wehub-resource-sync
73 lines
2.1 KiB
Python
73 lines
2.1 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from typing import TypeAlias
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import numpy as np
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from fastapi.responses import JSONResponse
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from vllm.entrypoints.openai.engine.protocol import UsageInfo
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from vllm.logger import init_logger
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from vllm.outputs import ClassificationOutput
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from ..base.serving import PoolingServing
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from ..typing import PoolingServeContext
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from .io_processor import ClassifyIOProcessor
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from .protocol import (
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ClassificationData,
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ClassificationRequest,
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ClassificationResponse,
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)
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logger = init_logger(__name__)
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ClassificationServeContext: TypeAlias = PoolingServeContext[ClassificationRequest]
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class ServingClassification(PoolingServing):
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request_id_prefix = "classify"
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def init_io_processor(self, *args, **kwargs) -> ClassifyIOProcessor:
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return ClassifyIOProcessor(*args, **kwargs)
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def _build_response(
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self,
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ctx: ClassificationServeContext,
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) -> JSONResponse:
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id2label = getattr(self.model_config.hf_config, "id2label", {})
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num_prompt_tokens = 0
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items: list[ClassificationData] = []
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for idx, final_res in enumerate(ctx.final_res_batch):
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classify_res = ClassificationOutput.from_base(final_res.outputs)
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probs = classify_res.probs
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predicted_index = int(np.argmax(probs))
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label = id2label.get(predicted_index)
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item = ClassificationData(
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index=idx,
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label=label,
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probs=probs,
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num_classes=len(probs),
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)
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items.append(item)
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prompt_token_ids = final_res.prompt_token_ids
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num_prompt_tokens += len(prompt_token_ids)
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usage = UsageInfo(
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prompt_tokens=num_prompt_tokens,
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total_tokens=num_prompt_tokens,
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)
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response = ClassificationResponse(
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id=ctx.request_id,
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created=ctx.created_time,
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model=ctx.model_name,
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data=items,
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usage=usage,
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)
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return JSONResponse(content=response.model_dump())
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