outputs.py

import time
from dataclasses import dataclass
from typing import Dict, Generic, List, MutableSequence, Optional
from typing import Sequence as GenericSequence
from typing import Union

import torch
from typing_extensions import TypeVar, deprecated

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:
        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()))

    @property
    @deprecated("`LLM.encode()` now stores raw outputs in the `data` "
                "attribute. To return embeddings, use `LLM.embed()`. "
                "To return class probabilities, use `LLM.classify()` "
                "and access the `probs` attribute. ")
    def embedding(self) -> list[float]:
        return self.data.tolist()


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,
        )

    def add(self, next_output: "RequestOutput") -> None:
        """Merge subsequent RequestOutput into this one"""

        self.prompt = next_output.prompt
        self.prompt_token_ids = next_output.prompt_token_ids
        self.prompt_logprobs = next_output.prompt_logprobs
        self.finished |= next_output.finished

        #TODO assuming n == 1 for now
        completion = self.outputs[0]
        next_completion = next_output.outputs[0]
        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

    @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
            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})")


_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):
        """
        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"{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):
        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):
        pooled_data = pooling_output.data
        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})"

    @property
    @deprecated("`LLM.score()` now returns scalar scores. "
                "Please access it via the `score` attribute. ")
    def embedding(self) -> list[float]:
        return [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,
        )