# 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 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 from src.utils import LossCallBack _current_dir = os.path.dirname(os.path.realpath(__file__)) 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) context.set_context(variable_memory_max_size="30GB") 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 = create_bert_dataset(1, 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) new_repeat_count = args_opt.epoch_size if args_opt.train_steps > 0: new_repeat_count = min(args_opt.epoch_size, 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()