# 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()
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