use private generation methods

parler_tts/modeling_parler_tts.py

@@ -1386,8 +1386,6 @@ class ParlerTTSForCausalLM(ParlerTTSPreTrainedModel):
         batch_size = input_ids.shape[0] // self.num_codebooks
 
         # 4. Define other model kwargs
-        model_kwargs["output_attentions"] = generation_config.output_attentions
-        model_kwargs["output_hidden_states"] = generation_config.output_hidden_states
         model_kwargs["use_cache"] = generation_config.use_cache
         model_kwargs["guidance_scale"] = generation_config.guidance_scale
 
@@ -1481,14 +1479,11 @@ class ParlerTTSForCausalLM(ParlerTTSPreTrainedModel):
                 )
 
             # 11. run greedy search
-            outputs = self.greedy_search(
+            outputs = self._greedy_search(
                 input_ids,
                 logits_processor=logits_processor,
                 stopping_criteria=stopping_criteria,
-                pad_token_id=generation_config.pad_token_id,
-                eos_token_id=generation_config.eos_token_id,
-                output_scores=generation_config.output_scores,
-                return_dict_in_generate=generation_config.return_dict_in_generate,
+                generation_config=generation_config,
                 synced_gpus=synced_gpus,
                 streamer=streamer,
                 **model_kwargs,
@@ -1506,15 +1501,12 @@ class ParlerTTSForCausalLM(ParlerTTSPreTrainedModel):
             )
 
             # 12. run sample
-            outputs = self.sample(
+            outputs = self._sample(
                 input_ids,
                 logits_processor=logits_processor,
                 logits_warper=logits_warper,
                 stopping_criteria=stopping_criteria,
-                pad_token_id=generation_config.pad_token_id,
-                eos_token_id=generation_config.eos_token_id,
-                output_scores=generation_config.output_scores,
-                return_dict_in_generate=generation_config.return_dict_in_generate,
+                generation_config=generation_config,
                 synced_gpus=synced_gpus,
                 streamer=streamer,
                 **model_kwargs,
@@ -2198,8 +2190,8 @@ class ParlerTTSForConditionalGeneration(PreTrainedModel):
         self,
         inputs_tensor: torch.Tensor,
         model_kwargs,
-        model_input_name: Optional[str] = None,
-        guidance_scale: Optional[float] = None,
+        model_input_name: Optional[str],
+        generation_config: GenerationConfig,
     ) -> Dict[str, Any]:
         # 1. get text encoder
         encoder = self.get_text_encoder()
@@ -2221,6 +2213,9 @@ class ParlerTTSForConditionalGeneration(PreTrainedModel):
             encoder_kwargs = {
                 argument: value for argument, value in encoder_kwargs.items() if argument in encoder_signature
             }
+        encoder_kwargs["output_attentions"] = generation_config.output_attentions
+        encoder_kwargs["output_hidden_states"] = generation_config.output_hidden_states
+        guidance_scale = generation_config.guidance_scale
 
         # 3. make sure that encoder returns `ModelOutput`
         model_input_name = model_input_name if model_input_name is not None else self.text_encoder.main_input_name
@@ -2452,8 +2447,6 @@ class ParlerTTSForConditionalGeneration(PreTrainedModel):
         batch_size = inputs_tensor.shape[0]
 
         # 4. Define other model kwargs
-        model_kwargs["output_attentions"] = generation_config.output_attentions
-        model_kwargs["output_hidden_states"] = generation_config.output_hidden_states
         model_kwargs["use_cache"] = generation_config.use_cache
         model_kwargs["guidance_scale"] = generation_config.guidance_scale
 
@@ -2467,10 +2460,7 @@ class ParlerTTSForConditionalGeneration(PreTrainedModel):
         if "encoder_outputs" not in model_kwargs:
             # encoder_outputs are created and added to `model_kwargs`
             model_kwargs = self._prepare_text_encoder_kwargs_for_generation(
-                inputs_tensor,
-                model_kwargs,
-                model_input_name,
-                guidance_scale=generation_config.guidance_scale,
+                inputs_tensor, model_kwargs, model_input_name, generation_config,
             )
 
         if "prompt_hidden_states" not in model_kwargs and "prompt_input_ids" in model_kwargs:
@@ -2579,14 +2569,11 @@ class ParlerTTSForConditionalGeneration(PreTrainedModel):
                 )
 
             # 11. run greedy search
-            outputs = self.greedy_search(
+            outputs = self._greedy_search(
                 input_ids,
                 logits_processor=logits_processor,
                 stopping_criteria=stopping_criteria,
-                pad_token_id=generation_config.pad_token_id,
-                eos_token_id=generation_config.eos_token_id,
-                output_scores=generation_config.output_scores,
-                return_dict_in_generate=generation_config.return_dict_in_generate,
+                generation_config=generation_config,
                 synced_gpus=synced_gpus,
                 streamer=streamer,
                 **model_kwargs,
@@ -2605,15 +2592,12 @@ class ParlerTTSForConditionalGeneration(PreTrainedModel):
             )
 
             # 12. run sample
-            outputs = self.sample(
+            outputs = self._sample(
                 input_ids,
                 logits_processor=logits_processor,
                 logits_warper=logits_warper,
                 stopping_criteria=stopping_criteria,
-                pad_token_id=generation_config.pad_token_id,
-                eos_token_id=generation_config.eos_token_id,
-                output_scores=generation_config.output_scores,
-                return_dict_in_generate=generation_config.return_dict_in_generate,
+                generation_config=generation_config,
                 synced_gpus=synced_gpus,
                 streamer=streamer,
                 **model_kwargs,