From 4e9f9514cbbd623f37ec521afdafc8e10496f0db Mon Sep 17 00:00:00 2001 From: Hongkun Yu Date: Tue, 2 Aug 2022 18:05:55 -0700 Subject: [PATCH] Internal change PiperOrigin-RevId: 464934071 --- official/nlp/modeling/layers/transformer_scaffold.py | 2 +- official/nlp/modeling/networks/albert_encoder.py | 2 +- official/nlp/modeling/networks/classification.py | 2 +- official/nlp/modeling/networks/encoder_scaffold.py | 2 +- official/nlp/modeling/networks/mobile_bert_encoder.py | 2 +- .../nlp/modeling/networks/packed_sequence_embedding.py | 8 ++++---- official/nlp/modeling/networks/span_labeling.py | 2 +- official/nlp/modeling/networks/xlnet_base.py | 8 ++++---- 8 files changed, 14 insertions(+), 14 deletions(-) diff --git a/official/nlp/modeling/layers/transformer_scaffold.py b/official/nlp/modeling/layers/transformer_scaffold.py index b00ee111a..8ce8bcf80 100644 --- a/official/nlp/modeling/layers/transformer_scaffold.py +++ b/official/nlp/modeling/layers/transformer_scaffold.py @@ -237,7 +237,7 @@ class TransformerScaffold(tf.keras.layers.Layer): self._output_layer_norm = tf.keras.layers.LayerNormalization( name="output_layer_norm", axis=-1, epsilon=1e-12, dtype=tf.float32) - super(TransformerScaffold, self).build(input_shape) + super().build(input_shape) logging.info("%s configs: %s", self.__class__.__name__, self.get_config()) def get_config(self): diff --git a/official/nlp/modeling/networks/albert_encoder.py b/official/nlp/modeling/networks/albert_encoder.py index a5fb97350..e7095de4e 100644 --- a/official/nlp/modeling/networks/albert_encoder.py +++ b/official/nlp/modeling/networks/albert_encoder.py @@ -173,7 +173,7 @@ class AlbertEncoder(tf.keras.Model): # created using the Functional API. Once super().__init__ is called, we # can assign attributes to `self` - note that all `self` assignments are # below this line. - super(AlbertEncoder, self).__init__( + super().__init__( inputs=[word_ids, mask, type_ids], outputs=outputs, **kwargs) config_dict = { 'vocab_size': vocab_size, diff --git a/official/nlp/modeling/networks/classification.py b/official/nlp/modeling/networks/classification.py index 67fa0dd26..ce8b1d704 100644 --- a/official/nlp/modeling/networks/classification.py +++ b/official/nlp/modeling/networks/classification.py @@ -74,7 +74,7 @@ class Classification(tf.keras.Model): ('Unknown `output` value "%s". `output` can be either "logits" or ' '"predictions"') % output) - super(Classification, self).__init__( + super().__init__( inputs=[cls_output], outputs=output_tensors, **kwargs) # b/164516224 diff --git a/official/nlp/modeling/networks/encoder_scaffold.py b/official/nlp/modeling/networks/encoder_scaffold.py index 1cfc3bcf3..72130d785 100644 --- a/official/nlp/modeling/networks/encoder_scaffold.py +++ b/official/nlp/modeling/networks/encoder_scaffold.py @@ -271,7 +271,7 @@ class EncoderScaffold(tf.keras.Model): # created using the Functional API. Once super().__init__ is called, we # can assign attributes to `self` - note that all `self` assignments are # below this line. - super(EncoderScaffold, self).__init__( + super().__init__( inputs=inputs, outputs=outputs, **kwargs) self._hidden_cls = hidden_cls diff --git a/official/nlp/modeling/networks/mobile_bert_encoder.py b/official/nlp/modeling/networks/mobile_bert_encoder.py index 09afa0bdb..46b2dbb21 100644 --- a/official/nlp/modeling/networks/mobile_bert_encoder.py +++ b/official/nlp/modeling/networks/mobile_bert_encoder.py @@ -163,7 +163,7 @@ class MobileBERTEncoder(tf.keras.Model): encoder_outputs=all_layer_outputs, attention_scores=all_attention_scores) - super(MobileBERTEncoder, self).__init__( + super().__init__( inputs=self.inputs, outputs=outputs, **kwargs) def get_embedding_table(self): diff --git a/official/nlp/modeling/networks/packed_sequence_embedding.py b/official/nlp/modeling/networks/packed_sequence_embedding.py index 604f9ff7c..6457e736b 100644 --- a/official/nlp/modeling/networks/packed_sequence_embedding.py +++ b/official/nlp/modeling/networks/packed_sequence_embedding.py @@ -143,7 +143,7 @@ class PackedSequenceEmbedding(tf.keras.Model): [attention_mask, sub_seq_mask]) outputs = [embeddings, attention_mask] - super(PackedSequenceEmbedding, self).__init__( + super().__init__( inputs=inputs, outputs=outputs, **kwargs) # TF does not track immutable attrs which do not contain Trackables, # so by creating a config namedtuple instead of a dict we avoid tracking it. @@ -221,7 +221,7 @@ class PositionEmbeddingWithSubSeqMask(tf.keras.layers.Layer): if 'dtype' not in kwargs: kwargs['dtype'] = 'float32' - super(PositionEmbeddingWithSubSeqMask, self).__init__(**kwargs) + super().__init__(**kwargs) if use_dynamic_slicing and max_sequence_length is None: raise ValueError( 'If `use_dynamic_slicing` is True, `max_sequence_length` must be set.' @@ -236,7 +236,7 @@ class PositionEmbeddingWithSubSeqMask(tf.keras.layers.Layer): 'initializer': tf.keras.initializers.serialize(self._initializer), 'use_dynamic_slicing': self._use_dynamic_slicing, } - base_config = super(PositionEmbeddingWithSubSeqMask, self).get_config() + base_config = super().get_config() return dict(list(base_config.items()) + list(config.items())) def build(self, input_shape): @@ -273,7 +273,7 @@ class PositionEmbeddingWithSubSeqMask(tf.keras.layers.Layer): shape=[weight_sequence_length, width], initializer=self._initializer) - super(PositionEmbeddingWithSubSeqMask, self).build(input_shape) + super().build(input_shape) def call(self, inputs, position_ids=None, sub_sequence_mask=None): """Implements call() for the layer. diff --git a/official/nlp/modeling/networks/span_labeling.py b/official/nlp/modeling/networks/span_labeling.py index eef8ffe0a..7da8a174e 100644 --- a/official/nlp/modeling/networks/span_labeling.py +++ b/official/nlp/modeling/networks/span_labeling.py @@ -81,7 +81,7 @@ class SpanLabeling(tf.keras.Model): # created using the Functional API. Once super().__init__ is called, we # can assign attributes to `self` - note that all `self` assignments are # below this line. - super(SpanLabeling, self).__init__( + super().__init__( inputs=[sequence_data], outputs=output_tensors, **kwargs) config_dict = { 'input_width': input_width, diff --git a/official/nlp/modeling/networks/xlnet_base.py b/official/nlp/modeling/networks/xlnet_base.py index 3fb01ef15..337fd8259 100644 --- a/official/nlp/modeling/networks/xlnet_base.py +++ b/official/nlp/modeling/networks/xlnet_base.py @@ -384,7 +384,7 @@ class RelativePositionEncoding(tf.keras.layers.Layer): """ def __init__(self, hidden_size, **kwargs): - super(RelativePositionEncoding, self).__init__(**kwargs) + super().__init__(**kwargs) self._hidden_size = hidden_size self._inv_freq = 1.0 / (10000.0**( tf.range(0, self._hidden_size, 2.0) / self._hidden_size)) @@ -476,7 +476,7 @@ class XLNetBase(tf.keras.layers.Layer): use_cls_mask=False, embedding_width=None, **kwargs): - super(XLNetBase, self).__init__(**kwargs) + super().__init__(**kwargs) self._vocab_size = vocab_size self._initializer = initializer @@ -574,7 +574,7 @@ class XLNetBase(tf.keras.layers.Layer): "embedding_width": self._embedding_width, } - base_config = super(XLNetBase, self).get_config() + base_config = super().get_config() return dict(list(base_config.items()) + list(config.items())) def get_embedding_lookup_table(self): @@ -601,7 +601,7 @@ class XLNetBase(tf.keras.layers.Layer): "target_mapping": target_mapping, "masked_tokens": masked_tokens } - return super(XLNetBase, self).__call__(inputs, **kwargs) + return super().__call__(inputs, **kwargs) def call(self, inputs): """Implements call() for the layer.""" -- GitLab