Neta Zmora 1d6fbbf45d [#9236][feature] Make sharing of activation_type across SW layers more robust (#9238) ... C++, Python and Python MoE layer all share the definition of ActivationType. Currently this is done thru redefinition which is fragile and can break when adding new activation function types. tensorrt_llm/_torch/utils.py cpp/tensorrt_llm/kernels/cutlass_kernels/include/common.h => tensorrt_llm/layers/moe.py cpp/tensorrt_llm/kernels/cutlass_kernels/moe_gemm/moe_kernels.cu Signed-off-by: Kaiyu Xie <26294424+kaiyux@users.noreply.github.com> Signed-off-by: Neta Zmora <96238833+nzmora-nvidia@users.noreply.github.com> Co-authored-by: Kaiyu Xie <26294424+kaiyux@users.noreply.github.com>		2025-11-20 16:06:58 +08:00
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allreduce_gemm_runner.h	opensource: Opensource MOE MXFP8-MXFP4 implementation (#5222)	2025-06-26 12:18:19 +08:00
common.h	[#9236][feature] Make sharing of activation_type across SW layers more robust (#9238)	2025-11-20 16:06:58 +08:00
cutlass_kernel_selector.h	opensource: Opensource MOE MXFP8-MXFP4 implementation (#5222)	2025-06-26 12:18:19 +08:00
fp4_gemm.h	feat: Add w4a8_mxfp4_fp8 quantization recipe. (#4867)	2025-06-16 11:30:57 +08:00
low_latency_gemm.h	refactoring: port customized kernels with public cutlass version (#5027)	2025-06-13 16:19:31 +08:00
moe_gemm_kernels.h	[TRTLLM-4629] [feat] Add support of CUDA13 and sm103 devices (#7568)	2025-09-16 09:56:18 +08:00
moe_kernels.h	[None][fix] Fix the performance issue of FP8 blockwise grouped GEMM when using attention DP (#8501)	2025-10-27 10:18:19 +08:00
moe_util_kernels.h	[TRTLLM-7319][perf] Fuse slicing into MoE. (#6728)	2025-08-25 16:52:30 -04:00