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

[digantdesai]

Stack from ghstack (oldest at bottom):

• [ExecuTorch][llm] Wire --use_moe_quantized_op through export pipeline and LLM config #21126
• [ExecuTorch][llm] Add MoE quantized custom op documentation #21125
• [ExecuTorch][llm] Fuse w1+w3 into single GEMM in quantized_moe_ffn #21124
• [ExecuTorch][llm] Add numerical kernel tests for quantized_moe_ffn #21123
• [ExecuTorch][llm] Add source-transform and Meta-kernel tests for MoE op #21122
• -> [ExecuTorch][llm] Add MoE source transformation with INT4 quantized packing #21121
• [ExecuTorch][llm] Add Meta kernel and registration probe for quantized_moe_ffn #21120
• [ExecuTorch][llm] Add quantized_moe_ffn custom op with C++ kernel #21119

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

[pytorch-bot]

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