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Diffstat (limited to 'delegate/opaque/src/FullyConnected.hpp')
| -rw-r--r-- | delegate/opaque/src/FullyConnected.hpp | 291 |
1 files changed, 291 insertions, 0 deletions
diff --git a/delegate/opaque/src/FullyConnected.hpp b/delegate/opaque/src/FullyConnected.hpp index e16969768e..3282cab543 100644 --- a/delegate/opaque/src/FullyConnected.hpp +++ b/delegate/opaque/src/FullyConnected.hpp @@ -2,3 +2,294 @@ // Copyright © 2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // + +#pragma once + +#include <OpaqueDelegateUtils.hpp> +#include <SharedFunctions.hpp> + +namespace armnnOpaqueDelegate +{ + +TfLiteStatus VisitFullyConnectedOperator(DelegateData& delegateData, + TfLiteOpaqueContext* tfLiteContext, + TfLiteOpaqueNode* tfLiteNode, + int nodeIndex, + int32_t operatorCode) +{ + auto numInputs = TfLiteOpaqueNodeNumberOfInputs(tfLiteNode); + if (numInputs < 2) + { + TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: Minimum number of inputs (%d != %d) in node #%d", + 2, numInputs, nodeIndex); + return kTfLiteError; + } + TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); + + // Gather input indices and use to get input tensor. + const int* inputTensors; + 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; + } + + const TfLiteOpaqueTensor* tfLiteInputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]); + if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex)) + { + return kTfLiteError; + } + + const TfLiteOpaqueTensor* tfLiteWeightsTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[1]); + if (!IsValid(tfLiteContext, tfLiteWeightsTensor, operatorCode, nodeIndex)) + { + return kTfLiteError; + } + + // Gather output indices and use to get output tensors. + int numOutputs = 0; + const int* outputTensors; + 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, operatorCode, nodeIndex)) + { + return kTfLiteError; + } + + const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor); + const armnn::TensorInfo& weightsTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteWeightsTensor); + const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true); + + // Check that we support fused activation before we attempt to create a layer + auto* tfLiteNodeParameters = + reinterpret_cast<TfLiteFullyConnectedParams*>(TfLiteOpaqueNodeGetBuiltinData(tfLiteNode)); + 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_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: Dimension #$d for Fully Connected weights is not supported by Armnn" + " in operator #%d node #%d: ", weightsDimension, operatorCode, nodeIndex); + return kTfLiteError; + } + + armnn::TensorInfo biasTensorInfo; + const TfLiteOpaqueTensor* tfLiteBiasTensor = nullptr; + + bool biasEnabled = IsOptionalOperandPresent(tfLiteNode, 2); + if (biasEnabled) + { + // Use input indices to get bias tensor. + tfLiteBiasTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[2]); + if (!IsValid(tfLiteContext, tfLiteBiasTensor, operatorCode, nodeIndex)) + { + return kTfLiteError; + } + biasTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteBiasTensor); + } + else + { + biasTensorInfo = armnn::TensorInfo(armnn::TensorShape({1}), GetDataType(tfLiteInputTensor)); + } + + armnn::TensorInfo reshapedTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(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_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: 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 = GetTensorInfoForTfLiteOpaqueTensor(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_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: 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_OPAQUE_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) + { + 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(tfLiteInputTensor, 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[inputTensors[0]]->Connect(reshapeLayer->GetInputSlot(0)); + reshapeLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0)); + + if (!descriptor.m_ConstantWeights) + { + delegateData.m_OutputSlotForNode[inputTensors[1]]->Connect(layer->GetInputSlot(1)); + } + + if (biasEnabled && !biasTensorInfo.IsConstant()) + { + delegateData.m_OutputSlotForNode[inputTensors[2]]->Connect(layer->GetInputSlot(2)); + } + delegateData.m_OutputSlotForNode[outputTensors[0]] = &outputSlot; + } + + if (reshapeLayer == nullptr) + { + if(Connect(layer, tfLiteContext, tfLiteNode, delegateData) != kTfLiteOk) + { + return kTfLiteError; + } + } + + if (outputTensorInfo.GetNumDimensions() > 2) + { + layer = AddReshapeLayer(tfLiteContext, + tfLiteNode, + layer, + reshapedOutputTensorInfo, + outputTensorInfo, + delegateData); + if (!layer) + { + TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnOpaqueDelegate: 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 armnnOpaqueDelegate |