export_model.py

# Copyright (c) 2020 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,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

import paddle
from run_glue_trainer import MODEL_CLASSES


def parse_args():
 parser = argparse.ArgumentParser()

 # Required parameters
 parser.add_argument(
 "--model_type",
 default=None,
 type=str,
 required=True,
 help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
 )
 parser.add_argument(
 "--model_path",
 default=None,
 type=str,
 required=True,
 help="Path of the trained model to be exported.",
 )
 parser.add_argument(
 "--output_path",
 default=None,
 type=str,
 required=True,
 help="The output file prefix used to save the exported inference model.",
 )
 args = parser.parse_args()
 return args


def main():
 args = parse_args()

 args.model_type = args.model_type.lower()
 model_class, tokenizer_class = MODEL_CLASSES[args.model_type]

 # build model and load trained parameters
 model = model_class.from_pretrained(args.model_path)
 # switch to eval model
 model.eval()
 # convert to static graph with specific input description
 model = paddle.jit.to_static(
 model,
 input_spec=[
 paddle.static.InputSpec(shape=[None, None], dtype="int64"), # input_ids
 paddle.static.InputSpec(shape=[None, None], dtype="int64"), # segment_ids
 ],
 )
 # save converted static graph model
 paddle.jit.save(model, args.output_path)
 # also save tokenizer for inference usage
 tokenizer = tokenizer_class.from_pretrained(args.model_path)
 tokenizer.save_pretrained(os.path.dirname(args.output_path))


if __name__ == "__main__":
 main()