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- # Copyright (c) 2022 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 time
- from functools import partial
-
- import paddle
- from evaluate import evaluate
- from utils import convert_example, create_data_loader, reader, set_seed
-
- from paddlenlp.datasets import load_dataset
- from paddlenlp.metrics import SpanEvaluator
- from paddlenlp.transformers import UIE, AutoTokenizer
- from paddlenlp.utils.log import logger
-
-
- 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)
-
- tokenizer = AutoTokenizer.from_pretrained(args.model)
- model = UIE.from_pretrained(args.model)
-
- train_ds = load_dataset(reader, data_path=args.train_path, max_seq_len=args.max_seq_len, lazy=False)
- dev_ds = load_dataset(reader, data_path=args.dev_path, max_seq_len=args.max_seq_len, lazy=False)
-
- trans_fn = partial(convert_example, tokenizer=tokenizer, max_seq_len=args.max_seq_len)
-
- train_data_loader = create_data_loader(train_ds, mode="train", batch_size=args.batch_size, trans_fn=trans_fn)
- dev_data_loader = create_data_loader(dev_ds, mode="dev", batch_size=args.batch_size, trans_fn=trans_fn)
-
- if args.init_from_ckpt and os.path.isfile(args.init_from_ckpt):
- logger.info("load model from path: {}".format(args.init_from_ckpt))
- state_dict = paddle.load(args.init_from_ckpt)
- model.set_dict(state_dict)
-
- if paddle.distributed.get_world_size() > 1:
- model = paddle.DataParallel(model)
-
- optimizer = paddle.optimizer.AdamW(learning_rate=args.learning_rate, parameters=model.parameters())
-
- criterion = paddle.nn.BCELoss()
- metric = SpanEvaluator()
-
- loss_list = []
- global_step = 0
- best_f1 = 0
- tic_train = time.time()
- for epoch in range(1, args.num_epochs + 1):
- for batch in train_data_loader:
- input_ids, token_type_ids, att_mask, pos_ids, start_ids, end_ids = batch
- start_prob, end_prob = model(input_ids, token_type_ids, att_mask, pos_ids)
- start_ids = paddle.cast(start_ids, "float32")
- end_ids = paddle.cast(end_ids, "float32")
- loss_start = criterion(start_prob, start_ids)
- loss_end = criterion(end_prob, end_ids)
- loss = (loss_start + loss_end) / 2.0
- loss.backward()
- optimizer.step()
- optimizer.clear_grad()
- loss_list.append(float(loss))
-
- global_step += 1
- if global_step % args.logging_steps == 0 and rank == 0:
- time_diff = time.time() - tic_train
- loss_avg = sum(loss_list) / len(loss_list)
- logger.info(
- "global step %d, epoch: %d, loss: %.5f, speed: %.2f step/s"
- % (global_step, epoch, loss_avg, args.logging_steps / time_diff)
- )
- tic_train = time.time()
-
- if global_step % args.valid_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)
- model_to_save = model._layers if isinstance(model, paddle.DataParallel) else model
- model_to_save.save_pretrained(save_dir)
- logger.disable()
- tokenizer.save_pretrained(save_dir)
- logger.enable()
-
- precision, recall, f1 = evaluate(model, metric, dev_data_loader)
- logger.info("Evaluation precision: %.5f, recall: %.5f, F1: %.5f" % (precision, recall, f1))
- if f1 > best_f1:
- logger.info(f"best F1 performence has been updated: {best_f1:.5f} --> {f1:.5f}")
- best_f1 = f1
- save_dir = os.path.join(args.save_dir, "model_best")
- model_to_save = model._layers if isinstance(model, paddle.DataParallel) else model
- model_to_save.save_pretrained(save_dir)
- logger.disable()
- tokenizer.save_pretrained(save_dir)
- logger.enable()
- tic_train = time.time()
-
-
- if __name__ == "__main__":
- # yapf: disable
- parser = argparse.ArgumentParser()
-
- parser.add_argument("--batch_size", default=16, type=int, help="Batch size per GPU/CPU for training.")
- parser.add_argument("--learning_rate", default=1e-5, type=float, help="The initial learning rate for Adam.")
- parser.add_argument("--train_path", default=None, type=str, help="The path of train set.")
- parser.add_argument("--dev_path", default=None, type=str, help="The path of dev set.")
- 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_len", default=512, type=int, help="The maximum input sequence length. Sequences longer than this will be split automatically.")
- parser.add_argument("--num_epochs", default=100, type=int, help="Total number of training epochs to perform.")
- parser.add_argument("--seed", default=1000, type=int, help="Random seed for initialization")
- parser.add_argument("--logging_steps", default=10, type=int, help="The interval steps to logging.")
- parser.add_argument("--valid_steps", default=100, type=int, help="The interval steps to evaluate model performance.")
- parser.add_argument('--device', choices=['cpu', 'gpu'], default="gpu", help="Select which device to train model, defaults to gpu.")
- parser.add_argument("--model", choices=["uie-senta-base", "uie-senta-medium", "uie-senta-mini", "uie-senta-micro", "uie-senta-nano"], default="uie-senta-base", type=str, help="Select the pretrained model for few-shot learning.")
- parser.add_argument("--init_from_ckpt", default=None, type=str, help="The path of model parameters for initialization.")
-
- args = parser.parse_args()
- # yapf: enable
-
- do_train()
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