Update deprecated type hinting in `platform`, `plugins`, `triton_utils`, `vllm_flash_attn` (#18129)

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

pyproject.toml

@@ -78,13 +78,8 @@ exclude = [
 "vllm/executor/**/*.py" = ["UP006", "UP035"]
 "vllm/model_executor/model_loader/**/*.py" = ["UP006", "UP035"]
 "vllm/model_executor/models/**/*.py" = ["UP006", "UP035"]
-"vllm/platforms/**/*.py" = ["UP006", "UP035"]
-"vllm/plugins/**/*.py" = ["UP006", "UP035"]
 "vllm/prompt_adapter/**/*.py" = ["UP006", "UP035"]
 "vllm/spec_decode/**/*.py" = ["UP006", "UP035"]
-"vllm/transformers_utils/**/*.py" = ["UP006", "UP035"]
-"vllm/triton_utils/**/*.py" = ["UP006", "UP035"]
-"vllm/vllm_flash_attn/**/*.py" = ["UP006", "UP035"]
 "vllm/worker/**/*.py" = ["UP006", "UP035"]
 "vllm/utils.py" = ["UP006", "UP035"]
 

vllm/platforms/cuda.py

@@ -5,8 +5,7 @@ pynvml. However, it should not initialize cuda context.
 
 import os
 from functools import wraps
-from typing import (TYPE_CHECKING, Callable, List, Optional, Tuple, TypeVar,
-                    Union)
+from typing import TYPE_CHECKING, Callable, Optional, TypeVar, Union
 
 import torch
 from typing_extensions import ParamSpec
@@ -56,7 +55,7 @@ class CudaPlatformBase(Platform):
     device_control_env_var: str = "CUDA_VISIBLE_DEVICES"
 
     @property
-    def supported_dtypes(self) -> List[torch.dtype]:
+    def supported_dtypes(self) -> list[torch.dtype]:
         if self.has_device_capability(80):
             # Ampere and Hopper or later NVIDIA GPUs.
             return [torch.bfloat16, torch.float16, torch.float32]
@@ -93,7 +92,7 @@ class CudaPlatformBase(Platform):
         return True
 
     @classmethod
-    def is_fully_connected(cls, device_ids: List[int]) -> bool:
+    def is_fully_connected(cls, device_ids: list[int]) -> bool:
         raise NotImplementedError
 
     @classmethod
@@ -335,7 +334,7 @@ class NvmlCudaPlatform(CudaPlatformBase):
     @with_nvml_context
     def has_device_capability(
         cls,
-        capability: Union[Tuple[int, int], int],
+        capability: Union[tuple[int, int], int],
         device_id: int = 0,
     ) -> bool:
         try:
@@ -365,7 +364,7 @@ class NvmlCudaPlatform(CudaPlatformBase):
 
     @classmethod
     @with_nvml_context
-    def is_fully_connected(cls, physical_device_ids: List[int]) -> bool:
+    def is_fully_connected(cls, physical_device_ids: list[int]) -> bool:
         """
         query if the set of gpus are fully connected by nvlink (1 hop)
         """
@@ -430,7 +429,7 @@ class NonNvmlCudaPlatform(CudaPlatformBase):
         return device_props.total_memory
 
     @classmethod
-    def is_fully_connected(cls, physical_device_ids: List[int]) -> bool:
+    def is_fully_connected(cls, physical_device_ids: list[int]) -> bool:
         logger.exception(
             "NVLink detection not possible, as context support was"
             " not found. Assuming no NVLink available.")

vllm/platforms/interface.py

@@ -4,7 +4,7 @@ import os
 import platform
 import random
 from platform import uname
-from typing import TYPE_CHECKING, NamedTuple, Optional, Tuple, Union
+from typing import TYPE_CHECKING, NamedTuple, Optional, Union
 
 import numpy as np
 import torch
@@ -200,7 +200,7 @@ class Platform:
     @classmethod
     def has_device_capability(
         cls,
-        capability: Union[Tuple[int, int], int],
+        capability: Union[tuple[int, int], int],
         device_id: int = 0,
     ) -> bool:
         """
@@ -362,7 +362,7 @@ class Platform:
         raise NotImplementedError
 
     @classmethod
-    def get_infinity_values(cls, dtype: torch.dtype) -> Tuple[float, float]:
+    def get_infinity_values(cls, dtype: torch.dtype) -> tuple[float, float]:
         """
         Return the platform specific values for (-inf, inf)
         """

vllm/platforms/rocm.py

@@ -2,7 +2,7 @@
 
 import os
 from functools import cache, lru_cache, wraps
-from typing import TYPE_CHECKING, Dict, List, Optional
+from typing import TYPE_CHECKING, Optional
 
 import torch
 
@@ -35,7 +35,7 @@ except ImportError as e:
     logger.warning("Failed to import from vllm._rocm_C with %r", e)
 
 # Models not supported by ROCm.
-_ROCM_UNSUPPORTED_MODELS: List[str] = []
+_ROCM_UNSUPPORTED_MODELS: list[str] = []
 
 # Models partially supported by ROCm.
 # Architecture -> Reason.
@@ -43,7 +43,7 @@ _ROCM_SWA_REASON = ("Sliding window attention (SWA) is not yet supported in "
                     "Triton flash attention. For half-precision SWA support, "
                     "please use CK flash attention by setting "
                     "`VLLM_USE_TRITON_FLASH_ATTN=0`")
-_ROCM_PARTIALLY_SUPPORTED_MODELS: Dict[str, str] = {
+_ROCM_PARTIALLY_SUPPORTED_MODELS: dict[str, str] = {
     "Qwen2ForCausalLM":
     _ROCM_SWA_REASON,
     "MistralForCausalLM":
@@ -58,7 +58,7 @@ _ROCM_PARTIALLY_SUPPORTED_MODELS: Dict[str, str] = {
      "excessive use of shared memory. If this happens, disable Triton FA "
      "by setting `VLLM_USE_TRITON_FLASH_ATTN=0`")
 }
-_ROCM_DEVICE_ID_NAME_MAP: Dict[str, str] = {
+_ROCM_DEVICE_ID_NAME_MAP: dict[str, str] = {
     "0x74a0": "AMD_Instinct_MI300A",
     "0x74a1": "AMD_Instinct_MI300X",
     "0x74b5": "AMD_Instinct_MI300X",  # MI300X VF
@@ -203,7 +203,7 @@ class RocmPlatform(Platform):
 
     @staticmethod
     @with_amdsmi_context
-    def is_fully_connected(physical_device_ids: List[int]) -> bool:
+    def is_fully_connected(physical_device_ids: list[int]) -> bool:
         """
         Query if the set of gpus are fully connected by xgmi (1 hop)
         """

vllm/platforms/tpu.py

 # SPDX-License-Identifier: Apache-2.0
 
-from typing import TYPE_CHECKING, Optional, Tuple, Union, cast
+from typing import TYPE_CHECKING, Optional, Union, cast
 
 import torch
 from tpu_info import device
@@ -73,7 +73,7 @@ class TpuPlatform(Platform):
         return "vllm.lora.punica_wrapper.punica_tpu.PunicaWrapperTPU"
 
     @classmethod
-    def get_infinity_values(cls, dtype: torch.dtype) -> Tuple[float, float]:
+    def get_infinity_values(cls, dtype: torch.dtype) -> tuple[float, float]:
         return torch.finfo(dtype).min, torch.finfo(dtype).max
 
     @classmethod

vllm/plugins/__init__.py

@@ -2,7 +2,7 @@
 
 import logging
 import os
-from typing import Callable, Dict
+from typing import Callable
 
 import torch
 
@@ -14,7 +14,7 @@ logger = logging.getLogger(__name__)
 plugins_loaded = False
 
 
-def load_plugins_by_group(group: str) -> Dict[str, Callable]:
+def load_plugins_by_group(group: str) -> dict[str, Callable]:
     import sys
     if sys.version_info < (3, 10):
         from importlib_metadata import entry_points