| // 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 --lower-affine \ |
| // RUN: --convert-vector-to-llvm --convert-memref-to-llvm --convert-std-to-llvm --reconcile-unrealized-casts | \ |
| // RUN: TENSOR0="%mlir_integration_test_dir/data/test_symmetric.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 |
| // |
| // Do the same run, but now with SIMDization as well. This should not change the outcome. |
| // |
| // RUN: mlir-opt %s \ |
| // RUN: --sparsification="vectorization-strategy=2 vl=2" --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 --lower-affine \ |
| // RUN: --convert-vector-to-llvm --convert-memref-to-llvm --convert-std-to-llvm --reconcile-unrealized-casts | \ |
| // RUN: TENSOR0="%mlir_integration_test_dir/data/test_symmetric.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> {linalg.inplaceable = true}) -> tensor<f64> { |
| %0 = linalg.generic #trait_sum_reduce |
| ins(%arga: tensor<?x?xf64, #SparseMatrix>) |
| outs(%argx: tensor<f64>) { |
| ^bb(%a: f64, %x: f64): |
| %0 = arith.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 = arith.constant 0.0 : f64 |
| %c0 = arith.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 = bufferization.to_tensor %xdata : memref<f64> |
| |
| // 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 @kernel_sum_reduce(%a, %x) |
| : (tensor<?x?xf64, #SparseMatrix>, tensor<f64>) -> tensor<f64> |
| |
| // Print the result for verification. |
| // |
| // CHECK: 30.2 |
| // |
| %m = bufferization.to_memref %0 : memref<f64> |
| %v = memref.load %m[] : memref<f64> |
| vector.print %v : f64 |
| |
| // Release the resources. |
| memref.dealloc %xdata : memref<f64> |
| sparse_tensor.release %a : tensor<?x?xf64, #SparseMatrix> |
| |
| return |
| } |
| } |