通过定义诸如 init_distributed_environment、initialize_model_parallel、ensure_model_parallel_initialized、destroy_model_parallel 和 destroy_distributed_environment 等函数,代码负责在程序启动时建立global ProcessGroup 以及不同并行
llm = LLM( model=model_name, dtype="bfloat16", gpu_memory_utilization=0.4, max_model_len=4096, # tensor_parallel_size=8 ) 如果需要多次初始化vllm模型,需要手动清空显存 from vllm import LLM, SamplingParams from vllm.distributed.parallel_state import destroy_model_parallel # Delete the llm ...
max_model_len=1024)outputs=llm.generate(prompts,sampling_params)foroutputinoutputs:prompt=output.promptgenerated_text=output.outputs[0].textprint(f"Prompt:{prompt!r}, Generated text:{generated_text!r}")#using a workaround given in Issue 1908destroy_model_parallel()delllm.llm_engine.model_...
importgcimporttorchfromvllmimportLLM,SamplingParamsfromvllm.model_executor.parallel_utils.parallel_stateimportdestroy_model_parallel# Load the model via vLLMllm=LLM(model=model_name,download_dir=saver_dir,tensor_parallel_size=num_gpus,gpu_memory_utilization=0.70)# Delete the llm object and free the...
如果程序崩溃,并且错误追踪显示在vllm/worker/model_runner.py中的self.graph.replay()附近,那么这是一个发生在 cudagraph 内部的 CUDA 错误。要知道引发错误的具体 CUDA 操作,您可以在命令行中添加--enforce-eager,或者在LLM类中设置enforce_eager=True,以禁用 cudagraph 优化。通过这种方式,您可以准确定位导致错误...
(rank, world_size, model_path, prompts): setup(rank, world_size) # 创建采样参数对象 sampling_params = SamplingParams(temperature=0.1, top_p=0.5, max_tokens=4096) # 加载vLLM模型 llm = LLM( model=model_path, trust_remote_code=True, tokenizer_mode="auto", tensor_parallel_size=1, # ...
vllm [Bug]: Ray内存泄漏实际上，我认为这不是Ray的问题，而是来自CUDA本身(即CUDA的缓存没有被清理)...
vllm [Bug]: Ray内存泄漏实际上，我认为这不是Ray的问题，而是来自CUDA本身(即CUDA的缓存没有被清理)...
# vllm/distributed/parallel_state.pydefinitialize_model_parallel(tensor_model_parallel_size:int=1,pipeline_model_parallel_size:int=1,enable_expert_parallel:bool=False,backend:Optional[str]=None,)->None:...# the layout order is: ExternalDP x DP x PP x TP# ExternalDP is the data parallel ...
python3 -m vllm.entrypoints.openai.api_server\--model=/workspace/DeepSeek-R1\--dtype=auto\--block-size32\--tokenizer-mode=slow\--max-model-len32768\--max-num-batched-tokens2048\--tensor-parallel-size8\--pipeline-parallel-size3\--gpu-memory-utilization 0.90\--max-num-seqs128\--trust-...