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 | // |
Teresa Charlin | ecebb0f | 2023-04-27 21:37:56 +0100 | [diff] [blame] | 5 | |
| 6 | #pragma once |
| 7 | |
| 8 | #include <OpaqueDelegateUtils.hpp> |
| 9 | |
| 10 | namespace armnnOpaqueDelegate |
| 11 | { |
| 12 | |
| 13 | TfLiteStatus VisitUnpackOperator(DelegateData& delegateData, |
| 14 | TfLiteOpaqueContext* tfLiteContext, |
| 15 | TfLiteOpaqueNode* tfLiteNode, |
| 16 | int nodeIndex, |
| 17 | int32_t operatorCode) |
| 18 | { |
| 19 | // Check inputs |
| 20 | TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); |
| 21 | |
| 22 | const int* inputTensors; |
| 23 | int numInputs; |
| 24 | if (TfLiteOpaqueNodeInputs(tfLiteNode, &inputTensors, &numInputs) != kTfLiteOk) |
| 25 | { |
| 26 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 27 | tfLiteContext, |
| 28 | "TfLiteArmnnOpaqueDelegate: Unable to gather input tensor indices from node #%d: ", |
| 29 | nodeIndex); |
| 30 | return kTfLiteError; |
| 31 | } |
| 32 | const TfLiteOpaqueTensor* tfLiteInputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, |
| 33 | inputTensors[0]); |
| 34 | if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex)) |
| 35 | { |
| 36 | return kTfLiteError; |
| 37 | } |
| 38 | |
| 39 | auto* tfLiteNodeParameters = reinterpret_cast<TfLiteUnpackParams*>(TfLiteOpaqueNodeGetBuiltinData(tfLiteNode)); |
| 40 | const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor); |
| 41 | |
| 42 | // Get Unpack Axis |
| 43 | const unsigned int unpackAxis = NonNegative(tfLiteNodeParameters->axis, nodeIndex); |
| 44 | |
| 45 | if (unpackAxis >= inputTensorInfo.GetNumDimensions()) |
| 46 | { |
| 47 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 48 | tfLiteContext, |
| 49 | "TfLiteArmnnOpaqueDelegate: The unpack axis #%d cannot be greater than or equal to " |
| 50 | "the number of input dimensions #%d in operator #%d node #%d", |
| 51 | unpackAxis, inputTensorInfo.GetNumDimensions(), operatorCode, nodeIndex); |
| 52 | return kTfLiteError; |
| 53 | } |
| 54 | |
| 55 | // Get Unpack Num |
| 56 | unsigned int unpackNum = NonNegative(tfLiteNodeParameters->num, nodeIndex); |
| 57 | |
| 58 | // If num is not defined, automatically infer from the length of the dimension axis. |
| 59 | if(unpackNum == 0) |
| 60 | { |
| 61 | unpackNum = inputTensorInfo.GetShape()[unpackAxis]; |
| 62 | } |
| 63 | |
| 64 | // If unpack number cannot be inferred and is still zero, return kTfLiteError. |
| 65 | if(unpackNum == 0) |
| 66 | { |
| 67 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 68 | tfLiteContext, |
| 69 | "TfLiteArmnnOpaqueDelegate: Number to unpack must greater than zero in operator #%d node #%d: ", |
| 70 | operatorCode, nodeIndex); |
| 71 | return kTfLiteError; |
| 72 | } |
| 73 | |
| 74 | // Check outputs |
| 75 | TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, unpackNum, nodeIndex)); |
| 76 | |
| 77 | auto inputDimSize = inputTensorInfo.GetNumDimensions(); |
| 78 | std::vector<unsigned int> unpackDimSizes(inputDimSize); |
| 79 | |
| 80 | // Add current input shape to unpackDimSizes |
| 81 | for (unsigned int i = 0; i < inputDimSize; ++i) |
| 82 | { |
| 83 | unpackDimSizes[i] = inputTensorInfo.GetShape()[i]; |
| 84 | } |
| 85 | |
| 86 | if (unpackDimSizes[unpackAxis] != unpackNum) |
| 87 | { |
| 88 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 89 | tfLiteContext, |
| 90 | "TfLiteArmnnOpaqueDelegate: Number to unpack must be the same as length " |
| 91 | "of the dimension to unpack along in operator #%d node #%d: ", |
| 92 | operatorCode, nodeIndex); |
| 93 | return kTfLiteError; |
| 94 | } |
| 95 | |
| 96 | unpackDimSizes[unpackAxis] /= unpackNum; |
| 97 | |
| 98 | armnn::SplitterDescriptor splitDesc(unpackNum, static_cast<unsigned int>(unpackDimSizes.size())); |
| 99 | for (unsigned int j = 0; j < unpackNum; ++j) |
| 100 | { |
| 101 | // Set the size of the views. |
| 102 | for (unsigned int dimIdx = 0; dimIdx < unpackDimSizes.size(); ++dimIdx) |
| 103 | { |
| 104 | splitDesc.SetViewSize(j, dimIdx, unpackDimSizes[dimIdx]); |
| 105 | } |
| 106 | splitDesc.SetViewOriginCoord(j, unpackAxis, unpackDimSizes[unpackAxis] * j); |
| 107 | } |
| 108 | |
| 109 | // Gather output indices and use to get output tensors. |
| 110 | const int* outputTensors; |
| 111 | int numOutputs; |
| 112 | if (TfLiteOpaqueNodeOutputs(tfLiteNode, &outputTensors, &numOutputs) != kTfLiteOk) |
| 113 | { |
| 114 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 115 | tfLiteContext, |
| 116 | "TfLiteArmnnOpaqueDelegate: Unable to gather output tensor indices from node #%d: ", |
| 117 | nodeIndex); |
| 118 | return kTfLiteError; |
| 119 | } |
| 120 | |
| 121 | // Validate all outputs and get TensorInfo |
| 122 | std::vector<armnn::TensorInfo> outputs; |
| 123 | for (unsigned int i = 0; i < unpackNum; ++i) |
| 124 | { |
| 125 | const TfLiteOpaqueTensor* tfLiteOutputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, |
| 126 | outputTensors[i]); |
| 127 | if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex)) |
| 128 | { |
| 129 | return kTfLiteError; |
| 130 | } |
| 131 | |
| 132 | outputs.push_back(GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true)); |
| 133 | } |
| 134 | |
| 135 | const std::vector<std::reference_wrapper<armnn::TensorInfo>> outputTensorInfos(outputs.begin(), outputs.end()); |
| 136 | |
| 137 | // Determine the shape of the Splitter layer outputs for validation |
| 138 | armnn::TensorShape splitOutShape = armnn::TensorShape(static_cast<unsigned int>(unpackDimSizes.size()), |
| 139 | unpackDimSizes.data()); |
| 140 | |
| 141 | std::vector<armnn::TensorInfo> splitterOutputs; |
| 142 | for (unsigned int outputIndex = 0; outputIndex < outputTensorInfos.size(); ++outputIndex) |
| 143 | { |
| 144 | splitterOutputs.push_back(armnn::TensorInfo(splitOutShape, |
| 145 | outputTensorInfos[outputIndex].get().GetDataType(), |
| 146 | outputTensorInfos[outputIndex].get().GetQuantizationScale(), |
| 147 | outputTensorInfos[outputIndex].get().GetQuantizationOffset())); |
| 148 | } |
| 149 | std::vector<std::reference_wrapper<armnn::TensorInfo>> splitterOutputTensorInfos(splitterOutputs.begin(), |
| 150 | splitterOutputs.end()); |
| 151 | |
| 152 | armnn::BackendId setBackendSplit; |
| 153 | if (!delegateData.m_Network) |
| 154 | { |
| 155 | // Check if splitter is supported |
| 156 | bool isSupported = false; |
| 157 | FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("UNPACK", |
| 158 | tfLiteContext, |
| 159 | IsSplitterSupported, |
| 160 | delegateData.m_Backends, |
| 161 | isSupported, |
| 162 | setBackendSplit, |
| 163 | inputTensorInfo, |
| 164 | splitterOutputTensorInfos, |
| 165 | splitDesc); |
| 166 | return isSupported ? kTfLiteOk : kTfLiteError; |
| 167 | } |
| 168 | |
| 169 | // Create Reshape descriptor from the first outputTensorInfo to validate a single Reshape layer |
| 170 | // Use this descriptor later when creating every ReshapeLayer as all Reshape Layers should be the same |
| 171 | armnn::ReshapeDescriptor reshapeDescriptor; |
| 172 | reshapeDescriptor.m_TargetShape = outputTensorInfos[0].get().GetShape(); |
| 173 | |
| 174 | armnn::BackendId setBackendReshape; |
| 175 | if (!delegateData.m_Network) |
| 176 | { |
| 177 | bool isSupported = false; |
| 178 | FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("RESHAPE", |
| 179 | tfLiteContext, |
| 180 | IsReshapeSupported, |
| 181 | delegateData.m_Backends, |
| 182 | isSupported, |
| 183 | setBackendReshape, |
| 184 | splitterOutputTensorInfos[0], |
| 185 | outputTensorInfos[0], |
| 186 | reshapeDescriptor); |
| 187 | return isSupported ? kTfLiteOk : kTfLiteError; |
| 188 | }; |
| 189 | |
Mike Kelly | a280650 | 2023-08-03 10:42:11 +0100 | [diff] [blame] | 190 | auto layerName = GetName(armnn::LayerType::Splitter, nodeIndex, "Unpack"); |
Teresa Charlin | ecebb0f | 2023-04-27 21:37:56 +0100 | [diff] [blame] | 191 | armnn::IConnectableLayer* splitterLayer = delegateData.m_Network->AddSplitterLayer(splitDesc, |
Mike Kelly | a280650 | 2023-08-03 10:42:11 +0100 | [diff] [blame] | 192 | layerName.c_str()); |
Teresa Charlin | ecebb0f | 2023-04-27 21:37:56 +0100 | [diff] [blame] | 193 | splitterLayer->SetBackendId(setBackendSplit); |
| 194 | ARMNN_ASSERT(splitterLayer != nullptr); |
| 195 | |
| 196 | for (unsigned int k = 0; k < splitterLayer->GetNumOutputSlots(); ++k) |
| 197 | { |
| 198 | splitterLayer->GetOutputSlot(k).SetTensorInfo(outputs[k]); |
| 199 | } |
| 200 | |
| 201 | // Connect the input slots |
| 202 | auto inputIndex = static_cast<unsigned int>(inputTensors[0]); |
| 203 | delegateData.m_OutputSlotForNode[inputIndex]->Connect(splitterLayer->GetInputSlot(0)); |
| 204 | |
| 205 | // Create reshape to remove the unpacked dimension for unpack operator of each output from Splitter. |
| 206 | for (unsigned int outputIndex = 0; outputIndex < splitterLayer->GetNumOutputSlots(); ++outputIndex) |
| 207 | { |
Mike Kelly | a280650 | 2023-08-03 10:42:11 +0100 | [diff] [blame] | 208 | auto reshapeLayerName = GetName(armnn::LayerType::Reshape, nodeIndex, "Unpack"); |
Teresa Charlin | ecebb0f | 2023-04-27 21:37:56 +0100 | [diff] [blame] | 209 | armnn::IConnectableLayer* reshapeLayer = delegateData.m_Network->AddReshapeLayer(reshapeDescriptor, |
| 210 | reshapeLayerName.c_str()); |
| 211 | reshapeLayer->SetBackendId(setBackendReshape); |
| 212 | ARMNN_ASSERT(reshapeLayer != nullptr); |
| 213 | |
| 214 | splitterLayer->GetOutputSlot(outputIndex).SetTensorInfo(splitterOutputTensorInfos[outputIndex]); |
| 215 | splitterLayer->GetOutputSlot(outputIndex).Connect(reshapeLayer->GetInputSlot(0)); |
| 216 | |
| 217 | armnn::TensorInfo outputTensorInfo = outputTensorInfos[outputIndex]; |
| 218 | reshapeLayer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| 219 | |
| 220 | armnn::IOutputSlot& slot = reshapeLayer->GetOutputSlot(0); |
| 221 | |
| 222 | delegateData.m_OutputSlotForNode[ |
| 223 | static_cast<unsigned long>(static_cast<unsigned int>(outputTensors[outputIndex]))] = &slot; |
| 224 | |
| 225 | } |
| 226 | |
| 227 | return kTfLiteOk; |
| 228 | } |
| 229 | |
| 230 | } // namespace armnnOpaqueDelegate |