import copy import time from functools import partial from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple, Union from vllm.config import (CacheConfig, ModelConfig, ParallelConfig, SchedulerConfig) from vllm.core.scheduler import Scheduler, SchedulerOutputs from vllm.engine.arg_utils import EngineArgs from vllm.engine.ray_utils import RayWorker, initialize_cluster, ray from vllm.logger import init_logger from vllm.outputs import RequestOutput from vllm.sampling_params import SamplingParams from vllm.sequence import (SamplerOutput, Sequence, SequenceGroup, SequenceGroupMetadata, SequenceOutputs, SequenceStatus) from vllm.transformers_utils.tokenizer import (detokenize_incrementally, get_tokenizer) from vllm.utils import Counter if ray: from ray.air.util.torch_dist import init_torch_dist_process_group from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy if TYPE_CHECKING: from ray.util.placement_group import PlacementGroup logger = init_logger(__name__) _LOGGING_INTERVAL_SEC = 5 class LLMEngine: """An LLM engine that receives requests and generates texts. This is the main class for the vLLM engine. It receives requests from clients and generates texts from the LLM. It includes a tokenizer, a language model (possibly distributed across multiple GPUs), and GPU memory space allocated for intermediate states (aka KV cache). This class utilizes iteration-level scheduling and efficient memory management to maximize the serving throughput. The `LLM` class wraps this class for offline batched inference and the `AsyncLLMEngine` class wraps this class for online serving. NOTE: The config arguments are derived from the `EngineArgs` class. For the comprehensive list of arguments, see `EngineArgs`. Args: model_config: The configuration related to the LLM model. cache_config: The configuration related to the KV cache memory management. parallel_config: The configuration related to distributed execution. scheduler_config: The configuration related to the request scheduler. distributed_init_method: The initialization method for distributed execution. See `torch.distributed.init_process_group` for details. stage_devices: The list of devices for each stage. Each stage is a list of (rank, node_resource, device) tuples. log_stats: Whether to log statistics. """ def __init__( self, model_config: ModelConfig, cache_config: CacheConfig, parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, distributed_init_method: str, placement_group: Optional["PlacementGroup"], log_stats: bool, ) -> None: logger.info( "Initializing an LLM engine with config: " f"model={model_config.model!r}, " f"tokenizer={model_config.tokenizer!r}, " f"tokenizer_mode={model_config.tokenizer_mode}, " f"revision={model_config.revision}, " f"trust_remote_code={model_config.trust_remote_code}, " f"dtype={model_config.dtype}, " f"download_dir={model_config.download_dir!r}, " f"load_format={model_config.load_format}, " f"tensor_parallel_size={parallel_config.tensor_parallel_size}, " f"quantization={model_config.quantization}, " f"seed={model_config.seed})") # TODO(woosuk): Print more configs in debug mode. self.model_config = model_config self.cache_config = cache_config self.parallel_config = parallel_config self.scheduler_config = scheduler_config self.log_stats = log_stats self._verify_args() self.tokenizer = get_tokenizer( model_config.tokenizer, tokenizer_mode=model_config.tokenizer_mode, trust_remote_code=model_config.trust_remote_code, revision=model_config.revision) self.seq_counter = Counter() # Create the parallel GPU workers. if self.parallel_config.worker_use_ray: self._init_workers_ray(placement_group) else: self._init_workers(distributed_init_method) # Profile the memory usage and initialize the cache. self._init_cache() # Create the scheduler. self.scheduler = Scheduler(scheduler_config, cache_config) # Logging. self.last_logging_time = 0.0 # List of (timestamp, num_tokens) self.num_prompt_tokens: List[Tuple[float, int]] = [] # List of (timestamp, num_tokens) self.num_generation_tokens: List[Tuple[float, int]] = [] def _init_workers(self, distributed_init_method: str): # Lazy import the Worker to avoid importing torch.cuda/xformers # before CUDA_VISIBLE_DEVICES is set in the Worker from vllm.worker.worker import Worker # pylint: disable=import-outside-toplevel assert self.parallel_config.world_size == 1, ( "Ray is required if parallel_config.world_size > 1.") self.workers: List[Worker] = [] worker = Worker( self.model_config, self.parallel_config, self.scheduler_config, 0, distributed_init_method, ) self.workers.append(worker) self._run_workers( "init_model", get_all_outputs=True, ) def _init_workers_ray(self, placement_group: "PlacementGroup", **ray_remote_kwargs): # Lazy import the Worker to avoid importing torch.cuda/xformers # before CUDA_VISIBLE_DEVICES is set in the Worker from vllm.worker.worker import Worker # pylint: disable=import-outside-toplevel self.workers: List[Worker] = [] for bundle in placement_group.bundle_specs: if not bundle.get("GPU", 0): continue worker = ray.remote( num_cpus=0, num_gpus=1, scheduling_strategy=PlacementGroupSchedulingStrategy( placement_group=placement_group, placement_group_capture_child_tasks=True), **ray_remote_kwargs, )(RayWorker).remote(self.model_config.trust_remote_code) self.workers.append(worker) # Initialize torch distributed process group for the workers. init_torch_dist_process_group(self.workers, backend="nccl") model_config = copy.deepcopy(self.model_config) parallel_config = copy.deepcopy(self.parallel_config) scheduler_config = copy.deepcopy(self.scheduler_config) self._run_workers("init_worker", get_all_outputs=True, worker_init_fn=lambda: Worker( model_config, parallel_config, scheduler_config, None, None, )) self._run_workers( "init_model", get_all_outputs=True, ) def _verify_args(self) -> None: self.model_config.verify_with_parallel_config(self.parallel_config) self.cache_config.verify_with_parallel_config(self.parallel_config) def _init_cache(self) -> None: """Profiles the memory usage and initializes the KV cache.""" # Get the maximum number of blocks that can be allocated on GPU and CPU. num_blocks = self._run_workers( "profile_num_available_blocks", get_all_outputs=True, block_size=self.cache_config.block_size, gpu_memory_utilization=self.cache_config.gpu_memory_utilization, cpu_swap_space=self.cache_config.swap_space_bytes, ) # Since we use a shared centralized controller, we take the minimum # number of blocks across all workers to make sure all the memory # operators can be applied to all workers. num_gpu_blocks = min(b[0] for b in num_blocks) num_cpu_blocks = min(b[1] for b in num_blocks) # FIXME(woosuk): Change to debug log. logger.info(f"# GPU blocks: {num_gpu_blocks}, " f"# CPU blocks: {num_cpu_blocks}") if num_gpu_blocks <= 0: raise ValueError("No available memory for the cache blocks. " "Try increasing `gpu_memory_utilization` when " "initializing the engine.") self.cache_config.num_gpu_blocks = num_gpu_blocks self.cache_config.num_cpu_blocks = num_cpu_blocks # Initialize the cache. self._run_workers("init_cache_engine", cache_config=self.cache_config) @classmethod def from_engine_args(cls, engine_args: EngineArgs) -> "LLMEngine": """Creates an LLM engine from the engine arguments.""" # Create the engine configs. engine_configs = engine_args.create_engine_configs() parallel_config = engine_configs[2] # Initialize the cluster. distributed_init_method, placement_group = initialize_cluster( parallel_config) # Create the LLM engine. engine = cls(*engine_configs, distributed_init_method, placement_group, log_stats=not engine_args.disable_log_stats) return engine def add_request( self, request_id: str, prompt: Optional[str], sampling_params: SamplingParams, prompt_token_ids: Optional[List[int]] = None, arrival_time: Optional[float] = None, ) -> None: """Add a request to the engine's request pool. The request is added to the request pool and will be processed by the scheduler as `engine.step()` is called. The exact scheduling policy is determined by the scheduler. Args: request_id: The unique ID of the request. prompt: The prompt string. Can be None if prompt_token_ids is provided. sampling_params: The sampling parameters for text generation. prompt_token_ids: The token IDs of the prompt. If None, we use the tokenizer to convert the prompts to token IDs. arrival_time: The arrival time of the request. If None, we use the current time. """ if arrival_time is None: arrival_time = time.time() if prompt_token_ids is None: assert prompt is not None prompt_token_ids = self.tokenizer.encode(prompt) # Create the sequences. block_size = self.cache_config.block_size seq_id = next(self.seq_counter) seq = Sequence(seq_id, prompt, prompt_token_ids, block_size) # Create the sequence group. seq_group = SequenceGroup(request_id, [seq], sampling_params, arrival_time) # Add the sequence group to the scheduler. self.scheduler.add_seq_group(seq_group) def abort_request(self, request_id: Union[str, Iterable[str]]) -> None: """Aborts a request(s) with the given ID. Args: request_id: The ID(s) of the request to abort. """ self.scheduler.abort_seq_group(request_id) def get_model_config(self) -> ModelConfig: """Gets the model configuration.""" return self.model_config def get_num_unfinished_requests(self) -> int: """Gets the number of unfinished requests.""" return self.scheduler.get_num_unfinished_seq_groups() def has_unfinished_requests(self) -> bool: """Returns True if there are unfinished requests.""" return self.scheduler.has_unfinished_seqs() def _schedule( self ) -> Tuple[List[SequenceGroupMetadata], SchedulerOutputs, List[RequestOutput]]: seq_group_metadata_list, scheduler_outputs = self.scheduler.schedule() return seq_group_metadata_list, scheduler_outputs, [ RequestOutput.from_seq_group(seq_group) for seq_group in scheduler_outputs.ignored_seq_groups ] def _check_beam_search_early_stopping( self, early_stopping: Union[bool, str], sampling_params: SamplingParams, best_running_seq: Sequence, current_worst_seq: Sequence, ) -> bool: assert sampling_params.use_beam_search length_penalty = sampling_params.length_penalty if early_stopping is True: return True current_worst_score = (current_worst_seq.get_beam_search_score( length_penalty=length_penalty, eos_token_id=self.tokenizer.eos_token_id)) if early_stopping is False: highest_attainable_score = (best_running_seq.get_beam_search_score( length_penalty=length_penalty, eos_token_id=self.tokenizer.eos_token_id)) else: assert early_stopping == "never" if length_penalty > 0.0: # If length_penalty > 0.0, beam search will prefer longer # sequences. The highest attainable score calculation is # based on the longest possible sequence length in this case. max_possible_length = max( best_running_seq.get_prompt_len() + sampling_params.max_tokens, self.scheduler_config.max_model_len) highest_attainable_score = ( best_running_seq.get_beam_search_score( length_penalty=length_penalty, eos_token_id=self.tokenizer.eos_token_id, seq_len=max_possible_length)) else: # Otherwise, beam search will prefer shorter sequences. The # highest attainable score calculation is based on the current # sequence length. highest_attainable_score = ( best_running_seq.get_beam_search_score( length_penalty=length_penalty, eos_token_id=self.tokenizer.eos_token_id)) return current_worst_score >= highest_attainable_score def _process_sequence_group_samples( self, seq_group: SequenceGroup, samples: List[SequenceOutputs]) -> None: parent_seqs = seq_group.get_seqs(status=SequenceStatus.RUNNING) existing_finished_seqs = seq_group.get_finished_seqs() parent_child_dict = { parent_seq.seq_id: [] for parent_seq in parent_seqs } for sample in samples: parent_child_dict[sample.parent_seq_id].append(sample) # List of (child, parent) child_seqs: List[Tuple[Sequence, Sequence]] = [] # Process the child samples for each parent sequence for parent in parent_seqs: child_samples: List[SequenceOutputs] = parent_child_dict[ parent.seq_id] if len(child_samples) == 0: # This parent sequence has no children samples. Remove # the parent sequence from the sequence group since it will # not be used in the future iterations. parent.status = SequenceStatus.FINISHED_ABORTED seq_group.remove(parent.seq_id) self.scheduler.free_seq(parent) continue # Fork the parent sequence if there are multiple child samples. for child_sample in child_samples[:-1]: new_child_seq_id = next(self.seq_counter) child = parent.fork(new_child_seq_id) child.append_token_id(child_sample.output_token, child_sample.logprobs) child_seqs.append((child, parent)) # Continue the parent sequence for the last child sample. # We reuse the parent sequence here to reduce redundant memory # copies, especially when using non-beam search sampling methods. last_child_sample = child_samples[-1] parent.append_token_id(last_child_sample.output_token, last_child_sample.logprobs) child_seqs.append((parent, parent)) for seq, _ in child_seqs: self._decode_sequence(seq) self._check_stop(seq, seq_group.sampling_params) # Non-beam search case if not seq_group.sampling_params.use_beam_search: # For newly created child sequences, add them to the sequence group # and fork them in block manager if they are not finished. for seq, parent in child_seqs: if seq is not parent: seq_group.add(seq) if not seq.is_finished(): self.scheduler.fork_seq(parent, seq) # Free the finished and selected parent sequences' memory in block # manager. Keep them in the sequence group as candidate output. # NOTE: we need to fork the new sequences before freeing the # old sequences. for seq, parent in child_seqs: if seq is parent and seq.is_finished(): self.scheduler.free_seq(seq) return # Beam search case # Select the child sequences to keep in the sequence group. selected_child_seqs = [] unselected_child_seqs = [] beam_width = seq_group.sampling_params.best_of length_penalty = seq_group.sampling_params.length_penalty # Select the newly finished sequences with the highest scores # to replace existing finished sequences. # Tuple of (seq, parent, is_new) existing_finished_seqs = [(seq, None, False) for seq in existing_finished_seqs] new_finished_seqs = [(seq, parent, True) for seq, parent in child_seqs if seq.is_finished()] all_finished_seqs = existing_finished_seqs + new_finished_seqs # Sort the finished sequences by their scores. all_finished_seqs.sort(key=lambda x: x[0].get_beam_search_score( length_penalty=length_penalty, eos_token_id=self.tokenizer.eos_token_id), reverse=True) for seq, parent, is_new in all_finished_seqs[:beam_width]: if is_new: # A newly generated child sequence finishes and has a high # score, so we will add it into the sequence group. selected_child_seqs.append((seq, parent)) for seq, parent, is_new in all_finished_seqs[beam_width:]: if is_new: # A newly generated child sequence finishes but has a low # score, so we will not add it into the sequence group. # Additionally, if this sequence is a continuation of a # parent sequence, we will need remove the parent sequence # from the sequence group. unselected_child_seqs.append((seq, parent)) else: # An existing finished sequence has a low score, so we will # remove it from the sequence group. seq_group.remove(seq.seq_id) # select the top beam_width sequences from the running # sequences for the next iteration to continue the beam # search. running_child_seqs = [(seq, parent) for seq, parent in child_seqs if not seq.is_finished()] # Sort the running sequences by their scores. running_child_seqs.sort(key=lambda x: x[0].get_beam_search_score( length_penalty=length_penalty, eos_token_id=self.tokenizer.eos_token_id), reverse=True) # Check if we can stop the beam search. if len(running_child_seqs) == 0: # No running sequences, stop the beam search. stop_beam_search = True elif len(all_finished_seqs) < beam_width: # Not enough finished sequences, continue the beam search. stop_beam_search = False else: # Check the early stopping criteria best_running_seq = running_child_seqs[0][0] current_worst_seq = all_finished_seqs[beam_width - 1][0] stop_beam_search = self._check_beam_search_early_stopping( seq_group.sampling_params.early_stopping, seq_group.sampling_params, best_running_seq, current_worst_seq) if stop_beam_search: # Stop the beam search and remove all the running sequences from # the sequence group. unselected_child_seqs.extend(running_child_seqs) else: # Continue the beam search and select the top beam_width sequences # to continue the beam search. selected_child_seqs.extend(running_child_seqs[:beam_width]) # The remaining running sequences will not be used in the next # iteration. Again, if these sequences are continuations of # parent sequences, we will need to remove the parent sequences # from the sequence group. unselected_child_seqs.extend(running_child_seqs[beam_width:]) # For newly created child sequences, add them to the sequence group # and fork them in block manager if they are not finished. for seq, parent in selected_child_seqs: if seq is not parent: seq_group.add(seq) if not seq.is_finished(): self.scheduler.fork_seq(parent, seq) # Free the finished and selected parent sequences' memory in block # manager. Keep them in the sequence group as candidate output. for seq, parent in selected_child_seqs: if seq is parent and seq.is_finished(): self.scheduler.free_seq(seq) # Remove the unselected parent sequences from the sequence group and # free their memory in block manager. for seq, parent in unselected_child_seqs: if seq is parent: # Remove the parent sequence if it is not selected for next # iteration seq_group.remove(seq.seq_id) self.scheduler.free_seq(seq) def _process_model_outputs( self, output: SamplerOutput, scheduler_outputs: SchedulerOutputs) -> List[RequestOutput]: # Update the scheduled sequence groups with the model outputs. scheduled_seq_groups = scheduler_outputs.scheduled_seq_groups for seq_group, samples in zip(scheduled_seq_groups, output): self._process_sequence_group_samples(seq_group, samples) # Free the finished sequence groups. self.scheduler.free_finished_seq_groups() # Create the outputs. request_outputs: List[RequestOutput] = [] for seq_group in (scheduled_seq_groups + scheduler_outputs.ignored_seq_groups): request_output = RequestOutput.from_seq_group(seq_group) request_outputs.append(request_output) if self.log_stats: # Log the system stats. self._log_system_stats(scheduler_outputs.prompt_run, scheduler_outputs.num_batched_tokens) return request_outputs def step(self) -> List[RequestOutput]: """Performs one decoding iteration and returns newly generated results. This function performs one decoding iteration of the engine. It first schedules the sequences to be executed in the next iteration and the token blocks to be swapped in/out/copy. Then, it executes the model and updates the scheduler with the model outputs. Finally, it decodes the sequences and returns the newly generated results. """ seq_group_metadata_list, scheduler_outputs, ignored = self._schedule() if scheduler_outputs.is_empty(): return ignored # Execute the model. output = self._run_workers( "execute_model", seq_group_metadata_list=seq_group_metadata_list, blocks_to_swap_in=scheduler_outputs.blocks_to_swap_in, blocks_to_swap_out=scheduler_outputs.blocks_to_swap_out, blocks_to_copy=scheduler_outputs.blocks_to_copy, ) return self._process_model_outputs(output, scheduler_outputs) + ignored def _log_system_stats( self, prompt_run: bool, num_batched_tokens: int, ) -> None: now = time.time() # Log the number of batched input tokens. if prompt_run: self.num_prompt_tokens.append((now, num_batched_tokens)) else: self.num_generation_tokens.append((now, num_batched_tokens)) elapsed_time = now - self.last_logging_time if elapsed_time < _LOGGING_INTERVAL_SEC: return # Discard the old stats. self.num_prompt_tokens = [(t, n) for t, n in self.num_prompt_tokens if now - t < _LOGGING_INTERVAL_SEC] self.num_generation_tokens = [(t, n) for t, n in self.num_generation_tokens if now - t < _LOGGING_INTERVAL_SEC] if len(self.num_prompt_tokens) > 1: total_num_tokens = sum(n for _, n in self.num_prompt_tokens[:-1]) window = now - self.num_prompt_tokens[0][0] avg_prompt_throughput = total_num_tokens / window else: avg_prompt_throughput = 0.0 if len(self.num_generation_tokens) > 1: total_num_tokens = sum(n for _, n in self.num_generation_tokens[:-1]) window = now - self.num_generation_tokens[0][0] avg_generation_throughput = total_num_tokens / window else: avg_generation_throughput = 0.0 total_num_gpu_blocks = self.cache_config.num_gpu_blocks num_free_gpu_blocks = ( self.scheduler.block_manager.get_num_free_gpu_blocks()) num_used_gpu_blocks = total_num_gpu_blocks - num_free_gpu_blocks gpu_cache_usage = num_used_gpu_blocks / total_num_gpu_blocks total_num_cpu_blocks = self.cache_config.num_cpu_blocks if total_num_cpu_blocks > 0: num_free_cpu_blocks = ( self.scheduler.block_manager.get_num_free_cpu_blocks()) num_used_cpu_blocks = total_num_cpu_blocks - num_free_cpu_blocks cpu_cache_usage = num_used_cpu_blocks / total_num_cpu_blocks else: cpu_cache_usage = 0.0 logger.info("Avg prompt throughput: " f"{avg_prompt_throughput:.1f} tokens/s, " "Avg generation throughput: " f"{avg_generation_throughput:.1f} tokens/s, " f"Running: {len(self.scheduler.running)} reqs, " f"Swapped: {len(self.scheduler.swapped)} reqs, " f"Pending: {len(self.scheduler.waiting)} reqs, " f"GPU KV cache usage: {gpu_cache_usage * 100:.1f}%, " f"CPU KV cache usage: {cpu_cache_usage * 100:.1f}%") self.last_logging_time = now def _decode_sequence(self, seq: Sequence) -> None: """Decodes the new token for a sequence.""" (new_tokens, new_output_text, prefix_offset, read_offset) = detokenize_incrementally( self.tokenizer, all_input_ids=seq.get_token_ids(), prev_tokens=seq.tokens, prefix_offset=seq.prefix_offset, read_offset=seq.read_offset, skip_special_tokens=True, ) if seq.tokens is None: seq.tokens = new_tokens else: seq.tokens.extend(new_tokens) seq.prefix_offset = prefix_offset seq.read_offset = read_offset seq.output_text += new_output_text def _check_stop(self, seq: Sequence, sampling_params: SamplingParams) -> None: """Stop the finished sequences.""" for stop_str in sampling_params.stop: if seq.output_text.endswith(stop_str): # Truncate the output text so that the stop string is # not included in the output. seq.output_text = seq.output_text[:-len(stop_str)] seq.status = SequenceStatus.FINISHED_STOPPED return if seq.get_last_token_id() in sampling_params.stop_token_ids: seq.status = SequenceStatus.FINISHED_STOPPED return # Check if the sequence has reached max_model_len. if seq.get_len() > self.scheduler_config.max_model_len: seq.status = SequenceStatus.FINISHED_LENGTH_CAPPED return # Check if the sequence has reached max_tokens. if seq.get_output_len() == sampling_params.max_tokens: seq.status = SequenceStatus.FINISHED_LENGTH_CAPPED return # Check if the sequence has generated the EOS token. if ((not sampling_params.ignore_eos) and seq.get_last_token_id() == self.tokenizer.eos_token_id): seq.status = SequenceStatus.FINISHED_STOPPED return def _run_workers( self, method: str, *args, get_all_outputs: bool = False, **kwargs, ) -> Any: """Runs the given method on all workers.""" all_outputs = [] for worker in self.workers: if self.parallel_config.worker_use_ray: executor = partial(worker.execute_method.remote, method) else: executor = getattr(worker, method) output = executor(*args, **kwargs) all_outputs.append(output) if self.parallel_config.worker_use_ray: all_outputs = ray.get(all_outputs) if get_all_outputs: return all_outputs # Make sure all workers have the same results. output = all_outputs[0] for other_output in all_outputs[1:]: assert output == other_output return output