06d7713db690b9b7a173eda46ecfcb58781cfed4
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
project_6
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41.8%
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31.6%
Python
22.2%
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2.1%
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1.1%
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1.1%