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implement of leftpadding #2242

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Merged
merged 9 commits into from
May 28, 2025
Merged

implement of leftpadding #2242

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299102c
implement of leftpadding
pass-lin May 2, 2025
59627a4
add doc
pass-lin May 3, 2025
5d1b2c0
update
pass-lin May 8, 2025
97cada7
fix
pass-lin May 20, 2025
85bb256
format
pass-lin May 20, 2025
6ab5ea9
format
pass-lin May 20, 2025
f1c55ac
update test
pass-lin May 20, 2025
8c40279
add left padding for segment
pass-lin May 21, 2025
7ceef48
add doc.
pass-lin May 23, 2025
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19 changes: 16 additions & 3 deletions keras_hub/src/layers/preprocessing/multi_segment_packer.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@
PreprocessingLayer,
)
from keras_hub.src.utils.tensor_utils import convert_to_ragged_batch
from keras_hub.src.utils.tensor_utils import pad
from keras_hub.src.utils.tensor_utils import preprocessing_function

try:
Expand Down Expand Up @@ -124,6 +125,7 @@ def __init__(
sep_value=None,
pad_value=None,
truncate="round_robin",
padding_side="right",
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please add a docstring

**kwargs,
):
super().__init__(**kwargs)
Expand Down Expand Up @@ -163,6 +165,8 @@ def check_special_value_type(value, value_name):

self.pad_value = pad_value

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nit: remove this empty line

self.padding_side = padding_side

def get_config(self):
config = super().get_config()
config.update(
Expand All @@ -173,6 +177,7 @@ def get_config(self):
"sep_value": self._sep_value,
"pad_value": self.pad_value,
"truncate": self.truncate,
"padding_side": self.padding_side,
}
)
return config
Expand Down Expand Up @@ -287,10 +292,18 @@ def call(
# Pad to dense tensor output.
sequence_length = sequence_length or self.sequence_length
shape = tf.cast([-1, sequence_length], "int64")
token_ids = token_ids.to_tensor(
shape=shape, default_value=self.pad_value
token_ids = pad(
token_ids,
shape=shape,
padding_side=self.padding_side,
pad_value=self.pad_value,
)
segment_ids = pad(
segment_ids,
shape=shape,
padding_side=self.padding_side,
pad_value=0,
)
segment_ids = segment_ids.to_tensor(shape=shape)
# Remove the batch dim if added.
if unbatched:
token_ids = tf.squeeze(token_ids, 0)
Expand Down
173 changes: 173 additions & 0 deletions keras_hub/src/layers/preprocessing/multi_segment_packer_test.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@

class MultiSegmentPackerTest(TestCase):
def test_trim_single_input_ints(self):
# right padding
input_data = np.arange(3, 10)
packer = MultiSegmentPacker(
sequence_length=8, start_value=1, end_value=2
Expand All @@ -16,7 +17,20 @@ def test_trim_single_input_ints(self):
self.assertAllEqual(token_ids, [1, 3, 4, 5, 6, 7, 8, 2])
self.assertAllEqual(segment_ids, [0, 0, 0, 0, 0, 0, 0, 0])

# left padding
input_data = np.arange(3, 10)
packer = MultiSegmentPacker(
sequence_length=8,
start_value=1,
end_value=2,
padding_side="left",
)
token_ids, segment_ids = packer(input_data)
self.assertAllEqual(token_ids, [1, 3, 4, 5, 6, 7, 8, 2])
self.assertAllEqual(segment_ids, [0, 0, 0, 0, 0, 0, 0, 0])

def test_trim_single_input_strings(self):
# right padding
input_data = ["a", "b", "c", "d"]
packer = MultiSegmentPacker(
sequence_length=5, start_value="[CLS]", end_value="[SEP]"
Expand All @@ -25,7 +39,19 @@ def test_trim_single_input_strings(self):
self.assertAllEqual(token_ids, ["[CLS]", "a", "b", "c", "[SEP]"])
self.assertAllEqual(segment_ids, [0, 0, 0, 0, 0])

# left padding
packer = MultiSegmentPacker(
sequence_length=5,
start_value="[CLS]",
end_value="[SEP]",
padding_side="left",
)
token_ids, segment_ids = packer(input_data)
self.assertAllEqual(token_ids, ["[CLS]", "a", "b", "c", "[SEP]"])
self.assertAllEqual(segment_ids, [0, 0, 0, 0, 0])

def test_trim_multiple_inputs_round_robin(self):
# right padding
seq1 = ["a", "b", "c"]
seq2 = ["x", "y", "z"]
packer = MultiSegmentPacker(
Expand All @@ -40,7 +66,22 @@ def test_trim_multiple_inputs_round_robin(self):
)
self.assertAllEqual(segment_ids, [0, 0, 0, 0, 1, 1, 1])

# left padding
packer = MultiSegmentPacker(
sequence_length=7,
start_value="[CLS]",
end_value="[SEP]",
truncate="round_robin",
padding_side="left",
)
token_ids, segment_ids = packer((seq1, seq2))
self.assertAllEqual(
token_ids, ["[CLS]", "a", "b", "[SEP]", "x", "y", "[SEP]"]
)
self.assertAllEqual(segment_ids, [0, 0, 0, 0, 1, 1, 1])

def test_trim_multiple_inputs_waterfall(self):
# right padding
seq1 = ["a", "b", "c"]
seq2 = ["x", "y", "z"]
packer = MultiSegmentPacker(
Expand All @@ -55,7 +96,22 @@ def test_trim_multiple_inputs_waterfall(self):
)
self.assertAllEqual(segment_ids, [0, 0, 0, 0, 0, 1, 1])

# left padding
packer = MultiSegmentPacker(
sequence_length=7,
start_value="[CLS]",
end_value="[SEP]",
truncate="waterfall",
padding_side="left",
)
token_ids, segment_ids = packer((seq1, seq2))
self.assertAllEqual(
token_ids, ["[CLS]", "a", "b", "c", "[SEP]", "x", "[SEP]"]
)
self.assertAllEqual(segment_ids, [0, 0, 0, 0, 0, 1, 1])

def test_trim_batched_inputs_round_robin(self):
# right padding
seq1 = [["a", "b", "c"], ["a", "b", "c"]]
seq2 = [["x", "y", "z"], ["x", "y", "z"]]
packer = MultiSegmentPacker(
Expand All @@ -80,7 +136,32 @@ def test_trim_batched_inputs_round_robin(self):
],
)

# left padding
packer = MultiSegmentPacker(
sequence_length=7,
start_value="[CLS]",
end_value="[SEP]",
truncate="round_robin",
padding_side="left",
)
token_ids, segment_ids = packer((seq1, seq2))
self.assertAllEqual(
token_ids,
[
["[CLS]", "a", "b", "[SEP]", "x", "y", "[SEP]"],
["[CLS]", "a", "b", "[SEP]", "x", "y", "[SEP]"],
],
)
self.assertAllEqual(
segment_ids,
[
[0, 0, 0, 0, 1, 1, 1],
[0, 0, 0, 0, 1, 1, 1],
],
)

def test_trim_batched_inputs_waterfall(self):
# right padding
seq1 = [["a", "b", "c"], ["a", "b"]]
seq2 = [["x", "y", "z"], ["x", "y", "z"]]
packer = MultiSegmentPacker(
Expand All @@ -105,7 +186,32 @@ def test_trim_batched_inputs_waterfall(self):
],
)

# left padding
packer = MultiSegmentPacker(
sequence_length=7,
start_value="[CLS]",
end_value="[SEP]",
truncate="waterfall",
padding_side="left",
)
token_ids, segment_ids = packer((seq1, seq2))
self.assertAllEqual(
token_ids,
[
["[CLS]", "a", "b", "c", "[SEP]", "x", "[SEP]"],
["[CLS]", "a", "b", "[SEP]", "x", "y", "[SEP]"],
],
)
self.assertAllEqual(
segment_ids,
[
[0, 0, 0, 0, 0, 1, 1],
[0, 0, 0, 0, 1, 1, 1],
],
)

def test_pad_inputs(self):
# right padding
seq1 = ["a"]
seq2 = ["x"]
packer = MultiSegmentPacker(
Expand All @@ -118,7 +224,23 @@ def test_pad_inputs(self):
)
self.assertAllEqual(segment_ids, [0, 0, 0, 1, 1, 0])

# left padding
packer = MultiSegmentPacker(
6,
start_value="[CLS]",
end_value="[SEP]",
pad_value="[PAD]",
padding_side="left",
)
token_ids, segment_ids = packer((seq1, seq2))
self.assertAllEqual(
token_ids,
["[PAD]", "[CLS]", "a", "[SEP]", "x", "[SEP]"],
)
self.assertAllEqual(segment_ids, [0, 0, 0, 0, 1, 1])

def test_pad_batched_inputs(self):
# right padding
seq1 = [["a"], ["a"]]
seq2 = [["x"], ["x", "y"]]
packer = MultiSegmentPacker(
Expand All @@ -143,7 +265,32 @@ def test_pad_batched_inputs(self):
],
)

# left padding
packer = MultiSegmentPacker(
sequence_length=7,
start_value="[CLS]",
end_value="[SEP]",
pad_value="[PAD]",
padding_side="left",
)
token_ids, segment_ids = packer((seq1, seq2))
self.assertAllEqual(
token_ids,
[
["[PAD]", "[PAD]", "[CLS]", "a", "[SEP]", "x", "[SEP]"],
["[PAD]", "[CLS]", "a", "[SEP]", "x", "y", "[SEP]"],
],
)
self.assertAllEqual(
segment_ids,
[
[0, 0, 0, 0, 0, 1, 1],
[0, 0, 0, 0, 1, 1, 1],
],
)

def test_list_special_tokens(self):
# right padding
seq1 = [["a", "b"], ["a", "b"]]
seq2 = [["x", "y"], ["x"]]
packer = MultiSegmentPacker(
Expand All @@ -170,6 +317,32 @@ def test_list_special_tokens(self):
],
)

# left padding
packer = MultiSegmentPacker(
8,
start_value="<s>",
end_value="</s>",
sep_value=["</s>", "</s>"],
pad_value="<pad>",
truncate="round_robin",
padding_side="left",
)
token_ids, segment_ids = packer((seq1, seq2))
self.assertAllEqual(
token_ids,
[
["<s>", "a", "b", "</s>", "</s>", "x", "y", "</s>"],
["<pad>", "<s>", "a", "b", "</s>", "</s>", "x", "</s>"],
],
)
self.assertAllEqual(
segment_ids,
[
[0, 0, 0, 0, 0, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 1, 1],
],
)

def test_config(self):
seq1 = [["a", "b", "c"], ["a", "b"]]
seq2 = [["x", "y", "z"], ["x", "y", "z"]]
Expand Down
30 changes: 24 additions & 6 deletions keras_hub/src/layers/preprocessing/start_end_packer.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@
PreprocessingLayer,
)
from keras_hub.src.utils.tensor_utils import convert_to_ragged_batch
from keras_hub.src.utils.tensor_utils import pad
from keras_hub.src.utils.tensor_utils import preprocessing_function

try:
Expand Down Expand Up @@ -39,6 +40,8 @@ class StartEndPacker(PreprocessingLayer):
0 or "" will be added depending on the dtype of the input tensor.
return_padding_mask: bool. Whether to return a boolean padding mask of
all locations that are filled in with the `pad_value`.
padding_side: str. Whether to pad the input on the "left" or "right".
Defaults to "right".

Call arguments:
inputs: A `tf.Tensor`, `tf.RaggedTensor`, or list of python strings.
Expand Down Expand Up @@ -111,6 +114,7 @@ def __init__(
pad_value=None,
return_padding_mask=False,
name=None,
padding_side="right",
**kwargs,
):
super().__init__(name=name, **kwargs)
Expand Down Expand Up @@ -139,6 +143,7 @@ def check_special_value_type(value, value_name):

self.pad_value = pad_value
self.return_padding_mask = return_padding_mask
self.padding_side = padding_side

@preprocessing_function
def call(
Expand All @@ -154,6 +159,13 @@ def call(
batch_size = tf.shape(x)[0]
sequence_length = sequence_length or self.sequence_length
dtype = inputs.dtype
# Truncate.
truncation_length = sequence_length
if add_start_value and self.start_value is not None:
truncation_length -= len(self.start_value)
if add_end_value and self.end_value is not None:
truncation_length -= len(self.end_value)
x = x[..., :truncation_length]

# Concatenate start and end tokens.
if add_start_value and self.start_value is not None:
Expand All @@ -167,23 +179,28 @@ def call(
end_token_id_tensor = tf.repeat(
end_value[tf.newaxis, :], repeats=batch_size, axis=0
)
# Trim to leave room for end token.
x = x[..., : sequence_length - len(self.end_value)]
x = tf.concat([x, end_token_id_tensor], axis=-1)

# Pad to desired length.
outputs = x.to_tensor(
default_value=self.pad_value,
outputs = pad(
x,
pad_value=self.pad_value,
padding_side=self.padding_side,
shape=(batch_size, sequence_length),
)
outputs = tf.squeeze(outputs, axis=0) if unbatched else outputs

if self.return_padding_mask:
mask = tf.ones_like(x, dtype="bool")
mask = mask.to_tensor(shape=(batch_size, sequence_length))

mask = pad(
mask,
pad_value=False,
padding_side=self.padding_side,
shape=(batch_size, sequence_length),
)
mask = tf.squeeze(mask, axis=0) if unbatched else mask
return outputs, mask

return outputs

def get_config(self):
Expand All @@ -195,6 +212,7 @@ def get_config(self):
"end_value": self._end_value,
"pad_value": self.pad_value,
"return_padding_mask": self.return_padding_mask,
"padding_side": self.padding_side,
}
)
return config
Expand Down
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