Merge branch 'main' into whatsnew2-7

Author: svekars

_templates/layout.html

@@ -211,14 +211,5 @@
 
 <img height="1" width="1" style="border-style:none;" alt="" src="https://www.googleadservices.com/pagead/conversion/795629140/?label=txkmCPmdtosBENSssfsC&amp;guid=ON&amp;script=0"/>
 
-<script>
-    //temporarily add a link to survey
-    var survey = '<div class="survey-banner"><p><i class="fas fa-poll" aria-hidden="true">&nbsp </i> Take the <a href="https://forms.gle/KZ4xGL65VRMYNbbG6">PyTorch Docs/Tutorials survey</a>.</p></div>'
-    if ($(".pytorch-call-to-action-links").length) {
-        $(".pytorch-call-to-action-links").before(survey);
-    } else {
-        $("#pytorch-article").prepend(survey);
-    }
-</script>
 
 {% endblock %}

prototype_source/inductor_windows.rst

@@ -44,18 +44,21 @@ Next, let's configure our environment.
     
     "C:/Program Files/Microsoft Visual Studio/2022/Community/VC/Auxiliary/Build/vcvars64.bat"
 #. Create and activate a virtual environment: ::
+
 #. Install `PyTorch 2.5 <https://pytorch.org/get-started/locally/>`_ or later for CPU Usage. Install PyTorch 2.7 or later refer to `Getting Started on Intel GPU <https://pytorch.org/docs/main/notes/get_start_xpu.html>`_ for XPU usage.
+
 #. Here is an example of how to use TorchInductor on Windows:
-.. code-block:: python
-
-    import torch
-    device="cpu" # or "xpu" for XPU
-    def foo(x, y):
-        a = torch.sin(x)
-        b = torch.cos(x)
-        return a + b
-    opt_foo1 = torch.compile(foo)
-    print(opt_foo1(torch.randn(10, 10).to(device), torch.randn(10, 10).to(device)))
+
+    .. code-block:: python
+
+        import torch
+        device="cpu" # or "xpu" for XPU
+        def foo(x, y):
+            a = torch.sin(x)
+            b = torch.cos(x)
+            return a + b
+        opt_foo1 = torch.compile(foo)
+        print(opt_foo1(torch.randn(10, 10).to(device), torch.randn(10, 10).to(device)))
 
 #. Below is the output of the above example::
 

recipes_source/foreach_map.py

@@ -1,6 +1,6 @@
 """
-(beta) Explicit horizontal fusion with foreach_map and torch.compile
-============================================================
+Explicit horizontal fusion with foreach_map and torch.compile
+===============================================================
 
 **Author:** `Michael Lazos <https://github.com/mlazos>`_
 """
@@ -13,11 +13,17 @@
 #  allows conversion of any pointwise op in ``torch`` to a horiztonally fused foreach
 #  variant. In this tutorial, we will demonstrate how to implement the Adam optimizer
 #  with ``foreach_map`` to generate a fully fused kernel.  
-# 
 #
 # .. note::
 #
-#    This tutorial requires PyTorch 2.7.0 or later.
+#    This recipe describes a prototype feature. Prototype features are typically
+#    at an early stage for feedback and testing and are subject to change.
+#
+# Prerequisites
+# -------------
+#
+# * PyTorch v2.7.0 or later
+#
 
 #####################################################################
 # Model Setup