Enable CUTLASS grouped GEMM for pretraining wgrad on GB200 and H100 (resubmit) #4913
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…resubmit) (pytorch#4913) Summary: Pull Request resolved: pytorch#4913 X-link: facebookresearch/FBGEMM#1937 - Enable CUTLASS grouped GEMM for llama4x pretraining wgrad on GB200 and H100 - Optimize performance of pretraining moe shapes on H100 - Support total_K in quantize_bench for wgrad - The FBGEMM relocation issue has been released for short-term, so resubmit. Passed all tests in T238469849 Differential Revision: D83001505
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…resubmit) (pytorch#4913) Summary: Pull Request resolved: pytorch#4913 X-link: facebookresearch/FBGEMM#1937 - Enable CUTLASS grouped GEMM for llama4x pretraining wgrad on GB200 and H100 - Optimize performance of pretraining moe shapes on H100 - Support total_K in quantize_bench for wgrad - The FBGEMM relocation issue has been released for short-term, so resubmit. Passed all tests in T238469849 Reviewed By: cthi Differential Revision: D83001505
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…resubmit) (pytorch#4913) Summary: Pull Request resolved: pytorch#4913 X-link: facebookresearch/FBGEMM#1937 - Enable CUTLASS grouped GEMM for llama4x pretraining wgrad on GB200 and H100 - Optimize performance of pretraining moe shapes on H100 - Support total_K in quantize_bench for wgrad - The FBGEMM relocation issue has been released for short-term, so resubmit. Passed all tests in T238469849 Reviewed By: cthi Differential Revision: D83001505
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Summary:
Differential Revision: D83001505