| Technical Overview |
| ==================== |
| |
| **TorchDynamo** is a Python-level JIT compiler designed to make unmodified |
| PyTorch programs faster. TorchDynamo hooks into the frame evaluation API |
| in CPython (`PEP 523 <https://peps.python.org/pep-0523/>`__) to |
| dynamically modify Python bytecode right before it is executed. It |
| rewrites Python bytecode in order to extract sequences of PyTorch |
| operations into an `FX Graph <https://pytorch.org/docs/stable/fx.html>`__ |
| which is then just-in-time compiled with a customizable backend. |
| It creates this FX Graph through bytecode analysis and is designed to |
| mix Python execution with compiled backends to get the best of both |
| worlds — usability and performance. |
| |
| TorchDynamo makes it easy to experiment with different compiler |
| backends to make PyTorch code faster with a single line decorator |
| ``torch._dynamo.optimize()`` which is wrapped for convenience by ``torch.compile()`` |
| |
| .. image:: ../_static/img/dynamo/TorchDynamo.png |
| |
| `TorchInductor` is one of the backends |
| supported by `TorchDynamo Graph <https://pytorch.org/docs/stable/fx.html>`__ |
| into `Triton <https://github.com/openai/triton>`__ for GPUs or |
| `C++/OpenMP <https://www.openmp.org/>`__ for CPUs. We have a |
| `training performance dashboard <https://github.com/pytorch/torchdynamo/issues/681#issuecomment-1233828468>`__ |
| that provides performance comparison for different training backends. You can read |
| more in the `TorchInductor post on PyTorch |
| dev-discuss <https://dev-discuss.pytorch.org/t/torchinductor-a-pytorch-native-compiler-with-define-by-run-ir-and-symbolic-shapes/747>`__. |
| |
| .. seealso:: |
| |
| * `TorchDynamo deep-dive video <https://www.youtube.com/watch?v=egZB5Uxki0I>`__ |
| * `dev-discuss topics <https://dev-discuss.pytorch.org/search?q=TorchDynamo%20order%3Alatest>`__ |
| |
| .. toctree:: |
| :maxdepth: 1 |
| |
| guards-overview |
| best-practices-for-backends |
| custom-backends |
| deep-dive |
