Avoid using tf.tile in embeddings for TF models (#14735)

* avoid tf.tile in embeddings

* remove more tf.tile in embeddings

* clean
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

src/transformers/models/albert/modeling_tf_albert.py

@@ -122,7 +122,6 @@ class TFAlbertEmbeddings(tf.keras.layers.Layer):
         self.embedding_size = config.embedding_size
         self.max_position_embeddings = config.max_position_embeddings
         self.initializer_range = config.initializer_range
-        self.embeddings_sum = tf.keras.layers.Add()
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
 
@@ -183,9 +182,8 @@ class TFAlbertEmbeddings(tf.keras.layers.Layer):
             )
 
         position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
-        position_embeds = tf.tile(input=position_embeds, multiples=(input_shape[0], 1, 1))
         token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
-        final_embeddings = self.embeddings_sum(inputs=[inputs_embeds, position_embeds, token_type_embeds])
+        final_embeddings = inputs_embeds + position_embeds + token_type_embeds
         final_embeddings = self.LayerNorm(inputs=final_embeddings)
         final_embeddings = self.dropout(inputs=final_embeddings, training=training)
 

src/transformers/models/bert/modeling_tf_bert.py

@@ -141,7 +141,6 @@ class TFBertEmbeddings(tf.keras.layers.Layer):
         self.hidden_size = config.hidden_size
         self.max_position_embeddings = config.max_position_embeddings
         self.initializer_range = config.initializer_range
-        self.embeddings_sum = tf.keras.layers.Add()
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
 
@@ -201,9 +200,8 @@ class TFBertEmbeddings(tf.keras.layers.Layer):
             )
 
         position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
-        position_embeds = tf.tile(input=position_embeds, multiples=(input_shape[0], 1, 1))
         token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
-        final_embeddings = self.embeddings_sum(inputs=[inputs_embeds, position_embeds, token_type_embeds])
+        final_embeddings = inputs_embeds + position_embeds + token_type_embeds
         final_embeddings = self.LayerNorm(inputs=final_embeddings)
         final_embeddings = self.dropout(inputs=final_embeddings, training=training)
 

src/transformers/models/convbert/modeling_tf_convbert.py

@@ -75,7 +75,6 @@ class TFConvBertEmbeddings(tf.keras.layers.Layer):
         self.embedding_size = config.embedding_size
         self.max_position_embeddings = config.max_position_embeddings
         self.initializer_range = config.initializer_range
-        self.embeddings_sum = tf.keras.layers.Add()
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
 
@@ -136,9 +135,8 @@ class TFConvBertEmbeddings(tf.keras.layers.Layer):
             )
 
         position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
-        position_embeds = tf.tile(input=position_embeds, multiples=(input_shape[0], 1, 1))
         token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
-        final_embeddings = self.embeddings_sum(inputs=[inputs_embeds, position_embeds, token_type_embeds])
+        final_embeddings = inputs_embeds + position_embeds + token_type_embeds
         final_embeddings = self.LayerNorm(inputs=final_embeddings)
         final_embeddings = self.dropout(inputs=final_embeddings, training=training)
 

src/transformers/models/deberta/modeling_tf_deberta.py

@@ -728,7 +728,6 @@ class TFDebertaEmbeddings(tf.keras.layers.Layer):
         self.max_position_embeddings = config.max_position_embeddings
         self.position_biased_input = getattr(config, "position_biased_input", True)
         self.initializer_range = config.initializer_range
-        self.embeddings_sum = tf.keras.layers.Add()
         if self.embedding_size != config.hidden_size:
             self.embed_proj = tf.keras.layers.Dense(config.hidden_size, bias=False)
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
@@ -795,7 +794,6 @@ class TFDebertaEmbeddings(tf.keras.layers.Layer):
         final_embeddings = inputs_embeds
         if self.position_biased_input:
             position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
-            position_embeds = tf.tile(input=position_embeds, multiples=(input_shape[0], 1, 1))
             final_embeddings += position_embeds
         if self.type_vocab_size > 0:
             token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)

src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py

@@ -845,7 +845,6 @@ class TFDebertaV2Embeddings(tf.keras.layers.Layer):
         self.max_position_embeddings = config.max_position_embeddings
         self.position_biased_input = getattr(config, "position_biased_input", True)
         self.initializer_range = config.initializer_range
-        self.embeddings_sum = tf.keras.layers.Add()
         if self.embedding_size != config.hidden_size:
             self.embed_proj = tf.keras.layers.Dense(config.hidden_size, bias=False)
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
@@ -912,7 +911,6 @@ class TFDebertaV2Embeddings(tf.keras.layers.Layer):
         final_embeddings = inputs_embeds
         if self.position_biased_input:
             position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
-            position_embeds = tf.tile(input=position_embeds, multiples=(input_shape[0], 1, 1))
             final_embeddings += position_embeds
         if self.type_vocab_size > 0:
             token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)

src/transformers/models/distilbert/modeling_tf_distilbert.py

@@ -77,8 +77,6 @@ class TFEmbeddings(tf.keras.layers.Layer):
         self.dim = config.dim
         self.initializer_range = config.initializer_range
         self.max_position_embeddings = config.max_position_embeddings
-
-        self.embeddings_sum = tf.keras.layers.Add()
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=1e-12, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.dropout)
 
@@ -117,8 +115,7 @@ class TFEmbeddings(tf.keras.layers.Layer):
             position_ids = tf.expand_dims(tf.range(start=0, limit=input_shape[-1]), axis=0)
 
         position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
-        position_embeds = tf.tile(input=position_embeds, multiples=(input_shape[0], 1, 1))
-        final_embeddings = self.embeddings_sum(inputs=[inputs_embeds, position_embeds])
+        final_embeddings = inputs_embeds + position_embeds
         final_embeddings = self.LayerNorm(inputs=final_embeddings)
         final_embeddings = self.dropout(inputs=final_embeddings, training=training)
 

src/transformers/models/electra/modeling_tf_electra.py

@@ -481,7 +481,6 @@ class TFElectraEmbeddings(tf.keras.layers.Layer):
         self.embedding_size = config.embedding_size
         self.max_position_embeddings = config.max_position_embeddings
         self.initializer_range = config.initializer_range
-        self.embeddings_sum = tf.keras.layers.Add()
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
 
@@ -542,9 +541,8 @@ class TFElectraEmbeddings(tf.keras.layers.Layer):
             )
 
         position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
-        position_embeds = tf.tile(input=position_embeds, multiples=(input_shape[0], 1, 1))
         token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
-        final_embeddings = self.embeddings_sum(inputs=[inputs_embeds, position_embeds, token_type_embeds])
+        final_embeddings = inputs_embeds + position_embeds + token_type_embeds
         final_embeddings = self.LayerNorm(inputs=final_embeddings)
         final_embeddings = self.dropout(inputs=final_embeddings, training=training)
 

src/transformers/models/layoutlm/modeling_tf_layoutlm.py

@@ -68,7 +68,6 @@ class TFLayoutLMEmbeddings(tf.keras.layers.Layer):
         self.max_position_embeddings = config.max_position_embeddings
         self.max_2d_position_embeddings = config.max_2d_position_embeddings
         self.initializer_range = config.initializer_range
-        self.embeddings_sum = tf.keras.layers.Add()
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
 
@@ -168,20 +167,17 @@ class TFLayoutLMEmbeddings(tf.keras.layers.Layer):
         w_position_embeddings = tf.gather(self.w_position_embeddings, bbox[:, :, 2] - bbox[:, :, 0])
 
         position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
-        position_embeds = tf.tile(input=position_embeds, multiples=(input_shape[0], 1, 1))
         token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
-        final_embeddings = self.embeddings_sum(
-            inputs=[
-                inputs_embeds,
-                position_embeds,
-                token_type_embeds,
-                left_position_embeddings,
-                upper_position_embeddings,
-                right_position_embeddings,
-                lower_position_embeddings,
-                h_position_embeddings,
-                w_position_embeddings,
-            ]
+        final_embeddings = (
+            inputs_embeds
+            + position_embeds
+            + token_type_embeds
+            + left_position_embeddings
+            + upper_position_embeddings
+            + right_position_embeddings
+            + lower_position_embeddings
+            + h_position_embeddings
+            + w_position_embeddings
         )
         final_embeddings = self.LayerNorm(inputs=final_embeddings)
         final_embeddings = self.dropout(inputs=final_embeddings, training=training)

src/transformers/models/longformer/modeling_tf_longformer.py

@@ -482,7 +482,6 @@ class TFLongformerEmbeddings(tf.keras.layers.Layer):
         self.hidden_size = config.hidden_size
         self.max_position_embeddings = config.max_position_embeddings
         self.initializer_range = config.initializer_range
-        self.embeddings_sum = tf.keras.layers.Add()
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
 
@@ -559,11 +558,10 @@ class TFLongformerEmbeddings(tf.keras.layers.Layer):
                 position_ids = tf.expand_dims(
                     tf.range(start=self.padding_idx + 1, limit=input_shape[-1] + self.padding_idx + 1), axis=0
                 )
-                position_ids = tf.tile(input=position_ids, multiples=(input_shape[0], 1))
 
         position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
         token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
-        final_embeddings = self.embeddings_sum(inputs=[inputs_embeds, position_embeds, token_type_embeds])
+        final_embeddings = inputs_embeds + position_embeds + token_type_embeds
         final_embeddings = self.LayerNorm(inputs=final_embeddings)
         final_embeddings = self.dropout(inputs=final_embeddings, training=training)
 

src/transformers/models/lxmert/modeling_tf_lxmert.py

@@ -188,7 +188,6 @@ class TFLxmertEmbeddings(tf.keras.layers.Layer):
         self.hidden_size = config.hidden_size
         self.max_position_embeddings = config.max_position_embeddings
         self.initializer_range = config.initializer_range
-        self.embeddings_sum = tf.keras.layers.Add()
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
 
@@ -235,9 +234,8 @@ class TFLxmertEmbeddings(tf.keras.layers.Layer):
 
         position_ids = tf.expand_dims(tf.range(start=0, limit=input_shape[-1]), axis=0)
         position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
-        position_embeds = tf.tile(input=position_embeds, multiples=(input_shape[0], 1, 1))
         token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
-        final_embeddings = self.embeddings_sum(inputs=[inputs_embeds, position_embeds, token_type_embeds])
+        final_embeddings = inputs_embeds + position_embeds + token_type_embeds
         final_embeddings = self.LayerNorm(inputs=final_embeddings)
         final_embeddings = self.dropout(inputs=final_embeddings, training=training)
 

src/transformers/models/mobilebert/modeling_tf_mobilebert.py

@@ -121,7 +121,6 @@ class TFMobileBertEmbeddings(tf.keras.layers.Layer):
         self.type_vocab_size = config.type_vocab_size
         self.max_position_embeddings = config.max_position_embeddings
         self.initializer_range = config.initializer_range
-        self.embeddings_sum = tf.keras.layers.Add()
         self.embedding_transformation = tf.keras.layers.Dense(config.hidden_size, name="embedding_transformation")
 
         # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
@@ -196,9 +195,8 @@ class TFMobileBertEmbeddings(tf.keras.layers.Layer):
             position_ids = tf.expand_dims(tf.range(start=0, limit=input_shape[-1]), axis=0)
 
         position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
-        position_embeds = tf.tile(input=position_embeds, multiples=(input_shape[0], 1, 1))
         token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
-        final_embeddings = self.embeddings_sum(inputs=[inputs_embeds, position_embeds, token_type_embeds])
+        final_embeddings = inputs_embeds + position_embeds + token_type_embeds
         final_embeddings = self.LayerNorm(inputs=final_embeddings)
         final_embeddings = self.dropout(inputs=final_embeddings, training=training)
 

src/transformers/models/mpnet/modeling_tf_mpnet.py

@@ -98,7 +98,6 @@ class TFMPNetEmbeddings(tf.keras.layers.Layer):
         self.hidden_size = config.hidden_size
         self.max_position_embeddings = config.max_position_embeddings
         self.initializer_range = config.initializer_range
-        self.embeddings_sum = tf.keras.layers.Add()
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
 
@@ -155,10 +154,9 @@ class TFMPNetEmbeddings(tf.keras.layers.Layer):
                 position_ids = tf.expand_dims(
                     tf.range(start=self.padding_idx + 1, limit=input_shape[-1] + self.padding_idx + 1), axis=0
                 )
-                position_ids = tf.tile(input=position_ids, multiples=(input_shape[0], 1))
 
         position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
-        final_embeddings = self.embeddings_sum(inputs=[inputs_embeds, position_embeds])
+        final_embeddings = inputs_embeds + position_embeds
         final_embeddings = self.LayerNorm(inputs=final_embeddings)
         final_embeddings = self.dropout(inputs=final_embeddings, training=training)
 

src/transformers/models/rembert/modeling_tf_rembert.py

@@ -79,7 +79,6 @@ class TFRemBertEmbeddings(tf.keras.layers.Layer):
         self.input_embedding_size = config.input_embedding_size
         self.max_position_embeddings = config.max_position_embeddings
         self.initializer_range = config.initializer_range
-        self.embeddings_sum = tf.keras.layers.Add()
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
 
@@ -138,9 +137,8 @@ class TFRemBertEmbeddings(tf.keras.layers.Layer):
             )
 
         position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
-        position_embeds = tf.tile(input=position_embeds, multiples=(input_shape[0], 1, 1))
         token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
-        final_embeddings = self.embeddings_sum(inputs=[inputs_embeds, position_embeds, token_type_embeds])
+        final_embeddings = inputs_embeds + position_embeds + token_type_embeds
         final_embeddings = self.LayerNorm(inputs=final_embeddings)
         final_embeddings = self.dropout(inputs=final_embeddings, training=training)
 

src/transformers/models/roberta/modeling_tf_roberta.py

@@ -87,7 +87,6 @@ class TFRobertaEmbeddings(tf.keras.layers.Layer):
         self.hidden_size = config.hidden_size
         self.max_position_embeddings = config.max_position_embeddings
         self.initializer_range = config.initializer_range
-        self.embeddings_sum = tf.keras.layers.Add()
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
 
@@ -164,11 +163,10 @@ class TFRobertaEmbeddings(tf.keras.layers.Layer):
                 position_ids = tf.expand_dims(
                     tf.range(start=self.padding_idx + 1, limit=input_shape[-1] + self.padding_idx + 1), axis=0
                 )
-                position_ids = tf.tile(input=position_ids, multiples=(input_shape[0], 1))
 
         position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
         token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
-        final_embeddings = self.embeddings_sum(inputs=[inputs_embeds, position_embeds, token_type_embeds])
+        final_embeddings = inputs_embeds + position_embeds + token_type_embeds
         final_embeddings = self.LayerNorm(inputs=final_embeddings)
         final_embeddings = self.dropout(inputs=final_embeddings, training=training)
 

src/transformers/models/roformer/modeling_tf_roformer.py

@@ -140,7 +140,6 @@ class TFRoFormerEmbeddings(tf.keras.layers.Layer):
         self.type_vocab_size = config.type_vocab_size
         self.embedding_size = config.embedding_size
         self.initializer_range = config.initializer_range
-        self.embeddings_sum = tf.keras.layers.Add()
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
 
@@ -186,7 +185,7 @@ class TFRoFormerEmbeddings(tf.keras.layers.Layer):
             token_type_ids = tf.fill(dims=input_shape, value=0)
 
         token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
-        final_embeddings = self.embeddings_sum(inputs=[inputs_embeds, token_type_embeds])
+        final_embeddings = inputs_embeds + token_type_embeds
         final_embeddings = self.LayerNorm(inputs=final_embeddings)
         final_embeddings = self.dropout(inputs=final_embeddings, training=training)
 

templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_tf_{{cookiecutter.lowercase_modelname}}.py

@@ -83,7 +83,6 @@ class TF{{cookiecutter.camelcase_modelname}}Embeddings(tf.keras.layers.Layer):
         self.hidden_size = config.hidden_size
         self.max_position_embeddings = config.max_position_embeddings
         self.initializer_range = config.initializer_range
-        self.embeddings_sum = tf.keras.layers.Add()
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
 
@@ -142,9 +141,8 @@ class TF{{cookiecutter.camelcase_modelname}}Embeddings(tf.keras.layers.Layer):
             )
 
         position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
-        position_embeds = tf.tile(input=position_embeds, multiples=(input_shape[0], 1, 1))
         token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
-        final_embeddings = self.embeddings_sum(inputs=[inputs_embeds, position_embeds, token_type_embeds])
+        final_embeddings = inputs_embeds + position_embeds + token_type_embeds
         final_embeddings = self.LayerNorm(inputs=final_embeddings)
         final_embeddings = self.dropout(inputs=final_embeddings, training=training)