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dataset.py
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dataset.py
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import random
import torch
from torch.utils.data import Dataset
game_splits = {'early': 0,
'mid': 16,
'late': 32}
class Finetune_Early(Dataset):
def __init__(self, data, block_size, pretrain_vocab):
assert pretrain_vocab, "Must have pretrain vocab for finetuning"
self.block_size = block_size
self.PAD_CHAR = u"\u25A1"
self.MASK_CHAR_1 = u"\u2047"
self.MASK_CHAR_2 = u"\u2047"
chars = list(sorted(list(set(data))))
if '\n' in chars:
chars.remove('\n')
# Check and insert pad and mask chars
if self.PAD_CHAR in chars:
chars.remove(self.PAD_CHAR)
chars.insert(0, self.PAD_CHAR)
if self.MASK_CHAR_1 in chars:
chars.remove(self.MASK_CHAR_1)
chars.insert(0, self.MASK_CHAR_1)
if self.MASK_CHAR_2 in chars:
chars.remove(self.MASK_CHAR_2)
chars.insert(0, self.MASK_CHAR_2)
self.stoi = pretrain_vocab['stoi']
self.itos = pretrain_vocab['itos']
assert len(self.stoi) == len(self.itos)
self.vocab_size = len(self.stoi)
self.data_size = len(data)
print('Data has %d characters, %d unique.' % (self.data_size, self.vocab_size))
self.data = list(data.encode('utf-8').decode('ascii', errors='ignore').split('\n'))[:-1]
# ensure all game strings are long enough for process
self.data = list(filter(lambda x: len(x.split(' ')) - 1 >= game_splits['early'], self.data))
self.min_pt = 0
self.max_pt = game_splits['mid']
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
game = self.data[idx]
spaces = [idx for idx, cur in enumerate(game) if cur == ' ']
n_spaces = len(spaces)
index = random.randint(self.min_pt, self.max_pt - 1)
m_start, m_stop = spaces[index] + 1, spaces[index + 1]
x = game[:m_start] + self.MASK_CHAR_1 + game[m_start:m_stop + 1] + self.MASK_CHAR_1
x = x + self.PAD_CHAR * (self.block_size - len(x))
y = self.PAD_CHAR * m_start + self.MASK_CHAR_1 + game[m_start:m_stop + 1] + self.MASK_CHAR_1
y = y + self.PAD_CHAR * (self.block_size - len(y))
assert len(x) == len(y) == self.block_size
x = x[:-1]
y = y[1:]
x = torch.tensor([self.stoi[c] for c in x], dtype=torch.long)
y = torch.tensor([self.stoi[c] for c in y], dtype=torch.long)
return x, y
class Finetune_Middle(Dataset):
def __init__(self, data, block_size, pretrain_vocab):
assert pretrain_vocab, "Must have pretrain vocab for finetuning"
self.block_size = block_size
self.PAD_CHAR = u"\u25A1"
self.MASK_CHAR_1 = u"\u2047"
self.MASK_CHAR_2 = u"\u2047"
chars = list(sorted(list(set(data))))
if '\n' in chars:
chars.remove('\n')
# Check and insert pad and mask chars
if self.PAD_CHAR in chars:
chars.remove(self.PAD_CHAR)
chars.insert(0, self.PAD_CHAR)
if self.MASK_CHAR_1 in chars:
chars.remove(self.MASK_CHAR_1)
chars.insert(0, self.MASK_CHAR_1)
if self.MASK_CHAR_2 in chars:
chars.remove(self.MASK_CHAR_2)
chars.insert(0, self.MASK_CHAR_2)
self.stoi = pretrain_vocab['stoi']
self.itos = pretrain_vocab['itos']
assert len(self.stoi) == len(self.itos)
breakpoint()
self.vocab_size = len(self.stoi)
self.data_size = len(data)
print('Data has %d characters, %d unique.' % (self.data_size, self.vocab_size))
self.data = list(data.encode('utf-8').decode('ascii', errors='ignore').split('\n'))[:-1]
self.data = list(filter(lambda x: len(x.split(' ')) - 1 >= game_splits['mid'], self.data))
self.min_pt = game_splits['mid']
self.max_pt = game_splits['late']
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
game = self.data[idx]
spaces = [idx for idx, cur in enumerate(game) if cur == ' ']
n_spaces = len(spaces)
index = random.randint(self.min_pt, self.max_pt)
m_start, m_stop = spaces[index] + 1, spaces[index + 1]
x = game[:m_start] + self.MASK_CHAR_1 + game[m_start:m_stop + 1] + self.MASK_CHAR_1
x = x + self.PAD_CHAR * (self.block_size - len(x))
y = self.PAD_CHAR * m_start + self.MASK_CHAR_1 + game[m_start:m_stop + 1] + self.MASK_CHAR_1
y = y + self.PAD_CHAR * (self.block_size - len(y))
assert len(x) == len(y) == self.block_size
x = x[:-1]
y = y[1:]
x = torch.tensor([self.stoi[c] for c in x], dtype=torch.long)
y = torch.tensor([self.stoi[c] for c in y], dtype=torch.long)
return x, y
class Finetune_Late(Dataset):
def __init__(self, data, block_size, pretrain_vocab):
assert pretrain_vocab, "Must have pretrain vocab for finetuning"
self.block_size = block_size
self.PAD_CHAR = u"\u25A1"
self.MASK_CHAR_1 = u"\u2047"
self.MASK_CHAR_2 = u"\u2047"
chars = list(sorted(list(set(data))))
if '\n' in chars:
chars.remove('\n')
# Check and insert pad and mask chars
if self.PAD_CHAR in chars:
chars.remove(self.PAD_CHAR)
chars.insert(0, self.PAD_CHAR)
if self.MASK_CHAR_1 in chars:
chars.remove(self.MASK_CHAR_1)
chars.insert(0, self.MASK_CHAR_1)
if self.MASK_CHAR_2 in chars:
chars.remove(self.MASK_CHAR_2)
chars.insert(0, self.MASK_CHAR_2)
self.stoi = pretrain_vocab['stoi']
self.itos = pretrain_vocab['itos']
assert len(self.stoi) == len(self.itos)
self.vocab_size = len(self.stoi)
self.data_size = len(data)
print('Data has %d characters, %d unique.' % (self.data_size, self.vocab_size))
self.data = list(data.encode('utf-8').decode('ascii', errors='ignore').split('\n'))[:-1]
self.data = list(filter(lambda x: len(x.split(' ')) - 1 >= game_splits['late'], self.data))
self.min_pt = game_splits['late']
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
game = self.data[idx]
spaces = [idx for idx, cur in enumerate(game) if cur == ' ']
n_spaces = len(spaces)
max_idx = n_spaces - 1
index = random.randint(self.min_pt, max_idx)
m_start, m_stop = spaces[index] + 1, spaces[index + 1]
x = game[:m_start] + self.MASK_CHAR_1 + game[m_start:m_stop + 1] + self.MASK_CHAR_1
x = x + self.PAD_CHAR * (self.block_size - len(x))
y = self.PAD_CHAR * m_start + self.MASK_CHAR_1 + game[m_start:m_stop + 1] + self.MASK_CHAR_1
y = y + self.PAD_CHAR * (self.block_size - len(y))
assert len(x) == len(y) == self.block_size
x = x[:-1]
y = y[1:]
x = torch.tensor([self.stoi[c] for c in x], dtype=torch.long)
y = torch.tensor([self.stoi[c] for c in y], dtype=torch.long)
return x, y
class Directory:
def __init__(self, data, version, config_args, pretrain_vocab=None):
self.data = data
self.version = version
self.config_args = config_args
self.pretrain_vocab = pretrain_vocab
self.direct = finetune_versions
self.direct.update({None: PretrainDataset})
def __call__(self):
return self.direct[self.version](self.data, self.config_args['block_size'], self.pretrain_vocab)
class PretrainDataset(Dataset):
def __init__(self, data,
block_size=1024,
pretrain_vocab=None):
self.block_size = block_size
self.PAD_CHAR = u"\u25A1"
self.MASK_CHAR_1 = u"\u2047"
self.MASK_CHAR_2 = u"\u2047"
chars = list(sorted(list(set(data))))
if '\n' in chars:
chars.remove('\n')
if pretrain_vocab is None:
self.stoi = {ch: i for i, ch in enumerate(chars)}
self.itos = {i: ch for i, ch in enumerate(chars)}
else:
self.stoi = pretrain_vocab['stoi']
self.itos = pretrain_vocab['itos']
assert len(self.stoi) == len(self.itos)
self.vocab_size = len(self.stoi)
self.data_size = len(data)
# Check and insert pad and mask chars
if self.PAD_CHAR in chars:
chars.remove(self.PAD_CHAR)
chars.insert(0, self.PAD_CHAR)
if self.MASK_CHAR_1 in chars:
chars.remove(self.MASK_CHAR_1)
chars.insert(0, self.MASK_CHAR_1)
if self.MASK_CHAR_2 in chars:
chars.remove(self.MASK_CHAR_2)
chars.insert(0, self.MASK_CHAR_2)
self.stoi = {ch: i for i, ch in enumerate(chars)}
self.itos = {i: ch for i, ch in enumerate(chars)}
data_size, vocab_size = len(data), len(chars)
print('Data has %d characters, %d unique.' % (data_size, vocab_size))
self.data = list(data.encode('utf-8').decode('ascii', errors='ignore').split('\n'))
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
game = self.data[idx]
game += self.PAD_CHAR * (self.block_size - len(game))
x = game[:-1]
y = game[1:]
x = torch.tensor([self.stoi[c] for c in x], dtype=torch.long)
y = torch.tensor([self.stoi[c] for c in y], dtype=torch.long)
return x, y
finetune_versions = {0: Finetune_Early,
1: Finetune_Middle,
2: Finetune_Late,}