---
myst:
  html_meta:
    description: PyTorch Library API in C++ — operator registration, custom classes, and versioning.
    keywords: PyTorch, C++, Library, operator registration, custom class, dispatch
---

# Torch Library API

The Torch Library API provides capabilities for extending PyTorch's core library
of operators with user-defined operators and data types. This is the primary
mechanism for registering custom C++ operators that can be called from both
Python and C++.

**When to use the Library API:**

- When creating custom operators for PyTorch
- When implementing backend-specific kernels (CPU, CUDA, etc.)
- When registering custom classes for use in TorchScript
- When extending PyTorch with new functionality

**Basic usage:**

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

// Define a custom operator
torch::Tensor my_add(const torch::Tensor& a, const torch::Tensor& b) {
    return a + b;
}

// Register the operator
TORCH_LIBRARY(myops, m) {
    m.def("add(Tensor a, Tensor b) -> Tensor", &my_add);
}

// Use from C++
auto result = torch::dispatcher::call("myops::add", tensor_a, tensor_b);
```

For a tutorial-style introduction to the library API, check out the
[Extending TorchScript with Custom C++ Operators](https://pytorch.org/tutorials/advanced/torch_script_custom_ops.html)
tutorial.

## Header Files

- `torch/library.h` - Main library API header
- `torch/custom_class.h` - Custom class registration

## Library API Categories

```{toctree}
:maxdepth: 1

registration
custom_classes
versioning
```

## See Also

- {doc}`../stable/index` - For stable ABI operator registration