Generate: inner decoding methods are no longer public (#29437)

docs/source/en/generation_strategies.md

@@ -389,3 +389,6 @@ just like in multinomial sampling. However, in assisted decoding, reducing the t
 >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
 ['Alice and Bob are going to the same party. It is a small party, in a small']
 ```
+
+Alternativelly, you can also set the `prompt_lookup_num_tokens` to trigger n-gram based assisted decoding, as opposed
+to model based assisted decoding. You can read more about it [here](https://twitter.com/joao_gante/status/1747322413006643259).

docs/source/en/internal/generation_utils.md

@@ -16,16 +16,7 @@ rendered properly in your Markdown viewer.
 
 # Utilities for Generation
 
-This page lists all the utility functions used by [`~generation.GenerationMixin.generate`],
-[`~generation.GenerationMixin.greedy_search`],
-[`~generation.GenerationMixin.contrastive_search`],
-[`~generation.GenerationMixin.sample`],
-[`~generation.GenerationMixin.beam_search`],
-[`~generation.GenerationMixin.beam_sample`],
-[`~generation.GenerationMixin.group_beam_search`], and
-[`~generation.GenerationMixin.constrained_beam_search`].
-
-Most of those are only useful if you are studying the code of the generate methods in the library.
+This page lists all the utility functions used by [`~generation.GenerationMixin.generate`].
 
 ## Generate Outputs
 
@@ -376,4 +367,4 @@ A [`Constraint`] can be used to force the generation to include specific tokens
 
 [[autodoc]] StaticCache
     - update
-    - get_seq_length
+    - get_seq_length

docs/source/en/main_classes/text_generation.md

@@ -43,13 +43,6 @@ like token streaming.
 [[autodoc]] generation.GenerationMixin
 	- generate
 	- compute_transition_scores
-	- greedy_search
-	- sample
-	- beam_search
-	- beam_sample
-	- contrastive_search
-	- group_beam_search
-	- constrained_beam_search
 
 ## TFGenerationMixin
 

docs/source/ja/internal/generation_utils.md

@@ -17,15 +17,6 @@ rendered properly in your Markdown viewer.
 # 発電用ユーティリティ
 
 このページには、[`~generation.GenerationMixin.generate`] で使用されるすべてのユーティリティ関数がリストされています。
-[`~generation.GenerationMixin.greedy_search`],
-[`~generation.GenerationMixin.contrastive_search`],
-[`~generation.GenerationMixin.sample`],
-[`~generation.GenerationMixin.beam_search`],
-[`~generation.GenerationMixin.beam_sample`],
-[`~generation.GenerationMixin.group_beam_search`]、および
-[`~generation.GenerationMixin.constrained_beam_search`]。
-
-これらのほとんどは、ライブラリ内の生成メソッドのコードを学習する場合にのみ役に立ちます。
 
 ## 出力を生成する
 

docs/source/ja/main_classes/text_generation.md

@@ -43,13 +43,6 @@ rendered properly in your Markdown viewer.
 [[autodoc]] generation.GenerationMixin
 	- generate
 	- compute_transition_scores
-	- greedy_search
-	- sample
-	- beam_search
-	- beam_sample
-	- contrastive_search
-	- group_beam_search
-	- constrained_beam_search
 
 ## TFGenerationMixin
 

docs/source/zh/internal/generation_utils.md

@@ -16,16 +16,7 @@ rendered properly in your Markdown viewer.
 
 # 用于生成的工具
 
-此页面列出了所有由 [`~generation.GenerationMixin.generate`],
-[`~generation.GenerationMixin.greedy_search`],
-[`~generation.GenerationMixin.contrastive_search`],
-[`~generation.GenerationMixin.sample`],
-[`~generation.GenerationMixin.beam_search`],
-[`~generation.GenerationMixin.beam_sample`],
-[`~generation.GenerationMixin.group_beam_search`], 和
-[`~generation.GenerationMixin.constrained_beam_search`]使用的实用函数。
-
-其中大多数仅在您研究库中生成方法的代码时才有用。
+此页面列出了所有由 [`~generation.GenerationMixin.generate`]。
 
 ## 生成输出
 

docs/source/zh/main_classes/text_generation.md

@@ -38,13 +38,6 @@ rendered properly in your Markdown viewer.
 [[autodoc]] generation.GenerationMixin
 	- generate
 	- compute_transition_scores
-	- greedy_search
-	- sample
-	- beam_search
-	- beam_sample
-	- contrastive_search
-	- group_beam_search
-	- constrained_beam_search
 
 ## TFGenerationMixin
 

src/transformers/generation/configuration_utils.py

@@ -43,22 +43,22 @@ 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
+        - *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.`
+        - *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
+        - *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
+        - *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
+        - *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
+        - *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
+        - *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` is passed to `.generate()`
+        - *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).

src/transformers/generation/utils.py

@@ -347,20 +347,22 @@ class GenerationMixin:
     A class containing all functions for auto-regressive text generation, to be used as a mixin in [`PreTrainedModel`].
 
     The class exposes [`~generation.GenerationMixin.generate`], which can be used for:
-        - *greedy decoding* by calling [`~generation.GenerationMixin.greedy_search`] if `num_beams=1` and
+        - *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
+        - *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
+        - *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
+        - *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`
+        - *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`
+        - *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
+        - *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' instead. To
     learn more about decoding strategies refer to the [text generation strategies guide](../generation_strategies).
@@ -1547,7 +1549,7 @@ class GenerationMixin:
             )
         if generation_mode == GenerationMode.GREEDY_SEARCH:
             # 11. run greedy search
-            result = self.greedy_search(
+            result = self._greedy_search(
                 input_ids,
                 logits_processor=prepared_logits_processor,
                 stopping_criteria=prepared_stopping_criteria,
@@ -1565,7 +1567,7 @@ class GenerationMixin:
             if not model_kwargs["use_cache"]:
                 raise ValueError("Contrastive search requires `use_cache=True`")
 
-            result = self.contrastive_search(
+            result = self._contrastive_search(
                 input_ids,
                 top_k=generation_config.top_k,
                 penalty_alpha=generation_config.penalty_alpha,
@@ -1595,7 +1597,7 @@ class GenerationMixin:
             )
 
             # 13. run sample
-            result = self.sample(
+            result = self._sample(
                 input_ids,
                 logits_processor=prepared_logits_processor,
                 logits_warper=logits_warper,
@@ -1629,7 +1631,7 @@ class GenerationMixin:
                 **model_kwargs,
             )
             # 13. run beam search
-            result = self.beam_search(
+            result = self._beam_search(
                 input_ids,
                 beam_scorer,
                 logits_processor=prepared_logits_processor,
@@ -1668,7 +1670,7 @@ class GenerationMixin:
             )
 
             # 14. run beam sample
-            result = self.beam_sample(
+            result = self._beam_sample(
                 input_ids,
                 beam_scorer,
                 logits_processor=prepared_logits_processor,
@@ -1703,7 +1705,7 @@ class GenerationMixin:
                 **model_kwargs,
             )
             # 13. run beam search
-            result = self.group_beam_search(
+            result = self._group_beam_search(
                 input_ids,
                 beam_scorer,
                 logits_processor=prepared_logits_processor,
@@ -1777,7 +1779,7 @@ class GenerationMixin:
                 **model_kwargs,
             )
             # 13. run beam search
-            result = self.constrained_beam_search(
+            result = self._constrained_beam_search(
                 input_ids,
                 constrained_beam_scorer=constrained_beam_scorer,
                 logits_processor=prepared_logits_processor,
@@ -1801,8 +1803,15 @@ class GenerationMixin:
 
         return result
 
+    def contrastive_search(self, *args, **kwargs):
+        logger.warning_once(
+            "Calling `contrastive_search` directly is deprecated and will be removed in v4.41. Use `generate` or a "
+            "custom generation loop instead.",
+        )
+        return self._contrastive_search(*args, **kwargs)
+
     @torch.no_grad()
-    def contrastive_search(
+    def _contrastive_search(
         self,
         input_ids: torch.LongTensor,
         top_k: Optional[int] = 1,
@@ -1828,7 +1837,7 @@ class GenerationMixin:
 
         <Tip warning={true}>
 
-        In most cases, you do not need to call [`~generation.GenerationMixin.contrastive_search`] directly. Use
+        In most cases, you do not need to call [`~generation.GenerationMixin._contrastive_search`] directly. Use
         generate() instead. For an overview of generation strategies and code examples, check the [following
         guide](../generation_strategies).
 
@@ -1902,7 +1911,7 @@ class GenerationMixin:
         >>> input_prompt = "DeepMind Company is"
         >>> input_ids = tokenizer(input_prompt, return_tensors="pt")
         >>> stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=64)])
-        >>> outputs = model.contrastive_search(
+        >>> outputs = model._contrastive_search(
         ...     **input_ids, penalty_alpha=0.6, top_k=4, stopping_criteria=stopping_criteria
         ... )
         >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
@@ -2243,7 +2252,14 @@ class GenerationMixin:
         else:
             return input_ids
 
-    def greedy_search(
+    def greedy_search(self, *args, **kwargs):
+        logger.warning_once(
+            "Calling `greedy_search` directly is deprecated and will be removed in v4.41. Use `generate` or a "
+            "custom generation loop instead.",
+        )
+        return self._greedy_search(*args, **kwargs)
+
+    def _greedy_search(
         self,
         input_ids: torch.LongTensor,
         logits_processor: Optional[LogitsProcessorList] = None,
@@ -2266,7 +2282,7 @@ class GenerationMixin:
 
         <Tip warning={true}>
 
-        In most cases, you do not need to call [`~generation.GenerationMixin.greedy_search`] directly. Use generate()
+        In most cases, you do not need to call [`~generation.GenerationMixin._greedy_search`] directly. Use generate()
         instead. For an overview of generation strategies and code examples, check the [following
         guide](../generation_strategies).
 
@@ -2348,7 +2364,7 @@ class GenerationMixin:
         ... )
         >>> stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=20)])
 
-        >>> outputs = model.greedy_search(
+        >>> outputs = model._greedy_search(
         ...     input_ids, logits_processor=logits_processor, stopping_criteria=stopping_criteria
         ... )
 
@@ -2514,7 +2530,14 @@ class GenerationMixin:
         else:
             return input_ids
 
-    def sample(
+    def sample(self, *args, **kwargs):
+        logger.warning_once(
+            "Calling `sample` directly is deprecated and will be removed in v4.41. Use `generate` or a "
+            "custom generation loop instead.",
+        )
+        return self._sample(*args, **kwargs)
+
+    def _sample(
         self,
         input_ids: torch.LongTensor,
         logits_processor: Optional[LogitsProcessorList] = None,
@@ -2538,7 +2561,7 @@ class GenerationMixin:
 
         <Tip warning={true}>
 
-        In most cases, you do not need to call [`~generation.GenerationMixin.sample`] directly. Use generate() instead.
+        In most cases, you do not need to call [`~generation.GenerationMixin._sample`] directly. Use generate() instead.
         For an overview of generation strategies and code examples, check the [following
         guide](../generation_strategies).
 
@@ -2635,7 +2658,7 @@ class GenerationMixin:
         >>> stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=20)])
 
         >>> torch.manual_seed(0)  # doctest: +IGNORE_RESULT
-        >>> outputs = model.sample(
+        >>> outputs = model._sample(
         ...     input_ids,
         ...     logits_processor=logits_processor,
         ...     logits_warper=logits_warper,
@@ -2832,7 +2855,14 @@ class GenerationMixin:
             past_key_values.reorder_cache(beam_idx)
         return past_key_values
 
-    def beam_search(
+    def beam_search(self, *args, **kwargs):
+        logger.warning_once(
+            "Calling `beam_search` directly is deprecated and will be removed in v4.41. Use `generate` or a "
+            "custom generation loop instead.",
+        )
+        return self._beam_search(*args, **kwargs)
+
+    def _beam_search(
         self,
         input_ids: torch.LongTensor,
         beam_scorer: BeamScorer,
@@ -2856,7 +2886,7 @@ class GenerationMixin:
 
         <Tip warning={true}>
 
-        In most cases, you do not need to call [`~generation.GenerationMixin.beam_search`] directly. Use generate()
+        In most cases, you do not need to call [`~generation.GenerationMixin._beam_search`] directly. Use generate()
         instead. For an overview of generation strategies and code examples, check the [following
         guide](../generation_strategies).
 
@@ -2958,7 +2988,7 @@ class GenerationMixin:
         ...     ]
         ... )
 
-        >>> outputs = model.beam_search(input_ids, beam_scorer, logits_processor=logits_processor, **model_kwargs)
+        >>> outputs = model._beam_search(input_ids, beam_scorer, logits_processor=logits_processor, **model_kwargs)
 
         >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
         ['Wie alt bist du?']
@@ -3214,7 +3244,14 @@ class GenerationMixin:
         else:
             return sequence_outputs["sequences"]
 
-    def beam_sample(
+    def beam_sample(self, *args, **kwargs):
+        logger.warning_once(
+            "Calling `beam_sample` directly is deprecated and will be removed in v4.41. Use `generate` or a "
+            "custom generation loop instead.",
+        )
+        return self._beam_sample(*args, **kwargs)
+
+    def _beam_sample(
         self,
         input_ids: torch.LongTensor,
         beam_scorer: BeamScorer,
@@ -3238,7 +3275,7 @@ class GenerationMixin:
 
         <Tip warning={true}>
 
-        In most cases, you do not need to call [`~generation.GenerationMixin.beam_sample`] directly. Use generate()
+        In most cases, you do not need to call [`~generation.GenerationMixin._beam_sample`] directly. Use generate()
         instead. For an overview of generation strategies and code examples, check the [following
         guide](../generation_strategies).
 
@@ -3346,7 +3383,7 @@ class GenerationMixin:
         ...     ]
         ... )
 
-        >>> outputs = model.beam_sample(
+        >>> outputs = model._beam_sample(
         ...     input_ids, beam_scorer, logits_processor=logits_processor, logits_warper=logits_warper, **model_kwargs
         ... )
 
@@ -3561,7 +3598,14 @@ class GenerationMixin:
         else:
             return sequence_outputs["sequences"]
 
-    def group_beam_search(
+    def group_beam_search(self, *args, **kwargs):
+        logger.warning_once(
+            "Calling `group_beam_search` directly is deprecated and will be removed in v4.41. Use `generate` or a "
+            "custom generation loop instead.",
+        )
+        return self._group_beam_search(*args, **kwargs)
+
+    def _group_beam_search(
         self,
         input_ids: torch.LongTensor,
         beam_scorer: BeamScorer,
@@ -3584,7 +3628,7 @@ class GenerationMixin:
 
         <Tip warning={true}>
 
-        In most cases, you do not need to call [`~generation.GenerationMixin.group_beam_search`] directly. Use
+        In most cases, you do not need to call [`~generation.GenerationMixin._group_beam_search`] directly. Use
         generate() instead. For an overview of generation strategies and code examples, check the [following
         guide](../generation_strategies).
 
@@ -3686,7 +3730,7 @@ class GenerationMixin:
         ...     ]
         ... )
 
-        >>> outputs = model.group_beam_search(
+        >>> outputs = model._group_beam_search(
         ...     input_ids, beam_scorer, logits_processor=logits_processor, **model_kwargs
         ... )
 
@@ -3958,7 +4002,14 @@ class GenerationMixin:
         else:
             return sequence_outputs["sequences"]
 
-    def constrained_beam_search(
+    def constrained_beam_search(self, *args, **kwargs):
+        logger.warning_once(
+            "Calling `constrained_beam_search` directly is deprecated and will be removed in v4.41. Use `generate` or a "
+            "custom generation loop instead.",
+        )
+        return self._constrained_beam_search(*args, **kwargs)
+
+    def _constrained_beam_search(
         self,
         input_ids: torch.LongTensor,
         constrained_beam_scorer: ConstrainedBeamSearchScorer,
@@ -3981,7 +4032,7 @@ class GenerationMixin:
 
         <Tip warning={true}>
 
-        In most cases, you do not need to call [`~generation.GenerationMixin.constrained_beam_search`] directly. Use
+        In most cases, you do not need to call [`~generation.GenerationMixin._constrained_beam_search`] directly. Use
         generate() instead. For an overview of generation strategies and code examples, check the [following
         guide](../generation_strategies).
 
@@ -4088,7 +4139,7 @@ class GenerationMixin:
         ...     ]
         ... )
 
-        >>> outputs = model.constrained_beam_search(
+        >>> outputs = model._constrained_beam_search(
         ...     input_ids, beam_scorer, constraints=constraints, logits_processor=logits_processor, **model_kwargs
         ... )
 
@@ -4311,7 +4362,14 @@ class GenerationMixin:
         else:
             return sequence_outputs["sequences"]
 
-    def assisted_decoding(
+    def assisted_decoding(self, *args, **kwargs):
+        logger.warning_once(
+            "Calling `_assisted_decoding` directly is deprecated and will be removed in v4.41. Use `generate` or a "
+            "custom generation loop instead.",
+        )
+        return self._assisted_decoding(*args, **kwargs)
+
+    def _assisted_decoding(
         self,
         input_ids: torch.LongTensor,
         candidate_generator: Optional["CandidateGenerator"] = None,
@@ -4338,7 +4396,7 @@ class GenerationMixin:
 
         <Tip warning={true}>
 
-        In most cases, you do not need to call [`~generation.GenerationMixin.candidate_decoding`] directly. Use
+        In most cases, you do not need to call [`~generation.GenerationMixin._assisted_decoding`] directly. Use
         generate() instead. For an overview of generation strategies and code examples, check the [following
         guide](../generation_strategies).
 
@@ -4429,7 +4487,7 @@ class GenerationMixin:
         ...     logits_processor=logits_processor,
         ...     model_kwargs={},
         ... )
-        >>> outputs = model.assisted_decoding(
+        >>> outputs = model._assisted_decoding(
         ...     input_ids,
         ...     candidate_generator=candidate_generator,
         ...     logits_processor=logits_processor,

src/transformers/models/musicgen/modeling_musicgen.py

@@ -1336,7 +1336,7 @@ class MusicgenForCausalLM(MusicgenPreTrainedModel):
                 )
 
             # 11. run greedy search
-            outputs = self.greedy_search(
+            outputs = self._greedy_search(
                 input_ids,
                 logits_processor=logits_processor,
                 stopping_criteria=stopping_criteria,
@@ -1361,7 +1361,7 @@ class MusicgenForCausalLM(MusicgenPreTrainedModel):
             )
 
             # 12. run sample
-            outputs = self.sample(
+            outputs = self._sample(
                 input_ids,
                 logits_processor=logits_processor,
                 logits_warper=logits_warper,
@@ -2402,7 +2402,7 @@ class MusicgenForConditionalGeneration(PreTrainedModel):
                 )
 
             # 11. run greedy search
-            outputs = self.greedy_search(
+            outputs = self._greedy_search(
                 input_ids,
                 logits_processor=logits_processor,
                 stopping_criteria=stopping_criteria,
@@ -2428,7 +2428,7 @@ class MusicgenForConditionalGeneration(PreTrainedModel):
             )
 
             # 12. run sample
-            outputs = self.sample(
+            outputs = self._sample(
                 input_ids,
                 logits_processor=logits_processor,
                 logits_warper=logits_warper,

src/transformers/models/rag/modeling_rag.py

@@ -1539,7 +1539,7 @@ class RagTokenForGeneration(RagPreTrainedModel):
                     f"num_return_sequences has to be 1, but is {generation_config.num_return_sequences} when doing"
                     " greedy search."
                 )
-            return self.greedy_search(
+            return self._greedy_search(
                 input_ids,
                 logits_processor=pre_processor,
                 max_length=generation_config.max_length,
@@ -1559,7 +1559,7 @@ class RagTokenForGeneration(RagPreTrainedModel):
                 num_beam_hyps_to_keep=generation_config.num_return_sequences,
                 max_length=generation_config.max_length,
             )
-            return self.beam_search(
+            return self._beam_search(
                 input_ids,
                 beam_scorer,
                 logits_processor=pre_processor,