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> |
| 13 | #include <armnn/utility/Assert.hpp> |
| 14 | #include <armnn/utility/NumericCast.hpp> |
| 15 | |
| 16 | #include <armnnUtils/Permute.hpp> |
| 17 | #include <armnnUtils/TensorUtils.hpp> |
| 18 | |
| 19 | #include <tensorflow/lite/builtin_ops.h> |
| 20 | #include <tensorflow/lite/c/builtin_op_data.h> |
| 21 | #include <tensorflow/lite/c/common.h> |
| 22 | #include <tensorflow/lite/c/c_api_opaque.h> |
| 23 | #include <tensorflow/lite/minimal_logging.h> |
| 24 | #include <tensorflow/lite/kernels/kernel_util.h> |
| 25 | |
| 26 | namespace |
| 27 | { |
| 28 | |
| 29 | // Macro to call an Is<layer_name>Supported function and log caller name together with reason for lack of support |
| 30 | #define FORWARD_LAYER_OPAQUE_SUPPORT_FUNC(opName, tfLiteContext, func, backends, supported, setBackend, ...) \ |
| 31 | try \ |
| 32 | { \ |
| 33 | for (auto&& backendId : backends) \ |
| 34 | { \ |
| 35 | auto layerSupportObject = armnn::GetILayerSupportByBackendId(backendId); \ |
| 36 | if (layerSupportObject.IsBackendRegistered()) \ |
| 37 | { \ |
| 38 | std::string reasonIfUnsupported; \ |
| 39 | supported = \ |
| 40 | layerSupportObject.func(__VA_ARGS__, armnn::Optional<std::string&>(reasonIfUnsupported)); \ |
| 41 | if (supported) \ |
| 42 | { \ |
| 43 | setBackend = backendId; \ |
| 44 | break; \ |
| 45 | } \ |
| 46 | else \ |
| 47 | { \ |
| 48 | if (reasonIfUnsupported.size() > 0) \ |
| 49 | { \ |
| 50 | TFLITE_LOG_PROD(tflite::TFLITE_LOG_WARNING, \ |
| 51 | "%s: not supported by armnn: %s", opName, reasonIfUnsupported.c_str()); \ |
| 52 | } \ |
| 53 | else \ |
| 54 | { \ |
| 55 | TFLITE_LOG_PROD(tflite::TFLITE_LOG_WARNING, \ |
| 56 | "%s: not supported by armnn", opName); \ |
| 57 | } \ |
| 58 | } \ |
| 59 | } \ |
| 60 | else \ |
| 61 | { \ |
| 62 | TF_LITE_OPAQUE_KERNEL_LOG(tfLiteContext, "%s: backend not registered: %s", \ |
| 63 | opName, backendId.Get().c_str()); \ |
| 64 | } \ |
| 65 | } \ |
| 66 | if (!supported) \ |
| 67 | { \ |
| 68 | TF_LITE_OPAQUE_KERNEL_LOG(tfLiteContext, "%s: not supported by any specified backend", opName); \ |
| 69 | } \ |
| 70 | } \ |
| 71 | catch (const armnn::InvalidArgumentException &e) \ |
| 72 | { \ |
| 73 | throw armnn::InvalidArgumentException(e, "Failed to check layer support", CHECK_LOCATION()); \ |
| 74 | } |
| 75 | |
| 76 | TfLiteStatus ValidateNumInputs(TfLiteOpaqueContext* tfLiteContext, |
| 77 | TfLiteOpaqueNode* tfLiteNode, |
| 78 | const unsigned int expectedSize, |
| 79 | int nodeIndex) |
| 80 | { |
| 81 | int numInputs = TfLiteOpaqueNodeNumberOfInputs(tfLiteNode); |
| 82 | if (static_cast<unsigned int>(numInputs) != expectedSize) |
| 83 | { |
| 84 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 85 | tfLiteContext, "TfLiteArmnnOpaqueDelegate: Unexpected number of inputs (%d != %d) in node #%d", |
| 86 | numInputs, expectedSize, nodeIndex); |
| 87 | return kTfLiteError; |
| 88 | } |
| 89 | return kTfLiteOk; |
| 90 | } |
| 91 | |
| 92 | TfLiteStatus ValidateNumOutputs(TfLiteOpaqueContext* tfLiteContext, |
| 93 | TfLiteOpaqueNode* tfLiteNode, |
| 94 | const unsigned int expectedSize, |
| 95 | int nodeIndex) |
| 96 | { |
| 97 | auto numOutputs = TfLiteOpaqueNodeNumberOfOutputs(tfLiteNode); |
| 98 | if (static_cast<unsigned int>(numOutputs) != expectedSize) |
| 99 | { |
| 100 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 101 | tfLiteContext, "TfLiteArmnnOpaqueDelegate: Unexpected number of outputs (%d != %d) in node #%d", |
| 102 | numOutputs, expectedSize, nodeIndex); |
| 103 | return kTfLiteError; |
| 104 | } |
| 105 | return kTfLiteOk; |
| 106 | } |
| 107 | |
| 108 | bool IsConstantTensor(const TfLiteOpaqueTensor* tfLiteTensor) |
| 109 | { |
| 110 | auto tensorAllocationType = TfLiteOpaqueTensorGetAllocationType(tfLiteTensor); |
| 111 | if (tensorAllocationType == kTfLiteMmapRo) |
| 112 | { |
| 113 | return true; |
| 114 | } |
| 115 | return false; |
| 116 | } |
| 117 | |
Ryan OShea | a37ccb0 | 2023-04-11 10:54:07 +0100 | [diff] [blame^] | 118 | bool IsDynamicTensor(const TfLiteOpaqueTensor* tfLiteTensor) |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 119 | { |
Ryan OShea | a37ccb0 | 2023-04-11 10:54:07 +0100 | [diff] [blame^] | 120 | auto tensorAllocationType = TfLiteOpaqueTensorGetAllocationType(tfLiteTensor); |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 121 | if (tensorAllocationType == kTfLiteDynamic) |
| 122 | { |
| 123 | return true; |
| 124 | } |
| 125 | return false; |
| 126 | } |
| 127 | |
| 128 | bool IsValid(const TfLiteOpaqueTensor* tfLiteTensor) |
| 129 | { |
| 130 | return tfLiteTensor == nullptr ? false : true; |
| 131 | } |
| 132 | |
| 133 | bool IsValid(TfLiteOpaqueContext* tfLiteContext, |
Ryan OShea | a37ccb0 | 2023-04-11 10:54:07 +0100 | [diff] [blame^] | 134 | const TfLiteOpaqueTensor* tfLiteTensor, |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 135 | int32_t operatorCode, |
| 136 | int32_t nodeIndex) |
| 137 | { |
Ryan OShea | a37ccb0 | 2023-04-11 10:54:07 +0100 | [diff] [blame^] | 138 | if(!IsValid(tfLiteTensor)) |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 139 | { |
| 140 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 141 | tfLiteContext, |
| 142 | "TfLiteArmnnDelegate: Invalid TfLite tensor in operator #%d node #%d: ", |
| 143 | operatorCode, nodeIndex); |
| 144 | return false; |
| 145 | } |
| 146 | if (IsDynamicTensor(tfLiteTensor)) |
| 147 | { |
| 148 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 149 | tfLiteContext, |
| 150 | "TfLiteArmnnDelegate: Dynamic tensors are not supported in operator #%d node #%d: ", |
| 151 | operatorCode, nodeIndex); |
| 152 | return false; |
| 153 | } |
| 154 | return true; |
| 155 | } |
| 156 | |
| 157 | bool IsAffineQuantization(const TfLiteOpaqueTensor& tfLiteTensor) |
| 158 | { |
| 159 | auto quantizationInfo = TfLiteOpaqueTensorGetQuantization(&tfLiteTensor); |
| 160 | if (quantizationInfo.type == kTfLiteAffineQuantization) |
| 161 | { |
| 162 | return true; |
| 163 | } |
| 164 | return false; |
| 165 | } |
| 166 | |
Ryan OShea | a37ccb0 | 2023-04-11 10:54:07 +0100 | [diff] [blame^] | 167 | // Connects the layer to the graph |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 168 | TfLiteStatus Connect(armnn::IConnectableLayer* layer, |
| 169 | TfLiteOpaqueContext* tfLiteContext, |
| 170 | TfLiteOpaqueNode* tfLiteNode, |
| 171 | armnnOpaqueDelegate::DelegateData& data) |
| 172 | { |
Ryan OShea | a37ccb0 | 2023-04-11 10:54:07 +0100 | [diff] [blame^] | 173 | // Get array of input indices, inputIndexArray is set from the TfLiteOpaqueNodeInputs function |
| 174 | // This function turns inputIndexArray into an int array of indices. These indices point to the index of the |
| 175 | // tensors for each input slot in the node. |
| 176 | const int* inputIndexArray; |
| 177 | int numInputs; |
| 178 | if(TfLiteOpaqueNodeInputs(tfLiteNode, &inputIndexArray, &numInputs) != kTfLiteOk) |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 179 | { |
| 180 | return kTfLiteError; |
| 181 | } |
Ryan OShea | a37ccb0 | 2023-04-11 10:54:07 +0100 | [diff] [blame^] | 182 | // numInputs is set from TfLiteOpaqueNodeInputs. |
| 183 | if(numInputs != static_cast<int>(layer->GetNumInputSlots())) |
| 184 | { |
| 185 | ARMNN_LOG(error) << "Layer: " << layer->GetName() << ": Expected number of input slots does not match actual " |
| 186 | "number of input slots."; |
| 187 | return kTfLiteError; |
| 188 | } |
| 189 | // Connect the input slots. |
| 190 | // For each input slot, get the index of the opaque tensor that was allocated for it. |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 191 | for (unsigned int inputIndex = 0; inputIndex < layer->GetNumInputSlots(); ++inputIndex) |
| 192 | { |
Ryan OShea | a37ccb0 | 2023-04-11 10:54:07 +0100 | [diff] [blame^] | 193 | if (data.m_OutputSlotForNode[inputIndexArray[inputIndex]] != nullptr) |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 194 | { |
Ryan OShea | a37ccb0 | 2023-04-11 10:54:07 +0100 | [diff] [blame^] | 195 | data.m_OutputSlotForNode[inputIndexArray[inputIndex]]->Connect(layer->GetInputSlot(inputIndex)); |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 196 | } |
| 197 | } |
| 198 | |
Ryan OShea | a37ccb0 | 2023-04-11 10:54:07 +0100 | [diff] [blame^] | 199 | // Get array of output indices, outputIndexArray is set from the TfLiteOpaqueNodeOutputs function |
| 200 | // This function turns outputIndexArray into an int array of indices. These indices point to the tensors for |
| 201 | // each output slot in the node. |
| 202 | const int* outputIndexArray; |
| 203 | int numOutputs; |
| 204 | if(TfLiteOpaqueNodeOutputs(tfLiteNode, &outputIndexArray, &numOutputs) != kTfLiteOk) |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 205 | { |
| 206 | return kTfLiteError; |
| 207 | } |
Ryan OShea | a37ccb0 | 2023-04-11 10:54:07 +0100 | [diff] [blame^] | 208 | // numOutputs is set from TfLiteOpaqueNodeOutputs. |
| 209 | if(numOutputs != static_cast<int>(layer->GetNumOutputSlots())) |
| 210 | { |
| 211 | ARMNN_LOG(error) << "Layer: " << layer->GetName() << ": Expected number of output slots does not match actual " |
| 212 | "number of output slots."; |
| 213 | return kTfLiteError; |
| 214 | } |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 215 | |
| 216 | // Prepare output slots |
| 217 | for (unsigned int outputIndex = 0; outputIndex < layer->GetNumOutputSlots(); ++outputIndex) |
| 218 | { |
| 219 | armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(outputIndex); |
Ryan OShea | a37ccb0 | 2023-04-11 10:54:07 +0100 | [diff] [blame^] | 220 | data.m_OutputSlotForNode[static_cast<unsigned long>(outputIndexArray[outputIndex])] = &outputSlot; |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 221 | } |
| 222 | |
| 223 | return kTfLiteOk; |
| 224 | } |
| 225 | |
| 226 | TfLiteStatus FusedActivation(TfLiteOpaqueContext* tfLiteContext, |
| 227 | TfLiteOpaqueNode* tfLiteNode, |
| 228 | TfLiteFusedActivation activationType, |
| 229 | armnn::IConnectableLayer* prevLayer, |
| 230 | unsigned int outputSlotIndex, |
| 231 | armnnOpaqueDelegate::DelegateData& data) |
| 232 | { |
| 233 | const armnn::TensorInfo& activationOutputInfo = prevLayer->GetOutputSlot(outputSlotIndex).GetTensorInfo(); |
| 234 | |
| 235 | armnn::ActivationDescriptor activationDesc; |
| 236 | |
| 237 | switch (activationType) |
| 238 | { |
| 239 | case kTfLiteActNone: |
| 240 | { |
| 241 | // No Activation |
| 242 | return kTfLiteOk; |
| 243 | } |
| 244 | case kTfLiteActRelu: |
| 245 | { |
| 246 | activationDesc.m_Function = armnn::ActivationFunction::ReLu; |
| 247 | break; |
| 248 | } |
| 249 | case kTfLiteActReluN1To1: |
| 250 | { |
| 251 | activationDesc.m_Function = armnn::ActivationFunction::BoundedReLu; |
| 252 | activationDesc.m_A = 1.0f; |
| 253 | activationDesc.m_B = -1.0f; |
| 254 | break; |
| 255 | } |
| 256 | case kTfLiteActRelu6: |
| 257 | { |
| 258 | activationDesc.m_Function = armnn::ActivationFunction::BoundedReLu; |
| 259 | activationDesc.m_A = 6.0f; |
| 260 | activationDesc.m_B = 0.0f; |
| 261 | break; |
| 262 | } |
| 263 | case kTfLiteActSigmoid: |
| 264 | { |
| 265 | activationDesc.m_Function = armnn::ActivationFunction::Sigmoid; |
| 266 | break; |
| 267 | } |
| 268 | case kTfLiteActTanh: |
| 269 | { |
| 270 | activationDesc.m_Function = armnn::ActivationFunction::TanH; |
| 271 | activationDesc.m_A = 1.0f; |
| 272 | activationDesc.m_B = 1.0f; |
| 273 | break; |
| 274 | } |
| 275 | default: |
| 276 | return kTfLiteError; |
| 277 | } |
| 278 | |
| 279 | bool isSupported = false; |
| 280 | armnn::BackendId setBackend; |
| 281 | FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("ACTIVATION", |
| 282 | tfLiteContext, |
| 283 | IsActivationSupported, |
| 284 | data.m_Backends, |
| 285 | isSupported, |
| 286 | setBackend, |
| 287 | activationOutputInfo, |
| 288 | activationOutputInfo, |
| 289 | activationDesc); |
| 290 | if (!isSupported) |
| 291 | { |
| 292 | return kTfLiteError; |
| 293 | } |
| 294 | armnn::IConnectableLayer* activationLayer = data.m_Network->AddActivationLayer(activationDesc); |
| 295 | activationLayer->SetBackendId(setBackend); |
| 296 | |
| 297 | ARMNN_ASSERT(activationLayer != nullptr); |
| 298 | activationLayer->GetOutputSlot(0).SetTensorInfo(activationOutputInfo); |
| 299 | |
Ryan OShea | a37ccb0 | 2023-04-11 10:54:07 +0100 | [diff] [blame^] | 300 | // Get array of output indices, outputIndexArray is set from the TfLiteOpaqueNodeOutputs function |
| 301 | // This function turns outputIndexArray into an int array of indices. These indices point to the tensors for |
| 302 | // each output slot in the node. |
| 303 | const int* outputIndexArray; |
| 304 | int numOutputs; |
| 305 | TfLiteStatus outputStatus = TfLiteOpaqueNodeOutputs(tfLiteNode, &outputIndexArray, &numOutputs); |
| 306 | if(outputStatus != kTfLiteOk) |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 307 | { |
| 308 | return kTfLiteError; |
| 309 | } |
| 310 | |
| 311 | // Connect and prepare output slots |
| 312 | for (unsigned int outputIndex = 0; outputIndex < activationLayer->GetNumOutputSlots(); ++outputIndex) |
| 313 | { |
| 314 | data.m_OutputSlotForNode[static_cast<unsigned long>( |
Ryan OShea | a37ccb0 | 2023-04-11 10:54:07 +0100 | [diff] [blame^] | 315 | outputIndexArray[outputIndex])]->Connect(activationLayer->GetInputSlot(0)); |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 316 | |
| 317 | armnn::IOutputSlot& outputSlot = activationLayer->GetOutputSlot(outputIndex); |
Ryan OShea | a37ccb0 | 2023-04-11 10:54:07 +0100 | [diff] [blame^] | 318 | data.m_OutputSlotForNode[static_cast<unsigned long>(outputIndexArray[outputIndex])] = &outputSlot; |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 319 | } |
| 320 | return kTfLiteOk; |
| 321 | } |
| 322 | |
| 323 | armnn::IConnectableLayer* AddReshapeLayer(TfLiteOpaqueContext* tfLiteContext, |
| 324 | TfLiteOpaqueNode* tfLiteNode, |
| 325 | armnn::IConnectableLayer* prevLayer, |
| 326 | armnn::TensorInfo reshapedOutputTensorInfo, |
| 327 | armnn::TensorInfo outputTensorInfo, |
| 328 | armnnOpaqueDelegate::DelegateData& data) |
| 329 | { |
| 330 | armnn::ReshapeDescriptor desc; |
| 331 | desc.m_TargetShape = outputTensorInfo.GetShape(); |
| 332 | |
| 333 | bool isSupported = false; |
| 334 | armnn::BackendId setBackend; |
| 335 | FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("RESHAPE", |
| 336 | tfLiteContext, |
| 337 | IsReshapeSupported, |
| 338 | data.m_Backends, |
| 339 | isSupported, |
| 340 | setBackend, |
| 341 | reshapedOutputTensorInfo, |
| 342 | outputTensorInfo, |
| 343 | desc); |
| 344 | |
| 345 | if (!isSupported) |
| 346 | { |
| 347 | return nullptr; |
| 348 | } |
| 349 | |
| 350 | armnn::IConnectableLayer* reshapeLayer = data.m_Network->AddReshapeLayer(desc); |
| 351 | reshapeLayer->SetBackendId(setBackend); |
| 352 | ARMNN_ASSERT(reshapeLayer != nullptr); |
| 353 | |
| 354 | prevLayer->GetOutputSlot(0).SetTensorInfo(reshapedOutputTensorInfo); |
| 355 | reshapeLayer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| 356 | |
| 357 | // Gather array of indices and it's length, replaces node->outputs->data[i] |
| 358 | const int* outputIndices = nullptr; |
| 359 | int numOutputs = 0; |
| 360 | |
| 361 | TfLiteStatus status = TfLiteOpaqueNodeOutputs(tfLiteNode, &outputIndices, &numOutputs); |
| 362 | if(status != kTfLiteOk) |
| 363 | { |
| 364 | throw armnn::Exception("TfLiteArmnnOpaqueDelegate: Unable to gather output information from node."); |
| 365 | } |
| 366 | |
| 367 | if (static_cast<unsigned int>(numOutputs) != reshapeLayer->GetNumOutputSlots()) |
| 368 | { |
| 369 | throw armnn::Exception("TfLiteArmnnOpaqueDelegate: Unexpected number of outputs (" + |
| 370 | std::to_string(numOutputs) + |
| 371 | "!= " + |
| 372 | std::to_string(reshapeLayer->GetNumOutputSlots()) + |
| 373 | ") in node."); |
| 374 | } |
| 375 | |
| 376 | // Connect and prepare output slots |
| 377 | for (unsigned int outputIndex = 0; outputIndex < reshapeLayer->GetNumOutputSlots(); ++outputIndex) |
| 378 | { |
| 379 | data.m_OutputSlotForNode[static_cast<unsigned long>( |
| 380 | outputIndices[outputIndex])]->Connect(reshapeLayer->GetInputSlot(0)); |
| 381 | |
| 382 | armnn::IOutputSlot& outputSlot = reshapeLayer->GetOutputSlot(outputIndex); |
| 383 | data.m_OutputSlotForNode[static_cast<unsigned long>(outputIndices[outputIndex])] = &outputSlot; |
| 384 | } |
| 385 | return reshapeLayer; |
| 386 | } |
| 387 | |
| 388 | armnn::DataType GetDataType(const TfLiteOpaqueTensor* tfLiteTensor) |
| 389 | { |
| 390 | switch (TfLiteOpaqueTensorType(tfLiteTensor)) |
| 391 | { |
| 392 | case kTfLiteBool: |
| 393 | return armnn::DataType::Boolean; |
| 394 | case kTfLiteFloat32: |
| 395 | return armnn::DataType::Float32; |
| 396 | case kTfLiteFloat16: |
| 397 | return armnn::DataType::Float16; |
| 398 | case kTfLiteUInt8: |
| 399 | return armnn::DataType::QAsymmU8; |
| 400 | case kTfLiteInt8: |
| 401 | { |
| 402 | auto quantizationInfo = TfLiteOpaqueTensorGetQuantization(tfLiteTensor); |
| 403 | if (quantizationInfo.type == kTfLiteAffineQuantization) |
| 404 | { |
| 405 | auto* quantization = |
| 406 | reinterpret_cast<TfLiteAffineQuantization*>(quantizationInfo.params); |
| 407 | |
| 408 | if (quantization->zero_point != nullptr && quantization->zero_point->size == 1) |
| 409 | { |
| 410 | return armnn::DataType::QAsymmS8; |
| 411 | } |
| 412 | else |
| 413 | { |
| 414 | return armnn::DataType::QSymmS8; |
| 415 | } |
| 416 | } |
| 417 | else |
| 418 | { |
| 419 | return armnn::DataType::QAsymmS8; |
| 420 | } |
| 421 | } |
| 422 | case kTfLiteInt16: |
| 423 | return armnn::DataType::QSymmS16; |
| 424 | case kTfLiteInt32: |
| 425 | return armnn::DataType::Signed32; |
| 426 | case kTfLiteInt64: |
| 427 | return armnn::DataType::Signed64; |
| 428 | default: |
| 429 | throw armnn::Exception( |
| 430 | &"TfLiteArmnnDelegate: Unsupported data type: " [ TfLiteOpaqueTensorType(tfLiteTensor) ]); |
| 431 | } |
| 432 | } |
| 433 | |
| 434 | armnn::TensorInfo GetTensorInfoForTfLiteOpaqueTensor(const TfLiteOpaqueTensor* tfLiteTensor, bool isOutput = false) |
| 435 | { |
| 436 | armnn::DataType type = GetDataType(tfLiteTensor); |
| 437 | armnn::TensorInfo ret; |
| 438 | |
| 439 | auto tensorDimensionSize = TfLiteOpaqueTensorNumDims(tfLiteTensor); |
| 440 | if (tensorDimensionSize == 0) |
| 441 | { |
| 442 | // If input tensor does not have a shape |
| 443 | // assuming that it has 1D tensor |
| 444 | if (!isOutput) |
| 445 | { |
| 446 | std::vector<unsigned int> safeShape = { 1 }; |
| 447 | bool dimensionsSpecificity[1] = { true }; |
| 448 | |
| 449 | armnn::TensorShape tensorShape(armnn::numeric_cast<unsigned int>(safeShape.size()), |
| 450 | safeShape.data(), |
| 451 | dimensionsSpecificity); |
| 452 | ret = armnn::TensorInfo(tensorShape, type); |
| 453 | |
| 454 | if(IsConstantTensor(tfLiteTensor)) |
| 455 | { |
| 456 | ret.SetConstant(true); |
| 457 | } |
| 458 | } |
| 459 | else |
| 460 | { |
| 461 | armnn::TensorShape tensorShape(armnn::Dimensionality::NotSpecified); |
| 462 | ret = armnn::TensorInfo(tensorShape, type); |
| 463 | } |
| 464 | } |
| 465 | else |
| 466 | { |
| 467 | std::vector<unsigned int> tensorDims(static_cast<unsigned int>(tensorDimensionSize)); |
| 468 | bool dimensionsSpecificity[5] = { true, true, true, true, true }; |
| 469 | |
| 470 | for (int32_t i = 0; i < tensorDimensionSize; ++i) |
| 471 | { |
| 472 | int32_t dim = TfLiteOpaqueTensorDim(tfLiteTensor, i); |
| 473 | |
| 474 | if (dim == 0) |
| 475 | { |
| 476 | dimensionsSpecificity[i] = false; |
| 477 | } |
| 478 | tensorDims[i] = static_cast<unsigned int>(dim); |
| 479 | } |
| 480 | |
| 481 | armnn::TensorShape tensorShape(static_cast<unsigned int>(tensorDimensionSize), |
| 482 | tensorDims.data(), |
| 483 | dimensionsSpecificity); |
| 484 | |
| 485 | if(IsConstantTensor(tfLiteTensor)) |
| 486 | { |
| 487 | ret = armnn::TensorInfo(tensorShape, type); |
| 488 | ret.SetConstant(true); |
| 489 | } |
| 490 | else |
| 491 | { |
| 492 | ret = armnn::TensorInfo(tensorShape, type); |
| 493 | } |
| 494 | } |
| 495 | |
| 496 | auto quantizationInfo = TfLiteOpaqueTensorGetQuantization(tfLiteTensor); |
| 497 | if (quantizationInfo.type == kTfLiteAffineQuantization) |
| 498 | { |
| 499 | // get per-channel quantization parameters |
| 500 | const auto* affineQuantization = |
| 501 | reinterpret_cast<TfLiteAffineQuantization*>(quantizationInfo.params); |
| 502 | if (affineQuantization->scale->size > 1) |
| 503 | { |
| 504 | std::vector<float> quantizationScales; |
| 505 | for (unsigned int i = 0; i < static_cast<unsigned int>(affineQuantization->scale->size); ++i) |
| 506 | { |
| 507 | quantizationScales.push_back(affineQuantization->scale->data[i]); |
| 508 | } |
| 509 | ret.SetQuantizationScales(quantizationScales); |
| 510 | ret.SetQuantizationDim(armnn::numeric_cast<unsigned int>(affineQuantization->quantized_dimension)); |
| 511 | } |
| 512 | else |
| 513 | { |
| 514 | ret.SetQuantizationScale(affineQuantization->scale->data[0]); |
| 515 | ret.SetQuantizationOffset(affineQuantization->zero_point->data[0]); |
| 516 | } |
| 517 | } |
| 518 | else |
| 519 | { |
| 520 | auto quantizationParameters = TfLiteOpaqueTensorGetQuantizationParams(tfLiteTensor); |
| 521 | ret.SetQuantizationScale(quantizationParameters.scale); |
| 522 | ret.SetQuantizationOffset(quantizationParameters.zero_point); |
| 523 | } |
| 524 | |
| 525 | return ret; |
| 526 | } |
| 527 | |
| 528 | armnn::ConstTensor CreateConstTensor(const TfLiteOpaqueTensor* tfLiteTensor, |
| 529 | const armnn::TensorInfo& tensorInfo) |
| 530 | { |
| 531 | auto allocType = TfLiteOpaqueTensorGetAllocationType(tfLiteTensor); |
| 532 | if (allocType != kTfLiteMmapRo) |
| 533 | { |
| 534 | throw armnn::Exception("TfLiteArmnnDelegate: Not constant allocation type: " + std::to_string(allocType)); |
| 535 | } |
| 536 | |
| 537 | return armnn::ConstTensor(tensorInfo, TfLiteOpaqueTensorData(tfLiteTensor)); |
| 538 | } |
| 539 | |
| 540 | armnn::ConstTensor* GetConstTensorForTfLiteTensor(const TfLiteOpaqueContext* tfLiteContext, |
| 541 | TfLiteOpaqueNode* tfLiteNode, |
| 542 | int index) |
| 543 | { |
| 544 | const TfLiteOpaqueTensor* tfLiteTensor = TfLiteOpaqueNodeGetInput(tfLiteContext, tfLiteNode, index); |
| 545 | armnn::TensorInfo tensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteTensor); |
| 546 | |
| 547 | return new armnn::ConstTensor(tensorInfo, TfLiteOpaqueTensorData(tfLiteTensor)); |
| 548 | } |
| 549 | |
| 550 | bool IsOptionalOperandPresent(TfLiteOpaqueNode* tfLiteNode, const int operandIndex) |
| 551 | { |
Ryan OShea | a37ccb0 | 2023-04-11 10:54:07 +0100 | [diff] [blame^] | 552 | // Get array of input indices, inputIndexArray is set from the TfLiteOpaqueNodeInputs function |
| 553 | // This function turns inputIndexArray into an int array of indices. These indices point to the index of the |
| 554 | // tensors for each input slot in the node. |
| 555 | const int* inputIndexArray; |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 556 | int numInputs = 0; |
| 557 | |
Ryan OShea | a37ccb0 | 2023-04-11 10:54:07 +0100 | [diff] [blame^] | 558 | TfLiteStatus status = TfLiteOpaqueNodeInputs(tfLiteNode, &inputIndexArray, &numInputs); |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 559 | if(status != kTfLiteOk) |
| 560 | { |
| 561 | throw armnn::Exception("TfLiteArmnnOpaqueDelegate: Unable to gather input information from node."); |
| 562 | } |
| 563 | |
| 564 | // If the inputs array has fewer than operandIndex entries or if the entry at operandIndex has a value of -1 or |
| 565 | // less then the input is not present. |
Ryan OShea | a37ccb0 | 2023-04-11 10:54:07 +0100 | [diff] [blame^] | 566 | if (numInputs > operandIndex && inputIndexArray[operandIndex] >= 0) |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 567 | { |
| 568 | return true; |
| 569 | } |
| 570 | return false; |
| 571 | } |
| 572 | |
| 573 | TfLiteStatus ProcessInputs(armnn::IConnectableLayer* layer, |
| 574 | armnnOpaqueDelegate::DelegateData& delegateData, |
| 575 | TfLiteOpaqueContext* tfLiteContext, |
| 576 | TfLiteOpaqueNode* tfLiteNode) |
| 577 | { |
Ryan OShea | a37ccb0 | 2023-04-11 10:54:07 +0100 | [diff] [blame^] | 578 | // Get array of input indices, inputIndexArray is set from the TfLiteOpaqueNodeInputs function |
| 579 | // This function turns inputIndexArray into an int array of indices. These indices point to the index of the |
| 580 | // tensors for each input slot in the node. |
| 581 | const int* inputIndexArray; |
| 582 | int numInputs = 0; |
| 583 | |
| 584 | TfLiteStatus status = TfLiteOpaqueNodeInputs(tfLiteNode, &inputIndexArray, &numInputs); |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 585 | if(status != kTfLiteOk) |
| 586 | { |
Ryan OShea | a37ccb0 | 2023-04-11 10:54:07 +0100 | [diff] [blame^] | 587 | throw armnn::Exception("TfLiteArmnnOpaqueDelegate: Unable to gather input information from node."); |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 588 | } |
| 589 | |
| 590 | // Process input tensors |
| 591 | // If input tensor is a Constant tensor create a constant layer and connect it to the network |
| 592 | for (int32_t inputIndex = 0; inputIndex < static_cast<int32_t>(layer->GetNumInputSlots()); ++inputIndex) |
| 593 | { |
| 594 | const TfLiteOpaqueTensor* tfLiteInputTensor = TfLiteOpaqueNodeGetInput(tfLiteContext, tfLiteNode, inputIndex); |
| 595 | |
| 596 | if (IsConstantTensor(tfLiteInputTensor)) |
| 597 | { |
| 598 | armnn::TensorInfo inputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor); |
| 599 | |
| 600 | bool isSupported = false; |
| 601 | armnn::BackendId setBackend; |
| 602 | FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("CONSTANT", |
| 603 | tfLiteContext, |
| 604 | IsConstantSupported, |
| 605 | delegateData.m_Backends, |
| 606 | isSupported, |
| 607 | setBackend, |
| 608 | inputTensorInfo); |
| 609 | if (!isSupported) |
| 610 | { |
| 611 | return kTfLiteError; |
| 612 | } |
| 613 | |
| 614 | auto constantInput = CreateConstTensor(tfLiteInputTensor, inputTensorInfo); |
| 615 | |
| 616 | armnn::IConnectableLayer* constantLayer = delegateData.m_Network->AddConstantLayer(constantInput); |
| 617 | constantLayer->SetBackendId(setBackend); |
| 618 | armnn::IOutputSlot& outputSlot = constantLayer->GetOutputSlot(0); |
| 619 | outputSlot.SetTensorInfo(inputTensorInfo); |
| 620 | |
Ryan OShea | a37ccb0 | 2023-04-11 10:54:07 +0100 | [diff] [blame^] | 621 | delegateData.m_OutputSlotForNode[inputIndexArray[inputIndex]] = &outputSlot; |
Matthew Sloyan | 1157232 | 2023-03-16 10:17:51 +0000 | [diff] [blame] | 622 | } |
| 623 | } |
| 624 | return kTfLiteOk; |
| 625 | } |
| 626 | |
| 627 | } // namespace anonymous |