Torch Function modes x torch.compile tutorial

recipes_source/recipes_index.rst

@@ -317,6 +317,15 @@ Recipes are bite-sized, actionable examples of how to use specific PyTorch featu
    :link: ../recipes/amx.html
    :tags: Model-Optimization
 
+.. (beta) Utilizing Torch Function modes with torch.compile
+
+.. customcarditem::
+   :header: (beta) Utilizing Torch Function modes with torch.compile
+   :card_description: Override torch operators with Torch Function modes and torch.compile
+   :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png
+   :link: ../recipes/torch_compile_torch_function_modes.html
+   :tags: Model-Optimization
+
 .. (beta) Compiling the Optimizer with torch.compile
 
 .. customcarditem::

recipes_source/torch_compile_torch_function_modes.py

@@ -0,0 +1,77 @@
+"""
+(beta) Utilizing Torch Function modes with torch.compile
+============================================================
+
+**Author:** `Michael Lazos <https://github.com/mlazos>`_
+"""
+
+#########################################################
+#  This tutorial covers how to use a key torch extensibility point, 
+#  torch function modes, in tandem with torch.compile to override 
+#  the behavior of torch ops at trace time, with no runtime overhead.
+#
+# .. note::
+#
+#    This tutorial requires PyTorch 2.7.0 or later.
+
+
+#####################################################################
+# Rewriting a torch op (torch.add -> torch.mul)
+# ~~~~~~~~~~~~~~~~~~~~~
+# For this example, we'll use torch function modes to rewrite occurences
+# of addition with multiply instead. This type of override can be common 
+# if a certain backend has a custom implementation that should be dispatched
+# for a given op. 
+import torch
+
+# exit cleanly if we are on a device that doesn't support ``torch.compile``
+if torch.cuda.get_device_capability() < (7, 0):
+    print("Exiting because torch.compile is not supported on this device.")
+    import sys
+    sys.exit(0)
+
+from torch.overrides import BaseTorchFunctionMode
+
+# Define our mode, Note: BaseTorchFunctionMode 
+# implements the actual invocation of func(..)
+class AddToMultiplyMode(BaseTorchFunctionMode):
+    def __torch_function__(self, func, types, args=(), kwargs=None):
+        if func == torch.Tensor.add:
+            func = torch.mul
+
+        return super().__torch_function__(func, types, args, kwargs)
+
+@torch.compile()
+def test_fn(x, y):
+    return x + y * x # Note: infix operators map to torch.Tensor.* methods
+
+x = torch.rand(2, 2)
+y = torch.rand_like(x)
+
+with AddToMultiplyMode():
+    z = test_fn(x, y)
+
+assert torch.allclose(z, x * y * x)
+
+# The mode can also be used within the compiled region as well like so
+
+@torch.compile()
+def test_fn(x, y):
+    with AddToMultiplyMode():
+        return x + y * x # Note: infix operators map to torch.Tensor.* methods
+
+x = torch.rand(2, 2)
+y = torch.rand_like(x)
+z = test_fn(x, y)
+
+assert torch.allclose(z, x * y * x)
+
+######################################################################
+# Conclusion
+# ~~~~~~~~~~
+# In this tutorial we demonstrated how to override the behavior of torch.* operators
+# using torch function modes from within torch.compile. This enables users to utilize
+# the extensibility benefits of torch function modes without the runtime overhead
+# of calling torch function on every op invocation. 
+# 
+# * `Extending Torch API with Modes <https://pytorch.org/docs/stable/notes/extending.html#extending-all-torch-api-with-modes>`__ - Other examples and backgroun on Torch Function modes.