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// DEFINE: %{option} = enable-runtime-library=true
// DEFINE: %{compile} = mlir-opt %s --sparse-compiler=%{option}
// DEFINE: %{run} = mlir-cpu-runner \
// DEFINE: -e entry -entry-point-result=void \
// DEFINE: -shared-libs=%mlir_c_runner_utils | \
// DEFINE: FileCheck %s
//
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation.
// REDEFINE: %{option} = enable-runtime-library=false
// RUN: %{compile} | %{run}
// Do the same run, but now with direct IR generation and, if available, VLA
// vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false vl=4 enable-arm-sve=%ENABLE_VLA"
// REDEFINE: %{run} = %lli_host_or_aarch64_cmd \
// REDEFINE: --entry-function=entry_lli \
// REDEFINE: --extra-module=%S/Inputs/main_for_lli.ll \
// REDEFINE: %VLA_ARCH_ATTR_OPTIONS \
// REDEFINE: --dlopen=%mlir_native_utils_lib_dir/libmlir_c_runner_utils%shlibext | \
// REDEFINE: FileCheck %s
// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
#map = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
#SparseMatrix = #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "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 = bufferization.alloc_tensor() : 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 = bufferization.alloc_tensor() : 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 @dump(%arg0: tensor<2x3x4xf64, #SparseMatrix>) {
%d0 = arith.constant 0.0 : f64
%c0 = arith.constant 0 : index
%dm = sparse_tensor.convert %arg0 : tensor<2x3x4xf64, #SparseMatrix> to tensor<2x3x4xf64>
%0 = vector.transfer_read %dm[%c0, %c0, %c0], %d0: tensor<2x3x4xf64>, vector<2x3x4xf64>
vector.print %0 : vector<2x3x4xf64>
return
}
func.func public @entry() {
%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: ( ( ( 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: ( ( ( 2, 3, 4, 5 ), ( 6, 7, 8, 9 ), ( 10, 11, 12, 13 ) ), ( ( 14, 15, 16, 17 ), ( 18, 19, 20, 21 ), ( 22, 23, 24, 25 ) ) )
call @dump(%sm_t) : (tensor<2x3x4xf64, #SparseMatrix>) -> ()
call @dump(%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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