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//--------------------------------------------------------------------------------------------------
// 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: %{sparse_compiler_opts} = enable-runtime-library=true
// DEFINE: %{sparse_compiler_opts_sve} = enable-arm-sve=true %{sparse_compiler_opts}
// DEFINE: %{compile} = mlir-opt %s --sparse-compiler="%{sparse_compiler_opts}"
// DEFINE: %{compile_sve} = mlir-opt %s --sparse-compiler="%{sparse_compiler_opts_sve}"
// DEFINE: %{run_libs} = -shared-libs=%mlir_c_runner_utils,%mlir_runner_utils
// DEFINE: %{run_opts} = -e entry -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}
//
// DEFINE: %{env} =
//--------------------------------------------------------------------------------------------------
// RUN: %{compile} | %{run} | FileCheck %s
//
// Do the same run, but now with direct IR generation.
// REDEFINE: %{sparse_compiler_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: %{sparse_compiler_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 %}
#SparseVector = #sparse_tensor.encoding<{map = (d0) -> (d0 : compressed)}>
#trait_op = {
indexing_maps = [
affine_map<(i) -> (i)>, // a (in)
affine_map<(i) -> (i)>, // b (in)
affine_map<(i) -> (i)> // x (out)
],
iterator_types = ["parallel"],
doc = "x(i) = a(i) OP b(i)"
}
module {
func.func @cadd(%arga: tensor<?xcomplex<f32>, #SparseVector>,
%argb: tensor<?xcomplex<f32>, #SparseVector>)
-> tensor<?xcomplex<f32>, #SparseVector> {
%c = arith.constant 0 : index
%d = tensor.dim %arga, %c : tensor<?xcomplex<f32>, #SparseVector>
%xv = bufferization.alloc_tensor(%d) : tensor<?xcomplex<f32>, #SparseVector>
%0 = linalg.generic #trait_op
ins(%arga, %argb: tensor<?xcomplex<f32>, #SparseVector>,
tensor<?xcomplex<f32>, #SparseVector>)
outs(%xv: tensor<?xcomplex<f32>, #SparseVector>) {
^bb(%a: complex<f32>, %b: complex<f32>, %x: complex<f32>):
%1 = complex.add %a, %b : complex<f32>
linalg.yield %1 : complex<f32>
} -> tensor<?xcomplex<f32>, #SparseVector>
return %0 : tensor<?xcomplex<f32>, #SparseVector>
}
func.func @cmul(%arga: tensor<?xcomplex<f32>, #SparseVector>,
%argb: tensor<?xcomplex<f32>, #SparseVector>)
-> tensor<?xcomplex<f32>, #SparseVector> {
%c = arith.constant 0 : index
%d = tensor.dim %arga, %c : tensor<?xcomplex<f32>, #SparseVector>
%xv = bufferization.alloc_tensor(%d) : tensor<?xcomplex<f32>, #SparseVector>
%0 = linalg.generic #trait_op
ins(%arga, %argb: tensor<?xcomplex<f32>, #SparseVector>,
tensor<?xcomplex<f32>, #SparseVector>)
outs(%xv: tensor<?xcomplex<f32>, #SparseVector>) {
^bb(%a: complex<f32>, %b: complex<f32>, %x: complex<f32>):
%1 = complex.mul %a, %b : complex<f32>
linalg.yield %1 : complex<f32>
} -> tensor<?xcomplex<f32>, #SparseVector>
return %0 : tensor<?xcomplex<f32>, #SparseVector>
}
func.func @dump(%arg0: tensor<?xcomplex<f32>, #SparseVector>, %d: index) {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%mem = sparse_tensor.values %arg0 : tensor<?xcomplex<f32>, #SparseVector> to memref<?xcomplex<f32>>
scf.for %i = %c0 to %d step %c1 {
%v = memref.load %mem[%i] : memref<?xcomplex<f32>>
%real = complex.re %v : complex<f32>
%imag = complex.im %v : complex<f32>
vector.print %real : f32
vector.print %imag : f32
}
return
}
// Driver method to call and verify complex kernels.
func.func @entry() {
// Setup sparse vectors.
%v1 = arith.constant sparse<
[ [0], [28], [31] ],
[ (511.13, 2.0), (3.0, 4.0), (5.0, 6.0) ] > : tensor<32xcomplex<f32>>
%v2 = arith.constant sparse<
[ [1], [28], [31] ],
[ (1.0, 0.0), (2.0, 0.0), (3.0, 0.0) ] > : tensor<32xcomplex<f32>>
%sv1 = sparse_tensor.convert %v1 : tensor<32xcomplex<f32>> to tensor<?xcomplex<f32>, #SparseVector>
%sv2 = sparse_tensor.convert %v2 : tensor<32xcomplex<f32>> to tensor<?xcomplex<f32>, #SparseVector>
// Call sparse vector kernels.
%0 = call @cadd(%sv1, %sv2)
: (tensor<?xcomplex<f32>, #SparseVector>,
tensor<?xcomplex<f32>, #SparseVector>) -> tensor<?xcomplex<f32>, #SparseVector>
%1 = call @cmul(%sv1, %sv2)
: (tensor<?xcomplex<f32>, #SparseVector>,
tensor<?xcomplex<f32>, #SparseVector>) -> tensor<?xcomplex<f32>, #SparseVector>
//
// Verify the results.
//
// CHECK: 511.13
// CHECK-NEXT: 2
// CHECK-NEXT: 1
// CHECK-NEXT: 0
// CHECK-NEXT: 5
// CHECK-NEXT: 4
// CHECK-NEXT: 8
// CHECK-NEXT: 6
// CHECK-NEXT: 6
// CHECK-NEXT: 8
// CHECK-NEXT: 15
// CHECK-NEXT: 18
//
%d1 = arith.constant 4 : index
%d2 = arith.constant 2 : index
call @dump(%0, %d1) : (tensor<?xcomplex<f32>, #SparseVector>, index) -> ()
call @dump(%1, %d2) : (tensor<?xcomplex<f32>, #SparseVector>, index) -> ()
// Release the resources.
bufferization.dealloc_tensor %sv1 : tensor<?xcomplex<f32>, #SparseVector>
bufferization.dealloc_tensor %sv2 : tensor<?xcomplex<f32>, #SparseVector>
bufferization.dealloc_tensor %0 : tensor<?xcomplex<f32>, #SparseVector>
bufferization.dealloc_tensor %1 : tensor<?xcomplex<f32>, #SparseVector>
return
}
}
|
