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1// RUN: mlir-opt %s -transform-interpreter -canonicalize --split-input-file | FileCheck %s2 3func.func @conv2d(%arg0: tensor<2x10x10x5xf32>, %arg1: tensor<2x3x3x5xf32>, %arg2: tensor<2x8x8x2xf32>) -> tensor<2x8x8x2xf32> {4 %0 = tensor.empty() : tensor<6x6x5x2xf32>5 %1 = linalg.winograd_filter_transform fmr(F_4_3) ins(%arg1 : tensor<2x3x3x5xf32>) outs(%0 : tensor<6x6x5x2xf32>) -> tensor<6x6x5x2xf32>6 %2 = tensor.empty() : tensor<6x6x2x2x2x5xf32>7 %3 = linalg.winograd_input_transform fmr(F_4_3) ins(%arg0 : tensor<2x10x10x5xf32>) outs(%2 : tensor<6x6x2x2x2x5xf32>) -> tensor<6x6x2x2x2x5xf32>8 %collapsed = tensor.collapse_shape %1 [[0, 1], [2], [3]] : tensor<6x6x5x2xf32> into tensor<36x5x2xf32>9 %collapsed_0 = tensor.collapse_shape %3 [[0, 1], [2, 3, 4], [5]] : tensor<6x6x2x2x2x5xf32> into tensor<36x8x5xf32>10 %4 = tensor.empty() : tensor<36x8x2xf32>11 %5 = linalg.batch_matmul ins(%collapsed_0, %collapsed : tensor<36x8x5xf32>, tensor<36x5x2xf32>) outs(%4 : tensor<36x8x2xf32>) -> tensor<36x8x2xf32>12 %expanded = tensor.expand_shape %5 [[0, 1], [2, 3, 4], [5]] output_shape [6, 6, 2, 2, 2, 2] : tensor<36x8x2xf32> into tensor<6x6x2x2x2x2xf32>13 %6 = linalg.winograd_output_transform fmr(F_4_3) ins(%expanded : tensor<6x6x2x2x2x2xf32>) outs(%arg2 : tensor<2x8x8x2xf32>) -> tensor<2x8x8x2xf32>14 return %6 : tensor<2x8x8x2xf32>15}16 17module attributes {transform.with_named_sequence} {18 transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {19 %0 = transform.structured.match ops{["linalg.winograd_filter_transform"]} in %arg1 : (!transform.any_op) -> !transform.any_op20 %2 = transform.structured.match ops{["linalg.winograd_input_transform"]} in %arg1 : (!transform.any_op) -> !transform.any_op21 %3, %loop3:2 = transform.structured.tile_using_for %2 tile_sizes [0, 0, 1, 1, 0, 0] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)22 %4 = transform.structured.match ops{["linalg.winograd_output_transform"]} in %arg1 : (!transform.any_op) -> !transform.any_op23 %5, %loop5:2 = transform.structured.tile_using_for %4 tile_sizes [0, 0, 1, 1, 0, 0] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)24 %7 = transform.structured.decompose_winograd_op %0 : (!transform.any_op) -> (!transform.any_op)25 %8 = transform.structured.match ops{["linalg.winograd_input_transform"]} in %3 : (!transform.any_op) -> !transform.any_op26 %9 = transform.structured.decompose_winograd_op %8 : (!transform.any_op) -> (!transform.any_op)27 %10 = transform.structured.match ops{["linalg.winograd_output_transform"]} in %5 : (!transform.any_op) -> !transform.any_op28 %11 = transform.structured.decompose_winograd_op %10 : (!transform.any_op) -> (!transform.any_op)29 transform.yield30 }31}32 33// CHECK: #[[$MAP0:.+]] = affine_map<(d0) -> (d0 * 4)>34// CHECK: #[[$MAP1:.+]] = affine_map<(d0, d1) -> ()>35// CHECK: #[[$MAP2:.+]] = affine_map<(d0, d1) -> (d0, d1)>36// CHECK-LABEL: func.func @conv2d37// CHECK-SAME: (%[[ARG0:.*]]: tensor<2x10x10x5xf32>, %[[ARG1:.*]]: tensor<2x3x3x5xf32>, %[[ARG2:.*]]: tensor<2x8x8x2xf32>) -> tensor<2x8x8x2xf32> {38// CHECK: %[[CST:.*]] = arith.constant 1.024000e+03 : f3239// CHECK: %[[CST_0:.*]] = arith.constant dense<{{.*}}> : tensor<6x4xf32>40// CHECK: %[[CST_1:.*]] = arith.constant dense<{{.*}}> : tensor<4x6xf32>41// CHECK: %[[CST_2:.*]] = arith.constant dense<{{.*}}> : tensor<6x6xf32>42// CHECK: %[[CST_3:.*]] = arith.constant dense<{{.*}}> : tensor<6x6xf32>43// CHECK: %[[CST_4:.*]] = arith.constant dense<{{.*}}> : tensor<3x6xf32>44// CHECK: %[[CST_5:.*]] = arith.constant dense<{{.*}}> : tensor<6x3xf32>45// CHECK: %[[CST_6:.*]] = arith.constant 0.000000e+00 : f3246// CHECK: %[[C1:.*]] = arith.constant 1 : index47// CHECK: %[[C5:.*]] = arith.constant 5 : index48// CHECK: %[[C2:.*]] = arith.constant 2 : index49// CHECK: %[[C0:.*]] = arith.constant 0 : index50// CHECK: %[[S0:.*]] = tensor.empty()51// CHECK: %[[S1:.*]] = scf.for %[[ARG3:.*]] = %[[C0]] to %[[C2]] step %[[C1]] iter_args(%[[ARG4:.*]] = %[[S0]])52// CHECK: %[[S9:.*]] = scf.for %[[ARG5:.*]] = %[[C0]] to %[[C5]] step %[[C1]] iter_args(%[[ARG6:.*]] = %[[ARG4]])53// CHECK: %[[EXTRACTED_SLICE:.*]] = tensor.extract_slice %[[ARG1]][%[[ARG3]], 0, 0, %[[ARG5]]] [1, 3, 3, 1] [1, 1, 1, 1]54// CHECK: %[[S10:.*]] = tensor.empty() : tensor<6x3xf32>55// CHECK: %[[S11:.*]] = linalg.fill ins(%[[CST_6]] : f32) outs(%[[S10]] : tensor<6x3xf32>) -> tensor<6x3xf32>56// CHECK: %[[S12:.*]] = linalg.matmul ins(%[[CST_5]], %[[EXTRACTED_SLICE]] : tensor<6x3xf32>, tensor<3x3xf32>) outs(%[[S11]] : tensor<6x3xf32>) -> tensor<6x3xf32>57// CHECK: %[[S13:.*]] = tensor.empty() : tensor<6x6xf32>58// CHECK: %[[S14:.*]] = linalg.fill ins(%[[CST_6]] : f32) outs(%[[S13]] : tensor<6x6xf32>) -> tensor<6x6xf32>59// CHECK: %[[S15:.*]] = linalg.matmul ins(%[[S12]], %[[CST_4]] : tensor<6x3xf32>, tensor<3x6xf32>) outs(%[[S14]] : tensor<6x6xf32>) -> tensor<6x6xf32>60// CHECK: %[[INSERTED_SLICE:.*]] = tensor.insert_slice %[[S15]] into %[[ARG6]][0, 0, %[[ARG5]], %[[ARG3]]] [6, 6, 1, 1] [1, 1, 1, 1]61// CHECK: scf.yield %[[INSERTED_SLICE]]62// CHECK: scf.yield %[[S9]]63// CHECK: %[[S2:.*]] = tensor.empty() : tensor<6x6x2x2x2x5xf32>64// CHECK: %[[S4:.*]] = scf.for %[[ARG3:.*]] = %[[C0]] to %[[C2]] step %[[C1]] iter_args(%[[ARG4:.*]] = %[[S2]])65// CHECK: %[[S9:.*]] = scf.for %[[ARG5:.*]] = %[[C0]] to %[[C2]] step %[[C1]] iter_args(%[[ARG6:.*]] = %[[ARG4]])66// CHECK: %[[S10:.*]] = affine.apply #[[$MAP0]](%[[ARG3]])67// CHECK: %[[S11:.*]] = affine.apply #[[$MAP0]](%[[ARG5]])68// CHECK: %[[EXTRACTED_SLICE:.*]] = tensor.extract_slice %[[ARG0]][0, %[[S10]], %[[S11]], 0] [2, 6, 6, 5] [1, 1, 1, 1]69// CHECK: %[[EXTRACTED_SLICE_7:.*]] = tensor.extract_slice %[[ARG6]][0, 0, %[[ARG3]], %[[ARG5]], 0, 0] [6, 6, 1, 1, 2, 5] [1, 1, 1, 1, 1, 1]70// CHECK: %[[S12:.*]] = scf.for %[[ARG7:.*]] = %[[C0]] to %[[C2]] step %[[C1]] iter_args(%[[ARG8:.*]] = %[[EXTRACTED_SLICE_7]])71// CHECK: %[[S13:.*]] = scf.for %[[ARG9:.*]] = %[[C0]] to %[[C5]] step %[[C1]] iter_args(%[[ARG10:.*]] = %[[ARG8]])72// CHECK: %[[EXTRACTED_SLICE_8:.*]] = tensor.extract_slice %[[EXTRACTED_SLICE]][%[[ARG7]], 0, 0, %[[ARG9]]] [1, 6, 6, 1] [1, 1, 1, 1]73// CHECK: %[[S14:.*]] = tensor.empty() : tensor<6x6xf32>74// CHECK: %[[S15:.*]] = linalg.fill ins(%[[CST_6]] : f32) outs(%[[S14]] : tensor<6x6xf32>) -> tensor<6x6xf32>75// CHECK: %[[S16:.*]] = linalg.matmul ins(%[[CST_3]], %[[EXTRACTED_SLICE_8]] : tensor<6x6xf32>, tensor<6x6xf32>) outs(%[[S15]] : tensor<6x6xf32>) -> tensor<6x6xf32>76// CHECK: %[[S17:.*]] = tensor.empty() : tensor<6x6xf32>77// CHECK: %[[S18:.*]] = linalg.fill ins(%[[CST_6]] : f32) outs(%[[S17]] : tensor<6x6xf32>) -> tensor<6x6xf32>78// CHECK: %[[S19:.*]] = linalg.matmul ins(%[[S16]], %[[CST_2]] : tensor<6x6xf32>, tensor<6x6xf32>) outs(%[[S18]] : tensor<6x6xf32>) -> tensor<6x6xf32>79// CHECK: %[[INSERTED_SLICE_9:.*]] = tensor.insert_slice %[[S19]] into %[[ARG10]][0, 0, 0, 0, %[[ARG7]], %[[ARG9]]] [6, 6, 1, 1, 1, 1] [1, 1, 1, 1, 1, 1]80// CHECK: scf.yield %[[INSERTED_SLICE_9]]81// CHECK: scf.yield %[[S13]]82// CHECK: %[[INSERTED_SLICE:.*]] = tensor.insert_slice %[[S12]] into %[[ARG6]][0, 0, %[[ARG3]], %[[ARG5]], 0, 0] [6, 6, 1, 1, 2, 5] [1, 1, 1, 1, 1, 1]83// CHECK: scf.yield %[[INSERTED_SLICE]]84// CHECK: scf.yield %[[S9]]85// CHECK: %[[COLLAPSED:.*]] = tensor.collapse_shape %[[S1]] {{\[}}[0, 1], [2], [3]]86// CHECK: %[[COLLAPSED_6:.*]] = tensor.collapse_shape %[[S4]] {{\[}}[0, 1], [2, 3, 4], [5]]87// CHECK: %[[S7:.*]] = tensor.empty()88// CHECK: %[[S6:.*]] = linalg.batch_matmul89// CHECK: %[[EXPANDED:.*]] = tensor.expand_shape %[[S6]] {{\[}}[0, 1], [2, 3, 4], [5]] output_shape [6, 6, 2, 2, 2, 2]90// CHECK: %[[S8:.*]] = scf.for %[[ARG3:.*]] = %[[C0]] to %[[C2]] step %[[C1]] iter_args(%[[ARG4:.*]] = %[[ARG2]])91// CHECK: %[[S9:.*]] = scf.for %[[ARG5:.*]] = %[[C0]] to %[[C2]] step %[[C1]] iter_args(%[[ARG6:.*]] = %[[ARG4]])92// CHECK: %[[EXTRACTED_SLICE:.*]] = tensor.extract_slice %[[EXPANDED]][0, 0, %[[ARG3]], %[[ARG5]], 0, 0] [6, 6, 1, 1, 2, 2] [1, 1, 1, 1, 1, 1]93// CHECK: %[[S10:.*]] = affine.apply #[[$MAP0]](%[[ARG3]])94// CHECK: %[[S11:.*]] = affine.apply #[[$MAP0]](%[[ARG5]])95// CHECK: %[[EXTRACTED_SLICE_7:.*]] = tensor.extract_slice %[[ARG6]][0, %[[S10]], %[[S11]], 0] [2, 4, 4, 2] [1, 1, 1, 1]96// CHECK: %[[S12:.*]] = scf.for %[[ARG7:.*]] = %[[C0]] to %[[C2]] step %[[C1]] iter_args(%[[ARG8:.*]] = %[[EXTRACTED_SLICE_7]])97// CHECK: %[[S15:.*]] = scf.for %[[ARG9:.*]] = %[[C0]] to %[[C2]] step %[[C1]] iter_args(%[[ARG10:.*]] = %[[ARG8]])98// CHECK: %[[EXTRACTED_SLICE_8:.*]] = tensor.extract_slice %[[EXTRACTED_SLICE]][0, 0, 0, 0, %[[ARG7]], %[[ARG9]]] [6, 6, 1, 1, 1, 1] [1, 1, 1, 1, 1, 1]99// CHECK: %[[S25:.*]] = tensor.extract_slice %[[ARG10]][%[[ARG7]], 0, 0, %[[ARG9]]] [1, 4, 4, 1] [1, 1, 1, 1]100// CHECK: %[[S16:.*]] = tensor.empty() : tensor<4x6xf32>101// CHECK: %[[S17:.*]] = linalg.fill ins(%[[CST_6]] : f32) outs(%[[S16]] : tensor<4x6xf32>) -> tensor<4x6xf32>102// CHECK: %[[S18:.*]] = linalg.matmul ins(%[[CST_1]], %[[EXTRACTED_SLICE_8]] : tensor<4x6xf32>, tensor<6x6xf32>) outs(%[[S17]] : tensor<4x6xf32>) -> tensor<4x6xf32>103// CHECK: %[[S19:.*]] = tensor.empty() : tensor<4x4xf32>104// CHECK: %[[S20:.*]] = linalg.fill ins(%[[CST_6]] : f32) outs(%[[S19]] : tensor<4x4xf32>) -> tensor<4x4xf32>105// CHECK: %[[S21:.*]] = linalg.matmul ins(%[[S18]], %[[CST_0]] : tensor<4x6xf32>, tensor<6x4xf32>) outs(%[[S20]] : tensor<4x4xf32>) -> tensor<4x4xf32>106// CHECK: %[[S23:.*]] = linalg.generic {indexing_maps = [#[[$MAP1]], #[[$MAP2]], #[[$MAP2]]], iterator_types = ["parallel", "parallel"]} ins(%[[CST]], %[[S21]] : f32, tensor<4x4xf32>) outs(%[[S25]] : tensor<4x4xf32>) {107// CHECK: ^bb0(%[[IN1:.*]]: f32, %[[IN2:.*]]: f32, %[[OUT:.*]]: f32):108// CHECK: %[[VAL_90:.*]] = arith.mulf %[[IN1]], %[[IN2]] : f32109// CHECK: %[[VAL_91:.*]] = arith.addf %[[VAL_90]], %[[OUT]] : f32110/// CHECK: linalg.yield %[[VAL_91]] : f32111// CHECK: } -> tensor<4x4xf32>112// CHECK: %[[INSERTED_SLICE_9:.*]] = tensor.insert_slice %[[S23]] into %[[ARG10]][%[[ARG7]], 0, 0, %[[ARG9]]] [1, 4, 4, 1] [1, 1, 1, 1]113// CHECK: scf.yield %[[INSERTED_SLICE_9]]114// CHECK: scf.yield %[[S15]]115// CHECK: %[[S13:.*]] = affine.apply #[[$MAP0]](%[[ARG3]])116// CHECK: %[[S14:.*]] = affine.apply #[[$MAP0]](%[[ARG5]])117// CHECK: %[[INSERTED_SLICE:.*]] = tensor.insert_slice %[[S12]] into %[[ARG6]][0, %[[S13]], %[[S14]], 0] [2, 4, 4, 2] [1, 1, 1, 1]118// CHECK: scf.yield %[[INSERTED_SLICE]]119// CHECK: scf.yield %[[S9]]120 121// -----122 123func.func @conv2d_unaligned(%arg0: tensor<2x11x11x5xf32>, %arg1: tensor<2x3x3x5xf32>, %arg2: tensor<2x9x9x2xf32>) -> tensor<2x9x9x2xf32> {124 %cst = arith.constant 0.000000e+00 : f32125 %0 = tensor.empty() : tensor<6x6x5x2xf32>126 %1 = linalg.winograd_filter_transform fmr(F_4_3) ins(%arg1 : tensor<2x3x3x5xf32>) outs(%0 : tensor<6x6x5x2xf32>) -> tensor<6x6x5x2xf32>127 %padded = tensor.pad %arg0 low[0, 0, 0, 0] high[0, 3, 3, 0] {128 ^bb0(%arg4: index, %arg5: index, %arg6: index, %arg7: index):129 tensor.yield %cst : f32130 } : tensor<2x11x11x5xf32> to tensor<2x14x14x5xf32>131 %2 = tensor.empty() : tensor<6x6x3x3x2x5xf32>132 %3 = linalg.winograd_input_transform fmr(F_4_3) ins(%padded : tensor<2x14x14x5xf32>) outs(%2 : tensor<6x6x3x3x2x5xf32>) -> tensor<6x6x3x3x2x5xf32>133 %collapsed = tensor.collapse_shape %1 [[0, 1], [2], [3]] : tensor<6x6x5x2xf32> into tensor<36x5x2xf32>134 %collapsed_0 = tensor.collapse_shape %3 [[0, 1], [2, 3, 4], [5]] : tensor<6x6x3x3x2x5xf32> into tensor<36x18x5xf32>135 %4 = tensor.empty() : tensor<36x18x2xf32>136 %5 = linalg.fill ins(%cst : f32) outs(%4 : tensor<36x18x2xf32>) -> tensor<36x18x2xf32>137 %6 = linalg.batch_matmul ins(%collapsed_0, %collapsed : tensor<36x18x5xf32>, tensor<36x5x2xf32>) outs(%5 : tensor<36x18x2xf32>) -> tensor<36x18x2xf32>138 %expanded = tensor.expand_shape %6 [[0, 1], [2, 3, 4], [5]] output_shape [6, 6, 3, 3, 2, 2] : tensor<36x18x2xf32> into tensor<6x6x3x3x2x2xf32>139 %padded_1 = tensor.pad %arg2 low[0, 0, 0, 0] high[0, 3, 3, 0] {140 ^bb0(%arg4: index, %arg5: index, %arg6: index, %arg7: index):141 tensor.yield %cst : f32142 } : tensor<2x9x9x2xf32> to tensor<2x12x12x2xf32>143 %7 = linalg.winograd_output_transform fmr(F_4_3) ins(%expanded : tensor<6x6x3x3x2x2xf32>) outs(%padded_1 : tensor<2x12x12x2xf32>) -> tensor<2x12x12x2xf32>144 %extracted_slice = tensor.extract_slice %7[0, 0, 0, 0] [2, 9, 9, 2] [1, 1, 1, 1] : tensor<2x12x12x2xf32> to tensor<2x9x9x2xf32>145 return %extracted_slice : tensor<2x9x9x2xf32>146}147 148module attributes {transform.with_named_sequence} {149 transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {150 %0 = transform.structured.match ops{["linalg.winograd_filter_transform"]} in %arg1 : (!transform.any_op) -> !transform.any_op151 %2 = transform.structured.match ops{["linalg.winograd_input_transform"]} in %arg1 : (!transform.any_op) -> !transform.any_op152 %3, %loop3:2 = transform.structured.tile_using_for %2 tile_sizes [0, 0, 1, 1, 0, 0] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)153 %4 = transform.structured.match ops{["linalg.winograd_output_transform"]} in %arg1 : (!transform.any_op) -> !transform.any_op154 %5, %loop5:2 = transform.structured.tile_using_for %4 tile_sizes [0, 0, 1, 1, 0, 0] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)155 %7 = transform.structured.decompose_winograd_op %0 : (!transform.any_op) -> (!transform.any_op)156 %8 = transform.structured.match ops{["linalg.winograd_input_transform"]} in %3 : (!transform.any_op) -> !transform.any_op157 %9 = transform.structured.decompose_winograd_op %8 : (!transform.any_op) -> (!transform.any_op)158 %10 = transform.structured.match ops{["linalg.winograd_output_transform"]} in %5 : (!transform.any_op) -> !transform.any_op159 %11 = transform.structured.decompose_winograd_op %10 : (!transform.any_op) -> (!transform.any_op)160 transform.yield161 }162}163 164// CHECK: #[[$MAP0:.+]] = affine_map<(d0) -> (d0 * 4)>165// CHECK: #[[$MAP1:.+]] = affine_map<(d0, d1) -> ()>166// CHECK: #[[$MAP2:.+]] = affine_map<(d0, d1) -> (d0, d1)>167// CHECK-LABEL: func.func @conv2d_unaligned168// CHECK-SAME: (%[[ARG0:.*]]: tensor<2x11x11x5xf32>, %[[ARG1:.*]]: tensor<2x3x3x5xf32>, %[[ARG2:.*]]: tensor<2x9x9x2xf32>) -> tensor<2x9x9x2xf32> {169// CHECK: %[[CST:.*]] = arith.constant 1.024000e+03 : f32170// CHECK: %[[CST_0:.*]] = arith.constant dense<{{.*}}> : tensor<6x4xf32>171// CHECK: %[[CST_1:.*]] = arith.constant dense<{{.*}}> : tensor<4x6xf32>172// CHECK: %[[CST_2:.*]] = arith.constant dense<{{.*}}> : tensor<6x6xf32>173// CHECK: %[[CST_3:.*]] = arith.constant dense<{{.*}}> : tensor<6x6xf32>174// CHECK: %[[C3:.*]] = arith.constant 3 : index175// CHECK: %[[CST_4:.*]] = arith.constant dense<{{.*}}> : tensor<3x6xf32>176// CHECK: %[[CST_5:.*]] = arith.constant dense<{{.*}}> : tensor<6x3xf32>177// CHECK: %[[C1:.*]] = arith.constant 1 : index178// CHECK: %[[C5:.*]] = arith.constant 5 : index179// CHECK: %[[C2:.*]] = arith.constant 2 : index180// CHECK: %[[C0:.*]] = arith.constant 0 : index181// CHECK: %[[CST_6:.*]] = arith.constant 0.000000e+00 : f32182// CHECK: %[[S0:.*]] = tensor.empty()183// CHECK: %[[S1:.*]] = scf.for %[[ARG4:.*]] = %[[C0]] to %[[C2]] step %[[C1]] iter_args(%[[ARG5:.*]] = %[[S0]])184// CHECK: %[[S9:.*]] = scf.for %[[ARG6:.*]] = %[[C0]] to %[[C5]] step %[[C1]] iter_args(%[[ARG7:.*]] = %[[ARG5]])185// CHECK: %[[EXTRACTED_SLICE_9:.*]] = tensor.extract_slice %[[ARG1]][%[[ARG4]], 0, 0, %[[ARG6]]] [1, 3, 3, 1] [1, 1, 1, 1]186// CHECK: %[[S11:.*]] = tensor.empty() : tensor<6x3xf32>187// CHECK: %[[S12:.*]] = linalg.fill ins(%[[CST_6]] : f32) outs(%[[S11]] : tensor<6x3xf32>) -> tensor<6x3xf32>188// CHECK: %[[S13:.*]] = linalg.matmul ins(%[[CST_5]], %[[EXTRACTED_SLICE_9]] : tensor<6x3xf32>, tensor<3x3xf32>) outs(%[[S12]] : tensor<6x3xf32>) -> tensor<6x3xf32>189// CHECK: %[[S14:.*]] = tensor.empty() : tensor<6x6xf32>190// CHECK: %[[S15:.*]] = linalg.fill ins(%[[CST_6]] : f32) outs(%[[S14]] : tensor<6x6xf32>) -> tensor<6x6xf32>191// CHECK: %[[S16:.*]] = linalg.matmul ins(%[[S13]], %[[CST_4]] : tensor<6x3xf32>, tensor<3x6xf32>) outs(%[[S15]] : tensor<6x6xf32>) -> tensor<6x6xf32>192// CHECK: %[[INSERTED_SLICE:.*]] = tensor.insert_slice %[[S16]] into %[[ARG7]][0, 0, %[[ARG6]], %[[ARG4]]] [6, 6, 1, 1] [1, 1, 1, 1]193// CHECK: scf.yield %[[INSERTED_SLICE]] : tensor<6x6x5x2xf32>194// CHECK: scf.yield %[[S9]] : tensor<6x6x5x2xf32>195// CHECK: %[[PADDED:.*]] = tensor.pad %[[ARG0]] low[0, 0, 0, 0] high[0, 3, 3, 0]196// CHECK: %[[S2:.*]] = tensor.empty() : tensor<6x6x3x3x2x5xf32>197// CHECK: %[[S4:.*]] = scf.for %[[ARG4:.*]] = %[[C0]] to %[[C3]] step %[[C1]] iter_args(%[[ARG5:.*]] = %[[S2]])198// CHECK: %[[S9:.*]] = scf.for %[[ARG6:.*]] = %[[C0]] to %[[C3]] step %[[C1]] iter_args(%[[ARG7:.*]] = %[[ARG5]])199// CHECK: %[[S10:.*]] = affine.apply #[[$MAP0]](%[[ARG4]])200// CHECK: %[[S11:.*]] = affine.apply #[[$MAP0]](%[[ARG6]])201// CHECK: %[[EXTRACTED_SLICE_9:.*]] = tensor.extract_slice %[[PADDED]][0, %[[S10]], %[[S11]], 0] [2, 6, 6, 5] [1, 1, 1, 1]202// CHECK: %[[EXTRACTED_SLICE_10:.*]] = tensor.extract_slice %[[ARG7]][0, 0, %[[ARG4]], %[[ARG6]], 0, 0] [6, 6, 1, 1, 2, 5] [1, 1, 1, 1, 1, 1]203// CHECK: %[[S12:.*]] = scf.for %[[ARG8:.*]] = %[[C0]] to %[[C2]] step %[[C1]] iter_args(%[[ARG9:.*]] = %[[EXTRACTED_SLICE_10]])204// CHECK: %[[S13:.*]] = scf.for %[[ARG10:.*]] = %[[C0]] to %[[C5]] step %[[C1]] iter_args(%[[ARG11:.*]] = %[[ARG9]])205// CHECK: %[[EXTRACTED_SLICE_11:.*]] = tensor.extract_slice %[[EXTRACTED_SLICE_9]][%[[ARG8]], 0, 0, %[[ARG10]]] [1, 6, 6, 1] [1, 1, 1, 1]206// CHECK: %[[S15:.*]] = tensor.empty() : tensor<6x6xf32>207// CHECK: %[[S16:.*]] = linalg.fill ins(%[[CST_6]] : f32) outs(%[[S15]] : tensor<6x6xf32>) -> tensor<6x6xf32>208// CHECK: %[[S17:.*]] = linalg.matmul ins(%[[CST_3]], %[[EXTRACTED_SLICE_11]] : tensor<6x6xf32>, tensor<6x6xf32>) outs(%[[S16]] : tensor<6x6xf32>) -> tensor<6x6xf32>209// CHECK: %[[S18:.*]] = tensor.empty() : tensor<6x6xf32>210// CHECK: %[[S19:.*]] = linalg.fill ins(%[[CST_6]] : f32) outs(%[[S18]] : tensor<6x6xf32>) -> tensor<6x6xf32>211// CHECK: %[[S20:.*]] = linalg.matmul ins(%[[S17]], %[[CST_2]] : tensor<6x6xf32>, tensor<6x6xf32>) outs(%[[S19]] : tensor<6x6xf32>) -> tensor<6x6xf32>212// CHECK: %[[INSERTED_SLICE_12:.*]] = tensor.insert_slice %[[S20]] into %[[ARG11]][0, 0, 0, 0, %[[ARG8]], %[[ARG10]]] [6, 6, 1, 1, 1, 1] [1, 1, 1, 1, 1, 1]213// CHECK: scf.yield %[[INSERTED_SLICE_12]] : tensor<6x6x1x1x2x5xf32>214// CHECK: scf.yield %[[S13]] : tensor<6x6x1x1x2x5xf32>215// CHECK: %[[INSERTED_SLICE:.*]] = tensor.insert_slice %[[S12]] into %[[ARG7]][0, 0, %[[ARG4]], %[[ARG6]], 0, 0] [6, 6, 1, 1, 2, 5] [1, 1, 1, 1, 1, 1]216// CHECK: scf.yield %[[INSERTED_SLICE]]217// CHECK: scf.yield %[[S9]]218// CHECK: %[[COLLAPSED:.*]] = tensor.collapse_shape %[[S1]] {{\[}}[0, 1], [2], [3]]219// CHECK: %[[COLLAPSED_7:.*]] = tensor.collapse_shape %[[S4]] {{\[}}[0, 1], [2, 3, 4], [5]]220// CHECK: %[[S7:.*]] = tensor.empty()221// CHECK: %[[S6:.*]] = linalg.batch_matmul222// CHECK: %[[EXPANDED:.*]] = tensor.expand_shape %[[S6]] {{\[}}[0, 1], [2, 3, 4], [5]] output_shape [6, 6, 3, 3, 2, 2]223// CHECK: %[[PADDED_8:.*]] = tensor.pad %[[ARG2]] low[0, 0, 0, 0] high[0, 3, 3, 0]224// CHECK: %[[S8:.*]] = scf.for %[[ARG4:.*]] = %[[C0]] to %[[C3]] step %[[C1]] iter_args(%[[ARG5:.*]] = %[[PADDED_8]])225// CHECK: %[[S9:.*]] = scf.for %[[ARG6:.*]] = %[[C0]] to %[[C3]] step %[[C1]] iter_args(%[[ARG7:.*]] = %[[ARG5]])226// CHECK: %[[EXTRACTED_SLICE_9:.*]] = tensor.extract_slice %[[EXPANDED]][0, 0, %[[ARG4]], %[[ARG6]], 0, 0] [6, 6, 1, 1, 2, 2] [1, 1, 1, 1, 1, 1]227// CHECK: %[[S10:.*]] = affine.apply #[[$MAP0]](%[[ARG4]])228// CHECK: %[[S11:.*]] = affine.apply #[[$MAP0]](%[[ARG6]])229// CHECK: %[[EXTRACTED_SLICE_10:.*]] = tensor.extract_slice %[[ARG7]][0, %[[S10]], %[[S11]], 0] [2, 4, 4, 2] [1, 1, 1, 1]230// CHECK: %[[S12:.*]] = scf.for %[[ARG8:.*]] = %[[C0]] to %[[C2]] step %[[C1]] iter_args(%[[ARG9:.*]] = %[[EXTRACTED_SLICE_10]])231// CHECK: %[[S15:.*]] = scf.for %[[ARG10:.*]] = %[[C0]] to %[[C2]] step %[[C1]] iter_args(%[[ARG11:.*]] = %[[ARG9]])232// CHECK: %[[EXTRACTED_SLICE_11:.*]] = tensor.extract_slice %[[EXTRACTED_SLICE_9]][0, 0, 0, 0, %[[ARG8]], %[[ARG10]]] [6, 6, 1, 1, 1, 1] [1, 1, 1, 1, 1, 1]233// CHECK: %[[S26:.*]] = tensor.extract_slice %[[ARG11]][%[[ARG8]], 0, 0, %[[ARG10]]] [1, 4, 4, 1] [1, 1, 1, 1]234// CHECK: %[[S17:.*]] = tensor.empty() : tensor<4x6xf32>235// CHECK: %[[S18:.*]] = linalg.fill ins(%[[CST_6]] : f32) outs(%[[S17]] : tensor<4x6xf32>) -> tensor<4x6xf32>236// CHECK: %[[S19:.*]] = linalg.matmul ins(%[[CST_1]], %[[EXTRACTED_SLICE_11]] : tensor<4x6xf32>, tensor<6x6xf32>) outs(%[[S18]] : tensor<4x6xf32>) -> tensor<4x6xf32>237// CHECK: %[[S20:.*]] = tensor.empty() : tensor<4x4xf32>238// CHECK: %[[S21:.*]] = linalg.fill ins(%[[CST_6]] : f32) outs(%[[S20]] : tensor<4x4xf32>) -> tensor<4x4xf32>239// CHECK: %[[S22:.*]] = linalg.matmul ins(%[[S19]], %[[CST_0]] : tensor<4x6xf32>, tensor<6x4xf32>) outs(%[[S21]] : tensor<4x4xf32>) -> tensor<4x4xf32>240// CHECK: %[[S24:.*]] = linalg.generic {indexing_maps = [#[[$MAP1]], #[[$MAP2]], #[[$MAP2]]], iterator_types = ["parallel", "parallel"]} ins(%[[CST]], %[[S22]] : f32, tensor<4x4xf32>) outs(%[[S26]] : tensor<4x4xf32>) {241// CHECK: ^bb0(%[[IN1:.*]]: f32, %[[IN2:.*]]: f32, %[[OUT:.*]]: f32):242// CHECK: %[[VAL_104:.*]] = arith.mulf %[[IN1]], %[[IN2]] : f32243// CHECK: %[[VAL_105:.*]] = arith.addf %[[VAL_104]], %[[OUT]] : f32244/// CHECK: linalg.yield %[[VAL_105]] : f32245// CHECK: } -> tensor<4x4xf32>246// CHECK: %[[INSERTED_SLICE_12:.*]] = tensor.insert_slice %[[S24]] into %[[ARG11]][%[[ARG8]], 0, 0, %[[ARG10]]] [1, 4, 4, 1] [1, 1, 1, 1]247// CHECK: scf.yield %[[INSERTED_SLICE_12]]248// CHECK: scf.yield %[[S15]] : tensor<2x4x4x2xf32>249// CHECK: %[[S13:.*]] = affine.apply #[[$MAP0]](%[[ARG4]])250// CHECK: %[[S14:.*]] = affine.apply #[[$MAP0]](%[[ARG6]])251// CHECK: %[[INSERTED_SLICE:.*]] = tensor.insert_slice %[[S12]] into %[[ARG7]][0, %[[S13]], %[[S14]], 0] [2, 4, 4, 2] [1, 1, 1, 1]252// CHECK: scf.yield %[[INSERTED_SLICE]]253// CHECK: scf.yield %[[S9]]254// CHECK: %[[EXTRACTED_SLICE:.*]] = tensor.extract_slice %[[S8]][0, 0, 0, 0] [2, 9, 9, 2] [1, 1, 1, 1]255// CHECK: return %[[EXTRACTED_SLICE]]256 257// -----258 259func.func @conv2d_mx1_rx1(%arg0: tensor<2x6x1x5xf32>, %arg1: tensor<2x3x1x5xf32>, %arg2: tensor<2x4x1x2xf32>) -> tensor<2x4x1x2xf32> {260 %cst = arith.constant 0.000000e+00 : f32261 %0 = tensor.empty() : tensor<6x1x5x2xf32>262 %1 = linalg.winograd_filter_transform fmr(F_4_3) ins(%arg1 : tensor<2x3x1x5xf32>) outs(%0 : tensor<6x1x5x2xf32>) -> tensor<6x1x5x2xf32>263 %2 = tensor.empty() : tensor<6x1x1x1x2x5xf32>264 %3 = linalg.winograd_input_transform fmr(F_4_3) ins(%arg0 : tensor<2x6x1x5xf32>) outs(%2 : tensor<6x1x1x1x2x5xf32>) -> tensor<6x1x1x1x2x5xf32>265 %collapsed = tensor.collapse_shape %1 [[0, 1], [2], [3]] : tensor<6x1x5x2xf32> into tensor<6x5x2xf32>266 %collapsed_0 = tensor.collapse_shape %3 [[0, 1], [2, 3, 4], [5]] : tensor<6x1x1x1x2x5xf32> into tensor<6x2x5xf32>267 %4 = tensor.empty() : tensor<6x2x2xf32>268 %5 = linalg.fill ins(%cst : f32) outs(%4 : tensor<6x2x2xf32>) -> tensor<6x2x2xf32>269 %6 = linalg.batch_matmul ins(%collapsed_0, %collapsed : tensor<6x2x5xf32>, tensor<6x5x2xf32>) outs(%5 : tensor<6x2x2xf32>) -> tensor<6x2x2xf32>270 %expanded = tensor.expand_shape %6 [[0, 1], [2, 3, 4], [5]] output_shape [6, 1, 1, 1, 2, 2] : tensor<6x2x2xf32> into tensor<6x1x1x1x2x2xf32>271 %7 = linalg.winograd_output_transform fmr(F_4_3) ins(%expanded : tensor<6x1x1x1x2x2xf32>) outs(%arg2 : tensor<2x4x1x2xf32>) -> tensor<2x4x1x2xf32>272 return %7 : tensor<2x4x1x2xf32>273}274 275module attributes {transform.with_named_sequence} {276 transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {277 %0 = transform.structured.match ops{["linalg.winograd_filter_transform"]} in %arg1 : (!transform.any_op) -> !transform.any_op278 %2 = transform.structured.match ops{["linalg.winograd_input_transform"]} in %arg1 : (!transform.any_op) -> !transform.any_op279 %3, %loop3:2 = transform.structured.tile_using_for %2 tile_sizes [0, 0, 1, 1, 0, 0] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)280 %4 = transform.structured.match ops{["linalg.winograd_output_transform"]} in %arg1 : (!transform.any_op) -> !transform.any_op281 %5, %loop5:2 = transform.structured.tile_using_for %4 tile_sizes [0, 0, 1, 1, 0, 0] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)282 %7 = transform.structured.decompose_winograd_op %0 : (!transform.any_op) -> (!transform.any_op)283 %8 = transform.structured.match ops{["linalg.winograd_input_transform"]} in %3 : (!transform.any_op) -> !transform.any_op284 %9 = transform.structured.decompose_winograd_op %8 : (!transform.any_op) -> (!transform.any_op)285 %10 = transform.structured.match ops{["linalg.winograd_output_transform"]} in %5 : (!transform.any_op) -> !transform.any_op286 %11 = transform.structured.decompose_winograd_op %10 : (!transform.any_op) -> (!transform.any_op)287 transform.yield288 }289}290 291// CHECK: #[[$MAP0:.+]] = affine_map<(d0, d1) -> ()>292// CHECK: #[[$MAP1:.+]] = affine_map<(d0, d1) -> (d0, d1)>293// CHECK-LABEL: func.func @conv2d_mx1_rx1294// CHECK-SAME: (%[[ARG0:.*]]: tensor<2x6x1x5xf32>, %[[ARG1:.*]]: tensor<2x3x1x5xf32>, %[[ARG2:.*]]: tensor<2x4x1x2xf32>) -> tensor<2x4x1x2xf32> {295// CHECK: %[[CST:.*]] = arith.constant 3.200000e+01 : f32296// CHECK: %[[CST_0:.*]] = arith.constant dense<{{.*}}> : tensor<4x6xf32>297// CHECK: %[[CST_1:.*]] = arith.constant dense<{{.*}}> : tensor<6x6xf32>298// CHECK: %[[CST_2:.*]] = arith.constant dense<{{.*}}> : tensor<6x3xf32>299// CHECK: %[[C1:.*]] = arith.constant 1 : index300// CHECK: %[[C5:.*]] = arith.constant 5 : index301// CHECK: %[[C2:.*]] = arith.constant 2 : index302// CHECK: %[[C0:.*]] = arith.constant 0 : index303// CHECK: %[[CST_3:.*]] = arith.constant 0.000000e+00 : f32304// CHECK: %[[S0:.*]] = tensor.empty() : tensor<6x1x5x2xf32>305// CHECK: %[[S1:.*]] = scf.for %[[ARG3:.*]] = %[[C0]] to %[[C2]] step %[[C1]] iter_args(%[[ARG4:.*]] = %[[S0]])306// CHECK: %[[S7:.*]] = scf.for %[[ARG5:.*]] = %[[C0]] to %[[C5]] step %[[C1]] iter_args(%[[ARG6:.*]] = %[[ARG4]])307// CHECK: %[[EXTRACTED_SLICE:.*]] = tensor.extract_slice %[[ARG1]][%[[ARG3]], 0, 0, %[[ARG5]]] [1, 3, 1, 1] [1, 1, 1, 1]308// CHECK: %[[S8:.*]] = tensor.empty() : tensor<6x1xf32>309// CHECK: %[[S9:.*]] = linalg.fill ins(%[[CST_3]] : f32) outs(%[[S8]] : tensor<6x1xf32>) -> tensor<6x1xf32>310// CHECK: %[[S10:.*]] = linalg.matmul ins(%[[CST_2]], %[[EXTRACTED_SLICE]] : tensor<6x3xf32>, tensor<3x1xf32>) outs(%[[S9]] : tensor<6x1xf32>) -> tensor<6x1xf32>311// CHECK: %[[INSERTED_SLICE:.*]] = tensor.insert_slice %[[S10]] into %[[ARG6]][0, 0, %[[ARG5]], %[[ARG3]]] [6, 1, 1, 1] [1, 1, 1, 1]312// CHECK: scf.yield %[[INSERTED_SLICE]]313// CHECK: scf.yield %[[S7]]314// CHECK: %[[S2:.*]] = tensor.empty() : tensor<6x1x1x1x2x5xf32>315// CHECK: %[[S3:.*]] = scf.for %[[ARG3:.*]] = %[[C0]] to %[[C2]] step %[[C1]] iter_args(%[[ARG4:.*]] = %[[S2]])316// CHECK: %[[S7:.*]] = scf.for %[[ARG5:.*]] = %[[C0]] to %[[C5]] step %[[C1]] iter_args(%[[ARG6:.*]] = %[[ARG4]])317// CHECK: %[[EXTRACTED_SLICE:.*]] = tensor.extract_slice %[[ARG0]][%[[ARG3]], 0, 0, %[[ARG5]]] [1, 6, 1, 1] [1, 1, 1, 1]318// CHECK: %[[S8:.*]] = tensor.empty() : tensor<6x1xf32>319// CHECK: %[[S9:.*]] = linalg.fill ins(%[[CST_3]] : f32) outs(%[[S8]] : tensor<6x1xf32>) -> tensor<6x1xf32>320// CHECK: %[[S10:.*]] = linalg.matmul ins(%[[CST_1]], %[[EXTRACTED_SLICE]] : tensor<6x6xf32>, tensor<6x1xf32>) outs(%[[S9]] : tensor<6x1xf32>) -> tensor<6x1xf32>321// CHECK: %[[INSERTED_SLICE:.*]] = tensor.insert_slice %[[S10]] into %[[ARG6]][0, 0, 0, 0, %[[ARG3]], %[[ARG5]]] [6, 1, 1, 1, 1, 1] [1, 1, 1, 1, 1, 1]322// CHECK: scf.yield %[[INSERTED_SLICE]]323// CHECK: scf.yield %[[S7]]324// CHECK: %[[COLLAPSED:.*]] = tensor.collapse_shape %[[S1]] {{\[}}[0, 1], [2], [3]]325// CHECK: %[[COLLAPSED_3:.*]] = tensor.collapse_shape %[[S3]] {{\[}}[0, 1], [2, 3, 4], [5]]326// CHECK: %[[S4:.*]] = tensor.empty() : tensor<6x2x2xf32>327// CHECK: %[[S5:.*]] = linalg.fill ins(%[[CST_3]] : f32) outs(%[[S4]] : tensor<6x2x2xf32>) -> tensor<6x2x2xf32>328// CHECK: %[[S6:.*]] = linalg.batch_matmul ins(%[[COLLAPSED_3]], %[[COLLAPSED]] : tensor<6x2x5xf32>, tensor<6x5x2xf32>) outs(%[[S5]] : tensor<6x2x2xf32>) -> tensor<6x2x2xf32>329// CHECK: %[[EXPANDED:.*]] = tensor.expand_shape %[[S6]] {{\[}}[0, 1], [2, 3, 4], [5]] output_shape [6, 1, 1, 1, 2, 2]330// CHECK: %[[S6:.*]] = scf.for %[[ARG3:.*]] = %[[C0]] to %[[C2]] step %[[C1]] iter_args(%[[ARG4:.*]] = %[[ARG2]])331// CHECK: %[[S7:.*]] = scf.for %[[ARG5:.*]] = %[[C0]] to %[[C2]] step %[[C1]] iter_args(%[[ARG6:.*]] = %[[ARG4]])332// CHECK: %[[EXTRACTED_SLICE:.*]] = tensor.extract_slice %[[EXPANDED]][0, 0, 0, 0, %[[ARG3]], %[[ARG5]]] [6, 1, 1, 1, 1, 1] [1, 1, 1, 1, 1, 1]333// CHECK: %[[S15:.*]] = tensor.extract_slice %[[ARG6]][%[[ARG3]], 0, 0, %[[ARG5]]] [1, 4, 1, 1] [1, 1, 1, 1]334// CHECK: %[[S9:.*]] = tensor.empty() : tensor<4x1xf32>335// CHECK: %[[S10:.*]] = linalg.fill ins(%[[CST_3]] : f32) outs(%[[S9]] : tensor<4x1xf32>) -> tensor<4x1xf32>336// CHECK: %[[S11:.*]] = linalg.matmul ins(%[[CST_0]], %[[EXTRACTED_SLICE]] : tensor<4x6xf32>, tensor<6x1xf32>) outs(%[[S10]] : tensor<4x1xf32>) -> tensor<4x1xf32>337// CHECK: %[[S13:.*]] = linalg.generic {indexing_maps = [#map, #map1, #map1], iterator_types = ["parallel", "parallel"]} ins(%[[CST]], %[[S11]] : f32, tensor<4x1xf32>) outs(%[[S15]] : tensor<4x1xf32>) {338// CHECK: ^bb0(%[[IN1:.*]]: f32, %[[IN2:.*]]: f32, %[[OUT:.*]]: f32):339// CHECK: %[[VAL_57:.*]] = arith.mulf %[[IN1]], %[[IN2]] : f32340// CHECK: %[[VAL_58:.*]] = arith.addf %[[VAL_57]], %[[OUT]] : f32341/// CHECK: linalg.yield %[[VAL_58]] : f32342// CHECK: } -> tensor<4x1xf32>343// CHECK: %[[INSERTED_SLICE:.*]] = tensor.insert_slice %[[S13]] into %[[ARG6]][%[[ARG3]], 0, 0, %[[ARG5]]] [1, 4, 1, 1] [1, 1, 1, 1]344// CHECK: scf.yield %[[INSERTED_SLICE]]345// CHECK: scf.yield %[[S7]]346// CHECK: return %[[S6]]347 348// -----349 350func.func @conv2d_mx1_rx1_2(%arg0: tensor<2x6x2x5xf32>, %arg1: tensor<2x3x1x5xf32>, %arg2: tensor<2x4x2x2xf32>) -> tensor<2x4x2x2xf32> {351 %cst = arith.constant 0.000000e+00 : f32352 %0 = tensor.empty() : tensor<6x1x5x2xf32>353 %1 = linalg.winograd_filter_transform fmr(F_4_3) ins(%arg1 : tensor<2x3x1x5xf32>) outs(%0 : tensor<6x1x5x2xf32>) -> tensor<6x1x5x2xf32>354 %2 = tensor.empty() : tensor<6x1x1x2x2x5xf32>355 %3 = linalg.winograd_input_transform fmr(F_4_3) ins(%arg0 : tensor<2x6x2x5xf32>) outs(%2 : tensor<6x1x1x2x2x5xf32>) -> tensor<6x1x1x2x2x5xf32>356 %collapsed = tensor.collapse_shape %1 [[0, 1], [2], [3]] : tensor<6x1x5x2xf32> into tensor<6x5x2xf32>357 %collapsed_0 = tensor.collapse_shape %3 [[0, 1], [2, 3, 4], [5]] : tensor<6x1x1x2x2x5xf32> into tensor<6x4x5xf32>358 %4 = tensor.empty() : tensor<6x4x2xf32>359 %5 = linalg.fill ins(%cst : f32) outs(%4 : tensor<6x4x2xf32>) -> tensor<6x4x2xf32>360 %6 = linalg.batch_matmul ins(%collapsed_0, %collapsed : tensor<6x4x5xf32>, tensor<6x5x2xf32>) outs(%5 : tensor<6x4x2xf32>) -> tensor<6x4x2xf32>361 %expanded = tensor.expand_shape %6 [[0, 1], [2, 3, 4], [5]] output_shape [6, 1, 1, 2, 2, 2] : tensor<6x4x2xf32> into tensor<6x1x1x2x2x2xf32>362 %7 = linalg.winograd_output_transform fmr(F_4_3) ins(%expanded : tensor<6x1x1x2x2x2xf32>) outs(%arg2 : tensor<2x4x2x2xf32>) -> tensor<2x4x2x2xf32>363 return %7 : tensor<2x4x2x2xf32>364}365 366module attributes {transform.with_named_sequence} {367 transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {368 %0 = transform.structured.match ops{["linalg.winograd_filter_transform"]} in %arg1 : (!transform.any_op) -> !transform.any_op369 %2 = transform.structured.match ops{["linalg.winograd_input_transform"]} in %arg1 : (!transform.any_op) -> !transform.any_op370 %3, %loop3:2 = transform.structured.tile_using_for %2 tile_sizes [0, 0, 1, 1, 0, 0] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)371 %4 = transform.structured.match ops{["linalg.winograd_output_transform"]} in %arg1 : (!transform.any_op) -> !transform.any_op372 %5, %loop5:2 = transform.structured.tile_using_for %4 tile_sizes [0, 0, 1, 1, 0, 0] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)373 %7 = transform.structured.decompose_winograd_op %0 : (!transform.any_op) -> (!transform.any_op)374 %8 = transform.structured.match ops{["linalg.winograd_input_transform"]} in %3 : (!transform.any_op) -> !transform.any_op375 %9 = transform.structured.decompose_winograd_op %8 : (!transform.any_op) -> (!transform.any_op)376 %10 = transform.structured.match ops{["linalg.winograd_output_transform"]} in %5 : (!transform.any_op) -> !transform.any_op377 %11 = transform.structured.decompose_winograd_op %10 : (!transform.any_op) -> (!transform.any_op)378 transform.yield379 }380}381 382// CHECK: #[[$MAP:.+]] = affine_map<(d0, d1) -> ()>383// CHECK: #[[$MAP1:.+]] = affine_map<(d0, d1) -> (d0, d1)>384// CHECK-LABEL: func.func @conv2d_mx1_rx1_2385// CHECK-SAME: (%[[ARG0:.*]]: tensor<2x6x2x5xf32>, %[[ARG1:.*]]: tensor<2x3x1x5xf32>, %[[ARG2:.*]]: tensor<2x4x2x2xf32>) -> tensor<2x4x2x2xf32> {386// CHECK: %[[CST:.*]] = arith.constant 3.200000e+01 : f32387// CHECK: %[[CST_0:.*]] = arith.constant dense<{{.*}}> : tensor<4x6xf32>388// CHECK: %[[CST_1:.*]] = arith.constant dense<{{.*}}> : tensor<6x6xf32>389// CHECK: %[[CST_2:.*]] = arith.constant dense<{{.*}}> : tensor<6x3xf32>390// CHECK: %[[C1:.*]] = arith.constant 1 : index391// CHECK: %[[C5:.*]] = arith.constant 5 : index392// CHECK: %[[C2:.*]] = arith.constant 2 : index393// CHECK: %[[C0:.*]] = arith.constant 0 : index394// CHECK: %[[CST_3:.*]] = arith.constant 0.000000e+00 : f32395// CHECK: %[[S0:.*]] = tensor.empty() : tensor<6x1x5x2xf32>396// CHECK: %[[S1:.*]] = scf.for %[[ARG3:.*]] = %[[C0]] to %[[C2]] step %[[C1]] iter_args(%[[ARG4:.*]] = %[[S0]])397// CHECK: %[[S7:.*]] = scf.for %[[ARG5:.*]] = %[[C0]] to %[[C5]] step %[[C1]] iter_args(%[[ARG6:.*]] = %[[ARG4]])398// CHECK: %[[EXTRACTED_SLICE:.*]] = tensor.extract_slice %[[ARG1]][%[[ARG3]], 0, 0, %[[ARG5]]] [1, 3, 1, 1] [1, 1, 1, 1]399// CHECK: %[[S8:.*]] = tensor.empty() : tensor<6x1xf32>400// CHECK: %[[S9:.*]] = linalg.fill ins(%[[CST_3]] : f32) outs(%[[S8]] : tensor<6x1xf32>) -> tensor<6x1xf32>401// CHECK: %[[S10:.*]] = linalg.matmul ins(%[[CST_2]], %[[EXTRACTED_SLICE]] : tensor<6x3xf32>, tensor<3x1xf32>) outs(%[[S9]] : tensor<6x1xf32>) -> tensor<6x1xf32>402// CHECK: %[[INSERTED_SLICE:.*]] = tensor.insert_slice %[[S10]] into %[[ARG6]][0, 0, %[[ARG5]], %[[ARG3]]] [6, 1, 1, 1] [1, 1, 1, 1]403// CHECK: scf.yield %[[INSERTED_SLICE]]404// CHECK: scf.yield %[[S7]]405// CHECK: %[[S2:.*]] = tensor.empty() : tensor<6x1x1x2x2x5xf32>406// CHECK: %[[S3:.*]] = scf.for %[[ARG3:.*]] = %[[C0]] to %[[C2]] step %[[C1]] iter_args(%[[ARG4:.*]] = %[[S2]])407// CHECK: %[[EXTRACTED_SLICE:.*]] = tensor.extract_slice %[[ARG0]][0, 0, %[[ARG3]], 0] [2, 6, 1, 5] [1, 1, 1, 1]408// CHECK: %[[EXTRACTED_SLICE_5:.*]] = tensor.extract_slice %[[ARG4]][0, 0, 0, %[[ARG3]], 0, 0] [6, 1, 1, 1, 2, 5] [1, 1, 1, 1, 1, 1]409// CHECK: %[[S9:.*]] = scf.for %[[ARG5:.*]] = %[[C0]] to %[[C2]] step %[[C1]] iter_args(%[[ARG6:.*]] = %[[EXTRACTED_SLICE_5]])410// CHECK: %[[S10:.*]] = scf.for %[[ARG7:.*]] = %[[C0]] to %[[C5]] step %[[C1]] iter_args(%[[ARG8:.*]] = %[[ARG6]])411// CHECK: %[[EXTRACTED_SLICE_6:.*]] = tensor.extract_slice %[[EXTRACTED_SLICE]][%[[ARG5]], 0, 0, %[[ARG7]]] [1, 6, 1, 1] [1, 1, 1, 1]412// CHECK: %[[S11:.*]] = tensor.empty() : tensor<6x1xf32>413// CHECK: %[[S12:.*]] = linalg.fill ins(%[[CST_3]] : f32) outs(%[[S11]] : tensor<6x1xf32>) -> tensor<6x1xf32>414// CHECK: %[[S13:.*]] = linalg.matmul ins(%[[CST_1]], %[[EXTRACTED_SLICE_6]] : tensor<6x6xf32>, tensor<6x1xf32>) outs(%[[S12]] : tensor<6x1xf32>) -> tensor<6x1xf32>415// CHECK: %[[INSERTED_SLICE_7:.*]] = tensor.insert_slice %[[S13]] into %[[ARG8]][0, 0, 0, 0, %[[ARG5]], %[[ARG7]]] [6, 1, 1, 1, 1, 1] [1, 1, 1, 1, 1, 1]416// CHECK: scf.yield %[[INSERTED_SLICE_7]]417// CHECK: scf.yield %[[S10]]418// CHECK: %[[INSERTED_SLICE:.*]] = tensor.insert_slice %[[S9]] into %[[ARG4]][0, 0, 0, %[[ARG3]], 0, 0] [6, 1, 1, 1, 2, 5] [1, 1, 1, 1, 1, 1]419// CHECK: scf.yield %[[INSERTED_SLICE]]420// CHECK: %[[COLLAPSED:.*]] = tensor.collapse_shape %[[S1]] {{\[}}[0, 1], [2], [3]]421// CHECK: %[[COLLAPSED_4:.*]] = tensor.collapse_shape %[[S3]] {{\[}}[0, 1], [2, 3, 4], [5]]422// CHECK: %[[S4:.*]] = tensor.empty() : tensor<6x4x2xf32>423// CHECK: %[[S5:.*]] = linalg.fill ins(%[[CST_3]] : f32) outs(%[[S4]] : tensor<6x4x2xf32>) -> tensor<6x4x2xf32>424// CHECK: %[[S6:.*]] = linalg.batch_matmul ins(%[[COLLAPSED_4]], %[[COLLAPSED]] : tensor<6x4x5xf32>, tensor<6x5x2xf32>) outs(%[[S5]] : tensor<6x4x2xf32>) -> tensor<6x4x2xf32>425// CHECK: %[[EXPANDED:.*]] = tensor.expand_shape %[[S6]] {{\[}}[0, 1], [2, 3, 4], [5]] output_shape [6, 1, 1, 2, 2, 2]426// CHECK: %[[S7:.*]] = scf.for %[[ARG3:.*]] = %[[C0]] to %[[C2]] step %[[C1]] iter_args(%[[ARG4:.*]] = %[[ARG2]])427// CHECK: %[[EXTRACTED_SLICE:.*]] = tensor.extract_slice %[[EXPANDED]][0, 0, 0, %[[ARG3]], 0, 0] [6, 1, 1, 1, 2, 2] [1, 1, 1, 1, 1, 1]428// CHECK: %[[EXTRACTED_SLICE_5:.*]] = tensor.extract_slice %[[ARG4]][0, 0, %[[ARG3]], 0] [2, 4, 1, 2] [1, 1, 1, 1]429// CHECK: %[[S8:.*]] = scf.for %[[ARG5:.*]] = %[[C0]] to %[[C2]] step %[[C1]] iter_args(%[[ARG6:.*]] = %[[EXTRACTED_SLICE_5]])430// CHECK: %[[S9:.*]] = scf.for %[[ARG7:.*]] = %[[C0]] to %[[C2]] step %[[C1]] iter_args(%[[ARG8:.*]] = %[[ARG6]])431// CHECK: %[[EXTRACTED_SLICE_6:.*]] = tensor.extract_slice %[[EXTRACTED_SLICE]][0, 0, 0, 0, %[[ARG5]], %[[ARG7]]] [6, 1, 1, 1, 1, 1] [1, 1, 1, 1, 1, 1]432// CHECK: %[[EXTRACTED_SLICE_7:.*]] = tensor.extract_slice %[[ARG8]][%[[ARG5]], 0, 0, %[[ARG7]]] [1, 4, 1, 1] [1, 1, 1, 1]433// CHECK: %[[S10:.*]] = tensor.empty() : tensor<4x1xf32>434// CHECK: %[[S11:.*]] = linalg.fill ins(%[[CST_3]] : f32) outs(%[[S10]] : tensor<4x1xf32>) -> tensor<4x1xf32>435// CHECK: %[[S12:.*]] = linalg.matmul ins(%[[CST_0]], %[[EXTRACTED_SLICE_6]] : tensor<4x6xf32>, tensor<6x1xf32>) outs(%[[S11]] : tensor<4x1xf32>) -> tensor<4x1xf32>436// CHECK: %[[S13:.*]] = linalg.generic {indexing_maps = [#[[$MAP]], #[[$MAP1]], #[[$MAP1]]], iterator_types = ["parallel", "parallel"]} ins(%[[CST]], %[[S12]] : f32, tensor<4x1xf32>) outs(%[[EXTRACTED_SLICE_7]] : tensor<4x1xf32>) {437// CHECK: ^bb0(%[[IN1:.*]]: f32, %[[IN2:.*]]: f32, %[[OUT:.*]]: f32):438// CHECK: %[[VAL_57:.*]] = arith.mulf %[[IN1]], %[[IN2]] : f32439// CHECK: %[[VAL_58:.*]] = arith.addf %[[VAL_57]], %[[OUT]] : f32440// CHECK: linalg.yield %[[VAL_58]] : f32441// CHECK: } -> tensor<4x1xf32>442// CHECK: %[[INSERTED_SLICE_8:.*]] = tensor.insert_slice %[[S13]] into %[[ARG8]][%[[ARG5]], 0, 0, %[[ARG7]]] [1, 4, 1, 1] [1, 1, 1, 1]443// CHECK: scf.yield %[[INSERTED_SLICE_8]]444// CHECK: scf.yield %[[S9]]445// CHECK: %[[INSERTED_SLICE:.*]] = tensor.insert_slice %[[S8]] into %[[ARG4]][0, 0, %[[ARG3]], 0] [2, 4, 1, 2] [1, 1, 1, 1]446// CHECK: scf.yield %[[INSERTED_SLICE]]447// CHECK: return %[[S7]]448