forked from mindspore-Ecosystem/mindspore
140 lines
7.9 KiB
Python
140 lines
7.9 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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#################pre_train bert example on zh-wiki########################
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python run_pretrain.py
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"""
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import os
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import argparse
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import mindspore.communication.management as D
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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.parallel_utils import ParallelMode
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from mindspore.nn.wrap.loss_scale import DynamicLossScaleUpdateCell
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from mindspore.train.callback import Callback, ModelCheckpoint, CheckpointConfig, TimeMonitor
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.model_zoo.Bert_NEZHA import BertNetworkWithLoss, BertTrainOneStepCell, BertTrainOneStepWithLossScaleCell
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from mindspore.nn.optim import Lamb, Momentum, AdamWeightDecayDynamicLR
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from dataset import create_bert_dataset
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from config import cfg, bert_net_cfg
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_current_dir = os.path.dirname(os.path.realpath(__file__))
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class LossCallBack(Callback):
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"""
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Monitor the loss in training.
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If the loss in NAN or INF terminating training.
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Note:
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if per_print_times is 0 do not print loss.
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Args:
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per_print_times (int): Print loss every times. Default: 1.
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"""
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def __init__(self, per_print_times=1):
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super(LossCallBack, self).__init__()
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if not isinstance(per_print_times, int) or per_print_times < 0:
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raise ValueError("print_step must be int and >= 0")
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self._per_print_times = per_print_times
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def step_end(self, run_context):
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cb_params = run_context.original_args()
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with open("./loss.log", "a+") as f:
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f.write("epoch: {}, step: {}, outputs are {}".format(cb_params.cur_epoch_num, cb_params.cur_step_num,
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str(cb_params.net_outputs)))
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f.write('\n')
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def run_pretrain():
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"""pre-train bert_clue"""
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parser = argparse.ArgumentParser(description='bert pre_training')
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parser.add_argument("--distribute", type=str, default="false", help="Run distribute, default is false.")
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parser.add_argument("--epoch_size", type=int, default="1", 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("--enable_save_ckpt", type=str, default="true", help="Enable save checkpoint, default is true.")
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parser.add_argument("--enable_lossscale", type=str, default="true", help="Use lossscale or not, default is not.")
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parser.add_argument("--do_shuffle", type=str, default="true", help="Enable shuffle for dataset, default is true.")
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parser.add_argument("--enable_data_sink", type=str, default="true", 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("--checkpoint_path", type=str, default="", help="Checkpoint file path")
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parser.add_argument("--save_checkpoint_steps", type=int, default=1000, help="Save checkpoint steps, "
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"default is 1000.")
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parser.add_argument("--save_checkpoint_num", type=int, default=1, help="Save checkpoint numbers, default is 1.")
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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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args_opt = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id)
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context.set_context(reserve_class_name_in_scope=False)
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if args_opt.distribute == "true":
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device_num = args_opt.device_num
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, mirror_mean=True,
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device_num=device_num)
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D.init()
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rank = args_opt.device_id % device_num
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else:
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rank = 0
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device_num = 1
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ds, new_repeat_count = create_bert_dataset(args_opt.epoch_size, device_num, rank, args_opt.do_shuffle,
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args_opt.enable_data_sink, args_opt.data_sink_steps,
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args_opt.data_dir, args_opt.schema_dir)
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netwithloss = BertNetworkWithLoss(bert_net_cfg, True)
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if cfg.optimizer == 'Lamb':
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optimizer = Lamb(netwithloss.trainable_params(), decay_steps=ds.get_dataset_size() * ds.get_repeat_count(),
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start_learning_rate=cfg.Lamb.start_learning_rate, end_learning_rate=cfg.Lamb.end_learning_rate,
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power=cfg.Lamb.power, warmup_steps=cfg.Lamb.warmup_steps, weight_decay=cfg.Lamb.weight_decay,
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eps=cfg.Lamb.eps)
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elif cfg.optimizer == 'Momentum':
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optimizer = Momentum(netwithloss.trainable_params(), learning_rate=cfg.Momentum.learning_rate,
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momentum=cfg.Momentum.momentum)
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elif cfg.optimizer == 'AdamWeightDecayDynamicLR':
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optimizer = AdamWeightDecayDynamicLR(netwithloss.trainable_params(),
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decay_steps=ds.get_dataset_size() * ds.get_repeat_count(),
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learning_rate=cfg.AdamWeightDecayDynamicLR.learning_rate,
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end_learning_rate=cfg.AdamWeightDecayDynamicLR.end_learning_rate,
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power=cfg.AdamWeightDecayDynamicLR.power,
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weight_decay=cfg.AdamWeightDecayDynamicLR.weight_decay,
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eps=cfg.AdamWeightDecayDynamicLR.eps,
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warmup_steps=cfg.AdamWeightDecayDynamicLR.warmup_steps)
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else:
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raise ValueError("Don't support optimizer {}, only support [Lamb, Momentum, AdamWeightDecayDynamicLR]".
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format(cfg.optimizer))
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callback = [TimeMonitor(ds.get_dataset_size()), LossCallBack()]
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if args_opt.enable_save_ckpt == "true":
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config_ck = CheckpointConfig(save_checkpoint_steps=args_opt.save_checkpoint_steps,
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keep_checkpoint_max=args_opt.save_checkpoint_num)
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ckpoint_cb = ModelCheckpoint(prefix='checkpoint_bert', config=config_ck)
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callback.append(ckpoint_cb)
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if args_opt.checkpoint_path:
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param_dict = load_checkpoint(args_opt.checkpoint_path)
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load_param_into_net(netwithloss, param_dict)
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if args_opt.enable_lossscale == "true":
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update_cell = DynamicLossScaleUpdateCell(loss_scale_value=cfg.loss_scale_value,
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scale_factor=cfg.scale_factor,
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scale_window=cfg.scale_window)
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netwithgrads = BertTrainOneStepWithLossScaleCell(netwithloss, optimizer=optimizer,
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scale_update_cell=update_cell)
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else:
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netwithgrads = BertTrainOneStepCell(netwithloss, optimizer=optimizer)
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model = Model(netwithgrads)
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model.train(new_repeat_count, ds, callbacks=callback, dataset_sink_mode=(args_opt.enable_data_sink == "true"))
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if __name__ == '__main__':
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run_pretrain()
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