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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 we’ll look at 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.

Basic autograd operations

(Adapted from this tutorial)

Create a tensor and set torch::requires_grad() to track computation with it

auto x = torch::ones({2, 2}, torch::requires_grad());
std::cout << x << std::endl;

Out:

1 1
1 1
[ CPUFloatType{2,2} ]

Do a tensor operation:

auto y = x + 2;
std::cout << y << std::endl;

Out:

 3  3
 3  3
[ CPUFloatType{2,2} ]

y was created as a result of an operation, so it has a grad_fn.

std::cout << y.grad_fn()->name() << std::endl;

Out:

AddBackward1

Do more operations on y

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:

 27  27
 27  27
[ CPUFloatType{2,2} ]
MulBackward1
27
[ CPUFloatType{} ]
MeanBackward0

.requires_grad_( ... ) changes an existing tensor’s requires_grad flag in-place.

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:

false
true
SumBackward0

Let’s backprop now. Because out contains a single scalar, out.backward() is equivalent to out.backward(torch::tensor(1.)).

out.backward();

Print gradients d(out)/dx

std::cout << x.grad() << std::endl;

Out:

 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.

Now let’s take a look at an example of vector-Jacobian product:

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:

-1021.4020
  314.6695
 -613.4944
[ CPUFloatType{3} ]
MulBackward1

If we want the vector-Jacobian product, pass the vector to backward as argument:

auto v = torch::tensor({0.1, 1.0, 0.0001}, torch::kFloat);
y.backward(v);

std::cout << x.grad() << std::endl;

Out:

  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

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:

true
true
false

Or by using .detach() to get a new tensor with the same content but that does not require gradients:

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:

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.

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:

#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:

-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) and torch::autograd::grad (link) for more information on how to use them.

Using custom autograd function in C++

(Adapted from this tutorial)

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 for the detailed requirements.

Below you can find code for a Linear function from torch::nn:

#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:

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:

 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:

#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:

auto x = torch::randn({2}).requires_grad_();
auto y = MulConstant::apply(x, 5.5);
y.sum().backward();

std::cout << x.grad() << std::endl;

Out:

 5.5000
 5.5000
[ CPUFloatType{2} ]

For more information on torch::autograd::Function, please see its documentation.

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)

torch.autograd.grad

torch::autograd::grad (link)

torch.Tensor.detach

torch::Tensor::detach (link)

torch.Tensor.detach_

torch::Tensor::detach_ (link)

torch.Tensor.backward

torch::Tensor::backward (link)

torch.Tensor.register_hook

torch::Tensor::register_hook (link)

torch.Tensor.requires_grad

torch::Tensor::requires_grad_ (link)

torch.Tensor.retain_grad

torch::Tensor::retain_grad (link)

torch.Tensor.grad

torch::Tensor::grad (link)

torch.Tensor.grad_fn

torch::Tensor::grad_fn (link)

torch.Tensor.set_data

torch::Tensor::set_data (link)

torch.Tensor.data

torch::Tensor::data (link)

torch.Tensor.output_nr

torch::Tensor::output_nr (link)

torch.Tensor.is_leaf

torch::Tensor::is_leaf (link)

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 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. As always, if you run into any problems or have questions, you can use our forum or GitHub issues to get in touch.


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