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[executorch][native-ggml] Size the scheduler hash for graph leafs too - #24991
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## Ulterior Motive Running Gemma 4 31B through ExecuTorch Native's GGML engine on CUDA, to find why it decodes slower than llama.cpp on the same GGUF. ## Rationale **What**: The backend scheduler is created for `2 * graph_size` tensors. **Why**: `ggml_backend_sched` hashes every node and every leaf of a graph it splits, and asserts that its hash set holds them all. It was sized for `graph_size`, which caps nodes and leafs separately, so a graph whose nodes and leafs together outgrew it failed that assertion, though either count alone fit. ## Details - **Before**: `ggml_backend_sched_new(..., graph_size, ...)`; ggml's own source notes the hash "needs to be size*2 to account for leafs". - **After**: `2 * graph_size`, room for a full graph of `graph_size` nodes and `graph_size` leafs. `test_graph_with_as_many_leafs_as_nodes` adds 2,100 separate constants to an input: 2,100 nodes and as many leafs, within the default graph size of 4,096. Authored with Claude Code. Differential Revision: [D130721991](https://our.internmc.facebook.com/intern/diff/D130721991/) ghstack-source-id: 469829014 Pull-Request: #24847
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This PR was created by the merge bot to help merge the original PR into the main branch.
ghstack PR number: #24847 by @SS-JIA
^ Please use this as the source of truth for the PR details, comments, and reviews
ghstack PR base: https://github.com/pytorch/executorch/tree/gh/SS-JIA/703/base
ghstack PR head: https://github.com/pytorch/executorch/tree/gh/SS-JIA/703/head
Merge bot PR base: https://github.com/pytorch/executorch/tree/gh/SS-JIA/702/orig
Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/SS-JIA/703/orig
Differential Revision: D130721991
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