def _cat_and_remove(tgt_filename, num_workers):
with file_io.open(tgt_filename, 'w') as writer:
for idx in range(num_workers):
src_filename = tgt_filename + str(idx)
with file_io.open(src_filename, 'r') as reader:
PathManager.copyfileobj(reader, writer)
PathManager.rm(src_filename)
def _concate(_tgt_filename, num_workers, tgt_filename):
src_filenames = [
_tgt_filename + str(idx) for idx in range(num_workers)
]
with file_io.open(tgt_filename, 'w') as writer:
for _src_fl in src_filenames:
with file_io.open(_src_fl, 'r') as reader:
shutil.copyfileobj(reader, writer)
PathManager.rm(_src_fl)
def merge_attr_files(flatten_dir, lang, mode, attrs):
"""shell cat"""
def _merge_files(src_files, tgt_file):
with file_io.open(tgt_file, 'w') as writer:
for src_fl in src_files:
with file_io.open(src_fl, 'r') as reader:
shutil.copyfileobj(reader, writer)
def _get_file_idx(filename):
filename = os.path.split(filename)[-1]
idx = int(filename[:str.rfind(filename, '.json')])
return idx
for attr in attrs:
attr_files = PathManager.ls(
os.path.join(flatten_dir, lang, mode, attr, '*.jsonl'))
attr_files = sorted(attr_files, key=_get_file_idx)
assert len(attr_files) > 0, RuntimeError(
'Attribute({}) files do not exist.'.format(attr))
dest_file = os.path.join(flatten_dir, lang, '{}.{}'.format(mode, attr))
_merge_files(attr_files, dest_file)
PathManager.rm(os.path.join(flatten_dir, lang, mode))
def single_main(args, init_distributed=False):
assert args['dataset']['max_tokens'] is not None or args['dataset']['max_sentences'] is not None, \
'Must specify batch size either with --max-tokens or --max-sentences'
metrics.reset()
# 0. Initialize CUDA and distributed training
if torch.cuda.is_available() and not args['common']['cpu']:
torch.cuda.set_device(args['distributed_training']['device_id'])
set_seed.set_seed(args['common']['seed'])
if init_distributed:
args['distributed_training'][
'distributed_rank'] = distributed_utils.distributed_init(args)
# Verify checkpoint directory
if distributed_utils.is_master(args):
save_dir = args['checkpoint']['save_dir']
checkpoint_utils.verify_checkpoint_directory(save_dir)
PathManager.rm(os.path.join(
save_dir, '*.pt')) # this code will remove pre-trained models
# 1. Setup task, e.g., translation, language modeling, etc.
task = tasks.setup_task(args)
# 2. Load valid dataset (we load training data below, based on the latest checkpoint)
task.load_dataset(args['dataset']['valid_subset'], combine=False, epoch=1)
# 3. Build model and criterion
model = task.build_model(args)
criterion = task.build_criterion(args)
LOGGER.info(model)
LOGGER.info('model {}, criterion {}'.format(args['model']['arch'],
criterion.__class__.__name__))
LOGGER.info('num. model params: {} (num. trained: {})'.format(
sum(p.numel() for p in model.parameters()),
sum(p.numel() for p in model.parameters() if p.requires_grad),
))
# 4. Build trainer
trainer = Trainer(args, task, model, criterion)
LOGGER.info('training on {} GPUs'.format(
args['distributed_training']['distributed_world_size']))
LOGGER.info(
'max tokens per GPU = {} and max sentences per GPU = {}'.format(
args['dataset']['max_tokens'],
args['dataset']['max_sentences'],
))
# 5. Load the latest checkpoint if one is available and restore the corresponding train iterator
extra_state, epoch_itr = checkpoint_utils.load_checkpoint(args,
trainer,
combine=False)
# 6. Train until the learning rate gets too small
max_epoch = args['optimization']['max_epoch'] or math.inf
max_update = args['optimization']['max_update'] or math.inf
lr = trainer.get_lr()
train_meter = meters.StopwatchMeter()
train_meter.start()
valid_subsets = args['dataset']['valid_subset'].split(',')
while (lr > args['optimization']['min_lr']
and epoch_itr.next_epoch_idx <= max_epoch
and trainer.get_num_updates() < max_update):
# train for one epoch
train(args, trainer, task, epoch_itr)
if not args['dataset']['disable_validation'] and epoch_itr.epoch % args[
'dataset']['validate_interval'] == 0:
valid_losses = validate(args, trainer, task, epoch_itr,
valid_subsets)
else:
valid_losses = [None]
# only use first validation loss to update the learning rate
lr = trainer.lr_step(epoch_itr.epoch, valid_losses[0])
# save checkpoint
if epoch_itr.epoch % args['checkpoint']['save_interval'] == 0:
checkpoint_utils.save_checkpoint(args, trainer, epoch_itr,
valid_losses[0])
# early stop
if should_stop_early(args, valid_losses[0]):
LOGGER.info(
'early stop since valid performance hasn\'t improved for last {} runs'
.format(args['checkpoint']['patience']))
break
epoch_itr = trainer.get_train_iterator(
epoch_itr.next_epoch_idx,
combine=False, # TODO to be checked
# sharded data: get train iterator for next epoch
load_dataset=(os.pathsep in args['task']['data']),
)
train_meter.stop()
LOGGER.info('done training in {:.1f} seconds'.format(train_meter.sum))
from ncc import (
__TREE_SITTER_LIBS_DIR__,
LOGGER,
)
from ncc.utils.path_manager import PathManager
# define your config
YOUR_LANGUAGE = 'csharp'
TREE_SITTER_LIB_URL = 'https://github.com/tree-sitter/tree-sitter-c-sharp/archive/master.zip'
os.makedirs(__TREE_SITTER_LIBS_DIR__, exist_ok=True)
so_file = os.path.join(__TREE_SITTER_LIBS_DIR__, f'{YOUR_LANGUAGE}.so')
# download
lib_filename = os.path.join(__TREE_SITTER_LIBS_DIR__, f'{YOUR_LANGUAGE}.zip')
if PathManager.exists(lib_filename):
PathManager.rm(lib_filename)
LOGGER.info(
f"Download TreeSitter-{YOUR_LANGUAGE}-Parser from {TREE_SITTER_LIB_URL}")
wget.download(TREE_SITTER_LIB_URL, lib_filename)
# decompress
decompress_dir = os.path.join(__TREE_SITTER_LIBS_DIR__, 'tmp')
with zipfile.ZipFile(lib_filename, 'r') as zip_file:
zip_file.extractall(path=decompress_dir)
lib_dir = os.path.join(decompress_dir, os.listdir(decompress_dir)[0])
# build
LOGGER.info(
f"Build {YOUR_LANGUAGE}.so, and save it at {__TREE_SITTER_LIBS_DIR__}")
Language.build_library(
# your language parser file, we recommend buidl *.so file for each language
