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Fix decoder inputs in BART preprocessor #984

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Merged
merged 4 commits into from
Apr 14, 2023
Merged

Fix decoder inputs in BART preprocessor #984

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5dc7908
Fix decoder inputs in BART preprocessor
abheesht17 Apr 13, 2023
cb7294f
Format code
abheesht17 Apr 13, 2023
c474b09
Handle rank-1 inps differently
abheesht17 Apr 14, 2023
4f9b2f6
Fix line length
mattdangerw Apr 14, 2023
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24 changes: 22 additions & 2 deletions keras_nlp/models/bart/bart_preprocessor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,8 @@

import copy

import tensorflow as tf

from keras_nlp.api_export import keras_nlp_export
from keras_nlp.layers.multi_segment_packer import MultiSegmentPacker
from keras_nlp.models.bart.bart_presets import backbone_presets
Expand Down Expand Up @@ -160,20 +162,26 @@ def __init__(
truncate=truncate,
sequence_length=encoder_sequence_length,
)
# The decoder is packed a bit differently; the format is as follows:
# `[end_token_id, start_token_id, tokens..., end_token_id, padding...]`.
# Hence, we pass `sequence_length - 1` to the packer.
self.decoder_packer = MultiSegmentPacker(
start_value=self.tokenizer.start_token_id,
end_value=self.tokenizer.end_token_id,
pad_value=self.tokenizer.pad_token_id,
truncate=truncate,
sequence_length=decoder_sequence_length,
sequence_length=decoder_sequence_length - 1,
)
# Maintain a private copy of `decoder_sequence_length` for config
# purposes.
self._decoder_sequence_length = decoder_sequence_length

def get_config(self):
config = super().get_config()
config.update(
{
"encoder_sequence_length": self.encoder_packer.sequence_length,
"decoder_sequence_length": self.decoder_packer.sequence_length,
"decoder_sequence_length": self._decoder_sequence_length,
"truncate": self.encoder_packer.truncate,
}
)
Expand Down Expand Up @@ -208,6 +216,18 @@ def call(self, x, y=None, sample_weight=None):
decoder_inputs = [self.tokenizer(segment) for segment in decoder_text]
decoder_token_ids, _ = self.decoder_packer(decoder_inputs)

# Append `end_token_id` to the beginning of `decoder_token_ids`.
input_is_1d = decoder_token_ids.shape.rank == 1
if input_is_1d:
decoder_token_ids = decoder_token_ids[tf.newaxis, :]
batch_size = tf.shape(decoder_token_ids)[0]
end_token_ids = tf.fill((batch_size, 1), self.tokenizer.end_token_id)
decoder_token_ids = tf.concat(
[end_token_ids, decoder_token_ids], axis=1
)
if input_is_1d:
decoder_token_ids = tf.squeeze(decoder_token_ids, axis=0)

x = {
"encoder_token_ids": encoder_token_ids,
"encoder_padding_mask": encoder_token_ids
Expand Down
18 changes: 9 additions & 9 deletions keras_nlp/models/bart/bart_preprocessor_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -54,7 +54,7 @@ def setUp(self):
merges=merges,
),
encoder_sequence_length=10,
decoder_sequence_length=8,
decoder_sequence_length=9,
)

def test_tokenize_strings(self):
Expand All @@ -71,10 +71,10 @@ def test_tokenize_strings(self):
output["encoder_padding_mask"], [1, 1, 1, 1, 1, 1, 1, 0, 0, 0]
)
self.assertAllEqual(
output["decoder_token_ids"], [0, 7, 8, 9, 10, 11, 2, 1]
output["decoder_token_ids"], [2, 0, 7, 8, 9, 10, 11, 2, 1]
)
self.assertAllEqual(
output["decoder_padding_mask"], [1, 1, 1, 1, 1, 1, 1, 0]
output["decoder_padding_mask"], [1, 1, 1, 1, 1, 1, 1, 1, 0]
)

def test_tokenize_list_of_strings(self):
Expand All @@ -91,10 +91,10 @@ def test_tokenize_list_of_strings(self):
output["encoder_padding_mask"], [[1, 1, 1, 1, 1, 1, 1, 0, 0, 0]] * 4
)
self.assertAllEqual(
output["decoder_token_ids"], [[0, 7, 8, 9, 10, 11, 2, 1]] * 4
output["decoder_token_ids"], [[2, 0, 7, 8, 9, 10, 11, 2, 1]] * 4
)
self.assertAllEqual(
output["decoder_padding_mask"], [[1, 1, 1, 1, 1, 1, 1, 0]] * 4
output["decoder_padding_mask"], [[1, 1, 1, 1, 1, 1, 1, 1, 0]] * 4
)

def test_tokenize_labeled_batch(self):
Expand All @@ -112,10 +112,10 @@ def test_tokenize_labeled_batch(self):
x_out["encoder_padding_mask"], [[1, 1, 1, 1, 1, 1, 1, 0, 0, 0]] * 4
)
self.assertAllEqual(
x_out["decoder_token_ids"], [[0, 7, 8, 9, 10, 11, 2, 1]] * 4
x_out["decoder_token_ids"], [[2, 0, 7, 8, 9, 10, 11, 2, 1]] * 4
)
self.assertAllEqual(
x_out["decoder_padding_mask"], [[1, 1, 1, 1, 1, 1, 1, 0]] * 4
x_out["decoder_padding_mask"], [[1, 1, 1, 1, 1, 1, 1, 1, 0]] * 4
)
self.assertAllEqual(y_out, y)
self.assertAllEqual(sw_out, sw)
Expand All @@ -137,10 +137,10 @@ def test_tokenize_labeled_dataset(self):
x_out["encoder_padding_mask"], [[1, 1, 1, 1, 1, 1, 1, 0, 0, 0]] * 4
)
self.assertAllEqual(
x_out["decoder_token_ids"], [[0, 7, 8, 9, 10, 11, 2, 1]] * 4
x_out["decoder_token_ids"], [[2, 0, 7, 8, 9, 10, 11, 2, 1]] * 4
)
self.assertAllEqual(
x_out["decoder_padding_mask"], [[1, 1, 1, 1, 1, 1, 1, 0]] * 4
x_out["decoder_padding_mask"], [[1, 1, 1, 1, 1, 1, 1, 1, 0]] * 4
)
self.assertAllEqual(y_out, y)
self.assertAllEqual(sw_out, sw)
Expand Down
18 changes: 9 additions & 9 deletions keras_nlp/models/bart/bart_seq_2_seq_lm_preprocessor_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -56,7 +56,7 @@ def setUp(self):
merges=merges,
),
encoder_sequence_length=10,
decoder_sequence_length=8,
decoder_sequence_length=9,
)

def test_tokenize_strings(self):
Expand All @@ -73,13 +73,13 @@ def test_tokenize_strings(self):
x_out["encoder_padding_mask"], [1, 1, 1, 1, 1, 1, 1, 0, 0, 0]
)
self.assertAllEqual(
x_out["decoder_token_ids"], [0, 7, 8, 9, 10, 11, 2, 1]
x_out["decoder_token_ids"], [2, 0, 7, 8, 9, 10, 11, 2, 1]
)
self.assertAllEqual(
x_out["decoder_padding_mask"], [1, 1, 1, 1, 1, 1, 1, 0]
x_out["decoder_padding_mask"], [1, 1, 1, 1, 1, 1, 1, 1, 0]
)
self.assertAllEqual(y_out, [7, 8, 9, 10, 11, 2, 1, 1])
self.assertAllEqual(sw_out, [1, 1, 1, 1, 1, 1, 0, 0])
self.assertAllEqual(y_out, [0, 7, 8, 9, 10, 11, 2, 1, 1])
self.assertAllEqual(sw_out, [1, 1, 1, 1, 1, 1, 1, 0, 0])

def test_tokenize_list_of_strings(self):
input_data = {
Expand All @@ -96,13 +96,13 @@ def test_tokenize_list_of_strings(self):
[[1, 1, 1, 1, 1, 1, 1, 0, 0, 0]] * 4,
)
self.assertAllEqual(
x_out["decoder_token_ids"], [[0, 7, 8, 9, 10, 11, 2, 1]] * 4
x_out["decoder_token_ids"], [[2, 0, 7, 8, 9, 10, 11, 2, 1]] * 4
)
self.assertAllEqual(
x_out["decoder_padding_mask"], [[1, 1, 1, 1, 1, 1, 1, 0]] * 4
x_out["decoder_padding_mask"], [[1, 1, 1, 1, 1, 1, 1, 1, 0]] * 4
)
self.assertAllEqual(y_out, [[7, 8, 9, 10, 11, 2, 1, 1]] * 4)
self.assertAllEqual(sw_out, [[1, 1, 1, 1, 1, 1, 0, 0]] * 4)
self.assertAllEqual(y_out, [[0, 7, 8, 9, 10, 11, 2, 1, 1]] * 4)
self.assertAllEqual(sw_out, [[1, 1, 1, 1, 1, 1, 1, 0, 0]] * 4)

def test_error_multi_segment_input(self):
input_data = {
Expand Down