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).

• Installation
  • Install PyTorch from Source
  • Install Intel PyTorch Extension from Source
• Getting Started
• Contribution
• License

1. Get PyTorch v1.5.0-rc3 source(Refer to PyTorch guide for more details)

   git clone --recursive https://github.com/pytorch/pytorch cd pytorch # checkout source code to the specified version git checkout v1.5.0-rc3 # update submodules for the specified pytorch version git submodule sync git submodule update --init --recursive

2. Get Intel PyTorch Extension source

   git clone --recursive https://github.com/intel/intel-extension-for-pytorch cd intel-extension-for-pytorch # if you are updating an existing checkout git submodule sync git submodule update --init --recursive

3. Add an new backend for Intel PyTorch Extension

   # Apply git patch to pytorch code cd ${intel_pytorch_extension_directory} git apply torch_patches/dpcpp-v1.5-rc3.patch ${pytorch_directory}

4. Build and install PyTorch (Refer to PyTorch guide for more details)

   cd ${pytorch_directory} python setup.py install

Install dependencies

pip install lark-parser hypothesis

Install the extension

cd ${intel_pytorch_extension_directory} python setup.py install

The user just needs to convert the model and input tensors to the extension device, then the extension will be enabled automatically. Take an example, the code as follows is a model without the extension.

import torch import torch.nn as nn class Model(nn.Module): def __init__(self): super(Model, self).__init__() self.linear = nn.Linear(4, 5) def forward(self, input): return self.linear(input) input = torch.randn(2, 4) model = Model() res = model(input)

If you want to explore the Intel PyTorch Extension, you just need to transform the above python script as follows.

import torch import torch.nn as nn # Import Intel PyTorch Extension import intel_pytorch_extension class Model(nn.Module): def __init__(self): super(Model, self).__init__() self.linear = nn.Linear(4, 5) def forward(self, input): return self.linear(input) # Convert the input tensor to Intel PyTorch Extension device input = torch.randn(2, 4).to('dpcpp') # Convert the model to Intel PyTorch Extension device model = Model().to('dpcpp') res = model(input)

In addition, Intel PyTorch Extension can auto dispatch an OP to DNNL if the OP is supported with DNNL. Currently, the feature is not enabled by default. If you want to enable the feature, you can refine the above code as follows.

import torch import torch.nn as nn # Import Intel PyTorch Extension import intel_pytorch_extension as ipex class Model(nn.Module): def __init__(self): super(Model, self).__init__() self.linear = nn.Linear(4, 5) def forward(self, input): return self.linear(input) # Convert the input tensor to Intel PyTorch Extension device input = torch.randn(2, 4).to('dpcpp') # Convert the model to Intel PyTorch Extension device model = Model().to('dpcpp') ipex.core.enable_auto_dnnl() res = model(input)

Please submit PR or issue to communicate with us or contribute code.

Apache License, Version 2.0. As found in LICENSE file.