[Bug] Qwen2-VL on NVIDIA L20 fails with Triton shared memory OutOfResources error

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#3,923 opened on Sep 2, 2025 4 comments 0 reactions 1 assignee Claimed by @grimoire View on GitHub

Dominant language: Python
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Merged PRs (30d): 54

Description

### Checklist

- [x] 1. I have searched related issues but cannot get the expected help.
- [x] 2. The bug has not been fixed in the latest version.
- [x] 3. Please note that if the bug-related issue you submitted lacks corresponding environment info and a minimal reproducible demo, it will be challenging for us to reproduce and resolve the issue, reducing the likelihood of receiving feedback.

### Describe the bug

Qwen2-VL on NVIDIA L20 fails with Triton shared memory OutOfResources error，Qwen2-vl-2B

### Reproduction

POST /v1/chat/completions HTTP/1.0

### Environment

```Shell
sys.platform: linux

Python: 3.10.12 (main, Feb 4 2025, 14:57:36) [GCC 11.4.0]

CUDA available: True

MUSA available: False

numpy_random_seed: 2147483648

GPU 0: NVIDIA L20

CUDA_HOME: /usr/local/cuda

NVCC: Cuda compilation tools, release 12.4, V12.4.131

GCC: x86_64-linux-gnu-gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0

PyTorch: 2.6.0+cu124

PyTorch compiling details: PyTorch built with:

- GCC 9.3

- C++ Version: 201703

- Intel(R) oneAPI Math Kernel Library Version 2024.2-Product Build 20240605 for Intel(R) 64 architecture applications

- Intel(R) MKL-DNN v3.5.3 (Git Hash 66f0cb9eb66affd2da3bf5f8d897376f04aae6af)

- OpenMP 201511 (a.k.a. OpenMP 4.5)

- LAPACK is enabled (usually provided by MKL)

- NNPACK is enabled

- CPU capability usage: AVX512

- CUDA Runtime 12.4

- NVCC architecture flags: -gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90

- CuDNN 90.1

- Magma 2.6.1

- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, COMMIT_SHA=2236df1770800ffea5697b11b0bb0d910b2e59e1, CUDA_VERSION=12.4, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DLIBKINETO_NOXPUPTI=ON -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, TORCH_VERSION=2.6.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF,

TorchVision: 0.21.0+cu124

LMDeploy: 0.8.0+

transformers: 4.50.0

gradio: 5.29.0

fastapi: 0.115.12

pydantic: 2.11.4

triton: 3.2.0

NVIDIA Topology:

GPU0 CPU Affinity NUMA Affinity GPU NUMA ID

GPU0 X 0-47,96-143 0 N/A

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
```

### Error traceback

```Shell
ret = await __forward(inputs)

File "/opt/lmdeploy/lmdeploy/pytorch/engine/model_agent.py", line 249, in __forward

return await self.async_forward(inputs, swap_in_map=swap_in_map, swap_out_map=swap_out_map)

File "/opt/lmdeploy/lmdeploy/pytorch/engine/model_agent.py", line 660, in async_forward

output = self._forward_impl(inputs, swap_in_map=swap_in_map, swap_out_map=swap_out_map)

File "/opt/lmdeploy/lmdeploy/pytorch/engine/model_agent.py", line 644, in _forward_impl

output = model_forward(

File "/opt/py3/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context

return func(*args, **kwargs)

File "/opt/lmdeploy/lmdeploy/pytorch/engine/model_agent.py", line 73, in model_forward

output = model(**input_dict)

File "/opt/lmdeploy/lmdeploy/pytorch/backends/cuda/graph_runner.py", line 161, in __call__

return self.model(**kwargs)

File "/opt/py3/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl

return self._call_impl(*args, **kwargs)