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.
- 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.
Verified Hardware Platforms:
-
Intel® Data Center GPU Max Series, Driver Version: 682
-
Intel® Data Center GPU Flex Series 170, Driver Version: 682
- Ubuntu 22.04, Red Hat 8.6/8.8/9.2 (64-bit)
- Intel® Data Center GPU Flex Series
- Ubuntu 22.04, Red Hat 8.6/8.8/9.2 (64-bit), SUSE Linux Enterprise Server(SLES) 15 SP4
- Intel® Data Center GPU Max Series
- Intel® oneAPI Base Toolkit 2023.2
- Jax/Jaxlib 0.4.13
- Python 3.9-3.11
- pip 19.0 or later (requires manylinux2014 support)
| OS | Intel GPU | Install Intel GPU Driver |
|---|---|---|
| Ubuntu 22.04, Red Hat 8.6/8.8/9.2 | 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 682, please append the specific version after components, such as sudo apt-get install intel-opencl-icd==23.22.26516.25-682~22.04 |
| Ubuntu 22.04, Red Hat 8.6/8.8/9.2, 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 682, please append the specific version after components, such as sudo apt-get install intel-opencl-icd==23.22.26516.25-682~22.04 |
Need to install components of Intel® oneAPI Base Toolkit:
- Intel® oneAPI DPC++ Compiler
- Intel® oneAPI Math Kernel Library (oneMKL)
- Intel® oneAPI Threading Building Blocks (TBB), dependency of DPC++ Compiler.
wget https://registrationcenter-download.intel.com/akdlm/IRC_NAS/992857b9-624c-45de-9701-f6445d845359/l_BaseKit_p_2023.2.0.49397_offline.sh
sudo sh ./l_BaseKit_p_2023.2.0.49397_offline.sh
# Source OneAPI env
source /opt/intel/oneapi/compiler/2023.2.0/env/vars.sh
source /opt/intel/oneapi/mkl/2023.2.0/env/vars.sh
source /opt/intel/oneapi/tbb/2021.9.0/env/vars.shpip install jax==0.4.13 jaxlib==0.4.13pip install --upgrade intel-extension-for-openxlagit 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-0.1.0-cp39-cp39-linux_x86_64.whlAditional 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_packageWhen 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())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]]]]-
If there is an error 'No visible XPU devices', print
jax.local_devices()to check which device is running. Setexport OCL_ICD_ENABLE_TRACE=1to check if there are driver error messages. The following code opens more debug log for JAX app.import logging logging.basicConfig(level = logging.DEBUG)
-
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
-
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