def verify_task_matches_fake_datasets(task,
use_cached,
token_preprocessed=False,
splits=("train", "validation"),
num_shards=None):
"""Assert all splits for both tokenized datasets are correct."""
for split in splits:
get_dataset = functools.partial(task.get_dataset,
_SEQUENCE_LENGTH,
split,
use_cached=use_cached,
shuffle=False)
if num_shards:
ds = get_dataset(
shard_info=dataset_providers.ShardInfo(0, num_shards))
for i in range(1, num_shards):
ds = ds.concatenate(
get_dataset(
shard_info=dataset_providers.ShardInfo(i, num_shards)))
else:
ds = get_dataset()
_assert_compare_to_fake_dataset(
ds,
split,
task.output_features,
token_preprocessed=token_preprocessed,
)
def test_get_dataset_enc_dec_sharded_and_packed(self):
mixture_or_task_name = "enc_dec_sharded_and_packed"
x = [{"inputs": [7, 8], "targets": [3, 9]},
{"inputs": [8, 4], "targets": [4]},
{"inputs": [5, 6, 7], "targets": [6]}]
dtypes = {"inputs": tf.int64, "targets": tf.int64}
ds = create_default_dataset(x, output_types=dtypes)
dataset_fn = lambda split, shuffle_files: ds
register_dummy_task(mixture_or_task_name, dataset_fn=dataset_fn)
task_feature_lengths = {"inputs": 7, "targets": 5}
converter = feature_converters.EncDecFeatureConverter(pack=True)
shard_info = dataset_providers.ShardInfo(index=0, num_shards=2)
output_ds = feature_converters.get_dataset(
mixture_or_task_name=mixture_or_task_name,
task_feature_lengths=task_feature_lengths,
dataset_split="train",
shuffle=False,
feature_converter=converter,
shard_info=shard_info)
# Packing should be done after the sharding.
expected = {
"encoder_input_token": [7, 8, 1, 5, 6, 7, 1],
"encoder_segment_id": [1, 1, 1, 2, 2, 2, 2],
"encoder_position": [0, 1, 2, 0, 1, 2, 3],
"decoder_target_token": [3, 9, 1, 6, 1],
"decoder_input_token": [0, 3, 9, 0, 6],
"decoder_loss_weight": [1, 1, 1, 1, 1],
"decoder_segment_id": [1, 1, 1, 2, 2],
"decoder_position": [0, 1, 2, 0, 1],
}
expected_dtypes = {feat: tf.int32 for feat in expected.keys()}
assert_dataset(output_ds, expected, expected_dtypes=expected_dtypes)
def test_get_dataset_enc_dec_sharded(self):
mixture_or_task_name = "enc_dec_sharded"
x = [{"inputs": [7, 8, 5, 6, 9, 4, 3], "targets": [3, 9]},
{"inputs": [8, 4], "targets": [4]},
{"inputs": [5, 6, 7], "targets": [6, 5]}]
dtypes = {"inputs": tf.int64, "targets": tf.int64}
ds = create_default_dataset(x, output_types=dtypes)
dataset_fn = lambda split, shuffle_files: ds
register_dummy_task(mixture_or_task_name, dataset_fn=dataset_fn)
task_feature_lengths = {"inputs": 7, "targets": 5}
converter = feature_converters.EncDecFeatureConverter(pack=False)
shard_info = dataset_providers.ShardInfo(index=0, num_shards=2)
output_ds = feature_converters.get_dataset(
mixture_or_task_name=mixture_or_task_name,
task_feature_lengths=task_feature_lengths,
dataset_split="train",
shuffle=False,
feature_converter=converter,
shard_info=shard_info)
# Example index 1 should not be present in the sharded dataset.
expected = [{
"encoder_input_token": [7, 8, 5, 6, 9, 4, 1],
"decoder_target_token": [3, 9, 1, 0, 0],
"decoder_input_token": [0, 3, 9, 1, 0],
"decoder_loss_weight": [1, 1, 1, 0, 0],
}, {
"encoder_input_token": [5, 6, 7, 1, 0, 0, 0],
"decoder_target_token": [6, 5, 1, 0, 0],
"decoder_input_token": [0, 6, 5, 1, 0],
"decoder_loss_weight": [1, 1, 1, 0, 0],
}]
expected_dtypes = {feat: tf.int32 for feat in expected[0].keys()}
assert_dataset(output_ds, expected, expected_dtypes=expected_dtypes)
