def test_optional_features(self):
def _dummy_preprocessor(output):
return lambda _: tf.data.Dataset.from_tensors(output)
default_vocab = test_utils.sentencepiece_vocab()
features = {
"inputs":
dataset_providers.Feature(vocabulary=default_vocab, required=False),
"targets":
dataset_providers.Feature(vocabulary=default_vocab, required=True),
}
test_utils.add_task(
"text_missing_optional_feature",
test_utils.get_fake_dataset,
output_features=features,
text_preprocessor=_dummy_preprocessor({"targets": "a"}))
TaskRegistry.get_dataset(
"text_missing_optional_feature", {"targets": 13},
"train", use_cached=False)
test_utils.add_task(
"text_missing_required_feature",
test_utils.get_fake_dataset,
output_features=features,
text_preprocessor=_dummy_preprocessor({"inputs": "a"}))
with self.assertRaisesRegex(
ValueError,
"Task dataset is missing expected output feature after preprocessing: "
"targets"):
TaskRegistry.get_dataset(
"text_missing_required_feature", {"inputs": 13},
"train", use_cached=False)
def test_vocab(self):
vocab = test_utils.sentencepiece_vocab()
self.assertEqual(26, vocab.vocab_size)
self.assertSequenceEqual(_TEST_TOKENS, vocab.encode(_TEST_STRING))
self.assertEqual(_TEST_STRING,
tf.compat.as_bytes(vocab.decode(_TEST_TOKENS)))
self.assertSequenceEqual(_TEST_TOKENS,
tuple(vocab.encode_tf(_TEST_STRING).numpy()))
self.assertEqual(_TEST_STRING, vocab.decode_tf(_TEST_TOKENS).numpy())
def test_vocab(self):
vocab = test_utils.sentencepiece_vocab()
self.assertEqual(26, vocab.vocab_size)
self.assertSequenceEqual(self.TEST_TOKENS,
vocab.encode(self.TEST_STRING))
self.assertEqual(self.TEST_STRING, vocab.decode(self.TEST_TOKENS))
self.assertSequenceEqual(
self.TEST_TOKENS, tuple(vocab.encode_tf(self.TEST_STRING).numpy()))
self.assertEqual(self.TEST_STRING, _decode_tf(vocab, self.TEST_TOKENS))
def test_no_eos(self):
default_vocab = test_utils.sentencepiece_vocab()
features = {
"inputs": utils.Feature(add_eos=True, vocabulary=default_vocab),
"targets": utils.Feature(add_eos=False, vocabulary=default_vocab),
}
test_utils.add_task("task_no_eos",
test_utils.get_fake_dataset,
output_features=features)
fn_task = TaskRegistry.get("task_no_eos")
test_utils.verify_task_matches_fake_datasets(fn_task, use_cached=False)
def test_triviaqa_truncate_text(self):
vocab = test_utils.sentencepiece_vocab()
def tokenize_and_prepare_dataset(inputs, targets):
tokenized_inputs = vocab.encode(inputs)
tokenized_targets = vocab.encode(targets)
dataset = tf.data.Dataset.from_tensors({
'inputs':
tokenized_inputs,
'targets':
tokenized_targets,
})
return dataset, tokenized_targets
inputs = 'This is a very very long string which must contain the answer.'
targets = 'long string'
og_dataset, tokenized_targets = tokenize_and_prepare_dataset(
inputs, targets)
for _ in range(0, 10):
dataset = prep.trivia_qa_truncate_inputs(
og_dataset,
output_features=None,
sequence_length={'inputs': 20})
for data in test_utils.dataset_as_text(dataset):
self.assertLen(data['inputs'], 20)
self.assertContainsSubset(tokenized_targets, data['inputs'])
# Dummy input which exists in the vocab to be able to compare strings after
# decoding.
inputs = 'w h d n r t v'
targets = 'h d'
og_dataset, _ = tokenize_and_prepare_dataset(inputs, targets)
for _ in range(0, 5):
dataset = prep.trivia_qa_truncate_inputs(
og_dataset,
output_features=None,
sequence_length={'inputs': 5})
for data in test_utils.dataset_as_text(dataset):
self.assertLen(data['inputs'], 5)
truncated_inputs = vocab.decode(data['inputs'].tolist())
new_targets = vocab.decode(data['targets'].tolist())
self.assertRegex(truncated_inputs, '.*' + targets + '.*')
self.assertEqual(targets, new_targets)
def test_dtype(self):
default_vocab = test_utils.sentencepiece_vocab()
features = {
"inputs":
# defaults to int32
dataset_providers.Feature(vocabulary=default_vocab),
"targets":
dataset_providers.Feature(dtype=tf.int64,
vocabulary=default_vocab),
}
test_utils.add_task("task_dtypes",
test_utils.get_fake_dataset,
output_features=features)
dtype_task = TaskRegistry.get("task_dtypes")
test_utils.verify_task_matches_fake_datasets(dtype_task,
use_cached=False)
def register_dummy_task(
task_name: str,
dataset_fn: Callable[[str, str], tf.data.Dataset],
output_feature_names: Sequence[str] = ("inputs", "targets")) -> None:
"""Register a dummy task for GetDatasetTest."""
dataset_providers.TaskRegistry.add(
task_name,
dataset_providers.TaskV3,
source=dataset_providers.FunctionSource(
dataset_fn=dataset_fn, splits=["train", "validation"]),
preprocessors=[dataset_providers.CacheDatasetPlaceholder()],
output_features={
feat: dataset_providers.Feature(test_utils.sentencepiece_vocab())
for feat in output_feature_names
},
metric_fns=[])
def test_v3_value_errors(self):
dataset_fn = (lambda split, shuffle_files: tf.data.Dataset.
from_tensors(["test"]))
output_features = {
"inputs":
dataset_providers.Feature(test_utils.sentencepiece_vocab())
}
with self.assertRaisesRegex(
ValueError,
"`CacheDatasetPlaceholder` can appear at most once in the "
"preprocessing pipeline. Found 2 in 'multiple_cache_placeholders'."
):
dataset_providers.TaskV3(
"multiple_cache_placeholders",
source=dataset_providers.FunctionSource(
dataset_fn=dataset_fn, splits=["train", "validation"]),
preprocessors=[
test_utils.test_text_preprocessor, preprocessors.tokenize,
dataset_providers.CacheDatasetPlaceholder(),
test_utils.test_token_preprocessor,
dataset_providers.CacheDatasetPlaceholder()
],
output_features=output_features,
metric_fns=[])
with self.assertRaisesRegex(
ValueError,
"'test_token_preprocessor' has a `sequence_length` argument but occurs "
"before `CacheDatasetPlaceholder` in 'sequence_length_pre_cache'. This "
"is not allowed since the sequence length is specified at run time."
):
dataset_providers.TaskV3(
"sequence_length_pre_cache",
dataset_providers.FunctionSource(
dataset_fn=dataset_fn,
splits=["train"],
),
preprocessors=[
test_utils.test_text_preprocessor, preprocessors.tokenize,
test_utils.test_token_preprocessor,
dataset_providers.CacheDatasetPlaceholder()
],
output_features=output_features,
metric_fns=[])
def test_denoise_nested_decorators(self):
"""Test whether gin and utils.map_over_dataset decorators are compatible."""
bindings = """
preprocessors.unsupervised.preprocessors = [@preprocessors.denoise]
preprocessors.denoise.noise_density = 0.15
preprocessors.denoise.noise_mask_fn = @preprocessors.iid_noise_mask
preprocessors.denoise.inputs_fn = @noise_token_to_sentinel
"""
gin.parse_config(bindings)
og_dataset = tf.data.Dataset.from_tensor_slices({'targets': [1, 2, 3]})
output_features = {
'targets': Feature(test_utils.sentencepiece_vocab())
}
# Test denoise function when it is used as a gin-configurable of another
# gin-configurable, prep.unsupervised.
dataset = prep.unsupervised(og_dataset,
output_features=output_features)
self.assertIsInstance(dataset, tf.data.Dataset)
def test_denoise(self):
tf.set_random_seed(55)
vocab = test_utils.sentencepiece_vocab()
target_tokens = vocab.encode('The quick brown fox.')
# This is what it encodes to.
self.assertEqual(
target_tokens,
[3, 2, 20, 4, 3, 2, 8, 13, 2, 3, 2, 23, 7, 19, 22, 3, 2, 7, 2])
og_dataset = tf.data.Dataset.from_tensor_slices({
'targets': [target_tokens],
})
output_features = {
'targets': utils.Feature(vocab),
}
# These are the parameters of denoise in the operative config of 'base'.
# Except noise_density, bumped up from 0.15 to 0.3 in order to demonstrate
# multiple corrupted spans.
denoised_dataset = prep.denoise(
og_dataset,
output_features,
noise_density=0.3,
noise_mask_fn=prep.random_spans_noise_mask,
inputs_fn=prep.noise_span_to_unique_sentinel,
targets_fn=prep.nonnoise_span_to_unique_sentinel)
# Two spans corrupted, [2] and [22, 3, 2, 7, 2], replaced by unique
# sentinels 25 and 24 respectively.
assert_dataset(denoised_dataset, [
{
'inputs': [
3, 25, 20, 4, 3, 2, 8, 13, 2, 3, 2, 23, 7, 19, 24
],
'targets': [
25, 2, 24, 22, 3, 2, 7, 2
],
},
])
def test_prefix_lm(self):
vocab = test_utils.sentencepiece_vocab()
inp = list(range(1, 101))
og_dataset = tf.data.Dataset.from_tensor_slices({'targets': [inp]})
og_dataset = og_dataset.repeat(100)
output_features = {'targets': Feature(vocab)}
output_dataset = prep.prefix_lm(
og_dataset,
{
'inputs': 100,
'targets': 100
},
output_features,
)
input_lengths = set()
for ex in output_dataset.as_numpy_iterator():
self.assertListEqual(
ex['inputs'].tolist() + ex['targets'].tolist(), inp)
input_lengths.add(len(ex['inputs']))
self.assertGreater(len(input_lengths), 1)
def test_not_equal(self):
vocab1 = test_utils.sentencepiece_vocab()
vocab2 = test_utils.sentencepiece_vocab(10)
self.assertNotEqual(vocab1, vocab2)
def test_extra_ids(self):
vocab = test_utils.sentencepiece_vocab(extra_ids=10)
self.assertEqual(36, vocab.vocab_size)
self.assertEqual("v", vocab.decode([25]))
self.assertEqual(_UNK_STRING, tf.compat.as_bytes(vocab.decode([35])))
self.assertEqual(_UNK_STRING, vocab.decode_tf([35]).numpy())
def test_extra_ids(self):
vocab = test_utils.sentencepiece_vocab(extra_ids=10)
self.assertEqual(36, vocab.vocab_size)
self.assertEqual("v", vocab.decode([25]))
self.assertEqual(self.UNK_STRING, vocab.decode([35]))
self.assertEqual(self.UNK_STRING, _decode_tf(vocab, [35]))
def test_get_sentencepiece_model_path(self):
self.assertEqual(
test_utils.sentencepiece_vocab().sentencepiece_model_file,
mesh_transformer.get_sentencepiece_model_path("cached_mixture")
)
