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203 lines
8.8 KiB
203 lines
8.8 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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"""FastText for train"""
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import os
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import time
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import argparse
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from mindspore import context
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from mindspore.nn.optim import Adam
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from mindspore.common import set_seed
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from mindspore.train.model import Model
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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.context import ParallelMode
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from mindspore.train.callback import Callback, TimeMonitor
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from mindspore.communication import management as MultiAscend
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from mindspore.train.callback import CheckpointConfig, ModelCheckpoint
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from src.load_dataset import load_dataset
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from src.lr_schedule import polynomial_decay_scheduler
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from src.fasttext_train import FastTextTrainOneStepCell, FastTextNetWithLoss
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parser = argparse.ArgumentParser()
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parser.add_argument('--data_path', type=str, required=True, help='FastText input data file path.')
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parser.add_argument('--data_name', type=str, required=True, default='ag', help='dataset name. eg. ag, dbpedia')
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args = parser.parse_args()
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if args.data_name == "ag":
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from src.config import config_ag as config
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elif args.data_name == 'dbpedia':
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from src.config import config_db as config
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elif args.data_name == 'yelp_p':
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from src.config import config_yelpp as config
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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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set_seed(5)
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time_stamp_init = False
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time_stamp_first = 0
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rank_id = os.getenv('DEVICE_ID')
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context.set_context(
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mode=context.GRAPH_MODE,
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save_graphs=False,
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device_target="Ascend")
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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, rank_ids=0):
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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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self.rank_id = rank_ids
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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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"""Monitor the loss in training."""
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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,
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cb_params.cur_step_num,
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str(cb_params.net_outputs)))
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with open("./loss_{}.log".format(self.rank_id), "a+") as f:
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f.write("time: {}, epoch: {}, step: {}, loss: {}".format(
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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.asnumpy())))
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f.write('\n')
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def _build_training_pipeline(pre_dataset):
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"""
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Build training pipeline
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Args:
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pre_dataset: preprocessed dataset
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"""
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net_with_loss = FastTextNetWithLoss(config.vocab_size, config.embedding_dims, config.num_class)
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net_with_loss.init_parameters_data()
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if config.pretrain_ckpt_dir:
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parameter_dict = load_checkpoint(config.pretrain_ckpt_dir)
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load_param_into_net(net_with_loss, parameter_dict)
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if pre_dataset is None:
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raise ValueError("pre-process dataset must be provided")
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#get learning rate
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update_steps = config.epoch * pre_dataset.get_dataset_size()
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decay_steps = pre_dataset.get_dataset_size()
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rank_size = os.getenv("RANK_SIZE")
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if isinstance(rank_size, int):
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raise ValueError("RANK_SIZE must be integer")
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if rank_size is not None and int(rank_size) > 1:
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base_lr = config.lr
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else:
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base_lr = config.lr / 10
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print("+++++++++++Total update steps ", update_steps)
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lr = Tensor(polynomial_decay_scheduler(lr=base_lr,
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min_lr=config.min_lr,
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decay_steps=decay_steps,
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total_update_num=update_steps,
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warmup_steps=config.warmup_steps,
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power=config.poly_lr_scheduler_power), dtype=mstype.float32)
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optimizer = Adam(net_with_loss.trainable_params(), lr, beta1=0.9, beta2=0.999)
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net_with_grads = FastTextTrainOneStepCell(net_with_loss, optimizer=optimizer)
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net_with_grads.set_train(True)
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model = Model(net_with_grads)
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loss_monitor = LossCallBack(rank_ids=rank_id)
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dataset_size = pre_dataset.get_dataset_size()
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time_monitor = TimeMonitor(data_size=dataset_size)
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ckpt_config = CheckpointConfig(save_checkpoint_steps=decay_steps * config.epoch,
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keep_checkpoint_max=config.keep_ckpt_max)
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callbacks = [time_monitor, loss_monitor]
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if rank_size is None or int(rank_size) == 1:
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ckpt_callback = ModelCheckpoint(prefix='fasttext',
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directory=os.path.join('./', 'ckpt_{}'.format(os.getenv("DEVICE_ID"))),
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config=ckpt_config)
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callbacks.append(ckpt_callback)
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if rank_size is not None and int(rank_size) > 1 and MultiAscend.get_rank() % 8 == 0:
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ckpt_callback = ModelCheckpoint(prefix='fasttext',
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directory=os.path.join('./', 'ckpt_{}'.format(os.getenv("DEVICE_ID"))),
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config=ckpt_config)
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callbacks.append(ckpt_callback)
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print("Prepare to Training....")
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epoch_size = pre_dataset.get_repeat_count()
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print("Epoch size ", epoch_size)
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if os.getenv("RANK_SIZE") is not None and int(os.getenv("RANK_SIZE")) > 1:
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print(f" | Rank {MultiAscend.get_rank()} Call model train.")
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model.train(epoch=config.epoch, train_dataset=pre_dataset, callbacks=callbacks, dataset_sink_mode=False)
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def train_single(input_file_path):
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"""
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Train model on single device
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Args:
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input_file_path: preprocessed dataset path
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"""
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print("Staring training on single device.")
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preprocessed_data = load_dataset(dataset_path=input_file_path,
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batch_size=config.batch_size,
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epoch_count=config.epoch_count,
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bucket=config.buckets)
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_build_training_pipeline(preprocessed_data)
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def set_parallel_env():
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context.reset_auto_parallel_context()
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MultiAscend.init()
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context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL,
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device_num=MultiAscend.get_group_size(),
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gradients_mean=True)
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def train_paralle(input_file_path):
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"""
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Train model on multi device
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Args:
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input_file_path: preprocessed dataset path
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"""
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set_parallel_env()
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print("Starting traning on mutiple devices. |~ _ ~| |~ _ ~| |~ _ ~| |~ _ ~|")
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preprocessed_data = load_dataset(dataset_path=input_file_path,
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batch_size=config.batch_size,
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epoch_count=config.epoch_count,
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rank_size=MultiAscend.get_group_size(),
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rank_id=MultiAscend.get_rank(),
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bucket=config.buckets,
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shuffle=False)
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_build_training_pipeline(preprocessed_data)
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if __name__ == "__main__":
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_rank_size = os.getenv("RANK_SIZE")
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if _rank_size is not None and int(_rank_size) > 1:
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train_paralle(args.data_path)
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else:
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train_single(args.data_path)
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