[ExecuTorch][llm] Add quantized_moe_ffn custom op with C++ kernel - #21533
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[ExecuTorch][llm] Add quantized_moe_ffn custom op with C++ kernel#21533pytorchbot wants to merge 1 commit into
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Pull Request resolved: #21119 Add a fused `quantized_moe_ffn` custom op for MoE feed-forward with INT4/INT8 weight quantization via torchao. The C++ runtime kernel implements router GEMM, scoring, top-k, counting-sort permutation, per-expert grouped GEMMs, SwiGLU, and weighted scatter-add unpermute. On aarch64, expert GEMMs use `torchao::linear_operator` from D112958457; otherwise a portable reference path unpacks the torchao blob, dequantizes to fp32, and calls `cpublas::gemm`. The AOT shim registers the schema, provides `CompositeExplicitAutograd` dispatch, and exposes a `_quantized_moe_ffn_active` sentinel op. Build integration covers Buck and CMake. ghstack-source-id: 409183072 @exported-using-ghexport Differential Revision: [D102382000](https://our.internmc.facebook.com/intern/diff/D102382000/)
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July 31, 2026 23:48
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/21533
Note: Links to docs will display an error until the docs builds have been completed. ⏳ No Failures, 1 PendingAs of commit a50a02f with merge base 250020b ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
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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: #21119 by @digantdesai
^ 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/digantdesai/69/base
ghstack PR head: https://github.com/pytorch/executorch/tree/gh/digantdesai/69/head
Merge bot PR base: https://github.com/pytorch/executorch/tree/main
Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/digantdesai/69/orig
Differential Revision: D102382000
@diff-train-skip-merge