"""A GPU worker class.""" import os from typing import Dict, List, Optional, Tuple import torch import torch.distributed from vllm.config import CacheConfig, ModelConfig, ParallelConfig, SchedulerConfig from vllm.model_executor import set_random_seed from vllm.model_executor.parallel_utils.communication_op import broadcast_object_list from vllm.model_executor.parallel_utils.parallel_state import initialize_model_parallel from vllm.sequence import SamplerOutput, SequenceGroupMetadata from vllm.worker.cache_engine import CacheEngine from .model_runner import ModelRunner class Worker: """A worker class that executes (a partition of) the model on a GPU. Each worker is associated with a single GPU. The worker is responsible for maintaining the KV cache and executing the model on the GPU. In case of distributed inference, each worker is assigned a partition of the model. """ def __init__( self, model_config: ModelConfig, parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, local_rank: int, rank: int, distributed_init_method: str, post_model_path: str, is_driver_worker: bool = False, ) -> None: self.model_config = model_config self.parallel_config = parallel_config self.scheduler_config = scheduler_config self.local_rank = local_rank self.rank = rank self.distributed_init_method = distributed_init_method self.is_driver_worker = is_driver_worker self.post_model_path = post_model_path 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, is_driver_worker, post_model_path, ) # 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: # torch.distributed.all_reduce does not free the input tensor until # the synchronization point. This causes the memory usage to grow # as the number of all_reduce calls increases. This env var disables # this behavior. # Related issue: # https://discuss.pytorch.org/t/cuda-allocation-lifetime-for-inputs-to-distributed-all-reduce/191573 os.environ["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1" # This env var set by Ray causes exceptions with graph building. os.environ.pop("NCCL_ASYNC_ERROR_HANDLING", None) self.device = torch.device(f"cuda:{self.local_rank}") torch.cuda.set_device(self.device) _check_if_gpu_supports_dtype(self.model_config.dtype) # Initialize the distributed environment. _init_distributed_environment( self.parallel_config, self.rank, self.distributed_init_method ) # 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, gpu_memory_utilization: float, cpu_swap_space: int, ) -> Tuple[int, int]: # Profile the memory usage of the model and get the maximum number of # cache blocks that can be allocated with the remaining free memory. torch.cuda.empty_cache() # Execute a forward pass with dummy inputs to profile the memory usage # of the model. self.model_runner.profile_run() # Calculate the number of blocks that can be allocated with the # profiled peak memory. torch.cuda.synchronize() free_gpu_memory, total_gpu_memory = torch.cuda.mem_get_info() peak_memory = total_gpu_memory - free_gpu_memory cache_block_size = CacheEngine.get_cache_block_size( block_size, self.model_config, self.parallel_config ) num_gpu_blocks = int( (total_gpu_memory * gpu_memory_utilization - peak_memory) // cache_block_size ) num_cpu_blocks = int(cpu_swap_space // cache_block_size) num_gpu_blocks = max(num_gpu_blocks, 0) num_cpu_blocks = max(num_cpu_blocks, 0) torch.cuda.empty_cache() 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.cache_events = self.cache_engine.events self.gpu_cache = self.cache_engine.gpu_cache self.model_runner.set_block_size(self.cache_engine.block_size) def warm_up_model(self) -> None: if not self.model_config.enforce_eager: self.model_runner.capture_model(self.gpu_cache) # Reset the seed to ensure that the random state is not affected by # the model initialization and profiling. set_random_seed(self.model_config.seed) 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. # TODO(woosuk): Profile swapping overhead and optimize if needed. if cache_events is not None: for event in cache_events: event.wait() @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 block_swapping_info = [ blocks_to_swap_in, blocks_to_swap_out, blocks_to_copy, ] broadcast_object_list([num_seq_groups] + block_swapping_info, src=0) else: # num_seq_groups, blocks_to_swap_in, blocks_to_swap_out, # blocks_to_copy (4 elements) recv_data = [None] * 4 broadcast_object_list(recv_data, src=0) num_seq_groups = recv_data[0] block_swapping_info = recv_data[1:] self.cache_swap(*block_swapping_info) # 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, ) -> 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: torch.distributed.init_process_group( backend="nccl", 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).cuda()) initialize_model_parallel( parallel_config.tensor_parallel_size, parallel_config.pipeline_parallel_size ) def _check_if_gpu_supports_dtype(torch_dtype: torch.dtype): # Check if the GPU supports the dtype. if torch_dtype == torch.bfloat16: compute_capability = torch.cuda.get_device_capability() if compute_capability[0] < 8: gpu_name = torch.cuda.get_device_name() raise ValueError( "Bfloat16 is only supported on GPUs with compute capability " f"of at least 8.0. Your {gpu_name} GPU has compute capability " f"{compute_capability[0]}.{compute_capability[1]}." )