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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 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=2 reassociate-fp-reductions=true enable-index-optimizations=true"
// RUN: %{compile} | %{run}
// Do the same run, but now with direct IR generation and, if available, VLA
// vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true vl=4 enable-arm-sve=%ENABLE_VLA"
// REDEFINE: %{run} = %lli_host_or_aarch64_cmd \
// REDEFINE: --entry-function=entry_lli \
// REDEFINE: --extra-module=%S/Inputs/main_for_lli.ll \
// REDEFINE: %VLA_ARCH_ATTR_OPTIONS \
// REDEFINE: --dlopen=%mlir_native_utils_lib_dir/libmlir_c_runner_utils%shlibext | \
// REDEFINE: FileCheck %s
// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
#SparseMatrix = #sparse_tensor.encoding<{
lvlTypes = [ "compressed", "compressed" ]
}>
#SparseTensor = #sparse_tensor.encoding<{
lvlTypes = [ "compressed", "compressed", "compressed" ]
}>
#redsum = {
indexing_maps = [
affine_map<(i,j,k) -> (i,j,k)>, // A
affine_map<(i,j,k) -> (i,j,k)>, // B
affine_map<(i,j,k) -> (i,j)> // X (out)
],
iterator_types = ["parallel", "parallel", "reduction"],
doc = "X(i,j) = SUM_k A(i,j,k) * B(i,j,k)"
}
module {
func.func @redsum(%arga: tensor<?x?x?xi32, #SparseTensor>,
%argb: tensor<?x?x?xi32, #SparseTensor>)
-> tensor<?x?xi32, #SparseMatrix> {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%d0 = tensor.dim %arga, %c0 : tensor<?x?x?xi32, #SparseTensor>
%d1 = tensor.dim %arga, %c1 : tensor<?x?x?xi32, #SparseTensor>
%xinit = bufferization.alloc_tensor(%d0, %d1): tensor<?x?xi32, #SparseMatrix>
%0 = linalg.generic #redsum
ins(%arga, %argb: tensor<?x?x?xi32, #SparseTensor>,
tensor<?x?x?xi32, #SparseTensor>)
outs(%xinit: tensor<?x?xi32, #SparseMatrix>) {
^bb(%a: i32, %b: i32, %x: i32):
%0 = arith.muli %a, %b : i32
%1 = arith.addi %x, %0 : i32
linalg.yield %1 : i32
} -> tensor<?x?xi32, #SparseMatrix>
return %0 : tensor<?x?xi32, #SparseMatrix>
}
// Driver method to call and verify tensor kernel.
func.func @entry() {
%c0 = arith.constant 0 : index
%i0 = arith.constant 0 : i32
// Setup very sparse 3-d tensors.
%t1 = arith.constant sparse<
[ [1,1,3], [2,0,0], [2,2,1], [2,2,2], [2,2,3] ], [ 1, 2, 3, 4, 5 ]
> : tensor<3x3x4xi32>
%t2 = arith.constant sparse<
[ [1,0,0], [1,1,3], [2,2,1], [2,2,3] ], [ 6, 7, 8, 9 ]
> : tensor<3x3x4xi32>
%st1 = sparse_tensor.convert %t1
: tensor<3x3x4xi32> to tensor<?x?x?xi32, #SparseTensor>
%st2 = sparse_tensor.convert %t2
: tensor<3x3x4xi32> to tensor<?x?x?xi32, #SparseTensor>
// Call kernel.
%0 = call @redsum(%st1, %st2)
: (tensor<?x?x?xi32, #SparseTensor>,
tensor<?x?x?xi32, #SparseTensor>) -> tensor<?x?xi32, #SparseMatrix>
//
// Verify results. Only two entries stored in result. Correct structure.
//
// CHECK: ( 7, 69, 0, 0 )
// CHECK-NEXT: ( ( 0, 0, 0 ), ( 0, 7, 0 ), ( 0, 0, 69 ) )
//
%val = sparse_tensor.values %0
: tensor<?x?xi32, #SparseMatrix> to memref<?xi32>
%vv = vector.transfer_read %val[%c0], %i0: memref<?xi32>, vector<4xi32>
vector.print %vv : vector<4xi32>
%dm = sparse_tensor.convert %0
: tensor<?x?xi32, #SparseMatrix> to tensor<?x?xi32>
%vm = vector.transfer_read %dm[%c0, %c0], %i0: tensor<?x?xi32>, vector<3x3xi32>
vector.print %vm : vector<3x3xi32>
// Release the resources.
bufferization.dealloc_tensor %st1 : tensor<?x?x?xi32, #SparseTensor>
bufferization.dealloc_tensor %st2 : tensor<?x?x?xi32, #SparseTensor>
bufferization.dealloc_tensor %0 : tensor<?x?xi32, #SparseMatrix>
return
}
}
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