import time from dataclasses import dataclass from typing import Dict, List, Optional from typing import Sequence as GenericSequence from typing import Union from vllm.lora.request import LoRARequest from vllm.multimodal.inputs import MultiModalPlaceholderDict from vllm.sampling_params import RequestOutputKind from vllm.sequence import (PromptLogprobs, RequestMetrics, SampleLogprobs, SequenceGroup, SequenceGroupBase, SequenceStatus) @dataclass class CompletionOutput: """The output data of one completion output of a request. Args: index: The index of the output in the request. text: The generated output text. token_ids: The token IDs of the generated output text. cumulative_logprob: The cumulative log probability of the generated output text. logprobs: The log probabilities of the top probability words at each position if the logprobs are requested. finish_reason: The reason why the sequence is finished. stop_reason: The stop string or token id that caused the completion to stop, None if the completion finished for some other reason including encountering the EOS token. lora_request: The LoRA request that was used to generate the output. """ index: int text: str token_ids: GenericSequence[int] cumulative_logprob: Optional[float] logprobs: Optional[SampleLogprobs] finish_reason: Optional[str] = None stop_reason: Union[int, str, None] = None lora_request: Optional[LoRARequest] = None def finished(self) -> bool: return self.finish_reason is not None def __repr__(self) -> str: return (f"CompletionOutput(index={self.index}, " f"text={self.text!r}, " f"token_ids={self.token_ids}, " f"cumulative_logprob={self.cumulative_logprob}, " f"logprobs={self.logprobs}, " f"finish_reason={self.finish_reason}, " f"stop_reason={self.stop_reason})") @dataclass class PoolingOutput: """The output data of one pooling output of a request. Args: embedding: The embedding vector, which is a list of floats. The length of vector depends on the model as listed in the embedding guide. """ embedding: List[float] def __repr__(self) -> str: return (f"PoolingOutput(" f"embedding={len(self.embedding)})") class RequestOutput: """The output data of a completion request to the LLM. Args: request_id: The unique ID of the request. prompt: The prompt string of the request. For encoder/decoder models, this is the decoder input prompt. prompt_token_ids: The token IDs of the prompt. For encoder/decoder models, this is the decoder input prompt token ids. prompt_logprobs: The log probabilities to return per prompt token. outputs: The output sequences of the request. finished: Whether the whole request is finished. metrics: Metrics associated with the request. lora_request: The LoRA request that was used to generate the output. encoder_prompt: The encoder prompt string of the request. None if decoder-only. encoder_prompt_token_ids: The token IDs of the encoder prompt. None if decoder-only. num_cached_tokens: The number of tokens with prefix cache hit. """ def __init__( self, request_id: str, prompt: Optional[str], prompt_token_ids: Optional[List[int]], prompt_logprobs: Optional[PromptLogprobs], outputs: List[CompletionOutput], finished: bool, metrics: Optional[RequestMetrics] = None, lora_request: Optional[LoRARequest] = None, encoder_prompt: Optional[str] = None, encoder_prompt_token_ids: Optional[List[int]] = None, num_cached_tokens: Optional[int] = None, *, multi_modal_placeholders: Optional[MultiModalPlaceholderDict] = None, ) -> None: self.request_id = request_id self.prompt = prompt self.prompt_token_ids = prompt_token_ids self.multi_modal_placeholders = multi_modal_placeholders or {} self.prompt_logprobs = prompt_logprobs self.outputs = outputs self.finished = finished self.metrics = metrics self.lora_request = lora_request self.encoder_prompt = encoder_prompt self.encoder_prompt_token_ids = encoder_prompt_token_ids self.num_cached_tokens = num_cached_tokens @classmethod def new( cls, request_id: str, prompt: Optional[str], prompt_token_ids: Optional[List[int]], text: str, token_ids: List[int], finished: bool = False, ) -> "RequestOutput": """Initialize a new RequestOutput object.""" # TODO: Support `n` > 1. completion_output = CompletionOutput( index=0, text=text, token_ids=token_ids, cumulative_logprob=None, logprobs=None, # TODO ) return RequestOutput( request_id=request_id, prompt=prompt, prompt_token_ids=prompt_token_ids, prompt_logprobs=None, # TODO outputs=[completion_output], finished=finished, ) @classmethod def from_seq_group( cls, seq_group: SequenceGroup, use_cache: bool, seq_id_to_seq_group: Dict[str, SequenceGroupBase] ) -> Optional["RequestOutput"]: finished = seq_group.is_finished() if seq_group.request_id in seq_id_to_seq_group: group: SequenceGroupBase = seq_id_to_seq_group[ seq_group.request_id] if finished: group.finish_seq(seq_group) assembled_seq_group = group.maybe_assemble_group(seq_group) if assembled_seq_group is None: return None return cls.from_seq_group(assembled_seq_group, use_cache, seq_id_to_seq_group) sampling_params = seq_group.sampling_params if sampling_params is None: raise ValueError( "Sampling parameters are missing for a CompletionRequest.") if sampling_params.output_kind == RequestOutputKind.FINAL_ONLY and ( not finished): return None # Init cache (if needed) if use_cache and seq_group.cached_request_output is None: seq_group.cached_request_output = RequestOutput( # type: ignore request_id="", prompt=None, prompt_token_ids=[], prompt_logprobs=None, outputs=[], finished=False) top_n_seqs = seq_group.get_seqs() # Create the outputs. # NOTE: We need omit logprobs here explicitly because the sequence # always has the logprobs of the sampled tokens even if the # logprobs are not requested. include_logprobs = sampling_params.logprobs is not None text_buffer_length = sampling_params.output_text_buffer_length delta = sampling_params.output_kind == RequestOutputKind.DELTA outputs = [] include_prompt = True # num_cached_tokens should be the same for all the sequences num_cached_tokens = None for i, seq in enumerate(top_n_seqs): output_text = seq.get_output_text_to_return( text_buffer_length, delta) output_token_ids = seq.get_output_token_ids_to_return(delta) num_output_tokens = 1 if isinstance(output_token_ids, int) else len(output_token_ids) num_cached_tokens = seq.data.get_num_cached_tokens() output_logprobs = seq.output_logprobs if include_logprobs else None if delta: # Slice logprobs delta if applicable if output_logprobs: output_logprobs = output_logprobs[-num_output_tokens:] # Don't include prompt if this is after the first output # containing decode token ids if include_prompt and seq.get_output_len() > num_output_tokens: include_prompt = False if use_cache: # Get cached output object cached_outputs = seq_group.cached_request_output.outputs # type: ignore if i >= len(cached_outputs): cached_outputs.append( CompletionOutput(index=i, text="", token_ids=[], cumulative_logprob=None, logprobs=None, finish_reason=None, stop_reason=None)) output = cached_outputs[i] # Init cached output object assert output.index == i output.text = output_text if isinstance(output_token_ids, int): output.token_ids.clear() output.token_ids.append(output_token_ids) else: output.token_ids = output_token_ids output.cumulative_logprob = seq.get_cumulative_logprob() \ if include_logprobs else None output.logprobs = output_logprobs output.finish_reason = SequenceStatus.get_finished_reason( seq.status) output.stop_reason = seq.stop_reason else: output = CompletionOutput( top_n_seqs.index(seq), output_text, [output_token_ids] if isinstance(output_token_ids, int) else output_token_ids, seq.get_cumulative_logprob() if include_logprobs else None, output_logprobs, SequenceStatus.get_finished_reason(seq.status), seq.stop_reason) outputs.append(output) # Every sequence in the sequence group should have the same prompt. if include_prompt: prompt = seq_group.prompt prompt_token_ids = seq_group.prompt_token_ids encoder_prompt = seq_group.encoder_prompt encoder_prompt_token_ids = seq_group.encoder_prompt_token_ids prompt_logprobs = seq_group.prompt_logprobs else: prompt = None prompt_token_ids = None encoder_prompt = None encoder_prompt_token_ids = None prompt_logprobs = None finished_time = time.time() if finished else None seq_group.set_finished_time(finished_time) init_kwargs = { "request_id": seq_group.request_id, "prompt": prompt, "prompt_token_ids": prompt_token_ids, "prompt_logprobs": prompt_logprobs, "outputs": outputs, "finished": finished, "metrics": seq_group.metrics, "lora_request": seq_group.lora_request, "encoder_prompt": encoder_prompt, "encoder_prompt_token_ids": encoder_prompt_token_ids, "num_cached_tokens": num_cached_tokens, "multi_modal_placeholders": seq_group.multi_modal_placeholders } if use_cache: request_output = seq_group.cached_request_output request_output.__init__(**init_kwargs) # type: ignore else: request_output = cls(**init_kwargs) # type: ignore return request_output def __repr__(self) -> str: return (f"RequestOutput(request_id={self.request_id}, " f"prompt={self.prompt!r}, " f"prompt_token_ids={self.prompt_token_ids}, " f"encoder_prompt={self.encoder_prompt!r}, " f"encoder_prompt_token_ids={self.encoder_prompt_token_ids}, " f"prompt_logprobs={self.prompt_logprobs}, " f"outputs={self.outputs}, " f"finished={self.finished}, " f"metrics={self.metrics}, " f"lora_request={self.lora_request}, " f"num_cached_tokens={self.num_cached_tokens}, " f"multi_modal_placeholders={self.multi_modal_placeholders})") class PoolingRequestOutput: """ The output data of a pooling request to the LLM. Args: request_id (str): A unique identifier for the pooling request. outputs (PoolingOutput): The pooling results for the given input. prompt_token_ids (List[int]): A list of token IDs used in the prompt. finished (bool): A flag indicating whether the pooling is completed. """ def __init__(self, request_id: str, outputs: "PoolingOutput", prompt_token_ids: List[int], finished: bool): self.request_id = request_id self.prompt_token_ids = prompt_token_ids self.finished = finished self.outputs = outputs @classmethod def from_seq_group(cls, seq_group: 'SequenceGroup') -> "PoolingRequestOutput": if seq_group.embeddings is None: raise ValueError( "Embeddings are missing in seq_group for EmbeddingRequest.") output = PoolingOutput(seq_group.embeddings) prompt_token_ids = seq_group.prompt_token_ids finished = seq_group.is_finished() return cls(seq_group.request_id, output, prompt_token_ids, finished) def __repr__(self): """ Returns a string representation of an PoolingRequestOutput instance. The representation includes the request_id and the number of outputs, providing a quick overview of the pooling request's results. Returns: str: A string representation of the PoolingRequestOutput instance. """ return (f"PoolingRequestOutput(request_id='{self.request_id}', " f"outputs={repr(self.outputs)}, " f"prompt_token_ids={self.prompt_token_ids}, " f"finished={self.finished})") @dataclass class ScoreOutput: """The output data of one completion output of a request. Args: score: The score, which is a list of floats. index: The correspondent text index of the score. """ index: int score: List[float] def __repr__(self) -> str: return (f"ScoreOutput(" f"score={self.score}), " f"index={self.index})") class ScoreRequestOutput: """ The output data of an score request to the LLM. Args: request_id (str): A unique identifier for the score request. outputs (score): The embedding results for the given input. """ def __init__(self, request_id: str, outputs: "ScoreOutput"): self.request_id = request_id self.outputs = outputs def __repr__(self): """ Returns a string representation of an ScoreRequestOutput instance. The representation includes the request_id and the number of outputs, providing a quick overview of the embedding request's results. Returns: str: A string representation of the ScoreRequestOutput instance. """ return (f"ScoreRequestOutput(request_id='{self.request_id}', " f"outputs={repr(self.outputs)}") class RequestOutputFactory: @staticmethod def create(seq_group: SequenceGroup, seq_id_to_seq_group: Dict[str, SequenceGroupBase], use_cache: bool = False): # Determine the type based on a condition, for example: if hasattr(seq_group, 'embeddings') and seq_group.embeddings is not None: return PoolingRequestOutput.from_seq_group(seq_group) else: return RequestOutput.from_seq_group(seq_group, use_cache, seq_id_to_seq_group) def __getattr__(name: str): import warnings if name == "EmbeddingOutput": msg = ("EmbeddingOutput has been renamed to PoolingOutput. " "The original name will be removed in an upcoming version.") warnings.warn(DeprecationWarning(msg), stacklevel=2) return PoolingOutput if name == "EmbeddingRequestOutput": msg = ("EmbeddingRequestOutput has been renamed to " "PoolingRequestOutput. " "The original name will be removed in an upcoming version.") warnings.warn(DeprecationWarning(msg), stacklevel=2) return PoolingRequestOutput raise AttributeError(f"module {__name__!r} has no attribute {name!r}")