mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_print_3d.mlir

//--------------------------------------------------------------------------------------------------
// 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} =
//--------------------------------------------------------------------------------------------------

// TODO: make this work with libgen

// 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
//

#BatchedCSR = #sparse_tensor.encoding<{
  map = (d0, d1, d2) -> (d0 : batch, d1 : dense, d2 : compressed)
}>

module {

  //
  // Main driver that tests 3-D sparse tensor printing.
  //
  func.func @main() {

    %pos = arith.constant dense<
      [[ 0, 8, 16, 24, 32],
       [ 0, 8, 16, 24, 32]]
    > : tensor<2x5xindex>

    %crd = arith.constant dense<
      [[0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7],
       [0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7]]
    > : tensor<2x32xindex>

    %val = arith.constant dense<
      [[ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.,
        12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22.,
        23., 24., 25., 26., 27., 28., 29., 30., 31., 32.],
       [33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43.,
        44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54.,
        55., 56., 57., 58., 59., 60., 61., 62., 63., 64.]]
    > : tensor<2x32xf64>

    %X = sparse_tensor.assemble (%pos, %crd), %val
      : (tensor<2x5xindex>, tensor<2x32xindex>), tensor<2x32xf64> to tensor<2x4x8xf64, #BatchedCSR>

    // CHECK:      ---- Sparse Tensor ----
    // CHECK-NEXT: nse = 32
    // CHECK-NEXT: dim = ( 2, 4, 8 )
    // CHECK-NEXT: lvl = ( 2, 4, 8 )
    // CHECK-NEXT: pos[2] : ( ( 0, 8, 16, 24, 32 )( 0, 8, 16, 24, 32 ) )
    // CHECK-NEXT: crd[2] : ( ( 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7 )
    // CHECK-SAME:            ( 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7 ) )
    // CHECK-NEXT: values : ( ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32 )
    // CHECK-SAME:            ( 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64 ) )
    // CHECK-NEXT: ----
    sparse_tensor.print %X : tensor<2x4x8xf64, #BatchedCSR>

    return
  }
}