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Documentation preview: https://vllm--40448.org.readthedocs.build/en/40448/ |
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This pull request updates the documentation and validation logic to clarify that per-attention-head KV-cache quantization is currently only supported with the FLASH_ATTN backend. It also adds tests to verify that using FLASHINFER with this feature correctly raises a ValueError. Feedback was provided regarding the test implementation, specifically that calling get_class() during test parameterization could cause import errors on non-CUDA platforms. Additionally, it was noted that calling the abstract method get_name() in the base class's validation logic could lead to a NotImplementedError and violates modularity by hardcoding backend names.
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…tization Raise a clear validation error when per-attention-head KV-cache quantization is requested with the FLASHINFER backend, add regression coverage for the FLASHINFER failure path, and document that this mode currently requires FLASH_ATTN. Signed-off-by: merlin <mer1inn@163.com>
Resolve FLASHINFER lazily in the backend registry test to avoid import-time failures during collection, and move the FLASHINFER-specific per-head KV quantization validation message out of the attention backend base class. Signed-off-by: merlin <mer1inn@163.com>
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@zhuohan123 @youkaichao Hi, I'm a new contributor. Could you please add the |
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This pull request has merge conflicts that must be resolved before it can be |
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Fixes #40444
Summary
FLASHINFERbackendFLASHINFERfailure path while keeping the existingFLASH_ATTNsuccess pathFLASH_ATTNWhy this is not duplicate work
#40444and did not find an open PR that already addresses this fixTest plan
uv run --no-project python -m py_compile vllm/v1/attention/backend.py tests/test_attention_backend_registry.py tests/quantization/test_compressed_tensors.py./.venv/bin/python -m pytest tests/test_attention_backend_registry.py -k per_head_quant_scale_support -v./.venv/bin/python -m pytest tests/quantization/test_compressed_tensors.py -k per_attn_head -vAI assistance