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reshape() keeps a strided view whenever it can (e.g. for a column-sliced tensor), and the quant kernel reads the buffer linearly, so a non-contiguous input is silently quantized from the wrong memory: row 0 is correct and every following row is garbage, with no error raised. Make the wrapper fall back to .contiguous() and add a regression test comparing a strided view against its contiguous copy (fails before this change: packed-byte match 0.50/0.07/0.01 at M=2/16/128 on SM120). Co-authored-by: Claude <noreply@anthropic.com> Signed-off-by: Zhirui <69025003+jszzr@users.noreply.github.com>
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waynehacking8
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Reproduced on an RTX PRO 6000 Blackwell (SM120) on a tree without this fix: quantizing a column-sliced view against its contiguous copy leaves row 0 correct and every following row wrong, with no error raised - 1/2, 15/16 and 127/128 rows bad for (2, 512), (16, 1024) and (128, 4096) respectively, and the block scales differ too.
That's exactly the failure mode described, and calling .contiguous() right after the reshape is the right spot, since the reshape is what preserves the strided view in the first place. LGTM.
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Purpose
scaled_fp4_quant's Python wrapper doesinput.reshape(other_dims, input.shape[-1]), which keeps a strided view whenever it can (e.g. for a column-sliced tensor), andtorch.ops._C.scaled_fp4_quant.outreads the input buffer linearly with no stride/contiguity check. A non-contiguous input is therefore silently quantized from the wrong memory: row 0 comes out correct and every following row is garbage, with no error raised.Found while auditing NVFP4 numerics on SM120 for #48898 (this is not that bug's root cause — in-engine activations turned out to be contiguous — but it is a latent silent-corruption hazard for any caller passing a view).
Changes
vllm/_custom_ops.py: fall back to.contiguous()when the reshaped input is not contiguous.tests/kernels/quantization/test_nvfp4_quant.py: regression test comparing a column-sliced strided view against its contiguous copy.Duplicate check
gh pr list --state open --search "scaled_fp4_quant contiguous"returns only this PR; no open PR touches this wrapper's input handling.Test
pytest tests/kernels/quantization/test_nvfp4_quant.py -v -k noncontiguouson RTX 6000D (SM120), CUDA 13.0, torch 2.11 cu130.Before the fix (same wrapper on 0.25.1 and current main), packed-byte match between a strided view and a contiguous copy of identical values:
After the fix: 1.0000 at every M. The new test fails before this change and passes after. No effect on contiguous inputs (the guard is a no-op for them), so no model-eval delta expected; GSM8K (limit 50) with NVFP4 Qwen2.5-VL-72B on the touched path was 0.66 flex before/after on TP1.
An additional
TORCH_CHECK(input.is_contiguous())in the csrc kernel entry would also protect non-wrapper callers; happy to add it here or as a follow-up if preferred.AI assistance was used for the investigation and drafting of this change; I reviewed every changed line and ran the tests above.