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168 lines
8.5 KiB
168 lines
8.5 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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"""Transformer training script."""
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import time
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import argparse
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import numpy as np
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import mindspore.common.dtype as mstype
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from mindspore.common.tensor import Tensor
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from mindspore.nn.optim import Adam
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from mindspore.train.model import Model
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from mindspore.train.loss_scale_manager import DynamicLossScaleManager
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from mindspore.train.callback import CheckpointConfig, ModelCheckpoint
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from mindspore.train.callback import Callback, TimeMonitor
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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import mindspore.communication.management as D
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from mindspore.train.parallel_utils import ParallelMode
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from mindspore import context
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from src.transformer_for_train import TransformerTrainOneStepCell, TransformerNetworkWithLoss, \
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TransformerTrainOneStepWithLossScaleCell
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from src.config import cfg, transformer_net_cfg
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from src.dataset import create_transformer_dataset
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from src.lr_schedule import create_dynamic_lr
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def get_ms_timestamp():
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t = time.time()
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return int(round(t * 1000))
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time_stamp_init = False
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time_stamp_first = 0
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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 is 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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global time_stamp_init, time_stamp_first
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if not time_stamp_init:
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time_stamp_first = get_ms_timestamp()
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time_stamp_init = True
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def step_end(self, run_context):
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global time_stamp_first
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time_stamp_current = get_ms_timestamp()
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cb_params = run_context.original_args()
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print("time: {}, epoch: {}, step: {}, outputs are {}".format(time_stamp_current - time_stamp_first,
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cb_params.cur_epoch_num, cb_params.cur_step_num,
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str(cb_params.net_outputs)))
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with open("./loss.log", "a+") as f:
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f.write("time: {}, epoch: {}, step: {}, outputs are {}".format(time_stamp_current - time_stamp_first,
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cb_params.cur_epoch_num,
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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 argparse_init():
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"""
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Argparse init.
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"""
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parser = argparse.ArgumentParser(description='transformer')
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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=52, help="Epoch size, default is 52.")
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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_lossscale", type=str, default="true", help="Use lossscale or not, default is true.")
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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="false", help="Enable data sink, default is false.")
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parser.add_argument("--checkpoint_path", type=str, default="", help="Checkpoint file path")
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parser.add_argument("--enable_save_ckpt", type=str, default="true", help="Enable save checkpoint, "
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"default is true.")
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parser.add_argument("--save_checkpoint_steps", type=int, default=2500, help="Save checkpoint steps, "
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"default is 2500.")
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parser.add_argument("--save_checkpoint_num", type=int, default=30, help="Save checkpoint numbers, default is 30.")
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parser.add_argument("--save_checkpoint_path", type=str, default="./checkpoint/", help="Save checkpoint file path, "
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"default is ./checkpoint/")
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parser.add_argument("--data_path", type=str, default="", help="Data path, it is better to use absolute path")
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return parser
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def run_transformer_train():
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"""
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Transformer training.
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"""
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parser = argparse_init()
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args, _ = parser.parse_known_args()
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args.device_id)
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context.set_context(reserve_class_name_in_scope=False, enable_auto_mixed_precision=False)
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if args.distribute == "true":
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device_num = args.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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parameter_broadcast=True, device_num=device_num)
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D.init()
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rank_id = args.device_id % device_num
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else:
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device_num = 1
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rank_id = 0
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dataset, repeat_count = create_transformer_dataset(epoch_count=args.epoch_size, rank_size=device_num,
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rank_id=rank_id, do_shuffle=args.do_shuffle,
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enable_data_sink=args.enable_data_sink,
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dataset_path=args.data_path)
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netwithloss = TransformerNetworkWithLoss(transformer_net_cfg, True)
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if args.checkpoint_path:
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parameter_dict = load_checkpoint(args.checkpoint_path)
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load_param_into_net(netwithloss, parameter_dict)
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lr = Tensor(create_dynamic_lr(schedule="constant*rsqrt_hidden*linear_warmup*rsqrt_decay",
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training_steps=dataset.get_dataset_size()*args.epoch_size,
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learning_rate=cfg.lr_schedule.learning_rate,
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warmup_steps=cfg.lr_schedule.warmup_steps,
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hidden_size=transformer_net_cfg.hidden_size,
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start_decay_step=cfg.lr_schedule.start_decay_step,
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min_lr=cfg.lr_schedule.min_lr), mstype.float32)
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optimizer = Adam(netwithloss.trainable_params(), lr)
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callbacks = [TimeMonitor(dataset.get_dataset_size()), LossCallBack()]
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if args.enable_save_ckpt == "true":
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ckpt_config = CheckpointConfig(save_checkpoint_steps=args.save_checkpoint_steps,
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keep_checkpoint_max=args.save_checkpoint_num)
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ckpoint_cb = ModelCheckpoint(prefix='transformer', directory=args.save_checkpoint_path, config=ckpt_config)
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callbacks.append(ckpoint_cb)
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if args.enable_lossscale == "true":
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scale_manager = DynamicLossScaleManager(init_loss_scale=cfg.init_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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update_cell = scale_manager.get_update_cell()
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netwithgrads = TransformerTrainOneStepWithLossScaleCell(netwithloss, optimizer=optimizer,
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scale_update_cell=update_cell)
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else:
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netwithgrads = TransformerTrainOneStepCell(netwithloss, optimizer=optimizer)
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netwithgrads.set_train(True)
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model = Model(netwithgrads)
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model.train(repeat_count, dataset, callbacks=callbacks, dataset_sink_mode=(args.enable_data_sink == "true"))
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if __name__ == '__main__':
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random_seed = 1
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np.random.seed(random_seed)
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run_transformer_train()
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