fix beam search in seq2seq (#1111)

* fix beam search in seq2seq

* add tests

optimum/intel/openvino/modeling_seq2seq.py

@@ -422,7 +422,7 @@ def get_encoder(self):
         return self.encoder
 
     def _reorder_cache(self, past, beam_idx) -> Tuple[Tuple[torch.FloatTensor]]:
-        self.decoder._reorder_cache(past, beam_idx)
+        return self.decoder._reorder_cache(past, beam_idx)
 
     def reshape(self, batch_size: int, sequence_length: int):
         """
@@ -627,6 +627,7 @@ def forward(
         if self.stateful and past_key_values is None:
             self.request.reset_state()
             self._past_length = 0
+            self.next_beam_idx = np.arange(input_ids.shape[0], dtype=int)
 
         if past_key_values is not None and not self.stateful:
             # Flatten the past_key_values
@@ -661,7 +662,6 @@ def forward(
             inputs["beam_idx"] = (
                 self.next_beam_idx if self.next_beam_idx is not None else np.arange(batch_size, dtype=np.int32)
             )
-
         # Run inference
         self.request.start_async(inputs, share_inputs=True)
         self.request.wait()
@@ -1016,7 +1016,6 @@ class _OVModelForWhisper(OVModelForSpeechSeq2Seq, WhisperForConditionalGeneratio
     auto_model_class = WhisperForConditionalGeneration
 
     # force the use of the WhisperForConditionalGeneration generate and prepare_inputs_for_generation methods
-    prepare_inputs_for_generation = WhisperForConditionalGeneration.prepare_inputs_for_generation
     generate = WhisperForConditionalGeneration.generate
 
     @classmethod
@@ -1083,7 +1082,7 @@ def prepare_inputs_for_generation(
 
         past_length = 0
         if past_key_values is not None:
-            self.decoder._get_past_length(past_key_values)
+            past_length = self.decoder._get_past_length(past_key_values)
 
             # Some generation methods already pass only the last input ID
             if decoder_input_ids.shape[1] > past_length:

tests/openvino/test_modeling.py

@@ -1658,6 +1658,21 @@ def test_compare_to_transformers(self, model_arch):
             transformers_outputs = transformers_model(**tokens, **decoder_inputs)
         # Compare tensor outputs
         self.assertTrue(torch.allclose(ov_outputs.logits, transformers_outputs.logits, atol=1e-4))
+        gen_config = GenerationConfig(
+            max_new_tokens=10,
+            min_new_tokens=10,
+            num_beams=2,
+            do_sample=False,
+            eos_token_id=None,
+        )
+
+        set_seed(SEED)
+        generated_tokens = transformers_model.generate(**tokens, generation_config=gen_config)
+        set_seed(SEED)
+        ov_generated_tokens = ov_model.generate(**tokens, generation_config=gen_config)
+
+        self.assertTrue(torch.equal(generated_tokens, ov_generated_tokens))
+
         del transformers_model
         del ov_model
 
@@ -2355,12 +2370,12 @@ def test_compare_to_transformers(self, model_arch):
 
         processor = get_preprocessor(model_id)
         data = self._generate_random_audio_data()
-        features = processor.feature_extractor(data, return_tensors="pt")
+        pt_features = processor.feature_extractor(data, return_tensors="pt")
         decoder_start_token_id = transformers_model.config.decoder_start_token_id
         decoder_inputs = {"decoder_input_ids": torch.ones((1, 1), dtype=torch.long) * decoder_start_token_id}
 
         with torch.no_grad():
-            transformers_outputs = transformers_model(**features, **decoder_inputs)
+            transformers_outputs = transformers_model(**pt_features, **decoder_inputs)
 
         for input_type in ["pt", "np"]:
             features = processor.feature_extractor(data, return_tensors=input_type)
@@ -2373,6 +2388,21 @@ def test_compare_to_transformers(self, model_arch):
             # Compare tensor outputs
             self.assertTrue(torch.allclose(torch.Tensor(ov_outputs.logits), transformers_outputs.logits, atol=1e-3))
 
+        gen_config = GenerationConfig(
+            max_new_tokens=10,
+            min_new_tokens=10,
+            num_beams=2,
+            do_sample=False,
+            eos_token_id=None,
+        )
+
+        set_seed(SEED)
+        generated_tokens = transformers_model.generate(**pt_features, generation_config=gen_config)
+        set_seed(SEED)
+        ov_generated_tokens = ov_model.generate(**pt_features, generation_config=gen_config)
+
+        self.assertTrue(torch.equal(generated_tokens, ov_generated_tokens))
+
         del transformers_model
         del ov_model
         gc.collect()