1// RUN: mlir-opt --transform-interpreter --mlir-print-local-scope --split-input-file --verify-diagnostics --cse %s | FileCheck %s 2 3module attributes {transform.with_named_sequence} { 4 transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { 5 %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op 6 %1, %loops:3 = transform.structured.tile_using_for %0 tile_sizes [4, 4, 4] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op) 7 transform.yield 8 } 9} 10 11// CHECK-LABEL: func @tile_linalg_matmul( 12// CHECK-SAME: %[[TA:[0-9a-z]+]]: tensor<128x128xf32> 13// CHECK-SAME: %[[TB:[0-9a-z]+]]: tensor<128x128xf32> 14// CHECK-SAME: %[[TC:[0-9a-z]+]]: tensor<128x128xf32> 15// CHECK-SAME: -> tensor<128x128xf32> { 16func.func @tile_linalg_matmul( 17 %arg0: tensor<128x128xf32>, %arg1: tensor<128x128xf32>, %arg2: tensor<128x128xf32>) 18 -> tensor<128x128xf32> { 19// CHECK: %[[TD0:.*]] = scf.for {{.*}} to {{.*}} step {{.*}} iter_args(%[[TC0:.*]] = %[[TC]]) -> (tensor<128x128xf32>) { 20// CHECK: %[[TD1:.*]] = scf.for {{.*}} to {{.*}} step {{.*}} iter_args(%[[TC1:.*]] = %[[TC0]]) -> (tensor<128x128xf32>) { 21// CHECK: %[[TD2:.*]] = scf.for {{.*}} to {{.*}} step {{.*}} iter_args(%[[TC2:.*]] = %[[TC1]]) -> (tensor<128x128xf32>) { 22// CHECK: %[[sTA:.*]] = tensor.extract_slice %[[TA]][{{.*}}] : tensor<128x128xf32> to tensor<4x4xf32> 23// CHECK: %[[sTB:.*]] = tensor.extract_slice %[[TB]][{{.*}}] : tensor<128x128xf32> to tensor<4x4xf32> 24// CHECK: %[[sTC:.*]] = tensor.extract_slice %[[TC2]][{{.*}}] : tensor<128x128xf32> to tensor<4x4xf32> 25// CHECK: %[[sTD:.*]] = linalg.matmul ins(%[[sTA]], %[[sTB]] : tensor<4x4xf32>, tensor<4x4xf32>) 26// CHECK-SAME: outs(%[[sTC]] : tensor<4x4xf32>) -> tensor<4x4xf32> 27// CHECK: %[[TD:.*]] = tensor.insert_slice %[[sTD]] into %[[TC2]][{{.*}}] : tensor<4x4xf32> into tensor<128x128xf32> 28// CHECK: scf.yield %[[TD]] : tensor<128x128xf32> 29// CHECK: scf.yield %[[TD2]] : tensor<128x128xf32> 30// CHECK: scf.yield %[[TD1]] : tensor<128x128xf32> 31 %0 = linalg.matmul ins(%arg0, %arg1: tensor<128x128xf32>, tensor<128x128xf32>) 32 outs(%arg2: tensor<128x128xf32>) 33 -> tensor<128x128xf32> 34 35// CHECK: return %[[TD0]] : tensor<128x128xf32> 36 return %0 : tensor<128x128xf32> 37} 38 39// ----- 40 41module attributes {transform.with_named_sequence} { 42 transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { 43 %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op 44 %1 = transform.structured.match ops{["func.call"]} in %arg1 : (!transform.any_op) -> !transform.any_op 45 %2, %loops:3 = transform.structured.tile_using_for %0 tile_sizes [%1, %1, 4] : (!transform.any_op, !transform.any_op, !transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op) 46 transform.yield 47 } 48} 49 50func.func private @get_dynamic_tile_size() -> index 51 52// CHECK-LABEL: func @tile_linalg_matmul_dynamic( 53// CHECK-SAME: %[[TA:[0-9a-z]+]]: tensor<128x128xf32> 54// CHECK-SAME: %[[TB:[0-9a-z]+]]: tensor<128x128xf32> 55// CHECK-SAME: %[[TC:[0-9a-z]+]]: tensor<128x128xf32> 56// CHECK-SAME: -> tensor<128x128xf32> { 57func.func @tile_linalg_matmul_dynamic( 58 %arg0: tensor<128x128xf32>, %arg1: tensor<128x128xf32>, %arg2: tensor<128x128xf32>) 59 -> tensor<128x128xf32> { 60// CHECK: %[[TD0:.*]] = scf.for {{.*}} to {{.*}} step {{.*}} iter_args(%[[TC0:.*]] = %[[TC]]) -> (tensor<128x128xf32>) { 61// CHECK: %[[TD1:.*]] = scf.for {{.*}} to {{.*}} step {{.*}} iter_args(%[[TC1:.*]] = %[[TC0]]) -> (tensor<128x128xf32>) { 62// CHECK: %[[TD2:.*]] = scf.for {{.*}} to {{.*}} step {{.*}} iter_args(%[[TC2:.*]] = %[[TC1]]) -> (tensor<128x128xf32>) { 63// CHECK: %[[sTA:.*]] = tensor.extract_slice %[[TA]][{{.*}}] : tensor<128x128xf32> to tensor<?x4xf32> 64// CHECK: %[[sTB:.*]] = tensor.extract_slice %[[TB]][{{.*}}] : tensor<128x128xf32> to tensor<4x?xf32> 65// CHECK: %[[sTC:.*]] = tensor.extract_slice %[[TC2]][{{.*}}] : tensor<128x128xf32> to tensor<?x?xf32> 66// CHECK: %[[sTD:.*]] = linalg.matmul ins(%[[sTA]], %[[sTB]] : tensor<?x4xf32>, tensor<4x?xf32>) 67// CHECK-SAME: outs(%[[sTC]] : tensor<?x?xf32>) -> tensor<?x?xf32> 68// CHECK: %[[TD:.*]] = tensor.insert_slice %[[sTD]] into %[[TC2]][{{.*}}] : tensor<?x?xf32> into tensor<128x128xf32> 69// CHECK: scf.yield %[[TD]] : tensor<128x128xf32> 70// CHECK: scf.yield %[[TD2]] : tensor<128x128xf32> 71// CHECK: scf.yield %[[TD1]] : tensor<128x128xf32> 72 %sz = func.call @get_dynamic_tile_size() : () -> index 73 %0 = linalg.matmul ins(%arg0, %arg1: tensor<128x128xf32>, tensor<128x128xf32>) 74 outs(%arg2: tensor<128x128xf32>) 75 -> tensor<128x128xf32> 76 77// CHECK: return %[[TD0]] : tensor<128x128xf32> 78 return %0 : tensor<128x128xf32> 79} 80 81// ----- 82 83module attributes {transform.with_named_sequence} { 84 transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { 85 %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op 86 // expected-note @below {{for this parameter}} 87 %1 = transform.test_produce_param (0 : i64) : !transform.param<i64> 88 // expected-error @below {{expected as many parameter values (0) as target ops (2)}} 89 transform.structured.tile_using_for %0 tile_sizes [%1, %1, %1] 90 : (!transform.any_op, !transform.param<i64>, !transform.param<i64>, !transform.param<i64>) 91 -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op) 92 transform.yield 93 } 94} 95 96func.func @tile_linalg_matmul( 97 %arg0: tensor<128x128xf32>, %arg1: tensor<128x128xf32>, %arg2: tensor<128x128xf32>) 98 -> (tensor<128x128xf32>, tensor<128x128xf32>) { 99 %0 = linalg.matmul ins(%arg0, %arg1: tensor<128x128xf32>, tensor<128x128xf32>) 100 outs(%arg2: tensor<128x128xf32>) 101 -> tensor<128x128xf32> 102 %1 = linalg.matmul ins(%0, %arg1: tensor<128x128xf32>, tensor<128x128xf32>) 103 outs(%arg2: tensor<128x128xf32>) 104 -> tensor<128x128xf32> 105 return %0, %1 : tensor<128x128xf32>, tensor<128x128xf32> 106} 107 108// ----- 109 110module attributes {transform.with_named_sequence} { 111 transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { 112 %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op 113 // expected-note @below {{for this handle}} 114 %1 = transform.structured.match ops{["arith.constant"]} in %arg1 : (!transform.any_op) -> !transform.any_op 115 // expected-error @below {{expected as many dynamic size-producing operations (0) as target ops (2)}} 116 transform.structured.tile_using_for %0 tile_sizes [%1, %1, 1] 117 : (!transform.any_op, !transform.any_op, !transform.any_op) 118 -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op) 119 transform.yield 120 } 121} 122 123func.func @tile_linalg_matmul( 124 %arg0: tensor<128x128xf32>, %arg1: tensor<128x128xf32>, %arg2: tensor<128x128xf32>) 125 -> (tensor<128x128xf32>, tensor<128x128xf32>) { 126 %0 = linalg.matmul ins(%arg0, %arg1: tensor<128x128xf32>, tensor<128x128xf32>) 127 outs(%arg2: tensor<128x128xf32>) 128 -> tensor<128x128xf32> 129 %1 = linalg.matmul ins(%0, %arg1: tensor<128x128xf32>, tensor<128x128xf32>) 130 outs(%arg2: tensor<128x128xf32>) 131 -> tensor<128x128xf32> 132 return %0, %1 : tensor<128x128xf32>, tensor<128x128xf32> 133} 134 135// ----- 136 137// CHECK-LABEL: tile_tensor_pad 138func.func @tile_tensor_pad( 139 %arg0 : tensor<?x?xf32>, %cst : f32, %low: index, %high: index) 140 -> tensor<20x40xf32> 141{ 142 // CHECK: scf.forall 143 // CHECK: scf.if 144 // CHECK: tensor.generate 145 // CHECK: else 146 // CHECK: tensor.pad {{.*}} nofold 147 %0 = tensor.pad %arg0 nofold low[%low, %low] high[%high, %high] { 148 ^bb0(%arg9: index, %arg10: index): 149 tensor.yield %cst : f32 150 } : tensor<?x?xf32> to tensor<20x40xf32> 151 return %0 : tensor<20x40xf32> 152} 153 154module attributes {transform.with_named_sequence} { 155 transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { 156 %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op 157 transform.structured.tile_using_forall %0 tile_sizes[1, 1] 158 : (!transform.any_op) -> (!transform.any_op, !transform.any_op) 159 transform.yield 160 } 161} 162 163// ----- 164 165#map = affine_map<(d0) -> (d0)> 166 167module { 168 func.func @scalable_tile(%arg0: tensor<?xf32>, %arg1: tensor<?xf32>, %arg2: tensor<?xf32>, %arg3: f32) -> tensor<?xf32> { 169 %0 = linalg.generic {indexing_maps = [#map, #map, #map], iterator_types = ["parallel"]} ins(%arg0, %arg1 : tensor<?xf32>, tensor<?xf32>) outs(%arg2 : tensor<?xf32>) { 170 ^bb0(%in_1: f32, %in_2: f32, %out: f32): 171 %1 = arith.addf %in_1, %in_2 : f32 172 %2 = arith.mulf %arg3, %1 : f32 173 linalg.yield %2 : f32 174 } -> tensor<?xf32> 175 return %0 : tensor<?xf32> 176 } 177} 178 179// CHECK-LABEL: func.func @scalable_tile( 180// CHECK-SAME: %[[ARG_0:.*]]: tensor<?xf32>, %[[ARG_1:.*]]: tensor<?xf32>, %[[ARG_2:.*]]: tensor<?xf32>, 181// CHECK: %[[C0:.*]] = arith.constant 0 : index 182// CHECK: %[[DIM:.*]] = tensor.dim %[[ARG_0]], %[[C0]] : tensor<?xf32> 183// CHECK: %[[VEC_SIZE:.*]] = arith.constant 4 : index 184// CHECK: %[[VS:.*]] = vector.vscale 185// CHECK: %[[STEP:.*]] = arith.muli %[[VEC_SIZE]], %[[VS]] : index 186// CHECK: scf.for %[[IV:.*]] = %[[C0]] to %[[DIM]] step %[[STEP]] iter_args(%[[VAL:.*]] = %[[ARG_2]]) -> (tensor<?xf32>) { 187// CHECK: %[[SIZE:.*]] = affine.min affine_map<(d0)[s0, s1] -> (-d0 + s0, s1)>(%[[IV]])[%[[DIM]], %[[STEP]]] 188// CHECK: %[[SLICE_ARG0:.*]] = tensor.extract_slice %[[ARG_0]][%[[IV]]] [%[[SIZE]]] [1] : tensor<?xf32> to tensor<?xf32> 189// CHECK: %[[SLICE_ARG1:.*]] = tensor.extract_slice %[[ARG_1]][%[[IV]]] [%[[SIZE]]] [1] : tensor<?xf32> to tensor<?xf32> 190// CHECK: %[[SLICE_ARG2:.*]] = tensor.extract_slice %[[VAL]][%[[IV]]] [%[[SIZE]]] [1] : tensor<?xf32> to tensor<?xf32> 191// CHECK: linalg.generic {indexing_maps = {{.*}}, iterator_types = ["parallel"]} ins(%[[SLICE_ARG0]], %[[SLICE_ARG1]] : tensor<?xf32>, tensor<?xf32>) outs(%[[SLICE_ARG2]] : tensor<?xf32>) { 192 193 module attributes {transform.with_named_sequence} { 194 transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { 195 %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op 196 %1, %loop = transform.structured.tile_using_for %0 tile_sizes [[4]] : (!transform.any_op) -> (!transform.any_op, !transform.any_op) 197 transform.yield 198 } 199 } 200 201// ----- 202 203// CHECK-LABEL: func.func @scalable_and_fixed_length_tile 204// CHECK-DAG: %[[C4:.*]] = arith.constant 4 : index 205// CHECK-DAG: %[[VS:.*]] = vector.vscale 206// CHECK-DAG: %[[STEP_2:.*]] = arith.muli %[[C4]], %[[VS]] : index 207// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index 208// CHECK-DAG: %[[C128:.*]] = arith.constant 128 : index 209// CHECK: scf.for %[[VAL_11:.*]] = %[[C0]] to %[[C128]] step %[[C4]] 210// CHECK: scf.for %[[VAL_16:.*]] = %[[C0]] to %[[C128]] step %[[C4]] 211// CHECK: scf.for %{{.*}} = %[[C0]] to %[[C128]] step %[[STEP_2]] 212 213func.func @scalable_and_fixed_length_tile( 214 %arg0: tensor<128x128xf32>, %arg1: tensor<128x128xf32>, %arg2: tensor<128x128xf32>) 215 -> tensor<128x128xf32> { 216 %0 = linalg.matmul ins(%arg0, %arg1: tensor<128x128xf32>, tensor<128x128xf32>) 217 outs(%arg2: tensor<128x128xf32>) 218 -> tensor<128x128xf32> 219 220 return %0 : tensor<128x128xf32> 221} 222 223module attributes {transform.with_named_sequence} { 224 transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { 225 %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op 226 %1, %loops:3 = transform.structured.tile_using_for %0 tile_sizes [4, 4, [4]] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op) 227 transform.yield 228 } 229} 230 231// ----- 232 233func.func @too_many_tiles(%arg0: tensor<128x128xf32>, %arg1: tensor<128x128xf32>, 234 %arg2: tensor<128x128xf32>) -> tensor<128x128xf32> { 235 // expected-note @below {{target op}} 236 %0 = linalg.matmul ins(%arg0, %arg1: tensor<128x128xf32>, tensor<128x128xf32>) 237 outs(%arg2: tensor<128x128xf32>) -> tensor<128x128xf32> 238 return %0 : tensor<128x128xf32> 239} 240 241module attributes {transform.with_named_sequence} { 242 transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { 243 %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op 244 // expected-error @below {{too many tiles provided, expected at most 3 found 4}} 245 %1, %loops = transform.structured.tile_using_for %0 tile_sizes [1, 0, 0, 0] : (!transform.any_op) -> (!transform.any_op, !transform.any_op) 246 transform.yield 247 } 248} 249 250// ----- 251 252module attributes {transform.with_named_sequence} { 253 transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { 254 %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op 255 // expected-error @below {{op expected number of loops to tile (3) to match number of `loops` results (1)}} 256 %1, %loops = transform.structured.tile_using_for %0 tile_sizes [4, 4, 4] : (!transform.any_op) -> (!transform.any_op, !transform.any_op) 257 transform.yield 258 } 259} 260 261func.func @tile_linalg_matmul( 262 %arg0: tensor<128x128xf32>, %arg1: tensor<128x128xf32>, %arg2: tensor<128x128xf32>) 263 -> tensor<128x128xf32> { 264 %0 = linalg.matmul ins(%arg0, %arg1: tensor<128x128xf32>, tensor<128x128xf32>) 265 outs(%arg2: tensor<128x128xf32>) 266 -> tensor<128x128xf32> 267 return %0 : tensor<128x128xf32> 268} 269