numba-cuda-mlir 0.4.0

numba-cuda-mlir Python Compiler

Numba-CUDA-MLIR

Numba-CUDA-MLIR provides a programming model similar to CUDA C++ in Python. It is evolved from Numba-CUDA, and is intended to be compatible with Numba-CUDA kernels.

Numba-CUDA-MLIR aims to interoperate well with existing programming models whilst also allowing experts sufficient control over code generation.

Quick Start

Install with pip:

pip install numba-cuda-mlir[cu13]  # or [cu12] if using CUDA 12

Writing and executing a simple vector add kernel:

import numpy as np
from numba_cuda_mlir import cuda

@cuda.jit
def vector_add(a, b, out):
    i = cuda.grid(1)
    if i < out.shape[0]:
        out[i] = a[i] + b[i]

n = 1_000_000
a = np.ones(n, dtype=np.float32)
b = np.ones(n, dtype=np.float32)
out = np.zeros(n, dtype=np.float32)

threads_per_block = 256
blocks = (n + threads_per_block - 1) // threads_per_block
vector_add[blocks, threads_per_block](a, b, out)

Migration from Numba / Numba-CUDA

Change imports to use the numba_cuda_mlir.cuda package instead of numba.cuda. For example:

from numba import cuda

becomes:

from numba_cuda_mlir import cuda

For the majority of code using Numba-CUDA, this should be a sufficient change to enable the use of Numba-CUDA-MLIR. For code using the extension APIs, modifications will be required as Numba-CUDA-MLIR uses MLIR in its code generation process instead of LLVM IR. See the Migration Guidance in the documentation for further details.

Installation Requirements

• Python >= 3.11, with:
  • The cuda.core and cuda-bindings packages
  • NumPy >= 1.22
• CUDA Toolkit components (CUDA Runtime, NVCC, NVRTC, nvJitLink, and CCCL) installed via pip or a system package manager (Linux).
• NVIDIA GPU with Compute Capability 7.0 or greater and a compatible driver:
  • >= r525 for CUDA 12.x
  • >= r580 for CUDA 13.x

Installation guidance

For full details of installation methods including from packages and building from source and testing, please see INSTALL.md.

Contributing to Numba-CUDA-MLIR

See the Contribution Guidelines for information on how to set up a development environment and follow the contribution process.

Benchmarks

A small suite of benchmarks can be executed from the source repository by running:

pytest tests/benchmarks/ --benchmark -s

Licensing

Numba-CUDA-MLIR is distributed under the Apache License 2.0.

It incorporates the following third-party projects, each retained under its original license:

1. numba-cuda — BSD 2-Clause License
2. cloudpickle — BSD 3-Clause License
3. appdirs — MIT License
4. LLVM Project / EUDSL — Apache License 2.0 WITH LLVM-exception

See NOTICE for the full attribution map and per-component locations in this repository, and THIRD-PARTY-LICENSES for the verbatim upstream license texts.

Contributions are accepted under the terms described in CONTRIBUTING.md.