昇思25天学习打卡营第23天|基于MindSpore的GPT2文本摘要

这节课主要学习基于MindSpore的GPT2文本摘要。主要包括环境安装、数据集加载与处理、模型构建、模型训练、模型推理五部分内容。

1.首先介绍环境安装

%%capture captured_output
# 实验环境已经预装了mindspore==2.2.14，如需更换mindspore版本，可更改下面mindspore的版本号
!pip uninstall mindspore -y
!pip install -i https://pypi.mirrors.ustc.edu.cn/simple mindspore==2.2.14

!pip install tokenizers==0.15.0 -i https://pypi.tuna.tsinghua.edu.cn/simple
# 该案例在 mindnlp 0.3.1 版本完成适配，如果发现案例跑不通，可以指定mindnlp版本，执行`!pip install mindnlp==0.3.1`
!pip install mindnlp

搭建mindspore深度学习环境。

2.数据集加载与处理
2.1 数据集加载
实验使用的是nlpcc2017摘要数据，内容为新闻正文及其摘要，总计50000个样本。

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)

2.2 数据预处理

原始数据格式：
article: [CLS] article_context [SEP]
summary: [CLS] summary_context [SEP]
预处理后的数据格式：
[CLS] article_context [SEP] summary_context [SEP]

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 articletokenized = 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 trainingdataset = dataset.map(merge_and_pad, ['article', 'summary'], ['input_ids', 'labels'])dataset = dataset.batch(batch_size)if shuffle:dataset = dataset.shuffle(batch_size)return dataset

因GPT2无中文的tokenizer，我们使用BertTokenizer替代。

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

train_dataset = process_dataset(train_dataset, tokenizer, batch_size=4)

next(train_dataset.create_tuple_iterator())

3.模型构建
3.1构建GPT2ForSummarization模型，注意shift right的操作。

from mindspore import ops
from mindnlp.transformers import GPT2LMHeadModelclass 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 tokensloss = ops.cross_entropy(shift_logits.view(-1, shift_logits.shape[-1]), shift_labels.view(-1), ignore_index=tokenizer.pad_token_id)return loss

3.2.动态学习率

from mindspore import ops
from mindspore.nn.learning_rate_schedule import LearningRateScheduleclass LinearWithWarmUp(LearningRateSchedule):"""Warmup-decay learning rate."""def __init__(self, learning_rate, num_warmup_steps, num_training_steps):super().__init__()self.learning_rate = learning_rateself.num_warmup_steps = num_warmup_stepsself.num_training_steps = num_training_stepsdef construct(self, global_step):if global_step < self.num_warmup_steps:return global_step / float(max(1, self.num_warmup_steps)) * self.learning_ratereturn ops.maximum(0.0, (self.num_training_steps - global_step) / (max(1, self.num_training_steps - self.num_warmup_steps))) * self.learning_rate

4.模型训练

num_epochs = 1
warmup_steps = 2000
learning_rate = 1.5e-4num_training_steps = num_epochs * train_dataset.get_dataset_size()

from mindspore import nn
from mindnlp.transformers import GPT2Config, GPT2LMHeadModelconfig = 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
5.、模型推理

数据处理，将向量数据变为中文数据

def process_test_dataset(dataset, tokenizer, batch_size=1, max_seq_len=1024, max_summary_len=100):def read_map(text):data = json.loads(text.tobytes())return np.array(data['article']), np.array(data['summarization'])def pad(article):tokenized = tokenizer(text=article, truncation=True, max_length=max_seq_len-max_summary_len)return tokenized['input_ids']dataset = dataset.map(read_map, 'text', ['article', 'summary'])dataset = dataset.map(pad, 'article', ['input_ids'])dataset = dataset.batch(batch_size)return dataset`

test_dataset = process_test_dataset(test_dataset, tokenizer, batch_size=1)

print(next(test_dataset.create_tuple_iterator(output_numpy=True)))

model = GPT2LMHeadModel.from_pretrained('./checkpoint/gpt2_summarization_epoch_0.ckpt', config=config)

model.set_train(False)
model.config.eos_token_id = model.config.sep_token_id
i = 0
for (input_ids, raw_summary) in test_dataset.create_tuple_iterator():output_ids = model.generate(input_ids, max_new_tokens=50, num_beams=5, no_repeat_ngram_size=2)output_text = tokenizer.decode(output_ids[0].tolist())print(output_text)i += 1if i == 1:break