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Update on "[ET-VK] Minor performance improvements to native layer norm."
This diff introduces minor performance improvements to the native layer norm function in the Vulkan backend of Executorch.
In this new approach:
The mean and variance values are calculated in 2 separate passes.
Shader is dispatched based on input texture size, and input texel is read and stored in shared memory.
Input stored in shard memory is then summed up using a reduce function.
This implementation better utilizes a GPUs parallel processing capabilities.
Differential Revision: [D72430290](https://our.internmc.facebook.com/intern/diff/D72430290/)
[ghstack-poisoned]
Copy file name to clipboardExpand all lines: CONTRIBUTING.md
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## Dev Install
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Set up your environment by following the instructions at
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https://pytorch.org/executorch/stable/getting-started-setup.html to clone
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https://pytorch.org/executorch/main/getting-started-setup to clone
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the repo and install the necessary requirements.
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Refer to this [document](https://pytorch.org/executorch/main/using-executorch-building-from-source.html) to build ExecuTorch from source.
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Refer to this [document](docs/source/using-executorch-building-from-source.md) to build ExecuTorch from source.
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### Dev Setup for Android
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For Android, please refer to the [Android documentation](docs/source/using-executorch-android.md).
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├── <ahref="devtools">devtools</a> - Model profiling, debugging, and inspection. Please refer to the <ahref="docs/source/devtools-overview.md">tools documentation</a> for more information.
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│ ├── <ahref="devtools/bundled_program">bundled_program</a> - a tool for validating ExecuTorch model. See <ahref="docs/source/bundled-io.md">doc</a>.
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│ ├── <ahref="devtools/etdump">etdump</a> - ETDump - a format for saving profiling and debugging data from runtime. See <ahref="docs/source/etdump.md">doc</a>.
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│ ├── <ahref="devtools/etrecord">etrecord</a> - ETRecord - AOT debug artifact for ExecuTorch. See <ahref="https://pytorch.org/executorch/main/etrecord.html">doc</a>.
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│ ├── <ahref="devtools/inspector">inspector</a> - Python API to inspect ETDump and ETRecord. See <ahref="https://pytorch.org/executorch/main/model-inspector.html">doc</a>.
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│ ├── <ahref="devtools/etrecord">etrecord</a> - ETRecord - AOT debug artifact for ExecuTorch. See <ahref="https://pytorch.org/executorch/main/etrecord">doc</a>.
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│ ├── <ahref="devtools/inspector">inspector</a> - Python API to inspect ETDump and ETRecord. See <ahref="https://pytorch.org/executorch/main/model-inspector">doc</a>.
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│ └── <ahref="devtools/visualization">visualization</a> - Visualization tools for representing model structure and performance metrics.
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├── <ahref="docs">docs</a> - Static docs tooling and documentation source files.
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├── <ahref="examples">examples</a> - Examples of various user flows, such as model export, delegates, and runtime execution.
│ ├── <ahref="exir/verification">verification</a> - IR verification.
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├── <ahref="extension">extension</a> - Extensions built on top of the runtime.
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│ ├── <ahref="extension/android">android</a> - ExecuTorch wrappers for Android apps. Please refer to the <ahref="docs/source/using-executorch-android.md">Android documentation</a> and <ahref="https://pytorch.org/executorch/main/javadoc/">Javadoc</a> for more information.
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│ ├── <ahref="extension/apple">apple</a> - ExecuTorch wrappers for iOS apps. Please refer to the <ahref="docs/source/using-executorch-ios.md">iOS documentation</a> and <ahref="https://pytorch.org/executorch/stable/apple-runtime.html">how to integrate into Apple platform</a> for more information.
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│ ├── <ahref="extension/android">android</a> - ExecuTorch wrappers for Android apps. Please refer to the <ahref="docs/source/using-executorch-android.md">Android documentation</a> and <ahref="https://pytorch.org/executorch/main/javadoc">Javadoc</a> for more information.
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│ ├── <ahref="extension/apple">apple</a> - ExecuTorch wrappers for iOS apps. Please refer to the <ahref="docs/source/using-executorch-ios.md">iOS documentation</a> on how to integrate into Apple platform</a> for more information.
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│ ├── <ahref="extension/aten_util">aten_util</a> - Converts to and from PyTorch ATen types.
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│ ├── <ahref="extension/data_loader">data_loader</a> - 1st party data loader implementations.
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│ ├── <ahref="extension/evalue_util">evalue_util</a> - Helpers for working with EValue objects.
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│ ├── <ahref="extension/memory_allocator">memory_allocator</a> - 1st party memory allocator implementations.
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│ ├── <ahref="extension/module">module</a> - A simplified C++ wrapper for the runtime. An abstraction that deserializes and executes an ExecuTorch artifact (.pte file). Refer to the <ahref="docs/source/extension-module.md">module documentation</a> for more information.
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│ ├── <ahref="extension/parallel">parallel</a> - C++ threadpool integration.
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│ ├── <ahref="extension/pybindings">pybindings</a> - Python API for executorch runtime. This is powering up the <ahref="https://pytorch.org/executorch/main/runtime-python-api-reference.html">runtime Python API</a> for ExecuTorch.
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│ ├── <ahref="extension/pybindings">pybindings</a> - Python API for executorch runtime. This is powering up the <ahref="docs/source/runtime-python-api-reference.md">runtime Python API</a> for ExecuTorch.
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│ ├── <ahref="extension/pytree">pytree</a> - C++ and Python flattening and unflattening lib for pytrees.
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│ ├── <ahref="extension/runner_util">runner_util</a> - Helpers for writing C++ PTE-execution tools.
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│ ├── <ahref="extension/tensor">tensor</a> - Tensor maker and <code>TensorPtr</code>, details in <ahref="/docs/source/extension-tensor.md">this documentation</a>. For how to use <code>TensorPtr</code> and <code>Module</code>, please refer to the <ahref="/docs/source/using-executorch-cpp.md">"Using ExecuTorch with C++"</a> doc.
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│ ├── <ahref="extension/tensor">tensor</a> - Tensor maker and <code>TensorPtr</code>, details in <ahref="docs/source/extension-tensor.md">this documentation</a>. For how to use <code>TensorPtr</code> and <code>Module</code>, please refer to the <ahref="docs/source/using-executorch-cpp.md">"Using ExecuTorch with C++"</a> doc.
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│ ├── <ahref="extension/testing_util">testing_util</a> - Helpers for writing C++ tests.
│ └── <ahref="extension/training">training</a> - Experimental libraries for on-device training.
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├── <ahref="runtime">runtime</a> - Core C++ runtime. These components are used to execute the ExecuTorch program. Please refer to the <ahref="docs/source/runtime-overview.md">runtime documentation</a> for more information.
│ ├── <ahref="runtime/core">core</a> - Core structures used across all levels of the runtime. Basic components such as <code>Tensor</code>, <code>EValue</code>, <code>Error</code> and <code>Result</code> etc.
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│ ├── <ahref="runtime/executor">executor</a> - Model loading, initialization, and execution. Runtime components that execute the ExecuTorch program, such as <code>Program</code>, <code>Method</code>. Refer to the <ahref="https://pytorch.org/executorch/main/executorch-runtime-api-reference.html">runtime API documentation</a> for more information.
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│ ├── <ahref="runtime/executor">executor</a> - Model loading, initialization, and execution. Runtime components that execute the ExecuTorch program, such as <code>Program</code>, <code>Method</code>. Refer to the <ahref="https://pytorch.org/executorch/main/executorch-runtime-api-reference">runtime API documentation</a> for more information.
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│ ├── <ahref="runtime/kernel">kernel</a> - Kernel registration and management.
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│ └── <ahref="runtime/platform">platform</a> - Layer between architecture specific code and portable C++.
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├── <ahref="schema">schema</a> - ExecuTorch PTE file format flatbuffer schemas.
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## Contributing workflow
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We actively welcome your pull requests (PRs).
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If you're completely new to open-source projects, GitHub, or ExecuTorch, please see our [New Contributor Guide](./docs/source/new-contributor-guide.md) for a step-by-step walkthrough on making your first contribution. Otherwise, read on.
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If you're completely new to open-source projects, GitHub, or ExecuTorch, please see our [New Contributor Guide](docs/source/new-contributor-guide.md) for a step-by-step walkthrough on making your first contribution. Otherwise, read on.
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1.[Claim an issue](#claiming-issues), if present, before starting work. If an
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issue doesn't cover the work you plan to do, consider creating one to provide
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### C++ Portability Guidelines
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See also [Portable C++ Programming](/docs/source/portable-cpp-programming.md)
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See also [Portable C++ Programming](docs/source/portable-cpp-programming.md)
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for detailed advice.
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#### C++ language version
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## For Backend Delegate Authors
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- Use [this](/docs/source/backend-delegates-integration.md) guide when
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- Use [this](docs/source/backend-delegates-integration.md) guide when
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integrating your delegate with ExecuTorch.
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- Refer to [this](/docs/source/backend-delegates-dependencies.md) set of
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- Refer to [this](docs/source/backend-delegates-dependencies.md) set of
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guidelines when including a third-party depenency for your delegate.
<ahref="https://pytorch.org/executorch/stable/index.html"><imgsrc="https://img.shields.io/badge/Documentation-000?logo=googledocs&logoColor=FFE165&style=for-the-badge"alt="Check out the documentation"></a>
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<ahref="https://pytorch.org/executorch/main/index"><imgsrc="https://img.shields.io/badge/Documentation-000?logo=googledocs&logoColor=FFE165&style=for-the-badge"alt="Check out the documentation"></a>
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<hr>
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</div>
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## Getting Started
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To get started you can:
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- Visit the [Step by Step Tutorial](https://pytorch.org/executorch/main/index.html) to get things running locally and deploy a model to a device
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- Use this [Colab Notebook](https://pytorch.org/executorch/stable/getting-started-setup.html#quick-setup-colab-jupyter-notebook-prototype) to start playing around right away
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- Jump straight into LLM use cases by following specific instructions for [Llama](./examples/models/llama/README.md) and [Llava](./examples/models/llava/README.md)
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- Visit the [Step by Step Tutorial](https://pytorch.org/executorch/main/index) to get things running locally and deploy a model to a device
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- Use this [Colab Notebook](https://pytorch.org/executorch/main/getting-started-setup#quick-setup-colab-jupyter-notebook-prototype) to start playing around right away
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- Jump straight into LLM use cases by following specific instructions for [Llama](examples/models/llama/README.md) and [Llava](examples/models/llava/README.md)
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