Files
clang-p2996/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_ds.mlir

124 lines
5.2 KiB
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_opts} = -e entry -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}
//
// DEFINE: %{env} =
//--------------------------------------------------------------------------------------------------
// REDEFINE: %{env} = TENSOR0="%mlir_src_dir/test/Integration/data/ds.mtx"
// RUN: %{compile} | env %{env} %{run} | FileCheck %s
//
// Do the same run, but now with direct IR generation.
// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false
// RUN: %{compile} | env %{env} %{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} | env %{env} %{run} | FileCheck %s
!Filename = !llvm.ptr
#CSR = #sparse_tensor.encoding<{
map = (i, j) -> ( i : dense, j : compressed)
}>
#CSR_hi = #sparse_tensor.encoding<{
map = (i, j) -> ( i : dense, j : loose_compressed)
}>
#NV_24 = #sparse_tensor.encoding<{
map = ( i, j ) -> ( i : dense,
j floordiv 4 : dense,
j mod 4 : block2_4),
crdWidth = 8
}>
module {
func.func private @getTensorFilename(index) -> (!Filename)
//
// Input matrix:
//
// [[0.0, 0.0, 1.0, 2.0, 0.0, 3.0, 0.0, 4.0],
// [0.0, 5.0, 6.0, 0.0, 7.0, 0.0, 0.0, 8.0],
// [9.0, 0.0, 10.0, 0.0, 11.0, 12.0, 0.0, 0.0]]
//
func.func @entry() {
%u0 = arith.constant 0 : i8
%c0 = arith.constant 0 : index
%f0 = arith.constant 0.0 : f64
%fileName = call @getTensorFilename(%c0) : (index) -> (!Filename)
%A1 = sparse_tensor.new %fileName : !Filename to tensor<?x?xf64, #CSR>
%A2 = sparse_tensor.new %fileName : !Filename to tensor<?x?xf64, #CSR_hi>
%A3 = sparse_tensor.new %fileName : !Filename to tensor<?x?xf64, #NV_24>
//
// CSR:
//
// CHECK: ( 0, 4, 8, 12 )
// CHECK-NEXT: ( 2, 3, 5, 7, 1, 2, 4, 7, 0, 2, 4, 5 )
// CHECK-NEXT: ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 )
//
%pos1 = sparse_tensor.positions %A1 {level = 1 : index } : tensor<?x?xf64, #CSR> to memref<?xindex>
%vecp1 = vector.transfer_read %pos1[%c0], %c0 : memref<?xindex>, vector<4xindex>
vector.print %vecp1 : vector<4xindex>
%crd1 = sparse_tensor.coordinates %A1 {level = 1 : index } : tensor<?x?xf64, #CSR> to memref<?xindex>
%vecc1 = vector.transfer_read %crd1[%c0], %c0 : memref<?xindex>, vector<12xindex>
vector.print %vecc1 : vector<12xindex>
%val1 = sparse_tensor.values %A1 : tensor<?x?xf64, #CSR> to memref<?xf64>
%vecv1 = vector.transfer_read %val1[%c0], %f0 : memref<?xf64>, vector<12xf64>
vector.print %vecv1 : vector<12xf64>
//
// CSR_hi:
//
// CHECK-NEXT: ( 0, 4, 4, 8, 8, 12 )
// CHECK-NEXT: ( 2, 3, 5, 7, 1, 2, 4, 7, 0, 2, 4, 5 )
// CHECK-NEXT: ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 )
//
%pos2 = sparse_tensor.positions %A2 {level = 1 : index } : tensor<?x?xf64, #CSR_hi> to memref<?xindex>
%vecp2 = vector.transfer_read %pos2[%c0], %c0 : memref<?xindex>, vector<6xindex>
vector.print %vecp2 : vector<6xindex>
%crd2 = sparse_tensor.coordinates %A2 {level = 1 : index } : tensor<?x?xf64, #CSR_hi> to memref<?xindex>
%vecc2 = vector.transfer_read %crd2[%c0], %c0 : memref<?xindex>, vector<12xindex>
vector.print %vecc2 : vector<12xindex>
%val2 = sparse_tensor.values %A2 : tensor<?x?xf64, #CSR_hi> to memref<?xf64>
%vecv2 = vector.transfer_read %val2[%c0], %f0 : memref<?xf64>, vector<12xf64>
vector.print %vecv2 : vector<12xf64>
//
// NV_24
//
// CHECK-NEXT: ( 2, 3, 1, 3, 1, 2, 0, 3, 0, 2, 0, 1 )
// CHECK-NEXT: ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 )
//
%crd3 = sparse_tensor.coordinates %A3 {level = 2 : index } : tensor<?x?xf64, #NV_24> to memref<?xi8>
%vecc3 = vector.transfer_read %crd3[%c0], %u0 : memref<?xi8>, vector<12xi8>
vector.print %vecc3 : vector<12xi8>
%val3 = sparse_tensor.values %A3 : tensor<?x?xf64, #NV_24> to memref<?xf64>
%vecv3 = vector.transfer_read %val3[%c0], %f0 : memref<?xf64>, vector<12xf64>
vector.print %vecv3 : vector<12xf64>
// Release the resources.
bufferization.dealloc_tensor %A1: tensor<?x?xf64, #CSR>
bufferization.dealloc_tensor %A2: tensor<?x?xf64, #CSR_hi>
bufferization.dealloc_tensor %A3: tensor<?x?xf64, #NV_24>
return
}
}