Update

With many PRs landed, we can run the first aot inductor example on Windows.

<img width="640" height="427" alt="image" src="https://github.com/user-attachments/assets/131db159-ce17-4857-a3d5-a4b03638f01d" />

Let's remove the Windows check on `AotCodeCompiler`.

CC: @angelayi , @desertfire , @jansel

Pull Request resolved: https://github.com/pytorch/pytorch/pull/158915
Approved by: https://github.com/desertfire

.github/ISSUE_TEMPLATE/release-feature-request.yml

@@ -58,9 +58,9 @@ body:
     description: |
       Select One of the following options
     options:
-      - Tutorial exists
-      - Will submit a PR to pytorch/tutorials
-      - Will submit a PR to a repo
+      - Tutorial exists (add a link)
+      - Will submit a PR to PyTorch tutorials repo
+      - Will submit a PR to a different repo (add link to repo)
       - Tutorial is not needed
   validations:
     required: true

torch/_inductor/codecache.py

@@ -1643,9 +1643,6 @@ class AotCodeCompiler:
         """
         generated_files: list[Union[str, Weights]] = additional_files  # type: ignore[assignment]
 
-        if sys.platform == "win32":
-            raise RuntimeError("AotCodeCompiler not yet supported for inductor")
-
         _set_gpu_runtime_env()  # cpp_extension consults the env
 
         picked_vec_isa = pick_vec_isa()

torch/nn/parameter.py

@@ -19,13 +19,13 @@ class _ParameterMeta(torch._C._TensorMeta):
 class Parameter(torch.Tensor, metaclass=_ParameterMeta):
     r"""A kind of Tensor that is to be considered a module parameter.
 
-    Parameters are {class}`~torch.Tensor` subclasses, that have a
-    very special property when used with {class}`Module` s - when they're
+    Parameters are :class:`~torch.Tensor` subclasses, that have a
+    very special property when used with :class:`Module` s - when they're
     assigned as Module attributes they are automatically added to the list of
-    its parameters, and will appear e.g. in {meth}`~Module.parameters` iterator.
+    its parameters, and will appear e.g. in :meth:`~Module.parameters` iterator.
     Assigning a Tensor doesn't have such effect. This is because one might
     want to cache some temporary state, like last hidden state of the RNN, in
-    the model. If there was no such class as {class}`Parameter`, these
+    the model. If there was no such class as :class:`Parameter`, these
     temporaries would get registered too.
 
     Args:
@@ -33,7 +33,7 @@ class Parameter(torch.Tensor, metaclass=_ParameterMeta):
         requires_grad (bool, optional): if the parameter requires gradient. Note that
             the torch.no_grad() context does NOT affect the default behavior of
             Parameter creation--the Parameter will still have `requires_grad=True` in
-            {class}`~no_grad` mode. See {ref}`locally-disable-grad-doc` for more
+            :class:`~no_grad` mode. See :ref:`locally-disable-grad-doc` for more
             details. Default: `True`
     """