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llvmlite

lightweight wrapper around basic LLVM functionality


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======== llvmlite

.. image:: https://dev.azure.com/numba/numba/_apis/build/status/numba.llvmlite?branchName=main :target: https://dev.azure.com/numba/numba/_build/latest?definitionId=2&branchName=main :alt: Azure Pipelines .. image:: https://codeclimate.com/github/numba/llvmlite/badges/gpa.svg :target: https://codeclimate.com/github/numba/llvmlite :alt: Code Climate .. image:: https://coveralls.io/repos/github/numba/llvmlite/badge.svg :target: https://coveralls.io/github/numba/llvmlite :alt: Coveralls.io .. image:: https://readthedocs.org/projects/llvmlite/badge/ :target: https://llvmlite.readthedocs.io :alt: Readthedocs.io

A Lightweight LLVM Python Binding for Writing JIT Compilers

.. _llvmpy: https://github.com/llvmpy/llvmpy

llvmlite is a project originally tailored for Numba_'s needs, using the following approach:

  • A small C wrapper around the parts of the LLVM C++ API we need that are not already exposed by the LLVM C API.
  • A ctypes Python wrapper around the C API.
  • A pure Python implementation of the subset of the LLVM IR builder that we need for Numba.

Why llvmlite

The old llvmpy_ binding exposes a lot of LLVM APIs but the mapping of C++-style memory management to Python is error prone. Numba_ and many JIT compilers do not need a full LLVM API. Only the IR builder, optimizer, and JIT compiler APIs are necessary.

Key Benefits

  • The IR builder is pure Python code and decoupled from LLVM's frequently-changing C++ APIs.
  • Materializing a LLVM module calls LLVM's IR parser which provides better error messages than step-by-step IR building through the C++ API (no more segfaults or process aborts).
  • Most of llvmlite uses the LLVM C API which is small but very stable (low maintenance when changing LLVM version).
  • The binding is not a Python C-extension, but a plain DLL accessed using ctypes (no need to wrestle with Python's compiler requirements and C++ 11 compatibility).
  • The Python binding layer has sane memory management.
  • llvmlite is faster than llvmpy thanks to a much simpler architecture (the Numba_ test suite is twice faster than it was).

Compatibility

llvmlite has been tested with Python 3.9 -- 3.12 and is likely to work with greater versions.

As of version 0.41.0, llvmlite requires LLVM 14.x.x on all architectures

Historical compatibility table:

================= ======================== llvmlite versions compatible LLVM versions ================= ======================== 0.41.0 - ... 14.x.x 0.40.0 - 0.40.1 11.x.x and 14.x.x (12.x.x and 13.x.x untested but may work) 0.37.0 - 0.39.1 11.x.x 0.34.0 - 0.36.0 10.0.x (9.0.x for aarch64 only) 0.33.0 9.0.x 0.29.0 - 0.32.0 7.0.x, 7.1.x, 8.0.x 0.27.0 - 0.28.0 7.0.x 0.23.0 - 0.26.0 6.0.x 0.21.0 - 0.22.0 5.0.x 0.17.0 - 0.20.0 4.0.x 0.16.0 - 0.17.0 3.9.x 0.13.0 - 0.15.0 3.8.x 0.9.0 - 0.12.1 3.7.x 0.6.0 - 0.8.0 3.6.x 0.1.0 - 0.5.1 3.5.x ================= ========================

Documentation

You'll find the documentation at http://llvmlite.pydata.org

Pre-built binaries

We recommend you use the binaries provided by the Numba_ team for the Conda_ package manager. You can find them in Numba's anaconda.org channel <https://anaconda.org/numba>_. For example::

$ conda install --channel=numba llvmlite

(or, simply, the official llvmlite package provided in the Anaconda_ distribution)

.. _Numba: http://numba.pydata.org/ .. _Conda: http://conda.pydata.org/ .. _Anaconda: http://docs.continuum.io/anaconda/index.html

Other build methods

If you don't want to use our pre-built packages, you can compile and install llvmlite yourself. The documentation will teach you how: http://llvmlite.pydata.org/en/latest/install/index.html

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