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nlpfun
2021-05-18 00:12:12 +08:00
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import argparse
import os
import sys
import numpy as np
import numba as nb
import faiss
import time
def _main():
# user-defined parameters
parser = argparse.ArgumentParser('Multilingual sentence alignment using BERT embeddings',
formatter_class = argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--job', type=str, required=True, help='Job file for alignment task.')
parser.add_argument('--src_embed', type=str, required=True, nargs=2, help='Source overlap and embedding files.')
parser.add_argument('--tgt_embed', type=str, required=True, nargs=2, help='Target overlap and embedding files.')
parser.add_argument('--max_align', type=int, default=5, help='Maximum alignment types, n + m <= this value.')
parser.add_argument('--win', type=int, default=5, help='Window size for the second-pass alignment.')
parser.add_argument('--top_k', type=int, default=3, help='Top-k target neighbors of each source sentence.')
parser.add_argument('--skip', type=float, default=-0.1, help='Similarity score for 0-1 and 1-0 alignment.')
parser.add_argument('--margin', action='store_true', help='Margin-based cosine similarity')
args = parser.parse_args()
# fixed parameters to determine the
# window size for the first-pass alignment
min_win_size = 10
max_win_size = 600
win_per_100 = 8
# read in embeddings
src_sent2line, src_line_embeddings = read_in_embeddings(args.src_embed[0], args.src_embed[1])
tgt_sent2line, tgt_line_embeddings = read_in_embeddings(args.tgt_embed[0], args.tgt_embed[1])
embedding_size = src_line_embeddings.shape[1]
# read in alignment jobs
job = read_job(args.job)
# start alignment
for rec in job:
src_file, tgt_file, align_file = rec.split("\t")
print("Aligning {} to {}".format(src_file, tgt_file))
# read in source and target sentences
src_lines = open(src_file, 'rt', encoding="utf-8").readlines()
tgt_lines = open(tgt_file, 'rt', encoding="utf-8").readlines()
# convert source and target texts into embeddings
# and calculate sentence length
t_0 = time.time()
src_vecs, src_lens = doc2feats(src_sent2line, src_line_embeddings, src_lines, args.max_align - 1)
tgt_vecs, tgt_lens = doc2feats(tgt_sent2line, tgt_line_embeddings, tgt_lines, args.max_align - 1)
char_ratio = np.sum(src_lens[0,]) / np.sum(tgt_lens[0,])
print("Reading embeddings takes {}".format(time.time() - t_0))
# using faiss, find in the target text
# the k nearest neighbors of each source sentence
#index = faiss.IndexFlatIP(embedding_size) # use inter product to build index
t_1 = time.time()
#index.add(tgt_vecs[0,:])
#xq = src_vecs[0,:]
#D,I = index.search(xq, args.top_k)
res = faiss.StandardGpuResources()
index = faiss.IndexFlatIP(embedding_size)
gpu_index = faiss.index_cpu_to_gpu(res, 0, index)
gpu_index.add(tgt_vecs[0,:])
xq = src_vecs[0,:]
D,I = gpu_index.search(xq,args.top_k)
print("Finding top-k neighbors takes {}".format(time.time() - t_1))
# find 1-to-1 alignment
t_2 = time.time()
src_len = len(src_lines)
tgt_len = len(tgt_lines)
first_alignment_types = make_alignment_types(2) # 0-0， 1-0 and 1-1
first_w, first_search_path = find_first_search_path(src_len, tgt_len, min_win_size, max_win_size, win_per_100)
first_pointers = first_pass_align(src_len, tgt_len, first_w, first_search_path, first_alignment_types, D, I, args.top_k)
first_alignment = first_back_track(src_len, tgt_len, first_pointers, first_search_path, first_alignment_types)
print("First pass alignment takes {}".format(time.time() - t_2))
# find m-to-n alignment
t_3 = time.time()
second_w, second_search_path = find_second_search_path(first_alignment, args.win, src_len, tgt_len)
second_alignment_types = make_alignment_types(args.max_align)
second_pointers = second_pass_align(src_vecs, tgt_vecs, src_lens, tgt_lens, second_w, second_search_path, second_alignment_types, char_ratio, args.skip, margin=args.margin)
second_alignment = second_back_track(src_len, tgt_len, second_pointers, second_search_path, second_alignment_types)
print("Second pass alignment takes {}".format(time.time() - t_3))
# save alignment
out_f = open(align_file, 'w', encoding="utf-8")
print_alignments(second_alignment, file=out_f)
def print_alignments(alignments, file=sys.stdout):
for x, y in alignments:
print('%s:%s' % (x, y), file=file)
def second_back_track(i, j, b, search_path, a_types):
alignment = []
while ( i !=0 and j != 0 ):
j_offset = j - search_path[i][0]
a = b[i][j_offset]
s = a_types[a][0]
t = a_types[a][1]
src_range = [i - offset - 1 for offset in range(s)][::-1]
tgt_range = [j - offset - 1 for offset in range(t)][::-1]
alignment.append((src_range, tgt_range))
i = i-s
j = j-t
return alignment[::-1]
@nb.jit(nopython=True, fastmath=True, cache=True)
def second_pass_align(src_vecs, tgt_vecs, src_lens, tgt_lens, w, search_path, align_types, char_ratio, skip, margin=False):
src_len = src_vecs.shape[1]
tgt_len = tgt_vecs.shape[1]
# intialize sum matrix
cost = np.zeros((src_len + 1, w))
#back = np.zeros((tgt_len + 1, w), dtype=nb.int64)
back = np.zeros((src_len + 1, w), dtype=nb.int64)
cost[0][0] = 0
back[0][0] = -1
for i in range(1, src_len + 1):
i_start = search_path[i][0]
i_end = search_path[i][1]
for j in range(i_start, i_end + 1):
if i + j == 0:
continue
best_score = -np.inf
best_a = -1
for a in range(align_types.shape[0]):
a_1 = align_types[a][0]
a_2 = align_types[a][1]
prev_i = i - a_1
prev_j = j - a_2
if prev_i < 0 or prev_j < 0 : # no previous cell in DP table
continue
prev_i_start = search_path[prev_i][0]
prev_i_end = search_path[prev_i][1]
if prev_j < prev_i_start or prev_j > prev_i_end: # out of bound of cost matrix
continue
prev_j_offset = prev_j - prev_i_start
score = cost[prev_i][prev_j_offset]
if score == -np.inf:
continue
if a_1 == 0 or a_2 == 0: # deletion or insertion
cur_score = skip
else:
src_v = src_vecs[a_1-1,i-1,:]
tgt_v = tgt_vecs[a_2-1,j-1,:]
src_l = src_lens[a_1-1, i-1]
tgt_l = tgt_lens[a_2-1, j-1]
cur_score = get_score(src_v, tgt_v, a_1, a_2, i, j, src_vecs, tgt_vecs, src_len, tgt_len, margin=margin)
tgt_l = tgt_l * char_ratio
min_len = min(src_l, tgt_l)
max_len = max(src_l, tgt_l)
len_p = np.log2(1 + min_len / max_len)
cur_score *= len_p
score += cur_score
if score > best_score:
best_score = score
best_a = a
j_offset = j - i_start
cost[i][j_offset] = best_score
back[i][j_offset] = best_a
return back
@nb.jit(nopython=True, fastmath=True, cache=True)
def get_score(src_v, tgt_v, a_1, a_2, i, j, src_vecs, tgt_vecs, src_len, tgt_len, margin=False):
similarity = nb_dot(src_v, tgt_v)
if margin:
tgt_neighbor_ave_sim = get_neighbor_sim(src_v, a_2, j, tgt_len, tgt_vecs)
src_neighbor_ave_sim = get_neighbor_sim(tgt_v, a_1, i, src_len, src_vecs)
neighbor_ave_sim = (tgt_neighbor_ave_sim + src_neighbor_ave_sim)/2
similarity -= neighbor_ave_sim
return similarity
@nb.jit(nopython=True, fastmath=True, cache=True)
def get_neighbor_sim(vec, a, j, len, db):
left_idx = j - a
right_idx = j + 1
if right_idx > len:
neighbor_right_sim = 0
else:
right_embed = db[0,right_idx-1,:]
neighbor_right_sim = nb_dot(vec, right_embed)
if left_idx == 0:
neighbor_left_sim = 0
else:
left_embed = db[0,left_idx-1,:]
neighbor_left_sim = nb_dot(vec, left_embed)
#if right_idx > LEN or left_idx < 0:
if right_idx > len or left_idx == 0:
neighbor_ave_sim = neighbor_left_sim + neighbor_right_sim
else:
neighbor_ave_sim = (neighbor_left_sim + neighbor_right_sim) / 2
return neighbor_ave_sim
@nb.jit(nopython=True, fastmath=True, cache=True)
def nb_dot(x, y):
return np.dot(x,y)
def find_second_search_path(align, w, src_len, tgt_len):
'''
Convert 1-1 alignment from first-pass to the path for second-pass alignment.
The index along X-axis and Y-axis must be consecutive.
'''
last_bead_src = align[-1][0]
last_bead_tgt = align[-1][1]
if last_bead_src != src_len:
if last_bead_tgt == tgt_len:
align.pop()
align.append((src_len, tgt_len))
else:
if last_bead_tgt != tgt_len:
align.pop()
align.append((src_len, tgt_len))
prev_src, prev_tgt = 0,0
path = []
max_w = -np.inf
for src, tgt in align:
lower_bound = max(0, prev_tgt - w)
upper_bound = min(tgt_len, tgt + w)
path.extend([(lower_bound, upper_bound) for id in range(prev_src+1, src+1)])
prev_src, prev_tgt = src, tgt
width = upper_bound - lower_bound
if width > max_w:
max_w = width
path = [path[0]] + path
return max_w + 1, np.array(path)
def first_back_track(i, j, b, search_path, a_types):
alignment = []
while ( i !=0 and j != 0 ):
j_offset = j - search_path[i][0]
a = b[i][j_offset]
s = a_types[a][0]
t = a_types[a][1]
if a == 2:
alignment.append((i, j))
i = i-s
j = j-t
return alignment[::-1]
@nb.jit(nopython=True, fastmath=True, cache=True)
def first_pass_align(src_len, tgt_len, w, search_path, align_types, dist, index, top_k):
#initialize cost and backpointer matrix
cost = np.zeros((src_len + 1, 2 * w + 1))
pointers = np.zeros((src_len + 1, 2 * w + 1), dtype=nb.int64)
cost[0][0] = 0
pointers[0][0] = -1
for i in range(1, src_len + 1):
i_start = search_path[i][0]
i_end = search_path[i][1]
for j in range(i_start, i_end + 1):
if i + j == 0:
continue
best_score = -np.inf
best_a = -1
for a in range(align_types.shape[0]):
a_1 = align_types[a][0]
a_2 = align_types[a][1]
prev_i = i - a_1
prev_j = j - a_2
if prev_i < 0 or prev_j < 0 : # no previous cell
continue
prev_i_start = search_path[prev_i][0]
prev_i_end = search_path[prev_i][1]
if prev_j < prev_i_start or prev_j > prev_i_end: # out of bound of cost matrix
continue
prev_j_offset = prev_j - prev_i_start
score = cost[prev_i][prev_j_offset]
if score == -np.inf:
continue
if a_1 > 0 and a_2 > 0:
for k in range(top_k):
if index[i-1][k] == j - 1:
score += dist[i-1][k]
if score > best_score:
best_score = score
best_a = a
j_offset = j - i_start
cost[i][j_offset] = best_score
pointers[i][j_offset] = best_a
return pointers
@nb.jit(nopython=True, fastmath=True, cache=True)
def find_first_search_path(src_len, tgt_len, min_win_size, max_win_size, win_per_100):
yx_ratio = tgt_len / src_len
win_size_1 = int(yx_ratio * tgt_len * win_per_100 / 100)
win_size_2 = int(abs(tgt_len - src_len) * 3/4)
w_1 = min(max(min_win_size, max(win_size_1, win_size_2)), max_win_size)
#w_2 = int(max(src_len, tgt_len) * 0.05)
w_2 = int(max(src_len, tgt_len) * 0.06)
w = max(w_1, w_2)
search_path = np.zeros((src_len + 1, 2), dtype=nb.int64)
for i in range(0, src_len + 1):
center = int(yx_ratio * i)
w_start = max(0, center - w)
w_end = min(center + w, tgt_len)
search_path[i] = [w_start, w_end]
return w, search_path
def doc2feats(sent2line, line_embeddings, lines, num_overlaps):
lines = [preprocess_line(line) for line in lines]
vecsize = line_embeddings.shape[1]
vecs0 = np.empty((num_overlaps, len(lines), vecsize), dtype=np.float32)
vecs1 = np.empty((num_overlaps, len(lines)), dtype=np.int)
for ii, overlap in enumerate(range(1, num_overlaps + 1)):
for jj, out_line in enumerate(layer(lines, overlap)):
try:
line_id = sent2line[out_line]
except KeyError:
logger.warning('Failed to find overlap=%d line "%s". Will use random vector.', overlap, out_line)
line_id = None
if line_id is not None:
vec = line_embeddings[line_id]
else:
vec = np.random.random(vecsize) - 0.5
vec = vec / np.linalg.norm(vec)
vecs0[ii, jj, :] = vec
vecs1[ii, jj] = len(out_line.encode("utf-8"))
return vecs0, vecs1
def preprocess_line(line):
line = line.strip()
if len(line) == 0:
line = 'BLANK_LINE'
return line
def layer(lines, num_overlaps, comb=' '):
"""
make front-padded overlapping sentences
"""
if num_overlaps < 1:
raise Exception('num_overlaps must be >= 1')
out = ['PAD', ] * min(num_overlaps - 1, len(lines))
for ii in range(len(lines) - num_overlaps + 1):
out.append(comb.join(lines[ii:ii + num_overlaps]))
return out
def read_in_embeddings(text_file, embed_file):
sent2line = dict()
with open(text_file, 'rt', encoding="utf-8") as fin:
for ii, line in enumerate(fin):
if line.strip() in sent2line:
raise Exception('got multiple embeddings for the same line')
sent2line[line.strip()] = ii
line_embeddings = np.fromfile(embed_file, dtype=np.float32, count=-1)
if line_embeddings.size == 0:
raise Exception('Got empty embedding file')
embedding_size = line_embeddings.size // len(sent2line)
line_embeddings.resize(line_embeddings.shape[0] // embedding_size, embedding_size)
return sent2line, line_embeddings
def make_alignment_types(max_alignment_size):
# Return list of all (n,m) where n+m <= this
alignment_types = []
for x in range(1, max_alignment_size):
for y in range(1, max_alignment_size):
if x + y <= max_alignment_size:
alignment_types.append([x, y])
alignment_types = [[0,1], [1,0]] + alignment_types
return np.array(alignment_types)
def read_job(file):
job = []
with open(file, 'r', encoding="utf-8") as f:
for line in f:
if not line.startswith("#"):
job.append(line.strip())
return job
if __name__ == '__main__':
_main()