mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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c86945ef49
* support pool * update pooling * add pooler_config and check * update * support AutoWeightsLoader load weight * fix * update * delete print * update pre-commit * fix * fix xpu * fix ModelRegistry->model_registry * fix Copilot review * fix pooler.py * delete StepPooler * fix abstract * fix default_loader_v1 * fix Pre Commit * support torch qwen3 dense * add test and fix torch-qwen * fix * fix * adapter ci: * fix review * fix pooling_params.py * fix * fix tasks.py 2025 * fix print and logger * Modefy ModelRegistry and delete AutoWeightsLoader * fix logger * fix test_embedding * fix ci bug * ernie4_5 model_registry * fix test * support Qwen3-Embedding-0.6B tp=1 load * fix extra code * fix * delete fix vocab_size * delete prepare_params_dict * fix:
55 lines
1.9 KiB
Python
55 lines
1.9 KiB
Python
# 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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from typing import Type
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from paddle import nn
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def is_text_generation_model(model_cls: Type[nn.Layer]) -> bool:
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from .model_base import ModelForCasualLM
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return issubclass(model_cls, ModelForCasualLM)
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def is_pooling_model(model_cls: Type[nn.Layer]) -> bool:
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class_name = model_cls.__name__
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pooling_indicators = ["Embedding", "ForSequenceClassification"]
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return (
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any(indicator in class_name for indicator in pooling_indicators)
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or hasattr(model_cls, "is_embedding_model")
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and model_cls.is_embedding_model
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)
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def is_multimodal_model(class_name: str) -> bool:
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multimodal_indicators = ["VL", "Vision", "ConditionalGeneration"]
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return any(indicator in class_name for indicator in multimodal_indicators)
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def determine_model_category(class_name: str):
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from fastdeploy.model_executor.models.model_base import ModelCategory
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if any(pattern in class_name for pattern in ["VL", "Vision", "ConditionalGeneration"]):
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return ModelCategory.MULTIMODAL
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elif any(pattern in class_name for pattern in ["Embedding", "ForSequenceClassification"]):
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return ModelCategory.EMBEDDING
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return ModelCategory.TEXT_GENERATION
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def get_default_pooling_type(model_cls: Type[nn.Layer] = None) -> str:
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if model_cls is not None:
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return getattr(model_cls, "default_pooling_type", "LAST")
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return "LAST"
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