import functools import time from collections import deque from contextlib import contextmanager from dataclasses import dataclass from typing import (TYPE_CHECKING, Any, ClassVar, Deque, Dict, Iterable, List, Mapping, NamedTuple, Optional) from typing import Sequence as GenericSequence from typing import Set, Tuple, Type, Union import torch from typing_extensions import TypeVar, assert_never import vllm.envs as envs from vllm.config import (CacheConfig, DecodingConfig, DeviceConfig, EngineConfig, LoadConfig, LoRAConfig, ModelConfig, ObservabilityConfig, ParallelConfig, PromptAdapterConfig, SchedulerConfig, SpeculativeConfig) from vllm.core.scheduler import (ScheduledSequenceGroup, Scheduler, 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.engine.output_processor.util import create_output_by_sequence_group from vllm.executor.executor_base import ExecutorBase from vllm.executor.ray_utils import initialize_ray_cluster from vllm.inputs import (INPUT_REGISTRY, EncoderDecoderLLMInputs, InputRegistry, LLMInputs, PromptInputs, SingletonPromptInputs) from vllm.inputs.parse import is_explicit_encoder_decoder_prompt from vllm.logger import init_logger from vllm.lora.request import LoRARequest from vllm.model_executor.layers.sampler import SamplerOutput from vllm.multimodal import MultiModalDataDict from vllm.outputs import (EmbeddingRequestOutput, RequestOutput, RequestOutputFactory) from vllm.pooling_params import PoolingParams from vllm.prompt_adapter.request import PromptAdapterRequest from vllm.sampling_params import SamplingParams from vllm.sequence import (EmbeddingSequenceGroupOutput, ExecuteModelRequest, Sequence, SequenceGroup, SequenceGroupMetadata, SequenceStatus) from vllm.tracing import (SpanAttributes, SpanKind, extract_trace_context, init_tracer) from vllm.transformers_utils.config import try_get_generation_config from vllm.transformers_utils.detokenizer import Detokenizer from vllm.transformers_utils.tokenizer import AnyTokenizer from vllm.transformers_utils.tokenizer_group import ( BaseTokenizerGroup, init_tokenizer_from_configs) from vllm.usage.usage_lib import (UsageContext, is_usage_stats_enabled, usage_message) from vllm.utils import Counter, Device from vllm.version import __version__ as VLLM_VERSION logger = init_logger(__name__) _LOCAL_LOGGING_INTERVAL_SEC = 5 def _load_generation_config_dict(model_config: ModelConfig) -> Dict[str, Any]: config = try_get_generation_config( model_config.model, trust_remote_code=model_config.trust_remote_code, revision=model_config.revision, ) if config is None: return {} return config.to_diff_dict() _G = TypeVar("_G", bound=BaseTokenizerGroup, default=BaseTokenizerGroup) _O = TypeVar("_O", RequestOutput, EmbeddingRequestOutput) PromptComponents = Tuple[Optional[str], List[int], Optional[MultiModalDataDict]] DecoderPromptComponents = Tuple[Optional[str], Optional[List[int]], Optional[MultiModalDataDict]] @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 skip: List[int] class SchedulerContext: def __init__(self): self.output_queue: Deque[OutputData] = deque() self.request_outputs: List[Union[RequestOutput, EmbeddingRequestOutput]] = [] 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): 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, 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 :class:`~vllm.LLM` class wraps this class for offline batched inference and the :class:`AsyncLLMEngine` class wraps this class for online serving. The config arguments are derived from :class:`~vllm.EngineArgs`. (See :ref:`engine_args`) 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. device_config: The configuration related to the device. lora_config (Optional): The configuration related to serving multi-LoRA. speculative_config (Optional): The configuration related to speculative decoding. executor_class: The model executor class for managing distributed execution. prompt_adapter_config (Optional): The configuration related to serving prompt adapters. 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 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[BaseTokenizerGroup] def __init__( self, model_config: ModelConfig, cache_config: CacheConfig, parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, device_config: DeviceConfig, load_config: LoadConfig, lora_config: Optional[LoRAConfig], speculative_config: Optional[SpeculativeConfig], decoding_config: Optional[DecodingConfig], observability_config: Optional[ObservabilityConfig], prompt_adapter_config: Optional[PromptAdapterConfig], executor_class: Type[ExecutorBase], log_stats: bool, usage_context: UsageContext = UsageContext.ENGINE_CONTEXT, stat_loggers: Optional[Dict[str, StatLoggerBase]] = None, input_registry: InputRegistry = INPUT_REGISTRY, # To improve performance, only final requests outputs may be required. # If this set to true, then no intermediate outputs will be returned. step_return_finished_only: bool = False, ) -> None: logger.info( "Initializing an LLM engine (v%s) with config: " "model=%r, speculative_config=%r, tokenizer=%r, " "skip_tokenizer_init=%s, tokenizer_mode=%s, revision=%s, " "override_neuron_config=%s, " "rope_scaling=%r, rope_theta=%r, tokenizer_revision=%s, " "trust_remote_code=%s, dtype=%s, max_seq_len=%d, " "download_dir=%r, load_format=%s, tensor_parallel_size=%d, " "pipeline_parallel_size=%d, " "disable_custom_all_reduce=%s, quantization=%s, " "enforce_eager=%s, kv_cache_dtype=%s, " "quantization_param_path=%s, device_config=%s, " "decoding_config=%r, observability_config=%r, " "seed=%d, served_model_name=%s, use_v2_block_manager=%s, " "num_scheduler_steps=%d, enable_prefix_caching=%s, " "use_async_output_proc=%s)", VLLM_VERSION, model_config.model, speculative_config, model_config.tokenizer, model_config.skip_tokenizer_init, model_config.tokenizer_mode, model_config.revision, model_config.override_neuron_config, model_config.rope_scaling, model_config.rope_theta, model_config.tokenizer_revision, model_config.trust_remote_code, model_config.dtype, model_config.max_model_len, load_config.download_dir, load_config.load_format, parallel_config.tensor_parallel_size, parallel_config.pipeline_parallel_size, parallel_config.disable_custom_all_reduce, model_config.quantization, model_config.enforce_eager, cache_config.cache_dtype, model_config.quantization_param_path, device_config.device, decoding_config, observability_config, model_config.seed, model_config.served_model_name, scheduler_config.use_v2_block_manager, scheduler_config.num_scheduler_steps, cache_config.enable_prefix_caching, model_config.use_async_output_proc, ) # TODO(woosuk): Print more configs in debug mode. from vllm.plugins import load_general_plugins load_general_plugins() self.model_config = model_config self.cache_config = cache_config self.lora_config = lora_config self.parallel_config = parallel_config self.scheduler_config = scheduler_config self.device_config = device_config self.speculative_config = speculative_config self.load_config = load_config self.decoding_config = decoding_config or DecodingConfig() self.prompt_adapter_config = prompt_adapter_config self.observability_config = observability_config or ObservabilityConfig( ) self.log_stats = log_stats self.step_return_finished_only = step_return_finished_only if not self.model_config.skip_tokenizer_init: self.tokenizer = self._init_tokenizer() self.detokenizer = Detokenizer(self.tokenizer) tokenizer_group = self.get_tokenizer_group() else: self.tokenizer = None self.detokenizer = None tokenizer_group = None # 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 = _load_generation_config_dict( model_config) self.input_registry = input_registry self.input_processor = input_registry.create_input_processor( model_config) self.model_executor = executor_class( model_config=model_config, cache_config=cache_config, parallel_config=parallel_config, scheduler_config=scheduler_config, device_config=device_config, lora_config=lora_config, speculative_config=speculative_config, load_config=load_config, prompt_adapter_config=prompt_adapter_config, observability_config=self.observability_config, ) if not self.model_config.embedding_mode: 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(model_config), usage_context, extra_kvs={ # Common configuration "dtype": str(model_config.dtype), "tensor_parallel_size": parallel_config.tensor_parallel_size, "block_size": cache_config.block_size, "gpu_memory_utilization": cache_config.gpu_memory_utilization, # Quantization "quantization": model_config.quantization, "kv_cache_dtype": str(cache_config.cache_dtype), # Feature flags "enable_lora": bool(lora_config), "enable_prompt_adapter": bool(prompt_adapter_config), "enable_prefix_caching": cache_config.enable_prefix_caching, "enforce_eager": model_config.enforce_eager, "disable_custom_all_reduce": parallel_config.disable_custom_all_reduce, }) if self.tokenizer: # Ping the tokenizer to ensure liveness if it runs in a # different process. self.tokenizer.ping() 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) ] self.async_callbacks = [ functools.partial(self._process_model_outputs, ctx=self.scheduler_contexts[v_id]) for v_id in range(self.parallel_config.pipeline_parallel_size) ] # Currently used by AsyncLLMEngine to ensure quick append # of request outputs to asyncio queues self.process_request_outputs_callback = 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. self.scheduler = [ Scheduler( scheduler_config, cache_config, lora_config, parallel_config.pipeline_parallel_size, self.async_callbacks[v_id] if model_config.use_async_output_proc else None) for v_id in range(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), "prometheus": PrometheusStatLogger( local_interval=_LOCAL_LOGGING_INTERVAL_SEC, labels=dict(model_name=model_config.served_model_name), max_model_len=self.model_config.max_model_len), } 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, ), )) 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. """ 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) @classmethod def _get_executor_cls(cls, engine_config: EngineConfig) -> Type[ExecutorBase]: 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}.") if distributed_executor_backend.uses_ray: # type: ignore initialize_ray_cluster(engine_config.parallel_config) executor_class = distributed_executor_backend elif engine_config.device_config.device_type == "neuron": from vllm.executor.neuron_executor import NeuronExecutor executor_class = NeuronExecutor elif engine_config.device_config.device_type == "tpu": if distributed_executor_backend == "ray": initialize_ray_cluster(engine_config.parallel_config) from vllm.executor.ray_tpu_executor import RayTPUExecutor executor_class = RayTPUExecutor else: assert distributed_executor_backend is None from vllm.executor.tpu_executor import TPUExecutor executor_class = TPUExecutor elif engine_config.device_config.device_type == "cpu": from vllm.executor.cpu_executor import CPUExecutor executor_class = CPUExecutor elif engine_config.device_config.device_type == "openvino": from vllm.executor.openvino_executor import OpenVINOExecutor executor_class = OpenVINOExecutor elif engine_config.device_config.device_type == "xpu": if distributed_executor_backend == "ray": initialize_ray_cluster(engine_config.parallel_config) from vllm.executor.ray_xpu_executor import RayXPUExecutor executor_class = RayXPUExecutor elif distributed_executor_backend == "mp": # FIXME(kunshang): # spawn needs calling `if __name__ == '__main__':`` # fork is not supported for xpu start new process. logger.error( "Both start methods (spawn and fork) have issue " "on XPU if you use mp backend, Please try ray instead.") else: from vllm.executor.xpu_executor import XPUExecutor executor_class = XPUExecutor elif distributed_executor_backend == "ray": initialize_ray_cluster(engine_config.parallel_config) from vllm.executor.ray_gpu_executor import RayGPUExecutor executor_class = RayGPUExecutor elif distributed_executor_backend == "mp": from vllm.executor.multiproc_gpu_executor import ( MultiprocessingGPUExecutor) assert not envs.VLLM_USE_RAY_SPMD_WORKER, ( "multiprocessing distributed executor backend does not " "support VLLM_USE_RAY_SPMD_WORKER=1") executor_class = MultiprocessingGPUExecutor else: from vllm.executor.gpu_executor import GPUExecutor executor_class = GPUExecutor return executor_class @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. engine_config = engine_args.create_engine_config() executor_class = cls._get_executor_cls(engine_config) # Create the LLM engine. engine = cls( **engine_config.to_dict(), executor_class=executor_class, log_stats=not engine_args.disable_log_stats, usage_context=usage_context, stat_loggers=stat_loggers, ) return engine 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() MISSING_TOKENIZER_GROUP_MSG = ("Unable to get tokenizer because " "skip_tokenizer_init is True") def get_tokenizer_group( self, group_type: Type[_G] = BaseTokenizerGroup, *, missing_msg: str = MISSING_TOKENIZER_GROUP_MSG, ) -> _G: tokenizer_group = self.tokenizer if tokenizer_group is None: raise ValueError(missing_msg) if not isinstance(tokenizer_group, group_type): raise TypeError("Invalid type of tokenizer group. " f"Expected type: {group_type}, but " f"found type: {type(tokenizer_group)}") return tokenizer_group def get_tokenizer( self, lora_request: Optional[LoRARequest] = None, ) -> AnyTokenizer: return self.get_tokenizer_group().get_lora_tokenizer(lora_request) def _init_tokenizer(self) -> BaseTokenizerGroup: return init_tokenizer_from_configs( model_config=self.model_config, scheduler_config=self.scheduler_config, parallel_config=self.parallel_config, enable_lora=bool(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) if self.prompt_adapter_config: self.prompt_adapter_config.verify_with_model_config( self.model_config) def _get_bos_token_id(self, lora_request: Optional[LoRARequest] = None ) -> Optional[int]: if self.tokenizer is None: logger.warning("Using None for BOS token id because tokenizer " "is not initialized") return None return self.tokenizer.get_lora_tokenizer(lora_request).bos_token_id def _get_eos_token_id(self, lora_request: Optional[LoRARequest] = None ) -> Optional[int]: if self.tokenizer is None: logger.warning("Using None for EOS token id because tokenizer " "is not initialized") return None return self.tokenizer.get_lora_tokenizer(lora_request).eos_token_id def _get_decoder_start_token_id(self) -> Optional[int]: ''' Obtain the decoder start token id employed by an encoder/decoder model. Returns None for non-encoder/decoder models or if the model config is unavailable. ''' if not self.is_encoder_decoder_model(): logger.warning("Using None for decoder start token id because " "this is not an encoder/decoder model.") return None if (self.model_config is None or self.model_config.hf_config is None): logger.warning("Using None for decoder start token id because " "model config is not available.") return None dec_start_token_id = getattr(self.model_config.hf_config, 'decoder_start_token_id', None) if dec_start_token_id is None: logger.warning("Falling back on for decoder start token id " "because decoder start token id is not available.") dec_start_token_id = self._get_bos_token_id() return dec_start_token_id def _add_processed_request( self, request_id: str, processed_inputs: Union[LLMInputs, EncoderDecoderLLMInputs], params: Union[SamplingParams, PoolingParams], arrival_time: float, lora_request: Optional[LoRARequest], prompt_adapter_request: Optional[PromptAdapterRequest], trace_headers: Optional[Mapping[str, str]] = None, ) -> None: self._validate_model_inputs(processed_inputs) # Create the sequences. block_size = self.cache_config.block_size seq_id = next(self.seq_counter) eos_token_id = self._get_eos_token_id(lora_request) seq = Sequence(seq_id, processed_inputs, block_size, eos_token_id, lora_request, prompt_adapter_request) encoder_seq = None if 'encoder_prompt_token_ids' in processed_inputs: encoder_seq = Sequence(seq_id, processed_inputs, block_size, eos_token_id, lora_request, prompt_adapter_request, from_decoder_prompt=False) # 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, prompt_adapter_request=prompt_adapter_request, encoder_seq=encoder_seq) elif isinstance(params, PoolingParams): seq_group = self._create_sequence_group_with_pooling( request_id, seq, params, arrival_time=arrival_time, lora_request=lora_request, prompt_adapter_request=prompt_adapter_request, encoder_seq=encoder_seq) 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) def stop_remote_worker_execution_loop(self) -> None: self.model_executor.stop_remote_worker_execution_loop() _LLMInputComponentsType = Tuple[str, List[int]] def _prepare_decoder_input_ids_for_generation( self, decoder_input_ids: Optional[List[int]], ) -> List[int]: """ Prepares `decoder_input_ids` for generation with encoder-decoder models. Based on https://github.com/huggingface/transformers/blob/ 4037a2b5b1278736e566aec12e169100275545ea/ src/transformers/generation/utils.py specifically GenerationMixin._prepare_decoder_input_ids_for_generation() Arguments: * decoder_input_ids: input token ids to preprocess Returns: * Processed token list """ decoder_start_token_id = self._get_decoder_start_token_id() assert decoder_start_token_id is not None if decoder_input_ids is None: # no decoder prompt input -> # use decoder_start_token_id as decoder_input_ids decoder_input_ids = self._get_default_enc_dec_decoder_prompt() if (len(decoder_input_ids) == 0 or decoder_input_ids[0] != decoder_start_token_id): decoder_input_ids = [decoder_start_token_id] + decoder_input_ids return decoder_input_ids def _tokenize_prompt( self, prompt: str, request_id: str, lora_request: Optional[LoRARequest], ) -> List[int]: ''' Wrapper around application of the model's tokenizer. Arguments: * prompt * request_id * lora_request Returns: * prompt token ids ''' tokenizer = self.get_tokenizer_group( missing_msg="prompts must be None if skip_tokenizer_init is True") return tokenizer.encode(request_id=request_id, prompt=prompt, lora_request=lora_request) def _extract_prompt_components( self, inputs: SingletonPromptInputs, request_id: str, lora_request: Optional[LoRARequest] = None, ) -> PromptComponents: ''' Extract the components of any single encoder or decoder input prompt. Arguments: * request_id * inputs: single encoder or decoder input prompt * lora_request: this is only valid for decoder prompts Returns: * prompt * prompt_token_ids * multi_modal_data ''' if isinstance(inputs, str): prompt = inputs prompt_token_ids = self._tokenize_prompt( prompt, request_id=request_id, lora_request=lora_request, ) multi_modal_data = None elif isinstance(inputs, dict): if "prompt_token_ids" in inputs: prompt = None prompt_token_ids = inputs["prompt_token_ids"] else: # NOTE: This extra assignment is required to pass mypy prompt = parsed_prompt = inputs["prompt"] prompt_token_ids = self._tokenize_prompt( parsed_prompt, request_id=request_id, lora_request=lora_request, ) multi_modal_data = inputs.get("multi_modal_data") else: assert_never(inputs) return prompt, prompt_token_ids, multi_modal_data def _apply_prompt_adapter( self, prompt_token_ids: List[int], prompt_adapter_request: Optional[PromptAdapterRequest], ) -> List[int]: if prompt_adapter_request: prompt_token_ids = ( [0] * prompt_adapter_request.prompt_adapter_num_virtual_tokens + prompt_token_ids) return prompt_token_ids def _get_default_enc_dec_decoder_prompt(self) -> List[int]: ''' Specifically for encoder/decoder models: generate a default decoder prompt for when the user specifies only the encoder prompt. Encoder/decoder models utilize the decoder prompt in different ways; as new models are added, it is intended that this function will be extended to produce differing default decoder prompts, depending on the model variety. Absent a special case, the default behavior of this method is to mirror the behavior of the HuggingFace (HF) GenerationMixin for a None decoder prompt, which is to employ a logit processor setting to force the first decoded token to be . Here, this behavior is approximated by having the "default" decoder prompt be . However, it is possible that in the future other models may have different or more complex logic for the default decoder prompt. This motivates having a special helper method for default decoder prompts. Returns: * prompt_token_ids ''' bos_token_id = self._get_bos_token_id() assert bos_token_id is not None return [bos_token_id] def _build_enc_dec_llm_inputs( self, encoder_comps: PromptComponents, decoder_comps: DecoderPromptComponents, ) -> EncoderDecoderLLMInputs: encoder_prompt, encoder_prompt_ids, encoder_mm_data = encoder_comps decoder_prompt, decoder_prompt_ids, decoder_mm_data = decoder_comps if encoder_mm_data is not None or decoder_mm_data is not None: raise ValueError("Multi-modal encoder-decoder models are " "not supported yet") decoder_prompt_ids = ( self._prepare_decoder_input_ids_for_generation(decoder_prompt_ids)) return EncoderDecoderLLMInputs( prompt_token_ids=decoder_prompt_ids, prompt=decoder_prompt, encoder_prompt_token_ids=encoder_prompt_ids, encoder_prompt=encoder_prompt, ) def _process_encoder_decoder_prompt( self, inputs: PromptInputs, request_id: str, ) -> EncoderDecoderLLMInputs: ''' For encoder/decoder models only: Process an input prompt into an :class:`EncoderDecoderLLMInputs` instance. There are two types of input prompts: singleton prompts which carry only the encoder prompt, and explicit encoder/decoder prompts which carry both the encoder and the decoder prompts as member variables. This function handles the following scenarios: * Singleton encoder prompt: extract encoder prompt token ids & infer default decoder prompt token ids * Explicit encoder/decoder prompt: extract encoder and decoder prompt token ids Note that for Explicit encoder/decoder prompts, each sub-prompt (encoder or decoder prompt) can have any possible singleton type; thus this method relies on helper functions to obtain token ids for the sub-prompts. Arguments: * inputs: an input prompt * request_id Returns: * :class:`EncoderDecoderLLMInputs` instance ''' encoder_comps: PromptComponents decoder_comps: DecoderPromptComponents if is_explicit_encoder_decoder_prompt(inputs): encoder_comps = self._extract_prompt_components( inputs["encoder_prompt"], request_id=request_id, ) if (decoder_input := inputs["decoder_prompt"]) is None: decoder_comps = None, None, None else: decoder_comps = self._extract_prompt_components( decoder_input, request_id=request_id, ) else: encoder_comps = self._extract_prompt_components( inputs, request_id=request_id, ) decoder_comps = None, None, None return self._build_enc_dec_llm_inputs(encoder_comps, decoder_comps) def _build_decoder_only_llm_inputs( self, prompt_comps: PromptComponents, prompt_adapter_request: Optional[PromptAdapterRequest], ) -> LLMInputs: prompt, prompt_token_ids, multi_modal_data = prompt_comps prompt_token_ids = self._apply_prompt_adapter( prompt_token_ids, prompt_adapter_request=prompt_adapter_request) return LLMInputs(prompt_token_ids=prompt_token_ids, prompt=prompt, multi_modal_data=multi_modal_data) def _process_decoder_only_prompt( self, inputs: SingletonPromptInputs, request_id: str, lora_request: Optional[LoRARequest] = None, prompt_adapter_request: Optional[PromptAdapterRequest] = None, ) -> LLMInputs: ''' For decoder-only models: Process an input prompt into an :class:`LLMInputs` instance. Arguments: * inputs: input prompt * request_id * lora_request * prompt_adapter_request Returns: * :class:`LLMInputs` instance ''' prompt_comps = self._extract_prompt_components( inputs, request_id=request_id, lora_request=lora_request, ) return self._build_decoder_only_llm_inputs( prompt_comps, prompt_adapter_request=prompt_adapter_request, ) def process_model_inputs( self, inputs: PromptInputs, request_id: str, lora_request: Optional[LoRARequest] = None, prompt_adapter_request: Optional[PromptAdapterRequest] = None, ) -> Union[LLMInputs, EncoderDecoderLLMInputs]: if self.is_encoder_decoder_model(): # Encoder-decoder model requires special mapping of # input prompts to encoder & decoder model_inputs = self._process_encoder_decoder_prompt( inputs, request_id=request_id, ) else: if is_explicit_encoder_decoder_prompt(inputs): raise ValueError("Cannot pass encoder-decoder prompt " "to decoder-only models") # Decoder-only operation model_inputs = self._process_decoder_only_prompt( inputs, request_id=request_id, lora_request=lora_request, prompt_adapter_request=prompt_adapter_request, ) return self.input_processor(model_inputs) def add_request( self, request_id: str, inputs: PromptInputs, params: Union[SamplingParams, PoolingParams], arrival_time: Optional[float] = None, lora_request: Optional[LoRARequest] = None, trace_headers: Optional[Mapping[str, str]] = None, prompt_adapter_request: Optional[PromptAdapterRequest] = 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. inputs: The inputs to the LLM. See :class:`~vllm.inputs.PromptInputs` for more details about the format of each input. params: Parameters for sampling or pooling. :class:`~vllm.SamplingParams` for text generation. :class:`~vllm.PoolingParams` for pooling. arrival_time: The arrival time of the request. If None, we use the current monotonic time. trace_headers: OpenTelemetry trace headers. 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 `best_of` number of :class:`~vllm.Sequence` objects. - Create a :class:`~vllm.SequenceGroup` object from the list of :class:`~vllm.Sequence`. - Add the :class:`~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 lora_request is not None and not self.lora_config: raise ValueError(f"Got lora_request {lora_request} but LoRA is " "not enabled!") if arrival_time is None: arrival_time = time.time() processed_inputs = self.process_model_inputs( inputs, request_id=request_id, lora_request=lora_request, prompt_adapter_request=prompt_adapter_request, ) self._add_processed_request( request_id=request_id, processed_inputs=processed_inputs, params=params, arrival_time=arrival_time, lora_request=lora_request, prompt_adapter_request=prompt_adapter_request, trace_headers=trace_headers, ) 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, prompt_adapter_request: Optional[PromptAdapterRequest] = None, encoder_seq: Optional[Sequence] = None, ) -> 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.") # 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. 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, prompt_adapter_request=prompt_adapter_request, encoder_seq=encoder_seq) 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], prompt_adapter_request: Optional[PromptAdapterRequest], encoder_seq: Optional[Sequence] = None, ) -> 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, prompt_adapter_request=prompt_adapter_request, encoder_seq=encoder_seq) 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 the :meth:`~vllm.core.scheduler.Scheduler.abort_seq_group` from class :class:`~vllm.core.scheduler.Scheduler`. 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) 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 _process_sequence_group_outputs( self, seq_group: SequenceGroup, outputs: List[EmbeddingSequenceGroupOutput], ) -> None: seq_group.embeddings = outputs[0].embeddings 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, skip) = ctx.output_queue[0] else: (outputs, seq_group_metadata_list, scheduler_outputs, is_async, is_last_step, skip) = ctx.output_queue.popleft() # Sanity check assert len(seq_group_metadata_list) == len( scheduler_outputs.scheduled_seq_groups) # Organize outputs by [step][sequence group] instead of # [sequence group][step]. if len(outputs) > 1: outputs_by_sequence_group = create_output_by_sequence_group( outputs, num_seq_groups=len(seq_group_metadata_list)) else: 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 = scheduled_seq_group.seq_group if seq_group.is_finished(): finished_before.append(i) continue if len(outputs) > 1: output = outputs_by_sequence_group[i] else: output = [outputs_by_sequence_group[0][i]] if not is_async: seq_group.update_num_computed_tokens( scheduled_seq_group.token_chunk_size) 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) 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) else: seq_group.metrics.model_execute_time = ( o.model_execute_time) if self.model_config.embedding_mode: 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) request_output = RequestOutputFactory.create(seq_group) 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() # For multi-step, do not create outputs each iteration if not is_last_step: # Immediately process request outputs here (if callback is given) 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 # 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 (seq_group.is_finished() if self.step_return_finished_only else True): request_output = RequestOutputFactory.create(seq_group) ctx.request_outputs.append(request_output) for seq_group in scheduler_outputs.ignored_seq_groups: request_output = RequestOutputFactory.create(seq_group) 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) # Tracing self.do_tracing(scheduler_outputs) return None def _advance_to_next_step( self, output: List[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 seq_group.update_num_computed_tokens( seq_group_metadata.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 and no " "sampling_params.best_of > 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) def step(self) -> List[Union[RequestOutput, EmbeddingRequestOutput]]: """Performs one decoding iteration and returns newly generated results. .. figure:: https://i.imgur.com/sv2HssD.png :alt: Overview of the step function :align: center 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. if not self._has_remaining_steps(seq_group_metadata_list): # 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 # 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) if (self.scheduler_config.is_multi_step and scheduler_outputs.num_lookahead_slots > 0): # cache the scheduler outputs for the next iteration if we have # lookahead slots self._cache_scheduler_outputs_for_multi_step( virtual_engine, seq_group_metadata_list, scheduler_outputs, allow_async_output_proc) assert seq_group_metadata_list is not None assert scheduler_outputs is not None if not scheduler_outputs.is_empty(): finished_requests_ids = self.scheduler[ virtual_engine].get_and_reset_finished_requests_ids() # 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] outputs = self.model_executor.execute_model( execute_model_req=execute_model_req) # We need to do this here so that last step's sampled_token_ids can # be passed to the next iteration for PP. if self.scheduler_config.is_multi_step: self._update_cached_scheduler_output(virtual_engine, outputs) 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 = [] # Finish the current step for all the sequence groups. if self.scheduler_config.is_multi_step: for seq_group in seq_group_metadata_list: seq_group.finish_step() if not self._has_remaining_steps(seq_group_metadata_list): # clear the cache if we have finished all the steps. if self.scheduler_config.is_multi_step: self.cached_scheduler_outputs[0] = SchedulerOutputState() # 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) 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. self.model_executor.stop_remote_worker_execution_loop() return ctx.request_outputs def _has_remaining_steps( self, seq_group_metadata_list: Optional[List[SequenceGroupMetadata]] ) -> bool: if (not self.scheduler_config.is_multi_step or not seq_group_metadata_list): return False # TODO(will) this is a sanity check for nowto make sure that all the # seqs are on the same steps. Eventually we will want to do some sort of # dynamic scheduling when doing multi-step decoding. ref_remaining_steps = seq_group_metadata_list[0].state.remaining_steps if any([ seq_group.state.remaining_steps != ref_remaining_steps for seq_group in seq_group_metadata_list[1:] ]): raise AssertionError(("All running sequence groups should " "have the same remaining steps.")) return ref_remaining_steps > 0 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]: cached_last_output = self.cached_scheduler_outputs[ virtual_engine].last_output if (self.scheduler_config.is_multi_step and self.parallel_config.pipeline_parallel_size > 1 and cached_last_output is not None and cached_last_output.sampled_token_ids_cpu is not None): return cached_last_output.sampled_token_ids_cpu 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) -> None: """Forced log when no requests active.""" if self.log_stats: stats = self._get_stats(scheduler_outputs, model_output, finished_before) 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) -> 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. """ 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 is not None: 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 is not None and num_total_cpu > 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) # Iteration stats num_prompt_tokens_iter = 0 num_generation_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] = [] # Metadata num_prompt_tokens_requests: List[int] = [] num_generation_tokens_requests: List[int] = [] best_of_requests: List[int] = [] n_requests: List[int] = [] finished_reason_requests: List[str] = [] # 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 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_latency(now) 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_latency(now) time_per_output_tokens_iter.append(latency) # 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) # 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() ]) if seq_group.sampling_params is not None: best_of_requests.append( seq_group.sampling_params.best_of) n_requests.append(seq_group.sampling_params.n) 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) # Spec decode, if enabled, emits specialized metrics from the worker in # sampler output. if model_output and (model_output[0].spec_decode_worker_metrics is not None): spec_decode_metrics = model_output[0].spec_decode_worker_metrics else: spec_decode_metrics = None 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, time_to_first_tokens_iter=time_to_first_tokens_iter, time_per_output_tokens_iter=time_per_output_tokens_iter, spec_decode_metrics=spec_decode_metrics, num_preemption_iter=num_preemption_iter, # Request stats # Latency time_e2e_requests=time_e2e_requests, # Metadata num_prompt_tokens_requests=num_prompt_tokens_requests, num_generation_tokens_requests=num_generation_tokens_requests, best_of_requests=best_of_requests, n_requests=n_requests, finished_reason_requests=finished_reason_requests, ) 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 add_prompt_adapter( self, prompt_adapter_request: PromptAdapterRequest) -> bool: return self.model_executor.add_prompt_adapter(prompt_adapter_request) def remove_prompt_adapter(self, prompt_adapter_id: int) -> bool: return self.model_executor.remove_prompt_adapter(prompt_adapter_id) def list_prompt_adapters(self) -> List[int]: return self.model_executor.list_prompt_adapters() def check_health(self) -> None: if self.tokenizer: self.tokenizer.check_health() self.model_executor.check_health() def start_profile(self) -> None: self.model_executor.start_profile() def stop_profile(self) -> None: self.model_executor.stop_profile() def is_tracing_enabled(self) -> bool: return self.tracer is not None def do_tracing(self, scheduler_outputs: SchedulerOutputs) -> None: if self.tracer is None: return for scheduled_seq_group in scheduler_outputs.scheduled_seq_groups: 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 ttft = metrics.first_token_time - metrics.arrival_time e2e_time = metrics.finished_time - metrics.arrival_time # attribute names are based on # https://github.com/open-telemetry/semantic-conventions/blob/main/docs/gen-ai/llm-spans.md seq_span.set_attribute(SpanAttributes.LLM_RESPONSE_MODEL, self.model_config.model) seq_span.set_attribute(SpanAttributes.LLM_REQUEST_ID, seq_group.request_id) seq_span.set_attribute(SpanAttributes.LLM_REQUEST_TEMPERATURE, seq_group.sampling_params.temperature) seq_span.set_attribute(SpanAttributes.LLM_REQUEST_TOP_P, seq_group.sampling_params.top_p) seq_span.set_attribute(SpanAttributes.LLM_REQUEST_MAX_TOKENS, seq_group.sampling_params.max_tokens) seq_span.set_attribute(SpanAttributes.LLM_REQUEST_BEST_OF, seq_group.sampling_params.best_of) seq_span.set_attribute(SpanAttributes.LLM_REQUEST_N, seq_group.sampling_params.n) seq_span.set_attribute(SpanAttributes.LLM_USAGE_NUM_SEQUENCES, seq_group.num_seqs()) seq_span.set_attribute(SpanAttributes.LLM_USAGE_PROMPT_TOKENS, len(seq_group.prompt_token_ids)) seq_span.set_attribute( SpanAttributes.LLM_USAGE_COMPLETION_TOKENS, sum([ seq.get_output_len() for seq in seq_group.get_finished_seqs() ])) seq_span.set_attribute(SpanAttributes.LLM_LATENCY_TIME_IN_QUEUE, metrics.time_in_queue) seq_span.set_attribute( SpanAttributes.LLM_LATENCY_TIME_TO_FIRST_TOKEN, ttft) seq_span.set_attribute(SpanAttributes.LLM_LATENCY_E2E, e2e_time) if metrics.scheduler_time is not None: seq_span.set_attribute( SpanAttributes.LLM_LATENCY_TIME_IN_SCHEDULER, metrics.scheduler_time) if metrics.model_forward_time is not None: seq_span.set_attribute( SpanAttributes.LLM_LATENCY_TIME_IN_MODEL_FORWARD, metrics.model_forward_time / 1000.0) if metrics.model_execute_time is not None: seq_span.set_attribute( SpanAttributes.LLM_LATENCY_TIME_IN_MODEL_EXECUTE, metrics.model_execute_time) def is_encoder_decoder_model(self): return self.model_config.is_encoder_decoder_model def is_embedding_model(self): return self.model_config.is_embedding_model def _validate_model_inputs(self, inputs: Union[LLMInputs, EncoderDecoderLLMInputs]): if self.is_encoder_decoder_model(): prompt_ids = inputs.get("encoder_prompt_token_ids") else: prompt_ids = inputs.get("prompt_token_ids") if prompt_ids is None or len(prompt_ids) == 0: raise ValueError("Prompt cannot be empty") if self.model_config.is_multimodal_model: max_prompt_len = self.model_config.max_model_len if len(prompt_ids) > max_prompt_len: raise ValueError( f"The prompt (total length {len(prompt_ids)}) is too long " f"to fit into the model (context length {max_prompt_len}). " "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.") # 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