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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 vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true"
// 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
// Reduction in this file _are_ supported by the AArch64 SVE backend
#SV = #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>
#trait_reduction = {
indexing_maps = [
affine_map<(i) -> (i)>, // a
affine_map<(i) -> ()> // x (scalar out)
],
iterator_types = ["reduction"],
doc = "x += OPER_i a(i)"
}
// An example of vector reductions.
module {
func.func @sum_reduction_i32(%arga: tensor<32xi32, #SV>,
%argx: tensor<i32>) -> tensor<i32> {
%0 = linalg.generic #trait_reduction
ins(%arga: tensor<32xi32, #SV>)
outs(%argx: tensor<i32>) {
^bb(%a: i32, %x: i32):
%0 = arith.addi %x, %a : i32
linalg.yield %0 : i32
} -> tensor<i32>
return %0 : tensor<i32>
}
func.func @sum_reduction_f32(%arga: tensor<32xf32, #SV>,
%argx: tensor<f32>) -> tensor<f32> {
%0 = linalg.generic #trait_reduction
ins(%arga: tensor<32xf32, #SV>)
outs(%argx: tensor<f32>) {
^bb(%a: f32, %x: f32):
%0 = arith.addf %x, %a : f32
linalg.yield %0 : f32
} -> tensor<f32>
return %0 : tensor<f32>
}
func.func @or_reduction_i32(%arga: tensor<32xi32, #SV>,
%argx: tensor<i32>) -> tensor<i32> {
%0 = linalg.generic #trait_reduction
ins(%arga: tensor<32xi32, #SV>)
outs(%argx: tensor<i32>) {
^bb(%a: i32, %x: i32):
%0 = arith.ori %x, %a : i32
linalg.yield %0 : i32
} -> tensor<i32>
return %0 : tensor<i32>
}
func.func @xor_reduction_i32(%arga: tensor<32xi32, #SV>,
%argx: tensor<i32>) -> tensor<i32> {
%0 = linalg.generic #trait_reduction
ins(%arga: tensor<32xi32, #SV>)
outs(%argx: tensor<i32>) {
^bb(%a: i32, %x: i32):
%0 = arith.xori %x, %a : i32
linalg.yield %0 : i32
} -> tensor<i32>
return %0 : tensor<i32>
}
func.func @dump_i32(%arg0 : tensor<i32>) {
%v = tensor.extract %arg0[] : tensor<i32>
vector.print %v : i32
return
}
func.func @dump_f32(%arg0 : tensor<f32>) {
%v = tensor.extract %arg0[] : tensor<f32>
vector.print %v : f32
return
}
func.func @entry() {
%ri = arith.constant dense< 7 > : tensor<i32>
%rf = arith.constant dense< 2.0 > : tensor<f32>
%c_0_i32 = arith.constant dense<[
0, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 4, 0, 0, 0,
0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0
]> : tensor<32xi32>
%c_0_f32 = arith.constant dense<[
0.0, 1.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0,
0.0, 0.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 2.5, 0.0, 0.0, 0.0,
2.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 9.0
]> : tensor<32xf32>
// Convert constants to annotated tensors.
%sparse_input_i32 = sparse_tensor.convert %c_0_i32
: tensor<32xi32> to tensor<32xi32, #SV>
%sparse_input_f32 = sparse_tensor.convert %c_0_f32
: tensor<32xf32> to tensor<32xf32, #SV>
// Call the kernels.
%0 = call @sum_reduction_i32(%sparse_input_i32, %ri)
: (tensor<32xi32, #SV>, tensor<i32>) -> tensor<i32>
%1 = call @sum_reduction_f32(%sparse_input_f32, %rf)
: (tensor<32xf32, #SV>, tensor<f32>) -> tensor<f32>
%2 = call @or_reduction_i32(%sparse_input_i32, %ri)
: (tensor<32xi32, #SV>, tensor<i32>) -> tensor<i32>
%3 = call @xor_reduction_i32(%sparse_input_i32, %ri)
: (tensor<32xi32, #SV>, tensor<i32>) -> tensor<i32>
// Verify results.
//
// CHECK: 26
// CHECK: 27.5
// CHECK: 15
// CHECK: 10
//
call @dump_i32(%0) : (tensor<i32>) -> ()
call @dump_f32(%1) : (tensor<f32>) -> ()
call @dump_i32(%2) : (tensor<i32>) -> ()
call @dump_i32(%3) : (tensor<i32>) -> ()
// Release the resources.
bufferization.dealloc_tensor %sparse_input_i32 : tensor<32xi32, #SV>
bufferization.dealloc_tensor %sparse_input_f32 : tensor<32xf32, #SV>
return
}
}
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