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// RUN: mlir-opt %s \
// RUN: --sparsification --sparse-tensor-conversion \
// RUN: --convert-vector-to-scf --convert-scf-to-std \
// RUN: --func-bufferize --tensor-constant-bufferize --tensor-bufferize \
// RUN: --std-bufferize --finalizing-bufferize \
// RUN: --convert-vector-to-llvm --convert-memref-to-llvm --convert-std-to-llvm | \
// RUN: TENSOR0="%mlir_integration_test_dir/data/test.mtx" \
// RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s
!Filename = type !llvm.ptr<i8>
#SparseMatrix = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ]
}>
#trait_sum_reduce = {
indexing_maps = [
affine_map<(i,j) -> (i,j)>, // A
affine_map<(i,j) -> ()> // x (out)
],
iterator_types = ["reduction", "reduction"],
doc = "x += A(i,j)"
}
//
// Integration test that lowers a kernel annotated as sparse to
// actual sparse code, initializes a matching sparse storage scheme
// from file, and runs the resulting code with the JIT compiler.
//
module {
//
// A kernel that sum-reduces a matrix to a single scalar.
//
func @kernel_sum_reduce(%arga: tensor<?x?xf64, #SparseMatrix>,
%argx: tensor<f64>) -> tensor<f64> {
%0 = linalg.generic #trait_sum_reduce
ins(%arga: tensor<?x?xf64, #SparseMatrix>)
outs(%argx: tensor<f64>) {
^bb(%a: f64, %x: f64):
%0 = addf %x, %a : f64
linalg.yield %0 : f64
} -> tensor<f64>
return %0 : tensor<f64>
}
func private @getTensorFilename(index) -> (!Filename)
//
// Main driver that reads matrix from file and calls the sparse kernel.
//
func @entry() {
%d0 = constant 0.0 : f64
%c0 = constant 0 : index
// Setup memory for a single reduction scalar,
// initialized to zero.
%xdata = memref.alloc() : memref<f64>
memref.store %d0, %xdata[] : memref<f64>
%x = memref.tensor_load %xdata : memref<f64>
// Read the sparse matrix from file, construct sparse storage.
%fileName = call @getTensorFilename(%c0) : (index) -> (!Filename)
%a = sparse_tensor.new %fileName : !llvm.ptr<i8> to tensor<?x?xf64, #SparseMatrix>
// Call the kernel.
%0 = call @kernel_sum_reduce(%a, %x)
: (tensor<?x?xf64, #SparseMatrix>, tensor<f64>) -> tensor<f64>
// Print the result for verification.
//
// CHECK: 28.2
//
%m = memref.buffer_cast %0 : memref<f64>
%v = memref.load %m[] : memref<f64>
vector.print %v : f64
// Release the resources.
memref.dealloc %xdata : memref<f64>
return
}
}
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