Spaces:
Runtime error
Runtime error
File size: 6,562 Bytes
334ea23 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 |
# Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import numpy as np
class ScoreParams:
def __init__(self, gap, match, mismatch):
self.gap = gap
self.match = match
self.mismatch = mismatch
def mis_match_char(self, x, y):
if x != y:
return self.mismatch
else:
return self.match
def get_matrix(size_x, size_y, gap):
matrix = []
for i in range(len(size_x) + 1):
sub_matrix = []
for j in range(len(size_y) + 1):
sub_matrix.append(0)
matrix.append(sub_matrix)
for j in range(1, len(size_y) + 1):
matrix[0][j] = j*gap
for i in range(1, len(size_x) + 1):
matrix[i][0] = i*gap
return matrix
def get_matrix(size_x, size_y, gap):
matrix = np.zeros((size_x + 1, size_y + 1), dtype=np.int32)
matrix[0, 1:] = (np.arange(size_y) + 1) * gap
matrix[1:, 0] = (np.arange(size_x) + 1) * gap
return matrix
def get_traceback_matrix(size_x, size_y):
matrix = np.zeros((size_x + 1, size_y +1), dtype=np.int32)
matrix[0, 1:] = 1
matrix[1:, 0] = 2
matrix[0, 0] = 4
return matrix
def global_align(x, y, score):
matrix = get_matrix(len(x), len(y), score.gap)
trace_back = get_traceback_matrix(len(x), len(y))
for i in range(1, len(x) + 1):
for j in range(1, len(y) + 1):
left = matrix[i, j - 1] + score.gap
up = matrix[i - 1, j] + score.gap
diag = matrix[i - 1, j - 1] + score.mis_match_char(x[i - 1], y[j - 1])
matrix[i, j] = max(left, up, diag)
if matrix[i, j] == left:
trace_back[i, j] = 1
elif matrix[i, j] == up:
trace_back[i, j] = 2
else:
trace_back[i, j] = 3
return matrix, trace_back
def get_aligned_sequences(x, y, trace_back):
x_seq = []
y_seq = []
i = len(x)
j = len(y)
mapper_y_to_x = []
while i > 0 or j > 0:
if trace_back[i, j] == 3:
x_seq.append(x[i-1])
y_seq.append(y[j-1])
i = i-1
j = j-1
mapper_y_to_x.append((j, i))
elif trace_back[i][j] == 1:
x_seq.append('-')
y_seq.append(y[j-1])
j = j-1
mapper_y_to_x.append((j, -1))
elif trace_back[i][j] == 2:
x_seq.append(x[i-1])
y_seq.append('-')
i = i-1
elif trace_back[i][j] == 4:
break
mapper_y_to_x.reverse()
return x_seq, y_seq, torch.tensor(mapper_y_to_x, dtype=torch.int64)
def get_mapper(x: str, y: str, tokenizer, max_len=77):
x_seq = tokenizer.encode(x)
y_seq = tokenizer.encode(y)
score = ScoreParams(0, 1, -1)
matrix, trace_back = global_align(x_seq, y_seq, score)
mapper_base = get_aligned_sequences(x_seq, y_seq, trace_back)[-1]
alphas = torch.ones(max_len)
alphas[: mapper_base.shape[0]] = mapper_base[:, 1].ne(-1).float()
mapper = torch.zeros(max_len, dtype=torch.int64)
mapper[:mapper_base.shape[0]] = mapper_base[:, 1]
mapper[mapper_base.shape[0]:] = len(y_seq) + torch.arange(max_len - len(y_seq))
return mapper, alphas
def get_refinement_mapper(prompts, tokenizer, max_len=77):
x_seq = prompts[0]
mappers, alphas = [], []
for i in range(1, len(prompts)):
mapper, alpha = get_mapper(x_seq, prompts[i], tokenizer, max_len)
mappers.append(mapper)
alphas.append(alpha)
return torch.stack(mappers), torch.stack(alphas)
def get_word_inds(text: str, word_place: int, tokenizer):
split_text = text.split(" ")
if type(word_place) is str:
word_place = [i for i, word in enumerate(split_text) if word_place == word]
elif type(word_place) is int:
word_place = [word_place]
out = []
if len(word_place) > 0:
words_encode = [tokenizer.decode([item]).strip("#") for item in tokenizer.encode(text)][1:-1]
cur_len, ptr = 0, 0
for i in range(len(words_encode)):
cur_len += len(words_encode[i])
if ptr in word_place:
out.append(i + 1)
if cur_len >= len(split_text[ptr]):
ptr += 1
cur_len = 0
return np.array(out)
def get_replacement_mapper_(x: str, y: str, tokenizer, max_len=77):
words_x = x.split(' ')
words_y = y.split(' ')
if len(words_x) != len(words_y):
raise ValueError(f"attention replacement edit can only be applied on prompts with the same length"
f" but prompt A has {len(words_x)} words and prompt B has {len(words_y)} words.")
inds_replace = [i for i in range(len(words_y)) if words_y[i] != words_x[i]]
inds_source = [get_word_inds(x, i, tokenizer) for i in inds_replace]
inds_target = [get_word_inds(y, i, tokenizer) for i in inds_replace]
mapper = np.zeros((max_len, max_len))
i = j = 0
cur_inds = 0
while i < max_len and j < max_len:
if cur_inds < len(inds_source) and inds_source[cur_inds][0] == i:
inds_source_, inds_target_ = inds_source[cur_inds], inds_target[cur_inds]
if len(inds_source_) == len(inds_target_):
mapper[inds_source_, inds_target_] = 1
else:
ratio = 1 / len(inds_target_)
for i_t in inds_target_:
mapper[inds_source_, i_t] = ratio
cur_inds += 1
i += len(inds_source_)
j += len(inds_target_)
elif cur_inds < len(inds_source):
mapper[i, j] = 1
i += 1
j += 1
else:
mapper[j, j] = 1
i += 1
j += 1
return torch.from_numpy(mapper).float()
def get_replacement_mapper(prompts, tokenizer, max_len=77):
x_seq = prompts[0]
mappers = []
for i in range(1, len(prompts)):
mapper = get_replacement_mapper_(x_seq, prompts[i], tokenizer, max_len)
mappers.append(mapper)
return torch.stack(mappers)
|