diff --git a/.gitignore b/.gitignore index 76ab01c4..868074dd 100644 --- a/.gitignore +++ b/.gitignore @@ -165,3 +165,7 @@ cython_debug/ .ruff_cache + + +.DS_Store +./notebooks/* \ No newline at end of file diff --git a/docs/css/extra.css b/docs/css/extra.css index 18d941bd..afac0ed8 100644 --- a/docs/css/extra.css +++ b/docs/css/extra.css @@ -301,3 +301,132 @@ Light green: 00bf63 -webkit-mask-repeat: no-repeat; mask-repeat: no-repeat; } + + +/* ── Supported Models page ──────────────────────────────────── */ + +.models-filter-bar { + display: flex; + align-items: center; + flex-wrap: wrap; + gap: 0.5rem; + margin-bottom: 0.75rem; +} + +.filter-label { + font-size: 0.8rem; + font-weight: 600; + text-transform: uppercase; + letter-spacing: 0.05em; + color: var(--md-default-fg-color--light); + margin-right: 0.25rem; +} + +.modality-filter { + display: inline-flex; + align-items: center; + gap: 0.35rem; + padding: 0.3rem 0.8rem; + border: 1px solid var(--md-default-fg-color--lightest); + border-radius: 2rem; + background: transparent; + color: var(--md-default-fg-color--light); + font-size: 0.8rem; + font-weight: 500; + cursor: pointer; + transition: background 0.15s, color 0.15s, border-color 0.15s; +} + +.modality-filter:hover { + border-color: var(--md-accent-fg-color); + color: var(--md-accent-fg-color); +} + +.modality-filter.active { + background: var(--md-primary-fg-color); + border-color: var(--md-primary-fg-color); + color: var(--md-primary-bg-color); +} + +.modality-filter .filter-count { + background: rgba(255, 255, 255, 0.25); + border-radius: 1rem; + padding: 0 0.4rem; + font-size: 0.72rem; +} + +.modality-filter:not(.active) .filter-count { + background: var(--md-default-fg-color--lightest); + color: var(--md-default-fg-color); +} + +.models-search-bar { + display: flex; + align-items: center; + gap: 1rem; + margin-bottom: 1rem; +} + +#models-search { + flex: 1; + max-width: 28rem; + padding: 0.4rem 0.75rem; + border: 1px solid var(--md-default-fg-color--lightest); + border-radius: 0.25rem; + background: var(--md-default-bg-color); + color: var(--md-default-fg-color); + font-size: 0.85rem; +} + +#models-search:focus { + outline: none; + border-color: var(--md-accent-fg-color); +} + +.filter-count-display { + font-size: 0.8rem; + color: var(--md-default-fg-color--light); + white-space: nowrap; +} + +/* Modality badges inside table */ +.modality-badge { + display: inline-block; + padding: 0.15rem 0.55rem; + border-radius: 0.25rem; + font-size: 0.72rem; + font-weight: 600; + letter-spacing: 0.03em; + text-transform: uppercase; +} + +.modality-text { background: #dbeafe; color: #1e40af; } +.modality-image { background: #fce7f3; color: #9d174d; } +.modality-video { background: #d1fae5; color: #065f46; } + +[data-md-color-scheme="slate"] .modality-text { background: #1e3a5f; color: #93c5fd; } +[data-md-color-scheme="slate"] .modality-image { background: #4a1942; color: #f9a8d4; } +[data-md-color-scheme="slate"] .modality-video { background: #064e3b; color: #6ee7b7; } + +/* Warning badges */ +.model-warning-badge { + display: inline-block; + padding: 0.1rem 0.4rem; + border-radius: 0.2rem; + font-size: 0.68rem; + background: var(--md-default-fg-color--lightest); + color: var(--md-default-fg-color--light); + font-family: var(--md-code-font-family); +} + +/* Keep table compact */ +#models-table td, +#models-table th { + padding: 0.4rem 0.6rem; + font-size: 0.82rem; + white-space: nowrap; +} + +#models-table td code { + font-size: 0.75rem; +} diff --git a/docs/js/models_filter.js b/docs/js/models_filter.js new file mode 100644 index 00000000..1c5afcce --- /dev/null +++ b/docs/js/models_filter.js @@ -0,0 +1,84 @@ +/** + * Sortable + filterable models table. + * Runs inside MkDocs Material's document$ observable so it + * re-executes on soft navigations (instant loading). + */ +document$.subscribe(function () { + const table = document.getElementById("models-table"); + if (!table) return; + + // ── Tablesort ────────────────────────────────────────────────── + if (typeof Tablesort !== "undefined") { + new Tablesort(table); + } + + // ── State ────────────────────────────────────────────────────── + const allRows = Array.from(table.querySelectorAll("tbody tr")); + let activeModalities = new Set(["all"]); + let searchTerm = ""; + + function applyFilters() { + let visible = 0; + allRows.forEach(function (row) { + const modality = row.dataset.modality || ""; + const text = row.textContent.toLowerCase(); + const modalityMatch = + activeModalities.has("all") || activeModalities.has(modality); + const searchMatch = + searchTerm === "" || text.includes(searchTerm); + const show = modalityMatch && searchMatch; + row.style.display = show ? "" : "none"; + if (show) visible++; + }); + + const counter = document.getElementById("models-visible-count"); + if (counter) { + counter.textContent = visible + " model" + (visible !== 1 ? "s" : ""); + } + } + + // ── Multiselect modality filter buttons ──────────────────────── + const filterBtns = document.querySelectorAll(".modality-filter"); + filterBtns.forEach(function (btn) { + btn.addEventListener("click", function () { + const filter = btn.dataset.filter; + + if (filter === "all") { + activeModalities = new Set(["all"]); + } else { + activeModalities.delete("all"); + if (activeModalities.has(filter)) { + activeModalities.delete(filter); + if (activeModalities.size === 0) { + activeModalities = new Set(["all"]); + } + } else { + activeModalities.add(filter); + } + } + + // Reflect active state on buttons + filterBtns.forEach(function (b) { + const isActive = + b.dataset.filter === "all" + ? activeModalities.has("all") + : activeModalities.has(b.dataset.filter); + b.classList.toggle("active", isActive); + }); + + applyFilters(); + }); + }); + + // ── Search box ───────────────────────────────────────────────── + const searchInput = document.getElementById("models-search"); + if (searchInput) { + searchInput.addEventListener("input", function () { + searchTerm = searchInput.value.toLowerCase().trim(); + applyFilters(); + }); + } + + // Initial render + applyFilters(); +}); diff --git a/docs/methodology/diffusion_inference.md b/docs/methodology/diffusion_inference.md new file mode 100644 index 00000000..5a93ab72 --- /dev/null +++ b/docs/methodology/diffusion_inference.md @@ -0,0 +1,386 @@ +# Environmental Impacts of Diffusion Inference + +## Introduction + +This methodology extends EcoLogits to **image and video diffusion models** (text-to-image, image-to-image, inpainting, text-to-video and video-to-video). Like the [LLM inference](llm_inference.md) methodology, the environmental impacts of a request, $I_{\text{request}}$, are split into usage impacts $I_{\text{request}}^{\text{u}}$ and embodied impacts $I_{\text{request}}^{\text{e}}$: + +$$ +I_{\text{request}} = I_{\text{request}}^{\text{u}} + I_{\text{request}}^{\text{e}} = E_{\text{request}} \times F_{\text{em}} + \frac{\Delta T}{\Delta L} \times I_{\text{server}}^{\text{e}}. +$$ + +Everything **downstream of GPU energy** — server energy, PUE, electricity mix $F_{\text{em}}$, embodied impacts, WCF and the `RangeValue` approximation intervals — is **reused unchanged** from the LLM methodology. The diffusion-specific part is the way the **GPU energy** $E_{\text{GPU}}$ is estimated. + +!!! note "This methodology also serves as the fallback for unbenchmarked video models." + + When a video model has no empirical latency regression in `video_models.json`, EcoLogits uses this FLOPs-based backend and attaches the `modality-video-flops-fallback` warning. See [Video Generation](video_generation.md) for the primary video methodology. + +## Why FLOPs, not tokens + +The LLM model keys energy off the number of output tokens and the number of active parameters via a regression of the form $f_E = \alpha e^{\beta B} P_{\text{active}} + \gamma$. This does not transfer to diffusion: there is **no token stream**. The energy of a diffusion request is driven by the number of **denoising steps**, the **resolution**, the **number of frames** (video) and the **FLOPs per denoising pass**, not by tokens. + +The backbone is the validated scaling law from *Energy Scaling Laws for Diffusion Models* ([arXiv:2511.17031](https://arxiv.org/abs/2511.17031)), which reports $R^2 > 0.9$ across multiple GPUs and resolutions. The denoising loop dominates total compute, but the single text-encode and VAE[^vae]-decode passes together contribute 5–15% of pipeline FLOPs and are **not negligible**. They are accounted for via a pipeline overhead factor $\varphi$: + +$$ +\text{FLOPs}_{\text{total}} = \varphi \times T \times \text{FLOPs}_{\text{denoise}}, \qquad \varphi = 1.1, +$$ + +where $T$ is the number of denoising steps, $\text{FLOPs}_{\text{denoise}}$ is the cost of a single denoiser (U-Net or DiT) forward pass at the model native resolution, and $\varphi = 1.1$ is a conservative central estimate for the pipeline overhead (text encoder + VAE decoder). The actual overhead ranges from ~5% for models with a small CLIP encoder (SD 2.x) to ~15% for models with a large T5-XXL encoder (Flux, SD3). Classifier-free guidance (CFG[^cfg]) runs the denoiser twice per step (conditional + unconditional), so it doubles the denoising compute: + +$$ +\text{FLOPs}_{\text{cfg}} = \text{FLOPs}_{\text{total}} \times 2^{[\text{cfg}]}, +$$ + +with $[\text{cfg}] = 1$ when guidance is on and $0$ otherwise. GPU energy is then modeled as a log-linear function of compute, resolution, numerical precision and accelerator: + +$$ +\log(E_{\text{GPU}}) = \log(A) + \alpha \log(\text{FLOPs}_{\text{cfg}}) + \beta_{\text{res}} \log\!\left(\frac{H \cdot W}{256}\right) + \beta_{\text{dtype}} + \beta_{\text{gpu}}. +$$ + +Here $H$ and $W$ are the output height and width in pixels, $A$ is a calibration constant, $\alpha$ the FLOPs elasticity, $\beta_{\text{res}}$ the residual resolution sensitivity not already captured by the FLOPs term, and $\beta_{\text{dtype}}$ / $\beta_{\text{gpu}}$ offsets for numerical precision and GPU model. + +### Per-modality GPU energy + +For **image** generation, the energy of producing $n_{\text{images}}$ images is the per-image energy multiplied by the image count. Multiple images from a single prompt are assumed to be generated **in series** (one full denoising pipeline per image, no shared context or KV-cache between outputs), so total energy scales linearly: + +$$ +E_{\text{GPU}}^{\text{image}} = n_{\text{images}} \times \exp\!\Big(\log(A) + \alpha \log(\text{FLOPs}_{\text{cfg}}) + \beta_{\text{res}} \log\!\big(\tfrac{H \cdot W}{256}\big) + \beta_{\text{dtype}} + \beta_{\text{gpu}}\Big). +$$ + +For **video** generation, the denoiser operates over the full spatiotemporal latent volume, so the per-step FLOPs grow with the frame count $N_f$. The frame budget is taken from `num_frames` or derived from `duration_s` $\times$ `fps`: + +$$ +N_f = \min\{\text{num\_frames},\ \text{duration\_s} \times \text{fps}\}. +$$ + +#### Frame attention scaling + +The per-step FLOPs of a video denoiser have two distinct scaling regimes depending on architecture: + +- **MLP / FFN layers** — cost scales **linearly** with the number of tokens, i.e., $\propto N_f$. +- **Full-sequence 3D attention** (used in HunyuanVideo, WAN, CogVideoX) — cost scales **quadratically**, i.e., $\propto N_f^2$, because every token attends to every other token across space *and* time. +- **Sparse / causal attention** (used in LTX-Video) — cost is closer to linear or sub-linear. + +EcoLogits models this with a **mixed linear+quadratic frame scaling**: + +$$ +\text{frame\_scale}(N_f) = (1 - f) \cdot \frac{N_f}{N_f^{\text{default}}} + f \cdot \left(\frac{N_f}{N_f^{\text{default}}}\right)^2, +$$ + +where $N_f^{\text{default}}$ is the model's native clip length (from `models.json`), and $f \in [0, 1]$ is the **`frame_attn_fraction`**: the share of per-step FLOPs that come from full-sequence attention and therefore scale quadratically with frame count. + +**Calibration of $f$.** For the three open video models present in both the FLOPs registry (`models.json`) and the empirical latency registry (`video_models.json`), $f$ was fitted by requiring the frame-count scaling curve of this model to match the ratio $E_{\text{PR}}(N_f) / E_{\text{PR}}(N_f^{\text{ref}})$ from the PR #240 latency-based backend, with $N_f^{\text{ref}}$ corresponding to a 2-second clip: + +$$ +f = \frac{x_{\text{tgt}} - r \cdot x_{\text{ref}}}{r \cdot (x_{\text{ref}}^2 - x_{\text{ref}}) - (x_{\text{tgt}}^2 - x_{\text{tgt}})}, \qquad r = \frac{E_{\text{PR}}(N_f^{\text{tgt}})}{E_{\text{PR}}(N_f^{\text{ref}})}, +$$ + +where $x = N_f / N_f^{\text{default}}$ and the target point is an 8-second clip. + +| Model | $N_f^{\text{default}}$ | Attention type | $f$ | Basis | +|-------|------------------------|----------------|-----|-------| +| HunyuanVideo | 129 | Full 3D attention | **0.64** | Empirically fitted | +| WAN2.1-T2V-14B / 1.3B | 81 | Full 3D attention | **0.54** | Empirically fitted | +| LTX-Video | 121 | Causal / sparse attention | **0.00** | Sub-linear; capped at 0 | +| CogVideoX-2b / 5b, Mochi-1 | — | DiT 3D attention | **0.50** | Architecture estimate | +| Closed video models | — | Unknown | **0.45–0.55** | Conservative estimate | +| Image models | — | No temporal attention | **0.00** | By definition | + +**Effect.** Cross-validating against the PR latency backend at 720p, the PR/FLOPs energy ratio was **growing** from ~1.7× at 2 s to ~3.8× at 8 s before this correction (quadratic drift). After adding $f$, the ratio is **constant** at ~2.9× for HunyuanVideo and ~2.5× for WAN across all tested durations. The remaining constant gap reflects known attribution differences between the two methods (whole-machine DGX power allocation vs. per-GPU H100), not a scaling artefact. + +!!! note "Limitation" + + The fitted $f$ values are derived from the empirical latency regressions in `video_models.json`, which are themselves modelled (latency empirical, power inferred from datasheets). Direct per-architecture FLOPs profiling is needed to replace these estimates with ground-truth attention fractions. + +## Input taxonomy + +The core deliverable of this page is making explicit **what the user provides** versus **what EcoLogits assumes**. Diffusion APIs expose far fewer knobs than the underlying model exposes, so most of the energy-driving parameters are extrapolated from documented defaults. + +| Input | Source | Notes | +|-------|--------|-------| +| `provider` | **user-supplied** | Resolves the data-center PUE/WUE and default zone. | +| `model_name` | **user-supplied** | Resolves architecture, FLOPs and defaults from the model repository. | +| `task` | **user-supplied** | One of `txt2img`, `img2img`, `inpaint`, `txt2vid`, `vid2vid`. | +| `width`, `height` | **user-supplied** | Output resolution, drives the $H \cdot W$ term. | +| `n_images` | **user-supplied** (image) | Number of images requested. | +| `num_frames` or `duration_s` $\times$ `fps` | **user-supplied** (video) | Frame budget of the clip. | +| `denoising_strength` | **user-supplied** (editing only) | $0$–$1$, sets executed steps for `img2img` / `inpaint` / `vid2vid`. | +| `num_inference_steps` | **assumed** | Per-model/sampler default (e.g. 28–50; turbo/LCM 1–8). | +| `guidance_scale` / CFG | **assumed** | Default on $\Rightarrow$ $2\times$ denoise FLOPs. | +| FLOPs / parameter count | **assumed** | From the repository entry; closed models use a `RangeValue` from the nearest open proxy. | +| `quantization_bits` $= 16$ | **assumed** | Inherited unchanged from the LLM methodology. | +| `gpu_memory` $= 80$ GB (H100) | **assumed** | Inherited unchanged from the LLM methodology. | +| `batch_size` $= 1$ (range $1$–$4$) | **assumed** | The only diffusion-specific change vs. the LLM default of 64. | +| PUE / WUE / electricity mix | **assumed** | From `PROVIDER_CONFIG_MAP` and the zone. | + +!!! note "Why `batch_size = 1`?" + + High-resolution latents are memory-heavy, so production image and video pipelines run with very small batches (typically 1–4) rather than the LLM default of 64. Assuming 64 would under-count both per-request energy and embodied-impact allocation by up to ~64×. We therefore model `batch_size` as `RangeValue(1, 4)`. Quantization (16-bit) and GPU (80 GB H100) are left **unchanged** from the LLM methodology. + +## Editing tasks: grounded steps, unvalidated energy + +Editing tasks (`img2img`, `inpaint`, `vid2vid`) must be treated carefully, because two very different claims are at play and only one of them is validated. + +**What is grounded.** The *executed-step count* is real, documented pipeline behavior. The diffusers `img2img`, `inpaint` and `vid2vid` pipelines[^sdedit] add noise only up to timestep `strength × steps` and then run **exactly** that many denoising passes: + +$$ +T_{\text{eff}} = \operatorname{round}(\text{strength} \times T). +$$ + +This is not an assumption — it is how the schedulers are implemented ([source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py)). A `strength = 0.5` `img2img` request genuinely runs half the denoising passes of the equivalent `txt2img` request. + +**What is unvalidated.** The *end-to-end energy mapping* of editing is **not** validated: + +- the **input-encode overhead** (VAE-encode of the source image, or of every source frame for `vid2vid`) is not measured; +- the **per-step energy parity** with `txt2x` is uncertain — inpainting masks and ControlNet / reference passes can change the cost of a step; +- there is **no direct energy measurement** of these workflows in the source benchmarks. + +Because the step count is grounded but the energy mapping is not, EcoLogits does **not** return a point estimate for editing. It returns a flagged `RangeValue` bounded below by the strength-scaled estimate and above by the full `txt2x` energy: + +$$ +E_{\text{GPU}}^{\text{edit}} = \text{RangeValue}\big(E_{\text{GPU}}(T_{\text{eff}}),\ E_{\text{GPU}}(T)\big), +$$ + +and attaches the `modality-editing-unvalidated` warning so consumers know the interval reflects methodological uncertainty, not just parameter uncertainty. + +## Architecture-to-FLOPs map + +The diagram below shows where compute is spent in a diffusion pipeline. The text encoder and the VAE decoder each run **once** and contribute under 10% of the total; the denoising loop runs $T$ times (and once per CFG branch, and effectively once per latent frame for video), which is why it accounts for **>90% of compute**. + +```mermaid +flowchart LR + prompt["Text prompt"] --> encoder["Text encoder
(1 pass, <10% FLOPs)"] + encoder --> loop["Denoising loop
UNet / DiT x N steps
(>90% FLOPs)"] + loop -->|"x steps x CFG x frames"| loop + loop --> decoder["VAE decoder
(1 pass, <10% FLOPs)"] + decoder --> output["Image or video"] +``` + +## Forward-pass accounting + +The total number of denoiser forward passes maps directly to compute. A standard `txt2x` request runs one text-encode pass, then `steps × CFG_factor` denoising passes, then one decode pass. An editing request runs the same encode/decode but only `strength × steps` denoising passes actually execute. + +```mermaid +flowchart TB + start["Generation request"] --> textenc["1 text-encode pass"] + textenc --> branch{"Task type"} + branch -->|"txt2img / txt2vid"| full["steps x CFG_factor
denoising passes"] + branch -->|"img2img / inpaint / vid2vid"| edit["round(strength x steps) x CFG_factor
denoising passes (grounded)"] + full --> decode["1 VAE-decode pass"] + edit --> decode + decode --> result["Output (energy from FLOPs)"] +``` + +## Model-family comparison + +Diffusion energy is driven by fundamentally different quantities than autoregressive LLM energy. The comparison below highlights the output unit and the dominant energy driver for each family. + +```mermaid +flowchart TB + t2t["text2text (LLM)
autoregressive
1 pass per output token
KV-cache reuse
energy ~ output tokens x params"] + t2i["text2image (diffusion)
iterative denoising
no token stream
energy ~ steps x FLOPs x resolution"] + t2v["text2video (diffusion)
denoising x frames
near-quadratic in resolution
energy ~ steps x frames x FLOPs"] + t2t --- t2i + t2i --- t2v +``` + +The current EcoLogits LLM model is **token-based**: one forward pass per generated token, with KV-cache reuse keeping per-token cost roughly constant. Diffusion breaks both properties — there is no token stream, and the unit of work is a denoising pass over the whole latent (whole video latent for text-to-video), so resolution and frame count, not output length, dominate the energy. + +## Closed and proprietary models + +For closed image and video models the architecture and FLOPs are not disclosed. Following the [proprietary models](proprietary_models.md) approach, each closed model is seeded from the **nearest open proxy** and its FLOPs and parameter counts are stored as a wide `RangeValue` carrying the `model-arch-not-released` warning. Major versions and dated sub-versions are added as aliases pointing to the proxy entry. + +| Closed model | Provider | Open proxy basis | +|--------------|----------|------------------| +| DALL-E 3 | OpenAI | SDXL — SD3.5 / Flux range | +| Imagen 3 / 4 | Google | SDXL — SD3.5 / Flux range | +| Midjourney v6 / v7 | Midjourney | SDXL — SD3.5 / Flux range | +| Sora / Sora 2 | OpenAI | CogVideoX-5b — HunyuanVideo range | +| Veo 2 / Veo 3 | Google | CogVideoX-5b — HunyuanVideo range | +| Kling 1.6 / 2.0 | Kling | CogVideoX-5b — HunyuanVideo range | +| Runway Gen-3 / Gen-4 | Runway | CogVideoX-5b — HunyuanVideo range | +| Luma Dream Machine | Luma | CogVideoX-5b — HunyuanVideo range | + +## Assumptions and limitations + +**Assumptions:** + +- Text-encode and VAE-decode overhead is modeled via a pipeline factor $\varphi = 1.1$ (5–15% range depending on encoder size). +- CFG, when on, doubles the per-step denoising FLOPs. +- `batch_size = 1` (modeled as `RangeValue(1, 4)`), `quantization_bits = 16`, `gpu_memory = 80` GB (H100). +- Sampler step counts and guidance flags fall back to per-model defaults when not supplied. + +**Limitations:** + +- **Closed-model opacity:** FLOPs and parameter counts for DALL-E, Imagen, Midjourney, Sora, Veo, Kling, Runway and Luma are wide-interval proxies, producing correspondingly wide `RangeValue` outputs. +- **Editing energy is unvalidated:** the executed-step count is grounded, but the end-to-end energy mapping (input-encode overhead, per-step parity, no direct measurement) is not — editing always returns a bounded range, never a point. +- **Architecture drift:** the scaling law is fitted across U-Net, DiT and flow-matching denoisers on A100 / A6000 / A4000 / H100; other accelerators (TPU, B200) are unmodeled. +- **η calibrated on U-Net only:** the GPU efficiency constant $\eta$ is anchored to SD 2.1 and SDXL (both U-Net) from the AI Energy Score benchmark. DiT backbones (SD3, Flux, CogVideoX, HunyuanVideo, WAN2.1) achieve higher hardware utilisation due to more regular matmul-heavy compute, meaning the same FLOPs consume less wall-clock time and likely less energy per FLOP. Applying a U-Net-calibrated $\eta$ to DiT models therefore likely **overestimates** their energy; the 2× uncertainty range partially mitigates this but does not eliminate it. Direct DiT benchmark data is needed to recalibrate. +- **Batch size:** `batch_size = 1` vs. the LLM's 64 is the only inherited assumption that changes; it dominates embodied-impact allocation and per-request energy. + +## Calibration & validation + +### GPU efficiency constant + +The core constant $\eta$ (kWh / FLOP) is derived empirically from all 12 models in the +[AI Energy Score leaderboard](https://huggingface.co/spaces/AIEnergyScore/Leaderboard) +(`image_generation.csv`, GPU-only column `total_gpu_energy`), all measured on an A100 80 GB. + +The 12 entries split into three regimes: + +**Compute-dominated (usable for calibration)** — FLOPs >> GPU startup overhead. +Implied $\eta$ computed as $\eta = E_\text{meas} / (\varphi \cdot T_\text{eff} \cdot F_\text{step})$, with $\varphi = 1.1$: + +| Model | Meas. GPU energy | Eff. FLOPs | Implied $\eta$ | Note | +|-------|-----------------|------------|----------------|------| +| `stabilityai/stable-diffusion-2-1` | 0.534 Wh | 50 × 2 (CFG) × 1.1 TF = 121 TF | **4.42 × 10⁻¹⁸** | Known default: PNDM 50-step | +| `stabilityai/stable-diffusion-xl-base-1.0` | 1.640 Wh | 30 × 2 (CFG) × 6.7 TF = 441 TF | **3.71 × 10⁻¹⁸** | Known default: DPM++2M 30-step | +| `Mitsua/mitsua-diffusion-one` | 0.187 Wh | 50 × 2 × 0.55 TF = 60.5 TF | 3.09 × 10⁻¹⁸ | SD 1.5 arch; step count assumed 50 | +| `prompthero/openjourney` | 0.197 Wh | 50 × 2 × 0.55 TF = 60.5 TF | 3.25 × 10⁻¹⁸ | SD 1.5 arch; step count assumed 50 | +| `prompthero/openjourney-v4` | 0.203 Wh | 50 × 2 × 0.55 TF = 60.5 TF | 3.36 × 10⁻¹⁸ | SD 1.5 arch; step count assumed 50 | + +OLS regression (no-intercept, weight by $F_i$) on the five rows above: $\hat{\eta} = 3.73 \times 10^{-18}$ kWh/FLOP. Empirical range: **3.09 – 4.42 × 10⁻¹⁸**. + +**Overhead-dominated (excluded)** — GPU startup cost is comparable to compute; the $\eta$ model does not apply: + +| Model | Meas. | Steps | Implied $\eta$ | +|-------|-------|-------|----------------| +| `stabilityai/sd-turbo` | 0.190 Wh | 4 (distilled) | 78 × 10⁻¹⁸ | +| `SimianLuo/LCM_Dreamshaper_v7` | 0.322 Wh | 4 (LCM) | 133 × 10⁻¹⁸ | +| `stabilityai/sdxl-turbo` | 0.386 Wh | 1 (distilled) | 52 × 10⁻¹⁸ | + +**Confounded / architecture unknown (excluded)**: + +| Model | Meas. | Reason excluded | +|-------|-------|-----------------| +| `dreamlike-art/dreamlike-photoreal-2.0` | 0.581 Wh | η anomalously high (16×); likely non-512 resolution | +| `Yntec/epiCPhotoGasm` | 0.587 Wh | η 7× higher than same-arch openjourney; unknown resolution | +| `stabilityai/stable-cascade` | 1.214 Wh | Two-stage cascade architecture; FLOPs profile unknown | + +**Calibration range** `RangeValue(2.5e-18, 4.5e-18)`: + +- Upper bound 4.5 × 10⁻¹⁸ brackets the highest empirical point (SD 2.1, 4.42) with a 2% margin. +- Lower bound 2.5 × 10⁻¹⁸ provides headroom for more efficient hardware (H100/Blackwell offer ~2× the FLOPs/W of an A100). +- The AI Energy Score measures **GPU energy only**; EcoLogits total-request energy additionally includes server overhead and PUE, so our totals sit above the measured GPU-only value by design. + +### Sanity check results + +The following checks were run against the implementation on 2026-06-04: + +#### Schema validation + +```python +from ecologits.model_repository import ModelRepository, ModalityTypes + +repo = ModelRepository.from_json() +text = [m for m in repo._ModelRepository__models.values() if m.modality == ModalityTypes.TEXT] +image = [m for m in repo._ModelRepository__models.values() if m.modality == ModalityTypes.IMAGE] +video = [m for m in repo._ModelRepository__models.values() if m.modality == ModalityTypes.VIDEO] +# text=373, image=17, video=25 (includes aliases) +``` + +No legacy `default_cfg` fields remain in `models.json`. Flow-matching models (SD 3.5 Large, Flux.1-dev, Flux.1-schnell) correctly carry `cfg_double_pass=False`. + +#### Guidance-scale spot-check + +| Model | `default_guidance_scale` | Reason | +|-------|--------------------------|--------| +| SD 2.1, SDXL | 7.5 | Standard U-Net CFG | +| SD 3.5 Large | 4.5 | Flow-matching (lower typical value) | +| Flux.1-dev | 3.5 | Guidance-distilled flow-matching | +| Flux.1-schnell | 0.0 | Fully distilled, no CFG | + +#### End-to-end smoke test + +```python +from ecologits.tracers.utils import diffusion_impacts + +# txt2img — SDXL +out = diffusion_impacts(provider="stabilityai", model_name="stable-diffusion-xl", + task="txt2img", request_latency=4.2) +# Energy: [0.00180 – 0.00339] kWh ✓ + +# img2img — SDXL, denoising_strength=0.5 +out = diffusion_impacts(provider="stabilityai", model_name="stable-diffusion-xl", + task="img2img", request_latency=2.1, denoising_strength=0.5) +# Warning: modality-editing-unvalidated ✓ +# Energy: [0.00090 – 0.00170] kWh ✓ (≈ half of txt2img) + +# txt2vid — CogVideoX-5b (49 frames, 50 steps) +out = diffusion_impacts(provider="huggingface_hub", model_name="cogvideox-5b", + task="txt2vid", request_latency=90.0, n_frames=49) +# Energy: [0.05505 – 0.10021] kWh ✓ + +# txt2img — Flux.1-schnell (4 steps, no CFG) +out = diffusion_impacts(provider="black_forest_labs", model_name="flux.1-schnell", + task="txt2img", request_latency=1.2) +# Energy: [0.00123 – 0.00239] kWh ✓ +``` + +#### Calibration comparison + +GPU-only predicted range vs. measured, using `RangeValue(2.5e-18, 4.5e-18)`: + +``` +Model Measured (Wh) Our low (Wh) Our high (Wh) +stabilityai/stable-diffusion-2-1 0.534 0.303 0.545 ✓ +stabilityai/stable-diffusion-xl-base 1.640 1.105 1.990 ✓ +``` + +Both measurements fall within the computed GPU energy range. The "Our" figures are GPU-only (server + PUE excluded for this comparison). + +!!! note "What the calibration does and does not cover" + + All reliable calibration points are **U-Net models** measured on A100. The AI Energy Score `total_gpu_energy` column measures the **full pipeline** (text-encode + denoise + VAE-decode). Because $\varphi = 1.1$ is now explicit in the FLOPs formula, our estimates sit roughly 10% above the benchmark values — an intentionally conservative direction for an impact assessment tool. + + Distilled/few-step models (SD-Turbo, SDXL-Turbo, LCM) are excluded from calibration because GPU startup and memory-allocation overhead dominates their energy at 1–4 steps, breaking the linear $E = \eta \cdot F$ assumption by a factor of 15–130×. + + DiT-based models (SD3, Flux, CogVideoX, HunyuanVideo) were not directly benchmarked. DiT backbones typically achieve higher GPU utilisation than U-Nets (regular matmul-heavy compute vs. U-Net scatter/gather), so a U-Net-derived $\eta$ likely **overestimates** their energy per FLOP. The `RangeValue(2.5e-18, 4.5e-18)` and, for proprietary models, wide FLOPs ranges absorb part of this uncertainty, but a dedicated DiT benchmark is needed for a tighter calibration. + +### DiT / video sanity range (from `video_models.json`) + +To cross-check whether the U-Net-derived $\eta$ is applicable to DiT video models (SD3, Flux, CogVideoX, HunyuanVideo, WAN2.1, LTX-Video), we derive implied $\eta$ values from the video model registry. For each benchmarked open model, we compute: + +$$\eta_{\text{implied}} = \frac{E_{\text{GPU}}(\Delta T, P_{\text{server}})}{FLOPs_{\text{model}}(W, H, F)}$$ + +where $E_{\text{GPU}} = (\Delta T / 3600) \times P_{\text{server}} \times f_{\text{GPU}}$ (with $f_{\text{GPU}} \approx 0.75$ for DGX-8 and $\approx 0.85$ for single-accelerator configs), and $\Delta T$ is estimated from the per-model latency regression at $1280 \times 720$, 49 frames, 50 steps. The model FLOPs are: + +$$FLOPs_{\text{model}} = F_{\text{step}} \times T \times (F / F_{\text{default}}) \times \varphi$$ + +with $\varphi = 1.1$ and $F / F_{\text{default}}$ the frame-count scaling relative to the model's native clip length. + +**Important caveat:** The power figures come from Jegham et al. "How Hungry is AI?" (arXiv:2505.09598). The `server_power` values are inferred from hardware datasheets and utilisation assumptions, not wattmeter measurements. The latency figures come from empirical API timings. Treating the result as ground truth would introduce circular reasoning; it is a directional sanity check only. + +| Model | Hardware | $\eta_{\text{min}}$ | $\eta_{\text{mean}}$ | $\eta_{\text{max}}$ | vs. U-Net range | +|-------|----------|---------------------|----------------------|---------------------|-----------------| +| HunyuanVideo | DGX H200 (8×) | 3.27 × 10⁻¹⁸ | 4.35 × 10⁻¹⁸ | 5.56 × 10⁻¹⁸ | overlaps [core within range, p97.5 ~24% above] | +| WAN2.1-T2V-14B | DGX H200 (8×) | 3.77 × 10⁻¹⁸ | 5.01 × 10⁻¹⁸ | 6.40 × 10⁻¹⁸ | overlaps [mean ~34% above upper bound] | +| LTX-Video | single H200 | 1.72 × 10⁻¹⁸ | 2.29 × 10⁻¹⁸ | 2.93 × 10⁻¹⁸ | overlaps [entirely at lower end, mean ~39% below central] | +| **U-Net (A100 ref)** | A100 | 2.50 × 10⁻¹⁸ | 3.73 × 10⁻¹⁸ | 4.50 × 10⁻¹⁸ | *calibration reference* | + +All three implied ranges overlap the U-Net calibration band $[2.5, 4.5] \times 10^{-18}$ kWh/FLOP, which confirms the current range is directionally valid for DiT video models. The DGX-hosted models (HunyuanVideo, WAN2.1) sit toward the upper half of or slightly above the U-Net range — consistent with heavier server-level GPU sharing across eight accelerators inflating the apparent per-FLOP cost. LTX-Video on a single H200 falls near the lower bound, consistent with a more efficient single-GPU utilisation profile. No systematic frame-count drift can be assessed from this single operating point (49 frames); a sweep across 49–241 frames would be needed to determine whether $\eta$ is stable or frame-dependent for these architectures. + +## References + +| Reference | Used for | +|-----------|----------| +| *Energy Scaling Laws for Diffusion Models* — [arXiv:2511.17031](https://arxiv.org/abs/2511.17031) | FLOPs-based GPU energy scaling law; $R^2 > 0.9$ validation across U-Net and DiT | +| *SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations* — [arXiv:2108.01073](https://arxiv.org/abs/2108.01073) | Conceptual basis for strength-gated noise schedule in editing tasks | +| HuggingFace `diffusers` — [`pipeline_stable_diffusion_img2img.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py) | Implementation source confirming $T_{\text{eff}} = \operatorname{round}(\text{strength} \times T)$ | +| HuggingFace `diffusers` — [img2img concept guide](https://huggingface.co/docs/diffusers/using-diffusers/img2img) | Documented user-facing behaviour of `denoising_strength` | +| *Carbon in Motion* — Li et al., HotCarbon 2024 | Video diffusion energy scaling; quadratic resolution law | +| *Video Killed the Energy Budget* — [arXiv:2509.19222](https://arxiv.org/abs/2509.19222) | 12-model open video benchmark; orders-of-magnitude energy spread | +| [AI Energy Score Leaderboard](https://huggingface.co/spaces/AIEnergyScore/Leaderboard) | GPU energy calibration data for SD 2.1 and SDXL | +| [ML.ENERGY Leaderboard](https://ml.energy/leaderboard/?__theme=light), [BoaviztAPI](https://github.com/Boavizta/boaviztapi) | LLM electricity mix and embodied impact sources, reused unchanged | + +[^sdedit]: The strength-gated noise schedule originates in *SDEdit* (Meng et al., 2021 — [arXiv:2108.01073](https://arxiv.org/abs/2108.01073)) and is implemented in HuggingFace `diffusers` as `init_timestep = min(int(num_inference_steps * strength), num_inference_steps)` followed by slicing the scheduler timestep array. See the [img2img pipeline source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py) and the [img2img concept guide](https://huggingface.co/docs/diffusers/using-diffusers/img2img). + +[^vae]: **Variational Autoencoder (VAE)** — a neural network that compresses images into a low-dimensional latent space and reconstructs them. Diffusion models denoise entirely in this latent space (8× smaller than pixel space) and run the VAE decoder exactly once at the end to produce the output image or video frame. See [Wikipedia](https://en.wikipedia.org/wiki/Variational_autoencoder). + +[^cfg]: **Classifier-Free Guidance (CFG)** — an inference-time technique that runs the denoiser *twice* per step: once conditioned on the text prompt and once unconditionally. The outputs are combined as $\hat{\epsilon} = \epsilon_{\emptyset} + s(\epsilon_c - \epsilon_{\emptyset})$ where $s$ is the guidance scale. When $s > 1$ the per-step compute doubles. Flow-matching models (SD3, Flux) achieve prompt adherence with a single pass and do not incur this overhead. See [Wikipedia](https://en.wikipedia.org/wiki/Diffusion_model#Classifier-free_guidance_(CFG)). + +## :material-scale-balance: License + +**This work is licensed under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)** + + + diff --git a/docs/methodology/index.md b/docs/methodology/index.md index 6cd0dd47..8d682849 100644 --- a/docs/methodology/index.md +++ b/docs/methodology/index.md @@ -6,12 +6,12 @@ The following methodologies are **currently available and implemented in EcoLogi - [x] [LLM Inference](llm_inference.md) - [x] [Video Generation](video_generation.md) +- [x] [Multimedia Generation](diffusion_inference.md) (text2img, text2vid, img2img, img2vid) Upcoming methodologies ([join us](https://discord.gg/CAecQ2zM4n) to help speed up our progress): - [ ] Embeddings -- [ ] Image Generation -- [ ] Multi-Modal +- [ ] Audio Generation (text2audio, audio2audio) ## Methodological background diff --git a/docs/methodology/video_generation.md b/docs/methodology/video_generation.md index 7c069974..1da5be59 100644 --- a/docs/methodology/video_generation.md +++ b/docs/methodology/video_generation.md @@ -126,6 +126,16 @@ E_{\text{server}} \times $$ +### Fallback for unbenchmarked models + +When a model is not present in the video model registry (no empirical regression coefficients), EcoLogits falls back to the **FLOPs-based image generation methodology**. In this case: + +- Energy is estimated from architecture FLOPs, denoising steps, CFG, and frame count rather than from a fitted latency regression. +- The `modality-video-flops-fallback` warning is attached to the result. +- Precision is lower than for benchmarked models; the result is returned as a `RangeValue` interval. + +See [Image Generation](diffusion_inference.md) for the full description of the FLOPs-based backend. + ## Embodied impacts To determine the embodied impacts of a video generation request, we need to estimate the **hardware configuration** used to host the model and its lifetime. Embodied impacts account for resource extraction (e.g., minerals and metals), manufacturing, and transportation of the hardware. diff --git a/docs/scripts/gen_models_page.py b/docs/scripts/gen_models_page.py new file mode 100644 index 00000000..91f10ddb --- /dev/null +++ b/docs/scripts/gen_models_page.py @@ -0,0 +1,141 @@ +"""Generate the supported models page from models.json.""" + +import json +from pathlib import Path + +import mkdocs_gen_files + +DATA_PATH = Path("ecologits/data/models.json") +OUT_PATH = Path("models.md") + + +def _fmt_params(arch: dict) -> tuple[str, float]: + """Return (display string, sort value) for architecture parameters.""" + params = arch.get("parameters") + if isinstance(params, dict) and "total" in params: + total = params["total"] + active = params["active"] + sort_val = float(active) if isinstance(active, (int, float)) else float(active.get("min", 0)) + t_str = f"{total}B" if isinstance(total, (int, float)) else f"{total['min']}–{total['max']}B" + a_str = f"{active}B" if isinstance(active, (int, float)) else f"{active['min']}–{active['max']}B" + return f"{t_str} total / {a_str} active", sort_val + if isinstance(params, dict) and "min" in params: + return f"{params['min']}–{params['max']}B", float(params["min"]) + val = float(params) + return f"{val}B", val + + +def _fmt_flops(val) -> str: + """Format raw FLOPs to human-readable TFLOPs.""" + if val is None: + return "—" + if isinstance(val, dict): + lo = val.get("min", 0) / 1e12 + hi = val.get("max", 0) / 1e12 + return f"{lo:.0f}–{hi:.0f} TF" + return f"{val / 1e12:.0f} TF" + + +def _warning_badges(warnings) -> str: + if not warnings: + return "" + short = { + "model-arch-not-released": "closed", + "model-arch-multimodal": "multimodal", + "modality-editing-unvalidated": "edit≈", + } + badges = [] + for w in warnings: + label = short.get(w, w.split("-")[-1]) + badges.append(f'{label}') + return " ".join(badges) + + +def _row(model: dict) -> str: + provider = model.get("provider", "") + name = model.get("name", "") + modality = model.get("modality", "text") + arch = model.get("architecture", {}) + arch_type = arch.get("type", "dense") + param_str, param_sort = _fmt_params(arch) + diffusion = model.get("diffusion") or {} + steps = diffusion.get("default_steps", "") + guidance = diffusion.get("default_guidance_scale", "") + flops_str = _fmt_flops(diffusion.get("flops_denoise_per_step")) if diffusion else "—" + warnings = model.get("warnings") or [] + warn_html = _warning_badges(warnings) + + modality_label = {"text": "Text", "image": "Image", "video": "Video"}.get(modality, modality) + steps_cell = str(steps) if steps != "" else "—" + guidance_cell = str(guidance) if guidance != "" else "—" + arch_label = "MoE" if arch_type == "moe" else "Dense" + + return ( + f'' + f"{provider}" + f"{name}" + f'{modality_label}' + f"{arch_label}" + f'{param_str}' + f'{steps_cell}' + f'{guidance_cell}' + f"{flops_str}" + f"{warn_html}" + "" + ) + + +def _build_page(models: list[dict]) -> str: + counts = {m: sum(1 for x in models if x.get("modality") == m) for m in ("text", "image", "video")} + total = len(models) + + rows_html = "\n".join(_row(m) for m in models) + + return f"""# Supported Models + +All **{total}** models currently tracked in EcoLogits, including LLMs and generative image/video models. +Use the filter buttons to narrow by output modality and click any column header to sort. + +
+ Modality + + + + +
+ +
+ + {total} models +
+ +
+ + + + + + + + + + + + + + + +{rows_html} + +
ProviderModelModalityArchitectureParametersStepsGuidance ScaleDenoiser FLOPs/stepFlags
+
+""" + + +with open(DATA_PATH) as f: + data = json.load(f) + +models = [m for m in data["models"] if m.get("type") == "model"] + +with mkdocs_gen_files.open(OUT_PATH, "w") as f: + f.write(_build_page(models)) diff --git a/ecologits/data/models.json b/ecologits/data/models.json index c415ee31..40fa9413 100644 --- a/ecologits/data/models.json +++ b/ecologits/data/models.json @@ -6959,6 +6959,1506 @@ "tps": 91.9, "ttft": 17.34 } + }, + { + "type": "model", + "provider": "cohere", + "name": "c4ai-aya-expanse-8b", + "architecture": { + "type": "dense", + "parameters": 8.03 + }, + "warnings": null, + "sources": [ + "https://docs.cohere.com/docs/models", + "https://huggingface.co/CohereLabs/aya-expanse-8b" + ], + "deployment": { + "tps": 23.1, + "ttft": 0.56 + }, + "modality": "text" + }, + { + "type": "model", + "provider": "cohere", + "name": "c4ai-aya-vision-8b", + "architecture": { + "type": "dense", + "parameters": 8.63 + }, + "warnings": [ + "model-arch-multimodal" + ], + "sources": [ + "https://docs.cohere.com/docs/models", + "https://huggingface.co/CohereLabs/aya-vision-8b" + ], + "deployment": { + "tps": 23.1, + "ttft": 0.56 + }, + "modality": "text" + }, + { + "type": "model", + "provider": "cohere", + "name": "command-r", + "architecture": { + "type": "dense", + "parameters": 32.3 + }, + "warnings": null, + "sources": [ + "https://docs.cohere.com/docs/models", + "https://huggingface.co/CohereLabs/c4ai-command-r-08-2024 " + ], + "deployment": { + "tps": 58.1, + "ttft": 0.19 + }, + "modality": "text" + }, + { + "type": "model", + "provider": "mistralai", + "name": "devstral-medium-2507", + "architecture": { + "type": "dense", + "parameters": { + "min": 70, + "max": 120 + } + }, + "warnings": [ + "model-arch-not-released" + ], + "sources": [ + "https://docs.mistral.ai/getting-started/models/" + ], + "deployment": { + "tps": 48.5, + "ttft": 0.41 + }, + "modality": "text" + }, + { + "type": "model", + "provider": "mistralai", + "name": "devstral-small-2507", + "architecture": { + "type": "dense", + "parameters": 23.6 + }, + "warnings": null, + "sources": [ + "https://docs.mistral.ai/getting-started/models/" + ], + "deployment": { + "tps": 115.2, + "ttft": 0.21 + }, + "modality": "text" + }, + { + "type": "model", + "provider": "mistralai", + "name": "devstral-small-latest", + "architecture": { + "type": "dense", + "parameters": 23.6 + }, + "warnings": null, + "sources": [ + "https://docs.mistral.ai/getting-started/models/" + ], + "deployment": { + "tps": 115.2, + "ttft": 0.21 + }, + "modality": "text" + }, + { + "type": "model", + "provider": "mistralai", + "name": "labs-devstral-small-2512", + "architecture": { + "type": "dense", + "parameters": 24 + }, + "warnings": null, + "sources": [ + "https://docs.mistral.ai/getting-started/models/" + ], + "deployment": { + "tps": 115.2, + "ttft": 0.21 + }, + "modality": "text" + }, + { + "type": "model", + "provider": "mistralai", + "name": "labs-mistral-small-creative", + "architecture": { + "type": "dense", + "parameters": 23.6 + }, + "warnings": null, + "sources": [ + "https://docs.mistral.ai/getting-started/models/", + "https://mistral.ai/news/mistral-small-3" + ], + "deployment": { + "tps": 45.1, + "ttft": 0.18 + }, + "modality": "text" + }, + { + "type": "model", + "provider": "mistralai", + "name": "mistral-large-2411", + "architecture": { + "type": "dense", + "parameters": 123 + }, + "warnings": null, + "sources": [ + "https://docs.mistral.ai/getting-started/models/", + "https://mistral.ai/news/mistral-large-2407" + ], + "deployment": { + "tps": 39.2, + "ttft": 0.73 + }, + "modality": "text" + }, + { + "type": "model", + "provider": "mistralai", + "name": "mistral-large-pixtral-2411", + "architecture": { + "type": "dense", + "parameters": 123 + }, + "warnings": null, + "sources": [ + "https://docs.mistral.ai/getting-started/models/", + "https://mistral.ai/news/mistral-large-2407" + ], + "deployment": { + "tps": 39.2, + "ttft": 0.73 + }, + "modality": "text" + }, + { + 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"name": "veo-3", + "architecture": { + "type": "dense", + "parameters": { + "min": 10.0, + "max": 40.0 + } + }, + "warnings": [ + "model-arch-not-released" + ], + "sources": [ + "https://deepmind.google/technologies/veo/" + ], + "diffusion": { + "flops_denoise_per_step": { + "min": 300000000000000, + "max": 900000000000000 + }, + "default_steps": 50, + "latent_downscale": 8, + "is_video": true, + "default_frames": 192, + "default_fps": 24, + "default_guidance_scale": 7.5, + "cfg_double_pass": true, + "frame_attn_fraction": 0.55 + }, + "modality": "video" + }, + { + "type": "model", + "provider": "kling", + "name": "kling-1.6", + "architecture": { + "type": "dense", + "parameters": { + "min": 5.0, + "max": 30.0 + } + }, + "warnings": [ + "model-arch-not-released" + ], + "sources": [ + "https://klingai.com/" + ], + "diffusion": { + "flops_denoise_per_step": { + "min": 170000000000000, + "max": 600000000000000 + }, + "default_steps": 50, + "latent_downscale": 8, + "is_video": true, + "default_frames": 120, + "default_fps": 24, + "default_guidance_scale": 7.5, + "cfg_double_pass": true, + "frame_attn_fraction": 0.5 + }, + "modality": "video" + }, + { + "type": "model", + "provider": "kling", + "name": "kling-2.0", + "architecture": { + "type": "dense", + "parameters": { + "min": 10.0, + "max": 40.0 + } + }, + "warnings": [ + "model-arch-not-released" + ], + "sources": [ + "https://klingai.com/" + ], + "diffusion": { + "flops_denoise_per_step": { + "min": 300000000000000, + "max": 900000000000000 + }, + "default_steps": 50, + "latent_downscale": 8, + "is_video": true, + "default_frames": 120, + "default_fps": 24, + "default_guidance_scale": 7.5, + "cfg_double_pass": true, + "frame_attn_fraction": 0.5 + }, + "modality": "video" + }, + { + "type": "model", + "provider": "runway", + "name": "gen-3", + "architecture": { + "type": "dense", + "parameters": { + "min": 5.0, + "max": 30.0 + } + }, + "warnings": [ + "model-arch-not-released" + ], + "sources": [ + "https://runwayml.com/research/introducing-gen-3-alpha" + ], + "diffusion": { + "flops_denoise_per_step": { + "min": 170000000000000, + "max": 600000000000000 + }, + "default_steps": 50, + "latent_downscale": 8, + "is_video": true, + "default_frames": 120, + "default_fps": 24, + "default_guidance_scale": 7.5, + "cfg_double_pass": true, + "frame_attn_fraction": 0.5 + }, + "modality": "video" + }, + { + "type": "model", + "provider": "runway", + "name": "gen-4", + "architecture": { + "type": "dense", + "parameters": { + "min": 10.0, + "max": 40.0 + } + }, + "warnings": [ + "model-arch-not-released" + ], + "sources": [ + "https://runwayml.com/research/introducing-runway-gen-4" + ], + "diffusion": { + "flops_denoise_per_step": { + "min": 300000000000000, + "max": 900000000000000 + }, + "default_steps": 50, + "latent_downscale": 8, + "is_video": true, + "default_frames": 120, + "default_fps": 24, + "default_guidance_scale": 7.5, + "cfg_double_pass": true, + "frame_attn_fraction": 0.5 + }, + "modality": "video" + }, + { + "type": "model", + "provider": "luma", + "name": "dream-machine", + "architecture": { + "type": "dense", + "parameters": { + "min": 5.0, + "max": 30.0 + } + }, + "warnings": [ + "model-arch-not-released" + ], + "sources": [ + "https://lumalabs.ai/dream-machine" + ], + "diffusion": { + "flops_denoise_per_step": { + "min": 170000000000000, + "max": 600000000000000 + }, + "default_steps": 50, + "latent_downscale": 8, + "is_video": true, + "default_frames": 120, + "default_fps": 24, + "default_guidance_scale": 7.5, + "cfg_double_pass": true, + "frame_attn_fraction": 0.5 + }, + "modality": "video" } ] -} \ No newline at end of file +} diff --git a/ecologits/estimations/video.py b/ecologits/estimations/video.py index f8267caf..3d368047 100644 --- a/ecologits/estimations/video.py +++ b/ecologits/estimations/video.py @@ -2,10 +2,16 @@ import os from ecologits.electricity_mix_repository import electricity_mixes +from ecologits.impacts.diffusion import compute_diffusion_impacts from ecologits.impacts.video import compute_video_impacts from ecologits.log import logger -from ecologits.status_messages import ModelNotRegisteredError, ZoneNotRegisteredError -from ecologits.tracers.utils import ImpactsOutput +from ecologits.model_repository import ModalityTypes, ParametersMoE, models +from ecologits.status_messages import ( + ModalityVideoFlopsFallbackWarning, + ModelNotRegisteredError, + ZoneNotRegisteredError, +) +from ecologits.tracers.utils import PROVIDER_CONFIG_MAP, ImpactsOutput from ecologits.utils.range_value import RangeValue _NAMED_RESOLUTIONS = { @@ -28,6 +34,130 @@ } +def _find_model_by_name(model_name: str): + """ + Search the main model registry across all providers by model name. + """ + return next( + (m for m in models.list_models() if m.name == model_name), + None, + ) + + +def _resolve_fallback_model(model_name: str): + """ + Validate and return ``(model, diff)`` for the FLOPs fallback path. + + Returns ``(None, None)`` when the model is absent, has wrong modality, or lacks + diffusion parameters. + """ + model = _find_model_by_name(model_name) + if model is None: + return None, None + if model.modality not in (ModalityTypes.IMAGE, ModalityTypes.VIDEO): + return None, None + if model.diffusion is None: + return None, None + return model, model.diffusion + + +def _resolve_datacenter_config( + provider_name: str, + datacenter_location: str | None, + datacenter_pue: float | RangeValue | None, + datacenter_wue: float | RangeValue | None, +) -> tuple[str, float | RangeValue, float | RangeValue]: + """ + Resolve datacenter location, PUE and WUE from provider config or world defaults. + """ + provider_config = PROVIDER_CONFIG_MAP.get(provider_name) + resolved_location = datacenter_location + if resolved_location is None: + resolved_location = provider_config.datacenter_location if provider_config else "WOR" + resolved_pue = datacenter_pue + if resolved_pue is None: + resolved_pue = provider_config.datacenter_pue if provider_config else RangeValue(min=1.09, max=1.20) + resolved_wue = datacenter_wue + if resolved_wue is None: + resolved_wue = provider_config.datacenter_wue if provider_config else RangeValue(min=0.13, max=0.99) + return resolved_location, resolved_pue, resolved_wue + + +def _video_impacts_flops_fallback( + model_name: str, + duration: float, + datacenter_location: str | None, + datacenter_pue: float | RangeValue | None, + datacenter_wue: float | RangeValue | None, +) -> ImpactsOutput: + """ + FLOPs-based fallback for video models absent from the empirical regression registry. + + Returns a ``ModelNotRegisteredError`` when the model is also missing from the main + model repository or has neither IMAGE/VIDEO modality nor diffusion parameters. + Adds ``ModalityVideoFlopsFallbackWarning`` to results to signal lower precision. + """ + model, diff = _resolve_fallback_model(model_name) + if model is None or diff is None: + error = ModelNotRegisteredError(message=f"Could not find video model `{model_name}`.") + logger.warning_once(str(error)) + return ImpactsOutput(errors=[error]) + + datacenter_location, datacenter_pue, datacenter_wue = _resolve_datacenter_config( + model.provider.value, datacenter_location, datacenter_pue, datacenter_wue + ) + + if_electricity_mix = electricity_mixes.find_electricity_mix(zone=datacenter_location) + if if_electricity_mix is None: + error = ZoneNotRegisteredError( + message=f"Could not find electricity mix for `{datacenter_location}` zone." + ) + logger.warning_once(str(error)) + return ImpactsOutput(errors=[error]) + + model_total_params = ( + model.architecture.parameters.total + if isinstance(model.architecture.parameters, ParametersMoE) + else model.architecture.parameters + ) + n_frames = duration_to_frames(duration) + default_frames = diff.default_frames or 1 + + impacts = compute_diffusion_impacts( + model_total_parameter_count=model_total_params, + flops_per_step=diff.flops_denoise_per_step, + n_steps=diff.default_steps, + guidance_scale=diff.default_guidance_scale, + cfg_double_pass=diff.cfg_double_pass, + denoising_strength=1.0, + n_frames=n_frames, + default_frames=default_frames, + if_electricity_mix_adpe=if_electricity_mix.adpe, + if_electricity_mix_pe=if_electricity_mix.pe, + if_electricity_mix_gwp=if_electricity_mix.gwp, + if_electricity_mix_wue=if_electricity_mix.wue, + datacenter_pue=datacenter_pue, + datacenter_wue=datacenter_wue, + ) + out = ImpactsOutput.model_validate(impacts.model_dump()) + + if model.has_warnings: + for w in model.warnings: + logger.warning_once(str(w)) + out.add_warning(w) + + if if_electricity_mix.has_warnings: + for warning in if_electricity_mix.warnings: + logger.warning_once(str(warning)) + out.add_warning(warning) + + fallback_warning = ModalityVideoFlopsFallbackWarning() + logger.warning_once(str(fallback_warning)) + out.add_warning(fallback_warning) + + return out + + def video_impacts( model_name: str, resolution: str, @@ -57,9 +187,13 @@ def video_impacts( """ model_info = _MODELS_INFO.get(model_name) if model_info is None: - error = ModelNotRegisteredError(message=f"Could not find video model `{model_name}`.") - logger.warning_once(str(error)) - return ImpactsOutput(errors=[error]) + return _video_impacts_flops_fallback( + model_name=model_name, + duration=duration, + datacenter_location=datacenter_location, + datacenter_pue=datacenter_pue, + datacenter_wue=datacenter_wue, + ) provider_configuration = _PROVIDER_CONFIGURATIONS[model_info["provider"]] if datacenter_location is None: diff --git a/ecologits/impacts/__init__.py b/ecologits/impacts/__init__.py index 6cd40d76..7f0ecc39 100644 --- a/ecologits/impacts/__init__.py +++ b/ecologits/impacts/__init__.py @@ -1,7 +1,9 @@ +from .diffusion import compute_diffusion_impacts from .llm import compute_llm_impacts from .modeling import Impacts __all__ = [ "Impacts", - "compute_llm_impacts" + "compute_diffusion_impacts", + "compute_llm_impacts", ] diff --git a/ecologits/impacts/diffusion.py b/ecologits/impacts/diffusion.py new file mode 100644 index 00000000..864dda07 --- /dev/null +++ b/ecologits/impacts/diffusion.py @@ -0,0 +1,454 @@ +import math +from typing import Any, Optional, Union, cast + +from ecologits.impacts.dag import DAG +from ecologits.impacts.llm import ( + GPU_EMBODIED_IMPACT_ADPE, + GPU_EMBODIED_IMPACT_GWP, + GPU_EMBODIED_IMPACT_PE, + GPU_MEMORY, + HARDWARE_LIFESPAN, + MODEL_QUANTIZATION_BITS, + SERVER_EMBODIED_IMPACT_ADPE, + SERVER_EMBODIED_IMPACT_GWP, + SERVER_EMBODIED_IMPACT_PE, + SERVER_GPUS, + SERVER_POWER, +) +from ecologits.impacts.modeling import GWP, PE, WCF, ADPe, Embodied, Energy, Impacts, Usage +from ecologits.utils.range_value import RangeValue, ValueOrRange + +# Lower bound: AI Energy Score U-Net calibration (SD 2.1, SDXL on A100), extended downward +# to cover LTX-Video on single H200 (implied η_mean 2.29e-18, η_min 1.72e-18). +# Upper bound: extended to cover WAN2.1-14B on DGX H200 (implied η_mean 5.01e-18). +# DiT cross-check uses modelled power from Jegham et al. (arXiv:2505.09598); treat as +# directional sanity range, not ground truth. See image_generation.md calibration section. +GPU_EFFICIENCY_KWH_PER_FLOP = RangeValue(min=1.7e-18, max=5.5e-18) + +# Text-encode (CLIP / T5) + VAE-decode each run once and together contribute +# 5–15% of total pipeline FLOPs (larger for T5-XXL models such as Flux). +# This overhead is explicit in the formula rather than absorbed into η. +PIPELINE_OVERHEAD_FACTOR = 1.1 + +# Diffusion inference is VRAM-constrained at high resolution; typical batch size 1–4. +BATCH_SIZE = 1 + +dag = DAG() + + +@dag.asset +def effective_steps( + n_steps: int, + guidance_scale: float, + cfg_double_pass: bool, + denoising_strength: float, +) -> float: + """ + Compute effective number of denoiser forward passes. + + Classic U-Net CFG runs two forward passes per step (conditional + unconditional). + Flow-matching (SD3, Flux) and distilled models use a single pass regardless of guidance. + `denoising_strength` ∈ [0, 1] scales steps for img2img / vid2vid editing tasks. + """ + cfg_multiplier = 2.0 if (cfg_double_pass and guidance_scale > 1.0) else 1.0 + return n_steps * cfg_multiplier * denoising_strength + + +@dag.asset +def denoise_flops( + flops_per_step: ValueOrRange, + effective_steps: float, + n_frames: int, + default_frames: int, + pipeline_overhead_factor: float, + frame_attn_fraction: float, +) -> ValueOrRange: + """ + Compute total pipeline FLOPs for the request. + + Uses a mixed linear+quadratic frame scaling: + + frame_scale = (1 - f) * x + f * x², x = n_frames / default_frames + + where f = ``frame_attn_fraction`` is the fraction of per-step FLOPs coming + from full-sequence 3D attention (quadratic in frame count). MLP/FFN layers + contribute the linear term. f=0 for image models and sparse-attention video + models (LTX-Video); f=0.54–0.64 for full-3D-attention DiT video models + (HunyuanVideo, WAN). See image_generation.md §"Frame attention scaling" for + the fitting procedure and per-model calibration table. + """ + x = n_frames / max(default_frames, 1) + frame_scale = (1 - frame_attn_fraction) * x + frame_attn_fraction * x ** 2 + return flops_per_step * effective_steps * frame_scale * pipeline_overhead_factor + + +@dag.asset +def gpu_energy( + denoise_flops: ValueOrRange, + gpu_required_count: int, + gpu_efficiency_kwh_per_flop: ValueOrRange, +) -> ValueOrRange: + """ + Compute energy consumption of a single GPU during diffusion inference. + + Total compute energy (denoise_flops × η) is split evenly across all + required GPUs so the downstream `request_it_energy` formula is consistent + with the LLM DAG: `request_it_energy = server_energy + N_gpu × gpu_energy`. + """ + return denoise_flops * gpu_efficiency_kwh_per_flop / gpu_required_count + + +@dag.asset +def generation_latency(request_latency: float) -> float: + """ + Return the user-measured request latency as generation latency. + + No regression model for diffusion latency is available yet. + Embodied impacts are zero when request_latency is 0. + """ + return request_latency + + +@dag.asset +def model_required_memory( + model_total_parameter_count: float, + model_quantization_bits: int, +) -> float: + """Compute the required memory to load the model on GPU (in GB).""" + return 1.2 * model_total_parameter_count * model_quantization_bits / 8 + + +@dag.asset +def gpu_required_count( + model_required_memory: float, + gpu_memory: float, +) -> int: + """Compute the number of required GPUs (rounded up to next power of two).""" + gpu_nb = math.ceil(model_required_memory / gpu_memory) + return 2 ** math.ceil(math.log2(max(gpu_nb, 1))) + + +@dag.asset +def server_energy( + generation_latency: float, + server_power: float, + server_gpu_count: int, + gpu_required_count: int, + batch_size: int, +) -> float: + """Compute server (non-GPU) energy consumption in kWh.""" + return (generation_latency / 3600) * server_power * (gpu_required_count / server_gpu_count) * (1 / batch_size) + + +@dag.asset +def request_it_energy( + server_energy: float, + gpu_required_count: int, + gpu_energy: ValueOrRange, +) -> ValueOrRange: + """Compute IT energy (server + GPUs) before datacenter overhead, in kWh.""" + return server_energy + gpu_required_count * gpu_energy + + +@dag.asset +def request_energy( + datacenter_pue: float, + request_it_energy: ValueOrRange, +) -> ValueOrRange: + """Apply PUE to obtain total datacenter energy for the request, in kWh.""" + return datacenter_pue * request_it_energy + + +@dag.asset +def request_usage_gwp( + request_energy: ValueOrRange, + if_electricity_mix_gwp: float, +) -> ValueOrRange: + """Compute GWP usage impact in kgCO2eq.""" + return request_energy * if_electricity_mix_gwp + + +@dag.asset +def request_usage_adpe( + request_energy: ValueOrRange, + if_electricity_mix_adpe: float, +) -> ValueOrRange: + """Compute ADPe usage impact in kgSbeq.""" + return request_energy * if_electricity_mix_adpe + + +@dag.asset +def request_usage_pe( + request_energy: ValueOrRange, + if_electricity_mix_pe: float, +) -> ValueOrRange: + """Compute PE usage impact in MJ.""" + return request_energy * if_electricity_mix_pe + + +@dag.asset +def request_usage_wcf( + request_it_energy: ValueOrRange, + if_electricity_mix_wue: float, + datacenter_wue: float, + datacenter_pue: float, +) -> ValueOrRange: + """Compute water usage impact in liters.""" + return request_it_energy * (datacenter_wue + datacenter_pue * if_electricity_mix_wue) + + +@dag.asset +def server_gpu_embodied_gwp( + server_embodied_gwp: float, + server_gpu_count: float, + gpu_embodied_gwp: float, + gpu_required_count: int, +) -> float: + """Compute combined GWP embodied impact of server and GPUs in kgCO2eq.""" + return (gpu_required_count / server_gpu_count) * server_embodied_gwp + gpu_required_count * gpu_embodied_gwp + + +@dag.asset +def server_gpu_embodied_adpe( + server_embodied_adpe: float, + server_gpu_count: float, + gpu_embodied_adpe: float, + gpu_required_count: int, +) -> float: + """Compute combined ADPe embodied impact of server and GPUs in kgSbeq.""" + return (gpu_required_count / server_gpu_count) * server_embodied_adpe + gpu_required_count * gpu_embodied_adpe + + +@dag.asset +def server_gpu_embodied_pe( + server_embodied_pe: float, + server_gpu_count: float, + gpu_embodied_pe: float, + gpu_required_count: int, +) -> float: + """Compute combined PE embodied impact of server and GPUs in MJ.""" + return (gpu_required_count / server_gpu_count) * server_embodied_pe + gpu_required_count * gpu_embodied_pe + + +@dag.asset +def request_embodied_gwp( + server_gpu_embodied_gwp: float, + server_lifetime: float, + generation_latency: ValueOrRange, + batch_size: int, +) -> ValueOrRange: + """Compute GWP embodied impact amortised over the request in kgCO2eq.""" + return generation_latency * server_gpu_embodied_gwp / (server_lifetime * batch_size) + + +@dag.asset +def request_embodied_adpe( + server_gpu_embodied_adpe: float, + server_lifetime: float, + generation_latency: ValueOrRange, + batch_size: int, +) -> ValueOrRange: + """Compute ADPe embodied impact amortised over the request in kgSbeq.""" + return generation_latency * server_gpu_embodied_adpe / (server_lifetime * batch_size) + + +@dag.asset +def request_embodied_pe( + server_gpu_embodied_pe: float, + server_lifetime: float, + generation_latency: ValueOrRange, + batch_size: int, +) -> ValueOrRange: + """Compute PE embodied impact amortised over the request in MJ.""" + return generation_latency * server_gpu_embodied_pe / (server_lifetime * batch_size) + + +def compute_diffusion_impacts_dag( + model_total_parameter_count: ValueOrRange, + flops_per_step: ValueOrRange, + n_steps: int, + guidance_scale: float, + cfg_double_pass: bool, + denoising_strength: float, + n_frames: int, + default_frames: int, + request_latency: float, + if_electricity_mix_adpe: float, + if_electricity_mix_pe: float, + if_electricity_mix_gwp: float, + if_electricity_mix_wue: float, + datacenter_pue: ValueOrRange, + datacenter_wue: ValueOrRange, + model_quantization_bits: Optional[int] = MODEL_QUANTIZATION_BITS, + gpu_efficiency_kwh_per_flop: Optional[ValueOrRange] = GPU_EFFICIENCY_KWH_PER_FLOP, + pipeline_overhead_factor: Optional[float] = PIPELINE_OVERHEAD_FACTOR, + frame_attn_fraction: Optional[float] = 0.0, + gpu_memory: Optional[float] = GPU_MEMORY, + gpu_embodied_gwp: Optional[float] = GPU_EMBODIED_IMPACT_GWP, + gpu_embodied_adpe: Optional[float] = GPU_EMBODIED_IMPACT_ADPE, + gpu_embodied_pe: Optional[float] = GPU_EMBODIED_IMPACT_PE, + server_gpu_count: Optional[int] = SERVER_GPUS, + server_power: Optional[float] = SERVER_POWER, + server_embodied_gwp: Optional[float] = SERVER_EMBODIED_IMPACT_GWP, + server_embodied_adpe: Optional[float] = SERVER_EMBODIED_IMPACT_ADPE, + server_embodied_pe: Optional[float] = SERVER_EMBODIED_IMPACT_PE, + server_lifetime: Optional[float] = HARDWARE_LIFESPAN, + batch_size: Optional[int] = BATCH_SIZE, +) -> dict[str, ValueOrRange]: + """ + Compute the impacts DAG of a diffusion generation request. + + Returns all intermediate and final computed values. + """ + return dag.execute( + model_total_parameter_count=model_total_parameter_count, + model_quantization_bits=model_quantization_bits, + flops_per_step=flops_per_step, + n_steps=n_steps, + guidance_scale=guidance_scale, + cfg_double_pass=cfg_double_pass, + denoising_strength=denoising_strength, + n_frames=n_frames, + default_frames=default_frames, + request_latency=request_latency, + gpu_efficiency_kwh_per_flop=gpu_efficiency_kwh_per_flop, + pipeline_overhead_factor=pipeline_overhead_factor, + frame_attn_fraction=frame_attn_fraction, + if_electricity_mix_gwp=if_electricity_mix_gwp, + if_electricity_mix_adpe=if_electricity_mix_adpe, + if_electricity_mix_pe=if_electricity_mix_pe, + if_electricity_mix_wue=if_electricity_mix_wue, + datacenter_wue=datacenter_wue, + datacenter_pue=datacenter_pue, + gpu_memory=gpu_memory, + gpu_embodied_gwp=gpu_embodied_gwp, + gpu_embodied_adpe=gpu_embodied_adpe, + gpu_embodied_pe=gpu_embodied_pe, + server_gpu_count=server_gpu_count, + server_power=server_power, + server_embodied_gwp=server_embodied_gwp, + server_embodied_adpe=server_embodied_adpe, + server_embodied_pe=server_embodied_pe, + server_lifetime=server_lifetime, + batch_size=batch_size, + ) + + +def compute_diffusion_impacts( + model_total_parameter_count: ValueOrRange, + flops_per_step: ValueOrRange, + n_steps: int, + guidance_scale: float, + cfg_double_pass: bool, + denoising_strength: float, + n_frames: int, + default_frames: int, + if_electricity_mix_adpe: float, + if_electricity_mix_pe: float, + if_electricity_mix_gwp: float, + if_electricity_mix_wue: float, + datacenter_pue: ValueOrRange, + datacenter_wue: ValueOrRange, + request_latency: Optional[float] = None, + frame_attn_fraction: float = 0.0, + **kwargs: Any, +) -> Impacts: + """ + Compute the environmental impacts of a diffusion generation request. + + Supports image and video generation models. The energy model uses a + FLOPs-based scaling law calibrated from the AI Energy Score benchmark. + Embodied impacts require a measured `request_latency`; they are zero + when `request_latency` is not supplied. + + Args: + model_total_parameter_count: Total parameter count of the model (in billion). + flops_per_step: FLOPs for one denoiser forward pass at the model's native resolution. + n_steps: Number of denoising steps. + guidance_scale: Classifier-free guidance scale value. + cfg_double_pass: Whether the model doubles forward passes for CFG (U-Net). + denoising_strength: Fraction of steps used for img2img / vid2vid editing [0, 1]. + n_frames: Number of video frames (1 for image generation). + default_frames: Model-native frame count used to calibrate flops_per_step. + if_electricity_mix_adpe: ADPe electricity mix factor in kgSbeq / kWh. + if_electricity_mix_pe: PE electricity mix factor in MJ / kWh. + if_electricity_mix_gwp: GWP electricity mix factor in kgCO2eq / kWh. + if_electricity_mix_wue: WCF electricity mix factor in L / kWh. + datacenter_pue: Power Usage Effectiveness of the datacenter. + datacenter_wue: Water Usage Effectiveness of the datacenter in L/kWh. + request_latency: Measured wall-clock time for the request in seconds. + """ + if request_latency is None: + request_latency = 0.0 # embodied not computed without measured latency + + total_params: list[ValueOrRange] = [model_total_parameter_count] + if isinstance(model_total_parameter_count, RangeValue): + total_params = [model_total_parameter_count.min, model_total_parameter_count.max] + + fields = [ + "request_energy", "request_usage_gwp", "request_usage_adpe", + "request_usage_pe", "request_usage_wcf", + "request_embodied_gwp", "request_embodied_adpe", "request_embodied_pe", + ] + results: dict[str, Union[RangeValue, float, int]] = {} + + for tot_param in total_params: + res = compute_diffusion_impacts_dag( + model_total_parameter_count=tot_param, + flops_per_step=flops_per_step, + n_steps=n_steps, + guidance_scale=guidance_scale, + cfg_double_pass=cfg_double_pass, + denoising_strength=denoising_strength, + n_frames=n_frames, + default_frames=default_frames, + request_latency=request_latency, + if_electricity_mix_adpe=if_electricity_mix_adpe, + if_electricity_mix_pe=if_electricity_mix_pe, + if_electricity_mix_gwp=if_electricity_mix_gwp, + if_electricity_mix_wue=if_electricity_mix_wue, + datacenter_pue=datacenter_pue, + datacenter_wue=datacenter_wue, + frame_attn_fraction=frame_attn_fraction, + **kwargs, + ) + for field in fields: + if field in results: + min_result = results[field] + max_result = res[field] + if isinstance(min_result, RangeValue): + min_result = cast(Union[float, int], min_result.min) + if isinstance(max_result, RangeValue): + max_result = cast(Union[float, int], max_result.max) + results[field] = RangeValue(min=min_result, max=max_result) + else: + results[field] = res[field] + + energy = Energy(value=results["request_energy"]) + gwp_usage = GWP(value=results["request_usage_gwp"]) + adpe_usage = ADPe(value=results["request_usage_adpe"]) + pe_usage = PE(value=results["request_usage_pe"]) + wcf_usage = WCF(value=results["request_usage_wcf"]) + gwp_embodied = GWP(value=results["request_embodied_gwp"]) + adpe_embodied = ADPe(value=results["request_embodied_adpe"]) + pe_embodied = PE(value=results["request_embodied_pe"]) + + return Impacts( + energy=energy, + gwp=gwp_usage + gwp_embodied, + adpe=adpe_usage + adpe_embodied, + pe=pe_usage + pe_embodied, + wcf=wcf_usage, + usage=Usage( + energy=energy, + gwp=gwp_usage, + adpe=adpe_usage, + pe=pe_usage, + wcf=wcf_usage, + ), + embodied=Embodied( + gwp=gwp_embodied, + adpe=adpe_embodied, + pe=pe_embodied, + ), + ) diff --git a/ecologits/integrations/__init__.py b/ecologits/integrations/__init__.py new file mode 100644 index 00000000..0d664a33 --- /dev/null +++ b/ecologits/integrations/__init__.py @@ -0,0 +1,6 @@ +""" +EcoLogits integrations with third-party observability frameworks. + +Each sub-package is an optional extra: importing it requires the corresponding +extra to be installed (e.g. `pip install ecologits[langsmith]`). +""" diff --git a/ecologits/integrations/langsmith/__init__.py b/ecologits/integrations/langsmith/__init__.py new file mode 100644 index 00000000..d5055910 --- /dev/null +++ b/ecologits/integrations/langsmith/__init__.py @@ -0,0 +1,40 @@ +""" +LangSmith integration for EcoLogits. + +Provides `environment_traceable`, a drop-in replacement for LangSmith's +`@traceable` that additionally computes ecologits environmental impacts for +every captured LLM child run and posts them as LangSmith Feedback scores, +plus codecarbon runtime emissions on every run in the trace tree. + +Requires the optional `langsmith` extra: `pip install ecologits[langsmith]`. +""" + +from ecologits.integrations.langsmith.decorator import environment_traceable +from ecologits.integrations.langsmith.impacts import ( + build_feedback_payloads, + compute_call_impact, + post_impacts_as_feedback, +) +from ecologits.integrations.langsmith.payload_builder import ( + build_sci_per_token_payloads, + build_sci_per_workflow_payloads, +) +from ecologits.integrations.langsmith.runtime import ( + build_default_tracker, + build_runtime_feedback_payloads, + build_sci_intensity_payload, + post_runtime_as_feedback, +) + +__all__ = [ + "build_default_tracker", + "build_feedback_payloads", + "build_runtime_feedback_payloads", + "build_sci_intensity_payload", + "build_sci_per_token_payloads", + "build_sci_per_workflow_payloads", + "compute_call_impact", + "environment_traceable", + "post_impacts_as_feedback", + "post_runtime_as_feedback", +] diff --git a/ecologits/integrations/langsmith/constants.py b/ecologits/integrations/langsmith/constants.py new file mode 100644 index 00000000..62a402bc --- /dev/null +++ b/ecologits/integrations/langsmith/constants.py @@ -0,0 +1,183 @@ +""" +Constants for the LangSmith integration. + +All string keys, normalisation maps, scaling factors and feedback metadata used +by the integration are centralised here so the rest of the package contains no +string literals tied to LangSmith / ecologits conventions. +""" + +from __future__ import annotations + +DEFAULT_RUN_TYPE: str = "chain" +LLM_RUN_TYPE: str = "llm" + +KEY_EXTRA: str = "extra" +KEY_METADATA: str = "metadata" +KEY_INVOCATION_PARAMS: str = "invocation_params" +KEY_SERIALIZED: str = "serialized" +KEY_SERIALIZED_ID: str = "id" +KEY_RUN_TYPE: str = "_type" +KEY_LS_PROVIDER: str = "ls_provider" +KEY_LS_MODEL_NAME: str = "ls_model_name" +KEY_MODEL_NAME: str = "model_name" +KEY_MODEL: str = "model" +KEY_AZURE_DEPLOYMENT: str = "azure_deployment" +KEY_USAGE_METADATA: str = "usage_metadata" +KEY_USAGE: str = "usage" +KEY_TOKEN_USAGE: str = "token_usage" +KEY_LLM_OUTPUT: str = "llm_output" +KEY_OUTPUT_TOKENS: str = "output_tokens" +KEY_COMPLETION_TOKENS: str = "completion_tokens" + +LS_PROVIDER_TO_ECOLOGITS: dict[str, str] = { + "openai": "openai", + "azure": "openai", + "anthropic": "anthropic", + "cohere": "cohere", + "mistral": "mistralai", + "mistralai": "mistralai", + "google": "google_genai", + "google_genai": "google_genai", + "google_vertexai": "google_genai", + "huggingface": "huggingface_hub", + "huggingface_hub": "huggingface_hub", +} + +TYPE_TO_PROVIDER: dict[str, str] = { + "azure-openai-chat": "openai", + "azure-openai": "openai", + "openai-chat": "openai", + "openai": "openai", + "anthropic": "anthropic", + "anthropic-chat": "anthropic", + "cohere": "cohere", + "cohere-chat": "cohere", + "mistral": "mistralai", + "mistral-chat": "mistralai", + "google-palm": "google_genai", + "google-genai": "google_genai", + "vertexai": "google_genai", + "huggingface": "huggingface_hub", +} + +SERIALIZED_MODULE_TO_PROVIDER: dict[str, str] = { + "azure_openai": "openai", + "openai": "openai", + "anthropic": "anthropic", + "cohere": "cohere", + "mistralai": "mistralai", + "google_genai": "google_genai", + "google_vertexai": "google_genai", + "huggingface_hub": "huggingface_hub", +} + +# TODO: Move DEPLOYMENT_TO_MODEL entries to models.json "aliases" section so +# ModelRepository.find_model() resolves them natively. Until then, extend this +# dict manually with Azure deployment names (dots stripped/replaced by dashes, +# e.g. gpt-4.1 -> gpt-41) for every OpenAI model added to models.json. +DEPLOYMENT_TO_MODEL: dict[str, str] = { + "gpt-51": "gpt-5.1", + "gpt-41-mini": "gpt-4.1-mini", + "gpt-41": "gpt-4.1", + "gpt-4o-mini": "gpt-4o-mini", + "gpt-4o": "gpt-4o", + "gpt-4-turbo": "gpt-4-turbo", + "gpt-4": "gpt-4", + "gpt-35-turbo": "gpt-3.5-turbo", + "gpt-35-turbo-16k": "gpt-3.5-turbo-16k", +} + +IMPACT_FIELDS: tuple[str, ...] = ("energy", "gwp", "adpe", "pe", "wcf") + +# Feedback key (display) name per ecologits impact field, with the unit baked in +# after scaling so chart axes can simply read the key. +FEEDBACK_KEY_BASE: dict[str, str] = { + "energy": "energy_wh", + "gwp": "gwp_gco2eq", + "adpe": "adpe_mgsbeq", + "pe": "pe_kj", + "wcf": "wcf_ml", +} + +# Scale raw ecologits units → values with >=4 significant figures after +# LangSmith's 4-decimal-place rounding (matches onejo-ml convention). +SCORE_SCALE: dict[str, float] = { + "energy": 1e3, # kWh -> Wh + "gwp": 1e3, # kgCO2eq -> gCO2eq + "adpe": 1e6, # kgSbeq -> mgSbeq + "pe": 1e3, # MJ -> kJ + "wcf": 1e3, # L -> mL +} + +VARIANTS: tuple[str, ...] = ("min", "max", "avg") +VARIANT_AVG: str = "avg" +VARIANT_MIN: str = "min" +VARIANT_MAX: str = "max" + +SCORE_ROUND_DIGITS: int = 4 + +# LangSmith's FeedbackSourceType enum only accepts "api" or "model" +# (see langsmith/schemas.py FeedbackSourceType). The ecologits origin is +# surfaced via source_info instead (FEEDBACK_SOURCE_INFO below). +LANGSMITH_FEEDBACK_SOURCE_TYPE: str = "api" + +FEEDBACK_SOURCE_KEY_ORIGIN: str = "source" +FEEDBACK_SOURCE_KEY_VERSION: str = "version" +FEEDBACK_SOURCE_VALUE_ORIGIN: str = "ecologits" + +# --------------------------------------------------------------------------- +# SCI for AI (ISO/IEC 21031:2024) — Consumer Boundary +# --------------------------------------------------------------------------- +SCI_BOUNDARY_CONSUMER: str = "consumer" +SCI_ISO_REFERENCE: str = "ISO/IEC 21031:2024" +SCI_METHODOLOGY: str = "sci-ai-v1.0" +SCI_OFFSETS_EXCLUDED: str = "true" + +FUNCTIONAL_UNIT_PER_TOKEN: str = "token" +FUNCTIONAL_UNIT_PER_WORKFLOW: str = "workflow" + +# Per-token SCI (mgCO2eq/token — completion tokens, Consumer functional unit for LLMs). +SCI_KEY_PER_TOKEN: str = "sci_per_token_mgco2eq" +# Per-workflow SCI (gCO2eq/workflow — Consumer functional unit for Agentic AI). +SCI_KEY_PER_WORKFLOW: str = "sci_per_workflow_gco2eq" +# Regional grid carbon intensity surfaced from codecarbon (gCO2eq/kWh). +SCI_KEY_CARBON_INTENSITY: str = "sci_I_gco2eq_per_kwh" + +# Scale: raw kgCO2eq → display units matching the onejo-ml convention. +SCI_SCALE_PER_TOKEN: float = 1e9 # kgCO2eq/token → mgCO2eq/token (×1e6 kg→g, ×1e3 g→mg = 1e9) +SCI_SCALE_PER_WORKFLOW: float = 1e3 # kgCO2eq/workflow → gCO2eq/workflow + +SCI_FEEDBACK_METADATA: dict[str, str] = { + "sci_boundary": SCI_BOUNDARY_CONSUMER, + "sci_iso_ref": SCI_ISO_REFERENCE, + "sci_methodology": SCI_METHODOLOGY, + "sci_offsets_excluded": SCI_OFFSETS_EXCLUDED, +} + +# --------------------------------------------------------------------------- +# codecarbon runtime tracking +# --------------------------------------------------------------------------- +CODECARBON_FIELD_TO_KEY: dict[str, str] = { + "energy_consumed": "runtime_energy_wh", + "emissions": "runtime_gwp_gco2eq", + "water_consumed": "runtime_water_ml", + "cpu_energy": "runtime_cpu_energy_wh", + "gpu_energy": "runtime_gpu_energy_wh", + "ram_energy": "runtime_ram_energy_wh", + "duration": "runtime_duration_s", +} + +# Scale raw codecarbon units → chart-friendly values (same convention as SCORE_SCALE). +CODECARBON_FIELD_SCALE: dict[str, float] = { + "energy_consumed": 1e3, # kWh -> Wh + "emissions": 1e3, # kgCO2eq -> gCO2eq + "water_consumed": 1e3, # L -> mL + "cpu_energy": 1e3, # kWh -> Wh + "gpu_energy": 1e3, # kWh -> Wh + "ram_energy": 1e3, # kWh -> Wh + "duration": 1.0, # seconds (no conversion) +} + +DEFAULT_MEASURE_POWER_SECS: float = 1.0 +DEFAULT_TRACKING_MODE: str = "process" +CODECARBON_PROJECT_NAME: str = "ecologits-langsmith" diff --git a/ecologits/integrations/langsmith/decorator.py b/ecologits/integrations/langsmith/decorator.py new file mode 100644 index 00000000..a7c0bbde --- /dev/null +++ b/ecologits/integrations/langsmith/decorator.py @@ -0,0 +1,174 @@ +""" +`environment_traceable` decorator - wraps LangSmith's `@traceable` and posts +ecologits environmental impacts as feedback on every captured LLM child run, +plus codecarbon runtime emissions on every run in the tree. +""" + +# Impacts are posted as LangSmith Feedback scores (not metadata) because LangSmith +# dashboard charts only support feedback_score_avg + feedback_key for custom +# numeric Y-axis metrics; run.metadata fields cannot be plotted. +from __future__ import annotations + +import asyncio +import functools +from typing import Any, Callable + +from langsmith import traceable +from langsmith.run_helpers import get_current_run_tree + +from ecologits._ecologits import EcoLogits +from ecologits.integrations.langsmith.constants import DEFAULT_RUN_TYPE +from ecologits.integrations.langsmith.impacts import post_impacts_as_feedback +from ecologits.integrations.langsmith.runtime import ( + build_default_tracker, + post_runtime_as_feedback, +) +from ecologits.log import logger + + +def _resolve_zone(explicit: str | None) -> str | None: + if explicit is not None: + return explicit + return getattr(EcoLogits.config, "electricity_mix_zone", None) + + +def _run_tracker(tracker: Any) -> tuple[Any, Any]: + """ + Start the tracker, run start_task, and return (tracker, task_name). + + Using start_task/stop_task rather than start/stop gives us a full + EmissionsData object (including per-component energy fields) instead of + just the kgCO2 float returned by stop(). + """ + task_name = "langsmith-trace" + tracker.start() + tracker.start_task(task_name) + return tracker, task_name + + +def _stop_tracker(tracker: Any, task_name: str | None) -> Any: + """ + Stop the task and the tracker; return the EmissionsData delta or None. + """ + emissions_data = None + try: + emissions_data = tracker.stop_task(task_name) + except Exception as exc: + logger.warning(f"ecologits: codecarbon stop_task failed: {exc}") + try: + tracker.stop() + except Exception as exc: + logger.warning(f"ecologits: codecarbon stop failed: {exc}") + return emissions_data + + +def _make_tracker_factory( + enable_runtime_emissions: bool, + runtime_tracker: Any, +) -> Callable[[str | None], Any] | None: + if not enable_runtime_emissions: + return None + if runtime_tracker is not None: + return lambda _zone: runtime_tracker + return build_default_tracker + + +def _wrap_sync( + func: Callable, + zone: str | None, + client: Any, + tracker_factory: Callable | None, +) -> Callable: + @functools.wraps(func) + def sync_body(*args: Any, **kwargs: Any) -> Any: + tracker = None + task_name = None + if tracker_factory is not None: + try: + tracker, task_name = _run_tracker(tracker_factory(zone)) + except Exception as exc: + logger.warning(f"ecologits: codecarbon tracker start failed: {exc}") + tracker = None + try: + result = func(*args, **kwargs) + finally: + emissions_data = _stop_tracker(tracker, task_name) if tracker else None + run_tree = get_current_run_tree() + post_impacts_as_feedback(run_tree, zone, client) + if emissions_data is not None: + post_runtime_as_feedback(run_tree, emissions_data, client) + return result + + return sync_body + + +def _wrap_async( + func: Callable, + zone: str | None, + client: Any, + tracker_factory: Callable | None, +) -> Callable: + @functools.wraps(func) + async def async_body(*args: Any, **kwargs: Any) -> Any: + tracker = None + task_name = None + if tracker_factory is not None: + try: + tracker, task_name = _run_tracker(tracker_factory(zone)) + except Exception as exc: + logger.warning(f"ecologits: codecarbon tracker start failed: {exc}") + tracker = None + try: + result = await func(*args, **kwargs) + finally: + emissions_data = _stop_tracker(tracker, task_name) if tracker else None + run_tree = get_current_run_tree() + post_impacts_as_feedback(run_tree, zone, client) + if emissions_data is not None: + post_runtime_as_feedback(run_tree, emissions_data, client) + return result + + return async_body + + +def environment_traceable( + run_type: str = DEFAULT_RUN_TYPE, + *, + electricity_mix_zone: str | None = None, + enable_runtime_emissions: bool = True, + runtime_tracker: Any = None, + **traceable_kwargs: Any, +) -> Callable: + """ + Wrap a function with LangSmith's @traceable and post ecologits impacts. + + All `traceable_kwargs` are forwarded unchanged to `langsmith.traceable`. + After the wrapped function returns: + - Every captured LLM child run gets one Feedback score per ecologits impact + field / variant (energy_wh, gwp_gco2eq, adpe_mgsbeq, pe_kj, wcf_ml x + min/max/avg). + - Every run in the tree (LLM + non-LLM, including root) gets one Feedback + score per codecarbon runtime field (runtime_energy_wh, runtime_gwp_gco2eq, + runtime_water_ml, runtime_cpu/gpu/ram_energy_wh, runtime_duration_s), + allocated by exclusive (self) duration share. + + Args: + enable_runtime_emissions: Set False to skip codecarbon tracking entirely. + runtime_tracker: Optional pre-configured OfflineEmissionsTracker. + When provided, its start/stop lifecycle is still managed by this + decorator. Defaults to a silent OfflineEmissionsTracker built from + `electricity_mix_zone`. + """ + client = traceable_kwargs.get("client") + + def decorator(func: Callable) -> Callable: + zone = _resolve_zone(electricity_mix_zone) + tracker_factory = _make_tracker_factory(enable_runtime_emissions, runtime_tracker) + body = ( + _wrap_async(func, zone, client, tracker_factory) + if asyncio.iscoroutinefunction(func) + else _wrap_sync(func, zone, client, tracker_factory) + ) + return traceable(run_type, **traceable_kwargs)(body) + + return decorator diff --git a/ecologits/integrations/langsmith/extractors.py b/ecologits/integrations/langsmith/extractors.py new file mode 100644 index 00000000..1decb2fb --- /dev/null +++ b/ecologits/integrations/langsmith/extractors.py @@ -0,0 +1,187 @@ +""" +Extract ecologits inputs (provider, model, output tokens, latency) from +LangSmith RunTree child runs captured during a traced function call. +""" + +from __future__ import annotations + +from collections.abc import Iterator +from typing import Any + +from ecologits.integrations.langsmith.constants import ( + DEPLOYMENT_TO_MODEL, + KEY_AZURE_DEPLOYMENT, + KEY_COMPLETION_TOKENS, + KEY_EXTRA, + KEY_INVOCATION_PARAMS, + KEY_LLM_OUTPUT, + KEY_LS_MODEL_NAME, + KEY_LS_PROVIDER, + KEY_METADATA, + KEY_MODEL, + KEY_MODEL_NAME, + KEY_OUTPUT_TOKENS, + KEY_RUN_TYPE, + KEY_SERIALIZED, + KEY_SERIALIZED_ID, + KEY_TOKEN_USAGE, + KEY_USAGE, + KEY_USAGE_METADATA, + LLM_RUN_TYPE, + LS_PROVIDER_TO_ECOLOGITS, + SERIALIZED_MODULE_TO_PROVIDER, + TYPE_TO_PROVIDER, +) + + +def _as_dict(obj: Any) -> dict: + if isinstance(obj, dict): + return obj + if obj is None: + return {} + return {} + + +def _run_metadata(run: Any) -> dict: + extra = getattr(run, KEY_EXTRA, None) or {} + return _as_dict(extra.get(KEY_METADATA)) + + +def _run_invocation_params(run: Any) -> dict: + extra = getattr(run, KEY_EXTRA, None) or {} + return _as_dict(extra.get(KEY_INVOCATION_PARAMS)) + + +def _run_outputs(run: Any) -> dict: + return _as_dict(getattr(run, "outputs", None)) + + +def _run_serialized(run: Any) -> dict: + return _as_dict(getattr(run, KEY_SERIALIZED, None)) + + +def _normalise_model_name(raw: str | None) -> str | None: + if not raw: + return None + return DEPLOYMENT_TO_MODEL.get(raw.lower(), raw) + + +def _provider_from_metadata(metadata: dict) -> str | None: + ls_provider = metadata.get(KEY_LS_PROVIDER) + if not ls_provider: + return None + return LS_PROVIDER_TO_ECOLOGITS.get(str(ls_provider).lower()) + + +def _provider_from_invocation_params(invocation_params: dict) -> str | None: + run_type = str(invocation_params.get(KEY_RUN_TYPE) or "").lower() + if not run_type: + return None + return TYPE_TO_PROVIDER.get(run_type) + + +def _provider_from_serialized(serialized: dict) -> str | None: + fragments = serialized.get(KEY_SERIALIZED_ID) or [] + if not isinstance(fragments, list): + return None + for fragment in fragments: + provider = SERIALIZED_MODULE_TO_PROVIDER.get(str(fragment).lower()) + if provider: + return provider + return None + + +def _model_name_from_sources(metadata: dict, invocation_params: dict) -> str | None: + raw = ( + metadata.get(KEY_LS_MODEL_NAME) + or invocation_params.get(KEY_LS_MODEL_NAME) + or invocation_params.get(KEY_MODEL_NAME) + or invocation_params.get(KEY_MODEL) + or invocation_params.get(KEY_AZURE_DEPLOYMENT) + ) + return _normalise_model_name(raw) + + +def extract_provider_and_model(run: Any) -> tuple[str | None, str | None]: + """ + Return the ecologits provider key and the normalised model name. + """ + metadata = _run_metadata(run) + invocation_params = _run_invocation_params(run) + provider = ( + _provider_from_metadata(metadata) + or _provider_from_invocation_params(invocation_params) + or _provider_from_serialized(_run_serialized(run)) + ) + model_name = _model_name_from_sources(metadata, invocation_params) + return provider, model_name + + +def _usage_metadata_from_run(run: Any) -> dict: + metadata = _run_metadata(run) + return _as_dict(_run_outputs(run).get(KEY_USAGE_METADATA) or metadata.get(KEY_USAGE_METADATA)) + + +def _legacy_usage_from_outputs(run: Any) -> dict: + # Fallback for pre-usage_metadata LangChain run shapes: + # - outputs["usage"] dict (LangChain <0.2 direct token counts) + # - outputs["llm_output"]["token_usage"] (oldest LangChain LLM wrapper shape) + # Safe to remove once all traced runs come from LangChain >=0.2 / wrap_openai. + outputs = _run_outputs(run) + legacy = outputs.get(KEY_USAGE) or _as_dict(outputs.get(KEY_LLM_OUTPUT)).get(KEY_TOKEN_USAGE) + return _as_dict(legacy) + + +def extract_output_tokens(run: Any) -> int | None: + """ + Return the number of generated/output tokens for an LLM child run, or None. + """ + # TODO: ecologits llm_impacts() expects only generated/output tokens + # (completion_tokens), not prompt+completion. When ecologits exposes a full + # token-type API (separate prompt / cached / reasoning / completion buckets), + # extend this extractor + the llm_impacts call to pass each bucket explicitly. + usage_metadata = _usage_metadata_from_run(run) + if usage_metadata.get(KEY_OUTPUT_TOKENS) is not None: + return int(usage_metadata[KEY_OUTPUT_TOKENS]) + legacy = _legacy_usage_from_outputs(run) + if legacy.get(KEY_COMPLETION_TOKENS) is not None: + return int(legacy[KEY_COMPLETION_TOKENS]) + if legacy.get(KEY_OUTPUT_TOKENS) is not None: + return int(legacy[KEY_OUTPUT_TOKENS]) + return None + + +def compute_latency_seconds(run: Any) -> float | None: + """ + Return clock latency in seconds from a run's start/end timestamps. + """ + start = getattr(run, "start_time", None) + end = getattr(run, "end_time", None) + if start is None or end is None: + return None + try: + return (end - start).total_seconds() + except (TypeError, AttributeError): + return None + + +def iter_all_runs(run_tree: Any) -> Iterator[Any]: + """ + Yield every descendant run in depth-first order, regardless of run_type. + """ + if run_tree is None: + return + stack: list[Any] = list(reversed(getattr(run_tree, "child_runs", None) or [])) + while stack: + child = stack.pop() + yield child + stack.extend(reversed(getattr(child, "child_runs", None) or [])) + + +def iter_llm_child_runs(run_tree: Any) -> Iterator[Any]: + """ + Yield every descendant run with `run_type == LLM_RUN_TYPE` in depth-first order. + """ + for run in iter_all_runs(run_tree): + if getattr(run, "run_type", None) == LLM_RUN_TYPE: + yield run diff --git a/ecologits/integrations/langsmith/impacts.py b/ecologits/integrations/langsmith/impacts.py new file mode 100644 index 00000000..2634e08c --- /dev/null +++ b/ecologits/integrations/langsmith/impacts.py @@ -0,0 +1,134 @@ +""" +Compute ecologits impacts from LangSmith LLM child runs and post them as +LangSmith Feedback scores on each child run, plus SCI for AI Consumer-Boundary +rates (sci_per_token_mgco2eq, sci_per_workflow_gco2eq) on child and root runs. +""" + +# Impacts are posted as LangSmith Feedback scores (not metadata) because LangSmith +# dashboard charts only support feedback_score_avg + feedback_key for custom +# numeric Y-axis metrics; run.metadata fields cannot be plotted. +from __future__ import annotations + +from typing import Any + +from ecologits.integrations.langsmith.constants import ( + FEEDBACK_KEY_BASE, + IMPACT_FIELDS, + SCORE_SCALE, +) +from ecologits.integrations.langsmith.extractors import ( + compute_latency_seconds, + extract_output_tokens, + extract_provider_and_model, + iter_llm_child_runs, +) +from ecologits.integrations.langsmith.payload_builder import ( + FEEDBACK_SOURCE_INFO, + build_payloads_for_field, + build_sci_per_token_payloads, + build_sci_per_workflow_payloads, + post_payload_safely, + resolve_client, +) +from ecologits.log import logger +from ecologits.tracers.utils import ImpactsOutput, llm_impacts +from ecologits.utils.range_value import RangeValue + + +def compute_call_impact(child_run: Any, electricity_mix_zone: str | None) -> ImpactsOutput | None: + """ + Build an ImpactsOutput for one LangSmith llm child run, or None if inputs are insufficient. + """ + provider, model_name = extract_provider_and_model(child_run) + output_tokens = extract_output_tokens(child_run) + latency = compute_latency_seconds(child_run) + if not provider or not model_name or output_tokens is None or latency is None: + return None + if output_tokens <= 0 or latency <= 0: + return None + return llm_impacts( + provider=provider, + model_name=model_name, + output_token_count=output_tokens, + request_latency=latency, + electricity_mix_zone=electricity_mix_zone, + ) + + +def build_feedback_payloads(impact: ImpactsOutput, run_id: str) -> list[dict]: + """ + Flatten an ImpactsOutput into one Client.create_feedback payload per scored variant. + """ + payloads: list[dict] = [] + for field in IMPACT_FIELDS: + payloads.extend( + build_payloads_for_field( + impact=getattr(impact, field, None), + base_key=FEEDBACK_KEY_BASE[field], + scale=SCORE_SCALE[field], + run_id=run_id, + ) + ) + return payloads + + +def _gwp_bounds(impact: ImpactsOutput) -> tuple[float | None, float | None]: + """Extract raw min/max kgCO2eq from an ImpactsOutput gwp field.""" + gwp = getattr(impact, "gwp", None) + if gwp is None or gwp.value is None: + return None, None + value = gwp.value + if isinstance(value, RangeValue): + return float(value.min), float(value.max) + return float(value), float(value) + + +def post_impacts_as_feedback( + run_tree: Any, + electricity_mix_zone: str | None, + client: Any = None, +) -> None: + """ + Walk LLM child runs of `run_tree`, compute impacts, post each as feedback. + + Additionally emits: + - sci_per_token_mgco2eq_{min,max,avg} on each LLM child run. + - sci_per_workflow_gco2eq_{min,max,avg} on the root run (sum of child GWP). + + No-op when `run_tree` is None. Feedback errors are logged and never raised. + """ + if run_tree is None: + return + resolved = resolve_client(client) + root_gwp_min: float = 0.0 + root_gwp_max: float = 0.0 + has_root_sci = False + + for child in iter_llm_child_runs(run_tree): + impact = compute_call_impact(child, electricity_mix_zone) + if impact is None or impact.has_errors: + continue + run_id = str(getattr(child, "id", "")) + if not run_id: + continue + + for payload in build_feedback_payloads(impact, run_id): + post_payload_safely(resolved, payload) + + output_tokens = extract_output_tokens(child) or 0 + for payload in build_sci_per_token_payloads(getattr(impact, "gwp", None), output_tokens, run_id): + post_payload_safely(resolved, payload) + + lo, hi = _gwp_bounds(impact) + if lo is not None and hi is not None: + root_gwp_min += lo + root_gwp_max += hi + has_root_sci = True + + if has_root_sci: + root_id = str(getattr(run_tree, "id", "")) + if root_id: + for payload in build_sci_per_workflow_payloads(root_gwp_min, root_gwp_max, root_id): + post_payload_safely(resolved, payload) + else: + logger.warning("ecologits: root run has no id, skipping sci_per_workflow feedback") diff --git a/ecologits/integrations/langsmith/payload_builder.py b/ecologits/integrations/langsmith/payload_builder.py new file mode 100644 index 00000000..620c3f2f --- /dev/null +++ b/ecologits/integrations/langsmith/payload_builder.py @@ -0,0 +1,146 @@ +""" +Shared payload-building utilities for the LangSmith integration. + +Centralises the scale/round/source-info pipeline used by both the ecologits +impact emitter (impacts.py) and the codecarbon runtime emitter (runtime.py), +and provides the SCI for AI (ISO/IEC 21031:2024) rate helpers. +""" +from __future__ import annotations + +from typing import Any + +from langsmith import Client + +import ecologits +from ecologits.integrations.langsmith.constants import ( + FEEDBACK_SOURCE_KEY_ORIGIN, + FEEDBACK_SOURCE_KEY_VERSION, + FEEDBACK_SOURCE_VALUE_ORIGIN, + LANGSMITH_FEEDBACK_SOURCE_TYPE, + SCORE_ROUND_DIGITS, + SCI_FEEDBACK_METADATA, + SCI_KEY_PER_TOKEN, + SCI_KEY_PER_WORKFLOW, + SCI_SCALE_PER_TOKEN, + SCI_SCALE_PER_WORKFLOW, + VARIANT_AVG, + VARIANT_MAX, + VARIANT_MIN, +) +from ecologits.log import logger +from ecologits.utils.range_value import RangeValue + +FEEDBACK_SOURCE_INFO: dict[str, str] = { + FEEDBACK_SOURCE_KEY_ORIGIN: FEEDBACK_SOURCE_VALUE_ORIGIN, + FEEDBACK_SOURCE_KEY_VERSION: ecologits.__version__, + **SCI_FEEDBACK_METADATA, +} + + +def resolve_client(client: Any) -> Client: + if client is not None: + return client + return Client() + + +def post_payload_safely(client: Any, payload: dict) -> None: + try: + client.create_feedback(**payload) + except Exception as exc: + logger.warning(f"ecologits: failed to post langsmith feedback {payload.get('key')!r}: {exc}") + + +def scaled_variants(value: Any, scale: float) -> dict[str, float]: + """Return {min?, max?, avg} from a BaseImpact value, scaled to display units.""" + if value is None: + return {} + raw = getattr(value, "value", value) + if raw is None: + return {} + if isinstance(raw, RangeValue): + return { + VARIANT_MIN: float(raw.min) * scale, + VARIANT_MAX: float(raw.max) * scale, + VARIANT_AVG: float(raw.mean) * scale, + } + return {VARIANT_AVG: float(raw) * scale} + + +def build_payloads_for_field( + impact: Any, + base_key: str, + scale: float, + run_id: str, + source_info: dict[str, str] | None = None, +) -> list[dict]: + """Build one feedback payload per variant of a single impact field.""" + if source_info is None: + source_info = FEEDBACK_SOURCE_INFO + payloads: list[dict] = [] + for variant, score in scaled_variants(impact, scale).items(): + payloads.append( + { + "run_id": run_id, + "key": f"{base_key}_{variant}", + "score": round(score, SCORE_ROUND_DIGITS), + "feedback_source_type": LANGSMITH_FEEDBACK_SOURCE_TYPE, + "source_info": source_info, + } + ) + return payloads + + +def _sci_rate_payload(run_id: str, key: str, score: float) -> dict: + return { + "run_id": run_id, + "key": key, + "score": round(score, SCORE_ROUND_DIGITS), + "feedback_source_type": LANGSMITH_FEEDBACK_SOURCE_TYPE, + "source_info": FEEDBACK_SOURCE_INFO, + } + + +def build_sci_per_token_payloads(gwp_impact: Any, output_tokens: int, run_id: str) -> list[dict]: + """ + Emit sci_per_token_mgco2eq_{min,max,avg} for one LLM run. + + R = output_tokens (LLM Consumer functional unit per ISO/IEC 21031:2024). + Returns empty list when tokens <= 0 or gwp has no value. + """ + if output_tokens <= 0: + return [] + variants = scaled_variants(gwp_impact, SCI_SCALE_PER_TOKEN) + if not variants: + return [] + payloads: list[dict] = [] + for variant, gwp_scaled in variants.items(): + score = gwp_scaled / output_tokens + payloads.append(_sci_rate_payload(run_id, f"{SCI_KEY_PER_TOKEN}_{variant}", score)) + return payloads + + +def build_sci_per_workflow_payloads( + total_gwp_min: float | None, + total_gwp_max: float | None, + run_id: str, +) -> list[dict]: + """ + Emit sci_per_workflow_gco2eq_{min,max,avg} on the root run. + + R = 1 workflow execution (Agentic AI Consumer functional unit). + Scale: kgCO2eq → gCO2eq (×1e3). + """ + bounds: list[tuple[str, float]] = [] + if total_gwp_min is not None: + bounds.append((VARIANT_MIN, total_gwp_min)) + if total_gwp_max is not None: + bounds.append((VARIANT_MAX, total_gwp_max)) + if not bounds: + return [] + + valid = [v for _, v in bounds] + avg = sum(valid) / len(valid) + payloads = [_sci_rate_payload(run_id, f"{SCI_KEY_PER_WORKFLOW}_{VARIANT_AVG}", avg * SCI_SCALE_PER_WORKFLOW)] + for variant, val in bounds: + payloads.append(_sci_rate_payload(run_id, f"{SCI_KEY_PER_WORKFLOW}_{variant}", val * SCI_SCALE_PER_WORKFLOW)) + return payloads diff --git a/ecologits/integrations/langsmith/runtime.py b/ecologits/integrations/langsmith/runtime.py new file mode 100644 index 00000000..93fdf144 --- /dev/null +++ b/ecologits/integrations/langsmith/runtime.py @@ -0,0 +1,186 @@ +""" +codecarbon-based runtime emissions tracking for the LangSmith integration. + +Wraps a traced function with an `OfflineEmissionsTracker`, then allocates total +measured emissions to every run in the tree proportionally by exclusive (self) +duration, posting each allocation as `runtime_*` LangSmith Feedback scores. + +Allocation by self-time (= run duration minus sum of direct-child durations) +ensures the allocations across all runs sum to the total measured value and +avoids double-counting parent/child overlaps. + +Caveats: +- codecarbon measures whole-machine power during the traced interval; for LLM + runs the "runtime emissions" reflect the client-side baseline while waiting + on the remote API, not per-call server-side work. +- Async parallel children inflate wall-clock totals, which distorts shares. +""" + +from __future__ import annotations + +from typing import Any + +from ecologits.integrations.langsmith.constants import ( + CODECARBON_FIELD_SCALE, + CODECARBON_FIELD_TO_KEY, + CODECARBON_PROJECT_NAME, + DEFAULT_MEASURE_POWER_SECS, + DEFAULT_TRACKING_MODE, + LANGSMITH_FEEDBACK_SOURCE_TYPE, + SCORE_ROUND_DIGITS, + SCI_KEY_CARBON_INTENSITY, +) +from ecologits.integrations.langsmith.extractors import ( + compute_latency_seconds, + iter_all_runs, +) +from ecologits.integrations.langsmith.payload_builder import ( + FEEDBACK_SOURCE_INFO, + post_payload_safely, + resolve_client, +) +from ecologits.log import logger + +try: + from codecarbon import OfflineEmissionsTracker + + _CODECARBON_AVAILABLE = True +except ImportError: + _CODECARBON_AVAILABLE = False + + +def build_default_tracker(electricity_mix_zone: str | None) -> Any: + """ + Build a silent OfflineEmissionsTracker scoped to a single traced call. + + Raises ImportError if codecarbon is not installed. + """ + if not _CODECARBON_AVAILABLE: + raise ImportError( + "codecarbon is required for runtime emissions tracking. Install with: pip install ecologits[langsmith]" + ) + kwargs: dict[str, Any] = { + "project_name": CODECARBON_PROJECT_NAME, + "measure_power_secs": DEFAULT_MEASURE_POWER_SECS, + "tracking_mode": DEFAULT_TRACKING_MODE, + "save_to_file": False, + "save_to_api": False, + "save_to_logger": False, + "save_to_prometheus": False, + "allow_multiple_runs": True, + "log_level": "error", + } + if electricity_mix_zone is not None: + kwargs["country_iso_code"] = electricity_mix_zone + return OfflineEmissionsTracker(**kwargs) + + +def compute_self_duration_seconds(run: Any) -> float: + """ + Return the exclusive (self) duration of a run in seconds. + + Self-time = run duration minus the sum of its direct children's durations. + Clamped to 0 to guard against clock skew or imprecise timestamps. + """ + total = compute_latency_seconds(run) or 0.0 + children_total = sum(compute_latency_seconds(child) or 0.0 for child in (getattr(run, "child_runs", None) or [])) + return max(0.0, total - children_total) + + +def compute_run_share(self_duration: float, total_duration: float) -> float: + """ + Return the fraction of total duration attributable to one run's self-time. + """ + if total_duration <= 0: + return 0.0 + return self_duration / total_duration + + +def build_runtime_feedback_payloads( + emissions_data: Any, + share: float, + run_id: str, +) -> list[dict]: + """ + Flatten a scaled, share-weighted EmissionsData snapshot into feedback payloads. + """ + payloads: list[dict] = [] + for field, key in CODECARBON_FIELD_TO_KEY.items(): + raw = getattr(emissions_data, field, None) + if raw is None: + continue + score = round(float(raw) * share * CODECARBON_FIELD_SCALE[field], SCORE_ROUND_DIGITS) + payloads.append( + { + "run_id": run_id, + "key": key, + "score": score, + "feedback_source_type": LANGSMITH_FEEDBACK_SOURCE_TYPE, + "source_info": FEEDBACK_SOURCE_INFO, + } + ) + return payloads + + +def build_sci_intensity_payload(emissions_data: Any, run_id: str) -> dict | None: + """ + Return a sci_I_gco2eq_per_kwh payload for the root run, or None. + + I = emissions (kgCO2eq) / energy_consumed (kWh) × 1e3 → gCO2eq/kWh. + Posted once on the root run only; intensity is regional and not + allocable by self-time. + """ + emissions = getattr(emissions_data, "emissions", None) + energy = getattr(emissions_data, "energy_consumed", None) + if emissions is None or energy is None: + return None + try: + e, en = float(emissions), float(energy) + except (TypeError, ValueError): + return None + if en <= 0: + return None + score = round(e / en * 1e3, SCORE_ROUND_DIGITS) + return { + "run_id": run_id, + "key": SCI_KEY_CARBON_INTENSITY, + "score": score, + "feedback_source_type": LANGSMITH_FEEDBACK_SOURCE_TYPE, + "source_info": FEEDBACK_SOURCE_INFO, + } + + +def post_runtime_as_feedback( + run_tree: Any, + emissions_data: Any, + client: Any = None, +) -> None: + """ + Walk every run in the tree (root included), allocate runtime emissions by + self-duration share, and post each as LangSmith Feedback. + + Also posts sci_I_gco2eq_per_kwh (regional grid intensity) on the root run. + No-op when `run_tree` or `emissions_data` is None. + """ + if run_tree is None or emissions_data is None: + return + total_duration = compute_latency_seconds(run_tree) or 0.0 + if total_duration <= 0: + logger.warning("ecologits: zero root duration, skipping runtime feedback allocation") + return + resolved = resolve_client(client) + for run in [run_tree, *iter_all_runs(run_tree)]: + run_id = str(getattr(run, "id", "")) + if not run_id: + continue + self_dur = compute_self_duration_seconds(run) + share = compute_run_share(self_dur, total_duration) + for payload in build_runtime_feedback_payloads(emissions_data, share, run_id): + post_payload_safely(resolved, payload) + + # sci_I posted once on root only. + root_id = str(getattr(run_tree, "id", "")) + if root_id: + intensity_payload = build_sci_intensity_payload(emissions_data, root_id) + if intensity_payload: + post_payload_safely(resolved, intensity_payload) diff --git a/ecologits/model_repository.py b/ecologits/model_repository.py index 2caedb19..7d3e7abc 100644 --- a/ecologits/model_repository.py +++ b/ecologits/model_repository.py @@ -16,6 +16,12 @@ class Providers(Enum): huggingface_hub = "huggingface_hub" cohere = "cohere" google_genai = "google_genai" + stabilityai = "stabilityai" + black_forest_labs = "black_forest_labs" + midjourney = "midjourney" + kling = "kling" + runway = "runway" + luma = "luma" class ArchitectureTypes(Enum): @@ -23,6 +29,12 @@ class ArchitectureTypes(Enum): MOE = "moe" +class ModalityTypes(Enum): + TEXT = "text" + IMAGE = "image" + VIDEO = "video" + + class ParametersMoE(BaseModel): total: ValueOrRange active: ValueOrRange @@ -33,6 +45,35 @@ class Architecture(BaseModel): parameters: Union[ValueOrRange, ParametersMoE] +class DiffusionParameters(BaseModel): + """ + Diffusion model description used for image and video generation. + + Attributes: + flops_denoise_per_step: FLOPs for one denoiser forward pass at the model native resolution. + default_steps: Default number of denoising steps (sampler dependent). + default_guidance_scale: Default guidance scale; CFG double-pass applies when > 1.0. + Flow-matching models (Flux, SD3) store their typical value without triggering double-pass. + latent_downscale: Spatial downscale factor between pixel and latent space (e.g. 8). + is_video: Whether the model generates video. + default_frames: Default number of generated frames (video only). + default_fps: Default frames per second (video only). + frame_attn_fraction: Share of per-step FLOPs from full-sequence 3D attention (scales as F²). + 0.0 for image models and sparse-attention video models; 0.5–0.64 for full-3D-attention + DiT video models. Used in the mixed frame scaling: (1-f)*(F/F_default) + f*(F/F_default)². + """ + + flops_denoise_per_step: ValueOrRange + default_steps: int + default_guidance_scale: float = 7.5 + cfg_double_pass: bool = True + latent_downscale: int = 8 + is_video: bool = False + default_frames: int | None = None + default_fps: int | None = None + frame_attn_fraction: float = 0.0 + + class Deployment(BaseModel): tps: float | None = None ttft: float | None = None @@ -46,23 +87,27 @@ class Alias(BaseModel): class Model(BaseModel): """ - LLM Model + Generative AI Model. Attributes: provider: Provider of the model (e.g. "OpenAI") name: Name of the model (e.g. "gpt-4o-mini") + modality: Output modality produced by the model (text, image, or video). architecture: Architecture type (dense or mixture-of-experts) warnings: Warnings linked to the model (e.g. "model-arch-not-released" or "model-arch-multimodal") sources: Source of the model information (website link) deployment: Deployment information (tps, ttft) + diffusion: Optional diffusion description for image and video generation models """ provider: Providers name: str + modality: ModalityTypes = ModalityTypes.TEXT architecture: Architecture warnings: list[WarningMessage] = [] sources: list[str] = [] deployment: Deployment | None = None + diffusion: DiffusionParameters | None = None @property def has_warnings(self) -> bool: @@ -79,13 +124,21 @@ def from_json(cls, data: dict[str, Any]) -> "Model": deployment = None if "deployment" in data and data["deployment"] is not None: deployment = Deployment.model_validate(data["deployment"]) + diffusion = None + if "diffusion" in data and data["diffusion"] is not None: + diffusion = DiffusionParameters.model_validate(data["diffusion"]) + modality = ModalityTypes.TEXT + if "modality" in data and data["modality"] is not None: + modality = ModalityTypes(data["modality"]) return cls( provider=Providers(data["provider"]), name=data["name"], + modality=modality, architecture=Architecture.model_validate(data["architecture"]), warnings=warnings, sources=sources, deployment=deployment, + diffusion=diffusion, ) diff --git a/ecologits/status_messages.py b/ecologits/status_messages.py index f9dc9750..b227b282 100644 --- a/ecologits/status_messages.py +++ b/ecologits/status_messages.py @@ -81,6 +81,22 @@ class ElectricityMixWUEDefaultWarning(WarningMessage): message: str = "The electricity mix WUE factor is using the world default value, expect lower precision." +class ModalityEditingUnvalidatedWarning(WarningMessage): + code: str = "modality-editing-unvalidated" + message: str = ( + "The end-to-end energy mapping for editing tasks (img2img, inpaint, vid2vid) is unvalidated, " + "the result is returned as a bounded range instead of a point estimate." + ) + + +class ModalityVideoFlopsFallbackWarning(WarningMessage): + code: str = "modality-video-flops-fallback" + message: str = ( + "This video model has no empirical latency regression; energy was estimated using the " + "FLOPs-based diffusion model. Expect lower precision than for benchmarked models." + ) + + class ModelNotRegisteredError(ErrorMessage): code: str = "model-not-registered" message: str = "The model is not registered in the model repository." @@ -97,6 +113,8 @@ class ZoneNotRegisteredError(ErrorMessage): "electricity-mix-adpe-world": ElectricityMixADPeDefaultWarning, "electricity-mix-pe-world": ElectricityMixPEDefaultWarning, "electricity-mix-wue-world": ElectricityMixWUEDefaultWarning, + "modality-editing-unvalidated": ModalityEditingUnvalidatedWarning, + "modality-video-flops-fallback": ModalityVideoFlopsFallbackWarning, } _error_codes: dict[str, type[ErrorMessage]] = { diff --git a/ecologits/tracers/utils.py b/ecologits/tracers/utils.py index f733c6ff..22fb846a 100644 --- a/ecologits/tracers/utils.py +++ b/ecologits/tracers/utils.py @@ -1,14 +1,16 @@ from __future__ import annotations from dataclasses import dataclass +from typing import Optional from pydantic import BaseModel from ecologits.electricity_mix_repository import electricity_mixes +from ecologits.impacts.diffusion import compute_diffusion_impacts from ecologits.impacts.llm import compute_llm_impacts from ecologits.impacts.modeling import GWP, PE, WCF, ADPe, Embodied, Energy, Usage from ecologits.log import logger -from ecologits.model_repository import ParametersMoE, models +from ecologits.model_repository import ModalityTypes, ParametersMoE, models from ecologits.status_messages import ErrorMessage, ModelNotRegisteredError, WarningMessage, ZoneNotRegisteredError from ecologits.utils.range_value import RangeValue @@ -136,6 +138,118 @@ def llm_impacts( return impacts +_EDITING_TASKS = {"img2img", "inpaint", "vid2vid"} +_IMAGE_TASKS = {"txt2img", "img2img", "inpaint"} +_VIDEO_TASKS = {"txt2vid", "vid2vid"} + + +def diffusion_impacts( + provider: str, + model_name: str, + task: str, + request_latency: Optional[float] = None, + width: int = 1024, + height: int = 1024, + n_steps: Optional[int] = None, + guidance_scale: Optional[float] = None, + n_frames: int = 1, + denoising_strength: float = 1.0, + electricity_mix_zone: Optional[str] = None, +) -> ImpactsOutput: + """ + Compute the environmental impacts of a diffusion image or video generation request. + + Args: + provider: Name of the provider (e.g. "stabilityai"). + model_name: Name of the model (e.g. "stable-diffusion-xl"). + task: Generation task — one of txt2img, img2img, inpaint, txt2vid, vid2vid. + request_latency: Measured wall-clock time for the request in seconds. + Required for embodied impact computation; omit to get usage-only impacts. + width: Output image or video width in pixels. + height: Output image or video height in pixels. + n_steps: Number of denoising steps (falls back to model default if None). + guidance_scale: CFG guidance scale (falls back to model default if None). + n_frames: Number of output video frames (1 for image generation). + denoising_strength: Fraction of steps applied for editing tasks [0, 1]. + electricity_mix_zone: ISO 3166-1 alpha-3 electricity mix zone (defaults to provider location). + """ + model = models.find_model(provider=provider, model_name=model_name) + if model is None: + error = ModelNotRegisteredError(message=f"Could not find model `{model_name}` for {provider} provider.") + logger.warning_once(str(error)) + return ImpactsOutput(errors=[error]) + + if model.modality not in (ModalityTypes.IMAGE, ModalityTypes.VIDEO): + error = ModelNotRegisteredError( + message=f"Model `{model_name}` is not a diffusion model (modality={model.modality.value})." + ) + return ImpactsOutput(errors=[error]) + + diff = model.diffusion + if diff is None: + error = ModelNotRegisteredError( + message=f"Model `{model_name}` has no diffusion parameters registered." + ) + return ImpactsOutput(errors=[error]) + + resolved_steps = n_steps if n_steps is not None else diff.default_steps + resolved_guidance = guidance_scale if guidance_scale is not None else diff.default_guidance_scale + resolved_frames = n_frames if diff.is_video else 1 + resolved_default_frames = diff.default_frames if (diff.is_video and diff.default_frames) else 1 + + model_total_params = ( + model.architecture.parameters.total + if isinstance(model.architecture.parameters, ParametersMoE) + else model.architecture.parameters + ) + + datacenter_location = PROVIDER_CONFIG_MAP[provider].datacenter_location + datacenter_pue = PROVIDER_CONFIG_MAP[provider].datacenter_pue + datacenter_wue = PROVIDER_CONFIG_MAP[provider].datacenter_wue + + if electricity_mix_zone is None: + electricity_mix_zone = datacenter_location + if electricity_mix_zone is None: + electricity_mix_zone = "WOR" + if_electricity_mix = electricity_mixes.find_electricity_mix(zone=electricity_mix_zone) + if if_electricity_mix is None: + error = ZoneNotRegisteredError(message=f"Could not find electricity mix for `{electricity_mix_zone}` zone.") + logger.warning_once(str(error)) + return ImpactsOutput(errors=[error]) + + impacts = compute_diffusion_impacts( + model_total_parameter_count=model_total_params, + flops_per_step=diff.flops_denoise_per_step, + n_steps=resolved_steps, + guidance_scale=resolved_guidance, + cfg_double_pass=diff.cfg_double_pass, + denoising_strength=denoising_strength, + n_frames=resolved_frames, + default_frames=resolved_default_frames, + request_latency=request_latency, + if_electricity_mix_adpe=if_electricity_mix.adpe, + if_electricity_mix_pe=if_electricity_mix.pe, + if_electricity_mix_gwp=if_electricity_mix.gwp, + if_electricity_mix_wue=if_electricity_mix.wue, + datacenter_pue=datacenter_pue, + datacenter_wue=datacenter_wue, + frame_attn_fraction=diff.frame_attn_fraction, + ) + out = ImpactsOutput.model_validate(impacts.model_dump()) + + if model.has_warnings: + for w in model.warnings: + logger.warning_once(str(w)) + out.add_warning(w) + + if task in _EDITING_TASKS: + editing_warning = WarningMessage.from_code("modality-editing-unvalidated") + logger.warning_once(str(editing_warning)) + out.add_warning(editing_warning) + + return out + + @dataclass class _ProviderConfig: datacenter_location: str @@ -173,5 +287,35 @@ class _ProviderConfig: datacenter_location="USA", datacenter_pue=1.20, datacenter_wue=0.569, + ), + "stabilityai": _ProviderConfig( + datacenter_location="WOR", + datacenter_pue=RangeValue(min=1.09, max=1.20), + datacenter_wue=RangeValue(min=0.13, max=0.99), + ), + "black_forest_labs": _ProviderConfig( + datacenter_location="WOR", + datacenter_pue=RangeValue(min=1.09, max=1.20), + datacenter_wue=RangeValue(min=0.13, max=0.99), + ), + "midjourney": _ProviderConfig( + datacenter_location="WOR", + datacenter_pue=RangeValue(min=1.09, max=1.20), + datacenter_wue=RangeValue(min=0.13, max=0.99), + ), + "kling": _ProviderConfig( + datacenter_location="WOR", + datacenter_pue=RangeValue(min=1.09, max=1.20), + datacenter_wue=RangeValue(min=0.13, max=0.99), + ), + "runway": _ProviderConfig( + datacenter_location="WOR", + datacenter_pue=RangeValue(min=1.09, max=1.20), + datacenter_wue=RangeValue(min=0.13, max=0.99), + ), + "luma": _ProviderConfig( + datacenter_location="WOR", + datacenter_pue=RangeValue(min=1.09, max=1.20), + datacenter_wue=RangeValue(min=0.13, max=0.99), ) } diff --git a/examples/diffusion_worked_example.py b/examples/diffusion_worked_example.py new file mode 100644 index 00000000..8cb22d50 --- /dev/null +++ b/examples/diffusion_worked_example.py @@ -0,0 +1,86 @@ +""" +Diffusion model impacts — worked examples. + +Demonstrates compute_diffusion_impacts() for: + 1. SDXL text-to-image (1024 × 1024, 40 steps, CFG=7.5) + 2. SDXL image-to-image editing (denoising_strength=0.6) + 3. CogVideoX-5b text-to-video (49 frames, 50 steps) + 4. Flux.1-schnell text-to-image (flow-matching, no CFG, 4 steps) + +All energy figures are returned as RangeValue intervals reflecting: + - GPU efficiency calibration uncertainty (2.5 – 5.0 × 10⁻¹⁸ kWh/FLOP) + - Proprietary-model FLOPs uncertainty (where applicable) +""" + +from ecologits.tracers.utils import diffusion_impacts + + +def fmt(label: str, out) -> None: + """Print a compact summary of an ImpactsOutput.""" + e = out.energy.value + gwp = out.gwp.value + print(f"\n{'─'*58}") + print(f" {label}") + print(f"{'─'*58}") + print(f" Energy : {e} kWh") + print(f" GWP : {gwp} kgCO2eq") + if out.warnings: + print(f" Warnings: {[w.code for w in out.warnings]}") + if out.errors: + print(f" Errors : {[e.code for e in out.errors]}") + + +# ── 1. SDXL text-to-image ──────────────────────────────────────────────────── +# 40 denoising steps × 2 (CFG double-pass) × 6.7 TFLOPs/step = 536 TFLOPs +# Measured by AI Energy Score on A100: 1.64 Wh — within our lower bound. +out1 = diffusion_impacts( + provider="stabilityai", + model_name="stable-diffusion-xl", + task="txt2img", + request_latency=4.2, # wall-clock seconds from your API call + width=1024, + height=1024, +) +fmt("SDXL • txt2img • 1024 × 1024 • 40 steps • 4.2 s", out1) + +# ── 2. SDXL image-to-image editing ───────────────────────────────────────── +# denoising_strength=0.6 → 40 × 0.6 × 2 = 48 effective forward passes. +# Energy returned as a RangeValue; editing-unvalidated warning is added. +out2 = diffusion_impacts( + provider="stabilityai", + model_name="stable-diffusion-xl", + task="img2img", + request_latency=2.5, + denoising_strength=0.6, +) +fmt("SDXL • img2img • denoising_strength=0.6 • 2.5 s", out2) + +# ── 3. CogVideoX-5b text-to-video ─────────────────────────────────────────── +# 170 TFLOPs/step × 50 steps × 2 (CFG) = 17 000 TFLOPs for 49 default frames. +# CogVideoX-5b has 5B parameters → requires 2 × H100-80 GB GPUs. +out3 = diffusion_impacts( + provider="huggingface_hub", + model_name="cogvideox-5b", + task="txt2vid", + request_latency=90.0, + n_frames=49, +) +fmt("CogVideoX-5b • txt2vid • 49 frames • 50 steps • 90 s", out3) + +# ── 4. Flux.1-schnell text-to-image ───────────────────────────────────────── +# Flow-matching, no CFG double-pass, only 4 steps → 4 × 98 TFLOPs = 392 TFLOPs. +# Despite similar per-step cost to large video models, the tiny step count +# makes it the most efficient image model in the catalogue. +out4 = diffusion_impacts( + provider="black_forest_labs", + model_name="flux.1-schnell", + task="txt2img", + request_latency=1.2, + width=1024, + height=1024, +) +fmt("Flux.1-schnell • txt2img • 1024 × 1024 • 4 steps • 1.2 s", out4) + +print(f"\n{'─'*58}") +print("All values include server overhead and PUE.") +print("GPU-only energy ≈ lower end of the Energy range.") diff --git a/mkdocs.yml b/mkdocs.yml index d3bb6c1e..3b4979b2 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -8,6 +8,7 @@ nav: - 'Get Started': - 'Welcome to EcoLogits': get_started.md - 'Why use EcoLogits': why.md + - 'Supported Models': models.md - 'Supported providers': tutorial/providers.md - 'FAQ': faq.md - 'Contributing': contributing.md @@ -34,6 +35,7 @@ nav: - 'Proprietary models': methodology/proprietary_models.md - 'LLM Inference': methodology/llm_inference.md - 'Video Generation': methodology/video_generation.md + - 'Multimedia Generation': methodology/diffusion_inference.md - 'API Reference': reference/ - 'Blog': - blog/index.md @@ -90,8 +92,11 @@ extra_javascript: - js/katex.js - js/installer.js - js/clipboard_copy.js + - js/models_filter.js - https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/katex.min.js - https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/contrib/auto-render.min.js + - https://unpkg.com/tablesort@5.3.0/dist/tablesort.min.js + - https://unpkg.com/tablesort@5.3.0/dist/sorts/tablesort.number.min.js watch: - docs @@ -106,6 +111,7 @@ plugins: - gen-files: scripts: - docs/scripts/gen_references.py + - docs/scripts/gen_models_page.py - literate-nav: nav_file: SUMMARY.md - mkdocstrings: @@ -147,7 +153,11 @@ markdown_extensions: - pymdownx.snippets: auto_append: - docs/_abbreviations - - pymdownx.superfences + - pymdownx.superfences: + custom_fences: + - name: mermaid + class: mermaid + format: !!python/name:pymdownx.superfences.fence_code_format - pymdownx.details - pymdownx.tabbed: alternate_style: true diff --git a/pyproject.toml b/pyproject.toml index ac14373c..11192b4e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -65,6 +65,7 @@ opentelemetry = [ "opentelemetry-exporter-otlp>=1.32.1,<2.0.0", "tiktoken>=0.8.0,<0.13.0", ] +langsmith = ["langsmith>=0.1.0,<1.0.0", "codecarbon>=2.4.0,<4.0.0"] [dependency-groups] dev = [ @@ -89,7 +90,7 @@ docs = [ ] [tool.uv] -exclude-newer = "2 days" +exclude-newer = "2026-06-04T00:00:00Z" [pytest] env_files = [".env"] diff --git a/tests/test_diffusion_impacts.py b/tests/test_diffusion_impacts.py new file mode 100644 index 00000000..a0f01ea7 --- /dev/null +++ b/tests/test_diffusion_impacts.py @@ -0,0 +1,92 @@ +import pytest + +from ecologits.impacts.diffusion import compute_diffusion_impacts +from ecologits.tracers.utils import diffusion_impacts +from ecologits.utils.range_value import RangeValue + +_SDXL_PARAMS = dict( + model_total_parameter_count=2.6, + flops_per_step=6.7e12, + n_steps=30, + guidance_scale=7.5, + cfg_double_pass=True, + denoising_strength=1.0, + n_frames=1, + default_frames=1, + if_electricity_mix_gwp=0.29, + if_electricity_mix_adpe=7.37e-7, + if_electricity_mix_pe=3.98, + if_electricity_mix_wue=1.0, + datacenter_pue=1.2, + datacenter_wue=0.5, +) + +_ENERGY_PLAUSIBLE_MIN = 0.0005 +_ENERGY_PLAUSIBLE_MAX = 0.01 + + +def _energy_mean(value): + if isinstance(value, RangeValue): + return (value.min + value.max) / 2 + return value + + +def test_diffusion_impacts_txt2img_sdxl_energy_is_range_value(): + result = compute_diffusion_impacts(**_SDXL_PARAMS) + + assert isinstance(result.energy.value, RangeValue) + + +def test_diffusion_impacts_txt2img_sdxl_gwp_positive(): + result = compute_diffusion_impacts(**_SDXL_PARAMS) + + gwp = result.gwp.value + gwp_min = gwp.min if isinstance(gwp, RangeValue) else gwp + assert gwp_min > 0 + + +def test_diffusion_impacts_txt2img_sdxl_energy_in_plausible_range(): + result = compute_diffusion_impacts(**_SDXL_PARAMS) + + energy = result.energy.value + assert isinstance(energy, RangeValue) + assert _ENERGY_PLAUSIBLE_MIN <= energy.min + assert energy.max <= _ENERGY_PLAUSIBLE_MAX + + +def test_diffusion_impacts_img2img_sdxl_has_editing_unvalidated_warning(): + result = diffusion_impacts( + provider="stabilityai", + model_name="stable-diffusion-xl", + task="img2img", + n_steps=30, + guidance_scale=7.5, + denoising_strength=0.5, + ) + + assert result.has_warnings + assert any(w.code == "modality-editing-unvalidated" for w in result.warnings) + + +def test_diffusion_impacts_img2img_sdxl_energy_approximately_half_of_txt2img(): + txt2img = diffusion_impacts( + provider="stabilityai", + model_name="stable-diffusion-xl", + task="txt2img", + n_steps=30, + guidance_scale=7.5, + denoising_strength=1.0, + ) + img2img = diffusion_impacts( + provider="stabilityai", + model_name="stable-diffusion-xl", + task="img2img", + n_steps=30, + guidance_scale=7.5, + denoising_strength=0.5, + ) + + txt2img_mean = _energy_mean(txt2img.energy.value) + img2img_mean = _energy_mean(img2img.energy.value) + ratio = img2img_mean / txt2img_mean + assert ratio == pytest.approx(0.5, rel=0.20) diff --git a/tests/test_video_estimations.py b/tests/test_video_estimations.py index a149d1ca..8753dfc5 100644 --- a/tests/test_video_estimations.py +++ b/tests/test_video_estimations.py @@ -175,3 +175,29 @@ def test_video_models_reference_provider_configurations(): for model_info in video_estimations._MODELS_INFO.values(): assert model_info["provider"] in provider_configurations + + +def test_video_impacts_flops_fallback_for_model_absent_from_video_registry(): + impacts = video_estimations.video_impacts( + model_name="cogvideox-5b", + resolution="720p", + duration=5, + with_audio=False, + ) + + assert impacts.errors is None + assert impacts.has_warnings + assert any(w.code == "modality-video-flops-fallback" for w in impacts.warnings) + assert impacts.energy is not None + + +def test_video_impacts_returns_error_for_completely_unknown_model(): + impacts = video_estimations.video_impacts( + model_name="completely-unknown-model-xyz", + resolution="720p", + duration=5, + with_audio=False, + ) + + assert impacts.has_errors + assert any(e.code == "model-not-registered" for e in impacts.errors) diff --git a/uv.lock b/uv.lock index 022ed367..96d20af5 100644 --- a/uv.lock +++ b/uv.lock @@ -2,28 +2,36 @@ version = 1 revision = 3 requires-python = ">=3.10, <4" resolution-markers = [ - "python_full_version >= '3.14'", - "python_full_version == '3.13.*'", - "python_full_version >= '3.11' and python_full_version < '3.13'", + "python_full_version >= '3.15' and sys_platform == 'win32'", + "python_full_version >= '3.15' and sys_platform == 'emscripten'", + "python_full_version >= '3.15' and sys_platform != 'emscripten' and sys_platform != 'win32'", + 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