diff options
author | surmeh01 <surabhi.mehta@arm.com> | 2018-03-29 16:29:27 +0100 |
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committer | surmeh01 <surabhi.mehta@arm.com> | 2018-03-29 16:29:27 +0100 |
commit | bceff2fb3fc68bb0aa88b886900c34b77340c826 (patch) | |
tree | d867d3e090d58d3012dfbbac456e9ea8c7f789bc /src/armnn | |
parent | 4fcda0101ec3d110c1d6d7bee5c83416b645528a (diff) | |
download | armnn-bceff2fb3fc68bb0aa88b886900c34b77340c826.tar.gz |
Release 18.03
Diffstat (limited to 'src/armnn')
47 files changed, 1645 insertions, 228 deletions
diff --git a/src/armnn/Graph.cpp b/src/armnn/Graph.cpp index 97f702e50f..af3b17ea8b 100644 --- a/src/armnn/Graph.cpp +++ b/src/armnn/Graph.cpp @@ -14,6 +14,9 @@ #include <boost/format.hpp> #include <unordered_map> +#include <DotSerializer.hpp> +#include <sstream> + namespace armnn { @@ -71,6 +74,80 @@ Status Graph::Print() const return Status::Success; } +Status Graph::SerializeToDot(std::ostream& stream) +{ + { + DotGraph graph(stream, "Optimized"); + + { + // Default node attributes: + DotDefaults nodes(stream, "node"); + nodes.GetAttributeSet() + .AddAttribute("shape", "record"); + } + + { + // Default edge attributes: + DotDefaults edges(stream, "edge"); + edges.GetAttributeSet() + .AddAttribute("fontsize", 8) + .AddAttribute("fontcolor", "blue") + .AddAttribute("fontname", "arial-bold"); + } + + // First declare the nodes + for (auto&& layer : m_Layers) + { + DotNode node(stream, layer->GetGuid(), GetLayerTypeAsCString(layer->GetType())); + // Extract the layer parameters + ParameterStringifyFunction extractParams = [&node](const std::string & name, const std::string & value){ + node.GetContents().AddContent(name + " : " + value); + }; + layer->SerializeLayerParameters(extractParams); + } + + // Second declare the edges + for (auto&& layer : m_Layers) + { + LayerGuid toId = layer->GetGuid(); + + for (unsigned int i=0;i<layer->GetNumInputSlots(); i++) + { + OutputSlot* outputSlot = static_cast<OutputSlot*>(layer->GetInputSlot(i).GetConnection()); + LayerGuid fromId = outputSlot->GetOwningLayer().GetGuid(); + DotEdge edge(stream, fromId, toId); + + // Now Print the tensor shape on the edge + { + // Construct the label attribute with HTML markup + std::stringstream ss; + { + ss << "< ["; + const TensorShape& shape = outputSlot->GetTensorInfo().GetShape(); + for (unsigned int i = 0; i < shape.GetNumDimensions(); i++) + { + if (i != 0) + { + ss << ","; + } + ss << shape[i]; + } + ss << "] >"; + } + + edge.GetAttributeSet().AddAttribute("label", ss); + } + } + } + } + + if (stream.bad()) + { + return Status::Failure; + } + return Status::Success; +} + Status Graph::AllocateDynamicBuffers() { for (auto&& layer : m_Layers) diff --git a/src/armnn/Graph.hpp b/src/armnn/Graph.hpp index 8888034197..34aefbf085 100644 --- a/src/armnn/Graph.hpp +++ b/src/armnn/Graph.hpp @@ -92,6 +92,8 @@ public: Status Print() const; + Status SerializeToDot(std::ostream& stream); + /// Adds a new layer of type LaterType to the graph constructed with the arguments passed. template <typename LayerT, typename... Args> LayerT* AddLayer(Args&&... args); @@ -121,6 +123,11 @@ public: /// Return const iterator pointing to end of list. Lowercase for range-based for loops. ConstIterator end() const { return {m_Layers.end(), &PtrCast<const Layer>}; } + /// Return const iterator pointing to begin of list. Lowercase for range-based for loops. + ConstIterator cbegin() const { return begin(); } + /// Return const iterator pointing to end of list. Lowercase for range-based for loops. + ConstIterator cend() const { return end(); } + /// Sort layers in topological order and return this. Graph& TopologicalSort() { const_cast<const Graph*>(this)->TopologicalSort(); return *this; } const Graph& TopologicalSort() const; @@ -154,13 +161,27 @@ private: template <typename LayerT> class LayerInGraph; + Iterator ForwardToEndOfInputs(Iterator it) const + { + while ((it != m_Layers.end()) && ((*it)->GetType() == LayerType::Input)) + { + ++it; + } + return it; + } + + Iterator RewindToBeginOfOutputs(Iterator it) const + { + while ((it != m_Layers.begin()) && ((*std::prev(it))->GetType() == LayerType::Output)) + { + --it; + } + return it; + } + /// Get the position of a layer in the graph. Iterator GetPosInGraph(Layer& layer); - /// Adds a new layer of type LaterType to the graph constructed with the arguments passed. - template <typename LayerT, typename... Args> - LayerInGraph<LayerT>* AddLayerImpl(Iterator insertBefore, Args&&... args); - std::unordered_set<LayerBindingId> m_InputIds; std::unordered_set<LayerBindingId> m_OutputIds; std::unordered_map<const Layer*, Iterator> m_PosInGraphMap; @@ -197,8 +218,19 @@ class Graph::LayerInGraph final : public LayerInGraphBase<LayerT> { public: template <typename... Args> + LayerInGraph(Graph& graph, Args&&... args) + : LayerInGraphBase<LayerT>(graph, + // Insert at the back of the intermediate layers (before outputs). + std::prev(graph.end(), IteratorDifference(graph.GetNumOutputs())), + std::forward<Args>(args)...) + { + } + template <typename... Args> LayerInGraph(Graph& graph, Iterator insertBefore, Args&&... args) - : LayerInGraphBase<LayerT>(graph, insertBefore, std::forward<Args>(args)...) + : LayerInGraphBase<LayerT>(graph, + // Make sure it's inserted after all inputs and before all outputs. + graph.ForwardToEndOfInputs(graph.RewindToBeginOfOutputs(insertBefore)), + std::forward<Args>(args)...) { } }; @@ -209,8 +241,11 @@ class Graph::LayerInGraph<InputLayer> final : public LayerInGraphBase<InputLayer { public: template <typename... Args> - LayerInGraph(Graph& graph, Iterator insertBefore, Args&&... args) - : LayerInGraphBase<InputLayer>(graph, insertBefore, std::forward<Args>(args)...) + LayerInGraph(Graph& graph, Args&&... args) + : LayerInGraphBase<InputLayer>(graph, + // Always add to the back of the inputs. + std::next(graph.begin(), IteratorDifference(graph.GetNumInputs())), + std::forward<Args>(args)...) { const bool isNewId = m_Graph.m_InputIds.emplace(GetBindingId()).second; if (!isNewId) @@ -218,6 +253,12 @@ public: throw InvalidArgumentException("A layer already exists with the specified id"); } } + template <typename... Args> + LayerInGraph(Graph& graph, Iterator insertBefore, Args&&... args) + // Ignore insertBefore. Always add to the back of the inputs. + : LayerInGraph(graph, std::forward<Args>(args)...) + { + } ~LayerInGraph() override { const size_t numErased = m_Graph.m_InputIds.erase(GetBindingId()); @@ -232,8 +273,11 @@ class Graph::LayerInGraph<OutputLayer> final : public LayerInGraphBase<OutputLay { public: template <typename... Args> - LayerInGraph(Graph& graph, Iterator insertBefore, Args&&... args) - : LayerInGraphBase<OutputLayer>(graph, insertBefore, std::forward<Args>(args)...) + LayerInGraph(Graph& graph, Args&&... args) + : LayerInGraphBase<OutputLayer>(graph, + // Always add to the back of the outputs. + graph.end(), + std::forward<Args>(args)...) { const bool isNewId = m_Graph.m_OutputIds.emplace(GetBindingId()).second; if (!isNewId) @@ -257,42 +301,22 @@ inline Graph::Iterator Graph::GetPosInGraph(Layer& layer) } template <typename LayerT, typename... Args> -inline Graph::LayerInGraph<LayerT>* Graph::AddLayerImpl(Iterator insertBefore, Args&&... args) -{ - return new LayerInGraph<LayerT>(*this, insertBefore, std::forward<Args>(args)...); -} - -/// Inputs are inserted at the front of the list, to keep the order correct if the list is sorted. -/// Outputs are inserted at the back of the list, to keep the order correct if the list is sorted. -/// Other layers are inserted before existing outputs, so the latter remain at the back of the list. -template <typename LayerT, typename... Args> inline LayerT* Graph::AddLayer(Args&&... args) { - switch (LayerEnumOf<LayerT>()) - { - case LayerType::Input: - { - return AddLayerImpl<LayerT>(begin(), std::forward<Args>(args)...); - } - case LayerType::Output: - { - return AddLayerImpl<LayerT>(end(), std::forward<Args>(args)...); - } - default: - { - m_LayersInOrder = false; - const auto pos = std::prev(end(), IteratorDifference(GetNumOutputs())); - return AddLayerImpl<LayerT>(pos, std::forward<Args>(args)...); - } - } + m_LayersInOrder = m_LayersInOrder && + ((LayerEnumOf<LayerT>() == LayerType::Input) || (LayerEnumOf<LayerT>() == LayerType::Output)); + return new LayerInGraph<LayerT>(*this, std::forward<Args>(args)...); } template <typename LayerT, typename... Args> inline LayerT* Graph::InsertNewLayer(InputSlot& insertBefore, Args&&... args) { - // Insert before the child layer so topological order is kept. - const Iterator pos = GetPosInGraph(insertBefore.GetOwningLayer()); - LayerT* const layer = AddLayerImpl<LayerT>(pos, std::forward<Args>(args)...); + // Insert after the parent if any, or before the child otherwise, so topological order is kept. + OutputSlot* parentOut = insertBefore.GetConnectedOutputSlot(); + const Iterator pos = (parentOut != nullptr) + ? std::next(GetPosInGraph(parentOut->GetOwningLayer())) + : GetPosInGraph(insertBefore.GetOwningLayer()); + LayerT* const layer = new LayerInGraph<LayerT>(*this, pos, std::forward<Args>(args)...); insertBefore.Insert(*layer); return layer; } diff --git a/src/armnn/Layer.cpp b/src/armnn/Layer.cpp index 20a8ba4926..fcf0656aeb 100644 --- a/src/armnn/Layer.cpp +++ b/src/armnn/Layer.cpp @@ -18,7 +18,6 @@ namespace armnn void InputSlot::Insert(Layer& layer) { - BOOST_ASSERT(layer.GetNumInputSlots() <= 1); BOOST_ASSERT(layer.GetNumOutputSlots() == 1); OutputSlot* const prevSlot = GetConnectedOutputSlot(); @@ -115,11 +114,21 @@ void OutputSlot::ValidateConnectionIndex(unsigned int index) const } } +namespace { +LayerGuid GenerateLayerGuid() +{ + //Note: Not thread safe. + static LayerGuid newGuid=0; + return newGuid++; +} +} //namespace + Layer::Layer(unsigned int numInputSlots, unsigned int numOutputSlots, LayerType type, const char* name) : m_OutputHandlers(numOutputSlots) , m_LayerName(name ? name : "") , m_Type(type) , m_ComputeDevice(Compute::Undefined) +, m_Guid(GenerateLayerGuid()) { m_InputSlots.reserve(numInputSlots); for (unsigned int i = 0; i < numInputSlots; ++i) diff --git a/src/armnn/Layer.hpp b/src/armnn/Layer.hpp index 1160f0ab09..f9f2f22bea 100644 --- a/src/armnn/Layer.hpp +++ b/src/armnn/Layer.hpp @@ -10,6 +10,7 @@ #include "backends/WorkloadDataCollector.hpp" #include "backends/WorkloadInfo.hpp" #include "InternalTypes.hpp" +#include "SerializeLayerParameters.hpp" #include <armnn/Types.hpp> #include <armnn/Tensor.hpp> @@ -218,6 +219,10 @@ public: virtual void ValidateTensorShapesFromInputs() = 0; + /// Helper to serialize the layer parameters to string + /// (currently used in DotSerializer and company) + virtual void SerializeLayerParameters(ParameterStringifyFunction & fn) const {} + // IConnectableLayer const char* GetName() const override { return m_LayerName.c_str(); } @@ -230,6 +235,9 @@ public: const OutputSlot& GetOutputSlot(unsigned int index = 0) const override { return m_OutputSlots.at(index); } OutputSlot& GetOutputSlot(unsigned int index = 0) override { return m_OutputSlots.at(index); } + void SetGuid(LayerGuid guid) { m_Guid = guid; } + LayerGuid GetGuid() const final { return m_Guid; } + protected: // Graph needs access to the virtual destructor friend class Graph; @@ -281,6 +289,8 @@ private: /// Used for sorting mutable LayerPriority m_Priority = 0; mutable bool m_Visiting = false; + + LayerGuid m_Guid; }; // A layer user-provided data can be bound to (e.g. inputs, outputs) diff --git a/src/armnn/Layers.cpp b/src/armnn/Layers.cpp index ddbc7d222c..48a02aba9c 100644 --- a/src/armnn/Layers.cpp +++ b/src/armnn/Layers.cpp @@ -11,6 +11,8 @@ #include "Permute.hpp" +#include <queue> + namespace armnn { @@ -21,6 +23,7 @@ LayerType* Layer::CloneBase(Graph& graph, Params&& ... params) const LayerType* const layer = graph.AddLayer<LayerType>(std::forward<Params>(params)...); layer->SetComputeDevice(m_ComputeDevice); + layer->SetGuid(GetGuid()); return layer; } @@ -82,12 +85,11 @@ void AdditionLayer::ValidateTensorShapesFromInputs() unsigned int dim1 = input1.GetShape()[i]; if (dim0 != dim1) { - BOOST_ASSERT_MSG(dim0 == 1 || dim1 == 1, "Dimensions should either match or one should be one length"); + BOOST_ASSERT_MSG(dim0 == 1 || dim1 == 1, "Dimensions should either match or one should be of size 1."); } } #endif - for (unsigned int i = 0; i < numDims; i++) { unsigned int dim0 = input0.GetShape()[i]; @@ -439,14 +441,31 @@ void MergerLayer::CreateTensorHandles(Graph& graph, const IWorkloadFactory& fact m_OutputHandlers[0].CreateTensorHandles(factory); if (factory.SupportsSubTensors()) { - const unsigned int numInputSlots = GetNumInputSlots(); - for (unsigned int i = 0; i < numInputSlots; ++i) + std::queue<MergerLayer*> m_MergerLayers; + + m_MergerLayers.push(this); + while (!m_MergerLayers.empty()) { - OutputHandler& outputHandler = GetInputSlot(i).GetConnectedOutputSlot()->GetOutputHandler(); + MergerLayer* currentLayer = m_MergerLayers.front(); + ITensorHandle* parentTensor = currentLayer->GetOutputHandler(0).GetData(); - outputHandler.SetData(factory.CreateSubTensorHandle(*m_OutputHandlers[0].GetData(), - outputHandler.GetTensorInfo().GetShape(), - m_Param.GetViewOrigin(i))); + m_MergerLayers.pop(); + + const unsigned int numInputSlots = currentLayer->GetNumInputSlots(); + for (unsigned int i = 0; i < numInputSlots; ++i) + { + OutputSlot* slot = currentLayer->GetInputSlot(i).GetConnectedOutputSlot(); + OutputHandler& outputHandler = slot->GetOutputHandler(); + outputHandler.SetData(factory.CreateSubTensorHandle(*parentTensor, + outputHandler.GetTensorInfo().GetShape(), + currentLayer->m_Param.GetViewOrigin(i))); + + Layer& inputLayer = slot->GetOwningLayer(); + if (inputLayer.GetType() == LayerType::Merger) + { + m_MergerLayers.push(boost::polymorphic_downcast<MergerLayer*>(&inputLayer)); + } + } } } } @@ -568,12 +587,36 @@ MultiplicationLayer* MultiplicationLayer::Clone(Graph& graph) const void MultiplicationLayer::ValidateTensorShapesFromInputs() { - ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() == - GetInputSlot(1).GetConnection()->GetTensorInfo().GetShape(), - "MultiplicationLayer: Inputs must match"); + auto& input0 = GetInputSlot(0).GetConnection()->GetTensorInfo(); + auto& input1 = GetInputSlot(1).GetConnection()->GetTensorInfo(); + + // Get the max of the inputs + BOOST_ASSERT(input0.GetNumDimensions() == input1.GetNumDimensions()); + unsigned int numDims = input0.GetNumDimensions(); + std::vector<unsigned int> dims(numDims); + + // validate inputs are broadcast compatible +#if !NDEBUG + for (unsigned int i = 0; i < numDims; i++) + { + unsigned int dim0 = input0.GetShape()[i]; + unsigned int dim1 = input1.GetShape()[i]; + if (dim0 != dim1) + { + BOOST_ASSERT_MSG(dim0 == 1 || dim1 == 1, "Dimensions should either match or one should be of size 1."); + } + } +#endif - TensorInfo infoOut(GetInputSlot(0).GetConnection()->GetTensorInfo()); - ConditionalThrow<LayerValidationException>(GetOutputSlot(0).ValidateTensorShape(infoOut.GetShape()), + for (unsigned int i = 0; i < numDims; i++) + { + unsigned int dim0 = input0.GetShape()[i]; + unsigned int dim1 = input1.GetShape()[i]; + dims[i] = std::max(dim0, dim1); + } + + TensorShape outShape(numDims, dims.data()); + ConditionalThrow<LayerValidationException>(GetOutputSlot(0).ValidateTensorShape(outShape), "MultiplicationLayer: TensorShape set on OutputSlot[0] does not match the inferred shape."); } diff --git a/src/armnn/Layers.hpp b/src/armnn/Layers.hpp index 5a1e3ca063..cb460e125f 100644 --- a/src/armnn/Layers.hpp +++ b/src/armnn/Layers.hpp @@ -22,10 +22,17 @@ template <typename Parameters> class LayerWithParameters : public Layer { public: - typedef Parameters DescriptorType; + using DescriptorType = Parameters; const Parameters& GetParameters() const { return m_Param; } + /// Helper to serialize the layer parameters to string + /// (currently used in DotSerializer and company) + void SerializeLayerParameters(ParameterStringifyFunction & fn) const + { + StringifyLayerParameters<Parameters>::Serialize(fn, m_Param); + } + protected: LayerWithParameters(unsigned int numInputSlots, unsigned int numOutputSlots, diff --git a/src/armnn/Network.cpp b/src/armnn/Network.cpp index 4ee68b3c48..77390cb0a4 100644 --- a/src/armnn/Network.cpp +++ b/src/armnn/Network.cpp @@ -58,6 +58,11 @@ Status OptimizedNetwork::PrintGraph() return Status::Success; } +Status OptimizedNetwork::SerializeToDot(std::ostream& stream) const +{ + return m_Graph->SerializeToDot(stream); +} + IOptimizedNetworkPtr Optimize(const INetwork& inNetwork, const DeviceSpec& deviceSpec) { const Network& network = *boost::polymorphic_downcast<const Network*>(&inNetwork); @@ -65,7 +70,7 @@ IOptimizedNetworkPtr Optimize(const INetwork& inNetwork, const DeviceSpec& devic OptimizedNetwork* optNet = new OptimizedNetwork(std::move(graph)); - Optimizer::Get().Optimize(optNet->GetGraph()); + Optimizer::Optimize(optNet->GetGraph()); // Infer the tensor infos for all output slots. Throws an exception on failure. optNet->GetGraph().InferTensorInfos(); diff --git a/src/armnn/Network.hpp b/src/armnn/Network.hpp index de0c1ecf2f..4eb67b1a15 100644 --- a/src/armnn/Network.hpp +++ b/src/armnn/Network.hpp @@ -135,6 +135,7 @@ public: ~OptimizedNetwork(); Status PrintGraph() override; + Status SerializeToDot(std::ostream& stream) const override; Graph& GetGraph() { return *m_Graph; } diff --git a/src/armnn/Optimizer.cpp b/src/armnn/Optimizer.cpp index 85b9f2803c..9b76c7fa72 100644 --- a/src/armnn/Optimizer.cpp +++ b/src/armnn/Optimizer.cpp @@ -8,7 +8,7 @@ namespace armnn { -const Optimizer& Optimizer::Get() +Optimizer::Optimizer() { // Add optimizations here static optimizations::SquashEqualPermuteSiblings squashEqualPermuteSiblings; @@ -19,28 +19,26 @@ const Optimizer& Optimizer::Get() static optimizations::OptimizeConsecutiveReshapes optimizeConsecutiveReshapes; // Set optimizations in desired order - static const Optimizer optimizer({ - &squashEqualPermuteSiblings, - &squashEqualReshapeSiblings, - &optimizeInversePermutes, - &movePermuteUp, - &permuteAsReshape, - &optimizeConsecutiveReshapes, - }); - - return optimizer; + m_Optimizations = {&squashEqualPermuteSiblings, + &squashEqualReshapeSiblings, + &optimizeInversePermutes, + &movePermuteUp, + &permuteAsReshape, + &optimizeConsecutiveReshapes, + }; } -void Optimizer::Optimize(Graph& graph) const +void Optimizer::Optimize(Graph& graph) { + Optimizer optimizer; auto it = graph.TopologicalSort().end(); // Call TopologicalSort() in every iteration to re-order the list in case layers where added/removed. while (it != graph.TopologicalSort().begin()) { --it; - for (auto&& optimization : m_Optimizations) + for (auto&& optimization : optimizer.m_Optimizations) { - optimization->Run(graph, it); + optimization->Run(graph, **it); if ((*it)->IsOutputUnconnected()) { diff --git a/src/armnn/Optimizer.hpp b/src/armnn/Optimizer.hpp index 262f264c28..1f5ed026fb 100644 --- a/src/armnn/Optimizer.hpp +++ b/src/armnn/Optimizer.hpp @@ -15,14 +15,13 @@ class Optimization; class Optimizer { public: - static const Optimizer& Get(); - void Optimize(Graph& graph) const; + static void Optimize(Graph& graph); private: ~Optimizer() = default; - Optimizer(std::initializer_list<Optimization*> optimizations) : m_Optimizations(optimizations) {} + Optimizer(); std::vector<Optimization*> m_Optimizations; }; diff --git a/src/armnn/Runtime.cpp b/src/armnn/Runtime.cpp index ea6d19bd31..e0d6a9add0 100644 --- a/src/armnn/Runtime.cpp +++ b/src/armnn/Runtime.cpp @@ -9,6 +9,7 @@ #ifdef ARMCOMPUTECL_ENABLED #include <arm_compute/core/CL/OpenCL.h> #include <arm_compute/core/CL/CLKernelLibrary.h> +#include <arm_compute/runtime/CL/CLScheduler.h> #endif #include <boost/log/trivial.hpp> @@ -58,18 +59,26 @@ Status Runtime::LoadNetwork(NetworkId& networkIdOut, IOptimizedNetworkPtr inNetw m_LoadedNetworks[networkIdOut] = std::move(loadedNetwork); return Status::Success; - } Status Runtime::UnloadNetwork(NetworkId networkId) { +#ifdef ARMCOMPUTECL_ENABLED + if (arm_compute::CLScheduler::get().context()() != NULL) + { + arm_compute::CLScheduler::get().sync(); + } +#endif if (m_LoadedNetworks.erase(networkId) == 0) { BOOST_LOG_TRIVIAL(warning) << "WARNING: Runtime::UnloadNetwork(): " << networkId << " not found!"; return Status::Failure; } #ifdef ARMCOMPUTECL_ENABLED - arm_compute::CLKernelLibrary::get().clear_programs_cache(); + if (arm_compute::CLScheduler::get().context()() != NULL && m_LoadedNetworks.empty()) + { + m_WorkloadFactories.m_GpuAcc.get()->LoadOpenClRuntime(); + } #endif BOOST_LOG_TRIVIAL(debug) << "Runtime::UnloadNetwork(): Unloaded network with ID: " << networkId; return Status::Success; @@ -87,11 +96,24 @@ Runtime::Runtime(const CreationOptions& options) m_WorkloadFactories.m_CpuRef = make_shared<RefWorkloadFactory>( options.m_DefaultComputeDevice == Compute::CpuRef ? true : options.m_UseCpuRefAsFallback); m_WorkloadFactories.m_CpuAcc = make_shared<NeonWorkloadFactory>(); - m_WorkloadFactories.m_GpuAcc = make_shared<ClWorkloadFactory>(); + m_WorkloadFactories.m_GpuAcc = make_shared<ClWorkloadFactory>(options.m_ClTunedParameters); if (options.m_DefaultComputeDevice == Compute::GpuAcc) { - m_WorkloadFactories.m_GpuAcc.get()->LoadOpenClRuntime(options.m_ClTunedParameters); + m_WorkloadFactories.m_GpuAcc.get()->LoadOpenClRuntime(); + } +} + +Runtime::~Runtime() +{ + std::vector<int> networkIDs; + std::transform(m_LoadedNetworks.begin(), m_LoadedNetworks.end(), + std::back_inserter(networkIDs), + [](const auto &pair) { return pair.first; }); + + for (auto networkID : networkIDs) + { + UnloadNetwork(networkID); } } diff --git a/src/armnn/Runtime.hpp b/src/armnn/Runtime.hpp index d3f3a578f3..86fd48d6d2 100644 --- a/src/armnn/Runtime.hpp +++ b/src/armnn/Runtime.hpp @@ -56,6 +56,8 @@ public: /// it cannot be setup for some reason. Runtime(const CreationOptions& options); + ~Runtime(); + private: friend void RuntimeLoadedNetworksReserve(armnn::Runtime* runtime); // see RuntimeTests.cpp diff --git a/src/armnn/SerializeLayerParameters.cpp b/src/armnn/SerializeLayerParameters.cpp new file mode 100644 index 0000000000..e8c2bba29b --- /dev/null +++ b/src/armnn/SerializeLayerParameters.cpp @@ -0,0 +1,156 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// See LICENSE file in the project root for full license information. +// +#include "SerializeLayerParameters.hpp" +#include <armnn/TypesUtils.hpp> +#include <string> +#include <iostream> +#include <sstream> + +namespace armnn +{ + +void +StringifyLayerParameters<PermuteDescriptor>::Serialize(ParameterStringifyFunction & fn, + const PermuteDescriptor & desc) +{ + std::stringstream ss; + ss << "["; + bool addComma = false; + for (auto it=desc.m_DimMappings.begin(); it!= desc.m_DimMappings.end(); ++it) + { + if (addComma) + { + ss << ","; + } + ss << *it; + addComma = true; + } + ss << "]"; + + fn("DimMappings",ss.str()); +} + +void +StringifyLayerParameters<ReshapeDescriptor>::Serialize(ParameterStringifyFunction & fn, + const ReshapeDescriptor & desc) +{ + std::stringstream ss; + ss << "["; + bool addComma = false; + for (unsigned int i=0; i<desc.m_TargetShape.GetNumDimensions(); ++i) + { + if (addComma) + { + ss << ","; + } + ss << desc.m_TargetShape[i]; + addComma = true; + } + ss << "]"; + + fn("TargetShape",ss.str()); +} + +void +StringifyLayerParameters<ActivationDescriptor>::Serialize(ParameterStringifyFunction & fn, + const ActivationDescriptor & desc) +{ + fn("Function",GetActivationFunctionAsCString(desc.m_Function)); + fn("A",std::to_string(desc.m_A)); + fn("B",std::to_string(desc.m_B)); +} + +void +StringifyLayerParameters<Convolution2dDescriptor>::Serialize(ParameterStringifyFunction & fn, + const Convolution2dDescriptor & desc) +{ + { + std::stringstream ss; + ss << "(" << desc.m_PadTop << "," << desc.m_PadLeft + << "," << desc.m_PadBottom << "," << desc.m_PadRight << ")"; + fn("Padding(T,L,B,R)",ss.str()); + } + + { + std::stringstream ss; + ss << "(" << desc.m_StrideX << "," << desc.m_StrideY << ")"; + fn("Stride(X,Y)", ss.str()); + } + + fn("BiasEnabled",(desc.m_BiasEnabled?"true":"false")); +} + +void +StringifyLayerParameters<BatchNormalizationDescriptor>::Serialize(ParameterStringifyFunction & fn, + const BatchNormalizationDescriptor & desc) +{ + fn("Eps",std::to_string(desc.m_Eps)); +} + +void +StringifyLayerParameters<DepthwiseConvolution2dDescriptor>::Serialize(ParameterStringifyFunction & fn, + const DepthwiseConvolution2dDescriptor & desc) +{ + { + std::stringstream ss; + ss << "(" << desc.m_PadTop << "," << desc.m_PadLeft + << "," << desc.m_PadBottom << "," << desc.m_PadRight << ")"; + fn("Padding(T,L,B,R)",ss.str()); + } + + { + std::stringstream ss; + ss << "(" << desc.m_StrideX << "," << desc.m_StrideY << ")"; + fn("Stride(X,Y)", ss.str()); + } + + fn("BiasEnabled",(desc.m_BiasEnabled?"true":"false")); +} + +void +StringifyLayerParameters<Pooling2dDescriptor>::Serialize(ParameterStringifyFunction & fn, + const Pooling2dDescriptor & desc) +{ + fn("Type", GetPoolingAlgorithmAsCString(desc.m_PoolType)); + { + std::stringstream ss; + ss << "(" << desc.m_PadTop << "," << desc.m_PadLeft + << "," << desc.m_PadBottom << "," << desc.m_PadRight << ")"; + fn("Padding(T,L,B,R)",ss.str()); + } + + { + std::stringstream ss; + ss << "(" << desc.m_PoolWidth << "," << desc.m_PoolHeight << ")"; + fn("(Width,Height)",ss.str()); + } + + { + std::stringstream ss; + ss << "(" << desc.m_StrideX << "," << desc.m_StrideY << ")"; + fn("Stride(X,Y)", ss.str()); + } + + fn("OutputShapeRounding", GetOutputShapeRoundingAsCString(desc.m_OutputShapeRounding)); + fn("PaddingMethod", GetPaddingMethodAsCString(desc.m_PaddingMethod)); +} + +void +StringifyLayerParameters<SoftmaxDescriptor>::Serialize(ParameterStringifyFunction & fn, + const SoftmaxDescriptor & desc) +{ + fn("Beta", std::to_string(desc.m_Beta)); +} + +void +StringifyLayerParameters<FullyConnectedDescriptor>::Serialize(ParameterStringifyFunction & fn, + const FullyConnectedDescriptor & desc) +{ + fn("BiasEnabled", (desc.m_BiasEnabled?"true":"false")); + fn("TransposeWeightMatrix", (desc.m_TransposeWeightMatrix?"true":"false")); +} + + +} diff --git a/src/armnn/SerializeLayerParameters.hpp b/src/armnn/SerializeLayerParameters.hpp new file mode 100644 index 0000000000..b00816067d --- /dev/null +++ b/src/armnn/SerializeLayerParameters.hpp @@ -0,0 +1,73 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// See LICENSE file in the project root for full license information. +// +#pragma once + +#include <string> +#include <functional> +#include <armnn/Descriptors.hpp> + +namespace armnn +{ + +using ParameterStringifyFunction = std::function<void(const std::string & name, const std::string & value)>; + +/// +/// StringifyLayerParameters allows serializing layer parameters to string. +/// The default implementation is a no-op because this operation is considered +/// non-vital for ArmNN and thus we allow adding new layer parameters without +/// supplying the corresponding stringify functionality. +/// +template <typename LayerParameter> +struct StringifyLayerParameters +{ + static void Serialize(ParameterStringifyFunction &, const LayerParameter &) {} +}; + +template <> struct StringifyLayerParameters<PermuteDescriptor> +{ + static void Serialize(ParameterStringifyFunction & fn, const PermuteDescriptor & desc); +}; + +template <> struct StringifyLayerParameters<ReshapeDescriptor> +{ + static void Serialize(ParameterStringifyFunction & fn, const ReshapeDescriptor & desc); +}; + +template <> struct StringifyLayerParameters<ActivationDescriptor> +{ + static void Serialize(ParameterStringifyFunction & fn, const ActivationDescriptor & desc); +}; + +template <> struct StringifyLayerParameters<Convolution2dDescriptor> +{ + static void Serialize(ParameterStringifyFunction & fn, const Convolution2dDescriptor & desc); +}; + +template <> struct StringifyLayerParameters<BatchNormalizationDescriptor> +{ + static void Serialize(ParameterStringifyFunction & fn, const BatchNormalizationDescriptor & desc); +}; + +template <> struct StringifyLayerParameters<DepthwiseConvolution2dDescriptor> +{ + static void Serialize(ParameterStringifyFunction & fn, const DepthwiseConvolution2dDescriptor & desc); +}; + +template <> struct StringifyLayerParameters<Pooling2dDescriptor> +{ + static void Serialize(ParameterStringifyFunction & fn, const Pooling2dDescriptor & desc); +}; + +template <> struct StringifyLayerParameters<SoftmaxDescriptor> +{ + static void Serialize(ParameterStringifyFunction & fn, const SoftmaxDescriptor & desc); +}; + +template <> struct StringifyLayerParameters<FullyConnectedDescriptor> +{ + static void Serialize(ParameterStringifyFunction & fn, const FullyConnectedDescriptor & desc); +}; + +}
\ No newline at end of file diff --git a/src/armnn/backends/ArmComputeTensorUtils.cpp b/src/armnn/backends/ArmComputeTensorUtils.cpp index 9f21c41a2f..f88ed2b4c3 100644 --- a/src/armnn/backends/ArmComputeTensorUtils.cpp +++ b/src/armnn/backends/ArmComputeTensorUtils.cpp @@ -78,6 +78,7 @@ arm_compute::PoolingLayerInfo BuildArmComputePoolingLayerInfo(const Pooling2dDes using arm_compute::DimensionRoundingType; using arm_compute::PadStrideInfo; using arm_compute::PoolingLayerInfo; + using arm_compute::Size2D; // Resolve ARM Compute layer parameters const PoolingType poolingType = ConvertPoolingAlgorithmToAclPoolingType(descriptor.m_PoolType); @@ -94,7 +95,9 @@ arm_compute::PoolingLayerInfo BuildArmComputePoolingLayerInfo(const Pooling2dDes const bool excludePadding = (descriptor.m_PaddingMethod == PaddingMethod::Exclude); - return arm_compute::PoolingLayerInfo(poolingType, descriptor.m_PoolWidth, padStrideInfo, excludePadding); + const Size2D poolSize(descriptor.m_PoolWidth, descriptor.m_PoolHeight); + + return arm_compute::PoolingLayerInfo(poolingType, poolSize, padStrideInfo, excludePadding); } arm_compute::NormalizationLayerInfo BuildArmComputeNormalizationLayerInfo(const NormalizationDescriptor& descriptor) @@ -114,7 +117,7 @@ arm_compute::PermutationVector BuildArmComputePermutationVector(const armnn::Per arm_compute::PermutationVector aclPerm; unsigned int start = 0; - while ((start == perm[start]) && (start < perm.GetSize())) + while ((start < perm.GetSize()) && (start == perm[start])) { ++start; } diff --git a/src/armnn/backends/ClWorkloadFactory.cpp b/src/armnn/backends/ClWorkloadFactory.cpp index 4e565a05d7..6af657b6b4 100644 --- a/src/armnn/backends/ClWorkloadFactory.cpp +++ b/src/armnn/backends/ClWorkloadFactory.cpp @@ -35,24 +35,62 @@ bool ClWorkloadFactory::IsLayerSupported(const Layer& layer, DataType dataType, #ifdef ARMCOMPUTECL_ENABLED -void ClWorkloadFactory::LoadOpenClRuntime(IClTunedParameters* clTunedParameters) +ClWorkloadFactory::ClWorkloadFactory(IClTunedParameters* clTunedParameters): + m_clTunedParameters(boost::polymorphic_downcast<ClTunedParameters*>(clTunedParameters)) { - ClTunedParameters* clTunedParametersImpl = boost::polymorphic_downcast<ClTunedParameters*>(clTunedParameters); + try + { + std::vector<cl::Platform> platforms; + cl::Platform::get(&platforms); + + // Select default platform as the first element + cl::Platform::setDefault(platforms[0]); + + std::vector<cl::Device> devices; + platforms[0].getDevices(CL_DEVICE_TYPE_GPU, &devices); + + // Select default device as the first element + cl::Device::setDefault(devices[0]); + } + catch (const cl::Error& clError) + { + throw ClRuntimeUnavailableException(boost::str(boost::format( + "Could not initialize the CL runtime. Error description: %1%. CL error code: %2%" + ) % clError.what() % clError.err())); + } + + // Remove the use of global CL context + cl::Context::setDefault(cl::Context{}); + BOOST_ASSERT(cl::Context::getDefault()() == NULL); - cl::Device device; + // Remove the use of global CL command queue + cl::CommandQueue::setDefault(cl::CommandQueue{}); + BOOST_ASSERT(cl::CommandQueue::getDefault()() == NULL); +} + +ClWorkloadFactory::~ClWorkloadFactory() +{ +} + +void ClWorkloadFactory::LoadOpenClRuntime() +{ + cl::Device device = cl::Device::getDefault(); cl::Context context; cl::CommandQueue commandQueue; try { - device = cl::Device::getDefault(); - context = cl::Context::getDefault(); + arm_compute::CLKernelLibrary::get().clear_programs_cache(); + arm_compute::CLScheduler::get().init(context, commandQueue, device); + arm_compute::CLKernelLibrary::get().init(".", context, device); + + context = cl::Context(device); bool enableProfiling = false; #if ARMNN_PROFILING_ENABLED enableProfiling = true; #endif - if (clTunedParametersImpl && clTunedParametersImpl->m_Mode == IClTunedParameters::Mode::UpdateTunedParameters) + if (m_clTunedParameters && m_clTunedParameters->m_Mode == IClTunedParameters::Mode::UpdateTunedParameters) { enableProfiling = true; // Needed for the CLTuner to work. } @@ -65,7 +103,7 @@ void ClWorkloadFactory::LoadOpenClRuntime(IClTunedParameters* clTunedParameters) else { // Use default queue - commandQueue = cl::CommandQueue::getDefault(); + commandQueue = cl::CommandQueue(context, device); } } catch (const cl::Error& clError) @@ -79,9 +117,9 @@ void ClWorkloadFactory::LoadOpenClRuntime(IClTunedParameters* clTunedParameters) arm_compute::CLKernelLibrary::get().init(".", context, device); arm_compute::ICLTuner* tuner = nullptr; - if (clTunedParameters) + if (m_clTunedParameters) { - tuner = &clTunedParametersImpl->m_Tuner; + tuner = &m_clTunedParameters->m_Tuner; } arm_compute::CLScheduler::get().init(context, commandQueue, device, tuner); } @@ -266,7 +304,16 @@ std::unique_ptr<IWorkload> ClWorkloadFactory::CreateFloor(const FloorQueueDescri #else // #if ARMCOMPUTECL_ENABLED -void ClWorkloadFactory::LoadOpenClRuntime(IClTunedParameters* clTunedParameters) +ClWorkloadFactory::ClWorkloadFactory(IClTunedParameters* clTunedParameters) +{ + // No CL support +} + +ClWorkloadFactory::~ClWorkloadFactory() +{ +} + +void ClWorkloadFactory::LoadOpenClRuntime() { // No CL support } diff --git a/src/armnn/backends/ClWorkloadFactory.hpp b/src/armnn/backends/ClWorkloadFactory.hpp index 2477e23eeb..e1e66c050b 100644 --- a/src/armnn/backends/ClWorkloadFactory.hpp +++ b/src/armnn/backends/ClWorkloadFactory.hpp @@ -23,18 +23,22 @@ namespace armnn { class IClTunedParameters; +class ClTunedParameters; // ARM Compute OpenCL workload factory class ClWorkloadFactory : public IWorkloadFactory { public: - virtual ~ClWorkloadFactory(){}; + + ClWorkloadFactory(IClTunedParameters* clTunedParameters = nullptr); + + virtual ~ClWorkloadFactory(); virtual Compute GetCompute() const override { return Compute::GpuAcc; } static bool IsLayerSupported(const Layer& layer, DataType dataType, std::string& outReasonIfUnsupported); - void LoadOpenClRuntime(IClTunedParameters* clTunedParameters = nullptr); + void LoadOpenClRuntime(); virtual bool SupportsSubTensors() const override { return true; } @@ -109,6 +113,9 @@ public: virtual std::unique_ptr<IWorkload> CreateFloor(const FloorQueueDescriptor& descriptor, const WorkloadInfo& info) const override; + +private: + ClTunedParameters* m_clTunedParameters; }; class ClTunedParameters : public IClTunedParameters diff --git a/src/armnn/backends/NeonLayerSupport.cpp b/src/armnn/backends/NeonLayerSupport.cpp index 382b15e277..d8a3366775 100644 --- a/src/armnn/backends/NeonLayerSupport.cpp +++ b/src/armnn/backends/NeonLayerSupport.cpp @@ -71,6 +71,22 @@ bool IsNeonDirectConvolutionPreferred(const TensorInfo& weightInfo, const Convol return preferDirectConvolution; } +bool IsNeonMultiplicationParamsSupported(std::string* reasonIfUnsupported, + const TensorInfo& info0, + const TensorInfo& info1) +{ + if (info0.GetShape() == info1.GetShape()) + { + return true; + } + + if (reasonIfUnsupported) + { + *reasonIfUnsupported = "Multiplication on Neon does not support implicit broadcast."; + } + return false; +} + bool IsNeonNormalizationDescParamsSupported(std::string* reasonIfUnsupported, const NormalizationDescriptor& parameters) { if (parameters.m_NormMethodType != NormalizationAlgorithmMethod::LocalBrightness) @@ -233,7 +249,7 @@ bool IsConvolution2dSupportedNeon(const TensorInfo& input, return IsSupportedForDataTypeNeon(reasonIfUnsupported, input.GetDataType(), &TrueFunc<>, - &FalseFuncU8<>); + &TrueFunc<>); } bool IsDepthwiseConvolutionSupportedNeon(const TensorInfo& input, @@ -293,11 +309,13 @@ bool IsMultiplicationSupportedNeon(const TensorInfo& input0, const TensorInfo& input1, std::string* reasonIfUnsupported) { - ignore_unused(input1); return IsSupportedForDataTypeNeon(reasonIfUnsupported, input0.GetDataType(), - &TrueFunc<>, - &FalseFuncU8<>); + &IsNeonMultiplicationParamsSupported, + &FalseFuncU8<const TensorInfo&, const TensorInfo&>, + input0, + input1 + ); } bool IsNormalizationSupportedNeon(const TensorInfo& input, diff --git a/src/armnn/backends/NeonWorkloadFactory.cpp b/src/armnn/backends/NeonWorkloadFactory.cpp index 384284114f..0f65a3dcd7 100644 --- a/src/armnn/backends/NeonWorkloadFactory.cpp +++ b/src/armnn/backends/NeonWorkloadFactory.cpp @@ -112,7 +112,7 @@ std::unique_ptr<armnn::IWorkload> NeonWorkloadFactory::CreatePooling2d(const Poo std::unique_ptr<armnn::IWorkload> NeonWorkloadFactory::CreateConvolution2d( const Convolution2dQueueDescriptor& descriptor, const WorkloadInfo& info) const { - return MakeWorkload<NeonConvolution2dFloat32Workload, NullWorkload>(descriptor, info); + return MakeWorkload<NeonConvolution2dFloat32Workload, NeonConvolution2dUint8Workload>(descriptor, info); } std::unique_ptr<IWorkload> NeonWorkloadFactory::CreateDepthwiseConvolution2d( diff --git a/src/armnn/backends/NeonWorkloads.hpp b/src/armnn/backends/NeonWorkloads.hpp index 7e9e885adc..83a3e9fd9b 100644 --- a/src/armnn/backends/NeonWorkloads.hpp +++ b/src/armnn/backends/NeonWorkloads.hpp @@ -13,7 +13,9 @@ #include "backends/NeonWorkloads/NeonBatchNormalizationFloat32Workload.hpp" #include "backends/NeonWorkloads/NeonConstantFloat32Workload.hpp" #include "backends/NeonWorkloads/NeonConstantUint8Workload.hpp" +#include "backends/NeonWorkloads/NeonConvolution2dBaseWorkload.hpp" #include "backends/NeonWorkloads/NeonConvolution2dFloat32Workload.hpp" +#include "backends/NeonWorkloads/NeonConvolution2dUint8Workload.hpp" #include "backends/NeonWorkloads/NeonDepthwiseConvolutionFloat32Workload.hpp" #include "backends/NeonWorkloads/NeonDepthwiseConvolutionUint8Workload.hpp" #include "backends/NeonWorkloads/NeonFloorFloat32Workload.hpp" diff --git a/src/armnn/backends/NeonWorkloads/NeonConvolution2dBaseWorkload.cpp b/src/armnn/backends/NeonWorkloads/NeonConvolution2dBaseWorkload.cpp index 5099965a24..10c96d82a6 100644 --- a/src/armnn/backends/NeonWorkloads/NeonConvolution2dBaseWorkload.cpp +++ b/src/armnn/backends/NeonWorkloads/NeonConvolution2dBaseWorkload.cpp @@ -73,10 +73,6 @@ NeonConvolution2dBaseWorkload<dataType>::NeonConvolution2dBaseWorkload(const Con using Type = ResolveType<dataType>; InitialiseArmComputeTensorData(m_KernelTensor, m_Data.m_Weight->template GetConstTensor<Type>()); - if (m_Data.m_Parameters.m_BiasEnabled) - { - InitialiseArmComputeTensorData(m_BiasTensor, m_Data.m_Bias->template GetConstTensor<Type>()); - } } // Generate known implementations for linker diff --git a/src/armnn/backends/NeonWorkloads/NeonConvolution2dBaseWorkload.hpp b/src/armnn/backends/NeonWorkloads/NeonConvolution2dBaseWorkload.hpp index 37740511ba..98d075a5ea 100644 --- a/src/armnn/backends/NeonWorkloads/NeonConvolution2dBaseWorkload.hpp +++ b/src/armnn/backends/NeonWorkloads/NeonConvolution2dBaseWorkload.hpp @@ -3,6 +3,8 @@ // See LICENSE file in the project root for full license information. // +#pragma once + #include <backends/Workload.hpp> #include <backends/NeonWorkloadUtils.hpp> diff --git a/src/armnn/backends/NeonWorkloads/NeonConvolution2dFloat32Workload.cpp b/src/armnn/backends/NeonWorkloads/NeonConvolution2dFloat32Workload.cpp index b4650ac011..a8c5c63683 100644 --- a/src/armnn/backends/NeonWorkloads/NeonConvolution2dFloat32Workload.cpp +++ b/src/armnn/backends/NeonWorkloads/NeonConvolution2dFloat32Workload.cpp @@ -15,7 +15,12 @@ using namespace armcomputetensorutils; NeonConvolution2dFloat32Workload::NeonConvolution2dFloat32Workload(const Convolution2dQueueDescriptor& descriptor, const WorkloadInfo& info) : NeonConvolution2dBaseWorkload(descriptor, info) -{} +{ + if (m_Data.m_Parameters.m_BiasEnabled) + { + InitialiseArmComputeTensorData(m_BiasTensor, m_Data.m_Bias->template GetConstTensor<float>()); + } +} void NeonConvolution2dFloat32Workload::Execute() const diff --git a/src/armnn/backends/NeonWorkloads/NeonConvolution2dUint8Workload.cpp b/src/armnn/backends/NeonWorkloads/NeonConvolution2dUint8Workload.cpp new file mode 100644 index 0000000000..ae20522361 --- /dev/null +++ b/src/armnn/backends/NeonWorkloads/NeonConvolution2dUint8Workload.cpp @@ -0,0 +1,33 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// See LICENSE file in the project root for full license information. +// + +#include "NeonConvolution2dUint8Workload.hpp" + + +namespace armnn +{ +NeonConvolution2dUint8Workload::NeonConvolution2dUint8Workload(const Convolution2dQueueDescriptor& descriptor, + const WorkloadInfo& info) + : NeonConvolution2dBaseWorkload(descriptor, info) +{ + if (m_Data.m_Parameters.m_BiasEnabled) + { + InitialiseArmComputeTensorData(m_BiasTensor, m_Data.m_Bias->template GetConstTensor<int32_t>()); + } +} + + +void NeonConvolution2dUint8Workload::Execute() const +{ + ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuAcc, NeonConvolution2dUint8Workload_Execute); + m_ConvolutionLayer->run(); +} + +void NeonConvolution2dUint8Workload::ValidateData() const +{ + m_Data.ValidateInputsOutputs("NeonConvolution2dUint8Workload", 1, 1); +} + +} //namespace armnn
\ No newline at end of file diff --git a/src/armnn/backends/NeonWorkloads/NeonConvolution2dUint8Workload.hpp b/src/armnn/backends/NeonWorkloads/NeonConvolution2dUint8Workload.hpp new file mode 100644 index 0000000000..319d574b1e --- /dev/null +++ b/src/armnn/backends/NeonWorkloads/NeonConvolution2dUint8Workload.hpp @@ -0,0 +1,27 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// See LICENSE file in the project root for full license information. +// + +#pragma once + +#include "NeonConvolution2dBaseWorkload.hpp" + +namespace armnn +{ + +class NeonConvolution2dUint8Workload : public NeonConvolution2dBaseWorkload<DataType::QuantisedAsymm8> +{ +public: + NeonConvolution2dUint8Workload(const Convolution2dQueueDescriptor& descriptor, const WorkloadInfo& info); + + virtual void ValidateData() const override; + virtual void Execute() const override; +private: +}; + +} //namespace armnnn + + + + diff --git a/src/armnn/backends/RefWorkloads/Addition.cpp b/src/armnn/backends/RefWorkloads/Addition.cpp index c26f82ecc2..6d53a702e4 100644 --- a/src/armnn/backends/RefWorkloads/Addition.cpp +++ b/src/armnn/backends/RefWorkloads/Addition.cpp @@ -8,9 +8,6 @@ #include <functional> -namespace armnn -{ - namespace { @@ -24,6 +21,9 @@ void ElementwiseAddition(unsigned int numElements, const float* inData0, const f } // namespace +namespace armnn +{ + void Addition(const TensorShape& inShape0, const TensorShape& inShape1, const TensorShape& outShape, diff --git a/src/armnn/backends/RefWorkloads/Merger.hpp b/src/armnn/backends/RefWorkloads/Merger.hpp index 9695e457e2..476ced76be 100644 --- a/src/armnn/backends/RefWorkloads/Merger.hpp +++ b/src/armnn/backends/RefWorkloads/Merger.hpp @@ -39,6 +39,7 @@ void Merger(const MergerQueueDescriptor& data) //split view extents are defined by the size of (the corresponding) input tensor const TensorInfo& inputInfo = GetTensorInfo(data.m_Inputs[viewIdx]); + BOOST_ASSERT(inputInfo.GetNumDimensions() == outputInfo0.GetNumDimensions()); // check all dimensions to see if this element is inside the given input view bool insideView = true; diff --git a/src/armnn/backends/RefWorkloads/Multiplication.cpp b/src/armnn/backends/RefWorkloads/Multiplication.cpp index 7f558d83c5..47c0f1cef1 100644 --- a/src/armnn/backends/RefWorkloads/Multiplication.cpp +++ b/src/armnn/backends/RefWorkloads/Multiplication.cpp @@ -4,18 +4,48 @@ // #include "Multiplication.hpp" +#include "Broadcast.hpp" -namespace armnn +#include <functional> + +namespace { -void Multiplication(const float* in0, - const float* in1, - unsigned int numElements, - float* out) +void ElementwiseMultiplication(unsigned int numElements, + const float* inData0, + const float* inData1, + float* outData) { for (unsigned int i = 0; i < numElements; ++i) { - out[i] = in0[i] * in1[i]; + outData[i] = inData0[i] * inData1[i]; + } +} + +} // namespace + +namespace armnn +{ + +void Multiplication(const TensorShape& inShape0, + const TensorShape& inShape1, + const TensorShape& outShape, + const float* inData0, + const float* inData1, + float* outData) +{ + if (inShape0 == inShape1) + { + ElementwiseMultiplication(inShape0.GetNumElements(), inData0, inData1, outData); + } + else + { + BroadcastLoop(inShape0, inShape1, outShape).Unroll( + std::multiplies<float>(), + 0, + inData0, + inData1, + outData); } } diff --git a/src/armnn/backends/RefWorkloads/Multiplication.hpp b/src/armnn/backends/RefWorkloads/Multiplication.hpp index d0b033e7ec..54fcac51c1 100644 --- a/src/armnn/backends/RefWorkloads/Multiplication.hpp +++ b/src/armnn/backends/RefWorkloads/Multiplication.hpp @@ -5,12 +5,16 @@ #pragma once +#include <armnn/Tensor.hpp> + namespace armnn { -void Multiplication(const float* in0, - const float* in1, - unsigned int numElements, - float* out); +void Multiplication(const TensorShape& inShape0, + const TensorShape& inShape1, + const TensorShape& outShape, + const float* inData0, + const float* inData1, + float* outData); } //namespace armnn diff --git a/src/armnn/backends/RefWorkloads/Pooling2d.cpp b/src/armnn/backends/RefWorkloads/Pooling2d.cpp index 6d15d8a436..a643e67690 100644 --- a/src/armnn/backends/RefWorkloads/Pooling2d.cpp +++ b/src/armnn/backends/RefWorkloads/Pooling2d.cpp @@ -186,8 +186,8 @@ void Pooling2d(const float* in, // Clamp the pooling region inside the valid input area (which includes the padding). // This is necessary because the final pooling in a row may overlap beyond the padding. - hend = std::min(hend, heightInput + padRight); - wend = std::min(wend, widthInput + padBottom); + hend = std::min(hend, heightInput + padBottom); + wend = std::min(wend, widthInput + padRight); float result = defaultInitializer; float poolAreaSize = boost::numeric_cast<float>((hend - hstart) * (wend - wstart)); diff --git a/src/armnn/backends/RefWorkloads/RefMultiplicationFloat32Workload.cpp b/src/armnn/backends/RefWorkloads/RefMultiplicationFloat32Workload.cpp index ed68b1f6db..d7c54d9aad 100644 --- a/src/armnn/backends/RefWorkloads/RefMultiplicationFloat32Workload.cpp +++ b/src/armnn/backends/RefWorkloads/RefMultiplicationFloat32Workload.cpp @@ -17,12 +17,15 @@ void RefMultiplicationFloat32Workload::Execute() const { ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefMultiplicationFloat32Workload_Execute"); - const TensorInfo& inputInfo0 = GetTensorInfo(m_Data.m_Inputs[0]); + const TensorShape& inShape0 = GetTensorInfo(m_Data.m_Inputs[0]).GetShape(); + const TensorShape& inShape1 = GetTensorInfo(m_Data.m_Inputs[1]).GetShape(); + const TensorShape& outShape = GetTensorInfo(m_Data.m_Outputs[0]).GetShape(); float* outputData = GetOutputTensorDataFloat(0, m_Data); const float* inputData0 = GetInputTensorDataFloat(0, m_Data); const float* inputData1 = GetInputTensorDataFloat(1, m_Data); - Multiplication(inputData0, inputData1, inputInfo0.GetNumElements(), outputData); + + Multiplication(inShape0, inShape1, outShape, inputData0, inputData1, outputData); } } //namespace armnn diff --git a/src/armnn/backends/RefWorkloads/RefMultiplicationUint8Workload.cpp b/src/armnn/backends/RefWorkloads/RefMultiplicationUint8Workload.cpp index 2e6f0e6c8b..d5c4afd87c 100644 --- a/src/armnn/backends/RefWorkloads/RefMultiplicationUint8Workload.cpp +++ b/src/armnn/backends/RefWorkloads/RefMultiplicationUint8Workload.cpp @@ -27,10 +27,9 @@ void RefMultiplicationUint8Workload::Execute() const auto dequant1 = Dequantize(GetInputTensorDataU8(1, m_Data), inputInfo1); std::vector<float> results(outputInfo.GetNumElements()); - Multiplication(dequant0.data(), - dequant1.data(), - inputInfo0.GetNumElements(), - results.data()); + Multiplication( + inputInfo0.GetShape(), inputInfo1.GetShape(), outputInfo.GetShape(), + dequant0.data(), dequant1.data(),results.data()); Quantize(GetOutputTensorDataU8(0, m_Data), results.data(), outputInfo); } diff --git a/src/armnn/backends/RefWorkloads/Splitter.hpp b/src/armnn/backends/RefWorkloads/Splitter.hpp index 67f6c100f9..74c4cb4e18 100644 --- a/src/armnn/backends/RefWorkloads/Splitter.hpp +++ b/src/armnn/backends/RefWorkloads/Splitter.hpp @@ -41,6 +41,7 @@ void Splitter(const SplitterQueueDescriptor& data) //split view extents are defined by the size of (the corresponding) input tensor const TensorInfo& outputInfo = GetTensorInfo(data.m_Outputs[viewIdx]); + BOOST_ASSERT(outputInfo.GetNumDimensions() == inputInfo0.GetNumDimensions()); // check all dimensions to see if this element is inside the given input view bool insideView = true; diff --git a/src/armnn/backends/WorkloadData.cpp b/src/armnn/backends/WorkloadData.cpp index 96a37802f1..c951fc5d8d 100644 --- a/src/armnn/backends/WorkloadData.cpp +++ b/src/armnn/backends/WorkloadData.cpp @@ -502,16 +502,13 @@ void MultiplicationQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) c { ValidateTwoInputs(workloadInfo, "MultiplicationQueueDescriptor"); ValidateSingleOutput(workloadInfo, "MultiplicationQueueDescriptor"); - ValidateTensorShapesMatch(workloadInfo.m_InputTensorInfos[0], - workloadInfo.m_InputTensorInfos[1], - "MultiplicationQueueDescriptor", - "first input", - "second input"); - ValidateTensorShapesMatch(workloadInfo.m_InputTensorInfos[0], - workloadInfo.m_OutputTensorInfos[0], - "MultiplicationQueueDescriptor", - "input", - "output"); + + ValidateBroadcastTensorShapesMatch(workloadInfo.m_InputTensorInfos[0], + workloadInfo.m_InputTensorInfos[1], + workloadInfo.m_OutputTensorInfos[0], + "MultiplicationQueueDescriptor", + "first input", + "second input"); } void BatchNormalizationQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const diff --git a/src/armnn/backends/test/ArmComputeCl.cpp b/src/armnn/backends/test/ArmComputeCl.cpp index 5933cebc80..c45a82db63 100644 --- a/src/armnn/backends/test/ArmComputeCl.cpp +++ b/src/armnn/backends/test/ArmComputeCl.cpp @@ -103,7 +103,7 @@ ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleAveragePooling2d, IgnorePaddingSimpleAve ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleAveragePooling2dUint8, IgnorePaddingSimpleAveragePooling2dUint8Test) ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleAveragePooling2dNoPadding, IgnorePaddingSimpleAveragePooling2dNoPaddingTest) ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleAveragePooling2dNoPaddingUint8, - IgnorePaddingSimpleAveragePooling2dNoPaddingUint8Test) + IgnorePaddingSimpleAveragePooling2dNoPaddingUint8Test) ARMNN_AUTO_TEST_CASE(IgnorePaddingAveragePooling2dSize3, IgnorePaddingAveragePooling2dSize3Test) ARMNN_AUTO_TEST_CASE(IgnorePaddingAveragePooling2dSize3Uint8, IgnorePaddingAveragePooling2dSize3Uint8Test) @@ -114,6 +114,12 @@ ARMNN_AUTO_TEST_CASE(UNSUPPORTED_IgnorePaddingL2Pooling2dSize3Uint8, IgnorePaddi ARMNN_AUTO_TEST_CASE(SimpleAveragePooling2d, SimpleAveragePooling2dTest) ARMNN_AUTO_TEST_CASE(SimpleAveragePooling2dUint8, SimpleAveragePooling2dUint8Test) +ARMNN_AUTO_TEST_CASE(IgnorePaddingAveragePooling2dSize3x2Stride2x2, + IgnorePaddingAveragePooling2dSize3x2Stride2x2Test, + false) +ARMNN_AUTO_TEST_CASE(IgnorePaddingAveragePooling2dSize3x2Stride2x2NoPadding, + IgnorePaddingAveragePooling2dSize3x2Stride2x2Test, + true) ARMNN_AUTO_TEST_CASE(LargeTensorsAveragePooling2d, LargeTensorsAveragePooling2dTest) ARMNN_AUTO_TEST_CASE(LargeTensorsAveragePooling2dUint8, LargeTensorsAveragePooling2dUint8Test) @@ -136,6 +142,8 @@ ARMNN_AUTO_TEST_CASE(AddBroadcast1Element, AdditionBroadcast1ElementTest) // Mul ARMNN_AUTO_TEST_CASE(SimpleMultiplication, MultiplicationTest) +ARMNN_AUTO_TEST_CASE(MultiplicationBroadcast1Element, MultiplicationBroadcast1ElementTest) +ARMNN_AUTO_TEST_CASE(MultiplicationBroadcast1DVector, MultiplicationBroadcast1DVectorTest) // Batch Norm ARMNN_AUTO_TEST_CASE(BatchNorm, BatchNormTest) @@ -194,6 +202,9 @@ ARMNN_AUTO_TEST_CASE(SimpleReshapeUint8, SimpleReshapeUint8Test) // Permute ARMNN_AUTO_TEST_CASE(SimplePermuteFloat32, SimplePermuteFloat32Test) ARMNN_AUTO_TEST_CASE(SimplePermuteUint8, SimplePermuteUint8Test) +ARMNN_AUTO_TEST_CASE(PermuteFloat32ValueSet1, PermuteFloat32ValueSet1Test) +ARMNN_AUTO_TEST_CASE(PermuteFloat32ValueSet2, PermuteFloat32ValueSet2Test) +ARMNN_AUTO_TEST_CASE(PermuteFloat32ValueSet3, PermuteFloat32ValueSet3Test) // ============================================================================ // COMPARE tests diff --git a/src/armnn/backends/test/ArmComputeNeon.cpp b/src/armnn/backends/test/ArmComputeNeon.cpp index dd8a668940..a81b7cdcd7 100644 --- a/src/armnn/backends/test/ArmComputeNeon.cpp +++ b/src/armnn/backends/test/ArmComputeNeon.cpp @@ -141,6 +141,7 @@ ARMNN_AUTO_TEST_CASE(SimpleMaxPooling2dSize3x3Stride2x4, SimpleMaxPooling2dSize3 ARMNN_AUTO_TEST_CASE(SimpleMaxPooling2dSize3x3Stride2x4Uint8, SimpleMaxPooling2dSize3x3Stride2x4Uint8Test, true) ARMNN_AUTO_TEST_CASE(SimpleAveragePooling2d, SimpleAveragePooling2dTest) ARMNN_AUTO_TEST_CASE(SimpleAveragePooling2dUint8, SimpleAveragePooling2dUint8Test) + ARMNN_AUTO_TEST_CASE(LargeTensorsAveragePooling2d, LargeTensorsAveragePooling2dTest) ARMNN_AUTO_TEST_CASE(LargeTensorsAveragePooling2dUint8, LargeTensorsAveragePooling2dUint8Test) @@ -170,6 +171,11 @@ ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleAveragePooling2dNoPaddingUint8, IgnorePaddingSimpleAveragePooling2dNoPaddingUint8Test) ARMNN_AUTO_TEST_CASE(IgnorePaddingAveragePooling2dSize3, IgnorePaddingAveragePooling2dSize3Test) ARMNN_AUTO_TEST_CASE(IgnorePaddingAveragePooling2dSize3Uint8, IgnorePaddingAveragePooling2dSize3Uint8Test) +ARMNN_AUTO_TEST_CASE(IgnorePaddingAveragePooling2dSize3x2Stride2x2, + IgnorePaddingAveragePooling2dSize3x2Stride2x2Test, false) +ARMNN_AUTO_TEST_CASE(IgnorePaddingAveragePooling2dSize3x2Stride2x2NoPadding, + IgnorePaddingAveragePooling2dSize3x2Stride2x2Test, + true) ARMNN_AUTO_TEST_CASE(IgnorePaddingSimpleL2Pooling2d, IgnorePaddingSimpleL2Pooling2dTest) ARMNN_AUTO_TEST_CASE(UNSUPPORTED_IgnorePaddingSimpleL2Pooling2dUint8, IgnorePaddingSimpleL2Pooling2dUint8Test) @@ -281,6 +287,10 @@ ARMNN_AUTO_TEST_CASE(SimpleReshapeUint8, SimpleReshapeUint8Test) // Permute ARMNN_AUTO_TEST_CASE(SimplePermuteFloat32, SimplePermuteFloat32Test) ARMNN_AUTO_TEST_CASE(SimplePermuteUint8, SimplePermuteUint8Test) +ARMNN_AUTO_TEST_CASE(PermuteFloat32ValueSet1, PermuteFloat32ValueSet1Test) +ARMNN_AUTO_TEST_CASE(PermuteFloat32ValueSet2, PermuteFloat32ValueSet2Test) +ARMNN_AUTO_TEST_CASE(PermuteFloat32ValueSet3, PermuteFloat32ValueSet3Test) + // ============================================================================ // COMPARE tests diff --git a/src/armnn/backends/test/LayerTests.cpp b/src/armnn/backends/test/LayerTests.cpp index 76681f9a93..9eed2dbf78 100644 --- a/src/armnn/backends/test/LayerTests.cpp +++ b/src/armnn/backends/test/LayerTests.cpp @@ -1005,31 +1005,22 @@ LayerTestResult<float,4> CompareAdditionTest(armnn::IWorkloadFactory& workloadFa return ret; } -LayerTestResult<float,4> MultiplicationTest(armnn::IWorkloadFactory& workloadFactory) -{ - const unsigned int width = 2; - const unsigned int height = 2; - const unsigned int channelCount = 2; - const unsigned int batchSize = 2; - - armnn::TensorInfo inputTensorInfo0; - armnn::TensorInfo inputTensorInfo1; - armnn::TensorInfo outputTensorInfo; - - constexpr unsigned int shape[] = { batchSize, channelCount, height, width }; - constexpr std::size_t dimensionCount = std::extent<decltype(shape)>::value; - - inputTensorInfo0 = armnn::TensorInfo(dimensionCount, shape, armnn::DataType::Float32); - inputTensorInfo1 = armnn::TensorInfo(dimensionCount, shape, armnn::DataType::Float32); - outputTensorInfo = armnn::TensorInfo(dimensionCount, shape, armnn::DataType::Float32); - - auto input0 = MakeTensor<float, 4>(inputTensorInfo0, std::vector<float>({ - 1, 1, 1, 1, 2, 2, 2, 2, - 3, 3, 3, 3, 4, 4, 4, 4 })); - - auto input1 = MakeTensor<float, 4>(inputTensorInfo1, std::vector<float>({ - 2, 2, 2, 2, 3, 3, 3, 3, - 4, 4, 4, 4, 5, 5, 5, 5 })); +namespace { +LayerTestResult<float,4> MultiplicationTestHelper(armnn::IWorkloadFactory& workloadFactory, + const unsigned int shape0[4], + const std::vector<float> & values0, + const unsigned int shape1[4], + const std::vector<float> & values1, + const unsigned int outShape[4], + const std::vector<float> & outValues) +{ + const size_t dimensionCount = 4; + armnn::TensorInfo inputTensorInfo0{dimensionCount, shape0, armnn::DataType::Float32}; + armnn::TensorInfo inputTensorInfo1{dimensionCount, shape1, armnn::DataType::Float32}; + armnn::TensorInfo outputTensorInfo{dimensionCount, outShape, armnn::DataType::Float32}; + + auto input0 = MakeTensor<float, 4>(inputTensorInfo0, values0); + auto input1 = MakeTensor<float, 4>(inputTensorInfo1, values1); LayerTestResult<float,4> ret(outputTensorInfo); @@ -1056,11 +1047,84 @@ LayerTestResult<float,4> MultiplicationTest(armnn::IWorkloadFactory& workloadFac CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - ret.outputExpected = MakeTensor<float, 4>(outputTensorInfo, std::vector<float>({ + ret.outputExpected = MakeTensor<float, 4>(outputTensorInfo, outValues); + return ret; +} +} // anonymous namespace + + +LayerTestResult<float,4> MultiplicationTest(armnn::IWorkloadFactory& workloadFactory) +{ + const unsigned int width = 2; + const unsigned int height = 2; + const unsigned int channelCount = 2; + const unsigned int batchSize = 2; + + unsigned int shape[] = { batchSize, channelCount, height, width }; + + std::vector<float> input0({ + 1, 1, 1, 1, 2, 2, 2, 2, + 3, 3, 3, 3, 4, 4, 4, 4 }); + + std::vector<float> input1({ + 2, 2, 2, 2, 3, 3, 3, 3, + 4, 4, 4, 4, 5, 5, 5, 5 }); + + std::vector<float> output({ 2, 2, 2, 2, 6, 6, 6, 6, - 12, 12, 12, 12, 20, 20, 20, 20 })); + 12, 12, 12, 12, 20, 20, 20, 20 }); - return ret; + return MultiplicationTestHelper(workloadFactory, + shape, + input0, + shape, + input1, + shape, + output); +} + +LayerTestResult<float, 4> MultiplicationBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory) +{ + unsigned int shape0[] = { 1, 2, 2, 2 }; + std::vector<float> input0({ 1, 2, 3, 4, 5, 6, 7, 8}); + + unsigned int shape1[] = { 1, 1, 1, 1 }; + std::vector<float> input1({ 2 }); + + std::vector<float> output({ 2, 4, 6, 8, 10, 12, 14, 16}); + + return MultiplicationTestHelper(workloadFactory, + shape0, + input0, + shape1, + input1, + shape0, + output); +} + +LayerTestResult<float, 4> MultiplicationBroadcast1DVectorTest(armnn::IWorkloadFactory& workloadFactory) +{ + unsigned int shape0[] = { 1, 3, 3, 2 }; + std::vector<float> input0({ + 1, 2, 3, 4, 5, 6, + 7, 8, 9, 10, 11, 12, + 13, 14, 15, 16, 17, 18}); + + unsigned int shape1[] = { 1, 1, 1, 2 }; + std::vector<float> input1({ 1, 2 }); + + std::vector<float> output({ + 1, 4, 3, 8, 5, 12, + 7, 16, 9, 20, 11, 24, + 13, 28, 15, 32, 17, 36}); + + return MultiplicationTestHelper(workloadFactory, + shape0, + input0, + shape1, + input1, + shape0, + output); } LayerTestResult<float,4> CompareMultiplicationTest(armnn::IWorkloadFactory& workloadFactory, @@ -3253,69 +3317,59 @@ LayerTestResult<uint8_t, 4> AdditionUint8Test(armnn::IWorkloadFactory& workloadF return result; } -LayerTestResult<uint8_t, 4> MultiplicationUint8Test(armnn::IWorkloadFactory& workloadFactory) +namespace { - unsigned int batchSize = 1; - unsigned int channels = 2; - unsigned int height = 2; - unsigned int width = 3; +LayerTestResult<uint8_t, 4> MultiplicationUint8TestHelper(armnn::IWorkloadFactory& workloadFactory, + const unsigned int shape0[4], + const std::vector<uint8_t> & values0, + float scale0, + int32_t offset0, + const unsigned int shape1[4], + const std::vector<uint8_t> & values1, + float scale1, + int32_t offset1, + const unsigned int outShape[4], + const std::vector<uint8_t> & outValues, + float outScale, + int32_t outOffset) +{ + armnn::TensorInfo inputTensorInfo0(4, shape0, armnn::DataType::QuantisedAsymm8); + armnn::TensorInfo inputTensorInfo1(4, shape1, armnn::DataType::QuantisedAsymm8); + armnn::TensorInfo outputTensorInfo(4, outShape, armnn::DataType::QuantisedAsymm8); - armnn::TensorInfo inputTensorInfo1, inputTensorInfo2; - armnn::TensorInfo outputTensorInfo; + inputTensorInfo0.SetQuantizationScale(scale0); + inputTensorInfo0.SetQuantizationOffset(offset0); - const unsigned int shape[] = { batchSize, channels, height, width }; - inputTensorInfo1 = armnn::TensorInfo(4, shape, armnn::DataType::QuantisedAsymm8); - inputTensorInfo1.SetQuantizationScale(4.0f); - inputTensorInfo1.SetQuantizationOffset(1); + inputTensorInfo1.SetQuantizationScale(scale1); + inputTensorInfo1.SetQuantizationOffset(offset1); - inputTensorInfo2 = armnn::TensorInfo(4, shape, armnn::DataType::QuantisedAsymm8); - inputTensorInfo2.SetQuantizationScale(3.0f); - inputTensorInfo2.SetQuantizationOffset(-2); + outputTensorInfo.SetQuantizationScale(outScale); + outputTensorInfo.SetQuantizationOffset(outOffset); - outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::QuantisedAsymm8); - outputTensorInfo.SetQuantizationScale(1366.255f); // Scale/offset chosen to have output values out of range - outputTensorInfo.SetQuantizationOffset(-5); + auto input0 = MakeTensor<uint8_t, 4>(inputTensorInfo0, values0); + auto input1 = MakeTensor<uint8_t, 4>(inputTensorInfo1, values1); - // See dequantized values to the right - auto input1 = MakeTensor<uint8_t, 4>(inputTensorInfo1, std::vector<uint8_t>( - { - 62, 37, 3, 172, 13, 111, // 244, 144, 8, 684, 48, 440, - 188, 20, 73, 31, 23, 31 // 748, 76, 288, 120, 88, 120 - })); - - // See dequantized values to the right - auto input2 = MakeTensor<uint8_t, 4>(inputTensorInfo1, std::vector<uint8_t>( - { - 126, 240, 252, 183, 121, 247, // 384, 726, 762, 555, 369, 747, - 48, 115, 151, 79, 78, 97 // 150, 351, 459, 243, 240, 297 - })); - - // See dequantized values to the right LayerTestResult<uint8_t, 4> result(outputTensorInfo); - result.outputExpected = MakeTensor<uint8_t, 4>(outputTensorInfo, std::vector<uint8_t>( - { - 64, 72, 0, 255, 8, 236, // 93696, 104544, 6096(clamped), 379620(clamped), 17712, 328680, - 77, 15, 92, 16, 10, 21, // 112200, 26676, 132192, 29160, 21120, 35640 - })); + result.outputExpected = MakeTensor<uint8_t, 4>(outputTensorInfo, outValues); + std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2); std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); armnn::MultiplicationQueueDescriptor data; armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); - AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); + AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get()); + AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMultiplication(data, info); + inputHandle0->Allocate(); inputHandle1->Allocate(); - inputHandle2->Allocate(); outputHandle->Allocate(); + CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]); CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); - CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]); workload->Execute(); @@ -3323,6 +3377,113 @@ LayerTestResult<uint8_t, 4> MultiplicationUint8Test(armnn::IWorkloadFactory& wor return result; } +} // anonymous namespace + +LayerTestResult<uint8_t, 4> MultiplicationUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + unsigned int batchSize = 1; + unsigned int channels = 2; + unsigned int height = 2; + unsigned int width = 3; + const unsigned int shape[] = { batchSize, channels, height, width }; + + // See dequantized values to the right + std::vector<uint8_t> input0({ + 62, 37, 3, 172, 13, 111, // 244, 144, 8, 684, 48, 440, + 188, 20, 73, 31, 23, 31 // 748, 76, 288, 120, 88, 120 + }); + + // See dequantized values to the right + std::vector<uint8_t> input1({ + 126, 240, 252, 183, 121, 247, // 384, 726, 762, 555, 369, 747, + 48, 115, 151, 79, 78, 97 // 150, 351, 459, 243, 240, 297 + }); + + // See dequantized values to the right + std::vector<uint8_t> output( + { + 64, 72, 0, 255, 8, 236, // 93696, 104544, 6096(clamped), 379620(clamped), 17712, 328680, + 77, 15, 92, 16, 10, 21, // 112200, 26676, 132192, 29160, 21120, 35640 + }); + + return MultiplicationUint8TestHelper(workloadFactory, + shape, + input0, + 4.0f, + 1, + shape, + input1, + 3.0f, + -2, + shape, + output, + 1366.255f, // Scale/offset chosen to have output values out of range + -5); +} + +LayerTestResult<uint8_t, 4> MultiplicationBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + const unsigned int shape0[] = { 1, 2, 2, 3 }; + const unsigned int shape1[] = { 1, 1, 1, 1 }; + + std::vector<uint8_t> input0({ + 1, 2, 3, 4, 5, 6, + 7, 8, 9, 10, 11, 12 + }); + + std::vector<uint8_t> input1({2}); + + std::vector<uint8_t> output({ + 2, 4, 6, 8, 10, 12, + 14, 16, 18, 20, 22, 24 + }); + + return MultiplicationUint8TestHelper(workloadFactory, + shape0, + input0, + 1.0f, + 0, + shape1, + input1, + 1.0f, + 0, + shape0, + output, + 1.0f, + 0); +} + +LayerTestResult<uint8_t, 4> MultiplicationBroadcast1DVectorUint8Test(armnn::IWorkloadFactory& workloadFactory) +{ + const unsigned int shape0[] = { 1, 2, 2, 3 }; + const unsigned int shape1[] = { 1, 1, 1, 3 }; + + std::vector<uint8_t> input0({ + 1, 2, 3, 4, 5, 6, + 7, 8, 9, 10, 11, 12 + }); + + std::vector<uint8_t> input1({1, 2, 3}); + + std::vector<uint8_t> output({ + 1, 4, 9, 4, 10, 18, + 7, 16, 27, 10, 22, 36 + }); + + return MultiplicationUint8TestHelper(workloadFactory, + shape0, + input0, + 1.0f, + 0, + shape1, + input1, + 1.0f, + 0, + shape0, + output, + 1.0f, + 0); +} LayerTestResult<uint8_t, 4> ResizeBilinearNopUint8Test(armnn::IWorkloadFactory& workloadFactory) { @@ -3702,6 +3863,12 @@ LayerTestResult<uint8_t, 4> SimpleAveragePooling2dUint8Test(armnn::IWorkloadFact return SimpleAveragePooling2dTestCommon<uint8_t>(workloadFactory, 0.5, -1); } +LayerTestResult<float, 4> IgnorePaddingAveragePooling2dSize3x2Stride2x2Test(armnn::IWorkloadFactory& workloadFactory, + bool forceNoPadding) +{ + return IgnorePaddingAveragePooling2dSize3x2Stride2x2TestCommon<float>(workloadFactory, forceNoPadding); +} + LayerTestResult<float, 4> LargeTensorsAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory) { return LargeTensorsAveragePooling2dTestCommon<float>(workloadFactory); @@ -3882,3 +4049,18 @@ LayerTestResult<uint8_t, 4> SimplePermuteUint8Test(armnn::IWorkloadFactory& work { return SimplePermuteUint8TestCommon(workloadFactory); }; + +LayerTestResult<float, 4> PermuteFloat32ValueSet1Test(armnn::IWorkloadFactory& workloadFactory) +{ + return PermuteFloat32ValueSet1TestCommon(workloadFactory); +}; + +LayerTestResult<float, 4> PermuteFloat32ValueSet2Test(armnn::IWorkloadFactory& workloadFactory) +{ + return PermuteFloat32ValueSet2TestCommon(workloadFactory); +}; + +LayerTestResult<float, 4> PermuteFloat32ValueSet3Test(armnn::IWorkloadFactory& workloadFactory) +{ + return PermuteFloat32ValueSet3TestCommon(workloadFactory); +}; diff --git a/src/armnn/backends/test/LayerTests.hpp b/src/armnn/backends/test/LayerTests.hpp index fc0c9c7b14..36e73e461c 100644 --- a/src/armnn/backends/test/LayerTests.hpp +++ b/src/armnn/backends/test/LayerTests.hpp @@ -82,6 +82,8 @@ LayerTestResult<uint8_t, 4> IgnorePaddingMaxPooling2dSize3Uint8Test(armnn::IWork LayerTestResult<float, 4> SimpleAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory); LayerTestResult<uint8_t, 4> SimpleAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult<float, 4> IgnorePaddingAveragePooling2dSize3x2Stride2x2Test(armnn::IWorkloadFactory& workloadFactory, + bool forceNoPadding); LayerTestResult<float, 4> IgnorePaddingSimpleAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory); LayerTestResult<uint8_t, 4> IgnorePaddingSimpleAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory); LayerTestResult<float, 4> IgnorePaddingSimpleAveragePooling2dNoPaddingTest(armnn::IWorkloadFactory& workloadFactory); @@ -187,6 +189,8 @@ LayerTestResult<float, 4> CompareActivationTest(armnn::IWorkloadFactory& worklo unsigned int batchSize); LayerTestResult<float, 4> MultiplicationTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult<float, 4> MultiplicationBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult<float, 4> MultiplicationBroadcast1DVectorTest(armnn::IWorkloadFactory& workloadFactory); LayerTestResult<float, 4> CompareMultiplicationTest(armnn::IWorkloadFactory& workloadFactory, armnn::IWorkloadFactory& refWorkloadFactory); @@ -260,6 +264,8 @@ LayerTestResult<uint8_t, 2> CompareSoftmaxUint8Test(armnn::IWorkloadFactory& wor float beta); LayerTestResult<uint8_t, 4> MultiplicationUint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult<uint8_t, 4> MultiplicationBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult<uint8_t, 4> MultiplicationBroadcast1DVectorUint8Test(armnn::IWorkloadFactory& workloadFactory); LayerTestResult<uint8_t, 4> SimpleConvolution2d3x5Uint8Test(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled); @@ -303,3 +309,6 @@ LayerTestResult<float, 2> FullyConnectedLargeTest(armnn::IWorkloadFactory& workl LayerTestResult<float, 4> SimplePermuteFloat32Test(armnn::IWorkloadFactory& workloadFactory); LayerTestResult<uint8_t, 4> SimplePermuteUint8Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult<float, 4> PermuteFloat32ValueSet1Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult<float, 4> PermuteFloat32ValueSet2Test(armnn::IWorkloadFactory& workloadFactory); +LayerTestResult<float, 4> PermuteFloat32ValueSet3Test(armnn::IWorkloadFactory& workloadFactory); diff --git a/src/armnn/backends/test/PermuteTestImpl.hpp b/src/armnn/backends/test/PermuteTestImpl.hpp index 4eafa1a211..4ecffedc91 100644 --- a/src/armnn/backends/test/PermuteTestImpl.hpp +++ b/src/armnn/backends/test/PermuteTestImpl.hpp @@ -119,3 +119,107 @@ LayerTestResult<uint8_t, 4> SimplePermuteUint8TestCommon(armnn::IWorkloadFactory return SimplePermuteTestImpl<uint8_t>(workloadFactory, descriptor, inputTensorInfo, outputTensorInfo, input, outputExpected); } + +LayerTestResult<float, 4> +PermuteFloat32ValueSet1TestCommon(armnn::IWorkloadFactory& workloadFactory) +{ + armnn::TensorInfo inputTensorInfo; + armnn::TensorInfo outputTensorInfo; + + unsigned int inputShape[] = { 1, 2, 2, 3 }; + unsigned int outputShape[] = { 1, 3, 2, 2 }; + + armnn::PermuteDescriptor descriptor; + descriptor.m_DimMappings = {0U, 2U, 3U, 1U}; + + inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); + outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); + + std::vector<float> input = std::vector<float>( + { + 1.0f, 2.0f, 3.0f, + 11.0f, 12.0f, 13.0f, + 21.0f, 22.0f, 23.0f, + 31.0f, 32.0f, 33.0f, + }); + + std::vector<float> outputExpected = std::vector<float>( + { + 1.0f, 11.0f, 21.0f, 31.0f, + 2.0f, 12.0f, 22.0f, 32.0f, + 3.0f, 13.0f, 23.0f, 33.0f, + }); + + return SimplePermuteTestImpl<float>(workloadFactory, descriptor, inputTensorInfo, + outputTensorInfo, input, outputExpected); +} + +LayerTestResult<float, 4> +PermuteFloat32ValueSet2TestCommon(armnn::IWorkloadFactory& workloadFactory) +{ + armnn::TensorInfo inputTensorInfo; + armnn::TensorInfo outputTensorInfo; + + unsigned int inputShape[] = { 1, 3, 2, 2 }; + unsigned int outputShape[] = { 1, 2, 2, 3 }; + + armnn::PermuteDescriptor descriptor; + descriptor.m_DimMappings = {0U, 3U, 1U, 2U}; + + inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); + outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); + + std::vector<float> input = std::vector<float>( + { + 1.0f, 11.0f, 21.0f, 31.0f, + 2.0f, 12.0f, 22.0f, 32.0f, + 3.0f, 13.0f, 23.0f, 33.0f, + }); + + std::vector<float> outputExpected = std::vector<float>( + { + 1.0f, 2.0f, 3.0f, + 11.0f, 12.0f, 13.0f, + 21.0f, 22.0f, 23.0f, + 31.0f, 32.0f, 33.0f, + }); + + return SimplePermuteTestImpl<float>(workloadFactory, descriptor, inputTensorInfo, + outputTensorInfo, input, outputExpected); +} + +LayerTestResult<float, 4> +PermuteFloat32ValueSet3TestCommon(armnn::IWorkloadFactory& workloadFactory) +{ + armnn::TensorInfo inputTensorInfo; + armnn::TensorInfo outputTensorInfo; + + unsigned int inputShape[] = { 1, 2, 3, 3 }; + unsigned int outputShape[] = { 1, 3, 2, 3 }; + + armnn::PermuteDescriptor descriptor; + descriptor.m_DimMappings = {0U, 2U, 3U, 1U}; + + inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); + outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); + + std::vector<float> input = std::vector<float>( + { + 1.0f, 2.0f, 3.0f, + 11.0f, 12.0f, 13.0f, + 21.0f, 22.0f, 23.0f, + 31.0f, 32.0f, 33.0f, + 41.0f, 42.0f, 43.0f, + 51.0f, 52.0f, 53.0f, + }); + + std::vector<float> outputExpected = std::vector<float>( + { + 1.0f, 11.0f, 21.0f, 31.0f, 41.0f, 51.0f, + 2.0f, 12.0f, 22.0f, 32.0f, 42.0f, 52.0f, + 3.0f, 13.0f, 23.0f, 33.0f, 43.0f, 53.0f, + }); + + return SimplePermuteTestImpl<float>(workloadFactory, descriptor, inputTensorInfo, + outputTensorInfo, input, outputExpected); +} diff --git a/src/armnn/backends/test/Pooling2dTestImpl.hpp b/src/armnn/backends/test/Pooling2dTestImpl.hpp index fc84ddb2ca..ab9fd6d6fb 100644 --- a/src/armnn/backends/test/Pooling2dTestImpl.hpp +++ b/src/armnn/backends/test/Pooling2dTestImpl.hpp @@ -720,6 +720,83 @@ LayerTestResult<T, 4> SimpleMaxPooling2dSize2x2Stride2x2TestCommon(armnn::IWorkl return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); } +// +// Tests max pooling with the following parameters: +// +// Pooling size: 3x2 +// Stride: (2,2) +// input size: 3x2 +// channels: 1 +// batch size: 1 +// +template<typename T> +LayerTestResult<T, 4> IgnorePaddingAveragePooling2dSize3x2Stride2x2TestCommon( + armnn::IWorkloadFactory& workloadFactory, + bool forceNoPadding, + float qScale = 1.0f, + int32_t qOffset = 0) +{ + armnn::Pooling2dDescriptor descriptor; + descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; + descriptor.m_PoolWidth = 3; + descriptor.m_PoolHeight = 2; + descriptor.m_StrideX = 2; + descriptor.m_StrideY = 2; + descriptor.m_PadLeft = (forceNoPadding) ? 0 : 1; + descriptor.m_PadRight = descriptor.m_PadLeft; + descriptor.m_PadTop = 0; + descriptor.m_PadBottom = 0; + descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; + descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; + + unsigned int inputWidth = 3; + unsigned int inputHeight = 2; + unsigned int outputWidth = + (inputWidth + descriptor.m_PadLeft + descriptor.m_PadRight + descriptor.m_StrideX - descriptor.m_PoolWidth) / + descriptor.m_StrideX; + unsigned int outputHeight = + (inputHeight + descriptor.m_PadTop + descriptor.m_PadBottom + descriptor.m_StrideY - descriptor.m_PoolHeight) / + descriptor.m_StrideY; + unsigned int channels = 1; + unsigned int batchSize = 1; + + std::vector<float> inputData = { + 3.0f, 6.0f, 9.0f, + 12.0f, 15.0f, 18.0f, + }; + + std::vector<float> expectedOutputDataWithPadding = { + 6.0f, 8.0f, + }; + + std::vector<float> expectedOutputDataNoPadding = { + 10.5f, + }; + + armnn::TensorInfo inputTensorInfo({ batchSize, channels, inputHeight, inputWidth }, armnn::GetDataType<T>()); + + // Scale and offset should match input - we're just calculating average values. + armnn::TensorInfo outputTensorInfo({ batchSize, channels, outputHeight, outputWidth }, armnn::GetDataType<T>()); + + // Set quantization parameters if the requested type is a quantized type. + if(armnn::IsQuantizedType<T>()) + { + inputTensorInfo.SetQuantizationScale(qScale); + inputTensorInfo.SetQuantizationOffset(qOffset); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + } + + auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData)); + + auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, + forceNoPadding ? QuantizedVector<T>(qScale, qOffset, expectedOutputDataNoPadding) : + QuantizedVector<T>(qScale, qOffset, expectedOutputDataWithPadding)); + + return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); +} + + template<typename T> LayerTestResult<T, 4> IgnorePaddingSimpleMaxPooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, float qScale = 1.0f, diff --git a/src/armnn/backends/test/Reference.cpp b/src/armnn/backends/test/Reference.cpp index 87d82f1781..89e5db8e43 100644 --- a/src/armnn/backends/test/Reference.cpp +++ b/src/armnn/backends/test/Reference.cpp @@ -76,6 +76,10 @@ ARMNN_AUTO_TEST_CASE(IgnorePaddingL2Pooling2dSize3Uint8, IgnorePaddingL2Pooling2 ARMNN_AUTO_TEST_CASE(SimpleAveragePooling2d, SimpleAveragePooling2dTest) ARMNN_AUTO_TEST_CASE(SimpleAveragePooling2dUint8, SimpleAveragePooling2dUint8Test) +ARMNN_AUTO_TEST_CASE(IgnorePaddingAveragePooling2dSize3x2Stride2x2, + IgnorePaddingAveragePooling2dSize3x2Stride2x2Test, false) +ARMNN_AUTO_TEST_CASE(IgnorePaddingAveragePooling2dSize3x2Stride2x2NoPadding, + IgnorePaddingAveragePooling2dSize3x2Stride2x2Test, true) ARMNN_AUTO_TEST_CASE(LargeTensorsAveragePooling2d, LargeTensorsAveragePooling2dTest) ARMNN_AUTO_TEST_CASE(LargeTensorsAveragePooling2dUint8, LargeTensorsAveragePooling2dUint8Test) @@ -158,7 +162,11 @@ ARMNN_AUTO_TEST_CASE(AddBroadcast1ElementUint8, AdditionBroadcast1ElementUint8Te // Mul ARMNN_AUTO_TEST_CASE(SimpleMultiplication, MultiplicationTest) +ARMNN_AUTO_TEST_CASE(MultiplicationBroadcast1Element, MultiplicationBroadcast1ElementTest) +ARMNN_AUTO_TEST_CASE(MultiplicationBroadcast1DVector, MultiplicationBroadcast1DVectorTest) ARMNN_AUTO_TEST_CASE(MultiplicationUint8, MultiplicationUint8Test) +ARMNN_AUTO_TEST_CASE(MultiplicationBroadcast1ElementUint8, MultiplicationBroadcast1ElementUint8Test) +ARMNN_AUTO_TEST_CASE(MultiplicationBroadcast1DVectorUint8, MultiplicationBroadcast1DVectorUint8Test) // Batch Norm ARMNN_AUTO_TEST_CASE(BatchNorm, BatchNormTest) @@ -227,5 +235,8 @@ ARMNN_AUTO_TEST_CASE(SimpleReshapeUint8, SimpleReshapeUint8Test) // Permute ARMNN_AUTO_TEST_CASE(SimplePermuteFloat32, SimplePermuteFloat32Test) ARMNN_AUTO_TEST_CASE(SimplePermuteUint8, SimplePermuteUint8Test) +ARMNN_AUTO_TEST_CASE(PermuteFloat32ValueSet1, PermuteFloat32ValueSet1Test) +ARMNN_AUTO_TEST_CASE(PermuteFloat32ValueSet2, PermuteFloat32ValueSet2Test) +ARMNN_AUTO_TEST_CASE(PermuteFloat32ValueSet3, PermuteFloat32ValueSet3Test) BOOST_AUTO_TEST_SUITE_END() diff --git a/src/armnn/optimizations/Optimization.hpp b/src/armnn/optimizations/Optimization.hpp index 89e03ff88d..f81071891b 100644 --- a/src/armnn/optimizations/Optimization.hpp +++ b/src/armnn/optimizations/Optimization.hpp @@ -13,7 +13,7 @@ namespace armnn class Optimization { public: - virtual void Run(Graph& graph, Graph::Iterator& pos) const = 0; + virtual void Run(Graph& graph, Layer& base) const = 0; protected: ~Optimization() = default; }; @@ -23,22 +23,20 @@ protected: // (curiously recurring template pattern). // For details, see https://en.wikipedia.org/wiki/Curiously_recurring_template_pattern -/// Wrapper Optimization base class that calls Wrapped::Run for every layer of type BaseType. -/// - Wrapped class mustn't remove the base layer. -/// - Base layer is removed if left unconnected after applying the wrapped optimization. +/// Wrapper Optimization base class that calls Wrapped::Run() for every layer of type BaseType. +/// - Wrapped class mustn't remove the base layer. The optimizer will remove it if left unconnected +/// after applying each optimization. template <typename BaseType, typename Wrapped> class OptimizeForTypeImpl : public armnn::Optimization, public Wrapped { public: using Wrapped::Wrapped; - void Run(Graph& graph, Graph::Iterator& pos) const override + void Run(Graph& graph, Layer& base) const override { - Layer* const base = *pos; - - if (base->GetType() == LayerEnumOf<BaseType>()) + if (base.GetType() == LayerEnumOf<BaseType>()) { - Wrapped::Run(graph, *boost::polymorphic_downcast<BaseType*>(base)); + Wrapped::Run(graph, *boost::polymorphic_downcast<BaseType*>(&base)); } } @@ -46,16 +44,16 @@ protected: ~OptimizeForTypeImpl() = default; }; -/// Specialization that calls Wrapped::Run for any layer type +/// Specialization that calls Wrapped::Run() for any layer type template <typename Wrapped> class OptimizeForTypeImpl<Layer, Wrapped> : public armnn::Optimization, public Wrapped { public: using Wrapped::Wrapped; - void Run(Graph& graph, Graph::Iterator& pos) const override + void Run(Graph& graph, Layer& base) const override { - Wrapped::Run(graph, **pos); + Wrapped::Run(graph, base); } protected: @@ -70,9 +68,10 @@ public: }; /// Wrapper Optimization class that calls Wrapped::Run for every connection BaseType -> ChildType. -/// - Wrapped class mustn't remove the base layer. +/// - Wrapped class mustn't remove the base layer. The optimizer will remove it if left unconnected +/// after applying each optimization. /// - Wrapped class mustn't affect existing connections in the same output. It might add new ones. -/// - Base and children layers are removed if left unconnected after applying the wrapped optimization. +/// - Children layers are removed if left unconnected after applying the wrapped optimization. template <typename BaseType, typename ChildType, typename Wrapped> class OptimizeForConnectionImpl : public Wrapped { diff --git a/src/armnn/optimizations/OptimizeConsecutiveReshapes.hpp b/src/armnn/optimizations/OptimizeConsecutiveReshapes.hpp index deb49c6884..9a926a57a4 100644 --- a/src/armnn/optimizations/OptimizeConsecutiveReshapes.hpp +++ b/src/armnn/optimizations/OptimizeConsecutiveReshapes.hpp @@ -18,8 +18,8 @@ public: /// Inserts an equivalent ReshapeLayer that bypasses both for that connection. void Run(Graph& graph, InputSlot& connection) const { - auto& base = connection.GetConnectedOutputSlot()->GetOwningLayer(); - auto& child = connection.GetOwningLayer(); + Layer& base = connection.GetConnectedOutputSlot()->GetOwningLayer(); + Layer& child = connection.GetOwningLayer(); BOOST_ASSERT(base.GetType() == LayerType::Reshape); BOOST_ASSERT(child.GetType() == LayerType::Reshape); diff --git a/src/armnn/optimizations/SquashEqualSiblings.hpp b/src/armnn/optimizations/SquashEqualSiblings.hpp index 2dfe91fdcc..c5ce28e723 100644 --- a/src/armnn/optimizations/SquashEqualSiblings.hpp +++ b/src/armnn/optimizations/SquashEqualSiblings.hpp @@ -26,19 +26,29 @@ public: if (!child.IsOutputUnconnected()) { OutputSlot& baseOutput = *connection.GetConnectedOutputSlot(); - auto& comparableChild = *boost::polymorphic_downcast<Comparable*>(&child); - for (auto&& it : baseOutput.GetConnections()) + if (baseOutput.GetNumConnections() > 1) { - Layer& sibling = it->GetOwningLayer(); - if ((&sibling != &child) && comparableChild.IsEqual(sibling)) + auto& comparableChild = *boost::polymorphic_downcast<Comparable*>(&child); + + Layer* lowestPriorityChild = &child; + for (auto&& it : baseOutput.GetConnections()) { - // Bypass sibling. It will be removed as it's left unconnected. - auto siblingOut = sibling.BeginOutputSlots(); - for (auto childOut = child.BeginOutputSlots(); childOut != child.EndOutputSlots(); ++childOut) + Layer* sibling = &it->GetOwningLayer(); + if ((sibling != lowestPriorityChild) && comparableChild.IsEqual(*sibling)) { - siblingOut->MoveAllConnections(*childOut); - ++siblingOut; + if (sibling->GetPriority() < lowestPriorityChild->GetPriority()) + { + std::swap(sibling, lowestPriorityChild); + } + // Bypass sibling. It will be removed as it's left unconnected. + auto siblingOut = sibling->BeginOutputSlots(); + for (auto lowestPriorityChildOut = lowestPriorityChild->BeginOutputSlots(); + lowestPriorityChildOut != lowestPriorityChild->EndOutputSlots(); ++lowestPriorityChildOut) + { + siblingOut->MoveAllConnections(*lowestPriorityChildOut); + ++siblingOut; + } } } } diff --git a/src/armnn/test/Network_test.cpp b/src/armnn/test/Network_test.cpp index 523d47b169..057caa0505 100644 --- a/src/armnn/test/Network_test.cpp +++ b/src/armnn/test/Network_test.cpp @@ -29,6 +29,64 @@ bool AreAllLayerInputSlotsConnected(const armnn::IConnectableLayer& layer) BOOST_AUTO_TEST_SUITE(Network) +BOOST_AUTO_TEST_CASE(LayerGuids) +{ + armnn::Network net; + armnn::LayerGuid inputId = net.AddInputLayer(0)->GetGuid(); + armnn::LayerGuid addId = net.AddAdditionLayer()->GetGuid(); + armnn::LayerGuid outputId = net.AddOutputLayer(0)->GetGuid(); + + BOOST_TEST(inputId != addId); + BOOST_TEST(addId != outputId); + BOOST_TEST(inputId != outputId); +} + +BOOST_AUTO_TEST_CASE(SerializeToDot) +{ + armnn::Network net; + + //define layers + auto input = net.AddInputLayer(0); + auto add = net.AddAdditionLayer(); + auto output = net.AddOutputLayer(0); + + // connect layers + input->GetOutputSlot(0).Connect(add->GetInputSlot(0)); + input->GetOutputSlot(0).Connect(add->GetInputSlot(1)); + add->GetOutputSlot(0).Connect(output->GetInputSlot(0)); + + armnn::TensorShape shape({4}); + armnn::TensorInfo info(shape, armnn::DataType::Float32); + input->GetOutputSlot(0).SetTensorInfo(info); + add->GetOutputSlot(0).SetTensorInfo(info); + + armnn::DeviceSpec spec; + spec.DefaultComputeDevice = armnn::Compute::CpuAcc; + armnn::IOptimizedNetworkPtr optimizedNet = armnn::Optimize(net, spec); + + std::ostringstream ss; + optimizedNet->SerializeToDot(ss); + + auto inputId = input->GetGuid(); + auto addId = add->GetGuid(); + auto outputId = output->GetGuid(); + + std::stringstream expected; + expected << + "digraph Optimized {\n" + " node [shape=\"record\"];\n" + " edge [fontsize=8 fontcolor=\"blue\" fontname=\"arial-bold\"];\n" + " " << inputId << " [label=\"{Input}\"];\n" + " " << addId << " [label=\"{Addition}\"];\n" + " " << outputId << " [label=\"{Output}\"];\n" + " " << inputId << " -> " << addId << " [label=< [4] >];\n" + " " << inputId << " -> " << addId << " [label=< [4] >];\n" + " " << addId << " -> " << outputId << " [label=< [4] >];\n" + "}\n"; + + BOOST_TEST(ss.str() == expected.str()); +} + BOOST_AUTO_TEST_CASE(NetworkBasic) { armnn::Network net; diff --git a/src/armnn/test/OptimizerTests.cpp b/src/armnn/test/OptimizerTests.cpp new file mode 100644 index 0000000000..da26fba76e --- /dev/null +++ b/src/armnn/test/OptimizerTests.cpp @@ -0,0 +1,334 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// See LICENSE file in the project root for full license information. +// +#include <boost/test/unit_test.hpp> + +#include "armnn/ArmNN.hpp" +#include "Graph.hpp" +#include "Optimizer.hpp" + +namespace +{ +template <typename LayerT> +bool IsLayerOfType(const armnn::Layer* const layer) +{ + return (layer->GetType() == armnn::LayerEnumOf<LayerT>()); +} + +bool CheckSequence(const armnn::Graph::ConstIterator first, const armnn::Graph::ConstIterator last) +{ + return (first == last); +} + +/// Check each unary function in Us evaluates true for each correspondent layer in the sequence [first, last) +template <typename U, typename... Us> +bool CheckSequence(const armnn::Graph::ConstIterator first, + const armnn::Graph::ConstIterator last, + U&& u, + Us&&... us) +{ + return u(*first) && CheckSequence(std::next(first), last, us...); +} +} + +BOOST_AUTO_TEST_SUITE(Optimizer) + +BOOST_AUTO_TEST_CASE(OptimizeInversePermutes) +{ + armnn::Graph graph; + + auto output = graph.AddLayer<armnn::OutputLayer>(0, "output"); + + graph.InsertNewLayer<armnn::InputLayer>(output->GetInputSlot(0), 0, "input"); + + // Insert two permutes, one the inverse of the other + graph.InsertNewLayer<armnn::PermuteLayer>(output->GetInputSlot(0), + armnn::PermuteDescriptor({0, 2, 3, 1}), + "perm0231"); + graph.InsertNewLayer<armnn::PermuteLayer>(output->GetInputSlot(0), + armnn::PermuteDescriptor({0, 3, 1, 2}), + "perm0312"); + + BOOST_TEST(CheckSequence(graph.cbegin(), + graph.cend(), + &IsLayerOfType<armnn::InputLayer>, + &IsLayerOfType<armnn::PermuteLayer>, + &IsLayerOfType<armnn::PermuteLayer>, + &IsLayerOfType<armnn::OutputLayer>)); + + armnn::Optimizer::Optimize(graph); + + // The permutes are removed + BOOST_TEST(CheckSequence(graph.cbegin(), + graph.cend(), + &IsLayerOfType<armnn::InputLayer>, + &IsLayerOfType<armnn::OutputLayer>)); +} + +BOOST_AUTO_TEST_CASE(MovePermuteUp) +{ + const armnn::TensorInfo info({ 1, 5, 2, 3 }, armnn::DataType::Float32); + const armnn::TensorInfo permuted({ 1, 3, 5, 2 }, armnn::DataType::Float32); + + armnn::Graph graph; + + armnn::LayerBindingId inputId = 0; + + armnn::Layer* head = graph.AddLayer<armnn::OutputLayer>(0, "output"); + + // Insert permute + head = graph.InsertNewLayer<armnn::PermuteLayer>(head->GetInputSlot(0), + armnn::PermuteDescriptor({ 0, 2, 3, 1 }), ""); + head->GetOutputHandler().SetTensorInfo(permuted); + + // Insert layers that don't care about data format + head = graph.InsertNewLayer<armnn::ActivationLayer>(head->GetInputSlot(0), + armnn::ActivationDescriptor{}, ""); + head->GetOutputHandler().SetTensorInfo(info); + + head = graph.InsertNewLayer<armnn::AdditionLayer>(head->GetInputSlot(0), ""); + head->GetOutputHandler().SetTensorInfo(info); + + // Insert input for 2nd input of Addition + graph.InsertNewLayer<armnn::InputLayer>(head->GetInputSlot(1), inputId++, "") + ->GetOutputHandler().SetTensorInfo(info); + + head = graph.InsertNewLayer<armnn::FakeQuantizationLayer>(head->GetInputSlot(0), + armnn::FakeQuantizationDescriptor{}, ""); + head->GetOutputHandler().SetTensorInfo(info); + + head = graph.InsertNewLayer<armnn::FloorLayer>(head->GetInputSlot(0), ""); + head->GetOutputHandler().SetTensorInfo(info); + + head = graph.InsertNewLayer<armnn::MemCopyLayer>(head->GetInputSlot(0), ""); + head->GetOutputHandler().SetTensorInfo(info); + + head = graph.InsertNewLayer<armnn::MultiplicationLayer>(head->GetInputSlot(0), ""); + head->GetOutputHandler().SetTensorInfo(info); + + // Insert input for 2nd input of Multiplication + graph.InsertNewLayer<armnn::InputLayer>(head->GetInputSlot(1), inputId++, "") + ->GetOutputHandler().SetTensorInfo(info); + + // Insert input + graph.InsertNewLayer<armnn::InputLayer>(head->GetInputSlot(0), inputId++, "") + ->GetOutputHandler().SetTensorInfo(info); + + BOOST_TEST(CheckSequence(graph.cbegin(), + graph.cend(), + &IsLayerOfType<armnn::InputLayer>, + &IsLayerOfType<armnn::InputLayer>, + &IsLayerOfType<armnn::InputLayer>, + &IsLayerOfType<armnn::MultiplicationLayer>, + &IsLayerOfType<armnn::MemCopyLayer>, + &IsLayerOfType<armnn::FloorLayer>, + &IsLayerOfType<armnn::FakeQuantizationLayer>, + &IsLayerOfType<armnn::AdditionLayer>, + &IsLayerOfType<armnn::ActivationLayer>, + &IsLayerOfType<armnn::PermuteLayer>, + &IsLayerOfType<armnn::OutputLayer>)); + + armnn::Optimizer::Optimize(graph); + + // The permute is moved to the top. New permutes for layers with multiple inputs + BOOST_TEST(CheckSequence(graph.cbegin(), + graph.cend(), + &IsLayerOfType<armnn::InputLayer>, + &IsLayerOfType<armnn::InputLayer>, + &IsLayerOfType<armnn::InputLayer>, + &IsLayerOfType<armnn::PermuteLayer>, + &IsLayerOfType<armnn::PermuteLayer>, + &IsLayerOfType<armnn::PermuteLayer>, + &IsLayerOfType<armnn::MultiplicationLayer>, + &IsLayerOfType<armnn::MemCopyLayer>, + &IsLayerOfType<armnn::FloorLayer>, + &IsLayerOfType<armnn::FakeQuantizationLayer>, + &IsLayerOfType<armnn::AdditionLayer>, + &IsLayerOfType<armnn::ActivationLayer>, + &IsLayerOfType<armnn::OutputLayer>)); +} + +BOOST_AUTO_TEST_CASE(PermuteAsReshape) +{ + armnn::Graph graph; + + const armnn::TensorInfo infoIn({ 1, 2, 3, 1 }, armnn::DataType::Float32); + const armnn::TensorInfo infoOut({ 1, 1, 2, 3 }, armnn::DataType::Float32); + + auto output = graph.AddLayer<armnn::OutputLayer>(0, "output"); + + graph.InsertNewLayer<armnn::InputLayer>(output->GetInputSlot(0), 0, "input") + ->GetOutputHandler().SetTensorInfo(infoIn); + + // Insert permute + graph.InsertNewLayer<armnn::PermuteLayer>(output->GetInputSlot(0), + armnn::PermuteDescriptor({ 0, 2, 3, 1 }), "") + ->GetOutputHandler().SetTensorInfo(infoOut); + + BOOST_TEST(CheckSequence(graph.cbegin(), + graph.cend(), + &IsLayerOfType<armnn::InputLayer>, + &IsLayerOfType<armnn::PermuteLayer>, + &IsLayerOfType<armnn::OutputLayer>)); + + armnn::Optimizer::Optimize(graph); + + // The permute is replaced by an equivalent reshape. + + auto checkReshape = [&infoOut](const armnn::Layer* const layer) -> bool + { + const auto reshapeLayer = static_cast<const armnn::ReshapeLayer*>(layer); + return IsLayerOfType<armnn::ReshapeLayer>(layer) && + (reshapeLayer->GetParameters().m_TargetShape == infoOut.GetShape()) && + (reshapeLayer->GetOutputHandler().GetTensorInfo().GetShape() == infoOut.GetShape()); + }; + + BOOST_TEST(CheckSequence(graph.cbegin(), + graph.cend(), + &IsLayerOfType<armnn::InputLayer>, + checkReshape, + &IsLayerOfType<armnn::OutputLayer>)); +} + +BOOST_AUTO_TEST_CASE(OptimizeConsecutiveReshapes) +{ + armnn::Graph graph; + + const armnn::TensorInfo info0({ 1, 2, 3, 5 }, armnn::DataType::Float32); + + auto output = graph.AddLayer<armnn::OutputLayer>(0, "output"); + auto input = graph.InsertNewLayer<armnn::InputLayer>(output->GetInputSlot(0), 0, "input"); + + input->GetOutputHandler().SetTensorInfo(info0); + + { + // Insert two reshapes + const armnn::TensorInfo info1({1, 30, 1, 1}, armnn::DataType::Float32); + const armnn::TensorInfo info2({1, 2, 1, 15}, armnn::DataType::Float32); + + auto reshape1 = graph.InsertNewLayer<armnn::ReshapeLayer>(output->GetInputSlot(0), + armnn::ReshapeDescriptor{ info1.GetShape() }, + "reshape1"); + auto reshape2 = graph.InsertNewLayer<armnn::ReshapeLayer>(output->GetInputSlot(0), + armnn::ReshapeDescriptor{ info2.GetShape() }, + "reshape2"); + + reshape1->GetOutputHandler().SetTensorInfo(info1); + reshape2->GetOutputHandler().SetTensorInfo(info2); + + BOOST_TEST(CheckSequence(graph.cbegin(), + graph.cend(), + &IsLayerOfType<armnn::InputLayer>, + &IsLayerOfType<armnn::ReshapeLayer>, + &IsLayerOfType<armnn::ReshapeLayer>, + &IsLayerOfType<armnn::OutputLayer>)); + + armnn::Optimizer::Optimize(graph); + + auto checkReshape = [&info2](const armnn::Layer* const layer) -> bool + { + const auto reshapeLayer = static_cast<const armnn::ReshapeLayer*>(layer); + return IsLayerOfType<armnn::ReshapeLayer>(layer) && + (reshapeLayer->GetParameters().m_TargetShape == info2.GetShape()) && + (reshapeLayer->GetOutputHandler().GetTensorInfo().GetShape() == info2.GetShape()); + }; + + // The two reshapes are replaced by a single equivalent reshape + BOOST_TEST(CheckSequence(graph.cbegin(), + graph.cend(), + &IsLayerOfType<armnn::InputLayer>, + checkReshape, + &IsLayerOfType<armnn::OutputLayer>)); + } + + { + // Insert a reshape to the input shape + auto reshapeToIn = graph.InsertNewLayer<armnn::ReshapeLayer>(output->GetInputSlot(0), + armnn::ReshapeDescriptor{ info0.GetShape() }, + "reshapeToIn"); + + reshapeToIn->GetOutputHandler().SetTensorInfo(info0); + + armnn::Optimizer::Optimize(graph); + + // The two reshapes are removed + BOOST_TEST(CheckSequence(graph.cbegin(), + graph.cend(), + &IsLayerOfType<armnn::InputLayer>, + &IsLayerOfType<armnn::OutputLayer>)); + } +} + +BOOST_AUTO_TEST_CASE(SquashEqualSiblings) +{ + armnn::Graph graph; + + armnn::LayerBindingId outputId = 0; + + const armnn::TensorInfo info({ 1, 2, 3, 5 }, armnn::DataType::Float32); + const armnn::TensorInfo permuted({ 1, 5, 2, 3 }, armnn::DataType::Float32); + + auto input = graph.AddLayer<armnn::InputLayer>(0, "input"); + input->GetOutputSlot().SetTensorInfo(info); + + // Insert equal permutes, equal reshapes and something else + const armnn::PermuteDescriptor permDesc({ 0, 2, 3, 1 }); + const armnn::ReshapeDescriptor reshapeDesc{ { 1, 3, 1, 5 } }; + + armnn::Layer* layer; + + layer = graph.AddLayer<armnn::PermuteLayer>(permDesc, ""); + layer->GetOutputSlot().SetTensorInfo(permuted); + layer->GetOutputSlot().Connect(graph.AddLayer<armnn::OutputLayer>(outputId++, "")->GetInputSlot(0)); + input->GetOutputSlot().Connect(layer->GetInputSlot(0)); + + layer = graph.AddLayer<armnn::ReshapeLayer>(reshapeDesc, ""); + layer->GetOutputSlot().Connect(graph.AddLayer<armnn::OutputLayer>(outputId++, "")->GetInputSlot(0)); + input->GetOutputSlot().Connect(layer->GetInputSlot(0)); + + layer = graph.AddLayer<armnn::FloorLayer>(""); + layer->GetOutputSlot().Connect(graph.AddLayer<armnn::OutputLayer>(outputId++, "")->GetInputSlot(0)); + input->GetOutputSlot().Connect(layer->GetInputSlot(0)); + + layer = graph.AddLayer<armnn::ReshapeLayer>(reshapeDesc, ""); + layer->GetOutputSlot().Connect(graph.AddLayer<armnn::OutputLayer>(outputId++, "")->GetInputSlot(0)); + input->GetOutputSlot().Connect(layer->GetInputSlot(0)); + + layer = graph.AddLayer<armnn::PermuteLayer>(permDesc, ""); + layer->GetOutputSlot().SetTensorInfo(permuted); + layer->GetOutputSlot().Connect(graph.AddLayer<armnn::OutputLayer>(outputId++, "")->GetInputSlot(0)); + input->GetOutputSlot().Connect(layer->GetInputSlot(0)); + + BOOST_TEST(CheckSequence(graph.cbegin(), + graph.cend(), + &IsLayerOfType<armnn::InputLayer>, + &IsLayerOfType<armnn::PermuteLayer>, + &IsLayerOfType<armnn::ReshapeLayer>, + &IsLayerOfType<armnn::FloorLayer>, + &IsLayerOfType<armnn::ReshapeLayer>, + &IsLayerOfType<armnn::PermuteLayer>, + &IsLayerOfType<armnn::OutputLayer>, + &IsLayerOfType<armnn::OutputLayer>, + &IsLayerOfType<armnn::OutputLayer>, + &IsLayerOfType<armnn::OutputLayer>, + &IsLayerOfType<armnn::OutputLayer>)); + + armnn::Optimizer::Optimize(graph); + + // The permutes and reshapes are squashed. + + BOOST_TEST(CheckSequence(graph.cbegin(), + graph.cend(), + &IsLayerOfType<armnn::InputLayer>, + &IsLayerOfType<armnn::PermuteLayer>, + &IsLayerOfType<armnn::ReshapeLayer>, + &IsLayerOfType<armnn::FloorLayer>, + &IsLayerOfType<armnn::OutputLayer>, + &IsLayerOfType<armnn::OutputLayer>, + &IsLayerOfType<armnn::OutputLayer>, + &IsLayerOfType<armnn::OutputLayer>, + &IsLayerOfType<armnn::OutputLayer>)); +} + +BOOST_AUTO_TEST_SUITE_END() diff --git a/src/armnn/test/RuntimeTests.cpp b/src/armnn/test/RuntimeTests.cpp index 117df5e55a..e42d71c37d 100644 --- a/src/armnn/test/RuntimeTests.cpp +++ b/src/armnn/test/RuntimeTests.cpp @@ -115,7 +115,7 @@ BOOST_AUTO_TEST_CASE(RuntimeMemoryUsage) BOOST_TEST(leakedBefore == leakedAfter); // Add resonable threshold after and before running valgrind with the ACL clear cache function. - BOOST_TEST(reachableAfter - reachableBefore < 30000); + BOOST_TEST(static_cast<long>(reachableAfter) - static_cast<long>(reachableBefore) < 1024); // these are needed because VALGRIND_COUNT_LEAKS is a macro that assigns to the parameters // so they are assigned to, but still considered unused, causing a warning @@ -178,7 +178,18 @@ BOOST_AUTO_TEST_CASE(RuntimeMemoryLeak) // if we're not running under Valgrind, these vars will have been initialised to 0, so this will always pass BOOST_TEST(leakedBefore == leakedAfter); - BOOST_TEST(reachableBefore == reachableAfter); + + #if defined(ARMCOMPUTECL_ENABLED) + // reachableBefore == reachableAfter should hold, but on OpenCL with Android we are still + // not entirely able to control the memory in the OpenCL driver. Testing is showing that + // after this test (which clears all OpenCL memory) we are clearing a little bit more than + // we expect, probably depending on the order in which other tests are run. + BOOST_TEST(reachableBefore - reachableAfter <= 24); + #else + BOOST_TEST(reachableBefore == reachableAfter); + #endif + + BOOST_TEST(reachableBefore >= reachableAfter); // these are needed because VALGRIND_COUNT_LEAKS is a macro that assigns to the parameters // so they are assigned to, but still considered unused, causing a warning |