[whisper] fix short-form output type

src/transformers/models/whisper/generation_whisper.py

@@ -498,7 +498,7 @@ def generate(
 
         # 3. Make sure generation config is correctly set
         # Make sure the generation config is correctly set depending on whether timestamps are to be returned or not
-        self._set_return_outputs(
+        return_dict_in_generate = self._set_return_outputs(
             return_dict_in_generate=return_dict_in_generate,
             return_token_timestamps=return_token_timestamps,
             logprob_threshold=logprob_threshold,
@@ -732,7 +732,7 @@ def generate(
             else:
                 outputs = sequences
 
-            if generation_config.return_dict_in_generate:
+            if return_dict_in_generate and generation_config.return_dict_in_generate:
                 dict_outputs = self._stack_split_outputs(seek_outputs, model_output_type, sequences.device, kwargs)
 
                 if num_return_sequences > 1:
@@ -1109,18 +1109,20 @@ def _maybe_warn_unused_inputs(
     def _set_return_outputs(return_dict_in_generate, return_token_timestamps, logprob_threshold, generation_config):
         if return_dict_in_generate is None:
             return_dict_in_generate = generation_config.return_dict_in_generate
+        else:
+            generation_config.return_dict_in_generate = return_dict_in_generate
 
         generation_config.return_token_timestamps = return_token_timestamps
         if return_token_timestamps:
-            return_dict_in_generate = True
+            generation_config.return_dict_in_generate = True
             generation_config.output_attentions = True
             generation_config.output_scores = True
 
         if logprob_threshold is not None:
-            return_dict_in_generate = True
+            generation_config.return_dict_in_generate = True
             generation_config.output_scores = True
 
-        generation_config.return_dict_in_generate = return_dict_in_generate
+        return return_dict_in_generate
 
     def _set_return_timestamps(self, return_timestamps, is_shortform, generation_config):
         if not is_shortform:

tests/models/whisper/test_modeling_whisper.py

@@ -26,6 +26,7 @@
 import numpy as np
 import pytest
 from huggingface_hub import hf_hub_download
+from parameterized import parameterized
 
 import transformers
 from transformers import WhisperConfig
@@ -72,6 +73,7 @@
         BeamSearchEncoderDecoderOutput,
         GenerateBeamDecoderOnlyOutput,
         GenerateBeamEncoderDecoderOutput,
+        GenerateEncoderDecoderOutput,
         PhrasalConstraint,
     )
     from transformers.generation.logits_process import LogitsProcessor
@@ -1820,6 +1822,26 @@ def test_custom_4d_attention_mask(self):
         normalized_1 = torch.nn.functional.softmax(out_shared_prefix_last_tokens)
         torch.testing.assert_close(normalized_0, normalized_1, rtol=1e-3, atol=1e-4)
 
+    @parameterized.expand([(True,), (False,)])
+    def test_generate_output_type(self, return_dict_in_generate):
+        expected_output_type = GenerateEncoderDecoderOutput if return_dict_in_generate else torch.Tensor
+        for model_class in self.all_generative_model_classes:
+            config, inputs = self.model_tester.prepare_config_and_inputs()
+            model = model_class(config).to(torch_device).eval()
+
+            # short-form generation without fallback
+            pred_ids = model.generate(**inputs, return_dict_in_generate=return_dict_in_generate)
+            assert isinstance(pred_ids, expected_output_type)
+
+            # short-form generation with fallback
+            pred_ids = model.generate(
+                **inputs,
+                logprob_threshold=-1.0,
+                temperature=[0.0, 0.1],
+                return_dict_in_generate=return_dict_in_generate,
+            )
+            assert isinstance(pred_ids, expected_output_type)
+
 
 @require_torch
 @require_torchaudio