wehub-resource-sync
78 lines
2.3 KiB
Python
78 lines
2.3 KiB
Python
# Copyright (c) 2020 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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import argparse
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import os
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import paddle
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from run_glue_trainer import MODEL_CLASSES
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def parse_args():
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parser = argparse.ArgumentParser()
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# Required parameters
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parser.add_argument(
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"--model_type",
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default=None,
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type=str,
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required=True,
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help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
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)
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parser.add_argument(
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"--model_path",
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default=None,
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type=str,
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required=True,
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help="Path of the trained model to be exported.",
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)
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parser.add_argument(
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"--output_path",
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default=None,
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type=str,
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required=True,
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help="The output file prefix used to save the exported inference model.",
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)
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args = parser.parse_args()
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return args
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def main():
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args = parse_args()
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args.model_type = args.model_type.lower()
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model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
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# build model and load trained parameters
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model = model_class.from_pretrained(args.model_path)
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# switch to eval model
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model.eval()
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# convert to static graph with specific input description
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model = paddle.jit.to_static(
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model,
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input_spec=[
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paddle.static.InputSpec(shape=[None, None], dtype="int64"), # input_ids
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paddle.static.InputSpec(shape=[None, None], dtype="int64"), # segment_ids
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],
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)
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# save converted static graph model
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paddle.jit.save(model, args.output_path)
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# also save tokenizer for inference usage
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tokenizer = tokenizer_class.from_pretrained(args.model_path)
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tokenizer.save_pretrained(os.path.dirname(args.output_path))
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if __name__ == "__main__":
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main()
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