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The pinned flash-attn 2.8.3+cu12torch2.7 wheel breaks at import time
once vllm 0.19.1 upgrades torch to its hard-pinned 2.10.0:
ImportError: .../flash_attn_2_cuda...so: undefined symbol:
_ZN3c104cuda29c10_cuda_check_implementationEiPKcS2_ib
That C10 CUDA symbol is libtorch-version-specific. Dao-AILab has not yet
published flash-attn wheels for torch 2.10 -- the latest release (2.8.3)
tops out at torch 2.8 -- so any wheel pinned here is silently ABI-broken
the moment vllm completes its install.
vllm 0.19.1 lists flashinfer-python==0.6.6 as a hard dep, which already
covers the attention path. The only other use of flash-attn in vllm is
the rotary apply_rotary import in
vllm/model_executor/layers/rotary_embedding/common.py, which is guarded
by find_spec("flash_attn") and falls back cleanly when absent.
Also unpin torch in requirements-cublas12.txt: the 2.7.0 pin only
existed to give the flash-attn wheel a matching torch to link against.
With flash-attn gone, vllm's own torch==2.10.0 dep is the binding
constraint regardless of what we put here.
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
10 lines
566 B
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10 lines
566 B
Plaintext
# flash-attn wheels are ABI-tied to a specific torch version. vllm forces
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# torch==2.10.0 as a hard dep, but flash-attn 2.8.3 (latest) only ships
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# prebuilt wheels up to torch 2.8 — any wheel we pin here gets silently
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# broken when vllm upgrades torch during install, producing an undefined
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# libc10_cuda symbol at import time. FlashInfer (required by vllm) covers
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# attention, and rotary_embedding/common.py guards the flash_attn import
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# with find_spec(), so skipping flash-attn is safe and the only stable
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# choice until upstream ships a torch-2.10 wheel.
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vllm
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