pynccl=PyNcclCommunicator(group=gloo_group,device=local_rank)pynccl.disabled=False s=torch.cuda.Stream()withtorch.cuda.stream(s):data.fill_(1)pynccl.all_reduce(data,stream=s)value=data.mean().item()assert value==world_size,f"Expected {world_size}, got {value}"print("vLLM NCCL is suc...
(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, # ...
1. 构建LLM engine时会对Ray集群进行初始化 # ray 集群初始化initialize_ray_cluster(engine_config.parallel_config) parallel_config的配置如下,pp=1,tp=2,world_size=2 {'pipeline_parallel_size': 1, 'tensor_parallel_size': 2, 'worker_use_ray': True, 'max_parallel_loading_workers': None, 'disa...
[rank8]: assert value == world_size, f"Expected {world_size}, got {value}" [rank8]: ^^^ [rank8]: AssertionError: Expected 16, got 1.0 [rank12]: Traceback (most recent call last): [rank12]: File "/vllm-workspace/test.py", line 43, in <module> [rank12]: assert value =...
# we will choose the backend based on the world size. 31- ifparallel_config.world_size>1: 32- distributed_executor_backend="mp" 33- else: 34- distributed_executor_backend="uni" 35- 36- ifdistributed_executor_backend=="ray": 28+ ...
Prompt:'Hello, my name is', Generated text:" Sherry and I'm a stay at home mom of three beautiful children."Prompt:'The president of the United States is', Generated text:' one of the most powerful people in the world, and yet, many people do'Prompt:'The capital of France is', ...
()assertvalue==world_size,f"Expected {world_size}, got {value}"print("vLLM NCCL is successful!")g=torch.cuda.CUDAGraph()withtorch.cuda.graph(cuda_graph=g,stream=s):out=pynccl.all_reduce(data,stream=torch.cuda.current_stream())data.fill_(1)g.replay()torch.cuda.current_stream()....
(model_dir)# Export the checkpoint to vLLM, prepare for inferenceexporter=vLLMExporter()exporter.export(nemo_checkpoint=checkpoint_file,model_dir=model_dir,model_type="llama",)# Run inference and print the outputoutput=exporter.forward(["What is the best city in the world?"],max_output_len...
(model_dir)# Export the checkpoint to vLLM, prepare for inferenceexporter=vLLMExporter()exporter.export(nemo_checkpoint=checkpoint_file,model_dir=model_dir,model_type="gemma",)# Run inference and print the outputoutput=exporter.forward(["What is the best city in the world?"],max_output_len...
importtorch,time,tqdmfromvllmimportLLM,SamplingParamsWORLD_SIZE=1BATCH_SIZE=2048llm=LLM(model="lmsys/vicuna-7b-v1.3",tokenizer="hf-internal-testing/llama-tokenizer",tensor_parallel_size=WORLD_SIZE,gpu_memory_utilization=0.85)start=time.perf_counter()batch=torch.randint(32000, (BATCH_SIZE,120))...