| commit | ab1c8aafc7d56b45945e5ec7dc8803a3aa96cceb | [log] [tgz] |
|---|---|---|
| author | Stephen Jia <ssjia@meta.com> | Wed May 22 06:41:39 2024 -0700 |
| committer | Facebook GitHub Bot <facebook-github-bot@users.noreply.github.com> | Wed May 22 06:41:39 2024 -0700 |
| tree | 48a63c238f8b358b4f15c3be3188e5d9b1a1b86b | |
| parent | 705ac963971cd125846aa7444e5b92544f003953 [diff] |
Fix zero size tensors (#3702) Summary: Pull Request resolved: https://github.com/pytorch/executorch/pull/3702 ## Context Dispatching a command buffer with a work group size that contains 0 is undefined behaviour. On some devices, this can cause the device to be lost. Fix this by setting the work group size to `{1, 1, 1}` right before dispatching a command buffer if the work group size contains a 0. Reviewed By: yipjustin Differential Revision: D57655257 fbshipit-source-id: 6209668c960f0e0afb0de0ab8b09c285e2de56b9
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 for the latest release (or the main branch).
We welcome any feedback, suggestions, and bug reports from the community to help us improve our technology. Please use the PyTorch Forums for discussion and feedback about ExecuTorch using the ExecuTorch category, and our GitHub repository for bug reporting.
We recommend using the latest release tag from the Releases page when developing.
executorch ├── backends # Backend delegate implementations. ├── build # Utilities for managing the build system. ├── bundled_program # Utilities for attaching reference inputs and outputs to models. ├── 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. | ├── 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. ├── runtime # Core cpp runtime | ├── 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 ├── 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
ExecuTorch is BSD licensed, as found in the LICENSE file.