| // RUN: mlir-opt %s -sparsification="vectorization-strategy=2 vl=16" -for-loop-peeling -canonicalize | \ |
| // RUN: FileCheck %s |
| |
| #SparseVector = #sparse_tensor.encoding<{ |
| dimLevelType = [ "compressed" ], |
| pointerBitWidth = 32, |
| indexBitWidth = 32 |
| }> |
| |
| #trait_mul_s = { |
| indexing_maps = [ |
| affine_map<(i) -> (i)>, // a |
| affine_map<(i) -> (i)>, // b |
| affine_map<(i) -> (i)> // x (out) |
| ], |
| iterator_types = ["parallel"], |
| doc = "x(i) = a(i) * b(i)" |
| } |
| |
| // CHECK-DAG: #[[$map0:.*]] = affine_map<()[s0, s1] -> (s0 + ((-s0 + s1) floordiv 16) * 16)> |
| // CHECK-DAG: #[[$map1:.*]] = affine_map<(d0)[s0] -> (-d0 + s0)> |
| // CHECK-LABEL: func @mul_s |
| // CHECK-DAG: %[[c0:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[c1:.*]] = arith.constant 1 : index |
| // CHECK-DAG: %[[c16:.*]] = arith.constant 16 : index |
| // CHECK: %[[p:.*]] = memref.load %{{.*}}[%[[c0]]] : memref<?xi32> |
| // CHECK: %[[a:.*]] = arith.extui %[[p]] : i32 to i64 |
| // CHECK: %[[q:.*]] = arith.index_cast %[[a]] : i64 to index |
| // CHECK: %[[r:.*]] = memref.load %{{.*}}[%[[c1]]] : memref<?xi32> |
| // CHECK: %[[b:.*]] = arith.extui %[[r]] : i32 to i64 |
| // CHECK: %[[s:.*]] = arith.index_cast %[[b]] : i64 to index |
| // CHECK: %[[boundary:.*]] = affine.apply #[[$map0]]()[%[[q]], %[[s]]] |
| // CHECK: scf.for %[[i:.*]] = %[[q]] to %[[boundary]] step %[[c16]] { |
| // CHECK: %[[mask:.*]] = vector.constant_mask [16] : vector<16xi1> |
| // CHECK: %[[li:.*]] = vector.load %{{.*}}[%[[i]]] : memref<?xi32>, vector<16xi32> |
| // CHECK: %[[zi:.*]] = arith.extui %[[li]] : vector<16xi32> to vector<16xi64> |
| // CHECK: %[[la:.*]] = vector.load %{{.*}}[%[[i]]] : memref<?xf32>, vector<16xf32> |
| // CHECK: %[[lb:.*]] = vector.gather %{{.*}}[%[[c0]]] [%[[zi]]], %[[mask]], %{{.*}} : memref<1024xf32>, vector<16xi64>, vector<16xi1>, vector<16xf32> into vector<16xf32> |
| // CHECK: %[[m:.*]] = arith.mulf %[[la]], %[[lb]] : vector<16xf32> |
| // CHECK: vector.scatter %{{.*}}[%[[c0]]] [%[[zi]]], %[[mask]], %[[m]] : memref<1024xf32>, vector<16xi64>, vector<16xi1>, vector<16xf32> |
| // CHECK: } |
| // CHECK: scf.for %[[i2:.*]] = %[[boundary]] to %[[s]] step %[[c16]] { |
| // CHECK: %[[sub:.*]] = affine.apply #[[$map1]](%[[i2]])[%[[s]]] |
| // CHECK: %[[mask2:.*]] = vector.create_mask %[[sub]] : vector<16xi1> |
| // CHECK: %[[li2:.*]] = vector.maskedload %{{.*}}[%[[i2]]], %[[mask2]], %{{.*}} : memref<?xi32>, vector<16xi1>, vector<16xi32> into vector<16xi32> |
| // CHECK: %[[zi2:.*]] = arith.extui %[[li2]] : vector<16xi32> to vector<16xi64> |
| // CHECK: %[[la2:.*]] = vector.maskedload %{{.*}}[%[[i2]]], %[[mask2]], %{{.*}} : memref<?xf32>, vector<16xi1>, vector<16xf32> into vector<16xf32> |
| // CHECK: %[[lb2:.*]] = vector.gather %{{.*}}[%[[c0]]] [%[[zi2]]], %[[mask2]], %{{.*}} : memref<1024xf32>, vector<16xi64>, vector<16xi1>, vector<16xf32> into vector<16xf32> |
| // CHECK: %[[m2:.*]] = arith.mulf %[[la2]], %[[lb2]] : vector<16xf32> |
| // CHECK: vector.scatter %{{.*}}[%[[c0]]] [%[[zi2]]], %[[mask2]], %[[m2]] : memref<1024xf32>, vector<16xi64>, vector<16xi1>, vector<16xf32> |
| // CHECK: } |
| // CHECK: return |
| // |
| func @mul_s(%arga: tensor<1024xf32, #SparseVector>, %argb: tensor<1024xf32>, %argx: tensor<1024xf32>) -> tensor<1024xf32> { |
| %0 = linalg.generic #trait_mul_s |
| ins(%arga, %argb: tensor<1024xf32, #SparseVector>, tensor<1024xf32>) |
| outs(%argx: tensor<1024xf32>) { |
| ^bb(%a: f32, %b: f32, %x: f32): |
| %0 = arith.mulf %a, %b : f32 |
| linalg.yield %0 : f32 |
| } -> tensor<1024xf32> |
| return %0 : tensor<1024xf32> |
| } |
