---
myst:
  html_meta:
    description: PyTorch C++ Autograd API — automatic differentiation for tensor computations.
    keywords: PyTorch, C++, autograd, automatic differentiation, gradient
---

# Autograd: Automatic Differentiation

PyTorch's autograd system provides automatic differentiation for all operations
on tensors. It records operations on tensors to build a computational graph,
then computes gradients automatically via backpropagation.

**When to use Autograd:**

- When training neural networks (gradients are computed automatically)
- When implementing custom backward passes for specialized operations
- When you need fine-grained control over gradient computation

**Basic usage:**

```cpp
#include <torch/torch.h>

// Create tensor with gradient tracking
auto x = torch::randn({2, 2}, torch::requires_grad());
auto y = x * x;
auto z = y.sum();

// Compute gradients via backpropagation
z.backward();
std::cout << x.grad() << std::endl;  // dz/dx = 2x

// Disable gradient tracking for inference
{
    torch::NoGradGuard no_grad;
    auto result = model->forward(input);  // No gradients computed
}
```

## Header Files

- `torch/csrc/autograd/autograd.h` - High-level autograd API
- `torch/csrc/autograd/function.h` - Custom autograd functions
- `torch/csrc/autograd/grad_mode.h` - Gradient computation modes
- `torch/csrc/api/include/torch/autograd.h` - C++ Frontend autograd

## Autograd Categories

```{toctree}
:maxdepth: 1

gradient
custom_functions
modes
```
