| // RUN: mlir-opt %s \ |
| // RUN: --sparsification --sparse-tensor-conversion \ |
| // RUN: --linalg-bufferize --convert-linalg-to-loops \ |
| // 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-math-to-llvm \ |
| // RUN: --convert-std-to-llvm --reconcile-unrealized-casts | \ |
| // 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 |
| |
| #ST1 = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed", "compressed"]}> |
| #ST2 = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed", "dense"]}> |
| |
| // |
| // Trait for 3-d tensor operation. |
| // |
| #trait_scale = { |
| indexing_maps = [ |
| affine_map<(i,j,k) -> (i,j,k)>, // A (in) |
| affine_map<(i,j,k) -> (i,j,k)> // X (out) |
| ], |
| iterator_types = ["parallel", "parallel", "parallel"], |
| doc = "X(i,j,k) = A(i,j,k) * 2.0" |
| } |
| |
| module { |
| // Scales a sparse tensor into a new sparse tensor. |
| func @tensor_scale(%arga: tensor<?x?x?xf64, #ST1>) -> tensor<?x?x?xf64, #ST2> { |
| %s = arith.constant 2.0 : f64 |
| %c0 = arith.constant 0 : index |
| %c1 = arith.constant 1 : index |
| %c2 = arith.constant 2 : index |
| %d0 = tensor.dim %arga, %c0 : tensor<?x?x?xf64, #ST1> |
| %d1 = tensor.dim %arga, %c1 : tensor<?x?x?xf64, #ST1> |
| %d2 = tensor.dim %arga, %c2 : tensor<?x?x?xf64, #ST1> |
| %xm = sparse_tensor.init [%d0, %d1, %d2] : tensor<?x?x?xf64, #ST2> |
| %0 = linalg.generic #trait_scale |
| ins(%arga: tensor<?x?x?xf64, #ST1>) |
| outs(%xm: tensor<?x?x?xf64, #ST2>) { |
| ^bb(%a: f64, %x: f64): |
| %1 = arith.mulf %a, %s : f64 |
| linalg.yield %1 : f64 |
| } -> tensor<?x?x?xf64, #ST2> |
| return %0 : tensor<?x?x?xf64, #ST2> |
| } |
| |
| // Driver method to call and verify tensor kernel. |
| func @entry() { |
| %c0 = arith.constant 0 : index |
| %d1 = arith.constant -1.0 : f64 |
| |
| // Setup sparse tensor. |
| %t = arith.constant dense< |
| [ [ [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], |
| [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], |
| [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], |
| [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0 ] ], |
| [ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], |
| [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], |
| [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], |
| [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] ], |
| [ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], |
| [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], |
| [0.0, 3.0, 4.0, 0.0, 0.0, 0.0, 0.0, 5.0 ], |
| [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] ] ]> : tensor<3x4x8xf64> |
| %st = sparse_tensor.convert %t : tensor<3x4x8xf64> to tensor<?x?x?xf64, #ST1> |
| |
| // Call sparse vector kernels. |
| %0 = call @tensor_scale(%st) : (tensor<?x?x?xf64, #ST1>) -> tensor<?x?x?xf64, #ST2> |
| |
| // Sanity check on stored values. |
| // |
| // CHECK: ( 1, 2, 3, 4, 5, -1, -1, -1 ) |
| // CHECK-NEXT: ( 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 6, 8, 0, 0, 0, 0, 10, -1, -1, -1, -1, -1, -1, -1, -1 ) |
| %m1 = sparse_tensor.values %st : tensor<?x?x?xf64, #ST1> to memref<?xf64> |
| %m2 = sparse_tensor.values %0 : tensor<?x?x?xf64, #ST2> to memref<?xf64> |
| %v1 = vector.transfer_read %m1[%c0], %d1: memref<?xf64>, vector<8xf64> |
| %v2 = vector.transfer_read %m2[%c0], %d1: memref<?xf64>, vector<32xf64> |
| vector.print %v1 : vector<8xf64> |
| vector.print %v2 : vector<32xf64> |
| |
| // Release the resources. |
| sparse_tensor.release %st : tensor<?x?x?xf64, #ST1> |
| sparse_tensor.release %0 : tensor<?x?x?xf64, #ST2> |
| return |
| } |
| } |