Autograd in C++ Frontend
========================
The ``autograd`` package is crucial for building highly flexible and dynamic neural
networks in PyTorch. Most of the autograd APIs in PyTorch Python frontend are also available
in C++ frontend, allowing easy translation of autograd code from Python to C++.
In this tutorial explore several examples of doing autograd in PyTorch C++ frontend.
Note that this tutorial assumes that you already have a basic understanding of
autograd in Python frontend. If that's not the case, please first read
`Autograd: Automatic Differentiation <https://pytorch.org/tutorials/beginner/blitz/autograd_tutorial.html>`_.
Basic autograd operations
-------------------------
(Adapted from `this tutorial <https://pytorch.org/tutorials/beginner/blitz/autograd_tutorial.html#autograd-automatic-differentiation>`_)
Create a tensor and set ``torch::requires_grad()`` to track computation with it
.. code-block:: cpp
auto x = torch::ones({2, 2}, torch::requires_grad());
std::cout << x << std::endl;
Out:
.. code-block:: shell
1 1
1 1
[ CPUFloatType{2,2} ]
Do a tensor operation:
.. code-block:: cpp
auto y = x + 2;
std::cout << y << std::endl;
Out:
.. code-block:: shell
3 3
3 3
[ CPUFloatType{2,2} ]
``y`` was created as a result of an operation, so it has a ``grad_fn``.
.. code-block:: cpp
std::cout << y.grad_fn()->name() << std::endl;
Out:
.. code-block:: shell
AddBackward1
Do more operations on ``y``
.. code-block:: cpp
auto z = y * y * 3;
auto out = z.mean();
std::cout << z << std::endl;
std::cout << z.grad_fn()->name() << std::endl;
std::cout << out << std::endl;
std::cout << out.grad_fn()->name() << std::endl;
Out:
.. code-block:: shell
27 27
27 27
[ CPUFloatType{2,2} ]
MulBackward1
27
[ CPUFloatType{} ]
MeanBackward0
``.requires_grad_( ... )`` changes an existing tensor's ``requires_grad`` flag in-place.
.. code-block:: cpp
auto a = torch::randn({2, 2});
a = ((a * 3) / (a - 1));
std::cout << a.requires_grad() << std::endl;
a.requires_grad_(true);
std::cout << a.requires_grad() << std::endl;
auto b = (a * a).sum();
std::cout << b.grad_fn()->name() << std::endl;
Out:
.. code-block:: shell
false
true
SumBackward0
Let's backprop now. Because ``out`` contains a single scalar, ``out.backward()``
is equivalent to ``out.backward(torch::tensor(1.))``.
.. code-block:: cpp
out.backward();
Print gradients d(out)/dx
.. code-block:: cpp
std::cout << x.grad() << std::endl;
Out:
.. code-block:: shell
4.5000 4.5000
4.5000 4.5000
[ CPUFloatType{2,2} ]
You should have got a matrix of ``4.5``. For explanations on how we arrive at this value,
please see `the corresponding section in this tutorial <https://pytorch.org/tutorials/beginner/blitz/autograd_tutorial.html#gradients>`_.
Now let's take a look at an example of vector-Jacobian product:
.. code-block:: cpp
x = torch::randn(3, torch::requires_grad());
y = x * 2;
while (y.norm().item<double>() < 1000) {
y = y * 2;
}
std::cout << y << std::endl;
std::cout << y.grad_fn()->name() << std::endl;
Out:
.. code-block:: shell
-1021.4020
314.6695
-613.4944
[ CPUFloatType{3} ]
MulBackward1
If we want the vector-Jacobian product, pass the vector to ``backward`` as argument:
.. code-block:: cpp
auto v = torch::tensor({0.1, 1.0, 0.0001}, torch::kFloat);
y.backward(v);
std::cout << x.grad() << std::endl;
Out:
.. code-block:: shell
102.4000
1024.0000
0.1024
[ CPUFloatType{3} ]
You can also stop autograd from tracking history on tensors that require gradients
either by putting ``torch::NoGradGuard`` in a code block
.. code-block:: cpp
std::cout << x.requires_grad() << std::endl;
std::cout << x.pow(2).requires_grad() << std::endl;
{
torch::NoGradGuard no_grad;
std::cout << x.pow(2).requires_grad() << std::endl;
}
Out:
.. code-block:: shell
true
true
false
Or by using ``.detach()`` to get a new tensor with the same content but that does
not require gradients:
.. code-block:: cpp
std::cout << x.requires_grad() << std::endl;
y = x.detach();
std::cout << y.requires_grad() << std::endl;
std::cout << x.eq(y).all().item<bool>() << std::endl;
Out:
.. code-block:: shell
true
false
true
For more information on C++ tensor autograd APIs such as ``grad`` / ``requires_grad`` /
``is_leaf`` / ``backward`` / ``detach`` / ``detach_`` / ``register_hook`` / ``retain_grad``,
please see `the corresponding C++ API docs <https://pytorch.org/cppdocs/api/classat_1_1_tensor.html>`_.
Computing higher-order gradients in C++
---------------------------------------
One of the applications of higher-order gradients is calculating gradient penalty.
Let's see an example of it using ``torch::autograd::grad``:
.. code-block:: cpp
#include <torch/torch.h>
auto model = torch::nn::Linear(4, 3);
auto input = torch::randn({3, 4}).requires_grad_(true);
auto output = model(input);
// Calculate loss
auto target = torch::randn({3, 3});
auto loss = torch::nn::MSELoss()(output, target);
// Use norm of gradients as penalty
auto grad_output = torch::ones_like(output);
auto gradient = torch::autograd::grad({output}, {input}, /*grad_outputs=*/{grad_output}, /*create_graph=*/true)[0];
auto gradient_penalty = torch::pow((gradient.norm(2, /*dim=*/1) - 1), 2).mean();
// Add gradient penalty to loss
auto combined_loss = loss + gradient_penalty;
combined_loss.backward();
std::cout << input.grad() << std::endl;
Out:
.. code-block:: shell
-0.1042 -0.0638 0.0103 0.0723
-0.2543 -0.1222 0.0071 0.0814
-0.1683 -0.1052 0.0355 0.1024
[ CPUFloatType{3,4} ]
Please see the documentation for ``torch::autograd::backward``
(`link <https://pytorch.org/cppdocs/api/function_namespacetorch_1_1autograd_1a1403bf65b1f4f8c8506a9e6e5312d030.html>`_)
and ``torch::autograd::grad``
(`link <https://pytorch.org/cppdocs/api/function_namespacetorch_1_1autograd_1ab9fa15dc09a8891c26525fb61d33401a.html>`_)
for more information on how to use them.
Using custom autograd function in C++
-------------------------------------
(Adapted from `this tutorial <https://pytorch.org/docs/stable/notes/extending.html#extending-torch-autograd>`_)
Adding a new elementary operation to ``torch::autograd`` requires implementing a new ``torch::autograd::Function``
subclass for each operation. ``torch::autograd::Function`` s are what ``torch::autograd``
uses to compute the results and gradients, and encode the operation history. Every
new function requires you to implement 2 methods: ``forward`` and ``backward``, and
please see `this link <https://pytorch.org/cppdocs/api/structtorch_1_1autograd_1_1_function.html>`_
for the detailed requirements.
Below you can find code for a ``Linear`` function from ``torch::nn``:
.. code-block:: cpp
#include <torch/torch.h>
using namespace torch::autograd;
// Inherit from Function
class LinearFunction : public Function<LinearFunction> {
public:
// Note that both forward and backward are static functions
// bias is an optional argument
static torch::Tensor forward(
AutogradContext *ctx, torch::Tensor input, torch::Tensor weight, torch::Tensor bias = torch::Tensor()) {
ctx->save_for_backward({input, weight, bias});
auto output = input.mm(weight.t());
if (bias.defined()) {
output += bias.unsqueeze(0).expand_as(output);
}
return output;
}
static tensor_list backward(AutogradContext *ctx, tensor_list grad_outputs) {
auto saved = ctx->get_saved_variables();
auto input = saved[0];
auto weight = saved[1];
auto bias = saved[2];
auto grad_output = grad_outputs[0];
auto grad_input = grad_output.mm(weight);
auto grad_weight = grad_output.t().mm(input);
auto grad_bias = torch::Tensor();
if (bias.defined()) {
grad_bias = grad_output.sum(0);
}
return {grad_input, grad_weight, grad_bias};
}
};
Then, we can use the ``LinearFunction`` in the following way:
.. code-block:: cpp
auto x = torch::randn({2, 3}).requires_grad_();
auto weight = torch::randn({4, 3}).requires_grad_();
auto y = LinearFunction::apply(x, weight);
y.sum().backward();
std::cout << x.grad() << std::endl;
std::cout << weight.grad() << std::endl;
Out:
.. code-block:: shell
0.5314 1.2807 1.4864
0.5314 1.2807 1.4864
[ CPUFloatType{2,3} ]
3.7608 0.9101 0.0073
3.7608 0.9101 0.0073
3.7608 0.9101 0.0073
3.7608 0.9101 0.0073
[ CPUFloatType{4,3} ]
Here, we give an additional example of a function that is parametrized by non-tensor arguments:
.. code-block:: cpp
#include <torch/torch.h>
using namespace torch::autograd;
class MulConstant : public Function<MulConstant> {
public:
static torch::Tensor forward(AutogradContext *ctx, torch::Tensor tensor, double constant) {
// ctx is a context object that can be used to stash information
// for backward computation
ctx->saved_data["constant"] = constant;
return tensor * constant;
}
static tensor_list backward(AutogradContext *ctx, tensor_list grad_outputs) {
// We return as many input gradients as there were arguments.
// Gradients of non-tensor arguments to forward must be `torch::Tensor()`.
return {grad_outputs[0] * ctx->saved_data["constant"].toDouble(), torch::Tensor()};
}
};
Then, we can use the ``MulConstant`` in the following way:
.. code-block:: cpp
auto x = torch::randn({2}).requires_grad_();
auto y = MulConstant::apply(x, 5.5);
y.sum().backward();
std::cout << x.grad() << std::endl;
Out:
.. code-block:: shell
5.5000
5.5000
[ CPUFloatType{2} ]
For more information on ``torch::autograd::Function``, please see
`its documentation <https://pytorch.org/cppdocs/api/structtorch_1_1autograd_1_1_function.html>`_.
Translating autograd code from Python to C++
--------------------------------------------
On a high level, the easiest way to use autograd in C++ is to have working
autograd code in Python first, and then translate your autograd code from Python to
C++ using the following table:
+--------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Python | C++ |
+================================+========================================================================================================================================================================+
| ``torch.autograd.backward`` | ``torch::autograd::backward`` (`link <https://pytorch.org/cppdocs/api/function_namespacetorch_1_1autograd_1a1403bf65b1f4f8c8506a9e6e5312d030.html>`_) |
+--------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| ``torch.autograd.grad`` | ``torch::autograd::grad`` (`link <https://pytorch.org/cppdocs/api/function_namespacetorch_1_1autograd_1ab9fa15dc09a8891c26525fb61d33401a.html>`_) |
+--------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| ``torch.Tensor.detach`` | ``torch::Tensor::detach`` (`link <https://pytorch.org/cppdocs/api/classat_1_1_tensor.html#_CPPv4NK2at6Tensor6detachEv>`_) |
+--------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| ``torch.Tensor.detach_`` | ``torch::Tensor::detach_`` (`link <https://pytorch.org/cppdocs/api/classat_1_1_tensor.html#_CPPv4NK2at6Tensor7detach_Ev>`_) |
+--------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| ``torch.Tensor.backward`` | ``torch::Tensor::backward`` (`link <https://pytorch.org/cppdocs/api/classat_1_1_tensor.html#_CPPv4NK2at6Tensor8backwardERK6Tensorbb>`_) |
+--------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| ``torch.Tensor.register_hook`` | ``torch::Tensor::register_hook`` (`link <https://pytorch.org/cppdocs/api/classat_1_1_tensor.html#_CPPv4I0ENK2at6Tensor13register_hookE18hook_return_void_tI1TERR1T>`_) |
+--------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| ``torch.Tensor.requires_grad`` | ``torch::Tensor::requires_grad_`` (`link <https://pytorch.org/cppdocs/api/classat_1_1_tensor.html#_CPPv4NK2at6Tensor14requires_grad_Eb>`_) |
+--------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| ``torch.Tensor.retain_grad`` | ``torch::Tensor::retain_grad`` (`link <https://pytorch.org/cppdocs/api/classat_1_1_tensor.html#_CPPv4NK2at6Tensor11retain_gradEv>`_) |
+--------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| ``torch.Tensor.grad`` | ``torch::Tensor::grad`` (`link <https://pytorch.org/cppdocs/api/classat_1_1_tensor.html#_CPPv4NK2at6Tensor4gradEv>`_) |
+--------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| ``torch.Tensor.grad_fn`` | ``torch::Tensor::grad_fn`` (`link <https://pytorch.org/cppdocs/api/classat_1_1_tensor.html#_CPPv4NK2at6Tensor7grad_fnEv>`_) |
+--------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| ``torch.Tensor.set_data`` | ``torch::Tensor::set_data`` (`link <https://pytorch.org/cppdocs/api/classat_1_1_tensor.html#_CPPv4NK2at6Tensor8set_dataERK6Tensor>`_) |
+--------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| ``torch.Tensor.data`` | ``torch::Tensor::data`` (`link <https://pytorch.org/cppdocs/api/classat_1_1_tensor.html#_CPPv4NK2at6Tensor4dataEv>`_) |
+--------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| ``torch.Tensor.output_nr`` | ``torch::Tensor::output_nr`` (`link <https://pytorch.org/cppdocs/api/classat_1_1_tensor.html#_CPPv4NK2at6Tensor9output_nrEv>`_) |
+--------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| ``torch.Tensor.is_leaf`` | ``torch::Tensor::is_leaf`` (`link <https://pytorch.org/cppdocs/api/classat_1_1_tensor.html#_CPPv4NK2at6Tensor7is_leafEv>`_) |
+--------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
After translation, most of your Python autograd code should just work in C++.
If that's not the case, please file a bug report at `GitHub issues <https://github.com/pytorch/pytorch/issues>`_
and we will fix it as soon as possible.
Conclusion
----------
You should now have a good overview of PyTorch's C++ autograd API.
You can find the code examples displayed in this note `here
<https://github.com/pytorch/examples/tree/master/cpp/autograd>`_. As always, if you run into any
problems or have questions, you can use our `forum <https://discuss.pytorch.org/>`_
or `GitHub issues <https://github.com/pytorch/pytorch/issues>`_ to get in touch.
