def load_token_dataset(
data_path, split, tgt, tgt_dict, dataset_impl,
attrs=None, attr_dict=None,
attrs_mapping=None, reversed_attrs_mapping=None,
truncate_target=False, max_target_positions=None,
shuffle=True,
):
# load tokens
tgt_path = os.path.join(data_path, '{}.{}'.format(split, tgt))
tgt_dataset = _load_dataset(tgt_path, dataset_impl)
if truncate_target:
tgt_dataset = TruncateDataset(tgt_dataset, max_target_positions)
LOGGER.info('Truncate dataset into max length: {}'.format(max_target_positions))
LOGGER.info('loaded {} examples from: {}'.format(len(tgt_dataset), tgt_path))
# load tokens.ext
tgt_ext_path = os.path.join(data_path, '{}.{}.ext'.format(split, tgt))
if indexed_dataset.SeqIndexedDataset.exists(tgt_ext_path):
tgt_ext_dataset = indexed_dataset.SeqIndexedDataset(tgt_ext_path)
if truncate_target:
tgt_ext_dataset.clip(max_position=max_target_positions)
assert len(tgt_dataset) == len(tgt_ext_dataset), (len(tgt_dataset), len(tgt_ext_dataset))
else:
tgt_ext_dataset = None
# load attrs
if attrs is None:
attr_dataset = None
else:
attr_path = os.path.join(data_path, '{}.code_types'.format(split))
attr_dataset = _load_dataset(attr_path, dataset_impl)
if truncate_target:
tgt_dataset = TruncateDataset(tgt_dataset, max_target_positions)
LOGGER.info('Truncate dataset\'s attributes into max length: {}'.format(max_target_positions))
LOGGER.info('loaded {} examples from: {}'.format(len(attr_dataset), attr_path))
# load attr.ext
attr_ext_path = os.path.join(data_path, '{}.code_types.ext'.format(split))
if indexed_dataset.SeqIndexedDataset.exists(attr_ext_path):
attr_ext_dataset = indexed_dataset.SeqIndexedDataset(attr_ext_path)
if truncate_target:
attr_ext_dataset.clip(max_position=max_target_positions)
assert np.all(tgt_ext_dataset == attr_ext_dataset)
del attr_ext_dataset
return CompletionDataset(
tgt_dataset, tgt_dataset.sizes, tgt_dict, extends=tgt_ext_dataset,
attrs=attrs, attr_indices=attr_dataset, attr_dict=attr_dict,
attrs_mapping=attrs_mapping, reversed_attrs_mapping=reversed_attrs_mapping,
max_target_positions=max_target_positions,
shuffle=shuffle,
)
def load_inference_token_dataset(
data_paths,
split,
tgt,
tgt_dict,
dataset_impl,
attrs=None,
attr_dict=None,
attrs_mapping=None,
reversed_attrs_mapping=None,
truncate_target=False,
max_target_positions=None,
):
# load tokens
tgt_dataset = []
for path in data_paths:
tgt_path = os.path.join(path, '{}.{}'.format(split, tgt))
tgt_dataset.append(_load_dataset(tgt_path, dataset_impl))
tgt_dataset = ConcatDataset(tgt_dataset)
if truncate_target:
tgt_dataset = TruncateDataset(tgt_dataset, max_target_positions)
LOGGER.info('Truncate dataset into max length: {}'.format(
max_target_positions))
LOGGER.info('loaded {} examples from: {}'.format(len(tgt_dataset),
data_paths))
return CompletionDataset(
tgt_dataset,
tgt_dataset.sizes,
tgt_dict,
extends=None,
attrs=None,
attr_indices=None,
attr_dict=None,
attrs_mapping=None,
reversed_attrs_mapping=None,
max_target_positions=max_target_positions,
)
def load_token_dataset(
data_path,
split,
tgt,
tgt_dict,
dataset_impl,
attrs=None,
attr_dict=None,
attrs_mapping=None,
reversed_attrs_mapping=None,
truncate_target=False,
max_target_positions=None,
# lifelong learning
prev_tasks=[],
cur_task=None,
sample_portion=None,
):
# load tokens
tgt_path = os.path.join(data_path, cur_task, '{}.{}'.format(split, tgt))
tgt_dataset = [_load_dataset(tgt_path, dataset_impl)]
if len(prev_tasks
) > 0 and cur_task is not None and sample_portion is not None:
sample_size_per_task = int(
len(tgt_dataset[0]) * sample_portion // len(prev_tasks))
else:
sample_size_per_task = -1
if sample_size_per_task > 0:
for p_task in prev_tasks:
p_path = os.path.join(data_path, p_task,
'{}.{}'.format(split, tgt))
p_dataset = _load_dataset(p_path, dataset_impl)
tgt_dataset.append(
SliceDataset(p_dataset, end=sample_size_per_task))
tgt_dataset = ConcatDataset(tgt_dataset)
if truncate_target:
tgt_dataset = TruncateDataset(tgt_dataset, max_target_positions)
LOGGER.info('Truncate dataset into max length: {}'.format(
max_target_positions))
LOGGER.info('loaded {} examples from: [{}](current task) + {}(previous tasks)'. \
format(len(tgt_dataset), cur_task, prev_tasks))
# load tokens.ext
tgt_ext_path = os.path.join(data_path, cur_task,
'{}.{}.ext'.format(split, tgt))
if indexed_dataset.SeqIndexedDataset.exists(tgt_ext_path):
tgt_ext_dataset = indexed_dataset.SeqIndexedDataset(tgt_ext_path)
if sample_size_per_task > 0:
for p_task in prev_tasks:
p_ext_path = os.path.join(data_path, p_task,
'{}.{}.ext'.format(split, tgt))
p_ext_dataset = indexed_dataset.SeqIndexedDataset(p_ext_path)
p_ext_dataset.truncate(end=sample_size_per_task)
tgt_ext_dataset.append(p_ext_dataset)
if truncate_target:
tgt_ext_dataset.clip(max_position=max_target_positions)
assert len(tgt_dataset) == len(tgt_ext_dataset), (len(tgt_dataset),
len(tgt_ext_dataset))
else:
tgt_ext_dataset = None
# load attrs
if attrs is None:
attr_dataset = None
else:
attr_path = os.path.join(data_path, cur_task,
'{}.code_types'.format(split))
attr_dataset = [_load_dataset(attr_path, dataset_impl)]
if sample_size_per_task > 0:
for p_task in prev_tasks:
p_attr_path = os.path.join(data_path, p_task,
'{}.code_types'.format(split))
p_attr_dataset = _load_dataset(p_attr_path, dataset_impl)
attr_dataset.append(
SliceDataset(p_attr_dataset, end=sample_size_per_task))
attr_dataset = ConcatDataset(attr_dataset)
if truncate_target:
tgt_dataset = TruncateDataset(tgt_dataset, max_target_positions)
LOGGER.info(
'Truncate dataset\'s attributes into max length: {}'.format(
max_target_positions))
LOGGER.info('loaded attributes {} examples from: [{}](current task) + {}(previous tasks)'. \
format(len(attr_dataset), cur_task, prev_tasks))
# load attr.ext
attr_ext_path = os.path.join(data_path, cur_task,
'{}.code_types.ext'.format(split))
if indexed_dataset.SeqIndexedDataset.exists(attr_ext_path):
attr_ext_dataset = indexed_dataset.SeqIndexedDataset(attr_ext_path)
if sample_size_per_task > 0:
for p_task in prev_tasks:
p_attr_ext_path = os.path.join(
data_path, p_task, '{}.code_types.ext'.format(split))
p_attr_ext_dataset = indexed_dataset.SeqIndexedDataset(
p_attr_ext_path)
p_attr_ext_dataset.truncate(end=sample_size_per_task)
attr_ext_dataset.append(p_attr_ext_dataset)
if truncate_target:
attr_ext_dataset.clip(max_position=max_target_positions)
assert np.all(tgt_ext_dataset == attr_ext_dataset)
del attr_ext_dataset
return CompletionDataset(
tgt_dataset,
tgt_dataset.sizes,
tgt_dict,
extends=tgt_ext_dataset,
attrs=attrs,
attr_indices=attr_dataset,
attr_dict=attr_dict,
attrs_mapping=attrs_mapping,
reversed_attrs_mapping=reversed_attrs_mapping,
max_target_positions=max_target_positions,
)
def build_dataset_for_inference(self, src_tokens, src_lengths):
return CompletionDataset(src_tokens, src_lengths,
self.target_dictionary) # TODO: bug