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Diffstat (limited to 'delegate/src/Split.hpp')
| -rw-r--r-- | delegate/src/Split.hpp | 331 |
1 files changed, 331 insertions, 0 deletions
diff --git a/delegate/src/Split.hpp b/delegate/src/Split.hpp new file mode 100644 index 0000000000..74da979666 --- /dev/null +++ b/delegate/src/Split.hpp @@ -0,0 +1,331 @@ +// +// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include "DelegateUtils.hpp" + +#include <algorithm> +#include <iterator> +#include <string> +#include <vector> + +namespace armnnDelegate +{ + +constexpr unsigned int MaxNumOfTensorDimensions = 5U; + +TfLiteStatus VisitSplitOperator(DelegateData& delegateData, + TfLiteContext* tfLiteContext, + TfLiteNode* tfLiteNode, + int nodeIndex, + int32_t tfLiteSplitOperatorCode) +{ + TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex)); + + auto* splitParameters = reinterpret_cast<TfLiteSplitParams*>(tfLiteNode->builtin_data); + const unsigned int numSplits = NonNegative(splitParameters->num_splits, nodeIndex); + + TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, numSplits, nodeIndex)); + + const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; + const TfLiteTensor& tfLiteAxisTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; + if (!IsValid(tfLiteContext, tfLiteAxisTensor, tfLiteSplitOperatorCode, nodeIndex)) + { + return kTfLiteError; + } + + const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; + if (!IsValid(tfLiteContext, tfLiteInputTensor, tfLiteSplitOperatorCode, nodeIndex)) + { + return kTfLiteError; + } + + const armnn::TensorInfo& axisTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteAxisTensor); + const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); + + ARMNN_ASSERT(axisTensorInfo.GetNumElements() == 1); + auto* axisTensorDataPtr = tflite::GetTensorData<int32_t>(&tfLiteAxisTensor); + std::vector<int32_t> axisTensorData(axisTensorDataPtr, axisTensorDataPtr + 1); + const unsigned int splitDim = axisTensorData[0]; + + std::vector<armnn::TensorInfo> outputs; + for (unsigned int i = 0; i < numSplits; ++i) + { + const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[i]]; + if (!IsValid(tfLiteContext, tfLiteOutputTensor, tfLiteSplitOperatorCode, nodeIndex)) + { + return kTfLiteError; + } + outputs.push_back(GetTensorInfoForTfLiteTensor(tfLiteOutputTensor)); + } + const std::vector<std::reference_wrapper<armnn::TensorInfo>> outputTensorInfos(outputs.begin(), outputs.end()); + + auto inputDimSize = inputTensorInfo.GetNumDimensions(); + if (inputDimSize > MaxNumOfTensorDimensions) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: The number of dimensions: #%d for input tensors of the split op cannot be greater " + "than #%d in node #%d: ", inputDimSize, MaxNumOfTensorDimensions, nodeIndex); + return kTfLiteError; + } + + std::vector<unsigned int> splitterDimSizes(inputDimSize); + + // Add current input shape to splitterDimSizes + for (unsigned int i = 0; i < inputDimSize; ++i) + { + splitterDimSizes[i] = inputTensorInfo.GetShape()[i]; + } + + if (splitterDimSizes[splitDim] % numSplits != 0) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Number of splits #%d must evenly divide the dimension #%d in node #%d: ", + numSplits, splitterDimSizes[splitDim], nodeIndex); + return kTfLiteError; + } + splitterDimSizes[splitDim] /= numSplits; + + armnn::SplitterDescriptor splitDescriptor(numSplits, inputDimSize); + for (unsigned int j = 0; j < numSplits; ++j) + { + // Set the size of the views. + for (unsigned int dimIdx = 0; dimIdx < splitterDimSizes.size(); ++dimIdx) + { + splitDescriptor.SetViewSize(j, dimIdx, splitterDimSizes[dimIdx]); + } + splitDescriptor.SetViewOriginCoord(j, splitDim, splitterDimSizes[splitDim] * j); + } + + if (!delegateData.m_Network) + { + // Check if supported + bool isSupported = false; + FORWARD_LAYER_SUPPORT_FUNC(__func__, + tfLiteContext, + IsSplitterSupported, + delegateData.m_Backends, + isSupported, + inputTensorInfo, + outputTensorInfos, + splitDescriptor); + return isSupported ? kTfLiteOk : kTfLiteError; + } + + armnn::IConnectableLayer* layer = delegateData.m_Network->AddSplitterLayer(splitDescriptor); + ARMNN_ASSERT(layer != nullptr); + + for (unsigned int k = 0; k < layer->GetNumOutputSlots(); ++k) + { + layer->GetOutputSlot(k).SetTensorInfo(outputs[k]); + } + + // Connect the input slots + delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[1]]->Connect(layer->GetInputSlot(0)); + + // Prepare output slots + for (unsigned int outputIndex = 0; outputIndex < layer->GetNumOutputSlots(); ++outputIndex) + { + armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(outputIndex); + delegateData.m_OutputSlotForNode[ + static_cast<unsigned long>(tfLiteNode->outputs->data[outputIndex])] = &outputSlot; + } + + return kTfLiteOk; +} + +TfLiteStatus VisitSplitVOperator(DelegateData& delegateData, + TfLiteContext* tfLiteContext, + TfLiteNode* tfLiteNode, + int nodeIndex, + int32_t tfLiteSplitVOperatorCode) +{ + TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 3, nodeIndex)); + + const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; + const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; + if (!IsValid(tfLiteContext, tfLiteInputTensor, tfLiteSplitVOperatorCode, nodeIndex)) + { + return kTfLiteError; + } + + const TfLiteTensor& tfLiteSplitsTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; + if (!IsValid(tfLiteContext, tfLiteSplitsTensor, tfLiteSplitVOperatorCode, nodeIndex)) + { + return kTfLiteError; + } + + const TfLiteTensor& tfLiteAxisTensor = tfLiteTensors[tfLiteNode->inputs->data[2]]; + if (!IsValid(tfLiteContext, tfLiteAxisTensor, tfLiteSplitVOperatorCode, nodeIndex)) + { + return kTfLiteError; + } + + const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); + const armnn::TensorInfo& splitsTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteSplitsTensor); + ARMNN_ASSERT(splitsTensorInfo.GetNumDimensions() == 1); + + const armnn::TensorInfo& axisTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteAxisTensor); + ARMNN_ASSERT(axisTensorInfo.GetNumElements() == 1); + auto* axisTensorDataPtr = tflite::GetTensorData<int32_t>(&tfLiteAxisTensor); + std::vector<int32_t> axisTensorData(axisTensorDataPtr, axisTensorDataPtr + 1); + + auto ComputeWrappedIndex = [](int index, unsigned int numDimensions) + { + int numDims = armnn::numeric_cast<int>(numDimensions); + int wrappedIndex = index < 0 ? numDims + index : index; + ARMNN_ASSERT(wrappedIndex >= 0); + ARMNN_ASSERT(wrappedIndex < numDims); + + return static_cast<unsigned int>(wrappedIndex); + }; + + const unsigned int splitDim = ComputeWrappedIndex(axisTensorData[0], + inputTensorInfo.GetNumDimensions()); + + auto* splitVParameters = reinterpret_cast<TfLiteSplitVParams*>(tfLiteNode->builtin_data); + unsigned int numSplits = 0; + if (splitVParameters) + { + numSplits = NonNegative(splitVParameters->num_splits, nodeIndex); + } + else + { + numSplits = splitsTensorInfo.GetNumElements(); + } + + if (numSplits <= 0) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, "TfLiteArmnnDelegate: Invalid number of splits %d in node #%d", + numSplits, nodeIndex); + return kTfLiteError; + } + + TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, numSplits, nodeIndex)); + std::vector<armnn::TensorInfo> outputs; + for (unsigned int i = 0; i < numSplits; ++i) + { + const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[i]]; + if (!IsValid(tfLiteContext, tfLiteOutputTensor, tfLiteSplitVOperatorCode, nodeIndex)) + { + return kTfLiteError; + } + outputs.push_back(GetTensorInfoForTfLiteTensor(tfLiteOutputTensor)); + } + const std::vector<std::reference_wrapper<armnn::TensorInfo>> outputTensorInfos(outputs.begin(), outputs.end()); + + auto inputDimSize = inputTensorInfo.GetNumDimensions(); + if (inputDimSize > MaxNumOfTensorDimensions) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: The number of dimensions: #%d for input tensors of the split op cannot be greater " + "than #%d in node #%d: ", inputDimSize, MaxNumOfTensorDimensions, nodeIndex); + return kTfLiteError; + } + + std::vector<int32_t> splitsTensorData(numSplits); +#ifdef __STDC_LIB_EXT1__ + ::memcpy_s(splitsTensorData.data(), sizeof(splitsTensorData), + tfLiteSplitsTensor.data.data, splitsTensorInfo.GetNumBytes()); +#else + ::memcpy(splitsTensorData.data(), tfLiteSplitsTensor.data.data, splitsTensorInfo.GetNumBytes()); +#endif + + unsigned int index = 0; + unsigned int inferredIndex = 0; + int numberOfInferred = 0; + int splitSum = 0; + + for (auto splitData : splitsTensorData) + { + if (splitData < 0) + { + ++numberOfInferred; + inferredIndex = index; + } + else + { + splitSum += splitData; + } + ++index; + } + + // Check for inferred axis + if (numberOfInferred == 0) + { + if (splitSum != armnn::numeric_cast<int>(inputTensorInfo.GetShape()[splitDim])) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, "TfLiteArmnnDelegate: SplitV split_sizes does not sum to the dimension of value along" + " split_dim in node #%d", nodeIndex); + return kTfLiteError; + } + } + else if (numberOfInferred == 1) + { + splitsTensorData[inferredIndex] = armnn::numeric_cast<int>(inputTensorInfo.GetShape()[splitDim]) - splitSum; + } + else + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, "TfLiteArmnnDelegate: SplitV cannot infer split size for more than one split in node #%d", + nodeIndex); + return kTfLiteError; + } + + armnn::SplitterDescriptor splitDescriptor(numSplits, inputDimSize); + unsigned int accumSplit = 0; + for (unsigned int j = 0; j < numSplits; ++j) + { + unsigned int splitSize = armnn::numeric_cast<unsigned int>(splitsTensorData[j]); + + // Set the size of the views. + for (unsigned int dimIdx = 0; dimIdx < inputTensorInfo.GetNumDimensions(); ++dimIdx) + { + unsigned int dimSize = inputTensorInfo.GetShape()[dimIdx]; + if (dimIdx == splitDim) + { + dimSize = splitSize; + } + splitDescriptor.SetViewSize(j, dimIdx, dimSize); + } + + splitDescriptor.SetViewOriginCoord(j, splitDim, accumSplit); + accumSplit += splitSize; + } + + if (!delegateData.m_Network) + { + // Check if supported + bool isSupported = false; + FORWARD_LAYER_SUPPORT_FUNC(__func__, + tfLiteContext, + IsSplitterSupported, + delegateData.m_Backends, + isSupported, + inputTensorInfo, + outputTensorInfos, + splitDescriptor); + return isSupported ? kTfLiteOk : kTfLiteError; + } + + armnn::IConnectableLayer* layer = delegateData.m_Network->AddSplitterLayer(splitDescriptor); + ARMNN_ASSERT(layer != nullptr); + + for (unsigned int k = 0; k < layer->GetNumOutputSlots(); ++k) + { + layer->GetOutputSlot(k).SetTensorInfo(outputs[k]); + } + + // Connect + return Connect(layer, tfLiteNode, delegateData); +} + +} // namespace armnnDelegate
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