remove logits warpers

docs/source/en/internal/generation_utils.md

@@ -421,4 +421,3 @@ A [`Constraint`] can be used to force the generation to include specific tokens
 
 [[autodoc]] WatermarkDetector
     - __call__
-

docs/source/ja/internal/generation_utils.md

@@ -157,9 +157,6 @@ generation_output[:2]
 [[autodoc]] LogitsProcessorList
     - __call__
 
-[[autodoc]] LogitsWarper
-    - __call__
-
 [[autodoc]] MinLengthLogitsProcessor
     - __call__
 

docs/source/zh/internal/generation_utils.md

@@ -151,9 +151,6 @@ generation_output[:2]
 [[autodoc]] LogitsProcessorList
     - __call__
 
-[[autodoc]] LogitsWarper
-    - __call__
-
 [[autodoc]] MinLengthLogitsProcessor
     - __call__
 

src/transformers/generation/logits_process.py

@@ -55,6 +55,12 @@ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> to
 class LogitsWarper:
     """Abstract base class for all logit warpers that can be applied during generation with multinomial sampling."""
 
+    def __init__(self):
+        logger.warning_once(
+            "`LogitsWarper` is deprecated and will be removed in v4.48. Your class should inherit `LogitsProcessor` "
+            "instead, which has the same properties and interface."
+        )
+
     @add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
     def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
         raise NotImplementedError(
@@ -64,9 +70,9 @@ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> to
 
 class LogitsProcessorList(list):
     """
-    This class can be used to create a list of [`LogitsProcessor`] or [`LogitsWarper`] to subsequently process a
-    `scores` input tensor. This class inherits from list and adds a specific *__call__* method to apply each
-    [`LogitsProcessor`] or [`LogitsWarper`] to the inputs.
+    This class can be used to create a list of [`LogitsProcessor`] to subsequently process a `scores` input tensor.
+    This class inherits from list and adds a specific *__call__* method to apply each [`LogitsProcessor`] to the
+    inputs.
     """
 
     def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> torch.FloatTensor:
@@ -233,9 +239,9 @@ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> to
         return scores_processed
 
 
-class TemperatureLogitsWarper(LogitsWarper):
+class TemperatureLogitsWarper(LogitsProcessor):
     r"""
-    [`LogitsWarper`] for temperature (exponential scaling output probability distribution), which effectively means
+    [`LogitsProcessor`] for temperature (exponential scaling output probability distribution), which effectively means
     that it can control the randomness of the predicted tokens. Often used together with [`TopPLogitsWarper`] and
     [`TopKLogitsWarper`].
 
@@ -408,10 +414,10 @@ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> to
         return scores_processed
 
 
-class TopPLogitsWarper(LogitsWarper):
+class TopPLogitsWarper(LogitsProcessor):
     """
-    [`LogitsWarper`] that performs top-p, i.e. restricting to top tokens summing to prob_cut_off <= prob_cut_off. Often
-    used together with [`TemperatureLogitsWarper`] and [`TopKLogitsWarper`].
+    [`LogitsProcessor`] that performs top-p, i.e. restricting to top tokens summing to prob_cut_off <= prob_cut_off.
+    Often used together with [`TemperatureLogitsWarper`] and [`TopKLogitsWarper`].
 
     Args:
         top_p (`float`):
@@ -475,10 +481,10 @@ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> to
         return scores_processed
 
 
-class TopKLogitsWarper(LogitsWarper):
+class TopKLogitsWarper(LogitsProcessor):
     r"""
-    [`LogitsWarper`] that performs top-k, i.e. restricting to the k highest probability elements. Often used together
-    with [`TemperatureLogitsWarper`] and [`TopPLogitsWarper`].
+    [`LogitsProcessor`] that performs top-k, i.e. restricting to the k highest probability elements. Often used
+    together with [`TemperatureLogitsWarper`] and [`TopPLogitsWarper`].
 
     Args:
         top_k (`int`):
@@ -528,9 +534,9 @@ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> to
         return scores_processed
 
 
-class MinPLogitsWarper(LogitsWarper):
+class MinPLogitsWarper(LogitsProcessor):
     """
-    [`LogitsWarper`] that performs min-p, i.e. keeps all tokens that are above a minimum probability, scaled by the
+    [`LogitsProcessor`] that performs min-p, i.e. keeps all tokens that are above a minimum probability, scaled by the
     probability of the most likely token. As a result, the filter becomes more agressive in the presence of
     high-probability tokens, which is a sign of a confident output that we shouldn't deviate from.
 
@@ -605,11 +611,11 @@ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> to
         return scores_processed
 
 
-class TypicalLogitsWarper(LogitsWarper):
+class TypicalLogitsWarper(LogitsProcessor):
     r"""
-    [`LogitsWarper`] that performs typical decoding. Inspired on how humans use language, it prioritizes tokens whose
-    log probability is close to the entropy of the token probability distribution. This means that the most likely
-    tokens may be discarded in the process.
+    [`LogitsProcessor`] that performs typical decoding. Inspired on how humans use language, it prioritizes tokens
+    whose log probability is close to the entropy of the token probability distribution. This means that the most
+    likely tokens may be discarded in the process.
 
     See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information.
 
@@ -693,9 +699,9 @@ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> to
         return scores_processed
 
 
-class EpsilonLogitsWarper(LogitsWarper):
+class EpsilonLogitsWarper(LogitsProcessor):
     r"""
-    [`LogitsWarper`] that performs epsilon-sampling, i.e. restricting to tokens with `prob >= epsilon`. Takes the
+    [`LogitsProcessor`] that performs epsilon-sampling, i.e. restricting to tokens with `prob >= epsilon`. Takes the
     largest min_tokens_to_keep tokens if no tokens satisfy this constraint. See [Truncation Sampling as Language Model
     Desmoothing](https://arxiv.org/abs/2210.15191) for more information.
 
@@ -762,15 +768,15 @@ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> to
         return scores_processed
 
 
-class EtaLogitsWarper(LogitsWarper):
+class EtaLogitsWarper(LogitsProcessor):
     r"""
-    [`LogitsWarper`] that performs eta-sampling, a technique to filter out tokens with probabilities below a dynamic
+    [`LogitsProcessor`] that performs eta-sampling, a technique to filter out tokens with probabilities below a dynamic
     cutoff value, `eta`, which is calculated based on a combination of the hyperparameter `epsilon` and the entropy of
     the token probabilities, i.e. `eta := min(epsilon, sqrt(epsilon * e^-entropy(probabilities)))`. Takes the largest
     min_tokens_to_keep tokens if no tokens satisfy this constraint. It addresses the issue of poor quality in long
     samples of text generated by neural language models leading to more coherent and fluent text. See [Truncation
     Sampling as Language Model Desmoothing](https://arxiv.org/abs/2210.15191) for more information. Note: `do_sample`
-    must be set to `True` for this `LogitsWarper` to work.
+    must be set to `True` for this `LogitsProcessor` to work.
 
 
     Args:
@@ -1708,9 +1714,9 @@ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> to
         return scores_processed
 
 
-class LogitNormalization(LogitsProcessor, LogitsWarper):
+class LogitNormalization(LogitsProcessor):
     r"""
-    [`LogitsWarper`] and [`LogitsProcessor`] for normalizing the scores using log-softmax. It's important to normalize
+    [`LogitsProcessor`] for normalizing the scores using log-softmax. It's important to normalize
     the scores during beam search, after applying the logits processors or warpers, since the search algorithm used in
     this library doesn't do it (it only does it before, but they may need re-normalization) but it still supposes that
     the scores are normalized when comparing the hypotheses.