Created On: Jul 28, 2023 | Last Updated On: Jul 20, 2026 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. 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. 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. 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 . In some cases, the terms torch.compile , TorchDynamo, torch.compiler might be used interchangeably in this documentation. torch.compiler also includes an ahead-of-time API, torch.compiler.precompile . It captures a whole computation fn(*example_inputs) – with the model(s) passed among example_inputs , e.g. precompile(lambda model, x: model(x), model, x) – and lowers it to a self-contained, runnable Python source string plus an acceleration cache. Reload the artifact with torch.compiler.precompile.load ; since no weights are baked in, you pass the model again at runtime. See the API reference for details. 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: 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

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 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 PyTorch on XLA Devices Access comprehensive developer documentation for PyTorch Get in-depth tutorials for beginners and advanced developers Find development resources and get your questions answered To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: Cookies Policy .