[Bug]: watchdog thread terminated with exception: CUDA error: an illegal memory access was encountered

[DreamGenX]

Your current environment

The output of `python collect_env.py`.

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 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.31

Python version: 3.10.14 (main, Apr  6 2024, 18:45:05) [GCC 9.4.0] (64-bit runtime)
Python platform: Linux-5.15.0-105-generic-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: Could not collect
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

Nvidia driver version: 550.54.15
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
Byte Order:                         Little Endian
Address sizes:                      46 bits physical, 57 bits virtual
CPU(s):                             120
On-line CPU(s) list:                0-119
Thread(s) per core:                 1
Core(s) per socket:                 1
Socket(s):                          120
NUMA node(s):                       1
Vendor ID:                          GenuineIntel
CPU family:                         6
Model:                              143
Model name:                         Intel(R) Xeon(R) Platinum 8462Y+
Stepping:                           8
CPU MHz:                            2800.000
BogoMIPS:                           5600.00
Virtualization:                     VT-x
Hypervisor vendor:                  KVM
Virtualization type:                full
L1d cache:                          3.8 MiB
L1i cache:                          3.8 MiB
L2 cache:                           480 MiB
L3 cache:                           1.9 GiB
NUMA node0 CPU(s):                  0-119
Vulnerability Gather data sampling: Not affected
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:             Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Mitigation; TSX disabled
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology cpuid tsc_known_freq pni pclmulqdq vmx ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd arat avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b fsrm md_clear serialize tsxldtrk avx512_fp16 arch_capabilities

Versions of relevant libraries:
[pip3] flashinfer==0.1.4+cu121torch2.4
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.1.3.1
[pip3] nvidia-cuda-cupti-cu12==12.1.105
[pip3] nvidia-cuda-nvrtc-cu12==12.1.105
[pip3] nvidia-cuda-runtime-cu12==12.1.105
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.0.2.54
[pip3] nvidia-curand-cu12==10.3.2.106
[pip3] nvidia-cusolver-cu12==11.4.5.107
[pip3] nvidia-cusparse-cu12==12.1.0.106
[pip3] nvidia-ml-py==12.560.30
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] nvidia-nvjitlink-cu12==12.6.20
[pip3] nvidia-nvtx-cu12==12.1.105
[pip3] pyzmq==26.2.0
[pip3] torch==2.4.0
[pip3] torchvision==0.19.0
[pip3] transformers==4.44.2
[pip3] triton==3.0.0
[conda] Could not collect
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.5.5@
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
�[4mGPU0	GPU1	GPU2	GPU3	CPU Affinity	NUMA Affinity	GPU NUMA ID�[0m
GPU0	 X 	NV6	NV6	NV6	0-119	0		N/A
GPU1	NV6	 X 	NV6	NV6	0-119	0		N/A
GPU2	NV6	NV6	 X 	NV6	0-119	0		N/A
GPU3	NV6	NV6	NV6	 X 	0-119	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

Environemnt summary: vLLM 0.5.5 docker on 4xH100 SXM
Model summary: Llama 3 70B in fp8 using AutoFP8
Runtime summary:

--gpu-memory-utilization=0.95 --tensor-parallel-size=4 --disable-log-requests --enable-chunked-prefill --max-num-batched-tokens=8192

INFO 09-03 19:10:35 config.py:813] Defaulting to use mp for distributed inference
INFO 09-03 19:10:35 config.py:911] Chunked prefill is enabled with max_num_batched_tokens=8192.
INFO 09-03 19:10:35 llm_engine.py:184] Initializing an LLM engine (v0.5.5) with config: model='/model', speculative_config=None, tokenizer='/model', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=8192, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=4, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=fp8, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=0, served_model_name=/model, use_v2_block_manager=False, enable_prefix_caching=False)
WARNING 09-03 19:10:36 multiproc_gpu_executor.py:59] Reducing Torch parallelism from 120 threads to 1 to avoid unnecessary CPU contention. Set OMP_NUM_THREADS in the external environment to tune this value as needed.
INFO 09-03 19:10:36 custom_cache_manager.py:17] Setting Triton cache manager to: vllm.triton_utils.custom_cache_manager:CustomCacheManager
�[1;36m(VllmWorkerProcess pid=140)�[0;0m INFO 09-03 19:10:36 multiproc_worker_utils.py:215] Worker ready; awaiting tasks
�[1;36m(VllmWorkerProcess pid=139)�[0;0m INFO 09-03 19:10:36 multiproc_worker_utils.py:215] Worker ready; awaiting tasks
�[1;36m(VllmWorkerProcess pid=138)�[0;0m INFO 09-03 19:10:36 multiproc_worker_utils.py:215] Worker ready; awaiting tasks
INFO 09-03 19:10:42 utils.py:975] Found nccl from library libnccl.so.2
�[1;36m(VllmWorkerProcess pid=138)�[0;0m INFO 09-03 19:10:42 utils.py:975] Found nccl from library libnccl.so.2
�[1;36m(VllmWorkerProcess pid=139)�[0;0m INFO 09-03 19:10:42 utils.py:975] Found nccl from library libnccl.so.2
INFO 09-03 19:10:42 pynccl.py:63] vLLM is using nccl==2.20.5
�[1;36m(VllmWorkerProcess pid=138)�[0;0m INFO 09-03 19:10:42 pynccl.py:63] vLLM is using nccl==2.20.5
�[1;36m(VllmWorkerProcess pid=139)�[0;0m INFO 09-03 19:10:42 pynccl.py:63] vLLM is using nccl==2.20.5
�[1;36m(VllmWorkerProcess pid=140)�[0;0m INFO 09-03 19:10:42 utils.py:975] Found nccl from library libnccl.so.2
�[1;36m(VllmWorkerProcess pid=140)�[0;0m INFO 09-03 19:10:42 pynccl.py:63] vLLM is using nccl==2.20.5

🐛 Describe the bug

AsyncLLMEngine causes Process group watchdog thread terminated with exception: CUDA error: an illegal memory access was encountered.

Click to see full logs

INFO:     172.18.0.1:35722 - "POST /generate HTTP/1.1" 200 OK
[rank0]:[E904 19:47:16.692386894 ProcessGroupNCCL.cpp:1515] [PG 3 Rank 0] Process group watchdog thread terminated with exception: CUDA error: an illegal memory access was encountered
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

Exception raised from c10_cuda_check_implementation at ../c10/cuda/CUDAException.cpp:43 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x96 (0x7f8d14d8df86 in /usr/local/lib/python3.10/dist-packages/torch/lib/libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x64 (0x7f8d14d3cd10 in /usr/local/lib/python3.10/dist-packages/torch/lib/libc10.so)
frame #2: c10::cuda::c10_cuda_check_implementation(int, char const*, char const*, int, bool) + 0x118 (0x7f8d14e68f08 in /usr/local/lib/python3.10/dist-packages/torch/lib/libc10_cuda.so)
frame #3: c10d::ProcessGroupNCCL::WorkNCCL::finishedGPUExecutionInternal() const + 0x56 (0x7f8d160853e6 in /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_cuda.so)
frame #4: c10d::ProcessGroupNCCL::WorkNCCL::isCompleted() + 0xa0 (0x7f8d1608a600 in /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_cuda.so)
frame #5: c10d::ProcessGroupNCCL::watchdogHandler() + 0x1da (0x7f8d160912ba in /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_cuda.so)
frame #6: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x10c (0x7f8d160936fc in /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_cuda.so)
frame #7: <unknown function> + 0xd6df4 (0x7f8d6381fdf4 in /usr/lib/x86_64-linux-gnu/libstdc++.so.6)
frame #8: <unknown function> + 0x8609 (0x7f8d66005609 in /usr/lib/x86_64-linux-gnu/libpthread.so.0)
frame #9: clone + 0x43 (0x7f8d6613f353 in /usr/lib/x86_64-linux-gnu/libc.so.6)

terminate called after throwing an instance of 'c10::DistBackendError'
ERROR 09-04 19:47:16 async_llm_engine.py:65] Engine background task failed
ERROR 09-04 19:47:16 async_llm_engine.py:65] Traceback (most recent call last):
ERROR 09-04 19:47:16 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 55, in _log_task_completion
ERROR 09-04 19:47:16 async_llm_engine.py:65]     return_value = task.result()
ERROR 09-04 19:47:16 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 930, in run_engine_loop
ERROR 09-04 19:47:16 async_llm_engine.py:65]     result = task.result()
ERROR 09-04 19:47:16 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 873, in engine_step
ERROR 09-04 19:47:16 async_llm_engine.py:65]     request_outputs = await self.engine.step_async(virtual_engine)
ERROR 09-04 19:47:16 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 337, in step_async
ERROR 09-04 19:47:16 async_llm_engine.py:65]     output = await self.model_executor.execute_model_async(
ERROR 09-04 19:47:16 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/vllm/executor/distributed_gpu_executor.py", line 175, in execute_model_async
ERROR 09-04 19:47:16 async_llm_engine.py:65]     return await self._driver_execute_model_async(execute_model_req)
ERROR 09-04 19:47:16 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/vllm/executor/multiproc_gpu_executor.py", line 224, in _driver_execute_model_async
ERROR 09-04 19:47:16 async_llm_engine.py:65]     return await self.driver_exec_model(execute_model_req)
ERROR 09-04 19:47:16 async_llm_engine.py:65]   File "/usr/lib/python3.10/concurrent/futures/thread.py", line 58, in run
ERROR 09-04 19:47:16 async_llm_engine.py:65]     result = self.fn(*self.args, **self.kwargs)
ERROR 09-04 19:47:16 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/vllm/worker/worker_base.py", line 322, in execute_model
ERROR 09-04 19:47:16 async_llm_engine.py:65]     output = self.model_runner.execute_model(
ERROR 09-04 19:47:16 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 116, in decorate_context
ERROR 09-04 19:47:16 async_llm_engine.py:65]     return func(*args, **kwargs)
ERROR 09-04 19:47:16 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py", line 1415, in execute_model
ERROR 09-04 19:47:16 async_llm_engine.py:65]     hidden_or_intermediate_states = model_executable(
ERROR 09-04 19:47:16 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
ERROR 09-04 19:47:16 async_llm_engine.py:65]     return self._call_impl(*args, **kwargs)
ERROR 09-04 19:47:16 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1562, in _call_impl
ERROR 09-04 19:47:16 async_llm_engine.py:65]     return forward_call(*args, **kwargs)
ERROR 09-04 19:47:16 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/models/llama.py", line 429, in forward
ERROR 09-04 19:47:16 async_llm_engine.py:65]     model_output = self.model(input_ids, positions, kv_caches,
ERROR 09-04 19:47:16 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
ERROR 09-04 19:47:16 async_llm_engine.py:65]     return self._call_impl(*args, **kwargs)
ERROR 09-04 19:47:16 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1562, in _call_impl
ERROR 09-04 19:47:16 async_llm_engine.py:65]     return forward_call(*args, **kwargs)
ERROR 09-04 19:47:16 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/models/llama.py", line 329, in forward
ERROR 09-04 19:47:16 async_llm_engine.py:65]     hidden_states, residual = layer(
ERROR 09-04 19:47:16 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
ERROR 09-04 19:47:16 async_llm_engine.py:65]     return self._call_impl(*args, **kwargs)
ERROR 09-04 19:47:16 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1562, in _call_impl
ERROR 09-04 19:47:16 async_llm_engine.py:65]     return forward_call(*args, **kwargs)
ERROR 09-04 19:47:16 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/models/llama.py", line 251, in forward
ERROR 09-04 19:47:16 async_llm_engine.py:65]     hidden_states = self.self_attn(
ERROR 09-04 19:47:16 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
ERROR 09-04 19:47:16 async_llm_engine.py:65]     return self._call_impl(*args, **kwargs)
ERROR 09-04 19:47:16 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1562, in _call_impl
ERROR 09-04 19:47:16 async_llm_engine.py:65]     return forward_call(*args, **kwargs)
ERROR 09-04 19:47:16 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/models/llama.py", line 181, in forward
ERROR 09-04 19:47:16 async_llm_engine.py:65]     attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
ERROR 09-04 19:47:16 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
ERROR 09-04 19:47:16 async_llm_engine.py:65]     return self._call_impl(*args, **kwargs)
ERROR 09-04 19:47:16 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1562, in _call_impl
ERROR 09-04 19:47:16 async_llm_engine.py:65]     return forward_call(*args, **kwargs)
ERROR 09-04 19:47:16 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/vllm/attention/layer.py", line 98, in forward
ERROR 09-04 19:47:16 async_llm_engine.py:65]     return self.impl.forward(query,
ERROR 09-04 19:47:16 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/vllm/attention/backends/flash_attn.py", line 692, in forward
ERROR 09-04 19:47:16 async_llm_engine.py:65]     num_prefill_tokens] = torch.ops.vllm.flash_attn_varlen_func(  # noqa
ERROR 09-04 19:47:16 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/torch/_ops.py", line 1061, in __call__
ERROR 09-04 19:47:16 async_llm_engine.py:65]     return self_._op(*args, **(kwargs or {}))
ERROR 09-04 19:47:16 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/torch/_library/custom_ops.py", line 236, in backend_impl
ERROR 09-04 19:47:16 async_llm_engine.py:65]     result = self._backend_fns[device_type](*args, **kwargs)
ERROR 09-04 19:47:16 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/vllm/attention/backends/flash_attn.py", line 48, in flash_attn_varlen_func
ERROR 09-04 19:47:16 async_llm_engine.py:65]     return _flash_attn_varlen_func(
ERROR 09-04 19:47:16 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/vllm_flash_attn/flash_attn_interface.py", line 1154, in flash_attn_varlen_func
ERROR 09-04 19:47:16 async_llm_engine.py:65]     return FlashAttnVarlenFunc.apply(
ERROR 09-04 19:47:16 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/torch/autograd/function.py", line 574, in apply
ERROR 09-04 19:47:16 async_llm_engine.py:65]     return super().apply(*args, **kwargs)  # type: ignore[misc]
ERROR 09-04 19:47:16 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/vllm_flash_attn/flash_attn_interface.py", line 632, in forward
ERROR 09-04 19:47:16 async_llm_engine.py:65]     out, q, k, v, out_padded, softmax_lse, S_dmask, rng_state = _flash_attn_varlen_forward(
ERROR 09-04 19:47:16 async_llm_engine.py:65]   File "/usr/local/lib/python3.10/dist-packages/vllm_flash_attn/flash_attn_interface.py", line 90, in _flash_attn_varlen_forward
ERROR 09-04 19:47:16 async_llm_engine.py:65]     out, q, k, v, out_padded, softmax_lse, S_dmask, rng_state = flash_attn_cuda.varlen_fwd(
ERROR 09-04 19:47:16 async_llm_engine.py:65] RuntimeError: CUDA error: an illegal memory access was encountered
ERROR 09-04 19:47:16 async_llm_engine.py:65] CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
ERROR 09-04 19:47:16 async_llm_engine.py:65] For debugging consider passing CUDA_LAUNCH_BLOCKING=1
ERROR 09-04 19:47:16 async_llm_engine.py:65] Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

ERROR 09-04 19:47:16 async_llm_engine.py:65] 
  what():  [PG 3 Rank 0] Process group watchdog thread terminated with exception: CUDA error: an illegal memory access was encountered
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

Exception raised from c10_cuda_check_implementation at ../c10/cuda/CUDAException.cpp:43 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x96 (0x7f8d14d8df86 in /usr/local/lib/python3.10/dist-packages/torch/lib/libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x64 (0x7f8d14d3cd10 in /usr/local/lib/python3.10/dist-packages/torch/lib/libc10.so)
frame #2: c10::cuda::c10_cuda_check_implementation(int, char const*, char const*, int, bool) + 0x118 (0x7f8d14e68f08 in /usr/local/lib/python3.10/dist-packages/torch/lib/libc10_cuda.so)
frame #3: c10d::ProcessGroupNCCL::WorkNCCL::finishedGPUExecutionInternal() const + 0x56 (0x7f8d160853e6 in /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_cuda.so)
frame #4: c10d::ProcessGroupNCCL::WorkNCCL::isCompleted() + 0xa0 (0x7f8d1608a600 in /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_cuda.so)
frame #5: c10d::ProcessGroupNCCL::watchdogHandler() + 0x1da (0x7f8d160912ba in /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_cuda.so)
frame #6: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x10c (0x7f8d160936fc in /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_cuda.so)
frame #7: <unknown function> + 0xd6df4 (0x7f8d6381fdf4 in /usr/lib/x86_64-linux-gnu/libstdc++.so.6)
frame #8: <unknown function> + 0x8609 (0x7f8d66005609 in /usr/lib/x86_64-linux-gnu/libpthread.so.0)
frame #9: clone + 0x43 (0x7f8d6613f353 in /usr/lib/x86_64-linux-gnu/libc.so.6)

Exception raised from ncclCommWatchdog at ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1521 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x96 (0x7f8d14d8df86 in /usr/local/lib/python3.10/dist-packages/torch/lib/libc10.so)
frame #1: <unknown function> + 0xe5aa84 (0x7f8d15d1ca84 in /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_cuda.so)
frame #2: <unknown function> + 0xd6df4 (0x7f8d6381fdf4 in /usr/lib/x86_64-linux-gnu/libstdc++.so.6)
frame #3: <unknown function> + 0x8609 (0x7f8d66005609 in /usr/lib/x86_64-linux-gnu/libpthread.so.0)
frame #4: clone + 0x43 (0x7f8d6613f353 in /usr/lib/x86_64-linux-gnu/libc.so.6)

[rank2]:[E904 19:47:16.699524824 ProcessGroupNCCL.cpp:1515] [PG 3 Rank 2] Process group watchdog thread terminated with exception: CUDA error: an illegal memory access was encountered
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

I did not find a way to consistently reporduce it, but it happens in production system under load regularly.

Interestingly, the process does not crash, but generate no longer works.

I have found some similar issues, but it's unclear if it's the same root cause. I tried to provide more details:

• [Bug]: vllm is hang after upgrade to v0.5.4 #7297

Before submitting a new issue...

• Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the documentation page, which can answer lots of frequently asked questions.

[onlinex]

Seeing this on both v0.5.4 and v0.5.5 with Mixtral 7B GPTQ 8bit with prefix caching enabled.

[DreamGenX]

@onlinex In my case prefix caching is not enabled

[eddiegaoo]

@DreamGenX Hi! I encounter exactly the same issue here (with no prefix caching enabled). I wonder if you have figured out a way to resolve this issue. In my case, I found that when I only utilized 2 GPUs (with p2p connections), no such issue happened; But when I increased the GPU number to 4 (still with p2p connections), the issue occurred :(

[DreamGenX]

@eddiegaoo try upgrading to 0.6.0 and migrate from autofp8 to llm-compressor

[eddiegaoo]

@eddiegaoo try upgrading to 0.6.0 and migrate from autofp8 to llm-compressor

Many thanks! I'll give it a try

[thies1006]

I get this error with v0.6.1 and model Meta-Llama-3.1-70B-Instruct-FP8-dynamic on 8xL4 (no enable-chunked-prefill).

[dhruvmullick]

I'm getting the same error on v0.6.3.post1 with neuralmagic/Llama-3.1-Nemotron-70B-Instruct-HF-FP8-dynamic

[gkm0120]

I had the same error using Qwen2.5-72B-Instruct on v0.6.0, enable prefix caching(NVIDIA-SMI 550.54.15、Driver Version: 550.54.15、CUDA Version: 12.4)

[rank0]:[E1115 12:10:49.629906572 ProcessGroupNCCL.cpp:1515] [PG 3 Rank 0] Process group watchdog thread terminated with exception: CUDA error: an illegal memory access was encountered

[TopIdiot]

Got similar bug in 0.6.3, change attention backend to flashinfer fixed it.

[FrankTheTank9]

We also have this issue. With Mistral Nemo 12b. What is the current status of this issue?

[holyseven]

similar bug in 0.7.1, changing attention backend to flashinfer does not work for me.

but kill all and rm -rf /tmp/*, then restart vllm, works for me.

---

update: it works for a while, but the issue raised again.

[FrankTheTank9]

How can we get attention to this issue? This fatal crash happens multiple times per day and makes it no longer suitable for production. This happens with both llama 3.1 8b and Mistral Nemo 12b.

[HelenaSak]

Hello!
Yes, I have the same problem today for some unknown reason. First time in six months. I use version 0.6.4.post1 and llama3.3.
Here is error log:
model_runner_base.py:120] Writing input of failed execution to /tmp/err_execute_model_input_20250219-121443.pkl...
[rank0]:[E219 12:14:43.351647859 ProcessGroupNCCL.cpp:1595] [PG ID 2 PG GUID 3 Rank 0] Process group watchdog thread terminated with exception: CUDA error: an illegal memory access was encountered
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with TORCH_USE_CUDA_DSA to enable device-side assertions.

Exception raised from c10_cuda_check_implementation at ../c10/cuda/CUDAException.cpp:43 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x96 (0x7fd4b596c446 in /usr/local/lib/python3.10/dist-packages/torch/lib/libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x64 (0x7fd4b59166e4 in /usr/local/lib/python3.10/dist-packages/torch/lib/libc10.so)
frame #2: c10::cuda::c10_cuda_check_implementation(int, char const*, char const*, int, bool) + 0x118 (0x7fd4b5d7fa18 in /usr/local/lib/python3.10/dist-packages/torch/lib/libc10_cuda.so)
frame #3: c10d::ProcessGroupNCCL::WorkNCCL::finishedGPUExecutionInternal() const + 0x56 (0x7fd46b825726 in /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_cuda.so)
frame #4: c10d::ProcessGroupNCCL::WorkNCCL::isCompleted() + 0xa0 (0x7fd46b82a3f0 in /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_cuda.so)
frame #5: c10d::ProcessGroupNCCL::watchdogHandler() + 0x1da (0x7fd46b831b5a in /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_cuda.so)
frame #6: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x14d (0x7fd46b83361d in /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_cuda.so)
frame #7: + 0x145c0 (0x7fd4b68355c0 in /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch.so)
frame #8: + 0x94ac3 (0x7fd4b6de5ac3 in /usr/lib/x86_64-linux-gnu/libc.so.6)
frame #9: clone + 0x44 (0x7fd4b6e76bf4 in /usr/lib/x86_64-linux-gnu/libc.so.6)

terminate called after throwing an instance of 'c10::DistBackendError'
what(): [PG ID 2 PG GUID 3 Rank 0] Process group watchdog thread terminated with exception: CUDA error: an illegal memory access was encountered
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with TORCH_USE_CUDA_DSA to enable device-side assertions.

Exception raised from c10_cuda_check_implementation at ../c10/cuda/CUDAException.cpp:43 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x96 (0x7fd4b596c446 in /usr/local/lib/python3.10/dist-packages/torch/lib/libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x64 (0x7fd4b59166e4 in /usr/local/lib/python3.10/dist-packages/torch/lib/libc10.so)
frame #2: c10::cuda::c10_cuda_check_implementation(int, char const*, char const*, int, bool) + 0x118 (0x7fd4b5d7fa18 in /usr/local/lib/python3.10/dist-packages/torch/lib/libc10_cuda.so)
frame #3: c10d::ProcessGroupNCCL::WorkNCCL::finishedGPUExecutionInternal() const + 0x56 (0x7fd46b825726 in /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_cuda.so)
frame #4: c10d::ProcessGroupNCCL::WorkNCCL::isCompleted() + 0xa0 (0x7fd46b82a3f0 in /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_cuda.so)
frame #5: c10d::ProcessGroupNCCL::watchdogHandler() + 0x1da (0x7fd46b831b5a in /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_cuda.so)
frame #6: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x14d (0x7fd46b83361d in /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_cuda.so)
frame #7: + 0x145c0 (0x7fd4b68355c0 in /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch.so)
frame #8: + 0x94ac3 (0x7fd4b6de5ac3 in /usr/lib/x86_64-linux-gnu/libc.so.6)
frame #9: clone + 0x44 (0x7fd4b6e76bf4 in /usr/lib/x86_64-linux-gnu/libc.so.6)

Exception raised from ncclCommWatchdog at ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1601 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x96 (0x7fd4b596c446 in /usr/local/lib/python3.10/dist-packages/torch/lib/libc10.so)
frame #1: + 0xe4271b (0x7fd46b4a071b in /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_cuda.so)
frame #2: + 0x145c0 (0x7fd4b68355c0 in /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch.so)
frame #3: + 0x94ac3 (0x7fd4b6de5ac3 in /usr/lib/x86_64-linux-gnu/libc.so.6)
frame #4: clone + 0x44 (0x7fd4b6e76bf4 in /usr/lib/x86_64-linux-gnu/libc.so.6)

[ZZXX-bit]

I'm having a similar problem, using vllm for inference online with the error: CUDA error: an illegal memory access was encountered, and there's an error message in the log: invalid character 'I' looking for beginning of value