# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # 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. import argparse import os import random import time from functools import partial import numpy as np import paddle from data import convert_example, create_dataloader, read_text_pair from model import QuestionMatching from paddlenlp.data import Pad, Stack, Tuple from paddlenlp.datasets import load_dataset from paddlenlp.transformers import AutoModel, AutoTokenizer, LinearDecayWithWarmup # fmt: off parser = argparse.ArgumentParser() parser.add_argument("--train_set", type=str, required=True, help="The full path of train_set_file") parser.add_argument("--dev_set", type=str, required=True, help="The full path of dev_set_file") parser.add_argument("--save_dir", default='./checkpoint', type=str, help="The output directory where the model checkpoints will be written.") parser.add_argument("--max_seq_length", default=256, type=int, help="The maximum total input sequence length after tokenization. Sequences longer than this will be truncated, sequences shorter will be padded.") parser.add_argument('--max_steps', default=-1, type=int, help="If > 0, set total number of training steps to perform.") parser.add_argument("--train_batch_size", default=32, type=int, help="Batch size per GPU/CPU for training.") parser.add_argument("--eval_batch_size", default=128, type=int, help="Batch size per GPU/CPU for training.") parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.") parser.add_argument("--epochs", default=3, type=int, help="Total number of training epochs to perform.") parser.add_argument("--eval_step", default=100, type=int, help="Step interval for evaluation.") parser.add_argument('--save_step', default=10000, type=int, help="Step interval for saving checkpoint.") parser.add_argument("--warmup_proportion", default=0.0, type=float, help="Linear warmup proportion over the training process.") parser.add_argument("--init_from_ckpt", type=str, default=None, help="The path of checkpoint to be loaded.") parser.add_argument("--seed", type=int, default=1000, help="Random seed for initialization.") parser.add_argument('--device', choices=['cpu', 'gpu'], default="gpu", help="Select which device to train model, defaults to gpu.") parser.add_argument("--rdrop_coef", default=0.0, type=float, help="The coefficient of KL-Divergence loss in R-Drop paper, for more detail please refer to https://arxiv.org/abs/2106.14448), if rdrop_coef > 0 then R-Drop works") args = parser.parse_args() # fmt: on def set_seed(seed): """sets random seed""" random.seed(seed) np.random.seed(seed) paddle.seed(seed) @paddle.no_grad() def evaluate(model, criterion, metric, data_loader): """ Given a dataset, it evals model and computes the metric. Args: model(obj:`paddle.nn.Layer`): A model to classify texts. data_loader(obj:`paddle.io.DataLoader`): The dataset loader which generates batches. criterion(obj:`paddle.nn.Layer`): It can compute the loss. metric(obj:`paddle.metric.Metric`): The evaluation metric. """ model.eval() metric.reset() losses = [] total_num = 0 for batch in data_loader: input_ids, token_type_ids, labels = batch total_num += len(labels) logits, _ = model(input_ids=input_ids, token_type_ids=token_type_ids, do_evaluate=True) loss = criterion(logits, labels) losses.append(loss.numpy()) correct = metric.compute(logits, labels) metric.update(correct) accu = metric.accumulate() print("dev_loss: {:.5}, accuracy: {:.5}, total_num:{}".format(np.mean(losses), accu, total_num)) model.train() metric.reset() return accu def do_train(): paddle.set_device(args.device) rank = paddle.distributed.get_rank() if paddle.distributed.get_world_size() > 1: paddle.distributed.init_parallel_env() set_seed(args.seed) train_ds = load_dataset(read_text_pair, data_path=args.train_set, is_test=False, lazy=False) dev_ds = load_dataset(read_text_pair, data_path=args.dev_set, is_test=False, lazy=False) pretrained_model = AutoModel.from_pretrained("ernie-3.0-medium-zh") tokenizer = AutoTokenizer.from_pretrained("ernie-3.0-medium-zh") trans_func = partial(convert_example, tokenizer=tokenizer, max_seq_length=args.max_seq_length) batchify_fn = lambda samples, fn=Tuple( Pad(axis=0, pad_val=tokenizer.pad_token_id), # text_pair_input Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # text_pair_segment Stack(dtype="int64"), # label ): [data for data in fn(samples)] train_data_loader = create_dataloader( train_ds, mode="train", batch_size=args.train_batch_size, batchify_fn=batchify_fn, trans_fn=trans_func ) dev_data_loader = create_dataloader( dev_ds, mode="dev", batch_size=args.eval_batch_size, batchify_fn=batchify_fn, trans_fn=trans_func ) model = QuestionMatching(pretrained_model, rdrop_coef=args.rdrop_coef) if args.init_from_ckpt and os.path.isfile(args.init_from_ckpt): state_dict = paddle.load(args.init_from_ckpt) model.set_dict(state_dict) model = paddle.DataParallel(model) num_training_steps = len(train_data_loader) * args.epochs lr_scheduler = LinearDecayWithWarmup(args.learning_rate, num_training_steps, args.warmup_proportion) # Generate parameter names needed to perform weight decay. # All bias and LayerNorm parameters are excluded. decay_params = [p.name for n, p in model.named_parameters() if not any(nd in n for nd in ["bias", "norm"])] optimizer = paddle.optimizer.AdamW( learning_rate=lr_scheduler, parameters=model.parameters(), weight_decay=args.weight_decay, apply_decay_param_fun=lambda x: x in decay_params, ) criterion = paddle.nn.loss.CrossEntropyLoss() metric = paddle.metric.Accuracy() global_step = 0 best_accuracy = 0.0 tic_train = time.time() for epoch in range(1, args.epochs + 1): for step, batch in enumerate(train_data_loader, start=1): input_ids, token_type_ids, labels = batch logits1, kl_loss = model(input_ids=input_ids, token_type_ids=token_type_ids) correct = metric.compute(logits1, labels) metric.update(correct) acc = metric.accumulate() ce_loss = criterion(logits1, labels) if kl_loss > 0: loss = ce_loss + kl_loss * args.rdrop_coef else: loss = ce_loss global_step += 1 if global_step % 10 == 0 and rank == 0: print( "global step %d, epoch: %d, batch: %d, loss: %.4f, ce_loss: %.4f., kl_loss: %.4f, accu: %.4f, speed: %.2f step/s" % (global_step, epoch, step, loss, ce_loss, kl_loss, acc, 10 / (time.time() - tic_train)) ) tic_train = time.time() loss.backward() optimizer.step() lr_scheduler.step() optimizer.clear_grad() if global_step % args.eval_step == 0 and rank == 0: accuracy = evaluate(model, criterion, metric, dev_data_loader) if accuracy > best_accuracy: save_dir = os.path.join(args.save_dir, "model_%d" % global_step) if not os.path.exists(save_dir): os.makedirs(save_dir) save_param_path = os.path.join(save_dir, "model_state.pdparams") paddle.save(model.state_dict(), save_param_path) tokenizer.save_pretrained(save_dir) best_accuracy = accuracy if global_step == args.max_steps: return if __name__ == "__main__": do_train()