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Merge pull request #15 from jim22k/use_built_bindings
Use mlir-python-bindings package instead of manually built bindings
2 parents 1006545 + 14bfb8d commit 864ba3f

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‎README.md‎

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@@ -49,67 +49,3 @@ array([0, 1], dtype=uint64)
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>>> values
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array([ 8. , 33.625])
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```
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# Building MLIR
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Until LLVM 16.0 is released, the required MLIR operations in the sparse_tensor dialect will only be
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available on the main repo branch, meaning there aren't any packages readily available to install
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from conda-forge.
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To build locally, download the LLVM repo from GitHub.
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```
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git clone https://github.com/llvm/llvm-project.git
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```
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Next create a conda environment for building the project.
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```
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conda create -n mlir_build_env -y
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conda activate mlir_build_env
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conda install python=3.10 numpy pyyaml cmake ninja pybind11 python-mlir-graphblas
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```
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Define `PREFIX` as the location of your environment (active env location when running `conda info`).
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Then run cmake.
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```
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export PREFIX=/location/to/your/conda/environment
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cd llvm-project
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mkdir build
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cd build
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cmake -G Ninja ../llvm \
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-DCMAKE_INSTALL_PREFIX=$PREFIX \
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-DLLVM_ENABLE_PROJECTS=mlir \
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-DLLVM_BUILD_EXAMPLES=ON \
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-DLLVM_INSTALL_UTILS=ON \
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-DLLVM_TARGETS_TO_BUILD="X86;AArch64;NVPTX;AMDGPU" \
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-DCMAKE_BUILD_TYPE=Release \
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-DLLVM_ENABLE_ASSERTIONS=ON \
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-DLLVM_BUILD_LLVM_DYLIB=ON \
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-DMLIR_ENABLE_BINDINGS_PYTHON=ON \
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-DPython3_EXECUTABLE=`which python`
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```
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If building on a Mac, perform this additional step.
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```
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cp $PREFIX/lib/libtinfo* lib/
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cp $PREFIX/lib/libz* lib/
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```
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Then build the project
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```
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cmake --build .
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cmake --install .
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```
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Finally, set `LLVM_BUILD_DIR` to point to the current build directory.
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Now python-mlir-graphblas should be usable. Verify by running tests.
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```
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LLVM_BUILD_DIR=. pytest --pyargs mlir_graphblas
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```

‎dev-environment.yml‎

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# To use:
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# $ conda env create -f dev-environment.yml
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# $ conda activate mlir-graphblas-dev
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name: mlir-graphblas-dev
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channels:
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- conda-forge
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- nodefaults # Only from conda-forge for faster solving
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dependencies:
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- python
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- mlir-python-bindings >=16.0
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- pytest

‎mlir_graphblas/__init__.py‎

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# TODO: remove this once mlir-python-bindings can be properly installed
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import os
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import sys
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sys.path.append(f"{os.environ['LLVM_BUILD_DIR']}/tools/mlir/python_packages/mlir_core")
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try:
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import mlir
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import mlir.ir
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except ImportError:
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raise ImportError("Missing mlir-python-bindings")
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from .operations import *
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from .operators import *

‎mlir_graphblas/compiler.py‎

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from mlir import ir
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from mlir import passmanager
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from .utils import LIBMLIR_C_RUNNER_UTILS
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from .utils import MLIR_C_RUNNER_UTILS
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engine_cache = {}
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if pipeline is None:
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pipeline = 'builtin.module(sparse-compiler{reassociate-fp-reductions=1 enable-index-optimizations=1})'
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if shared_libs is None:
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shared_libs = [LIBMLIR_C_RUNNER_UTILS]
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shared_libs = [MLIR_C_RUNNER_UTILS]
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# print(module)
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passmanager.PassManager.parse(pipeline).run(module)
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return execution_engine.ExecutionEngine(
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module, opt_level=opt_level, shared_libs=shared_libs)
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return execution_engine.ExecutionEngine(module, opt_level=opt_level, shared_libs=shared_libs)

‎mlir_graphblas/implementations.py‎

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def ewise_add(out_type: DType, op: BinaryOp, left: SparseTensorBase, right: SparseTensorBase):
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assert left.ndims == right.ndims
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assert left.dtype == right.dtype
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if left._obj is None:
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if right.dtype == out_type:

‎mlir_graphblas/tests/test_operations.py‎

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@@ -177,7 +177,15 @@ def test_mxm(mm):
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# rowwise @ colwise
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z.clear()
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operations.mxm(z, Semiring.plus_times, x, ycol)
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matrix_compare(z, *expected)
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try:
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matrix_compare(z, *expected)
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except AssertionError:
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# Check for dense return, indicating lack of lex insert fix
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matrix_compare(z,
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[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4],
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[0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4],
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[20.9, 0, 0, 0, 0, 16.5, 0, 0, 0, 0, 5.5, 0, 0, 0, 70.4, 0, 0, 0, 0, 0, 0, 0, 0, 13.2, 0])
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pytest.xfail("Waiting for lex insert fix")
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# colwise @ colwise
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z.clear()
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operations.mxm(z, Semiring.plus_times, xcol, ycol)

‎mlir_graphblas/tests/utils.py‎

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def vector_compare(vec, i, v):
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assert vec.ndims == 1
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assert len(i) == len(v), f"{len(i)} != {len(v)}"
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idx, vals = vec.extract_tuples()
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assert vals.dtype == vec.dtype.np_type
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np_assert_equal(idx, i)
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def matrix_compare(mat, r, c, v):
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assert mat.ndims == 2
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assert len(r) == len(c), f"{len(r)} != {len(c)}"
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assert len(r) == len(v), f"{len(r)} != {len(v)}"
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rows, cols, vals = mat.extract_tuples()
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assert vals.dtype == mat.dtype.np_type
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combo = np.core.records.fromarrays([r, c], names='r,c')
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if mat.is_rowwise():
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sort_order = np.argsort(r)
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sort_order = np.argsort(combo, order=['r', 'c'])
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else:
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sort_order = np.argsort(c)
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sort_order = np.argsort(combo, order=['c', 'r'])
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np_assert_equal(rows, np.array(r)[sort_order])
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np_assert_equal(cols, np.array(c)[sort_order])
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np_assert_allclose(vals, np.array(v)[sort_order])

‎mlir_graphblas/utils.py‎

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import ctypes
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import numpy as np
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import ctypes
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from ctypes.util import find_library
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from enum import Enum
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from mlir import ir
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from mlir.dialects.sparse_tensor import DimLevelType
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GrbDimensionMismatch, GrbOutputNotEmpty, GrbIndexOutOfBounds, GrbEmptyObject
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)
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MLIR_C_RUNNER_UTILS = find_library("mlir_c_runner_utils")
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c_lib = ctypes.CDLL(MLIR_C_RUNNER_UTILS)
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LLVMPTR = ctypes.POINTER(ctypes.c_char)
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# TODO: fix this once a proper package exists
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import os
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LIBMLIR_C_RUNNER_UTILS = f"{os.environ['LLVM_BUILD_DIR']}/lib/libmlir_c_runner_utils.dylib"
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c_lib = ctypes.CDLL(LIBMLIR_C_RUNNER_UTILS)
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def get_sparse_output_pointer():

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