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- Installation for IPEX-1.8, to remove the recompilation for PT and add the installation for dependency package.
- Add the supported customized ops & fusion patterns.
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README.md

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Intel Extension for PyTorch is a Python package to extend official PyTorch. It is designed to make the Out-of-Box user experience of PyTorch CPU better while achieving good performance. The extension also will be the PR(Pull-Request) buffer for the Intel PyTorch framework dev team. The PR buffer will not only contain functions, but also optimization (for example, take advantage of Intel's new hardware features).
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- [Installation](#installation)
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- [Install PyTorch from Source](#install-pytorch-from-source)
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- [Install PyTorch](#install-pytorch)
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- [Install Intel Extension for PyTorch from Source](#install-intel-extension-for-pytorch-from-source)
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- [Getting Started](#getting-started)
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- [Automatically Mix Precison](#automatically-mix-precision)
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## Installation
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### Install PyTorch from Source
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### Install PyTorch
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|IPEX Version|PyTorch Version|
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|--|--|
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|[v1.8.0](https://github.com/intel/intel-extension-for-pytorch/tree/v1.8.0)|[v1.8.0](https://github.com/pytorch/pytorch/tree/v1.8.0 "v1.8.0")|
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|[v1.2.0](https://github.com/intel/intel-extension-for-pytorch/tree/v1.2.0)|[v1.7.0](https://github.com/pytorch/pytorch/tree/v1.7.0 "v1.7.0")|
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|[v1.1.0](https://github.com/intel/intel-extension-for-pytorch/tree/v1.1.0)|[v1.5.0-rc3](https://github.com/pytorch/pytorch/tree/v1.5.0-rc3 "v1.5.0-rc3")|
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|[v1.0.2](https://github.com/intel/intel-extension-for-pytorch/tree/v1.0.2)|[v1.5.0-rc3](https://github.com/pytorch/pytorch/tree/v1.5.0-rc3 "v1.5.0-rc3")|
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|[v1.0.1](https://github.com/intel/intel-extension-for-pytorch/tree/v1.0.1)|[v1.5.0-rc3](https://github.com/pytorch/pytorch/tree/v1.5.0-rc3 "v1.5.0-rc3")|
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|[v1.0.0](https://github.com/intel/intel-extension-for-pytorch/tree/v1.0.0)|[v1.5.0-rc3](https://github.com/pytorch/pytorch/tree/v1.5.0-rc3 "v1.5.0-rc3")|
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Take Intel-Extension-for-Pytorch v1.2.0 as the example.
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Take Intel-Extension-for-Pytorch v1.8.0 as the example.
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1. Get PyTorch v1.7.0 source(Refer to [PyTorch guide](https://github.com/pytorch/pytorch#get-the-pytorch-source) for more details)
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1. Install PyTorch from binary
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```bash
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conda install pytorch torchvision torchaudio cpuonly -c pytorch
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```
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2. Install PyTorch from source
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Get PyTorch v1.8.0 source(Refer to [PyTorch guide](https://github.com/pytorch/pytorch#get-the-pytorch-source) for more details)
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```bash
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git clone --recursive https://github.com/pytorch/pytorch
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```
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Checkout PyTorch to the specified version
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```bash
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cd pytorch
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# checkout source code to the specified version
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git checkout v1.7.0
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# update submodules for the specified PyTorch version
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git submodule sync
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git submodule update --init --recursive
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git checkout v1.8.0
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```
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2. Get the source code of Intel Extension for PyTorch
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Update submodules
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```bash
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git clone --recursive https://github.com/intel/intel-extension-for-pytorch
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cd intel-extension-for-pytorch
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# if you are updating an existing checkout
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git submodule sync
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git submodule update --init --recursive
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```
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3. Add an new backend for Intel Extension for PyTorch
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Build and install PyTorch (Refer to [PyTorch guide](https://github.com/pytorch/pytorch#install-pytorch) for more details)
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```bash
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# Apply git patch to pytorch code
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cd ${pytorch_directory}
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git apply ${intel_extension_for_pytorch_directory}/torch_patches/xpu-1.7.patch
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```
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4. Build and install PyTorch (Refer to [PyTorch guide](https://github.com/pytorch/pytorch#install-pytorch) for more details)
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```bash
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cd ${pytorch_directory}
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python setup.py install
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```
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### Install Intel Extension for PyTorch from Source
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Get the source code of Intel Extension for PyTorch
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```bash
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git clone --recursive https://github.com/intel/intel-extension-for-pytorch
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cd intel-extension-for-pytorch
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# if you are updating an existing checkout
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git submodule sync
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git submodule update --init --recursive
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```
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Install dependencies
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```bash
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pip install lark-parser hypothesis
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### Supported Customized Operators
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* ROIAlign
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* NMS
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* BatchScoreNMS
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* MLP
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* Interaction
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* FrozenBatchNorm2d
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### Supported Fusion Patterns
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* Conv2D + ReLU
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* Conv2D + SUM
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* Conv2D + SUM + ReLU
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* Conv2D + Sigmoid
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* Conv2D + Sigmoid + MUL
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* Conv2D + HardTanh
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* Conv2D + ELU
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* Conv3D + ReLU
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* Conv3D + SUM
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* Conv3D + SUM + ReLU
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* Linear + ReLU
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* Linear + GELU
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* View + Transpose + Contiguous + View
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## Tutorials
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* [Performance Tuning](tutorials/Performance_Tuning.md)

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