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Intel® Extension for OpenXLA*

Python PyPI version version license

The OpenXLA Project brings together a community of developers and leading AI/ML teams to accelerate ML and address infrastructure fragmentation across ML frameworks and hardware.

Intel® Extension for OpenXLA includes PJRT plugin implementation, which seamlessly runs JAX models on Intel GPU. The PJRT API simplified the integration, which allowed the Intel GPU plugin to be developed separately and quickly integrated into JAX. This same PJRT implementation also enables initial Intel GPU support for TensorFlow and PyTorch models with XLA acceleration. Refer to OpenXLA PJRT Plugin RFC for more details.

This guide introduces the overview of OpenXLA high level integration structure and demonstrates how to build Intel® Extension for OpenXLA and run JAX example with OpenXLA on Intel GPU. JAX is the first supported front-end.

1. Overview

  • JAX provides a familiar NumPy-style API, includes composable function transformations for compilation, batching, automatic differentiation, and parallelization, and the same code executes on multiple backends.
  • TensorFlow and PyTorch support is on the way.

2. Requirements

Hardware Requirements

Verified Hardware Platforms:

  • Intel® Data Center GPU Max Series, Driver Version: 803

  • Intel® Data Center GPU Flex Series 170, Driver Version: 803

Software Requirements

  • Ubuntu 22.04 (64-bit)
    • Intel® Data Center GPU Flex Series
  • Ubuntu 22.04, SUSE Linux Enterprise Server(SLES) 15 SP4
    • Intel® Data Center GPU Max Series
  • Intel® oneAPI Base Toolkit 2024.2
  • Jax/Jaxlib 0.4.26
  • Python 3.9-3.12
  • pip 19.0 or later (requires manylinux2014 support)

NOTE: Since JAX has its own platform limitation (Ubuntu 20.04 or later), real software requirements is restricted when works with JAX.

Install Intel GPU Drivers

OS Intel GPU Install Intel GPU Driver
Ubuntu 22.04 Intel® Data Center GPU Flex Series Refer to the Installation Guides for latest driver installation. If install the verified Intel® Data Center GPU Max Series/Intel® Data Center GPU Flex Series 803, please append the specific version after components, such as sudo apt-get install intel-opencl-icd==23.43.27642.52-803~22.04
Ubuntu 22.04, SLES 15 SP4 Intel® Data Center GPU Max Series Refer to the Installation Guides for latest driver installation. If install the verified Intel® Data Center GPU Max Series/Intel® Data Center GPU Flex Series 803, please append the specific version after components, such as sudo apt-get install intel-opencl-icd==23.43.27642.52-803~22.04

Install oneAPI Base Toolkit Packages

Need to install components of Intel® oneAPI Base Toolkit:

  • Intel® oneAPI DPC++ Compiler
  • Intel® oneAPI Math Kernel Library (oneMKL)
$ wget https://registrationcenter-download.intel.com/akdlm/IRC_NAS/e6ff8e9c-ee28-47fb-abd7-5c524c983e1c/l_BaseKit_p_2024.2.1.100_offline.sh
# 2 components are necessary: DPC++/C++ Compiler and oneMKL
sudo sh l_BaseKit_p_2024.2.1.100_offline.sh

# Source OneAPI env
source /opt/intel/oneapi/compiler/2024.2/env/vars.sh
source /opt/intel/oneapi/mkl/2024.2/env/vars.sh

Backup: Recommend to rollback to Toolkit 2024.1 if meet performance issue. See Release Notes for more details.

wget https://registrationcenter-download.intel.com/akdlm/IRC_NAS/fdc7a2bc-b7a8-47eb-8876-de6201297144/l_BaseKit_p_2024.1.0.596.sh
# 2 components are necessary: DPC++/C++ Compiler and oneMKL
sudo sh l_BaseKit_p_2024.1.0.596.sh

# Source OneAPI env
source /opt/intel/oneapi/compiler/2024.1/env/vars.sh
source /opt/intel/oneapi/mkl/2024.1/env/vars.sh

Install Jax and Jaxlib

pip install -r https://raw.githubusercontent.com/intel/intel-extension-for-openxla/main/test/requirements.txt

Please refer to test/requirements.txt for the version dependency of jax, jaxlib and flax.

The following table tracks intel-extension-for-openxla versions and compatible versions of jax and jaxlib. The compatibility between jax and jaxlib is maintained through JAX. This version restriction will be relaxed over time as the plugin API matures.

intel-extension-for-openxla jaxlib jax
0.4.0 0.4.26 >= 0.4.26, <= 0.4.27
0.3.0 0.4.24 >= 0.4.24, <= 0.4.27
0.2.1 0.4.20 >= 0.4.20, <= 0.4.26
0.2.0 0.4.20 >= 0.4.20, <= 0.4.26
0.1.0 0.4.13 >= 0.4.13, <= 0.4.14

3. Install

Install via PyPI wheel

pip install --upgrade intel-extension-for-openxla

Install from Source Build

NOTE: Extra software (GCC 10.0.0 or later) is required if want to build from source.

git clone https://github.com/intel/intel-extension-for-openxla.git
./configure        # Choose Yes for all.
bazel build //xla/tools/pip_package:build_pip_package
./bazel-bin/xla/tools/pip_package/build_pip_package ./
pip install intel_extension_for_openxla-0.4.0-cp39-cp39-linux_x86_64.whl

Aditional Build Option:

This repo pulls public XLA code as its third party build dependency. As an openxla developer, you may need to modify and override this specific XLA repo with a local checkout version by the following command:

bazel build --override_repository=xla=/path/to/xla //xla/tools/pip_package:build_pip_package

Custom Library Path:

By default, bazel will automatically search for the required libraries on your system. This eliminates the need for manual configuration in most cases. For more advanced use cases, you can specify a custom location for the libraries using environment variables:

export MKL_INSTALL_PATH=/opt/intel/oneapi/mkl/2024.2
export L0_INSTALL_PATH=/usr
bazel build //xla/tools/pip_package:build_pip_package

4. Run JAX Example

Run the below jax python code

When running jax code, jax.local_devices() can check which device is running.

import jax
import jax.numpy as jnp
import jax
print("jax.local_devices(): ", jax.local_devices())

@jax.jit
def lax_conv():
  key = jax.random.PRNGKey(0)
  lhs = jax.random.uniform(key, (2,1,9,9), jnp.float32)
  rhs = jax.random.uniform(key, (1,1,4,4), jnp.float32)
  side = jax.random.uniform(key, (1,1,1,1), jnp.float32)
  out = jax.lax.conv_with_general_padding(lhs, rhs, (1,1), ((0,0),(0,0)), (1,1), (1,1))
  out = jax.nn.relu(out)
  out = jnp.multiply(out, side)
  return out

print(lax_conv())

Reference result

jax.local_devices():  [xpu(id=0), xpu(id=1)]
[[[[2.0449753 2.093208  2.1844783 1.9769732 1.5857391 1.6942389]
   [1.9218378 2.2862523 2.1549542 1.8367321 1.3978379 1.3860377]
   [1.9456574 2.062028  2.0365305 1.901286  1.5255247 1.1421617]
   [2.0621    2.2933435 2.1257985 2.1095486 1.5584903 1.1229166]
   [1.7746235 2.2446113 1.7870374 1.8216239 1.557919  0.9832508]
   [2.0887792 2.5433128 1.9749291 2.2580051 1.6096935 1.264905 ]]]
 [[[2.175818  2.0094342 2.005763  1.6559253 1.3896458 1.4036925]
   [2.1342552 1.8239582 1.6091168 1.434404  1.671778  1.7397764]
   [1.930626  1.659667  1.6508744 1.3305787 1.4061482 2.0829628]
   [2.130649  1.6637266 1.594426  1.2636002 1.7168686 1.8598001]
   [1.9009514 1.7938274 1.4870623 1.6193901 1.5297288 2.0247464]
   [2.0905268 1.7598859 1.9362347 1.9513799 1.9403584 2.1483061]]]]

5. FAQ

  1. If there is an error 'No visible XPU devices', print jax.local_devices() to check which device is running. Set export OCL_ICD_ENABLE_TRACE=1 to check if there are driver error messages. The following code opens more debug log for JAX app.

    import logging
logging.basicConfig(level = logging.DEBUG)

  2. If there is an error 'version GLIBCXX_3.4.30' not found, upgrade libstdc++ to the latest, for example for conda

    conda install libstdcxx-ng==12.2.0 -c conda-forge

  3. If there is an error '/usr/bin/ld: cannot find -lstdc++: No such file or directory' during source build under Ubuntu 22.04, check the selected GCC-toolchain path and the installed libstdc++.so library path, then create symbolic link of the selected GCC-toolchain path to the libstdc++.so path, for example:

    icx -v # For example, the output of the "Selected GCC installation" is "/usr/lib/gcc/x86_64-linux-gnu/12".
sudo apt install plocate
locate libstdc++.so |grep /usr/lib/ # For example, the output of the library path is "/usr/lib/x86_64-linux-gnu/libstdc++.so.6".
sudo ln -s /usr/lib/x86_64-linux-gnu/libstdc++.so.6 /usr/lib/gcc/x86_64-linux-gnu/12/libstdc++.so