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android / platform / external / executorch / a8da94a08
commita8da94a08e76e903bea344c8b1144265f7f9f912[log] [tgz]
authorMax Ren <maxren@meta.com>Thu Jan 11 17:41:15 2024 -0800
committerFacebook GitHub Bot <facebook-github-bot@users.noreply.github.com>Thu Jan 11 17:41:15 2024 -0800
tree8aa6784270e5afea92375a666d0083fb3e40fd7c
parent99d0ea1db63fbb9a7a836ab4ac3b68d211fe0b27 [diff]
Handle XNNHeader in XNNPACK Runtime (#1543)

Summary:
Pull Request resolved: https://github.com/pytorch/executorch/pull/1543

We introduce XNNHeader on runtime side to handle the newly introduced XNNHeader ahead of time. XNNHeader manages the offsets and sizes of the flatbuffer payload and the constant data payload so that it is accessible by the XNNCompiler

It is important to note that on serialization side, we have not yet switched our serialization method to `serialize_xnnpack_binary` so this does not yet use the new serialization format. However, passing tests on this illustrates BC as old models will still be able to run on this new runtime.

Passing tests here show that the Header Magic correctly works in discerning between using the XNNHeader and the Flatbuffer header

Reviewed By: digantdesai

Differential Revision: D52556131

fbshipit-source-id: 240e88f891d9b4ec48ab7a8b6c93aaddf0bacdde
3 files changed
tree: 8aa6784270e5afea92375a666d0083fb3e40fd7c
  1. .ci/
  2. .github/
  3. backends/
  4. build/
  5. codegen/
  6. configurations/
  7. docs/
  8. examples/
  9. exir/
  10. extension/
  11. kernels/
  12. profiler/
  13. runtime/
  14. schema/
  15. scripts/
  16. sdk/
  17. shim/
  18. test/
  19. third-party/
  20. util/
  21. .buckconfig
  22. .clang-format
  23. .clang-tidy
  24. .cmakelintrc
  25. .flake8
  26. .gitignore
  27. .gitmodules
  28. .lintrunner.toml
  29. CMakeLists.txt
  30. CODE_OF_CONDUCT.md
  31. CONTRIBUTING.md
  32. install_requirements.sh
  33. LICENSE
  34. pyproject.toml
  35. pytest.ini
  36. README.md
  37. requirements-lintrunner.txt
  38. setup.py
README.md

ExecuTorch

ExecuTorch is an end-to-end solution for enabling on-device inference capabilities across mobile and edge devices including wearables, embedded devices and microcontrollers. It is part of the PyTorch Edge ecosystem and enables efficient deployment of PyTorch models to edge devices.

Key value propositions of ExecuTorch are:

  • Portability: Compatibility with a wide variety of computing platforms, from high-end mobile phones to highly constrained embedded systems and microcontrollers.
  • Productivity: Enabling developers to use the same toolchains and SDK from PyTorch model authoring and conversion, to debugging and deployment to a wide variety of platforms.
  • Performance: Providing end users with a seamless and high-performance experience due to a lightweight runtime and utilizing full hardware capabilities such as CPUs, NPUs, and DSPs.

For a comprehensive technical overview of ExecuTorch and step-by-step tutorials, please visit our documentation website.

Important: This is a preview release

This is a preview version of ExecuTorch and should be used for testing and evaluation purposes only. It is not recommended for use in production settings. We welcome any feedback, suggestions, and bug reports from the community to help us improve the technology. Please use the PyTorch Forums for discussion and feedback about ExecuTorch using the ExecuTorch category, and our GitHub repository for bug reporting.

The ExecuTorch code and APIs are still changing quickly, and there are not yet any guarantees about forward/backward source compatibility. We recommend using the latest v#.#.# release tag from the Releases page when experimenting with this preview release.

Directory Structure

executorch
├── backends                        #  Backend delegate implementations.
├── build                           #  Utilities for managing the build system.
├── bundled_program                 #  Utilities for attaching reference inputs and outputs to models. TODO move to extension
├── codegen                         #  Tooling to autogenerate bindings between kernels and the runtime. TODO move to tool
├── configurations                  #  TODO delete this
├── docs                            #  Static docs tooling
├── examples                        #  Examples of various user flows, such as model export, delegates, and runtime execution.
├── exir                            #  Ahead of time library, model capture and lowering apis.
|   ├── _serialize                  #  Serialize final export artifact.
|   ├── backend                     #  Backend delegate ahead of time APIs
|   ├── capture                     #  Program capture.
|   ├── dialects                    #  Op sets for various dialects in the export process.
|   ├── emit                        #  Conversion from ExportedProgram to ExecuTorch execution instructions.
|   ├── passes                      #  Built-in compiler passes.
|   ├── program                     #  Export artifacts.
|   ├── verification                #  IR verification.
├── extension                       #  Extensions built on top of the runtime.
|   ├── aten_util
|   ├── data_loader                 #  1st party data loader implementations.
|   ├── memory_allocator            #  1st party memory allocator implementations.
|   ├── pybindings                  #  Python api for executorch runtime.
|   ├── pytree                      #  C++ and Python flattening and unflattening lib for pytrees.
|   ├── testing_util
├── kernels                         #  1st party kernel implementations.
|   ├── aten
|   ├── optimized
|   ├── portable                    #  Reference implementations of ATen operators.
|   ├── prim_ops                    #  Special ops used in executorch runtime for control flow and symbolic primitives.
|   ├── quantized
├── profiler                        #  Utilities for profiling. TODO delete in favor of ETDump in sdk/
├── runtime                         #  core cpp runtime of executorch
|   ├── backend                     #  Backend delegate runtime APIs
|   ├── core                        #  Core structures used across all levels of the runtime
|   ├── executor                    #  Model loading, initalization, and execution.
|   ├── kernel                      #  Kernel registration and management.
|   ├── platform                    #  Layer between architecture specific code and user calls.
├── schema                          #  ExecuTorch program definition, TODO move under serialization/
├── scripts                         #  Utility scripts for size management, dependency management, etc.
├── sdk                             #  Model profiling, debugging, and introspection.
├── shim                            #  Compatibility layer between OSS and Internal builds
├── test                            #  Broad scoped end2end tests
├── third-party                     #  third-party dependencies
├── util                            #  TODO delete this

License

ExecuTorch is BSD licensed, as found in the LICENSE file.