[Bug] lmdeploy CUDA runtime error: out of memory

AmazDeng commented 2 weeks ago

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

I followed the official documentation for InternVL2 and used lmdeploy to load the 40B model（https://internvl.readthedocs.io/en/latest/internvl2.0/deployment.html）, but I encountered an error：RuntimeError: [TM][ERROR] CUDA runtime error: out of memory /lmdeploy/src/turbomind/utils/memory_utils.cu:32. My machine is an A100 80G. What could be the issue? lmdeploy officially supports the InternVL2 model.

Reproduction

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

model = '/media/star/disk2/pretrained_model/InternVL2-40B'
image = load_image('/media/star/8T/tmp/2.jpg')
pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=8192))
response = pipe(('describe this image', image))
print(response.text)

Environment

sys.platform: linux
Python: 3.10.14 | packaged by conda-forge | (main, Mar 20 2024, 12:45:18) [GCC 12.3.0]
CUDA available: True
MUSA available: False
numpy_random_seed: 2147483648
GPU 0: NVIDIA A100-SXM4-80GB
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 12.2, V12.2.91
GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
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+
transformers: 4.44.2
gradio: Not Found
fastapi: 0.112.2
pydantic: 2.8.2
triton: 2.3.1
NVIDIA Topology: 
        GPU0    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      20-39,60-79     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

```Shell
Error message:

(lmdeploy) star@star-SYS-7049GP-TRT:/media/star/8T/PycharmProjects/github/gpt/InternVL/jupyter$ python lmdeploy_test2.py 
Unrecognized keys in `rope_scaling` for 'rope_type'='dynamic': {'type'}
Unrecognized keys in `rope_scaling` for 'rope_type'='dynamic': {'type'}
Unrecognized keys in `rope_scaling` for 'rope_type'='dynamic': {'type'}
Flash Attention 2.0 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in LlamaForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `torch_dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", torch_dtype=torch.float16)`
Flash Attention 2.0 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in LlamaModel is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `torch_dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", torch_dtype=torch.float16)`
Unrecognized keys in `rope_scaling` for 'rope_type'='dynamic': {'type'}
Unrecognized keys in `rope_scaling` for 'rope_type'='dynamic': {'type'}
Unrecognized keys in `rope_scaling` for 'rope_type'='dynamic': {'type'}
Unrecognized keys in `rope_scaling` for 'rope_type'='dynamic': {'type'}
Unrecognized keys in `rope_scaling` for 'rope_type'='dynamic': {'type'}
Unrecognized keys in `rope_scaling` for 'rope_type'='dynamic': {'type'}
Unrecognized keys in `rope_scaling` for 'rope_type'='dynamic': {'type'}
Traceback (most recent call last):
  File "/media/star/8T/PycharmProjects/github/gpt/InternVL/jupyter/lmdeploy_test2.py", line 8, in <module>
    pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=8192))
  File "/home/star/miniconda3/envs/lmdeploy/lib/python3.10/site-packages/lmdeploy/api.py", line 89, in pipeline
    return pipeline_class(model_path,
  File "/home/star/miniconda3/envs/lmdeploy/lib/python3.10/site-packages/lmdeploy/serve/vl_async_engine.py", line 24, in __init__
    super().__init__(model_path, **kwargs)
  File "/home/star/miniconda3/envs/lmdeploy/lib/python3.10/site-packages/lmdeploy/serve/async_engine.py", line 190, in __init__
    self._build_turbomind(model_path=model_path,
  File "/home/star/miniconda3/envs/lmdeploy/lib/python3.10/site-packages/lmdeploy/serve/async_engine.py", line 235, in _build_turbomind
    self.engine = tm.TurboMind.from_pretrained(
  File "/home/star/miniconda3/envs/lmdeploy/lib/python3.10/site-packages/lmdeploy/turbomind/turbomind.py", line 340, in from_pretrained
    return cls(model_path=pretrained_model_name_or_path,
  File "/home/star/miniconda3/envs/lmdeploy/lib/python3.10/site-packages/lmdeploy/turbomind/turbomind.py", line 144, in __init__
    self.model_comm = self._from_hf(model_source=model_source,
  File "/home/star/miniconda3/envs/lmdeploy/lib/python3.10/site-packages/lmdeploy/turbomind/turbomind.py", line 251, in _from_hf
    self._create_weight(model_comm)
  File "/home/star/miniconda3/envs/lmdeploy/lib/python3.10/site-packages/lmdeploy/turbomind/turbomind.py", line 170, in _create_weight
    future.result()
  File "/home/star/miniconda3/envs/lmdeploy/lib/python3.10/concurrent/futures/_base.py", line 458, in result
    return self.__get_result()
  File "/home/star/miniconda3/envs/lmdeploy/lib/python3.10/concurrent/futures/_base.py", line 403, in __get_result
    raise self._exception
  File "/home/star/miniconda3/envs/lmdeploy/lib/python3.10/concurrent/futures/thread.py", line 58, in run
    result = self.fn(*self.args, **self.kwargs)
  File "/home/star/miniconda3/envs/lmdeploy/lib/python3.10/site-packages/lmdeploy/turbomind/turbomind.py", line 163, in _create_weight_func
    model_comm.create_shared_weights(device_id, rank)
RuntimeError: [TM][ERROR] CUDA runtime error: out of memory /lmdeploy/src/turbomind/utils/memory_utils.cu:32

AmazDeng commented 2 weeks ago

@czczup @whai362 @ErfeiCui @hjh0119 @lvhan028 @Adushar @Weiyun1025 @cg1177 @opengvlab-admin @qishisuren123 @dlutwy Could you please take a look at this issue?

G-z-w commented 2 weeks ago

@czczup @whai362 @ErfeiCui @hjh0119 @lvhan028 @Adushar @Weiyun1025 @cg1177 @opengvlab-admin @qishisuren123 @dlutwy Could you please take a look at this issue?

Please refer to link to view the minimum gpu memory requirements.

lvhan028 commented 2 weeks ago

Hi, @AmazDeng 40B model costs about 80G memory. One A100 GPU is not enough. Two GPUs are required at least

AmazDeng commented 2 weeks ago

@G-z-w @lvhan028 Thank you, I understand. Under the condition of an A100 GPU, lmdeploy can only load the InternVL2-40B-AWQ version

OpenGVLab / InternVL