forked from mindspore-Ecosystem/mindspore
59 lines
2.7 KiB
Python
59 lines
2.7 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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Data operations, will be used in run_pretrain.py
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"""
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import os
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import mindspore.common.dtype as mstype
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import mindspore.dataset.engine.datasets as de
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import mindspore.dataset.transforms.c_transforms as C
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from mindspore import log as logger
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from config import bert_net_cfg
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def create_bert_dataset(epoch_size=1, device_num=1, rank=0, do_shuffle="true", enable_data_sink="true",
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data_sink_steps=1, data_dir=None, schema_dir=None):
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"""create train dataset"""
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# apply repeat operations
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repeat_count = epoch_size
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files = os.listdir(data_dir)
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data_files = []
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for file_name in files:
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data_files.append(os.path.join(data_dir, file_name))
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ds = de.TFRecordDataset(data_files, schema_dir,
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columns_list=["input_ids", "input_mask", "segment_ids", "next_sentence_labels",
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"masked_lm_positions", "masked_lm_ids", "masked_lm_weights"],
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shuffle=(do_shuffle == "true"), num_shards=device_num, shard_id=rank,
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shard_equal_rows=True)
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ori_dataset_size = ds.get_dataset_size()
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new_size = ori_dataset_size
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if enable_data_sink == "true":
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new_size = data_sink_steps * bert_net_cfg.batch_size
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ds.set_dataset_size(new_size)
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repeat_count = int(repeat_count * ori_dataset_size // ds.get_dataset_size())
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type_cast_op = C.TypeCast(mstype.int32)
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ds = ds.map(input_columns="masked_lm_ids", operations=type_cast_op)
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ds = ds.map(input_columns="masked_lm_positions", operations=type_cast_op)
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ds = ds.map(input_columns="next_sentence_labels", operations=type_cast_op)
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ds = ds.map(input_columns="segment_ids", operations=type_cast_op)
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ds = ds.map(input_columns="input_mask", operations=type_cast_op)
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ds = ds.map(input_columns="input_ids", operations=type_cast_op)
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# apply batch operations
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ds = ds.batch(bert_net_cfg.batch_size, drop_remainder=True)
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ds = ds.repeat(repeat_count)
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logger.info("data size: {}".format(ds.get_dataset_size()))
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logger.info("repeatcount: {}".format(ds.get_repeat_count()))
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return ds
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