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Transpose FP8 GEMM inputs for better tuning#3866

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Transpose FP8 GEMM inputs for better tuning#3866
jwfromm wants to merge 3 commits into
pytorch:mainfrom
jwfromm:export-D71564657

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@jwfromm jwfromm commented Mar 21, 2025

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Summary: This diff switches does an implicit layout transpose in cutlass FP8 grouped gemm. The reason this is desirable is that having A be the right side matrix allow us to use very small M tiles. Cutlass allows tile shapes like [128, 16, 128] (M, N, K). For most of our LLM use cases, M can be very small but N and K tend to be large. Thus, we benefit a lot from having a small M tile.

Differential Revision: D71564657

jwfromm added 3 commits March 21, 2025 11:08
Summary:
X-link: facebookresearch/FBGEMM#945


In cases where there are many groups, but few have a non-zero amount of routed tokens, it turns out we pay a high overhead. For example if a single token is routed to one of 128 experts, while the compute is the same as 1 token being routed to one expert the runtime is much lower.

Presumably there are some kernel inefficiencies involved in looping over the empty groups. This diff changes how kernel arguments are set up so that we do grouped gemm over min(total_M, groups). This allows us to ignore many of the groups where no compute is required and improves performance in those cases considerably.

As an example of the effect of this diff, when total_M is 1 and there are 128 groups, latency will be 3X smaller thanks to this change.

Reviewed By: jiawenliu64

Differential Revision: D71510967
Summary:
X-link: facebookresearch/FBGEMM#953


This diff does an overdue refactor and cleanup of the cutlass FP8 Gemm. There's no functional difference but we use more strict and accurate typing and are a bit more careful with buffer allocation.

Differential Revision: D71349695
Summary: This diff switches does an implicit layout transpose in cutlass FP8 grouped gemm. The reason this is desirable is that having A be the right side matrix allow us to use very small M tiles. Cutlass allows tile shapes like [128, 16, 128] (M, N, K). For most of our LLM use cases, M can be very small but N and K tend to be large. Thus, we benefit a lot from having a small M tile.

Differential Revision: D71564657
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This pull request was exported from Phabricator. Differential Revision: D71564657

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jwfromm added a commit to jwfromm/FBGEMM that referenced this pull request Mar 24, 2025
Summary:
X-link: facebookresearch/FBGEMM#954


This diff switches does an implicit layout transpose in cutlass FP8 grouped gemm. The reason this is desirable is that having A be the right side matrix allow us to use very small M tiles. Cutlass allows tile shapes like [128, 16, 128] (M, N, K). For most of our LLM use cases, M can be very small but N and K tend to be large. Thus, we benefit a lot from having a small M tile.

Reviewed By: jiawenliu64

Differential Revision: D71564657
jwfromm added a commit to jwfromm/FBGEMM that referenced this pull request Mar 24, 2025
Summary:
X-link: facebookresearch/FBGEMM#954


This diff switches does an implicit layout transpose in cutlass FP8 grouped gemm. The reason this is desirable is that having A be the right side matrix allow us to use very small M tiles. Cutlass allows tile shapes like [128, 16, 128] (M, N, K). For most of our LLM use cases, M can be very small but N and K tend to be large. Thus, we benefit a lot from having a small M tile.

Reviewed By: jiawenliu64

Differential Revision: D71564657
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This pull request has been merged in 37f5287.

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3 participants