# 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. # ============================================================================ """Transformer training script.""" import os import time import argparse import ast import mindspore.common.dtype as mstype from mindspore.common.tensor import Tensor from mindspore.nn.optim import Adam from mindspore.train.model import Model from mindspore.train.loss_scale_manager import DynamicLossScaleManager from mindspore.train.callback import CheckpointConfig, ModelCheckpoint from mindspore.train.callback import Callback, TimeMonitor from mindspore.train.serialization import load_checkpoint, load_param_into_net import mindspore.communication.management as D from mindspore.communication.management import get_rank from mindspore.context import ParallelMode from mindspore import context from mindspore.common import set_seed from src.transformer_for_train import TransformerTrainOneStepCell, TransformerNetworkWithLoss, \ TransformerTrainOneStepWithLossScaleCell from src.config import cfg, transformer_net_cfg, transformer_net_cfg_gpu from src.dataset import create_transformer_dataset from src.lr_schedule import create_dynamic_lr set_seed(1) def get_ms_timestamp(): t = time.time() return int(round(t * 1000)) time_stamp_init = False time_stamp_first = 0 class LossCallBack(Callback): """ Monitor the loss in training. If the loss is 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, rank_id=0): 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 self.rank_id = rank_id global time_stamp_init, time_stamp_first if not time_stamp_init: time_stamp_first = get_ms_timestamp() time_stamp_init = True def step_end(self, run_context): """Monitor the loss in training.""" global time_stamp_first time_stamp_current = get_ms_timestamp() cb_params = run_context.original_args() print("time: {}, epoch: {}, step: {}, outputs are {}".format(time_stamp_current - time_stamp_first, cb_params.cur_epoch_num, cb_params.cur_step_num, str(cb_params.net_outputs))) with open("./loss_{}.log".format(self.rank_id), "a+") as f: f.write("time: {}, epoch: {}, step: {}, loss: {}, overflow: {}, loss_scale: {}".format( time_stamp_current - time_stamp_first, cb_params.cur_epoch_num, cb_params.cur_step_num, str(cb_params.net_outputs[0].asnumpy()), str(cb_params.net_outputs[1].asnumpy()), str(cb_params.net_outputs[2].asnumpy()))) f.write('\n') def argparse_init(): """ Argparse init. """ parser = argparse.ArgumentParser(description='transformer') parser.add_argument("--distribute", type=str, default="false", choices=['true', 'false'], help="Run distribute, default is false.") parser.add_argument("--epoch_size", type=int, default=52, help="Epoch size, default is 52.") parser.add_argument("--device_target", type=str, default="Ascend", help="device where the code will be implemented, default is Ascend") 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_lossscale", type=str, default="true", choices=['true', 'false'], help="Use lossscale or not, default is true.") parser.add_argument("--do_shuffle", type=str, default="true", choices=['true', 'false'], help="Enable shuffle for dataset, default is true.") parser.add_argument("--checkpoint_path", type=str, default="", help="Checkpoint file path") parser.add_argument("--enable_save_ckpt", type=str, default="true", choices=['true', 'false'], help="Enable save checkpoint, default is true.") parser.add_argument("--save_checkpoint_steps", type=int, default=2500, help="Save checkpoint steps, " "default is 2500.") parser.add_argument("--save_checkpoint_num", type=int, default=30, help="Save checkpoint numbers, default is 30.") parser.add_argument("--save_checkpoint_path", type=str, default="./", help="Save checkpoint file path") parser.add_argument("--data_path", type=str, default="", help="Data path, it is better to use absolute path") parser.add_argument("--bucket_boundaries", type=ast.literal_eval, default=[16, 32, 48, 64, 128], help="sequence length for different bucket") return parser def run_transformer_train(): """ Transformer training. """ parser = argparse_init() args, _ = parser.parse_known_args() context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, device_id=args.device_id) context.set_context(reserve_class_name_in_scope=False, enable_auto_mixed_precision=False) if args.distribute == "true": if args.device_target == "Ascend": device_num = args.device_num D.init('hccl') else: D.init('nccl') device_num = D.get_group_size() rank = get_rank() args.device_id = rank context.reset_auto_parallel_context() context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True, device_num=device_num) rank_id = args.device_id % device_num save_ckpt_path = os.path.join(args.save_checkpoint_path, 'ckpt_' + str(get_rank()) + '/') else: device_num = 1 rank_id = 0 save_ckpt_path = os.path.join(args.save_checkpoint_path, 'ckpt_0/') dataset = create_transformer_dataset(epoch_count=1, rank_size=device_num, rank_id=rank_id, do_shuffle=args.do_shuffle, dataset_path=args.data_path, bucket_boundaries=args.bucket_boundaries, device_target=args.device_target) if args.device_target == "Ascend": netwithloss = TransformerNetworkWithLoss(transformer_net_cfg, True) else: netwithloss = TransformerNetworkWithLoss(transformer_net_cfg_gpu, True) if args.checkpoint_path: parameter_dict = load_checkpoint(args.checkpoint_path) load_param_into_net(netwithloss, parameter_dict) hidden_size = transformer_net_cfg.hidden_size if args.device_target == "Ascend" \ else transformer_net_cfg_gpu.hidden_size lr = Tensor(create_dynamic_lr(schedule="constant*rsqrt_hidden*linear_warmup*rsqrt_decay", training_steps=dataset.get_dataset_size()*args.epoch_size, learning_rate=cfg.lr_schedule.learning_rate, warmup_steps=cfg.lr_schedule.warmup_steps, hidden_size=hidden_size, start_decay_step=cfg.lr_schedule.start_decay_step, min_lr=cfg.lr_schedule.min_lr), mstype.float32) if args.device_target == "GPU" and cfg.transformer_network == "large": optimizer = Adam(netwithloss.trainable_params(), lr, beta2=cfg.optimizer_adam_beta2) else: optimizer = Adam(netwithloss.trainable_params(), lr) callbacks = [TimeMonitor(dataset.get_dataset_size()), LossCallBack(rank_id=rank_id)] if args.enable_save_ckpt == "true": if device_num == 1 or (device_num > 1 and rank_id == 0): if args.device_target == "Ascend": ckpt_config = CheckpointConfig(save_checkpoint_steps=args.save_checkpoint_steps, keep_checkpoint_max=args.save_checkpoint_num) else: ckpt_config = CheckpointConfig(save_checkpoint_steps=dataset.get_dataset_size(), keep_checkpoint_max=args.save_checkpoint_num) ckpoint_cb = ModelCheckpoint(prefix='transformer', directory=save_ckpt_path, config=ckpt_config) callbacks.append(ckpoint_cb) if args.enable_lossscale == "true": scale_manager = DynamicLossScaleManager(init_loss_scale=cfg.init_loss_scale_value, scale_factor=cfg.scale_factor, scale_window=cfg.scale_window) update_cell = scale_manager.get_update_cell() netwithgrads = TransformerTrainOneStepWithLossScaleCell(netwithloss, optimizer=optimizer, scale_update_cell=update_cell) else: netwithgrads = TransformerTrainOneStepCell(netwithloss, optimizer=optimizer) netwithgrads.set_train(True) model = Model(netwithgrads) model.train(args.epoch_size, dataset, callbacks=callbacks, dataset_sink_mode=False) if __name__ == '__main__': run_transformer_train()