Google Git
Sign in
android / platform / external / executorch / 691400301
commit6914003012f55aa29a9860811c23b377431b7b37[log] [tgz]
authorDave Bort <dbort@meta.com>Mon Sep 25 18:01:36 2023 -0700
committerFacebook GitHub Bot <facebook-github-bot@users.noreply.github.com>Mon Sep 25 18:01:36 2023 -0700
tree50b1f59853e80218d263d9f14d34e85daf99a16a
parentbd26dbf58dbfea7666eddda80e366c2ba7b4ecdc [diff]
Make operator<<() wrap long EValue lists (#480)

Summary:
Pull Request resolved: https://github.com/pytorch/executorch/pull/480

Wrap long lists at ten items per line. I considered adding another stream manipulator to let users modify this, but it seemed less immediately useful than the "edge_items" manipulator.

Also considered wrapping at a particular column number, but it's way easier to count items then to count characters.

The indenting on some of the nested elements like Tensors and Tensor lists aren't the best, but we can fix that in a future diff.
ghstack-source-id: 201868941
exported-using-ghexport

Reviewed By: JacobSzwejbka

Differential Revision: D49607605

fbshipit-source-id: 499472554ec899ea2bb51da26bae6b3559bab6fb
2 files changed
tree: 50b1f59853e80218d263d9f14d34e85daf99a16a
  1. .ci/
  2. .github/
  3. backends/
  4. build/
  5. bundled_program/
  6. codegen/
  7. configurations/
  8. docs/
  9. examples/
  10. exir/
  11. extension/
  12. kernels/
  13. profiler/
  14. runtime/
  15. schema/
  16. scripts/
  17. sdk/
  18. shim/
  19. test/
  20. third-party/
  21. util/
  22. requirements-lintrunner.txt ⇨ .ci/docker/requirements-lintrunner.txt
  23. .buckconfig
  24. .clang-format
  25. .clang-tidy
  26. .cmakelintrc
  27. .flake8
  28. .gitignore
  29. .gitmodules
  30. .lintrunner.toml
  31. CMakeLists.txt
  32. CODE_OF_CONDUCT.md
  33. CONTRIBUTING.md
  34. install_requirements.sh
  35. LICENSE
  36. pyproject.toml
  37. pytest.ini
  38. README.md
  39. setup.py
README.md

ExecuTorch

A unified ML software stack within the PyTorch platform for edge devices. It defines new compiler entry points as well as a state-of-art runtime.

Why ExecuTorch?

Compared to the legacy Lite Interpreter, there are some major benefits:

  • Performance wins compared to Lite Interpreter
    • Faster (orders of magnitude lower framework tax in both DSP and CPU)
    • Much smaller binary size, 1.5 MB vs 30 KB without operators.
    • Smaller memory footprint because we do ahead of time memory planning in ExecuTorch and also have clear granular control over where the runtime allocations are done.
  • Long term alignment with the direction of PyTorch infrastructure
    • Lite Interpreter relies on TorchScript, which is being phased out; ExecuTorch is the planned replacement for Lite Interpreter.
  • Model Authoring & Productivity gains
    • More and better defined entry points to perform model, device, and/or use-case specific optimizations (e.g. better backend delegation, user-defined compiler transformations, default or user-defined memory planning, etc)
    • Ability to lower constructs like dynamic control flow to run on device.

Design goals

  • Minimal binary size (< 50KB not including kernels)
  • Minimal framework tax: loading program, initializing executor, kernel and backend-delegate dispatch, runtime memory utilization
  • Portable (cross-compile across many toolchains)
  • Executes ATen kernels (or ATen custom kernels)
  • Executes custom op kernels
  • Supports inter op asynchronous execution
  • Supports static memory allocation (heapless)
  • Supports custom allocation across memory hierarchies
  • Supports control flow needed by models
  • Allows selective build of kernels
  • Allows backend delegation with lightweight interface

Quick Links

Quick Links for Partners

Directory Structure [WIP]

executorch
├── backends                        #  1st party backend implementations.
|   ├── xnnpack
|   ├── vulkan
├── 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.
|   ├── executor_runner
|   ├── export
|   ├── models
├── exir                            #  Ahead of time library, model capture and lowering apis.
|   ├── 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.
|   ├── program                     #  Export artifacts.
|   ├── serialize                   #  Serialize final export artifact.
├── 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: NOT READY YET FOR OSS USE
├── 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.