Description
Checklist
- 1. I have searched related issues but cannot get the expected help.
- 2. The bug has not been fixed in the latest version.
- 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
在8卡V100机器上使用如下命令启动时，卡在某个位置一直没有反应
Reproduction
启动命令：
lmdeploy serve api_server /data/liyongzhi/hf_models/QwQ-32B/ --server-port 8080 --server-name qwq32B --tp=8 --dtype=float16 --log-level DEBUG
最后打印的日志:
[TM][DEBUG] void turbomind::UnifiedAttentionLayer::Forward(turbomind::UnifiedAttentionLayer::ForwardParam)
[TM][DEBUG] void turbomind::UnifiedAttentionLayer::Forward(turbomind::UnifiedAttentionLayer::ForwardParam)
[TM][DEBUG] void turbomind::UnifiedAttentionLayer::Forward(turbomind::UnifiedAttentionLayer::ForwardParam)
[TM][DEBUG] void turbomind::UnifiedAttentionLayer::Forward(turbomind::UnifiedAttentionLayer::ForwardParam)
[TM][DEBUG] void turbomind::UnifiedAttentionLayer::Forward(turbomind::UnifiedAttentionLayer::ForwardParam)
[TM][DEBUG] void turbomind::UnifiedAttentionLayer::Forward(turbomind::UnifiedAttentionLayer::ForwardParam)
[TM][DEBUG] void turbomind::UnifiedAttentionLayer::Forward(turbomind::UnifiedAttentionLayer::ForwardParam)
[TM][DEBUG] void turbomind::UnifiedAttentionLayer::Forward(turbomind::UnifiedAttentionLayer::ForwardParam)
Environment
lmdeploy check_env
sys.platform: linux Python: 3.10.16 (main, Dec 4 2024, 08:53:37) [GCC 9.4.0] CUDA available: True MUSA available: False numpy_random_seed: 2147483648 GPU 0,1,2,3,4,5,6,7: Tesla V100-SXM2-32GB CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 12.1, V12.1.105 GCC: x86_64-linux-gnu-gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0 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=comp
ute_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_FBGEM
M -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-fi
eld-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: 4.51.3
gradio: Not Found
fastapi: 0.115.8
pydantic: 2.10.6
triton: 3.2.0
NVIDIA Topology: TorchVision: 0.21.0+cu124
LMDeploy: 0.8.0+
transformers: 4.51.3
gradio: Not Found
fastapi: 0.115.8
pydantic: 2.10.6
triton: 3.2.0
NVIDIA Topology:
GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 NIC0 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X NV1 NV2 NV1 SYS SYS SYS NV2 NODE 0-19,40-59 0 N/A
GPU1 NV1 X NV1 NV2 SYS SYS NV2 SYS NODE 0-19,40-59 0 N/A
GPU2 NV2 NV1 X NV2 SYS NV1 SYS SYS PIX 0-19,40-59 0 N/A
GPU3 NV1 NV2 NV2 X NV1 SYS SYS SYS PIX 0-19,40-59 0 N/A
GPU4 SYS SYS SYS NV1 X NV2 NV2 NV1 SYS 20-39,60-79 1 N/A
GPU5 SYS SYS NV1 SYS NV2 X NV1 NV2 SYS 20-39,60-79 1 N/A
GPU6 SYS NV2 SYS SYS NV2 NV1 X NV1 SYS 20-39,60-79 1 N/A
GPU7 NV2 SYS SYS SYS NV1 NV2 NV1 X SYS 20-39,60-79 1 N/A
NIC0 NODE NODE PIX PIX SYS SYS SYS SYS 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