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- # 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.
-
- from functools import partial
- import argparse
- import os
- import random
- import time
-
- import numpy as np
- import paddle
- import paddle.nn.functional as F
- from paddlenlp.data import Stack, Tuple, Pad
- from paddlenlp.datasets import load_dataset
- from paddlenlp.metrics import ChunkEvaluator
- from paddlenlp.transformers import SkepCrfForTokenClassification, SkepModel, SkepTokenizer
-
- # 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("--learning_rate", default=5e-7, 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("--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', 'xpu'], default="gpu", help="Select which device to train model, defaults to gpu.")
- args = parser.parse_args()
- # yapf: enable
-
-
- def set_seed(seed):
- """Sets random seed."""
- random.seed(seed)
- np.random.seed(seed)
- paddle.seed(seed)
-
-
- def convert_example_to_feature(example,
- tokenizer,
- max_seq_len=512,
- no_entity_label="O",
- is_test=False):
- """
- Builds model inputs from a sequence or a pair of sequence for sequence classification tasks
- by concatenating and adding special tokens. And creates a mask from the two sequences passed
- to be used in a sequence-pair classification task.
-
- A skep_ernie_1.0_large_ch/skep_ernie_2.0_large_en sequence has the following format:
- ::
- - single sequence: ``[CLS] X [SEP]``
- - pair of sequences: ``[CLS] A [SEP] B [SEP]``
-
- A skep_ernie_1.0_large_ch/skep_ernie_2.0_large_en sequence pair mask has the following format:
- ::
-
- 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
- | first sequence | second sequence |
-
- If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
-
- Args:
- example(obj:`list[str]`): List of input data, containing text and label if it have label.
- tokenizer(obj:`PretrainedTokenizer`): This tokenizer inherits from :class:`~paddlenlp.transformers.PretrainedTokenizer`
- which contains most of the methods. Users should refer to the superclass for more information regarding methods.
- max_seq_len(obj:`int`): The maximum total input sequence length after tokenization.
- Sequences longer than this will be truncated, sequences shorter will be padded.
- no_entity_label(obj:`str`, defaults to "O"): The label represents that the token isn't an entity.
- is_test(obj:`False`, defaults to `False`): Whether the example contains label or not.
-
- Returns:
- input_ids(obj:`list[int]`): The list of token ids.
- token_type_ids(obj: `list[int]`): List of sequence pair mask.
- label(obj:`list[int]`, optional): The input label if not test data.
- """
- tokens = example['tokens']
- labels = example['labels']
- tokenized_input = tokenizer(
- tokens,
- return_length=True,
- is_split_into_words=True,
- max_seq_len=max_seq_len)
-
- input_ids = np.array(tokenized_input['input_ids'], dtype="int64")
- token_type_ids = np.array(tokenized_input['token_type_ids'], dtype="int64")
- seq_len = np.array(tokenized_input['seq_len'], dtype="int64")
-
- if is_test:
- return input_ids, token_type_ids, seq_len
- else:
- labels = labels[:(max_seq_len - 2)]
- encoded_label = np.array(
- [no_entity_label] + labels + [no_entity_label], dtype="int64")
-
- return input_ids, token_type_ids, seq_len, encoded_label
-
-
- def create_dataloader(dataset,
- mode='train',
- batch_size=1,
- batchify_fn=None,
- trans_fn=None):
- if trans_fn:
- dataset = dataset.map(trans_fn)
-
- shuffle = True if mode == 'train' else False
- if mode == 'train':
- batch_sampler = paddle.io.DistributedBatchSampler(
- dataset, batch_size=batch_size, shuffle=shuffle)
- else:
- batch_sampler = paddle.io.BatchSampler(
- dataset, batch_size=batch_size, shuffle=shuffle)
-
- return paddle.io.DataLoader(
- dataset=dataset,
- batch_sampler=batch_sampler,
- collate_fn=batchify_fn,
- return_list=True)
-
-
- if __name__ == "__main__":
- paddle.set_device(args.device)
- rank = paddle.distributed.get_rank()
- if paddle.distributed.get_world_size() > 1:
- paddle.distributed.init_parallel_env()
-
- train_ds = load_dataset("cote", "dp", splits=['train'])
- # The COTE_DP dataset labels with "BIO" schema.
- label_map = {label: idx for idx, label in enumerate(train_ds.label_list)}
- # `no_entity_label` represents that the token isn't an entity.
- no_entity_label_idx = label_map.get("O", 2)
-
- set_seed(args.seed)
- skep = SkepModel.from_pretrained('skep_ernie_1.0_large_ch')
- model = SkepCrfForTokenClassification(
- skep, num_classes=len(train_ds.label_list))
- tokenizer = SkepTokenizer.from_pretrained('skep_ernie_1.0_large_ch')
-
- trans_func = partial(
- convert_example_to_feature,
- tokenizer=tokenizer,
- max_seq_len=args.max_seq_length,
- no_entity_label=no_entity_label_idx,
- is_test=False)
- batchify_fn = lambda samples, fn=Tuple(
- Pad(axis=0, pad_val=tokenizer.vocab[tokenizer.pad_token]), # input ids
- Pad(axis=0, pad_val=tokenizer.vocab[tokenizer.pad_token]), # token type ids
- Stack(dtype='int64'), # sequence lens
- Pad(axis=0, pad_val=no_entity_label_idx) # 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)
-
- 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
- # 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=args.learning_rate,
- parameters=model.parameters(),
- weight_decay=args.weight_decay,
- apply_decay_param_fun=lambda x: x in decay_params)
- metric = ChunkEvaluator(label_list=train_ds.label_list, suffix=True)
-
- 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):
- input_ids, token_type_ids, seq_lens, labels = batch
- loss = model(
- input_ids, token_type_ids, seq_lens=seq_lens, labels=labels)
- avg_loss = paddle.mean(loss)
- 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, avg_loss,
- 10 / (time.time() - tic_train)))
- tic_train = time.time()
- loss.backward()
- optimizer.step()
- optimizer.clear_grad()
- if global_step % 100 == 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)
- file_name = os.path.join(save_dir, "model_state.pdparam")
- # Need better way to get inner model of DataParallel
- paddle.save(model._layers.state_dict(), file_name)
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