//--------------------------------------------------------------------------------------------------
// 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} =
//--------------------------------------------------------------------------------------------------
// RUN: %{compile} | %{run} | FileCheck %s
//
// Do the same run, but now with direct IR generation.
// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false enable-buffer-initialization=true
// RUN: %{compile} | %{run} | FileCheck %s
//
// Do the same run, but now with direct IR generation and vectorization.
// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false enable-buffer-initialization=true vl=2 reassociate-fp-reductions=true enable-index-optimizations=true
// RUN: %{compile} | %{run} | FileCheck %s
//
// Do the same run, but now with direct IR generation and VLA vectorization.
// RUN: %if mlir_arm_sve_tests %{ %{compile_sve} | %{run_sve} | FileCheck %s %}
#DCSR = #sparse_tensor.encoding<{
map = (d0, d1) -> (d0 : compressed, d1 : compressed)
}>
#trait_mult_elt = {
indexing_maps = [
affine_map<(i,j) -> (i,j)>, // A
affine_map<(i,j) -> (i,j)>, // B
affine_map<(i,j) -> (i,j)> // X (out)
],
iterator_types = ["parallel", "parallel"],
doc = "X(i,j) = A(i,j) * B(i,j)"
}
module {
// Sparse kernel.
func.func @sparse_mult_elt(
%arga: tensor<32x16xf32, #DCSR>, %argb: tensor<32x16xf32, #DCSR>) -> tensor<32x16xf32, #DCSR> {
%argx = tensor.empty() : tensor<32x16xf32, #DCSR>
%0 = linalg.generic #trait_mult_elt
ins(%arga, %argb: tensor<32x16xf32, #DCSR>, tensor<32x16xf32, #DCSR>)
outs(%argx: tensor<32x16xf32, #DCSR>) {
^bb(%a: f32, %b: f32, %x: f32):
%1 = arith.mulf %a, %b : f32
linalg.yield %1 : f32
} -> tensor<32x16xf32, #DCSR>
return %0 : tensor<32x16xf32, #DCSR>
}
// Driver method to call and verify kernel.
func.func @main() {
%c0 = arith.constant 0 : index
%f0 = arith.constant 0.0 : f32
// Setup very sparse matrices.
%ta = arith.constant sparse<
[ [2,2], [15,15], [31,0], [31,14] ], [ 2.0, 3.0, -2.0, 4.0 ]
> : tensor<32x16xf32>
%tb = arith.constant sparse<
[ [1,1], [2,0], [2,2], [2,15], [31,0], [31,15] ], [ 5.0, 6.0, 7.0, 8.0, -10.0, 9.0 ]
> : tensor<32x16xf32>
%sta = sparse_tensor.convert %ta
: tensor<32x16xf32> to tensor<32x16xf32, #DCSR>
%stb = sparse_tensor.convert %tb
: tensor<32x16xf32> to tensor<32x16xf32, #DCSR>
// Call kernel.
%0 = call @sparse_mult_elt(%sta, %stb)
: (tensor<32x16xf32, #DCSR>,
tensor<32x16xf32, #DCSR>) -> tensor<32x16xf32, #DCSR>
//
// Verify results. Only two entries stored in result!
//
// CHECK: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 2
// CHECK-NEXT: dim = ( 32, 16 )
// CHECK-NEXT: lvl = ( 32, 16 )
// CHECK-NEXT: pos[0] : ( 0, 2 )
// CHECK-NEXT: crd[0] : ( 2, 31 )
// CHECK-NEXT: pos[1] : ( 0, 1, 2 )
// CHECK-NEXT: crd[1] : ( 2, 0 )
// CHECK-NEXT: values : ( 14, 20 )
// CHECK-NEXT: ----
//
sparse_tensor.print %0 : tensor<32x16xf32, #DCSR>
// Release the resources.
bufferization.dealloc_tensor %sta : tensor<32x16xf32, #DCSR>
bufferization.dealloc_tensor %stb : tensor<32x16xf32, #DCSR>
bufferization.dealloc_tensor %0 : tensor<32x16xf32, #DCSR>
return
}
}
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