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https://github.com/PaddlePaddle/FastDeploy.git
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fe540f6caa
* support custom model&&model_runner * fix merge * add test && update doc * fix codestyle * fix unittest * load model in rl
53 lines
1.7 KiB
Python
53 lines
1.7 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 paddleformers.transformers import PretrainedModel
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from fastdeploy import ModelRegistry
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from fastdeploy.config import ErnieArchitectures
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from fastdeploy.model_executor.models.model_base import ModelForCasualLM
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class MyPretrainedModel(PretrainedModel):
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@classmethod
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def arch_names(cls):
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return "MyModelForCasualLM"
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class MyModelForCasualLM(ModelForCasualLM):
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def __init__(self, fd_config):
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"""
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Args:
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fd_config : Configurations for the LLM model.
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"""
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super().__init__(fd_config)
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print("init done")
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@classmethod
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def name(cls):
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return "MyModelForCasualLM"
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def compute_logits(self, logits):
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logits[:, 0] += 1.0
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return logits
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def register():
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if "MyModelForCasualLM" not in ModelRegistry.get_supported_archs():
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if MyModelForCasualLM.name().startswith("Ernie"):
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ErnieArchitectures.register_ernie_model_arch(MyModelForCasualLM)
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ModelRegistry.register_model_class(MyModelForCasualLM)
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ModelRegistry.register_pretrained_model(MyPretrainedModel)
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