[Bug] Use triton to deploy minicpm-v-2_6 GPU memory keeps increasing until it overflows

### Checklist

- [X] 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

When I use lmdeploy to deploy minicpm-V-2_6 in triton, I find that the GPU memory keeps increasing until an exception occurs due to GPU memory issues, but this does not happen when I using a python script for loop inference. I experimented with the official model weights（[https://huggingface.co/openbmb/MiniCPM-V-2_6](https://huggingface.co/openbmb/MiniCPM-V-2_6)） and still had the same problem. When I use torch.cuda.empty_cache() to free unused GPU memory in each request, I can solve this problem, but it will cause the inference time to increase.

### Reproduction

The triton service startup script is as follow：
```
#!/bin/bash
cd ~
export OMP_NUM_THREADS=1
tritonserver --log-dir ./serving-logs \
--model-control-mode "explicit" \
--load-model "minicpm-v-2_6" \
--whale-rpc-port 26381 \
--grpc-port 26384 \
--mport 26382 \
--whale-rpc-use-async 1 \
--log-info false \
--allow-grpc true \
--allow-whale-rpc true \
--allow-metrics 0 \
--model-repository /home/hadoop-platcv/model_repos/minicpm-v-2_6/1 \
--exit-timeout-secs 0 \
--log-verbose 0 \
--server-appkey xxx
```
The python backend inference script is as follow:
```
import os
import triton_python_backend_utils as pb_utils
import json
import time

os.system(f"pip3 list")

from lmdeploy import pipeline, TurbomindEngineConfig, GenerationConfig
from lmdeploy.vl import load_image
import torch
import numpy as np
import io
from PIL import Image
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:32"

class TritonPythonModel:
"""Your Python model must use the same class name. Every Python model
that is created must have "TritonPythonModel" as the class name.
"""

def initialize(self, args):
"""`initialize` is called only once when the model is being loaded.
Implementing `initialize` function is optional. This function allows
the model to intialize any state associated with this model.

Parameters
----------
args : dict
Both keys and values are strings. The dictionary keys and values are:
* model_config: A JSON string containing the model configuration
* model_instance_kind: A string containing model instance kind
* model_instance_device_id: A string containing model instance device ID
* model_repository: Model repository path
* model_version: Model version
* model_name: Model name
"""
# You must parse model_config. JSON string is not parsed here
pwd = os.path.abspath(os.path.dirname(__file__))
self.ckpt_path = f'{pwd}/MiniCPM-V-2_6/'
print('self.ckpt_path', self.ckpt_path)
engine_config = TurbomindEngineConfig(session_len=8192, max_batch_size=1)
self.pipeline = pipeline(self.ckpt_path, backend_config=engine_config)
self.gen_config = GenerationConfig(top_k=1)
print(os.environ['LD_LIBRARY_PATH'])

def execute(self, requests):
"""`execute` must be implemented in every Python model. `execute`
function receives a list of pb_utils.InferenceRequest as the only
argument. This function is called when an inference request is made
for this model. Depending on the batching configuration (e.g. Dynamic
Batching) used, `requests` may contain multiple requests. Every
Python model, must create one pb_utils.InferenceResponse for every
pb_utils.InferenceRequest in `requests`. If there is an error, you can
set the error argument when creating a pb_utils.InferenceResponse

Parameters
----------
requests : list
A list of pb_utils.InferenceRequest

Returns
-------
list
A list of pb_utils.InferenceResponse. The length of this list must
be the same as `requests`
"""
responses = []

# 后处理模型输出，并为每个请求创建响应
for request in requests:
#tic_pre = time.time()
query = pb_utils.get_input_tensor_by_name(request, "query")
query = query.as_numpy()

image = pb_utils.get_input_tensor_by_name(request, "image")
image = image.as_numpy()

#pil_image = load_image(image[0][0].decode('utf-8'))
pil_image = Image.open(io.BytesIO(image[0][0])).convert('RGB')
messages = []
message = dict()
message['role'] = 'user'
lm_content = []
lm_content.append(dict(type='text', text=query[0][0].decode('utf-8')))
lm_content.append(dict(type='image_data', image_data=dict(max_slice_nums=1, data=pil_image)))
message['content'] = lm_content
messages.append(message)
#toc_pre = time.time()
#print(f'preprocess cost {toc_pre-tic_pre}')

#tic_infer = time.time()
result = self.pipeline(messages, gen_config=self.gen_config)
output_bytes = result.text.encode("utf-8")
output_bytes = np.array(output_bytes, dtype=bytes).reshape([1, -1])
output_tensor = pb_utils.Tensor("response", output_bytes)
responses.append(pb_utils.InferenceResponse(output_tensors=[output_tensor]))

# torch.cuda.empty_cache()

return responses

def finalize(self):
"""`finalize` is called only once when the model is being unloaded.
Implementing `finalize` function is OPTIONAL. This function allows
the model to perform any necessary clean ups before exit.
"""
print('Cleaning up...')
```
triton config.txt is as follow:
```
name: "minicpm-v-2_6"
backend: "python"
default_model_filename: "model.py"
max_batch_size: 1

input [
{
name: "image"
data_type: TYPE_STRING
dims: [-1]
},
{
name: "query"
data_type: TYPE_STRING
dims: [-1]
}
]

output [
{
name: "response"
data_type: TYPE_STRING
dims: [-1]
}
]

instance_group [
{
count: 1
kind: KIND_GPU
}
]

parameters {
key: "pip_cmd"
value: {
string_value: "pip3 install lmdeploy==0.6.1"
}
}
```

### Environment

```Shell
sys.platform: linux
Python: 3.9.16 (main, Apr 2 2024, 20:40:25) [GCC 10.2.1 20210130 (Red Hat 10.2.1-11)]
CUDA available: True
MUSA available: False
numpy_random_seed: 2147483648
GPU 0: NVIDIA L40
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 12.2, V12.2.91
GCC: gcc (GCC) 4.8.5 20150623 (Red Hat 4.8.5-44)
PyTorch: 2.2.1
PyTorch compiling details: PyTorch built with:
- GCC 10.2
- C++ Version: 201703
- Intel(R) oneAPI Math Kernel Library Version 2024.1-Product Build 20240215 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 12.2
- NVCC architecture flags: -gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_89,code=sm_89;-gencode;arch=compute_90,code=sm_90
- CuDNN 8.9.6
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.2, CUDNN_VERSION=8.9.6, CXX_COMPILER=/opt/rh/devtoolset-10/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=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF,

TorchVision: 0.17.1
LMDeploy: 0.6.1+
transformers: 4.41.2
gradio: Not Found
fastapi: 0.115.0
pydantic: 2.9.2
triton: 2.3.1
NVIDIA Topology:
GPU0 NIC0 NIC1 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X SYS SYS N/A
NIC0 SYS X SYS
NIC1 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_bond_0
NIC1: mlx5_bond_1
```

### Error traceback

```Shell
terminate called after throwing an instance of 'std::runtime_error'
what(): [TM][ERROR] CUDA runtime error: out of memory /lmdeploy/src/turbomind/utils/allocator.h:246
```