This project provides integrations between JAX and OpenAI Triton, enabling users to write custom, high-performance kernels for JAX arrays. It targets researchers and engineers needing to optimize complex computations beyond standard JAX operations, offering significant speedups through custom GPU kernels.

How It Works

The core of jax-triton is the jax_triton.triton_call function, which allows seamless integration of Triton kernels within JAX workflows, including jax.jit. This enables users to define custom kernels using Triton's Pythonic API and apply them directly to JAX arrays, leveraging Triton's low-level control over GPU hardware for performance.

Quick Start & Requirements

• Install with: pip install jax-triton
• Requires a CUDA-compatible JAX installation (e.g., pip install "jax[cuda12]").
• Currently requires building the latest version of Triton from source.
• Official documentation: https://github.com/jax-ml/jax-triton
• Examples: https://github.com/jax-ml/jax-triton/tree/main/examples

Highlighted Details

• Enables writing custom GPU kernels using Triton's domain-specific language.
• Supports integration within jax.jit-compiled functions for seamless performance optimization.
• Provides examples for advanced use cases like fused attention.

Maintenance & Community

• This is not an officially supported Google product.
• Development can be done via editable install after cloning the repository.
• Tests are available via pytest.

Licensing & Compatibility

• The repository does not explicitly state a license in the README.

Limitations & Caveats

The project requires building Triton from source, which can add complexity to the setup process. Compatibility with specific CUDA versions or JAX builds may require manual verification.