# Copyright 2021 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. # ============================================================================ """task distill script""" import os import argparse from mindspore import context from mindspore.train.model import Model from mindspore.nn.optim import AdamWeightDecay from mindspore import set_seed from src.dataset import create_dataset from src.utils import StepCallBack, ModelSaveCkpt, EvalCallBack, BertLearningRate from src.config import train_cfg, eval_cfg, teacher_net_cfg, student_net_cfg, task_cfg from src.cell_wrapper import BertNetworkWithLoss, BertTrainCell WEIGHTS_NAME = 'eval_model.ckpt' EVAL_DATA_NAME = 'eval.tf_record' TRAIN_DATA_NAME = 'train.tf_record' def parse_args(): """ parse args """ parser = argparse.ArgumentParser(description='ternarybert task distill') parser.add_argument('--device_target', type=str, default='GPU', choices=['Ascend', 'GPU'], help='Device where the code will be implemented. (Default: GPU)') parser.add_argument('--do_eval', type=str, default='true', choices=['true', 'false'], help='Do eval task during training or not. (Default: true)') parser.add_argument('--epoch_size', type=int, default=3, help='Epoch size for train phase. (Default: 3)') parser.add_argument('--device_id', type=int, default=0, help='Device id. (Default: 0)') parser.add_argument('--do_shuffle', type=str, default='true', choices=['true', 'false'], help='Enable shuffle for train dataset. (Default: true)') parser.add_argument('--enable_data_sink', type=str, default='true', choices=['true', 'false'], help='Enable data sink. (Default: true)') parser.add_argument('--save_ckpt_step', type=int, default=50, help='If do_eval is false, the checkpoint will be saved every save_ckpt_step. (Default: 50)') parser.add_argument('--eval_ckpt_step', type=int, default=50, help='If do_eval is true, the evaluation will be ran every eval_ckpt_step. (Default: 50)') parser.add_argument('--max_ckpt_num', type=int, default=10, help='The number of checkpoints will not be larger than max_ckpt_num. (Default: 10)') parser.add_argument('--data_sink_steps', type=int, default=1, help='Sink steps for each epoch. (Default: 1)') parser.add_argument('--teacher_model_dir', type=str, default='', help='The checkpoint directory of teacher model.') parser.add_argument('--student_model_dir', type=str, default='', help='The checkpoint directory of student model.') parser.add_argument('--data_dir', type=str, default='', help='Data directory.') parser.add_argument('--output_dir', type=str, default='./', help='The output checkpoint directory.') parser.add_argument('--task_name', type=str, default='sts-b', choices=['sts-b', 'qnli', 'mnli'], help='The name of the task to train. (Default: sts-b)') parser.add_argument('--dataset_type', type=str, default='tfrecord', choices=['tfrecord', 'mindrecord'], help='The name of the task to train. (Default: tfrecord)') parser.add_argument('--seed', type=int, default=1, help='The random seed') parser.add_argument('--train_batch_size', type=int, default=16, help='Batch size for training') parser.add_argument('--eval_batch_size', type=int, default=32, help='Eval Batch size in callback') return parser.parse_args() def run_task_distill(args_opt): """ run task distill """ task = task_cfg[args_opt.task_name] teacher_net_cfg.seq_length = task.seq_length student_net_cfg.seq_length = task.seq_length train_cfg.batch_size = args_opt.train_batch_size eval_cfg.batch_size = args_opt.eval_batch_size teacher_ckpt = os.path.join(args_opt.teacher_model_dir, args_opt.task_name, WEIGHTS_NAME) student_ckpt = os.path.join(args_opt.student_model_dir, args_opt.task_name, WEIGHTS_NAME) train_data_dir = os.path.join(args_opt.data_dir, args_opt.task_name, TRAIN_DATA_NAME) eval_data_dir = os.path.join(args_opt.data_dir, args_opt.task_name, EVAL_DATA_NAME) save_ckpt_dir = os.path.join(args_opt.output_dir, args_opt.task_name) context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target, device_id=args.device_id) rank = 0 device_num = 1 train_dataset = create_dataset(batch_size=train_cfg.batch_size, device_num=device_num, rank=rank, do_shuffle=args_opt.do_shuffle, data_dir=train_data_dir, data_type=args_opt.dataset_type, seq_length=task.seq_length, task_type=task.task_type, drop_remainder=True) dataset_size = train_dataset.get_dataset_size() print('train dataset size:', dataset_size) eval_dataset = create_dataset(batch_size=eval_cfg.batch_size, device_num=device_num, rank=rank, do_shuffle=args_opt.do_shuffle, data_dir=eval_data_dir, data_type=args_opt.dataset_type, seq_length=task.seq_length, task_type=task.task_type, drop_remainder=False) print('eval dataset size:', eval_dataset.get_dataset_size()) if args_opt.enable_data_sink == 'true': repeat_count = args_opt.epoch_size * dataset_size // args_opt.data_sink_steps else: repeat_count = args_opt.epoch_size netwithloss = BertNetworkWithLoss(teacher_config=teacher_net_cfg, teacher_ckpt=teacher_ckpt, student_config=student_net_cfg, student_ckpt=student_ckpt, is_training=True, task_type=task.task_type, num_labels=task.num_labels) params = netwithloss.trainable_params() optimizer_cfg = train_cfg.optimizer_cfg lr_schedule = BertLearningRate(learning_rate=optimizer_cfg.AdamWeightDecay.learning_rate, end_learning_rate=optimizer_cfg.AdamWeightDecay.end_learning_rate, warmup_steps=int(dataset_size * args_opt.epoch_size * optimizer_cfg.AdamWeightDecay.warmup_ratio), decay_steps=int(dataset_size * args_opt.epoch_size), power=optimizer_cfg.AdamWeightDecay.power) decay_params = list(filter(optimizer_cfg.AdamWeightDecay.decay_filter, params)) other_params = list(filter(lambda x: not optimizer_cfg.AdamWeightDecay.decay_filter(x), params)) group_params = [{'params': decay_params, 'weight_decay': optimizer_cfg.AdamWeightDecay.weight_decay}, {'params': other_params, 'weight_decay': 0.0}, {'order_params': params}] optimizer = AdamWeightDecay(group_params, learning_rate=lr_schedule, eps=optimizer_cfg.AdamWeightDecay.eps) netwithgrads = BertTrainCell(netwithloss, optimizer=optimizer) if args_opt.do_eval == 'true': eval_dataset = list(eval_dataset.create_dict_iterator()) callback = [EvalCallBack(network=netwithloss.bert, dataset=eval_dataset, eval_ckpt_step=args_opt.eval_ckpt_step, save_ckpt_dir=save_ckpt_dir, embedding_bits=student_net_cfg.embedding_bits, weight_bits=student_net_cfg.weight_bits, clip_value=student_net_cfg.weight_clip_value, metrics=task.metrics)] else: callback = [StepCallBack(), ModelSaveCkpt(network=netwithloss.bert, save_ckpt_step=args_opt.save_ckpt_step, max_ckpt_num=args_opt.max_ckpt_num, output_dir=save_ckpt_dir, embedding_bits=student_net_cfg.embedding_bits, weight_bits=student_net_cfg.weight_bits, clip_value=student_net_cfg.weight_clip_value)] model = Model(netwithgrads) model.train(repeat_count, train_dataset, callbacks=callback, dataset_sink_mode=(args_opt.enable_data_sink == 'true'), sink_size=args_opt.data_sink_steps) if __name__ == '__main__': args = parse_args() set_seed(args.seed) run_task_distill(args)