autograd_tutorial.py

# -*- coding: utf-8 -*-
"""
Autograd
========

Autograd is now a core torch package for automatic differentiation.
It uses a tape based system for automatic differentiation.

In the forward phase, the autograd tape will remember all the operations
it executed, and in the backward phase, it will replay the operations.

Variable
--------

In autograd, we introduce a ``Variable`` class, which is a very thin
wrapper around a ``Tensor``. You can access the raw tensor through the
``.data`` attribute, and after computing the backward pass, a gradient
w.r.t. this variable is accumulated into ``.grad`` attribute.

.. figure:: /_static/img/Variable.png
   :alt: Variable

   Variable

There’s one more class which is very important for autograd
implementation - a ``Function``. ``Variable`` and ``Function`` are
interconnected and build up an acyclic graph, that encodes a complete
history of computation. Each variable has a ``.creator`` attribute that
references a function that has created a function (except for Variables
created by the user - these have ``None`` as ``.creator``).

If you want to compute the derivatives, you can call ``.backward()`` on
a ``Variable``. If ``Variable`` is a scalar (i.e. it holds a one element
tensor), you don’t need to specify any arguments to ``backward()``,
however if it has more elements, you need to specify a ``grad_output``
argument that is a tensor of matching shape.
"""

import torch
from torch.autograd import Variable
x = Variable(torch.ones(2, 2), requires_grad=True)
print(x)  # notice the "Variable containing" line

###############################################################
#
print(x.data)

###############################################################
#
print(x.grad)

###############################################################
#

print(x.creator)  # we've created x ourselves

###############################################################
# Do an operation of x:

y = x + 2
print(y)

###############################################################
# y was created as a result of an operation,
# so it has a creator
print(y.creator)

###############################################################
# More operations on y:

z = y * y * 3
out = z.mean()

print(z, out)

###############################################################
# Gradients
# ---------
#
# let's backprop now and print gradients d(out)/dx

out.backward()
print(x.grad)


###############################################################
# By default, gradient computation flushes all the internal buffers
# contained in the graph, so if you even want to do the backward on some
# part of the graph twice, you need to pass in ``retain_variables = True``
# during the first pass.

x = Variable(torch.ones(2, 2), requires_grad=True)
y = x + 2
y.backward(torch.ones(2, 2), retain_variables=True)
# the retain_variables flag will prevent the internal buffers from being freed
print(x.grad)

###############################################################
#
z = y * y
print(z)

###############################################################
#
# just backprop random gradients

gradient = torch.randn(2, 2)

# this would fail if we didn't specify
# that we want to retain variables
y.backward(gradient)

print(x.grad)