StableHLO

StableHLO is an operation set for high-level operations (HLO) in machine learning (ML) models. Essentially, it's a portability layer between different ML frameworks and ML compilers: ML frameworks that produce StableHLO programs are compatible with ML compilers that consume StableHLO programs.

Our goal is to simplify and accelerate ML development by creating more interoperability between various ML frameworks (such as TensorFlow, JAX and PyTorch) and ML compilers (such as XLA and IREE).

StableHLO is based on the MHLO dialect and enhances it with additional functionality, including serialization and versioning. This provides backward and forward compatibility guarantees for StableHLO programs and ensures compatibility between frameworks and compilers, even as StableHLO continues to evolve.

This repository includes the StableHLO specification along with an MLIR-based implementation in C++ and Python, which you can use to define StableHLO programs for consumption by compilers such as XLA and IREE.

Build steps

Here's how to build the StableHLO repo:

1. Make sure you have the LLVM-based linker lld installed:

   sudo apt update && sudo apt install lld

2. Clone this repo and the LLVM git repository:

   git clone https://github.com/openxla/stablehlo

   cd stablehlo && git clone https://github.com/llvm/llvm-project.git

3. Make sure you check out the correct commit in the LLVM repository:

   (cd llvm-project && git fetch && git checkout $(cat ../build_tools/llvm_version.txt))

   You need to do this every time llvm_version.txt changes.

4. Configure and build MLIR:

   build_tools/build_mlir.sh ${PWD}/llvm-project/ ${PWD}/llvm-build

   This will take several minutes.

   Again, you need to do this every time llvm_version.txt changes.

5. Build StableHLO as a standalone library:

   mkdir -p build && cd build
   
   cmake .. -GNinja \
     -DLLVM_ENABLE_LLD=ON \
     -DCMAKE_BUILD_TYPE=Release \
     -DLLVM_ENABLE_ASSERTIONS=On \
     -DMLIR_DIR=${PWD}/../llvm-build/lib/cmake/mlir

6. Now you can make sure it works by running some tests:

   ninja check-stablehlo

   You should see results like this:

   Testing Time: 5.99s
     Passed: 47

   This runs all the tests in stablehlo/tests/.

Community

Building an amazing portability layer between ML frameworks and ML compilers requires collaboration across the whole ML industry, so we're happy to have your help on the StableHLO project.

We're using GitHub issues / pull requests to organize development and GitHub discussions to have longer discussions. We also have a #stablehlo channel on the OpenXLA Discord server.

Roadmap

• Workstream #1: Stable version of HLO/MHLO, including the spec, the corresponding dialect with high-quality implementations of prettyprinting, verification and type inference, and the interpeter. ETA: H2 2022.
• Workstream #2: Evolution beyond what's currently in HLO/MHLO. Ongoing work on dynamism, sparsity, quantization and extensibility. ETA: H2 2022.
• Workstream #3: Support for ML frameworks (TensorFlow, JAX, PyTorch) and ML compilers (XLA and IREE). ETA: H2 2022.