Fix `serving_output` for TF composite models (encoder-decoder like models) (#22743)

* fix

* style

* fix

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

src/transformers/models/encoder_decoder/modeling_tf_encoder_decoder.py

@@ -633,14 +633,18 @@ class TFEncoderDecoderModel(TFPreTrainedModel, TFCausalLanguageModelingLoss):
         )
 
     def serving_output(self, output):
-        pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
-        dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
-        dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
-        enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
-        enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None
+        pkv = tf.tuple(output.past_key_values)[1] if self.config.decoder.use_cache else None
+        dec_hs = (
+            tf.convert_to_tensor(output.decoder_hidden_states) if self.config.decoder.output_hidden_states else None
+        )
+        dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.decoder.output_attentions else None
+        enc_hs = (
+            tf.convert_to_tensor(output.encoder_hidden_states) if self.config.encoder.output_hidden_states else None
+        )
+        enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.encoder.output_attentions else None
         cross_attns = (
             tf.convert_to_tensor(output.cross_attentions)
-            if self.config.output_attentions and output.cross_attentions is not None
+            if self.config.decoder.output_attentions and output.cross_attentions is not None
             else None
         )
 

src/transformers/models/vision_encoder_decoder/modeling_tf_vision_encoder_decoder.py

@@ -662,14 +662,18 @@ class TFVisionEncoderDecoderModel(TFPreTrainedModel, TFCausalLanguageModelingLos
         )
 
     def serving_output(self, output):
-        pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
-        dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
-        dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
-        enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
-        enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None
+        pkv = tf.tuple(output.past_key_values)[1] if self.config.decoder.use_cache else None
+        dec_hs = (
+            tf.convert_to_tensor(output.decoder_hidden_states) if self.config.decoder.output_hidden_states else None
+        )
+        dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.decoder.output_attentions else None
+        enc_hs = (
+            tf.convert_to_tensor(output.encoder_hidden_states) if self.config.encoder.output_hidden_states else None
+        )
+        enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.encoder.output_attentions else None
         cross_attns = (
             tf.convert_to_tensor(output.cross_attentions)
-            if self.config.output_attentions and output.cross_attentions is not None
+            if self.config.decoder.output_attentions and output.cross_attentions is not None
             else None
         )