# 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_simcse_text, read_text_pair, word_repetition, ) from model import SimCSE from scipy import stats from paddlenlp.data import Pad, Stack, Tuple from paddlenlp.datasets import load_dataset from paddlenlp.transformers import AutoModel, AutoTokenizer, LinearDecayWithWarmup # yapf: disable parser = argparse.ArgumentParser() 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=128, 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("--batch_size", default=32, type=int, help="Batch size per GPU/CPU for training.") parser.add_argument("--output_emb_size", default=0, type=int, help="Output_embedding_size, 0 means use hidden_size as output embedding size.") parser.add_argument("--learning_rate", default=1e-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=1, type=int, help="Total number of training epochs to perform.") 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', 'npu'], default="gpu", help="Select which device to train model, defaults to gpu.") parser.add_argument('--save_steps', type=int, default=10000, help="Step interval for saving checkpoint.") parser.add_argument("--max_steps", default=-1, type=int, help="If > 0: set total number of training steps to perform. Override ecpochs.") parser.add_argument('--eval_steps', type=int, default=10000, help="Step interval for evaluation.") parser.add_argument("--train_set_file", type=str, required=True, help="The full path of train_set_file.") parser.add_argument("--test_set_file", type=str, required=True, help="The full path of test_set_file.") parser.add_argument("--margin", default=0.0, type=float, help="Margin between pos_sample and neg_samples.") parser.add_argument("--scale", default=20, type=int, help="Scale for pair-wise margin_rank_loss.") parser.add_argument("--dropout", default=0.1, type=float, help="Dropout for pretrained model encoder.") parser.add_argument("--dup_rate", default=0.32, type=float, help="duplicate rate for word repetition.") parser.add_argument("--infer_with_fc_pooler", action='store_true', help="Whether use fc layer after cls embedding or not for when infer.") args = parser.parse_args() def set_seed(seed): """sets random seed""" random.seed(seed) np.random.seed(seed) paddle.seed(seed) def do_evaluate(model, tokenizer, data_loader, with_pooler=False): model.eval() total_num = 0 spearman_corr = 0.0 sims = [] labels = [] for batch in data_loader: query_input_ids, query_token_type_ids, title_input_ids, title_token_type_ids, label = batch total_num += len(label) query_cls_embedding = model.get_pooled_embedding( query_input_ids, query_token_type_ids, with_pooler=with_pooler) title_cls_embedding = model.get_pooled_embedding(title_input_ids, title_token_type_ids, with_pooler=with_pooler) cosine_sim = paddle.sum(query_cls_embedding * title_cls_embedding, axis=-1) sims.append(cosine_sim.numpy()) labels.append(label.numpy()) sims = np.concatenate(sims, axis=0) labels = np.concatenate(labels, axis=0) spearman_corr = stats.spearmanr(labels, sims).correlation model.train() return spearman_corr, total_num 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_simcse_text, data_path=args.train_set_file, lazy=False) dev_ds = load_dataset( read_text_pair, data_path=args.test_set_file, lazy=False) pretrained_model = AutoModel.from_pretrained( 'ernie-3.0-medium-zh', hidden_dropout_prob=args.dropout, attention_probs_dropout_prob=args.dropout) 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), # query_input Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # query_segment Pad(axis=0, pad_val=tokenizer.pad_token_id), # title_input Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # title_segment ): [data for data in fn(samples)] dev_batchify_fn = lambda samples, fn=Tuple( Pad(axis=0, pad_val=tokenizer.pad_token_id), # query_input Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # query_segment Pad(axis=0, pad_val=tokenizer.pad_token_id), # title_input Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # title_segment Stack(dtype="int64"), # labels ): [data for data in fn(samples)] train_data_loader = create_dataloader( train_ds, mode='train', batch_size=args.batch_size, batchify_fn=batchify_fn, trans_fn=trans_func) dev_data_loader = create_dataloader( dev_ds, mode='eval', batch_size=args.batch_size, batchify_fn=dev_batchify_fn, trans_fn=trans_func) model = SimCSE( pretrained_model, margin=args.margin, scale=args.scale, output_emb_size=args.output_emb_size) 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) print("warmup from:{}".format(args.init_from_ckpt)) model = paddle.DataParallel(model) num_training_steps = args.max_steps if args.max_steps > 0 else 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) global_step = 0 tic_train = time.time() for epoch in range(1, args.epochs + 1): for step, batch in enumerate(train_data_loader, start=1): query_input_ids, query_token_type_ids, title_input_ids, title_token_type_ids = batch if args.dup_rate > 0: query_input_ids, query_token_type_ids = word_repetition(query_input_ids, query_token_type_ids, args.dup_rate) title_input_ids, title_token_type_ids = word_repetition(title_input_ids, title_token_type_ids, args.dup_rate) loss = model( query_input_ids=query_input_ids, title_input_ids=title_input_ids, query_token_type_ids=query_token_type_ids, title_token_type_ids=title_token_type_ids) global_step += 1 if global_step % 10 == 0 and rank == 0: print( "global step %d, epoch: %d, batch: %d, loss: %.5f, speed: %.2f step/s" % (global_step, epoch, step, loss, 10 / (time.time() - tic_train))) tic_train = time.time() if global_step % args.eval_steps == 0 and rank == 0: # need better way to get model Layers spearman_corr, total_num = do_evaluate(model._layers, tokenizer, dev_data_loader, args.infer_with_fc_pooler) print("global step: {}, spearman_corr: {:.4f}, total_num: {}".format(global_step, spearman_corr, total_num)) loss.backward() optimizer.step() lr_scheduler.step() optimizer.clear_grad() if global_step % args.save_steps == 0 and rank == 0: 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) if args.max_steps > 0 and global_step >= args.max_steps: return if __name__ == "__main__": do_train()