[TRTLLM-5812][feat] support FP8 row-wise dense GEMM in torch flow #5615
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@tomeras91 Thanks for the comments. I’ve implemented all the suggested changes. Please feel free to approve or let me know if further changes are needed. |
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Signed-off-by: Dylan Chen <[email protected]>
Signed-off-by: Dylan Chen <[email protected]>
Signed-off-by: Dylan Chen <[email protected]>
Signed-off-by: Dylan Chen <[email protected]>
Signed-off-by: Dylan Chen <[email protected]>
Signed-off-by: Dylan Chen <[email protected]>
Signed-off-by: Dylan Chen <[email protected]>
Signed-off-by: Dylan Chen <[email protected]>
Signed-off-by: Dylan Chen <[email protected]>
Signed-off-by: Dylan Chen <[email protected]>
Signed-off-by: Dylan Chen <[email protected]>
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…IDIA#5615) Signed-off-by: Dylan Chen <[email protected]> Signed-off-by: Yuxin <[email protected]>
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[TRTLLM-5812][feat] support FP8 row-wise dense GEMM in torch flow
Description
Adapter existing FP8 row-wise dense GEMM kernels (sm89/sm90/sm120) to torch workflow.
Support FP8 row-wise GEMM as a TunableRunner for better performance.
Test Coverage
tests/unittest/_torch/thop/test_fp8_rowwise_linear.py: Test the output correctness of row-wise torch op
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