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colbert.py
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colbert.py
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from colbert.infra.config.config import ColBERTConfig
from colbert.search.strided_tensor import StridedTensor
from colbert.utils.utils import print_message, flatten
from colbert.modeling.base_colbert import BaseColBERT
import torch
import string
import os
import pathlib
from torch.utils.cpp_extension import load
class ColBERT(BaseColBERT):
"""
This class handles the basic encoding and scoring operations in ColBERT. It is used for training.
"""
def __init__(self, name='bert-base-uncased', colbert_config=None):
super().__init__(name, colbert_config)
self.use_gpu = colbert_config.total_visible_gpus > 0
ColBERT.try_load_torch_extensions(self.use_gpu)
if self.colbert_config.mask_punctuation:
self.skiplist = {w: True
for symbol in string.punctuation
for w in [symbol, self.raw_tokenizer.encode(symbol, add_special_tokens=False)[0]]}
self.pad_token = self.raw_tokenizer.pad_token_id
@classmethod
def try_load_torch_extensions(cls, use_gpu):
if hasattr(cls, "loaded_extensions") or use_gpu:
return
print_message(f"Loading segmented_maxsim_cpp extension (set COLBERT_LOAD_TORCH_EXTENSION_VERBOSE=True for more info)...")
segmented_maxsim_cpp = load(
name="segmented_maxsim_cpp",
sources=[
os.path.join(
pathlib.Path(__file__).parent.resolve(), "segmented_maxsim.cpp"
),
],
extra_cflags=["-O3"],
verbose=os.getenv("COLBERT_LOAD_TORCH_EXTENSION_VERBOSE", "False") == "True",
)
cls.segmented_maxsim = segmented_maxsim_cpp.segmented_maxsim_cpp
cls.loaded_extensions = True
def forward(self, Q, D):
Q = self.query(*Q)
D, D_mask = self.doc(*D, keep_dims='return_mask')
# Repeat each query encoding for every corresponding document.
Q_duplicated = Q.repeat_interleave(self.colbert_config.nway, dim=0).contiguous()
scores = self.score(Q_duplicated, D, D_mask)
if self.colbert_config.use_ib_negatives:
ib_loss = self.compute_ib_loss(Q, D, D_mask)
return scores, ib_loss
return scores
def compute_ib_loss(self, Q, D, D_mask):
# TODO: Organize the code below! Quite messy.
scores = (D.unsqueeze(0) @ Q.permute(0, 2, 1).unsqueeze(1)).flatten(0, 1) # query-major unsqueeze
scores = colbert_score_reduce(scores, D_mask.repeat(Q.size(0), 1, 1), self.colbert_config)
nway = self.colbert_config.nway
all_except_self_negatives = [list(range(qidx*D.size(0), qidx*D.size(0) + nway*qidx+1)) +
list(range(qidx*D.size(0) + nway * (qidx+1), qidx*D.size(0) + D.size(0)))
for qidx in range(Q.size(0))]
scores = scores[flatten(all_except_self_negatives)]
scores = scores.view(Q.size(0), -1) # D.size(0) - self.colbert_config.nway + 1)
labels = torch.arange(0, Q.size(0), device=scores.device) * (self.colbert_config.nway)
return torch.nn.CrossEntropyLoss()(scores, labels)
def query(self, input_ids, attention_mask):
input_ids, attention_mask = input_ids.to(self.device), attention_mask.to(self.device)
Q = self.bert(input_ids, attention_mask=attention_mask)[0]
Q = self.linear(Q)
mask = torch.tensor(self.mask(input_ids, skiplist=[]), device=self.device).unsqueeze(2).float()
Q = Q * mask
return torch.nn.functional.normalize(Q, p=2, dim=2)
def doc(self, input_ids, attention_mask, keep_dims=True):
assert keep_dims in [True, False, 'return_mask']
input_ids, attention_mask = input_ids.to(self.device), attention_mask.to(self.device)
D = self.bert(input_ids, attention_mask=attention_mask)[0]
D = self.linear(D)
mask = torch.tensor(self.mask(input_ids, skiplist=self.skiplist), device=self.device).unsqueeze(2).float()
D = D * mask
D = torch.nn.functional.normalize(D, p=2, dim=2)
if self.use_gpu:
D = D.half()
if keep_dims is False:
D, mask = D.cpu(), mask.bool().cpu().squeeze(-1)
D = [d[mask[idx]] for idx, d in enumerate(D)]
elif keep_dims == 'return_mask':
return D, mask.bool()
return D
def score(self, Q, D_padded, D_mask):
# assert self.colbert_config.similarity == 'cosine'
if self.colbert_config.similarity == 'l2':
assert self.colbert_config.interaction == 'colbert'
return (-1.0 * ((Q.unsqueeze(2) - D_padded.unsqueeze(1))**2).sum(-1)).max(-1).values.sum(-1)
return colbert_score(Q, D_padded, D_mask, config=self.colbert_config)
def mask(self, input_ids, skiplist):
mask = [[(x not in skiplist) and (x != self.pad_token) for x in d] for d in input_ids.cpu().tolist()]
return mask
# TODO: In Query/DocTokenizer, use colbert.raw_tokenizer
# TODO: The masking below might also be applicable in the kNN part
def colbert_score_reduce(scores_padded, D_mask, config: ColBERTConfig):
D_padding = ~D_mask.view(scores_padded.size(0), scores_padded.size(1)).bool()
scores_padded[D_padding] = -9999
scores = scores_padded.max(1).values
assert config.interaction in ['colbert', 'flipr'], config.interaction
if config.interaction == 'flipr':
assert config.query_maxlen == 64, ("for now", config)
# assert scores.size(1) == config.query_maxlen, scores.size()
K1 = config.query_maxlen // 2
K2 = 8
A = scores[:, :config.query_maxlen].topk(K1, dim=-1).values.sum(-1)
B = 0
if K2 <= scores.size(1) - config.query_maxlen:
B = scores[:, config.query_maxlen:].topk(K2, dim=-1).values.sum(1)
return A + B
return scores.sum(-1)
# TODO: Wherever this is called, pass `config=`
def colbert_score(Q, D_padded, D_mask, config=ColBERTConfig()):
"""
Supply sizes Q = (1 | num_docs, *, dim) and D = (num_docs, *, dim).
If Q.size(0) is 1, the matrix will be compared with all passages.
Otherwise, each query matrix will be compared against the *aligned* passage.
EVENTUALLY: Consider masking with -inf for the maxsim (or enforcing a ReLU).
"""
use_gpu = config.total_visible_gpus > 0
if use_gpu:
Q, D_padded, D_mask = Q.cuda(), D_padded.cuda(), D_mask.cuda()
assert Q.dim() == 3, Q.size()
assert D_padded.dim() == 3, D_padded.size()
assert Q.size(0) in [1, D_padded.size(0)]
scores = D_padded @ Q.to(dtype=D_padded.dtype).permute(0, 2, 1)
return colbert_score_reduce(scores, D_mask, config)
def colbert_score_packed(Q, D_packed, D_lengths, config=ColBERTConfig()):
"""
Works with a single query only.
"""
use_gpu = config.total_visible_gpus > 0
if use_gpu:
Q, D_packed, D_lengths = Q.cuda(), D_packed.cuda(), D_lengths.cuda()
Q = Q.squeeze(0)
assert Q.dim() == 2, Q.size()
assert D_packed.dim() == 2, D_packed.size()
scores = D_packed @ Q.to(dtype=D_packed.dtype).T
if use_gpu or config.interaction == "flipr":
scores_padded, scores_mask = StridedTensor(scores, D_lengths, use_gpu=use_gpu).as_padded_tensor()
return colbert_score_reduce(scores_padded, scores_mask, config)
else:
return ColBERT.segmented_maxsim(scores, D_lengths)