Francis Murtagh | c4fb0dd | 2023-03-16 17:01:56 +0000 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2023 Arm Ltd and Contributors. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
Matthew Sloyan | 0bd4c62 | 2023-04-27 11:48:26 +0100 | [diff] [blame] | 5 | |
| 6 | #pragma once |
| 7 | |
| 8 | #include <OpaqueDelegateUtils.hpp> |
| 9 | #include <SharedFunctions.hpp> |
| 10 | |
| 11 | namespace armnnOpaqueDelegate |
| 12 | { |
| 13 | |
| 14 | TfLiteStatus VisitFullyConnectedOperator(DelegateData& delegateData, |
| 15 | TfLiteOpaqueContext* tfLiteContext, |
| 16 | TfLiteOpaqueNode* tfLiteNode, |
| 17 | int nodeIndex, |
| 18 | int32_t operatorCode) |
| 19 | { |
| 20 | auto numInputs = TfLiteOpaqueNodeNumberOfInputs(tfLiteNode); |
| 21 | if (numInputs < 2) |
| 22 | { |
| 23 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 24 | tfLiteContext, |
| 25 | "TfLiteArmnnOpaqueDelegate: Minimum number of inputs (%d != %d) in node #%d", |
| 26 | 2, numInputs, nodeIndex); |
| 27 | return kTfLiteError; |
| 28 | } |
| 29 | TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); |
| 30 | |
| 31 | // Gather input indices and use to get input tensor. |
| 32 | const int* inputTensors; |
| 33 | if (TfLiteOpaqueNodeInputs(tfLiteNode, &inputTensors, &numInputs) != kTfLiteOk) |
| 34 | { |
| 35 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 36 | tfLiteContext, |
| 37 | "TfLiteArmnnOpaqueDelegate: Unable to gather input tensor indices from node #%d: ", |
| 38 | nodeIndex); |
| 39 | return kTfLiteError; |
| 40 | } |
| 41 | |
| 42 | const TfLiteOpaqueTensor* tfLiteInputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]); |
| 43 | if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex)) |
| 44 | { |
| 45 | return kTfLiteError; |
| 46 | } |
| 47 | |
| 48 | const TfLiteOpaqueTensor* tfLiteWeightsTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[1]); |
| 49 | if (!IsValid(tfLiteContext, tfLiteWeightsTensor, operatorCode, nodeIndex)) |
| 50 | { |
| 51 | return kTfLiteError; |
| 52 | } |
| 53 | |
| 54 | // Gather output indices and use to get output tensors. |
| 55 | int numOutputs = 0; |
| 56 | const int* outputTensors; |
| 57 | if (TfLiteOpaqueNodeOutputs(tfLiteNode, &outputTensors, &numOutputs) != kTfLiteOk) |
| 58 | { |
| 59 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 60 | tfLiteContext, |
| 61 | "TfLiteArmnnOpaqueDelegate: Unable to gather output tensor indices from node #%d: ", |
| 62 | nodeIndex); |
| 63 | return kTfLiteError; |
| 64 | } |
| 65 | |
| 66 | const TfLiteOpaqueTensor* tfLiteOutputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[0]); |
| 67 | if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex)) |
| 68 | { |
| 69 | return kTfLiteError; |
| 70 | } |
| 71 | |
| 72 | const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor); |
| 73 | const armnn::TensorInfo& weightsTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteWeightsTensor); |
| 74 | const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true); |
| 75 | |
| 76 | // Check that we support fused activation before we attempt to create a layer |
| 77 | auto* tfLiteNodeParameters = |
| 78 | reinterpret_cast<TfLiteFullyConnectedParams*>(TfLiteOpaqueNodeGetBuiltinData(tfLiteNode)); |
| 79 | TfLiteFusedActivation activationType=kTfLiteActNone; |
| 80 | if (tfLiteNodeParameters) |
| 81 | { |
| 82 | activationType = tfLiteNodeParameters->activation; |
| 83 | TfLiteStatus activationStatus = ValidateFusedActivationOperator(delegateData, tfLiteContext, outputTensorInfo, |
| 84 | outputTensorInfo, activationType); |
| 85 | if(activationStatus != kTfLiteOk) |
| 86 | { |
| 87 | return kTfLiteError; |
| 88 | } |
| 89 | } |
| 90 | |
| 91 | // Fully Connected Layer accepts two dimensional weights input |
| 92 | int32_t weightsDimension = static_cast<int32_t>(weightsTensorInfo.GetNumDimensions()); |
| 93 | if (weightsDimension != 2) |
| 94 | { |
| 95 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 96 | tfLiteContext, |
| 97 | "TfLiteArmnnOpaqueDelegate: Dimension #$d for Fully Connected weights is not supported by Armnn" |
| 98 | " in operator #%d node #%d: ", weightsDimension, operatorCode, nodeIndex); |
| 99 | return kTfLiteError; |
| 100 | } |
| 101 | |
| 102 | armnn::TensorInfo biasTensorInfo; |
| 103 | const TfLiteOpaqueTensor* tfLiteBiasTensor = nullptr; |
| 104 | |
| 105 | bool biasEnabled = IsOptionalOperandPresent(tfLiteNode, 2); |
| 106 | if (biasEnabled) |
| 107 | { |
| 108 | // Use input indices to get bias tensor. |
| 109 | tfLiteBiasTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[2]); |
| 110 | if (!IsValid(tfLiteContext, tfLiteBiasTensor, operatorCode, nodeIndex)) |
| 111 | { |
| 112 | return kTfLiteError; |
| 113 | } |
| 114 | biasTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteBiasTensor); |
| 115 | } |
| 116 | else |
| 117 | { |
| 118 | biasTensorInfo = armnn::TensorInfo(armnn::TensorShape({1}), GetDataType(tfLiteInputTensor)); |
| 119 | } |
| 120 | |
| 121 | armnn::TensorInfo reshapedTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor); |
| 122 | if (inputTensorInfo.GetNumDimensions() > 2) |
| 123 | { |
| 124 | // Calculate reshape to flatten to 2D [batch_size, input_size] |
| 125 | std::vector<unsigned int> reshapedDimensions(2); |
| 126 | reshapedDimensions[1] = weightsTensorInfo.GetShape()[1]; |
| 127 | reshapedDimensions[0] = inputTensorInfo.GetNumElements() / reshapedDimensions[1]; |
| 128 | |
| 129 | if (inputTensorInfo.GetNumElements() % reshapedDimensions[1] != 0) |
| 130 | { |
| 131 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 132 | tfLiteContext, |
| 133 | "TfLiteArmnnOpaqueDelegate: Failed to deduce input tensor shape from filter size #%d #%d node #%d: ", |
| 134 | reshapedDimensions[1], operatorCode, nodeIndex); |
| 135 | return kTfLiteError; |
| 136 | } |
| 137 | |
| 138 | reshapedTensorInfo.SetShape(armnn::TensorShape{ 2, reshapedDimensions.data() }); |
| 139 | } |
| 140 | armnn::TensorInfo reshapedOutputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor); |
| 141 | |
| 142 | if (outputTensorInfo.GetNumDimensions() > 2) |
| 143 | { |
| 144 | // Calculate reshape to flatten to 2D [batch_size, input_size] |
| 145 | std::vector<unsigned int> reshapedDimensions(2); |
| 146 | reshapedDimensions[1] = weightsTensorInfo.GetShape()[0]; |
| 147 | reshapedDimensions[0] = outputTensorInfo.GetNumElements() / reshapedDimensions[1]; |
| 148 | |
| 149 | if (outputTensorInfo.GetNumElements() % reshapedDimensions[1] != 0) |
| 150 | { |
| 151 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 152 | tfLiteContext, |
| 153 | "TfLiteArmnnOpaqueDelegate: Failed to deduce output tensor shape from filter size #%d #%d node #%d: ", |
| 154 | reshapedDimensions[1], operatorCode, nodeIndex); |
| 155 | return kTfLiteError; |
| 156 | } |
| 157 | reshapedOutputTensorInfo.SetShape(armnn::TensorShape{ 2, reshapedDimensions.data() }); |
| 158 | } |
| 159 | |
| 160 | armnn::FullyConnectedDescriptor descriptor; |
| 161 | descriptor.m_TransposeWeightMatrix = true; |
| 162 | descriptor.m_BiasEnabled = biasEnabled; |
| 163 | descriptor.m_ConstantWeights = weightsTensorInfo.IsConstant(); |
| 164 | |
| 165 | bool isSupported = false; |
| 166 | armnn::BackendId setBackend; |
| 167 | auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) |
| 168 | { |
| 169 | |
| 170 | FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("FULLY_CONNECTED", |
| 171 | tfLiteContext, |
| 172 | IsFullyConnectedSupported, |
| 173 | delegateData.m_Backends, |
| 174 | isSupported, |
| 175 | setBackend, |
| 176 | reshapedTensorInfo, |
| 177 | outputTensorInfo, |
| 178 | weightsTensorInfo, |
| 179 | biasTensorInfo, |
| 180 | descriptor); |
| 181 | }; |
| 182 | |
| 183 | if (!delegateData.m_Network) |
| 184 | { |
| 185 | validateFunc(reshapedOutputTensorInfo, isSupported); |
| 186 | return isSupported ? kTfLiteOk : kTfLiteError; |
| 187 | } |
| 188 | |
| 189 | armnn::IConnectableLayer* layer = delegateData.m_Network->AddFullyConnectedLayer(descriptor); |
| 190 | layer->SetBackendId(setBackend); |
| 191 | ARMNN_ASSERT(layer != nullptr); |
| 192 | |
| 193 | // Add a constant layer for weights and biases if inputs are constant. |
| 194 | if (weightsTensorInfo.IsConstant()) |
| 195 | { |
| 196 | auto weightsTensor = CreateConstTensor(tfLiteWeightsTensor, weightsTensorInfo); |
| 197 | |
| 198 | armnn::IConnectableLayer* weightsLayer = delegateData.m_Network->AddConstantLayer(weightsTensor); |
| 199 | |
| 200 | weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u)); |
| 201 | weightsLayer->GetOutputSlot(0).SetTensorInfo(weightsTensorInfo); |
| 202 | } |
| 203 | |
| 204 | if (biasEnabled) |
| 205 | { |
| 206 | if(biasTensorInfo.IsConstant()) |
| 207 | { |
| 208 | auto biasTensor = CreateConstTensor(tfLiteBiasTensor, biasTensorInfo); |
| 209 | |
| 210 | armnn::IConnectableLayer* biasLayer = delegateData.m_Network->AddConstantLayer(biasTensor); |
| 211 | ARMNN_ASSERT(biasLayer != nullptr); |
| 212 | |
| 213 | biasLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(2u)); |
| 214 | biasLayer->GetOutputSlot(0).SetTensorInfo(biasTensorInfo); |
| 215 | } |
| 216 | } |
| 217 | |
| 218 | // The data input can also be constant, so we must check that this is also allocated to an input slot |
| 219 | if(inputTensorInfo.IsConstant()) |
| 220 | { |
| 221 | auto input = CreateConstTensor(tfLiteInputTensor, inputTensorInfo); |
| 222 | |
| 223 | armnn::IConnectableLayer* inputLayer = delegateData.m_Network->AddConstantLayer(input); |
| 224 | inputLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0u)); |
| 225 | inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo); |
| 226 | } |
| 227 | |
| 228 | armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); |
| 229 | outputSlot.SetTensorInfo(outputTensorInfo); |
| 230 | |
| 231 | armnn::IConnectableLayer* reshapeLayer = nullptr; |
| 232 | if (inputTensorInfo.GetNumDimensions() > 2) |
| 233 | { |
| 234 | // Add reshape to flatten to 2D [batch_size, input_size] |
| 235 | armnn::ReshapeDescriptor reshapeDescriptor; |
| 236 | reshapeDescriptor.m_TargetShape = reshapedTensorInfo.GetShape(); |
| 237 | reshapeLayer = delegateData.m_Network->AddReshapeLayer(reshapeDescriptor); |
| 238 | ARMNN_ASSERT(reshapeLayer != nullptr); |
| 239 | |
| 240 | reshapeLayer->GetOutputSlot(0).SetTensorInfo(reshapedTensorInfo); |
| 241 | |
| 242 | // Connect |
| 243 | delegateData.m_OutputSlotForNode[inputTensors[0]]->Connect(reshapeLayer->GetInputSlot(0)); |
| 244 | reshapeLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0)); |
| 245 | |
| 246 | if (!descriptor.m_ConstantWeights) |
| 247 | { |
| 248 | delegateData.m_OutputSlotForNode[inputTensors[1]]->Connect(layer->GetInputSlot(1)); |
| 249 | } |
| 250 | |
| 251 | if (biasEnabled && !biasTensorInfo.IsConstant()) |
| 252 | { |
| 253 | delegateData.m_OutputSlotForNode[inputTensors[2]]->Connect(layer->GetInputSlot(2)); |
| 254 | } |
| 255 | delegateData.m_OutputSlotForNode[outputTensors[0]] = &outputSlot; |
| 256 | } |
| 257 | |
| 258 | if (reshapeLayer == nullptr) |
| 259 | { |
| 260 | if(Connect(layer, tfLiteContext, tfLiteNode, delegateData) != kTfLiteOk) |
| 261 | { |
| 262 | return kTfLiteError; |
| 263 | } |
| 264 | } |
| 265 | |
| 266 | if (outputTensorInfo.GetNumDimensions() > 2) |
| 267 | { |
| 268 | layer = AddReshapeLayer(tfLiteContext, |
| 269 | tfLiteNode, |
| 270 | layer, |
| 271 | reshapedOutputTensorInfo, |
| 272 | outputTensorInfo, |
| 273 | delegateData); |
| 274 | if (!layer) |
| 275 | { |
| 276 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 277 | tfLiteContext, |
| 278 | "TfLiteArmnnOpaqueDelegate: Failed to add reshape for FullyConnected #%d node #%d: ", |
| 279 | operatorCode, |
| 280 | nodeIndex); |
| 281 | return kTfLiteError; |
| 282 | } |
| 283 | } |
| 284 | |
| 285 | if (!tfLiteNodeParameters) |
| 286 | { |
| 287 | // No Activation |
| 288 | return kTfLiteOk; |
| 289 | } |
| 290 | |
| 291 | // Check and Create Activation |
| 292 | return FusedActivation(tfLiteContext, tfLiteNode, activationType, layer, 0, delegateData); |
| 293 | } |
| 294 | |
| 295 | } // namespace armnnOpaqueDelegate |