def mix_datasets(datasets, props=None, new_dataset=None):
if len(set(datasets)) == 1:
return datasets[0]
if props is None:
props = [1 / len(datasets)] * len(datasets)
assert len(props) == len(datasets)
assert all([get_dataset_dir(dataset).exists() for dataset in datasets])
# Sort in unison according to dataset names
datasets, props = zip(*sorted(zip(datasets, props)))
if new_dataset is None:
new_dataset = 'mix-' + '-'.join([
f'{dataset}_{prop:.2f}' for dataset, prop in zip(datasets, props)
])
with create_directory_or_skip(get_dataset_dir(new_dataset)):
print('Mixing datasets...')
for phase, language in product(PHASES, LANGUAGES):
input_files = [
get_data_filepath(dataset, phase, language)
for dataset in datasets
]
# If one of the input files does not exist, we remove it and its prop and renormalize
input_files, current_props = zip(
*[(input_file, prop)
for input_file, prop in zip(input_files, props)
if input_file.exists()])
current_props = np.array(current_props) / np.sum(current_props)
output_file = get_data_filepath(new_dataset, phase, language)
# TODO: Jointly mix files
# The seed is set everytime mix is called, therefore they should be mixed in the same order
mix_files(input_files, current_props, output_file)
shuffle_file_lines(output_file)
return new_dataset
def create_preprocessed_dataset_one_preprocessor(dataset, preprocessor,
n_jobs):
new_dataset = get_preprocessed_dataset_name(dataset, preprocessor)
with create_directory_or_skip(get_dataset_dir(new_dataset)):
print(
f'Creating preprocessed dataset with {preprocessor}: {dataset} -> {new_dataset}'
)
new_dataset_dir = get_dataset_dir(new_dataset)
filepaths_dict = get_filepaths_dict(dataset)
new_filepaths_dict = get_filepaths_dict(new_dataset)
for phase in PHASES:
if not filepaths_dict[phase, 'complex'].exists(
) or not filepaths_dict[phase, 'complex'].exists():
continue
parallel_file_pair_preprocessor = get_parallel_file_pair_preprocessor(
preprocessor.encode_file_pair,
n_jobs=n_jobs,
)
parallel_file_pair_preprocessor(
filepaths_dict[phase, 'complex'],
filepaths_dict[phase, 'simple'],
new_filepaths_dict[phase, 'complex'],
new_filepaths_dict[phase, 'simple'],
)
previous_preprocessors = load_preprocessors(get_dataset_dir(dataset))
if previous_preprocessors is not None:
preprocessors = previous_preprocessors + [preprocessor]
else:
preprocessors = [preprocessor]
dump_preprocessors(preprocessors, new_dataset_dir)
with open(new_dataset_dir / 'original_dataset', 'w') as f:
f.write(dataset + '\n')
if hasattr(preprocessor, 'copy_sentencepiece_files_to_dir'):
preprocessor.copy_sentencepiece_files_to_dir(new_dataset_dir)
return new_dataset
def combine_simplifications_in_dataset(simplification_pairs, dataset):
with create_directory_or_skip(get_dataset_dir(dataset)):
assert len(simplification_pairs) > 30000, f'Not enough pairs: {len(simplification_pairs)}'
indexes = np.random.permutation(len(simplification_pairs))
for phase, start_index, end_index in [
('test', 10000, 20000),
('valid', 20000, 30000),
('train', 30000, len(indexes)),
]:
with write_lines_in_parallel(
[get_data_filepath(dataset, phase, 'complex'), get_data_filepath(dataset, phase, 'simple')]
) as files:
for idx in tqdm(indexes[start_index:end_index]):
files.write(simplification_pairs[idx])
return get_dataset_dir(dataset)
def finetune_and_predict_on_dataset(finetuning_dataset, exp_dir, **kwargs):
kwargs['train_kwargs']['ngpus'] = 1
prefix = 'finetune'
if kwargs.get('fast_parametrization_search', False):
prefix += '_fast'
pred_filepaths = [
exp_dir /
f'{prefix}_{finetuning_dataset}_valid-test_{finetuning_dataset}_valid.pred',
exp_dir /
f'{prefix}_{finetuning_dataset}_valid-test_{finetuning_dataset}_test.pred',
]
if all([path.exists() for path in pred_filepaths]):
return
for phase, pred_filepath in zip(['valid', 'test'], pred_filepaths):
orig_sents_path = get_data_filepath(finetuning_dataset, phase,
'complex')
refs_sents_paths = list(
get_dataset_dir(finetuning_dataset).glob(f'{phase}.simple*'))
kwargs['evaluate_kwargs'] = {
'test_set': 'custom',
'orig_sents_path': orig_sents_path,
'refs_sents_paths': refs_sents_paths,
}
if phase == 'valid':
# Finetune preprocessors_kwargs only on valid
kwargs['preprocessors_kwargs'] = find_best_parametrization(
exp_dir, **kwargs)
shutil.copyfile(
fairseq_get_simplifier(exp_dir, **kwargs)(orig_sents_path),
pred_filepath)
def get_scores_on_dataset(pred_path, dataset, phase):
orig_sents_path = get_data_filepath(dataset, phase, 'complex')
refs_sents_paths = list(get_dataset_dir(dataset).glob(f'{phase}.simple*'))
return evaluate_system_output(
'custom',
sys_sents_path=pred_path,
orig_sents_path=orig_sents_path,
refs_sents_paths=refs_sents_paths,
metrics=['sari', 'bleu', 'fkgl', 'sari_by_operation'],
quality_estimation=False,
)
def apply_line_function_to_dataset(line_function,
dataset,
new_dataset,
languages=LANGUAGES):
'''Provided function signature: line_function(line) -> line'''
with create_directory_or_skip(get_dataset_dir(new_dataset)):
for phase, language in product(PHASES, languages):
source_filepath = get_data_filepath(dataset, phase, language)
target_filepath = get_data_filepath(new_dataset, phase, language)
if not source_filepath.exists():
continue
apply_line_function_to_file(line_function, source_filepath,
target_filepath)
return new_dataset
def create_smaller_dataset(dataset, n_lines):
new_dataset = f'{dataset}-lines{n_lines}'
with create_directory_or_skip(get_dataset_dir(new_dataset)):
filepaths_dict = get_filepaths_dict(dataset)
new_filepaths_dict = get_filepaths_dict(new_dataset)
for phase, language in product(['train'], LANGUAGES):
with open(new_filepaths_dict[(phase, language)],
'w') as output_file:
for line in yield_lines(filepaths_dict[(phase, language)],
n_lines=n_lines):
output_file.write(line + '\n')
for phase, language in product(['valid', 'test'], LANGUAGES):
shutil.copy(filepaths_dict[(phase, language)],
new_filepaths_dict[(phase, language)])
return new_dataset
def mlm_fairseq_preprocess(dataset):
'''Too specific for ts.fairseq.base.fairseq_preprocess'''
dataset_dir = get_dataset_dir(dataset)
with lock_directory(dataset_dir):
preprocessed_dir = dataset_dir / 'fairseq_preprocessed'
with create_directory_or_skip(preprocessed_dir):
vocab_path = get_data_filepath(dataset, 'vocab', 'fr')
assert vocab_path.exists()
trainpref = get_data_filepath(dataset, 'train', 'fr')
validpref = get_data_filepath(dataset, 'valid', 'fr')
testpref = get_data_filepath(dataset, 'test', 'fr')
command = f'fairseq-preprocess --only-source --trainpref {trainpref} --validpref {validpref} --testpref {testpref} --destdir {preprocessed_dir} --workers 64 --srcdict {vocab_path}' # noqa
print(command)
run_command(command)
return preprocessed_dir
def prepare_asset():
print('ASSET')
dataset = 'asset'
with create_directory_or_skip(get_dataset_dir(dataset)):
for phase in ('valid', 'test'):
for i in range(10):
for (old_language_name,
new_language_name) in [('orig', 'complex'),
(f'simp.{i}', f'simple.{i}')]:
url = f'https://raw.githubusercontent.com/facebookresearch/asset/master/dataset/asset.{phase}.{old_language_name}'
old_path = download(url)
new_path = get_data_filepath(dataset, phase,
new_language_name)
shutil.copyfile(old_path, new_path)
add_newline_at_end_of_file(new_path)
print('Done.')
def fairseq_prepare_and_train(dataset, **kwargs):
check_dataset(dataset)
kwargs = check_and_resolve_args(kwargs)
exp_dir = prepare_exp_dir()
preprocessors_kwargs = kwargs.get('preprocessors_kwargs', {})
preprocessors = get_preprocessors(preprocessors_kwargs)
if len(preprocessors) > 0:
dataset = create_preprocessed_dataset(dataset, preprocessors, n_jobs=8)
dataset_dir = get_dataset_dir(dataset)
shutil.copy(dataset_dir / 'preprocessors.pickle', exp_dir)
if hasattr(preprocessors[-1], 'copy_sentencepiece_files_to_dir'):
preprocessors[-1].copy_sentencepiece_files_to_dir(dataset_dir)
model_symlink_path = exp_dir / 'model.pt'
if not model_symlink_path.exists():
model_symlink_path.symlink_to('checkpoints/checkpoint_best.pt')
preprocessed_dir = fairseq_preprocess(
dataset, **kwargs.get('preprocess_kwargs', {}))
train_kwargs = kwargs.get('train_kwargs', {})
fairseq_train(preprocessed_dir, exp_dir=exp_dir, **train_kwargs)
return exp_dir
def fairseq_preprocess(dataset,
dict_path=None,
source_lang='complex',
target_lang='simple'):
dataset_dir = get_dataset_dir(dataset)
with lock_directory(dataset_dir):
preprocessed_dir = dataset_dir / f'fairseq_preprocessed_{source_lang}-{target_lang}'
with create_directory_or_skip(preprocessed_dir):
# HACK
for phase in PHASES:
for language, new_language in zip(LANGUAGES,
[source_lang, target_lang]):
symlink_path = get_data_filepath(dataset, phase,
new_language)
if not symlink_path.exists():
symlink_path.symlink_to(
get_data_filepath(dataset, phase, language))
trainpref = str(get_data_filepath(dataset, 'train',
'dummy')).replace('.dummy', '')
validpref = str(get_data_filepath(dataset, 'valid',
'dummy')).replace('.dummy', '')
testpref = str(get_data_filepath(dataset, 'test',
'dummy')).replace('.dummy', '')
args = f'''
--source-lang {source_lang} --target-lang {target_lang} --trainpref {trainpref} --validpref {validpref} --testpref {testpref}
--destdir {preprocessed_dir} --bpe sentencepiece
--joined-dictionary --workers 32
'''
if dict_path is not None:
args = f'{args} --srcdict {dict_path}'
args = remove_multiple_whitespaces(args.replace('\n',
' ')).strip(' ')
print(f'fairseq-preprocess {args}')
args = shlex.split(args)
with mock_cli_args(args):
preprocess.cli_main()
return preprocessed_dir
def prepare_wikilarge():
print('WikiLarge')
dataset = 'wikilarge' # dataset = wikismall works as well
with create_directory_or_skip(get_dataset_dir(dataset)):
url = 'https://github.com/louismartin/dress-data/raw/master/data-simplification.tar.bz2'
extracted_path = download_and_extract(url)[0]
# Process
print('Processing...')
# Only rename files and put them in local directory architecture
# FIXME: Wikilarge validations set only has 992 sentences
for phase in PHASES:
for (old_language_name, new_language_name) in [('src', 'complex'),
('dst', 'simple')]:
old_path_glob = os.path.join(
extracted_path, dataset,
f'*.ori.{phase}.{old_language_name}')
globs = glob(old_path_glob)
assert len(globs) == 1
old_path = globs[0]
new_path = get_data_filepath(dataset, phase, new_language_name)
shutil.copyfile(old_path, new_path)
shutil.move(replace_lrb_rrb_file(new_path), new_path)
add_newline_at_end_of_file(new_path)
print('Done.')
def get_original_dataset(dataset):
filepath = get_dataset_dir(dataset) / 'original_dataset'
if not filepath.exists():
return None
[original_dataset] = read_lines(filepath)
return original_dataset
def train_roberta(
dataset,
sample_break_mode='complete',
batch_size=8192,
max_sentences=16,
max_tokens=12000,
tokens_per_sample=512,
checkpoints_dir=None,
distributed_world_size=None,
sentencepiece_model_path=None,
arch='roberta_base',
dropout=0.1,
total_updates=500000,
log_interval=100,
peak_lr=0.0007,
clip_norm=None,
no_epoch_checkpoint=False,
validate_interval=1,
save_interval=1,
save_interval_updates=5000,
warmup_updates=10000,
):
preprocessed_dir = mlm_fairseq_preprocess(dataset)
if checkpoints_dir is None:
checkpoints_dir = get_fairseq_exp_dir() / 'checkpoints'
checkpoints_dir = Path(checkpoints_dir)
checkpoints_dir.mkdir(parents=True, exist_ok=True)
shutil.copy(
get_dataset_dir(dataset) / 'sentencepiece.bpe.model', checkpoints_dir)
shutil.copy(
get_dataset_dir(dataset) / 'fairseq_preprocessed/dict.txt',
checkpoints_dir)
effective_batch_size = max_sentences * distributed_world_size
# assert batch_size % effective_batch_size == 0
update_freq = int(round(batch_size / effective_batch_size, 0))
print(f'batch_size={effective_batch_size * update_freq}')
command = f'''
eval "$(conda shell.bash hook)" # Needed to use conda activate in subshells
conda activate bert_fr
fairseq-train {preprocessed_dir} \
--save-dir {checkpoints_dir} \
--task masked_lm --criterion masked_lm \
--arch {arch} --sample-break-mode {sample_break_mode} --tokens-per-sample {tokens_per_sample} \
--optimizer adam --adam-betas '(0.9, 0.98)' --adam-eps 1e-6 --clip-norm 0.0 \
--lr-scheduler polynomial_decay --lr {peak_lr} --warmup-updates {warmup_updates} --total-num-update {total_updates} \
--dropout {dropout} --attention-dropout {dropout} --weight-decay 0.01 \
--max-sentences {max_sentences} --update-freq {update_freq} \
--max-update {total_updates} --log-format simple --log-interval {log_interval} \
--skip-invalid-size-inputs-valid-test \
--save-interval-updates {save_interval_updates} \
--validate-interval {validate_interval} --save-interval {save_interval} \
--tensorboard-logdir {TENSORBOARD_LOGS_DIR} --fast-stat-sync \
--fp16 --seed 1
''' # noqa
command = command.strip(' ').strip('\n')
if distributed_world_size is not None:
command += f' --distributed-world-size {distributed_world_size} --distributed-port 53005' # noqa
if sentencepiece_model_path is not None:
command += f' --bpe sentencepiece --sentencepiece-vocab {sentencepiece_model_path} --mask-whole-words'
if max_tokens is not None:
command += f' --max-tokens {max_tokens}'
if clip_norm is not None:
command += f' --clip-norm {clip_norm}'
if no_epoch_checkpoint:
command += ' --no-epoch-checkpoints'
command = re.sub(' +', ' ', command) # Remove multiple whitespaces
print(command)
run_command(command)
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import faiss
from muss.mining.preprocessing import create_base_index, get_index_name, get_sentences_paths
from muss.utils.helpers import yield_lines
from muss.laser import get_laser_embeddings
from muss.resources.paths import get_dataset_dir
# Create index
language = 'en'
n_train_sentences = 1000000
train_sentences = []
for sentences_path in get_sentences_paths(language='en'):
for sentence in yield_lines(sentences_path):
train_sentences.append(sentence)
if len(train_sentences) == n_train_sentences:
break
if len(train_sentences) == n_train_sentences:
break
get_embeddings = lambda sentences: get_laser_embeddings(
sentences, max_tokens=3000, language=language) # noqa: E731
output_dir = get_dataset_dir('uts') / f'base_indexes/laser_{language}'
output_dir.mkdir(exist_ok=True)
create_base_index(train_sentences, get_index_name(), get_embeddings,
faiss.METRIC_INNER_PRODUCT, output_dir)
compute_and_save_simplification_pairs,
get_index_path,
compute_and_save_embeddings,
get_filter_string_representation,
combine_simplifications_in_dataset,
get_simplification_pairs_paths,
)
from muss.mining.filtering import SimplicityScorer
ccnet_dir = Path(
input(
'Please download the CCNet corpus from https://github.com/facebookresearch/cc_net and enter the path to the downloaded data: '
))
language = input('What language do you want to process? (en/fr/es): ')
cluster = 'local'
dataset_dir = get_dataset_dir('uts') / language
# For large jobs only
slurm_partition = 'dev,scavenge'
slurm_array_parallelism = 1024
# Split CCNet shards into subshards
with log_action('Splitting CCNet shards into smaller subshards'):
# We need to split each shard even more for the LASER embeddings to fit in memory
n_shards = { # Number of shards to take for each languages for ~1B sentences
'en': 15,
'fr': 25,
'es':
13, # We would need about 20 shards for 1B sentences, but there are only 13
}[language]
ccnet_filepaths = [
ccnet_dir / f'{language}_head_{i:04d}.json.gz' for i in range(n_shards)
