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/graph/nodes/ConvolutionLayer.cpp | 363 ----------------------------------- 1 file changed, 363 deletions(-) delete mode 100644 src/graph/nodes/ConvolutionLayer.cpp (limited to 'src/graph/nodes/ConvolutionLayer.cpp') diff --git a/src/graph/nodes/ConvolutionLayer.cpp b/src/graph/nodes/ConvolutionLayer.cpp deleted file mode 100644 index 5b3a84a4ad..0000000000 --- a/src/graph/nodes/ConvolutionLayer.cpp +++ /dev/null @@ -1,363 +0,0 @@ -/* - * Copyright (c) 2017-2018 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/graph/nodes/ConvolutionLayer.h" - -#include "arm_compute/graph/Error.h" -#include "arm_compute/runtime/CL/functions/CLConvolutionLayer.h" -#include "arm_compute/runtime/CL/functions/CLDirectConvolutionLayer.h" -#include "arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h" -#include "arm_compute/runtime/IFunction.h" -#include "arm_compute/runtime/NEON/functions/NEConvolutionLayer.h" -#include "arm_compute/runtime/NEON/functions/NEDirectConvolutionLayer.h" -#include "arm_compute/runtime/NEON/functions/NEWinogradLayer.h" -#include "support/ToolchainSupport.h" -#include "utils/GraphTypePrinter.h" -#include "utils/TypePrinter.h" - -#include -#include - -using namespace arm_compute::graph; - -namespace -{ -/** Calculates the output shaped of the convolution layer - * - * @param[in] input_shape Input tensor shape - * @param[in] weights_shape Weights shape - * @param[in] conv_info Convolution information (padding, stride, etc.) - * - * @return The expected output tensor shape - */ -TensorShape calculate_convolution_layer_output_shape(const TensorShape &input_shape, const TensorShape &weights_shape, const PadStrideInfo &conv_info) -{ - unsigned int output_width = 0; - unsigned int output_height = 0; - - // Get output width and height - std::tie(output_width, output_height) = arm_compute::scaled_dimensions(input_shape.x(), input_shape.y(), weights_shape.x(), weights_shape.y(), conv_info); - - // Create output shape - TensorShape output_shape = input_shape; - output_shape.set(0, output_width); - output_shape.set(1, output_height); - output_shape.set(2, weights_shape[3]); - - return output_shape; -} - -// Instantiate GEMM based convolution layer -template -std::unique_ptr instantiate_function(arm_compute::ITensor *input, arm_compute::ITensor *weights, arm_compute::ITensor *biases, arm_compute::ITensor *output, - const PadStrideInfo &conv_info, const WeightsInfo &weights_info) -{ - auto conv = arm_compute::support::cpp14::make_unique(); - conv->configure( - dynamic_cast(input), - dynamic_cast(weights), - dynamic_cast(biases), - dynamic_cast(output), - conv_info, weights_info); - return std::move(conv); -} - -// Instantiate direct convolution layer -template -std::unique_ptr instantiate_direct_function(arm_compute::ITensor *input, arm_compute::ITensor *weights, arm_compute::ITensor *biases, arm_compute::ITensor *output, - const PadStrideInfo &conv_info) -{ - auto conv = arm_compute::support::cpp14::make_unique(); - conv->configure( - dynamic_cast(input), - dynamic_cast(weights), - dynamic_cast(biases), - dynamic_cast(output), - conv_info); - return std::move(conv); -} - -template -std::unique_ptr instantiate(arm_compute::ITensor *input, arm_compute::ITensor *weights, arm_compute::ITensor *biases, arm_compute::ITensor *output, - const PadStrideInfo &conv_info, const WeightsInfo &weights_info, - ConvolutionMethodHint conv_method); - -template <> -std::unique_ptr instantiate(arm_compute::ITensor *input, arm_compute::ITensor *weights, arm_compute::ITensor *biases, arm_compute::ITensor *output, - const PadStrideInfo &conv_info, - const WeightsInfo &weights_info, - ConvolutionMethodHint conv_method) -{ - if((conv_method == ConvolutionMethodHint::WINOGRAD) - && arm_compute::CLWinogradConvolutionLayer::validate(input->info(), weights->info(), biases != nullptr ? biases->info() : nullptr, output->info(), conv_info)) // NOLINT - { - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLWinogradConvolutionLayer"); - return instantiate_direct_function(input, weights, biases, output, conv_info); - } - else if((conv_method == ConvolutionMethodHint::DIRECT) - && arm_compute::CLDirectConvolutionLayer::validate(input->info(), weights->info(), biases != nullptr ? biases->info() : nullptr, output->info(), conv_info)) // NOLINT - { - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLDirectConvolutionLayer"); - return instantiate_direct_function(input, weights, biases, output, conv_info); - } - else - { - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLConvolutionLayer"); - return instantiate_function(input, weights, biases, output, conv_info, weights_info); - } -} - -template <> -std::unique_ptr instantiate(arm_compute::ITensor *input, arm_compute::ITensor *weights, arm_compute::ITensor *biases, arm_compute::ITensor *output, - const PadStrideInfo &conv_info, - const WeightsInfo &weights_info, - ConvolutionMethodHint conv_method) -{ - const unsigned int kernel_size_x = weights->info()->tensor_shape().x(); - const unsigned int kernel_size_y = weights->info()->tensor_shape().y(); - const unsigned int conv_stride_x = conv_info.stride().first; - const unsigned int conv_stride_y = conv_info.stride().second; - - bool is_square_kernel = (kernel_size_x == kernel_size_y); - bool has_same_stride = (conv_stride_x == conv_stride_y); - - // TODO (COMPID-765) : Winograd should have a validate function - if(conv_method == ConvolutionMethodHint::WINOGRAD && is_square_kernel && ((kernel_size_x == 3) || (kernel_size_x == 5)) && has_same_stride && (conv_info.stride().first == 1)) - { - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEWinogradConvolutionLayer"); - return instantiate_direct_function(input, weights, biases, output, conv_info); - } - else if((conv_method == ConvolutionMethodHint::DIRECT) - && arm_compute::NEDirectConvolutionLayer::validate(input->info(), weights->info(), biases != nullptr ? biases->info() : nullptr, output->info(), conv_info)) // NOLINT - { - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEDirectConvolutionLayer"); - return instantiate_direct_function(input, weights, biases, output, conv_info); - } - else - { - ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEConvolutionLayer"); - return instantiate_function(input, weights, biases, output, conv_info, weights_info); - } -} -} // namespace - -/** Grouped Convolution function */ -class GroupedConvolutionFunction final : public arm_compute::IFunction -{ -public: - /** Default Constructor */ - GroupedConvolutionFunction() = default; - /** Default Destructor */ - ~GroupedConvolutionFunction() final = default; - /** Prevent instances from being copy constructed */ - GroupedConvolutionFunction(const GroupedConvolutionFunction &) = delete; - /** Prevent instances from being copy assigned */ - GroupedConvolutionFunction &operator=(const GroupedConvolutionFunction &) = delete; - /** Allow instances to be move constructed */ - GroupedConvolutionFunction(GroupedConvolutionFunction &&) noexcept = default; - /** Allow instances to be move assigned */ - GroupedConvolutionFunction &operator=(GroupedConvolutionFunction &&) noexcept = default; - /** Adds a convolution - * - * @param convolution Convolution function to add - */ - void add_convolution_function(std::unique_ptr convolution) // NOLINT - { - _convolutions.emplace_back(std::move(convolution)); - } - - // Inherited methods overridden: - void run() override - { - for(auto &c : _convolutions) - { - c->run(); - } - } - -private: - std::vector> _convolutions{}; -}; - -std::unique_ptr ConvolutionLayer::instantiate_node(GraphContext &ctx, ITensorObject *input, ITensorObject *output) -{ - ARM_COMPUTE_ERROR_ON_UNALLOCATED_TENSOR_OBJECT(input, output); - - arm_compute::ITensor *in = input->tensor(); - arm_compute::ITensor *out = output->tensor(); - - // Set weights and biases info - if(_weights.tensor() == nullptr) - { - TensorInfo info = TensorInfo(TensorShape(_conv_width, _conv_height, in->info()->dimension(2) / _num_groups, _ofm), - in->info()->num_channels(), - in->info()->data_type(), - in->info()->fixed_point_position()); - info.set_quantization_info(_weights_quant_info); - _weights.set_info(std::move(info)); - } - if(_biases.has_accessor() && _biases.tensor() == nullptr) - { - DataType dt = in->info()->data_type(); - _biases.set_info(TensorInfo(TensorShape(_ofm), in->info()->num_channels(), is_data_type_quantized_asymmetric(dt) ? DataType::S32 : dt, in->info()->fixed_point_position())); - } - - std::unique_ptr func; - _target_hint = ctx.hints().target_hint(); - const ConvolutionMethodHint conv_method_hint = ctx.hints().convolution_method_hint(); - - // Check if the weights and biases are loaded - bool weights_are_loaded = _weights.tensor() != nullptr; - bool biases_are_loaded = _biases.has_accessor() ? _biases.tensor() != nullptr : true; - - // Set bias and weights target - _weights.set_target(_target_hint); - if(_biases.has_accessor()) - { - _biases.set_target(_target_hint); - } - - // Calculate output shape - TensorShape output_shape = calculate_convolution_layer_output_shape(in->info()->tensor_shape(), _weights.info().tensor_shape(), _conv_info); - - // Output auto inizialitation if not yet initialized - arm_compute::auto_init_if_empty(*out->info(), output_shape, 1, in->info()->data_type(), in->info()->fixed_point_position(), - (_out_quant_info.empty()) ? in->info()->quantization_info() : _out_quant_info); - - // Create appropriate convolution function - if(_num_groups == 1) - { - func = instantiate_convolution(in, out, conv_method_hint); - } - else - { - func = instantiate_grouped_convolution(in, out, conv_method_hint); - } - - // Fill weights - if(!weights_are_loaded) - { - _weights.allocate_and_fill_if_needed(); - } - // Fill biases - if(!biases_are_loaded) - { - _biases.allocate_and_fill_if_needed(); - } - - ARM_COMPUTE_LOG_GRAPH_INFO(" Data Type: " << in->info()->data_type() - << " Input Shape: " << in->info()->tensor_shape() - << " Weights shape: " << _weights.info().tensor_shape() - << " Biases Shape: " << _biases.info().tensor_shape() - << " Output Shape: " << out->info()->tensor_shape() - << " PadStrideInfo: " << _conv_info - << " Groups: " << _num_groups - << " WeightsInfo: " << _weights_info - << std::endl); - - return func; -} - -std::unique_ptr ConvolutionLayer::instantiate_convolution(ITensor *input, ITensor *output, ConvolutionMethodHint conv_method_hint) -{ - std::unique_ptr func; - if(_target_hint == TargetHint::OPENCL) - { - func = instantiate(input, _weights.tensor(), _biases.tensor(), output, _conv_info, _weights_info, conv_method_hint); - } - else - { - func = instantiate(input, _weights.tensor(), _biases.tensor(), output, _conv_info, _weights_info, conv_method_hint); - } - return func; -} - -std::unique_ptr ConvolutionLayer::instantiate_grouped_convolution(ITensor *input, ITensor *output, ConvolutionMethodHint conv_method_hint) -{ - // Get tensor shapes - TensorShape input_shape = input->info()->tensor_shape(); - TensorShape output_shape = output->info()->tensor_shape(); - TensorShape weights_shape = _weights.info().tensor_shape(); - TensorShape biases_shape = _biases.info().tensor_shape(); - - ARM_COMPUTE_ERROR_ON_MSG((input_shape.z() % _num_groups) != 0, "Input depth not multiple of the number of groups!"); - ARM_COMPUTE_ERROR_ON_MSG((output_shape.z() % _num_groups) != 0, "Output depth not multiple of the number of groups!"); - ARM_COMPUTE_ERROR_ON_MSG((weights_shape[3] % _num_groups) != 0, "Number of kernels not multiple of the number of groups!"); - ARM_COMPUTE_ERROR_ON_MSG((biases_shape.x() % _num_groups) != 0, "Biases not multiple of the number of groups!"); - - // Create a grouped convolution function - auto grouped_conv = arm_compute::support::cpp14::make_unique(); - - // Create sub-tensors vectors - _is = arm_compute::support::cpp14::make_unique(_num_groups); - _os = arm_compute::support::cpp14::make_unique(_num_groups); - _ws = arm_compute::support::cpp14::make_unique(_num_groups); - _bs = arm_compute::support::cpp14::make_unique(_num_groups); - - // Calculate sub-tensor splits - const int input_split = input_shape.z() / _num_groups; - const int output_split = output_shape.z() / _num_groups; - const int weights_split = weights_shape[3] / _num_groups; - const int biases_split = biases_shape.x() / _num_groups; - - // Calculate sub-tensor shapes - input_shape.set(2, input_split); - output_shape.set(2, output_split); - weights_shape.set(3, weights_split); - biases_shape.set(0, biases_split); - - // Configure sub-tensors - for(int i = 0; i < static_cast(_num_groups); ++i) - { - // Create convolution function - std::unique_ptr func; - - // Calculate sub-tensors starting coordinates - Coordinates input_coord(0, 0, input_split * i); - Coordinates output_coord(0, 0, output_split * i); - Coordinates weights_coord(0, 0, 0, weights_split * i); - Coordinates biases_coord(biases_split * i); - - // Create sub-tensors for input, output, weights and bias - auto hint_to_use = (_target_hint == TargetHint::OPENCL) ? TargetHint::OPENCL : TargetHint::NEON; - _is[i] = SubTensor(input, input_shape, input_coord, hint_to_use); - _os[i] = SubTensor(output, output_shape, output_coord, hint_to_use); - _ws[i] = SubTensor(_weights.tensor(), weights_shape, weights_coord, hint_to_use); - _bs[i] = SubTensor(_biases.tensor(), biases_shape, biases_coord, hint_to_use); - - // Instantiate convolution function - if(_target_hint == TargetHint::OPENCL) - { - func = instantiate(_is[i].tensor(), _ws[i].tensor(), _bs[i].tensor(), _os[i].tensor(), _conv_info, _weights_info, conv_method_hint); - } - else - { - func = instantiate(_is[i].tensor(), _ws[i].tensor(), _bs[i].tensor(), _os[i].tensor(), _conv_info, _weights_info, conv_method_hint); - } - - // Add convolution function to the list of convolutions for the grouped convolution - grouped_conv->add_convolution_function(std::move(func)); - } - - return std::move(grouped_conv); -} -- cgit v1.2.1