torch.compiler

Created On: Jul 28, 2023 | Last Updated On: Dec 07, 2025

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.

To better understand how torch.compile tracing behavior on your code, or to learn more about the internals of torch.compile, please refer to the torch.compile programming model.

Note

In some cases, the terms torch.compile, TorchDynamo, torch.compiler might be used interchangeably in this documentation.

Warning

torch.compile may not support recently released major versions of Python.

If you attempt to use @torch.compile in an unsupported Python environment, you may encounter an error similar to:

RuntimeError: torch.compile is not supported on Python 3.xx.0+

Please ensure that your current Python version is within the range supported by PyTorch for torch.compile.

If you have installed PyTorch on a Python version that is too new, you will need to switch to an earlier Python version in order to use torch.compile.

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

Backend	Description
torch.compile(m, backend="inductor")	Uses the TorchInductor backend. Read more
torch.compile(m, backend="cudagraphs")	CUDA graphs with AOT Autograd. Read more
torch.compile(m, backend="ipex")	Uses IPEX on CPU. Read more

Inference-only backends

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
torch.compile(m, backend="ipex")	Uses IPEX for inference on CPU. Read more
torch.compile(m, backend="tvm")	Uses Apache TVM for inference optimizations. Read more
torch.compile(m, backend="openvino")	Uses OpenVINO for inference optimizations. Read more