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228 lines
13 KiB
228 lines
13 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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"""
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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.communication.management import get_rank
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import mindspore.common.dtype as mstype
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from mindspore import context
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from mindspore.train.model import Model
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from mindspore.context import ParallelMode
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from mindspore.nn.wrap.loss_scale import DynamicLossScaleUpdateCell
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from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, TimeMonitor
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.nn.optim import Lamb, Momentum, AdamWeightDecay
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from mindspore import log as logger
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from mindspore.common import set_seed
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from src import BertNetworkWithLoss, BertTrainOneStepCell, BertTrainOneStepWithLossScaleCell, \
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BertTrainAccumulateStepsWithLossScaleCell
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from src.dataset import create_bert_dataset
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from src.config import cfg, bert_net_cfg
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from src.utils import LossCallBack, BertLearningRate
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_current_dir = os.path.dirname(os.path.realpath(__file__))
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def _set_bert_all_reduce_split():
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"""set bert all_reduce fusion split, support num_hidden_layers is 12 and 24."""
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if bert_net_cfg.num_hidden_layers == 12:
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if bert_net_cfg.use_relative_positions:
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context.set_auto_parallel_context(all_reduce_fusion_config=[29, 58, 87, 116, 145, 174, 203, 217])
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else:
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context.set_auto_parallel_context(all_reduce_fusion_config=[28, 55, 82, 109, 136, 163, 190, 205])
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elif bert_net_cfg.num_hidden_layers == 24:
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if bert_net_cfg.use_relative_positions:
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context.set_auto_parallel_context(all_reduce_fusion_config=[30, 90, 150, 210, 270, 330, 390, 421])
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else:
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context.set_auto_parallel_context(all_reduce_fusion_config=[38, 93, 148, 203, 258, 313, 368, 397])
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def _get_optimizer(args_opt, network):
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"""get bert optimizer, support Lamb, Momentum, AdamWeightDecay."""
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if cfg.optimizer == 'Lamb':
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lr_schedule = BertLearningRate(learning_rate=cfg.Lamb.learning_rate,
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end_learning_rate=cfg.Lamb.end_learning_rate,
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warmup_steps=cfg.Lamb.warmup_steps,
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decay_steps=args_opt.train_steps,
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power=cfg.Lamb.power)
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params = network.trainable_params()
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decay_params = list(filter(cfg.Lamb.decay_filter, params))
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other_params = list(filter(lambda x: not cfg.Lamb.decay_filter(x), params))
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group_params = [{'params': decay_params, 'weight_decay': cfg.Lamb.weight_decay},
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{'params': other_params},
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{'order_params': params}]
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optimizer = Lamb(group_params, learning_rate=lr_schedule, eps=cfg.Lamb.eps)
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elif cfg.optimizer == 'Momentum':
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optimizer = Momentum(network.trainable_params(), learning_rate=cfg.Momentum.learning_rate,
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momentum=cfg.Momentum.momentum)
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elif cfg.optimizer == 'AdamWeightDecay':
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lr_schedule = BertLearningRate(learning_rate=cfg.AdamWeightDecay.learning_rate,
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end_learning_rate=cfg.AdamWeightDecay.end_learning_rate,
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warmup_steps=cfg.AdamWeightDecay.warmup_steps,
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decay_steps=args_opt.train_steps,
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power=cfg.AdamWeightDecay.power)
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params = network.trainable_params()
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decay_params = list(filter(cfg.AdamWeightDecay.decay_filter, params))
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other_params = list(filter(lambda x: not cfg.AdamWeightDecay.decay_filter(x), params))
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group_params = [{'params': decay_params, 'weight_decay': cfg.AdamWeightDecay.weight_decay},
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{'params': other_params, 'weight_decay': 0.0},
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{'order_params': params}]
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optimizer = AdamWeightDecay(group_params, learning_rate=lr_schedule, eps=cfg.AdamWeightDecay.eps)
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else:
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raise ValueError("Don't support optimizer {}, only support [Lamb, Momentum, AdamWeightDecay]".
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format(cfg.optimizer))
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return optimizer
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def _auto_enable_graph_kernel(device_target, graph_kernel_mode):
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"""Judge whether is suitable to enable graph kernel."""
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return graph_kernel_mode in ("auto", "true") and device_target == 'GPU' and \
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cfg.bert_network == 'base' and (cfg.batch_size == 32 or cfg.batch_size == 64) and \
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cfg.optimizer == 'AdamWeightDecay'
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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('--device_target', type=str, default='Ascend', choices=['Ascend', 'GPU'],
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help='device where the code will be implemented. (Default: Ascend)')
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parser.add_argument("--distribute", type=str, default="false", choices=["true", "false"],
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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", choices=["true", "false"],
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help="Enable save checkpoint, default is true.")
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parser.add_argument("--enable_lossscale", type=str, default="true", choices=["true", "false"],
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help="Use lossscale or not, default is not.")
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parser.add_argument("--do_shuffle", type=str, default="true", choices=["true", "false"],
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help="Enable shuffle for dataset, default is true.")
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parser.add_argument("--enable_data_sink", type=str, default="true", choices=["true", "false"],
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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("--accumulation_steps", type=int, default="1",
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help="Accumulating gradients N times before weight update, default is 1.")
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parser.add_argument("--save_checkpoint_path", type=str, default="", help="Save checkpoint path")
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parser.add_argument("--load_checkpoint_path", type=str, default="", help="Load 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("--train_steps", type=int, default=-1, help="Training Steps, default is -1, "
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"meaning run all steps according to epoch number.")
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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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parser.add_argument("--enable_graph_kernel", type=str, default="auto", choices=["auto", "true", "false"],
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help="Accelerate by graph kernel, default is auto.")
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args_opt = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target, device_id=args_opt.device_id)
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context.set_context(reserve_class_name_in_scope=False)
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ckpt_save_dir = args_opt.save_checkpoint_path
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if args_opt.distribute == "true":
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if args_opt.device_target == 'Ascend':
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D.init()
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device_num = args_opt.device_num
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rank = args_opt.device_id % device_num
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else:
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D.init()
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device_num = D.get_group_size()
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rank = D.get_rank()
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ckpt_save_dir = args_opt.save_checkpoint_path + 'ckpt_' + str(get_rank()) + '/'
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True,
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device_num=device_num)
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if args_opt.device_target == 'Ascend':
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_set_bert_all_reduce_split()
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else:
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rank = 0
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device_num = 1
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is_auto_enable_graph_kernel = _auto_enable_graph_kernel(args_opt.device_target, args_opt.enable_graph_kernel)
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if args_opt.enable_graph_kernel == "true" or is_auto_enable_graph_kernel:
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context.set_context(enable_graph_kernel=True)
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if args_opt.device_target == 'GPU' and bert_net_cfg.compute_type != mstype.float32 and \
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not is_auto_enable_graph_kernel:
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logger.warning('Gpu only support fp32 temporarily, run with fp32.')
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bert_net_cfg.compute_type = mstype.float32
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if args_opt.accumulation_steps > 1:
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logger.info("accumulation steps: {}".format(args_opt.accumulation_steps))
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logger.info("global batch size: {}".format(cfg.batch_size * args_opt.accumulation_steps))
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if args_opt.enable_data_sink == "true":
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args_opt.data_sink_steps *= args_opt.accumulation_steps
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logger.info("data sink steps: {}".format(args_opt.data_sink_steps))
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if args_opt.enable_save_ckpt == "true":
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args_opt.save_checkpoint_steps *= args_opt.accumulation_steps
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logger.info("save checkpoint steps: {}".format(args_opt.save_checkpoint_steps))
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ds = create_bert_dataset(device_num, rank, args_opt.do_shuffle, args_opt.data_dir, args_opt.schema_dir)
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net_with_loss = BertNetworkWithLoss(bert_net_cfg, True)
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new_repeat_count = args_opt.epoch_size * ds.get_dataset_size() // args_opt.data_sink_steps
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if args_opt.train_steps > 0:
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train_steps = args_opt.train_steps * args_opt.accumulation_steps
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new_repeat_count = min(new_repeat_count, train_steps // args_opt.data_sink_steps)
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else:
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args_opt.train_steps = args_opt.epoch_size * ds.get_dataset_size() // args_opt.accumulation_steps
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logger.info("train steps: {}".format(args_opt.train_steps))
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optimizer = _get_optimizer(args_opt, net_with_loss)
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callback = [TimeMonitor(args_opt.data_sink_steps), LossCallBack(ds.get_dataset_size())]
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if args_opt.enable_save_ckpt == "true" and args_opt.device_id % min(8, device_num) == 0:
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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',
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directory=None if ckpt_save_dir == "" else ckpt_save_dir, config=config_ck)
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callback.append(ckpoint_cb)
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if args_opt.load_checkpoint_path:
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param_dict = load_checkpoint(args_opt.load_checkpoint_path)
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load_param_into_net(net_with_loss, 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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if args_opt.accumulation_steps <= 1:
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net_with_grads = BertTrainOneStepWithLossScaleCell(net_with_loss, optimizer=optimizer,
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scale_update_cell=update_cell)
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else:
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accumulation_steps = args_opt.accumulation_steps
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net_with_grads = BertTrainAccumulateStepsWithLossScaleCell(net_with_loss, optimizer=optimizer,
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scale_update_cell=update_cell,
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accumulation_steps=accumulation_steps,
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enable_global_norm=cfg.enable_global_norm)
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else:
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net_with_grads = BertTrainOneStepCell(net_with_loss, optimizer=optimizer)
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model = Model(net_with_grads)
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model.train(new_repeat_count, ds, callbacks=callback,
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dataset_sink_mode=(args_opt.enable_data_sink == "true"), sink_size=args_opt.data_sink_steps)
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if __name__ == '__main__':
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set_seed(0)
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run_pretrain()
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