pytorch/docs/source/torch.compiler.md

(torch.compiler_overview)=

# torch.compiler

`torch.compiler` is a namespace through which some of the internal compiler
methods are surfaced for user consumption. The main function and the feature in
this namespace is `torch.compile`.

`torch.compile` is a PyTorch function introduced in PyTorch 2.x that aims to
solve the problem of accurate graph capturing in PyTorch and ultimately enable
software engineers to run their PyTorch programs faster. `torch.compile` is
written in Python and it marks the transition of PyTorch from C++ to Python.

`torch.compile` leverages the following underlying technologies:

- **TorchDynamo (torch._dynamo)** is an internal API that uses a CPython
  feature called the Frame Evaluation API to safely capture PyTorch graphs.
  Methods that are available externally for PyTorch users are surfaced
  through the `torch.compiler` namespace.
- **TorchInductor** is the default `torch.compile` deep learning compiler
  that generates fast code for multiple accelerators and backends. You
  need to use a backend compiler to make speedups through `torch.compile`
  possible. For NVIDIA, AMD and Intel GPUs, it leverages OpenAI Triton as the key
  building block.
- **AOT Autograd** captures not only the user-level code, but also backpropagation,
  which results in capturing the backwards pass "ahead-of-time". This enables
  acceleration of both forwards and backwards pass using TorchInductor.

:::{note}
In some cases, the terms `torch.compile`, TorchDynamo, `torch.compiler`
might be used interchangeably in this documentation.
:::

As mentioned above, to run your workflows faster, `torch.compile` through
TorchDynamo requires a backend that converts the captured graphs into a fast
machine code. Different backends can result in various optimization gains.
The default backend is called TorchInductor, also known as *inductor*,
TorchDynamo has a list of supported backends developed by our partners,
which can be seen by running `torch.compiler.list_backends()` each of which
with its optional dependencies.

Some of the most commonly used backends include:

**Training & inference backends**

```{eval-rst}
.. list-table::
   :widths: 50 50
   :header-rows: 1

   * - Backend
     - Description
   * - ``torch.compile(m, backend="inductor")``
     - Uses the TorchInductor backend. `Read more <https://dev-discuss.pytorch.org/t/torchinductor-a-pytorch-native-compiler-with-define-by-run-ir-and-symbolic-shapes/747>`__
   * - ``torch.compile(m, backend="cudagraphs")``
     - CUDA graphs with AOT Autograd. `Read more <https://github.com/pytorch/torchdynamo/pull/757>`__
   * - ``torch.compile(m, backend="ipex")``
     - Uses IPEX on CPU. `Read more <https://github.com/intel/intel-extension-for-pytorch>`__
   * - ``torch.compile(m, backend="onnxrt")``
     - Uses ONNX Runtime for training on CPU/GPU. :doc:`Read more <onnx_dynamo_onnxruntime_backend>`
```

**Inference-only backends**

```{eval-rst}
.. list-table::
   :widths: 50 50
   :header-rows: 1

   * - Backend
     - Description
   * - ``torch.compile(m, backend="tensorrt")``
     - Uses Torch-TensorRT for inference optimizations. Requires ``import torch_tensorrt`` in the calling script to register backend. `Read more <https://github.com/pytorch/TensorRT>`__
   * - ``torch.compile(m, backend="ipex")``
     - Uses IPEX for inference on CPU. `Read more <https://github.com/intel/intel-extension-for-pytorch>`__
   * - ``torch.compile(m, backend="tvm")``
     - Uses Apache TVM for inference optimizations. `Read more <https://tvm.apache.org/>`__
   * - ``torch.compile(m, backend="openvino")``
     - Uses OpenVINO for inference optimizations. `Read more <https://docs.openvino.ai/torchcompile>`__
```

## Read More

```{eval-rst}
.. toctree::
   :caption: Getting Started for PyTorch Users
   :maxdepth: 1

   torch.compiler_get_started
   torch.compiler_api
   torch.compiler.config
   torch.compiler_fine_grain_apis
   torch.compiler_backward
   torch.compiler_aot_inductor
   torch.compiler_inductor_profiling
   torch.compiler_profiling_torch_compile
   torch.compiler_faq
   torch.compiler_troubleshooting
   torch.compiler_performance_dashboard
   torch.compiler_inductor_provenance
```

% _If you want to contribute a developer-level topic
%  that provides in-depth overview of a torch._dynamo feature,
%  add in the below toc.

```{eval-rst}
.. toctree::
   :caption: Deep Dive for PyTorch Developers
   :maxdepth: 1

   torch.compiler_dynamo_overview
   torch.compiler_dynamo_deepdive
   torch.compiler_dynamic_shapes
   torch.compiler_nn_module
   torch.compiler_cudagraph_trees
   torch.compiler_fake_tensor
```

```{eval-rst}
.. toctree::
   :caption: HowTo for PyTorch Backend Vendors
   :maxdepth: 1

   torch.compiler_custom_backends
   torch.compiler_transformations
   torch.compiler_ir
```