def _step_preprocess(self,
bilingual_corpora,
monolingual_corpora,
_,
skip=False,
logger=None,
delete_on_exit=False):
preprocessed_folder = self._get_tempdir('preprocessed')
cleaned_folder = self._get_tempdir('clean_corpora')
# if skip is true, then we are in resume mode, so return the already existing results
if skip:
processed_bicorpora, processed_monocorpora = BilingualCorpus.splitlist(
self._engine.source_lang,
self._engine.target_lang,
roots=preprocessed_folder)
cleaned_bicorpora = BilingualCorpus.list(cleaned_folder)
else:
processed_bicorpora, processed_monocorpora = self._engine.training_preprocessor.process(
bilingual_corpora + monolingual_corpora,
preprocessed_folder,
(self._engine.data_path if self._split_trainingset else None),
log=logger.stream)
cleaned_bicorpora = self._engine.training_preprocessor.clean(
processed_bicorpora, cleaned_folder)
return processed_bicorpora, processed_monocorpora, cleaned_bicorpora
def process(self, corpora, dest_folder, print_tags=True, print_placeholders=False, original_spacing=False):
for corpus in corpora:
for lang in corpus.langs:
source = corpus.get_file(lang)
dest = BilingualCorpus.make_parallel(corpus.name, dest_folder, [lang])
self.__process_file(source, dest, lang, print_tags, print_placeholders, original_spacing)
return BilingualCorpus.list(dest_folder)
def process_corpora(self, corpora, dest_folder):
fileutils.makedirs(dest_folder, exist_ok=True)
for corpus in corpora:
for lang in corpus.langs:
source = corpus.get_file(lang)
dest = BilingualCorpus.make_parallel(corpus.name, dest_folder, [lang])
self.process_file(source, dest, lang)
return BilingualCorpus.list(dest_folder)
def process_corpora(self, corpora, dest_folder):
fileutils.makedirs(dest_folder, exist_ok=True)
for corpus in corpora:
for lang in corpus.langs:
source = corpus.get_file(lang)
dest = BilingualCorpus.make_parallel(corpus.name, dest_folder, [lang])
self.process_file(source, dest, lang)
return BilingualCorpus.list(dest_folder)
def encode(self, corpora, dest_folder):
if not os.path.isdir(dest_folder):
fileutils.makedirs(dest_folder, exist_ok=True)
for corpus in corpora:
for lang in corpus.langs:
source = corpus.get_file(lang)
dest_file = BilingualCorpus.make_parallel(corpus.name, dest_folder, [lang]).get_file(lang)
self.encode_file(source, dest_file, delete_nl=True)
return BilingualCorpus.list(dest_folder)
def encode(self, corpora, dest_folder):
if not os.path.isdir(dest_folder):
fileutils.makedirs(dest_folder, exist_ok=True)
for corpus in corpora:
for lang in corpus.langs:
source = corpus.get_file(lang)
dest_file = BilingualCorpus.make_parallel(corpus.name, dest_folder, [lang]).get_file(lang)
self.encode_file(source, dest_file, delete_nl=True)
return BilingualCorpus.list(dest_folder)
def _make_training_folder(self, bilingual_corpora, monolingual_corpora, domains, folder):
for corpus in bilingual_corpora:
dest_corpus = BilingualCorpus.make_parallel(domains[corpus.name], folder, corpus.langs)
for lang in corpus.langs:
os.symlink(corpus.get_file(lang), dest_corpus.get_file(lang))
for corpus in monolingual_corpora:
dest_corpus = BilingualCorpus.make_parallel(corpus.name, folder, corpus.langs)
for lang in corpus.langs:
os.symlink(corpus.get_file(lang), dest_corpus.get_file(lang))
return BilingualCorpus.splitlist(self._source_lang, self._target_lang, roots=folder)
def _make_training_folder(self, bilingual_corpora, monolingual_corpora, domains, folder):
for corpus in bilingual_corpora:
dest_corpus = BilingualCorpus.make_parallel(domains[corpus.name], folder, corpus.langs)
for lang in corpus.langs:
os.symlink(corpus.get_file(lang), dest_corpus.get_file(lang))
for corpus in monolingual_corpora:
dest_corpus = BilingualCorpus.make_parallel(corpus.name, folder, corpus.langs)
for lang in corpus.langs:
os.symlink(corpus.get_file(lang), dest_corpus.get_file(lang))
return BilingualCorpus.splitlist(self._source_lang, self._target_lang, roots=folder)
def main_sweep(argv):
parser = argparse.ArgumentParser(description='Sweep SA sample size and measure BLEU scores at various settings.')
parser.add_argument('-e', '--engine', dest='engine', help='the engine name, \'default\' will be used if absent',
default=None)
parser.add_argument('--path', dest='corpora_path', metavar='CORPORA', default=None,
help='the path to the test corpora (default is the automatically splitted sample)')
args = parser.parse_args(argv)
samples = [int(e) for e in '10 20 50 70 80 90 100 110 120 150 200 350 500 800 1000 2000 5000'.split()]
injector = dependency.Injector()
#injector.read_args(args)
engine = MMTEngine(args.engine)
injector.inject(engine)
node = ClusterNode(engine, api_port=DEFAULT_MMT_API_PORT)
# more or less copy-pasted from mmt evaluate:
evaluator = Evaluator(node, google_key='1234', use_sessions=True)
corpora = BilingualCorpus.list(args.corpora_path) if args.corpora_path is not None \
else BilingualCorpus.list(os.path.join(node.engine.data_path, TrainingPreprocessor.TEST_FOLDER_NAME))
lines = 0
for corpus in corpora:
lines += corpus.count_lines()
# end copy-paste
print('sample bleu')
for sample in samples:
node.engine.set_config_option('suffixarrays', 'sample', sample)
injector.read_config(node.engine.config) # to get engine.set() to affect MosesFeatures -> moses.ini
injector.inject(node.engine)
node.engine.write_configs()
node.restart()
scores = evaluator.evaluate(corpora=corpora, heval_output=None,
debug=False)
engine_scores = [r for r in scores if r.id == 'MMT'][0]
if engine_scores.error:
raise RuntimeError(engine_scores.error)
bleu = engine_scores.bleu
print(sample, '%.2f' % (bleu * 100))
def translate(self, corpora, output):
"""
Translate the given corpora in parallel processing fashion.
:param corpora: list of ParallelCorpus
:param output: path to output directory
:return: ([ParallelCorpus, ...], time_per_sentence, parallelism)
"""
pool = multithread.Pool(self._threads)
try:
translations = []
start_time = datetime.now()
for corpus in corpora:
self._before_translate(corpus)
with open(corpus.get_file(self.source_lang)) as source:
output_path = os.path.join(
output, corpus.name + '.' + self.target_lang)
for line in source:
translation = pool.apply_async(self._get_translation,
(line, corpus))
translations.append((translation, output_path))
self._after_translate(corpus)
elapsed_time = 0
translation_count = 0
path = None
stream = None
for translation_job, output_path in translations:
translation, elapsed = translation_job.get()
if output_path != path:
if stream is not None:
stream.close()
stream = open(output_path, 'wb')
path = output_path
stream.write(translation.encode('utf-8'))
stream.write('\n')
elapsed_time += elapsed
translation_count += 1
if stream is not None:
stream.close()
end_time = datetime.now()
total_time = end_time - start_time
return BilingualCorpus.list(output), (
elapsed_time / translation_count), (elapsed_time /
total_time.total_seconds())
finally:
pool.terminate()
def process(self, corpora, output_path, data_path=None):
args = [
'-s', self._source_lang, '-t', self._target_lang, '-v',
self._vocabulary_path, '--output', output_path, '--input'
]
input_paths = set([corpus.get_folder() for corpus in corpora])
for root in input_paths:
args.append(root)
if data_path is not None:
args.append('--dev')
args.append(
os.path.join(data_path, TrainingPreprocessor.DEV_FOLDER_NAME))
args.append('--test')
args.append(
os.path.join(data_path, TrainingPreprocessor.TEST_FOLDER_NAME))
command = mmt_javamain(self._java_mainclass, args)
shell.execute(command,
stdin=shell.DEVNULL,
stdout=shell.DEVNULL,
stderr=shell.DEVNULL)
return BilingualCorpus.splitlist(self._source_lang,
self._target_lang,
roots=output_path)
def build(self, corpora, working_dir='.', log=None):
if log is None:
log = shell.DEVNULL
shutil.rmtree(self._model, ignore_errors=True)
fileutils.makedirs(self._model, exist_ok=True)
if not os.path.isdir(working_dir):
fileutils.makedirs(working_dir, exist_ok=True)
merged_corpus = BilingualCorpus.make_parallel(
'merge', working_dir, (self._source_lang, self._target_lang))
fileutils.merge(
[corpus.get_file(self._source_lang) for corpus in corpora],
merged_corpus.get_file(self._source_lang))
fileutils.merge(
[corpus.get_file(self._target_lang) for corpus in corpora],
merged_corpus.get_file(self._target_lang))
command = [
self._build_bin, '-s',
merged_corpus.get_file(self._source_lang), '-t',
merged_corpus.get_file(self._target_lang), '-m', self._model, '-I',
'4'
]
shell.execute(command, stdout=log, stderr=log)
def train(self, corpora, aligner, working_dir='.', log=None):
if log is None:
log = shell.DEVNULL
if os.path.isdir(self._model) and len(os.listdir(self._model)) > 0:
raise Exception('Model already exists at ' + self._model)
if not os.path.isdir(self._model):
fileutils.makedirs(self._model, exist_ok=True)
if not os.path.isdir(working_dir):
fileutils.makedirs(working_dir, exist_ok=True)
train_corpora = [] # Prepare training folder
for corpus in corpora:
dest_corpus = BilingualCorpus.make_parallel(corpus.name, working_dir,
(self._source_lang, self._target_lang))
source_file = corpus.get_file(self._source_lang)
target_file = corpus.get_file(self._target_lang)
os.symlink(source_file, dest_corpus.get_file(self._source_lang))
os.symlink(target_file, dest_corpus.get_file(self._target_lang))
train_corpora.append(dest_corpus)
# Align corpora
aligner.align(train_corpora, working_dir, log=log)
# Build models
command = [self._build_bin, '--input', working_dir, '--model', self._model,
'-s', self._source_lang, '-t', self._target_lang]
shell.execute(command, stdout=log, stderr=log)
def train(self, corpora, aligner, working_dir='.', log_file=None):
if os.path.isdir(self._model) and len(os.listdir(self._model)) > 0:
raise Exception('Model already exists at ' + self._model)
if not os.path.isdir(self._model):
fileutils.makedirs(self._model, exist_ok=True)
if not os.path.isdir(working_dir):
fileutils.makedirs(working_dir, exist_ok=True)
log = shell.DEVNULL
try:
if log_file is not None:
log = open(log_file, 'a')
# Prepare training folder
for corpus in corpora:
dest_corpus = BilingualCorpus.make_parallel(corpus.name, working_dir,
(self._source_lang, self._target_lang))
source_file = corpus.get_file(self._source_lang)
target_file = corpus.get_file(self._target_lang)
os.symlink(source_file, dest_corpus.get_file(self._source_lang))
os.symlink(target_file, dest_corpus.get_file(self._target_lang))
aligner.align(corpus, os.path.join(working_dir, corpus.name + '.align'))
# Build models
command = [self._build_bin, '--input', working_dir, '--model', self._model,
'-s', self._source_lang, '-t', self._target_lang]
shell.execute(command, stdout=log, stderr=log)
finally:
if log_file is not None:
log.close()
def build(self, corpora, working_dir='.', log_file=None):
if not os.path.isdir(working_dir):
fileutils.makedirs(working_dir, exist_ok=True)
if not os.path.isdir(self._model):
fileutils.makedirs(self._model, exist_ok=True)
merged_corpus = BilingualCorpus.make_parallel(
'merge', working_dir, (self._source_lang, self._target_lang))
fileutils.merge(
[corpus.get_file(self._source_lang) for corpus in corpora],
merged_corpus.get_file(self._source_lang))
fileutils.merge(
[corpus.get_file(self._target_lang) for corpus in corpora],
merged_corpus.get_file(self._target_lang))
log = shell.DEVNULL
try:
if log_file is not None:
log = open(log_file, 'a')
# Train model
command = [
self._build_bin, '-s',
merged_corpus.get_file(self._source_lang), '-t',
merged_corpus.get_file(self._target_lang), '-m', self._model,
'-I', '4'
]
shell.execute(command, stderr=log)
finally:
if log_file is not None:
log.close()
def _step_init(self,
bilingual_corpora,
monolingual_corpora,
skip=False,
logger=None,
delete_on_exit=False):
training_folder = self._get_tempdir('training_corpora')
# if skip is true, then we are in resume mode, so return the already existing results
if skip:
bilingual_corpora, monolingual_corpora = BilingualCorpus.splitlist(
self._engine.source_lang,
self._engine.target_lang,
roots=training_folder)
# else perform the baseline domains extraction and domain mapping, and return its result
else:
domains = self._engine.db.insert(bilingual_corpora)
bilingual_corpora = [
domain.corpus.symlink(training_folder, name=str(domain.id))
for domain in domains
]
monolingual_corpora = [
corpus.symlink(training_folder)
for corpus in monolingual_corpora
]
return bilingual_corpora, monolingual_corpora
def build(self, corpora, working_dir='.', log_file=None):
if not os.path.isdir(working_dir):
fileutils.makedirs(working_dir, exist_ok=True)
if not os.path.isdir(self._model):
fileutils.makedirs(self._model, exist_ok=True)
merged_corpus = BilingualCorpus.make_parallel('merge', working_dir, (self._source_lang, self._target_lang))
fileutils.merge([corpus.get_file(self._source_lang) for corpus in corpora],
merged_corpus.get_file(self._source_lang))
fileutils.merge([corpus.get_file(self._target_lang) for corpus in corpora],
merged_corpus.get_file(self._target_lang))
log = shell.DEVNULL
try:
if log_file is not None:
log = open(log_file, 'a')
# Train model
command = [self._build_bin,
'-s', merged_corpus.get_file(self._source_lang), '-t', merged_corpus.get_file(self._target_lang),
'-m', self._model, '-I', '4']
shell.execute(command, stderr=log)
finally:
if log_file is not None:
log.close()
def translate(self, corpora, output):
"""
Translate the given corpora in parallel processing fashion.
:param corpora: list of ParallelCorpus
:param output: path to output directory
:return: ([ParallelCorpus, ...], time_per_sentence, parallelism)
"""
pool = multithread.Pool(self._threads)
try:
translations = []
start_time = datetime.now()
for corpus in corpora:
self._before_translate(corpus)
with open(corpus.get_file(self.source_lang)) as source:
output_path = os.path.join(output, corpus.name + '.' + self.target_lang)
for line in source:
translation = pool.apply_async(self._get_translation, (line, corpus))
translations.append((translation, output_path))
self._after_translate(corpus)
elapsed_time = 0
translation_count = 0
path = None
stream = None
for translation_job, output_path in translations:
translation, elapsed = translation_job.get()
if output_path != path:
if stream is not None:
stream.close()
stream = open(output_path, 'wb')
path = output_path
stream.write(translation.encode('utf-8'))
stream.write('\n')
elapsed_time += elapsed
translation_count += 1
if stream is not None:
stream.close()
end_time = datetime.now()
total_time = end_time - start_time
return BilingualCorpus.list(output), (elapsed_time / translation_count), (
elapsed_time / total_time.total_seconds())
finally:
pool.terminate()
def clean(self, source, target, input_paths, output_path):
args = ['-s', source, '-t', target, '--output', output_path, '--input']
for root in input_paths:
args.append(root)
command = mmt_javamain(self._java_mainclass, args)
shell.execute(command, stdin=shell.DEVNULL, stdout=shell.DEVNULL, stderr=shell.DEVNULL)
return BilingualCorpus.splitlist(source, target, roots=output_path)[0]
def main_sweep(argv):
parser = argparse.ArgumentParser(description='Sweep SA sample size and measure BLEU scores at various settings.')
parser.add_argument('-e', '--engine', dest='engine', help='the engine name, \'default\' will be used if absent',
default=None)
parser.add_argument('--path', dest='corpora_path', metavar='CORPORA', default=None,
help='the path to the test corpora (default is the automatically splitted sample)')
args = parser.parse_args(argv)
samples = [int(e) for e in '10 20 50 70 80 90 100 110 120 150 200 350 500 800 1000 2000 5000'.split()]
node = ConfiguredClusterNode(args.engine)
# more or less copy-pasted from mmt evaluate:
evaluator = Evaluator(node.engine, node)
corpora = BilingualCorpus.list(args.corpora_path) if args.corpora_path is not None \
else BilingualCorpus.list(os.path.join(node.engine.data_path, TrainingPreprocessor.TEST_FOLDER_NAME))
lines = 0
for corpus in corpora:
lines += corpus.count_lines()
# end copy-paste
print('sample bleu')
for sample in samples:
node.set('suffixarrays', 'sample', sample)
node.apply_configs()
scores = evaluator.evaluate(corpora=corpora, google_key='1234', heval_output=None,
use_sessions=True, debug=False)
engine_scores = scores['MMT']
if isinstance(engine_scores, str):
raise RuntimeError(engine_scores)
bleu = engine_scores['bleu']
print(sample, '%.2f' % (bleu * 100))
def clean(self, corpora, output_path):
args = ['-s', self._source_lang, '-t', self._target_lang, '--output', output_path, '--input']
input_paths = set([corpus.get_folder() for corpus in corpora])
for root in input_paths:
args.append(root)
command = mmt_javamain(self._java_mainclass, args)
shell.execute(command, stdin=shell.DEVNULL, stdout=shell.DEVNULL, stderr=shell.DEVNULL)
return BilingualCorpus.list(output_path)
def clean(self, corpora, output_path, log=None):
if log is None:
log = shell.DEVNULL
args = ['-s', self._source_lang, '-t', self._target_lang, '--output', output_path, '--input']
input_paths = set([corpus.get_folder() for corpus in corpora])
for root in input_paths:
args.append(root)
command = mmt_javamain(self._java_mainclass, args)
shell.execute(command, stdout=log, stderr=log)
return BilingualCorpus.list(output_path)
def process(self, source, target, input_paths, output_path, data_path=None):
args = ['-s', source, '-t', target, '--output', output_path, '--input']
for root in input_paths:
args.append(root)
if data_path is not None:
args.append('--dev')
args.append(os.path.join(data_path, TrainingPreprocessor.DEV_FOLDER_NAME))
args.append('--test')
args.append(os.path.join(data_path, TrainingPreprocessor.TEST_FOLDER_NAME))
command = mmt_javamain(self._java_mainclass, args)
shell.execute(command, stdin=shell.DEVNULL, stdout=shell.DEVNULL, stderr=shell.DEVNULL)
return BilingualCorpus.splitlist(source, target, roots=output_path)
def _step_tm_cleanup(self,
corpora,
skip=False,
logger=None,
delete_on_exit=True):
# the folder where tm_cleanup results are to be stored
folder = self._get_tempdir('clean_tms')
# if skip is true, then we are in resume mode, so use the already existing results
if skip:
clean_tms = BilingualCorpus.list(folder)
# else perform the cleaning on the corpora and use the clean corpora
else:
clean_tms = self._engine.cleaner.clean(corpora,
folder,
log=logger.stream)
return clean_tms
def process(self, corpora, output_path, data_path=None):
args = ['-s', self._source_lang, '-t', self._target_lang, '-v', self._vocabulary_path, '--output', output_path,
'--input']
input_paths = set([corpus.get_folder() for corpus in corpora])
for root in input_paths:
args.append(root)
if data_path is not None:
args.append('--dev')
args.append(os.path.join(data_path, TrainingPreprocessor.DEV_FOLDER_NAME))
args.append('--test')
args.append(os.path.join(data_path, TrainingPreprocessor.TEST_FOLDER_NAME))
command = mmt_javamain(self._java_mainclass, args)
shell.execute(command, stdin=shell.DEVNULL, stdout=shell.DEVNULL, stderr=shell.DEVNULL)
return BilingualCorpus.splitlist(self._source_lang, self._target_lang, roots=output_path)
def train(self, corpora, aligner, working_dir='.', log_file=None):
if os.path.isdir(self._model) and len(os.listdir(self._model)) > 0:
raise Exception('Model already exists at ' + self._model)
if not os.path.isdir(self._model):
fileutils.makedirs(self._model, exist_ok=True)
if not os.path.isdir(working_dir):
fileutils.makedirs(working_dir, exist_ok=True)
log = shell.DEVNULL
try:
if log_file is not None:
log = open(log_file, 'a')
# Prepare training folder
for corpus in corpora:
dest_corpus = BilingualCorpus.make_parallel(
corpus.name, working_dir,
(self._source_lang, self._target_lang))
source_file = corpus.get_file(self._source_lang)
target_file = corpus.get_file(self._target_lang)
os.symlink(source_file,
dest_corpus.get_file(self._source_lang))
os.symlink(target_file,
dest_corpus.get_file(self._target_lang))
aligner.align(
corpus, os.path.join(working_dir, corpus.name + '.align'))
# Build models
command = [
self._build_bin, '--input', working_dir, '--model',
self._model, '-s', self._source_lang, '-t', self._target_lang
]
shell.execute(command, stdout=log, stderr=log)
finally:
if log_file is not None:
log.close()
def clean(self, corpora, output_path, log=None):
if log is None:
log = shell.DEVNULL
# read memory size
mem_bytes = os.sysconf('SC_PAGE_SIZE') * os.sysconf('SC_PHYS_PAGES') # e.g. 4015976448
mem_mb = mem_bytes / (1024. ** 2) # e.g. 3.74
extended_heap_mb = int(mem_mb*90/100)
args = ['-s', self._source_lang, '-t', self._target_lang, '--output', output_path, '--input']
input_paths = set([corpus.get_folder() for corpus in corpora])
for root in input_paths:
args.append(root)
command = mmt_javamain(self._java_mainclass, args=args, max_heap_mb=extended_heap_mb)
shell.execute(command, stdout=log, stderr=log)
return BilingualCorpus.list(output_path)
def clean(self, corpora, dest_folder):
langs = (self._source_lang, self._target_lang)
_pool_exec(self._clean_file, [(corpus, dest_folder, langs) for corpus in corpora])
return BilingualCorpus.list(dest_folder)
def clean(self, corpora, dest_folder, langs=None):
if langs is None and len(corpora) > 0:
langs = (corpora[0].langs[0], corpora[0].langs[1])
self._pool_exec(self._clean_file, [(corpus, dest_folder, langs) for corpus in corpora])
return BilingualCorpus.list(dest_folder)
def _build(self, resume=False):
self._temp_dir = self._engine.get_tempdir('training',
ensure=(not resume))
self._checkpoint_path = os.path.join(self._temp_dir, 'checkpoint.json')
self._passed_steps = []
if resume:
self.load_checkpoint()
else:
self.save_checkpoint()
# initialize thee checkpoint manager
source_lang = self._engine.source_lang
target_lang = self._engine.target_lang
# separate bilingual and monolingual corpora in separate lists, reading them from roots
bilingual_corpora, monolingual_corpora = BilingualCorpus.splitlist(
source_lang, target_lang, roots=self._roots)
# if no bilingual corpora are found, it is not possible to train the translation system
if len(bilingual_corpora) == 0:
raise IllegalArgumentException(
'you project does not include %s-%s data.' %
(source_lang.upper(), target_lang.upper()))
# if no old engines (i.e. engine folders) can be found, create a new one from scratch
# if we are not trying to resume an old one, create from scratch anyway
if not os.path.isdir(self._engine.path) or not resume:
shutil.rmtree(self._engine.path, ignore_errors=True)
os.makedirs(self._engine.path)
# Check if all requirements are fulfilled before launching engine training
self._check_constraints()
# Create a new logger for the building activities,
# passing it the amount of steps to perform (plus a non user-decidable step)
# and the name of the log file to create
logger = _builder_logger(
len(self._scheduled_steps) + 1,
self._engine.get_logfile('training'))
delete_on_exit = not self._debug
# Start the engine building (training) phases
try:
# tell the logger that the engine training has started
logger.start(self._engine, bilingual_corpora, monolingual_corpora)
# ~~~~~~~~~~~~~~~~~~~~~ RUN ALL STEPS ~~~~~~~~~~~~~~~~~~~~~
# Note: if resume is true, a step is only run if it was not in the previous attempt
# run tm_cleanup step on the bilingual_corpora if required.
# Obtain cleaned bicorpora
cleaned_bicorpora = self._run_step('tm_cleanup',
self._step_tm_cleanup,
logger=logger,
values=[bilingual_corpora],
delete_on_exit=delete_on_exit)
# run __db_map step (always: user can't skip it)
# on the cleaned bicorpora and the original monocorpora;
# obtain base bicorpora and base monocorpora
base_bicorpora, base_monocorpora = self._run_step(
'__db_map',
self._step_init,
forced=True,
values=[cleaned_bicorpora, monolingual_corpora],
delete_on_exit=delete_on_exit)
# run preprocess step if required.
# Return processed bi and mono corpora and cleaned bicorpora
processed_bicorpora, processed_monocorpora, cleaned_bicorpora = \
self._run_step('preprocess',
self._step_preprocess,
logger=logger,
values=[base_bicorpora, base_monocorpora, base_bicorpora],
delete_on_exit=delete_on_exit)
# run context_analyzer step base_bicorpora if required.
_ = self._run_step('context_analyzer',
self._step_context_analyzer,
logger=logger,
values=[base_bicorpora],
delete_on_exit=delete_on_exit)
# run aligner step cleaned_bicorpora if required.
_ = self._run_step('aligner',
self._step_aligner,
logger=logger,
values=[cleaned_bicorpora],
delete_on_exit=delete_on_exit)
# run tm step cleaned_bicorpora if required.
_ = self._run_step('tm',
self._step_tm,
logger=logger,
values=[cleaned_bicorpora],
delete_on_exit=delete_on_exit)
# run lm step on the joint list of processed_bicorpora and processed_monocorpora
_ = self._run_step(
'lm',
self._step_lm,
logger=logger,
values=[processed_bicorpora + processed_monocorpora],
delete_on_exit=delete_on_exit)
# Writing config file
with logger.step('Writing config files') as _:
self._engine.write_configs()
# tell the logger that the engine training has completed
logger.completed()
# if this is not debug mode, then the training temporary folder must be deleted
if not self._debug:
self._engine.clear_tempdir('training')
except:
logger.error()
raise
finally:
logger.close()
def train(self, corpora, aligner, working_dir='.', log_file=None):
if os.path.isdir(self._model) and len(os.listdir(self._model)) > 0:
raise Exception('Model already exists at ' + self._model)
if not os.path.isdir(self._model):
fileutils.makedirs(self._model, exist_ok=True)
if not os.path.isdir(working_dir):
fileutils.makedirs(working_dir, exist_ok=True)
l1 = self._source_lang
l2 = self._target_lang
langs = (l1, l2)
langs_suffix = l1 + '-' + l2
mct_base = self._get_model_basename()
dmp_file = mct_base + '.dmp'
mam_file = mct_base + '.' + langs_suffix + '.mam'
lex_file = mct_base + '.' + langs_suffix + '.lex'
log = shell.DEVNULL
try:
if log_file is not None:
log = open(log_file, 'a')
# Clean corpus for training
clean_output = os.path.join(working_dir, 'clean_corpora')
fileutils.makedirs(clean_output, exist_ok=True)
corpora = self._cleaner.clean(corpora, clean_output, (self._source_lang, self._target_lang))
# Create merged corpus and domains list file (dmp)
merged_corpus = BilingualCorpus.make_parallel(os.path.basename(mct_base), working_dir, langs)
fileutils.merge([corpus.get_file(l1) for corpus in corpora], merged_corpus.get_file(l1))
fileutils.merge([corpus.get_file(l2) for corpus in corpora], merged_corpus.get_file(l2))
with open(dmp_file, 'w') as dmp:
for corpus in corpora:
dmp.write(str(corpus.name) + ' ' + str(corpus.count_lines()) + '\n')
# Create alignments in 'bal' file and symmetrize
bal_file = aligner.align(merged_corpus, langs, self._model, working_dir, log_file)
symal_file = os.path.join(working_dir, 'alignments.' + langs_suffix + '.symal')
symal_command = [self._symal_bin, '-a=g', '-d=yes', '-f=yes', '-b=yes']
with open(bal_file) as stdin:
with open(symal_file, 'w') as stdout:
shell.execute(symal_command, stdin=stdin, stdout=stdout, stderr=log)
# Execute mtt-build
mttbuild_command = self._get_mttbuild_command(mct_base, dmp_file, l1)
with open(merged_corpus.get_file(l1)) as stdin:
shell.execute(mttbuild_command, stdin=stdin, stdout=log, stderr=log)
mttbuild_command = self._get_mttbuild_command(mct_base, dmp_file, l2)
with open(merged_corpus.get_file(l2)) as stdin:
shell.execute(mttbuild_command, stdin=stdin, stdout=log, stderr=log)
# Create 'mam' file
mam_command = [self._symal2mam_bin, mam_file]
with open(symal_file) as stdin:
shell.execute(mam_command, stdin=stdin, stdout=log, stderr=log)
# Create 'lex' file
lex_command = [self._mmlexbuild_bin, mct_base + '.', l1, l2, '-o', lex_file]
shell.execute(lex_command, stdout=log, stderr=log)
finally:
if log_file is not None:
log.close()
def tune(self,
corpora=None,
debug=False,
context_enabled=True,
random_seeds=False,
max_iterations=25,
early_stopping_value=None):
if corpora is None:
corpora = BilingualCorpus.list(
os.path.join(self.engine.data_path,
TrainingPreprocessor.DEV_FOLDER_NAME))
target_lang = self.engine.target_lang
source_lang = self.engine.source_lang
corpora = [
corpus for corpus in corpora
if source_lang in corpus.langs and target_lang in corpus.langs
]
if len(corpora) == 0:
raise IllegalArgumentException(
'No %s > %s corpora found into specified path' %
(source_lang, target_lang))
source_corpora = [
BilingualCorpus.make_parallel(corpus.name, corpus.get_folder(),
[source_lang]) for corpus in corpora
]
reference_corpora = [
BilingualCorpus.make_parallel(corpus.name, corpus.get_folder(),
[target_lang]) for corpus in corpora
]
cmdlogger = _tuning_logger(4)
cmdlogger.start(self, corpora)
working_dir = self.engine.get_tempdir('tuning')
mert_wd = os.path.join(working_dir, 'mert')
try:
# Tokenization
tokenizer = Tokenizer(target_lang)
tokenized_output = os.path.join(working_dir, 'reference_corpora')
fileutils.makedirs(tokenized_output, exist_ok=True)
with cmdlogger.step('Corpora tokenization') as _:
reference_corpora = tokenizer.process_corpora(
reference_corpora, tokenized_output)
# Create merged corpus
with cmdlogger.step('Merging corpus') as _:
# source
source_merged_corpus = os.path.join(working_dir,
'corpus.' + source_lang)
with open(source_merged_corpus, 'wb') as out:
for corpus in source_corpora:
out.write(corpus.get_file(source_lang) + '\n')
# target
target_merged_corpus = os.path.join(working_dir,
'corpus.' + target_lang)
fileutils.merge([
corpus.get_file(target_lang)
for corpus in reference_corpora
], target_merged_corpus)
# Run MERT algorithm
with cmdlogger.step('Tuning') as _:
# Start MERT
decoder_flags = ['--port', str(self.api.port)]
if self.api.root is not None:
decoder_flags += ['--root', self.api.root]
if not context_enabled:
decoder_flags.append('--skip-context-analysis')
decoder_flags.append('1')
fileutils.makedirs(mert_wd, exist_ok=True)
with tempfile.NamedTemporaryFile() as runtime_moses_ini:
command = [
self._mert_script, source_merged_corpus,
target_merged_corpus, self._mert_i_script,
runtime_moses_ini.name, '--threads',
str(multiprocessing.cpu_count()), '--mertdir',
cli.BIN_DIR, '--mertargs',
'\'--binary --sctype BLEU\'', '--working-dir', mert_wd,
'--nbest', '100', '--decoder-flags',
'"' + ' '.join(decoder_flags) + '"', '--nonorm',
'--closest', '--no-filter-phrase-table'
]
if early_stopping_value is not None:
command += [
'--bleuscorer', self._scorer_script,
'--bleuscorer-flags "-nt" --early-stopping-value %d'
% early_stopping_value
]
if not random_seeds:
command.append('--predictable-seeds')
if max_iterations > 0:
command.append('--maximum-iterations={num}'.format(
num=max_iterations))
with open(self.engine.get_logfile('mert'), 'wb') as log:
shell.execute(' '.join(command),
stdout=log,
stderr=log)
# Read optimized configuration
with cmdlogger.step('Applying changes') as _:
bleu_score = 0
weights = {}
found_weights = False
with open(os.path.join(mert_wd, 'moses.ini')) as moses_ini:
for line in moses_ini:
line = line.strip()
if len(line) == 0:
continue
elif found_weights:
tokens = line.split()
weights[tokens[0].rstrip('=')] = [
float(val) for val in tokens[1:]
]
elif line.startswith('# BLEU'):
bleu_score = float(line.split()[2])
elif line == '[weight]':
found_weights = True
_ = self.api.update_features(weights)
cmdlogger.completed(bleu_score)
finally:
if not debug:
self.engine.clear_tempdir("tuning")
def clean(self, corpora, dest_folder):
langs = (self._source_lang, self._target_lang)
_pool_exec(self._clean_file, [(corpus, dest_folder, langs) for corpus in corpora])
return BilingualCorpus.list(dest_folder)
def build(self, roots, debug=False, steps=None, split_trainingset=True):
self._temp_dir = self._engine.get_tempdir('training', ensure=True)
source_lang = self._engine.source_lang
target_lang = self._engine.target_lang
bilingual_corpora, monolingual_corpora = BilingualCorpus.splitlist(source_lang, target_lang, roots=roots)
if len(bilingual_corpora) == 0:
raise IllegalArgumentException(
'you project does not include %s-%s data.' % (source_lang.upper(), target_lang.upper()))
if steps is None:
steps = self._engine.training_steps
else:
unknown_steps = [step for step in steps if step not in self._engine.training_steps]
if len(unknown_steps) > 0:
raise IllegalArgumentException('Unknown training steps: ' + str(unknown_steps))
cmdlogger = _builder_logger(len(steps) + 1)
cmdlogger.start(self._engine, bilingual_corpora, monolingual_corpora)
shutil.rmtree(self._engine.path, ignore_errors=True)
os.makedirs(self._engine.path)
# Check disk space constraints
free_space_on_disk = fileutils.df(self._engine.path)[2]
corpus_size_on_disk = 0
for root in roots:
corpus_size_on_disk += fileutils.du(root)
free_memory = fileutils.free()
recommended_mem = self.__GB * corpus_size_on_disk / (350 * self.__MB) # 1G RAM every 350M on disk
recommended_disk = 10 * corpus_size_on_disk
if free_memory < recommended_mem or free_space_on_disk < recommended_disk:
if free_memory < recommended_mem:
print '> WARNING: more than %.fG of RAM recommended, only %.fG available' % \
(recommended_mem / self.__GB, free_memory / self.__GB)
if free_space_on_disk < recommended_disk:
print '> WARNING: more than %.fG of storage recommended, only %.fG available' % \
(recommended_disk / self.__GB, free_space_on_disk / self.__GB)
print
try:
unprocessed_bicorpora = bilingual_corpora
unprocessed_monocorpora = monolingual_corpora
# TM draft-translations cleanup
if 'tm_cleanup' in steps:
with cmdlogger.step('TMs clean-up') as _:
unprocessed_bicorpora = self._engine.cleaner.clean(
unprocessed_bicorpora, self._get_tempdir('clean_tms')
)
cleaned_bicorpora = unprocessed_bicorpora
processed_bicorpora = unprocessed_bicorpora
processed_monocorpora = unprocessed_monocorpora
# Preprocessing
if 'preprocess' in steps:
with cmdlogger.step('Corpora preprocessing') as _:
unprocessed_bicorpora, unprocessed_monocorpora = self._engine.db.generate(
unprocessed_bicorpora, unprocessed_monocorpora, self._get_tempdir('training_corpora')
)
processed_bicorpora, processed_monocorpora = self._engine.training_preprocessor.process(
unprocessed_bicorpora + unprocessed_monocorpora, self._get_tempdir('preprocessed'),
(self._engine.data_path if split_trainingset else None)
)
cleaned_bicorpora = self._engine.training_preprocessor.clean(
processed_bicorpora, self._get_tempdir('clean_corpora')
)
# Training Context Analyzer
if 'context_analyzer' in steps:
with cmdlogger.step('Context Analyzer training') as _:
log_file = self._engine.get_logfile('training.context')
self._engine.analyzer.create_index(unprocessed_bicorpora, source_lang, log_file=log_file)
# Aligner
if 'aligner' in steps:
with cmdlogger.step('Aligner training') as _:
log_file = self._engine.get_logfile('training.aligner')
working_dir = self._get_tempdir('aligner')
self._engine.aligner.build(cleaned_bicorpora, working_dir, log_file)
# Training Translation Model
if 'tm' in steps:
with cmdlogger.step('Translation Model training') as _:
working_dir = self._get_tempdir('tm')
log_file = self._engine.get_logfile('training.tm')
self._engine.pt.train(cleaned_bicorpora, self._engine.aligner, working_dir, log_file)
# Training Adaptive Language Model
if 'lm' in steps:
with cmdlogger.step('Language Model training') as _:
working_dir = self._get_tempdir('lm')
log_file = self._engine.get_logfile('training.lm')
self._engine.lm.train(processed_bicorpora + processed_monocorpora, target_lang,
working_dir, log_file)
# Writing config file
with cmdlogger.step('Writing config files') as _:
self._engine.write_configs()
cmdlogger.completed()
finally:
if not debug:
self._engine.clear_tempdir('training')
def main_sweep(argv):
parser = argparse.ArgumentParser(
description=
'Sweep SA sample size and measure BLEU scores at various settings.')
parser.add_argument(
'-e',
'--engine',
dest='engine',
help='the engine name, \'default\' will be used if absent',
default=None)
parser.add_argument(
'--path',
dest='corpora_path',
metavar='CORPORA',
default=None,
help=
'the path to the test corpora (default is the automatically splitted sample)'
)
args = parser.parse_args(argv)
samples = [
int(e) for e in
'10 20 50 70 80 90 100 110 120 150 200 350 500 800 1000 2000 5000'.
split()
]
injector = dependency.DependencyInjector()
#injector.read_args(args)
engine = MMTEngine(args.engine)
injector.inject(engine)
node = ClusterNode(engine, api_port=DEFAULT_MMT_API_PORT)
# more or less copy-pasted from mmt evaluate:
evaluator = Evaluator(node, google_key='1234', use_sessions=True)
corpora = BilingualCorpus.list(args.corpora_path) if args.corpora_path is not None \
else BilingualCorpus.list(os.path.join(node.engine.data_path, TrainingPreprocessor.TEST_FOLDER_NAME))
lines = 0
for corpus in corpora:
lines += corpus.count_lines()
# end copy-paste
print('sample bleu')
for sample in samples:
node.engine.set_config_option('suffixarrays', 'sample', sample)
injector.read_config(
node.engine.config
) # to get engine.set() to affect MosesFeatures -> moses.ini
injector.inject(node.engine)
node.engine.write_configs()
node.restart()
scores = evaluator.evaluate(corpora=corpora,
heval_output=None,
debug=False)
engine_scores = [r for r in scores if r.id == 'MMT'][0]
if engine_scores.error:
raise RuntimeError(engine_scores.error)
bleu = engine_scores.bleu
print(sample, '%.2f' % (bleu * 100))
def tune(self, corpora=None, debug=False, context_enabled=True, random_seeds=False, max_iterations=25):
if corpora is None:
corpora = BilingualCorpus.list(os.path.join(self.engine.data_path, TrainingPreprocessor.DEV_FOLDER_NAME))
if len(corpora) == 0:
raise IllegalArgumentException('empty corpora')
if not self.is_running():
raise IllegalStateException('No MMT Server running, start the engine first')
tokenizer = Tokenizer()
target_lang = self.engine.target_lang
source_lang = self.engine.source_lang
source_corpora = [BilingualCorpus.make_parallel(corpus.name, corpus.get_folder(), [source_lang])
for corpus in corpora]
reference_corpora = [BilingualCorpus.make_parallel(corpus.name, corpus.get_folder(), [target_lang])
for corpus in corpora]
cmdlogger = _tuning_logger(4)
cmdlogger.start(self, corpora)
working_dir = self.engine.get_tempdir('tuning')
mert_wd = os.path.join(working_dir, 'mert')
try:
# Tokenization
tokenized_output = os.path.join(working_dir, 'reference_corpora')
fileutils.makedirs(tokenized_output, exist_ok=True)
with cmdlogger.step('Corpora tokenization') as _:
reference_corpora = tokenizer.process_corpora(reference_corpora, tokenized_output)
# Create merged corpus
with cmdlogger.step('Merging corpus') as _:
# source
source_merged_corpus = os.path.join(working_dir, 'corpus.' + source_lang)
with open(source_merged_corpus, 'wb') as out:
for corpus in source_corpora:
out.write(corpus.get_file(source_lang) + '\n')
# target
target_merged_corpus = os.path.join(working_dir, 'corpus.' + target_lang)
fileutils.merge([corpus.get_file(target_lang) for corpus in reference_corpora], target_merged_corpus)
# Run MERT algorithm
with cmdlogger.step('Tuning') as _:
# Start MERT
decoder_flags = ['--port', str(self.api.port)]
if not context_enabled:
decoder_flags.append('--skip-context-analysis')
decoder_flags.append('1')
fileutils.makedirs(mert_wd, exist_ok=True)
with tempfile.NamedTemporaryFile() as runtime_moses_ini:
command = [self._mert_script, source_merged_corpus, target_merged_corpus,
self._mert_i_script, runtime_moses_ini.name, '--threads',
str(multiprocessing.cpu_count()), '--mertdir', cli.BIN_DIR,
'--mertargs', '\'--binary --sctype BLEU\'', '--working-dir', mert_wd, '--nbest', '100',
'--decoder-flags', '"' + ' '.join(decoder_flags) + '"', '--nonorm', '--closest',
'--no-filter-phrase-table']
if not random_seeds:
command.append('--predictable-seeds')
if max_iterations > 0:
command.append('--maximum-iterations={num}'.format(num=max_iterations))
with open(self.engine.get_logfile('mert'), 'wb') as log:
shell.execute(' '.join(command), stdout=log, stderr=log)
# Read optimized configuration
with cmdlogger.step('Applying changes') as _:
bleu_score = 0
weights = {}
found_weights = False
with open(os.path.join(mert_wd, 'moses.ini')) as moses_ini:
for line in moses_ini:
line = line.strip()
if len(line) == 0:
continue
elif found_weights:
tokens = line.split()
weights[tokens[0].rstrip('=')] = [float(val) for val in tokens[1:]]
elif line.startswith('# BLEU'):
bleu_score = float(line.split()[2])
elif line == '[weight]':
found_weights = True
_ = self.api.update_features(weights)
cmdlogger.completed(bleu_score)
finally:
if not debug:
self.engine.clear_tempdir()
def build(self, roots, debug=False, steps=None, split_trainingset=True):
self._temp_dir = self._engine.get_tempdir('training', ensure=True)
source_lang = self._engine.source_lang
target_lang = self._engine.target_lang
bilingual_corpora, monolingual_corpora = BilingualCorpus.splitlist(
source_lang, target_lang, roots=roots)
if len(bilingual_corpora) == 0:
raise IllegalArgumentException(
'you project does not include %s-%s data.' %
(source_lang.upper(), target_lang.upper()))
if steps is None:
steps = self._engine.training_steps
else:
unknown_steps = [
step for step in steps
if step not in self._engine.training_steps
]
if len(unknown_steps) > 0:
raise IllegalArgumentException('Unknown training steps: ' +
str(unknown_steps))
shutil.rmtree(self._engine.path, ignore_errors=True)
os.makedirs(self._engine.path)
# Check disk space constraints
free_space_on_disk = fileutils.df(self._engine.path)[2]
corpus_size_on_disk = 0
for root in roots:
corpus_size_on_disk += fileutils.du(root)
free_memory = fileutils.free()
recommended_mem = self.__GB * corpus_size_on_disk / (
350 * self.__MB) # 1G RAM every 350M on disk
recommended_disk = 10 * corpus_size_on_disk
if free_memory < recommended_mem or free_space_on_disk < recommended_disk:
if free_memory < recommended_mem:
print '> WARNING: more than %.fG of RAM recommended, only %.fG available' % \
(recommended_mem / self.__GB, free_memory / self.__GB)
if free_space_on_disk < recommended_disk:
print '> WARNING: more than %.fG of storage recommended, only %.fG available' % \
(recommended_disk / self.__GB, free_space_on_disk / self.__GB)
print
logger = _builder_logger(
len(steps) + 1, self._engine.get_logfile('training'))
try:
logger.start(self._engine, bilingual_corpora, monolingual_corpora)
unprocessed_bicorpora = bilingual_corpora
unprocessed_monocorpora = monolingual_corpora
# TM draft-translations cleanup
if 'tm_cleanup' in steps:
with logger.step('TMs clean-up') as _:
unprocessed_bicorpora = self._engine.cleaner.clean(
unprocessed_bicorpora,
self._get_tempdir('clean_tms'),
log=logger.stream)
cleaned_bicorpora = unprocessed_bicorpora
processed_bicorpora = unprocessed_bicorpora
processed_monocorpora = unprocessed_monocorpora
# Preprocessing
if 'preprocess' in steps:
with logger.step('Corpora preprocessing') as _:
unprocessed_bicorpora, unprocessed_monocorpora = self._engine.db.generate(
unprocessed_bicorpora,
unprocessed_monocorpora,
self._get_tempdir('training_corpora'),
log=logger.stream)
processed_bicorpora, processed_monocorpora = self._engine.training_preprocessor.process(
unprocessed_bicorpora + unprocessed_monocorpora,
self._get_tempdir('preprocessed'),
(self._engine.data_path
if split_trainingset else None),
log=logger.stream)
cleaned_bicorpora = self._engine.training_preprocessor.clean(
processed_bicorpora,
self._get_tempdir('clean_corpora'))
# Training Context Analyzer
if 'context_analyzer' in steps:
with logger.step('Context Analyzer training') as _:
self._engine.analyzer.create_index(unprocessed_bicorpora,
log=logger.stream)
# Aligner
if 'aligner' in steps:
with logger.step('Aligner training') as _:
working_dir = self._get_tempdir('aligner')
self._engine.aligner.build(cleaned_bicorpora,
working_dir,
log=logger.stream)
# Training Translation Model
if 'tm' in steps:
with logger.step('Translation Model training') as _:
working_dir = self._get_tempdir('tm')
self._engine.pt.train(cleaned_bicorpora,
self._engine.aligner,
working_dir,
log=logger.stream)
# Training Adaptive Language Model
if 'lm' in steps:
with logger.step('Language Model training') as _:
working_dir = self._get_tempdir('lm')
self._engine.lm.train(processed_bicorpora +
processed_monocorpora,
target_lang,
working_dir,
log=logger.stream)
# Writing config file
with logger.step('Writing config files') as _:
self._engine.write_configs()
logger.completed()
except:
logger.error()
raise
finally:
logger.close()
if not debug:
self._engine.clear_tempdir('training')
