//--------------------------------------------------------------------------------------------------
// 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
// RUN: %{compile} | %{run} | FileCheck %s
//
// Do the same run, but now with vectorization.
// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false vl=4
// RUN: %{compile} | %{run} | FileCheck %s
//
// Do the same run, but now with VLA vectorization.
// RUN: %if mlir_arm_sve_tests %{ %{compile_sve} | %{run_sve} | FileCheck %s %}
#map = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
#SparseMatrix = #sparse_tensor.encoding<{ map = (d0, d1, d2) -> (d0 : compressed, d1 : compressed, d2 : compressed) }>
module @func_sparse.2 {
// Do elementwise x+1 when true, x-1 when false
func.func public @condition(%cond: i1, %arg0: tensor<2x3x4xf64, #SparseMatrix>) -> tensor<2x3x4xf64, #SparseMatrix> {
%1 = scf.if %cond -> (tensor<2x3x4xf64, #SparseMatrix>) {
%cst_2 = arith.constant dense<1.000000e+00> : tensor<f64>
%cst_3 = arith.constant dense<1.000000e+00> : tensor<2x3x4xf64>
%2 = tensor.empty() : tensor<2x3x4xf64, #SparseMatrix>
%3 = linalg.generic {
indexing_maps = [#map, #map, #map],
iterator_types = ["parallel", "parallel", "parallel"]}
ins(%arg0, %cst_3 : tensor<2x3x4xf64, #SparseMatrix>, tensor<2x3x4xf64>)
outs(%2 : tensor<2x3x4xf64, #SparseMatrix>) {
^bb0(%arg1: f64, %arg2: f64, %arg3: f64):
%4 = arith.subf %arg1, %arg2 : f64
linalg.yield %4 : f64
} -> tensor<2x3x4xf64, #SparseMatrix>
scf.yield %3 : tensor<2x3x4xf64, #SparseMatrix>
} else {
%cst_2 = arith.constant dense<1.000000e+00> : tensor<f64>
%cst_3 = arith.constant dense<1.000000e+00> : tensor<2x3x4xf64>
%2 = tensor.empty() : tensor<2x3x4xf64, #SparseMatrix>
%3 = linalg.generic {
indexing_maps = [#map, #map, #map],
iterator_types = ["parallel", "parallel", "parallel"]}
ins(%arg0, %cst_3 : tensor<2x3x4xf64, #SparseMatrix>, tensor<2x3x4xf64>)
outs(%2 : tensor<2x3x4xf64, #SparseMatrix>) {
^bb0(%arg1: f64, %arg2: f64, %arg3: f64):
%4 = arith.addf %arg1, %arg2 : f64
linalg.yield %4 : f64
} -> tensor<2x3x4xf64, #SparseMatrix>
scf.yield %3 : tensor<2x3x4xf64, #SparseMatrix>
}
return %1 : tensor<2x3x4xf64, #SparseMatrix>
}
func.func public @main() {
%src = arith.constant dense<[
[ [ 1.0, 2.0, 3.0, 4.0 ],
[ 5.0, 6.0, 7.0, 8.0 ],
[ 9.0, 10.0, 11.0, 12.0 ] ],
[ [ 13.0, 14.0, 15.0, 16.0 ],
[ 17.0, 18.0, 19.0, 20.0 ],
[ 21.0, 22.0, 23.0, 24.0 ] ]
]> : tensor<2x3x4xf64>
%t = arith.constant 1 : i1
%f = arith.constant 0 : i1
%sm = sparse_tensor.convert %src : tensor<2x3x4xf64> to tensor<2x3x4xf64, #SparseMatrix>
%sm_t = call @condition(%t, %sm) : (i1, tensor<2x3x4xf64, #SparseMatrix>) -> tensor<2x3x4xf64, #SparseMatrix>
%sm_f = call @condition(%f, %sm) : (i1, tensor<2x3x4xf64, #SparseMatrix>) -> tensor<2x3x4xf64, #SparseMatrix>
//
// CHECK: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 24
// CHECK-NEXT: dim = ( 2, 3, 4 )
// CHECK-NEXT: lvl = ( 2, 3, 4 )
// CHECK-NEXT: pos[0] : ( 0, 2 )
// CHECK-NEXT: crd[0] : ( 0, 1 )
// CHECK-NEXT: pos[1] : ( 0, 3, 6 )
// CHECK-NEXT: crd[1] : ( 0, 1, 2, 0, 1, 2 )
// CHECK-NEXT: pos[2] : ( 0, 4, 8, 12, 16, 20, 24 )
// CHECK-NEXT: crd[2] : ( 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3 )
// CHECK-NEXT: values : ( 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 )
// CHECK-NEXT: ----
// CHECK: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 24
// CHECK-NEXT: dim = ( 2, 3, 4 )
// CHECK-NEXT: lvl = ( 2, 3, 4 )
// CHECK-NEXT: pos[0] : ( 0, 2 )
// CHECK-NEXT: crd[0] : ( 0, 1 )
// CHECK-NEXT: pos[1] : ( 0, 3, 6 )
// CHECK-NEXT: crd[1] : ( 0, 1, 2, 0, 1, 2 )
// CHECK-NEXT: pos[2] : ( 0, 4, 8, 12, 16, 20, 24 )
// CHECK-NEXT: crd[2] : ( 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3 )
// CHECK-NEXT: values : ( 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25 )
// CHECK-NEXT: ----
//
sparse_tensor.print %sm_t : tensor<2x3x4xf64, #SparseMatrix>
sparse_tensor.print %sm_f : tensor<2x3x4xf64, #SparseMatrix>
bufferization.dealloc_tensor %sm : tensor<2x3x4xf64, #SparseMatrix>
bufferization.dealloc_tensor %sm_t : tensor<2x3x4xf64, #SparseMatrix>
bufferization.dealloc_tensor %sm_f : tensor<2x3x4xf64, #SparseMatrix>
return
}
}
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