本次打卡基于gpt2的文本摘要

数据加载及预处理

from mindnlp.utils import http_get

# download dataset
url = 'https://download.mindspore.cn/toolkits/mindnlp/dataset/text_generation/nlpcc2017/train_with_summ.txt'
path = http_get(url, './')

from mindspore.dataset import TextFileDataset

# load dataset
dataset = TextFileDataset(str(path), shuffle=False)
dataset.get_dataset_size()

# split into training and testing dataset
train_dataset, test_dataset = dataset.split([0.9, 0.1], randomize=False)

import json
import numpy as np

# preprocess dataset
def process_dataset(dataset, tokenizer, batch_size=6, max_seq_len=1024, shuffle=False):
    def read_map(text):
        data = json.loads(text.tobytes())
        return np.array(data['article']), np.array(data['summarization'])

    def merge_and_pad(article, summary):
        # tokenization
        # pad to max_seq_length, only truncate the article
        tokenized = tokenizer(text=article, text_pair=summary,
                              padding='max_length', truncation='only_first', max_length=max_seq_len)
        return tokenized['input_ids'], tokenized['input_ids']
    
    dataset = dataset.map(read_map, 'text', ['article', 'summary'])
    # change column names to input_ids and labels for the following training
    dataset = dataset.map(merge_and_pad, ['article', 'summary'], ['input_ids', 'labels'])

    dataset = dataset.batch(batch_size)
    if shuffle:
        dataset = dataset.shuffle(batch_size)

    return dataset

from mindnlp.transformers import BertTokenizer

# We use BertTokenizer for tokenizing chinese context.
tokenizer = BertTokenizer.from_pretrained('bert-base-chinese')
len(tokenizer)

 模型构建¶

from mindspore import ops
from mindnlp.transformers import GPT2LMHeadModel

class GPT2ForSummarization(GPT2LMHeadModel):
    def construct(
        self,
        input_ids = None,
        attention_mask = None,
        labels = None,
    ):
        outputs = super().construct(input_ids=input_ids, attention_mask=attention_mask)
        shift_logits = outputs.logits[..., :-1, :]
        shift_labels = labels[..., 1:]
        # Flatten the tokens
        loss = ops.cross_entropy(shift_logits.view(-1, shift_logits.shape[-1]), shift_labels.view(-1), ignore_index=tokenizer.pad_token_id)
        return loss

from mindspore import ops
from mindspore.nn.learning_rate_schedule import LearningRateSchedule

class LinearWithWarmUp(LearningRateSchedule):
    """
    Warmup-decay learning rate.
    """
    def __init__(self, learning_rate, num_warmup_steps, num_training_steps):
        super().__init__()
        self.learning_rate = learning_rate
        self.num_warmup_steps = num_warmup_steps
        self.num_training_steps = num_training_steps

    def construct(self, global_step):
        if global_step < self.num_warmup_steps:
            return global_step / float(max(1, self.num_warmup_steps)) * self.learning_rate
        return ops.maximum(
            0.0, (self.num_training_steps - global_step) / (max(1, self.num_training_steps - self.num_warmup_steps))
        ) * self.learning_rate

num_epochs = 1
warmup_steps = 2000
learning_rate = 1.5e-4

num_training_steps = num_epochs * train_dataset.get_dataset_size()

from mindspore import nn
from mindnlp.transformers import GPT2Config, GPT2LMHeadModel

config = GPT2Config(vocab_size=len(tokenizer))
model = GPT2ForSummarization(config)

lr_scheduler = LinearWithWarmUp(learning_rate=learning_rate, num_warmup_steps=warmup_steps, num_training_steps=num_training_steps)
optimizer = nn.AdamWeightDecay(model.trainable_params(), learning_rate=lr_scheduler)

# 记录模型参数数量
print('number of model parameters: {}'.format(model.num_parameters()))

from mindnlp._legacy.engine import Trainer
from mindnlp._legacy.engine.callbacks import CheckpointCallback

ckpoint_cb = CheckpointCallback(save_path='checkpoint', ckpt_name='gpt2_summarization',
                                epochs=1, keep_checkpoint_max=2)

trainer = Trainer(network=model, train_dataset=train_dataset,
                  epochs=1, optimizer=optimizer, callbacks=ckpoint_cb)
trainer.set_amp(level='O1')  # 开启混合精度

trainer.run(tgt_columns="labels")

 结论

gpt2相较bert等模型，在文本识别、文本摘要、命名体识别中有着优秀的表现，但其模型规模相对较大，训练时间较长，打卡中展示的没有完成训练，这里需要更好的gpu来辅助训练。