]*128).to("cuda")dist.all_reduce(data,op=dist.ReduceOp.SUM)torch.cuda.synchronize()value=data.mean().item()world_size=dist.get_world_size()assertvalue==world_size
world_size = pipeline_parallel_size * self.tensor_parallel_size if self.world_size > 1: self.worker_use_ray = True self._verify_args() SchedulerConfig class SchedulerConfig: """Scheduler configuration. Args: max_num_batched_tokens: Maximum number of tokens to be processed in a single ...
(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, # ...
# 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+ ...
self.scheduler_config=scheduler_configifdevice_configisNone:device_config=DeviceConfig()self.device_config=device_configifparallel_configisNone:parallel_config=ParallelConfig()self.parallel_config=parallel_config# 2. 初始化策略配置self.num_nodes=parallel_config.world_size//parallel_config.tensor_parallel_...
[rank14]: assert value == world_size, f"Expected {world_size}, got {value}" [rank14]: ^^^ [rank14]: AssertionError: Expected 16, got 1.0 DG-DGX6:1452:1554 [0] NCCL INFO [Service thread] Connection closed by localRank 1 DG-DGX6:1453:1555 [1] NCCL INFO [Service thread] Conne...
当启动参数--tensor-parallel-size > 1 时,会自动触发ray分布式部署。 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, '...
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', ...
(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...
world_size == 1): 122 119 return tensor_dict @@ -190,8 +187,6 @@ def broadcast_tensor_dict( 190 187 tensor_dict[key] = value 191 188 for async_handle in async_handles: 192 189 async_handle.wait() 193 _pynative_executor.sync() 194 _pynative_executor.set_async_for_...