Tracy Narine | 10403ec | 2023-11-28 11:55:08 +0000 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2024 Arm Ltd and Contributors. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | // |
| 6 | // Copyright © 2020 The TensorFlow Authors. All Rights Reserved. |
| 7 | // SPDX-License-Identifier: Apache-2.0 |
| 8 | // |
| 9 | |
| 10 | #include "ActivationOperator.hpp" |
| 11 | #include "TosaRescaleOperatorUtils.hpp" |
| 12 | |
| 13 | #include <layers/ActivationLayer.hpp> |
| 14 | |
| 15 | // This function is paraphrased from: |
| 16 | // tensorflow/compiler/mlir/tosa/transforms/legalize_tfl.cc from function ConvertTFLLeakyReluOp |
| 17 | TosaSerializationBasicBlock* ConvertActivationToTosaOperator(const Layer* layer, |
| 18 | const std::vector<const TensorInfo*>& inputs, |
| 19 | const std::vector<const TensorInfo*>& outputs, |
| 20 | const ActivationDescriptor* activationDescriptor) |
| 21 | { |
| 22 | if (inputs.size() != 1) |
| 23 | { |
| 24 | throw armnn::Exception("ConvertActivationToTosaOperator: 1 input tensors required."); |
| 25 | } |
| 26 | |
| 27 | if (outputs.size() != 1) |
| 28 | { |
| 29 | throw armnn::Exception("ConvertActivationToTosaOperator: 1 output tensor required."); |
| 30 | } |
| 31 | |
| 32 | std::string inputName = std::string("input0_"); |
| 33 | std::string outputNameAlpha = std::string("intermediate1_") + GetUniqueTosaMappingID(); |
| 34 | std::string outputNameMul = std::string("intermediate2_") + GetUniqueTosaMappingID(); |
| 35 | std::string outputName = std::string("output0_"); |
| 36 | std::string blockName = std::string("Op_ACTIVATION_block_") + GetUniqueTosaMappingID(); |
| 37 | |
| 38 | // If a layer is present then the block will be used for execution, so input and output names need to be determined |
| 39 | // using the previous and following layers so the graph is connected correctly. For validation this doesn't matter. |
| 40 | if (layer != nullptr) |
| 41 | { |
| 42 | // Get the layers connected to the input slots and determine unique tensors names. |
| 43 | Layer& connectedInputLayer = layer->GetInputSlot(0).GetConnectedOutputSlot()->GetOwningLayer(); |
| 44 | inputName = GenerateUniqueName(connectedInputLayer, 0); |
| 45 | |
| 46 | // Determine unique output tensor name. |
| 47 | outputName = GenerateUniqueOutputName(*layer, 0); |
| 48 | } |
| 49 | |
| 50 | std::vector<TosaSerializationTensor*> tensors; |
| 51 | |
| 52 | // Only add input tensors if connected layer is an input layer. |
| 53 | // As intermediate or constant tensors will be created separately. |
| 54 | // There also can't be duplicate tensor. |
| 55 | std::vector<int32_t> inputShape0; |
| 56 | DType inputDType0 = DType::DType_UNKNOWN; |
| 57 | if(inputName.find("input0_") != std::string::npos) |
| 58 | { |
| 59 | inputShape0 = GetTosaTensorShape(inputs[0]->GetShape()); |
| 60 | inputDType0 = ArmNNToDType(inputs[0]->GetDataType()); |
| 61 | tensors.push_back(new TosaSerializationTensor(inputName, inputShape0, inputDType0, {})); |
| 62 | } |
| 63 | |
| 64 | std::vector<int32_t> outputShape0 = GetTosaTensorShape(outputs[0]->GetShape()); |
| 65 | DType outputDType0 = ArmNNToDType(outputs[0]->GetDataType()); |
| 66 | tensors.push_back(new TosaSerializationTensor(outputName, outputShape0, outputDType0, {})); |
| 67 | |
Tracy Narine | 91ffe3d | 2024-01-23 09:40:00 +0000 | [diff] [blame] | 68 | #if TOSA_COMPAT_VERSION(0, 60, 0) |
Tracy Narine | 10403ec | 2023-11-28 11:55:08 +0000 | [diff] [blame] | 69 | std::string outputNameMAXMIN= std::string("intermediate3_") + GetUniqueTosaMappingID(); |
| 70 | |
Teresa Charlin | a42e006 | 2024-04-23 13:03:40 +0100 | [diff] [blame^] | 71 | if (inputDType0 == DType::DType_FP32 || |
| 72 | inputDType0 == DType::DType_FP16) |
Tracy Narine | 10403ec | 2023-11-28 11:55:08 +0000 | [diff] [blame] | 73 | { |
| 74 | // const_alpha |
| 75 | TosaSerializationOperator* alphaOp = nullptr; |
| 76 | TosaSerializationTensor* alphaTensor = nullptr; |
| 77 | CreateConstTosaOperator<float>(outputNameAlpha, |
| 78 | activationDescriptor->m_A, |
| 79 | inputDType0, |
| 80 | inputShape0, |
| 81 | alphaOp, |
| 82 | alphaTensor); |
| 83 | tensors.push_back(alphaTensor); |
| 84 | |
| 85 | // mul |
| 86 | int32_t shift = 0; |
| 87 | TosaMulAttribute mulAttribute(shift); |
| 88 | TosaSerializationOperator* mulOp = new TosaSerializationOperator(Op_MUL, |
| 89 | Attribute_MulAttribute, |
| 90 | &mulAttribute, |
| 91 | {inputName, outputNameAlpha}, |
| 92 | {outputNameMul}); |
| 93 | tensors.push_back(new TosaSerializationTensor(outputNameMul, inputShape0, inputDType0, {})); |
| 94 | |
| 95 | TosaSerializationOperator* op = nullptr; |
| 96 | if (activationDescriptor->m_A <= 1.0) |
| 97 | { |
| 98 | op = new TosaSerializationOperator(Op_MAXIMUM, |
| 99 | Attribute_NONE, |
| 100 | nullptr, |
| 101 | {inputName, outputNameMul}, |
| 102 | {outputName}); |
| 103 | } |
| 104 | else |
| 105 | { |
| 106 | op = new TosaSerializationOperator(Op_MINIMUM, |
| 107 | Attribute_NONE, |
| 108 | nullptr, |
| 109 | {inputName, outputNameMul}, |
| 110 | {outputName}); |
| 111 | |
| 112 | } |
| 113 | |
| 114 | // operatorInputNames/operatorOutputNames ends up being the same as |
| 115 | // blockInputNames/blockOutputNames for one-to-one ArmNN to Tosa mappings |
| 116 | return new TosaSerializationBasicBlock(blockName, // name |
| 117 | mainName, // region name |
| 118 | {alphaOp, mulOp, op}, // operators |
| 119 | tensors, // tensors |
| 120 | {inputName}, // inputs |
| 121 | {outputName}); // outputs |
| 122 | } |
| 123 | else |
| 124 | { |
| 125 | std::string outputNameRescaleAlpha = std::string("intermediate3_") + GetUniqueTosaMappingID(); |
| 126 | std::string outputNameRescaleIdentity = std::string("intermediate4_") + GetUniqueTosaMappingID(); |
| 127 | std::string outputNameRescaleMaxMin = std::string("intermediate5_") + GetUniqueTosaMappingID(); |
| 128 | |
| 129 | DType rescale_type = DType::DType_INT32; |
| 130 | float alpha = activationDescriptor->m_A; |
| 131 | double scale_alpha = inputs[0]->GetQuantizationScale() * alpha / outputs[0]->GetQuantizationScale(); |
| 132 | double scale_identity = inputs[0]->GetQuantizationScale() / outputs[0]->GetQuantizationScale(); |
| 133 | int32_t input_zp = inputs[0]->GetQuantizationOffset(); |
| 134 | int32_t output_zp = outputs[0]->GetQuantizationOffset(); |
| 135 | |
| 136 | // Value op_rescale_alpha_in = |
| 137 | // buildRescale(rewriter, op, rescale_type, input, scale_alpha, |
| 138 | // input_qtype.getZeroPoint(), 0, true, true); |
| 139 | TosaSerializationOperator* rescaleAlphaOp = nullptr; |
| 140 | TosaSerializationTensor* rescaleAlphaTensor = nullptr; |
| 141 | CreateRescaleTosaOperator(inputName, |
| 142 | outputNameRescaleAlpha, |
| 143 | rescale_type, |
| 144 | inputShape0, |
| 145 | scale_alpha, |
| 146 | input_zp, |
| 147 | 0, |
| 148 | true, |
| 149 | true, |
| 150 | &rescaleAlphaOp, |
| 151 | &rescaleAlphaTensor); |
| 152 | tensors.push_back(rescaleAlphaTensor); |
| 153 | |
| 154 | // Value op_rescale_identity_in = |
| 155 | // buildRescale(rewriter, op, rescale_type, input, scale_identity, |
| 156 | // input_qtype.getZeroPoint(), 0, true, true); |
| 157 | TosaSerializationOperator* rescaleIdentityOp = nullptr; |
| 158 | TosaSerializationTensor* rescaleIdentityTensor = nullptr; |
| 159 | CreateRescaleTosaOperator(inputName, |
| 160 | outputNameRescaleIdentity, |
| 161 | rescale_type, |
| 162 | inputShape0, |
| 163 | scale_identity, |
| 164 | input_zp, |
| 165 | 0, |
| 166 | true, |
| 167 | true, |
| 168 | &rescaleIdentityOp, |
| 169 | &rescaleIdentityTensor); |
| 170 | tensors.push_back(rescaleIdentityTensor); |
| 171 | |
| 172 | // Value result_int32; |
| 173 | // if (alpha <= 1.0) { |
| 174 | // auto max_op = CreateOpAndInfer<tosa::MaximumOp>( |
| 175 | // rewriter, op->getLoc(), rescale_type, op_rescale_identity_in, |
| 176 | // op_rescale_alpha_in); |
| 177 | // result_int32 = max_op.getResult(); |
| 178 | // } else { |
| 179 | // auto min_op = CreateOpAndInfer<tosa::MinimumOp>( |
| 180 | // rewriter, op->getLoc(), rescale_type, op_rescale_identity_in, |
| 181 | // op_rescale_alpha_in); |
| 182 | // result_int32 = min_op.getResult(); |
| 183 | // } |
| 184 | TosaSerializationOperator* op = nullptr; |
| 185 | if (alpha <= 1.0) |
| 186 | { |
| 187 | op = new TosaSerializationOperator(Op_MAXIMUM, |
| 188 | Attribute_NONE, |
| 189 | nullptr, |
| 190 | {outputNameRescaleAlpha, outputNameRescaleIdentity}, |
| 191 | {outputNameRescaleMaxMin}); |
| 192 | } |
| 193 | else |
| 194 | { |
| 195 | op = new TosaSerializationOperator(Op_MINIMUM, |
| 196 | Attribute_NONE, |
| 197 | nullptr, |
| 198 | {outputNameRescaleAlpha, outputNameRescaleIdentity}, |
| 199 | {outputNameRescaleMaxMin}); |
| 200 | |
| 201 | } |
| 202 | tensors.push_back(new TosaSerializationTensor(outputNameRescaleMaxMin, inputShape0, rescale_type, {})); |
| 203 | |
| 204 | // Value output = buildRescaleFromInt32(rewriter, op, output_type, result_int32, |
| 205 | // 1.0, output_qtype.getZeroPoint()); |
| 206 | TosaSerializationOperator* rescaleOutputOp = nullptr; |
| 207 | CreateFromInt32RescaleTosaOperator(outputNameRescaleMaxMin, |
| 208 | outputName, |
| 209 | outputDType0, |
| 210 | outputShape0, |
| 211 | 1.0, |
| 212 | output_zp, |
| 213 | &rescaleOutputOp, |
| 214 | nullptr); |
| 215 | |
| 216 | // operatorInputNames/operatorOutputNames ends up being the same as |
| 217 | // blockInputNames/blockOutputNames for one-to-one ArmNN to Tosa mappings |
| 218 | return new TosaSerializationBasicBlock(blockName, // name |
| 219 | mainName, // region name |
| 220 | {rescaleAlphaOp, rescaleIdentityOp, op, rescaleOutputOp}, // operators |
| 221 | tensors, // tensors |
| 222 | {inputName}, // inputs |
| 223 | {outputName}); // outputs |
| 224 | } |
| 225 | #else |
| 226 | std::string outputNameZero = std::string("intermediate3_") + GetUniqueTosaMappingID(); |
| 227 | std::string outputNameGE = std::string("intermediate4_") + GetUniqueTosaMappingID(); |
| 228 | |
| 229 | // const_zero |
| 230 | TosaSerializationOperator* zeroOp = nullptr; |
| 231 | TosaSerializationTensor* zeroTensor = nullptr; |
| 232 | CreateConstTosaOperator<float>(outputNameZero, |
| 233 | 0.0f, |
| 234 | inputDType0, |
| 235 | inputShape0, |
| 236 | zeroOp, |
| 237 | zeroTensor); |
| 238 | tensors.push_back(zeroTensor); |
| 239 | |
| 240 | // const_alpha |
| 241 | TosaSerializationOperator* alphaOp = nullptr; |
| 242 | TosaSerializationTensor* alphaTensor = nullptr; |
| 243 | CreateConstTosaOperator<float>(outputNameAlpha, |
| 244 | activationDescriptor->m_A, |
| 245 | inputDType0, |
| 246 | inputShape0, |
| 247 | alphaOp, |
| 248 | alphaTensor); |
| 249 | tensors.push_back(alphaTensor); |
| 250 | |
| 251 | // mul |
| 252 | int32_t shift = 0; |
| 253 | TosaMulAttribute mulAttribute(shift); |
| 254 | TosaSerializationOperator* mulOp = new TosaSerializationOperator(Op_MUL, |
| 255 | Attribute_MulAttribute, |
| 256 | &mulAttribute, |
| 257 | {inputName, outputNameAlpha}, |
| 258 | {outputNameMul}); |
| 259 | tensors.push_back(new TosaSerializationTensor(outputNameMul, inputShape0, inputDType0, {})); |
| 260 | |
| 261 | // greater_equal |
| 262 | TosaSerializationOperator* geOp = new TosaSerializationOperator(Op_GREATER_EQUAL, |
| 263 | Attribute_NONE, |
| 264 | nullptr, |
| 265 | {inputName, outputNameZero}, |
| 266 | {outputNameGE}); |
| 267 | tensors.push_back(new TosaSerializationTensor(outputNameGE, outputShape0, DType::DType_BOOL, {})); |
| 268 | |
| 269 | // select |
| 270 | TosaSerializationOperator* selOp = new TosaSerializationOperator(Op_SELECT, |
| 271 | Attribute_NONE, |
| 272 | nullptr, |
| 273 | {outputNameGE, inputName, outputNameMul}, |
| 274 | {outputName}); |
| 275 | |
| 276 | // operatorInputNames/operatorOutputNames ends up being the same as |
| 277 | // blockInputNames/blockOutputNames for one-to-one ArmNN to Tosa mappings |
| 278 | return new TosaSerializationBasicBlock(blockName, // name |
| 279 | mainName, // region name |
| 280 | {zeroOp, alphaOp, mulOp, geOp, selOp}, // operators |
| 281 | tensors, // tensors |
| 282 | {inputName}, // inputs |
| 283 | {outputName}); // outputs |
| 284 | #endif |
| 285 | } |