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|
// DEFINE: %{option} = enable-runtime-library=true
// DEFINE: %{run_option} =
// DEFINE: %{compile} = mlir-opt %s --sparse-compiler=%{option}
// DEFINE: %{run} = mlir-cpu-runner \
// DEFINE: -e entry -entry-point-result=void \
// DEFINE: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext,%mlir_lib_dir/libmlir_runner_utils%shlibext %{run_option} | \
// DEFINE: FileCheck %s
//
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation.
// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true"
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation and vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true vl=4 reassociate-fp-reductions=true enable-index-optimizations=true"
// RUN: %{compile} | %{run}
#MAT_C_C = #sparse_tensor.encoding<{lvlTypes = ["compressed", "compressed"]}>
#MAT_D_C = #sparse_tensor.encoding<{lvlTypes = ["dense", "compressed"]}>
#MAT_C_D = #sparse_tensor.encoding<{lvlTypes = ["compressed", "dense"]}>
#MAT_D_D = #sparse_tensor.encoding<{
lvlTypes = ["dense", "dense"],
dimToLvl = affine_map<(i,j) -> (j,i)>
}>
#MAT_C_C_P = #sparse_tensor.encoding<{
lvlTypes = [ "compressed", "compressed" ],
dimToLvl = affine_map<(i,j) -> (j,i)>
}>
#MAT_C_D_P = #sparse_tensor.encoding<{
lvlTypes = [ "compressed", "dense" ],
dimToLvl = affine_map<(i,j) -> (j,i)>
}>
#MAT_D_C_P = #sparse_tensor.encoding<{
lvlTypes = [ "dense", "compressed" ],
dimToLvl = affine_map<(i,j) -> (j,i)>
}>
module {
func.func private @printMemrefF64(%ptr : tensor<*xf64>)
func.func private @printMemref1dF64(%ptr : memref<?xf64>) attributes { llvm.emit_c_interface }
//
// Tests without permutation (concatenate on dimension 1)
//
// Concats all sparse matrices (with different encodings) to a sparse matrix.
func.func @concat_sparse_sparse_dim1(%arg0: tensor<4x2xf64, #MAT_C_C>, %arg1: tensor<4x3xf64, #MAT_C_D>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64, #MAT_C_C> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 1 : index}
: tensor<4x2xf64, #MAT_C_C>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C> to tensor<4x9xf64, #MAT_C_C>
return %0 : tensor<4x9xf64, #MAT_C_C>
}
// Concats all sparse matrices (with different encodings) to a dense matrix.
func.func @concat_sparse_dense_dim1(%arg0: tensor<4x2xf64, #MAT_C_C>, %arg1: tensor<4x3xf64, #MAT_C_D>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 1 : index}
: tensor<4x2xf64, #MAT_C_C>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C> to tensor<4x9xf64>
return %0 : tensor<4x9xf64>
}
// Concats mix sparse and dense matrices to a sparse matrix.
func.func @concat_mix_sparse_dim1(%arg0: tensor<4x2xf64>, %arg1: tensor<4x3xf64, #MAT_C_D>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64, #MAT_C_C> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 1 : index}
: tensor<4x2xf64>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C> to tensor<4x9xf64, #MAT_C_C>
return %0 : tensor<4x9xf64, #MAT_C_C>
}
// Concats mix sparse and dense matrices to a dense matrix.
func.func @concat_mix_dense_dim1(%arg0: tensor<4x2xf64>, %arg1: tensor<4x3xf64, #MAT_C_D>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 1 : index}
: tensor<4x2xf64>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C> to tensor<4x9xf64>
return %0 : tensor<4x9xf64>
}
func.func @dump_mat_4x9(%A: tensor<4x9xf64, #MAT_C_C>) {
%c = sparse_tensor.convert %A : tensor<4x9xf64, #MAT_C_C> to tensor<4x9xf64>
%cu = tensor.cast %c : tensor<4x9xf64> to tensor<*xf64>
call @printMemrefF64(%cu) : (tensor<*xf64>) -> ()
%n = sparse_tensor.number_of_entries %A : tensor<4x9xf64, #MAT_C_C>
vector.print %n : index
%1 = sparse_tensor.values %A : tensor<4x9xf64, #MAT_C_C> to memref<?xf64>
call @printMemref1dF64(%1) : (memref<?xf64>) -> ()
return
}
func.func @dump_mat_dense_4x9(%A: tensor<4x9xf64>) {
%1 = tensor.cast %A : tensor<4x9xf64> to tensor<*xf64>
call @printMemrefF64(%1) : (tensor<*xf64>) -> ()
return
}
// Driver method to call and verify kernels.
func.func @entry() {
%m42 = arith.constant dense<
[ [ 1.0, 0.0 ],
[ 3.1, 0.0 ],
[ 0.0, 2.0 ],
[ 0.0, 0.0 ] ]> : tensor<4x2xf64>
%m43 = arith.constant dense<
[ [ 1.0, 0.0, 1.0 ],
[ 1.0, 0.0, 0.5 ],
[ 0.0, 0.0, 1.0 ],
[ 5.0, 2.0, 0.0 ] ]> : tensor<4x3xf64>
%m44 = arith.constant dense<
[ [ 0.0, 0.0, 1.5, 1.0],
[ 0.0, 3.5, 0.0, 0.0],
[ 1.0, 5.0, 2.0, 0.0],
[ 1.0, 0.5, 0.0, 0.0] ]> : tensor<4x4xf64>
%sm42cc = sparse_tensor.convert %m42 : tensor<4x2xf64> to tensor<4x2xf64, #MAT_C_C>
%sm43cd = sparse_tensor.convert %m43 : tensor<4x3xf64> to tensor<4x3xf64, #MAT_C_D>
%sm44dc = sparse_tensor.convert %m44 : tensor<4x4xf64> to tensor<4x4xf64, #MAT_D_C>
// CHECK: {{\[}}[1, 0, 1, 0, 1, 0, 0, 1.5, 1],
// CHECK-NEXT: [3.1, 0, 1, 0, 0.5, 0, 3.5, 0, 0],
// CHECK-NEXT: [0, 2, 0, 0, 1, 1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 5, 2, 0, 1, 0.5, 0, 0]]
// CHECK-NEXT: 18
// CHECK: [1, 1, 1, 1.5, 1, 3.1, 1, 0.5, 3.5, 2, 1, 1, 5, 2, 5, 2, 1, 0.5
%8 = call @concat_sparse_sparse_dim1(%sm42cc, %sm43cd, %sm44dc)
: (tensor<4x2xf64, #MAT_C_C>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64, #MAT_C_C>
call @dump_mat_4x9(%8) : (tensor<4x9xf64, #MAT_C_C>) -> ()
// CHECK: {{\[}}[1, 0, 1, 0, 1, 0, 0, 1.5, 1],
// CHECK-NEXT: [3.1, 0, 1, 0, 0.5, 0, 3.5, 0, 0],
// CHECK-NEXT: [0, 2, 0, 0, 1, 1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 5, 2, 0, 1, 0.5, 0, 0]]
%9 = call @concat_sparse_dense_dim1(%sm42cc, %sm43cd, %sm44dc)
: (tensor<4x2xf64, #MAT_C_C>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64>
call @dump_mat_dense_4x9(%9) : (tensor<4x9xf64>) -> ()
// CHECK: {{\[}}[1, 0, 1, 0, 1, 0, 0, 1.5, 1],
// CHECK-NEXT: [3.1, 0, 1, 0, 0.5, 0, 3.5, 0, 0],
// CHECK-NEXT: [0, 2, 0, 0, 1, 1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 5, 2, 0, 1, 0.5, 0, 0]]
// CHECK-NEXT: 18
// CHECK: [1, 1, 1, 1.5, 1, 3.1, 1, 0.5, 3.5, 2, 1, 1, 5, 2, 5, 2, 1, 0.5
%10 = call @concat_mix_sparse_dim1(%m42, %sm43cd, %sm44dc)
: (tensor<4x2xf64>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64, #MAT_C_C>
call @dump_mat_4x9(%10) : (tensor<4x9xf64, #MAT_C_C>) -> ()
// CHECK: {{\[}}[1, 0, 1, 0, 1, 0, 0, 1.5, 1],
// CHECK-NEXT: [3.1, 0, 1, 0, 0.5, 0, 3.5, 0, 0],
// CHECK-NEXT: [0, 2, 0, 0, 1, 1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 5, 2, 0, 1, 0.5, 0, 0]]
%11 = call @concat_mix_dense_dim1(%m42, %sm43cd, %sm44dc)
: (tensor<4x2xf64>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64>
call @dump_mat_dense_4x9(%11) : (tensor<4x9xf64>) -> ()
// Release resources.
bufferization.dealloc_tensor %sm42cc : tensor<4x2xf64, #MAT_C_C>
bufferization.dealloc_tensor %sm43cd : tensor<4x3xf64, #MAT_C_D>
bufferization.dealloc_tensor %sm44dc : tensor<4x4xf64, #MAT_D_C>
bufferization.dealloc_tensor %8 : tensor<4x9xf64, #MAT_C_C>
bufferization.dealloc_tensor %9 : tensor<4x9xf64>
bufferization.dealloc_tensor %10 : tensor<4x9xf64, #MAT_C_C>
bufferization.dealloc_tensor %11 : tensor<4x9xf64>
return
}
}
|
