//--------------------------------------------------------------------------------------------------
// 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} =
//--------------------------------------------------------------------------------------------------
// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false
// RUN: %{compile} | %{run} | FileCheck %s
//
// Do the same run, but now with 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 VLA vectorization.
// RUN: %if mlir_arm_sve_tests %{ %{compile_sve} | %{run_sve} | FileCheck %s %}
#Dense = #sparse_tensor.encoding<{
map = (d0, d1) -> (d0 : dense, d1 : dense)
}>
#SortedCOO = #sparse_tensor.encoding<{
map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton(soa))
}>
#CSR = #sparse_tensor.encoding<{
map = (d0, d1) -> (d0 : dense, d1 : compressed)
}>
#DCSR = #sparse_tensor.encoding<{
map = (d0, d1) -> (d0 : compressed, d1 : compressed)
}>
#Row = #sparse_tensor.encoding<{
map = (d0, d1) -> (d0 : compressed, d1 : dense)
}>
module {
//
// Main driver. We test the contents of various sparse tensor
// schemes when they are still empty and after a few insertions.
//
func.func @main() {
%c0 = arith.constant 0 : index
%c2 = arith.constant 2 : index
%c3 = arith.constant 3 : index
%f1 = arith.constant 1.0 : f64
%f2 = arith.constant 2.0 : f64
%f3 = arith.constant 3.0 : f64
%f4 = arith.constant 4.0 : f64
//
// Dense case.
//
// CHECK: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 12
// CHECK-NEXT: dim = ( 4, 3 )
// CHECK-NEXT: lvl = ( 4, 3 )
// CHECK-NEXT: values : ( 1, 0, 0, 0, 0, 0, 0, 0, 2, 3, 0, 4 )
// CHECK-NEXT: ----
//
%densea = tensor.empty() : tensor<4x3xf64, #Dense>
%dense1 = tensor.insert %f1 into %densea[%c0, %c0] : tensor<4x3xf64, #Dense>
%dense2 = tensor.insert %f2 into %dense1[%c2, %c2] : tensor<4x3xf64, #Dense>
%dense3 = tensor.insert %f3 into %dense2[%c3, %c0] : tensor<4x3xf64, #Dense>
%dense4 = tensor.insert %f4 into %dense3[%c3, %c2] : tensor<4x3xf64, #Dense>
%densem = sparse_tensor.load %dense4 hasInserts : tensor<4x3xf64, #Dense>
sparse_tensor.print %densem : tensor<4x3xf64, #Dense>
//
// COO case.
//
// CHECK-NEXT: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 4
// CHECK-NEXT: dim = ( 4, 3 )
// CHECK-NEXT: lvl = ( 4, 3 )
// CHECK-NEXT: pos[0] : ( 0, 4 )
// CHECK-NEXT: crd[0] : ( 0, 2, 3, 3 )
// CHECK-NEXT: crd[1] : ( 0, 2, 0, 2 )
// CHECK-NEXT: values : ( 1, 2, 3, 4 )
// CHECK-NEXT: ----
//
%cooa = tensor.empty() : tensor<4x3xf64, #SortedCOO>
%coo1 = tensor.insert %f1 into %cooa[%c0, %c0] : tensor<4x3xf64, #SortedCOO>
%coo2 = tensor.insert %f2 into %coo1[%c2, %c2] : tensor<4x3xf64, #SortedCOO>
%coo3 = tensor.insert %f3 into %coo2[%c3, %c0] : tensor<4x3xf64, #SortedCOO>
%coo4 = tensor.insert %f4 into %coo3[%c3, %c2] : tensor<4x3xf64, #SortedCOO>
%coom = sparse_tensor.load %coo4 hasInserts : tensor<4x3xf64, #SortedCOO>
sparse_tensor.print %coom : tensor<4x3xf64, #SortedCOO>
//
// CSR case.
//
// CHECK-NEXT: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 4
// CHECK-NEXT: dim = ( 4, 3 )
// CHECK-NEXT: lvl = ( 4, 3 )
// CHECK-NEXT: pos[1] : ( 0, 1, 1, 2, 4 )
// CHECK-NEXT: crd[1] : ( 0, 2, 0, 2 )
// CHECK-NEXT: values : ( 1, 2, 3, 4 )
// CHECK-NEXT: ----
//
%csra = tensor.empty() : tensor<4x3xf64, #CSR>
%csr1 = tensor.insert %f1 into %csra[%c0, %c0] : tensor<4x3xf64, #CSR>
%csr2 = tensor.insert %f2 into %csr1[%c2, %c2] : tensor<4x3xf64, #CSR>
%csr3 = tensor.insert %f3 into %csr2[%c3, %c0] : tensor<4x3xf64, #CSR>
%csr4 = tensor.insert %f4 into %csr3[%c3, %c2] : tensor<4x3xf64, #CSR>
%csrm = sparse_tensor.load %csr4 hasInserts : tensor<4x3xf64, #CSR>
sparse_tensor.print %csrm : tensor<4x3xf64, #CSR>
//
// DCSR case.
//
// CHECK-NEXT: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 4
// CHECK-NEXT: dim = ( 4, 3 )
// CHECK-NEXT: lvl = ( 4, 3 )
// CHECK-NEXT: pos[0] : ( 0, 3 )
// CHECK-NEXT: crd[0] : ( 0, 2, 3 )
// CHECK-NEXT: pos[1] : ( 0, 1, 2, 4 )
// CHECK-NEXT: crd[1] : ( 0, 2, 0, 2 )
// CHECK-NEXT: values : ( 1, 2, 3, 4 )
// CHECK-NEXT: ----
//
%dcsra = tensor.empty() : tensor<4x3xf64, #DCSR>
%dcsr1 = tensor.insert %f1 into %dcsra[%c0, %c0] : tensor<4x3xf64, #DCSR>
%dcsr2 = tensor.insert %f2 into %dcsr1[%c2, %c2] : tensor<4x3xf64, #DCSR>
%dcsr3 = tensor.insert %f3 into %dcsr2[%c3, %c0] : tensor<4x3xf64, #DCSR>
%dcsr4 = tensor.insert %f4 into %dcsr3[%c3, %c2] : tensor<4x3xf64, #DCSR>
%dcsrm = sparse_tensor.load %dcsr4 hasInserts : tensor<4x3xf64, #DCSR>
sparse_tensor.print %dcsrm : tensor<4x3xf64, #DCSR>
//
// Row case.
//
// CHECK-NEXT: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 9
// CHECK-NEXT: dim = ( 4, 3 )
// CHECK-NEXT: lvl = ( 4, 3 )
// CHECK-NEXT: pos[0] : ( 0, 3 )
// CHECK-NEXT: crd[0] : ( 0, 2, 3 )
// CHECK-NEXT: values : ( 1, 0, 0, 0, 0, 2, 3, 0, 4 )
// CHECK-NEXT: ----
//
%rowa = tensor.empty() : tensor<4x3xf64, #Row>
%row1 = tensor.insert %f1 into %rowa[%c0, %c0] : tensor<4x3xf64, #Row>
%row2 = tensor.insert %f2 into %row1[%c2, %c2] : tensor<4x3xf64, #Row>
%row3 = tensor.insert %f3 into %row2[%c3, %c0] : tensor<4x3xf64, #Row>
%row4 = tensor.insert %f4 into %row3[%c3, %c2] : tensor<4x3xf64, #Row>
%rowm = sparse_tensor.load %row4 hasInserts : tensor<4x3xf64, #Row>
sparse_tensor.print %rowm : tensor<4x3xf64, #Row>
// Release resources.
bufferization.dealloc_tensor %densem : tensor<4x3xf64, #Dense>
bufferization.dealloc_tensor %coom : tensor<4x3xf64, #SortedCOO>
bufferization.dealloc_tensor %csrm : tensor<4x3xf64, #CSR>
bufferization.dealloc_tensor %dcsrm : tensor<4x3xf64, #DCSR>
bufferization.dealloc_tensor %rowm : tensor<4x3xf64, #Row>
return
}
}
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