mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-20 21:14:14 +08:00
faff387bfd
as title Pull Request resolved: https://github.com/pytorch/pytorch/pull/152211 Approved by: https://github.com/svekars, https://github.com/eellison, https://github.com/desertfire
121 lines
4.7 KiB
ReStructuredText
121 lines
4.7 KiB
ReStructuredText
.. _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 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**
|
|
|
|
.. 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**
|
|
|
|
.. 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
|
|
~~~~~~~~~
|
|
|
|
.. 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_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.
|
|
|
|
.. 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_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
|