fix bug in PT speech-encoder-decoder (#15699)

* fix bug in PT speech-encoder-decoder

* add pt test for `inputs is not None`

* fix test

* new pt test

* Update tests/test_modeling_speech_encoder_decoder.py

* make fixup

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

src/transformers/models/speech_encoder_decoder/modeling_speech_encoder_decoder.py

@@ -490,15 +490,16 @@ class SpeechEncoderDecoderModel(PreTrainedModel):
             argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
         }
 
-        if encoder_outputs is None and inputs is None:
+        if encoder_outputs is None:
-            if input_values is not None and input_features is not None:
+            if inputs is None:
-                raise ValueError("You cannot specify both input_values and input_features at the same time")
+                if input_values is not None and input_features is not None:
-            elif input_values is not None:
+                    raise ValueError("You cannot specify both input_values and input_features at the same time")
-                inputs = input_values
+                elif input_values is not None:
-            elif input_features is not None:
+                    inputs = input_values
-                inputs = input_features
+                elif input_features is not None:
-            else:
+                    inputs = input_features
-                raise ValueError("You have to specify either input_values or input_features")
+                else:
+                    raise ValueError("You have to specify either input_values or input_features")
 
             encoder_outputs = self.encoder(
                 inputs,

tests/test_modeling_speech_encoder_decoder.py

@@ -125,6 +125,43 @@ class EncoderDecoderMixin:
             outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
         )
 
+    def check_encoder_decoder_model_with_inputs(
+        self,
+        config,
+        attention_mask,
+        decoder_config,
+        decoder_input_ids,
+        decoder_attention_mask,
+        input_values=None,
+        input_features=None,
+        **kwargs
+    ):
+        inputs = input_values if input_features is None else input_features
+        encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
+        enc_dec_model = SpeechEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
+        enc_dec_model.to(torch_device)
+
+        outputs_encoder_decoder = enc_dec_model(
+            inputs,
+            decoder_input_ids=decoder_input_ids,
+            attention_mask=attention_mask,
+            decoder_attention_mask=decoder_attention_mask,
+            output_hidden_states=True,
+        )
+        self.assertEqual(
+            outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
+        )
+        outputs_encoder_decoder_kwarg = enc_dec_model(
+            inputs=inputs,
+            decoder_input_ids=decoder_input_ids,
+            attention_mask=attention_mask,
+            decoder_attention_mask=decoder_attention_mask,
+            output_hidden_states=True,
+        )
+        self.assertEqual(
+            outputs_encoder_decoder_kwarg["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
+        )
+
     def check_encoder_decoder_model_from_pretrained(
         self,
         config,
@@ -325,6 +362,10 @@ class EncoderDecoderMixin:
         input_ids_dict = self.prepare_config_and_inputs()
         self.check_encoder_decoder_model(**input_ids_dict)
 
+    def test_encoder_decoder_model_with_inputs(self):
+        input_ids_dict = self.prepare_config_and_inputs()
+        self.check_encoder_decoder_model_with_inputs(**input_ids_dict)
+
     def test_encoder_decoder_model_from_pretrained_configs(self):
         input_ids_dict = self.prepare_config_and_inputs()
         self.check_encoder_decoder_model_from_pretrained_configs(**input_ids_dict)