Tianle Cheng | 92ce35c | 2023-07-25 16:41:00 +0100 | [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 <OpaqueDelegateUtils.hpp> |
| 9 | |
| 10 | namespace armnnOpaqueDelegate |
| 11 | { |
| 12 | TfLiteStatus ValidateTileOperator(DelegateData& delegateData, |
| 13 | TfLiteOpaqueContext *tfLiteContext, |
| 14 | const armnn::TensorInfo& inputInfo, |
| 15 | const armnn::TensorInfo& outputInfo, |
| 16 | const armnn::TileDescriptor& descriptor) |
| 17 | { |
| 18 | bool isSupported = false; |
| 19 | FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("TILE", |
| 20 | tfLiteContext, |
| 21 | IsTileSupported, |
| 22 | delegateData.m_Backends, |
| 23 | isSupported, |
| 24 | armnn::BackendId(), |
| 25 | inputInfo, |
| 26 | outputInfo, |
| 27 | descriptor); |
| 28 | return isSupported ? kTfLiteOk : kTfLiteError; |
| 29 | } |
| 30 | |
| 31 | TfLiteStatus VisitTileOperator(DelegateData& delegateData, |
| 32 | TfLiteOpaqueContext* tfLiteContext, |
| 33 | TfLiteOpaqueNode* tfLiteNode, |
| 34 | int nodeIndex, |
| 35 | int32_t tileOperatorCode) |
| 36 | { |
| 37 | TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex)); |
| 38 | TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); |
| 39 | |
| 40 | // Gather input tensors |
| 41 | auto numInputs = TfLiteOpaqueNodeNumberOfInputs(tfLiteNode); |
| 42 | const int* inputTensors; |
| 43 | if (TfLiteOpaqueNodeInputs(tfLiteNode, &inputTensors, &numInputs) != kTfLiteOk) |
| 44 | { |
| 45 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 46 | tfLiteContext, |
| 47 | "TfLiteArmnnOpaqueDelegate: Unable to gather input tensor indices from node #%d: ", |
| 48 | nodeIndex); |
| 49 | return kTfLiteError; |
| 50 | } |
| 51 | |
| 52 | // Gather output tensors |
| 53 | int numOutputs = 0; |
| 54 | const int* outputTensors; |
| 55 | if (TfLiteOpaqueNodeOutputs(tfLiteNode, &outputTensors, &numOutputs) != kTfLiteOk) |
| 56 | { |
| 57 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 58 | tfLiteContext, |
| 59 | "TfLiteArmnnOpaqueDelegate: Unable to gather output tensor indices from node #%d: ", |
| 60 | nodeIndex); |
| 61 | return kTfLiteError; |
| 62 | } |
| 63 | |
| 64 | // The input contains the data that should be tiled |
| 65 | const TfLiteOpaqueTensor* tfLiteInputTensor = |
| 66 | TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]); |
| 67 | if (IsDynamicTensor(tfLiteInputTensor)) |
| 68 | { |
| 69 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 70 | tfLiteContext, |
| 71 | "TfLiteArmnnOpaqueDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", |
| 72 | tileOperatorCode, nodeIndex); |
| 73 | return kTfLiteError; |
| 74 | } |
| 75 | |
| 76 | // The multiples tensor contains the number of copies for each axis |
| 77 | const TfLiteOpaqueTensor* tfLiteMultiplesTensor = |
| 78 | TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[1]);; |
| 79 | if (IsDynamicTensor(tfLiteMultiplesTensor)) |
| 80 | { |
| 81 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 82 | tfLiteContext, |
| 83 | "TfLiteArmnnOpaqueDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", |
| 84 | tileOperatorCode, nodeIndex); |
| 85 | return kTfLiteError; |
| 86 | } |
| 87 | |
| 88 | // The output tensor |
| 89 | const TfLiteOpaqueTensor* tfLiteOutputTensor = |
| 90 | TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[0]); |
| 91 | if (IsDynamicTensor(tfLiteOutputTensor)) |
| 92 | { |
| 93 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 94 | tfLiteContext, |
| 95 | "TfLiteArmnnOpaqueDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ", |
| 96 | tileOperatorCode, nodeIndex); |
| 97 | return kTfLiteError; |
| 98 | } |
| 99 | |
| 100 | const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor); |
| 101 | const armnn::TensorInfo& multiplesTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteMultiplesTensor); |
| 102 | const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true); |
| 103 | |
| 104 | // Multiples length must be the same as the number of dimension in input tensor |
| 105 | if (multiplesTensorInfo.GetNumElements() != inputTensorInfo.GetNumDimensions()) |
| 106 | { |
| 107 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 108 | tfLiteContext, |
| 109 | "TfLiteArmnnOpaqueDelegate:", |
| 110 | "The Multiples length must be the same as the number of dimension in input tensor", |
| 111 | "Operator: #%d node #%d: ", |
| 112 | tileOperatorCode, nodeIndex); |
| 113 | return kTfLiteError; |
| 114 | } |
| 115 | |
| 116 | // Get the Multiples data: In armnn, the values of the multiples input tensor is saved in the operator descriptor |
| 117 | // We have to read it from the input tensor and write it the descriptor |
| 118 | auto* multiplesTensorDataPtr = static_cast<int32_t*>(TfLiteOpaqueTensorData(tfLiteMultiplesTensor)); |
| 119 | auto multiplesTensorNum = TfLiteOpaqueTensorDim(tfLiteMultiplesTensor, 0); |
| 120 | std::vector<int32_t> multiplesIntData(multiplesTensorDataPtr, multiplesTensorDataPtr + multiplesTensorNum); |
| 121 | |
| 122 | // The multiples must be positive |
| 123 | for (auto multiple : multiplesIntData) |
| 124 | { |
| 125 | if (multiple < 0) |
| 126 | { |
| 127 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 128 | tfLiteContext, |
| 129 | "TfLiteArmnnOpaqueDelegate: The Multiples must be positive values", |
| 130 | "Operator: #%d node #%d: ", |
| 131 | tileOperatorCode, nodeIndex); |
| 132 | return kTfLiteError; |
| 133 | } |
| 134 | } |
| 135 | |
| 136 | // The original input from TFLite is int32, and we have to make it as uint32 for our descriptor |
| 137 | std::vector<uint32_t> multiplesUintData; |
| 138 | std::transform(multiplesIntData.begin(), |
| 139 | multiplesIntData.end(), |
| 140 | std::back_inserter(multiplesUintData), |
| 141 | [] (const int value) |
| 142 | { |
| 143 | return static_cast<uint32_t>(value); |
| 144 | }); |
| 145 | |
| 146 | armnn::TileDescriptor tileDescriptor; |
| 147 | tileDescriptor.m_Multiples = multiplesUintData; |
| 148 | |
| 149 | // Check output dimensions |
| 150 | if (inputTensorInfo.GetNumDimensions() != outputTensorInfo.GetNumDimensions()) |
| 151 | { |
| 152 | TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| 153 | tfLiteContext, |
| 154 | "TfLiteArmnnOpaqueDelegate: Input tensor dimension and output tensor dimension differ", |
| 155 | "Operator: #%d node #%d: ", |
| 156 | tileOperatorCode, nodeIndex); |
| 157 | return kTfLiteError; |
| 158 | } |
| 159 | |
| 160 | // No network pointer indicates that only support for this operator should be checked |
| 161 | if (!delegateData.m_Network) |
| 162 | { |
| 163 | return ValidateTileOperator(delegateData, |
| 164 | tfLiteContext, |
| 165 | inputTensorInfo, |
| 166 | outputTensorInfo, |
| 167 | tileDescriptor); |
| 168 | } |
| 169 | |
Mike Kelly | a280650 | 2023-08-03 10:42:11 +0100 | [diff] [blame] | 170 | auto layerName = GetName(armnn::LayerType::Tile, nodeIndex); |
Tianle Cheng | 92ce35c | 2023-07-25 16:41:00 +0100 | [diff] [blame] | 171 | armnn::IConnectableLayer* layer = delegateData.m_Network->AddTileLayer(tileDescriptor, layerName.c_str()); |
| 172 | |
| 173 | if (layer == nullptr) |
| 174 | { |
| 175 | return kTfLiteError; |
| 176 | } |
| 177 | |
| 178 | layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); |
| 179 | |
Mike Kelly | a280650 | 2023-08-03 10:42:11 +0100 | [diff] [blame] | 180 | if (ProcessInputs(layer, delegateData, tfLiteContext, tfLiteNode, nodeIndex) != kTfLiteOk) |
Tianle Cheng | 92ce35c | 2023-07-25 16:41:00 +0100 | [diff] [blame] | 181 | { |
| 182 | return kTfLiteError; |
| 183 | } |
| 184 | |
| 185 | return Connect(layer, tfLiteContext, tfLiteNode, delegateData); |
| 186 | } |
| 187 | |
| 188 | } // namespace armnnOpaqueDelegate |