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import torch
from colbert.modeling.hf_colbert import HF_ColBERT
from colbert.infra import ColBERTConfig
from colbert.modeling.tokenization.utils import _split_into_batches
from colbert.utils.utils import batch
class QueryTokenizer():
def __init__(self, config: ColBERTConfig):
self.tok = HF_ColBERT.raw_tokenizer_from_pretrained(config.checkpoint)
self.config = config
self.query_maxlen = config.query_maxlen
self.background_maxlen = 512 - self.query_maxlen + 1 # FIXME: Make this configurable
self.Q_marker_token, self.Q_marker_token_id = '[Q]', self.tok.convert_tokens_to_ids('[unused0]')
self.cls_token, self.cls_token_id = self.tok.cls_token, self.tok.cls_token_id
self.sep_token, self.sep_token_id = self.tok.sep_token, self.tok.sep_token_id
self.mask_token, self.mask_token_id = self.tok.mask_token, self.tok.mask_token_id
# assert self.Q_marker_token_id == 1 and self.mask_token_id == 103
self.used = False
def tokenize(self, batch_text, add_special_tokens=False):
assert type(batch_text) in [list, tuple], (type(batch_text))
tokens = [self.tok.tokenize(x, add_special_tokens=False) for x in batch_text]
if not add_special_tokens:
return tokens
prefix, suffix = [self.cls_token, self.Q_marker_token], [self.sep_token]
tokens = [prefix + lst + suffix + [self.mask_token] * (self.query_maxlen - (len(lst)+3)) for lst in tokens]
return tokens
def encode(self, batch_text, add_special_tokens=False):
assert type(batch_text) in [list, tuple], (type(batch_text))
ids = self.tok(batch_text, add_special_tokens=False)['input_ids']
if not add_special_tokens:
return ids
prefix, suffix = [self.cls_token_id, self.Q_marker_token_id], [self.sep_token_id]
ids = [prefix + lst + suffix + [self.mask_token_id] * (self.query_maxlen - (len(lst)+3)) for lst in ids]
return ids
def tensorize(self, batch_text, bsize=None, context=None):
assert type(batch_text) in [list, tuple], (type(batch_text))
# add placehold for the [Q] marker
batch_text = ['. ' + x for x in batch_text]
obj = self.tok(batch_text, padding='max_length', truncation=True,
return_tensors='pt', max_length=self.query_maxlen)
ids, mask = obj['input_ids'], obj['attention_mask']
# postprocess for the [Q] marker and the [MASK] augmentation
ids[:, 1] = self.Q_marker_token_id
ids[ids == 0] = self.mask_token_id
if context is not None:
assert len(context) == len(batch_text), (len(context), len(batch_text))
obj_2 = self.tok(context, padding='longest', truncation=True,
return_tensors='pt', max_length=self.background_maxlen)
ids_2, mask_2 = obj_2['input_ids'][:, 1:], obj_2['attention_mask'][:, 1:] # Skip the first [SEP]
ids = torch.cat((ids, ids_2), dim=-1)
mask = torch.cat((mask, mask_2), dim=-1)
if self.config.attend_to_mask_tokens:
mask[ids == self.mask_token_id] = 1
assert mask.sum().item() == mask.size(0) * mask.size(1), mask
if bsize:
batches = _split_into_batches(ids, mask, bsize)
return batches
if self.used is False:
self.used = True
firstbg = (context is None) or context[0]
print()
print("#> QueryTokenizer.tensorize(batch_text[0], batch_background[0], bsize) ==")
print(f"#> Input: {batch_text[0]}, \t\t {firstbg}, \t\t {bsize}")
print(f"#> Output IDs: {ids[0].size()}, {ids[0]}")
print(f"#> Output Mask: {mask[0].size()}, {mask[0]}")
print()
return ids, mask
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