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import torch
from tqdm import tqdm
from colbert.modeling.tokenization import QueryTokenizer, DocTokenizer
from colbert.utils.amp import MixedPrecisionManager
from colbert.modeling.colbert import ColBERT
class Checkpoint(ColBERT):
"""
Easy inference with ColBERT.
TODO: Add .cast() accepting [also] an object instance-of(Checkpoint) as first argument.
"""
def __init__(self, name, colbert_config=None):
super().__init__(name, colbert_config)
assert self.training is False
self.query_tokenizer = QueryTokenizer(self.colbert_config)
self.doc_tokenizer = DocTokenizer(self.colbert_config)
self.amp_manager = MixedPrecisionManager(True)
def query(self, *args, to_cpu=False, **kw_args):
with torch.no_grad():
with self.amp_manager.context():
Q = super().query(*args, **kw_args)
return Q.cpu() if to_cpu else Q
def doc(self, *args, to_cpu=False, **kw_args):
with torch.no_grad():
with self.amp_manager.context():
D = super().doc(*args, **kw_args)
if to_cpu:
return (D[0].cpu(), *D[1:]) if isinstance(D, tuple) else D.cpu()
return D
def queryFromText(self, queries, bsize=None, to_cpu=False, context=None):
if bsize:
batches = self.query_tokenizer.tensorize(queries, context=context, bsize=bsize)
batches = [self.query(input_ids, attention_mask, to_cpu=to_cpu) for input_ids, attention_mask in batches]
return torch.cat(batches)
input_ids, attention_mask = self.query_tokenizer.tensorize(queries, context=context)
return self.query(input_ids, attention_mask)
def docFromText(self, docs, bsize=None, keep_dims=True, to_cpu=False, showprogress=False, return_tokens=False):
assert keep_dims in [True, False, 'flatten']
if bsize:
text_batches, reverse_indices = self.doc_tokenizer.tensorize(docs, bsize=bsize)
returned_text = []
if return_tokens:
returned_text = [text for batch in text_batches for text in batch[0]]
returned_text = [returned_text[idx] for idx in reverse_indices.tolist()]
returned_text = [returned_text]
keep_dims_ = 'return_mask' if keep_dims == 'flatten' else keep_dims
batches = [self.doc(input_ids, attention_mask, keep_dims=keep_dims_, to_cpu=to_cpu)
for input_ids, attention_mask in tqdm(text_batches, disable=not showprogress)]
if keep_dims is True:
D = _stack_3D_tensors(batches)
return (D[reverse_indices], *returned_text)
elif keep_dims == 'flatten':
D, mask = [], []
for D_, mask_ in batches:
D.append(D_)
mask.append(mask_)
D, mask = torch.cat(D)[reverse_indices], torch.cat(mask)[reverse_indices]
doclens = mask.squeeze(-1).sum(-1).tolist()
D = D.view(-1, self.colbert_config.dim)
D = D[mask.bool().flatten()].cpu()
return (D, doclens, *returned_text)
assert keep_dims is False
D = [d for batch in batches for d in batch]
return ([D[idx] for idx in reverse_indices.tolist()], *returned_text)
input_ids, attention_mask = self.doc_tokenizer.tensorize(docs)
return self.doc(input_ids, attention_mask, keep_dims=keep_dims, to_cpu=to_cpu)
def lazy_rank(self, queries, docs):
Q = self.queryFromText(queries, bsize=128, to_cpu=True)
D = self.docFromText(docs, bsize=128, to_cpu=True)
assert False, "Implement scoring"
def score(self, Q, D, mask=None, lengths=None):
assert False, "Call colbert_score"
# EVENTUALLY: Just call the colbert_score function!
if lengths is not None:
assert mask is None, "don't supply both mask and lengths"
mask = torch.arange(D.size(1), device=self.device) + 1
mask = mask.unsqueeze(0) <= lengths.to(self.device).unsqueeze(-1)
scores = (D @ Q)
scores = scores if mask is None else scores * mask.unsqueeze(-1)
scores = scores.max(1)
return scores.values.sum(-1).cpu()
def _stack_3D_tensors(groups):
bsize = sum([x.size(0) for x in groups])
maxlen = max([x.size(1) for x in groups])
hdim = groups[0].size(2)
output = torch.zeros(bsize, maxlen, hdim, device=groups[0].device, dtype=groups[0].dtype)
offset = 0
for x in groups:
endpos = offset + x.size(0)
output[offset:endpos, :x.size(1)] = x
offset = endpos
return output
"""
TODO:
def tokenize_and_encode(checkpoint, passages):
embeddings, token_ids = checkpoint.docFromText(passages, bsize=128, keep_dims=False, showprogress=True, return_tokens=True)
tokens = [checkpoint.doc_tokenizer.tok.convert_ids_to_tokens(ids.tolist()) for ids in token_ids]
tokens = [tokens[:tokens.index('[PAD]') if '[PAD]' in tokens else -1] for tokens in tokens]
tokens = [[tok for tok in tokens if tok not in checkpoint.skiplist] for tokens in tokens]
return embeddings, tokens
"""
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