[Bug]: Encountered issues when deploying Llama-3.2-11B-Vision-Instruct for online inference.

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 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: 3.9.20 (main, Oct 3 2024, 07:27:41) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-5.15.0-119-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: 12.5.40 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA H100 PCIe GPU 1: NVIDIA H100 PCIe Nvidia driver version: 550.54.14 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: 52 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) Gold 6430 CPU family: 6 Model: 143 Thread(s) per core: 2 Core(s) per socket: 32 Socket(s): 2 Stepping: 8 Frequency boost: enabled CPU max MHz: 2101.0000 CPU min MHz: 800.0000 BogoMIPS: 4200.00 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 cpuid aperfmperf tsc_known_freq 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 cpuid_fault epb cat_l3 cat_l2 cdp_l3 invpcid_single intel_ppin cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities Virtualization: VT-x L1d cache: 3 MiB (64 instances) L1i cache: 2 MiB (64 instances) L2 cache: 128 MiB (64 instances) L3 cache: 120 MiB (2 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0-31,64-95 NUMA node1 CPU(s): 32-63,96-127 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 Reg file data sampling: 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; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Versions of relevant libraries: [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==12.4.127 [pip3] nvidia-nvtx-cu12==12.1.105 [pip3] pyzmq==26.2.0 [pip3] torch==2.4.0 [pip3] torchvision==0.19.0 [pip3] transformers==4.45.2 [pip3] triton==3.0.0 [conda] numpy 1.26.4 pypi_0 pypi [conda] nvidia-cublas-cu12 12.1.3.1 pypi_0 pypi [conda] nvidia-cuda-cupti-cu12 12.1.105 pypi_0 pypi [conda] nvidia-cuda-nvrtc-cu12 12.1.105 pypi_0 pypi [conda] nvidia-cuda-runtime-cu12 12.1.105 pypi_0 pypi [conda] nvidia-cudnn-cu12 9.1.0.70 pypi_0 pypi [conda] nvidia-cufft-cu12 11.0.2.54 pypi_0 pypi [conda] nvidia-curand-cu12 10.3.2.106 pypi_0 pypi [conda] nvidia-cusolver-cu12 11.4.5.107 pypi_0 pypi [conda] nvidia-cusparse-cu12 12.1.0.106 pypi_0 pypi [conda] nvidia-ml-py 12.560.30 pypi_0 pypi [conda] nvidia-nccl-cu12 2.20.5 pypi_0 pypi [conda] nvidia-nvjitlink-cu12 12.4.127 pypi_0 pypi [conda] nvidia-nvtx-cu12 12.1.105 pypi_0 pypi [conda] pyzmq 26.2.0 pypi_0 pypi [conda] torch 2.4.0 pypi_0 pypi [conda] torchvision 0.19.0 pypi_0 pypi [conda] transformers 4.45.2 pypi_0 pypi [conda] triton 3.0.0 pypi_0 pypi ROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: 0.6.3.post1 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 NIC0 NIC1 NIC2 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X SYS SYS SYS SYS 32-63,96-127 1 N/A GPU1 SYS X SYS SYS SYS 32-63,96-127 1 N/A NIC0 SYS SYS X PIX SYS NIC1 SYS SYS PIX X SYS NIC2 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 NVIDIA_VISIBLE_DEVICES=all CUBLAS_VERSION=12.5.2.13 NVIDIA_REQUIRE_CUDA=cuda>=9.0 CUDA_CACHE_DISABLE=1 TORCH_CUDA_ARCH_LIST=5.2 6.0 6.1 7.0 7.2 7.5 8.0 8.6 8.7 9.0+PTX NCCL_VERSION=2.21.5 NVIDIA_DRIVER_CAPABILITIES=compute,utility,video NVIDIA_PRODUCT_NAME=PyTorch CUDA_VERSION=12.5.0.023 PYTORCH_VERSION=2.4.0a0+f70bd71 PYTORCH_BUILD_NUMBER=0 CUDNN_VERSION=9.1.0.70+cuda12.4 PYTORCH_HOME=/opt/pytorch/pytorch LD_LIBRARY_PATH=/root/miniconda3/envs/kvsort/lib/python3.9/site-packages/cv2/../../lib64:/usr/local/lib/python3.10/dist-packages/torch/lib:/usr/local/lib/python3.10/dist-packages/torch_tensorrt/lib:/usr/local/cuda/compat/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64 NVIDIA_BUILD_ID=96418707 CUDA_DRIVER_VERSION=555.42.02 PYTORCH_BUILD_VERSION=2.4.0a0+f70bd71 CUDA_HOME=/usr/local/cuda CUDA_HOME=/usr/local/cuda CUDA_MODULE_LOADING=LAZY NVIDIA_REQUIRE_JETPACK_HOST_MOUNTS= NVIDIA_PYTORCH_VERSION=24.06 TORCH_ALLOW_TF32_CUBLAS_OVERRIDE=1

Model Input Dumps

vllm serve /root/lzd/Llama-3.2-11B-Vision-Instruct --dtype auto --api-key token-abc123 --max-num-seqs 64 --max-model-len 8000 --gpu-memory-utilization 0.9

🐛 Describe the bug

seem like the model Llamma-vision is unable to serving online. I try to use different methods employing this model. But meet the same bug output. The output details is below:

Traceback (most recent call last):
  File "/root/miniconda3/envs/kvsort/lib/python3.9/site-packages/vllm/worker/model_runner.py", line 1795, in capture
    output_hidden_or_intermediate_states = self.model(
  File "/root/miniconda3/envs/kvsort/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/root/miniconda3/envs/kvsort/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
    return forward_call(*args, **kwargs)
  File "/root/miniconda3/envs/kvsort/lib/python3.9/site-packages/vllm/model_executor/models/mllama.py", line 1233, in forward
    **skip_cross_attention = max(attn_metadata.encoder_seq_lens) == 0**
RuntimeError: CUDA error: operation not permitted when stream is capturing
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.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/root/miniconda3/envs/kvsort/lib/python3.9/multiprocessing/process.py", line 315, in _bootstrap
    self.run()
  File "/root/miniconda3/envs/kvsort/lib/python3.9/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/root/miniconda3/envs/kvsort/lib/python3.9/site-packages/vllm/engine/multiprocessing/engine.py", line 390, in run_mp_engine
    engine = MQLLMEngine.from_engine_args(engine_args=engine_args,
  File "/root/miniconda3/envs/kvsort/lib/python3.9/site-packages/vllm/engine/multiprocessing/engine.py", line 139, in from_engine_args
    return cls(
  File "/root/miniconda3/envs/kvsort/lib/python3.9/site-packages/vllm/engine/multiprocessing/engine.py", line 78, in __init__
    self.engine = LLMEngine(*args, **kwargs)
  File "/root/miniconda3/envs/kvsort/lib/python3.9/site-packages/vllm/engine/llm_engine.py", line 348, in __init__
    self._initialize_kv_caches()
  File "/root/miniconda3/envs/kvsort/lib/python3.9/site-packages/vllm/engine/llm_engine.py", line 496, in _initialize_kv_caches
    self.model_executor.initialize_cache(num_gpu_blocks, num_cpu_blocks)
  File "/root/miniconda3/envs/kvsort/lib/python3.9/site-packages/vllm/executor/gpu_executor.py", line 129, in initialize_cache
    self.driver_worker.initialize_cache(num_gpu_blocks, num_cpu_blocks)
  File "/root/miniconda3/envs/kvsort/lib/python3.9/site-packages/vllm/worker/worker.py", line 271, in initialize_cache
    self._warm_up_model()
  File "/root/miniconda3/envs/kvsort/lib/python3.9/site-packages/vllm/worker/worker.py", line 287, in _warm_up_model
    self.model_runner.capture_model(self.gpu_cache)
  File "/root/miniconda3/envs/kvsort/lib/python3.9/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
    return func(*args, **kwargs)
  File "/root/miniconda3/envs/kvsort/lib/python3.9/site-packages/vllm/worker/model_runner.py", line 1515, in capture_model
    graph_runner.capture(**capture_inputs)
  File "/root/miniconda3/envs/kvsort/lib/python3.9/site-packages/vllm/worker/model_runner.py", line 1818, in capture
    gc.collect()
  File "/root/miniconda3/envs/kvsort/lib/python3.9/site-packages/torch/cuda/graphs.py", line 185, in __exit__
    self.cuda_graph.capture_end()
  File "/root/miniconda3/envs/kvsort/lib/python3.9/site-packages/torch/cuda/graphs.py", line 83, in capture_end
    super().capture_end()
RuntimeError: CUDA error: operation failed due to a previous error during capture
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.

Traceback (most recent call last):
  File "/root/miniconda3/envs/kvsort/bin/vllm", line 8, in <module>
    sys.exit(main())
  File "/root/miniconda3/envs/kvsort/lib/python3.9/site-packages/vllm/scripts.py", line 195, in main
    args.dispatch_function(args)
  File "/root/miniconda3/envs/kvsort/lib/python3.9/site-packages/vllm/scripts.py", line 41, in serve
    uvloop.run(run_server(args))
  File "/root/miniconda3/envs/kvsort/lib/python3.9/site-packages/uvloop/__init__.py", line 82, in run
    return loop.run_until_complete(wrapper())
  File "uvloop/loop.pyx", line 1518, in uvloop.loop.Loop.run_until_complete
  File "/root/miniconda3/envs/kvsort/lib/python3.9/site-packages/uvloop/__init__.py", line 61, in wrapper
    return await main
  File "/root/miniconda3/envs/kvsort/lib/python3.9/site-packages/vllm/entrypoints/openai/api_server.py", line 552, in run_server
    async with build_async_engine_client(args) as engine_client:
  File "/root/miniconda3/envs/kvsort/lib/python3.9/contextlib.py", line 181, in __aenter__
    return await self.gen.__anext__()
  File "/root/miniconda3/envs/kvsort/lib/python3.9/site-packages/vllm/entrypoints/openai/api_server.py", line 107, in build_async_engine_client
    async with build_async_engine_client_from_engine_args(
  File "/root/miniconda3/envs/kvsort/lib/python3.9/contextlib.py", line 181, in __aenter__
    return await self.gen.__anext__()
  File "/root/miniconda3/envs/kvsort/lib/python3.9/site-packages/vllm/entrypoints/openai/api_server.py", line 194, in build_async_engine_client_from_engine_args
    raise RuntimeError(
RuntimeError: Engine process failed to start

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