Spaces:
Sleeping
Sleeping
File size: 11,568 Bytes
05d3571 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 |
#!/usr/bin/python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
#
# LASER Language-Agnostic SEntence Representations
# is a toolkit to calculate multilingual sentence embeddings
# and to use them for document classification, bitext filtering
# and mining
#
# --------------------------------------------------------
#
# Tool to calculate to embed a text file
# The functions can be also imported into another Python code
import os
import sys
import faiss
import argparse
import torch
import numpy as np
# get environment
assert os.environ.get('LASER'), 'Please set the enviornment variable LASER'
LASER = os.environ['LASER']
sys.path.append(LASER + '/source')
sys.path.append(LASER + '/source/tools')
from embed import SentenceEncoder, EncodeLoad, EncodeFile, EmbedLoad
from lib.text_processing import Token, BPEfastApply
###############################################################################
#
# Load texts and remove duplicates
#
###############################################################################
def TextLoadUnify(fname, args):
if args.verbose:
print(' - loading texts {:s}: '.format(fname), end='')
fin = open(fname, encoding=args.encoding, errors='surrogateescape')
inds = []
sents = []
sent2ind = {}
n = 0
nu = 0
for line in fin:
new_ind = len(sent2ind)
inds.append(sent2ind.setdefault(line, new_ind))
if args.unify:
if inds[-1] == new_ind:
sents.append(line[:-1])
nu += 1
else:
sents.append(line[:-1])
nu += 1
n += 1
if args.verbose:
print('{:d} lines, {:d} unique'.format(n, nu))
del sent2ind
return inds, sents
###############################################################################
#
# Wrapper for knn on CPU/GPU
#
###############################################################################
def knn(x, y, k, use_gpu):
return knnGPU(x, y, k) if use_gpu else knnCPU(x, y, k)
###############################################################################
#
# Perform knn on GPU
#
###############################################################################
def knnGPU(x, y, k, mem=5*1024*1024*1024):
dim = x.shape[1]
batch_size = mem // (dim*4)
sim = np.zeros((x.shape[0], k), dtype=np.float32)
ind = np.zeros((x.shape[0], k), dtype=np.int64)
for xfrom in range(0, x.shape[0], batch_size):
xto = min(xfrom + batch_size, x.shape[0])
bsims, binds = [], []
for yfrom in range(0, y.shape[0], batch_size):
yto = min(yfrom + batch_size, y.shape[0])
# print('{}-{} -> {}-{}'.format(xfrom, xto, yfrom, yto))
idx = faiss.IndexFlatIP(dim)
idx = faiss.index_cpu_to_all_gpus(idx)
idx.add(y[yfrom:yto])
bsim, bind = idx.search(x[xfrom:xto], min(k, yto-yfrom))
bsims.append(bsim)
binds.append(bind + yfrom)
del idx
bsims = np.concatenate(bsims, axis=1)
binds = np.concatenate(binds, axis=1)
aux = np.argsort(-bsims, axis=1)
for i in range(xfrom, xto):
for j in range(k):
sim[i, j] = bsims[i-xfrom, aux[i-xfrom, j]]
ind[i, j] = binds[i-xfrom, aux[i-xfrom, j]]
return sim, ind
###############################################################################
#
# Perform knn on CPU
#
###############################################################################
def knnCPU(x, y, k):
dim = x.shape[1]
idx = faiss.IndexFlatIP(dim)
idx.add(y)
sim, ind = idx.search(x, k)
return sim, ind
###############################################################################
#
# Scoring
#
###############################################################################
def score(x, y, fwd_mean, bwd_mean, margin):
return margin(x.dot(y), (fwd_mean + bwd_mean) / 2)
def score_candidates(x, y, candidate_inds, fwd_mean, bwd_mean, margin, verbose=False):
if verbose:
print(' - scoring {:d} candidates'.format(x.shape[0]))
scores = np.zeros(candidate_inds.shape)
for i in range(scores.shape[0]):
for j in range(scores.shape[1]):
k = candidate_inds[i, j]
scores[i, j] = score(x[i], y[k], fwd_mean[i], bwd_mean[k], margin)
return scores
###############################################################################
#
# Main
#
###############################################################################
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='LASER: Mine bitext')
parser.add_argument('src',
help='Source language corpus')
parser.add_argument('trg',
help='Target language corpus')
parser.add_argument('--encoding', default='utf-8',
help='Character encoding for input/output')
parser.add_argument('--src-lang', required=True,
help='Source language id')
parser.add_argument('--trg-lang', required=True,
help='Target language id')
parser.add_argument('--output', required=True,
help='Output file')
parser.add_argument('--threshold', type=float, default=0,
help='Threshold on extracted bitexts')
# mining params
parser.add_argument('--mode',
choices=['search', 'score', 'mine'], required=True,
help='Execution mode')
parser.add_argument('-k', '--neighborhood',
type=int, default=4,
help='Neighborhood size')
parser.add_argument('--margin',
choices=['absolute', 'distance', 'ratio'], default='ratio',
help='Margin function')
parser.add_argument('--retrieval',
choices=['fwd', 'bwd', 'max', 'intersect'], default='max',
help='Retrieval strategy')
parser.add_argument('--unify', action='store_true',
help='Unify texts')
parser.add_argument('--gpu', action='store_true',
help='Run knn on all available GPUs')
parser.add_argument('--verbose', action='store_true',
help='Detailed output')
# embeddings
parser.add_argument('--src-embeddings', required=True,
help='Precomputed source sentence embeddings')
parser.add_argument('--trg-embeddings', required=True,
help='Precomputed target sentence embeddings')
parser.add_argument('--dim', type=int, default=1024,
help='Embedding dimensionality')
parser.add_argument('--fp16', action='store_true',
help='Load precomputed embeddings in float16 format')
args = parser.parse_args()
print('LASER: tool to search, score or mine bitexts')
use_gpu = torch.cuda.is_available() and args.gpu
if use_gpu:
print(' - knn will run on all available GPUs (recommended)')
else:
print(' - knn will run on CPU (slow)')
src_inds, src_sents = TextLoadUnify(args.src, args)
trg_inds, trg_sents = TextLoadUnify(args.trg, args)
def unique_embeddings(emb, ind, verbose=False):
aux = {j: i for i, j in enumerate(ind)}
if verbose:
print(' - unify embeddings: {:d} -> {:d}'.format(len(emb), len(aux)))
return emb[[aux[i] for i in range(len(aux))]]
# load the embeddings and store as np.float32 (required for FAISS)
x = EmbedLoad(args.src_embeddings, args.dim, verbose=args.verbose, fp16=args.fp16).astype(np.float32)
if args.unify:
x = unique_embeddings(x, src_inds, args.verbose)
faiss.normalize_L2(x)
y = EmbedLoad(args.trg_embeddings, args.dim, verbose=args.verbose, fp16=args.fp16).astype(np.float32)
if args.unify:
y = unique_embeddings(y, trg_inds, args.verbose)
faiss.normalize_L2(y)
# calculate knn in both directions
if args.retrieval != 'bwd':
if args.verbose:
print(' - perform {:d}-nn source against target'.format(args.neighborhood))
x2y_sim, x2y_ind = knn(x, y, min(y.shape[0], args.neighborhood), use_gpu)
x2y_mean = x2y_sim.mean(axis=1)
if args.retrieval != 'fwd':
if args.verbose:
print(' - perform {:d}-nn target against source'.format(args.neighborhood))
y2x_sim, y2x_ind = knn(y, x, min(x.shape[0], args.neighborhood), use_gpu)
y2x_mean = y2x_sim.mean(axis=1)
# margin function
if args.margin == 'absolute':
margin = lambda a, b: a
elif args.margin == 'distance':
margin = lambda a, b: a - b
else: # args.margin == 'ratio':
margin = lambda a, b: a / b
fout = open(args.output, mode='w', encoding=args.encoding, errors='surrogateescape')
if args.mode == 'search':
if args.verbose:
print(' - Searching for closest sentences in target')
print(' - writing alignments to {:s}'.format(args.output))
scores = score_candidates(x, y, x2y_ind, x2y_mean, y2x_mean, margin, args.verbose)
best = x2y_ind[np.arange(x.shape[0]), scores.argmax(axis=1)]
nbex = x.shape[0]
ref = np.linspace(0, nbex-1, nbex).astype(int) # [0, nbex)
err = nbex - np.equal(best.reshape(nbex), ref).astype(int).sum()
print(' - errors: {:d}={:.2f}%'.format(err, 100*err/nbex))
for i in src_inds:
print(trg_sents[best[i]], file=fout)
elif args.mode == 'score':
for i, j in zip(src_inds, trg_inds):
s = score(x[i], y[j], x2y_mean[i], y2x_mean[j], margin)
print(s, src_sents[i], trg_sents[j], sep='\t', file=fout)
elif args.mode == 'mine':
if args.verbose:
print(' - mining for parallel data')
fwd_scores = score_candidates(x, y, x2y_ind, x2y_mean, y2x_mean, margin, args.verbose)
bwd_scores = score_candidates(y, x, y2x_ind, y2x_mean, x2y_mean, margin, args.verbose)
fwd_best = x2y_ind[np.arange(x.shape[0]), fwd_scores.argmax(axis=1)]
bwd_best = y2x_ind[np.arange(y.shape[0]), bwd_scores.argmax(axis=1)]
if args.verbose:
print(' - writing alignments to {:s}'.format(args.output))
if args.threshold > 0:
print(' - with threshold of {:f}'.format(args.threshold))
if args.retrieval == 'fwd':
for i, j in enumerate(fwd_best):
print(fwd_scores[i].max(), src_sents[i], trg_sents[j], sep='\t', file=fout)
if args.retrieval == 'bwd':
for j, i in enumerate(bwd_best):
print(bwd_scores[j].max(), src_sents[i], trg_sents[j], sep='\t', file=fout)
if args.retrieval == 'intersect':
for i, j in enumerate(fwd_best):
if bwd_best[j] == i:
print(fwd_scores[i].max(), src_sents[i], trg_sents[j], sep='\t', file=fout)
if args.retrieval == 'max':
indices = np.stack((np.concatenate((np.arange(x.shape[0]), bwd_best)),
np.concatenate((fwd_best, np.arange(y.shape[0])))), axis=1)
scores = np.concatenate((fwd_scores.max(axis=1), bwd_scores.max(axis=1)))
seen_src, seen_trg = set(), set()
for i in np.argsort(-scores):
src_ind, trg_ind = indices[i]
if not src_ind in seen_src and not trg_ind in seen_trg:
seen_src.add(src_ind)
seen_trg.add(trg_ind)
if scores[i] > args.threshold:
print(scores[i], src_sents[src_ind], trg_sents[trg_ind], sep='\t', file=fout)
fout.close()
|