| commit | 76cbfb7cce22b6794a5c7787d9a5caca049204ea | [log] [tgz] |
|---|---|---|
| author | Kimish Patel <kimishpatel@meta.com> | Thu Feb 29 09:50:29 2024 -0800 |
| committer | Facebook GitHub Bot <facebook-github-bot@users.noreply.github.com> | Thu Feb 29 09:50:29 2024 -0800 |
| tree | 2f0f1a69f7bb8e173fc52e8b5abe6ad1421a1b37 | |
| parent | c67f0ff57993239c5881d979e267a2ec560f208f [diff] |
Dont memory plan for inputs (#2155) Summary: Pull Request resolved: https://github.com/pytorch/executorch/pull/2155 For KV cache with IO tHis results in 1. allocating kv cache in the memory plan but also allocated by llama runner 2. Doing actual copy of kv cache Also we should really make plan_input = false by default. I dont imagine a case where this does not result in making copies. Planning for output is fine but still dangerous as people may assume having reference to output tensor is all good without realizing the underlying memory being shared. ghstack-source-id: 216889056 exported-using-ghexport validated oss ci is clean. have to by pass because ci think its needs internal linter to pass. bypass-github-export-checks Reviewed By: mergennachin Differential Revision: D54161288 fbshipit-source-id: b5e7aa42d4a72e455550af5d7467f46f2a1017f8
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:
For a comprehensive technical overview of ExecuTorch and step-by-step tutorials, please visit our documentation website.
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.
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
ExecuTorch is BSD licensed, as found in the LICENSE file.