Fix the encoder-decoder / dual-encoder classes

src/transformers/models/convbert/modeling_tf_convbert.py

@@ -458,7 +458,6 @@ def build(self, input_shape=None):
                 self.dense.build(self.config.hidden_size)
 
 
-
 class TFConvBertOutput(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
         super().__init__(**kwargs)

src/transformers/models/encoder_decoder/modeling_tf_encoder_decoder.py

@@ -674,3 +674,9 @@ def build(self, input_shape=None):
         if getattr(self, "enc_to_dec_proj", None) is not None:
             with tf.name_scope(self.enc_to_dec_proj.name):
                 self.enc_to_dec_proj.build(self.encoder.config.hidden_size)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "decoder", None) is not None:
+            with tf.name_scope(self.decoder.name):
+                self.decoder.build(None)

src/transformers/models/vision_encoder_decoder/modeling_tf_vision_encoder_decoder.py

@@ -725,3 +725,9 @@ def build(self, input_shape=None):
         if getattr(self, "enc_to_dec_proj", None) is not None:
             with tf.name_scope(self.enc_to_dec_proj.name):
                 self.enc_to_dec_proj.build(self.encoder.config.hidden_size)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "decoder", None) is not None:
+            with tf.name_scope(self.decoder.name):
+                self.decoder.build(None)

src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py

@@ -223,19 +223,23 @@ def __init__(
         self.config = config
 
     def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
         # Build in the build() method to make sure the names are right
         initializer = tf.keras.initializers.Constant(self.config.logit_scale_init_value)
         self.logit_scale = self.add_weight(shape=(1,), initializer=initializer, name="logit_scale")
 
-        if self.built:
-            return
-        self.built = True
         if getattr(self, "visual_projection", None) is not None:
             with tf.name_scope(self.visual_projection.name):
                 self.visual_projection.build(self.vision_embed_dim)
         if getattr(self, "text_projection", None) is not None:
             with tf.name_scope(self.text_projection.name):
                 self.text_projection.build(self.text_embed_dim)
+        with tf.name_scope(self.vision_model.name):
+            self.vision_model.build(None)
+        with tf.name_scope(self.text_model.name):
+            self.text_model.build(None)
 
     @classmethod
     def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):

src/transformers/models/xlm/modeling_tf_xlm.py

@@ -384,7 +384,6 @@ def build(self, input_shape=None):
             with tf.name_scope(layer.name):
                 layer.build([None, None, self.dim])
 
-
     def get_input_embeddings(self):
         return self.embeddings
 

tests/models/encoder_decoder/test_modeling_tf_encoder_decoder.py

@@ -1071,9 +1071,9 @@ def test_encoder_decoder_save_load_from_encoder_decoder(self):
 
         # create two random BERT models for bert2bert & initialize weights (+cross_attention weights)
         encoder = TFBertModel(config.encoder)
-        encoder.build()
+        encoder.build_in_name_scope()
         decoder = TFBertLMHeadModel(config.decoder)
-        decoder.build()
+        decoder.build_in_name_scope()
 
         encoder_decoder_orig = TFEncoderDecoderModel(encoder=encoder, decoder=decoder)
 

tests/models/vision_encoder_decoder/test_modeling_tf_vision_encoder_decoder.py

@@ -729,9 +729,9 @@ def test_encoder_decoder_save_load_from_encoder_decoder(self):
 
         # create two random ViT/GPT2 models for vit-gpt2 & initialize weights (+cross_attention weights)
         encoder = TFViTModel(config.encoder)
-        encoder.build()
+        encoder.build_in_name_scope()
         decoder = TFGPT2LMHeadModel(config.decoder)
-        decoder.build()
+        decoder.build_in_name_scope()
 
         encoder_decoder_orig = TFVisionEncoderDecoderModel(encoder=encoder, decoder=decoder)