| // NOTE: Assertions have been autogenerated by utils/generate-test-checks.py |
| |
| // RUN: mlir-opt %s -sparsification="vectorization-strategy=2 vl=8" -canonicalize | \ |
| // RUN: FileCheck %s |
| |
| #SparseMatrix = #sparse_tensor.encoding<{dimLevelType = ["dense","compressed"]}> |
| |
| #trait = { |
| indexing_maps = [ |
| affine_map<(i,j) -> (i,j)>, // a (in) |
| affine_map<(i,j) -> (i,j)>, // b (in) |
| affine_map<(i,j) -> ()> // x (out) |
| ], |
| iterator_types = ["reduction", "reduction"] |
| } |
| |
| // Verifies that the SIMD reductions in the two for-loops after the |
| // while-loop are chained before horizontally reducing these back to scalar. |
| // |
| // CHECK-LABEL: func @sparse_matrix_sum( |
| // CHECK-SAME: %[[VAL_0:.*]]: tensor<f64> {linalg.inplaceable = true}, |
| // CHECK-SAME: %[[VAL_1:.*]]: tensor<64x32xf64, #sparse_tensor.encoding<{{{.*}}}>>, |
| // CHECK-SAME: %[[VAL_2:.*]]: tensor<64x32xf64, #sparse_tensor.encoding<{{{.*}}}>>) -> tensor<f64> { |
| // CHECK-DAG: %[[VAL_3:.*]] = arith.constant dense<0.000000e+00> : vector<8xf64> |
| // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 8 : index |
| // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[VAL_7:.*]] = arith.constant 64 : index |
| // CHECK-DAG: %[[VAL_8:.*]] = arith.constant 1 : index |
| // CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_8]] : tensor<64x32xf64, #sparse_tensor.encoding<{{{.*}}}>> |
| // CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_8]] : tensor<64x32xf64, #sparse_tensor.encoding<{{{.*}}}>> |
| // CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<64x32xf64, #sparse_tensor.encoding<{{{.*}}}>> |
| // CHECK: %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_2]], %[[VAL_8]] : tensor<64x32xf64, #sparse_tensor.encoding<{{{.*}}}>> |
| // CHECK: %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_2]], %[[VAL_8]] : tensor<64x32xf64, #sparse_tensor.encoding<{{{.*}}}>> |
| // CHECK: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_2]] : tensor<64x32xf64, #sparse_tensor.encoding<{{{.*}}}>> |
| // CHECK: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_0]] : memref<f64> |
| // CHECK: %[[VAL_16:.*]] = tensor.extract %[[VAL_0]][] : tensor<f64> |
| // CHECK: %[[VAL_17:.*]] = scf.for %[[VAL_18:.*]] = %[[VAL_6]] to %[[VAL_7]] step %[[VAL_8]] iter_args(%[[VAL_19:.*]] = %[[VAL_16]]) -> (f64) { |
| // CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_18]]] : memref<?xindex> |
| // CHECK: %[[VAL_21:.*]] = arith.addi %[[VAL_18]], %[[VAL_8]] : index |
| // CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_21]]] : memref<?xindex> |
| // CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_18]]] : memref<?xindex> |
| // CHECK: %[[VAL_24:.*]] = arith.addi %[[VAL_18]], %[[VAL_8]] : index |
| // CHECK: %[[VAL_25:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_24]]] : memref<?xindex> |
| // CHECK: %[[VAL_26:.*]]:3 = scf.while (%[[VAL_27:.*]] = %[[VAL_20]], %[[VAL_28:.*]] = %[[VAL_23]], %[[VAL_29:.*]] = %[[VAL_19]]) : (index, index, f64) -> (index, index, f64) { |
| // CHECK: %[[VAL_30:.*]] = arith.cmpi ult, %[[VAL_27]], %[[VAL_22]] : index |
| // CHECK: %[[VAL_31:.*]] = arith.cmpi ult, %[[VAL_28]], %[[VAL_25]] : index |
| // CHECK: %[[VAL_32:.*]] = arith.andi %[[VAL_30]], %[[VAL_31]] : i1 |
| // CHECK: scf.condition(%[[VAL_32]]) %[[VAL_27]], %[[VAL_28]], %[[VAL_29]] : index, index, f64 |
| // CHECK: } do { |
| // CHECK: ^bb0(%[[VAL_33:.*]]: index, %[[VAL_34:.*]]: index, %[[VAL_35:.*]]: f64): |
| // CHECK: %[[VAL_36:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_33]]] : memref<?xindex> |
| // CHECK: %[[VAL_37:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_34]]] : memref<?xindex> |
| // CHECK: %[[VAL_38:.*]] = arith.cmpi ult, %[[VAL_37]], %[[VAL_36]] : index |
| // CHECK: %[[VAL_39:.*]] = select %[[VAL_38]], %[[VAL_37]], %[[VAL_36]] : index |
| // CHECK: %[[VAL_40:.*]] = arith.cmpi eq, %[[VAL_36]], %[[VAL_39]] : index |
| // CHECK: %[[VAL_41:.*]] = arith.cmpi eq, %[[VAL_37]], %[[VAL_39]] : index |
| // CHECK: %[[VAL_42:.*]] = arith.andi %[[VAL_40]], %[[VAL_41]] : i1 |
| // CHECK: %[[VAL_43:.*]] = scf.if %[[VAL_42]] -> (f64) { |
| // CHECK: %[[VAL_44:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_33]]] : memref<?xf64> |
| // CHECK: %[[VAL_45:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_34]]] : memref<?xf64> |
| // CHECK: %[[VAL_46:.*]] = arith.addf %[[VAL_44]], %[[VAL_45]] : f64 |
| // CHECK: %[[VAL_47:.*]] = arith.addf %[[VAL_35]], %[[VAL_46]] : f64 |
| // CHECK: scf.yield %[[VAL_47]] : f64 |
| // CHECK: } else { |
| // CHECK: %[[VAL_48:.*]] = arith.cmpi eq, %[[VAL_36]], %[[VAL_39]] : index |
| // CHECK: %[[VAL_49:.*]] = scf.if %[[VAL_48]] -> (f64) { |
| // CHECK: %[[VAL_50:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_33]]] : memref<?xf64> |
| // CHECK: %[[VAL_51:.*]] = arith.addf %[[VAL_35]], %[[VAL_50]] : f64 |
| // CHECK: scf.yield %[[VAL_51]] : f64 |
| // CHECK: } else { |
| // CHECK: %[[VAL_52:.*]] = arith.cmpi eq, %[[VAL_37]], %[[VAL_39]] : index |
| // CHECK: %[[VAL_53:.*]] = scf.if %[[VAL_52]] -> (f64) { |
| // CHECK: %[[VAL_54:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_34]]] : memref<?xf64> |
| // CHECK: %[[VAL_55:.*]] = arith.addf %[[VAL_35]], %[[VAL_54]] : f64 |
| // CHECK: scf.yield %[[VAL_55]] : f64 |
| // CHECK: } else { |
| // CHECK: scf.yield %[[VAL_35]] : f64 |
| // CHECK: } |
| // CHECK: scf.yield %[[VAL_56:.*]] : f64 |
| // CHECK: } |
| // CHECK: scf.yield %[[VAL_57:.*]] : f64 |
| // CHECK: } |
| // CHECK: %[[VAL_58:.*]] = arith.cmpi eq, %[[VAL_36]], %[[VAL_39]] : index |
| // CHECK: %[[VAL_59:.*]] = arith.addi %[[VAL_33]], %[[VAL_8]] : index |
| // CHECK: %[[VAL_60:.*]] = select %[[VAL_58]], %[[VAL_59]], %[[VAL_33]] : index |
| // CHECK: %[[VAL_61:.*]] = arith.cmpi eq, %[[VAL_37]], %[[VAL_39]] : index |
| // CHECK: %[[VAL_62:.*]] = arith.addi %[[VAL_34]], %[[VAL_8]] : index |
| // CHECK: %[[VAL_63:.*]] = select %[[VAL_61]], %[[VAL_62]], %[[VAL_34]] : index |
| // CHECK: scf.yield %[[VAL_60]], %[[VAL_63]], %[[VAL_64:.*]] : index, index, f64 |
| // CHECK: } |
| // CHECK: %[[VAL_65:.*]] = vector.insertelement %[[VAL_66:.*]]#2, %[[VAL_3]]{{\[}}%[[VAL_6]] : index] : vector<8xf64> |
| // CHECK: %[[VAL_67:.*]] = scf.for %[[VAL_68:.*]] = %[[VAL_66]]#0 to %[[VAL_22]] step %[[VAL_4]] iter_args(%[[VAL_69:.*]] = %[[VAL_65]]) -> (vector<8xf64>) { |
| // CHECK: %[[VAL_70:.*]] = affine.min #map(%[[VAL_22]], %[[VAL_68]]) |
| // CHECK: %[[VAL_71:.*]] = vector.create_mask %[[VAL_70]] : vector<8xi1> |
| // CHECK: %[[VAL_72:.*]] = vector.maskedload %[[VAL_11]]{{\[}}%[[VAL_68]]], %[[VAL_71]], %[[VAL_3]] : memref<?xf64>, vector<8xi1>, vector<8xf64> into vector<8xf64> |
| // CHECK: %[[VAL_73:.*]] = arith.addf %[[VAL_69]], %[[VAL_72]] : vector<8xf64> |
| // CHECK: %[[VAL_74:.*]] = select %[[VAL_71]], %[[VAL_73]], %[[VAL_69]] : vector<8xi1>, vector<8xf64> |
| // CHECK: scf.yield %[[VAL_74]] : vector<8xf64> |
| // CHECK: } |
| // CHECK: %[[VAL_75:.*]] = scf.for %[[VAL_76:.*]] = %[[VAL_66]]#1 to %[[VAL_25]] step %[[VAL_4]] iter_args(%[[VAL_77:.*]] = %[[VAL_78:.*]]) -> (vector<8xf64>) { |
| // CHECK: %[[VAL_79:.*]] = affine.min #map(%[[VAL_25]], %[[VAL_76]]) |
| // CHECK: %[[VAL_80:.*]] = vector.create_mask %[[VAL_79]] : vector<8xi1> |
| // CHECK: %[[VAL_81:.*]] = vector.maskedload %[[VAL_14]]{{\[}}%[[VAL_76]]], %[[VAL_80]], %[[VAL_3]] : memref<?xf64>, vector<8xi1>, vector<8xf64> into vector<8xf64> |
| // CHECK: %[[VAL_82:.*]] = arith.addf %[[VAL_77]], %[[VAL_81]] : vector<8xf64> |
| // CHECK: %[[VAL_83:.*]] = select %[[VAL_80]], %[[VAL_82]], %[[VAL_77]] : vector<8xi1>, vector<8xf64> |
| // CHECK: scf.yield %[[VAL_83]] : vector<8xf64> |
| // CHECK: } |
| // CHECK: %[[VAL_84:.*]] = vector.reduction "add", %[[VAL_85:.*]] : vector<8xf64> into f64 |
| // CHECK: scf.yield %[[VAL_84]] : f64 |
| // CHECK: } |
| // CHECK: memref.store %[[VAL_86:.*]], %[[VAL_15]][] : memref<f64> |
| // CHECK: %[[VAL_87:.*]] = bufferization.to_tensor %[[VAL_15]] : memref<f64> |
| // CHECK: return %[[VAL_87]] : tensor<f64> |
| // CHECK: } |
| func @sparse_matrix_sum(%argx: tensor<f64> {linalg.inplaceable = true}, |
| %arga: tensor<64x32xf64, #SparseMatrix>, |
| %argb: tensor<64x32xf64, #SparseMatrix>) -> tensor<f64> { |
| %0 = linalg.generic #trait |
| ins(%arga, %argb: tensor<64x32xf64, #SparseMatrix>, |
| tensor<64x32xf64, #SparseMatrix>) |
| outs(%argx: tensor<f64>) { |
| ^bb(%a: f64, %b: f64, %x: f64): |
| %m = arith.addf %a, %b : f64 |
| %t = arith.addf %x, %m : f64 |
| linalg.yield %t : f64 |
| } -> tensor<f64> |
| return %0 : tensor<f64> |
| } |
