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Published 3 months ago • verified publisher icon OpenGVLab

同样的客户端调用脚本，多张图片推理，InternVL2-Llama3-76B-AWQ运行正常，InternVL2-Llama3-76B运行出错返回内容为空，token溢出

Open AI-Mart opened this issue 1 year ago • 4 comments

Checklist

  • [X] 1. I have searched related issues but cannot get the expected help.
  • [X] 2. The bug has not been fixed in the latest version.
  • [X] 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

image 以上是部署InternVL2-Llama3-76B-AWQ的结果，可以正常输出 image 以上是部署InternVL2-Llama3-76B的结果，输出异常，总token变成0了

Reproduction

InternVL2-Llama3-76B-AWQ服务运行脚本： lmdeploy serve api_server /usr/local/serving/models/OpenGVLab/InternVL2-Llama3-76B-AWQ --backend turbomind --server-port 23333 --model-format awq --tp 2 --cache-max-entry-count 0.1

InternVL2-Llama3-76B服务运行脚本 lmdeploy serve api_server /usr/local/serving/models/OpenGVLab/InternVL2-Llama3-76B --backend turbomind --server-port 23333 --tp 4 --cache-max-entry-count 0.05 --session-len 256000

Environment

InternVL2-Llama3-76B-AWQ服务运行环境：
sys.platform: linux
Python: 3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0]
CUDA available: True
MUSA available: False
numpy_random_seed: 2147483648
GPU 0,1: NVIDIA RTX A6000
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 12.1, V12.1.105
GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
PyTorch: 2.1.2
PyTorch compiling details: PyTorch built with:
  - GCC 9.3
  - C++ Version: 201703
  - Intel(R) oneAPI Math Kernel Library Version 2023.1-Product Build 20230303 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v3.1.1 (Git Hash 64f6bcbcbab628e96f33a62c3e975f8535a7bde4)
  - 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_61,code=sm_61;-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 -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-invalid-partial-specialization -Wno-unused-private-field -Wno-aligned-allocation-unavailable -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.1.2, 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,

TorchVision: 0.16.2
LMDeploy: 0.5.3+unknown
transformers: 4.37.2
gradio: 3.35.2
fastapi: 0.112.2
pydantic: 2.8.2
triton: 2.1.0
NVIDIA Topology:
        GPU0    GPU1    CPU Affinity    NUMA Affinity
GPU0     X      SYS     0-11,24-35      0
GPU1    SYS      X      12-23,36-47     1

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





InternVL2-Llama3-76B服务运行环境：
# lmdeploy check_env
sys.platform: linux
Python: 3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0]
CUDA available: True
MUSA available: False
numpy_random_seed: 2147483648
GPU 0,1,2,3: NVIDIA L40
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 12.1, V12.1.105
GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
PyTorch: 2.1.2
PyTorch compiling details: PyTorch built with:
  - GCC 9.3
  - C++ Version: 201703
  - Intel(R) oneAPI Math Kernel Library Version 2023.1-Product Build 20230303 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v3.1.1 (Git Hash 64f6bcbcbab628e96f33a62c3e975f8535a7bde4)
  - 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_61,code=sm_61;-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 -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-invalid-partial-specialization -Wno-unused-private-field -Wno-aligned-allocation-unavailable -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.1.2, 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, 

TorchVision: 0.16.2
LMDeploy: 0.5.3+unknown
transformers: 4.37.2
gradio: 4.42.0
fastapi: 0.114.1
pydantic: 2.9.1
triton: 2.1.0
NVIDIA Topology: 
        GPU0    GPU1    GPU2    GPU3    CPU Affinity    NUMA Affinity
GPU0     X      PIX     PXB     PXB     0-15,32-47      0
GPU1    PIX      X      PXB     PXB     0-15,32-47      0
GPU2    PXB     PXB      X      PXB     0-15,32-47      0
GPU3    PXB     PXB     PXB      X      0-15,32-47      0

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

AI-Mart avatar Sep 24 '24 07:09 AI-Mart

这还挺奇怪，我没遇到过这个问题。请问您的环境中用安装apex和flasn attn吗。如果有安装apex，建议卸载了；如果没安装flash attn可以安装一个试试看，因为lmdeploy中运行vlm的ViT部分应该还是跑的pytorch后端。

czczup avatar Sep 25 '24 06:09 czczup

accelerate 0.34.2 addict 2.4.0 aiofiles 23.2.1 aiohappyeyeballs 2.4.0 aiohttp 3.10.5 aiosignal 1.3.1 altair 5.4.1 annotated-types 0.7.0 anyio 4.4.0 archspec 0.2.1 asttokens 2.0.5 astunparse 1.6.3 async-timeout 4.0.3 attrs 23.1.0 backcall 0.2.0 beautifulsoup4 4.12.2 bitsandbytes 0.41.0 blinker 1.8.2 boltons 23.0.0 Brotli 1.0.9 cachetools 5.5.0 certifi 2023.11.17 cffi 1.16.0 chardet 4.0.0 charset-normalizer 2.0.4 click 8.1.7 conda 23.9.0 conda-build 3.28.1 conda-content-trust 0.2.0 conda_index 0.3.0 conda-libmamba-solver 23.7.0 conda-package-handling 2.2.0 conda_package_streaming 0.9.0 contourpy 1.3.0 cryptography 41.0.7 cycler 0.12.1 decorator 5.1.1 decord 0.6.0 deepspeed 0.13.5 distro 1.8.0 dnspython 2.4.2 einops 0.6.1 einops-exts 0.0.4 exceptiongroup 1.0.4 executing 0.8.3 expecttest 0.1.6 fastapi 0.114.1 ffmpy 0.4.0 filelock 3.13.1 fire 0.6.0 flash-attn 2.3.6 fonttools 4.53.1 frozenlist 1.4.1 fsspec 2023.12.2 gitdb 4.0.11 GitPython 3.1.43 gmpy2 2.1.2 gradio 4.42.0 gradio_client 1.3.0 h11 0.14.0 hjson 3.1.0 httpcore 1.0.5 httpx 0.27.2 huggingface-hub 0.24.6 hypothesis 6.92.0 idna 3.4 imageio 2.35.1 importlib_metadata 8.5.0 importlib_resources 6.4.5 ipython 8.15.0 jedi 0.18.1 Jinja2 3.1.2 jiter 0.5.0 joblib 1.4.2 jsonpatch 1.32 jsonpointer 2.1 jsonschema 4.19.2 jsonschema-specifications 2023.7.1 kiwisolver 1.4.7 latex2mathml 3.77.0 libarchive-c 2.9 libmambapy 1.5.3 linkify-it-py 2.0.3 lmdeploy 0.5.3 markdown-it-py 2.2.0 markdown2 2.5.0 MarkupSafe 2.1.1 matplotlib 3.9.2 matplotlib-inline 0.1.6 mdit-py-plugins 0.3.3 mdurl 0.1.2 menuinst 2.0.1 mkl-fft 1.3.8 mkl-random 1.2.4 mkl-service 2.4.0 mmengine-lite 0.10.4 more-itertools 10.1.0 mpmath 1.3.0 multidict 6.1.0 narwhals 1.7.0 networkx 3.1 ninja 1.11.1.1 numpy 1.26.2 nvidia-cublas-cu12 12.6.1.4 nvidia-cuda-runtime-cu12 12.6.68 nvidia-curand-cu12 10.3.7.68 nvidia-nccl-cu12 2.23.4 openai 1.42.0 opencv-python-headless 4.10.0.84 orjson 3.10.7 packaging 23.1 pandas 2.2.2 parso 0.8.3 peft 0.11.1 pexpect 4.8.0 pickleshare 0.7.5 Pillow 10.0.1 pip 23.3.1 pkginfo 1.9.6 platformdirs 3.10.0 pluggy 1.0.0 prompt-toolkit 3.0.36 protobuf 5.28.1 psutil 5.9.0 ptyprocess 0.7.0 pure-eval 0.2.2 py-cpuinfo 9.0.0 pyarrow 17.0.0 pycocoevalcap 1.2 pycocotools 2.0.8 pycosat 0.6.6 pycparser 2.21 pydantic 2.9.1 pydantic_core 2.23.3 pydeck 0.9.1 pydub 0.25.1 Pygments 2.15.1 pynvml 11.5.3 pyOpenSSL 23.2.0 pyparsing 3.1.4 PySocks 1.7.1 python-dateutil 2.9.0.post0 python-etcd 0.4.5 python-multipart 0.0.9 pytz 2023.3.post1 PyYAML 6.0.1 referencing 0.30.2 regex 2024.9.11 requests 2.31.0 rich 13.8.1 rpds-py 0.10.6 ruamel.yaml 0.17.21 ruamel.yaml.clib 0.2.6 ruff 0.6.4 safetensors 0.4.5 scikit-learn 1.5.2 scipy 1.14.1 semantic-version 2.10.0 sentencepiece 0.1.99 setuptools 68.2.2 shellingham 1.5.4 shortuuid 1.0.13 six 1.16.0 smmap 5.0.1 sniffio 1.3.1 sortedcontainers 2.4.0 soupsieve 2.5 stack-data 0.2.0 starlette 0.38.5 streamlit 1.38.0 streamlit-image-select 0.6.0 svgwrite 1.4.3 sympy 1.12 tenacity 8.5.0 tensorboardX 2.6.2.2 termcolor 2.4.0 threadpoolctl 3.5.0 tiktoken 0.7.0 timm 0.9.12 tokenizers 0.15.1 toml 0.10.2 tomli 2.0.1 tomlkit 0.12.0 toolz 0.12.0 torch 2.1.2 torchaudio 2.1.2 torchelastic 0.2.2 torchvision 0.16.2 tornado 6.4.1 tqdm 4.65.0 traitlets 5.7.1 transformers 4.37.2 triton 2.1.0 truststore 0.8.0 typer 0.12.5 types-dataclasses 0.6.6 typing_extensions 4.12.2 tzdata 2024.1 uc-micro-py 1.0.3 urllib3 2.2.2 uvicorn 0.30.6 watchdog 4.0.2 wavedrom 2.0.3.post3 wcwidth 0.2.5 websockets 12.0 wheel 0.41.2 yacs 0.1.8 yapf 0.40.2 yarl 1.11.1 zipp 3.20.1 zstandard 0.19.0

AI-Mart avatar Sep 25 '24 06:09 AI-Mart

安装了Name: flash-attn Version: 2.3.6 Summary: Flash Attention: Fast and Memory-Efficient Exact Attention Home-page: https://github.com/Dao-AILab/flash-attention Author: Tri Dao Author-email: [email protected] License: Location: /opt/conda/lib/python3.10/site-packages Requires: einops, ninja, packaging, torch Required-by:

AI-Mart avatar Sep 25 '24 06:09 AI-Mart

没有安装apex

AI-Mart avatar Sep 25 '24 06:09 AI-Mart