-
Notifications
You must be signed in to change notification settings - Fork 12
/
datautils.py
178 lines (153 loc) · 6.29 KB
/
datautils.py
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
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
import numpy as np
import torch
def set_seed(seed):
np.random.seed(seed)
torch.random.manual_seed(seed)
def get_wikitext2(nsamples, seed, seqlen, model, tokenizer=None):
from datasets import load_dataset
traindata = load_dataset('wikitext', 'wikitext-2-raw-v1', split='train')
testdata = load_dataset('wikitext', 'wikitext-2-raw-v1', split='test')
if tokenizer is None:
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(model, use_fast=False)
trainenc = tokenizer("\n\n".join(traindata['text']), return_tensors='pt')
testenc = tokenizer("\n\n".join(testdata['text']), return_tensors='pt')
import random
random.seed(seed)
trainloader = []
for _ in range(nsamples):
i = random.randint(0, trainenc.input_ids.shape[1] - seqlen - 1)
j = i + seqlen
inp = trainenc.input_ids[:, i:j]
tar = inp.clone()
tar[:, :-1] = -100
trainloader.append((inp, tar))
return trainloader, testenc
def get_ptb(nsamples, seed, seqlen, model, tokenizer=None):
from datasets import load_dataset
traindata = load_dataset('ptb_text_only', 'penn_treebank', split='train')
valdata = load_dataset('ptb_text_only', 'penn_treebank', split='validation')
if tokenizer is None:
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(model, use_fast=False)
trainenc = tokenizer("\n\n".join(traindata['sentence']), return_tensors='pt')
testenc = tokenizer("\n\n".join(valdata['sentence']), return_tensors='pt')
import random
random.seed(seed)
trainloader = []
for _ in range(nsamples):
i = random.randint(0, trainenc.input_ids.shape[1] - seqlen - 1)
j = i + seqlen
inp = trainenc.input_ids[:, i:j]
tar = inp.clone()
tar[:, :-1] = -100
trainloader.append((inp, tar))
return trainloader, testenc
def get_c4(nsamples, seed, seqlen, model, tokenizer=None):
from datasets import load_dataset
traindata = load_dataset(
'allenai/c4', 'allenai--c4', data_files={'train': 'en/c4-train.00000-of-01024.json.gz'}, split='train'
)
valdata = load_dataset(
'allenai/c4', 'allenai--c4', data_files={'validation': 'en/c4-validation.00000-of-00008.json.gz'}, split='validation'
)
if tokenizer is None:
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(model, use_fast=False)
import random
random.seed(seed)
trainloader = []
for _ in range(nsamples):
while True:
i = random.randint(0, len(traindata) - 1)
trainenc = tokenizer(traindata[i]['text'], return_tensors='pt')
if trainenc.input_ids.shape[1] >= seqlen:
break
i = random.randint(0, trainenc.input_ids.shape[1] - seqlen - 1)
j = i + seqlen
inp = trainenc.input_ids[:, i:j]
tar = inp.clone()
tar[:, :-1] = -100
trainloader.append((inp, tar))
import random
random.seed(0)
valenc = []
for _ in range(256):
while True:
i = random.randint(0, len(valdata) - 1)
tmp = tokenizer(valdata[i]['text'], return_tensors='pt')
if tmp.input_ids.shape[1] >= seqlen:
break
i = random.randint(0, tmp.input_ids.shape[1] - seqlen - 1)
j = i + seqlen
valenc.append(tmp.input_ids[:, i:j])
valenc = torch.hstack(valenc)
class TokenizerWrapper:
def __init__(self, input_ids):
self.input_ids = input_ids
valenc = TokenizerWrapper(valenc)
return trainloader, valenc
def get_ptb_new(nsamples, seed, seqlen, model):
from datasets import load_dataset
traindata = load_dataset('ptb_text_only', 'penn_treebank', split='train')
testdata = load_dataset('ptb_text_only', 'penn_treebank', split='test')
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(model, use_fast=False)
trainenc = tokenizer(" ".join(traindata['sentence']), return_tensors='pt')
testenc = tokenizer(" ".join(testdata['sentence']), return_tensors='pt')
import random
random.seed(seed)
trainloader = []
for _ in range(nsamples):
i = random.randint(0, trainenc.input_ids.shape[1] - seqlen - 1)
j = i + seqlen
inp = trainenc.input_ids[:, i:j]
tar = inp.clone()
tar[:, :-1] = -100
trainloader.append((inp, tar))
return trainloader, testenc
def get_c4_new(nsamples, seed, seqlen, model):
from datasets import load_dataset
traindata = load_dataset(
'allenai/c4', 'allenai--c4', data_files={'train': 'en/c4-train.00000-of-01024.json.gz'}, split='train'
)
valdata = load_dataset(
'allenai/c4', 'allenai--c4', data_files={'validation': 'en/c4-validation.00000-of-00008.json.gz'}, split='validation'
)
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(model, use_fast=False)
import random
random.seed(seed)
trainloader = []
for _ in range(nsamples):
while True:
i = random.randint(0, len(traindata) - 1)
trainenc = tokenizer(traindata[i]['text'], return_tensors='pt')
if trainenc.input_ids.shape[1] >= seqlen:
break
i = random.randint(0, trainenc.input_ids.shape[1] - seqlen - 1)
j = i + seqlen
inp = trainenc.input_ids[:, i:j]
tar = inp.clone()
tar[:, :-1] = -100
trainloader.append((inp, tar))
valenc = tokenizer(' '.join(valdata[:1100]['text']), return_tensors='pt')
valenc = valenc.input_ids[:, :(256 * seqlen)]
class TokenizerWrapper:
def __init__(self, input_ids):
self.input_ids = input_ids
valenc = TokenizerWrapper(valenc)
return trainloader, valenc
def get_loaders(
name, nsamples=128, seed=0, seqlen=2048, model='', tokenizer=None
):
if 'wikitext2' in name:
return get_wikitext2(nsamples, seed, seqlen, model, tokenizer)
if 'ptb' in name:
if 'new' in name:
return get_ptb_new(nsamples, seed, seqlen, model, tokenizer)
return get_ptb(nsamples, seed, seqlen, model)
if 'c4' in name:
if 'new' in name:
return get_c4_new(nsamples, seed, seqlen, model, tokenizer)
return get_c4(nsamples, seed, seqlen, model)