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synthetics.py
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synthetics.py
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'''Synthetic datasets to test in-context learning ability.'''
import os
import torch
from torch.utils.data import TensorDataset, Dataset, DataLoader
from typing import Dict
import numpy as np
from tqdm import tqdm
from collections import Counter
from src.dataloaders.base import SequenceDataset
class Vocab:
"""Custom vocab."""
def __init__(self, vocab_size: int, special_vocabs: Dict):
# Special tokens hold copy_prefix and noop/pad token etc
assert "copy_prefix" in special_vocabs
self.special_vocabs = special_vocabs
vocab = [str(v) for v in list(range(vocab_size))]
self.non_special_vocab = sorted(list(vocab))
self.vocab = sorted(list(set(vocab + list(self.special_vocabs.values()))))
self.v2id = {v:i for i,v in enumerate(self.vocab)}
self.vocab_size = len(vocab)
def get_next_vocab(self, token: str):
"""Gets next token excluding special_vocabs."""
id = (self.get_id(token) + 1) % self.vocab_size
while self.get_vocab(id) in self.special_vocabs:
id = (id + 1) % self.vocab_size
return self.get_vocab(id)
@property
def copy_prefix(self):
return self.special_vocabs["copy_prefix"]
@property
def noop(self):
return self.special_vocabs["noop"]
@property
def special_tokens(self):
return set(self.special_vocabs.values())
def get_id(self, token: str):
return self.v2id[token]
def get_vocab(self, id: int):
return self.vocab[id]
def __len__(self):
return len(self.vocab)
class Tokenizer:
"""Custom Tokenizer for our own vocab."""
def __init__(self, vocab: Vocab):
self.vocab = vocab
def tokenize(self, text: str, return_tensor=False, mask_input=False):
input_ids = [self.vocab.get_id(t) for t in text.split()]
if self.vocab.get_id(self.vocab.copy_prefix) not in input_ids:
raise ValueError("Input text must contain copy_prefix token.")
copy_prefix_pos = input_ids.index(self.vocab.get_id(self.vocab.copy_prefix))
labels = input_ids
if mask_input:
# Mask the input tokens for loss but do not mask the copied token
labels = [-100] * (copy_prefix_pos+1) + labels[copy_prefix_pos+1:]
if return_tensor:
input_ids = torch.LongTensor(input_ids)
labels = torch.LongTensor(labels)
return {
"input_ids": input_ids,
"labels": labels,
}
def decode(self, ids: list):
return " ".join([self.vocab.get_vocab(id) for id in ids])
def generate_start_seq(vocab: Vocab, input_seq_len: int, rng: np.random.Generator):
"""Generate token sequence up to and including the copy_prefix token."""
vocab_seq = rng.choice(
vocab.vocab,
input_seq_len,
replace=True,
# Do not generate any special tokens
p=[1/(len(vocab)-len(vocab.special_tokens)) if p not in vocab.special_tokens else 0 for p in vocab.vocab])
vocab_seq = np.append(vocab_seq, vocab.copy_prefix)
return vocab_seq.tolist()
def generate_induction_head(
vocab: Vocab,
input_seq_len: int,
copy_prefix: str,
induction_len: int,
num_triggers: int,
rng: np.random.Generator,
valid_chars: list = None,
):
"""Generate sequence where the copy prefix is inserted into the input
and then the character after the copy prefix is copied at the end.
"""
if valid_chars is not None:
raise NotImplementedError("Valid chars not implemented for induction heads.")
vocab_seq = generate_start_seq(vocab, input_seq_len, rng)
if rng.uniform() < 0.5:
num_triggers = 1
pos = sorted(rng.integers(
input_seq_len - (1 + induction_len), size=num_triggers
))
pos_filtered = []
for i, p in enumerate(pos):
if i == 0:
pos_filtered.append(p)
elif p - pos_filtered[-1] > induction_len:
pos_filtered.append(p)
to_copy = [
vocab_seq[pos_filtered[0]+1+i]
for i in range(induction_len)
]
for pos in pos_filtered:
vocab_seq[pos] = copy_prefix
for i in range(induction_len):
vocab_seq[pos+1+i] = to_copy[i]
# if valid_chars is not None and to_copy not in valid_chars:
# vocab_seq[pos+1] = rng.choice(valid_chars)
# to_copy = vocab_seq[pos+1]
vocab_seq = vocab_seq + to_copy
return " ".join(vocab_seq)
def generate_assoc_recall(
vocab: Vocab,
input_seq_len: int,
num_keys: int,
rng: np.random.Generator,
allow_dot: bool = True,
valid_chars: list = None,
):
"""Generate sequence where the input has a sequence of key value pairs
and the copy prefix at the end, and then a key value pair is inserted
after the copy prefix."""
non_special_vocab_size = len(vocab.non_special_vocab)
keys = vocab.non_special_vocab[:non_special_vocab_size // 2]
values = vocab.non_special_vocab[non_special_vocab_size // 2:]
keys_multi = [ [key] for key in keys ]
for i in range(num_keys-1):
keys_multi = [ key + [key2] for key in keys_multi for key2 in keys ]
kv_map = {
tuple(k): rng.choice(values) for k in keys_multi
}
key_present = {}
vocab_seq = []
for _ in range(input_seq_len // (num_keys + 1)):
k = tuple(rng.choice(list(kv_map.keys())))
v = kv_map[k]
vocab_seq += list(k) + [v]
key_present[k] = True
# vocab_seq.append(v)
k = tuple(rng.choice(list(kv_map.keys())))
if not allow_dot:
while k not in key_present:
k = tuple(rng.choice(list(key_present.keys())))
to_copy = [vocab.copy_prefix] + list(k) + [ kv_map[k] if k in key_present else vocab.noop ]
vocab_seq = vocab_seq + to_copy
return " ".join(vocab_seq)
class ICLDataModule(SequenceDataset):
_name_ = "icl_synthetics"
def __init__(
self,
num_examples: int,
num_test_examples: int,
vocab_size: int,
input_seq_len: int,
copy_method: str,
number_duplicates_per_epoch: int = 0,
seed: int = 0,
batch_size: int = 32,
split_train_test: bool = False,
induction_len: int = 1,
induction_num_triggers: int = 1,
allow_dot: bool = False,
max_copy_len: int = 10,
test_seq_len: int = None,
num_keys: int = 1, # number of keys for associative recall,
data_dir: str = None,
*args, **kwargs
):
self.num_examples = num_examples
self.num_test_examples = num_test_examples
self.input_seq_len = input_seq_len
self.vocab_size = vocab_size
self.copy_method = copy_method
assert copy_method in ["induction_head", "assoc_recall"]
self.number_duplicates_per_epoch = number_duplicates_per_epoch
self.seed = seed
self.batch_size = batch_size
self.split_train_test = split_train_test # let the same copy chars appear in train/test
self.induction_len = induction_len
self.induction_num_triggers = induction_num_triggers
self.allow_dot = allow_dot
self.max_copy_len = max_copy_len
self.data_dir = data_dir
if test_seq_len is not None:
self.test_seq_len = test_seq_len
else:
self.test_seq_len = input_seq_len
self.num_keys = num_keys
special_vocabs = {
"copy_prefix": "=>",
"noop": "."
}
self.special_vocabs = special_vocabs
self.vocab = Vocab(vocab_size-len(special_vocabs), special_vocabs=special_vocabs)
self.tokenizer = Tokenizer(self.vocab)
self.num_extra_seq_len = 2
if self.copy_method == "induction_head":
self.copy_f = self.generate_induction_head
self.num_extra_seq_len = 1 + self.induction_len
elif self.copy_method == "assoc_recall":
self.copy_f = self.generate_assoc_recall
self.num_extra_seq_len = 1 + self.num_keys
else:
self.copy_f = None
if self.number_duplicates_per_epoch > 0:
self.duplicate_ex = self.generate_example()
self.duplicate_index = max(int(self.num_examples / self.number_duplicates_per_epoch), 1)
else:
self.duplicate_ex = None
self.duplicate_index = -1
self.total_seq_len = self.input_seq_len + self.num_extra_seq_len
def generate_induction_head(self, seqlen=None, valid_chars=None):
return generate_induction_head(self.vocab, seqlen if seqlen is not None else self.input_seq_len, self.special_vocabs["copy_prefix"], self.induction_len, self.induction_num_triggers, self.rng, valid_chars=valid_chars)
def generate_assoc_recall(self, seqlen=None, valid_chars=None):
return generate_assoc_recall(self.vocab, seqlen if seqlen is not None else self.input_seq_len, self.num_keys, self.rng, allow_dot = self.allow_dot, valid_chars=valid_chars)
def generate_example(self, seqlen=None, valid_chars=None):
vocab_seq = self.copy_f(seqlen=seqlen, valid_chars=valid_chars)
return self.tokenizer.tokenize(vocab_seq, return_tensor=True)
def setup(self, stage=None):
train_tensor = test_tensor = None
if self.data_dir is not None:
try:
train_tensor = torch.load(os.path.join(self.data_dir,
f"train_{self.copy_method}_{self.num_examples}_{self.vocab_size}_{self.input_seq_len}.pt"))
test_tensor = torch.load(os.path.join(self.data_dir,
f"test_{self.copy_method}_{self.num_examples}_{self.vocab_size}_{self.input_seq_len}.pt"))
except:
pass
if train_tensor is None or test_tensor is None:
if hasattr(self, 'dataset'):
return
self.rng = np.random.default_rng(self.seed)
if self.split_train_test:
all_vocab = self.vocab.non_special_vocab
train_vocab = set(self.rng.choice(all_vocab, size=len(all_vocab) // 2, replace=False))
test_vocab = set(all_vocab) - train_vocab
train_vocab = list(train_vocab)
test_vocab = list(test_vocab)
else:
train_vocab = None
test_vocab = None
all_examples = []
for i, (example_count, valid_vocab) in enumerate(zip([self.num_examples, self.num_test_examples], [train_vocab, test_vocab])):
examples = torch.stack([self.generate_example(
seqlen=self.input_seq_len if i == 0 else self.test_seq_len,
valid_chars=valid_vocab
)['input_ids'] for _ in tqdm(range(example_count))])
examples = torch.unique(examples, dim=0, sorted=False).tolist()
while len(examples) < example_count:
new_example = self.generate_example(
seqlen=self.input_seq_len if i == 0 else self.test_seq_len,
valid_chars=valid_vocab
)['input_ids'].tolist()
if new_example not in examples:
examples.append(new_example)
self.rng.shuffle(examples)
all_examples.append(torch.LongTensor(examples))
# all_examples = torch.concat(all_examples)
train_tensor = torch.stack([torch.stack([example[:-1], example[1:]]) for example in all_examples[0]])
test_tensor = torch.stack([torch.stack([example[:-1], example[1:]]) for example in all_examples[1]])
test_tensor[:, 1, :-1 * (self.num_extra_seq_len - 1)] = -100
if self.copy_method in ["assoc_recall"]:
test_tensor[:, 1, :-1] = -100
if self.copy_method in ["majority", "fom1"]:
train_tensor[:, 1, :-1 * (self.num_extra_seq_len - 1)] = -100
if self.data_dir is not None:
torch.save(train_tensor, os.path.join(self.data_dir,
f"train_{self.copy_method}_{self.num_examples}_{self.vocab_size}_{self.input_seq_len}.pt")
)
torch.save(test_tensor, os.path.join(self.data_dir,
f"test_{self.copy_method}_{self.num_examples}_{self.vocab_size}_{self.input_seq_len}.pt")
)
self.dataset = {
'train': TensorDataset(train_tensor[:, 0, :], train_tensor[:, 1, :]),
'test': TensorDataset(test_tensor[:, 0, :], test_tensor[:, 1, :])
}
def train_dataloader(self, *args, **kwargs):
return self._data_loader(self.dataset['train'], shuffle=True)
def val_dataloader(self, *args, **kwargs):
return self._data_loader(self.dataset['test'], shuffle=False)
def test_dataloader(self, *args, **kwargs):
return self._data_loader(self.dataset['test'], shuffle=False)
def _data_loader(self, dataset: Dataset, shuffle: bool = False) -> DataLoader:
return DataLoader(
dataset,
batch_size=self.batch_size,
num_workers=10,
shuffle=shuffle,
persistent_workers=True
)