README.md

python-mlir-graphblas

Implementation of the GraphBLAS spec using MLIR python bindings

A full GraphBLAS implementation is possible in relatively few lines of Python code because we rely heavily on the linalg.generic operation in MLIR, specifically its appropriate handling of different Sparse Tensor layouts. Each GraphBLAS operation becomes essentially a single linalg.generic operation with some minor pre- and post-handling of object creation.

Usage

Create a Matrix or Vector

>>> from mlir_graphblas import types, operations, operators, Matrix, Vector

>>> m = Matrix.new(types.FP64, 2, 5)
>>> m.build([0, 0, 1, 1, 1], [1, 3, 0, 3, 4], [1., 2., 3., 4., 5.75])
>>> m
Matrix<FP64, shape=(2, 5), format=CSR>

>>> v = Vector.new(types.FP64, 5)
>>> v.build([0, 2, 3, 4], [3., -2., 4., 1.5])
>>> v
Vector<FP64, size=5>

Perform Operations

Each operation requires an output object to be passed in.

>>> from mlir_graphblas.operators import Semiring

>>> z = Vector.new(types.FP64, 2)
>>> operations.mxv(z, Semiring.plus_times, m, v)
>>> z
Vector<FP64, size=2)

View Results

>>> indices, values = z.extract_tuples()
>>> indices
array([0, 1], dtype=uint64)
>>> values
array([ 8.   , 33.625])