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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 and AMD 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 see 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
torch.compile(m, backend="onnxrt") Uses ONNX Runtime for training on CPU/GPU. :doc:`Read more <onnx_dynamo_onnxruntime_backend>`

Inference-only backends

Backend Description
torch.compile(m, backend="tensorrt") Uses ONNX Runtime to run TensorRT for inference optimizations. 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

Read More

.. toctree::
   :caption: Getting Started for PyTorch Users
   :maxdepth: 1

   torch.compiler_get_started
   torch.compiler_api
   torch.compiler_performance_dashboard
   torch.compiler_fine_grain_apis
   torch.compiler_inductor_profiling
   torch.compiler_profiling_torch_compile
   torch.compiler_faq
   torch.compiler_troubleshooting


.. toctree::
   :caption: Deep Dive for PyTorch Developers
   :maxdepth: 1

   torch.compiler_deepdive
   torch.compiler_guards_overview
   torch.compiler_dynamic_shapes
   torch.compiler_nn_module
   torch.compiler_best_practices_for_backends
   torch.compiler_cudagraph_trees
   torch.compiler_fake_tensor


.. toctree::
   :caption: HowTo for PyTorch Backend Vendors
   :maxdepth: 1

   torch.compiler_custom_backends
   torch.compiler_transformations
   torch.compiler_ir