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ict_dataset.py
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ict_dataset.py
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import itertools
import random
import numpy as np
from torch.utils.data import Dataset
from megatron.training import get_tokenizer
from megatron.training import get_args
from megatron.legacy.data.dataset_utils import get_indexed_dataset_
from megatron.legacy.data.realm_dataset_utils import get_block_samples_mapping
def make_attention_mask(source_block, target_block):
"""
Returns a 2-dimensional (2-D) attention mask
:param source_block: 1-D array
:param target_block: 1-D array
"""
mask = (target_block[None, :] >= 1) * (source_block[:, None] >= 1)
mask = mask.astype(np.int64)
# (source_length, target_length)
return mask
def get_ict_dataset(use_titles=True, query_in_block_prob=1):
"""Get a dataset which uses block samples mappings to get ICT/block indexing data (via get_block())
rather than for training, since it is only built with a single epoch sample mapping.
"""
args = get_args()
block_dataset = get_indexed_dataset_(args.data_path, 'mmap', True)
titles_dataset = get_indexed_dataset_(args.titles_data_path, 'mmap', True)
kwargs = dict(
name='full',
block_dataset=block_dataset,
title_dataset=titles_dataset,
data_prefix=args.data_path,
num_epochs=1,
max_num_samples=None,
max_seq_length=args.seq_length,
seed=1,
query_in_block_prob=query_in_block_prob,
use_titles=use_titles,
use_one_sent_docs=args.use_one_sent_docs
)
dataset = ICTDataset(**kwargs)
return dataset
class ICTDataset(Dataset):
"""Dataset containing sentences and their blocks for an inverse cloze task."""
def __init__(self, name, block_dataset, title_dataset, data_prefix,
num_epochs, max_num_samples, max_seq_length, query_in_block_prob,
seed, use_titles=True, use_one_sent_docs=False, binary_head=False):
self.name = name
self.seed = seed
self.max_seq_length = max_seq_length
self.query_in_block_prob = query_in_block_prob
self.block_dataset = block_dataset
self.title_dataset = title_dataset
self.rng = random.Random(self.seed)
self.use_titles = use_titles
self.use_one_sent_docs = use_one_sent_docs
self.samples_mapping = get_block_samples_mapping(
block_dataset, title_dataset, data_prefix, num_epochs,
max_num_samples, max_seq_length, seed, name, use_one_sent_docs)
self.tokenizer = get_tokenizer()
self.vocab_id_list = list(self.tokenizer.inv_vocab.keys())
self.vocab_id_to_token_list = self.tokenizer.inv_vocab
self.cls_id = self.tokenizer.cls
self.sep_id = self.tokenizer.sep
self.mask_id = self.tokenizer.mask
self.pad_id = self.tokenizer.pad
def __len__(self):
return len(self.samples_mapping)
def __getitem__(self, idx):
"""Get an ICT example of a pseudo-query and the block of text from which it was extracted"""
sample_data = self.samples_mapping[idx]
start_idx, end_idx, doc_idx, block_idx = sample_data.as_tuple()
if self.use_titles:
title = self.title_dataset[int(doc_idx)]
title_pad_offset = 3 + len(title)
else:
title = None
title_pad_offset = 2
block = [self.block_dataset[i] for i in range(start_idx, end_idx)]
assert len(block) > 1 or self.use_one_sent_docs or self.query_in_block_prob == 1
# randint() is inclusive for Python rng
rand_sent_idx = self.rng.randint(0, len(block) - 1)
# keep the query in the context query_in_block_prob fraction of the time.
if self.rng.random() < self.query_in_block_prob:
query = block[rand_sent_idx].copy()
else:
query = block.pop(rand_sent_idx)
# still need to truncate because blocks are concluded when
# the sentence lengths have exceeded max_seq_length.
query = query[:self.max_seq_length - 2]
block = list(itertools.chain(*block))[:self.max_seq_length - title_pad_offset]
query_tokens, query_pad_mask = self.concat_and_pad_tokens(query)
context_tokens, context_pad_mask = self.concat_and_pad_tokens(block, title)
query_mask = make_attention_mask(query_tokens, query_tokens)
context_mask = make_attention_mask(context_tokens, context_tokens)
block_data = sample_data.as_array()
sample = {
'query_tokens': query_tokens,
'query_mask': query_mask,
'query_pad_mask': query_pad_mask,
'context_tokens': context_tokens,
'context_mask': context_mask,
'context_pad_mask': context_pad_mask,
'block_data': block_data,
}
return sample
def get_block(self, start_idx, end_idx, doc_idx):
"""Get the IDs for an evidence block plus the title of the corresponding document"""
block = [self.block_dataset[i] for i in range(start_idx, end_idx)]
title = self.title_dataset[int(doc_idx)]
block = list(itertools.chain(*block))[:self.max_seq_length - (3 + len(title))]
block_tokens, block_pad_mask = self.concat_and_pad_tokens(block, title)
return block_tokens, block_pad_mask
def get_null_block(self):
"""Get empty block and title - used in REALM pretraining"""
block, title = [], []
block_tokens, block_pad_mask = self.concat_and_pad_tokens(block, title)
return block_tokens, block_pad_mask
def concat_and_pad_tokens(self, tokens, title=None):
"""Concat with special tokens and pad sequence to self.max_seq_length"""
tokens = list(tokens)
if title is None:
tokens = [self.cls_id] + tokens + [self.sep_id]
else:
title = list(title)
tokens = [self.cls_id] + title + [self.sep_id] + tokens + [self.sep_id]
assert len(tokens) <= self.max_seq_length
num_pad = self.max_seq_length - len(tokens)
pad_mask = [1] * len(tokens) + [0] * num_pad
tokens += [self.pad_id] * num_pad
return np.array(tokens), np.array(pad_mask)