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/*
* Copyright (c) 2017 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/core/CL/kernels/CLDirectConvolutionLayerKernel.h"
#include "arm_compute/core/AccessWindowStatic.h"
#include "arm_compute/core/CL/CLHelpers.h"
#include "arm_compute/core/CL/CLKernelLibrary.h"
#include "arm_compute/core/CL/ICLTensor.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/IAccessWindow.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Validate.h"
#include "support/ToolchainSupport.h"
using namespace arm_compute;
CLDirectConvolutionLayerKernel::CLDirectConvolutionLayerKernel()
: _input(nullptr), _biases(nullptr), _weights(nullptr), _output(nullptr), _border_size(0), _conv_pad_x(0), _conv_pad_y(0), _conv_stride_x(0), _conv_stride_y(0)
{
}
BorderSize CLDirectConvolutionLayerKernel::border_size() const
{
return _border_size;
}
void CLDirectConvolutionLayerKernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info)
{
const unsigned int kernel_size = weights->info()->dimension(0);
ARM_COMPUTE_ERROR_ON_MSG(kernel_size != 1 && kernel_size != 3,
"Kernel sizes other than 1x1 or 3x3 are not supported");
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
ARM_COMPUTE_ERROR_ON(weights->info()->dimension(2) != input->info()->dimension(2));
ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != weights->info()->dimension(1));
ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
ARM_COMPUTE_ERROR_ON_MSG(weights->info()->dimension(0) == 1 && (std::get<0>(conv_info.pad()) || std::get<1>(conv_info.pad())),
"Pad > 0 not supported for 1x1 weights");
ARM_COMPUTE_ERROR_ON_MSG(weights->info()->dimension(0) == 3 && (std::get<0>(conv_info.pad()) > 1 || std::get<1>(conv_info.pad()) > 1),
"Pad > 1 not supported for 3x3 weights");
ARM_COMPUTE_ERROR_ON_MSG(std::get<0>(conv_info.stride()) > 3, "Strides larger than 3 not supported.");
ARM_COMPUTE_ERROR_ON_MSG((kernel_size == 3 && std::get<0>(conv_info.stride()) > 2), "Strides larger than 2 not supported in 3x3 direct convolution!");
if(biases != nullptr)
{
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3));
ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
}
_conv_stride_x = std::get<0>(conv_info.stride());
_conv_stride_y = std::get<1>(conv_info.stride());
_conv_pad_x = std::get<0>(conv_info.pad());
_conv_pad_y = std::get<1>(conv_info.pad());
_input = input;
_weights = weights;
_output = output;
_biases = biases;
_border_size = BorderSize(_conv_pad_y, _conv_pad_x);
std::stringstream kernel_name;
std::set<std::string> options;
kernel_name << "direct_convolution" << kernel_size << "x" << kernel_size;
options.insert("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
options.insert("-DDATA_SIZE=" + get_data_size_from_data_type(input->info()->data_type()));
options.emplace("-DSTRIDE_X=" + support::cpp11::to_string(_conv_stride_x));
if(_biases != nullptr)
{
options.emplace("-DHAS_BIAS");
}
_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name.str(), options));
unsigned int idx = (_biases == nullptr) ? 3 * num_arguments_per_3D_tensor() : (num_arguments_per_1D_tensor() + 3 * num_arguments_per_3D_tensor());
_kernel.setArg<cl_uint>(idx++, _weights->info()->strides_in_bytes()[3]); // weights_stride_w
_kernel.setArg<cl_uint>(idx++, _weights->info()->dimension(2)); // filter depth
// Using this local workgroup size gives better performance over others that have been tried.
_lws_hint = cl::NDRange(4, 1, 8);
// Configure kernel window
Window win = calculate_max_window(*output->info());
unsigned int num_elems_read_per_iteration = 16 * _conv_stride_x;
unsigned int num_elems_written_per_iteration = 8;
// Calculate right and bottom border
const int input_width = input->info()->dimension(0);
const int input_height = input->info()->dimension(1);
const int upper_bound_w = ceil_to_multiple(((output->info()->dimension(0) - 1) * _conv_stride_x + kernel_size), num_elems_read_per_iteration) - _conv_pad_x - input_width;
const int upper_bound_h = ((output->info()->dimension(1) - 1) * _conv_stride_y - _conv_pad_y + kernel_size) - input_height;
const int padding_right = std::max(upper_bound_w, static_cast<int>(kernel_size));
const int padding_bottom = std::max(upper_bound_h, static_cast<int>(kernel_size));
// Create window and update padding
win = calculate_max_window(*output->info(), Steps(num_elems_written_per_iteration));
AccessWindowStatic input_access(input->info(), -_conv_pad_x, -_conv_pad_y, input_width + padding_right, input_height + padding_bottom);
AccessWindowStatic weights_access(weights->info(), 0, 0, kernel_size, kernel_size);
AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration);
update_window_and_padding(win, input_access, weights_access, output_access);
output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
ICLKernel::configure(win);
}
void CLDirectConvolutionLayerKernel::run(const Window &window, cl::CommandQueue &queue)
{
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
// Get initial windows
Window slice = window.first_slice_window_3D();
Window win_in = window;
win_in.adjust(Window::DimX, -_conv_pad_x, true);
win_in.adjust(Window::DimY, -_conv_pad_y, true);
win_in.set_dimension_step(Window::DimX, window.x().step() * _conv_stride_x);
win_in.set_dimension_step(Window::DimY, window.y().step() * _conv_stride_y);
Window slice_in = win_in.first_slice_window_3D();
unsigned int idx1 = 2 * num_arguments_per_3D_tensor();
add_3D_tensor_argument(idx1, _weights, slice);
if(_biases != nullptr)
{
Window slice_biases;
slice_biases.use_tensor_dimensions(_biases->info());
add_1D_tensor_argument(idx1, _biases, slice_biases);
}
do
{
unsigned int idx = 0;
add_3D_tensor_argument(idx, _input, slice_in);
add_3D_tensor_argument(idx, _output, slice);
enqueue(queue, *this, slice, _lws_hint);
}
while(window.slide_window_slice_3D(slice) && win_in.slide_window_slice_3D(slice_in));
}
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