//--------------------------------------------------------------------------------------------------
// 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 %}
#Row = #sparse_tensor.encoding<{
map = (d0, d1) -> (d0 : compressed, d1 : dense)
}>
#CSR = #sparse_tensor.encoding<{
map = (d0, d1) -> (d0 : dense, d1 : compressed)
}>
#DCSC = #sparse_tensor.encoding<{
map = (d0, d1) -> (d1 : compressed, d0 : compressed)
}>
#SortedCOO = #sparse_tensor.encoding<{
map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton)
}>
#SortedCOOPerm = #sparse_tensor.encoding<{
map = (d0, d1) -> (d1 : compressed(nonunique), d0 : singleton)
}>
#CCCPerm = #sparse_tensor.encoding<{
map = (d0, d1, d2) -> (d1 : compressed, d2 : compressed, d0 : compressed)
}>
module {
/// uses foreach operator to print coords and values.
func.func @foreach_print_const() {
// Initialize a tensor.
%0 = arith.constant sparse<[[0, 0], [1, 6]], [1.0, 5.0]> : tensor<8x7xf32>
sparse_tensor.foreach in %0 : tensor<8x7xf32> do {
^bb0(%1: index, %2: index, %v: f32) :
vector.print %1: index
vector.print %2: index
vector.print %v: f32
}
return
}
/// uses foreach operator to print coords and values.
func.func @foreach_print_1(%arg0: tensor<2x2xf64, #Row>) {
sparse_tensor.foreach in %arg0 : tensor<2x2xf64, #Row> do {
^bb0(%1: index, %2: index, %v: f64) :
vector.print %1: index
vector.print %2: index
vector.print %v: f64
}
return
}
func.func @foreach_print_2(%arg0: tensor<2x2xf64, #CSR>) {
sparse_tensor.foreach in %arg0 : tensor<2x2xf64, #CSR> do {
^bb0(%1: index, %2: index, %v: f64) :
vector.print %1: index
vector.print %2: index
vector.print %v: f64
}
return
}
func.func @foreach_print_3(%arg0: tensor<2x2xf64, #DCSC>) {
sparse_tensor.foreach in %arg0 : tensor<2x2xf64, #DCSC> do {
^bb0(%1: index, %2: index, %v: f64) :
vector.print %1: index
vector.print %2: index
vector.print %v: f64
}
return
}
func.func @foreach_print_4(%arg0: tensor<2x2xf64, #SortedCOO>) {
sparse_tensor.foreach in %arg0 : tensor<2x2xf64, #SortedCOO> do {
^bb0(%1: index, %2: index, %v: f64) :
vector.print %1: index
vector.print %2: index
vector.print %v: f64
}
return
}
func.func @foreach_print_5(%arg0: tensor<2x2xf64, #SortedCOOPerm>) {
sparse_tensor.foreach in %arg0 : tensor<2x2xf64, #SortedCOOPerm> do {
^bb0(%1: index, %2: index, %v: f64) :
vector.print %1: index
vector.print %2: index
vector.print %v: f64
}
return
}
func.func @foreach_print_3d(%arg0: tensor<7x8x9xf64, #CCCPerm>) {
sparse_tensor.foreach in %arg0 : tensor<7x8x9xf64, #CCCPerm> do {
^bb0(%1: index, %2: index, %3: index, %v: f64) :
vector.print %1: index
vector.print %2: index
vector.print %3: index
vector.print %v: f64
}
return
}
func.func @foreach_print_dense(%arg0: tensor<2x2xf64>) {
sparse_tensor.foreach in %arg0 : tensor<2x2xf64> do {
^bb0(%1: index, %2: index, %v: f64) :
vector.print %1: index
vector.print %2: index
vector.print %v: f64
}
return
}
//
// Main driver.
//
func.func @main() {
//
// Initialize a 3-dim dense tensor.
//
%src = arith.constant dense<
[[ 1.0, 2.0],
[ 5.0, 6.0]]
> : tensor<2x2xf64>
%src3d = arith.constant sparse<
[[1, 2, 3], [4, 5, 6]], [1.0, 2.0]
> : tensor<7x8x9xf64>
//
// Convert dense tensor directly to various sparse tensors.
//
%s1 = sparse_tensor.convert %src : tensor<2x2xf64> to tensor<2x2xf64, #Row>
%s2 = sparse_tensor.convert %src : tensor<2x2xf64> to tensor<2x2xf64, #CSR>
%s3 = sparse_tensor.convert %src : tensor<2x2xf64> to tensor<2x2xf64, #DCSC>
%s4 = sparse_tensor.convert %src : tensor<2x2xf64> to tensor<2x2xf64, #SortedCOO>
%s5 = sparse_tensor.convert %src : tensor<2x2xf64> to tensor<2x2xf64, #SortedCOOPerm>
%s6 = sparse_tensor.convert %src3d : tensor<7x8x9xf64> to tensor<7x8x9xf64, #CCCPerm>
// CHECK: 0
// CHECK-NEXT: 0
// CHECK-NEXT: 1
// CHECK-NEXT: 1
// CHECK-NEXT: 6
// CHECK-NEXT: 5
call @foreach_print_const() : () -> ()
// CHECK-NEXT: 0
// CHECK-NEXT: 0
// CHECK-NEXT: 1
// CHECK-NEXT: 0
// CHECK-NEXT: 1
// CHECK-NEXT: 2
// CHECK-NEXT: 1
// CHECK-NEXT: 0
// CHECK-NEXT: 5
// CHECK-NEXT: 1
// CHECK-NEXT: 1
// CHECK-NEXT: 6
call @foreach_print_dense(%src) : (tensor<2x2xf64>) -> ()
// CHECK-NEXT: 0
// CHECK-NEXT: 0
// CHECK-NEXT: 1
// CHECK-NEXT: 0
// CHECK-NEXT: 1
// CHECK-NEXT: 2
// CHECK-NEXT: 1
// CHECK-NEXT: 0
// CHECK-NEXT: 5
// CHECK-NEXT: 1
// CHECK-NEXT: 1
// CHECK-NEXT: 6
call @foreach_print_1(%s1) : (tensor<2x2xf64, #Row>) -> ()
// CHECK-NEXT: 0
// CHECK-NEXT: 0
// CHECK-NEXT: 1
// CHECK-NEXT: 0
// CHECK-NEXT: 1
// CHECK-NEXT: 2
// CHECK-NEXT: 1
// CHECK-NEXT: 0
// CHECK-NEXT: 5
// CHECK-NEXT: 1
// CHECK-NEXT: 1
// CHECK-NEXT: 6
call @foreach_print_2(%s2) : (tensor<2x2xf64, #CSR>) -> ()
// CHECK-NEXT: 0
// CHECK-NEXT: 0
// CHECK-NEXT: 1
// CHECK-NEXT: 1
// CHECK-NEXT: 0
// CHECK-NEXT: 5
// CHECK-NEXT: 0
// CHECK-NEXT: 1
// CHECK-NEXT: 2
// CHECK-NEXT: 1
// CHECK-NEXT: 1
// CHECK-NEXT: 6
call @foreach_print_3(%s3) : (tensor<2x2xf64, #DCSC>) -> ()
// CHECK-NEXT: 0
// CHECK-NEXT: 0
// CHECK-NEXT: 1
// CHECK-NEXT: 0
// CHECK-NEXT: 1
// CHECK-NEXT: 2
// CHECK-NEXT: 1
// CHECK-NEXT: 0
// CHECK-NEXT: 5
// CHECK-NEXT: 1
// CHECK-NEXT: 1
// CHECK-NEXT: 6
call @foreach_print_4(%s4) : (tensor<2x2xf64, #SortedCOO>) -> ()
// CHECK-NEXT: 0
// CHECK-NEXT: 0
// CHECK-NEXT: 1
// CHECK-NEXT: 1
// CHECK-NEXT: 0
// CHECK-NEXT: 5
// CHECK-NEXT: 0
// CHECK-NEXT: 1
// CHECK-NEXT: 2
// CHECK-NEXT: 1
// CHECK-NEXT: 1
// CHECK-NEXT: 6
call @foreach_print_5(%s5) : (tensor<2x2xf64, #SortedCOOPerm>) -> ()
// CHECK-NEXT: 1
// CHECK-NEXT: 2
// CHECK-NEXT: 3
// CHECK-NEXT: 1
// CHECK-NEXT: 4
// CHECK-NEXT: 5
// CHECK-NEXT: 6
// CHECK-NEXT: 2
call @foreach_print_3d(%s6): (tensor<7x8x9xf64, #CCCPerm>) -> ()
bufferization.dealloc_tensor %s1 : tensor<2x2xf64, #Row>
bufferization.dealloc_tensor %s2 : tensor<2x2xf64, #CSR>
bufferization.dealloc_tensor %s3 : tensor<2x2xf64, #DCSC>
bufferization.dealloc_tensor %s4 : tensor<2x2xf64, #SortedCOO>
bufferization.dealloc_tensor %s5 : tensor<2x2xf64, #SortedCOOPerm>
bufferization.dealloc_tensor %s6 : tensor<7x8x9xf64, #CCCPerm>
return
}
}
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