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Diffstat (limited to 'delegate/opaque/src/Slice.hpp')
| -rw-r--r-- | delegate/opaque/src/Slice.hpp | 156 |
1 files changed, 156 insertions, 0 deletions
diff --git a/delegate/opaque/src/Slice.hpp b/delegate/opaque/src/Slice.hpp index e16969768e..2064b2e7e4 100644 --- a/delegate/opaque/src/Slice.hpp +++ b/delegate/opaque/src/Slice.hpp @@ -2,3 +2,159 @@ // Copyright © 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // + +#pragma once + +#include <OpaqueDelegateUtils.hpp> + +namespace armnnOpaqueDelegate +{ + +TfLiteStatus VisitSliceOperator(DelegateData& delegateData, + TfLiteOpaqueContext* tfLiteContext, + TfLiteOpaqueNode* tfLiteNode, + int nodeIndex, + int32_t tfLiteSliceOperatorCode) +{ + + TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 3, nodeIndex)); + TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); + + // Read inputs [input, begin, size] + // Gather input indices and use to get input tensor. + const int* inputTensors; + int numInputs; + if (TfLiteOpaqueNodeInputs(tfLiteNode, &inputTensors, &numInputs) != kTfLiteOk) + { + TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: Unable to gather input tensor indices from node #%d: ", + nodeIndex); + return kTfLiteError; + } + + std::vector<const TfLiteOpaqueTensor*> tfLiteInputTensors; + tfLiteInputTensors.reserve(numInputs); + for (int i = 0; i < numInputs; i++) + { + const TfLiteOpaqueTensor* inputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[i]); + tfLiteInputTensors.push_back(inputTensor); + if (!IsValid(tfLiteContext, inputTensor, tfLiteSliceOperatorCode, nodeIndex)) + { + return kTfLiteError; + } + } + + const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensors[0]); + + // We save the begin and size tensors in our descriptor. Therefore we have to read those values from inputs + unsigned int inputRank = inputTensorInfo.GetNumDimensions(); + auto ReadInt32Input = [&](int inputIndex, std::vector<uint32_t>& outputData) -> TfLiteStatus + { + if (TfLiteOpaqueTensorType(tfLiteInputTensors[inputIndex]) != kTfLiteInt32) + { + TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: The Begin- and Size-Tensors of the Slice operation need to " + "be of type int32. Operator: #%d node #%d: ", + tfLiteSliceOperatorCode, nodeIndex); + return kTfLiteError; + } + uint32_t rank = TfLiteOpaqueTensorNumDims(tfLiteInputTensors[inputIndex]); + if (rank != 1) + { + TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: The Begin- and Size-Tensors of the Slice operation need to " + "be a 1D-Tensor. Operator: #%d node #%d: ", + tfLiteSliceOperatorCode, nodeIndex); + return kTfLiteError; + } + uint32_t numValues = TfLiteOpaqueTensorDim(tfLiteInputTensors[inputIndex], 0); + if (numValues != inputRank) + { + TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: The number of values in the Begin- and Size-Tensors of the " + "Slice operation need to be equal to the rank of the Input-Tensor. Operator: #%d node #%d: ", + tfLiteSliceOperatorCode, nodeIndex); + return kTfLiteError; + } + // return tensor data + auto* tensorDataPtr = static_cast<uint32_t*>(TfLiteOpaqueTensorData(tfLiteInputTensors[inputIndex])); + outputData.assign(tensorDataPtr, tensorDataPtr + numValues); + return kTfLiteOk; + }; + + std::vector<uint32_t> begin; + if (ReadInt32Input(1, begin) != kTfLiteOk) + return kTfLiteError; + std::vector<uint32_t> size; + if (ReadInt32Input(2, size) != kTfLiteOk) + return kTfLiteError; + + // Write all data to the descriptor + armnn::SliceDescriptor descriptor(begin, size); + + // Validate output + // Gather output indices and use to get output tensor. + const int* outputTensors; + int numOutputs; + if (TfLiteOpaqueNodeOutputs(tfLiteNode, &outputTensors, &numOutputs) != kTfLiteOk) + { + TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: Unable to gather output tensor indices from node #%d: ", + nodeIndex); + return kTfLiteError; + } + + const TfLiteOpaqueTensor* tfLiteOutputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[0]); + if (!IsValid(tfLiteContext, tfLiteOutputTensor, tfLiteSliceOperatorCode, nodeIndex)) + { + return kTfLiteError; + } + + const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true); + + bool isSupported = false; + armnn::BackendId setBackend; + auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported) + { + FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("SLICE", + tfLiteContext, + IsSliceSupported, + delegateData.m_Backends, + isSupported, + setBackend, + inputTensorInfo, + outInfo, + descriptor); + }; + + if (!delegateData.m_Network) + { + validateFunc(outputTensorInfo, isSupported); + return isSupported ? kTfLiteOk : kTfLiteError; + } + + // Add a Slice layer + armnn::IConnectableLayer* layer = delegateData.m_Network->AddSliceLayer(descriptor); + layer->SetBackendId(setBackend); + ARMNN_ASSERT(layer != nullptr); + + armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); + outputSlot.SetTensorInfo(outputTensorInfo); + + // try to connect the Constant Inputs if there are any + if(ProcessInputs(layer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) + { + return kTfLiteError; + } + + // Connect + return Connect(layer, tfLiteContext, tfLiteNode, delegateData); +} + +} // namespace armnnOpaqueDelegate + |