9048ea28da
This reverts commit 5561e174117ff395d65b6978d04b62c1a1275138 The logic was moved from cmake into lit fixing the issue that lead to the revert and potentially others with multi-config cmake generators Differential Revision: https://reviews.llvm.org/D143925
120 lines
4.1 KiB
MLIR
120 lines
4.1 KiB
MLIR
// DEFINE: %{option} = enable-runtime-library=true
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// DEFINE: %{compile} = mlir-opt %s --sparse-compiler=%{option}
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// DEFINE: %{run} = TENSOR0="%mlir_src_dir/test/Integration/data/wide.mtx" \
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// DEFINE: mlir-cpu-runner \
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// DEFINE: -e entry -entry-point-result=void \
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// DEFINE: -shared-libs=%mlir_c_runner_utils | \
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// DEFINE: FileCheck %s
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//
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// RUN: %{compile} | %{run}
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//
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// Do the same run, but now with direct IR generation.
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// REDEFINE: %{option} = enable-runtime-library=false
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// RUN: %{compile} | %{run}
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//
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// Do the same run, but now with direct IR generation and vectorization.
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// REDEFINE: %{option} = "enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true"
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// RUN: %{compile} | %{run}
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// Do the same run, but now with direct IR generation and, if available, VLA
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// vectorization.
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// REDEFINE: %{option} = "enable-runtime-library=false vl=4 enable-arm-sve=%ENABLE_VLA"
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// REDEFINE: %{run} = TENSOR0="%mlir_src_dir/test/Integration/data/wide.mtx" \
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// REDEFINE: lli \
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// REDEFINE: --entry-function=entry_lli \
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// REDEFINE: --extra-module=%S/Inputs/main_for_lli.ll \
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// REDEFINE: %VLA_ARCH_ATTR_OPTIONS \
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// REDEFINE: --dlopen=%mlir_native_utils_lib_dir/libmlir_c_runner_utils%shlibext | \
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// REDEFINE: FileCheck %s
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// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
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!Filename = !llvm.ptr<i8>
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#SparseMatrix = #sparse_tensor.encoding<{
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dimLevelType = [ "dense", "compressed" ]
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}>
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#spmm = {
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indexing_maps = [
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affine_map<(i,j,k) -> (i,k)>, // A
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affine_map<(i,j,k) -> (k,j)>, // B
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affine_map<(i,j,k) -> (i,j)> // X (out)
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],
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iterator_types = ["parallel", "parallel", "reduction"],
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doc = "X(i,j) += A(i,k) * B(k,j)"
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}
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//
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// Integration test that lowers a kernel annotated as sparse to
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// actual sparse code, initializes a matching sparse storage scheme
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// from file, and runs the resulting code with the JIT compiler.
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//
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module {
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//
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// A kernel that multiplies a sparse matrix A with a dense matrix B
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// into a dense matrix X.
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//
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func.func @kernel_spmm(%arga: tensor<?x?xf64, #SparseMatrix>,
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%argb: tensor<?x?xf64>,
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%argx: tensor<?x?xf64>) -> tensor<?x?xf64> {
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%0 = linalg.generic #spmm
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ins(%arga, %argb: tensor<?x?xf64, #SparseMatrix>, tensor<?x?xf64>)
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outs(%argx: tensor<?x?xf64>) {
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^bb(%a: f64, %b: f64, %x: f64):
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%0 = arith.mulf %a, %b : f64
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%1 = arith.addf %x, %0 : f64
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linalg.yield %1 : f64
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} -> tensor<?x?xf64>
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return %0 : tensor<?x?xf64>
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}
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func.func private @getTensorFilename(index) -> (!Filename)
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//
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// Main driver that reads matrix from file and calls the sparse kernel.
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//
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func.func @entry() {
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%i0 = arith.constant 0.0 : f64
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%c0 = arith.constant 0 : index
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%c1 = arith.constant 1 : index
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%c4 = arith.constant 4 : index
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%c256 = arith.constant 256 : index
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// Read the sparse matrix from file, construct sparse storage.
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%fileName = call @getTensorFilename(%c0) : (index) -> (!Filename)
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%a = sparse_tensor.new %fileName : !Filename to tensor<?x?xf64, #SparseMatrix>
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// Initialize dense tensors.
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%b = tensor.generate %c256, %c4 {
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^bb0(%i : index, %j : index):
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%k0 = arith.muli %i, %c4 : index
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%k1 = arith.addi %j, %k0 : index
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%k2 = arith.index_cast %k1 : index to i32
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%k = arith.sitofp %k2 : i32 to f64
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tensor.yield %k : f64
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} : tensor<?x?xf64>
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%x = tensor.generate %c4, %c4 {
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^bb0(%i : index, %j : index):
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tensor.yield %i0 : f64
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} : tensor<?x?xf64>
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// Call kernel.
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%0 = call @kernel_spmm(%a, %b, %x)
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: (tensor<?x?xf64, #SparseMatrix>, tensor<?x?xf64>, tensor<?x?xf64>) -> tensor<?x?xf64>
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// Print the result for verification.
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//
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// CHECK: ( ( 3548, 3550, 3552, 3554 ), ( 6052, 6053, 6054, 6055 ), ( -56, -63, -70, -77 ), ( -13704, -13709, -13714, -13719 ) )
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//
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%v = vector.transfer_read %0[%c0, %c0], %i0: tensor<?x?xf64>, vector<4x4xf64>
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vector.print %v : vector<4x4xf64>
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// Release the resources.
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bufferization.dealloc_tensor %a : tensor<?x?xf64, #SparseMatrix>
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return
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}
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}
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