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1// RUN: mlir-opt --split-input-file --tosa-optional-decompositions %s | FileCheck %s2 3// CHECK-LABEL: @transpose_conv2d4func.func @transpose_conv2d(%arg0: tensor<2x16x14x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>) -> tensor<2x18x19x5xf32> {5 // CHECK-DAG: %[[ZP:.+]] = "tosa.const"() <{values = dense<0.000000e+00> : tensor<1xf32>}> : () -> tensor<1xf32>6 // CHECK: %[[REV1:.+]] = tosa.reverse %arg1 {axis = 1 : i32}7 // CHECK: %[[REV2:.+]] = tosa.reverse %[[REV1]] {axis = 2 : i32}8 // CHECK: tosa.conv2d %arg0, %[[REV2]], %arg2, %[[ZP]], %[[ZP]]9 // CHECK-SAME: dilation = array<i64: 1, 1>, pad = array<i64: 2, 2, 5, 5>, stride = array<i64: 1, 1>10 %zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>11 %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %zp, %zp {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<2x16x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x18x19x5xf32>12 return %0 : tensor<2x18x19x5xf32>13}14 15// -----16 17// CHECK-LABEL: @transpose_conv2d_quantized18 19func.func @transpose_conv2d_quantized(%arg0: tensor<2x16x14x3xi8>, %arg1: tensor<5x3x6x3xi8>, %arg2: tensor<5xi32>) -> (tensor<2x18x19x5xi32>) {20 // CHECK-DAG: %[[INPUT_ZP:.+]] = "tosa.const"() <{values = dense<-6> : tensor<1xi8>}21 // CHECK-DAG: %[[WEIGHT_ZP:.+]] = "tosa.const"() <{values = dense<11> : tensor<1xi8>}22 // CHECK: %[[REV1:.+]] = tosa.reverse %arg1 {axis = 1 : i32}23 // CHECK: %[[REV2:.+]] = tosa.reverse %[[REV1]] {axis = 2 : i32}24 // CHECK: tosa.conv2d %arg0, %[[REV2]], %arg2, %[[INPUT_ZP]], %[[WEIGHT_ZP]] {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 2, 2, 5, 5>, stride = array<i64: 1, 1>}25 %input_zp = "tosa.const"() {values = dense<-6> : tensor<1xi8>} : () -> tensor<1xi8>26 %weight_zp = "tosa.const"() {values = dense<11> : tensor<1xi8>} : () -> tensor<1xi8>27 %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = i32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<2x16x14x3xi8>, tensor<5x3x6x3xi8>, tensor<5xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<2x18x19x5xi32>28 return %0 : tensor<2x18x19x5xi32>29}30 31// -----32 33// CHECK-LABEL: @transpose_conv2d_quantized_padded34func.func @transpose_conv2d_quantized_padded(%arg0: tensor<2x16x14x3xi8>, %arg1: tensor<5x3x6x3xi8>, %arg2: tensor<5xi32>) -> (tensor<2x21x26x5xi32>) {35 // CHECK-DAG: %[[INPUT_ZP:.+]] = "tosa.const"() <{values = dense<-22> : tensor<1xi8>}> : () -> tensor<1xi8>36 // CHECK-DAG: %[[WEIGHT_ZP:.+]] = "tosa.const"() <{values = dense<42> : tensor<1xi8>}> : () -> tensor<1xi8>37 // CHECK-DAG: %[[REV0:.+]] = tosa.reverse %arg1 {axis = 1 : i32}38 // CHECK-DAG: %[[REV1:.+]] = tosa.reverse %[[REV0]] {axis = 2 : i32}39 // CHECK: tosa.conv2d %arg0, %[[REV1]], %arg2, %[[INPUT_ZP]], %[[WEIGHT_ZP]] {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 3, 4, 8, 9>, stride = array<i64: 1, 1>}40 %input_zp = "tosa.const"() <{values = dense<-22> : tensor<1xi8>}> : () -> tensor<1xi8>41 %weight_zp = "tosa.const"() <{values = dense<42> : tensor<1xi8>}> : () -> tensor<1xi8>42 %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {43 acc_type = i32,44 out_pad = array<i64: 1, 2, 3, 4>,45 stride = array<i64: 1, 1>} : (tensor<2x16x14x3xi8>, tensor<5x3x6x3xi8>, tensor<5xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<2x21x26x5xi32>46 return %0 : tensor<2x21x26x5xi32>47}48 49// -----50 51// CHECK-LABEL: @transpose_conv2d_strided52 53func.func @transpose_conv2d_strided(%arg0: tensor<2x17x15x3xf32>, %arg1: tensor<5x3x5x3xf32>, %arg2: tensor<5xf32>) -> tensor<2x?x?x5xf32> {54 // Manipulate the weight matrix to handle striding.55 // CHECK-DAG: %[[PADV:.+]] = tosa.const_shape {values = dense<[0, 0, 0, 1, 0, 1, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>56 // CHECK-DAG: %[[PADW:.+]] = tosa.pad %arg1, %[[PADV]]57 // CHECK-DAG: %[[CONST1:.+]] = tosa.const_shape {values = dense<[5, 2, 2, 2, 3, 3]> : tensor<6xindex>}58 // CHECK-DAG: %[[RESW1:.+]] = tosa.reshape %[[PADW]], %[[CONST1]]59 // CHECK-DAG: %[[TRANS:.+]] = tosa.transpose %[[RESW1]] {perms = array<i32: 2, 4, 0, 1, 3, 5>}60 // CHECK-DAG: %[[CONST3:.+]] = tosa.const_shape {values = dense<[30, 2, 2, 3]> : tensor<4xindex>}61 // CHECK-DAG: %[[RESW2:.+]] = tosa.reshape %[[TRANS]], %[[CONST3]]62 // CHECK-DAG: %[[REV1:.+]] = tosa.reverse %[[RESW2]] {axis = 1 : i32}63 // CHECK-DAG: %[[NEWWEIGHT:.+]] = tosa.reverse %[[REV1]] {axis = 2 : i32}64 // CHECK-DAG: %[[SIZE:.*]] = tosa.const_shape {values = dense<[2, 35, 47, 5]> : tensor<4xindex>} : () -> !tosa.shape<4>65 // CHECK-DAG: %[[START:.*]] = tosa.const_shape {values = dense<0> : tensor<4xindex>} : () -> !tosa.shape<4>66 67 // Pad out the input matrix to handle the transpose conv.68 // CHECK-DAG: %[[PAD:.+]] = tosa.const_shape {values = dense<[0, 0, 1, 1, 1, 1, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>69 // CHECK-DAG: %[[NEWINPUT:.+]] = tosa.pad %arg0, %[[PAD]]70 71 // Manipulate the final shape.72 // CHECK-DAG: %[[ZP:.+]] = "tosa.const"() <{values = dense<0.000000e+00> : tensor<1xf32>}> : () -> tensor<1xf32>73 // CHECK-DAG: %[[BIAS:.+]] = "tosa.const"() <{values = dense<0.000000e+00> : tensor<30xf32>}74 // CHECK-DAG: %[[CONV:.+]] = tosa.conv2d %[[NEWINPUT]], %[[NEWWEIGHT]], %[[BIAS]], %[[ZP]], %[[ZP]] {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}75 // CHECK-DAG: %[[CONST6:.+]] = tosa.const_shape {values = dense<[2, 18, 16, 2, 3, 5]> : tensor<6xindex>}76 // CHECK-DAG: %[[RESHAPE_OUT_1:.+]] = tosa.reshape %[[CONV]], %[[CONST6]]77 // CHECK-DAG: %[[TRANS_OUT:.+]] = tosa.transpose %[[RESHAPE_OUT_1]] {perms = array<i32: 0, 1, 3, 2, 4, 5>}78 // CHECK-DAG: %[[CONST8:.+]] = tosa.const_shape {values = dense<[2, 36, 48, 5]> : tensor<4xindex>79 // CHECK-DAG: %[[RESHAPE_OUT_2:.+]] = tosa.reshape %[[TRANS_OUT]], %[[CONST8]]80 // CHECK-DAG: %[[SLICE:.+]] = tosa.slice %[[RESHAPE_OUT_2]], %[[START]], %[[SIZE]]81 // CHECK-DAG: %[[CONST9:.+]] = tosa.const_shape {values = dense<[1, 1, 1, 5]> : tensor<4xindex>}82 // CHECK-DAG: %[[RESHAPE_ARG2:.+]] = tosa.reshape %arg2, %[[CONST9]]83 // CHECK: %[[ADD:.+]] = tosa.add %[[SLICE]], %[[RESHAPE_ARG2]]84 %zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>85 %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %zp, %zp {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 3>} : (tensor<2x17x15x3xf32>, tensor<5x3x5x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<2x35x47x5xf32>86 %1 = tensor.cast %0 : tensor<2x35x47x5xf32> to tensor<2x?x?x5xf32>87 return %1 : tensor<2x?x?x5xf32>88}89 90// -----91 92// CHECK-LABEL: @transpose_conv2d_strided_quantized93 94func.func @transpose_conv2d_strided_quantized(%arg0: tensor<2x17x15x3xi8>, %arg1: tensor<5x3x5x3xi8>, %arg2: tensor<5xi32>) -> (tensor<2x35x47x5xi32>) {95 // Manipulate the weight matrix to handle striding.96 // CHECK-DAG: %[[INPUT_ZP:.+]] = "tosa.const"() <{values = dense<-22> : tensor<1xi8>}> : () -> tensor<1xi8>97 // CHECK-DAG: %[[WEIGHT_ZP:.+]] = "tosa.const"() <{values = dense<42> : tensor<1xi8>}> : () -> tensor<1xi8>98 // CHECK-DAG: %[[PADV:.+]] = tosa.const_shape {values = dense<[0, 0, 0, 1, 0, 1, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>99 // CHECK-DAG: %[[PADW:.+]] = tosa.pad %arg1, %[[PADV]], %[[WEIGHT_ZP]]100 // CHECK-DAG: %[[CONST1:.+]] = tosa.const_shape {values = dense<[5, 2, 2, 2, 3, 3]> : tensor<6xindex>}101 // CHECK-DAG: %[[RESW1:.+]] = tosa.reshape %[[PADW]], %[[CONST1]]102 // CHECK-DAG: %[[TRANS:.+]] = tosa.transpose %[[RESW1]] {perms = array<i32: 2, 4, 0, 1, 3, 5>}103 // CHECK-DAG: %[[CONST3:.+]] = tosa.const_shape {values = dense<[30, 2, 2, 3]> : tensor<4xindex>}104 // CHECK-DAG: %[[RESW2:.+]] = tosa.reshape %[[TRANS]], %[[CONST3]]105 // CHECK-DAG: %[[REV1:.+]] = tosa.reverse %[[RESW2]] {axis = 1 : i32}106 // CHECK-DAG: %[[NEWWEIGHT:.+]] = tosa.reverse %[[REV1]] {axis = 2 : i32}107 108 // Pad out the input matrix to handle the transpose conv.109 // CHECK-DAG: %[[PAD:.+]] = tosa.const_shape {values = dense<[0, 0, 1, 1, 1, 1, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>110 // CHECK-DAG: %[[NEWINPUT:.+]] = tosa.pad %arg0, %[[PAD]], %[[INPUT_ZP]]111 112 // Manipulate the final shape.113 // CHECK-DAG: %[[BIAS:.+]] = "tosa.const"() <{values = dense<0> : tensor<30xi32>}114 // CHECK-DAG: %[[CONV:.+]] = tosa.conv2d %[[NEWINPUT]], %[[NEWWEIGHT]], %[[BIAS]], %[[INPUT_ZP]], %[[WEIGHT_ZP]] {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}115 // CHECK-DAG: %[[CONST6:.+]] = tosa.const_shape {values = dense<[2, 18, 16, 2, 3, 5]> : tensor<6xindex>}116 // CHECK-DAG: %[[RESHAPE_OUT_1:.+]] = tosa.reshape %[[CONV]], %[[CONST6]]117 // CHECK-DAG: %[[TRANS_OUT:.+]] = tosa.transpose %[[RESHAPE_OUT_1]] {perms = array<i32: 0, 1, 3, 2, 4, 5>}118 // CHECK-DAG: %[[CONST8:.+]] = tosa.const_shape {values = dense<[2, 36, 48, 5]> : tensor<4xindex>}119 // CHECK-DAG: %[[RESHAPE_OUT_2:.+]] = tosa.reshape %[[TRANS_OUT]], %[[CONST8]]120 // CHECK-DAG: %[[START:.*]] = tosa.const_shape {values = dense<0> : tensor<4xindex>}121 // CHECK-DAG: %[[SIZE:.*]] = tosa.const_shape {values = dense<[2, 35, 47, 5]> : tensor<4xindex>}122 // CHECK-DAG: %[[SLICE:.*]] = tosa.slice %[[RESHAPE_OUT_2]], %[[START]], %[[SIZE]]123 // CHECK-DAG: %[[CONST9:.+]] = tosa.const_shape {values = dense<[1, 1, 1, 5]> : tensor<4xindex>}124 // CHECK-DAG: %[[RESHAPE_ARG2:.+]] = tosa.reshape %arg2, %[[CONST9]]125 // CHECK: %[[ADD:.+]] = tosa.add %[[SLICE]], %[[RESHAPE_ARG2]]126 %input_zp = "tosa.const"() <{values = dense<-22> : tensor<1xi8>}> : () -> tensor<1xi8>127 %weight_zp = "tosa.const"() <{values = dense<42> : tensor<1xi8>}> : () -> tensor<1xi8>128 %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = i32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 3>} : (tensor<2x17x15x3xi8>, tensor<5x3x5x3xi8>, tensor<5xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<2x35x47x5xi32>129 return %0 : tensor<2x35x47x5xi32>130}131 132// -----133 134// CHECK-LABEL: @transpose_conv2d_strided_quantized_quant_input135func.func @transpose_conv2d_strided_quantized_quant_input(%arg0: tensor<2x17x15x3x!quant.uniform<i8:f32, 0.015684274956583977:-1>>, %arg1: tensor<5x3x5x3x!quant.uniform<i8:f32, 0.015684274956583977:-1>>, %arg2: tensor<5xi32>) -> (tensor<2x35x47x5xi32>) {136 // Checks a regression. A typo in `createPadConstTensor` caused the conversion to crash137 // CHECK-DAG: %[[PAD_SHAPE:.+]] = tosa.const_shape {values = dense<[0, 0, 1, 1, 1, 1, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>138 // CHECK-DAG: %[[PAD_CONST:.+]] = "tosa.const"() <{values = dense<-22> : tensor<1xi8>}> : () -> tensor<1x!quant.uniform<i8:f32, 0.015684274956583977:-1>>139 // CHECK: %[[PAD:.+]] = tosa.pad %arg0, %[[PAD_SHAPE]], %[[PAD_CONST]] : (tensor<2x17x15x3x!quant.uniform<i8:f32, 0.015684274956583977:-1>>, !tosa.shape<8>, tensor<1x!quant.uniform<i8:f32, 0.015684274956583977:-1>>)140 %input_zp = "tosa.const"() <{values = dense<-22> : tensor<1xi8>}> : () -> tensor<1xi8>141 %weight_zp = "tosa.const"() <{values = dense<42> : tensor<1xi8>}> : () -> tensor<1xi8>142 %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = i32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 3>} : (tensor<2x17x15x3x!quant.uniform<i8:f32, 0.015684274956583977:-1>>, tensor<5x3x5x3x!quant.uniform<i8:f32, 0.015684274956583977:-1>>, tensor<5xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<2x35x47x5xi32>143 return %0 : tensor<2x35x47x5xi32>144}145 146// -----147 148// CHECK-LABEL: @transpose_conv2d_strided_overpad149func.func @transpose_conv2d_strided_overpad(%arg0 : tensor<1x16x1x1xi8>, %arg1 : tensor<1x2x1x1xi8>, %arg2 : tensor<1xi32>) -> (tensor<1x19x2x1xi32>) {150 // CHECK-DAG: %[[WEIGHT_PAD:.+]] = tosa.const_shape {values = dense<[0, 0, 0, 0, 0, 1, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>151 // CHECK-DAG: %[[CONST1:.+]] = tosa.const_shape {values = dense<[1, 2, 1, 1, 2, 1]> : tensor<6xindex>}152 // CHECK-DAG: %[[INPUT_ZP:.+]] = "tosa.const"() <{values = dense<-103> : tensor<1xi8>}> : () -> tensor<1xi8>153 // CHECK-DAG: %[[WEIGHT_ZP:.+]] = "tosa.const"() <{values = dense<93> : tensor<1xi8>}> : () -> tensor<1xi8>154 // CHECK-DAG: %[[CONST3:.+]] = tosa.const_shape {values = dense<[2, 2, 1, 1]> : tensor<4xindex>}155 // CHECK-DAG: %[[INPUT_PAD:.+]] = tosa.const_shape {values = dense<[0, 0, 1, 1, 0, 0, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>156 // CHECK-DAG: %[[ZERO:.+]] = "tosa.const"() <{values = dense<0> : tensor<2xi32>}157 // CHECK-DAG: %[[CONST6:.+]] = tosa.const_shape {values = dense<[1, 17, 1, 1, 2, 1]> : tensor<6xindex>}158 // CHECK-DAG: %[[CONST8:.+]] = tosa.const_shape {values = dense<[1, 17, 2, 1]> : tensor<4xindex>}159 // CHECK-DAG: %[[RESULT_PAD:.+]] = tosa.const_shape {values = dense<[0, 0, 2, 0, 0, 0, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>160 // CHECK-DAG: %[[CONST10:.+]] = tosa.const_shape {values = dense<1> : tensor<4xindex>}161 // CHECK: %[[PAD_WEIGHT:.+]] = tosa.pad %arg1, %[[WEIGHT_PAD]], %[[WEIGHT_ZP]]162 // CHECK: %[[RESHAPE_WEIGHT_0:.+]] = tosa.reshape %[[PAD_WEIGHT]], %[[CONST1]]163 // CHECK: %[[TRANSPOSE_WEIGHT:.+]] = tosa.transpose %[[RESHAPE_WEIGHT_0]] {perms = array<i32: 2, 4, 0, 1, 3, 5>}164 // CHECK: %[[RESHAPE_WEIGHT_1:.+]] = tosa.reshape %[[TRANSPOSE_WEIGHT]], %[[CONST3]]165 // CHECK: %[[REVERSE:.+]] = tosa.reverse %[[RESHAPE_WEIGHT_1]] {axis = 1 : i32}166 // CHECK: %[[PAD_INPUT:.+]] = tosa.pad %arg0, %[[INPUT_PAD]], %[[INPUT_ZP]]167 // CHECK: %[[CONV:.+]] = tosa.conv2d %[[PAD_INPUT]], %[[REVERSE]], %[[ZERO]], %[[INPUT_ZP]], %[[WEIGHT_ZP]]168 // CHECK-SAME{literal}: dilation = [1, 1], pad = [0, 0, 0, 0], stride = [1, 1]}169 // CHECK: %[[RESHAPE_RESULT_0:.+]] = tosa.reshape %[[CONV]], %[[CONST6]]170 // CHECK: %[[TRANSPOSE_RESULT:.+]] = tosa.transpose %[[RESHAPE_RESULT_0]] {perms = array<i32: 0, 1, 3, 2, 4, 5>}171 // CHECK: %[[RESHAPE_RESULT_1:.+]] = tosa.reshape %[[TRANSPOSE_RESULT]], %[[CONST8]]172 // CHECK: %[[PAD_RESULT:.+]] = tosa.pad %[[RESHAPE_RESULT_1]], %[[RESULT_PAD]]173 // CHECK: %[[RESHAPE_ARG2:.+]] = tosa.reshape %arg2, %[[CONST10]]174 // CHECK: %[[ADD:.+]] = tosa.add %[[PAD_RESULT]], %[[RESHAPE_ARG2]]175 %input_zp = "tosa.const"() <{values = dense<-103> : tensor<1xi8>}> : () -> tensor<1xi8>176 %weight_zp = "tosa.const"() <{values = dense<93> : tensor<1xi8>}> : () -> tensor<1xi8>177 %2 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {178 acc_type = i32,179 out_pad = array<i64: 2, 0, 0, 1>,180 stride = array<i64: 1, 2>} :181 (tensor<1x16x1x1xi8>, tensor<1x2x1x1xi8>, tensor<1xi32>, tensor<1xi8>, tensor<1xi8>) -> tensor<1x19x2x1xi32>182 "func.return" (%2) : (tensor<1x19x2x1xi32>) -> ()183}184 185 186// -----187// CHECK-LABEL: @transpose_conv2d_non_strided_dynamic_batch188// CHECK: tosa.conv2d189// CHECK-NOT: tosa.transpose_conv2d190func.func @transpose_conv2d_non_strided_dynamic_batch(%arg0: tensor<?x16x14x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>) -> tensor<?x18x19x5xf32> {191 %zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>192 %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %zp, %zp {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<?x16x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x18x19x5xf32>193 return %0 : tensor<?x18x19x5xf32>194}195 196// -----197// CHECK-LABEL: @transpose_conv2d_strided_dynamic_batch198// CHECK: tosa.conv2d199// CHECK-NOT: tosa.transpose_conv2d200func.func @transpose_conv2d_strided_dynamic_batch(%arg0: tensor<?x17x15x3xf32>, %arg1: tensor<5x3x5x3xf32>, %arg2: tensor<5xf32>) -> tensor<?x35x47x5xf32> {201 %zp = "tosa.const"() <{values = dense<0.0> : tensor<1xf32>}> : () -> tensor<1xf32>202 %0 = tosa.transpose_conv2d %arg0, %arg1, %arg2, %zp, %zp {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 2, 3>} : (tensor<?x17x15x3xf32>, tensor<5x3x5x3xf32>, tensor<5xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x35x47x5xf32>203 return %0 : tensor<?x35x47x5xf32>204}205