[docs] update input documentation for MAMBA2 and MISTRAL models to include cache_position and attention_mask details (#34322)

* [docs] update input documentation for MAMBA2 and MISTRAL models to include cache_position and attention_mask details

* [docs] correct input documentation for MISTRAL model to reference `input_ids` instead of `decoder_input_ids`

* [docs] clarify cache_position description in MISTRAL model documentation

src/transformers/models/mamba2/modeling_mamba2.py

@@ -805,6 +805,16 @@ MAMBA2_INPUTS_DOCSTRING = r"""
             more detail.
         return_dict (`bool`, *optional*):
             Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+        cache_position (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            The position of the current input in the cache. This is used to ensure that the cache is correctly updated.
+            If `cache_params` is passed, `cache_position` should also be passed.
+        attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
 """
 
 

src/transformers/models/mistral/modeling_mistral.py

@@ -619,7 +619,7 @@ MISTRAL_INPUTS_DOCSTRING = r"""
             Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
             [`PreTrainedTokenizer.__call__`] for details.
 
-            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+            If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
             `past_key_values`).
 
             If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
@@ -666,6 +666,10 @@ MISTRAL_INPUTS_DOCSTRING = r"""
             more detail.
         return_dict (`bool`, *optional*):
             Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+        cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+            Indices indicating the position of the input sequence tokens in the sequence. Unlike `position_ids`,
+            this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
+            the complete sequence length.
 """