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Qwen2_5OmniProcessor.__init__() got multiple values for argument 'image_processor' #38898

@WenmuZhou

Description

@WenmuZhou

System Info

Your current environment

INFO 06-19 02:57:16 [__init__.py:244] Automatically detected platform cuda.
Collecting environment information...
==============================
        System Info
==============================
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 3.30.2
Libc version                 : glibc-2.35

==============================
       PyTorch Info
==============================
PyTorch version              : 2.7.0+cu126
Is debug build               : False
CUDA used to build PyTorch   : 12.6
ROCM used to build PyTorch   : N/A

==============================
      Python Environment
==============================
Python version               : 3.10.12 (main, Jul 29 2024, 16:56:48) [GCC 11.4.0] (64-bit runtime)
Python platform              : Linux-5.4.0-167-generic-x86_64-with-glibc2.35

==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : 12.6.20
CUDA_MODULE_LOADING set to   : LAZY
GPU models and configuration : GPU 0: NVIDIA A100-SXM4-40GB
Nvidia driver version        : 535.216.03
cuDNN version                : Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.3.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.3.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.3.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.3.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.3.0
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.3.0
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.3.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.3.0
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:                      43 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             128
On-line CPU(s) list:                0-127
Vendor ID:                          AuthenticAMD
Model name:                         AMD EPYC 7763 64-Core Processor
CPU family:                         25
Model:                              1
Thread(s) per core:                 1
Core(s) per socket:                 64
Socket(s):                          2
Stepping:                           1
Frequency boost:                    enabled
CPU max MHz:                        2450.0000
CPU min MHz:                        1500.0000
BogoMIPS:                           4890.72
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 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 wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca sme sev sev_es
Virtualization:                     AMD-V
L1d cache:                          4 MiB (128 instances)
L1i cache:                          4 MiB (128 instances)
L2 cache:                           64 MiB (128 instances)
L3 cache:                           512 MiB (16 instances)
NUMA node(s):                       4
NUMA node0 CPU(s):                  0-31
NUMA node1 CPU(s):                  32-63
NUMA node2 CPU(s):                  64-95
NUMA node3 CPU(s):                  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 Retbleed:             Not affected
Vulnerability Spec store bypass:    Vulnerable
Vulnerability Spectre v1:           Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers
Vulnerability Spectre v2:           Vulnerable, IBPB: disabled, STIBP: disabled, PBRSB-eIBRS: Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

==============================
Versions of relevant libraries
==============================
[pip3] flake8==7.1.1
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.6.4.1
[pip3] nvidia-cuda-cupti-cu12==12.6.80
[pip3] nvidia-cuda-nvrtc-cu12==12.6.77
[pip3] nvidia-cuda-runtime-cu12==12.6.77
[pip3] nvidia-cudnn-cu12==9.5.1.17
[pip3] nvidia-cudnn-frontend==1.5.2
[pip3] nvidia-cufft-cu12==11.3.0.4
[pip3] nvidia-cufile-cu12==1.11.1.6
[pip3] nvidia-curand-cu12==10.3.7.77
[pip3] nvidia-cusolver-cu12==11.7.1.2
[pip3] nvidia-cusparse-cu12==12.5.4.2
[pip3] nvidia-cusparselt-cu12==0.6.3
[pip3] nvidia-dali-cuda120==1.40.0
[pip3] nvidia-ml-py==12.575.51
[pip3] nvidia-ml-py3==7.352.0
[pip3] nvidia-modelopt==0.15.0
[pip3] nvidia-nccl-cu12==2.26.2
[pip3] nvidia-nvimgcodec-cu12==0.3.0.5
[pip3] nvidia-nvjitlink-cu12==12.6.85
[pip3] nvidia-nvtx-cu12==12.6.77
[pip3] nvidia-pyindex==1.0.9
[pip3] nvidia-smi==0.1.3
[pip3] onnx==1.16.1
[pip3] onnxruntime-gpu==1.17.1
[pip3] onnxsim==0.4.36
[pip3] open-clip-torch==2.24.0
[pip3] optree==0.13.0
[pip3] pynvml==12.0.0
[pip3] pytorch-lightning==2.2.4
[pip3] pytorch-triton==3.0.0+dedb7bdf3
[pip3] pyzmq==26.2.0
[pip3] sentence-transformers==4.1.0
[pip3] torch==2.7.0
[pip3] torchaudio==2.7.0
[pip3] torchmetrics==1.4.0.post0
[pip3] torchpack==0.3.1
[pip3] torchprofile==0.0.4
[pip3] torchvision==0.22.0
[pip3] transformers==f7b21822e32fba8bd92a939db7f352d1623f09e4
[pip3] transformers-stream-generator==0.0.5
[pip3] triton==3.3.0
[conda] Could not collect

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
Neuron SDK Version           : N/A
vLLM Version                 : 0.9.1
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    NIC0    NIC1    NIC2    NIC3    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      PXB     SYS     SYS     SYS     32-63   1               N/A
NIC0    PXB      X      SYS     SYS     SYS
NIC1    SYS     SYS      X      SYS     SYS
NIC2    SYS     SYS     SYS      X      PIX
NIC3    SYS     SYS     SYS     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

==============================
     Environment Variables
==============================
NVIDIA_VISIBLE_DEVICES=GPU-e77165e1-0491-57a4-d10c-852cbce0cf61
CUBLAS_VERSION=12.6.0.22
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=2.22.3
NVIDIA_DRIVER_CAPABILITIES=video,compute,utility,graphics
NVIDIA_PRODUCT_NAME=PyTorch
CUDA_VERSION=12.6.0.022
PYTORCH_VERSION=2.5.0a0+872d972
PYTORCH_BUILD_NUMBER=0
CUDNN_FRONTEND_VERSION=1.5.2
CUDNN_VERSION=9.3.0.75
PYTORCH_HOME=/opt/pytorch/pytorch
LD_LIBRARY_PATH=/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=107063150
CUDA_DRIVER_VERSION=560.35.03
PYTORCH_BUILD_VERSION=2.5.0a0+872d972
CUDA_HOME=/usr/local/cuda
CUDA_HOME=/usr/local/cuda
CUDA_MODULE_LOADING=LAZY
NVIDIA_REQUIRE_JETPACK_HOST_MOUNTS=
NVIDIA_PYTORCH_VERSION=24.08
TORCH_ALLOW_TF32_CUBLAS_OVERRIDE=1
NCCL_CUMEM_ENABLE=0
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1

Who can help?

No response

Information

  • The official example scripts
  • My own modified scripts

Tasks

  • An officially supported task in the examples folder (such as GLUE/SQuAD, ...)
  • My own task or dataset (give details below)

Reproduction

🐛 Describe the bug

When I use vllm to infer Qwen/Qwen2.5-Omni-3B model , I meet an init error

from vllm import LLM, SamplingParams
llm = LLM(model="Qwen/Qwen2.5-Omni-3B")

outputs = llm.generate('你是谁', SamplingParams(temperature=0.8, top_p=0.95))
print(outputs)

The complete log is as follows

INFO 06-19 02:48:20 [__init__.py:244] Automatically detected platform cuda.
Unrecognized keys in `rope_scaling` for 'rope_type'='default': {'mrope_section'}
INFO 06-19 02:49:07 [config.py:823] This model supports multiple tasks: {'classify', 'reward', 'score', 'generate', 'embed'}. Defaulting to 'generate'.
INFO 06-19 02:49:08 [config.py:2195] Chunked prefill is enabled with max_num_batched_tokens=8192.
INFO 06-19 02:49:16 [core.py:455] Waiting for init message from front-end.
INFO 06-19 02:49:16 [core.py:70] Initializing a V1 LLM engine (v0.9.1) with config: model='Qwen/Qwen2.5-Omni-3B', speculative_config=None, tokenizer='Qwen/Qwen2.5-Omni-3B', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config={}, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto,  device_config=cuda, decoding_config=DecodingConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_backend=''), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None), seed=0, served_model_name=Qwen/Qwen2.5-Omni-3B, num_scheduler_steps=1, multi_step_stream_outputs=True, enable_prefix_caching=True, chunked_prefill_enabled=True, use_async_output_proc=True, pooler_config=None, compilation_config={"level":3,"debug_dump_path":"","cache_dir":"","backend":"","custom_ops":["none"],"splitting_ops":["vllm.unified_attention","vllm.unified_attention_with_output"],"use_inductor":true,"compile_sizes":[],"inductor_compile_config":{"enable_auto_functionalized_v2":false},"inductor_passes":{},"use_cudagraph":true,"cudagraph_num_of_warmups":1,"cudagraph_capture_sizes":[512,504,496,488,480,472,464,456,448,440,432,424,416,408,400,392,384,376,368,360,352,344,336,328,320,312,304,296,288,280,272,264,256,248,240,232,224,216,208,200,192,184,176,168,160,152,144,136,128,120,112,104,96,88,80,72,64,56,48,40,32,24,16,8,4,2,1],"cudagraph_copy_inputs":false,"full_cuda_graph":false,"max_capture_size":512,"local_cache_dir":null}
WARNING 06-19 02:49:17 [utils.py:2737] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in <vllm.v1.worker.gpu_worker.Worker object at 0x7f531dc0a050>
INFO 06-19 02:49:18 [parallel_state.py:1065] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, TP rank 0, EP rank 0
Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
You have video processor config saved in `preprocessor.json` file which is deprecated. Video processor configs should be saved in their own `video_preprocessor.json` file. You can rename the file or load and save the processor back which renames it automatically. Loading from `preprocessor.json` will be removed in v5.0.
ERROR 06-19 02:49:35 [core.py:515] EngineCore failed to start.
ERROR 06-19 02:49:35 [core.py:515] Traceback (most recent call last):
ERROR 06-19 02:49:35 [core.py:515]   File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/v1/engine/core.py", line 506, in run_engine_core
ERROR 06-19 02:49:35 [core.py:515]     engine_core = EngineCoreProc(*args, **kwargs)
ERROR 06-19 02:49:35 [core.py:515]   File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/v1/engine/core.py", line 390, in __init__
ERROR 06-19 02:49:35 [core.py:515]     super().__init__(vllm_config, executor_class, log_stats,
ERROR 06-19 02:49:35 [core.py:515]   File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/v1/engine/core.py", line 76, in __init__
ERROR 06-19 02:49:35 [core.py:515]     self.model_executor = executor_class(vllm_config)
ERROR 06-19 02:49:35 [core.py:515]   File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/executor/executor_base.py", line 53, in __init__
ERROR 06-19 02:49:35 [core.py:515]     self._init_executor()
ERROR 06-19 02:49:35 [core.py:515]   File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/executor/uniproc_executor.py", line 47, in _init_executor
ERROR 06-19 02:49:35 [core.py:515]     self.collective_rpc("init_device")
ERROR 06-19 02:49:35 [core.py:515]   File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/executor/uniproc_executor.py", line 57, in collective_rpc
ERROR 06-19 02:49:35 [core.py:515]     answer = run_method(self.driver_worker, method, args, kwargs)
ERROR 06-19 02:49:35 [core.py:515]   File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/utils.py", line 2671, in run_method
ERROR 06-19 02:49:35 [core.py:515]     return func(*args, **kwargs)
ERROR 06-19 02:49:35 [core.py:515]   File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/worker/worker_base.py", line 606, in init_device
ERROR 06-19 02:49:35 [core.py:515]     self.worker.init_device()  # type: ignore
ERROR 06-19 02:49:35 [core.py:515]   File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/v1/worker/gpu_worker.py", line 160, in init_device
ERROR 06-19 02:49:35 [core.py:515]     self.model_runner: GPUModelRunner = GPUModelRunner(
ERROR 06-19 02:49:35 [core.py:515]   File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/v1/worker/gpu_model_runner.py", line 129, in __init__
ERROR 06-19 02:49:35 [core.py:515]     encoder_compute_budget, encoder_cache_size = compute_encoder_budget(
ERROR 06-19 02:49:35 [core.py:515]   File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/v1/core/encoder_cache_manager.py", line 95, in compute_encoder_budget
ERROR 06-19 02:49:35 [core.py:515]     ) = _compute_encoder_budget_multimodal(
ERROR 06-19 02:49:35 [core.py:515]   File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/v1/core/encoder_cache_manager.py", line 125, in _compute_encoder_budget_multimodal
ERROR 06-19 02:49:35 [core.py:515]     .get_max_tokens_per_item_by_nonzero_modality(model_config)
ERROR 06-19 02:49:35 [core.py:515]   File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/multimodal/registry.py", line 153, in get_max_tokens_per_item_by_nonzero_modality
ERROR 06-19 02:49:35 [core.py:515]     mm_limits = self.get_mm_limits_per_prompt(model_config)
ERROR 06-19 02:49:35 [core.py:515]   File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/multimodal/registry.py", line 206, in get_mm_limits_per_prompt
ERROR 06-19 02:49:35 [core.py:515]     processor = self.create_processor(model_config, disable_cache=False)
ERROR 06-19 02:49:35 [core.py:515]   File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/multimodal/registry.py", line 281, in create_processor
ERROR 06-19 02:49:35 [core.py:515]     return factories.build_processor(ctx, cache=cache)
ERROR 06-19 02:49:35 [core.py:515]   File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/multimodal/registry.py", line 88, in build_processor
ERROR 06-19 02:49:35 [core.py:515]     return self.processor(info, dummy_inputs_builder, cache=cache)
ERROR 06-19 02:49:35 [core.py:515]   File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/multimodal/processing.py", line 1131, in __init__
ERROR 06-19 02:49:35 [core.py:515]     self.data_parser = self._get_data_parser()
ERROR 06-19 02:49:35 [core.py:515]   File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/model_executor/models/qwen2_5_omni_thinker.py", line 238, in _get_data_parser
ERROR 06-19 02:49:35 [core.py:515]     feature_extractor = self.info.get_feature_extractor()
ERROR 06-19 02:49:35 [core.py:515]   File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/model_executor/models/qwen2_5_omni_thinker.py", line 170, in get_feature_extractor
ERROR 06-19 02:49:35 [core.py:515]     hf_processor = self.get_hf_processor(sampling_rate=sampling_rate)
ERROR 06-19 02:49:35 [core.py:515]   File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/model_executor/models/qwen2_5_omni_thinker.py", line 147, in get_hf_processor
ERROR 06-19 02:49:35 [core.py:515]     processor = self.ctx.get_hf_processor(
ERROR 06-19 02:49:35 [core.py:515]   File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/inputs/registry.py", line 131, in get_hf_processor
ERROR 06-19 02:49:35 [core.py:515]     return super().get_hf_processor(
ERROR 06-19 02:49:35 [core.py:515]   File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/inputs/registry.py", line 94, in get_hf_processor
ERROR 06-19 02:49:35 [core.py:515]     return cached_processor_from_config(
ERROR 06-19 02:49:35 [core.py:515]   File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/transformers_utils/processor.py", line 110, in cached_processor_from_config
ERROR 06-19 02:49:35 [core.py:515]     return cached_get_processor(
ERROR 06-19 02:49:35 [core.py:515]   File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/transformers_utils/processor.py", line 72, in get_processor
ERROR 06-19 02:49:35 [core.py:515]     processor = processor_factory.from_pretrained(
ERROR 06-19 02:49:35 [core.py:515]   File "/home/jun.zhou10/.local/lib/python3.10/site-packages/transformers/processing_utils.py", line 1213, in from_pretrained
ERROR 06-19 02:49:35 [core.py:515]     return cls.from_args_and_dict(args, processor_dict, **kwargs)
ERROR 06-19 02:49:35 [core.py:515]   File "/home/jun.zhou10/.local/lib/python3.10/site-packages/transformers/processing_utils.py", line 1014, in from_args_and_dict
ERROR 06-19 02:49:35 [core.py:515]     processor = cls(*args, **valid_kwargs)
ERROR 06-19 02:49:35 [core.py:515] TypeError: Qwen2_5OmniProcessor.__init__() got multiple values for argument 'image_processor'
Process EngineCore_0:
Traceback (most recent call last):
  File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
    self.run()
  File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/v1/engine/core.py", line 519, in run_engine_core
    raise e
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/v1/engine/core.py", line 506, in run_engine_core
    engine_core = EngineCoreProc(*args, **kwargs)
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/v1/engine/core.py", line 390, in __init__
    super().__init__(vllm_config, executor_class, log_stats,
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/v1/engine/core.py", line 76, in __init__
    self.model_executor = executor_class(vllm_config)
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/executor/executor_base.py", line 53, in __init__
    self._init_executor()
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/executor/uniproc_executor.py", line 47, in _init_executor
    self.collective_rpc("init_device")
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/executor/uniproc_executor.py", line 57, in collective_rpc
    answer = run_method(self.driver_worker, method, args, kwargs)
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/utils.py", line 2671, in run_method
    return func(*args, **kwargs)
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/worker/worker_base.py", line 606, in init_device
    self.worker.init_device()  # type: ignore
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/v1/worker/gpu_worker.py", line 160, in init_device
    self.model_runner: GPUModelRunner = GPUModelRunner(
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/v1/worker/gpu_model_runner.py", line 129, in __init__
    encoder_compute_budget, encoder_cache_size = compute_encoder_budget(
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/v1/core/encoder_cache_manager.py", line 95, in compute_encoder_budget
    ) = _compute_encoder_budget_multimodal(
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/v1/core/encoder_cache_manager.py", line 125, in _compute_encoder_budget_multimodal
    .get_max_tokens_per_item_by_nonzero_modality(model_config)
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/multimodal/registry.py", line 153, in get_max_tokens_per_item_by_nonzero_modality
    mm_limits = self.get_mm_limits_per_prompt(model_config)
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/multimodal/registry.py", line 206, in get_mm_limits_per_prompt
    processor = self.create_processor(model_config, disable_cache=False)
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/multimodal/registry.py", line 281, in create_processor
    return factories.build_processor(ctx, cache=cache)
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/multimodal/registry.py", line 88, in build_processor
    return self.processor(info, dummy_inputs_builder, cache=cache)
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/multimodal/processing.py", line 1131, in __init__
    self.data_parser = self._get_data_parser()
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/model_executor/models/qwen2_5_omni_thinker.py", line 238, in _get_data_parser
    feature_extractor = self.info.get_feature_extractor()
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/model_executor/models/qwen2_5_omni_thinker.py", line 170, in get_feature_extractor
    hf_processor = self.get_hf_processor(sampling_rate=sampling_rate)
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/model_executor/models/qwen2_5_omni_thinker.py", line 147, in get_hf_processor
    processor = self.ctx.get_hf_processor(
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/inputs/registry.py", line 131, in get_hf_processor
    return super().get_hf_processor(
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/inputs/registry.py", line 94, in get_hf_processor
    return cached_processor_from_config(
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/transformers_utils/processor.py", line 110, in cached_processor_from_config
    return cached_get_processor(
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/transformers_utils/processor.py", line 72, in get_processor
    processor = processor_factory.from_pretrained(
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/transformers/processing_utils.py", line 1213, in from_pretrained
    return cls.from_args_and_dict(args, processor_dict, **kwargs)
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/transformers/processing_utils.py", line 1014, in from_args_and_dict
    processor = cls(*args, **valid_kwargs)
TypeError: Qwen2_5OmniProcessor.__init__() got multiple values for argument 'image_processor'
Traceback (most recent call last):
  File "/dc-hl/jun.zhou10/swift/eval/infer_vllm.py", line 65, in <module>
    llm = LLM(model="Qwen/Qwen2.5-Omni-3B")
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/entrypoints/llm.py", line 243, in __init__
    self.llm_engine = LLMEngine.from_engine_args(
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/engine/llm_engine.py", line 501, in from_engine_args
    return engine_cls.from_vllm_config(
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/v1/engine/llm_engine.py", line 124, in from_vllm_config
    return cls(vllm_config=vllm_config,
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/v1/engine/llm_engine.py", line 101, in __init__
    self.engine_core = EngineCoreClient.make_client(
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/v1/engine/core_client.py", line 75, in make_client
    return SyncMPClient(vllm_config, executor_class, log_stats)
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/v1/engine/core_client.py", line 558, in __init__
    super().__init__(
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/v1/engine/core_client.py", line 422, in __init__
    self._init_engines_direct(vllm_config, local_only,
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/v1/engine/core_client.py", line 491, in _init_engines_direct
    self._wait_for_engine_startup(handshake_socket, input_address,
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/v1/engine/core_client.py", line 511, in _wait_for_engine_startup
    wait_for_engine_startup(
  File "/home/jun.zhou10/.local/lib/python3.10/site-packages/vllm/v1/utils.py", line 494, in wait_for_engine_startup
    raise RuntimeError("Engine core initialization failed. "
RuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {}

But it runs normally under 4.52.4

Expected behavior

Get the correct output

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