"""A GPU worker class.""" from typing import Dict, List, Tuple import torch from cacheflow.config import (CacheConfig, ModelConfig, ParallelConfig, SchedulerConfig) from cacheflow.model_executor import get_model, InputMetadata, set_random_seed from cacheflow.model_executor.parallel_utils.parallel_state import ( initialize_model_parallel, initialize_all_reduce_launcher) from cacheflow.sampling_params import SamplingParams from cacheflow.sequence import (SequenceData, SequenceGroupMetadata, SequenceOutputs) from cacheflow.worker.cache_engine import CacheEngine from cacheflow.utils import get_gpu_memory 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, rank: int, distributed_init_method: str, ) -> None: self.model_config = model_config self.parallel_config = parallel_config self.scheduler_config = scheduler_config self.rank = rank self.distributed_init_method = distributed_init_method # Initialize the distributed environment. _init_distributed_environment(parallel_config, rank, distributed_init_method) # Initialize the model. set_random_seed(self.model_config.seed) self.model = get_model(model_config) initialize_all_reduce_launcher( self.scheduler_config.max_num_batched_tokens, self.model_config.get_hidden_size(), self.model_config.dtype, ) # Uninitialized cache engine. Will be initialized by # self.init_cache_engine(). self.cache_config = None self.block_size = None self.cache_engine = None self.cache_events = None self.gpu_cache = None @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() torch.cuda.reset_peak_memory_stats() # Profile memory usage with max_num_sequences sequences and the total # number of tokens equal to max_num_batched_tokens. # Enable top-k sampling to reflect the accurate memory usage. sampling_params = SamplingParams(top_p=0.99, top_k=self.model.config.vocab_size - 1) max_num_batched_tokens = self.scheduler_config.max_num_batched_tokens max_num_seqs = self.scheduler_config.max_num_seqs seqs = [] for group_id in range(max_num_seqs): seq_len = (max_num_batched_tokens // max_num_seqs + (group_id < max_num_batched_tokens % max_num_seqs)) seq_data = SequenceData([0] * seq_len) seq = SequenceGroupMetadata( request_id=str(group_id), is_prompt=True, seq_data={group_id: seq_data}, sampling_params=sampling_params, block_tables=None, ) seqs.append(seq) input_tokens, input_positions, input_metadata = self._prepare_inputs(seqs) # Execute the model. num_layers = self.model_config.get_num_layers(self.parallel_config) self.model( input_ids=input_tokens, positions=input_positions, kv_caches=[(None, None)] * num_layers, input_metadata=input_metadata, cache_events=None, ) # Calculate the number of blocks that can be allocated with the # profiled peak memory. torch.cuda.synchronize() peak_memory = torch.cuda.max_memory_allocated() total_gpu_memory = get_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) torch.cuda.empty_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) return num_gpu_blocks, num_cpu_blocks def init_cache_engine(self, cache_config: CacheConfig) -> None: self.cache_config = cache_config self.block_size = cache_config.block_size 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 def _prepare_inputs( self, seq_group_metadata_list: List[SequenceGroupMetadata], ) -> Tuple[torch.LongTensor, torch.LongTensor, InputMetadata]: seq_groups: List[Tuple[List[int], SamplingParams]] = [] input_tokens: List[int] = [] input_positions: List[int] = [] slot_mapping: List[int] = [] # Add prompt tokens. prompt_lens: List[int] = [] for seq_group_metadata in seq_group_metadata_list: if not seq_group_metadata.is_prompt: continue seq_ids = list(seq_group_metadata.seq_data.keys()) sampling_params = seq_group_metadata.sampling_params seq_groups.append((seq_ids, sampling_params)) # Use any sequence in the group. seq_id = seq_ids[0] seq_data = seq_group_metadata.seq_data[seq_id] prompt_tokens = seq_data.get_token_ids() prompt_len = len(prompt_tokens) prompt_lens.append(prompt_len) input_tokens.extend(prompt_tokens) # NOTE(woosuk): Here we assume that the first token in the prompt # is always the first token in the sequence. input_positions.extend(range(len(prompt_tokens))) if seq_group_metadata.block_tables is None: # During memory profiling, the block tables are not initialized # yet. In this case, we just use a dummy slot mapping. slot_mapping.extend([0] * prompt_len) continue # Compute the slot mapping. block_table = seq_group_metadata.block_tables[seq_id] for i in range(prompt_len): block_number = block_table[i // self.block_size] block_offset = i % self.block_size slot = block_number * self.block_size + block_offset slot_mapping.append(slot) # Add generation tokens. max_context_len = 0 max_num_blocks_per_seq = 0 context_lens: List[int] = [] generation_block_tables: List[List[int]] = [] for seq_group_metadata in seq_group_metadata_list: if seq_group_metadata.is_prompt: continue seq_ids = list(seq_group_metadata.seq_data.keys()) sampling_params = seq_group_metadata.sampling_params seq_groups.append((seq_ids, sampling_params)) for seq_id in seq_ids: seq_data = seq_group_metadata.seq_data[seq_id] generation_token = seq_data.get_last_token_id() input_tokens.append(generation_token) context_len = seq_data.get_len() position = context_len - 1 input_positions.append(position) block_table = seq_group_metadata.block_tables[seq_id] generation_block_tables.append(block_table) max_context_len = max(max_context_len, context_len) max_num_blocks_per_seq = max( max_num_blocks_per_seq, len(block_table)) context_lens.append(context_len) block_number = block_table[position // self.block_size] block_offset = position % self.block_size slot = block_number * self.block_size + block_offset slot_mapping.append(slot) # Optimization: Pad the input length to be a multiple of 8. # This is required for utilizing the Tensor Cores in NVIDIA GPUs. input_tokens = _pad_to_alignment(input_tokens, multiple_of=8) input_positions = _pad_to_alignment(input_positions, multiple_of=8) # Convert to tensors. tokens_tensor = torch.tensor( input_tokens, dtype=torch.long, device='cuda') positions_tensor = torch.tensor( input_positions, dtype=torch.long, device='cuda') slot_mapping_tensor = torch.tensor( slot_mapping, dtype=torch.int, device='cuda') context_lens_tensor = torch.tensor( context_lens, dtype=torch.int, device='cuda') padded_block_tables = [ _pad_to_max(block_table, max_num_blocks_per_seq) for block_table in generation_block_tables] block_tables_tensor = torch.tensor( padded_block_tables, dtype=torch.int, device='cuda') seq_data: Dict[int, SequenceData] = {} for seq_group_metadata in seq_group_metadata_list: seq_data.update(seq_group_metadata.seq_data) input_metadata = InputMetadata( seq_groups=seq_groups, seq_data=seq_data, prompt_lens=prompt_lens, slot_mapping=slot_mapping_tensor, context_lens=context_lens_tensor, max_context_len=max_context_len, block_tables=block_tables_tensor, ) return tokens_tensor, positions_tensor, input_metadata @torch.inference_mode() def execute_model( self, seq_group_metadata_list: List[SequenceGroupMetadata], blocks_to_swap_in: Dict[int, int], blocks_to_swap_out: Dict[int, int], blocks_to_copy: Dict[int, List[int]], ) -> Dict[int, SequenceOutputs]: # 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 if issued_cache_op: cache_events = self.cache_events else: cache_events = None # If there is no input, we don't need to execute the model. if not seq_group_metadata_list: if cache_events is not None: for event in cache_events: event.wait() return {} # Prepare input tensors. input_tokens, input_positions, input_metadata = self._prepare_inputs( seq_group_metadata_list) # Execute the model. output = self.model( input_ids=input_tokens, positions=input_positions, kv_caches=self.gpu_cache, input_metadata=input_metadata, cache_events=cache_events, ) return output def _init_distributed_environment( parallel_config: ParallelConfig, rank: int, distributed_init_method: str, ) -> None: """Initialize the distributed environment.""" 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 _pad_to_alignment(x: List[int], multiple_of: int) -> List[int]: return x + [0] * ((-len(x)) % multiple_of) def _pad_to_max(x: List[int], max_len: int) -> List[int]: return x + [0] * (max_len - len(x))