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89 lines
3.7 KiB
89 lines
3.7 KiB
# 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 air models"""
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import re
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import argparse
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import numpy as np
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from mindspore import Tensor, context
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from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
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from src.td_config import td_student_net_cfg
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from src.tinybert_model import BertModelCLS
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parser = argparse.ArgumentParser(description='tinybert task distill')
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parser.add_argument("--device_id", type=int, default=0, help="Device id")
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parser.add_argument("--ckpt_file", type=str, required=True, help="tinybert ckpt file.")
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parser.add_argument("--file_name", type=str, default="tinybert", help="output file 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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parser.add_argument('--task_name', type=str, default='SST-2', choices=['SST-2', 'QNLI', 'MNLI'], help='task name')
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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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DEFAULT_NUM_LABELS = 2
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DEFAULT_SEQ_LENGTH = 128
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DEFAULT_BS = 32
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task_params = {"SST-2": {"num_labels": 2, "seq_length": 64},
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"QNLI": {"num_labels": 2, "seq_length": 128},
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"MNLI": {"num_labels": 3, "seq_length": 128}}
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class Task:
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"""
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Encapsulation class of get the task parameter.
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"""
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def __init__(self, task_name):
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self.task_name = task_name
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@property
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def num_labels(self):
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if self.task_name in task_params and "num_labels" in task_params[self.task_name]:
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return task_params[self.task_name]["num_labels"]
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return DEFAULT_NUM_LABELS
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@property
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def seq_length(self):
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if self.task_name in task_params and "seq_length" in task_params[self.task_name]:
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return task_params[self.task_name]["seq_length"]
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return DEFAULT_SEQ_LENGTH
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if __name__ == '__main__':
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task = Task(args.task_name)
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td_student_net_cfg.seq_length = task.seq_length
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td_student_net_cfg.batch_size = DEFAULT_BS
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eval_model = BertModelCLS(td_student_net_cfg, False, task.num_labels, 0.0, phase_type="student")
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param_dict = load_checkpoint(args.ckpt_file)
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new_param_dict = {}
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for key, value in param_dict.items():
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new_key = re.sub('tinybert_', 'bert_', key)
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new_key = re.sub('^bert.', '', new_key)
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new_param_dict[new_key] = value
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load_param_into_net(eval_model, new_param_dict)
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eval_model.set_train(False)
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input_ids = Tensor(np.zeros((td_student_net_cfg.batch_size, task.seq_length), np.int32))
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token_type_id = Tensor(np.zeros((td_student_net_cfg.batch_size, task.seq_length), np.int32))
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input_mask = Tensor(np.zeros((td_student_net_cfg.batch_size, task.seq_length), np.int32))
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input_data = [input_ids, token_type_id, input_mask]
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export(eval_model, *input_data, file_name=args.file_name, file_format=args.file_format)
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