File size: 4,335 Bytes
34e468d | 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 | import os
import pickle
import numpy as np
import re
import argparse
parser = argparse.ArgumentParser(description='Create the dataset based on the given parameters.')
parser.add_argument('--num_nodes', type=int, default=100, help='Number of nodes in the graph')
parser.add_argument('--num_of_paths', type=int, default=20, help='Number of paths per pair nodes in training dataset')
args = parser.parse_args()
num_nodes = args.num_nodes
if(args.num_of_paths == 0):
train_file_path = os.path.join(os.path.dirname(__file__), f'{args.num_nodes}/train.txt')
val_file_path = os.path.join(os.path.dirname(__file__), f'{args.num_nodes}/test.txt')
else:
train_file_path = os.path.join(os.path.dirname(__file__), f'{args.num_nodes}/train_{args.num_of_paths}.txt')
val_file_path = os.path.join(os.path.dirname(__file__), f'{args.num_nodes}/test.txt')
# test_file_path = os.path.join(os.path.dirname(__file__), 'test.txt')
with open(train_file_path, 'r') as f:
train_data = f.read()
print(f"length of train dataset in characters: {len(train_data):,}")
with open(val_file_path, 'r') as f:
val_data = f.read()
print(f"length of val dataset in characters: {len(val_data):,}")
all_data = train_data + val_data
def find_characters(data_string):
pattern = r'\d+|\D'
matches = re.findall(pattern, data_string)
return set(matches)
def process_reasoning(s):
split_text = s.split('\n')
#split_text = [s + '\n' for s in split_text if s != ""]
ret = []
for st in split_text:
if(st != ""):
enc_str = encode(st) + [1]
ret += enc_str +[0] * (block_size + 1 - len(enc_str))
return ret
def get_block_size(s):
split_text = s.split('\n')
#split_text = [s + '\n' for s in split_text if s != ""]
ret = []
bs = 0
for st in split_text:
if(st != ""):
enc_str = encode(st) + [1]
bs = max(bs, len(enc_str))
return bs
def encode_string(s, stonum):
ss = s.split(" ")
encoded_string = [stonum[ch] for ch in ss]
return encoded_string
def decode_string(l, numtos):
dec = ""
for i in l:
dec = dec + numtos[i] + " "
return dec[:-1]
# get all the unique characters that occur in this text
chars = sorted(list(find_characters(all_data)))
vocab_size = num_nodes+2
print("all the unique characters:", ' '.join(chars))
print(f"vocab size: {vocab_size:,}")
# create a mapping from characters to integers
stoi = {}
itos = {}
for i in range(num_nodes):
stoi[str(i)] = i+2
itos[i+2] = str(i)
stoi['[PAD]'] = 0
itos[0] = '[PAD]'
stoi['\n'] = 1
itos[1] = '\n'
def encode(s):
return encode_string(s, stoi) # encoder: take a string, output a list of integers
def decode(l):
return decode_string(l, itos) # decoder: take a list of integers, output a string
# encode both to integers
block_size = (max(get_block_size(train_data), get_block_size(val_data)) // 32 + 1) * 32
print(f"the block size is {block_size}")
train_ids = process_reasoning(train_data)
val_ids = process_reasoning(val_data)
print(f"train has {len(train_ids):,} tokens")
print(f"val has {len(val_ids):,} tokens")
# export to bin files
train_ids = np.array(train_ids, dtype=np.uint16)
val_ids = np.array(val_ids, dtype=np.uint16)
if(args.num_of_paths == 0):
train_ids.tofile(os.path.join(os.path.dirname(__file__), f'{args.num_nodes}/train.bin'))
val_ids.tofile(os.path.join(os.path.dirname(__file__), f'{args.num_nodes}/val.bin'))
else:
train_ids.tofile(os.path.join(os.path.dirname(__file__), f'{args.num_nodes}/train_{args.num_of_paths}.bin'))
val_ids.tofile(os.path.join(os.path.dirname(__file__), f'{args.num_nodes}/val.bin'))
unreachable = False; simple_format = True
if 'x' in chars:
unreachable = True
if ':' in chars:
simple_format = False
# save the meta information as well, to help us encode/decode later
meta = {
'unreachable': unreachable,
'simple_format': simple_format,
'block_size': block_size,
'vocab_size': vocab_size,
'itos': itos,
'stoi': stoi,
}
print(stoi)
print(itos)
with open(os.path.join(os.path.dirname(__file__), f'{args.num_nodes}/meta.pkl'), 'wb') as f:
pickle.dump(meta, f) |