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1// RUN: mlir-opt %s --lower-sparse-foreach-to-scf --canonicalize | FileCheck %s2 3// CHECK-LABEL: func.func @sparse_foreach_constant4// CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index5// CHECK-DAG: %[[V1:.*]] = arith.constant 5.000000e+00 : f326// CHECK-DAG: %[[C2:.*]] = arith.constant 2 : index7// CHECK-DAG: %[[V3:.*]] = arith.constant 1.000000e+00 : f328// CHECK-DAG: %[[V4:.*]] = arith.constant 6.000000e+00 : f329// (1, 1) -> (2, 1) -> (2, 2)10// CHECK-NEXT: "test.use"(%[[C1]], %[[C1]], %[[V1]])11// CHECK-NEXT: "test.use"(%[[C2]], %[[C1]], %[[V3]])12// CHECK-NEXT: "test.use"(%[[C1]], %[[C2]], %[[V4]])13// (1, 1) -> (1, 2) -> (2, 1)14// CHECK-NEXT: "test.use"(%[[C1]], %[[C1]], %[[V1]])15// CHECK-NEXT: "test.use"(%[[C1]], %[[C2]], %[[V4]])16// CHECK-NEXT: "test.use"(%[[C2]], %[[C1]], %[[V3]])17func.func @sparse_foreach_constant() -> () {18 %cst = arith.constant sparse<[[2, 1], [1, 1], [1, 2]], [1.0, 5.0, 6.0]> : tensor<8x7xf32>19 // Make use the sparse constant are properly sorted based on the requested order.20 sparse_tensor.foreach in %cst { order = affine_map<(d0, d1) -> (d1, d0)> } : tensor<8x7xf32> do {21 ^bb0(%arg0: index, %arg1: index, %arg2: f32):22 "test.use" (%arg0, %arg1, %arg2): (index,index,f32)->()23 }24 sparse_tensor.foreach in %cst : tensor<8x7xf32> do {25 ^bb0(%arg0: index, %arg1: index, %arg2: f32):26 "test.use" (%arg0, %arg1, %arg2): (index,index,f32)->()27 }28 return29}30 31#CSR_SLICE = #sparse_tensor.encoding<{32 map = (d0 : #sparse_tensor<slice(0, 4, 1)>, d1 : #sparse_tensor<slice(2, 4, 1)>) -> (d0 : compressed, d1 : compressed)33}>34 35#CSR_SLICE_DYN = #sparse_tensor.encoding<{36 map = (d0 : #sparse_tensor<slice(?, ?, ?)>, d1 : #sparse_tensor<slice(?, ?, ?)>) -> (d0 : compressed, d1 : compressed)37}>38 39// TODO: re-enable after lowering coo.next to function call (such that loop structure is more clear).40 41// C_HECK-LABEL: func.func @foreach_print_slice_dyn(42// C_HECK-SAME: %[[VAL_0:.*]]: tensor<?x?xf64,43// C_HECK-DAG: %[[VAL_1:.*]] = arith.constant 0 : index44// C_HECK-DAG: %[[VAL_2:.*]] = arith.constant 1 : index45// C_HECK-DAG: %[[VAL_3:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<?x?xf64,46// C_HECK-DAG: %[[VAL_4:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<?x?xf64,47// C_HECK-DAG: %[[VAL_5:.*]] = sparse_tensor.lvl %[[VAL_0]], %[[VAL_1]] : tensor<?x?xf64,48// C_HECK-DAG: %[[VAL_6:.*]] = sparse_tensor.slice.offset %[[VAL_0]] at 0 : tensor<?x?xf64,49// C_HECK-DAG: %[[VAL_7:.*]] = sparse_tensor.slice.stride %[[VAL_0]] at 0 : tensor<?x?xf64,50// C_HECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<?x?xf64,51// C_HECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<?x?xf64,52// C_HECK-DAG: %[[VAL_10:.*]] = sparse_tensor.lvl %[[VAL_0]], %[[VAL_2]] : tensor<?x?xf64,53// C_HECK-DAG: %[[VAL_11:.*]] = sparse_tensor.slice.offset %[[VAL_0]] at 1 : tensor<?x?xf64,54// C_HECK-DAG: %[[VAL_12:.*]] = sparse_tensor.slice.stride %[[VAL_0]] at 1 : tensor<?x?xf64,55// C_HECK-DAG: %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xf64,56// C_HECK: %[[VAL_14:.*]] = memref.load %[[VAL_3]]{{\[}}%[[VAL_1]]] : memref<?xindex>57// C_HECK: %[[VAL_15:.*]] = memref.load %[[VAL_3]]{{\[}}%[[VAL_2]]] : memref<?xindex>58// C_HECK: scf.for %[[VAL_16:.*]] = %[[VAL_14]] to %[[VAL_15]] step %[[VAL_2]] {59// C_HECK: %[[VAL_17:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_16]]] : memref<?xindex>60// C_HECK: %[[VAL_18:.*]] = arith.subi %[[VAL_17]], %[[VAL_6]] : index61// C_HECK: %[[VAL_19:.*]] = arith.remui %[[VAL_18]], %[[VAL_7]] : index62// C_HECK: %[[VAL_20:.*]] = arith.divui %[[VAL_18]], %[[VAL_7]] : index63// C_HECK: %[[VAL_21:.*]] = arith.cmpi uge, %[[VAL_17]], %[[VAL_6]] : index64// C_HECK: %[[VAL_22:.*]] = arith.cmpi ult, %[[VAL_20]], %[[VAL_5]] : index65// C_HECK: %[[VAL_23:.*]] = arith.cmpi eq, %[[VAL_19]], %[[VAL_1]] : index66// C_HECK: %[[VAL_24:.*]] = arith.andi %[[VAL_21]], %[[VAL_22]] : i167// C_HECK: %[[VAL_25:.*]] = arith.andi %[[VAL_24]], %[[VAL_23]] : i168// C_HECK: scf.if %[[VAL_25]] {69// C_HECK: %[[VAL_26:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref<?xindex>70// C_HECK: %[[VAL_27:.*]] = arith.addi %[[VAL_16]], %[[VAL_2]] : index71// C_HECK: %[[VAL_28:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_27]]] : memref<?xindex>72// C_HECK: scf.for %[[VAL_29:.*]] = %[[VAL_26]] to %[[VAL_28]] step %[[VAL_2]] {73// C_HECK: %[[VAL_30:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_29]]] : memref<?xindex>74// C_HECK: %[[VAL_31:.*]] = arith.subi %[[VAL_30]], %[[VAL_11]] : index75// C_HECK: %[[VAL_32:.*]] = arith.remui %[[VAL_31]], %[[VAL_12]] : index76// C_HECK: %[[VAL_33:.*]] = arith.divui %[[VAL_31]], %[[VAL_12]] : index77// C_HECK: %[[VAL_34:.*]] = arith.cmpi uge, %[[VAL_30]], %[[VAL_11]] : index78// C_HECK: %[[VAL_35:.*]] = arith.cmpi ult, %[[VAL_33]], %[[VAL_10]] : index79// C_HECK: %[[VAL_36:.*]] = arith.cmpi eq, %[[VAL_32]], %[[VAL_1]] : index80// C_HECK: %[[VAL_37:.*]] = arith.andi %[[VAL_34]], %[[VAL_35]] : i181// C_HECK: %[[VAL_38:.*]] = arith.andi %[[VAL_37]], %[[VAL_36]] : i182// C_HECK: scf.if %[[VAL_38]] {83// C_HECK: %[[VAL_39:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_29]]] : memref<?xf64>84// C_HECK: "test.use"(%[[VAL_39]]) : (f64) -> ()85// C_HECK: }86// C_HECK: }87// C_HECK: }88// C_HECK: }89// C_HECK: return90//91func.func @foreach_print_slice_dyn(%A: tensor<?x?xf64, #CSR_SLICE_DYN>) {92 sparse_tensor.foreach in %A : tensor<?x?xf64, #CSR_SLICE_DYN> do {93 ^bb0(%1: index, %2: index, %v: f64) :94 "test.use" (%v) : (f64) -> ()95 }96 return97}98 99// C_HECK-LABEL: func.func @foreach_print_slice(100// C_HECK-SAME: %[[VAL_0:.*]]: tensor<4x4xf64,101// C_HECK-DAG: %[[VAL_1:.*]] = arith.constant 4 : index102// C_HECK-DAG: %[[VAL_2:.*]] = arith.constant 2 : index103// C_HECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index104// C_HECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index105// C_HECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<4x4xf64,106// C_HECK-DAG: %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<4x4xf64,107// C_HECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<4x4xf64,108// C_HECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<4x4xf64,109// C_HECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<4x4xf64,110// C_HECK-DAG: %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>111// C_HECK: %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>112// C_HECK: scf.for %[[VAL_12:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_4]] {113// C_HECK: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref<?xindex>114// C_HECK: %[[VAL_14:.*]] = arith.cmpi ult, %[[VAL_13]], %[[VAL_1]] : index115// C_HECK: scf.if %[[VAL_14]] {116// C_HECK: %[[VAL_15:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_12]]] : memref<?xindex>117// C_HECK: %[[VAL_16:.*]] = arith.addi %[[VAL_12]], %[[VAL_4]] : index118// C_HECK: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_16]]] : memref<?xindex>119// C_HECK: scf.for %[[VAL_18:.*]] = %[[VAL_15]] to %[[VAL_17]] step %[[VAL_4]] {120// C_HECK: %[[VAL_19:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_18]]] : memref<?xindex>121// C_HECK: %[[VAL_20:.*]] = arith.subi %[[VAL_19]], %[[VAL_2]] : index122// C_HECK: %[[VAL_21:.*]] = arith.cmpi uge, %[[VAL_19]], %[[VAL_2]] : index123// C_HECK: %[[VAL_22:.*]] = arith.cmpi ult, %[[VAL_20]], %[[VAL_1]] : index124// C_HECK: %[[VAL_23:.*]] = arith.andi %[[VAL_21]], %[[VAL_22]] : i1125// C_HECK: scf.if %[[VAL_23]] {126// C_HECK: %[[VAL_24:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_18]]] : memref<?xf64>127// C_HECK: "test.use"(%[[VAL_24]]) : (f64) -> ()128// C_HECK: }129// C_HECK: }130// C_HECK: }131// C_HECK: }132// C_HECK: return133//134func.func @foreach_print_slice(%A: tensor<4x4xf64, #CSR_SLICE>) {135 sparse_tensor.foreach in %A : tensor<4x4xf64, #CSR_SLICE> do {136 ^bb0(%1: index, %2: index, %v: f64) :137 "test.use" (%v) : (f64) -> ()138 }139 return140}141 142#BCOO = #sparse_tensor.encoding<{143 map = (d0, d1, d2) -> (d0 : dense, d1 : loose_compressed(nonunique), d2 : singleton)144}>145 146// C_HECK-LABEL: func.func @foreach_bcoo(147// C_HECK-SAME: %[[VAL_0:.*]]: tensor<4x4x4xf64, #sparse{{[0-9]*}}>) {148// C_HECK-DAG: %[[VAL_1:.*]] = arith.constant 4 : index149// C_HECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index150// C_HECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index151// C_HECK-DAG: %[[VAL_4:.*]] = arith.constant 2 : index152// C_HECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<4x4x4xf64, #sparse{{[0-9]*}}> to memref<?xindex>153// C_HECK-DAG: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<4x4x4xf64, #sparse{{[0-9]*}}> to memref<?xf64>154// C_HECK: scf.for %[[VAL_7:.*]] = %[[VAL_2]] to %[[VAL_1]] step %[[VAL_3]] {155// C_HECK: %[[VAL_8:.*]] = arith.muli %[[VAL_7]], %[[VAL_4]] : index156// C_HECK: %[[VAL_9:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_8]]] : memref<?xindex>157// C_HECK: %[[VAL_10:.*]] = arith.addi %[[VAL_8]], %[[VAL_3]] : index158// C_HECK: %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_10]]] : memref<?xindex>159// C_HECK: scf.for %[[VAL_12:.*]] = %[[VAL_9]] to %[[VAL_11]] step %[[VAL_3]] {160// C_HECK: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref<?xf64>161// C_HECK: "test.use"(%[[VAL_13]]) : (f64) -> ()162// C_HECK: } {"Emitted from" = "sparse_tensor.foreach"}163// C_HECK: } {"Emitted from" = "sparse_tensor.foreach"}164// C_HECK: return165// C_HECK: }166func.func @foreach_bcoo(%A: tensor<4x4x4xf64, #BCOO>) {167 sparse_tensor.foreach in %A : tensor<4x4x4xf64, #BCOO> do {168 ^bb0(%1: index, %2: index, %3: index, %v: f64) :169 "test.use" (%v) : (f64) -> ()170 }171 return172}173