src/graph/backends/NEON/NEFunctionFactory.cpp

/*
 * Copyright (c) 2018-2019 ARM Limited.
 *
 * SPDX-License-Identifier: MIT
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to
 * deal in the Software without restriction, including without limitation the
 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
 * sell copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in all
 * copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */
#include "arm_compute/graph/backends/NEON/NEFunctionFactory.h"

#include "arm_compute/core/utils/misc/Cast.h"
#include "arm_compute/graph/Graph.h"
#include "arm_compute/graph/GraphContext.h"
#include "arm_compute/graph/Logger.h"
#include "arm_compute/graph/TypePrinter.h"
#include "arm_compute/graph/backends/FunctionHelpers.h"
#include "arm_compute/graph/backends/Utils.h"
#include "arm_compute/graph/nodes/Nodes.h"
#include "arm_compute/runtime/CPP/CPPFunctions.h"
#include "arm_compute/runtime/NEON/NEFunctions.h"
#include "support/ToolchainSupport.h"

using namespace arm_compute::utils::cast;

namespace arm_compute
{
namespace graph
{
namespace backends
{
/** Target specific information structure used to pass information to the layer templates */
struct NETargetInfo
{
    using TensorType         = arm_compute::ITensor;
    using TensorConcreteType = arm_compute::Tensor;
    static Target TargetType;
};

Target NETargetInfo::TargetType = Target::NEON;

/** Collection of CL convolution functions */
struct NEConvolutionLayerFunctions
{
    using GenericConvolutionLayer  = NEConvolutionLayer;
    using GEMMConvolutionLayer     = NEGEMMConvolutionLayer;
    using DirectConvolutionLayer   = NEDirectConvolutionLayer;
    using WinogradConvolutionLayer = NEWinogradConvolutionLayer;
};

/** Collection of CL depthwise convolution functions */
struct NEDepthwiseConvolutionLayerFunctions
{
    using GenericDepthwiseConvolutionLayer   = NEDepthwiseConvolutionLayer;
    using OptimizedDepthwiseConvolutionLayer = NEDepthwiseConvolutionLayerOptimized;
};

/** Collection of CL element-wise functions */
struct NEEltwiseFunctions
{
    using Addition       = NEArithmeticAddition;
    using Subtraction    = NEArithmeticSubtraction;
    using Multiplication = NEPixelWiseMultiplication;
};

/** Function and tensor types to be used inside a NEON fused convolution/batch normalization layer */
struct NEFusedLayerTypes
{
    using ConvolutionLayer          = NEConvolutionLayer;
    using DepthwiseConvolutionLayer = NEDepthwiseConvolutionLayer;
    using FuseBatchNormalization    = NEFuseBatchNormalization;
};

namespace detail
{
// Specialized functions
template <>
std::unique_ptr<IFunction> create_convolution_layer<NEConvolutionLayerFunctions, NETargetInfo>(ConvolutionLayerNode &node,
                                                                                               GraphContext &ctx)
{
    validate_node<NETargetInfo>(node, 3 /* expected inputs */, 1 /* expected outputs */);

    // Extract IO and info
    NETargetInfo::TensorType *input   = get_backing_tensor<NETargetInfo>(node.input(0));
    NETargetInfo::TensorType *weights = get_backing_tensor<NETargetInfo>(node.input(1));
    NETargetInfo::TensorType *biases  = get_backing_tensor<NETargetInfo>(node.input(2));
    NETargetInfo::TensorType *output  = get_backing_tensor<NETargetInfo>(node.output(0));

    const bool is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type());

    if(is_quantized)
    {
        biases->info()->set_data_type(DataType::S32);
    }

    const PadStrideInfo       conv_info      = node.convolution_info();
    const ConvolutionMethod   conv_algorithm = node.convolution_method();
    const ActivationLayerInfo fused_act      = node.fused_activation();

    // Create and configure function (we assume that functions have been validated before creation)
    std::shared_ptr<IMemoryManager> mm = get_memory_manager(ctx, Target::NEON);
    std::unique_ptr<IFunction>      func;
    std::string                     func_name;
    if(conv_algorithm == ConvolutionMethod::Direct)
    {
        std::tie(func, func_name) = create_named_memory_managed_function<NEDirectConvolutionLayer>(
                                        std::string("DirectConvolutionLayer"), mm, input, weights, biases, output, conv_info, fused_act);
    }
    else if(conv_algorithm == ConvolutionMethod::GEMM)
    {
        std::tie(func, func_name) = create_named_memory_managed_function<NEGEMMConvolutionLayer>(
                                        std::string("GEMMConvolutionLayer"), mm, input, weights, biases, output, conv_info, WeightsInfo(), Size2D(1, 1), fused_act);
    }
    else if(conv_algorithm == ConvolutionMethod::Winograd)
    {
        std::tie(func, func_name) = create_named_memory_managed_function<NEWinogradConvolutionLayer>(
                                        std::string("WinogradConvolutionLayer"), mm, input, weights, biases, output, conv_info, fused_act);
    }
    else
    {
        std::tie(func, func_name) = create_named_memory_managed_function<NEConvolutionLayer>(
                                        std::string("ConvolutionLayer"), mm, input, weights, biases, output, conv_info, WeightsInfo(), Size2D(1, 1), fused_act);
    }

    // Log info
    std::ostringstream qss;
    if(is_quantized)
    {
        qss << " Input QuantInfo: " << input->info()->quantization_info()
            << " Weights QuantInfo: " << weights->info()->quantization_info()
            << " Output QuantInfo: " << output->info()->quantization_info();
    }
    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
                               << node.name()
                               << " Type: " << func_name
                               << " Target: " << NETargetInfo::TargetType
                               << " Data Type: " << input->info()->data_type()
                               << qss.str()
                               << " Input shape: " << input->info()->tensor_shape()
                               << " Weights shape: " << weights->info()->tensor_shape()
                               << " Output shape: " << output->info()->tensor_shape()
                               << (fused_act.enabled() ? " " + to_string(fused_act.activation()) : "")
                               << std::endl);
    return func;
}

template <>
std::unique_ptr<IFunction> create_normalization_layer<NENormalizationLayer, NETargetInfo>(NormalizationLayerNode &node, GraphContext &ctx)
{
    validate_node<NETargetInfo>(node, 1 /* expected inputs */, 1 /* expected outputs */);

    // Extract IO and info
    NETargetInfo::TensorType    *input     = get_backing_tensor<NETargetInfo>(node.input(0));
    NETargetInfo::TensorType    *output    = get_backing_tensor<NETargetInfo>(node.output(0));
    const NormalizationLayerInfo norm_info = node.normalization_info();
    ARM_COMPUTE_ERROR_ON(input == nullptr);
    ARM_COMPUTE_ERROR_ON(output == nullptr);

    // Create and configure function
    auto func = support::cpp14::make_unique<NENormalizationLayer>(get_memory_manager(ctx, NETargetInfo::TargetType));
    func->configure(input, output, norm_info);

    // Log info
    ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated "
                               << node.name()
                               << " Type: " << node.type()
                               << " Target: " << NETargetInfo::TargetType
                               << " Data Type: " << input->info()->data_type()
                               << " Input shape: " << input->info()->tensor_shape()
                               << " Output shape: " << output->info()->tensor_shape()
                               << " Normalization info: " << norm_info.type()
                               << std::endl);

    return std::move(func);
}
} // namespace detail

std::unique_ptr<IFunction> NEFunctionFactory::create(INode *node, GraphContext &ctx)
{
    if(node == nullptr)
    {
        return nullptr;
    }

    NodeType type = node->type();
    switch(type)
    {
        case NodeType::ActivationLayer:
            return detail::create_activation_layer<NEActivationLayer, NETargetInfo>(*polymorphic_downcast<ActivationLayerNode *>(node));
        case NodeType::BatchNormalizationLayer:
            return detail::create_batch_normalization_layer<NEBatchNormalizationLayer, NETargetInfo>(*polymorphic_downcast<BatchNormalizationLayerNode *>(node));
        case NodeType::ChannelShuffleLayer:
            return detail::create_channel_shuffle_layer<NEChannelShuffleLayer, NETargetInfo>(*polymorphic_downcast<ChannelShuffleLayerNode *>(node));
        case NodeType::ConvolutionLayer:
            return detail::create_convolution_layer<NEConvolutionLayerFunctions, NETargetInfo>(*polymorphic_downcast<ConvolutionLayerNode *>(node), ctx);
        case NodeType::DeconvolutionLayer:
            return detail::create_deconvolution_layer<NEDeconvolutionLayer, NETargetInfo>(*polymorphic_downcast<DeconvolutionLayerNode *>(node), ctx);
        case NodeType::ConcatenateLayer:
            return detail::create_concatenate_layer<NEConcatenateLayer, NETargetInfo>(*polymorphic_downcast<ConcatenateLayerNode *>(node));
        case NodeType::DepthwiseConvolutionLayer:
            return detail::create_depthwise_convolution_layer<NEDepthwiseConvolutionLayerFunctions, NETargetInfo>(*polymorphic_downcast<DepthwiseConvolutionLayerNode *>(node));
        case NodeType::DetectionOutputLayer:
            return detail::create_detection_output_layer<CPPDetectionOutputLayer, NETargetInfo>(*polymorphic_downcast<DetectionOutputLayerNode *>(node));
        case NodeType::DetectionPostProcessLayer:
            return detail::create_detection_post_process_layer<CPPDetectionPostProcessLayer, NETargetInfo>(*polymorphic_downcast<DetectionPostProcessLayerNode *>(node));
        case NodeType::EltwiseLayer:
            return detail::create_eltwise_layer<NEEltwiseFunctions, NETargetInfo>(*polymorphic_downcast<EltwiseLayerNode *>(node));
        case NodeType::FlattenLayer:
            return detail::create_flatten_layer<NEFlattenLayer, NETargetInfo>(*polymorphic_downcast<FlattenLayerNode *>(node));
        case NodeType::FullyConnectedLayer:
            return detail::create_fully_connected_layer<NEFullyConnectedLayer, NETargetInfo>(*polymorphic_downcast<FullyConnectedLayerNode *>(node), ctx);
        case NodeType::FusedConvolutionBatchNormalizationLayer:
            return detail::create_fused_convolution_batch_normalization_layer<NEFusedLayerTypes, NETargetInfo>(*polymorphic_downcast<FusedConvolutionBatchNormalizationNode *>(node));
        case NodeType::FusedDepthwiseConvolutionBatchNormalizationLayer:
            return detail::create_fused_depthwise_convolution_batch_normalization_layer<NEFusedLayerTypes, NETargetInfo>(*polymorphic_downcast<FusedDepthwiseConvolutionBatchNormalizationNode *>(node));
        case NodeType::NormalizationLayer:
            return detail::create_normalization_layer<NENormalizationLayer, NETargetInfo>(*polymorphic_downcast<NormalizationLayerNode *>(node), ctx);
        case NodeType::PermuteLayer:
            return detail::create_permute_layer<NEPermute, NETargetInfo>(*polymorphic_downcast<PermuteLayerNode *>(node));
        case NodeType::PoolingLayer:
            return detail::create_pooling_layer<NEPoolingLayer, NETargetInfo>(*polymorphic_downcast<PoolingLayerNode *>(node));
        case NodeType::PriorBoxLayer:
            return detail::create_priorbox_layer<NEPriorBoxLayer, NETargetInfo>(*polymorphic_downcast<PriorBoxLayerNode *>(node));
        case NodeType::QuantizationLayer:
            return detail::create_quantization_layer<NEQuantizationLayer, NETargetInfo>(*polymorphic_downcast<QuantizationLayerNode *>(node));
        case NodeType::ReorgLayer:
            return detail::create_reorg_layer<NEReorgLayer, NETargetInfo>(*polymorphic_downcast<ReorgLayerNode *>(node));
        case NodeType::ReshapeLayer:
            return detail::create_reshape_layer<NEReshapeLayer, NETargetInfo>(*polymorphic_downcast<ReshapeLayerNode *>(node));
        case NodeType::ResizeLayer:
            return detail::create_resize_layer<NEScale, NETargetInfo>(*polymorphic_downcast<ResizeLayerNode *>(node));
        case NodeType::SoftmaxLayer:
            return detail::create_softmax_layer<NESoftmaxLayer, NETargetInfo>(*polymorphic_downcast<SoftmaxLayerNode *>(node), ctx);
        case NodeType::StackLayer:
            return detail::create_stack_layer<NEStackLayer, NETargetInfo>(*polymorphic_downcast<StackLayerNode *>(node));
        case NodeType::UpsampleLayer:
            return detail::create_upsample_layer<NEUpsampleLayer, NETargetInfo>(*polymorphic_downcast<UpsampleLayerNode *>(node), ctx);
        case NodeType::YOLOLayer:
            return detail::create_yolo_layer<NEYOLOLayer, NETargetInfo>(*polymorphic_downcast<YOLOLayerNode *>(node), ctx);
        default:
            return nullptr;
    }
}
} // namespace backends
} // namespace graph
} // namespace arm_compute