執行者

執行火炬

ExecuTorch是一種端到端解決方案，用於跨移動和邊緣裝置（包括穿戴式裝置、嵌入式裝置和微控制器）實現裝置上推理功能。它是 PyTorch Edge 生態系統的一部分，支援將 PyTorch 模型高效部署到邊緣設備。

ExecuTorch 的主要價值主張是：

• 便攜性：與各種運算平台相容，從高階行動電話到高度受限的嵌入式系統和微控制器。
• 生產力：使開發人員能夠使用相同的工具鏈和開發人員工具，從 PyTorch 模型創作和轉換，到調試和部署到各種平台。
• 效能：由於輕量級運行時間並利用 CPU、NPU 和 DSP 等完整硬體功能，為最終用戶提供無縫的高效能體驗。

有關 ExecuTorch 的全面技術概述和逐步教程，請訪問我們的文件網站以獲取最新版本（或主分支）。

查看入門頁面以快速瀏覽。

查看使用 ExecuTorch 在邊緣設備上運行的 Llama、Llava 和其他模型的範例。

[更新 - 10/24]我們添加了對透過 ExecuTorch 運行 Llama 3.2 量化 1B/3B 模型的支援。

回饋

我們歡迎來自社群的任何回饋、建議和錯誤報告，以幫助我們改進技術。請使用 PyTorch 論壇進行使用ExecuTorch類別的 ExecuTorch 的討論和回饋，並使用我們的 GitHub 儲存庫進行錯誤報告。

我們建議在開發時使用發布頁面中的最新版本標籤。

貢獻

有關問題、PR、代碼風格、CI 作業和其他開發主題的詳細信息，請參閱 CONTRIBUTING.md。

為了與我們和其他社群成員聯繫，我們邀請您填寫此表格加入 PyTorch Slack 社群。加入後，您可以：

• 前往#executorch-general頻道了解一般問題、討論和社群支援。
• 如果您有興趣直接為專案開發做出貢獻，請加入#executorch-contributors頻道。

目錄結構

 executorch
├── backends                        #  Backend delegate implementations.
├── build                           #  Utilities for managing the build system.
├── codegen                         #  Tooling to autogenerate bindings between kernels and the runtime.
├── configurations
├── 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.
|   ├── operator                    #  Operator node manipulation utilities.
|   ├── passes                      #  Built-in compiler passes.
|   ├── program                     #  Export artifacts.
|   ├── serde                       #  Graph module
serialization/deserialization.
|   ├── verification                #  IR verification.
├── extension                       #  Extensions built on top of the runtime.
|   ├── android                     #  ExecuTorch wrappers for Android apps.
|   ├── apple                       #  ExecuTorch wrappers for iOS apps.
|   ├── aten_util                   #  Converts to and from PyTorch ATen types.
|   ├── data_loader                 #  1st party data loader implementations.
|   ├── evalue_util                 #  Helpers for working with EValue objects.
|   ├── gguf_util                   #  Tools to convert from the GGUF format.
|   ├── kernel_util                 #  Helpers for registering kernels.
|   ├── memory_allocator            #  1st party memory allocator implementations.
|   ├── module                      #  A simplified C++ wrapper for the runtime.
|   ├── parallel                    #  C++ threadpool integration.
|   ├── pybindings                  #  Python API for executorch runtime.
|   ├── pytree                      #  C++ and Python flattening and unflattening lib for pytrees.
|   ├── runner_util                 #  Helpers for writing C++ PTE-execution
tools.
|   ├── testing_util                #  Helpers for writing C++ tests.
|   ├── training                    #  Experimental libraries for on-device training
├── 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 runtime execution.
├── runtime                         #  Core C++ runtime.
|   ├── backend                     #  Backend delegate runtime APIs.
|   ├── core                        #  Core structures used across all levels of the runtime.
|   ├── executor                    #  Model loading, initialization, and execution.
|   ├── kernel                      #  Kernel registration and management.
|   ├── platform                    #  Layer between architecture specific code and portable C++.
├── schema                          #  ExecuTorch PTE file format flatbuffer
schemas.
├── scripts                         #  Utility scripts for size management, dependency management, etc.
├── devtools                        #  Model profiling, debugging, and introspection.
├── shim                            #  Compatibility layer between OSS and Internal builds
├── test                            #  Broad scoped end-to-end tests.
├── third-party                     #  Third-party dependencies.
├── util                            #  Various helpers and scripts.

執照

ExecuTorch 已獲得 BSD 許可，如 LICENSE 文件所示。