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train_model_summarization.py
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train_model_summarization.py
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import torch
from torch.utils.data import Dataset, DataLoader
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from transformers import AdamW, get_scheduler
from tqdm.auto import tqdm
from rouge import Rouge
import random
import numpy as np
import os
import json
def seed_everything(seed=1029):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(f'Using {device} device')
seed_everything(5)
max_dataset_size = 200000
max_input_length = 512
max_target_length = 32
batch_size = 32
learning_rate = 1e-5
epoch_num = 3
beam_size = 4
no_repeat_ngram_size = 2
class LCSTS(Dataset):
def __init__(self, data_file):
self.data = self.load_data(data_file)
def load_data(self, data_file):
Data = {}
with open(data_file, 'rt', encoding='utf-8') as f:
for idx, line in enumerate(f):
if idx >= max_dataset_size:
break
items = line.strip().split('!=!')
assert len(items) == 2
Data[idx] = {
'title': items[0],
'content': items[1]
}
return Data
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
return self.data[idx]
train_data = LCSTS('data/lcsts_tsv/data1.tsv')
valid_data = LCSTS('data/lcsts_tsv/data2.tsv')
test_data = LCSTS('data/lcsts_tsv/data3.tsv')
model_checkpoint = "csebuetnlp/mT5_multilingual_XLSum"
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
model = model.to(device)
def collote_fn(batch_samples):
batch_inputs, batch_targets = [], []
for sample in batch_samples:
batch_inputs.append(sample['content'])
batch_targets.append(sample['title'])
batch_data = tokenizer(
batch_inputs,
padding=True,
max_length=max_input_length,
truncation=True,
return_tensors="pt"
)
with tokenizer.as_target_tokenizer():
labels = tokenizer(
batch_targets,
padding=True,
max_length=max_target_length,
truncation=True,
return_tensors="pt"
)["input_ids"]
batch_data['decoder_input_ids'] = model.prepare_decoder_input_ids_from_labels(labels)
end_token_index = torch.where(labels == tokenizer.eos_token_id)[1]
for idx, end_idx in enumerate(end_token_index):
labels[idx][end_idx+1:] = -100
batch_data['labels'] = labels
return batch_data
train_dataloader = DataLoader(train_data, batch_size=batch_size, shuffle=True, collate_fn=collote_fn)
valid_dataloader = DataLoader(valid_data, batch_size=batch_size, shuffle=False, collate_fn=collote_fn)
test_dataloader = DataLoader(test_data, batch_size=batch_size, shuffle=False, collate_fn=collote_fn)
def train_loop(dataloader, model, optimizer, lr_scheduler, epoch, total_loss):
progress_bar = tqdm(range(len(dataloader)))
progress_bar.set_description(f'loss: {0:>7f}')
finish_batch_num = (epoch-1) * len(dataloader)
model.train()
for batch, batch_data in enumerate(dataloader, start=1):
batch_data = batch_data.to(device)
outputs = model(**batch_data)
loss = outputs.loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
lr_scheduler.step()
total_loss += loss.item()
progress_bar.set_description(f'loss: {total_loss/(finish_batch_num + batch):>7f}')
progress_bar.update(1)
return total_loss
rouge = Rouge()
def test_loop(dataloader, model):
preds, labels = [], []
model.eval()
for batch_data in tqdm(dataloader):
batch_data = batch_data.to(device)
with torch.no_grad():
generated_tokens = model.generate(
batch_data["input_ids"],
attention_mask=batch_data["attention_mask"],
max_length=max_target_length,
num_beams=beam_size,
no_repeat_ngram_size=no_repeat_ngram_size,
).cpu().numpy()
if isinstance(generated_tokens, tuple):
generated_tokens = generated_tokens[0]
label_tokens = batch_data["labels"].cpu().numpy()
decoded_preds = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
label_tokens = np.where(label_tokens != -100, label_tokens, tokenizer.pad_token_id)
decoded_labels = tokenizer.batch_decode(label_tokens, skip_special_tokens=True)
preds += [' '.join(pred.strip()) for pred in decoded_preds]
labels += [' '.join(label.strip()) for label in decoded_labels]
scores = rouge.get_scores(hyps=preds, refs=labels, avg=True)
result = {key: value['f'] * 100 for key, value in scores.items()}
result['avg'] = np.mean(list(result.values()))
print(f"Rouge1: {result['rouge-1']:>0.2f} Rouge2: {result['rouge-2']:>0.2f} RougeL: {result['rouge-l']:>0.2f}\n")
return result
optimizer = AdamW(model.parameters(), lr=learning_rate)
lr_scheduler = get_scheduler(
"linear",
optimizer=optimizer,
num_warmup_steps=0,
num_training_steps=epoch_num*len(train_dataloader),
)
total_loss = 0.
best_avg_rouge = 0.
for t in range(epoch_num):
print(f"Epoch {t+1}/{epoch_num}\n-------------------------------")
total_loss = train_loop(train_dataloader, model, optimizer, lr_scheduler, t+1, total_loss)
valid_rouge = test_loop(valid_dataloader, model)
rouge_avg = valid_rouge['avg']
if rouge_avg > best_avg_rouge:
best_avg_rouge = rouge_avg
print('saving new weights...\n')
torch.save(model.state_dict(), f'epoch_{t+1}_valid_rouge_{rouge_avg:0.4f}_model_weights.bin')
print("Done!")
# model.load_state_dict(torch.load('epoch_1_valid_rouge_6.6667_model_weights.bin'))
# model.eval()
# with torch.no_grad():
# print('evaluating on test set...')
# sources, preds, labels = [], [], []
# for batch_data in tqdm(test_dataloader):
# batch_data = batch_data.to(device)
# generated_tokens = model.generate(
# batch_data["input_ids"],
# attention_mask=batch_data["attention_mask"],
# max_length=max_target_length,
# num_beams=beam_size,
# no_repeat_ngram_size=no_repeat_ngram_size,
# ).cpu().numpy()
# if isinstance(generated_tokens, tuple):
# generated_tokens = generated_tokens[0]
# label_tokens = batch_data["labels"].cpu().numpy()
# decoded_sources = tokenizer.batch_decode(
# batch_data["input_ids"].cpu().numpy(),
# skip_special_tokens=True,
# use_source_tokenizer=True
# )
# decoded_preds = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
# label_tokens = np.where(label_tokens != -100, label_tokens, tokenizer.pad_token_id)
# decoded_labels = tokenizer.batch_decode(label_tokens, skip_special_tokens=True)
# sources += [source.strip() for source in decoded_sources]
# preds += [pred.strip() for pred in decoded_preds]
# labels += [label.strip() for label in decoded_labels]
# scores = rouge.get_scores(
# hyps=[' '.join(pred) for pred in preds],
# refs=[' '.join(label) for label in labels],
# avg=True
# )
# rouges = {key: value['f'] * 100 for key, value in scores.items()}
# rouges['avg'] = np.mean(list(rouges.values()))
# print(f"Test Rouge1: {rouges['rouge-1']:>0.2f} Rouge2: {rouges['rouge-2']:>0.2f} RougeL: {rouges['rouge-l']:>0.2f}\n")
# results = []
# print('saving predicted results...')
# for source, pred, label in zip(sources, preds, labels):
# results.append({
# "document": source,
# "prediction": pred,
# "summarization": label
# })
# with open('test_data_pred.json', 'wt', encoding='utf-8') as f:
# for exapmle_result in results:
# f.write(json.dumps(exapmle_result, ensure_ascii=False) + '\n')