78 lines
2.5 KiB
Python
78 lines
2.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import time
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from typing import Any, TypeAlias
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from pydantic import Field
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from vllm.config import ModelConfig
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from vllm.entrypoints.openai.engine.protocol import OpenAIBaseModel, UsageInfo
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from vllm.entrypoints.pooling.base.protocol import (
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ChatRequestMixin,
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ClassifyRequestMixin,
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CompletionRequestMixin,
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PoolingBasicRequestMixin,
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)
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from vllm.renderers import TokenizeParams
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from vllm.utils import random_uuid
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class ClassificationCompletionRequest(
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PoolingBasicRequestMixin, CompletionRequestMixin, ClassifyRequestMixin
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):
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def build_tok_params(self, model_config: ModelConfig) -> TokenizeParams:
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encoder_config = model_config.encoder_config or {}
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return TokenizeParams(
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max_total_tokens=model_config.max_model_len,
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max_output_tokens=0,
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truncate_prompt_tokens=self.truncate_prompt_tokens,
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do_lower_case=encoder_config.get("do_lower_case", False),
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add_special_tokens=self.add_special_tokens,
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max_total_tokens_param="max_model_len",
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)
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class ClassificationChatRequest(
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PoolingBasicRequestMixin, ChatRequestMixin, ClassifyRequestMixin
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):
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# --8<-- [start:chat-classification-extra-params]
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mm_processor_kwargs: dict[str, Any] | None = Field(
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default=None,
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description=("Additional kwargs to pass to the HF processor."),
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)
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def build_tok_params(self, model_config: ModelConfig) -> TokenizeParams:
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encoder_config = model_config.encoder_config or {}
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return TokenizeParams(
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max_total_tokens=model_config.max_model_len,
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max_output_tokens=0,
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truncate_prompt_tokens=self.truncate_prompt_tokens,
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do_lower_case=encoder_config.get("do_lower_case", False),
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add_special_tokens=self.add_special_tokens,
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max_total_tokens_param="max_model_len",
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)
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ClassificationRequest: TypeAlias = (
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ClassificationCompletionRequest | ClassificationChatRequest
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)
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class ClassificationData(OpenAIBaseModel):
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index: int
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label: str | None
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probs: list[float]
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num_classes: int
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class ClassificationResponse(OpenAIBaseModel):
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id: str = Field(default_factory=lambda: f"classify-{random_uuid()}")
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object: str = "list"
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created: int = Field(default_factory=lambda: int(time.time()))
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model: str
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data: list[ClassificationData]
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usage: UsageInfo
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