.. _autograd-mechanics:
Autograd mechanics
==================
This note will present an overview of how autograd works and records the
operations. It's not strictly necessary to understand all this, but we recommend
getting familiar with it, as it will help you write more efficient, cleaner
programs, and can aid you in debugging.
.. _how-autograd-encodes-history:
How autograd encodes the history
--------------------------------
Autograd is reverse automatic differentiation system. Conceptually,
autograd records a graph recording all of the operations that created
the data as you execute operations, giving you a directed acyclic graph
whose leaves are the input tensors and roots are the output tensors.
By tracing this graph from roots to leaves, you can automatically
compute the gradients using the chain rule.
Internally, autograd represents this graph as a graph of
:class:`Function` objects (really expressions), which can be
:meth:`~torch.autograd.Function.apply` ed to compute the result of
evaluating the graph. When computing the forwards pass, autograd
simultaneously performs the requested computations and builds up a graph
representing the function that computes the gradient (the ``.grad_fn``
attribute of each :class:`torch.Tensor` is an entry point into this graph).
When the forwards pass is completed, we evaluate this graph in the
backwards pass to compute the gradients.
An important thing to note is that the graph is recreated from scratch at every
iteration, and this is exactly what allows for using arbitrary Python control
flow statements, that can change the overall shape and size of the graph at
every iteration. You don't have to encode all possible paths before you
launch the training - what you run is what you differentiate.
.. _locally-disable-grad-doc:
Locally disabling gradient computation
--------------------------------------
There are several mechanisms available from Python to locally disable gradient
computation:
To disable gradients across entire blocks of code, there are context managers
like no-grad mode and inference mode.
For more fine-grained exclusion of subgraphs from gradient computation,
there is setting the ``requires_grad`` field of a tensor.
Below, in addition to discussing the mechanisms above, we also describe
evaluation mode (:meth:`nn.Module.eval()`), a method that is not actually used
to disable gradient computation but, because of its name, is often mixed up with the three.
Setting ``requires_grad``
^^^^^^^^^^^^^^^^^^^^^^^^^
:attr:`requires_grad` is a flag that allows for fine-grained exclusion of
subgraphs from gradient computation. It takes effect in both the forward
and backward passes:
During the forward pass, an operation is only recorded in the backward graph if
at least one of its input tensors require grad.
During the backward pass (``.backward()``), only leaf tensors with
``requires_grad=True`` will have gradients accumulated into their ``.grad``
fields.
It is important to note that even though every tensor has this flag,
*setting* it only makes sense for leaf tensors (tensors that do not have a
``grad_fn``, e.g., a ``nn.Module``'s parameters).
Non-leaf tensors (tensors that do have ``grad_fn``) are tensors that have a
backward graph associated with them. Thus their gradients will be needed
as an intermediary result to compute the gradient for a leaf tensor that
requires grad. From this definition, it is clear that all non-leaf tensors
will automatically have ``require_grad=True``.
Setting ``requires_grad`` should be the main way you control which parts
of the model are part of the gradient computation, for example, if you need to
freeze parts of your pretrained model during model fine-tuning.
To freeze parts of your model, simply apply ``.requires_grad_(False)`` to
the parameters that you don't want updated. And as described above,
since computations that use these parameters as inputs would not be recorded in
the forward pass, they won't have their ``.grad`` fields updated in the backward
pass because they won't be part of the backward graph in the first place, as
desired.
Because this is such a common pattern, ``requires_grad`` can also be set at
the module level with :meth:`nn.Module.requires_grad_()`.
When applied to a module, ``.requires_grad_()`` takes effect on all
of the module's parameters (which have ``requires_grad=True`` by default).
Grad Modes
^^^^^^^^^^
Apart from setting ``requires_grad`` there are also three possible modes
enableable from Python that can affect how computations in PyTorch are
processed by autograd internally: default mode (grad mode), no-grad mode,
and inference mode, all of which can be togglable via context managers and
decorators.
Default Mode (Grad Mode)
^^^^^^^^^^^^^^^^^^^^^^^^
The "default mode" is actually the mode we are implicitly in when no other modes like
no-grad and inference mode are enabled. To be contrasted with
"no-grad mode" the default mode is also sometimes called "grad mode".
The most important thing to know about the default mode is that it is the only
mode in which ``requires_grad`` takes effect. ``requires_grad`` is always overridden
to be ``False`` in both the two other modes.
No-grad Mode
^^^^^^^^^^^^
Computations in no-grad mode behave as if none of the inputs require grad.
In other words, computations in no-grad mode are never recorded in the backward graph
even if there are inputs that have ``require_grad=True``.
Enable no-grad mode when you need to perform operations that should not be
recorded by autograd, but you’d still like to use the outputs of these
computations in grad mode later. This context manager makes it convenient to
disable gradients for a block of code or function without
having to temporarily set tensors to have ``requires_grad=False``, and then
back to ``True``.
For example, no-grad mode might be useful when writing an optimizer: when
performing the training update you’d like to update parameters
in-place without the update being recorded by autograd.
You also intend to use the updated parameters for computations in
grad mode in the next forward pass.
The implementations in :ref:`nn-init-doc` also
rely on no-grad mode when initializing the parameters as to avoid
autograd tracking when updating the intialized parameters in-place.
Inference Mode
^^^^^^^^^^^^^^
Inference mode is the extreme version of no-grad mode. Just like in no-grad
mode, computations in inference mode are not recorded in the backward graph, but
enabling inference mode will allow PyTorch to speed up your model even more.
This better runtime comes with a drawback: tensors created in inference mode
will not be able to be used in computations to be recorded by autograd after
exiting inference mode.
Enable inference mode when you are performing computations that don’t need
to be recorded in the backward graph, AND you don’t plan on using the tensors
created in inference mode in any computation that is to be recorded by autograd later.
It is recommended that you try out inference mode in the parts of your code
that do not require autograd tracking (e.g., data processing and model evaluation).
If it works out of the box
for your use case it’s a free performance win. If you run into errors after
enabling inference mode, check that you are not using tensors created in
inference mode in computations that are recorded by autograd after exiting inference
mode. If you cannot avoid such use in your case, you can always switch back
to no-grad mode.
For details on inference mode please see
`Inference Mode <https://pytorch.org/cppdocs/notes/inference_mode.html>`_.
For implementation details of inference mode see
`RFC-0011-InferenceMode <https://github.com/pytorch/rfcs/pull/17>`_.
Evaluation Mode (``nn.Module.eval()``)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Evaluation mode is not actually a mechanism to locally disable gradient computation.
It is included here anyway because it is sometimes confused to be such a mechanism.
Functionally, ``module.eval()`` (or equivalently ``module.train()``) are completely
orthogonal to no-grad mode and inference mode. How ``model.eval()`` affects
your model depends entirely on the specific modules used in your model and
whether they define any training-mode specific behavior.
You are responsible for calling ``model.eval()`` and ``model.train()`` if your
model relies on modules such as :class:`torch.nn.Dropout` and
:class:`torch.nn.BatchNorm2d` that may behave
differently depending on training mode, for example, to avoid updating your
BatchNorm running statistics on validation data.
It is recommended that you always use ``model.train()`` when
training and ``model.eval()`` when evaluating your model (validation/testing) even
if you aren’t sure your model has training-mode specific behavior, because a
module you are using might be updated to behave differently in training and
eval modes.
In-place operations with autograd
---------------------------------
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.
There are two main reasons that limit the applicability of in-place operations:
1. In-place operations can potentially overwrite values required to compute
gradients.
2. Every in-place operation actually requires the implementation to rewrite the
computational graph. Out-of-place versions simply allocate new objects and
keep references to the old graph, while in-place operations, require
changing the creator of all inputs to the :class:`Function` representing
this operation. This can be tricky, especially if there are many Tensors
that reference the same storage (e.g. created by indexing or transposing),
and in-place functions will actually raise an error if the storage of
modified inputs is referenced by any other :class:`Tensor`.
In-place correctness checks
^^^^^^^^^^^^^^^^^^^^^^^^^^^
Every tensor keeps a version counter, that is incremented every time it is
marked dirty in any operation. When a Function saves any tensors for backward,
a version counter of their containing Tensor is saved as well. Once you access
``self.saved_tensors`` it is checked, and if it is greater than the saved value
an error is raised. 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.
Multithreaded Autograd
----------------------
The autograd engine is responsible for running all the backward operations
necessary to compute the backward pass. This section will describe all the details
that can help you make the best use of it in a multithreaded environment.(this is
relevant only for PyTorch 1.6+ as the behavior in previous version was different).
User could train their model with multithreading code (e.g. Hogwild training), and
does not block on the concurrent backward computations, example code could be:
.. code::
# Define a train function to be used in different threads
def train_fn():
x = torch.ones(5, 5, requires_grad=True)
# forward
y = (x + 3) * (x + 4) * 0.5
# backward
y.sum().backward()
# potential optimizer update
# User write their own threading code to drive the train_fn
threads = []
for _ in range(10):
p = threading.Thread(target=train_fn, args=())
p.start()
threads.append(p)
for p in threads:
p.join()
Note that some behaviors that user should be aware of:
Concurrency on CPU
^^^^^^^^^^^^^^^^^^
When you run ``backward()`` or ``grad()`` via python or C++ API in multiple
threads on CPU, you are expecting to see extra concurrency instead of
serializing all the backward calls in a specific order during execution
(behavior before PyTorch 1.6).
Non-determinism
^^^^^^^^^^^^^^^
If you are calling ``backward()`` on multiple thread concurrently but with
shared inputs (i.e. Hogwild CPU training). Since parameters are automatically
shared across threads, gradient accumulation might become non-deterministic on
backward calls across threads, because two backward calls might access and try
to accumulate the same ``.grad`` attribute. This is technically not safe, and
it might result in racing condition and the result might be invalid to use.
But this is expected pattern if you are using the multithreading approach to
drive the whole training process but using shared parameters, user who use
multithreading should have the threading model in mind and should expect this
to happen. User could use the functional API :func:`torch.autograd.grad` to
calculate the gradients instead of ``backward()`` to avoid non-determinism.
Graph retaining
^^^^^^^^^^^^^^^
If part of the autograd graph is shared between threads, i.e. run first
part of forward single thread, then run second part in multiple threads,
then the first part of graph is shared. In this case different threads
execute ``grad()`` or ``backward()`` on the same graph might have issue of
destroying the graph on the fly of one thread, and the other thread will
crash in this case. Autograd will error out to the user similar to what call
``backward()`` twice with out ``retain_graph=True``, and let the user know
they should use ``retain_graph=True``.
Thread Safety on Autograd Node
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Since Autograd allows the caller thread to drive its backward execution for
potential parallelism, it's important that we ensure thread safety on CPU with
parallel backwards that share part/whole of the GraphTask.
Custom Python ``autograd.function`` is automatically thread safe because of GIL.
for built-in C++ Autograd Nodes(e.g. AccumulateGrad, CopySlices) and custom
``autograd::Function``, the Autograd Engine uses thread mutex locking to protect
thread safety on autograd Nodes that might have state write/read.
No thread safety on C++ hooks
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Autograd relies on the user to write thread safe C++ hooks. If you want the hook
to be correctly applied in multithreading environment, you will need to write
proper thread locking code to ensure the hooks are thread safe.
.. _complex_autograd-doc:
Autograd for Complex Numbers
----------------------------
The short version:
- When you use PyTorch to differentiate any function :math:`f(z)` with complex domain and/or codomain,
the gradients are computed under the assumption that the function is a part of a larger real-valued
loss function :math:`g(input)=L`. The gradient computed is :math:`\frac{\partial L}{\partial z^*}`
(note the conjugation of z), the negative of which is precisely the direction of steepest descent
used in Gradient Descent algorithm. Thus, all the existing optimizers work out of
the box with complex parameters.
- This convention matches TensorFlow's convention for complex
differentiation, but is different from JAX (which computes
:math:`\frac{\partial L}{\partial z}`).
- If you have a real-to-real function which internally uses complex
operations, the convention here doesn't matter: you will always get
the same result that you would have gotten if it had been implemented
with only real operations.
If you are curious about the mathematical details, or want to know how
to define complex derivatives in PyTorch, read on.
What are complex derivatives?
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The mathematical definition of complex-differentiability takes the
limit definition of a derivative and generalizes it to operate on
complex numbers. Consider a function :math:`f: ℂ → ℂ`,
.. math::
`f(z=x+yj) = u(x, y) + v(x, y)j`
where :math:`u` and :math:`v` are two variable real valued functions.
Using the derivative definition, we can write:
.. math::
f'(z) = \lim_{h \to 0, h \in C} \frac{f(z+h) - f(z)}{h}
In order for this limit to exist, not only must :math:`u` and :math:`v` must be
real differentiable, but :math:`f` must also satisfy the Cauchy-Riemann `equations
<https://en.wikipedia.org/wiki/Cauchy%E2%80%93Riemann_equations>`_. In
other words: the limit computed for real and imaginary steps (:math:`h`)
must be equal. This is a more restrictive condition.
The complex differentiable functions are commonly known as holomorphic
functions. They are well behaved, have all the nice properties that
you've seen from real differentiable functions, but are practically of no
use in the optimization world. For optimization problems, only real valued objective
functions are used in the research community since complex numbers are not part of any
ordered field and so having complex valued loss does not make much sense.
It also turns out that no interesting real-valued objective fulfill the
Cauchy-Riemann equations. So the theory with homomorphic function cannot be
used for optimization and most people therefore use the Wirtinger calculus.
Wirtinger Calculus comes in picture ...
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
So, we have this great theory of complex differentiability and
holomorphic functions, and we can’t use any of it at all, because many
of the commonly used functions are not holomorphic. What’s a poor
mathematician to do? Well, Wirtinger observed that even if :math:`f(z)`
isn’t holomorphic, one could rewrite it as a two variable function
:math:`f(z, z*)` which is always holomorphic. This is because real and
imaginary of the components of :math:`z` can be expressed in terms of
:math:`z` and :math:`z^*` as:
.. math::
\begin{aligned}
Re(z) &= \frac {z + z^*}{2} \\
Im(z) &= \frac {z - z^*}{2j}
\end{aligned}
Wirtinger calculus suggests to study :math:`f(z, z^*)` instead, which is
guaranteed to be holomorphic if :math:`f` was real differentiable (another
way to think of it is as a change of coordinate system, from :math:`f(x, y)`
to :math:`f(z, z^*)`.) This function has partial derivatives
:math:`\frac{\partial }{\partial z}` and :math:`\frac{\partial}{\partial z^{*}}`.
We can use the chain rule to establish a
relationship between these partial derivatives and the partial
derivatives w.r.t., the real and imaginary components of :math:`z`.
.. math::
\begin{aligned}
\frac{\partial }{\partial x} &= \frac{\partial z}{\partial x} * \frac{\partial }{\partial z} + \frac{\partial z^*}{\partial x} * \frac{\partial }{\partial z^*} \\
&= \frac{\partial }{\partial z} + \frac{\partial }{\partial z^*} \\
\\
\frac{\partial }{\partial y} &= \frac{\partial z}{\partial y} * \frac{\partial }{\partial z} + \frac{\partial z^*}{\partial y} * \frac{\partial }{\partial z^*} \\
&= 1j * (\frac{\partial }{\partial z} - \frac{\partial }{\partial z^*})
\end{aligned}
From the above equations, we get:
.. math::
\begin{aligned}
\frac{\partial }{\partial z} &= 1/2 * (\frac{\partial }{\partial x} - 1j * \frac{\partial }{\partial y}) \\
\frac{\partial }{\partial z^*} &= 1/2 * (\frac{\partial }{\partial x} + 1j * \frac{\partial }{\partial y})
\end{aligned}
which is the classic definition of Wirtinger calculus that you would find on `Wikipedia <https://en.wikipedia.org/wiki/Wirtinger_derivatives>`_.
There are a lot of beautiful consequences of this change.
- For one, the Cauchy-Riemann equations translate into simply saying that :math:`\frac{\partial f}{\partial z^*} = 0` (that is to say, the function :math:`f` can be written
entirely in terms of :math:`z`, without making reference to :math:`z^*`).
- Another important (and somewhat counterintuitive) result, as we’ll see later, is that when we do optimization on a real-valued loss, the step we should
take while making variable update is given by :math:`\frac{\partial Loss}{\partial z^*}` (not :math:`\frac{\partial Loss}{\partial z}`).
For more reading, check out: https://arxiv.org/pdf/0906.4835.pdf
How is Wirtinger Calculus useful in optimization?
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Researchers in audio and other fields, more commonly, use gradient
descent to optimize real valued loss functions with complex variables.
Typically, these people treat the real and imaginary values as separate
channels that can be updated. For a step size :math:`\alpha/2` and loss
:math:`L`, we can write the following equations in :math:`ℝ^2`:
.. math::
\begin{aligned}
x_{n+1} &= x_n - (\alpha/2) * \frac{\partial L}{\partial x} \\
y_{n+1} &= y_n - (\alpha/2) * \frac{\partial L}{\partial y}
\end{aligned}
How do these equations translate into complex space :math:`ℂ`?
.. math::
\begin{aligned}
z_{n+1} &= x_n - (\alpha/2) * \frac{\partial L}{\partial x} + 1j * (y_n - (\alpha/2) * \frac{\partial L}{\partial y}) \\
&= z_n - \alpha * 1/2 * (\frac{\partial L}{\partial x} + j \frac{\partial L}{\partial y}) \\
&= z_n - \alpha * \frac{\partial L}{\partial z^*}
\end{aligned}
Something very interesting has happened: Wirtinger calculus tells us
that we can simplify the complex variable update formula above to only
refer to the conjugate Wirtinger derivative
:math:`\frac{\partial L}{\partial z^*}`, giving us exactly the step we take in optimization.
Because the conjugate Wirtinger derivative gives us exactly the correct step for a real valued loss function, PyTorch gives you this derivative
when you differentiate a function with a real valued loss.
How does PyTorch compute the conjugate Wirtinger derivative?
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Typically, our derivative formulas take in `grad_output` as an input,
representing the incoming Vector-Jacobian product that we’ve already
computed, aka, :math:`\frac{\partial L}{\partial s^*}`, where :math:`L`
is the loss of the entire computation (producing a real loss) and
:math:`s` is the output of our function. The goal here is to compute
:math:`\frac{\partial L}{\partial z^*}`, where :math:`z` is the input of
the function. It turns out that in the case of real loss, we can
get away with *only* calculating :math:`\frac{\partial L}{\partial z^*}`,
even though the chain rule implies that we also need to
have access to :math:`\frac{\partial L}{\partial z^*}`. If you want
to skip this derivation, look at the last equation in this section
and then skip to the next section.
Let’s continue working with :math:`f: ℂ → ℂ` defined as
:math:`f(z) = f(x+yj) = u(x, y) + v(x, y)j`. As discussed above,
autograd’s gradient convention is centered around optimization for real
valued loss functions, so let’s assume :math:`f` is a part of larger
real valued loss function :math:`g`. Using chain rule, we can write:
.. math::
\frac{\partial L}{\partial z^*} = \frac{\partial L}{\partial u} * \frac{\partial u}{\partial z^*} + \frac{\partial L}{\partial v} * \frac{\partial v}{\partial z^*}
:label: [1]
Now using Wirtinger derivative definition, we can write:
.. math::
\begin{aligned}
\frac{\partial L}{\partial s} = 1/2 * (\frac{\partial L}{\partial u} - \frac{\partial L}{\partial v} j) \\
\frac{\partial L}{\partial s^*} = 1/2 * (\frac{\partial L}{\partial u} + \frac{\partial L}{\partial v} j)
\end{aligned}
It should be noted here that since :math:`u` and :math:`v` are real
functions, and :math:`L` is real by our assumption that :math:`f` is a
part of a real valued function, we have:
.. math::
(\frac{\partial L}{\partial s})^* = \frac{\partial L}{\partial s^*}
:label: [2]
i.e., :math:`\frac{\partial L}{\partial s}` equals to :math:`grad\_output^*`.
Solving the above equations for :math:`\frac{\partial L}{\partial u}` and :math:`\frac{\partial L}{\partial v}`, we get:
.. math::
\begin{aligned}
\frac{\partial L}{\partial u} = \frac{\partial L}{\partial s} + \frac{\partial L}{\partial s^*} \\
\frac{\partial L}{\partial v} = -1j * (\frac{\partial L}{\partial s} - \frac{\partial L}{\partial s^*})
\end{aligned}
:label: [3]
Substituting :eq:`[3]` in :eq:`[1]`, we get:
.. math::
\begin{aligned}
\frac{\partial L}{\partial z^*} &= (\frac{\partial L}{\partial s} + \frac{\partial L}{\partial s^*}) * \frac{\partial u}{\partial z^*} - 1j * (\frac{\partial L}{\partial s} - \frac{\partial L}{\partial s^*}) * \frac{\partial v}{\partial z^*} \\
&= \frac{\partial L}{\partial s} * (\frac{\partial u}{\partial z^*} + \frac{\partial v}{\partial z^*} j) + \frac{\partial L}{\partial s^*} * (\frac{\partial u}{\partial z^*} - \frac{\partial v}{\partial z^*} j) \\
&= \frac{\partial L}{\partial s^*} * \frac{\partial (u + vj)}{\partial z^*} + \frac{\partial L}{\partial s} * \frac{\partial (u + vj)^*}{\partial z^*} \\
&= \frac{\partial L}{\partial s} * \frac{\partial s}{\partial z^*} + \frac{\partial L}{\partial s^*} * \frac{\partial s^*}{\partial z^*} \\
\end{aligned}
Using :eq:`[2]`, we get:
.. math::
\begin{aligned}
\frac{\partial L}{\partial z^*} &= (\frac{\partial L}{\partial s^*})^* * \frac{\partial s}{\partial z^*} + \frac{\partial L}{\partial s^*} * (\frac{\partial s}{\partial z})^* \\
&= \boxed{ (grad\_output)^* * \frac{\partial s}{\partial z^*} + grad\_output * {(\frac{\partial s}{\partial z})}^* } \\
\end{aligned}
:label: [4]
This last equation is the important one for writing your own gradients,
as it decomposes our derivative formula into a simpler one that is easy
to compute by hand.
How can I write my own derivative formula for a complex function?
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The above boxed equation gives us the general formula for all
derivatives on complex functions. However, we still need to
compute :math:`\frac{\partial s}{\partial z}` and :math:`\frac{\partial s}{\partial z^*}`.
There are two ways you could do this:
- The first way is to just use the definition of Wirtinger derivatives directly and calculate :math:`\frac{\partial s}{\partial z}` and :math:`\frac{\partial s}{\partial z^*}` by
using :math:`\frac{\partial s}{\partial x}` and :math:`\frac{\partial s}{\partial y}`
(which you can compute in the normal way).
- The second way is to use the change of variables trick and rewrite :math:`f(z)` as a two variable function :math:`f(z, z^*)`, and compute
the conjugate Wirtinger derivatives by treating :math:`z` and :math:`z^*` as independent variables. This is often easier; for example, if the function in question is holomorphic, only :math:`z` will be used (and :math:`\frac{\partial s}{\partial z^*}` will be zero).
Let's consider the function :math:`f(z = x + yj) = c * z = c * (x+yj)` as an example, where :math:`c \in ℝ`.
Using the first way to compute the Wirtinger derivatives, we have.
.. math::
\begin{aligned}
\frac{\partial s}{\partial z} &= 1/2 * (\frac{\partial s}{\partial x} - \frac{\partial s}{\partial y} j) \\
&= 1/2 * (c - (c * 1j) * 1j) \\
&= c \\
\\
\\
\frac{\partial s}{\partial z^*} &= 1/2 * (\frac{\partial s}{\partial x} + \frac{\partial s}{\partial y} j) \\
&= 1/2 * (c + (c * 1j) * 1j) \\
&= 0 \\
\end{aligned}
Using :eq:`[4]`, and `grad\_output = 1.0` (which is the default grad output value used when :func:`backward` is called on a scalar output in PyTorch), we get:
.. math::
\frac{\partial L}{\partial z^*} = 1 * 0 + 1 * c = c
Using the second way to compute Wirtinger derivatives, we directly get:
.. math::
\begin{aligned}
\frac{\partial s}{\partial z} &= \frac{\partial (c*z)}{\partial z} \\
&= c \\
\frac{\partial s}{\partial z^*} &= \frac{\partial (c*z)}{\partial z^*} \\
&= 0
\end{aligned}
And using :eq:`[4]` again, we get :math:`\frac{\partial L}{\partial z^*} = c`. As you can see, the second way involves lesser calculations, and comes
in more handy for faster calculations.
What about cross-domain functions?
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Some functions map from complex inputs to real outputs, or vice versa.
These functions form a special case of :eq:`[4]`, which we can derive using the
chain rule:
- For :math:`f: ℂ → ℝ`, we get:
.. math::
\frac{\partial L}{\partial z^*} = 2 * grad\_output * \frac{\partial s}{\partial z^{*}}
- For :math:`f: ℝ → ℂ`, we get:
.. math::
\frac{\partial L}{\partial z^*} = 2 * Re(grad\_out^* * \frac{\partial s}{\partial z^{*}})