def load_dataset(self, split, epoch=0, combine=False, **kwargs):
"""Load a given dataset split.
Args:
split (str): name of the split (e.g., train, valid, test)
"""
dataset_map = OrderedDict()
for lang in self.langs2id.keys():
# Datasets are expected to be in "split.lang" format (Eg: train.en)
language_split = '{}.{}'.format(split, lang)
block_dataset, sizes = self._load_single_lang_dataset(split=language_split, epoch=epoch)
dataset_map[lang] = MaskedLMDataset(
dataset=block_dataset,
sizes=sizes,
vocab=self.dictionary,
pad_idx=self.dictionary.pad(),
mask_idx=self.dictionary.mask(),
classif_token_idx=self.dictionary.eos(),
sep_token_idx=self.dictionary.eos(),
shuffle=getattr(self.args, 'shuffle', False),
has_pairs=False,
segment_id=self.langs2id[lang],
seed=self.seed,
)
self.datasets[split] = MultiCorpusSampledDataset(dataset_map)
print('| {} {} {} examples'.format(
self.args.data.split(os.pathsep)[epoch], split, len(self.datasets[split]))
)
def _load_dataset_multi_path(
self,
split: str,
src_multiple_bin_paths: Dict[str, str],
tgt_multiple_bin_paths: Dict[str, str],
dataset_upsampling: Optional[Dict[str, float]] = None,
dataset_relative_ratio: Optional[Tuple[str, float]] = None,
seed: Optional[int] = None,
noiser: Optional[Dict[str, UnsupervisedMTNoising]] = None,
):
corpora_map = pytorch_translate_data.ParallelCorporaMapConfig(
src_files=src_multiple_bin_paths, tgt_files=tgt_multiple_bin_paths)
datasets = OrderedDict()
for key in corpora_map.src_files:
src, tgt = corpora_map.src_files[key], corpora_map.tgt_files[key]
src_dataset, tgt_dataset = (
pytorch_translate_data.InMemoryNumpyDataset.create_from_file(
src),
pytorch_translate_data.InMemoryNumpyDataset.create_from_file(
tgt),
)
src_sizes = src_dataset.sizes
if noiser is not None and key in noiser:
src_dataset = NoisingDataset(
src_dataset=src_dataset,
src_dict=self.source_dictionary,
seed=seed,
noiser=noiser[key],
)
datasets[key] = LanguagePairDataset(
src=src_dataset,
src_sizes=src_sizes,
src_dict=self.source_dictionary,
tgt=tgt_dataset,
tgt_sizes=tgt_dataset.sizes,
tgt_dict=self.target_dictionary,
left_pad_source=False,
)
total_line_count = sum(len(datasets[key]) for key in datasets)
if dataset_relative_ratio is not None:
ds, ratio = dataset_relative_ratio
line_count = len(datasets[ds])
# By definition ratio = u * line_count / sum(#lines of other datasets)
u = (total_line_count - line_count) / line_count * ratio
dataset_upsampling = {key: u}
dataset_weights = {
key: 1.0 * len(datasets[key]) / total_line_count
for key in src_multiple_bin_paths.keys()
}
if dataset_upsampling is not None:
for k, v in dataset_upsampling.items():
dataset_weights[k] *= v
print(f"|dataset_weights:{dataset_weights}")
self.datasets[split] = MultiCorpusSampledDataset(
datasets=datasets,
default_key=list(dataset_weights.keys())[0],
sampling_func=self._normalized_weighted_sampling(dataset_weights),
)
def _test_sample_helper(
self,
expected_sample_from_first_ds_percentage,
num_samples=1000,
sampling_func=None,
):
# To make sure test is not flaky
np.random.seed(0)
if sampling_func is None:
m = MultiCorpusSampledDataset(
OrderedDict({
0: self.dataset_1,
1: self.dataset_2
}), )
else:
m = MultiCorpusSampledDataset(
OrderedDict({
0: self.dataset_1,
1: self.dataset_2
}),
sampling_func=sampling_func,
)
m.ordered_indices()
count_sample_from_first_dataset = 0
for _ in range(num_samples):
if m.collater([m[0], m[1]])["net_input"]["src_tokens"][0] == 1:
count_sample_from_first_dataset += 1
sample_from_first_ds_percentage = (1.0 *
count_sample_from_first_dataset /
num_samples)
self.assertLess(
abs(sample_from_first_ds_percentage -
expected_sample_from_first_ds_percentage),
0.01,
)
def load_dataset(self, split, combine=False):
"""Load a given dataset split.
Args:
split (str): name of the split (e.g., train, valid, test)
"""
dataset_map = OrderedDict()
for lang in self.langs2id.keys():
if self.default_key is None:
self.default_key = lang
# Datasets are expected to be in "split.lang" format (Eg: train.en)
language_split = '{}.{}'.format(split, lang)
path = os.path.join(self.args.data, language_split)
if self.args.raw_text and IndexedRawTextDataset.exists(path):
ds = IndexedRawTextDataset(path, self.dictionary)
elif not self.args.raw_text and IndexedDataset.exists(path):
if self.args.lazy_load:
ds = IndexedDataset(path, fix_lua_indexing=True)
else:
ds = IndexedCachedDataset(path, fix_lua_indexing=True)
else:
raise FileNotFoundError('Dataset not found: {} ({})'.format(
language_split, self.args.data))
# Since we append each block with the classification_token,
# we need to effectively create blocks of length
# tokens_per_sample-1
block_dataset = TokenBlockDataset(
dataset=ds,
sizes=ds.sizes,
block_size=self.args.tokens_per_sample - 1,
pad=self.dictionary.pad(),
eos=self.dictionary.eos())
dataset_map[lang] = MaskedLMDataset(
dataset=block_dataset,
sizes=block_dataset.sizes,
vocab=self.dictionary,
pad_idx=self.dictionary.pad(),
mask_idx=self.dictionary.mask(),
classif_token_idx=self.dictionary.eos(),
sep_token_idx=self.dictionary.eos(),
shuffle=getattr(self.args, 'shuffle', False),
has_pairs=False,
segment_id=self.langs2id[lang],
seed=self.seed,
)
self.datasets[split] = MultiCorpusSampledDataset(
dataset_map, default_key=self.default_key)
print('| {} {} {} examples'.format(self.args.data, split,
len(self.datasets[split])))
def _load_dataset_multi_path(
self,
split: str,
src_multiple_bin_paths: Dict[str, str],
tgt_multiple_bin_paths: Dict[str, str],
dataset_upsampling: Optional[Dict[str, float]],
):
corpora_map = pytorch_translate_data.ParallelCorporaMapConfig(
src_files=src_multiple_bin_paths, tgt_files=tgt_multiple_bin_paths)
datasets = OrderedDict()
for key in corpora_map.src_files:
src, tgt = corpora_map.src_files[key], corpora_map.tgt_files[key]
src_dataset, tgt_dataset = (
pytorch_translate_data.InMemoryNumpyDataset.create_from_file(
src),
pytorch_translate_data.InMemoryNumpyDataset.create_from_file(
tgt),
)
datasets[key] = LanguagePairDataset(
src=src_dataset,
src_sizes=src_dataset.sizes,
src_dict=self.source_dictionary,
tgt=tgt_dataset,
tgt_sizes=tgt_dataset.sizes,
tgt_dict=self.target_dictionary,
left_pad_source=False,
)
dataset_weights = {
key: 1.0 / len(src_multiple_bin_paths)
for key in src_multiple_bin_paths.keys()
}
if dataset_upsampling is not None:
for k, v in dataset_upsampling.items():
dataset_weights[k] *= v
self.datasets[split] = MultiCorpusSampledDataset(
datasets=datasets,
default_key=list(dataset_weights.keys())[0],
sampling_func=self._normalized_weighted_sampling(dataset_weights),
)
