// DEFINE: %{option} = enable-runtime-library=true
// DEFINE: %{compile} = mlir-opt %s --sparse-compiler=%{option}
// DEFINE: %{run} = mlir-cpu-runner \
// DEFINE:  -e entry -entry-point-result=void  \
// DEFINE:  -shared-libs=%mlir_c_runner_utils | \
// DEFINE: FileCheck %s
//
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation.
// REDEFINE: %{option} = enable-runtime-library=false
// RUN: %{compile} | %{run}

// Do the same run, but now with direct IR generation and, if available, VLA
// vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false vl=4 enable-arm-sve=%ENABLE_VLA"
// REDEFINE: %{run} = %lli_host_or_aarch64_cmd \
// REDEFINE:   --entry-function=entry_lli \
// REDEFINE:   --extra-module=%S/Inputs/main_for_lli.ll \
// REDEFINE:   %VLA_ARCH_ATTR_OPTIONS \
// REDEFINE:   --dlopen=%mlir_native_utils_lib_dir/libmlir_c_runner_utils%shlibext | \
// REDEFINE: FileCheck %s
// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}

#CSR = #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>

#trait_scale = {
  indexing_maps = [
    affine_map<(i,j) -> (i,j)>   // X (out)
  ],
  iterator_types = ["parallel", "parallel"],
  doc = "X(i,j) = X(i,j) * 2"
}

//
// Integration test that lowers a kernel annotated as sparse to actual sparse
// code, initializes a matching sparse storage scheme from a dense tensor,
// and runs the resulting code with the JIT compiler.
//
module {
  //
  // A kernel that scales a sparse matrix A by a factor of 2.0.
  //
  func.func @sparse_scale(%argx: tensor<8x8xf32, #CSR>) -> tensor<8x8xf32, #CSR> {
    %c = arith.constant 2.0 : f32
    %0 = linalg.generic #trait_scale
      outs(%argx: tensor<8x8xf32, #CSR>) {
        ^bb(%x: f32):
          %1 = arith.mulf %x, %c : f32
          linalg.yield %1 : f32
    } -> tensor<8x8xf32, #CSR>
    return %0 : tensor<8x8xf32, #CSR>
  }

  //
  // Main driver that converts a dense tensor into a sparse tensor
  // and then calls the sparse scaling kernel with the sparse tensor
  // as input argument.
  //
  func.func @entry() {
    %c0 = arith.constant 0 : index
    %f0 = arith.constant 0.0 : f32

    // Initialize a dense tensor.
    %0 = arith.constant dense<[
       [1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0],
       [0.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
       [0.0, 0.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0],
       [0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0],
       [0.0, 1.0, 0.0, 0.0, 5.0, 0.0, 0.0, 0.0],
       [0.0, 1.0, 1.0, 0.0, 0.0, 6.0, 0.0, 0.0],
       [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 7.0, 1.0],
       [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 8.0]
    ]> : tensor<8x8xf32>

    // Convert dense tensor to sparse tensor and call sparse kernel.
    %1 = sparse_tensor.convert %0 : tensor<8x8xf32> to tensor<8x8xf32, #CSR>
    %2 = call @sparse_scale(%1)
      : (tensor<8x8xf32, #CSR>) -> tensor<8x8xf32, #CSR>

    // Print the resulting compacted values for verification.
    //
    // CHECK: ( 2, 2, 2, 4, 6, 8, 2, 10, 2, 2, 12, 2, 14, 2, 2, 16 )
    //
    %m = sparse_tensor.values %2 : tensor<8x8xf32, #CSR> to memref<?xf32>
    %v = vector.transfer_read %m[%c0], %f0: memref<?xf32>, vector<16xf32>
    vector.print %v : vector<16xf32>

    // Release the resources.
    bufferization.dealloc_tensor %1 : tensor<8x8xf32, #CSR>

    return
  }
}