "weight"), {n_embd}, 0); + + for (int i = 0; i < n_layer; ++i) { + auto & layer = layers[i]; + + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + + // AttnRes per-layer mixtures + layer.attn_res_norm = create_tensor(tn(LLM_TENSOR_ATTN_RES_NORM, "weight", i), {n_embd}, 0); + layer.attn_res_proj = create_tensor(tn(LLM_TENSOR_ATTN_RES_PROJ, "weight", i), {n_embd}, 0); + layer.ffn_res_norm = create_tensor(tn(LLM_TENSOR_FFN_RES_NORM, "weight", i), {n_embd}, 0); + layer.ffn_res_proj = create_tensor(tn(LLM_TENSOR_FFN_RES_PROJ, "weight", i), {n_embd}, 0); + + const int64_t n_embd_head_k_kda = hparams.n_embd_head_kda; + const int64_t n_embd_head_v_kda = hparams.n_embd_head_kda; + const int64_t ssm_d_conv = hparams.ssm_d_conv; + const int64_t d_inner = n_embd_head_k_kda * n_head; + + if (hparams.is_recr(i)) { + // === KDA layer === + // Conv1d weights: 4D [d_conv, 1, d_inner, 1] with 3D fallback + layer.ssm_q_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_Q, "weight", i), {ssm_d_conv, 1, d_inner, 1}, TENSOR_NOT_REQUIRED); + if (!layer.ssm_q_conv) { + layer.ssm_q_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_Q, "weight", i), {ssm_d_conv, 1, d_inner}, 0); + } + layer.ssm_k_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_K, "weight", i), {ssm_d_conv, 1, d_inner, 1}, TENSOR_NOT_REQUIRED); + if (!layer.ssm_k_conv) { + layer.ssm_k_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_K, "weight", i), {ssm_d_conv, 1, d_inner}, 0); + } + layer.ssm_v_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_V, "weight", i), {ssm_d_conv, 1, d_inner, 1}, TENSOR_NOT_REQUIRED); + if (!layer.ssm_v_conv) { + layer.ssm_v_conv = create_tensor(tn(LLM_TENSOR_SSM_CONV1D_V, "weight", i), {ssm_d_conv, 1, d_inner}, 0); + } + + // q, k, v projections + create_tensor_qkv(layer, i, n_embd, d_inner, d_inner, d_inner, 0); + + // forget gate projections (low-rank) + layer.ssm_f_a = create_tensor(tn(LLM_TENSOR_SSM_F_A, "weight", i), {n_embd, n_embd_head_k_kda}, 0); + layer.ssm_f_b = create_tensor(tn(LLM_TENSOR_SSM_F_B, "weight", i), {n_embd_head_k_kda, d_inner}, 0); + + // b_proj (beta mixing coefficient) + layer.ssm_beta = create_tensor(tn(LLM_TENSOR_SSM_BETA, "weight", i), {n_embd, n_head}, 0); + + // exp(A_log), per head (sliced from [head_dim] to [n_head] at conversion) + layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {n_head}, TENSOR_NOT_REQUIRED); + if (!layer.ssm_a) { + layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, n_head}, 0); + } + + // dt_bias + layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {d_inner}, 0); + + // full-rank output gate (use_full_rank_gate = true) + layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, d_inner}, 0); + + // o_norm + layer.ssm_o_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), {n_embd_head_k_kda}, 0); + + // o_proj + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_v_kda * n_head, n_embd}, 0); + } else { + // === Gated MLA layer (NoPE) === + const int64_t q_lora_rank = hparams.n_lora_q; + const int64_t kv_lora_rank = hparams.n_lora_kv; + const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla(); + const int64_t n_embd_head_v_mla = hparams.n_embd_head_v_mla(); + const int64_t qk_rope_head_dim = hparams.n_rot(); + + layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, 0); + layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, 0); + + layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, 0); + layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k_mla}, 0); + + layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + qk_rope_head_dim}, 0); + // Support legacy GGUFs that don't split wkv_b (MLA KV cache disabled) + layer.wkv_b = create_tensor(tn(LLM_TENSOR_ATTN_KV_B, "weight", i), + {kv_lora_rank, n_head * (n_embd_head_k_mla - qk_rope_head_dim + n_embd_head_v_mla)}, TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL); + if (!layer.wkv_b) { // MLA KV cache enabled + layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K_B, "weight", i), {n_embd_head_k_mla - qk_rope_head_dim, kv_lora_rank, n_head}, 0); + layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V_B, "weight", i), {kv_lora_rank, n_embd_head_v_mla, n_head}, 0); + } + + // output gate: attn = attn * sigmoid(g_proj(x)) before o_proj + layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_head * n_embd_head_v_mla}, 0); + + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * n_embd_head_v_mla, n_embd}, 0); + } + + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + + const int64_t n_ff_exp = hparams.n_ff_exp; + + if (i < (int) hparams.n_layer_dense_lead) { + // Dense FFN layer (SiTU-GLU) + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + } else { + // Stable LatentMoE: router on full hidden, experts in the latent space + layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0); + layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0); + + layer.ffn_latent_down = create_tensor(tn(LLM_TENSOR_FFN_LATENT_DOWN, "weight", i), {n_embd, moe_latent}, 0); + layer.ffn_latent_norm = create_tensor(tn(LLM_TENSOR_FFN_LATENT_NORM, "weight", i), {moe_latent}, 0); + layer.ffn_latent_up = create_tensor(tn(LLM_TENSOR_FFN_LATENT_UP, "weight", i), {moe_latent, n_embd}, 0); + + layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {moe_latent, n_ff_exp, n_expert}, 0); + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, moe_latent, n_expert}, 0); + layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {moe_latent, n_ff_exp, n_expert}, 0); + + // Shared experts operate on the full hidden state + const int64_t n_ff_shexp = n_ff_exp * (hparams.n_expert_shared > 0 ? hparams.n_expert_shared : 1); + layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp}, 0); + layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, 0); + layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_shexp}, 0); + } + } +} + +std::unique_ptr llama_model_kimi_k3::build_arch_graph(const llm_graph_params & params) const { + return std::make_unique(*this, params); +} + +// Causal Conv1d for Q/K/V (identical to Kimi Linear) +// qkv: 0 = Q, 1 = K, 2 = V +static ggml_tensor * causal_conv1d(ggml_cgraph * gf, ggml_context * ctx0, ggml_tensor * conv_states_all, ggml_tensor * conv_state_all, int64_t qkv, ggml_tensor * x, ggml_tensor * proj_w, ggml_tensor * conv_w, int64_t d_conv, int64_t head_dim, int64_t n_head, int64_t n_seq_tokens, int64_t n_seqs, int64_t n_tokens, int64_t kv_head) { + const int64_t d_inner = head_dim * n_head; + const int64_t conv_state_size = (d_conv - 1) * d_inner; + const int64_t n_embd_r_total = 3 * conv_state_size; // Q + K + V + + ggml_tensor * conv_state_x = ggml_view_3d(ctx0, conv_state_all, d_conv - 1, d_inner, n_seqs, + (d_conv - 1) * ggml_element_size(conv_state_all), + n_embd_r_total * ggml_element_size(conv_state_all), + qkv * conv_state_size * ggml_element_size(conv_state_all)); + + ggml_tensor * x_proj = ggml_mul_mat(ctx0, proj_w, x); + + ggml_tensor * x_3d = ggml_reshape_3d(ctx0, x_proj, d_inner, n_seq_tokens, n_seqs); + + ggml_tensor * conv_x = ggml_concat(ctx0, conv_state_x, ggml_transpose(ctx0, x_3d), 0); + + // Save last (d_conv-1) columns back to the conv state + ggml_tensor * last_conv_x = ggml_view_3d(ctx0, conv_x, d_conv - 1, d_inner, n_seqs, + conv_x->nb[1], conv_x->nb[2], n_seq_tokens * conv_x->nb[0]); + ggml_build_forward_expand(gf, + ggml_cpy(ctx0, last_conv_x, + ggml_view_3d(ctx0, conv_states_all, + d_conv - 1, d_inner, n_seqs, + (d_conv - 1) * ggml_element_size(conv_states_all), + n_embd_r_total * ggml_element_size(conv_states_all), + (kv_head * n_embd_r_total + qkv * conv_state_size) * ggml_element_size(conv_states_all)))); + + ggml_tensor * conv_weight = ggml_reshape_2d(ctx0, conv_w, d_conv, d_inner); + + ggml_tensor * Xcur = ggml_ssm_conv(ctx0, conv_x, conv_weight); + Xcur = ggml_reshape_2d(ctx0, Xcur, d_inner, n_tokens); + Xcur = ggml_silu(ctx0, Xcur); + + return ggml_reshape_4d(ctx0, Xcur, head_dim, n_head, n_seq_tokens, n_seqs); +} + +// AttnRes mixture: softmax over [bank rows..., prefix] scored by a scalar projection +// of the RMS-normed rows; the mixture combines the raw (unnormalized) rows. +// k = rms_norm(v) +// scores = sum(k * (norm_w * proj_w), dim=embd) +// probs = softmax(scores over rows) +// out = sum_r probs_r * v_r +ggml_tensor * llama_model_kimi_k3::graph::build_attn_res_mix( + ggml_tensor * prefix, + const std::vector & bank, + ggml_tensor * norm_w, + ggml_tensor * proj_w, + int il) { + const int64_t n_embd = prefix->ne[0]; + const int64_t n_toks = prefix->ne[1]; + const int64_t n_rows = (int64_t) bank.size() + 1; + + // v: [n_embd, n_rows, n_toks] + ggml_tensor * v = nullptr; + for (ggml_tensor * b : bank) { + ggml_tensor * r = ggml_reshape_3d(ctx0, b, n_embd, 1, n_toks); + v = v ? ggml_concat(ctx0, v, r, 1) : r; + } + { + ggml_tensor * r = ggml_reshape_3d(ctx0, prefix, n_embd, 1, n_toks); + v = v ? ggml_concat(ctx0, v, r, 1) : r; + } + + ggml_tensor * k = ggml_rms_norm(ctx0, v, hparams.f_norm_rms_eps); + cb(k, "attn_res_k", il); + + // score weight: norm.weight * proj.weight, [n_embd] + ggml_tensor * sw = ggml_mul(ctx0, norm_w, proj_w); + + // scores: [1, n_rows, n_toks] + ggml_tensor * scores = ggml_mul_mat(ctx0, ggml_reshape_2d(ctx0, sw, n_embd, 1), k); + scores = ggml_reshape_2d(ctx0, scores, n_rows, n_toks); + cb(scores, "attn_res_scores", il); + + ggml_tensor * probs = ggml_soft_max(ctx0, scores); + cb(probs, "attn_res_probs", il); + + // weighted sum of raw rows + ggml_tensor * w = ggml_mul(ctx0, v, ggml_reshape_3d(ctx0, probs, 1, n_rows, n_toks)); + w = ggml_cont(ctx0, ggml_permute(ctx0, w, 1, 0, 2, 3)); // [n_rows, n_embd, n_toks] + ggml_tensor * out = ggml_sum_rows(ctx0, w); // [1, n_embd, n_toks] + out = ggml_reshape_2d(ctx0, out, n_embd, n_toks); + cb(out, "attn_res_mix", il); + + return out; +} + +llama_model_kimi_k3::graph::graph(const llama_model & model, const llm_graph_params & params) : + llm_build_delta_net_base(params), model(model) { + ggml_tensor * cur; + ggml_tensor * inpL; + + inpL = build_inp_embd(model.tok_embd); + cb(inpL, "model.embed_tokens", -1); + + // K3 uses no positional embeddings anywhere (KDA recurrence + NoPE MLA) + + auto * inp_kv = !hparams.is_mla() ? build_inp_mem_hybrid() : nullptr; + auto * inp_k = hparams.is_mla() ? build_inp_mem_hybrid_k() : nullptr; + auto * inp_rs = hparams.is_mla() ? inp_k->get_recr() : inp_kv->get_recr(); + auto * inp_attn_kv = !hparams.is_mla() ? inp_kv->get_attn() : nullptr; + auto * inp_attn_k = hparams.is_mla() ? inp_k->get_attn() : nullptr; + + ggml_tensor * inp_out_ids = build_inp_out_ids(); + + const int64_t n_head = hparams.n_head(); + const int64_t head_dim = hparams.n_embd_head_kda; + const int64_t d_conv = hparams.ssm_d_conv; + const int64_t d_inner = n_head * head_dim; + const int64_t n_seqs = ubatch.n_seqs; + const int64_t n_seq_tokens = ubatch.n_seq_tokens; + + GGML_ASSERT(n_seqs != 0); + GGML_ASSERT(ubatch.equal_seqs()); + GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs); + + // MLA params + const int64_t n_embd_head_k_mla = hparams.n_embd_head_k_mla(); + const int64_t n_embd_head_v_mla = hparams.n_embd_head_v_mla(); + const int64_t kv_lora_rank = hparams.n_lora_kv; + const int64_t n_embd_head_qk_rope = hparams.n_rot(); + const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope; + const float kq_scale_mla = 1.0f / sqrtf((float)n_embd_head_k_mla); + + const uint32_t res_block = hparams.attn_res_block_size; + GGML_ASSERT(res_block > 0); + + // AttnRes state: snapshot bank + current prefix sum of the residual stream + std::vector res_bank; + ggml_tensor * prefix = inpL; + + for (int il = 0; il < n_layer; ++il) { + const auto & layer = model.layers[il]; + + // pre-attention mixture (bank is empty only before the first snapshot) + ggml_tensor * h = res_bank.empty() + ? prefix + : build_attn_res_mix(prefix, res_bank, layer.attn_res_norm, layer.attn_res_proj, il); + + // snapshot + restart of the residual stream + const bool snapshot = (il % (int) res_block) == 0; + if (snapshot) { + res_bank.push_back(prefix); + } + + cur = build_norm(h, layer.attn_norm, NULL, LLM_NORM_RMS, il); + cb(cur, "attn_norm", il); + + ggml_build_forward_expand(gf, cur); + + if (hparams.is_recr(il)) { + // === KDA layer (Kimi Delta Attention) === + const auto * mctx_cur = inp_rs->mctx; + const auto kv_head = mctx_cur->get_head(); + + ggml_tensor * conv_states_all = mctx_cur->get_r_l(il); + cb(conv_states_all, "conv_states_all", il); + ggml_tensor * conv_state_all = build_rs(inp_rs, conv_states_all, hparams.n_embd_r(), n_seqs); + ggml_tensor * Qcur = causal_conv1d(gf, ctx0, conv_states_all, conv_state_all, 0, cur, layer.wq, layer.ssm_q_conv, d_conv, head_dim, n_head, n_seq_tokens, n_seqs, n_tokens, kv_head); + ggml_tensor * Kcur = causal_conv1d(gf, ctx0, conv_states_all, conv_state_all, 1, cur, layer.wk, layer.ssm_k_conv, d_conv, head_dim, n_head, n_seq_tokens, n_seqs, n_tokens, kv_head); + ggml_tensor * Vcur = causal_conv1d(gf, ctx0, conv_states_all, conv_state_all, 2, cur, layer.wv, layer.ssm_v_conv, d_conv, head_dim, n_head, n_seq_tokens, n_seqs, n_tokens, kv_head); + + // K3 safe gate: g1 = lower_bound * sigmoid(exp(A_log) * (f_b(f_a(x)) + dt_bias)) + // ssm_a stores exp(A_log) (positive), per head + ggml_tensor * f_a = ggml_mul_mat(ctx0, layer.ssm_f_a, cur); + ggml_tensor * g1 = ggml_mul_mat(ctx0, layer.ssm_f_b, f_a); + cb(g1, "kda_g1_raw", il); + g1 = ggml_add(ctx0, g1, layer.ssm_dt_b); + g1 = ggml_reshape_3d(ctx0, g1, head_dim, n_head, n_tokens); + + ggml_tensor * A = ggml_reshape_3d(ctx0, layer.ssm_a, 1, n_head, 1); + g1 = ggml_mul(ctx0, g1, A); + g1 = ggml_sigmoid(ctx0, g1); + g1 = ggml_scale(ctx0, g1, hparams.kda_gate_lower_bound); + cb(g1, "kda_g1", il); + + g1 = ggml_reshape_4d(ctx0, g1, head_dim, n_head, n_seq_tokens, n_seqs); + + // beta (mixing coefficient) + ggml_tensor * beta = ggml_mul_mat(ctx0, layer.ssm_beta, cur); + beta = ggml_reshape_4d(ctx0, beta, 1, n_head, n_seq_tokens, n_seqs); + beta = ggml_sigmoid(ctx0, beta); + cb(beta, "kda_beta", il); + + // KDA recurrence + ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il); + ggml_tensor * state = build_rs(inp_rs, ssm_states_all, hparams.n_embd_s(), n_seqs); + state = ggml_reshape_4d(ctx0, state, head_dim, head_dim, n_head, n_seqs); + + const float eps_norm = hparams.f_norm_rms_eps; + + Qcur = ggml_l2_norm(ctx0, Qcur, eps_norm); + Kcur = ggml_l2_norm(ctx0, Kcur, eps_norm); + + auto attn_out = build_delta_net(Qcur, Kcur, Vcur, g1, beta, state, il); + + ggml_tensor * output = ggml_cont(ctx0, attn_out.first); + ggml_tensor * new_state = attn_out.second; + cb(output, "attn_output", il); + + ggml_build_forward_expand(gf, + ggml_cpy(ctx0, new_state, + ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs, + kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all)))); + + // full-rank output gate g2 = g_proj(x) + ggml_tensor * g2 = ggml_mul_mat(ctx0, layer.wqkv_gate, cur); + cb(g2, "kda_g2", il); + g2 = ggml_reshape_3d(ctx0, g2, head_dim, n_head, n_tokens); + + // o_norm with sigmoid gating: out = RMSNorm(o) * sigmoid(g2) + ggml_tensor * attn_out_final = ggml_reshape_3d(ctx0, output, head_dim, n_head, n_tokens); + ggml_tensor * normed = build_norm(attn_out_final, layer.ssm_o_norm, nullptr, LLM_NORM_RMS, il); + cb(normed, "kda_normed", il); + ggml_tensor * gated = ggml_mul(ctx0, normed, ggml_sigmoid(ctx0, g2)); + + gated = ggml_cont_2d(ctx0, gated, d_inner, n_tokens); + cur = ggml_mul_mat(ctx0, layer.wo, gated); + cb(cur, "kda_out", il); + } else { + // === Gated MLA layer (NoPE) === + // Q: q_b(q_a_norm(q_a(x))) + ggml_tensor * Qcur = ggml_mul_mat(ctx0, layer.wq_a, cur); + Qcur = build_norm(Qcur, layer.attn_q_a_norm, nullptr, LLM_NORM_RMS, il); + Qcur = ggml_mul_mat(ctx0, layer.wq_b, Qcur); + cb(Qcur, "mla_q", il); + + // KV compression + ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, layer.wkv_a_mqa, cur); + + ggml_tensor * kv_cmpr = ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens, + ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0); + ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens, + ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), + ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), + ggml_row_size(kv_cmpr_pe->type, kv_lora_rank)); + // NoPE: k_pe is a positional-encoding-free shared key dimension, no RoPE applied + kv_cmpr = build_norm(kv_cmpr, layer.attn_kv_a_norm, nullptr, LLM_NORM_RMS, il); + + ggml_tensor * attn_pregate = nullptr; + + if (layer.wk_b && layer.wv_b) { // MLA KV cache enabled (absorption) + ggml_tensor * q_nope = + ggml_view_3d(ctx0, Qcur, n_embd_head_qk_nope, n_head, n_tokens, ggml_row_size(Qcur->type, n_embd_head_k_mla), + ggml_row_size(Qcur->type, n_embd_head_k_mla) * n_head, 0); + ggml_tensor * q_pe = ggml_view_3d( + ctx0, Qcur, n_embd_head_qk_rope, n_head, n_tokens, ggml_row_size(Qcur->type, n_embd_head_k_mla), + ggml_row_size(Qcur->type, n_embd_head_k_mla) * n_head, ggml_row_size(Qcur->type, n_embd_head_qk_nope)); + + q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3); + ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, layer.wk_b, q_nope); + q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3); + + ggml_tensor * Qmla = ggml_concat(ctx0, q_nope_absorbed, q_pe, 0); + cb(Qmla, "mla_q_absorbed", il); + + kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens); + ggml_tensor * Kcur = ggml_concat(ctx0, kv_cmpr, k_pe, 0); + ggml_tensor * Vcur = kv_cmpr; + + attn_pregate = build_attn(inp_attn_k, nullptr, nullptr, nullptr, Qmla, Kcur, Vcur, nullptr, nullptr, layer.wv_b, kq_scale_mla, il); + } else { // MLA KV cache disabled: fall back to MHA + ggml_tensor * Qmla = ggml_reshape_3d(ctx0, Qcur, n_embd_head_k_mla, n_head, n_tokens); + ggml_tensor * kv = ggml_mul_mat(ctx0, layer.wkv_b, kv_cmpr); + const int64_t kv_per_head = n_embd_head_qk_nope + n_embd_head_v_mla; + + ggml_tensor * k_nope = ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens, + ggml_row_size(kv->type, kv_per_head), + ggml_row_size(kv->type, kv_per_head * n_head), 0); + ggml_tensor * Vcur = ggml_view_3d(ctx0, kv, n_embd_head_v_mla, n_head, n_tokens, + ggml_row_size(kv->type, kv_per_head), + ggml_row_size(kv->type, kv_per_head * n_head), + ggml_row_size(kv->type, n_embd_head_qk_nope)); + Vcur = ggml_cont(ctx0, Vcur); + + ggml_tensor * k_pe_target = ggml_new_tensor_3d(ctx0, k_pe->type, n_embd_head_qk_rope, n_head, n_tokens); + ggml_tensor * k_pe_repeated = ggml_repeat(ctx0, k_pe, k_pe_target); + ggml_tensor * Kcur = ggml_concat(ctx0, k_pe_repeated, k_nope, 0); + + attn_pregate = build_attn(inp_attn_kv, nullptr, nullptr, nullptr, Qmla, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale_mla, il); + } + cb(attn_pregate, "mla_pregate", il); + + // output gate: attn = attn * sigmoid(g_proj(x)), then o_proj + ggml_tensor * gate = ggml_mul_mat(ctx0, layer.wqkv_gate, cur); + gate = ggml_sigmoid(ctx0, gate); + cb(gate, "mla_gate", il); + + cur = ggml_mul(ctx0, attn_pregate, gate); + cur = build_lora_mm(layer.wo, cur, layer.wo_s); + cb(cur, "mla_out", il); + } + + // residual stream update (restarts at snapshot layers) + prefix = snapshot ? cur : ggml_add(ctx0, prefix, cur); + cb(prefix, "attn_prefix", il); + + // pre-FFN mixture (unconditional: bank is never empty here) + ggml_tensor * h2 = build_attn_res_mix(prefix, res_bank, layer.ffn_res_norm, layer.ffn_res_proj, il); + + cur = build_norm(h2, layer.ffn_norm, NULL, LLM_NORM_RMS, il); + cb(cur, "ffn_norm", il); + + if ((uint32_t) il < hparams.n_layer_dense_lead) { + // Dense FFN (SiTU-GLU) + cur = build_ffn(cur, + layer.ffn_up, NULL, NULL, + layer.ffn_gate, NULL, NULL, + layer.ffn_down, NULL, NULL, + NULL, LLM_FFN_SITU, LLM_FFN_PAR, il); + cb(cur, "ffn_out", il); + } else { + // Stable LatentMoE + // router operates on the full hidden state + ggml_tensor * router_logits = build_lora_mm(layer.ffn_gate_inp, cur); + cb(router_logits, "ffn_moe_logits", il); + + // experts run in the latent space + ggml_tensor * latent = ggml_mul_mat(ctx0, layer.ffn_latent_down, cur); + cb(latent, "ffn_moe_latent", il); + + ggml_tensor * moe_out = build_moe_ffn(latent, + layer.ffn_gate_inp, + layer.ffn_up_exps, + layer.ffn_gate_exps, + layer.ffn_down_exps, + layer.ffn_exp_probs_b, + hparams.n_expert, + hparams.n_expert_used, + LLM_FFN_SITU, + hparams.expert_weights_norm, + hparams.expert_weights_scale, + (llama_expert_gating_func_type) hparams.expert_gating_func, + il, + router_logits); + cb(moe_out, "ffn_moe_out", il); + + // latent norm applies AFTER the weighted expert sum, then project back up + moe_out = build_norm(moe_out, layer.ffn_latent_norm, NULL, LLM_NORM_RMS, il); + moe_out = ggml_mul_mat(ctx0, layer.ffn_latent_up, moe_out); + cb(moe_out, "ffn_moe_out_up", il); + + // shared experts on the full hidden state (SiTU-GLU) + ggml_tensor * ffn_shexp = build_ffn(cur, + layer.ffn_up_shexp, NULL, NULL, + layer.ffn_gate_shexp, NULL, NULL, + layer.ffn_down_shexp, NULL, NULL, + NULL, LLM_FFN_SITU, LLM_FFN_PAR, il); + cb(ffn_shexp, "ffn_shexp", il); + + cur = ggml_add(ctx0, moe_out, ffn_shexp); + cb(cur, "ffn_out", il); + } + + // residual stream update + prefix = ggml_add(ctx0, prefix, cur); + prefix = build_cvec(prefix, il); + cb(prefix, "l_out", il); + } + + // output mixture over the final prefix sum and the snapshot bank + cur = build_attn_res_mix(prefix, res_bank, model.output_res_norm, model.output_res_proj, -1); + + // select only the output tokens (AttnRes needs the full token set until here) + if (inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + } + + cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1); + cb(cur, "result_norm", -1); + res->t_embd = cur; + + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + res->t_logits = cur; + + ggml_build_forward_expand(gf, cur); +} diff --git a/src/models/laguna.cpp b/src/models/laguna.cpp index d4e913bb2724..82c9a9538cd4 100644 --- a/src/models/laguna.cpp +++ b/src/models/laguna.cpp @@ -11,15 +11,21 @@ void llama_model_laguna::load_arch_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead); ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false); - ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale); - ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm); + ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false); + ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false); // Laguna ships one shared expert and stores its size directly (routed and // shared experts may differ), so read the size from expert_shared_feed_forward_length. // The count is not in the config; default to 1 but read the key if present. hparams.n_expert_shared = 1; ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared, false); - ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp); + ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false); + if (hparams.n_ff_shexp == 0) { + // Weightless fixtures (test-llama-archs) omit this key; derive a nonzero + // size so the shared expert is still built. Real GGUFs always carry the + // exact value (routed and shared FF lengths may differ). + hparams.n_ff_shexp = hparams.n_ff_exp * hparams.n_expert_shared; + } // Sliding-window attention is OPTIONAL. XS.2 is hybrid (full / SWA / SWA / // SWA repeating, period 4 starting with full); M.1 has no sliding window @@ -52,6 +58,7 @@ void llama_model_laguna::load_arch_hparams(llama_model_loader & ml) { switch (hparams.n_layer()) { case 40: type = LLM_TYPE_30B_A3B; break; // Laguna-XS.2 + case 48: type = LLM_TYPE_118B_A8B; break; // Laguna-S.2 case 70: type = LLM_TYPE_230B_A10B; break; // Laguna-M.1 default: type = LLM_TYPE_UNKNOWN; } @@ -95,22 +102,24 @@ void llama_model_laguna::load_arch_tensors(llama_model_loader & ml) { // per head broadcast over head_dim at multiply time); M.1 is per-element // (g_proj -> n_head*head_dim, like afmoe). Detect from the stored tensor // shape so a single arch handles both; the graph mirrors this check. - // Gate width selects per-head vs per-element. Read it from the tensor and - // require EXACTLY one of the two valid widths -- never silently fall back. - // (The converter also cross-checks this against the config's declared - // `gating` type and fails at conversion time on a mismatch.) + // Gate width selects per-head vs per-element. Real GGUFs always carry the + // gate tensor, so read the width from it and require EXACTLY one of the two + // valid widths -- never guess between them. Weightless fixtures + // (test-llama-archs) have no gate tensor; fall back to the per-head layout so + // the per-head reshape path is still exercised. const int64_t n_gate_per_head = n_head_il; const int64_t n_gate_per_elem = n_embd_head_k * n_head_il; - // Metadata-only loads (test-llama-archs synthesizes models without a - // tensor map) have no meta to inspect -- default to per-head there. A - // real file with the gate tensor missing still fails hard in the - // required create_tensor below. const ggml_tensor * gate_meta = ml.get_tensor_meta(tn(LLM_TENSOR_ATTN_GATE, "weight", i).str().c_str()); - const int64_t n_gate_out = gate_meta != nullptr ? gate_meta->ne[1] : n_gate_per_head; - if (n_gate_out != n_gate_per_head && n_gate_out != n_gate_per_elem) { - GGML_ABORT("Laguna: unexpected attention gate width %lld at layer %d " - "(expected %lld per-head or %lld per-element)", - (long long) n_gate_out, i, (long long) n_gate_per_head, (long long) n_gate_per_elem); + int64_t n_gate_out; + if (gate_meta != nullptr) { + n_gate_out = gate_meta->ne[1]; + if (n_gate_out != n_gate_per_head && n_gate_out != n_gate_per_elem) { + GGML_ABORT("Laguna: unexpected attention gate width %lld at layer %d " + "(expected %lld per-head or %lld per-element)", + (long long) n_gate_out, i, (long long) n_gate_per_head, (long long) n_gate_per_elem); + } + } else { + n_gate_out = n_gate_per_head; } layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_gate_out}, 0); diff --git a/src/models/mimo2.cpp b/src/models/mimo2.cpp index 889891605701..d50e186cce92 100644 --- a/src/models/mimo2.cpp +++ b/src/models/mimo2.cpp @@ -25,9 +25,17 @@ void llama_model_mimo2::load_arch_hparams(llama_model_loader & ml) { } } -void llama_model_mimo2::load_arch_tensors(llama_model_loader &) { +void llama_model_mimo2::load_arch_tensors(llama_model_loader & ml) { LLAMA_LOAD_LOCALS; + const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight"; + const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr); + int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0; + + if (!ml.load_mtp) { + mtp_flags |= TENSOR_SKIP; + } + tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // output @@ -40,41 +48,46 @@ void llama_model_mimo2::load_arch_tensors(llama_model_loader &) { uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i); uint32_t n_head = hparams.n_head(i); - // NextN/MTP layers (the last n_nextn blocks) are preserved but disabled pending support const bool is_nextn = i >= n_layer; - const int skip = is_nextn ? TENSOR_SKIP : 0; + const int flags = is_nextn ? mtp_flags : 0; - create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, skip); - layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_v * n_head, n_embd }, skip); + create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, flags); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_v * n_head, n_embd }, flags); - layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, skip); - layer.attn_sinks = create_tensor(tn(LLM_TENSOR_ATTN_SINKS, "weight", i), {n_head}, TENSOR_NOT_REQUIRED | skip); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags); + layer.attn_sinks = create_tensor(tn(LLM_TENSOR_ATTN_SINKS, "weight", i), {n_head}, TENSOR_NOT_REQUIRED | flags); - layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, skip); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags); // non-MoE branch - layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED | skip); - layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, TENSOR_NOT_REQUIRED | skip); - layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED | skip); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED | flags); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, TENSOR_NOT_REQUIRED | flags); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED | flags); // MoE branch int64_t n_ff_exp = hparams.n_ff_exp; - layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, TENSOR_NOT_REQUIRED | skip); - layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, TENSOR_NOT_REQUIRED | skip); - layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, TENSOR_NOT_REQUIRED | skip); - layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, TENSOR_NOT_REQUIRED | skip); - layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED | skip); + layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, TENSOR_NOT_REQUIRED | flags); + layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, TENSOR_NOT_REQUIRED | flags); + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, TENSOR_NOT_REQUIRED | flags); + layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, TENSOR_NOT_REQUIRED | flags); + layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED | flags); if (is_nextn) { - layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), {2 * n_embd, n_embd}, skip); - layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), {n_embd}, skip); - layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), {n_embd}, skip); - layer.layer_out_norm = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, skip); + layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), {2 * n_embd, n_embd}, flags); + layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), {n_embd}, flags); + layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), {n_embd}, flags); + layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED | flags); + layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED | flags); + layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED | flags); + layer.layer_out_norm = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED | flags); } } } std::unique_ptr llama_model_mimo2::build_arch_graph(const llm_graph_params & params) const { + if (params.gtype == LLM_GRAPH_TYPE_DECODER_MTP) { + return std::make_unique(*this, params); + } return std::make_unique(*this, params); } @@ -89,6 +102,8 @@ llama_model_mimo2::graph::graph(const llama_model & model, const llm_graph_param ggml_tensor * inp_out_ids = build_inp_out_ids(); const float v_scale = hparams.f_attn_value_scale; + const bool emit_h_nextn = cparams.embeddings_nextn; + const bool crop_last_layer = inp_out_ids && (!emit_h_nextn || cparams.embeddings_nextn_masked); for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -168,7 +183,7 @@ llama_model_mimo2::graph::graph(const llama_model & model, const llm_graph_param } } - if (il == n_layer - 1 && inp_out_ids) { + if (il == n_layer - 1 && crop_last_layer) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -218,6 +233,15 @@ llama_model_mimo2::graph::graph(const llama_model & model, const llm_graph_param cur = inpL; + if (emit_h_nextn) { + cb(cur, "h_nextn", -1); + res->t_h_nextn = cur; + + if (!cparams.embeddings_nextn_masked && inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + } + } + cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1); @@ -233,3 +257,143 @@ llama_model_mimo2::graph::graph(const llama_model & model, const llm_graph_param ggml_build_forward_expand(gf, cur); } + +// Mirrors MiMo's appended NextN block: normalize and fuse token and hidden inputs, run the decoder block, +// expose its pre-head-norm state to the next draft step, then apply the shared output norm and LM head. +// Converted checkpoints may store that shared norm as layer_out_norm, so it remains in the fallback chain. +llama_model_mimo2::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params) + : llm_graph_context(params) { + GGML_ASSERT(hparams.n_layer_nextn > 0 && "MIMO2 MTP requires n_layer_nextn > 0"); + + const int il = hparams.n_layer() + cparams.nextn_layer_offset; + GGML_ASSERT(cparams.nextn_layer_offset >= 0 && + cparams.nextn_layer_offset < (int) hparams.n_layer_nextn && + "nextn_layer_offset out of range [0, n_layer_nextn)"); + + const auto & layer = model.layers[il]; + GGML_ASSERT(layer.nextn.eh_proj && "MIMO2 MTP block missing nextn.eh_proj"); + GGML_ASSERT(layer.nextn.enorm && "MIMO2 MTP block missing nextn.enorm"); + GGML_ASSERT(layer.nextn.hnorm && "MIMO2 MTP block missing nextn.hnorm"); + GGML_ASSERT(layer.wqkv && "MIMO2 MTP requires fused attn_qkv"); + + const uint32_t n_head_l = hparams.n_head(il); + const uint32_t n_head_kv_l = hparams.n_head_kv(il); + + const float freq_base_l = model.get_rope_freq_base(cparams, il); + const float freq_scale_l = model.get_rope_freq_scale(cparams, il); + const float v_scale = hparams.f_attn_value_scale; + + auto inp = std::make_unique(hparams.n_embd); + + inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + ggml_set_input(inp->tokens); + + inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd, n_tokens); + ggml_set_input(inp->embd); + ggml_set_name(inp->embd, "mtp_h_input"); + + ggml_tensor * tok_embd_w = layer.nextn.embed_tokens ? layer.nextn.embed_tokens : model.tok_embd; + ggml_tensor * h_input = inp->embd; + ggml_tensor * tok_embd = ggml_get_rows(ctx0, tok_embd_w, inp->tokens); + cb(tok_embd, "mtp_tok_embd", il); + + res->add_input(std::move(inp)); + + ggml_tensor * inp_pos = build_inp_pos(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + auto * inp_attn = build_attn_inp_kv_iswa(); + + ggml_tensor * h_norm = build_norm(h_input, layer.nextn.hnorm, nullptr, LLM_NORM_RMS, il); + cb(h_norm, "mtp_hnorm", il); + + ggml_tensor * e_norm = build_norm(tok_embd, layer.nextn.enorm, nullptr, LLM_NORM_RMS, il); + cb(e_norm, "mtp_enorm", il); + + ggml_tensor * concat = ggml_concat(ctx0, e_norm, h_norm, /*dim=*/ 0); + cb(concat, "mtp_concat", il); + + ggml_tensor * cur = build_lora_mm(layer.nextn.eh_proj, concat, layer.nextn.eh_proj_s); + cb(cur, "mtp_eh_proj", il); + + ggml_tensor * inpSA = cur; + + cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il); + cb(cur, "mtp_attn_norm", il); + + ggml_tensor * qkv = build_lora_mm(layer.wqkv, cur, layer.wqkv_s); + cb(qkv, "mtp_wqkv", il); + + const size_t row_k = ggml_row_size(qkv->type, n_embd_head_k); + const size_t row_v = ggml_row_size(qkv->type, n_embd_head_v); + const size_t row_full = qkv->nb[1]; + const size_t k_off = row_k * n_head_l; + const size_t v_off = k_off + row_k * n_head_kv_l; + + ggml_tensor * Qcur = ggml_view_3d(ctx0, qkv, n_embd_head_k, n_head_l, n_tokens, row_k, row_full, 0); + ggml_tensor * Kcur = ggml_view_3d(ctx0, qkv, n_embd_head_k, n_head_kv_l, n_tokens, row_k, row_full, k_off); + ggml_tensor * Vcur = ggml_view_3d(ctx0, qkv, n_embd_head_v, n_head_kv_l, n_tokens, row_v, row_full, v_off); + + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l, + ext_factor, attn_factor, beta_fast, beta_slow); + + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l, + ext_factor, attn_factor, beta_fast, beta_slow); + + cb(Qcur, "mtp_Qcur", il); + cb(Kcur, "mtp_Kcur", il); + cb(Vcur, "mtp_Vcur", il); + + cur = build_attn(inp_attn, + layer.wo, nullptr, layer.wo_s, + Qcur, Kcur, Vcur, nullptr, layer.attn_sinks, nullptr, + 1.0f / sqrtf(float(n_embd_head_k)), il); + cb(cur, "mtp_attn_out", il); + + if (v_scale) { + cur = ggml_scale(ctx0, cur, v_scale); + cb(cur, "mtp_attn_out_scaled", il); + } + + ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "mtp_ffn_inp", il); + + cur = build_norm(ffn_inp, layer.ffn_norm, nullptr, LLM_NORM_RMS, il); + cb(cur, "mtp_ffn_norm", il); + + GGML_ASSERT(layer.ffn_gate && layer.ffn_down && layer.ffn_up && "MIMO2 MTP requires dense FFN tensors"); + cur = build_ffn(cur, + layer.ffn_up, layer.ffn_up_b, nullptr, + layer.ffn_gate, layer.ffn_gate_b, nullptr, + layer.ffn_down, layer.ffn_down_b, nullptr, + nullptr, + LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(cur, "mtp_ffn_out", il); + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "mtp_post_ffn", il); + + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + + cb(cur, "h_nextn", -1); + res->t_h_nextn = cur; + + ggml_tensor * head_norm_w = layer.nextn.shared_head_norm + ? layer.nextn.shared_head_norm + : (layer.layer_out_norm ? layer.layer_out_norm : model.output_norm); + GGML_ASSERT(head_norm_w && "MIMO2 MTP missing head norm fallback"); + cur = build_norm(cur, head_norm_w, nullptr, LLM_NORM_RMS, -1); + cb(cur, "mtp_shared_head_norm", -1); + + ggml_tensor * head_w = layer.nextn.shared_head_head ? layer.nextn.shared_head_head : model.output; + ggml_tensor * head_s = layer.nextn.shared_head_head ? layer.nextn.shared_head_head_s : model.output_s; + GGML_ASSERT(head_w && "MIMO2 MTP missing LM head fallback"); + cur = build_lora_mm(head_w, cur, head_s); + cb(cur, "result_output", -1); + + res->t_logits = cur; + ggml_build_forward_expand(gf, cur); +} diff --git a/src/models/minimax-m3.cpp b/src/models/minimax-m3.cpp new file mode 100644 index 000000000000..854d5aed0f82 --- /dev/null +++ b/src/models/minimax-m3.cpp @@ -0,0 +1,601 @@ +#include "models.h" +#include "llama-kv-cache-msa.h" +#include +#include +#include + +// MiniMax-M3: MiniMax-M2 style GQA (per-head QK-norm, partial rotary) with +// DeepSeek-V3 leading-dense + routed/shared experts (sigmoid gating, routed scaling), +// swigluoai activation, and MiniMax Sparse Attention (MSA). MTP is not in released model weights. +// MSA blocks are defined over token positions. The graph translates between position space (block +// selection) and cell space (K/V/indexer storage) via per-ubatch pos<->cell maps populated from llama_kv_cells + +void llama_model_minimax_m3::load_arch_hparams(llama_model_loader & ml) { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false); + ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); + ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared); + ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false); + ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false); + ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func); + ml.get_key(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, hparams.indexer_n_head); + ml.get_key(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, hparams.indexer_head_size); + ml.get_key(LLM_KV_ATTENTION_INDEXER_TOP_K, hparams.indexer_top_k); + ml.get_key(LLM_KV_ATTENTION_INDEXER_BLOCK_SIZE, hparams.indexer_block_size); + ml.get_key(LLM_KV_ATTENTION_INDEXER_LOCAL_BLOCKS, hparams.indexer_local_blocks); + msa_p = { (int) hparams.indexer_block_size, (int) hparams.indexer_top_k, (int) hparams.indexer_local_blocks }; + + switch (hparams.n_layer()) { + case 60: type = LLM_TYPE_428B_A23B; break; + default: type = LLM_TYPE_UNKNOWN; + } +} + +void llama_model_minimax_m3::load_arch_tensors(llama_model_loader &) { + LLAMA_LOAD_LOCALS; + const int64_t n_expert_shared = hparams.n_expert_shared; + const int64_t n_ff_exp = hparams.n_ff_exp; + + tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + + // output + output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); + + for (int i = 0; i < n_layer; ++i) { + auto & layer = layers[i]; + + create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0); + + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + // per-head QK-norm: a single head_dim vector applied to every head + layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0); + layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0); + + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + + if (i < (int) hparams.n_layer_dense_lead) { + // leading dense layers + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + } else { + // routed experts + layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0); + layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0); + layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0); + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0); + layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0); + + // shared expert + layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0); + layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd}, 0); + layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0); + + // indexer + layer.index_q_proj = create_tensor(tn(LLM_TENSOR_INDEXER_Q_PROJ, "weight", i), {n_embd, hparams.indexer_n_head * hparams.indexer_head_size}, 0); + layer.index_k_proj = create_tensor(tn(LLM_TENSOR_INDEXER_K_PROJ, "weight", i), {n_embd, hparams.indexer_head_size}, 0); + layer.index_q_norm = create_tensor(tn(LLM_TENSOR_INDEXER_Q_NORM, "weight", i), {hparams.indexer_head_size}, 0); + layer.index_k_norm = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM, "weight", i), {hparams.indexer_head_size}, 0); + } + } +} + +std::unique_ptr llama_model_minimax_m3::build_arch_graph(const llm_graph_params & params) const { + return std::make_unique(*this, params); +} + +class llm_graph_input_msa : public llm_graph_input_i { +public: + llm_graph_input_msa(const llama_kv_cache_msa_context * mctx, int blk, int local) : + mctx(mctx), blk(blk), local(local) {} + + void set_input(const llama_ubatch * ubatch) override { + if (pos_slot_i) { mctx->set_input_pos_slot(pos_slot_i, ubatch); } + if (pos_slot_f) { mctx->set_input_pos_slot(pos_slot_f, ubatch); } + if (cell_blk) { mctx->set_input_cell_pos(cell_blk, ubatch, blk); } + if (pos_mask) { mctx->set_input_pos_mask(pos_mask, ubatch); } + + // local-force bias over position blocks + if (bias && ubatch->pos) { + const int64_t n_tokens = ubatch->n_tokens; + const int64_t nblk = bias->ne[0]; + std::vector data((size_t) nblk * n_tokens, 0.0f); + for (int64_t i = 0; i < n_tokens; ++i) { + const int64_t L = ubatch->pos[i] / blk; + for (int l = 0; l < local && L - l >= 0; ++l) { + if (L - l < nblk) { + data[(size_t) i * nblk + (L - l)] = 1e30f; + } + } + } + ggml_backend_tensor_set(bias, data.data(), 0, data.size() * sizeof(float)); + } + } + + // valid as long as the tensor dims still match the new ubatch/cache window and the + // ubatch is in the same regime (decode graphs have pos_slot_f, batch graphs cell_blk) + bool can_reuse(const llm_graph_params & params) override { + const auto * mctx_new = static_cast(params.mctx); + + this->mctx = mctx_new; + + const int64_t n_ps = GGML_PAD((int64_t) mctx_new->get_n_pos(), blk); + const int64_t ns = params.cparams.kv_unified ? 1 : params.ubatch.n_seqs_unq; + + const bool decode = params.ubatch.n_tokens == ns; // one token per stream + + bool res = true; + + res &= bias->ne[0] * blk == n_ps; + res &= bias->ne[1] == params.ubatch.n_tokens; + + res &= pos_mask->ne[0] == n_ps; + res &= pos_mask->ne[1] == params.ubatch.n_tokens; + + res &= pos_slot_i->ne[0] == n_ps; + res &= pos_slot_i->ne[1] == ns; + + res &= decode == (pos_slot_f != nullptr); + res &= decode == (cell_blk == nullptr); + + if (pos_slot_f) { + res &= pos_slot_f->ne[0] == n_ps; + res &= pos_slot_f->ne[1] == ns; + } + + if (cell_blk) { + res &= cell_blk->ne[0] == (int64_t) mctx_new->get_base()->get_n_kv(); + res &= cell_blk->ne[1] == ns; + } + + return res; + } + + ggml_tensor * bias = nullptr; // F32 [nblk, n_tokens] local-force bias (position blocks) + ggml_tensor * pos_mask = nullptr; // F32 [n_ps, n_tokens] 0/-inf visibility, by position + ggml_tensor * pos_slot_i = nullptr; // I32 [n_ps, ns] pos -> cell (get_rows index) + ggml_tensor * pos_slot_f = nullptr; // F32 [n_ps, ns] pos -> cell (gatherable values, decode) + ggml_tensor * cell_blk = nullptr; // I32 [n_kv, ns] cell -> position block (batch) + + const llama_kv_cache_msa_context * mctx; + + int blk; + int local; +}; + +// One FA call for all GQA groups (and at multi-stream decode, all streams) by mapping them onto the FA sequence dim (ne[3]) +ggml_tensor * llama_model_minimax_m3::graph::build_attn_msa_fa( + ggml_tensor * q_cur, // [D, HQ, T] + ggml_tensor * k, // [D, n_keys, 1, C] + ggml_tensor * v, // [D, n_keys, 1, C] + ggml_tensor * mask, // [n_keys, R, 1, C] f16, contiguous + int64_t Gp, float kq_scale, int il) const { + + const int64_t D = q_cur->ne[0]; + const int64_t HQ = q_cur->ne[1]; + const int64_t T = q_cur->ne[2]; + const int64_t C = k->ne[3]; + const int64_t R = HQ*T/(Gp*C); + GGML_ASSERT(Gp*C*R == HQ*T); + GGML_ASSERT(mask->type == GGML_TYPE_F16); + + // [D, HQ, T] -> [D, Gp, C, R] -> [D, R, Gp, C] + // batch (C=HKV, R=T): channel = group + // decode (C=HKV*ns, R=1): channel = (group, stream), group innermost + ggml_tensor * q = ggml_reshape_4d(ctx0, q_cur, D, Gp, C, R); + q = ggml_permute(ctx0, q, 0, 2, 3, 1); + + ggml_tensor * o = ggml_flash_attn_ext(ctx0, q, k, v, mask, kq_scale, + hparams.f_max_alibi_bias, 0.0f); + ggml_flash_attn_ext_set_prec(o, GGML_PREC_F32); + cb(o, "msa_fattn", il); + + // [D, Gp, R, C] -> [D, Gp, C, R] -> [n_embd, T] + o = ggml_permute(ctx0, o, 0, 1, 3, 2); + if (!ggml_is_contiguous(o)) { + o = ggml_cont(ctx0, o); // no-op layout at decode (R == 1), copy at batch + } + return ggml_reshape_2d(ctx0, o, D*HQ, T); +} + +llama_model_minimax_m3::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { + const int64_t n_embd_head = hparams.n_embd_head_v(); + const auto & mm = static_cast(model); + + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + // partial rotary: head_dim != n_rot, so don't assert n_embd_head == n_rot + + ggml_tensor * cur; + ggml_tensor * inpL; + + inpL = build_inp_embd(model.tok_embd); + + ggml_tensor * inp_pos = build_inp_pos(); + + // ========================================== + // TODO: avoid such kind of complexity in the model graphs + + // MSA calls ggml_flash_attn_ext directly and assumes the non-transposed V layout that + // llama.cpp only provides when flash attention is enabled. Block selection is anchored + // to absolute KV cache slots, which equal positions only for append-only per-stream + // caches either a single sequence, or multiple sequences with kv_unified == false (each + // stream then has its own slot space). A unified cache with multiple sequences + // interleaves slots and would silently break block anchoring so it falls back to dense. + const bool fa_on = cparams.flash_attn; + const bool streams_ok = cparams.n_seq_max == 1 || !cparams.kv_unified; + const bool msa_enabled = fa_on && streams_ok; + + auto * inp_attn = build_attn_inp_kv_msa(msa_enabled); + + static bool warned_no_fa = false; + if (!fa_on && !warned_no_fa) { + LLAMA_LOG_WARN("%s: flash attention disabled; MSA requires it -> running DENSE attention " + "(output may be degraded). Enable flash attention for MSA.\n", __func__); + warned_no_fa = true; + } + static bool warned_unified = false; + if (fa_on && !streams_ok && !warned_unified) { + LLAMA_LOG_WARN("%s: unified KV cache with n_seq_max > 1; MSA needs per-sequence streams " + "-> running DENSE attention. Output may be degraded. Drop --kv-unified to enable MSA.\n", __func__); + warned_unified = true; + } + // ========================================== + + // hoisted per-graph MSA state (shared by every sparse layer) + llm_graph_input_msa * msa = nullptr; + ggml_tensor * msa_kqm = nullptr; + ggml_tensor * msa_mf = nullptr; // F32 copy of the FA mask for the final mask add + int64_t n_kv = 0, n_ps = 0, nblk = 0, ns = 1, n_tps = 0; + bool msa_decode = false; // gather (1 token per stream) vs mask + const int blk = mm.msa_p.blk; + const int64_t Hd = hparams.indexer_n_head; // one indexer head per GQA group + + if (msa_enabled) { + const auto * mctx_msa = static_cast(mctx); + + msa_kqm = inp_attn->get_kq_mask(); + n_kv = msa_kqm->ne[0]; + n_tps = msa_kqm->ne[1]; // tokens per stream + ns = msa_kqm->ne[3]; // streams in this ubatch + GGML_ASSERT(msa_kqm->type == GGML_TYPE_F16 && "MSA requires the FA (f16) mask"); + GGML_ASSERT(n_tps*ns == n_tokens); + + // the position axis covers every position currently in the cache and is padded to whole blocks + n_ps = GGML_PAD((int64_t) mctx_msa->get_n_pos(), blk); + nblk = n_ps / blk; + msa_decode = n_tps == 1; + + auto inp = std::make_unique(mctx_msa, blk, mm.msa_p.local); + + inp->bias = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, nblk, n_tokens); // stream-grouped tokens + ggml_set_input(inp->bias); + + inp->pos_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_ps, n_tokens); + ggml_set_input(inp->pos_mask); + + inp->pos_slot_i = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_ps, ns); + ggml_set_input(inp->pos_slot_i); + + if (msa_decode) { + inp->pos_slot_f = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_ps, ns); + ggml_set_input(inp->pos_slot_f); + } else { + inp->cell_blk = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_kv, ns); + ggml_set_input(inp->cell_blk); + + msa_mf = ggml_cast(ctx0, msa_kqm, GGML_TYPE_F32); + } + + msa = (llm_graph_input_msa *) res->add_input(std::move(inp)); + } + + ggml_tensor * inp_out_ids = build_inp_out_ids(); + + for (int il = 0; il < n_layer; ++il) { + ggml_tensor * inpSA = inpL; + + // self-attention + { + cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il); + cb(cur, "attn_norm", il); + + auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur, + n_embd_head, n_head, n_head_kv, il); + + // per-head QK RMSNorm (weights already include Gemma's +1) + Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il); + cb(Qcur, "Qcur_normed", il); + Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il); + cb(Kcur, "Kcur_normed", il); + + // partial rotary: only the first n_rot dims are rotated + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow); + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + const bool is_sparse = msa_enabled && il >= (int) hparams.n_layer_dense_lead; + + if (!is_sparse) { + cur = build_attn(inp_attn, model.layers[il].wo, NULL, model.layers[il].wo_s, + Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, + 1.0f/sqrtf(float(n_embd_head)), il); + } else { + const int64_t n_idx_dim = hparams.indexer_head_size; // 128 + + // Index Branch, project, norm, partial RoPE, cache + ggml_tensor * iq = build_lora_mm(model.layers[il].index_q_proj, cur); + ggml_tensor * ik = build_lora_mm(model.layers[il].index_k_proj, cur); + iq = ggml_reshape_3d(ctx0, iq, n_idx_dim, Hd, n_tokens); + ik = ggml_reshape_3d(ctx0, ik, n_idx_dim, 1, n_tokens); + iq = build_norm(iq, model.layers[il].index_q_norm, NULL, LLM_NORM_RMS, il); // +1 baked + ik = build_norm(ik, model.layers[il].index_k_norm, NULL, LLM_NORM_RMS, il); + iq = ggml_rope_ext(ctx0, iq, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, + freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); + ik = ggml_rope_ext(ctx0, ik, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, + freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); + + const auto * mctx_msa_l = static_cast(mctx); + const auto * mctx_cur = mctx_msa_l->get_base(); + const auto * mctx_idx = mctx_msa_l->get_idx(); + ggml_build_forward_expand(gf, mctx_idx->cpy_k(ctx0, ik, inp_attn->get_k_idxs_idx(), il)); + ggml_tensor * ik_kv = mctx_idx->get_k(ctx0, il); + + if (inp_attn->self_k_rot) { + Qcur = llama_mul_mat_hadamard(ctx0, Qcur, inp_attn->self_k_rot); + Kcur = llama_mul_mat_hadamard(ctx0, Kcur, inp_attn->self_k_rot); + } + if (inp_attn->self_v_rot) { + Vcur = llama_mul_mat_hadamard(ctx0, Vcur, inp_attn->self_v_rot); + } + + // Main branch: store K/V, take cache views + ggml_build_forward_expand(gf, Qcur); + ggml_build_forward_expand(gf, Kcur); + ggml_build_forward_expand(gf, Vcur); + ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, Kcur, inp_attn->get_k_idxs(), il)); + ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, Vcur, inp_attn->get_v_idxs(), il)); + ggml_tensor * k = mctx_cur->get_k(ctx0, il); + ggml_tensor * v = mctx_cur->get_v(ctx0, il); + GGML_ASSERT(!(v->nb[1] > v->nb[2]) && "MSA assumes v_trans=false (FA on)"); + + const int64_t D = k->ne[0]; + const int64_t HKV = k->ne[1]; + const int64_t Gp = n_head/HKV; + GGML_ASSERT(HKV == Hd && "MSA: one indexer head per GQA group"); + GGML_ASSERT(k->ne[3] == ns); + const int K = mm.msa_p.topk_blocks < (int) nblk ? mm.msa_p.topk_blocks : (int) nblk; + + const float kq_scale = 1.0f/sqrtf(float(n_embd_head)); + + if (msa_decode) { + // decode: batched over streams top-k + gather, one grouped FA + // gather the indexer keys through the pos -> cell map + ggml_tensor * ik3 = ggml_view_3d(ctx0, ik_kv, n_idx_dim, n_kv, ns, + ik_kv->nb[2], ik_kv->nb[3], 0); + ggml_tensor * ikp = ggml_get_rows(ctx0, ik3, msa->pos_slot_i); // [n_idx_dim, n_ps, ns] + ggml_tensor * iq4 = ggml_reshape_4d(ctx0, iq, n_idx_dim, Hd, 1, ns); + ggml_tensor * sc = ggml_mul_mat(ctx0, + ggml_reshape_4d(ctx0, ikp, n_idx_dim, n_ps, 1, ns), iq4); + ggml_mul_mat_set_prec(sc, GGML_PREC_F32); + // unmapped positions come out -inf, so they can never rank into the top-k + sc = ggml_add_inplace(ctx0, sc, + ggml_reshape_4d(ctx0, msa->pos_mask, n_ps, 1, 1, ns)); + ggml_tensor * bs = ggml_pool_2d(ctx0, sc, GGML_OP_POOL_MAX, blk, 1, blk, 1, 0, 0); + cb(bs, "msa_bs", il); + + ggml_tensor * bsf = ggml_add(ctx0, bs, + ggml_reshape_4d(ctx0, msa->bias, nblk, 1, 1, ns)); + ggml_tensor * idx = ggml_top_k(ctx0, bsf, K); // position blocks + + // pos idx: tj[t,k,h,s] = blk*idx[k,h,s] + t (positions - mask gather) + // cell idx: cs[t,k,h,s] = pos_slot[tj] (pos -> cell translation) + // row idx: tr[t,k,h,s] = cs*HKV + h (per-stream K/V gather) + ggml_tensor * a = ggml_scale(ctx0, ggml_cast(ctx0, idx, GGML_TYPE_F32), (float) blk); + a = ggml_reshape_4d(ctx0, a, 1, K, Hd, ns); + ggml_tensor * tj = ggml_add(ctx0, + ggml_repeat_4d(ctx0, a, blk, K, Hd, ns), + ggml_reshape_3d(ctx0, ggml_arange(ctx0, 0.0f, (float) blk, 1.0f), blk, 1, 1)); + + ggml_tensor * tokj = ggml_cast(ctx0, ggml_reshape_2d(ctx0, tj, (int64_t) blk*K*Hd, ns), GGML_TYPE_I32); + + ggml_tensor * cs = ggml_get_rows(ctx0, + ggml_reshape_3d(ctx0, msa->pos_slot_f, 1, n_ps, ns), tokj); // [1, blk*K*Hd, ns] + cs = ggml_reshape_4d(ctx0, cs, blk, K, Hd, ns); + + ggml_tensor * tr = ggml_add(ctx0, + ggml_scale(ctx0, cs, (float) HKV), + ggml_reshape_3d(ctx0, ggml_arange(ctx0, 0.0f, (float) HKV, 1.0f), 1, 1, Hd)); + + ggml_tensor * tokr = ggml_cast(ctx0, ggml_reshape_2d(ctx0, tr, (int64_t) blk*K*Hd, ns), GGML_TYPE_I32); + + ggml_tensor * k3 = ggml_view_3d(ctx0, k, D, HKV*n_kv, ns, k->nb[1], k->nb[3], 0); + ggml_tensor * v3 = ggml_view_3d(ctx0, v, D, HKV*n_kv, ns, v->nb[1], v->nb[3], 0); + ggml_tensor * mp = ggml_reshape_3d(ctx0, msa->pos_mask, 1, n_ps, ns); + + ggml_tensor * kg = ggml_get_rows(ctx0, k3, tokr); + ggml_tensor * vg = ggml_get_rows(ctx0, v3, tokr); + ggml_tensor * mg = ggml_get_rows(ctx0, mp, tokj); + + // fold (group, stream) onto the FA channel dim + const ggml_type kt = ggml_is_quantized(k->type) ? GGML_TYPE_F16 : k->type; + const ggml_type vt = ggml_is_quantized(v->type) ? GGML_TYPE_F16 : v->type; + ggml_tensor * kfa = ggml_reshape_4d(ctx0, kg, D, (int64_t) blk*K, 1, Hd*ns); + ggml_tensor * vfa = ggml_reshape_4d(ctx0, vg, D, (int64_t) blk*K, 1, Hd*ns); + if (kfa->type != kt) { kfa = ggml_cast(ctx0, kfa, kt); } + if (vfa->type != vt) { vfa = ggml_cast(ctx0, vfa, vt); } + // the FA mask must be F16 + ggml_tensor * mfa = ggml_cast(ctx0, ggml_reshape_4d(ctx0, mg, (int64_t) blk*K, 1, 1, Hd*ns), GGML_TYPE_F16); + + cur = build_attn_msa_fa(Qcur, kfa, vfa, mfa, Gp, kq_scale, il); + } else { + // batch: per-stream loop + std::vector outs(ns); + for (int64_t st = 0; st < ns; ++st) { + ggml_tensor * iq_s = ggml_view_3d(ctx0, iq, n_idx_dim, Hd, n_tps, + iq->nb[1], iq->nb[2], st*n_tps*iq->nb[2]); + ggml_tensor * ik_s = ggml_view_2d(ctx0, ik_kv, n_idx_dim, n_kv, + ik_kv->nb[2], st*ik_kv->nb[3]); + ggml_tensor * psl_s = ggml_view_1d(ctx0, msa->pos_slot_i, n_ps, + st*msa->pos_slot_i->nb[1]); + ggml_tensor * pm_s = ggml_view_3d(ctx0, msa->pos_mask, n_ps, 1, n_tps, + msa->pos_mask->nb[1], msa->pos_mask->nb[1], st*n_tps*msa->pos_mask->nb[1]); + ggml_tensor * cb_s = ggml_view_1d(ctx0, msa->cell_blk, n_kv, + st*msa->cell_blk->nb[1]); + ggml_tensor * mf_s = ggml_view_3d(ctx0, msa_mf, n_kv, n_tps, 1, + msa_mf->nb[1], msa_mf->nb[3], st*msa_mf->nb[3]); + ggml_tensor * bias_s = ggml_view_3d(ctx0, msa->bias, nblk, 1, n_tps, + msa->bias->nb[1], msa->bias->nb[1], st*n_tps*msa->bias->nb[1]); + ggml_tensor * q_s = ggml_view_3d(ctx0, Qcur, D, n_head, n_tps, + Qcur->nb[1], Qcur->nb[2], st*n_tps*Qcur->nb[2]); + ggml_tensor * k_s = ggml_view_4d(ctx0, k, D, HKV, n_kv, 1, + k->nb[1], k->nb[2], k->nb[3], st*k->nb[3]); + ggml_tensor * v_s = ggml_view_4d(ctx0, v, D, HKV, n_kv, 1, + v->nb[1], v->nb[2], v->nb[3], st*v->nb[3]); + + // block scores: the indexer keys are gathered through the pos -> cell map first + // scores are unscaled, only the top-k ordering matters + ggml_tensor * ikp = ggml_get_rows(ctx0, ik_s, psl_s); // [n_idx_dim, n_ps] + ggml_tensor * sc = ggml_mul_mat(ctx0, ikp, + ggml_reshape_2d(ctx0, iq_s, n_idx_dim, Hd*n_tps)); + // indexer scores run in F32 + ggml_mul_mat_set_prec(sc, GGML_PREC_F32); + sc = ggml_reshape_3d(ctx0, sc, n_ps, Hd, n_tps); + // unmapped positions (holes, padding, empty cells) come out -inf + sc = ggml_add_inplace(ctx0, sc, pm_s); + ggml_tensor * bs = ggml_pool_2d(ctx0, sc, GGML_OP_POOL_MAX, blk, 1, blk, 1, 0, 0); + cb(bs, "msa_bs", il); + + // bias the scores so locally-forced blocks always rank first + ggml_tensor * bsf = ggml_add(ctx0, bs, bias_s); // [nblk, Hd, n_tps] + cb(bsf, "msa_bsf", il); + + ggml_tensor * idx = ggml_top_k(ctx0, bsf, K); // [K, Hd, n_tps] i32 + + ggml_tensor * ninf = ggml_cast(ctx0, + ggml_scale_bias(ctx0, bias_s, 0.0f, -1e30f), + GGML_TYPE_F16); // [nblk, 1, n_tps] + ninf = ggml_repeat_4d(ctx0, ninf, nblk, Hd, n_tps, 1); + ggml_tensor * zero = ggml_scale(ctx0, + ggml_cast(ctx0, idx, GGML_TYPE_F32), 0.0f); + ggml_tensor * bm = ggml_set_rows(ctx0, + ggml_reshape_3d(ctx0, ninf, 1, nblk, Hd*n_tps), + ggml_reshape_3d(ctx0, zero, 1, K, Hd*n_tps), + ggml_reshape_2d(ctx0, idx, K, Hd*n_tps)); + bm = ggml_reshape_3d(ctx0, bm, nblk, Hd, n_tps); + bm = ggml_cont(ctx0, ggml_permute(ctx0, bm, 0, 2, 1, 3)); // [nblk, n_tps, Hd] + cb(bm, "msa_block_mask", il); + + // expand block -> cell granularity through the cell -> position block + // map, then combine with the causal mask. empty cells are masked by the causal mask. + ggml_tensor * bm2 = ggml_cont(ctx0, ggml_transpose(ctx0, + ggml_reshape_2d(ctx0, bm, nblk, n_tps*Hd))); // [n_tps*Hd, nblk] + ggml_tensor * bmc = ggml_get_rows(ctx0, bm2, cb_s); // [n_tps*Hd, n_kv] F32 + ggml_tensor * bmx = ggml_cont(ctx0, ggml_transpose(ctx0, bmc)); + bmx = ggml_reshape_3d(ctx0, bmx, n_kv, n_tps, Hd); + ggml_tensor * mask4 = ggml_add_inplace(ctx0, bmx, mf_s); + mask4 = ggml_cast(ctx0, + ggml_reshape_4d(ctx0, mask4, n_kv, n_tps, 1, Hd), GGML_TYPE_F16); + cb(mask4, "msa_mask4", il); + + // cache views with groups on ne[3]; + ggml_tensor * kfa = ggml_permute(ctx0, k_s, 0, 3, 1, 2); + ggml_tensor * vfa = ggml_permute(ctx0, v_s, 0, 3, 1, 2); + + outs[st] = build_attn_msa_fa(q_s, kfa, vfa, mask4, Gp, kq_scale, il); + } + cur = outs[0]; + for (int64_t st = 1; st < ns; ++st) { + cur = ggml_concat(ctx0, cur, outs[st], 1); + } + } + if (inp_attn->self_v_rot) { + cur = llama_mul_mat_hadamard(ctx0, cur, inp_attn->self_v_rot); + } + cb(cur, "kqv_out", il); + if (model.layers[il].wo) { + cur = build_lora_mm(model.layers[il].wo, cur, model.layers[il].wo_s); + } + } + } + + if (il == n_layer - 1 && inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + + ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il); + cb(cur, "ffn_norm", il); + + if ((uint32_t) il < hparams.n_layer_dense_lead) { + // leading dense FFN (swigluoai) + cur = build_ffn(cur, + model.layers[il].ffn_up, NULL, NULL, + model.layers[il].ffn_gate, NULL, NULL, + model.layers[il].ffn_down, NULL, NULL, + NULL, + LLM_FFN_SWIGLU_OAI_MOE, LLM_FFN_PAR, il); + cb(cur, "ffn_out", il); + } else { + // routed experts (swigluoai MoE) + ggml_tensor * moe_out = build_moe_ffn(cur, + model.layers[il].ffn_gate_inp, + model.layers[il].ffn_up_exps, + model.layers[il].ffn_gate_exps, + model.layers[il].ffn_down_exps, + model.layers[il].ffn_exp_probs_b, + n_expert, n_expert_used, + LLM_FFN_SWIGLU_OAI_MOE, hparams.expert_weights_norm, + hparams.expert_weights_scale, + (llama_expert_gating_func_type) hparams.expert_gating_func, + il); + cb(moe_out, "ffn_moe_out", il); + + // shared expert (swigluoai) + ggml_tensor * ffn_shexp = build_ffn(cur, + model.layers[il].ffn_up_shexp, NULL, NULL, + model.layers[il].ffn_gate_shexp, NULL, NULL, + model.layers[il].ffn_down_shexp, NULL, NULL, + NULL, + LLM_FFN_SWIGLU_OAI_MOE, LLM_FFN_PAR, il); + cb(ffn_shexp, "ffn_shexp", il); + + cur = ggml_add(ctx0, moe_out, ffn_shexp); + cb(cur, "ffn_out", il); + } + + cur = ggml_add(ctx0, cur, ffn_inp); + + cur = build_cvec(cur, il); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1); + cb(cur, "result_norm", -1); + res->t_embd = cur; + + // lm_head + cur = build_lora_mm(model.output, cur, model.output_s); + cb(cur, "result_output", -1); + res->t_logits = cur; + + ggml_build_forward_expand(gf, cur); +} diff --git a/src/models/models.h b/src/models/models.h index fb3cb1076598..d797b3477a40 100644 --- a/src/models/models.h +++ b/src/models/models.h @@ -424,6 +424,22 @@ struct llama_model_mellum : public llama_model_base { std::unique_ptr build_arch_graph(const llm_graph_params & params) const override; }; +struct llama_model_nanbeige : public llama_model_base { + llama_model_nanbeige(const struct llama_model_params & params) : llama_model_base(params) {} + void load_arch_hparams(llama_model_loader & ml) override; + void load_arch_tensors(llama_model_loader & ml) override; + + int n_loops = 1; + int n_layer_phys = 0; + bool skip_loop_final_norm = false; + + struct graph : public llm_graph_context { + graph(const llama_model & model, const llm_graph_params & params); + }; + + std::unique_ptr build_arch_graph(const llm_graph_params & params) const override; +}; + struct llama_model_qwen : public llama_model_base { llama_model_qwen(const struct llama_model_params & params) : llama_model_base(params) {} void load_arch_hparams(llama_model_loader & ml) override; @@ -1068,6 +1084,10 @@ struct llama_model_deepseek2 : public llama_model_base { graph(const llama_model & model, const llm_graph_params & params); }; + struct graph_mtp : public llm_graph_context { + graph_mtp(const llama_model & model, const llm_graph_params & params); + }; + std::unique_ptr build_arch_graph(const llm_graph_params & params) const override; }; @@ -1081,6 +1101,10 @@ struct llama_model_deepseek32 : public llama_model_base { graph(const llama_model & model, const llm_graph_params & params); }; + struct graph_mtp : public llm_graph_context { + graph_mtp(const llama_model & model, const llm_graph_params & params); + }; + std::unique_ptr build_arch_graph(const llm_graph_params & params) const override; }; @@ -1091,6 +1115,7 @@ struct llama_model_deepseek4 : public llama_model_base { void load_arch_tensors(llama_model_loader & ml) override; struct graph : public llm_graph_context { + graph(const llm_graph_params & params) : llm_graph_context(params) {} graph(const llama_model & model, const llm_graph_params & params); ggml_tensor * build_hc_pre( @@ -1122,6 +1147,21 @@ struct llama_model_deepseek4 : public llama_model_base { ggml_tensor * inp_pos, int il) const; + ggml_tensor * build_attention( + const llama_model & model, + llm_graph_input_attn_k_iswa * inp_mtp, + ggml_tensor * cur, + ggml_tensor * inp_pos, + int il) const; + + ggml_tensor * build_attention_impl( + const llama_model & model, + llm_graph_input_dsv4 * inp_dsv4, + llm_graph_input_attn_k_iswa * inp_mtp, + ggml_tensor * cur, + ggml_tensor * inp_pos, + int il) const; + ggml_tensor * build_hca_compressed_kv_from_state( ggml_tensor * kv_state, ggml_tensor * score_state, @@ -1187,15 +1227,20 @@ struct llama_model_deepseek4 : public llama_model_base { float kq_scale, int il) const; - ggml_tensor * build_hc_weighted_sum( + ggml_tensor * build_hc_pre( ggml_tensor * x, - ggml_tensor * weights) const; + ggml_tensor * weights, + int il) const; ggml_tensor * build_hc_sinkhorn( ggml_tensor * comb, int il) const; }; + struct graph_mtp : public graph { + graph_mtp(const llama_model & model, const llm_graph_params & params); + }; + std::unique_ptr build_arch_graph(const llm_graph_params & params) const override; }; @@ -1216,7 +1261,13 @@ struct llama_model_glm_dsa : public llama_model_base { void load_arch_hparams(llama_model_loader & ml) override; void load_arch_tensors(llama_model_loader & ml) override; - using graph = llama_model_deepseek2::graph; + struct graph : public llm_graph_context { + graph(const llama_model & model, const llm_graph_params & params); + }; + + struct graph_mtp : public llm_graph_context { + graph_mtp(const llama_model & model, const llm_graph_params & params); + }; std::unique_ptr build_arch_graph(const llm_graph_params & params) const override; }; @@ -1249,6 +1300,10 @@ struct llama_model_dflash : public llama_model_base { ggml_tensor * build_inp_embd_enc() const; }; + struct graph_dsv4 : public llama_model_deepseek4::graph { + graph_dsv4(const llama_model & model, const llm_graph_params & params); + }; + std::unique_ptr build_arch_graph(const llm_graph_params & params) const override; }; @@ -1912,6 +1967,29 @@ struct llama_model_minimax_m2 : public llama_model_base { std::unique_ptr build_arch_graph(const llm_graph_params & params) const override; }; +struct msa_params { + int blk; + int topk_blocks; + int local; +}; + +struct llama_model_minimax_m3 : public llama_model_base { + llama_model_minimax_m3(const struct llama_model_params & params) : llama_model_base(params) {} + void load_arch_hparams(llama_model_loader & ml) override; + void load_arch_tensors(llama_model_loader & ml) override; + msa_params msa_p; + struct graph : public llm_graph_context { + graph(const llama_model & model, const llm_graph_params & params); + + ggml_tensor * build_attn_msa_fa( + ggml_tensor * q_cur, // [D, HQ, S] f32 + ggml_tensor * k, // [D, n_keys, 1, C] C = HKV or HKV*n_stream + ggml_tensor * v, // [D, n_keys, 1, C] + ggml_tensor * mask, // [n_keys, R, 1, C] f16, R = HQ*T/(Gp*C) + int64_t Gp, float kq_scale, int il) const; + }; + std::unique_ptr build_arch_graph(const llm_graph_params & params) const override; +}; struct llama_model_cogvlm : public llama_model_base { llama_model_cogvlm(const struct llama_model_params & params) : llama_model_base(params) {} @@ -1976,6 +2054,10 @@ struct llama_model_qwen3next : public llama_model_base { const llama_model & model; }; + struct graph_mtp : public llm_graph_context { + graph_mtp(const llama_model & model, const llm_graph_params & params); + }; + std::unique_ptr build_arch_graph(const llm_graph_params & params) const override; }; @@ -2094,6 +2176,10 @@ struct llama_model_mimo2 : public llama_model_base { graph(const llama_model & model, const llm_graph_params & params); }; + struct graph_mtp : public llm_graph_context { + graph_mtp(const llama_model & model, const llm_graph_params & params); + }; + std::unique_ptr build_arch_graph(const llm_graph_params & params) const override; }; @@ -2134,6 +2220,29 @@ struct llama_model_kimi_linear : public llama_model_base { }; +struct llama_model_kimi_k3 : public llama_model_base { + llama_model_kimi_k3(const struct llama_model_params & params) : llama_model_base(params) {} + void load_arch_hparams(llama_model_loader & ml) override; + void load_arch_tensors(llama_model_loader & ml) override; + + struct graph : public llm_build_delta_net_base { + graph(const llama_model & model, const llm_graph_params & params); + + // AttnRes: softmax mixture over residual-stream snapshots + current prefix sum + ggml_tensor * build_attn_res_mix( + ggml_tensor * prefix, + const std::vector & bank, + ggml_tensor * norm_w, + ggml_tensor * proj_w, + int il); + + const llama_model & model; + }; + + std::unique_ptr build_arch_graph(const llm_graph_params & params) const override; +}; + + struct llama_model_step35 : public llama_model_base { llama_model_step35(const struct llama_model_params & params) : llama_model_base(params) {} void load_arch_hparams(llama_model_loader & ml) override; diff --git a/src/models/nanbeige.cpp b/src/models/nanbeige.cpp new file mode 100644 index 000000000000..3a546600fa27 --- /dev/null +++ b/src/models/nanbeige.cpp @@ -0,0 +1,184 @@ +#include "models.h" + +void llama_model_nanbeige::load_arch_hparams(llama_model_loader & ml) { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + + uint32_t n_loops_u = 1; + ml.get_key(LLM_KV_NUM_LOOPS, n_loops_u, false); + GGML_ASSERT(n_loops_u >= 1); + + skip_loop_final_norm = false; + ml.get_key(LLM_KV_SKIP_LOOP_FINAL_NORM, skip_loop_final_norm, false); + + n_layer_phys = (int) hparams.n_layer(); + + // Bound-check before casting: signed int mul can overflow and bypass the guard. + GGML_ASSERT((size_t) n_layer_phys * (size_t) n_loops_u <= (size_t) LLAMA_MAX_LAYERS); + n_loops = (int) n_loops_u; + + // Expand logical layer count before load_tensors() allocates layers / KV. + if (n_loops > 1) { + for (int j = 1; j < n_loops; ++j) { + for (int i = 0; i < n_layer_phys; ++i) { + const int dst = i + j * n_layer_phys; + hparams.n_head_arr[dst] = hparams.n_head_arr[i]; + hparams.n_head_kv_arr[dst] = hparams.n_head_kv_arr[i]; + hparams.n_ff_arr[dst] = hparams.n_ff_arr[i]; + hparams.is_swa_impl[dst] = hparams.is_swa_impl[i]; + hparams.is_recr_impl[dst] = hparams.is_recr_impl[i]; + } + } + hparams.n_layer_all = (uint32_t) ((size_t) n_layer_phys * (size_t) n_loops); + } + + type = LLM_TYPE_UNKNOWN; +} + +void llama_model_nanbeige::load_arch_tensors(llama_model_loader &) { + LLAMA_LOAD_LOCALS; + + tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + + output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED); + if (output == NULL) { + output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); + } + + const int n_phys = n_layer_phys > 0 ? n_layer_phys : n_layer; + for (int i = 0; i < n_phys; ++i) { + auto & layer = layers[i]; + + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + + create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0); + + layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, + TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0)); + + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + } + + // Share physical weights across loops; each slot still has its own KV index. + if (n_loops > 1) { + for (int j = 1; j < n_loops; ++j) { + for (int i = 0; i < n_phys; ++i) { + layers[i + j * n_phys] = layers[i]; + } + } + } +} + +std::unique_ptr llama_model_nanbeige::build_arch_graph(const llm_graph_params & params) const { + return std::make_unique(*this, params); +} + +llama_model_nanbeige::graph::graph(const llama_model & model, const llm_graph_params & params) : + llm_graph_context(params) { + const auto & nb = static_cast(model); + + const int64_t n_embd_head = hparams.n_embd_head_v(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + + const int n_phys = nb.n_layer_phys > 0 ? nb.n_layer_phys : (int) n_layer; + const int n_loops = nb.n_loops > 0 ? nb.n_loops : 1; + + ggml_tensor * cur; + ggml_tensor * inpL; + + inpL = build_inp_embd(model.tok_embd); + + ggml_tensor * inp_pos = build_inp_pos(); + + auto * inp_attn = build_attn_inp_kv(); + + const float kq_scale = hparams.f_attention_scale == 0.0f + ? 1.0f / sqrtf(float(n_embd_head)) + : hparams.f_attention_scale; + + ggml_tensor * inp_out_ids = build_inp_out_ids(); + + for (int il = 0; il < n_layer; ++il) { + ggml_tensor * inpSA = inpL; + + cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il); + cb(cur, "attn_norm", il); + + { + ggml_tensor * rope_factors = model.get_rope_factors(cparams, il); + + auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur, + n_embd_head, n_head, n_head_kv, il); + + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, rope_factors, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow); + + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, rope_factors, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + cur = build_attn(inp_attn, + model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s, + Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il); + cb(cur, "attn_out", il); + } + + if (il == n_layer - 1 && inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + + ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il); + cb(cur, "ffn_norm", il); + + cur = build_ffn(cur, + model.layers[il].ffn_up, model.layers[il].ffn_up_b, model.layers[il].ffn_up_s, + model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, model.layers[il].ffn_gate_s, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, model.layers[il].ffn_down_s, + NULL, LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(cur, "ffn_out", il); + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "ffn_out", il); + + cur = build_cvec(cur, il); + cb(cur, "l_out", il); + + inpL = cur; + + if (n_loops > 1 && + ((il + 1) % n_phys) == 0 && + (il + 1) < n_layer && + !nb.skip_loop_final_norm) { + cur = build_norm(inpL, model.output_norm, NULL, LLM_NORM_RMS, il); + cb(cur, "loop_norm", il); + inpL = cur; + } + } + + cur = inpL; + + cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1); + cb(cur, "result_norm", -1); + res->t_embd = cur; + + cur = build_lora_mm(model.output, cur, model.output_s); + cb(cur, "result_output", -1); + res->t_logits = cur; + + ggml_build_forward_expand(gf, cur); +} diff --git a/src/models/openai-moe.cpp b/src/models/openai-moe.cpp index 6d74f9c7e6ef..c91bae1c35c6 100644 --- a/src/models/openai-moe.cpp +++ b/src/models/openai-moe.cpp @@ -116,7 +116,7 @@ llama_model_openai_moe::graph::graph(const llama_model & model, const llm_graph_ cb(cur, "attn_out", il); } - if (il == n_layer - 1) { + if (il == n_layer - 1 && inp_out_ids && cparams.embeddings_nextn_masked) { // skip computing output for unused tokens cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); @@ -154,6 +154,12 @@ llama_model_openai_moe::graph::graph(const llama_model & model, const llm_graph_ } cur = inpL; + res->t_h_nextn = cur; + + if (!cparams.embeddings_nextn_masked && inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + } + cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1); diff --git a/src/models/qwen35.cpp b/src/models/qwen35.cpp index d8ffe43ae76c..309dd432447c 100644 --- a/src/models/qwen35.cpp +++ b/src/models/qwen35.cpp @@ -39,6 +39,7 @@ void llama_model_qwen35::load_arch_tensors(llama_model_loader & ml) { const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr); const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0; + int mtp_flags = !ml.load_mtp ? TENSOR_SKIP : 0; tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0); @@ -97,25 +98,25 @@ void llama_model_qwen35::load_arch_tensors(llama_model_loader & ml) { auto & layer = layers[il]; // MTP block looks like a full-attention Qwen3.5 decoder block. - layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", il), { n_embd }, 0); - layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", il), { n_embd }, 0); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", il), { n_embd }, mtp_flags); + layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", il), { n_embd }, mtp_flags); - create_tensor_qkv(layer, il, n_embd, n_embd_head_k * n_head * 2, n_embd_k_gqa, n_embd_v_gqa, 0); - layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", il), { n_embd_head_k * n_head, n_embd }, 0); - layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", il), { n_embd_head_k }, 0); - layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", il), { n_embd_head_k }, 0); + create_tensor_qkv(layer, il, n_embd, n_embd_head_k * n_head * 2, n_embd_k_gqa, n_embd_v_gqa, mtp_flags); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", il), { n_embd_head_k * n_head, n_embd }, mtp_flags); + layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", il), { n_embd_head_k }, mtp_flags); + layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", il), { n_embd_head_k }, mtp_flags); - layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", il), {n_embd, n_ff}, 0); - layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", il), { n_ff, n_embd}, 0); - layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", il), {n_embd, n_ff}, 0); + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", il), {n_embd, n_ff}, mtp_flags); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", il), { n_ff, n_embd}, mtp_flags); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", il), {n_embd, n_ff}, mtp_flags); // NextN-specific tensors that define the MTP block. - layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", il), { 2 * n_embd, n_embd }, 0); - layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", il), { n_embd }, 0); - layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", il), { n_embd }, 0); - layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", il), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED); - layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", il), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED); - layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", il), { n_embd }, TENSOR_NOT_REQUIRED); + layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", il), { 2 * n_embd, n_embd }, mtp_flags); + layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", il), { n_embd }, mtp_flags); + layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", il), { n_embd }, mtp_flags); + layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", il), { n_embd, n_vocab }, mtp_flags|TENSOR_NOT_REQUIRED); + layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", il), { n_embd, n_vocab }, mtp_flags|TENSOR_NOT_REQUIRED); + layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", il), { n_embd }, mtp_flags|TENSOR_NOT_REQUIRED); }; for (int i = 0; i < n_layer; ++i) { diff --git a/src/models/qwen35moe.cpp b/src/models/qwen35moe.cpp index 7b0876cbb04b..38f2a57985a9 100644 --- a/src/models/qwen35moe.cpp +++ b/src/models/qwen35moe.cpp @@ -42,6 +42,7 @@ void llama_model_qwen35moe::load_arch_tensors(llama_model_loader & ml) { const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr); const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0; + int mtp_flags = !ml.load_mtp ? TENSOR_SKIP : 0; tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0); @@ -113,32 +114,32 @@ void llama_model_qwen35moe::load_arch_tensors(llama_model_loader & ml) { const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff; // MTP block looks like a full-attention Qwen3.5 decoder block with MoE FFN. - layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", il), { n_embd }, 0); - layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", il), { n_embd }, 0); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", il), { n_embd }, mtp_flags); + layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", il), { n_embd }, mtp_flags); - create_tensor_qkv(layer, il, n_embd, n_embd_head_k * n_head * 2, n_embd_k_gqa, n_embd_v_gqa, 0); - layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", il), { n_embd_head_k * n_head, n_embd }, 0); - layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", il), { n_embd_head_k }, 0); - layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", il), { n_embd_head_k }, 0); + create_tensor_qkv(layer, il, n_embd, n_embd_head_k * n_head * 2, n_embd_k_gqa, n_embd_v_gqa, mtp_flags); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", il), { n_embd_head_k * n_head, n_embd }, mtp_flags); + layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", il), { n_embd_head_k }, mtp_flags); + layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", il), { n_embd_head_k }, mtp_flags); // Routed experts - layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", il), { n_embd, n_expert }, 0); - layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", il), { n_ff_exp, n_embd, n_expert }, 0); - create_tensor_gate_up_exps(layer, il, n_embd, n_ff_exp, n_expert, 0); + layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", il), { n_embd, n_expert }, mtp_flags); + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", il), { n_ff_exp, n_embd, n_expert }, mtp_flags); + create_tensor_gate_up_exps(layer, il, n_embd, n_ff_exp, n_expert, mtp_flags); // Shared experts - layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", il), { n_embd }, 0); - layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", il), { n_embd, n_ff_shexp }, 0); - layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", il), { n_embd, n_ff_shexp }, 0); - layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", il), { n_ff_shexp, n_embd }, 0); + layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", il), { n_embd }, mtp_flags); + layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", il), { n_embd, n_ff_shexp }, mtp_flags); + layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", il), { n_embd, n_ff_shexp }, mtp_flags); + layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", il), { n_ff_shexp, n_embd }, mtp_flags); // NextN-specific tensors that define the MTP block. - layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", il), { 2 * n_embd, n_embd }, 0); - layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", il), { n_embd }, 0); - layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", il), { n_embd }, 0); - layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", il), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED); - layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", il), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED); - layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", il), { n_embd }, TENSOR_NOT_REQUIRED); + layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", il), { 2 * n_embd, n_embd }, mtp_flags); + layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", il), { n_embd }, mtp_flags); + layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", il), { n_embd }, mtp_flags); + layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", il), { n_embd, n_vocab }, mtp_flags|TENSOR_NOT_REQUIRED); + layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", il), { n_embd, n_vocab }, mtp_flags|TENSOR_NOT_REQUIRED); + layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", il), { n_embd }, mtp_flags|TENSOR_NOT_REQUIRED); }; for (int i = 0; i < n_layer; ++i) { diff --git a/src/models/qwen3next.cpp b/src/models/qwen3next.cpp index 09b66423d5a8..0808fd87aa0e 100644 --- a/src/models/qwen3next.cpp +++ b/src/models/qwen3next.cpp @@ -13,7 +13,11 @@ void llama_model_qwen3next::load_arch_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank); ml.get_key(LLM_KV_SSM_GROUP_COUNT, hparams.ssm_n_group); - // Mark recurrent layers (linear attention layers) + // NextN/MTP: extra decoder block appended beyond the main stack + ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false); + GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_all"); + + // Mark recurrent layers (linear attention layers). if (!ml.get_key_or_arr(LLM_KV_ATTENTION_RECURRENT_LAYERS, hparams.is_recr_impl, hparams.n_layer_all, false)) { uint32_t full_attn_interval = 4; ml.get_key(LLM_KV_FULL_ATTENTION_INTERVAL, full_attn_interval, false); @@ -28,13 +32,17 @@ void llama_model_qwen3next::load_arch_hparams(llama_model_loader & ml) { } } -void llama_model_qwen3next::load_arch_tensors(llama_model_loader &) { +void llama_model_qwen3next::load_arch_tensors(llama_model_loader & ml) { LLAMA_LOAD_LOCALS; if (n_expert == 0) { throw std::runtime_error(arch_name() + " model cannot have zero experts"); } + const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr); + const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0; + int mtp_flags = !ml.load_mtp ? TENSOR_SKIP : 0; + tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0); // output @@ -61,49 +69,73 @@ void llama_model_qwen3next::load_arch_tensors(llama_model_loader &) { const int64_t qkvz_dim = key_dim * 2 + value_dim * 2; const int64_t ba_dim = n_v_heads * 2; - for (int i = 0; i < n_layer; ++i) { - auto & layer = layers[i]; - const uint32_t n_ff_shexp = hparams.n_ff_shexp > 0 ? hparams.n_ff_shexp : hparams.n_ff(i); + auto load_block_trunk = [&](int il, int flags) { + auto & layer = layers[il]; + const uint32_t n_ff_shexp = hparams.n_ff_shexp > 0 ? hparams.n_ff_shexp : hparams.n_ff(il); - layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0); - layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), { n_embd }, 0); + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", il), { n_embd }, flags); + layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", il), { n_embd }, flags); - if (!hparams.is_recr(i)) { + if (!hparams.is_recr(il)) { // Attention layers - create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head * 2, n_embd_k_gqa, n_embd_v_gqa, 0); - layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0); - + create_tensor_qkv(layer, il, n_embd, n_embd_head_k * n_head * 2, n_embd_k_gqa, n_embd_v_gqa, flags); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", il), { n_embd_head_k * n_head, n_embd }, flags); // Q/K normalization for attention layers - layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, 0); - layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, 0); + layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", il), { n_embd_head_k }, flags); + layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", il), { n_embd_head_k }, flags); } else { // Linear attention (gated delta net) specific tensors // Create tensors with calculated dimensions // note: ssm_in is used by legacy GGUF - layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), { n_embd, qkvz_dim }, TENSOR_NOT_REQUIRED); - layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, key_dim * 2 + value_dim }, TENSOR_NOT_REQUIRED); - layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), { n_embd, value_dim }, TENSOR_NOT_REQUIRED); - layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), { hparams.ssm_d_conv, conv_dim }, 0); - layer.ssm_dt = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), { hparams.ssm_dt_rank }, 0); - layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A_NOSCAN, i), { hparams.ssm_dt_rank }, 0); - layer.ssm_beta_alpha = create_tensor(tn(LLM_TENSOR_SSM_BETA_ALPHA, "weight", i), { n_embd, ba_dim }, 0); - layer.ssm_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), { head_v_dim }, 0); - layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), { value_dim, n_embd }, 0); + layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", il), { n_embd, qkvz_dim }, TENSOR_NOT_REQUIRED | flags); + layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", il), { n_embd, key_dim * 2 + value_dim }, TENSOR_NOT_REQUIRED | flags); + layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", il), { n_embd, value_dim }, TENSOR_NOT_REQUIRED | flags); + layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", il), { hparams.ssm_d_conv, conv_dim }, flags); + layer.ssm_dt = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", il), { hparams.ssm_dt_rank }, flags); + layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A_NOSCAN, il), { hparams.ssm_dt_rank }, flags); + layer.ssm_beta_alpha = create_tensor(tn(LLM_TENSOR_SSM_BETA_ALPHA, "weight", il), { n_embd, ba_dim }, flags); + layer.ssm_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", il), { head_v_dim }, flags); + layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", il), { value_dim, n_embd }, flags); } - layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert }, 0); - layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, 0); - create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, 0); + layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", il), { n_embd, n_expert }, flags); + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", il), { n_ff_exp, n_embd, n_expert }, flags); + create_tensor_gate_up_exps(layer, il, n_embd, n_ff_exp, n_expert, flags); // Shared experts - layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), { n_embd }, 0); - layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp }, 0); - layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, n_ff_shexp }, 0); - layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_shexp, n_embd }, 0); + layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", il), { n_embd }, flags); + layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", il), { n_embd, n_ff_shexp }, flags); + layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", il), { n_embd, n_ff_shexp }, flags); + layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", il), { n_ff_shexp, n_embd }, flags); + }; + + auto load_block_mtp = [&](int il) { + // MTP head is identical to the trunk block (full attention + FFN) + load_block_trunk(il, mtp_flags); + + auto & layer = layers[il]; + + // NextN-specific tensors that define the MTP block. + layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", il), { 2 * n_embd, n_embd }, mtp_flags); + layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", il), { n_embd }, mtp_flags); + layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", il), { n_embd }, mtp_flags); + layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", il), { n_embd, n_vocab }, mtp_flags | TENSOR_NOT_REQUIRED); + layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", il), { n_embd, n_vocab }, mtp_flags | TENSOR_NOT_REQUIRED); + layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", il), { n_embd }, mtp_flags | TENSOR_NOT_REQUIRED); + }; + + for (int i = 0; i < n_layer; i++) { + load_block_trunk(i, trunk_flags); + } + for (int i = n_layer; i < n_layer_all; i++) { + load_block_mtp(i); } } std::unique_ptr llama_model_qwen3next::build_arch_graph(const llm_graph_params & params) const { + if (params.gtype == LLM_GRAPH_TYPE_DECODER_MTP) { + return std::make_unique(*this, params); + } return std::make_unique(*this, params); } @@ -120,6 +152,7 @@ llama_model_qwen3next::graph::graph(const llama_model & model, const llm_graph_p ggml_tensor * inp_pos = build_inp_pos(); ggml_tensor * inp_out_ids = build_inp_out_ids(); + // MTP/NextN layers are loaded as extra decoder blocks but not executed in the main pass. for (int il = 0; il < n_layer; ++il) { res->t_layer_inp[il] = inpL; @@ -139,7 +172,7 @@ llama_model_qwen3next::graph::graph(const llama_model & model, const llm_graph_p cur = build_layer_attn(inp->get_attn(), cur, inp_pos, il); } - if (il == n_layer - 1 && inp_out_ids) { + if (il == n_layer - 1 && inp_out_ids && cparams.embeddings_nextn_masked) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } @@ -171,9 +204,16 @@ llama_model_qwen3next::graph::graph(const llama_model & model, const llm_graph_p } cur = inpL; - // Final norm + // post-norm hidden state is input to both the LM head and the MTP head cur = build_norm(cur, model.output_norm, nullptr, LLM_NORM_RMS, -1); + cb(cur, "h_nextn", -1); + res->t_h_nextn = cur; + + if (!cparams.embeddings_nextn_masked && inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + } + cb(cur, "result_norm", -1); res->t_embd = cur; @@ -186,14 +226,6 @@ llama_model_qwen3next::graph::graph(const llama_model & model, const llm_graph_p ggml_build_forward_expand(gf, cur); } -// utility to get one slice from the third dimension -// input dim: [x, y, c, b] -// output dim: [x, y, 1, b] -static ggml_tensor * get_slice_2d(ggml_context * ctx0, ggml_tensor * t, int64_t c) { - return ggml_view_4d(ctx0, t, t->ne[0], t->ne[1], 1, t->ne[3], - t->nb[1], t->nb[2], t->nb[3], t->nb[2] * c); -} - ggml_tensor * llama_model_qwen3next::graph::build_norm_gated( ggml_tensor * input, ggml_tensor * weights, @@ -216,7 +248,7 @@ ggml_tensor * llama_model_qwen3next::graph::build_layer_attn( // Order: joint QG projection, QG split, Q norm, KV projection, K norm, RoPE, attention // Qwen3Next uses a single Q projection that outputs query + gate - ggml_tensor * Qcur_full = build_lora_mm(model.layers[il].wq, cur); + ggml_tensor * Qcur_full = build_lora_mm(model.layers[il].wq, cur, model.layers[il].wq_s); cb(Qcur_full, "Qcur_full", il); Qcur_full = ggml_reshape_4d(ctx0, Qcur_full, n_embd_head * 2, n_head, n_tokens, 1); @@ -232,10 +264,10 @@ ggml_tensor * llama_model_qwen3next::graph::build_layer_attn( Qcur_full->nb[1], Qcur_full->nb[2], Qcur_full->nb[3], n_embd_head * ggml_element_size(Qcur_full)); cb(gate, "gate", il); - ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur); + ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur, model.layers[il].wk_s); cb(Kcur, "Kcur", il); - ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur); + ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur, model.layers[il].wv_s); cb(Vcur, "Vcur", il); Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); @@ -274,8 +306,6 @@ ggml_tensor * llama_model_qwen3next::graph::build_layer_attn( gate = ggml_sigmoid(ctx0, gate); cb(gate, "gate_sigmoid", il); - gate = ggml_reshape_2d(ctx0, gate, n_embd_head * n_head, n_tokens); - cur = ggml_mul(ctx0, cur, gate); cb(cur, "attn_gated", il); @@ -550,16 +580,19 @@ ggml_tensor * llama_model_qwen3next::graph::build_layer_ffn(ggml_tensor * cur, c LLM_FFN_SILU, true, hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il, - nullptr, model.layers[il].ffn_gate_up_exps); + nullptr, model.layers[il].ffn_gate_up_exps, + model.layers[il].ffn_up_exps_s, + model.layers[il].ffn_gate_exps_s, + model.layers[il].ffn_down_exps_s); cb(moe_out, "ffn_moe_out", il); // Add shared experts if present - following Qwen3Next reference implementation if (model.layers[il].ffn_up_shexp != nullptr) { ggml_tensor * ffn_shexp = build_ffn(cur, - model.layers[il].ffn_up_shexp, NULL, NULL, - model.layers[il].ffn_gate_shexp, NULL, NULL, - model.layers[il].ffn_down_shexp, NULL, NULL, + model.layers[il].ffn_up_shexp, NULL, model.layers[il].ffn_up_shexp_s, + model.layers[il].ffn_gate_shexp, NULL, model.layers[il].ffn_gate_shexp_s, + model.layers[il].ffn_down_shexp, NULL, model.layers[il].ffn_down_shexp_s, NULL, LLM_FFN_SILU, LLM_FFN_PAR, il); cb(ffn_shexp, "ffn_shexp", il); @@ -593,3 +626,198 @@ ggml_tensor * llama_model_qwen3next::graph::build_layer_ffn(ggml_tensor * cur, c } return cur; } + +// LLM_GRAPH_TYPE_DECODER_MTP draft head for Qwen3-Next +llama_model_qwen3next::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params) + : llm_graph_context(params) { + GGML_ASSERT(hparams.n_layer_nextn > 0 && "QWEN3NEXT MTP requires n_layer_nextn > 0"); + GGML_ASSERT(hparams.n_layer_nextn == 1 && "QWEN3NEXT MTP currently only supports a single MTP block"); + + const int64_t n_embd_head = hparams.n_embd_head_v(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + + const int il = hparams.n_layer(); + const auto & layer = model.layers[il]; + + GGML_ASSERT(layer.nextn.eh_proj && "MTP block missing nextn.eh_proj"); + GGML_ASSERT(layer.nextn.enorm && "MTP block missing nextn.enorm"); + GGML_ASSERT(layer.nextn.hnorm && "MTP block missing nextn.hnorm"); + GGML_ASSERT(layer.ffn_gate_inp && "MTP block missing ffn_gate_inp"); + + // TODO: extract in a common llm_graph_context::build_inp_embd_h() + auto inp = std::make_unique(hparams.n_embd); + + inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + ggml_set_input(inp->tokens); + + inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd_inp(), n_tokens); + ggml_set_input(inp->embd); + + // TODO: make static using `ggml_build_forward_select()` + // see llm_graph_context::build_inp_embd() for reference + ggml_tensor * tok_embd; + if (ubatch.token) { + ggml_tensor * tok_embd_w = layer.nextn.embed_tokens ? layer.nextn.embed_tokens : model.tok_embd; + + tok_embd = ggml_get_rows(ctx0, tok_embd_w, inp->tokens); + } else { + tok_embd = inp->embd; + } + cb(tok_embd, "mtp_tok_embd", il); + + inp->h = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd, n_tokens); + ggml_set_input(inp->h); + ggml_set_name(inp->h, "mtp_h_input"); + + ggml_tensor * h_embd = inp->h; + + res->add_input(std::move(inp)); + + ggml_tensor * inp_pos = build_inp_pos(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + + auto * inp_attn = build_attn_inp_kv(); + + ggml_tensor * h_norm = build_norm(h_embd, layer.nextn.hnorm, nullptr, LLM_NORM_RMS, il); + cb(h_norm, "mtp_hnorm", il); + + ggml_tensor * e_norm = build_norm(tok_embd, layer.nextn.enorm, nullptr, LLM_NORM_RMS, il); + cb(e_norm, "mtp_enorm", il); + + ggml_tensor * concat = ggml_concat(ctx0, e_norm, h_norm, /*dim=*/ 0); + cb(concat, "mtp_concat", il); + + ggml_tensor * cur = build_lora_mm(layer.nextn.eh_proj, concat, layer.nextn.eh_proj_s); + cb(cur, "mtp_eh_proj", il); + + ggml_tensor * inpSA = cur; + + cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il); + cb(cur, "mtp_attn_norm", il); + + ggml_tensor * Qcur_full = build_lora_mm(layer.wq, cur, layer.wq_s); + cb(Qcur_full, "mtp_Qcur_full", il); + + ggml_tensor * Qcur = ggml_view_3d(ctx0, Qcur_full, + n_embd_head, n_head, n_tokens, + ggml_element_size(Qcur_full) * n_embd_head * 2, + ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head, + 0); + Qcur = build_norm(Qcur, layer.attn_q_norm, nullptr, LLM_NORM_RMS, il); + cb(Qcur, "mtp_Qcur_normed", il); + + ggml_tensor * Kcur = build_lora_mm(layer.wk, cur, layer.wk_s); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); + Kcur = build_norm(Kcur, layer.attn_k_norm, nullptr, LLM_NORM_RMS, il); + cb(Kcur, "mtp_Kcur_normed", il); + + ggml_tensor * Vcur = build_lora_mm(layer.wv, cur, layer.wv_s); + Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens); + + Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow); + Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow); + + cb(Qcur, "mtp_Qcur", il); + cb(Kcur, "mtp_Kcur", il); + cb(Vcur, "mtp_Vcur", il); + + const float kq_scale = hparams.f_attention_scale == 0.0f + ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale; + + cur = build_attn(inp_attn, + nullptr, nullptr, nullptr, + Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il); + cb(cur, "mtp_attn_pregate", il); + + ggml_tensor * gate = ggml_view_3d(ctx0, Qcur_full, + n_embd_head, n_head, n_tokens, + ggml_element_size(Qcur_full) * n_embd_head * 2, + ggml_element_size(Qcur_full) * n_embd_head * 2 * n_head, + ggml_element_size(Qcur_full) * n_embd_head); + + // TODO: CUDA is missing non-contiguous unary ops. when implemented: remove this cont + gate = ggml_cont_2d(ctx0, gate, n_embd_head * n_head, n_tokens); + cb(gate, "mtp_gate", il); + + cur = ggml_mul(ctx0, cur, ggml_sigmoid(ctx0, gate)); + cur = build_lora_mm(layer.wo, cur, layer.wo_s); + cb(cur, "mtp_attn_out", il); + + if (inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + + cur = ggml_add(ctx0, cur, inpSA); + cb(cur, "mtp_attn_residual", il); + + ggml_tensor * ffn_residual = cur; + cur = build_norm(cur, layer.attn_post_norm, nullptr, LLM_NORM_RMS, il); + cb(cur, "mtp_attn_post_norm", il); + + // MoE FFN — routed experts plus gated shared expert (mirrors the trunk). + ggml_tensor * moe_out = + build_moe_ffn(cur, + layer.ffn_gate_inp, + layer.ffn_up_exps, + layer.ffn_gate_exps, + layer.ffn_down_exps, + nullptr, + n_expert, n_expert_used, + LLM_FFN_SILU, true, + hparams.expert_weights_scale, + LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il, + nullptr, layer.ffn_gate_up_exps, + layer.ffn_up_exps_s, + layer.ffn_gate_exps_s, + layer.ffn_down_exps_s); + cb(moe_out, "mtp_ffn_moe_out", il); + + if (layer.ffn_up_shexp != nullptr) { + ggml_tensor * ffn_shexp = + build_ffn(cur, + layer.ffn_up_shexp, nullptr, layer.ffn_up_shexp_s, + layer.ffn_gate_shexp, nullptr, layer.ffn_gate_shexp_s, + layer.ffn_down_shexp, nullptr, layer.ffn_down_shexp_s, + nullptr, + LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(ffn_shexp, "mtp_ffn_shexp", il); + + ggml_tensor * shared_gate = build_lora_mm(layer.ffn_gate_inp_shexp, cur); + shared_gate = ggml_sigmoid(ctx0, shared_gate); + cb(shared_gate, "mtp_shared_expert_gate_sigmoid", il); + + ffn_shexp = ggml_mul(ctx0, ffn_shexp, shared_gate); + cb(ffn_shexp, "mtp_ffn_shexp_gated", il); + + cur = ggml_add(ctx0, moe_out, ffn_shexp); + } else { + cur = moe_out; + } + cb(cur, "mtp_ffn_out", il); + + cur = ggml_add(ctx0, cur, ffn_residual); + cb(cur, "mtp_post_ffn", il); + + ggml_tensor * head_norm_w = layer.nextn.shared_head_norm + ? layer.nextn.shared_head_norm + : model.output_norm; + GGML_ASSERT(head_norm_w && "QWEN3NEXT MTP: missing both nextn.shared_head_norm and output_norm"); + cur = build_norm(cur, head_norm_w, nullptr, LLM_NORM_RMS, -1); + + cb(cur, "h_nextn", -1); + res->t_h_nextn = cur; + + ggml_tensor * head_w = layer.nextn.shared_head_head ? layer.nextn.shared_head_head : model.output; + ggml_tensor * head_s = layer.nextn.shared_head_head ? layer.nextn.shared_head_head_s : model.output_s; + GGML_ASSERT(head_w && "QWEN3NEXT MTP: missing LM head (nextn.shared_head_head or model.output)"); + cur = build_lora_mm(head_w, cur, head_s); + cb(cur, "result_output", -1); + + res->t_logits = cur; + ggml_build_forward_expand(gf, cur); +} diff --git a/src/models/step35.cpp b/src/models/step35.cpp index 9b7b18a3678b..5b1d902581e6 100644 --- a/src/models/step35.cpp +++ b/src/models/step35.cpp @@ -48,7 +48,11 @@ void llama_model_step35::load_arch_tensors(llama_model_loader & ml) { const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight"; const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr); const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0; - const int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0; + int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0; + + if (!ml.load_mtp) { + mtp_flags |= TENSOR_SKIP; + } tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index d5d64bd2ed62..7c72a318fb10 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -87,7 +87,7 @@ function(llama_build_and_test source) set(multiValueArgs ARGS) cmake_parse_arguments(LLAMA_TEST "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) - set(TEST_SOURCES ${source} ${LLAMA_TEST_UNPARSED_ARGUMENTS} get-model.cpp) + set(TEST_SOURCES ${source} ${LLAMA_TEST_UNPARSED_ARGUMENTS}) if (NOT DEFINED LLAMA_TEST_LABEL) set(LLAMA_TEST_LABEL "main") @@ -148,6 +148,8 @@ if (LLAMA_LLGUIDANCE) llama_build_and_test(test-grammar-llguidance.cpp ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-llama-bpe.gguf) endif () +llama_build(test-recurrent-state-rollback.cpp) + if (NOT WIN32 OR NOT BUILD_SHARED_LIBS) # these tests are disabled on Windows because they use internal functions not exported with LLAMA_API (when building with shared libraries) llama_build_and_test(test-sampling.cpp) @@ -193,6 +195,28 @@ if (NOT WIN32 OR NOT BUILD_SHARED_LIBS) # llama_build_and_test(test-double-float.cpp) # SLOW llama_build_and_test(test-llama-archs.cpp) + + set(MODEL_DIR "${CMAKE_CURRENT_BINARY_DIR}/test-models/") + file(MAKE_DIRECTORY "${MODEL_DIR}") + + llama_test( + test-llama-archs + NAME test-generate-models + LABEL main + ARGS -o "${MODEL_DIR}" + ) + set_tests_properties(test-generate-models PROPERTIES + FIXTURES_SETUP generate-models + ) + + llama_test( + test-recurrent-state-rollback + LABEL main + ARGS -m "${MODEL_DIR}/qwen35-dense.gguf" + ) + set_tests_properties(test-recurrent-state-rollback PROPERTIES + FIXTURES_REQUIRED generate-models + ) endif() llama_build_and_test(test-chat-peg-parser.cpp peg-parser/simple-tokenize.cpp) @@ -234,6 +258,9 @@ llama_build_and_test(test-thread-safety.cpp ARGS -m "${MODEL_DEST}" -ngl 99 -p " set_tests_properties(test-thread-safety PROPERTIES FIXTURES_REQUIRED test-download-model) llama_build_and_test(test-arg-parser.cpp) +llama_build_and_test(test-model-resolution.cpp) +# the test serves its repos from an httplib server, and the library links it privately +target_link_libraries(test-model-resolution PRIVATE cpp-httplib) if (NOT LLAMA_SANITIZE_ADDRESS AND NOT GGML_SCHED_NO_REALLOC) # TODO: repair known memory leaks @@ -258,13 +285,14 @@ llama_build_and_test(test-backend-sampler.cpp LABEL "model") llama_build_and_test(test-state-restore-fragmented.cpp LABEL "model" ARGS -m "${MODEL_DEST}") set_tests_properties(test-state-restore-fragmented PROPERTIES FIXTURES_REQUIRED test-download-model) -llama_build_and_test(test-recurrent-state-rollback.cpp LABEL "model" ARGS -m "${MODEL_DEST}") -set_tests_properties(test-recurrent-state-rollback PROPERTIES FIXTURES_REQUIRED test-download-model) - # Test state save/load functionality llama_build_and_test(test-save-load-state.cpp LABEL "model" ARGS -m "${MODEL_DEST}") set_tests_properties(test-save-load-state PROPERTIES FIXTURES_REQUIRED test-download-model) +if (APPLE) + llama_build(test-rset-release.cpp) +endif() + if (NOT GGML_BACKEND_DL) # these tests use the backends directly and cannot be built with dynamic loading llama_build_and_test(test-barrier.cpp) diff --git a/tests/get-model.cpp b/tests/get-model.cpp deleted file mode 100644 index 4edb685f0fbf..000000000000 --- a/tests/get-model.cpp +++ /dev/null @@ -1,21 +0,0 @@ -#include -#include -#include - -#include "get-model.h" - -char * get_model_or_exit(int argc, char *argv[]) { - char * model_path; - if (argc > 1) { - model_path = argv[1]; - - } else { - model_path = getenv("LLAMACPP_TEST_MODELFILE"); - if (!model_path || strlen(model_path) == 0) { - fprintf(stderr, "\033[33mWARNING: No model file provided. Skipping this test. Set LLAMACPP_TEST_MODELFILE= to silence this warning and run this test.\n\033[0m"); - exit(EXIT_SUCCESS); - } - } - - return model_path; -} diff --git a/tests/get-model.h b/tests/get-model.h deleted file mode 100644 index 81a3a0fefdab..000000000000 --- a/tests/get-model.h +++ /dev/null @@ -1,2 +0,0 @@ -#pragma once -char * get_model_or_exit(int, char*[]); diff --git a/tests/snapshots/qwen3.5-27b.schema b/tests/snapshots/qwen3.6-27b.schema similarity index 100% rename from tests/snapshots/qwen3.5-27b.schema rename to tests/snapshots/qwen3.6-27b.schema diff --git a/tests/test-arg-parser.cpp b/tests/test-arg-parser.cpp index e83ee85dd4ba..fd5adb740eab 100644 --- a/tests/test-arg-parser.cpp +++ b/tests/test-arg-parser.cpp @@ -1,6 +1,7 @@ #include "arg.h" #include "common.h" #include "download.h" +#include "llama.h" #include #include @@ -98,15 +99,41 @@ static void test(void) { argv = {"binary_name", "-sm", "hello"}; assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON)); + { + common_params penalty_params; + + argv = {"binary_name", "--repeat-penalty", "0"}; + assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), penalty_params, LLAMA_EXAMPLE_COMMON)); + + argv = {"binary_name", "--repeat-penalty", "-1"}; + assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), penalty_params, LLAMA_EXAMPLE_COMMON)); + + argv = {"binary_name", "--repeat-penalty", "nan"}; + assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), penalty_params, LLAMA_EXAMPLE_COMMON)); + + argv = {"binary_name", "--repeat-penalty", "inf"}; + assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), penalty_params, LLAMA_EXAMPLE_COMMON)); + + argv = {"binary_name", "--repeat-penalty", "-inf"}; + assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), penalty_params, LLAMA_EXAMPLE_COMMON)); + + const char * penalty_options[] = {"--frequency-penalty", "--presence-penalty"}; + const char * nonfinite_values[] = {"nan", "inf", "-inf"}; + for (const char * option : penalty_options) { + for (const char * value : nonfinite_values) { + argv = {"binary_name", option, value}; + assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), penalty_params, LLAMA_EXAMPLE_COMMON)); + } + } + } + // non-existence arg in specific example (--draft cannot be used outside llama-speculative) argv = {"binary_name", "--draft", "123"}; assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_EMBEDDING)); - // negated arg - argv = {"binary_name", "--no-mmap"}; + argv = {"binary_name", "-lm", "hello"}; assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON)); - printf("test-arg-parser: test valid usage\n\n"); argv = {"binary_name", "-m", "model_file.gguf"}; @@ -132,6 +159,26 @@ static void test(void) { assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_SPECULATIVE)); assert(params.speculative.draft.n_max == 123); + argv = {"binary_name", "-lm", "none"}; + assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON)); + assert(params.load_mode == LLAMA_LOAD_MODE_NONE); + + argv = {"binary_name", "-lm", "mmap"}; + assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON)); + assert(params.load_mode == LLAMA_LOAD_MODE_MMAP); + + argv = {"binary_name", "-lm", "mlock"}; + assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON)); + assert(params.load_mode == LLAMA_LOAD_MODE_MLOCK); + + argv = {"binary_name", "-lm", "mmap+mlock"}; + assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON)); + assert(params.load_mode == LLAMA_LOAD_MODE_MMAP_MLOCK); + + argv = {"binary_name", "-lm", "dio"}; + assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON)); + assert(params.load_mode == LLAMA_LOAD_MODE_DIRECT_IO); + // multi-value args (CSV) argv = {"binary_name", "--lora", "file1.gguf,\"file2,2.gguf\",\"file3\"\"3\"\".gguf\",file4\".gguf"}; assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON)); @@ -158,13 +205,37 @@ static void test(void) { assert(params.model.path == "blah.gguf"); assert(params.cpuparams.n_threads == 1010); + setenv("LLAMA_ARG_LOAD_MODE", "blah", true); + argv = {"binary_name"}; + assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON)); + + setenv("LLAMA_ARG_LOAD_MODE", "mmap", true); + argv = {"binary_name"}; + assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON)); + assert(params.load_mode == LLAMA_LOAD_MODE_MMAP); + + setenv("LLAMA_ARG_LOAD_MODE", "mlock", true); + argv = {"binary_name"}; + assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON)); + assert(params.load_mode == LLAMA_LOAD_MODE_MLOCK); + + setenv("LLAMA_ARG_LOAD_MODE", "mmap+mlock", true); + argv = {"binary_name"}; + assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON)); + assert(params.load_mode == LLAMA_LOAD_MODE_MMAP_MLOCK); + + setenv("LLAMA_ARG_LOAD_MODE", "dio", true); + argv = {"binary_name"}; + assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON)); + assert(params.load_mode == LLAMA_LOAD_MODE_DIRECT_IO); + printf("test-arg-parser: test negated environment variables\n\n"); - setenv("LLAMA_ARG_MMAP", "0", true); + setenv("LLAMA_ARG_LOAD_MODE", "none", true); setenv("LLAMA_ARG_NO_PERF", "1", true); // legacy format argv = {"binary_name"}; assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON)); - assert(params.use_mmap == false); + assert(params.load_mode == LLAMA_LOAD_MODE_NONE); assert(params.no_perf == true); printf("test-arg-parser: test environment variables being overwritten\n\n"); diff --git a/tests/test-autorelease.cpp b/tests/test-autorelease.cpp index ca87c56a8fd3..370428809f25 100644 --- a/tests/test-autorelease.cpp +++ b/tests/test-autorelease.cpp @@ -1,15 +1,13 @@ // ref: https://github.com/ggml-org/llama.cpp/issues/4952#issuecomment-1892864763 -#include -#include #include #include "llama.h" -#include "get-model.h" +#include "common.h" // This creates a new context inside a pthread and then tries to exit cleanly. int main(int argc, char ** argv) { - auto * model_path = get_model_or_exit(argc, argv); + auto * model_path = common_get_model_or_exit(argc, argv); std::thread([&model_path]() { llama_backend_init(); diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 144dceeff94e..a37cf4cf7ae7 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -1350,18 +1350,22 @@ struct test_case { // check if the backends support the ops bool supported = true; + std::string unsupported_str; for (ggml_backend_t backend : {backend1, backend2}) { for (ggml_tensor * t = ggml_get_first_tensor(ctx.get()); t != NULL; t = ggml_get_next_tensor(ctx.get(), t)) { if (!ggml_backend_supports_op(backend, t)) { supported = false; - break; + if (unsupported_str.empty()) { + unsupported_str = std::string(ggml_backend_name(backend)); + } else { + unsupported_str += ", " + std::string(ggml_backend_name(backend)); + } } } } if (!supported) { - // Create test result for unsupported operation - test_result result(ggml_backend_name(backend1), current_op_name, vars(), "test", + test_result result(unsupported_str, current_op_name, vars(), "test", false, false, "not supported"); print_test_result_locked(output_printer, result); @@ -2435,13 +2439,17 @@ struct test_set_rows : public test_case { } double max_nmse_err() override { - if (type_dst == GGML_TYPE_Q4_0 || type_dst == GGML_TYPE_Q4_1 || type_dst == GGML_TYPE_IQ4_NL || + if (type_dst == GGML_TYPE_Q2_0 || type_dst == GGML_TYPE_Q4_0 || type_dst == GGML_TYPE_Q4_1 || + type_dst == GGML_TYPE_IQ4_NL || type_dst == GGML_TYPE_Q5_0 || type_dst == GGML_TYPE_Q5_1 || type_dst == GGML_TYPE_Q8_0) { // estimate what the max nmse error would be if one quantized value is // off by one. The test values are distributed in [-1,1], so it'll be // roughly (2.0 / 2^bits)^2, divided by the mean square value of the reference, // which is roughly 0.25 times the number of elements. double err_estimate = 1.0f/8.0f; + if (type_src == GGML_TYPE_F16 && type_dst == GGML_TYPE_Q2_0) { + err_estimate *= 4.0f; + } if (type_dst == GGML_TYPE_Q5_0 || type_dst == GGML_TYPE_Q5_1) { err_estimate /= 2.0f; } @@ -3756,6 +3764,167 @@ struct test_snake_fuse : public test_case { } }; + +struct test_dsv4_hc : public test_case { + static constexpr int64_t hc = 4; + + ggml_tensor * out = nullptr; + + static uint32_t tensor_seed(const ggml_tensor * t) { + uint32_t seed = 2166136261u; + for (const char * p = ggml_get_name(t); *p; ++p) { + seed ^= (uint8_t) *p; + seed *= 16777619u; + } + for (int i = 0; i < GGML_MAX_DIMS; ++i) { + seed ^= (uint32_t) t->ne[i]; + seed *= 16777619u; + } + return seed; + } + + static bool tensor_range(const std::string & name, float & lo, float & hi) { + if (name == "mixes") { + lo = -2.0f; hi = 2.0f; return true; + } + if (name == "scale") { + lo = -0.5f; hi = 0.5f; return true; + } + if (name == "base") { + lo = -0.25f; hi = 0.25f; return true; + } + if (name == "weights" || name == "comb") { + lo = 0.0f; hi = 1.0f; return true; + } + if (name == "post") { + lo = 0.0f; hi = 2.0f; return true; + } + if (name == "x" || name == "residual") { + lo = -1.0f; hi = 1.0f; return true; + } + return false; + } + + void initialize_tensors(ggml_context * ctx) override { + for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) { + const std::string name = ggml_get_name(t); + float lo; + float hi; + if (!tensor_range(name, lo, hi)) { + init_tensor_uniform(t); + continue; + } + + GGML_ASSERT(t->type == GGML_TYPE_F32); + std::mt19937 rng(tensor_seed(t)); + std::uniform_real_distribution dist(lo, hi); + std::vector data(ggml_nelements(t)); + for (float & v : data) { + v = dist(rng); + } + ggml_backend_tensor_set(t, data.data(), 0, data.size()*sizeof(float)); + } + } +}; + +struct test_dsv4_hc_comb : public test_dsv4_hc { + const int64_t n_tokens; + const int32_t n_iter; + const float eps; + + std::string op_desc(ggml_tensor * t) override { + GGML_UNUSED(t); + return "DSV4_HC_COMB"; + } + + std::string vars() override { + return VARS_TO_STR3(n_tokens, n_iter, eps); + } + + test_dsv4_hc_comb(int64_t n_tokens = 17, int32_t n_iter = 4, float eps = 1e-6f) + : n_tokens(n_tokens), n_iter(n_iter), eps(eps) {} + + ggml_tensor * build_graph(ggml_context * ctx) override { + ggml_tensor * mixes = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, (2 + hc)*hc, n_tokens); + ggml_set_name(mixes, "mixes"); + + ggml_tensor * scale = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 3); + ggml_set_name(scale, "scale"); + + ggml_tensor * base = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, (2 + hc)*hc); + ggml_set_name(base, "base"); + + out = ggml_dsv4_hc_comb(ctx, mixes, scale, base, eps, n_iter); + ggml_set_name(out, "out"); + return out; + } +}; + +struct test_dsv4_hc_pre : public test_dsv4_hc { + const int64_t n_embd; + const int64_t n_tokens; + + std::string op_desc(ggml_tensor * t) override { + GGML_UNUSED(t); + return "DSV4_HC_PRE"; + } + + std::string vars() override { + return VARS_TO_STR2(n_embd, n_tokens); + } + + test_dsv4_hc_pre(int64_t n_embd = 31, int64_t n_tokens = 17) + : n_embd(n_embd), n_tokens(n_tokens) {} + + ggml_tensor * build_graph(ggml_context * ctx) override { + ggml_tensor * x = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd, hc, n_tokens); + ggml_set_name(x, "x"); + + ggml_tensor * weights = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, hc, n_tokens); + ggml_set_name(weights, "weights"); + + out = ggml_dsv4_hc_pre(ctx, x, weights); + ggml_set_name(out, "out"); + return out; + } +}; + +struct test_dsv4_hc_post : public test_dsv4_hc { + const int64_t n_embd; + const int64_t n_tokens; + + std::string op_desc(ggml_tensor * t) override { + GGML_UNUSED(t); + return "DSV4_HC_POST"; + } + + std::string vars() override { + return VARS_TO_STR2(n_embd, n_tokens); + } + + test_dsv4_hc_post(int64_t n_embd = 31, int64_t n_tokens = 17) + : n_embd(n_embd), n_tokens(n_tokens) {} + + ggml_tensor * build_graph(ggml_context * ctx) override { + ggml_tensor * x = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_tokens); + ggml_set_name(x, "x"); + + ggml_tensor * residual = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd, hc, n_tokens); + ggml_set_name(residual, "residual"); + + ggml_tensor * post = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, hc, n_tokens); + ggml_set_name(post, "post"); + + ggml_tensor * comb = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hc, hc, n_tokens); + ggml_set_name(comb, "comb"); + + out = ggml_dsv4_hc_post(ctx, x, residual, post, comb); + ggml_set_name(out, "out"); + return out; + } +}; + + // GGML_OP_SSM_CONV struct test_ssm_conv : public test_case { const ggml_type type; @@ -3840,7 +4009,7 @@ struct test_ssm_scan : public test_case { test_ssm_scan(ggml_type type = GGML_TYPE_F32, int64_t d_state = 32, - int64_t head_dim = 1, // non-zero for Mamba-2 + int64_t head_dim = 1, // 1 = Mamba-1; > 1 = Mamba-2 (scalar A per head) int64_t n_head = 32, int64_t n_group = 1, int64_t n_seq_tokens = 32, @@ -3848,6 +4017,11 @@ struct test_ssm_scan : public test_case { bool xbc_overlap = false) : type(type), d_state(d_state), head_dim(head_dim), n_head(n_head), n_group(n_group), n_seq_tokens(n_seq_tokens), n_seqs(n_seqs), xbc_overlap(xbc_overlap) {} + double max_nmse_err() override { + // SSD path (head_dim > 1) uses FP16 intermediates (M matrix, X_dt); Mamba-1 is pure FP32. + return (head_dim > 1) ? 2e-7 : 1e-7; + } + ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * s = ggml_new_tensor_4d(ctx, type, d_state, head_dim, n_head, n_seqs); ggml_tensor * dt = ggml_new_tensor_3d(ctx, type, n_head, n_seq_tokens, n_seqs); @@ -3874,14 +4048,14 @@ struct test_ssm_scan : public test_case { return out; } - // similar to test_mul_mat_id + void initialize_tensors(ggml_context * ctx) override { std::random_device rd; std::default_random_engine rng(rd()); for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { if (t->type == GGML_TYPE_I32) { if (ggml_is_view_op(t->op)) { continue; } - // ids + // ids: permutation of [0..n_seqs) for (int64_t r = 0; r < ggml_nrows(t); r++) { std::vector data(t->ne[0]); for (int i = 0; i < t->ne[0]; i++) { @@ -3890,6 +4064,11 @@ struct test_ssm_scan : public test_case { std::shuffle(data.begin(), data.end(), rng); ggml_backend_tensor_set(t, data.data(), r * t->nb[1], t->ne[0] * sizeof(int32_t)); } + } else if (ggml_is_view_op(t->op)) { + continue; + } else if (t->ne[1] == n_head && t->ne[2] == 1) { + // A {1 or d_state, n_head}: negative decay (2-D tensor, ne[2]==1 distinguishes from 3-D/4-D tensors) + init_tensor_uniform(t, -1.0f, -0.5f); } else { init_tensor_uniform(t); } @@ -5560,7 +5739,7 @@ struct test_concat : public test_case { const std::array ne_a; const int64_t ne_b_d; const int dim; - const int v; // view (1 << 0: non-cont a, 1 << 1: non-cont b) + const int v; // view (1 << 0: non-cont a (first 3 dim), 1 << 1: non-cont b (first 3 dim), 1 << 2: non-cont a (last 2 dim), 1 << 3: non-cont b (last 2 dim)) std::string vars() override { return VARS_TO_STR5(type, ne_a, ne_b_d, dim, v); @@ -5581,6 +5760,13 @@ struct test_concat : public test_case { a = ggml_new_tensor(ctx, type, 4, ne.data()); ggml_set_name(a, "a"); + a = ggml_view_4d(ctx, a, ne_a[0], ne_a[1], ne_a[2], ne_a[3], a->nb[1], a->nb[2], a->nb[3], 0); + ggml_set_name(a, "view_of_a"); + } else if (v & 4) { + auto ne = ne_a; ne[2] *= 2; ne[3] *= 4; + a = ggml_new_tensor(ctx, type, 4, ne.data()); + ggml_set_name(a, "a"); + a = ggml_view_4d(ctx, a, ne_a[0], ne_a[1], ne_a[2], ne_a[3], a->nb[1], a->nb[2], a->nb[3], 0); ggml_set_name(a, "view_of_a"); } else { @@ -5593,6 +5779,13 @@ struct test_concat : public test_case { b = ggml_new_tensor(ctx, type, 4, ne.data()); ggml_set_name(b, "b"); + b = ggml_view_4d(ctx, b, ne_b[0], ne_b[1], ne_b[2], ne_b[3], b->nb[1], b->nb[2], b->nb[3], 0); + ggml_set_name(b, "view_of_b"); + } else if (v & 8) { + auto ne = ne_b; ne[2] *= 3; ne[3] *= 2; + b = ggml_new_tensor(ctx, type, 4, ne.data()); + ggml_set_name(b, "b"); + b = ggml_view_4d(ctx, b, ne_b[0], ne_b[1], ne_b[2], ne_b[3], b->nb[1], b->nb[2], b->nb[3], 0); ggml_set_name(b, "view_of_b"); } else { @@ -5786,6 +5979,7 @@ enum MoeGatingFunc { GATING_FUNC_SOFTMAX, GATING_FUNC_SIGMOID, GATING_FUNC_SOFTMAX_WEIGHT, + GATING_FUNC_SQRT_SOFTPLUS, }; struct test_topk_moe : public test_case { @@ -5829,7 +6023,8 @@ struct test_topk_moe : public test_case { ggml_tensor * logits = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne.data()); ggml_tensor * probs = (gating_func == GATING_FUNC_SOFTMAX) ? ggml_soft_max(ctx, logits) : - (gating_func == GATING_FUNC_SIGMOID) ? ggml_sigmoid(ctx, logits) : logits; + (gating_func == GATING_FUNC_SIGMOID) ? ggml_sigmoid(ctx, logits) : + (gating_func == GATING_FUNC_SQRT_SOFTPLUS) ? ggml_sqrt(ctx, ggml_softplus(ctx, logits)) : logits; ggml_set_name(probs, "probs"); ggml_tensor * selection_probs = probs; @@ -8080,6 +8275,7 @@ static const ggml_type all_types[] = { GGML_TYPE_Q5_0, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0, GGML_TYPE_Q1_0, + GGML_TYPE_Q2_0, GGML_TYPE_MXFP4, GGML_TYPE_NVFP4, GGML_TYPE_Q2_K, GGML_TYPE_Q3_K, GGML_TYPE_Q4_K, GGML_TYPE_Q5_K, @@ -8095,6 +8291,7 @@ static const ggml_type base_types[] = { GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_Q8_0, // for I8MM tests GGML_TYPE_Q1_0, + GGML_TYPE_Q2_0, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1, // for I8MM tests GGML_TYPE_Q4_K, @@ -8107,6 +8304,7 @@ static const ggml_type other_types[] = { GGML_TYPE_Q5_0, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0, GGML_TYPE_Q1_0, + GGML_TYPE_Q2_0, GGML_TYPE_Q2_K, GGML_TYPE_Q3_K, GGML_TYPE_Q5_K, GGML_TYPE_Q6_K, @@ -8159,6 +8357,21 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_snake_fuse(type, { 64, 32, 2, 3})); // ne[2] > 1 and ne[3] > 1 } + test_cases.emplace_back(new test_dsv4_hc_comb(1, 1)); + test_cases.emplace_back(new test_dsv4_hc_comb(17, 4)); + test_cases.emplace_back(new test_dsv4_hc_comb(257, 8)); + test_cases.emplace_back(new test_dsv4_hc_comb(17, 20)); + + test_cases.emplace_back(new test_dsv4_hc_pre(1, 1)); + test_cases.emplace_back(new test_dsv4_hc_pre(31, 17)); + test_cases.emplace_back(new test_dsv4_hc_pre(128, 257)); + test_cases.emplace_back(new test_dsv4_hc_pre(4096, 21)); + + test_cases.emplace_back(new test_dsv4_hc_post(1, 1)); + test_cases.emplace_back(new test_dsv4_hc_post(31, 17)); + test_cases.emplace_back(new test_dsv4_hc_post(128, 257)); + test_cases.emplace_back(new test_dsv4_hc_post(4096, 21)); + // glu ops for (ggml_type type : {GGML_TYPE_F16, GGML_TYPE_F32}) { for (int v : {0, 1}) { @@ -8272,9 +8485,9 @@ static std::vector> make_test_cases_eval() { for (ggml_type type_input : {GGML_TYPE_F32}) { for (ggml_op_pool pool_type : {GGML_OP_POOL_AVG, GGML_OP_POOL_MAX}) { - for (int k0 : {1, 3}) { - for (int s0 : {1, 2}) { - for (int p0 : {0, 1}) { + for (int k0 : {1, 2, 3}) { + for (int s0 : {1, 2, 3}) { + for (int p0 : {0, 1, 2, 3}) { test_cases.emplace_back(new test_pool1d(pool_type, type_input, { 10, 3, 2, 1 }, k0, s0, p0)); test_cases.emplace_back(new test_pool1d(pool_type, type_input, { 11, 1, 3, 2 }, k0, s0, p0)); test_cases.emplace_back(new test_pool1d(pool_type, type_input, { 128, 2, 1, 3 }, k0, s0, p0)); @@ -8409,7 +8622,11 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_conv_2d( { act_case[iwh_idx], act_case[iwh_idx], act_case[Cin_idx], act_case[B_idx] }, { act_case[kwh_idx], act_case[kwh_idx], act_case[Cin_idx], act_case[Cout_idx] }, - kernel_type, 1, 1, 0, 0, 1, 1, false)); + kernel_type, 1, 1, 0, 0, 1, 1, false)); // bool cwhn = false + test_cases.emplace_back(new test_conv_2d( + { act_case[iwh_idx], act_case[iwh_idx], act_case[Cin_idx], act_case[B_idx] }, + { act_case[kwh_idx], act_case[kwh_idx], act_case[Cin_idx], act_case[Cout_idx] }, + kernel_type, 1, 1, 0, 0, 1, 1, true)); // bool cwhn = true } } #endif @@ -8438,7 +8655,9 @@ static std::vector> make_test_cases_eval() { calc_conv_output_size(H, KH, s1, p1, d1) > 0) { for (auto kernel_type : {GGML_TYPE_F32, GGML_TYPE_F16}) { test_cases.emplace_back(new test_conv_2d( - { W, H, Cin, 2 }, { KW, KH, Cin, Cout }, kernel_type, s0, s1, p0, p1, d0, d1, false)); + { W, H, Cin, 2 }, { KW, KH, Cin, Cout }, kernel_type, s0, s1, p0, p1, d0, d1, false)); // bool cwhn = false + test_cases.emplace_back(new test_conv_2d( + { W, H, Cin, 2 }, { KW, KH, Cin, Cout }, kernel_type, s0, s1, p0, p1, d0, d1, true)); // bool cwhn = true } } } @@ -8450,7 +8669,8 @@ static std::vector> make_test_cases_eval() { } } for (auto kernel_type : {GGML_TYPE_F32, GGML_TYPE_F16}) { - test_cases.emplace_back(new test_conv_2d({ 256, 256, 192, 1 }, { 3, 3, 192, 96 }, kernel_type, 1, 1, 1, 1, 1, 1, false)); + test_cases.emplace_back(new test_conv_2d({ 256, 256, 192, 1 }, { 3, 3, 192, 96 }, kernel_type, 1, 1, 1, 1, 1, 1, false)); // bool cwhn = false + test_cases.emplace_back(new test_conv_2d({ 256, 256, 192, 1 }, { 3, 3, 192, 96 }, kernel_type, 1, 1, 1, 1, 1, 1, true)); // bool cwhn = true } // sycl backend will limit task global_range < MAX_INT @@ -8584,6 +8804,7 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 5, 4, ne3}, {1, 2, 1, 1})); test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 5, 4, ne3}, {1, 1, 2, 1})); test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 5, 4, ne3}, {1, 1, 1, 2})); + test_cases.emplace_back(new test_repeat(GGML_TYPE_F16, {10, 5, 4, ne3}, {2, 1, 1, 1})); test_cases.emplace_back(new test_repeat(GGML_TYPE_I32, {10, 5, 4, ne3}, {2, 1, 1, 1})); test_cases.emplace_back(new test_repeat(GGML_TYPE_I16, {10, 5, 4, ne3}, {1, 1, 1, 2})); test_cases.emplace_back(new test_repeat(GGML_TYPE_BF16, {10, 5, 4, ne3}, {2, 1, 1, 1})); @@ -8865,6 +9086,9 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_ssm_scan(GGML_TYPE_F32, 128, 64, 16, 2, 32, 4)); // Mamba-2 test_cases.emplace_back(new test_ssm_scan(GGML_TYPE_F32, 256, 64, 8, 2, 32, 4)); // Falcon-H1 test_cases.emplace_back(new test_ssm_scan(GGML_TYPE_F32, 128, 128, 4, 4, 16, 2, true)); // x/B/C overlap + test_cases.emplace_back(new test_ssm_scan(GGML_TYPE_F32, 128, 80, 128, 1, 256, 1)); // Nemotron-9B SSD path + test_cases.emplace_back(new test_ssm_scan(GGML_TYPE_F32, 128, 80, 128, 1, 512, 1)); // Nemotron-9B SSD multi-chunk (2 aligned chunks) + test_cases.emplace_back(new test_ssm_scan(GGML_TYPE_F32, 128, 64, 80, 8, 300, 2)); // Mamba-2 SSD multi-chunk (partial 2nd chunk, 2 seqs) test_cases.emplace_back(new test_rwkv_wkv6(GGML_TYPE_F32, 32, 64, 1, 1)); test_cases.emplace_back(new test_rwkv_wkv6(GGML_TYPE_F32, 32, 64, 32, 1)); @@ -8888,6 +9112,9 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 512, 1, 512)); test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 128, 32, 128)); test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 128, 4, 128, {2, 3})); + test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 256, 512, 256)); // many rows + test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 32, 1, 32)); // too small (N<64) + test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 1024, 1, 1024)); // too big (N>512) #if 0 // > 4GB A matrix. Too slow to be enabled by default. @@ -8936,6 +9163,10 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_mul_mat(GGML_TYPE_Q8_0, GGML_TYPE_F32, 2880, 32, 2880, {1, 1}, {1, 1})); test_cases.emplace_back(new test_mul_mat(GGML_TYPE_MXFP4, GGML_TYPE_F32, 2880, 32, 2880, {1, 1}, {1, 1})); + // m == 1, with n on both sides of MMVF_MAX_BATCH_SIZE (8): mmvf below, operand swap above + for (int64_t n : {1, 7, 8, 9, 16, 128, 512}) { + test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F32, GGML_TYPE_F32, 1, n, 2048, {1, 1}, {1, 1})); + } #if 0 { @@ -9404,8 +9635,10 @@ static std::vector> make_test_cases_eval() { } for (ggml_type type_a : { GGML_TYPE_Q4_0, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0 }) { - for (int dim : { 0, 1, 2, 3, }) { - test_cases.emplace_back(new test_concat(type_a, {128, 12, 13, 14}, dim == 0 ? 256 : 7, dim, 0)); + for (int v : { 0, 4, 8, 12 }) { + for (int dim : { 0, 1, 2, 3, }) { + test_cases.emplace_back(new test_concat(type_a, {128, 12, 13, 14}, dim == 0 ? 256 : 7, dim, v)); + } } } @@ -9607,6 +9840,18 @@ static std::vector> make_test_cases_eval() { } } + // prefill-shaped cases with long KV (nb >= 32, kv >= 1024): covers the + // XMX/GEMM-accelerated SYCL FA path which only activates for these shapes. + for (int kv : { 1024, 2048, }) { + for (int hs : { 64, 128, 256, }) { + for (int nb : { 32, 64, }) { + for (ggml_type type_KV : { GGML_TYPE_F16, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0, }) { + test_cases.emplace_back(new test_flash_attn_ext(hs, hs, 8, {4, 1}, kv, nb, true, false, 0, 0, GGML_PREC_F32, type_KV, type_KV)); + } + } + } + } + // TURBO_WHT round-trip tests (forward then inverse = identity) for (int64_t hd : {128, 256, 512}) { for (int64_t nh : {1, 4, 8}) { @@ -9640,7 +9885,6 @@ static std::vector> make_test_cases_eval() { // Large tensor test_cases.emplace_back(new test_set_rows_tq4_1s(GGML_TYPE_I32, 128, 256, 64)); - for (int hsk : { 40, 64, 72, 80, 96, 128, 192, 256, 320, 512, 576 }) { for (int hsv : { 40, 64, 72, 80, 96, 128, 192, 256, 512 }) { if (hsk != 192 && hsk != 320 && hsk != 576 && hsk != hsv) continue; @@ -9704,6 +9948,19 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_flash_attn_ext(128, 64, 4, {1, 1}, 128, 2, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_Q1_0, GGML_TYPE_Q4_0)); test_cases.emplace_back(new test_flash_attn_ext(64, 128, 4, {1, 1}, 128, 2, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_Q4_0, GGML_TYPE_Q1_0)); test_cases.emplace_back(new test_flash_attn_ext(128, 64, 4, {1, 1}, 64, 2, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_Q1_0, GGML_TYPE_F16)); + test_cases.emplace_back(new test_flash_attn_ext(128, 128, 4, {1, 1}, 96, 2, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_Q2_0, GGML_TYPE_Q2_0)); + test_cases.emplace_back(new test_flash_attn_ext(128, 64, 4, {1, 1}, 128, 2, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_Q2_0, GGML_TYPE_Q4_0)); + test_cases.emplace_back(new test_flash_attn_ext(64, 128, 4, {1, 1}, 128, 2, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_Q4_0, GGML_TYPE_Q2_0)); + test_cases.emplace_back(new test_flash_attn_ext(128, 64, 4, {1, 1}, 64, 2, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_Q2_0, GGML_TYPE_F16)); + + // large-KV F16 cases (Qwen3.6-27B geometry and a llama-class control): the upstream matrix + // stops at kv=1024, blind to long-context FA bugs (e.g. the oneDNN SDPA ordering race on BMG). + for (int64_t kv : { 4096, 16384 }) { + test_cases.emplace_back(new test_flash_attn_ext(256, 256, 4, {6, 1}, kv, 512, true, false, 0, 0, + GGML_PREC_F32, GGML_TYPE_F16, GGML_TYPE_F16)); + test_cases.emplace_back(new test_flash_attn_ext(128, 128, 8, {4, 1}, kv, 512, true, false, 0, 0, + GGML_PREC_F32, GGML_TYPE_F16, GGML_TYPE_F16)); + } // banded score-bias coverage: band edges, masks, decode offset, GQA, head sizes, table types test_cases.emplace_back(new test_flash_attn_ext_banded( 64, 2, 1, 8, 8, 8, 1, GGML_TYPE_F32, GGML_TYPE_F32)); @@ -9763,7 +10020,7 @@ static std::vector> make_test_cases_eval() { } } - for (auto gate : {GATING_FUNC_SOFTMAX, GATING_FUNC_SIGMOID, GATING_FUNC_SOFTMAX_WEIGHT}) { + for (auto gate : {GATING_FUNC_SOFTMAX, GATING_FUNC_SIGMOID, GATING_FUNC_SOFTMAX_WEIGHT, GATING_FUNC_SQRT_SOFTPLUS}) { for (bool with_norm : {false, true}) { for (bool bias_probs : {false, true}) { for (float scale_w : {0.0f, 2.0f}) { @@ -9775,6 +10032,7 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_topk_moe({128, 1, 1, 1}, 128, with_norm, bias_probs, gate, scale_w)); test_cases.emplace_back(new test_topk_moe({129, 1, 1, 1}, 128, with_norm, bias_probs, gate, scale_w)); test_cases.emplace_back(new test_topk_moe({160, 4, 1, 1}, 160, with_norm, bias_probs, gate, scale_w)); + test_cases.emplace_back(new test_topk_moe({256, 22, 1, 1}, 6, with_norm, bias_probs, gate, scale_w)); // Used by DeepSeek-V4 test_cases.emplace_back(new test_topk_moe({288, 22, 1, 1}, 8, with_norm, bias_probs, gate, scale_w)); // Used by StepFun 3.7 } } @@ -9847,6 +10105,12 @@ static std::vector> make_test_cases_eval() { } } + for (int kv : { 1, 7, 8, 63, 64, 65 }) { + for (ggml_type type_K : {GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_BF16, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0}) { + test_cases.emplace_back(new test_lightning_indexer(128, 64, kv, 32, 4, 1, type_K)); + } + } + return test_cases; } #ifdef _MSC_VER @@ -9895,7 +10159,11 @@ static std::vector> make_test_cases_perf() { test_cases.emplace_back(new test_conv_2d( { act_case[iwh_idx], act_case[iwh_idx], act_case[Cin_idx], act_case[B_idx] }, { act_case[kwh_idx], act_case[kwh_idx], act_case[Cin_idx], act_case[Cout_idx] }, - kernel_type, 1, 1, 0, 0, 1, 1, false)); + kernel_type, 1, 1, 0, 0, 1, 1, false)); // bool cwhn = false + test_cases.emplace_back(new test_conv_2d( + { act_case[iwh_idx], act_case[iwh_idx], act_case[Cin_idx], act_case[B_idx] }, + { act_case[kwh_idx], act_case[kwh_idx], act_case[Cin_idx], act_case[Cout_idx] }, + kernel_type, 1, 1, 0, 0, 1, 1, true)); // bool cwhn = true } } @@ -9979,6 +10247,10 @@ static std::vector> make_test_cases_perf() { test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 64, 1, 64)); test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 256, 1, 256)); test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 128, 32, 128)); + test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 64, 2048, 64)); + test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 128, 2048, 128)); + test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 256, 2048, 256)); + test_cases.emplace_back(new test_mul_mat_hadamard(GGML_TYPE_F32, GGML_TYPE_F32, 512, 2048, 512)); test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 4, 4 }, { 32, 64, 4, 4 })); test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 4, 2 }, { 32, 128, 4, 2 })); @@ -10150,6 +10422,8 @@ static std::vector> make_test_cases_perf() { test_cases.emplace_back(new test_ssm_conv_bias_silu(GGML_TYPE_F32, {4, 3328, 1, 1}, {4, 3328, 1, 1}, true)); // generate test_cases.emplace_back(new test_ssm_scan(GGML_TYPE_F32, 128, 64, 48, 1, 512, 1)); // prefill test_cases.emplace_back(new test_ssm_scan(GGML_TYPE_F32, 128, 64, 48, 1, 1, 1)); // generate + test_cases.emplace_back(new test_ssm_scan(GGML_TYPE_F32, 128, 80, 128, 1, 512, 1)); // Nemotron-9B prefill + test_cases.emplace_back(new test_ssm_scan(GGML_TYPE_F32, 128, 80, 128, 1, 1, 1)); // Nemotron-9B generate // acc test_cases.emplace_back(new test_acc(GGML_TYPE_F32, {256, 17, 1, 1}, {256, 16, 1, 1}, -1)); diff --git a/tests/test-backend-sampler.cpp b/tests/test-backend-sampler.cpp index 61ddf91feaa4..1165f46f0c92 100644 --- a/tests/test-backend-sampler.cpp +++ b/tests/test-backend-sampler.cpp @@ -1,7 +1,6 @@ #include "ggml.h" #include "llama.h" #include "llama-cpp.h" -#include "get-model.h" #include "common.h" #ifdef NDEBUG @@ -9,12 +8,15 @@ #endif #include +#include #include #include #include +#include #include #include #include +#include #include struct test_args { @@ -762,6 +764,564 @@ static void test_backend_logit_bias_sampling(const test_params & params) { printf("backend logit bias sampling test PASSED\n"); } +static void accept_prompt(llama_sampler * smpl, const llama_vocab * vocab, const std::string & prompt) { + const llama_token bos = llama_vocab_bos(vocab); + if (bos != LLAMA_TOKEN_NULL) { + llama_sampler_accept(smpl, bos); + } + + std::vector tokens(64); + int32_t n_tokens = llama_tokenize(vocab, prompt.c_str(), (int32_t) prompt.size(), + tokens.data(), (int32_t) tokens.size(), false, false); + if (n_tokens < 0) { + tokens.resize(-n_tokens); + n_tokens = llama_tokenize(vocab, prompt.c_str(), (int32_t) prompt.size(), + tokens.data(), (int32_t) tokens.size(), false, false); + } + + for (int32_t i = 0; i < n_tokens; ++i) { + llama_sampler_accept(smpl, tokens[i]); + } +} + +static std::vector decode_raw_logits(const test_params & params, const std::string & prompt) { + const int seq_id = 0; + const int n_vocab = llama_vocab_n_tokens(llama_model_get_vocab(params.model.get())); + std::vector empty_configs; + test_context ctx(params, empty_configs); + + GGML_ASSERT(ctx.decode({{ seq_id, prompt }})); + + float * logits = llama_get_logits_ith(ctx.ctx.get(), ctx.idx_for_seq(seq_id)); + GGML_ASSERT(logits != nullptr); + return std::vector(logits, logits + n_vocab); +} + +static std::vector apply_cpu_sampler( + const std::vector & raw_logits, + llama_sampler * sampler) { + std::vector data; + data.reserve(raw_logits.size()); + for (llama_token token = 0; token < (llama_token) raw_logits.size(); ++token) { + data.push_back({ token, raw_logits[token], 0.0f }); + } + + llama_token_data_array cur_p = { data.data(), data.size(), -1, false }; + llama_sampler_apply(sampler, &cur_p); + data.resize(cur_p.size); + return data; +} + +using sampler_setup_fn = std::function; +using sampler_init_fn = std::function; + +enum class penalties_position { + before_filter, + after_filter, +}; + +static void add_filter_and_penalties( + llama_sampler * chain, + const sampler_init_fn & init_filter, + int32_t n_vocab, + int32_t penalty_last_n, + float penalty_repeat, + float penalty_freq, + float penalty_present, + penalties_position position) { + const auto add_penalties = [&]() { + llama_sampler_chain_add(chain, llama_sampler_init_penalties( + n_vocab, penalty_last_n, penalty_repeat, penalty_freq, penalty_present)); + }; + + if (position == penalties_position::before_filter) { + add_penalties(); + llama_sampler_chain_add(chain, init_filter()); + } else { + llama_sampler_chain_add(chain, init_filter()); + add_penalties(); + } +} + +static llama_sampler_ptr make_sampler_chain( + const sampler_setup_fn & add_samplers, + const sampler_setup_fn & accept_history) { + llama_sampler_ptr chain(llama_sampler_chain_init(llama_sampler_chain_default_params())); + add_samplers(chain.get()); + accept_history(chain.get()); + return chain; +} + +struct backend_sampler_output { + std::vector logits; + std::vector candidates; +}; + +static backend_sampler_output run_backend_sampler( + const test_params & params, + const std::string & prompt, + llama_sampler * sampler) { + const int seq_id = 0; + std::vector configs = {{ seq_id, sampler }}; + test_context ctx(params, configs); + + GGML_ASSERT(ctx.decode({{ seq_id, prompt }})); + llama_synchronize(ctx.ctx.get()); + + const int32_t idx = ctx.idx_for_seq(seq_id); + const uint32_t n_logits = llama_get_sampled_logits_count_ith(ctx.ctx.get(), idx); + const uint32_t n_candidates = llama_get_sampled_candidates_count_ith(ctx.ctx.get(), idx); + float * logits = llama_get_sampled_logits_ith(ctx.ctx.get(), idx); + llama_token * candidates = llama_get_sampled_candidates_ith(ctx.ctx.get(), idx); + GGML_ASSERT(logits != nullptr); + + backend_sampler_output result; + result.logits.assign(logits, logits + n_logits); + result.candidates.resize(n_logits); + + if (n_candidates == 0) { + for (uint32_t i = 0; i < n_logits; ++i) { + result.candidates[i] = (llama_token) i; + } + } else { + GGML_ASSERT(candidates != nullptr); + GGML_ASSERT(n_candidates == n_logits); + std::memcpy(result.candidates.data(), candidates, n_candidates * sizeof(llama_token)); + } + + return result; +} + +struct sampler_comparison_output { + std::vector expected; + backend_sampler_output actual; +}; + +static sampler_comparison_output run_sampler_comparison( + const test_params & params, + const std::string & prompt, + const std::vector & raw_logits, + const sampler_setup_fn & add_samplers, + const sampler_setup_fn & accept_history) { + llama_sampler_ptr cpu_chain = make_sampler_chain(add_samplers, accept_history); + llama_sampler_ptr backend_chain = make_sampler_chain(add_samplers, accept_history); + return { + apply_cpu_sampler(raw_logits, cpu_chain.get()), + run_backend_sampler(params, prompt, backend_chain.get()), + }; +} + +static std::unordered_map map_logits(const std::vector & data) { + std::unordered_map result; + result.reserve(data.size()); + for (const auto & item : data) { + result[item.id] = item.logit; + } + return result; +} + +struct sampler_comparison_stats { + int n_mismatch = 0; + int n_masked = 0; + float max_diff = 0.0f; +}; + +static sampler_comparison_stats compare_sampler_outputs( + const char * name, + const std::unordered_map & expected, + const backend_sampler_output & actual, + bool allow_extra_candidates = false) { + GGML_ASSERT(actual.logits.size() == actual.candidates.size()); + + sampler_comparison_stats result; + std::unordered_set seen; + seen.reserve(actual.candidates.size()); + + for (size_t i = 0; i < actual.logits.size(); ++i) { + const llama_token token = actual.candidates[i]; + const float logit = actual.logits[i]; + if (!seen.insert(token).second || std::isnan(logit)) { + if (result.n_mismatch < 5) { + printf("%s token %d has invalid backend output\n", name, token); + } + ++result.n_mismatch; + continue; + } + + const auto it = expected.find(token); + if (it == expected.end()) { + if (std::isinf(logit) && logit < 0.0f) { + ++result.n_masked; + } else if (!allow_extra_candidates) { + if (result.n_mismatch < 5) { + printf("%s token %d was not masked\n", name, token); + } + ++result.n_mismatch; + } + continue; + } + + const float diff = fabsf(it->second - logit); + result.max_diff = std::max(result.max_diff, diff); + if (!std::isfinite(logit) || diff > 1e-3f) { + if (result.n_mismatch < 5) { + printf("%s mismatch token %d: cpu=%.6f backend=%.6f diff=%.6f\n", + name, token, it->second, logit, diff); + } + ++result.n_mismatch; + } + } + + for (const auto & item : expected) { + if (seen.find(item.first) == seen.end()) { + if (result.n_mismatch < 5) { + printf("%s missing backend token %d\n", name, item.first); + } + ++result.n_mismatch; + } + } + + printf("%s logits: max_diff=%.6f n_masked=%d n_mismatch=%d\n", + name, result.max_diff, result.n_masked, result.n_mismatch); + return result; +} + +static float find_backend_logit(const backend_sampler_output & output, llama_token token) { + for (size_t i = 0; i < output.candidates.size(); ++i) { + if (output.candidates[i] == token) { + return output.logits[i]; + } + } + GGML_ABORT("backend token not found"); +} + +static sampler_comparison_output run_penalties_comparison( + const test_params & params, + int32_t penalty_last_n, + float penalty_repeat, + float penalty_freq, + float penalty_present, + const std::string & prompt, + const std::function & extra_accept = {}) { + const auto * vocab = llama_model_get_vocab(params.model.get()); + const std::vector raw_logits = decode_raw_logits(params, prompt); + const auto add_samplers = [&](llama_sampler * chain) { + llama_sampler_chain_add(chain, llama_sampler_init_penalties( + llama_vocab_n_tokens(vocab), penalty_last_n, penalty_repeat, penalty_freq, penalty_present)); + }; + const auto accept_history = [&](llama_sampler * chain) { + accept_prompt(chain, vocab, prompt); + if (extra_accept) { + extra_accept(chain); + } + }; + + return run_sampler_comparison( + params, prompt, raw_logits, add_samplers, accept_history); +} + +static void compare_penalties_logits( + const test_params & params, + int32_t penalty_last_n, + float penalty_repeat, + float penalty_freq, + float penalty_present, + const std::string & prompt, + const std::function & extra_accept = {}) { + const sampler_comparison_output output = run_penalties_comparison( + params, penalty_last_n, penalty_repeat, penalty_freq, penalty_present, prompt, extra_accept); + + GGML_ASSERT(output.expected.size() == output.actual.logits.size()); + + const sampler_comparison_stats stats = compare_sampler_outputs( + "penalties", map_logits(output.expected), output.actual); + GGML_ASSERT(stats.n_masked == 0); + GGML_ASSERT(stats.n_mismatch == 0); +} + +static void test_penalty_parameter_values(const test_params & params) { + struct penalty_test_case { + const char * name; + float repeat; + float frequency; + float presence; + }; + + const penalty_test_case cases[] = { + { "frequency -1", 1.0f, -1.0f, 0.0f }, + { "frequency 0", 1.0f, 0.0f, 0.0f }, + { "frequency 1", 1.0f, 1.0f, 0.0f }, + { "presence -1", 1.0f, 0.0f, -1.0f }, + { "presence 0", 1.0f, 0.0f, 0.0f }, + { "presence 1", 1.0f, 0.0f, 1.0f }, + { "repeat 1", 1.0f, 0.0f, 0.0f }, + }; + + int n_failed = 0; + for (const auto & test : cases) { + const sampler_comparison_output output = run_penalties_comparison( + params, 64, test.repeat, test.frequency, test.presence, "Hello Hello world"); + GGML_ASSERT(output.expected.size() == output.actual.logits.size()); + const sampler_comparison_stats stats = compare_sampler_outputs( + test.name, map_logits(output.expected), output.actual); + n_failed += stats.n_mismatch != 0; + } + + GGML_ASSERT(n_failed == 0); +} + +static void compare_top_k_penalties_logits( + const test_params & params, + int32_t k, + int32_t penalty_last_n, + float penalty_repeat, + float penalty_freq, + float penalty_present, + const std::string & prompt, + penalties_position position) { + const auto * vocab = llama_model_get_vocab(params.model.get()); + const std::vector raw_logits = decode_raw_logits(params, prompt); + const int n_vocab = (int) raw_logits.size(); + + GGML_ASSERT(n_vocab > k); + + const sampler_init_fn init_top_k = [k]() { + return llama_sampler_init_top_k(k); + }; + llama_sampler_ptr top_k(init_top_k()); + const std::vector top_k_data = apply_cpu_sampler(raw_logits, top_k.get()); + GGML_ASSERT(top_k_data.size() == (size_t) k); + const llama_token retained_history_token = top_k_data[0].id; + + llama_token excluded_history_token = LLAMA_TOKEN_NULL; + for (llama_token token = 0; token < n_vocab; ++token) { + const auto it = std::find_if(top_k_data.begin(), top_k_data.end(), [token](const llama_token_data & data) { + return data.id == token; + }); + if (it == top_k_data.end()) { + excluded_history_token = token; + break; + } + } + GGML_ASSERT(excluded_history_token != LLAMA_TOKEN_NULL); + + const auto add_samplers = [&](llama_sampler * chain) { + add_filter_and_penalties(chain, init_top_k, n_vocab, + penalty_last_n, penalty_repeat, penalty_freq, penalty_present, position); + }; + + auto accept_history = [&](llama_sampler * smpl) { + accept_prompt(smpl, vocab, prompt); + llama_sampler_accept(smpl, excluded_history_token); + llama_sampler_accept(smpl, excluded_history_token); + llama_sampler_accept(smpl, retained_history_token); + llama_sampler_accept(smpl, retained_history_token); + }; + + const sampler_comparison_output output = run_sampler_comparison( + params, prompt, raw_logits, add_samplers, accept_history); + + GGML_ASSERT(output.expected.size() == (size_t) k); + GGML_ASSERT(output.actual.logits.size() == (size_t) k); + + const std::unordered_map expected_logits = map_logits(output.expected); + + if (position == penalties_position::after_filter) { + GGML_ASSERT(expected_logits.find(retained_history_token) != expected_logits.end()); + GGML_ASSERT(fabsf(expected_logits.at(retained_history_token) - raw_logits[retained_history_token]) > 1e-6f); + GGML_ASSERT(expected_logits.find(excluded_history_token) == expected_logits.end()); + GGML_ASSERT(std::find(output.actual.candidates.begin(), output.actual.candidates.end(), + excluded_history_token) == output.actual.candidates.end()); + } else { + const std::unordered_map unpenalized_logits = map_logits(top_k_data); + bool changed = false; + for (const auto & item : expected_logits) { + const auto it = unpenalized_logits.find(item.first); + if (it == unpenalized_logits.end() || fabsf(it->second - item.second) > 1e-6f) { + changed = true; + break; + } + } + GGML_ASSERT(changed); + } + + const char * name = position == penalties_position::before_filter + ? "penalties top-k" + : "top-k penalties"; + const sampler_comparison_stats stats = compare_sampler_outputs( + name, expected_logits, output.actual); + GGML_ASSERT(stats.n_masked == 0); + GGML_ASSERT(stats.n_mismatch == 0); +} + +static void compare_masking_penalties_logits( + const test_params & params, + const char * filter_name, + const sampler_init_fn & init_filter, + int32_t penalty_last_n, + float penalty_repeat, + float penalty_freq, + float penalty_present, + const std::string & prompt, + penalties_position position, + bool allow_extra_candidates, + bool add_history = true) { + const auto * vocab = llama_model_get_vocab(params.model.get()); + const std::vector raw_logits = decode_raw_logits(params, prompt); + const int n_vocab = (int) raw_logits.size(); + llama_sampler_ptr filter(init_filter()); + const std::vector filtered_data = apply_cpu_sampler(raw_logits, filter.get()); + GGML_ASSERT(!filtered_data.empty()); + GGML_ASSERT(filtered_data.size() < (size_t) n_vocab); + + const llama_token penalized_token = filtered_data[0].id; + std::unordered_set retained_tokens; + retained_tokens.reserve(filtered_data.size()); + for (const auto & data : filtered_data) { + retained_tokens.insert(data.id); + } + + llama_token masked_token = LLAMA_TOKEN_NULL; + for (llama_token token = 0; token < n_vocab; ++token) { + if (retained_tokens.find(token) == retained_tokens.end()) { + masked_token = token; + break; + } + } + GGML_ASSERT(masked_token != LLAMA_TOKEN_NULL); + + const auto add_samplers = [&](llama_sampler * chain) { + add_filter_and_penalties(chain, init_filter, n_vocab, + penalty_last_n, penalty_repeat, penalty_freq, penalty_present, position); + }; + auto accept_history = [&](llama_sampler * smpl) { + if (!add_history) { + return; + } + accept_prompt(smpl, vocab, prompt); + llama_sampler_accept(smpl, penalized_token); + llama_sampler_accept(smpl, penalized_token); + llama_sampler_accept(smpl, masked_token); + llama_sampler_accept(smpl, masked_token); + }; + + const sampler_comparison_output output = run_sampler_comparison( + params, prompt, raw_logits, add_samplers, accept_history); + + GGML_ASSERT(output.actual.logits.size() == (size_t) n_vocab); + + const std::unordered_map expected_logits = map_logits(output.expected); + + GGML_ASSERT(expected_logits.find(masked_token) == expected_logits.end()); + if (add_history) { + if (position == penalties_position::after_filter) { + GGML_ASSERT(expected_logits.find(penalized_token) != expected_logits.end()); + GGML_ASSERT(fabsf(expected_logits.at(penalized_token) - raw_logits[penalized_token]) > 1e-6f); + } else { + llama_sampler_ptr penalties(llama_sampler_init_penalties( + n_vocab, penalty_last_n, penalty_repeat, penalty_freq, penalty_present)); + accept_history(penalties.get()); + const std::unordered_map penalized_logits = + map_logits(apply_cpu_sampler(raw_logits, penalties.get())); + GGML_ASSERT(fabsf(penalized_logits.at(penalized_token) - raw_logits[penalized_token]) > 1e-6f); + } + } + + const std::string name = position == penalties_position::before_filter + ? "penalties " + std::string(filter_name) + : std::string(filter_name) + " penalties"; + const sampler_comparison_stats stats = compare_sampler_outputs( + name.c_str(), expected_logits, output.actual, allow_extra_candidates); + const float masked_logit = find_backend_logit(output.actual, masked_token); + GGML_ASSERT(stats.n_masked > 0); + GGML_ASSERT(std::isinf(masked_logit) && masked_logit < 0.0f); + GGML_ASSERT(stats.n_mismatch == 0); +} + +static void test_backend_penalties_sampling(const test_params & params) { + printf("Testing backend penalties (repeat + freq + presence)\n"); + compare_penalties_logits(params, 64, 1.1f, 0.5f, 0.25f, "Hello Hello world"); + + printf("Testing backend penalties with penalty_last_n > 64\n"); + const auto * vocab = llama_model_get_vocab(params.model.get()); + std::vector tokens(8); + int32_t n_tok = llama_tokenize(vocab, "a", 1, tokens.data(), (int32_t) tokens.size(), false, false); + if (n_tok < 0) { + tokens.resize(-n_tok); + n_tok = llama_tokenize(vocab, "a", 1, tokens.data(), (int32_t) tokens.size(), false, false); + } + GGML_ASSERT(n_tok > 0); + const llama_token tok = tokens[0]; + + compare_penalties_logits(params, 80, 1.15f, 0.1f, 0.05f, "a", [tok](llama_sampler * smpl) { + // accept_prompt already accepted BOS + one 'a'; fill the ring to n=80 + for (int i = 0; i < 78; ++i) { + llama_sampler_accept(smpl, tok); + } + }); + + printf("Testing backend penalties without filler entries\n"); + compare_penalties_logits(params, 64, 1.1f, 0.5f, 0.25f, "Hello", [](llama_sampler * smpl) { + for (llama_token token = 0; token < 64; ++token) { + llama_sampler_accept(smpl, token); + } + }); + + printf("Testing backend top-k followed by penalties\n"); + compare_top_k_penalties_logits(params, 8, 64, 1.1f, 0.5f, 0.25f, "Hello", + penalties_position::after_filter); + + printf("Testing backend penalties followed by top-k\n"); + compare_top_k_penalties_logits(params, 8, 64, 1.1f, 0.5f, 0.25f, "Hello", + penalties_position::before_filter); + + printf("Testing backend top-p followed by penalties\n"); + compare_masking_penalties_logits(params, "top-p", []() { + return llama_sampler_init_top_p(0.9f, 0); + }, 64, 1.1f, 0.5f, 0.25f, "Hello", penalties_position::after_filter, true); + + printf("Testing backend top-p followed by penalties with a large history window\n"); + compare_masking_penalties_logits(params, "top-p large-window", []() { + return llama_sampler_init_top_p(0.9f, 0); + }, 4096, 1.1f, 0.5f, 0.25f, "Hello", penalties_position::after_filter, true); + + printf("Testing backend penalties followed by top-p\n"); + compare_masking_penalties_logits(params, "top-p", []() { + return llama_sampler_init_top_p(0.9f, 0); + }, 64, 1.1f, 0.5f, 0.25f, "Hello", penalties_position::before_filter, true); + + printf("Testing backend min-p followed by penalties\n"); + compare_masking_penalties_logits(params, "min-p", []() { + return llama_sampler_init_min_p(0.1f, 0); + }, 64, 1.1f, 0.5f, 0.25f, "Hello", penalties_position::after_filter, false); + + printf("Testing backend penalties followed by min-p\n"); + compare_masking_penalties_logits(params, "min-p", []() { + return llama_sampler_init_min_p(0.1f, 0); + }, 64, 1.1f, 0.5f, 0.25f, "Hello", penalties_position::before_filter, false); + + printf("Testing backend top-p followed by penalties with empty history\n"); + compare_masking_penalties_logits(params, "top-p empty", []() { + return llama_sampler_init_top_p(0.9f, 0); + }, 64, 1.1f, 0.5f, 0.25f, "Hello", penalties_position::after_filter, true, false); + + printf("Testing backend top-p followed by individual penalties\n"); + compare_masking_penalties_logits(params, "top-p repeat", []() { + return llama_sampler_init_top_p(0.9f, 0); + }, 64, 1.1f, 0.0f, 0.0f, "Hello", penalties_position::after_filter, true); + compare_masking_penalties_logits(params, "top-p frequency", []() { + return llama_sampler_init_top_p(0.9f, 0); + }, 64, 1.0f, 0.5f, 0.0f, "Hello", penalties_position::after_filter, true); + compare_masking_penalties_logits(params, "top-p presence", []() { + return llama_sampler_init_top_p(0.9f, 0); + }, 64, 1.0f, 0.0f, 0.25f, "Hello", penalties_position::after_filter, true); + + printf("Testing backend penalty parameter values\n"); + test_penalty_parameter_values(params); + + printf("backend penalties sampling test PASSED\n"); +} + // This test verifies that it is possible to have two different backend samplers, // one that uses the backend dist sampler, and another that uses CPU dist sampler. static void test_backend_mixed_sampling(const test_params & params) { @@ -1015,6 +1575,7 @@ struct backend_test_case { static const backend_test_case BACKEND_TESTS[] = { { "greedy", test_backend_greedy_sampling, true }, { "logit_bias", test_backend_logit_bias_sampling, true }, + { "penalties", test_backend_penalties_sampling, true }, { "temp", test_backend_temp_sampling, true }, { "temp_ext", test_backend_temp_ext_sampling, true }, { "top_k", test_backend_top_k_sampling, true }, @@ -1136,7 +1697,7 @@ int main(int argc, char ** argv) { test_args args = parse_cli(argc, argv); if (args.model.empty()) { - args.model = get_model_or_exit(1, argv); + args.model = common_get_model_or_exit(1, argv); } { diff --git a/tests/test-chat-auto-parser.cpp b/tests/test-chat-auto-parser.cpp index 78e42c65a50c..4218f8d5747d 100644 --- a/tests/test-chat-auto-parser.cpp +++ b/tests/test-chat-auto-parser.cpp @@ -57,6 +57,15 @@ static void test_seed_oss_tool_with_reasoning(testing & t); static void test_nemotron_analysis(testing & t); static void test_nemotron_reasoning_detection(testing & t); static void test_nemotron_tool_format(testing & t); +static void test_laguna_analysis(testing & t); +static void test_laguna_reasoning_detection(testing & t); +static void test_laguna_tool_format(testing & t); +static void test_laguna_s_analysis(testing & t); +static void test_laguna_s_reasoning_detection(testing & t); +static void test_laguna_s_tool_format(testing & t); +static void test_laguna_xs2_analysis(testing & t); +static void test_laguna_xs2_reasoning_detection(testing & t); +static void test_laguna_xs2_tool_format(testing & t); // CohereForAI template analysis tests static void test_cohere_reasoning_detection(testing & t); @@ -101,6 +110,9 @@ int main(int argc, char * argv[]) { t.test("seed_oss_diffs", test_seed_oss_tool_analysis); t.test("cohere", test_cohere_analysis); t.test("nemotron", test_nemotron_analysis); + t.test("laguna", test_laguna_analysis); + t.test("laguna-s", test_laguna_s_analysis); + t.test("laguna-xs2", test_laguna_xs2_analysis); t.test("smollm3", test_smollm3_analysis); t.test("standard_json_tools", test_standard_json_tools_formats); t.test("normalize_quotes_to_json", test_normalize_quotes_to_json); @@ -1378,6 +1390,94 @@ static void test_nemotron_tool_format(testing & t) { t.assert_true("should support tools", analysis.jinja_caps.supports_tools); } +// ============================================================================ +// Laguna Template Analysis Tests +// ============================================================================ +static common_chat_template load_laguna_template(testing & t) { + return load_template(t, "models/templates/poolside-Laguna-XS-2.1.jinja"); +} + +static void test_laguna_reasoning_detection(testing & t) { + common_chat_template tmpl = load_laguna_template(t); + struct autoparser analysis; + analysis.analyze_template(tmpl); + // Laguna's template renders reasoning delimiters with formatting whitespace + // ("\n") that the model does not emit; the Laguna patch trims them. + t.assert_equal("reasoning_start should be ''", "", analysis.reasoning.start); + t.assert_equal("reasoning_end should be ''", "", analysis.reasoning.end); + t.assert_equal("reasoning should be TAG_BASED", reasoning_mode::TAG_BASED, analysis.reasoning.mode); +} + +static void test_laguna_tool_format(testing & t) { + common_chat_template tmpl = load_laguna_template(t); + struct autoparser analysis; + analysis.analyze_template(tmpl); + t.assert_equal("arg_value_suffix should be ''", "", analysis.tools.arguments.value_suffix); +} + +static void test_laguna_stop_string(testing & t) { + // The turn terminator can be emitted as ordinary text tokens + // (not the single eot token), so it must also be a literal stop string. + common_chat_template tmpl = load_laguna_template(t); + struct autoparser analysis; + analysis.analyze_template(tmpl); + bool has_stop = false; + for (const auto & stop : analysis.additional_stops) { + if (stop == "") { has_stop = true; break; } + } + t.assert_true("Laguna additional_stops contains ", has_stop); +} + +static void test_laguna_analysis(testing & t) { + t.test("Laguna reasoning detection", test_laguna_reasoning_detection); + t.test("Laguna tool format", test_laguna_tool_format); + t.test("Laguna stop string", test_laguna_stop_string); +} + +static common_chat_template load_laguna_s_template(testing & t) { + return load_template(t, "models/templates/poolside-Laguna-S-2.1.jinja"); +} +static void test_laguna_s_reasoning_detection(testing & t) { + common_chat_template tmpl = load_laguna_s_template(t); + struct autoparser analysis; + analysis.analyze_template(tmpl); + t.assert_equal("Laguna-S(v8) reasoning_start should be ''", "", analysis.reasoning.start); + t.assert_equal("Laguna-S(v8) reasoning_end should be ''", "", analysis.reasoning.end); + t.assert_equal("Laguna-S(v8) reasoning should be TAG_BASED", reasoning_mode::TAG_BASED, analysis.reasoning.mode); +} +static void test_laguna_s_tool_format(testing & t) { + common_chat_template tmpl = load_laguna_s_template(t); + struct autoparser analysis; + analysis.analyze_template(tmpl); + t.assert_equal("Laguna-S(v8) arg_value_suffix should be ''", "", analysis.tools.arguments.value_suffix); +} +static void test_laguna_s_analysis(testing & t) { + t.test("Laguna-S(v8) reasoning detection", test_laguna_s_reasoning_detection); + t.test("Laguna-S(v8) tool format", test_laguna_s_tool_format); +} + +static common_chat_template load_laguna_xs2_template(testing & t) { + return load_template(t, "models/templates/poolside-Laguna-XS.2.jinja"); +} +static void test_laguna_xs2_reasoning_detection(testing & t) { + common_chat_template tmpl = load_laguna_xs2_template(t); + struct autoparser analysis; + analysis.analyze_template(tmpl); + t.assert_equal("Laguna-XS.2(v5) reasoning_start should be ''", "", analysis.reasoning.start); + t.assert_equal("Laguna-XS.2(v5) reasoning_end should be ''", "", analysis.reasoning.end); + t.assert_equal("Laguna-XS.2(v5) reasoning should be TAG_BASED", reasoning_mode::TAG_BASED, analysis.reasoning.mode); +} +static void test_laguna_xs2_tool_format(testing & t) { + common_chat_template tmpl = load_laguna_xs2_template(t); + struct autoparser analysis; + analysis.analyze_template(tmpl); + t.assert_equal("Laguna-XS.2(v5) arg_value_suffix should be ''", "", analysis.tools.arguments.value_suffix); +} +static void test_laguna_xs2_analysis(testing & t) { + t.test("Laguna-XS.2(v5) reasoning detection", test_laguna_xs2_reasoning_detection); + t.test("Laguna-XS.2(v5) tool format", test_laguna_xs2_tool_format); +} + static common_chat_template load_cohere_template(testing & t) { return load_template(t, "models/templates/CohereForAI-c4ai-command-r7b-12-2024-tool_use.jinja"); } diff --git a/tests/test-chat-peg-parser.cpp b/tests/test-chat-peg-parser.cpp index 908b13fd0ca7..3ab7a67b6a82 100644 --- a/tests/test-chat-peg-parser.cpp +++ b/tests/test-chat-peg-parser.cpp @@ -8,6 +8,7 @@ #include #include +#include #include #include "nlohmann/json.hpp" @@ -21,6 +22,7 @@ static void test_example_qwen3_non_coder(testing & t); static void test_command7_parser_compare(testing & t); static void test_prefix_tool_names(testing & t); static void test_tagged_peg_parser(testing & t); +static void test_permute(testing & t); int main(int argc, char * argv[]) { testing t(std::cout); @@ -39,6 +41,7 @@ int main(int argc, char * argv[]) { t.test("comparison", test_command7_parser_compare); t.test("prefix tool names", test_prefix_tool_names); t.test("tagged peg parser", test_tagged_peg_parser); + t.test("permute", test_permute); return t.summary(); } @@ -981,3 +984,103 @@ static void test_tagged_peg_parser(testing & t) { t.assert_equal("fun_post should be '>'", ">", result.tags["fun_post"]); }); } + +static void test_permute(testing & t) { + auto accepts = [](const common_peg_arena & parser, const std::string & input) { + common_peg_parse_context ctx(input); + return parser.parse(ctx).success(); + }; + + auto gbnf_of = [](const common_peg_arena & parser) { + return build_grammar([&](const common_grammar_builder & builder) { parser.build_grammar(builder); }); + }; + + auto assert_gbnf_equal = [](testing & t, const std::string & expected, const std::string & actual) { + static const std::regex leading_ws_re = std::regex(R"((^|\n)\s+)"); + t.assert_equal("gbnf are equal", std::regex_replace(expected, leading_ws_re, "$1"), actual); + }; + + auto count_rules = [](const std::string & gbnf, const std::string & prefix) { + size_t count = 0; + for (const auto & line : string_split(gbnf, '\n')) { + if (line.rfind(prefix, 0) == 0) { + count++; + } + } + return count; + }; + + t.test("accepts every ordering", [&](testing & t) { + auto parser = build_chat_peg_parser([](common_chat_peg_builder & p) { + return p.permute("abc", { p.literal("a"), p.literal("b"), p.literal("c") }) + p.end(); + }); + + for (const std::string input : { "abc", "acb", "bac", "bca", "cab", "cba" }) { + t.assert_true("accepts " + input, accepts(parser, input)); + } + }); + + t.test("single element", [&](testing & t) { + auto parser = build_chat_peg_parser([](common_chat_peg_builder & p) { + return p.permute("a", { p.literal("a") }) + p.end(); + }); + + t.assert_true("accepts a", accepts(parser, "a")); + t.assert_true("rejects aa", !accepts(parser, "aa")); + }); + + t.test("grammar left-factorizes shared tails", [&](testing & t) { + auto parser = build_chat_peg_parser([](common_chat_peg_builder & p) { + return p.permute("abc", { p.literal("a"), p.literal("b"), p.literal("c") }) + p.end(); + }); + + // Every rule is one remaining subset, keyed by bitmask: abc-3 is {a,b}, abc-7 is {a,b,c}. + // Each subset is emitted once and shared by every branch that leads into it. + assert_gbnf_equal(t, R"""( + abc-1 ::= "a" + abc-2 ::= "b" + abc-3 ::= "a" abc-2 | "b" abc-1 + abc-4 ::= "c" + abc-5 ::= "a" abc-4 | "c" abc-1 + abc-6 ::= "b" abc-4 | "c" abc-2 + abc-7 ::= "a" abc-6 | "b" abc-5 | "c" abc-3 + root ::= abc-7 + space ::= | " " | "\n"{1,2} [ \t]{0,20} + )""", gbnf_of(parser)); + }); + + t.test("grammar emits one rule per remaining subset", [&](testing & t) { + auto parser = build_chat_peg_parser([](common_chat_peg_builder & p) { + return p.permute("abcd", { p.literal("a"), p.literal("b"), p.literal("c"), p.literal("d") }) + p.end(); + }); + + // 2^4 - 1 non-empty subsets, one rule each - not the 4! = 24 orderings. + t.assert_equal("permute rule count", 15u, count_rules(gbnf_of(parser), "abcd-")); + }); + + t.test("grammar emits no rules for a single element", [&](testing & t) { + auto parser = build_chat_peg_parser([](common_chat_peg_builder & p) { + return p.permute("a", { p.literal("a") }) + p.end(); + }); + + assert_gbnf_equal(t, R"""( + root ::= "a" + space ::= | " " | "\n"{1,2} [ \t]{0,20} + )""", gbnf_of(parser)); + }); + + t.test("grammar falls back to the given order when too large", [&](testing & t) { + auto parser = build_chat_peg_parser([](common_chat_peg_builder & p) { + std::vector parsers; + for (size_t i = 0; i <= COMMON_CHAT_MAX_PERMUTE; i++) { + parsers.push_back(p.literal(std::string(1, (char) ('a' + i)))); + } + return p.permute("big", parsers) + p.end(); + }); + + assert_gbnf_equal(t, R"""( + root ::= "a" "b" "c" "d" "e" "f" "g" + space ::= | " " | "\n"{1,2} [ \t]{0,20} + )""", gbnf_of(parser)); + }); +} diff --git a/tests/test-chat.cpp b/tests/test-chat.cpp index 407426f9f3bd..445b06187928 100644 --- a/tests/test-chat.cpp +++ b/tests/test-chat.cpp @@ -109,6 +109,15 @@ static void assert_contains(const std::string & haystack, const std::string & ne } } +static void assert_not_contains(const std::string & haystack, const std::string & needle) { + if (haystack.find(needle) != std::string::npos) { + LOG_ERR("Expected NOT to contain: %s\n", needle.c_str()); + LOG_ERR("Actual: %s\n", haystack.c_str()); + common_log_flush(common_log_main()); + throw std::runtime_error("Test failed"); + } +} + static void assert_ends_with(const std::string & str, const std::string & suffix) { if (str.size() < suffix.size() || str.compare(str.size() - suffix.size(), suffix.size(), suffix) != 0) { @@ -721,6 +730,71 @@ static common_chat_tool imaginary_number_tool{ })", }; +static common_chat_tool nested_args_tool{ + /* .name = */ "nested_args", + /* .description = */ "Tool with nested array arguments", + /* .parameters = */ R"({ + "type": "object", + "properties": { + "tags": { + "type": "array", + "items": { "type": "string" } + }, + "entries": { + "type": "array", + "items": { + "type": "object", + "properties": { + "id": { "type": "integer" }, + "label": { "type": "string" } + }, + "required": ["id", "label"] + } + } + }, + "required": ["tags", "entries"] + })", +}; + +static common_chat_tool union_args_tool{ + /* .name = */ "union_args", + /* .description = */ "Tool with union arguments", + /* .parameters = */ R"({ + "type": "object", + "properties": { + "filter": { + "anyOf": [ + { "type": "array", "items": { "type": "string" } }, + { + "type": "object", + "properties": { + "field": { "type": "string" }, + "op": { "type": "string" } + }, + "required": ["field", "op"] + } + ] + }, + "label": { + "oneOf": [ + { "type": "string" }, + { "type": "object", "properties": { "text": { "type": "string" } } } + ] + }, + "limit": { + "oneOf": [ + { "type": "integer" }, + { + "type": "object", + "properties": { "max": { "type": "integer" } }, + "required": ["max"] + } + ] + } + } + })", +}; + static common_chat_tool nullable_string_tool{ /* .name = */ "set_nullable_str", /* .description = */ "Set a nullable string value", @@ -1135,7 +1209,7 @@ static void test_peg_parser(common_chat_templates * tmpls, // budget sampler inhibits grammar application while inside thinking blocks — // triggers inside ... are suppressed. bool use_reasoning_budget_path = false; - if (parser.params_.grammar_lazy && !parser.params_.thinking_end_tag.empty()) { + if (parser.params_.grammar_lazy && !parser.params_.thinking_end_tags.empty()) { use_reasoning_budget_path = true; for (const auto & trigger : parser.params_.grammar_triggers) { if (trigger.type != COMMON_GRAMMAR_TRIGGER_TYPE_WORD) { @@ -1153,7 +1227,7 @@ static void test_peg_parser(common_chat_templates * tmpls, // Walk through full_input tracking thinking state; only match triggers // when outside thinking blocks. const auto & think_start = parser.params_.thinking_start_tag; - const auto & think_end = parser.params_.thinking_end_tag; + const auto & think_ends = parser.params_.thinking_end_tags; bool in_thinking = false; for (size_t i = 0; i < full_input.size(); ++i) { @@ -1163,12 +1237,14 @@ static void test_peg_parser(common_chat_templates * tmpls, i += think_start.size() - 1; continue; } - if (in_thinking && full_input.compare(i, think_end.size(), think_end) == 0) { - in_thinking = false; - i += think_end.size() - 1; - continue; - } if (in_thinking) { + for (const auto & think_end : think_ends) { + if (full_input.compare(i, think_end.size(), think_end) == 0) { + in_thinking = false; + i += think_end.size() - 1; + break; + } + } continue; } // Outside thinking — check if any trigger word starts here @@ -2280,46 +2356,39 @@ static void test_template_output_peg_parsers(bool detailed_debug) { .expect_content(R"({"amount": 123.45, "date": "2025-12-03"})") .run(); - // tool call segment in reasoning + // a tool call ends the prefilled thinking block, with or without a closing tst.test( - "Let's call a tool: \n" - "\n" - "\n" - "def hello():\n" - " print(\"Not the real call!\")\n" - "\n" - "hello()\n" + "\n" + "\n" + "\n" + "pwd\n" "\n" "\n" - "\n\n\n" + "") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ run_in_terminal_tool }) + .expect_tool_calls({ + { "run_in_terminal", R"({"command": "pwd"})", {} }, + }) + .run(); + + // ...including after the model has thought about it + tst.test( + "Need to inspect the current directory.\n" "\n" - "\n" - "\n" - "def hello():\n" - " print(\"Hello, world!\")\n" - "\n" - "hello()\n" + "\n" + "\n" + "pwd\n" "\n" "\n" "") .enable_thinking(true) .reasoning_format(COMMON_REASONING_FORMAT_AUTO) - .tools({ - python_tool - }) - .expect_reasoning( - "Let's call a tool: \n" - "\n" - "\n" - "def hello():\n" - " print(\"Not the real call!\")\n" - "\n" - "hello()\n" - "\n" - "\n" - "") + .tools({ run_in_terminal_tool }) + .expect_reasoning("Need to inspect the current directory.") .expect_tool_calls({ - { "python", "{\"code\": \"def hello():\\n print(\\\"Hello, world!\\\")\\n\\nhello()\"}", {} }, + { "run_in_terminal", R"({"command": "pwd"})", {} }, }) .run(); @@ -2463,17 +2532,6 @@ static void test_template_output_peg_parsers(bool detailed_debug) { }) .run(); - tst.test( - "I might call later, but I am still thinking.\n" - "\n\n" - "Final answer without tools.") - .reasoning_format(COMMON_REASONING_FORMAT_AUTO) - .enable_thinking(true) - .tools({ run_in_terminal_tool }) - .expect_reasoning("I might call later, but I am still thinking.") - .expect_content("Final answer without tools.") - .run(); - // Continuation tests tst.test("world!\nWhat's up?") .reasoning_format(COMMON_REASONING_FORMAT_AUTO) @@ -2778,49 +2836,6 @@ static void test_template_output_peg_parsers(bool detailed_debug) { .expect_content(R"({"amount": 123.45, "date": "2025-12-03"})") .run(); - // tool call segment in reasoning - tst.test( - "Let's call a tool: \n" - "\n" - "\n" - "def hello():\n" - " print(\"Not the real call!\")\n" - "\n" - "hello()\n" - "\n" - "\n" - "\n\n" - "\n" - "\n" - "\n" - "def hello():\n" - " print(\"Hello, world!\")\n" - "\n" - "hello()\n" - "\n" - "\n" - "\n" - ) - .enable_thinking(true) - .reasoning_format(COMMON_REASONING_FORMAT_AUTO) - .tools({ - python_tool - }) - .expect_reasoning("Let's call a tool: \n" - "\n" - "\n" - "def hello():\n" - " print(\"Not the real call!\")\n" - "\n" - "hello()\n" - "\n" - "\n" - "\n") - .expect_tool_calls({ - { "python", "{\"code\": \"def hello():\\n print(\\\"Hello, world!\\\")\\n\\nhello()\"}", {} }, - }) - .run(); - // Continuation tests tst.test("world!\nWhat's up?") .reasoning_format(COMMON_REASONING_FORMAT_AUTO) @@ -2918,6 +2933,17 @@ static void test_template_output_peg_parsers(bool detailed_debug) { .expect(message_assist) .run(); + // JSON output schema + tst.test( + "I need to output the invoice details in JSON<|END_THINKING|>" + "<|START_TEXT|>{\"amount\": 123.45, \"date\": \"2025-12-03\"}<|END_TEXT|>") + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .json_schema(invoice_schema) + .tools({ special_function_tool }) + .expect_reasoning("I need to output the invoice details in JSON") + .expect_content(R"({"amount": 123.45, "date": "2025-12-03"})") + .run(); + // Single tool call with reasoning. tst.test( "I'm\nthinking<|END_THINKING|>" @@ -3642,6 +3668,61 @@ static void test_template_output_peg_parsers(bool detailed_debug) { .expect_reconstruction() .run(); + // Some models skip the opening and go straight to + tst.test( + "\n" + "\n" + "1\n" + "\n" + "\n" + "") + .tools({ special_function_tool }) + .expect(message_assist_call) + .run(); + + tst.test( + "Let me call it.\n" + "\n" + "\n" + "1\n" + "\n" + "\n" + "") + .tools({ special_function_tool }) + .expect_content("Let me call it.\n") + .expect_tool_calls({ + { "special_function", R"({"arg1": 1})", {} }, + }) + .run(); + + // Only the first call may omit it, the rest keep the \n separator + tst.test( + "\n" + "\n" + "1\n" + "\n" + "\n" + "\n" + "\n" + "\n" + "\n" + "1\n" + "\n" + "\n" + "2\n" + "\n" + "\n" + "") + .parallel_tool_calls(true) + .tools({ + special_function_tool, special_function_tool_with_optional_param + }) + .expect_tool_calls({ + { "special_function", R"({"arg1": 1})", {} }, + { "special_function_with_opt", R"({"arg1": 1, "arg2": 2})", {} }, + }) + .run(); + tst.test( "\n" "\n" @@ -3750,6 +3831,37 @@ static void test_template_output_peg_parsers(bool detailed_debug) { .expect_reconstruction() .run(); + // Test flexible required argument ordering (required args still come first, in any order) + tst.test( + "\n" + "\n" + "\n#include\n\n" + "\nfoo.c\n\n" + "\n#iclunde\n\n" + "\n" + "") + .tools({ edit_tool }) + .expect_tool_calls({ + { "edit", R"({"newString": "#include", "filename": "foo.c", "oldString": "#iclunde"})", {} }, + }) + .expect_reconstruction() + .run(); + + tst.test( + "\n" + "\n" + "\n42\n\n" + "\nhello\n\n" + "\n200\n\n" + "\n" + "") + .tools({ tool_2req_4opt }) + .expect_tool_calls({ + { "tool_2req_4opt", R"({"req2": 42, "req1": "hello", "opt2": 200})", {} }, + }) + .expect_reconstruction() + .run(); + // Test flexible optional argument ordering (2 required + 4 optional, reversed optional order) tst.test( "\n" @@ -4032,6 +4144,7 @@ static void test_template_output_peg_parsers(bool detailed_debug) { .expect_tool_calls({ { "special_function", R"({"arg1": 1})", {} }, }) + .expect_reconstruction() .run(); // Tool call with negative number @@ -4173,64 +4286,223 @@ static void test_template_output_peg_parsers(bool detailed_debug) { .run(); } - // GLM-4.6 tests - format: function_name\n...\n...\n - { - auto tst = peg_tester("models/templates/GLM-4.6.jinja", detailed_debug); - tst.test( - "special_function\n" - "arg1\n1\n" - "") - .tools({ special_function_tool }) - .expect(message_assist_call) - .run(); - } - - // GLM-4.7-Flash tests - format: function_name...... - // Note: Template uses forced-open thinking mode (prompt ends with ) + // DeepSeek V4 tests - same DSML markup as V3.2, but the tool call block is named + // "tool_calls" and the non-thinking generation prompt ends in a bare + // instead of an empty pair. { - auto tst = peg_tester("models/templates/GLM-4.7-Flash.jinja", detailed_debug); + auto tst = peg_tester("models/templates/deepseek-ai-DeepSeek-V4.jinja", detailed_debug); - // Pure content (no reasoning) + // Pure content (non-thinking mode; generation prompt ends with ) tst.test("Hello, world!\nWhat's up?") .enable_thinking(false) + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) .expect(message_assist) - .expect_reconstruction() .run(); - // Reasoning with content (forced-open mode - input starts after ) + // Thinking + content tst.test("I'm\nthinkingHello, world!\nWhat's up?") .enable_thinking(true) .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) .expect(message_assist_thoughts) - .expect_reconstruction() .run(); - // Tool call without reasoning + // Thinking + tool call (single, string param) tst.test( - "special_function" - "arg11" - "") - .enable_thinking(false) - .tools({ special_function_tool }) - .expect(message_assist_call) - .expect_reconstruction() + "Let me check the time\n\n" + "<｜DSML｜tool_calls>\n" + "<｜DSML｜invoke name=\"get_time\">\n" + "<｜DSML｜parameter name=\"city\" string=\"true\">Tokyo\n" + "\n" + "") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .tools({ get_time_tool }) + .expect(message_with_tool_calls_and_reasoning("get_time", R"({"city": "Tokyo"})", "Let me check the time")) .run(); - // Tool call with reasoning (forced-open mode) + // Tool call without reasoning (non-thinking mode), integer param (string="false") tst.test( - "I'm\nthinking" - "special_function" - "arg11" - "") - .enable_thinking(true) + "<｜DSML｜tool_calls>\n" + "<｜DSML｜invoke name=\"special_function\">\n" + "<｜DSML｜parameter name=\"arg1\" string=\"false\">1\n" + "\n" + "") + .enable_thinking(false) .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) .tools({ special_function_tool }) - .expect(message_assist_call_thoughts) - .expect_reconstruction() + .expect(message_assist_call) .run(); + // Multiple parallel tool calls with reasoning tst.test( - "special_function" + "Calling both\n\n" + "<｜DSML｜tool_calls>\n" + "<｜DSML｜invoke name=\"get_time\">\n" + "<｜DSML｜parameter name=\"city\" string=\"true\">Paris\n" + "\n" + "<｜DSML｜invoke name=\"get_weather\">\n" + "<｜DSML｜parameter name=\"city\" string=\"true\">Paris\n" + "\n" + "") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .parallel_tool_calls(true) + .tools({ get_time_tool, get_weather_tool }) + .expect(message_with_reasoning_content_and_multiple_tool_calls( + "Calling both", "", + { { "get_time", R"({"city": "Paris"})" }, { "get_weather", R"({"city": "Paris"})" } })) + .run(); + + // Tool call with content before tool calls + tst.test( + "Thinking about it" + "Let me call the function.\n\n" + "<｜DSML｜tool_calls>\n" + "<｜DSML｜invoke name=\"special_function\">\n" + "<｜DSML｜parameter name=\"arg1\" string=\"false\">1\n" + "\n" + "") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .tools({ special_function_tool }) + .expect_reasoning("Thinking about it") + .expect_content("Let me call the function.") + .expect_tool_calls({ + { "special_function", R"({"arg1": 1})", {} }, + }) + .expect_reconstruction() + .run(); + + // Tool call with multiple params (mixed types) + tst.test( + "Multi-arg call\n\n" + "<｜DSML｜tool_calls>\n" + "<｜DSML｜invoke name=\"magic_int\">\n" + "<｜DSML｜parameter name=\"ref\" string=\"false\">42\n" + "<｜DSML｜parameter name=\"name\" string=\"true\">foo bar\n" + "\n" + "") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .tools({ magic_int_tool }) + .expect_reasoning("Multi-arg call") + .expect_tool_calls({ + { "magic_int", R"({"ref": 42, "name": "foo bar"})", {} }, + }) + .run(); + + // Continuation tests + tst.test("world!\nWhat's up?") + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .enable_thinking(true) + .messages({ message_user, message_assist_prefill_content }) + .add_generation_prompt(false) + .continue_final_message(COMMON_CHAT_CONTINUATION_CONTENT) + .expect_reasoning("I'm thinking") + .expect_content("Hello, world!\nWhat's up?") + .run(); + + tst.test(" thinkingHello, world!\nWhat's up?") + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .enable_thinking(true) + .messages({ message_user, message_assist_prefill_reasoning }) + .add_generation_prompt(false) + .continue_final_message(COMMON_CHAT_CONTINUATION_REASONING) + .expect_reasoning("I'm thinking") + .expect_content("Hello, world!\nWhat's up?") + .run(); + + tst.test( + "Let me check the time\n\n" + "<｜DSML｜tool_calls>\n" + "<｜DSML｜invoke name=\"get_time\">\n" + "<｜DSML｜parameter name=\"city\" string=\"true\">Tokyo\n" + "\n" + "") // no after the TC close because the grammar will immediately constrain it to end + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .tools({ get_time_tool }) + .expect_reasoning("Let me check the time") + .expect_tool_calls({ { "get_time", R"({"city": "Tokyo"})", {} } }) + .run(); + } + + { + // The DSML separator belongs to the tool call block, not assistant content. + auto tst = peg_tester("models/templates/deepseek-ai-DeepSeek-V4-Flash-0731.jinja", detailed_debug); + tst.test( + "\n\n" + "<｜DSML｜tool_calls>\n" + "<｜DSML｜invoke name=\"special_function\">\n" + "<｜DSML｜parameter name=\"arg1\" string=\"false\">1\n" + "\n" + "") + .enable_thinking(false) + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .tools({ special_function_tool }) + .expect(message_assist_call) + .expect_reconstruction() + .run(); + } + + // GLM-4.6 tests - format: function_name\n...\n...\n + { + auto tst = peg_tester("models/templates/GLM-4.6.jinja", detailed_debug); + tst.test( + "special_function\n" + "arg1\n1\n" + "") + .tools({ special_function_tool }) + .expect(message_assist_call) + .run(); + } + + // GLM-4.7-Flash tests - format: function_name...... + // Note: Template uses forced-open thinking mode (prompt ends with ) + { + auto tst = peg_tester("models/templates/GLM-4.7-Flash.jinja", detailed_debug); + + // Pure content (no reasoning) + tst.test("Hello, world!\nWhat's up?") + .enable_thinking(false) + .expect(message_assist) + .expect_reconstruction() + .run(); + + // Reasoning with content (forced-open mode - input starts after ) + tst.test("I'm\nthinkingHello, world!\nWhat's up?") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .expect(message_assist_thoughts) + .expect_reconstruction() + .run(); + + // Tool call without reasoning + tst.test( + "special_function" + "arg11" + "") + .enable_thinking(false) + .tools({ special_function_tool }) + .expect(message_assist_call) + .expect_reconstruction() + .run(); + + // Tool call with reasoning (forced-open mode) + tst.test( + "I'm\nthinking" + "special_function" + "arg11" + "") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK) + .tools({ special_function_tool }) + .expect(message_assist_call_thoughts) + .expect_reconstruction() + .run(); + + tst.test( + "special_function" "arg11" "" "special_function_with_opt" @@ -4347,6 +4619,111 @@ static void test_template_output_peg_parsers(bool detailed_debug) { } } + // Kimi-K3 tests - custom parser + // Unique feature: XTML-ish tags built from <|open|>/<|close|>/<|sep|>, and a + // generation prompt that leaves the think section already open. + { + auto tst = peg_tester("models/templates/Kimi-K3.jinja", detailed_debug); + + // Content only. The response section is explicit even with no reasoning. + tst.test("<|open|>response<|sep|>Hello, world!\nWhat's up?<|close|>response<|sep|>" + "<|close|>message<|sep|>") + .expect(message_assist) + .run(); + + // Reasoning with NO opening tag - the generation prompt already opened + // it. This is the case that silently loses reasoning if unhandled. + tst.test("I'm thinking about this<|close|>think<|sep|>" + "<|open|>response<|sep|>Hello, world!\nWhat's up?<|close|>response<|sep|>" + "<|close|>message<|sep|>") + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .expect(simple_assist_msg("Hello, world!\nWhat's up?", "I'm thinking about this")) + .run(); + + // Prose that mentions the tag names must survive intact. + tst.test("<|open|>response<|sep|>Use the response tag, then message the handler." + "<|close|>response<|sep|><|close|>message<|sep|>") + .expect(simple_assist_msg("Use the response tag, then message the handler.")) + .run(); + + // Truncated mid-reasoning (hit the token budget): keep the reasoning. + tst.test("I was still thinking when the budget ran out") + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .expect_reasoning("I was still thinking when the budget ran out") + .run(); + + // Single tool call, one argument. + tst.test("<|open|>response<|sep|><|close|>response<|sep|>" + "<|open|>tools<|sep|>" + "<|open|>call tool=\"special_function\" index=\"1\"<|sep|>" + "<|open|>argument key=\"arg1\" type=\"number\"<|sep|>1<|close|>argument<|sep|>" + "<|close|>call<|sep|><|close|>tools<|sep|><|close|>message<|sep|>") + .tools({ special_function_tool }) + .expect_tool_calls({ + { "special_function", R"({"arg1":1})", "" }, + }) + .run(); + + // Tool call preceded by reasoning (no opening think tag) and content. + tst.test("I should call it<|close|>think<|sep|>" + "<|open|>response<|sep|>On it.<|close|>response<|sep|>" + "<|open|>tools<|sep|>" + "<|open|>call tool=\"special_function\" index=\"1\"<|sep|>" + "<|open|>argument key=\"arg1\" type=\"number\"<|sep|>1<|close|>argument<|sep|>" + "<|close|>call<|sep|><|close|>tools<|sep|><|close|>message<|sep|>") + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ special_function_tool }) + .expect(simple_assist_msg("On it.", "I should call it", "special_function", + R"({"arg1":1})", "")) + .run(); + + // Multiple typed arguments: the type lives in an attribute, and the + // value must come back as a JSON number, not the string "2". + tst.test("<|open|>response<|sep|><|close|>response<|sep|>" + "<|open|>tools<|sep|>" + "<|open|>call tool=\"special_function_with_opt\" index=\"1\"<|sep|>" + "<|open|>argument key=\"arg1\" type=\"number\"<|sep|>1<|close|>argument<|sep|>" + "<|open|>argument key=\"arg2\" type=\"number\"<|sep|>2<|close|>argument<|sep|>" + "<|close|>call<|sep|><|close|>tools<|sep|><|close|>message<|sep|>") + .tools({ special_function_tool_with_optional_param }) + .expect_tool_calls({ + { "special_function_with_opt", R"({"arg1":1,"arg2":2})", "" }, + }) + .run(); + + // Parallel tool calls in one <|open|>tools<|sep|> section. + tst.test("<|open|>response<|sep|><|close|>response<|sep|>" + "<|open|>tools<|sep|>" + "<|open|>call tool=\"special_function\" index=\"1\"<|sep|>" + "<|open|>argument key=\"arg1\" type=\"number\"<|sep|>1<|close|>argument<|sep|>" + "<|close|>call<|sep|>" + "<|open|>call tool=\"special_function_with_opt\" index=\"2\"<|sep|>" + "<|open|>argument key=\"arg1\" type=\"number\"<|sep|>1<|close|>argument<|sep|>" + "<|open|>argument key=\"arg2\" type=\"number\"<|sep|>2<|close|>argument<|sep|>" + "<|close|>call<|sep|><|close|>tools<|sep|><|close|>message<|sep|>") + .parallel_tool_calls(true) + .tools({ special_function_tool, special_function_tool_with_optional_param }) + .expect_tool_calls({ + { "special_function", R"({"arg1":1})", "" }, + { "special_function_with_opt", R"({"arg1":1,"arg2":2})", "" }, + }) + .run(); + + // String-typed argument keeps its literal text (no JSON coercion). + tst.test("<|open|>response<|sep|><|close|>response<|sep|>" + "<|open|>tools<|sep|>" + "<|open|>call tool=\"python\" index=\"1\"<|sep|>" + "<|open|>argument key=\"code\" type=\"string\"<|sep|>print('hey')" + "<|close|>argument<|sep|>" + "<|close|>call<|sep|><|close|>tools<|sep|><|close|>message<|sep|>") + .tools({ python_tool }) + .expect_tool_calls({ + // custom delimiter: the payload itself contains )" + { "python", R"JSON({"code":"print('hey')"})JSON", "" }, + }) + .run(); + } + // Kimi-K2-Thinking tests - custom parser // Unique feature: tool call ID embeds function name as functions.: { @@ -4863,6 +5240,370 @@ static void test_template_output_peg_parsers(bool detailed_debug) { .run(); } + // MiniMax-M3 tests - namespaced XML invoke format, the parameter name is the tag + // Format: + // ]<]minimax[>[ + // ]<]minimax[>[]<]minimax[>[Tokyo]<]minimax[>[]<]minimax[>[ + // ]<]minimax[>[ + // Reasoning uses .... The generation prompt is only "]~b]ai\n", so the model + // opens the thinking block itself; a turn without reasoning is prefixed with a bare . + { + auto tst = peg_tester("models/templates/MiniMax-M3.jinja", detailed_debug); + + // Content only (bare prefix) + tst.test("Hello, world!\nWhat's up?") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .expect(message_assist) + .expect_reconstruction() + .run(); + + // Thinking + content + tst.test("I'm\nthinkingHello, world!\nWhat's up?") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .expect(message_assist_thoughts) + .expect_reconstruction() + .run(); + + // Thinking + tool call (single, string param) + tst.test( + "Let me check the time" + "]<]minimax[>[\n" + "]<]minimax[>[" + "]<]minimax[>[Tokyo]<]minimax[>[" + "]<]minimax[>[\n" + "]<]minimax[>[") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ get_time_tool }) + .expect(message_with_tool_calls_and_reasoning("get_time", R"({"city": "Tokyo"})", "Let me check the time")) + .expect_reconstruction() + .run(); + + // Tool call without reasoning, integer param + tst.test( + "" + "]<]minimax[>[\n" + "]<]minimax[>[" + "]<]minimax[>[1]<]minimax[>[" + "]<]minimax[>[\n" + "]<]minimax[>[") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ special_function_tool }) + .expect(message_assist_call) + .expect_reconstruction() + .run(); + + // Tool call with no parameters + tst.test( + "Let's call a tool:" + "]<]minimax[>[\n" + "]<]minimax[>[" + "]<]minimax[>[\n" + "]<]minimax[>[") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ empty_args_tool }) + .expect(message_with_reasoning_and_tool_call("Let's call a tool:", "empty_args", "{}")) + .expect_reconstruction() + .run(); + + // Multiple parallel tool calls in one block + tst.test( + "Calling both" + "]<]minimax[>[\n" + "]<]minimax[>[" + "]<]minimax[>[Paris]<]minimax[>[" + "]<]minimax[>[\n" + "]<]minimax[>[" + "]<]minimax[>[Paris]<]minimax[>[" + "]<]minimax[>[\n" + "]<]minimax[>[") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .parallel_tool_calls(true) + .tools({ get_time_tool, get_weather_tool }) + .expect(message_with_reasoning_content_and_multiple_tool_calls( + "Calling both", "", + { { "get_time", R"({"city": "Paris"})" }, { "get_weather", R"({"city": "Paris"})" } })) + .expect_reconstruction() + .run(); + + // Content before the tool call block + tst.test( + "Thinking about it" + "Let me call the function." + "]<]minimax[>[\n" + "]<]minimax[>[" + "]<]minimax[>[1]<]minimax[>[" + "]<]minimax[>[\n" + "]<]minimax[>[") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ special_function_tool }) + .expect_reasoning("Thinking about it") + .expect_content("Let me call the function.") + .expect_tool_calls({ + { "special_function", R"({"arg1": 1})", {} }, + }) + .expect_reconstruction() + .run(); + + // Negative number + tst.test( + "Test negative" + "]<]minimax[>[\n" + "]<]minimax[>[" + "]<]minimax[>[-14]<]minimax[>[" + "]<]minimax[>[\n" + "]<]minimax[>[") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ magic_int_tool }) + .expect_reasoning("Test negative") + .expect_tool_calls({ + { "magic_int", R"({"ref": -14})", {} }, + }) + .expect_reconstruction() + .run(); + + // Decimal number + tst.test( + "Test decimal" + "]<]minimax[>[\n" + "]<]minimax[>[" + "]<]minimax[>[3.14]<]minimax[>[" + "]<]minimax[>[\n" + "]<]minimax[>[") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ amount_tool }) + .expect_reasoning("Test decimal") + .expect_tool_calls({ + { "amount", R"({"orig": 3.14})", {} }, + }) + .expect_reconstruction() + .run(); + + // Boolean + tst.test( + "Test boolean" + "]<]minimax[>[\n" + "]<]minimax[>[" + "]<]minimax[>[true]<]minimax[>[" + "]<]minimax[>[\n" + "]<]minimax[>[") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ toggle_tool }) + .expect_reasoning("Test boolean") + .expect_tool_calls({ + { "toggle", R"({"enabled": true})", {} }, + }) + .expect_reconstruction() + .run(); + + // Multiple params of mixed types (required int first, then optional string) + tst.test( + "Multi-arg call" + "]<]minimax[>[\n" + "]<]minimax[>[" + "]<]minimax[>[42]<]minimax[>[" + "]<]minimax[>[foo bar]<]minimax[>[" + "]<]minimax[>[\n" + "]<]minimax[>[") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ magic_int_tool }) + .expect_reasoning("Multi-arg call") + .expect_tool_calls({ + { "magic_int", R"({"ref": 42, "name": "foo bar"})", {} }, + }) + .expect_reconstruction() + .run(); + + // Nested object param, expanded into one element per key + tst.test( + "Nested object" + "]<]minimax[>[\n" + "]<]minimax[>[" + "]<]minimax[>[" + "]<]minimax[>[1.5]<]minimax[>[" + "]<]minimax[>[-2.5]<]minimax[>[" + "]<]minimax[>[" + "]<]minimax[>[\n" + "]<]minimax[>[") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ imaginary_number_tool }) + .expect_reasoning("Nested object") + .expect_tool_calls({ + { "imaginary_number", R"({"number": {"real": 1.5, "imaginary": -2.5}})", {} }, + }) + .expect_reconstruction() + .run(); + + // Array params, expanded into elements (of scalars and of objects) + tst.test( + "Nested arrays" + "]<]minimax[>[\n" + "]<]minimax[>[" + "]<]minimax[>[" + "]<]minimax[>[alpha]<]minimax[>[" + "]<]minimax[>[beta]<]minimax[>[" + "]<]minimax[>[" + "]<]minimax[>[" + "]<]minimax[>[" + "]<]minimax[>[1]<]minimax[>[" + "]<]minimax[>[" + "]<]minimax[>[" + "]<]minimax[>[" + "]<]minimax[>[\n" + "]<]minimax[>[") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ nested_args_tool }) + .expect_reasoning("Nested arrays") + .expect_tool_calls({ + { "nested_args", R"({"tags": ["alpha", "beta"], "entries": [{"id": 1, "label": "one"}]})", {} }, + }) + .expect_reconstruction() + .run(); + + // Union params (anyOf/oneOf), expanded as a choice of the alternatives + tst.test( + "Union array" + "]<]minimax[>[\n" + "]<]minimax[>[" + "]<]minimax[>[" + "]<]minimax[>[alpha]<]minimax[>[" + "]<]minimax[>[beta]<]minimax[>[" + "]<]minimax[>[" + "]<]minimax[>[\n" + "]<]minimax[>[") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ union_args_tool }) + .expect_reasoning("Union array") + .expect_tool_calls({ + { "union_args", R"({"filter": ["alpha", "beta"]})", {} }, + }) + .expect_reconstruction() + .run(); + + // oneOf between a scalar and an object + tst.test( + "Union scalar" + "]<]minimax[>[\n" + "]<]minimax[>[" + "]<]minimax[>[5]<]minimax[>[" + "]<]minimax[>[\n" + "]<]minimax[>[") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ union_args_tool }) + .expect_reasoning("Union scalar") + .expect_tool_calls({ + { "union_args", R"({"limit": 5})", {} }, + }) + .expect_reconstruction() + .run(); + + tst.test( + "Union nested" + "]<]minimax[>[\n" + "]<]minimax[>[" + "]<]minimax[>[" + "]<]minimax[>[10]<]minimax[>[" + "]<]minimax[>[" + "]<]minimax[>[\n" + "]<]minimax[>[") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ union_args_tool }) + .expect_reasoning("Union nested") + .expect_tool_calls({ + { "union_args", R"({"limit": {"max": 10}})", {} }, + }) + .expect_reconstruction() + .run(); + + // A union with a string alternative is a string + tst.test( + "Union string" + "]<]minimax[>[\n" + "]<]minimax[>[" + "]<]minimax[>[" + "]<]minimax[>[\n" + "]<]minimax[>[") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ union_args_tool }) + .expect_reasoning("Union string") + .expect_tool_calls({ + { "union_args", R"({"label": "hi"})", {} }, + }) + .expect_reconstruction() + .run(); + + // ... even when the value looks structured + tst.test( + "Union string" + "]<]minimax[>[\n" + "]<]minimax[>[" + "]<]minimax[>[" + "]<]minimax[>[\n" + "]<]minimax[>[") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ union_args_tool }) + .expect_reasoning("Union string") + .expect_tool_calls({ + { "union_args", R"({"label": "]<]minimax[>[hi]<]minimax[>["})", {} }, + }) + .expect_reconstruction() + .run(); + + // Edge case: empty reasoning followed by a tool call + tst.test( + "" + "]<]minimax[>[\n" + "]<]minimax[>[" + "]<]minimax[>[XYZCITY]<]minimax[>[" + "]<]minimax[>[\n" + "]<]minimax[>[") + .enable_thinking(true) + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .tools({ get_time_tool }) + .expect(message_with_tool_calls("get_time", R"({"city": "XYZCITY"})")) + .run(); + + // Continuation tests + tst.test("world!\nWhat's up?") + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .enable_thinking(true) + .messages({ message_user, message_assist_prefill_content }) + .add_generation_prompt(false) + .continue_final_message(COMMON_CHAT_CONTINUATION_CONTENT) + .expect_reasoning("I'm thinking") + .expect_content("Hello, world!\nWhat's up?") + .run(); + + tst.test(" thinkingHello, world!\nWhat's up?") + .reasoning_format(COMMON_REASONING_FORMAT_AUTO) + .enable_thinking(true) + .messages({ message_user, message_assist_prefill_reasoning }) + .add_generation_prompt(false) + .continue_final_message(COMMON_CHAT_CONTINUATION_REASONING) + .expect_reasoning("I'm thinking") + .expect_content("Hello, world!\nWhat's up?") + .run(); + } + // NVIDIA-Nemotron-Nano-v2 tests - ... format // Format: [{"name": "func", "arguments": {...}}] { @@ -5904,6 +6645,7 @@ static void test_template_generation_prompt() { std::vector messages; bool add_generation_prompt = true; common_chat_continuation continue_final_message = COMMON_CHAT_CONTINUATION_NONE; + bool enable_thinking = true; }; auto basic = [&]() { @@ -5935,6 +6677,7 @@ static void test_template_generation_prompt() { inputs.messages = opts.messages; inputs.add_generation_prompt = opts.add_generation_prompt; inputs.continue_final_message = opts.continue_final_message; + inputs.enable_thinking = opts.enable_thinking; auto params = common_chat_templates_apply(tmpls.get(), inputs); @@ -6033,6 +6776,156 @@ static void test_template_generation_prompt() { check(tmpls, continuation_reasoning(), "<｜Assistant｜>I'm"); } + const std::string deepseek_v4_reasoning_effort_max = "Reasoning Effort: Absolute maximum"; + const std::string deepseek_v4_flash_0731_reasoning_effort_max = "Reasoning Effort: Beyond maximum"; + + { + auto tmpls = read_templates("models/templates/deepseek-ai-DeepSeek-V4.jinja"); + check(tmpls, basic(), "<｜Assistant｜>"); + check(tmpls, continuation_content(), "<｜Assistant｜>I'm thinkingHello, "); + check(tmpls, continuation_reasoning(), "<｜Assistant｜>I'm"); + + auto continuation_content_no_thinking = continuation_content(); + continuation_content_no_thinking.messages = { system_msg, message_user, simple_assist_msg("Hello, ") }; + continuation_content_no_thinking.enable_thinking = false; + check(tmpls, continuation_content_no_thinking, "<｜Assistant｜>Hello, "); + + common_chat_templates_inputs max_inputs; + max_inputs.messages = { system_msg, message_user }; + max_inputs.chat_template_kwargs["reasoning_effort"] = R"("max")"; + auto max_params = common_chat_templates_apply(tmpls.get(), max_inputs); + assert_contains(max_params.prompt, deepseek_v4_reasoning_effort_max); + + auto high_inputs = max_inputs; + high_inputs.chat_template_kwargs["reasoning_effort"] = R"("high")"; + auto high_params = common_chat_templates_apply(tmpls.get(), high_inputs); + assert_not_contains(high_params.prompt, deepseek_v4_reasoning_effort_max); + + auto low_inputs = max_inputs; + low_inputs.chat_template_kwargs["reasoning_effort"] = R"("low")"; + auto low_params = common_chat_templates_apply(tmpls.get(), low_inputs); + assert_not_contains(low_params.prompt, deepseek_v4_reasoning_effort_max); + + common_chat_templates_inputs default_effort_inputs; + default_effort_inputs.messages = { system_msg, message_user }; + auto default_effort_params = common_chat_templates_apply(tmpls.get(), default_effort_inputs); + assert_not_contains(default_effort_params.prompt, deepseek_v4_reasoning_effort_max); + + auto non_thinking_max_inputs = max_inputs; + non_thinking_max_inputs.enable_thinking = false; + auto non_thinking_max_params = common_chat_templates_apply(tmpls.get(), non_thinking_max_inputs); + assert_not_contains(non_thinking_max_params.prompt, deepseek_v4_reasoning_effort_max); + + common_chat_templates_inputs response_format_inputs; + response_format_inputs.messages = { system_msg, message_user }; + response_format_inputs.tools = { get_time_tool }; + response_format_inputs.json_schema = + R"({"type":"object","properties":{"answer":{"type":"string"}}})"; + auto response_format_params = common_chat_templates_apply(tmpls.get(), response_format_inputs); + const auto tools_pos = response_format_params.prompt.find("## Tools"); + const auto response_format_pos = response_format_params.prompt.find( + "## Response Format:\n\nYou MUST strictly adhere to the following schema to reply:\n"); + if (tools_pos == std::string::npos || response_format_pos == std::string::npos || tools_pos > response_format_pos) { + LOG_ERR("Expected response format after tools\nActual: %s\n", response_format_params.prompt.c_str()); + common_log_flush(common_log_main()); + throw std::runtime_error("Test failed"); + } + assert_contains(response_format_params.prompt, R"("answer": {"type": "string"})"); + + response_format_inputs.json_schema = "{}"; + auto json_object_params = common_chat_templates_apply(tmpls.get(), response_format_inputs); + assert_contains(json_object_params.prompt, + "## Response Format:\n\nYou MUST strictly adhere to the following schema to reply:\n{}"); + + common_chat_msg assistant_history; + assistant_history.role = "assistant"; + assistant_history.content = "Previous answer"; + assistant_history.reasoning_content = "Previous reasoning"; + + common_chat_msg user_followup; + user_followup.role = "user"; + user_followup.content = "Follow up"; + + common_chat_templates_inputs default_history_inputs; + default_history_inputs.messages = { message_user, assistant_history, user_followup }; + auto default_history_params = common_chat_templates_apply(tmpls.get(), default_history_inputs); + assert_contains(default_history_params.prompt, "<｜Assistant｜>Previous answer"); + + auto drop_thinking_inputs = default_history_inputs; + drop_thinking_inputs.chat_template_kwargs["drop_thinking"] = "false"; + auto drop_thinking_params = common_chat_templates_apply(tmpls.get(), drop_thinking_inputs); + assert_contains(drop_thinking_params.prompt, "<｜Assistant｜>Previous reasoningPrevious answer"); + + auto preserve_reasoning_inputs = default_history_inputs; + preserve_reasoning_inputs.chat_template_kwargs["preserve_reasoning"] = "true"; + auto preserve_reasoning_params = common_chat_templates_apply(tmpls.get(), preserve_reasoning_inputs); + assert_contains(preserve_reasoning_params.prompt, "<｜Assistant｜>Previous reasoningPrevious answer"); + assert_equals(true, common_chat_templates_get_caps(tmpls.get()).at("supports_preserve_reasoning")); + + auto no_preserve_reasoning_inputs = default_history_inputs; + no_preserve_reasoning_inputs.chat_template_kwargs["preserve_reasoning"] = "false"; + auto no_preserve_reasoning_params = common_chat_templates_apply(tmpls.get(), no_preserve_reasoning_inputs); + assert_contains(no_preserve_reasoning_params.prompt, "<｜Assistant｜>Previous answer"); + + common_chat_msg empty_tool_call = simple_assist_msg("", "", "empty_args", "{}"); + common_chat_templates_inputs empty_tool_inputs; + empty_tool_inputs.messages = { message_user, empty_tool_call }; + empty_tool_inputs.tools = { empty_args_tool }; + auto empty_tool_params = common_chat_templates_apply(tmpls.get(), empty_tool_inputs); + assert_contains(empty_tool_params.prompt, + "<｜DSML｜invoke name=\"empty_args\">\n\n"); + } + + { + auto tmpls = read_templates("models/templates/deepseek-ai-DeepSeek-V4-Flash-0731.jinja"); + check(tmpls, basic(), "<｜Assistant｜>"); + check(tmpls, continuation_content(), "<｜Assistant｜>I'm thinkingHello, "); + check(tmpls, continuation_reasoning(), "<｜Assistant｜>I'm"); + + auto continuation_content_no_thinking = continuation_content(); + continuation_content_no_thinking.messages = { system_msg, message_user, simple_assist_msg("Hello, ") }; + continuation_content_no_thinking.enable_thinking = false; + check(tmpls, continuation_content_no_thinking, "<｜Assistant｜>Hello, "); + + common_chat_templates_inputs high_inputs; + high_inputs.messages = { system_msg, message_user }; + high_inputs.chat_template_kwargs["reasoning_effort"] = R"("high")"; + auto high_params = common_chat_templates_apply(tmpls.get(), high_inputs); + assert_contains(high_params.prompt, deepseek_v4_reasoning_effort_max); + + auto max_inputs = high_inputs; + max_inputs.chat_template_kwargs["reasoning_effort"] = R"("max")"; + auto max_params = common_chat_templates_apply(tmpls.get(), max_inputs); + assert_contains(max_params.prompt, deepseek_v4_flash_0731_reasoning_effort_max); + + auto low_inputs = high_inputs; + low_inputs.chat_template_kwargs["reasoning_effort"] = R"("low")"; + auto low_params = common_chat_templates_apply(tmpls.get(), low_inputs); + assert_not_contains(low_params.prompt, deepseek_v4_reasoning_effort_max); + assert_not_contains(low_params.prompt, deepseek_v4_flash_0731_reasoning_effort_max); + + common_chat_templates_inputs default_effort_inputs; + default_effort_inputs.messages = { system_msg, message_user }; + auto default_effort_params = common_chat_templates_apply(tmpls.get(), default_effort_inputs); + assert_not_contains(default_effort_params.prompt, deepseek_v4_reasoning_effort_max); + assert_not_contains(default_effort_params.prompt, deepseek_v4_flash_0731_reasoning_effort_max); + + auto non_thinking_max_inputs = max_inputs; + non_thinking_max_inputs.enable_thinking = false; + auto non_thinking_max_params = common_chat_templates_apply(tmpls.get(), non_thinking_max_inputs); + assert_not_contains(non_thinking_max_params.prompt, deepseek_v4_flash_0731_reasoning_effort_max); + + common_chat_templates_inputs response_format_inputs; + response_format_inputs.messages = { system_msg, message_user }; + response_format_inputs.tools = { get_time_tool }; + response_format_inputs.json_schema = + R"({"type":"object","properties":{"answer":{"type":"string"}}})"; + auto response_format_params = common_chat_templates_apply(tmpls.get(), response_format_inputs); + assert_contains(response_format_params.prompt, + "## Response Format:\n\nYou MUST strictly adhere to the following schema to reply:\n"); + assert_contains(response_format_params.prompt, R"("answer": {"type": "string"})"); + } + { auto tmpls = read_templates("models/templates/openbmb-MiniCPM5-1B.jinja"); check(tmpls, basic(), "<|im_start|>assistant\n\n"); @@ -6079,6 +6972,144 @@ static void test_developer_role_to_system_workaround() { } } +// Verify reasoning-trace retention rules in the DeepSeek-V4 template: +// all traces are retained unless drop_thinking is true AND the conversation +// has no tool calls, in which case only the last (after-final-user) trace is +// kept and earlier ones are dropped. +static void test_deepseek_v4_thinking_retention() { + LOG_DBG("%s\n", __func__); + + auto tmpls = read_templates("models/templates/deepseek-ai-DeepSeek-V4.jinja"); + + common_chat_msg user_q1; user_q1.role = "user"; user_q1.content = "Question 1"; + common_chat_msg user_q2; user_q2.role = "user"; user_q2.content = "Question 2"; + common_chat_msg asst_a1 = simple_assist_msg("Answer 1", "thinking A1"); + common_chat_msg asst_a2 = simple_assist_msg("Answer 2", "thinking A2"); + + common_chat_msg tool_assist = message_with_tool_calls("special_function", "{\"arg1\": 1}"); + common_chat_msg tool_result; tool_result.role = "tool"; + tool_result.tool_name = "special_function"; tool_result.tool_call_id = "0"; tool_result.content = "result"; + + // The template uses U+FF5C as the role separator and literal think tags + // for the reasoning block. + const std::string asst_marker = "<\xef\xbd\x9c" "Assistant" "\xef\xbd\x9c>"; + // Built via concatenation so the thinking tokens are not interpreted by + // tooling processing this source file. + const std::string think_start = "<" "think" ">"; + const std::string think_end = ""; + + const std::string think_a1 = asst_marker + think_start + "thinking A1" + think_end; + const std::string think_a2 = asst_marker + think_start + "thinking A2" + think_end; + const std::string asst_no_think = asst_marker + think_end; + + auto render = [&](const std::vector & messages, bool drop_thinking) { + common_chat_templates_inputs inputs; + inputs.messages = messages; + inputs.add_generation_prompt = false; + inputs.chat_template_kwargs["thinking"] = "true"; + inputs.chat_template_kwargs["drop_thinking"] = drop_thinking ? "true" : "false"; + return common_chat_templates_apply(tmpls.get(), inputs).prompt; + }; + + // No tools, drop_thinking=false: all reasoning is retained. + { + auto prompt = render({ user_q1, asst_a1, user_q2, asst_a2 }, /* drop_thinking = */ false); + assert_contains(prompt, think_a1); + assert_contains(prompt, think_a2); + } + + // No tools, drop_thinking=true: only the last reasoning trace is kept, + // earlier ones are dropped (the assistant block emits just the end token). + { + auto prompt = render({ user_q1, asst_a1, user_q2, asst_a2 }, /* drop_thinking = */ true); + assert_not_contains(prompt, think_a1); + assert_contains(prompt, think_a2); + // The dropped assistant turn still opens with the marker + bare end token. + assert_contains(prompt, asst_no_think + "Answer 1"); + } + + // Single assistant turn, drop_thinking=true: the only trace is the last + // one, so it must be retained even with drop_thinking set. + { + auto prompt = render({ user_q1, asst_a1 }, /* drop_thinking = */ true); + assert_contains(prompt, think_a1); + } + + // Single assistant turn, drop_thinking=false: reasoning is retained. + { + auto prompt = render({ user_q1, asst_a1 }, /* drop_thinking = */ false); + assert_contains(prompt, think_a1); + } + + // With tool calls, drop_thinking=true: tool presence forces all reasoning + // to be retained, including the pre-tool-call trace. + { + auto prompt = render({ user_q1, asst_a1, user_q2, tool_assist, tool_result, asst_a2 }, + /* drop_thinking = */ true); + assert_contains(prompt, think_a1); + assert_contains(prompt, think_a2); + } + + // With tool calls, drop_thinking=false: all reasoning retained. + { + auto prompt = render({ user_q1, asst_a1, user_q2, tool_assist, tool_result, asst_a2 }, + /* drop_thinking = */ false); + assert_contains(prompt, think_a1); + assert_contains(prompt, think_a2); + } +} + +// Verify that consecutive tool results are rendered in the tool call order of the +// preceding assistant message (matched by tool call id), as required by the reference +// DeepSeek-V4 implementation. +static void test_deepseek_v4_tool_result_ordering() { + LOG_DBG("%s\n", __func__); + + auto tmpls = read_templates("models/templates/deepseek-ai-DeepSeek-V4.jinja"); + + common_chat_msg user_q; user_q.role = "user"; user_q.content = "Question"; + + common_chat_msg assist_calls; + assist_calls.role = "assistant"; + assist_calls.tool_calls.push_back({ "get_time", "{\"city\": \"Paris\"}", "call_1" }); + assist_calls.tool_calls.push_back({ "get_weather", "{\"city\": \"Paris\"}", "call_2" }); + + common_chat_msg time_result; time_result.role = "tool"; + time_result.tool_name = "get_time"; time_result.tool_call_id = "call_1"; time_result.content = "12:00"; + common_chat_msg weather_result; weather_result.role = "tool"; + weather_result.tool_name = "get_weather"; weather_result.tool_call_id = "call_2"; weather_result.content = "sunny"; + + auto render = [&](const std::vector & messages) { + common_chat_templates_inputs inputs; + inputs.messages = messages; + inputs.add_generation_prompt = false; + return common_chat_templates_apply(tmpls.get(), inputs).prompt; + }; + + // Results sent out of order are reordered to match the tool call order. + { + auto prompt = render({ user_q, assist_calls, weather_result, time_result }); + assert_contains(prompt, "12:00\n\nsunny"); + } + + // Results already in call order stay put. + { + auto prompt = render({ user_q, assist_calls, time_result, weather_result }); + assert_contains(prompt, "12:00\n\nsunny"); + } + + // Without tool call ids there is nothing to match against; order is preserved. + { + auto no_id_calls = assist_calls; + no_id_calls.tool_calls[0].id = ""; + no_id_calls.tool_calls[1].id = ""; + auto no_id_weather = weather_result; no_id_weather.tool_call_id = ""; + auto no_id_time = time_result; no_id_time.tool_call_id = ""; + auto prompt = render({ user_q, no_id_calls, no_id_weather, no_id_time }); + assert_contains(prompt, "sunny\n\n12:00"); + } +} + static void test_reasoning_budget_tokens_per_request() { LOG_DBG("%s\n", __func__); // Use Qwen3 template which has ... reasoning markers. @@ -6300,6 +7331,8 @@ int main(int argc, char ** argv) { test_tools_oaicompat_json_conversion(); test_convert_responses_to_chatcmpl(); test_developer_role_to_system_workaround(); + test_deepseek_v4_thinking_retention(); + test_deepseek_v4_tool_result_ordering(); test_template_generation_prompt(); test_reasoning_budget_tokens_per_request(); test_reasoning_budget_message_per_request(); diff --git a/tests/test-jinja.cpp b/tests/test-jinja.cpp index 90bdbc445d52..1ac5b57decca 100644 --- a/tests/test-jinja.cpp +++ b/tests/test-jinja.cpp @@ -4,7 +4,7 @@ #include #include -#include +#include "subproc.h" #include "jinja/runtime.h" #include "jinja/parser.h" @@ -2135,21 +2135,20 @@ static void test_template_py(testing & t, const std::string & name, const std::s const char * python_executable = "python3"; #endif - const char * command_line[] = {python_executable, "-c", py_script.c_str(), NULL}; + std::vector args = {python_executable, "-c", py_script, }; - struct subprocess_s subprocess; + common_subproc subprocess; int options = subprocess_option_combined_stdout_stderr | subprocess_option_no_window | subprocess_option_inherit_environment | subprocess_option_search_user_path; - int result = subprocess_create(command_line, options, &subprocess); - if (result != 0) { - t.log("Failed to create subprocess, error code: " + std::to_string(result)); + if (!subprocess.create(args, options)) { + t.log("Failed to create subprocess"); t.assert_true("subprocess creation", false); return; } - FILE * p_stdin = subprocess_stdin(&subprocess); + FILE * p_stdin = subprocess.stdin_file(); // Write input std::string input = merged.dump(); @@ -2157,24 +2156,22 @@ static void test_template_py(testing & t, const std::string & name, const std::s if (written != input.size()) { t.log("Failed to write complete input to subprocess stdin"); t.assert_true("subprocess stdin write", false); - subprocess_destroy(&subprocess); + subprocess.close_stdin(); + subprocess.join(); return; } fflush(p_stdin); - fclose(p_stdin); // Close stdin to signal EOF to the Python process - subprocess.stdin_file = nullptr; + subprocess.close_stdin(); // Close stdin to signal EOF to the Python process // Read output std::string output; char buffer[1024]; - FILE * p_stdout = subprocess_stdout(&subprocess); + FILE * p_stdout = subprocess.stdout_file(); while (fgets(buffer, sizeof(buffer), p_stdout)) { output += buffer; } - int process_return; - subprocess_join(&subprocess, &process_return); - subprocess_destroy(&subprocess); + int process_return = subprocess.join(); if (process_return != 0) { t.log("Python script failed with exit code: " + std::to_string(process_return)); diff --git a/tests/test-llama-archs.cpp b/tests/test-llama-archs.cpp index 7112970f3482..0825c5ecd837 100644 --- a/tests/test-llama-archs.cpp +++ b/tests/test-llama-archs.cpp @@ -40,8 +40,10 @@ static double nmse(const std::vector & a, const std::vector & b) { } static void set_tensor_data(struct ggml_tensor * tensor, void * userdata) { + size_t seed = *(const size_t *) userdata; std::hash hasher; - std::mt19937 gen(hasher(tensor->name) + *(const size_t *) userdata); + seed ^= hasher(tensor->name); + std::mt19937 gen(seed); std::normal_distribution dis(0.0f, 1.0e-2f); const int64_t ne = ggml_nelements(tensor); @@ -166,6 +168,9 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) { ms.add_kv(LLM_KV_ROPE_DIMENSION_COUNT, uint32_t(64)); ms.add_kv(LLM_KV_ATTENTION_KEY_LENGTH_MLA, uint32_t(192)); ms.add_kv(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, uint32_t(128)); + } else if (arch == LLM_ARCH_MINIMAX_M3) { + // partial rotary: n_rot must not exceed the indexer key length (64) + ms.add_kv(LLM_KV_ROPE_DIMENSION_COUNT, uint32_t(64)); } ms.add_kv(LLM_KV_ATTENTION_CLAMP_KQV, 1.0f); ms.add_kv(LLM_KV_ATTENTION_LAYERNORM_EPS, 1e-5f); @@ -196,9 +201,13 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) { ms.add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, uint32_t(2)); } - ms.add_kv(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, uint32_t(1)); - ms.add_kv(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, uint32_t(64)); - ms.add_kv(LLM_KV_ATTENTION_INDEXER_TOP_K, uint32_t(8)); + // MSA requires one indexer head per GQA (KV) head, unlike the DSA archs where the + // indexer head count is independent of the main attention head count. + ms.add_kv(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, arch == LLM_ARCH_MINIMAX_M3 ? n_head : uint32_t(1)); + ms.add_kv(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, uint32_t(64)); + ms.add_kv(LLM_KV_ATTENTION_INDEXER_TOP_K, uint32_t(8)); + ms.add_kv(LLM_KV_ATTENTION_INDEXER_BLOCK_SIZE, uint32_t(4)); + ms.add_kv(LLM_KV_ATTENTION_INDEXER_LOCAL_BLOCKS, uint32_t(1)); ms.add_kv(LLM_KV_ROPE_DIMENSION_SECTIONS, std::vector({n_embd_head/4, n_embd_head/4, n_embd_head/4, n_embd_head/4})); ms.add_kv(LLM_KV_TOKENIZER_MODEL, "no_vocab"); // ms.add_kv(LLM_KV_DENSE_2_FEAT_OUT, n_embd); @@ -357,6 +366,7 @@ static bool moe_mandatory(const llm_arch arch) { case LLM_ARCH_LLADA_MOE: case LLM_ARCH_GROVEMOE: case LLM_ARCH_MINIMAX_M2: + case LLM_ARCH_MINIMAX_M3: case LLM_ARCH_RND1: case LLM_ARCH_PADDLEOCR: case LLM_ARCH_MIMO2: @@ -420,13 +430,16 @@ static bool arch_supported(const llm_arch arch) { if (arch == LLM_ARCH_DEEPSEEK4) { return false; } + if (arch == LLM_ARCH_KIMI_K3) { + return false; // TODO fixture params for the arch-specific hparams (kda, situ, attn_res, moe_latent) + } if (arch == LLM_ARCH_INKLING) { return false; // TODO fixture params for the arch-specific hparams (d_rel, rel_extent, shortconv, logit_scale_denom) } - // FIXME some models are segfaulting with WebGPU: + // FIXME: these hit scheduler/view-backed-output issues with WebGPU on CI. #ifdef GGML_USE_WEBGPU - if (arch == LLM_ARCH_QWEN3NEXT || arch == LLM_ARCH_QWEN35 || arch == LLM_ARCH_QWEN35MOE || arch == LLM_ARCH_KIMI_LINEAR) { + if (arch == LLM_ARCH_DEEPSEEK32 || arch == LLM_ARCH_GLM_DSA || arch == LLM_ARCH_MINIMAX_M3) { return false; } #endif // GGML_USE_WEBGPU @@ -472,7 +485,7 @@ static int save_models(const llm_arch target_arch, const size_t seed, const ggml if (!moe && moe_mandatory(arch)) { continue; } - if (!llama_model_saver_supports_arch(arch)) { + if (!llama_model_saver_supports_arch(arch) || !arch_supported(arch)) { LOG_INF("%s: %s model (%s) is unsupported, skipping\n", __func__, llm_arch_name(arch), moe ? "MoE" : "dense"); continue; } @@ -596,9 +609,6 @@ static int test_backends(const llm_arch target_arch, const size_t seed, const gg std::string status_roundtrip = "\033[1;33mSKIP\033[0m"; char nmse_str[12] = {0}; bool skip = !arch_supported(arch) || (dc.split_mode == LLAMA_SPLIT_MODE_TENSOR && dc.devs.empty()); -#if defined(GGML_USE_WEBGPU) - skip = true; // FIXME -#endif // GGML_USE_WEBGPU if (!skip) { if (logits_cpu.empty()) { model_and_ctx_cpu = get_model_and_ctx(gguf_ctx.get(), nullptr, seed, {}, LLAMA_SPLIT_MODE_LAYER, encode); diff --git a/tests/test-model-load-cancel.cpp b/tests/test-model-load-cancel.cpp index 9095826fa988..f8139b26d791 100644 --- a/tests/test-model-load-cancel.cpp +++ b/tests/test-model-load-cancel.cpp @@ -1,10 +1,10 @@ #include "llama.h" -#include "get-model.h" +#include "common.h" #include int main(int argc, char *argv[] ) { - auto * model_path = get_model_or_exit(argc, argv); + auto * model_path = common_get_model_or_exit(argc, argv); auto * file = fopen(model_path, "r"); if (file == nullptr) { fprintf(stderr, "no model at '%s' found\n", model_path); @@ -16,7 +16,7 @@ int main(int argc, char *argv[] ) { llama_backend_init(); auto params = llama_model_params{}; - params.use_mmap = false; + params.load_mode = LLAMA_LOAD_MODE_NONE; params.progress_callback = [](float progress, void * ctx){ (void) ctx; return progress > 0.50; diff --git a/tests/test-model-resolution.cpp b/tests/test-model-resolution.cpp new file mode 100644 index 000000000000..a96e40bb2bf8 --- /dev/null +++ b/tests/test-model-resolution.cpp @@ -0,0 +1,506 @@ +// tests the HF model resolution and the model handler assembly end-to-end on +// synthetic repo listings: a local httplib server bound to the loopback +// serves hardcoded HF API responses, so the real client, hf_cache, resolution +// and CLI parsing run against them without external network access + +#include "arg.h" +#include "common.h" +#include "download.h" +#include "http.h" +#include "log.h" + +#include + +#include +#include +#include +#include +#include +#include +#include +#include + +// the case and reordering being checked, printed with every failure +static std::string g_context; + +// independent of NDEBUG, so the checks stay alive in Release builds +#define REQUIRE(x) do { \ + if (!(x)) { \ + fprintf(stderr, "%s:%d: [%s] REQUIRE(%s) failed\n", \ + __FILE__, __LINE__, g_context.c_str(), #x); \ + std::abort(); \ + } \ +} while (0) + +#define REQUIRE_EQ(actual, expected) do { \ + if (!((actual) == (expected))) { \ + fprintf(stderr, "%s:%d: [%s] REQUIRE_EQ(%s, %s) failed\n actual: '%s'\n expected: '%s'\n", \ + __FILE__, __LINE__, g_context.c_str(), #actual, #expected, \ + std::string(actual).c_str(), std::string(expected).c_str()); \ + std::abort(); \ + } \ +} while (0) + +// +// synthetic repos keyed by repo id, served over the loopback by a real +// httplib server, so the tested code runs its own client and transport +// + +static std::map> g_repos; + +static const char * COMMIT = "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa"; + +// the server lives in main, so its destructor runs before the static teardown +// tears down the winsock state httplib brings in +static void serve_repos(httplib::Server & server) { + server.Get(R"(/api/models/(.+)/refs)", [](const httplib::Request & req, httplib::Response & res) { + if (g_repos.count(req.matches[1])) { + res.set_content(nlohmann::json{{"branches", {{{"name", "main"}, {"targetCommit", COMMIT}}}}}.dump(), + "application/json"); + } else { + res.status = 404; + } + }); + server.Get(R"(/api/models/(.+)/tree/.+)", [](const httplib::Request & req, httplib::Response & res) { + if (!g_repos.count(req.matches[1])) { + res.status = 404; + return; + } + auto files = nlohmann::json::array(); + size_t i = 0; + for (const auto & p : g_repos[req.matches[1]]) { + char oid[41]; + snprintf(oid, sizeof(oid), "%040lx", (unsigned long) ++i); + files.push_back({{"type", "file"}, {"path", p}, {"size", 1}, {"oid", oid}}); + } + res.set_content(files.dump(), "application/json"); + }); +} + +static common_params_model model_ref(const std::string & hf_repo, const std::string & hf_file = "") { + common_params_model m; + m.hf_repo = hf_repo; + m.hf_file = hf_file; + return m; +} + +// the model cache is isolated under a temporary directory named after the +// loopback port, so concurrent runs on a shared machine keep their own, and +// the local path the handler wires for a file is snapshots// +static std::filesystem::path cache_dir; + +static std::string cached(std::string repo_id, const std::string & path) { + string_replace_all(repo_id, "/", "--"); + return (cache_dir / ("models--" + repo_id) / "snapshots" / COMMIT / path).string(); +} + +// +// fixtures mimicking real repo layouts +// + +// flat layout in the style of ggml-org/gemma-4-31B-it-GGUF +static const std::vector flat = { + "README.md", + "model-BF16.gguf", + "model-Q4_K_M.gguf", + "model-Q8_0.gguf", + "mmproj-model-BF16.gguf", + "mmproj-model-Q8_0.gguf", + "mtp-model-BF16.gguf", + "mtp-model-Q4_0.gguf", + "mtp-model-Q8_0.gguf", + "dflash-model-BF16.gguf", + "dflash-model-Q8_0.gguf", +}; + +// quants in subdirectories with sharded files and root sidecars, +// in the style of stepfun-ai/Step-3.7-Flash-GGUF +static const std::vector subdir = { + "mmproj-model-f16.gguf", + "model-mtp-BF16.gguf", + "model-mtp-Q8_0.gguf", + "Q3_K_M/model-Q3_K_M-00001-of-00003.gguf", + "Q3_K_M/model-Q3_K_M-00002-of-00003.gguf", + "Q3_K_M/model-Q3_K_M-00003-of-00003.gguf", + "Q8_0/model-Q8_0-00001-of-00002.gguf", + "Q8_0/model-Q8_0-00002-of-00002.gguf", +}; + +// sidecar quants exist where the full model quant does not, +// in the style of ggml-org/Qwen3.6-27B-GGUF +static const std::vector hole = { + "model-BF16.gguf", + "model-Q4_K_M.gguf", + "model-Q8_0.gguf", + "mtp-model-BF16.gguf", + "mtp-model-Q4_0.gguf", + "mtp-model-Q8_0.gguf", + "dflash-model-BF16.gguf", + "dflash-model-Q8_0.gguf", +}; + +// unsloth-style naming with UD quants and a suffix MTP file +static const std::vector unsloth = { + "model-UD-Q8_K_XL.gguf", + "mmproj-BF16.gguf", + "model-MTP-BF16.gguf", +}; + +// bartowski-style vendor prefix and mradermacher-style dot quant +static const std::vector vendors = { + "TheDrummer_Model-24B-v4.1-Q8_0.gguf", + "BlackSheep-24B.Q8_0.gguf", +}; + +// every speculative sidecar type at the same quant +static const std::vector quad = { + "model-Q8_0.gguf", + "mtp-model-Q8_0.gguf", + "dflash-model-Q8_0.gguf", + "eagle3-model-Q8_0.gguf", + "dspark-model-Q8_0.gguf", +}; + +static const std::vector dflash_only = { + "model-Q8_0.gguf", + "dflash-model-Q8_0.gguf", +}; + +static const std::vector eagle3_only = { + "model-Q8_0.gguf", + "eagle3-model-Q8_0.gguf", +}; + +// a single full quant with dspark sidecars at other quants, +// in the style of ggml-org/DeepSeek-V4-Flash-0731-GGUF +static const std::vector spark = { + "README.md", + "model-MXFP4.gguf", + "dspark-model-BF16.gguf", + "dspark-model-MXFP4.gguf", +}; + +// dspark outranks dflash in the type auto-selection +static const std::vector dspark_dflash = { + "model-Q8_0.gguf", + "dflash-model-Q8_0.gguf", + "dspark-model-Q8_0.gguf", +}; + +// +// table-driven plan resolution through the real entry point, +// each case replayed on multiple deterministic reorderings of the listing, +// except the cases whose pick legitimately depends on the listing order +// + +struct plan_case { + const char * name; + const std::vector & files; + const char * hf_repo; + const char * hf_file; + bool sidecars; // request mmproj + mtp + dflash + eagle3 + dspark + bool order_dependent; // the expected pick depends on the listing order + const char * primary; + std::vector model_files; + const char * mmproj; + const char * mtp; + const char * dflash; + const char * eagle3; + const char * dspark; +}; + +static const plan_case plan_cases[] = { + // exact tag picks the matching primary, sidecars follow the tag + {"flat exact tag", flat, "test/repo:Q8_0", "", true, false, + "model-Q8_0.gguf", {"model-Q8_0.gguf"}, + "mmproj-model-Q8_0.gguf", "mtp-model-Q8_0.gguf", "dflash-model-Q8_0.gguf", "", ""}, + + // no tag falls back to the default quant preference + {"flat default", flat, "test/repo", "", false, false, + "model-Q4_K_M.gguf", {"model-Q4_K_M.gguf"}, + "", "", "", "", ""}, + + // no tag and no default match falls back to the first model in the listing + {"unsloth fallback", unsloth, "test/repo", "", true, true, + "model-UD-Q8_K_XL.gguf", {"model-UD-Q8_K_XL.gguf"}, + "mmproj-BF16.gguf", "", "", "", ""}, + + // explicit hf_file picks that exact file + {"flat hf_file", flat, "test/repo", "model-BF16.gguf", false, false, + "model-BF16.gguf", {"model-BF16.gguf"}, + "", "", "", "", ""}, + + // missing hf_file resolves nothing + {"flat missing hf_file", flat, "test/repo", "nope.gguf", false, false, + "", {}, + "", "", "", "", ""}, + + // a sharded primary brings all its parts, a subdir primary finds the root sidecar + {"subdir shards", subdir, "test/repo:Q3_K_M", "", true, false, + "Q3_K_M/model-Q3_K_M-00001-of-00003.gguf", + {"Q3_K_M/model-Q3_K_M-00001-of-00003.gguf", + "Q3_K_M/model-Q3_K_M-00002-of-00003.gguf", + "Q3_K_M/model-Q3_K_M-00003-of-00003.gguf"}, + "mmproj-model-f16.gguf", "model-mtp-Q8_0.gguf", "", "", ""}, + + // a tag with no matching full model still resolves the requested sidecars + {"hole tag sidecar", hole, "test/repo:Q4_0", "", true, false, + "", {}, + "", "mtp-model-Q4_0.gguf", "dflash-model-Q8_0.gguf", "", ""}, + + // the same tag without a requested sidecar resolves nothing + {"hole tag alone", hole, "test/repo:Q4_0", "", false, false, + "", {}, + "", "", "", "", ""}, + + // no tag anchors the sidecars on the primary quant + {"hole default anchor", hole, "test/repo", "", true, false, + "model-Q4_K_M.gguf", {"model-Q4_K_M.gguf"}, + "", "mtp-model-Q4_0.gguf", "dflash-model-Q8_0.gguf", "", ""}, + + // the mtp- keyword is case sensitive, a suffix -MTP file is not discovered + {"unsloth suffix mtp", unsloth, "test/repo:Q8_K_XL", "", true, false, + "model-UD-Q8_K_XL.gguf", {"model-UD-Q8_K_XL.gguf"}, + "mmproj-BF16.gguf", "", "", "", ""}, + + // vendor prefixes and the dot quant convention both match the tag, + // first match wins between two files at the same quant + {"vendor prefix", vendors, "test/repo:Q8_0", "", false, true, + "TheDrummer_Model-24B-v4.1-Q8_0.gguf", {"TheDrummer_Model-24B-v4.1-Q8_0.gguf"}, + "", "", "", "", ""}, + + // every sidecar type resolves at the tag + {"quad exact tag", quad, "test/repo:Q8_0", "", true, false, + "model-Q8_0.gguf", {"model-Q8_0.gguf"}, + "", "mtp-model-Q8_0.gguf", "dflash-model-Q8_0.gguf", "eagle3-model-Q8_0.gguf", "dspark-model-Q8_0.gguf"}, + + // no tag anchors the dspark sidecar on the only full quant + {"spark default anchor", spark, "test/repo", "", true, false, + "model-MXFP4.gguf", {"model-MXFP4.gguf"}, + "", "", "", "", "dspark-model-MXFP4.gguf"}, + + // a tag with no matching full model still resolves the exact dspark sidecar + {"spark tag sidecar", spark, "test/repo:BF16", "", true, false, + "", {}, + "", "", "", "", "dspark-model-BF16.gguf"}, +}; + +static void check_plan(const plan_case & c) { + common_download_opts opts; + opts.download_mmproj = c.sidecars; + opts.download_mtp = c.sidecars; + opts.download_dflash = c.sidecars; + opts.download_eagle3 = c.sidecars; + opts.download_dspark = c.sidecars; + + auto plan = common_download_get_hf_plan(model_ref(c.hf_repo, c.hf_file), opts); + + REQUIRE_EQ(plan.primary.path, c.primary); + REQUIRE_EQ(plan.mmproj.path, c.mmproj); + REQUIRE_EQ(plan.mtp.path, c.mtp); + REQUIRE_EQ(plan.dflash.path, c.dflash); + REQUIRE_EQ(plan.eagle3.path, c.eagle3); + REQUIRE_EQ(plan.dspark.path, c.dspark); + + // exact shard set, order insensitive; the primary must be the first split + std::vector actual; + for (const auto & f : plan.model_files) { + actual.push_back(f.path); + } + std::sort(actual.begin(), actual.end()); + auto expected = c.model_files; + std::sort(expected.begin(), expected.end()); + REQUIRE(actual == expected); + if (!expected.empty()) { + REQUIRE(plan.primary.path == expected.front()); + } +} + +static void test_plan_resolution() { + printf("test-model-resolution: plan resolution on %zu cases\n", sizeof(plan_cases) / sizeof(plan_cases[0])); + + for (const auto & c : plan_cases) { + printf(" %s\n", c.name); + // invariant: the resolution is insensitive to the listing order + for (size_t rot = 0; rot < c.files.size(); ++rot) { + if (c.order_dependent && rot > 0) { + continue; + } + g_context = std::string(c.name) + ", reordering " + std::to_string(rot); + auto files = c.files; + std::rotate(files.begin(), files.begin() + rot, files.end()); + if (rot % 2 == 1) { + std::reverse(files.begin(), files.end()); + } + g_repos["test/repo"] = files; + check_plan(c); + } + } + g_repos.clear(); +} + +// +// end-to-end assembly: real CLI parsing, real handler init resolving over the +// loopback, downloads skipped by flipping offline before apply +// + +static void assemble(std::vector argv, common_params & params) { + std::vector cargv; + g_context.clear(); + for (auto & a : argv) { + g_context += g_context.empty() ? a : " " + a; + cargv.push_back(a.data()); + } + bool ok = common_params_parse((int) cargv.size(), cargv.data(), params, LLAMA_EXAMPLE_SERVER); + REQUIRE(ok); + + auto handler = common_models_handler_init(params, LLAMA_EXAMPLE_SERVER); + + // skip the network execution, on_done still wires the params + params.offline = true; + common_models_handler_apply(handler, params); +} + +static void test_task_assembly() { + printf("test-model-resolution: end-to-end assembly\n"); + + g_repos["test/main"] = flat; + g_repos["test/hole"] = hole; + g_repos["test/quad"] = quad; + g_repos["test/dflash"] = dflash_only; + g_repos["test/eagle3"] = eagle3_only; + g_repos["test/spark"] = spark; + g_repos["test/pair"] = dspark_dflash; + g_repos["test/small"] = {"draft-model-Q4_K_M.gguf"}; + g_repos["test/preset"] = {"preset.ini", "model-Q8_0.gguf"}; + + { + // plain -hf wires the model and its mmproj, nothing speculative + common_params params; + assemble({"server", "-hf", "test/main:Q8_0"}, params); + REQUIRE_EQ(params.model.path, cached("test/main", "model-Q8_0.gguf")); + REQUIRE_EQ(params.mmproj.path, cached("test/main", "mmproj-model-Q8_0.gguf")); + REQUIRE(params.speculative.draft.mparams.path.empty()); + } + { + // --no-mmproj disables the mmproj discovery + common_params params; + assemble({"server", "-hf", "test/main:Q8_0", "--no-mmproj"}, params); + REQUIRE(params.mmproj.path.empty()); + } + { + // an explicit --mmproj wins over the discovery + common_params params; + assemble({"server", "-hf", "test/main:Q8_0", "--mmproj", "/local/mmproj.gguf"}, params); + REQUIRE(params.mmproj.path == "/local/mmproj.gguf"); + } + { + // -hf with a spec type wires the sidecar of the main repo as fallback draft + common_params params; + assemble({"server", "-hf", "test/main:Q8_0", "--spec-type", "draft-mtp"}, params); + REQUIRE_EQ(params.speculative.draft.mparams.path, cached("test/main", "mtp-model-Q8_0.gguf")); + } + { + // -hfd with a spec type wires the draft repo sidecar at its tag, + // not its full model, and suppresses the main repo fallback + common_params params; + assemble({"server", "-hf", "test/hole:Q8_0", "-hfd", "test/hole:Q4_0", "--spec-type", "draft-mtp"}, params); + REQUIRE_EQ(params.speculative.draft.mparams.path, cached("test/hole", "mtp-model-Q4_0.gguf")); + } + { + // an explicit -md file wins over the sidecar resolution + common_params params; + assemble({"server", "-hf", "test/main:Q8_0", "-hfd", "test/main", "-md", "mtp-model-BF16.gguf", "--spec-type", "draft-mtp"}, params); + REQUIRE_EQ(params.speculative.draft.mparams.path, cached("test/main", "mtp-model-BF16.gguf")); + } + { + // -hfd without a spec type auto-selects the type, mtp first when all ship + common_params params; + assemble({"server", "-hf", "test/main:Q8_0", "-hfd", "test/quad:Q8_0"}, params); + REQUIRE(params.speculative.types == std::vector{COMMON_SPECULATIVE_TYPE_DRAFT_MTP}); + REQUIRE_EQ(params.speculative.draft.mparams.path, cached("test/quad", "mtp-model-Q8_0.gguf")); + } + { + // auto-selection with only a dflash sidecar + common_params params; + assemble({"server", "-hf", "test/main:Q8_0", "-hfd", "test/dflash:Q8_0"}, params); + REQUIRE(params.speculative.types == std::vector{COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH}); + REQUIRE_EQ(params.speculative.draft.mparams.path, cached("test/dflash", "dflash-model-Q8_0.gguf")); + } + { + // auto-selection with only an eagle3 sidecar + common_params params; + assemble({"server", "-hf", "test/main:Q8_0", "-hfd", "test/eagle3:Q8_0"}, params); + REQUIRE(params.speculative.types == std::vector{COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3}); + REQUIRE_EQ(params.speculative.draft.mparams.path, cached("test/eagle3", "eagle3-model-Q8_0.gguf")); + } + { + // auto-selection prefers dspark over dflash when both ship + common_params params; + assemble({"server", "-hf", "test/main:Q8_0", "-hfd", "test/pair:Q8_0"}, params); + REQUIRE(params.speculative.types == std::vector{COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK}); + REQUIRE_EQ(params.speculative.draft.mparams.path, cached("test/pair", "dspark-model-Q8_0.gguf")); + } + { + // -hf with the dspark spec type wires the sidecar of the main repo, + // anchored on the only full quant + common_params params; + assemble({"server", "-hf", "test/spark", "--spec-type", "draft-dspark"}, params); + REQUIRE_EQ(params.model.path, cached("test/spark", "model-MXFP4.gguf")); + REQUIRE_EQ(params.speculative.draft.mparams.path, cached("test/spark", "dspark-model-MXFP4.gguf")); + } + { + // -hfd on a repo without sidecars keeps resolving a full model as draft + common_params params; + assemble({"server", "-hf", "test/main:Q8_0", "-hfd", "test/small"}, params); + REQUIRE(params.speculative.types == std::vector{COMMON_SPECULATIVE_TYPE_NONE}); + REQUIRE_EQ(params.speculative.draft.mparams.path, cached("test/small", "draft-model-Q4_K_M.gguf")); + } + { + // a preset repo wires the preset and clears the model for router mode + common_params params; + assemble({"server", "-hf", "test/preset"}, params); + REQUIRE_EQ(params.models_preset, cached("test/preset", "preset.ini")); + REQUIRE(params.model.path.empty()); + REQUIRE(params.model.hf_repo.empty()); + } + + g_repos.clear(); +} + +int main(void) { + // unbuffered, so a crash cannot swallow the reports already printed + setvbuf(stdout, nullptr, _IONBF, 0); + setvbuf(stderr, nullptr, _IONBF, 0); + + // the negative cases legitimately log errors on every reordering, + // keep the output down to the reports + common_log_pause(common_log_main()); + + // the loopback endpoint also keeps the client init from rejecting + // https on the builds without TLS support + httplib::Server server; + serve_repos(server); + int port = server.bind_to_any_port("127.0.0.1"); + + // isolate the cache, its location is read once so it is set + // before anything else + cache_dir = std::filesystem::temp_directory_path() / + ("test-model-resolution-cache-" + std::to_string(port)); + std::filesystem::remove_all(cache_dir); + common_set_env("LLAMA_CACHE", cache_dir.string()); + + std::thread server_thread([&server] { server.listen_after_bind(); }); + server.wait_until_ready(); + common_set_env("MODEL_ENDPOINT", "http://127.0.0.1:" + std::to_string(port) + "/"); + + test_plan_resolution(); + test_task_assembly(); + + server.stop(); + server_thread.join(); + + std::filesystem::remove_all(cache_dir); + printf("test-model-resolution: all tests OK\n"); + return 0; +} diff --git a/tests/test-quant-type-selection.cpp b/tests/test-quant-type-selection.cpp index 3c8983360e26..9a5f5e53e119 100644 --- a/tests/test-quant-type-selection.cpp +++ b/tests/test-quant-type-selection.cpp @@ -216,18 +216,18 @@ static std::string snapshot_file_from_name(const std::string & name) { } static const remote_model_spec model_specs[] = { - { "ggml-org/Qwen3-0.6B-GGUF", "Q8_0" }, - { "ggml-org/GLM-4.6V-GGUF", "Q8_0" }, - { "ggml-org/Step-3.5-Flash-GGUF", "Q4_K" }, - { "ggml-org/Qwen3-Coder-Next-GGUF", "Q8_0" }, - { "ggml-org/Qwen3-14B-GGUF", "Q8_0" }, - { "ggml-org/Nemotron-Nano-3-30B-A3B-GGUF", "Q8_0" }, - { "ggml-org/gpt-oss-120b-GGUF", "mxfp4" }, - { "ggml-org/gemma-3-4b-it-GGUF", "Q8_0" }, - { "bartowski/Meta-Llama-3.1-70B-Instruct-GGUF", "Q4_K_M" }, - { "bartowski/deepseek-ai_DeepSeek-V3.1-GGUF", "IQ1_M" }, - { "bartowski/Qwen_Qwen3.5-397B-A17B-GGUF", "IQ1_S" }, // TODO: swap with ggml-org if/when it's released - { "bartowski/Qwen_Qwen3.5-27B-GGUF", "Q8_0" }, // TODO: swap with ggml-org if/when it's released + { "ggml-org/Qwen3-0.6B-GGUF", "Q8_0" }, + { "ggml-org/GLM-4.6V-GGUF", "Q8_0" }, + { "ggml-org/Step-3.5-Flash-GGUF", "Q4_K" }, + { "ggml-org/Qwen3-Coder-Next-GGUF", "Q8_0" }, + { "ggml-org/Qwen3-14B-GGUF", "Q8_0" }, + { "ggml-org/NVIDIA-Nemotron-Nano-3-30B-A3B-GGUF", "Q8_0" }, + { "ggml-org/gpt-oss-120b-GGUF", "mxfp4" }, + { "ggml-org/gemma-3-4b-it-GGUF", "Q8_0" }, + { "bartowski/Meta-Llama-3.1-70B-Instruct-GGUF", "Q4_K_M" }, + { "bartowski/deepseek-ai_DeepSeek-V3.1-GGUF", "IQ1_M" }, + //{ "bartowski/Qwen_Qwen3.5-397B-A17B-GGUF", "IQ1_S" }, // TODO: swap with ggml-org if/when it's released + { "ggml-org/Qwen3.6-27B-GGUF", "Q8_0" }, }; static const int n_model_specs = (int) (sizeof(model_specs) / sizeof(model_specs[0])); diff --git a/tests/test-quantize-stats.cpp b/tests/test-quantize-stats.cpp index e53a7b355318..c65557534025 100644 --- a/tests/test-quantize-stats.cpp +++ b/tests/test-quantize-stats.cpp @@ -312,7 +312,7 @@ int main(int argc, char ** argv) { { auto mparams = llama_model_default_params(); - mparams.use_mlock = false; + mparams.load_mode = LLAMA_LOAD_MODE_NONE; model = llama_model_load_from_file(params.model.c_str(), mparams); diff --git a/tests/test-reasoning-budget.cpp b/tests/test-reasoning-budget.cpp index f54cff4f8a27..3bcc77e1733c 100644 --- a/tests/test-reasoning-budget.cpp +++ b/tests/test-reasoning-budget.cpp @@ -20,8 +20,8 @@ static void test_reasoning_budget( const char * test_name, const std::vector & sequence, - const std::vector & start_tokens, - const std::vector & end_tokens, + const std::vector & start_seqs, + const std::vector & end_seqs, const std::vector & forced_tokens, int32_t budget, common_reasoning_budget_state initial_state, @@ -31,8 +31,12 @@ static void test_reasoning_budget( // Find the maximum token ID to ensure our vocab covers all tokens llama_token max_token = 0; for (auto t : sequence) max_token = std::max(max_token, t); - for (auto t : start_tokens) max_token = std::max(max_token, t); - for (auto t : end_tokens) max_token = std::max(max_token, t); + for (const auto & seq : start_seqs) { + for (auto t : seq) max_token = std::max(max_token, t); + } + for (const auto & seq : end_seqs) { + for (auto t : seq) max_token = std::max(max_token, t); + } for (auto t : forced_tokens) max_token = std::max(max_token, t); // Create a minimal sampler with mock vocabulary @@ -40,8 +44,8 @@ static void test_reasoning_budget( // The UTF-8 boundary check will treat all tokens as complete (safe fallback) auto * sampler = common_reasoning_budget_init( nullptr, // vocab - not used for basic state machine tests - start_tokens, - end_tokens, + start_seqs, + end_seqs, forced_tokens, budget, initial_state @@ -152,7 +156,7 @@ static void test_reasoning_budget_clone_mid_counting() { const std::vector end = {101}; const std::vector forced = {102, 101}; - auto * sampler = common_reasoning_budget_init(nullptr, start, end, forced, 2, REASONING_BUDGET_IDLE); + auto * sampler = common_reasoning_budget_init(nullptr, {start}, {end}, forced, 2, REASONING_BUDGET_IDLE); llama_sampler_accept(sampler, 100); // COUNTING, remaining=2 llama_sampler_accept(sampler, 50); // COUNTING, remaining=1 @@ -171,7 +175,7 @@ static void test_reasoning_budget_clone_mid_forcing() { const std::vector end = {101}; const std::vector forced = {102, 101}; - auto * sampler = common_reasoning_budget_init(nullptr, start, end, forced, 0, REASONING_BUDGET_FORCING); + auto * sampler = common_reasoning_budget_init(nullptr, {start}, {end}, forced, 0, REASONING_BUDGET_FORCING); GGML_ASSERT(get_forced_token(sampler, 102) == 102); llama_sampler_accept(sampler, 102); // advance to the second forced token @@ -191,7 +195,7 @@ static void test_reasoning_budget_force_manual() { // if COUNTING, force() succeeds and begins forcing the end sequence from the start { - auto * sampler = common_reasoning_budget_init(nullptr, start, end, forced, 5, REASONING_BUDGET_IDLE); + auto * sampler = common_reasoning_budget_init(nullptr, {start}, {end}, forced, 5, REASONING_BUDGET_IDLE); llama_sampler_accept(sampler, 100); // COUNTING, remaining=5 llama_sampler_accept(sampler, 50); // COUNTING, remaining=4 @@ -212,7 +216,7 @@ static void test_reasoning_budget_force_manual() { // if IDLE, force() is a no-op { - auto * sampler = common_reasoning_budget_init(nullptr, start, end, forced, 5, REASONING_BUDGET_IDLE); + auto * sampler = common_reasoning_budget_init(nullptr, {start}, {end}, forced, 5, REASONING_BUDGET_IDLE); GGML_ASSERT(!common_reasoning_budget_force(sampler) && "force() must not transition from IDLE"); GGML_ASSERT(common_reasoning_budget_get_state(sampler) == REASONING_BUDGET_IDLE); @@ -222,7 +226,7 @@ static void test_reasoning_budget_force_manual() { // if DONE, force() is a no-op { - auto * sampler = common_reasoning_budget_init(nullptr, start, end, forced, 5, REASONING_BUDGET_IDLE); + auto * sampler = common_reasoning_budget_init(nullptr, {start}, {end}, forced, 5, REASONING_BUDGET_IDLE); llama_sampler_accept(sampler, 100); // COUNTING llama_sampler_accept(sampler, 101); // natural end -> DONE @@ -236,7 +240,7 @@ static void test_reasoning_budget_force_manual() { // if FORCING, force() is a no-op and must not rewind the force position { - auto * sampler = common_reasoning_budget_init(nullptr, start, end, forced, 0, REASONING_BUDGET_FORCING); + auto * sampler = common_reasoning_budget_init(nullptr, {start}, {end}, forced, 0, REASONING_BUDGET_FORCING); GGML_ASSERT(get_forced_token(sampler, 102) == 102); llama_sampler_accept(sampler, 102); // advance to the second forced token (force_pos=1) @@ -254,6 +258,81 @@ static void test_reasoning_budget_force_manual() { fprintf(stderr, " Test 'manual force transition' passed\n"); } +static void test_reasoning_budget_end_match() { + const std::vector start = {{100}}; + const std::vector end = {{101}, {103, 104}}; + + // natural end records the sequence that matched; re-arming clears it + { + auto * sampler = common_reasoning_budget_init(nullptr, start, end, {102, 101}, 5, REASONING_BUDGET_IDLE); + + GGML_ASSERT(common_reasoning_budget_get_end_match(sampler) == nullptr); + + llama_sampler_accept(sampler, 100); // COUNTING + llama_sampler_accept(sampler, 50); + llama_sampler_accept(sampler, 103); + llama_sampler_accept(sampler, 104); // end matched via {103, 104}, DONE + + const llama_tokens * matched = common_reasoning_budget_get_end_match(sampler); + GGML_ASSERT(matched != nullptr); + GGML_ASSERT(*matched == llama_tokens({103, 104})); + + llama_sampler_accept(sampler, 100); // re-arm, COUNTING + GGML_ASSERT(common_reasoning_budget_get_end_match(sampler) == nullptr); + + llama_sampler_free(sampler); + } + + // overlapping end sequences: the longest one ending at the position wins + { + const std::vector end_overlap = {{104}, {103, 104}}; + + auto * sampler = common_reasoning_budget_init(nullptr, start, end_overlap, {102, 104}, 5, REASONING_BUDGET_IDLE); + + llama_sampler_accept(sampler, 100); // COUNTING + llama_sampler_accept(sampler, 103); + llama_sampler_accept(sampler, 104); // both {104} and {103, 104} end here + + const llama_tokens * matched = common_reasoning_budget_get_end_match(sampler); + GGML_ASSERT(matched != nullptr); + GGML_ASSERT(*matched == llama_tokens({103, 104})); + + llama_sampler_free(sampler); + } + + // forcing records the end sequence terminating forced_tokens + { + auto * sampler = common_reasoning_budget_init(nullptr, start, end, {102, 103, 104}, 0, REASONING_BUDGET_FORCING); + + llama_sampler_accept(sampler, 102); + llama_sampler_accept(sampler, 103); + GGML_ASSERT(common_reasoning_budget_get_end_match(sampler) == nullptr); + llama_sampler_accept(sampler, 104); // forced sequence complete, DONE + + const llama_tokens * matched = common_reasoning_budget_get_end_match(sampler); + GGML_ASSERT(matched != nullptr); + GGML_ASSERT(*matched == llama_tokens({103, 104})); + + llama_sampler_free(sampler); + } + + // forced_tokens not ending with a known end sequence records nothing + { + auto * sampler = common_reasoning_budget_init(nullptr, start, end, {102}, 0, REASONING_BUDGET_FORCING); + + llama_sampler_accept(sampler, 102); // forced sequence complete, DONE + GGML_ASSERT(common_reasoning_budget_get_state(sampler) == REASONING_BUDGET_DONE); + GGML_ASSERT(common_reasoning_budget_get_end_match(sampler) == nullptr); + + llama_sampler_free(sampler); + } + + // a null sampler is safely ignored + GGML_ASSERT(common_reasoning_budget_get_end_match(nullptr) == nullptr); + + fprintf(stderr, " Test 'matched end sequence' passed\n"); +} + // UTF-8 boundary detection unit test // Tests common_utf8_is_complete() from reasoning-budget.h static void test_utf8_boundary_detection() { @@ -290,7 +369,7 @@ int main(void) { const std::vector forced = {102}; // forced token (not used in this test) const std::vector sequence = {100, 50, 51, 101, 52}; // start, two tokens, end, one more - test_reasoning_budget("natural end before budget exhausted", sequence, start, end, forced, + test_reasoning_budget("natural end before budget exhausted", sequence, {start}, {end}, forced, 5, // budget of 5 tokens REASONING_BUDGET_IDLE, SIZE_MAX, SIZE_MAX); // no forcing expected (natural end) @@ -306,7 +385,7 @@ int main(void) { const std::vector forced = {102, 101}; // forced message + end const std::vector sequence = {100, 50, 51, 52, 53}; // start + 4 tokens (budget=2) - test_reasoning_budget("budget exhausted forcing", sequence, start, end, forced, + test_reasoning_budget("budget exhausted forcing", sequence, {start}, {end}, forced, 2, // budget of 2 tokens REASONING_BUDGET_IDLE, 3, // forcing starts at i=3 (accept at i=2 depletes budget, apply at i=3 forces) @@ -321,7 +400,7 @@ int main(void) { const std::vector forced = {102, 101}; const std::vector sequence = {100, 50, 51, 52}; // start token first, then 3 tokens - test_reasoning_budget("activate immediately budget=0", sequence, start, end, forced, + test_reasoning_budget("activate immediately budget=0", sequence, {start}, {end}, forced, 0, // budget of 0 tokens REASONING_BUDGET_COUNTING, // starts counting, promoted to FORCING since budget=0 0, // forcing starts at i=0 (initialized in FORCING, apply forces immediately) @@ -335,7 +414,7 @@ int main(void) { const std::vector forced = {102}; const std::vector sequence = {50, 51, 52, 53}; - test_reasoning_budget("no start/end configured", sequence, start, end, forced, + test_reasoning_budget("no start/end configured", sequence, {start}, {end}, forced, 2, // budget REASONING_BUDGET_IDLE, SIZE_MAX, SIZE_MAX); // no forcing (no start/end configured) @@ -350,7 +429,7 @@ int main(void) { const std::vector forced = {102, 101}; const std::vector sequence = {50, 51, 52, 53}; - test_reasoning_budget("activate immediately with budget", sequence, start, end, forced, + test_reasoning_budget("activate immediately with budget", sequence, {start}, {end}, forced, 2, // budget of 2 tokens REASONING_BUDGET_COUNTING, 2, // forcing starts at i=2 (after 2 accepts deplete budget, apply at i=2 forces) @@ -373,18 +452,50 @@ int main(void) { const std::vector forced = {102, 101}; const std::vector sequence = {100, 50, 101, 100, 60, 61, 62, 63}; - test_reasoning_budget("multi-block re-arms budget after DONE", sequence, start, end, forced, + test_reasoning_budget("multi-block re-arms budget after DONE", sequence, {start}, {end}, forced, 2, // budget of 2 tokens (per block) REASONING_BUDGET_IDLE, 6, // forcing starts at i=6 (after second block exhausts at i=5) 7); // forcing continues through i=7 } + // Test 7: Multiple start sequences - the second sequence activates counting + // Flow: i=0 accept(110), i=1 accept(111)->COUNTING rem=2; i=2 accept(50)->rem=1; + // i=3 accept(51)->rem=0->FORCING; i=4..5 apply() forces the end sequence + { + const std::vector start = {{100}, {110, 111}}; + const std::vector end = {{101}}; + const std::vector forced = {102, 101}; + const std::vector sequence = {110, 111, 50, 51, 52, 53}; + + test_reasoning_budget("multiple start sequences", sequence, start, end, forced, + 2, // budget of 2 tokens + REASONING_BUDGET_IDLE, + 4, // forcing starts at i=4 (accept at i=3 depletes budget) + 5); // forcing continues through i=5 + } + + // Test 8: Multiple end sequences - natural end via the second sequence + // Flow: i=0 accept(100)->COUNTING rem=5; i=1 accept(50)->rem=4; + // i=2 accept(103)->partial end, rem=3; i=3 accept(104)->end matched, DONE + { + const std::vector start = {{100}}; + const std::vector end = {{101}, {103, 104}}; + const std::vector forced = {102, 101}; + const std::vector sequence = {100, 50, 103, 104, 52}; + + test_reasoning_budget("multiple end sequences", sequence, start, end, forced, + 5, // budget of 5 tokens + REASONING_BUDGET_IDLE, + SIZE_MAX, SIZE_MAX); // no forcing expected (natural end) + } + test_reasoning_budget_clone_mid_counting(); test_reasoning_budget_clone_mid_forcing(); test_reasoning_budget_force_manual(); + test_reasoning_budget_end_match(); - printf("OK (9 tests passed)\n"); + printf("OK (12 tests passed)\n"); printf("Testing UTF-8 boundary detection... "); test_utf8_boundary_detection(); diff --git a/tests/test-recurrent-state-rollback.cpp b/tests/test-recurrent-state-rollback.cpp index be19316db8a7..5d1f0140b623 100644 --- a/tests/test-recurrent-state-rollback.cpp +++ b/tests/test-recurrent-state-rollback.cpp @@ -20,7 +20,7 @@ static llama_context * make_ctx(const common_params & params, llama_model * mode static bool decode_tokens(llama_context * ctx, const std::vector & tokens, uint32_t count) { llama_batch batch = llama_batch_init(count, 0, 1); for (uint32_t pos = 0; pos < count; ++pos) { - common_batch_add(batch, tokens[pos], pos, { 0 }, false); + common_batch_add(batch, tokens[pos], pos, { 0 }, pos + 1 == count); } const bool ok = llama_decode(ctx, batch) == 0; llama_batch_free(batch); @@ -79,26 +79,37 @@ int main(int argc, char ** argv) { return 0; } - std::vector tokens = common_tokenize(ctx_src, "The quick brown fox jumps", true); + std::vector tokens; + if (llama_vocab_type(vocab) == LLAMA_VOCAB_TYPE_NONE) { + tokens = { 1, 2, 3, 4, 5, 6, 7, 8, 9 }; + } else { + tokens = common_tokenize(ctx_src, "The quick brown fox jumps over the lazy dog", true); + } const uint32_t n_rs_seq = llama_n_rs_seq(ctx_src); - if (tokens.size() > n_rs_seq + 1) { - tokens.resize(n_rs_seq + 1); + constexpr uint32_t n_rollback = 3; + if (n_rs_seq < n_rollback) { + fprintf(stderr, "%s : skipping because n_rs_seq is too small\n", __func__); + llama_free(ctx_src); + llama_free(ctx_dst); + return 0; } - if (tokens.size() < 2) { + if (tokens.empty()) { fprintf(stderr, "%s : not enough prompt tokens\n", __func__); return 1; } - const uint32_t n_tokens = tokens.size(); - const llama_token last_tok = tokens.back(); - const llama_pos last_pos = (llama_pos) n_tokens - 2; + tokens.resize(n_rs_seq + 1, tokens.back()); + + const uint32_t n_tokens = tokens.size(); + const llama_pos rollback_pos = (llama_pos) n_tokens - n_rollback; - // Decode the full prompt on the source, then roll back the last position. + // Decode the full prompt on the source, then roll back three positions. + // Replaying them crosses DSV4's ratio-4 compressor boundary. // Rollback leaves the recurrent memory in a snapshot state (rs_idx != 0). if (!decode_tokens(ctx_src, tokens, n_tokens)) { fprintf(stderr, "%s : failed to decode prompt\n", __func__); return 1; } - if (!llama_memory_seq_rm(llama_get_memory(ctx_src), 0, last_pos, -1)) { + if (!llama_memory_seq_rm(llama_get_memory(ctx_src), 0, rollback_pos, -1)) { fprintf(stderr, "%s : rollback failed\n", __func__); return 1; } @@ -108,31 +119,56 @@ int main(int argc, char ** argv) { ckpt.update_tgt(ctx_src, 0, 0); ckpt.load_tgt(ctx_dst, 0, 0); - // Replay the rolled-back token on both contexts and compare logits. - if (!decode_one(ctx_src, last_tok, last_pos) || - !decode_one(ctx_dst, last_tok, last_pos)) { - fprintf(stderr, "%s : replay failed\n", __func__); + constexpr float eps = 1e-5f; + std::vector> logits_src_replay(n_rollback); + const auto replay_and_compare = [&](const char * mode) { + for (uint32_t i = 0; i < n_rollback; ++i) { + const llama_pos pos = rollback_pos + i; + if (!decode_one(ctx_src, tokens[pos], pos) || + !decode_one(ctx_dst, tokens[pos], pos)) { + fprintf(stderr, "%s : %s replay failed at position %d\n", __func__, mode, pos); + return false; + } + + const float * logits_src = llama_get_logits_ith(ctx_src, 0); + const float * logits_dst = llama_get_logits_ith(ctx_dst, 0); + if (logits_src == nullptr || logits_dst == nullptr) { + fprintf(stderr, "%s : missing %s logits at position %d\n", __func__, mode, pos); + return false; + } + + logits_src_replay[i].assign(logits_src, logits_src + n_vocab); + for (int token = 0; token < n_vocab; ++token) { + if (std::fabs(logits_src[token] - logits_dst[token]) > eps) { + fprintf(stderr, "%s : %s logits mismatch at position %d, token %d (%g != %g)\n", + __func__, mode, pos, token, (double) logits_src[token], (double) logits_dst[token]); + return false; + } + } + } + return true; + }; + if (!replay_and_compare("full")) { return 1; } - const float * logits_src = llama_get_logits_ith(ctx_src, 0); - const float * logits_dst = llama_get_logits_ith(ctx_dst, 0); - if (logits_src == nullptr || logits_dst == nullptr) { - fprintf(stderr, "%s : missing logits\n", __func__); + if (!llama_memory_seq_rm(llama_get_memory(ctx_src), 0, rollback_pos, -1) || + !llama_memory_seq_rm(llama_get_memory(ctx_dst), 0, rollback_pos, -1)) { + fprintf(stderr, "%s : partial rollback failed\n", __func__); return 1; } - constexpr float eps = 1e-5f; - for (int i = 0; i < n_vocab; ++i) { - if (std::fabs(logits_src[i] - logits_dst[i]) > eps) { - fprintf(stderr, "%s : logits mismatch at token %d (%g != %g)\n", - __func__, i, (double) logits_src[i], (double) logits_dst[i]); - return 1; - } + constexpr llama_state_seq_flags partial_flags = LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY; + common_prompt_checkpoint ckpt_partial; + ckpt_partial.update_tgt(ctx_src, 0, partial_flags); + ckpt_partial.load_tgt(ctx_dst, 0, partial_flags); + + if (!replay_and_compare("partial")) { + return 1; } // Repeat the load into a context that already has its own rollback state: - // groups 1..n_rs_seq hold a *different* prompt's history, and rs_idx[0] is + // groups 1..n_rs_seq hold a different prompt's history, and rs_idx[0] is // non-zero at load time. The restore must wipe that state and still match. llama_context * ctx_dirty = make_ctx(params, model); if (ctx_dirty == nullptr) { @@ -151,30 +187,33 @@ int main(int argc, char ** argv) { fprintf(stderr, "%s : dirty prompt decode failed\n", __func__); return 1; } - if (!llama_memory_seq_rm(llama_get_memory(ctx_dirty), 0, last_pos, -1)) { + if (!llama_memory_seq_rm(llama_get_memory(ctx_dirty), 0, rollback_pos, -1)) { fprintf(stderr, "%s : dirty rollback failed\n", __func__); return 1; } ckpt.load_tgt(ctx_dirty, 0, 0); - if (!decode_one(ctx_dirty, last_tok, last_pos)) { - fprintf(stderr, "%s : dirty replay failed\n", __func__); - return 1; - } - - const float * logits_dirty = llama_get_logits_ith(ctx_dirty, 0); - if (logits_dirty == nullptr) { - fprintf(stderr, "%s : missing dirty logits\n", __func__); - return 1; - } + for (uint32_t i = 0; i < n_rollback; ++i) { + const llama_pos pos = rollback_pos + i; + if (!decode_one(ctx_dirty, tokens[pos], pos)) { + fprintf(stderr, "%s : dirty replay failed at position %d\n", __func__, pos); + return 1; + } - for (int i = 0; i < n_vocab; ++i) { - if (std::fabs(logits_src[i] - logits_dirty[i]) > eps) { - fprintf(stderr, "%s : dirty-ctx logits mismatch at token %d (%g != %g)\n", - __func__, i, (double) logits_src[i], (double) logits_dirty[i]); + const float * logits_dirty = llama_get_logits_ith(ctx_dirty, 0); + if (logits_dirty == nullptr) { + fprintf(stderr, "%s : missing dirty logits at position %d\n", __func__, pos); return 1; } + + for (int token = 0; token < n_vocab; ++token) { + if (std::fabs(logits_src_replay[i][token] - logits_dirty[token]) > eps) { + fprintf(stderr, "%s : dirty-ctx logits mismatch at position %d, token %d (%g != %g)\n", + __func__, pos, token, (double) logits_src_replay[i][token], (double) logits_dirty[token]); + return 1; + } + } } fprintf(stderr, "%s : recurrent rollback checkpoint restored successfully\n", __func__); diff --git a/tests/test-rset-release.cpp b/tests/test-rset-release.cpp new file mode 100644 index 000000000000..c60801c115ea --- /dev/null +++ b/tests/test-rset-release.cpp @@ -0,0 +1,53 @@ +// ref: https://github.com/ggml-org/llama.cpp/issues/25937 +// only works reliably when run with a large model that occupies 3GB+ of wired memory +// thus, this test is not run by default +// example model to run with: google/gemma-4-E4B-it-qat-q4_0-gguf + +#include "llama.h" +#include "common.h" + +#include +#include +#include +#include + +static uint64_t wired_memory() { + vm_statistics64_data_t vmstat; + mach_msg_type_number_t count = HOST_VM_INFO64_COUNT; + if (host_statistics64(mach_host_self(), HOST_VM_INFO64, (host_info64_t)&vmstat, &count) != KERN_SUCCESS) { + return UINT64_MAX; + } + return static_cast(vmstat.wire_count) * vm_kernel_page_size; +} + +int main(int argc, char ** argv) { + auto * model_path = common_get_model_or_exit(argc, argv); + + llama_backend_init(); + + const uint64_t wired_initial = wired_memory(); + + llama_model_params params = llama_model_default_params(); + params.load_mode = LLAMA_LOAD_MODE_NONE; + struct llama_model* model = llama_model_load_from_file(model_path, params); + + const uint64_t wired_loaded = wired_memory(); + const uint64_t wired_delta = wired_loaded - wired_initial; + // system memory fluctuates, so we need to allocate enough to reliably detect the release + GGML_ASSERT(wired_delta > 2'000'000'000); // 2GB + + llama_model_free(model); + + const uint64_t t_start_ms = ggml_time_ms(); + + // expect most of the allocated memory to be released within 10 seconds + // we allow for some tolerance due to system-wide memory fluctuations + while (wired_memory() > wired_loaded - 0.75 * wired_delta) { + GGML_ASSERT(ggml_time_ms() - t_start_ms < 10'000); + usleep(100'000); // 100ms + } + + llama_backend_free(); + + return 0; +} diff --git a/tests/test-sampling.cpp b/tests/test-sampling.cpp index 2aecff90e7bb..297f760157df 100644 --- a/tests/test-sampling.cpp +++ b/tests/test-sampling.cpp @@ -144,7 +144,7 @@ static void test_penalties( sampler_tester tester(probs, probs_expected); - auto * sampler = llama_sampler_init_penalties(last_tokens.size(), repeat_penalty, alpha_frequency, alpha_presence); + auto * sampler = llama_sampler_init_penalties((int32_t) probs.size(), (int32_t) last_tokens.size(), repeat_penalty, alpha_frequency, alpha_presence); for (size_t i = 0; i < last_tokens.size(); i++) { llama_sampler_accept(sampler, last_tokens[i]); diff --git a/tools/cli/README.md b/tools/cli/README.md index f93ae914ce27..bcddd05702bb 100644 --- a/tools/cli/README.md +++ b/tools/cli/README.md @@ -54,9 +54,11 @@ | `-ctv, --cache-type-v TYPE` | KV cache data type for V
allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
(default: f16)
(env: LLAMA_ARG_CACHE_TYPE_V) | | `-dt, --defrag-thold N` | KV cache defragmentation threshold (DEPRECATED)
(env: LLAMA_ARG_DEFRAG_THOLD) | | `-np, --parallel N` | number of parallel sequences to decode (default: 1)
(env: LLAMA_ARG_N_PARALLEL) | -| `--mlock` | force system to keep model in RAM rather than swapping or compressing
(env: LLAMA_ARG_MLOCK) | -| `--mmap, --no-mmap` | whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: enabled)
(env: LLAMA_ARG_MMAP) | -| `-dio, --direct-io, -ndio, --no-direct-io` | use DirectIO if available. (default: disabled)
(env: LLAMA_ARG_DIO) | +| `--rpc SERVERS` | comma-separated list of RPC servers (host:port)
(env: LLAMA_ARG_RPC) | +| `--mlock` | DEPRECATED in favor of `--load-mode`: force system to keep model in RAM rather than swapping or compressing
(env: LLAMA_ARG_MLOCK) | +| `--mmap, --no-mmap` | DEPRECATED in favor of `--load-mode`: whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock)
(env: LLAMA_ARG_MMAP) | +| `-dio, --direct-io, -ndio, --no-direct-io` | DEPRECATED in favor of `--load-mode`: use DirectIO if available
(env: LLAMA_ARG_DIO) | +| `-lm, --load-mode MODE` | model loading mode (default: mmap)
- none: no special loading mode
- mmap: memory-map model (if mmap disabled, slower load but may reduce pageouts if not using mlock)
- mlock: force system to keep model in RAM rather than swapping or compressing
- mmap+mlock: mmap + force system to keep model in RAM rather than swapping or compressing
- dio: use DirectIO if available

(env: LLAMA_ARG_LOAD_MODE) | | `--numa TYPE` | attempt optimizations that help on some NUMA systems
- distribute: spread execution evenly over all nodes
- isolate: only spawn threads on CPUs on the node that execution started on
- numactl: use the CPU map provided by numactl
if run without this previously, it is recommended to drop the system page cache before using this
see https://github.com/ggml-org/llama.cpp/issues/1437
(env: LLAMA_ARG_NUMA) | | `-dev, --device ` | comma-separated list of devices to use for offloading (none = don't offload)
use --list-devices to see a list of available devices
(env: LLAMA_ARG_DEVICE) | | `--list-devices` | print list of available devices and exit | @@ -142,6 +144,7 @@ | Argument | Explanation | | -------- | ----------- | +| `--server-base URL` | connect to this server instead of starting a new one, example: 'http://localhost:8080' (default: none) | | `--verbose-prompt` | print a verbose prompt before generation (default: false) | | `--display-prompt, --no-display-prompt` | whether to print prompt at generation (default: true) | | `-co, --color [on\|off\|auto]` | Colorize output to distinguish prompt and user input from generations ('on', 'off', or 'auto', default: 'auto')
'auto' enables colors when output is to a terminal | @@ -164,17 +167,19 @@ | `--image, --audio, --video FILE` | path to an image, audio, or video file. use with multimodal models, use comma-separated values for multiple files | | `--image-min-tokens N` | minimum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)
(env: LLAMA_ARG_IMAGE_MIN_TOKENS) | | `--image-max-tokens N` | maximum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)
(env: LLAMA_ARG_IMAGE_MAX_TOKENS) | +| `-o, --output, --output-file FNAME` | output file (default: '') | | `--chat-template-kwargs STRING` | sets additional params for the json template parser, must be a valid json object string, e.g. '{"key1":"value1","key2":"value2"}'
(env: LLAMA_ARG_CHAT_TEMPLATE_KWARGS) | | `--jinja, --no-jinja` | whether to use jinja template engine for chat (default: enabled)
(env: LLAMA_ARG_JINJA) | | `--reasoning-format FORMAT` | controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:
- none: leaves thoughts unparsed in `message.content`
- deepseek: puts thoughts in `message.reasoning_content`
- deepseek-legacy: keeps `` tags in `message.content` while also populating `message.reasoning_content`
(default: auto)
(env: LLAMA_ARG_THINK) | | `-rea, --reasoning [on\|off\|auto]` | Use reasoning/thinking in the chat ('on', 'off', or 'auto', default: 'auto' (detect from template))
(env: LLAMA_ARG_REASONING) | | `--reasoning-budget N` | token budget for thinking: -1 for unrestricted, 0 for immediate end, N>0 for token budget (default: -1)
(env: LLAMA_ARG_THINK_BUDGET) | | `--reasoning-budget-message MESSAGE` | message injected before the end-of-thinking tag when reasoning budget is exhausted (default: none)
(env: LLAMA_ARG_THINK_BUDGET_MESSAGE) | +| `--reasoning-preserve, --no-reasoning-preserve` | preserve reasoning trace in the full history, not just the last assistant message (default: template default)
compatible with certain templates having 'supports_preserve_reasoning' capability
example: https://docs.z.ai/guides/capabilities/thinking-mode#preserved-thinking
(env: LLAMA_ARG_REASONING_PRESERVE) | | `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, granite-4.0, granite-4.1, grok-2, hunyuan-dense, hunyuan-moe, hunyuan-vl, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE) | | `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, granite-4.0, granite-4.1, grok-2, hunyuan-dense, hunyuan-moe, hunyuan-vl, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) | | `--skip-chat-parsing, --no-skip-chat-parsing` | force a pure content parser, even if a Jinja template is specified; model will output everything in the content section, including any reasoning and/or tool calls (default: disabled)
(env: LLAMA_ARG_SKIP_CHAT_PARSING) | | `--simple-io` | use basic IO for better compatibility in subprocesses and limited consoles | -| `--log-prompts-dir PATH` | Log prompts to directory (only used for debugging, default: disabled) | +| `--log-prompts-dir PATH` | Log prompts to directory (auto-created if not present; only used for debugging, default: disabled) | | `--spec-draft-hf, -hfd, -hfrd, --hf-repo-draft /[:quant]` | Same as --hf-repo, but for the draft model (default: unused)
(env: LLAMA_ARG_SPEC_DRAFT_HF_REPO) | | `--spec-draft-threads, -td, --threads-draft N` | number of threads to use during generation (default: same as --threads) | | `--spec-draft-threads-batch, -tbd, --threads-batch-draft N` | number of threads to use during batch and prompt processing (default: same as --threads-draft) | @@ -198,7 +203,7 @@ | `--spec-draft-device, -devd, --device-draft ` | comma-separated list of devices to use for offloading the draft model (none = don't offload)
use --list-devices to see a list of available devices | | `--spec-draft-ngl, -ngld, --gpu-layers-draft, --n-gpu-layers-draft N` | max. number of draft model layers to store in VRAM, either an exact number, 'auto', or 'all' (default: auto)
(env: LLAMA_ARG_N_GPU_LAYERS_DRAFT) | | `--spec-draft-model, -md, --model-draft FNAME` | draft model for speculative decoding (default: unused)
(env: LLAMA_ARG_SPEC_DRAFT_MODEL) | -| `--spec-type none,draft-simple,draft-eagle3,draft-mtp,ngram-simple,ngram-map-k,ngram-map-k4v,ngram-mod,ngram-cache` | comma-separated list of types of speculative decoding to use (default: none)

(env: LLAMA_ARG_SPEC_TYPE) | +| `--spec-type none,draft-simple,draft-eagle3,draft-mtp,draft-dflash,draft-dspark,ngram-simple,ngram-map-k,ngram-map-k4v,ngram-mod,ngram-cache` | comma-separated list of types of speculative decoding to use (default: none)

(env: LLAMA_ARG_SPEC_TYPE) | | `--spec-ngram-mod-n-min N` | minimum number of ngram tokens to use for ngram-based speculative decoding (default: 48) | | `--spec-ngram-mod-n-max N` | maximum number of ngram tokens to use for ngram-based speculative decoding (default: 64) | | `--spec-ngram-mod-n-match N` | ngram-mod lookup length (default: 24) | diff --git a/tools/cli/cli-context.cpp b/tools/cli/cli-context.cpp index 0de8f69025ce..3d801b73d4c1 100644 --- a/tools/cli/cli-context.cpp +++ b/tools/cli/cli-context.cpp @@ -624,10 +624,14 @@ int cli_context::run() { generated_content content; generate_completion(content, timings); - impl->messages.push_back({ + json assistant_msg = { {"role", "assistant"}, {"content", content.content} - }); + }; + if (!content.reasoning.empty()) { + assistant_msg["reasoning_content"] = content.reasoning; + } + impl->messages.push_back(std::move(assistant_msg)); if (output_file) { std::string out_content = "Assistant:\n"; diff --git a/tools/completion/README.md b/tools/completion/README.md index d90f81748662..bce71d68d949 100644 --- a/tools/completion/README.md +++ b/tools/completion/README.md @@ -137,9 +137,11 @@ llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1 | `-ctv, --cache-type-v TYPE` | KV cache data type for V
allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
(default: f16)
(env: LLAMA_ARG_CACHE_TYPE_V) | | `-dt, --defrag-thold N` | KV cache defragmentation threshold (DEPRECATED)
(env: LLAMA_ARG_DEFRAG_THOLD) | | `-np, --parallel N` | number of parallel sequences to decode (default: 1)
(env: LLAMA_ARG_N_PARALLEL) | -| `--mlock` | force system to keep model in RAM rather than swapping or compressing
(env: LLAMA_ARG_MLOCK) | -| `--mmap, --no-mmap` | whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: enabled)
(env: LLAMA_ARG_MMAP) | -| `-dio, --direct-io, -ndio, --no-direct-io` | use DirectIO if available. (default: disabled)
(env: LLAMA_ARG_DIO) | +| `--rpc SERVERS` | comma-separated list of RPC servers (host:port)
(env: LLAMA_ARG_RPC) | +| `--mlock` | DEPRECATED in favor of `--load-mode`: force system to keep model in RAM rather than swapping or compressing
(env: LLAMA_ARG_MLOCK) | +| `--mmap, --no-mmap` | DEPRECATED in favor of `--load-mode`: whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock)
(env: LLAMA_ARG_MMAP) | +| `-dio, --direct-io, -ndio, --no-direct-io` | DEPRECATED in favor of `--load-mode`: use DirectIO if available
(env: LLAMA_ARG_DIO) | +| `-lm, --load-mode MODE` | model loading mode (default: mmap)
- none: no special loading mode
- mmap: memory-map model (if mmap disabled, slower load but may reduce pageouts if not using mlock)
- mlock: force system to keep model in RAM rather than swapping or compressing
- mmap+mlock: mmap + force system to keep model in RAM rather than swapping or compressing
- dio: use DirectIO if available

(env: LLAMA_ARG_LOAD_MODE) | | `--numa TYPE` | attempt optimizations that help on some NUMA systems
- distribute: spread execution evenly over all nodes
- isolate: only spawn threads on CPUs on the node that execution started on
- numactl: use the CPU map provided by numactl
if run without this previously, it is recommended to drop the system page cache before using this
see https://github.com/ggml-org/llama.cpp/issues/1437
(env: LLAMA_ARG_NUMA) | | `-dev, --device ` | comma-separated list of devices to use for offloading (none = don't offload)
use --list-devices to see a list of available devices
(env: LLAMA_ARG_DEVICE) | | `--list-devices` | print list of available devices and exit | @@ -253,6 +255,7 @@ llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1 | `-rea, --reasoning [on\|off\|auto]` | Use reasoning/thinking in the chat ('on', 'off', or 'auto', default: 'auto' (detect from template))
(env: LLAMA_ARG_REASONING) | | `--reasoning-budget N` | token budget for thinking: -1 for unrestricted, 0 for immediate end, N>0 for token budget (default: -1)
(env: LLAMA_ARG_THINK_BUDGET) | | `--reasoning-budget-message MESSAGE` | message injected before the end-of-thinking tag when reasoning budget is exhausted (default: none)
(env: LLAMA_ARG_THINK_BUDGET_MESSAGE) | +| `--reasoning-preserve, --no-reasoning-preserve` | preserve reasoning trace in the full history, not just the last assistant message (default: template default)
compatible with certain templates having 'supports_preserve_reasoning' capability
example: https://docs.z.ai/guides/capabilities/thinking-mode#preserved-thinking
(env: LLAMA_ARG_REASONING_PRESERVE) | | `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, granite-4.0, granite-4.1, grok-2, hunyuan-dense, hunyuan-moe, hunyuan-vl, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE) | | `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, granite-4.0, granite-4.1, grok-2, hunyuan-dense, hunyuan-moe, hunyuan-vl, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) | | `--skip-chat-parsing, --no-skip-chat-parsing` | force a pure content parser, even if a Jinja template is specified; model will output everything in the content section, including any reasoning and/or tool calls (default: disabled)
(env: LLAMA_ARG_SKIP_CHAT_PARSING) | diff --git a/tools/llama-bench/llama-bench.cpp b/tools/llama-bench/llama-bench.cpp index a099bddbec65..46a1511427d3 100644 --- a/tools/llama-bench/llama-bench.cpp +++ b/tools/llama-bench/llama-bench.cpp @@ -26,6 +26,7 @@ #include "fit.h" #include "ggml.h" #include "llama.h" +#include "log.h" #ifdef _WIN32 # define WIN32_LEAN_AND_MEAN @@ -339,14 +340,13 @@ struct cmd_params { std::vector n_gpu_layers; std::vector n_cpu_moe; std::vector split_mode; + std::vector load_mode; std::vector main_gpu; std::vector no_kv_offload; std::vector flash_attn; std::vector> devices; std::vector> tensor_split; std::vector> tensor_buft_overrides; - std::vector use_mmap; - std::vector use_direct_io; std::vector embeddings; std::vector no_op_offload; std::vector no_host; @@ -384,14 +384,13 @@ static const cmd_params cmd_params_defaults = { /* n_gpu_layers */ { -1 }, /* n_cpu_moe */ { 0 }, /* split_mode */ { LLAMA_SPLIT_MODE_LAYER }, + /* load_mode */ { LLAMA_LOAD_MODE_MMAP }, /* main_gpu */ { 0 }, /* no_kv_offload */ { false }, /* flash_attn */ { LLAMA_FLASH_ATTN_TYPE_AUTO }, /* devices */ { {} }, /* tensor_split */ { std::vector(llama_max_devices(), 0.0f) }, /* tensor_buft_overrides*/ { std::vector{ { nullptr, nullptr } } }, - /* use_mmap */ { true }, - /* use_direct_io */ { false }, /* embeddings */ { false }, /* no_op_offload */ { false }, /* no_host */ { false }, @@ -430,44 +429,45 @@ static void print_usage(int /* argc */, char ** argv) { } printf("\n"); printf("test parameters:\n"); - printf(" -m, --model (default: %s)\n", join(cmd_params_defaults.model, ",").c_str()); - printf(" -hf, -hfr, --hf-repo /[:quant] Hugging Face model repository; quant is optional, case-insensitive\n"); - printf(" default to Q4_K_M, or falls back to the first file in the repo if Q4_K_M doesn't exist.\n"); - printf(" example: ggml-org/GLM-4.7-Flash-GGUF:Q4_K_M\n"); - printf(" (default: unused)\n"); - printf(" -hff, --hf-file Hugging Face model file. If specified, it will override the quant in --hf-repo\n"); - printf(" (default: unused)\n"); - printf(" -hft, --hf-token Hugging Face access token\n"); - printf(" (default: value from HF_TOKEN environment variable)\n"); - printf(" --offline Offline mode: forces use of cache, prevents network access\n"); - printf(" (default: disabled)\n"); - printf(" -p, --n-prompt (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str()); - printf(" -n, --n-gen (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str()); - printf(" -pg (default: %s)\n", join(transform_to_str(cmd_params_defaults.n_pg, pair_str), ",").c_str()); - printf(" -d, --n-depth (default: %s)\n", join(cmd_params_defaults.n_depth, ",").c_str()); - printf(" -b, --batch-size (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str()); - printf(" -ub, --ubatch-size (default: %s)\n", join(cmd_params_defaults.n_ubatch, ",").c_str()); - printf(" -ctk, --cache-type-k (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str()); - printf(" -ctv, --cache-type-v (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str()); - printf(" -t, --threads (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str()); - printf(" -C, --cpu-mask (default: %s)\n", join(cmd_params_defaults.cpu_mask, ",").c_str()); - printf(" --cpu-strict <0|1> (default: %s)\n", join(cmd_params_defaults.cpu_strict, ",").c_str()); - printf(" --poll <0...100> (default: %s)\n", join(cmd_params_defaults.poll, ",").c_str()); - printf(" -ngl, --n-gpu-layers (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str()); - printf(" -ncmoe, --n-cpu-moe (default: %s)\n", join(cmd_params_defaults.n_cpu_moe, ",").c_str()); - printf(" -sm, --split-mode (default: %s)\n", join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str()); - printf(" -mg, --main-gpu (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str()); - printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str()); - printf(" -fa, --flash-attn (default: %s)\n", join(transform_to_str(cmd_params_defaults.flash_attn, llama_flash_attn_type_name), ",").c_str()); - printf(" -dev, --device (default: auto)\n"); - printf(" -mmp, --mmap <0|1> (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str()); - printf(" -dio, --direct-io <0|1> (default: %s)\n", join(cmd_params_defaults.use_direct_io, ",").c_str()); - printf(" -embd, --embeddings <0|1> (default: %s)\n", join(cmd_params_defaults.embeddings, ",").c_str()); - printf(" -ts, --tensor-split (default: 0)\n"); + printf(" -m, --model (default: %s)\n", join(cmd_params_defaults.model, ",").c_str()); + printf(" -hf, -hfr, --hf-repo /[:quant] Hugging Face model repository; quant is optional, case-insensitive\n"); + printf(" default to Q4_K_M, or falls back to the first file in the repo if Q4_K_M doesn't exist.\n"); + printf(" example: ggml-org/GLM-4.7-Flash-GGUF:Q4_K_M\n"); + printf(" (default: unused)\n"); + printf(" -hff, --hf-file Hugging Face model file. If specified, it will override the quant in --hf-repo\n"); + printf(" (default: unused)\n"); + printf(" -hft, --hf-token Hugging Face access token\n"); + printf(" (default: value from HF_TOKEN environment variable)\n"); + printf(" --offline Offline mode: forces use of cache, prevents network access\n"); + printf(" (default: disabled)\n"); + printf(" -p, --n-prompt (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str()); + printf(" -n, --n-gen (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str()); + printf(" -pg (default: %s)\n", join(transform_to_str(cmd_params_defaults.n_pg, pair_str), ",").c_str()); + printf(" -d, --n-depth (default: %s)\n", join(cmd_params_defaults.n_depth, ",").c_str()); + printf(" -b, --batch-size (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str()); + printf(" -ub, --ubatch-size (default: %s)\n", join(cmd_params_defaults.n_ubatch, ",").c_str()); + printf(" -ctk, --cache-type-k (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str()); + printf(" -ctv, --cache-type-v (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str()); + printf(" -t, --threads (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str()); + printf(" -C, --cpu-mask (default: %s)\n", join(cmd_params_defaults.cpu_mask, ",").c_str()); + printf(" --cpu-strict <0|1> (default: %s)\n", join(cmd_params_defaults.cpu_strict, ",").c_str()); + printf(" --poll <0...100> (default: %s)\n", join(cmd_params_defaults.poll, ",").c_str()); + printf(" -ngl, --n-gpu-layers (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str()); + printf(" -ncmoe, --n-cpu-moe (default: %s)\n", join(cmd_params_defaults.n_cpu_moe, ",").c_str()); + printf(" -sm, --split-mode (default: %s)\n", join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str()); + printf(" -mg, --main-gpu (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str()); + printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str()); + printf(" -fa, --flash-attn (default: %s)\n", join(transform_to_str(cmd_params_defaults.flash_attn, llama_flash_attn_type_name), ",").c_str()); + printf(" -dev, --device (default: auto)\n"); + printf(" -lm, --load-mode (default: %s)\n", join(transform_to_str(cmd_params_defaults.load_mode, llama_load_mode_name), ",").c_str()); + printf(" -mmp, --mmap <0|1> (DEPRECATED IN FAVOUR OF --load-mode)\n"); + printf(" -dio, --direct-io <0|1> (DEPRECATED IN FAVOUR OF --load-mode)\n"); + printf(" -embd, --embeddings <0|1> (default: %s)\n", join(cmd_params_defaults.embeddings, ",").c_str()); + printf(" -ts, --tensor-split (default: 0)\n"); printf(" -ot --override-tensor =;...\n"); - printf(" (default: disabled)\n"); - printf(" -nopo, --no-op-offload <0|1> (default: 0)\n"); - printf(" --no-host <0|1> (default: %s)\n", join(cmd_params_defaults.no_host, ",").c_str()); + printf(" (default: disabled)\n"); + printf(" -nopo, --no-op-offload <0|1> (default: 0)\n"); + printf(" --no-host <0|1> (default: %s)\n", join(cmd_params_defaults.no_host, ",").c_str()); printf("\n"); printf( "Multiple values can be given for each parameter by separating them with ','\n" @@ -679,22 +679,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { break; } } else if (arg == "--list-devices") { - std::vector devices; - for (size_t i = 0; i < ggml_backend_dev_count(); ++i) { - auto * dev = ggml_backend_dev_get(i); - if (ggml_backend_dev_type(dev) != GGML_BACKEND_DEVICE_TYPE_CPU) { - devices.push_back(dev); - } - } - printf("Available devices:\n"); - if (devices.empty()) { - printf(" (none)\n"); - } - for (auto * dev : devices) { - size_t free, total; - ggml_backend_dev_memory(dev, &free, &total); - printf(" %s: %s (%zu MiB, %zu MiB free)\n", ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), total / 1024 / 1024, free / 1024 / 1024); - } + common_print_available_devices(); exit(0); } else if (arg == "-t" || arg == "--threads") { if (++i >= argc) { @@ -778,6 +763,36 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { break; } params.split_mode.insert(params.split_mode.end(), modes.begin(), modes.end()); + } else if (arg == "-lm" || arg == "--load-mode") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = string_split(argv[i], split_delim); + + std::vector modes; + for (const auto & m : p) { + llama_load_mode mode; + if (m == "none") { + mode = LLAMA_LOAD_MODE_NONE; + } else if (m == "mmap") { + mode = LLAMA_LOAD_MODE_MMAP; + } else if (m == "mlock") { + mode = LLAMA_LOAD_MODE_MLOCK; + } else if (m == "mmap+mlock") { + mode = LLAMA_LOAD_MODE_MMAP_MLOCK; + } else if (m == "dio") { + mode = LLAMA_LOAD_MODE_DIRECT_IO; + } else { + invalid_param = true; + break; + } + modes.push_back(mode); + } + if (invalid_param) { + break; + } + params.load_mode.insert(params.load_mode.end(), modes.begin(), modes.end()); } else if (arg == "-mg" || arg == "--main-gpu") { if (++i >= argc) { invalid_param = true; @@ -838,15 +853,39 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { invalid_param = true; break; } + LOG_WRN("DEPRECATED: -mmp and --mmap are deprecated in favour of --load-mode. Please use --load-mode mmap instead."); auto p = string_split(argv[i], split_delim); - params.use_mmap.insert(params.use_mmap.end(), p.begin(), p.end()); + + std::vector modes; + for (const auto & m : p) { + llama_load_mode mode; + if (m) { + mode = LLAMA_LOAD_MODE_MMAP; + } else { + mode = LLAMA_LOAD_MODE_NONE; + } + modes.push_back(mode); + } + params.load_mode.insert(params.load_mode.end(), modes.begin(), modes.end()); } else if (arg == "-dio" || arg == "--direct-io") { if (++i >= argc) { invalid_param = true; break; } + LOG_WRN("DEPRECATED: -dio and --direct-io are deprecated in favour of --load-mode. Please use --load-mode dio instead."); auto p = string_split(argv[i], split_delim); - params.use_direct_io.insert(params.use_direct_io.end(), p.begin(), p.end()); + + std::vector modes; + for (const auto & m : p) { + llama_load_mode mode; + if (m) { + mode = LLAMA_LOAD_MODE_DIRECT_IO; + } else { + mode = LLAMA_LOAD_MODE_NONE; + } + modes.push_back(mode); + } + params.load_mode.insert(params.load_mode.end(), modes.begin(), modes.end()); } else if (arg == "-embd" || arg == "--embeddings") { if (++i >= argc) { invalid_param = true; @@ -1102,6 +1141,9 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { if (params.split_mode.empty()) { params.split_mode = cmd_params_defaults.split_mode; } + if (params.load_mode.empty()) { + params.load_mode = cmd_params_defaults.load_mode; + } if (params.main_gpu.empty()) { params.main_gpu = cmd_params_defaults.main_gpu; } @@ -1120,12 +1162,6 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { if (params.tensor_buft_overrides.empty()) { params.tensor_buft_overrides = cmd_params_defaults.tensor_buft_overrides; } - if (params.use_mmap.empty()) { - params.use_mmap = cmd_params_defaults.use_mmap; - } - if (params.use_direct_io.empty()) { - params.use_direct_io = cmd_params_defaults.use_direct_io; - } if (params.embeddings.empty()) { params.embeddings = cmd_params_defaults.embeddings; } @@ -1173,14 +1209,13 @@ struct cmd_params_instance { int n_gpu_layers; int n_cpu_moe; llama_split_mode split_mode; + llama_load_mode load_mode; int main_gpu; bool no_kv_offload; llama_flash_attn_type flash_attn; std::vector devices; std::vector tensor_split; std::vector tensor_buft_overrides; - bool use_mmap; - bool use_direct_io; bool embeddings; bool no_op_offload; bool no_host; @@ -1195,10 +1230,9 @@ struct cmd_params_instance { mparams.devices = const_cast(devices.data()); } mparams.split_mode = split_mode; + mparams.load_mode = load_mode; mparams.main_gpu = main_gpu; mparams.tensor_split = tensor_split.data(); - mparams.use_mmap = use_mmap; - mparams.use_direct_io = use_direct_io; mparams.no_host = no_host; if (n_cpu_moe <= 0) { @@ -1244,9 +1278,7 @@ struct cmd_params_instance { return model == other.model && n_gpu_layers == other.n_gpu_layers && n_cpu_moe == other.n_cpu_moe && split_mode == other.split_mode && main_gpu == other.main_gpu && tensor_split == other.tensor_split && - use_mmap == other.use_mmap && use_direct_io == other.use_direct_io && - devices == other.devices && - no_host == other.no_host && + load_mode == other.load_mode && devices == other.devices && no_host == other.no_host && vec_tensor_buft_override_equal(tensor_buft_overrides, other.tensor_buft_overrides); } @@ -1279,12 +1311,11 @@ static std::vector get_cmd_params_instances(const cmd_param for (const auto & nl : params.n_gpu_layers) for (const auto & ncmoe : params.n_cpu_moe) for (const auto & sm : params.split_mode) + for (const auto & lm : params.load_mode) for (const auto & mg : params.main_gpu) for (const auto & devs : params.devices) for (const auto & ts : params.tensor_split) for (const auto & ot : params.tensor_buft_overrides) - for (const auto & mmp : params.use_mmap) - for (const auto & dio : params.use_direct_io) for (const auto & noh : params.no_host) for (const auto & embd : params.embeddings) for (const auto & nopo : params.no_op_offload) @@ -1304,34 +1335,33 @@ static std::vector get_cmd_params_instances(const cmd_param continue; } cmd_params_instance instance = { - /* .model = */ m, - /* .n_prompt = */ n_prompt, - /* .n_gen = */ 0, - /* .n_depth = */ nd, - /* .n_batch = */ nb, - /* .n_ubatch = */ nub, - /* .type_k = */ tk, - /* .type_v = */ tv, - /* .n_threads = */ nt, - /* .cpu_mask = */ cm, - /* .cpu_strict = */ cs, - /* .poll = */ pl, - /* .n_gpu_layers = */ nl, - /* .n_cpu_moe = */ ncmoe, - /* .split_mode = */ sm, - /* .main_gpu = */ mg, - /* .no_kv_offload= */ nkvo, - /* .flash_attn = */ fa, - /* .devices = */ devs, - /* .tensor_split = */ ts, + /* .model = */ m, + /* .n_prompt = */ n_prompt, + /* .n_gen = */ 0, + /* .n_depth = */ nd, + /* .n_batch = */ nb, + /* .n_ubatch = */ nub, + /* .type_k = */ tk, + /* .type_v = */ tv, + /* .n_threads = */ nt, + /* .cpu_mask = */ cm, + /* .cpu_strict = */ cs, + /* .poll = */ pl, + /* .n_gpu_layers = */ nl, + /* .n_cpu_moe = */ ncmoe, + /* .split_mode = */ sm, + /* .load_mode = */ lm, + /* .main_gpu = */ mg, + /* .no_kv_offload = */ nkvo, + /* .flash_attn = */ fa, + /* .devices = */ devs, + /* .tensor_split = */ ts, /* .tensor_buft_overrides = */ ot, - /* .use_mmap = */ mmp, - /* .use_direct_io= */ dio, - /* .embeddings = */ embd, - /* .no_op_offload= */ nopo, - /* .no_host = */ noh, - /* .fit_target = */ fpt, - /* .fit_min_ctx = */ fpc, + /* .embeddings = */ embd, + /* .no_op_offload = */ nopo, + /* .no_host = */ noh, + /* .fit_target = */ fpt, + /* .fit_min_ctx = */ fpc, }; instances.push_back(instance); } @@ -1341,34 +1371,33 @@ static std::vector get_cmd_params_instances(const cmd_param continue; } cmd_params_instance instance = { - /* .model = */ m, - /* .n_prompt = */ 0, - /* .n_gen = */ n_gen, - /* .n_depth = */ nd, - /* .n_batch = */ nb, - /* .n_ubatch = */ nub, - /* .type_k = */ tk, - /* .type_v = */ tv, - /* .n_threads = */ nt, - /* .cpu_mask = */ cm, - /* .cpu_strict = */ cs, - /* .poll = */ pl, - /* .n_gpu_layers = */ nl, - /* .n_cpu_moe = */ ncmoe, - /* .split_mode = */ sm, - /* .main_gpu = */ mg, - /* .no_kv_offload= */ nkvo, - /* .flash_attn = */ fa, - /* .devices = */ devs, - /* .tensor_split = */ ts, + /* .model = */ m, + /* .n_prompt = */ 0, + /* .n_gen = */ n_gen, + /* .n_depth = */ nd, + /* .n_batch = */ nb, + /* .n_ubatch = */ nub, + /* .type_k = */ tk, + /* .type_v = */ tv, + /* .n_threads = */ nt, + /* .cpu_mask = */ cm, + /* .cpu_strict = */ cs, + /* .poll = */ pl, + /* .n_gpu_layers = */ nl, + /* .n_cpu_moe = */ ncmoe, + /* .split_mode = */ sm, + /* .load_mode = */ lm, + /* .main_gpu = */ mg, + /* .no_kv_offload = */ nkvo, + /* .flash_attn = */ fa, + /* .devices = */ devs, + /* .tensor_split = */ ts, /* .tensor_buft_overrides = */ ot, - /* .use_mmap = */ mmp, - /* .use_direct_io= */ dio, - /* .embeddings = */ embd, - /* .no_op_offload= */ nopo, - /* .no_host = */ noh, - /* .fit_target = */ fpt, - /* .fit_min_ctx = */ fpc, + /* .embeddings = */ embd, + /* .no_op_offload = */ nopo, + /* .no_host = */ noh, + /* .fit_target = */ fpt, + /* .fit_min_ctx = */ fpc, }; instances.push_back(instance); } @@ -1378,34 +1407,33 @@ static std::vector get_cmd_params_instances(const cmd_param continue; } cmd_params_instance instance = { - /* .model = */ m, - /* .n_prompt = */ n_pg.first, - /* .n_gen = */ n_pg.second, - /* .n_depth = */ nd, - /* .n_batch = */ nb, - /* .n_ubatch = */ nub, - /* .type_k = */ tk, - /* .type_v = */ tv, - /* .n_threads = */ nt, - /* .cpu_mask = */ cm, - /* .cpu_strict = */ cs, - /* .poll = */ pl, - /* .n_gpu_layers = */ nl, - /* .n_cpu_moe = */ ncmoe, - /* .split_mode = */ sm, - /* .main_gpu = */ mg, - /* .no_kv_offload= */ nkvo, - /* .flash_attn = */ fa, - /* .devices = */ devs, - /* .tensor_split = */ ts, + /* .model = */ m, + /* .n_prompt = */ n_pg.first, + /* .n_gen = */ n_pg.second, + /* .n_depth = */ nd, + /* .n_batch = */ nb, + /* .n_ubatch = */ nub, + /* .type_k = */ tk, + /* .type_v = */ tv, + /* .n_threads = */ nt, + /* .cpu_mask = */ cm, + /* .cpu_strict = */ cs, + /* .poll = */ pl, + /* .n_gpu_layers = */ nl, + /* .n_cpu_moe = */ ncmoe, + /* .split_mode = */ sm, + /* .load_mode = */ lm, + /* .main_gpu = */ mg, + /* .no_kv_offload = */ nkvo, + /* .flash_attn = */ fa, + /* .devices = */ devs, + /* .tensor_split = */ ts, /* .tensor_buft_overrides = */ ot, - /* .use_mmap = */ mmp, - /* .use_direct_io= */ dio, - /* .embeddings = */ embd, - /* .no_op_offload= */ nopo, - /* .no_host = */ noh, - /* .fit_target = */ fpt, - /* .fit_min_ctx = */ fpc, + /* .embeddings = */ embd, + /* .no_op_offload = */ nopo, + /* .no_host = */ noh, + /* .fit_target = */ fpt, + /* .fit_min_ctx = */ fpc, }; instances.push_back(instance); } @@ -1435,14 +1463,13 @@ struct test { int n_gpu_layers; int n_cpu_moe; llama_split_mode split_mode; + llama_load_mode load_mode; int main_gpu; bool no_kv_offload; llama_flash_attn_type flash_attn; std::vector devices; std::vector tensor_split; std::vector tensor_buft_overrides; - bool use_mmap; - bool use_direct_io; bool embeddings; bool no_op_offload; bool no_host; @@ -1475,14 +1502,13 @@ struct test { n_gpu_layers = inst.n_gpu_layers; n_cpu_moe = inst.n_cpu_moe; split_mode = inst.split_mode; + load_mode = inst.load_mode; main_gpu = inst.main_gpu; no_kv_offload = inst.no_kv_offload; flash_attn = inst.flash_attn; devices = inst.devices; tensor_split = inst.tensor_split; tensor_buft_overrides = inst.tensor_buft_overrides; - use_mmap = inst.use_mmap; - use_direct_io = inst.use_direct_io; embeddings = inst.embeddings; no_op_offload = inst.no_op_offload; no_host = inst.no_host; @@ -1544,8 +1570,8 @@ struct test { "n_ubatch", "n_threads", "cpu_mask", "cpu_strict", "poll", "type_k", "type_v", "n_gpu_layers", "n_cpu_moe", "split_mode", "main_gpu", "no_kv_offload", "flash_attn", "devices", "tensor_split", - "tensor_buft_overrides", "use_mmap", "use_direct_io", "embeddings", - "no_op_offload", "no_host", "fit_target", "fit_min_ctx", + "tensor_buft_overrides", "load_mode", "embeddings", + "no_op_offload", "no_host", "fit_target", "fit_min_ctx", "n_prompt", "n_gen", "n_depth", "test_time", "avg_ns", "stddev_ns", "avg_ts", "stddev_ts" }; @@ -1563,12 +1589,15 @@ struct test { return INT; } if (field == "f16_kv" || field == "no_kv_offload" || field == "cpu_strict" || - field == "use_mmap" || field == "use_direct_io" || field == "embeddings" || field == "no_host") { + field == "embeddings" || field == "no_host") { return BOOL; } if (field == "avg_ts" || field == "stddev_ts") { return FLOAT; } + if (field == "load_mode") { + return STRING; + } return STRING; } @@ -1635,8 +1664,7 @@ struct test { devices_to_string(devices), tensor_split_str, tensor_buft_overrides_str, - std::to_string(use_mmap), - std::to_string(use_direct_io), + llama_load_mode_name(load_mode), std::to_string(embeddings), std::to_string(no_op_offload), std::to_string(no_host), @@ -1815,18 +1843,15 @@ struct markdown_printer : public printer { if (field == "split_mode") { return 6; } + if (field == "load_mode") { + return 10; + } if (field == "flash_attn") { return 3; } if (field == "devices") { return -12; } - if (field == "use_mmap") { - return 4; - } - if (field == "use_direct_io") { - return 3; - } if (field == "test") { return 15; } @@ -1861,11 +1886,8 @@ struct markdown_printer : public printer { if (field == "flash_attn") { return "fa"; } - if (field == "use_mmap") { - return "mmap"; - } - if (field == "use_direct_io") { - return "dio"; + if (field == "load_mode") { + return "lm"; } if (field == "embeddings") { return "embd"; @@ -1954,11 +1976,8 @@ struct markdown_printer : public printer { if (params.tensor_buft_overrides.size() > 1 || !vec_vec_tensor_buft_override_equal(params.tensor_buft_overrides, cmd_params_defaults.tensor_buft_overrides)) { fields.emplace_back("tensor_buft_overrides"); } - if (params.use_mmap.size() > 1 || params.use_mmap != cmd_params_defaults.use_mmap) { - fields.emplace_back("use_mmap"); - } - if (params.use_direct_io.size() > 1 || params.use_direct_io != cmd_params_defaults.use_direct_io) { - fields.emplace_back("use_direct_io"); + if (params.load_mode.size() > 1 || params.load_mode != cmd_params_defaults.load_mode) { + fields.emplace_back("load_mode"); } if (params.embeddings.size() > 1 || params.embeddings != cmd_params_defaults.embeddings) { fields.emplace_back("embeddings"); diff --git a/tools/mtmd/CMakeLists.txt b/tools/mtmd/CMakeLists.txt index 12ff725e37ca..b62c15c1570d 100644 --- a/tools/mtmd/CMakeLists.txt +++ b/tools/mtmd/CMakeLists.txt @@ -1,8 +1,15 @@ # mtmd -set(MTMD_VIDEO ON CACHE BOOL "enable video support in mtmd (requires ffmpeg binary in PATH)") +set(MTMD_VIDEO_HELP "enable video support in mtmd (requires ffmpeg binary in PATH)") + +set(MTMD_VIDEO ON CACHE BOOL "${MTMD_VIDEO_HELP}") # TODO: add MTMD_VIDEO_METHOD in the future to select between ffmpeg and other backends +if (MTMD_VIDEO AND NOT LLAMA_SUBPROCESS) + message(STATUS "Disabling MTMD_VIDEO because LLAMA_SUBPROCESS is OFF") + set(MTMD_VIDEO OFF CACHE BOOL "${MTMD_VIDEO_HELP}" FORCE) +endif() + find_package(Threads REQUIRED) add_library(mtmd @@ -31,6 +38,7 @@ add_library(mtmd models/granite4-vision.cpp models/hunyuanvl.cpp models/internvl.cpp + models/kimik3.cpp models/inkling.cpp models/kimivl.cpp models/kimik25.cpp @@ -41,9 +49,11 @@ add_library(mtmd models/paddleocr.cpp models/pixtral.cpp models/qwen2vl.cpp + models/minimax-m3.cpp models/qwen3vl.cpp models/mimovl.cpp models/qwen3a.cpp + models/mimo-audio.cpp models/step3vl.cpp models/siglip.cpp models/whisper-enc.cpp @@ -52,6 +62,7 @@ add_library(mtmd models/mobilenetv5.cpp models/youtuvl.cpp models/yasa2.cpp + models/parakeet.cpp ) set_target_properties(mtmd PROPERTIES diff --git a/tools/mtmd/clip-graph.h b/tools/mtmd/clip-graph.h index a95de20a3122..29352abb4c0b 100644 --- a/tools/mtmd/clip-graph.h +++ b/tools/mtmd/clip-graph.h @@ -13,6 +13,14 @@ struct build_vit_opts { ggml_tensor * attn_mask = nullptr; + // TODO @ngxson : merge attn_mask and attn_mask_layers into one call + std::vector attn_mask_layers; // one per layer + + // hook at layer output embeddings + std::function callback_layer_out = nullptr; + + // whether to skip the automatic post-layernorm (model.post_ln_w) applied at the end + bool skip_post_ln = false; }; struct clip_graph { diff --git a/tools/mtmd/clip-impl.h b/tools/mtmd/clip-impl.h index c874a2c7c99c..7844a6a81f96 100644 --- a/tools/mtmd/clip-impl.h +++ b/tools/mtmd/clip-impl.h @@ -41,6 +41,7 @@ #define KEY_PROJ_DIM "clip.%s.projection_dim" #define KEY_N_HEAD "clip.%s.attention.head_count" #define KEY_N_HEAD_KV "clip.%s.attention.head_count_kv" +#define KEY_N_EMBD_HEAD "clip.%s.attention.head_dim" #define KEY_LAYER_NORM_EPS "clip.%s.attention.layer_norm_epsilon" #define KEY_FEATURE_LAYERS "clip.%s.feature_layer" @@ -82,6 +83,13 @@ #define KEY_A_PROJ_WINDOW_SIZE "clip.audio.projector.window_size" #define KEY_A_PROJ_DOWNSAMPLE_RATE "clip.audio.projector.downsample_rate" #define KEY_A_PROJ_HEAD_COUNT "clip.audio.projector.head_count" +#define KEY_A_RVQ_NUM_QUANTIZERS "clip.audio.rvq.num_quantizers" // mimo-audio-tokenizer +#define KEY_A_RVQ_CODEBOOK_SIZE "clip.audio.rvq.codebook_size" // mimo-audio-tokenizer: per-quantizer bin count +#define KEY_A_WA_PATTERN_MODE "clip.audio.wa_pattern_mode" // mimo-audio-tokenizer, per-layer -1 (full) / 0 (windowed) +#define KEY_A_ATTN_WINDOW_SIZE "clip.audio.window_size" // mimo-audio-tokenizer: sliding-window radius +#define KEY_A_LOCAL_BLOCK_COUNT "clip.audio.local_block_count" // mimo-v2.5: input_local_transformer layer count +#define KEY_A_LOCAL_GROUP_SIZE "clip.audio.local_group_size" // mimo-v2.5: input_local_transformer grouping size +#define KEY_AUDIO_SUBSAMPLING_FACTOR "clip.audio.subsampling_factor" // // tensor name constants @@ -131,6 +139,8 @@ #define TN_MM_SOFT_EMB_N "mm.soft_emb_norm.weight" // gemma3 #define TN_MM_PROJECTOR "mm.model.fc.%s" // idefics3, deepseekocr #define TN_MM_PATCH_MERGER "mm.patch_merger.%s" // mistral small 3.1, glm4v +#define TN_MM_MERGER_FC1 "mm.merger.fc1.%s" // minimax-m3 patch-merge MLP +#define TN_MM_MERGER_FC2 "mm.merger.fc2.%s" #define TN_TOK_IMG_BREAK "v.token_embd.img_break" // pixtral #define TN_TOK_GLM_BOI "adapter.boi" // glm-edge (these embeddings are not in text model) #define TN_TOK_GLM_EOI "adapter.eoi" // glm-edge (these embeddings are not in text model) @@ -173,6 +183,24 @@ #define TN_MM_NORM_PRE "mm.a.norm_pre.%s" #define TN_MM_NORM_MID "mm.a.norm_mid.%s" +// mimo-audio-tokenizer +#define TN_A_DOWNSAMPLE_CONV "a.downsample.conv.%s" +#define TN_A_DOWNSAMPLE_NORM "a.downsample.norm.%s" +#define TN_A_RVQ_CODEBOOK "a.rvq.codebook.%s" +// mimo-v2.5: text-side RVQ code embedding ("text codebook") +#define TN_MM_A_CODE_EMBD "mm.a.code_embd.%s" +// mimo-v2.5: LLM-side connector (input_local_transformer) +#define TN_MM_A_LOCAL_ATTN_Q "mm.a.local_blk.%d.attn_q.%s" +#define TN_MM_A_LOCAL_ATTN_K "mm.a.local_blk.%d.attn_k.%s" +#define TN_MM_A_LOCAL_ATTN_V "mm.a.local_blk.%d.attn_v.%s" +#define TN_MM_A_LOCAL_ATTN_OUT "mm.a.local_blk.%d.attn_out.%s" +#define TN_MM_A_LOCAL_FFN_GATE "mm.a.local_blk.%d.ffn_gate.%s" +#define TN_MM_A_LOCAL_FFN_UP "mm.a.local_blk.%d.ffn_up.%s" +#define TN_MM_A_LOCAL_FFN_DOWN "mm.a.local_blk.%d.ffn_down.%s" +#define TN_MM_A_LOCAL_LN1 "mm.a.local_blk.%d.ln1.%s" +#define TN_MM_A_LOCAL_LN2 "mm.a.local_blk.%d.ln2.%s" +#define TN_MM_A_LOCAL_NORM "mm.a.local_norm.%s" + // cogvlm #define TN_MM_POST_FC_NORM "mm.post_fc_norm.%s" #define TN_MM_H_TO_4H "mm.up.%s" @@ -319,6 +347,12 @@ #define TN_YASA_STAGE_DOWN_CONV "v.stage.%d.down.conv.%s" #define TN_YASA_STAGE_BLK "v.stage.%d.blk.%d.%s.%s" +// parakeet +#define TN_MEL_FILTERS "a.mel_filters" +#define TN_WINDOW "a.window" +#define TN_CONV_NORM_MEAN "%s.blk.%d.conv_norm_mean" +#define TN_CONV_NORM_VAR "%s.blk.%d.conv_norm_var" + // align x to upper multiple of n #define CLIP_ALIGN(x, n) ((((x) + (n) - 1) / (n)) * (n)) @@ -372,13 +406,17 @@ enum projector_type { PROJECTOR_TYPE_YOUTUVL, PROJECTOR_TYPE_YASA2, PROJECTOR_TYPE_KIMIK25, + PROJECTOR_TYPE_KIMIK3, PROJECTOR_TYPE_NEMOTRON_V2_VL, PROJECTOR_TYPE_HUNYUANVL, + PROJECTOR_TYPE_PARAKEET, PROJECTOR_TYPE_EXAONE4_5, PROJECTOR_TYPE_MINICPMV4_6, PROJECTOR_TYPE_GRANITE_SPEECH, PROJECTOR_TYPE_MIMOVL, + PROJECTOR_TYPE_MINIMAX_M3, PROJECTOR_TYPE_GRANITE4_VISION, + PROJECTOR_TYPE_MIMO_AUDIO, PROJECTOR_TYPE_UNKNOWN, }; @@ -427,13 +465,17 @@ static std::map PROJECTOR_TYPE_NAMES = { { PROJECTOR_TYPE_YOUTUVL, "youtuvl"}, { PROJECTOR_TYPE_YASA2, "yasa2"}, { PROJECTOR_TYPE_KIMIK25, "kimik25"}, + { PROJECTOR_TYPE_KIMIK3, "kimik3"}, { PROJECTOR_TYPE_NEMOTRON_V2_VL, "nemotron_v2_vl"}, { PROJECTOR_TYPE_EXAONE4_5, "exaone4_5"}, { PROJECTOR_TYPE_HUNYUANVL, "hunyuanvl"}, { PROJECTOR_TYPE_MINICPMV4_6, "minicpmv4_6"}, { PROJECTOR_TYPE_GRANITE_SPEECH, "granite_speech"}, { PROJECTOR_TYPE_MIMOVL, "mimovl"}, + { PROJECTOR_TYPE_MINIMAX_M3, "minimax_m3"}, { PROJECTOR_TYPE_GRANITE4_VISION, "granite4_vision"}, + { PROJECTOR_TYPE_MIMO_AUDIO, "mimo_audio"}, + { PROJECTOR_TYPE_PARAKEET, "parakeet"}, }; static projector_type clip_projector_type_from_string(const std::string & str) { diff --git a/tools/mtmd/clip-model.h b/tools/mtmd/clip-model.h index 9b5cb09a8f6f..ee6b09879b44 100644 --- a/tools/mtmd/clip-model.h +++ b/tools/mtmd/clip-model.h @@ -54,6 +54,8 @@ struct clip_hparams { int32_t projection_dim = 0; int32_t n_head = 0; int32_t n_head_kv = 0; + // 0 = derive from n_embd; set when qkv width != n_embd + int32_t n_embd_head = 0; int32_t n_layer = 0; int32_t n_merge = 1; // number of patch merges **per-side** @@ -110,6 +112,8 @@ struct clip_hparams { // audio int32_t n_mel_bins = 0; // whisper preprocessor int32_t proj_stack_factor = 0; // ultravox + int32_t subsampling_factor = 0; // parakeet + int32_t audio_chunk_size = 0; int32_t audio_conv_kernel_size = 0; int32_t audio_max_pos_emb = 0; @@ -124,6 +128,18 @@ struct clip_hparams { int32_t audio_window_len = -1; int32_t audio_hop_len = -1; + // parakeet + std::vector mel_filters; + std::vector window; + + // mimo-audio-tokenizer: residual vector quantizer + int32_t rvq_num_quantizers = 0; + std::vector rvq_codebook_size; // per-quantizer bin count (ragged, e.g. 1024/1024/256/128x17) + + // mimo-v2.5: LLM-side connector (input_local_transformer) + int32_t audio_local_n_layer = 0; + int32_t audio_local_group_size = 0; + // legacy bool has_llava_projector = false; int minicpmv_version = 0; @@ -237,14 +253,16 @@ struct clip_layer { ggml_tensor * norm_conv_b = nullptr; ggml_tensor * linear_pos_w = nullptr; - ggml_tensor * conv_norm_w = nullptr; - ggml_tensor * conv_norm_b = nullptr; - ggml_tensor * conv_dw_w = nullptr; - ggml_tensor * conv_dw_b = nullptr; - ggml_tensor * conv_pw1_w = nullptr; - ggml_tensor * conv_pw1_b = nullptr; - ggml_tensor * conv_pw2_w = nullptr; - ggml_tensor * conv_pw2_b = nullptr; + ggml_tensor * conv_norm_w = nullptr; + ggml_tensor * conv_norm_b = nullptr; + ggml_tensor * conv_norm_mean = nullptr; // parakeet + ggml_tensor * conv_norm_var = nullptr; // parakeet + ggml_tensor * conv_dw_w = nullptr; + ggml_tensor * conv_dw_b = nullptr; + ggml_tensor * conv_pw1_w = nullptr; + ggml_tensor * conv_pw1_b = nullptr; + ggml_tensor * conv_pw2_w = nullptr; + ggml_tensor * conv_pw2_b = nullptr; // gemma4 audio conformer per-layer ggml_tensor * attn_pre_norm_w = nullptr; @@ -408,6 +426,10 @@ struct clip_model { ggml_tensor * mm_0_b = nullptr; ggml_tensor * mm_2_w = nullptr; ggml_tensor * mm_2_b = nullptr; + ggml_tensor * mm_merger_fc1_w = nullptr; // minimax-m3 + ggml_tensor * mm_merger_fc1_b = nullptr; + ggml_tensor * mm_merger_fc2_w = nullptr; + ggml_tensor * mm_merger_fc2_b = nullptr; ggml_tensor * image_newline = nullptr; ggml_tensor * view_seperator = nullptr; @@ -544,6 +566,20 @@ struct clip_model { ggml_tensor * mm_norm_pre_b = nullptr; ggml_tensor * mm_norm_mid_w = nullptr; + // mimo-audio-tokenizer: post-transformer downsample + RVQ codebook + ggml_tensor * downsample_conv_w = nullptr; // no bias + ggml_tensor * downsample_norm_w = nullptr; + ggml_tensor * downsample_norm_b = nullptr; + ggml_tensor * rvq_codebook = nullptr; // merged 3D [n_q, max_bins, dim] + + // mimo-v2.5: text-side RVQ code embedding ("text codebook") + ggml_tensor * mm_a_code_embd = nullptr; // merged 3D [n_channels, vocab, dim] + + // mimo-v2.5: LLM-side connector (input_local_transformer, separate from the + // audio_tokenizer's own encoder `layers`) + std::vector mm_a_local_layers; + ggml_tensor * mm_a_local_norm_w = nullptr; + // qwen3a ggml_tensor * conv2d_1_w = nullptr; ggml_tensor * conv2d_1_b = nullptr; diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp index dbd07081bf73..c0c0e2104b1f 100644 --- a/tools/mtmd/clip.cpp +++ b/tools/mtmd/clip.cpp @@ -253,7 +253,7 @@ clip_graph::clip_graph(clip_ctx * ctx, const clip_image_f32 & img) : n_embd(hparams.n_embd), n_head(hparams.n_head), n_head_kv(hparams.n_head_kv), - d_head(n_head > 0 ? n_embd / n_head : 0), + d_head(hparams.n_embd_head > 0 ? hparams.n_embd_head : (n_head > 0 ? n_embd / n_head : 0)), n_layer(hparams.n_layer), n_mmproj_embd(clip_n_mmproj_embd(ctx)), eps(hparams.eps), @@ -340,6 +340,11 @@ ggml_tensor * clip_graph::build_vit( auto & layer = model.layers[il]; ggml_tensor * cur = inpL; // inpL = residual, cur = hidden_states + ggml_tensor * attn_mask = opts.attn_mask; + if (opts.attn_mask_layers.size() > (size_t) il) { + attn_mask = opts.attn_mask_layers[il]; + } + // layernorm1 cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, norm_t, eps, il); cb(cur, "layer_inp_normed", il); @@ -367,13 +372,13 @@ ggml_tensor * clip_graph::build_vit( /* nb1 */ ggml_row_size(cur->type, d_head), /* nb2 */ cur->nb[1], /* nb3 */ cur->nb[1] * n_pos, - /* offset */ ggml_row_size(cur->type, n_embd)); + /* offset */ ggml_row_size(cur->type, n_head * d_head)); Vcur = ggml_view_4d(ctx0, cur, d_head, n_head, n_pos, B, /* nb1 */ ggml_row_size(cur->type, d_head), /* nb2 */ cur->nb[1], /* nb3 */ cur->nb[1] * n_pos, - /* offset */ ggml_row_size(cur->type, 2 * n_embd)); + /* offset */ ggml_row_size(cur->type, 2 * n_head * d_head)); if (layer.q_norm) { GGML_ASSERT(layer.q_norm->ne[0] == Qcur->ne[0]); @@ -452,7 +457,7 @@ ggml_tensor * clip_graph::build_vit( // build_attn returns a flat 2D [n_embd, n_pos*B] cur = build_attn(layer.o_w, layer.o_b, - Qcur, Kcur, Vcur, opts.attn_mask, kq_scale, il); + Qcur, Kcur, Vcur, attn_mask, kq_scale, il); cb(cur, "attn_out", il); } @@ -471,6 +476,10 @@ ggml_tensor * clip_graph::build_vit( inpL = cur; // inpL = residual, cur = hidden_states + if (opts.callback_layer_out) { + opts.callback_layer_out(cur, il); + } + cb(cur, "ffn_inp", il); // layernorm2 (pre-ffn norm) @@ -519,7 +528,7 @@ ggml_tensor * clip_graph::build_vit( } // post-layernorm - if (model.post_ln_w) { + if (model.post_ln_w && !opts.skip_post_ln) { inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, norm_t, eps, -1); } @@ -919,6 +928,10 @@ static std::unique_ptr clip_get_graph_builder(clip_ctx * ctx, const { builder = std::make_unique(ctx, img); } break; + case PROJECTOR_TYPE_MINIMAX_M3: + { + builder = std::make_unique(ctx, img); + } break; case PROJECTOR_TYPE_STEP3VL: { builder = std::make_unique(ctx, img); @@ -964,6 +977,10 @@ static std::unique_ptr clip_get_graph_builder(clip_ctx * ctx, const { builder = std::make_unique(ctx, img); } break; + case PROJECTOR_TYPE_KIMIK3: + { + builder = std::make_unique(ctx, img); + } break; case PROJECTOR_TYPE_COGVLM: { builder = std::make_unique(ctx, img); @@ -1012,6 +1029,10 @@ static std::unique_ptr clip_get_graph_builder(clip_ctx * ctx, const { builder = std::make_unique(ctx, img); } break; + case PROJECTOR_TYPE_MIMO_AUDIO: + { + builder = std::make_unique(ctx, img); + } break; case PROJECTOR_TYPE_YOUTUVL: { builder = std::make_unique(ctx, img); @@ -1020,6 +1041,10 @@ static std::unique_ptr clip_get_graph_builder(clip_ctx * ctx, const { builder = std::make_unique(ctx, img); } break; + case PROJECTOR_TYPE_PARAKEET: + { + builder = std::make_unique(ctx, img); + } break; case PROJECTOR_TYPE_GRANITE4_VISION: { builder = std::make_unique(ctx, img); @@ -1173,6 +1198,7 @@ struct clip_model_loader { const char * prefix = is_vision ? "vision" : "audio"; get_u32(string_format(KEY_N_EMBD, prefix), hparams.n_embd); get_u32(string_format(KEY_N_HEAD, prefix), hparams.n_head); + get_u32(string_format(KEY_N_EMBD_HEAD, prefix), hparams.n_embd_head, false); get_u32(string_format(KEY_N_FF, prefix), hparams.n_ff); get_u32(string_format(KEY_N_BLOCK, prefix), hparams.n_layer); get_u32(string_format(KEY_PROJ_DIM, prefix), hparams.projection_dim); @@ -1334,6 +1360,7 @@ struct clip_model_loader { // ViT merger 2x2 + final merger 2x2 = 4x spatial merge per dimension hparams.n_merge = 4; get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false); + GGML_ASSERT(hparams.n_merge == 2 || hparams.n_merge == 4); // borrow wa_layer_indexes for vit_merger insertion point std::vector wa_layer_indexes_vec; @@ -1358,6 +1385,20 @@ struct clip_model_loader { { get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false); } break; + case PROJECTOR_TYPE_PARAKEET: + { + get_u32(KEY_AUDIO_SUBSAMPLING_FACTOR, hparams.subsampling_factor); + GGML_ASSERT(hparams.subsampling_factor == 8 && + "subsampling_factor must match the conv strides in clip_graph_parakeet::build()"); + get_u32(KEY_A_CONV_KERNEL_SIZE, hparams.audio_conv_kernel_size); + GGML_ASSERT(hparams.audio_conv_kernel_size > 0 && hparams.audio_conv_kernel_size % 2 == 1 && + "audio_conv_kernel_size must be a positive odd integer"); + hparams.audio_chunk_len = 0; + hparams.audio_sample_rate = 16000; + hparams.audio_n_fft = 512; + hparams.audio_window_len = 400; + hparams.audio_hop_len = 160; + } break; case PROJECTOR_TYPE_IDEFICS3: { // use default llava-uhd preprocessing params @@ -1425,6 +1466,23 @@ struct clip_model_loader { hparams.rope_theta = 10000.0f; get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false); + int min_pixels = 0, max_pixels = 0; + get_u32(KEY_IMAGE_MIN_PIXELS, min_pixels, false); + get_u32(KEY_IMAGE_MAX_PIXELS, max_pixels, false); + if (min_pixels > 0 && max_pixels > 0) { + hparams.image_min_pixels = min_pixels; + hparams.image_max_pixels = max_pixels; + hparams.warmup_image_size = static_cast(std::sqrt(max_pixels)); + } else { + hparams.set_limit_image_tokens(2, 4096); + } + } break; + case PROJECTOR_TYPE_KIMIK3: + { + hparams.image_resize_algo = RESIZE_ALGO_BILINEAR; + hparams.rope_theta = 10000.0f; + get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false); + int min_pixels = 0, max_pixels = 0; get_u32(KEY_IMAGE_MIN_PIXELS, min_pixels, false); get_u32(KEY_IMAGE_MAX_PIXELS, max_pixels, false); @@ -1488,6 +1546,17 @@ struct clip_model_loader { LOG_WRN("%s: more info: https://github.com/ggml-org/llama.cpp/issues/16842\n\n", __func__); } } break; + case PROJECTOR_TYPE_MINIMAX_M3: + { + hparams.n_merge = 2; // spatial_merge_size + hparams.image_resize_algo = RESIZE_ALGO_BICUBIC_PILLOW; + hparams.image_resize_pad = PAD_NONE; + get_u32(KEY_SPATIAL_MERGE_SIZE, hparams.n_merge, false); + hparams.rope_theta = 10000.0f; // vision_config.rope_theta + // MiniMax-M3: max_pixels 451584 (=672^2) -> 576 merged tokens (image_seq_length) + hparams.set_limit_image_tokens(8, 576); + hparams.set_warmup_n_tokens(16*16); + } break; case PROJECTOR_TYPE_MIMOVL: { hparams.n_merge = 2; // spatial_merge_size @@ -1579,6 +1648,45 @@ struct clip_model_loader { hparams.audio_window_len = 400; hparams.audio_hop_len = 160; } break; + case PROJECTOR_TYPE_MIMO_AUDIO: + { + get_u32(KEY_A_RVQ_NUM_QUANTIZERS, hparams.rvq_num_quantizers, false); + get_arr_int(KEY_A_RVQ_CODEBOOK_SIZE, hparams.rvq_codebook_size, false); + if (hparams.rvq_num_quantizers <= 0) { + throw std::runtime_error(string_format("%s: mimo_audio: missing %s\n", __func__, KEY_A_RVQ_NUM_QUANTIZERS)); + } + if ((int) hparams.rvq_codebook_size.size() != hparams.rvq_num_quantizers) { + throw std::runtime_error(string_format( + "%s: mimo_audio: %s length (%zu) must equal %s (%d)\n", __func__, + KEY_A_RVQ_CODEBOOK_SIZE, hparams.rvq_codebook_size.size(), + KEY_A_RVQ_NUM_QUANTIZERS, hparams.rvq_num_quantizers)); + } + hparams.ffn_op = FFN_GELU_ERF; // PyTorch F.gelu default (approximate="none") + hparams.rope_theta = 10000.0f; + + // audio preprocessing params (mel spectrogram) + hparams.audio_sample_rate = 24000; + hparams.audio_n_fft = 960; + hparams.audio_window_len = 960; + hparams.audio_hop_len = 240; + + get_u32(KEY_A_ATTN_WINDOW_SIZE, hparams.attn_window_size); + std::vector wa_pattern; + get_arr_int(KEY_A_WA_PATTERN_MODE, wa_pattern, true); + if ((int) wa_pattern.size() != hparams.n_layer) { + throw std::runtime_error(string_format( + "%s: mimo_audio: %s length (%zu) must equal n_layer (%d)\n", __func__, + KEY_A_WA_PATTERN_MODE, wa_pattern.size(), hparams.n_layer)); + } + hparams.wa_pattern_mode.assign(wa_pattern.begin(), wa_pattern.end()); + + get_u32(KEY_A_LOCAL_BLOCK_COUNT, hparams.audio_local_n_layer); + get_u32(KEY_A_LOCAL_GROUP_SIZE, hparams.audio_local_group_size); + if (hparams.audio_local_group_size <= 0) { + throw std::runtime_error(string_format( + "%s: mimo_audio: %s must be > 0\n", __func__, KEY_A_LOCAL_GROUP_SIZE)); + } + } break; case PROJECTOR_TYPE_PADDLEOCR: { hparams.n_merge = 2; @@ -1845,16 +1953,46 @@ struct clip_model_loader { return cur; }; - auto get_scalar = [&](const std::string & name, float default_val) { + auto get_vector = [&](const std::string & name) { + std::vector result; auto it = tensor_offset.find(name); if (it == tensor_offset.end()) { + return result; + } + + const int64_t idx = gguf_find_tensor(ctx_gguf.get(), name.c_str()); + if (idx < 0) { + throw std::runtime_error(string_format("%s: failed to find tensor %s\n", __func__, name.c_str())); + } + + if (const auto type = gguf_get_tensor_type(ctx_gguf.get(), idx); type != GGML_TYPE_F32) { + throw std::runtime_error(string_format("%s: %s must be %s, was %s\n", __func__, + name.c_str(), ggml_type_name(GGML_TYPE_F32), ggml_type_name(type))); + } + + const size_t n_bytes = gguf_get_tensor_size(ctx_gguf.get(), idx); + if (n_bytes == 0) { + throw std::runtime_error(string_format("%s: tensor %s is empty\n", __func__, name.c_str())); + } + + const size_t n_elems = n_bytes / sizeof(float); + result.resize(n_elems); + fin.seekg(it->second, std::ios::beg); + fin.read(reinterpret_cast(result.data()), n_bytes); + return result; + }; + + auto get_scalar = [&](const std::string & name, float default_val) { + auto v = get_vector(name); + if (v.empty()) { return default_val; } - size_t offset = it->second; - fin.seekg(offset, std::ios::beg); - float value; - fin.read(reinterpret_cast(&value), sizeof(float)); - return value; + if (v.size() != 1) { + throw std::runtime_error(string_format("%s: expected scalar tensor '%s' but got %d elements\n", + __func__, name.c_str(), (int) v.size())); + } + + return v[0]; }; model.class_embedding = get_tensor(TN_CLASS_EMBD, false); @@ -2064,24 +2202,29 @@ struct clip_model_loader { } break; case PROJECTOR_TYPE_MINICPMV4_6: { + const bool merger_required = hparams.n_merge == 4; + auto get_merger_tensor = [&](const std::string & name, bool required = true) { + return get_tensor(name, merger_required && required); + }; + // ViT merger: window self-attention - model.vit_merger_ln1_w = get_tensor(string_format(TN_VIT_MERGER_LN1, "weight")); - model.vit_merger_ln1_b = get_tensor(string_format(TN_VIT_MERGER_LN1, "bias")); - model.vit_merger_attn_q_w = get_tensor(string_format(TN_VIT_MERGER_ATTN_Q, "weight")); - model.vit_merger_attn_q_b = get_tensor(string_format(TN_VIT_MERGER_ATTN_Q, "bias"), false); - model.vit_merger_attn_k_w = get_tensor(string_format(TN_VIT_MERGER_ATTN_K, "weight")); - model.vit_merger_attn_k_b = get_tensor(string_format(TN_VIT_MERGER_ATTN_K, "bias"), false); - model.vit_merger_attn_v_w = get_tensor(string_format(TN_VIT_MERGER_ATTN_V, "weight")); - model.vit_merger_attn_v_b = get_tensor(string_format(TN_VIT_MERGER_ATTN_V, "bias"), false); - model.vit_merger_attn_o_w = get_tensor(string_format(TN_VIT_MERGER_ATTN_O, "weight")); - model.vit_merger_attn_o_b = get_tensor(string_format(TN_VIT_MERGER_ATTN_O, "bias"), false); + model.vit_merger_ln1_w = get_merger_tensor(string_format(TN_VIT_MERGER_LN1, "weight")); + model.vit_merger_ln1_b = get_merger_tensor(string_format(TN_VIT_MERGER_LN1, "bias")); + model.vit_merger_attn_q_w = get_merger_tensor(string_format(TN_VIT_MERGER_ATTN_Q, "weight")); + model.vit_merger_attn_q_b = get_merger_tensor(string_format(TN_VIT_MERGER_ATTN_Q, "bias"), false); + model.vit_merger_attn_k_w = get_merger_tensor(string_format(TN_VIT_MERGER_ATTN_K, "weight")); + model.vit_merger_attn_k_b = get_merger_tensor(string_format(TN_VIT_MERGER_ATTN_K, "bias"), false); + model.vit_merger_attn_v_w = get_merger_tensor(string_format(TN_VIT_MERGER_ATTN_V, "weight")); + model.vit_merger_attn_v_b = get_merger_tensor(string_format(TN_VIT_MERGER_ATTN_V, "bias"), false); + model.vit_merger_attn_o_w = get_merger_tensor(string_format(TN_VIT_MERGER_ATTN_O, "weight")); + model.vit_merger_attn_o_b = get_merger_tensor(string_format(TN_VIT_MERGER_ATTN_O, "bias"), false); // ViT merger: MLP downsample - model.vit_merger_ds_ln_w = get_tensor(string_format(TN_VIT_MERGER_DS_LN, "weight")); - model.vit_merger_ds_ln_b = get_tensor(string_format(TN_VIT_MERGER_DS_LN, "bias")); - model.vit_merger_ds_up_w = get_tensor(string_format(TN_VIT_MERGER_DS_UP, "weight")); - model.vit_merger_ds_up_b = get_tensor(string_format(TN_VIT_MERGER_DS_UP, "bias"), false); - model.vit_merger_ds_down_w = get_tensor(string_format(TN_VIT_MERGER_DS_DOWN, "weight")); - model.vit_merger_ds_down_b = get_tensor(string_format(TN_VIT_MERGER_DS_DOWN, "bias"), false); + model.vit_merger_ds_ln_w = get_merger_tensor(string_format(TN_VIT_MERGER_DS_LN, "weight")); + model.vit_merger_ds_ln_b = get_merger_tensor(string_format(TN_VIT_MERGER_DS_LN, "bias")); + model.vit_merger_ds_up_w = get_merger_tensor(string_format(TN_VIT_MERGER_DS_UP, "weight")); + model.vit_merger_ds_up_b = get_merger_tensor(string_format(TN_VIT_MERGER_DS_UP, "bias"), false); + model.vit_merger_ds_down_w = get_merger_tensor(string_format(TN_VIT_MERGER_DS_DOWN, "weight")); + model.vit_merger_ds_down_b = get_merger_tensor(string_format(TN_VIT_MERGER_DS_DOWN, "bias"), false); // Final Merger (DownsampleMLP) model.mm_input_norm_w = get_tensor(TN_MM_INP_NORM); model.mm_input_norm_b = get_tensor(TN_MM_INP_NORM_B, false); @@ -2126,6 +2269,19 @@ struct clip_model_loader { model.mm_1_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight")); model.mm_1_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias"), false); } break; + case PROJECTOR_TYPE_MINIMAX_M3: + { + // per-patch MLP: mm.1 -> gelu -> mm.2 + model.mm_1_w = get_tensor(string_format(TN_LLAVA_PROJ, 1, "weight")); + model.mm_1_b = get_tensor(string_format(TN_LLAVA_PROJ, 1, "bias")); + model.mm_2_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight")); + model.mm_2_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias")); + // 2x2 merge MLP: mm.merge.fc1 -> gelu -> mm.merge.fc2 + model.mm_merger_fc1_w = get_tensor(string_format(TN_MM_MERGER_FC1, "weight")); + model.mm_merger_fc1_b = get_tensor(string_format(TN_MM_MERGER_FC1, "bias")); + model.mm_merger_fc2_w = get_tensor(string_format(TN_MM_MERGER_FC2, "weight")); + model.mm_merger_fc2_b = get_tensor(string_format(TN_MM_MERGER_FC2, "bias")); + } break; case PROJECTOR_TYPE_STEP3VL: { model.mm_0_w = get_tensor(string_format(TN_LLAVA_PROJ, 0, "weight")); @@ -2352,6 +2508,13 @@ struct clip_model_loader { model.mm_2_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight")); model.mm_2_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias")); } break; + case PROJECTOR_TYPE_KIMIK3: + { + // patchmergerv2, bias-free, norm after the projection + model.mm_1_w = get_tensor(string_format(TN_LLAVA_PROJ, 1, "weight")); + model.mm_2_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight")); + model.mm_post_norm_w = get_tensor(string_format(TN_MM_POST_NORM, "weight")); + } break; case PROJECTOR_TYPE_KIMIVL: case PROJECTOR_TYPE_PADDLEOCR: case PROJECTOR_TYPE_KIMIK25: @@ -2453,6 +2616,54 @@ struct clip_model_loader { model.mm_2_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 2, "weight")); model.mm_2_b = get_tensor(string_format(TN_MM_AUDIO_MLP, 2, "bias")); } break; + case PROJECTOR_TYPE_MIMO_AUDIO: + { + model.conv1d_1_w = get_tensor(string_format(TN_CONV1D, 1, "weight")); + model.conv1d_1_b = get_tensor(string_format(TN_CONV1D, 1, "bias")); + model.conv1d_2_w = get_tensor(string_format(TN_CONV1D, 2, "weight")); + model.conv1d_2_b = get_tensor(string_format(TN_CONV1D, 2, "bias")); + model.downsample_conv_w = get_tensor(string_format(TN_A_DOWNSAMPLE_CONV, "weight")); + model.downsample_norm_w = get_tensor(string_format(TN_A_DOWNSAMPLE_NORM, "weight")); + model.downsample_norm_b = get_tensor(string_format(TN_A_DOWNSAMPLE_NORM, "bias")); + model.rvq_codebook = get_tensor(string_format(TN_A_RVQ_CODEBOOK, "weight"), false); + model.mm_a_code_embd = get_tensor(string_format(TN_MM_A_CODE_EMBD, "weight"), false); + if (!model.rvq_codebook || !model.mm_a_code_embd) { + throw std::runtime_error(string_format("%s: mimo_audio: missing %s or %s\n", __func__, + TN_A_RVQ_CODEBOOK, TN_MM_A_CODE_EMBD)); + } + // hparams.rvq_codebook_size comes from GGUF metadata and is independent of the + // tensors' actual shapes - bound it so codebook/code_embd views built from it + // (mimo-audio.cpp) can never read past either tensor's allocated bins/vocab. + for (int32_t bins : hparams.rvq_codebook_size) { + if (bins <= 0 || bins > model.rvq_codebook->ne[1] || bins > model.mm_a_code_embd->ne[1]) { + throw std::runtime_error(string_format( + "%s: mimo_audio: %s entry (%d) out of range for codebook/code_embd tensors\n", + __func__, KEY_A_RVQ_CODEBOOK_SIZE, bins)); + } + } + + // LLM-side connector: input_local_transformer + projection + model.mm_a_local_layers.resize(hparams.audio_local_n_layer); + for (int il = 0; il < hparams.audio_local_n_layer; il++) { + auto & layer = model.mm_a_local_layers[il]; + layer.q_w = get_tensor(string_format(TN_MM_A_LOCAL_ATTN_Q, il, "weight")); + layer.q_b = get_tensor(string_format(TN_MM_A_LOCAL_ATTN_Q, il, "bias")); + layer.k_w = get_tensor(string_format(TN_MM_A_LOCAL_ATTN_K, il, "weight")); + layer.k_b = get_tensor(string_format(TN_MM_A_LOCAL_ATTN_K, il, "bias")); + layer.v_w = get_tensor(string_format(TN_MM_A_LOCAL_ATTN_V, il, "weight")); + layer.v_b = get_tensor(string_format(TN_MM_A_LOCAL_ATTN_V, il, "bias")); + layer.o_w = get_tensor(string_format(TN_MM_A_LOCAL_ATTN_OUT, il, "weight")); + layer.ff_gate_w = get_tensor(string_format(TN_MM_A_LOCAL_FFN_GATE, il, "weight")); + layer.ff_up_w = get_tensor(string_format(TN_MM_A_LOCAL_FFN_UP, il, "weight")); + layer.ff_down_w = get_tensor(string_format(TN_MM_A_LOCAL_FFN_DOWN, il, "weight")); + layer.ln_1_w = get_tensor(string_format(TN_MM_A_LOCAL_LN1, il, "weight")); + layer.ln_2_w = get_tensor(string_format(TN_MM_A_LOCAL_LN2, il, "weight")); + } + model.mm_a_local_norm_w = get_tensor(string_format(TN_MM_A_LOCAL_NORM, "weight")); + + model.mm_1_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 1, "weight")); + model.mm_2_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 2, "weight")); + } break; case PROJECTOR_TYPE_VOXTRAL: { model.conv1d_1_w = get_tensor(string_format(TN_CONV1D, 1, "weight")); @@ -2709,6 +2920,68 @@ struct clip_model_loader { layer.conv_pw2_b = get_tensor(string_format(TN_CONV_PW2, prefix, il, "bias")); } } break; + case PROJECTOR_TYPE_PARAKEET: + { + + hparams.mel_filters = get_vector(TN_MEL_FILTERS); + hparams.window = get_vector(TN_WINDOW); + + // Subsampling layers (conv1d) + for (int i : {0, 2, 3, 5, 6}) { + model.pre_encode_conv_X_w[i] = get_tensor(string_format(TN_CONV1D, i, "weight")); + model.pre_encode_conv_X_b[i] = get_tensor(string_format(TN_CONV1D, i, "bias")); + } + model.pre_encode_out_w = get_tensor(string_format(TN_PRE_ENCODE_OUT, "weight")); + model.pre_encode_out_b = get_tensor(string_format(TN_PRE_ENCODE_OUT, "bias")); + + // Projection layers + model.mm_norm_pre_w = get_tensor(string_format(TN_MM_NORM_PRE, "weight"), false); + model.mm_0_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 1, "weight"), false); + model.mm_1_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 2, "weight"), false); + + // Encoder layers + for (int il = 0; il < hparams.n_layer; ++il) { + auto & layer = model.layers[il]; + + // Attention (from shared above) + + // Relative position encoding + layer.linear_pos_w = get_tensor(string_format(TN_LINEAR_POS, prefix, il, "weight")); + layer.pos_bias_u = get_tensor(string_format(TN_POS_BIAS_U, prefix, il)); + layer.pos_bias_v = get_tensor(string_format(TN_POS_BIAS_V, prefix, il)); + + // Convolution module + layer.conv_pw1_w = get_tensor(string_format(TN_CONV_PW1, prefix, il, "weight")); + layer.conv_pw1_b = get_tensor(string_format(TN_CONV_PW1, prefix, il, "bias"), false); + layer.conv_dw_w = get_tensor(string_format(TN_CONV_DW, prefix, il, "weight")); + layer.conv_dw_b = get_tensor(string_format(TN_CONV_DW, prefix, il, "bias"), false); + layer.conv_norm_w = get_tensor(string_format(TN_CONV_NORM, prefix, il, "weight")); + layer.conv_norm_b = get_tensor(string_format(TN_CONV_NORM, prefix, il, "bias")); + layer.conv_norm_mean = get_tensor(string_format(TN_CONV_NORM_MEAN, prefix, il)); + layer.conv_norm_var = get_tensor(string_format(TN_CONV_NORM_VAR, prefix, il)); + layer.conv_pw2_w = get_tensor(string_format(TN_CONV_PW2, prefix, il, "weight")); + layer.conv_pw2_b = get_tensor(string_format(TN_CONV_PW2, prefix, il, "bias"), false); + + // Feed-forward networks + layer.ff_norm_w = get_tensor(string_format(TN_FFN_NORM, prefix, il, "weight")); + layer.ff_norm_b = get_tensor(string_format(TN_FFN_NORM, prefix, il, "bias")); + + layer.ff_norm_1_w = get_tensor(string_format(TN_FFN_NORM_1, prefix, il, "weight")); + layer.ff_norm_1_b = get_tensor(string_format(TN_FFN_NORM_1, prefix, il, "bias")); + layer.ff_up_1_w = get_tensor(string_format(TN_FFN_UP_1, prefix, il, "weight")); + layer.ff_up_1_b = get_tensor(string_format(TN_FFN_UP_1, prefix, il, "bias"), false); + layer.ff_down_1_w = get_tensor(string_format(TN_FFN_DOWN_1, prefix, il, "weight")); + layer.ff_down_1_b = get_tensor(string_format(TN_FFN_DOWN_1, prefix, il, "bias"), false); + + // Layer norms + layer.norm_conv_w = get_tensor(string_format(TN_NORM_CONV, prefix, il, "weight")); + layer.norm_conv_b = get_tensor(string_format(TN_NORM_CONV, prefix, il, "bias")); + } + + model.mm_model_mlp_1_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 0, "weight")); + model.mm_model_mlp_2_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 1, "weight")); + model.mm_model_mlp_3_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 3, "weight")); + } break; case PROJECTOR_TYPE_GRANITE_SPEECH: { model.inp_proj_w = get_tensor(string_format(TN_INP_PROJ, "weight")); @@ -3404,14 +3677,14 @@ int clip_n_output_tokens(const clip_ctx * ctx, const clip_image_f32 * img) { } break; case PROJECTOR_TYPE_MINICPMV4_6: { - // ViT merger 4x + final merger 4x = 16x total spatial downsample - n_patches = n_patches / 16; + n_patches /= params.n_merge * params.n_merge; } break; case PROJECTOR_TYPE_QWEN2VL: case PROJECTOR_TYPE_QWEN25VL: case PROJECTOR_TYPE_QWEN3VL: case PROJECTOR_TYPE_EXAONE4_5: case PROJECTOR_TYPE_MIMOVL: + case PROJECTOR_TYPE_MINIMAX_M3: case PROJECTOR_TYPE_GLM4V: case PROJECTOR_TYPE_YOUTUVL: { @@ -3447,6 +3720,7 @@ int clip_n_output_tokens(const clip_ctx * ctx, const clip_image_f32 * img) { case PROJECTOR_TYPE_LFM2: case PROJECTOR_TYPE_KIMIVL: case PROJECTOR_TYPE_KIMIK25: + case PROJECTOR_TYPE_KIMIK3: { // dynamic size int out_patch_size = params.patch_size * ctx->model.hparams.n_merge; @@ -3568,10 +3842,23 @@ int clip_n_output_tokens(const clip_ctx * ctx, const clip_image_f32 * img) { } n_patches = n; } break; + case PROJECTOR_TYPE_PARAKEET: + { + n_patches = (img->nx() + (params.subsampling_factor - 1)) / params.subsampling_factor; + } break; case PROJECTOR_TYPE_GEMMA4UA: { n_patches = img->nx(); // no downsampling: one token per raw waveform frame } break; + case PROJECTOR_TYPE_MIMO_AUDIO: + { + // conv1(s=1) + conv2(s=2) -> RVQ-encoder downsample conv(k=2,s=2) + int n = img->nx(); + n = (n - 1) / 2 + 1; // conv1 + conv2 + n = (n - 2) / 2 + 1; // downsample conv + const int group_size = params.audio_local_group_size; + n_patches = (n + group_size - 1) / group_size; + } break; case PROJECTOR_TYPE_GRANITE_SPEECH: { const int ws = ctx->model.hparams.audio_proj_window_size; @@ -3777,6 +4064,8 @@ bool clip_image_batch_encode(clip_ctx * ctx, int n_threads, const clip_image_f32 } break; case PROJECTOR_TYPE_MINICPMV4_6: { + const bool is_4x = hparams.n_merge == 2; + // SigLIP position buckets (same as resampler path) std::vector positions(pos_h * pos_w); int bucket_coords_h[1024]; @@ -3797,40 +4086,6 @@ bool clip_image_batch_encode(clip_ctx * ctx, int n_threads, const clip_image_f32 const int half_h = pos_h / 2; const int half_w = pos_w / 2; - // window reorder indices for 2x2 windows - std::vector window_idx(n_pos); - std::vector inv_window_idx(n_pos); - { - int k = 0; - for (int wi = 0; wi < half_h; wi++) { - for (int wj = 0; wj < half_w; wj++) { - window_idx[k++] = (2*wi ) * pos_w + (2*wj ); - window_idx[k++] = (2*wi ) * pos_w + (2*wj + 1); - window_idx[k++] = (2*wi + 1) * pos_w + (2*wj ); - window_idx[k++] = (2*wi + 1) * pos_w + (2*wj + 1); - } - } - for (int i = 0; i < n_pos; i++) { - inv_window_idx[window_idx[i]] = i; - } - } - set_input_i32("vit_merger_window_idx", window_idx); - set_input_i32("vit_merger_inv_window_idx", inv_window_idx); - - // block-diagonal attention mask: tokens in the same 4-token - // window attend to each other (mask = 0), all other positions - // are masked out (-inf). matches the window-major reorder above. - std::vector window_mask_data(n_pos * n_pos, std::numeric_limits::lowest()); - for (int wi = 0; wi < n_pos / 4; wi++) { - for (int i = 0; i < 4; i++) { - for (int j = 0; j < 4; j++) { - window_mask_data[(wi*4 + i) * n_pos + (wi*4 + j)] = 0.0f; - } - } - } - set_input_f32("vit_merger_window_mask", window_mask_data); - - // ViT merger 2x2 downsample indices auto make_ds_idx = [](int off_r, int off_c, int ds_h, int ds_w, int stride_w) { std::vector idx(ds_h * ds_w); for (int i = 0; i < ds_h; i++) { @@ -3840,22 +4095,58 @@ bool clip_image_batch_encode(clip_ctx * ctx, int n_threads, const clip_image_f32 } return idx; }; - auto vit_merger_ds_0 = make_ds_idx(0, 0, half_h, half_w, pos_w); - auto vit_merger_ds_1 = make_ds_idx(0, 1, half_h, half_w, pos_w); - auto vit_merger_ds_2 = make_ds_idx(1, 0, half_h, half_w, pos_w); - auto vit_merger_ds_3 = make_ds_idx(1, 1, half_h, half_w, pos_w); - set_input_i32("vit_merger_ds_idx_0", vit_merger_ds_0); - set_input_i32("vit_merger_ds_idx_1", vit_merger_ds_1); - set_input_i32("vit_merger_ds_idx_2", vit_merger_ds_2); - set_input_i32("vit_merger_ds_idx_3", vit_merger_ds_3); - - // final merger 2x2 downsample indices (operates on half_h x half_w grid) - const int qh = half_h / 2; - const int qw = half_w / 2; - auto m_ds_0 = make_ds_idx(0, 0, qh, qw, half_w); - auto m_ds_1 = make_ds_idx(0, 1, qh, qw, half_w); - auto m_ds_2 = make_ds_idx(1, 0, qh, qw, half_w); - auto m_ds_3 = make_ds_idx(1, 1, qh, qw, half_w); + + if (!is_4x) { + // window reorder indices for 2x2 windows + std::vector window_idx(n_pos); + std::vector inv_window_idx(n_pos); + { + int k = 0; + for (int wi = 0; wi < half_h; wi++) { + for (int wj = 0; wj < half_w; wj++) { + window_idx[k++] = (2*wi ) * pos_w + (2*wj ); + window_idx[k++] = (2*wi ) * pos_w + (2*wj + 1); + window_idx[k++] = (2*wi + 1) * pos_w + (2*wj ); + window_idx[k++] = (2*wi + 1) * pos_w + (2*wj + 1); + } + } + for (int i = 0; i < n_pos; i++) { + inv_window_idx[window_idx[i]] = i; + } + } + set_input_i32("vit_merger_window_idx", window_idx); + set_input_i32("vit_merger_inv_window_idx", inv_window_idx); + + // block-diagonal attention mask: tokens in the same 4-token + // window attend to each other (mask = 0), all other positions + // are masked out (-inf). matches the window-major reorder above. + std::vector window_mask_data(n_pos * n_pos, std::numeric_limits::lowest()); + for (int wi = 0; wi < n_pos / 4; wi++) { + for (int i = 0; i < 4; i++) { + for (int j = 0; j < 4; j++) { + window_mask_data[(wi*4 + i) * n_pos + (wi*4 + j)] = 0.0f; + } + } + } + set_input_f32("vit_merger_window_mask", window_mask_data); + + // ViT merger 2x2 downsample indices + auto vit_merger_ds_0 = make_ds_idx(0, 0, half_h, half_w, pos_w); + auto vit_merger_ds_1 = make_ds_idx(0, 1, half_h, half_w, pos_w); + auto vit_merger_ds_2 = make_ds_idx(1, 0, half_h, half_w, pos_w); + auto vit_merger_ds_3 = make_ds_idx(1, 1, half_h, half_w, pos_w); + set_input_i32("vit_merger_ds_idx_0", vit_merger_ds_0); + set_input_i32("vit_merger_ds_idx_1", vit_merger_ds_1); + set_input_i32("vit_merger_ds_idx_2", vit_merger_ds_2); + set_input_i32("vit_merger_ds_idx_3", vit_merger_ds_3); + } + + const int merger_h = is_4x ? pos_h : half_h; + const int merger_w = is_4x ? pos_w : half_w; + auto m_ds_0 = make_ds_idx(0, 0, merger_h / 2, merger_w / 2, merger_w); + auto m_ds_1 = make_ds_idx(0, 1, merger_h / 2, merger_w / 2, merger_w); + auto m_ds_2 = make_ds_idx(1, 0, merger_h / 2, merger_w / 2, merger_w); + auto m_ds_3 = make_ds_idx(1, 1, merger_h / 2, merger_w / 2, merger_w); set_input_i32("merger_ds_idx_0", m_ds_0); set_input_i32("merger_ds_idx_1", m_ds_1); set_input_i32("merger_ds_idx_2", m_ds_2); @@ -3922,6 +4213,24 @@ bool clip_image_batch_encode(clip_ctx * ctx, int n_threads, const clip_image_f32 set_input_i32("positions", positions); } break; + case PROJECTOR_TYPE_MINIMAX_M3: + { + const int n_merge = hparams.n_merge; + const int gh = image_size_height / patch_size; + const int gw = image_size_width / patch_size; + std::vector pos_h, pos_w; + pos_h.reserve(gh * gw); + pos_w.reserve(gh * gw); + for (int bh = 0; bh < gh / n_merge; bh++) + for (int bw = 0; bw < gw / n_merge; bw++) + for (int mh = 0; mh < n_merge; mh++) + for (int mw = 0; mw < n_merge; mw++) { + pos_h.push_back(bh * n_merge + mh); + pos_w.push_back(bw * n_merge + mw); + } + set_input_i32("minimax_pos_h", pos_h); + set_input_i32("minimax_pos_w", pos_w); + } break; case PROJECTOR_TYPE_DOTS_OCR: { const int pw = image_size_width / patch_size; @@ -4117,6 +4426,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, int n_threads, const clip_image_f32 case PROJECTOR_TYPE_PIXTRAL: case PROJECTOR_TYPE_KIMIVL: case PROJECTOR_TYPE_KIMIK25: + case PROJECTOR_TYPE_KIMIK3: case PROJECTOR_TYPE_LIGHTONOCR: { // set the 2D positions @@ -4385,6 +4695,58 @@ bool clip_image_batch_encode(clip_ctx * ctx, int n_threads, const clip_image_f32 set_input_f32("pos_emb", pos_emb); } } break; + case PROJECTOR_TYPE_MIMO_AUDIO: + { + GGML_ASSERT(imgs.entries.size() == 1); + const int n_frames = imgs.entries.front().nx(); + const int n_pos = (n_frames - 1) / 2 + 1; // matches conv1(s=1)+conv2(s=2) output length + + std::vector positions(n_pos); + for (int i = 0; i < n_pos; i++) { + positions[i] = i; + } + set_input_i32("mimo_audio_positions", positions); + + const int window = hparams.attn_window_size; + GGML_ASSERT(window > 0); + + const float neg_inf = std::numeric_limits::lowest(); + std::vector full_mask((size_t) n_pos * n_pos); + std::vector window_mask((size_t) n_pos * n_pos); + for (int q = 0; q < n_pos; q++) { + for (int k = 0; k < n_pos; k++) { + const bool causal_ok = k <= q; + full_mask[(size_t) q * n_pos + k] = causal_ok ? 0.0f : neg_inf; + window_mask[(size_t) q * n_pos + k] = (causal_ok && (q - k) <= window) ? 0.0f : neg_inf; + } + } + set_input_f32("mimo_audio_full_mask", full_mask); + set_input_f32("mimo_audio_window_mask", window_mask); + + // input_local_transformer: block-diagonal mask + in-group positions + { + const int n_pos_ds = (n_pos - 2) / 2 + 1; // matches downsample conv (k=2,s=2,p=0) + const int group_size = hparams.audio_local_group_size; + GGML_ASSERT(group_size > 0); + const int n_groups = (n_pos_ds + group_size - 1) / group_size; + const int n_padded = n_groups * group_size; + + std::vector local_positions(n_padded); + for (int i = 0; i < n_padded; i++) { + local_positions[i] = i % group_size; + } + set_input_i32("mimo_audio_local_positions", local_positions); + + std::vector local_mask((size_t) n_padded * n_padded); + for (int q = 0; q < n_padded; q++) { + for (int k = 0; k < n_padded; k++) { + const bool same_group = (q / group_size) == (k / group_size); + local_mask[(size_t) q * n_padded + k] = same_group ? 0.0f : neg_inf; + } + } + set_input_f32("mimo_audio_local_mask", local_mask); + } + } break; case PROJECTOR_TYPE_LFM2A: { GGML_ASSERT(imgs.entries.size() == 1); @@ -4406,6 +4768,88 @@ bool clip_image_batch_encode(clip_ctx * ctx, int n_threads, const clip_image_f32 } set_input_f32("pos_emb", pos_emb); } break; + case PROJECTOR_TYPE_PARAKEET: + { + GGML_ASSERT(imgs.entries.size() == 1); + struct ggml_tensor * attn_mask = ggml_graph_get_tensor(gf, "attn_mask"); + const int n_q = attn_mask->ne[1]; + const int n_k = attn_mask->ne[0]; + const int n_frames = imgs.entries.front().nx(); + const int n_tokens_real = (n_frames + hparams.subsampling_factor-1) / hparams.subsampling_factor; + const float mask_value = -1e30f; + + std::vector mask_data(n_q * n_k); + if (n_k == n_q) { + // full attention: mask keys that are padding + for (int q = 0; q < n_q; ++q) { + for (int k = 0; k < n_k; ++k) { + mask_data[q * n_k + k] = (k >= n_tokens_real) ? mask_value : 0.0f; + } + } + } else { + // local attention: mask keys outside the valid window + const int att_left = n_k / 2; + for (int q = 0; q < n_q; ++q) { + for (int k = 0; k < n_k; ++k) { + const int key = q - att_left + k; + mask_data[q * n_k + k] = (key >= 0 && key < n_tokens_real) ? 0.0f : mask_value; + } + } + } + set_input_f32(attn_mask->name, mask_data); + + // local attention skew mask: zeroes out the probs that were + // computed for keys outside the valid sliding window. + if (struct ggml_tensor * local_mask = ggml_graph_get_tensor(gf, "local_mask")) { + const int lm_k = local_mask->ne[0]; + const int lm_q = local_mask->ne[1]; + const int window_size = lm_k - lm_q + 1; + std::vector lm_data(lm_q * lm_k); + for (int q = 0; q < lm_q; ++q) { + for (int k = 0; k < lm_k; ++k) { + const int rel = k - q; + lm_data[q * lm_k + k] = (rel >= 0 && rel < window_size) ? 1.0f : 0.0f; + } + } + set_input_f32(local_mask->name, lm_data); + } + + // Generate rotation frequencies for relative positional encoding. + { + const int n_state = hparams.n_embd; + const int d_half = n_state / 2; + const float log_10000 = logf(10000.0f); + std::vector freqs(d_half); + for (int k = 0; k < d_half; ++k) { + freqs[k] = expf(-(float(k * 2) * log_10000 / float(n_state))); + } + set_input_f32("pos_freqs", freqs); + } + + // Generate relative positional distance values which scaled by + // the frequency to produce the angles for sin/cos. + { + // window_size is only known after graph construction since it depends on + // n_time from the conv output, so we read it back from the graph tensor. + struct ggml_tensor * rel_pos = ggml_graph_get_tensor(gf, "rel_positions"); + const int window_size = rel_pos->ne[1]; + std::vector pos(window_size); + // local attention: window is fixed at [att_left, att_right] + // full attention: window covers the full sequence, centered + if (ggml_graph_get_tensor(gf, "local_mask")) { + const int att_left = window_size / 2; + for (int t = 0; t < window_size; ++t) { + pos[t] = float(att_left - t); + } + } else { + const int n_time = (window_size + 1) / 2; + for (int t = 0; t < window_size; ++t) { + pos[t] = float(n_time - 1 - t); + } + } + set_input_f32(rel_pos->name, pos); + } + } break; case PROJECTOR_TYPE_GRANITE_SPEECH: { const int context_size = ctx->model.hparams.audio_chunk_size; @@ -4629,6 +5073,8 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) { return ctx->model.mm_ffn_down_w->ne[1]; case PROJECTOR_TYPE_GLM_EDGE: return ctx->model.mm_model_mlp_3_w->ne[1]; + case PROJECTOR_TYPE_MINIMAX_M3: + return ctx->model.mm_merger_fc2_b->ne[0]; case PROJECTOR_TYPE_QWEN2VL: case PROJECTOR_TYPE_QWEN25VL: case PROJECTOR_TYPE_EXAONE4_5: @@ -4672,6 +5118,7 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) { case PROJECTOR_TYPE_KIMIVL: case PROJECTOR_TYPE_PADDLEOCR: case PROJECTOR_TYPE_KIMIK25: + case PROJECTOR_TYPE_KIMIK3: case PROJECTOR_TYPE_YASA2: return ctx->model.mm_2_w->ne[1]; case PROJECTOR_TYPE_HUNYUANVL: @@ -4689,6 +5136,10 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) { return ctx->model.qf_proj_blocks.size() * ctx->model.hparams.projection_dim; case PROJECTOR_TYPE_GLM4V: return ctx->model.mm_ffn_down_w->ne[1]; + case PROJECTOR_TYPE_MIMO_AUDIO: + return ctx->model.mm_2_w->ne[1]; + case PROJECTOR_TYPE_PARAKEET: + return ctx->model.mm_1_w->ne[1]; default: GGML_ABORT("Unknown projector type"); } diff --git a/tools/mtmd/models/kimik3.cpp b/tools/mtmd/models/kimik3.cpp new file mode 100644 index 000000000000..fe06c7fdbb38 --- /dev/null +++ b/tools/mtmd/models/kimik3.cpp @@ -0,0 +1,80 @@ +#include "models.h" + +#include +#include + +// Kimi-K3 MoonViT-3d, image path. +// Follows clip_graph_kimik25, but with RMSNorm, no biases, qkv width != n_embd, and a post-norm patchmergerv2 projector. +// Images only: at t == 1 the temporal pool and the temporal position term vanish. + +ggml_tensor * clip_graph_kimik3::resize_position_embeddings_3d(uint32_t interpolation_mode) { + ggml_tensor * pos_embd = model.position_embeddings; + const int height = img.ny() / patch_size; + const int width = img.nx() / patch_size; + + GGML_ASSERT(pos_embd); + + const int64_t stored_c = pos_embd->ne[0]; + const int64_t orig_w = pos_embd->ne[1]; + const int64_t orig_h = pos_embd->ne[2]; + + GGML_ASSERT(stored_c == n_embd); + + if (height == (int) orig_h && width == (int) orig_w) { + return ggml_cont_2d(ctx0, pos_embd, n_embd, width * height); + } + + pos_embd = ggml_permute(ctx0, pos_embd, 2, 1, 0, 3); + pos_embd = ggml_interpolate(ctx0, pos_embd, height, width, n_embd, 1, interpolation_mode); + pos_embd = ggml_permute(ctx0, pos_embd, 2, 1, 0, 3); + pos_embd = ggml_cont_2d(ctx0, pos_embd, n_embd, width * height); + return pos_embd; +} + +ggml_cgraph * clip_graph_kimik3::build() { + ggml_tensor * pos_h = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches); + ggml_set_name(pos_h, "pos_h"); + ggml_set_input(pos_h); + + ggml_tensor * pos_w = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches); + ggml_set_name(pos_w, "pos_w"); + ggml_set_input(pos_w); + + ggml_tensor * learned_pos_embd = resize_position_embeddings_3d(GGML_SCALE_MODE_BILINEAR); + + // Q/K are de-interleaved during conversion. + auto add_pos = [&](ggml_tensor * cur, const clip_layer &) { + return build_rope_2d(ctx0, cur, pos_w, pos_h, hparams.rope_theta, false); + }; + + ggml_tensor * inp = build_inp(); + inp = ggml_add(ctx0, inp, learned_pos_embd); + + ggml_tensor * cur = build_vit( + inp, n_patches, + NORM_TYPE_RMS, + hparams.ffn_op, + nullptr, + add_pos); + cb(cur, "vit_out", -1); + + { + const int scale_factor = model.hparams.n_merge; + cur = build_patch_merge_permute(cur, scale_factor); + + cur = build_ffn(cur, + model.mm_1_w, nullptr, + nullptr, nullptr, + model.mm_2_w, nullptr, + FFN_GELU, + -1); + cb(cur, "proj_mlp_out", -1); + + cur = build_norm(cur, model.mm_post_norm_w, nullptr, NORM_TYPE_RMS, hparams.eps, -1); + cb(cur, "proj_out", -1); + } + + ggml_build_forward_expand(gf, cur); + + return gf; +} diff --git a/tools/mtmd/models/mimo-audio.cpp b/tools/mtmd/models/mimo-audio.cpp new file mode 100644 index 000000000000..481b36cc8d60 --- /dev/null +++ b/tools/mtmd/models/mimo-audio.cpp @@ -0,0 +1,218 @@ +#include "models.h" + +ggml_cgraph * clip_graph_mimo_audio::build() { + ggml_tensor * inp = build_inp_raw(1); // [n_frames, n_mel, 1] + + ggml_tensor * cur = ggml_conv_1d_ph(ctx0, model.conv1d_1_w, inp, 1, 1); + cur = ggml_add(ctx0, cur, model.conv1d_1_b); + cur = ggml_gelu_erf(ctx0, cur); + + cur = ggml_conv_1d_ph(ctx0, model.conv1d_2_w, cur, 2, 1); + cur = ggml_add(ctx0, cur, model.conv1d_2_b); + cur = ggml_gelu_erf(ctx0, cur); + + ggml_tensor * inpL = ggml_cont(ctx0, ggml_transpose(ctx0, cur)); // [n_embd, n_pos] + const int64_t n_pos = inpL->ne[1]; + cb(inpL, "after_conv1d", -1); + + GGML_ASSERT((int) hparams.wa_pattern_mode.size() == n_layer); + + ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos); + ggml_set_name(inp_pos, "mimo_audio_positions"); + ggml_set_input(inp_pos); + + ggml_tensor * full_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_pos, n_pos); + ggml_set_name(full_mask, "mimo_audio_full_mask"); + ggml_set_input(full_mask); + + ggml_tensor * window_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_pos, n_pos); + ggml_set_name(window_mask, "mimo_audio_window_mask"); + ggml_set_input(window_mask); + + build_vit_opts opts; + opts.attn_mask_layers.resize(n_layer); + for (int il = 0; il < n_layer; il++) { + opts.attn_mask_layers[il] = hparams.wa_pattern_mode[il] == -1 ? full_mask : window_mask; + } + // the skip connection below must be added before the post-transformer norm, + // so build_vit must not apply that norm itself + opts.skip_post_ln = true; + + // encoder_skip_layer_id=3 (1-indexed) -> capture output of layer index 2 + const int skip_capture_il = 2; + GGML_ASSERT(n_layer > skip_capture_il); + ggml_tensor * skip_hidden = nullptr; + opts.callback_layer_out = [&](ggml_tensor * layer_cur, int il) { + if (il == skip_capture_il) { + skip_hidden = layer_cur; + } + }; + + auto add_pos = [&](ggml_tensor * x, const clip_layer &) { + return ggml_rope_ext(ctx0, x, inp_pos, nullptr, d_head, + GGML_ROPE_TYPE_NEOX, 0, hparams.rope_theta, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f); + }; + + inpL = build_vit(inpL, n_pos, NORM_TYPE_NORMAL, hparams.ffn_op, nullptr, add_pos, opts); + inpL = ggml_reshape_2d(ctx0, inpL, n_embd, n_pos); // build_vit restores a (size-1) batch dim + + GGML_ASSERT(skip_hidden != nullptr); + inpL = ggml_add(ctx0, inpL, skip_hidden); + + inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, NORM_TYPE_NORMAL, eps, -1); + cb(inpL, "after_transformer", -1); + + // downsample: strided conv (no bias) + gelu + layernorm + { + ggml_tensor * ds = ggml_cont(ctx0, ggml_transpose(ctx0, inpL)); // [n_pos, n_embd] + ds = ggml_conv_1d(ctx0, model.downsample_conv_w, ds, 2, 0, 1); + ds = ggml_gelu_erf(ctx0, ds); + ds = ggml_cont(ctx0, ggml_transpose(ctx0, ds)); // [n_embd, n_pos/2] + ds = build_norm(ds, model.downsample_norm_w, model.downsample_norm_b, NORM_TYPE_NORMAL, eps, -1); + inpL = ds; + } + cb(inpL, "after_downsample", -1); + + // RVQ quantize: codebook ne=[dim, max_bins, n_q] + // quantize input vector to codes (type=I32) + std::vector codes; + { + GGML_ASSERT(model.rvq_codebook != nullptr); + const int64_t dim = model.rvq_codebook->ne[0]; + GGML_ASSERT(dim == inpL->ne[0]); + GGML_ASSERT((int64_t) hparams.rvq_codebook_size.size() == model.rvq_codebook->ne[2]); + + ggml_tensor * residual = inpL; // [dim, n_pos_ds] + + for (size_t q = 0; q < hparams.rvq_codebook_size.size(); q++) { + const int64_t bins = hparams.rvq_codebook_size[q]; + ggml_tensor * codebook_q = ggml_view_2d(ctx0, model.rvq_codebook, dim, bins, + model.rvq_codebook->nb[1], q * model.rvq_codebook->nb[2]); + codebook_q = ggml_cont(ctx0, codebook_q); + + ggml_tensor * codebook_norm = ggml_sum_rows(ctx0, ggml_sqr(ctx0, codebook_q)); // [1, bins] + codebook_norm = ggml_cont(ctx0, ggml_transpose(ctx0, codebook_norm)); // [bins, 1] + + ggml_tensor * dot = ggml_mul_mat(ctx0, codebook_q, residual); // [bins, n_pos_ds] + ggml_tensor * scores = ggml_sub(ctx0, ggml_scale(ctx0, dot, 2.0f), codebook_norm); + + ggml_tensor * idx = ggml_argmax(ctx0, scores); // [n_pos_ds] + codes.push_back(idx); + + ggml_tensor * quant = ggml_get_rows(ctx0, codebook_q, idx); // [dim, n_pos_ds] + residual = ggml_sub(ctx0, residual, quant); + cb(idx, "rvq_code", (int) q); + } + } + + // convert codes to LLM embeddings + ggml_tensor * code_embd_sum = nullptr; + { + GGML_ASSERT(model.mm_a_code_embd != nullptr); + const int64_t dim = model.mm_a_code_embd->ne[0]; + const int64_t vocab = model.mm_a_code_embd->ne[1]; + GGML_ASSERT((int64_t) codes.size() == model.mm_a_code_embd->ne[2]); + GGML_ASSERT(dim == inpL->ne[0]); + + for (size_t i = 0; i < codes.size(); i++) { + ggml_tensor * table_i = ggml_view_2d(ctx0, model.mm_a_code_embd, dim, vocab, + model.mm_a_code_embd->nb[1], i * model.mm_a_code_embd->nb[2]); + table_i = ggml_cont(ctx0, table_i); + + ggml_tensor * embd_i = ggml_get_rows(ctx0, table_i, codes[i]); // [dim, n_pos_ds] + code_embd_sum = code_embd_sum ? ggml_add(ctx0, code_embd_sum, embd_i) : embd_i; + } + cb(code_embd_sum, "code_embd_sum", -1); + } + + // input_local_transformer + // groups of `group_size` consecutive downsampled frames are processed together, attending only within their own group. + // Implemented as a block-diagonal mask + in-group-repeating positions + // (rather than a real batch dim) - same technique as the encoder's masks above, and as gemma4a's / deepseekocr2's chunked attention. + + // note: hand-rolled here instead of build_vit() because this is a second, independent layer stack + // (own layer array/count, RMSNorm instead of LN, SiLU FFN, own RoPE theta) + + ggml_tensor * projected; + { + const int group_size = hparams.audio_local_group_size; + GGML_ASSERT(group_size > 0); + const int64_t n_pos_ds = code_embd_sum->ne[1]; + const int64_t n_groups = (n_pos_ds + group_size - 1) / group_size; + const int64_t n_padded = n_groups * group_size; + + ggml_tensor * cur_local = code_embd_sum; + if (n_padded != n_pos_ds) { + cur_local = ggml_pad(ctx0, cur_local, 0, (int) (n_padded - n_pos_ds), 0, 0); + } + + ggml_tensor * local_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_padded); + ggml_set_name(local_pos, "mimo_audio_local_positions"); + ggml_set_input(local_pos); + + ggml_tensor * local_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_padded, n_padded); + ggml_set_name(local_mask, "mimo_audio_local_mask"); + ggml_set_input(local_mask); + + const float local_rope_theta = 640000.0f; // audio_config.rope_theta (differs from the encoder's) + auto apply_local_rope = [&](ggml_tensor * x) { + return ggml_rope_ext(ctx0, x, local_pos, nullptr, d_head, + GGML_ROPE_TYPE_NEOX, 0, local_rope_theta, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f); + }; + + for (int il = 0; il < hparams.audio_local_n_layer; il++) { + auto & layer = model.mm_a_local_layers[il]; + + ggml_tensor * attn_in = build_norm(cur_local, layer.ln_1_w, nullptr, NORM_TYPE_RMS, eps, il); + + ggml_tensor * Qcur = build_mm(layer.q_w, attn_in); + if (layer.q_b) { + Qcur = ggml_add(ctx0, Qcur, layer.q_b); + } + ggml_tensor * Kcur = build_mm(layer.k_w, attn_in); + if (layer.k_b) { + Kcur = ggml_add(ctx0, Kcur, layer.k_b); + } + ggml_tensor * Vcur = build_mm(layer.v_w, attn_in); + if (layer.v_b) { + Vcur = ggml_add(ctx0, Vcur, layer.v_b); + } + + Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_padded); + Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_padded); + Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_padded); + + Qcur = apply_local_rope(Qcur); + Kcur = apply_local_rope(Kcur); + + ggml_tensor * attn_out = build_attn(layer.o_w, nullptr, Qcur, Kcur, Vcur, local_mask, kq_scale, il); + cur_local = ggml_add(ctx0, cur_local, attn_out); + + ggml_tensor * ffn_in = build_norm(cur_local, layer.ln_2_w, nullptr, NORM_TYPE_RMS, eps, il); + ggml_tensor * ffn_out = build_ffn(ffn_in, + layer.ff_up_w, nullptr, + layer.ff_gate_w, nullptr, + layer.ff_down_w, nullptr, + FFN_SILU, il); + cur_local = ggml_add(ctx0, cur_local, ffn_out); + } + + cur_local = build_norm(cur_local, model.mm_a_local_norm_w, nullptr, NORM_TYPE_RMS, eps, -1); + cb(cur_local, "after_local_transformer", -1); + + // flatten each group of `group_size` frames into one (group_size*n_embd)-dim vector + // (matching AudioProjection's flattened input) + ggml_tensor * grouped = ggml_reshape_2d(ctx0, cur_local, n_embd * group_size, n_groups); + + // AudioProjection: Linear (no bias) -> GELU -> Linear (no bias) + projected = build_ffn(grouped, + model.mm_1_w, nullptr, + nullptr, nullptr, + model.mm_2_w, nullptr, + FFN_GELU_ERF, -1); + cb(projected, "after_projection", -1); + } + + ggml_build_forward_expand(gf, projected); + return gf; +} diff --git a/tools/mtmd/models/minicpmv.cpp b/tools/mtmd/models/minicpmv.cpp index bac087ffdfce..3e9c4c2a1114 100644 --- a/tools/mtmd/models/minicpmv.cpp +++ b/tools/mtmd/models/minicpmv.cpp @@ -114,14 +114,12 @@ ggml_cgraph * clip_graph_minicpmv::build() { } ggml_cgraph * clip_graph_minicpmv4_6::build() { - const int insert_lid = hparams.insert_layer_id; - const int n_pos = n_patches; - const int half_h = n_patches_y / 2; - const int half_w = n_patches_x / 2; - const int n_ds = half_h * half_w; // after ViT merger 2x2 downsample - const int qh = half_h / 2; - const int qw = half_w / 2; - const int n_ds2 = qh * qw; // after final merger 2x2 downsample + const bool is_4x = hparams.n_merge == 2; + const int n_pos = n_patches; + const int half_h = n_patches_y / 2; + const int half_w = n_patches_x / 2; + const int n_ds = half_h * half_w; + const int n_out = is_4x ? n_ds : (half_h / 2) * (half_w / 2); auto add_i32_input = [&](const char * name, int n) { ggml_tensor * t = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n); @@ -134,29 +132,39 @@ ggml_cgraph * clip_graph_minicpmv4_6::build() { ggml_tensor * positions = add_i32_input("positions", n_pos); ggml_tensor * learned_pos_embd = ggml_get_rows(ctx0, model.position_embeddings, positions); - // ViT merger window reorder indices + block-diagonal mask - // (mask layout follows qwen2vl: -inf except for 4x4 blocks on the diagonal, - // so each window-major group of 4 tokens only attends to itself) - ggml_tensor * vit_merger_window_idx = add_i32_input("vit_merger_window_idx", n_pos); - ggml_tensor * vit_merger_inv_window_idx = add_i32_input("vit_merger_inv_window_idx", n_pos); - ggml_tensor * vit_merger_window_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_pos, n_pos); - ggml_set_name(vit_merger_window_mask, "vit_merger_window_mask"); - ggml_set_input(vit_merger_window_mask); - if (flash_attn_type == CLIP_FLASH_ATTN_TYPE_ENABLED) { - vit_merger_window_mask = ggml_cast(ctx0, vit_merger_window_mask, GGML_TYPE_F16); - } + ggml_tensor * vit_merger_window_idx = nullptr; + ggml_tensor * vit_merger_inv_window_idx = nullptr; + ggml_tensor * vit_merger_window_mask = nullptr; + ggml_tensor * vit_merger_ds_idx_0 = nullptr; + ggml_tensor * vit_merger_ds_idx_1 = nullptr; + ggml_tensor * vit_merger_ds_idx_2 = nullptr; + ggml_tensor * vit_merger_ds_idx_3 = nullptr; + + if (!is_4x) { + // ViT merger window reorder indices + block-diagonal mask + // (mask layout follows qwen2vl: -inf except for 4x4 blocks on the diagonal, + // so each window-major group of 4 tokens only attends to itself) + vit_merger_window_idx = add_i32_input("vit_merger_window_idx", n_pos); + vit_merger_inv_window_idx = add_i32_input("vit_merger_inv_window_idx", n_pos); + vit_merger_window_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_pos, n_pos); + ggml_set_name(vit_merger_window_mask, "vit_merger_window_mask"); + ggml_set_input(vit_merger_window_mask); + if (flash_attn_type == CLIP_FLASH_ATTN_TYPE_ENABLED) { + vit_merger_window_mask = ggml_cast(ctx0, vit_merger_window_mask, GGML_TYPE_F16); + } - // ViT merger 2x2 downsample gather indices - ggml_tensor * vit_merger_ds_idx_0 = add_i32_input("vit_merger_ds_idx_0", n_ds); - ggml_tensor * vit_merger_ds_idx_1 = add_i32_input("vit_merger_ds_idx_1", n_ds); - ggml_tensor * vit_merger_ds_idx_2 = add_i32_input("vit_merger_ds_idx_2", n_ds); - ggml_tensor * vit_merger_ds_idx_3 = add_i32_input("vit_merger_ds_idx_3", n_ds); + // ViT merger 2x2 downsample gather indices + vit_merger_ds_idx_0 = add_i32_input("vit_merger_ds_idx_0", n_ds); + vit_merger_ds_idx_1 = add_i32_input("vit_merger_ds_idx_1", n_ds); + vit_merger_ds_idx_2 = add_i32_input("vit_merger_ds_idx_2", n_ds); + vit_merger_ds_idx_3 = add_i32_input("vit_merger_ds_idx_3", n_ds); + } // final merger 2x2 downsample gather indices - ggml_tensor * merger_ds_idx_0 = add_i32_input("merger_ds_idx_0", n_ds2); - ggml_tensor * merger_ds_idx_1 = add_i32_input("merger_ds_idx_1", n_ds2); - ggml_tensor * merger_ds_idx_2 = add_i32_input("merger_ds_idx_2", n_ds2); - ggml_tensor * merger_ds_idx_3 = add_i32_input("merger_ds_idx_3", n_ds2); + ggml_tensor * merger_ds_idx_0 = add_i32_input("merger_ds_idx_0", n_out); + ggml_tensor * merger_ds_idx_1 = add_i32_input("merger_ds_idx_1", n_out); + ggml_tensor * merger_ds_idx_2 = add_i32_input("merger_ds_idx_2", n_out); + ggml_tensor * merger_ds_idx_3 = add_i32_input("merger_ds_idx_3", n_out); // patch embedding + positional embedding ggml_tensor * inp = build_inp(); @@ -169,150 +177,10 @@ ggml_cgraph * clip_graph_minicpmv4_6::build() { cb(inpL, "pre_ln", -1); } - // ViT layers 0..insert_layer_id (inclusive) - // Mirrors the separate-qkv path of clip_graph::build_vit so the two manually - // unrolled segments around the ViT merger read like build_vit() expansions. - for (int il = 0; il <= insert_lid; il++) { - auto & layer = model.layers[il]; - ggml_tensor * cur = inpL; - - cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, NORM_TYPE_NORMAL, eps, il); - cb(cur, "layer_inp_normed", il); - - { - ggml_tensor * Qcur = build_mm(layer.q_w, cur); - if (layer.q_b) { - Qcur = ggml_add(ctx0, Qcur, layer.q_b); - } - ggml_tensor * Kcur = build_mm(layer.k_w, cur); - if (layer.k_b) { - Kcur = ggml_add(ctx0, Kcur, layer.k_b); - } - ggml_tensor * Vcur = build_mm(layer.v_w, cur); - if (layer.v_b) { - Vcur = ggml_add(ctx0, Vcur, layer.v_b); - } - - Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_pos); - Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_pos); - Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_pos); - cb(Qcur, "Qcur", il); - cb(Kcur, "Kcur", il); - cb(Vcur, "Vcur", il); - - cur = build_attn(layer.o_w, layer.o_b, Qcur, Kcur, Vcur, nullptr, kq_scale, il); - cb(cur, "attn_out", il); - } - - if (layer.ls_1_w) { - cur = ggml_mul(ctx0, cur, layer.ls_1_w); - cb(cur, "attn_out_scaled", il); - } - cur = ggml_add(ctx0, cur, inpL); - inpL = cur; - cb(cur, "ffn_inp", il); - - cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, NORM_TYPE_NORMAL, eps, il); - cb(cur, "ffn_inp_normed", il); - - cur = build_ffn(cur, layer.ff_up_w, layer.ff_up_b, layer.ff_gate_w, layer.ff_gate_b, - layer.ff_down_w, layer.ff_down_b, hparams.ffn_op, il); - cb(cur, "ffn_out", il); - - if (layer.ls_2_w) { - cur = ggml_mul(ctx0, cur, layer.ls_2_w); - cb(cur, "ffn_out_scaled", il); - } - cur = ggml_add(ctx0, inpL, cur); - cb(cur, "layer_out", il); - - inpL = cur; - } - - // ViT merger: window self-attention - // Tokens are reordered to window-major (4 tokens per window are contiguous), - // and a block-diagonal mask restricts attention to within each window. This - // mirrors the qwen2vl windowed-attention pattern so build_attn() can pick the - // flash-attention path when available. - { - ggml_tensor * residual = inpL; - ggml_tensor * cur = build_norm(inpL, - model.vit_merger_ln1_w, model.vit_merger_ln1_b, - NORM_TYPE_NORMAL, eps, -1); - cb(cur, "vit_merger_attn_inp_normed", -1); - - cur = ggml_get_rows(ctx0, cur, vit_merger_window_idx); - cb(cur, "vit_merger_window_reorder", -1); - - ggml_tensor * Qcur = build_mm(model.vit_merger_attn_q_w, cur); - if (model.vit_merger_attn_q_b) { - Qcur = ggml_add(ctx0, Qcur, model.vit_merger_attn_q_b); - } - ggml_tensor * Kcur = build_mm(model.vit_merger_attn_k_w, cur); - if (model.vit_merger_attn_k_b) { - Kcur = ggml_add(ctx0, Kcur, model.vit_merger_attn_k_b); - } - ggml_tensor * Vcur = build_mm(model.vit_merger_attn_v_w, cur); - if (model.vit_merger_attn_v_b) { - Vcur = ggml_add(ctx0, Vcur, model.vit_merger_attn_v_b); - } - - Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_pos); - Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_pos); - Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_pos); - cb(Qcur, "vit_merger_Qcur", -1); - cb(Kcur, "vit_merger_Kcur", -1); - cb(Vcur, "vit_merger_Vcur", -1); - - cur = build_attn(model.vit_merger_attn_o_w, model.vit_merger_attn_o_b, - Qcur, Kcur, Vcur, vit_merger_window_mask, kq_scale, -1); - cb(cur, "vit_merger_attn_out", -1); - - cur = ggml_get_rows(ctx0, cur, vit_merger_inv_window_idx); - inpL = ggml_add(ctx0, cur, residual); - cb(inpL, "vit_merger_attn_residual", -1); - } - - // ViT merger: 2x2 spatial downsample + MLP (4 tokens -> 1) - { - ggml_tensor * p0 = ggml_get_rows(ctx0, inpL, vit_merger_ds_idx_0); - ggml_tensor * p1 = ggml_get_rows(ctx0, inpL, vit_merger_ds_idx_1); - ggml_tensor * p2 = ggml_get_rows(ctx0, inpL, vit_merger_ds_idx_2); - ggml_tensor * p3 = ggml_get_rows(ctx0, inpL, vit_merger_ds_idx_3); - - ggml_tensor * mean_res = ggml_add(ctx0, p0, p1); - mean_res = ggml_add(ctx0, mean_res, p2); - mean_res = ggml_add(ctx0, mean_res, p3); - mean_res = ggml_scale(ctx0, mean_res, 0.25f); - cb(mean_res, "vit_merger_ds_mean_res", -1); - - ggml_tensor * cat = ggml_concat(ctx0, p0, p1, 0); - cat = ggml_concat(ctx0, cat, p2, 0); - cat = ggml_concat(ctx0, cat, p3, 0); - - ggml_tensor * cur = build_norm(cat, - model.vit_merger_ds_ln_w, model.vit_merger_ds_ln_b, - NORM_TYPE_NORMAL, eps, -1); - cb(cur, "vit_merger_ds_normed", -1); - - // ViTWindowAttentionMerger downsample MLP uses gelu_pytorch_tanh (FFN_GELU) - cur = build_ffn(cur, - model.vit_merger_ds_up_w, model.vit_merger_ds_up_b, - nullptr, nullptr, - model.vit_merger_ds_down_w, model.vit_merger_ds_down_b, - FFN_GELU, -1); - cb(cur, "vit_merger_ds_mlp_out", -1); - - inpL = ggml_add(ctx0, cur, mean_res); - cb(inpL, "vit_merger_ds_out", -1); - } - - // ViT layers (insert_layer_id+1)..n_layer-1, operating on the downsampled tokens - { - const int64_t n_pos_ds = n_ds; - for (int il = insert_lid + 1; il < n_layer; il++) { + auto build_vit_layers = [&](ggml_tensor * input, int il_begin, int il_end, int64_t n_pos_layer) { + for (int il = il_begin; il < il_end; il++) { auto & layer = model.layers[il]; - ggml_tensor * cur = inpL; + ggml_tensor * cur = input; cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, NORM_TYPE_NORMAL, eps, il); cb(cur, "layer_inp_normed", il); @@ -331,9 +199,9 @@ ggml_cgraph * clip_graph_minicpmv4_6::build() { Vcur = ggml_add(ctx0, Vcur, layer.v_b); } - Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_pos_ds); - Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_pos_ds); - Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_pos_ds); + Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_pos_layer); + Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_pos_layer); + Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_pos_layer); cb(Qcur, "Qcur", il); cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); @@ -346,8 +214,8 @@ ggml_cgraph * clip_graph_minicpmv4_6::build() { cur = ggml_mul(ctx0, cur, layer.ls_1_w); cb(cur, "attn_out_scaled", il); } - cur = ggml_add(ctx0, cur, inpL); - inpL = cur; + cur = ggml_add(ctx0, cur, input); + input = cur; cb(cur, "ffn_inp", il); cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, NORM_TYPE_NORMAL, eps, il); @@ -361,11 +229,98 @@ ggml_cgraph * clip_graph_minicpmv4_6::build() { cur = ggml_mul(ctx0, cur, layer.ls_2_w); cb(cur, "ffn_out_scaled", il); } - cur = ggml_add(ctx0, inpL, cur); - cb(cur, "layer_out", il); + input = ggml_add(ctx0, input, cur); + cb(input, "layer_out", il); + } + return input; + }; + + if (!is_4x) { + const int insert_lid = hparams.insert_layer_id; + + inpL = build_vit_layers(inpL, 0, insert_lid + 1, n_pos); + + // ViT merger: window self-attention + // Tokens are reordered to window-major (4 tokens per window are contiguous), + // and a block-diagonal mask restricts attention to within each window. This + // mirrors the qwen2vl windowed-attention pattern so build_attn() can pick the + // flash-attention path when available. + { + ggml_tensor * residual = inpL; + ggml_tensor * cur = build_norm(inpL, + model.vit_merger_ln1_w, model.vit_merger_ln1_b, + NORM_TYPE_NORMAL, eps, -1); + cb(cur, "vit_merger_attn_inp_normed", -1); + + cur = ggml_get_rows(ctx0, cur, vit_merger_window_idx); + cb(cur, "vit_merger_window_reorder", -1); + + ggml_tensor * Qcur = build_mm(model.vit_merger_attn_q_w, cur); + if (model.vit_merger_attn_q_b) { + Qcur = ggml_add(ctx0, Qcur, model.vit_merger_attn_q_b); + } + ggml_tensor * Kcur = build_mm(model.vit_merger_attn_k_w, cur); + if (model.vit_merger_attn_k_b) { + Kcur = ggml_add(ctx0, Kcur, model.vit_merger_attn_k_b); + } + ggml_tensor * Vcur = build_mm(model.vit_merger_attn_v_w, cur); + if (model.vit_merger_attn_v_b) { + Vcur = ggml_add(ctx0, Vcur, model.vit_merger_attn_v_b); + } - inpL = cur; + Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_pos); + Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_pos); + Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_pos); + cb(Qcur, "vit_merger_Qcur", -1); + cb(Kcur, "vit_merger_Kcur", -1); + cb(Vcur, "vit_merger_Vcur", -1); + + cur = build_attn(model.vit_merger_attn_o_w, model.vit_merger_attn_o_b, + Qcur, Kcur, Vcur, vit_merger_window_mask, kq_scale, -1); + cb(cur, "vit_merger_attn_out", -1); + + cur = ggml_get_rows(ctx0, cur, vit_merger_inv_window_idx); + inpL = ggml_add(ctx0, cur, residual); + cb(inpL, "vit_merger_attn_residual", -1); } + + // ViT merger: 2x2 spatial downsample + MLP (4 tokens -> 1) + { + ggml_tensor * p0 = ggml_get_rows(ctx0, inpL, vit_merger_ds_idx_0); + ggml_tensor * p1 = ggml_get_rows(ctx0, inpL, vit_merger_ds_idx_1); + ggml_tensor * p2 = ggml_get_rows(ctx0, inpL, vit_merger_ds_idx_2); + ggml_tensor * p3 = ggml_get_rows(ctx0, inpL, vit_merger_ds_idx_3); + + ggml_tensor * mean_res = ggml_add(ctx0, p0, p1); + mean_res = ggml_add(ctx0, mean_res, p2); + mean_res = ggml_add(ctx0, mean_res, p3); + mean_res = ggml_scale(ctx0, mean_res, 0.25f); + cb(mean_res, "vit_merger_ds_mean_res", -1); + + ggml_tensor * cat = ggml_concat(ctx0, p0, p1, 0); + cat = ggml_concat(ctx0, cat, p2, 0); + cat = ggml_concat(ctx0, cat, p3, 0); + + ggml_tensor * cur = build_norm(cat, + model.vit_merger_ds_ln_w, model.vit_merger_ds_ln_b, + NORM_TYPE_NORMAL, eps, -1); + cb(cur, "vit_merger_ds_normed", -1); + + // ViTWindowAttentionMerger downsample MLP uses gelu_pytorch_tanh (FFN_GELU) + cur = build_ffn(cur, + model.vit_merger_ds_up_w, model.vit_merger_ds_up_b, + nullptr, nullptr, + model.vit_merger_ds_down_w, model.vit_merger_ds_down_b, + FFN_GELU, -1); + cb(cur, "vit_merger_ds_mlp_out", -1); + + inpL = ggml_add(ctx0, cur, mean_res); + cb(inpL, "vit_merger_ds_out", -1); + } + + inpL = build_vit_layers(inpL, insert_lid + 1, n_layer, n_ds); + } else { + inpL = build_vit_layers(inpL, 0, n_layer, n_pos); } if (model.post_ln_w) { diff --git a/tools/mtmd/models/minimax-m3.cpp b/tools/mtmd/models/minimax-m3.cpp new file mode 100644 index 000000000000..447621754e69 --- /dev/null +++ b/tools/mtmd/models/minimax-m3.cpp @@ -0,0 +1,84 @@ +#include "models.h" + +ggml_tensor * clip_graph_minimax_m3::apply_rope( + ggml_tensor * x, ggml_tensor * pos_h, ggml_tensor * pos_w) { + const int64_t Hn = x->ne[1]; + const int64_t P = x->ne[2]; + const size_t es = ggml_element_size(x); + const int dh = (int) x->ne[0]; + const int axd = 2 * ((2 * (dh / 2) / 3) / 2); + + GGML_ASSERT(x->nb[0] == es); + GGML_ASSERT(3 * axd <= dh); + + const float th = hparams.rope_theta; + + // layout of x is [t, h, w, pad] + // t is unrotated, h and w are rotated, pad is unrotated + // note: everything from n_dims onward untouched, so w and pad are rotated in one call. + auto sl = [&](int off, int n) { + return ggml_cont(ctx0, ggml_view_3d(ctx0, x, n, Hn, P, x->nb[1], x->nb[2], (size_t) off * es)); + }; + ggml_tensor * t = sl(0, axd); + ggml_tensor * h = sl(axd, axd); + ggml_tensor * w = sl(2 * axd, dh - 2 * axd); // w + pad + + h = ggml_rope_ext(ctx0, h, pos_h, nullptr, axd, GGML_ROPE_TYPE_NEOX, 0, th, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f); + w = ggml_rope_ext(ctx0, w, pos_w, nullptr, axd, GGML_ROPE_TYPE_NEOX, 0, th, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f); + return ggml_concat(ctx0, ggml_concat(ctx0, t, h, 0), w, 0); +} + +ggml_cgraph * clip_graph_minimax_m3::build() { + GGML_ASSERT(model.patch_bias == nullptr); + GGML_ASSERT(model.class_embedding == nullptr); + GGML_ASSERT(model.patch_embeddings_0 && model.patch_embeddings_1); + GGML_ASSERT(model.mm_1_w && model.mm_2_w); + GGML_ASSERT(model.mm_merger_fc1_w && model.mm_merger_fc2_w); + + const int batch_size = 1; + const int n_pos = n_patches; + const int merge = hparams.n_merge; + + // patch embedding + ggml_tensor * inp_raw = build_inp_raw(); + ggml_tensor * inp = ggml_add(ctx0, + ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1), + ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1)); + + // spatial merge + { + inp = ggml_permute(ctx0, inp, 1, 2, 0, 3); + inp = ggml_cont_4d(ctx0, inp, n_embd * merge, n_patches_x / merge, n_patches_y, batch_size); + inp = ggml_reshape_4d(ctx0, inp, n_embd * merge, n_patches_x / merge, merge, batch_size * (n_patches_y / merge)); + inp = ggml_permute(ctx0, inp, 0, 2, 1, 3); + inp = ggml_cont_3d(ctx0, inp, n_embd, n_patches_x * n_patches_y, batch_size); + } + + // t (time axis) is always 0 for now, so we leave it unrotated + ggml_tensor * pos_h = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos); + ggml_set_name(pos_h, "minimax_pos_h"); ggml_set_input(pos_h); + ggml_tensor * pos_w = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos); + ggml_set_name(pos_w, "minimax_pos_w"); ggml_set_input(pos_w); + + ggml_tensor * inpL = build_vit( + inp, n_pos, NORM_TYPE_NORMAL, FFN_GELU_ERF, nullptr, + [&](ggml_tensor * c, const clip_layer &) { + return apply_rope(c, pos_h, pos_w); + }); + + // projector + ggml_tensor * emb = inpL; + emb = build_ffn(emb, model.mm_1_w, model.mm_1_b, + nullptr, nullptr, + model.mm_2_w, model.mm_2_b, FFN_GELU_ERF, -1); + + const int64_t proj = emb->ne[0]; + emb = ggml_reshape_2d(ctx0, emb, proj * merge * merge, n_pos / (merge * merge)); + + emb = build_ffn(emb, model.mm_merger_fc1_w, model.mm_merger_fc1_b, + nullptr, nullptr, + model.mm_merger_fc2_w, model.mm_merger_fc2_b, FFN_GELU_ERF, -1); + + ggml_build_forward_expand(gf, emb); + return gf; +} diff --git a/tools/mtmd/models/models.h b/tools/mtmd/models/models.h index e50785e525bf..542949de3ddf 100644 --- a/tools/mtmd/models/models.h +++ b/tools/mtmd/models/models.h @@ -51,6 +51,12 @@ struct clip_graph_qwen3vl : clip_graph_qwen2vl { ggml_cgraph * build() override; }; +struct clip_graph_minimax_m3 : clip_graph { + clip_graph_minimax_m3(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} + ggml_cgraph * build() override; + ggml_tensor * apply_rope(ggml_tensor * x, ggml_tensor * pos_h, ggml_tensor * pos_w); +}; + struct clip_graph_mimovl : clip_graph { clip_graph_mimovl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} ggml_cgraph * build() override; @@ -215,6 +221,11 @@ struct clip_graph_qwen3a : clip_graph { ggml_cgraph * build() override; }; +struct clip_graph_mimo_audio : clip_graph { + clip_graph_mimo_audio(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} + ggml_cgraph * build() override; +}; + struct clip_graph_kimik25 : clip_graph { clip_graph_kimik25(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} ggml_cgraph * build() override; @@ -222,6 +233,18 @@ struct clip_graph_kimik25 : clip_graph { ggml_tensor * resize_position_embeddings_3d(uint32_t interpolation_mode); }; +struct clip_graph_kimik3 : clip_graph { + clip_graph_kimik3(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} + ggml_cgraph * build() override; + + ggml_tensor * resize_position_embeddings_3d(uint32_t interpolation_mode); +}; + +struct clip_graph_parakeet : clip_graph { + clip_graph_parakeet(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} + ggml_cgraph * build() override; +}; + struct clip_graph_exaone4_5 : clip_graph { clip_graph_exaone4_5(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} ggml_cgraph * build() override; diff --git a/tools/mtmd/models/parakeet.cpp b/tools/mtmd/models/parakeet.cpp new file mode 100644 index 000000000000..8be141d93b37 --- /dev/null +++ b/tools/mtmd/models/parakeet.cpp @@ -0,0 +1,421 @@ +#include "models.h" + +static constexpr int PARAKEET_LOCAL_ATTN_THRESHOLD = 8192; +static constexpr int PARAKEET_LOCAL_ATTN_WINDOW = 128; + +// conv subsampling + conformer encoder +ggml_cgraph * clip_graph_parakeet::build() { + + // Conv subsampling + ggml_tensor * inp = build_inp_raw(1); + inp = ggml_cont(ctx0, ggml_transpose(ctx0, inp)); + + // [freq, time, channels, batch] + ggml_tensor * cur = ggml_conv_2d(ctx0, model.pre_encode_conv_X_w[0], inp, 2, 2, 1, 1, 1, 1); + cur = ggml_add(ctx0, cur, model.pre_encode_conv_X_b[0]); + cb(cur, "pre_conv_0", -1); + + cur = ggml_relu(ctx0, cur); + cb(cur, "pre_conv_0_relu", -1); + + // [freq, time, channels, batch] + cur = ggml_conv_2d_dw_direct(ctx0, model.pre_encode_conv_X_w[2], cur, 2, 2, 1, 1, 1, 1); + cur = ggml_add(ctx0, cur, model.pre_encode_conv_X_b[2]); + cb(cur, "pre_conv_2", -1); + + // [freq, time, channels, batch] + cur = ggml_conv_2d(ctx0, model.pre_encode_conv_X_w[3], cur, 1, 1, 0, 0, 1, 1); + cur = ggml_add(ctx0, cur, model.pre_encode_conv_X_b[3]); + cb(cur, "pre_conv_3", -1); + + cur = ggml_relu(ctx0, cur); + cb(cur, "pre_conv_3_relu", -1); + + // [freq, time, channels, batch] + cur = ggml_conv_2d_dw_direct(ctx0, model.pre_encode_conv_X_w[5], cur, 2, 2, 1, 1, 1, 1); + cb(cur, "pre_conv_5_direct", -1); + cur = ggml_add(ctx0, cur, model.pre_encode_conv_X_b[5]); + cb(cur, "pre_conv_5", -1); + + // [freq, time, channels, batch] + cur = ggml_conv_2d(ctx0, model.pre_encode_conv_X_w[6], cur, 1, 1, 0, 0, 1, 1); + cur = ggml_add(ctx0, cur, model.pre_encode_conv_X_b[6]); + cb(cur, "pre_conv_6", -1); + + cur = ggml_relu(ctx0, cur); + cb(cur, "pre_conv_6_relu", -1); + + // [freq, time, chan] + cur = ggml_permute(ctx0, cur, 0, 2, 1, 3); + // [freq, chan, time] + cur = ggml_cont(ctx0, cur); + + const int n_freq = cur->ne[0]; + const int n_chan = cur->ne[1]; + const int n_frames = cur->ne[2]; + + // [freq, time, chan, batch] -> [(freq * chan), time] + cur = ggml_reshape_2d(ctx0, cur, n_freq * n_chan, n_frames); + + cur = build_mm(model.pre_encode_out_w, cur); + cur = ggml_add(ctx0, cur, model.pre_encode_out_b); + + ggml_set_name(cur, "pre_enc_out"); + + // Encoder + + const auto & hparams = model.hparams; + const int n_layer = hparams.n_layer; + const int n_state = hparams.n_embd; + const float fc_factor = 0.5f; + + const int n_time = cur->ne[1]; + const bool local_attn = n_time > PARAKEET_LOCAL_ATTN_THRESHOLD; + const int att_left = local_attn ? PARAKEET_LOCAL_ATTN_WINDOW : n_time - 1; + const int att_right = local_attn ? PARAKEET_LOCAL_ATTN_WINDOW : n_time - 1; + const int window_size = local_attn ? att_left + att_right + 1 : 2 * n_time - 1; + const int d_half = n_state / 2; + const int mask_dim = local_attn ? window_size : n_time; + + // mask [key, n_time] + struct ggml_tensor * attn_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, mask_dim, n_time); + ggml_set_name(attn_mask, "attn_mask"); + ggml_set_input(attn_mask); + + struct ggml_tensor * local_mask = nullptr; + if (local_attn) { + const int chunk = att_left + att_right; + local_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, chunk + window_size - 1, chunk); + ggml_set_name(local_mask, "local_mask"); + ggml_set_input(local_mask); + } + + struct ggml_tensor * pos_freqs = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, d_half); + ggml_set_name(pos_freqs, "pos_freqs"); + ggml_set_input(pos_freqs); + + struct ggml_tensor * rel_positions = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 1, window_size); + ggml_set_name(rel_positions, "rel_positions"); + ggml_set_input(rel_positions); + + struct ggml_tensor * freqs = ggml_repeat_4d(ctx0, pos_freqs, d_half, window_size, 1, 1); + struct ggml_tensor * theta = ggml_mul(ctx0, freqs, rel_positions); + + struct ggml_tensor * sin = ggml_reshape_3d(ctx0, ggml_sin(ctx0, theta), 1, d_half, window_size); + struct ggml_tensor * cos = ggml_reshape_3d(ctx0, ggml_cos(ctx0, theta), 1, d_half, window_size); + struct ggml_tensor * pos_emb = ggml_reshape_2d(ctx0, ggml_cont(ctx0, ggml_concat(ctx0, sin, cos, 0)), n_state, window_size); + ggml_set_name(pos_emb, "pos_emb"); + + for (int il = 0; il < n_layer; ++il) { + const auto & layer = model.layers[il]; + // FFN1 + { + struct ggml_tensor * residual = cur; + ggml_format_name(cur, "enc_%d_res", il); + + // norm + cur = ggml_norm(ctx0, cur, hparams.eps); + cur = ggml_add(ctx0, ggml_mul(ctx0, cur, layer.ff_norm_w), layer.ff_norm_b); + ggml_format_name(cur, "enc_%d_ffn_norm_1", il); + + cur = build_ffn(cur, layer.ff_up_w, nullptr, nullptr, nullptr, layer.ff_down_w, nullptr, FFN_SILU, il); + ggml_format_name(cur, "enc_%d_ffn_1", il); + + cur = ggml_add(ctx0, residual, ggml_scale(ctx0, cur, fc_factor)); + ggml_format_name(cur, "enc_%d_res_ffn", il); + } + + // self attention block using relative positional encoding from model.position_embedding. + { + // [feat, time_frames, 1, 1] + struct ggml_tensor * residual = cur; + + cur = ggml_norm(ctx0, cur, hparams.eps); + cur = ggml_add(ctx0, ggml_mul(ctx0, cur, layer.ln_1_w), layer.ln_1_b); + ggml_format_name(cur, "enc_%d_attn_norm", il); + + const int n_head = hparams.n_head; + const int d_head = n_state / n_head; + + // [feat, time_frames, 1, 1] + struct ggml_tensor * Q_cur = build_mm(layer.q_w, cur); + struct ggml_tensor * K_cur = build_mm(layer.k_w, cur); + struct ggml_tensor * V_cur = build_mm(layer.v_w, cur); + + // [d_head, n_heads, n_time, 1] + Q_cur = ggml_reshape_3d(ctx0, Q_cur, d_head, n_head, n_time); + K_cur = ggml_reshape_3d(ctx0, K_cur, d_head, n_head, n_time); + V_cur = ggml_reshape_3d(ctx0, V_cur, d_head, n_head, n_time); + + // [n_state, window_size] + struct ggml_tensor * pos = build_mm(layer.linear_pos_w, pos_emb); + // [feat, head, window_size, 1] + pos = ggml_reshape_3d(ctx0, pos, d_head, n_head, pos_emb->ne[1]); + // [feat, window_size, head, 1] + pos = ggml_cont(ctx0, ggml_permute(ctx0, pos, 0, 2, 1, 3)); + ggml_format_name(pos, "enc_%d_attn_pos", il); + + if (local_attn) { + const int chunk = att_left + att_right; + const int n_group = (n_time + chunk - 1) / chunk; + const int n_time_padded = n_group * chunk; + const int n_kv_chunk = chunk + window_size - 1; + const int n_kv_dense = n_kv_chunk * n_group; + const bool need_padding = n_time_padded > n_time; + + Q_cur = ggml_cont(ctx0, ggml_permute(ctx0, Q_cur, 0, 2, 1, 3)); + K_cur = ggml_cont(ctx0, ggml_permute(ctx0, K_cur, 0, 2, 1, 3)); + V_cur = ggml_cont(ctx0, ggml_permute(ctx0, V_cur, 0, 2, 1, 3)); + + // content bias + struct ggml_tensor * bias_u = ggml_reshape_3d(ctx0, layer.pos_bias_u, d_head, 1, n_head); + struct ggml_tensor * Q_u = ggml_add(ctx0, Q_cur, bias_u); + + // position bias + struct ggml_tensor * bias_v = ggml_reshape_3d(ctx0, layer.pos_bias_v, d_head, 1, n_head); + struct ggml_tensor * Q_v = ggml_add(ctx0, Q_cur, bias_v); + + // right pad the time dimension + struct ggml_tensor * Q_u_padded = need_padding ? + ggml_pad_ext(ctx0, Q_u, 0, 0, 0, n_time_padded - n_time, 0, 0, 0, 0) : Q_u; + Q_u_padded = ggml_reshape_4d(ctx0, Q_u_padded, d_head, chunk, n_group, n_head); + + // pad front and back for the first and last time frames + struct ggml_tensor * K_padded = ggml_pad_ext(ctx0, K_cur, 0, 0, att_left, att_right, 0, 0, 0, 0); + if (n_kv_dense > K_padded->ne[1]) { + K_padded = ggml_pad_ext(ctx0, K_padded, 0, 0, 0, n_kv_dense - K_padded->ne[1], 0, 0, 0, 0); + } + + // sliding window view: each group spans n_kv_chunk keys but steps by chunk + struct ggml_tensor * K_chunk = ggml_view_4d(ctx0, K_padded, + d_head, n_kv_chunk, n_group, n_head, + K_padded->nb[1], + (size_t) chunk * K_padded->nb[1], + K_padded->nb[2], + 0); + K_chunk = ggml_cont(ctx0, K_chunk); + + struct ggml_tensor * content_scores = ggml_mul_mat(ctx0, K_chunk, Q_u_padded); + + // trim the dense output down to window_size scores per query + content_scores = ggml_view_4d(ctx0, content_scores, + window_size, chunk, n_group, n_head, + (size_t) (chunk + window_size) * content_scores->nb[0], + content_scores->nb[2], + content_scores->nb[3], + 0); + content_scores = ggml_cont(ctx0, content_scores); + + // ungroup: [window_size, n_time_padded, n_head] + content_scores = ggml_reshape_3d(ctx0, content_scores, window_size, n_time_padded, n_head); + if (need_padding) { + content_scores = ggml_view_3d(ctx0, content_scores, + window_size, n_time, n_head, + content_scores->nb[1], + content_scores->nb[2], + 0); + } + + // Q_v: [d_head, time, head] + Q_v = ggml_cont(ctx0, ggml_permute(ctx0, Q_v, 0, 2, 1, 3)); + struct ggml_tensor * rel_pos_scores = ggml_mul_mat(ctx0, pos, Q_v); + + struct ggml_tensor * attn_scores = ggml_add(ctx0, content_scores, rel_pos_scores); + attn_scores = ggml_soft_max_ext(ctx0, attn_scores, attn_mask, 1.0f / std::sqrt(d_head), 0.0f); + ggml_format_name(attn_scores, "enc_%d_attn_probs", il); + + // expand probs back to n_kv_chunk width for the V matmul + struct ggml_tensor * probs_padded = need_padding ? + ggml_pad_ext(ctx0, attn_scores, 0, 0, 0, n_time_padded - n_time, 0, 0, 0, 0) : attn_scores; + + probs_padded = ggml_reshape_4d(ctx0, probs_padded, window_size, chunk, n_group, n_head); + probs_padded = ggml_pad_ext(ctx0, probs_padded, 0, chunk, 0, 0, 0, 0, 0, 0); + probs_padded = ggml_view_4d(ctx0, probs_padded, + n_kv_chunk, chunk, n_group, n_head, + (size_t) n_kv_chunk * probs_padded->nb[0], + probs_padded->nb[2], + probs_padded->nb[3], + 0); + probs_padded = ggml_cont(ctx0, probs_padded); + probs_padded = ggml_mul(ctx0, probs_padded, local_mask); + + struct ggml_tensor * V_padded = ggml_pad_ext(ctx0, V_cur, 0, 0, att_left, att_right, 0, 0, 0, 0); + if (n_kv_dense > V_padded->ne[1]) { + V_padded = ggml_pad_ext(ctx0, V_padded, 0, 0, 0, n_kv_dense - V_padded->ne[1], 0, 0, 0, 0); + } + V_padded = ggml_cont(ctx0, ggml_transpose(ctx0, V_padded)); + + struct ggml_tensor * V_chunk = ggml_view_4d(ctx0, V_padded, + n_kv_chunk, d_head, n_group, n_head, + V_padded->nb[1], + (size_t) chunk * V_padded->nb[0], + V_padded->nb[2], + 0); + V_chunk = ggml_cont(ctx0, V_chunk); + + cur = ggml_mul_mat(ctx0, V_chunk, probs_padded); + cur = ggml_reshape_3d(ctx0, cur, d_head, n_time_padded, n_head); + if (need_padding) { + cur = ggml_view_3d(ctx0, cur, d_head, n_time, n_head, cur->nb[1], cur->nb[2], 0); + } + cur = ggml_cont(ctx0, ggml_permute(ctx0, cur, 0, 2, 1, 3)); + cur = ggml_reshape_2d(ctx0, cur, n_state, n_time); + cur = build_mm(layer.o_w, cur); + } else { + // full attention + struct ggml_tensor * Q_u = ggml_add(ctx0, Q_cur, layer.pos_bias_u); + ggml_format_name(Q_u, "enc_%d_attn_q_u", il); + + struct ggml_tensor * K_prep = ggml_permute(ctx0, K_cur, 0, 2, 1, 3); + struct ggml_tensor * Q_prep = ggml_permute(ctx0, Q_u, 0, 2, 1, 3); + struct ggml_tensor * content_scores = ggml_mul_mat(ctx0, K_prep, Q_prep); + ggml_format_name(content_scores, "enc_%d_attn_content_scores", il); + + struct ggml_tensor * Q_v = ggml_add(ctx0, Q_cur, layer.pos_bias_v); + ggml_format_name(Q_v, "enc_%d_attn_q_v", il); + + Q_v = ggml_permute(ctx0, Q_v, 0, 2, 1, 3); + Q_v = ggml_cont(ctx0, Q_v); + ggml_format_name(Q_v, "enc_%d_attn_q_v_perm", il); + + struct ggml_tensor * rel_pos_scores = ggml_mul_mat(ctx0, pos, Q_v); + ggml_format_name(rel_pos_scores, "enc_%d_attn_rel_pos", il); + + // Relative positional shift + { + const auto pos_window = rel_pos_scores->ne[0]; + const auto n_frame = rel_pos_scores->ne[1]; + const auto n_head = rel_pos_scores->ne[2]; + + rel_pos_scores = ggml_pad(ctx0, rel_pos_scores, 1, 0, 0, 0); + rel_pos_scores = ggml_roll(ctx0, rel_pos_scores, 1, 0, 0, 0); + + rel_pos_scores = ggml_reshape_3d(ctx0, rel_pos_scores, n_frame, pos_window + 1, n_head); + rel_pos_scores = ggml_cont(ctx0, rel_pos_scores); + ggml_format_name(rel_pos_scores, "enc_%d_attn_rel_pos_reshaped", il); + + int center = pos_window / 2; + size_t offset = rel_pos_scores->nb[0] * (center+1); + + rel_pos_scores = ggml_view_3d(ctx0, rel_pos_scores, + n_frame, pos_window, n_head, + (pos_window) * 4, + rel_pos_scores->nb[2], + offset); + rel_pos_scores = ggml_cont(ctx0, rel_pos_scores); + ggml_format_name(rel_pos_scores, "enc_%d_attn_rel_pos_shifted", il); + + rel_pos_scores = ggml_view_3d(ctx0, rel_pos_scores, + content_scores->ne[0], + content_scores->ne[1], + rel_pos_scores->ne[2], + rel_pos_scores->nb[1], + rel_pos_scores->nb[2], + 0); + rel_pos_scores = ggml_cont(ctx0, rel_pos_scores); + ggml_format_name(rel_pos_scores, "enc_%d_attn_rel_pos_shifted_view", il); + } + + struct ggml_tensor * attn_scores = ggml_add(ctx0, content_scores, rel_pos_scores); + ggml_format_name(attn_scores, "enc_%d_attn_scores", il); + attn_scores = ggml_scale(ctx0, attn_scores, 1.0f / std::sqrt(d_head)); + attn_scores = ggml_add(ctx0, attn_scores, attn_mask); + ggml_format_name(attn_scores, "enc_%d_attn_scores_scaled", il); + + struct ggml_tensor * probs = ggml_soft_max(ctx0, attn_scores); + ggml_format_name(probs, "enc_%d_attn_probs", il); + + V_cur = ggml_cont(ctx0, ggml_permute(ctx0, V_cur, 1, 2, 0, 3)); + ggml_format_name(V_cur, "enc_%d_attn_v_cur", il); + cur = ggml_mul_mat(ctx0, probs, V_cur); + ggml_format_name(cur, "enc_%d_attn_inp", il); + + cur = ggml_permute(ctx0, cur, 2, 0, 1, 3); + cur = ggml_cont_2d(ctx0, cur, n_state, n_time); + cur = build_mm(layer.o_w, cur); + } + ggml_format_name(cur, "enc_%d_attn_out", il); + + cur = ggml_add(ctx0, residual, cur); + ggml_format_name(cur, "enc_%d_attn_res", il); + } + + // Convolution + { + struct ggml_tensor * residual = cur; + ggml_format_name(cur, "enc_%d_residual_conv", il); + + cur = ggml_norm(ctx0, cur, hparams.eps); + cur = ggml_add(ctx0, ggml_mul(ctx0, cur, layer.norm_conv_w), layer.norm_conv_b); + ggml_format_name(cur, "enc_%d_norm_conv", il); + + // pointwise 1d convolution: + cur = build_mm(layer.conv_pw1_w, cur); + ggml_format_name(cur, "enc_%d_conv_pw1", il); + + { + int64_t d = cur->ne[0] / 2; + struct ggml_tensor * signal = ggml_view_2d(ctx0, cur, d, cur->ne[1], cur->nb[1], 0); + struct ggml_tensor * gate = ggml_view_2d(ctx0, cur, d, cur->ne[1], cur->nb[1], d * cur->nb[0]); + + cur = ggml_mul(ctx0, signal, ggml_sigmoid(ctx0, gate)); + ggml_format_name(cur, "enc_%d_conv_glu", il); + } + + cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur)); + + // use ggml_ssm_conv for f32 precision + const int dw_pad = (hparams.audio_conv_kernel_size - 1) / 2; + cur = ggml_pad(ctx0, cur, dw_pad, 0, 0, 0); + cur = ggml_roll(ctx0, cur, dw_pad, 0, 0, 0); + cur = ggml_pad(ctx0, cur, dw_pad, 0, 0, 0); + ggml_format_name(cur, "enc_%d_conv_dw_pad", il); + + cur = ggml_ssm_conv(ctx0, cur, layer.conv_dw_w); + ggml_format_name(cur, "enc_%d_conv_1d_dw", il); + + cur = ggml_sub(ctx0, cur, layer.conv_norm_mean); + struct ggml_tensor * std = ggml_sqrt(ctx0, layer.conv_norm_var); + cur = ggml_div(ctx0, cur, std); + cur = ggml_add(ctx0, ggml_mul(ctx0, cur, layer.conv_norm_w), layer.conv_norm_b); + ggml_format_name(cur, "enc_%d_conv_bn", il); + + cur = ggml_silu(ctx0, cur); + ggml_format_name(cur, "enc_%d_conv_silu", il); + + cur = build_mm(layer.conv_pw2_w, cur); + ggml_format_name(cur, "enc_%d_conv_pw2", il); + + cur = ggml_add(ctx0, residual, cur); + ggml_format_name(cur, "enc_%d_conv_res", il); + } + + // FFN2 + { + struct ggml_tensor * residual = cur; + cur = ggml_norm(ctx0, cur, hparams.eps); + cur = ggml_add(ctx0, ggml_mul(ctx0, cur, layer.ff_norm_1_w), layer.ff_norm_1_b); + ggml_format_name(cur, "enc_%d_ffn_norm_2", il); + + cur = build_ffn(cur, layer.ff_up_1_w, nullptr, nullptr, nullptr, layer.ff_down_1_w, nullptr, FFN_SILU, il); + cur = ggml_add(ctx0, residual, ggml_scale(ctx0, cur, 0.5)); + ggml_format_name(cur, "enc_%d_ffn_res", il); + } + + cur = ggml_norm(ctx0, cur, hparams.eps); + cur = ggml_add(ctx0, ggml_mul(ctx0, cur, layer.ln_2_w), layer.ln_2_b); + } + + cb(cur, "encoder_out", -1); + + cur = ggml_rms_norm(ctx0, cur, 1e-6); + cur = ggml_mul(ctx0, cur, model.mm_norm_pre_w); + cb(cur, "sound_projection.norm", -1); + + cur = build_ffn(cur, model.mm_0_w, model.mm_0_b, nullptr, nullptr, model.mm_1_w, model.mm_1_b, FFN_RELU_SQR, -1); + cb(cur, "projected", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; +} diff --git a/tools/mtmd/models/qwen3vl.cpp b/tools/mtmd/models/qwen3vl.cpp index 261e77a198af..48626b221fbb 100644 --- a/tools/mtmd/models/qwen3vl.cpp +++ b/tools/mtmd/models/qwen3vl.cpp @@ -37,7 +37,7 @@ ggml_cgraph * clip_graph_qwen3vl::build() { } // calculate absolute position embedding and apply - ggml_tensor * learned_pos_embd = resize_position_embeddings(); + ggml_tensor * learned_pos_embd = resize_position_embeddings(GGML_SCALE_MODE_BILINEAR | GGML_SCALE_FLAG_ALIGN_CORNERS); learned_pos_embd = ggml_cont_4d( ctx0, learned_pos_embd, n_embd * 2, n_patches_x / 2, n_patches_y, batch_size); diff --git a/tools/mtmd/mtmd-audio.cpp b/tools/mtmd/mtmd-audio.cpp index 7d6cc83961d8..13e31ff54e9a 100644 --- a/tools/mtmd/mtmd-audio.cpp +++ b/tools/mtmd/mtmd-audio.cpp @@ -796,6 +796,72 @@ bool mtmd_audio_preprocessor_qwen3a::preprocess(const float * sa return true; } +// +// mtmd_audio_preprocessor_mimo_audio +// +// Matches torchaudio.transforms.MelSpectrogram(power=1.0, center=True) followed by +// log(clip(spec, min=1e-7)): HTK mel scale, no Slaney area norm, magnitude (not power) +// spectrogram, natural log, reflect-padded by n_fft/2 on each side. +// + +void mtmd_audio_preprocessor_mimo_audio::initialize() { + cache.fill_sin_cos_table(hparams.audio_n_fft); + cache.fill_hann_window(hparams.audio_window_len, true); + cache.fill_mel_filterbank_matrix( + hparams.n_mel_bins, hparams.audio_n_fft, hparams.audio_sample_rate, + 0.0f, hparams.audio_sample_rate / 2.0f, + /*slaney_area_norm=*/ false, + /*scale=*/ 1.0f, + /*use_htk=*/ true + ); +} + +bool mtmd_audio_preprocessor_mimo_audio::preprocess(const float * samples, + size_t n_samples, + std::vector & output) { + if (n_samples == 0) { + return false; + } + + GGML_ASSERT(!cache.sin_vals.empty()); + GGML_ASSERT(!cache.cos_vals.empty()); + GGML_ASSERT(!cache.filters.data.empty()); + + const int pad = hparams.audio_n_fft / 2; + + std::vector padded(n_samples + 2 * pad, 0.0f); + for (int i = 0; i < pad; i++) { + int src = pad - i; + padded[i] = (src < (int)n_samples) ? samples[src] : 0.0f; + } + std::copy(samples, samples + n_samples, padded.begin() + pad); + for (int i = 0; i < pad; i++) { + int src = (int)n_samples - 2 - i; + padded[n_samples + pad + i] = (src >= 0) ? samples[src] : 0.0f; + } + + filter_params params; + params.n_mel = hparams.n_mel_bins; + params.n_fft_bins = 1 + (hparams.audio_n_fft / 2); + params.hann_window_size = hparams.audio_window_len; + params.hop_length = hparams.audio_hop_len; + params.sample_rate = hparams.audio_sample_rate; + params.no_padding = true; // reflect padding already applied above + params.use_natural_log = true; + params.use_magnitude = true; + params.mel_floor = 1e-7f; + params.norm_per_feature = false; + + mtmd_audio_mel out; + bool ok = log_mel_spectrogram(padded.data(), (int)padded.size(), 4, params, cache, out); + if (!ok) { + return false; + } + + output.push_back(std::move(out)); + return true; +} + // // mtmd_audio_preprocessor_conformer // @@ -1027,6 +1093,209 @@ bool mtmd_audio_preprocessor_gemma4a::preprocess(const float * s } // +// mtmd_audio_preprocessor_parakeet implementation +// + +void mtmd_audio_preprocessor_parakeet::worker_thread( + int ith, + const float * window_func, + int window_size, + const std::vector & samples, + int n_samples, + int frame_size, + int frame_step, + int n_threads, + int n_fft_bins, + const mtmd_audio_cache & cache, + mtmd_audio_mel & mel) { + std::vector fft_in(frame_size * 2, 0.0); + std::vector fft_out(frame_size * 2 * 2 * 2); + + int n_fb = n_fft_bins; + int i = ith; + + GGML_ASSERT(n_fb == 1 + (frame_size / 2)); + + const double eps = 5.960464477539063e-08; + + for (; i < std::min(n_samples / frame_step + 1, (int) mel.n_len); i += n_threads) { + const int offset = i * frame_step; + const int window_pad_left = (frame_size - window_size) / 2; + + // Zero-pad left. + std::fill(fft_in.begin(), fft_in.begin() + window_pad_left, 0.0f); + + // Apply windowed samples in the center. + const int n_to_process = std::min({window_size, n_samples - offset}); + for (int j = 0; j < n_to_process; j++) { + fft_in[window_pad_left + j] = window_func[j] * samples[offset + window_pad_left + j]; + } + + // Zero-pad right. + std::fill(fft_in.begin() + window_pad_left + n_to_process, fft_in.begin() + frame_size, 0.0f); + + // FFT. + fft(cache, fft_in.data(), frame_size, fft_out.data()); + + // Calculate modulus^2 of complex numbers. + for (int j = 0; j < n_fb; j++) { + fft_out[j] = (fft_out[2 * j + 0] * fft_out[2 * j + 0] + fft_out[2 * j + 1] * fft_out[2 * j + 1]); + } + + // mel spectrogram. + for (int j = 0; j < mel.n_mel; j++) { + double sum = 0.0; + int k = 0; + for (k = 0; k < n_fb - 3; k += 4) { + sum += + fft_out[k + 0] * cache.filters.data[j * n_fb + k + 0] + + fft_out[k + 1] * cache.filters.data[j * n_fb + k + 1] + + fft_out[k + 2] * cache.filters.data[j * n_fb + k + 2] + + fft_out[k + 3] * cache.filters.data[j * n_fb + k + 3]; + } + for (; k < n_fb; k++) { + sum += fft_out[k] * cache.filters.data[j * n_fb + k]; + } + mel.data[j * mel.n_len + i] = std::log(sum + eps); + } + } + + // Otherwise fft_out are all zero. + const double empty_sum = std::log(eps); + for (; i < mel.n_len; i += n_threads) { + for (int j = 0; j < mel.n_mel; j++) { + mel.data[j * mel.n_len + i] = empty_sum; + } + } +} + +void mtmd_audio_preprocessor_parakeet::initialize() { + cache.fill_sin_cos_table(hparams.audio_n_fft); + + const size_t n_fft = hparams.audio_n_fft / 2 + 1; + GGML_ASSERT(hparams.mel_filters.size() == (size_t)hparams.n_mel_bins * n_fft); + cache.filters.n_mel = hparams.n_mel_bins; + cache.filters.n_fft = n_fft; + cache.filters.data = hparams.mel_filters; + + GGML_ASSERT(hparams.window.size() == (size_t)hparams.audio_window_len); + GGML_ASSERT(hparams.window.size() <= (size_t) hparams.audio_n_fft); + cache.hann_window = hparams.window; +} + +bool mtmd_audio_preprocessor_parakeet::preprocess(const float * samples, + size_t n_samples_in, + std::vector & output) { + if (n_samples_in == 0) { + return false; + } + + filter_params params; + params.n_mel = hparams.n_mel_bins; + params.n_fft_bins = 1 + (hparams.audio_n_fft / 2); + params.hann_window_size = hparams.audio_window_len; + params.hop_length = hparams.audio_hop_len; + params.sample_rate = hparams.audio_sample_rate; + + GGML_ASSERT(!cache.sin_vals.empty()); + GGML_ASSERT(!cache.cos_vals.empty()); + GGML_ASSERT(!cache.filters.data.empty()); + + const float * window_func = cache.hann_window.data(); + const int window_size = params.hann_window_size; + const int frame_size = (params.n_fft_bins - 1) * 2; + const int frame_step = params.hop_length; + + // Apply preemphasis filter (high-pass): x[i] = x[i] - 0.97 * x[i-1] + std::vector samples_preprocessed(samples, samples + n_samples_in); + { + const float preemph = 0.97f; + for (int i = n_samples_in - 1; i > 0; i--) { + samples_preprocessed[i] = samples_preprocessed[i] - preemph * samples_preprocessed[i - 1]; + } + } + + // Parakeet uses centered constant padding + const size_t pad = (size_t)(frame_size / 2); + std::vector samples_padded(n_samples_in + 2 * pad, 0.0f); + std::copy(samples_preprocessed.begin(), samples_preprocessed.end(), samples_padded.begin() + pad); + + mtmd_audio_mel out_full; + out_full.n_mel = params.n_mel; + out_full.n_len = (samples_padded.size() - frame_size) / frame_step + 1; + out_full.n_len_org = out_full.n_len; + out_full.data.resize(out_full.n_mel * out_full.n_len); + + const int n_threads = 4; + std::vector workers(n_threads - 1); + for (int iw = 0; iw < n_threads - 1; ++iw) { + workers[iw] = std::thread( + worker_thread, iw + 1, + window_func, + window_size, + std::cref(samples_padded), + samples_padded.size(), + frame_size, + frame_step, + n_threads, + params.n_fft_bins, + std::cref(cache), + std::ref(out_full) + ); + } + + worker_thread(0, + window_func, + window_size, + samples_padded, + samples_padded.size(), + frame_size, + frame_step, + n_threads, + params.n_fft_bins, + cache, + out_full); + + for (int iw = 0; iw < n_threads - 1; ++iw) { + workers[iw].join(); + } + + // Per-feature normalization (only on valid frames) + { + const double eps = 1e-5; + int valid_frames = n_samples_in / frame_step; + + for (int j = 0; j < out_full.n_mel; j++) { + double sum = 0.0; + double sq_diff_sum = 0.0; + + // Calculate Mean ONLY on valid audio frames + for (int i = 0; i < valid_frames; i++) { + sum += (double)out_full.data[j * out_full.n_len + i]; + } + double mean = sum / valid_frames; + + // Calculate Variance ONLY on valid audio frames + for (int i = 0; i < valid_frames; i++) { + double diff = (double)out_full.data[j * out_full.n_len + i] - mean; + sq_diff_sum += diff * diff; + } + + double std_dev = std::sqrt(sq_diff_sum / (valid_frames - 1.0)); + double denominator = std_dev + eps; + + // Apply to ALL frames (including the padded ones) + for (int i = 0; i < out_full.n_len; i++) { + out_full.data[j * out_full.n_len + i] = (float)((out_full.data[j * out_full.n_len + i] - mean) / denominator); + } + } + } + + output.push_back(std::move(out_full)); + return true; +} + + // mtmd_audio_preprocessor_gemma4ua // diff --git a/tools/mtmd/mtmd-audio.h b/tools/mtmd/mtmd-audio.h index 29613d11146c..e79519553e9b 100644 --- a/tools/mtmd/mtmd-audio.h +++ b/tools/mtmd/mtmd-audio.h @@ -121,6 +121,30 @@ struct mtmd_audio_preprocessor_qwen3a : mtmd_audio_preprocessor { mtmd_audio_cache cache; }; +struct mtmd_audio_preprocessor_mimo_audio : mtmd_audio_preprocessor { + mtmd_audio_preprocessor_mimo_audio(const clip_ctx * ctx) : mtmd_audio_preprocessor(ctx) {} + void initialize() override; + bool preprocess(const float * samples, size_t n_samples, std::vector & output) override; + + private: + mtmd_audio_cache cache; +}; + +struct mtmd_audio_preprocessor_parakeet : mtmd_audio_preprocessor { + mtmd_audio_preprocessor_parakeet(clip_ctx * ctx) : mtmd_audio_preprocessor(ctx) { } + void initialize() override; + bool preprocess(const float * samples, size_t n_samples, std::vector & output) override; + + private: + mtmd_audio_cache cache; + + static void worker_thread(int ith, const float * window_func, int window_size, + const std::vector & samples, int n_samples, + int frame_size, int frame_step, int n_threads, + int n_fft_bins, + const mtmd_audio_cache & cache, mtmd_audio_mel & mel); +}; + // // streaming ISTFT - converts spectrogram frames back to audio one frame at a time // diff --git a/tools/mtmd/mtmd-helper.cpp b/tools/mtmd/mtmd-helper.cpp index 3c73db4431e7..90451d02ebd8 100644 --- a/tools/mtmd/mtmd-helper.cpp +++ b/tools/mtmd/mtmd-helper.cpp @@ -238,6 +238,29 @@ struct decode_embd_batch { } }; +// Helper class to set non-causal attention via RAII +class scope_non_causal { +public: + scope_non_causal(llama_context * context, bool enabled) : context_(context), enabled_(enabled) { + if (enabled_) { + // TODO @ngxson : need to make sure only one image is processed at a time, and n_ubatch must be enough to hold the image + llama_set_causal_attn(context_, false); + } + } + ~scope_non_causal() { + if (enabled_) { + llama_set_causal_attn(context_, true); + } + } + + scope_non_causal(const scope_non_causal &) = delete; + scope_non_causal & operator=(const scope_non_causal &) = delete; + +private: + llama_context * context_; + bool enabled_; +}; + // Helper function for decoding an image whose embeddings have already been calculated int32_t mtmd_helper_decode_image_chunk( mtmd_context * ctx, @@ -288,10 +311,7 @@ int32_t mtmd_helper_decode_image_chunk( } const bool use_non_causal = mtmd_decode_use_non_causal(ctx, chunk); - if (use_non_causal) { - llama_set_causal_attn(lctx, false); - // TODO @ngxson : need to make sure only one image is processed at a time, and n_ubatch must be enough to hold the image - } + const scope_non_causal non_causal(lctx, use_non_causal); while (i_batch < n_img_batches) { // split into batches int pos_offset = i_batch*n_batch; @@ -304,9 +324,6 @@ int32_t mtmd_helper_decode_image_chunk( int32_t ret = llama_decode(lctx, batch_embd_view); if (ret != 0) { LOG_ERR("failed to decode %s\n", name); - if (use_non_causal) { - llama_set_causal_attn(lctx, true); - } return ret; } @@ -314,9 +331,6 @@ int32_t mtmd_helper_decode_image_chunk( ret = callback(batch_embd_view, user_data); if (ret != 0) { LOG_ERR("post-decode callback failed\n"); - if (use_non_causal) { - llama_set_causal_attn(lctx, true); - } return ret; } } @@ -329,9 +343,6 @@ int32_t mtmd_helper_decode_image_chunk( n_past += mtmd_input_chunk_get_n_pos(chunk); *new_n_past = n_past; - if (use_non_causal) { - llama_set_causal_attn(lctx, true); - } return 0; } @@ -629,6 +640,7 @@ bool mtmd_helper_support_video(mtmd_context * ctx) { #ifdef MTMD_VIDEO return mtmd_support_vision(ctx); #else + GGML_UNUSED(ctx); return false; #endif } @@ -996,6 +1008,9 @@ mtmd_helper_video * mtmd_helper_video_init( return ctx; #else + GGML_UNUSED(mctx); + GGML_UNUSED(path); + GGML_UNUSED(params); LOG_ERR("%s: video is not supported in this build (MTMD_VIDEO is set to OFF)\n", __func__); return nullptr; #endif @@ -1028,6 +1043,10 @@ mtmd_helper_video * mtmd_helper_video_init_from_buf( return ctx; #else + GGML_UNUSED(mctx); + GGML_UNUSED(buf); + GGML_UNUSED(len); + GGML_UNUSED(params); LOG_ERR("%s: video is not supported in this build (MTMD_VIDEO is set to OFF)\n", __func__); return nullptr; #endif @@ -1039,6 +1058,7 @@ void mtmd_helper_video_free(mtmd_helper_video * ctx) { ctx->stop_ffmpeg(); delete ctx; #else + GGML_UNUSED(ctx); LOG_ERR("%s: video is not supported in this build (MTMD_VIDEO is set to OFF)\n", __func__); #endif } @@ -1047,6 +1067,7 @@ mtmd_helper_video_info mtmd_helper_video_get_info(const mtmd_helper_video * ctx) #ifdef MTMD_VIDEO return ctx->info; #else + GGML_UNUSED(ctx); GGML_ASSERT(false && "video is not supported in this build (MTMD_VIDEO is set to OFF)"); #endif } @@ -1057,6 +1078,9 @@ int32_t mtmd_helper_video_read_next(mtmd_helper_video * ctx, if (!ctx) return -2; return ctx->read_next(out_bitmap, out_text); #else + GGML_UNUSED(ctx); + GGML_UNUSED(out_bitmap); + GGML_UNUSED(out_text); GGML_ASSERT(false && "video is not supported in this build (MTMD_VIDEO is set to OFF)"); #endif } diff --git a/tools/mtmd/mtmd-image.cpp b/tools/mtmd/mtmd-image.cpp index 01cb9f59228f..d46da68862e8 100644 --- a/tools/mtmd/mtmd-image.cpp +++ b/tools/mtmd/mtmd-image.cpp @@ -343,19 +343,20 @@ struct img_tool { } } - // Bicubic resize function using Pillow's ImagingResample algorithm + // Pillow-compatible separable resampling (Bicubic and Lanczos) // Adapted from https://github.com/python-pillow/Pillow/blob/main/src/libImaging/Resample.c // - // Key Difference with resize_bicubic: - // 1. Uses separable filtering: horizontal pass followed by vertical pass + // Key properties: + // 1. Separable filtering: horizontal pass followed by vertical pass // 2. Pre-computes normalized filter coefficients for each output pixel - // 3. Applies convolution using fixed-point integer arithmetic for performance + // 3. Fixed-point integer arithmetic (22 fractional bits) for speed and determinism static bool resize_bicubic_pillow(const clip_image_u8 & img, clip_image_u8 & dst, int target_width, int target_height) { - return resize_pillow(img, dst, target_width, target_height, false); + return resize_pillow(img, dst, target_width, target_height, /*use_lanczos=*/false); } + // Lanczos-3 (support radius 3), matches Pillow's Image.LANCZOS static bool resize_lanczos_pillow(const clip_image_u8 & img, clip_image_u8 & dst, int target_width, int target_height) { - return resize_pillow(img, dst, target_width, target_height, true); + return resize_pillow(img, dst, target_width, target_height, /*use_lanczos=*/true); } static bool resize_pillow( @@ -368,18 +369,18 @@ struct img_tool { // This allows encoding fractional weights as integers: weight * 2^22 const int PRECISION_BITS = 32 - 8 - 2; - // Bicubic filter function with a = -0.5 (Note that GGML/PyTorch takes a = -0.75) + // Resample filter: Lanczos-3 (support [-3, 3]) or bicubic with a = -0.5 (support [-2, 2]) + // Note: GGML/PyTorch bicubic uses a = -0.75, Pillow uses a = -0.5 // Returns filter weight for distance x from pixel center - // Support: [-2, 2], meaning the filter influences pixels within 2 units of distance auto resample_filter = [use_lanczos](double x) -> double { if (use_lanczos) { if (-3.0 <= x && x < 3.0) { - auto sinc = [](double value) { - if (value == 0.0) { + auto sinc = [](double v) { + if (v == 0.0) { return 1.0; } - const double pix = value * 3.141592653589793238462643383279502884; - return std::sin(pix) / pix; + const double pi_v = v * 3.141592653589793238462643383279502884; + return std::sin(pi_v) / pi_v; }; return sinc(x) * sinc(x / 3.0); } @@ -399,7 +400,7 @@ struct img_tool { return 0.0; // Zero outside [-2, 2] }; - // Filter support radius: bicubic extends 2 pixels in each direction + // Filter support radius: 2 for bicubic, 3 for lanczos const double filter_support = use_lanczos ? 3.0 : 2.0; // Clipping function for 8-bit values @@ -496,9 +497,23 @@ struct img_tool { const double fxp_scale = std::ldexp(1.0, PRECISION_BITS); // 1.0 * 2^PRECISION_BITS for (int i = 0; i < outSize * ksize; i++) { - // Pillow adds +/- 0.5 then truncates toward zero; std::round would round twice - const double rounded = pre_weights[i] * fxp_scale + (pre_weights[i] < 0 ? -0.5 : 0.5); - weights[i] = static_cast(rounded); + if (use_lanczos) { + // Pillow adds +/- 0.5 then truncates toward zero; std::round would round twice + const double rounded = pre_weights[i] * fxp_scale + (pre_weights[i] < 0 ? -0.5 : 0.5); + weights[i] = static_cast(rounded); + continue; + } + double tmp_val = pre_weights[i] * fxp_scale; + if (pre_weights[i] < 0) { + tmp_val -= 0.5; + } else { + tmp_val += 0.5; + } + tmp_val = std::round(tmp_val); + tmp_val = std::clamp(tmp_val, + static_cast(std::numeric_limits::min()), + static_cast(std::numeric_limits::max())); + weights[i] = static_cast(tmp_val); } return ksize; @@ -1107,6 +1122,26 @@ mtmd_image_preproc_out mtmd_image_preprocessor_longest_edge::preprocess(const cl return output; } +// +// mtmd_image_preprocessor_minicpmv +// + +mtmd_image_preprocessor_llava_uhd::slice_instructions mtmd_image_preprocessor_minicpmv::get_slice_instructions(const clip_image_size & original_size) { + if (hparams.n_merge == 2) { + const int slice_size = hparams.image_size; + const float ratio = (float)original_size.width * original_size.height / (slice_size * slice_size); + if (ratio <= 1.0f) { + mtmd_image_preprocessor_llava_uhd::slice_instructions inst; + const int patch_size = hparams.patch_size * hparams.n_merge; + inst.overview_size = get_best_resize(original_size, slice_size, patch_size, true); + inst.refined_size = clip_image_size{0, 0}; + inst.grid_size = clip_image_size{0, 0}; + return inst; + } + } + return mtmd_image_preprocessor_llava_uhd::get_slice_instructions(original_size); +} + // // mtmd_image_preprocessor_lfm2 // diff --git a/tools/mtmd/mtmd-image.h b/tools/mtmd/mtmd-image.h index 044d72f61375..30f112f6a2b8 100644 --- a/tools/mtmd/mtmd-image.h +++ b/tools/mtmd/mtmd-image.h @@ -88,7 +88,6 @@ struct mtmd_image_preprocessor_llava_uhd : mtmd_image_preprocessor { std::vector slices; }; - // LFM2 override this function to implement its custom slicing logic virtual slice_instructions get_slice_instructions(const clip_image_size & original_size); struct slice_output { @@ -97,9 +96,10 @@ struct mtmd_image_preprocessor_llava_uhd : mtmd_image_preprocessor { }; slice_output slice_image(const clip_image_u8 & img, const slice_instructions & inst); -private: +protected: clip_image_size get_best_resize(const clip_image_size & original_size, int scale_resolution, int patch_size, bool allow_upscale = false); +private: clip_image_size resize_maintain_aspect_ratio(const clip_image_size & orig, const clip_image_size & target_max); /** @@ -149,6 +149,12 @@ struct mtmd_image_preprocessor_longest_edge : mtmd_image_preprocessor { mtmd_image_preproc_out preprocess(const clip_image_u8 & img) override; }; +// custom llava-uhd slicing logic for MiniCPM-V +struct mtmd_image_preprocessor_minicpmv : mtmd_image_preprocessor_llava_uhd { + using mtmd_image_preprocessor_llava_uhd::mtmd_image_preprocessor_llava_uhd; + slice_instructions get_slice_instructions(const clip_image_size & original_size) override; +}; + // custom llava-uhd slicing logic for LFM2 // ref: https://github.com/huggingface/transformers/blob/v5.1.0/src/transformers/models/lfm2_vl/image_processing_lfm2_vl_fast.py struct mtmd_image_preprocessor_lfm2 : mtmd_image_preprocessor_llava_uhd { diff --git a/tools/mtmd/mtmd.cpp b/tools/mtmd/mtmd.cpp index 3e81e44143fa..34ae8c071b4b 100644 --- a/tools/mtmd/mtmd.cpp +++ b/tools/mtmd/mtmd.cpp @@ -457,7 +457,7 @@ struct mtmd_context { tok_row_end = {lookup_token("\n")}; tok_row_end_trail = false; // no trailing end-of-row token ov_img_first = true; - image_preproc = std::make_unique(ctx_v); + image_preproc = std::make_unique(ctx_v); } break; case PROJECTOR_TYPE_QWEN2VL: case PROJECTOR_TYPE_QWEN25VL: @@ -469,6 +469,13 @@ struct mtmd_context { img_end = "<|vision_end|>"; image_preproc = std::make_unique(ctx_v); } break; + case PROJECTOR_TYPE_MINIMAX_M3: + { + // ]<]start of image[>[ ... (image embeddings) ... ]<]end of image[>[ + img_beg = "]<]start of image[>["; + img_end = "]<]end of image[>["; + image_preproc = std::make_unique(ctx_v); + } break; case PROJECTOR_TYPE_YOUTUVL: { // <|vision_start|> ... (image embeddings) ... <|vision_end|> @@ -560,10 +567,19 @@ struct mtmd_context { image_preproc = std::make_unique(ctx_v); } break; case PROJECTOR_TYPE_KIMIK25: + case PROJECTOR_TYPE_KIMIK3: { - // <|media_begin|> ... (image embeddings) ... <|media_end|> - img_beg = "<|media_begin|>"; - img_end = "<|media_end|>"; + // GLM-5.2-V reuses the Kimi-K2.5 vision encoder and projector, but marks + // images with its own tokens, so decide based on the text model vocab + if (lookup_token("<|begin_of_image|>") != LLAMA_TOKEN_NULL) { + // <|begin_of_image|> ... (image embeddings) ... <|end_of_image|> + img_beg = "<|begin_of_image|>"; + img_end = "<|end_of_image|>"; + } else { + // <|media_begin|> ... (image embeddings) ... <|media_end|> + img_beg = "<|media_begin|>"; + img_end = "<|media_end|>"; + } image_preproc = std::make_unique(ctx_v); } break; case PROJECTOR_TYPE_LIGHTONOCR: @@ -722,12 +738,22 @@ struct mtmd_context { aud_end = ""; audio_preproc = std::make_unique(ctx_a); } break; + case PROJECTOR_TYPE_PARAKEET: + { + audio_preproc = std::make_unique(ctx_a); + } break; case PROJECTOR_TYPE_GEMMA4UA: { aud_beg = "<|audio>"; aud_end = ""; audio_preproc = std::make_unique(ctx_a); } break; + case PROJECTOR_TYPE_MIMO_AUDIO: + { + aud_beg = "<|mimo_audio_start|>"; + aud_end = "<|mimo_audio_end|>"; + audio_preproc = std::make_unique(ctx_a); + } break; default: throw std::runtime_error(string_format("%s: unexpected audio projector type %d\n", __func__, proj)); } diff --git a/tools/parser/debug-template-parser.cpp b/tools/parser/debug-template-parser.cpp index 50e8f1efb7d6..8a916f79c78e 100644 --- a/tools/parser/debug-template-parser.cpp +++ b/tools/parser/debug-template-parser.cpp @@ -9,6 +9,7 @@ #include "peg-parser.h" #include +#include #include #include #include @@ -398,7 +399,7 @@ int main(int argc, char ** argv) { if (std::optional spec_tmpl = common_chat_try_specialized_template(chat_template, template_source, params)) { LOG_ERR("\n"); - LOG_ERR("This template uses a specialized parser, analysis results will not be available."); + LOG_ERR("This template uses a specialized parser, analysis results will not be available.\n"); parser_data = *spec_tmpl; } else { // Render template scenarios if requested @@ -426,7 +427,9 @@ int main(int argc, char ** argv) { // Generate Parser parser_data = autoparser::peg_generator::generate_parser(chat_template, params, analysis); } + } + if (!std::empty(parser_data.parser)) { LOG_ERR("\n=== Generated Parser ===\n"); common_peg_arena arena; arena.load(parser_data.parser); diff --git a/tools/server/CMakeLists.txt b/tools/server/CMakeLists.txt index b5c40884fd6e..280bd9e19dca 100644 --- a/tools/server/CMakeLists.txt +++ b/tools/server/CMakeLists.txt @@ -19,6 +19,8 @@ add_library(${TARGET} STATIC server-stream.h server-tools.cpp server-tools.h + server-mcp.cpp + server-mcp.h server-schema.cpp server-schema.h ) diff --git a/tools/server/README-dev.md b/tools/server/README-dev.md index e81336e5e26b..45bcdcca7699 100644 --- a/tools/server/README-dev.md +++ b/tools/server/README-dev.md @@ -136,13 +136,13 @@ Producer side: `server_res_generator` extends `server_res_spipe`, which keeps al Lifetime safety: the session holds no back reference to the response, so `spipe` is a plain `unique_ptr` touched only by the http worker. `cancel` raises an atomic the producer polls; the producer finalizes the session from its destructor, which also runs `~server_response_reader::stop()` to cancel the generation at the queue level. A `DELETE` stops work by raising the flag and letting the worker unwind. -Consumer side: `GET /v1/stream/?from=N` opens a `text/event-stream` that replays buffered bytes from offset `N` and blocks for live bytes, so the browser reattaches like a fresh EventSource. An offset below the dropped prefix returns 400. +Consumer side: `GET /v1/stream?conv_id=&from=N` opens a `text/event-stream` that replays buffered bytes from offset `N` and blocks for live bytes, so the browser reattaches like a fresh EventSource. An offset below the dropped prefix returns 400. Routes: -- `GET /v1/stream/:conv_id?from=N`: replay or live reattach. +- `GET /v1/stream?conv_id=&from=N`: replay or live reattach. The id travels in the query string because it can embed a model name containing slashes. - `POST /v1/streams/lookup` with `{"conversation_ids": [...]}`: returns session status only for ids the caller already owns. There is no listing route, so live sessions cannot be enumerated (an earlier `GET /v1/streams` was removed for exactly this reason). -- `DELETE /v1/stream/:conv_id`: explicit Stop, idempotent (`evict_and_cancel`). +- `DELETE /v1/stream?conv_id=`: explicit Stop, idempotent (`evict_and_cancel`). Router mode binds the same paths to proxy handlers. A `conv_id -> child` map (`conv_models`), populated when a POST is routed, resolves the owning child in one lookup with no polling. The lookup groups ids per child; GET and DELETE proxy straight to the owner. This loopback REST hop is expected to move to a websocket IPC later, swapping only the transport. @@ -166,8 +166,8 @@ graph TD GC[GC thread] -- drop after TTL --> Sess end Sess -- read_from offset --> Cons[stream_pipe_consumer] - Cons -- "GET /v1/stream/:id?from=N" --> Client - DEL[DELETE /v1/stream/:id] -- evict_and_cancel --> Sess + Cons -- "GET /v1/stream?conv_id=id&from=N" --> Client + DEL[DELETE /v1/stream?conv_id=id] -- evict_and_cancel --> Sess ``` The diagram shows the buffer touch points. The live wire (chunks streamed to the original client during a normal generation) is the producer's default output, described under "Producer side" above. @@ -189,7 +189,7 @@ This endpoint is intended to be used internally by the Web UI and subject to cha Get a list of tools, each tool has these fields: - `tool` (string): the ID name of the tool, to be used in POST call. Example: `read_file` - `display_name` (string): the name to be displayed on UI. Example: `Read file` -- `type` (string): always be `"builtin"` for now +- `type` (string): `"builtin"` for a built-in tool, or `"mcp"` for a tool exposed by an MCP server - `permissions` (object): a mapping string --> boolean that indicates the permission required by this tool. This is useful for the UI to ask the user before calling the tool. For now, the only permission supported is `"write"` - `definition` (object): the OAI-compat definition of this tool @@ -199,7 +199,10 @@ Invoke a tool call, request body is a JSON object with: - `tool` (string): the name of the tool - `params` (object): a mapping from argument name (string) to argument value -Returns JSON object. There are two response formats: +Headers: +- `x-tool-cwd`: optional; if set, use as the CWD for tool; this is not part of tool's params because it's meant to be set by the runtime, not the LLM itself + +Returns JSON object. There are two response formats (MCP tools use the same two formats: their result content is concatenated into `plain_text_response`, and RPC or tool errors are surfaced as the `error` string): Format 1: Plain text. The text will be placed into a field called `plain_text_response`, example: diff --git a/tools/server/README.md b/tools/server/README.md index 52222843dde9..97b546272d7d 100644 --- a/tools/server/README.md +++ b/tools/server/README.md @@ -71,9 +71,11 @@ For the full list of features, please refer to [server's changelog](https://gith | `-ctk, --cache-type-k TYPE` | KV cache data type for K
allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
(default: f16)
(env: LLAMA_ARG_CACHE_TYPE_K) | | `-ctv, --cache-type-v TYPE` | KV cache data type for V
allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
(default: f16)
(env: LLAMA_ARG_CACHE_TYPE_V) | | `-dt, --defrag-thold N` | KV cache defragmentation threshold (DEPRECATED)
(env: LLAMA_ARG_DEFRAG_THOLD) | -| `--mlock` | force system to keep model in RAM rather than swapping or compressing
(env: LLAMA_ARG_MLOCK) | -| `--mmap, --no-mmap` | whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: enabled)
(env: LLAMA_ARG_MMAP) | -| `-dio, --direct-io, -ndio, --no-direct-io` | use DirectIO if available. (default: disabled)
(env: LLAMA_ARG_DIO) | +| `--rpc SERVERS` | comma-separated list of RPC servers (host:port)
(env: LLAMA_ARG_RPC) | +| `--mlock` | DEPRECATED in favor of `--load-mode`: force system to keep model in RAM rather than swapping or compressing
(env: LLAMA_ARG_MLOCK) | +| `--mmap, --no-mmap` | DEPRECATED in favor of `--load-mode`: whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock)
(env: LLAMA_ARG_MMAP) | +| `-dio, --direct-io, -ndio, --no-direct-io` | DEPRECATED in favor of `--load-mode`: use DirectIO if available
(env: LLAMA_ARG_DIO) | +| `-lm, --load-mode MODE` | model loading mode (default: mmap)
- none: no special loading mode
- mmap: memory-map model (if mmap disabled, slower load but may reduce pageouts if not using mlock)
- mlock: force system to keep model in RAM rather than swapping or compressing
- mmap+mlock: mmap + force system to keep model in RAM rather than swapping or compressing
- dio: use DirectIO if available

(env: LLAMA_ARG_LOAD_MODE) | | `--numa TYPE` | attempt optimizations that help on some NUMA systems
- distribute: spread execution evenly over all nodes
- isolate: only spawn threads on CPUs on the node that execution started on
- numactl: use the CPU map provided by numactl
if run without this previously, it is recommended to drop the system page cache before using this
see https://github.com/ggml-org/llama.cpp/issues/1437
(env: LLAMA_ARG_NUMA) | | `-dev, --device ` | comma-separated list of devices to use for offloading (none = don't offload)
use --list-devices to see a list of available devices
(env: LLAMA_ARG_DEVICE) | | `--list-devices` | print list of available devices and exit | @@ -162,7 +164,7 @@ For the full list of features, please refer to [server's changelog](https://gith | `-lcs, --lookup-cache-static FNAME` | path to static lookup cache to use for lookup decoding (not updated by generation) | | `-lcd, --lookup-cache-dynamic FNAME` | path to dynamic lookup cache to use for lookup decoding (updated by generation) | | `-ctxcp, --ctx-checkpoints, --swa-checkpoints N` | max number of context checkpoints to create per slot (default: 32)[(more info)](https://github.com/ggml-org/llama.cpp/pull/15293)
(env: LLAMA_ARG_CTX_CHECKPOINTS) | -| `-cms, --checkpoint-min-step N` | minimum spacing between context checkpoints in tokens (default: 256, 0 = no minimum)
(env: LLAMA_ARG_CHECKPOINT_MIN_SPACING_NT) | +| `-cms, --checkpoint-min-step N` | minimum spacing between context checkpoints in tokens (default: 8192, 0 = no minimum)
(env: LLAMA_ARG_CHECKPOINT_MIN_SPACING_NT) | | `-cram, --cache-ram N` | set the maximum cache size in MiB (default: 8192, -1 - no limit, 0 - disable)[(more info)](https://github.com/ggml-org/llama.cpp/pull/16391)
(env: LLAMA_ARG_CACHE_RAM) | | `-kvu, --kv-unified, -no-kvu, --no-kv-unified` | use single unified KV buffer shared across all sequences (default: enabled if number of slots is auto)
(env: LLAMA_ARG_KV_UNIFIED) | | `--cache-idle-slots, --no-cache-idle-slots` | save idle slots to the prompt cache on new task, and clear them when using unified KV (default: enabled, requires cache-ram)
(env: LLAMA_ARG_CACHE_IDLE_SLOTS) | @@ -188,12 +190,18 @@ For the full list of features, please refer to [server's changelog](https://gith | `--port PORT` | port to listen (default: 8080)
(env: LLAMA_ARG_PORT) | | `--reuse-port` | allow multiple sockets to bind to the same port (default: disabled)
(env: LLAMA_ARG_REUSE_PORT) | | `--path PATH` | path to serve static files from (default: )
(env: LLAMA_ARG_STATIC_PATH) | +| `--cors-origins ORIGINS` | comma-separated list of allowed origins for CORS (default: *)
if set to special value 'localhost', reflect the Origin header only if it is localhost
(env: LLAMA_ARG_CORS_ORIGINS) | +| `--cors-methods METHODS` | comma-separated list of allowed methods for CORS (default: GET, POST, DELETE, OPTIONS)
(env: LLAMA_ARG_CORS_METHODS) | +| `--cors-headers HEADERS` | comma-separated list of allowed headers for CORS (default: *)
(env: LLAMA_ARG_CORS_HEADERS) | +| `--cors-credentials, --no-cors-credentials` | whether to allow credentials for CORS (default: enabled)
note: if this is enabled and --cors-origins is set to * (default), the Origin header will be echoed back, and credentials will always be allowed
(env: LLAMA_ARG_CORS_CREDENTIALS) | | `--api-prefix PREFIX` | prefix path the server serves from, without the trailing slash (default: )
(env: LLAMA_ARG_API_PREFIX) | | `--ui-config, --webui-config JSON` | JSON that provides default UI settings (overrides UI defaults)
(env: LLAMA_ARG_UI_CONFIG) | | `--ui-config-file, --webui-config-file PATH` | JSON file that provides default UI settings (overrides UI defaults)
(env: LLAMA_ARG_UI_CONFIG_FILE) | | `--ui-mcp-proxy, --webui-mcp-proxy, --no-ui-mcp-proxy, --no-webui-mcp-proxy` | experimental: whether to enable MCP CORS proxy - do not enable in untrusted environments (default: disabled)
(env: LLAMA_ARG_UI_MCP_PROXY) | -| `--tools TOOL1,TOOL2,...` | experimental: whether to enable built-in tools for AI agents - do not enable in untrusted environments (default: no tools)
specify "all" to enable all tools
available tools: read_file, file_glob_search, grep_search, exec_shell_command, write_file, edit_file, apply_diff, get_datetime
(env: LLAMA_ARG_TOOLS) | -| `-ag, --agent, -no-ag, --no-agent` | whether to enable CORS proxy and all built-in tools - do not enable in untrusted environments (default: disabled)
(env: LLAMA_ARG_AGENT) | +| `--tools TOOL1,TOOL2,...` | experimental: whether to enable built-in tools for AI agents - do not enable in untrusted environments (default: no tools)
specify "all" to enable all tools
available tools: read_file, file_glob_search, grep_search, exec_shell_command, write_file, edit_file, get_datetime, get_info
note: for security reasons, this will limit --cors-origins to localhost by default
(env: LLAMA_ARG_TOOLS) | +| `--mcp-servers-config PATH` | experimental: path to JSON file with MCP server definitions (Cursor-compatible format) - do not enable in untrusted environments (default: none)
note: for security reasons, this will limit --cors-origins to localhost by default
(env: LLAMA_ARG_MCP_SERVERS_CONFIG) | +| `--mcp-servers-json JSON` | experimental: inline JSON with MCP server definitions (Cursor-compatible format) - do not enable in untrusted environments (default: none)
note: for security reasons, this will limit --cors-origins to localhost by default
(env: LLAMA_ARG_MCP_SERVERS_JSON) | +| `-ag, --agent, -no-ag, --no-agent` | whether to enable CORS proxy and all built-in tools - do not enable in untrusted environments (default: disabled)
note: for security reasons, this will limit --cors-origins to localhost by default
(env: LLAMA_ARG_AGENT) | | `--ui, --webui, --no-ui, --no-webui` | whether to enable the Web UI (default: enabled)
(env: LLAMA_ARG_UI) | | `--embedding, --embeddings` | restrict to only support embedding use case; use only with dedicated embedding models (default: disabled)
(env: LLAMA_ARG_EMBEDDINGS) | | `--rerank, --reranking` | enable reranking endpoint on server (default: disabled)
(env: LLAMA_ARG_RERANKING) | @@ -221,6 +229,7 @@ For the full list of features, please refer to [server's changelog](https://gith | `-rea, --reasoning [on\|off\|auto]` | Use reasoning/thinking in the chat ('on', 'off', or 'auto', default: 'auto' (detect from template))
(env: LLAMA_ARG_REASONING) | | `--reasoning-budget N` | token budget for thinking: -1 for unrestricted, 0 for immediate end, N>0 for token budget (default: -1)
(env: LLAMA_ARG_THINK_BUDGET) | | `--reasoning-budget-message MESSAGE` | message injected before the end-of-thinking tag when reasoning budget is exhausted (default: none)
(env: LLAMA_ARG_THINK_BUDGET_MESSAGE) | +| `--reasoning-preserve, --no-reasoning-preserve` | preserve reasoning trace in the full history, not just the last assistant message (default: template default)
compatible with certain templates having 'supports_preserve_reasoning' capability
example: https://docs.z.ai/guides/capabilities/thinking-mode#preserved-thinking
(env: LLAMA_ARG_REASONING_PRESERVE) | | `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, granite-4.0, granite-4.1, grok-2, hunyuan-dense, hunyuan-moe, hunyuan-vl, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE) | | `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, granite-4.0, granite-4.1, grok-2, hunyuan-dense, hunyuan-moe, hunyuan-vl, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) | | `--skip-chat-parsing, --no-skip-chat-parsing` | force a pure content parser, even if a Jinja template is specified; model will output everything in the content section, including any reasoning and/or tool calls (default: disabled)
(env: LLAMA_ARG_SKIP_CHAT_PARSING) | @@ -254,7 +263,7 @@ For the full list of features, please refer to [server's changelog](https://gith | `--spec-draft-device, -devd, --device-draft ` | comma-separated list of devices to use for offloading the draft model (none = don't offload)
use --list-devices to see a list of available devices | | `--spec-draft-ngl, -ngld, --gpu-layers-draft, --n-gpu-layers-draft N` | max. number of draft model layers to store in VRAM, either an exact number, 'auto', or 'all' (default: auto)
(env: LLAMA_ARG_N_GPU_LAYERS_DRAFT) | | `--spec-draft-model, -md, --model-draft FNAME` | draft model for speculative decoding (default: unused)
(env: LLAMA_ARG_SPEC_DRAFT_MODEL) | -| `--spec-type none,draft-simple,draft-eagle3,draft-mtp,ngram-simple,ngram-map-k,ngram-map-k4v,ngram-mod,ngram-cache` | comma-separated list of types of speculative decoding to use (default: none)

(env: LLAMA_ARG_SPEC_TYPE) | +| `--spec-type none,draft-simple,draft-eagle3,draft-mtp,draft-dflash,draft-dspark,ngram-simple,ngram-map-k,ngram-map-k4v,ngram-mod,ngram-cache` | comma-separated list of types of speculative decoding to use (default: none)

(env: LLAMA_ARG_SPEC_TYPE) | | `--spec-ngram-mod-n-min N` | minimum number of ngram tokens to use for ngram-based speculative decoding (default: 48) | | `--spec-ngram-mod-n-max N` | maximum number of ngram tokens to use for ngram-based speculative decoding (default: 64) | | `--spec-ngram-mod-n-match N` | ngram-mod lookup length (default: 24) | @@ -1242,6 +1251,8 @@ The `response_format` parameter supports both plain JSON output (e.g. `{"type": `chat_template_kwargs`: Allows sending additional parameters to the json templating system. For example: `{"enable_thinking": false}` +`reasoning_effort`: If set to `none`, reasoning will be disabled for this request. Other values (e.g., `low`, `max`) have no effect on reasoning. + `reasoning_format`: The reasoning format to be parsed. If set to `none`, it will output the raw generated text. `reasoning_control`: Arms realtime reasoning control for this completion so it can be ended early via `/v1/chat/completions/control`. Defaults to `false`. diff --git a/tools/server/server-chat.cpp b/tools/server/server-chat.cpp index 31f94e023307..0322e54ccea8 100644 --- a/tools/server/server-chat.cpp +++ b/tools/server/server-chat.cpp @@ -283,6 +283,15 @@ json server_chat_convert_responses_to_chatcmpl(const json & response_body) { chatcmpl_body["max_tokens"] = response_body["max_output_tokens"]; } + if (response_body.contains("reasoning")) { + // Only "effort" is handled so far + const json & reasoning = response_body.at("reasoning"); + if (reasoning.contains("effort")) { + chatcmpl_body["reasoning_effort"] = reasoning.at("effort"); + } + chatcmpl_body.erase("reasoning"); + } + return chatcmpl_body; } diff --git a/tools/server/server-common.cpp b/tools/server/server-common.cpp index 78454823db5d..933cffd95c8e 100644 --- a/tools/server/server-common.cpp +++ b/tools/server/server-common.cpp @@ -1119,6 +1119,14 @@ json oaicompat_chat_params_parse( throw std::invalid_argument("invalid type for \"enable_thinking\" (expected boolean, got string)"); } + // Parse also the OAI "reasoning_effort": "none" specific value + if (body.contains("reasoning_effort")) { + auto reasoning_effort = json_value(body, "reasoning_effort", std::string("")); + if (reasoning_effort == "none") { + inputs.enable_thinking = false; + } // other reasoning_effort values are model-specific and not yet handled + } + inputs.force_pure_content = opt.force_pure_content; // Apply chat template to the list of messages @@ -1156,10 +1164,10 @@ json oaicompat_chat_params_parse( reasoning_budget = opt.reasoning_budget; } - if (!chat_params.thinking_end_tag.empty()) { + if (!chat_params.thinking_end_tags.empty()) { llama_params["reasoning_budget_tokens"] = reasoning_budget; llama_params["reasoning_budget_start_tag"] = chat_params.thinking_start_tag; - llama_params["reasoning_budget_end_tag"] = chat_params.thinking_end_tag; + llama_params["reasoning_budget_end_tags"] = chat_params.thinking_end_tags; llama_params["reasoning_budget_message"] = json_value(body, "reasoning_budget_message", opt.reasoning_budget_message); llama_params["reasoning_control"] = json_value(body, "reasoning_control", false); } diff --git a/tools/server/server-common.h b/tools/server/server-common.h index eb0e8f78022b..850eed83d0f6 100644 --- a/tools/server/server-common.h +++ b/tools/server/server-common.h @@ -9,9 +9,15 @@ #define JSON_ASSERT GGML_ASSERT #include +#include +#include +#include +#include +#include +#include +#include #include #include -#include using json = nlohmann::ordered_json; @@ -211,6 +217,9 @@ struct server_tokens { bool empty() const { return tokens.empty(); } + // true if the sequence actually contains image/audio chunks. + bool has_media() const { return !map_idx_to_media.empty(); } + void clear() { map_idx_to_media.clear(); tokens.clear(); @@ -386,3 +395,67 @@ server_tokens format_prompt_rerank( mtmd_context * mctx, const std::string & query, const std::string & doc); + +// simple implementation of a pipe +// used for streaming data between threads +template +struct server_pipe { + std::mutex mutex; + std::condition_variable cv; + std::queue queue; + std::atomic writer_closed{false}; + std::atomic reader_closed{false}; + + // 0 = unbounded (default) + // > 0, write() drops the oldest item once the queue is full + size_t max_size = 0; + + void close_write() { + writer_closed.store(true, std::memory_order_relaxed); + cv.notify_all(); + } + + void close_read() { + reader_closed.store(true, std::memory_order_relaxed); + cv.notify_all(); + } + + // close_on_stop = true: should_stop means the reader is gone for good, so the writer is told the pipe is broken. + // close_on_stop = false: should_stop is a per-read deadline and further reads still come, so the pipe stays usable. + bool read(T & output, const std::function & should_stop, bool close_on_stop = true) { + std::unique_lock lk(mutex); + constexpr auto poll_interval = std::chrono::milliseconds(500); + while (true) { + if (!queue.empty()) { + output = std::move(queue.front()); + queue.pop(); + return true; + } + if (writer_closed.load()) { + return false; // clean EOF + } + if (should_stop && should_stop()) { // a null should_stop means "never stop" + if (close_on_stop) { + close_read(); // signal broken pipe to writer + } + return false; // cancelled / deadline reached + } + cv.wait_for(lk, poll_interval); + } + } + + bool write(T && data) { + std::lock_guard lk(mutex); + if (reader_closed.load()) { + return false; // broken pipe + } + if (max_size > 0) { + while (queue.size() >= max_size) { + queue.pop(); // drop oldest to stay bounded + } + } + queue.push(std::move(data)); + cv.notify_one(); + return true; + } +}; diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp index 7b09098476e2..68201ff83b32 100644 --- a/tools/server/server-context.cpp +++ b/tools/server/server-context.cpp @@ -81,31 +81,41 @@ struct server_batch { }; std::vector tokens; int32_t n_tokens_alloc = 0; + int32_t n_embd = 0; // track if given slot can be batched with slots already in the batch server_slot * slot_batched = nullptr; + // in embd mode, we temporarily swap out the tokens arr and restore it on clear() + bool has_embd = false; + llama_token * tokens_ptr = nullptr; + std::vector embd; + float alora_scale = -1.0f; size_t alora_disabled_id = 0; server_batch() { - batch.token = nullptr; // sentinel: uninitialized batch + batch.pos = nullptr; // sentinel: uninitialized batch } ~server_batch() { - if (batch.token != nullptr) { + if (batch.pos != nullptr) { + clear(); llama_batch_free(batch); } } - void init(int32_t n_tokens_alloc) { + void init(int32_t n_tokens_alloc, int32_t n_embd) { this->n_tokens_alloc = n_tokens_alloc; + this->n_embd = n_embd; batch = llama_batch_init(n_tokens_alloc, 0, 1); + tokens_ptr = batch.token; tokens.reserve(n_tokens_alloc); } bool add(int32_t id_slot, llama_token token, llama_pos pos, bool output) { - GGML_ASSERT(batch.token != nullptr); + GGML_ASSERT(!has_embd); // cannot mix tokens + embd in same batch + GGML_ASSERT(batch.pos != nullptr); if ((int32_t)tokens.size() >= n_tokens_alloc) { return false; } @@ -113,13 +123,30 @@ struct server_batch { return true; } + bool add(int32_t id_slot, const std::vector & embd_in, llama_pos pos, bool output) { + GGML_ASSERT(batch.pos != nullptr); + if ((int32_t)tokens.size() >= n_tokens_alloc) { + return false; + } + tokens.push_back({ id_slot, LLAMA_TOKEN_NULL, pos, output }); + has_embd = true; + embd.insert(embd.end(), embd_in.begin(), embd_in.end()); + return true; + } + void clear() { tokens.clear(); + embd.clear(); common_batch_clear(batch); slot_batched = nullptr; alora_scale = -1.0f; alora_disabled_id = 0; batch_rendered = false; + has_embd = false; + if (batch.token == nullptr) { + batch.token = tokens_ptr; + batch.embd = nullptr; + } } int32_t size() const { @@ -132,25 +159,33 @@ struct server_batch { } void render() { - GGML_ASSERT(batch.token != nullptr); + GGML_ASSERT(!batch_rendered); + GGML_ASSERT(batch.pos != nullptr); common_batch_clear(batch); for (int32_t i = 0; i < size(); i++) { const auto & t = tokens[i]; common_batch_add(batch, t.token, t.pos, { t.id_slot }, t.output); } + if (has_embd) { + batch.token = nullptr; // will be restored on clear() + batch.embd = embd.data(); + } batch_rendered = true; } llama_batch get_view(int32_t off, int32_t n_tokens) const { - GGML_ASSERT(batch.token != nullptr); + GGML_ASSERT(batch.pos != nullptr); GGML_ASSERT(batch_rendered); GGML_ASSERT(off >= 0 && off < size()); GGML_ASSERT(n_tokens > 0 && off + n_tokens <= size()); + auto * token = batch.token ? batch.token + off : nullptr; + auto * embd = batch.embd ? batch.embd + off * n_embd : nullptr; + llama_batch view = { n_tokens, - batch.token + off, - nullptr, + token, + embd, batch.pos + off, batch.n_seq_id + off, batch.seq_id + off, @@ -167,6 +202,8 @@ struct server_slot { llama_context * ctx_tgt = nullptr; llama_context * ctx_dft = nullptr; + common_memory mem; + // multimodal mtmd_context * mctx = nullptr; mtmd::batch_ptr mbatch = nullptr; @@ -178,6 +215,7 @@ struct server_slot { llama_tokens spec_prompt; std::vector spec_i_batch; common_prompt_checkpoint spec_ckpt; + bool spec_is_replay = false; // TODO: move members that belong to the task (such as `generated_text`, `has_new_line`) to task_results_state // see https://github.com/ggml-org/llama.cpp/pull/18283#issuecomment-3710175837 @@ -256,10 +294,7 @@ struct server_slot { void prompt_clear() { SLT_TRC(*this, "clearing prompt with %zu tokens\n", prompt.tokens.size()); - common_context_seq_rm(ctx_tgt, id, -1, -1); - if (ctx_dft) { - common_context_seq_rm(ctx_dft, id, -1, -1); - } + mem.seq_rm(id, -1, -1); prompt.clear(); } @@ -274,6 +309,10 @@ struct server_slot { llama_token sampled; // in speculative mode, this is the last accepted token + // for TTS models, this is the embd generated from prev step, decode this to generate next hidden state + // corresponding to one token position (size = n_embd) + std::vector inp_embd; + // stats size_t n_sent_text = 0; // number of sent text character @@ -297,6 +336,8 @@ struct server_slot { void reset() { SLT_DBG(*this, "%s", "\n"); + spec_is_replay = false; + n_prompt_tokens_cache = 0; last_nl_pos = 0; @@ -382,7 +423,9 @@ struct server_slot { bool can_batch_with(server_slot & other_slot) const { GGML_ASSERT(task); - return task->type == other_slot.task->type && are_lora_equal(lora, other_slot.lora); + return task->type == other_slot.task->type + && inp_embd.size() == other_slot.inp_embd.size() + && are_lora_equal(lora, other_slot.lora); } bool has_budget(const common_params & global_params) { @@ -448,7 +491,11 @@ struct server_slot { // no speculative decoding i_batch = batch.size(); - add_ok &= batch.add(id, sampled, prompt.tokens.pos_next(), true); + if (!inp_embd.empty()) { + add_ok &= batch.add(id, inp_embd, prompt.tokens.pos_next(), true); + } else { + add_ok &= batch.add(id, sampled, prompt.tokens.pos_next(), true); + } SLT_DBG(*this, "slot decode token, id=%d, n_ctx = %d, n_tokens = %d, truncated = %d\n", sampled, n_ctx, prompt.n_tokens(), truncated); @@ -671,13 +718,8 @@ struct server_slot { void copy_state_to(server_slot & other) const { GGML_ASSERT(state == SLOT_STATE_DONE_PROMPT); - common_context_seq_rm(ctx_tgt, other.id, -1, -1); - common_context_seq_cp(ctx_tgt, id, other.id, -1, -1); - - if (ctx_dft) { - common_context_seq_rm(ctx_dft, other.id, -1, -1); - common_context_seq_cp(ctx_dft, id, other.id, -1, -1); - } + mem.seq_rm(other.id, -1, -1); + mem.seq_cp(id, other.id, -1, -1); other.n_decoded = n_decoded; other.n_remaining = n_remaining; @@ -1158,6 +1200,11 @@ struct server_context_impl { return false; } + if (ctx_tgt == nullptr) { + SRV_ERR("failed to create_context with model '%s'\n", params_base.model.path.c_str()); + return false; + } + vocab = llama_model_get_vocab(model_tgt); n_ctx = llama_n_ctx(ctx_tgt); @@ -1305,6 +1352,7 @@ struct server_context_impl { slot.id = i; slot.ctx_tgt = ctx_tgt; slot.ctx_dft = ctx_dft; + slot.mem.init(ctx_tgt, ctx_dft); slot.spec = spec.get(); slot.n_ctx = n_ctx_slot; @@ -1342,7 +1390,8 @@ struct server_context_impl { // note that n_batch can be > n_ctx (e.g. for non-causal attention models such as BERT where the KV cache is not used) { const int32_t n_batch = llama_n_batch(ctx_tgt); - batch.init(std::max(n_batch, params_base.n_parallel)); + const int32_t n_embd = llama_model_n_embd_inp(model_tgt); + batch.init(std::max(n_batch, params_base.n_parallel), n_embd); } if (params_base.cache_ram_mib != 0) { @@ -1614,7 +1663,7 @@ struct server_context_impl { // find the slot that has at least n% prompt similarity if (allow_prompt_similarity && ret == nullptr && slot_prompt_similarity != 0.0f) { - float sim_best = 0; + float f_sim_best = 0; for (server_slot & slot : slots) { if (task.id_slot != -1 && slot.id != task.id_slot) { @@ -1623,6 +1672,7 @@ struct server_context_impl { // skip the slot if it is not available if (slot.is_processing()) { + SLT_TRC(slot, " - skipping, is_processing = %d\n", slot.is_processing()); continue; } @@ -1630,26 +1680,30 @@ struct server_context_impl { // skip the slot if it does not contains cached tokens if (tokens.empty()) { + SLT_TRC(slot, "%s", " - skipping, slot is empty\n"); continue; } // fraction of the Longest Common Prefix length with respect to the input prompt length - const float sim_cur = float(tokens.get_common_prefix(task.tokens)) / task.tokens.size(); + const size_t lcp_len = tokens.get_common_prefix(task.tokens); + const float f_sim_cur = float(lcp_len) / task.tokens.size(); + + SLT_TRC(slot, " - checking sim = %.3f (%zu/%zu) > %.3f\n", f_sim_cur, lcp_len, task.tokens.size(), slot_prompt_similarity); // select the current slot if the criteria match - if (sim_cur > sim_best && sim_cur > slot_prompt_similarity) { - sim_best = sim_cur; + if (f_sim_cur > f_sim_best && f_sim_cur > slot_prompt_similarity) { + f_sim_best = f_sim_cur; ret = &slot; } } if (ret != nullptr) { - const float f_keep = (sim_best*task.tokens.size()) / ret->prompt.tokens.size(); + const float f_keep = (f_sim_best*task.tokens.size()) / ret->prompt.tokens.size(); if (task.id_slot == -1) { - SLT_INF(*ret, "selected slot by LCP similarity, sim_best = %.3f (> %.3f thold), f_keep = %.3f\n", - sim_best, slot_prompt_similarity, f_keep); + SLT_INF(*ret, "selected slot by LCP similarity, f_sim_best = %.3f (> %.3f thold), f_keep = %.3f\n", + f_sim_best, slot_prompt_similarity, f_keep); } // if we are about to lose a large portion of the existing context - save it in the prompt cache @@ -1830,7 +1884,8 @@ struct server_context_impl { // initialize samplers if (task.need_sampling()) { try { - slot.smpl.reset(common_sampler_init(model_tgt, task.params.sampling)); + slot.smpl.reset(common_sampler_init( + model_tgt, task.params.sampling, (int32_t) llama_n_ctx(ctx_tgt))); } catch (std::exception & e) { std::string err_msg = std::string("Failed to initialize samplers: ") + e.what(); send_error(task, err_msg, ERROR_TYPE_INVALID_REQUEST); @@ -2090,10 +2145,13 @@ struct server_context_impl { queue_results.send(std::move(res)); } - // if multimodal is enabled, send an error and return false - bool check_no_mtmd(const int id_task) { - if (mctx) { - send_error(id_task, "This feature is not supported by multimodal", ERROR_TYPE_NOT_SUPPORTED); + // Gate slot save/restore/erase on slot content (does it hold media), + // not model capability: a multimodal model may hold a pure-text slot. + bool check_slot_no_media(const server_slot & slot, const int id_task) { + if (slot.prompt.tokens.has_media()) { + send_error(id_task, + "This operation is not supported while the slot holds image/audio tokens (a pure-text prefix is supported)", + ERROR_TYPE_NOT_SUPPORTED); return false; } return true; @@ -2593,16 +2651,15 @@ struct server_context_impl { } break; case SERVER_TASK_TYPE_SLOT_SAVE: { - if (!check_no_mtmd(task.id)) { - break; - } - const int id_slot = task.slot_action.id_slot; server_slot * slot = get_slot_by_id(id_slot); if (slot == nullptr) { send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST); break; } + if (!check_slot_no_media(*slot, task.id)) { + break; + } if (slot->is_processing()) { // if requested slot is unavailable, we defer this task for processing later SRV_DBG("requested slot is unavailable, defer task, id_task = %d\n", task.id); @@ -2610,13 +2667,13 @@ struct server_context_impl { break; } - const size_t token_count = slot->prompt.tokens.size(); const int64_t t_start = ggml_time_us(); std::string filename = task.slot_action.filename; std::string filepath = task.slot_action.filepath; - const llama_tokens & tokens = slot->prompt.tokens.get_tokens(); + const llama_tokens tokens = slot->prompt.tokens.get_text_tokens(); + const size_t token_count = tokens.size(); const size_t nwrite = llama_state_seq_save_file(ctx_tgt, filepath.c_str(), slot->id, tokens.data(), token_count); const int64_t t_end = ggml_time_us(); @@ -2634,7 +2691,6 @@ struct server_context_impl { } break; case SERVER_TASK_TYPE_SLOT_RESTORE: { - if (!check_no_mtmd(task.id)) break; const int id_slot = task.slot_action.id_slot; server_slot * slot = get_slot_by_id(id_slot); if (slot == nullptr) { @@ -2681,15 +2737,16 @@ struct server_context_impl { } break; case SERVER_TASK_TYPE_SLOT_ERASE: { - if (!check_no_mtmd(task.id)) { - break; - } const int id_slot = task.slot_action.id_slot; server_slot * slot = get_slot_by_id(id_slot); if (slot == nullptr) { send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST); break; } + // Gate on slot content, consistent with save/restore. + if (!check_slot_no_media(*slot, task.id)) { + break; + } if (slot->is_processing()) { // if requested slot is unavailable, we defer this task for processing later SRV_DBG("requested slot is unavailable, defer task, id_task = %d\n", task.id); @@ -2965,13 +3022,8 @@ struct server_context_impl { SLT_WRN(slot, "slot context shift, n_keep = %d, n_left = %d, n_discard = %d\n", n_keep, n_left, n_discard); - common_context_seq_rm (ctx_tgt, slot.id, n_keep , n_keep + n_discard); - common_context_seq_add(ctx_tgt, slot.id, n_keep + n_discard, slot.prompt.n_tokens(), -n_discard); - - if (ctx_dft) { - common_context_seq_rm (ctx_dft, slot.id, n_keep , n_keep + n_discard); - common_context_seq_add(ctx_dft, slot.id, n_keep + n_discard, slot.prompt.tokens.pos_next(), -n_discard); - } + slot.mem.seq_rm (slot.id, n_keep , n_keep + n_discard); + slot.mem.seq_add(slot.id, n_keep + n_discard, slot.prompt.tokens.pos_next(), -n_discard); // add generated tokens to cache // ref: https://github.com/ggml-org/llama.cpp/pull/16818#discussion_r2473269481 @@ -3082,7 +3134,9 @@ struct server_context_impl { ckpt.load_dft(ctx_dft, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY); } - common_context_seq_rm(ctx_dft, slot.id, ckpt.pos_max + 1, -1); + if (!llama_memory_seq_rm(llama_get_memory(ctx_dft), slot.id, ckpt.pos_max + 1, -1)) { + GGML_ABORT("failed to remove sequence %d\n", slot.id); + } } if (!draft.empty()) { @@ -3285,13 +3339,8 @@ struct server_context_impl { const int64_t kv_shift = (int64_t) head_p - (int64_t) head_c; - common_context_seq_rm (ctx_tgt, slot.id, head_p, head_c); - common_context_seq_add(ctx_tgt, slot.id, head_c, head_c + n_match, kv_shift); - - if (ctx_dft) { - common_context_seq_rm (ctx_dft, slot.id, head_p, head_c); - common_context_seq_add(ctx_dft, slot.id, head_c, head_c + n_match, kv_shift); - } + slot.mem.seq_rm (slot.id, head_p, head_c); + slot.mem.seq_add(slot.id, head_c, head_c + n_match, kv_shift); for (size_t i = 0; i < n_match; i++) { slot.prompt.tokens.set_token(head_p + i, slot.prompt.tokens[head_c + i]); @@ -3463,10 +3512,7 @@ struct server_context_impl { SLT_TRC(slot, "cached n_tokens = %d, memory_seq_rm [%d, end)\n", slot.prompt.n_tokens(), p0); - common_context_seq_rm(ctx_tgt, slot.id, p0, -1); - if (ctx_dft) { - common_context_seq_rm(ctx_dft, slot.id, p0, -1); - } + slot.mem.seq_rm(slot.id, p0, -1); // If using an alora, there may be uncached tokens that come // before the invocation sequence. When this happens, the @@ -3684,6 +3730,15 @@ struct server_context_impl { n_empty_consecutive = 0; } + // TODO @ngxson : dft model may have different n_embd than the tgt model, so we check & reject if that's the case + // this case is not currently used by any models, but may need to be supported in the future + if (spec && batch.has_embd) { + if (llama_model_n_embd_inp(model_dft) != llama_model_n_embd_inp(model_tgt)) { + SRV_ERR("%s", "unsupported batch.has_embd + spec case\n"); + throw std::runtime_error("unsupported batch.has_embd + spec case"); + } + } + const int ret = llama_decode(ctx_tgt, batch_view); metrics.on_decoded(slots); @@ -3926,24 +3981,21 @@ struct server_context_impl { } // partial acceptance is not supported by the context -> truncate the draft and restore the state + slot.spec_is_replay = true; slot.spec_draft = std::move(accepted); const auto & ckpt = slot.spec_ckpt; SLT_DBG(slot, "restoring speculative checkpoint (pos_min = %d, pos_max = %d, size = %zu)\n", ckpt.pos_min, ckpt.pos_max, ckpt.size()); - { - ckpt.load_tgt(slot.ctx_tgt, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY); - - common_context_seq_rm(slot.ctx_tgt, slot.id, ckpt.pos_max + 1, -1); - } + ckpt.load_tgt(slot.ctx_tgt, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY); if (slot.ctx_dft) { ckpt.load_dft(slot.ctx_dft, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY); - - common_context_seq_rm(slot.ctx_dft, slot.id, ckpt.pos_max + 1, -1); } + slot.mem.seq_rm(slot.id, ckpt.pos_max + 1, -1); + slot.prompt.tokens.keep_first(ckpt.n_tokens); slot.smpl = std::move(smpl_save); @@ -3964,16 +4016,22 @@ struct server_context_impl { const auto ids = std::move(slot.spec_draft); + size_t n_accepted = ids.size() - 1; + if (slot.spec_is_replay && n_accepted > 0) { + n_accepted--; + } + slot.spec_is_replay = false; + slot.t_token_generation = std::max(1, t_now - slot.t_start_generation) / 1e3; // update how many tokens out of those tested were accepted - slot.n_draft_accepted += ids.size() - 1; + slot.n_draft_accepted += n_accepted; slot.n_draft_verif_steps += 1; if (slot.n_accepted_per_pos.empty()) { slot.n_accepted_per_pos.resize(common_speculative_n_max(¶ms_base.speculative), 0); } - for (size_t i = 0; i < ids.size() - 1 && i < slot.n_accepted_per_pos.size(); ++i) { + for (size_t i = 0; i < n_accepted && i < slot.n_accepted_per_pos.size(); ++i) { slot.n_accepted_per_pos[i]++; } @@ -3984,10 +4042,7 @@ struct server_context_impl { slot.sampled = ids.back(); // last accepted token SLT_DBG(slot, "add accepted tokens: sampled=%d, ids.size=%zu, n_draft=%zu\n", slot.sampled, ids.size(), n_draft); - common_context_seq_rm(slot.ctx_tgt, slot.id, slot.prompt.tokens.pos_next(), -1); - if (slot.ctx_dft) { - common_context_seq_rm(slot.ctx_dft, slot.id, slot.prompt.tokens.pos_next(), -1); - } + slot.mem.seq_rm(slot.id, slot.prompt.tokens.pos_next(), -1); for (size_t i = 0; i < ids.size(); ++i) { completion_token_output result; @@ -4012,7 +4067,7 @@ struct server_context_impl { slot.print_timings_tg(); - SLT_DBG(slot, "accepted %d/%d draft tokens, new n_tokens = %d\n", (int) ids.size() - 1, (int) n_draft, slot.prompt.n_tokens()); + SLT_DBG(slot, "accepted %d/%d draft tokens, new n_tokens = %d\n", (int) n_accepted, (int) n_draft, slot.prompt.n_tokens()); }); } diff --git a/tools/server/server-http.cpp b/tools/server/server-http.cpp index 24a38452aab7..783b01b82d11 100644 --- a/tools/server/server-http.cpp +++ b/tools/server/server-http.cpp @@ -283,9 +283,9 @@ bool server_http_context::init(const common_params & params) { } else if (params.cors_origins == "localhost") { // special case: only reflect the Origin header if it is a localhost origin std::string origin = req.get_header_value("Origin"); - if (origin_is_localhost(origin)) { + if (!origin.empty() && origin_is_localhost(origin)) { res.set_header("Access-Control-Allow-Origin", origin); - } else { + } else if (!origin.empty()) { SRV_WRN("(CORS) skip non-localhost origin: %s\n", origin.c_str()); } } else { diff --git a/tools/server/server-mcp.cpp b/tools/server/server-mcp.cpp new file mode 100644 index 000000000000..93db6164d34e --- /dev/null +++ b/tools/server/server-mcp.cpp @@ -0,0 +1,820 @@ +#include "server-mcp.h" + +#include "subproc.h" + +#include +#include +#include +#include +#include +#include +#include + +#if defined(_WIN32) +# include +# include +#else +# include +# include +# include +# include +extern char ** environ; +#endif + +// read NDJSON lines from a child pipe, calling on_line per line until `running` clears, EOF/error, or on_line returns false. +// polled, not blocking: a grandchild can inherit the pipe's write end and hold it open (terminate() kills only the direct child), so a blocking read would hang teardown on an EOF that never comes. +static void mcp_pump_ndjson(FILE * f, std::atomic & running, + const std::function & on_line) { + if (!f) { + return; + } + const int poll_ms = 50; + const size_t max_line = 8 * 1024 * 1024; // drop any single NDJSON line larger than this, so a child that never emits '\n' can't grow buf without bound +#if defined(_WIN32) + HANDLE h = (HANDLE) _get_osfhandle(_fileno(f)); +#else + int fd = fileno(f); + int fl = fcntl(fd, F_GETFL, 0); + if (fl >= 0) { + fcntl(fd, F_SETFL, fl | O_NONBLOCK); + } +#endif + std::string buf; + bool skipping = false; // discarding an over-long line until its terminating newline + char chunk[4096]; + while (running.load()) { + size_t n = 0; +#if defined(_WIN32) + DWORD avail = 0; + if (!PeekNamedPipe(h, NULL, 0, NULL, &avail, NULL)) { + break; // pipe broken / child gone + } + if (avail == 0) { + std::this_thread::sleep_for(std::chrono::milliseconds(poll_ms)); + continue; + } + DWORD to_read = avail < (DWORD) sizeof(chunk) ? avail : (DWORD) sizeof(chunk); + DWORD got = 0; + if (!ReadFile(h, chunk, to_read, &got, NULL) || got == 0) { + break; + } + n = (size_t) got; +#else + struct pollfd pfd; + pfd.fd = fd; + pfd.events = POLLIN; + pfd.revents = 0; + int pr = poll(&pfd, 1, poll_ms); + if (pr < 0) { + if (errno == EINTR) { + continue; + } + break; + } + if (pr == 0) { + continue; // timeout -> re-check running + } + if (pfd.revents & (POLLERR | POLLNVAL)) { + break; + } + ssize_t r = read(fd, chunk, sizeof(chunk)); + if (r < 0) { + if (errno == EINTR || errno == EAGAIN || errno == EWOULDBLOCK) { + continue; + } + break; + } + if (r == 0) { + break; // EOF: child (and any pipe writers) closed the stream + } + n = (size_t) r; +#endif + buf.append(chunk, n); + + // resync after an over-long, unterminated line: discard bytes until the next newline + if (skipping) { + size_t nl = buf.find('\n'); + if (nl == std::string::npos) { + if (buf.size() > max_line) { + buf.clear(); // stay bounded while waiting for a terminator + } + continue; + } + buf.erase(0, nl + 1); + skipping = false; + } + + size_t pos; + while ((pos = buf.find('\n')) != std::string::npos) { + std::string line = buf.substr(0, pos); + buf.erase(0, pos + 1); + if (!line.empty() && line.back() == '\r') { + line.pop_back(); + } + if (line.empty()) { + continue; + } + if (!on_line(std::move(line))) { + return; + } + } + + // a partial line already larger than the cap and still no newline: drop it to avoid unbounded growth + if (buf.size() > max_line) { + SRV_WRN("MCP: dropping oversized line (> %zu bytes) from child pipe\n", max_line); + buf.clear(); + skipping = true; + } + } +} + +// +// server_mcp_server_config +// + +std::vector server_mcp_server_config::parse_from_json(const std::string & json_str) { + return parse_cursor_format(json::parse(json_str)); +} + +std::vector server_mcp_server_config::parse_cursor_format(const json & j) { + std::vector result; + + if (!j.contains("mcpServers") || !j.at("mcpServers").is_object()) { + return result; + } + + for (const auto & [name, cfg] : j.at("mcpServers").items()) { + server_mcp_server_config sc; + sc.name = name; + sc.command = cfg.value("command", std::string()); + sc.cwd = cfg.value("cwd", std::string()); + sc.timeout_ms = cfg.value("timeout_ms", sc.timeout_ms); + + if (cfg.contains("args") && cfg.at("args").is_array()) { + for (const auto & a : cfg.at("args")) { + sc.args.push_back(a.get()); + } + } + if (cfg.contains("env") && cfg.at("env").is_object()) { + for (const auto & [k, v] : cfg.at("env").items()) { + sc.env[k] = v.get(); + } + } + + if (sc.command.empty()) { + SRV_WRN("MCP server '%s' has no command, skipping\n", name.c_str()); + continue; + } + result.push_back(std::move(sc)); + } + + return result; +} + + +// +// server_mcp_transport +// + +static constexpr const char * MCP_PROTOCOL_VERSION = "2024-11-05"; + +static std::string rpc_error_message(const json & resp) { + if (resp.contains("error")) { + const json & e = resp.at("error"); + if (e.is_object()) { + return e.value("message", "unknown error"); + } + if (e.is_string()) { + return e.get(); + } + } + return "unknown error"; +} + +// normalize an MCP tools/call result to the /tools contract (see README-dev.md): +// concat text parts of result.content[], and surface an isError result +static json mcp_result_to_response(const json & result) { + std::string text; + if (result.contains("content") && result.at("content").is_array()) { + for (const auto & part : result.at("content")) { + if (part.is_object() && part.value("type", "") == "text") { + if (!text.empty()) { + text += "\n"; + } + text += part.value("text", ""); + } + } + } + if (result.is_object() && result.value("isError", false)) { + return {{"error", text.empty() ? "MCP tool returned an error" : text}}; + } + return {{"plain_text_response", text}}; +} + +json server_mcp_transport::send_rpc(const json & request, const std::function & should_stop) { + if (!to_server.write(request.dump())) { + return {{"error", {{"code", -32603}, {"message", "transport closed"}}}}; + } + + const bool has_id = request.contains("id"); + const auto deadline = std::chrono::steady_clock::now() + std::chrono::milliseconds(timeout_ms); + auto stop = [&]() { + return (should_stop && should_stop()) || std::chrono::steady_clock::now() >= deadline; + }; + + std::string frame; + while (from_server.read(frame, stop, false)) { + json reply; + try { + reply = json::parse(frame); + } catch (...) { + if (std::chrono::steady_clock::now() >= deadline) { + break; + } + continue; // skip malformed frame + } + // no id: a notification. mismatched id: a stale reply from a timed-out request (ids are monotonic, never a future one) + if (!has_id || (reply.contains("id") && reply.at("id") == request.at("id"))) { + return reply; + } + if (std::chrono::steady_clock::now() >= deadline) { + break; // a flood of notifications must not outrun the deadline + } + } + + if (should_stop && should_stop()) { + return {{"error", {{"code", -32603}, {"message", "cancelled"}}}}; + } + if (std::chrono::steady_clock::now() >= deadline) { + return {{"error", {{"code", -32603}, {"message", "request timed out"}}}}; + } + return {{"error", {{"code", -32603}, {"message", "transport closed"}}}}; +} + +bool server_mcp_transport::ensure_init(const std::function & should_stop) { + if (initialized) { + return true; + } + + json init_req = { + {"jsonrpc", "2.0"}, + {"id", next_id++}, + {"method", "initialize"}, + {"params", { + {"protocolVersion", MCP_PROTOCOL_VERSION}, + {"capabilities", json::object()}, + {"clientInfo", {{"name", "llama.cpp"}, {"version", "1.0"}}}, + }}, + }; + json resp = send_rpc(init_req, should_stop); + if (!resp.contains("result")) { + last_error = "initialize failed: " + rpc_error_message(resp); + return false; + } + + // notifications/initialized: no id, no reply expected + json notif = {{"jsonrpc", "2.0"}, {"method", "notifications/initialized"}}; + to_server.write(notif.dump()); + + initialized = true; + return true; +} + +std::vector server_mcp_transport::list_tools(const std::function & should_stop) { + std::lock_guard lock(rpc_mutex); + if (!ensure_init(should_stop)) { + return {}; + } + if (!tools.empty()) { + return tools; + } + + json req = {{"jsonrpc", "2.0"}, {"id", next_id++}, {"method", "tools/list"}}; + json resp = send_rpc(req, should_stop); + if (!resp.contains("result")) { + last_error = "tools/list failed: " + rpc_error_message(resp); + return {}; + } + + const json & result = resp.at("result"); + if (result.contains("tools") && result.at("tools").is_array()) { + for (const auto & t : result.at("tools")) { + server_mcp_tool_def def; + def.server_name = name; + def.name = t.value("name", ""); + def.description = t.value("description", ""); + if (t.contains("inputSchema")) { + def.input_schema = t.at("inputSchema"); + } + tools.push_back(std::move(def)); + } + } + return tools; +} + +json server_mcp_transport::call_tool(const std::string & tool_name, + const json & arguments, + const std::function & should_stop) { + std::lock_guard lock(rpc_mutex); + if (!ensure_init(should_stop)) { + return {{"error", last_error}}; + } + + json req = { + {"jsonrpc", "2.0"}, + {"id", next_id++}, + {"method", "tools/call"}, + {"params", {{"name", tool_name}, {"arguments", arguments}}}, + }; + json resp = send_rpc(req, should_stop); + if (resp.contains("error")) { + return {{"error", rpc_error_message(resp)}}; + } + if (resp.contains("result")) { + return mcp_result_to_response(resp.at("result")); + } + return {{"error", "invalid response from MCP server"}}; +} + +// +// server_mcp_stdio +// + +struct server_mcp_stdio::process_handle { + common_subproc sp; + FILE * in = nullptr; // child stdin + FILE * out = nullptr; // child stdout + FILE * err = nullptr; // child stderr +}; + +#if defined(_WIN32) +// config strings are UTF-8 (from JSON) and subprocess.h converts them with CP_UTF8, so inputs must be UTF-8, not the active code page +static std::wstring windows_utf8_to_wide(const std::string & s) { + if (s.empty()) { + return std::wstring(); + } + int n = MultiByteToWideChar(CP_UTF8, 0, s.data(), (int) s.size(), NULL, 0); + if (n <= 0) { + return std::wstring(); + } + std::wstring w((size_t) n, L'\0'); + MultiByteToWideChar(CP_UTF8, 0, s.data(), (int) s.size(), &w[0], n); + return w; +} + +static std::string windows_wide_to_utf8(const wchar_t * s, int len /* -1 for NUL-terminated */) { + int n = WideCharToMultiByte(CP_UTF8, 0, s, len, NULL, 0, NULL, NULL); + if (n <= 0) { + return std::string(); + } + std::string out((size_t) n, '\0'); + WideCharToMultiByte(CP_UTF8, 0, s, len, &out[0], n, NULL, NULL); + if (len == -1 && !out.empty() && out.back() == '\0') { + out.pop_back(); // drop the terminator WideCharToMultiByte counts for -1 + } + return out; +} +#endif + +static std::string mcp_resolve_command(const std::string & command) { +#if defined(_WIN32) + // For Windows: make sure we handle ".exe" correctly, as well as UTF-8 + std::wstring wcmd = windows_utf8_to_wide(command); + wchar_t buf[MAX_PATH * 4]; + const DWORD cap = (DWORD) (sizeof(buf) / sizeof(buf[0])); + + auto search = [&](const wchar_t * ext) -> std::string { + DWORD n = SearchPathW(NULL, wcmd.c_str(), ext, cap, buf, NULL); + return (n > 0 && n < cap) ? windows_wide_to_utf8(buf, (int) n) : std::string(); + }; + + std::string found = search(NULL); // exact path / already-extensioned / .exe on PATH + if (!found.empty()) { + return found; + } + + std::wstring pathext; + DWORD need = GetEnvironmentVariableW(L"PATHEXT", NULL, 0); + if (need > 0) { + pathext.resize(need); + DWORD got = GetEnvironmentVariableW(L"PATHEXT", &pathext[0], need); + pathext.resize(got); + } + if (pathext.empty()) { + pathext = L".COM;.EXE;.BAT;.CMD"; + } + for (size_t start = 0; start <= pathext.size();) { + size_t sep = pathext.find(L';', start); + std::wstring ext = pathext.substr(start, sep == std::wstring::npos ? std::wstring::npos : sep - start); + if (!ext.empty()) { + found = search(ext.c_str()); + if (!found.empty()) { + return found; + } + } + if (sep == std::wstring::npos) { + break; + } + start = sep + 1; + } + return command; // give up and let subprocess.h report the spawn error +#else + return command; +#endif // _WIN32 +} + +static std::vector mcp_parent_env() { + std::vector env; +#if defined(_WIN32) + LPWCH block = GetEnvironmentStringsW(); + if (block) { + for (LPWCH e = block; *e; e += wcslen(e) + 1) { + env.emplace_back(windows_wide_to_utf8(e, -1)); + } + FreeEnvironmentStringsW(block); + } +#else + if (environ) { + for (char ** e = environ; *e; ++e) { + env.emplace_back(*e); + } + } +#endif + return env; +} + +// parent env with the config overrides applied, in "KEY=VALUE" form +static std::vector mcp_build_env(const std::map & overrides) { + std::vector env; + for (auto & e : mcp_parent_env()) { + size_t eq = e.find('='); + std::string key = eq == std::string::npos ? e : e.substr(0, eq); + if (overrides.find(key) == overrides.end()) { + env.push_back(e); + } + } + for (auto & [k, v] : overrides) { + env.push_back(k + "=" + v); + } + return env; +} + +server_mcp_stdio::server_mcp_stdio(const server_mcp_server_config & config) : config(config) { + name = config.name; + timeout_ms = config.timeout_ms; + // bound the reply queue: send_rpc only drains during a call, so unsolicited notifications would otherwise grow it without limit + from_server.max_size = 65536; +} + +server_mcp_stdio::~server_mcp_stdio() { + join_pumps(); +} + +bool server_mcp_stdio::start() { + std::vector argv_s; + argv_s.push_back(mcp_resolve_command(config.command)); + argv_s.insert(argv_s.end(), config.args.begin(), config.args.end()); + + int options = subprocess_option_no_window | subprocess_option_search_user_path; + std::vector envp_s; + if (config.env.empty()) { + options |= subprocess_option_inherit_environment; + } else { + envp_s = mcp_build_env(config.env); + } + + auto handle = std::make_unique(); + bool ok = handle->sp.create(argv_s, options, envp_s, config.cwd.empty() ? nullptr : config.cwd.c_str()); + if (!ok) { + SRV_WRN("MCP '%s': failed to spawn '%s'\n", config.name.c_str(), config.command.c_str()); + return false; + } + handle->in = handle->sp.stdin_file(); + handle->out = handle->sp.stdout_file(); + handle->err = handle->sp.stderr_file(); + + proc = std::move(handle); + running.store(true); + reader = std::thread([this] { reader_loop(); }); + writer = std::thread([this] { writer_loop(); }); + errlog = std::thread([this] { errlog_loop(); }); + return true; +} + +void server_mcp_stdio::close() { + join_pumps(); +} + +bool server_mcp_stdio::is_alive() const { + return running.load(); +} + +std::string server_mcp_stdio::diagnostics() { + std::string out; + { + std::lock_guard lock(rpc_mutex); // last_error is written by send_rpc's callers + out = last_error; + } + std::lock_guard lk(err_mu); + if (!err_tail.empty()) { + if (!out.empty()) { + out += "; "; + } + out += "last stderr: " + err_tail; + } + return out; +} + +void server_mcp_stdio::reader_loop() { + mcp_pump_ndjson(proc->out, running, [this](std::string && line) { + return from_server.write(std::move(line)); // false => consumer gone, stop + }); + running.store(false); + to_server.close_write(); // stop the writer + from_server.close_write(); // EOF to any waiting caller +} + +// write all of `data` to child stdin, non-blocking and polled so teardown never hangs (a grandchild can hold the read end of a full pipe open). returns false on error/close/shutdown. +static bool mcp_write_all(FILE * f, const std::string & data, std::atomic & running) { + if (!f) { + return false; + } + size_t total = 0; +#if defined(_WIN32) + HANDLE h = (HANDLE) _get_osfhandle(_fileno(f)); + DWORD nowait = PIPE_NOWAIT; + SetNamedPipeHandleState(h, &nowait, NULL, NULL); + while (total < data.size() && running.load()) { + DWORD written = 0; + BOOL ok = WriteFile(h, data.data() + total, (DWORD) (data.size() - total), &written, NULL); + if (ok && written > 0) { + total += written; + continue; + } + if (!ok) { + DWORD err = GetLastError(); + if (err != ERROR_NO_DATA && err != ERROR_PIPE_BUSY) { + return false; + } + } + // backpressure (pipe full) is rare for small JSON-RPC frames; sleep rather than spin. + // no writable-wait exists for a PIPE_NOWAIT anonymous pipe, so this polls like the POSIX poll() path. + std::this_thread::sleep_for(std::chrono::milliseconds(10)); + } +#else + int fd = fileno(f); + int fl = fcntl(fd, F_GETFL, 0); + if (fl >= 0) { + fcntl(fd, F_SETFL, fl | O_NONBLOCK); + } + while (total < data.size() && running.load()) { + ssize_t n = write(fd, data.data() + total, data.size() - total); + if (n > 0) { + total += (size_t) n; + continue; + } + if (n == 0) { + return false; + } + if (errno == EINTR) { + continue; + } + if (errno != EAGAIN && errno != EWOULDBLOCK) { + return false; + } + struct pollfd pfd; + pfd.fd = fd; + pfd.events = POLLOUT; + pfd.revents = 0; + int pr = poll(&pfd, 1, 50); + if (pr < 0) { + if (errno == EINTR) { + continue; + } + return false; + } + if (pfd.revents & (POLLERR | POLLNVAL | POLLHUP)) { + return false; + } + } +#endif + return total == data.size(); +} + +void server_mcp_stdio::writer_loop() { + auto should_stop = [this] { return !running.load(); }; + std::string msg; + while (to_server.read(msg, should_stop)) { + msg.push_back('\n'); + if (!mcp_write_all(proc->in, msg, running)) { + break; // child gone or shutting down + } + } + running.store(false); + to_server.close_read(); // fail fast on any further send_rpc write + from_server.close_write(); // wake any caller waiting for a reply +} + +void server_mcp_stdio::errlog_loop() { + static constexpr size_t ERR_TAIL_MAX = 4096; + // drain stderr (an undrained pipe blocks the child): + // log it, and keep a bounded tail for reporting when the server dies + mcp_pump_ndjson(proc->err, running, [this](std::string && line) { + SRV_DBG("MCP '%s' stderr: %s\n", name.c_str(), line.c_str()); + std::lock_guard lk(err_mu); + err_tail += line; + err_tail += '\n'; + if (err_tail.size() > ERR_TAIL_MAX) { + err_tail.erase(0, err_tail.size() - ERR_TAIL_MAX); + } + return true; + }); +} + +void server_mcp_stdio::join_pumps() { + if (!proc) { + return; + } + running.store(false); + to_server.close_write(); // wake the writer if it waits for a message + from_server.close_write(); // wake any caller waiting for a reply + + proc->sp.terminate(); // child death unblocks the blocked fread/fwrite + + if (writer.joinable()) writer.join(); + if (reader.joinable()) reader.join(); + if (errlog.joinable()) errlog.join(); + + proc->sp.join(); // reap the child: never waiting would leave the pid a zombie for the process lifetime + proc.reset(); +} + + +// +// server_mcp +// + +static constexpr int MCP_COOLDOWN_SECONDS = 5; +static constexpr int MCP_WARMUP_TIMEOUT_SECONDS = 10; // cap per-server tool discovery at startup + +server_mcp::~server_mcp() { + shutdown(); + + std::vector> to_close; + { + std::lock_guard lock(mutex); + for (auto & [name, t] : transports) { + to_close.push_back(std::move(t)); + } + transports.clear(); + } + for (auto & t : to_close) { + t->close(); + } +} + +std::shared_ptr server_mcp::create_transport(const server_mcp_server_config & cfg) { + return std::make_shared(cfg); +} + +void server_mcp::shutdown() { + stopping.store(true); +} + +const server_mcp_server_config * server_mcp::find_config(const std::string & name) const { + for (const auto & c : configs) { + if (c.name == name) { + return &c; + } + } + return nullptr; +} + +void server_mcp::start(const common_params & params) { + auto append = [this](const std::string & json_str) { + try { + auto parsed = server_mcp_server_config::parse_from_json(json_str); + if (parsed.empty()) { + SRV_WRN("%s", "MCP config: no servers found in JSON\n"); + } + for (auto & p : parsed) { + // names must be unique across both config sources: get_or_create / find_config key on the name + if (find_config(p.name)) { + SRV_WRN("MCP config: duplicate server name '%s', skipping\n", p.name.c_str()); + continue; + } + configs.push_back(std::move(p)); + } + } catch (const std::exception & e) { + throw std::runtime_error(std::string("failed to parse MCP config JSON: ") + e.what()); + } + }; + if (!params.mcp_servers_config.empty()) { + std::ifstream f = fs_open_ifstream(params.mcp_servers_config, std::ios::in); + if (!f) { + throw std::runtime_error("failed to open MCP config file: " + params.mcp_servers_config); + } + std::stringstream ss; + ss << f.rdbuf(); + append(ss.str()); + } + if (!params.mcp_servers_json.empty()) { + append(params.mcp_servers_json); + } + + if (configs.empty()) { + return; + } + + std::vector discovered; + for (const auto & cfg : configs) { + auto t = create_transport(cfg); + if (!t->start()) { + SRV_WRN("MCP warmup: failed to spawn '%s': %s\n", cfg.name.c_str(), t->diagnostics().c_str()); + continue; + } + // bound warmup per server so an unresponsive one can't stall startup for the full per-call timeout + const auto deadline = std::chrono::steady_clock::now() + std::chrono::seconds(MCP_WARMUP_TIMEOUT_SECONDS); + auto should_stop = [this, deadline]() { + return stopping.load() || std::chrono::steady_clock::now() >= deadline; + }; + auto tools = t->list_tools(should_stop); + SRV_INF("MCP warmup: '%s' discovered %zu tools\n", cfg.name.c_str(), tools.size()); + discovered.insert(discovered.end(), tools.begin(), tools.end()); + t->close(); + } + + std::lock_guard lock(mutex); + registry.swap(discovered); +} + +std::vector server_mcp::list_tools() const { + std::lock_guard lock(mutex); + return registry; +} + +json server_mcp::call_tool(const std::string & server_name, + const std::string & tool_name, + const json & arguments, + const std::function & should_stop) { + auto transport = get_or_create(server_name); + if (!transport) { + return {{"error", "MCP server unavailable: " + server_name}}; + } + + auto stop = [this, &should_stop]() { + return stopping.load() || (should_stop && should_stop()); + }; + return transport->call_tool(tool_name, arguments, stop); +} + +std::shared_ptr server_mcp::get_or_create(const std::string & name) { + std::vector> to_close; // closed after unlock + std::shared_ptr result; + + { + std::lock_guard lock(mutex); + if (stopping.load()) { + return nullptr; + } + + auto now = std::chrono::steady_clock::now(); + auto dead_it = dead_servers.find(name); + if (dead_it != dead_servers.end()) { + if (now < dead_it->second) { + return nullptr; + } + dead_servers.erase(dead_it); + } + + auto it = transports.find(name); + if (it != transports.end()) { + if (it->second->is_alive()) { + return it->second; + } + SRV_WRN("MCP '%s' is no longer alive: %s\n", name.c_str(), it->second->diagnostics().c_str()); + to_close.push_back(std::move(it->second)); + transports.erase(it); + } + + const server_mcp_server_config * cfg = find_config(name); + if (cfg) { + auto fresh = create_transport(*cfg); + if (fresh->start() && fresh->is_alive()) { + transports[name] = fresh; + result = fresh; + } else { + SRV_WRN("MCP '%s': failed to start: %s\n", name.c_str(), fresh->diagnostics().c_str()); + to_close.push_back(std::move(fresh)); + dead_servers[name] = now + std::chrono::seconds(MCP_COOLDOWN_SECONDS); + } + } + } + + for (auto & t : to_close) { + t->close(); // blocking call, no leaks + } + + return result; +} + diff --git a/tools/server/server-mcp.h b/tools/server/server-mcp.h new file mode 100644 index 000000000000..c7f33a3f797d --- /dev/null +++ b/tools/server/server-mcp.h @@ -0,0 +1,176 @@ +#pragma once + +#include "server-common.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +// +// Configuration (Cursor-compatible "mcpServers" JSON) +// + +struct server_mcp_server_config { + std::string name; // config key, e.g. "filesystem" + std::string command; + std::vector args; + std::map env; // merged over the parent env + std::string cwd; + int timeout_ms = 30000; // per-tool-call timeout + + // throw on parse errors; missing "mcpServers" yields an empty list; entries without a "command" are skipped + static std::vector parse_from_json(const std::string & json_str); + static std::vector parse_cursor_format(const json & j); +}; + +// a tool advertised by an MCP server +struct server_mcp_tool_def { + std::string server_name; + std::string name; // bare tool name, no "_" prefix + std::string description; + json input_schema; // JSON Schema for the arguments, or null +}; + +// +// server_mcp_transport: one MCP server session. +// +// caller --send_rpc--> to_server --[writer]--> framing --> server +// caller <--send_rpc-- from_server <--[reader]-- framing <-- server +// +// each queue item is one complete serialized JSON message. +// subclass owns byte I/O and framing; base owns JSON and the JSON-RPC session (handshake, id correlation). +// + +struct server_mcp_transport { + std::string name; + int timeout_ms = 30000; + + server_pipe to_server; // serialized messages we send to the server + server_pipe from_server; // serialized messages read from the server + + virtual ~server_mcp_transport() = default; + + virtual bool start() = 0; + virtual void close() = 0; // blocking and idempotent + virtual bool is_alive() const = 0; // never blocks behind an in-flight send_rpc() + + // human-readable diagnostics for logging when the transport fails/dies + // (example: last RPC error, plus any transport-specific detail) + // may run on a different thread than send_rpc(), so last_error is read under rpc_mutex + virtual std::string diagnostics() { + std::lock_guard lock(rpc_mutex); + return last_error; + } + + std::vector list_tools(const std::function & should_stop); + + json call_tool(const std::string & tool_name, + const json & arguments, + const std::function & should_stop); + +protected: + // per-transport: send_rpc() holds it across the reply wait, so sharing it would stall every server behind one slow call. guards all members below. + std::mutex rpc_mutex; + uint64_t next_id = 1; // reset to 1 per (re)spawn + bool initialized = false; + std::string last_error; + std::vector tools; + + // both assume rpc_mutex is already held by the public caller + bool ensure_init(const std::function & should_stop); // initialize handshake, once + json send_rpc(const json & request, const std::function & should_stop); // returns the reply or an {"error": ...} +}; + +// +// server_mcp_stdio: child process, NDJSON JSON-RPC over stdio (stderr drained to the debug log) +// + +struct server_mcp_stdio : server_mcp_transport { + explicit server_mcp_stdio(const server_mcp_server_config & config); + ~server_mcp_stdio() override; + + bool start() override; + void close() override; + bool is_alive() const override; + std::string diagnostics() override; + +private: + server_mcp_server_config config; + + // defined in the .cpp so stays out of this header + struct process_handle; + std::unique_ptr proc; + + std::thread reader; // child stdout -> NDJSON de-framing -> from_server + std::thread writer; // to_server -> NDJSON framing -> child stdin + std::thread errlog; // child stderr -> debug log (must be drained or the child blocks) + + // cleared by close() or by the reader on stdout EOF; read without rpc_mutex + std::atomic running{false}; + + // bounded tail of the child's stderr, for diagnostics when it dies + std::mutex err_mu; + std::string err_tail; + + void reader_loop(); + void writer_loop(); + void errlog_loop(); + void join_pumps(); +}; + +// +// server_mcp +// declare before the HTTP context so it outlives every /tools handler. +// + +class server_mcp { +public: + server_mcp() = default; + ~server_mcp(); + + // parse the MCP config from params (file and/or inline JSON), + // then spawn each server once, list its tools, and shut it down + // throws on config parse errors; spawn failures are logged. + void start(const common_params & params); + + // true until start() has parsed at least one server from the config + bool empty() const { return configs.empty(); } + + std::vector list_tools() const; + + // lazily (re)spawns the transport. returns the MCP result or an {"error": ...}. should_stop is OR-ed with the manager's cancel flag. + json call_tool(const std::string & server_name, + const std::string & tool_name, + const json & arguments, + const std::function & should_stop = nullptr); + + // flip the cancel flag so in-flight calls return; blocking teardown is in the destructor. call before the HTTP server drains. + // note: multiple calls are idempotent + void shutdown(); + +private: + std::vector configs; + + mutable std::mutex mutex; // guards transports, dead_servers, registry + + // shared_ptr: call_tool() hands a transport to the caller and drops the lock for the blocking RPC, so a concurrent evict/respawn must not destroy it mid-call + std::map> transports; + std::map dead_servers; // spawn-failure cooldown + std::vector registry; + + std::atomic stopping{false}; + + const server_mcp_server_config * find_config(const std::string & name) const; + + // the only place that names a concrete transport + std::shared_ptr create_transport(const server_mcp_server_config & cfg); + + // nullptr during cooldown or shutdown + std::shared_ptr get_or_create(const std::string & name); +}; diff --git a/tools/server/server-models.cpp b/tools/server/server-models.cpp index 191db64dbfef..b75e30065f2b 100644 --- a/tools/server/server-models.cpp +++ b/tools/server/server-models.cpp @@ -8,10 +8,10 @@ #include "preset.h" #include "download.h" #include "http.h" +#include "subproc.h" #include // TODO: remove this once we use HTTP client from download.h #include -#include #include #include @@ -50,43 +50,24 @@ extern char **environ; #define CHILD_ADDR "127.0.0.1" struct server_subproc { - std::optional sproc; // empty while in DOWNLOADING state + common_subproc sproc; // not yet spawned while in DOWNLOADING state std::atomic stopped{false}; // set to cancel a download or signal child process exit - subprocess_s & get() { - GGML_ASSERT(sproc.has_value() && "subprocess not initialized"); - return sproc.value(); - } - bool is_alive() { - return sproc.has_value() && subprocess_alive(&sproc.value()); + return sproc.alive(); } void request_exit() { - if (sproc.has_value()) { - FILE * stdin_file = subprocess_stdin(&sproc.value()); - if (stdin_file) { - fprintf(stdin_file, "%s\n", CMD_ROUTER_TO_CHILD_EXIT); - fflush(stdin_file); - } + FILE * stdin_file = sproc.stdin_file(); + if (stdin_file) { + fprintf(stdin_file, "%s\n", CMD_ROUTER_TO_CHILD_EXIT); + fflush(stdin_file); } stopped.store(true, std::memory_order_relaxed); } void terminate() { - if (!sproc.has_value()) { - return; - } -#if defined(_WIN32) - if (sproc->hProcess == NULL) { - return; - } -#else - if (sproc->child <= 0) { - return; - } -#endif - subprocess_terminate(&sproc.value()); + sproc.terminate(); } }; @@ -712,18 +693,6 @@ std::optional server_models::get_meta(const std::string & nam return std::nullopt; } -// helper to convert vector to char ** -// pointers are only valid as long as the original vector is valid -static std::vector to_char_ptr_array(const std::vector & vec) { - std::vector result; - result.reserve(vec.size() + 1); - for (const auto & s : vec) { - result.push_back(const_cast(s.c_str())); - } - result.push_back(nullptr); - return result; -} - std::vector server_models::get_all_meta() { std::unique_lock lk(mutex); if (need_reload) { @@ -846,15 +815,10 @@ void server_models::load(const std::string & name, const load_options & opts) { } inst.meta.args = child_args; // save for debugging - std::vector argv = to_char_ptr_array(child_args); - std::vector envp = to_char_ptr_array(child_env); - // TODO @ngxson : maybe separate stdout and stderr in the future // so that we can use stdout for commands and stderr for logging int options = subprocess_option_no_window | subprocess_option_combined_stdout_stderr; - inst.subproc->sproc.emplace(); - int result = subprocess_create_ex(argv.data(), options, envp.data(), &inst.subproc->get()); - if (result != 0) { + if (!inst.subproc->sproc.create(child_args, options, child_env)) { throw std::runtime_error("failed to spawn server instance"); } } @@ -868,8 +832,8 @@ void server_models::load(const std::string & name, const load_options & opts) { stop_timeout = inst.meta.stop_timeout, child_mode = opts.mode ]() { - FILE * stdin_file = subprocess_stdin(&child_proc->get()); - FILE * stdout_file = subprocess_stdout(&child_proc->get()); // combined stdout/stderr + FILE * stdin_file = child_proc->sproc.stdin_file(); + FILE * stdout_file = child_proc->sproc.stdout_file(); // combined stdout/stderr std::thread log_thread([&]() { // read stdout/stderr and forward to main server log @@ -971,9 +935,7 @@ void server_models::load(const std::string & name, const load_options & opts) { } // get the exit code - int exit_code = 0; - subprocess_join(&child_proc->get(), &exit_code); - subprocess_destroy(&child_proc->get()); + int exit_code = child_proc->sproc.join(); // update status and exit code if (child_mode == SERVER_CHILD_MODE_DOWNLOAD) { @@ -1247,7 +1209,7 @@ bool server_models::ensure_model_ready(const std::string & name) { return true; } -server_http_res_ptr server_models::proxy_request(const server_http_req & req, const std::string & method, const std::string & name, bool update_last_used) { +server_http_res_ptr server_models::proxy_request(const server_http_req & req, const std::string & method, const std::string & name, bool update_last_used, bool detached) { auto meta = get_meta(name); if (!meta.has_value()) { throw std::runtime_error("model name=" + name + " is not found"); @@ -1273,7 +1235,10 @@ server_http_res_ptr server_models::proxy_request(const server_http_req & req, co req.headers, req.body, req.files, - req.should_stop, + // a detached request belongs to a replay session that outlives the client socket: + // it reaches the child even when the downstream died during the load wait, the + // session buffer is the recipient and DELETE remains the stop + detached ? std::function([]() { return false; }) : req.should_stop, base_params.timeout_read, base_params.timeout_write ); @@ -1544,13 +1509,9 @@ static bool router_validate_model(std::string & name, server_models & models, bo } // resolve alias to canonical model name name = meta->name; - if (models_autoload) { - models.ensure_model_ready(name); - } else { - if (!meta->is_running()) { - res_err(res, format_error_response("model is not loaded", ERROR_TYPE_INVALID_REQUEST)); - return false; - } + if (!models_autoload && !meta->is_running()) { + res_err(res, format_error_response("model is not loaded", ERROR_TYPE_INVALID_REQUEST)); + return false; } return true; } @@ -1604,6 +1565,10 @@ static std::optional resolve_child_for_conv( } void server_models_routes::init_routes() { + if (!common_subproc::is_supported()) { + throw std::runtime_error("subprocess is not enabled on this build"); + } + this->get_router_props = [this](const server_http_req & req) { std::string name = req.get_param("model"); if (name.empty()) { @@ -1639,6 +1604,9 @@ void server_models_routes::init_routes() { if (!router_validate_model(name, models, autoload, error_res)) { return error_res; } + if (autoload) { + models.ensure_model_ready(name); + } return models.proxy_request(req, method, name, false); }; @@ -1652,12 +1620,23 @@ void server_models_routes::init_routes() { return error_res; } // remember which child serves this conversation so the stream routes can route straight - // to it without polling, keyed on the exact conv id from the header + // to it without polling, keyed on the exact conv id from the header. registered before + // the load wait so a stop issued while the model loads can erase the entry and cancel + // this request instead of leaving an orphan generation std::string conv_id = server_stream_conv_id_from_headers(req.headers); - if (!conv_id.empty()) { - models.conv_models.remember(conv_id, name); + uint64_t ticket = models.conv_models.remember(conv_id, name); + bool waited = autoload && models.ensure_model_ready(name); + if (ticket != 0 && !models.conv_models.alive(conv_id, ticket)) { + SRV_INF("request for conv_id=%s cancelled while model name=%s was loading\n", + conv_id.c_str(), name.c_str()); + res_err(error_res, format_error_response( + "request cancelled by a stop while the model was loading", ERROR_TYPE_INVALID_REQUEST)); + return error_res; } - return models.proxy_request(req, method, name, true); // update last usage for POST request only + // a session request that waited for a load detaches from the client socket: the + // client may have dropped during the wait (page reload) and the session buffer must + // still receive the generation for a later resume + return models.proxy_request(req, method, name, true, waited && ticket != 0); // update last usage for POST request only }; this->post_router_models_load = [this](const server_http_req & req) { @@ -1856,7 +1835,7 @@ void server_models_routes::init_routes() { }; this->router_stream_get = [this](const server_http_req & req) { - // GET /v1/stream/?from=N. resolve the owning child from the conv_id -> model + // GET /v1/stream?conv_id=&from=N. resolve the owning child from the conv_id -> model // map, 404 when nothing maps auto res = std::make_unique(); std::string conv_id = req.get_param("conv_id"); @@ -1866,13 +1845,24 @@ void server_models_routes::init_routes() { } std::optional owner = resolve_child_for_conv(models, conv_id); if (!owner.has_value()) { - res_err(res, format_error_response("Stream not found or expired", ERROR_TYPE_NOT_FOUND)); + // a registered conv whose model is still loading earns a retry: the session appears + // once the load ends and the pending request reaches the child + auto tracked = models.conv_models.lookup(conv_id); + auto meta = tracked.has_value() ? models.get_meta(*tracked) : std::nullopt; + bool transient = meta.has_value() && (meta->status == SERVER_MODEL_STATUS_LOADING || + meta->status == SERVER_MODEL_STATUS_DOWNLOADING || + meta->status == SERVER_MODEL_STATUS_DOWNLOADED); + if (transient) { + res_err(res, format_error_response("Stream owner model is loading, retry later", ERROR_TYPE_UNAVAILABLE)); + } else { + res_err(res, format_error_response("Stream not found or expired", ERROR_TYPE_NOT_FOUND)); + } return res; } std::string from = req.get_param("from"); - std::string child_path = "/v1/stream/" + encode_qs(conv_id); + std::string child_path = "/v1/stream?conv_id=" + encode_qs(conv_id); if (!from.empty()) { - child_path += "?from=" + from; + child_path += "&from=" + from; } SRV_TRC("proxying stream resume to model %s on port %d, path=%s\n", owner->name.c_str(), owner->port, child_path.c_str()); @@ -1952,7 +1942,7 @@ void server_models_routes::init_routes() { }; this->router_stream_delete = [this](const server_http_req & req) { - // DELETE /v1/stream/. resolve the owning child via the map and forward only to + // DELETE /v1/stream?conv_id=. resolve the owning child via the map and forward only to // it, evict_and_cancel is idempotent on the child auto res = std::make_unique(); std::string conv_id = req.get_param("conv_id"); @@ -1960,7 +1950,7 @@ void server_models_routes::init_routes() { res_err(res, format_error_response("Missing conversation id in path", ERROR_TYPE_INVALID_REQUEST)); return res; } - std::string child_path = "/v1/stream/" + encode_qs(conv_id); + std::string child_path = "/v1/stream?conv_id=" + encode_qs(conv_id); auto owner = resolve_child_for_conv(models, conv_id); if (owner.has_value()) { httplib::Client cli(CHILD_ADDR, owner->port); @@ -1969,6 +1959,11 @@ void server_models_routes::init_routes() { cli.set_write_timeout(0, STREAM_LOOKUP_TIMEOUT_MS * 1000); auto resp = cli.Delete(child_path.c_str()); (void) resp; // the child logs its own miss when the session is unknown there + } else if (auto tracked = models.conv_models.lookup(conv_id); tracked.has_value()) { + // the entry exists but its model is still loading: the forget below erases it, + // which cancels the request parked in proxy_post before the generation starts + SRV_INF("router stop for conv_id=%s while model name=%s is loading, cancelling the pending request\n", + conv_id.c_str(), tracked->c_str()); } else { SRV_WRN("router stop for unknown conv_id=%s, no owning child in the conv map\n", conv_id.c_str()); @@ -1987,53 +1982,6 @@ void server_models_routes::init_routes() { // server_http_proxy // -// simple implementation of a pipe -// used for streaming data between threads -template -struct pipe_t { - std::mutex mutex; - std::condition_variable cv; - std::queue queue; - std::atomic writer_closed{false}; - std::atomic reader_closed{false}; - void close_write() { - writer_closed.store(true, std::memory_order_relaxed); - cv.notify_all(); - } - void close_read() { - reader_closed.store(true, std::memory_order_relaxed); - cv.notify_all(); - } - bool read(T & output, const std::function & should_stop) { - std::unique_lock lk(mutex); - constexpr auto poll_interval = std::chrono::milliseconds(500); - while (true) { - if (!queue.empty()) { - output = std::move(queue.front()); - queue.pop(); - return true; - } - if (writer_closed.load()) { - return false; // clean EOF - } - if (should_stop()) { - close_read(); // signal broken pipe to writer - return false; // cancelled / reader no longer alive - } - cv.wait_for(lk, poll_interval); - } - } - bool write(T && data) { - std::lock_guard lk(mutex); - if (reader_closed.load()) { - return false; // broken pipe - } - queue.push(std::move(data)); - cv.notify_one(); - return true; - } -}; - static std::string to_lower_copy(const std::string & value) { std::string lowered(value.size(), '\0'); std::transform(value.begin(), value.end(), lowered.begin(), [](unsigned char c) { return std::tolower(c); }); @@ -2143,7 +2091,7 @@ server_http_proxy::server_http_proxy( ) { // shared between reader and writer threads auto cli = std::make_shared(host, port); - auto pipe = std::make_shared>(); + auto pipe = std::make_shared>(); if (scheme == "https") { #ifdef CPPHTTPLIB_OPENSSL_SUPPORT diff --git a/tools/server/server-models.h b/tools/server/server-models.h index b41f98eb796c..ba40e4fc3870 100644 --- a/tools/server/server-models.h +++ b/tools/server/server-models.h @@ -153,12 +153,24 @@ struct server_models { // proxy_request forwards a POST carrying an X-Conversation-Id. best effort: a stale entry just // makes the child answer not found and the client recovers. owns its lock, one mutex per struct struct conv_model_tracker { - void remember(const std::string & conv_id, const std::string & model) { + // returns the ticket of this registration, 0 when nothing was registered. erasing or + // replacing the entry invalidates the ticket, which is how a stop cancels a request + // parked in the model load wait + uint64_t remember(const std::string & conv_id, const std::string & model) { if (conv_id.empty() || model.empty()) { - return; + return 0; } std::lock_guard lock(mu); - map[conv_id] = model; + uint64_t ticket = next_ticket++; + map[conv_id] = { model, ticket }; + return ticket; + } + + // false means a stop erased the entry or a newer request replaced it + bool alive(const std::string & conv_id, uint64_t ticket) { + std::lock_guard lock(mu); + auto it = map.find(conv_id); + return it != map.end() && it->second.ticket == ticket; } std::optional lookup(const std::string & conv_id) { @@ -170,7 +182,7 @@ struct server_models { if (it == map.end()) { return std::nullopt; } - return it->second; + return it->second.model; } void forget(const std::string & conv_id) { @@ -182,8 +194,13 @@ struct server_models { } private: - std::mutex mu; - std::unordered_map map; + struct entry_t { + std::string model; + uint64_t ticket; + }; + std::mutex mu; + uint64_t next_ticket = 1; + std::unordered_map map; }; common_preset_context ctx_preset; @@ -272,7 +289,7 @@ struct server_models { bool ensure_model_ready(const std::string & name); // proxy an HTTP request to the model instance - server_http_res_ptr proxy_request(const server_http_req & req, const std::string & method, const std::string & name, bool update_last_used); + server_http_res_ptr proxy_request(const server_http_req & req, const std::string & method, const std::string & name, bool update_last_used, bool detached = false); // handle message sent from server_child::notify_to_router() // raw input must starts with CMD_CHILD_TO_ROUTER_STATE, followed by a JSON string diff --git a/tools/server/server-schema.cpp b/tools/server/server-schema.cpp index 89026eb4e3f0..674d3ba337bc 100644 --- a/tools/server/server-schema.cpp +++ b/tools/server/server-schema.cpp @@ -209,6 +209,7 @@ std::vector> make_llama_cmpl_schema(const common_params & ->set_hard_limits(0.0f, 1.0f) ->set_desc("Minimum speculative decoding probability for draft tokens (0 = greedy)")); + add((new field_str("speculative.type")) ->set_desc("Speculative decoding method (for debugging and research purposes)") ->set_handler([&](field_eval_context & ctx, const json & data) { @@ -390,21 +391,40 @@ std::vector> make_llama_cmpl_schema(const common_params & ctx.params.sampling.reasoning_budget_start = common_tokenize(ctx.vocab, data.at("reasoning_budget_start_tag").get(), false, true); })); - add((new field_str("reasoning_budget_end_tag")) - ->set_desc("Token string marking the end of the reasoning budget section") + add((new field_json("reasoning_budget_end_tags")) + ->add_alias("reasoning_budget_end_tag") + ->set_desc("Token strings marking the end of the reasoning budget section; the first is forced when the budget expires") ->set_handler([&](field_eval_context & ctx, const json & data) { GGML_ASSERT(ctx.vocab != nullptr); - std::string end_tag = data.at("reasoning_budget_end_tag").get(); - ctx.params.sampling.reasoning_budget_end = common_tokenize(ctx.vocab, end_tag, false, true); + ctx.params.sampling.reasoning_budget_end.clear(); + if (data.contains("reasoning_budget_end_tags")) { + for (const auto & t : data.at("reasoning_budget_end_tags")) { + std::string tag = t.get(); + if (!tag.empty()) { + ctx.params.sampling.reasoning_budget_end.push_back(common_tokenize(ctx.vocab, tag, false, true)); + } + } + } else if (data.contains("reasoning_budget_end_tag")) { + std::string tag = data.at("reasoning_budget_end_tag").get(); + if (!tag.empty()) { + ctx.params.sampling.reasoning_budget_end.push_back(common_tokenize(ctx.vocab, tag, false, true)); + } + } })); add((new field_str("reasoning_budget_message")) ->set_desc("Message to prepend to the reasoning budget end tag when forcing it") ->set_handler([&](field_eval_context & ctx, const json & data) { GGML_ASSERT(ctx.vocab != nullptr); - std::string end_tag = json_value(data, "reasoning_budget_end_tag", std::string()); - std::string message = data.at("reasoning_budget_message").get(); - ctx.params.sampling.reasoning_budget_forced = common_tokenize(ctx.vocab, message + end_tag, false, true); + if (!ctx.params.sampling.reasoning_budget_end.empty()) { + llama_tokens end_tag = ctx.params.sampling.reasoning_budget_end.front(); + std::string message = json_value(data, "reasoning_budget_message", std::string()); + if (!message.empty()) { + llama_tokens message_tokens = common_tokenize(ctx.vocab, message, false, true); + end_tag.insert(end_tag.begin(), message_tokens.begin(), message_tokens.end()); + } + ctx.params.sampling.reasoning_budget_forced = std::move(end_tag); + } })); add((new field_json("logit_bias")) @@ -546,7 +566,7 @@ task_params eval_llama_cmpl_schema( // debugging { auto budget = params.sampling.reasoning_budget_tokens; - SRV_DBG("reasoning budget: tokens=%d, generation_prompt='%s', start=%zu toks, end=%zu toks, forced=%zu toks\n", + SRV_DBG("reasoning budget: tokens=%d, generation_prompt='%s', start=%zu toks, end=%zu seqs, forced=%zu toks\n", budget, params.sampling.generation_prompt.c_str(), params.sampling.reasoning_budget_start.size(), params.sampling.reasoning_budget_end.size(), diff --git a/tools/server/server-stream.cpp b/tools/server/server-stream.cpp index 19db04988d81..f6b9b8a9f4cc 100644 --- a/tools/server/server-stream.cpp +++ b/tools/server/server-stream.cpp @@ -453,7 +453,7 @@ static server_http_res_ptr make_error_response(int status, const std::string & m server_http_context::handler_t server_stream_make_get_handler() { return [](const server_http_req & req) -> server_http_res_ptr { - // GET /v1/stream/?from=N replays buffered SSE bytes then blocks for live + // GET /v1/stream?conv_id=&from=N replays buffered SSE bytes then blocks for live // bytes until the session finalizes, streamed as text/event-stream for EventSource std::string conv_id = req.get_param("conv_id"); if (conv_id.empty()) { @@ -560,13 +560,13 @@ server_http_context::handler_t server_stream_make_lookup_handler() { server_http_context::handler_t server_stream_make_delete_handler() { return [](const server_http_req & req) -> server_http_res_ptr { - // DELETE /v1/stream/ is the explicit user Stop, cancels the producer and evicts + // DELETE /v1/stream?conv_id= is the explicit user Stop, cancels the producer and evicts // the buffer. idempotent, returns 204 even if the session was already gone std::string conv_id = req.get_param("conv_id"); if (conv_id.empty()) { return make_error_response(400, "Missing conversation id in path", ERROR_TYPE_INVALID_REQUEST); } - SRV_TRC("DELETE /v1/stream/%s -> evict_and_cancel\n", conv_id.c_str()); + SRV_TRC("DELETE /v1/stream conv_id=%s -> evict_and_cancel\n", conv_id.c_str()); g_stream_sessions.evict_and_cancel(conv_id); auto res = std::make_unique(); res->status = 204; @@ -621,7 +621,7 @@ bool server_res_spipe::conn_alive() { bool server_res_spipe::should_stop() { if (spipe) { - // note: if DELETE /v1/stream/ is called, is_cancelled() will be true + // note: if DELETE /v1/stream is called for this conv, is_cancelled() will be true return spipe->is_cancelled(); } else { return !conn_alive(); @@ -632,6 +632,13 @@ void server_res_spipe::on_complete() { if (!spipe || next_finished) { return; } + // an empty next_orig means set_next() never ran: the request failed before streaming + // started, typically a params validation throw. evict the session installed by set_req() + // so the failed request leaves nothing behind for discovery or replay + if (!next_orig) { + g_stream_sessions.evict(server_stream_conv_id_from_headers(req->headers)); + return; + } std::string chunk; while (!spipe->is_cancelled()) { chunk.clear(); diff --git a/tools/server/server-stream.h b/tools/server/server-stream.h index 9753140dd601..1e7461285f43 100644 --- a/tools/server/server-stream.h +++ b/tools/server/server-stream.h @@ -45,7 +45,13 @@ void server_stream_session_manager_start(); void server_stream_session_manager_stop(); // route handler factories wired under /v1/stream/* by server.cpp +// child-side handlers for the resumable stream routes. the conv id travels in the conv_id +// query string because it can embed a model name containing slashes (org/repo), which the +// decoded path would split before the param is captured server_http_context::handler_t server_stream_make_get_handler(); +// POST /v1/streams/lookup with body {"conversation_ids": [...]}: only answers for ids the +// caller already owns (the WebUI passes the convs visible in its sidebar), the server never +// lists ids it has not been asked about, so a random caller cannot enumerate live sessions server_http_context::handler_t server_stream_make_lookup_handler(); server_http_context::handler_t server_stream_make_delete_handler(); diff --git a/tools/server/server-task.cpp b/tools/server/server-task.cpp index f01780100c0d..070f1ade241c 100644 --- a/tools/server/server-task.cpp +++ b/tools/server/server-task.cpp @@ -63,6 +63,8 @@ json task_params::to_json(bool only_metrics) const { {"mirostat", sampling.mirostat}, {"mirostat_tau", sampling.mirostat_tau}, {"mirostat_eta", sampling.mirostat_eta}, + {"adaptive_target", sampling.adaptive_target}, + {"adaptive_decay", sampling.adaptive_decay}, {"max_tokens", n_predict}, {"n_predict", n_predict}, // TODO: deduplicate? {"n_keep", n_keep}, @@ -114,6 +116,8 @@ json task_params::to_json(bool only_metrics) const { {"mirostat", sampling.mirostat}, {"mirostat_tau", sampling.mirostat_tau}, {"mirostat_eta", sampling.mirostat_eta}, + {"adaptive_target", sampling.adaptive_target}, + {"adaptive_decay", sampling.adaptive_decay}, {"stop", antiprompt}, {"max_tokens", n_predict}, {"n_predict", n_predict}, // TODO: deduplicate? @@ -1738,9 +1742,9 @@ bool server_prompt_cache::load(server_prompt & prompt, const server_tokens & tok const int lcp_best = prompt.tokens.get_common_prefix(tokens_new); float f_keep_best = prompt.tokens.size() > 0 ? float(lcp_best) / prompt.tokens.size() : -1.0f; // empty slot: any cache entry wins - float sim_best = float(lcp_best) / tokens_new.size(); + float f_sim_best = float(lcp_best) / tokens_new.size(); - SRV_TRC(" - looking for better prompt, base f_keep = %.3f, sim = %.3f\n", f_keep_best, sim_best); + SRV_TRC(" - looking for better prompt, base f_keep = %.3f, f_sim = %.3f\n", f_keep_best, f_sim_best); auto it_best = states.end(); @@ -1749,23 +1753,25 @@ bool server_prompt_cache::load(server_prompt & prompt, const server_tokens & tok const int lcp_cur = it->prompt.tokens.get_common_prefix(tokens_new); const float f_keep_cur = float(lcp_cur) / it->prompt.tokens.size(); - const float sim_cur = float(lcp_cur) / tokens_new.size(); + const float f_sim_cur = float(lcp_cur) / tokens_new.size(); + + SRV_TRC(" - prompt with length %7zu, lcp = %7d, f_keep = %.3f, f_sim = %.3f\n", it->prompt.tokens.size(), lcp_cur, f_keep_cur, f_sim_cur); // don't trash large prompts if (f_keep_cur < 0.25f) { continue; } - if (f_keep_best < f_keep_cur && sim_best < sim_cur) { + if (f_keep_best < f_keep_cur && f_sim_best < f_sim_cur) { f_keep_best = f_keep_cur; - sim_best = sim_cur; + f_sim_best = f_sim_cur; it_best = it; } } if (it_best != states.end()) { - SRV_TRC(" - found better prompt with f_keep = %.3f, sim = %.3f\n", f_keep_best, sim_best); + SRV_TRC(" - found better prompt with f_keep = %.3f, f_sim = %.3f\n", f_keep_best, f_sim_best); { auto & data = it_best->data.main; diff --git a/tools/server/server-task.h b/tools/server/server-task.h index 98e473ae3eba..d939fa37862d 100644 --- a/tools/server/server-task.h +++ b/tools/server/server-task.h @@ -652,7 +652,7 @@ struct server_prompt_cache { server_prompt_cache_state * alloc(const server_prompt & prompt, size_t state_size_main, size_t state_size_drft); - bool load(server_prompt & prompt, const server_tokens & tokens_new, llama_context * ctx_main, llama_context * ctx_drft, int32_t id_slot); + bool load(server_prompt & prompt, const server_tokens & tokens_new, llama_context * ctx_tgt, llama_context * ctx_dft, int32_t id_slot); void update(); }; diff --git a/tools/server/server-tools.cpp b/tools/server/server-tools.cpp index a8216d7dbc54..984bb478ea23 100644 --- a/tools/server/server-tools.cpp +++ b/tools/server/server-tools.cpp @@ -1,18 +1,19 @@ #include "server-tools.h" -#include +#include "subproc.h" #include #include #include #include #include +#include #include #include -#include #include #include #include +#include namespace fs = std::filesystem; @@ -24,7 +25,7 @@ json server_tool::to_json() const { return { {"display_name", display_name}, {"tool", name}, - {"type", "builtin"}, + {"type", type()}, {"permissions", json{ {"write", permission_write} }}, @@ -63,24 +64,27 @@ class tools_io { class tools_io_basic : public tools_io { public: + // cwd, if non-empty, is used to resolve relative paths and as the working directory for run() + explicit tools_io_basic(std::string cwd = "") : cwd(std::move(cwd)) {} + bool is_directory(const std::string & path) const override { std::error_code ec; - return fs::is_directory(path, ec) && !ec; + return fs::is_directory(resolve(path), ec) && !ec; } bool is_regular_file(const std::string & path) const override { std::error_code ec; - return fs::is_regular_file(path, ec) && !ec; + return fs::is_regular_file(resolve(path), ec) && !ec; } bool file_size(const std::string & path, uintmax_t & out_size) const override { std::error_code ec; - out_size = fs::file_size(path, ec); + out_size = fs::file_size(resolve(path), ec); return !ec; } bool read_file(const std::string & path, std::string & out) const override { - std::ifstream f(path, std::ios::binary); + std::ifstream f(resolve(path), std::ios::binary); if (!f) return false; std::ostringstream ss; ss << f.rdbuf(); @@ -90,12 +94,12 @@ class tools_io_basic : public tools_io { bool write_file(const std::string & path, const std::string & content) const override { std::error_code ec; - fs::path fpath(path); + fs::path fpath(resolve(path)); if (fpath.has_parent_path()) { fs::create_directories(fpath.parent_path(), ec); if (ec) return false; } - std::ofstream f(path, std::ios::binary); + std::ofstream f(fpath, std::ios::binary); if (!f) return false; f << content; return (bool) f; @@ -103,13 +107,14 @@ class tools_io_basic : public tools_io { std::vector list_files(const std::string & base, std::string & err) const override { err.clear(); + std::string abs_base = resolve(base); if (!is_directory(base)) { err = "path does not exist or is not a directory: " + base; return {}; } auto res = run( - {"git", "-C", base, "ls-files", "--cached", "--others", "--exclude-standard"}, + {"git", "-C", abs_base, "ls-files", "--cached", "--others", "--exclude-standard"}, SERVER_TOOL_GIT_LS_FILES_MAX_OUTPUT, SERVER_TOOL_GIT_LS_FILES_TIMEOUT); if (res.exit_code == 0 && !res.timed_out) { @@ -127,7 +132,7 @@ class tools_io_basic : public tools_io { return result; } - return list_files_fallback(base); + return list_files_fallback(abs_base); } exec_result run( @@ -137,15 +142,14 @@ class tools_io_basic : public tools_io { const std::function & on_chunk = nullptr) const override { exec_result res; - subprocess_s proc; - auto argv = to_cstr_vec(args); + common_subproc proc; int options = subprocess_option_no_window | subprocess_option_combined_stdout_stderr | subprocess_option_inherit_environment | subprocess_option_search_user_path; - if (subprocess_create(argv.data(), options, &proc) != 0) { + if (!proc.create(args, options, {}, cwd.empty() ? nullptr : cwd.c_str())) { res.output = "failed to spawn process"; return res; } @@ -158,14 +162,14 @@ class tools_io_basic : public tools_io { while (!done.load()) { if (std::chrono::steady_clock::now() >= deadline) { timed_out.store(true); - subprocess_terminate(&proc); + proc.terminate(); return; } std::this_thread::sleep_for(std::chrono::milliseconds(100)); } }); - FILE * f = subprocess_stdout(&proc); + FILE * f = proc.stdout_file(); std::string output; bool truncated = false; if (f) { @@ -176,7 +180,7 @@ class tools_io_basic : public tools_io { if (output.size() + len <= max_output) { output.append(buf, len); if (on_chunk && !on_chunk(std::string(buf, len))) { - subprocess_terminate(&proc); + proc.terminate(); break; } } else { @@ -194,8 +198,7 @@ class tools_io_basic : public tools_io { timeout_thread.join(); } - subprocess_join(&proc, &res.exit_code); - subprocess_destroy(&proc); + res.exit_code = proc.join(); res.output = output; res.timed_out = timed_out.load(); @@ -206,14 +209,14 @@ class tools_io_basic : public tools_io { } private: - static std::vector to_cstr_vec(const std::vector & v) { - std::vector r; - r.reserve(v.size() + 1); - for (const auto & s : v) { - r.push_back(const_cast(s.c_str())); - } - r.push_back(nullptr); - return r; + std::string cwd; + + // resolves `path` against `cwd` if `path` is relative and `cwd` is set; otherwise returns `path` unchanged + std::string resolve(const std::string & path) const { + if (cwd.empty() || fs::path(path).is_absolute()) { + return path; + } + return (fs::path(cwd) / path).string(); } static const std::unordered_set & junk_dir_names() { @@ -255,8 +258,8 @@ class tools_io_basic : public tools_io { }; static std::unique_ptr make_tools_io(const json & params) { - GGML_UNUSED(params); // TODO in follow-up PR - return std::make_unique(); + std::string cwd = json_value(params, "cwd", std::string()); + return std::make_unique(cwd); } // no '/' in pattern -> match basename at any depth; else match full relative path @@ -289,7 +292,7 @@ struct server_tool_read_file : server_tool { {"function", { {"name", name}, {"description", "Read the contents of a file. Optionally specify a 1-based line range. " - "If append_loc is true, each line is prefixed with its line number (e.g. \"1\u2192 ...\")."}, + "If append_loc is true, each line is prefixed with its line number (e.g. \"1\u2192...\")."}, {"parameters", { {"type", "object"}, {"properties", { @@ -339,7 +342,7 @@ struct server_tool_read_file : server_tool { std::string out_line; if (append_loc) { - out_line = std::to_string(lineno) + "\u2192 " + line + "\n"; + out_line = std::to_string(lineno) + "\u2192" + line + "\n"; } else { out_line = line + "\n"; } @@ -1048,16 +1051,92 @@ struct server_tool_get_datetime : server_tool { {"type", "function"}, {"function", { {"name", name}, - {"description", "Returns the current date and time"}, + {"description", "Returns the current date and time in UTC"}, + {"parameters", { + {"type", "object"}, + {"properties", { + {"format", { + {"type", "string"}, + {"description", + "strftime()-style format string for the output (default: \"%Y-%m-%dT%H:%M:%SZ\", " + "e.g. ISO 8601). Choose your own format if you need something else, " + "e.g. \"%A, %B %d %Y\" for a human-readable date."}, + }}, + }}, + }}, }}, }; } - json invoke(json, server_tool::stream *) const override { - auto now = std::chrono::system_clock::now(); + json invoke(json params, server_tool::stream *) const override { + std::string format = json_value(params, "format", std::string("%Y-%m-%dT%H:%M:%SZ")); + + auto now = std::chrono::system_clock::now(); auto time = std::chrono::system_clock::to_time_t(now); + std::tm tm_utc; +#ifdef _WIN32 + gmtime_s(&tm_utc, &time); +#else + gmtime_r(&time, &tm_utc); +#endif + + char buf[256]; + size_t len = std::strftime(buf, sizeof(buf), format.c_str(), &tm_utc); + if (len == 0) { + return {{"error", "invalid format string"}}; + } - return {{"result", std::ctime(&time)}}; + return {{"result", std::string(buf, len)}}; + } +}; + +// +// get_info: returns runtime info (OS name/version and cwd) +// + +struct server_tool_get_info : server_tool { + server_tool_get_info() { + name = "get_info"; + display_name = "Get Runtime Info"; + permission_write = false; + } + + json get_definition() const override { + return { + {"type", "function"}, + {"function", { + {"name", name}, + {"description", "Returns runtime info: the OS name/version and the current working directory"}, + {"parameters", { + {"type", "object"}, + {"properties", json::object()}, + }}, + }}, + }; + } + + json invoke(json params, server_tool::stream *) const override { + auto io = make_tools_io(params); + +#ifdef _WIN32 + auto res = io->run({"cmd", "/c", "ver"}, 4096, 5); +#else + auto res = io->run({"uname", "-a"}, 4096, 5); +#endif + // "ver" prints a blank line before the version, so the output is stripped on both ends; + // a failed spawn or a timeout leaves a diagnostic in res.output, which is not an OS name + std::string os_info = res.exit_code == 0 && !res.timed_out ? string_strip(res.output) : "unknown"; + + std::string cwd = json_value(params, "cwd", std::string()); + if (cwd.empty()) { + std::error_code ec; + cwd = fs::current_path(ec).string(); + } + + return { + {"os", os_info}, + {"cwd", cwd}, + }; } }; @@ -1102,6 +1181,49 @@ struct server_tools_res : server_http_res { } }; +// +// server_mcp_tool: exposes one tool from a running MCP server as a server_tool. +// +struct server_mcp_tool : server_tool { + std::string server_name; + std::string tool_name; + server_mcp_tool_def def; + server_mcp & mcp_mgr; + + server_mcp_tool(server_mcp_tool_def d, server_mcp & mgr) + : server_name(d.server_name) + , tool_name(d.name) + , def(std::move(d)) + , mcp_mgr(mgr) + { + name = server_name + "_" + tool_name; + display_name = name; + permission_write = false; + support_stream = false; + } + + std::string type() const override { return "mcp"; } + + json get_definition() const override { + json schema = def.input_schema; + if (schema.is_null() || !schema.is_object()) { + schema = json::object(); + } + return { + {"type", "function"}, + {"function", { + {"name", name}, + {"description", def.description}, + {"parameters", schema}, + }}, + }; + } + + json invoke(json params, server_tool::stream *) const override { + return mcp_mgr.call_tool(server_name, tool_name, params); + } +}; + static server_tool & find_tool(std::vector> & tools, const std::string & name, bool require_stream) { for (auto & t : tools) { if (t->name == name) { @@ -1127,11 +1249,33 @@ static std::vector> build_tools() { tools.push_back(std::make_unique()); tools.push_back(std::make_unique()); tools.push_back(std::make_unique()); + tools.push_back(std::make_unique()); return tools; } -void server_tools::setup(const std::vector & enabled_tools) { +static std::string str_to_lower(const std::string & value) { + std::string lowered(value.size(), '\0'); + std::transform(value.begin(), value.end(), lowered.begin(), [](unsigned char c) { return std::tolower(c); }); + return lowered; +} + +static std::string get_header(const std::map & headers, const std::string & key, std::string default_value = "") { + const auto lowered_key = str_to_lower(key); + for (const auto & h : headers) { + if (str_to_lower(h.first) == lowered_key) { + return h.second; + } + } + return default_value; +} + +void server_tools::setup(const std::vector & enabled_tools, + server_mcp & mcp_mgr) { if (!enabled_tools.empty()) { + if (!common_subproc::is_supported()) { + throw std::runtime_error("subprocess is not enabled on this build"); + } + std::unordered_set enabled_set(enabled_tools.begin(), enabled_tools.end()); auto all_tools = build_tools(); @@ -1161,6 +1305,29 @@ void server_tools::setup(const std::vector & enabled_tools) { } } + // append MCP tools, skipping any that collide with a built-in or another MCP tool of the same "_" name + if (!mcp_mgr.empty()) { + std::unordered_set seen_names; + for (auto & t : tools) { + seen_names.insert(t->name); + } + size_t n_added = 0; + for (const auto & def : mcp_mgr.list_tools()) { + std::string mcp_name = def.server_name + "_" + def.name; + if (seen_names.count(mcp_name)) { + SRV_WRN("MCP tool \"%s\" from server \"%s\" collides with an existing tool, skipping\n", + mcp_name.c_str(), def.server_name.c_str()); + continue; + } + seen_names.insert(mcp_name); + tools.push_back(std::make_unique(def, mcp_mgr)); + n_added++; + } + if (n_added > 0) { + SRV_INF("Added %zu MCP tools\n", n_added); + } + } + handle_get = [this](const server_http_req &) -> server_http_res_ptr { auto res = std::make_unique(); try { @@ -1185,6 +1352,12 @@ void server_tools::setup(const std::vector & enabled_tools) { json params = body.value("params", json::object()); bool stream = body.value("stream", false); + // accept x-tool-cwd header to override of the process + auto cwd = get_header(req.headers, "x-tool-cwd"); + if (!cwd.empty()) { + params["cwd"] = cwd; + } + server_tool & tool = find_tool(tools, tool_name, stream); if (stream) { diff --git a/tools/server/server-tools.h b/tools/server/server-tools.h index 6f6528f484f8..601399ee9392 100644 --- a/tools/server/server-tools.h +++ b/tools/server/server-tools.h @@ -3,9 +3,11 @@ #include "server-common.h" #include "server-http.h" #include "server-queue.h" +#include "server-mcp.h" #include #include +#include struct server_tool { std::string name; @@ -15,6 +17,7 @@ struct server_tool { virtual ~server_tool() = default; virtual json get_definition() const = 0; + virtual std::string type() const { return "builtin"; } struct stream { server_response & qr; @@ -34,7 +37,8 @@ struct server_tools { server_response queue_res; std::atomic res_id{0}; - void setup(const std::vector & enabled_tools); + void setup(const std::vector & enabled_tools, + server_mcp & mcp_mgr); server_http_context::handler_t handle_get; server_http_context::handler_t handle_post; diff --git a/tools/server/server.cpp b/tools/server/server.cpp index f1caf691ef26..4d119306ee74 100644 --- a/tools/server/server.cpp +++ b/tools/server/server.cpp @@ -88,6 +88,11 @@ static server_http_context::handler_t ex_wrapper(server_http_context::handler_t int llama_server(int argc, char ** argv) { std::setlocale(LC_NUMERIC, "C"); +#ifndef _WIN32 + // Ignore SIGPIPE so the server does not crash if an MCP child exits while we are writing to its stdin + signal(SIGPIPE, SIG_IGN); +#endif + // own arguments required by this example common_params params; @@ -175,6 +180,9 @@ int llama_server(common_params & params, int argc, char ** argv) { params.model_alias.insert(model_name); } + // note: this is guaranteed to out-live ctx_http and tools + server_mcp mcp_mgr; + // struct that contains llama context and inference server_context ctx_server; @@ -282,10 +290,8 @@ int llama_server(common_params & params, int argc, char ** argv) { ctx_http.get ("/slots", ex_wrapper(routes.get_slots)); ctx_http.post("/slots/:id_slot", ex_wrapper(routes.post_slots)); - // resumable streaming, the conversation_id is the session identity end to end. router and - // child wire different handlers under the same paths: a child binds the local session - // factories, the router binds proxies that resolve the owning child through the - // conv_id -> model map + // resumable streaming: a child binds the local session factories, the router binds + // proxies that resolve the owning child, see server-stream.h server_http_context::handler_t stream_get_h; server_http_context::handler_t streams_lookup_h; server_http_context::handler_t stream_delete_h; @@ -298,12 +304,9 @@ int llama_server(common_params & params, int argc, char ** argv) { streams_lookup_h = server_stream_make_lookup_handler(); stream_delete_h = server_stream_make_delete_handler(); } - ctx_http.get ("/v1/stream/:conv_id", ex_wrapper(stream_get_h)); - // POST /v1/streams/lookup with body {"conversation_ids": [...]}. you can only ask for ids - // you already own (the WebUI passes the convs visible in its sidebar). the server never - // lists ids it has not been asked about, so a random caller cannot enumerate live sessions + ctx_http.get ("/v1/stream", ex_wrapper(stream_get_h)); ctx_http.post("/v1/streams/lookup", ex_wrapper(streams_lookup_h)); - ctx_http.del ("/v1/stream/:conv_id", ex_wrapper(stream_delete_h)); + ctx_http.del ("/v1/stream", ex_wrapper(stream_delete_h)); // Google Cloud Platform (Vertex AI) compat ctx_http.register_gcp_compat(); @@ -344,17 +347,28 @@ int llama_server(common_params & params, int argc, char ** argv) { ctx_http.post("/cors-proxy", ex_wrapper(res_403)); } - // EXPERIMENTAL built-in tools - if (!params.server_tools.empty()) { + try { + mcp_mgr.start(params); + } catch (const std::exception & e) { + SRV_ERR("MCP starting failed: %s\n", e.what()); + return 1; + } + + if (!params.server_tools.empty() || !mcp_mgr.empty()) { try { - tools.setup(params.server_tools); + tools.setup(params.server_tools, mcp_mgr); } catch (const std::exception & e) { SRV_ERR("tools setup failed: %s\n", e.what()); return 1; } ctx_http.get ("/tools", ex_wrapper(tools.handle_get)); ctx_http.post("/tools", ex_wrapper(tools.handle_post)); - warn_names.push_back("built-in tools (experimental)"); + if (!params.server_tools.empty()) { + warn_names.push_back("built-in tools (experimental)"); + } + if (!mcp_mgr.empty()) { + warn_names.push_back("MCP servers (experimental)"); + } } else { ctx_http.get ("/tools", ex_wrapper(res_403)); ctx_http.post("/tools", ex_wrapper(res_403)); @@ -396,7 +410,7 @@ int llama_server(common_params & params, int argc, char ** argv) { if (is_router_server) { SRV_INF("%s", "starting server in router mode. models will be automatically loaded on-demand\n"); - clean_up = [&models_routes]() { + clean_up = [&models_routes, &mcp_mgr]() { SRV_INF("%s: cleaning up before exit...\n", __func__); // stop the session GC first, it finalizes live sessions and wakes pending readers server_stream_session_manager_stop(); @@ -404,6 +418,7 @@ int llama_server(common_params & params, int argc, char ** argv) { models_routes->stopping.store(true); // maybe redundant, but just to be safe models_routes->models.unload_all(); } + mcp_mgr.shutdown(); llama_backend_free(); }; @@ -419,17 +434,19 @@ int llama_server(common_params & params, int argc, char ** argv) { // important to disconnect any SSE clients models_routes->stopping.store(true); } + mcp_mgr.shutdown(); ctx_http.stop(); }; } else { // setup clean up function, to be called before exit - clean_up = [&ctx_http, &ctx_server]() { + clean_up = [&ctx_http, &ctx_server, &mcp_mgr]() { SRV_INF("%s: cleaning up before exit...\n", __func__); // stop the session GC first, it finalizes live sessions and wakes pending readers server_stream_session_manager_stop(); ctx_http.stop(); ctx_server.terminate(); + mcp_mgr.shutdown(); llama_backend_free(); }; @@ -462,6 +479,7 @@ int llama_server(common_params & params, int argc, char ** argv) { SRV_INF("%s", "model loaded\n"); shutdown_handler = [&](int) { + mcp_mgr.shutdown(); // this will unblock start_loop() ctx_server.terminate(); }; @@ -486,6 +504,13 @@ int llama_server(common_params & params, int argc, char ** argv) { SRV_INF("listening on %s\n", ctx_http.listening_address.c_str()); + // TODO: remove this in the future + // check the string to also handle the .sock case + if (string_ends_with(ctx_http.listening_address, ":8080")) { + SRV_WRN("%s", "NOTICE: server default port will be changed to :9931 in a future release\n"); + SRV_WRN("%s", " ref: https://github.com/ggml-org/llama.cpp/pull/26508\n"); + } + if (is_router_server) { if (!params.models_preset_hf.empty()) { SRV_WRN( "NOTE: using preset.ini from HF repo '%s'\n", params.models_preset_hf.c_str()); diff --git a/tools/server/tests/fixtures/mcp_burst_server.py b/tools/server/tests/fixtures/mcp_burst_server.py new file mode 100644 index 000000000000..22892d9a1d72 --- /dev/null +++ b/tools/server/tests/fixtures/mcp_burst_server.py @@ -0,0 +1,118 @@ +#!/usr/bin/env python3 +""" +Minimal MCP server that writes notification + response in a single write() with no flush. +This reproduces the buffering bug where read_message() can strand the response. +""" +import json +import sys +import os + +TOOLS = [ + { + "name": "echo", + "description": "Echo back the input message", + "inputSchema": { + "type": "object", + "properties": { + "message": {"type": "string"} + }, + "required": ["message"] + } + } +] + +def handle_initialize(params, req_id): + return { + "jsonrpc": "2.0", + "id": req_id, + "result": { + "protocolVersion": "2024-11-05", + "capabilities": {"tools": {}}, + "serverInfo": {"name": "burst-test", "version": "1.0"} + } + } + +def handle_tools_list(params, req_id): + return { + "jsonrpc": "2.0", + "id": req_id, + "result": {"tools": TOOLS} + } + +def handle_tools_call(params, req_id): + tool_name = params.get("name") + arguments = params.get("arguments", {}) + + if tool_name == "echo": + message = arguments.get("message", "") + notif = { + "jsonrpc": "2.0", + "method": "notifications/progress", + "params": {"progress": 50, "total": 100} + } + response = { + "jsonrpc": "2.0", + "id": req_id, + "result": { + "content": [{"type": "text", "text": f"echo: {message}"}] + } + } + # Single os.write() call: both lines land in one pipe packet atomically. + # This is the key difference from mcp_malformed_server.py which flushes between writes. + data = (json.dumps(notif) + "\n" + json.dumps(response) + "\n").encode("utf-8") + os.write(sys.stdout.fileno(), data) + return None # already written + else: + response = { + "jsonrpc": "2.0", + "id": req_id, + "error": {"code": -32602, "message": f"Unknown tool: {tool_name}"} + } + return response + +HANDLERS = { + "initialize": handle_initialize, + "tools/list": handle_tools_list, + "tools/call": handle_tools_call, +} + +def main(): + # Use line-buffered text mode for regular responses, but the burst write + # uses os.write() directly to guarantee a single kernel write(). + sys.stdout = os.fdopen(sys.stdout.fileno(), "w", buffering=1) + sys.stderr = os.fdopen(sys.stderr.fileno(), "w", buffering=1) + + for line in sys.stdin: + line = line.strip() + if not line: + continue + try: + request = json.loads(line) + except json.JSONDecodeError: + continue + + method = request.get("method") + req_id = request.get("id") + params = request.get("params", {}) + + # JSON-RPC 2.0: a message without an id is a notification and must not receive a response + if req_id is None: + continue + + handler = HANDLERS.get(method) + if handler: + response = handler(params, req_id) + if response is not None: + sys.stdout.write(json.dumps(response) + "\n") + sys.stdout.flush() + else: + response = { + "jsonrpc": "2.0", + "id": req_id, + "error": {"code": -32601, "message": f"Method not found: {method}"} + } + sys.stdout.write(json.dumps(response) + "\n") + sys.stdout.flush() + +if __name__ == "__main__": + main() diff --git a/tools/server/tests/fixtures/mcp_crash_server.py b/tools/server/tests/fixtures/mcp_crash_server.py new file mode 100644 index 000000000000..8dffdc61c0e9 --- /dev/null +++ b/tools/server/tests/fixtures/mcp_crash_server.py @@ -0,0 +1,114 @@ +#!/usr/bin/env python3 +""" +MCP server that crashes after receiving a specific tool call. +""" +import json +import sys +import os + +def handle_initialize(params, req_id): + return { + "jsonrpc": "2.0", + "id": req_id, + "result": { + "protocolVersion": "2024-11-05", + "capabilities": {"tools": {}}, + "serverInfo": {"name": "crash-test", "version": "1.0"} + } + } + +def handle_tools_list(params, req_id): + return { + "jsonrpc": "2.0", + "id": req_id, + "result": { + "tools": [ + { + "name": "echo", + "description": "Echo back the input message", + "inputSchema": { + "type": "object", + "properties": { + "message": {"type": "string"} + } + } + }, + { + "name": "crash", + "description": "Crash the server", + "inputSchema": { + "type": "object", + "properties": {} + } + } + ] + } + } + +def handle_tools_call(params, req_id): + tool_name = params.get("name") + arguments = params.get("arguments", {}) + + if tool_name == "echo": + message = arguments.get("message", "") + return { + "jsonrpc": "2.0", + "id": req_id, + "result": { + "content": [{"type": "text", "text": f"echo: {message}"}] + } + } + elif tool_name == "crash": + # Send a partial response then exit + sys.stdout.write(json.dumps({"jsonrpc": "2.0", "id": req_id, "result": {"content": [{"type": "text", "text": "crashing..."}]}}) + "\n") + sys.stdout.flush() + os._exit(1) + else: + return { + "jsonrpc": "2.0", + "id": req_id, + "error": {"code": -32602, "message": f"Unknown tool: {tool_name}"} + } + +HANDLERS = { + "initialize": handle_initialize, + "tools/list": handle_tools_list, + "tools/call": handle_tools_call, +} + +def main(): + sys.stdout = os.fdopen(sys.stdout.fileno(), "w", buffering=1) + sys.stderr = os.fdopen(sys.stderr.fileno(), "w", buffering=1) + + for line in sys.stdin: + line = line.strip() + if not line: + continue + try: + request = json.loads(line) + except json.JSONDecodeError: + continue + + method = request.get("method") + req_id = request.get("id") + params = request.get("params", {}) + + # JSON-RPC 2.0: a message without an id is a notification and must not receive a response + if req_id is None: + continue + + handler = HANDLERS.get(method) + if handler: + response = handler(params, req_id) + else: + response = { + "jsonrpc": "2.0", + "id": req_id, + "error": {"code": -32601, "message": f"Method not found: {method}"} + } + + sys.stdout.write(json.dumps(response) + "\n") + sys.stdout.flush() + +if __name__ == "__main__": + main() diff --git a/tools/server/tests/fixtures/mcp_echo_server.py b/tools/server/tests/fixtures/mcp_echo_server.py new file mode 100755 index 000000000000..7acfb358881a --- /dev/null +++ b/tools/server/tests/fixtures/mcp_echo_server.py @@ -0,0 +1,164 @@ +#!/usr/bin/env python3 +""" +Minimal MCP server for testing. +Implements JSON-RPC 2.0 over stdio (line-delimited JSON). +""" +import json +import sys +import os + +# Ensure we use python3 from the current environment +if sys.platform == "win32": + # On Windows, we need to use the same python interpreter + pass + +TOOLS = [ + { + "name": "echo", + "description": "Echo back the input message", + "inputSchema": { + "type": "object", + "properties": { + "message": {"type": "string", "description": "Message to echo"} + }, + "required": ["message"] + } + }, + { + "name": "add", + "description": "Add two numbers", + "inputSchema": { + "type": "object", + "properties": { + "a": {"type": "number"}, + "b": {"type": "number"} + }, + "required": ["a", "b"] + } + }, + { + "name": "fail_once", + "description": "Fails on first call, succeeds on subsequent calls", + "inputSchema": { + "type": "object", + "properties": {} + } + } +] + +_state = {"fail_once_called": False} + +def handle_initialize(params, req_id): + return { + "jsonrpc": "2.0", + "id": req_id, + "result": { + "protocolVersion": "2024-11-05", + "capabilities": {"tools": {}}, + "serverInfo": {"name": "echo-test", "version": "1.0"} + } + } + +def handle_tools_list(params, req_id): + return { + "jsonrpc": "2.0", + "id": req_id, + "result": {"tools": TOOLS} + } + +def handle_tools_call(params, req_id): + tool_name = params.get("name") + arguments = params.get("arguments", {}) + + if tool_name == "echo": + message = arguments.get("message", "") + return { + "jsonrpc": "2.0", + "id": req_id, + "result": { + "content": [{"type": "text", "text": f"echo: {message}"}] + } + } + elif tool_name == "add": + a = arguments.get("a", 0) + b = arguments.get("b", 0) + return { + "jsonrpc": "2.0", + "id": req_id, + "result": { + "content": [{"type": "text", "text": str(a + b)}] + } + } + elif tool_name == "fail_once": + if not _state["fail_once_called"]: + _state["fail_once_called"] = True + return { + "jsonrpc": "2.0", + "id": req_id, + "error": {"code": -32000, "message": "transient error"} + } + return { + "jsonrpc": "2.0", + "id": req_id, + "result": { + "content": [{"type": "text", "text": "ok"}] + } + } + else: + return { + "jsonrpc": "2.0", + "id": req_id, + "error": {"code": -32602, "message": f"Unknown tool: {tool_name}"} + } + +def handle_ping(params, req_id): + return { + "jsonrpc": "2.0", + "id": req_id, + "result": {} + } + +HANDLERS = { + "initialize": handle_initialize, + "tools/list": handle_tools_list, + "tools/call": handle_tools_call, + "ping": handle_ping, +} + +def main(): + # Use unbuffered output + sys.stdout = os.fdopen(sys.stdout.fileno(), "w", buffering=1) + sys.stderr = os.fdopen(sys.stderr.fileno(), "w", buffering=1) + + for line in sys.stdin: + line = line.strip() + if not line: + continue + try: + request = json.loads(line) + except json.JSONDecodeError: + continue + + method = request.get("method") + req_id = request.get("id") + params = request.get("params", {}) + + # JSON-RPC 2.0: a message without an id is a notification and must not receive a response + if req_id is None: + continue + + handler = HANDLERS.get(method) + if handler: + response = handler(params, req_id) + else: + response = { + "jsonrpc": "2.0", + "id": req_id, + "error": {"code": -32601, "message": f"Method not found: {method}"} + } + + sys.stdout.write(json.dumps(response) + "\n") + sys.stdout.flush() + +if __name__ == "__main__": + main() diff --git a/tools/server/tests/fixtures/mcp_grandchild_server.py b/tools/server/tests/fixtures/mcp_grandchild_server.py new file mode 100644 index 000000000000..2604a77ee80e --- /dev/null +++ b/tools/server/tests/fixtures/mcp_grandchild_server.py @@ -0,0 +1,100 @@ +#!/usr/bin/env python3 +""" +MCP server (NDJSON JSON-RPC over stdio) that spawns a long-lived grandchild which inherits +this process's stdin/stdout/stderr and keeps them open. + +This reproduces the reader-teardown deadlock: killing the direct MCP child (SIGKILL, which is +all subprocess_terminate() does) does NOT close the stdout/stderr pipe write ends, because the +grandchild still holds them. A server that reads those pipes with a blocking read would then +wait forever for an EOF that never arrives, hanging teardown (both warmup shutdown at startup +and process shutdown). The polled, running-aware reader must exit regardless. +""" +import json +import os +import subprocess +import sys + +# Spawn a grandchild that inherits our std handles (fds 0/1/2 = the MCP pipes) and lives well +# past any teardown in the tests. We do NOT redirect its stdio, so it keeps the pipe write ends +# open even after this process is killed. +subprocess.Popen([sys.executable, "-c", "import time; time.sleep(30)"]) + +TOOLS = [ + { + "name": "echo", + "description": "Echo back the input message", + "inputSchema": { + "type": "object", + "properties": {"message": {"type": "string", "description": "Message to echo"}}, + "required": ["message"], + }, + } +] + + +def handle_initialize(params, req_id): + return { + "jsonrpc": "2.0", + "id": req_id, + "result": { + "protocolVersion": "2024-11-05", + "capabilities": {"tools": {}}, + "serverInfo": {"name": "grandchild-test", "version": "1.0"}, + }, + } + + +def handle_tools_list(params, req_id): + return {"jsonrpc": "2.0", "id": req_id, "result": {"tools": TOOLS}} + + +def handle_tools_call(params, req_id): + if params.get("name") == "echo": + message = params.get("arguments", {}).get("message", "") + return { + "jsonrpc": "2.0", + "id": req_id, + "result": {"content": [{"type": "text", "text": f"echo: {message}"}]}, + } + return {"jsonrpc": "2.0", "id": req_id, "error": {"code": -32602, "message": "Unknown tool"}} + + +HANDLERS = { + "initialize": handle_initialize, + "tools/list": handle_tools_list, + "tools/call": handle_tools_call, +} + + +def main(): + sys.stdout = os.fdopen(sys.stdout.fileno(), "w", buffering=1) + sys.stderr = os.fdopen(sys.stderr.fileno(), "w", buffering=1) + + for line in sys.stdin: + line = line.strip() + if not line: + continue + try: + request = json.loads(line) + except json.JSONDecodeError: + continue + + method = request.get("method") + req_id = request.get("id") + params = request.get("params", {}) + + if req_id is None: + continue # notification, no response + + handler = HANDLERS.get(method) + if handler: + response = handler(params, req_id) + else: + response = {"jsonrpc": "2.0", "id": req_id, "error": {"code": -32601, "message": f"Method not found: {method}"}} + + sys.stdout.write(json.dumps(response) + "\n") + sys.stdout.flush() + + +if __name__ == "__main__": + main() diff --git a/tools/server/tests/fixtures/mcp_malformed_server.py b/tools/server/tests/fixtures/mcp_malformed_server.py new file mode 100644 index 000000000000..743333c5fdaa --- /dev/null +++ b/tools/server/tests/fixtures/mcp_malformed_server.py @@ -0,0 +1,113 @@ +#!/usr/bin/env python3 +""" +MCP server that sends malformed responses and notifications during requests. +""" +import json +import sys +import os + +def handle_initialize(params, req_id): + return { + "jsonrpc": "2.0", + "id": req_id, + "result": { + "protocolVersion": "2024-11-05", + "capabilities": {"tools": {}}, + "serverInfo": {"name": "malformed-test", "version": "1.0"} + } + } + +def handle_tools_list(params, req_id): + return { + "jsonrpc": "2.0", + "id": req_id, + "result": { + "tools": [ + { + "name": "echo", + "description": "Echo back the input message", + "inputSchema": { + "type": "object", + "properties": { + "message": {"type": "string"} + } + } + } + ] + } + } + +def handle_tools_call(params, req_id): + tool_name = params.get("name") + arguments = params.get("arguments", {}) + + if tool_name == "echo": + message = arguments.get("message", "") + # Send a notification first (no id field) + notif = { + "jsonrpc": "2.0", + "method": "notifications/progress", + "params": {"progress": 50, "total": 100} + } + sys.stdout.write(json.dumps(notif) + "\n") + sys.stdout.flush() + # Then send the actual response + return { + "jsonrpc": "2.0", + "id": req_id, + "result": { + "content": [{"type": "text", "text": f"echo: {message}"}] + } + } + else: + return { + "jsonrpc": "2.0", + "id": req_id, + "error": {"code": -32602, "message": f"Unknown tool: {tool_name}"} + } + +HANDLERS = { + "initialize": handle_initialize, + "tools/list": handle_tools_list, + "tools/call": handle_tools_call, +} + +def main(): + sys.stdout = os.fdopen(sys.stdout.fileno(), "w", buffering=1) + sys.stderr = os.fdopen(sys.stderr.fileno(), "w", buffering=1) + + for line in sys.stdin: + line = line.strip() + if not line: + continue + try: + request = json.loads(line) + except json.JSONDecodeError: + # Send malformed JSON response + sys.stdout.write("THIS IS NOT JSON\n") + sys.stdout.flush() + continue + + method = request.get("method") + req_id = request.get("id") + params = request.get("params", {}) + + # JSON-RPC 2.0: a message without an id is a notification and must not receive a response + if req_id is None: + continue + + handler = HANDLERS.get(method) + if handler: + response = handler(params, req_id) + else: + response = { + "jsonrpc": "2.0", + "id": req_id, + "error": {"code": -32601, "message": f"Method not found: {method}"} + } + + sys.stdout.write(json.dumps(response) + "\n") + sys.stdout.flush() + +if __name__ == "__main__": + main() diff --git a/tools/server/tests/fixtures/mcp_slow_server.py b/tools/server/tests/fixtures/mcp_slow_server.py new file mode 100644 index 000000000000..7f8e67835acc --- /dev/null +++ b/tools/server/tests/fixtures/mcp_slow_server.py @@ -0,0 +1,132 @@ +#!/usr/bin/env python3 +""" +MCP server that sleeps before responding, for timeout testing. +""" +import json +import sys +import os +import time +import argparse + +TOOLS = [ + { + "name": "sleep", + "description": "Sleep for a given number of seconds", + "inputSchema": { + "type": "object", + "properties": { + "seconds": {"type": "number", "description": "Seconds to sleep"} + }, + "required": ["seconds"] + } + } +] + +def handle_initialize(params, req_id): + return { + "jsonrpc": "2.0", + "id": req_id, + "result": { + "protocolVersion": "2024-11-05", + "capabilities": {"tools": {}}, + "serverInfo": {"name": "slow-test", "version": "1.0"} + } + } + +def handle_tools_list(params, req_id): + return { + "jsonrpc": "2.0", + "id": req_id, + "result": {"tools": TOOLS} + } + +def handle_tools_call(params, req_id): + tool_name = params.get("name") + arguments = params.get("arguments", {}) + + if tool_name == "sleep": + seconds = arguments.get("seconds", 1) + time.sleep(seconds) + return { + "jsonrpc": "2.0", + "id": req_id, + "result": { + "content": [{"type": "text", "text": f"slept {seconds}s"}] + } + } + else: + return { + "jsonrpc": "2.0", + "id": req_id, + "error": {"code": -32602, "message": f"Unknown tool: {tool_name}"} + } + +HANDLERS = { + "initialize": handle_initialize, + "tools/list": handle_tools_list, + "tools/call": handle_tools_call, +} + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--delay", type=float, default=5.0, help="Delay in seconds for sleep tool") + args = parser.parse_args() + + # Override the sleep duration + global handle_tools_call + def handle_tools_call(params, req_id): + tool_name = params.get("name") + arguments = params.get("arguments", {}) + + if tool_name == "sleep": + seconds = arguments.get("seconds", args.delay) + time.sleep(seconds) + return { + "jsonrpc": "2.0", + "id": req_id, + "result": { + "content": [{"type": "text", "text": f"slept {seconds}s"}] + } + } + else: + return { + "jsonrpc": "2.0", + "id": req_id, + "error": {"code": -32602, "message": f"Unknown tool: {tool_name}"} + } + + sys.stdout = os.fdopen(sys.stdout.fileno(), "w", buffering=1) + sys.stderr = os.fdopen(sys.stderr.fileno(), "w", buffering=1) + + for line in sys.stdin: + line = line.strip() + if not line: + continue + try: + request = json.loads(line) + except json.JSONDecodeError: + continue + + method = request.get("method") + req_id = request.get("id") + params = request.get("params", {}) + + # JSON-RPC 2.0: a message without an id is a notification and must not receive a response + if req_id is None: + continue + + handler = HANDLERS.get(method) + if handler: + response = handler(params, req_id) + else: + response = { + "jsonrpc": "2.0", + "id": req_id, + "error": {"code": -32601, "message": f"Method not found: {method}"} + } + + sys.stdout.write(json.dumps(response) + "\n") + sys.stdout.flush() + +if __name__ == "__main__": + main() diff --git a/tools/server/tests/unit/test_completion.py b/tools/server/tests/unit/test_completion.py index 1e0891987a9d..9375e0110e53 100644 --- a/tools/server/tests/unit/test_completion.py +++ b/tools/server/tests/unit/test_completion.py @@ -66,6 +66,8 @@ def test_completion_stream(prompt: str, n_predict: int, re_content: str, n_promp assert server.n_predict is not None assert data["generation_settings"]["n_predict"] == min(n_predict, server.n_predict) assert data["generation_settings"]["seed"] == server.seed + assert "adaptive_target" in data["generation_settings"] + assert "adaptive_decay" in data["generation_settings"] assert match_regex(re_content, content) else: assert len(data["tokens"]) > 0 diff --git a/tools/server/tests/unit/test_mcp_servers.py b/tools/server/tests/unit/test_mcp_servers.py new file mode 100644 index 000000000000..9ad2241bd029 --- /dev/null +++ b/tools/server/tests/unit/test_mcp_servers.py @@ -0,0 +1,718 @@ +#!/usr/bin/env python3 +""" +Tests for MCP server integration via the /tools endpoint. + +Invariants verified: +1. MCP tools appear in /tools listing when configured +2. MCP tools use _ naming +3. MCP tools can be invoked and return correct results +4. Misconfigured MCP servers do not crash the server +5. Multiple MCP servers can be configured simultaneously +6. Warmup populates the tool list at startup +""" +import json +import os +import sys +import tempfile +import time + +import pytest + +from utils import * + +# Path to the test MCP server fixture +FIXTURES_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "fixtures") +MCP_ECHO_SERVER = os.path.join(FIXTURES_DIR, "mcp_echo_server.py") + +server: ServerProcess + + +def _mcp_config_json(servers: dict) -> str: + """Create a JSON config string for --mcp-servers-json.""" + return json.dumps({"mcpServers": servers}) + + +def _start_server_with_mcp(mcp_json: str, **kwargs) -> ServerProcess: + """Helper to start a router server with MCP config.""" + srv = ServerPreset.router() + srv.server_tools = "all" + srv.no_ui = True + srv.server_port = 8085 # avoid conflict with load_all() which uses 8080 + srv.mcp_servers_json = mcp_json + for k, v in kwargs.items(): + setattr(srv, k, v) + srv.start() + return srv + + +def test_mcp_tools_listed_in_tools_endpoint(): + """MCP tools should appear in GET /tools with server:tool naming.""" + global server + mcp_json = _mcp_config_json({ + "echo": { + "command": sys.executable, + "args": [MCP_ECHO_SERVER], + } + }) + server = _start_server_with_mcp(mcp_json) + + try: + res = server.make_request("GET", "/tools") + assert res.status_code == 200, res.body + + tools = res.body + assert isinstance(tools, list), f"Expected list, got {type(tools)}" + + # Find MCP tools - name is in "tool" field or definition.function.name + def get_tool_name(t): + return t.get("tool", "") or t.get("definition", {}).get("function", {}).get("name", "") + + mcp_tools = [t for t in tools if get_tool_name(t).startswith("echo_")] + assert len(mcp_tools) >= 2, f"Expected at least 2 echo_ tools, got {len(mcp_tools)}: {mcp_tools}" + + tool_names = {get_tool_name(t) for t in mcp_tools} + assert "echo_echo" in tool_names + assert "echo_add" in tool_names + + # Verify tool structure + echo_tool = next(t for t in mcp_tools if get_tool_name(t) == "echo_echo") + assert "description" in echo_tool or "definition" in echo_tool + finally: + server.stop() + + +def test_mcp_tool_invocation(): + """MCP tools should be callable via POST /tools and return correct results.""" + global server + mcp_json = _mcp_config_json({ + "echo": { + "command": sys.executable, + "args": [MCP_ECHO_SERVER], + } + }) + server = _start_server_with_mcp(mcp_json) + + try: + # Call echo_echo + res = server.make_request("POST", "/tools", data={ + "tool": "echo_echo", + "params": {"message": "hello world"} + }) + assert res.status_code == 200, res.body + body = res.body + assert "error" not in body, body + # The result format depends on the tool implementation + # For MCP tools, it should contain the tool result + assert "plain_text_response" in body or "result" in body or "content" in body, body + + # Call echo_add + res = server.make_request("POST", "/tools", data={ + "tool": "echo_add", + "params": {"a": 3, "b": 5} + }) + assert res.status_code == 200, res.body + body = res.body + assert "error" not in body, body + finally: + server.stop() + + +def test_mcp_bad_command_does_not_crash(): + """A misconfigured MCP server should not crash the llama-server.""" + global server + mcp_json = _mcp_config_json({ + "nonexistent": { + "command": "this_executable_does_not_exist_12345", + "args": [], + } + }) + server = _start_server_with_mcp(mcp_json) + + try: + # Server should still be healthy + res = server.make_request("GET", "/health") + assert res.status_code == 200, res.body + + # Builtin tools should still work + res = server.make_request("GET", "/tools") + assert res.status_code == 200, res.body + tools = res.body + # Should have builtin tools but no MCP tools from the bad server + mcp_tools = [t for t in tools if t.get("name", "").startswith("nonexistent_")] + assert len(mcp_tools) == 0, f"Expected no nonexistent_ tools, got {mcp_tools}" + finally: + server.stop() + + +def test_mcp_multiple_servers(): + """Multiple MCP servers can be configured simultaneously.""" + global server + mcp_json = _mcp_config_json({ + "echo": { + "command": sys.executable, + "args": [MCP_ECHO_SERVER], + }, + "echo2": { + "command": sys.executable, + "args": [MCP_ECHO_SERVER], + } + }) + server = _start_server_with_mcp(mcp_json) + + try: + res = server.make_request("GET", "/tools") + assert res.status_code == 200, res.body + + tools = res.body + + def get_tool_name(t): + return t.get("tool", "") or t.get("definition", {}).get("function", {}).get("name", "") + + echo_tools = [t for t in tools if get_tool_name(t).startswith("echo_")] + echo2_tools = [t for t in tools if get_tool_name(t).startswith("echo2_")] + + assert len(echo_tools) >= 2, f"Expected echo_ tools, got {echo_tools}" + assert len(echo2_tools) >= 2, f"Expected echo2_ tools, got {echo2_tools}" + finally: + server.stop() + + +def test_mcp_tools_not_listed_when_not_configured(): + """Without MCP config, no MCP tools should appear.""" + global server + server = ServerPreset.router() + server.server_tools = "all" + server.no_ui = True + server.server_port = 8085 + server.start() + + try: + res = server.make_request("GET", "/tools") + assert res.status_code == 200, res.body + + tools = res.body + + def get_tool_name(t): + return t.get("tool", "") or t.get("definition", {}).get("function", {}).get("name", "") + + # Should only have builtin tools, no server: prefixed tools + mcp_tools = [t for t in tools if ":" in get_tool_name(t)] + assert len(mcp_tools) == 0, f"Expected no MCP tools, got {mcp_tools}" + finally: + server.stop() + + +def test_mcp_fail_once_tool_eventual_success(): + """Test that a tool that fails once eventually succeeds (tests instance respawn).""" + global server + mcp_json = _mcp_config_json({ + "echo": { + "command": sys.executable, + "args": [MCP_ECHO_SERVER], + } + }) + server = _start_server_with_mcp(mcp_json) + + try: + # First call should succeed (warmup already spawned and shut down the instance, + # but the first actual tool call will spawn a fresh instance) + res = server.make_request("POST", "/tools", data={ + "tool": "echo_fail_once", + "params": {} + }) + # It might fail on first call if the warmup instance was shut down + # and a new instance is spawned. The fail_once state is per-process, + # so a fresh process will fail once then succeed. + # Actually, warmup spawns, lists, then shuts down. So the first tool call + # spawns a new process which will fail once. + assert res.status_code in (200, 500), res.body + finally: + server.stop() + + +def test_mcp_tools_via_json_config_file(): + """Test that --mcp-servers-config (file) works as well as --mcp-servers-json.""" + global server + config = { + "mcpServers": { + "echo": { + "command": sys.executable, + "args": [MCP_ECHO_SERVER], + } + } + } + + with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as f: + json.dump(config, f) + config_path = f.name + + try: + server = ServerPreset.router() + server.server_tools = "all" + server.no_ui = True + server.server_port = 8085 + server.mcp_servers_config = config_path + server.start() + + res = server.make_request("GET", "/tools") + assert res.status_code == 200, res.body + + tools = res.body + + def get_tool_name(t): + return t.get("tool", "") or t.get("definition", {}).get("function", {}).get("name", "") + + mcp_tools = [t for t in tools if get_tool_name(t).startswith("echo_")] + assert len(mcp_tools) >= 2, f"Expected echo_ tools, got {mcp_tools}" + finally: + os.unlink(config_path) + server.stop() + + +def test_mcp_tools_slot_independent(): + """MCP tools should work without any slot concept; /tools is slot-independent.""" + global server + mcp_json = _mcp_config_json({ + "echo": { + "command": sys.executable, + "args": [MCP_ECHO_SERVER], + } + }) + server = _start_server_with_mcp(mcp_json) + + try: + # Call /tools without any slot binding - should succeed + res = server.make_request("POST", "/tools", data={ + "tool": "echo_echo", + "params": {"message": "hello"} + }) + assert res.status_code == 200, res.body + body = res.body + assert "error" not in body, body + finally: + server.stop() + + +def test_mcp_concurrent_tool_calls(): + """Concurrent POST /tools to same MCP server should all succeed.""" + global server + mcp_json = _mcp_config_json({ + "echo": { + "command": sys.executable, + "args": [MCP_ECHO_SERVER], + } + }) + server = _start_server_with_mcp(mcp_json) + + try: + def call_tool(): + return server.make_request("POST", "/tools", data={ + "tool": "echo_echo", + "params": {"message": "hi"} + }) + + with ThreadPoolExecutor(max_workers=10) as executor: + futures = [executor.submit(call_tool) for _ in range(10)] + results = [f.result() for f in futures] + + for res in results: + assert res.status_code == 200, res.body + assert "error" not in res.body, res.body + finally: + server.stop() + + +def test_mcp_tool_timeout(): + """Tool call should timeout if MCP server is too slow.""" + global server + MCP_SLOW_SERVER = os.path.join(FIXTURES_DIR, "mcp_slow_server.py") + mcp_json = _mcp_config_json({ + "slow": { + "command": sys.executable, + "args": [MCP_SLOW_SERVER, "--delay", "5"], + "timeout_ms": 500 + } + }) + server = _start_server_with_mcp(mcp_json) + + try: + res = server.make_request("POST", "/tools", data={ + "tool": "slow_sleep", + "params": {"seconds": 5} + }) + assert res.status_code == 200, res.body + body = res.body + assert "error" in body, body + finally: + server.stop() + + +def test_mcp_warmup_partial_failure(): + """Good server's tools should appear even if bad server fails warmup.""" + global server + mcp_json = _mcp_config_json({ + "good": { + "command": sys.executable, + "args": [MCP_ECHO_SERVER], + }, + "bad": { + "command": "nonexistent", + "args": [] + } + }) + server = _start_server_with_mcp(mcp_json) + + try: + res = server.make_request("GET", "/tools") + assert res.status_code == 200, res.body + tools = res.body + + def get_tool_name(t): + return t.get("tool", "") or t.get("definition", {}).get("function", {}).get("name", "") + + # good server tools should be present + assert any("good_" in get_tool_name(t) for t in tools), f"Expected good: tools in {tools}" + finally: + server.stop() + + +def test_mcp_notification_during_request(): + """Notification during request should not be returned as response.""" + global server + MCP_MALFORMED_SERVER = os.path.join(FIXTURES_DIR, "mcp_malformed_server.py") + mcp_json = _mcp_config_json({ + "notifying": { + "command": sys.executable, + "args": [MCP_MALFORMED_SERVER], + } + }) + server = _start_server_with_mcp(mcp_json) + + try: + res = server.make_request("POST", "/tools", data={ + "tool": "notifying_echo", + "params": {"message": "hi"} + }) + assert res.status_code == 200, res.body + body = res.body + assert "error" not in body, body + finally: + server.stop() + + +def test_mcp_instance_respawn_after_crash(): + """Tool call after process crash should respawn and succeed.""" + global server + MCP_CRASH_SERVER = os.path.join(FIXTURES_DIR, "mcp_crash_server.py") + mcp_json = _mcp_config_json({ + "crash": { + "command": sys.executable, + "args": [MCP_CRASH_SERVER], + } + }) + server = _start_server_with_mcp(mcp_json) + + try: + # First call succeeds + res1 = server.make_request("POST", "/tools", data={ + "tool": "crash_echo", + "params": {"message": "hi"} + }) + assert res1.status_code == 200, res1.body + assert "error" not in res1.body, res1.body + + # Second call should also succeed (respawned instance) + res2 = server.make_request("POST", "/tools", data={ + "tool": "crash_echo", + "params": {"message": "hi2"} + }) + assert res2.status_code == 200, res2.body + assert "error" not in res2.body, res2.body + finally: + server.stop() + + + + +def test_mcp_fail_once_eventual_success_verified(): + """Verify that fail_once tool eventually succeeds after respawn.""" + global server + mcp_json = _mcp_config_json({ + "echo": { + "command": sys.executable, + "args": [MCP_ECHO_SERVER], + } + }) + server = _start_server_with_mcp(mcp_json) + + try: + # First call may fail (fresh process) + res1 = server.make_request("POST", "/tools", data={ + "tool": "echo_fail_once", + "params": {} + }) + # Second call should succeed + res2 = server.make_request("POST", "/tools", data={ + "tool": "echo_fail_once", + "params": {} + }) + assert res2.status_code == 200, res2.body + assert "error" not in res2.body, res2.body + finally: + server.stop() + + +def test_mcp_config_file_errors(): + """Invalid JSON config and missing file should cause server to fail to start.""" + # Invalid JSON - server should fail to start + server = ServerPreset.router() + server.server_tools = "all" + server.no_ui = True + server.server_port = 8085 + server.mcp_servers_json = "not valid json" + try: + server.start() + assert False, "Server should not have started with invalid MCP JSON config" + except RuntimeError: + pass # Expected: server process dies due to bad config + + # Missing file - server should fail to start + server = ServerPreset.router() + server.server_tools = "all" + server.no_ui = True + server.server_port = 8085 + server.mcp_servers_config = "/nonexistent/path.json" + try: + server.start() + assert False, "Server should not have started with missing config file" + except RuntimeError: + pass # Expected: server process dies due to missing config + + +def test_mcp_empty_tool_list(): + """MCP server reporting zero tools should result in empty tool list.""" + global server + # Create a minimal server that returns empty tools list + empty_server = os.path.join(FIXTURES_DIR, "_empty_mcp_server.py") + with open(empty_server, "w") as f: + f.write('''#!/usr/bin/env python3 +import json, sys, os +def main(): + sys.stdout = os.fdopen(sys.stdout.fileno(), "w", buffering=1) + for line in sys.stdin: + line = line.strip() + if not line: continue + try: request = json.loads(line) + except: continue + method = request.get("method") + req_id = request.get("id") + if method == "initialize": + resp = {"jsonrpc": "2.0", "id": req_id, "result": {"protocolVersion": "2024-11-05", "capabilities": {"tools": {}}, "serverInfo": {"name": "empty", "version": "1.0"}}} + elif method == "tools/list": + resp = {"jsonrpc": "2.0", "id": req_id, "result": {"tools": []}} + else: + resp = {"jsonrpc": "2.0", "id": req_id, "error": {"code": -32601, "message": "Method not found"}} + sys.stdout.write(json.dumps(resp) + "\\n") + sys.stdout.flush() +if __name__ == "__main__": + main() +''') + try: + mcp_json = _mcp_config_json({ + "empty": { + "command": sys.executable, + "args": [empty_server], + } + }) + server = _start_server_with_mcp(mcp_json) + res = server.make_request("GET", "/tools") + assert res.status_code == 200, res.body + tools = res.body + def get_tool_name(t): + return t.get("tool", "") or t.get("definition", {}).get("function", {}).get("name", "") + mcp_tools = [t for t in tools if get_tool_name(t).startswith("empty:")] + assert len(mcp_tools) == 0, f"Expected no empty: tools, got {mcp_tools}" + finally: + os.unlink(empty_server) + server.stop() + + +def test_mcp_rapid_succession_calls(): + """Many rapid calls should increment next_id correctly and correlate responses.""" + global server + mcp_json = _mcp_config_json({ + "echo": { + "command": sys.executable, + "args": [MCP_ECHO_SERVER], + } + }) + server = _start_server_with_mcp(mcp_json) + + try: + for i in range(20): + res = server.make_request("POST", "/tools", data={ + "tool": "echo_echo", + "params": {"message": f"msg{i}"} + }) + assert res.status_code == 200, res.body + assert "error" not in res.body, res.body + finally: + server.stop() + + +def test_mcp_notification_burst(): + """Notification + response in a single write() with no flush should not strand the response.""" + global server + MCP_BURST_SERVER = os.path.join(FIXTURES_DIR, "mcp_burst_server.py") + mcp_json = _mcp_config_json({ + "burst": { + "command": sys.executable, + "args": [MCP_BURST_SERVER], + } + }) + server = _start_server_with_mcp(mcp_json) + + try: + res = server.make_request("POST", "/tools", data={ + "tool": "burst_echo", + "params": {"message": "burst test"} + }) + assert res.status_code == 200, res.body + body = res.body + assert "error" not in body, body + finally: + server.stop() + + +def test_mcp_tool_definition_shape_via_chat_completions(): + """MCP tool definitions returned by GET /tools should have the correct shape for chat/completions.""" + global server + mcp_json = _mcp_config_json({ + "echo": { + "command": sys.executable, + "args": [MCP_ECHO_SERVER], + } + }) + server = _start_server_with_mcp(mcp_json) + + try: + # Get MCP tool definitions + res = server.make_request("GET", "/tools") + assert res.status_code == 200, res.body + tools = res.body + + def get_tool_name(t): + return t.get("tool", "") or t.get("definition", {}).get("function", {}).get("name", "") + + echo_tools = [t for t in tools if get_tool_name(t).startswith("echo_")] + assert len(echo_tools) >= 2, f"Expected echo_ tools, got {echo_tools}" + + echo_tool = next(t for t in echo_tools if get_tool_name(t) == "echo_echo") + definition = echo_tool.get("definition", echo_tool) + + # Verify the definition has the standard function-calling shape + assert definition.get("type") == "function", f"Expected type=function, got {definition.get('type')}" + func = definition.get("function", {}) + assert "name" in func, "Missing function.name" + assert "description" in func, "Missing function.description" + assert "parameters" in func, f"Missing function.parameters, got keys: {list(func.keys())}" + params = func["parameters"] + assert params.get("type") == "object", f"Expected parameters.type=object, got {params.get('type')}" + assert "properties" in params, "Missing parameters.properties" + finally: + server.stop() + + +def test_mcp_slow_tool_call_slot_release(): + """A slow tool call should not stall server shutdown for the full I/O timeout.""" + global server + MCP_SLOW_SERVER = os.path.join(FIXTURES_DIR, "mcp_slow_server.py") + mcp_json = _mcp_config_json({ + "slow": { + "command": sys.executable, + "args": [MCP_SLOW_SERVER, "--delay", "10"], + "timeout_ms": 30000 + } + }) + server = _start_server_with_mcp(mcp_json) + + try: + # Start a slow tool call in a background thread + def slow_call(): + return server.make_request("POST", "/tools", data={ + "tool": "slow_sleep", + "params": {"seconds": 10} + }) + + with ThreadPoolExecutor(max_workers=1) as executor: + future = executor.submit(slow_call) + + # Wait a moment for the call to start + time.sleep(2) + + # Stop the server while the tool call is in progress. + # With global MCP instances, close_all() is called explicitly at shutdown + # (not from slot release), so shutdown should complete promptly. + start_time = time.time() + server.stop() + elapsed = time.time() - start_time + + # The server should stop quickly, not wait for the full 30s I/O timeout. + # With the terminating flag, send_rpc() bails out within one select() + # slice (~50ms). This threshold MUST stay below the 5s force-kill + # fallback in ServerProcess.stop(): without the flag, shutdown stalls + # on the instance mutex and only completes when stop() sends SIGKILL + # at ~5s -- which any threshold above 5 would still accept. + assert elapsed < 3, f"Server stop took {elapsed:.1f}s, expected < 3s" + + # Wait for the future to complete (it will get an error response or timeout) + try: + res = future.result(timeout=5) + # If we got a response, it should be an error since the server stopped + if hasattr(res, 'status_code'): + assert res.status_code in (200, 500, 502, 503, 504), f"Unexpected status: {res.status_code}" + except Exception: + # Thread may have raised due to connection error - that's acceptable + pass + finally: + server.stop() + + +def test_mcp_grandchild_holding_pipes_does_not_deadlock(): + """An MCP server that leaves a grandchild inheriting its stdout/stderr must not deadlock + teardown. + + subprocess_terminate() only SIGKILLs the direct MCP child, so the inherited pipe write ends + stay open and a blocking read on them would never see EOF. That hung both warmup shutdown + (the server would never reach "ready") and process shutdown. The polled, running-aware reader + must exit regardless, so the server both starts and stops promptly here. + """ + global server + MCP_GRANDCHILD_SERVER = os.path.join(FIXTURES_DIR, "mcp_grandchild_server.py") + mcp_json = _mcp_config_json({ + "gc": { + "command": sys.executable, + "args": [MCP_GRANDCHILD_SERVER], + } + }) + + # If warmup teardown deadlocked, the server would never become ready and start() would time out. + server = _start_server_with_mcp(mcp_json) + + try: + # invoking the tool spawns a live transport whose reader thread holds the inherited pipe + res = server.make_request("POST", "/tools", data={ + "tool": "gc_echo", + "params": {"message": "hello"} + }) + assert res.status_code == 200, res.body + assert "error" not in res.body, res.body + + # shutdown must be prompt: a deadlocked reader-join would stall until the 5s SIGKILL + # fallback in ServerProcess.stop(), so the threshold has to stay below that + start = time.time() + server.stop() + elapsed = time.time() - start + assert elapsed < 3, f"server shutdown took {elapsed:.1f}s (expected < 3s) — teardown likely deadlocked" + finally: + server.stop() diff --git a/tools/server/tests/unit/test_slot_save.py b/tools/server/tests/unit/test_slot_save.py index 1b428cc2a840..be22d9859efd 100644 --- a/tools/server/tests/unit/test_slot_save.py +++ b/tools/server/tests/unit/test_slot_save.py @@ -1,5 +1,7 @@ import pytest from utils import * +import base64 +import requests server = ServerPreset.tinyllama2() @@ -96,3 +98,127 @@ def test_slot_erase(): assert res.status_code == 200 assert match_regex("(Whiskers|Flana)+", res.body["content"]) assert res.body["timings"]["prompt_n"] == 21 # all tokens are processed + + +# +# Multimodal server (mmproj loaded) slot save/restore. +# +# Regression coverage for issue #21133: slot save/restore/erase must be gated on +# the slot's CONTENT (does it actually hold image/audio tokens) rather than the +# model's CAPABILITY (is an mmproj loaded). A pure-text slot on a multimodal +# server must save/restore/erase normally; a slot that actually holds an image +# must be rejected with ERROR_TYPE_NOT_SUPPORTED (HTTP 501). +# + +IMG_URL_CAT = "https://huggingface.co/ggml-org/tinygemma3-GGUF/resolve/main/test/91_cat.png" + + +def _get_img_base64(url: str) -> str: + response = requests.get(url) + response.raise_for_status() # Raise an exception for bad status codes + return base64.b64encode(response.content).decode("utf-8") + + +@pytest.fixture +def mmproj_server(): + # tinygemma3 is a small multimodal model: the mmproj is provided by the HF + # registry API and auto-downloaded on first run. + os.environ['LLAMA_MEDIA_MARKER'] = '<__media__>' + mm_server = ServerPreset.tinygemma3() + mm_server.slot_save_path = "./tmp" + mm_server.temperature = 0.0 + return mm_server + + +def test_slot_save_restore_text_only_on_multimodal(mmproj_server): + server = mmproj_server + server.start() + + # A pure-text prompt processed on slot 1 of a multimodal server. + res = server.make_request("POST", "/completion", data={ + "prompt": "The quick brown fox jumps over the lazy dog.", + "id_slot": 1, + "cache_prompt": True, + }) + assert res.status_code == 200 + prompt_n = res.body["timings"]["prompt_n"] + assert prompt_n > 0 # all tokens are processed + + # Saving a pure-text slot must succeed even though an mmproj is loaded. + res = server.make_request("POST", "/slots/1?action=save", data={ + "filename": "mm_slot1.bin", + }) + assert res.status_code == 200 + n_saved = res.body["n_saved"] + assert n_saved > 0 # the slot KV (prompt + generated tokens) was written + + # Restore the saved state into slot 0; it must round-trip exactly. + res = server.make_request("POST", "/slots/0?action=restore", data={ + "filename": "mm_slot1.bin", + }) + assert res.status_code == 200 + assert res.body["n_restored"] == n_saved + + # The restored slot is usable for a follow-up completion. We do NOT assert + # prefix reuse here: tinygemma3 is a SWA model, which forces full prompt + # re-processing after a restore (a model property, not the save/restore gate + # under test). + res = server.make_request("POST", "/completion", data={ + "prompt": "The quick brown fox jumps over the lazy dog.", + "id_slot": 0, + "cache_prompt": True, + }) + assert res.status_code == 200 + + +def test_slot_save_rejected_when_slot_holds_image(mmproj_server): + server = mmproj_server + server.start() + + # Process a prompt that actually contains an image on slot 1. + res = server.make_request("POST", "/completions", data={ + "temperature": 0.0, + "top_k": 1, + "id_slot": 1, + "cache_prompt": True, + "prompt": { + "prompt_string": "What is this: <__media__>\n", + "multimodal_data": [ _get_img_base64(IMG_URL_CAT) ], + }, + }) + assert res.status_code == 200 + + # Saving a slot that holds image tokens must be rejected (HTTP 501, + # not_supported_error). + res = server.make_request("POST", "/slots/1?action=save", data={ + "filename": "mm_slot_image.bin", + }) + assert res.status_code != 200 + assert res.body["error"]["type"] == "not_supported_error" + + +def test_slot_erase_text_only_on_multimodal(mmproj_server): + server = mmproj_server + server.start() + + res = server.make_request("POST", "/completion", data={ + "prompt": "The quick brown fox jumps over the lazy dog.", + "id_slot": 1, + "cache_prompt": True, + }) + assert res.status_code == 200 + prompt_n = res.body["timings"]["prompt_n"] + assert prompt_n > 0 # all tokens are processed + + # Erasing a pure-text slot must succeed even though an mmproj is loaded. + res = server.make_request("POST", "/slots/1?action=erase") + assert res.status_code == 200 + + # Re-running the same prompt should process all tokens again. + res = server.make_request("POST", "/completion", data={ + "prompt": "The quick brown fox jumps over the lazy dog.", + "id_slot": 1, + "cache_prompt": True, + }) + assert res.status_code == 200 + assert res.body["timings"]["prompt_n"] == prompt_n # all tokens are processed again diff --git a/tools/server/tests/unit/test_speculative.py b/tools/server/tests/unit/test_speculative.py index 84cd77e6f2ed..c6568479ca4a 100644 --- a/tools/server/tests/unit/test_speculative.py +++ b/tools/server/tests/unit/test_speculative.py @@ -12,8 +12,9 @@ def create_server(): server = ServerPreset.stories15m_moe() # set default values server.model_draft = download_file(MODEL_DRAFT_FILE_URL) - server.draft_min = 4 - server.draft_max = 8 + server.spec_type = "draft-simple" + server.spec_draft_n_min = 4 + server.spec_draft_n_max = 8 server.fa = "off" @@ -25,6 +26,7 @@ def fixture_create_server(): def test_with_and_without_draft(): global server server.model_draft = None # disable draft model + server.spec_type = None server.start() res = server.make_request("POST", "/completion", data={ "prompt": "I believe the meaning of life is", @@ -46,6 +48,7 @@ def test_with_and_without_draft(): "n_predict": 16, }) assert res.status_code == 200 + assert res.body["timings"]["draft_n"] > 0 content_draft = res.body["content"] assert content_no_draft == content_draft @@ -63,8 +66,8 @@ def test_different_draft_min_draft_max(): last_content = None for draft_min, draft_max in test_values: server.stop() - server.draft_min = draft_min - server.draft_max = draft_max + server.spec_draft_n_min = draft_min + server.spec_draft_n_max = draft_max server.start() res = server.make_request("POST", "/completion", data={ "prompt": "I believe the meaning of life is", diff --git a/tools/server/tests/unit/test_stream.py b/tools/server/tests/unit/test_stream.py new file mode 100644 index 000000000000..a1ef55567bc7 --- /dev/null +++ b/tools/server/tests/unit/test_stream.py @@ -0,0 +1,153 @@ +import json +import socket +import threading +import time +from urllib.parse import quote +import pytest +from utils import * + +server: ServerProcess + +# a model name with slashes exercises the query string routing of the stream routes: the id +# cannot travel as a path param because the decoded slash would split it before capture +MODEL = "ggml-org/tinygemma3-GGUF:Q8_0" +STREAM_ID = f"conv-stream-test::{MODEL}" +QS = "conv_id=" + quote(STREAM_ID, safe="") + + +@pytest.fixture(autouse=True) +def create_server(): + global server + server = ServerPreset.router() + + +def test_stream_resume_and_stop_with_slashed_model_name(): + global server + server.start() + + content = "" + for data in server.make_stream_request("POST", "/chat/completions", data={ + "model": MODEL, + "stream": True, + "max_tokens": 16, + "messages": [{"role": "user", "content": "hello"}], + }, headers={"X-Conversation-Id": STREAM_ID}): + if data["choices"]: + content += data["choices"][0]["delta"].get("content") or "" + assert len(content) > 0 + + # the finished session replays from the beginning through the router + res = server.make_request("GET", f"/v1/stream?{QS}&from=0") + assert res.status_code == 200 + assert "data: " in str(res.body) + + # the explicit stop reaches the owning child and evicts the session + res = server.make_request("DELETE", f"/v1/stream?{QS}") + assert res.status_code == 204 + res = server.make_request("GET", f"/v1/stream?{QS}&from=0") + assert res.status_code == 404 + + +def test_stream_stop_during_model_load(): + global server + server.start() + + thread_error: list[ServerError] = [] + thread_done = threading.Event() + + def fire_post(): + try: + for _ in server.make_stream_request("POST", "/chat/completions", data={ + "model": MODEL, + "stream": True, + "max_tokens": 512, + "messages": [{"role": "user", "content": "Count from 1 to 1000."}], + }, headers={"X-Conversation-Id": STREAM_ID}): + pass + except ServerError as e: + thread_error.append(e) + finally: + thread_done.set() + + t = threading.Thread(target=fire_post) + t.start() + + # catch the autoload window, tiny models load fast so poll aggressively + saw_loading = False + deadline = time.time() + 5.0 + while time.time() < deadline and not thread_done.is_set(): + res = server.make_request("GET", "/models") + status = next(m["status"]["value"] for m in res.body["data"] if m["id"] == MODEL) + if status == "loading": + saw_loading = True + break + time.sleep(0.002) + if not saw_loading: + t.join() + pytest.skip("load window too short to be observed on this machine") # ty: ignore[too-many-positional-arguments] + + # a stop during the load cancels the parked request instead of leaving an orphan + res = server.make_request("DELETE", f"/v1/stream?{QS}") + assert res.status_code == 204 + assert thread_done.wait(timeout=60) + t.join() + assert len(thread_error) == 1 + assert thread_error[0].code == 400 + assert "cancelled" in json.dumps(thread_error[0].body) + res = server.make_request("GET", f"/v1/stream?{QS}&from=0") + assert res.status_code == 404 + + +def test_stream_resumes_after_reload_during_model_load(): + global server + server.start() + + # raw socket client so the connection can be dropped mid load like a page reload + body = json.dumps({ + "model": MODEL, + "stream": True, + "max_tokens": 16, + "messages": [{"role": "user", "content": "hello"}], + }) + request = ( + f"POST /v1/chat/completions HTTP/1.1\r\n" + f"Host: {server.server_host}:{server.server_port}\r\n" + f"Content-Type: application/json\r\n" + f"X-Conversation-Id: {STREAM_ID}\r\n" + f"Content-Length: {len(body)}\r\n" + f"Connection: close\r\n\r\n{body}" + ) + sock = socket.create_connection((server.server_host, server.server_port)) + sock.sendall(request.encode()) + + # drop the client while the model loads, poll aggressively to catch the window + saw_loading = False + saw_503 = False + deadline = time.time() + 5.0 + while time.time() < deadline: + res = server.make_request("GET", "/models") + status = next(m["status"]["value"] for m in res.body["data"] if m["id"] == MODEL) + if status == "loading": + saw_loading = True + break + if status == "loaded": + break + time.sleep(0.002) + sock.close() + if not saw_loading: + pytest.skip("load window too short to be observed on this machine") # ty: ignore[too-many-positional-arguments] + + # while the model loads the resume route answers retry later, then the session appears, + # receives the whole generation despite the dead client, and replays from the beginning + deadline = time.time() + 60.0 + replay = None + while time.time() < deadline: + res = server.make_request("GET", f"/v1/stream?{QS}&from=0") + if res.status_code == 503: + saw_503 = True + elif res.status_code == 200 and "data: " in str(res.body): + replay = res + break + time.sleep(0.1) + assert saw_503, "resume during the load did not answer 503" + assert replay is not None, "session never became resumable after the client disconnect" diff --git a/tools/server/tests/unit/test_tools_builtin.py b/tools/server/tests/unit/test_tools_builtin.py index 1b2d0db43231..fb194cac669a 100755 --- a/tools/server/tests/unit/test_tools_builtin.py +++ b/tools/server/tests/unit/test_tools_builtin.py @@ -19,8 +19,8 @@ def create_server(): server.server_tools = "all" -def call_tool(name: str, params: dict) -> dict: - res = server.make_request("POST", "/tools", data={"tool": name, "params": params}) +def call_tool(name: str, params: dict, headers: dict | None = None) -> dict: + res = server.make_request("POST", "/tools", data={"tool": name, "params": params}, headers=headers) assert res.status_code == 200, res.body assert "error" not in res.body, res.body return res.body @@ -123,6 +123,29 @@ def test_tools_builtin_exec_shell_command_stream(): assert "[exit code: 0]" in chunks +def test_tools_builtin_cwd_header(): + global server + server.start() + + cwd_dir = os.path.join(PROJECT_ROOT, "tools", "server", "tests", "unit") + headers = {"x-tool-cwd": cwd_dir} + + res = call_tool("read_file", {"path": "test_tools_builtin.py"}, headers=headers) + assert GREP_MARKER in res["plain_text_response"] + + # exec_shell_command should also run with that directory as its working directory: + # writing to a relative filename must land inside cwd_dir + marker_name = "llama_cpp_test_tools_builtin_cwd_marker.txt" + marker_path = os.path.join(cwd_dir, marker_name) + try: + command = f"echo hello > {marker_name}" + call_tool("exec_shell_command", {"command": command}, headers=headers) + assert os.path.exists(marker_path) + finally: + if os.path.exists(marker_path): + os.remove(marker_path) + + def test_tools_builtin_edit_file_rejects_overlapping_edits(): global server server.start() diff --git a/tools/server/tests/utils.py b/tools/server/tests/utils.py index f4f0e61e6106..ae56bc70a15a 100644 --- a/tools/server/tests/utils.py +++ b/tools/server/tests/utils.py @@ -95,6 +95,7 @@ class ServerProcess: no_models_autoload: bool | None = None lora_files: List[str] | None = None enable_ctx_shift: int | None = False + spec_type: str | None = None spec_draft_n_min: int | None = None spec_draft_n_max: int | None = None no_ui: bool | None = None @@ -114,6 +115,8 @@ class ServerProcess: backend_sampling: bool = False gcp_compat: bool = False server_tools: str | None = None + mcp_servers_config: str | None = None + mcp_servers_json: str | None = None cors_origins: str | None = None # session variables @@ -226,6 +229,8 @@ def start(self, timeout_seconds: int = DEFAULT_HTTP_TIMEOUT) -> None: server_args.extend(["--lora", lora_file]) if self.enable_ctx_shift: server_args.append("--context-shift") + if self.spec_type: + server_args.extend(["--spec-type", self.spec_type]) if self.api_key: server_args.extend(["--api-key", self.api_key]) if self.spec_draft_n_max: @@ -262,6 +267,10 @@ def start(self, timeout_seconds: int = DEFAULT_HTTP_TIMEOUT) -> None: server_args.append("--ui-mcp-proxy") if self.server_tools: server_args.extend(["--tools", self.server_tools]) + if self.mcp_servers_config: + server_args.extend(["--mcp-servers-config", self.mcp_servers_config]) + if self.mcp_servers_json: + server_args.extend(["--mcp-servers-json", self.mcp_servers_json]) if self.backend_sampling: server_args.append("--backend_sampling") if self.gcp_compat: diff --git a/tools/tokenize/tokenize.cpp b/tools/tokenize/tokenize.cpp index 23120ad2e182..77b33c4a4657 100644 --- a/tools/tokenize/tokenize.cpp +++ b/tools/tokenize/tokenize.cpp @@ -103,19 +103,11 @@ int main(int argc, char ** argv) { return 1; } - // which prompt source was requested? - // -p/--prompt and -f/--file both end up in params.prompt (common's -f also - // strips a single trailing newline), but -f additionally records the path - // in params.prompt_file, so we use that to tell them apart. + // -f and -p both land in params.prompt; -f also sets prompt_file. -f and -p + // resolve like the other tools (no mutual exclusion), --stdin takes precedence. const bool use_stdin = params.tokenize_stdin; const bool use_file = !params.prompt_file.empty(); - // sanity check: --stdin is mutually exclusive with -f/--file and -p/--prompt - if (use_stdin && (use_file || !params.prompt.empty())) { - LOG_ERR("error: --stdin is mutually exclusive with --file and --prompt\n"); - return 1; - } - // must have some prompt if (!use_stdin && !use_file && params.prompt.empty()) { LOG_ERR("error: must specify one of: --stdin, --file or --prompt\n"); diff --git a/tools/ui/.gitignore b/tools/ui/.gitignore index 0bb8c9b3c218..7cd35376e199 100644 --- a/tools/ui/.gitignore +++ b/tools/ui/.gitignore @@ -36,3 +36,7 @@ static/favicon* *storybook.log storybook-static *.code-workspace + +# Vitest browser mode failure artifacts +.vitest-attachments/ +tests/**/__screenshots__/ diff --git a/tools/ui/.prettierignore b/tools/ui/.prettierignore index 7bbdcf6a0963..635cf99c7e9c 100644 --- a/tools/ui/.prettierignore +++ b/tools/ui/.prettierignore @@ -16,3 +16,6 @@ build/ /build/ /.svelte-kit/ test-results + +# Vendored third party sources, kept byte identical to upstream +src/lib/vendors/ diff --git a/tools/ui/CMakeLists.txt b/tools/ui/CMakeLists.txt index 74bca417e372..208b46a5c15a 100644 --- a/tools/ui/CMakeLists.txt +++ b/tools/ui/CMakeLists.txt @@ -61,12 +61,30 @@ if(CMAKE_CROSSCOMPILING) # phony target to tie it into the dependency graph add_custom_target(llama-ui-embed DEPENDS "${LLAMA_UI_EMBED_EXE}") else() + # exclude llama-ui-embed from sanitizer flags, + # it's a build-time-only tool, no need to instrument it + # this is to fix TSan "memory layout is incompatible" error on CI + get_directory_property(_llama_ui_dir_co COMPILE_OPTIONS) + get_directory_property(_llama_ui_dir_ll LINK_LIBRARIES) + set(_llama_ui_embed_co ${_llama_ui_dir_co}) + set(_llama_ui_embed_ll ${_llama_ui_dir_ll}) + list(FILTER _llama_ui_embed_co EXCLUDE REGEX ".*-fsanitize=.*") + list(FILTER _llama_ui_embed_ll EXCLUDE REGEX ".*-fsanitize=.*") + set_directory_properties(PROPERTIES + COMPILE_OPTIONS "${_llama_ui_embed_co}" + LINK_LIBRARIES "${_llama_ui_embed_ll}") + add_executable(llama-ui-embed embed.cpp) target_compile_features(llama-ui-embed PRIVATE cxx_std_17) set_target_properties(llama-ui-embed PROPERTIES RUNTIME_OUTPUT_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}" ) set(LLAMA_UI_EMBED_EXE "$") + + # restore so the llama-ui library below keeps sanitizer instrumentation + set_directory_properties(PROPERTIES + COMPILE_OPTIONS "${_llama_ui_dir_co}" + LINK_LIBRARIES "${_llama_ui_dir_ll}") endif() # Run the provisioning script every build so source changes in tools/ui/ are diff --git a/tools/ui/eslint.config.js b/tools/ui/eslint.config.js index b90376b6c156..fcbf7ee95489 100644 --- a/tools/ui/eslint.config.js +++ b/tools/ui/eslint.config.js @@ -29,6 +29,13 @@ export default ts.config( // This app uses hash-based routing (#/) where resolve() from $app/paths does not apply 'svelte/no-navigation-without-resolve': 'off', + // Snippet bodies often ignore one or more of the parent's params + // (e.g. `{#snippet children(_meta, ctx)}` when only ctx is read). + '@typescript-eslint/no-unused-vars': [ + 'error', + { argsIgnorePattern: '^_', varsIgnorePattern: '^_' } + ], + // Enforce empty line at end of file 'eol-last': 'error' } @@ -52,7 +59,8 @@ export default ts.config( '.svelte-kit/**', 'test-results/**', '.storybook/**/*', - 'src/lib/services/sandbox-worker.js' + 'src/lib/services/sandbox-worker.js', + 'src/lib/vendors/**' ] }, storybook.configs['flat/recommended'] diff --git a/tools/ui/package-lock.json b/tools/ui/package-lock.json index 7216de682340..2fe44f4c34f7 100644 --- a/tools/ui/package-lock.json +++ b/tools/ui/package-lock.json @@ -12,7 +12,7 @@ "@eslint/compat": "1.4.1", "@eslint/js": "9.39.2", "@internationalized/date": "3.12.2", - "@lucide/svelte": "0.515.0", + "@lucide/svelte": "1.25.0", "@modelcontextprotocol/sdk": "1.26.0", "@playwright/test": "1.56.1", "@storybook/addon-a11y": "10.2.4", @@ -3065,9 +3065,9 @@ } }, "node_modules/@lucide/svelte": { - "version": "0.515.0", - "resolved": "https://registry.npmjs.org/@lucide/svelte/-/svelte-0.515.0.tgz", - "integrity": "sha512-CEAyqcZmNBfYzVgaRmK2RFJP5tnbXxekRyDk0XX/eZQRfsJmkDvmQwXNX8C869BgNeryzmrRyjHhUL6g9ZOHNA==", + "version": "1.25.0", + "resolved": "https://registry.npmjs.org/@lucide/svelte/-/svelte-1.25.0.tgz", + "integrity": "sha512-v9m+dD68jxVnqkU3K59mG/RSRFlPGzmKCGSyMfnXcaGv9jODDQMyQkcp1CGvk3Y/cUj9v7f8rw1n//K0B53xGQ==", "dev": true, "license": "ISC", "peerDependencies": { diff --git a/tools/ui/package.json b/tools/ui/package.json index 8b3516a02cf2..4ea2bf703c35 100644 --- a/tools/ui/package.json +++ b/tools/ui/package.json @@ -31,7 +31,7 @@ "@eslint/compat": "1.4.1", "@eslint/js": "9.39.2", "@internationalized/date": "3.12.2", - "@lucide/svelte": "0.515.0", + "@lucide/svelte": "1.25.0", "@modelcontextprotocol/sdk": "1.26.0", "@playwright/test": "1.56.1", "@storybook/addon-a11y": "10.2.4", diff --git a/tools/ui/scripts/vite-plugin-nerdamer.ts b/tools/ui/scripts/vite-plugin-nerdamer.ts new file mode 100644 index 000000000000..218c2fa233d0 --- /dev/null +++ b/tools/ui/scripts/vite-plugin-nerdamer.ts @@ -0,0 +1,49 @@ +import { build } from 'esbuild'; +import { dirname, resolve } from 'path'; +import { fileURLToPath } from 'url'; +import type { Plugin } from 'vite'; + +const __dirname = dirname(fileURLToPath(import.meta.url)); + +const VENDORS_DIR = resolve(__dirname, '../src/lib/vendors'); +const VIRTUAL_ID = 'virtual:nerdamer'; +const RESOLVED_ID = '\0' + VIRTUAL_ID; + +/** + * Bundle the vendored nerdamer-prime source into a minified IIFE string, + * exposed as the `virtual:nerdamer` module. Flags mirror the upstream + * build (esbuild --bundle --minify --format=iife --global-name=nerdamer), + * so only human readable source lives in the repo and minification is a + * build artifact. Vendored under src/lib/vendors/, upstream snapshot: + * https://github.com/together-science/nerdamer-prime/commit/1936145f8af306ec0d883b9bfd7730aedd175c24 + */ +export function nerdamerPlugin(): Plugin { + let bundled: string | null = null; + + return { + name: 'llamacpp:nerdamer', + resolveId(id) { + return id === VIRTUAL_ID ? RESOLVED_ID : undefined; + }, + async load(id) { + if (id !== RESOLVED_ID) return undefined; + if (bundled === null) { + const result = await build({ + entryPoints: [resolve(VENDORS_DIR, 'nerdamer-prime/all.js')], + bundle: true, + minify: true, + format: 'iife', + globalName: 'nerdamer', + alias: { + 'big-integer': resolve(VENDORS_DIR, 'big-integer/BigInteger.js'), + 'decimal.js': resolve(VENDORS_DIR, 'decimal.js/decimal.js') + }, + write: false, + logLevel: 'silent' + }); + bundled = result.outputFiles[0].text; + } + return `export default ${JSON.stringify(bundled)};`; + } + }; +} diff --git a/tools/ui/src/app.css b/tools/ui/src/app.css index 8c4056477dbf..f9b544bebc56 100644 --- a/tools/ui/src/app.css +++ b/tools/ui/src/app.css @@ -193,6 +193,33 @@ -ms-overflow-style: none; scrollbar-width: none; } + + .shimmer-text { + background: linear-gradient( + 90deg, + var(--muted-foreground), + var(--foreground), + var(--muted-foreground) + ); + background-size: 200% 100%; + background-clip: text; + -webkit-background-clip: text; + -webkit-text-fill-color: transparent; + font-weight: 500; + animation: shimmer 1s linear infinite; + } + + @keyframes shimmer { + to { + background-position: -200% 0; + } + } + + @media (prefers-reduced-motion: reduce) { + .shimmer-text { + animation: none; + } + } } .mermaidTooltip { diff --git a/tools/ui/src/lib/actions/fade-in-view.svelte.ts b/tools/ui/src/lib/actions/fade-in-view.svelte.ts deleted file mode 100644 index 9a5918131aa9..000000000000 --- a/tools/ui/src/lib/actions/fade-in-view.svelte.ts +++ /dev/null @@ -1,49 +0,0 @@ -import { isElementInViewport } from '$lib/utils/viewport'; - -/** - * Svelte action that fades in an element when it enters the viewport. - * Uses IntersectionObserver for efficient viewport detection. - * - * If skipIfVisible is set and the element is already visible in the viewport - * when the action attaches (e.g. a markdown block promoted from unstable - * during streaming), the fade is skipped entirely to avoid a flash. - */ -export function fadeInView( - node: HTMLElement, - options: { duration?: number; y?: number; delay?: number; skipIfVisible?: boolean } = {} -) { - const { duration = 300, y = 0, delay = 0, skipIfVisible = false } = options; - - if (skipIfVisible && isElementInViewport(node)) { - return; - } - - node.style.opacity = '0'; - node.style.transform = `translateY(${y}px)`; - node.style.transition = `opacity ${duration}ms ease-out, transform ${duration}ms ease-out`; - - $effect(() => { - const observer = new IntersectionObserver( - (entries) => { - for (const entry of entries) { - if (entry.isIntersecting) { - setTimeout(() => { - requestAnimationFrame(() => { - node.style.opacity = '1'; - node.style.transform = 'translateY(0)'; - }); - }, delay); - observer.disconnect(); - } - } - }, - { threshold: 0.05 } - ); - - observer.observe(node); - - return () => { - observer.disconnect(); - }; - }); -} diff --git a/tools/ui/src/lib/components/app/actions/ActionIcon.svelte b/tools/ui/src/lib/components/app/actions/ActionIcon.svelte index 8a86557bb98d..608ff6fab4b2 100644 --- a/tools/ui/src/lib/components/app/actions/ActionIcon.svelte +++ b/tools/ui/src/lib/components/app/actions/ActionIcon.svelte @@ -66,7 +66,14 @@ {#snippet child({ props })} - {@render button(props)} + {#if disabled} + + + {@render button({})} + + {:else} + {@render button(props)} + {/if} {/snippet} diff --git a/tools/ui/src/lib/components/app/actions/ActionIconCopyToClipboard.svelte b/tools/ui/src/lib/components/app/actions/ActionIconCopyToClipboard.svelte index 999f0cba9e78..9b7b370ad085 100644 --- a/tools/ui/src/lib/components/app/actions/ActionIconCopyToClipboard.svelte +++ b/tools/ui/src/lib/components/app/actions/ActionIconCopyToClipboard.svelte @@ -1,4 +1,5 @@ {#if reasoning.modelSupportsThinking} - + {#if reasoning.thinkingEnabled} - + + {:else if reasoning.isOff} + {:else} - + {/if} - {reasoning.thinkingEnabled ? reasoning.currentEffort : 'off'} + {reasoning.currentEffort} @@ -36,19 +37,18 @@ > {#each reasoning.levels as level (level.value)} {@const tokenLabel = reasoning.tokenLabel(level)} - + {/each} diff --git a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddSheet.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddSheet.svelte index 6ee8eb578f89..1c6bb0c1c6ee 100644 --- a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddSheet.svelte +++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddSheet.svelte @@ -1,4 +1,5 @@ - -{#if modelSupportsThinking} - - - {#if thinkingEnabled} - - {:else} - - {/if} - - Thinking - - {#if thinkingEnabled} - {currentEffort} - {:else} - off - {/if} - - - - {#each REASONING_EFFORT_LEVELS as level (level.value)} - - {/each} - - -{/if} diff --git a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormContextGauge/ChatFormContextGauge.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormContextGauge/ChatFormContextGauge.svelte index 855cf6ce783d..ff6d39fdd48c 100644 --- a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormContextGauge/ChatFormContextGauge.svelte +++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormContextGauge/ChatFormContextGauge.svelte @@ -1,14 +1,16 @@ - - - - - - -
-
- Context - · - - {formatParameters(gauge.contextUsed)} - / {gauge.contextTotal !== null ? formatParameters(gauge.contextTotal) : '-'} - -
- - {#if gauge.activeModelId !== null && !gauge.isActiveModelLoaded} - - {:else if showProgressBar} -
-
-
- -
- - {gauge.contextPercent}% used - - - {formatParameters((gauge.contextTotal ?? 0) - gauge.contextUsed)} remaining - -
- {:else} -
No context info available
- {/if} - - {#if gauge.hasAnyUsage} - - {/if} -
- - +
+ +
diff --git a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormContextGauge/ContextGaugePopup.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormContextGauge/ContextGaugePopup.svelte new file mode 100644 index 000000000000..af9ad010e3a3 --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormContextGauge/ContextGaugePopup.svelte @@ -0,0 +1,113 @@ + + +{#if gaugePopup.open} +
+
+
+ Context + · + + {formatParameters(gauge.contextUsed)} + / {gauge.contextTotal !== null ? formatParameters(gauge.contextTotal) : '-'} + +
+ + {#if gauge.activeModelId !== null && !gauge.isActiveModelLoaded} + + {:else if showProgressBar} +
+
+
+ +
+ + {gauge.contextPercent}% used + + + {formatParameters((gauge.contextTotal ?? 0) - gauge.contextUsed)} remaining + +
+ {:else} +
No context info available
+ {/if} + + {#if gauge.hasAnyUsage} + + {/if} +
+
+{/if} diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessage.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessage.svelte index 2b4a3f9d393c..b8068f790734 100644 --- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessage.svelte +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessage.svelte @@ -2,12 +2,12 @@ import { goto } from '$app/navigation'; import { getChatActionsContext, setMessageEditContext } from '$lib/contexts'; import { chatStore, pendingEditMessageId } from '$lib/stores/chat.svelte'; + import { isMobile } from '$lib/stores/viewport.svelte'; import { conversationsStore } from '$lib/stores/conversations.svelte'; import { DatabaseService } from '$lib/services/database.service'; import { SYSTEM_MESSAGE_PLACEHOLDER } from '$lib/constants'; import { REASONING_TAGS } from '$lib/constants/agentic'; import { MessageRole, AttachmentType, AgenticSectionType } from '$lib/enums'; - import { fadeInView } from '$lib/actions/fade-in-view.svelte'; import { ChatMessageAssistant, ChatMessageUser, @@ -47,7 +47,14 @@ assistantMessages: number; messageTypes: string[]; } | null>(null); - let editedContent = $derived(message.content); + // The system message placeholder must never surface as editable content; keeping + // it in the derived (not just in handleEdit) guards against prop invalidation + // reverting the override while editing + let editedContent = $derived( + message.role === MessageRole.SYSTEM && message.content === SYSTEM_MESSAGE_PLACEHOLDER + ? '' + : message.content + ); let rawEditContent = $derived.by(() => { if (message.role !== MessageRole.ASSISTANT) return undefined; @@ -266,6 +273,12 @@ chatActions.navigateToSibling(siblingId); } + // After the system message flow ends, hand focus to the main chat form + function focusMainChatForm() { + if (isMobile.current) return; + document.querySelector('.chat-screen-form-wrapper textarea')?.focus(); + } + async function handleSaveEdit() { if (message.role === MessageRole.SYSTEM) { // System messages: update in place without branching @@ -277,6 +290,8 @@ isEditing = false; if (conversationDeleted) { goto(ROUTES.START); + } else { + focusMainChatForm(); } return; } @@ -286,6 +301,7 @@ if (index !== -1) { conversationsStore.updateMessageAtIndex(index, { content: newContent }); } + focusMainChatForm(); } else if (message.role === MessageRole.USER) { const finalExtras = await getMergedExtras(); chatActions.editWithBranching(message, editedContent.trim(), finalExtras); @@ -328,7 +344,7 @@ } -
+
{#if message.role === MessageRole.SYSTEM} {/if}
+ + diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistant.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistant.svelte index 6670d9302dc9..199d75fcec95 100644 --- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistant.svelte +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistant.svelte @@ -2,23 +2,20 @@ import { ChatMessageAgenticContent, ChatMessageActionIcons, - ChatMessageEditForm, - ChatMessageStatistics, - ModelBadge, - ModelsSelectorDropdown + ChatMessageAssistantModel, + ChatMessageAssistantProcessingInfo, + ChatMessageAssistantRawOutput, + ChatMessageAssistantStatistics, + ChatMessageEditForm } from '$lib/components/app'; import { getMessageEditContext } from '$lib/contexts'; import { useProcessingState } from '$lib/hooks/use-processing-state.svelte'; - import { isLoading, isChatStreaming } from '$lib/stores/chat.svelte'; - import { copyToClipboard, deriveAgenticSections, modelLoadProgressText } from '$lib/utils'; - import { AgenticSectionType, ChatMessageStatisticsMode } from '$lib/enums'; - import { REASONING_TAGS } from '$lib/constants/agentic'; - import { fade } from 'svelte/transition'; + import { chatStore, isLoading, isChatStreaming } from '$lib/stores/chat.svelte'; + import { modelLoadProgressText } from '$lib/utils'; import { MessageRole } from '$lib/enums'; import { config } from '$lib/stores/settings.svelte'; import { isRouterMode } from '$lib/stores/server.svelte'; import { modelsStore } from '$lib/stores/models.svelte'; - import { ServerModelStatus } from '$lib/enums'; import { hasAgenticContent } from '$lib/utils'; @@ -33,7 +30,6 @@ isLastAssistantMessage?: boolean; message: DatabaseMessage; toolMessages?: DatabaseMessage[]; - messageContent: string | undefined; onCopy: () => void; onConfirmDelete: () => void; onContinue?: () => void; @@ -54,7 +50,6 @@ isLastAssistantMessage = false, message, toolMessages = [], - messageContent, onConfirmDelete, onContinue, onCopy, @@ -77,62 +72,21 @@ let currentConfig = $derived(config()); let isRouter = $derived(isRouterMode()); - let showRawOutput = $state(false); - - let rawOutputContent = $derived.by(() => { - const sections = deriveAgenticSections(message, toolMessages, [], false); - const parts: string[] = []; - - for (const section of sections) { - switch (section.type) { - case AgenticSectionType.REASONING: - case AgenticSectionType.REASONING_PENDING: - parts.push(`${REASONING_TAGS.START}\n${section.content}\n${REASONING_TAGS.END}`); - break; - - case AgenticSectionType.TEXT: - parts.push(section.content); - break; - - case AgenticSectionType.TOOL_CALL: - case AgenticSectionType.TOOL_CALL_PENDING: - case AgenticSectionType.TOOL_CALL_STREAMING: { - const callObj: Record = { name: section.toolName }; - - if (section.toolArgs) { - try { - callObj.arguments = JSON.parse(section.toolArgs); - } catch { - callObj.arguments = section.toolArgs; - } - } - - parts.push(JSON.stringify(callObj, null, 2)); - - if (section.toolResult) { - parts.push(`[Tool Result]\n${section.toolResult}`); - } - - break; - } - } - } - return parts.join('\n\n\n'); - }); + let showRawOutput = $state(false); let displayedModel = $derived(message.model ?? null); - // model being switched to while it loads, so the selector bar tracks it - let pendingModel = $state(null); - let isCurrentlyLoading = $derived(isLoading()); let isStreaming = $derived(isChatStreaming()); let hasNoContent = $derived(!message?.content?.trim()); let isActivelyProcessing = $derived(isCurrentlyLoading || isStreaming); - // during a router auto-load the message has no model yet, so target the selected one - let loadTargetModel = $derived(message.model ?? modelsStore.selectedModelName); + // during a router auto-load the message has no model yet: target the model frozen in the + // persisted stream state (survives a reload), then fall back to the dropdown selection + let loadTargetModel = $derived( + message.model ?? chatStore.getResumeModel(message.convId) ?? modelsStore.selectedModelName + ); let modelLoadProgress = $derived( isRouter && loadTargetModel ? modelsStore.getLoadProgress(loadTargetModel) : null ); @@ -189,10 +143,6 @@ }; }); - function handleCopyModel() { - void copyToClipboard(displayedModel ?? ''); - } - $effect(() => { if (showProcessingInfoTop || showProcessingInfoBottom) { processingState.startMonitoring(); @@ -211,23 +161,14 @@ aria-label="Assistant message with actions" > {#if showProcessingInfoTop} -
-
- - {modelLoadingText ?? - processingState.getPromptProgressText() ?? - processingState.getProcessingMessage() ?? - 'Processing...'} - -
-
+ {/if} {#if editCtx.isEditing} - {:else if message.role === MessageRole.ASSISTANT} + {:else} {#if showRawOutput} - 
{rawOutputContent || ''}
+ {:else} {/if} - {:else} -
- {messageContent} -
{/if} {#if showProcessingInfoBottom} -
-
- - {modelLoadingText ?? - processingState.getPromptProgressText() ?? - processingState.getProcessingMessage() ?? - 'Processing...'} - -
-
+ {/if}
{#if displayedModel}
- {#if isRouter} - { - const status = modelsStore.getModelStatus(modelId); - - if (status !== ServerModelStatus.LOADED) { - pendingModel = modelId; - - try { - await modelsStore.loadModel(modelId); - } finally { - pendingModel = null; - } - } - - onRegenerate(modelName); - return true; - }} - /> - {:else} - - {/if} - - {#if currentConfig.showMessageStats && message.timings && message.timings.predicted_n && message.timings.predicted_ms} - {@const agentic = message.timings.agentic} - - {:else if isLoading() && currentConfig.showMessageStats} - {@const liveStats = processingState.getLiveProcessingStats()} - {@const genStats = processingState.getLiveGenerationStats()} - - {#if genStats} - - {/if} - {/if} + + +
{/if}
@@ -353,47 +244,4 @@ ); } } - - .processing-container { - display: flex; - flex-direction: column; - align-items: flex-start; - gap: 0.5rem; - } - - .processing-text { - background: linear-gradient( - 90deg, - var(--muted-foreground), - var(--foreground), - var(--muted-foreground) - ); - background-size: 200% 100%; - background-clip: text; - -webkit-background-clip: text; - -webkit-text-fill-color: transparent; - animation: shine 1s linear infinite; - font-weight: 500; - font-size: 0.875rem; - } - - @keyframes shine { - to { - background-position: -200% 0; - } - } - - .raw-output { - width: 100%; - max-width: 48rem; - margin-top: 1.5rem; - padding: 1rem 1.25rem; - border-radius: 1rem; - background: hsl(var(--muted) / 0.3); - color: var(--foreground); - font-size: 0.875rem; - line-height: 1.6; - white-space: pre-wrap; - word-break: break-word; - } diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistantModel.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistantModel.svelte new file mode 100644 index 000000000000..76b45ec94e09 --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistantModel.svelte @@ -0,0 +1,46 @@ + + +{#if isRouter} + { + const status = modelsStore.getModelStatus(modelId); + + if (status !== ServerModelStatus.LOADED) { + pendingModel = modelId; + + try { + await modelsStore.loadModel(modelId); + } finally { + pendingModel = null; + } + } + + onRegenerate(modelName); + return true; + }} + /> +{:else} + +{/if} diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistantProcessingInfo.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistantProcessingInfo.svelte new file mode 100644 index 000000000000..356512ecb73d --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistantProcessingInfo.svelte @@ -0,0 +1,25 @@ + + +
+
+ + {modelLoadingText ?? + processingState.getPromptProgressText() ?? + processingState.getProcessingMessage() ?? + 'Processing...'} + +
+
diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistantRawOutput.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistantRawOutput.svelte new file mode 100644 index 000000000000..30ce16be934e --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistantRawOutput.svelte @@ -0,0 +1,33 @@ + + +
{rawOutputContent || ''}
+ + diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistantStatistics.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistantStatistics.svelte new file mode 100644 index 000000000000..4cc4080c3b7c --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistantStatistics.svelte @@ -0,0 +1,40 @@ + + +{#if showMessageStats && message.timings && message.timings.predicted_n && message.timings.predicted_ms} + {@const agentic = message.timings.agentic} + +{:else if isLoading && showMessageStats} + {@const liveStats = processingState.getLiveProcessingStats()} + {@const genStats = processingState.getLiveGenerationStats()} + + {#if genStats} + + {/if} +{/if} diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageSystem/ChatMessageSystem.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageSystem/ChatMessageSystem.svelte index 9d3d07a2730b..24b3be4c5ff7 100644 --- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageSystem/ChatMessageSystem.svelte +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageSystem/ChatMessageSystem.svelte @@ -7,7 +7,7 @@ import { getMessageEditContext } from '$lib/contexts'; import { KeyboardKey, MessageRole } from '$lib/enums'; import { config } from '$lib/stores/settings.svelte'; - import { isIMEComposing } from '$lib/utils'; + import { autoResizeTextarea, isIMEComposing } from '$lib/utils'; interface Props { class?: string; @@ -91,6 +91,11 @@ resizeObserver.disconnect(); }; }); + $effect(() => { + if (editCtx.isEditing && textareaElement) { + autoResizeTextarea(textareaElement); + } + }); function toggleExpand() { isExpanded = !isExpanded; @@ -105,11 +110,15 @@ {#if editCtx.isEditing}
@@ -157,10 +166,7 @@ > {#if currentConfig.renderUserContentAsMarkdown}
- +
{:else} + import { BuiltInTool } from '$lib/enums'; + import { + extractSearchQuery, + extractSearchResults, + isWebSearchToolName, + type AgenticSection + } from '$lib/utils'; + import type { DatabaseMessageExtra } from '$lib/types'; + import ChatMessageToolCallBlockDefault from './ChatMessageToolCallBlockDefault.svelte'; + import ChatMessageToolCallBlockEditFile from './ChatMessageToolCallBlockEditFile.svelte'; + import ChatMessageToolCallBlockExecShellCommand from './ChatMessageToolCallBlockExecShellCommand.svelte'; + import ChatMessageToolCallBlockFileGlobSearch from './ChatMessageToolCallBlockFileGlobSearch.svelte'; + import ChatMessageToolCallBlockGetDatetime from './ChatMessageToolCallBlockGetDatetime.svelte'; + import ChatMessageToolCallBlockGrepSearch from './ChatMessageToolCallBlockGrepSearch.svelte'; + import ChatMessageToolCallBlockReadFile from './ChatMessageToolCallBlockReadFile.svelte'; + import ChatMessageToolCallBlockRunJavascript from './ChatMessageToolCallBlockRunJavascript.svelte'; + import ChatMessageToolCallBlockSearchResults from './ChatMessageToolCallBlockSearchResults.svelte'; + import ChatMessageToolCallBlockWriteFile from './ChatMessageToolCallBlockWriteFile.svelte'; + + interface Props { + section: AgenticSection; + attachments?: DatabaseMessageExtra[]; + open: boolean; + isStreaming: boolean; + isExecuting?: boolean; + onToggle?: () => void; + } + + let { section, attachments, open, isStreaming, isExecuting, onToggle }: Props = $props(); + + const searchResults = $derived(extractSearchResults(section.toolResult)); + const searchQuery = $derived(extractSearchQuery(section.toolArgs)); + const isSearchCall = $derived( + searchResults.length > 0 || (searchQuery.length > 0 && isWebSearchToolName(section.toolName)) + ); + + +{#if isSearchCall} + +{:else if section.toolName === BuiltInTool.GET_DATETIME} + +{:else if section.toolName === BuiltInTool.READ_FILE} + +{:else if section.toolName === BuiltInTool.EDIT_FILE} + +{:else if section.toolName === BuiltInTool.WRITE_FILE} + +{:else if section.toolName === BuiltInTool.EXEC_SHELL_COMMAND} + +{:else if section.toolName === BuiltInTool.FILE_GLOB_SEARCH} + +{:else if section.toolName === BuiltInTool.GREP_SEARCH} + +{:else if section.toolName === BuiltInTool.RUN_JAVASCRIPT} + +{:else} + +{/if} diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockDefault.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockDefault.svelte new file mode 100644 index 000000000000..92652a0a86ab --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockDefault.svelte @@ -0,0 +1,122 @@ + + + + {#snippet children(_meta, ctx)} + {#if ctx.isStreamingCall} +
+ Input + {#if ctx.isStreaming} + + {/if} +
+ {#if section.toolArgs} + + {:else if ctx.isStreaming} +
+ Receiving arguments... +
+ {:else} +
+ Response was truncated +
+ {/if} + {:else} + {@const showInput = Boolean(section.toolArgs)} + {#if showInput} +
+ Input +
+ + {/if} +
+ Output + {#if ctx.isPending} + + {/if} +
+ {#if ctx.isPending} +
+ Waiting for result... +
+ {:else if section.toolResult} + {#if outputKind === ToolResultKind.JSON} + + {:else if outputKind === ToolResultKind.MARKDOWN} + + {:else} +
+ {#each parsedLines as line, i (i)} +
+ {line.text} +
+ {#if line.image} + {line.image.name} + {/if} + {/each} +
+ {/if} + {:else} +
No output
+ {/if} + {/if} + {/snippet} + diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockEditFile.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockEditFile.svelte new file mode 100644 index 000000000000..b990c3898b23 --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockEditFile.svelte @@ -0,0 +1,165 @@ + + + + {#snippet titleSnippet()} + Edit file + {editFileMeta?.filePath} + {#if editFileMeta?.errorMessage} + (failed) + {/if} + {/snippet} + + {#snippet children(meta, _ctx)} + {#if meta?.errorMessage} +
+ + {meta.errorMessage} +
+ {:else if meta && meta.edits.length > 0} + {#each editDiffs as diffLines, ei (ei)} +
+
+ Edit {ei + 1} of {meta.edits.length} +
+
+
+ {#each diffLines as line, li (li)} +
+ {line.oldLine ?? ''} + {prefixFor(line.kind)} + {line.newLine ?? ''} + {line.text || ' '} +
+ {/each} +
+
+
+ {/each} +
+ {#if meta.resultMessage} + {meta.resultMessage}{meta.editsApplied != null ? RESULT_STAT_SEPARATOR : ''}{/if} + {#if meta.editsApplied != null} + {meta.editsApplied} + {meta.editsApplied === 1 ? 'edit' : 'edits'} applied + {/if} +
+ {:else} +
No edits
+ {/if} + {/snippet} + + + diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockExecShellCommand.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockExecShellCommand.svelte new file mode 100644 index 000000000000..5de801d39a48 --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockExecShellCommand.svelte @@ -0,0 +1,293 @@ + + +{#snippet execShellTitle()} + {#if highlightedCommandHtml} + {@html highlightedCommandHtml} + {:else} + {execShellMeta?.command} + {/if} +{/snippet} + + + {#snippet titleSnippet()} + {@render execShellTitle()} + {/snippet} + + {#snippet children(_meta, ctx)} + {#if ctx.isPending} +
+ + Running... +
+ {:else if execShellError} +
+ + {execShellError} +
+ {:else if section.toolResult} +
+ {#each outputLines as line, i (i)} +
{line.text}
+ {#if line.image} + {line.image.name} + {/if} + {/each} + + {#if isExitCodeFinalLine && execShellExitStatus} +
+ {#if execShellExitStatus.timedOut} + + timed out + · + exit {execShellExitStatus.code} + {:else if execShellExitStatus.code === 0} + + exit 0 + {:else} + + exit {execShellExitStatus.code} + {/if} +
+ {/if} +
+ {/if} + {/snippet} + + + diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockFileGlobSearch.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockFileGlobSearch.svelte new file mode 100644 index 000000000000..ad082039ff60 --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockFileGlobSearch.svelte @@ -0,0 +1,61 @@ + + + + {#snippet titleSnippet()} + {#if fileGlobMeta} + {fileGlobMeta.include === '**' ? 'List files' : 'Search files'}  + {#if fileGlobMeta.include !== '**'} + {fileGlobMeta.include} + {/if} +  in  + {fileGlobMeta.path} + {/if} + {/snippet} + + {#snippet children(meta, ctx)} + {#if ctx.isPending} +
+ Searching... +
+ {:else if meta?.errorMessage} +
+ + {meta.errorMessage} +
+ {:else if meta && meta.matches.length > 0} +
+ {#each meta.matches as match, i (i)} +
{match}
+ {/each} +
+
+ Total matches: {meta.totalMatches ?? meta.matches.length} +
+ {:else} +
No matches
+
+ Total matches: {meta?.totalMatches ?? 0} +
+ {/if} + {/snippet} + diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockGetDatetime.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockGetDatetime.svelte new file mode 100644 index 000000000000..e0c701deaa58 --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockGetDatetime.svelte @@ -0,0 +1,57 @@ + + +
+ + {#if showSpinner} + Current time + + {:else if dateMeta.errorMessage} + Current time  + - {dateMeta.errorMessage} + {:else if dateMeta.dateString} + Current time is  + {dateMeta.dateString} + {:else} + Current time + {/if} +
diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockGrepSearch.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockGrepSearch.svelte new file mode 100644 index 000000000000..afb06fef7168 --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockGrepSearch.svelte @@ -0,0 +1,67 @@ + + + + {#snippet titleSnippet()} + {#if grepMeta} + Search for  + {grepMeta.pattern} +  in  + {grepMeta.path} + {/if} + {/snippet} + + {#snippet children(meta, ctx)} + {#if ctx.isPending} +
+ Searching... +
+ {:else if meta?.errorMessage} +
+ + {meta.errorMessage} +
+ {:else if meta && meta.matches.length > 0} +
+ {#each meta.matches as match, mi (mi)} +
+ {match.file} + {#if meta.showLineNumbers && match.line != null} + :{match.line} + {/if} + : + {match.content} +
+ {/each} +
+
+ Total matches: {meta.totalMatches ?? meta.matches.length} + {#if meta.showLineNumbers} +  (with line numbers) + {/if} +
+ {:else} +
No matches
+
+ Total matches: {meta?.totalMatches ?? 0} +
+ {/if} + {/snippet} + diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockReadFile.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockReadFile.svelte new file mode 100644 index 000000000000..a99ff9ceedcd --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockReadFile.svelte @@ -0,0 +1,44 @@ + + + + {#snippet titleSnippet()} + Read file + {readFileMeta?.fileName} + {#if readFileMeta?.lineRange} +  (lines {readFileMeta.lineRange.start}-{readFileMeta.lineRange.end}) + {/if} + {/snippet} + + {#snippet children(_meta, _ctx)} + {#if section.toolResult} + + {:else} +
+ Waiting for file content... +
+ {/if} + {/snippet} + diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockRunJavascript.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockRunJavascript.svelte new file mode 100644 index 000000000000..707d83d7377e --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockRunJavascript.svelte @@ -0,0 +1,69 @@ + + + + {#snippet children(meta, ctx)} + {#if ctx.isPending} +
Running...
+ {:else if meta?.errorMessage} +
+ + {meta.errorMessage} +
+
+ +
+ {:else if meta} + +
+ + Console + {#if meta.timeoutMs != null} + · timeout {meta.timeoutMs} ms + {/if} +
+ {#if section.toolResult} +
+ +
+ {:else} +
No output
+ {/if} + {/if} + {/snippet} + diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockSearchResults.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockSearchResults.svelte new file mode 100644 index 000000000000..60862dd06353 --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockSearchResults.svelte @@ -0,0 +1,167 @@ + + +{#snippet pill(result: SearchResult)} + {@const faviconUrl = faviconForUrl(result.url)} + {@const safeUrl = sanitizeExternalUrl(result.url)} + {@const showHoverCard = safeUrl !== null && hasDetails(result)} + {#if safeUrl} + + + {#if faviconUrl} + + {:else} + + {/if} + {result.title} + + {#if showHoverCard} + {@const publishDate = formatPublishDate(result.published)} + {@const host = hostFor(safeUrl)} + +
+ {result.title} + {#if publishDate || result.author} +
+ {#if publishDate} + {publishDate} + {/if} + {#if publishDate && result.author} + · + {/if} + {#if result.author} + {result.author} + {/if} +
+ {/if} + {#if result.highlights} +

+ {result.highlights} +

+ {/if} + {#if host} +
{host}
+ {/if} +
+ + {/if} + + {/if} +{/snippet} + + + {#if results.length > 0} +
+ {#each results as result (result.url)} + {@render pill(result)} + {/each} +
+ {:else if showSpinner} +
+ + Searching... +
+ {:else} +
No results
+ {/if} + diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockWriteFile.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockWriteFile.svelte new file mode 100644 index 000000000000..eda067662305 --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ChatMessageToolCallBlockWriteFile.svelte @@ -0,0 +1,55 @@ + + + + {#snippet titleSnippet()} + Write file + {writeFileMeta?.filePath} + {#if writeFileMeta?.errorMessage} + (failed) + {/if} + {/snippet} + + {#snippet children(meta, ctx)} + {#if meta?.errorMessage} +
+ + {meta.errorMessage} +
+ {:else if meta} + +
+ {#if meta.resultMessage} + {meta.resultMessage}{meta.bytesWritten != null ? RESULT_STAT_SEPARATOR : ''}{/if} + {#if meta.bytesWritten != null} + {meta.bytesWritten} + bytes + {/if} +
+ {/if} + {/snippet} + diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ToolCallBlock.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ToolCallBlock.svelte new file mode 100644 index 000000000000..a17a74e16126 --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/ToolCallBlock.svelte @@ -0,0 +1,129 @@ + + + + {@render children(meta, { + isStreaming, + isPending, + isStreamingCall, + isCodeStreaming + })} + diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/_shared.ts b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/_shared.ts new file mode 100644 index 000000000000..6114f17b5bde --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/_shared.ts @@ -0,0 +1,49 @@ +// Helpers shared by the per-tool meta parsers under +// `src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/`. +// Each tool needs the same first three steps (tool-name check, +// args-present check, JSON parse) - keeping them here lets each parser +// stay focused on its own format quirks. + +import { BuiltInTool } from '$lib/enums'; +import { parsePartialJsonArgs } from '$lib/utils/parse-partial-json-args'; +import type { AgenticSection } from '$lib/utils/agentic'; + +/** + * Strict (final-state) JSON parser for a tool-args blob. Mirrors the + * behaviour the per-tool components used before extraction: an + * invalid JSON blob, a JSON array, or a JSON primitive all map to + * `null` so callers don't have to guard against surprise shapes. + */ +function parseFinalToolArgs(blob: string): Record | null { + try { + const parsed: unknown = JSON.parse(blob); + if (parsed && typeof parsed === 'object' && !Array.isArray(parsed)) { + return parsed as Record; + } + return null; + } catch { + return null; + } +} + +/** + * Parse a section's toolArgs against an expected tool name. Returns + * `null` when: + * - the section's toolName doesn't match (component isn't for this + * tool); + * - the section has no args yet (call hasn't started streaming); + * - or the args blob can't be parsed. + * + * Pass `{ partial: true }` for tools that need to render incrementally + * as each token lands (read_file, edit_file, write_file). + */ +export function parseToolArgs( + expected: BuiltInTool, + section: AgenticSection, + options: { partial?: boolean } = {} +): Record | null { + if (section.toolName !== expected || !section.toolArgs) return null; + return options.partial + ? parsePartialJsonArgs(section.toolArgs) + : parseFinalToolArgs(section.toolArgs); +} diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/edit-file.ts b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/edit-file.ts new file mode 100644 index 000000000000..4bff25bb54cd --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/edit-file.ts @@ -0,0 +1,71 @@ +// Meta parser for `edit_file` tool calls. Reads the file path and the +// array of edits from the streamed args (partial JSON for incremental +// rendering), plus the result blob for `result` / `edits_applied` / +// `error` fields. + +import { BuiltInTool } from '$lib/enums'; +import { FILE_PATH_SEPARATOR_REGEX } from '$lib/constants'; +import { tryParseToolResultObject, type AgenticSection } from '$lib/utils'; +import { parseToolArgs } from './_shared'; + +export type EditFileEdit = { + oldText: string; + newText: string; +}; + +export type EditFileMeta = { + fileName: string; + filePath: string; + edits: EditFileEdit[]; + resultMessage?: string; + editsApplied?: number; + errorMessage?: string; +}; + +export function parseEditFileMeta(section: AgenticSection): EditFileMeta | null { + const args = parseToolArgs(BuiltInTool.EDIT_FILE, section, { partial: true }); + if (!args) return null; + + const rawPath = args.path ?? args.file_path ?? args.filePath; + if (typeof rawPath !== 'string' || !rawPath) return null; + + const fileName = rawPath.split(FILE_PATH_SEPARATOR_REGEX).pop() || rawPath; + + // Filter the streamed edits array strictly: each entry must be an + // object with a non-empty `old_text`. Edits without an old_text + // would diff against empty and render as a full re-write. + const rawEdits = Array.isArray(args.edits) ? args.edits : []; + const edits: EditFileEdit[] = []; + for (const e of rawEdits) { + if (!e || typeof e !== 'object' || Array.isArray(e)) continue; + const obj = e as Record; + const oldText = typeof obj.old_text === 'string' ? obj.old_text : ''; + if (!oldText) continue; + const newText = typeof obj.new_text === 'string' ? obj.new_text : ''; + edits.push({ oldText, newText }); + } + + const resultObj = tryParseToolResultObject(section.toolResult); + let resultMessage: string | undefined; + let editsApplied: number | undefined; + let errorMessage: string | undefined; + if (typeof resultObj?.error === 'string') { + errorMessage = resultObj.error; + } else if (resultObj) { + if (typeof resultObj.result === 'string') { + resultMessage = resultObj.result; + } + if (Number.isFinite(Number(resultObj.edits_applied))) { + editsApplied = Number(resultObj.edits_applied); + } + } + + return { + fileName, + filePath: rawPath, + edits, + resultMessage, + editsApplied, + errorMessage + }; +} diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/exec-shell-command.ts b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/exec-shell-command.ts new file mode 100644 index 000000000000..e8adbd18b06b --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/exec-shell-command.ts @@ -0,0 +1,23 @@ +// Meta parser for `exec_shell_command` tool calls. Surfaces the +// command text from args `command` / `cmd` / `shell_command` aliases. +// The exit-status and error parsing live in their own utilities +// (`parse-exec-shell-status.ts` / `parse-exec-shell-error.ts`) - this +// file only deals with what's strictly about *calling* the tool, since +// the error / exit status elide from call-section to result-section. + +import { BuiltInTool } from '$lib/enums'; +import type { AgenticSection } from '$lib/utils'; +import { parseToolArgs } from './_shared'; + +export type ExecShellCommandMeta = { + command: string; +}; + +export function parseExecShellCommandMeta(section: AgenticSection): ExecShellCommandMeta | null { + const args = parseToolArgs(BuiltInTool.EXEC_SHELL_COMMAND, section); + if (!args) return null; + + const commandRaw = args.command ?? args.cmd ?? args.shell_command; + if (typeof commandRaw !== 'string' || !commandRaw) return null; + return { command: commandRaw }; +} diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/file-glob-search.ts b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/file-glob-search.ts new file mode 100644 index 000000000000..1ad92b74cf05 --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/file-glob-search.ts @@ -0,0 +1,58 @@ +// Meta parser for `file_glob_search` tool calls. Reads the path, +// include pattern, and optional exclude from the args (strict parsing) +// and the matches from the result blob. Like grep_search, the result +// parser keeps the original raw-text fallback for MCP servers that +// emit unparseable output. + +import { BuiltInTool } from '$lib/enums'; +import { splitSearchSummaryList, type AgenticSection } from '$lib/utils'; +import { parseToolArgs } from './_shared'; + +export type FileGlobSearchMeta = { + path: string; + include: string; + exclude?: string; + matches: string[]; + totalMatches?: number; + errorMessage?: string; +}; + +export function parseFileGlobSearchMeta(section: AgenticSection): FileGlobSearchMeta | null { + const args = parseToolArgs(BuiltInTool.FILE_GLOB_SEARCH, section); + if (!args) return null; + + const path = typeof args.path === 'string' ? args.path : ''; + const include = typeof args.include === 'string' && args.include ? args.include : '**'; + const exclude = typeof args.exclude === 'string' && args.exclude ? args.exclude : undefined; + if (!path) return null; + + let matches: string[] = []; + let totalMatches: number | undefined; + let errorMessage: string | undefined; + + const toolResultString = section.toolResult; + if (toolResultString) { + try { + const parsed: unknown = JSON.parse(toolResultString); + if (parsed && typeof parsed === 'object' && !Array.isArray(parsed)) { + const obj = parsed as Record; + if (typeof obj.error === 'string') { + errorMessage = obj.error; + } else if (typeof obj.plain_text_response === 'string') { + const split = splitSearchSummaryList(obj.plain_text_response, (total) => { + totalMatches = total; + }); + matches = split.lines; + } + } + } catch { + // See grep-search.ts: same fallback used there. + const split = splitSearchSummaryList(toolResultString, (total) => { + totalMatches = total; + }); + matches = split.lines; + } + } + + return { path, include, exclude, matches, totalMatches, errorMessage }; +} diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/grep-search.ts b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/grep-search.ts new file mode 100644 index 000000000000..0e606e193c68 --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/grep-search.ts @@ -0,0 +1,108 @@ +// Meta parser for `grep_search` tool calls. Reads the path/pattern +// triplet from args (strict parsing - we wait for the args to +// complete) and the matches from the result blob. The result parser +// keeps the original "scan result as raw text on JSON.parse failure" +// fallback so MCP servers that return unparseable output still get +// surfaced. + +import { BuiltInTool } from '$lib/enums'; +import { splitSearchSummaryList, type AgenticSection } from '$lib/utils'; +import { parseToolArgs } from './_shared'; + +export type GrepSearchMatch = { + file: string; + line?: number; + content: string; +}; + +export type GrepSearchMeta = { + path: string; + pattern: string; + include: string; + exclude?: string; + showLineNumbers: boolean; + matches: GrepSearchMatch[]; + totalMatches?: number; + errorMessage?: string; +}; + +export function parseGrepSearchMeta(section: AgenticSection): GrepSearchMeta | null { + const args = parseToolArgs(BuiltInTool.GREP_SEARCH, section); + if (!args) return null; + + const path = typeof args.path === 'string' ? args.path : ''; + const pattern = typeof args.pattern === 'string' ? args.pattern : ''; + if (!path || !pattern) return null; + + const include = typeof args.include === 'string' && args.include ? args.include : '**'; + const exclude = typeof args.exclude === 'string' && args.exclude ? args.exclude : undefined; + const showLineNumbers = args.return_line_numbers === true; + + let matches: GrepSearchMatch[] = []; + let totalMatches: number | undefined; + let errorMessage: string | undefined; + + const toolResultString = section.toolResult; + if (toolResultString) { + try { + const parsed: unknown = JSON.parse(toolResultString); + if (parsed && typeof parsed === 'object' && !Array.isArray(parsed)) { + const obj = parsed as Record; + if (typeof obj.error === 'string') { + errorMessage = obj.error; + } else if (typeof obj.plain_text_response === 'string') { + const split = splitSearchSummaryList(obj.plain_text_response, (total) => { + totalMatches = total; + }); + matches = split.lines.map((line) => parseGrepLine(line, showLineNumbers)); + } + } + } catch { + // Result wasn't JSON: keep behaviour for MCP servers that + // emit raw text and treat each line as a `:` + // (or `::`) match. + const split = splitSearchSummaryList(toolResultString, (total) => { + totalMatches = total; + }); + matches = split.lines.map((line) => parseGrepLine(line, showLineNumbers)); + } + } + + return { + path, + pattern, + include, + exclude, + showLineNumbers, + matches, + totalMatches, + errorMessage + }; +} + +function parseGrepLine(line: string, showLineNumbers: boolean): GrepSearchMatch { + // Server output: + // : when return_line_numbers=false + // :: when return_line_numbers=true + const firstColon = line.indexOf(':'); + if (firstColon === -1) { + return { file: line, content: '' }; + } + const file = line.slice(0, firstColon); + const tail = line.slice(firstColon + 1); + + if (!showLineNumbers) { + return { file, content: tail }; + } + + const secondColon = tail.indexOf(':'); + if (secondColon === -1) { + return { file, content: tail }; + } + const lineNum = parseInt(tail.slice(0, secondColon), 10); + return { + file, + line: Number.isFinite(lineNum) ? lineNum : undefined, + content: tail.slice(secondColon + 1) + }; +} diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/read-file.ts b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/read-file.ts new file mode 100644 index 000000000000..d37dcf5010ea --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/read-file.ts @@ -0,0 +1,52 @@ +// Meta parser for `read_file` tool calls. Reads the file path and an +// optional line range (either `start_line`+`end_line` or +// `start_line`+`line_count`). Args are parsed partially so a header +// can render incrementally as the file path streams in. + +import { BuiltInTool } from '$lib/enums'; +import { + DEFAULT_LANGUAGE, + FILE_PATH_SEPARATOR_REGEX, + TEXT_LANGUAGE_PREFIX_REGEX +} from '$lib/constants'; +import { getFileTypeByExtension, type AgenticSection } from '$lib/utils'; +import { parseToolArgs } from './_shared'; + +export type ReadFileMeta = { + fileName: string; + lineRange: { start: number; end: number } | null; + language: string; +}; + +export function parseReadFileMeta(section: AgenticSection): ReadFileMeta | null { + const args = parseToolArgs(BuiltInTool.READ_FILE, section, { partial: true }); + if (!args) return null; + + const rawPath = args.path ?? args.file_path ?? args.filePath; + if (typeof rawPath !== 'string' || !rawPath) return null; + + const fileName = rawPath.split(FILE_PATH_SEPARATOR_REGEX).pop() || rawPath; + + // Models emit range arguments under several aliases. Accept all to + // stay forgiving across prompt variations. + const startRaw = args.start_line ?? args.line_start ?? args.startLine ?? args.from_line; + const endRaw = args.end_line ?? args.line_end ?? args.endLine ?? args.to_line; + const countRaw = args.line_count ?? args.count ?? args.num_lines; + + let lineRange: { start: number; end: number } | null = null; + const sNum = Number(startRaw); + const eNum = Number(endRaw); + if (startRaw != null && endRaw != null && Number.isFinite(sNum) && Number.isFinite(eNum)) { + lineRange = { start: sNum, end: eNum }; + } else if (startRaw != null && countRaw != null) { + const cNum = Number(countRaw); + if (Number.isFinite(sNum) && Number.isFinite(cNum)) { + lineRange = { start: sNum, end: sNum + cNum - 1 }; + } + } + + const fileType = getFileTypeByExtension(fileName); + const language = fileType ? fileType.replace(TEXT_LANGUAGE_PREFIX_REGEX, '') : DEFAULT_LANGUAGE; + + return { fileName, lineRange, language }; +} diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/run-javascript.ts b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/run-javascript.ts new file mode 100644 index 000000000000..9bcba8f03cc4 --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/run-javascript.ts @@ -0,0 +1,56 @@ +// Meta parser for `run_javascript` tool calls. Reads the JS code and +// optional timeout from args (strict parsing) and surfaces any error +// from the result blob. SandboxService.formatReply emits a JSON object +// containing an `error` field on failure, but a partial/non-JSON +// failure renders as a flat line beginning with `Error:`. Both shapes +// are handled. + +import { BuiltInTool } from '$lib/enums'; +import type { AgenticSection } from '$lib/utils'; +import { parseToolArgs } from './_shared'; + +export type RunJavascriptMeta = { + code: string; + timeoutMs?: number; + errorMessage?: string; +}; + +export function parseRunJavascriptMeta(section: AgenticSection): RunJavascriptMeta | null { + const args = parseToolArgs(BuiltInTool.RUN_JAVASCRIPT, section); + if (!args) return null; + + const code = typeof args.code === 'string' ? args.code : ''; + if (!code) return null; + + const timeoutRaw = Number(args.timeout_ms); + const timeoutMs = Number.isFinite(timeoutRaw) && timeoutRaw > 0 ? timeoutRaw : undefined; + + let errorMessage: string | undefined; + const toolResultString = section.toolResult; + if (toolResultString) { + // Branches matter here: a JSON object can carry `error`, but a + // JSON array always represents successful output (sandbox returns + // the array of values). Only when the result isn't a JSON object + // do we scan raw lines for the `Error:` prefix. + let parsedObject: Record | null = null; + try { + const parsed: unknown = JSON.parse(toolResultString); + if (parsed && typeof parsed === 'object' && !Array.isArray(parsed)) { + parsedObject = parsed as Record; + } + } catch { + parsedObject = null; + } + if (typeof parsedObject?.error === 'string') { + errorMessage = parsedObject.error; + } else if (!parsedObject) { + const errorLine = toolResultString + .split('\n') + .map((line) => line.trim()) + .find((line) => line.startsWith('Error:')); + if (errorLine) errorMessage = errorLine.slice('Error:'.length).trim(); + } + } + + return { code, timeoutMs, errorMessage }; +} diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/write-file.ts b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/write-file.ts new file mode 100644 index 000000000000..95edc3d95eaf --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageToolCall/parsers/write-file.ts @@ -0,0 +1,54 @@ +// Meta parser for `write_file` tool calls. Reads the path/content from +// the streamed args (partial JSON so we can render before the call +// finishes) and surfaces `bytes`, `result`, and `error` from the +// result blob. + +import { BuiltInTool } from '$lib/enums'; +import { + DEFAULT_LANGUAGE, + FILE_PATH_SEPARATOR_REGEX, + TEXT_LANGUAGE_PREFIX_REGEX +} from '$lib/constants'; +import { getFileTypeByExtension, tryParseToolResultObject, type AgenticSection } from '$lib/utils'; +import { parseToolArgs } from './_shared'; + +export type WriteFileMeta = { + fileName: string; + filePath: string; + language: string; + content: string; + bytesWritten?: number; + resultMessage?: string; + errorMessage?: string; +}; + +export function parseWriteFileMeta(section: AgenticSection): WriteFileMeta | null { + const args = parseToolArgs(BuiltInTool.WRITE_FILE, section, { partial: true }); + if (!args) return null; + + // Tool contracts drifted over time: some models emit `path`, + // others `file_path` / `filePath`. Accept all three. + const rawPath = args.path ?? args.file_path ?? args.filePath; + if (typeof rawPath !== 'string' || !rawPath) return null; + + const fileName = rawPath.split(FILE_PATH_SEPARATOR_REGEX).pop() || rawPath; + const content = typeof args.content === 'string' ? args.content : ''; + const language = + getFileTypeByExtension(rawPath)?.replace(TEXT_LANGUAGE_PREFIX_REGEX, '') ?? DEFAULT_LANGUAGE; + + const resultObj = tryParseToolResultObject(section.toolResult); + const bytesWritten = + resultObj && Number.isFinite(Number(resultObj.bytes)) ? Number(resultObj.bytes) : undefined; + const resultMessage = typeof resultObj?.result === 'string' ? resultObj.result : undefined; + const errorMessage = typeof resultObj?.error === 'string' ? resultObj.error : undefined; + + return { + fileName, + filePath: rawPath, + language, + content, + bytesWritten, + resultMessage, + errorMessage + }; +} diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserBubble.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserBubble.svelte index 01fc9d365505..04e6715bf058 100644 --- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserBubble.svelte +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserBubble.svelte @@ -65,7 +65,7 @@ > {#if renderMarkdown && currentConfig.renderUserContentAsMarkdown}
- +
{:else} diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserPending.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserPending.svelte index 58b3a42e07ac..1cc79fe6bca4 100644 --- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserPending.svelte +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserPending.svelte @@ -1,6 +1,5 @@
+ import { ICON_CLASS_DEFAULT } from '$lib/constants/css-classes'; import type { Snippet, Component } from 'svelte'; interface Props { @@ -12,7 +13,7 @@
- + {@render message()} diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageAgenticContent.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageAgenticContent.svelte index f0d03f547862..751d13756271 100644 --- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageAgenticContent.svelte +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageAgenticContent.svelte @@ -1,40 +1,29 @@ -{#snippet renderSection(section: (typeof sectionsParsed)[number], index: number)} +{#snippet renderSection(section: AgenticSection, index: number)} {#if section.type === AgenticSectionType.TEXT}
- {:else if section.type === AgenticSectionType.TOOL_CALL_STREAMING} - {@const streamingIcon = isStreaming ? Loader2 : Loader2} - {@const streamingIconClass = isStreaming ? 'h-4 w-4 animate-spin' : 'h-4 w-4'} - - toggleExpanded(index, section)} - > -
-
- Arguments: - - {#if isStreaming} - - {/if} -
- {#if section.toolArgs} - - {:else if isStreaming} -
- Receiving arguments... -
- {:else} -
- Response was truncated -
- {/if} -
- - {:else if section.type === AgenticSectionType.TOOL_CALL || section.type === AgenticSectionType.TOOL_CALL_PENDING} - {@const isPending = section.type === AgenticSectionType.TOOL_CALL_PENDING} - {@const toolIcon = isPending ? Loader2 : Wrench} - {@const toolIconClass = isPending ? 'h-4 w-4 animate-spin' : 'h-4 w-4'} - - toggleExpanded(index, section)} - > - {#if section.toolArgs && section.toolArgs !== '{}'} -
-
Arguments:
- - -
- {/if} - -
-
- Result: - - {#if isPending} - - {/if} -
- {#if isPending} -
- Waiting for result... -
- {:else if section.toolResult} -
- {#each section.parsedLines as line, i (i)} -
- {line.text} -
- {#if line.image} - {line.image.name} - {/if} - {/each} -
- {:else} -
No output
- {/if} -
- - {:else if section.type === AgenticSectionType.REASONING} - {@const reasoningSubtitle = section.wasInterrupted - ? hasReasoningError - ? 'Error' - : 'Cancelled' - : isStreaming - ? '' - : undefined} - - toggleExpanded(index, section)} - > -
- {#if renderThinkingAsMarkdown} - - {:else} -
- {section.content} -
- {/if} -
- - {:else if section.type === AgenticSectionType.REASONING_PENDING} - {@const reasoningTitle = isStreaming ? 'Reasoning...' : 'Reasoning'} - {@const reasoningSubtitle = isStreaming ? '' : hasReasoningError ? 'Error' : 'Cancelled'} - - + {:else if section.type === AgenticSectionType.TOOL_CALL || section.type === AgenticSectionType.TOOL_CALL_PENDING || section.type === AgenticSectionType.TOOL_CALL_STREAMING} + toggleExpanded(index, section)} - > -
- {#if renderThinkingAsMarkdown} - - {:else} -
- {section.content} -
- {/if} -
- + /> {/if} {/snippet} -
+
{#if turnGroups.length > 1} {#each turnGroups as turn, turnIndex (turnIndex)} {@const turnStats = message?.timings?.agentic?.perTurn?.[turnIndex]} -
+
{#each turn.sections as section, sIdx (turn.flatIndices[sIdx])} {@render renderSection(section, turn.flatIndices[sIdx])} {/each} - {#if turnStats && showMessageStats} -
+ {#if turnStats && showAgenticTurnStats} +
{/each} {:else} - {#each sectionsParsed as section, index (index)} + {#each sections as section, index (index)} {@render renderSection(section, index)} {/each} {/if} @@ -404,7 +256,6 @@ flex-direction: column; width: 100%; max-width: 48rem; - gap: 1rem; } .agentic-content > :global(*), diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageReasoningBlock.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageReasoningBlock.svelte new file mode 100644 index 000000000000..833cae5db529 --- /dev/null +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageReasoningBlock.svelte @@ -0,0 +1,151 @@ + + + +
+ {#if renderThinkingAsMarkdown} + + {:else} +
+ {section.content} +
+ {/if} +
+ + + diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte index dce0edd03138..2b5ccb978e16 100644 --- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte +++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte @@ -1,6 +1,4 @@ -
+
{#each displayMessages as { message, toolMessages, isLastAssistantMessage, isLastUserMessage, nextAssistantMessage, siblingInfo } (message.id)} { + if (autoScroll.userScrolledUp) return; + if (activeConversation()?.id !== id) return; + autoScroll.scrollToBottom(); + const height = container.scrollHeight; + stableFrames = height === lastHeight ? stableFrames + 1 : 0; + lastHeight = height; + if (stableFrames >= LANDING_STABLE_FRAMES) return; + if (performance.now() - started > LANDING_SETTLE_MAX_MS) return; + requestAnimationFrame(settle); + }; + requestAnimationFrame(settle); + } + function handleSendLikeScroll() { if (!isMobile.current) { autoScroll.enable(); @@ -246,6 +281,7 @@ {#if !isEmpty} { handleSendLikeScroll(); }} diff --git a/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenActionScrollDown.svelte b/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenActionScrollDown.svelte index c470ac729690..dca24afd440f 100644 --- a/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenActionScrollDown.svelte +++ b/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenActionScrollDown.svelte @@ -1,4 +1,5 @@ {#if hasError} -
+
{#if isLoadingModel} - + {:else} - + {/if} diff --git a/tools/ui/src/lib/components/app/chat/index.ts b/tools/ui/src/lib/components/app/chat/index.ts index 4f826841e4b7..cd06ec036619 100644 --- a/tools/ui/src/lib/components/app/chat/index.ts +++ b/tools/ui/src/lib/components/app/chat/index.ts @@ -571,6 +571,10 @@ export { default as ChatMessageMcpPromptContent } from './ChatMessages/ChatMessa * Handles streaming state with real-time content updates. */ export { default as ChatMessageAssistant } from './ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistant.svelte'; +export { default as ChatMessageAssistantModel } from './ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistantModel.svelte'; +export { default as ChatMessageAssistantProcessingInfo } from './ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistantProcessingInfo.svelte'; +export { default as ChatMessageAssistantRawOutput } from './ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistantRawOutput.svelte'; +export { default as ChatMessageAssistantStatistics } from './ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistantStatistics.svelte'; /** * Inline message editing form. Provides textarea for editing message content with diff --git a/tools/ui/src/lib/components/app/content/CollapsibleContentBlock.svelte b/tools/ui/src/lib/components/app/content/CollapsibleContentBlock.svelte index 3875b449a186..ad703226192a 100644 --- a/tools/ui/src/lib/components/app/content/CollapsibleContentBlock.svelte +++ b/tools/ui/src/lib/components/app/content/CollapsibleContentBlock.svelte @@ -1,12 +1,8 @@ @@ -76,59 +45,59 @@ open = value; onToggle?.(); }} - class="{className} my-0!" + class={cn('group/collapsible', 'my-0!', className)} > - - -
-
- {#if IconComponent} - - {/if} - - {title} - - {#if subtitle} - {subtitle} - {/if} -
- - {#if displayedPreview && !showThoughtInProgress} -
-
- {displayedPreview} -
- {#if displayedOverflow > 0} - {displayedOverflow}+ chars - {/if} -
+ +
+ {#if iconUrl} + + {:else if IconComponent} + + {/if} + + + {#if titleSnippet} + {@render titleSnippet()} + {:else} + {title} {/if} + + + {#if subtitle} + {subtitle} + {/if} +
+ + + + Toggle content + + + + + {#if open} +
+
+ {@render children()} +
- -
- - - Toggle content -
- - - -
- {@render children()} -
- - + {/if} + diff --git a/tools/ui/src/lib/components/app/content/CollapsibleTerminalBlock.svelte b/tools/ui/src/lib/components/app/content/CollapsibleTerminalBlock.svelte new file mode 100644 index 000000000000..5cbe003bd7ad --- /dev/null +++ b/tools/ui/src/lib/components/app/content/CollapsibleTerminalBlock.svelte @@ -0,0 +1,101 @@ + + + { + open = value; + onToggle?.(); + }} + class={cn('group/collapsible', 'overflow-hidden rounded-md', className)} + style="background: var(--code-background); border: 1px solid color-mix(in oklch, var(--border) 30%, transparent);" +> + +
+ {#if iconUrl} + + {:else if IconComponent} + + {/if} + + + {#if titleSnippet} + {@render titleSnippet()} + {:else} + {title} + {/if} + + + {#if subtitle} + {subtitle} + {/if} +
+ + + + Toggle content + + + + + {#if open} +
+ {@render children()} +
+ {/if} + + diff --git a/tools/ui/src/lib/components/app/content/MarkdownContent/MarkdownContent.svelte b/tools/ui/src/lib/components/app/content/MarkdownContent/MarkdownContent.svelte index 8ac7f94483a2..fc7e314122f7 100644 --- a/tools/ui/src/lib/components/app/content/MarkdownContent/MarkdownContent.svelte +++ b/tools/ui/src/lib/components/app/content/MarkdownContent/MarkdownContent.svelte @@ -78,7 +78,6 @@ import { createAutoScrollController } from '$lib/hooks/use-auto-scroll.svelte'; import type { DatabaseMessageExtra } from '$lib/types/database'; import { config } from '$lib/stores/settings.svelte'; - import { fadeInView } from '$lib/actions/fade-in-view.svelte'; interface Props { attachments?: DatabaseMessageExtra[]; @@ -108,6 +107,15 @@ return null; }); const liveSvgHtml = $derived(streamingSvgCode !== null ? sanitizeSvg(streamingSvgCode) : ''); + + // Derived rather than called inline in the template so it only recomputes when + // the block actually changes. Auto-detection is disabled while streaming: it + // costs ~38ms a call and re-guesses the language on every chunk. + const streamingCodeHtml = $derived( + incompleteCodeBlock + ? highlightCode(incompleteCodeBlock.code, incompleteCodeBlock.language || 'text', false) + : '' + ); let previewDialogOpen = $state(false); let previewCode = $state(''); let previewLanguage = $state('text'); @@ -828,7 +836,7 @@ : ''}" > {#each renderedBlocks as block (block.id)} -
+
{@html block.html}
{/each} @@ -904,10 +912,7 @@ > 
{@html highlightCode(
-								incompleteCodeBlock.code,
-								incompleteCodeBlock.language || 'text'
-							)}{@html streamingCodeHtml}
diff --git a/tools/ui/src/lib/components/app/content/MarkdownContent/markdown-content.css b/tools/ui/src/lib/components/app/content/MarkdownContent/markdown-content.css index b0e04ca6209a..41813f4fda76 100644 --- a/tools/ui/src/lib/components/app/content/MarkdownContent/markdown-content.css +++ b/tools/ui/src/lib/components/app/content/MarkdownContent/markdown-content.css @@ -19,8 +19,16 @@ line-height: 1.75; } +.markdown-content :global(.markdown-block:first-child p:first-child) { + margin-block-start: 0; +} + +.markdown-content :global(.markdown-block:last-child p:last-child) { + margin-block-end: 0; +} + .markdown-content :global(:is(h1, h2, h3, h4, h5, h6):first-child) { - margin-top: 0; + margin-top: 0.5rem; } /* Headers with consistent spacing */ diff --git a/tools/ui/src/lib/components/app/content/MermaidPreviewControls.svelte b/tools/ui/src/lib/components/app/content/MermaidPreviewControls.svelte index bb3185f40eda..39540e7a8cd5 100644 --- a/tools/ui/src/lib/components/app/content/MermaidPreviewControls.svelte +++ b/tools/ui/src/lib/components/app/content/MermaidPreviewControls.svelte @@ -1,4 +1,5 @@
- + 
{@html highlightedHtml}
diff --git a/tools/ui/src/lib/components/app/content/index.ts b/tools/ui/src/lib/components/app/content/index.ts index 5d2884bb214c..5cfdd1b9c1e0 100644 --- a/tools/ui/src/lib/components/app/content/index.ts +++ b/tools/ui/src/lib/components/app/content/index.ts @@ -68,7 +68,6 @@ export { default as SyntaxHighlightedCode } from './SyntaxHighlightedCode.svelte * ```svelte * @@ -78,6 +77,22 @@ export { default as SyntaxHighlightedCode } from './SyntaxHighlightedCode.svelte */ export { default as CollapsibleContentBlock } from './CollapsibleContentBlock.svelte'; +/** + * **CollapsibleTerminalBlock** - Expandable content card with a terminal-style frame + * + * Same shape as CollapsibleContentBlock, but with a `code-background` + * fill, subtle border, and tightened padding suited for shell command + * output and similar dense / monospace content. + * + * @example + * ```svelte + * + * 
{output}
+ * + * ``` + */ +export { default as CollapsibleTerminalBlock } from './CollapsibleTerminalBlock.svelte'; + /** * **MermaidPreview** - Interactive Mermaid diagram viewer * diff --git a/tools/ui/src/lib/components/app/dialogs/DialogConversationRename.svelte b/tools/ui/src/lib/components/app/dialogs/DialogConversationRename.svelte new file mode 100644 index 000000000000..d85340f3fb6a --- /dev/null +++ b/tools/ui/src/lib/components/app/dialogs/DialogConversationRename.svelte @@ -0,0 +1,85 @@ + + + + + + + + Rename conversation + + + Choose a new title for this conversation. + + +
+ + + +
+ + + Cancel + + + + + diff --git a/tools/ui/src/lib/components/app/dialogs/DialogConversationSelection.svelte b/tools/ui/src/lib/components/app/dialogs/DialogConversationSelection.svelte index 7373250850cc..5f5b2f4ab315 100644 --- a/tools/ui/src/lib/components/app/dialogs/DialogConversationSelection.svelte +++ b/tools/ui/src/lib/components/app/dialogs/DialogConversationSelection.svelte @@ -37,9 +37,9 @@ - + - + Select Conversations to {mode === 'export' ? 'Export' : 'Import'} @@ -58,6 +58,7 @@ - import * as AlertDialog from '$lib/components/ui/alert-dialog'; - import { Button } from '$lib/components/ui/button'; - - interface Props { - open: boolean; - currentTitle: string; - newTitle: string; - onConfirm: () => void; - onCancel: () => void; - } - - let { open = $bindable(), currentTitle, newTitle, onConfirm, onCancel }: Props = $props(); - - - - - - Update Conversation Title? - - - Do you want to update the conversation title to match the first message content? - - - -
-
-

Current title:

- -

{currentTitle}

-
- -
-

New title would be:

- -

{newTitle}

-
-
- - - - - - - - diff --git a/tools/ui/src/lib/components/app/dialogs/DialogExportSettings.svelte b/tools/ui/src/lib/components/app/dialogs/DialogExportSettings.svelte index c112bde9f682..fe36dce56e52 100644 --- a/tools/ui/src/lib/components/app/dialogs/DialogExportSettings.svelte +++ b/tools/ui/src/lib/components/app/dialogs/DialogExportSettings.svelte @@ -68,6 +68,7 @@ Cancel + + import { ICON_CLASS_DEFAULT } from '$lib/constants/css-classes'; import { FolderOpen, Plus, Loader2, Braces } from '@lucide/svelte'; import { toast } from 'svelte-sonner'; import * as Dialog from '$lib/components/ui/dialog'; @@ -289,7 +290,7 @@ {#if selectedTemplate && !templatePreviewContent}
- + {selectedTemplate.title || selectedTemplate.name} @@ -371,9 +372,9 @@ {#if hasTemplateResult} +
+ +
+ {#each recommendationsToShow as recommendation (recommendation.id)} + handleRecommendationClick(recommendation.id)} + selected={selectedRecommendationId === recommendation.id} + dimmed={hasSelection && selectedRecommendationId !== recommendation.id} + /> + {/each} +
+
+ {/if} +
(newServerUrl = v)} @@ -83,6 +282,8 @@ onUseProxyChange={(v) => (newServerUseProxy = v)} urlError={newServerUrl ? newServerUrlError : null} id="new-server" + bind:wantsAuthorization={newServerWantsAuthorization} + required={authRequired} />
diff --git a/tools/ui/src/lib/components/app/dialogs/DialogModelNotAvailable.svelte b/tools/ui/src/lib/components/app/dialogs/DialogModelNotAvailable.svelte index a6c20291fa0a..89d23cd4b292 100644 --- a/tools/ui/src/lib/components/app/dialogs/DialogModelNotAvailable.svelte +++ b/tools/ui/src/lib/components/app/dialogs/DialogModelNotAvailable.svelte @@ -1,4 +1,5 @@ + + +
+ {#if activeIconUrl} + + {/if} + +

{server.name}

+
+ +

{server.description}

+ diff --git a/tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCardEditForm.svelte b/tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCardEditForm.svelte index 8ed4ee8b8023..19778f95b006 100644 --- a/tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCardEditForm.svelte +++ b/tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCardEditForm.svelte @@ -7,13 +7,23 @@ serverId: string; serverUrl: string; serverUseProxy?: boolean; - onSave: (url: string, headers: string, useProxy: boolean) => void; + /** Current automatic label, prefilled so the user can customize it. */ + serverLabel?: string; + onSave: (url: string, headers: string, useProxy: boolean, name?: string) => void; onCancel: () => void; } - let { serverId, serverUrl, serverUseProxy = false, onSave, onCancel }: Props = $props(); + let { + serverId, + serverUrl, + serverUseProxy = false, + serverLabel = '', + onSave, + onCancel + }: Props = $props(); let editUrl = $derived(serverUrl); + let editName = $derived(serverLabel); let editHeaders = $state(''); let editUseProxy = $derived(serverUseProxy); @@ -34,7 +44,12 @@ function handleSave() { if (!canSave) return; - onSave(editUrl.trim(), editHeaders.trim(), editUseProxy); + + // An unchanged prefill keeps following the automatic label; only an + // actual edit becomes a persisted custom display name. + const name = editName.trim() !== serverLabel.trim() ? editName.trim() : undefined; + + onSave(editUrl.trim(), editHeaders.trim(), editUseProxy, name); } function handleSubmit(event: SubmitEvent) { @@ -42,10 +57,11 @@ handleSave(); } - export function setInitialValues(url: string, headers: string, useProxy: boolean) { + export function setInitialValues(url: string, headers: string, useProxy: boolean, name = '') { editUrl = url; editHeaders = headers; editUseProxy = useProxy; + editName = name; } @@ -55,6 +71,8 @@ (editName = v)} headers={editHeaders} useProxy={editUseProxy} onUrlChange={(v) => (editUrl = v)} diff --git a/tools/ui/src/lib/components/app/mcp/McpServerForm.svelte b/tools/ui/src/lib/components/app/mcp/McpServerForm.svelte index 7f05d5fef31f..2b8e1226bab8 100644 --- a/tools/ui/src/lib/components/app/mcp/McpServerForm.svelte +++ b/tools/ui/src/lib/components/app/mcp/McpServerForm.svelte @@ -5,30 +5,60 @@ import type { KeyValuePair } from '$lib/types'; import { parseHeadersToArray, serializeHeaders } from '$lib/utils'; import { UrlProtocol } from '$lib/enums'; - import { MCP_SERVER_URL_PLACEHOLDER } from '$lib/constants'; + import { + AUTHORIZATION_HEADER, + BEARER_PREFIX, + CLI_FLAGS, + MCP_SERVER_URL_PLACEHOLDER, + REDACTED_HEADERS + } from '$lib/constants'; import { mcpStore } from '$lib/stores/mcp.svelte'; - import { CLI_FLAGS } from '$lib/constants'; interface Props { url: string; headers: string; + name?: string; + onNameChange?: (name: string) => void; + /** Shown in the empty display name field, e.g. the current automatic label. */ + namePlaceholder?: string; useProxy?: boolean; onUrlChange: (url: string) => void; onHeadersChange: (headers: string) => void; onUseProxyChange?: (useProxy: boolean) => void; urlError?: string | null; id?: string; + /** + * "Wants Authorization" is the user's *intent* to add a Bearer token + * (separate from `hasAuthorization` which reflects what's already in + * the headers). Bindable so a parent - e.g. the recommendation cards + * on the "Add New Server" dialog - can flip the switch on when the + * picked server ships a `needsAuthorization: true` flag. + */ + wantsAuthorization?: boolean; + /** + * Marks the "Authorization" field as required. Locks the toggle so the + * user can't dismiss it, and visually marks the field with a red + * asterisk. The parent is expected to gate its submit affordance on + * the bearer token actually being filled. Used by the "Add New Server" + * dialog for recommendations whose `needsAuthorization` flag is true. + */ + required?: boolean; } let { url, headers, + name = '', + onNameChange, + namePlaceholder = 'Name reported by the server', useProxy = false, onUrlChange, onHeadersChange, onUseProxyChange, urlError = null, - id = 'server' + id = 'server', + wantsAuthorization = $bindable(false), + required = false }: Props = $props(); let isWebSocket = $derived( @@ -38,14 +68,11 @@ let headerPairs = $derived(parseHeadersToArray(headers)); - const AUTHORIZATION_HEADER = 'Authorization'; - const BEARER_PREFIX = 'Bearer '; - // Heuristic: this dedicated UI only owns Authorization headers that already // carry a Bearer scheme. Anything else (e.g. Basic, raw tokens) stays in the // KV section so the user can still edit those values verbatim. const matchesAuthorizationKey = (key: string): boolean => - key.trim().toLowerCase() === AUTHORIZATION_HEADER.toLowerCase(); + REDACTED_HEADERS.has(key.trim().toLowerCase()); const isBearerScheme = (value: string): boolean => value.trim().toLowerCase().startsWith(BEARER_PREFIX.toLowerCase()); @@ -55,8 +82,6 @@ let hasAuthorization = $derived(headerPairs.some(ownedByBearerUi)); - let wantsAuthorization = $state(false); - let showAuthorization = $derived(hasAuthorization || wantsAuthorization); let urlInput: HTMLInputElement | null = $state(null); @@ -119,7 +144,7 @@
-
-
- + @@ -139,6 +115,7 @@ + {#if filteredConversations.length === 0} @@ -152,23 +129,28 @@ {:else} {#each filteredConversations as conv (conv.id)} + {@const checked = selectedIds.has(conv.id)} toggleConversation(conv.id, event.shiftKey)} + class="cursor-pointer border-b transition-colors hover:bg-muted/50 {checked + ? 'bg-muted/75' + : ''}" + data-conversation-row={conv.id} + onmousedown={(event) => marquee.rowMouseDown(conv.id, event)} + onclick={(event) => marquee.rowClick(conv.id, event.shiftKey)} > diff --git a/tools/ui/src/lib/components/app/misc/HorizontalScrollCarousel.svelte b/tools/ui/src/lib/components/app/misc/HorizontalScrollCarousel.svelte index a04f3956f8a5..d5665901a1a4 100644 --- a/tools/ui/src/lib/components/app/misc/HorizontalScrollCarousel.svelte +++ b/tools/ui/src/lib/components/app/misc/HorizontalScrollCarousel.svelte @@ -1,4 +1,5 @@ {#snippet itemIcon(IconComponent: Component)} - + {/snippet} {#if isSearchModeActive} @@ -118,9 +119,7 @@ : onSearchClick} {@const itemTransition = { duration: ICON_STRIP_TRANSITION_DURATION, - delay: !initialized - ? ICON_STRIP_TRANSITION_DELAY_MULTIPLIER + i * ICON_STRIP_TRANSITION_DELAY_MULTIPLIER - : 0, + delay: !initialized ? i * ICON_STRIP_TRANSITION_DELAY_MULTIPLIER : 0, easing: circIn }} @@ -139,10 +138,8 @@ {@render itemIcon(item.icon)} {#if showIcons} - {item.tooltip}{item.tooltip} {/if} @@ -170,9 +167,7 @@ : onSearchClick} {@const itemTransition = { duration: ICON_STRIP_TRANSITION_DURATION, - delay: !initialized - ? ICON_STRIP_TRANSITION_DELAY_MULTIPLIER + i * ICON_STRIP_TRANSITION_DELAY_MULTIPLIER - : 0, + delay: !initialized ? i * ICON_STRIP_TRANSITION_DELAY_MULTIPLIER : 0, easing: circIn }} @@ -183,7 +178,7 @@ tooltip={item.tooltip} tooltipSide={TooltipSide.RIGHT} size="lg" - iconSize="h-4 w-4" + iconSize={ICON_CLASS_DEFAULT} class="h-9 w-9 rounded-full hover:bg-accent! {isActive ? 'bg-accent text-accent-foreground' : ''}" diff --git a/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationConversationItem.svelte b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationConversationItem.svelte index b1c2b78f65ea..7204d7fec293 100644 --- a/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationConversationItem.svelte +++ b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationConversationItem.svelte @@ -1,4 +1,5 @@ -{#if isSearchModeActive} - -{:else} - {#if pinnedConversations.length > 0} -
-
- +
+ {#if isSearchModeActive} + + {:else} + {#if pinnedConversations.length > 0} +
+
+ - Pinned + Pinned +
-
- -
    - {#each pinnedConversations as { conversation, depth } (conversation.id)} -
  • - -
    • - {/each} -
- {/if} - -
- {#if filteredConversations.length > 0} -
- Recent conversations -
- {/if} -
-
    - {#each unpinnedConversations as { conversation, depth } (conversation.id)} +
      + {#each pinnedConversations as { conversation, depth } (conversation.id)}
      • {/each} - - {#if unpinnedConversations.length === 0} -
    • -

      - {recentEmptyMessage} -

      -
      • - {/if}
    + {/if} + +
    + {#if filteredConversations.length > 0} +
    + Recent conversations +
    + {/if} + +
    +
      + {#each unpinnedConversations as { conversation, depth } (conversation.id)} +
    • + +
      • + {/each} + + {#if unpinnedConversations.length === 0} +
    • +

      + {recentEmptyMessage} +

      +
      • + {/if} +
    +
    -
-{/if} + + {#if isSelectionMode} + + {/if} + {/if} +
diff --git a/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationSearchResults.svelte b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationSearchResults.svelte index 92d8fd0bda88..68d6c214366b 100644 --- a/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationSearchResults.svelte +++ b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationSearchResults.svelte @@ -7,10 +7,16 @@ searchQuery: string; filteredConversations: DatabaseConversation[]; currentChatId: string | undefined; + isSelectionMode?: boolean; + selectedIds?: Set; onSelect: (id: string) => void; onEdit: (id: string) => void; onDelete: (id: string) => void; onStop: (id: string) => void; + onToggleSelect?: (id: string) => void; + onEnterSelectionMode?: (id: string) => void; + onSelectionClick?: (id: string, options: { shiftKey: boolean }) => void; + onRowMouseDown?: (id: string, event: MouseEvent) => void; } let { @@ -18,10 +24,16 @@ searchQuery, filteredConversations, currentChatId, + isSelectionMode = false, + selectedIds = new Set(), onSelect, onEdit, onDelete, - onStop + onStop, + onToggleSelect, + onEnterSelectionMode, + onSelectionClick, + onRowMouseDown }: Props = $props(); let tree = $derived(buildConversationTree(filteredConversations)); @@ -56,10 +68,16 @@ }} {depth} isActive={currentChatId === conversation.id} + {isSelectionMode} + isSelected={selectedIds.has(conversation.id)} {onSelect} {onEdit} {onDelete} {onStop} + {onToggleSelect} + {onEnterSelectionMode} + {onSelectionClick} + {onRowMouseDown} /> {/each} diff --git a/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationSelectionBar.svelte b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationSelectionBar.svelte new file mode 100644 index 000000000000..15412e57b62e --- /dev/null +++ b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationSelectionBar.svelte @@ -0,0 +1,163 @@ + + + + + diff --git a/tools/ui/src/lib/components/app/navigation/index.ts b/tools/ui/src/lib/components/app/navigation/index.ts index e07dde6bc901..ea5ad1794012 100644 --- a/tools/ui/src/lib/components/app/navigation/index.ts +++ b/tools/ui/src/lib/components/app/navigation/index.ts @@ -114,6 +114,36 @@ export { default as SidebarNavigation } from './SidebarNavigation/SidebarNavigat */ export { default as SidebarNavigationConversationItem } from './SidebarNavigation/SidebarNavigationConversationItem.svelte'; +/** + * **SidebarNavigationSelectionBar** - Bulk action toolbar for selection mode + * + * Rendered above the conversation list when the sidebar enters selection mode. + * Hosts a master checkbox (with select-all / clear-all semantics over the + * currently-visible items), a selected-count caption, and bulk actions for + * pin/unpin, export, and delete. Delete uses + * {@link DialogConfirmation} before invoking the bulk store method. + * + * Pure-presentational; all operations are delegated via callbacks so the + * sidebar owns selection state and persistence. + * + * @example + * ```svelte + * + * ``` + */ +export { default as SidebarNavigationSelectionBar } from './SidebarNavigation/SidebarNavigationSelectionBar.svelte'; + /** * **SidebarNavigationConversationList** - Grouped conversation list * diff --git a/tools/ui/src/lib/components/app/server/ServerErrorSplash.svelte b/tools/ui/src/lib/components/app/server/ServerErrorSplash.svelte index 4da0d1ddfa80..d9c4386e4c10 100644 --- a/tools/ui/src/lib/components/app/server/ServerErrorSplash.svelte +++ b/tools/ui/src/lib/components/app/server/ServerErrorSplash.svelte @@ -1,4 +1,5 @@ {#each fields as field (field.key)} -
- {#if field.type === SettingsFieldType.INPUT} - {@const currentValue = String(localConfig[field.key] ?? '')} - {@const serverDefault = currentModelParams[field.key]} - {@const isCustomRealTime = (() => { - if (serverDefault == null) return false; - if (currentValue === '') return false; + {#if !field.dependsOn || Boolean(localConfig[field.dependsOn])} +
+ {#if field.type === SettingsFieldType.INPUT} + {@const currentValue = String(localConfig[field.key] ?? '')} + {@const serverDefault = currentModelParams[field.key]} + {@const isCustomRealTime = (() => { + if (serverDefault == null) return false; + if (currentValue === '') return false; - const numericInput = parseFloat(currentValue); - const normalizedInput = !isNaN(numericInput) - ? Math.round(numericInput * 1000000) / 1000000 - : currentValue; - const normalizedDefault = - typeof serverDefault === 'number' - ? Math.round(serverDefault * 1000000) / 1000000 - : serverDefault; + const numericInput = parseFloat(currentValue); + const normalizedInput = !isNaN(numericInput) + ? Math.round(numericInput * 1000000) / 1000000 + : currentValue; + const normalizedDefault = + typeof serverDefault === 'number' + ? Math.round(serverDefault * 1000000) / 1000000 + : serverDefault; - return normalizedInput !== normalizedDefault; - })()} + return normalizedInput !== normalizedDefault; + })()} -
- - {#if isCustomRealTime} - - {/if} -
- -
- { - // Update local config immediately for real-time badge feedback - onConfigChange(field.key, e.currentTarget.value); - }} - placeholder={currentModelParams[field.key] != null - ? `Default: ${normalizeFloatingPoint(currentModelParams[field.key])}` - : ''} - class="w-full {isCustomRealTime ? 'pr-8' : ''}" - /> - {#if isCustomRealTime} - - {/if} -
- {#if field.help || SETTING_CONFIG_INFO[field.key]} -

- {@html field.help || SETTING_CONFIG_INFO[field.key]} -

- {/if} - {:else if field.type === SettingsFieldType.TEXTAREA} - {#if field.label} - - {/if} - -
Messages
{ event.preventDefault(); event.stopPropagation(); - toggleConversation(conv.id, event.shiftKey); + marquee.rowClick(conv.id, event.shiftKey); }} /> -
+
{conv.name || 'Untitled conversation'}