Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2023 Arm Ltd and Contributors. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #pragma once |
| 7 | |
| 8 | #include <armnn_delegate.hpp> |
| 9 | #include <DelegateUtils.hpp> |
| 10 | |
| 11 | #include <armnn/ArmNN.hpp> |
| 12 | #include <armnn/BackendHelper.hpp> |
Mike Kelly | 07169c8 | 2023-08-02 13:23:09 +0100 | [diff] [blame^] | 13 | #include <armnn/TypesUtils.hpp> |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 14 | #include <armnn/utility/Assert.hpp> |
| 15 | #include <armnn/utility/NumericCast.hpp> |
| 16 | |
| 17 | #include <armnnUtils/Permute.hpp> |
| 18 | #include <armnnUtils/TensorUtils.hpp> |
| 19 | |
| 20 | #include <tensorflow/lite/builtin_ops.h> |
| 21 | #include <tensorflow/lite/c/builtin_op_data.h> |
| 22 | #include <tensorflow/lite/c/common.h> |
| 23 | #include <tensorflow/lite/minimal_logging.h> |
| 24 | #include <tensorflow/lite/kernels/kernel_util.h> |
| 25 | |
Mike Kelly | 07169c8 | 2023-08-02 13:23:09 +0100 | [diff] [blame^] | 26 | #include <fmt/format.h> |
| 27 | |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 28 | namespace |
| 29 | { |
| 30 | |
| 31 | // Macro to call an Is<layer_name>Supported function and log caller name together with reason for lack of support |
| 32 | #define FORWARD_LAYER_SUPPORT_FUNC(opName, tfLiteContext, func, backends, supported, setBackend, ...) \ |
| 33 | try \ |
| 34 | { \ |
| 35 | for (auto&& backendId : backends) \ |
| 36 | { \ |
| 37 | auto layerSupportObject = armnn::GetILayerSupportByBackendId(backendId); \ |
| 38 | if (layerSupportObject.IsBackendRegistered()) \ |
| 39 | { \ |
| 40 | std::string reasonIfUnsupported; \ |
| 41 | supported = \ |
| 42 | layerSupportObject.func(__VA_ARGS__, armnn::Optional<std::string&>(reasonIfUnsupported)); \ |
| 43 | if (supported) \ |
| 44 | { \ |
| 45 | setBackend = backendId; \ |
| 46 | break; \ |
| 47 | } \ |
| 48 | else \ |
| 49 | { \ |
| 50 | if (reasonIfUnsupported.size() > 0) \ |
| 51 | { \ |
| 52 | TFLITE_LOG_PROD(tflite::TFLITE_LOG_WARNING, \ |
| 53 | "%s: not supported by armnn: %s", opName, reasonIfUnsupported.c_str()); \ |
| 54 | } \ |
| 55 | else \ |
| 56 | { \ |
| 57 | TFLITE_LOG_PROD(tflite::TFLITE_LOG_WARNING, \ |
| 58 | "%s: not supported by armnn", opName); \ |
| 59 | } \ |
| 60 | } \ |
| 61 | } \ |
| 62 | else \ |
| 63 | { \ |
| 64 | TF_LITE_KERNEL_LOG(tfLiteContext, "%s: backend not registered: %s", opName, backendId.Get().c_str()); \ |
| 65 | } \ |
| 66 | } \ |
| 67 | if (!supported) \ |
| 68 | { \ |
| 69 | TF_LITE_KERNEL_LOG(tfLiteContext, "%s: not supported by any specified backend", opName); \ |
| 70 | } \ |
| 71 | } \ |
| 72 | catch (const armnn::InvalidArgumentException &e) \ |
| 73 | { \ |
| 74 | throw armnn::InvalidArgumentException(e, "Failed to check layer support", CHECK_LOCATION()); \ |
| 75 | } |
| 76 | |
Mike Kelly | 07169c8 | 2023-08-02 13:23:09 +0100 | [diff] [blame^] | 77 | std::string GetLayerName(armnn::ActivationFunction function, int nodeIndex) |
| 78 | { |
| 79 | return fmt::format("{}:{}", GetActivationFunctionAsCString(function), nodeIndex); |
| 80 | } |
| 81 | |
| 82 | std::string GetLayerName(armnn::ArgMinMaxFunction function, int nodeIndex) |
| 83 | { |
| 84 | return fmt::format("{}:{}", GetArgMinMaxFunctionAsCString(function), nodeIndex); |
| 85 | } |
| 86 | |
| 87 | std::string GetLayerName(armnn::BinaryOperation opType, int nodeIndex) |
| 88 | { |
| 89 | return fmt::format("{}:{}", GetBinaryOperationAsCString(opType), nodeIndex); |
| 90 | } |
| 91 | |
| 92 | std::string GetLayerName(armnn::ComparisonOperation layerType, int nodeIndex) |
| 93 | { |
| 94 | return fmt::format("{}:{}", GetComparisonOperationAsCString(layerType), nodeIndex); |
| 95 | } |
| 96 | |
| 97 | std::string GetLayerName(armnn::LogicalBinaryOperation operation, int nodeIndex) |
| 98 | { |
| 99 | return fmt::format("{}:{}", GetLogicalBinaryOperationAsCString(operation), nodeIndex); |
| 100 | } |
| 101 | |
| 102 | std::string GetLayerName(armnn::UnaryOperation opType, int nodeIndex) |
| 103 | { |
| 104 | return fmt::format("{}:{}", GetUnaryOperationAsCString(opType), nodeIndex); |
| 105 | } |
| 106 | |
| 107 | std::string GetLayerName(armnn::LayerType layerType, int nodeIndex, std::string name = "") |
| 108 | { |
| 109 | return fmt::format("{}{}:{}", GetLayerTypeAsCString(layerType), name, nodeIndex); |
| 110 | } |
| 111 | |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 112 | TfLiteStatus ValidateNumInputs(TfLiteContext* tfLiteContext, |
| 113 | TfLiteNode* tfLiteNode, |
| 114 | const unsigned int expectedSize, |
| 115 | int nodeIndex) |
| 116 | { |
| 117 | auto numInputs = tfLiteNode->inputs->size; |
| 118 | if (static_cast<unsigned int >(numInputs) != expectedSize) |
| 119 | { |
| 120 | TF_LITE_MAYBE_KERNEL_LOG( |
| 121 | tfLiteContext, "TfLiteArmnnDelegate: Unexpected number of inputs (%d != %d) in node #%d", |
| 122 | numInputs, expectedSize, nodeIndex); |
| 123 | return kTfLiteError; |
| 124 | } |
| 125 | return kTfLiteOk; |
| 126 | } |
| 127 | |
| 128 | TfLiteStatus ValidateNumOutputs(TfLiteContext* tfLiteContext, |
| 129 | TfLiteNode* tfLiteNode, |
| 130 | const unsigned int expectedSize, |
| 131 | int nodeIndex) |
| 132 | { |
| 133 | auto numOutputs = tfLiteNode->outputs->size; |
| 134 | if (static_cast<unsigned int >(numOutputs) != expectedSize) |
| 135 | { |
| 136 | TF_LITE_MAYBE_KERNEL_LOG( |
| 137 | tfLiteContext, "TfLiteArmnnDelegate: Unexpected number of outputs (%d != %d) in node #%d", |
| 138 | numOutputs, expectedSize, nodeIndex); |
| 139 | return kTfLiteError; |
| 140 | } |
| 141 | return kTfLiteOk; |
| 142 | } |
| 143 | |
| 144 | bool IsDynamicTensor(const TfLiteTensor& tfLiteTensor) |
| 145 | { |
| 146 | auto tensorAllocationType = tfLiteTensor.allocation_type; |
| 147 | if (tensorAllocationType == kTfLiteDynamic) |
| 148 | { |
| 149 | return true; |
| 150 | } |
| 151 | return false; |
| 152 | } |
| 153 | |
| 154 | bool IsValid(const TfLiteTensor* tfLiteTensor) |
| 155 | { |
| 156 | return tfLiteTensor == nullptr ? false : true; |
| 157 | } |
| 158 | |
| 159 | bool IsValid(TfLiteContext* tfLiteContext, const TfLiteTensor& tfLiteTensor, int32_t operatorCode, int32_t nodeIndex) |
| 160 | { |
| 161 | if(!IsValid(&tfLiteTensor)) |
| 162 | { |
| 163 | std::cout << "..Is Not Valid" << std::endl; |
| 164 | TF_LITE_MAYBE_KERNEL_LOG( |
| 165 | tfLiteContext, |
| 166 | "TfLiteArmnnDelegate: Invalid TfLite tensor in operator #%d node #%d: ", |
| 167 | operatorCode, nodeIndex); |
| 168 | return false; |
| 169 | } |
| 170 | if (IsDynamicTensor(tfLiteTensor)) |
| 171 | { |
| 172 | std::cout << "..IsDynamicTensor" << std::endl; |
| 173 | TF_LITE_MAYBE_KERNEL_LOG( |
| 174 | tfLiteContext, |
| 175 | "TfLiteArmnnDelegate: Dynamic tensors are not supported in operator #%d node #%d: ", |
| 176 | operatorCode, nodeIndex); |
| 177 | return false; |
| 178 | } |
| 179 | return true; |
| 180 | } |
| 181 | |
| 182 | bool IsAffineQuantization(const TfLiteTensor& tfLiteTensor) |
| 183 | { |
| 184 | auto quantizationInfo = tfLiteTensor.quantization; |
| 185 | if (quantizationInfo.type == kTfLiteAffineQuantization) |
| 186 | { |
| 187 | return true; |
| 188 | } |
| 189 | return false; |
| 190 | } |
| 191 | |
| 192 | TfLiteStatus Connect(armnn::IConnectableLayer* layer, |
| 193 | TfLiteNode* tfLiteNode, |
| 194 | armnnDelegate::DelegateData& data) |
| 195 | { |
| 196 | ARMNN_ASSERT(static_cast<unsigned int>(tfLiteNode->outputs->size) == layer->GetNumOutputSlots()); |
| 197 | |
| 198 | // Connect the input slots |
| 199 | for (unsigned int inputIndex = 0; inputIndex < layer->GetNumInputSlots(); ++inputIndex) |
| 200 | { |
| 201 | if (data.m_OutputSlotForNode[tfLiteNode->inputs->data[inputIndex]] != nullptr) |
| 202 | { |
| 203 | data.m_OutputSlotForNode[tfLiteNode->inputs->data[inputIndex]]->Connect(layer->GetInputSlot(inputIndex)); |
| 204 | } |
| 205 | } |
| 206 | |
| 207 | // Prepare output slots |
| 208 | for (unsigned int outputIndex = 0; outputIndex < layer->GetNumOutputSlots(); ++outputIndex) |
| 209 | { |
| 210 | armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(outputIndex); |
| 211 | data.m_OutputSlotForNode[static_cast<unsigned long>(tfLiteNode->outputs->data[outputIndex])] = &outputSlot; |
| 212 | } |
| 213 | |
| 214 | return kTfLiteOk; |
| 215 | } |
| 216 | |
| 217 | TfLiteStatus FusedActivation(TfLiteContext* tfLiteContext, |
| 218 | TfLiteNode* tfLiteNode, |
| 219 | TfLiteFusedActivation activationType, |
| 220 | armnn::IConnectableLayer* prevLayer, |
| 221 | unsigned int outputSlotIndex, |
Mike Kelly | 07169c8 | 2023-08-02 13:23:09 +0100 | [diff] [blame^] | 222 | armnnDelegate::DelegateData& data, |
| 223 | int nodeIndex) |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 224 | { |
| 225 | |
| 226 | const armnn::TensorInfo& activationOutputInfo = prevLayer->GetOutputSlot(outputSlotIndex).GetTensorInfo(); |
| 227 | |
| 228 | armnn::ActivationDescriptor activationDesc; |
| 229 | |
| 230 | switch (activationType) |
| 231 | { |
| 232 | case kTfLiteActNone: |
| 233 | { |
| 234 | // No Activation |
| 235 | return kTfLiteOk; |
| 236 | } |
| 237 | case kTfLiteActRelu: |
| 238 | { |
| 239 | activationDesc.m_Function = armnn::ActivationFunction::ReLu; |
| 240 | break; |
| 241 | } |
| 242 | // The name of kTfLiteActRelu1 changed after TF Lite v2.3 |
| 243 | #if defined(ARMNN_POST_TFLITE_2_3) |
| 244 | case kTfLiteActReluN1To1: |
| 245 | #else |
| 246 | case kTfLiteActRelu1: |
| 247 | #endif |
| 248 | { |
| 249 | activationDesc.m_Function = armnn::ActivationFunction::BoundedReLu; |
| 250 | activationDesc.m_A = 1.0f; |
| 251 | activationDesc.m_B = -1.0f; |
| 252 | break; |
| 253 | } |
| 254 | case kTfLiteActRelu6: |
| 255 | { |
| 256 | activationDesc.m_Function = armnn::ActivationFunction::BoundedReLu; |
| 257 | activationDesc.m_A = 6.0f; |
| 258 | activationDesc.m_B = 0.0f; |
| 259 | break; |
| 260 | } |
| 261 | case kTfLiteActSigmoid: |
| 262 | { |
| 263 | activationDesc.m_Function = armnn::ActivationFunction::Sigmoid; |
| 264 | break; |
| 265 | } |
| 266 | case kTfLiteActTanh: |
| 267 | { |
| 268 | activationDesc.m_Function = armnn::ActivationFunction::TanH; |
| 269 | activationDesc.m_A = 1.0f; |
| 270 | activationDesc.m_B = 1.0f; |
| 271 | break; |
| 272 | } |
| 273 | default: |
| 274 | return kTfLiteError; |
| 275 | } |
| 276 | |
| 277 | bool isSupported = false; |
| 278 | armnn::BackendId setBackend; |
| 279 | FORWARD_LAYER_SUPPORT_FUNC("ACTIVATION", |
| 280 | tfLiteContext, |
| 281 | IsActivationSupported, |
| 282 | data.m_Backends, |
| 283 | isSupported, |
| 284 | setBackend, |
| 285 | activationOutputInfo, |
| 286 | activationOutputInfo, |
| 287 | activationDesc); |
| 288 | if (!isSupported) |
| 289 | { |
| 290 | return kTfLiteError; |
| 291 | } |
Mike Kelly | 07169c8 | 2023-08-02 13:23:09 +0100 | [diff] [blame^] | 292 | auto layerName = GetLayerName(activationDesc.m_Function, nodeIndex); |
| 293 | armnn::IConnectableLayer* activationLayer = data.m_Network->AddActivationLayer(activationDesc, layerName.c_str()); |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 294 | activationLayer->SetBackendId(setBackend); |
| 295 | |
| 296 | ARMNN_ASSERT(activationLayer != nullptr); |
| 297 | activationLayer->GetOutputSlot(0).SetTensorInfo(activationOutputInfo); |
| 298 | |
| 299 | // Connect and prepare output slots |
| 300 | for (unsigned int outputIndex = 0; outputIndex < activationLayer->GetNumOutputSlots(); ++outputIndex) |
| 301 | { |
| 302 | data.m_OutputSlotForNode[static_cast<unsigned long>( |
| 303 | tfLiteNode->outputs->data[outputIndex])]->Connect(activationLayer->GetInputSlot(0)); |
| 304 | armnn::IOutputSlot& outputSlot = activationLayer->GetOutputSlot(outputIndex); |
| 305 | data.m_OutputSlotForNode[static_cast<unsigned long>( |
| 306 | tfLiteNode->outputs->data[outputIndex])] = &outputSlot; |
| 307 | } |
| 308 | return kTfLiteOk; |
| 309 | } |
| 310 | |
| 311 | armnn::IConnectableLayer* AddReshapeLayer(TfLiteContext* tfLiteContext, |
| 312 | TfLiteNode* tfLiteNode, |
| 313 | armnn::IConnectableLayer* prevLayer, |
| 314 | armnn::TensorInfo reshapedOutputTensorInfo, |
| 315 | armnn::TensorInfo outputTensorInfo, |
Mike Kelly | 07169c8 | 2023-08-02 13:23:09 +0100 | [diff] [blame^] | 316 | armnnDelegate::DelegateData& data, |
| 317 | int nodeIndex) |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 318 | { |
| 319 | armnn::ReshapeDescriptor desc; |
| 320 | desc.m_TargetShape = outputTensorInfo.GetShape(); |
| 321 | |
| 322 | bool isSupported = false; |
| 323 | armnn::BackendId setBackend; |
| 324 | FORWARD_LAYER_SUPPORT_FUNC("RESHAPE", |
| 325 | tfLiteContext, |
| 326 | IsReshapeSupported, |
| 327 | data.m_Backends, |
| 328 | isSupported, |
| 329 | setBackend, |
| 330 | reshapedOutputTensorInfo, |
| 331 | outputTensorInfo, |
| 332 | desc); |
| 333 | |
| 334 | if (!isSupported) |
| 335 | { |
| 336 | return nullptr; |
| 337 | } |
| 338 | |
Mike Kelly | 07169c8 | 2023-08-02 13:23:09 +0100 | [diff] [blame^] | 339 | auto layerName = GetLayerName(armnn::LayerType::Reshape, nodeIndex); |
| 340 | armnn::IConnectableLayer* reshapeLayer = data.m_Network->AddReshapeLayer(desc, layerName.c_str()); |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 341 | reshapeLayer->SetBackendId(setBackend); |
| 342 | ARMNN_ASSERT(reshapeLayer != nullptr); |
| 343 | |
| 344 | prevLayer->GetOutputSlot(0).SetTensorInfo(reshapedOutputTensorInfo); |
| 345 | reshapeLayer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| 346 | |
| 347 | // Connect and prepare output slots |
| 348 | for (unsigned int outputIndex = 0; outputIndex < reshapeLayer->GetNumOutputSlots(); ++outputIndex) |
| 349 | { |
| 350 | data.m_OutputSlotForNode[static_cast<unsigned long>( |
| 351 | tfLiteNode->outputs->data[outputIndex])]->Connect(reshapeLayer->GetInputSlot(0)); |
| 352 | armnn::IOutputSlot& outputSlot = reshapeLayer->GetOutputSlot(outputIndex); |
| 353 | data.m_OutputSlotForNode[static_cast<unsigned long>( |
| 354 | tfLiteNode->outputs->data[outputIndex])] = &outputSlot; |
| 355 | } |
| 356 | return reshapeLayer; |
| 357 | } |
| 358 | |
| 359 | armnn::DataType GetDataType(const TfLiteTensor& tfLiteTensor) |
| 360 | { |
| 361 | switch (tfLiteTensor.type) |
| 362 | { |
| 363 | case kTfLiteBool: |
| 364 | return armnn::DataType::Boolean; |
| 365 | case kTfLiteFloat32: |
| 366 | return armnn::DataType::Float32; |
| 367 | case kTfLiteFloat16: |
| 368 | return armnn::DataType::Float16; |
| 369 | case kTfLiteUInt8: |
| 370 | return armnn::DataType::QAsymmU8; |
| 371 | case kTfLiteInt8: |
| 372 | { |
| 373 | auto quantizationInfo = tfLiteTensor.quantization; |
| 374 | if (quantizationInfo.type == kTfLiteAffineQuantization) |
| 375 | { |
| 376 | auto* quantization = |
| 377 | reinterpret_cast<TfLiteAffineQuantization*>(tfLiteTensor.quantization.params); |
| 378 | if (quantization->zero_point != nullptr && quantization->zero_point->size == 1) |
| 379 | { |
| 380 | return armnn::DataType::QAsymmS8; |
| 381 | } |
| 382 | else |
| 383 | { |
| 384 | return armnn::DataType::QSymmS8; |
| 385 | } |
| 386 | } |
| 387 | else |
| 388 | { |
| 389 | return armnn::DataType::QAsymmS8; |
| 390 | } |
| 391 | } |
| 392 | case kTfLiteInt16: |
| 393 | return armnn::DataType::QSymmS16; |
| 394 | case kTfLiteInt32: |
| 395 | return armnn::DataType::Signed32; |
| 396 | case kTfLiteInt64: |
| 397 | return armnn::DataType::Signed64; |
| 398 | default: |
| 399 | throw armnn::Exception(&"TfLiteArmnnDelegate: Unsupported data type: " [ tfLiteTensor.type]); |
| 400 | } |
| 401 | } |
| 402 | |
| 403 | armnn::TensorInfo GetTensorInfoForTfLiteTensor(const TfLiteTensor& tfLiteTensor, bool isOutput = false) |
| 404 | { |
| 405 | armnn::DataType type = GetDataType(tfLiteTensor); |
| 406 | armnn::TensorInfo ret; |
| 407 | auto tensorDimensionSize = tfLiteTensor.dims->size; |
| 408 | if (tensorDimensionSize == 0) |
| 409 | { |
| 410 | // If input tensor does not have a shape |
| 411 | // assuming that it has 1D tensor |
| 412 | if (!isOutput) |
| 413 | { |
| 414 | std::vector<unsigned int> safeShape = { 1 }; |
| 415 | bool dimensionsSpecificity[1] = { true }; |
Mike Kelly | 460a179 | 2023-08-01 11:31:55 +0100 | [diff] [blame] | 416 | armnn::TensorShape tensorShape(safeShape.size(), |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 417 | safeShape.data(), |
| 418 | dimensionsSpecificity); |
| 419 | ret = armnn::TensorInfo(tensorShape, type); |
| 420 | if(tflite::IsConstantTensor(&tfLiteTensor)) |
| 421 | { |
| 422 | ret.SetConstant(true); |
| 423 | } |
| 424 | } |
| 425 | else |
| 426 | { |
| 427 | armnn::TensorShape tensorShape(armnn::Dimensionality::NotSpecified); |
| 428 | ret = armnn::TensorInfo(tensorShape, type); |
| 429 | } |
| 430 | } |
| 431 | else |
| 432 | { |
Mike Kelly | 460a179 | 2023-08-01 11:31:55 +0100 | [diff] [blame] | 433 | std::vector<unsigned int> tensorDims(tensorDimensionSize); |
| 434 | std::vector<unsigned char> dimensionsSpecificity(tensorDimensionSize, true); |
| 435 | for (int i = 0; i < tensorDimensionSize; ++i) { |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 436 | auto dim = tfLiteTensor.dims->data[i]; |
Mike Kelly | 460a179 | 2023-08-01 11:31:55 +0100 | [diff] [blame] | 437 | if (dim <= 0) |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 438 | { |
| 439 | dimensionsSpecificity[i] = false; |
| 440 | } |
| 441 | tensorDims[i] = static_cast<unsigned int>(dim); |
| 442 | } |
Mike Kelly | 460a179 | 2023-08-01 11:31:55 +0100 | [diff] [blame] | 443 | armnn::TensorShape tensorShape(tensorDimensionSize, |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 444 | tensorDims.data(), |
Mike Kelly | 460a179 | 2023-08-01 11:31:55 +0100 | [diff] [blame] | 445 | reinterpret_cast<const bool *>(dimensionsSpecificity.data())); |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 446 | |
Mike Kelly | 460a179 | 2023-08-01 11:31:55 +0100 | [diff] [blame] | 447 | if (tflite::IsConstantTensor(&tfLiteTensor)) |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 448 | { |
| 449 | ret = armnn::TensorInfo(tensorShape, type); |
| 450 | ret.SetConstant(true); |
| 451 | } |
| 452 | else |
| 453 | { |
| 454 | ret = armnn::TensorInfo(tensorShape, type); |
| 455 | } |
| 456 | } |
| 457 | |
| 458 | auto quantizationInfo = tfLiteTensor.quantization; |
| 459 | if (quantizationInfo.type == kTfLiteAffineQuantization) |
| 460 | { |
| 461 | // get per-channel quantization parameters |
| 462 | const auto* affineQuantization = |
| 463 | reinterpret_cast<TfLiteAffineQuantization*>(tfLiteTensor.quantization.params); |
| 464 | if (affineQuantization->scale->size > 1) |
| 465 | { |
| 466 | std::vector<float> quantizationScales; |
| 467 | for (unsigned int i = 0; i < static_cast<unsigned int>(affineQuantization->scale->size); ++i) |
| 468 | { |
| 469 | quantizationScales.push_back(affineQuantization->scale->data[i]); |
| 470 | } |
| 471 | ret.SetQuantizationScales(quantizationScales); |
| 472 | ret.SetQuantizationDim(armnn::numeric_cast<unsigned int>(affineQuantization->quantized_dimension)); |
| 473 | } |
| 474 | else |
| 475 | { |
| 476 | ret.SetQuantizationScale(affineQuantization->scale->data[0]); |
| 477 | ret.SetQuantizationOffset(affineQuantization->zero_point->data[0]); |
| 478 | } |
| 479 | } |
| 480 | else |
| 481 | { |
| 482 | auto quantizationParameters = tfLiteTensor.params; |
| 483 | ret.SetQuantizationScale(quantizationParameters.scale); |
| 484 | ret.SetQuantizationOffset(quantizationParameters.zero_point); |
| 485 | } |
| 486 | |
| 487 | return ret; |
| 488 | } |
| 489 | |
| 490 | armnn::ConstTensor CreateConstTensor(const TfLiteTensor* tfLiteTensor, |
| 491 | const armnn::TensorInfo& tensorInfo) |
| 492 | { |
| 493 | if (tfLiteTensor->allocation_type != kTfLiteMmapRo) |
| 494 | { |
| 495 | throw armnn::Exception( |
| 496 | "TfLiteArmnnDelegate: Not constant allocation type: " + std::to_string(tfLiteTensor->allocation_type)); |
| 497 | } |
| 498 | |
| 499 | return armnn::ConstTensor(tensorInfo, tfLiteTensor->data.data); |
| 500 | } |
| 501 | |
| 502 | armnn::ConstTensor* GetConstTensorForTfLiteTensor(const TfLiteTensor* tfLiteTensors, TfLiteNode* tfLiteNode, int index) |
| 503 | { |
| 504 | const TfLiteTensor &tfLiteTensor = tfLiteTensors[tfLiteNode->inputs->data[index]]; |
| 505 | armnn::TensorInfo tensorInfo = GetTensorInfoForTfLiteTensor(tfLiteTensor); |
| 506 | return new armnn::ConstTensor(tensorInfo, tfLiteTensor.data.data); |
| 507 | } |
| 508 | |
| 509 | bool IsOptionalOperandPresent(TfLiteNode* tfLiteNode, const int operandIndex) |
| 510 | { |
| 511 | // If the inputs array has fewer than operandIndex entries or if the entry at operandIndex has a value of -1 or |
| 512 | // less then the input is not present. |
| 513 | if (tfLiteNode->inputs->size > operandIndex && tfLiteNode->inputs->data[operandIndex] >= 0) |
| 514 | { |
| 515 | return true; |
| 516 | } |
| 517 | return false; |
| 518 | } |
| 519 | |
| 520 | TfLiteStatus ProcessInputs(armnn::IConnectableLayer* layer, |
| 521 | armnnDelegate::DelegateData& delegateData, |
| 522 | TfLiteContext* tfLiteContext, |
Mike Kelly | 07169c8 | 2023-08-02 13:23:09 +0100 | [diff] [blame^] | 523 | TfLiteNode* tfLiteNode, |
| 524 | int nodeIndex) |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 525 | { |
| 526 | const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; |
| 527 | // Process input tensors |
| 528 | // If input tensor is a Constant tensor create a constant layer and connect it to the network |
| 529 | for (unsigned int inputIndex = 0; inputIndex < layer->GetNumInputSlots(); ++inputIndex) |
| 530 | { |
| 531 | const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[inputIndex]]; |
| 532 | if (tflite::IsConstantTensor(&tfLiteInputTensor)) |
| 533 | { |
| 534 | armnn::TensorInfo inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); |
| 535 | bool isSupported = false; |
| 536 | armnn::BackendId setBackend; |
| 537 | FORWARD_LAYER_SUPPORT_FUNC("CONSTANT", |
| 538 | tfLiteContext, |
| 539 | IsConstantSupported, |
| 540 | delegateData.m_Backends, |
| 541 | isSupported, |
| 542 | setBackend, |
| 543 | inputTensorInfo); |
| 544 | if (!isSupported) |
| 545 | { |
| 546 | return kTfLiteError; |
| 547 | } |
| 548 | auto constantInput = CreateConstTensor(&tfLiteInputTensor, |
| 549 | inputTensorInfo); |
Mike Kelly | 07169c8 | 2023-08-02 13:23:09 +0100 | [diff] [blame^] | 550 | |
| 551 | auto layerName = GetLayerName(armnn::LayerType::Constant, nodeIndex); |
| 552 | armnn::IConnectableLayer* constantLayer = delegateData.m_Network->AddConstantLayer(constantInput, |
| 553 | layerName.c_str()); |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 554 | constantLayer->SetBackendId(setBackend); |
| 555 | armnn::IOutputSlot& outputSlot = constantLayer->GetOutputSlot(0); |
| 556 | outputSlot.SetTensorInfo(inputTensorInfo); |
| 557 | |
| 558 | delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[inputIndex]] = &outputSlot; |
| 559 | } |
| 560 | } |
| 561 | return kTfLiteOk; |
| 562 | } |
| 563 | |
| 564 | } // namespace anonymous |