# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import time from collections import Counter as collectionsCounter from collections import deque from contextlib import contextmanager from dataclasses import dataclass from functools import partial from typing import (TYPE_CHECKING, Any, Callable, ClassVar, Deque, Dict, Iterable, List, Literal, Mapping, NamedTuple, Optional) from typing import Sequence as GenericSequence from typing import Set, Type, Union, cast import torch from typing_extensions import TypeVar import vllm.envs as envs from vllm.config import (DecodingConfig, LoRAConfig, ModelConfig, ObservabilityConfig, ParallelConfig, SchedulerConfig, VllmConfig) from vllm.core.scheduler import ScheduledSequenceGroup, SchedulerOutputs from vllm.engine.arg_utils import EngineArgs from vllm.engine.metrics_types import StatLoggerBase, Stats from vllm.engine.output_processor.interfaces import ( SequenceGroupOutputProcessor) from vllm.engine.output_processor.stop_checker import StopChecker from vllm.entrypoints.openai.logits_processors import ( get_logits_processors as get_openai_logits_processors) from vllm.executor.executor_base import ExecutorBase from vllm.inputs import ProcessorInputs, PromptType, SingletonInputs from vllm.inputs.parse import split_enc_dec_inputs from vllm.inputs.preprocess import InputPreprocessor from vllm.logger import init_logger from vllm.logits_process import get_bad_words_logits_processors from vllm.lora.request import LoRARequest from vllm.model_executor.layers.sampler import SamplerOutput from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry from vllm.multimodal.processing import EncDecMultiModalProcessor from vllm.outputs import (PoolingRequestOutput, RequestOutput, RequestOutputFactory) from vllm.pooling_params import PoolingParams from vllm.sampling_params import RequestOutputKind, SamplingParams from vllm.sequence import (ExecuteModelRequest, ParallelSampleSequenceGroup, PoolingSequenceGroupOutput, Sequence, SequenceGroup, SequenceGroupBase, SequenceGroupMetadata, SequenceGroupOutput, SequenceStatus) from vllm.tracing import (SpanAttributes, SpanKind, extract_trace_context, init_tracer) from vllm.transformers_utils.detokenizer import Detokenizer from vllm.transformers_utils.tokenizer import AnyTokenizer from vllm.transformers_utils.tokenizer_group import ( TokenizerGroup, init_tokenizer_from_configs) from vllm.usage.usage_lib import (UsageContext, is_usage_stats_enabled, usage_message) from vllm.utils import Counter, Device, resolve_obj_by_qualname, weak_bind from vllm.version import __version__ as VLLM_VERSION from vllm.worker.model_runner_base import InputProcessingError logger = init_logger(__name__) _LOCAL_LOGGING_INTERVAL_SEC = 5 _O = TypeVar("_O", RequestOutput, PoolingRequestOutput) _R = TypeVar("_R", default=Any) @dataclass class SchedulerOutputState: """Caches the scheduler outputs for a virtual engine. Used for Multi-Step""" seq_group_metadata_list: Optional[List[SequenceGroupMetadata]] = None scheduler_outputs: Optional[SchedulerOutputs] = None allow_async_output_proc: bool = False last_output: Optional[SamplerOutput] = None class OutputData(NamedTuple): outputs: List[SamplerOutput] seq_group_metadata_list: List[SequenceGroupMetadata] scheduler_outputs: SchedulerOutputs is_async: bool is_last_step: bool # Indicates if this output is from the first step of the # multi-step. When multi-step is disabled, this is always # set to True. # is_first_step_output is invalid when `outputs` has # outputs from multiple steps. is_first_step_output: Optional[bool] skip: List[int] class SchedulerContext: def __init__(self) -> None: self.output_queue: Deque[OutputData] = deque() self.request_outputs: List[Union[RequestOutput, PoolingRequestOutput]] = [] self.seq_group_metadata_list: Optional[ List[SequenceGroupMetadata]] = None self.scheduler_outputs: Optional[SchedulerOutputs] = None def append_output(self, outputs: List[SamplerOutput], seq_group_metadata_list: List[SequenceGroupMetadata], scheduler_outputs: SchedulerOutputs, is_async: bool, is_last_step: bool, is_first_step_output: Optional[bool]): self.output_queue.append( OutputData(outputs=outputs, seq_group_metadata_list=seq_group_metadata_list, scheduler_outputs=scheduler_outputs, is_async=is_async, is_last_step=is_last_step, is_first_step_output=is_first_step_output, skip=[])) 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`][vllm.LLM] class wraps this class for offline batched inference and the [`AsyncLLMEngine`][vllm.engine.async_llm_engine.AsyncLLMEngine] class wraps this class for online serving. The config arguments are derived from [`EngineArgs`][vllm.EngineArgs]. Args: vllm_config: The configuration for initializing and running vLLM. executor_class: The model executor class for managing distributed execution. log_stats: Whether to log statistics. usage_context: Specified entry point, used for usage info collection. """ DO_VALIDATE_OUTPUT: ClassVar[bool] = False """A flag to toggle whether to validate the type of request output.""" @classmethod @contextmanager def enable_output_validation(cls): cls.DO_VALIDATE_OUTPUT = True yield cls.DO_VALIDATE_OUTPUT = False @classmethod def validate_output( cls, output: object, output_type: Type[_O], ) -> _O: do_validate = cls.DO_VALIDATE_OUTPUT if ((TYPE_CHECKING or do_validate) and not isinstance(output, output_type)): raise TypeError(f"Expected output of type {output_type}, " f"but found type {type(output)}") return cast(_O, output) @classmethod def validate_outputs( cls, outputs: GenericSequence[object], output_type: Type[_O], ) -> List[_O]: do_validate = cls.DO_VALIDATE_OUTPUT outputs_: List[_O] if TYPE_CHECKING or do_validate: outputs_ = [] for output in outputs: if not isinstance(output, output_type): raise TypeError(f"Expected output of type {output_type}, " f"but found type {type(output)}") outputs_.append(output) else: outputs_ = outputs return outputs_ tokenizer: Optional[TokenizerGroup] def __init__( self, vllm_config: VllmConfig, executor_class: Type[ExecutorBase], log_stats: bool, usage_context: UsageContext = UsageContext.ENGINE_CONTEXT, stat_loggers: Optional[Dict[str, StatLoggerBase]] = None, mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY, use_cached_outputs: bool = False, ) -> None: if envs.VLLM_USE_V1: raise ValueError( "Using V0 LLMEngine, but envs.VLLM_USE_V1=True. " "This should not happen. As a workaround, try using " "LLMEngine.from_vllm_config(...) or explicitly set " "VLLM_USE_V1=0 or 1 and report this issue on Github.") self.vllm_config = vllm_config self.model_config = vllm_config.model_config self.cache_config = vllm_config.cache_config self.lora_config = vllm_config.lora_config self.parallel_config = vllm_config.parallel_config self.scheduler_config = vllm_config.scheduler_config self.device_config = vllm_config.device_config self.speculative_config = vllm_config.speculative_config # noqa self.load_config = vllm_config.load_config self.decoding_config = vllm_config.decoding_config or DecodingConfig( # noqa ) self.observability_config = vllm_config.observability_config or ObservabilityConfig( # noqa ) logger.info( "Initializing a V0 LLM engine (v%s) with config: %s, " "use_cached_outputs=%s, ", VLLM_VERSION, vllm_config, use_cached_outputs, ) self.log_stats = log_stats self.use_cached_outputs = use_cached_outputs if self.model_config.skip_tokenizer_init: self.tokenizer = None self.detokenizer = None tokenizer_group = None else: self.tokenizer = self._init_tokenizer() self.detokenizer = Detokenizer(self.tokenizer) tokenizer_group = self.get_tokenizer_group() # Ensure that the function doesn't contain a reference to self, # to avoid engine GC issues def get_tokenizer_for_seq(sequence: Sequence) -> AnyTokenizer: assert tokenizer_group, ("tokenizer_group cannot be None, " "make sure skip_tokenizer_init is False") return tokenizer_group.get_lora_tokenizer(sequence.lora_request) self.seq_counter = Counter() self.generation_config_fields = ( self.model_config.try_get_generation_config()) self.input_preprocessor = InputPreprocessor(self.model_config, self.tokenizer, mm_registry) self.model_executor = executor_class(vllm_config=vllm_config) if self.model_config.runner_type != "pooling": self._initialize_kv_caches() # If usage stat is enabled, collect relevant info. if is_usage_stats_enabled(): from vllm.model_executor.model_loader import ( get_architecture_class_name) usage_message.report_usage( get_architecture_class_name(self.model_config), usage_context, extra_kvs={ # Common configuration "dtype": str(self.model_config.dtype), "tensor_parallel_size": self.parallel_config.tensor_parallel_size, "block_size": self.cache_config.block_size, "gpu_memory_utilization": self.cache_config.gpu_memory_utilization, # Quantization "quantization": self.model_config.quantization, "kv_cache_dtype": str(self.cache_config.cache_dtype), # Feature flags "enable_lora": bool(self.lora_config), "enable_prefix_caching": self.cache_config.enable_prefix_caching, "enforce_eager": self.model_config.enforce_eager, "disable_custom_all_reduce": self.parallel_config.disable_custom_all_reduce, }) self.cached_scheduler_outputs = [ SchedulerOutputState() for _ in range(self.parallel_config.pipeline_parallel_size) ] self.scheduler_contexts = [ SchedulerContext() for _ in range(self.parallel_config.pipeline_parallel_size) ] if self.model_config.use_async_output_proc: process_model_outputs = weak_bind(self._process_model_outputs) self.async_callbacks = [ partial(process_model_outputs, ctx=self.scheduler_contexts[v_id]) for v_id in range(self.parallel_config.pipeline_parallel_size) ] else: self.async_callbacks = [] # Currently used by AsyncLLMEngine to ensure quick append # of request outputs to asyncio queues self.process_request_outputs_callback: Optional[Callable] = None # Create the scheduler. # NOTE: the cache_config here have been updated with the numbers of # GPU and CPU blocks, which are profiled in the distributed executor. if isinstance(self.vllm_config.scheduler_config.scheduler_cls, str): Scheduler = resolve_obj_by_qualname( self.vllm_config.scheduler_config.scheduler_cls) else: Scheduler = self.vllm_config.scheduler_config.scheduler_cls self.scheduler = [ Scheduler( self.scheduler_config, self.cache_config, self.lora_config, self.parallel_config.pipeline_parallel_size, self.async_callbacks[v_id] if self.model_config.use_async_output_proc else None) for v_id in range(self.parallel_config.pipeline_parallel_size) ] # Metric Logging. if self.log_stats: if stat_loggers is not None: self.stat_loggers = stat_loggers else: # Lazy import for prometheus multiprocessing. # We need to set PROMETHEUS_MULTIPROC_DIR environment variable # before prometheus_client is imported. # See https://prometheus.github.io/client_python/multiprocess/ from vllm.engine.metrics import (LoggingStatLogger, PrometheusStatLogger) self.stat_loggers = { "logging": LoggingStatLogger( local_interval=_LOCAL_LOGGING_INTERVAL_SEC, vllm_config=vllm_config), "prometheus": PrometheusStatLogger( local_interval=_LOCAL_LOGGING_INTERVAL_SEC, labels=dict( model_name=self.model_config.served_model_name), vllm_config=vllm_config), } self.stat_loggers["prometheus"].info("cache_config", self.cache_config) self.tracer = None if self.observability_config.otlp_traces_endpoint: self.tracer = init_tracer( "vllm.llm_engine", self.observability_config.otlp_traces_endpoint) # Create sequence output processor, e.g. for beam search or # speculative decoding. self.output_processor = ( SequenceGroupOutputProcessor.create_output_processor( self.scheduler_config, self.detokenizer, self.scheduler, self.seq_counter, get_tokenizer_for_seq, stop_checker=StopChecker(self.scheduler_config.max_model_len, get_tokenizer_for_seq), )) self.seq_id_to_seq_group: Dict[str, SequenceGroupBase] = {} # Flag to set when an input fails to process and the engine should run # the next step without re-scheduling. self._skip_scheduling_next_step = False # Don't keep the dummy data in memory self.reset_mm_cache() def _initialize_kv_caches(self) -> None: """Initialize the KV cache in the worker(s). The workers will determine the number of blocks in both the GPU cache and the swap CPU cache. """ start = time.time() num_gpu_blocks, num_cpu_blocks = ( self.model_executor.determine_num_available_blocks()) if self.cache_config.num_gpu_blocks_override is not None: num_gpu_blocks_override = self.cache_config.num_gpu_blocks_override logger.info( "Overriding num_gpu_blocks=%d with " "num_gpu_blocks_override=%d", num_gpu_blocks, num_gpu_blocks_override) num_gpu_blocks = num_gpu_blocks_override self.cache_config.num_gpu_blocks = num_gpu_blocks self.cache_config.num_cpu_blocks = num_cpu_blocks self.model_executor.initialize_cache(num_gpu_blocks, num_cpu_blocks) elapsed = time.time() - start logger.info(("init engine (profile, create kv cache, " "warmup model) took %.2f seconds"), elapsed) @classmethod def _get_executor_cls(cls, engine_config: VllmConfig) -> Type[ExecutorBase]: # distributed_executor_backend must be set in VllmConfig.__post_init__ distributed_executor_backend = ( engine_config.parallel_config.distributed_executor_backend) # Initialize the cluster and specify the executor class. if isinstance(distributed_executor_backend, type): if not issubclass(distributed_executor_backend, ExecutorBase): raise TypeError( "distributed_executor_backend must be a subclass of " f"ExecutorBase. Got {distributed_executor_backend}.") executor_class = distributed_executor_backend elif distributed_executor_backend == "ray": from vllm.executor.ray_distributed_executor import ( RayDistributedExecutor) executor_class = RayDistributedExecutor elif distributed_executor_backend == "mp": from vllm.executor.mp_distributed_executor import ( MultiprocessingDistributedExecutor) assert not envs.VLLM_USE_RAY_SPMD_WORKER, ( "multiprocessing distributed executor backend does not " "support VLLM_USE_RAY_SPMD_WORKER=1") executor_class = MultiprocessingDistributedExecutor elif distributed_executor_backend == "uni": # JAX-style, single-process, multi-device executor. from vllm.executor.uniproc_executor import UniProcExecutor executor_class = UniProcExecutor elif distributed_executor_backend == "external_launcher": # executor with external launcher from vllm.executor.uniproc_executor import ( # noqa ExecutorWithExternalLauncher) executor_class = ExecutorWithExternalLauncher else: raise ValueError("unrecognized distributed_executor_backend: " f"{distributed_executor_backend}") return executor_class @classmethod def from_vllm_config( cls, vllm_config: VllmConfig, usage_context: UsageContext = UsageContext.ENGINE_CONTEXT, stat_loggers: Optional[Dict[str, StatLoggerBase]] = None, disable_log_stats: bool = False, ) -> "LLMEngine": return cls( vllm_config=vllm_config, executor_class=cls._get_executor_cls(vllm_config), log_stats=(not disable_log_stats), usage_context=usage_context, stat_loggers=stat_loggers, ) @classmethod def from_engine_args( cls, engine_args: EngineArgs, usage_context: UsageContext = UsageContext.ENGINE_CONTEXT, stat_loggers: Optional[Dict[str, StatLoggerBase]] = None, ) -> "LLMEngine": """Creates an LLM engine from the engine arguments.""" # Create the engine configs. vllm_config = engine_args.create_engine_config(usage_context) engine_cls = cls if envs.VLLM_USE_V1: from vllm.v1.engine.llm_engine import LLMEngine as V1LLMEngine engine_cls = V1LLMEngine return engine_cls.from_vllm_config( vllm_config=vllm_config, usage_context=usage_context, stat_loggers=stat_loggers, disable_log_stats=engine_args.disable_log_stats, ) def __reduce__(self): # This is to ensure that the LLMEngine is not referenced in # the closure used to initialize Ray worker actors raise RuntimeError("LLMEngine should not be pickled!") def __del__(self): # Shutdown model executor when engine is garbage collected # Use getattr since __init__ can fail before the field is set if model_executor := getattr(self, "model_executor", None): model_executor.shutdown() def get_tokenizer_group(self) -> TokenizerGroup: if self.tokenizer is None: raise ValueError("Unable to get tokenizer because " "skip_tokenizer_init is True") return self.tokenizer def get_tokenizer( self, lora_request: Optional[LoRARequest] = None, ) -> AnyTokenizer: return self.get_tokenizer_group().get_lora_tokenizer(lora_request) def _init_tokenizer(self) -> TokenizerGroup: return init_tokenizer_from_configs( model_config=self.model_config, scheduler_config=self.scheduler_config, lora_config=self.lora_config) 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) if self.lora_config: self.lora_config.verify_with_model_config(self.model_config) self.lora_config.verify_with_scheduler_config( self.scheduler_config) def _add_processed_request( self, request_id: str, processed_inputs: ProcessorInputs, params: Union[SamplingParams, PoolingParams], arrival_time: float, lora_request: Optional[LoRARequest], trace_headers: Optional[Mapping[str, str]] = None, priority: int = 0, ) -> Optional[SequenceGroup]: """Add a processed request to the engine's request pool. return the created sequence group. """ if isinstance(params, SamplingParams) and params.n > 1: ParallelSampleSequenceGroup.add_request( request_id, self, params, processed_inputs=processed_inputs, arrival_time=arrival_time, lora_request=lora_request, trace_headers=trace_headers, priority=priority, ) return None self._validate_model_inputs(processed_inputs, lora_request) # Create the sequences. block_size = self.cache_config.block_size seq_id = next(self.seq_counter) eos_token_id = self.input_preprocessor.get_eos_token_id(lora_request) encoder_inputs, decoder_inputs = split_enc_dec_inputs(processed_inputs) seq = Sequence(seq_id, decoder_inputs, block_size, eos_token_id, lora_request) encoder_seq = (None if encoder_inputs is None else Sequence( seq_id, encoder_inputs, block_size, eos_token_id, lora_request)) # Create a SequenceGroup based on SamplingParams or PoolingParams if isinstance(params, SamplingParams): seq_group = self._create_sequence_group_with_sampling( request_id, seq, params, arrival_time=arrival_time, lora_request=lora_request, trace_headers=trace_headers, encoder_seq=encoder_seq, priority=priority) elif isinstance(params, PoolingParams): seq_group = self._create_sequence_group_with_pooling( request_id, seq, params, arrival_time=arrival_time, lora_request=lora_request, encoder_seq=encoder_seq, priority=priority) else: raise ValueError( "Either SamplingParams or PoolingParams must be provided.") # Add the sequence group to the scheduler with least unfinished seqs. costs = [ scheduler.get_num_unfinished_seq_groups() for scheduler in self.scheduler ] min_cost_scheduler = self.scheduler[costs.index(min(costs))] min_cost_scheduler.add_seq_group(seq_group) return seq_group def stop_remote_worker_execution_loop(self) -> None: self.model_executor.stop_remote_worker_execution_loop() def add_request( self, request_id: str, prompt: PromptType, params: Union[SamplingParams, PoolingParams], arrival_time: Optional[float] = None, lora_request: Optional[LoRARequest] = None, tokenization_kwargs: Optional[dict[str, Any]] = None, trace_headers: Optional[Mapping[str, str]] = None, priority: int = 0, ) -> 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 to the LLM. See [PromptType][vllm.inputs.PromptType] for more details about the format of each input. params: Parameters for sampling or pooling. [SamplingParams][vllm.SamplingParams] for text generation. [PoolingParams][vllm.PoolingParams] for pooling. arrival_time: The arrival time of the request. If None, we use the current monotonic time. lora_request: The LoRA request to add. trace_headers: OpenTelemetry trace headers. priority: The priority of the request. Only applicable with priority scheduling. Details: - Set arrival_time to the current time if it is None. - Set prompt_token_ids to the encoded prompt if it is None. - Create `n` number of [Sequence][vllm.Sequence] objects. - Create a [SequenceGroup][vllm.SequenceGroup] object from the list of [Sequence][vllm.Sequence]. - Add the [SequenceGroup][vllm.SequenceGroup] object to the scheduler. Example: >>> # initialize engine >>> engine = LLMEngine.from_engine_args(engine_args) >>> # set request arguments >>> example_prompt = "Who is the president of the United States?" >>> sampling_params = SamplingParams(temperature=0.0) >>> request_id = 0 >>> >>> # add the request to the engine >>> engine.add_request( >>> str(request_id), >>> example_prompt, >>> SamplingParams(temperature=0.0)) >>> # continue the request processing >>> ... """ if not isinstance(request_id, str): raise TypeError( f"request_id must be a string, got {type(request_id)}") if lora_request is not None and not self.lora_config: raise ValueError(f"Got lora_request {lora_request} but LoRA is " "not enabled!") if priority != 0 and not self.scheduler_config.policy == "priority": raise ValueError(f"Got priority {priority} but " "Priority scheduling is not enabled.") if isinstance(params, SamplingParams) \ and params.logits_processors: raise ValueError( "Logits processors are not supported in multi-step decoding") if arrival_time is None: arrival_time = time.time() if (isinstance(prompt, dict) and prompt.get("prompt_embeds", None) is not None and not prompt.get("prompt_token_ids", None)): seq_len = prompt["prompt_embeds"].shape[0] prompt["prompt_token_ids"] = [0] * seq_len processed_inputs = self.input_preprocessor.preprocess( prompt, tokenization_kwargs=tokenization_kwargs, lora_request=lora_request, ) self._add_processed_request( request_id=request_id, processed_inputs=processed_inputs, params=params, arrival_time=arrival_time, lora_request=lora_request, trace_headers=trace_headers, priority=priority, ) def _create_sequence_group_with_sampling( self, request_id: str, seq: Sequence, sampling_params: SamplingParams, arrival_time: float, lora_request: Optional[LoRARequest], trace_headers: Optional[Mapping[str, str]] = None, encoder_seq: Optional[Sequence] = None, priority: int = 0, ) -> SequenceGroup: """Creates a SequenceGroup with SamplingParams.""" max_logprobs = self.get_model_config().max_logprobs if (sampling_params.logprobs and sampling_params.logprobs > max_logprobs) or ( sampling_params.prompt_logprobs and sampling_params.prompt_logprobs > max_logprobs): raise ValueError(f"Cannot request more than " f"{max_logprobs} logprobs.") sampling_params = self._build_logits_processors( sampling_params, lora_request) # Defensive copy of SamplingParams, which are used by the sampler, # this doesn't deep-copy LogitsProcessor objects sampling_params = sampling_params.clone() sampling_params.update_from_generation_config( self.generation_config_fields, seq.eos_token_id) # Create the sequence group. draft_size = 1 if self.vllm_config.speculative_config is not None: draft_size = \ self.vllm_config.speculative_config.num_speculative_tokens + 1 seq_group = SequenceGroup(request_id=request_id, seqs=[seq], arrival_time=arrival_time, sampling_params=sampling_params, lora_request=lora_request, trace_headers=trace_headers, encoder_seq=encoder_seq, priority=priority, draft_size=draft_size) return seq_group def _create_sequence_group_with_pooling( self, request_id: str, seq: Sequence, pooling_params: PoolingParams, arrival_time: float, lora_request: Optional[LoRARequest], encoder_seq: Optional[Sequence] = None, priority: int = 0, ) -> SequenceGroup: """Creates a SequenceGroup with PoolingParams.""" # Defensive copy of PoolingParams, which are used by the pooler pooling_params = pooling_params.clone() # Create the sequence group. seq_group = SequenceGroup(request_id=request_id, seqs=[seq], arrival_time=arrival_time, lora_request=lora_request, pooling_params=pooling_params, encoder_seq=encoder_seq, priority=priority) return 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. Details: - Refer to [vllm.core.scheduler.Scheduler.abort_seq_group][]. Example: >>> # initialize engine and add a request with request_id >>> request_id = str(0) >>> # abort the request >>> engine.abort_request(request_id) """ for scheduler in self.scheduler: scheduler.abort_seq_group( request_id, seq_id_to_seq_group=self.seq_id_to_seq_group) def get_vllm_config(self) -> VllmConfig: """Gets the vllm configuration.""" return self.vllm_config def get_model_config(self) -> ModelConfig: """Gets the model configuration.""" return self.model_config def get_parallel_config(self) -> ParallelConfig: """Gets the parallel configuration.""" return self.parallel_config def get_decoding_config(self) -> DecodingConfig: """Gets the decoding configuration.""" return self.decoding_config def get_scheduler_config(self) -> SchedulerConfig: """Gets the scheduler configuration.""" return self.scheduler_config def get_lora_config(self) -> LoRAConfig: """Gets the LoRA configuration.""" return self.lora_config def get_num_unfinished_requests(self) -> int: """Gets the number of unfinished requests.""" return sum(scheduler.get_num_unfinished_seq_groups() for scheduler in self.scheduler) def has_unfinished_requests(self) -> bool: """Returns True if there are unfinished requests.""" return any(scheduler.has_unfinished_seqs() for scheduler in self.scheduler) def has_unfinished_requests_for_virtual_engine( self, virtual_engine: int) -> bool: """ Returns True if there are unfinished requests for the virtual engine. """ return self.scheduler[virtual_engine].has_unfinished_seqs() def reset_mm_cache(self) -> bool: """Reset the multi-modal cache.""" return self.input_preprocessor.mm_registry.reset_processor_cache( self.model_config) def reset_prefix_cache(self, device: Optional[Device] = None) -> bool: """Reset prefix cache for all devices.""" success = True for scheduler in self.scheduler: success = success and scheduler.reset_prefix_cache(device) return success @staticmethod def _process_sequence_group_outputs( seq_group: SequenceGroup, outputs: List[PoolingSequenceGroupOutput], ) -> None: seq_group.pooled_data = outputs[0].data for seq in seq_group.get_seqs(): seq.status = SequenceStatus.FINISHED_STOPPED return def _process_model_outputs(self, ctx: SchedulerContext, request_id: Optional[str] = None) -> None: """Apply the model output to the sequences in the scheduled seq groups and return responses. ctx: The virtual engine context to work on request_id: If provided, then only this request is going to be processed """ now = time.time() if len(ctx.output_queue) == 0: return None # Get pending async postprocessor if request_id: # When we process only one request, no pop is required # (since later we will process all of the rest) (outputs, seq_group_metadata_list, scheduler_outputs, is_async, is_last_step, is_first_step_output, skip) = ctx.output_queue[0] else: (outputs, seq_group_metadata_list, scheduler_outputs, is_async, is_last_step, is_first_step_output, skip) = ctx.output_queue.popleft() # Sanity check assert len(seq_group_metadata_list) == len( scheduler_outputs.scheduled_seq_groups) has_multiple_outputs: bool = len(outputs) > 1 outputs_by_sequence_group: List[List[SequenceGroupOutput]] assert not has_multiple_outputs outputs_by_sequence_group = outputs # Determine the requests we need to operate on if request_id: indices = [] for i, seq_group_meta in enumerate(seq_group_metadata_list): if seq_group_meta.request_id == request_id: assert i not in skip # Cannot be called twice indices.append(i) break # If the request_id was not found, then it means that # this is a new request that has no pending async # postprocessor if not indices: return else: indices = range(len(seq_group_metadata_list)) # type: ignore finished_before: List[int] = [] finished_now: List[int] = [] for i in indices: if i in skip: continue seq_group_meta = seq_group_metadata_list[i] scheduled_seq_group = scheduler_outputs.scheduled_seq_groups[i] seq_group: SequenceGroup = scheduled_seq_group.seq_group if seq_group.is_finished(): finished_before.append(i) continue output: List[SequenceGroupOutput] if has_multiple_outputs: output = outputs_by_sequence_group[i] else: output = [outputs_by_sequence_group[0][i]] if not is_async: seq_group.update_num_computed_tokens( seq_group_meta.token_chunk_size or 0) if outputs: for o in outputs: if (isinstance(o, SamplerOutput) and seq_group.metrics is not None): if seq_group.metrics.model_forward_time is not None: seq_group.metrics.model_forward_time += ( o.model_forward_time or 0) else: seq_group.metrics.model_forward_time = ( o.model_forward_time) if seq_group.metrics.model_execute_time is not None: seq_group.metrics.model_execute_time += ( o.model_execute_time or 0) else: seq_group.metrics.model_execute_time = ( o.model_execute_time) if self.model_config.runner_type == "pooling": self._process_sequence_group_outputs(seq_group, output) else: self.output_processor.process_prompt_logprob(seq_group, output) if seq_group_meta.do_sample: self.output_processor.process_outputs( seq_group, output, is_async) if seq_group.is_finished(): finished_now.append(i) # Generate outputs for the requests that finished this iteration for i in finished_now: scheduled_seq_group = scheduler_outputs.scheduled_seq_groups[i] seq_group = scheduled_seq_group.seq_group seq_group.maybe_set_first_token_time(now) if not seq_group.is_prefill(): seq_group.set_last_token_time(now) request_output = RequestOutputFactory.create( seq_group, self.seq_id_to_seq_group, use_cache=self.use_cached_outputs) if request_output: ctx.request_outputs.append(request_output) # When we process a single request, we skip it for the next time, # and invoke the request output callback (if there was final output) if request_id: assert len(indices) == 1 skip.append(indices[0]) if (finished_now and self.process_request_outputs_callback is not None): self.process_request_outputs_callback(ctx.request_outputs) ctx.request_outputs.clear() return # Free currently finished requests if finished_now: for scheduler in self.scheduler: scheduler.free_finished_seq_groups() # Create the outputs for i in indices: if i in skip or i in finished_before or i in finished_now: continue # Avoids double processing scheduled_seq_group = scheduler_outputs.scheduled_seq_groups[i] seq_group = scheduled_seq_group.seq_group seq_group.maybe_set_first_token_time(now) if not seq_group.is_prefill(): seq_group.set_last_token_time(now) request_output = RequestOutputFactory.create( seq_group, self.seq_id_to_seq_group, use_cache=self.use_cached_outputs) if request_output: ctx.request_outputs.append(request_output) # Create outputs only after processing the scheduler's results for seq_group in scheduler_outputs.ignored_seq_groups: params = seq_group.sampling_params if params is not None and params.output_kind == ( RequestOutputKind.DELTA) and not seq_group.is_finished(): continue request_output = RequestOutputFactory.create( seq_group, self.seq_id_to_seq_group, use_cache=self.use_cached_outputs, ) if request_output: ctx.request_outputs.append(request_output) # Immediately process request outputs here (if callback is given) if (ctx.request_outputs and self.process_request_outputs_callback is not None): self.process_request_outputs_callback(ctx.request_outputs) ctx.request_outputs.clear() # For async case, we need to record the stats here. # For non-async case, the stats are done in the # LLMEngine/AsyncLLMEngine directly if is_async: # Log stats. self.do_log_stats(scheduler_outputs, outputs, finished_before, skip) # Tracing self.do_tracing(scheduler_outputs, finished_before) return None def _advance_to_next_step( self, output: SamplerOutput, seq_group_metadata_list: List[SequenceGroupMetadata], scheduled_seq_groups: List[ScheduledSequenceGroup]) -> None: """Given model output from a single run, append the tokens to the sequences. This is normally done inside output processor, but it is required if the worker is to perform async forward pass to next step. """ for seq_group_metadata, sequence_group_outputs, scheduled_seq_group in \ zip(seq_group_metadata_list, output, scheduled_seq_groups): seq_group = scheduled_seq_group.seq_group if seq_group.is_finished(): continue token_chunk_size = (seq_group_metadata.token_chunk_size if seq_group_metadata.token_chunk_size is not None else 0) seq_group.update_num_computed_tokens(token_chunk_size) if seq_group_metadata.do_sample: assert len(sequence_group_outputs.samples) == 1, ( "Async output processor expects a single sample" " (i.e sampling_params.n == 1)") sample = sequence_group_outputs.samples[0] assert len(seq_group.seqs) == 1 seq = seq_group.seqs[0] seq.append_token_id(sample.output_token, sample.logprobs, sample.output_embed) def step(self) -> List[Union[RequestOutput, PoolingRequestOutput]]: """Performs one decoding iteration and returns newly generated results.
![Overview of the step function](https://i.imgur.com/sv2HssD.png)
Overview of the step function
Details: - Step 1: Schedules the sequences to be executed in the next iteration and the token blocks to be swapped in/out/copy. - Depending on the scheduling policy, sequences may be `preempted/reordered`. - A Sequence Group (SG) refer to a group of sequences that are generated from the same prompt. - Step 2: Calls the distributed executor to execute the model. - Step 3: Processes the model output. This mainly includes: - Decodes the relevant outputs. - Updates the scheduled sequence groups with model outputs based on its `sampling parameters` (`use_beam_search` or not). - Frees the finished sequence groups. - Finally, it creates and returns the newly generated results. Example: ``` # Please see the example/ folder for more detailed examples. # initialize engine and request arguments engine = LLMEngine.from_engine_args(engine_args) example_inputs = [(0, "What is LLM?", SamplingParams(temperature=0.0))] # Start the engine with an event loop while True: if example_inputs: req_id, prompt, sampling_params = example_inputs.pop(0) engine.add_request(str(req_id),prompt,sampling_params) # continue the request processing request_outputs = engine.step() for request_output in request_outputs: if request_output.finished: # return or show the request output if not (engine.has_unfinished_requests() or example_inputs): break ``` """ if self.parallel_config.pipeline_parallel_size > 1: raise NotImplementedError( "Pipeline parallelism is only supported through AsyncLLMEngine " "as performance will be severely degraded otherwise.") # For llm_engine, there is no pipeline parallel support, so the engine # used is always 0. virtual_engine = 0 # These are cached outputs from previous iterations. None if on first # iteration cached_outputs = self.cached_scheduler_outputs[virtual_engine] seq_group_metadata_list = cached_outputs.seq_group_metadata_list scheduler_outputs = cached_outputs.scheduler_outputs allow_async_output_proc = cached_outputs.allow_async_output_proc ctx = self.scheduler_contexts[virtual_engine] # Clear outputs for each new scheduler iteration ctx.request_outputs.clear() # Skip the scheduler if there are any remaining steps in the seq groups. # This ensures that the scheduler is only called again when the current # batch has completed. # The scheduler is also skipped if a single request caused the last # engine step to fail, and the previous schedule needs to be rerun. if not self._has_remaining_steps( seq_group_metadata_list ) and not self._skip_scheduling_next_step: # Schedule iteration (seq_group_metadata_list, scheduler_outputs, allow_async_output_proc ) = self.scheduler[virtual_engine].schedule() ctx.seq_group_metadata_list = seq_group_metadata_list ctx.scheduler_outputs = scheduler_outputs finished_requests_ids = self.scheduler[ virtual_engine].get_and_reset_finished_requests_ids() # When n>1, elements in self.seq_id_to_seq_group should be deleted # here, otherwise memory leaks. for finished_request_id in finished_requests_ids: if finished_request_id in self.seq_id_to_seq_group: del self.seq_id_to_seq_group[finished_request_id] # Maybe switch from async mode to sync mode if not allow_async_output_proc and len(ctx.output_queue) > 0: self._process_model_outputs(ctx=ctx) else: finished_requests_ids = list() assert seq_group_metadata_list is not None assert scheduler_outputs is not None if not scheduler_outputs.is_empty(): # Check if we have a cached last_output from the previous iteration. # For supporting PP this is probably the best way to pass the # sampled_token_ids, as a separate broadcast over all the PP stages # will cause one virtual engine's microbatch to block the pipeline. last_sampled_token_ids = \ self._get_last_sampled_token_ids(virtual_engine) execute_model_req = ExecuteModelRequest( 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, num_lookahead_slots=scheduler_outputs.num_lookahead_slots, running_queue_size=scheduler_outputs.running_queue_size, finished_requests_ids=finished_requests_ids, # We use ExecuteModelRequest to pass the last sampled_token_ids # to each of the non-last PP stages for in-place prepare_input. last_sampled_token_ids=last_sampled_token_ids) if allow_async_output_proc: execute_model_req.async_callback = self.async_callbacks[ virtual_engine] try: outputs = self.model_executor.execute_model( execute_model_req=execute_model_req) self._skip_scheduling_next_step = False except InputProcessingError as e: # The input for this request cannot be processed, so we must # abort it. If there are remaining requests in the batch that # have been scheduled, they will be retried on the next step. invalid_request_id = e.request_id self._abort_and_cache_schedule( request_id=invalid_request_id, virtual_engine=virtual_engine, seq_group_metadata_list=seq_group_metadata_list, scheduler_outputs=scheduler_outputs, allow_async_output_proc=allow_async_output_proc) # Raise so the caller is notified that this request failed raise else: # Nothing scheduled => If there is pending async postprocessor, # then finish it here. if len(ctx.output_queue) > 0: self._process_model_outputs(ctx=ctx) # No outputs in this case outputs = [] if not self._has_remaining_steps(seq_group_metadata_list): # is_first_step_output is True only when the num_steps of all # the sequences are 1. is_first_step_output: bool = False if not seq_group_metadata_list \ else seq_group_metadata_list[0].state.num_steps == 1 # Add results to the output_queue ctx.append_output(outputs=outputs, seq_group_metadata_list=seq_group_metadata_list, scheduler_outputs=scheduler_outputs, is_async=allow_async_output_proc, is_last_step=True, is_first_step_output=is_first_step_output) if outputs and allow_async_output_proc: assert len(outputs) == 1, ( "Async postprocessor expects only a single output set") self._advance_to_next_step( outputs[0], seq_group_metadata_list, scheduler_outputs.scheduled_seq_groups) # Check if need to run the usual non-async path if not allow_async_output_proc: self._process_model_outputs(ctx=ctx) # Log stats. self.do_log_stats(scheduler_outputs, outputs) # Tracing self.do_tracing(scheduler_outputs) else: # Multi-step case return ctx.request_outputs if not self.has_unfinished_requests(): # Drain async postprocessor (if exists) if len(ctx.output_queue) > 0: self._process_model_outputs(ctx=ctx) assert len(ctx.output_queue) == 0 # Stop the execute model loop in parallel workers until there are # more requests to process. This avoids waiting indefinitely in # torch.distributed ops which may otherwise timeout, and unblocks # the RPC thread in the workers so that they can process any other # queued control plane messages, such as add/remove lora adapters. logger.debug("Stopping remote worker execution loop.") self.model_executor.stop_remote_worker_execution_loop() return ctx.request_outputs def _abort_and_cache_schedule( self, request_id: str, virtual_engine: int, seq_group_metadata_list: List[SequenceGroupMetadata], scheduler_outputs: SchedulerOutputs, allow_async_output_proc: bool) -> None: """Aborts a single request, and caches the scheduler outputs minus that request. This allows the next step to continue processing the remaining requests without having to re-run the scheduler.""" # Abort the request and remove its sequence group from the current # schedule self.abort_request(request_id) for i, metadata in enumerate(seq_group_metadata_list): if metadata.request_id == request_id: del seq_group_metadata_list[i] break for i, group in enumerate(scheduler_outputs.scheduled_seq_groups): if group.seq_group.request_id == request_id: del scheduler_outputs.scheduled_seq_groups[i] break # If there are still other sequence groups left in the schedule, cache # them and flag the engine to reuse the schedule. if len(seq_group_metadata_list) > 0: self._skip_scheduling_next_step = True # Reuse multi-step caching logic self._cache_scheduler_outputs_for_multi_step( virtual_engine=virtual_engine, scheduler_outputs=scheduler_outputs, seq_group_metadata_list=seq_group_metadata_list, allow_async_output_proc=allow_async_output_proc) def _has_remaining_steps( self, seq_group_metadata_list: Optional[List[SequenceGroupMetadata]] ) -> bool: return False def _cache_scheduler_outputs_for_multi_step( self, virtual_engine: int, seq_group_metadata_list: Optional[List[SequenceGroupMetadata]], scheduler_outputs: SchedulerOutputs, allow_async_output_proc: bool) -> None: co = self.cached_scheduler_outputs[virtual_engine] co.seq_group_metadata_list = seq_group_metadata_list co.scheduler_outputs = scheduler_outputs co.allow_async_output_proc = allow_async_output_proc co.last_output = None def _update_cached_scheduler_output( self, virtual_engine: int, output: List[Optional[SamplerOutput]]) -> None: if (self.parallel_config.pipeline_parallel_size > 1 and len(output) > 0 and output[0] is not None): last_output = output[-1] assert last_output is not None assert last_output.sampled_token_ids_cpu is not None assert last_output.sampled_token_ids is None assert last_output.sampled_token_probs is None self.cached_scheduler_outputs[ virtual_engine].last_output = last_output def _get_last_sampled_token_ids( self, virtual_engine: int) -> Optional[torch.Tensor]: return None def add_logger(self, logger_name: str, logger: StatLoggerBase) -> None: if not self.log_stats: raise RuntimeError( "Stat logging is disabled. Set `disable_log_stats=False` " "argument to enable.") if logger_name in self.stat_loggers: raise KeyError(f"Logger with name {logger_name} already exists.") self.stat_loggers[logger_name] = logger def remove_logger(self, logger_name: str) -> None: if not self.log_stats: raise RuntimeError( "Stat logging is disabled. Set `disable_log_stats=False` " "argument to enable.") if logger_name not in self.stat_loggers: raise KeyError(f"Logger with name {logger_name} does not exist.") del self.stat_loggers[logger_name] def do_log_stats(self, scheduler_outputs: Optional[SchedulerOutputs] = None, model_output: Optional[List[SamplerOutput]] = None, finished_before: Optional[List[int]] = None, skip: Optional[List[int]] = None) -> None: """Forced log when no requests active.""" if self.log_stats: stats = self._get_stats(scheduler_outputs, model_output, finished_before, skip) for logger in self.stat_loggers.values(): logger.log(stats) def _get_stats(self, scheduler_outputs: Optional[SchedulerOutputs], model_output: Optional[List[SamplerOutput]] = None, finished_before: Optional[List[int]] = None, skip: Optional[List[int]] = None) -> Stats: """Get Stats to be Logged to Prometheus. Args: scheduler_outputs: Optional, used to populate metrics related to the scheduled batch, model_output: Optional, used to emit speculative decoding metrics which are created by the workers. finished_before: Optional, indices of sequences that were finished before. These sequences will be ignored. skip: Optional, indices of sequences that were preempted. These sequences will be ignored. """ now = time.time() # System State # Scheduler State num_running_sys = sum( len(scheduler.running) for scheduler in self.scheduler) num_swapped_sys = sum( len(scheduler.swapped) for scheduler in self.scheduler) num_waiting_sys = sum( len(scheduler.waiting) for scheduler in self.scheduler) # KV Cache Usage in % num_total_gpu = self.cache_config.num_gpu_blocks gpu_cache_usage_sys = 0. if num_total_gpu: # Guard against both None and 0 num_free_gpu = sum( scheduler.block_manager.get_num_free_gpu_blocks() for scheduler in self.scheduler) gpu_cache_usage_sys = 1.0 - (num_free_gpu / num_total_gpu) num_total_cpu = self.cache_config.num_cpu_blocks cpu_cache_usage_sys = 0. if num_total_cpu: # Guard against both None and 0 num_free_cpu = sum( scheduler.block_manager.get_num_free_cpu_blocks() for scheduler in self.scheduler) cpu_cache_usage_sys = 1.0 - (num_free_cpu / num_total_cpu) # Prefix Cache Hit Rate. Note that we always use # the cache hit rate of the first virtual engine. cpu_prefix_cache_hit_rate = self.scheduler[ 0].get_prefix_cache_hit_rate(Device.CPU) gpu_prefix_cache_hit_rate = self.scheduler[ 0].get_prefix_cache_hit_rate(Device.GPU) # Exchange the uasge and cache hit stats between gpu and cpu when # running on cpu because the cpu_worker.py intentionally reports the # number of cpu blocks as gpu blocks in favor of cache management. if self.device_config.device_type == "cpu": num_total_gpu, num_total_cpu = num_total_cpu, num_total_gpu gpu_cache_usage_sys, cpu_cache_usage_sys = ( cpu_cache_usage_sys, gpu_cache_usage_sys, ) gpu_prefix_cache_hit_rate, cpu_prefix_cache_hit_rate = ( cpu_prefix_cache_hit_rate, gpu_prefix_cache_hit_rate, ) # Iteration stats num_prompt_tokens_iter = 0 num_generation_tokens_iter = 0 num_tokens_iter = 0 time_to_first_tokens_iter: List[float] = [] time_per_output_tokens_iter: List[float] = [] num_preemption_iter = (0 if scheduler_outputs is None else scheduler_outputs.preempted) # Request stats # Latency time_e2e_requests: List[float] = [] time_queue_requests: List[float] = [] time_inference_requests: List[float] = [] time_prefill_requests: List[float] = [] time_decode_requests: List[float] = [] # Metadata num_prompt_tokens_requests: List[int] = [] num_generation_tokens_requests: List[int] = [] n_requests: List[int] = [] max_num_generation_tokens_requests: List[int] = [] max_tokens_requests: List[int] = [] finished_reason_requests: List[str] = [] # LoRA requests running_lora_adapters = dict( collectionsCounter([ running_request.lora_request.lora_name for scheduler in self.scheduler for running_request in scheduler.running if running_request.lora_request ])) waiting_lora_adapters = dict( collectionsCounter([ waiting_request.lora_request.lora_name for scheduler in self.scheduler for waiting_request in scheduler.waiting if waiting_request.lora_request ])) max_lora_stat = "0" if self.lora_config: max_lora_stat = str(self.lora_config.max_loras) # NOTE: This loop assumes prefill seq_groups are before # decode seq_groups in scheduled_seq_groups. if scheduler_outputs is not None: # For async postprocessor, already finished sequences need to be # not counted (to avoid double counting) actual_num_batched_tokens = scheduler_outputs.num_batched_tokens # type: ignore num_generation_tokens_from_prefill_groups = 0 # NOTE: if scheduler_outputs.num_prefill_groups > 0 and # the len of scheduler_outputs.scheduled_seq_groups is != # scheduler_outputs.num_prefill_groups, this means that # chunked prefills have been detected. for idx, scheduled_seq_group in enumerate( scheduler_outputs.scheduled_seq_groups): # Skip double logging when using async output proc if finished_before and idx in finished_before: actual_num_batched_tokens -= 1 continue # Currently, skip == preempted sequences, so we need to skip # their log stats if skip and idx in skip: continue group_was_prefill = idx < scheduler_outputs.num_prefill_groups seq_group = scheduled_seq_group.seq_group # NOTE: a seq_group that completed all of its prefill tokens # in the last iteration will have seq_group.is_prefill() = False # with group_was_prefill = True if group_was_prefill: # Number of prompt tokens. num_prompt_tokens_iter += ( scheduled_seq_group.token_chunk_size) # If the seq_group just finished the prefill state # get TTFT. if not seq_group.is_prefill(): latency = seq_group.get_last_token_latency() time_to_first_tokens_iter.append(latency) # One generation token per finished prefill. num_generation_tokens_from_prefill_groups += ( seq_group.num_seqs()) else: # TPOTs. latency = seq_group.get_last_token_latency() time_per_output_tokens_iter.append(latency) if seq_group.state.current_step == 0: # For async_output_proc, the do_log_stats() # is called following init_multi_step(), which # sets the current_step to zero. actual_num_batched_tokens +=\ seq_group.state.num_steps - 1 else: actual_num_batched_tokens +=\ seq_group.state.current_step - 1 # Because of chunked prefill, we can have a single sequence # group that does multiple prompt_runs. To prevent logging # the same metadata more than once per request, we standardize # on logging request level information for finished requests, # which can only happen once. if seq_group.is_finished(): # Latency timings time_e2e_requests.append(now - seq_group.metrics.arrival_time) if (seq_group.metrics.first_scheduled_time is not None and seq_group.metrics.first_token_time is not None): time_queue_requests.append( seq_group.metrics.first_scheduled_time - seq_group.metrics.arrival_time) time_prefill_requests.append( seq_group.metrics.first_token_time - seq_group.metrics.first_scheduled_time) time_decode_requests.append( now - seq_group.metrics.first_token_time) time_inference_requests.append( now - seq_group.metrics.first_scheduled_time) # Metadata num_prompt_tokens_requests.append( len(seq_group.prompt_token_ids)) num_generation_tokens_requests.extend([ seq.get_output_len() for seq in seq_group.get_finished_seqs() ]) max_num_generation_tokens_requests.append( max(seq.get_output_len() for seq in seq_group.get_seqs())) if seq_group.sampling_params is not None: n_requests.append(seq_group.sampling_params.n) max_tokens_requests.append( seq_group.sampling_params.max_tokens) finished_reason_requests.extend([ SequenceStatus.get_finished_reason(seq.status) for seq in seq_group.get_finished_seqs() ]) # Number of generation tokens. # num_batched_tokens equals the number of prompt_tokens plus the # number of decode_tokens in a single iteration. So, # num_generation_tokens = num_batched_tokens - num_prompt_tokens # + num_generation_tokens_from_prefill_groups (since we generate # one token on prefills on iters where the prefill finishes). num_generation_tokens_iter = ( actual_num_batched_tokens - num_prompt_tokens_iter + num_generation_tokens_from_prefill_groups) num_tokens_iter = (num_generation_tokens_iter + num_prompt_tokens_iter) return Stats( now=now, # System stats # Scheduler State num_running_sys=num_running_sys, num_swapped_sys=num_swapped_sys, num_waiting_sys=num_waiting_sys, # KV Cache Usage in % gpu_cache_usage_sys=gpu_cache_usage_sys, cpu_cache_usage_sys=cpu_cache_usage_sys, # Prefix Cache Hit Rate cpu_prefix_cache_hit_rate=cpu_prefix_cache_hit_rate, gpu_prefix_cache_hit_rate=gpu_prefix_cache_hit_rate, # Iteration stats num_prompt_tokens_iter=num_prompt_tokens_iter, num_generation_tokens_iter=num_generation_tokens_iter, num_tokens_iter=num_tokens_iter, time_to_first_tokens_iter=time_to_first_tokens_iter, time_per_output_tokens_iter=time_per_output_tokens_iter, num_preemption_iter=num_preemption_iter, # Request stats # Latency time_e2e_requests=time_e2e_requests, time_queue_requests=time_queue_requests, time_inference_requests=time_inference_requests, time_prefill_requests=time_prefill_requests, time_decode_requests=time_decode_requests, # Metadata num_prompt_tokens_requests=num_prompt_tokens_requests, num_generation_tokens_requests=num_generation_tokens_requests, max_num_generation_tokens_requests= max_num_generation_tokens_requests, n_requests=n_requests, max_tokens_requests=max_tokens_requests, finished_reason_requests=finished_reason_requests, max_lora=str(max_lora_stat), waiting_lora_adapters=list(waiting_lora_adapters.keys()), running_lora_adapters=list(running_lora_adapters.keys())) def add_lora(self, lora_request: LoRARequest) -> bool: return self.model_executor.add_lora(lora_request) def remove_lora(self, lora_id: int) -> bool: return self.model_executor.remove_lora(lora_id) def list_loras(self) -> Set[int]: return self.model_executor.list_loras() def pin_lora(self, lora_id: int) -> bool: return self.model_executor.pin_lora(lora_id) def start_profile(self) -> None: self.model_executor.start_profile() def stop_profile(self) -> None: self.model_executor.stop_profile() def sleep(self, level: int = 1) -> None: assert self.vllm_config.model_config.enable_sleep_mode, ( "Sleep mode is not enabled in the model config") self.model_executor.sleep(level=level) def wake_up(self, tags: Optional[list[str]] = None) -> None: assert self.vllm_config.model_config.enable_sleep_mode, ( "Sleep mode is not enabled in the model config") self.model_executor.wake_up(tags) def is_sleeping(self) -> bool: return self.model_executor.is_sleeping def check_health(self) -> None: self.model_executor.check_health() def is_tracing_enabled(self) -> bool: return self.tracer is not None def do_tracing(self, scheduler_outputs: SchedulerOutputs, finished_before: Optional[List[int]] = None) -> None: if self.tracer is None: return for idx, scheduled_seq_group in enumerate( scheduler_outputs.scheduled_seq_groups): # Skip double tracing when using async output proc if finished_before and idx in finished_before: continue seq_group = scheduled_seq_group.seq_group if seq_group.is_finished(): self.create_trace_span(seq_group) def create_trace_span(self, seq_group: SequenceGroup) -> None: if self.tracer is None or seq_group.sampling_params is None: return arrival_time_nano_seconds = int(seq_group.metrics.arrival_time * 1e9) trace_context = extract_trace_context(seq_group.trace_headers) with self.tracer.start_as_current_span( "llm_request", kind=SpanKind.SERVER, context=trace_context, start_time=arrival_time_nano_seconds) as seq_span: metrics = seq_group.metrics # Handle potential None values for cancelled/aborted requests ttft = (metrics.first_token_time - metrics.arrival_time if metrics.first_token_time is not None else None) e2e_time = (metrics.finished_time - metrics.arrival_time if metrics.finished_time is not None else None) seq_span.set_attribute(SpanAttributes.GEN_AI_RESPONSE_MODEL, self.model_config.model) seq_span.set_attribute(SpanAttributes.GEN_AI_REQUEST_ID, seq_group.request_id) seq_span.set_attribute(SpanAttributes.GEN_AI_REQUEST_TEMPERATURE, seq_group.sampling_params.temperature) seq_span.set_attribute(SpanAttributes.GEN_AI_REQUEST_TOP_P, seq_group.sampling_params.top_p) seq_span.set_attribute(SpanAttributes.GEN_AI_REQUEST_MAX_TOKENS, seq_group.sampling_params.max_tokens) seq_span.set_attribute(SpanAttributes.GEN_AI_REQUEST_N, seq_group.sampling_params.n) seq_span.set_attribute(SpanAttributes.GEN_AI_USAGE_NUM_SEQUENCES, seq_group.num_seqs()) seq_span.set_attribute(SpanAttributes.GEN_AI_USAGE_PROMPT_TOKENS, len(seq_group.prompt_token_ids)) seq_span.set_attribute( SpanAttributes.GEN_AI_USAGE_COMPLETION_TOKENS, sum([ seq.get_output_len() for seq in seq_group.get_finished_seqs() ])) # Only set timing attributes if the values are available if metrics.time_in_queue is not None: seq_span.set_attribute( SpanAttributes.GEN_AI_LATENCY_TIME_IN_QUEUE, metrics.time_in_queue) if ttft is not None: seq_span.set_attribute( SpanAttributes.GEN_AI_LATENCY_TIME_TO_FIRST_TOKEN, ttft) if e2e_time is not None: seq_span.set_attribute(SpanAttributes.GEN_AI_LATENCY_E2E, e2e_time) if metrics.scheduler_time is not None: seq_span.set_attribute( SpanAttributes.GEN_AI_LATENCY_TIME_IN_SCHEDULER, metrics.scheduler_time) if metrics.model_forward_time is not None: seq_span.set_attribute( SpanAttributes.GEN_AI_LATENCY_TIME_IN_MODEL_FORWARD, metrics.model_forward_time / 1000.0) if metrics.model_execute_time is not None: seq_span.set_attribute( SpanAttributes.GEN_AI_LATENCY_TIME_IN_MODEL_EXECUTE, metrics.model_execute_time) def _validate_model_inputs(self, inputs: ProcessorInputs, lora_request: Optional[LoRARequest]): encoder_inputs, decoder_inputs = split_enc_dec_inputs(inputs) if encoder_inputs is not None: self._validate_model_input(encoder_inputs, lora_request, prompt_type="encoder") self._validate_model_input(decoder_inputs, lora_request, prompt_type="decoder") def _validate_model_input( self, prompt_inputs: SingletonInputs, lora_request: Optional[LoRARequest], *, prompt_type: Literal["encoder", "decoder"], ): model_config = self.model_config tokenizer = (None if self.tokenizer is None else self.tokenizer.get_lora_tokenizer(lora_request)) prompt_ids = prompt_inputs.get("prompt_token_ids", []) if not prompt_ids: if prompt_type == "encoder" and model_config.is_multimodal_model: pass # Mllama may have empty encoder inputs for text-only data elif prompt_inputs["type"] == "embeds": pass else: raise ValueError(f"The {prompt_type} prompt cannot be empty") if tokenizer is not None: max_input_id = max(prompt_ids, default=0) if max_input_id > tokenizer.max_token_id: raise ValueError( f"Token id {max_input_id} is out of vocabulary") max_prompt_len = self.model_config.max_model_len if len(prompt_ids) > max_prompt_len: if prompt_type == "encoder" and model_config.is_multimodal_model: mm_registry = self.input_preprocessor.mm_registry mm_processor = mm_registry.create_processor( model_config, tokenizer=tokenizer or object(), # Dummy if no tokenizer ) assert isinstance(mm_processor, EncDecMultiModalProcessor) if mm_processor.pad_dummy_encoder_prompt: return # Skip encoder length check for Whisper if model_config.is_multimodal_model: suggestion = ( "Make sure that `max_model_len` is no smaller than the " "number of text tokens plus multimodal tokens. For image " "inputs, the number of image tokens depends on the number " "of images, and possibly their aspect ratios as well.") else: suggestion = ( "Make sure that `max_model_len` is no smaller than the " "number of text tokens.") raise ValueError( f"The {prompt_type} prompt (length {len(prompt_ids)}) is " f"longer than the maximum model length of {max_prompt_len}. " f"{suggestion}") # TODO: Find out how many placeholder tokens are there so we can # check that chunked prefill does not truncate them # max_batch_len = self.scheduler_config.max_num_batched_tokens def _build_logits_processors( self, sampling_params: SamplingParams, lora_request: Optional[LoRARequest]) -> SamplingParams: """Constructs logits processors based on the logits_bias, and allowed_token_ids fields in sampling_params. Deletes those fields and adds the constructed logits processors to the logits_processors field. Returns the modified sampling params.""" logits_processors = [] if (sampling_params.logit_bias or sampling_params.allowed_token_ids): tokenizer = self.get_tokenizer(lora_request=lora_request) processors = get_openai_logits_processors( logit_bias=sampling_params.logit_bias, allowed_token_ids=sampling_params.allowed_token_ids, tokenizer=tokenizer) logits_processors.extend(processors) # Unset so these don't get passed down to the model sampling_params.logit_bias = None sampling_params.allowed_token_ids = None if len(sampling_params.bad_words) > 0: tokenizer = self.get_tokenizer(lora_request) processors = get_bad_words_logits_processors( bad_words=sampling_params.bad_words, tokenizer=tokenizer) logits_processors.extend(processors) if logits_processors: if sampling_params.logits_processors is None: sampling_params.logits_processors = logits_processors else: sampling_params.logits_processors.extend(logits_processors) return sampling_params def collective_rpc(self, method: Union[str, Callable[..., _R]], timeout: Optional[float] = None, args: tuple = (), kwargs: Optional[dict[str, Any]] = None) -> list[_R]: return self.model_executor.collective_rpc(method, timeout, args, kwargs) if envs.is_set("VLLM_USE_V1") and envs.VLLM_USE_V1: from vllm.v1.engine.llm_engine import LLMEngine as V1LLMEngine LLMEngine = V1LLMEngine # type: ignore