diff options
Diffstat (limited to 'src/armnn')
| -rw-r--r-- | src/armnn/BackendHelper.cpp | 16 | ||||
| -rw-r--r-- | src/armnn/Descriptors.cpp | 82 | ||||
| -rw-r--r-- | src/armnn/ILayerSupport.cpp | 2 | ||||
| -rw-r--r-- | src/armnn/LayersFwd.hpp | 4 | ||||
| -rw-r--r-- | src/armnn/Network.cpp | 11 | ||||
| -rw-r--r-- | src/armnn/Network.hpp | 3 | ||||
| -rw-r--r-- | src/armnn/layers/BatchMatMulLayer.cpp | 97 | ||||
| -rw-r--r-- | src/armnn/layers/BatchMatMulLayer.hpp | 46 |
8 files changed, 259 insertions, 2 deletions
diff --git a/src/armnn/BackendHelper.cpp b/src/armnn/BackendHelper.cpp index 5b5bece783..6638709d6f 100644 --- a/src/armnn/BackendHelper.cpp +++ b/src/armnn/BackendHelper.cpp @@ -179,6 +179,22 @@ bool LayerSupportHandle::IsArgMinMaxSupported(const TensorInfo& input, reasonIfUnsupported); } +bool LayerSupportHandle::IsBatchMatMulSupported(const TensorInfo& input0, + const TensorInfo& input1, + const TensorInfo& output, + const BatchMatMulDescriptor& descriptor, + Optional<std::string&> reasonIfUnsupported) +{ + TensorInfos infos{input0, input1, output}; + + return m_LayerSupport->IsLayerSupported(LayerType::BatchMatMul, + infos, + descriptor, + EmptyOptional(), + EmptyOptional(), + reasonIfUnsupported); +} + bool LayerSupportHandle::IsBatchNormalizationSupported(const TensorInfo& input, const TensorInfo& output, const TensorInfo& mean, diff --git a/src/armnn/Descriptors.cpp b/src/armnn/Descriptors.cpp index c740fd03ad..f9576271d5 100644 --- a/src/armnn/Descriptors.cpp +++ b/src/armnn/Descriptors.cpp @@ -455,4 +455,86 @@ uint32_t DepthwiseConvolution2dDescriptor::GetNumInputs() const return armnn::GetNumInputs(m_BiasEnabled); } +std::pair<std::pair<unsigned int, unsigned int>, std::pair<unsigned int, unsigned int>> +BatchMatMulDescriptor::GetAxesToMul( + const BatchMatMulDescriptor& desc, + const TensorShape& tensorXShape, + const TensorShape& tensorYShape) +{ + // May refactor to just work on one input per call - makes it less confusing and also + // allows more flexibility (i.e. in Layer output shape inference) + + auto xNumDims = tensorXShape.GetNumDimensions(); + auto yNumDims = tensorYShape.GetNumDimensions(); + + std::pair<unsigned int, unsigned int> xAxes = { xNumDims-2, xNumDims-1 }; + std::pair<unsigned int, unsigned int> yAxes = { yNumDims-2, yNumDims-1 }; + + if(desc.m_DataLayoutX.has_value()) + { + switch(desc.m_DataLayoutX.value()) + { + case DataLayout::NDHWC: + case DataLayout::NHWC: + xAxes.first -= 1; + xAxes.second -= 1; + break; + case DataLayout::NCDHW: + case DataLayout::NCHW: + default: + break; + } + } + + if(desc.m_DataLayoutY.has_value()) + { + switch(desc.m_DataLayoutY.value()) + { + case DataLayout::NDHWC: + case DataLayout::NHWC: + yAxes.first -= 1; + yAxes.second -= 1; + break; + case DataLayout::NCDHW: + case DataLayout::NCHW: + default: + break; + } + } + + return { xAxes, yAxes}; +} + +std::pair<std::vector<unsigned int>, std::vector<unsigned int>> BatchMatMulDescriptor::GetAxesNotMul( + const BatchMatMulDescriptor& desc, + const TensorShape& inputXShape, + const TensorShape& inputYShape) +{ + // May refactor to just work on one input per call - makes it less confusing and also + // allows more flexibility (i.e. in Layer output shape inference) + auto axesToMul = BatchMatMulDescriptor::GetAxesToMul(desc, inputXShape, inputYShape); + + std::vector<unsigned int> axesXNotMul; + std::vector<unsigned int> axesYNotMul; + + for(unsigned int i = 0; i < inputXShape.GetNumDimensions(); i++) + { + if(i == axesToMul.first.first || i == axesToMul.first.second) + { + continue; + } + axesXNotMul.push_back(i); + } + for(unsigned int i = 0; i < inputYShape.GetNumDimensions(); i++) + { + if(i == axesToMul.second.first || i == axesToMul.second.second) + { + continue; + } + axesYNotMul.push_back(i); + } + + return { axesXNotMul, axesYNotMul }; +} + } diff --git a/src/armnn/ILayerSupport.cpp b/src/armnn/ILayerSupport.cpp index 5366b13088..8099782750 100644 --- a/src/armnn/ILayerSupport.cpp +++ b/src/armnn/ILayerSupport.cpp @@ -13,7 +13,7 @@ namespace armnn { ARMNN_NO_DEPRECATE_WARN_BEGIN -// IsLayerSupport() forwards to the deprecated virtual methods depending on input LayerType. +// IsLayerSupported() forwards to the deprecated virtual methods depending on input LayerType. // Allows backends continue to behave as before maintaining backward compatibility. bool ILayerSupport::IsLayerSupported(const LayerType& type, const std::vector<TensorInfo>& infos, diff --git a/src/armnn/LayersFwd.hpp b/src/armnn/LayersFwd.hpp index dcfb91b65a..acac1f9988 100644 --- a/src/armnn/LayersFwd.hpp +++ b/src/armnn/LayersFwd.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2017 Arm Ltd. All rights reserved. +// Copyright © 2017 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // #pragma once @@ -9,6 +9,7 @@ #include "layers/ActivationLayer.hpp" #include "layers/AdditionLayer.hpp" #include "layers/ArgMinMaxLayer.hpp" +#include "layers/BatchMatMulLayer.hpp" #include "layers/BatchNormalizationLayer.hpp" #include "layers/BatchToSpaceNdLayer.hpp" #include "layers/CastLayer.hpp" @@ -110,6 +111,7 @@ constexpr LayerType LayerEnumOf(const T* = nullptr); DECLARE_LAYER(Activation) DECLARE_LAYER(Addition) DECLARE_LAYER(ArgMinMax) +DECLARE_LAYER(BatchMatMul) DECLARE_LAYER(BatchNormalization) DECLARE_LAYER(BatchToSpaceNd) DECLARE_LAYER(Cast) diff --git a/src/armnn/Network.cpp b/src/armnn/Network.cpp index 5d443068ce..ef9f4e7522 100644 --- a/src/armnn/Network.cpp +++ b/src/armnn/Network.cpp @@ -456,6 +456,12 @@ IConnectableLayer* INetwork::AddChannelShuffleLayer(const ChannelShuffleDescript return pNetworkImpl->AddChannelShuffleLayer(descriptor, name); } +IConnectableLayer* INetwork::AddBatchMatMulLayer(const BatchMatMulDescriptor &descriptor, + const char* name) +{ + return pNetworkImpl->AddBatchMatMulLayer(descriptor, name); +} + void INetwork::ExecuteStrategy(IStrategy& strategy) const { return pNetworkImpl->ExecuteStrategy(strategy); @@ -2876,6 +2882,11 @@ IConnectableLayer* NetworkImpl::AddUnidirectionalSequenceLstmLayer( return layer; } +IConnectableLayer* NetworkImpl::AddBatchMatMulLayer(const BatchMatMulDescriptor& desc, const char* name) +{ + return m_Graph->AddLayer<BatchMatMulLayer>(desc, name); +} + IConnectableLayer* NetworkImpl::AddPrecompiledLayer(const PreCompiledDescriptor& preCompiledDescriptor, CompiledBlobPtr compiledBlobPtr, const Optional<BackendId>& backend, diff --git a/src/armnn/Network.hpp b/src/armnn/Network.hpp index a4387e65c0..19a0286e95 100644 --- a/src/armnn/Network.hpp +++ b/src/armnn/Network.hpp @@ -49,6 +49,9 @@ public: IConnectableLayer* AddArgMinMaxLayer(const ArgMinMaxDescriptor& desc, const char* name = nullptr); + IConnectableLayer* AddBatchMatMulLayer(const BatchMatMulDescriptor& desc, + const char* name = nullptr); + IConnectableLayer* AddBatchNormalizationLayer(const BatchNormalizationDescriptor& desc, const ConstTensor& mean, const ConstTensor& variance, diff --git a/src/armnn/layers/BatchMatMulLayer.cpp b/src/armnn/layers/BatchMatMulLayer.cpp new file mode 100644 index 0000000000..501de2d091 --- /dev/null +++ b/src/armnn/layers/BatchMatMulLayer.cpp @@ -0,0 +1,97 @@ +// +// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// +#include "BatchMatMulLayer.hpp" + +#include <armnn/backends/WorkloadFactory.hpp> +#include "layers/LayerCloneBase.hpp" + +namespace armnn +{ + +BatchMatMulLayer::BatchMatMulLayer(const BatchMatMulDescriptor& param, const char* name) + : LayerWithParameters(2, 1, LayerType::BatchMatMul, param, name) +{} + +std::unique_ptr<IWorkload> BatchMatMulLayer::CreateWorkload(const IWorkloadFactory& factory) const +{ + BatchMatMulQueueDescriptor descriptor; + SetAdditionalInfo(descriptor); + + return factory.CreateWorkload(LayerType::BatchMatMul, descriptor, PrepInfoAndDesc(descriptor)); +} + +BatchMatMulLayer* BatchMatMulLayer::Clone(Graph& graph) const +{ + auto layer = CloneBase<BatchMatMulLayer>(graph, m_Param, GetName()); + + return std::move(layer); +} + +std::vector<TensorShape> BatchMatMulLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const +{ + ARMNN_ASSERT(inputShapes.size() == 2); + + TensorShape inputXShape = inputShapes[0]; + TensorShape inputYShape = inputShapes[1]; + + // Note: Take into account what pre-adjoint or pre-transposing will do to the inferred output shape + + TensorShape& longerInput = inputXShape.GetNumDimensions() >= inputYShape.GetNumDimensions()? + inputXShape:inputYShape; + TensorShape& shorterInput = inputXShape.GetNumDimensions() >= inputYShape.GetNumDimensions()? + inputYShape:inputXShape; + + unsigned int inputNumDimsOffset = longerInput.GetNumDimensions() - shorterInput.GetNumDimensions(); + + unsigned int outputNumDimensions = longerInput.GetNumDimensions(); + + std::vector<unsigned int> tensorDimensions(outputNumDimensions, 0); + + auto axesToMul = BatchMatMulDescriptor::GetAxesToMul(m_Param, inputXShape, inputYShape); + const auto& longerAxesToMul = (axesToMul.first.first >= axesToMul.second.first && + axesToMul.first.second >= axesToMul.second.second) ? + axesToMul.first : axesToMul.second; + + for (unsigned int i = 0; i < outputNumDimensions; ++i) + { + if (i == longerAxesToMul.first) + { + tensorDimensions[i] = &shorterInput == &inputXShape ? inputXShape[i - inputNumDimsOffset] : inputXShape[i]; + } + else if(i == longerAxesToMul.second) + { + tensorDimensions[i] = &shorterInput == &inputYShape ? inputYShape[i - inputNumDimsOffset] : inputYShape[i]; + } + else // The other dimensions not to be multiplied (but may be broadcasted) + { + // Does NOT validate whether it's a valid broadcast - that's done in the validate func in WorkloadData.cpp + tensorDimensions[i] = static_cast<int>(i) - static_cast<int>(inputNumDimsOffset) < 0 ? + longerInput[i] : + std::max(longerInput[i], shorterInput[i - inputNumDimsOffset]); + } + } + + auto outputShape = TensorShape(outputNumDimensions, tensorDimensions.data()); + return std::vector<TensorShape>({ outputShape }); +} + +void BatchMatMulLayer::ValidateTensorShapesFromInputs() +{ + VerifyLayerConnections(2, CHECK_LOCATION()); + + const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape(); + + VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod); + + auto inferredShapes = InferOutputShapes({ + GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(), + GetInputSlot(1).GetConnection()->GetTensorInfo().GetShape() }); + + ARMNN_ASSERT(inferredShapes.size() == 1); + + ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "BatchMatMulLayer"); +} + +} // namespace armnn
\ No newline at end of file diff --git a/src/armnn/layers/BatchMatMulLayer.hpp b/src/armnn/layers/BatchMatMulLayer.hpp new file mode 100644 index 0000000000..8dc79d33c4 --- /dev/null +++ b/src/armnn/layers/BatchMatMulLayer.hpp @@ -0,0 +1,46 @@ +// +// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include "LayerWithParameters.hpp" + +namespace armnn +{ + +class BatchMatMulLayer : public LayerWithParameters<BatchMatMulDescriptor> +{ +public: + /// Makes a workload for the BatchMatMul type. + /// @param [in] graph The graph where this layer can be found. + /// @param [in] factory The workload factory which will create the workload. + /// @return A pointer to the created workload, or nullptr if not created. + virtual std::unique_ptr<IWorkload> CreateWorkload(const IWorkloadFactory &factory) const override; + + /// Creates a dynamically-allocated copy of this layer. + /// @param [in] graph The graph into which this layer is being cloned. + BatchMatMulLayer* Clone(Graph &graph) const override; + + /// Infers the output shape from the given input shapes. + /// @param [in] inputShapes The vector of input shapes for BatchMatMul. + /// @return A vector of inferred output shape. + std::vector<TensorShape> InferOutputShapes(const std::vector<TensorShape>& inputShapes) const override; + + /// Check if the input tensor shapes + /// will lead to a valid configuration of @ref BatchMatMulLayer. + /// @param [in] shapeInferenceMethod Indicates if output shape shall be overwritten or just validated. + void ValidateTensorShapesFromInputs() override; + +protected: + /// Constructor to create a BatchMatMulLayer. + /// @param [in] param BatchMatMulDescriptor to configure optional parameters for batch matrix multiplication + /// @param [in] name Optional name for the layer + BatchMatMulLayer(const BatchMatMulDescriptor& param, const char* name); + + /// Default destructor + ~BatchMatMulLayer() = default; +}; + +}
\ No newline at end of file |