Enable fast FP8 GEMM for memory bound #3577
Closed
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
|
This pull request was exported from Phabricator. Differential Revision: D68193920 |
✅ Deploy Preview for pytorch-fbgemm-docs ready!
To edit notification comments on pull requests, go to your Netlify site configuration. |
Summary: X-link: facebookresearch/FBGEMM#659 This Diff enables fast FP8 gemm for memory bound with adding TRT-LLM FP8 cuda gemm to fbgemm. In addition to the original kernel, this Diff extends the kernel to: - Support pytorch operations - Support cuda graph with handling scale as tensor - Support larger dim M - Support benchmark/unittest For decode attn linear shapes: - When BS=1, TRT-LLM FP8 gemm brings 2x speedup compared to BF16, while FP8 cutlass gemm’s perf is similar to BF16 - When BS>4, TRT-LLM FP8 gemm does not bring perf gain This TRT-LLM kernel is based on tensorwise quantization not rowwise. Reviewed By: jwfromm Differential Revision: D68193920
jiawenliu64
force-pushed
the
export-D68193920
branch
from
17573a1 to
b76d104
Compare
|
This pull request was exported from Phabricator. Differential Revision: D68193920 |
|
This pull request has been merged in 497bad6. |
|
This pull request has been reverted by 2d025dc. |
q10
pushed a commit
to q10/FBGEMM
that referenced
this pull request
Apr 10, 2025
Summary: X-link: pytorch#3577 Pull Request resolved: facebookresearch/FBGEMM#659 This Diff enables fast FP8 gemm for memory bound with adding TRT-LLM FP8 cuda gemm to fbgemm. In addition to the original kernel, this Diff extends the kernel to: - Support pytorch operations - Support cuda graph with handling scale as tensor - Support larger dim M - Support benchmark/unittest For decode attn linear shapes: - When BS=1, TRT-LLM FP8 gemm brings 2x speedup compared to BF16, while FP8 cutlass gemm’s perf is similar to BF16 - When BS>4, TRT-LLM FP8 gemm does not bring perf gain This TRT-LLM kernel is based on tensorwise quantization not rowwise. Reviewed By: jwfromm Differential Revision: D68193920 fbshipit-source-id: fbf34e283e9430a8fed63ddb91781ade321012e3
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Summary:
X-link: https://github.com/facebookresearch/FBGEMM/pull/659
This Diff enables fast FP8 gemm for memory bound with adding TRT-LLM FP8 cuda gemm to fbgemm. In addition to the original kernel, this Diff extends the kernel to:
For decode attn linear shapes:
This TRT-LLM kernel is based on tensorwise quantization not rowwise
Reviewed By: jwfromm
Differential Revision: D68193920