//--------------------------------------------------------------------------------------------------
// 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 %}
#SparseVector = #sparse_tensor.encoding<{ map = (d0) -> (d0 : compressed) }>
#trait_mul_s = {
indexing_maps = [
affine_map<(i) -> (i)> // x (out)
],
iterator_types = ["parallel"],
doc = "x(i) = x(i) * 2.0"
}
module {
func.func @main() {
%f1 = arith.constant 1.0 : f32
%f2 = arith.constant 2.0 : f32
%f3 = arith.constant 3.0 : f32
%f4 = arith.constant 4.0 : f32
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%c3 = arith.constant 3 : index
%c8 = arith.constant 8 : index
%c1023 = arith.constant 1023 : index
// Build the sparse vector from straightline code.
%0 = tensor.empty() : tensor<1024xf32, #SparseVector>
%1 = tensor.insert %f1 into %0[%c0] : tensor<1024xf32, #SparseVector>
%2 = tensor.insert %f2 into %1[%c1] : tensor<1024xf32, #SparseVector>
%3 = tensor.insert %f3 into %2[%c3] : tensor<1024xf32, #SparseVector>
%4 = tensor.insert %f4 into %3[%c1023] : tensor<1024xf32, #SparseVector>
%5 = sparse_tensor.load %4 hasInserts : tensor<1024xf32, #SparseVector>
//
// CHECK: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 4
// CHECK-NEXT: dim = ( 1024 )
// CHECK-NEXT: lvl = ( 1024 )
// CHECK-NEXT: pos[0] : ( 0, 4 )
// CHECK-NEXT: crd[0] : ( 0, 1, 3, 1023 )
// CHECK-NEXT: values : ( 1, 2, 3, 4 )
// CHECK-NEXT: ----
//
sparse_tensor.print %5 : tensor<1024xf32, #SparseVector>
// Build another sparse vector in a loop.
%6 = tensor.empty() : tensor<1024xf32, #SparseVector>
%7 = scf.for %i = %c0 to %c8 step %c1 iter_args(%vin = %6) -> tensor<1024xf32, #SparseVector> {
%ii = arith.muli %i, %c3 : index
%vout = tensor.insert %f1 into %vin[%ii] : tensor<1024xf32, #SparseVector>
scf.yield %vout : tensor<1024xf32, #SparseVector>
}
%8 = sparse_tensor.load %7 hasInserts : tensor<1024xf32, #SparseVector>
//
// CHECK-NEXT: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 8
// CHECK-NEXT: dim = ( 1024 )
// CHECK-NEXT: lvl = ( 1024 )
// CHECK-NEXT: pos[0] : ( 0, 8 )
// CHECK-NEXT: crd[0] : ( 0, 3, 6, 9, 12, 15, 18, 21 )
// CHECK-NEXT: values : ( 1, 1, 1, 1, 1, 1, 1, 1 )
// CHECK-NEXT: ----
//
sparse_tensor.print %8 : tensor<1024xf32, #SparseVector>
// Free resources.
bufferization.dealloc_tensor %5 : tensor<1024xf32, #SparseVector>
bufferization.dealloc_tensor %8 : tensor<1024xf32, #SparseVector>
return
}
}
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