[ExecuTorch][llm] Add MoE source transformation with INT4 quantized packing - #21535
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…acking Pull Request resolved: #21121 Add `moe.py` with a `QuantizedMoEFFN` module wrapping `llama::quantized_moe_ffn`, INT4 symmetric group quantization, torchao packing, and recursive module replacement. Preserve fp32 buffers and output dtype across model casts. Dequantize a torchao-quantized gate into the custom op fp32 buffer so QAT models can be captured by `torch.export` without tensor-subclass dispatch failures. ghstack-source-id: 409183078 @exported-using-ghexport Differential Revision: [D102381994](https://our.internmc.facebook.com/intern/diff/D102381994/)
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/21535
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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: #21121 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/71/base
ghstack PR head: https://github.com/pytorch/executorch/tree/gh/digantdesai/71/head
Merge bot PR base: https://github.com/pytorch/executorch/tree/gh/digantdesai/70/orig
Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/digantdesai/71/orig
Differential Revision: D102381994
@diff-train-skip-merge