[Bug] InternVL2-2B的推理速度慢，发现是视觉特征提取的耗时很长

### 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

使用Transformer、vllm、LMdeploy对InternVL2-2B进行推理，max_num_patch都设置为12，推理结果发现:
Transformer平均691ms/条
VLLM平均308ms/条
LMdeploy平均523ms/条
对VLLM和LMdeploy耗时进行分析发现，vllm的vit部分平均耗时9ms，LMdeploy的vit部分平均耗时323ms。
LMdeploy的vit统计时间在VLAsyncEngine类的_get_prompt_input中统计``

### Reproduction

from lmdeploy import pipeline, TurbomindEngineConfig, PytorchEngineConfig
from lmdeploy.vl import load_image
import os
import time

PROMPT_SYSTEM = """
根据图片，判断该文档所属的文档类别。 请严格按照如下的格式进行回复，不要输出多余的解释（注意不要强行给文档分一个不正确的类别：对于不属于特定类别的文档，判别为‘其他文档’）：
文档类别：该文档所属的文档类别
"""

# model = 'model/OpenGVLab/InternVL2-1B'
model = 'model/OpenGVLab/InternVL2-2B'
pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=8192,model_format='hf'))
img_path = './cs_function_recommendation_bak/test_data/image'
imgs = os.listdir(img_path)
totle_time = 0
vit_time_total =0
for img in imgs[:100]:
image = load_image(os.path.join(img_path,img))

start = time.time()
response = pipe((PROMPT_SYSTEM, image))
end = time.time()
time_ = end - start
totle_time += time_
vit_time_total += response.vit_time
print(response.text,f"\nvit 时间：{response.vit_time},总耗时：{time_}")
print(vit_time_total)
print(totle_time)

### Environment

```Shell
sys.platform: linux
Python: 3.11.0 | packaged by conda-forge | (main, Jan 14 2023, 12:27:40) [GCC 11.3.0]
CUDA available: True
MUSA available: False
numpy_random_seed: 2147483648
GPU 0,1,2,3,4,5,6,7,8,9: NVIDIA A100 80GB PCIe
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 11.8, V11.8.89
GCC: gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
PyTorch: 2.3.1+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 v3.3.6 (Git Hash 86e6af5974177e513fd3fee58425e1063e7f1361)
- 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=2.3.1, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, 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: 0.18.1+cu121
LMDeploy: 0.5.3+aa00ed0
transformers: 4.45.0.dev0
gradio: Not Found
fastapi: 0.115.0
pydantic: 2.9.2
triton: 2.3.1
```

### Error traceback

_No response_