[Bug] internv2系列模型使用pipeline报错

[wssywh]

Checklist

• [ ] 1. I have searched related issues but cannot get the expected help.
• [ ] 2. The bug has not been fixed in the latest version.
• [ ] 3. Please note that if the bug-related issue you submitted lacks corresponding environment info and a minimal reproducible demo, it will be challenging for us to reproduce and resolve the issue, reducing the likelihood of receiving feedback.

Describe the bug

InternVL2系列模型使用pipeline会报错

Reproduction

使用InternVL2-26B模型会报错，使用InternVL-Chat-V1-5模型正常

from lmdeploy import pipeline, TurbomindEngineConfig
from lmdeploy.vl import load_image

# model_path = "/data/workspace/models/InternVL-Chat-V1-5"
model_path = "/data/workspace/models/InternVL2-26B"
img_path = "tmp/558170523.jpg"
pipe = pipeline(model_path, backend_config=TurbomindEngineConfig(tp=2, cache_max_entry_count=0.5))
question = "请述这张图片."
responses = pipe([(question, load_image(img_path)), (question, load_image(img_path))])
for res in responses:
    print(res.text)

程序输出为

Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.                                                                                     
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
[WARNING] gemm_config.in is not found; using default GEMM algo                                                                                                                                            
[WARNING] gemm_config.in is not found; using default GEMM algo
Aborted (core dumped)

Environment

sys.platform: linux
Python: 3.10.14 (main, May  6 2024, 19:42:50) [GCC 11.2.0]
CUDA available: True
MUSA available: False
numpy_random_seed: 2147483648
GPU 0,1,2,3,4,5: NVIDIA A100-PCIE-40GB
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 12.2, V12.2.140
GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
PyTorch: 2.2.1+cu121
PyTorch compiling details: PyTorch built with:
  - GCC 9.3
  - C++ Version: 201703
  - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v3.3.2 (Git Hash 2dc95a2ad0841e29db8b22fbccaf3e5da7992b01)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX512
  - CUDA Runtime 12.1
  - NVCC architecture flags: -gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90
  - CuDNN 8.9.2
  - Magma 2.6.1
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=8.9.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=2.2.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, 

TorchVision: 0.17.1+cu121
LMDeploy: 0.5.2.post1+e53fa70
transformers: 4.40.2
gradio: 3.50.2
fastapi: 0.111.0
pydantic: 2.7.1
triton: 2.2.0
NVIDIA Topology: 
        GPU0    GPU1    GPU2    GPU3    GPU4    GPU5    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      PIX     PXB     SYS     SYS     SYS     0-27,56-83      0               N/A
GPU1    PIX      X      PXB     SYS     SYS     SYS     0-27,56-83      0               N/A
GPU2    PXB     PXB      X      SYS     SYS     SYS     0-27,56-83      0               N/A
GPU3    SYS     SYS     SYS      X      PXB     PXB     28-55,84-111    1               N/A
GPU4    SYS     SYS     SYS     PXB      X      PXB     28-55,84-111    1               N/A
GPU5    SYS     SYS     SYS     PXB     PXB      X      28-55,84-111    1               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

Error traceback

No response