1// NOTE: Assertions have been autogenerated by utils/generate-test-checks.py 2 3// The script is designed to make adding checks to 4// a test case fast, it is *not* designed to be authoritative 5// about what constitutes a good test! The CHECK should be 6// minimized and named to reflect the test intent. 7 8// RUN: mlir-opt %s -sparsification="vectorization-strategy=2 vl=8" -canonicalize | \ 9// RUN: FileCheck %s 10 11#SparseVector = #sparse_tensor.encoding<{ 12 dimLevelType = ["compressed"] 13}> 14 15#trait_1d = { 16 indexing_maps = [ 17 affine_map<(i) -> (i)>, // a 18 affine_map<(i) -> (i)> // x (out) 19 ], 20 iterator_types = ["parallel"], 21 doc = "X(i) = a(i) op i" 22} 23 24// CHECK-LABEL: func @sparse_index_1d_conj( 25// CHECK-SAME: %[[VAL_0:.*]]: tensor<8xi64, #sparse_tensor.encoding<{{{.*}}}>>) -> tensor<8xi64> { 26// CHECK-DAG: %[[VAL_1:.*]] = arith.constant dense<0> : vector<8xi64> 27// CHECK-DAG: %[[VAL_2:.*]] = arith.constant dense<0> : vector<8xindex> 28// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 8 : index 29// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index 30// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : i64 31// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index 32// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_6]] : tensor<8xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex> 33// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_6]] : tensor<8xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex> 34// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<8xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xi64> 35// CHECK-DAG: %[[VAL_10:.*]] = memref.alloc() : memref<8xi64> 36// CHECK-DAG: linalg.fill ins(%[[VAL_5]] : i64) outs(%[[VAL_10]] : memref<8xi64>) 37// CHECK-DAG: %[[VAL_11:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_6]]] : memref<?xindex> 38// CHECK-DAG: %[[VAL_12:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref<?xindex> 39// CHECK: scf.for %[[VAL_13:.*]] = %[[VAL_11]] to %[[VAL_12]] step %[[VAL_3]] { 40// CHECK: %[[VAL_14:.*]] = affine.min #map0(%[[VAL_13]]){{\[}}%[[VAL_12]]] 41// CHECK: %[[VAL_15:.*]] = vector.create_mask %[[VAL_14]] : vector<8xi1> 42// CHECK: %[[VAL_16:.*]] = vector.maskedload %[[VAL_8]]{{\[}}%[[VAL_13]]], %[[VAL_15]], %[[VAL_2]] : memref<?xindex>, vector<8xi1>, vector<8xindex> into vector<8xindex> 43// CHECK: %[[VAL_17:.*]] = vector.maskedload %[[VAL_9]]{{\[}}%[[VAL_13]]], %[[VAL_15]], %[[VAL_1]] : memref<?xi64>, vector<8xi1>, vector<8xi64> into vector<8xi64> 44// CHECK: %[[VAL_18:.*]] = arith.index_cast %[[VAL_16]] : vector<8xindex> to vector<8xi64> 45// CHECK: %[[VAL_19:.*]] = arith.muli %[[VAL_17]], %[[VAL_18]] : vector<8xi64> 46// CHECK: vector.scatter %[[VAL_10]]{{\[}}%[[VAL_6]]] {{\[}}%[[VAL_16]]], %[[VAL_15]], %[[VAL_19]] : memref<8xi64>, vector<8xindex>, vector<8xi1>, vector<8xi64> 47// CHECK: } 48// CHECK: %[[VAL_20:.*]] = bufferization.to_tensor %[[VAL_10]] : memref<8xi64> 49// CHECK: return %[[VAL_20]] : tensor<8xi64> 50// CHECK: } 51func.func @sparse_index_1d_conj(%arga: tensor<8xi64, #SparseVector>) -> tensor<8xi64> { 52 %init = linalg.init_tensor [8] : tensor<8xi64> 53 %r = linalg.generic #trait_1d 54 ins(%arga: tensor<8xi64, #SparseVector>) 55 outs(%init: tensor<8xi64>) { 56 ^bb(%a: i64, %x: i64): 57 %i = linalg.index 0 : index 58 %ii = arith.index_cast %i : index to i64 59 %m1 = arith.muli %a, %ii : i64 60 linalg.yield %m1 : i64 61 } -> tensor<8xi64> 62 return %r : tensor<8xi64> 63} 64 65// CHECK-LABEL: func @sparse_index_1d_disj( 66// CHECK-SAME: %[[VAL_0:.*]]: tensor<8xi64, #sparse_tensor.encoding<{{{.*}}}>>) -> tensor<8xi64> { 67// CHECK-DAG: %[[VAL_1:.*]] = arith.constant dense<[0, 1, 2, 3, 4, 5, 6, 7]> : vector<8xindex> 68// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 1 : index 69// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : i64 70// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 8 : index 71// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index 72// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<8xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex> 73// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<8xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex> 74// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<8xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xi64> 75// CHECK-DAG: %[[VAL_9:.*]] = memref.alloc() : memref<8xi64> 76// CHECK-DAG: linalg.fill ins(%[[VAL_3]] : i64) outs(%[[VAL_9]] : memref<8xi64>) 77// CHECK-DAG: %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex> 78// CHECK-DAG: %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_2]]] : memref<?xindex> 79// CHECK: %[[VAL_12:.*]]:2 = scf.while (%[[VAL_13:.*]] = %[[VAL_10]], %[[VAL_14:.*]] = %[[VAL_5]]) : (index, index) -> (index, index) { 80// CHECK: %[[VAL_15:.*]] = arith.cmpi ult, %[[VAL_13]], %[[VAL_11]] : index 81// CHECK: scf.condition(%[[VAL_15]]) %[[VAL_13]], %[[VAL_14]] : index, index 82// CHECK: } do { 83// CHECK: ^bb0(%[[VAL_16:.*]]: index, %[[VAL_17:.*]]: index): 84// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_16]]] : memref<?xindex> 85// CHECK: %[[VAL_19:.*]] = arith.cmpi eq, %[[VAL_18]], %[[VAL_17]] : index 86// CHECK: scf.if %[[VAL_19]] { 87// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref<?xi64> 88// CHECK: %[[VAL_21:.*]] = arith.index_cast %[[VAL_17]] : index to i64 89// CHECK: %[[VAL_22:.*]] = arith.addi %[[VAL_20]], %[[VAL_21]] : i64 90// CHECK: memref.store %[[VAL_22]], %[[VAL_9]]{{\[}}%[[VAL_17]]] : memref<8xi64> 91// CHECK: } else { 92// CHECK: %[[VAL_23:.*]] = arith.index_cast %[[VAL_17]] : index to i64 93// CHECK: memref.store %[[VAL_23]], %[[VAL_9]]{{\[}}%[[VAL_17]]] : memref<8xi64> 94// CHECK: } 95// CHECK: %[[VAL_24:.*]] = arith.cmpi eq, %[[VAL_18]], %[[VAL_17]] : index 96// CHECK: %[[VAL_25:.*]] = arith.addi %[[VAL_16]], %[[VAL_2]] : index 97// CHECK: %[[VAL_26:.*]] = arith.select %[[VAL_24]], %[[VAL_25]], %[[VAL_16]] : index 98// CHECK: %[[VAL_27:.*]] = arith.addi %[[VAL_17]], %[[VAL_2]] : index 99// CHECK: scf.yield %[[VAL_26]], %[[VAL_27]] : index, index 100// CHECK: } 101// CHECK: scf.for %[[VAL_28:.*]] = %[[VAL_29:.*]]#1 to %[[VAL_4]] step %[[VAL_4]] { 102// CHECK: %[[VAL_30:.*]] = affine.min #map1(%[[VAL_28]]) 103// CHECK: %[[VAL_31:.*]] = vector.create_mask %[[VAL_30]] : vector<8xi1> 104// CHECK: %[[VAL_32:.*]] = vector.broadcast %[[VAL_28]] : index to vector<8xindex> 105// CHECK: %[[VAL_33:.*]] = arith.addi %[[VAL_32]], %[[VAL_1]] : vector<8xindex> 106// CHECK: %[[VAL_34:.*]] = arith.index_cast %[[VAL_33]] : vector<8xindex> to vector<8xi64> 107// CHECK: vector.maskedstore %[[VAL_9]]{{\[}}%[[VAL_28]]], %[[VAL_31]], %[[VAL_34]] : memref<8xi64>, vector<8xi1>, vector<8xi64> 108// CHECK: } 109// CHECK: %[[VAL_35:.*]] = bufferization.to_tensor %[[VAL_9]] : memref<8xi64> 110// CHECK: return %[[VAL_35]] : tensor<8xi64> 111// CHECK: } 112func.func @sparse_index_1d_disj(%arga: tensor<8xi64, #SparseVector>) -> tensor<8xi64> { 113 %init = linalg.init_tensor [8] : tensor<8xi64> 114 %r = linalg.generic #trait_1d 115 ins(%arga: tensor<8xi64, #SparseVector>) 116 outs(%init: tensor<8xi64>) { 117 ^bb(%a: i64, %x: i64): 118 %i = linalg.index 0 : index 119 %ii = arith.index_cast %i : index to i64 120 %m1 = arith.addi %a, %ii : i64 121 linalg.yield %m1 : i64 122 } -> tensor<8xi64> 123 return %r : tensor<8xi64> 124} 125