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// 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 vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true"
// RUN: %{compile} | %{run}
#SparseVector = #sparse_tensor.encoding<{
dimLevelType = ["compressed"]
}>
#SparseMatrix = #sparse_tensor.encoding<{
dimLevelType = ["compressed", "compressed"]
}>
#Sparse3dTensor = #sparse_tensor.encoding<{
dimLevelType = ["compressed", "compressed", "compressed"]
}>
module {
func.func @reshape0(%arg0: tensor<3x4xf64, #SparseMatrix>) -> tensor<2x6xf64, #SparseMatrix> {
%shape = arith.constant dense <[ 2, 6 ]> : tensor<2xi32>
%0 = tensor.reshape %arg0(%shape) : (tensor<3x4xf64, #SparseMatrix>, tensor<2xi32>) -> tensor<2x6xf64, #SparseMatrix>
return %0 : tensor<2x6xf64, #SparseMatrix>
}
func.func @reshape1(%arg0: tensor<3x4xf64, #SparseMatrix>) -> tensor<12xf64, #SparseVector> {
%shape = arith.constant dense <[ 12 ]> : tensor<1xi32>
%0 = tensor.reshape %arg0(%shape) : (tensor<3x4xf64, #SparseMatrix>, tensor<1xi32>) -> tensor<12xf64, #SparseVector>
return %0 : tensor<12xf64, #SparseVector>
}
func.func @reshape2(%arg0: tensor<3x4xf64, #SparseMatrix>) -> tensor<2x3x2xf64, #Sparse3dTensor> {
%shape = arith.constant dense <[ 2, 3, 2 ]> : tensor<3xi32>
%0 = tensor.reshape %arg0(%shape) : (tensor<3x4xf64, #SparseMatrix>, tensor<3xi32>) -> tensor<2x3x2xf64, #Sparse3dTensor>
return %0 : tensor<2x3x2xf64, #Sparse3dTensor>
}
func.func @entry() {
%m = arith.constant dense <[ [ 1.1, 0.0, 1.3, 0.0 ],
[ 2.1, 0.0, 2.3, 0.0 ],
[ 3.1, 0.0, 3.3, 0.0 ]]> : tensor<3x4xf64>
%sm = sparse_tensor.convert %m : tensor<3x4xf64> to tensor<3x4xf64, #SparseMatrix>
%reshaped0 = call @reshape0(%sm) : (tensor<3x4xf64, #SparseMatrix>) -> tensor<2x6xf64, #SparseMatrix>
%reshaped1 = call @reshape1(%sm) : (tensor<3x4xf64, #SparseMatrix>) -> tensor<12xf64, #SparseVector>
%reshaped2 = call @reshape2(%sm) : (tensor<3x4xf64, #SparseMatrix>) -> tensor<2x3x2xf64, #Sparse3dTensor>
%c0 = arith.constant 0 : index
%df = arith.constant -1.0 : f64
// CHECK: ( 1.1, 1.3, 2.1, 2.3, 3.1, 3.3
%b0 = sparse_tensor.values %reshaped0: tensor<2x6xf64, #SparseMatrix> to memref<?xf64>
%v0 = vector.transfer_read %b0[%c0], %df: memref<?xf64>, vector<12xf64>
vector.print %v0 : vector<12xf64>
// CHECK: ( 1.1, 1.3, 2.1, 2.3, 3.1, 3.3
%b1 = sparse_tensor.values %reshaped1: tensor<12xf64, #SparseVector> to memref<?xf64>
%v1 = vector.transfer_read %b1[%c0], %df: memref<?xf64>, vector<12xf64>
vector.print %v1 : vector<12xf64>
// CHECK: ( 1.1, 1.3, 2.1, 2.3, 3.1, 3.3
%b2 = sparse_tensor.values %reshaped2: tensor<2x3x2xf64, #Sparse3dTensor> to memref<?xf64>
%v2 = vector.transfer_read %b2[%c0], %df: memref<?xf64>, vector<12xf64>
vector.print %v2: vector<12xf64>
bufferization.dealloc_tensor %sm : tensor<3x4xf64, #SparseMatrix>
bufferization.dealloc_tensor %reshaped0 : tensor<2x6xf64, #SparseMatrix>
bufferization.dealloc_tensor %reshaped1 : tensor<12xf64, #SparseVector>
bufferization.dealloc_tensor %reshaped2 : tensor<2x3x2xf64, #Sparse3dTensor>
return
}
}
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