[docs] fix xref to PreTrainedModel.generate (#11049)

* fix xref to generate

* do the same for search methods

* style

* style

docs/source/internal/generation_utils.rst

@@ -13,19 +13,21 @@
 Utilities for Generation
 -----------------------------------------------------------------------------------------------------------------------
 
-This page lists all the utility functions used by :meth:`~transformers.PreTrainedModel.generate`,
+This page lists all the utility functions used by :meth:`~transformers.generation_utils.GenerationMixin.generate`,
-:meth:`~transformers.PreTrainedModel.greedy_search`, :meth:`~transformers.PreTrainedModel.sample`,
+:meth:`~transformers.generation_utils.GenerationMixin.greedy_search`,
-:meth:`~transformers.PreTrainedModel.beam_search`, :meth:`~transformers.PreTrainedModel.beam_sample`, and
+:meth:`~transformers.generation_utils.GenerationMixin.sample`,
-:meth:`~transformers.PreTrainedModel.group_beam_search`.
+:meth:`~transformers.generation_utils.GenerationMixin.beam_search`,
+:meth:`~transformers.generation_utils.GenerationMixin.beam_sample`, and
+:meth:`~transformers.generation_utils.GenerationMixin.group_beam_search`.
 
 Most of those are only useful if you are studying the code of the generate methods in the library.
 
 Generate Outputs
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The output of :meth:`~transformers.PreTrainedModel.generate` is an instance of a subclass of
+The output of :meth:`~transformers.generation_utils.GenerationMixin.generate` is an instance of a subclass of
 :class:`~transformers.file_utils.ModelOutput`. This output is a data structure containing all the information returned
-by :meth:`~transformers.PreTrainedModel.generate`, but that can also be used as tuple or dictionary.
+by :meth:`~transformers.generation_utils.GenerationMixin.generate`, but that can also be used as tuple or dictionary.
 
 Here's an example:
 

docs/source/model_doc/bart.rst

@@ -61,7 +61,7 @@ Implementation Notes
 - Model predictions are intended to be identical to the original implementation when
   :obj:`force_bos_token_to_be_generated=True`. This only works, however, if the string you pass to
   :func:`fairseq.encode` starts with a space.
-- :meth:`~transformers.BartForConditionalGeneration.generate` should be used for conditional generation tasks like
+- :meth:`~transformers.generation_utils.GenerationMixin.generate` should be used for conditional generation tasks like
   summarization, see the example in that docstrings.
 - Models that load the `facebook/bart-large-cnn` weights will not have a :obj:`mask_token_id`, or be able to perform
   mask-filling tasks.

docs/source/model_doc/t5.rst

@@ -44,9 +44,9 @@ Tips:
 
   For more information about which prefix to use, it is easiest to look into Appendix D of the `paper
   <https://arxiv.org/pdf/1910.10683.pdf>`__. - For sequence-to-sequence generation, it is recommended to use
-  :obj:`T5ForConditionalGeneration.generate()`. This method takes care of feeding the encoded input via cross-attention
+  :meth:`~transformers.generation_utils.GenerationMixin.generate`. This method takes care of feeding the encoded input
-  layers to the decoder and auto-regressively generates the decoder output. - T5 uses relative scalar embeddings.
+  via cross-attention layers to the decoder and auto-regressively generates the decoder output. - T5 uses relative
-  Encoder input padding can be done on the left and on the right.
+  scalar embeddings. Encoder input padding can be done on the left and on the right.
 
 This model was contributed by `thomwolf <https://huggingface.co/thomwolf>`__. The original code can be found `here
 <https://github.com/google-research/text-to-text-transfer-transformer>`__.

docs/source/task_summary.rst

@@ -505,8 +505,8 @@ This outputs a (hopefully) coherent next token following the original sequence,
     >>> print(resulting_string)
     Hugging Face is based in DUMBO, New York City, and has
 
-In the next section, we show how :func:`~transformers.PreTrainedModel.generate` can be used to generate multiple tokens
+In the next section, we show how :func:`~transformers.generation_utils.GenerationMixin.generate` can be used to
-up to a specified length instead of one token at a time.
+generate multiple tokens up to a specified length instead of one token at a time.
 
 Text Generation
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

src/transformers/models/rag/modeling_rag.py

@@ -906,8 +906,9 @@ class RagSequenceForGeneration(RagPreTrainedModel):
         **model_kwargs
     ):
         """
-        Implements RAG sequence "thorough" decoding. Read the :meth:`~transformers.PreTrainedModel.generate``
+        Implements RAG sequence "thorough" decoding. Read the
-        documentation for more information on how to set other generate input parameters.
+        :meth:`~transformers.generation_utils.GenerationMixin.generate`` documentation for more information on how to
+        set other generate input parameters.
 
         Args:
             input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
@@ -942,14 +943,15 @@ class RagSequenceForGeneration(RagPreTrainedModel):
                 to be set to :obj:`False` if used while training with distributed backend.
             num_return_sequences(:obj:`int`, `optional`, defaults to 1):
                 The number of independently computed returned sequences for each element in the batch. Note that this
-                is not the value we pass to the ``generator``'s `:func:`~transformers.PreTrainedModel.generate``
+                is not the value we pass to the ``generator``'s
-                function, where we set ``num_return_sequences`` to :obj:`num_beams`.
+                `:func:`~transformers.generation_utils.GenerationMixin.generate`` function, where we set
+                ``num_return_sequences`` to :obj:`num_beams`.
             num_beams (:obj:`int`, `optional`, defaults to 1):
                 Number of beams for beam search. 1 means no beam search.
             n_docs (:obj:`int`, `optional`, defaults to :obj:`config.n_docs`)
                 Number of documents to retrieve and/or number of documents for which to generate an answer.
             kwargs:
-                Additional kwargs will be passed to :meth:`~transformers.PreTrainedModel.generate`.
+                Additional kwargs will be passed to :meth:`~transformers.generation_utils.GenerationMixin.generate`.
 
         Return:
             :obj:`torch.LongTensor` of shape :obj:`(batch_size * num_return_sequences, sequence_length)`: The generated
@@ -1452,8 +1454,9 @@ class RagTokenForGeneration(RagPreTrainedModel):
                 enabled.
             num_return_sequences(:obj:`int`, `optional`, defaults to 1):
                 The number of independently computed returned sequences for each element in the batch. Note that this
-                is not the value we pass to the ``generator``'s `:func:`~transformers.PreTrainedModel.generate`
+                is not the value we pass to the ``generator``'s
-                function, where we set ``num_return_sequences`` to :obj:`num_beams`.
+                `:func:`~transformers.generation_utils.GenerationMixin.generate` function, where we set
+                ``num_return_sequences`` to :obj:`num_beams`.
             decoder_start_token_id (:obj:`int`, `optional`):
                 If an encoder-decoder model starts decoding with a different token than `bos`, the id of that token.
             n_docs (:obj:`int`, `optional`, defaults to :obj:`config.n_docs`)

src/transformers/models/rag/modeling_tf_rag.py

@@ -1130,8 +1130,9 @@ class TFRagTokenForGeneration(TFRagPreTrainedModel, TFCausalLanguageModelingLoss
                 Number of beams for beam search. 1 means no beam search.
             num_return_sequences(:obj:`int`, `optional`, defaults to 1):
                 The number of independently computed returned sequences for each element in the batch. Note that this
-                is not the value we pass to the ``generator``'s `:func:`~transformers.PreTrainedModel.generate`
+                is not the value we pass to the ``generator``'s
-                function, where we set ``num_return_sequences`` to :obj:`num_beams`.
+                `:func:`~transformers.generation_utils.GenerationMixin.generate` function, where we set
+                ``num_return_sequences`` to :obj:`num_beams`.
             decoder_start_token_id (:obj:`int`, `optional`):
                 If an encoder-decoder model starts decoding with a different token than `bos`, the id of that token.
             n_docs (:obj:`int`, `optional`, defaults to :obj:`config.n_docs`)
@@ -1682,8 +1683,9 @@ class TFRagSequenceForGeneration(TFRagPreTrainedModel, TFCausalLanguageModelingL
         **model_kwargs
     ):
         """
-        Implements RAG sequence "thorough" decoding. Read the :meth:`~transformers.PreTrainedModel.generate``
+        Implements RAG sequence "thorough" decoding. Read the
-        documentation for more information on how to set other generate input parameters
+        :meth:`~transformers.generation_utils.GenerationMixin.generate`` documentation for more information on how to
+        set other generate input parameters
 
         Args:
             input_ids (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
@@ -1711,14 +1713,15 @@ class TFRagSequenceForGeneration(TFRagPreTrainedModel, TFCausalLanguageModelingL
                 to be set to :obj:`False` if used while training with distributed backend.
             num_return_sequences(:obj:`int`, `optional`, defaults to 1):
                 The number of independently computed returned sequences for each element in the batch. Note that this
-                is not the value we pass to the ``generator``'s `:func:`~transformers.PreTrainedModel.generate``
+                is not the value we pass to the ``generator``'s
-                function, where we set ``num_return_sequences`` to :obj:`num_beams`.
+                `:func:`~transformers.generation_utils.GenerationMixin.generate`` function, where we set
+                ``num_return_sequences`` to :obj:`num_beams`.
             num_beams (:obj:`int`, `optional`, defaults to 1):
                 Number of beams for beam search. 1 means no beam search.
             n_docs (:obj:`int`, `optional`, defaults to :obj:`config.n_docs`)
                 Number of documents to retrieve and/or number of documents for which to generate an answer.
             kwargs:
-                Additional kwargs will be passed to :meth:`~transformers.PreTrainedModel.generate`
+                Additional kwargs will be passed to :meth:`~transformers.generation_utils.GenerationMixin.generate`
 
         Return:
             :obj:`tf.Tensor` of shape :obj:`(batch_size * num_return_sequences, sequence_length)`: The generated