mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-12-24 13:28:13 +08:00
YuanRisheng
102 lines
4.3 KiB
Python
102 lines
4.3 KiB
Python
"""
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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from typing import Any, Dict, Optional
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from fastdeploy.config import ErnieArchitectures
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from fastdeploy.engine.config import ModelConfig
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from fastdeploy.reasoning import ReasoningParserManager
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class InputPreprocessor:
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"""
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Args:
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model_name_or_path (str):
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Model name or path to the pretrained model. If a model name is provided, it should be a
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key in the Hugging Face Transformers' model registry (https://huggingface.co/models).
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The model will be downloaded from the Hugging Face model hub if necessary.
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If a path is provided, the model will be loaded from that path.
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reasoning_parser (str, optional):
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Reasoning parser type. Defaults to None.
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Flag specifies the reasoning parser to use for extracting reasoning content from the model output
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enable_mm (bool, optional):
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Whether to use the multi-modal model processor. Defaults to False.
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Raises:
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ValueError:
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If the model name is not found in the Hugging Face Transformers' model registry and the path does not
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exist.
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"""
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def __init__(
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self,
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model_name_or_path: str,
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reasoning_parser: str = None,
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limit_mm_per_prompt: Optional[Dict[str, Any]] = None,
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mm_processor_kwargs: Optional[Dict[str, Any]] = None,
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enable_mm: bool = False,
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) -> None:
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self.model_name_or_path = model_name_or_path
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self.reasoning_parser = reasoning_parser
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self.enable_mm = enable_mm
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self.limit_mm_per_prompt = limit_mm_per_prompt
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self.mm_processor_kwargs = mm_processor_kwargs
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def create_processor(self):
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"""
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创建数据处理器。如果启用了多模态注册表，则使用该表中的模型；否则，使用传递给构造函数的模型名称或路径。
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返回值：DataProcessor（如果不启用多模态注册表）或MultiModalRegistry.Processor（如果启用多模态注册表）。
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Args:
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无参数。
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Returns:
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DataProcessor or MultiModalRegistry.Processor (Union[DataProcessor, MultiModalRegistry.Processor]): 数据处理器。
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"""
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reasoning_parser_obj = None
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if self.reasoning_parser:
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reasoning_parser_obj = ReasoningParserManager.get_reasoning_parser(self.reasoning_parser)
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architectures = ModelConfig({"model": self.model_name_or_path}).architectures[0]
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if not self.enable_mm:
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if not ErnieArchitectures.contains_ernie_arch(architectures):
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from fastdeploy.input.text_processor import DataProcessor
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self.processor = DataProcessor(
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model_name_or_path=self.model_name_or_path,
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reasoning_parser_obj=reasoning_parser_obj,
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)
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else:
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from fastdeploy.input.ernie_processor import ErnieProcessor
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self.processor = ErnieProcessor(
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model_name_or_path=self.model_name_or_path,
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reasoning_parser_obj=reasoning_parser_obj,
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)
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else:
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if not architectures.startswith("Ernie4_5_VLMoeForConditionalGeneration"):
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raise ValueError(f"Model {self.model_name_or_path} is not a valid Ernie4_5_VLMoe model.")
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else:
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from fastdeploy.input.ernie_vl_processor import ErnieMoEVLProcessor
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self.processor = ErnieMoEVLProcessor(
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model_name_or_path=self.model_name_or_path,
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limit_mm_per_prompt=self.limit_mm_per_prompt,
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mm_processor_kwargs=self.mm_processor_kwargs,
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reasoning_parser_obj=reasoning_parser_obj,
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)
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return self.processor
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