Update deprecated Python 3.8 typing (#13971)

This commit is contained in:
Harry Mellor
2025-03-03 01:34:51 +00:00
committed by GitHub
parent bf33700ecd
commit cf069aa8aa
300 changed files with 2294 additions and 2347 deletions

View File

@@ -1,6 +1,6 @@
# SPDX-License-Identifier: Apache-2.0
from typing import TYPE_CHECKING, Dict, List, Set, Tuple
from typing import TYPE_CHECKING
from vllm.logger import init_logger
from vllm.multimodal import MULTIMODAL_REGISTRY
@@ -18,9 +18,9 @@ class EncoderCacheManager:
self.cache_size = cache_size
self.num_free_slots = cache_size
# req_id -> cached input ids
self.cached: Dict[str, Set[int]] = {}
# List of [req_id, input_id]
self.freed: List[Tuple[str, int]] = []
self.cached: dict[str, set[int]] = {}
# list of [req_id, input_id]
self.freed: list[tuple[str, int]] = []
def has_cache(self, request: Request, input_id: int) -> bool:
req_id = request.request_id
@@ -37,7 +37,7 @@ class EncoderCacheManager:
self.cached[req_id].add(input_id)
self.num_free_slots -= request.get_num_encoder_tokens(input_id)
def get_cached_input_ids(self, request: Request) -> Set[int]:
def get_cached_input_ids(self, request: Request) -> set[int]:
return self.cached.get(request.request_id, set())
def free_encoder_input(self, request: Request, input_id: int) -> None:
@@ -58,7 +58,7 @@ class EncoderCacheManager:
for input_id in input_ids:
self.free_encoder_input(request, input_id)
def get_freed_ids(self) -> List[Tuple[str, int]]:
def get_freed_ids(self) -> list[tuple[str, int]]:
freed = self.freed
self.freed = []
return freed
@@ -67,7 +67,7 @@ class EncoderCacheManager:
def compute_encoder_budget(
model_config: "ModelConfig",
scheduler_config: "SchedulerConfig",
) -> Tuple[int, int]:
) -> tuple[int, int]:
"""Compute the encoder cache budget based on the model and scheduler
configurations.
@@ -97,7 +97,7 @@ def compute_encoder_budget(
def _compute_encoder_budget_multimodal(
model_config: "ModelConfig",
scheduler_config: "SchedulerConfig",
) -> Tuple[int, int]:
) -> tuple[int, int]:
"""Compute the encoder cache budget based on the model and scheduler
configurations for a multimodal model.