[Bug]: Latest v0.10.1 image can not run qwen2.5vl on 5090 due to xformer bug

[Godricly]

Your current environment

root@d7a36f5fe4f9:/vllm-workspace# python3 collect_env.py Collecting environment information... ============================== System Info ============================== OS : Ubuntu 22.04.5 LTS (x86_64) GCC version : (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 Clang version : Could not collect CMake version : version 4.1.0 Libc version : glibc-2.35

==============================
PyTorch Info

PyTorch version : 2.7.1+cu128
Is debug build : False
CUDA used to build PyTorch : 12.8
ROCM used to build PyTorch : N/A

==============================
Python Environment

Python version : 3.12.11 (main, Jun 4 2025, 08:56:18) [GCC 11.4.0] (64-bit runtime)
Python platform : Linux-5.15.0-94-generic-x86_64-with-glibc2.35

==============================
CUDA / GPU Info

Is CUDA available : True
CUDA runtime version : 12.8.93
CUDA_MODULE_LOADING set to : LAZY
GPU models and configuration :
GPU 0: NVIDIA GeForce RTX 5090
GPU 1: NVIDIA GeForce RTX 5090
GPU 2: NVIDIA GeForce RTX 5090
GPU 3: NVIDIA GeForce RTX 5090
GPU 4: NVIDIA GeForce RTX 5090
GPU 5: NVIDIA GeForce RTX 5090

Nvidia driver version : 570.133.20
cuDNN version : Could not collect
HIP runtime version : N/A
MIOpen runtime version : N/A
Is XNNPACK available : True

==============================
CPU Info

Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 52 bits physical, 57 bits virtual
Byte Order: Little Endian
CPU(s): 128
On-line CPU(s) list: 0-127
Vendor ID: GenuineIntel
Model name: INTEL(R) XEON(R) GOLD 6530
CPU family: 6
Model: 207
Thread(s) per core: 2
Core(s) per socket: 32
Socket(s): 2
Stepping: 2
CPU max MHz: 4000.0000
CPU min MHz: 800.0000
BogoMIPS: 4200.00
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 invpcid_single intel_ppin cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities
Virtualization: VT-x
L1d cache: 3 MiB (64 instances)
L1i cache: 2 MiB (64 instances)
L2 cache: 128 MiB (64 instances)
L3 cache: 320 MiB (2 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-31,64-95
NUMA node1 CPU(s): 32-63,96-127
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected

==============================
Versions of relevant libraries

[pip3] numpy==2.2.6
[pip3] nvidia-cublas-cu12==12.8.3.14
[pip3] nvidia-cuda-cupti-cu12==12.8.57
[pip3] nvidia-cuda-nvrtc-cu12==12.8.61
[pip3] nvidia-cuda-runtime-cu12==12.8.57
[pip3] nvidia-cudnn-cu12==9.7.1.26
[pip3] nvidia-cufft-cu12==11.3.3.41
[pip3] nvidia-cufile-cu12==1.13.0.11
[pip3] nvidia-curand-cu12==10.3.9.55
[pip3] nvidia-cusolver-cu12==11.7.2.55
[pip3] nvidia-cusparse-cu12==12.5.7.53
[pip3] nvidia-cusparselt-cu12==0.6.3
[pip3] nvidia-nccl-cu12==2.26.2
[pip3] nvidia-nvjitlink-cu12==12.8.61
[pip3] nvidia-nvtx-cu12==12.8.55
[pip3] pyzmq==27.0.1
[pip3] torch==2.7.1+cu128
[pip3] torchaudio==2.7.1+cu128
[pip3] torchvision==0.22.1+cu128
[pip3] transformers==4.55.2
[pip3] triton==3.3.1
[conda] Could not collect

==============================
vLLM Info

ROCM Version : Could not collect
Neuron SDK Version : N/A
vLLM Version : 0.10.1
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 NIC0 NIC1 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X NODE NODE NODE SYS SYS NODE NODE 0-31,64-95 0 N/A
GPU1 NODE X NODE NODE SYS SYS NODE NODE 0-31,64-95 0 N/A
GPU2 NODE NODE X NODE SYS SYS NODE NODE 0-31,64-95 0 N/A
GPU3 NODE NODE NODE X SYS SYS NODE NODE 0-31,64-95 0 N/A
GPU4 SYS SYS SYS SYS X NODE SYS SYS 32-63,96-127 1 N/A
GPU5 SYS SYS SYS SYS NODE X SYS SYS 32-63,96-127 1 N/A
NIC0 NODE NODE NODE NODE SYS SYS X PIX
NIC1 NODE NODE NODE NODE SYS SYS PIX X

Legend:

X = Self
SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
PIX = Connection traversing at most a single PCIe bridge
NV# = Connection traversing a bonded set of # NVLinks

NIC Legend:

NIC0: mlx5_0
NIC1: mlx5_1

==============================
Environment Variables

NVIDIA_VISIBLE_DEVICES=0,1,2,3,4,5
NVIDIA_REQUIRE_CUDA=cuda>=12.8 brand=unknown,driver>=470,driver<471 brand=grid,driver>=470,driver<471 brand=tesla,driver>=470,driver<471 brand=nvidia,driver>=470,driver<471 brand=quadro,driver>=470,driver<471 brand=quadrortx,driver>=470,driver<471 brand=nvidiartx,driver>=470,driver<471 brand=vapps,driver>=470,driver<471 brand=vpc,driver>=470,driver<471 brand=vcs,driver>=470,driver<471 brand=vws,driver>=470,driver<471 brand=cloudgaming,driver>=470,driver<471 brand=unknown,driver>=535,driver<536 brand=grid,driver>=535,driver<536 brand=tesla,driver>=535,driver<536 brand=nvidia,driver>=535,driver<536 brand=quadro,driver>=535,driver<536 brand=quadrortx,driver>=535,driver<536 brand=nvidiartx,driver>=535,driver<536 brand=vapps,driver>=535,driver<536 brand=vpc,driver>=535,driver<536 brand=vcs,driver>=535,driver<536 brand=vws,driver>=535,driver<536 brand=cloudgaming,driver>=535,driver<536 brand=unknown,driver>=550,driver<551 brand=grid,driver>=550,driver<551 brand=tesla,driver>=550,driver<551 brand=nvidia,driver>=550,driver<551 brand=quadro,driver>=550,driver<551 brand=quadrortx,driver>=550,driver<551 brand=nvidiartx,driver>=550,driver<551 brand=vapps,driver>=550,driver<551 brand=vpc,driver>=550,driver<551 brand=vcs,driver>=550,driver<551 brand=vws,driver>=550,driver<551 brand=cloudgaming,driver>=550,driver<551 brand=unknown,driver>=560,driver<561 brand=grid,driver>=560,driver<561 brand=tesla,driver>=560,driver<561 brand=nvidia,driver>=560,driver<561 brand=quadro,driver>=560,driver<561 brand=quadrortx,driver>=560,driver<561 brand=nvidiartx,driver>=560,driver<561 brand=vapps,driver>=560,driver<561 brand=vpc,driver>=560,driver<561 brand=vcs,driver>=560,driver<561 brand=vws,driver>=560,driver<561 brand=cloudgaming,driver>=560,driver<561 brand=unknown,driver>=565,driver<566 brand=grid,driver>=565,driver<566 brand=tesla,driver>=565,driver<566 brand=nvidia,driver>=565,driver<566 brand=quadro,driver>=565,driver<566 brand=quadrortx,driver>=565,driver<566 brand=nvidiartx,driver>=565,driver<566 brand=vapps,driver>=565,driver<566 brand=vpc,driver>=565,driver<566 brand=vcs,driver>=565,driver<566 brand=vws,driver>=565,driver<566 brand=cloudgaming,driver>=565,driver<566
NCCL_VERSION=2.25.1-1
NVIDIA_DRIVER_CAPABILITIES=compute,utility
NVIDIA_PRODUCT_NAME=CUDA
VLLM_USAGE_SOURCE=production-docker-image
CUDA_VERSION=12.8.1
LD_LIBRARY_PATH=/usr/local/cuda/lib64
NCCL_CUMEM_ENABLE=0
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
CUDA_MODULE_LOADING=LAZY

🐛 Describe the bug

flash attention fails in model initialization.

docker image info

docker pull vllm/vllm-openai:v0.10.1

how to run

start vllm with a qwen2.5vl model.

A related issue can be found here. Dao-AILab/flash-attention#1763

output

(EngineCore_0 pid=1113) INFO 08-19 20:50:42 [gpu_model_runner.py:2007] Model loading took 15.6269 GiB and 3.074475 seconds
(EngineCore_0 pid=1113) INFO 08-19 20:50:42 [gpu_model_runner.py:2591] Encoder cache will be initialized with a budget of 16384 tokens, and profiled with 1 image items of the maximum feature size.
CUDA error (/__w/xformers/xformers/third_party/flash-attention/hopper/flash_fwd_launch_template.h:188): invalid argument
(APIServer pid=849) Traceback (most recent call last):
(APIServer pid=849)   File "<frozen runpy>", line 198, in _run_module_as_main
(APIServer pid=849)   File "<frozen runpy>", line 88, in _run_code
(APIServer pid=849)   File "/usr/local/lib/python3.12/dist-packages/vllm/entrypoints/openai/api_server.py", line 1918, in <module>
(APIServer pid=849)     uvloop.run(run_server(args))
(APIServer pid=849)   File "/usr/local/lib/python3.12/dist-packages/uvloop/__init__.py", line 109, in run
(APIServer pid=849)     return __asyncio.run(
(APIServer pid=849)            ^^^^^^^^^^^^^^
(APIServer pid=849)   File "/usr/lib/python3.12/asyncio/runners.py", line 195, in run
(APIServer pid=849)     return runner.run(main)
(APIServer pid=849)            ^^^^^^^^^^^^^^^^
(APIServer pid=849)   File "/usr/lib/python3.12/asyncio/runners.py", line 118, in run
(APIServer pid=849)     return self._loop.run_until_complete(task)
(APIServer pid=849)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(APIServer pid=849)   File "uvloop/loop.pyx", line 1518, in uvloop.loop.Loop.run_until_complete
(APIServer pid=849)   File "/usr/local/lib/python3.12/dist-packages/uvloop/__init__.py", line 61, in wrapper
(APIServer pid=849)     return await main
(APIServer pid=849)            ^^^^^^^^^^
(APIServer pid=849)   File "/usr/local/lib/python3.12/dist-packages/vllm/entrypoints/openai/api_server.py", line 1850, in run_server
(APIServer pid=849)     await run_server_worker(listen_address, sock, args, **uvicorn_kwargs)
(APIServer pid=849)   File "/usr/local/lib/python3.12/dist-packages/vllm/entrypoints/openai/api_server.py", line 1870, in run_server_worker
(APIServer pid=849)     async with build_async_engine_client(
(APIServer pid=849)                ^^^^^^^^^^^^^^^^^^^^^^^^^^
(APIServer pid=849)   File "/usr/lib/python3.12/contextlib.py", line 210, in __aenter__
(APIServer pid=849)     return await anext(self.gen)
(APIServer pid=849)            ^^^^^^^^^^^^^^^^^^^^^
(APIServer pid=849)   File "/usr/local/lib/python3.12/dist-packages/vllm/entrypoints/openai/api_server.py", line 178, in build_async_engine_client
(APIServer pid=849)     async with build_async_engine_client_from_engine_args(
(APIServer pid=849)                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(APIServer pid=849)   File "/usr/lib/python3.12/contextlib.py", line 210, in __aenter__
(APIServer pid=849)     return await anext(self.gen)
(APIServer pid=849)            ^^^^^^^^^^^^^^^^^^^^^
(APIServer pid=849)   File "/usr/local/lib/python3.12/dist-packages/vllm/entrypoints/openai/api_server.py", line 220, in build_async_engine_client_from_engine_args
(APIServer pid=849)     async_llm = AsyncLLM.from_vllm_config(
(APIServer pid=849)                 ^^^^^^^^^^^^^^^^^^^^^^^^^^
(APIServer pid=849)   File "/usr/local/lib/python3.12/dist-packages/vllm/utils/__init__.py", line 1557, in inner
(APIServer pid=849)     return fn(*args, **kwargs)
(APIServer pid=849)            ^^^^^^^^^^^^^^^^^^^
(APIServer pid=849)   File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/async_llm.py", line 174, in from_vllm_config
(APIServer pid=849)     return cls(
(APIServer pid=849)            ^^^^
(APIServer pid=849)   File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/async_llm.py", line 120, in __init__
(APIServer pid=849)     self.engine_core = EngineCoreClient.make_async_mp_client(
(APIServer pid=849)                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(APIServer pid=849)   File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core_client.py", line 102, in make_async_mp_client
(APIServer pid=849)     return AsyncMPClient(*client_args)
(APIServer pid=849)            ^^^^^^^^^^^^^^^^^^^^^^^^^^^
(APIServer pid=849)   File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core_client.py", line 767, in __init__
(APIServer pid=849)     super().__init__(
(APIServer pid=849)   File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core_client.py", line 446, in __init__
(APIServer pid=849)     with launch_core_engines(vllm_config, executor_class,
(APIServer pid=849)          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(APIServer pid=849)   File "/usr/lib/python3.12/contextlib.py", line 144, in __exit__
(APIServer pid=849)     next(self.gen)
(APIServer pid=849)   File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/utils.py", line 706, in launch_core_engines
(APIServer pid=849)     wait_for_engine_startup(
(APIServer pid=849)   File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/utils.py", line 759, in wait_for_engine_startup
(APIServer pid=849)     raise RuntimeError("Engine core initialization failed. "
(APIServer pid=849) RuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {}
/usr/lib/python3.12/multiprocessing/resource_tracker.py:279: UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown
  warnings.warn('resource_tracker: There appear to be %d '

Before submitting a new issue...

• Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the documentation page, which can answer lots of frequently asked questions.

[DarkLight1337]

From my understanding, this is just a problem with vLLM's dependencies rather than vLLM itself. What do you propose as the solution?

[Godricly]

So what is the proper way to report this kind of issue?
I tried to upgrade xformers to latest version, which upgraded torch to 2.8.0. But the vllm requires rebuilding. I'll try rebuilding the docker.

[DarkLight1337]

I think you just have to keep using an older version of vLLM until a new version of vLLM comes out where its dependencies no longer have this problem. You can also try building vLLM from source with the patched versions of the dependencies.

[DarkLight1337]

cc @LucasWilkinson @Isotr0py maybe you know more about this

[Godricly]

downgrading vllm to 0.9.2 solves my problem.

[jeejeelee]

We also encountered similar problems locally, and manual build xformer solved this issue.

[okamiRvS]

I’m experiencing a similar problem with a variant of Qwen2.5VL, specifically:
RedHatAI/Qwen2.5-VL-72B-Instruct-quantized.w4a16

I am running with these initialization arguments:

"args": [
  "--model", "RedHatAI/Qwen2.5-VL-72B-Instruct-quantized.w4a16",
  "--port", "8081",
  "--download-dir", "/data/datascience/.shared/.transformers_cache",
  "--gpu-memory-utilization", "0.9",
  "--max-model-len", "30720",
  "--enable-prefix-caching",
  "--guided-decoding-backend", "xgrammar",
  "--limit-mm-per-prompt", "{\"images\": 3}",
  "--enable-auto-tool-choice",
  "--tool-call-parser", "hermes",
  "--api-key", "token-abc123",
  "--trust-remote-code"
],

When launching, I get the following error:

RuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {}

Full error log

Exception has occurred: RuntimeError       (note: full exception trace is shown but execution is paused at: _run_module_as_main)
Engine core initialization failed. See root cause above. Failed core proc(s): {}
  File "../workarea/test_vllm_tools_issue/vllm/vllm/v1/engine/utils.py", line 759, in wait_for_engine_startup
    raise RuntimeError("Engine core initialization failed. "
  File "../workarea/test_vllm_tools_issue/vllm/vllm/v1/engine/utils.py", line 706, in launch_core_engines
    wait_for_engine_startup(
  File "../miniconda3/envs/test_vllm_tools_issue/lib/python3.10/contextlib.py", line 142, in __exit__
    next(self.gen)
  File "../workarea/test_vllm_tools_issue/vllm/vllm/v1/engine/core_client.py", line 446, in __init__
    with launch_core_engines(vllm_config, executor_class,
  File "../workarea/test_vllm_tools_issue/vllm/vllm/v1/engine/core_client.py", line 767, in __init__
    super().__init__(
  File "../workarea/test_vllm_tools_issue/vllm/vllm/v1/engine/core_client.py", line 102, in make_async_mp_client
    return AsyncMPClient(*client_args)
  File "../workarea/test_vllm_tools_issue/vllm/vllm/v1/engine/async_llm.py", line 120, in __init__
    self.engine_core = EngineCoreClient.make_async_mp_client(
  File "../workarea/test_vllm_tools_issue/vllm/vllm/v1/engine/async_llm.py", line 174, in from_vllm_config
    return cls(
  File "../workarea/test_vllm_tools_issue/vllm/vllm/utils/__init__.py", line 1557, in inner
    return fn(*args, **kwargs)
  File "../workarea/test_vllm_tools_issue/vllm/vllm/entrypoints/openai/api_server.py", line 220, in build_async_engine_client_from_engine_args
    async_llm = AsyncLLM.from_vllm_config(
  File "../miniconda3/envs/test_vllm_tools_issue/lib/python3.10/contextlib.py", line 199, in __aenter__
    return await anext(self.gen)
  File "../workarea/test_vllm_tools_issue/vllm/vllm/entrypoints/openai/api_server.py", line 178, in build_async_engine_client
    async with build_async_engine_client_from_engine_args(
  File "../miniconda3/envs/test_vllm_tools_issue/lib/python3.10/contextlib.py", line 199, in __aenter__
    return await anext(self.gen)
  File "../workarea/test_vllm_tools_issue/vllm/vllm/entrypoints/openai/api_server.py", line 1870, in run_server_worker
    async with build_async_engine_client(
  File "../workarea/test_vllm_tools_issue/vllm/vllm/entrypoints/openai/api_server.py", line 1850, in run_server
    await run_server_worker(listen_address, sock, args, **uvicorn_kwargs)
  File "../miniconda3/envs/test_vllm_tools_issue/lib/python3.10/site-packages/uvloop/__init__.py", line 61, in wrapper
    return await main
  File "../miniconda3/envs/test_vllm_tools_issue/lib/python3.10/site-packages/uvloop/loop.pyx", line 1518, in uvloop.loop.Loop.run_until_complete
    return future.result()
  File "../miniconda3/envs/test_vllm_tools_issue/lib/python3.10/site-packages/uvloop/__init__.py", line 82, in run
    return loop.run_until_complete(wrapper())
  File "../workarea/test_vllm_tools_issue/vllm/vllm/entrypoints/openai/api_server.py", line 1918, in <module>
    uvloop.run(run_server(args))
  File "../miniconda3/envs/test_vllm_tools_issue/lib/python3.10/runpy.py", line 86, in _run_code
    exec(code, run_globals)
  File "../miniconda3/envs/test_vllm_tools_issue/lib/python3.10/runpy.py", line 196, in _run_module_as_main (Current frame)
    return _run_code(code, main_globals, None,
RuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {}

Important details:

• On v0.9.2 this works fine.
• On v0.10.1 (latest) I consistently hit the error.
• I’m running on NVIDIA A100 80GB PCIe GPUs, not on RTX 5090 as in the issue title.

[Godricly]

@okamiRvS maybe you can start another issue? Mine is caused by xformer. But yours look different.

[Godricly]

This problem still exists in 0.10.1.1. Just FYI.

[Zedong-Liu]

This problem still exists in 0.10.1.1. Just FYI.

I manually built flash_attn on the 5090 and successfully ran inference (0.10.1.1) on qwen2.5-vl without using the xformers backend.

[DeoLeung]

failing on 0.10.2 with 5090

[zhy272711]

这个问题在 0.10.1.1 中仍然存在。仅供参考。

我在 5090 上手动构建了 flash_attn，并在 qwen2.5-vl 上成功运行了推理 （0.10.1.1），而无需使用 xformers 后端。

Did you download flash-attention from https://github.com/Dao-AILab/flash-attention/releases?page=1? Which version did you download?

[Zedong-Liu]

这个问题在 0.10.1.1 中仍然存在。仅供参考。

我在 5090 上手动构建了 flash_attn，并在 qwen2.5-vl 上成功运行了推理 （0.10.1.1），而无需使用 xformers 后端。

Did you download flash-attention from https://github.com/Dao-AILab/flash-attention/releases?page=1? Which version did you download?
flash-attn 2.8.1