biondizzle/vllm
1
0
Fork 0
Code Issues Pull Requests Actions 2 Packages Projects Releases Wiki Activity

Update deprecated Python 3.8 typing (#13971)

Browse Source
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
Download Patch File Download Diff File Expand all files Collapse all files

18
benchmarks/kernels/benchmark_moe.py
View File

@@ -4,7 +4,7 @@ import argparse
import time
from datetime import datetime
from itertools import product
from typing import Any, Dict, List, Tuple, TypedDict
from typing import Any, TypedDict
import ray
import torch
@@ -132,7 +132,7 @@ def benchmark_config(
start_event = torch.cuda.Event(enable_timing=True)
end_event = torch.cuda.Event(enable_timing=True)
latencies: List[float] = []
latencies: list[float] = []
for i in range(num_iters):
prepare(i)
torch.cuda.synchronize()
@@ -175,8 +175,8 @@ def get_rocm_tuning_space(use_fp16):
return param_ranges
def get_configs_compute_bound(use_fp16) -> List[Dict[str, int]]:
configs: List[BenchmarkConfig] = []
def get_configs_compute_bound(use_fp16) -> list[dict[str, int]]:
configs: list[BenchmarkConfig] = []
if current_platform.is_rocm():
param_ranges = get_rocm_tuning_space(use_fp16)
@@ -335,7 +335,7 @@ class BenchmarkWorker:
dtype: torch.dtype,
use_fp8_w8a8: bool,
use_int8_w8a16: bool,
) -> Tuple[Dict[str, int], float]:
) -> tuple[dict[str, int], float]:
current_platform.seed_everything(self.seed)
dtype_str = get_config_dtype_str(dtype,
use_int8_w8a16=use_int8_w8a16,
@@ -371,8 +371,8 @@ class BenchmarkWorker:
dtype: torch.dtype,
use_fp8_w8a8: bool,
use_int8_w8a16: bool,
search_space: List[Dict[str, int]],
) -> Dict[str, int]:
search_space: list[dict[str, int]],
) -> dict[str, int]:
best_config = None
best_time = float("inf")
if current_platform.is_rocm():
@@ -434,7 +434,7 @@ def sort_config(config: BenchmarkConfig) -> BenchmarkConfig:
}
def save_configs(configs: Dict[int, BenchmarkConfig], num_experts: int,
def save_configs(configs: dict[int, BenchmarkConfig], num_experts: int,
shard_intermediate_size: int, hidden_size: int, topk: int,
dtype: torch.dtype, use_fp8_w8a8: bool,
use_int8_w8a16: bool) -> None:
@@ -498,7 +498,7 @@ def main(args: argparse.Namespace):
num_gpus = int(ray.available_resources()["GPU"])
workers = [BenchmarkWorker.remote(args.seed) for _ in range(num_gpus)]
def _distribute(method: str, inputs: List[Any]) -> List[Any]:
def _distribute(method: str, inputs: list[Any]) -> list[Any]:
outputs = []
worker_idx = 0
for input_args in inputs: