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Revert "[Bugfix] Fix scaled_mm output narrowing for 3D input tensors" (#38093) - #40423
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…llm-project#38093)" This reverts commit 58631d7.
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Code Review
This pull request removes the _get_num_tokens helper and replaces it with output_shape[0] in several torch.narrow calls. Reviewers pointed out that this breaks support for 3D tensors by failing to account for the sequence length, which will lead to shape mismatch errors.
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| num_tokens = _get_num_tokens(output_shape) | ||
| return torch.narrow(output, 0, 0, num_tokens).view(*output_shape) | ||
| return torch.narrow(output, 0, 0, output_shape[0]).view(*output_shape) |
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Reverting this change re-introduces a bug for 3D input tensors. When the input x is 3D (e.g., [batch, seq, hidden]), output_shape is [batch, seq, out_features]. Using output_shape[0] as the narrowing size only selects the first batch tokens, whereas the flattened GEMM output contains batch * seq tokens. This will cause a RuntimeError in the subsequent .view(*output_shape) call due to a size mismatch (unless seq == 1). Since the base class FP8ScaledMMLinearKernel.apply_weights explicitly supports 3D tensors by flattening them, this implementation should correctly unpad all tokens using the product of all dimensions except the last one (e.g., math.prod(output_shape[:-1])).
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| num_tokens = _get_num_tokens(output_shape) | ||
| return torch.narrow(output, 0, 0, num_tokens).view(*output_shape) | ||
| return torch.narrow(output, 0, 0, output_shape[0]).view(*output_shape) |
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Similar to the issue in PerTensorTorchFP8ScaledMMLinearKernel, using output_shape[0] here is incorrect for 3D tensors and will lead to a shape mismatch error during .view(*output_shape) if the sequence length is greater than 1.
| output = torch.narrow(output, 0, 0, output_shape[0]) | ||
| x_scale = torch.narrow(As, 0, 0, output_shape[0]) |
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For 3D tensors, narrowing output and x_scale to output_shape[0] (the batch size) instead of the total number of tokens (batch * seq) is incorrect. This will result in a size mismatch during the dequantization step or the final .view(*output_shape) call.
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Revert of #38093
This reverts the merge commit 58631d7 from PR #38093.
Reason: This PR is suspected of causing the following CI failure in build #62198:
test_mla_attention_quant_patternfailed withAssertionError: Tensor-likes are not close!(NaN values, 4.3% mismatched elements) in the FP8 static quantization pathThe PR modified the FP8
scaled_mmpytorch kernel (vllm/model_executor/kernels/linear/scaled_mm/pytorch.py), which is in the code path used by the MLA attention quantization fusion test on B200 GPUs.Auto-generated by CI failure analyzer. 1 new failure linked to this PR.