# 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 ance.model import SemanticIndexANCE from data import ( convert_example, create_dataloader, get_latest_ann_data, get_latest_checkpoint, read_text_triplet, ) from paddlenlp.data import Pad, Tuple from paddlenlp.datasets import load_dataset from paddlenlp.transformers import AutoModel, AutoTokenizer, LinearDecayWithWarmup from paddlenlp.utils.log import logger # fmt: off parser = argparse.ArgumentParser() parser.add_argument("--save_dir", default='./checkpoints', type=str, help="The output directory where the model checkpoints will be written.") parser.add_argument("--ann_data_dir", default='./ann_data', type=str, help="The output directory where the ann generated training data will be saved.") 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("--max_training_steps", default=1000000, type=int, help="The maximum total steps for training") parser.add_argument("--batch_size", default=32, type=int, help="Batch size per GPU/CPU for training.") parser.add_argument("--output_emb_size", default=None, type=int, help="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=10, 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'], default="gpu", help="Select which device to train model, defaults to gpu.") parser.add_argument('--save_steps', type=int, default=10000, help="Interval steps to save checkpoint") parser.add_argument("--train_set_file", type=str, required=True, help="The full path of train_set_file") parser.add_argument("--margin", default=0.3, type=float, help="Margin for pair-wise margin_rank_loss") args = parser.parse_args() # fmt: on def set_seed(seed): """sets random seed""" random.seed(seed) np.random.seed(seed) paddle.seed(seed) 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) pretrained_model = AutoModel.from_pretrained("ernie-3.0-medium-zh") latest_checkpoint, latest_global_step = get_latest_checkpoint(args) logger.info("get latest_checkpoint:{}".format(latest_checkpoint)) model = SemanticIndexANCE(pretrained_model, margin=args.margin, output_emb_size=args.output_emb_size) if latest_checkpoint: state_dict = paddle.load(latest_checkpoint) model.set_dict(state_dict) print("warmup from:{}".format(latest_checkpoint)) model = paddle.DataParallel(model) 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_input Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # text_segment Pad(axis=0, pad_val=tokenizer.pad_token_id), # pos_sample_input Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # pos_sample_segment Pad(axis=0, pad_val=tokenizer.pad_token_id), # neg_sample_input Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # neg_sample_segment ): [data for data in fn(samples)] global_step = 0 while global_step < args.max_training_steps: latest_ann_data, latest_ann_data_step = get_latest_ann_data(args.ann_data_dir) if latest_ann_data_step == -1: # No ann_data generated yet latest_ann_data = args.train_set_file logger.info("No ann_data generated yet, Use training_set:{}".format(args.train_set_file)) else: # Using ann_data to training model logger.info("Latest ann_data is ready for training: [{}]".format(latest_ann_data)) train_ds = load_dataset(read_text_triplet, data_path=latest_ann_data, lazy=False) train_data_loader = create_dataloader( train_ds, mode="train", batch_size=args.batch_size, batchify_fn=batchify_fn, trans_fn=trans_func ) 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"])] clip = paddle.nn.ClipGradByGlobalNorm(clip_norm=1.0) 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, grad_clip=clip, ) tic_train = time.time() for epoch in range(1, args.epochs + 1): for step, batch in enumerate(train_data_loader, start=1): ( text_input_ids, text_token_type_ids, pos_sample_input_ids, pos_sample_token_type_ids, neg_sample_input_ids, neg_sample_token_type_ids, ) = batch loss = model( text_input_ids=text_input_ids, pos_sample_input_ids=pos_sample_input_ids, neg_sample_input_ids=neg_sample_input_ids, text_token_type_ids=text_token_type_ids, pos_sample_token_type_ids=pos_sample_token_type_ids, neg_sample_token_type_ids=neg_sample_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, trainning_file: %s" % (global_step, epoch, step, loss, 10 / (time.time() - tic_train), latest_ann_data) ) tic_train = time.time() 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, str(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) # Flag to indicate succeefully save model succeed_flag_file = os.path.join(save_dir, "succeed_flag_file") open(succeed_flag_file, "a").close() if __name__ == "__main__": do_train()