# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import time from collections.abc import MutableSequence from collections.abc import Sequence as GenericSequence from dataclasses import dataclass from typing import Any, Generic, Optional, Union import torch from typing_extensions import TypeVar from vllm.logger import init_logger from vllm.logprobs import PromptLogprobs, SampleLogprobs from vllm.lora.request import LoRARequest from vllm.multimodal.inputs import MultiModalPlaceholderDict from vllm.sampling_params import RequestOutputKind from vllm.sequence import (RequestMetrics, SequenceGroup, SequenceGroupBase, SequenceStatus) logger = init_logger(__name__) @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: data: The extracted hidden states. """ data: torch.Tensor def __repr__(self) -> str: return (f"PoolingOutput(data={self.data})") def __eq__(self, other: object) -> bool: return (isinstance(other, self.__class__) and bool( (self.data == other.data).all())) 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. kv_transfer_params: The params for remote K/V transfer. """ 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, kv_transfer_params: Optional[dict[str, Any]] = None, # Forward compatibility, code that uses args added in new release can # still run with older versions of vLLM without breaking. **kwargs: Any, ) -> None: if kwargs: logger.warning_once("RequestOutput: Ignoring extra arguments: %s", str(kwargs)) 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 self.kv_transfer_params = kv_transfer_params def add(self, next_output: "RequestOutput", aggregate: bool) -> None: """Merge subsequent RequestOutput into this one""" self.finished |= next_output.finished self.kv_transfer_params = next_output.kv_transfer_params for next_completion in next_output.outputs: for i, completion in enumerate(self.outputs): if completion.index == next_completion.index: if aggregate: # Merge outputs with same index completion.text += next_completion.text if not isinstance(completion.token_ids, MutableSequence): completion.token_ids = list(completion.token_ids) completion.token_ids.extend(next_completion.token_ids) if next_completion.logprobs: assert completion.logprobs is not None completion.logprobs.extend( next_completion.logprobs) completion.cumulative_logprob = ( next_completion.cumulative_logprob) completion.finish_reason = next_completion.finish_reason completion.stop_reason = next_completion.stop_reason else: # Replace the output with the new one self.outputs[i] = next_completion break else: self.outputs.append(next_completion) @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] assembled_seq_group = group.maybe_assemble_group(seq_group) if finished: group.finish_seq(seq_group) if assembled_seq_group is None: return None # clear finished seq in seq_id_to_seq_group if len(group.to_be_finished) == 0: for sub_request_id in list(group.seq_id_to_index.keys()): if sub_request_id in seq_id_to_seq_group: del seq_id_to_seq_group[sub_request_id] 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: # num_output_tokens can be 0 when n > 1 and request finishes # before the others if num_output_tokens > 0: output_logprobs = output_logprobs[-num_output_tokens:] else: output_logprobs = None # 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})") _O = TypeVar("_O", default=PoolingOutput) class PoolingRequestOutput(Generic[_O]): """ 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: _O, 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 @staticmethod def from_seq_group(seq_group: SequenceGroup) -> "PoolingRequestOutput": pooled_data = seq_group.pooled_data assert pooled_data is not None data = pooled_data.to(dtype=torch.float32, device="cpu") output = PoolingOutput(data) prompt_token_ids = seq_group.prompt_token_ids finished = seq_group.is_finished() return PoolingRequestOutput(seq_group.request_id, output, prompt_token_ids, finished) def __repr__(self): return (f"{type(self).__name__}(request_id={self.request_id!r}, " f"outputs={self.outputs!r}, " f"prompt_token_ids={self.prompt_token_ids}, " f"finished={self.finished})") class RequestOutputFactory: @staticmethod def create(seq_group: SequenceGroup, seq_id_to_seq_group: dict[str, SequenceGroupBase], use_cache: bool = False): if seq_group.pooled_data 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) @dataclass class EmbeddingOutput: """The output data of one embedding output of a request. Args: embedding: The embedding vector, which is a list of floats. Its length depends on the hidden dimension of the model. """ embedding: list[float] @staticmethod def from_base(pooling_output: PoolingOutput): pooled_data = pooling_output.data if pooled_data.ndim != 1: raise ValueError("pooled_data should be a 1-D embedding vector") return EmbeddingOutput(pooled_data.tolist()) @property def hidden_size(self) -> int: return len(self.embedding) def __repr__(self) -> str: return f"EmbeddingOutput(hidden_size={self.hidden_size})" class EmbeddingRequestOutput(PoolingRequestOutput[EmbeddingOutput]): @staticmethod def from_base(request_output: PoolingRequestOutput): return EmbeddingRequestOutput( request_id=request_output.request_id, outputs=EmbeddingOutput.from_base(request_output.outputs), prompt_token_ids=request_output.prompt_token_ids, finished=request_output.finished, ) @dataclass class ClassificationOutput: """The output data of one classification output of a request. Args: probs: The probability vector, which is a list of floats. Its length depends on the number of classes. """ probs: list[float] @staticmethod def from_base(pooling_output: PoolingOutput): # pooling_output shape: (num_classes) pooled_data = pooling_output.data if pooled_data.ndim != 1: raise ValueError("pooled_data should be a 1-D probability vector") return ClassificationOutput(pooled_data.tolist()) @property def num_classes(self) -> int: return len(self.probs) def __repr__(self) -> str: return f"ClassificationOutput(num_classes={self.num_classes})" class ClassificationRequestOutput(PoolingRequestOutput[ClassificationOutput]): @staticmethod def from_base(request_output: PoolingRequestOutput): return ClassificationRequestOutput( request_id=request_output.request_id, outputs=ClassificationOutput.from_base(request_output.outputs), prompt_token_ids=request_output.prompt_token_ids, finished=request_output.finished, ) @dataclass class ScoringOutput: """The output data of one scoring output of a request. Args: score: The similarity score, which is a scalar value. """ score: float @staticmethod def from_base(pooling_output: PoolingOutput): # pooling_output shape: # classify task: (num_classes) num_classes == 1 # embed task: a scalar value pooled_data = pooling_output.data.squeeze() if pooled_data.ndim != 0: raise ValueError("pooled_data should be a scalar score") return ScoringOutput(pooled_data.item()) def __repr__(self) -> str: return f"ScoringOutput(score={self.score})" class ScoringRequestOutput(PoolingRequestOutput[ScoringOutput]): @staticmethod def from_base(request_output: PoolingRequestOutput): return ScoringRequestOutput( request_id=request_output.request_id, outputs=ScoringOutput.from_base(request_output.outputs), prompt_token_ids=request_output.prompt_token_ids, finished=request_output.finished, )