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Diffstat (limited to 'delegate/classic/src/FullyConnected.hpp')
| -rw-r--r-- | delegate/classic/src/FullyConnected.hpp | 275 |
1 files changed, 275 insertions, 0 deletions
diff --git a/delegate/classic/src/FullyConnected.hpp b/delegate/classic/src/FullyConnected.hpp new file mode 100644 index 0000000000..28d43d06df --- /dev/null +++ b/delegate/classic/src/FullyConnected.hpp @@ -0,0 +1,275 @@ +// +// Copyright © 2020-2023 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include <DelegateUtils.hpp> +#include "armnnUtils/TensorUtils.hpp" +#include <armnn/utility/IgnoreUnused.hpp> + +#include <tensorflow/lite/builtin_ops.h> +#include <tensorflow/lite/c/builtin_op_data.h> +#include <tensorflow/lite/c/common.h> +#include <tensorflow/lite/minimal_logging.h> + +namespace armnnDelegate +{ + +TfLiteStatus VisitFullyConnectedOperator(DelegateData& delegateData, + TfLiteContext* tfLiteContext, + TfLiteNode* tfLiteNode, + int nodeIndex, + int32_t operatorCode) +{ + auto numInputs = tfLiteNode->inputs->size; + if (numInputs < 2) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, "TfLiteArmnnDelegate: Minimum number of inputs (%d != %d) in node #%d", + 2, numInputs, nodeIndex); + return kTfLiteError; + } + TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); + bool biasEnabled = IsOptionalOperandPresent(tfLiteNode, 2); + + const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; + const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; + if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex)) + { + return kTfLiteError; + } + + const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; + if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex)) + { + return kTfLiteError; + } + + const TfLiteTensor& tfLiteWeightsTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; + if (!IsValid(tfLiteContext, tfLiteWeightsTensor, operatorCode, nodeIndex)) + { + return kTfLiteError; + } + + const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); + const armnn::TensorInfo& weightsTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteWeightsTensor); + const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); + + // Check that we support fused activation before we attempt to create a layer + auto* tfLiteNodeParameters = reinterpret_cast<TfLiteFullyConnectedParams *>(tfLiteNode->builtin_data); + TfLiteFusedActivation activationType=kTfLiteActNone; + if (tfLiteNodeParameters) + { + activationType = tfLiteNodeParameters->activation; + TfLiteStatus activationStatus = ValidateFusedActivationOperator(delegateData, tfLiteContext, outputTensorInfo, + outputTensorInfo, activationType); + if(activationStatus != kTfLiteOk) + { + return kTfLiteError; + } + } + + // Fully Connected Layer accepts two dimensional weights input + int32_t weightsDimension = static_cast<int32_t>(weightsTensorInfo.GetNumDimensions()); + if (weightsDimension != 2) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dimension #$d for Fully Connected weights is not supported by Armnn" + " in operator #%d node #%d: ", weightsDimension, operatorCode, nodeIndex); + return kTfLiteError; + } + + armnn::TensorInfo biasTensorInfo; + if (biasEnabled) + { + const TfLiteTensor& tfLiteBiasTensor = tfLiteTensors[tfLiteNode->inputs->data[2]]; + if (!IsValid(tfLiteContext, tfLiteBiasTensor, operatorCode, nodeIndex)) + { + return kTfLiteError; + } + biasTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteBiasTensor); + } + else + { + biasTensorInfo = armnn::TensorInfo(armnn::TensorShape({1}), GetDataType(tfLiteInputTensor)); + } + + armnn::TensorInfo reshapedTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); + if (inputTensorInfo.GetNumDimensions() > 2) + { + // Calculate reshape to flatten to 2D [batch_size, input_size] + std::vector<unsigned int> reshapedDimensions(2); + reshapedDimensions[1] = weightsTensorInfo.GetShape()[1]; + reshapedDimensions[0] = inputTensorInfo.GetNumElements() / reshapedDimensions[1]; + + if (inputTensorInfo.GetNumElements() % reshapedDimensions[1] != 0) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Failed to deduce input tensor shape from filter size #%d #%d node #%d: ", + reshapedDimensions[1], operatorCode, nodeIndex); + return kTfLiteError; + } + + reshapedTensorInfo.SetShape(armnn::TensorShape{ 2, reshapedDimensions.data() }); + } + armnn::TensorInfo reshapedOutputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor); + + if (outputTensorInfo.GetNumDimensions() > 2) + { + // Calculate reshape to flatten to 2D [batch_size, input_size] + std::vector<unsigned int> reshapedDimensions(2); + reshapedDimensions[1] = weightsTensorInfo.GetShape()[0]; + reshapedDimensions[0] = outputTensorInfo.GetNumElements() / reshapedDimensions[1]; + + if (outputTensorInfo.GetNumElements() % reshapedDimensions[1] != 0) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Failed to deduce output tensor shape from filter size #%d #%d node #%d: ", + reshapedDimensions[1], operatorCode, nodeIndex); + return kTfLiteError; + } + reshapedOutputTensorInfo.SetShape(armnn::TensorShape{ 2, reshapedDimensions.data() }); + } + + armnn::FullyConnectedDescriptor descriptor; + descriptor.m_TransposeWeightMatrix = true; + descriptor.m_BiasEnabled = biasEnabled; + descriptor.m_ConstantWeights = weightsTensorInfo.IsConstant(); + + bool isSupported = false; + armnn::BackendId setBackend; + auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) + { + + FORWARD_LAYER_SUPPORT_FUNC("FULLY_CONNECTED", + tfLiteContext, + IsFullyConnectedSupported, + delegateData.m_Backends, + isSupported, + setBackend, + reshapedTensorInfo, + outputTensorInfo, + weightsTensorInfo, + biasTensorInfo, + descriptor); + }; + + if (!delegateData.m_Network) + { + validateFunc(reshapedOutputTensorInfo, isSupported); + return isSupported ? kTfLiteOk : kTfLiteError; + } + + armnn::IConnectableLayer* layer = delegateData.m_Network->AddFullyConnectedLayer(descriptor); + layer->SetBackendId(setBackend); + ARMNN_ASSERT(layer != nullptr); + + // Add a constant layer for weights and biases if inputs are constant. + if (weightsTensorInfo.IsConstant()) + { + auto weightsTensor = CreateConstTensor(&tfLiteWeightsTensor, + weightsTensorInfo); + + armnn::IConnectableLayer* weightsLayer = delegateData.m_Network->AddConstantLayer(weightsTensor); + + weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u)); + weightsLayer->GetOutputSlot(0).SetTensorInfo(weightsTensorInfo); + } + + if (biasEnabled) + { + const TfLiteTensor& tfLiteBiasTensor = tfLiteTensors[tfLiteNode->inputs->data[2]]; + if(biasTensorInfo.IsConstant()) + { + auto biasTensor = CreateConstTensor(&tfLiteBiasTensor, + biasTensorInfo); + + armnn::IConnectableLayer* biasLayer = delegateData.m_Network->AddConstantLayer(biasTensor); + ARMNN_ASSERT(biasLayer != nullptr); + + biasLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(2u)); + biasLayer->GetOutputSlot(0).SetTensorInfo(biasTensorInfo); + } + } + + // The data input can also be constant, so we must check that this is also allocated to an input slot + if(inputTensorInfo.IsConstant()) + { + auto input = + CreateConstTensor(&tfLiteContext->tensors[tfLiteNode->inputs->data[0]], + inputTensorInfo); + + armnn::IConnectableLayer *inputLayer = delegateData.m_Network->AddConstantLayer(input); + inputLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0u)); + inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo); + } + + armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); + outputSlot.SetTensorInfo(outputTensorInfo); + + armnn::IConnectableLayer* reshapeLayer = nullptr; + if (inputTensorInfo.GetNumDimensions() > 2) + { + // Add reshape to flatten to 2D [batch_size, input_size] + armnn::ReshapeDescriptor reshapeDescriptor; + reshapeDescriptor.m_TargetShape = reshapedTensorInfo.GetShape(); + reshapeLayer = delegateData.m_Network->AddReshapeLayer(reshapeDescriptor); + ARMNN_ASSERT(reshapeLayer != nullptr); + + reshapeLayer->GetOutputSlot(0).SetTensorInfo(reshapedTensorInfo); + + // Connect + delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[0]]->Connect(reshapeLayer->GetInputSlot(0)); + reshapeLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0)); + + if (!descriptor.m_ConstantWeights) + { + delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[1]]->Connect(layer->GetInputSlot(1)); + } + + if (biasEnabled && !biasTensorInfo.IsConstant()) + { + delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[2]]->Connect(layer->GetInputSlot(2)); + } + delegateData.m_OutputSlotForNode[tfLiteNode->outputs->data[0]] = &outputSlot; + } + + if (reshapeLayer == nullptr) + { + if(Connect(layer, tfLiteNode, delegateData) != kTfLiteOk) + { + return kTfLiteError; + } + } + + if (outputTensorInfo.GetNumDimensions() > 2) + { + layer = AddReshapeLayer(tfLiteContext, tfLiteNode, layer, reshapedOutputTensorInfo, outputTensorInfo, + delegateData); + if (!layer) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Failed to add reshape for FullyConnected #%d node #%d: ", + operatorCode, + nodeIndex); + return kTfLiteError; + } + } + + if (!tfLiteNodeParameters) + { + // No Activation + return kTfLiteOk; + } + + // Check and Create Activation + return FusedActivation(tfLiteContext, tfLiteNode, activationType, layer, 0, delegateData); +} + +} // namespace armnnDelegate
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