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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 | \
// 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 | \
// REDEFINE: FileCheck %s
// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
!Filename = !llvm.ptr<i8>
#DCSR = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ],
dimOrdering = affine_map<(i,j) -> (i,j)>
}>
#eltwise_mult = {
indexing_maps = [
affine_map<(i,j) -> (i,j)> // X (out)
],
iterator_types = ["parallel", "parallel"],
doc = "X(i,j) *= X(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 multiplies a sparse matrix A with itself
// in an element-wise fashion. In this operation, we have
// a sparse tensor as output, but although the values of the
// sparse tensor change, its nonzero structure remains the same.
//
func.func @kernel_eltwise_mult(%argx: tensor<?x?xf64, #DCSR>)
-> tensor<?x?xf64, #DCSR> {
%0 = linalg.generic #eltwise_mult
outs(%argx: tensor<?x?xf64, #DCSR>) {
^bb(%x: f64):
%0 = arith.mulf %x, %x : f64
linalg.yield %0 : f64
} -> tensor<?x?xf64, #DCSR>
return %0 : tensor<?x?xf64, #DCSR>
}
func.func private @getTensorFilename(index) -> (!Filename)
//
// Main driver that reads matrix from file and calls the sparse kernel.
//
func.func @entry() {
%d0 = arith.constant 0.0 : f64
%c0 = arith.constant 0 : index
// Read the sparse matrix from file, construct sparse storage.
%fileName = call @getTensorFilename(%c0) : (index) -> (!Filename)
%x = sparse_tensor.new %fileName : !Filename to tensor<?x?xf64, #DCSR>
// Call kernel.
%0 = call @kernel_eltwise_mult(%x) : (tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR>
// Print the result for verification.
//
// CHECK: ( 1, 1.96, 4, 6.25, 9, 16.81, 16, 27.04, 25 )
//
%m = sparse_tensor.values %0 : tensor<?x?xf64, #DCSR> to memref<?xf64>
%v = vector.transfer_read %m[%c0], %d0: memref<?xf64>, vector<9xf64>
vector.print %v : vector<9xf64>
// Release the resources.
bufferization.dealloc_tensor %x : tensor<?x?xf64, #DCSR>
return
}
}
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