| // 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 --reconcile-unrealized-casts | \ |
| // RUN: TENSOR0="%mlir_integration_test_dir/data/test.mtx" \ |
| // RUN: TENSOR1="%mlir_integration_test_dir/data/zero.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> |
| |
| #DenseMatrix = #sparse_tensor.encoding<{ |
| dimLevelType = [ "dense", "dense" ], |
| dimOrdering = affine_map<(i,j) -> (i,j)> |
| }> |
| |
| #SparseMatrix = #sparse_tensor.encoding<{ |
| dimLevelType = [ "dense", "compressed" ], |
| dimOrdering = affine_map<(i,j) -> (i,j)> |
| }> |
| |
| #trait_assign = { |
| indexing_maps = [ |
| affine_map<(i,j) -> (i,j)>, // A |
| affine_map<(i,j) -> (i,j)> // X (out) |
| ], |
| iterator_types = ["parallel", "parallel"], |
| doc = "X(i,j) = A(i,j)" |
| } |
| |
| // |
| // Integration test that demonstrates assigning a sparse tensor |
| // to an all-dense annotated "sparse" tensor, which effectively |
| // result in inserting the nonzero elements into a linearized array. |
| // |
| // Note that there is a subtle difference between a non-annotated |
| // tensor and an all-dense annotated tensor. Both tensors are assumed |
| // dense, but the former remains an n-dimensional memref whereas the |
| // latter is linearized into a one-dimensional memref that is further |
| // lowered into a storage scheme that is backed by the runtime support |
| // library. |
| module { |
| // |
| // A kernel that assigns elements from A to an initially zero X. |
| // |
| func @dense_output(%arga: tensor<?x?xf64, #SparseMatrix>, |
| %argx: tensor<?x?xf64, #DenseMatrix> |
| {linalg.inplaceable = true}) |
| -> tensor<?x?xf64, #DenseMatrix> { |
| %0 = linalg.generic #trait_assign |
| ins(%arga: tensor<?x?xf64, #SparseMatrix>) |
| outs(%argx: tensor<?x?xf64, #DenseMatrix>) { |
| ^bb(%a: f64, %x: f64): |
| linalg.yield %a : f64 |
| } -> tensor<?x?xf64, #DenseMatrix> |
| return %0 : tensor<?x?xf64, #DenseMatrix> |
| } |
| |
| func private @getTensorFilename(index) -> (!Filename) |
| |
| // |
| // Main driver that reads matrix from file and calls the kernel. |
| // |
| func @entry() { |
| %d0 = arith.constant 0.0 : f64 |
| %c0 = arith.constant 0 : index |
| %c1 = arith.constant 1 : 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 all-dense annotated "sparse" matrix to all zeros. |
| %fileZero = call @getTensorFilename(%c1) : (index) -> (!Filename) |
| %x = sparse_tensor.new %fileZero |
| : !Filename to tensor<?x?xf64, #DenseMatrix> |
| |
| // Call the kernel. |
| %0 = call @dense_output(%a, %x) |
| : (tensor<?x?xf64, #SparseMatrix>, |
| tensor<?x?xf64, #DenseMatrix>) -> tensor<?x?xf64, #DenseMatrix> |
| |
| // |
| // Print the linearized 5x5 result for verification. |
| // |
| // CHECK: ( 1, 0, 0, 1.4, 0, 0, 2, 0, 0, 2.5, 0, 0, 3, 0, 0, 4.1, 0, 0, 4, 0, 0, 5.2, 0, 0, 5 ) |
| // |
| %m = sparse_tensor.values %0 |
| : tensor<?x?xf64, #DenseMatrix> to memref<?xf64> |
| %v = vector.load %m[%c0] : memref<?xf64>, vector<25xf64> |
| vector.print %v : vector<25xf64> |
| |
| // Release the resources. |
| sparse_tensor.release %a : tensor<?x?xf64, #SparseMatrix> |
| sparse_tensor.release %x : tensor<?x?xf64, #DenseMatrix> |
| |
| return |
| } |
| } |