"""A Neuron worker class.""" from typing import Dict, List, Optional, Tuple import torch import torch.distributed from vllm.config import (CacheConfig, DeviceConfig, ModelConfig, ParallelConfig, SchedulerConfig, LoRAConfig) from vllm.model_executor import set_random_seed from vllm.model_executor.parallel_utils.communication_op import ( broadcast_tensor_dict) from vllm.model_executor.parallel_utils.parallel_state import ( ensure_model_parallel_initialized) from vllm.sequence import SamplerOutput, SequenceGroupMetadata from vllm.worker.cache_engine import CacheEngine from vllm.worker.model_runner import ModelRunner class Worker: """A worker class that executes the model on a group of neuron cores. """ def __init__( self, model_config: ModelConfig, parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, device_config: DeviceConfig, local_rank: int, rank: int, distributed_init_method: str, lora_config: Optional[LoRAConfig] = None, kv_cache_dtype: Optional[str] = "auto", is_driver_worker: bool = False, ) -> None: self.model_config = model_config self.parallel_config = parallel_config self.scheduler_config = scheduler_config self.device_config = device_config self.local_rank = local_rank self.rank = rank self.distributed_init_method = distributed_init_method self.lora_config = lora_config self.is_driver_worker = is_driver_worker if self.is_driver_worker: assert self.rank == 0, "The driver worker must have rank 0." self.model_runner = ModelRunner(model_config, parallel_config, scheduler_config, device_config, lora_config=self.lora_config, is_driver_worker=is_driver_worker) # Uninitialized cache engine. Will be initialized by # self.init_cache_engine(). self.cache_config = None self.cache_engine = None self.cache_events = None self.gpu_cache = None def init_model(self) -> None: # Initialize the distributed environment. _init_distributed_environment(self.parallel_config, self.rank, self.distributed_init_method, distributed_backend="gloo") # Initialize the model. set_random_seed(self.model_config.seed) def load_model(self): self.model_runner.load_model() @torch.inference_mode() def profile_num_available_blocks( self, block_size: int = 128, gpu_memory_utilization: float = 0.9, cpu_swap_space: int = 0, cache_dtype: str = "float16", ) -> Tuple[int, int]: """Simply returns max_num_seqs as num_gpu_blocks, 0 as num_cpu_blocks.""" num_gpu_blocks = self.scheduler_config.max_num_seqs num_cpu_blocks = 0 return num_gpu_blocks, num_cpu_blocks def init_cache_engine(self, cache_config: CacheConfig) -> None: self.cache_config = cache_config self.cache_engine = CacheEngine(self.cache_config, self.model_config, self.parallel_config) self.model_runner.set_block_size(self.cache_engine.block_size) def warm_up_model(self) -> None: # Warm up is maintained in transformers-neuronx pass def cache_swap( self, blocks_to_swap_in: Dict[int, int], blocks_to_swap_out: Dict[int, int], blocks_to_copy: Dict[int, List[int]], ) -> None: # Issue cache operations. issued_cache_op = False if blocks_to_swap_in: self.cache_engine.swap_in(blocks_to_swap_in) issued_cache_op = True if blocks_to_swap_out: self.cache_engine.swap_out(blocks_to_swap_out) issued_cache_op = True if blocks_to_copy: self.cache_engine.copy(blocks_to_copy) issued_cache_op = True cache_events = self.cache_events if issued_cache_op else None # Wait for cache operations to finish. if cache_events is not None: raise NotImplementedError( "cache operations are not implemented for neuron backend.") @torch.inference_mode() def execute_model( self, seq_group_metadata_list: Optional[List[SequenceGroupMetadata]] = None, blocks_to_swap_in: Optional[Dict[int, int]] = None, blocks_to_swap_out: Optional[Dict[int, int]] = None, blocks_to_copy: Optional[Dict[int, List[int]]] = None, ) -> Optional[SamplerOutput]: if self.is_driver_worker: assert seq_group_metadata_list is not None num_seq_groups = len(seq_group_metadata_list) assert blocks_to_swap_in is not None assert blocks_to_swap_out is not None assert blocks_to_copy is not None data = { "num_seq_groups": num_seq_groups, "blocks_to_swap_in": blocks_to_swap_in, "blocks_to_swap_out": blocks_to_swap_out, "blocks_to_copy": blocks_to_copy, } broadcast_tensor_dict(data, src=0) else: data = broadcast_tensor_dict(src=0) num_seq_groups = data["num_seq_groups"] blocks_to_swap_in = data["blocks_to_swap_in"] blocks_to_swap_out = data["blocks_to_swap_out"] blocks_to_copy = data["blocks_to_copy"] self.cache_swap(blocks_to_swap_in, blocks_to_swap_out, blocks_to_copy) # If there is no input, we don't need to execute the model. if num_seq_groups == 0: return {} output = self.model_runner.execute_model(seq_group_metadata_list, self.gpu_cache) return output def _init_distributed_environment( parallel_config: ParallelConfig, rank: int, distributed_init_method: Optional[str] = None, distributed_backend: Optional[str] = None, ) -> None: """Initialize the distributed environment.""" if torch.distributed.is_initialized(): torch_world_size = torch.distributed.get_world_size() if torch_world_size != parallel_config.world_size: raise RuntimeError( "torch.distributed is already initialized but the torch world " "size does not match parallel_config.world_size " f"({torch_world_size} vs. {parallel_config.world_size}).") elif not distributed_init_method: raise ValueError( "distributed_init_method must be set if torch.distributed " "is not already initialized") else: distributed_backend = distributed_backend if distributed_backend else "nccl" torch.distributed.init_process_group( backend=distributed_backend, world_size=parallel_config.world_size, rank=rank, init_method=distributed_init_method, ) # A small all_reduce for warmup. torch.distributed.all_reduce(torch.zeros(1)) ensure_model_parallel_initialized(parallel_config.tensor_parallel_size, parallel_config.pipeline_parallel_size)