mlir/test/Integration/Dialect/SparseTensor/python/test_SDDMM.py

# RUN: env SUPPORT_LIB=%mlir_c_runner_utils \
# RUN:   %PYTHON %s | FileCheck %s

import ctypes
import numpy as np
import os
import sys

from mlir import ir
from mlir import runtime as rt

from mlir.dialects import sparse_tensor as st
from mlir.dialects import builtin
from mlir.dialects import func
from mlir.dialects.linalg.opdsl import lang as dsl

_SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__))
sys.path.append(_SCRIPT_PATH)
from tools import sparsifier


@dsl.linalg_structured_op
def sddmm_dsl(
    A=dsl.TensorDef(dsl.T, dsl.S.M, dsl.S.K),
    B=dsl.TensorDef(dsl.T, dsl.S.K, dsl.S.N),
    S=dsl.TensorDef(dsl.T, dsl.S.M, dsl.S.N),
    C=dsl.TensorDef(dsl.T, dsl.S.M, dsl.S.N, output=True),
):
    C[dsl.D.m, dsl.D.n] += (
        S[dsl.D.m, dsl.D.n] * A[dsl.D.m, dsl.D.k] * B[dsl.D.k, dsl.D.n]
    )


def build_SDDMM(attr: st.EncodingAttr):
    """Build SDDMM kernel.

    This method generates a linalg op with for matrix multiplication using
    just the Python API. Effectively, a generic linalg op is constructed
    that computes C(i,j) += S(i,j) SUM_k A(i,k) B(k,j) for sparse S.
    """
    module = ir.Module.create()
    f64 = ir.F64Type.get()
    a = ir.RankedTensorType.get([8, 8], f64)
    b = ir.RankedTensorType.get([8, 8], f64)
    c = ir.RankedTensorType.get([8, 8], f64)
    s = ir.RankedTensorType.get([8, 8], f64, attr)
    arguments = [a, b, s, c]
    with ir.InsertionPoint(module.body):

        @func.FuncOp.from_py_func(*arguments)
        def sddmm(*args):
            return sddmm_dsl(args[0], args[1], args[2], outs=[args[3]])

    return module


def boilerplate(attr: st.EncodingAttr):
    """Returns boilerplate code for main driver."""
    return f"""
func.func @main(%a: tensor<8x8xf64>,
           %b: tensor<8x8xf64>,
           %c: tensor<8x8xf64>) -> tensor<8x8xf64> attributes {{ llvm.emit_c_interface }} {{
  %t = arith.constant sparse<[[0,0], [0,2], [4,1]], [1.0, 2.0, 3.0]> : tensor<8x8xf64>
  %s = sparse_tensor.convert %t : tensor<8x8xf64> to tensor<8x8xf64, {attr}>
  %0 = call @sddmm(%a, %b, %s, %c) : (tensor<8x8xf64>,
                                      tensor<8x8xf64>,
                                      tensor<8x8xf64, {attr}>,
                                      tensor<8x8xf64>) -> tensor<8x8xf64>
  return %0 : tensor<8x8xf64>
}}
"""


def build_compile_and_run_SDDMMM(attr: st.EncodingAttr, compiler):
    # Build.
    module = build_SDDMM(attr)
    func = str(module.operation.regions[0].blocks[0].operations[0].operation)
    module = ir.Module.parse(func + boilerplate(attr))

    # Compile.
    engine = compiler.compile_and_jit(module)

    # Set up numpy input and buffer for output.
    a = np.array(
        [
            [1.1, 2.1, 3.1, 4.1, 5.1, 6.1, 7.1, 8.1],
            [1.2, 2.2, 3.2, 4.2, 5.2, 6.2, 7.2, 8.2],
            [1.3, 2.3, 3.3, 4.3, 5.3, 6.3, 7.3, 8.3],
            [1.4, 2.4, 3.4, 4.4, 5.4, 6.4, 7.4, 8.4],
            [1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5],
            [1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6],
            [1.7, 2.7, 3.7, 4.7, 5.7, 6.7, 7.7, 8.7],
            [1.8, 2.8, 3.8, 4.8, 5.8, 6.8, 7.8, 8.8],
        ],
        np.float64,
    )
    b = np.ones((8, 8), np.float64)
    c = np.zeros((8, 8), np.float64)

    mem_a = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(a)))
    mem_b = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(b)))
    mem_c = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(c)))

    # Allocate a MemRefDescriptor to receive the output tensor.
    # The buffer itself is allocated inside the MLIR code generation.
    ref_out = rt.make_nd_memref_descriptor(2, ctypes.c_double)()
    mem_out = ctypes.pointer(ctypes.pointer(ref_out))

    # Invoke the kernel and get numpy output.
    # Built-in bufferization uses in-out buffers.
    engine.invoke("main", mem_out, mem_a, mem_b, mem_c)

    # Sanity check on computed result. Only a few elements
    # are sampled from the full dense matrix multiplication.
    full_matmul = np.matmul(a, b)
    expected = np.zeros((8, 8), np.float64)
    expected[0, 0] = 1.0 * full_matmul[0, 0]
    expected[0, 2] = 2.0 * full_matmul[0, 2]
    expected[4, 1] = 3.0 * full_matmul[4, 1]
    c = rt.ranked_memref_to_numpy(mem_out[0])
    if np.allclose(c, expected):
        pass
    else:
        quit(f"FAILURE")


def main():
    support_lib = os.getenv("SUPPORT_LIB")
    assert support_lib is not None, "SUPPORT_LIB is undefined"
    if not os.path.exists(support_lib):
        raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), support_lib)

    # CHECK-LABEL: TEST: testSDDMMM
    print("\nTEST: testSDDMMM")
    count = 0
    with ir.Context() as ctx, ir.Location.unknown():
        # Loop over various ways to compile and annotate the SDDMM kernel with
        # a *single* sparse tensor. Note that we deliberate do not exhaustively
        # search the full state space to reduce runtime of the test. It is
        # straightforward to adapt the code below to explore more combinations.
        # For these simple orderings, dim2lvl and lvl2dim are the same.
        builder = st.EncodingAttr.build_level_type
        fmt = st.LevelFormat
        prop = st.LevelProperty
        levels = [
            [builder(fmt.compressed, [prop.non_unique]), builder(fmt.singleton)],
            [builder(fmt.dense), builder(fmt.dense)],
            [builder(fmt.dense), builder(fmt.compressed)],
            [builder(fmt.compressed), builder(fmt.dense)],
            [builder(fmt.compressed), builder(fmt.compressed)],
        ]
        orderings = [
            ir.AffineMap.get_permutation([0, 1]),
            ir.AffineMap.get_permutation([1, 0]),
        ]
        for level in levels:
            for ordering in orderings:
                for pwidth in [32]:
                    for iwidth in [32]:
                        for e in [True]:
                            attr = st.EncodingAttr.get(
                                level, ordering, ordering, pwidth, iwidth
                            )
                            opt = f"parallelization-strategy=none"
                            compiler = sparsifier.Sparsifier(
                                extras="",
                                options=opt,
                                opt_level=0,
                                shared_libs=[support_lib],
                            )
                            build_compile_and_run_SDDMMM(attr, compiler)
                            count = count + 1
    # CHECK: Passed 10 tests
    print("Passed ", count, "tests")


if __name__ == "__main__":
    main()