[Bug]: vllm v0.5.0 internal assert failed

[changshivek]

Your current environment

Collecting environment information...
PyTorch version: 2.3.0+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 3.29.3
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-3.10.0-1160.45.1.el7.x86_64-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA A800-SXM4-80GB
GPU 1: NVIDIA A800-SXM4-80GB

Nvidia driver version: 535.104.12
cuDNN version: Could not collect
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):              64
On-line CPU(s) list: 0-63
Vendor ID:           GenuineIntel
Model name:          Intel(R) Xeon(R) Platinum 8358 CPU @ 2.60GHz
CPU family:          6
Model:               106
Thread(s) per core:  1
Core(s) per socket:  32
Socket(s):           2
Stepping:            6
Frequency boost:     enabled
CPU max MHz:         3400.0000
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 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):        2
NUMA node0 CPU(s):   0-31
NUMA node1 CPU(s):   32-63

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] torch==2.3.0
[pip3] transformers==4.41.2
[pip3] triton==2.3.0
[conda] Could not collect
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.4.3
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0    GPU1    NIC0    NIC1    NIC2    NIC3    NIC4    NIC5    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      NV8     SYS     SYS     SYS     PXB     NODE    SYS     32-63   1               N/A
GPU1    NV8      X      SYS     SYS     SYS     PXB     NODE    SYS     32-63   1               N/A
NIC0    SYS     SYS      X      NODE    NODE    SYS     SYS     NODE
NIC1    SYS     SYS     NODE     X      NODE    SYS     SYS     PIX
NIC2    SYS     SYS     NODE    NODE     X      SYS     SYS     NODE
NIC3    PXB     PXB     SYS     SYS     SYS      X      NODE    SYS
NIC4    NODE    NODE    SYS     SYS     SYS     NODE     X      SYS
NIC5    SYS     SYS     NODE    PIX     NODE    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_4
  NIC3: mlx5_5
  NIC4: mlx5_6
  NIC5: mlx5_bond_0

🐛 Describe the bug

I use vllm/vllm-openai:v0.5.0 on k8s to deploy qwen 2 72b instruct, with tensor parallel size = 4, args looks like:

        command: ["/bin/bash", "-c"] 
        args: [
        "python3 -m vllm.entrypoints.openai.api_server \
        --host 0.0.0.0 \
        --model /fl/nlp/common/qwen/Qwen2-72B-Instruct \
        --trust-remote-code \
        --enforce-eager \
        --max-model-len 32768 \
        --gpu-memory-utilization 0.98 \
        --served-model-name qwen2-72bc \
        --tensor-parallel-size 4"
         ]

then I got the following error:

Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/cuda/__init__.py", line 306, in _lazy_init
    queued_call()
  File "/usr/local/lib/python3.10/dist-packages/torch/cuda/__init__.py", line 174, in _check_capability
    capability = get_device_capability(d)
  File "/usr/local/lib/python3.10/dist-packages/torch/cuda/__init__.py", line 430, in get_device_capability
    prop = get_device_properties(device)
  File "/usr/local/lib/python3.10/dist-packages/torch/cuda/__init__.py", line 448, in get_device_properties
    return _get_device_properties(device)  # type: ignore[name-defined]
RuntimeError: device >= 0 && device < num_gpus INTERNAL ASSERT FAILED at "../aten/src/ATen/cuda/CUDAContext.cpp":50, please report a bug to PyTorch. device=, num_gpus=

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
    exec(code, run_globals)
  File "/usr/local/lib/python3.10/dist-packages/vllm/entrypoints/openai/api_server.py", line 196, in <module>
    engine = AsyncLLMEngine.from_engine_args(
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 395, in from_engine_args
    engine = cls(
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 349, in __init__
    self.engine = self._init_engine(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 470, in _init_engine
    return engine_class(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/llm_engine.py", line 223, in __init__
    self.model_executor = executor_class(
  File "/usr/local/lib/python3.10/dist-packages/vllm/executor/multiproc_gpu_executor.py", line 142, in __init__
    super().__init__(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/vllm/executor/distributed_gpu_executor.py", line 25, in __init__
    super().__init__(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/vllm/executor/executor_base.py", line 41, in __init__
    self._init_executor()
  File "/usr/local/lib/python3.10/dist-packages/vllm/executor/multiproc_gpu_executor.py", line 63, in _init_executor
    self.driver_worker = self._create_worker(
  File "/usr/local/lib/python3.10/dist-packages/vllm/executor/gpu_executor.py", line 67, in _create_worker
    wrapper.init_worker(**self._get_worker_kwargs(local_rank, rank,
  File "/usr/local/lib/python3.10/dist-packages/vllm/worker/worker_base.py", line 134, in init_worker
    self.worker = worker_class(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/vllm/worker/worker.py", line 74, in __init__
    self.model_runner = ModelRunnerClass(
  File "/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py", line 118, in __init__
    self.attn_backend = get_attn_backend(
  File "/usr/local/lib/python3.10/dist-packages/vllm/attention/selector.py", line 42, in get_attn_backend
    backend = which_attn_to_use(num_heads, head_size, num_kv_heads,
  File "/usr/local/lib/python3.10/dist-packages/vllm/attention/selector.py", line 117, in which_attn_to_use
    if torch.cuda.get_device_capability()[0] < 8:
  File "/usr/local/lib/python3.10/dist-packages/torch/cuda/__init__.py", line 430, in get_device_capability
    prop = get_device_properties(device)
  File "/usr/local/lib/python3.10/dist-packages/torch/cuda/__init__.py", line 444, in get_device_properties
    _lazy_init()  # will define _get_device_properties
  File "/usr/local/lib/python3.10/dist-packages/torch/cuda/__init__.py", line 312, in _lazy_init
    raise DeferredCudaCallError(msg) from e
torch.cuda.DeferredCudaCallError: CUDA call failed lazily at initialization with error: device >= 0 && device < num_gpus INTERNAL ASSERT FAILED at "../aten/src/ATen/cuda/CUDAContext.cpp":50, please report a bug to PyTorch. device=, num_gpus=

CUDA call was originally invoked at:

  File "/usr/lib/python3.10/runpy.py", line 187, in _run_module_as_main
    mod_name, mod_spec, code = _get_module_details(mod_name, _Error)
  File "/usr/lib/python3.10/runpy.py", line 110, in _get_module_details
    __import__(pkg_name)
  File "<frozen importlib._bootstrap>", line 1027, in _find_and_load
  File "<frozen importlib._bootstrap>", line 992, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
  File "<frozen importlib._bootstrap>", line 1027, in _find_and_load
  File "<frozen importlib._bootstrap>", line 992, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
  File "<frozen importlib._bootstrap>", line 1027, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1006, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 688, in _load_unlocked
  File "<frozen importlib._bootstrap_external>", line 883, in exec_module
  File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
  File "/usr/local/lib/python3.10/dist-packages/vllm/__init__.py", line 3, in <module>
    from vllm.engine.arg_utils import AsyncEngineArgs, EngineArgs
  File "<frozen importlib._bootstrap>", line 1027, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1006, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 688, in _load_unlocked
  File "<frozen importlib._bootstrap_external>", line 883, in exec_module
  File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/arg_utils.py", line 8, in <module>
    from vllm.config import (CacheConfig, DecodingConfig, DeviceConfig,
  File "<frozen importlib._bootstrap>", line 1027, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1006, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 688, in _load_unlocked
  File "<frozen importlib._bootstrap_external>", line 883, in exec_module
  File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
  File "/usr/local/lib/python3.10/dist-packages/vllm/config.py", line 7, in <module>
    import torch
  File "<frozen importlib._bootstrap>", line 1027, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1006, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 688, in _load_unlocked
  File "<frozen importlib._bootstrap_external>", line 883, in exec_module
  File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
  File "/usr/local/lib/python3.10/dist-packages/torch/__init__.py", line 1478, in <module>
    _C._initExtension(manager_path())
  File "<frozen importlib._bootstrap>", line 1027, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1006, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 688, in _load_unlocked
  File "<frozen importlib._bootstrap_external>", line 883, in exec_module
  File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
  File "/usr/local/lib/python3.10/dist-packages/torch/cuda/__init__.py", line 238, in <module>
    _lazy_call(_check_capability)
  File "/usr/local/lib/python3.10/dist-packages/torch/cuda/__init__.py", line 235, in _lazy_call
    _queued_calls.append((callable, traceback.format_stack()))

INFO 06-12 10:22:22 multiproc_worker_utils.py:123] Killing local vLLM worker processes

This same config works normally with vllm/vllm-openai:v0.4.3.
I tried to set tensor parallel size = 8, then I got a bunch of exceptions like #5439 ,and it takes very long time to launch, I did not wait to see if it starts successfully.

[youkaichao]

GPU models and configuration:
GPU 0: NVIDIA A800-SXM4-80GB
GPU 1: NVIDIA A800-SXM4-80GB

You only have 2 GPUs, why use tensor parallel size = 4?

[Yimi81]

@youkaichao

same question

[youkaichao]

If you use a tensor parallel size different from the number of GPUs you have, then this is indeed a known issue. #5473 should solve it.

[changshivek]

If you use a tensor parallel size different from the number of GPUs you have, then this is indeed a known issue. #5473 should solve it.

No, I actually run vLLM on Kubernetes. Every time I modify the tensor parallel size, I manually adjust the number of GPUs simultaneously. The environment description shows only 2 GPUs because I copied it from another issue I had raised previously, where I encountered a similar problem on the same computing cluster. Therefore, I reused the environment description.

[youkaichao]

you can take a look at #6056