| // RUN: mlir-opt %s --sparse-reinterpret-map -sparsification -cse -sparse-vectorization="vl=8" -cse -split-input-file | \ |
| // RUN: FileCheck %s --check-prefix=CHECK-ON |
| // RUN: mlir-opt %s --sparse-reinterpret-map -sparsification -cse -split-input-file | \ |
| // RUN: FileCheck %s --check-prefix=CHECK-OFF |
| |
| // ----- |
| |
| // Check that we vectorize reductions with ori. |
| |
| // CHECK-ON-LABEL: func.func @sparse_reduction_ori( |
| // CHECK-ON-SAME: %[[VAL_0:.*]]: tensor<i13>, |
| // CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?xi13, #sparse{{[0-9]*}}>) -> tensor<i13> { |
| // CHECK-ON-DAG: %[[VAL_2:.*]] = arith.constant 8 : index |
| // CHECK-ON-DAG: %[[VAL_3:.*]] = arith.constant dense<0> : vector<8xi13> |
| // CHECK-ON-DAG: %[[VAL_4:.*]] = arith.constant 0 : index |
| // CHECK-ON-DAG: %[[VAL_5:.*]] = arith.constant 1 : index |
| // CHECK-ON: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi13, #sparse{{[0-9]*}}> to memref<?xindex> |
| // CHECK-ON: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi13, #sparse{{[0-9]*}}> to memref<?xi13> |
| // CHECK-ON: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i13> |
| // CHECK-ON: %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<i13> |
| // CHECK-ON: %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex> |
| // CHECK-ON: %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex> |
| // CHECK-ON: %[[VAL_12:.*]] = vector.broadcast %[[VAL_9]] : i13 to vector<8xi13> |
| // CHECK-ON: %[[VAL_13:.*]] = scf.for %[[VAL_14:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_2]] iter_args(%[[VAL_15:.*]] = %[[VAL_12]]) -> (vector<8xi13>) { |
| // CHECK-ON: %[[VAL_16:.*]] = affine.min #map(%[[VAL_11]], %[[VAL_14]]){{\[}}%[[VAL_2]]] |
| // CHECK-ON: %[[VAL_17:.*]] = vector.create_mask %[[VAL_16]] : vector<8xi1> |
| // CHECK-ON: %[[VAL_18:.*]] = vector.maskedload %[[VAL_7]]{{\[}}%[[VAL_14]]], %[[VAL_17]], %[[VAL_3]] : memref<?xi13>, vector<8xi1>, vector<8xi13> into vector<8xi13> |
| // CHECK-ON: %[[VAL_19:.*]] = arith.ori %[[VAL_15]], %[[VAL_18]] : vector<8xi13> |
| // CHECK-ON: %[[VAL_20:.*]] = arith.select %[[VAL_17]], %[[VAL_19]], %[[VAL_15]] : vector<8xi1>, vector<8xi13> |
| // CHECK-ON: scf.yield %[[VAL_20]] : vector<8xi13> |
| // CHECK-ON: } {"Emitted from" = "linalg.generic"} |
| // CHECK-ON: %[[VAL_21:.*]] = vector.reduction <or>, %[[VAL_22:.*]] : vector<8xi13> into i13 |
| // CHECK-ON: memref.store %[[VAL_21]], %[[VAL_8]][] : memref<i13> |
| // CHECK-ON: %[[VAL_23:.*]] = bufferization.to_tensor %[[VAL_8]] : memref<i13> |
| // CHECK-ON: return %[[VAL_23]] : tensor<i13> |
| // CHECK-ON: } |
| // |
| // CHECK-OFF-LABEL: func.func @sparse_reduction_ori( |
| // CHECK-OFF-SAME: %[[VAL_0:.*]]: tensor<i13>, |
| // CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?xi13, #sparse{{[0-9]*}}>) -> tensor<i13> { |
| // CHECK-OFF-DAG: %[[VAL_2:.*]] = arith.constant 0 : index |
| // CHECK-OFF-DAG: %[[VAL_3:.*]] = arith.constant 1 : index |
| // CHECK-OFF: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi13, #sparse{{[0-9]*}}> to memref<?xindex> |
| // CHECK-OFF: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi13, #sparse{{[0-9]*}}> to memref<?xi13> |
| // CHECK-OFF: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i13> |
| // CHECK-OFF: %[[VAL_7:.*]] = memref.load %[[VAL_6]][] : memref<i13> |
| // CHECK-OFF: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex> |
| // CHECK-OFF: %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex> |
| // CHECK-OFF: %[[VAL_10:.*]] = scf.for %[[VAL_11:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] iter_args(%[[VAL_12:.*]] = %[[VAL_7]]) -> (i13) { |
| // CHECK-OFF: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_11]]] : memref<?xi13> |
| // CHECK-OFF: %[[VAL_14:.*]] = arith.ori %[[VAL_12]], %[[VAL_13]] : i13 |
| // CHECK-OFF: scf.yield %[[VAL_14]] : i13 |
| // CHECK-OFF: } {"Emitted from" = "linalg.generic"} |
| // CHECK-OFF: memref.store %[[VAL_15:.*]], %[[VAL_6]][] : memref<i13> |
| // CHECK-OFF: %[[VAL_16:.*]] = bufferization.to_tensor %[[VAL_6]] : memref<i13> |
| // CHECK-OFF: return %[[VAL_16]] : tensor<i13> |
| // CHECK-OFF: } |
| #SparseVector = #sparse_tensor.encoding<{map = (d0) -> (d0 : compressed)}> |
| |
| #trait = { |
| indexing_maps = [ |
| affine_map<(i) -> (i)>, // a (in) |
| affine_map<(i) -> ()> // x (out) |
| ], |
| iterator_types = ["reduction"] |
| } |
| |
| func.func @sparse_reduction_ori(%argx: tensor<i13>, |
| %arga: tensor<?xi13, #SparseVector>) |
| -> tensor<i13> { |
| %0 = linalg.generic #trait |
| ins(%arga: tensor<?xi13, #SparseVector>) |
| outs(%argx: tensor<i13>) { |
| ^bb(%a: i13, %x: i13): |
| %t = arith.ori %x, %a: i13 |
| linalg.yield %t : i13 |
| } -> tensor<i13> |
| return %0 : tensor<i13> |
| } |
| |
| // ----- |
| |
| // Same test as sparse_reduction_ori except that the accumulator is on the |
| // rhs of the operation. This checks that we can recognize a reduction |
| // irrespective to where the accumulator appears on commutative operations. |
| |
| // CHECK-ON-LABEL: func.func @sparse_reduction_ori_accumulator_on_rhs( |
| // CHECK-ON-SAME: %[[VAL_0:.*]]: tensor<i13>, |
| // CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?xi13, #sparse{{[0-9]*}}>) -> tensor<i13> { |
| // CHECK-ON-DAG: %[[VAL_2:.*]] = arith.constant 8 : index |
| // CHECK-ON-DAG: %[[VAL_3:.*]] = arith.constant dense<0> : vector<8xi13> |
| // CHECK-ON-DAG: %[[VAL_4:.*]] = arith.constant 0 : index |
| // CHECK-ON-DAG: %[[VAL_5:.*]] = arith.constant 1 : index |
| // CHECK-ON: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi13, #sparse{{[0-9]*}}> to memref<?xindex> |
| // CHECK-ON: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi13, #sparse{{[0-9]*}}> to memref<?xi13> |
| // CHECK-ON: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i13> |
| // CHECK-ON: %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<i13> |
| // CHECK-ON: %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex> |
| // CHECK-ON: %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex> |
| // CHECK-ON: %[[VAL_12:.*]] = vector.broadcast %[[VAL_9]] : i13 to vector<8xi13> |
| // CHECK-ON: %[[VAL_13:.*]] = scf.for %[[VAL_14:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_2]] iter_args(%[[VAL_15:.*]] = %[[VAL_12]]) -> (vector<8xi13>) { |
| // CHECK-ON: %[[VAL_16:.*]] = affine.min #map(%[[VAL_11]], %[[VAL_14]]){{\[}}%[[VAL_2]]] |
| // CHECK-ON: %[[VAL_17:.*]] = vector.create_mask %[[VAL_16]] : vector<8xi1> |
| // CHECK-ON: %[[VAL_18:.*]] = vector.maskedload %[[VAL_7]]{{\[}}%[[VAL_14]]], %[[VAL_17]], %[[VAL_3]] : memref<?xi13>, vector<8xi1>, vector<8xi13> into vector<8xi13> |
| // CHECK-ON: %[[VAL_19:.*]] = arith.ori %[[VAL_18]], %[[VAL_15]] : vector<8xi13> |
| // CHECK-ON: %[[VAL_20:.*]] = arith.select %[[VAL_17]], %[[VAL_19]], %[[VAL_15]] : vector<8xi1>, vector<8xi13> |
| // CHECK-ON: scf.yield %[[VAL_20]] : vector<8xi13> |
| // CHECK-ON: } {"Emitted from" = "linalg.generic"} |
| // CHECK-ON: %[[VAL_21:.*]] = vector.reduction <or>, %[[VAL_22:.*]] : vector<8xi13> into i13 |
| // CHECK-ON: memref.store %[[VAL_21]], %[[VAL_8]][] : memref<i13> |
| // CHECK-ON: %[[VAL_23:.*]] = bufferization.to_tensor %[[VAL_8]] : memref<i13> |
| // CHECK-ON: return %[[VAL_23]] : tensor<i13> |
| // CHECK-ON: } |
| // |
| // CHECK-OFF-LABEL: func.func @sparse_reduction_ori_accumulator_on_rhs( |
| // CHECK-OFF-SAME: %[[VAL_0:.*]]: tensor<i13>, |
| // CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?xi13, #sparse{{[0-9]*}}>) -> tensor<i13> { |
| // CHECK-OFF-DAG: %[[VAL_2:.*]] = arith.constant 0 : index |
| // CHECK-OFF-DAG: %[[VAL_3:.*]] = arith.constant 1 : index |
| // CHECK-OFF: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi13, #sparse{{[0-9]*}}> to memref<?xindex> |
| // CHECK-OFF: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi13, #sparse{{[0-9]*}}> to memref<?xi13> |
| // CHECK-OFF: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i13> |
| // CHECK-OFF: %[[VAL_7:.*]] = memref.load %[[VAL_6]][] : memref<i13> |
| // CHECK-OFF: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex> |
| // CHECK-OFF: %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex> |
| // CHECK-OFF: %[[VAL_10:.*]] = scf.for %[[VAL_11:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] iter_args(%[[VAL_12:.*]] = %[[VAL_7]]) -> (i13) { |
| // CHECK-OFF: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_11]]] : memref<?xi13> |
| // CHECK-OFF: %[[VAL_14:.*]] = arith.ori %[[VAL_13]], %[[VAL_12]] : i13 |
| // CHECK-OFF: scf.yield %[[VAL_14]] : i13 |
| // CHECK-OFF: } {"Emitted from" = "linalg.generic"} |
| // CHECK-OFF: memref.store %[[VAL_15:.*]], %[[VAL_6]][] : memref<i13> |
| // CHECK-OFF: %[[VAL_16:.*]] = bufferization.to_tensor %[[VAL_6]] : memref<i13> |
| // CHECK-OFF: return %[[VAL_16]] : tensor<i13> |
| // CHECK-OFF: } |
| #SparseVector = #sparse_tensor.encoding<{map = (d0) -> (d0 : compressed)}> |
| |
| #trait = { |
| indexing_maps = [ |
| affine_map<(i) -> (i)>, // a (in) |
| affine_map<(i) -> ()> // x (out) |
| ], |
| iterator_types = ["reduction"] |
| } |
| |
| func.func @sparse_reduction_ori_accumulator_on_rhs(%argx: tensor<i13>, |
| %arga: tensor<?xi13, #SparseVector>) |
| -> tensor<i13> { |
| %0 = linalg.generic #trait |
| ins(%arga: tensor<?xi13, #SparseVector>) |
| outs(%argx: tensor<i13>) { |
| ^bb(%a: i13, %x: i13): |
| %t = arith.ori %a, %x: i13 |
| linalg.yield %t : i13 |
| } -> tensor<i13> |
| return %0 : tensor<i13> |
| } |
| |
| // ----- |
| |
| // Check that we vectorize reductions with subi. |
| // |
| // CHECK-ON-LABEL: func.func @sparse_reduction_subi( |
| // CHECK-ON-SAME: %[[VAL_0:.*]]: tensor<i32>, |
| // CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse{{[0-9]*}}>) -> tensor<i32> { |
| // CHECK-ON-DAG: %[[VAL_2:.*]] = arith.constant 8 : index |
| // CHECK-ON-DAG: %[[VAL_3:.*]] = arith.constant 0 : index |
| // CHECK-ON-DAG: %[[VAL_4:.*]] = arith.constant dense<0> : vector<8xi32> |
| // CHECK-ON-DAG: %[[VAL_5:.*]] = arith.constant 1 : index |
| // CHECK-ON: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse{{[0-9]*}}> to memref<?xindex> |
| // CHECK-ON: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse{{[0-9]*}}> to memref<?xi32> |
| // CHECK-ON: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i32> |
| // CHECK-ON: %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<i32> |
| // CHECK-ON: %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_3]]] : memref<?xindex> |
| // CHECK-ON: %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex> |
| // CHECK-ON: %[[VAL_12:.*]] = vector.insertelement %[[VAL_9]], %[[VAL_4]]{{\[}}%[[VAL_3]] : index] : vector<8xi32> |
| // CHECK-ON: %[[VAL_13:.*]] = scf.for %[[VAL_14:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_2]] iter_args(%[[VAL_15:.*]] = %[[VAL_12]]) -> (vector<8xi32>) { |
| // CHECK-ON: %[[VAL_16:.*]] = affine.min #map(%[[VAL_11]], %[[VAL_14]]){{\[}}%[[VAL_2]]] |
| // CHECK-ON: %[[VAL_17:.*]] = vector.create_mask %[[VAL_16]] : vector<8xi1> |
| // CHECK-ON: %[[VAL_18:.*]] = vector.maskedload %[[VAL_7]]{{\[}}%[[VAL_14]]], %[[VAL_17]], %[[VAL_4]] : memref<?xi32>, vector<8xi1>, vector<8xi32> into vector<8xi32> |
| // CHECK-ON: %[[VAL_19:.*]] = arith.subi %[[VAL_15]], %[[VAL_18]] : vector<8xi32> |
| // CHECK-ON: %[[VAL_20:.*]] = arith.select %[[VAL_17]], %[[VAL_19]], %[[VAL_15]] : vector<8xi1>, vector<8xi32> |
| // CHECK-ON: scf.yield %[[VAL_20]] : vector<8xi32> |
| // CHECK-ON: } {"Emitted from" = "linalg.generic"} |
| // CHECK-ON: %[[VAL_21:.*]] = vector.reduction <add>, %[[VAL_22:.*]] : vector<8xi32> into i32 |
| // CHECK-ON: memref.store %[[VAL_21]], %[[VAL_8]][] : memref<i32> |
| // CHECK-ON: %[[VAL_23:.*]] = bufferization.to_tensor %[[VAL_8]] : memref<i32> |
| // CHECK-ON: return %[[VAL_23]] : tensor<i32> |
| // CHECK-ON: } |
| // |
| // CHECK-OFF-LABEL: func.func @sparse_reduction_subi( |
| // CHECK-OFF-SAME: %[[VAL_0:.*]]: tensor<i32>, |
| // CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse{{[0-9]*}}>) -> tensor<i32> { |
| // CHECK-OFF-DAG: %[[VAL_2:.*]] = arith.constant 0 : index |
| // CHECK-OFF-DAG: %[[VAL_3:.*]] = arith.constant 1 : index |
| // CHECK-OFF: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse{{[0-9]*}}> to memref<?xindex> |
| // CHECK-OFF: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse{{[0-9]*}}> to memref<?xi32> |
| // CHECK-OFF: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i32> |
| // CHECK-OFF: %[[VAL_7:.*]] = memref.load %[[VAL_6]][] : memref<i32> |
| // CHECK-OFF: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex> |
| // CHECK-OFF: %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex> |
| // CHECK-OFF: %[[VAL_10:.*]] = scf.for %[[VAL_11:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] iter_args(%[[VAL_12:.*]] = %[[VAL_7]]) -> (i32) { |
| // CHECK-OFF: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_11]]] : memref<?xi32> |
| // CHECK-OFF: %[[VAL_14:.*]] = arith.subi %[[VAL_12]], %[[VAL_13]] : i32 |
| // CHECK-OFF: scf.yield %[[VAL_14]] : i32 |
| // CHECK-OFF: } {"Emitted from" = "linalg.generic"} |
| // CHECK-OFF: memref.store %[[VAL_15:.*]], %[[VAL_6]][] : memref<i32> |
| // CHECK-OFF: %[[VAL_16:.*]] = bufferization.to_tensor %[[VAL_6]] : memref<i32> |
| // CHECK-OFF: return %[[VAL_16]] : tensor<i32> |
| // CHECK-OFF: } |
| #SparseVector = #sparse_tensor.encoding<{map = (d0) -> (d0 : compressed)}> |
| |
| #trait = { |
| indexing_maps = [ |
| affine_map<(i) -> (i)>, // a (in) |
| affine_map<(i) -> ()> // x (out) |
| ], |
| iterator_types = ["reduction"] |
| } |
| |
| func.func @sparse_reduction_subi(%argx: tensor<i32>, |
| %arga: tensor<?xi32, #SparseVector>) |
| -> tensor<i32> { |
| %0 = linalg.generic #trait |
| ins(%arga: tensor<?xi32, #SparseVector>) |
| outs(%argx: tensor<i32>) { |
| ^bb(%a: i32, %x: i32): |
| %t = arith.subi %x, %a: i32 |
| linalg.yield %t : i32 |
| } -> tensor<i32> |
| return %0 : tensor<i32> |
| } |
| |
| // ----- |
| |
| // Check that we vectorize reductions with xor. |
| |
| // CHECK-ON-LABEL: func.func @sparse_reduction_xor( |
| // CHECK-ON-SAME: %[[VAL_0:.*]]: tensor<i32>, |
| // CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse{{[0-9]*}}>) -> tensor<i32> { |
| // CHECK-ON-DAG: %[[VAL_2:.*]] = arith.constant 8 : index |
| // CHECK-ON-DAG: %[[VAL_3:.*]] = arith.constant dense<0> : vector<8xi32> |
| // CHECK-ON-DAG: %[[VAL_4:.*]] = arith.constant 0 : index |
| // CHECK-ON-DAG: %[[VAL_5:.*]] = arith.constant 1 : index |
| // CHECK-ON: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse{{[0-9]*}}> to memref<?xindex> |
| // CHECK-ON: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse{{[0-9]*}}> to memref<?xi32> |
| // CHECK-ON: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i32> |
| // CHECK-ON: %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<i32> |
| // CHECK-ON: %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex> |
| // CHECK-ON: %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex> |
| // CHECK-ON: %[[VAL_12:.*]] = vector.insertelement %[[VAL_9]], %[[VAL_3]]{{\[}}%[[VAL_4]] : index] : vector<8xi32> |
| // CHECK-ON: %[[VAL_13:.*]] = scf.for %[[VAL_14:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_2]] iter_args(%[[VAL_15:.*]] = %[[VAL_12]]) -> (vector<8xi32>) { |
| // CHECK-ON: %[[VAL_16:.*]] = affine.min #map(%[[VAL_11]], %[[VAL_14]]){{\[}}%[[VAL_2]]] |
| // CHECK-ON: %[[VAL_17:.*]] = vector.create_mask %[[VAL_16]] : vector<8xi1> |
| // CHECK-ON: %[[VAL_18:.*]] = vector.maskedload %[[VAL_7]]{{\[}}%[[VAL_14]]], %[[VAL_17]], %[[VAL_3]] : memref<?xi32>, vector<8xi1>, vector<8xi32> into vector<8xi32> |
| // CHECK-ON: %[[VAL_19:.*]] = arith.xori %[[VAL_15]], %[[VAL_18]] : vector<8xi32> |
| // CHECK-ON: %[[VAL_20:.*]] = arith.select %[[VAL_17]], %[[VAL_19]], %[[VAL_15]] : vector<8xi1>, vector<8xi32> |
| // CHECK-ON: scf.yield %[[VAL_20]] : vector<8xi32> |
| // CHECK-ON: } {"Emitted from" = "linalg.generic"} |
| // CHECK-ON: %[[VAL_21:.*]] = vector.reduction <xor>, %[[VAL_22:.*]] : vector<8xi32> into i32 |
| // CHECK-ON: memref.store %[[VAL_21]], %[[VAL_8]][] : memref<i32> |
| // CHECK-ON: %[[VAL_23:.*]] = bufferization.to_tensor %[[VAL_8]] : memref<i32> |
| // CHECK-ON: return %[[VAL_23]] : tensor<i32> |
| // CHECK-ON: } |
| // |
| // CHECK-OFF-LABEL: func.func @sparse_reduction_xor( |
| // CHECK-OFF-SAME: %[[VAL_0:.*]]: tensor<i32>, |
| // CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse{{[0-9]*}}>) -> tensor<i32> { |
| // CHECK-OFF-DAG: %[[VAL_2:.*]] = arith.constant 0 : index |
| // CHECK-OFF-DAG: %[[VAL_3:.*]] = arith.constant 1 : index |
| // CHECK-OFF: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse{{[0-9]*}}> to memref<?xindex> |
| // CHECK-OFF: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse{{[0-9]*}}> to memref<?xi32> |
| // CHECK-OFF: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i32> |
| // CHECK-OFF: %[[VAL_7:.*]] = memref.load %[[VAL_6]][] : memref<i32> |
| // CHECK-OFF: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex> |
| // CHECK-OFF: %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex> |
| // CHECK-OFF: %[[VAL_10:.*]] = scf.for %[[VAL_11:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] iter_args(%[[VAL_12:.*]] = %[[VAL_7]]) -> (i32) { |
| // CHECK-OFF: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_11]]] : memref<?xi32> |
| // CHECK-OFF: %[[VAL_14:.*]] = arith.xori %[[VAL_12]], %[[VAL_13]] : i32 |
| // CHECK-OFF: scf.yield %[[VAL_14]] : i32 |
| // CHECK-OFF: } {"Emitted from" = "linalg.generic"} |
| // CHECK-OFF: memref.store %[[VAL_15:.*]], %[[VAL_6]][] : memref<i32> |
| // CHECK-OFF: %[[VAL_16:.*]] = bufferization.to_tensor %[[VAL_6]] : memref<i32> |
| // CHECK-OFF: return %[[VAL_16]] : tensor<i32> |
| // CHECK-OFF: } |
| |
| #SparseVector = #sparse_tensor.encoding<{map = (d0) -> (d0 : compressed)}> |
| |
| #trait = { |
| indexing_maps = [ |
| affine_map<(i) -> (i)>, // a (in) |
| affine_map<(i) -> ()> // x (out) |
| ], |
| iterator_types = ["reduction"] |
| } |
| |
| func.func @sparse_reduction_xor(%argx: tensor<i32>, |
| %arga: tensor<?xi32, #SparseVector>) |
| -> tensor<i32> { |
| %0 = linalg.generic #trait |
| ins(%arga: tensor<?xi32, #SparseVector>) |
| outs(%argx: tensor<i32>) { |
| ^bb(%a: i32, %x: i32): |
| %t = arith.xori %x, %a: i32 |
| linalg.yield %t : i32 |
| } -> tensor<i32> |
| return %0 : tensor<i32> |
| } |
| |
| // ----- |
| |
| // Check that we vectorize reductions with addi. |
| |
| // CHECK-ON-LABEL: func.func @sparse_reduction_addi( |
| // CHECK-ON-SAME: %[[VAL_0:.*]]: tensor<i32>, |
| // CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse{{[0-9]*}}>) -> tensor<i32> { |
| // CHECK-ON-DAG: %[[VAL_2:.*]] = arith.constant 8 : index |
| // CHECK-ON-DAG: %[[VAL_3:.*]] = arith.constant dense<0> : vector<8xi32> |
| // CHECK-ON-DAG: %[[VAL_4:.*]] = arith.constant 0 : index |
| // CHECK-ON-DAG: %[[VAL_5:.*]] = arith.constant 1 : index |
| // CHECK-ON: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse{{[0-9]*}}> to memref<?xindex> |
| // CHECK-ON: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse{{[0-9]*}}> to memref<?xi32> |
| // CHECK-ON: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i32> |
| // CHECK-ON: %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<i32> |
| // CHECK-ON: %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex> |
| // CHECK-ON: %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex> |
| // CHECK-ON: %[[VAL_12:.*]] = vector.insertelement %[[VAL_9]], %[[VAL_3]]{{\[}}%[[VAL_4]] : index] : vector<8xi32> |
| // CHECK-ON: %[[VAL_13:.*]] = scf.for %[[VAL_14:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_2]] iter_args(%[[VAL_15:.*]] = %[[VAL_12]]) -> (vector<8xi32>) { |
| // CHECK-ON: %[[VAL_16:.*]] = affine.min #map(%[[VAL_11]], %[[VAL_14]]){{\[}}%[[VAL_2]]] |
| // CHECK-ON: %[[VAL_17:.*]] = vector.create_mask %[[VAL_16]] : vector<8xi1> |
| // CHECK-ON: %[[VAL_18:.*]] = vector.maskedload %[[VAL_7]]{{\[}}%[[VAL_14]]], %[[VAL_17]], %[[VAL_3]] : memref<?xi32>, vector<8xi1>, vector<8xi32> into vector<8xi32> |
| // CHECK-ON: %[[VAL_19:.*]] = arith.addi %[[VAL_15]], %[[VAL_18]] : vector<8xi32> |
| // CHECK-ON: %[[VAL_20:.*]] = arith.select %[[VAL_17]], %[[VAL_19]], %[[VAL_15]] : vector<8xi1>, vector<8xi32> |
| // CHECK-ON: scf.yield %[[VAL_20]] : vector<8xi32> |
| // CHECK-ON: } {"Emitted from" = "linalg.generic"} |
| // CHECK-ON: %[[VAL_21:.*]] = vector.reduction <add>, %[[VAL_22:.*]] : vector<8xi32> into i32 |
| // CHECK-ON: memref.store %[[VAL_21]], %[[VAL_8]][] : memref<i32> |
| // CHECK-ON: %[[VAL_23:.*]] = bufferization.to_tensor %[[VAL_8]] : memref<i32> |
| // CHECK-ON: return %[[VAL_23]] : tensor<i32> |
| // CHECK-ON: } |
| // |
| // CHECK-OFF-LABEL: func.func @sparse_reduction_addi( |
| // CHECK-OFF-SAME: %[[VAL_0:.*]]: tensor<i32>, |
| // CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse{{[0-9]*}}>) -> tensor<i32> { |
| // CHECK-OFF-DAG: %[[VAL_2:.*]] = arith.constant 0 : index |
| // CHECK-OFF-DAG: %[[VAL_3:.*]] = arith.constant 1 : index |
| // CHECK-OFF: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse{{[0-9]*}}> to memref<?xindex> |
| // CHECK-OFF: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse{{[0-9]*}}> to memref<?xi32> |
| // CHECK-OFF: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i32> |
| // CHECK-OFF: %[[VAL_7:.*]] = memref.load %[[VAL_6]][] : memref<i32> |
| // CHECK-OFF: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex> |
| // CHECK-OFF: %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex> |
| // CHECK-OFF: %[[VAL_10:.*]] = scf.for %[[VAL_11:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] iter_args(%[[VAL_12:.*]] = %[[VAL_7]]) -> (i32) { |
| // CHECK-OFF: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_11]]] : memref<?xi32> |
| // CHECK-OFF: %[[VAL_14:.*]] = arith.addi %[[VAL_12]], %[[VAL_13]] : i32 |
| // CHECK-OFF: scf.yield %[[VAL_14]] : i32 |
| // CHECK-OFF: } {"Emitted from" = "linalg.generic"} |
| // CHECK-OFF: memref.store %[[VAL_15:.*]], %[[VAL_6]][] : memref<i32> |
| // CHECK-OFF: %[[VAL_16:.*]] = bufferization.to_tensor %[[VAL_6]] : memref<i32> |
| // CHECK-OFF: return %[[VAL_16]] : tensor<i32> |
| // CHECK-OFF: } |
| |
| #SparseVector = #sparse_tensor.encoding<{map = (d0) -> (d0 : compressed)}> |
| |
| #trait = { |
| indexing_maps = [ |
| affine_map<(i) -> (i)>, // a (in) |
| affine_map<(i) -> ()> // x (out) |
| ], |
| iterator_types = ["reduction"] |
| } |
| |
| func.func @sparse_reduction_addi(%argx: tensor<i32>, |
| %arga: tensor<?xi32, #SparseVector>) |
| -> tensor<i32> { |
| %0 = linalg.generic #trait |
| ins(%arga: tensor<?xi32, #SparseVector>) |
| outs(%argx: tensor<i32>) { |
| ^bb(%a: i32, %x: i32): |
| %t = arith.addi %x, %a: i32 |
| linalg.yield %t : i32 |
| } -> tensor<i32> |
| return %0 : tensor<i32> |
| } |
| |
| // ----- |
| |
| // Check that we vectorize reductions with subf. |
| |
| // CHECK-ON-LABEL: func.func @sparse_reduction_subf( |
| // CHECK-ON-SAME: %[[VAL_0:.*]]: tensor<f32>, |
| // CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?xf32, #sparse{{[0-9]*}}>) -> tensor<f32> { |
| // CHECK-ON-DAG: %[[VAL_2:.*]] = arith.constant 8 : index |
| // CHECK-ON-DAG: %[[VAL_3:.*]] = arith.constant dense<0.000000e+00> : vector<8xf32> |
| // CHECK-ON-DAG: %[[VAL_4:.*]] = arith.constant 0 : index |
| // CHECK-ON-DAG: %[[VAL_5:.*]] = arith.constant 1 : index |
| // CHECK-ON: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xf32, #sparse{{[0-9]*}}> to memref<?xindex> |
| // CHECK-ON: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xf32, #sparse{{[0-9]*}}> to memref<?xf32> |
| // CHECK-ON: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref<f32> |
| // CHECK-ON: %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<f32> |
| // CHECK-ON: %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex> |
| // CHECK-ON: %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex> |
| // CHECK-ON: %[[VAL_12:.*]] = vector.insertelement %[[VAL_9]], %[[VAL_3]]{{\[}}%[[VAL_4]] : index] : vector<8xf32> |
| // CHECK-ON: %[[VAL_13:.*]] = scf.for %[[VAL_14:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_2]] iter_args(%[[VAL_15:.*]] = %[[VAL_12]]) -> (vector<8xf32>) { |
| // CHECK-ON: %[[VAL_16:.*]] = affine.min #map(%[[VAL_11]], %[[VAL_14]]){{\[}}%[[VAL_2]]] |
| // CHECK-ON: %[[VAL_17:.*]] = vector.create_mask %[[VAL_16]] : vector<8xi1> |
| // CHECK-ON: %[[VAL_18:.*]] = vector.maskedload %[[VAL_7]]{{\[}}%[[VAL_14]]], %[[VAL_17]], %[[VAL_3]] : memref<?xf32>, vector<8xi1>, vector<8xf32> into vector<8xf32> |
| // CHECK-ON: %[[VAL_19:.*]] = arith.subf %[[VAL_15]], %[[VAL_18]] : vector<8xf32> |
| // CHECK-ON: %[[VAL_20:.*]] = arith.select %[[VAL_17]], %[[VAL_19]], %[[VAL_15]] : vector<8xi1>, vector<8xf32> |
| // CHECK-ON: scf.yield %[[VAL_20]] : vector<8xf32> |
| // CHECK-ON: } {"Emitted from" = "linalg.generic"} |
| // CHECK-ON: %[[VAL_21:.*]] = vector.reduction <add>, %[[VAL_22:.*]] : vector<8xf32> into f32 |
| // CHECK-ON: memref.store %[[VAL_21]], %[[VAL_8]][] : memref<f32> |
| // CHECK-ON: %[[VAL_23:.*]] = bufferization.to_tensor %[[VAL_8]] : memref<f32> |
| // CHECK-ON: return %[[VAL_23]] : tensor<f32> |
| // CHECK-ON: } |
| // |
| // CHECK-OFF-LABEL: func.func @sparse_reduction_subf( |
| // CHECK-OFF-SAME: %[[VAL_0:.*]]: tensor<f32>, |
| // CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?xf32, #sparse{{[0-9]*}}>) -> tensor<f32> { |
| // CHECK-OFF-DAG: %[[VAL_2:.*]] = arith.constant 0 : index |
| // CHECK-OFF-DAG: %[[VAL_3:.*]] = arith.constant 1 : index |
| // CHECK-OFF: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xf32, #sparse{{[0-9]*}}> to memref<?xindex> |
| // CHECK-OFF: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xf32, #sparse{{[0-9]*}}> to memref<?xf32> |
| // CHECK-OFF: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<f32> |
| // CHECK-OFF: %[[VAL_7:.*]] = memref.load %[[VAL_6]][] : memref<f32> |
| // CHECK-OFF: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex> |
| // CHECK-OFF: %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex> |
| // CHECK-OFF: %[[VAL_10:.*]] = scf.for %[[VAL_11:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] iter_args(%[[VAL_12:.*]] = %[[VAL_7]]) -> (f32) { |
| // CHECK-OFF: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_11]]] : memref<?xf32> |
| // CHECK-OFF: %[[VAL_14:.*]] = arith.subf %[[VAL_12]], %[[VAL_13]] : f32 |
| // CHECK-OFF: scf.yield %[[VAL_14]] : f32 |
| // CHECK-OFF: } {"Emitted from" = "linalg.generic"} |
| // CHECK-OFF: memref.store %[[VAL_15:.*]], %[[VAL_6]][] : memref<f32> |
| // CHECK-OFF: %[[VAL_16:.*]] = bufferization.to_tensor %[[VAL_6]] : memref<f32> |
| // CHECK-OFF: return %[[VAL_16]] : tensor<f32> |
| // CHECK-OFF: } |
| |
| #SparseVector = #sparse_tensor.encoding<{map = (d0) -> (d0 : compressed)}> |
| |
| #trait = { |
| indexing_maps = [ |
| affine_map<(i) -> (i)>, // a (in) |
| affine_map<(i) -> ()> // x (out) |
| ], |
| iterator_types = ["reduction"] |
| } |
| |
| func.func @sparse_reduction_subf(%argx: tensor<f32>, |
| %arga: tensor<?xf32, #SparseVector>) |
| -> tensor<f32> { |
| %0 = linalg.generic #trait |
| ins(%arga: tensor<?xf32, #SparseVector>) |
| outs(%argx: tensor<f32>) { |
| ^bb(%a: f32, %x: f32): |
| %t = arith.subf %x, %a: f32 |
| linalg.yield %t : f32 |
| } -> tensor<f32> |
| return %0 : tensor<f32> |
| } |
| |
| // ----- |
| |
| // Check that we vectorize reductions with addf. |
| |
| // CHECK-ON-LABEL: func.func @sparse_reduction_addf( |
| // CHECK-ON-SAME: %[[VAL_0:.*]]: tensor<f32>, |
| // CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?xf32, #sparse{{[0-9]*}}>) -> tensor<f32> { |
| // CHECK-ON-DAG: %[[VAL_2:.*]] = arith.constant 8 : index |
| // CHECK-ON-DAG: %[[VAL_3:.*]] = arith.constant dense<0.000000e+00> : vector<8xf32> |
| // CHECK-ON-DAG: %[[VAL_4:.*]] = arith.constant 0 : index |
| // CHECK-ON-DAG: %[[VAL_5:.*]] = arith.constant 1 : index |
| // CHECK-ON: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xf32, #sparse{{[0-9]*}}> to memref<?xindex> |
| // CHECK-ON: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xf32, #sparse{{[0-9]*}}> to memref<?xf32> |
| // CHECK-ON: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref<f32> |
| // CHECK-ON: %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<f32> |
| // CHECK-ON: %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex> |
| // CHECK-ON: %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex> |
| // CHECK-ON: %[[VAL_12:.*]] = vector.insertelement %[[VAL_9]], %[[VAL_3]]{{\[}}%[[VAL_4]] : index] : vector<8xf32> |
| // CHECK-ON: %[[VAL_13:.*]] = scf.for %[[VAL_14:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_2]] iter_args(%[[VAL_15:.*]] = %[[VAL_12]]) -> (vector<8xf32>) { |
| // CHECK-ON: %[[VAL_16:.*]] = affine.min #map(%[[VAL_11]], %[[VAL_14]]){{\[}}%[[VAL_2]]] |
| // CHECK-ON: %[[VAL_17:.*]] = vector.create_mask %[[VAL_16]] : vector<8xi1> |
| // CHECK-ON: %[[VAL_18:.*]] = vector.maskedload %[[VAL_7]]{{\[}}%[[VAL_14]]], %[[VAL_17]], %[[VAL_3]] : memref<?xf32>, vector<8xi1>, vector<8xf32> into vector<8xf32> |
| // CHECK-ON: %[[VAL_19:.*]] = arith.addf %[[VAL_15]], %[[VAL_18]] : vector<8xf32> |
| // CHECK-ON: %[[VAL_20:.*]] = arith.select %[[VAL_17]], %[[VAL_19]], %[[VAL_15]] : vector<8xi1>, vector<8xf32> |
| // CHECK-ON: scf.yield %[[VAL_20]] : vector<8xf32> |
| // CHECK-ON: } {"Emitted from" = "linalg.generic"} |
| // CHECK-ON: %[[VAL_21:.*]] = vector.reduction <add>, %[[VAL_22:.*]] : vector<8xf32> into f32 |
| // CHECK-ON: memref.store %[[VAL_21]], %[[VAL_8]][] : memref<f32> |
| // CHECK-ON: %[[VAL_23:.*]] = bufferization.to_tensor %[[VAL_8]] : memref<f32> |
| // CHECK-ON: return %[[VAL_23]] : tensor<f32> |
| // CHECK-ON: } |
| // |
| // CHECK-OFF-LABEL: func.func @sparse_reduction_addf( |
| // CHECK-OFF-SAME: %[[VAL_0:.*]]: tensor<f32>, |
| // CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?xf32, #sparse{{[0-9]*}}>) -> tensor<f32> { |
| // CHECK-OFF-DAG: %[[VAL_2:.*]] = arith.constant 0 : index |
| // CHECK-OFF-DAG: %[[VAL_3:.*]] = arith.constant 1 : index |
| // CHECK-OFF: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xf32, #sparse{{[0-9]*}}> to memref<?xindex> |
| // CHECK-OFF: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xf32, #sparse{{[0-9]*}}> to memref<?xf32> |
| // CHECK-OFF: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<f32> |
| // CHECK-OFF: %[[VAL_7:.*]] = memref.load %[[VAL_6]][] : memref<f32> |
| // CHECK-OFF: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex> |
| // CHECK-OFF: %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex> |
| // CHECK-OFF: %[[VAL_10:.*]] = scf.for %[[VAL_11:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] iter_args(%[[VAL_12:.*]] = %[[VAL_7]]) -> (f32) { |
| // CHECK-OFF: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_11]]] : memref<?xf32> |
| // CHECK-OFF: %[[VAL_14:.*]] = arith.addf %[[VAL_12]], %[[VAL_13]] : f32 |
| // CHECK-OFF: scf.yield %[[VAL_14]] : f32 |
| // CHECK-OFF: } {"Emitted from" = "linalg.generic"} |
| // CHECK-OFF: memref.store %[[VAL_15:.*]], %[[VAL_6]][] : memref<f32> |
| // CHECK-OFF: %[[VAL_16:.*]] = bufferization.to_tensor %[[VAL_6]] : memref<f32> |
| // CHECK-OFF: return %[[VAL_16]] : tensor<f32> |
| // CHECK-OFF: } |
| |
| #SparseVector = #sparse_tensor.encoding<{map = (d0) -> (d0 : compressed)}> |
| |
| #trait = { |
| indexing_maps = [ |
| affine_map<(i) -> (i)>, // a (in) |
| affine_map<(i) -> ()> // x (out) |
| ], |
| iterator_types = ["reduction"] |
| } |
| |
| func.func @sparse_reduction_addf(%argx: tensor<f32>, |
| %arga: tensor<?xf32, #SparseVector>) |
| -> tensor<f32> { |
| %0 = linalg.generic #trait |
| ins(%arga: tensor<?xf32, #SparseVector>) |
| outs(%argx: tensor<f32>) { |
| ^bb(%a: f32, %x: f32): |
| %t = arith.addf %x, %a: f32 |
| linalg.yield %t : f32 |
| } -> tensor<f32> |
| return %0 : tensor<f32> |
| } |
