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// DEFINE: %{option} = enable-runtime-library=true
// DEFINE: %{compile} = mlir-opt %s --sparse-compiler=%{option}
// DEFINE: %{run} = TENSOR0="%mlir_src_dir/test/Integration/data/test.mtx" \
// DEFINE: mlir-cpu-runner \
// DEFINE: -e entry -entry-point-result=void \
// DEFINE: -shared-libs=%mlir_c_runner_utils,%mlir_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}
// Do the same run, but now with direct IR generation and, if available, VLA
// vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false vl=4 enable-arm-sve=%ENABLE_VLA"
// REDEFINE: %{run} = TENSOR0="%mlir_src_dir/test/Integration/data/test.mtx" \
// REDEFINE: %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 --dlopen=%mlir_runner_utils | \
// REDEFINE: FileCheck %s
// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
!Filename = !llvm.ptr<i8>
#DenseMatrix = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "dense" ],
dimOrdering = affine_map<(i,j) -> (i,j)>
}>
#SparseMatrix = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ],
dimOrdering = affine_map<(i,j) -> (i,j)>
}>
#trait_assign = {
indexing_maps = [
affine_map<(i,j) -> (i,j)>, // A
affine_map<(i,j) -> (i,j)> // X (out)
],
iterator_types = ["parallel", "parallel"],
doc = "X(i,j) = A(i,j) * 2"
}
//
// Integration test that demonstrates assigning a sparse tensor
// to an all-dense annotated "sparse" tensor, which effectively
// result in inserting the nonzero elements into a linearized array.
//
// Note that there is a subtle difference between a non-annotated
// tensor and an all-dense annotated tensor. Both tensors are assumed
// dense, but the former remains an n-dimensional memref whereas the
// latter is linearized into a one-dimensional memref that is further
// lowered into a storage scheme that is backed by the runtime support
// library.
module {
//
// A kernel that assigns multiplied elements from A to X.
//
func.func @dense_output(%arga: tensor<?x?xf64, #SparseMatrix>) -> tensor<?x?xf64, #DenseMatrix> {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%c2 = arith.constant 2.0 : f64
%d0 = tensor.dim %arga, %c0 : tensor<?x?xf64, #SparseMatrix>
%d1 = tensor.dim %arga, %c1 : tensor<?x?xf64, #SparseMatrix>
%init = bufferization.alloc_tensor(%d0, %d1) : tensor<?x?xf64, #DenseMatrix>
%0 = linalg.generic #trait_assign
ins(%arga: tensor<?x?xf64, #SparseMatrix>)
outs(%init: tensor<?x?xf64, #DenseMatrix>) {
^bb(%a: f64, %x: f64):
%0 = arith.mulf %a, %c2 : f64
linalg.yield %0 : f64
} -> tensor<?x?xf64, #DenseMatrix>
return %0 : tensor<?x?xf64, #DenseMatrix>
}
func.func private @getTensorFilename(index) -> (!Filename)
func.func private @printMemref1dF64(%ptr : memref<?xf64>) attributes { llvm.emit_c_interface }
//
// Main driver that reads matrix from file and calls the kernel.
//
func.func @entry() {
%d0 = arith.constant 0.0 : f64
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
// Read the sparse matrix from file, construct sparse storage.
%fileName = call @getTensorFilename(%c0) : (index) -> (!Filename)
%a = sparse_tensor.new %fileName
: !Filename to tensor<?x?xf64, #SparseMatrix>
// Call the kernel.
%0 = call @dense_output(%a)
: (tensor<?x?xf64, #SparseMatrix>) -> tensor<?x?xf64, #DenseMatrix>
//
// Print the linearized 5x5 result for verification.
// CHECK: 25
// CHECK: [2, 0, 0, 2.8, 0, 0, 4, 0, 0, 5, 0, 0, 6, 0, 0, 8.2, 0, 0, 8, 0, 0, 10.4, 0, 0, 10
//
%n = sparse_tensor.number_of_entries %0 : tensor<?x?xf64, #DenseMatrix>
vector.print %n : index
%m = sparse_tensor.values %0
: tensor<?x?xf64, #DenseMatrix> to memref<?xf64>
call @printMemref1dF64(%m) : (memref<?xf64>) -> ()
// Release the resources.
bufferization.dealloc_tensor %a : tensor<?x?xf64, #SparseMatrix>
bufferization.dealloc_tensor %0 : tensor<?x?xf64, #DenseMatrix>
return
}
}
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