• What Is AKG?
• Hardware Backends Support
• Build
  • Build With MindSpore
  • Build Standalone
• Run
• Contributing
• Release Notes
• License

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What Is AKG

AKG(Auto Kernel Generator) is an optimizer for operators in Deep Learning Networks. It provides the ability to automatically fuse ops with specific patterns. AKG works with MindSpore-GraphKernel to improve the performance of networks running on different hardware backends.

AKG composes with three basic optimization module, normalization, auto schedule and backend optimization.

• normalization. In order to solve the limitation in expression ability of polyhedral(which can only process static linear programs), the computation IR needs to be normalized first. The mainly optimization of normalization module includes auto-inline, loop fusing, common subexpression elimination and so on.
• auto schedule. Base on polyhedral technology, the auto schedule module mainly have auto-vectorization, auto-tiling, thread/block mapping, dependency analysis and memory promotion.

• backend optimization. The backend optimization module mainly consists of TensorCore acceleration, double buffer optimization, storage flatten optimization and inject sync optimization.

  

Hardware Backends Support

At present, Ascend910, NVIDIA V100/A100 and CPU are supported. More Backends are on the list.

Build

Build With MindSpore

See MindSpore README.md for details.

Build Standalone

We suggest you build and run akg together with MindSpore. And we also provide a way to run case in standalone mode for convenience sake. Refer to MindSpore Installation for more information about compilation dependencies.

• Build on Ascend910

  git-lfs needs to be installed before cloning the source codes.

  git clone https://gitee.com/mindspore/akg.git
  cd akg
  bash build.sh -e ascend -j8

• Build on GPU

  git clone https://gitee.com/mindspore/akg.git
  cd akg
  bash build.sh -e gpu -j8

• Build on CPU

  git clone https://gitee.com/mindspore/akg.git
  cd akg
  bash build.sh -e cpu -j8

Run Standalone

1. Set Environment

• Ascend910

  cd tests
  source ./test_env.sh

• NVIDIA V100/A100

  cd tests
  source ./test_env.sh gpu

• CPU

  cd tests
  source ./test_env.sh cpu

2. Run test

• Use script:

  cd tests/st
  python run.py -e gpu -o add -l level0  # run add operator on GPU

  Detailed instructions see:python run.py -h

• Use specific case:

  • Ascend910

    cd tests/st/ops/
    pytest -s test_abs.py -m "level0 and platform_x86_ascend_training" # run level0 testcases on Ascend

  • NVIDIA V100/A100

    cd tests/st/ops/
    pytest -s test_abs.py -m "level0 and platform_x86_gpu_training" # run level0 testcases on GPU

  • CPU

    cd tests/st/ops/
    pytest -s test_abs.py -m "level0 and platform_x86_cpu" # run level0 testcases on CPU

Using AKG to generate high performance kernels

See Wiki.

Contributing

Welcome contributions. See MindSpore Contributor Wiki for more details.

Release Notes

The release notes, see our RELEASE.

License

Apache License 2.0