# 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. # ============================================================================ """general distill script""" import os import argparse import datetime 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.callback import TimeMonitor from mindspore.context import ParallelMode from mindspore.nn.optim import AdamWeightDecay from mindspore.nn.wrap.loss_scale import DynamicLossScaleUpdateCell from mindspore import log as logger from mindspore.common import set_seed from src.dataset import create_tinybert_dataset, DataType from src.utils import LossCallBack, ModelSaveCkpt, BertLearningRate from src.gd_config import common_cfg, bert_teacher_net_cfg, bert_student_net_cfg from src.tinybert_for_gd_td import BertTrainWithLossScaleCell, BertNetworkWithLoss_gd, BertTrainCell def get_argument(): """Tinybert general distill argument parser.""" parser = argparse.ArgumentParser(description='tinybert general distill') parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'GPU', 'CPU'], help='device where the code will be implemented. (Default: Ascend)') 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="3", 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("--save_ckpt_step", type=int, default=100, help="Enable data sink, default is true.") parser.add_argument("--max_ckpt_num", type=int, default=1, help="Enable data sink, 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("--enable_data_sink", type=str, default="true", choices=["true", "false"], 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_ckpt_path", type=str, default="", help="Save checkpoint path") parser.add_argument("--load_teacher_ckpt_path", type=str, default="", help="Load checkpoint file path") 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") parser.add_argument("--dataset_type", type=str, default="tfrecord", help="dataset type tfrecord/mindrecord, default is tfrecord") args_opt = parser.parse_args() return args_opt def run_general_distill(): """ run general distill """ args_opt = get_argument() context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target, reserve_class_name_in_scope=False) if args_opt.device_target == "Ascend": context.set_context(device_id=args_opt.device_id) save_ckpt_dir = os.path.join(args_opt.save_ckpt_path, datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S')) if args_opt.distribute == "true": if args_opt.device_target == 'Ascend': D.init() device_num = args_opt.device_num rank = args_opt.device_id % device_num else: D.init() device_num = D.get_group_size() rank = D.get_rank() save_ckpt_dir = save_ckpt_dir + '_ckpt_' + str(rank) context.reset_auto_parallel_context() context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True, device_num=device_num) else: rank = 0 device_num = 1 if not os.path.exists(save_ckpt_dir): os.makedirs(save_ckpt_dir) enable_loss_scale = True if args_opt.device_target == "GPU": if bert_student_net_cfg.compute_type != mstype.float32: logger.warning('Compute about the student only support float32 temporarily, run with float32.') bert_student_net_cfg.compute_type = mstype.float32 # Backward of the network are calculated using fp32, # and the loss scale is not necessary enable_loss_scale = False if args_opt.device_target == "CPU": logger.warning('CPU only support float32 temporarily, run with float32.') bert_teacher_net_cfg.dtype = mstype.float32 bert_teacher_net_cfg.compute_type = mstype.float32 bert_student_net_cfg.dtype = mstype.float32 bert_student_net_cfg.compute_type = mstype.float32 enable_loss_scale = False netwithloss = BertNetworkWithLoss_gd(teacher_config=bert_teacher_net_cfg, teacher_ckpt=args_opt.load_teacher_ckpt_path, student_config=bert_student_net_cfg, is_training=True, use_one_hot_embeddings=False) if args_opt.dataset_type == "tfrecord": dataset_type = DataType.TFRECORD elif args_opt.dataset_type == "mindrecord": dataset_type = DataType.MINDRECORD else: raise Exception("dataset format is not supported yet") dataset = create_tinybert_dataset('gd', common_cfg.batch_size, device_num, rank, args_opt.do_shuffle, args_opt.data_dir, args_opt.schema_dir, data_type=dataset_type) dataset_size = dataset.get_dataset_size() print('dataset size: ', dataset_size) print("dataset repeatcount: ", dataset.get_repeat_count()) if args_opt.enable_data_sink == "true": repeat_count = args_opt.epoch_size * dataset_size // args_opt.data_sink_steps time_monitor_steps = args_opt.data_sink_steps else: repeat_count = args_opt.epoch_size time_monitor_steps = dataset_size lr_schedule = BertLearningRate(learning_rate=common_cfg.AdamWeightDecay.learning_rate, end_learning_rate=common_cfg.AdamWeightDecay.end_learning_rate, warmup_steps=int(dataset_size * args_opt.epoch_size / 10), decay_steps=int(dataset_size * args_opt.epoch_size), power=common_cfg.AdamWeightDecay.power) params = netwithloss.trainable_params() decay_params = list(filter(common_cfg.AdamWeightDecay.decay_filter, params)) other_params = list(filter(lambda x: not common_cfg.AdamWeightDecay.decay_filter(x), params)) group_params = [{'params': decay_params, 'weight_decay': common_cfg.AdamWeightDecay.weight_decay}, {'params': other_params, 'weight_decay': 0.0}, {'order_params': params}] optimizer = AdamWeightDecay(group_params, learning_rate=lr_schedule, eps=common_cfg.AdamWeightDecay.eps) callback = [TimeMonitor(time_monitor_steps), LossCallBack(), ModelSaveCkpt(netwithloss.bert, args_opt.save_ckpt_step, args_opt.max_ckpt_num, save_ckpt_dir)] if enable_loss_scale: update_cell = DynamicLossScaleUpdateCell(loss_scale_value=common_cfg.loss_scale_value, scale_factor=common_cfg.scale_factor, scale_window=common_cfg.scale_window) netwithgrads = BertTrainWithLossScaleCell(netwithloss, optimizer=optimizer, scale_update_cell=update_cell) else: netwithgrads = BertTrainCell(netwithloss, optimizer=optimizer) model = Model(netwithgrads) model.train(repeat_count, dataset, callbacks=callback, dataset_sink_mode=(args_opt.enable_data_sink == "true"), sink_size=args_opt.data_sink_steps) if __name__ == '__main__': set_seed(0) run_general_distill()