forked from mindspore-Ecosystem/mindspore
97 lines
3.1 KiB
Python
97 lines
3.1 KiB
Python
# 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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"""
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network config setting, will be used in dataset.py, run_pretrain.py
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"""
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from easydict import EasyDict as edict
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import mindspore.common.dtype as mstype
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from mindspore.model_zoo.Bert_NEZHA import BertConfig
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cfg = edict({
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'bert_network': 'base',
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'loss_scale_value': 2**32,
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'scale_factor': 2,
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'scale_window': 1000,
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'optimizer': 'Lamb',
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'AdamWeightDecayDynamicLR': edict({
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'learning_rate': 3e-5,
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'end_learning_rate': 1e-7,
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'power': 5.0,
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'weight_decay': 1e-5,
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'eps': 1e-6,
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'warmup_steps': 10000,
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}),
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'Lamb': edict({
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'start_learning_rate': 3e-5,
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'end_learning_rate': 1e-7,
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'power': 10.0,
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'warmup_steps': 10000,
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'weight_decay': 0.01,
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'eps': 1e-6,
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}),
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'Momentum': edict({
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'learning_rate': 2e-5,
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'momentum': 0.9,
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}),
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})
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'''
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Including two kinds of network: \
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base: Goole BERT-base(the base version of BERT model).
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large: BERT-NEZHA(a Chinese pretrained language model developed by Huawei, which introduced a improvement of \
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Functional Relative Posetional Encoding as an effective positional encoding scheme).
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'''
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if cfg.bert_network == 'base':
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bert_net_cfg = BertConfig(
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batch_size=32,
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seq_length=128,
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vocab_size=21128,
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hidden_size=768,
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num_hidden_layers=12,
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num_attention_heads=12,
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intermediate_size=3072,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=2,
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initializer_range=0.02,
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use_relative_positions=False,
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input_mask_from_dataset=True,
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token_type_ids_from_dataset=True,
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dtype=mstype.float32,
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compute_type=mstype.float16,
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)
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if cfg.bert_network == 'nezha':
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bert_net_cfg = BertConfig(
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batch_size=32,
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seq_length=128,
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vocab_size=21128,
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hidden_size=1024,
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num_hidden_layers=24,
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num_attention_heads=16,
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intermediate_size=4096,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=2,
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initializer_range=0.02,
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use_relative_positions=True,
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input_mask_from_dataset=True,
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token_type_ids_from_dataset=True,
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dtype=mstype.float32,
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compute_type=mstype.float16,
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)
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