Source code for paddlenlp.transformers.export

# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# See the License for the specific language governing permissions and
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import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import paddle

from ..utils.log import logger
from .model_utils import PretrainedModel, unwrap_model

__all__ = ["export_model"]


[docs]def export_model( model: "PretrainedModel", input_spec=None, path: Optional[str] = None, model_format: Optional[str] = "paddle" ) -> Tuple[List[str], List[str]]: """ Export paddle inference model or onnx model. Args: model ([`PretrainedModel`]: The model to export. input_spec (paddle.static.InputSpec, optional): Describes the input of the saved model’s forward method, which can be described by InputSpec or example Tensor. Default None. path (Optional[str], optional): Output dir to save the exported model. Defaults to None. model_format (Optional[str], optional): Export model format. There are two options: paddle or onnx, defaults to paddle. """ if path is None: path = "./" logger.info("Export path is missing, set default path to current dir.") if issubclass(type(model), PretrainedModel): model = unwrap_model(model) model.eval() model_format = model_format.lower() file_prefix = "model" if model_format == "paddle": # Convert to static graph with specific input description model = paddle.jit.to_static(model, input_spec=input_spec) # Save in static graph model. save_path = os.path.join(path, file_prefix) logger.info("Exporting inference model to %s" % save_path) paddle.jit.save(model, save_path) logger.info("Inference model exported.") elif model_format == "onnx": # Export ONNX model. save_path = os.path.join(path, file_prefix) logger.info("Exporting ONNX model to %s" % save_path) paddle.onnx.export(model, save_path, input_spec=input_spec) logger.info("ONNX model exported.") else: logger.info("This export format is not supported, please select paddle or onnx!")