Huawei_Technology
/
mindspore

# 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.
# ============================================================================
"""
#################pre_train bert example on zh-wiki########################
python run_pretrain.py
"""

import os
import argparse
import numpy
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.parallel_utils import ParallelMode
from mindspore.nn.wrap.loss_scale import DynamicLossScaleUpdateCell
from mindspore.train.callback import Callback, ModelCheckpoint, CheckpointConfig, TimeMonitor
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.nn.optim import Lamb, Momentum, AdamWeightDecayDynamicLR
from mindspore import log as logger
from src import BertNetworkWithLoss, BertTrainOneStepCell, BertTrainOneStepWithLossScaleCell
from src.dataset import create_bert_dataset
from src.config import cfg, bert_net_cfg
_current_dir = os.path.dirname(os.path.realpath(__file__))

class LossCallBack(Callback):
    """
    Monitor the loss in training.
    If the loss in NAN or INF terminating training.
    Note:
        if per_print_times is 0 do not print loss.
    Args:
        per_print_times (int): Print loss every times. Default: 1.
    """
    def __init__(self, per_print_times=1):
        super(LossCallBack, self).__init__()
        if not isinstance(per_print_times, int) or per_print_times < 0:
            raise ValueError("print_step must be int and >= 0")
        self._per_print_times = per_print_times
    def step_end(self, run_context):
        cb_params = run_context.original_args()
        print("epoch: {}, step: {}, outputs are {}".format(cb_params.cur_epoch_num, cb_params.cur_step_num,
                                                           str(cb_params.net_outputs)))

def run_pretrain():
    """pre-train bert_clue"""
    parser = argparse.ArgumentParser(description='bert pre_training')
    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", help="Run distribute, default is false.")
    parser.add_argument("--epoch_size", type=int, default="1", 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("--enable_save_ckpt", type=str, default="true", help="Enable save checkpoint, default is true.")
    parser.add_argument("--enable_lossscale", type=str, default="true", help="Use lossscale or not, default is not.")
    parser.add_argument("--do_shuffle", type=str, default="true", help="Enable shuffle for dataset, default is true.")
    parser.add_argument("--enable_data_sink", type=str, default="true", 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_checkpoint_path", type=str, default="", help="Save checkpoint path")
    parser.add_argument("--load_checkpoint_path", type=str, default="", help="Load checkpoint file path")
    parser.add_argument("--save_checkpoint_steps", type=int, default=1000, help="Save checkpoint steps, "
                                                                                "default is 1000.")
    parser.add_argument("--train_steps", type=int, default=-1, help="Training Steps, default is -1, "
                                                                    "meaning run all steps according to epoch number.")
    parser.add_argument("--save_checkpoint_num", type=int, default=1, help="Save checkpoint numbers, default is 1.")
    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")

    args_opt = parser.parse_args()
    context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target, device_id=args_opt.device_id)
    context.set_context(reserve_class_name_in_scope=False)

    ckpt_save_dir = args_opt.save_checkpoint_path
    if args_opt.distribute == "true":
        if args_opt.device_target == 'Ascend':
            D.init('hccl')
            device_num = args_opt.device_num
            rank = args_opt.device_id % device_num
        else:
            D.init('nccl')
            device_num = D.get_group_size()
            rank = D.get_rank()
            ckpt_save_dir = args_opt.save_checkpoint_path + 'ckpt_' + str(rank) + '/'

        context.reset_auto_parallel_context()
        context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, mirror_mean=True,
                                          device_num=device_num)
        from mindspore.parallel._auto_parallel_context import auto_parallel_context
        if bert_net_cfg.num_hidden_layers == 12:
            if bert_net_cfg.use_relative_positions:
                auto_parallel_context().set_all_reduce_fusion_split_indices([29, 58, 87, 116, 145, 174, 203, 217])
            else:
                auto_parallel_context().set_all_reduce_fusion_split_indices([28, 55, 82, 109, 136, 163, 190, 205])
        elif bert_net_cfg.num_hidden_layers == 24:
            if bert_net_cfg.use_relative_positions:
                auto_parallel_context().set_all_reduce_fusion_split_indices([30, 90, 150, 210, 270, 330, 390, 421])
            else:
                auto_parallel_context().set_all_reduce_fusion_split_indices([38, 93, 148, 203, 258, 313, 368, 397])
    else:
        rank = 0
        device_num = 1

    if args_opt.device_target == 'GPU' and bert_net_cfg.compute_type != mstype.float32:
        logger.warning('Gpu only support fp32 temporarily, run with fp32.')
        bert_net_cfg.compute_type = mstype.float32


    ds, new_repeat_count = create_bert_dataset(args_opt.epoch_size, device_num, rank, args_opt.do_shuffle,
                                               args_opt.enable_data_sink, args_opt.data_sink_steps,
                                               args_opt.data_dir, args_opt.schema_dir)
    if args_opt.train_steps > 0:
        new_repeat_count = min(new_repeat_count, args_opt.train_steps // args_opt.data_sink_steps)
    netwithloss = BertNetworkWithLoss(bert_net_cfg, True)

    if cfg.optimizer == 'Lamb':
        optimizer = Lamb(netwithloss.trainable_params(), decay_steps=ds.get_dataset_size() * new_repeat_count,
                         start_learning_rate=cfg.Lamb.start_learning_rate, end_learning_rate=cfg.Lamb.end_learning_rate,
                         power=cfg.Lamb.power, warmup_steps=cfg.Lamb.warmup_steps, weight_decay=cfg.Lamb.weight_decay,
                         eps=cfg.Lamb.eps)
    elif cfg.optimizer == 'Momentum':
        optimizer = Momentum(netwithloss.trainable_params(), learning_rate=cfg.Momentum.learning_rate,
                             momentum=cfg.Momentum.momentum)
    elif cfg.optimizer == 'AdamWeightDecayDynamicLR':
        optimizer = AdamWeightDecayDynamicLR(netwithloss.trainable_params(),
                                             decay_steps=ds.get_dataset_size() * new_repeat_count,
                                             learning_rate=cfg.AdamWeightDecayDynamicLR.learning_rate,
                                             end_learning_rate=cfg.AdamWeightDecayDynamicLR.end_learning_rate,
                                             power=cfg.AdamWeightDecayDynamicLR.power,
                                             weight_decay=cfg.AdamWeightDecayDynamicLR.weight_decay,
                                             eps=cfg.AdamWeightDecayDynamicLR.eps,
                                             warmup_steps=cfg.AdamWeightDecayDynamicLR.warmup_steps)
    else:
        raise ValueError("Don't support optimizer {}, only support [Lamb, Momentum, AdamWeightDecayDynamicLR]".
                         format(cfg.optimizer))
    callback = [TimeMonitor(ds.get_dataset_size()), LossCallBack()]
    if args_opt.enable_save_ckpt == "true":
        config_ck = CheckpointConfig(save_checkpoint_steps=args_opt.save_checkpoint_steps,
                                     keep_checkpoint_max=args_opt.save_checkpoint_num)
        ckpoint_cb = ModelCheckpoint(prefix='checkpoint_bert', directory=ckpt_save_dir, config=config_ck)
        callback.append(ckpoint_cb)

    if args_opt.load_checkpoint_path:
        param_dict = load_checkpoint(args_opt.load_checkpoint_path)
        load_param_into_net(netwithloss, param_dict)

    if args_opt.enable_lossscale == "true":
        update_cell = DynamicLossScaleUpdateCell(loss_scale_value=cfg.loss_scale_value,
                                                 scale_factor=cfg.scale_factor,
                                                 scale_window=cfg.scale_window)
        netwithgrads = BertTrainOneStepWithLossScaleCell(netwithloss, optimizer=optimizer,
                                                         scale_update_cell=update_cell)
    else:
        netwithgrads = BertTrainOneStepCell(netwithloss, optimizer=optimizer)

    model = Model(netwithgrads)
    model.train(new_repeat_count, ds, callbacks=callback, dataset_sink_mode=(args_opt.enable_data_sink == "true"))
if __name__ == '__main__':
    numpy.random.seed(0)
    run_pretrain()