mirror of https://github.com/hpcaitech/ColossalAI
44 lines
1.6 KiB
Python
44 lines
1.6 KiB
Python
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import json
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import os
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from typing import Optional
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import torch
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from torch.utils.data import Dataset
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from transformers import GPT2Tokenizer
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from colossalai.registry import DATASETS
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@DATASETS.register_module
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class WebtextDataset(Dataset):
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def __init__(self, path: Optional[str] = None, seq_len=1024) -> None:
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super().__init__()
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if path is not None:
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root = os.path.dirname(path)
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encoded_data_cache_path = os.path.join(root, f'gpt_webtext_{seq_len}.pt')
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if os.path.isfile(encoded_data_cache_path):
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seq_len_, data, attention_mask = torch.load(encoded_data_cache_path)
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if seq_len_ == seq_len:
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self.data = data
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self.attention_mask = attention_mask
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return
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raw_data = []
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with open(path) as f:
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for line in f.readlines():
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raw_data.append(json.loads(line)['text'])
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tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
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tokenizer.pad_token = tokenizer.unk_token
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encoded_data = tokenizer(raw_data, padding=True, truncation=True, max_length=seq_len, return_tensors='pt')
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self.data = encoded_data['input_ids']
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self.attention_mask = encoded_data['attention_mask']
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else:
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self.data = torch.randint(0, 50257, (10240, seq_len))
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self.attention_mask = torch.ones_like(self.data)
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def __len__(self):
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return len(self.data)
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def __getitem__(self, index):
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return {'input_ids': self.data[index], 'attention_mask': self.attention_mask[index]}, self.data[index]
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