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_encoder_decoder_pretokenized_field(self):
x = [{
"inputs": [7, 8, 5, 1],
"targets": [3, 9, 1],
"targets_pretokenized": "abc"
}, {
"inputs": [8, 4, 9, 3, 1],
"targets": [4, 1],
"targets_pretokenized": "def"
}]
types = {
"inputs": tf.int32,
"targets": tf.int32,
"targets_pretokenized": tf.string
}
shapes = {
"inputs": [None],
"targets": [None],
"targets_pretokenized": []
}
ds = tf.data.Dataset.from_generator(lambda: x,
output_types=types,
output_shapes=shapes)
task_feature_lengths = {"inputs": 10, "targets": 7}
converter = feature_converters.EncDecFeatureConverter(pack=True)
# Check whether convert_features raise error because targets_pretokenized is
# present in the ds but not in the task_feature_lengths
converter(ds, task_feature_lengths)
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)
def test_encoder_decoder_packed_long_sequences(self):
x = [{"inputs": [7, 8, 5, 6, 9, 4, 1], "targets": [3, 9, 1]},
{"inputs": [8, 4, 9, 3, 5, 1], "targets": [4, 1]}]
ds = create_default_dataset(x)
task_feature_lengths = {"inputs": 7, "targets": 3}
converter = feature_converters.EncDecFeatureConverter(pack=True)
converted_ds = converter(ds, task_feature_lengths)
# Corner case: packing is true but task_feature_lengths are too long for
# packing to happen. We should still get the *_segment_id, *_position
# fields.
expected = [{
"encoder_input_token": [7, 8, 5, 6, 9, 4, 1],
"encoder_segment_id": [1, 1, 1, 1, 1, 1, 1],
"encoder_position": [0, 1, 2, 3, 4, 5, 6],
"decoder_target_token": [3, 9, 1],
"decoder_input_token": [0, 3, 9],
"decoder_loss_weight": [1, 1, 1],
"decoder_segment_id": [1, 1, 1],
"decoder_position": [0, 1, 2],
}, {
"encoder_input_token": [8, 4, 9, 3, 5, 1, 0],
"encoder_segment_id": [1, 1, 1, 1, 1, 1, 0],
"encoder_position": [0, 1, 2, 3, 4, 5, 0],
"decoder_target_token": [4, 1, 0],
"decoder_input_token": [0, 4, 0],
"decoder_loss_weight": [1, 1, 0],
"decoder_segment_id": [1, 1, 0],
"decoder_position": [0, 1, 0],
}]
assert_dataset(converted_ds, expected)
def test_encoder_decoder_extra_long_inputs(self):
x = [{"inputs": [9, 4, 3, 8, 4, 5, 1], "targets": [3, 9, 4, 7, 8, 1]}]
ds = create_default_dataset(x)
task_feature_lengths = {"inputs": 5, "targets": 8}
expected_msg = (
r".*Feature \\'inputs\\' has length not less than or equal to the "
r"expected length of 5 during input_validation.*")
with self.assertRaisesRegex(tf.errors.InvalidArgumentError, expected_msg):
converter = feature_converters.EncDecFeatureConverter(pack=False)
converted_ds = converter(ds, task_feature_lengths)
list(converted_ds.as_numpy_iterator())
def test_get_dataset_enc_dec_packed(self):
mixture_or_task_name = "enc_dec_packed"
x = [{
"inputs": [7, 8, 5, 6, 9, 4, 3],
"targets": [3, 9]
}, {
"inputs": [8, 4],
"targets": [4]
}, {
"inputs": [5, 6, 7],
"targets": [6, 5]
}]
ds = create_default_dataset(x)
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)
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)
expected = [
{
# Example 1 is trimmed
"encoder_input_tokens": [7, 8, 5, 6, 9, 4, 1],
"encoder_segment_ids": [1, 1, 1, 1, 1, 1, 1],
"encoder_positions": [0, 1, 2, 3, 4, 5, 6],
"decoder_target_tokens": [3, 9, 1, 0, 0],
"decoder_input_tokens": [0, 3, 9, 0, 0],
"decoder_loss_weights": [1, 1, 1, 0, 0],
"decoder_segment_ids": [1, 1, 1, 0, 0],
"decoder_positions": [0, 1, 2, 0, 0],
},
{
# Example 2 and 3 are packed together
"encoder_input_tokens": [8, 4, 1, 5, 6, 7, 1],
"encoder_segment_ids": [1, 1, 1, 2, 2, 2, 2],
"encoder_positions": [0, 1, 2, 0, 1, 2, 3],
"decoder_target_tokens": [4, 1, 6, 5, 1],
"decoder_input_tokens": [0, 4, 0, 6, 5],
"decoder_loss_weights": [1, 1, 1, 1, 1],
"decoder_segment_ids": [1, 1, 2, 2, 2],
"decoder_positions": [0, 1, 0, 1, 2],
}
]
expected_dtypes = {feat: tf.int32 for feat in expected[0].keys()}
assert_dataset(output_ds, expected, expected_dtypes=expected_dtypes)
def test_encoder_decoder_targets_max_length(self):
x = [{"inputs": [9, 4, 3, 8, 1], "targets": [3, 9, 4, 5, 1]}]
ds = create_default_dataset(x)
task_feature_lengths = {"inputs": 5, "targets": 5}
converter = feature_converters.EncDecFeatureConverter(pack=False)
converted_ds = converter(ds, task_feature_lengths)
expected = {
"encoder_input_tokens": [9, 4, 3, 8, 1],
"decoder_target_tokens": [3, 9, 4, 5, 1],
"decoder_input_tokens": [0, 3, 9, 4, 5],
"decoder_loss_weights": [1, 1, 1, 1, 1],
}
assert_dataset(converted_ds, expected)
def test_encoder_decoder_unpacked(self):
x = [{"inputs": [9, 4, 3, 8, 1], "targets": [3, 9, 4, 1]}]
ds = create_default_dataset(x)
task_feature_lengths = {"inputs": 7, "targets": 5}
converter = feature_converters.EncDecFeatureConverter(pack=False)
converted_ds = converter(ds, task_feature_lengths)
expected = {
"encoder_input_tokens": [9, 4, 3, 8, 1, 0, 0],
"decoder_target_tokens": [3, 9, 4, 1, 0],
# mtf.transformer.autoregressive_inputs does not zero out the last eos
# when the data is not packed. This test mimic the behavior.
"decoder_input_tokens": [0, 3, 9, 4, 1],
"decoder_loss_weights": [1, 1, 1, 1, 0],
}
assert_dataset(converted_ds, expected)
def test_encoder_decoder_packed(self):
x = [{"inputs": [7, 8, 5, 1], "targets": [3, 9, 1]},
{"inputs": [8, 4, 9, 3, 1], "targets": [4, 1]}]
ds = create_default_dataset(x)
task_feature_lengths = {"inputs": 10, "targets": 7}
converter = feature_converters.EncDecFeatureConverter(pack=True)
converted_ds = converter(ds, task_feature_lengths)
expected = {
"encoder_input_token": [7, 8, 5, 1, 8, 4, 9, 3, 1, 0],
"encoder_segment_id": [1, 1, 1, 1, 2, 2, 2, 2, 2, 0],
"encoder_position": [0, 1, 2, 3, 0, 1, 2, 3, 4, 0],
"decoder_target_token": [3, 9, 1, 4, 1, 0, 0],
"decoder_input_token": [0, 3, 9, 0, 4, 0, 0],
"decoder_loss_weight": [1, 1, 1, 1, 1, 0, 0],
"decoder_segment_id": [1, 1, 1, 2, 2, 0, 0],
"decoder_position": [0, 1, 2, 0, 1, 0, 0],
}
assert_dataset(converted_ds, expected)
def test_get_dataset_both_train_and_validation_splits(self):
mixture_or_task_name = "both_train_and_validation_splits"
x_train = [{"inputs": [7, 8, 5, 6, 9, 4, 3], "targets": [3, 9]}]
x_val = [{"inputs": [8, 4], "targets": [4]}]
datasets = {
"train": create_default_dataset(x_train),
"validation": create_default_dataset(x_val)
}
dataset_fn = lambda split, shuffle_files: datasets[split]
register_dummy_task(mixture_or_task_name, dataset_fn=dataset_fn)
task_feature_lengths = {"inputs": 7, "targets": 5}
output_ds = {}
for split in ["train", "validation"]:
converter = feature_converters.EncDecFeatureConverter(pack=False)
output_ds[split] = feature_converters.get_dataset(
mixture_or_task_name=mixture_or_task_name,
task_feature_lengths=task_feature_lengths,
dataset_split=split,
shuffle=False,
feature_converter=converter)
expected_train = {
"encoder_input_tokens": [7, 8, 5, 6, 9, 4, 1],
"decoder_target_tokens": [3, 9, 1, 0, 0],
"decoder_input_tokens": [0, 3, 9, 1, 0],
"decoder_loss_weights": [1, 1, 1, 0, 0],
}
expected_val = {
"encoder_input_tokens": [8, 4, 1, 0, 0, 0, 0],
"decoder_target_tokens": [4, 1, 0, 0, 0],
"decoder_input_tokens": [0, 4, 1, 0, 0],
"decoder_loss_weights": [1, 1, 0, 0, 0],
}
expected_dtypes = {feat: tf.int32 for feat in expected_train.keys()}
assert_dataset(output_ds["train"],
expected_train,
expected_dtypes=expected_dtypes)
assert_dataset(output_ds["validation"],
expected_val,
expected_dtypes=expected_dtypes)
def test_get_dataset_enc_dec_unpacked(self):
mixture_or_task_name = "enc_dec_unpacked"
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)
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)
expected = [{
"encoder_input_tokens": [7, 8, 5, 6, 9, 4, 1],
"decoder_target_tokens": [3, 9, 1, 0, 0],
"decoder_input_tokens": [0, 3, 9, 1, 0],
"decoder_loss_weights": [1, 1, 1, 0, 0],
}, {
"encoder_input_tokens": [8, 4, 1, 0, 0, 0, 0],
"decoder_target_tokens": [4, 1, 0, 0, 0],
"decoder_input_tokens": [0, 4, 1, 0, 0],
"decoder_loss_weights": [1, 1, 0, 0, 0],
}, {
"encoder_input_tokens": [5, 6, 7, 1, 0, 0, 0],
"decoder_target_tokens": [6, 5, 1, 0, 0],
"decoder_input_tokens": [0, 6, 5, 1, 0],
"decoder_loss_weights": [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)
