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// RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s
#SparseVector = #sparse_tensor.encoding<{
dimLevelType = ["compressed"]
}>
#SparseMatrix = #sparse_tensor.encoding<{
dimLevelType = ["compressed", "compressed"]
}>
//
// Test with various forms of the two most elementary reshape
// operations: expand/collapse.
//
module {
func.func @expand_dense(%arg0: tensor<12xf64>) -> tensor<3x4xf64> {
%0 = tensor.expand_shape %arg0 [[0, 1]] : tensor<12xf64> into tensor<3x4xf64>
return %0 : tensor<3x4xf64>
}
func.func @expand_from_sparse(%arg0: tensor<12xf64, #SparseVector>) -> tensor<3x4xf64> {
%0 = tensor.expand_shape %arg0 [[0, 1]] : tensor<12xf64, #SparseVector> into tensor<3x4xf64>
return %0 : tensor<3x4xf64>
}
func.func @expand_to_sparse(%arg0: tensor<12xf64>) -> tensor<3x4xf64, #SparseMatrix> {
%0 = tensor.expand_shape %arg0 [[0, 1]] : tensor<12xf64> into tensor<3x4xf64, #SparseMatrix>
return %0 : tensor<3x4xf64, #SparseMatrix>
}
func.func @expand_sparse2sparse(%arg0: tensor<12xf64, #SparseVector>) -> tensor<3x4xf64, #SparseMatrix> {
%0 = tensor.expand_shape %arg0 [[0, 1]] : tensor<12xf64, #SparseVector> into tensor<3x4xf64, #SparseMatrix>
return %0 : tensor<3x4xf64, #SparseMatrix>
}
func.func @collapse_dense(%arg0: tensor<3x4xf64>) -> tensor<12xf64> {
%0 = tensor.collapse_shape %arg0 [[0, 1]] : tensor<3x4xf64> into tensor<12xf64>
return %0 : tensor<12xf64>
}
func.func @collapse_from_sparse(%arg0: tensor<3x4xf64, #SparseMatrix>) -> tensor<12xf64> {
%0 = tensor.collapse_shape %arg0 [[0, 1]] : tensor<3x4xf64, #SparseMatrix> into tensor<12xf64>
return %0 : tensor<12xf64>
}
func.func @collapse_to_sparse(%arg0: tensor<3x4xf64>) -> tensor<12xf64, #SparseVector> {
%0 = tensor.collapse_shape %arg0 [[0, 1]] : tensor<3x4xf64> into tensor<12xf64, #SparseVector>
return %0 : tensor<12xf64, #SparseVector>
}
func.func @collapse_sparse2sparse(%arg0: tensor<3x4xf64, #SparseMatrix>) -> tensor<12xf64, #SparseVector> {
%0 = tensor.collapse_shape %arg0 [[0, 1]] : tensor<3x4xf64, #SparseMatrix> into tensor<12xf64, #SparseVector>
return %0 : tensor<12xf64, #SparseVector>
}
//
// Main driver.
//
func.func @entry() {
%c0 = arith.constant 0 : index
%df = arith.constant -1.0 : f64
// Setup test vectors and matrices..
%v = arith.constant dense <[ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0,
7.0, 8.0, 9.0, 10.0, 11.0, 12.0]> : tensor<12xf64>
%m = arith.constant dense <[ [ 1.1, 1.2, 1.3, 1.4 ],
[ 2.1, 2.2, 2.3, 2.4 ],
[ 3.1, 3.2, 3.3, 3.4 ]]> : tensor<3x4xf64>
%sv = sparse_tensor.convert %v : tensor<12xf64> to tensor<12xf64, #SparseVector>
%sm = sparse_tensor.convert %m : tensor<3x4xf64> to tensor<3x4xf64, #SparseMatrix>
// Call the kernels.
%expand0 = call @expand_dense(%v) : (tensor<12xf64>) -> tensor<3x4xf64>
%expand1 = call @expand_from_sparse(%sv) : (tensor<12xf64, #SparseVector>) -> tensor<3x4xf64>
%expand2 = call @expand_to_sparse(%v) : (tensor<12xf64>) -> tensor<3x4xf64, #SparseMatrix>
%expand3 = call @expand_sparse2sparse(%sv) : (tensor<12xf64, #SparseVector>) -> tensor<3x4xf64, #SparseMatrix>
%collapse0 = call @collapse_dense(%m) : (tensor<3x4xf64>) -> tensor<12xf64>
%collapse1 = call @collapse_from_sparse(%sm) : (tensor<3x4xf64, #SparseMatrix>) -> tensor<12xf64>
%collapse2 = call @collapse_to_sparse(%m) : (tensor<3x4xf64>) -> tensor<12xf64, #SparseVector>
%collapse3 = call @collapse_sparse2sparse(%sm) : (tensor<3x4xf64, #SparseMatrix>) -> tensor<12xf64, #SparseVector>
//
// Verify result.
//
// CHECK: ( ( 1, 2, 3, 4 ), ( 5, 6, 7, 8 ), ( 9, 10, 11, 12 ) )
// CHECK-NEXT: ( ( 1, 2, 3, 4 ), ( 5, 6, 7, 8 ), ( 9, 10, 11, 12 ) )
// CHECK-NEXT: ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, -1, -1, -1, -1 )
// CHECK-NEXT: ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, -1, -1, -1, -1 )
// CHECK-NEXT: ( 1.1, 1.2, 1.3, 1.4, 2.1, 2.2, 2.3, 2.4, 3.1, 3.2, 3.3, 3.4 )
// CHECK-NEXT: ( 1.1, 1.2, 1.3, 1.4, 2.1, 2.2, 2.3, 2.4, 3.1, 3.2, 3.3, 3.4 )
// CHECK-NEXT: ( 1.1, 1.2, 1.3, 1.4, 2.1, 2.2, 2.3, 2.4, 3.1, 3.2, 3.3, 3.4, -1, -1, -1, -1 )
// CHECK-NEXT: ( 1.1, 1.2, 1.3, 1.4, 2.1, 2.2, 2.3, 2.4, 3.1, 3.2, 3.3, 3.4, -1, -1, -1, -1 )
//
%m0 = vector.transfer_read %expand0[%c0, %c0], %df: tensor<3x4xf64>, vector<3x4xf64>
vector.print %m0 : vector<3x4xf64>
%m1 = vector.transfer_read %expand1[%c0, %c0], %df: tensor<3x4xf64>, vector<3x4xf64>
vector.print %m1 : vector<3x4xf64>
%a2 = sparse_tensor.values %expand2 : tensor<3x4xf64, #SparseMatrix> to memref<?xf64>
%m2 = vector.transfer_read %a2[%c0], %df: memref<?xf64>, vector<16xf64>
vector.print %m2 : vector<16xf64>
%a3 = sparse_tensor.values %expand3 : tensor<3x4xf64, #SparseMatrix> to memref<?xf64>
%m3 = vector.transfer_read %a3[%c0], %df: memref<?xf64>, vector<16xf64>
vector.print %m3 : vector<16xf64>
%v0 = vector.transfer_read %collapse0[%c0], %df: tensor<12xf64>, vector<12xf64>
vector.print %v0 : vector<12xf64>
%v1 = vector.transfer_read %collapse1[%c0], %df: tensor<12xf64>, vector<12xf64>
vector.print %v1 : vector<12xf64>
%b2 = sparse_tensor.values %collapse2 : tensor<12xf64, #SparseVector> to memref<?xf64>
%v2 = vector.transfer_read %b2[%c0], %df: memref<?xf64>, vector<16xf64>
vector.print %v2 : vector<16xf64>
%b3 = sparse_tensor.values %collapse3 : tensor<12xf64, #SparseVector> to memref<?xf64>
%v3 = vector.transfer_read %b3[%c0], %df: memref<?xf64>, vector<16xf64>
vector.print %v3 : vector<16xf64>
// Release sparse resources.
sparse_tensor.release %sv : tensor<12xf64, #SparseVector>
sparse_tensor.release %sm : tensor<3x4xf64, #SparseMatrix>
sparse_tensor.release %expand2 : tensor<3x4xf64, #SparseMatrix>
sparse_tensor.release %expand3 : tensor<3x4xf64, #SparseMatrix>
sparse_tensor.release %collapse2 : tensor<12xf64, #SparseVector>
sparse_tensor.release %collapse3 : tensor<12xf64, #SparseVector>
// Release dense resources.
%meme1 = bufferization.to_memref %expand1 : memref<3x4xf64>
memref.dealloc %meme1 : memref<3x4xf64>
%memc1 = bufferization.to_memref %collapse1 : memref<12xf64>
memref.dealloc %memc1 : memref<12xf64>
return
}
}
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