//--------------------------------------------------------------------------------------------------
// WHEN CREATING A NEW TEST, PLEASE JUST COPY & PASTE WITHOUT EDITS.
//
// Set-up that's shared across all tests in this directory. In principle, this
// config could be moved to lit.local.cfg. However, there are downstream users that
// do not use these LIT config files. Hence why this is kept inline.
//
// DEFINE: %{sparsifier_opts} = enable-runtime-library=true
// DEFINE: %{sparsifier_opts_sve} = enable-arm-sve=true %{sparsifier_opts}
// DEFINE: %{compile} = mlir-opt %s --sparsifier="%{sparsifier_opts}"
// DEFINE: %{compile_sve} = mlir-opt %s --sparsifier="%{sparsifier_opts_sve}"
// DEFINE: %{run_libs} = -shared-libs=%mlir_c_runner_utils,%mlir_runner_utils
// DEFINE: %{run_libs_sve} = -shared-libs=%native_mlir_runner_utils,%native_mlir_c_runner_utils
// DEFINE: %{run_opts} = -e main -entry-point-result=void
// DEFINE: %{run} = mlir-cpu-runner %{run_opts} %{run_libs}
// DEFINE: %{run_sve} = %mcr_aarch64_cmd --march=aarch64 --mattr="+sve" %{run_opts} %{run_libs_sve}
//
// DEFINE: %{env} =
//--------------------------------------------------------------------------------------------------
// REDEFINE: %{env} = TENSOR0="%mlir_src_dir/test/Integration/data/test_symmetric_complex.mtx"
// RUN: %{compile} | env %{env} %{run} | FileCheck %s
//
// Do the same run, but now with direct IR generation.
// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false
// RUN: %{compile} | env %{env} %{run} | FileCheck %s
//
// Do the same run, but now with vectorization.
// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true
// RUN: %{compile} | env %{env} %{run} | FileCheck %s
//
// Do the same run, but now with VLA vectorization.
// RUN: %if mlir_arm_sve_tests %{ %{compile_sve} | env %{env} %{run_sve} | FileCheck %s %}
// TODO: The test currently only operates on the triangular part of the
// symmetric matrix.
!Filename = !llvm.ptr
#SparseMatrix = #sparse_tensor.encoding<{
map = (d0, d1) -> (d0 : compressed, d1 : compressed)
}>
#trait_sum_reduce = {
indexing_maps = [
affine_map<(i,j) -> (i,j)>, // A
affine_map<(i,j) -> ()> // x (out)
],
iterator_types = ["reduction", "reduction"],
doc = "x += A(i,j)"
}
//
// Integration test that lowers a kernel annotated as sparse to
// actual sparse code, initializes a matching sparse storage scheme
// from file, and runs the resulting code with the JIT compiler.
//
module {
//
// A kernel that sum-reduces a matrix to a single scalar.
//
func.func @kernel_sum_reduce(%arga: tensor<?x?xcomplex<f64>, #SparseMatrix>,
%argx: tensor<complex<f64>>) -> tensor<complex<f64>> {
%0 = linalg.generic #trait_sum_reduce
ins(%arga: tensor<?x?xcomplex<f64>, #SparseMatrix>)
outs(%argx: tensor<complex<f64>>) {
^bb(%a: complex<f64>, %x: complex<f64>):
%0 = complex.add %x, %a : complex<f64>
linalg.yield %0 : complex<f64>
} -> tensor<complex<f64>>
return %0 : tensor<complex<f64>>
}
func.func private @getTensorFilename(index) -> (!Filename)
//
// Main driver that reads matrix from file and calls the sparse kernel.
//
func.func @main() {
//%d0 = arith.constant 0.0 : complex<f64>
%d0 = complex.constant [0.0 : f64, 0.0 : f64] : complex<f64>
%c0 = arith.constant 0 : index
// Setup memory for a single reduction scalar,
// initialized to zero.
// TODO: tensor.from_elements does not support complex.
%alloc = tensor.empty() : tensor<complex<f64>>
%x = tensor.insert %d0 into %alloc[] : tensor<complex<f64>>
// Read the sparse matrix from file, construct sparse storage.
%fileName = call @getTensorFilename(%c0) : (index) -> (!Filename)
%a = sparse_tensor.new %fileName : !Filename to tensor<?x?xcomplex<f64>, #SparseMatrix>
// Call the kernel.
%0 = call @kernel_sum_reduce(%a, %x)
: (tensor<?x?xcomplex<f64>, #SparseMatrix>, tensor<complex<f64>>) -> tensor<complex<f64>>
// Print the result for verification.
//
// CHECK: 24.1
// CHECK-NEXT: 16.1
//
%v = tensor.extract %0[] : tensor<complex<f64>>
%real = complex.re %v : complex<f64>
%imag = complex.im %v : complex<f64>
vector.print %real : f64
vector.print %imag : f64
// Release the resources.
bufferization.dealloc_tensor %0 : tensor<complex<f64>>
bufferization.dealloc_tensor %a : tensor<?x?xcomplex<f64>, #SparseMatrix>
return
}
}
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