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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/wide.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/wide.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>
#SparseMatrix = #sparse_tensor.encoding<{
lvlTypes = [ "dense", "compressed" ]
}>
#spmm = {
indexing_maps = [
affine_map<(i,j,k) -> (i,k)>, // A
affine_map<(i,j,k) -> (k,j)>, // B
affine_map<(i,j,k) -> (i,j)> // X (out)
],
iterator_types = ["parallel", "parallel", "reduction"],
doc = "X(i,j) += A(i,k) * B(k,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 a dense matrix B
// into a dense matrix X.
//
func.func @kernel_spmm(%arga: tensor<?x?xf64, #SparseMatrix>,
%argb: tensor<?x?xf64>,
%argx: tensor<?x?xf64>) -> tensor<?x?xf64> {
%0 = linalg.generic #spmm
ins(%arga, %argb: tensor<?x?xf64, #SparseMatrix>, tensor<?x?xf64>)
outs(%argx: tensor<?x?xf64>) {
^bb(%a: f64, %b: f64, %x: f64):
%0 = arith.mulf %a, %b : f64
%1 = arith.addf %x, %0 : f64
linalg.yield %1 : f64
} -> tensor<?x?xf64>
return %0 : tensor<?x?xf64>
}
func.func private @getTensorFilename(index) -> (!Filename)
//
// Main driver that reads matrix from file and calls the sparse kernel.
//
func.func @entry() {
%i0 = arith.constant 0.0 : f64
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%c4 = arith.constant 4 : index
%c256 = arith.constant 256 : 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>
// Initialize dense tensors.
%b = tensor.generate %c256, %c4 {
^bb0(%i : index, %j : index):
%k0 = arith.muli %i, %c4 : index
%k1 = arith.addi %j, %k0 : index
%k2 = arith.index_cast %k1 : index to i32
%k = arith.sitofp %k2 : i32 to f64
tensor.yield %k : f64
} : tensor<?x?xf64>
%x = tensor.generate %c4, %c4 {
^bb0(%i : index, %j : index):
tensor.yield %i0 : f64
} : tensor<?x?xf64>
// Call kernel.
%0 = call @kernel_spmm(%a, %b, %x)
: (tensor<?x?xf64, #SparseMatrix>, tensor<?x?xf64>, tensor<?x?xf64>) -> tensor<?x?xf64>
// Print the result for verification.
//
// CHECK: ( ( 3548, 3550, 3552, 3554 ), ( 6052, 6053, 6054, 6055 ), ( -56, -63, -70, -77 ), ( -13704, -13709, -13714, -13719 ) )
//
%v = vector.transfer_read %0[%c0, %c0], %i0: tensor<?x?xf64>, vector<4x4xf64>
vector.print %v : vector<4x4xf64>
// Release the resources.
bufferization.dealloc_tensor %a : tensor<?x?xf64, #SparseMatrix>
return
}
}
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