Remove unused function patameters

src/transformers/convert_slow_tokenizer.py

@@ -1413,7 +1413,7 @@ def converted(self) -> Tokenizer:
 class MoshiConverter(SpmConverter):
     handle_byte_fallback = True
 
-    def __init__(self, vocab_file, model_max_length=None, **kwargs):
+    def __init__(self, vocab_file, **kwargs):
         requires_backends(self, "protobuf")
 
         Converter.__init__(self, vocab_file)

src/transformers/generation/continuous_batching/cache.py

@@ -14,7 +14,7 @@
 # limitations under the License.
 from collections import deque
 from math import floor, gcd, sqrt
-from typing import Optional, Union
+from typing import Optional
 
 import torch
 
@@ -123,7 +123,6 @@ def __init__(
         generation_config: GenerationConfig,
         device: torch.device,
         dtype: torch.dtype = torch.float16,
-        layer_device_map: Optional[dict[int, Union[str, torch.device, int]]] = None,
         tp_size: Optional[int] = None,
     ) -> None:
         """Initialize a paged attention cache for efficient memory usage.
@@ -133,7 +132,6 @@ def __init__(
             generation_config: Generation configuration containing cache parameters
             device: Device for the cache tensors
             dtype: Data type of the cache
-            layer_device_map: Optional mapping of layer indices to devices
             tp_size: Tensor parallelism size
         """
         self.config = config

src/transformers/modeling_utils.py

@@ -3259,11 +3259,11 @@ def _get_resized_embeddings(
 
                 with deepspeed.zero.GatheredParameters([old_embeddings.weight], modifier_rank=None):
                     self._init_added_embeddings_weights_with_mean(
-                        old_embeddings, new_embeddings, old_embedding_dim, old_num_tokens, added_num_tokens
+                        old_embeddings, new_embeddings, old_num_tokens, added_num_tokens
                     )
             else:
                 self._init_added_embeddings_weights_with_mean(
-                    old_embeddings, new_embeddings, old_embedding_dim, old_num_tokens, added_num_tokens
+                    old_embeddings, new_embeddings, old_num_tokens, added_num_tokens
                 )
 
         # Copy token embeddings from the previous weights
@@ -3433,7 +3433,7 @@ def _get_resized_lm_head(
         return new_lm_head
 
     def _init_added_embeddings_weights_with_mean(
-        self, old_embeddings, new_embeddings, old_embedding_dim, old_num_tokens, added_num_tokens
+        self, old_embeddings, new_embeddings, old_num_tokens, added_num_tokens
     ):
         old_embeddings_weight = old_embeddings.weight.data.to(torch.float32)
         mean_embeddings = torch.mean(old_embeddings_weight, axis=0)
@@ -3472,9 +3472,7 @@ def _init_added_lm_head_weights_with_mean(
             old_lm_head.weight.data = old_lm_head.weight.data.T
 
         # The same initialization logic as Embeddings.
-        self._init_added_embeddings_weights_with_mean(
-            old_lm_head, new_lm_head, old_lm_head_dim, old_num_tokens, added_num_tokens
-        )
+        self._init_added_embeddings_weights_with_mean(old_lm_head, new_lm_head, old_num_tokens, added_num_tokens)
 
         if transposed:
             # Transpose again to the correct shape.