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 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 setUp(self):
super().setUp()
self.maxDiff = None # pylint:disable=invalid-name
# Mock TFDS
# Note we don't use mock.Mock since they fail to pickle.
fake_tfds_paths = {
"train": [
{ # pylint:disable=g-complex-comprehension
"filename": "train.tfrecord-%05d-of-00002" % i,
"skip": 0,
"take": -1
} for i in range(2)
],
"validation": [{
"filename": "validation.tfrecord-00000-of-00001",
"skip": 0,
"take": -1
}],
}
def _load_shard(shard_instruction, shuffle_files, seed):
del shuffle_files
del seed
fname = shard_instruction["filename"]
if "train" in fname:
if fname.endswith("00000-of-00002"):
return get_fake_dataset("train").take(2)
else:
return get_fake_dataset("train").skip(2)
else:
return get_fake_dataset("validation")
fake_tfds = FakeLazyTfds(name="fake:0.0.0",
load=get_fake_dataset,
load_shard=_load_shard,
info=FakeTfdsInfo(splits={
"train": None,
"validation": None
}),
files=fake_tfds_paths.get,
size=lambda x: 30 if x == "train" else 10)
self._tfds_patcher = mock.patch("t5.data.utils.LazyTfdsLoader",
new=mock.Mock(return_value=fake_tfds))
self._tfds_patcher.start()
# Set up data directory.
self.test_tmpdir = self.get_tempdir()
self.test_data_dir = os.path.join(self.test_tmpdir, "test_data")
shutil.copytree(TEST_DATA_DIR, self.test_data_dir)
for root, dirs, _ in os.walk(self.test_data_dir):
for d in dirs + [""]:
os.chmod(os.path.join(root, d), 0o777)
# Register a cached test Task.
dataset_utils.set_global_cache_dirs([self.test_data_dir])
clear_tasks()
add_tfds_task("cached_task",
token_preprocessor=test_token_preprocessor)
add_tfds_task("cached_task_no_token_prep")
# Prepare cached tasks.
self.cached_task = TaskRegistry.get("cached_task")
cached_task_dir = os.path.join(self.test_data_dir, "cached_task")
_dump_fake_dataset(os.path.join(cached_task_dir, "train.tfrecord"),
_FAKE_TOKENIZED_DATASET["train"], [2, 1],
_dump_examples_to_tfrecord)
_dump_fake_dataset(
os.path.join(cached_task_dir, "validation.tfrecord"),
_FAKE_TOKENIZED_DATASET["validation"], [2],
_dump_examples_to_tfrecord)
shutil.copytree(
cached_task_dir,
os.path.join(self.test_data_dir, "cached_task_no_token_prep"))
# Prepare uncached TfdsTask.
add_tfds_task("uncached_task",
token_preprocessor=test_token_preprocessor)
add_tfds_task("uncached_task_no_token_prep")
self.uncached_task = TaskRegistry.get("uncached_task")
# Prepare uncached, random TfdsTask
add_tfds_task("uncached_random_task",
token_preprocessor=random_token_preprocessor)
self.uncached_random_task = TaskRegistry.get("uncached_random_task")
# Prepare uncached TextLineTask.
_dump_fake_dataset(os.path.join(self.test_data_dir,
"train.tsv"), _FAKE_DATASET["train"],
[2, 1], _dump_examples_to_tsv)
TaskRegistry.add("text_line_task",
dataset_providers.TextLineTask,
split_to_filepattern={
"train":
os.path.join(self.test_data_dir, "train.tsv*"),
},
skip_header_lines=1,
text_preprocessor=[
_split_tsv_preprocessor, test_text_preprocessor
],
output_features=dataset_providers.Feature(
sentencepiece_vocab()),
metric_fns=[])
self.text_line_task = TaskRegistry.get("text_line_task")
# Prepare uncached TFExampleTask
_dump_fake_dataset(os.path.join(self.test_data_dir, "train.tfrecord"),
_FAKE_DATASET["train"], [2, 1],
_dump_examples_to_tfrecord)
TaskRegistry.add(
"tf_example_task",
dataset_providers.TFExampleTask,
split_to_filepattern={
"train": os.path.join(self.test_data_dir, "train.tfrecord*"),
},
feature_description={
"prefix": tf.io.FixedLenFeature([], tf.string),
"suffix": tf.io.FixedLenFeature([], tf.string),
},
text_preprocessor=[test_text_preprocessor],
output_features=dataset_providers.Feature(sentencepiece_vocab()),
metric_fns=[])
self.tf_example_task = TaskRegistry.get("tf_example_task")
# Prepare uncached Task.
def _dataset_fn(split,
shuffle_files,
filepattern=os.path.join(self.test_data_dir,
"train.tsv*")):
del split
files = tf.data.Dataset.list_files(filepattern,
shuffle=shuffle_files)
return files.interleave(
lambda f: tf.data.TextLineDataset(f).skip(1),
num_parallel_calls=tf.data.experimental.AUTOTUNE)
TaskRegistry.add("general_task",
dataset_providers.Task,
dataset_fn=_dataset_fn,
splits=["train"],
text_preprocessor=[
_split_tsv_preprocessor, test_text_preprocessor
],
output_features=dataset_providers.Feature(
sentencepiece_vocab()),
metric_fns=[])
self.general_task = TaskRegistry.get("general_task")
# Prepare uncached TaskV3.
TaskRegistry.add("task_v3",
dataset_providers.TaskV3,
source=dataset_providers.FunctionSource(
dataset_fn=get_fake_dataset,
splits=["train", "validation"]),
preprocessors=[
test_text_preprocessor,
preprocessors.tokenize,
token_preprocessor_no_sequence_length,
dataset_providers.CacheDatasetPlaceholder(),
],
output_features={
"inputs":
dataset_providers.Feature(sentencepiece_vocab()),
"targets":
dataset_providers.Feature(sentencepiece_vocab()),
},
metric_fns=[])
self.task_v3 = TaskRegistry.get("task_v3")
# Prepare uncached TaskV3 with no caching before tokenization.
TaskRegistry.add("task_v3_tokenized_postcache",
dataset_providers.TaskV3,
source=dataset_providers.FunctionSource(
dataset_fn=get_fake_dataset,
splits=["train", "validation"]),
preprocessors=[
test_text_preprocessor,
dataset_providers.CacheDatasetPlaceholder(),
preprocessors.tokenize,
token_preprocessor_no_sequence_length,
],
output_features={
"inputs":
dataset_providers.Feature(sentencepiece_vocab()),
"targets":
dataset_providers.Feature(sentencepiece_vocab()),
},
metric_fns=[])