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169 lines
8.9 KiB
169 lines
8.9 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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"""general distill script"""
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import os
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import argparse
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import datetime
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import mindspore.communication.management as D
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import mindspore.common.dtype as mstype
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from mindspore import context
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from mindspore.train.model import Model
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from mindspore.train.callback import TimeMonitor
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from mindspore.context import ParallelMode
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from mindspore.nn.optim import AdamWeightDecay
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from mindspore.nn.wrap.loss_scale import DynamicLossScaleUpdateCell
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from mindspore import log as logger
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from mindspore.common import set_seed
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from src.dataset import create_tinybert_dataset, DataType
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from src.utils import LossCallBack, ModelSaveCkpt, BertLearningRate
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from src.gd_config import common_cfg, bert_teacher_net_cfg, bert_student_net_cfg
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from src.tinybert_for_gd_td import BertTrainWithLossScaleCell, BertNetworkWithLoss_gd, BertTrainCell
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def get_argument():
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"""Tinybert general distill argument parser."""
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parser = argparse.ArgumentParser(description='tinybert general distill')
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parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'GPU', 'CPU'],
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help='device where the code will be implemented. (Default: Ascend)')
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parser.add_argument("--distribute", type=str, default="false", choices=["true", "false"],
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help="Run distribute, default is false.")
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parser.add_argument("--epoch_size", type=int, default="3", help="Epoch size, default is 1.")
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parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
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parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default is 1.")
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parser.add_argument("--save_ckpt_step", type=int, default=100, help="Enable data sink, default is true.")
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parser.add_argument("--max_ckpt_num", type=int, default=1, help="Enable data sink, default is true.")
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parser.add_argument("--do_shuffle", type=str, default="true", choices=["true", "false"],
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help="Enable shuffle for dataset, default is true.")
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parser.add_argument("--enable_data_sink", type=str, default="true", choices=["true", "false"],
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help="Enable data sink, default is true.")
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parser.add_argument("--data_sink_steps", type=int, default=1, help="Sink steps for each epoch, default is 1.")
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parser.add_argument("--save_ckpt_path", type=str, default="", help="Save checkpoint path")
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parser.add_argument("--load_teacher_ckpt_path", type=str, default="", help="Load checkpoint file path")
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parser.add_argument("--data_dir", type=str, default="", help="Data path, it is better to use absolute path")
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parser.add_argument("--schema_dir", type=str, default="", help="Schema path, it is better to use absolute path")
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parser.add_argument("--dataset_type", type=str, default="tfrecord",
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help="dataset type tfrecord/mindrecord, default is tfrecord")
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args_opt = parser.parse_args()
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return args_opt
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def run_general_distill():
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"""
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run general distill
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"""
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args_opt = get_argument()
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context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target,
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reserve_class_name_in_scope=False)
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if args_opt.device_target == "Ascend":
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context.set_context(device_id=args_opt.device_id)
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save_ckpt_dir = os.path.join(args_opt.save_ckpt_path,
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datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
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if args_opt.distribute == "true":
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if args_opt.device_target == 'Ascend':
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D.init()
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device_num = args_opt.device_num
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rank = args_opt.device_id % device_num
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else:
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D.init()
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device_num = D.get_group_size()
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rank = D.get_rank()
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save_ckpt_dir = save_ckpt_dir + '_ckpt_' + str(rank)
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True,
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device_num=device_num)
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else:
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rank = 0
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device_num = 1
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if not os.path.exists(save_ckpt_dir):
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os.makedirs(save_ckpt_dir)
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enable_loss_scale = True
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if args_opt.device_target == "GPU":
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if bert_student_net_cfg.compute_type != mstype.float32:
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logger.warning('Compute about the student only support float32 temporarily, run with float32.')
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bert_student_net_cfg.compute_type = mstype.float32
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# Backward of the network are calculated using fp32,
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# and the loss scale is not necessary
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enable_loss_scale = False
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if args_opt.device_target == "CPU":
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logger.warning('CPU only support float32 temporarily, run with float32.')
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bert_teacher_net_cfg.dtype = mstype.float32
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bert_teacher_net_cfg.compute_type = mstype.float32
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bert_student_net_cfg.dtype = mstype.float32
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bert_student_net_cfg.compute_type = mstype.float32
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enable_loss_scale = False
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netwithloss = BertNetworkWithLoss_gd(teacher_config=bert_teacher_net_cfg,
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teacher_ckpt=args_opt.load_teacher_ckpt_path,
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student_config=bert_student_net_cfg,
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is_training=True, use_one_hot_embeddings=False)
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if args_opt.dataset_type == "tfrecord":
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dataset_type = DataType.TFRECORD
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elif args_opt.dataset_type == "mindrecord":
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dataset_type = DataType.MINDRECORD
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else:
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raise Exception("dataset format is not supported yet")
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dataset = create_tinybert_dataset('gd', common_cfg.batch_size, device_num, rank,
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args_opt.do_shuffle, args_opt.data_dir, args_opt.schema_dir,
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data_type=dataset_type)
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dataset_size = dataset.get_dataset_size()
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print('dataset size: ', dataset_size)
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print("dataset repeatcount: ", dataset.get_repeat_count())
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if args_opt.enable_data_sink == "true":
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repeat_count = args_opt.epoch_size * dataset_size // args_opt.data_sink_steps
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time_monitor_steps = args_opt.data_sink_steps
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else:
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repeat_count = args_opt.epoch_size
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time_monitor_steps = dataset_size
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lr_schedule = BertLearningRate(learning_rate=common_cfg.AdamWeightDecay.learning_rate,
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end_learning_rate=common_cfg.AdamWeightDecay.end_learning_rate,
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warmup_steps=int(dataset_size * args_opt.epoch_size / 10),
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decay_steps=int(dataset_size * args_opt.epoch_size),
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power=common_cfg.AdamWeightDecay.power)
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params = netwithloss.trainable_params()
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decay_params = list(filter(common_cfg.AdamWeightDecay.decay_filter, params))
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other_params = list(filter(lambda x: not common_cfg.AdamWeightDecay.decay_filter(x), params))
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group_params = [{'params': decay_params, 'weight_decay': common_cfg.AdamWeightDecay.weight_decay},
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{'params': other_params, 'weight_decay': 0.0},
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{'order_params': params}]
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optimizer = AdamWeightDecay(group_params, learning_rate=lr_schedule, eps=common_cfg.AdamWeightDecay.eps)
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callback = [TimeMonitor(time_monitor_steps), LossCallBack(), ModelSaveCkpt(netwithloss.bert,
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args_opt.save_ckpt_step,
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args_opt.max_ckpt_num,
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save_ckpt_dir)]
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if enable_loss_scale:
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update_cell = DynamicLossScaleUpdateCell(loss_scale_value=common_cfg.loss_scale_value,
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scale_factor=common_cfg.scale_factor,
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scale_window=common_cfg.scale_window)
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netwithgrads = BertTrainWithLossScaleCell(netwithloss, optimizer=optimizer, scale_update_cell=update_cell)
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else:
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netwithgrads = BertTrainCell(netwithloss, optimizer=optimizer)
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model = Model(netwithgrads)
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model.train(repeat_count, dataset, callbacks=callback,
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dataset_sink_mode=(args_opt.enable_data_sink == "true"),
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sink_size=args_opt.data_sink_steps)
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if __name__ == '__main__':
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set_seed(0)
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run_general_distill()
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