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Automatic differentiation package - torch.autograd

.. automodule:: torch.autograd

.. currentmodule:: torch.autograd


.. autosummary::
    :toctree: generated
    :nosignatures:

    backward
    grad

Forward-mode Automatic Differentiation

Warning

This API is in beta. Even though the function signatures are very unlikely to change, improved operator coverage is planned before we consider this stable.

Please see the forward-mode AD tutorial for detailed steps on how to use this API.

.. autosummary::
    :toctree: generated
    :nosignatures:

    forward_ad.dual_level
    forward_ad.make_dual
    forward_ad.unpack_dual

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).

.. autosummary::
    :toctree: generated
    :nosignatures:

    functional.jacobian
    functional.hessian
    functional.vjp
    functional.jvp
    functional.vhp
    functional.hvp

Locally disabling gradient computation

See :ref:`locally-disable-grad-doc` for more information on the differences between no-grad and inference mode as well as other related mechanisms that may be confused with the two. Also see :ref:`torch-rst-local-disable-grad` for a list of functions that can be used to locally disable gradients.

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:

  1. If create_graph=False, backward() accumulates into .grad in-place, which preserves its strides.
  2. 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 .grads 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 .grads 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

.. autosummary::
    :nosignatures:

   torch.Tensor.grad
   torch.Tensor.requires_grad
   torch.Tensor.is_leaf
   torch.Tensor.backward
   torch.Tensor.detach
   torch.Tensor.detach_
   torch.Tensor.register_hook
   torch.Tensor.retain_grad

Function

.. autoclass:: Function


.. autosummary::
    :toctree: generated
    :nosignatures:

    Function.forward
    Function.backward
    Function.jvp
    Function.vmap

Context method mixins

When creating a new :class:`Function`, the following methods are available to ctx.

.. autosummary::
    :toctree: generated
    :nosignatures:

    function.FunctionCtx.mark_dirty
    function.FunctionCtx.mark_non_differentiable
    function.FunctionCtx.save_for_backward
    function.FunctionCtx.set_materialize_grads

Numerical gradient checking

.. autosummary::
    :toctree: generated
    :nosignatures:

    gradcheck
    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 three modes implemented at the moment - CPU-only using :class:`~torch.autograd.profiler.profile`. nvprof based (registers both CPU and GPU activity) using :class:`~torch.autograd.profiler.emit_nvtx`. and vtune profiler based using :class:`~torch.autograd.profiler.emit_itt`.

.. autoclass:: torch.autograd.profiler.profile


.. autosummary::
    :toctree: generated
    :nosignatures:

    profiler.profile.export_chrome_trace
    profiler.profile.key_averages
    profiler.profile.self_cpu_time_total
    profiler.profile.total_average


.. autoclass:: torch.autograd.profiler.emit_nvtx

.. autoclass:: torch.autograd.profiler.emit_itt



.. autosummary::
    :toctree: generated
    :nosignatures:

    profiler.load_nvprof

Anomaly detection

.. autoclass:: detect_anomaly


.. autoclass:: set_detect_anomaly

Autograd graph

Autograd exposes methods that allow one to inspect the graph and interpose behavior during the backward pass.

The grad_fn attribute of a :class:`torch.Tensor` holds a :class:`torch.autograd.graph.Node` if the tensor is the output of a operation that was recorded by autograd (i.e., grad_mode is enabled and at least one of the inputs required gradients), or None otherwise.

.. autosummary::
    :toctree: generated
    :nosignatures:

    graph.Node.name
    graph.Node.metadata
    graph.Node.next_functions
    graph.Node.register_hook
    graph.Node.register_prehook

Some operations need intermediary results to be saved during the forward pass in order to execute the backward pass. These intermediary results are saved as attributes on the grad_fn and can be accessed. For example:

>>> a = torch.tensor([0., 0., 0.], requires_grad=True)
>>> b = a.exp()
>>> print(isinstance(b.grad_fn, torch.autograd.graph.Node))
True
>>> print(dir(b.grad_fn))
['__call__', '__class__', '__delattr__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '_raw_saved_result', '_register_hook_dict', '_saved_result', 'metadata', 'name', 'next_functions', 'register_hook', 'register_prehook', 'requires_grad']
>>> print(torch.allclose(b.grad_fn._saved_result, b))
True

You can also define how these saved tensors should be packed / unpacked using hooks. A common application is to trade compute for memory by saving those intermediary results to disk or to CPU instead of leaving them on the GPU. This is especially useful if you notice your model fits on GPU during evaluation, but not training. Also see :ref:`saved-tensors-hooks-doc`.

.. autoclass:: torch.autograd.graph.saved_tensors_hooks


.. autoclass:: torch.autograd.graph.save_on_cpu


.. autoclass:: torch.autograd.graph.disable_saved_tensors_hooks


.. autoclass:: torch.autograd.graph.register_multi_grad_hook


.. autoclass:: torch.autograd.graph.allow_mutation_on_saved_tensors