[ExecuTorch][llm] Fuse w1+w3 into single GEMM in quantized_moe_ffn

[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

Fuse the up-projection (w1) and gate-projection (w3) into a single [2F, D] GEMM per expert. This halves the number of torchao activation quantizations per expert (from 2 to 1) and reduces total GEMM calls from 3 to 2 per active expert.

At AOT time, w1 and w3 are concatenated before packing: pack_fn(cat([w1, w3], dim=0)). At runtime, a single expert_linear_dispatch produces [m_e, 2F], then a fused swiglu_and_compact pass reads the interleaved h1/h3 and writes [m_e, F] for the w2 down-projection.

Schema changes from (packed_w1, packed_w3, packed_w2) to (packed_w13, packed_w2) — one fewer tensor arg (14 -> 13).

Differential Revision: D102799854

[pytorch-bot]

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