[BE] Enable ruff's UP rules in pyproject.toml (pytorch#105437)

Signed-off-by: Justin Chu <justinchu@microsoft.com>

Pull Request resolved: pytorch#105437
Approved by: https://github.com/huydhn, https://github.com/malfet, https://github.com/Skylion007

Author: justinchuby

pyproject.toml

@@ -55,6 +55,8 @@ ignore = [
     "SIM116", # Disable Use a dictionary instead of consecutive `if` statements
     "SIM117",
     "SIM118",
+    "UP006", # keep-runtime-typing
+    "UP007", # keep-runtime-typing
 ]
 line-length = 120
 select = [
@@ -66,13 +68,21 @@ select = [
     "SIM1",
     "W",
     # Not included in flake8
+    "UP",
     "PERF",
     "PLE",
     "TRY302",
 ]
 
 [tool.ruff.per-file-ignores]
 "__init__.py" = ["F401"]
+"test/jit/**" = [
+    "UP", # We don't want to modify the jit test as they test specify syntax
+]
+"torch/onnx/**" = [
+    "UP037", # ONNX does runtime type checking
+]
+
 "torchgen/api/types/__init__.py" = [
     "F401",
     "F403",
@@ -81,3 +91,6 @@ select = [
     "F401",
     "F403",
 ]
+"torch/utils/collect_env.py" = [
+    "UP", # collect_env.py needs to work with older versions of Python
+]

test/inductor/test_cpu_repro.py

@@ -518,7 +518,7 @@ def fn(x):
 
         numerical_testsuit = [4.4, 4.5, 4.6, 5.5]
         for numerical_number in numerical_testsuit:
-            x = torch.ones((17)) * numerical_number
+            x = torch.ones(17) * numerical_number
             with config.patch({"cpp.simdlen": None}):
                 torch._dynamo.reset()
                 metrics.reset()

test/mobile/test_lite_script_type.py

@@ -14,7 +14,7 @@
 class TestLiteScriptModule(TestCase):
 
     def test_typing_namedtuple(self):
-        myNamedTuple = NamedTuple('myNamedTuple', [('a', List[torch.Tensor])])
+        myNamedTuple = NamedTuple('myNamedTuple', [('a', List[torch.Tensor])])  # noqa: UP014
 
         class MyTestModule(torch.nn.Module):
             def forward(self, a: torch.Tensor):

test/nn/test_pooling.py

@@ -1209,9 +1209,10 @@ def expected_output(dim, dtype):
                 return torch.stack([col, col + 2], 1).view(2, 2, 2, 2)
 
         if adaptive:
-            cls_name = 'AdaptiveMaxPool{}d'.format(num_dim)
+            cls_name = 'AdaptiveMaxPool{}d'.format(num_dim)  # noqa: UP032
         else:
-            cls_name = 'MaxPool{}d'.format(num_dim)
+            # FIXME(#105716): Test fails when using f-string
+            cls_name = 'MaxPool{}d'.format(num_dim)  # noqa: UP032
         module_cls = getattr(nn, cls_name)
         module = module_cls(2, return_indices=True).to(device, dtype=dtype)
         numel = 4 ** (num_dim + 1)

test/test_jit.py

@@ -14257,7 +14257,7 @@ def test_non_primitive_types(x):
         self.assertEqual(out, torch.tensor(6.0))
 
     def test_namedtuple_type_inference(self):
-        _AnnotatedNamedTuple = NamedTuple('_NamedTupleAnnotated', [('value', int)])
+        _AnnotatedNamedTuple = NamedTuple('_NamedTupleAnnotated', [('value', int)])  # noqa: UP014
         _UnannotatedNamedTuple = namedtuple('_NamedTupleUnAnnotated', ['value'])
 
         def test_check_named_tuple_value():