protocol.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import time
from typing import Any, TypeAlias

from pydantic import (
    Field,
)

from vllm import PoolingParams
from vllm.config.pooler import get_use_activation
from vllm.entrypoints.chat_utils import ChatCompletionMessageParam
from vllm.entrypoints.openai.engine.protocol import OpenAIBaseModel, UsageInfo
from vllm.entrypoints.pooling.base.protocol import (
    CompletionRequestMixin,
    PoolingBasicRequestMixin,
)
from vllm.utils import random_uuid


class ClassificationCompletionRequest(PoolingBasicRequestMixin, CompletionRequestMixin):
    # --8<-- [start:classification-extra-params]
    softmax: bool | None = Field(
        default=None,
        description="softmax will be deprecated, please use use_activation instead.",
    )

    activation: bool | None = Field(
        default=None,
        description="activation will be deprecated, please use use_activation instead.",
    )

    use_activation: bool | None = Field(
        default=None,
        description="Whether to use activation for classification outputs. "
        "Default is True.",
    )
    # --8<-- [end:classification-extra-params]

    def to_pooling_params(self):
        return PoolingParams(
            truncate_prompt_tokens=self.truncate_prompt_tokens,
            use_activation=get_use_activation(self),
        )


class ClassificationChatRequest(PoolingBasicRequestMixin):
    messages: list[ChatCompletionMessageParam]

    # --8<-- [start:chat-classification-extra-params]
    add_generation_prompt: bool = Field(
        default=False,
        description=(
            "If true, the generation prompt will be added to the chat template. "
            "This is a parameter used by chat template in tokenizer config of the "
            "model."
        ),
    )

    add_special_tokens: bool = Field(
        default=False,
        description=(
            "If true, special tokens (e.g. BOS) will be added to the prompt "
            "on top of what is added by the chat template. "
            "For most models, the chat template takes care of adding the "
            "special tokens so this should be set to false (as is the "
            "default)."
        ),
    )

    chat_template: str | None = Field(
        default=None,
        description=(
            "A Jinja template to use for this conversion. "
            "As of transformers v4.44, default chat template is no longer "
            "allowed, so you must provide a chat template if the tokenizer "
            "does not define one."
        ),
    )

    chat_template_kwargs: dict[str, Any] | None = Field(
        default=None,
        description=(
            "Additional keyword args to pass to the template renderer. "
            "Will be accessible by the chat template."
        ),
    )

    mm_processor_kwargs: dict[str, Any] | None = Field(
        default=None,
        description=("Additional kwargs to pass to the HF processor."),
    )

    softmax: bool | None = Field(
        default=None,
        description="softmax will be deprecated, please use use_activation instead.",
    )

    activation: bool | None = Field(
        default=None,
        description="activation will be deprecated, please use use_activation instead.",
    )

    use_activation: bool | None = Field(
        default=None,
        description="Whether to use activation for classification outputs. "
        "Default is True.",
    )
    # --8<-- [end:chat-classification-extra-params]

    def to_pooling_params(self):
        return PoolingParams(
            truncate_prompt_tokens=self.truncate_prompt_tokens,
            use_activation=get_use_activation(self),
        )


ClassificationRequest: TypeAlias = (
    ClassificationCompletionRequest | ClassificationChatRequest
)


class ClassificationData(OpenAIBaseModel):
    index: int
    label: str | None
    probs: list[float]
    num_classes: int


class ClassificationResponse(OpenAIBaseModel):
    id: str = Field(default_factory=lambda: f"classify-{random_uuid()}")
    object: str = "list"
    created: int = Field(default_factory=lambda: int(time.time()))
    model: str
    data: list[ClassificationData]
    usage: UsageInfo