352 lines · plain
1// RUN: mlir-opt -transform-interpreter -split-input-file --cse %s | FileCheck %s2 3func.func @simple_matmul(%arg0 : tensor<?x?xf32>, %arg1 : tensor<?x?xf32>,4 %arg2 : tensor<?x?xf32>) -> tensor<?x?xf32> {5 %0 = linalg.matmul6 ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>)7 outs(%arg2 : tensor<?x?xf32>) -> tensor<?x?xf32>8 return %0 : tensor<?x?xf32>9}10 11module attributes {transform.with_named_sequence} {12 transform.named_sequence @__transform_main(%arg1 : !transform.any_op {transform.readonly}) {13 %matmul = transform.structured.match ops{["linalg.matmul"]} in %arg114 : (!transform.any_op) -> !transform.any_op15 %a, %b = transform.test.tile_using_forall %matmul [10, 20] mapping = [#gpu.block<y>, #gpu.block<x>]16 : (!transform.any_op) -> (!transform.any_op, !transform.any_op)17 transform.yield18 }19}20// CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0)[s0] -> (-d0 + s0, 10)>21// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0)[s0] -> (-d0 + s0, 20)>22// CHECK: func.func @simple_matmul(23// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<?x?xf32>24// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: tensor<?x?xf32>25// CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: tensor<?x?xf32>26// CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index27// CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index28// CHECK-DAG: %[[M:.+]] = tensor.dim %[[ARG0]], %[[C0]]29// CHECK-DAG: %[[K:.+]] = tensor.dim %[[ARG0]], %[[C1]]30// CHECK-DAG: %[[N:.+]] = tensor.dim %[[ARG1]], %[[C1]]31// CHECK: %[[RESULT:.+]] = scf.forall (%[[IV0:[a-zA-Z0-9]+]], %[[IV1:[a-zA-Z0-9]+]]) =32// CHECK-SAME: (0, 0) to (%[[M]], %[[N]]) step (10, 20) shared_outs(%[[INIT:.+]] = %[[ARG2]])33// CHECK: %[[TS_Y:.+]] = affine.min #[[MAP0]](%[[IV0]])[%[[M]]]34// CHECK: %[[TS_X:.+]] = affine.min #[[MAP1]](%[[IV1]])[%[[N]]]35// CHECK: %[[LHS_TILE:.+]] = tensor.extract_slice %[[ARG0]]36// CHECK-SAME: [%[[IV0]], 0] [%[[TS_Y]], %[[K]]] [1, 1]37// CHECK: %[[RHS_TILE:.+]] = tensor.extract_slice %[[ARG1]]38// CHECK-SAME: [0, %[[IV1]]] [%[[K]], %[[TS_X]]] [1, 1]39// CHECK: %[[INIT_TILE:.+]] = tensor.extract_slice %[[INIT]]40// CHECK-SAME: [%[[IV0]], %[[IV1]]] [%[[TS_Y]], %[[TS_X]]] [1, 1]41// CHECK: %[[GEMM_TILE:.+]] = linalg.matmul42// CHECK-SAME: ins(%[[LHS_TILE]], %[[RHS_TILE]] :43// CHECK-SAME: outs(%[[INIT_TILE]] :44// CHECK: scf.forall.in_parallel {45// CHECK: tensor.parallel_insert_slice %[[GEMM_TILE]] into %[[INIT]]46// CHECK-SAME: [%[[IV0]], %[[IV1]]] [%[[TS_Y]], %[[TS_X]]] [1, 1]47// CHECK: mapping = [#gpu.block<y>, #gpu.block<x>]48// CHECK: return %[[RESULT]]49 50// -----51 52func.func @simple_matmul_memref(%arg0 : memref<?x?xf32>, %arg1 : memref<?x?xf32>,53 %arg2 : memref<?x?xf32>) {54 linalg.matmul ins(%arg0, %arg1 : memref<?x?xf32>, memref<?x?xf32>)55 outs(%arg2 : memref<?x?xf32>)56 return57}58 59module attributes {transform.with_named_sequence} {60 transform.named_sequence @__transform_main(%arg1 : !transform.any_op {transform.readonly}) {61 %matmul = transform.structured.match ops{["linalg.matmul"]} in %arg162 : (!transform.any_op) -> !transform.any_op63 %a, %b = transform.test.tile_using_forall %matmul [10, 20]64 : (!transform.any_op) -> (!transform.any_op, !transform.any_op)65 transform.yield66 }67}68// CHECK-DAG: #[[$MAP0:.+]] = affine_map<(d0)[s0] -> (-d0 + s0, 10)>69// CHECK-DAG: #[[$MAP1:.+]] = affine_map<(d0)[s0] -> (-d0 + s0, 20)>70// CHECK-LABEL: func.func @simple_matmul_memref(71// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: memref<?x?xf32>72// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: memref<?x?xf32>73// CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: memref<?x?xf32>74// CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index75// CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index76// CHECK-DAG: %[[M:.+]] = memref.dim %[[ARG0]], %[[C0]]77// CHECK-DAG: %[[K:.+]] = memref.dim %[[ARG0]], %[[C1]]78// CHECK-DAG: %[[N:.+]] = memref.dim %[[ARG1]], %[[C1]]79// CHECK: scf.forall (%[[IV0:[a-zA-Z0-9]+]], %[[IV1:[a-zA-Z0-9]+]]) = (0, 0) to (%[[M]], %[[N]]) step (10, 20) {80// CHECK-DAG: %[[TS_M:.+]] = affine.min #[[$MAP0]](%[[IV0]])[%[[M]]]81// CHECK-DAG: %[[TS_N:.+]] = affine.min #[[$MAP1]](%[[IV1]])[%[[N]]]82// CHECK-DAG: %[[LHS_TILE:.+]] = memref.subview %[[ARG0]]83// CHECK-SAME: [%[[IV0]], 0] [%[[TS_M]], %[[K]]] [1, 1]84// CHECK-DAG: %[[RHS_TILE:.+]] = memref.subview %[[ARG1]]85// CHECK-SAME: [0, %[[IV1]]] [%[[K]], %[[TS_N]]] [1, 1]86// CHECK-DAG: %[[OUT_TILE:.+]] = memref.subview %[[ARG2]]87// CHECK-SAME: [%[[IV0]], %[[IV1]]] [%[[TS_M]], %[[TS_N]]] [1, 1]88// CHECK: linalg.matmul89// CHECK-SAME: ins(%[[LHS_TILE]], %[[RHS_TILE]] :90// CHECK-SAME: outs(%[[OUT_TILE]] :91 92// -----93 94#map0 = affine_map<(d0, d1, d2) -> (d0, d1, d2)>95#map1 = affine_map<(d0, d1, d2) -> (d0, d2, d1)>96#map2 = affine_map<(d0, d1, d2) -> (d2, d0, d1)>97func.func @multi_result(%arg0 : tensor<128x200x300xf32>) -> (tensor<128x300x200xf32>, tensor<300x128x200xf32>) {98 %init0 = tensor.empty() : tensor<128x300x200xf32>99 %init1 = tensor.empty() : tensor<300x128x200xf32>100 %0:2 = linalg.generic {101 indexing_maps = [#map0, #map1, #map2],102 iterator_types = ["parallel", "parallel", "parallel"]}103 ins(%arg0 : tensor<128x200x300xf32>)104 outs(%init0, %init1 : tensor<128x300x200xf32>, tensor<300x128x200xf32>) {105 ^bb0(%b0 : f32, %b1 : f32, %b2 : f32):106 linalg.yield %b0, %b0 : f32, f32107 } -> (tensor<128x300x200xf32>, tensor<300x128x200xf32>)108 return %0#0, %0#1 : tensor<128x300x200xf32>, tensor<300x128x200xf32>109}110 111module attributes {transform.with_named_sequence} {112 transform.named_sequence @__transform_main(%arg1 : !transform.any_op {transform.readonly}) {113 %generic = transform.structured.match ops{["linalg.generic"]} in %arg1114 : (!transform.any_op) -> !transform.any_op115 %a, %b = transform.test.tile_using_forall %generic [10, 0, 20]116 : (!transform.any_op) -> (!transform.any_op, !transform.any_op)117 transform.yield118 }119}120// CHECK-DAG: #[[$MAP0:.+]] = affine_map<(d0) -> (-d0 + 128, 10)>121// CHECK-LABEL: func.func @multi_result(122// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<128x200x300xf32>)123// CHECK-DAG: %[[INIT0:.+]] = tensor.empty()124// CHECK-DAG: %[[INIT1:.+]] = tensor.empty()125// CHECK: %[[OUTER:[a-zA-Z0-9]+]]:2 = scf.forall (%[[IV0:[a-zA-Z0-9]+]], %[[IV1:[a-zA-Z0-9]+]]) = (0, 0) to (128, 300) step (10, 20)126// CHECK-SAME: shared_outs(%[[ARG1:[a-zA-Z0-9]+]] = %[[INIT0]], %[[ARG2:[a-zA-Z0-9]+]] = %[[INIT1]])127// CHECK: %[[TS_Y:.+]] = affine.min #[[$MAP0]](%[[IV0]])128// CHECK: %[[ARG_TILE:.+]] = tensor.extract_slice %[[ARG0]]129// CHECK-SAME: [%[[IV0]], 0, %[[IV1]]] [%[[TS_Y]], 200, 20] [1, 1, 1]130// CHECK-DAG: %[[INIT0_TILE:.+]] = tensor.extract_slice %[[ARG1]]131// CHECK-SAME: [%[[IV0]], %[[IV1]], 0] [%[[TS_Y]], 20, 200] [1, 1, 1]132// CHECK-DAG: %[[INIT1_TILE:.+]] = tensor.extract_slice %[[ARG2]]133// CHECK-SAME: [%[[IV1]], %[[IV0]], 0] [20, %[[TS_Y]], 200] [1, 1, 1]134// CHECK: %[[RESULT_TILE:.+]]:2 = linalg.generic135// CHECK-SAME: ins(%[[ARG_TILE]] :136// CHECK-SAME: outs(%[[INIT0_TILE]], %[[INIT1_TILE]] :137// CHECK: scf.forall.in_parallel {138// CHECK-DAG: tensor.parallel_insert_slice %[[RESULT_TILE]]#0 into %[[ARG1]][%[[IV0]], %[[IV1]], 0] [%[[TS_Y]], 20, 200] [1, 1, 1]139// CHECK-DAG: tensor.parallel_insert_slice %[[RESULT_TILE]]#1 into %[[ARG2]][%[[IV1]], %[[IV0]], 0] [20, %[[TS_Y]], 200] [1, 1, 1]140// CHECK: }141// CHECK: return %[[OUTER]]#0, %[[OUTER]]#1142 143// -----144 145func.func @conv2D(%arg0 : tensor<?x?x?x?xf32>, %arg1 : tensor<?x?x?x?xf32>,146 %arg2 : tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> {147 %0 = linalg.conv_2d_nhwc_hwcf {148 strides = dense<[2, 3]> : tensor<2xi64>,149 dilation = dense<[4, 5]> : tensor<2xi64>}150 ins(%arg0, %arg1 : tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>)151 outs(%arg2 : tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>152 return %0 : tensor<?x?x?x?xf32>153}154 155module attributes {transform.with_named_sequence} {156 transform.named_sequence @__transform_main(%arg1 : !transform.any_op {transform.readonly}) {157 %conv = transform.structured.match ops{["linalg.conv_2d_nhwc_hwcf"]} in %arg1158 : (!transform.any_op) -> !transform.any_op159 %a, %b = transform.test.tile_using_forall %conv [0, 0, 0, 0, 10, 20, 30]160 : (!transform.any_op) -> (!transform.any_op, !transform.any_op)161 transform.yield162 }163}164// CHECK-DAG: #[[$MAP0:.+]] = affine_map<(d0)[s0] -> (-d0 + s0, 10)>165// CHECK-DAG: #[[$MAP1:.+]] = affine_map<(d0)[s0] -> (-d0 + s0, 20)>166// CHECK-DAG: #[[$MAP2:.+]] = affine_map<(d0)[s0] -> (-d0 + s0, 30)>167// CHECK-DAG: #[[$MAP3:.+]] = affine_map<(d0)[s0] -> (d0 + s0 * 2 - 2)>168// CHECK-DAG: #[[$MAP4:.+]] = affine_map<(d0)[s0] -> (d0 + s0 * 3 - 3)>169// CHECK-LABEL: func.func @conv2D(170// CHECK-SAME: %[[INPUT:[a-zA-Z0-9]+]]: tensor<?x?x?x?xf32>171// CHECK-SAME: %[[FILTER:[a-zA-Z0-9]+]]: tensor<?x?x?x?xf32>172// CHECK-SAME: %[[INIT:[a-zA-Z0-9]+]]: tensor<?x?x?x?xf32>173// CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index174// CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index175// CHECK-DAG: %[[C2:.+]] = arith.constant 2 : index176// CHECK-DAG: %[[C3:.+]] = arith.constant 3 : index177// CHECK-DAG: %[[N:.+]] = tensor.dim %[[INPUT]], %[[C0]]178// CHECK-DAG: %[[C:.+]] = tensor.dim %[[INPUT]], %[[C3]]179// CHECK-DAG: %[[P:.+]] = tensor.dim %[[FILTER]], %[[C0]]180// CHECK-DAG: %[[Q:.+]] = tensor.dim %[[FILTER]], %[[C1]]181// CHECK-DAG: %[[F:.+]] = tensor.dim %[[FILTER]], %[[C3]]182// CHECK-DAG: %[[R:.+]] = tensor.dim %[[INIT]], %[[C1]]183// CHECK-DAG: %[[S:.+]] = tensor.dim %[[INIT]], %[[C2]]184// CHECK: %[[RESULT:.+]] = scf.forall (%[[IV0:[a-zA-Z0-9]+]], %[[IV1:[a-zA-Z0-9]+]], %[[IV2:[a-zA-Z0-9]+]]) =185// CHECK-SAME: (0, 0, 0) to (%[[P]], %[[Q]], %[[C]]) step (10, 20, 30) shared_outs(%[[INIT0:.+]] = %[[INIT]])186// CHECK-DAG: %[[TS_P:.+]] = affine.min #[[$MAP0]](%[[IV0]])[%[[P]]]187// CHECK-DAG: %[[TS_Q:.+]] = affine.min #[[$MAP1]](%[[IV1]])[%[[Q]]]188// CHECK-DAG: %[[TS_C:.+]] = affine.min #[[$MAP2]](%[[IV2]])[%[[C]]]189// CHECK-DAG: %[[TS_H:.+]] = affine.apply #[[$MAP3]](%[[TS_P]])[%[[R]]]190// CHECK-DAG: %[[TS_W:.+]] = affine.apply #[[$MAP4]](%[[TS_Q]])[%[[S]]]191// CHECK-DAG: %[[INPUT_TILE:.+]] = tensor.extract_slice %[[INPUT]]192// CHECK-SAME: [0, %[[IV0]], %[[IV1]], %[[IV2]]] [%[[N]], %[[TS_H]], %[[TS_W]], %[[TS_C]]]193// CHECK-DAG: %[[FILTER_TILE:.+]] = tensor.extract_slice %[[FILTER]]194// CHECK-SAME: [%[[IV0]], %[[IV1]], %[[IV2]], 0] [%[[TS_P]], %[[TS_Q]], %[[TS_C]], %[[F]]]195// CHECK-DAG: %[[INIT_TILE:.+]] = tensor.extract_slice %[[INIT0]]196// CHECK-SAME: [0, 0, 0, 0] [%[[N]], %[[R]], %[[S]], %[[F]]]197// CHECK: %[[CONV_TILE:.+]] = linalg.conv_2d_nhwc_hwcf198// CHECK-SAME: dilation = dense<[4, 5]> : tensor<2xi64>, strides = dense<[2, 3]> : tensor<2xi64>199// CHECK-SAME: ins(%[[INPUT_TILE]], %[[FILTER_TILE]] :200// CHECK-SAME: outs(%[[INIT_TILE]] :201// CHECK: scf.forall.in_parallel202// CHECK: tensor.parallel_insert_slice %[[CONV_TILE]] into %[[INIT0]]203// CHECK-SAME: [0, 0, 0, 0] [%[[N]], %[[R]], %[[S]], %[[F]]] [1, 1, 1, 1]204// CHECK: return %[[RESULT]]205 206// -----207 208// CHECK: #[[$MAP_ADD:.+]] = affine_map<(d0)[s0] -> (d0 + s0)>209 210func.func @indexed_semantics(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>) -> tensor<?x?xf32> {211 // Check that we correctly amend "linalg.index" results.212 213 %0 = linalg.generic {214 indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,215 affine_map<(d0, d1) -> (d0, d1)>],216 iterator_types = ["parallel", "parallel"]}217 ins(%arg0: tensor<?x?xf32>)218 outs(%arg1: tensor<?x?xf32>) {219 ^bb0(%arg2: f32, %arg3: f32):220 %1 = linalg.index 0 : index221 %2 = linalg.index 1 : index222 %3 = arith.addi %1, %2 : index223 %4 = arith.index_cast %3 : index to i64224 %5 = arith.uitofp %4 : i64 to f32225 %6 = arith.addf %5, %arg2 : f32226 linalg.yield %6 : f32227 } -> (tensor<?x?xf32>)228 return %0 : tensor<?x?xf32>229}230 231module attributes {transform.with_named_sequence} {232 transform.named_sequence @__transform_main(%arg1 : !transform.any_op {transform.readonly}) {233 %generic = transform.structured.match ops{["linalg.generic"]} in %arg1234 : (!transform.any_op) -> !transform.any_op235 %a, %b = transform.test.tile_using_forall %generic [10, 20]236 : (!transform.any_op) -> (!transform.any_op, !transform.any_op)237 transform.yield238 }239}240 241// CHECK-LABEL: @indexed_semantics242// CHECK: scf.forall (%[[I0:.+]], %[[I1:.+]]) =243// CHECK: %[[INDEX0:.+]] = linalg.index 0244// CHECK: %[[INDEX0_AMENDED:.+]] = affine.apply #[[$MAP_ADD]](%[[I0]])[%[[INDEX0]]]245// CHECK: %[[INDEX1:.+]] = linalg.index 1246// CHECK: %[[INDEX1_AMENDED:.+]] = affine.apply #[[$MAP_ADD]](%[[I1]])[%[[INDEX1]]]247// CHECK: arith.addi %[[INDEX0_AMENDED]], %[[INDEX1_AMENDED]]248 249// -----250 251func.func @interchange_matmul(%arg0 : tensor<?x?xf32>, %arg1 : tensor<?x?xf32>,252 %arg2 : tensor<?x?xf32>) -> tensor<?x?xf32> {253 %0 = linalg.matmul ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>)254 outs(%arg2 : tensor<?x?xf32>) -> tensor<?x?xf32>255 return %0 : tensor<?x?xf32>256}257 258module attributes {transform.with_named_sequence} {259 transform.named_sequence @__transform_main(%arg1 : !transform.any_op {transform.readonly}) {260 %matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1261 : (!transform.any_op) -> !transform.any_op262 %a, %b = transform.test.tile_using_forall %matmul [10, 20] interchange = [1, 0] mapping = [#gpu.block<y>, #gpu.block<x>]263 : (!transform.any_op) -> (!transform.any_op, !transform.any_op)264 transform.yield265 }266}267// CHECK-DAG: #[[$MAP0:.+]] = affine_map<(d0)[s0] -> (-d0 + s0, 20)>268// CHECK-DAG: #[[$MAP2:.+]] = affine_map<(d0)[s0] -> (-d0 + s0, 10)>269// CHECK-LABEL: func.func @interchange_matmul(270// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<?x?xf32>271// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: tensor<?x?xf32>272// CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: tensor<?x?xf32>273// CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index274// CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index275// CHECK-DAG: %[[M:.+]] = tensor.dim %[[ARG0]], %[[C0]]276// CHECK-DAG: %[[K:.+]] = tensor.dim %[[ARG0]], %[[C1]]277// CHECK-DAG: %[[N:.+]] = tensor.dim %[[ARG1]], %[[C1]]278// CHECK: %[[OUTER:[a-zA-Z0-9]+]] = scf.forall (%[[IV0:[a-zA-Z0-9]+]], %[[IV1:[a-zA-Z0-9]+]]279// CHECK-SAME: (0, 0) to (%[[N]], %[[M]]) step (20, 10)280// CHECK-SAME: shared_outs(%[[INIT0:.+]] = %[[ARG2]])281// CHECK-DAG: %[[TS_N:.+]] = affine.min #[[$MAP0]](%[[IV0]])[%[[N]]]282// CHECK-DAG: %[[TS_M:.+]] = affine.min #[[$MAP2]](%[[IV1]])[%[[M]]]283// CHECK-DAG: %[[LHS_TILE:.+]] = tensor.extract_slice %[[ARG0]]284// CHECK-SAME: [%[[IV1]], 0] [%[[TS_M]], %[[K]]] [1, 1]285// CHECK-DAG: %[[RHS_TILE:.+]] = tensor.extract_slice %[[ARG1]]286// CHECK-SAME: [0, %[[IV0]]] [%[[K]], %[[TS_N]]] [1, 1]287// CHECK-DAG: %[[INIT_TILE:.+]] = tensor.extract_slice %[[INIT0]]288// CHECK-SAME: [%[[IV1]], %[[IV0]]] [%[[TS_M]], %[[TS_N]]] [1, 1]289// CHECK: %[[GEMM_TILE:.+]] = linalg.matmul290// CHECK-SAME: ins(%[[LHS_TILE]], %[[RHS_TILE]] :291// CHECK-SAME: outs(%[[INIT_TILE]] :292// CHECK: scf.forall.in_parallel {293// CHECK: tensor.parallel_insert_slice %[[GEMM_TILE]] into %[[INIT0]]294// CHECK-SAME: [%[[IV1]], %[[IV0]]] [%[[TS_M]], %[[TS_N]]] [1, 1]295// CHECK: } {mapping = [#gpu.block<y>, #gpu.block<x>]}296// CHECK: return %[[OUTER]]297 298// -----299 300func.func @check_scalar_operation(%arg0 : tensor<f32>) -> tensor<f32> {301 %init = tensor.empty() : tensor<f32>302 %0 = linalg.generic {303 indexing_maps = [affine_map<() -> ()>, affine_map<() -> ()>],304 iterator_types = []} 305 ins(%arg0 : tensor<f32>) outs(%init : tensor<f32>){306 ^bb0(%b0 : f32, %b1 : f32):307 %1 = arith.mulf %b0, %b0 : f32308 linalg.yield %1 : f32309 } -> tensor<f32>310 return %0 : tensor<f32>311}312 313module attributes {transform.with_named_sequence} {314 transform.named_sequence @__transform_main(%arg1 : !transform.any_op {transform.readonly}) {315 %generic = transform.structured.match ops{["linalg.generic"]} in %arg1316 : (!transform.any_op) -> !transform.any_op317 %a = transform.test.tile_using_forall %generic []318 : (!transform.any_op) -> (!transform.any_op)319 transform.yield320 }321}322// CHECK-LABEL: func @check_scalar_operation323// CHECK-NOT: scf.for324// CHECK: linalg.generic325 326// -----327 328func.func @check_scalar_memref_operation(%arg0 : memref<f32>, %arg1 : memref<f32>){329 linalg.generic {330 indexing_maps = [affine_map<() -> ()>, affine_map<() -> ()>],331 iterator_types = []} 332 ins(%arg0 : memref<f32>) outs(%arg1 : memref<f32>){333 ^bb0(%b0 : f32, %b1 : f32):334 %1 = arith.mulf %b0, %b0 : f32335 linalg.yield %1 : f32336 }337 return338}339 340module attributes {transform.with_named_sequence} {341 transform.named_sequence @__transform_main(%arg1 : !transform.any_op {transform.readonly}) {342 %generic = transform.structured.match ops{["linalg.generic"]} in %arg1343 : (!transform.any_op) -> !transform.any_op344 %a = transform.test.tile_using_forall %generic []345 : (!transform.any_op) -> (!transform.any_op)346 transform.yield347 }348}349// CHECK-LABEL: func @check_scalar_memref_operation350// CHECK-NOT: scf.for351// CHECK: linalg.generic352