[Bug]: ngc24.05 "RuntimeError: Cannot re-initialize CUDA in forked subprocess."

[LSC527]

Your current environment

Collecting environment information...
PyTorch version: 2.4.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.29.2
Libc version: glibc-2.35

Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-3.10.0-1062.9.1.el7.x86_64-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.4.131
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA A30
GPU 1: NVIDIA A30
GPU 2: NVIDIA A30
GPU 3: NVIDIA A30

Nvidia driver version: 525.85.12
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.1.0
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                    x86_64
CPU op-mode(s):                  32-bit, 64-bit
Address sizes:                   46 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) Platinum 8350C CPU @ 2.60GHz
CPU family:                      6
Model:                           106
Thread(s) per core:              2
Core(s) per socket:              32
Socket(s):                       2
Stepping:                        6
Frequency boost:                 enabled
CPU max MHz:                     3500.0000
CPU min MHz:                     800.0000
BogoMIPS:                        5200.00
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 aperfmperf eagerfpu pni pclmulqdq dtes64 monitor 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 epb cat_l3 invpcid_single intel_pt ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq md_clear pconfig spec_ctrl intel_stibp flush_l1d arch_capabilities
Virtualization:                  VT-x
L1d cache:                       3 MiB (64 instances)
L1i cache:                       2 MiB (64 instances)
L2 cache:                        80 MiB (64 instances)
L3 cache:                        96 MiB (2 instances)
NUMA node(s):                    1
NUMA node0 CPU(s):               0-127
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1:        Vulnerable: Load fences, __user pointer sanitization and usercopy barriers only; no swapgs barriers
Vulnerability Spectre v2:        Vulnerable, IBPB
Vulnerability Tsx async abort:   Not affected

Versions of relevant libraries:
[pip3] numpy==1.24.4
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] onnx==1.16.0
[pip3] optree==0.11.0
[pip3] pytorch-quantization==2.1.2
[pip3] pytorch-triton==3.0.0+989adb9a2
[pip3] torch==2.4.0
[pip3] torch-tensorrt==2.4.0a0
[pip3] torchvision==0.19.0
[pip3] transformers==4.43.4
[pip3] triton==3.0.0
[conda] Could not collect
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.5.4
vLLM Build Flags:
CUDA Archs: 5.2 6.0 6.1 7.0 7.2 7.5 8.0 8.6 8.7 9.0+PTX; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0	GPU1	GPU2	GPU3	CPU Affinity	NUMA Affinity
GPU0	 X 	SYS	SYS	SYS	0-127		N/A
GPU1	SYS	 X 	SYS	SYS	0-127		N/A
GPU2	SYS	SYS	 X 	SYS	0-127		N/A
GPU3	SYS	SYS	SYS	 X 	0-127		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

🐛 Describe the bug

pip install vllm==0.5.4
export VLLM_WORKER_MULTIPROC_METHOD=spawn
CUDA_VISIBLE_DEVICES=0 vllm serve openbmb/MiniCPM-Llama3-V-2_5 --trust-remote-code --tensor-parallel-size 1 --max-model-len 2048

The following error happens in ngc24.05 no matter set VLLM_WORKER_MULTIPROC_METHOD=spawn or not.

Traceback (most recent call last):
  File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
    self.run()
  File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/usr/local/lib/python3.10/dist-packages/vllm/entrypoints/openai/rpc/server.py", line 217, in run_rpc_server
    server = AsyncEngineRPCServer(async_engine_args, usage_context, port)
  File "/usr/local/lib/python3.10/dist-packages/vllm/entrypoints/openai/rpc/server.py", line 25, in __init__
    self.engine = AsyncLLMEngine.from_engine_args(async_engine_args,
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 471, in from_engine_args
    engine = cls(
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 381, in __init__
    self.engine = self._init_engine(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 552, in _init_engine
    return engine_class(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/llm_engine.py", line 249, in __init__
    self.model_executor = executor_class(
  File "/usr/local/lib/python3.10/dist-packages/vllm/executor/executor_base.py", line 47, in __init__
    self._init_executor()
  File "/usr/local/lib/python3.10/dist-packages/vllm/executor/gpu_executor.py", line 35, in _init_executor
    self.driver_worker.init_device()
  File "/usr/local/lib/python3.10/dist-packages/vllm/worker/worker.py", line 123, in init_device
    torch.cuda.set_device(self.device)
  File "/usr/local/lib/python3.10/dist-packages/torch/cuda/__init__.py", line 420, in set_device
    torch._C._cuda_setDevice(device)
  File "/usr/local/lib/python3.10/dist-packages/torch/cuda/__init__.py", line 300, in _lazy_init
    raise RuntimeError(
RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method

However, everything went fine using ngc24.02/ngc24.04 (tested) or any ngc image version lower than 24.05 (I guess).

[youkaichao]

cc @njhill @robertgshaw2-neuralmagic

this looks like the api server process somehow initialize the cuda context.

@LSC527 how about trying to add --disable-frontend-multiprocessing ?

[LSC527]

cc @njhill @robertgshaw2-neuralmagic

this looks like the api server process somehow initialize the cuda context.

@LSC527 how about trying to add --disable-frontend-multiprocessing ?

@youkaichao add --disable-frontend-multiprocessing can fix it when tp=1 but won't work if tp>1.

[robertgshaw2-redhat]

@LSC527 are these the images you are talking about?

• nvcr.io/nvidia/pytorch:24.04-py3
• nvcr.io/nvidia/pytorch:24.05-py3

[LSC527]

@robertgshaw2-neuralmagic yes.

[robertgshaw2-redhat]

Are you able to install vllm inside these? I have been getting conflicts with flashattention inside this image

ImportError: /usr/local/lib/python3.10/dist-packages/flash_attn_2_cuda.cpython-310-x86_64-linux-gnu.so: undefined symbol: _ZN3c105ErrorC2ENS_14SourceLocationENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE

[LSC527]

Are you able to install vllm inside these? I have been getting conflicts with flashattention inside this image

ImportError: /usr/local/lib/python3.10/dist-packages/flash_attn_2_cuda.cpython-310-x86_64-linux-gnu.so: undefined symbol: _ZN3c105ErrorC2ENS_14SourceLocationENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE

just pip uninstall flash_attn. flash_attn is not required by vllm.

[robertgshaw2-redhat]

cool

[robertgshaw2-redhat]

I am unable to reproduce this issue.

• Dockefile:

FROM nvcr.io/nvidia/pytorch:24.05-py3

RUN pip uninstall torch -y

RUN pip uninstall flash_attn -y

RUN pip install vllm==0.5.4

RUN huggingface-cli download openbmb/MiniCPM-Llama3-V-2_5

ENTRYPOINT ["vllm", "serve", "openbmb/MiniCPM-Llama3-V-2_5", "--trust-remote-code", "--tensor-parallel-size", "2", "--max-model-len", "2048"]

docker build -t ngc-24.05 .

• Launch commands:

docker run --gpus all --shm-size 2g -e CUDA_VISIBLE_DEVICES=6,7 -e VLLM_WORKER_MULTIPROC_METHOD=spawn -p 8000:8000 ngc-24.05

docker run --gpus all --shm-size 2g -e CUDA_VISIBLE_DEVICES=6,7 -p 8000:8000 ngc-24.05

Both of there have been working for me + I am able to send requests.

Do I have something different here than your setup?

[youkaichao]

I find that the container nvcr.io/nvidia/pytorch:24.05-py3 uses pytorch 2.4.0a0+07cecf4168.nv24.05 by default. this seems to be a dev-version of pytorch.

@LSC527 did you try to uninstall the existing pytorch inside the container?

[robertgshaw2-redhat]

I find that the container nvcr.io/nvidia/pytorch:24.05-py3 uses pytorch 2.4.0a0+07cecf4168.nv24.05 by default. this seems to be a dev-version of pytorch.

@LSC527 did you try to uninstall the existing pytorch inside the container?

pip install vllm==0.5.4 will uninstall that version