你当前的环境

Collecting environment information...

PyTorch version: 2.3.1+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 3.30.1
Libc version: glibc-2.35

Python version: 3.10.14 (main, May  6 2024, 19:42:50) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-6.5.0-41-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA H100 NVL
GPU 1: NVIDIA H100 NVL

Nvidia driver version: 535.183.01
cuDNN version: Probably one of the following:
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn.so.8.5.0
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.5.0
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.5.0
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.5.0
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.5.0
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.5.0
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.5.0
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:                      48 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             48
On-line CPU(s) list:                0-47
Vendor ID:                          AuthenticAMD
Model name:                         AMD EPYC 7443P 24-Core Processor
CPU family:                         25
Model:                              1
Thread(s) per core:                 2
Core(s) per socket:                 24
Socket(s):                          1
Stepping:                           1
Frequency boost:                    enabled
CPU max MHz:                        4035.6440
CPU min MHz:                        1500.0000
BogoMIPS:                           5688.96
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 bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin brs arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca fsrm
Virtualisation:                     AMD-V
L1d cache:                          768 KiB (24 instances)
L1i cache:                          768 KiB (24 instances)
L2 cache:                           12 MiB (24 instances)
L3 cache:                           128 MiB (4 instances)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-47
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: Mitigation; Safe RET
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl
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; BHI Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] torch==2.3.1
[pip3] torchvision==0.18.1
[pip3] transformers==4.43.3
[pip3] triton==2.3.1
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] nvidia-nccl-cu12          2.20.5                   pypi_0    pypi
[conda] torch                     2.3.1                    pypi_0    pypi
[conda] torchvision               0.18.1                   pypi_0    pypi
[conda] transformers              4.43.3                   pypi_0    pypi
[conda] triton                    2.3.1                    pypi_0    pypi
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.5.3.post1
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0    GPU1    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      NV12    0-47    0               N/A
GPU1    NV12     X      0-47    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```

### 🐛 Describe the bug

[rank0]: Traceback (most recent call last):
[rank0]:   File "/home/zhhc/proj/vllm_inference/examples/InternVL2-8B/q1.py", line 72, in <module>
[rank0]:     main(args.input_file, args.image_directory, args.output_file, args.start_idx, args.end_idx)
[rank0]:   File "/home/zhhc/proj/vllm_inference/examples/InternVL2-8B/q1.py", line 9, in main
[rank0]:     llm = LLM(model="OpenGVLab/InternVL2-4B", trust_remote_code=True, max_num_seqs=5) #max_num_seqs
[rank0]:   File "/home/zhhc/proj/open-source-hf/CT-Release_v1/vllm/vllm/vllm/entrypoints/llm.py", line 155, in __init__
[rank0]:     self.llm_engine = LLMEngine.from_engine_args(
[rank0]:   File "/home/zhhc/proj/open-source-hf/CT-Release_v1/vllm/vllm/vllm/engine/llm_engine.py", line 446, in from_engine_args
[rank0]:     engine = cls(
[rank0]:   File "/home/zhhc/proj/open-source-hf/CT-Release_v1/vllm/vllm/vllm/engine/llm_engine.py", line 264, in __init__
[rank0]:     self._initialize_kv_caches()
[rank0]:   File "/home/zhhc/proj/open-source-hf/CT-Release_v1/vllm/vllm/vllm/engine/llm_engine.py", line 363, in _initialize_kv_caches
[rank0]:     self.model_executor.determine_num_available_blocks())
[rank0]:   File "/home/zhhc/proj/open-source-hf/CT-Release_v1/vllm/vllm/vllm/executor/gpu_executor.py", line 94, in determine_num_available_blocks
[rank0]:     return self.driver_worker.determine_num_available_blocks()
[rank0]:   File "/home/zhhc/anaconda3/envs/vllm/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
[rank0]:     return func(*args, **kwargs)
[rank0]:   File "/home/zhhc/proj/open-source-hf/CT-Release_v1/vllm/vllm/vllm/worker/worker.py", line 179, in determine_num_available_blocks
[rank0]:     self.model_runner.profile_run()
[rank0]:   File "/home/zhhc/anaconda3/envs/vllm/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
[rank0]:     return func(*args, **kwargs)
[rank0]:   File "/home/zhhc/proj/open-source-hf/CT-Release_v1/vllm/vllm/vllm/worker/model_runner.py", line 934, in profile_run
[rank0]:     self.execute_model(model_input, kv_caches, intermediate_tensors)
[rank0]:   File "/home/zhhc/anaconda3/envs/vllm/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
[rank0]:     return func(*args, **kwargs)
[rank0]:   File "/home/zhhc/proj/open-source-hf/CT-Release_v1/vllm/vllm/vllm/worker/model_runner.py", line 1357, in execute_model
[rank0]:     hidden_or_intermediate_states = model_executable(
[rank0]:   File "/home/zhhc/anaconda3/envs/vllm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[rank0]:     return self._call_impl(*args, **kwargs)
[rank0]:   File "/home/zhhc/anaconda3/envs/vllm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[rank0]:     return forward_call(*args, **kwargs)
[rank0]:   File "/home/zhhc/proj/open-source-hf/CT-Release_v1/vllm/vllm/vllm/model_executor/models/internvl.py", line 389, in forward
[rank0]:     vit_embeds = self.extract_feature(image_input["data"])
[rank0]:   File "/home/zhhc/proj/open-source-hf/CT-Release_v1/vllm/vllm/vllm/model_executor/models/internvl.py", line 316, in extract_feature
[rank0]:     vit_embeds = self.vision_model(pixel_values=pixel_values)
[rank0]:   File "/home/zhhc/anaconda3/envs/vllm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[rank0]:     return self._call_impl(*args, **kwargs)
[rank0]:   File "/home/zhhc/anaconda3/envs/vllm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[rank0]:     return forward_call(*args, **kwargs)
[rank0]:   File "/home/zhhc/proj/open-source-hf/CT-Release_v1/vllm/vllm/vllm/model_executor/models/intern_vit.py", line 263, in forward
[rank0]:     hidden_states = self.embeddings(pixel_values)
[rank0]:   File "/home/zhhc/anaconda3/envs/vllm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[rank0]:     return self._call_impl(*args, **kwargs)
[rank0]:   File "/home/zhhc/anaconda3/envs/vllm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[rank0]:     return forward_call(*args, **kwargs)
[rank0]:   File "/home/zhhc/proj/open-source-hf/CT-Release_v1/vllm/vllm/vllm/model_executor/models/intern_vit.py", line 63, in forward
[rank0]:     patch_embeds = self.patch_embedding(pixel_values.to(
[rank0]:   File "/home/zhhc/anaconda3/envs/vllm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
[rank0]:     return self._call_impl(*args, **kwargs)
[rank0]:   File "/home/zhhc/anaconda3/envs/vllm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
[rank0]:     return forward_call(*args, **kwargs)
[rank0]:   File "/home/zhhc/anaconda3/envs/vllm/lib/python3.10/site-packages/torch/nn/modules/conv.py", line 460, in forward
[rank0]:     return self._conv_forward(input, self.weight, self.bias)
[rank0]:   File "/home/zhhc/anaconda3/envs/vllm/lib/python3.10/site-packages/torch/nn/modules/conv.py", line 456, in _conv_forward
[rank0]:     return F.conv2d(input, weight, bias, self.stride,
[rank0]: RuntimeError: GET was unable to find an engine to execute this computation