[Performance][ROCm] Add skinny gemms for unquantized linear on ROCm #15830
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Signed-off-by: charlifu <[email protected]>
Signed-off-by: charlifu <[email protected]>
Signed-off-by: charlifu <[email protected]>
Signed-off-by: charlifu <[email protected]>
Signed-off-by: charlifu <[email protected]>
Signed-off-by: charlifu <[email protected]>
Signed-off-by: charlifu <[email protected]>
Signed-off-by: charlifu <[email protected]>
Signed-off-by: charlifu <[email protected]>
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Signed-off-by: charlifu <[email protected]>
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Can you merge from main to fix the Docker build issue? |
Signed-off-by: charlifu <[email protected]>
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| # use rocm skinny gemms | ||
| "VLLM_ROCM_USE_SKINNY_GEMM": |
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I'm somewhat hesitant to have this on by default. It looks like it only gives modest gains in low batch scenarios? Is this generally true or just for llama?
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We discussed this a bit offline. Since this has been on by default in the rocm fork, which is deployed in customer environments, for some time, I'm fine with having it on by default.
Signed-off-by: charlifu <[email protected]>
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This pull request has merge conflicts that must be resolved before it can be |
Signed-off-by: charlifu <[email protected]>
Signed-off-by: charlifu <[email protected]>
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@SageMoore Unit test added. Let me you know you wanna add more shapes to test. BTW, for the dispatch logic to select different gemm method, I am copying #14916. We might need to merge aiter code path with this PR. |
Signed-off-by: charlifu <[email protected]>
Signed-off-by: charlifu <[email protected]>
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| # fp8 rowwise scaling in torch._scaled_mm is introduced in | ||
| # https://github.com/pytorch/pytorch/pull/144432 using | ||
| # hipBLASLt and ROCm 6.3, which only exists in torch 2.7 and above. | ||
| # For CUDA platform please validate if the | ||
| # torch._scaled_mm support rowwise scaled GEMM | ||
| # Fused GEMM_DQ Rowwise GEMM |
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I see this is already in vLLM so not a problem with this PR in particular but why are landing comments like this one?
Who is the audience for this message? Who is supposed to "please validate if torch._scaled_mm support rowwise scaled GEMM" on CUDA? The user?
Head branch was pushed to by a user without write access
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This pull request has merge conflicts that must be resolved before it can be |
Co-authored-by: Tyler Michael Smith <[email protected]> Signed-off-by: charlifu <[email protected]>
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Signed-off-by: charlifu <[email protected]>
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PR looks good to me.
Kernel test timeout is a known issue, and could be force merged. I want to make sure the other test failures are not problems with this PR, so have re-run them. (The lm-eval-small-models test failure is suspicious but it passes locally for me using this branch)
…llm-project#15830) Signed-off-by: charlifu <[email protected]> Co-authored-by: Tyler Michael Smith <[email protected]> Signed-off-by: Frieda (Jingying) Huang <[email protected]>
…llm-project#15830) Signed-off-by: charlifu <[email protected]> Co-authored-by: Tyler Michael Smith <[email protected]>
…llm-project#15830) Signed-off-by: charlifu <[email protected]> Co-authored-by: Tyler Michael Smith <[email protected]>
…llm-project#15830) Signed-off-by: charlifu <[email protected]> Co-authored-by: Tyler Michael Smith <[email protected]> Signed-off-by: Agata Dobrzyniewicz <[email protected]>
…llm-project#15830) Signed-off-by: charlifu <[email protected]> Co-authored-by: Tyler Michael Smith <[email protected]> Signed-off-by: Mu Huai <[email protected]>
This PR adds skinny gemms for unquantized linear (bf16 and fp16) op on ROCm to achieve better performance when the batch size is <= 2.
Llama 3.1 70b, fp16