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[Bug]: Segment fault when import decord before import vllm #9993

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litianjian opened this issue Nov 4, 2024 · 4 comments
Closed
1 task done

[Bug]: Segment fault when import decord before import vllm #9993

litianjian opened this issue Nov 4, 2024 · 4 comments
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bug Something isn't working

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@litianjian
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litianjian commented Nov 4, 2024 •
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Your current environment

The output of `python collect_env.py` 
Collecting environment information...
PyTorch version: 2.5.0+cu124
Is debug build: False
CUDA used to build PyTorch: 12.4
ROCM used to build PyTorch: N/A

OS: Debian GNU/Linux 11 (bullseye) (x86_64)
GCC version: (Debian 10.2.1-6) 10.2.1 20210110
Clang version: Could not collect
CMake version: version 3.29.6
Libc version: glibc-2.31

Python version: 3.12.7 | packaged by Anaconda, Inc. | (main, Oct  4 2024, 13:27:36) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.120.bsk.2-amd64-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 12.2.140
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA H100 80GB HBM3
GPU 1: NVIDIA H100 80GB HBM3
GPU 2: NVIDIA H100 80GB HBM3
GPU 3: NVIDIA H100 80GB HBM3
GPU 4: NVIDIA H100 80GB HBM3
GPU 5: NVIDIA H100 80GB HBM3
GPU 6: NVIDIA H100 80GB HBM3
GPU 7: NVIDIA H100 80GB HBM3

Nvidia driver version: 535.161.08
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.4
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.4
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.4
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.4
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.4
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.4
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.4
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
Byte Order:                      Little Endian
Address sizes:                   46 bits physical, 57 bits virtual
CPU(s):                          96
On-line CPU(s) list:             0-95
Thread(s) per core:              1
Core(s) per socket:              48
Socket(s):                       2
NUMA node(s):                    2
Vendor ID:                       GenuineIntel
CPU family:                      6
Model:                           143
Model name:                      Intel(R) Xeon(R) Platinum 8480C
Stepping:                        8
CPU MHz:                         2387.141
BogoMIPS:                        4000.00
Hypervisor vendor:               Microsoft
Virtualization type:             full
L1d cache:                       4.5 MiB
L1i cache:                       3 MiB
L2 cache:                        192 MiB
L3 cache:                        210 MiB
NUMA node0 CPU(s):               0-47
NUMA node1 CPU(s):               48-95
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
Vulnerability Mmio stale data:   Unknown: No mitigations
Vulnerability Retbleed:          Vulnerable
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1:        Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:        Vulnerable, STIBP: disabled, RSB filling, PBRSB-eIBRS: Not affected
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected
Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology tsc_reliable nonstop_tsc cpuid aperfmperf pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 avx512vbmi umip waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid cldemote movdiri movdir64b fsrm serialize amx_bf16 avx512_fp16 amx_tile amx_int8 arch_capabilities

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.4.5.8
[pip3] nvidia-cuda-cupti-cu12==12.4.127
[pip3] nvidia-cuda-nvrtc-cu12==12.4.127
[pip3] nvidia-cuda-runtime-cu12==12.4.127
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.2.1.3
[pip3] nvidia-curand-cu12==10.3.5.147
[pip3] nvidia-cusolver-cu12==11.6.1.9
[pip3] nvidia-cusparse-cu12==12.3.1.170
[pip3] nvidia-ml-py==12.560.30
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.4.127
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] pyzmq==26.2.0
[pip3] torch==2.5.0
[pip3] torchvision==0.20.0
[pip3] transformers==4.46.1
[pip3] triton==3.1.0
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] nvidia-cublas-cu12        12.4.5.8                 pypi_0    pypi
[conda] nvidia-cuda-cupti-cu12    12.4.127                 pypi_0    pypi
[conda] nvidia-cuda-nvrtc-cu12    12.4.127                 pypi_0    pypi
[conda] nvidia-cuda-runtime-cu12  12.4.127                 pypi_0    pypi
[conda] nvidia-cudnn-cu12         9.1.0.70                 pypi_0    pypi
[conda] nvidia-cufft-cu12         11.2.1.3                 pypi_0    pypi
[conda] nvidia-curand-cu12        10.3.5.147               pypi_0    pypi
[conda] nvidia-cusolver-cu12      11.6.1.9                 pypi_0    pypi
[conda] nvidia-cusparse-cu12      12.3.1.170               pypi_0    pypi
[conda] nvidia-ml-py              12.560.30                pypi_0    pypi
[conda] nvidia-nccl-cu12          2.21.5                   pypi_0    pypi
[conda] nvidia-nvjitlink-cu12     12.4.127                 pypi_0    pypi
[conda] nvidia-nvtx-cu12          12.4.127                 pypi_0    pypi
[conda] pyzmq                     26.2.0                   pypi_0    pypi
[conda] torch                     2.5.0                    pypi_0    pypi
[conda] torchvision               0.20.0                   pypi_0    pypi
[conda] transformers              4.46.1                   pypi_0    pypi
[conda] triton                    3.1.0                    pypi_0    pypi
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.6.3.post2.dev217+gccb5376a
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
�[4mGPU0	GPU1	GPU2	GPU3	GPU4	GPU5	GPU6	GPU7	NIC0	NIC1	NIC2	NIC3	NIC4	NIC5	NIC6	NIC7	NIC8	CPU Affinity	NUMA Affinity	GPU NUMA ID�[0m
GPU0	 X 	NV18	NV18	NV18	NV18	NV18	NV18	NV18	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	NODE	0-47	0		N/A
GPU1	NV18	 X 	NV18	NV18	NV18	NV18	NV18	NV18	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	NODE	0-47	0		N/A
GPU2	NV18	NV18	 X 	NV18	NV18	NV18	NV18	NV18	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	NODE	0-47	0		N/A
GPU3	NV18	NV18	NV18	 X 	NV18	NV18	NV18	NV18	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	NODE	0-47	0		N/A
GPU4	NV18	NV18	NV18	NV18	 X 	NV18	NV18	NV18	SYS	SYS	SYS	SYS	NODE	NODE	NODE	NODE	SYS	48-95	1		N/A
GPU5	NV18	NV18	NV18	NV18	NV18	 X 	NV18	NV18	SYS	SYS	SYS	SYS	NODE	NODE	NODE	NODE	SYS	48-95	1		N/A
GPU6	NV18	NV18	NV18	NV18	NV18	NV18	 X 	NV18	SYS	SYS	SYS	SYS	NODE	NODE	NODE	NODE	SYS	48-95	1		N/A
GPU7	NV18	NV18	NV18	NV18	NV18	NV18	NV18	 X 	SYS	SYS	SYS	SYS	NODE	NODE	NODE	NODE	SYS	48-95	1		N/A
NIC0	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	 X 	NODE	NODE	NODE	SYS	SYS	SYS	SYS	NODE				
NIC1	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	NODE	 X 	NODE	NODE	SYS	SYS	SYS	SYS	NODE				
NIC2	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	NODE	NODE	 X 	NODE	SYS	SYS	SYS	SYS	NODE				
NIC3	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	NODE	NODE	NODE	 X 	SYS	SYS	SYS	SYS	NODE				
NIC4	SYS	SYS	SYS	SYS	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	 X 	NODE	NODE	NODE	SYS				
NIC5	SYS	SYS	SYS	SYS	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	NODE	 X 	NODE	NODE	SYS				
NIC6	SYS	SYS	SYS	SYS	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	NODE	NODE	 X 	NODE	SYS				
NIC7	SYS	SYS	SYS	SYS	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	NODE	NODE	NODE	 X 	SYS				
NIC8	NODE	NODE	NODE	NODE	SYS	SYS	SYS	SYS	NODE	NODE	NODE	NODE	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
  NIC1: mlx5_1
  NIC2: mlx5_2
  NIC3: mlx5_3
  NIC4: mlx5_4
  NIC5: mlx5_5
  NIC6: mlx5_6
  NIC7: mlx5_7
  NIC8: mlx5_8

Model Input Dumps

No response

🐛 Describe the bug

Importing decord before vllm causes the program to encounter a segmentation fault

import decord 
from vllm import LLM, SamplingParams
prompts = [
    "Hello, my name is",
    "The president of the United States is",
    "The capital of France is",
    "The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

llm = LLM(model="facebook/opt-125m")

outputs = llm.generate(prompts, sampling_params)

# Print the outputs.
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

Output

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@litianjian litianjian added the bug Something isn't working label Nov 4, 2024
@russellb
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russellb commented Nov 5, 2024

I was able to reproduce this. Here's the python backtrace:

(gdb) py-bt
Traceback (most recent call first):
  <built-in method _cuda_init of module object at remote 0x7fff7d3445e0>
  File "/home/ec2-user/venv/lib64/python3.11/site-packages/torch/cuda/__init__.py", line 319, in _lazy_init
    torch._C._cuda_init()
  <built-in method _cuda_setDevice of module object at remote 0x7fff7d3445e0>
  File "/home/ec2-user/venv/lib64/python3.11/site-packages/torch/cuda/__init__.py", line 478, in set_device
    torch._C._cuda_setDevice(device)
  File "/home/ec2-user/vllm/vllm/worker/worker.py", line 138, in init_device
    torch.cuda.set_device(self.device)
  File "/home/ec2-user/vllm/vllm/executor/gpu_executor.py", line 39, in _init_executor
    self.driver_worker.init_device()
  File "/home/ec2-user/vllm/vllm/executor/executor_base.py", line 36, in __init__
    self._init_executor()
  File "/home/ec2-user/vllm/vllm/engine/llm_engine.py", line ?, in __init__
    (failed to get frame line number)
  File "/home/ec2-user/vllm/vllm/engine/llm_engine.py", line 577, in from_engine_args
    engine = cls(
  File "/home/ec2-user/vllm/vllm/entrypoints/llm.py", line 209, in __init__
    self.llm_engine = LLMEngine.from_engine_args(
  File "/home/ec2-user/vllm/vllm/utils.py", line 1025, in inner
    return fn(*args, **kwargs)
  File "/home/ec2-user/vllm/test.py", line 11, in <module>
    llm = LLM(model="facebook/opt-125m")

@russellb
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russellb commented Nov 5, 2024

It looks like when you install decord using pip, you're getting a binary package that isn't built with the same versions of dependencies. I haven't tried it myself, but I would suggest trying to build decord from source to ensure it's built against the same versions of things as vllm and its other dependencies.

@XiaobingSuper
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I think it is a similiar issue with dmlc/decord#293

@russellb
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russellb commented Nov 6, 2024

I think it is a similiar issue with dmlc/decord#293

thanks! Yes, swapping the import order to import vllm first (which imports torch), and then record, this test script works. I'm going to close this out, but feel free to reopen if you feel there's something vllm needs to do here.

@russellb russellb closed this as completed Nov 6, 2024
@korkinof korkinof mentioned this issue Jan 3, 2025
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@russellb @XiaobingSuper @litianjian