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.coreandcuda-bindingspackages - NumPy >= 1.22
- The
- CUDA Toolkit components (CUDA Runtime, NVCC, NVRTC, and nvJitLink) 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:
- numba-cuda — BSD 2-Clause License
- cloudpickle — BSD 3-Clause License
- appdirs — MIT License
- 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.
1 maintainer
Release files for numba-cuda-mlir 0.5.3
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Release files / numba_cuda_mlir-0.5.3-cp314-cp314t-win_amd64.whl
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Transparency logRelease files / numba_cuda_mlir-0.5.3-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
| Download URL | numba_cuda_mlir-0.5.3-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl |
|---|---|
| Size | 67.2 MB |
| Tags | CPython 3.10 Linux glibc 2.27+ ARM64 Linux glibc 2.28+ ARM64 |
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SHA-256 checksum How to use checksums |
42f54977fca90614a5f8ff0371a342926b1e17c0b4ea8268bb9035a236fe76a1
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BLAKE2b-256 checksum How to use checksums |
fc64bfc041cb372e400885f10b6d1f4ee51c77a285ae59f9cb87b9100b051161
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Yes |
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twine/7.0.0 CPython/3.13.14
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