diff options
Diffstat (limited to '1.2/HalPolicy.cpp')
| -rw-r--r-- | 1.2/HalPolicy.cpp | 2372 |
1 files changed, 21 insertions, 2351 deletions
diff --git a/1.2/HalPolicy.cpp b/1.2/HalPolicy.cpp index ca92318e..9e547fab 100644 --- a/1.2/HalPolicy.cpp +++ b/1.2/HalPolicy.cpp @@ -4,17 +4,6 @@ // #include "HalPolicy.hpp" -#include "Utils.hpp" - -#include <armnn/TypesUtils.hpp> - -#include <armnnUtils/DataLayoutIndexed.hpp> -#include <armnnUtils/TensorUtils.hpp> - -#include <Half.hpp> - -#include <cmath> -#include <string> namespace armnn_driver { @@ -26,58 +15,6 @@ using namespace armnn; namespace { -bool IsQSymmDequantizeForWeights(const HalPolicy::Operation& operation, const HalPolicy::Model& model) -{ - const HalPolicy::Operand* operand = GetInputOperand<hal_1_2::HalPolicy>(operation, 0, model); - if (!operand) - { - return false; - } - - if(!IsQSymm8(*operand)) - { - // Only QSymm8 weights are dequantized on the fly by the driver - return false; - } - - if (!IsOperandConstant<hal_1_2::HalPolicy>(*operand)) - { - // Non-const input is not accepted for weights - return false; - } - - // Iterate through all the operations and find the operation feeding from the Dequantize output - const size_t outputIndex = operation.outputs[0]; - for (uint32_t operationIdx = 0; operationIdx < model.operations.size(); ++operationIdx) - { - const auto& operationIt = model.operations[operationIdx]; - switch (operationIt.type) - { - case HalPolicy::OperationType::FULLY_CONNECTED: - if (outputIndex == operationIt.inputs[1]) // Weights are bound to slot 1 - { - // If the output is going into the FC weights return true - return true; - } - break; - case HalPolicy::OperationType::LSTM: - for (size_t k = 0; k < operationIt.inputs.size(); ++k) - { - if (outputIndex == operationIt.inputs[k]) - { - // If the output is going into the LSTM weights return true - return true; - } - } - break; - default: - break; - } - } - - return false; -} - } // anonymous namespace bool HalPolicy::ConvertOperation(const Operation& operation, const Model& model, ConversionData& data) @@ -237,57 +174,7 @@ bool HalPolicy::ConvertComparison(const Operation& operation, ComparisonOperation comparisonOperation) { ALOGV("hal_1_2::HalPolicy::ConvertComparison()"); - ALOGV("comparisonOperation = %s", GetComparisonOperationAsCString(comparisonOperation)); - - LayerInputHandle input0 = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data); - LayerInputHandle input1 = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 1, model, data); - - if (!(input0.IsValid() && input1.IsValid())) - { - return Fail("%s: Operation has invalid inputs", __func__); - } - - const Operand* output = GetOutputOperand<hal_1_2::HalPolicy>(operation, 0, model); - if (!output) - { - return Fail("%s: Could not read output 0", __func__); - } - - const TensorInfo& inputInfo0 = input0.GetTensorInfo(); - const TensorInfo& inputInfo1 = input1.GetTensorInfo(); - const TensorInfo& outputInfo = GetTensorInfoForOperand(*output); - - if (IsDynamicTensor(outputInfo)) - { - return Fail("%s: Dynamic output tensors are not supported", __func__); - } - - ComparisonDescriptor descriptor(comparisonOperation); - - bool isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC(__func__, - IsComparisonSupported, - data.m_Backends, - isSupported, - inputInfo0, - inputInfo1, - outputInfo, - descriptor); - - if (!isSupported) - { - return false; - } - - IConnectableLayer* layer = data.m_Network->AddComparisonLayer(descriptor); - assert(layer != nullptr); - bool isReshapeSupported = BroadcastTensor(input0, input1, layer, data); - if (!isReshapeSupported) - { - return false; - } - - return SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 0, *layer, model, data); + return ::ConvertComparison_1_2<hal_1_2::HalPolicy>(operation, model, data, comparisonOperation); } bool HalPolicy::ConvertConcatenation(const Operation& operation, const Model& model, ConversionData& data) @@ -299,153 +186,7 @@ bool HalPolicy::ConvertConcatenation(const Operation& operation, const Model& mo bool HalPolicy::ConvertConv2d(const Operation& operation, const Model& model, ConversionData& data) { ALOGV("hal_1_2::HalPolicy::ConvertConv2d()"); - - LayerInputHandle input = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data); - if (!input.IsValid()) - { - return Fail("%s: Operation has invalid inputs", __func__); - } - - const Operand* output = GetOutputOperand<hal_1_2::HalPolicy>(operation, 0, model); - if (!output) - { - return Fail("%s: Could not read output 0", __func__); - } - - const TensorInfo& inputInfo = input.GetTensorInfo(); - const TensorInfo& outputInfo = GetTensorInfoForOperand(*output); - - if (IsDynamicTensor(outputInfo)) - { - return Fail("%s: Dynamic output tensors are not supported", __func__); - } - - Convolution2dDescriptor desc; - desc.m_DataLayout = DataLayout::NHWC; - - // Determine whether padding is implicit or explicit - bool implicitPadding = operation.inputs.size() == 7 || - (operation.inputs.size() >= 8 && - GetInputOperand<hal_1_2::HalPolicy>(operation, 7, model)->type == OperandType::BOOL); - - if (implicitPadding) - { - desc.m_DataLayout = OptionalDataLayout<hal_1_2::HalPolicy>(operation, 7, model, data); - } - else if (operation.inputs.size() >= 10) - { - desc.m_DataLayout = OptionalDataLayout<hal_1_2::HalPolicy>(operation, 10, model, data); - } - - const PermutationVector OHWIToOIHW = {0, 2, 3, 1}; - - // ArmNN does not currently support non-fixed weights or bias - // The NNAPI filter is always OHWI [depth_out, filter_height, filter_width, depth_in] but ArmNN expects the - // filter's height and width indices to match the input's height and width indices so we permute it to OIHW if - // the DataLayout is NCHW - const ConstTensorPin weightsPin = (desc.m_DataLayout == DataLayout::NCHW) ? - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 1, model, data, OHWIToOIHW) : - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 1, model, data); - const ConstTensorPin biasPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 2, model, data); - - if (!weightsPin.IsValid()) - { - return Fail("%s: Operation has invalid weights", __func__); - } - - if (!biasPin.IsValid()) - { - return Fail("%s: Operation has invalid biases", __func__); - } - - ConstTensor weights = weightsPin.GetConstTensor(); - ConstTensor bias = biasPin.GetConstTensor(); - SanitizeBiasQuantizationScale(bias.GetInfo(), weights.GetInfo(), inputInfo); - - ActivationFn activation; - - if (implicitPadding) - { - android::nn::PaddingScheme paddingScheme; - if (!GetInputPaddingScheme<hal_1_2::HalPolicy>(operation, 3, paddingScheme, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 4, OperandType::INT32, desc.m_StrideX, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 5, OperandType::INT32, desc.m_StrideY, model, data) || - !GetInputActivationFunction<hal_1_2::HalPolicy>(operation, 6, activation, model, data) || - !GetOptionalConvolutionDilationParams<hal_1_2::HalPolicy>(operation, 8, desc, model, data)) - { - return Fail("%s: Operation has invalid inputs (implicit padding)", __func__); - } - - armnnUtils::DataLayoutIndexed dataLayoutIndexed(desc.m_DataLayout); - unsigned int widthIndex = dataLayoutIndexed.GetWidthIndex(); - unsigned int heightIndex = dataLayoutIndexed.GetHeightIndex(); - const uint32_t kernelX = weights.GetShape()[widthIndex]; - const uint32_t kernelY = weights.GetShape()[heightIndex]; - const uint32_t inputX = inputInfo.GetShape()[widthIndex]; - const uint32_t inputY = inputInfo.GetShape()[heightIndex]; - - CalcPadding(inputX, kernelX, desc.m_StrideX, desc.m_DilationX, desc.m_PadLeft, desc.m_PadRight, paddingScheme); - CalcPadding(inputY, kernelY, desc.m_StrideY, desc.m_DilationY, desc.m_PadTop, desc.m_PadBottom, paddingScheme); - - } - else if (operation.inputs.size() >= 10) - { - // explicit padding - if (!GetInputScalar<hal_1_2::HalPolicy>(operation, 3, OperandType::INT32, desc.m_PadLeft, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 4, OperandType::INT32, desc.m_PadRight, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 5, OperandType::INT32, desc.m_PadTop, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 6, OperandType::INT32, desc.m_PadBottom, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 7, OperandType::INT32, desc.m_StrideX, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 8, OperandType::INT32, desc.m_StrideY, model, data) || - !GetInputActivationFunction<hal_1_2::HalPolicy>(operation, 9, activation, model, data) || - !GetOptionalConvolutionDilationParams<hal_1_2::HalPolicy>(operation, 11, desc, model, data)) - { - return Fail("%s: Operation has invalid inputs (explicit padding)", __func__); - } - } - else - { - return Fail("%s: Unsupported number of operation inputs", __func__); - } - - desc.m_BiasEnabled = true; - Optional<TensorInfo> biases(bias.GetInfo()); - - bool isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC(__func__, - IsConvolution2dSupported, - data.m_Backends, - isSupported, - inputInfo, - outputInfo, - desc, - weights.GetInfo(), - biases); - - if (!isSupported) - { - return false; - } - - IConnectableLayer* startLayer = - data.m_Network->AddConvolution2dLayer(desc, weights, Optional<ConstTensor>(bias)); - - if (!startLayer) - { - return Fail("%s: AddConvolution2dLayer failed", __func__); - } - - IConnectableLayer* endLayer = ProcessActivation(outputInfo, activation, startLayer, data); - - if (!endLayer) - { - return Fail("%s: ProcessActivation failed", __func__); - } - - input.Connect(startLayer->GetInputSlot(0)); - - return SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 0, *endLayer, model, data); + return ::ConvertConv2d_1_2<hal_1_2::HalPolicy>(operation, model, data); } bool HalPolicy::ConvertDepthToSpace(const Operation& operation, const Model& model, ConversionData& data) @@ -457,187 +198,13 @@ bool HalPolicy::ConvertDepthToSpace(const Operation& operation, const Model& mod bool HalPolicy::ConvertDepthwiseConv2d(const Operation& operation, const Model& model, ConversionData& data) { ALOGV("hal_1_2::HalPolicy::ConvertDepthwiseConv2d()"); - - LayerInputHandle input = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data); - - if (!input.IsValid()) - { - return Fail("%s: Operation has invalid inputs", __func__); - } - - const Operand* output = GetOutputOperand<hal_1_2::HalPolicy>(operation, 0, model); - - if (!output) - { - return Fail("%s: Could not read output 0", __func__); - } - - const TensorInfo& inputInfo = input.GetTensorInfo(); - const TensorInfo& outputInfo = GetTensorInfoForOperand(*output); - - if (IsDynamicTensor(outputInfo)) - { - return Fail("%s: Dynamic output tensors are not supported", __func__); - } - - // ArmNN does not currently support non-fixed weights or bias - // Find the shape of the weights tensor. In AndroidNN this will be [ 1, H, W, I * M ] - const Operand* weightsOperand = GetInputOperand<hal_1_2::HalPolicy>(operation, 1, model); - - if (weightsOperand == nullptr) - { - return Fail("%s: Operand is invalid", __func__); - } - if ( weightsOperand->dimensions[0] != 1) - { - return Fail("%s: Invalid weights; for depthwise convolution, dimension 0 must be 1 but it is %i", - __func__, weightsOperand->dimensions[0] ); - } - - DepthwiseConvolution2dDescriptor desc; - desc.m_DataLayout = DataLayout::NHWC; - - // Determine whether padding is implicit or explicit - bool implicitPadding = operation.inputs.size() == 8 || - (operation.inputs.size() >= 9 && - GetInputOperand<hal_1_2::HalPolicy>(operation, 8, model)->type == OperandType::BOOL); - - // Look ahead to find the optional DataLayout, if present - const uint32_t dataLayoutFlagIndex = implicitPadding ? 8 : 11; - desc.m_DataLayout = OptionalDataLayout<hal_1_2::HalPolicy>(operation, dataLayoutFlagIndex, model, data); - - armnnUtils::DataLayoutIndexed dataLayoutIndexed(desc.m_DataLayout); - unsigned int channelsIndex = dataLayoutIndexed.GetChannelsIndex(); - unsigned int widthIndex = dataLayoutIndexed.GetWidthIndex(); - unsigned int heightIndex = dataLayoutIndexed.GetHeightIndex(); - - // Reinterpret weight data as [ H, W, I, M ] - TensorShape weightsShape({ weightsOperand->dimensions[1], - weightsOperand->dimensions[2], - inputInfo.GetShape()[channelsIndex], - weightsOperand->dimensions[3] / inputInfo.GetShape()[channelsIndex] }); - - // Swizzle weight data [ H, W, I, M ] -> [ M, I, H, W ] - const PermutationVector HWIMToMIHW = { 2U, 3U, 1U, 0U }; - - const ConstTensorPin weightsPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, - 1, - model, - data, - HWIMToMIHW, - &weightsShape); - - // Bias is a 1D tensor - const ConstTensorPin biasPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 2, model, data); - - if (!weightsPin.IsValid()) - { - return Fail("%s: Operation has invalid weights", __func__); - } - - if (!biasPin.IsValid()) - { - return Fail("%s: Operation has invalid biases", __func__); - } - - ConstTensor weights = weightsPin.GetConstTensor(); - ConstTensor bias = biasPin.GetConstTensor(); - SanitizeBiasQuantizationScale(bias.GetInfo(), weights.GetInfo(), inputInfo); - - ActivationFn activation; - - if (implicitPadding) - { - android::nn::PaddingScheme paddingScheme; - if (!GetInputPaddingScheme<hal_1_2::HalPolicy>(operation, 3, paddingScheme, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 4, OperandType::INT32, desc.m_StrideX, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 5, OperandType::INT32, desc.m_StrideY, model, data) || - !GetInputActivationFunction<hal_1_2::HalPolicy>(operation, 7, activation, model, data) || - !GetOptionalConvolutionDilationParams<hal_1_2::HalPolicy>(operation, 9, desc, model, data)) - { - return Fail("%s: Operation has invalid inputs (implicit padding)", __func__); - } - - const uint32_t kernelX = weights.GetShape()[3]; - const uint32_t kernelY = weights.GetShape()[2]; - const uint32_t inputX = inputInfo.GetShape()[widthIndex]; - const uint32_t inputY = inputInfo.GetShape()[heightIndex]; - - CalcPadding(inputX, kernelX, desc.m_StrideX, desc.m_DilationX, desc.m_PadLeft, desc.m_PadRight, paddingScheme); - CalcPadding(inputY, kernelY, desc.m_StrideY, desc.m_DilationY, desc.m_PadTop, desc.m_PadBottom, paddingScheme); - } - else if (operation.inputs.size() >= 11) - { - // explicit padding - if (!GetInputScalar<hal_1_2::HalPolicy>(operation, 3, OperandType::INT32, desc.m_PadLeft, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 4, OperandType::INT32, desc.m_PadRight, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 5, OperandType::INT32, desc.m_PadTop, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 6, OperandType::INT32, desc.m_PadBottom, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 7, OperandType::INT32, desc.m_StrideX, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 8, OperandType::INT32, desc.m_StrideY, model, data) || - !GetInputActivationFunction<hal_1_2::HalPolicy>(operation, 10, activation, model, data) || - !GetOptionalConvolutionDilationParams<hal_1_2::HalPolicy>(operation, 12, desc, model, data)) - { - return Fail("%s: Operation has invalid inputs (explicit padding)", __func__); - } - } - else - { - return Fail("%s: Unsupported number of operation inputs", __func__); - } - - desc.m_BiasEnabled = true; - Optional<TensorInfo> biases(bias.GetInfo()); - - bool isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC(__func__, - IsDepthwiseConvolutionSupported, - data.m_Backends, - isSupported, - inputInfo, - outputInfo, - desc, - weights.GetInfo(), - biases); - - if (!isSupported) - { - return false; - } - - IConnectableLayer* startLayer = - data.m_Network->AddDepthwiseConvolution2dLayer(desc, weights, Optional<ConstTensor>(bias)); - - if (!startLayer) - { - return Fail("%s: AddDepthwiseConvolution2dLayer failed", __func__); - } - - IConnectableLayer* endLayer = ProcessActivation(outputInfo, activation, startLayer, data); - if (!endLayer) - { - return Fail("%s: ProcessActivation failed", __func__); - } - - input.Connect(startLayer->GetInputSlot(0)); - - return SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 0, *endLayer, model, data); + return ::ConvertDepthwiseConv2d_1_2<hal_1_2::HalPolicy>(operation, model, data); } bool HalPolicy::ConvertDequantize(const Operation& operation, const Model& model, ConversionData& data) { ALOGV("hal_1_2::HalPolicy::ConvertDequantize()"); - - if (IsQSymmDequantizeForWeights(operation, model)) - { - // NOTE: QSymm8 weights are dequantized internally by the driver, - // therefore this type of Dequantize is implicitly supported - return true; - } - - return ::ConvertDequantize<hal_1_2::HalPolicy>(operation, model, data); + return ::ConvertDequantize_1_2<hal_1_2::HalPolicy>(operation, model, data); } bool HalPolicy::ConvertDiv(const Operation& operation, const Model& model, ConversionData& data) @@ -652,120 +219,13 @@ bool HalPolicy::ConvertElementwiseUnary(const Operation& operation, UnaryOperation unaryOperation) { ALOGV("hal_1_2::HalPolicy::ConvertElementwiseUnary()"); - ALOGV("unaryOperation = %s", GetUnaryOperationAsCString(unaryOperation)); - - LayerInputHandle input = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data); - - if (!input.IsValid()) - { - return Fail("%s: Operation has invalid input", __func__); - } - - const Operand* output = GetOutputOperand<hal_1_2::HalPolicy>(operation, 0, model); - if (!output) - { - return Fail("%s: Could not read output 0", __func__); - } - - const TensorInfo& inputInfo = input.GetTensorInfo(); - const TensorInfo& outputInfo = GetTensorInfoForOperand(*output); - - if (IsDynamicTensor(outputInfo)) - { - return Fail("%s: Dynamic output tensors are not supported", __func__); - } - - ElementwiseUnaryDescriptor descriptor(unaryOperation); - - bool isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC(__func__, - IsElementwiseUnarySupported, - data.m_Backends, - isSupported, - inputInfo, - outputInfo, - descriptor); - - if (!isSupported) - { - return false; - } - - IConnectableLayer* layer = data.m_Network->AddElementwiseUnaryLayer(descriptor); - assert(layer != nullptr); - - input.Connect(layer->GetInputSlot(0)); - - return SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 0, *layer, model, data); + return ::ConvertElementwiseUnary<hal_1_2::HalPolicy>(operation, model, data, unaryOperation); } bool HalPolicy::ConvertExpandDims(const Operation& operation, const Model& model, ConversionData& data) { ALOGV("hal_1_2::HalPolicy::ConvertExpandDims()"); - - LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data); - - if (!input.IsValid()) - { - return Fail("%s: Operation has invalid input", __func__); - } - - const Operand* output = GetOutputOperand<HalPolicy>(operation, 0, model); - if (!output) - { - return Fail("%s: Operation has invalid output", __func__); - } - - const TensorInfo& outputInfo = GetTensorInfoForOperand(*output); - if (IsDynamicTensor(outputInfo)) - { - return Fail("%s: Dynamic output tensors are not supported", __func__); - } - - int32_t axis; - if (!GetInputScalar<HalPolicy>(operation, 1, OperandType::INT32, axis, model, data)) - { - return Fail("%s: failed to get axis input value", __func__); - } - - TensorShape targetShape; - - try - { - targetShape = armnnUtils::ExpandDims(input.GetTensorInfo().GetShape(), axis); - } - catch (const std::exception &e) - { - return Fail("%s: %s", __func__, e.what()); - } - - if (targetShape != outputInfo.GetShape()) - { - return Fail("%s: Shape of the output operand does not match the resolved expanded shape", __func__); - } - - ReshapeDescriptor reshapeDescriptor; - reshapeDescriptor.m_TargetShape = targetShape; - - bool isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC(__func__, - IsReshapeSupported, - data.m_Backends, - isSupported, - input.GetTensorInfo(), - outputInfo, - reshapeDescriptor); - - if (!isSupported) - { - return false; - } - - IConnectableLayer* layer = data.m_Network->AddReshapeLayer(reshapeDescriptor); - assert(layer != nullptr); - input.Connect(layer->GetInputSlot(0)); - - return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, model, data); + return ::ConvertExpandDims<hal_1_2::HalPolicy>(operation, model, data); } bool HalPolicy::ConvertFloor(const Operation& operation, const Model& model, ConversionData& data) @@ -783,416 +243,13 @@ bool HalPolicy::ConvertFullyConnected(const Operation& operation, const Model& m bool HalPolicy::ConvertGroupedConv2d(const Operation& operation, const Model& model, ConversionData& data) { ALOGV("hal_1_2::HalPolicy::ConvertGroupedConv2d()"); - - // - // Parse data - // - LayerInputHandle input = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data); - if (!input.IsValid()) - { - return Fail("%s: Operation has invalid inputs", __func__); - } - const TensorInfo& inputInfo = input.GetTensorInfo(); - - const Operand* output = GetOutputOperand<hal_1_2::HalPolicy>(operation, 0, model); - if (!output) - { - return Fail("%s: Could not read output 0", __func__); - } - const TensorInfo& outputInfo = GetTensorInfoForOperand(*output); - if (IsDynamicTensor(outputInfo)) - { - return Fail("%s: Dynamic output tensors are not supported", __func__); - } - - // Look ahead to determine data layout - DataLayout dataLayout = DataLayout::NHWC; - if (operation.inputs.size() == 12) - { - dataLayout = OptionalDataLayout<hal_1_2::HalPolicy>(operation, 11, model, data); - } - else - { - dataLayout = OptionalDataLayout<hal_1_2::HalPolicy>(operation, 8, model, data); - } - - // NOTE: - // NNAPI weights are always OHWI, i.e. [depth_out, filter_height, filter_width, depth_group], - // but Arm NN expects the filter's height and width indices to match the input's height and - // width indices so when the DataLayout is NCHW, we need to permute the weights to OIHW - const PermutationVector ohwiToOihw = { 0u, 2u, 3u, 1u }; - const ConstTensorPin weightsPin = (dataLayout == DataLayout::NCHW) ? - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 1, model, data, ohwiToOihw) : - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 1, model, data); - const ConstTensorPin biasesPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 2, model, data); - if (!weightsPin.IsValid() || !biasesPin.IsValid()) - { - return Fail("%s: Operation has invalid inputs", __func__); - } - - ConstTensor weights = weightsPin.GetConstTensor(); - ConstTensor biases = biasesPin.GetConstTensor(); - SanitizeBiasQuantizationScale(biases.GetInfo(), weights.GetInfo(), inputInfo); - - const TensorShape& inputShape = inputInfo.GetShape(); - const TensorShape& outputShape = outputInfo.GetShape(); - const TensorShape& weightsShape = weights.GetShape(); - const TensorShape& biasesShape = biases.GetShape(); - - armnnUtils::DataLayoutIndexed dataLayoutIndexed(dataLayout); - const unsigned int channelsIndex = dataLayoutIndexed.GetChannelsIndex(); - const unsigned int heightIndex = dataLayoutIndexed.GetHeightIndex(); - const unsigned int widthIndex = dataLayoutIndexed.GetWidthIndex(); - - Convolution2dDescriptor desc; - desc.m_DataLayout = dataLayout; - desc.m_BiasEnabled = true; - - int numGroups; - ActivationFn activation; - - if (operation.inputs.size() == 12) - { - if (!GetInputScalar<hal_1_2::HalPolicy>(operation, 3, OperandType::INT32, desc.m_PadLeft, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 4, OperandType::INT32, desc.m_PadRight, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 5, OperandType::INT32, desc.m_PadTop, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 6, OperandType::INT32, desc.m_PadBottom, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 7, OperandType::INT32, desc.m_StrideX, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 8, OperandType::INT32, desc.m_StrideY, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 9, OperandType::INT32, numGroups, model, data) || - !GetInputActivationFunction<hal_1_2::HalPolicy>(operation, 10, activation, model, data)) - { - return Fail("%s: Operation has invalid inputs (explicit padding)", __func__); - } - - } - else if (operation.inputs.size() == 9) - { - android::nn::PaddingScheme paddingScheme; - if (!GetInputPaddingScheme<hal_1_2::HalPolicy>(operation, 3, paddingScheme, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 4, OperandType::INT32, desc.m_StrideX, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 5, OperandType::INT32, desc.m_StrideY, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 6, OperandType::INT32, numGroups, model, data) || - !GetInputActivationFunction<hal_1_2::HalPolicy>(operation, 7, activation, model, data)) - { - return Fail("%s: Operation has invalid inputs (implicit padding)", __func__); - } - - const uint32_t inputX = inputInfo.GetShape()[widthIndex]; - const uint32_t inputY = inputInfo.GetShape()[heightIndex]; - - const uint32_t kernelX = weightsShape[widthIndex]; - const uint32_t kernelY = weightsShape[heightIndex]; - - CalcPadding(inputX, kernelX, desc.m_StrideX, desc.m_PadLeft, desc.m_PadRight, paddingScheme); - CalcPadding(inputY, kernelY, desc.m_StrideY, desc.m_PadTop, desc.m_PadBottom, paddingScheme); - } - else - { - return Fail("%s: Unsupported number of operation inputs", __func__); - } - - const unsigned int outputChannels = outputShape[channelsIndex]; - - const unsigned int channelsPerGroup = weightsShape[channelsIndex]; - const unsigned int channelMultiplier = outputChannels / numGroups; - - // - // Validate all relevant inputs - // - if (numGroups <= 0) - { - return Fail("%s: Number of groups must be greater than 0. Got: %d", __func__, numGroups); - } - - if (outputChannels % numGroups != 0u) - { - return Fail("%s: Output channels must be divisible by the number of groups", __func__); - } - - // - // Set up Splitter layer - // - unsigned int splitterDimSizes[4] = { inputShape[0], inputShape[1], inputShape[2], inputShape[3] }; - splitterDimSizes[channelsIndex] /= numGroups; // split in depth - - TensorInfo splitterOutputInfo(4, - splitterDimSizes, - inputInfo.GetDataType(), - inputInfo.GetQuantizationScale(), - inputInfo.GetQuantizationOffset()); - - std::vector<std::reference_wrapper<TensorInfo>> splitterOutputInfos(numGroups, std::ref(splitterOutputInfo)); - - ViewsDescriptor splitterDesc(numGroups); - for (unsigned int group = 0u; group < numGroups; ++group) - { - splitterDesc.SetViewOriginCoord(group, channelsIndex, splitterDimSizes[channelsIndex] * group); - for (unsigned int dimIdx = 0u; dimIdx < 4u; dimIdx++) - { - splitterDesc.SetViewSize(group, dimIdx, splitterDimSizes[dimIdx]); - } - } - - bool isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC(__func__, - IsSplitterSupported, - data.m_Backends, - isSupported, - inputInfo, - splitterOutputInfos, - splitterDesc); - if (!isSupported) - { - return false; - } - - IConnectableLayer* splitterLayer = data.m_Network->AddSplitterLayer(splitterDesc); - if (!splitterLayer) - { - return Fail("%s: Failed to add SplitterLayer", __func__); - } - - input.Connect(splitterLayer->GetInputSlot(0)); - for (unsigned int group = 0u; group < splitterLayer->GetNumOutputSlots(); ++group) - { - splitterLayer->GetOutputSlot(group).SetTensorInfo(splitterOutputInfo); - } - - // - // Set up Convolution2d layers for each group - // - - // Set up group tensor shapes - TensorShape groupInputShape(inputShape); - groupInputShape[channelsIndex] = channelsPerGroup; - - TensorShape groupOutputShape(outputShape); - groupOutputShape[channelsIndex] = 1; - - TensorShape groupWeightsShape(weightsShape); - groupWeightsShape[0] /= channelMultiplier * numGroups; - - TensorShape groupBiasesShape({ 1 }); - - // Set up group tensor infos - TensorInfo groupInputInfo(inputInfo); - groupInputInfo.SetShape(groupInputShape); - - const TensorInfo& weightsInfo = weights.GetInfo(); - TensorInfo groupWeightsInfo(weightsInfo); - groupWeightsInfo.SetShape(groupWeightsShape); - - const TensorInfo& biasesInfo = biases.GetInfo(); - TensorInfo groupBiasesInfo(biasesInfo); - groupBiasesInfo.SetShape(groupBiasesShape); - - TensorInfo groupOutputInfo(outputInfo); - groupOutputInfo.SetShape(groupOutputShape); - - const unsigned int weightsDataTypeSize = GetDataTypeSize(groupWeightsInfo.GetDataType()); - const unsigned int biasesDataTypeSize = GetDataTypeSize(groupBiasesInfo.GetDataType()); - - std::vector<IConnectableLayer*> convLayers(numGroups * channelMultiplier, nullptr); - for (unsigned int group = 0u; group < numGroups; ++group) - { - for (unsigned int m = 0u; m < channelMultiplier; ++m) - { - auto index = group * channelMultiplier + m; - - const unsigned int weightsDataOffset = groupWeightsShape.GetNumElements() * index * weightsDataTypeSize; - const unsigned int biasesDataOffset = groupBiasesShape.GetNumElements() * index * biasesDataTypeSize; - - if (weightsInfo.HasPerAxisQuantization()) - { - // Extract per-axis quantization scales for group weights - const std::vector<float>& weightsQuantScales = weightsInfo.GetQuantizationScales(); - groupWeightsInfo.SetQuantizationScales( - std::vector<float>(weightsQuantScales.begin() + index, - weightsQuantScales.begin() + index + groupWeightsShape[0])); - - // Extract per-axis quantization scales for group biases - const std::vector<float>& biasesQuantScales = biasesInfo.GetQuantizationScales(); - groupBiasesInfo.SetQuantizationScales( - std::vector<float>(biasesQuantScales.begin() + index, - biasesQuantScales.begin() + index + groupWeightsShape[0])); - } - - // Extract weights and biases data for current group convolution - ConstTensor groupWeights(groupWeightsInfo, - static_cast<const void *>(reinterpret_cast<const char *>(weights.GetMemoryArea()) + - weightsDataOffset)); - ConstTensor groupBiases(groupBiasesInfo, - static_cast<const void *>(reinterpret_cast<const char *>(biases.GetMemoryArea()) + - biasesDataOffset)); - - isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC(__func__, - IsConvolution2dSupported, - data.m_Backends, - isSupported, - groupInputInfo, - groupOutputInfo, - desc, - groupWeightsInfo, - Optional<TensorInfo>(groupBiasesInfo)); - if (!isSupported) - { - return false; - } - - IConnectableLayer *convLayer = - data.m_Network->AddConvolution2dLayer(desc, groupWeights, Optional<ConstTensor>(groupBiases)); - if (!convLayer) - { - return Fail("%s: AddConvolution2dLayer failed", __func__); - } - - splitterLayer->GetOutputSlot(group).Connect(convLayer->GetInputSlot(0)); - convLayer->GetOutputSlot(0).SetTensorInfo(groupOutputInfo); - - convLayers[index] = convLayer; - } - } - - // - // Set up Concat layer - // - ConcatDescriptor concatDescriptor(outputInfo.GetShape()[channelsIndex]); - for (unsigned int group = 0u; group < numGroups; ++group) - { - for (unsigned int m = 0u; m < channelMultiplier; ++m) - { - auto index = group * channelMultiplier + m; - concatDescriptor.SetViewOriginCoord(index, channelsIndex, index); - concatDescriptor.SetConcatAxis(channelsIndex); - } - } - - isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC(__func__, - IsConcatSupported, - data.m_Backends, - isSupported, - std::vector<const TensorInfo*>(numGroups * channelMultiplier, &groupOutputInfo), - outputInfo, - concatDescriptor); - if (!isSupported) - { - return false; - } - - IConnectableLayer* concatLayer = data.m_Network->AddConcatLayer(concatDescriptor); - if (!concatLayer) - { - return Fail("%s: AddConcatLayer failed", __func__); - } - - for (unsigned int group = 0u; group < numGroups; ++group) - { - for (unsigned int m = 0u; m < channelMultiplier; ++m) - { - auto index = group * channelMultiplier + m; - convLayers[index]->GetOutputSlot(0).Connect(concatLayer->GetInputSlot(index)); - } - } - concatLayer->GetOutputSlot(0).SetTensorInfo(outputInfo); - - // - // Set up Activation layer (if it is set) - // - IConnectableLayer* endLayer = ProcessActivation(outputInfo, activation, concatLayer, data); - if (!endLayer) - { - return Fail("%s: ProcessActivation failed", __func__); - } - - return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *endLayer, model, data); + return ::ConvertGroupedConv2d<hal_1_2::HalPolicy>(operation, model, data); } bool HalPolicy::ConvertInstanceNormalization(const Operation& operation, const Model& model, ConversionData& data) { ALOGV("hal_1_2::HalPolicy::ConvertInstanceNormalization()"); - - LayerInputHandle input = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data); - if (!input.IsValid()) - { - return Fail("%s: Operation has an invalid input 0", __func__); - } - - const Operand* output = GetOutputOperand<HalPolicy>(operation, 0, model); - if (!output) - { - return Fail("%s: Operation has an invalid output", __func__); - } - - const TensorInfo& outputInfo = GetTensorInfoForOperand(*output); - if (IsDynamicTensor(outputInfo)) - { - return Fail("%s: Dynamic output tensors are not supported", __func__); - } - - // Determine data type of input tensor - OperandType inputType; - if (!GetOperandType<hal_1_2::HalPolicy>(operation, 0, model, inputType)) - { - return Fail("%s: Operation has invalid inputs", __func__); - } - - InstanceNormalizationDescriptor desc; - - // Read gamma, beta & epsilon - if (inputType == OperandType::TENSOR_FLOAT16) - { - Half fp16Gamma; - Half fp16Beta; - Half fp16Epsilon; - - if (!GetInputScalar<hal_1_2::HalPolicy>(operation, 1, OperandType::FLOAT16, fp16Gamma, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 2, OperandType::FLOAT16, fp16Beta, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 3, OperandType::FLOAT16, fp16Epsilon, model, data)) - { - return Fail("%s: Operation has invalid inputs (FLOAT16)", __func__); - } - - desc.m_Gamma = static_cast<float>(fp16Gamma); - desc.m_Beta = static_cast<float>(fp16Beta); - desc.m_Eps = static_cast<float>(fp16Epsilon); - } - else if (inputType == OperandType::TENSOR_FLOAT32) - { - if (!GetInputScalar<hal_1_2::HalPolicy>(operation, 1, OperandType::FLOAT32, desc.m_Gamma, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 2, OperandType::FLOAT32, desc.m_Beta, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 3, OperandType::FLOAT32, desc.m_Eps, model, data)) - { - return Fail("%s: Operation has invalid inputs (FLOAT32)", __func__); - } - } - else - { - return Fail("%s: Unsupported input tensor type: %d", __func__, inputType); - } - - desc.m_DataLayout = OptionalDataLayout<hal_1_2::HalPolicy>(operation, 4, model, data); - - bool isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC(__func__, - IsInstanceNormalizationSupported, - data.m_Backends, - isSupported, - input.GetTensorInfo(), - outputInfo, - desc); - if (!isSupported) - { - return false; - } - - IConnectableLayer* layer = data.m_Network->AddInstanceNormalizationLayer(desc); - input.Connect(layer->GetInputSlot(0)); - - return SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 0, *layer, model, data); + return ::ConvertInstanceNormalization<hal_1_2::HalPolicy>(operation, model, data); } bool HalPolicy::ConvertL2Normalization(const Operation& operation, const Model& model, ConversionData& data) @@ -1224,85 +281,7 @@ bool HalPolicy::ConvertLogistic(const Operation& operation, const Model& model, bool HalPolicy::ConvertLogSoftmax(const Operation& operation, const Model& model, ConversionData& data) { ALOGV("hal_1_2::HalPolicy::ConvertLogSoftmax()"); - - LayerInputHandle input = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data); - if (!input.IsValid()) - { - return Fail("%s: Failed to read input 0", __func__); - } - - const Operand* output = GetOutputOperand<hal_1_2::HalPolicy>(operation, 0, model); - if (!output) - { - return Fail("%s: Failed to read output", __func__); - } - - const TensorInfo& outputInfo = GetTensorInfoForOperand(*output); - if (IsDynamicTensor(outputInfo)) - { - return Fail("%s: Dynamic output tensors are not supported", __func__); - } - - // Determine data type of input tensor - OperandType inputType; - if (!GetOperandType<hal_1_2::HalPolicy>(operation, 0, model, inputType)) - { - return Fail("%s: Operation has invalid inputs", __func__); - } - - LogSoftmaxDescriptor descriptor; - - // Read beta - if (inputType == OperandType::TENSOR_FLOAT16) - { - Half fp16Beta; - if (!GetInputScalar<hal_1_2::HalPolicy>(operation, 1, OperandType::FLOAT16, fp16Beta, model, data)) - { - return Fail("%s: Failed to read input 1 (FLOAT16)", __func__); - } - - descriptor.m_Beta = static_cast<float>(fp16Beta); - } - else if (inputType == OperandType::TENSOR_FLOAT32) - { - if (!GetInputScalar<hal_1_2::HalPolicy>(operation, 1, OperandType::FLOAT32, descriptor.m_Beta, model, data)) - { - return Fail("%s: Failed to read input 1 (FLOAT32)", __func__); - } - } - else - { - return Fail("%s: Unsupported input tensor type: %d", __func__, inputType); - } - - // Read axis - if (!GetInputInt32<hal_1_2::HalPolicy>(operation, 2, descriptor.m_Axis, model, data)) - { - return Fail("%s: Failed to read input 2", __func__); - } - - bool isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC(__func__, - IsLogSoftmaxSupported, - data.m_Backends, - isSupported, - input.GetTensorInfo(), - outputInfo, - descriptor); - if (!isSupported) - { - return false; - } - - IConnectableLayer* layer = data.m_Network->AddLogSoftmaxLayer(descriptor); - if (!layer) - { - return Fail("%s: AddLogSoftmaxLayer() returned nullptr", __func__); - } - - input.Connect(layer->GetInputSlot(0)); - - return SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, model, data); + return ::ConvertLogSoftmax<hal_1_2::HalPolicy>(operation, model, data); } bool HalPolicy::ConvertMaxPool2d(const Operation& operation, const Model& model, ConversionData& data) @@ -1314,50 +293,7 @@ bool HalPolicy::ConvertMaxPool2d(const Operation& operation, const Model& model, bool HalPolicy::ConvertMaximum(const Operation& operation, const Model& model, ConversionData& data) { ALOGV("hal_1_2::HalPolicy::ConvertMaximum()"); - - LayerInputHandle input0 = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data); - LayerInputHandle input1 = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 1, model, data); - - if (!input0.IsValid() || !input1.IsValid()) - { - return Fail("%s: Operation has invalid inputs", __func__); - } - - const Operand* outputOperand = GetOutputOperand<hal_1_2::HalPolicy>(operation, 0, model); - if (!outputOperand) - { - return Fail("%s: Could not read output", __func__); - } - - const TensorInfo& outInfo = GetTensorInfoForOperand(*outputOperand); - if (IsDynamicTensor(outInfo)) - { - return Fail("%s: Dynamic output tensors are not supported", __func__); - } - - bool isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC(__func__, - IsMaximumSupported, - data.m_Backends, - isSupported, - input0.GetTensorInfo(), - input1.GetTensorInfo(), - outInfo); - - if (!isSupported) - { - return false; - } - - IConnectableLayer* layer = data.m_Network->AddMaximumLayer(); - assert(layer != nullptr); - bool isReshapeSupported = BroadcastTensor(input0, input1, layer, data); - if (!isReshapeSupported) - { - return false; - } - - return SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 0, *layer, model, data); + return ::ConvertMaximum<hal_1_2::HalPolicy>(operation, model, data); } bool HalPolicy::ConvertMean(const Operation& operation, const Model& model, ConversionData& data) @@ -1369,50 +305,7 @@ bool HalPolicy::ConvertMean(const Operation& operation, const Model& model, Conv bool HalPolicy::ConvertMinimum(const Operation& operation, const Model& model, ConversionData& data) { ALOGV("hal_1_2::HalPolicy::ConvertMinimum()"); - - LayerInputHandle input0 = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data); - LayerInputHandle input1 = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 1, model, data); - - if (!input0.IsValid() || !input1.IsValid()) - { - return Fail("%s: Operation has invalid inputs", __func__); - } - - const Operand* output = GetOutputOperand<hal_1_2::HalPolicy>(operation, 0, model); - if (!output) - { - return Fail("%s: Could not read output 0", __func__); - } - - const TensorInfo& outputInfo = GetTensorInfoForOperand(*output); - if (IsDynamicTensor(outputInfo)) - { - return Fail("%s: Dynamic output tensors are not supported", __func__); - } - - bool isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC(__func__, - IsMinimumSupported, - data.m_Backends, - isSupported, - input0.GetTensorInfo(), - input1.GetTensorInfo(), - outputInfo); - - if (!isSupported) - { - return false; - } - - IConnectableLayer* const layer = data.m_Network->AddMinimumLayer(); - assert(layer != nullptr); - bool isReshapeSupported = BroadcastTensor(input0, input1, layer, data); - if (!isReshapeSupported) - { - return false; - } - - return SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 0, *layer, model, data); + return ::ConvertMinimum<hal_1_2::HalPolicy>(operation, model, data); } bool HalPolicy::ConvertMul(const Operation& operation, const Model& model, ConversionData& data) @@ -1430,401 +323,25 @@ bool HalPolicy::ConvertPad(const Operation& operation, const Model& model, Conve bool HalPolicy::ConvertPadV2(const Operation& operation, const Model& model, ConversionData& data) { ALOGV("hal_1_2::HalPolicy::ConvertPadV2()"); - - LayerInputHandle input = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data); - if (!input.IsValid()) - { - return Fail("%s: Could not read input 0", __func__); - } - - const Operand* output = GetOutputOperand<hal_1_2::HalPolicy>(operation, 0, model); - if (!output) - { - return Fail("%s: Could not read output", __func__); - } - - const TensorInfo& inputInfo = input.GetTensorInfo(); - unsigned int rank = inputInfo.GetNumDimensions(); - - PadDescriptor descriptor; - if (!ConvertPaddings<hal_1_2::HalPolicy>(operation, model, data, rank, descriptor)) - { - return Fail("%s: Could not convert paddings", __func__); - } - - const TensorInfo& outputInfo = GetTensorInfoForOperand(*output); - if (IsDynamicTensor(outputInfo)) - { - return Fail("%s: Dynamic output tensors are not supported", __func__); - } - - // Determine type of padding value - OperandType operandType0; - OperandType operandType2; - - if (!GetOperandType<hal_1_2::HalPolicy>(operation, 0, model, operandType0) || - !GetOperandType<hal_1_2::HalPolicy>(operation, 2, model, operandType2)) - { - return Fail("%s: Operation has invalid inputs", __func__); - } - - // Read value to use for padding - if (operandType0 == OperandType::TENSOR_FLOAT16 && operandType2 == OperandType::FLOAT16) - { - Half f16PadValue; - if (!GetInputScalar<hal_1_2::HalPolicy>(operation, 2, operandType2, f16PadValue, model, data)) - { - return Fail("%s: Could not read input 2 (FLOAT16)", __func__); - } - - descriptor.m_PadValue = f16PadValue; - } - else if (operandType0 == OperandType::TENSOR_FLOAT32 && operandType2 == OperandType::FLOAT32) - { - if (!GetInputFloat32<hal_1_2::HalPolicy>(operation, 2, descriptor.m_PadValue, model, data)) - { - return Fail("%s: Could not read input 2 (FLOAT32)", __func__); - } - } - else if (operandType0 == OperandType::TENSOR_QUANT8_ASYMM && operandType2 == OperandType::INT32) - { - int32_t intPadValue = 0; - if (!GetInputInt32<hal_1_2::HalPolicy>(operation, 2, intPadValue, model, data)) - { - return Fail("%s: Could not read input 2 (INT32)", __func__); - } - descriptor.m_PadValue = intPadValue; - } - else - { - return Fail("%s: Operation has invalid inputs: type mismatch", __func__); - } - - bool isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC(__func__, - IsPadSupported, - data.m_Backends, - isSupported, - inputInfo, - outputInfo, - descriptor); - if (!isSupported) - { - return false; - } - - IConnectableLayer* const layer = data.m_Network->AddPadLayer(descriptor); - assert(layer != nullptr); - input.Connect(layer->GetInputSlot(0)); - layer->GetOutputSlot(0).SetTensorInfo(outputInfo); - - return SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 0, *layer, model, data); + return ::ConvertPadV2<hal_1_2::HalPolicy>(operation, model, data); } bool HalPolicy::ConvertPrelu(const Operation& operation, const Model& model, ConversionData& data) { ALOGV("hal_1_2::HalPolicy::ConvertPrelu()"); - - LayerInputHandle input = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data); - LayerInputHandle alpha = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 1, model, data); - - if (!input.IsValid() || !alpha.IsValid()) - { - return Fail("%s: Operation has invalid inputs", __func__); - } - - const Operand* output = GetOutputOperand<hal_1_2::HalPolicy>(operation, 0, model); - - if (!output) - { - return Fail("%s: Could not read output", __func__); - } - - const TensorInfo& inputInfo = input.GetTensorInfo(); - const TensorInfo& alphaInfo = alpha.GetTensorInfo(); - const TensorInfo& outputInfo = GetTensorInfoForOperand(*output); - - if (IsDynamicTensor(outputInfo)) - { - return Fail("%s: Dynamic output tensors are not supported", __func__); - } - - bool isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC(__func__, - IsPreluSupported, - data.m_Backends, - isSupported, - inputInfo, - alphaInfo, - outputInfo); - if (!isSupported) - { - return false; - } - - IConnectableLayer* const layer = data.m_Network->AddPreluLayer(); - - if (!layer) - { - return Fail("%s: AddPreluLayer failed", __func__); - } - - bool isReshapeSupported = BroadcastTensor(input, alpha, layer, data); - if (!isReshapeSupported) - { - return false; - } - - return SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 0, *layer, model, data); + return ::ConvertPrelu<hal_1_2::HalPolicy>(operation, model, data); } bool HalPolicy::ConvertQuantize(const Operation& operation, const Model& model, ConversionData& data) { ALOGV("hal_1_2::HalPolicy::ConvertQuantize()"); - - LayerInputHandle input = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data); - if (!input.IsValid()) - { - return Fail("%s: Operation has invalid input", __func__); - } - - const Operand* const outputOperand = GetOutputOperand<hal_1_2::HalPolicy>(operation, 0, model); - if (!outputOperand) - { - return Fail("%s: Operation has invalid outputs", __func__); - } - - const TensorInfo& outputInfo = GetTensorInfoForOperand(*outputOperand); - if (IsDynamicTensor(outputInfo)) - { - return Fail("%s: Dynamic output tensors are not supported", __func__); - } - - bool isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC(__func__, - IsQuantizeSupported, - data.m_Backends, - isSupported, - input.GetTensorInfo(), - outputInfo); - if (!isSupported) - { - return false; - } - - IConnectableLayer* const layer = data.m_Network->AddQuantizeLayer(); - assert(layer != nullptr); - input.Connect(layer->GetInputSlot(0)); - - return SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 0, *layer, model, data); + return ::ConvertQuantize<hal_1_2::HalPolicy>(operation, model, data); } bool HalPolicy::ConvertQuantizedLstm(const Operation& operation, const Model& model, ConversionData& data) { ALOGV("hal_1_2::HalPolicy::ConvertQuantizedLstm()"); - - //Inputs: - // 0: The input: A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape [numBatches, inputSize] - // specifying the input to the LSTM cell. Tensor is quantized with a fixed quantization range of -1, 127/128. - LayerInputHandle input = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data); - if (!input.IsValid()) - { - return Fail("%s: Could not read input 0: input", __func__); - } - - //13: The previous cell state: A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT16_SYMM and shape - // [numBatches, outputSize] specifying the cell state from the previous time step of the LSTM cell. - // It is quantized using a quantization range of -2^4, 2^4 * 32767/32768. - LayerInputHandle previousCellStateIn = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 13, model, data); - if (!previousCellStateIn.IsValid()) - { - return Fail("%s: Could not read input 13: previousCellStateIn", __func__); - } - - // 14: The previous output state: A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape - // [numBathes, outputSize] specifying the output of the LSTM cell from previous time-step. Tensor - // is quantized with a fixed quantization range of -1, 127/128. - LayerInputHandle previousOutputIn = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 14, model, data); - if (!previousOutputIn.IsValid()) - { - return Fail("%s: Could not read input 14: previousOutputIn", __func__); - } - - // Get the input tensors: - // 1: The input-to-input weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape - // [outputSize, inputSize] specifying input-to-input part of weights for fully-connected layer inside the - // LSTM cell. Quantization zero point and scale must be the same across all the weights. - const ConstTensorPin inputToInputWeightsPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 1, model, data); - - // 2: The input-to-forget weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape - // [outputSize, inputSize] specifying input-to-forget part of weights for fully-connected layer inside the - // LSTM cell. Quantization zero point and scale must be the same across all the weights. - const ConstTensorPin inputToForgetWeightsPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 2, model, data); - - // 3: The input-to-cell weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape - // [outputSize, inputSize] specifying input-to-cell part of weights for fully-connected layer inside the - // LSTM cell. Quantization zero point and scale must be the same across all the weights. - const ConstTensorPin inputToCellWeightsPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 3, model, data); - - // 4: The input-to-output weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape - // [outputSize, inputSize] specifying input-to-output part of weights for fully-connected layer inside the - // LSTM cell. Quantization zero point and scale must be the same across all the weights. - const ConstTensorPin inputToOutputWeightsPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 4, model, data); - - // 5: The recurrent-to-input weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape - // [outputSize, outputSize] specifying recurrent-to-input part of weights for fully-connected layer inside - // the LSTM cell. Quantization zero point and scale must be the same across all the weights. - const ConstTensorPin recurrentToInputWeightsPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 5, model, data); - - // 6: The recurrent-to-forget weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape - // [outputSize, outputSize] specifying recurrent-to-forget part of weights for fully-connected layer inside - // the LSTM cell. Quantization zero point and scale must be the same across all the weights. - const ConstTensorPin recurrentToForgetWeightsPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 6, model, data); - - // 7: The recurrent-to-cell weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape - // [outputSize, outputSize] specifying recurrent-to-cell part of weights for fully-connected layer inside - // the LSTM cell. Quantization zero point and scale must be the same across all the weights. - const ConstTensorPin recurrentToCellWeightsPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 7, model, data); - - // 8: The recurrent-to-output weights. A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape - // [outputSize, outputSize] specifying recurrent-to-output part of weights for fully-connected layer inside - // the LSTM cell. Quantization zero point and scale must be the same across all the weights. - const ConstTensorPin recurrentToOutputWeightsPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 8, model, data); - - // 9: The input gate bias. A 1-D tensor of type ANEURALNETWORKS_TENSOR_INT32 and shape [outputSize] specifying the - // bias for the fully-connected layer inside the LSTM cell. Bias is quantized with scale being a product - // of input and weights scales and zeroPoint equal to 0. - const ConstTensorPin inputGateBiasPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 9, model, data); - - // 10: The forget gate bias. A 1-D tensor of type ANEURALNETWORKS_TENSOR_INT32 and shape [outputSize] specifying - // the bias for the fully-connected layer inside the LSTM cell. Bias is quantized with scale being a product - // of input and weights scales and zeroPoint equal to 0. - const ConstTensorPin forgetGateBiasPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 10, model, data); - - // 11:The cell bias. A 1-D tensor of type ANEURALNETWORKS_TENSOR_INT32 and shape [outputSize] specifying the bias - // for the fully-connected layer inside the LSTM cell. Bias is quantized with scale being a product of input - // and weights scales and zeroPoint equal to 0. - const ConstTensorPin cellBiasPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 11, model, data); - - // 12:The output gate bias. A 1-D tensor of type ANEURALNETWORKS_TENSOR_INT32 and shape [outputSize] specifying - // the bias for the fully-connected layer inside the LSTM cell. Bias is quantized with scale being a product - // of input and weights scales and zeroPoint equal to 0. - const ConstTensorPin outputGateBiasPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 12, model, data); - - if (!inputToInputWeightsPin.IsValid() || - !inputToForgetWeightsPin.IsValid() || - !inputToCellWeightsPin.IsValid() || - !inputToOutputWeightsPin.IsValid() || - !recurrentToInputWeightsPin.IsValid() || - !recurrentToForgetWeightsPin.IsValid() || - !recurrentToCellWeightsPin.IsValid() || - !recurrentToOutputWeightsPin.IsValid() || - !inputGateBiasPin.IsValid() || - !forgetGateBiasPin.IsValid() || - !cellBiasPin.IsValid() || - !outputGateBiasPin.IsValid()) - { - return Fail("%s: Operation has invalid tensor inputs", __func__); - } - - // Outputs: - // 0: The cell state: A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT16_SYMM and shape [numBatches, outputSize] - // which contains a cell state from the current time step. Tensor is quantized using a quantization range - // of -2^4, 2^4 * 32767/32768. - const Operand* cellStateOut = GetOutputOperand<hal_1_2::HalPolicy>(operation, 0, model); - if (!cellStateOut) - { - return Fail("%s: Could not read output 0: cellStateOut", __func__); - } - - // 1: The output: A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape [numBathes, outputSize] which - // contains the output value. Tensor is quantized with a fixed quantization range of -1, 127/128. - const Operand* output = GetOutputOperand<hal_1_2::HalPolicy>(operation, 1, model); - if (!output) - { - return Fail("%s: Could not read output 1: output", __func__); - } - - // Inputs - const TensorInfo& inputInfo = input.GetTensorInfo(); - const TensorInfo& previousCellStateInInfo = previousCellStateIn.GetTensorInfo(); - const TensorInfo& previousOutputInInfo = previousOutputIn.GetTensorInfo(); - - // Outputs - const TensorInfo& cellStateOutInfo = GetTensorInfoForOperand(*cellStateOut); - const TensorInfo& outputInfo = GetTensorInfoForOperand(*output); - - // Dynamic tensors currently not supported - if (IsDynamicTensor(cellStateOutInfo) || IsDynamicTensor(outputInfo)) - { - return Fail("%s: Dynamic output tensors are not supported", __func__); - } - - QuantizedLstmInputParams params; - - params.m_InputToInputWeights = inputToInputWeightsPin.GetConstTensorPtr(); - params.m_InputToForgetWeights = inputToForgetWeightsPin.GetConstTensorPtr(); - params.m_InputToCellWeights = inputToCellWeightsPin.GetConstTensorPtr(); - params.m_InputToOutputWeights = inputToOutputWeightsPin.GetConstTensorPtr(); - params.m_RecurrentToInputWeights = recurrentToInputWeightsPin.GetConstTensorPtr(); - params.m_RecurrentToForgetWeights = recurrentToForgetWeightsPin.GetConstTensorPtr(); - params.m_RecurrentToCellWeights = recurrentToCellWeightsPin.GetConstTensorPtr(); - params.m_RecurrentToOutputWeights = recurrentToOutputWeightsPin.GetConstTensorPtr(); - params.m_InputGateBias = inputGateBiasPin.GetConstTensorPtr(); - params.m_ForgetGateBias = forgetGateBiasPin.GetConstTensorPtr(); - params.m_CellBias = cellBiasPin.GetConstTensorPtr(); - params.m_OutputGateBias = outputGateBiasPin.GetConstTensorPtr(); - - QuantizedLstmInputParamsInfo paramsInfo; - paramsInfo.m_InputToInputWeights = &(params.m_InputToInputWeights->GetInfo()); - paramsInfo.m_InputToForgetWeights = &(params.m_InputToForgetWeights->GetInfo()); - paramsInfo.m_InputToCellWeights = &(params.m_InputToCellWeights->GetInfo()); - paramsInfo.m_InputToOutputWeights = &(params.m_InputToOutputWeights->GetInfo()); - paramsInfo.m_RecurrentToInputWeights = &(params.m_RecurrentToInputWeights->GetInfo()); - paramsInfo.m_RecurrentToForgetWeights = &(params.m_RecurrentToForgetWeights->GetInfo()); - paramsInfo.m_RecurrentToCellWeights = &(params.m_RecurrentToCellWeights->GetInfo()); - paramsInfo.m_RecurrentToOutputWeights = &(params.m_RecurrentToOutputWeights->GetInfo()); - paramsInfo.m_InputGateBias = &(params.m_InputGateBias->GetInfo()); - paramsInfo.m_ForgetGateBias = &(params.m_ForgetGateBias->GetInfo()); - paramsInfo.m_CellBias = &(params.m_CellBias->GetInfo()); - paramsInfo.m_OutputGateBias = &(params.m_OutputGateBias->GetInfo()); - - bool isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC(__func__, - IsQuantizedLstmSupported, - data.m_Backends, - isSupported, - inputInfo, - previousCellStateInInfo, - previousOutputInInfo, - cellStateOutInfo, - outputInfo, - paramsInfo); - - if (!isSupported) - { - return false; - } - - IConnectableLayer* const layer = data.m_Network->AddQuantizedLstmLayer(params, "QuantizedLstm"); - input.Connect(layer->GetInputSlot(0)); - previousCellStateIn.Connect(layer->GetInputSlot(1)); - previousOutputIn.Connect(layer->GetInputSlot(2)); - - return (SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 0, *layer, 0, model, data) && - SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 1, *layer, 1, model, data)); + return ::ConvertQuantizedLstm<hal_1_2::HalPolicy>(operation, model, data); } bool HalPolicy::ConvertReLu(const Operation& operation, const Model& model, ConversionData& data) @@ -1857,134 +374,7 @@ bool HalPolicy::ConvertResize(const Operation& operation, ResizeMethod resizeMethod) { ALOGV("hal_1_2::HalPolicy::ConvertResize()"); - ALOGV("resizeMethod = %s", GetResizeMethodAsCString(resizeMethod)); - - LayerInputHandle input = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data); - if (!input.IsValid()) - { - return Fail("%s: Could not read input 0", __func__); - } - - const Operand* output = GetOutputOperand<hal_1_2::HalPolicy>(operation, 0, model); - if (!output) - { - return Fail("%s: Could not read output 0", __func__); - } - - const TensorInfo& inputInfo = input.GetTensorInfo(); - const TensorInfo& outputInfo = GetTensorInfoForOperand(*output); - - if (IsDynamicTensor(outputInfo)) - { - return Fail("%s: Dynamic output tensors are not supported", __func__); - } - - ResizeDescriptor descriptor; - descriptor.m_Method = resizeMethod; - descriptor.m_DataLayout = OptionalDataLayout<hal_1_2::HalPolicy>(operation, 3, model, data); - - OperandType operandType1; - OperandType operandType2; - - if (!GetOperandType<hal_1_2::HalPolicy>(operation, 1, model, operandType1) || - !GetOperandType<hal_1_2::HalPolicy>(operation, 2, model, operandType2)) - { - return Fail("%s: Operation has invalid inputs", __func__); - } - - if (operandType1 != operandType2) - { - return Fail("%s: Operation has invalid inputs. Type of input 1 and 2 should be the same", __func__); - } - - if (operandType1 == OperandType::INT32) - { - // Case 1: resizing by shape - int32_t targetWidth = 0; - int32_t targetHeight = 0; - - if (!GetInputInt32<hal_1_2::HalPolicy>(operation, 1, targetWidth, model, data) || - !GetInputInt32<hal_1_2::HalPolicy>(operation, 2, targetHeight, model, data)) - { - return Fail("%s: Operation has invalid inputs for resizing by shape", __func__); - } - - if (targetWidth < 0 || targetHeight < 0) - { - return Fail("%s: Operation has invalid inputs for resizing by shape. " - "Target width/height cannot be < 0", __func__); - } - - descriptor.m_TargetWidth = static_cast<uint32_t>(targetWidth); - descriptor.m_TargetHeight = static_cast<uint32_t>(targetHeight); - } - else if (operandType1 == OperandType::FLOAT32) - { - // Case 2: resizing by scale - float widthScale = 1.0f; - float heightScale = 1.0f; - - if (!GetInputFloat32<hal_1_2::HalPolicy>(operation, 1, widthScale, model, data) || - !GetInputFloat32<hal_1_2::HalPolicy>(operation, 2, heightScale, model, data)) - { - return Fail("%s: Operation has invalid inputs for resizing by scale", __func__); - } - - const TensorShape& inputShape = inputInfo.GetShape(); - armnnUtils::DataLayoutIndexed dataLayoutIndexed(descriptor.m_DataLayout); - - float width = inputShape[dataLayoutIndexed.GetWidthIndex()]; - float height = inputShape[dataLayoutIndexed.GetHeightIndex()]; - - descriptor.m_TargetWidth = std::floor(width * widthScale); - descriptor.m_TargetHeight = std::floor(height * heightScale); - } - else if (operandType1 == OperandType::FLOAT16) - { - Half widthScale; - Half heightScale; - - if (!GetInputScalar<HalPolicy>(operation, 1, HalPolicy::OperandType::FLOAT16, widthScale, model, data) || - !GetInputScalar<HalPolicy>(operation, 2, HalPolicy::OperandType::FLOAT16, heightScale, model, data)) - { - return Fail("%s: Operation has invalid inputs for resizing by scale", __func__); - } - - const TensorShape& inputShape = inputInfo.GetShape(); - armnnUtils::DataLayoutIndexed dataLayoutIndexed(descriptor.m_DataLayout); - - Half width = static_cast<Half>(inputShape[dataLayoutIndexed.GetWidthIndex()]); - Half height = static_cast<Half>(inputShape[dataLayoutIndexed.GetHeightIndex()]); - - descriptor.m_TargetWidth = std::floor(width * widthScale); - descriptor.m_TargetHeight = std::floor(height * heightScale); - } - else - { - return Fail("%s: Operand has invalid data type for resizing by scale", __func__); - } - - bool isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC(__func__, - IsResizeSupported, - data.m_Backends, - isSupported, - inputInfo, - outputInfo, - descriptor); - - if (!isSupported) - { - return false; - } - - IConnectableLayer* layer = data.m_Network->AddResizeLayer(descriptor); - - assert(layer != nullptr); - - input.Connect(layer->GetInputSlot(0)); - - return SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 0, *layer, model, data); + return ::ConvertResize<hal_1_2::HalPolicy>(operation, model, data, resizeMethod); } bool HalPolicy::ConvertSpaceToBatchNd(const Operation& operation, const Model& model, ConversionData& data) @@ -1996,126 +386,13 @@ bool HalPolicy::ConvertSpaceToBatchNd(const Operation& operation, const Model& m bool HalPolicy::ConvertSpaceToDepth(const Operation& operation, const Model& model, ConversionData& data) { ALOGV("hal_1_2::HalPolicy::ConvertSpaceToDepth()"); - - LayerInputHandle input = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data); - if (!input.IsValid() ) - { - return Fail("%s: Operation has invalid inputs", __func__); - } - - const TensorInfo& inputInfo = input.GetTensorInfo(); - unsigned int rank = inputInfo.GetNumDimensions(); - if (rank != 4) - { - return Fail("%s: Only inputs with rank 4 are supported", __func__); - } - - const Operand* output = GetOutputOperand<hal_1_2::HalPolicy>(operation, 0, model); - if (!output) - { - return Fail("%s: Could not read output 0", __func__); - } - - const TensorInfo& outputInfo = GetTensorInfoForOperand(*output); - if (IsDynamicTensor(outputInfo)) - { - return Fail("%s: Dynamic output tensors are not supported", __func__); - } - - SpaceToDepthDescriptor desc; - - GetInputScalar<hal_1_2::HalPolicy>(operation, 1, OperandType::INT32, desc.m_BlockSize, model, data); - - if (desc.m_BlockSize <= 1) - { - return Fail("%s: Block size must be at least 1 in all dimensions"); - } - - desc.m_DataLayout = OptionalDataLayout<hal_1_2::HalPolicy>(operation, 2, model, data); - - bool isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC(__func__, - IsSpaceToDepthSupported, - data.m_Backends, - isSupported, - inputInfo, - outputInfo, - desc); - if (!isSupported) - { - return false; - } - - IConnectableLayer* const layer = data.m_Network->AddSpaceToDepthLayer(desc); - assert(layer != nullptr); - input.Connect(layer->GetInputSlot(0)); - - return SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 0, *layer, model, data); + return ::ConvertSpaceToDepth<hal_1_2::HalPolicy>(operation, model, data); } bool HalPolicy::ConvertSoftmax(const Operation& operation, const Model& model, ConversionData& data) { ALOGV("hal_1_2::HalPolicy::ConvertSoftmax()"); - - LayerInputHandle input = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data); - if (!input.IsValid()) - { - return Fail("%s: Operation has invalid inputs", __func__); - } - - const Operand* outputOperand = GetOutputOperand<hal_1_2::HalPolicy>(operation, 0, model); - if (!outputOperand) - { - return Fail("%s: Operation has no outputs", __func__); - } - - const TensorInfo& outputInfo = GetTensorInfoForOperand(*outputOperand); - if (IsDynamicTensor(outputInfo)) - { - return Fail("%s: Dynamic output tensors are not supported", __func__); - } - - SoftmaxDescriptor desc; - if (!GetInputFloat32<hal_1_2::HalPolicy>(operation, 1, desc.m_Beta, model, data)) - { - return Fail("%s: Operation has invalid inputs", __func__); - } - - if (operation.inputs.size() > 2 && !GetInputScalar<hal_1_2::HalPolicy>(operation, - 2, - HalPolicy::OperandType::INT32, - desc.m_Axis, - model, - data)) - { - return Fail("%s: Operation has invalid inputs", __func__); - } - - if (input.GetTensorInfo().GetNumDimensions() > 2 || - !(desc.m_Axis == 1 || - (desc.m_Axis < 0 && static_cast<int>(input.GetTensorInfo().GetNumDimensions()) + desc.m_Axis == 1))) - { - return Fail("%s: Unsupported input greater than 2D or axis != 1", __func__); - } - - bool isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC(__func__, - IsSoftmaxSupported, - data.m_Backends, - isSupported, - input.GetTensorInfo(), - outputInfo, - desc); - if (!isSupported) - { - return false; - } - - IConnectableLayer* layer = data.m_Network->AddSoftmaxLayer(desc); - assert(layer != nullptr); - input.Connect(layer->GetInputSlot(0)); - - return SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 0, *layer, model, data); + return ::ConvertSoftmax<hal_1_2::HalPolicy>(operation, model, data); } bool HalPolicy::ConvertSub(const Operation& operation, const Model& model, ConversionData& data) @@ -2130,450 +407,10 @@ bool HalPolicy::ConvertTanH(const Operation& operation, const Model& model, Conv return ::ConvertTanH<hal_1_2::HalPolicy>(operation, model, data); } -template<typename HalPolicy, - typename HalOperation = typename HalPolicy::Operation, - typename HalModel = typename HalPolicy::Model> -bool SetupAndTrackLayerOutputSlotAndOverrideTensorInfo(const HalOperation& operation, - uint32_t operationOutputIndex, - armnn::IConnectableLayer& layer, - uint32_t layerOutputIndex, - const HalModel& model, - ConversionData& data, - const armnn::TensorInfo tensor_info) -{ - using HalOperand = typename HalPolicy::Operand; - - const HalOperand* outputOperand = GetOutputOperand<HalPolicy>(operation, operationOutputIndex, model); - if ((outputOperand == nullptr) || (operationOutputIndex >= layer.GetNumOutputSlots())) - { - return false; - } - - armnn::IOutputSlot& outputSlot = layer.GetOutputSlot(layerOutputIndex); - - const uint32_t operandIndex = operation.outputs[operationOutputIndex]; - data.m_OutputSlotForOperand[operandIndex] = &outputSlot; - - outputSlot.SetTensorInfo(tensor_info); - - return true; -} - - bool HalPolicy::ConvertLstm(const Operation& operation, const Model& model, ConversionData& data) { ALOGV("hal_1_2::HalPolicy::ConvertLstm()"); - - // Inputs: - // 00: The input: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, input_size], where - // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. - LayerInputHandle input = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data); - if (!input.IsValid()) - { - return Fail("%s: Could not read input 0: input", __func__); - } - // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. - LayerInputHandle outputStateIn = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 18, model, data); - if (!outputStateIn.IsValid()) - { - return Fail("%s: Could not read input 18: outputStateIn", __func__); - } - // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. - LayerInputHandle cellStateIn = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 19, model, data); - if (!cellStateIn.IsValid()) - { - return Fail("%s: Could not read input 19: cellStateIn", __func__); - } - - // Get the mandatory input tensors: - // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, input_size]. - const ConstTensorPin inputToForgetWeightsPin = - (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 2)); - // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, input_size]. - const ConstTensorPin inputToCellWeightsPin = - (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 3)); - // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, input_size]. - const ConstTensorPin inputToOutputWeightsPin = - (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 4)); - // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, output_size]. - const ConstTensorPin recurrentToForgetWeightsPin = - (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 6)); - // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, output_size]. - const ConstTensorPin recurrentToCellWeightsPin = - (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 7)); - // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, output_size]. - const ConstTensorPin recurrentToOutputWeightsPin = - (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 8)); - // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - const ConstTensorPin forgetGateBiasPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 13, model, data); - // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - const ConstTensorPin cellBiasPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 14, model, data); - // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - const ConstTensorPin outputGateBiasPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 15, model, data); - - if (!inputToForgetWeightsPin.IsValid() || - !inputToCellWeightsPin.IsValid() || - !inputToOutputWeightsPin.IsValid() || - !recurrentToForgetWeightsPin.IsValid() || - !recurrentToCellWeightsPin.IsValid() || - !recurrentToOutputWeightsPin.IsValid() || - !forgetGateBiasPin.IsValid() || - !cellBiasPin.IsValid() || - !outputGateBiasPin.IsValid()) - { - return Fail("%s: Operation has invalid tensor inputs", __func__); - } - - // Get the optional input tensors: - // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, input_size], where “num_units” corresponds to the number of cell units. - const ConstTensorPin inputToInputWeightsPin = - (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 1, true)); - // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., - // “num_units”), or the second dimension of the “projection_weights”, if defined. - const ConstTensorPin recurrentToInputWeightsPin = - (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 5, true)); - // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - const ConstTensorPin cellToInputWeightsPin = - (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 9, true)); - // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - const ConstTensorPin cellToForgetWeightsPin = - (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 10, true)); - // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - const ConstTensorPin cellToOutputWeightsPin = - (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 11, true)); - // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. - const ConstTensorPin inputGateBiasPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, - 12, - model, - data, - g_DontPermute, - nullptr, - true); - - // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape - // [output_size, num_units]. - const ConstTensorPin projectionWeightsPin = - (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 16, true)); - // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. - const ConstTensorPin projectionBiasPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, - 17, - model, - data, - g_DontPermute, - nullptr, - true); - - if ((!inputToInputWeightsPin.IsValid() && !inputToInputWeightsPin.IsOptional()) || - (!recurrentToInputWeightsPin.IsValid() && !recurrentToInputWeightsPin.IsOptional()) || - (!cellToInputWeightsPin.IsValid() && !cellToInputWeightsPin.IsOptional()) || - (!cellToForgetWeightsPin.IsValid() && !cellToForgetWeightsPin.IsOptional()) || - (!cellToOutputWeightsPin.IsValid() && !cellToOutputWeightsPin.IsOptional()) || - (!inputGateBiasPin.IsValid() && !inputGateBiasPin.IsOptional()) || - (!projectionWeightsPin.IsValid() && !projectionWeightsPin.IsOptional()) || - (!projectionBiasPin.IsValid() && !projectionBiasPin.IsOptional())) - { - return Fail("%s: Operation has invalid tensor inputs", __func__); - } - - // Get the mandatory input scalars (actually 1-D tensors of size 1): - // 20: The activation function: A value indicating the activation function: - // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. - // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip]. - // If set to 0.0 then clipping is disabled. - // 22: The clipping threshold: for the output from the projection layer, such that values are bound within - // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. - ActivationFn activation; - float cellClip; - float projClip; - if (!GetInputActivationFunctionFromTensor<hal_1_2::HalPolicy>(operation, 20, activation, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 21, OperandType::FLOAT32, cellClip, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 22, OperandType::FLOAT32, projClip, model, data)) - { - return Fail("%s: Operation has invalid scalar inputs", __func__); - } - - // Get the normalization tensors - // 23: The input layer normalization weights. A 1-D tensor of shape [num_units]. - // Used to rescale normalized inputs to activation at input gate. - const ConstTensorPin inputLayerNormWeightsPin - (DequantizeAndMakeConstTensorPin<hal_1_2::HalPolicy>(operation, model, data, 23, true)); - - // 24: The forget layer normalization weights. A 1-D tensor of shape [num_units]. - // Used to rescale normalized inputs to activation at forget gate. - const ConstTensorPin forgetLayerNormWeightsPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, - 24, - model, - data, - g_DontPermute, - nullptr, - true); - - // 25: The cell layer normalization weights. A 1-D tensor of shape [num_units]. - // Used to rescale normalized inputs to activation at cell gate. - const ConstTensorPin cellLayerNormWeightsPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, - 25, - model, - data, - g_DontPermute, - nullptr, - true); - - // 26: The output layer normalization weights. A 1-D tensor of shape [num_units]. - // Used to rescale normalized inputs to activation at output gate. - const ConstTensorPin outputLayerNormWeightsPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, - 26, - model, - data, - g_DontPermute, - nullptr, - true); - - // Outputs: - // 00: The scratch buffer: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units * 4] - // with CIFG, or [batch_size, num_units * 3] without CIFG. - const Operand* scratchBuffer = GetOutputOperand<hal_1_2::HalPolicy>(operation, 0, model); - if (!scratchBuffer) - { - return Fail("%s: Could not read output 0: scratchBuffer", __func__); - } - // 01: The output state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. - const Operand* outputStateOut = GetOutputOperand<hal_1_2::HalPolicy>(operation, 1, model); - if (!outputStateOut) - { - return Fail("%s: Could not read output 1: outputStateOut", __func__); - } - // 02: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. - const Operand* cellStateOut = GetOutputOperand<hal_1_2::HalPolicy>(operation, 2, model); - if (!cellStateOut) - { - return Fail("%s: Could not read output 2: cellStateOut", __func__); - } - // 03: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. This is - // effectively the same as the current “output state (out)” value. - const Operand* output = GetOutputOperand<hal_1_2::HalPolicy>(operation, 3, model); - if (!output) - { - return Fail("%s: Could not read output 3: output", __func__); - } - - // set the params structure for the AddLstmLayer call - LstmInputParams params; - params.m_InputToInputWeights = inputToInputWeightsPin.GetConstTensorPtr(); - params.m_InputToForgetWeights = inputToForgetWeightsPin.GetConstTensorPtr(); - params.m_InputToCellWeights = inputToCellWeightsPin.GetConstTensorPtr(); - params.m_InputToOutputWeights = inputToOutputWeightsPin.GetConstTensorPtr(); - params.m_RecurrentToInputWeights = recurrentToInputWeightsPin.GetConstTensorPtr(); - params.m_RecurrentToForgetWeights = recurrentToForgetWeightsPin.GetConstTensorPtr(); - params.m_RecurrentToCellWeights = recurrentToCellWeightsPin.GetConstTensorPtr(); - params.m_RecurrentToOutputWeights = recurrentToOutputWeightsPin.GetConstTensorPtr(); - params.m_CellToInputWeights = cellToInputWeightsPin.GetConstTensorPtr(); - params.m_CellToForgetWeights = cellToForgetWeightsPin.GetConstTensorPtr(); - params.m_CellToOutputWeights = cellToOutputWeightsPin.GetConstTensorPtr(); - params.m_InputGateBias = inputGateBiasPin.GetConstTensorPtr(); - params.m_ForgetGateBias = forgetGateBiasPin.GetConstTensorPtr(); - params.m_CellBias = cellBiasPin.GetConstTensorPtr(); - params.m_OutputGateBias = outputGateBiasPin.GetConstTensorPtr(); - params.m_ProjectionWeights = projectionWeightsPin.GetConstTensorPtr(); - params.m_ProjectionBias = projectionBiasPin.GetConstTensorPtr(); - params.m_InputLayerNormWeights = inputLayerNormWeightsPin.GetConstTensorPtr(); - params.m_ForgetLayerNormWeights = forgetLayerNormWeightsPin.GetConstTensorPtr(); - params.m_CellLayerNormWeights = cellLayerNormWeightsPin.GetConstTensorPtr(); - params.m_OutputLayerNormWeights = outputLayerNormWeightsPin.GetConstTensorPtr(); - - // set the layer descriptor - LstmDescriptor desc; - desc.m_ActivationFunc = activation; - desc.m_ClippingThresCell = cellClip; - desc.m_ClippingThresProj = projClip; - desc.m_CifgEnabled = (params.m_InputToInputWeights == nullptr || - params.m_RecurrentToInputWeights == nullptr || - params.m_InputGateBias == nullptr); - desc.m_PeepholeEnabled = (params.m_CellToForgetWeights != nullptr || - params.m_CellToOutputWeights != nullptr); - desc.m_ProjectionEnabled = (params.m_ProjectionWeights != nullptr); - desc.m_LayerNormEnabled = (params.m_InputLayerNormWeights != nullptr || - params.m_ForgetLayerNormWeights != nullptr || - params.m_CellLayerNormWeights != nullptr || - params.m_OutputLayerNormWeights != nullptr); - - // validate the optional input groups - if (desc.m_CifgEnabled && - (params.m_InputToInputWeights != nullptr || - params.m_RecurrentToInputWeights != nullptr || - params.m_InputGateBias != nullptr)) - { - return Fail("%s: All, or none, of input-to-input weights, recurrent-to-input weights," - " and input gate bias must be provided", __func__); - } - - if (!desc.m_ProjectionEnabled && params.m_ProjectionBias != nullptr) - { - return Fail("%s: projection bias should not be provided without projection weights", __func__); - } - - if (desc.m_PeepholeEnabled && - (params.m_CellToForgetWeights == nullptr || - params.m_CellToOutputWeights == nullptr || - (!desc.m_CifgEnabled && params.m_CellToInputWeights == nullptr))) - { - return Fail("%s: All, or none, of cell-to-forget weights and cell-to-output weights must be provided" - " and, if CIFG is not enabled, cell-to-input weights must also be provided", __func__); - } - - if (desc.m_LayerNormEnabled && - (params.m_ForgetLayerNormWeights == nullptr || - params.m_CellLayerNormWeights == nullptr || - params.m_OutputLayerNormWeights == nullptr || - (!desc.m_CifgEnabled && params.m_InputLayerNormWeights == nullptr))) - { - return Fail("%s: All, or none, of forget-norm weights, cell-norm weights and output-norm weights must be" - " provided and, if CIFG is not enabled, input-norm weights must also be provided", __func__); - } - - // Check if the layer is supported - // Inputs - const TensorInfo& inputInfo = input.GetTensorInfo(); - const TensorInfo& outputStateInInfo = outputStateIn.GetTensorInfo(); - const TensorInfo& cellStateInInfo = cellStateIn.GetTensorInfo(); - - // Outputs - const TensorInfo& scratchBufferInfo = GetTensorInfoForOperand(*scratchBuffer); - const TensorInfo& outputStateOutInfo = GetTensorInfoForOperand(*outputStateOut); - const TensorInfo& cellStateOutInfo = GetTensorInfoForOperand(*cellStateOut); - const TensorInfo& outputInfo = GetTensorInfoForOperand(*output); - - // Check if the scratch buffer shape was initialized, - // In some cases the shape could be (0,0) which requires the driver - // to infer the shape and set it up accordingly. - // The code below does that. - TensorInfo fixSbInfo = scratchBufferInfo; - if (IsDynamicTensor(scratchBufferInfo)) - { - auto & s = fixSbInfo.GetShape(); - s[0] = outputStateInInfo.GetShape()[0]; - if (desc.m_CifgEnabled) - { - // 2D tensor with dimensions [num_units * 3, batch_size] with CIFG - s[1] = cellStateOutInfo.GetShape()[1]*3; - } - else - { - // scratch_buffer [num_units * 4, batch_size] without CIFG - s[1] = cellStateOutInfo.GetShape()[1]*4; - } - } - - if (IsDynamicTensor(outputStateOutInfo) || - IsDynamicTensor(cellStateOutInfo) || - IsDynamicTensor(outputInfo)) - { - return Fail("%s: Dynamic output tensors are not supported %d %d %d %d", __func__, - IsDynamicTensor(scratchBufferInfo), IsDynamicTensor(outputStateOutInfo), - IsDynamicTensor(cellStateOutInfo), IsDynamicTensor(outputInfo)); - } - - // Basic parameters - LstmInputParamsInfo paramsInfo; - paramsInfo.m_InputToForgetWeights = &(params.m_InputToForgetWeights->GetInfo()); - paramsInfo.m_InputToCellWeights = &(params.m_InputToCellWeights->GetInfo()); - paramsInfo.m_InputToOutputWeights = &(params.m_InputToOutputWeights->GetInfo()); - paramsInfo.m_RecurrentToForgetWeights = &(params.m_RecurrentToForgetWeights->GetInfo()); - paramsInfo.m_RecurrentToCellWeights = &(params.m_RecurrentToCellWeights->GetInfo()); - paramsInfo.m_RecurrentToOutputWeights = &(params.m_RecurrentToOutputWeights->GetInfo()); - paramsInfo.m_ForgetGateBias = &(params.m_ForgetGateBias->GetInfo()); - paramsInfo.m_CellBias = &(params.m_CellBias->GetInfo()); - paramsInfo.m_OutputGateBias = &(params.m_OutputGateBias->GetInfo()); - - // Optional parameters - if(!desc.m_CifgEnabled) - { - paramsInfo.m_InputToInputWeights = &(params.m_InputToInputWeights->GetInfo()); - paramsInfo.m_RecurrentToInputWeights = &(params.m_RecurrentToInputWeights->GetInfo()); - if (params.m_CellToInputWeights != nullptr) - { - paramsInfo.m_CellToInputWeights = &(params.m_CellToInputWeights->GetInfo()); - } - paramsInfo.m_InputGateBias = &(params.m_InputGateBias->GetInfo()); - } - - if(desc.m_ProjectionEnabled) - { - paramsInfo.m_ProjectionWeights = &(params.m_ProjectionWeights->GetInfo()); - if (params.m_ProjectionBias != nullptr) - { - paramsInfo.m_ProjectionBias = &(params.m_ProjectionBias->GetInfo()); - } - } - - if(desc.m_PeepholeEnabled) - { - paramsInfo.m_CellToForgetWeights = &(params.m_CellToForgetWeights->GetInfo()); - paramsInfo.m_CellToOutputWeights = &(params.m_CellToOutputWeights->GetInfo()); - } - - if (desc.m_LayerNormEnabled) - { - if(!desc.m_CifgEnabled) - { - paramsInfo.m_InputLayerNormWeights = &(params.m_InputLayerNormWeights->GetInfo()); - } - paramsInfo.m_ForgetLayerNormWeights = &(params.m_ForgetLayerNormWeights->GetInfo()); - paramsInfo.m_CellLayerNormWeights = &(params.m_CellLayerNormWeights->GetInfo()); - paramsInfo.m_OutputLayerNormWeights = &(params.m_OutputLayerNormWeights->GetInfo()); - } - - bool isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC(__func__, - IsLstmSupported, - data.m_Backends, - isSupported, - inputInfo, - outputStateInInfo, - cellStateInInfo, - fixSbInfo, - outputStateOutInfo, - cellStateOutInfo, - outputInfo, - desc, - paramsInfo); - if (!isSupported) - { - return false; - } - - // Add the layer - IConnectableLayer* layer = data.m_Network->AddLstmLayer(desc, params, "Lstm"); - - input.Connect(layer->GetInputSlot(0)); - outputStateIn.Connect(layer->GetInputSlot(1)); - cellStateIn.Connect(layer->GetInputSlot(2)); - - - return ( - (IsDynamicTensor(scratchBufferInfo)? - SetupAndTrackLayerOutputSlotAndOverrideTensorInfo<hal_1_2::HalPolicy>( - operation, 0, *layer, 0, model, data,fixSbInfo): - SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>( - operation, 0, *layer, 0, model, data)) && - SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 1, *layer, 1, model, data) && - SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 2, *layer, 2, model, data) && - SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 3, *layer, 3, model, data)); + return ::ConvertLstm<hal_1_2::HalPolicy>(operation, model, data); } bool HalPolicy::ConvertSqrt(const Operation& operation, const Model& model, ConversionData& data) @@ -2605,175 +442,8 @@ bool HalPolicy::ConvertTranspose(const Operation& operation, const Model& model, bool HalPolicy::ConvertTransposeConv2d(const Operation& operation, const Model& model, ConversionData& data) { - LayerInputHandle input = ConvertToLayerInputHandle<hal_1_2::HalPolicy>(operation, 0, model, data); - - if (!input.IsValid()) - { - return Fail("%s: Operation has invalid inputs", __func__); - } - - const Operand* output = GetOutputOperand<hal_1_2::HalPolicy>(operation, 0, model); - - if (!output) - { - return Fail("%s: Could not read output 0", __func__); - } - - const TensorInfo& inputInfo = input.GetTensorInfo(); - const TensorInfo& outputInfo = GetTensorInfoForOperand(*output); - if (IsDynamicTensor(outputInfo)) - { - return Fail("%s: Dynamic output tensors are not supported", __func__); - } - - // ArmNN does not currently support non-fixed weights or bias - // Find the shape of the weights tensor. In AndroidNN this will be [ 1, H, W, I * M ] - const Operand* weightsOperand = GetInputOperand<hal_1_2::HalPolicy>(operation, 1, model); - - if (weightsOperand == nullptr) - { - return Fail("%s: Operand is invalid", __func__); - } - TransposeConvolution2dDescriptor desc; - desc.m_DataLayout = DataLayout::NHWC; - - // Determine whether padding is implicit or explicit - bool implicitPadding = operation.inputs.size() == 9; - - if (implicitPadding ) - { - desc.m_DataLayout = OptionalDataLayout<hal_1_2::HalPolicy>(operation, 8, model, data); - } - else - { - desc.m_DataLayout = OptionalDataLayout<hal_1_2::HalPolicy>(operation, 10, model, data); - } - - armnnUtils::DataLayoutIndexed dataLayoutIndexed(desc.m_DataLayout); - unsigned int widthIndex = dataLayoutIndexed.GetWidthIndex(); - unsigned int heightIndex = dataLayoutIndexed.GetHeightIndex(); - - const PermutationVector OHWIToOIHW = {0, 2, 3, 1}; - - // The shape of the weight is [depth_out, filter_height, filter_width, depth_in]. - // We have to permute it to OIHW if the data layout is NCHW. - const ConstTensorPin weightsPin = (desc.m_DataLayout == DataLayout::NCHW) ? - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 1, model, data, OHWIToOIHW) : - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 1, model, data); - - // Bias is a 1D tensor - const ConstTensorPin biasPin = - ConvertOperationInputToConstTensorPin<hal_1_2::HalPolicy>(operation, 2, model, data); - - if (!weightsPin.IsValid()) - { - return Fail("%s: Operation has invalid weights", __func__); - } - - if (!biasPin.IsValid()) - { - return Fail("%s: Operation has invalid biases", __func__); - } - - ConstTensor weights = weightsPin.GetConstTensor(); - ConstTensor bias = biasPin.GetConstTensor(); - SanitizeBiasQuantizationScale(bias.GetInfo(), weights.GetInfo(), inputInfo); - - ActivationFn activation; - - if (implicitPadding) - { - int32_t strideX{0}; - int32_t strideY{0}; - int32_t padLeft{0}; - int32_t padRight{0}; - int32_t padTop{0}; - int32_t padBottom{0}; - - android::nn::PaddingScheme paddingScheme; - if (!GetInputPaddingScheme<hal_1_2::HalPolicy>(operation, 4, paddingScheme, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 5, OperandType::INT32, strideX, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 6, OperandType::INT32, strideY, model, data) || - !GetInputActivationFunction<hal_1_2::HalPolicy>(operation, 7, activation, model, data)) - { - return Fail("%s: Operation has invalid inputs (implicit padding)", __func__); - } - - const uint32_t kernelX = weights.GetShape()[widthIndex]; - const uint32_t kernelY = weights.GetShape()[heightIndex]; - const uint32_t outputX = outputInfo.GetShape()[widthIndex]; - const uint32_t outputY = outputInfo.GetShape()[heightIndex]; - - CalcPaddingTransposeConv(outputX, kernelX, strideX, padLeft, padRight, paddingScheme); - CalcPaddingTransposeConv(outputY, kernelY, strideY, padTop, padBottom, paddingScheme); - - // NOTE: The Android NN API allows for negative padding values in TransposeConv2d, - // but Arm NN only supports values >= 0 - if (padLeft < 0 || padRight < 0 || padTop < 0 || padBottom < 0) - { - return Fail("%s: Negative padding values are not supported", __func__); - } - - desc.m_StrideX = boost::numeric_cast<uint32_t>(strideX); - desc.m_StrideY = boost::numeric_cast<uint32_t>(strideY); - desc.m_PadLeft = boost::numeric_cast<uint32_t>(padLeft); - desc.m_PadRight = boost::numeric_cast<uint32_t>(padRight); - desc.m_PadTop = boost::numeric_cast<uint32_t>(padTop); - desc.m_PadBottom = boost::numeric_cast<uint32_t>(padBottom); - } - else if (operation.inputs.size() == 11) - { - // explicit padding - if (!GetInputScalar<hal_1_2::HalPolicy>(operation, 3, OperandType::INT32, desc.m_PadLeft, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 4, OperandType::INT32, desc.m_PadRight, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 5, OperandType::INT32, desc.m_PadTop, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 6, OperandType::INT32, desc.m_PadBottom, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 7, OperandType::INT32, desc.m_StrideX, model, data) || - !GetInputScalar<hal_1_2::HalPolicy>(operation, 8, OperandType::INT32, desc.m_StrideY, model, data) || - !GetInputActivationFunction<hal_1_2::HalPolicy>(operation, 9, activation, model, data)) - { - return Fail("%s: Operation has invalid inputs (explicit padding)", __func__); - } - } - else - { - return Fail("%s: Unsupported number of operation inputs", __func__); - } - - desc.m_BiasEnabled = true; - Optional<TensorInfo> biases(bias.GetInfo()); - - bool isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC(__func__, - IsTransposeConvolution2dSupported, - data.m_Backends, - isSupported, - inputInfo, - outputInfo, - desc, - weights.GetInfo(), - biases); - if (!isSupported) - { - return false; - } - - IConnectableLayer* startLayer = - data.m_Network->AddTransposeConvolution2dLayer(desc, weights, Optional<ConstTensor>(bias)); - if (!startLayer) - { - return Fail("%s: AddTransposeConvolution2dLayer failed", __func__); - } - - IConnectableLayer* endLayer = ProcessActivation(outputInfo, activation, startLayer, data); - if (!endLayer) - { - return Fail("%s: ProcessActivation failed", __func__); - } - - input.Connect(startLayer->GetInputSlot(0)); - - return SetupAndTrackLayerOutputSlot<hal_1_2::HalPolicy>(operation, 0, *endLayer, model, data); + ALOGV("hal_1_2::HalPolicy::ConvertTransposeConv2d()"); + return ::ConvertTransposeConv2d<hal_1_2::HalPolicy>(operation, model, data); } } // namespace hal_1_2 |