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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.
- # ============================================================================
-
- """general distill script"""
-
- import os
- import argparse
- import datetime
- import mindspore.communication.management as D
- import mindspore.common.dtype as mstype
- from mindspore import context
- from mindspore.train.model import Model
- from mindspore.train.callback import TimeMonitor
- from mindspore.context import ParallelMode
- from mindspore.nn.optim import AdamWeightDecay
- from mindspore.nn.wrap.loss_scale import DynamicLossScaleUpdateCell
- from mindspore import log as logger
- from mindspore.common import set_seed
- from src.dataset import create_tinybert_dataset, DataType
- from src.utils import LossCallBack, ModelSaveCkpt, BertLearningRate
- from src.gd_config import common_cfg, bert_teacher_net_cfg, bert_student_net_cfg
- from src.tinybert_for_gd_td import BertTrainWithLossScaleCell, BertNetworkWithLoss_gd, BertTrainCell
-
-
-
- def run_general_distill():
- """
- run general distill
- """
- parser = argparse.ArgumentParser(description='tinybert general distill')
- parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'GPU'],
- help='device where the code will be implemented. (Default: Ascend)')
- parser.add_argument("--distribute", type=str, default="false", choices=["true", "false"],
- help="Run distribute, default is false.")
- parser.add_argument("--epoch_size", type=int, default="3", help="Epoch size, default is 1.")
- parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
- parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default is 1.")
- parser.add_argument("--save_ckpt_step", type=int, default=100, help="Enable data sink, default is true.")
- parser.add_argument("--max_ckpt_num", type=int, default=1, help="Enable data sink, default is true.")
- parser.add_argument("--do_shuffle", type=str, default="true", choices=["true", "false"],
- help="Enable shuffle for dataset, default is true.")
- parser.add_argument("--enable_data_sink", type=str, default="true", choices=["true", "false"],
- help="Enable data sink, default is true.")
- parser.add_argument("--data_sink_steps", type=int, default=1, help="Sink steps for each epoch, default is 1.")
- parser.add_argument("--save_ckpt_path", type=str, default="", help="Save checkpoint path")
- parser.add_argument("--load_teacher_ckpt_path", type=str, default="", help="Load checkpoint file path")
- parser.add_argument("--data_dir", type=str, default="", help="Data path, it is better to use absolute path")
- parser.add_argument("--schema_dir", type=str, default="", help="Schema path, it is better to use absolute path")
- parser.add_argument("--dataset_type", type=str, default="tfrecord",
- help="dataset type tfrecord/mindrecord, default is tfrecord")
- args_opt = parser.parse_args()
-
- if args_opt.device_target == "Ascend":
- context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target, device_id=args_opt.device_id)
- elif args_opt.device_target == "GPU":
- context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
- else:
- raise Exception("Target error, GPU or Ascend is supported.")
-
- context.set_context(reserve_class_name_in_scope=False)
- context.set_context(variable_memory_max_size="30GB")
-
- save_ckpt_dir = os.path.join(args_opt.save_ckpt_path,
- datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
-
-
- if args_opt.distribute == "true":
- if args_opt.device_target == 'Ascend':
- D.init()
- device_num = args_opt.device_num
- rank = args_opt.device_id % device_num
- else:
- D.init()
- device_num = D.get_group_size()
- rank = D.get_rank()
- save_ckpt_dir = save_ckpt_dir + '_ckpt_' + str(rank)
- context.reset_auto_parallel_context()
- context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True,
- device_num=device_num)
- else:
- rank = 0
- device_num = 1
-
- if not os.path.exists(save_ckpt_dir):
- os.makedirs(save_ckpt_dir)
-
- enable_loss_scale = True
- if args_opt.device_target == "GPU":
- if bert_student_net_cfg.compute_type != mstype.float32:
- logger.warning('Compute about the student only support float32 temporarily, run with float32.')
- bert_student_net_cfg.compute_type = mstype.float32
- # Backward of the network are calculated using fp32,
- # and the loss scale is not necessary
- enable_loss_scale = False
-
- netwithloss = BertNetworkWithLoss_gd(teacher_config=bert_teacher_net_cfg,
- teacher_ckpt=args_opt.load_teacher_ckpt_path,
- student_config=bert_student_net_cfg,
- is_training=True, use_one_hot_embeddings=False)
-
- if args_opt.dataset_type == "tfrecord":
- dataset_type = DataType.TFRECORD
- elif args_opt.dataset_type == "mindrecord":
- dataset_type = DataType.MINDRECORD
- else:
- raise Exception("dataset format is not supported yet")
- dataset = create_tinybert_dataset('gd', common_cfg.batch_size, device_num, rank,
- args_opt.do_shuffle, args_opt.data_dir, args_opt.schema_dir,
- data_type=dataset_type)
- dataset_size = dataset.get_dataset_size()
- print('dataset size: ', dataset_size)
- print("dataset repeatcount: ", dataset.get_repeat_count())
- if args_opt.enable_data_sink == "true":
- repeat_count = args_opt.epoch_size * dataset_size // args_opt.data_sink_steps
- time_monitor_steps = args_opt.data_sink_steps
- else:
- repeat_count = args_opt.epoch_size
- time_monitor_steps = dataset_size
-
- lr_schedule = BertLearningRate(learning_rate=common_cfg.AdamWeightDecay.learning_rate,
- end_learning_rate=common_cfg.AdamWeightDecay.end_learning_rate,
- warmup_steps=int(dataset_size * args_opt.epoch_size / 10),
- decay_steps=int(dataset_size * args_opt.epoch_size),
- power=common_cfg.AdamWeightDecay.power)
- params = netwithloss.trainable_params()
- decay_params = list(filter(common_cfg.AdamWeightDecay.decay_filter, params))
- other_params = list(filter(lambda x: not common_cfg.AdamWeightDecay.decay_filter(x), params))
- group_params = [{'params': decay_params, 'weight_decay': common_cfg.AdamWeightDecay.weight_decay},
- {'params': other_params, 'weight_decay': 0.0},
- {'order_params': params}]
-
- optimizer = AdamWeightDecay(group_params, learning_rate=lr_schedule, eps=common_cfg.AdamWeightDecay.eps)
-
- callback = [TimeMonitor(time_monitor_steps), LossCallBack(), ModelSaveCkpt(netwithloss.bert,
- args_opt.save_ckpt_step,
- args_opt.max_ckpt_num,
- save_ckpt_dir)]
- if enable_loss_scale:
- update_cell = DynamicLossScaleUpdateCell(loss_scale_value=common_cfg.loss_scale_value,
- scale_factor=common_cfg.scale_factor,
- scale_window=common_cfg.scale_window)
- netwithgrads = BertTrainWithLossScaleCell(netwithloss, optimizer=optimizer, scale_update_cell=update_cell)
- else:
- netwithgrads = BertTrainCell(netwithloss, optimizer=optimizer)
- model = Model(netwithgrads)
- model.train(repeat_count, dataset, callbacks=callback,
- dataset_sink_mode=(args_opt.enable_data_sink == "true"),
- sink_size=args_opt.data_sink_steps)
-
- if __name__ == '__main__':
- set_seed(0)
- run_general_distill()
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