Apply ruff fixes to tests (#146140)

Fixes #ISSUE_NUMBER

Pull Request resolved: https://github.com/pytorch/pytorch/pull/146140
Approved by: https://github.com/albanD

test/conftest.py

@@ -135,8 +135,7 @@ class _NodeReporterReruns(_NodeReporter):
         else:
             assert isinstance(report.longrepr, tuple)
             filename, lineno, skipreason = report.longrepr
-            if skipreason.startswith("Skipped: "):
-                skipreason = skipreason[9:]
+            skipreason = skipreason.removeprefix("Skipped: ")
             details = f"{filename}:{lineno}: {skipreason}"
 
             skipped = ET.Element(

test/dynamo/test_python_autograd.py

@@ -1,5 +1,5 @@
 # Owner(s): ["module: dynamo"]
-from typing import Callable, List, NamedTuple, Optional
+from typing import Callable, NamedTuple, Optional
 
 import torch
 import torch._dynamo
@@ -81,7 +81,7 @@ def grad(L, desired_results: list[Variable]) -> list[Variable]:
 
     # look up dL_dentries. If a variable is never used to compute the loss,
     # we consider its gradient None, see the note below about zeros for more information.
-    def gather_grad(entries: List[str]):
+    def gather_grad(entries: list[str]):
         return [dL_d[entry] if entry in dL_d else None for entry in entries]
 
     # propagate the gradient information backward
@@ -127,7 +127,7 @@ def operator_mul(self: Variable, rhs: Variable) -> Variable:
     outputs = [r.name]
 
     # define backprop
-    def propagate(dL_doutputs: List[Variable]):
+    def propagate(dL_doutputs: list[Variable]):
         (dL_dr,) = dL_doutputs
 
         dr_dself = rhs  # partial derivative of r = self*rhs
@@ -150,7 +150,7 @@ def operator_add(self: Variable, rhs: Variable) -> Variable:
     r = Variable(self.value + rhs.value)
     # print(f'{r.name} = {self.name} + {rhs.name}')
 
-    def propagate(dL_doutputs: List[Variable]):
+    def propagate(dL_doutputs: list[Variable]):
         (dL_dr,) = dL_doutputs
         dr_dself = 1.0
         dr_drhs = 1.0
@@ -168,7 +168,7 @@ def operator_sum(self: Variable, name: Optional[str]) -> "Variable":
     r = Variable(torch.sum(self.value), name=name)
     # print(f'{r.name} = {self.name}.sum()')
 
-    def propagate(dL_doutputs: List[Variable]):
+    def propagate(dL_doutputs: list[Variable]):
         (dL_dr,) = dL_doutputs
         size = self.value.size()
         return [dL_dr.expand(*size)]
@@ -179,12 +179,12 @@ def operator_sum(self: Variable, name: Optional[str]) -> "Variable":
     return r
 
 
-def operator_expand(self: Variable, sizes: List[int]) -> "Variable":
+def operator_expand(self: Variable, sizes: list[int]) -> "Variable":
     assert self.value.dim() == 0  # only works for scalars
     r = Variable(self.value.expand(sizes))
     # print(f'{r.name} = {self.name}.expand({sizes})')
 
-    def propagate(dL_doutputs: List[Variable]):
+    def propagate(dL_doutputs: list[Variable]):
         (dL_dr,) = dL_doutputs
         return [dL_dr.sum()]
 

test/functorch/discover_coverage.py

@@ -44,7 +44,7 @@ def get_public_overridable_apis(pytorch_root="/raid/rzou/pt/debug-cpu"):
             if line.startswith(".. autofunction::")
         ]
         lines = api_lines1 + api_lines2
-        lines = [line[7:] if line.startswith("Tensor.") else line for line in lines]
+        lines = [line.removeprefix("Tensor.") for line in lines]
         lines = [line for line in lines if hasattr(module, line)]
         for line in lines:
             api = getattr(module, line)

test/onnx/internal/test_diagnostics.py

@@ -6,7 +6,7 @@ import dataclasses
 import io
 import logging
 import typing
-from typing import AbstractSet, Protocol
+from typing import Protocol
 
 import torch
 from torch.onnx import errors
@@ -27,7 +27,7 @@ class _SarifLogBuilder(Protocol):
 
 def _assert_has_diagnostics(
     sarif_log_builder: _SarifLogBuilder,
-    rule_level_pairs: AbstractSet[tuple[infra.Rule, infra.Level]],
+    rule_level_pairs: set[tuple[infra.Rule, infra.Level]],
 ):
     sarif_log = sarif_log_builder.sarif_log()
     unseen_pairs = {(rule.id, level.name.lower()) for rule, level in rule_level_pairs}
@@ -62,7 +62,7 @@ class _RuleCollectionForTest(infra.RuleCollection):
 def assert_all_diagnostics(
     test_suite: unittest.TestCase,
     sarif_log_builder: _SarifLogBuilder,
-    rule_level_pairs: AbstractSet[tuple[infra.Rule, infra.Level]],
+    rule_level_pairs: set[tuple[infra.Rule, infra.Level]],
 ):
     """Context manager to assert that all diagnostics are emitted.
 

test/test_fx_experimental.py

@@ -12,7 +12,7 @@ import tempfile
 import typing
 import unittest
 from types import BuiltinFunctionType
-from typing import Callable, NamedTuple, Optional, Union, List
+from typing import Callable, List, NamedTuple, Optional, Union
 
 import torch
 import torch.fx.experimental.meta_tracer

test/test_overrides.py

@@ -706,8 +706,7 @@ def generate_tensor_like_override_tests(cls):
             for arg in annotated_args[func]:
                 # Guess valid input to aten function based on type of argument
                 t = arg["simple_type"]
-                if t.endswith("?"):
-                    t = t[:-1]
+                t = t.removesuffix("?")
                 if t == "Tensor" and is_method and arg["name"] == "self":
                     # See "Note: properties and __get__"
                     func = func.__get__(instance_gen())