v1.0
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345
outputs.py
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345
outputs.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from collections.abc import MutableSequence
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from collections.abc import Sequence as GenericSequence
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from dataclasses import dataclass
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from typing import Any, Generic
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import torch
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from typing_extensions import TypeVar
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from vllm.logger import init_logger
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from vllm.logprobs import PromptLogprobs, SampleLogprobs
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from vllm.lora.request import LoRARequest
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from vllm.multimodal.inputs import MultiModalPlaceholderDict
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from vllm.sequence import RequestMetrics
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from vllm.v1.metrics.stats import RequestStateStats
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logger = init_logger(__name__)
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@dataclass
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class CompletionOutput:
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"""The output data of one completion output of a request.
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Args:
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index: The index of the output in the request.
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text: The generated output text.
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token_ids: The token IDs of the generated output text.
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cumulative_logprob: The cumulative log probability of the generated
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output text.
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logprobs: The log probabilities of the top probability words at each
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position if the logprobs are requested.
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finish_reason: The reason why the sequence is finished.
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stop_reason: The stop string or token id that caused the completion
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to stop, None if the completion finished for some other reason
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including encountering the EOS token.
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lora_request: The LoRA request that was used to generate the output.
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"""
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index: int
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text: str
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token_ids: GenericSequence[int]
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cumulative_logprob: float | None
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logprobs: SampleLogprobs | None
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finish_reason: str | None = None
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stop_reason: int | str | None = None
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lora_request: LoRARequest | None = None
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def finished(self) -> bool:
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return self.finish_reason is not None
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def __repr__(self) -> str:
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return (
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f"CompletionOutput(index={self.index}, "
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f"text={self.text!r}, "
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f"token_ids={self.token_ids}, "
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f"cumulative_logprob={self.cumulative_logprob}, "
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f"logprobs={self.logprobs}, "
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f"finish_reason={self.finish_reason}, "
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f"stop_reason={self.stop_reason})"
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)
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@dataclass
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class PoolingOutput:
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"""The output data of one pooling output of a request.
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Args:
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data: The extracted hidden states.
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"""
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data: torch.Tensor
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def __repr__(self) -> str:
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return f"PoolingOutput(data={self.data})"
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def __eq__(self, other: object) -> bool:
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return isinstance(other, self.__class__) and bool(
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(self.data == other.data).all()
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)
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class RequestOutput:
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"""The output data of a completion request to the LLM.
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Args:
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request_id: The unique ID of the request.
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prompt: The prompt string of the request.
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For encoder/decoder models, this is the
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decoder input prompt.
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prompt_token_ids: The token IDs of the prompt.
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For encoder/decoder models, this is the
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decoder input prompt token ids.
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prompt_logprobs: The log probabilities to return per prompt token.
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outputs: The output sequences of the request.
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finished: Whether the whole request is finished.
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metrics: Metrics associated with the request.
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lora_request: The LoRA request that was used to generate the output.
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encoder_prompt: The encoder prompt string of the request.
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None if decoder-only.
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encoder_prompt_token_ids: The token IDs of the encoder prompt.
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None if decoder-only.
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num_cached_tokens: The number of tokens with prefix cache hit.
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kv_transfer_params: The params for remote K/V transfer.
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"""
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def __init__(
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self,
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request_id: str,
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prompt: str | None,
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prompt_token_ids: list[int] | None,
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prompt_logprobs: PromptLogprobs | None,
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outputs: list[CompletionOutput],
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finished: bool,
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metrics: RequestMetrics | RequestStateStats | None = None,
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lora_request: LoRARequest | None = None,
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encoder_prompt: str | None = None,
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encoder_prompt_token_ids: list[int] | None = None,
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num_cached_tokens: int | None = None,
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*,
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multi_modal_placeholders: MultiModalPlaceholderDict | None = None,
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kv_transfer_params: dict[str, Any] | None = None,
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# Forward compatibility, code that uses args added in new release can
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# still run with older versions of vLLM without breaking.
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**kwargs: Any,
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) -> None:
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if kwargs:
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logger.warning_once(
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"RequestOutput: Ignoring extra arguments: %s", str(kwargs)
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)
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self.request_id = request_id
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self.prompt = prompt
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self.prompt_token_ids = prompt_token_ids
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self.multi_modal_placeholders = multi_modal_placeholders or {}
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self.prompt_logprobs = prompt_logprobs
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self.outputs = outputs
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self.finished = finished
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self.metrics = metrics
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self.lora_request = lora_request
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self.encoder_prompt = encoder_prompt
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self.encoder_prompt_token_ids = encoder_prompt_token_ids
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self.num_cached_tokens = num_cached_tokens
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self.kv_transfer_params = kv_transfer_params
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def add(self, next_output: "RequestOutput", aggregate: bool) -> None:
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"""Merge subsequent RequestOutput into this one"""
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self.finished |= next_output.finished
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self.kv_transfer_params = next_output.kv_transfer_params
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for next_completion in next_output.outputs:
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for i, completion in enumerate(self.outputs):
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if completion.index == next_completion.index:
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if aggregate:
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# Merge outputs with same index
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completion.text += next_completion.text
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if not isinstance(completion.token_ids, MutableSequence):
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completion.token_ids = list(completion.token_ids)
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completion.token_ids.extend(next_completion.token_ids)
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if next_completion.logprobs:
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assert completion.logprobs is not None
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completion.logprobs.extend(next_completion.logprobs)
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completion.cumulative_logprob = (
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next_completion.cumulative_logprob
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)
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completion.finish_reason = next_completion.finish_reason
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completion.stop_reason = next_completion.stop_reason
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else:
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# Replace the output with the new one
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self.outputs[i] = next_completion
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break
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else:
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self.outputs.append(next_completion)
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def __repr__(self) -> str:
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return (
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f"RequestOutput(request_id={self.request_id}, "
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f"prompt={self.prompt!r}, "
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f"prompt_token_ids={self.prompt_token_ids}, "
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f"encoder_prompt={self.encoder_prompt!r}, "
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f"encoder_prompt_token_ids={self.encoder_prompt_token_ids}, "
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f"prompt_logprobs={self.prompt_logprobs}, "
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f"outputs={self.outputs}, "
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f"finished={self.finished}, "
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f"metrics={self.metrics}, "
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f"lora_request={self.lora_request}, "
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f"num_cached_tokens={self.num_cached_tokens}, "
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f"multi_modal_placeholders={self.multi_modal_placeholders})"
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)
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_O = TypeVar("_O", default=PoolingOutput)
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class PoolingRequestOutput(Generic[_O]):
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"""
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The output data of a pooling request to the LLM.
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Args:
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request_id (str): A unique identifier for the pooling request.
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outputs (PoolingOutput): The pooling results for the given input.
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prompt_token_ids (list[int]): A list of token IDs used in the prompt.
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num_cached_tokens: The number of tokens with prefix cache hit.
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finished (bool): A flag indicating whether the pooling is completed.
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"""
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def __init__(
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self,
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request_id: str,
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outputs: _O,
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prompt_token_ids: list[int],
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num_cached_tokens: int,
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finished: bool,
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):
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self.request_id = request_id
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self.prompt_token_ids = prompt_token_ids
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self.num_cached_tokens = num_cached_tokens
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self.finished = finished
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self.outputs = outputs
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def __repr__(self):
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return (
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f"{type(self).__name__}(request_id={self.request_id!r}, "
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f"outputs={self.outputs!r}, "
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f"prompt_token_ids={self.prompt_token_ids}, "
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f"num_cached_tokens={self.num_cached_tokens}, "
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f"finished={self.finished})"
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)
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@dataclass
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class EmbeddingOutput:
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"""The output data of one embedding output of a request.
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Args:
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embedding: The embedding vector, which is a list of floats.
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Its length depends on the hidden dimension of the model.
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"""
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embedding: list[float]
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@staticmethod
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def from_base(pooling_output: PoolingOutput):
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pooled_data = pooling_output.data
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if pooled_data.ndim != 1:
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raise ValueError("pooled_data should be a 1-D embedding vector")
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return EmbeddingOutput(pooled_data.tolist())
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@property
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def hidden_size(self) -> int:
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return len(self.embedding)
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def __repr__(self) -> str:
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return f"EmbeddingOutput(hidden_size={self.hidden_size})"
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class EmbeddingRequestOutput(PoolingRequestOutput[EmbeddingOutput]):
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@staticmethod
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def from_base(request_output: PoolingRequestOutput):
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return EmbeddingRequestOutput(
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request_id=request_output.request_id,
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outputs=EmbeddingOutput.from_base(request_output.outputs),
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prompt_token_ids=request_output.prompt_token_ids,
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num_cached_tokens=request_output.num_cached_tokens,
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finished=request_output.finished,
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)
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@dataclass
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class ClassificationOutput:
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"""The output data of one classification output of a request.
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Args:
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probs: The probability vector, which is a list of floats.
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Its length depends on the number of classes.
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"""
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probs: list[float]
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@staticmethod
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def from_base(pooling_output: PoolingOutput):
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# pooling_output shape: (num_classes)
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pooled_data = pooling_output.data
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if pooled_data.ndim != 1:
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raise ValueError("pooled_data should be a 1-D probability vector")
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return ClassificationOutput(pooled_data.tolist())
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@property
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def num_classes(self) -> int:
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return len(self.probs)
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def __repr__(self) -> str:
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return f"ClassificationOutput(num_classes={self.num_classes})"
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class ClassificationRequestOutput(PoolingRequestOutput[ClassificationOutput]):
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@staticmethod
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def from_base(request_output: PoolingRequestOutput):
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return ClassificationRequestOutput(
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request_id=request_output.request_id,
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outputs=ClassificationOutput.from_base(request_output.outputs),
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prompt_token_ids=request_output.prompt_token_ids,
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num_cached_tokens=request_output.num_cached_tokens,
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finished=request_output.finished,
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)
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@dataclass
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class ScoringOutput:
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"""The output data of one scoring output of a request.
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Args:
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score: The similarity score, which is a scalar value.
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"""
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score: float
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@staticmethod
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def from_base(pooling_output: PoolingOutput):
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# pooling_output shape:
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# classify task: (num_classes) num_classes == 1
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# embed task: a scalar value
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pooled_data = pooling_output.data.squeeze()
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if pooled_data.ndim != 0:
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raise ValueError("pooled_data should be a scalar score")
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return ScoringOutput(pooled_data.item())
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def __repr__(self) -> str:
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return f"ScoringOutput(score={self.score})"
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class ScoringRequestOutput(PoolingRequestOutput[ScoringOutput]):
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@staticmethod
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def from_base(request_output: PoolingRequestOutput):
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return ScoringRequestOutput(
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request_id=request_output.request_id,
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outputs=ScoringOutput.from_base(request_output.outputs),
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prompt_token_ids=request_output.prompt_token_ids,
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num_cached_tokens=request_output.num_cached_tokens,
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finished=request_output.finished,
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)
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