-
Notifications
You must be signed in to change notification settings - Fork 355
/
index_ranker.py
164 lines (109 loc) · 5.81 KB
/
index_ranker.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
import os
import math
import torch
import ujson
import traceback
from itertools import accumulate
from colbert.parameters import DEVICE
from colbert.utils.utils import print_message, dotdict, flatten
BSIZE = 1 << 14
class IndexRanker():
def __init__(self, tensor, doclens):
self.tensor = tensor
self.doclens = doclens
self.maxsim_dtype = torch.float32
self.doclens_pfxsum = [0] + list(accumulate(self.doclens))
self.doclens = torch.tensor(self.doclens)
self.doclens_pfxsum = torch.tensor(self.doclens_pfxsum)
self.dim = self.tensor.size(-1)
self.strides = [torch_percentile(self.doclens, p) for p in [90]]
self.strides.append(self.doclens.max().item())
self.strides = sorted(list(set(self.strides)))
print_message(f"#> Using strides {self.strides}..")
self.views = self._create_views(self.tensor)
self.buffers = self._create_buffers(BSIZE, self.tensor.dtype, {'cpu', 'cuda:0'})
def _create_views(self, tensor):
views = []
for stride in self.strides:
outdim = tensor.size(0) - stride + 1
view = torch.as_strided(tensor, (outdim, stride, self.dim), (self.dim, self.dim, 1))
views.append(view)
return views
def _create_buffers(self, max_bsize, dtype, devices):
buffers = {}
for device in devices:
buffers[device] = [torch.zeros(max_bsize, stride, self.dim, dtype=dtype,
device=device, pin_memory=(device == 'cpu'))
for stride in self.strides]
return buffers
def rank(self, Q, pids, views=None, shift=0):
assert len(pids) > 0
assert Q.size(0) in [1, len(pids)]
Q = Q.contiguous().to(DEVICE).to(dtype=self.maxsim_dtype)
views = self.views if views is None else views
VIEWS_DEVICE = views[0].device
D_buffers = self.buffers[str(VIEWS_DEVICE)]
raw_pids = pids if type(pids) is list else pids.tolist()
pids = torch.tensor(pids) if type(pids) is list else pids
doclens, offsets = self.doclens[pids], self.doclens_pfxsum[pids]
assignments = (doclens.unsqueeze(1) > torch.tensor(self.strides).unsqueeze(0) + 1e-6).sum(-1)
one_to_n = torch.arange(len(raw_pids))
output_pids, output_scores, output_permutation = [], [], []
for group_idx, stride in enumerate(self.strides):
locator = (assignments == group_idx)
if locator.sum() < 1e-5:
continue
group_pids, group_doclens, group_offsets = pids[locator], doclens[locator], offsets[locator]
group_Q = Q if Q.size(0) == 1 else Q[locator]
group_offsets = group_offsets.to(VIEWS_DEVICE) - shift
group_offsets_uniq, group_offsets_expand = torch.unique_consecutive(group_offsets, return_inverse=True)
D_size = group_offsets_uniq.size(0)
D = torch.index_select(views[group_idx], 0, group_offsets_uniq, out=D_buffers[group_idx][:D_size])
D = D.to(DEVICE)
D = D[group_offsets_expand.to(DEVICE)].to(dtype=self.maxsim_dtype)
mask = torch.arange(stride, device=DEVICE) + 1
mask = mask.unsqueeze(0) <= group_doclens.to(DEVICE).unsqueeze(-1)
scores = (D @ group_Q) * mask.unsqueeze(-1)
scores = scores.max(1).values.sum(-1).cpu()
output_pids.append(group_pids)
output_scores.append(scores)
output_permutation.append(one_to_n[locator])
output_permutation = torch.cat(output_permutation).sort().indices
output_pids = torch.cat(output_pids)[output_permutation].tolist()
output_scores = torch.cat(output_scores)[output_permutation].tolist()
assert len(raw_pids) == len(output_pids)
assert len(raw_pids) == len(output_scores)
assert raw_pids == output_pids
return output_scores
def batch_rank(self, all_query_embeddings, all_query_indexes, all_pids, sorted_pids):
assert sorted_pids is True
######
scores = []
range_start, range_end = 0, 0
for pid_offset in range(0, len(self.doclens), 50_000):
pid_endpos = min(pid_offset + 50_000, len(self.doclens))
range_start = range_start + (all_pids[range_start:] < pid_offset).sum()
range_end = range_end + (all_pids[range_end:] < pid_endpos).sum()
pids = all_pids[range_start:range_end]
query_indexes = all_query_indexes[range_start:range_end]
print_message(f"###--> Got {len(pids)} query--passage pairs in this sub-range {(pid_offset, pid_endpos)}.")
if len(pids) == 0:
continue
print_message(f"###--> Ranking in batches the pairs #{range_start} through #{range_end} in this sub-range.")
tensor_offset = self.doclens_pfxsum[pid_offset].item()
tensor_endpos = self.doclens_pfxsum[pid_endpos].item() + 512
collection = self.tensor[tensor_offset:tensor_endpos].to(DEVICE)
views = self._create_views(collection)
print_message(f"#> Ranking in batches of {BSIZE} query--passage pairs...")
for batch_idx, offset in enumerate(range(0, len(pids), BSIZE)):
if batch_idx % 100 == 0:
print_message("#> Processing batch #{}..".format(batch_idx))
endpos = offset + BSIZE
batch_query_index, batch_pids = query_indexes[offset:endpos], pids[offset:endpos]
Q = all_query_embeddings[batch_query_index]
scores.extend(self.rank(Q, batch_pids, views, shift=tensor_offset))
return scores
def torch_percentile(tensor, p):
assert p in range(1, 100+1)
assert tensor.dim() == 1
return tensor.kthvalue(int(p * tensor.size(0) / 100.0)).values.item()