From d9eb27597eabe5b7c17520f4f9b3f8a282d72573 Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Tue, 3 Apr 2018 13:44:29 +0100 Subject: COMPMID-797: Switch to new graph. - Cleaned up build system Change-Id: If2faa27ee5b31fa8b972836960ab3ef671059c8d Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/126435 Tested-by: Jenkins Reviewed-by: Pablo Tello --- src/graph2/backends/NEON/NEFunctionFactory.cpp | 563 ------------------------- 1 file changed, 563 deletions(-) delete mode 100644 src/graph2/backends/NEON/NEFunctionFactory.cpp (limited to 'src/graph2/backends/NEON/NEFunctionFactory.cpp') diff --git a/src/graph2/backends/NEON/NEFunctionFactory.cpp b/src/graph2/backends/NEON/NEFunctionFactory.cpp deleted file mode 100644 index 933210377d..0000000000 --- a/src/graph2/backends/NEON/NEFunctionFactory.cpp +++ /dev/null @@ -1,563 +0,0 @@ -/* - * Copyright (c) 2018 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/graph2/backends/NEON/NEFunctionFactory.h" - -#include "arm_compute/core/utils/misc/Cast.h" -#include "arm_compute/graph2/Graph.h" -#include "arm_compute/graph2/GraphContext.h" -#include "arm_compute/graph2/Logger.h" -#include "arm_compute/graph2/TypePrinter.h" -#include "arm_compute/graph2/backends/Utils.h" -#include "arm_compute/graph2/nodes/Nodes.h" -#include "arm_compute/runtime/NEON/NEFunctions.h" -#include "support/ToolchainSupport.h" - -using namespace arm_compute::utils::cast; - -namespace arm_compute -{ -namespace graph2 -{ -namespace backends -{ -namespace -{ -/** Returns backing tensor of a given tensor - * - * @param[in] tensor Tensor to extract the backing tensor from - * - * @return Backing tensor if present else nullptr - */ -arm_compute::ITensor *get_backing_tensor(arm_compute::graph2::Tensor *tensor) -{ - return ((tensor == nullptr) || (tensor->handle() == nullptr)) ? nullptr : &tensor->handle()->tensor(); -} - -/** Create a backend activation layer function - * - * @param[in] node Node to create the backend function for - * - * @return Backend activation layer function - */ -std::unique_ptr create_activation_layer(ActivationLayerNode &node) -{ - ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating NEON ActivationLayerNode node with ID : " << node.id() << " and Name: " << node.name() << std::endl); - ARM_COMPUTE_ERROR_ON(node.num_inputs() != 1); - ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1); - - // Extract IO and info - ITensor *input = get_backing_tensor(node.input(0)); - ITensor *output = get_backing_tensor(node.output(0)); - const ActivationLayerInfo act_info = node.activation_info(); - - // Create function - auto func = support::cpp14::make_unique(); - func->configure(input, output, act_info); - - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated NEActivationLayer" - << " Data Type: " << input->info()->data_type() - << " Shape: " << input->info()->tensor_shape() - << " Activation function: " << act_info.activation() - << " a: " << act_info.a() - << " b: " << act_info.b() - << " InPlace : " << is_in_place_operation(input, output) - << std::endl); - - return std::move(func); -} - -/** Create a backend batch normalization layer function - * - * @param[in] node Node to create the backend function for - * - * @return Backend batch normalization layer function - */ -std::unique_ptr create_batch_normalization_layer(BatchNormalizationLayerNode &node) -{ - ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating NEON BatchNormalization node with ID : " << node.id() << " and Name: " << node.name() << std::endl); - - // TODO (geopin01) : Var and mean are compulsory, switch function to accept nullptr as beta and/or gamma - ARM_COMPUTE_ERROR_ON(node.num_inputs() != 5); - ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1); - - // Extract IO and info - ITensor *input = get_backing_tensor(node.input(0)); - ITensor *mean = get_backing_tensor(node.input(1)); - ITensor *var = get_backing_tensor(node.input(2)); - ITensor *beta = get_backing_tensor(node.input(3)); - ITensor *gamma = get_backing_tensor(node.input(4)); - ITensor *output = get_backing_tensor(node.output(0)); - const float epsilon = node.epsilon(); - const ActivationLayerInfo fused_act = node.fused_activation(); - - // Create and configure function - auto func = support::cpp14::make_unique(); - func->configure(input, output, mean, var, beta, gamma, epsilon, fused_act); - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated NEBatchNormalizationLayer" - << " Data Type: " << input->info()->data_type() - << " Shape: " << input->info()->tensor_shape() - << " Epsilon: " << epsilon << " " - << (fused_act.enabled() ? to_string(fused_act.activation()) : "") - << " InPlace : " << is_in_place_operation(input, output) - << std::endl); - - return std::move(func); -} - -/** Create a backend convolution layer function - * - * @param[in] node Node to create the backend function for - * - * @return Backend convolution layer function - */ -std::unique_ptr create_convolution_layer(ConvolutionLayerNode &node, GraphContext &ctx) -{ - ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating NEON ConvolutionLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl); - ARM_COMPUTE_ERROR_ON(node.num_inputs() != 3); - ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1); - - // Extract IO and info - ITensor *input = get_backing_tensor(node.input(0)); - ITensor *weights = get_backing_tensor(node.input(1)); - ITensor *biases = get_backing_tensor(node.input(2)); - ITensor *output = get_backing_tensor(node.output(0)); - const PadStrideInfo conv_info = node.convolution_info(); - const ConvolutionMethod conv_algorithm = node.convolution_method(); - - // Create and configure function (we assume that functions have been validated before creation) - std::shared_ptr mm = get_memory_manager(ctx, Target::NEON); - std::unique_ptr func; - std::string func_name; - if(conv_algorithm == ConvolutionMethod::DIRECT) - { - std::tie(func, func_name) = create_named_memory_managed_function(std::string("NEDirectConvolutionLayer"), mm, - input, weights, biases, output, conv_info); - } - else if(conv_algorithm == ConvolutionMethod::GEMM) - { - std::tie(func, func_name) = create_named_memory_managed_function(std::string("NEGEMMConvolutionLayer"), mm, - input, weights, biases, output, conv_info); - } - else if(conv_algorithm == ConvolutionMethod::WINOGRAD) - { - std::tie(func, func_name) = create_named_memory_managed_function(std::string("NEWinogradLayer"), mm, - input, weights, biases, output, conv_info); - } - else - { - std::tie(func, func_name) = create_named_memory_managed_function(std::string("NEConvolutionLayer"), mm, - input, weights, biases, output, conv_info); - } - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << func_name - << " Data Type: " << input->info()->data_type() - << " Input shape: " << input->info()->tensor_shape() - << " Weights shape: " << weights->info()->tensor_shape() - << " Output shape: " << output->info()->tensor_shape() - << std::endl); - return func; -} - -/** Create a backend layer depth concatenate function - * - * @param[in] node Node to create the backend function for - * - * @return Backend depth concatenate layer function - */ -std::unique_ptr create_depth_concatenate_layer(DepthConcatenateLayerNode &node) -{ - ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating NEON DepthConcatenate node with ID : " << node.id() << " and Name: " << node.name() << std::endl); - ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1); - - // Return nullptr if depth concatenate is switched off - if(!node.is_enabled()) - { - return nullptr; - } - - // Extract IO and info - std::vector inputs; - for(unsigned int i = 0; i < node.num_inputs(); ++i) - { - inputs.push_back(get_backing_tensor(node.input(i))); - } - ITensor *output = get_backing_tensor(node.output(0)); - - // Create and configure function - auto func = support::cpp14::make_unique(); - func->configure(inputs, output); - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated NEDepthConcatenateLayer" - << " Data Type: " << output->info()->data_type() - << " Shape: " << output->info()->tensor_shape() - << " Num Inputs: " << inputs.size() - << std::endl); - - return std::move(func); -} - -/** Create a backend layer depth-wise convolution function - * - * @param[in] node Node to create the backend function for - * - * @return Backend depth-wise convolution layer function - */ -std::unique_ptr create_depthwise_convolution_layer(DepthwiseConvolutionLayerNode &node) -{ - ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating NEON DepthwiseConvolutionLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl); - ARM_COMPUTE_ERROR_ON(node.num_inputs() != 3); - ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1); - - // Extract IO and info - ITensor *input = get_backing_tensor(node.input(0)); - ITensor *weights = get_backing_tensor(node.input(1)); - ITensor *biases = get_backing_tensor(node.input(2)); - ITensor *output = get_backing_tensor(node.output(0)); - const PadStrideInfo conv_info = node.convolution_info(); - const DepthwiseConvolutionMethod dwc_algorithm = node.depthwise_convolution_method(); - - // Create and configure function (we assume that functions have been validated before creation) - std::unique_ptr func; - std::string func_name; - if(dwc_algorithm == DepthwiseConvolutionMethod::OPTIMIZED_3x3) - { - std::tie(func, func_name) = create_named_function(std::string("NEDepthwiseConvolutionLayer3x3"), - input, weights, biases, output, conv_info); - } - else - { - std::tie(func, func_name) = create_named_function(std::string("NEDepthwiseConvolutionLayer"), - input, weights, biases, output, conv_info); - } - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << func_name - << " Data Type: " << input->info()->data_type() - << " Input shape: " << input->info()->tensor_shape() - << " Weights shape: " << weights->info()->tensor_shape() - << " Output shape: " << output->info()->tensor_shape() - << std::endl); - return func; -} - -/** Create a backend element-wise operation layer function - * - * @param[in] node Node to create the backend function for - * - * @return Backend element-wise operation layer function - */ -std::unique_ptr create_eltwise_layer(EltwiseLayerNode &node) -{ - ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating NEON EltwiseLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl); - ARM_COMPUTE_ERROR_ON(node.num_inputs() != 2); - ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1); - - // Extract IO and info - ITensor *input1 = get_backing_tensor(node.input(0)); - ITensor *input2 = get_backing_tensor(node.input(1)); - ITensor *output = get_backing_tensor(node.output(0)); - const EltwiseOperation eltwise_op = node.eltwise_operation(); - ARM_COMPUTE_ERROR_ON(input1 == nullptr); - ARM_COMPUTE_ERROR_ON(input2 == nullptr); - ARM_COMPUTE_ERROR_ON(output == nullptr); - - std::unique_ptr func = nullptr; - std::string func_name; - if(eltwise_op == EltwiseOperation::ADD) - { - std::tie(func, func_name) = create_named_function(std::string("NEArithmeticAddition"), - input1, input2, output, ConvertPolicy::SATURATE); - } - else if(eltwise_op == EltwiseOperation::SUB) - { - std::tie(func, func_name) = create_named_function(std::string("NEArithmeticSubtraction"), - input1, input2, output, ConvertPolicy::SATURATE); - } - else if(eltwise_op == EltwiseOperation::MUL) - { - std::tie(func, func_name) = create_named_function(std::string("NEPixelWiseMultiplication"), - input1, input2, output, 1.f, - ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN); - } - else - { - ARM_COMPUTE_ERROR("Unsupported element-wise operation!"); - } - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << func_name - << " Data Type: " << input1->info()->data_type() - << " Shape : " << input1->info()->tensor_shape() - << std::endl); - - return func; -} - -/** Create a backend flatten layer function - * - * @param[in] node Node to create the backend function for - * - * @return Backend flatten layer function - */ -std::unique_ptr create_flatten_layer(FlattenLayerNode &node) -{ - ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating NEON FlattenLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl); - ARM_COMPUTE_ERROR_ON(node.num_inputs() != 1); - ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1); - - // Extract IO and info - ITensor *input = get_backing_tensor(node.input(0)); - ITensor *output = get_backing_tensor(node.output(0)); - - // Create and configure function - auto func = support::cpp14::make_unique(); - func->configure(input, output); - ARM_COMPUTE_ERROR_ON(input == nullptr); - ARM_COMPUTE_ERROR_ON(output == nullptr); - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated NEFlattenLayer" - << " Data Type: " << input->info()->data_type() - << " Input shape: " << input->info()->tensor_shape() - << " Output shape: " << output->info()->tensor_shape() - << std::endl); - - return std::move(func); -} - -/** Create a backend fully connected layer function - * - * @param[in] node Node to create the backend function for - * - * @return Backend fully connected layer function - */ -std::unique_ptr create_fully_connected_layer(FullyConnectedLayerNode &node, GraphContext &ctx) -{ - ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating NEON FullyConnectedLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl); - ARM_COMPUTE_ERROR_ON(node.num_inputs() != 3); - ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1); - - // Extract IO and info - ITensor *input = get_backing_tensor(node.input(0)); - ITensor *weights = get_backing_tensor(node.input(1)); - ITensor *biases = get_backing_tensor(node.input(2)); - ITensor *output = get_backing_tensor(node.output(0)); - - // Create and configure function - auto func = support::cpp14::make_unique(get_memory_manager(ctx, Target::NEON)); - func->configure(input, weights, biases, output); - ARM_COMPUTE_ERROR_ON(input == nullptr); - ARM_COMPUTE_ERROR_ON(weights == nullptr); - ARM_COMPUTE_ERROR_ON(output == nullptr); - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated NEFullyConnectedLayer" - << " Data Type: " << input->info()->data_type() - << " Input shape: " << input->info()->tensor_shape() - << " Weights shape: " << weights->info()->tensor_shape() - << " Output shape: " << output->info()->tensor_shape() - << std::endl); - - return std::move(func); -} - -/** Create a backend normalization layer function - * - * @param[in] node Node to create the backend function for - * - * @return Backend normalization layer function - */ -std::unique_ptr create_normalization_layer(NormalizationLayerNode &node, GraphContext &ctx) -{ - ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating NEON NormalizationLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl); - ARM_COMPUTE_ERROR_ON(node.num_inputs() != 1); - ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1); - - // Extract IO and info - ITensor *input = get_backing_tensor(node.input(0)); - ITensor *output = get_backing_tensor(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(get_memory_manager(ctx, Target::NEON)); - func->configure(input, output, norm_info); - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated NENormalizationLayer" - << " 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); -} - -/** Create a backend pooling layer function - * - * @param[in] node Node to create the backend function for - * - * @return Backend pooling layer function - */ -std::unique_ptr create_pooling_layer(PoolingLayerNode &node) -{ - ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating NEON PoolingLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl); - ARM_COMPUTE_ERROR_ON(node.num_inputs() != 1); - ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1); - - // Extract IO and info - ITensor *input = get_backing_tensor(node.input(0)); - ITensor *output = get_backing_tensor(node.output(0)); - const PoolingLayerInfo pool_info = node.pooling_info(); - ARM_COMPUTE_ERROR_ON(input == nullptr); - ARM_COMPUTE_ERROR_ON(output == nullptr); - - // Create and configure function - auto func = support::cpp14::make_unique(); - func->configure(input, output, pool_info); - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated NEPoolingLayer" - << " Data Type: " << input->info()->data_type() - << " Input shape: " << input->info()->tensor_shape() - << " Output shape: " << output->info()->tensor_shape() - << " Pooling info: " << pool_info.pool_type() - << std::endl); - - return std::move(func); -} - -/** Create a backend reshape layer function - * - * @param[in] node Node to create the backend function for - * - * @return Backend reshape layer function - */ -std::unique_ptr create_reshape_layer(ReshapeLayerNode &node) -{ - ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating NEON ReshapeLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl); - ARM_COMPUTE_ERROR_ON(node.num_inputs() != 1); - ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1); - - // Extract IO and info - ITensor *input = get_backing_tensor(node.input(0)); - ITensor *output = get_backing_tensor(node.output(0)); - ARM_COMPUTE_ERROR_ON(input == nullptr); - ARM_COMPUTE_ERROR_ON(output == nullptr); - - // Create and configure function - auto func = support::cpp14::make_unique(); - func->configure(input, output); - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated NEReshapeLayer" - << " Data Type: " << input->info()->data_type() - << " Input shape: " << input->info()->tensor_shape() - << " Output shape: " << output->info()->tensor_shape() - << std::endl); - - return std::move(func); -} - -/** Create a backend softmax layer function - * - * @param[in] node Node to create the backend function for - * - * @return Backend softmax layer function - */ -std::unique_ptr create_softmax_layer(SoftmaxLayerNode &node, GraphContext &ctx) -{ - ARM_COMPUTE_LOG_GRAPH_VERBOSE("Creating NEON SoftmaxLayer node with ID : " << node.id() << " and Name: " << node.name() << std::endl); - ARM_COMPUTE_ERROR_ON(node.num_inputs() != 1); - ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1); - - // Extract IO and info - ITensor *input = get_backing_tensor(node.input(0)); - ITensor *output = get_backing_tensor(node.output(0)); - const float beta = node.beta(); - ARM_COMPUTE_ERROR_ON(input == nullptr); - ARM_COMPUTE_ERROR_ON(output == nullptr); - - // Create and configure function - auto func = support::cpp14::make_unique(get_memory_manager(ctx, Target::NEON)); - func->configure(input, output, beta); - - // Log info - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated NESoftmaxLayer" - << " Data Type: " << input->info()->data_type() - << " Input shape: " << input->info()->tensor_shape() - << " Output shape: " << output->info()->tensor_shape() - << std::endl); - - return std::move(func); -} -} // namespace - -std::unique_ptr NEFunctionFactory::create(INode *node, GraphContext &ctx) -{ - if(node == nullptr) - { - return nullptr; - } - - NodeType type = node->type(); - switch(type) - { - case NodeType::ActivationLayer: - return create_activation_layer(*polymorphic_downcast(node)); - case NodeType::BatchNormalizationLayer: - return create_batch_normalization_layer(*polymorphic_downcast(node)); - case NodeType::ConvolutionLayer: - return create_convolution_layer(*polymorphic_downcast(node), ctx); - case NodeType::DepthConcatenateLayer: - return create_depth_concatenate_layer(*polymorphic_downcast(node)); - case NodeType::DepthwiseConvolutionLayer: - return create_depthwise_convolution_layer(*polymorphic_downcast(node)); - case NodeType::EltwiseLayer: - return create_eltwise_layer(*polymorphic_downcast(node)); - case NodeType::FlattenLayer: - return create_flatten_layer(*polymorphic_downcast(node)); - case NodeType::FullyConnectedLayer: - return create_fully_connected_layer(*polymorphic_downcast(node), ctx); - case NodeType::NormalizationLayer: - return create_normalization_layer(*polymorphic_downcast(node), ctx); - case NodeType::PoolingLayer: - return create_pooling_layer(*polymorphic_downcast(node)); - case NodeType::ReshapeLayer: - return create_reshape_layer(*polymorphic_downcast(node)); - case NodeType::SoftmaxLayer: - return create_softmax_layer(*polymorphic_downcast(node), ctx); - default: - return nullptr; - } -} -} // namespace backends -} // namespace graph2 -} // namespace arm_compute \ No newline at end of file -- cgit v1.2.1