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android / platform / external / executorch / 1693e652b
commit1693e652b46eba6b52744d4a05554f3aa329d37e[log] [tgz]
authorHuy Do <huydhn@gmail.com>Mon Nov 27 10:53:12 2023 -0800
committerFacebook GitHub Bot <facebook-github-bot@users.noreply.github.com>Mon Nov 27 10:53:12 2023 -0800
tree843bcb098759e4ddc84f19454761feaff220c91f
parent6dc33910f63f60fa3316dad0cc6fe1ac05a7c88a [diff]
Use python 3.11 and the correct conda env on MacOS runners (#1285)

Summary:
While working on https://github.com/pytorch/executorch/pull/1247, I discover an issue where MacOS jobs don't use the correct temporary conda environment and try to install packages directly into the runner base conda setup.  Because the base env is persisted across jobs, this could lead to cryptic issues elsewhere like mismatched PyTorch version.  Also, using a temp environment allows MacOS jobs to use any python it needs instead of the default 3.9 from the base env.

This fixes the root cause of some MacOS flaky issues:
* https://github.com/pytorch/executorch/actions/runs/7000671520/job/19041660402
* https://github.com/pytorch/executorch/actions/runs/6962965183/job/18947693161

Note that `$CONDA_RUN` is a preset command to make sure that the temporary env is used. For example, in https://github.com/pytorch/executorch/actions/runs/7002529232/job/19046476459, `$CONDA_RUN` is set [here](https://github.com/pytorch/executorch/actions/runs/7002529232/job/19046476459#step:4:647)

Pull Request resolved: https://github.com/pytorch/executorch/pull/1285

Reviewed By: digantdesai

Differential Revision: D51582046

Pulled By: huydhn

fbshipit-source-id: c146be51ed6fa0c458cfe80fbb80f3fc800fdc58
6 files changed
tree: 843bcb098759e4ddc84f19454761feaff220c91f
  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.