//--------------------------------------------------------------------------------------------------
// 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)
}>
#SparseMatrix = #sparse_tensor.encoding<{
map = (d0, d1) -> (d0 : compressed, d1 : compressed)
}>
#trait_1d = {
indexing_maps = [
affine_map<(i) -> (i)>, // a
affine_map<(i) -> (i)> // x (out)
],
iterator_types = ["parallel"],
doc = "X(i) = a(i) op i"
}
#trait_2d = {
indexing_maps = [
affine_map<(i,j) -> (i,j)>, // A
affine_map<(i,j) -> (i,j)> // X (out)
],
iterator_types = ["parallel", "parallel"],
doc = "X(i,j) = A(i,j) op i op j"
}
//
// Test with indices and sparse inputs. All outputs are dense.
//
module {
//
// Kernel that uses index in the index notation (conjunction).
//
func.func @sparse_index_1d_conj(%arga: tensor<8xi64, #SparseVector>,
%out: tensor<8xi64>) -> tensor<8xi64> {
%r = linalg.generic #trait_1d
ins(%arga: tensor<8xi64, #SparseVector>)
outs(%out: tensor<8xi64>) {
^bb(%a: i64, %x: i64):
%i = linalg.index 0 : index
%ii = arith.index_cast %i : index to i64
%m1 = arith.muli %a, %ii : i64
linalg.yield %m1 : i64
} -> tensor<8xi64>
return %r : tensor<8xi64>
}
//
// Kernel that uses index in the index notation (disjunction).
//
func.func @sparse_index_1d_disj(%arga: tensor<8xi64, #SparseVector>,
%out: tensor<8xi64>) -> tensor<8xi64> {
%r = linalg.generic #trait_1d
ins(%arga: tensor<8xi64, #SparseVector>)
outs(%out: tensor<8xi64>) {
^bb(%a: i64, %x: i64):
%i = linalg.index 0 : index
%ii = arith.index_cast %i : index to i64
%m1 = arith.addi %a, %ii : i64
linalg.yield %m1 : i64
} -> tensor<8xi64>
return %r : tensor<8xi64>
}
//
// Kernel that uses indices in the index notation (conjunction).
//
func.func @sparse_index_2d_conj(%arga: tensor<3x4xi64, #SparseMatrix>,
%out: tensor<3x4xi64>) -> tensor<3x4xi64> {
%r = linalg.generic #trait_2d
ins(%arga: tensor<3x4xi64, #SparseMatrix>)
outs(%out: tensor<3x4xi64>) {
^bb(%a: i64, %x: i64):
%i = linalg.index 0 : index
%j = linalg.index 1 : index
%ii = arith.index_cast %i : index to i64
%jj = arith.index_cast %j : index to i64
%m1 = arith.muli %ii, %a : i64
%m2 = arith.muli %jj, %m1 : i64
linalg.yield %m2 : i64
} -> tensor<3x4xi64>
return %r : tensor<3x4xi64>
}
//
// Kernel that uses indices in the index notation (disjunction).
//
func.func @sparse_index_2d_disj(%arga: tensor<3x4xi64, #SparseMatrix>,
%out: tensor<3x4xi64>) -> tensor<3x4xi64> {
%r = linalg.generic #trait_2d
ins(%arga: tensor<3x4xi64, #SparseMatrix>)
outs(%out: tensor<3x4xi64>) {
^bb(%a: i64, %x: i64):
%i = linalg.index 0 : index
%j = linalg.index 1 : index
%ii = arith.index_cast %i : index to i64
%jj = arith.index_cast %j : index to i64
%m1 = arith.addi %ii, %a : i64
%m2 = arith.addi %jj, %m1 : i64
linalg.yield %m2 : i64
} -> tensor<3x4xi64>
return %r : tensor<3x4xi64>
}
//
// Main driver.
//
func.func @main() {
%c0 = arith.constant 0 : index
%du = arith.constant -1 : i64
// Setup input sparse vector.
%v1 = arith.constant sparse<[[2], [4]], [ 10, 20]> : tensor<8xi64>
%sv = sparse_tensor.convert %v1 : tensor<8xi64> to tensor<8xi64, #SparseVector>
// Setup input "sparse" vector.
%v2 = arith.constant dense<[ 1, 2, 4, 8, 16, 32, 64, 128 ]> : tensor<8xi64>
%dv = sparse_tensor.convert %v2 : tensor<8xi64> to tensor<8xi64, #SparseVector>
// Setup input sparse matrix.
%m1 = arith.constant sparse<[[1,1], [2,3]], [10, 20]> : tensor<3x4xi64>
%sm = sparse_tensor.convert %m1 : tensor<3x4xi64> to tensor<3x4xi64, #SparseMatrix>
// Setup input "sparse" matrix.
%m2 = arith.constant dense <[ [ 1, 1, 1, 1 ],
[ 1, 2, 1, 1 ],
[ 1, 1, 3, 4 ] ]> : tensor<3x4xi64>
%dm = sparse_tensor.convert %m2 : tensor<3x4xi64> to tensor<3x4xi64, #SparseMatrix>
// Setup out tensors.
// Note: Constants bufferize to read-only buffers.
%init_8 = tensor.empty() : tensor<8xi64>
%init_3_4 = tensor.empty() : tensor<3x4xi64>
// Call the kernels.
%0 = call @sparse_index_1d_conj(%sv, %init_8) : (tensor<8xi64, #SparseVector>, tensor<8xi64>) -> tensor<8xi64>
%1 = call @sparse_index_1d_disj(%sv, %init_8) : (tensor<8xi64, #SparseVector>, tensor<8xi64>) -> tensor<8xi64>
%2 = call @sparse_index_1d_conj(%dv, %init_8) : (tensor<8xi64, #SparseVector>, tensor<8xi64>) -> tensor<8xi64>
%3 = call @sparse_index_1d_disj(%dv, %init_8) : (tensor<8xi64, #SparseVector>, tensor<8xi64>) -> tensor<8xi64>
%4 = call @sparse_index_2d_conj(%sm, %init_3_4) : (tensor<3x4xi64, #SparseMatrix>, tensor<3x4xi64>) -> tensor<3x4xi64>
%5 = call @sparse_index_2d_disj(%sm, %init_3_4) : (tensor<3x4xi64, #SparseMatrix>, tensor<3x4xi64>) -> tensor<3x4xi64>
%6 = call @sparse_index_2d_conj(%dm, %init_3_4) : (tensor<3x4xi64, #SparseMatrix>, tensor<3x4xi64>) -> tensor<3x4xi64>
%7 = call @sparse_index_2d_disj(%dm, %init_3_4) : (tensor<3x4xi64, #SparseMatrix>, tensor<3x4xi64>) -> tensor<3x4xi64>
//
// Verify result.
//
// CHECK: ( 0, 0, 20, 0, 80, 0, 0, 0 )
// CHECK-NEXT: ( 0, 1, 12, 3, 24, 5, 6, 7 )
// CHECK-NEXT: ( 0, 2, 8, 24, 64, 160, 384, 896 )
// CHECK-NEXT: ( 1, 3, 6, 11, 20, 37, 70, 135 )
// CHECK-NEXT: ( ( 0, 0, 0, 0 ), ( 0, 10, 0, 0 ), ( 0, 0, 0, 120 ) )
// CHECK-NEXT: ( ( 0, 1, 2, 3 ), ( 1, 12, 3, 4 ), ( 2, 3, 4, 25 ) )
// CHECK-NEXT: ( ( 0, 0, 0, 0 ), ( 0, 2, 2, 3 ), ( 0, 2, 12, 24 ) )
// CHECK-NEXT: ( ( 1, 2, 3, 4 ), ( 2, 4, 4, 5 ), ( 3, 4, 7, 9 ) )
//
%vv0 = vector.transfer_read %0[%c0], %du: tensor<8xi64>, vector<8xi64>
%vv1 = vector.transfer_read %1[%c0], %du: tensor<8xi64>, vector<8xi64>
%vv2 = vector.transfer_read %2[%c0], %du: tensor<8xi64>, vector<8xi64>
%vv3 = vector.transfer_read %3[%c0], %du: tensor<8xi64>, vector<8xi64>
%vv4 = vector.transfer_read %4[%c0,%c0], %du: tensor<3x4xi64>, vector<3x4xi64>
%vv5 = vector.transfer_read %5[%c0,%c0], %du: tensor<3x4xi64>, vector<3x4xi64>
%vv6 = vector.transfer_read %6[%c0,%c0], %du: tensor<3x4xi64>, vector<3x4xi64>
%vv7 = vector.transfer_read %7[%c0,%c0], %du: tensor<3x4xi64>, vector<3x4xi64>
vector.print %vv0 : vector<8xi64>
vector.print %vv1 : vector<8xi64>
vector.print %vv2 : vector<8xi64>
vector.print %vv3 : vector<8xi64>
vector.print %vv4 : vector<3x4xi64>
vector.print %vv5 : vector<3x4xi64>
vector.print %vv6 : vector<3x4xi64>
vector.print %vv7 : vector<3x4xi64>
// Release resources.
bufferization.dealloc_tensor %sv : tensor<8xi64, #SparseVector>
bufferization.dealloc_tensor %dv : tensor<8xi64, #SparseVector>
bufferization.dealloc_tensor %sm : tensor<3x4xi64, #SparseMatrix>
bufferization.dealloc_tensor %dm : tensor<3x4xi64, #SparseMatrix>
bufferization.dealloc_tensor %0 : tensor<8xi64>
bufferization.dealloc_tensor %1 : tensor<8xi64>
bufferization.dealloc_tensor %2 : tensor<8xi64>
bufferization.dealloc_tensor %3 : tensor<8xi64>
bufferization.dealloc_tensor %4 : tensor<3x4xi64>
bufferization.dealloc_tensor %5 : tensor<3x4xi64>
bufferization.dealloc_tensor %6 : tensor<3x4xi64>
bufferization.dealloc_tensor %7 : tensor<3x4xi64>
return
}
}
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