PEP585: Add noqa to necessary tests (#146391)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/146391
Approved by: https://github.com/justinchuby, https://github.com/Skylion007

test/onnx/test_onnxscript_runtime.py

@@ -2,7 +2,7 @@
 
 """Test the support on onnxscript in PyTorch-ONNX converter with onnxruntime."""
 
-from typing import List
+from typing import Sequence
 
 import onnx_test_common
 import onnxscript
@@ -90,7 +90,11 @@ class TestONNXScriptRuntime(onnx_test_common._TestONNXRuntime):
 
         @onnxscript.script(custom_opset)
         def layer_norm(
-            X, axes: List[int], weight: FLOAT[...], bias: FLOAT[...], eps: float
+            X,
+            axes: Sequence[int],
+            weight: FLOAT[...],
+            bias: FLOAT[...],
+            eps: float,
         ):
             mean = op.ReduceMean(X, axes=axes)
             D = X - mean  # op.Sub(X, mean)

test/test_cpp_extensions_aot.py

@@ -231,7 +231,7 @@ class TestPybindTypeCasters(common.TestCase):
         Our Pybind functions have a signature of the form `() -> return_type`.
         """
         # Imports needed for the `eval` below.
-        from typing import List, Tuple  # noqa: F401
+        from typing import List, Tuple  # noqa: F401, UP035
 
         return eval(re.search("-> (.*)\n", func.__doc__).group(1))
 

test/test_fx.py

@@ -22,7 +22,7 @@ import warnings
 from collections import namedtuple
 from copy import deepcopy
 from math import sqrt
-from typing import Any, Callable, List, NamedTuple, Optional, Tuple, Union
+from typing import Any, Callable, NamedTuple, Optional, Union
 
 import torch
 import torch.fx._pytree as fx_pytree
@@ -2270,10 +2270,19 @@ class TestFX(JitTestCase):
         graph: torch.fx.Graph = torch.fx.Graph()
         x: torch.fx.Node = graph.create_node("placeholder", "x")
         b: torch.fx.Node = graph.create_node(
-            "call_function", target=torch.relu, args=(x,), type_expr=List[float]
+            "call_function", target=torch.relu, args=(x,), type_expr=list[float]
         )
         output: torch.fx.Node = graph.output(b)
 
+        self.assertTrue('list[float]' in str(graph))
+
+    def test_typename_print_pre_pep585(self):
+        graph : torch.fx.Graph = torch.fx.Graph()
+        x : torch.fx.Node = graph.create_node('placeholder', 'x')
+        b : torch.fx.Node = graph.create_node('call_function', target=torch.relu, args=(x,),
+                                              type_expr=typing.List[float])  # noqa: UP006
+        output : torch.fx.Node = graph.output(b)
+
         self.assertTrue("typing.List[float]" in str(graph))
 
     def test_layout(self):
@@ -2922,6 +2931,19 @@ class TestFX(JitTestCase):
             def forward(self, x: list[str]) -> list[str]:
                 return self.other(x)
 
+        traced = symbolic_trace(ReturnTypeModule())
+        self.assertIn("-> list[str]", traced._code)
+        scripted = torch.jit.script(traced)
+        self.assertIn("-> List[str]", scripted.code)
+
+    def test_return_type_exists_pre_pep585(self):
+        class ReturnTypeModule(torch.nn.Module):
+            def other(self, x: typing.List[str]) -> typing.List[str]:  # noqa: UP006
+                return x
+
+            def forward(self, x: typing.List[str]) -> typing.List[str]:  # noqa: UP006
+                return self.other(x)
+
         traced = symbolic_trace(ReturnTypeModule())
         self.assertIn("-> typing_List[str]", traced._code)
         scripted = torch.jit.script(traced)
@@ -3735,7 +3757,7 @@ class TestFX(JitTestCase):
     @unittest.skipIf(sys.version_info > (3, 11), "Does not work in 3.11")
     def test_annotations_empty_tuple(self):
         class Foo(torch.nn.Module):
-            def forward(self, x: Tuple[()], y: Tuple[str, Tuple[()]]):
+            def forward(self, x: typing.Tuple[()], y: typing.Tuple[str, typing.Tuple[()]]):  # noqa: UP006
                 return "foo"
 
         traced = torch.fx.symbolic_trace(Foo())
@@ -4320,10 +4342,10 @@ class TestFXAPIBackwardCompatibility(JitTestCase):
         tuple,
         type,
         typing.Callable,
-        typing.Dict,
-        typing.List,
-        typing.Tuple,
-        typing.Type,
+        typing.Dict,  # noqa: UP006
+        typing.List,  # noqa: UP006
+        typing.Tuple,  # noqa: UP006
+        typing.Type,  # noqa: UP006
         typing.Union,
     }
 

test/test_fx_experimental.py

@@ -12,7 +12,7 @@ import tempfile
 import typing
 import unittest
 from types import BuiltinFunctionType
-from typing import Callable, List, NamedTuple, Optional, Union
+from typing import Callable, NamedTuple, Optional, Union
 
 import torch
 import torch.fx.experimental.meta_tracer
@@ -1548,25 +1548,25 @@ class {test_classname}(torch.nn.Module):
             (Optional[list[int]], list[int]),
         ] + [
             # pre-PEP585 signatures
-            (typing.List[int], int),
-            (typing.List[int], create_type_hint([int, int])),
-            (typing.List[int], create_type_hint((int, int))),
-            (typing.List[torch.Tensor], create_type_hint([torch.Tensor, torch.Tensor])),
+            (typing.List[int], int),  # noqa: UP006
+            (typing.List[int], create_type_hint([int, int])),  # noqa: UP006
+            (typing.List[int], create_type_hint((int, int))),  # noqa: UP006
+            (typing.List[torch.Tensor], create_type_hint([torch.Tensor, torch.Tensor])),  # noqa: UP006
             (
-                typing.List[torch.Tensor],
+                typing.List[torch.Tensor],  # noqa: UP006
                 create_type_hint([torch.nn.Parameter, torch.nn.Parameter]),
             ),
-            (typing.List[torch.Tensor], create_type_hint([torch.nn.Parameter, torch.Tensor])),
-            (typing.List[torch.Tensor], create_type_hint([torch.Tensor, torch.nn.Parameter])),
-            (typing.List[torch.Tensor], create_type_hint((torch.Tensor, torch.Tensor))),
+            (typing.List[torch.Tensor], create_type_hint([torch.nn.Parameter, torch.Tensor])),  # noqa: UP006
+            (typing.List[torch.Tensor], create_type_hint([torch.Tensor, torch.nn.Parameter])),  # noqa: UP006
+            (typing.List[torch.Tensor], create_type_hint((torch.Tensor, torch.Tensor))),  # noqa: UP006
             (
-                typing.List[torch.Tensor],
+                typing.List[torch.Tensor],  # noqa: UP006
                 create_type_hint((torch.nn.Parameter, torch.nn.Parameter)),
             ),
-            (typing.List[torch.Tensor], create_type_hint((torch.nn.Parameter, torch.Tensor))),
-            (typing.List[torch.Tensor], create_type_hint((torch.Tensor, torch.nn.Parameter))),
-            (Optional[typing.List[torch.Tensor]], typing.List[torch.Tensor]),
-            (Optional[typing.List[int]], typing.List[int]),
+            (typing.List[torch.Tensor], create_type_hint((torch.nn.Parameter, torch.Tensor))),  # noqa: UP006
+            (typing.List[torch.Tensor], create_type_hint((torch.Tensor, torch.nn.Parameter))),  # noqa: UP006
+            (Optional[typing.List[torch.Tensor]], typing.List[torch.Tensor]),  # noqa: UP006
+            (Optional[typing.List[int]], typing.List[int]),  # noqa: UP006
         ]
 
         for sig_type, arg_type in should_be_equal:
@@ -1575,7 +1575,7 @@ class {test_classname}(torch.nn.Module):
         should_fail = [
             (int, float),
             (Union[int, float], str),
-            (list[torch.Tensor], List[int]),
+            (list[torch.Tensor], typing.List[int]),  # noqa: UP006
         ] + [
             # pre-PEP585 signatures
             (list[torch.Tensor], list[int]),

torch/autograd/grad_mode.py

@@ -1,5 +1,5 @@
 # mypy: allow-untyped-defs
-from typing import Any, Tuple, Union
+from typing import Any, Union
 
 import torch
 from torch.utils._contextlib import (
@@ -386,7 +386,7 @@ class _unsafe_preserve_version_counter(_DecoratorContextManager):
 
     """
 
-    def __init__(self, tensors: Union[torch.Tensor, Tuple[torch.Tensor, ...]]) -> None:
+    def __init__(self, tensors: Union[torch.Tensor, tuple[torch.Tensor, ...]]) -> None:
         self.tensors = (tensors,) if isinstance(tensors, torch.Tensor) else tensors
         assert isinstance(self.tensors, tuple)
         self.prev_versions = tuple(t._version for t in self.tensors)

torch/fx/graph.py

@@ -455,9 +455,13 @@ class CodeGen:
 
             typename = _type_repr(o)
 
-            if hasattr(o, "__origin__"):
-                # This is a generic type, e.g. typing.List[torch.Tensor]
-                origin_type = _origin_type_map.get(o.__origin__, o.__origin__)
+            if origin_type := getattr(o, "__origin__", None):
+                # list[...], typing.List[...], TensorType[...]
+
+                if isinstance(o, typing._GenericAlias):  # type: ignore[attr-defined]
+                    # This is a generic pre-PEP585 type, e.g. typing.List[torch.Tensor]
+                    origin_type = _origin_type_map.get(origin_type, origin_type)
+
                 origin_typename = add_global(_type_repr(origin_type), origin_type)
 
                 if hasattr(o, "__args__"):

torch/fx/node.py

@@ -126,7 +126,9 @@ def _type_repr(obj: object) -> str:
     typically enough to uniquely identify a type.  For everything
     else, we fall back on repr(obj).
     """
-    if isinstance(obj, type):
+    # Extension: If we don't ignore GenericAlias then `list[int]` will print
+    # simply "list".
+    if isinstance(obj, type) and not isinstance(obj, types.GenericAlias):
         if obj.__module__ == "builtins":
             return obj.__qualname__
         return f"{obj.__module__}.{obj.__qualname__}"