claude 06d7713db6 feat: batched MoE expert GEMM — replaces Python for-loop
ixformer probe results:
  ✗ moe_w16a16_group_gemm NOT in ixformer .so
  ✗ CUTLASS grouped GEMM needs cuda/std (variadic function error on corex)
  ✓ ixformer_linear EXISTS (fused matmul)
  ✓ torch.mm works (uses corex cublas)

Solution: moe_batched_gemm.cu
  - C++ loop over experts (eliminates Python overhead)
  - torch::mm for GEMM (corex cublas, not F.linear Python)
  - Fused silu_and_mul CUDA kernel (not PyTorch ops)
  - Weighted scatter-add in C++
  - Skips empty experts (no wasted compute)

Integration in qwen3_5.py:
  _USE_XLLM_MOE_GEMM dispatches to moe_experts_forward()
  Falls back to Python for-loop if not available

Build: bash qwen3_6_scripts/build_xllm_kernels.sh
2026-08-14 11:43:46 +00:00
2026-08-07 08:43:51 +00:00
2026-07-30 17:03:23 +08:00
2026-08-13 02:29:40 +00:00

project_6

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