.. role:: hidden
:class: hidden-section
Automatic differentiation package - torch.autograd
==================================================
.. automodule:: torch.autograd
.. currentmodule:: torch.autograd
.. autofunction:: backward
.. autofunction:: grad
.. _functional-api:
Functional higher level API
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. warning::
This API is in beta. Even though the function signatures are very unlikely to change, major
improvements to performances are planned before we consider this stable.
This section contains the higher level API for the autograd that builds on the basic API above
and allows you to compute jacobians, hessians, etc.
This API works with user-provided functions that take only Tensors as input and return
only Tensors.
If your function takes other arguments that are not Tensors or Tensors that don't have requires_grad set,
you can use a lambda to capture them.
For example, for a function ``f`` that takes three inputs, a Tensor for which we want the jacobian, another
tensor that should be considered constant and a boolean flag as ``f(input, constant, flag=flag)``
you can use it as ``functional.jacobian(lambda x: f(x, constant, flag=flag), input)``.
.. autofunction:: torch.autograd.functional.jacobian
.. autofunction:: torch.autograd.functional.hessian
.. autofunction:: torch.autograd.functional.vjp
.. autofunction:: torch.autograd.functional.jvp
.. autofunction:: torch.autograd.functional.vhp
.. autofunction:: torch.autograd.functional.hvp
.. _locally-disable-grad:
Locally disabling gradient computation
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. autoclass:: no_grad
.. autoclass:: enable_grad
.. autoclass:: set_grad_enabled
.. _default-grad-layouts:
Default gradient layouts
^^^^^^^^^^^^^^^^^^^^^^^^
When a non-sparse ``param`` receives a non-sparse gradient during
:func:`torch.autograd.backward` or :func:`torch.Tensor.backward`
``param.grad`` is accumulated as follows.
If ``param.grad`` is initially ``None``:
1. If ``param``'s memory is non-overlapping and dense, ``.grad`` is
created with strides matching ``param`` (thus matching ``param``'s
layout).
2. Otherwise, ``.grad`` is created with rowmajor-contiguous strides.
If ``param`` already has a non-sparse ``.grad`` attribute:
3. If ``create_graph=False``, ``backward()`` accumulates into ``.grad``
in-place, which preserves its strides.
4. If ``create_graph=True``, ``backward()`` replaces ``.grad`` with a
new tensor ``.grad + new grad``, which attempts (but does not guarantee)
matching the preexisting ``.grad``'s strides.
The default behavior (letting ``.grad``\ s be ``None`` before the first
``backward()``, such that their layout is created according to 1 or 2,
and retained over time according to 3 or 4) is recommended for best performance.
Calls to ``model.zero_grad()`` or ``optimizer.zero_grad()`` will not affect ``.grad``
layouts.
In fact, resetting all ``.grad``\ s to ``None`` before each
accumulation phase, e.g.::
for iterations...
...
for param in model.parameters():
param.grad = None
loss.backward()
such that they're recreated according to 1 or 2 every time,
is a valid alternative to ``model.zero_grad()`` or ``optimizer.zero_grad()``
that may improve performance for some networks.
Manual gradient layouts
-----------------------
If you need manual control over ``.grad``'s strides,
assign ``param.grad =`` a zeroed tensor with desired strides
before the first ``backward()``, and never reset it to ``None``.
3 guarantees your layout is preserved as long as ``create_graph=False``.
4 indicates your layout is *likely* preserved even if ``create_graph=True``.
In-place operations on Tensors
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Supporting in-place operations in autograd is a hard matter, and we discourage
their use in most cases. Autograd's aggressive buffer freeing and reuse makes
it very efficient and there are very few occasions when in-place operations
actually lower memory usage by any significant amount. Unless you're operating
under heavy memory pressure, you might never need to use them.
In-place correctness checks
---------------------------
All :class:`Tensor` s keep track of in-place operations applied to them, and
if the implementation detects that a tensor was saved for backward in one of
the functions, but it was modified in-place afterwards, an error will be raised
once backward pass is started. This ensures that if you're using in-place
functions and not seeing any errors, you can be sure that the computed
gradients are correct.
Variable (deprecated)
^^^^^^^^^^^^^^^^^^^^^
.. warning::
The Variable API has been deprecated: Variables are no longer necessary to
use autograd with tensors. Autograd automatically supports Tensors with
``requires_grad`` set to ``True``. Below please find a quick guide on what
has changed:
- ``Variable(tensor)`` and ``Variable(tensor, requires_grad)`` still work as expected,
but they return Tensors instead of Variables.
- ``var.data`` is the same thing as ``tensor.data``.
- Methods such as ``var.backward(), var.detach(), var.register_hook()`` now work on tensors
with the same method names.
In addition, one can now create tensors with ``requires_grad=True`` using factory
methods such as :func:`torch.randn`, :func:`torch.zeros`, :func:`torch.ones`, and others
like the following:
``autograd_tensor = torch.randn((2, 3, 4), requires_grad=True)``
Tensor autograd functions
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. autoclass:: torch.Tensor
:noindex:
.. autoattribute:: grad
.. autoattribute:: requires_grad
.. autoattribute:: is_leaf
.. automethod:: backward
.. automethod:: detach
.. automethod:: detach_
.. automethod:: register_hook
.. automethod:: retain_grad
:hidden:`Function`
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. autoclass:: Function
:members:
Context method mixins
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
When creating a new :class:`Function`, the following methods are available to `ctx`.
.. autoclass:: torch.autograd.function._ContextMethodMixin
:members:
.. _grad-check:
Numerical gradient checking
^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. autofunction:: gradcheck
.. autofunction:: gradgradcheck
Profiler
^^^^^^^^
Autograd includes a profiler that lets you inspect the cost of different
operators inside your model - both on the CPU and GPU. There are two modes
implemented at the moment - CPU-only using :class:`~torch.autograd.profiler.profile`.
and nvprof based (registers both CPU and GPU activity) using
:class:`~torch.autograd.profiler.emit_nvtx`.
.. autoclass:: torch.autograd.profiler.profile
:members:
.. autoclass:: torch.autograd.profiler.emit_nvtx
:members:
.. autofunction:: torch.autograd.profiler.load_nvprof
Anomaly detection
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. autoclass:: detect_anomaly
.. autoclass:: set_detect_anomaly