Update deprecated type hinting in model_executor/layers (#18056)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

vllm/model_executor/layers/quantization/bitsandbytes.py

@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 
-from typing import Any, Dict, List, Optional
+from typing import Any, Optional
 
 import torch
 
@@ -29,7 +29,7 @@ class BitsAndBytesConfig(QuantizationConfig):
         bnb_4bit_use_double_quant: bool = False,
         llm_int8_enable_fp32_cpu_offload: bool = False,
         llm_int8_has_fp16_weight: bool = False,
-        llm_int8_skip_modules: Optional[List[str]] = None,
+        llm_int8_skip_modules: Optional[list[str]] = None,
         llm_int8_threshold: float = 6.0,
     ) -> None:
         super().__init__()
@@ -61,7 +61,7 @@ class BitsAndBytesConfig(QuantizationConfig):
         return "bitsandbytes"
 
     @classmethod
-    def get_supported_act_dtypes(self) -> List[torch.dtype]:
+    def get_supported_act_dtypes(self) -> list[torch.dtype]:
         return [torch.float32, torch.float16, torch.bfloat16]
 
     @classmethod
@@ -69,13 +69,13 @@ class BitsAndBytesConfig(QuantizationConfig):
         return 70
 
     @staticmethod
-    def get_config_filenames() -> List[str]:
+    def get_config_filenames() -> list[str]:
         return [
             "adapter_config.json",
         ]
 
     @classmethod
-    def from_config(cls, config: Dict[str, Any]) -> "BitsAndBytesConfig":
+    def from_config(cls, config: dict[str, Any]) -> "BitsAndBytesConfig":
 
         def get_safe_value(config, keys, default_value=None):
             try:
@@ -130,7 +130,7 @@ class BitsAndBytesConfig(QuantizationConfig):
         return None
 
 
-def is_layer_skipped_bnb(prefix: str, llm_int8_skip_modules: List[str]):
+def is_layer_skipped_bnb(prefix: str, llm_int8_skip_modules: list[str]):
     # Split the prefix into its dot-separated components
     components = prefix.split('.')
 
@@ -169,7 +169,7 @@ class BitsAndBytesLinearMethod(LinearMethodBase):
 
     def create_weights(self, layer: torch.nn.Module,
                        input_size_per_partition: int,
-                       output_partition_sizes: List[int], input_size: int,
+                       output_partition_sizes: list[int], input_size: int,
                        output_size: int, params_dtype: torch.dtype,
                        **extra_weight_attrs):
         from bitsandbytes.nn import Int8Params