diff options
Diffstat (limited to 'src')
63 files changed, 14 insertions, 2612 deletions
diff --git a/src/armnn/BackendHelper.cpp b/src/armnn/BackendHelper.cpp index 6638709d6f..ff899d49ea 100644 --- a/src/armnn/BackendHelper.cpp +++ b/src/armnn/BackendHelper.cpp @@ -307,34 +307,6 @@ bool LayerSupportHandle::IsConstantSupported(const TensorInfo& output, reasonIfUnsupported); } -bool LayerSupportHandle::IsConvertBf16ToFp32Supported(const TensorInfo& input, - const TensorInfo& output, - Optional<std::string&> reasonIfUnsupported) -{ - TensorInfos infos{input, output}; - - return m_LayerSupport->IsLayerSupported(LayerType::ConvertBf16ToFp32, - infos, - BaseDescriptor(), - EmptyOptional(), - EmptyOptional(), - reasonIfUnsupported); -} - -bool LayerSupportHandle::IsConvertFp32ToBf16Supported(const TensorInfo& input, - const TensorInfo& output, - Optional<std::string&> reasonIfUnsupported) -{ - TensorInfos infos{input, output}; - - return m_LayerSupport->IsLayerSupported(LayerType::ConvertFp32ToBf16, - infos, - BaseDescriptor(), - EmptyOptional(), - EmptyOptional(), - reasonIfUnsupported); -} - bool LayerSupportHandle::IsConvertFp16ToFp32Supported(const TensorInfo& input, const TensorInfo& output, Optional<std::string&> reasonIfUnsupported) diff --git a/src/armnn/ILayerSupport.cpp b/src/armnn/ILayerSupport.cpp index 8099782750..3ef367ee16 100644 --- a/src/armnn/ILayerSupport.cpp +++ b/src/armnn/ILayerSupport.cpp @@ -77,18 +77,10 @@ bool ILayerSupport::IsLayerSupported(const LayerType& type, case LayerType::Constant: return IsConstantSupported(infos[0], reasonIfUnsupported); - case LayerType::ConvertBf16ToFp32: - return IsConvertBf16ToFp32Supported(infos[0], - infos[1], - reasonIfUnsupported); case LayerType::ConvertFp16ToFp32: return IsConvertFp16ToFp32Supported(infos[0], infos[1], reasonIfUnsupported); - case LayerType::ConvertFp32ToBf16: - return IsConvertFp32ToBf16Supported(infos[0], - infos[1], - reasonIfUnsupported); case LayerType::ConvertFp32ToFp16: return IsConvertFp32ToFp16Supported(infos[0], infos[1], @@ -634,22 +626,6 @@ bool ILayerSupport::IsConstantSupported(const TensorInfo& output, return false; } -bool ILayerSupport::IsConvertBf16ToFp32Supported(const TensorInfo& input, - const TensorInfo& output, - Optional<std::string&> reasonIfUnsupported) const -{ - IgnoreUnused(input, output, reasonIfUnsupported); - return false; -} - -bool ILayerSupport::IsConvertFp32ToBf16Supported(const TensorInfo& input, - const TensorInfo& output, - Optional<std::string&> reasonIfUnsupported) const -{ - IgnoreUnused(input, output, reasonIfUnsupported); - return false; -} - bool ILayerSupport::IsConvertFp16ToFp32Supported(const TensorInfo& input, const TensorInfo& output, Optional<std::string&> reasonIfUnsupported) const diff --git a/src/armnn/LayersFwd.hpp b/src/armnn/LayersFwd.hpp index acac1f9988..43862d5072 100644 --- a/src/armnn/LayersFwd.hpp +++ b/src/armnn/LayersFwd.hpp @@ -17,9 +17,7 @@ #include "layers/ComparisonLayer.hpp" #include "layers/ConcatLayer.hpp" #include "layers/ConstantLayer.hpp" -#include "layers/ConvertBf16ToFp32Layer.hpp" #include "layers/ConvertFp16ToFp32Layer.hpp" -#include "layers/ConvertFp32ToBf16Layer.hpp" #include "layers/ConvertFp32ToFp16Layer.hpp" #include "layers/Convolution2dLayer.hpp" #include "layers/Convolution3dLayer.hpp" @@ -119,9 +117,7 @@ DECLARE_LAYER(ChannelShuffle) DECLARE_LAYER(Comparison) DECLARE_LAYER(Concat) DECLARE_LAYER(Constant) -DECLARE_LAYER(ConvertBf16ToFp32) DECLARE_LAYER(ConvertFp16ToFp32) -DECLARE_LAYER(ConvertFp32ToBf16) DECLARE_LAYER(ConvertFp32ToFp16) DECLARE_LAYER(Convolution2d) DECLARE_LAYER(Convolution3d) diff --git a/src/armnn/Network.cpp b/src/armnn/Network.cpp index 9d00a69518..6d3058c670 100644 --- a/src/armnn/Network.cpp +++ b/src/armnn/Network.cpp @@ -604,30 +604,6 @@ bool CheckScaleSetOnQuantizedType(Layer* layer, Optional<std::vector<std::string return noErrors; } -template <typename LayerT> -LayerT* ConvertBf16ToFp32Weight(Layer* l) -{ - LayerT* layer = PolymorphicDowncast<LayerT*>(l); - if ((layer->GetType() == LayerType::Convolution2d || layer->GetType() == LayerType::FullyConnected) - && layer->m_Weight) - { - const TensorInfo& info = layer->m_Weight->GetTensorInfo(); - - if (info.GetDataType() == DataType::BFloat16) - { - std::vector<float> newValues(info.GetNumElements()); - - armnnUtils::FloatingPointConverter::ConvertBFloat16ToFloat32( - layer->m_Weight->template GetConstTensor<armnn::BFloat16>(), info.GetNumElements(), newValues.data()); - - TensorInfo newInfo(info.GetShape(), DataType::Float32); - ConstTensor newInput(newInfo, newValues); - layer->m_Weight.reset(new ScopedTensorHandle(newInput)); - } - } - return layer; -} - OptimizationResult AttemptBackendAssignment(BackendSettings& backendSettings, Graph& graph, Layer* layer, @@ -772,98 +748,6 @@ OptimizationResult AttemptBackendAssignment(BackendSettings& backendSettings, return result; } } - else if (dataTypeIn == DataType::BFloat16 || dataTypeOut == DataType::BFloat16) - { - const auto layerType = layer->GetType(); - if (IWorkloadFactory::IsLayerSupported(*layer, DataType::Float32, reasonIfUnsupported) - && layerType != LayerType::ConvertFp32ToBf16 - && layerType != LayerType::ConvertBf16ToFp32) - { - bool revertConstantWeightsConversion = RevertConstantWeightsToFP32(layer); - - // Insert BF16 -> FP32 conversion layer before current layer. - // Unless we have reverted Constant Weights Type above. - std::vector<ConvertBf16ToFp32Layer*> convertBf16ToFp32Layers; - if (dataTypeIn == DataType::BFloat16 && dataTypeOut != DataType::BFloat16 - && !revertConstantWeightsConversion) - { - convertBf16ToFp32Layers = - InsertConvertBf16ToFp32LayersBefore(graph, *layer); - if (layer->GetType() == LayerType::Convolution2d) - { - ConvertBf16ToFp32Weight<Convolution2dLayer>(layer); - } - else if (layer->GetType() == LayerType::FullyConnected) - { - ConvertBf16ToFp32Weight<FullyConnectedLayer>(layer); - } - } - - // Insert FP32 -> BF16 conversion layer after current layer - std::vector<ConvertFp32ToBf16Layer*> convertFp32ToBf16Layers; - if (dataTypeOut == DataType::BFloat16) - { - convertFp32ToBf16Layers = - InsertConvertFp32ToBf16LayersAfter(graph, *layer); - } - - // Assign a supported backend to the newly introduced conversion layers - auto AssignFirstSupportedBackend = [&](Layer* layer, BackendId preferredBackend) - { - bool supportedBackendFound = false; - std::string reasonIfUnsupported; - - // Try preferred backend first - layer->SetBackendId(preferredBackend); - if (IWorkloadFactory::IsLayerSupported(*layer, - EmptyOptional(), - reasonIfUnsupported)) - { - supportedBackendFound = true; - } - else - { - for (const auto& backend : availablePreferredBackends) - { - // Skip preferred backend (we already determined that it is not supported) - if (backend == preferredBackend) - { - continue; - } - - layer->SetBackendId(backend); - if (IWorkloadFactory::IsLayerSupported(*layer, - EmptyOptional(), - reasonIfUnsupported)) - { - supportedBackendFound = true; - break; - } - } - } - - return supportedBackendFound; - }; - - for (ConvertBf16ToFp32Layer* convertLayer : convertBf16ToFp32Layers) - { - if (!AssignFirstSupportedBackend(convertLayer, backend)) - { - return ReturnError(convertLayer); - } - } - - for (ConvertFp32ToBf16Layer* convertLayer : convertFp32ToBf16Layers) - { - if (!AssignFirstSupportedBackend(convertLayer, backend)) - { - return ReturnError(convertLayer); - } - } - - return result; - } - } std::stringstream warningMsg; warningMsg << "Layer of type " << GetLayerTypeAsCString(layer->GetType()) @@ -1669,6 +1553,12 @@ IOptimizedNetworkPtr Optimize(const Graph& inGraph, throw InvalidArgumentException("Invoked Optimize with no backends specified"); } + if (options.m_ReduceFp32ToBf16) + { + throw InvalidArgumentException("BFloat16 optimization is currently ignored. In order to use Bf16 optimization " + "Please use the FastMathEnabled backend option for CpuAcc or GpuAcc."); + } + if (options.m_ReduceFp32ToFp16 && options.m_ReduceFp32ToBf16) { throw InvalidArgumentException("BFloat16 and Float16 optimization cannot be enabled at the same time."); @@ -1745,17 +1635,6 @@ IOptimizedNetworkPtr Optimize(const Graph& inGraph, Optimizer::Pass(optGraph, MakeOptimizations(ConvertConstantsFloatToHalf())); } - // If Fp32 to Bf16 optimization is set convert Fp32 network to Bf16 - // Convert input of Convolution2d and FullyConnected from Fp32 to Bf16 - // Only Constant weight of Convolution2d and FullyConnected are converted from Fp32 to Bf16 - // Constant and Fp32ToBf16 layers will also be fused so conversion is no longer needed at inference time - if (options.m_ReduceFp32ToBf16) - { - ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "Optimizer_ReduceFp32ToBf16"); - Optimizer::Pass(optGraph, MakeOptimizations(Fp32NetworkToBf16Converter())); - Optimizer::Pass(optGraph, MakeOptimizations(FuseConversionLayersIntoConstLayers())); - } - // Initialize backend settings BackendSettings backendSettings(backendPreferences, deviceSpec); if (backendSettings.GetAvailablePreferredBackends().empty()) diff --git a/src/armnn/NetworkUtils.cpp b/src/armnn/NetworkUtils.cpp index aaee4eba1a..1d46f029dc 100644 --- a/src/armnn/NetworkUtils.cpp +++ b/src/armnn/NetworkUtils.cpp @@ -5,8 +5,6 @@ #include "NetworkUtils.hpp" -#include <armnnUtils/FloatingPointConverter.hpp> -#include <BFloat16.hpp> #include "SubgraphViewSelector.hpp" #include <armnn/Exceptions.hpp> @@ -26,17 +24,6 @@ void UpdateOutputSlotToFp32(OutputSlot& outputSlot) outputSlot.SetTensorInfo(newTensorInfo); } -void ChangeOutputBf16ToFp32(Layer& layer) -{ - for (auto&& outputSlot = layer.BeginOutputSlots(); outputSlot != layer.EndOutputSlots(); ++outputSlot) - { - if (outputSlot->GetTensorInfo().GetDataType() == DataType::BFloat16) - { - UpdateOutputSlotToFp32(*outputSlot); - } - } -} - void ChangeOutputFp16ToFp32(Layer& layer) { for (auto&& outputSlot = layer.BeginOutputSlots(); outputSlot != layer.EndOutputSlots(); ++outputSlot) @@ -50,93 +37,6 @@ void ChangeOutputFp16ToFp32(Layer& layer) } // anonymous namespace -std::vector<ConvertBf16ToFp32Layer*> InsertConvertBf16ToFp32LayersBefore(Graph& graph, - Layer& layer, - bool expectCorrectInputType) -{ - std::vector<ConvertBf16ToFp32Layer*> convertLayers; - convertLayers.reserve(layer.GetNumInputSlots()); - - // Insert a ConvertBf16ToFp32Layer before each input slot - for (auto&& inputSlot = layer.BeginInputSlots(); inputSlot != layer.EndInputSlots(); ++inputSlot) - { - bool allowInsert = true; - if (expectCorrectInputType) - { - // Only insert ConvertBf16ToFp32Layer before BF16 input slots - OutputSlot* connectedOutputSlot = inputSlot->GetConnectedOutputSlot(); - allowInsert = - connectedOutputSlot && connectedOutputSlot->GetTensorInfo().GetDataType() == DataType::BFloat16; - } - - if (allowInsert) - { - const std::string name = - std::string("convert_bf16_to_fp32-" + std::to_string(inputSlot->GetSlotIndex()) + "-") + - layer.GetName(); - ConvertBf16ToFp32Layer* convertLayer = - graph.InsertNewLayer<ConvertBf16ToFp32Layer>(*inputSlot, name.c_str()); - - TensorInfo convertInfo = convertLayer->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo(); - convertInfo.SetDataType(DataType::Float32); - - convertLayer->GetOutputSlot().SetTensorInfo(convertInfo); - - convertLayers.emplace_back(convertLayer); - } - } - - return convertLayers; -} - -std::vector<ConvertFp32ToBf16Layer*> InsertConvertFp32ToBf16LayersBefore(Graph& graph, - Layer& layer, - bool expectCorrectInputType) -{ - std::vector<ConvertFp32ToBf16Layer*> convertLayers; - convertLayers.reserve(layer.GetNumInputSlots()); - - // Insert a ConvertFp32ToBf16Layer before each input slot - for (auto&& inputSlot = layer.BeginInputSlots(); inputSlot != layer.EndInputSlots(); ++inputSlot) - { - bool allowInsert = true; - - if ((layer.GetType() == LayerType::Convolution2d || - layer.GetType() == LayerType::FullyConnected || - layer.GetType() == LayerType::DepthwiseConvolution2d) - && inputSlot->GetSlotIndex() == 2) - { - // Refrain from reducing bias to Bf16 - continue; - } - if (expectCorrectInputType) - { - // Only insert ConvertFp32ToBf16Layer before FP32 input slots - OutputSlot* connectedOutputSlot = inputSlot->GetConnectedOutputSlot(); - allowInsert = - connectedOutputSlot && connectedOutputSlot->GetTensorInfo().GetDataType() == DataType::Float32; - } - - if (allowInsert) - { - const std::string name = - std::string("convert_fp32_to_bf16-" + std::to_string(inputSlot->GetSlotIndex()) + "-") + - layer.GetName(); - ConvertFp32ToBf16Layer* convertLayer = - graph.InsertNewLayer<ConvertFp32ToBf16Layer>(*inputSlot, name.c_str()); - - TensorInfo convertInfo = convertLayer->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo(); - convertInfo.SetDataType(DataType::BFloat16); - - convertLayer->GetOutputSlot().SetTensorInfo(convertInfo); - - convertLayers.emplace_back(convertLayer); - } - } - - return convertLayers; -} - std::vector<ConvertFp16ToFp32Layer*> InsertConvertFp16ToFp32LayersBefore(Graph& graph, Layer& layer, bool expectCorrectInputType) @@ -176,39 +76,6 @@ std::vector<ConvertFp16ToFp32Layer*> InsertConvertFp16ToFp32LayersBefore(Graph& return convertLayers; } -std::vector<ConvertFp32ToBf16Layer*> InsertConvertFp32ToBf16LayersAfter(Graph& graph, Layer& layer) -{ - const unsigned int numOutputSlots = layer.GetNumOutputSlots(); - - std::vector<ConvertFp32ToBf16Layer*> convertLayers; - convertLayers.reserve(numOutputSlots); - - // Update Bf16 output slots to FP32 on current layer - ChangeOutputBf16ToFp32(layer); - - // Insert a ConvertFp32ToBf16Layer after each FP32 output slot - for (unsigned int slotIndex = 0u; slotIndex < numOutputSlots; ++slotIndex) - { - OutputSlot& outputSlot = layer.GetOutputSlot(slotIndex); - if(outputSlot.GetTensorInfo().GetDataType() == DataType::Float32) - { - const std::string name = - std::string("convert_fp32_to_bf16-" + std::to_string(slotIndex) + "-") + layer.GetName(); - ConvertFp32ToBf16Layer* convertLayer = - graph.InsertNewLayer<ConvertFp32ToBf16Layer>(outputSlot, name.c_str()); - - TensorInfo convertInfo = convertLayer->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo(); - convertInfo.SetDataType(DataType::BFloat16); - - convertLayer->GetOutputSlot().SetTensorInfo(convertInfo); - - convertLayers.emplace_back(convertLayer); - } - } - - return convertLayers; -} - std::vector<ConvertFp32ToFp16Layer*> InsertConvertFp32ToFp16LayersAfter(Graph& graph, Layer& layer) { const unsigned int numOutputSlots = layer.GetNumOutputSlots(); @@ -274,50 +141,4 @@ std::vector<DebugLayer*> InsertDebugLayerAfter(Graph& graph, Layer& layer, bool return debugLayers; } -bool RevertConstantWeightsToFP32(Layer* layer) -{ - if (layer->GetType() == LayerType::Convolution2d || layer->GetType() == LayerType::FullyConnected) - { - // Revert Weights on Constant Layer to FP32 so they can be accessed by Conv2d or FullyConnected - // This prevents a conversion layer being added in during backend assignment which blocks - // the RedirectMembersToConstantInputs backward compatibility workaround/optimization. - auto constantLayerInfo = layer->GetInputSlot(1).GetConnection()->GetTensorInfo(); - - if (constantLayerInfo.IsConstant() && constantLayerInfo.GetDataType() == DataType::BFloat16) - { - std::vector<float> newValues(constantLayerInfo.GetNumElements()); - - auto weightLayer = PolymorphicDowncast<ConstantLayer*>( - &layer->GetInputSlot(1).GetConnection()->GetOwningIConnectableLayer()); - armnnUtils::FloatingPointConverter::ConvertBFloat16ToFloat32( - weightLayer->m_LayerOutput->GetConstTensor<BFloat16>(), - constantLayerInfo.GetNumElements(), - newValues.data()); - - TensorInfo newInfo(constantLayerInfo.GetShape(), DataType::Float32); - newInfo.SetConstant(true); - ConstTensor newInput(newInfo, newValues); - weightLayer->m_LayerOutput.reset(new ScopedTensorHandle(newInput)); - weightLayer->GetOutputSlot(0).SetTensorInfo(newInfo); - - // Connect Conv2d/FullyConnected to InputLayer directly leaving out - // the ConversionLayer to be cleaned up later - auto& conversionLayer = layer->GetInputSlot(0).GetConnection()->GetOwningIConnectableLayer(); - auto actualInputOutputSlot = conversionLayer.GetInputSlot(0).GetConnection(); - - auto& conversionLayerOutputSlot = - layer->GetInputSlot(0).GetConnection()->GetOwningIConnectableLayer().GetOutputSlot(0); - auto& conversionLayerInputSlot = - layer->GetInputSlot(0).GetConnection()->GetOwningIConnectableLayer().GetInputSlot(0); - actualInputOutputSlot->Disconnect(conversionLayerInputSlot); - conversionLayerOutputSlot.Disconnect(layer->GetInputSlot(0)); - - actualInputOutputSlot->Connect(layer->GetInputSlot(0)); - - return true; - } - } - return false; -} - } // namespace armnn diff --git a/src/armnn/NetworkUtils.hpp b/src/armnn/NetworkUtils.hpp index 38e0aabaf9..74e872cfbc 100644 --- a/src/armnn/NetworkUtils.hpp +++ b/src/armnn/NetworkUtils.hpp @@ -11,16 +11,6 @@ namespace armnn { -std::vector<ConvertBf16ToFp32Layer*> InsertConvertBf16ToFp32LayersBefore(Graph& graph, - Layer& layer, - bool expectCorrectInputType = true); - -std::vector<ConvertFp32ToBf16Layer*> InsertConvertFp32ToBf16LayersBefore(Graph& graph, - Layer& layer, - bool expectCorrectInputType = true); - -std::vector<ConvertFp32ToBf16Layer*> InsertConvertFp32ToBf16LayersAfter(Graph& graph, Layer& layer); - std::vector<ConvertFp16ToFp32Layer*> InsertConvertFp16ToFp32LayersBefore(Graph& graph, Layer& layer, bool expectCorrectInputType = true); diff --git a/src/armnn/layers/ConvertBf16ToFp32Layer.cpp b/src/armnn/layers/ConvertBf16ToFp32Layer.cpp deleted file mode 100644 index a0958e36cb..0000000000 --- a/src/armnn/layers/ConvertBf16ToFp32Layer.cpp +++ /dev/null @@ -1,55 +0,0 @@ -// -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "ConvertBf16ToFp32Layer.hpp" -#include "LayerCloneBase.hpp" - -#include <armnn/TypesUtils.hpp> - -#include <armnn/backends/WorkloadData.hpp> -#include <armnn/backends/WorkloadFactory.hpp> - -namespace armnn -{ - -ConvertBf16ToFp32Layer::ConvertBf16ToFp32Layer(const char* name) - : Layer(1, 1, LayerType::ConvertBf16ToFp32, name) -{ -} - -std::unique_ptr<IWorkload> ConvertBf16ToFp32Layer::CreateWorkload(const IWorkloadFactory& factory) const -{ - ConvertBf16ToFp32QueueDescriptor descriptor; - SetAdditionalInfo(descriptor); - - return factory.CreateWorkload(LayerType::ConvertBf16ToFp32, descriptor, PrepInfoAndDesc(descriptor)); -} - -ConvertBf16ToFp32Layer* ConvertBf16ToFp32Layer::Clone(Graph& graph) const -{ - return CloneBase<ConvertBf16ToFp32Layer>(graph, GetName()); -} - -void ConvertBf16ToFp32Layer::ValidateTensorShapesFromInputs() -{ - VerifyLayerConnections(1, CHECK_LOCATION()); - - const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape(); - - VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod); - - auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() }); - - ARMNN_ASSERT(inferredShapes.size() == 1); - - ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "ConvertBf16ToFp32Layer"); -} - -void ConvertBf16ToFp32Layer::ExecuteStrategy(IStrategy& strategy) const -{ - strategy.ExecuteStrategy(this, GetParameters(), {}, GetName()); -} - -} // namespace armnn diff --git a/src/armnn/layers/ConvertBf16ToFp32Layer.hpp b/src/armnn/layers/ConvertBf16ToFp32Layer.hpp deleted file mode 100644 index 71312758e4..0000000000 --- a/src/armnn/layers/ConvertBf16ToFp32Layer.hpp +++ /dev/null @@ -1,42 +0,0 @@ -// -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include <Layer.hpp> - -namespace armnn -{ - -/// This layer converts data type BFloat16 to Float32. -class ConvertBf16ToFp32Layer : public Layer -{ -public: - /// Makes a workload for the ConvertBf16ToFp32 type. - /// @param [in] factory The workload factory which will create the workload. - /// @return A pointer to the created workload, or nullptr if not created. - virtual std::unique_ptr<IWorkload> CreateWorkload(const IWorkloadFactory& factory) const override; - - /// Creates a dynamically-allocated copy of this layer. - /// @param [in] graph The graph into which this layer is being cloned. - ConvertBf16ToFp32Layer* Clone(Graph& graph) const override; - - /// Check if the input tensor shape(s) - /// will lead to a valid configuration of @ref ConvertBf16ToFp32Layer. - /// @param [in] shapeInferenceMethod Indicates if output shape shall be overwritten or just validated. - void ValidateTensorShapesFromInputs() override; - - void ExecuteStrategy(IStrategy& strategy) const override; - -protected: - /// Constructor to create a ConvertBf16ToFp32Layer. - /// @param [in] name Optional name for the layer. - ConvertBf16ToFp32Layer(const char* name); - - /// Default destructor - ~ConvertBf16ToFp32Layer() = default; -}; - -} // namespace diff --git a/src/armnn/layers/ConvertFp32ToBf16Layer.cpp b/src/armnn/layers/ConvertFp32ToBf16Layer.cpp deleted file mode 100644 index 7c98eea239..0000000000 --- a/src/armnn/layers/ConvertFp32ToBf16Layer.cpp +++ /dev/null @@ -1,56 +0,0 @@ -// -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "ConvertFp32ToBf16Layer.hpp" -#include "LayerCloneBase.hpp" - -#include <armnn/TypesUtils.hpp> - -#include <armnn/backends/WorkloadData.hpp> -#include <armnn/backends/WorkloadFactory.hpp> - -namespace armnn -{ - -ConvertFp32ToBf16Layer::ConvertFp32ToBf16Layer(const char* name) - : Layer(1, 1, LayerType::ConvertFp32ToBf16, name) -{ -} - -std::unique_ptr<IWorkload> ConvertFp32ToBf16Layer::CreateWorkload(const IWorkloadFactory& factory) const -{ - ConvertFp32ToBf16QueueDescriptor descriptor; - SetAdditionalInfo(descriptor); - - return factory.CreateWorkload(LayerType::ConvertFp32ToBf16, descriptor, PrepInfoAndDesc(descriptor)); -} - -ConvertFp32ToBf16Layer* ConvertFp32ToBf16Layer::Clone(Graph& graph) const -{ - return CloneBase<ConvertFp32ToBf16Layer>(graph, GetName()); -} - -void ConvertFp32ToBf16Layer::ValidateTensorShapesFromInputs() -{ - - VerifyLayerConnections(1, CHECK_LOCATION()); - - const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape(); - - VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod); - - auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() }); - - ARMNN_ASSERT(inferredShapes.size() == 1); - - ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "LayerName"); -} - -void ConvertFp32ToBf16Layer::ExecuteStrategy(IStrategy& strategy) const -{ - strategy.ExecuteStrategy(this, GetParameters(), {}, GetName()); -} - -} // namespace armnn diff --git a/src/armnn/layers/ConvertFp32ToBf16Layer.hpp b/src/armnn/layers/ConvertFp32ToBf16Layer.hpp deleted file mode 100644 index 71de4fbcda..0000000000 --- a/src/armnn/layers/ConvertFp32ToBf16Layer.hpp +++ /dev/null @@ -1,42 +0,0 @@ -// -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include <Layer.hpp> - -namespace armnn -{ - -/// This layer converts data type Float32 to BFloat16. -class ConvertFp32ToBf16Layer : public Layer -{ -public: - /// Makes a workload for the ConvertFp32ToBf16Layer type. - /// @param [in] factory The workload factory which will create the workload. - /// @return A pointer to the created workload, or nullptr if not created. - virtual std::unique_ptr<IWorkload> CreateWorkload(const IWorkloadFactory& factory) const override; - - /// Creates a dynamically-allocated copy of this layer. - /// @param [in] graph The graph into which this layer is being cloned. - ConvertFp32ToBf16Layer* Clone(Graph& graph) const override; - - /// Check if the input tensor shape(s) - /// will lead to a valid configuration of @ref ConvertFp32ToBf16Layer. - /// @param [in] shapeInferenceMethod Indicates if output shape shall be overwritten or just validated. - void ValidateTensorShapesFromInputs() override; - - void ExecuteStrategy(IStrategy& strategy) const override; - -protected: - /// Constructor to create a ConvertFp32ToBf16Layer. - /// @param [in] name Optional name for the layer. - ConvertFp32ToBf16Layer(const char* name); - - /// Default destructor - ~ConvertFp32ToBf16Layer() = default; -}; - -} // namespace diff --git a/src/armnn/optimizations/All.hpp b/src/armnn/optimizations/All.hpp index 0421f31973..a11dec9446 100644 --- a/src/armnn/optimizations/All.hpp +++ b/src/armnn/optimizations/All.hpp @@ -9,8 +9,6 @@ #include "ConvertConstants.hpp" #include "ConvertConstDequantisationLayersToConstLayers.hpp" #include "ConvertConstPermuteLayersToConstLayers.hpp" -#include "FuseConvertFp32ToBf16IntoConstLayers.hpp" -#include "ConvertFp32NetworkToBf16.hpp" #include "ConvertFp32NetworkToFp16.hpp" #include "FoldPadIntoLayer2d.hpp" #include "FuseBatchNorm.hpp" diff --git a/src/armnn/optimizations/ConvertConstants.hpp b/src/armnn/optimizations/ConvertConstants.hpp index 54c14e5c89..7b2f1fd291 100644 --- a/src/armnn/optimizations/ConvertConstants.hpp +++ b/src/armnn/optimizations/ConvertConstants.hpp @@ -11,7 +11,6 @@ #include <armnn/backends/TensorHandle.hpp> #include <armnn/utility/IgnoreUnused.hpp> -#include <BFloat16.hpp> #include <Half.hpp> namespace armnn @@ -19,27 +18,6 @@ namespace armnn namespace optimizations { -struct BFloat16ToFloat32 -{ - static void Func(std::shared_ptr<ConstTensorHandle>& handle) - { - const TensorInfo& info = handle->GetTensorInfo(); - - if (info.GetDataType() == DataType::BFloat16) - { - std::vector<float> newValues(info.GetNumElements()); - - armnnUtils::FloatingPointConverter::ConvertBFloat16ToFloat32(handle->GetConstTensor<BFloat16>(), - info.GetNumElements(), - newValues.data()); - - TensorInfo newInfo(info.GetShape(), DataType::Float32, 0.0f, 0, true); - ConstTensor newInput(newInfo, newValues); - handle.reset(new ScopedTensorHandle(newInput)); - } - } -}; - struct Float16ToFloat32 { static void Func(std::shared_ptr<ConstTensorHandle>& handle) @@ -61,27 +39,6 @@ struct Float16ToFloat32 } }; -struct Float32ToBFloat16 -{ - static void Func(std::shared_ptr<ConstTensorHandle>& handle) - { - const TensorInfo& info = handle->GetTensorInfo(); - - if (info.GetDataType() == DataType::Float32) - { - std::vector<BFloat16> newValues(info.GetNumElements()); - - armnnUtils::FloatingPointConverter::ConvertFloat32ToBFloat16(handle->GetConstTensor<float>(), - info.GetNumElements(), - newValues.data()); - - TensorInfo newInfo(info.GetShape(), DataType::BFloat16, 0.0f, 0, true); - ConstTensor newInput(newInfo, newValues); - handle.reset(new ScopedTensorHandle(newInput)); - } - } -}; - struct Float32ToFloat16 { static void Func(std::shared_ptr<ConstTensorHandle>& handle) @@ -138,17 +95,6 @@ struct IsFloat16Layer } }; -struct IsBFloat16Layer -{ - static bool Test(const Layer& layer) - { - return layer.GetDataType() == DataType::BFloat16; - } -}; - -using ConvertConstantsBFloatToFloat = ConvertConstants<BFloat16ToFloat32, IsFloat32Layer>; -using ConvertConstantsFloatToBFloat = ConvertConstants<Float32ToBFloat16, IsBFloat16Layer>; - using ConvertConstantsHalfToFloat = ConvertConstants<Float16ToFloat32, IsFloat32Layer>; using ConvertConstantsFloatToHalf = ConvertConstants<Float32ToFloat16, IsFloat16Layer>; diff --git a/src/armnn/optimizations/ConvertFp32NetworkToBf16.hpp b/src/armnn/optimizations/ConvertFp32NetworkToBf16.hpp deleted file mode 100644 index 6c80e740be..0000000000 --- a/src/armnn/optimizations/ConvertFp32NetworkToBf16.hpp +++ /dev/null @@ -1,79 +0,0 @@ -// -// Copyright © 2020 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#pragma once - -#include "NetworkUtils.hpp" -#include "Optimization.hpp" - -#include <armnn/utility/PolymorphicDowncast.hpp> - -namespace armnn -{ -namespace optimizations -{ - -template <typename LayerT> -inline LayerT* ConvertWeight(Layer* l) -{ - LayerT* layer = PolymorphicDowncast<LayerT*>(l); - if ((layer->GetType() == LayerType::Convolution2d || layer->GetType() == LayerType::FullyConnected) - && layer->m_Weight) - { - const TensorInfo& info = layer->m_Weight->GetTensorInfo(); - - if (info.GetDataType() == DataType::Float32) - { - std::vector<BFloat16> newValues(info.GetNumElements()); - - armnnUtils::FloatingPointConverter::ConvertFloat32ToBFloat16( - layer->m_Weight->template GetConstTensor<float>(), - info.GetNumElements(), - newValues.data()); - - TensorInfo newInfo(info); - newInfo.SetDataType(DataType::BFloat16); - ConstTensor newInput(newInfo, newValues); - layer->m_Weight.reset(new ScopedTensorHandle(newInput)); - } - } - return layer; -} - -class ConvertFp32NetworkToBf16Impl -{ -public: - - void Run(Graph& graph, Layer& layer) const - { - // Only convert Float32 To BFloat16 for the Input of Convolution2d layer and FullyConnected layer. - // And also convert weight data type from Float32 to Bfloat16. - // Do not convert bias data type. - if (layer.GetType() == LayerType::Convolution2d) - { - if (layer.GetDataType() == DataType::Float32) - { - InsertConvertFp32ToBf16LayersBefore(graph,layer); - ConvertWeight<Convolution2dLayer>(&layer); - } - } - else if (layer.GetType() == LayerType::FullyConnected) - { - if (layer.GetDataType() == DataType::Float32) - { - InsertConvertFp32ToBf16LayersBefore(graph,layer); - ConvertWeight<FullyConnectedLayer>(&layer); - } - } - } - -protected: - ConvertFp32NetworkToBf16Impl() = default; - ~ConvertFp32NetworkToBf16Impl() = default; -}; - -using Fp32NetworkToBf16Converter = OptimizeForType<Layer, ConvertFp32NetworkToBf16Impl>; - -} // namespace optimizations -} // namespace armnn diff --git a/src/armnn/optimizations/FuseConvertFp32ToBf16IntoConstLayers.hpp b/src/armnn/optimizations/FuseConvertFp32ToBf16IntoConstLayers.hpp deleted file mode 100644 index d112010539..0000000000 --- a/src/armnn/optimizations/FuseConvertFp32ToBf16IntoConstLayers.hpp +++ /dev/null @@ -1,89 +0,0 @@ -// -// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "Optimization.hpp" -#include <armnnUtils/Permute.hpp> -#include <ResolveType.hpp> - -namespace armnn -{ -namespace optimizations -{ - -class FuseConvertFp32ToBf16IntoConstLayers -{ -public: - void Run(Graph& graph, InputSlot& connection) const - { - Layer& base = connection.GetConnectedOutputSlot()->GetOwningLayer(); - Layer& child = connection.GetOwningLayer(); - - ARMNN_ASSERT(base.GetType() == LayerType::Constant); - ARMNN_ASSERT(child.GetType() == LayerType::ConvertFp32ToBf16); - - auto dataType = base.GetDataType(); - switch (dataType) - { - case DataType::Float32: - ReplaceConvertFp32ToBf16Layer<DataType::BFloat16>( - graph, - PolymorphicDowncast<ConstantLayer*>(&base), - PolymorphicDowncast<ConvertFp32ToBf16Layer*>(&child)); - break; - default: - throw InvalidArgumentException(GetDataTypeName(dataType) + - std::string(" Constant Layer cannot be fused into ") + - GetDataTypeName(child.GetDataType()) + - std::string(" conversion layer.")); - } - } -protected: - FuseConvertFp32ToBf16IntoConstLayers() = default; - ~FuseConvertFp32ToBf16IntoConstLayers() = default; -private: - template<armnn::DataType ArmnnType, - typename T = armnn::ResolveType<ArmnnType>> - static void ReplaceConvertFp32ToBf16Layer(Graph& graph, - ConstantLayer* constantLayer, - ConvertFp32ToBf16Layer* convertFp32ToBf16layer) - { - IgnoreUnused(graph); - /** - * This optimisation is to find situations where a constant set of inputs is being provided to a - * ConvertFp32ToBf16 layer. In this case we don't want the overhead of Converting the values on - * every inference, instead we want to Convert them once and store them in a Const layer to be - * used everytime as they will not change. - */ - TensorInfo outputConvertFp32ToBf16Info = convertFp32ToBf16layer->GetOutputSlot(0).GetTensorInfo(); - std::vector<T> newValues(outputConvertFp32ToBf16Info.GetNumElements()); - - armnnUtils::FloatingPointConverter::ConvertFloat32ToBFloat16( - constantLayer->m_LayerOutput->GetConstTensor<float>(), - outputConvertFp32ToBf16Info.GetNumElements(), - newValues.data()); - TensorInfo newInfo = outputConvertFp32ToBf16Info; - newInfo.SetConstant(true); - ConstTensor newInput(newInfo, newValues); - - constantLayer->m_LayerOutput.reset(new ScopedTensorHandle(newInput)); - - // Moves connections in convertFp32ToBf16layer output slot to the constant layer. - // ConvertFp32ToBf16layer layer will be removed if left unconnected. - convertFp32ToBf16layer->GetOutputSlot().MoveAllConnections(constantLayer->GetOutputSlot()); - - // Updating the output tensor - constantLayer->GetOutputSlot(0).SetTensorInfo(newInfo); - ARMNN_ASSERT(constantLayer->GetOutputSlot(0).GetTensorInfo().IsConstant() == true); - } -}; - -using FuseConversionLayersIntoConstLayers = OptimizeForConnection<ConstantLayer, - ConvertFp32ToBf16Layer, - FuseConvertFp32ToBf16IntoConstLayers>; - -} // namespace optimizations -} // namespace armnn
\ No newline at end of file diff --git a/src/armnn/test/FloatingPointConverterTest.cpp b/src/armnn/test/FloatingPointConverterTest.cpp index 21a16a3cc0..81384cefae 100644 --- a/src/armnn/test/FloatingPointConverterTest.cpp +++ b/src/armnn/test/FloatingPointConverterTest.cpp @@ -5,7 +5,6 @@ #include <armnnUtils/FloatingPointConverter.hpp> -#include <BFloat16.hpp> #include <Half.hpp> #include <vector> @@ -55,73 +54,4 @@ TEST_CASE("TestConvertFp16ToFp32") } } -TEST_CASE("TestConvertFloat32ToBFloat16") -{ - float floatArray[] = { 1.704735E38f, // 0x7F004000 round down - 0.0f, // 0x00000000 round down - 2.2959E-41f, // 0x00004000 round down - 1.7180272E38f, // 0x7F014000 round down - 9.18355E-41f, // 0x00010000 round down - 1.14794E-40f, // 0x00014000 round down - 4.5918E-41f, // 0x00008000 round down - -1.708058E38f, // 0xFF008000 round down - -4.3033756E37f, // 0xFE018000 round up - 1.60712E-40f, // 0x0001C000 round up - -2.0234377f, // 0xC0018001 round up - -1.1800863E-38f,// 0x80808001 round up - 4.843037E-35f, // 0x0680C000 round up - 3.9999998f, // 0x407FFFFF round up - std::numeric_limits<float>::max(), // 0x7F7FFFFF max positive value - std::numeric_limits<float>::lowest(), // 0xFF7FFFFF max negative value - 1.1754942E-38f, // 0x007FFFFF min positive value - -1.1754942E-38f // 0x807FFFFF min negative value - }; - uint16_t expectedResult[] = { 0x7F00, - 0x0000, - 0x0000, - 0x7F01, - 0x0001, - 0x0001, - 0x0000, - 0xFF00, - 0xFE02, - 0x0002, - 0xC002, - 0x8081, - 0x0681, - 0x4080, - 0x7F80, - 0xFF80, - 0x0080, - 0x8080 - }; - size_t numFloats = sizeof(floatArray) / sizeof(floatArray[0]); - - std::vector<armnn::BFloat16> convertedBuffer(numFloats); - - armnnUtils::FloatingPointConverter::ConvertFloat32ToBFloat16(floatArray, numFloats, convertedBuffer.data()); - - for (size_t i = 0; i < numFloats; i++) - { - armnn::BFloat16 actual = convertedBuffer[i]; - CHECK_EQ(expectedResult[i], actual.Val()); - } -} - -TEST_CASE("TestConvertBFloat16ToFloat32") -{ - uint16_t bf16Array[] = { 16256, 16320, 38699, 16384, 49156, 32639 }; - size_t numFloats = sizeof(bf16Array) / sizeof(bf16Array[0]); - float expectedResult[] = { 1.0f, 1.5f, -5.525308E-25f, 2.0f, -2.0625f, 3.3895314E38f }; - std::vector<float> convertedBuffer(numFloats, 0.0f); - - armnnUtils::FloatingPointConverter::ConvertBFloat16ToFloat32(bf16Array, numFloats, convertedBuffer.data()); - - for (size_t i = 0; i < numFloats; i++) - { - float actual = convertedBuffer[i]; - CHECK_EQ(expectedResult[i], actual); - } -} - } diff --git a/src/armnn/test/ShapeInferenceTests.cpp b/src/armnn/test/ShapeInferenceTests.cpp index a3800ade09..1035a3b6fd 100644 --- a/src/armnn/test/ShapeInferenceTests.cpp +++ b/src/armnn/test/ShapeInferenceTests.cpp @@ -250,17 +250,6 @@ TEST_CASE("ConstantTest") CHECK(layer->GetOutputSlot(0).GetTensorInfo().GetShape() == outputShape); } -TEST_CASE("ConvertBf16ToFp32Test") -{ - CreateGraphAndRunTest<ConvertBf16ToFp32Layer>({{ 5, 7, 6, 2 }}, {{ 5, 7, 6, 2 }}, "floor"); -} - -TEST_CASE("ConvertFp16ToBf16Test") -{ - const TensorShape tensorShape{5, 7, 6, 2}; - CreateGraphAndRunTest<ConvertFp32ToBf16Layer>({{ 5, 7, 6, 2 }}, {{ 5, 7, 6, 2 }}, "floor"); -} - TEST_CASE("ConvertFp16ToFp32Test") { CreateGraphAndRunTest<ConvertFp16ToFp32Layer>({{ 5, 7, 6, 2 }}, {{ 5, 7, 6, 2 }}, "floor"); diff --git a/src/armnn/test/UtilsTests.cpp b/src/armnn/test/UtilsTests.cpp index 63884374b3..067c8612fe 100644 --- a/src/armnn/test/UtilsTests.cpp +++ b/src/armnn/test/UtilsTests.cpp @@ -123,54 +123,6 @@ TEST_CASE("BFloatType") CHECK((GetDataTypeName(armnn::DataType::BFloat16) == std::string("BFloat16"))); } -TEST_CASE("Float32ToBFloat16Test") -{ - // LSB = 0, R = 0 -> round down - armnn::BFloat16 roundDown0 = armnn::BFloat16::Float32ToBFloat16(1.704735E38f); // 0x7F004000 - CHECK_EQ(roundDown0.Val(), 0x7F00); - // LSB = 1, R = 0 -> round down - armnn::BFloat16 roundDown1 = armnn::BFloat16::Float32ToBFloat16(9.18355E-41f); // 0x00010000 - CHECK_EQ(roundDown1.Val(), 0x0001); - // LSB = 0, R = 1 all 0 -> round down - armnn::BFloat16 roundDown2 = armnn::BFloat16::Float32ToBFloat16(1.14794E-40f); // 0x00014000 - CHECK_EQ(roundDown2.Val(), 0x0001); - // LSB = 1, R = 1 -> round up - armnn::BFloat16 roundUp = armnn::BFloat16::Float32ToBFloat16(-2.0234377f); // 0xC0018001 - CHECK_EQ(roundUp.Val(), 0xC002); - // LSB = 0, R = 1 -> round up - armnn::BFloat16 roundUp1 = armnn::BFloat16::Float32ToBFloat16(4.843037E-35f); // 0x0680C000 - CHECK_EQ(roundUp1.Val(), 0x0681); - // Max positive value -> infinity - armnn::BFloat16 maxPositive = armnn::BFloat16::Float32ToBFloat16(std::numeric_limits<float>::max()); // 0x7F7FFFFF - CHECK_EQ(maxPositive, armnn::BFloat16::Inf()); - // Max negative value -> -infinity - armnn::BFloat16 maxNeg = armnn::BFloat16::Float32ToBFloat16(std::numeric_limits<float>::lowest()); // 0xFF7FFFFF - CHECK_EQ(maxNeg.Val(), 0xFF80); - // Min positive value - armnn::BFloat16 minPositive = armnn::BFloat16::Float32ToBFloat16(1.1754942E-38f); // 0x007FFFFF - CHECK_EQ(minPositive.Val(), 0x0080); - // Min negative value - armnn::BFloat16 minNeg = armnn::BFloat16::Float32ToBFloat16(-1.1754942E-38f); // 0x807FFFFF - CHECK_EQ(minNeg.Val(), 0x8080); -} - -TEST_CASE("BFloat16ToFloat32Test") -{ - armnn::BFloat16 bf0(1.5f); - CHECK_EQ(bf0.ToFloat32(), 1.5f); - armnn::BFloat16 bf1(-5.525308E-25f); - CHECK_EQ(bf1.ToFloat32(), -5.525308E-25f); - armnn::BFloat16 bf2(-2.0625f); - CHECK_EQ(bf2.ToFloat32(), -2.0625f); - uint16_t v = 32639; - armnn::BFloat16 bf3(v); - CHECK_EQ(bf3.ToFloat32(), 3.3895314E38f); - // Infinity - CHECK_EQ(armnn::BFloat16::Inf().ToFloat32(), std::numeric_limits<float>::infinity()); - // NaN - CHECK(std::isnan(armnn::BFloat16::Nan().ToFloat32())); -} - TEST_CASE("GraphTopologicalSortSimpleTest") { std::map<int, std::vector<int>> graph; diff --git a/src/armnn/test/optimizations/ConvertConstantsBFloatTests.cpp b/src/armnn/test/optimizations/ConvertConstantsBFloatTests.cpp deleted file mode 100644 index 4aacf7f4fe..0000000000 --- a/src/armnn/test/optimizations/ConvertConstantsBFloatTests.cpp +++ /dev/null @@ -1,128 +0,0 @@ -// -// Copyright © 2020 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include <TestUtils.hpp> - -#include <BFloat16.hpp> -#include <Optimizer.hpp> - -#include <doctest/doctest.h> - -using namespace armnn; - -TEST_SUITE("Optimizer") -{ -using namespace armnn::optimizations; - -TEST_CASE("ConvertConstantsFloatToBFloatTest") -{ - armnn::Graph graph; - - const armnn::TensorInfo info({ 1, 1, 1, 2 }, armnn::DataType::BFloat16); - - // Create const tensor from fp32 data - unsigned int dims[] = { 4, 2, 1, 1 }; - std::vector<float> floatWeights{ 0.0f, -1.0f, - 3.8f, // 0x40733333 Round down - 3.1055E+29f, // 0x707ADC3C Round up - 9.149516E-10f, // 0x307B7FFF Round down - -3.8f, // 0xC0733333 Round down - -3.1055E+29f, // 0xF07ADC3C Round up - -9.149516E-10f // 0xB07B7FFF Round down - }; - armnn::ConstTensor weights(armnn::TensorInfo(4, dims, armnn::DataType::Float32, 0.0f, 0, true), floatWeights); - - // Create simple test network - auto input = graph.AddLayer<armnn::InputLayer>(0, "input"); - input->GetOutputSlot().SetTensorInfo(info); - - auto fc = graph.AddLayer<armnn::FullyConnectedLayer>(armnn::FullyConnectedDescriptor(), "fc"); - fc->m_Weight = std::make_unique<armnn::ScopedTensorHandle>(weights); - fc->GetOutputSlot().SetTensorInfo(info); - - auto output = graph.AddLayer<armnn::OutputLayer>(1, "output"); - - // Connect up the layers - input->GetOutputSlot().Connect(fc->GetInputSlot(0)); - fc->GetOutputSlot().Connect(output->GetInputSlot(0)); - - // Check tensor data type before conversion - CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::Float32); - - // Run the optimizer - armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(ConvertConstantsFloatToBFloat())); - - // Check tensor data type after conversion - CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::BFloat16); - - // Check whether data matches expected Bf16 data - const BFloat16* data = fc->m_Weight->GetConstTensor<BFloat16>(); - CHECK(data[0] == BFloat16(0.0f)); - CHECK(data[1] == BFloat16(-1.0f)); - CHECK(data[2] == BFloat16(3.796875f)); // 0x4073 - CHECK(data[3] == BFloat16(3.1072295E29f)); // 0x707B - CHECK(data[4] == BFloat16(9.131327E-10f)); // 0x307B - CHECK(data[5] == BFloat16(-3.796875f)); // 0xC073 - CHECK(data[6] == BFloat16(-3.1072295E29f)); // 0xF07B - CHECK(data[7] == BFloat16(-9.131327E-10f)); // 0xB07B -} - -TEST_CASE("ConvertConstantsBFloatToFloatTest") -{ - armnn::Graph graph; - - const armnn::TensorInfo info({ 1, 1, 1, 2 }, armnn::DataType::Float32); - - // Create the BFloat16 precision input data - unsigned int dims[] = { 4, 2, 1, 1 }; - std::vector<float> convWeightsData{ 0.f, -1.f, - 3.796875f, // 0x4073 - 3.1072295E29f, // 0x707B - 9.131327E-10f, // 0x307B - -3.796875f, // 0xC073 - -3.1072295E29f, // 0xF07B - -9.131327E-10f // 0xB07B - }; - std::vector<uint16_t> bfWeights(8); - armnnUtils::FloatingPointConverter::ConvertFloat32ToBFloat16(convWeightsData.data(), convWeightsData.size(), - bfWeights.data()); - armnn::ConstTensor weights(armnn::TensorInfo(4, dims, armnn::DataType::BFloat16, 0.0f, 0, true), bfWeights); - - //Create the simple test network - auto input = graph.AddLayer<armnn::InputLayer>(0, "input"); - input->GetOutputSlot().SetTensorInfo(info); - - auto fc = graph.AddLayer<armnn::FullyConnectedLayer>(armnn::FullyConnectedDescriptor(), "fc"); - fc->m_Weight = std::make_unique<armnn::ScopedTensorHandle>(weights); - fc->GetOutputSlot().SetTensorInfo(info); - - auto output = graph.AddLayer<armnn::OutputLayer>(1, "output"); - - //Connect up the layers - input->GetOutputSlot().Connect(fc->GetInputSlot(0)); - fc->GetOutputSlot().Connect(output->GetInputSlot(0)); - - //Test the tensor info is correct. - CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::BFloat16); - - // Run the optimizer - armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(ConvertConstantsBFloatToFloat())); - - //Test the tensor info is correct. - CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::Float32); - - // Now test the data matches float32 data - const float* data = fc->m_Weight->GetConstTensor<float>(); - CHECK(data[0] == 0.0f); - CHECK(data[1] == -1.0f); - CHECK(data[2] == 3.796875f); - CHECK(data[3] == 3.1072295E29f); - CHECK(data[4] == 9.131327E-10f); - CHECK(data[5] == -3.796875f); - CHECK(data[6] == -3.1072295E29f); - CHECK(data[7] == -9.131327E-10f); -} - -}
\ No newline at end of file diff --git a/src/armnn/test/optimizations/Fp32NetworkToBf16ConverterTests.cpp b/src/armnn/test/optimizations/Fp32NetworkToBf16ConverterTests.cpp deleted file mode 100644 index 66893ce1f5..0000000000 --- a/src/armnn/test/optimizations/Fp32NetworkToBf16ConverterTests.cpp +++ /dev/null @@ -1,229 +0,0 @@ -// -// Copyright © 2020 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include <TestUtils.hpp> - -#include <Optimizer.hpp> - -#include <doctest/doctest.h> - -TEST_SUITE("Optimizer") -{ -using namespace armnn::optimizations; - -TEST_CASE("Fp32NetworkToBf16OptimizationNoConversionTest") -{ - armnn::Graph graph; - - const armnn::TensorInfo infoFP32({ 2, 2, 1, 3 }, armnn::DataType::Float32); - - // Create the simple test network without Conv2D/FullyConnected. - auto input = graph.AddLayer<armnn::InputLayer>(0, "input"); - input->GetOutputSlot().SetTensorInfo(infoFP32); - - auto floor = graph.AddLayer<armnn::FloorLayer>("floor"); - floor->GetOutputSlot().SetTensorInfo(infoFP32); - - auto output = graph.AddLayer<armnn::OutputLayer>(1, "output"); - - // Connect up the layers - input->GetOutputSlot().Connect(floor->GetInputSlot(0)); - floor->GetOutputSlot().Connect(output->GetInputSlot(0)); - - CHECK(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<armnn::FloorLayer>, &IsLayerOfType<armnn::OutputLayer>)); - - // Run the optimizer - armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(Fp32NetworkToBf16Converter())); - - CHECK(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<armnn::FloorLayer>, - &IsLayerOfType<armnn::OutputLayer>)); -} - -TEST_CASE("Fp32NetworkToBf16OptimizationConv2DTest") -{ - armnn::Graph graph; - - const armnn::TensorInfo infoFP32({ 2, 3, 8, 1 }, armnn::DataType::Float32); - - // Create const tensor fp32 data - unsigned int dims[] = { 4, 2, 1, 1 }; - std::vector<float> floatWeights{ 0.0f, -1.0f, - 3.8f, // 0x40733333 Round down - 3.1055E+29f, // 0x707ADC3C Round up - 9.149516E-10f, // 0x307B7FFF Round down - -3.8f, // 0xC0733333 Round down - -3.1055E+29f, // 0xF07ADC3C Round up - -9.149516E-10f // 0xB07B7FFF Round down - }; - armnn::ConstTensor weights(armnn::TensorInfo(4, dims, armnn::DataType::Float32, 0.0f, 0, true), floatWeights); - - // Create const bias fp32 data - unsigned int biasDims[] {4}; - std::vector<float> floatBias{ 1.0f, 2.0f, 3.0f, 4.0f }; - armnn::ConstTensor bias(armnn::TensorInfo(1, biasDims, armnn::DataType::Float32, 0.0f, 0, true), floatBias); - - // A network with Convolution2d layer - auto input = graph.AddLayer<armnn::InputLayer>(0, "input"); - input->GetOutputSlot().SetTensorInfo(infoFP32); - - armnn::Convolution2dDescriptor descriptor; - descriptor.m_BiasEnabled = true; - auto conv = graph.AddLayer<armnn::Convolution2dLayer>(descriptor, "conv2d"); - conv->GetOutputSlot().SetTensorInfo(infoFP32); - - auto weightsLayer = graph.AddLayer<armnn::ConstantLayer>("Weights"); - weightsLayer->m_LayerOutput = std::make_shared<armnn::ScopedTensorHandle>(weights); - weightsLayer->GetOutputSlot(0).SetTensorInfo(weightsLayer->m_LayerOutput->GetTensorInfo()); - - auto biasLayer = graph.AddLayer<armnn::ConstantLayer>("Bias"); - biasLayer->m_LayerOutput = std::make_shared<armnn::ScopedTensorHandle>(bias); - biasLayer->GetOutputSlot(0).SetTensorInfo(biasLayer->m_LayerOutput->GetTensorInfo()); - - auto output = graph.AddLayer<armnn::OutputLayer>(1, "output"); - - // Connect up the layers - input->GetOutputSlot().Connect(conv->GetInputSlot(0)); - weightsLayer->GetOutputSlot(0).Connect(conv->GetInputSlot(1)); - biasLayer->GetOutputSlot(0).Connect(conv->GetInputSlot(2)); - conv->GetOutputSlot().Connect(output->GetInputSlot(0)); - - CHECK(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<armnn::ConstantLayer>, - &IsLayerOfType<armnn::ConstantLayer>, - &IsLayerOfType<armnn::Convolution2dLayer>, - &IsLayerOfType<armnn::OutputLayer>)); - - // Run the optimizer - armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(RedirectMembersToConstantInputs(), - Fp32NetworkToBf16Converter())); - - CHECK(7 == graph.GetNumLayers()); - CHECK(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<armnn::ConstantLayer>, - &IsLayerOfType<armnn::ConstantLayer>, - &IsLayerOfType<armnn::ConvertFp32ToBf16Layer>, - &IsLayerOfType<armnn::ConvertFp32ToBf16Layer>, - &IsLayerOfType<armnn::Convolution2dLayer>, - &IsLayerOfType<armnn::OutputLayer>)); - - armnn::TensorInfo inputTensor = conv->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo(); - armnn::TensorInfo weightTensor = conv->GetInputSlot(1).GetConnectedOutputSlot()->GetTensorInfo(); - armnn::TensorInfo biasTensor = conv->GetInputSlot(2).GetConnectedOutputSlot()->GetTensorInfo(); - armnn::TensorInfo outputTensor = conv->GetOutputSlot(0).GetTensorInfo(); - CHECK((conv->GetDataType() == armnn::DataType::BFloat16)); - CHECK((conv->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::BFloat16)); - CHECK((conv->m_Bias->GetTensorInfo().GetDataType() == armnn::DataType::Float32)); - CHECK((inputTensor.GetDataType() == armnn::DataType::BFloat16)); - CHECK((weightTensor.GetDataType() == armnn::DataType::BFloat16)); - CHECK((biasTensor.GetDataType() == armnn::DataType::Float32)); - CHECK((outputTensor.GetDataType() == armnn::DataType::Float32)); - - // Check whether data matches expected Bf16 data - const armnn::BFloat16* data = conv->m_Weight->GetConstTensor<armnn::BFloat16>(); - CHECK(data[0] == armnn::BFloat16(0.0f)); - CHECK(data[1] == armnn::BFloat16(-1.0f)); - CHECK(data[2] == armnn::BFloat16(3.796875f)); // 0x4073 - CHECK(data[3] == armnn::BFloat16(3.1072295E29f)); // 0x707B - CHECK(data[4] == armnn::BFloat16(9.131327E-10f)); // 0x307B - CHECK(data[5] == armnn::BFloat16(-3.796875f)); // 0xC073 - CHECK(data[6] == armnn::BFloat16(-3.1072295E29f)); // 0xF07B - CHECK(data[7] == armnn::BFloat16(-9.131327E-10f)); // 0xB07B -} - -TEST_CASE("Fp32NetworkToBf16OptimizationFullyConnectedTest") -{ - armnn::Graph graph; - - const armnn::TensorInfo infoFP32({ 2, 3, 8, 1 }, armnn::DataType::Float32); - - // Create const tensor fp32 data - unsigned int dims[] = { 4, 2, 1, 1 }; - std::vector<float> floatWeights{ 0.0f, -1.0f, - 3.8f, // 0x40733333 Round down - 3.1055E+29f, // 0x707ADC3C Round up - 9.149516E-10f, // 0x307B7FFF Round down - -3.8f, // 0xC0733333 Round down - -3.1055E+29f, // 0xF07ADC3C Round up - -9.149516E-10f // 0xB07B7FFF Round down - }; - armnn::ConstTensor weights(armnn::TensorInfo(4, dims, armnn::DataType::Float32, 0.0f, 0, true), floatWeights); - - // Create const bias fp32 data - unsigned int biasDims[] {4}; - std::vector<float> floatBias{ 1.0f, 2.0f, 3.0f, 4.0f }; - armnn::ConstTensor bias(armnn::TensorInfo(1, biasDims, armnn::DataType::Float32, 0.0f, 0, true), floatBias); - - // A network with FullyConnected layer - auto input = graph.AddLayer<armnn::InputLayer>(0, "input"); - input->GetOutputSlot().SetTensorInfo(infoFP32); - - armnn::FullyConnectedDescriptor descriptor; - descriptor.m_BiasEnabled = true; - - auto fc = graph.AddLayer<armnn::FullyConnectedLayer>(descriptor, "fully"); - fc->GetOutputSlot().SetTensorInfo(infoFP32); - - auto weightsLayer = graph.AddLayer<armnn::ConstantLayer>("Weights"); - weightsLayer->m_LayerOutput = std::make_shared<armnn::ScopedTensorHandle>(weights); - weightsLayer->GetOutputSlot(0).SetTensorInfo(weightsLayer->m_LayerOutput->GetTensorInfo()); - - auto biasLayer = graph.AddLayer<armnn::ConstantLayer>("Bias"); - biasLayer->m_LayerOutput = std::make_shared<armnn::ScopedTensorHandle>(bias); - biasLayer->GetOutputSlot(0).SetTensorInfo(biasLayer->m_LayerOutput->GetTensorInfo()); - - auto output = graph.AddLayer<armnn::OutputLayer>(1, "output"); - - // Connect up the layers - input->GetOutputSlot().Connect(fc->GetInputSlot(0)); - weightsLayer->GetOutputSlot(0).Connect(fc->GetInputSlot(1)); - biasLayer->GetOutputSlot(0).Connect(fc->GetInputSlot(2)); - fc->GetOutputSlot().Connect(output->GetInputSlot(0)); - - CHECK(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<armnn::ConstantLayer>, - &IsLayerOfType<armnn::ConstantLayer>, - &IsLayerOfType<armnn::FullyConnectedLayer>, - &IsLayerOfType<armnn::OutputLayer>)); - - // Run the optimizer - armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(RedirectMembersToConstantInputs(), - Fp32NetworkToBf16Converter())); - - CHECK(7 == graph.GetNumLayers()); - CHECK(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<armnn::ConstantLayer>, - &IsLayerOfType<armnn::ConstantLayer>, - &IsLayerOfType<armnn::ConvertFp32ToBf16Layer>, - &IsLayerOfType<armnn::ConvertFp32ToBf16Layer>, - &IsLayerOfType<armnn::FullyConnectedLayer>, - &IsLayerOfType<armnn::OutputLayer>)); - - armnn::TensorInfo inputTensor = fc->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo(); - armnn::TensorInfo weightTensor = fc->GetInputSlot(1).GetConnectedOutputSlot()->GetTensorInfo(); - armnn::TensorInfo biasTensor = fc->GetInputSlot(2).GetConnectedOutputSlot()->GetTensorInfo(); - armnn::TensorInfo outputTensor = fc->GetOutputSlot(0).GetTensorInfo(); - CHECK((fc->GetDataType() == armnn::DataType::BFloat16)); - CHECK((fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::BFloat16)); - CHECK((fc->m_Bias->GetTensorInfo().GetDataType() == armnn::DataType::Float32)); - CHECK((inputTensor.GetDataType() == armnn::DataType::BFloat16)); - CHECK((weightTensor.GetDataType() == armnn::DataType::BFloat16)); - CHECK((biasTensor.GetDataType() == armnn::DataType::Float32)); - CHECK((outputTensor.GetDataType() == armnn::DataType::Float32)); - - // Check whether data matches expected Bf16 data - const armnn::BFloat16* data = fc->m_Weight->GetConstTensor<armnn::BFloat16>(); - CHECK(data[0] == armnn::BFloat16(0.0f)); - CHECK(data[1] == armnn::BFloat16(-1.0f)); - CHECK(data[2] == armnn::BFloat16(3.796875f)); // 0x4073 - CHECK(data[3] == armnn::BFloat16(3.1072295E29f)); // 0x707B - CHECK(data[4] == armnn::BFloat16(9.131327E-10f)); // 0x307B - CHECK(data[5] == armnn::BFloat16(-3.796875f)); // 0xC073 - CHECK(data[6] == armnn::BFloat16(-3.1072295E29f)); // 0xF07B - CHECK(data[7] == armnn::BFloat16(-9.131327E-10f)); // 0xB07B -} - -}
\ No newline at end of file diff --git a/src/armnn/test/optimizations/FuseConvertF32BF16IntoConstLayerTests.cpp b/src/armnn/test/optimizations/FuseConvertF32BF16IntoConstLayerTests.cpp deleted file mode 100644 index 93d5948d61..0000000000 --- a/src/armnn/test/optimizations/FuseConvertF32BF16IntoConstLayerTests.cpp +++ /dev/null @@ -1,151 +0,0 @@ -// -// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include <LayersFwd.hpp> -#include <Network.hpp> -#include <NetworkUtils.hpp> -#include <Optimizer.hpp> -#include <TestUtils.hpp> - -#include <armnn/backends/TensorHandle.hpp> - -#include <doctest/doctest.h> - -TEST_SUITE("Optimizer") -{ -using namespace armnn; -using namespace armnn::optimizations; - -TEST_CASE("FuseConvertFp32Fp16intoConst") -{ - Graph graph; - const unsigned int shape[] = {1, 2, 2, 3}; - - const TensorInfo constTensorInfo(4, shape, DataType::Float32, 1.0, 0, true); - const TensorInfo outputConvertInfo(4, shape, DataType::BFloat16, 1.0, 0, true); - - ConstantLayer* constantLayer = graph.AddLayer<ConstantLayer>("constant"); - std::vector<float> constantValues(constTensorInfo.GetNumElements(), 3.1416f); - ConstTensor constTensor(constTensorInfo, constantValues.data()); - constantLayer->m_LayerOutput = std::make_shared<ScopedTensorHandle>(constTensor); - constantLayer->GetOutputSlot().SetTensorInfo(constTensorInfo); - - ConvertFp32ToBf16Layer* convertLayer = graph.AddLayer<ConvertFp32ToBf16Layer>("convert"); - convertLayer->GetOutputSlot().SetTensorInfo(outputConvertInfo); - - OutputLayer* output = graph.AddLayer<OutputLayer>(0, "output"); - - // Connect up constant -> convert -> output - constantLayer->GetOutputSlot().Connect(convertLayer->GetInputSlot(0)); - convertLayer->GetOutputSlot().Connect(output->GetInputSlot(0)); - - auto checkConstantFloat32 = [](const armnn::Layer *const layer) -> bool { - return IsLayerOfType<ConstantLayer>(layer) && - (layer->GetDataType() == DataType::Float32); - }; - auto checkConstantBFloat16 = [](const armnn::Layer *const layer) -> bool { - return IsLayerOfType<ConstantLayer>(layer) && - (layer->GetDataType() == DataType::BFloat16); - }; - - CHECK(CheckSequence(graph.cbegin(), graph.cend(), - checkConstantFloat32, - &IsLayerOfType<ConvertFp32ToBf16Layer>, - &IsLayerOfType<OutputLayer>)); - - armnn::Optimizer::Pass(graph, MakeOptimizations(FuseConversionLayersIntoConstLayers())); - - CHECK(CheckSequence(graph.cbegin(), graph.cend(), - checkConstantBFloat16, - &IsLayerOfType<OutputLayer>)); -} - -TEST_CASE("RevertConstantWeightsToFP32") -{ - Graph graph; - const unsigned int shape[] = {1, 2, 2, 3}; - - const TensorInfo constTensorInfo(4, shape, DataType::Float32, 1.0, 0, true); - const TensorInfo outputConvertInfo(4, shape, DataType::BFloat16, 1.0, 0, true); - - TensorInfo inputInfo(4, shape, DataType::Float32); - auto* input = graph.AddLayer<InputLayer>(0, "input0"); - input->GetOutputSlot().SetTensorInfo(inputInfo); - - auto* constantLayer = graph.AddLayer<ConstantLayer>("constant"); - std::vector<float> constantValues(constTensorInfo.GetNumElements(), 3.1416f); - ConstTensor constTensor(constTensorInfo, constantValues.data()); - constantLayer->m_LayerOutput = std::make_shared<ScopedTensorHandle>(constTensor); - constantLayer->GetOutputSlot().SetTensorInfo(constTensorInfo); - - ConvertFp32ToBf16Layer* convertLayerInputs = graph.AddLayer<ConvertFp32ToBf16Layer>("convert"); - convertLayerInputs->GetOutputSlot().SetTensorInfo(outputConvertInfo); - ConvertFp32ToBf16Layer* convertLayerWeights = graph.AddLayer<ConvertFp32ToBf16Layer>("convert2"); - convertLayerWeights->GetOutputSlot().SetTensorInfo(outputConvertInfo); - ConvertFp32ToBf16Layer* convertLayerBiases = graph.AddLayer<ConvertFp32ToBf16Layer>("convert3"); - convertLayerBiases->GetOutputSlot().SetTensorInfo(outputConvertInfo); - - auto* biases = graph.AddLayer<armnn::ConstantLayer>("Biases"); - biases->m_LayerOutput = std::make_unique<armnn::ScopedTensorHandle>(constTensor); - biases->GetOutputSlot().SetTensorInfo(constTensorInfo); - - armnn::Convolution2dDescriptor descriptor; - descriptor.m_BiasEnabled = true; - auto* conv = graph.AddLayer<armnn::Convolution2dLayer>(descriptor, "conv2d"); - const armnn::TensorInfo infoFP32({ 2, 3, 8, 1 }, armnn::DataType::Float32); - conv->GetOutputSlot().SetTensorInfo(infoFP32); - - auto* output = graph.AddLayer<OutputLayer>(0, "output"); - - // Connect up Input -> Convert -> - // Constant -> Convert -> Conv2d -> Output - // Constant -> Convert -> - input->GetOutputSlot().Connect(convertLayerInputs->GetInputSlot(0)); - constantLayer->GetOutputSlot().Connect(convertLayerWeights->GetInputSlot(0)); - biases->GetOutputSlot().Connect(convertLayerBiases->GetInputSlot(0)); - - convertLayerInputs->GetOutputSlot().Connect(conv->GetInputSlot(0)); - convertLayerWeights->GetOutputSlot().Connect(conv->GetInputSlot(1)); - convertLayerBiases->GetOutputSlot().Connect(conv->GetInputSlot(2)); - - conv->GetOutputSlot().Connect(output->GetInputSlot(0)); - - auto checkConstantFloat32 = [](const armnn::Layer *const layer) -> bool { - return IsLayerOfType<ConstantLayer>(layer) && - (layer->GetDataType() == DataType::Float32); - }; - auto checkConstantBFloat16 = [](const armnn::Layer *const layer) -> bool { - return IsLayerOfType<ConstantLayer>(layer) && - (layer->GetDataType() == DataType::BFloat16); - }; - - CHECK(CheckSequence(graph.cbegin(), graph.cend(), - &IsLayerOfType<InputLayer>, - checkConstantFloat32, - checkConstantFloat32, - &IsLayerOfType<ConvertFp32ToBf16Layer>, - &IsLayerOfType<ConvertFp32ToBf16Layer>, - &IsLayerOfType<ConvertFp32ToBf16Layer>, - &IsLayerOfType<Convolution2dLayer>, - &IsLayerOfType<OutputLayer>)); - - armnn::Optimizer::Pass(graph, MakeOptimizations(FuseConversionLayersIntoConstLayers())); - - bool revert = RevertConstantWeightsToFP32(conv); - - // Erase unconnected layer as occurs during Topological Sort. - graph.EraseLayer(convertLayerInputs); - - CHECK(revert); - CHECK(constantLayer->GetDataType() == DataType::Float32); - - CHECK(CheckSequence(graph.cbegin(), graph.cend(), - &IsLayerOfType<InputLayer>, - checkConstantBFloat16, - checkConstantFloat32, - &IsLayerOfType<Convolution2dLayer>, - &IsLayerOfType<OutputLayer>)); -} -} diff --git a/src/armnnUtils/FloatingPointConverter.cpp b/src/armnnUtils/FloatingPointConverter.cpp index 8123cf3577..7a684f1eb0 100644 --- a/src/armnnUtils/FloatingPointConverter.cpp +++ b/src/armnnUtils/FloatingPointConverter.cpp @@ -43,34 +43,4 @@ void FloatingPointConverter::ConvertFloat16To32(const void* srcFloat16Buffer, } } -void FloatingPointConverter::ConvertFloat32ToBFloat16(const float* srcFloat32Buffer, - size_t numElements, - void* dstBFloat16Buffer) -{ - ARMNN_ASSERT(srcFloat32Buffer != nullptr); - ARMNN_ASSERT(dstBFloat16Buffer != nullptr); - - armnn::BFloat16* bf16 = static_cast<armnn::BFloat16*>(dstBFloat16Buffer); - - for (size_t i = 0; i < numElements; i++) - { - bf16[i] = armnn::BFloat16(srcFloat32Buffer[i]); - } -} - -void FloatingPointConverter::ConvertBFloat16ToFloat32(const void* srcBFloat16Buffer, - size_t numElements, - float* dstFloat32Buffer) -{ - ARMNN_ASSERT(srcBFloat16Buffer != nullptr); - ARMNN_ASSERT(dstFloat32Buffer != nullptr); - - const armnn::BFloat16* bf16 = static_cast<const armnn::BFloat16*>(srcBFloat16Buffer); - - for (size_t i = 0; i < numElements; i++) - { - dstFloat32Buffer[i] = bf16[i].ToFloat32(); - } -} - } //namespace armnnUtils diff --git a/src/backends/backendsCommon/LayerSupportBase.cpp b/src/backends/backendsCommon/LayerSupportBase.cpp index 001037908d..26137f51b0 100644 --- a/src/backends/backendsCommon/LayerSupportBase.cpp +++ b/src/backends/backendsCommon/LayerSupportBase.cpp @@ -164,13 +164,6 @@ bool LayerSupportBase::IsConstantSupported(const TensorInfo&, // output return DefaultLayerSupport(__func__, __FILE__, __LINE__, reasonIfUnsupported); } -bool LayerSupportBase::IsConvertBf16ToFp32Supported(const TensorInfo&, // input - const TensorInfo&, // output - Optional<std::string&> reasonIfUnsupported) const -{ - return DefaultLayerSupport(__func__, __FILE__, __LINE__, reasonIfUnsupported); -} - bool LayerSupportBase::IsConvertFp16ToFp32Supported(const TensorInfo&, // input const TensorInfo&, // output Optional<std::string&> reasonIfUnsupported) const @@ -178,14 +171,6 @@ bool LayerSupportBase::IsConvertFp16ToFp32Supported(const TensorInfo&, // input return DefaultLayerSupport(__func__, __FILE__, __LINE__, reasonIfUnsupported); } -bool LayerSupportBase::IsConvertFp32ToBf16Supported(const TensorInfo&, // input - const TensorInfo&, // output - Optional<std::string&> reasonIfUnsupported) const -{ - return DefaultLayerSupport(__func__, __FILE__, __LINE__, reasonIfUnsupported); -} - - bool LayerSupportBase::IsConvertFp32ToFp16Supported(const TensorInfo&, // input const TensorInfo&, // output Optional<std::string&> reasonIfUnsupported) const diff --git a/src/backends/backendsCommon/LayerSupportBase.hpp b/src/backends/backendsCommon/LayerSupportBase.hpp index b18af35967..acf24a2d3a 100644 --- a/src/backends/backendsCommon/LayerSupportBase.hpp +++ b/src/backends/backendsCommon/LayerSupportBase.hpp @@ -83,19 +83,11 @@ public: bool IsConstantSupported(const TensorInfo& output, Optional<std::string&> reasonIfUnsupported = EmptyOptional()) const override; - bool IsConvertBf16ToFp32Supported(const TensorInfo& input, - const TensorInfo& output, - Optional<std::string&> reasonIfUnsupported = EmptyOptional()) const override; - ARMNN_DEPRECATED_MSG_REMOVAL_DATE("This method is deprecated. Use IsLayerSupported instead.", "23.08") bool IsConvertFp16ToFp32Supported(const TensorInfo& input, const TensorInfo& output, Optional<std::string&> reasonIfUnsupported = EmptyOptional()) const override; - bool IsConvertFp32ToBf16Supported(const TensorInfo& input, - const TensorInfo& output, - Optional<std::string&> reasonIfUnsupported = EmptyOptional()) const override; - ARMNN_DEPRECATED_MSG_REMOVAL_DATE("This method is deprecated. Use IsLayerSupported instead.", "23.08") bool IsConvertFp32ToFp16Supported( const TensorInfo& input, diff --git a/src/backends/backendsCommon/WorkloadData.cpp b/src/backends/backendsCommon/WorkloadData.cpp index 753fe06edb..62dfc6a38b 100644 --- a/src/backends/backendsCommon/WorkloadData.cpp +++ b/src/backends/backendsCommon/WorkloadData.cpp @@ -2222,52 +2222,6 @@ void LstmQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const } } -void ConvertBf16ToFp32QueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const -{ - const std::string descriptorName{"ConvertBf16ToFp32QueueDescriptor"}; - - ValidateNumInputs(workloadInfo, descriptorName, 1); - ValidateNumOutputs(workloadInfo, descriptorName, 1); - - const TensorInfo& inputTensorInfo = workloadInfo.m_InputTensorInfos[0]; - const TensorInfo& outputTensorInfo = workloadInfo.m_OutputTensorInfos[0]; - - if (inputTensorInfo.GetDataType() != DataType::BFloat16) - { - throw InvalidArgumentException(descriptorName + ": Input tensor type must be BFloat16."); - } - - if (outputTensorInfo.GetDataType() != DataType::Float32) - { - throw InvalidArgumentException(descriptorName + ": Output tensor type must be Float32."); - } - - ValidateTensorShapesMatch(inputTensorInfo, outputTensorInfo, descriptorName, "input", "output"); -} - -void ConvertFp32ToBf16QueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const -{ - const std::string descriptorName{"ConvertFp32ToBf16QueueDescriptor"}; - - ValidateNumInputs(workloadInfo, descriptorName, 1); - ValidateNumOutputs(workloadInfo, descriptorName, 1); - - const TensorInfo& inputTensorInfo = workloadInfo.m_InputTensorInfos[0]; - const TensorInfo& outputTensorInfo = workloadInfo.m_OutputTensorInfos[0]; - - if (inputTensorInfo.GetDataType() != DataType::Float32) - { - throw InvalidArgumentException(descriptorName + ": Input tensor type must be Float32."); - } - - if (outputTensorInfo.GetDataType() != DataType::BFloat16) - { - throw InvalidArgumentException(descriptorName + ": Output tensor type must be BFloat16."); - } - - ValidateTensorShapesMatch(inputTensorInfo, outputTensorInfo, descriptorName, "input", "output"); -} - void ConvertFp32ToFp16QueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const { const std::string descriptorName{"ConvertFp32ToFp16QueueDescriptor"}; diff --git a/src/backends/backendsCommon/WorkloadFactory.cpp b/src/backends/backendsCommon/WorkloadFactory.cpp index 665ab3f86c..1283f67660 100644 --- a/src/backends/backendsCommon/WorkloadFactory.cpp +++ b/src/backends/backendsCommon/WorkloadFactory.cpp @@ -227,13 +227,6 @@ bool IWorkloadFactory::IsLayerConfigurationSupported(const BackendId& backendId, result = layerSupportObject.IsConstantSupported(OverrideDataType(output, dataType), reason); break; } - case LayerType::ConvertBf16ToFp32: - { - const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); - const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); - result = layerSupportObject.IsConvertBf16ToFp32Supported(input, output, reason); - break; - } case LayerType::ConvertFp16ToFp32: { const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); @@ -241,13 +234,6 @@ bool IWorkloadFactory::IsLayerConfigurationSupported(const BackendId& backendId, result = layerSupportObject.IsConvertFp16ToFp32Supported(input, output, reason); break; } - case LayerType::ConvertFp32ToBf16: - { - const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); - const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); - result = layerSupportObject.IsConvertFp32ToBf16Supported(input, output, reason); - break; - } case LayerType::ConvertFp32ToFp16: { const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); @@ -1630,24 +1616,12 @@ std::unique_ptr<IWorkload> IWorkloadFactory::CreateWorkload(LayerType type, auto constantQueueDescriptor = PolymorphicDowncast<const ConstantQueueDescriptor*>(&descriptor); return CreateConstant(*constantQueueDescriptor, info); } - case LayerType::ConvertBf16ToFp32 : - { - auto convertBf16ToFp32QueueDescriptor - = PolymorphicDowncast<const ConvertBf16ToFp32QueueDescriptor*>(&descriptor); - return CreateConvertBf16ToFp32(*convertBf16ToFp32QueueDescriptor, info); - } case LayerType::ConvertFp16ToFp32: { auto convertFp16ToFp32QueueDescriptor = PolymorphicDowncast<const ConvertFp16ToFp32QueueDescriptor*>(&descriptor); return CreateConvertFp16ToFp32(*convertFp16ToFp32QueueDescriptor, info); } - case LayerType::ConvertFp32ToBf16: - { - auto convertFp32ToBf16QueueDescriptor - = PolymorphicDowncast<const ConvertFp32ToBf16QueueDescriptor*>(&descriptor); - return CreateConvertFp32ToBf16(*convertFp32ToBf16QueueDescriptor, info); - } case LayerType::ConvertFp32ToFp16: { auto convertFp32ToFp16QueueDescriptor @@ -1992,24 +1966,12 @@ std::unique_ptr<IWorkload> IWorkloadFactory::CreateConstant(const ConstantQueueD return std::unique_ptr<IWorkload>(); } -std::unique_ptr<IWorkload> IWorkloadFactory::CreateConvertBf16ToFp32(const ConvertBf16ToFp32QueueDescriptor& /*desc*/, - const WorkloadInfo& /*info*/) const -{ - return std::unique_ptr<IWorkload>(); -} - std::unique_ptr<IWorkload> IWorkloadFactory::CreateConvertFp16ToFp32(const ConvertFp16ToFp32QueueDescriptor& /*desc*/, const WorkloadInfo& /*info*/) const { return std::unique_ptr<IWorkload>(); } -std::unique_ptr<IWorkload> IWorkloadFactory::CreateConvertFp32ToBf16(const ConvertFp32ToBf16QueueDescriptor& /*desc*/, - const WorkloadInfo& /*info*/) const -{ - return std::unique_ptr<IWorkload>(); -} - std::unique_ptr<IWorkload> IWorkloadFactory::CreateConvertFp32ToFp16(const ConvertFp32ToFp16QueueDescriptor& /*desc*/, const WorkloadInfo& /*info*/) const { diff --git a/src/backends/backendsCommon/common.mk b/src/backends/backendsCommon/common.mk index 007cca57fa..3545331c8f 100644 --- a/src/backends/backendsCommon/common.mk +++ b/src/backends/backendsCommon/common.mk @@ -55,9 +55,7 @@ COMMON_TEST_SOURCES := \ test/layerTests/ConstantTestImpl.cpp \ test/layerTests/Conv2dTestImpl.cpp \ test/layerTests/Conv3dTestImpl.cpp \ - test/layerTests/ConvertBf16ToFp32TestImpl.cpp \ test/layerTests/ConvertFp16ToFp32TestImpl.cpp \ - test/layerTests/ConvertFp32ToBf16TestImpl.cpp \ test/layerTests/ConvertFp32ToFp16TestImpl.cpp \ test/layerTests/DebugTestImpl.cpp \ test/layerTests/DepthToSpaceTestImpl.cpp \ diff --git a/src/backends/backendsCommon/test/CMakeLists.txt b/src/backends/backendsCommon/test/CMakeLists.txt index 5e283990f7..232226b5df 100644 --- a/src/backends/backendsCommon/test/CMakeLists.txt +++ b/src/backends/backendsCommon/test/CMakeLists.txt @@ -83,12 +83,8 @@ list(APPEND armnnBackendsCommonUnitTests_sources layerTests/Conv2dTestImpl.hpp layerTests/Conv3dTestImpl.cpp layerTests/Conv3dTestImpl.hpp - layerTests/ConvertBf16ToFp32TestImpl.cpp - layerTests/ConvertBf16ToFp32TestImpl.hpp layerTests/ConvertFp16ToFp32TestImpl.cpp layerTests/ConvertFp16ToFp32TestImpl.hpp - layerTests/ConvertFp32ToBf16TestImpl.cpp - layerTests/ConvertFp32ToBf16TestImpl.hpp layerTests/ConvertFp32ToFp16TestImpl.cpp layerTests/ConvertFp32ToFp16TestImpl.hpp layerTests/DebugTestImpl.cpp diff --git a/src/backends/backendsCommon/test/IsLayerSupportedTestImpl.hpp b/src/backends/backendsCommon/test/IsLayerSupportedTestImpl.hpp index 5fdcd9c57a..18f11a542e 100644 --- a/src/backends/backendsCommon/test/IsLayerSupportedTestImpl.hpp +++ b/src/backends/backendsCommon/test/IsLayerSupportedTestImpl.hpp @@ -630,12 +630,8 @@ DECLARE_LAYER_POLICY_2_PARAM(Concat) DECLARE_LAYER_POLICY_1_PARAM(Constant) -DECLARE_LAYER_POLICY_1_PARAM(ConvertBf16ToFp32) - DECLARE_LAYER_POLICY_1_PARAM(ConvertFp16ToFp32) -DECLARE_LAYER_POLICY_1_PARAM(ConvertFp32ToBf16) - DECLARE_LAYER_POLICY_1_PARAM(ConvertFp32ToFp16) DECLARE_LAYER_POLICY_2_PARAM(Convolution2d) diff --git a/src/backends/backendsCommon/test/LayerTests.hpp b/src/backends/backendsCommon/test/LayerTests.hpp index 25435b24ec..00bfea5452 100644 --- a/src/backends/backendsCommon/test/LayerTests.hpp +++ b/src/backends/backendsCommon/test/LayerTests.hpp @@ -16,9 +16,7 @@ #include <backendsCommon/test/layerTests/ChannelShuffleTestImpl.hpp> #include <backendsCommon/test/layerTests/ComparisonTestImpl.hpp> #include <backendsCommon/test/layerTests/ConcatTestImpl.hpp> -#include <backendsCommon/test/layerTests/ConvertBf16ToFp32TestImpl.hpp> #include <backendsCommon/test/layerTests/ConvertFp16ToFp32TestImpl.hpp> -#include <backendsCommon/test/layerTests/ConvertFp32ToBf16TestImpl.hpp> #include <backendsCommon/test/layerTests/ConvertFp32ToFp16TestImpl.hpp> #include <backendsCommon/test/layerTests/Conv2dTestImpl.hpp> #include <backendsCommon/test/layerTests/Conv3dTestImpl.hpp> diff --git a/src/backends/backendsCommon/test/layerTests/ConvertBf16ToFp32TestImpl.cpp b/src/backends/backendsCommon/test/layerTests/ConvertBf16ToFp32TestImpl.cpp deleted file mode 100644 index 0dd8b598ac..0000000000 --- a/src/backends/backendsCommon/test/layerTests/ConvertBf16ToFp32TestImpl.cpp +++ /dev/null @@ -1,62 +0,0 @@ -// -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "ConvertBf16ToFp32TestImpl.hpp" - -#include <armnnTestUtils/TensorCopyUtils.hpp> -#include <armnnTestUtils/WorkloadTestUtils.hpp> - -#include <armnnTestUtils/TensorHelpers.hpp> - -LayerTestResult<float, 4> ConvertBf16ToFp32Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::ITensorHandleFactory& tensorHandleFactory) -{ - IgnoreUnused(memoryManager); - - const armnn::TensorInfo inputTensorInfo({1, 3, 2, 3}, armnn::DataType::BFloat16); - const armnn::TensorInfo outputTensorInfo({1, 3, 2, 3}, armnn::DataType::Float32); - - std::vector<armnn::BFloat16> inputValues = armnnUtils::QuantizedVector<armnn::BFloat16>( - { - -37.5f, -15.2f, -8.76f, -2.0f, -1.5f, -1.3f, -0.5f, -0.4f, 0.0f, - 1.0f, 0.4f, 0.5f, 1.3f, 1.5f, 2.0f, 8.76f, 15.2f, 37.5f - }, - 1.0f, 0); - - std::vector<float> actualOutput(outputTensorInfo.GetNumElements()); - std::vector<float> expectedOutput = - { - -37.5f, -15.2f, -8.76f, -2.0f, -1.5f, -1.3f, -0.5f, -0.4f, 0.0f, - 1.0f, 0.4f, 0.5f, 1.3f, 1.5f, 2.0f, 8.76f, 15.2f, 37.5f - }; - - std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ConvertBf16ToFp32QueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateWorkload(armnn::LayerType::ConvertBf16ToFp32, - data, - info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), inputValues.data()); - - workload->Execute(); - - CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); - - return LayerTestResult<float, 4>(actualOutput, - expectedOutput, - outputHandle->GetShape(), - outputTensorInfo.GetShape()); -} diff --git a/src/backends/backendsCommon/test/layerTests/ConvertBf16ToFp32TestImpl.hpp b/src/backends/backendsCommon/test/layerTests/ConvertBf16ToFp32TestImpl.hpp deleted file mode 100644 index bcb0d6f124..0000000000 --- a/src/backends/backendsCommon/test/layerTests/ConvertBf16ToFp32TestImpl.hpp +++ /dev/null @@ -1,18 +0,0 @@ -// -// Copyright © 2020 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include <armnnTestUtils/LayerTestResult.hpp> - -#include <BFloat16.hpp> - -#include <armnn/backends/IBackendInternal.hpp> -#include <armnn/backends/WorkloadFactory.hpp> - -LayerTestResult<float, 4> ConvertBf16ToFp32Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::ITensorHandleFactory& tensorHandleFactory); diff --git a/src/backends/backendsCommon/test/layerTests/ConvertFp32ToBf16TestImpl.cpp b/src/backends/backendsCommon/test/layerTests/ConvertFp32ToBf16TestImpl.cpp deleted file mode 100644 index 5ee8f1dd9a..0000000000 --- a/src/backends/backendsCommon/test/layerTests/ConvertFp32ToBf16TestImpl.cpp +++ /dev/null @@ -1,84 +0,0 @@ -// -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "ConvertFp32ToBf16TestImpl.hpp" - -#include <armnnTestUtils/TensorCopyUtils.hpp> -#include <armnnTestUtils/WorkloadTestUtils.hpp> - -#include <armnnTestUtils/TensorHelpers.hpp> - -LayerTestResult<armnn::BFloat16, 4> ConvertFp32ToBf16Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::ITensorHandleFactory& tensorHandleFactory) -{ - IgnoreUnused(memoryManager); - - const armnn::TensorInfo inputTensorInfo({1, 2, 4, 3}, armnn::DataType::Float32); - const armnn::TensorInfo outputTensorInfo({1, 2, 4, 3}, armnn::DataType::BFloat16); - - std::vector<float> input = - { - -37.5f, -15.2f, -8.76f, - -2.0f, -1.5f, -1.3f, - -0.5f, -0.4f, 0.0f, - 1.0f, 0.4f, 0.5f, - 1.3f, 1.5f, 2.0f, - 8.76f, 15.2f, 37.5f, - 3.8f, // 0x40733333 Round down - 3.1055E+29f, // 0x707ADC3C Round up - 9.149516E-10f, // 0x307B7FFF Round down - -3.8f, // 0xC0733333 Round down - -3.1055E+29f, // 0xF07ADC3C Round up - -9.149516E-10f // 0xB07B7FFF Round down - }; - - std::vector<armnn::BFloat16> expectedOutput = armnnUtils::QuantizedVector<armnn::BFloat16>( - { - -37.5f, -15.2f, -8.76f, - -2.0f, -1.5f, -1.3f, - -0.5f, -0.4f, 0.0f, - 1.0f, 0.4f, 0.5f, - 1.3f, 1.5f, 2.0f, - 8.76f, 15.2f, 37.5f, - 3.796875f, // 0x4073 - 3.1072295E29f, // 0x707B - 9.131327E-10f, // 0x307B - -3.796875f, // 0xC073 - -3.1072295E29f, // 0xF07B - -9.131327E-10f // 0xB07B - }, - 1.0f, 0); - - std::vector<armnn::BFloat16> actualOutput(outputTensorInfo.GetNumElements()); - - std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ConvertFp32ToBf16QueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateWorkload(armnn::LayerType::ConvertFp32ToBf16, - data, - info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), input.data()); - - workload->Execute(); - - CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); - - return LayerTestResult<armnn::BFloat16, 4>(actualOutput, - expectedOutput, - outputHandle->GetShape(), - outputTensorInfo.GetShape()); - -} diff --git a/src/backends/backendsCommon/test/layerTests/ConvertFp32ToBf16TestImpl.hpp b/src/backends/backendsCommon/test/layerTests/ConvertFp32ToBf16TestImpl.hpp deleted file mode 100644 index c2286d9c41..0000000000 --- a/src/backends/backendsCommon/test/layerTests/ConvertFp32ToBf16TestImpl.hpp +++ /dev/null @@ -1,18 +0,0 @@ -// -// Copyright © 2020 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include <armnnTestUtils/LayerTestResult.hpp> - -#include <BFloat16.hpp> - -#include <armnn/backends/IBackendInternal.hpp> -#include <armnn/backends/WorkloadFactory.hpp> - -LayerTestResult<armnn::BFloat16, 4> ConvertFp32ToBf16Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::ITensorHandleFactory& tensorHandleFactory); diff --git a/src/backends/cl/ClLayerSupport.cpp b/src/backends/cl/ClLayerSupport.cpp index 9c40391f1a..a61a5bb640 100644 --- a/src/backends/cl/ClLayerSupport.cpp +++ b/src/backends/cl/ClLayerSupport.cpp @@ -247,14 +247,6 @@ bool ClLayerSupport::IsLayerSupported(const LayerType& type, return IsConvertFp16ToFp32Supported(infos[0], infos[1], reasonIfUnsupported); case LayerType::ConvertFp32ToFp16: return IsConvertFp32ToFp16Supported(infos[0], infos[1], reasonIfUnsupported); - case LayerType::ConvertBf16ToFp32: - return LayerSupportBase::IsConvertBf16ToFp32Supported(infos[0], - infos[1], - reasonIfUnsupported); - case LayerType::ConvertFp32ToBf16: - return LayerSupportBase::IsConvertFp32ToBf16Supported(infos[0], - infos[1], - reasonIfUnsupported); case LayerType::Convolution2d: { if (infos.size() != 4) diff --git a/src/backends/neon/NeonLayerSupport.cpp b/src/backends/neon/NeonLayerSupport.cpp index 7f311d8684..4c97855668 100644 --- a/src/backends/neon/NeonLayerSupport.cpp +++ b/src/backends/neon/NeonLayerSupport.cpp @@ -220,12 +220,8 @@ bool NeonLayerSupport::IsLayerSupported(const LayerType& type, } case LayerType::Constant: return IsConstantSupported(infos[0], reasonIfUnsupported); - case LayerType::ConvertBf16ToFp32: - return IsConvertBf16ToFp32Supported(infos[0], infos[1], reasonIfUnsupported); case LayerType::ConvertFp16ToFp32: return IsConvertFp16ToFp32Supported(infos[0], infos[1], reasonIfUnsupported); - case LayerType::ConvertFp32ToBf16: - return IsConvertFp32ToBf16Supported(infos[0], infos[1], reasonIfUnsupported); case LayerType::ConvertFp32ToFp16: return IsConvertFp32ToFp16Supported(infos[0], infos[1], reasonIfUnsupported); case LayerType::Convolution2d: @@ -765,16 +761,6 @@ bool NeonLayerSupport::IsConstantSupported(const TensorInfo& output, output); } -bool NeonLayerSupport::IsConvertBf16ToFp32Supported(const TensorInfo& input, - const TensorInfo& output, - Optional<std::string&> reasonIfUnsupported) const -{ - armnn::IgnoreUnused(input); - armnn::IgnoreUnused(output); - armnn::IgnoreUnused(reasonIfUnsupported); - return true; -} - bool NeonLayerSupport::IsConvertFp16ToFp32Supported(const TensorInfo& input, const TensorInfo& output, Optional<std::string&> reasonIfUnsupported) const @@ -785,16 +771,6 @@ bool NeonLayerSupport::IsConvertFp16ToFp32Supported(const TensorInfo& input, return true; } -bool NeonLayerSupport::IsConvertFp32ToBf16Supported(const TensorInfo& input, - const TensorInfo& output, - Optional<std::string&> reasonIfUnsupported) const -{ - armnn::IgnoreUnused(input); - armnn::IgnoreUnused(output); - armnn::IgnoreUnused(reasonIfUnsupported); - return true; -} - bool NeonLayerSupport::IsConvertFp32ToFp16Supported(const TensorInfo& input, const TensorInfo& output, Optional<std::string&> reasonIfUnsupported) const diff --git a/src/backends/neon/NeonLayerSupport.hpp b/src/backends/neon/NeonLayerSupport.hpp index e916162f93..374a9049c8 100644 --- a/src/backends/neon/NeonLayerSupport.hpp +++ b/src/backends/neon/NeonLayerSupport.hpp @@ -84,18 +84,10 @@ public: bool IsConstantSupported(const TensorInfo& output, Optional<std::string&> reasonIfUnsupported = EmptyOptional()) const override; - bool IsConvertBf16ToFp32Supported(const TensorInfo& input, - const TensorInfo& output, - Optional<std::string&> reasonIfUnsupported = EmptyOptional()) const override; - bool IsConvertFp16ToFp32Supported(const TensorInfo& input, const TensorInfo& output, Optional<std::string&> reasonIfUnsupported = EmptyOptional()) const override; - bool IsConvertFp32ToBf16Supported(const TensorInfo& input, - const TensorInfo& output, - Optional<std::string&> reasonIfUnsupported = EmptyOptional()) const override; - bool IsConvertFp32ToFp16Supported(const TensorInfo& input, const TensorInfo& output, Optional<std::string&> reasonIfUnsupported = EmptyOptional()) const override; diff --git a/src/backends/neon/NeonWorkloadFactory.cpp b/src/backends/neon/NeonWorkloadFactory.cpp index d5a7c684d3..dccd4a3a36 100644 --- a/src/backends/neon/NeonWorkloadFactory.cpp +++ b/src/backends/neon/NeonWorkloadFactory.cpp @@ -194,24 +194,12 @@ std::unique_ptr<IWorkload> NeonWorkloadFactory::CreateWorkload(LayerType type, auto constantQueueDescriptor = PolymorphicDowncast<const ConstantQueueDescriptor*>(&descriptor); return std::make_unique<NeonConstantWorkload>(*constantQueueDescriptor, info); } - case LayerType::ConvertBf16ToFp32 : - { - auto convertBf16ToFp32QueueDescriptor - = PolymorphicDowncast<const ConvertBf16ToFp32QueueDescriptor*>(&descriptor); - return std::make_unique<NeonConvertBf16ToFp32Workload>(*convertBf16ToFp32QueueDescriptor, info); - } case LayerType::ConvertFp16ToFp32 : { auto convertFp16ToFp32QueueDescriptor = PolymorphicDowncast<const ConvertFp16ToFp32QueueDescriptor*>(&descriptor); return std::make_unique<NeonConvertFp16ToFp32Workload>(*convertFp16ToFp32QueueDescriptor, info); } - case LayerType::ConvertFp32ToBf16 : - { - auto convertFp32ToBf16QueueDescriptor - = PolymorphicDowncast<const ConvertFp32ToBf16QueueDescriptor*>(&descriptor); - return std::make_unique<NeonConvertFp32ToBf16Workload>(*convertFp32ToBf16QueueDescriptor, info); - } case LayerType::ConvertFp32ToFp16 : { auto convertFp32ToFp16QueueDescriptor @@ -655,13 +643,6 @@ std::unique_ptr<IWorkload> NeonWorkloadFactory::CreateConstant(const ConstantQue return std::make_unique<NeonConstantWorkload>(descriptor, info); } -std::unique_ptr<IWorkload> NeonWorkloadFactory::CreateConvertBf16ToFp32( - const ConvertBf16ToFp32QueueDescriptor& descriptor, - const WorkloadInfo& info) const -{ - return std::make_unique<NeonConvertBf16ToFp32Workload>(descriptor, info); -} - std::unique_ptr<IWorkload> NeonWorkloadFactory::CreateConvertFp16ToFp32( const ConvertFp16ToFp32QueueDescriptor& descriptor, const WorkloadInfo& info) const @@ -669,13 +650,6 @@ std::unique_ptr<IWorkload> NeonWorkloadFactory::CreateConvertFp16ToFp32( return std::make_unique<NeonConvertFp16ToFp32Workload>(descriptor, info); } -std::unique_ptr<IWorkload> NeonWorkloadFactory::CreateConvertFp32ToBf16( - const ConvertFp32ToBf16QueueDescriptor& descriptor, - const WorkloadInfo& info) const -{ - return std::make_unique<NeonConvertFp32ToBf16Workload>(descriptor, info); -} - std::unique_ptr<IWorkload> NeonWorkloadFactory::CreateConvertFp32ToFp16( const ConvertFp32ToFp16QueueDescriptor& descriptor, const WorkloadInfo& info) const diff --git a/src/backends/neon/NeonWorkloadFactory.hpp b/src/backends/neon/NeonWorkloadFactory.hpp index 0c116086d4..e4f545900a 100644 --- a/src/backends/neon/NeonWorkloadFactory.hpp +++ b/src/backends/neon/NeonWorkloadFactory.hpp @@ -108,21 +108,11 @@ public: ARMNN_DEPRECATED_MSG_REMOVAL_DATE("Use ABI stable " "CreateWorkload(LayerType, const QueueDescriptor&, const WorkloadInfo& info) instead.", "23.08") - std::unique_ptr<IWorkload> CreateConvertBf16ToFp32(const ConvertBf16ToFp32QueueDescriptor& descriptor, - const WorkloadInfo& info) const override; - - ARMNN_DEPRECATED_MSG_REMOVAL_DATE("Use ABI stable " - "CreateWorkload(LayerType, const QueueDescriptor&, const WorkloadInfo& info) instead.", "23.08") std::unique_ptr<IWorkload> CreateConvertFp16ToFp32(const ConvertFp16ToFp32QueueDescriptor& descriptor, const WorkloadInfo& info) const override; ARMNN_DEPRECATED_MSG_REMOVAL_DATE("Use ABI stable " "CreateWorkload(LayerType, const QueueDescriptor&, const WorkloadInfo& info) instead.", "23.08") - std::unique_ptr<IWorkload> CreateConvertFp32ToBf16(const ConvertFp32ToBf16QueueDescriptor& descriptor, - const WorkloadInfo& info) const override; - - ARMNN_DEPRECATED_MSG_REMOVAL_DATE("Use ABI stable " - "CreateWorkload(LayerType, const QueueDescriptor&, const WorkloadInfo& info) instead.", "23.08") std::unique_ptr<IWorkload> CreateConvertFp32ToFp16(const ConvertFp32ToFp16QueueDescriptor& descriptor, const WorkloadInfo& info) const override; diff --git a/src/backends/neon/backend.mk b/src/backends/neon/backend.mk index b1c0103426..bbc55547a0 100644 --- a/src/backends/neon/backend.mk +++ b/src/backends/neon/backend.mk @@ -34,8 +34,6 @@ BACKEND_SOURCES := \ workloads/NeonComparisonWorkload.cpp \ workloads/NeonConcatWorkload.cpp \ workloads/NeonConstantWorkload.cpp \ - workloads/NeonConvertBf16ToFp32Workload.cpp \ - workloads/NeonConvertFp32ToBf16Workload.cpp \ workloads/NeonConvertFp16ToFp32Workload.cpp \ workloads/NeonConvertFp32ToFp16Workload.cpp \ workloads/NeonConvolution2dWorkload.cpp \ diff --git a/src/backends/neon/test/NeonLayerTests.cpp b/src/backends/neon/test/NeonLayerTests.cpp index 88e513e62f..2512821a85 100644 --- a/src/backends/neon/test/NeonLayerTests.cpp +++ b/src/backends/neon/test/NeonLayerTests.cpp @@ -743,12 +743,6 @@ ARMNN_AUTO_TEST_CASE_WITH_THF(ConcatUint8, ConcatUint8Test) ARMNN_AUTO_TEST_CASE_WITH_THF(ConcatUint8DifferentInputOutputQParam, ConcatDifferentInputOutputQParamTest<DataType::QAsymmU8>, false) -// Convert from BFloat16 to Float32 -ARMNN_AUTO_TEST_CASE_WITH_THF(ConvertBf16ToFp32, ConvertBf16ToFp32Test) - -// Convert from Float32 to BFloat16 -ARMNN_AUTO_TEST_CASE_WITH_THF(ConvertFp32ToBf16, ConvertFp32ToBf16Test) - // Fully Connected ARMNN_AUTO_TEST_CASE_WITH_THF(SimpleFullyConnected, FullyConnectedFloat32Test, false, false) ARMNN_AUTO_TEST_CASE_WITH_THF(SimpleFullyConnectedWithBias, FullyConnectedFloat32Test, true, false) @@ -798,7 +792,6 @@ ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize1QAsymmU8, RankDimSize1Test<DataType::Q ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize1Signed32, RankDimSize1Test<DataType::Signed32>) ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize1QSymmS16, RankDimSize1Test<DataType::QSymmS16>) ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize1QAsymmS8, RankDimSize1Test<DataType::QAsymmS8>) -ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize1BFloat16, RankDimSize1Test<DataType::BFloat16>) ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize2Float16, RankDimSize2Test<DataType::Float16>) ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize2Float32, RankDimSize2Test<DataType::Float32>) @@ -806,7 +799,6 @@ ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize2QAsymmU8, RankDimSize2Test<DataType::Q ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize2Signed32, RankDimSize2Test<DataType::Signed32>) ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize2QSymmS16, RankDimSize2Test<DataType::QSymmS16>) ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize2QAsymmS8, RankDimSize2Test<DataType::QAsymmS8>) -ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize2BFloat16, RankDimSize2Test<DataType::BFloat16>) ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize3Float16, RankDimSize3Test<DataType::Float16>) ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize3Float32, RankDimSize3Test<DataType::Float32>) @@ -814,7 +806,6 @@ ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize3QAsymmU8, RankDimSize3Test<DataType::Q ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize3Signed32, RankDimSize3Test<DataType::Signed32>) ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize3QSymmS16, RankDimSize3Test<DataType::QSymmS16>) ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize3QAsymmS8, RankDimSize3Test<DataType::QAsymmS8>) -ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize3BFloat16, RankDimSize3Test<DataType::BFloat16>) ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize4Float16, RankDimSize4Test<DataType::Float16>) ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize4Float32, RankDimSize4Test<DataType::Float32>) @@ -822,7 +813,6 @@ ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize4QAsymmU8, RankDimSize4Test<DataType::Q ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize4Signed32, RankDimSize4Test<DataType::Signed32>) ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize4QSymmS16, RankDimSize4Test<DataType::QSymmS16>) ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize4QAsymmS8, RankDimSize4Test<DataType::QAsymmS8>) -ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize4BFloat16, RankDimSize4Test<DataType::BFloat16>) // InstanceNormalization ARMNN_AUTO_TEST_CASE_WITH_THF(InstanceNormFloat32Nchw, InstanceNormFloat32Test, DataLayout::NCHW); diff --git a/src/backends/neon/workloads/CMakeLists.txt b/src/backends/neon/workloads/CMakeLists.txt index dd09ecf015..a3eb883079 100644 --- a/src/backends/neon/workloads/CMakeLists.txt +++ b/src/backends/neon/workloads/CMakeLists.txt @@ -28,12 +28,8 @@ list(APPEND armnnNeonBackendWorkloads_sources NeonConcatWorkload.hpp NeonConstantWorkload.cpp NeonConstantWorkload.hpp - NeonConvertBf16ToFp32Workload.cpp - NeonConvertBf16ToFp32Workload.hpp NeonConvertFp16ToFp32Workload.cpp NeonConvertFp16ToFp32Workload.hpp - NeonConvertFp32ToBf16Workload.cpp - NeonConvertFp32ToBf16Workload.hpp NeonConvertFp32ToFp16Workload.cpp NeonConvertFp32ToFp16Workload.hpp NeonConvolution2dWorkload.cpp diff --git a/src/backends/neon/workloads/NeonConvertBf16ToFp32Workload.cpp b/src/backends/neon/workloads/NeonConvertBf16ToFp32Workload.cpp deleted file mode 100644 index 7a2ff9ac1a..0000000000 --- a/src/backends/neon/workloads/NeonConvertBf16ToFp32Workload.cpp +++ /dev/null @@ -1,81 +0,0 @@ -// -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "NeonConvertBf16ToFp32Workload.hpp" - -#include <armnnUtils/FloatingPointConverter.hpp> - -#include <BFloat16.hpp> - -#include <backendsCommon/WorkloadUtils.hpp> - -namespace armnn -{ - -NeonConvertBf16ToFp32Workload::NeonConvertBf16ToFp32Workload(const ConvertBf16ToFp32QueueDescriptor& descriptor, - const WorkloadInfo& info) - : BFloat16ToFloat32Workload<ConvertBf16ToFp32QueueDescriptor>(descriptor, info) -{ - this->m_Data.ValidateInputsOutputs("NeonConvertBf16ToFp32Workload", 1, 1); - GatherTensorHandlePairs(descriptor, m_TensorHandlePairs); -} - -void NeonConvertBf16ToFp32Workload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonConvertBf16ToFp32Workload_Execute", this->GetGuid()); - - auto convertFunc = [](uint8_t* dst, const uint8_t* src, size_t size) - { - auto input = reinterpret_cast<const BFloat16*>(src); - auto output = reinterpret_cast<float*>(dst); - size_t numElements = size/2; // 2 bytes per Bf16 - armnnUtils::FloatingPointConverter::ConvertBFloat16ToFloat32(input, numElements, output); - }; - - for (const auto& pair : m_TensorHandlePairs) - { - CopyTensorContentsGeneric(pair.first, pair.second, convertFunc); - } -} - -void NeonConvertBf16ToFp32Workload::ReplaceInputTensorHandle(ITensorHandle* tensorHandle, unsigned int slot) -{ - ITensorHandle* backupHandle = this->m_Data.m_Inputs[slot]; - this->m_Data.m_Inputs[slot] = tensorHandle; - try - { - Reconfigure(); - } - catch(armnn::UnimplementedException& e) - { - // Cannot reconfigure, revert the slot back and throw the exception. - this->m_Data.m_Inputs[slot] = backupHandle; - throw e; - } -} - -// Replace output tensor handle with the given TensorHandle -void NeonConvertBf16ToFp32Workload::ReplaceOutputTensorHandle(ITensorHandle* tensorHandle, unsigned int slot) -{ - ITensorHandle* backupHandle = this->m_Data.m_Inputs[slot]; - this->m_Data.m_Inputs[slot] = tensorHandle; - try - { - Reconfigure(); - } - catch(armnn::UnimplementedException& e) - { - // Cannot reconfigure, revert the slot back and throw the exception. - this->m_Data.m_Inputs[slot] = backupHandle; - throw e; - } -} - -void NeonConvertBf16ToFp32Workload::Reconfigure() -{ - throw armnn::UnimplementedException("Reconfigure not implemented for this workload"); -} - -} //namespace armnn diff --git a/src/backends/neon/workloads/NeonConvertBf16ToFp32Workload.hpp b/src/backends/neon/workloads/NeonConvertBf16ToFp32Workload.hpp deleted file mode 100644 index 9d44ad2cac..0000000000 --- a/src/backends/neon/workloads/NeonConvertBf16ToFp32Workload.hpp +++ /dev/null @@ -1,31 +0,0 @@ -// -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include <armnn/backends/Workload.hpp> -#include <armnn/backends/WorkloadData.hpp> -#include <neon/workloads/NeonWorkloadUtils.hpp> - -namespace armnn -{ - -class NeonConvertBf16ToFp32Workload : public BFloat16ToFloat32Workload<ConvertBf16ToFp32QueueDescriptor> -{ -public: - NeonConvertBf16ToFp32Workload(const ConvertBf16ToFp32QueueDescriptor& descriptor, const WorkloadInfo& info); - virtual void Execute() const override; - // Replace input tensor handle with the given TensorHandle - void ReplaceInputTensorHandle(ITensorHandle* tensorHandle, unsigned int slot) override; - - // Replace output tensor handle with the given TensorHandle - void ReplaceOutputTensorHandle(ITensorHandle* tensorHandle, unsigned int slot) override; -private: - using TensorHandlePair = std::pair<const ITensorHandle*, ITensorHandle*>; - std::vector<TensorHandlePair> m_TensorHandlePairs; - virtual void Reconfigure(); -}; - -} //namespace armnn diff --git a/src/backends/neon/workloads/NeonConvertFp32ToBf16Workload.cpp b/src/backends/neon/workloads/NeonConvertFp32ToBf16Workload.cpp deleted file mode 100644 index acd1a1ea8f..0000000000 --- a/src/backends/neon/workloads/NeonConvertFp32ToBf16Workload.cpp +++ /dev/null @@ -1,82 +0,0 @@ -// -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "NeonConvertFp32ToBf16Workload.hpp" - -#include <BFloat16.hpp> -#include <Profiling.hpp> - -#include <armnnUtils/FloatingPointConverter.hpp> - -#include <backendsCommon/WorkloadUtils.hpp> - -namespace armnn -{ - -NeonConvertFp32ToBf16Workload::NeonConvertFp32ToBf16Workload(const ConvertFp32ToBf16QueueDescriptor& descriptor, - const WorkloadInfo& info) - : Float32ToBFloat16Workload<ConvertFp32ToBf16QueueDescriptor>(descriptor, info) -{ - this->m_Data.ValidateInputsOutputs("NeonConvertFp32ToBf16Workload", 1, 1); - GatherTensorHandlePairs(descriptor, m_TensorHandlePairs); -} - -void NeonConvertFp32ToBf16Workload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonConvertFp32ToBf16Workload_Execute", this->GetGuid()); - - auto convertFunc = [](uint8_t* dst, const uint8_t* src, size_t size) - { - auto input = reinterpret_cast<const float*>(src); - auto output = reinterpret_cast<BFloat16*>(dst); - size_t numElements = size/2; // 2 bytes per bf16 - armnnUtils::FloatingPointConverter::ConvertFloat32ToBFloat16(input, numElements, output); - }; - - for (const auto& pair : m_TensorHandlePairs) - { - CopyTensorContentsGeneric(pair.first, pair.second, convertFunc); - } -} - -void NeonConvertFp32ToBf16Workload::ReplaceInputTensorHandle(ITensorHandle* tensorHandle, unsigned int slot) -{ - ITensorHandle* backupHandle = this->m_Data.m_Inputs[slot]; - this->m_Data.m_Inputs[slot] = tensorHandle; - try - { - Reconfigure(); - } - catch(armnn::UnimplementedException& e) - { - // Cannot reconfigure, revert the slot back and throw the exception. - this->m_Data.m_Inputs[slot] = backupHandle; - throw e; - } -} - -// Replace output tensor handle with the given TensorHandle -void NeonConvertFp32ToBf16Workload::ReplaceOutputTensorHandle(ITensorHandle* tensorHandle, unsigned int slot) -{ - ITensorHandle* backupHandle = this->m_Data.m_Inputs[slot]; - this->m_Data.m_Inputs[slot] = tensorHandle; - try - { - Reconfigure(); - } - catch(armnn::UnimplementedException& e) - { - // Cannot reconfigure, revert the slot back and throw the exception. - this->m_Data.m_Inputs[slot] = backupHandle; - throw e; - } -} - -void NeonConvertFp32ToBf16Workload::Reconfigure() -{ - throw armnn::UnimplementedException("Reconfigure not implemented for this workload"); -} - -} //namespace armnn diff --git a/src/backends/neon/workloads/NeonConvertFp32ToBf16Workload.hpp b/src/backends/neon/workloads/NeonConvertFp32ToBf16Workload.hpp deleted file mode 100644 index 2304f8a1d4..0000000000 --- a/src/backends/neon/workloads/NeonConvertFp32ToBf16Workload.hpp +++ /dev/null @@ -1,31 +0,0 @@ -// -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include <armnn/backends/Workload.hpp> -#include <armnn/backends/WorkloadData.hpp> -#include <neon/workloads/NeonWorkloadUtils.hpp> - -namespace armnn -{ - -class NeonConvertFp32ToBf16Workload : public Float32ToBFloat16Workload<ConvertFp32ToBf16QueueDescriptor> -{ -public: - NeonConvertFp32ToBf16Workload(const ConvertFp32ToBf16QueueDescriptor& descriptor, const WorkloadInfo& info); - virtual void Execute() const override; - // Replace input tensor handle with the given TensorHandle - void ReplaceInputTensorHandle(ITensorHandle* tensorHandle, unsigned int slot) override; - - // Replace output tensor handle with the given TensorHandle - void ReplaceOutputTensorHandle(ITensorHandle* tensorHandle, unsigned int slot) override; -private: - using TensorHandlePair = std::pair<const ITensorHandle*, ITensorHandle*>; - std::vector<TensorHandlePair> m_TensorHandlePairs; - virtual void Reconfigure(); -}; - -} //namespace armnn diff --git a/src/backends/neon/workloads/NeonWorkloads.hpp b/src/backends/neon/workloads/NeonWorkloads.hpp index c9c5421804..01fd2f7dba 100644 --- a/src/backends/neon/workloads/NeonWorkloads.hpp +++ b/src/backends/neon/workloads/NeonWorkloads.hpp @@ -16,9 +16,7 @@ #include "NeonComparisonWorkload.hpp" #include "NeonConcatWorkload.hpp" #include "NeonConstantWorkload.hpp" -#include "NeonConvertBf16ToFp32Workload.hpp" #include "NeonConvertFp16ToFp32Workload.hpp" -#include "NeonConvertFp32ToBf16Workload.hpp" #include "NeonConvertFp32ToFp16Workload.hpp" #include "NeonConvolution2dWorkload.hpp" #include "NeonConvolution3dWorkload.hpp" diff --git a/src/backends/reference/RefLayerSupport.cpp b/src/backends/reference/RefLayerSupport.cpp index 40909019ba..669c91d628 100644 --- a/src/backends/reference/RefLayerSupport.cpp +++ b/src/backends/reference/RefLayerSupport.cpp @@ -120,12 +120,8 @@ bool RefLayerSupport::IsLayerSupported(const LayerType& type, } case LayerType::Constant: return IsConstantSupported(infos[0], reasonIfUnsupported); - case LayerType::ConvertBf16ToFp32: - return IsConvertBf16ToFp32Supported(infos[0], infos[1], reasonIfUnsupported); case LayerType::ConvertFp16ToFp32: return IsConvertFp16ToFp32Supported(infos[0], infos[1], reasonIfUnsupported); - case LayerType::ConvertFp32ToBf16: - return IsConvertFp32ToBf16Supported(infos[0], infos[1], reasonIfUnsupported); case LayerType::ConvertFp32ToFp16: return IsConvertFp32ToFp16Supported(infos[0], infos[1], reasonIfUnsupported); case LayerType::Convolution2d: @@ -518,7 +514,6 @@ bool RefLayerSupport::IsActivationSupported(const TensorInfo& input, // Define supported types. std::array<DataType,6> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -585,7 +580,6 @@ bool RefLayerSupport::IsAdditionSupported(const TensorInfo& input0, bool supported = true; std::array<DataType,7> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -623,7 +617,6 @@ bool RefLayerSupport::IsArgMinMaxSupported(const armnn::TensorInfo &input, const std::array<DataType, 8> supportedInputTypes = { - DataType::BFloat16, DataType::Float16, DataType::Float32, DataType::QAsymmS8, @@ -658,7 +651,6 @@ bool RefLayerSupport::IsBatchMatMulSupported(const TensorInfo& inputX, std::array<DataType, 6> supportedTypes = { - DataType::BFloat16, DataType::Float16, DataType::Float32, DataType::QAsymmS8, @@ -707,7 +699,6 @@ bool RefLayerSupport::IsBatchNormalizationSupported(const TensorInfo& input, std::array<DataType, 6> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -757,7 +748,6 @@ bool RefLayerSupport::IsBatchToSpaceNdSupported(const TensorInfo& input, // Define supported types. std::array<DataType,6> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -797,7 +787,6 @@ bool RefLayerSupport::IsCastSupported(const TensorInfo& input, { std::array<DataType, 9> supportedInputTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QSymmS8, @@ -832,7 +821,6 @@ bool RefLayerSupport::IsChannelShuffleSupported(const TensorInfo& input, // Define supported output and inputs types. std::array<DataType, 7> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -864,7 +852,6 @@ bool RefLayerSupport::IsComparisonSupported(const TensorInfo& input0, std::array<DataType, 8> supportedInputTypes = { DataType::Boolean, - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -896,7 +883,6 @@ bool RefLayerSupport::IsConcatSupported(const std::vector<const TensorInfo*> inp bool supported = true; std::array<DataType,7> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -925,7 +911,6 @@ bool RefLayerSupport::IsConstantSupported(const TensorInfo& output, { std::array<DataType,8> supportedTypes = { - DataType::BFloat16, DataType::Float16, DataType::Float32, DataType::QAsymmS8, @@ -939,21 +924,6 @@ bool RefLayerSupport::IsConstantSupported(const TensorInfo& output, "Reference constant: output is not a supported type."); } -bool RefLayerSupport::IsConvertBf16ToFp32Supported(const TensorInfo& input, - const TensorInfo& output, - Optional<std::string&> reasonIfUnsupported) const -{ - bool supported = true; - - supported &= CheckSupportRule(TypeIs(input, DataType::BFloat16), reasonIfUnsupported, - "Reference for ConvertBf16ToFp32 layer: input type not supported"); - - supported &= CheckSupportRule(TypeIs(output, DataType::Float32), reasonIfUnsupported, - "Reference for ConvertBf16ToFp32 layer: output type not supported"); - - return supported; -} - bool RefLayerSupport::IsConvertFp16ToFp32Supported(const TensorInfo& input, const TensorInfo& output, Optional<std::string&> reasonIfUnsupported) const @@ -974,21 +944,6 @@ bool RefLayerSupport::IsConvertFp16ToFp32Supported(const TensorInfo& input, &FalseFuncU8<>)); } -bool RefLayerSupport::IsConvertFp32ToBf16Supported(const TensorInfo& input, - const TensorInfo& output, - Optional<std::string&> reasonIfUnsupported) const -{ - bool supported = true; - - supported &= CheckSupportRule(TypeIs(input, DataType::Float32), reasonIfUnsupported, - "Reference for ConvertFp32ToBf16 layer: input type not supported"); - - supported &= CheckSupportRule(TypeIs(output, DataType::BFloat16), reasonIfUnsupported, - "Reference for ConvertFp32ToBf16 layer: output type not supported"); - - return supported; -} - bool RefLayerSupport::IsConvertFp32ToFp16Supported(const TensorInfo& input, const TensorInfo& output, Optional<std::string&> reasonIfUnsupported) const @@ -1021,7 +976,6 @@ bool RefLayerSupport::IsConvolution2dSupported(const TensorInfo& input, // Define supported types. std::array<DataType,7> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -1036,20 +990,9 @@ bool RefLayerSupport::IsConvolution2dSupported(const TensorInfo& input, supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, "Reference Convolution2d: output is not a supported type."); - // For Convolution2d, we allow to have BFloat16 input with Float32 output for optimization. - if (input.GetDataType() == DataType::BFloat16) - { - if (output.GetDataType() != DataType::BFloat16 && output.GetDataType() != DataType::Float32) - { - reasonIfUnsupported.value() += "Output tensor type must be BFloat16 or Float32 for BFloat16 input.\n"; - supported = false; - } - } - else - { - supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, - "Reference Convolution2d: input and output types mismatched."); - } + supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, + "Reference Convolution2d: input and output types mismatched."); + const DataType inputType = input.GetDataType(); if (IsQuantized8BitType(inputType)) @@ -1077,7 +1020,6 @@ bool RefLayerSupport::IsConvolution2dSupported(const TensorInfo& input, { std::array<DataType,4> biasesSupportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::Signed32 @@ -1103,7 +1045,6 @@ bool RefLayerSupport::IsConvolution3dSupported(const TensorInfo& input, // Define supported types. std::array<DataType,7> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -1147,7 +1088,6 @@ bool RefLayerSupport::IsConvolution3dSupported(const TensorInfo& input, { std::array<DataType,4> biasesSupportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::Signed32 @@ -1201,7 +1141,6 @@ bool RefLayerSupport::IsDepthToSpaceSupported(const TensorInfo& input, std::array<DataType,6> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -1234,7 +1173,6 @@ bool RefLayerSupport::IsDepthwiseConvolutionSupported(const TensorInfo& input, // Define supported types. std::array<DataType,7> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -1279,7 +1217,6 @@ bool RefLayerSupport::IsDepthwiseConvolutionSupported(const TensorInfo& input, { std::array<DataType,4> biasesSupportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::Signed32 @@ -1313,7 +1250,6 @@ bool RefLayerSupport::IsDequantizeSupported(const TensorInfo& input, "Reference for Dequantize layer: per-axis quantized input not supported."); std::array<DataType,3> supportedOutputTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16 }; @@ -1344,7 +1280,6 @@ bool RefLayerSupport::IsDetectionPostProcessSupported(const TensorInfo& boxEncod std::array<DataType,6> supportedInputTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -1379,7 +1314,6 @@ bool RefLayerSupport::IsDivisionSupported(const TensorInfo& input0, bool supported = true; std::array<DataType,7> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -1418,7 +1352,6 @@ bool RefLayerSupport::IsElementwiseUnarySupported(const TensorInfo& input, std::array<DataType, 7> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -1513,7 +1446,6 @@ bool RefLayerSupport::IsFloorSupported(const TensorInfo& input, std::array<DataType,3> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16 }; @@ -1539,7 +1471,6 @@ bool RefLayerSupport::IsFullyConnectedSupported(const TensorInfo& input, // Define supported types. std::array<DataType,6> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -1556,20 +1487,8 @@ bool RefLayerSupport::IsFullyConnectedSupported(const TensorInfo& input, supported &= CheckSupportRule(TypeAnyOf(weights, supportedTypes), reasonIfUnsupported, "Reference Fully Connected: weights type not supported."); - // For FullyConnected, we allow to have BFloat16 input with Float32 output for optimization. - if (input.GetDataType() == DataType::BFloat16) - { - if (output.GetDataType() != DataType::BFloat16 && output.GetDataType() != DataType::Float32) - { - reasonIfUnsupported.value() += "Output tensor type must be BFloat16 or Float32 for BFloat16 input.\n"; - supported = false; - } - } - else - { - supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, - "Reference Fully Connected: input and output types mismatched."); - } + supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, + "Reference Fully Connected: input and output types mismatched."); supported &= CheckSupportRule(TypeAnyOf(weights, supportedTypes), reasonIfUnsupported, "Reference Fully Connected: weights is not a supported type."); @@ -1583,7 +1502,6 @@ bool RefLayerSupport::IsFullyConnectedSupported(const TensorInfo& input, std::array<DataType, 5> supportedBiasTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::Signed32, @@ -1615,7 +1533,6 @@ bool RefLayerSupport::IsGatherNdSupported(const armnn::TensorInfo& input0, bool supported = true; std::array<DataType,7> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -1648,7 +1565,6 @@ bool RefLayerSupport::IsGatherSupported(const armnn::TensorInfo& input0, bool supported = true; std::array<DataType,7> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -1692,7 +1608,6 @@ bool RefLayerSupport::IsInstanceNormalizationSupported(const TensorInfo& input, // Define supported types std::array<DataType, 3> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16 }; @@ -1724,7 +1639,6 @@ bool RefLayerSupport::IsL2NormalizationSupported(const TensorInfo& input, // Define supported types std::array<DataType, 6> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -1784,7 +1698,6 @@ bool RefLayerSupport::IsLogSoftmaxSupported(const TensorInfo& input, std::array<DataType, 3> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16 }; @@ -1819,7 +1732,6 @@ bool RefLayerSupport::IsLstmSupported(const TensorInfo& input, bool supported = true; std::array<DataType,3> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::QSymmS16 }; @@ -1922,7 +1834,6 @@ bool RefLayerSupport::IsMaximumSupported(const TensorInfo& input0, bool supported = true; std::array<DataType,7> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -1963,7 +1874,6 @@ bool RefLayerSupport::IsMeanSupported(const TensorInfo& input, std::array<DataType,6> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -2052,7 +1962,6 @@ bool RefLayerSupport::IsMinimumSupported(const TensorInfo& input0, bool supported = true; std::array<DataType,7> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -2090,7 +1999,6 @@ bool RefLayerSupport::IsMultiplicationSupported(const TensorInfo& input0, bool supported = true; std::array<DataType,7> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -2130,7 +2038,6 @@ bool RefLayerSupport::IsNormalizationSupported(const TensorInfo& input, // Define supported types std::array<DataType, 6> supportedTypes = { - DataType::BFloat16, DataType::Float16, DataType::Float32, DataType::QAsymmS8, @@ -2170,7 +2077,6 @@ bool RefLayerSupport::IsPadSupported(const TensorInfo& input, // Define supported output and inputs types. std::array<DataType,6> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -2232,7 +2138,6 @@ bool RefLayerSupport::IsPooling2dSupported(const TensorInfo& input, // Define supported output and inputs types. std::array<DataType,6> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -2263,7 +2168,6 @@ bool RefLayerSupport::IsPooling3dSupported(const TensorInfo& input, // Define supported output and inputs types. std::array<DataType,6> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -2316,7 +2220,6 @@ bool RefLayerSupport::IsQuantizeSupported(const TensorInfo& input, // Define supported input types. std::array<DataType,7> supportedInputTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -2368,7 +2271,6 @@ bool RefLayerSupport::IsReduceSupported(const TensorInfo& input, bool supported = true; std::array<DataType,7> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -2470,7 +2372,6 @@ bool RefLayerSupport::IsSliceSupported(const TensorInfo& input, std::array<DataType, 5> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::QAsymmS8, DataType::QAsymmU8, @@ -2498,7 +2399,6 @@ bool RefLayerSupport::IsSoftmaxSupported(const TensorInfo& input, bool supported = true; std::array<DataType,7> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QSymmS8, @@ -2528,7 +2428,6 @@ bool RefLayerSupport::IsSpaceToBatchNdSupported(const TensorInfo& input, bool supported = true; std::array<DataType,6> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -2559,7 +2458,6 @@ bool RefLayerSupport::IsSpaceToDepthSupported(const TensorInfo& input, std::array<DataType,6> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -2588,7 +2486,6 @@ bool RefLayerSupport::IsSplitterSupported(const TensorInfo& input, bool supported = true; std::array<DataType,6> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -2620,7 +2517,6 @@ bool RefLayerSupport::IsStackSupported(const std::vector<const TensorInfo*>& inp bool supported = true; std::array<DataType,7> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -2654,7 +2550,6 @@ bool RefLayerSupport::IsStridedSliceSupported(const TensorInfo& input, std::array<DataType,5> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::QAsymmS8, DataType::QAsymmU8, @@ -2681,7 +2576,6 @@ bool RefLayerSupport::IsSubtractionSupported(const TensorInfo& input0, bool supported = true; std::array<DataType,7> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -2720,7 +2614,6 @@ bool RefLayerSupport::IsPreluSupported(const TensorInfo& input, std::array<DataType, 6> supportedTypes { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -2758,7 +2651,6 @@ bool RefLayerSupport::IsTransposeConvolution2dSupported(const TensorInfo& input, std::array<DataType,7> supportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::QAsymmS8, @@ -2804,7 +2696,6 @@ bool RefLayerSupport::IsTransposeConvolution2dSupported(const TensorInfo& input, { std::array<DataType,4> biasesSupportedTypes = { - DataType::BFloat16, DataType::Float32, DataType::Float16, DataType::Signed32 diff --git a/src/backends/reference/RefLayerSupport.hpp b/src/backends/reference/RefLayerSupport.hpp index b64244db24..f0e9e35978 100644 --- a/src/backends/reference/RefLayerSupport.hpp +++ b/src/backends/reference/RefLayerSupport.hpp @@ -77,17 +77,10 @@ public: bool IsConstantSupported(const TensorInfo& output, Optional<std::string&> reasonIfUnsupported = EmptyOptional()) const override; - bool IsConvertBf16ToFp32Supported(const TensorInfo& input, - const TensorInfo& output, - Optional<std::string&> reasonIfUnsupported = EmptyOptional()) const override; - bool IsConvertFp16ToFp32Supported(const TensorInfo& input, const TensorInfo& output, Optional<std::string&> reasonIfUnsupported = EmptyOptional()) const override; - bool IsConvertFp32ToBf16Supported(const TensorInfo& input, - const TensorInfo& output, - Optional<std::string&> reasonIfUnsupported = EmptyOptional()) const override; bool IsConvertFp32ToFp16Supported(const TensorInfo& input, const TensorInfo& output, diff --git a/src/backends/reference/RefWorkloadFactory.cpp b/src/backends/reference/RefWorkloadFactory.cpp index 093d0d5e20..69f75cae8a 100644 --- a/src/backends/reference/RefWorkloadFactory.cpp +++ b/src/backends/reference/RefWorkloadFactory.cpp @@ -212,24 +212,12 @@ std::unique_ptr<IWorkload> RefWorkloadFactory::CreateWorkload(LayerType type, auto constantQueueDescriptor = PolymorphicDowncast<const ConstantQueueDescriptor*>(&descriptor); return std::make_unique<RefConstantWorkload>(*constantQueueDescriptor, info); } - case LayerType::ConvertBf16ToFp32 : - { - auto convertBf16ToFp32QueueDescriptor - = PolymorphicDowncast<const ConvertBf16ToFp32QueueDescriptor*>(&descriptor); - return std::make_unique<RefConvertBf16ToFp32Workload>(*convertBf16ToFp32QueueDescriptor, info); - } case LayerType::ConvertFp16ToFp32: { auto convertFp16ToFp32QueueDescriptor = PolymorphicDowncast<const ConvertFp16ToFp32QueueDescriptor*>(&descriptor); return std::make_unique<RefConvertFp16ToFp32Workload>(*convertFp16ToFp32QueueDescriptor, info); } - case LayerType::ConvertFp32ToBf16: - { - auto convertFp32ToBf16QueueDescriptor - = PolymorphicDowncast<const ConvertFp32ToBf16QueueDescriptor*>(&descriptor); - return std::make_unique<RefConvertFp32ToBf16Workload>(*convertFp32ToBf16QueueDescriptor, info); - } case LayerType::ConvertFp32ToFp16: { auto convertFp32ToFp16QueueDescriptor @@ -724,13 +712,6 @@ std::unique_ptr<IWorkload> RefWorkloadFactory::CreateConstant(const ConstantQueu return std::make_unique<RefConstantWorkload>(descriptor, info); } -std::unique_ptr<IWorkload> RefWorkloadFactory::CreateConvertBf16ToFp32( - const ConvertBf16ToFp32QueueDescriptor& descriptor, - const WorkloadInfo& info) const -{ - return std::make_unique<RefConvertBf16ToFp32Workload>(descriptor, info); -} - std::unique_ptr<IWorkload> RefWorkloadFactory::CreateConvertFp16ToFp32( const ConvertFp16ToFp32QueueDescriptor& descriptor, const WorkloadInfo& info) const @@ -738,13 +719,6 @@ std::unique_ptr<IWorkload> RefWorkloadFactory::CreateConvertFp16ToFp32( return std::make_unique<RefConvertFp16ToFp32Workload>(descriptor, info); } -std::unique_ptr<IWorkload> RefWorkloadFactory::CreateConvertFp32ToBf16( - const ConvertFp32ToBf16QueueDescriptor& descriptor, - const WorkloadInfo& info) const -{ - return std::make_unique<RefConvertFp32ToBf16Workload>(descriptor, info); -} - std::unique_ptr<IWorkload> RefWorkloadFactory::CreateConvertFp32ToFp16( const ConvertFp32ToFp16QueueDescriptor& descriptor, const WorkloadInfo& info) const diff --git a/src/backends/reference/RefWorkloadFactory.hpp b/src/backends/reference/RefWorkloadFactory.hpp index 53d0806ffe..22dc35a8c8 100644 --- a/src/backends/reference/RefWorkloadFactory.hpp +++ b/src/backends/reference/RefWorkloadFactory.hpp @@ -122,21 +122,11 @@ public: ARMNN_DEPRECATED_MSG_REMOVAL_DATE("Use ABI stable " "CreateWorkload(LayerType, const QueueDescriptor&, const WorkloadInfo& info) instead.", "23.08") - std::unique_ptr<IWorkload> CreateConvertBf16ToFp32(const ConvertBf16ToFp32QueueDescriptor& descriptor, - const WorkloadInfo& info) const override; - - ARMNN_DEPRECATED_MSG_REMOVAL_DATE("Use ABI stable " - "CreateWorkload(LayerType, const QueueDescriptor&, const WorkloadInfo& info) instead.", "23.08") std::unique_ptr<IWorkload> CreateConvertFp16ToFp32(const ConvertFp16ToFp32QueueDescriptor& descriptor, const WorkloadInfo& info) const override; ARMNN_DEPRECATED_MSG_REMOVAL_DATE("Use ABI stable " "CreateWorkload(LayerType, const QueueDescriptor&, const WorkloadInfo& info) instead.", "23.08") - std::unique_ptr<IWorkload> CreateConvertFp32ToBf16(const ConvertFp32ToBf16QueueDescriptor& descriptor, - const WorkloadInfo& info) const override; - - ARMNN_DEPRECATED_MSG_REMOVAL_DATE("Use ABI stable " - "CreateWorkload(LayerType, const QueueDescriptor&, const WorkloadInfo& info) instead.", "23.08") std::unique_ptr<IWorkload> CreateConvertFp32ToFp16(const ConvertFp32ToFp16QueueDescriptor& descriptor, const WorkloadInfo& info) const override; diff --git a/src/backends/reference/backend.mk b/src/backends/reference/backend.mk index ed942e67cd..eb2ec2df44 100644 --- a/src/backends/reference/backend.mk +++ b/src/backends/reference/backend.mk @@ -58,9 +58,7 @@ BACKEND_SOURCES := \ workloads/RefComparisonWorkload.cpp \ workloads/RefConcatWorkload.cpp \ workloads/RefConstantWorkload.cpp \ - workloads/RefConvertBf16ToFp32Workload.cpp \ workloads/RefConvertFp16ToFp32Workload.cpp \ - workloads/RefConvertFp32ToBf16Workload.cpp \ workloads/RefConvertFp32ToFp16Workload.cpp \ workloads/RefConvolution2dWorkload.cpp \ workloads/RefConvolution3dWorkload.cpp \ diff --git a/src/backends/reference/test/RefEndToEndTests.cpp b/src/backends/reference/test/RefEndToEndTests.cpp index 369e98adb1..a8c0634186 100644 --- a/src/backends/reference/test/RefEndToEndTests.cpp +++ b/src/backends/reference/test/RefEndToEndTests.cpp @@ -634,11 +634,6 @@ TEST_CASE("RefEluEndToEndTestFloat16") EluEndToEndTest<armnn::DataType::Float16>(defaultBackends); } -TEST_CASE("RefEluEndToEndTestBFloat16") -{ - EluEndToEndTest<armnn::DataType::BFloat16>(defaultBackends); -} - TEST_CASE("RefEluEndToEndTestQAsymmS8") { EluEndToEndTest<armnn::DataType::QAsymmS8>(defaultBackends); @@ -1006,11 +1001,6 @@ TEST_CASE("RefHardSwishEndToEndTestFloat16") HardSwishEndToEndTest<armnn::DataType::Float16>(defaultBackends); } -TEST_CASE("RefHardSwishEndToEndTestBFloat16") -{ - HardSwishEndToEndTest<armnn::DataType::BFloat16>(defaultBackends); -} - TEST_CASE("RefHardSwishEndToEndTestQAsymmS8") { HardSwishEndToEndTest<armnn::DataType::QAsymmS8>(defaultBackends); diff --git a/src/backends/reference/test/RefLayerSupportTests.cpp b/src/backends/reference/test/RefLayerSupportTests.cpp index 9a27c7c0b3..e671496ce2 100644 --- a/src/backends/reference/test/RefLayerSupportTests.cpp +++ b/src/backends/reference/test/RefLayerSupportTests.cpp @@ -49,12 +49,6 @@ TEST_CASE("IsLayerSupportedReferenceAddition") CHECK(supportChecker.IsAdditionSupported(in0, in1, out, reasonNotSupported)); } -TEST_CASE("IsLayerSupportedBFloat16Reference") -{ - armnn::RefWorkloadFactory factory; - IsLayerSupportedTests<armnn::RefWorkloadFactory, armnn::DataType::BFloat16>(&factory); -} - TEST_CASE("IsLayerSupportedFloat16Reference") { armnn::RefWorkloadFactory factory; @@ -117,70 +111,6 @@ TEST_CASE("IsConvertFp16ToFp32SupportedFp16OutputReference") CHECK_EQ(reasonIfUnsupported, "Layer is not supported with float16 data type output"); } -TEST_CASE("IsConvertBf16ToFp32SupportedReference") -{ - std::string reasonIfUnsupported; - - bool result = IsConvertLayerSupportedTests<armnn::RefWorkloadFactory, armnn::ConvertBf16ToFp32Layer, - armnn::DataType::BFloat16, armnn::DataType::Float32>(reasonIfUnsupported); - - CHECK(result); -} - -TEST_CASE("IsConvertBf16ToFp32SupportedFp32InputReference") -{ - std::string reasonIfUnsupported; - - bool result = IsConvertLayerSupportedTests<armnn::RefWorkloadFactory, armnn::ConvertBf16ToFp32Layer, - armnn::DataType::Float32, armnn::DataType::Float32>(reasonIfUnsupported); - - CHECK(!result); - CHECK_EQ(reasonIfUnsupported, "Reference for ConvertBf16ToFp32 layer: input type not supported\n"); -} - -TEST_CASE("IsConvertBf16ToFp32SupportedBf16OutputReference") -{ - std::string reasonIfUnsupported; - - bool result = IsConvertLayerSupportedTests<armnn::RefWorkloadFactory, armnn::ConvertBf16ToFp32Layer, - armnn::DataType::BFloat16, armnn::DataType::BFloat16>(reasonIfUnsupported); - - CHECK(!result); - CHECK_EQ(reasonIfUnsupported, "Reference for ConvertBf16ToFp32 layer: output type not supported\n"); -} - -TEST_CASE("IsConvertFp32ToBf16SupportedReference") -{ - std::string reasonIfUnsupported; - - bool result = IsConvertLayerSupportedTests<armnn::RefWorkloadFactory, armnn::ConvertFp32ToBf16Layer, - armnn::DataType::Float32, armnn::DataType::BFloat16>(reasonIfUnsupported); - - CHECK(result); -} - -TEST_CASE("IsConvertFp32ToBf16SupportedBf16InputReference") -{ - std::string reasonIfUnsupported; - - bool result = IsConvertLayerSupportedTests<armnn::RefWorkloadFactory, armnn::ConvertFp32ToBf16Layer, - armnn::DataType::BFloat16, armnn::DataType::BFloat16>(reasonIfUnsupported); - - CHECK(!result); - CHECK_EQ(reasonIfUnsupported, "Reference for ConvertFp32ToBf16 layer: input type not supported\n"); -} - -TEST_CASE("IsConvertFp32ToBf16SupportedFp32OutputReference") -{ - std::string reasonIfUnsupported; - - bool result = IsConvertLayerSupportedTests<armnn::RefWorkloadFactory, armnn::ConvertFp32ToBf16Layer, - armnn::DataType::Float32, armnn::DataType::Float32>(reasonIfUnsupported); - - CHECK(!result); - CHECK_EQ(reasonIfUnsupported, "Reference for ConvertFp32ToBf16 layer: output type not supported\n"); -} - TEST_CASE("IsConvertFp32ToFp16SupportedReference") { std::string reasonIfUnsupported; @@ -271,7 +201,9 @@ TEST_CASE("IsConstantSupportedRef") result = IsConstantLayerSupportedTests<armnn::RefWorkloadFactory, armnn::DataType::BFloat16>(reasonIfUnsupported); - CHECK(result); + CHECK(!result); + CHECK(reasonIfUnsupported.find("Reference constant: output is not a supported type.") != std::string::npos); + } } diff --git a/src/backends/reference/test/RefLayerTests.cpp b/src/backends/reference/test/RefLayerTests.cpp index 7375847602..750da8fba2 100644 --- a/src/backends/reference/test/RefLayerTests.cpp +++ b/src/backends/reference/test/RefLayerTests.cpp @@ -72,14 +72,6 @@ ARMNN_AUTO_TEST_CASE_WITH_THF(SimpleConvolution2dAsymmetricPaddingNhwc, ARMNN_AUTO_TEST_CASE_WITH_THF(SimpleConvolution2dSquareNhwc, SimpleConvolution2d3x3NhwcTest, false) -ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution2d3x3Dilation3x3BFloat16, - Convolution2d3x3Dilation3x3Test<DataType::BFloat16, DataType::BFloat16>, - false, - DataLayout::NCHW) -ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution2d3x3Dilation3x3NhwcBFloat16, - Convolution2d3x3Dilation3x3Test<DataType::BFloat16, DataType::BFloat16>, - false, - DataLayout::NHWC) ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution2d3x3Dilation3x3, Convolution2d3x3Dilation3x3Test<DataType::Float32, DataType::Float32>, false, @@ -113,14 +105,6 @@ ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution2d3x3Dilation3x3NhwcInt16, false, DataLayout::NHWC) -ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution2d2x3x3Dilation3x3BFloat16, - Convolution2d2x3x3Dilation3x3Test<DataType::BFloat16, DataType::BFloat16>, - false, - DataLayout::NCHW) -ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution2d2x3x3Dilation3x3NhwcBFloat16, - Convolution2d2x3x3Dilation3x3Test<DataType::BFloat16, DataType::BFloat16>, - false, - DataLayout::NHWC) ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution2d2x3x3Dilation3x3, Convolution2d2x3x3Dilation3x3Test<DataType::Float32, DataType::Float32>, false, @@ -154,15 +138,6 @@ ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution2d2x3x3Dilation3x3NhwcInt16, false, DataLayout::NHWC) -ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution2d2x2Dilation2x2Padding2x2Stride3x3BFloat16, - Convolution2d2x2Dilation2x2Padding2x2Stride3x3Test<DataType::BFloat16, DataType::BFloat16>, - false, - DataLayout::NCHW) - -ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution2d2x2Dilation2x2Padding2x2Stride3x3NhwcBFloat16, - Convolution2d2x2Dilation2x2Padding2x2Stride3x3Test<DataType::BFloat16, DataType::BFloat16>, - false, - DataLayout::NHWC) ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution2d2x2Dilation2x2Padding2x2Stride3x3, Convolution2d2x2Dilation2x2Padding2x2Stride3x3Test<DataType::Float32, DataType::Float32>, false, @@ -199,15 +174,6 @@ ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution2d2x2Dilation2x2Padding2x2Stride3x3Nhwc ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution2dPerAxisQuantTestNchw, Convolution2dPerAxisQuantTest, DataLayout::NCHW); ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution2dPerAxisQuantTestNhwc, Convolution2dPerAxisQuantTest, DataLayout::NHWC); -ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution2d3x3Stride2x2Bf16, - Convolution2d3x3Stride2x2BFloat16Test, - false, - DataLayout::NHWC); -ARMNN_AUTO_TEST_CASE_WITH_THF(Convolution2d3x3Stride2x2BFloat16SmallValue, - Convolution2d3x3Stride2x2BFloat16SmallValueTest, - false, - DataLayout::NHWC); - // Convolution 3d - NDHWC ARMNN_AUTO_TEST_CASE_WITH_THF(SimpleConvolution3d3x3x3Float32, SimpleConvolution3d3x3x3Float32Test, @@ -354,14 +320,6 @@ ARMNN_AUTO_TEST_CASE_WITH_THF(DepthwiseConvolution2d3x3Dilation3x3Nhwc, DepthwiseConvolution2d3x3Dilation3x3Test<DataType::Float32, DataType::Float32>, false, DataLayout::NHWC) -ARMNN_AUTO_TEST_CASE_WITH_THF(DepthwiseConvolution2d3x3Dilation3x3BFloat16, - DepthwiseConvolution2d3x3Dilation3x3Test<DataType::BFloat16, DataType::BFloat16>, - false, - DataLayout::NCHW) -ARMNN_AUTO_TEST_CASE_WITH_THF(DepthwiseConvolution2d3x3Dilation3x3NhwcBFloat16, - DepthwiseConvolution2d3x3Dilation3x3Test<DataType::BFloat16, DataType::BFloat16>, - false, - DataLayout::NHWC) ARMNN_AUTO_TEST_CASE_WITH_THF(DepthwiseConvolution2d3x3Dilation3x3Int8, DepthwiseConvolution2d3x3Dilation3x3Test<DataType::QAsymmS8, DataType::Signed32>, false, @@ -395,14 +353,6 @@ ARMNN_AUTO_TEST_CASE_WITH_THF(DepthwiseConvolution2d2x3x3Dilation3x3Nhwc, DepthwiseConvolution2d2x3x3Dilation3x3Test<DataType::Float32, DataType::Float32>, false, DataLayout::NHWC) -ARMNN_AUTO_TEST_CASE_WITH_THF(DepthwiseConvolution2d2x3x3Dilation3x3BFloat16, - DepthwiseConvolution2d2x3x3Dilation3x3Test<DataType::BFloat16, DataType::BFloat16>, - false, - DataLayout::NCHW) -ARMNN_AUTO_TEST_CASE_WITH_THF(DepthwiseConvolution2d2x3x3Dilation3x3NhwcBFloat16, - DepthwiseConvolution2d2x3x3Dilation3x3Test<DataType::BFloat16, DataType::BFloat16>, - false, - DataLayout::NHWC) ARMNN_AUTO_TEST_CASE_WITH_THF(DepthwiseConvolution2d2x3x3Dilation3x3Int8, DepthwiseConvolution2d2x3x3Dilation3x3Test<DataType::QAsymmS8, DataType::Signed32>, false, @@ -435,14 +385,6 @@ ARMNN_AUTO_TEST_CASE_WITH_THF(DepthwiseConvolution2dMult2, DepthwiseConvolution2dMult2Test<armnn::DataType::Float32, armnn::DataType::Float32>, false, armnn::DataLayout::NCHW) -ARMNN_AUTO_TEST_CASE_WITH_THF(DepthwiseConvolution2dMult4BFloat16, - DepthwiseConvolution2dMult4Test<armnn::DataType::BFloat16, armnn::DataType::BFloat16>, - false, - armnn::DataLayout::NCHW) -ARMNN_AUTO_TEST_CASE_WITH_THF(DepthwiseConvolution2dMult2BFloat16, - DepthwiseConvolution2dMult2Test<armnn::DataType::BFloat16, armnn::DataType::BFloat16>, - false, - armnn::DataLayout::NCHW) ARMNN_AUTO_TEST_CASE_WITH_THF(DepthwiseConvolution2dDepthMul1, DepthwiseConvolution2dDepthMul1Test, @@ -864,7 +806,6 @@ ARMNN_AUTO_TEST_CASE_WITH_THF(CopyViaSplitterInt16, CopyViaSplitterInt16Test) // Concat ARMNN_AUTO_TEST_CASE_WITH_THF(SimpleConcat, ConcatTest) -ARMNN_AUTO_TEST_CASE_WITH_THF(ConcatBFloat16, ConcatBFloat16Test) ARMNN_AUTO_TEST_CASE_WITH_THF(ConcatFloat16, ConcatFloat16Test) ARMNN_AUTO_TEST_CASE_WITH_THF(ConcatInt32, ConcatInt32Test) ARMNN_AUTO_TEST_CASE_WITH_THF(ConcatUint8, ConcatUint8Test) @@ -1063,91 +1004,78 @@ ARMNN_AUTO_TEST_CASE_WITH_THF(MultiplicationBroadcast1DVectorInt32, Multiplicati ARMNN_AUTO_TEST_CASE_WITH_THF(Multiplication5d, Multiplication5dTest) // Batch Mat Mul -ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul2DSimpleBFloat16, BatchMatMul2DSimpleTest<DataType::BFloat16>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul2DSimpleFloat32, BatchMatMul2DSimpleTest<DataType::Float32>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul2DSimpleFloat16, BatchMatMul2DSimpleTest<DataType::Float16>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul2DSimpleQAsymmS8, BatchMatMul2DSimpleTest<DataType::QAsymmS8>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul2DSimpleQAsymmU8, BatchMatMul2DSimpleTest<DataType::QAsymmU8>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul2DSimpleQASymmS16, BatchMatMul2DSimpleTest<DataType::QSymmS16>); -ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul3DSimpleBFloat16, BatchMatMul3DSimpleTest<DataType::BFloat16>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul3DSimpleFloat32, BatchMatMul3DSimpleTest<DataType::Float32>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul3DSimpleFloat16, BatchMatMul3DSimpleTest<DataType::Float16>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul3DSimpleQAsymmS8, BatchMatMul3DSimpleTest<DataType::QAsymmS8>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul3DSimpleQAsymmU8, BatchMatMul3DSimpleTest<DataType::QAsymmU8>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul3DSimpleQASymmS16, BatchMatMul3DSimpleTest<DataType::QSymmS16>); -ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMulNCHWSimpleBFloat16, BatchMatMulNCHWSimpleTest<DataType::BFloat16>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMulNCHWSimpleFloat32, BatchMatMulNCHWSimpleTest<DataType::Float32>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMulNCHWSimpleFloat16, BatchMatMulNCHWSimpleTest<DataType::Float16>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMulNCHWSimpleQAsymmS8, BatchMatMulNCHWSimpleTest<DataType::QAsymmS8>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMulNCHWSimpleQAsymmU8, BatchMatMulNCHWSimpleTest<DataType::QAsymmU8>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMulNCHWSimpleQASymmS16, BatchMatMulNCHWSimpleTest<DataType::QSymmS16>); -ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMulNHWCSimpleBFloat16, BatchMatMulNHWCSimpleTest<DataType::BFloat16>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMulNHWCSimpleFloat32, BatchMatMulNHWCSimpleTest<DataType::Float32>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMulNHWCSimpleFloat16, BatchMatMulNHWCSimpleTest<DataType::Float16>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMulNHWCSimpleQAsymmS8, BatchMatMulNHWCSimpleTest<DataType::QAsymmS8>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMulNHWCSimpleQAsymmU8, BatchMatMulNHWCSimpleTest<DataType::QAsymmU8>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMulNHWCSimpleQASymmS16, BatchMatMulNHWCSimpleTest<DataType::QSymmS16>); -ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul3DBatchBFloat16, BatchMatMul3DBatchTest<DataType::BFloat16>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul3DBatchFloat32, BatchMatMul3DBatchTest<DataType::Float32>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul3DBatchFloat16, BatchMatMul3DBatchTest<DataType::Float16>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul3DBatchQAsymmS8, BatchMatMul3DBatchTest<DataType::QAsymmS8>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul3DBatchQAsymmU8, BatchMatMul3DBatchTest<DataType::QAsymmU8>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul3DBatchQASymmS16, BatchMatMul3DBatchTest<DataType::QSymmS16>); -ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul3DBroadcastBFloat16, BatchMatMul3DBroadcastTest<DataType::BFloat16>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul3DBroadcastFloat32, BatchMatMul3DBroadcastTest<DataType::Float32>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul3DBroadcastFloat16, BatchMatMul3DBroadcastTest<DataType::Float16>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul3DBroadcastQAsymmS8, BatchMatMul3DBroadcastTest<DataType::QAsymmS8>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul3DBroadcastQAsymmU8, BatchMatMul3DBroadcastTest<DataType::QAsymmU8>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul3DBroadcastQASymmS16, BatchMatMul3DBroadcastTest<DataType::QSymmS16>); -ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul3D2DBroadcastBFloat16, BatchMatMul3D2DBroadcastTest<DataType::BFloat16>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul3D2DBroadcastFloat32, BatchMatMul3D2DBroadcastTest<DataType::Float32>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul3D2DBroadcastFloat16, BatchMatMul3D2DBroadcastTest<DataType::Float16>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul3D2DBroadcastQAsymmS8, BatchMatMul3D2DBroadcastTest<DataType::QAsymmS8>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul3D2DBroadcastQAsymmU8, BatchMatMul3D2DBroadcastTest<DataType::QAsymmU8>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul3D2DBroadcastQASymmSS16, BatchMatMul3D2DBroadcastTest<DataType::QSymmS16>); -ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMulNDHWCNHWCBFloat16, BatchMatMulNDHWCNHWCTest<DataType::BFloat16>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMulNDHWCNHWCFloat32, BatchMatMulNDHWCNHWCTest<DataType::Float32>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMulNDHWCNHWCFloat16, BatchMatMulNDHWCNHWCTest<DataType::Float16>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMulNDHWCNHWCQAsymmS8, BatchMatMulNDHWCNHWCTest<DataType::QAsymmS8>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMulNDHWCNHWCQAsymmU8, BatchMatMulNDHWCNHWCTest<DataType::QAsymmU8>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMulNDHWCNHWCQASymmSS16, BatchMatMulNDHWCNHWCTest<DataType::QSymmS16>); -ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul2DTinyBFloat16, BatchMatMul2DTinyTest<DataType::BFloat16>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul2DTinyFloat32, BatchMatMul2DTinyTest<DataType::Float32>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul2DTinyFloat16, BatchMatMul2DTinyTest<DataType::Float16>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul2DTinyQAsymmS8, BatchMatMul2DTinyTest<DataType::QAsymmS8>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul2DTinyQAsymmU8, BatchMatMul2DTinyTest<DataType::QAsymmU8>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul2DTinyQASymmS16, BatchMatMul2DTinyTest<DataType::QSymmS16>); -ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul3DNonSquareBFloat16, BatchMatMul3DNonSquareTest<DataType::BFloat16>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul3DNonSquareFloat32, BatchMatMul3DNonSquareTest<DataType::Float32>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul3DNonSquareFloat16, BatchMatMul3DNonSquareTest<DataType::Float16>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul3DNonSquareQAsymmS8, BatchMatMul3DNonSquareTest<DataType::QAsymmS8>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul3DNonSquareQAsymmU8, BatchMatMul3DNonSquareTest<DataType::QAsymmU8>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul3DNonSquareQASymmS16, BatchMatMul3DNonSquareTest<DataType::QSymmS16>); -ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul2DTranspSimpleBFloat16, BatchMatMul2DTranspSimpleTest<DataType::BFloat16>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul2DTranspSimpleFloat32, BatchMatMul2DTranspSimpleTest<DataType::Float32>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul2DTranspSimpleFloat16, BatchMatMul2DTranspSimpleTest<DataType::Float16>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul2DTranspSimpleQAsymmS8, BatchMatMul2DTranspSimpleTest<DataType::QAsymmS8>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul2DTranspSimpleQAsymmU8, BatchMatMul2DTranspSimpleTest<DataType::QAsymmU8>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul2DTranspSimpleQASymmS16,BatchMatMul2DTranspSimpleTest<DataType::QSymmS16>); -ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul2DAdjointSimpleBFloat16, BatchMatMul2DAdjointSimpleTest<DataType::BFloat16>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul2DAdjointSimpleFloat32, BatchMatMul2DAdjointSimpleTest<DataType::Float32>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul2DAdjointSimpleFloat16, BatchMatMul2DAdjointSimpleTest<DataType::Float16>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul2DAdjointSimpleQAsymmS8, BatchMatMul2DAdjointSimpleTest<DataType::QAsymmS8>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul2DAdjointSimpleQAsymmU8, BatchMatMul2DAdjointSimpleTest<DataType::QAsymmU8>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMul2DAdjointSimpleQASymmS16,BatchMatMul2DAdjointSimpleTest<DataType::QSymmS16>); -ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMulNHWCParamsBFloat16, BatchMatMulNHWCParamsTest<DataType::BFloat16>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMulNHWCParamsFloat32, BatchMatMulNHWCParamsTest<DataType::Float32>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMulNHWCParamsFloat16, BatchMatMulNHWCParamsTest<DataType::Float16>); ARMNN_AUTO_TEST_CASE_WITH_THF(BatchMatMulNHWCParamsQAsymmS8, BatchMatMulNHWCParamsTest<DataType::QAsymmS8>); @@ -1172,7 +1100,6 @@ ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize1Signed32, RankDimSize1Test<DataType::S ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize1QSymmS16, RankDimSize1Test<DataType::QSymmS16>) ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize1QSymmS8, RankDimSize1Test<DataType::QSymmS8>) ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize1QAsymmS8, RankDimSize1Test<DataType::QAsymmS8>) -ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize1BFloat16, RankDimSize1Test<DataType::BFloat16>) ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize2Float16, RankDimSize2Test<DataType::Float16>) ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize2Float32, RankDimSize2Test<DataType::Float32>) @@ -1181,7 +1108,6 @@ ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize2Signed32, RankDimSize2Test<DataType::S ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize2QSymmS16, RankDimSize2Test<DataType::QSymmS16>) ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize2QSymmS8, RankDimSize2Test<DataType::QSymmS8>) ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize2QAsymmS8, RankDimSize2Test<DataType::QAsymmS8>) -ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize2BFloat16, RankDimSize2Test<DataType::BFloat16>) ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize3Float16, RankDimSize3Test<DataType::Float16>) ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize3Float32, RankDimSize3Test<DataType::Float32>) @@ -1190,7 +1116,6 @@ ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize3Signed32, RankDimSize3Test<DataType::S ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize3QSymmS16, RankDimSize3Test<DataType::QSymmS16>) ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize3QSymmS8, RankDimSize3Test<DataType::QSymmS8>) ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize3QAsymmS8, RankDimSize3Test<DataType::QAsymmS8>) -ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize3BFloat16, RankDimSize3Test<DataType::BFloat16>) ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize4Float16, RankDimSize4Test<DataType::Float16>) ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize4Float32, RankDimSize4Test<DataType::Float32>) @@ -1199,7 +1124,6 @@ ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize4Signed32, RankDimSize4Test<DataType::S ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize4QSymmS16, RankDimSize4Test<DataType::QSymmS16>) ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize4QSymmS8, RankDimSize4Test<DataType::QSymmS8>) ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize4QAsymmS8, RankDimSize4Test<DataType::QAsymmS8>) -ARMNN_AUTO_TEST_CASE_WITH_THF(RankDimSize4BFloat16, RankDimSize4Test<DataType::BFloat16>) // Resize Bilinear - NCHW ARMNN_AUTO_TEST_CASE_WITH_THF(SimpleResizeBilinear, @@ -1650,11 +1574,6 @@ ARMNN_AUTO_TEST_CASE_WITH_THF(LogSoftmaxFloat16_3, LogSoftmaxTest3<DataType::Flo ARMNN_AUTO_TEST_CASE_WITH_THF(LogSoftmaxFloat16_4, LogSoftmaxTest4<DataType::Float16>) // Pad - Constant -ARMNN_AUTO_TEST_CASE_WITH_THF(PadBFloat162d, PadBFloat162dTest) -ARMNN_AUTO_TEST_CASE_WITH_THF(PadBFloat162dCustomPadding, PadBFloat162dCustomPaddingTest) -ARMNN_AUTO_TEST_CASE_WITH_THF(PadBFloat163d, PadBFloat163dTest) -ARMNN_AUTO_TEST_CASE_WITH_THF(PadBFloat164d, PadBFloat164dTest) - ARMNN_AUTO_TEST_CASE_WITH_THF(PadFloat322d, PadFloat322dTest) ARMNN_AUTO_TEST_CASE_WITH_THF(PadFloat322dCustomPadding, PadFloat322dCustomPaddingTest) ARMNN_AUTO_TEST_CASE_WITH_THF(PadFloat323d, PadFloat323dTest) @@ -1692,8 +1611,6 @@ ARMNN_AUTO_TEST_CASE_WITH_THF(PadReflect3dInt8, PadReflect3dInt8Test) ARMNN_AUTO_TEST_CASE_WITH_THF(PadSymmetric4dFloat32, PadSymmetric4dFloat32Test) ARMNN_AUTO_TEST_CASE_WITH_THF(PadReflect4dFloat32, PadReflect4dFloat32Test) -ARMNN_AUTO_TEST_CASE_WITH_THF(PadSymmetric4dBFloat16, PadSymmetric4dBFloat16Test) -ARMNN_AUTO_TEST_CASE_WITH_THF(PadReflect4dBFloat16, PadReflect4dBFloat16Test) ARMNN_AUTO_TEST_CASE_WITH_THF(PadSymmetric4dUint8, PadSymmetric4dUint8Test) ARMNN_AUTO_TEST_CASE_WITH_THF(PadReflect4dUint8, PadReflect4dUint8Test) ARMNN_AUTO_TEST_CASE_WITH_THF(PadSymmetric4dInt8, PadSymmetric4dInt8Test) @@ -1878,17 +1795,10 @@ ARMNN_AUTO_TEST_CASE_WITH_THF(CastUIntToFloat, CastUInt8ToFloat2dTest) ARMNN_AUTO_TEST_CASE_WITH_THF(CastInt8ToUInt, CastInt8ToUInt82dTest) ARMNN_AUTO_TEST_CASE_WITH_THF(CastInt8AsymmToUInt, CastInt8AsymmToUInt82dTest) ARMNN_AUTO_TEST_CASE_WITH_THF(CastFloat16ToFloat32, CastFloat16ToFloat322dTest) -ARMNN_AUTO_TEST_CASE_WITH_THF(CastBFloat16ToFloat32, CastBFloat16ToFloat322dTest) ARMNN_AUTO_TEST_CASE_WITH_THF(CastFloatToFloat16, CastFloat32ToFloat162dTest) ARMNN_AUTO_TEST_CASE_WITH_THF(CastFloatToIn8, CastFloat32ToInt82dTest) ARMNN_AUTO_TEST_CASE_WITH_THF(CastFloatToUInt8, CastFloat32ToUInt82dTest) -// Convert from BFloat16 to Float32 -ARMNN_AUTO_TEST_CASE_WITH_THF(ConvertBf16ToFp32, ConvertBf16ToFp32Test) - -// Convert from Float32 to BFloat16 -ARMNN_AUTO_TEST_CASE_WITH_THF(ConvertFp32ToBf16, ConvertFp32ToBf16Test) - // Convert from Float16 to Float32 ARMNN_AUTO_TEST_CASE_WITH_THF(SimpleConvertFp16ToFp32, SimpleConvertFp16ToFp32Test) // Convert from Float32 to Float16 @@ -2139,7 +2049,6 @@ ARMNN_AUTO_TEST_CASE_WITH_THF(ShapeDimSize1Signed32, ShapeDimSize1Test<DataType: ARMNN_AUTO_TEST_CASE_WITH_THF(ShapeDimSize1QSymmS16, ShapeDimSize1Test<DataType::QSymmS16>) ARMNN_AUTO_TEST_CASE_WITH_THF(ShapeDimSize1QSymmS8, ShapeDimSize1Test<DataType::QSymmS8>) ARMNN_AUTO_TEST_CASE_WITH_THF(ShapeDimSize1QAsymmS8, ShapeDimSize1Test<DataType::QAsymmS8>) -ARMNN_AUTO_TEST_CASE_WITH_THF(ShapeDimSize1BFloat16, ShapeDimSize1Test<DataType::BFloat16>) ARMNN_AUTO_TEST_CASE_WITH_THF(ShapeDimSize2Float16, ShapeDimSize2Test<DataType::Float16>) ARMNN_AUTO_TEST_CASE_WITH_THF(ShapeDimSize2Float32, ShapeDimSize2Test<DataType::Float32>) @@ -2148,7 +2057,6 @@ ARMNN_AUTO_TEST_CASE_WITH_THF(ShapeDimSize2Signed32, ShapeDimSize2Test<DataType: ARMNN_AUTO_TEST_CASE_WITH_THF(ShapeDimSize2QSymmS16, ShapeDimSize2Test<DataType::QSymmS16>) ARMNN_AUTO_TEST_CASE_WITH_THF(ShapeDimSize2QSymmS8, ShapeDimSize2Test<DataType::QSymmS8>) ARMNN_AUTO_TEST_CASE_WITH_THF(ShapeDimSize2QAsymmS8, ShapeDimSize2Test<DataType::QAsymmS8>) -ARMNN_AUTO_TEST_CASE_WITH_THF(ShapeDimSize2BFloat16, ShapeDimSize2Test<DataType::BFloat16>) ARMNN_AUTO_TEST_CASE_WITH_THF(ShapeDimSize3Float16, ShapeDimSize3Test<DataType::Float16>) ARMNN_AUTO_TEST_CASE_WITH_THF(ShapeDimSize3Float32, ShapeDimSize3Test<DataType::Float32>) @@ -2157,7 +2065,6 @@ ARMNN_AUTO_TEST_CASE_WITH_THF(ShapeDimSize3Signed32, ShapeDimSize3Test<DataType: ARMNN_AUTO_TEST_CASE_WITH_THF(ShapeDimSize3QSymmS16, ShapeDimSize3Test<DataType::QSymmS16>) ARMNN_AUTO_TEST_CASE_WITH_THF(ShapeDimSize3QSymmS8, ShapeDimSize3Test<DataType::QSymmS8>) ARMNN_AUTO_TEST_CASE_WITH_THF(ShapeDimSize3QAsymmS8, ShapeDimSize3Test<DataType::QAsymmS8>) -ARMNN_AUTO_TEST_CASE_WITH_THF(ShapeDimSize3BFloat16, ShapeDimSize3Test<DataType::BFloat16>) ARMNN_AUTO_TEST_CASE_WITH_THF(ShapeDimSize4Float16, ShapeDimSize4Test<DataType::Float16>) ARMNN_AUTO_TEST_CASE_WITH_THF(ShapeDimSize4Float32, ShapeDimSize4Test<DataType::Float32>) @@ -2166,7 +2073,6 @@ ARMNN_AUTO_TEST_CASE_WITH_THF(ShapeDimSize4Signed32, ShapeDimSize4Test<DataType: ARMNN_AUTO_TEST_CASE_WITH_THF(ShapeDimSize4QSymmS16, ShapeDimSize4Test<DataType::QSymmS16>) ARMNN_AUTO_TEST_CASE_WITH_THF(ShapeDimSize4QSymmS8, ShapeDimSize4Test<DataType::QSymmS8>) ARMNN_AUTO_TEST_CASE_WITH_THF(ShapeDimSize4QAsymmS8, ShapeDimSize4Test<DataType::QAsymmS8>) -ARMNN_AUTO_TEST_CASE_WITH_THF(ShapeDimSize4BFloat16, ShapeDimSize4Test<DataType::BFloat16>) // SpaceToDepth ARMNN_AUTO_TEST_CASE_WITH_THF(SpaceToDepthNchwAsymmQ8, SpaceToDepthNchwAsymmQ8Test) diff --git a/src/backends/reference/workloads/BaseIterator.hpp b/src/backends/reference/workloads/BaseIterator.hpp index e09371fd96..2d27951b73 100644 --- a/src/backends/reference/workloads/BaseIterator.hpp +++ b/src/backends/reference/workloads/BaseIterator.hpp @@ -260,44 +260,6 @@ private: }; -class BFloat16Decoder : public TypedIterator<const BFloat16, Decoder<float>> -{ -public: - BFloat16Decoder(const BFloat16* data) - : TypedIterator(data) {} - - BFloat16Decoder() - : BFloat16Decoder(nullptr) {} - - float Get() const override - { - float val = 0.f; - armnnUtils::FloatingPointConverter::ConvertBFloat16ToFloat32(m_Iterator, 1, &val); - return val; - } - std::vector<float> DecodeTensor (const TensorShape& tensorShape, - const bool isDepthwise) override - { - IgnoreUnused(isDepthwise); - - const unsigned int size = tensorShape.GetNumElements(); - std::vector<float> decodedTensor; - decodedTensor.reserve(size); - - for (uint32_t i = 0; i < size; ++i) - { - this->operator[](i); - - float val = 0.f; - armnnUtils::FloatingPointConverter::ConvertBFloat16ToFloat32(m_Iterator, 1, &val); - decodedTensor.emplace_back(val); - } - - return decodedTensor; - } - -}; - class Float16Decoder : public TypedIterator<const Half, Decoder<float>> { public: @@ -624,28 +586,6 @@ private: const int32_t m_Offset; }; -class BFloat16Encoder : public TypedIterator<armnn::BFloat16, Encoder<float>> -{ -public: - BFloat16Encoder(armnn::BFloat16* data) - : TypedIterator(data) {} - - BFloat16Encoder() - : BFloat16Encoder(nullptr) {} - - void Set(float right) override - { - armnnUtils::FloatingPointConverter::ConvertFloat32ToBFloat16(&right, 1, m_Iterator); - } - - float Get() const override - { - float val = 0.f; - armnnUtils::FloatingPointConverter::ConvertBFloat16ToFloat32(m_Iterator, 1, &val); - return val; - } -}; - class Float16Encoder : public TypedIterator<Half, Encoder<float>> { public: diff --git a/src/backends/reference/workloads/CMakeLists.txt b/src/backends/reference/workloads/CMakeLists.txt index b8835e3cdb..de6c042959 100644 --- a/src/backends/reference/workloads/CMakeLists.txt +++ b/src/backends/reference/workloads/CMakeLists.txt @@ -88,12 +88,8 @@ list(APPEND armnnRefBackendWorkloads_sources RefConcatWorkload.hpp RefConstantWorkload.cpp RefConstantWorkload.hpp - RefConvertBf16ToFp32Workload.cpp - RefConvertBf16ToFp32Workload.hpp RefConvertFp16ToFp32Workload.cpp RefConvertFp16ToFp32Workload.hpp - RefConvertFp32ToBf16Workload.cpp - RefConvertFp32ToBf16Workload.hpp RefConvertFp32ToFp16Workload.cpp RefConvertFp32ToFp16Workload.hpp RefConvolution2dWorkload.cpp diff --git a/src/backends/reference/workloads/Decoders.hpp b/src/backends/reference/workloads/Decoders.hpp index c2a456bfce..54e7008d50 100644 --- a/src/backends/reference/workloads/Decoders.hpp +++ b/src/backends/reference/workloads/Decoders.hpp @@ -88,10 +88,6 @@ inline std::unique_ptr<Decoder<float>> MakeDecoder(const TensorInfo& info, const info.GetQuantizationScale(), info.GetQuantizationOffset()); } - case DataType::BFloat16: - { - return std::make_unique<BFloat16Decoder>(static_cast<const BFloat16*>(data)); - } case DataType::Float16: { return std::make_unique<Float16Decoder>(static_cast<const Half*>(data)); diff --git a/src/backends/reference/workloads/Encoders.hpp b/src/backends/reference/workloads/Encoders.hpp index a7be9e172b..d6d611494d 100644 --- a/src/backends/reference/workloads/Encoders.hpp +++ b/src/backends/reference/workloads/Encoders.hpp @@ -65,10 +65,6 @@ inline std::unique_ptr<Encoder<float>> MakeEncoder(const TensorInfo& info, void* { return std::make_unique<Int32Encoder>(static_cast<int32_t*>(data)); } - case armnn::DataType::BFloat16: - { - return std::make_unique<BFloat16Encoder>(static_cast<armnn::BFloat16*>(data)); - } case armnn::DataType::Float16: { return std::make_unique<Float16Encoder>(static_cast<Half*>(data)); diff --git a/src/backends/reference/workloads/RefConvertBf16ToFp32Workload.cpp b/src/backends/reference/workloads/RefConvertBf16ToFp32Workload.cpp deleted file mode 100644 index 2fe2eafb9b..0000000000 --- a/src/backends/reference/workloads/RefConvertBf16ToFp32Workload.cpp +++ /dev/null @@ -1,39 +0,0 @@ -// -// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefConvertBf16ToFp32Workload.hpp" -#include "RefWorkloadUtils.hpp" - -#include <armnnUtils/FloatingPointConverter.hpp> - -#include <BFloat16.hpp> - -namespace armnn -{ - -void RefConvertBf16ToFp32Workload::Execute() const -{ - Execute(m_Data.m_Inputs, m_Data.m_Outputs); -} - -void RefConvertBf16ToFp32Workload::ExecuteAsync(ExecutionData& executionData) -{ - WorkingMemDescriptor* workingMemDescriptor = static_cast<WorkingMemDescriptor*>(executionData.m_Data); - Execute(workingMemDescriptor->m_Inputs, workingMemDescriptor->m_Outputs); -} - -void RefConvertBf16ToFp32Workload::Execute(std::vector<ITensorHandle*> inputs, - std::vector<ITensorHandle*> outputs) const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefConvertBf16ToFp32Workload_Execute"); - - const BFloat16* const input = reinterpret_cast<const BFloat16*>(inputs[0]->Map()); - float* const output = reinterpret_cast<float*>(outputs[0]->Map()); - - unsigned int numElements = GetTensorInfo(inputs[0]).GetNumElements(); - armnnUtils::FloatingPointConverter::ConvertBFloat16ToFloat32(input, numElements, output); -} - -} //namespace armnn diff --git a/src/backends/reference/workloads/RefConvertBf16ToFp32Workload.hpp b/src/backends/reference/workloads/RefConvertBf16ToFp32Workload.hpp deleted file mode 100644 index 24dcb0f682..0000000000 --- a/src/backends/reference/workloads/RefConvertBf16ToFp32Workload.hpp +++ /dev/null @@ -1,24 +0,0 @@ -// -// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "RefBaseWorkload.hpp" -#include <armnn/backends/WorkloadData.hpp> - -namespace armnn -{ - -class RefConvertBf16ToFp32Workload : public BFloat16ToFloat32Workload<ConvertBf16ToFp32QueueDescriptor> -{ -public: - using BFloat16ToFloat32Workload<ConvertBf16ToFp32QueueDescriptor>::BFloat16ToFloat32Workload; - void Execute() const override; - void ExecuteAsync(ExecutionData& executionData) override; -private: - void Execute(std::vector<ITensorHandle*> inputs, std::vector<ITensorHandle*> outputs) const; -}; - -} //namespace armnn diff --git a/src/backends/reference/workloads/RefConvertFp32ToBf16Workload.cpp b/src/backends/reference/workloads/RefConvertFp32ToBf16Workload.cpp deleted file mode 100644 index 71ee95b2aa..0000000000 --- a/src/backends/reference/workloads/RefConvertFp32ToBf16Workload.cpp +++ /dev/null @@ -1,39 +0,0 @@ -// -// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefConvertFp32ToBf16Workload.hpp" -#include "RefWorkloadUtils.hpp" - -#include <armnnUtils/FloatingPointConverter.hpp> - -#include <BFloat16.hpp> - -namespace armnn -{ - -void RefConvertFp32ToBf16Workload::Execute() const -{ - Execute(m_Data.m_Inputs, m_Data.m_Outputs); -} - -void RefConvertFp32ToBf16Workload::ExecuteAsync(ExecutionData& executionData) -{ - WorkingMemDescriptor* workingMemDescriptor = static_cast<WorkingMemDescriptor*>(executionData.m_Data); - Execute(workingMemDescriptor->m_Inputs, workingMemDescriptor->m_Outputs); -} - -void RefConvertFp32ToBf16Workload::Execute(std::vector<ITensorHandle*> inputs, - std::vector<ITensorHandle*> outputs) const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefConvertFp32ToBf16Workload_Execute"); - - const float* const input = reinterpret_cast<const float*>(inputs[0]->Map()); - BFloat16* const output = reinterpret_cast<BFloat16*>(outputs[0]->Map()); - - unsigned int numElements = GetTensorInfo(inputs[0]).GetNumElements(); - armnnUtils::FloatingPointConverter::ConvertFloat32ToBFloat16(input, numElements, output); -} - -} //namespace armnn diff --git a/src/backends/reference/workloads/RefConvertFp32ToBf16Workload.hpp b/src/backends/reference/workloads/RefConvertFp32ToBf16Workload.hpp deleted file mode 100644 index c1e57ec37e..0000000000 --- a/src/backends/reference/workloads/RefConvertFp32ToBf16Workload.hpp +++ /dev/null @@ -1,24 +0,0 @@ -// -// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "RefBaseWorkload.hpp" -#include <armnn/backends/WorkloadData.hpp> - -namespace armnn -{ - -class RefConvertFp32ToBf16Workload : public Float32ToBFloat16Workload<ConvertFp32ToBf16QueueDescriptor> -{ -public: - using Float32ToBFloat16Workload<ConvertFp32ToBf16QueueDescriptor>::Float32ToBFloat16Workload; - void Execute() const override; - void ExecuteAsync(ExecutionData& executionData) override; -private: - void Execute(std::vector<ITensorHandle*> inputs, std::vector<ITensorHandle*> outputs) const; -}; - -} //namespace armnn diff --git a/src/backends/reference/workloads/RefWorkloads.hpp b/src/backends/reference/workloads/RefWorkloads.hpp index e049d8db2c..afed71bfff 100644 --- a/src/backends/reference/workloads/RefWorkloads.hpp +++ b/src/backends/reference/workloads/RefWorkloads.hpp @@ -17,9 +17,7 @@ #include "RefConvolution3dWorkload.hpp" #include "RefConstantWorkload.hpp" #include "RefConcatWorkload.hpp" -#include "RefConvertBf16ToFp32Workload.hpp" #include "RefConvertFp16ToFp32Workload.hpp" -#include "RefConvertFp32ToBf16Workload.hpp" #include "RefConvertFp32ToFp16Workload.hpp" #include "RefDebugWorkload.hpp" #include "RefDepthToSpaceWorkload.hpp" |