Generate: remove deprecated public decoding functions and streamline logic 🧼 (#29956)

src/transformers/generation/candidate_generator.py

@@ -123,7 +123,7 @@ class AssistedCandidateGenerator(CandidateGenerator):
                 inputs_tensor, assistant_model.generation_config.bos_token_id, assistant_kwargs
             )
             assistant_kwargs = assistant_model._prepare_encoder_decoder_kwargs_for_generation(
-                inputs_tensor, assistant_kwargs, model_input_name
+                inputs_tensor, assistant_kwargs, model_input_name, assistant_model.generation_config
             )
         elif "encoder_outputs" in model_kwargs:
             assistant_kwargs["encoder_outputs"] = model_kwargs["encoder_outputs"]

src/transformers/generation/configuration_utils.py

@@ -65,25 +65,16 @@ class GenerationConfig(PushToHubMixin):
     Class that holds a configuration for a generation task. A `generate` call supports the following generation methods
     for text-decoder, text-to-text, speech-to-text, and vision-to-text models:
 
-        - *greedy decoding* by calling [`~generation.GenerationMixin._greedy_search`] if `num_beams=1` and
-            `do_sample=False`
-        - *contrastive search* by calling [`~generation.GenerationMixin._contrastive_search`] if `penalty_alpha>0.`
-            and `top_k>1`
-        - *multinomial sampling* by calling [`~generation.GenerationMixin._sample`] if `num_beams=1` and
-            `do_sample=True`
-        - *beam-search decoding* by calling [`~generation.GenerationMixin._beam_search`] if `num_beams>1` and
-            `do_sample=False`
-        - *beam-search multinomial sampling* by calling [`~generation.GenerationMixin._beam_sample`] if
-            `num_beams>1` and `do_sample=True`
-        - *diverse beam-search decoding* by calling [`~generation.GenerationMixin._group_beam_search`], if
-            `num_beams>1` and `num_beam_groups>1`
-        - *constrained beam-search decoding* by calling [`~generation.GenerationMixin._constrained_beam_search`], if
-            `constraints!=None` or `force_words_ids!=None`
-        - *assisted decoding* by calling [`~generation.GenerationMixin._assisted_decoding`], if
-            `assistant_model` or `prompt_lookup_num_tokens` is passed to `.generate()`
-
-    You do not need to call any of the above methods directly. Pass custom parameter values to '.generate()'. To learn
-    more about decoding strategies refer to the [text generation strategies guide](../generation_strategies).
+        - *greedy decoding* if `num_beams=1` and `do_sample=False`
+        - *contrastive search* if `penalty_alpha>0.` and `top_k>1`
+        - *multinomial sampling* if `num_beams=1` and `do_sample=True`
+        - *beam-search decoding* if `num_beams>1` and `do_sample=False`
+        - *beam-search multinomial sampling* if `num_beams>1` and `do_sample=True`
+        - *diverse beam-search decoding* if `num_beams>1` and `num_beam_groups>1`
+        - *constrained beam-search decoding* if `constraints!=None` or `force_words_ids!=None`
+        - *assisted decoding* if `assistant_model` or `prompt_lookup_num_tokens` is passed to `.generate()`
+
+    To learn more about decoding strategies refer to the [text generation strategies guide](../generation_strategies).
 
     <Tip>
 

src/transformers/models/musicgen/modeling_musicgen.py

@@ -1650,8 +1650,6 @@ class MusicgenForCausalLM(MusicgenPreTrainedModel):
         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
 
@@ -1748,10 +1746,7 @@ class MusicgenForCausalLM(MusicgenPreTrainedModel):
                 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,
@@ -1774,10 +1769,7 @@ class MusicgenForCausalLM(MusicgenPreTrainedModel):
                 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,
@@ -2423,8 +2415,8 @@ class MusicgenForConditionalGeneration(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()
@@ -2446,6 +2438,9 @@ class MusicgenForConditionalGeneration(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
@@ -2708,8 +2703,6 @@ class MusicgenForConditionalGeneration(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
 
@@ -2723,10 +2716,7 @@ class MusicgenForConditionalGeneration(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 "decoder_input_ids" not in model_kwargs and "input_values" in model_kwargs:
@@ -2831,10 +2821,7 @@ class MusicgenForConditionalGeneration(PreTrainedModel):
                 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,
@@ -2858,10 +2845,7 @@ class MusicgenForConditionalGeneration(PreTrainedModel):
                 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,

src/transformers/models/musicgen_melody/modeling_musicgen_melody.py

@@ -1586,8 +1586,6 @@ class MusicgenMelodyForCausalLM(MusicgenMelodyPreTrainedModel):
         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
 
@@ -1684,10 +1682,7 @@ class MusicgenMelodyForCausalLM(MusicgenMelodyPreTrainedModel):
                 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,
@@ -1710,10 +1705,7 @@ class MusicgenMelodyForCausalLM(MusicgenMelodyPreTrainedModel):
                 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,
@@ -2318,12 +2310,13 @@ class MusicgenMelodyForConditionalGeneration(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]:
         encoder_hidden_states = None
         # attention mask is consumed once to produce text conditional hidden states through the text encoder
         encoder_attention_mask = model_kwargs.pop("attention_mask")
+        guidance_scale = generation_config.guidance_scale
 
         # 1. condition on text
         if inputs_tensor is not None:
@@ -2346,6 +2339,8 @@ class MusicgenMelodyForConditionalGeneration(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
 
             # 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
@@ -2572,8 +2567,6 @@ class MusicgenMelodyForConditionalGeneration(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
 
@@ -2585,10 +2578,7 @@ class MusicgenMelodyForConditionalGeneration(PreTrainedModel):
         if "encoder_hidden_states" not in model_kwargs:
             # encoder_hidden_states are created and added to `model_kwargs`
             model_kwargs = self._prepare_encoder_hidden_states_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
             )
 
         # 5. Prepare `input_ids` which will be used for auto-regressive generation
@@ -2684,14 +2674,11 @@ class MusicgenMelodyForConditionalGeneration(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,
@@ -2710,15 +2697,12 @@ class MusicgenMelodyForConditionalGeneration(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,

src/transformers/models/rag/modeling_rag.py

@@ -1537,6 +1537,10 @@ class RagTokenForGeneration(RagPreTrainedModel):
             logits_processor=logits_processor,
         )
 
+        prepared_stopping_criteria = self._get_stopping_criteria(
+            generation_config=generation_config, stopping_criteria=stopping_criteria
+        )
+
         if generation_config.num_beams == 1:
             if generation_config.num_return_sequences > 1:
                 raise ValueError(
@@ -1546,9 +1550,10 @@ class RagTokenForGeneration(RagPreTrainedModel):
             return self._greedy_search(
                 input_ids,
                 logits_processor=pre_processor,
-                max_length=generation_config.max_length,
-                pad_token_id=generation_config.pad_token_id,
-                eos_token_id=generation_config.eos_token_id,
+                stopping_criteria=prepared_stopping_criteria,
+                generation_config=generation_config,
+                synced_gpus=False,
+                streamer=None,
                 **model_kwargs,
             )
         elif generation_config.num_beams > 1:
@@ -1567,9 +1572,9 @@ class RagTokenForGeneration(RagPreTrainedModel):
                 input_ids,
                 beam_scorer,
                 logits_processor=pre_processor,
-                max_length=generation_config.max_length,
-                pad_token_id=generation_config.pad_token_id,
-                eos_token_id=generation_config.eos_token_id,
+                stopping_criteria=prepared_stopping_criteria,
+                generation_config=generation_config,
+                synced_gpus=False,
                 **model_kwargs,
             )
         else: