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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
// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
#trait_op = {
indexing_maps = [
affine_map<(i) -> (i)>, // a (in)
affine_map<(i) -> (i)> // x (out)
],
iterator_types = ["parallel"],
doc = "x(i) = OP a(i)"
}
module {
func.func @cre(%arga: tensor<?xcomplex<f32>, #SparseVector>)
-> tensor<?xf32, #SparseVector> {
%c = arith.constant 0 : index
%d = tensor.dim %arga, %c : tensor<?xcomplex<f32>, #SparseVector>
%xv = bufferization.alloc_tensor(%d) : tensor<?xf32, #SparseVector>
%0 = linalg.generic #trait_op
ins(%arga: tensor<?xcomplex<f32>, #SparseVector>)
outs(%xv: tensor<?xf32, #SparseVector>) {
^bb(%a: complex<f32>, %x: f32):
%1 = complex.re %a : complex<f32>
linalg.yield %1 : f32
} -> tensor<?xf32, #SparseVector>
return %0 : tensor<?xf32, #SparseVector>
}
func.func @cim(%arga: tensor<?xcomplex<f32>, #SparseVector>)
-> tensor<?xf32, #SparseVector> {
%c = arith.constant 0 : index
%d = tensor.dim %arga, %c : tensor<?xcomplex<f32>, #SparseVector>
%xv = bufferization.alloc_tensor(%d) : tensor<?xf32, #SparseVector>
%0 = linalg.generic #trait_op
ins(%arga: tensor<?xcomplex<f32>, #SparseVector>)
outs(%xv: tensor<?xf32, #SparseVector>) {
^bb(%a: complex<f32>, %x: f32):
%1 = complex.im %a : complex<f32>
linalg.yield %1 : f32
} -> tensor<?xf32, #SparseVector>
return %0 : tensor<?xf32, #SparseVector>
}
func.func @dump(%arg0: tensor<?xf32, #SparseVector>) {
%c0 = arith.constant 0 : index
%d0 = arith.constant -1.0 : f32
%n = sparse_tensor.number_of_entries %arg0 : tensor<?xf32, #SparseVector>
vector.print %n : index
%values = sparse_tensor.values %arg0 : tensor<?xf32, #SparseVector> to memref<?xf32>
%0 = vector.transfer_read %values[%c0], %d0: memref<?xf32>, vector<3xf32>
vector.print %0 : vector<3xf32>
%coordinates = sparse_tensor.coordinates %arg0 { level = 0 : index } : tensor<?xf32, #SparseVector> to memref<?xindex>
%1 = vector.transfer_read %coordinates[%c0], %c0: memref<?xindex>, vector<3xindex>
vector.print %1 : vector<3xindex>
return
}
// Driver method to call and verify functions cim and cre.
func.func @entry() {
// Setup sparse vectors.
%v1 = arith.constant sparse<
[ [0], [20], [31] ],
[ (5.13, 2.0), (3.0, 4.0), (5.0, 6.0) ] > : tensor<32xcomplex<f32>>
%sv1 = sparse_tensor.convert %v1 : tensor<32xcomplex<f32>> to tensor<?xcomplex<f32>, #SparseVector>
// Call sparse vector kernels.
%0 = call @cre(%sv1)
: (tensor<?xcomplex<f32>, #SparseVector>) -> tensor<?xf32, #SparseVector>
%1 = call @cim(%sv1)
: (tensor<?xcomplex<f32>, #SparseVector>) -> tensor<?xf32, #SparseVector>
//
// Verify the results.
//
// CHECK: 3
// CHECK-NEXT: ( 5.13, 3, 5 )
// CHECK-NEXT: ( 0, 20, 31 )
// CHECK-NEXT: 3
// CHECK-NEXT: ( 2, 4, 6 )
// CHECK-NEXT: ( 0, 20, 31 )
//
call @dump(%0) : (tensor<?xf32, #SparseVector>) -> ()
call @dump(%1) : (tensor<?xf32, #SparseVector>) -> ()
// Release the resources.
bufferization.dealloc_tensor %sv1 : tensor<?xcomplex<f32>, #SparseVector>
bufferization.dealloc_tensor %0 : tensor<?xf32, #SparseVector>
bufferization.dealloc_tensor %1 : tensor<?xf32, #SparseVector>
return
}
}
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