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#!/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
#
# --------------------------------------------------------
#
# tools for indexing and search with FAISS
import faiss
import os.path
import sys
import numpy as np
#-------------------------------------------------------------
# Get list of fnames:
# - we loop over the list of given languages
# - for each language, we also check if there are splitted files .%03d
def SplitFnames(par_fname, langs):
fnames = []
for l in langs:
fname = par_fname + '.' + l
if os.path.isfile(fname):
fnames.append(fname)
for i in range(1000):
fname = par_fname + '.' + l + '.{:03d}'.format(i)
if os.path.isfile(fname):
fnames.append(fname)
if len(fnames) == 0:
print("ERROR: no embeddings found in {:s}*".format(par_fname))
sys.exit(1)
return fnames
def SplitOpen(par_fname, langs, dim, dtype, verbose=False):
M = []
nf = 0
nc = 0
print('Reading sentence embeddings')
print(' - memory mapped files {:s}'.format(par_fname))
for fname in SplitFnames(par_fname, langs):
n = int(os.path.getsize(fname) / dim / np.dtype(dtype).itemsize)
if verbose:
print(' - {:s}: {:d} x {:d}'.format(fname, n, dim))
Mi = np.memmap(fname, mode='r', dtype=dtype, shape=(n, dim))
nc += n
nf += 1
M.append(Mi)
print(' - total of {:d} files: {:d} x {:d}'.format(nf, nc, dim))
return M
def SplitAccess(M, idx):
i = idx
for Mi in M:
n = Mi.shape[0]
if i < n:
return Mi[i,:]
i -= n
print('ERROR: index {:d} is too large form memory mapped files'.format(idx))
sys.exit(1)
###############################################################################
# create an FAISS index on the given data
def IndexCreate(dname, idx_type,
verbose=False, normalize=True, save_index=False, dim=1024):
assert idx_type == 'FlatL2', 'only FlatL2 index is currently supported'
x = np.fromfile(dname, dtype=np.float32, count=-1)
nbex = x.shape[0] // dim
print(' - embedding: {:s} {:d} examples of dim {:d}'
.format(dname, nbex, dim))
x.resize(nbex, dim)
print(' - creating FAISS index')
idx = faiss.IndexFlatL2(dim)
if normalize:
faiss.normalize_L2(x)
idx.add(x)
if save_index:
iname = 'TODO'
print(' - saving index into ' + iname)
faiss.write_index(idx, iname)
return x, idx
###############################################################################
# search closest vector for all languages pairs and calculate error rate
def IndexSearchMultiple(data, idx, langs, verbose=False, texts=None, print_errors=False):
nl = len(data)
nbex = data[0].shape[0]
err = np.zeros((nl, nl)).astype(float)
ref = np.linspace(0, nbex-1, nbex).astype(int) # [0, nbex)
if verbose:
if texts is None:
print('Calculating similarity error (indices):')
else:
print('Calculating similarity error (textual):')
for i1 in range(nl):
for i2 in range(nl):
if i1 != i2:
D, I = idx[i2].search(data[i1], 1)
if texts: # do textual comparison
e1 = 0
for p in range(I.shape[0]):
if texts[i2][p] != texts[i2][I[p,0]]:
e1 += 1
if print_errors:
print('Error {:s}\n {:s}'
.format(texts[i2][p].strip(), texts[i2][I[p,0]].strip()))
err[i1, i2] = e1 / nbex
else: # do index based comparision
err[i1, i2] \
= (nbex - np.equal(I.reshape(nbex), ref)
.astype(int).sum()) / nbex
if verbose:
print(' - similarity error {:s}/{:s}: {:5.2f}%'
.format(langs[i1], langs[i2],
100.0 * err[i1, i2]))
return err
###############################################################################
# print confusion matrix
def IndexPrintConfusionMatrix(err, langs):
nl = len(langs)
assert nl == err.shape[0], 'size of errror matrix doesn not match'
print('Confusion matrix:')
print('{:8s}'.format('langs'), end='')
for i2 in range(nl):
print('{:8s} '.format(langs[i2]), end='')
print('{:8s}'.format('avg'))
for i1 in range(nl):
print('{:3s}'.format(langs[i1]), end='')
for i2 in range(nl):
print('{:8.2f}%'.format(100 * err[i1, i2]), end='')
print('{:8.2f}%'.format(100 * err[i1, :].sum() / (nl-1)))
print('avg', end='')
for i2 in range(nl):
print('{:8.2f}%'.format(100 * err[:, i2].sum() / (nl-1)), end='')
# global average
print('{:8.2f}%'.format(100 * err.sum() / (nl-1) / nl))
###############################################################################
# Load an FAISS index
def IndexLoad(idx_name, nprobe, gpu=False):
print('Reading FAISS index')
print(' - index: {:s}'.format(idx_name))
index = faiss.read_index(idx_name)
print(' - found {:d} sentences of dim {:d}'.format(index.ntotal, index.d))
print(' - setting nbprobe to {:d}'.format(nprobe))
if gpu:
print(' - transfer index to %d GPUs ' % faiss.get_num_gpus())
#co = faiss.GpuMultipleClonerOptions()
#co.shard = True
index = faiss.index_cpu_to_all_gpus(index) # co=co
faiss.GpuParameterSpace().set_index_parameter(index, 'nprobe', nprobe)
return index
###############################################################################
# Opens a text file with the sentences corresponding to the indices used
# by an FAISS index
# We also need the reference files with the byte offsets to the beginning
# of each sentence
# optionnally: array with number of words per sentence
# All arrays are memory mapped
def IndexTextOpen(txt_fname):
print('Reading text corpus')
print(' - texts: {:s}'.format(txt_fname))
txt_mmap = np.memmap(txt_fname, mode='r', dtype=np.uint8)
fname = txt_fname.replace('.txt', '.ref.bin32')
if os.path.isfile(fname):
print(' - sentence start offsets (32 bit): {}'.format(fname))
ref_mmap = np.memmap(fname, mode='r', dtype=np.uint32)
else:
fname = txt_fname.replace('.txt', '.ref.bin64')
if os.path.isfile(fname):
print(' - sentence start offsets (64 bit): {}'.format(fname))
ref_mmap = np.memmap(fname, mode='r', dtype=np.uint64)
else:
print('ERROR: no file with sentence start offsets found')
sys.exit(1)
print(' - found {:d} sentences'.format(ref_mmap.shape[0]))
nbw_mmap = None
fname = txt_fname.replace('.txt', '.nw.bin8')
if os.path.isfile(fname):
print(' - word counts: {:s}'.format(fname))
nbw_mmap = np.memmap(fname, mode='r', dtype=np.uint8)
M = None
fname = txt_fname.replace('.txt', '.meta')
if os.path.isfile(fname):
M = []
n = 0
print(' - metafile: {:s}'.format(fname))
with open(fname, 'r') as fp:
for line in fp:
fields = line.strip().split()
if len(fields) != 2:
print('ERROR: format error in meta file')
sys.exit(1)
n += int(fields[1])
M.append({'lang': fields[0], 'n': n})
print(' - found {:d} languages:'.format(len(M)), end='')
for L in M:
print(' {:s}'.format(L['lang']), end='')
print('')
return txt_mmap, ref_mmap, nbw_mmap, M
###############################################################################
# Return the text for the given index
def IndexTextQuery(txt_mmap, ref_mmap, idx):
p = int(ref_mmap[idx]) # get starting byte position
i = 0
dim = 10000 # max sentence length in bytes
b = bytearray(dim)
# find EOL
while txt_mmap[p+i] != 10 and i < dim:
b[i] = txt_mmap[p+i]
i += 1
return b[0:i].decode('utf-8')
###############################################################################
# Search the [k] nearest vectors of [x] in the given index
# and return the text lines
def IndexSearchKNN(index, x, T, R, kmax=1, Dmax=1.0, dedup=True):
D, I = index.search(x, kmax)
prev = {} # for depuplication
res = []
for n in range(x.shape[0]):
for i in range(kmax):
txt = IndexTextQuery(T, R, I[n, i])
if (dedup and txt not in prev) and D[n, i] <= Dmax:
prev[txt] = 1
res.append([txt, D[n, i]])
return res
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