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- # Copyright 2020 Huawei Technologies Co., Ltd
- #
- # 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 random
- import numpy as np
- import mindspore.common.dtype as mstype
- import mindspore.dataset as de
- from mindspore import Tensor, context
- from mindspore.train.serialization import export
- from tests.st.networks.models.bert.src.bert_model import BertModel, BertConfig
-
- bert_net_cfg = BertConfig(
- batch_size=2,
- seq_length=32,
- vocab_size=12,
- hidden_size=12,
- num_hidden_layers=12,
- num_attention_heads=12,
- intermediate_size=3072,
- hidden_act="gelu",
- hidden_dropout_prob=0.1,
- attention_probs_dropout_prob=0.1,
- max_position_embeddings=512,
- type_vocab_size=2,
- initializer_range=0.02,
- use_relative_positions=False,
- input_mask_from_dataset=True,
- token_type_ids_from_dataset=True,
- dtype=mstype.float32,
- compute_type=mstype.float16
- )
-
- context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-
- random.seed(1)
- np.random.seed(1)
- de.config.set_seed(1)
-
- def export_bert_model():
- input_ids = np.random.randint(0, 1000, size=(2, 32), dtype=np.int32)
- segment_ids = np.zeros((2, 32), dtype=np.int32)
- input_mask = np.zeros((2, 32), dtype=np.int32)
- net = BertModel(bert_net_cfg, False)
- export(net, Tensor(input_ids), Tensor(segment_ids), Tensor(input_mask),
- file_name='bert.mindir', file_format='MINDIR')
-
- if __name__ == '__main__':
- export_bert_model()
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