Stuck during parallel inference.

[Yanyx44]

I am performing parallel inference with a batch size of 8 on a machine with 4 * A6000 GPUs. However, after running inference for a while, it gets stuck and stops responding. Meanwhile, nvidia-smi shows the following situation:

[lvhan028]

Please share the env information by running lmdeploy check_env

[Yanyx44]

Please share the env information by running lmdeploy check_env

Here is the output of lmdeploy check_env `Python: 3.9.0 (default, Nov 15 2020, 14:28:56) [GCC 7.3.0] [15/1901] CUDA available: True
MUSA available: False
numpy_random_seed: 2147483648
GPU 0,1,2,3: NVIDIA RTX A6000
CUDA_HOME: /usr
NVCC: Cuda compilation tools, release 12.1, V12.1.66
GCC: gcc (Ubuntu 8.4.0-3ubuntu2) 8.4.0
PyTorch: 2.4.1+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 v3.4.2 (Git Hash 1137e04ec0b5251ca2b4400a4fd3c667ce843d67)
• OpenMP 201511 (a.k.a. OpenMP 4.5)
• LAPACK is enabled (usually provided by MKL)
• NNPACK is enabled
• CPU capability usage: AVX2
• 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 90.1 (built against CUDA 12.4)
• Magma 2.6.1
• Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=9.1.0, 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_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=2.4.1, 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: 0.19.1+cu121 LMDeploy: 0.7.0+6cd35d5 transformers: 4.47.0 gradio: Not Found fastapi: 0.115.6 pydantic: 2.10.3 triton: 3.0.0 NVIDIA Topology: GPU0 GPU1 GPU2 GPU3 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X NV4 NODE NODE 0-127 0 N/A GPU1 NV4 X NODE NODE 0-127 0 N/A GPU2 NODE NODE X NV4 0-127 0 N/A GPU3 NODE NODE NV4 X 0-127 0 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`

[lvhan028]

Can you share the reproducible code snippet too?

[Yanyx44]

Can you share the reproducible code snippet too?

I may not be able to provide the complete code because this is a complex project, but essentially, I am performing normal inference using a VLM with a batch size of 8. Each input in a batch consists of 8 images along with the same prompt. This issue occurs with both Internvl-8B and QwenVL-7B models. It seems that reducing the batch size to 4 can alleviate the occurrence of this problem.

`def batch_generate_entities_and_relations( llm: BaseVideoModel, events: list[dict], video: VideoRepresentation, file_path: str, global_config: dict, max_retries: int = 5, batch_size: int = 4, ): ... try: batch_responses = llm.batch_generate_response(batch_inputs=batch_inputs) ...

model : class InternVL_Pipe: def batch_generate_response(self, batch_inputs, timestamps=None): prompts = [] gen_config = GenerationConfig(do_sample=True, max_new_tokens=2048) if "video" in batch_inputs[0].keys(): for inputs in batch_inputs: images = inputs["video"] video_prefix = ''.join([f'Frame-{i}: {IMAGE_TOKEN}\n' for i in range(len(images))]) if timestamps is None else
''.join([f'Timestamp {timestamps[i]}s : {IMAGE_TOKEN}\n' for i in range(len(images))]) question = video_prefix + inputs["text"] prompts.append((question, images))

        responses = self.pipe(prompts, gen_config=gen_config)
    else:
        for inputs in batch_inputs:
            question = inputs["text"]
            prompts.append(question)
        
        responses = self.pipe(prompts, gen_config=gen_config)
    
    responses = [response.text for response in responses]

    return responses

`

[lvhan028]

Can you set log_level=INFO when creating the pipeline? Hope we can get some clues from the info log. Another way to debug the issue is to use gdb, for instance:

gdb attach <pid>
set logging on
thread apply all bt
c
set logging off

You can find gdb.txt in the working directory. May share it with us. Meanshile, we will try to reproduce it on our A100 device.

[LaoWangGB]

Same problem when Infer 78B with H800 * 4. When I use lmdeploy ==0.6.3, it occurs occasionally, but definitely occurs using lmdeploy ==0.7.0.

[Yanyx44]

Can you set log_level=INFO when creating the pipeline? Hope we can get some clues from the info log. Another way to debug the issue is to use gdb, for instance:

gdb attach <pid>
set logging on
thread apply all bt
c
set logging off

You can find gdb.txt in the working directory. May share it with us. Meanshile, we will try to reproduce it on our A100 device.

So, do I need to keep running until the “stuck” situation occurs, or do I only need the INFO logs when creating the pipeline?

[lvhan028]

Set log_level="INFO" when creating the pipeline and then run the reproducible code. When the stuck happens, please share the whole log with us

[Yanyx44]

Set log_level="INFO" when creating the pipeline and then run the reproducible code. When the stuck happens, please share the whole log with us

Here is the log

[fschiffers]

Hello, just wanted to mention that I encounter exactly the same problem! Has there been any solution on this?

[xuanmingcui]

Same issue here with lmdeploy==0.8.0. The pipeline randomly (and very frequently) hang with InternVL3-78B, 4xA100 GPUs.

[lzhangzz]

The current solution to this problem is #3126, however it hurts performance a lot.

Recommend to use the pytorch engine which does not have the problem. (by replacing TurbomindEngineConfig with PytorchEngineConfig