change DynamicCache function names from "split" to "batch_split" for …

…readability + apply coding style

src/transformers/cache_utils.py

@@ -201,7 +201,7 @@ def crop(self, maximum_length: int):
             self.key_cache[idx] = self.key_cache[idx][..., :maximum_length, :]
             self.value_cache[idx] = self.value_cache[idx][..., :maximum_length, :]
 
-    def split(self, full_batch_size: int, split_size: int) -> List["DynamicCache"]:
+    def batch_split(self, full_batch_size: int, split_size: int) -> List["DynamicCache"]:
         """Split the current instance into a list of `DynamicCache` by the batch size. This will be used by
         `_split_model_inputs()` in `generation.utils`"""
         out = []
@@ -214,8 +214,8 @@ def split(self, full_batch_size: int, split_size: int) -> List["DynamicCache"]:
         return out
 
     @classmethod
-    def from_splits(cls, splits: List["DynamicCache"]) -> "DynamicCache":
-        """This is the opposite of the above `split()` method. This will be used by `stack_model_outputs` in
+    def from_batch_splits(cls, splits: List["DynamicCache"]) -> "DynamicCache":
+        """This is the opposite of the above `batch_split()` method. This will be used by `stack_model_outputs` in
         `generation.utils`"""
         cache = cls()
         for idx in range(len(splits[0])):

src/transformers/generation/candidate_generator.py

@@ -115,10 +115,13 @@ def __init__(
                 assistant_kwargs[key] = (
                     value.detach().to(device) if isinstance(value, torch.Tensor) else copy.deepcopy(value)
                 )
-        
+
         # Remove potential default DynamicCache if assistant does not support it
         if "past_key_values" in assistant_kwargs.keys():
-            if isinstance(assistant_kwargs["past_key_values"], DynamicCache) and not self.assistant_model._supports_dynamic_cache_class:
+            if (
+                isinstance(assistant_kwargs["past_key_values"], DynamicCache)
+                and not self.assistant_model._supports_dynamic_cache_class
+            ):
                 # Cache is empty -> remove it from kwargs
                 if len(assistant_kwargs["past_key_values"]) == 0:
                     del assistant_kwargs["past_key_values"]

src/transformers/generation/utils.py

@@ -3793,9 +3793,9 @@ def _split(data, full_batch_size: int, split_size: int = None):
         return [None] * (full_batch_size // split_size)
     if isinstance(data, torch.Tensor):
         return [data[i : i + split_size] for i in range(0, full_batch_size, split_size)]
-    # New efficient cache
+    # New cache format
     elif isinstance(data, DynamicCache):
-        return data.split(full_batch_size, split_size)
+        return data.batch_split(full_batch_size, split_size)
     elif isinstance(data, tuple):
         # If the elements of the tuple are also tuples (e.g., past_key_values in our earlier example)
         if isinstance(data[0], tuple):
@@ -3899,9 +3899,9 @@ def _concat(data):
             return None
         if isinstance(data[0], torch.Tensor):
             return torch.cat(data, dim=0)
-        # New efficient cache
+        # New cache format
         elif isinstance(data[0], DynamicCache):
-            return DynamicCache.from_splits(data)
+            return DynamicCache.from_batch_splits(data)
         elif isinstance(data[0], tuple):
             # If the elements of the tuple are also tuples (e.g., past_key_values in our earlier example)
             if isinstance(data[0][0], tuple):