//--------------------------------------------------------------------------------------------------
// 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 enable-buffer-initialization=true
// RUN: %{compile} | %{run} | FileCheck %s
//
// Do the same run, but now with direct IR generation and vectorization.
// REDEFINE: %{sparsifier_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 %}
#DCSR = #sparse_tensor.encoding<{
map = (d0, d1) -> (d0 : compressed, d1 : compressed)
}>
#DCSC = #sparse_tensor.encoding<{
map = (d0, d1) -> (d1 : compressed, d0 : compressed)
}>
//
// Integration test that tests conversions between sparse tensors,
// where the dynamic sizes of the shape of the enveloping tensor
// may change (the actual underlying sizes obviously never change).
//
module {
func.func @main() {
%t1 = arith.constant sparse<
[ [0,0], [0,1], [0,63], [1,0], [1,1], [31,0], [31,63] ],
[ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0 ]> : tensor<32x64xf64>
%t2 = tensor.cast %t1 : tensor<32x64xf64> to tensor<?x?xf64>
// Four dense to sparse conversions.
%1 = sparse_tensor.convert %t1 : tensor<32x64xf64> to tensor<?x?xf64, #DCSR>
%2 = sparse_tensor.convert %t1 : tensor<32x64xf64> to tensor<?x?xf64, #DCSC>
%3 = sparse_tensor.convert %t2 : tensor<?x?xf64> to tensor<?x?xf64, #DCSR>
%4 = sparse_tensor.convert %t2 : tensor<?x?xf64> to tensor<?x?xf64, #DCSC>
// Two cross conversions.
%5 = sparse_tensor.convert %3 : tensor<?x?xf64, #DCSR> to tensor<?x?xf64, #DCSC>
%6 = sparse_tensor.convert %4 : tensor<?x?xf64, #DCSC> to tensor<?x?xf64, #DCSR>
//
// Verify the outputs.
//
// CHECK: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 7
// CHECK-NEXT: dim = ( 32, 64 )
// CHECK-NEXT: lvl = ( 32, 64 )
// CHECK-NEXT: pos[0] : ( 0, 3 )
// CHECK-NEXT: crd[0] : ( 0, 1, 31 )
// CHECK-NEXT: pos[1] : ( 0, 3, 5, 7 )
// CHECK-NEXT: crd[1] : ( 0, 1, 63, 0, 1, 0, 63 )
// CHECK-NEXT: values : ( 1, 2, 3, 4, 5, 6, 7 )
// CHECK-NEXT: ----
//
// CHECK: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 7
// CHECK-NEXT: dim = ( 32, 64 )
// CHECK-NEXT: lvl = ( 64, 32 )
// CHECK-NEXT: pos[0] : ( 0, 3 )
// CHECK-NEXT: crd[0] : ( 0, 1, 63 )
// CHECK-NEXT: pos[1] : ( 0, 3, 5, 7 )
// CHECK-NEXT: crd[1] : ( 0, 1, 31, 0, 1, 0, 31 )
// CHECK-NEXT: values : ( 1, 4, 6, 2, 5, 3, 7 )
// CHECK-NEXT: ----
//
// CHECK: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 7
// CHECK-NEXT: dim = ( 32, 64 )
// CHECK-NEXT: lvl = ( 32, 64 )
// CHECK-NEXT: pos[0] : ( 0, 3 )
// CHECK-NEXT: crd[0] : ( 0, 1, 31 )
// CHECK-NEXT: pos[1] : ( 0, 3, 5, 7 )
// CHECK-NEXT: crd[1] : ( 0, 1, 63, 0, 1, 0, 63 )
// CHECK-NEXT: values : ( 1, 2, 3, 4, 5, 6, 7 )
// CHECK-NEXT: ----
//
// CHECK: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 7
// CHECK-NEXT: dim = ( 32, 64 )
// CHECK-NEXT: lvl = ( 64, 32 )
// CHECK-NEXT: pos[0] : ( 0, 3 )
// CHECK-NEXT: crd[0] : ( 0, 1, 63 )
// CHECK-NEXT: pos[1] : ( 0, 3, 5, 7 )
// CHECK-NEXT: crd[1] : ( 0, 1, 31, 0, 1, 0, 31 )
// CHECK-NEXT: values : ( 1, 4, 6, 2, 5, 3, 7 )
// CHECK-NEXT: ----
//
// CHECK: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 7
// CHECK-NEXT: dim = ( 32, 64 )
// CHECK-NEXT: lvl = ( 64, 32 )
// CHECK-NEXT: pos[0] : ( 0, 3 )
// CHECK-NEXT: crd[0] : ( 0, 1, 63 )
// CHECK-NEXT: pos[1] : ( 0, 3, 5, 7 )
// CHECK-NEXT: crd[1] : ( 0, 1, 31, 0, 1, 0, 31 )
// CHECK-NEXT: values : ( 1, 4, 6, 2, 5, 3, 7 )
// CHECK-NEXT: ----
//
// CHECK: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 7
// CHECK-NEXT: dim = ( 32, 64 )
// CHECK-NEXT: lvl = ( 32, 64 )
// CHECK-NEXT: pos[0] : ( 0, 3 )
// CHECK-NEXT: crd[0] : ( 0, 1, 31 )
// CHECK-NEXT: pos[1] : ( 0, 3, 5, 7 )
// CHECK-NEXT: crd[1] : ( 0, 1, 63, 0, 1, 0, 63 )
// CHECK-NEXT: values : ( 1, 2, 3, 4, 5, 6, 7 )
// CHECK-NEXT: ----
//
sparse_tensor.print %1 : tensor<?x?xf64, #DCSR>
sparse_tensor.print %2 : tensor<?x?xf64, #DCSC>
sparse_tensor.print %3 : tensor<?x?xf64, #DCSR>
sparse_tensor.print %4 : tensor<?x?xf64, #DCSC>
sparse_tensor.print %5 : tensor<?x?xf64, #DCSC>
sparse_tensor.print %6 : tensor<?x?xf64, #DCSR>
// Release the resources.
bufferization.dealloc_tensor %1 : tensor<?x?xf64, #DCSR>
bufferization.dealloc_tensor %2 : tensor<?x?xf64, #DCSC>
bufferization.dealloc_tensor %3 : tensor<?x?xf64, #DCSR>
bufferization.dealloc_tensor %4 : tensor<?x?xf64, #DCSC>
bufferization.dealloc_tensor %5 : tensor<?x?xf64, #DCSC>
bufferization.dealloc_tensor %6 : tensor<?x?xf64, #DCSR>
return
}
}
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