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export ids during tokenisation #30

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export ids during tokenisation #30

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37 changes: 28 additions & 9 deletions colbert/modeling/inference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,30 +28,49 @@ def doc(self, *args, to_cpu=False, **kw_args):
D = self.colbert.doc(*args, **kw_args)
return D.cpu() if to_cpu else D

def queryFromText(self, queries, bsize=None, to_cpu=False):
def queryFromText(self, queries, bsize=None, to_cpu=False, with_ids=False):
if bsize:
batches = self.query_tokenizer.tensorize(queries, bsize=bsize)
batches = [self.query(input_ids, attention_mask, to_cpu=to_cpu) for input_ids, attention_mask in batches]
return torch.cat(batches)
batchesEmbs = [self.query(input_ids, attention_mask, to_cpu=to_cpu) for input_ids, attention_mask in batches]
if with_ids:
return torch.cat(batchesEmbs), torch.cat([ids for ids, _ in batches]), torch.cat([masks for _, masks in batches])
return torch.cat(batchesEmbs)

input_ids, attention_mask = self.query_tokenizer.tensorize(queries)
if with_ids:
return (self.query(input_ids, attention_mask), input_ids, attention_mask)
return self.query(input_ids, attention_mask)

def docFromText(self, docs, bsize=None, keep_dims=True, to_cpu=False):
def docFromText(self, docs, bsize=None, keep_dims=True, to_cpu=False, with_ids=False):
if bsize:
batches, reverse_indices = self.doc_tokenizer.tensorize(docs, bsize=bsize)

batch_ids, reverse_indices = self.doc_tokenizer.tensorize(docs, bsize=bsize)
# batch_ids contain batches; each batch is a 2-tuple, of which the left is
# the ids of each document, and the right is the masks of each document

batches = [self.doc(input_ids, attention_mask, keep_dims=keep_dims, to_cpu=to_cpu)
for input_ids, attention_mask in batches]

for input_ids, attention_mask in batch_ids]
if keep_dims:
D = _stack_3D_tensors(batches)
if with_ids:
Dids = _stack_3D_tensors(batch_ids)
return D[reverse_indices], Dids
return D[reverse_indices]

D = [d for batch in batches for d in batch]
if with_ids:
#the masking code assumes that args.mask_punctuation is false.
assert len(self.colbert.skiplist) == 0

D_i = [ d[(mask > 0) & (d != 0)] for input_ids, attention_masks in batch_ids for d, mask in zip(input_ids,attention_masks) ]

left = [D[idx] for idx in reverse_indices.tolist()]
right = [D_i[idx] for idx in reverse_indices.tolist()]
return left, right
return [D[idx] for idx in reverse_indices.tolist()]

input_ids, attention_mask = self.doc_tokenizer.tensorize(docs)
if with_ids:
return self.doc(input_ids, attention_mask, keep_dims=keep_dims), input_ids
return self.doc(input_ids, attention_mask, keep_dims=keep_dims)

def score(self, Q, D, mask=None, lengths=None, explain=False):
Expand Down