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# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ============================================================================
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"""export checkpoint file into models"""
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import argparse
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import numpy as np
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import mindspore.common.dtype as mstype
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from mindspore import Tensor, context, load_checkpoint, export
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from src.finetune_eval_model import BertCLSModel, BertSquadModel, BertNERModel
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from src.finetune_eval_config import bert_net_cfg
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from src.bert_for_finetune import BertNER
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from src.utils import convert_labels_to_index
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parser = argparse.ArgumentParser(description="Bert export")
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parser.add_argument("--device_id", type=int, default=0, help="Device id")
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parser.add_argument("--use_crf", type=str, default="false", help="Use cfg, default is false.")
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parser.add_argument("--downstream_task", type=str, choices=["NER", "CLS", "SQUAD"], default="NER",
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help="at present，support NER only")
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parser.add_argument("--batch_size", type=int, default=16, help="batch size")
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parser.add_argument("--label_file_path", type=str, default="", help="label file path, used in clue benchmark.")
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parser.add_argument("--ckpt_file", type=str, required=True, help="Bert ckpt file.")
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parser.add_argument("--file_name", type=str, default="Bert", help="bert output air name.")
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parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], default="AIR", help="file format")
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parser.add_argument("--device_target", type=str, default="Ascend",
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choices=["Ascend", "GPU", "CPU"], help="device target (default: Ascend)")
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args = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
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if args.device_target == "Ascend":
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context.set_context(device_id=args.device_id)
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label_list = []
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with open(args.label_file_path) as f:
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for label in f:
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label_list.append(label.strip())
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tag_to_index = convert_labels_to_index(label_list)
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if args.use_crf.lower() == "true":
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max_val = max(tag_to_index.values())
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tag_to_index["<START>"] = max_val + 1
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tag_to_index["<STOP>"] = max_val + 2
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number_labels = len(tag_to_index)
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else:
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number_labels = len(tag_to_index)
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if __name__ == "__main__":
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if args.downstream_task == "NER":
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if args.use_crf.lower() == "true":
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net = BertNER(bert_net_cfg, args.batch_size, False, num_labels=number_labels,
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use_crf=True, tag_to_index=tag_to_index)
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else:
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net = BertNERModel(bert_net_cfg, False, number_labels, use_crf=(args.use_crf.lower() == "true"))
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elif args.downstream_task == "CLS":
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net = BertCLSModel(bert_net_cfg, False, num_labels=number_labels)
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elif args.downstream_task == "SQUAD":
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net = BertSquadModel(bert_net_cfg, False)
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else:
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raise ValueError("unsupported downstream task")
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load_checkpoint(args.ckpt_file, net=net)
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net.set_train(False)
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input_ids = Tensor(np.zeros([args.batch_size, bert_net_cfg.seq_length]), mstype.int32)
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input_mask = Tensor(np.zeros([args.batch_size, bert_net_cfg.seq_length]), mstype.int32)
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token_type_id = Tensor(np.zeros([args.batch_size, bert_net_cfg.seq_length]), mstype.int32)
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label_ids = Tensor(np.zeros([args.batch_size, bert_net_cfg.seq_length]), mstype.int32)
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if args.downstream_task == "NER" and args.use_crf.lower() == "true":
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input_data = [input_ids, input_mask, token_type_id, label_ids]
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else:
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input_data = [input_ids, input_mask, token_type_id]
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export(net, *input_data, file_name=args.file_name, file_format=args.file_format)
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