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# coding=utf8
from torch.utils.data import Dataset, DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
import json
import torch
import pytorch_lightning as pl
import os
class AbstractCollator:
"""
collector for summary task
"""
def __init__(self, tokenizer, max_enc_length, max_dec_length, prompt):
self.tokenizer = tokenizer
self.max_enc_length = max_enc_length
self.max_dec_length = max_dec_length
self.prompt = prompt
def __call__(self, samples):
labels = []
attn_mask = []
# decoder_attn_mask = []
source_inputs = []
for sample in samples:
encode_dict = self.tokenizer.encode_plus(
self.prompt + sample['text'],
max_length=self.max_enc_length,
padding='max_length',
truncation=True,
return_tensors='pt')
decode_dict = self.tokenizer.encode_plus(
sample['summary'],
max_length=self.max_dec_length,
padding='max_length',
truncation=True,
return_tensors='pt')
source_inputs.append(encode_dict['input_ids'].squeeze())
labels.append(decode_dict['input_ids'].squeeze())
attn_mask.append(encode_dict['attention_mask'].squeeze())
# decoder_attn_mask.append(decode_dict['attention_mask'].squeeze())
# labels = torch.tensor(decode_dict['input'])
source_inputs = torch.stack(source_inputs)
labels = torch.stack(labels)
attn_mask = torch.stack(attn_mask)
# decoder_attn_mask = torch.stack(decoder_attn_mask)
# decode_input_idxs = shift_tokens_right(labels, self.tokenizer.pad_token_id, self.tokenizer.pad_token_id)
end_token_index = torch.where(labels == self.tokenizer.eos_token_id)[1]
for idx, end_idx in enumerate(end_token_index):
labels[idx][end_idx + 1:] = -100
return {
"input_ids": source_inputs,
"attention_mask": attn_mask,
"labels": labels,
"text": [sample['text'] for sample in samples],
"summary": [sample['summary'] for sample in samples]
}
class LCSTSDataset(Dataset):
'''
Dataset Used for LCSTS summary task.
'''
def __init__(self, data_path, args):
super().__init__()
self.tokenizer = AutoTokenizer.from_pretrained(
args.pretrained_model_path, use_fast=False)
self.data = self.load_data(data_path)
self.prompt = args.prompt
self.max_enc_length = args.max_enc_length
self.max_dec_length = args.max_dec_length
def __len__(self):
return len(self.data)
def __getitem__(self, index):
return self.encode(self.data[index])
def load_data(self, data_path):
with open(data_path, "r", encoding='utf8') as f:
lines = f.readlines()
samples = []
for line in tqdm(lines):
obj = json.loads(line)
source = obj['text']
target = obj['summary']
samples.append({
"text": source,
"summary": target
})
return samples
def cal_data(self, data_path):
with open(data_path, "r", encoding='utf8') as f:
lines = f.readlines()
samples = []
enc_sizes = []
dec_sizes = []
for line in tqdm(lines):
obj = json.loads(line.strip())
source = obj['text']
target = obj['summary']
enc_input_ids = self.tokenizer.encode(source)
target = self.tokenizer.encode(target)
enc_sizes.append(len(enc_input_ids))
dec_sizes.append(len(target)-1)
samples.append({
"enc_input_ids": enc_input_ids,
"dec_input_ids": target[:-1],
"label_ids": target[1:]
})
max_enc_len = max(enc_sizes)
max_dec_len = max(dec_sizes)
import numpy as np
# mean of len(enc_input_ids): 74.68041911345998
# mean of len(dec_input_ids): 14.02265483791283
# max of len(enc_input_ids): 132
# max of len(dec_input_ids): 31
print('mean of len(enc_input_ids):', np.mean(enc_sizes),
'mean of len(dec_input_ids):', np.mean(dec_sizes),
'max of len(enc_input_ids):', max_enc_len,
'max of len(dec_input_ids):', max_dec_len)
return samples
def encode(self, item):
encode_dict = self.tokenizer.encode_plus(
self.prompt + item['text'],
max_length=self.max_enc_length,
padding='max_length',
truncation=True,
return_tensors='pt')
decode_dict = self.tokenizer.encode_plus(
item['summary'],
max_length=self.max_dec_length,
padding='max_length',
truncation=True)
target = decode_dict['input_ids']
# print('encode_dict shape:', encode_dict['input_ids'].shape)
labels = torch.tensor(target)
labels[target == self.tokenizer.pad_token_id] = -100
return {
"input_ids": encode_dict['input_ids'].squeeze(),
"attention_mask": encode_dict['attention_mask'].squeeze(),
"labels": labels.squeeze(),
"text": item['text'],
"summary": item['summary']
}
class LCSTSDataModel(pl.LightningDataModule):
@staticmethod
def add_data_specific_args(parent_args):
parser = parent_args.add_argument_group('LCSTSDataModel')
parser.add_argument(
'--data_dir', default='/cognitive_comp/ganruyi/data_datasets_LCSTS_LCSTS/', type=str)
parser.add_argument('--num_workers', default=8, type=int)
parser.add_argument('--train_data', default='train.jsonl', type=str)
parser.add_argument('--valid_data', default='valid.jsonl', type=str)
parser.add_argument('--test_data', default='test_public.jsonl', type=str)
parser.add_argument('--train_batchsize', default=128, type=int)
parser.add_argument('--valid_batchsize', default=128, type=int)
parser.add_argument('--max_enc_length', default=128, type=int)
parser.add_argument('--max_dec_length', default=30, type=int)
parser.add_argument('--prompt', default='summarize:', type=str)
return parent_args
def __init__(self, args):
super().__init__()
self.args = args
self.train_batchsize = args.train_batchsize
self.valid_batchsize = args.valid_batchsize
if not args.do_eval_only:
self.train_data = LCSTSDataset(os.path.join(
args.data_dir, args.train_data), args)
self.valid_data = LCSTSDataset(os.path.join(
args.data_dir, args.valid_data), args)
self.test_data = LCSTSDataset(os.path.join(
args.data_dir, args.test_data), args)
def train_dataloader(self):
return DataLoader(self.train_data,
shuffle=True,
batch_size=self.train_batchsize,
pin_memory=False,
num_workers=self.args.num_workers)
def val_dataloader(self):
return DataLoader(self.valid_data,
shuffle=False,
batch_size=self.valid_batchsize,
pin_memory=False,
num_workers=self.args.num_workers)
def predict_dataloader(self):
return DataLoader(self.test_data,
shuffle=False,
batch_size=self.valid_batchsize,
pin_memory=False,
num_workers=self.args.num_workers)
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