Intel® Extension for PyTorch*

Intel® Extension for PyTorch* extends PyTorch* with up-to-date features optimizations for an extra performance boost on Intel hardware. Optimizations take advantage of AVX-512 Vector Neural Network Instructions (AVX512 VNNI) and Intel® Advanced Matrix Extensions (Intel® AMX) on Intel CPUs as well as Intel X^e Matrix Extensions (XMX) AI engines on Intel discrete GPUs. Moreover, through PyTorch* xpu device, Intel® Extension for PyTorch* provides easy GPU acceleration for Intel discrete GPUs with PyTorch*.

Intel® Extension for PyTorch* provides optimizations for both eager mode and graph mode, however, compared to eager mode, graph mode in PyTorch* normally yields better performance from optimization techniques, such as operation fusion. Intel® Extension for PyTorch* amplifies them with more comprehensive graph optimizations. Therefore we recommend you to take advantage of Intel® Extension for PyTorch* with TorchScript whenever your workload supports it. You could choose to run with torch.jit.trace() function or torch.jit.script() function, but based on our evaluation, torch.jit.trace() supports more workloads so we recommend you to use torch.jit.trace() as your first choice.

The extension can be loaded as a Python module for Python programs or linked as a C++ library for C++ programs. In Python scripts users can enable it dynamically by importing intel_extension_for_pytorch.

In the current technological landscape, Generative AI (GenAI) workloads and models have gained widespread attention and popularity. Large Language Models (LLMs) have emerged as the dominant models driving these GenAI applications. Starting from 2.1.0, specific optimizations for certain LLM models are introduced in the Intel® Extension for PyTorch*. Check LLM optimizations for details.

Check CPU tutorial for detailed information of Intel® Extension for PyTorch* for Intel® CPUs. Source code is available at the main branch.
Check GPU tutorial for detailed information of Intel® Extension for PyTorch* for Intel® GPUs. Source code is available at the xpu-main branch.

Installation

CPU version

You can use either of the following 2 commands to install Intel® Extension for PyTorch* CPU version.

python -m pip install intel_extension_for_pytorch

python -m pip install intel_extension_for_pytorch -f https://developer.intel.com/ipex-whl-stable-cpu

Note: Intel® Extension for PyTorch* has PyTorch version requirement. Please check more detailed information via the URL below.

More installation methods can be found at CPU Installation Guide.

Compilation instruction of the latest CPU code base main branch can be found at Installation Guide.

GPU version

You can install Intel® Extension for PyTorch* for GPU via command below.

python -m pip install torch==2.0.1a0 torchvision==0.15.2a0 intel_extension_for_pytorch==2.0.110+xpu -f https://developer.intel.com/ipex-whl-stable-xpu

Note: The patched PyTorch 2.0.1a0 is required to work with Intel® Extension for PyTorch* on Intel® graphics card for now.

More installation methods can be found at GPU Installation Guide.

Compilation instruction of the latest GPU code base xpu-main branch can be found at Installation Guide.

Getting Started

Minor code changes are required for users to get start with Intel® Extension for PyTorch*. Both PyTorch imperative mode and TorchScript mode are supported. You just need to import Intel® Extension for PyTorch* package and apply its optimize function against the model object. If it is a training workload, the optimize function also needs to be applied against the optimizer object.

The following code snippet shows an inference code with FP32 data type. More examples on CPU, including training and C++ examples, are available at CPU Example page. More examples on GPU are available at GPU Example page.

NOTE: More detailed information about torch.compile() with ipex backend can be found at Tutorial features page.

Inference on CPU

import torch
import torchvision.models as models

model = models.resnet50(pretrained=True)
model.eval()
data = torch.rand(1, 3, 224, 224)

import intel_extension_for_pytorch as ipex
model = ipex.optimize(model)

with torch.no_grad():
  model(data)

Inference on GPU

import torch
import torchvision.models as models

model = models.resnet50(pretrained=True)
model.eval()
data = torch.rand(1, 3, 224, 224)

import intel_extension_for_pytorch as ipex
model = model.to('xpu')
data = data.to('xpu')
model = ipex.optimize(model)

with torch.no_grad():
  model(data)

Model Zoo

Use cases that had already been optimized by Intel engineers are available at Model Zoo for Intel® Architecture. A bunch of PyTorch use cases for benchmarking are also available on the Github page. You can get performance benefits out-of-box by simply running scripts in the Model Zoo.

License

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

Security

See Intel's Security Center for information on how to report a potential security issue or vulnerability.

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