1// RUN: mlir-opt %s \ 2// RUN: --linalg-generalize-named-ops --linalg-fuse-elementwise-ops \ 3// RUN: --sparse-reinterpret-map --sparsification | FileCheck %s 4 5#SparseVector = #sparse_tensor.encoding<{ map = (d0) -> (d0 : compressed) }> 6 7#DCSR = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : compressed, d1 : compressed) }> 8 9// CHECK-LABEL: func.func @matmul1( 10// CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20xf32, #sparse{{[0-9]*}}>, 11// CHECK-SAME: %[[VAL_1:.*]]: tensor<20x30xf32>, 12// CHECK-SAME: %[[VAL_2:.*]]: tensor<10x30xf32>) -> tensor<10x30xf32> { 13// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 30 : index 14// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index 15// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index 16// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<10x20xf32, #sparse{{[0-9]*}}> to memref<?xindex> 17// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<10x20xf32, #sparse{{[0-9]*}}> to memref<?xindex> 18// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<10x20xf32, #sparse{{[0-9]*}}> to memref<?xindex> 19// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<10x20xf32, #sparse{{[0-9]*}}> to memref<?xindex> 20// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20xf32, #sparse{{[0-9]*}}> to memref<?xf32> 21// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : tensor<20x30xf32> to memref<20x30xf32> 22// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : tensor<10x30xf32> to memref<10x30xf32> 23// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex> 24// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex> 25// CHECK: scf.for %[[VAL_15:.*]] = %[[VAL_13]] to %[[VAL_14]] step %[[VAL_5]] { 26// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_15]]] : memref<?xindex> 27// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_15]]] : memref<?xindex> 28// CHECK: %[[VAL_18:.*]] = arith.addi %[[VAL_15]], %[[VAL_5]] : index 29// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_18]]] : memref<?xindex> 30// CHECK: scf.for %[[VAL_20:.*]] = %[[VAL_17]] to %[[VAL_19]] step %[[VAL_5]] { 31// CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_20]]] : memref<?xindex> 32// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_20]]] : memref<?xf32> 33// CHECK: scf.for %[[VAL_23:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] { 34// CHECK: %[[VAL_24:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_16]], %[[VAL_23]]] : memref<10x30xf32> 35// CHECK: %[[VAL_25:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_21]], %[[VAL_23]]] : memref<20x30xf32> 36// CHECK: %[[VAL_26:.*]] = arith.mulf %[[VAL_22]], %[[VAL_25]] : f32 37// CHECK: %[[VAL_27:.*]] = arith.addf %[[VAL_24]], %[[VAL_26]] : f32 38// CHECK: memref.store %[[VAL_27]], %[[VAL_12]]{{\[}}%[[VAL_16]], %[[VAL_23]]] : memref<10x30xf32> 39// CHECK: } 40// CHECK: } 41// CHECK: } 42// CHECK: %[[VAL_28:.*]] = bufferization.to_tensor %[[VAL_12]] : memref<10x30xf32> 43// CHECK: return %[[VAL_28]] : tensor<10x30xf32> 44// CHECK: } 45func.func @matmul1(%a: tensor<10x20xf32, #DCSR>, 46 %b: tensor<20x30xf32>, 47 %c: tensor<10x30xf32>) -> tensor<10x30xf32> { 48 %0 = linalg.matmul 49 ins(%a, %b: tensor<10x20xf32, #DCSR>, tensor<20x30xf32>) 50 outs(%c: tensor<10x30xf32>) -> tensor<10x30xf32> 51 return %0 : tensor<10x30xf32> 52} 53 54// CHECK-LABEL: func.func @matmul_sparse_rhs( 55// CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20xf32>, 56// CHECK-SAME: %[[VAL_1:.*]]: tensor<20x30xf32, #sparse{{[0-9]*}}>, 57// CHECK-SAME: %[[VAL_2:.*]]: tensor<10x30xf32>) -> tensor<10x30xf32> { 58// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 10 : index 59// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index 60// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index 61// CHECK-DAG: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : tensor<10x20xf32> to memref<10x20xf32> 62// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} 63// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} 64// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} 65// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} 66// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] 67// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : tensor<10x30xf32> to memref<10x30xf32> 68// CHECK: scf.for %[[VAL_13:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] { 69// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref<?xindex> 70// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_5]]] : memref<?xindex> 71// CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_14]] to %[[VAL_15]] step %[[VAL_5]] { 72// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref<?xindex> 73// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_13]], %[[VAL_17]]] : memref<10x20xf32> 74// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_16]]] : memref<?xindex> 75// CHECK: %[[VAL_20:.*]] = arith.addi %[[VAL_16]], %[[VAL_5]] : index 76// CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_20]]] : memref<?xindex> 77// CHECK: scf.for %[[VAL_22:.*]] = %[[VAL_19]] to %[[VAL_21]] step %[[VAL_5]] { 78// CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_22]]] : memref<?xindex> 79// CHECK: %[[VAL_24:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_13]], %[[VAL_23]]] : memref<10x30xf32> 80// CHECK: %[[VAL_25:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_22]]] : memref<?xf32> 81// CHECK: %[[VAL_26:.*]] = arith.mulf %[[VAL_18]], %[[VAL_25]] : f32 82// CHECK: %[[VAL_27:.*]] = arith.addf %[[VAL_24]], %[[VAL_26]] : f32 83// CHECK: memref.store %[[VAL_27]], %[[VAL_12]]{{\[}}%[[VAL_13]], %[[VAL_23]]] : memref<10x30xf32> 84// CHECK: } {"Emitted from" = "linalg.generic"} 85// CHECK: } {"Emitted from" = "linalg.generic"} 86// CHECK: } {"Emitted from" = "linalg.generic"} 87// CHECK: %[[VAL_28:.*]] = bufferization.to_tensor %[[VAL_12]] : memref<10x30xf32> 88// CHECK: return %[[VAL_28]] : tensor<10x30xf32> 89// CHECK: } 90// IMPORTANT! Ensures that dense input are visit in row-major order. 91func.func @matmul_sparse_rhs(%a: tensor<10x20xf32>, 92 %b: tensor<20x30xf32, #DCSR>, 93 %c: tensor<10x30xf32>) -> tensor<10x30xf32> { 94 %0 = linalg.matmul 95 ins(%a, %b: tensor<10x20xf32>, tensor<20x30xf32,#DCSR>) 96 outs(%c: tensor<10x30xf32>) -> tensor<10x30xf32> 97 return %0 : tensor<10x30xf32> 98} 99 100 101// 102// Computes C = A x B with all matrices sparse (SpMSpM) in DCSR. 103// 104// CHECK-LABEL: func.func @matmul2( 105// CHECK-SAME: %[[VAL_0:.*]]: tensor<4x8xf64, #sparse{{[0-9]*}}>, 106// CHECK-SAME: %[[VAL_1:.*]]: tensor<8x4xf64, #sparse{{[0-9]*}}>) -> tensor<4x4xf64, #sparse{{[0-9]*}}> { 107// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index 108// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index 109// CHECK-DAG: %[[VAL_4:.*]] = arith.constant false 110// CHECK-DAG: %[[VAL_5:.*]] = arith.constant true 111// CHECK-DAG: %[[VAL_6:.*]] = tensor.empty() : tensor<4x4xf64, #sparse{{[0-9]*}}> 112// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<4x8xf64, #sparse{{[0-9]*}}> to memref<?xindex> 113// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<4x8xf64, #sparse{{[0-9]*}}> to memref<?xindex> 114// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<4x8xf64, #sparse{{[0-9]*}}> to memref<?xindex> 115// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<4x8xf64, #sparse{{[0-9]*}}> to memref<?xindex> 116// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<4x8xf64, #sparse{{[0-9]*}}> to memref<?xf64> 117// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<8x4xf64, #sparse{{[0-9]*}}> to memref<?xindex> 118// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<8x4xf64, #sparse{{[0-9]*}}> to memref<?xindex> 119// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<8x4xf64, #sparse{{[0-9]*}}> to memref<?xindex> 120// CHECK-DAG: %[[VAL_15:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<8x4xf64, #sparse{{[0-9]*}}> to memref<?xindex> 121// CHECK-DAG: %[[VAL_16:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<8x4xf64, #sparse{{[0-9]*}}> to memref<?xf64> 122// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_2]]] : memref<?xindex> 123// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_3]]] : memref<?xindex> 124// CHECK: %[[VAL_19:.*]] = scf.for %[[VAL_20:.*]] = %[[VAL_17]] to %[[VAL_18]] step %[[VAL_3]] iter_args(%[[VAL_21:.*]] = %[[VAL_6]]) -> (tensor<4x4xf64, #sparse{{[0-9]*}}>) { 125// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_20]]] : memref<?xindex> 126// CHECK: %[[VAL_23:.*]], %[[VAL_24:.*]], %[[VAL_25:.*]], %[[VAL_26:.*]] = sparse_tensor.expand %[[VAL_6]] : tensor<4x4xf64, #sparse{{[0-9]*}}> to memref<?xf64>, memref<?xi1>, memref<?xindex> 127// CHECK: %[[VAL_27:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_20]]] : memref<?xindex> 128// CHECK: %[[VAL_28:.*]] = arith.addi %[[VAL_20]], %[[VAL_3]] : index 129// CHECK: %[[VAL_29:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_28]]] : memref<?xindex> 130// CHECK: %[[VAL_30:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_2]]] : memref<?xindex> 131// CHECK: %[[VAL_31:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_3]]] : memref<?xindex> 132// CHECK: %[[VAL_32:.*]]:4 = scf.while (%[[VAL_33:.*]] = %[[VAL_27]], %[[VAL_34:.*]] = %[[VAL_30]], %[[VAL_35:.*]] = %[[VAL_26]], %[[VAL_36:.*]] = %[[VAL_21]]) : (index, index, index, tensor<4x4xf64, #sparse{{[0-9]*}}>) -> (index, index, index, tensor<4x4xf64, #sparse{{[0-9]*}}>) { 133// CHECK: %[[VAL_37:.*]] = arith.cmpi ult, %[[VAL_33]], %[[VAL_29]] : index 134// CHECK: %[[VAL_38:.*]] = arith.cmpi ult, %[[VAL_34]], %[[VAL_31]] : index 135// CHECK: %[[VAL_39:.*]] = arith.andi %[[VAL_37]], %[[VAL_38]] : i1 136// CHECK: scf.condition(%[[VAL_39]]) %[[VAL_33]], %[[VAL_34]], %[[VAL_35]], %[[VAL_36]] : index, index, index, tensor<4x4xf64, #sparse{{[0-9]*}}> 137// CHECK: } do { 138// CHECK: ^bb0(%[[VAL_40:.*]]: index, %[[VAL_41:.*]]: index, %[[VAL_42:.*]]: index, %[[VAL_43:.*]]: tensor<4x4xf64, #sparse{{[0-9]*}}>): 139// CHECK: %[[VAL_44:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_40]]] : memref<?xindex> 140// CHECK: %[[VAL_45:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_41]]] : memref<?xindex> 141// CHECK: %[[VAL_46:.*]] = arith.cmpi ult, %[[VAL_45]], %[[VAL_44]] : index 142// CHECK: %[[VAL_47:.*]] = arith.select %[[VAL_46]], %[[VAL_45]], %[[VAL_44]] : index 143// CHECK: %[[VAL_48:.*]] = arith.cmpi eq, %[[VAL_44]], %[[VAL_47]] : index 144// CHECK: %[[VAL_49:.*]] = arith.cmpi eq, %[[VAL_45]], %[[VAL_47]] : index 145// CHECK: %[[VAL_50:.*]] = arith.andi %[[VAL_48]], %[[VAL_49]] : i1 146// CHECK: %[[VAL_51:.*]]:2 = scf.if %[[VAL_50]] -> (index, tensor<4x4xf64, #sparse{{[0-9]*}}>) { 147// CHECK: %[[VAL_52:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_40]]] : memref<?xf64> 148// CHECK: %[[VAL_53:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_41]]] : memref<?xindex> 149// CHECK: %[[VAL_54:.*]] = arith.addi %[[VAL_41]], %[[VAL_3]] : index 150// CHECK: %[[VAL_55:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_54]]] : memref<?xindex> 151// CHECK: %[[VAL_56:.*]] = scf.for %[[VAL_57:.*]] = %[[VAL_53]] to %[[VAL_55]] step %[[VAL_3]] iter_args(%[[VAL_58:.*]] = %[[VAL_42]]) -> (index) { 152// CHECK: %[[VAL_59:.*]] = memref.load %[[VAL_15]]{{\[}}%[[VAL_57]]] : memref<?xindex> 153// CHECK: %[[VAL_60:.*]] = memref.load %[[VAL_23]]{{\[}}%[[VAL_59]]] : memref<?xf64> 154// CHECK: %[[VAL_61:.*]] = memref.load %[[VAL_16]]{{\[}}%[[VAL_57]]] : memref<?xf64> 155// CHECK: %[[VAL_62:.*]] = arith.mulf %[[VAL_52]], %[[VAL_61]] : f64 156// CHECK: %[[VAL_63:.*]] = arith.addf %[[VAL_60]], %[[VAL_62]] : f64 157// CHECK: %[[VAL_64:.*]] = memref.load %[[VAL_24]]{{\[}}%[[VAL_59]]] : memref<?xi1> 158// CHECK: %[[VAL_65:.*]] = arith.cmpi eq, %[[VAL_64]], %[[VAL_4]] : i1 159// CHECK: %[[VAL_66:.*]] = scf.if %[[VAL_65]] -> (index) { 160// CHECK: memref.store %[[VAL_5]], %[[VAL_24]]{{\[}}%[[VAL_59]]] : memref<?xi1> 161// CHECK: memref.store %[[VAL_59]], %[[VAL_25]]{{\[}}%[[VAL_58]]] : memref<?xindex> 162// CHECK: %[[VAL_67:.*]] = arith.addi %[[VAL_58]], %[[VAL_3]] : index 163// CHECK: scf.yield %[[VAL_67]] : index 164// CHECK: } else { 165// CHECK: scf.yield %[[VAL_58]] : index 166// CHECK: } 167// CHECK: memref.store %[[VAL_63]], %[[VAL_23]]{{\[}}%[[VAL_59]]] : memref<?xf64> 168// CHECK: scf.yield %[[VAL_68:.*]] : index 169// CHECK: } 170// CHECK: scf.yield %[[VAL_69:.*]], %[[VAL_43]] : index, tensor<4x4xf64, #sparse{{[0-9]*}}> 171// CHECK: } else { 172// CHECK: scf.yield %[[VAL_42]], %[[VAL_43]] : index, tensor<4x4xf64, #sparse{{[0-9]*}}> 173// CHECK: } 174// CHECK: %[[VAL_70:.*]] = arith.cmpi eq, %[[VAL_44]], %[[VAL_47]] : index 175// CHECK: %[[VAL_71:.*]] = arith.addi %[[VAL_40]], %[[VAL_3]] : index 176// CHECK: %[[VAL_72:.*]] = arith.select %[[VAL_70]], %[[VAL_71]], %[[VAL_40]] : index 177// CHECK: %[[VAL_73:.*]] = arith.cmpi eq, %[[VAL_45]], %[[VAL_47]] : index 178// CHECK: %[[VAL_74:.*]] = arith.addi %[[VAL_41]], %[[VAL_3]] : index 179// CHECK: %[[VAL_75:.*]] = arith.select %[[VAL_73]], %[[VAL_74]], %[[VAL_41]] : index 180// CHECK: scf.yield %[[VAL_72]], %[[VAL_75]], %[[VAL_76:.*]]#0, %[[VAL_76]]#1 : index, index, index, tensor<4x4xf64, #sparse{{[0-9]*}}> 181// CHECK: } 182// CHECK: %[[VAL_77:.*]] = sparse_tensor.compress %[[VAL_23]], %[[VAL_24]], %[[VAL_25]], %[[VAL_78:.*]]#2 into %[[VAL_78]]#3{{\[}}%[[VAL_22]]] : memref<?xf64>, memref<?xi1>, memref<?xindex>, tensor<4x4xf64, #sparse{{[0-9]*}}> 183// CHECK: scf.yield %[[VAL_77]] : tensor<4x4xf64, #sparse{{[0-9]*}}> 184// CHECK: } 185// CHECK: %[[VAL_79:.*]] = sparse_tensor.load %[[VAL_80:.*]] hasInserts : tensor<4x4xf64, #sparse{{[0-9]*}}> 186// CHECK: return %[[VAL_79]] : tensor<4x4xf64, #sparse{{[0-9]*}}> 187// CHECK: } 188func.func @matmul2(%A: tensor<4x8xf64, #DCSR>, 189 %B: tensor<8x4xf64, #DCSR>) -> tensor<4x4xf64, #DCSR> { 190 %c4 = arith.constant 4 : index 191 %C = tensor.empty() : tensor<4x4xf64, #DCSR> 192 %D = linalg.matmul 193 ins(%A, %B: tensor<4x8xf64, #DCSR>, tensor<8x4xf64, #DCSR>) 194 outs(%C: tensor<4x4xf64, #DCSR>) -> tensor<4x4xf64, #DCSR> 195 return %D: tensor<4x4xf64, #DCSR> 196} 197 198 199// CHECK-LABEL: func.func @conv2d( 200// CHECK-SAME: %[[VAL_0:.*]]: tensor<8x8xi32>, 201// CHECK-SAME: %[[VAL_1:.*]]: tensor<3x3xi32, #sparse{{[0-9]*}}>, 202// CHECK-SAME: %[[VAL_2:.*]]: tensor<6x6xi32>) -> tensor<6x6xi32> { 203// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 6 : index 204// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index 205// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index 206// CHECK-DAG: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : tensor<8x8xi32> to memref<8x8xi32> 207// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<3x3xi32, #sparse{{[0-9]*}}> to memref<?xindex> 208// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<3x3xi32, #sparse{{[0-9]*}}> to memref<?xindex> 209// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<3x3xi32, #sparse{{[0-9]*}}> to memref<?xindex> 210// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<3x3xi32, #sparse{{[0-9]*}}> to memref<?xindex> 211// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<3x3xi32, #sparse{{[0-9]*}}> to memref<?xi32> 212// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : tensor<6x6xi32> to memref<6x6xi32> 213// CHECK: scf.for %[[VAL_13:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] { 214// CHECK: scf.for %[[VAL_14:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] { 215// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_13]], %[[VAL_14]]] : memref<6x6xi32> 216// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref<?xindex> 217// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_5]]] : memref<?xindex> 218// CHECK: %[[VAL_18:.*]] = scf.for %[[VAL_19:.*]] = %[[VAL_16]] to %[[VAL_17]] step %[[VAL_5]] iter_args(%[[VAL_20:.*]] = %[[VAL_15]]) -> (i32) { 219// CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_19]]] : memref<?xindex> 220// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_19]]] : memref<?xindex> 221// CHECK: %[[VAL_23:.*]] = arith.addi %[[VAL_19]], %[[VAL_5]] : index 222// CHECK: %[[VAL_24:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_23]]] : memref<?xindex> 223// CHECK: %[[VAL_25:.*]] = scf.for %[[VAL_26:.*]] = %[[VAL_22]] to %[[VAL_24]] step %[[VAL_5]] iter_args(%[[VAL_27:.*]] = %[[VAL_20]]) -> (i32) { 224// CHECK: %[[VAL_28:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_26]]] : memref<?xindex> 225// CHECK: %[[VAL_29:.*]] = arith.addi %[[VAL_13]], %[[VAL_21]] : index 226// CHECK: %[[VAL_30:.*]] = arith.addi %[[VAL_14]], %[[VAL_28]] : index 227// CHECK: %[[VAL_31:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_29]], %[[VAL_30]]] : memref<8x8xi32> 228// CHECK: %[[VAL_32:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_26]]] : memref<?xi32> 229// CHECK: %[[VAL_33:.*]] = arith.muli %[[VAL_31]], %[[VAL_32]] : i32 230// CHECK: %[[VAL_34:.*]] = arith.addi %[[VAL_27]], %[[VAL_33]] : i32 231// CHECK: scf.yield %[[VAL_34]] : i32 232// CHECK: } {"Emitted from" = "linalg.generic"} 233// CHECK: scf.yield %[[VAL_25]] : i32 234// CHECK: } {"Emitted from" = "linalg.generic"} 235// CHECK: memref.store %[[VAL_18]], %[[VAL_12]]{{\[}}%[[VAL_13]], %[[VAL_14]]] : memref<6x6xi32> 236// CHECK: } {"Emitted from" = "linalg.generic"} 237// CHECK: } {"Emitted from" = "linalg.generic"} 238// CHECK: %[[VAL_35:.*]] = bufferization.to_tensor %[[VAL_12]] : memref<6x6xi32> 239// CHECK: return %[[VAL_35]] : tensor<6x6xi32> 240// CHECK: } 241func.func @conv2d(%input: tensor<8x8xi32>, 242 %filter: tensor<3x3xi32, #DCSR>, 243 %output: tensor<6x6xi32>) -> tensor<6x6xi32> { 244 %0 = linalg.conv_2d 245 ins (%input, %filter: tensor<8x8xi32>, tensor<3x3xi32, #DCSR>) 246 outs (%output: tensor<6x6xi32>) -> tensor<6x6xi32> 247 return %0 : tensor<6x6xi32> 248} 249 250// CHECK-LABEL: func.func @quantized_matmul( 251// CHECK-SAME: %[[VAL_0:.*]]: tensor<5x3xi8>, 252// CHECK-SAME: %[[VAL_1:.*]]: tensor<3x6xi8, #sparse{{[0-9]*}}>, 253// CHECK-SAME: %[[VAL_2:.*]]: tensor<5x6xi64>) -> tensor<5x6xi64> { 254// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 5 : index 255// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index 256// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index 257// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 2 : i64 258// CHECK-DAG: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_0]] : tensor<5x3xi8> to memref<5x3xi8> 259// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<3x6xi8, #sparse{{[0-9]*}}> to memref<?xindex> 260// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<3x6xi8, #sparse{{[0-9]*}}> to memref<?xindex> 261// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<3x6xi8, #sparse{{[0-9]*}}> to memref<?xindex> 262// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<3x6xi8, #sparse{{[0-9]*}}> to memref<?xindex> 263// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<3x6xi8, #sparse{{[0-9]*}}> to memref<?xi8> 264// CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : tensor<5x6xi64> to memref<5x6xi64> 265// CHECK: scf.for %[[VAL_14:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] { 266// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref<?xindex> 267// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_5]]] : memref<?xindex> 268// CHECK: scf.for %[[VAL_17:.*]] = %[[VAL_15]] to %[[VAL_16]] step %[[VAL_5]] { 269// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_17]]] : memref<?xindex> 270// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_14]], %[[VAL_18]]] : memref<5x3xi8> 271// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_17]]] : memref<?xindex> 272// CHECK: %[[VAL_21:.*]] = arith.addi %[[VAL_17]], %[[VAL_5]] : index 273// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_21]]] : memref<?xindex> 274// CHECK: scf.for %[[VAL_23:.*]] = %[[VAL_20]] to %[[VAL_22]] step %[[VAL_5]] { 275// CHECK: %[[VAL_24:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_23]]] : memref<?xindex> 276// CHECK: %[[VAL_25:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_14]], %[[VAL_24]]] : memref<5x6xi64> 277// CHECK: %[[VAL_26:.*]] = arith.extsi %[[VAL_19]] : i8 to i64 278// CHECK: %[[VAL_27:.*]] = arith.subi %[[VAL_26]], %[[VAL_6]] : i64 279// CHECK: %[[VAL_28:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_23]]] : memref<?xi8> 280// CHECK: %[[VAL_29:.*]] = arith.extsi %[[VAL_28]] : i8 to i64 281// CHECK: %[[VAL_30:.*]] = arith.muli %[[VAL_27]], %[[VAL_29]] : i64 282// CHECK: %[[VAL_31:.*]] = arith.addi %[[VAL_25]], %[[VAL_30]] : i64 283// CHECK: memref.store %[[VAL_31]], %[[VAL_13]]{{\[}}%[[VAL_14]], %[[VAL_24]]] : memref<5x6xi64> 284// CHECK: } {"Emitted from" = "linalg.generic"} 285// CHECK: } {"Emitted from" = "linalg.generic"} 286// CHECK: } {"Emitted from" = "linalg.generic"} 287// CHECK: %[[VAL_32:.*]] = bufferization.to_tensor %[[VAL_13]] : memref<5x6xi64> 288// CHECK: return %[[VAL_32]] : tensor<5x6xi64> 289// CHECK: } 290func.func @quantized_matmul(%input1: tensor<5x3xi8>, 291 %input2: tensor<3x6xi8, #DCSR>, 292 %output: tensor<5x6xi64>) -> tensor<5x6xi64> { 293 %c0 = arith.constant 0 : i32 294 %c2 = arith.constant 2 : i32 295 %0 = linalg.quantized_matmul 296 ins(%input1, %input2, %c2, %c0 : tensor<5x3xi8>, tensor<3x6xi8, #DCSR>, i32, i32) 297 outs(%output : tensor<5x6xi64>) -> tensor<5x6xi64> 298 return %0: tensor<5x6xi64> 299} 300 301// CHECK-LABEL: func.func @sparse_dot( 302// CHECK-SAME: %[[VAL_0:.*0]]: tensor<1024xf32, #sparse{{[0-9]*}}>, %[[VAL_1:.*1]]: tensor<1024xf32, #sparse{{[0-9]*}}>, 303// CHECK-SAME: %[[VAL_2:.*2]]: tensor<f32>) -> tensor<f32> { 304// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index 305// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index 306// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<1024xf32, #sparse{{[0-9]*}}> to memref<?xindex> 307// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<1024xf32, #sparse{{[0-9]*}}> to memref<?xindex> 308// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<1024xf32, #sparse{{[0-9]*}}> to memref<?xf32> 309// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<1024xf32, #sparse{{[0-9]*}}> to memref<?xindex> 310// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<1024xf32, #sparse{{[0-9]*}}> to memref<?xindex> 311// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<1024xf32, #sparse{{[0-9]*}}> to memref<?xf32> 312// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : tensor<f32> to memref<f32> 313// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_11]][] : memref<f32> 314// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex> 315// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex> 316// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_3]]] : memref<?xindex> 317// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref<?xindex> 318// CHECK: %[[VAL_17:.*]]:3 = scf.while (%[[VAL_18:.*]] = %[[VAL_13]], %[[VAL_19:.*]] = %[[VAL_15]], %[[VAL_20:.*]] = %[[VAL_12]]) : (index, index, f32) -> (index, index, f32) { 319// CHECK: %[[VAL_21:.*]] = arith.cmpi ult, %[[VAL_18]], %[[VAL_14]] : index 320// CHECK: %[[VAL_22:.*]] = arith.cmpi ult, %[[VAL_19]], %[[VAL_16]] : index 321// CHECK: %[[VAL_23:.*]] = arith.andi %[[VAL_21]], %[[VAL_22]] : i1 322// CHECK: scf.condition(%[[VAL_23]]) %[[VAL_18]], %[[VAL_19]], %[[VAL_20]] : index, index, f32 323// CHECK: } do { 324// CHECK: ^bb0(%[[VAL_24:.*]]: index, %[[VAL_25:.*]]: index, %[[VAL_26:.*]]: f32): 325// CHECK: %[[VAL_27:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_24]]] : memref<?xindex> 326// CHECK: %[[VAL_28:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_25]]] : memref<?xindex> 327// CHECK: %[[VAL_29:.*]] = arith.cmpi ult, %[[VAL_28]], %[[VAL_27]] : index 328// CHECK: %[[VAL_30:.*]] = arith.select %[[VAL_29]], %[[VAL_28]], %[[VAL_27]] : index 329// CHECK: %[[VAL_31:.*]] = arith.cmpi eq, %[[VAL_27]], %[[VAL_30]] : index 330// CHECK: %[[VAL_32:.*]] = arith.cmpi eq, %[[VAL_28]], %[[VAL_30]] : index 331// CHECK: %[[VAL_33:.*]] = arith.andi %[[VAL_31]], %[[VAL_32]] : i1 332// CHECK: %[[VAL_34:.*]] = scf.if %[[VAL_33]] -> (f32) { 333// CHECK: %[[VAL_35:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_24]]] : memref<?xf32> 334// CHECK: %[[VAL_36:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_25]]] : memref<?xf32> 335// CHECK: %[[VAL_37:.*]] = arith.mulf %[[VAL_35]], %[[VAL_36]] : f32 336// CHECK: %[[VAL_38:.*]] = arith.addf %[[VAL_26]], %[[VAL_37]] : f32 337// CHECK: scf.yield %[[VAL_38]] : f32 338// CHECK: } else { 339// CHECK: scf.yield %[[VAL_26]] : f32 340// CHECK: } 341// CHECK: %[[VAL_39:.*]] = arith.cmpi eq, %[[VAL_27]], %[[VAL_30]] : index 342// CHECK: %[[VAL_40:.*]] = arith.addi %[[VAL_24]], %[[VAL_4]] : index 343// CHECK: %[[VAL_41:.*]] = arith.select %[[VAL_39]], %[[VAL_40]], %[[VAL_24]] : index 344// CHECK: %[[VAL_42:.*]] = arith.cmpi eq, %[[VAL_28]], %[[VAL_30]] : index 345// CHECK: %[[VAL_43:.*]] = arith.addi %[[VAL_25]], %[[VAL_4]] : index 346// CHECK: %[[VAL_44:.*]] = arith.select %[[VAL_42]], %[[VAL_43]], %[[VAL_25]] : index 347// CHECK: scf.yield %[[VAL_41]], %[[VAL_44]], %[[VAL_45:.*]] : index, index, f32 348// CHECK: } 349// CHECK: memref.store %[[VAL_46:.*]]#2, %[[VAL_11]][] : memref<f32> 350// CHECK: %[[VAL_47:.*]] = bufferization.to_tensor %[[VAL_11]] : memref<f32> 351// CHECK: return %[[VAL_47]] : tensor<f32> 352// CHECK: } 353func.func @sparse_dot(%a: tensor<1024xf32, #SparseVector>, 354 %b: tensor<1024xf32, #SparseVector>, 355 %x: tensor<f32>) -> tensor<f32> { 356 %dot = linalg.dot ins(%a, %b: tensor<1024xf32, #SparseVector>, 357 tensor<1024xf32, #SparseVector>) 358 outs(%x: tensor<f32>) -> tensor<f32> 359 return %dot : tensor<f32> 360} 361