[Bug]: DeepSeekv4 flash on RTXpro6000 vllm benchmark input 8192 output 1024 encounter a err

[iceCreeam]

Your current environment

The output of python collect_env.py

Your output of `python collect_env.py` here

🐛 Describe the bug

when run the server will crash

vllm bench serve --backend openai --base-url http://localhost:8005 --model /data/nfs_63_200g/DeepSeek-V4-Flash/ --num-prompts 256 --max-concurrency 8 --dataset-name random --served-model-name VLLM-MODEL --input-len 8192 --output-len 1024

change the input-len 8192 to 2048，the server is ok

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[elinacse]

@iceCreeam Can I take it up ?

[elinacse]

@iceCreeam
Can you provide these info.

1. python collect_env.py output (vLLM version/commit, CUDA/driver, torch version)
2. Full server startup log / launch command (attention backend, --max-num-batched-tokens, chunked prefill on/off, TP size)
3. Whether it reproduces with --enforce-eager
4. The full traceback text if you still have the terminal output (the image cuts off above the first line)

Thanks !

[iceCreeam]

@elinacse
python3 collect_env.py
Collecting environment information...

    System Info

==============================
OS : Ubuntu 22.04.4 LTS (x86_64)
GCC version : (Ubuntu 11.4.0-1ubuntu1~22.04.3) 11.4.0
Clang version : Could not collect
CMake version : version 3.22.1
Libc version : glibc-2.35

==============================
PyTorch Info

PyTorch version : 2.11.0+cu129
Is debug build : False
CUDA used to build PyTorch : 12.9
ROCM used to build PyTorch : N/A
XPU used to build PyTorch : N/A

==============================
Python Environment

Python version : 3.10.12 (main, Mar 3 2026, 11:56:32) [GCC 11.4.0] (64-bit runtime)
Python platform : Linux-5.15.0-94-generic-x86_64-with-glibc2.35

==============================
CUDA / GPU Info

Is CUDA available : True
CUDA runtime version : 12.9.41
CUDA_MODULE_LOADING set to :
GPU models and configuration :
GPU 0: NVIDIA RTX PRO 6000 Blackwell Server Edition
GPU 1: NVIDIA RTX PRO 6000 Blackwell Server Edition
GPU 2: NVIDIA RTX PRO 6000 Blackwell Server Edition
GPU 3: NVIDIA RTX PRO 6000 Blackwell Server Edition
GPU 4: NVIDIA RTX PRO 6000 Blackwell Server Edition
GPU 5: NVIDIA RTX PRO 6000 Blackwell Server Edition
GPU 6: NVIDIA RTX PRO 6000 Blackwell Server Edition
GPU 7: NVIDIA RTX PRO 6000 Blackwell Server Edition

Nvidia driver version : 580.76.05
cuDNN version : Could not collect
HIP runtime version : N/A
MIOpen runtime version : N/A
Is XNNPACK available : True

==============================
CPU Info

Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 52 bits physical, 57 bits virtual
Byte Order: Little Endian
CPU(s): 224
On-line CPU(s) list: 0-223
Vendor ID: AuthenticAMD
Model name: AMD EPYC 9555 64-Core Processor
CPU family: 26
Model: 2
Thread(s) per core: 2
Core(s) per socket: 56
Socket(s): 2
Stepping: 1
Frequency boost: enabled
CPU max MHz: 4409.3750
CPU min MHz: 1500.0000
BogoMIPS: 6400.07
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local avx_vnni avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid bus_lock_detect movdiri movdir64b overflow_recov succor smca fsrm avx512_vp2intersect flush_l1d
Virtualization: AMD-V
L1d cache: 5.3 MiB (112 instances)
L1i cache: 3.5 MiB (112 instances)
L2 cache: 112 MiB (112 instances)
L3 cache: 512 MiB (16 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-55,112-167
NUMA node1 CPU(s): 56-111,168-223
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: Not affected
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; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected

==============================
Versions of relevant libraries

[pip3] flashinfer-python==0.6.14
[pip3] numpy==2.2.6
[pip3] nvidia-cublas-cu12==12.9.1.4
[pip3] nvidia-cuda-cccl==13.3.3.4.1
[pip3] nvidia-cuda-cccl-cu12==12.9.27
[pip3] nvidia-cuda-crt==13.3.73
[pip3] nvidia-cuda-cupti-cu12==12.9.79
[pip3] nvidia-cuda-nvcc==13.2.78
[pip3] nvidia-cuda-nvcc-cu12==12.9.86
[pip3] nvidia-cuda-nvrtc==13.3.33
[pip3] nvidia-cuda-nvrtc-cu12==12.9.86
[pip3] nvidia-cuda-runtime==13.3.29
[pip3] nvidia-cuda-runtime-cu12==12.9.79
[pip3] nvidia-cuda-tileiras==13.2.78
[pip3] nvidia-cudnn-cu12==9.17.1.4
[pip3] nvidia-cudnn-frontend==1.26.0
[pip3] nvidia-cufft-cu12==11.4.1.4
[pip3] nvidia-cufile-cu12==1.14.1.1
[pip3] nvidia-curand-cu12==10.3.10.19
[pip3] nvidia-cusolver-cu12==11.7.5.82
[pip3] nvidia-cusparse-cu12==12.5.10.65
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-cutlass-dsl==4.5.2
[pip3] nvidia-cutlass-dsl-libs-base==4.5.2
[pip3] nvidia-cutlass-dsl-libs-cu13==4.5.2
[pip3] nvidia-ml-py==13.610.43
[pip3] nvidia-nccl-cu12==2.30.7
[pip3] nvidia-nccl-cu13==2.30.7
[pip3] nvidia-nvjitlink-cu12==12.9.86
[pip3] nvidia-nvshmem-cu12==3.4.5
[pip3] nvidia-nvtx-cu12==12.9.79
[pip3] nvidia-nvvm==13.2.78
[pip3] pyzmq==27.1.0
[pip3] tokenspeed-triton==3.8.10.post20260709
[pip3] torch==2.11.0+cu129
[pip3] torchaudio==2.11.0+cu129
[pip3] torchcodec==0.11.1+cu129
[pip3] torchvision==0.26.0+cu129
[pip3] transformers==5.13.1
[pip3] triton==3.6.0
[conda] No relevant packages

==============================
vLLM Info

ROCM Version : Could not collect
vLLM Version : 0.25.0
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; XPU: Disabled
GPU Topology:
GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 NIC0 NIC1 NIC2 NIC3 NIC4 NIC5 NIC6 NIC7 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X PIX NODE NODE SYS SYS SYS SYS PIX PIX NODE NODE SYS SYS SYS SYS 0-55,112-167 0 N/A
GPU1 PIX X NODE NODE SYS SYS SYS SYS PIX PIX NODE NODE SYS SYS SYS SYS 0-55,112-167 0 N/A
GPU2 NODE NODE X PIX SYS SYS SYS SYS NODE NODE PIX PIX SYS SYS SYS SYS 0-55,112-167 0 N/A
GPU3 NODE NODE PIX X SYS SYS SYS SYS NODE NODE PIX PIX SYS SYS SYS SYS 0-55,112-167 0 N/A
GPU4 SYS SYS SYS SYS X PIX NODE NODE SYS SYS SYS SYS PIX PIX NODE NODE 56-111,168-223 1 N/A
GPU5 SYS SYS SYS SYS PIX X NODE NODE SYS SYS SYS SYS PIX PIX NODE NODE 56-111,168-223 1 N/A
GPU6 SYS SYS SYS SYS NODE NODE X PIX SYS SYS SYS SYS NODE NODE PIX PIX 56-111,168-223 1 N/A
GPU7 SYS SYS SYS SYS NODE NODE PIX X SYS SYS SYS SYS NODE NODE PIX PIX 56-111,168-223 1 N/A
NIC0 PIX PIX NODE NODE SYS SYS SYS SYS X PIX NODE NODE SYS SYS SYS SYS
NIC1 PIX PIX NODE NODE SYS SYS SYS SYS PIX X NODE NODE SYS SYS SYS SYS
NIC2 NODE NODE PIX PIX SYS SYS SYS SYS NODE NODE X PIX SYS SYS SYS SYS
NIC3 NODE NODE PIX PIX SYS SYS SYS SYS NODE NODE PIX X SYS SYS SYS SYS
NIC4 SYS SYS SYS SYS PIX PIX NODE NODE SYS SYS SYS SYS X PIX NODE NODE
NIC5 SYS SYS SYS SYS PIX PIX NODE NODE SYS SYS SYS SYS PIX X NODE NODE
NIC6 SYS SYS SYS SYS NODE NODE PIX PIX SYS SYS SYS SYS NODE NODE X PIX
NIC7 SYS SYS SYS SYS NODE NODE PIX PIX SYS SYS SYS SYS NODE NODE PIX 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

==============================
Environment Variables

LD_LIBRARY_PATH=/usr/local/nixl/lib/x86_64-linux-gnu:/usr/local/lib/python3.10/dist-packages/torch/lib:/opt/nvshmem-cx7/lib::/usr/local/cuda-12.9/lib64
CUDA_HOME=/usr/local/cuda
CUDA_HOME=/usr/local/cuda
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_root

export VLLM_SM120_REFERENCE_DEEPSEEK_V4_ATTENTION=1
export VLLM_SM120_REFERENCE_TOPK_CHUNK_SIZE=256
export VLLM_SM120_REFERENCE_QUERY_CHUNK_SIZE=128
export VLLM_ENABLE_CUDA_COMPATIBILITY=1
export VLLM_CUDA_COMPATIBILITY_PATH="${CONDA_PREFIX}/cuda-compat"
export VLLM_ATTENTION_BACKEND=FLASHMLA
vllm serve
/data/nfs_63_200g/DeepSeek-V4-Flash/
--trust-remote-code
--enforce-eager
--kv-cache-dtype fp8
--block-size 256
--tensor-parallel-size 8
--tokenizer-mode deepseek_v4
--tool-call-parser deepseek_v4
--enable-auto-tool-choice
--reasoning-parser deepseek_v4
--host 0.0.0.0 --port 8005
--served-model-name VLLM-MODEL
--gpu-memory-utilization 0.9
--max-num-seqs 64
--max-model-len 512000
--compilation-config '{"cudagraph_mode":"FULL_AND_PIECEWISE", "custom_ops":["all"]}'
--async-scheduling
--enable-prefix-caching
--load-format auto
--pipeline-parallel-size 1
--data-parallel-size 1
--max-cudagraph-capture-size 256

when use --enforce-eager also has the same err

(Worker_TP0 pid=105949) WARNING 07-15 08:34:03 [jit_monitor.py:129] Triton kernel JIT compilation during inference: _compute_global_topk_indices_and_lens_kernel. This causes a latency spike; consider extending warmup to cover this shape/config.
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] WorkerProc hit an exception.
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] Traceback (most recent call last):
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/vllm/v1/executor/multiproc_executor.py", line 996, in worker_busy_loop
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] output = func(*args, **kwargs)
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/vllm/v1/worker/worker_base.py", line 351, in execute_model
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] return self.worker.execute_model(scheduler_output)
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 124, in decorate_context
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] return func(*args, **kwargs)
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/vllm/v1/worker/gpu_worker.py", line 1015, in execute_model
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] output = self.model_runner.execute_model(
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 124, in decorate_context
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] return func(*args, **kwargs)
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/vllm/v1/worker/gpu_model_runner.py", line 4353, in execute_model
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] model_output = self._model_forward(
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/vllm/v1/worker/gpu_model_runner.py", line 3807, in _model_forward
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] return self.model(
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1779, in _wrapped_call_impl
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] return self._call_impl(*args, **kwargs)
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1790, in _call_impl
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] return forward_call(*args, **kwargs)
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/vllm/models/deepseek_v4/nvidia/model.py", line 1428, in forward
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] hidden_states = self.model(
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1779, in _wrapped_call_impl
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] return self._call_impl(*args, **kwargs)
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1790, in _call_impl
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] return forward_call(*args, **kwargs)
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/vllm/models/deepseek_v4/nvidia/model.py", line 1088, in forward
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] hidden_states, residual, post_mix, res_mix = layer(
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1779, in _wrapped_call_impl
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] return self._call_impl(*args, **kwargs)
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1790, in _call_impl
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] return forward_call(*args, **kwargs)
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/vllm/models/deepseek_v4/nvidia/model.py", line 914, in forward
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] x = self.attn(positions, x, None)
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1779, in _wrapped_call_impl
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] return self._call_impl(*args, **kwargs)
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1790, in _call_impl
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] return forward_call(*args, **kwargs)
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/vllm/models/deepseek_v4/attention.py", line 357, in forward
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] self.attention_impl(
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/vllm/compilation/breakable_cudagraph.py", line 97, in wrapper
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] return fn(*args, **kwargs)
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/vllm/models/deepseek_v4/attention.py", line 511, in attention_impl
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] self.forward_mqa(q, kv, positions, out)
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/vllm/models/deepseek_v4/nvidia/flashinfer_sparse.py", line 691, in forward_mqa
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] self._forward_sparse_impl(
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/vllm/models/deepseek_v4/nvidia/flashinfer_sparse.py", line 628, in _forward_sparse_impl
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] self._forward_prefill(
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/vllm/models/deepseek_v4/nvidia/flashinfer_sparse.py", line 834, in _forward_prefill
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] compute_global_topk_indices_and_lens(
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/vllm/models/deepseek_v4/common/ops/cache_utils.py", line 443, in compute_global_topk_indices_and_lens
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] _compute_global_topk_indices_and_lens_kernel[(num_tokens,)](
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/triton/runtime/jit.py", line 370, in
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs)
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/triton/runtime/jit.py", line 743, in run
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] launch_metadata = kernel.launch_metadata(grid, stream, *bound_args.values())
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/triton/compiler/compiler.py", line 482, in launch_metadata
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] self._init_handles()
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/triton/compiler/compiler.py", line 465, in _init_handles
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] self.module, self.function, self.n_regs, self.n_spills, self.n_max_threads = driver.active.utils.load_binary(
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] RuntimeError: Triton Error [CUDA]: an illegal memory access was encountered
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] WorkerProc hit an exception.
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] Traceback (most recent call last):
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/vllm/v1/executor/multiproc_executor.py", line 996, in worker_busy_loop
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] output = func(*args, **kwargs)
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/vllm/v1/worker/worker_base.py", line 351, in execute_model
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] return self.worker.execute_model(scheduler_output)
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 124, in decorate_context
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] return func(*args, **kwargs)
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/vllm/v1/worker/gpu_worker.py", line 1015, in execute_model
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] output = self.model_runner.execute_model(
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 124, in decorate_context
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] return func(*args, **kwargs)
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/vllm/v1/worker/gpu_model_runner.py", line 4107, in execute_model
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] with (
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/lib/python3.10/contextlib.py", line 135, in enter
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] return next(self.gen)
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/vllm/v1/worker/gpu_model_runner.py", line 3777, in synchronize_input_prep
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] self.prepare_inputs_event.synchronize()
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] File "/usr/local/lib/python3.10/dist-packages/torch/cuda/streams.py", line 254, in synchronize
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] super().synchronize()
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] torch.AcceleratorError: CUDA error: an illegal memory access was encountered
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] Search for cudaErrorIllegalAddress' in https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html for more information. (Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. (Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] For debugging consider passing CUDA_LAUNCH_BLOCKING=1 (Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] Compile with TORCH_USE_CUDA_DSA` to enable device-side assertions.
(Worker_TP5 pid=105954) ERROR 07-15 08:34:03 [multiproc_executor.py:1004]
(Worker_TP1 pid=105950) ERROR 07-15 08:34:03 [multiproc_executor.py:1004] WorkerProc hit an exception.