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/utils/fp8_utils.py

@@ -4,7 +4,7 @@
 import functools
 import json
 import os
-from typing import Any, Dict, List, Optional, Tuple, Union
+from typing import Any, Optional, Union
 
 import torch
 
@@ -32,7 +32,7 @@ def is_fp8(x: Union[torch.dtype, torch.Tensor]) -> bool:
 def apply_w8a8_block_fp8_linear(
     input: torch.Tensor,
     weight: torch.Tensor,
-    block_size: List[int],
+    block_size: list[int],
     weight_scale: torch.Tensor,
     input_scale: Optional[torch.Tensor] = None,
     bias: Optional[torch.Tensor] = None,
@@ -95,7 +95,7 @@ def apply_w8a8_block_fp8_linear(
 def apply_w8a8_block_fp8_linear_fake(
     input: torch.Tensor,
     weight: torch.Tensor,
-    block_size: List[int],
+    block_size: list[int],
     weight_scale: torch.Tensor,
     input_scale: Optional[torch.Tensor] = None,
 ) -> torch.Tensor:
@@ -114,7 +114,7 @@ direct_register_custom_op(
 def input_to_float8(
         x: torch.Tensor,
         dtype: Optional[torch.dtype] = None
-) -> Tuple[torch.Tensor, torch.Tensor]:
+) -> tuple[torch.Tensor, torch.Tensor]:
     """This function quantizes input values to float8 values "
     "with tensor-wise quantization."""
     dtype = current_platform.fp8_dtype() if dtype is None else dtype
@@ -129,7 +129,7 @@ def input_to_float8(
 def block_quant_to_tensor_quant(
     x_q_block: torch.Tensor,
     x_s: torch.Tensor,
-) -> Tuple[torch.Tensor, torch.Tensor]:
+) -> tuple[torch.Tensor, torch.Tensor]:
     """This function converts block-wise quantization to tensor-wise
     quantization. The inputs are block-wise quantization tensor `x_q_block`,
     block-wise quantization scale and the block size.
@@ -247,7 +247,7 @@ def per_token_group_quant_fp8(
     eps: float = 1e-10,
     dtype: Optional[torch.dtype] = None,
     column_major_scales: bool = False,
-) -> Tuple[torch.Tensor, torch.Tensor]:
+) -> tuple[torch.Tensor, torch.Tensor]:
     """Function to perform per-token-group quantization on an input tensor `x`.
     It converts the tensor values into signed float8 values and returns the
     quantized tensor along with the scaling factor used for quantization.
@@ -258,7 +258,7 @@ def per_token_group_quant_fp8(
         dtype: The dype of output tensor. Note that only `torch.float8_e4m3fn`
         is supported for now.
     Returns:
-        Tuple[torch.Tensor, torch.Tensor]: The quantized tensor and the
+        tuple[torch.Tensor, torch.Tensor]: The quantized tensor and the
         scaling factor for quantization.
     """
     dtype = current_platform.fp8_dtype() if dtype is None else dtype
@@ -412,7 +412,7 @@ def _w8a8_block_fp8_matmul(
 
 @functools.lru_cache
 def get_w8a8_block_fp8_configs(N: int, K: int, block_n: int,
-                               block_k: int) -> Optional[Dict[int, Any]]:
+                               block_k: int) -> Optional[dict[int, Any]]:
     """
     Return optimized configurations for the w8a8 block fp8 kernel.
     The return value will be a dictionary that maps an irregular grid of
@@ -452,7 +452,7 @@ def w8a8_block_fp8_matmul(
     B: torch.Tensor,
     As: torch.Tensor,
     Bs: torch.Tensor,
-    block_size: List[int],
+    block_size: list[int],
     output_dtype: torch.dtype = torch.float16,
 ) -> torch.Tensor:
     """This function performs matrix multiplication with block-wise