File size: 3,075 Bytes
778d47d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | # saves the SQL corpus to several binary files for pre-training CodeGen. following was helpful:
# https://github.com/karpathy/nanoGPT/blob/master/data/openwebtext/prepare.py
import numpy as np
import torch
import random
from torch.utils.data import IterableDataset, Dataset, DataLoader
# class PretrainDataset(IterableDataset):
# def __init__(self, pt_data_dir, block_size, epochs):
# super().__init__()
# self.corpus = np.memmap(pt_data_dir, dtype = np.uint16, mode = 'r')
# self.block_size = block_size
# self.epochs = epochs
# self.length = len(self.corpus) // self.block_size
# # return a tokenized sequence
# def __iter__(self):
# for _ in range(self.epochs):
# start_idx_list = list(range(0, len(self.corpus), self.block_size))
# # for each epoch, shuffle the order of sequences
# random.shuffle(start_idx_list)
# for start_idx in start_idx_list:
# input_ids = self.corpus[start_idx: start_idx + self.block_size]
# # skip the sequence whose length is not equal to `block_size`
# if len(input_ids) != self.block_size:
# continue
# input_ids = torch.from_numpy(input_ids.astype(np.int64))
# attention_mask = torch.ones(len(input_ids))
# yield {"input_ids": input_ids, "attention_mask": attention_mask, "labels": input_ids}
# # # return a tokenized sequence
# # def __iter__(self):
# # for _ in range(self.dataset_length):
# # # randomly select a sequence of token ids from the tokenized corpus
# # idx = random.randint(0, len(self.corpus) - self.block_size)
# # input_ids = self.corpus[idx: idx + self.block_size]
# # input_ids = torch.from_numpy(input_ids.astype(np.int64))
# # attention_mask = torch.ones(len(input_ids))
# # yield {"input_ids": input_ids, "attention_mask": attention_mask, "labels": input_ids}
# def __len__(self):
# return self.length
class PretrainDataset(Dataset):
def __init__(self, pt_data_dir, block_size):
super().__init__()
self.corpus = np.memmap(pt_data_dir, dtype = np.uint16, mode = 'r')
self.block_size = block_size
self.length = len(self.corpus) // self.block_size
# return a list of token ids in the corpus
def __getitem__(self, index):
input_ids = self.corpus[index * self.block_size : (index + 1) * self.block_size]
input_ids = torch.from_numpy(input_ids.astype(np.int64))
attention_mask = torch.ones(len(input_ids))
return {"input_ids": input_ids, "attention_mask": attention_mask, "labels": input_ids}
def __len__(self):
return self.length
if __name__ == "__main__":
dataset = PretrainDataset("./data/pt_corpus/starcoder_corpus.bin", 6144)
dataloader = DataLoader(dataset, batch_size = 4, shuffle = False, drop_last = True)
for batch in dataloader:
print("-"*20)
print(len(dataset)) |