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import torch
from torch.utils.data import Dataset
import string
from generate import generate_seq
from transformers import AutoTokenizer
#################################################################
# Tokenizer
class Tokenizer:
def __init__(self, TO_TOKEN, vocab_tokens, number_tokens):
self.TO_TOKEN = TO_TOKEN
self.TO_STR = {v:k for k, v in TO_TOKEN.items()}
self.vocab = np.array(list(TO_TOKEN.keys()))
self.vocab_tokens = vocab_tokens
self.number_tokens = number_tokens
self.num_vocab = len(self.vocab_tokens)
self.num_numbers = len(self.number_tokens)
self.bos_token = self.TO_TOKEN['<bos>']
self.eos_token = self.TO_TOKEN['<eos>']
self.null = '<null>'
# Human readible printing
vocab_part = {self.TO_TOKEN[k]: v for (k, v) in zip(self.vocab_tokens, string.ascii_lowercase[:self.num_vocab])}
number_part = {self.TO_TOKEN[t]: t[1:] for t in self.number_tokens}
self.TO_STRING = {**vocab_part, **number_part}
self.TO_STRING[self.bos_token] = "$"
self.TO_STRING[self.eos_token] = "."
self.TO_STRING[self.TO_TOKEN[self.null]] = "_"
def __call__(self, x):
encoded = [self.TO_TOKEN[c] for c in x]
return torch.tensor(encoded, dtype=torch.int64)
def decode(self, x):
x = x.detach().cpu().numpy()
decoded = [str(t) if t not in self.TO_STR else self.TO_STR[t] for t in x]
return decoded
def __len__(self):
return len(self.TO_TOKEN)
# def to_string(self, x, pytorch=True):
# if pytorch:
# return "".join([self.TO_STRING[t.item()] for t in x])
# else:
# return "".join([self.TO_STRING[self.TO_TOKEN[t]] for t in x])
def get_tokenizer(args):
if args.model == "pretrained":
tokenizer = AutoTokenizer.from_pretrained(args.pretrained_model)
return tokenizer
# Vocab tokens look like 'V' + number
vocab_tokens = ["V%d" % i for i in range(args.num_vocab)]
# Create number tokens. Variable copy tasks start with 5, not 0
if args.train_task.startswith("var-copy"):
number_tokens = ["#%d" % (5+i) for i in range(args.num_numbers)]
else:
number_tokens = ["#%d" % i for i in range(args.num_numbers)]
vocab = vocab_tokens + number_tokens + ["<bos>", "<eos>", "<null>"]
TO_TOKEN = dict(zip(vocab, range(len(vocab))))
tokenizer = Tokenizer(TO_TOKEN, vocab_tokens, number_tokens)
return tokenizer
#################################################################
# Datasets
class TrainDataset(Dataset):
def __init__(self,
tokenizer,
task="var_copy",
sequence_length=220,
min_subseq_length=20,
max_subseq_length=50,
num_examples=1000,
batch_size=8,
p=0.2,
pack_examples=False,
mixed=False):
self.tokenizer = tokenizer
self.task = task
self.num_vocab = self.tokenizer.num_vocab
self.num_numbers = self.tokenizer.num_numbers
self.sequence_length = sequence_length
self.min_subseq_length = min_subseq_length
self.max_subseq_length = max_subseq_length
self.num_examples = num_examples
self.batch_size = batch_size
self.p = p
self.pack_examples = pack_examples
self.mixed = mixed
def __len__(self):
return self.num_examples
def __getitem__(self, idx):
if idx >= self.num_examples:
raise IndexError("Index out of range in dataset")
batch = {'input': [], 'input_ids': [], 'output': [], 'output_ids': [], 'mask': []}
for _ in range(self.batch_size):
# Fill the context with subsequences of the desired task
prospective_len = 0
input_seq = []
output_seq = []
mask = []
if self.pack_examples:
while prospective_len < self.sequence_length:
# Sample for the task
length = np.random.randint(self.min_subseq_length, self.max_subseq_length+1)
input_sample, output_sample = generate_seq_and_mask(self.tokenizer, length, self.task, self.p, mixed=self.mixed)
input_sample = ["<bos>"] + input_sample + ["<eos>"]
output_sample = ["<bos>"] + output_sample + ["<eos>"]
mask_sample = [0 if i in ["<bos>", "<eos>", "<null>"] else 1 for i in output_seq]
# Add the sample to the context
if prospective_len + len(input_sample) <= self.sequence_length:
prospective_len += len(input_sample)
input_seq += input_sample
output_seq += output_sample
mask += mask_sample
# Not enough room for another sample
else:
remaining_len = self.sequence_length - prospective_len
remaining_mask_len = self.sequence_length - prospective_len
input_seq += input_sample[:remaining_len]
output_seq += output_sample[:remaining_len]
mask += [0] * (remaining_mask_len) # Just mask it
break
else:
input_seq, output_seq = generate_seq(self.tokenizer, self.sequence_length, self.task, self.p, mixed=self.mixed)
mask = [0 if i in ["<bos>", "<eos>", "<null>"] else 1 for i in output_seq]
# Add the sequence to the sampled dataset
assert len(input_seq) == len(mask)
input_ids = self.tokenizer(input_seq)
output_ids = self.tokenizer(output_seq)
mask = torch.tensor(mask)
batch['input'].append(input_seq)
batch['input_ids'].append(input_ids)
batch['output'].append(output_seq)
batch['output_ids'].append(output_ids)
batch['mask'].append(mask)
batch['input_ids'] = torch.stack(batch['input_ids'], dim=0)
batch['output_ids'] = torch.stack(batch['output_ids'], dim=0)
batch['mask'] = torch.stack(batch['mask'], dim=0)
return batch
class EvalDataset(Dataset):
def __init__(self,
tokenizer,
train_task="var_copy",
sequence_length=220,
min_subseq_length=20,
max_subseq_length=50,
num_examples=1000,
batch_size=8,
p=0.2,
mixed=False):
self.tokenizer = tokenizer
self.train_task = train_task
self.sequence_length = sequence_length
self.min_subseq_length = min_subseq_length
self.max_subseq_length = max_subseq_length
self.num_examples = num_examples
self.batch_size = batch_size
self.p = p
self.mixed = mixed
def __len__(self):
return self.num_examples
def __getitem__(self, idx):
batch = {'input': [], 'input_ids': [], 'output': [], 'output_ids': [], 'mask': []}
for _ in range(self.batch_size):
# Fill the context with subsequences of the desired task
prospective_len = 0
input_seq = []
output_seq = []
mask = []
# Sample for the task
length = np.random.randint(self.min_subseq_length, self.max_subseq_length+1)
input_seq, output_seq = generate_seq(self.tokenizer, length, self.train_task, self.p, mixed=self.mixed)
mask = [0 if i in ["<bos>", "<eos>", "<null>"] else 1 for i in output_seq]
# DO NOT REPLACE
# Fill the context with null tokens
input_seq += ["<null>"] * (self.sequence_length - len(input_seq))
output_seq += ["<null>"] * (self.sequence_length - len(output_seq))
mask += [0] * (self.sequence_length - len(mask))
# Add the sequence to the sampled dataset
assert len(input_seq) == len(mask)
input_ids = self.tokenizer(input_seq)
output_ids = self.tokenizer(output_seq)
mask = torch.tensor(mask)
batch['input'].append(input_seq)
batch['input_ids'].append(input_ids)
batch['output'].append(output_seq)
batch['output_ids'].append(output_ids)
batch['mask'].append(mask)
batch['input_ids'] = torch.stack(batch['input_ids'], dim=0)
batch['output_ids'] = torch.stack(batch['output_ids'], dim=0)
batch['mask'] = torch.stack(batch['mask'], dim=0)
return batch
#################################################################
# Util functions
def get_train_dataset(args, tokenizer):
train_dataset = TrainDataset(
tokenizer=tokenizer,
task=args.train_task,
sequence_length=args.sequence_length,
min_subseq_length=args.min_train_length,
max_subseq_length=args.max_train_length,
num_examples=args.num_examples,
batch_size=args.train_batch_size,
p=args.p,
pack_examples=args.pack_examples,
mixed=args.mixed
)
return train_dataset
def get_eval_dataset(args, tokenizer, min_length, max_length):
eval_dataset = EvalDataset(
tokenizer=tokenizer,
train_task=args.train_task,
sequence_length=args.sequence_length,
min_subseq_length=min_length,
max_subseq_length=max_length,
num_examples=args.num_eval_examples,
batch_size=args.eval_batch_size,
p=args.eval_p,
mixed=args.mixed
)
return eval_dataset
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