//--------------------------------------------------------------------------------------------------
// 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 direct IR generation and vectorization.
// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false 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_op1 = {
indexing_maps = [
affine_map<(i) -> (i)>, // a (in)
affine_map<(i) -> (i)> // x (out)
],
iterator_types = ["parallel"],
doc = "x(i) = OP a(i)"
}
#trait_op2 = {
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 @cops(%arga: tensor<?xcomplex<f64>, #SparseVector>,
%argb: tensor<?xcomplex<f64>, #SparseVector>)
-> tensor<?xcomplex<f64>, #SparseVector> {
%c0 = arith.constant 0 : index
%d = tensor.dim %arga, %c0 : tensor<?xcomplex<f64>, #SparseVector>
%xv = tensor.empty(%d) : tensor<?xcomplex<f64>, #SparseVector>
%0 = linalg.generic #trait_op2
ins(%arga, %argb: tensor<?xcomplex<f64>, #SparseVector>,
tensor<?xcomplex<f64>, #SparseVector>)
outs(%xv: tensor<?xcomplex<f64>, #SparseVector>) {
^bb(%a: complex<f64>, %b: complex<f64>, %x: complex<f64>):
%1 = complex.neg %b : complex<f64>
%2 = complex.sub %a, %1 : complex<f64>
linalg.yield %2 : complex<f64>
} -> tensor<?xcomplex<f64>, #SparseVector>
return %0 : tensor<?xcomplex<f64>, #SparseVector>
}
func.func @csin(%arga: tensor<?xcomplex<f64>, #SparseVector>)
-> tensor<?xcomplex<f64>, #SparseVector> {
%c0 = arith.constant 0 : index
%d = tensor.dim %arga, %c0 : tensor<?xcomplex<f64>, #SparseVector>
%xv = tensor.empty(%d) : tensor<?xcomplex<f64>, #SparseVector>
%0 = linalg.generic #trait_op1
ins(%arga: tensor<?xcomplex<f64>, #SparseVector>)
outs(%xv: tensor<?xcomplex<f64>, #SparseVector>) {
^bb(%a: complex<f64>, %x: complex<f64>):
%1 = complex.sin %a : complex<f64>
linalg.yield %1 : complex<f64>
} -> tensor<?xcomplex<f64>, #SparseVector>
return %0 : tensor<?xcomplex<f64>, #SparseVector>
}
func.func @complex_sqrt(%arga: tensor<?xcomplex<f64>, #SparseVector>)
-> tensor<?xcomplex<f64>, #SparseVector> {
%c0 = arith.constant 0 : index
%d = tensor.dim %arga, %c0 : tensor<?xcomplex<f64>, #SparseVector>
%xv = tensor.empty(%d) : tensor<?xcomplex<f64>, #SparseVector>
%0 = linalg.generic #trait_op1
ins(%arga: tensor<?xcomplex<f64>, #SparseVector>)
outs(%xv: tensor<?xcomplex<f64>, #SparseVector>) {
^bb(%a: complex<f64>, %x: complex<f64>):
%1 = complex.sqrt %a : complex<f64>
linalg.yield %1 : complex<f64>
} -> tensor<?xcomplex<f64>, #SparseVector>
return %0 : tensor<?xcomplex<f64>, #SparseVector>
}
func.func @complex_tanh(%arga: tensor<?xcomplex<f64>, #SparseVector>)
-> tensor<?xcomplex<f64>, #SparseVector> {
%c0 = arith.constant 0 : index
%d = tensor.dim %arga, %c0 : tensor<?xcomplex<f64>, #SparseVector>
%xv = tensor.empty(%d) : tensor<?xcomplex<f64>, #SparseVector>
%0 = linalg.generic #trait_op1
ins(%arga: tensor<?xcomplex<f64>, #SparseVector>)
outs(%xv: tensor<?xcomplex<f64>, #SparseVector>) {
^bb(%a: complex<f64>, %x: complex<f64>):
%1 = complex.tanh %a : complex<f64>
linalg.yield %1 : complex<f64>
} -> tensor<?xcomplex<f64>, #SparseVector>
return %0 : tensor<?xcomplex<f64>, #SparseVector>
}
func.func @clog1p_expm1(%arga: tensor<?xcomplex<f64>, #SparseVector>)
-> tensor<?xcomplex<f64>, #SparseVector> {
%c0 = arith.constant 0 : index
%d = tensor.dim %arga, %c0 : tensor<?xcomplex<f64>, #SparseVector>
%xv = tensor.empty(%d) : tensor<?xcomplex<f64>, #SparseVector>
%0 = linalg.generic #trait_op1
ins(%arga: tensor<?xcomplex<f64>, #SparseVector>)
outs(%xv: tensor<?xcomplex<f64>, #SparseVector>) {
^bb(%a: complex<f64>, %x: complex<f64>):
%1 = complex.log1p %a : complex<f64>
%2 = complex.expm1 %1 : complex<f64>
linalg.yield %2 : complex<f64>
} -> tensor<?xcomplex<f64>, #SparseVector>
return %0 : tensor<?xcomplex<f64>, #SparseVector>
}
func.func @cdiv(%arga: tensor<?xcomplex<f64>, #SparseVector>)
-> tensor<?xcomplex<f64>, #SparseVector> {
%c0 = arith.constant 0 : index
%d = tensor.dim %arga, %c0 : tensor<?xcomplex<f64>, #SparseVector>
%xv = tensor.empty(%d) : tensor<?xcomplex<f64>, #SparseVector>
%c = complex.constant [2.0 : f64, 0.0 : f64] : complex<f64>
%0 = linalg.generic #trait_op1
ins(%arga: tensor<?xcomplex<f64>, #SparseVector>)
outs(%xv: tensor<?xcomplex<f64>, #SparseVector>) {
^bb(%a: complex<f64>, %x: complex<f64>):
%1 = complex.div %a, %c : complex<f64>
linalg.yield %1 : complex<f64>
} -> tensor<?xcomplex<f64>, #SparseVector>
return %0 : tensor<?xcomplex<f64>, #SparseVector>
}
func.func @cabs(%arga: tensor<?xcomplex<f64>, #SparseVector>)
-> tensor<?xf64, #SparseVector> {
%c0 = arith.constant 0 : index
%d = tensor.dim %arga, %c0 : tensor<?xcomplex<f64>, #SparseVector>
%xv = tensor.empty(%d) : tensor<?xf64, #SparseVector>
%0 = linalg.generic #trait_op1
ins(%arga: tensor<?xcomplex<f64>, #SparseVector>)
outs(%xv: tensor<?xf64, #SparseVector>) {
^bb(%a: complex<f64>, %x: f64):
%1 = complex.abs %a : complex<f64>
linalg.yield %1 : f64
} -> tensor<?xf64, #SparseVector>
return %0 : tensor<?xf64, #SparseVector>
}
// Driver method to call and verify complex kernels.
func.func @main() {
// Setup sparse vectors.
%v1 = arith.constant sparse<
[ [0], [28], [31] ],
[ (-5.13, 2.0), (3.0, 4.0), (5.0, 6.0) ] > : tensor<32xcomplex<f64>>
%v2 = arith.constant sparse<
[ [1], [28], [31] ],
[ (1.0, 0.0), (-2.0, 0.0), (3.0, 0.0) ] > : tensor<32xcomplex<f64>>
%sv1 = sparse_tensor.convert %v1 : tensor<32xcomplex<f64>> to tensor<?xcomplex<f64>, #SparseVector>
%sv2 = sparse_tensor.convert %v2 : tensor<32xcomplex<f64>> to tensor<?xcomplex<f64>, #SparseVector>
// Call sparse vector kernels.
%0 = call @cops(%sv1, %sv2)
: (tensor<?xcomplex<f64>, #SparseVector>,
tensor<?xcomplex<f64>, #SparseVector>) -> tensor<?xcomplex<f64>, #SparseVector>
%1 = call @csin(%sv1)
: (tensor<?xcomplex<f64>, #SparseVector>) -> tensor<?xcomplex<f64>, #SparseVector>
%2 = call @complex_sqrt(%sv1)
: (tensor<?xcomplex<f64>, #SparseVector>) -> tensor<?xcomplex<f64>, #SparseVector>
%3 = call @complex_tanh(%sv2)
: (tensor<?xcomplex<f64>, #SparseVector>) -> tensor<?xcomplex<f64>, #SparseVector>
%4 = call @clog1p_expm1(%sv1)
: (tensor<?xcomplex<f64>, #SparseVector>) -> tensor<?xcomplex<f64>, #SparseVector>
%5 = call @cdiv(%sv1)
: (tensor<?xcomplex<f64>, #SparseVector>) -> tensor<?xcomplex<f64>, #SparseVector>
%6 = call @cabs(%sv1)
: (tensor<?xcomplex<f64>, #SparseVector>) -> tensor<?xf64, #SparseVector>
//
// Verify the results.
//
// CHECK: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 4
// CHECK-NEXT: dim = ( 32 )
// CHECK-NEXT: lvl = ( 32 )
// CHECK-NEXT: pos[0] : ( 0, 4 )
// CHECK-NEXT: crd[0] : ( 0, 1, 28, 31 )
// CHECK-NEXT: values : ( ( -5.13, 2 ), ( 1, 0 ), ( 1, 4 ), ( 8, 6 ) )
// CHECK-NEXT: ----
//
// CHECK-NEXT: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 3
// CHECK-NEXT: dim = ( 32 )
// CHECK-NEXT: lvl = ( 32 )
// CHECK-NEXT: pos[0] : ( 0, 3 )
// CHECK-NEXT: crd[0] : ( 0, 28, 31 )
// CHECK-NEXT: values : ( ( 3.43887, 1.47097 ), ( 3.85374, -27.0168 ), ( -193.43, 57.2184 ) )
// CHECK-NEXT: ----
//
// CHECK-NEXT: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 3
// CHECK-NEXT: dim = ( 32 )
// CHECK-NEXT: lvl = ( 32 )
// CHECK-NEXT: pos[0] : ( 0, 3 )
// CHECK-NEXT: crd[0] : ( 0, 28, 31 )
// CHECK-NEXT: values : ( ( 0.433635, 2.30609 ), ( 2, 1 ), ( 2.53083, 1.18538 ) )
// CHECK-NEXT: ----
//
// CHECK-NEXT: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 3
// CHECK-NEXT: dim = ( 32 )
// CHECK-NEXT: lvl = ( 32 )
// CHECK-NEXT: pos[0] : ( 0, 3 )
// CHECK-NEXT: crd[0] : ( 1, 28, 31 )
// CHECK-NEXT: values : ( ( 0.761594, 0 ), ( -0.964028, 0 ), ( 0.995055, 0 ) )
// CHECK-NEXT: ----
//
// CHECK-NEXT: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 3
// CHECK-NEXT: dim = ( 32 )
// CHECK-NEXT: lvl = ( 32 )
// CHECK-NEXT: pos[0] : ( 0, 3 )
// CHECK-NEXT: crd[0] : ( 0, 28, 31 )
// CHECK-NEXT: values : ( ( -5.13, 2 ), ( 3, 4 ), ( 5, 6 ) )
// CHECK-NEXT: ----
//
// CHECK-NEXT: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 3
// CHECK-NEXT: dim = ( 32 )
// CHECK-NEXT: lvl = ( 32 )
// CHECK-NEXT: pos[0] : ( 0, 3 )
// CHECK-NEXT: crd[0] : ( 0, 28, 31 )
// CHECK-NEXT: values : ( ( -2.565, 1 ), ( 1.5, 2 ), ( 2.5, 3 ) )
// CHECK-NEXT: ----
//
// CHECK-NEXT: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 3
// CHECK-NEXT: dim = ( 32 )
// CHECK-NEXT: lvl = ( 32 )
// CHECK-NEXT: pos[0] : ( 0, 3 )
// CHECK-NEXT: crd[0] : ( 0, 28, 31 )
// CHECK-NEXT: values : ( 5.50608, 5, 7.81025 )
// CHECK-NEXT: ----
//
sparse_tensor.print %0 : tensor<?xcomplex<f64>, #SparseVector>
sparse_tensor.print %1 : tensor<?xcomplex<f64>, #SparseVector>
sparse_tensor.print %2 : tensor<?xcomplex<f64>, #SparseVector>
sparse_tensor.print %3 : tensor<?xcomplex<f64>, #SparseVector>
sparse_tensor.print %4 : tensor<?xcomplex<f64>, #SparseVector>
sparse_tensor.print %5 : tensor<?xcomplex<f64>, #SparseVector>
sparse_tensor.print %6 : tensor<?xf64, #SparseVector>
// Release the resources.
bufferization.dealloc_tensor %sv1 : tensor<?xcomplex<f64>, #SparseVector>
bufferization.dealloc_tensor %sv2 : tensor<?xcomplex<f64>, #SparseVector>
bufferization.dealloc_tensor %0 : tensor<?xcomplex<f64>, #SparseVector>
bufferization.dealloc_tensor %1 : tensor<?xcomplex<f64>, #SparseVector>
bufferization.dealloc_tensor %2 : tensor<?xcomplex<f64>, #SparseVector>
bufferization.dealloc_tensor %3 : tensor<?xcomplex<f64>, #SparseVector>
bufferization.dealloc_tensor %4 : tensor<?xcomplex<f64>, #SparseVector>
bufferization.dealloc_tensor %5 : tensor<?xcomplex<f64>, #SparseVector>
bufferization.dealloc_tensor %6 : tensor<?xf64, #SparseVector>
return
}
}
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