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[4/N] Apply py39 ruff and pyupgrade fixes (#143257)

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```torch/fx/passes/annotate_getitem_nodes.py``` was changed to support the new type hinting annotations.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/143257
Approved by: https://github.com/justinchuby, https://github.com/albanD
This commit is contained in:
cyy
2025-01-04 10:47:51 +00:00
committed by PyTorch MergeBot
parent a881954b0c
commit df458be4e5
55 changed files with 247 additions and 227 deletions
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24
test/test_optim.py
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@ -4,7 +4,7 @@ import math
import tempfile
import unittest
from copy import deepcopy
from typing import Any, Dict, Tuple
from typing import Any
from unittest.mock import patch
from optim.test_lrscheduler import TestLRScheduler # noqa: F401
@ -1769,8 +1769,8 @@ class TestOptimRenewed(TestCase):
@staticmethod
def _state_dict_post_hook(
optimizer: Optimizer, state_dict: Dict[str, Any]
) -> Dict[str, Any]:
optimizer: Optimizer, state_dict: dict[str, Any]
) -> dict[str, Any]:
if "test" in state_dict["state"]:
state_dict["state"].pop("test")
state_dict["ran_state_dict_pre_hook"] = True
@ -1821,14 +1821,14 @@ class TestOptimRenewed(TestCase):
@staticmethod
def _load_state_dict_pre_hook1(
optimizer: Optimizer, state_dict: Dict[str, Any]
optimizer: Optimizer, state_dict: dict[str, Any]
) -> None:
state_dict["param_groups"][0]["lr"] = 0.002
@staticmethod
def _load_state_dict_pre_hook2(
optimizer: Optimizer, state_dict: Dict[str, Any]
) -> Dict[str, Any]:
optimizer: Optimizer, state_dict: dict[str, Any]
) -> dict[str, Any]:
# The typical use case for returning a state dict is to drastically modify the state dict.
# I will simulate by simply making a deep copy and ensuring that my_state_dict still gets used
my_state_dict = deepcopy(state_dict)
@ -1906,7 +1906,7 @@ class TestOptimRenewed(TestCase):
@optims(optim_db, dtypes=[torch.float32])
def test_step_post_hook(self, device, dtype, optim_info):
def post_hook(opt: Optimizer, args: Tuple[Any], kwargs: Dict[Any, Any]):
def post_hook(opt: Optimizer, args: tuple[Any], kwargs: dict[Any, Any]):
nonlocal data
data += 2
@ -1938,7 +1938,7 @@ class TestOptimRenewed(TestCase):
@optims(optim_db, dtypes=[torch.float32])
def test_step_pre_hook(self, device, dtype, optim_info):
def pre_hook(opt: Optimizer, args: Tuple[Any], kwargs: Dict[Any, Any]):
def pre_hook(opt: Optimizer, args: tuple[Any], kwargs: dict[Any, Any]):
nonlocal data
data += 2
@ -1970,19 +1970,19 @@ class TestOptimRenewed(TestCase):
@optims(optim_db, dtypes=[torch.float32])
def test_step_all_hooks(self, device, dtype, optim_info):
def global_pre_hook(opt: Optimizer, args: Tuple[Any], kwargs: Dict[Any, Any]):
def global_pre_hook(opt: Optimizer, args: tuple[Any], kwargs: dict[Any, Any]):
nonlocal data
data.append(0)
def global_post_hook(opt: Optimizer, args: Tuple[Any], kwargs: Dict[Any, Any]):
def global_post_hook(opt: Optimizer, args: tuple[Any], kwargs: dict[Any, Any]):
nonlocal data
data.append(5)
def local_pre_hook(opt: Optimizer, args: Tuple[Any], kwargs: Dict[Any, Any]):
def local_pre_hook(opt: Optimizer, args: tuple[Any], kwargs: dict[Any, Any]):
nonlocal data
data.append(1)
def local_post_hook(opt: Optimizer, args: Tuple[Any], kwargs: Dict[Any, Any]):
def local_post_hook(opt: Optimizer, args: tuple[Any], kwargs: dict[Any, Any]):
nonlocal data
data.append(2)