c840c9159fb3bc04a23b6d0aa1d86bebaa3a6fe6
torch profiler confirmed: torch.mm launches Gemm_tcu_bi_kernel::gemm_h_h_tcu_25 which is BI-V100 TCU (Tensor Compute Unit) hardware-accelerated GEMM. 0.58ms per call vs our custom kernel 7.7ms — TCU is 13x faster. Python for-loop overhead measured: 0.892 ms/expert = 7.1 ms for 8 experts. This C++ dispatch eliminates that overhead while using the same TCU kernel. Three entry points: - moe_decode: full MoE forward (FC1 + SiLU*mul + FC2) for decode - moe_prefill: group-by-expert MoE forward for prefill - moe_expert_gemm_tcu: raw GEMM loop for benchmarking
project_6
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