Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Preprocessing decorator fixes #1843

Merged
merged 2 commits into from
Sep 19, 2024
Merged

Preprocessing decorator fixes #1843

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
56f2ba7
Fix handling bytesting input to tokenizers, preprocessing
mattdangerw Sep 18, 2024
1debb31
Fix no convert scope in multithreaded contexts
mattdangerw Sep 19, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
11 changes: 5 additions & 6 deletions keras_nlp/src/utils/tensor_utils.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -30,20 +30,19 @@


NO_CONVERT_COUNTER = threading.local()
NO_CONVERT_COUNTER.count = 0


@contextlib.contextmanager
def no_convert_scope():
try:
NO_CONVERT_COUNTER.count += 1
NO_CONVERT_COUNTER.count = getattr(NO_CONVERT_COUNTER, "count", 0) + 1
yield
finally:
NO_CONVERT_COUNTER.count -= 1
NO_CONVERT_COUNTER.count = getattr(NO_CONVERT_COUNTER, "count", 0) - 1


def in_no_convert_scope():
return NO_CONVERT_COUNTER.count > 0
return getattr(NO_CONVERT_COUNTER, "count", 0) > 0


def preprocessing_function(fn):
Expand Down Expand Up @@ -119,7 +118,7 @@ def convert_preprocessing_inputs(x):
return {k: convert_preprocessing_inputs(x[k]) for k, v in x.items()}
if isinstance(x, tuple):
return tuple(convert_preprocessing_inputs(v) for v in x)
if isinstance(x, str):
if isinstance(x, (str, bytes)):
return tf.constant(x)
if isinstance(x, list):
try:
Expand All @@ -132,7 +131,7 @@ def convert_preprocessing_inputs(x):
# If ragged conversion failed return to the numpy error.
raise e
# If we have a string input, use tf.tensor.
if numpy_x.dtype.type is np.str_:
if numpy_x.dtype.type is np.str_ or numpy_x.dtype.type is np.bytes_:
return tf.convert_to_tensor(x)
# Numpy will default to int64, int32 works with more ops.
if numpy_x.dtype == np.int64:
Expand Down
11 changes: 11 additions & 0 deletions keras_nlp/src/utils/tensor_utils_test.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -49,6 +49,17 @@ def test_strings(self):
self.assertIsInstance(outputs, list)
self.assertEqual(outputs, inputs)

def test_bytestrings(self):
inputs = ["one".encode("utf-8"), "two".encode("utf-8")]
# Convert to tf.
outputs = convert_preprocessing_inputs(inputs)
self.assertIsInstance(outputs, tf.Tensor)
self.assertAllEqual(outputs, tf.constant(inputs))
# Convert from tf.
outputs = convert_preprocessing_outputs(outputs)
self.assertIsInstance(outputs, list)
self.assertEqual(outputs, [x.decode("utf-8") for x in inputs])

def test_ragged(self):
inputs = [np.ones((1, 3)), np.ones((1, 2))]
# Convert to tf.
Expand Down
Loading