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Fix forward AD tutorial directory and add to index #1758

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Dec 7, 2021
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Fix forward AD tutorial directory and add to index #1758

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8 changes: 8 additions & 0 deletions index.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -387,6 +387,13 @@ Welcome to PyTorch Tutorials
:link: intermediate/custom_function_conv_bn_tutorial.html
:tags: Extending-PyTorch,Frontend-APIs

.. customcarditem::
:header: Forward-mode Automatic Differentiation
:card_description: Learn how to use forward-mode automatic differentiation
:image: _static/img/thumbnails/cropped/generic-pytorch-logo.PNG
:link: intermediate/forward_ad_usage.html
:tags: Frontend-APIs

.. Model Optimization

.. customcarditem::
Expand Down Expand Up @@ -746,6 +753,7 @@ Additional Resources
:caption: Frontend APIs

intermediate/memory_format_tutorial
intermediate/forward_ad_usage
advanced/cpp_frontend
advanced/torch-script-parallelism
advanced/cpp_autograd
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10 changes: 8 additions & 2 deletions forward_ad_usage.py → intermediate_source/forward_ad_usage.py
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@@ -1,11 +1,17 @@
# -*- coding: utf-8 -*-
"""
Forward-mode Automatic Differentiation
======================================
Forward-mode Automatic Differentiation (Beta)
=============================================

This tutorial demonstrates how to use forward-mode AD to compute
directional derivatives (or equivalently, Jacobian-vector products).

The tutorial below uses some APIs only available in versions >= 1.11
(or nightly builds).

Also note that forward-mode AD is currently in beta. The API is
subject to change and operator coverage is still incomplete.

Basic Usage
--------------------------------------------------------------------
Unlike reverse-mode AD, forward-mode AD computes gradients eagerly
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