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[ExecuTorch][llm] Add MoE source transformation with INT4 quantized packing#21121

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[ExecuTorch][llm] Add MoE source transformation with INT4 quantized packing#21121
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@digantdesai digantdesai commented Jul 22, 2026

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Stack from ghstack (oldest at bottom):

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.

Differential Revision: D102381994

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🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/21121

Note: Links to docs will display an error until the docs builds have been completed.

❗ 1 Active SEVs

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❌ 4 New Failures, 1 Unrelated Failure

As of commit 11b27ac with merge base bfed808 (image):

NEW FAILURES - The following jobs have failed:

BROKEN TRUNK - The following job failed but were present on the merge base:

👉 Rebase onto the `viable/strict` branch to avoid these failures

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