src/core/CL/kernels/CLIm2ColKernel.cpp - platform/external/ComputeLibrary - Git at Google

 /*
  * 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/CLIm2ColKernel.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/CL/OpenCL.h"
 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/Size2D.h"
 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/Validate.h"
 #include "support/ToolchainSupport.h"

 #include <cmath>
 #include <tuple>

 using namespace arm_compute;

 CLIm2ColKernel::CLIm2ColKernel()
     : _input(nullptr), _output(nullptr), _convolved_dims(), _num_elems_processed_per_iteration(1), _run_func(nullptr)
 {
 }

 void CLIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias)
 {
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);

     _input  = input;
     _output = output;

     // Create kernel
     std::set<std::string> build_opts;
     build_opts.emplace(("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())));
     build_opts.emplace((has_bias ? "-DHAS_BIAS" : ""));

     if(is_data_type_fixed_point(input->info()->data_type()))
     {
         build_opts.emplace("-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position()));
     }

     int pad_x    = 0;
     int pad_y    = 0;
     int stride_x = 0;
     int stride_y = 0;
     std::tie(pad_x, pad_y)       = conv_info.pad();
     std::tie(stride_x, stride_y) = conv_info.stride();

     const bool run_img2col_reduced = (output->info()->dimension(0) == (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2))) && (TensorShape::num_max_dimensions >= 4)
                                      && (std::equal(input->info()->tensor_shape().cbegin() + 3,
                                                     input->info()->tensor_shape().cend(),
                                                     output->info()->tensor_shape().cbegin() + 1))
                                      && ((stride_x == 1) && (stride_y == 1) && (pad_x == 0) && (pad_y == 0));

     if(!run_img2col_reduced)
     {
         _convolved_dims = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1),
                                             kernel_dims.width, kernel_dims.height,
                                             conv_info);
         _num_elems_processed_per_iteration = output->info()->dimension(0);

         build_opts.emplace("-DKERNEL_WIDTH=" + support::cpp11::to_string(kernel_dims.width));
         build_opts.emplace("-DKERNEL_HEIGHT=" + support::cpp11::to_string(kernel_dims.height));
         build_opts.emplace("-DKERNEL_DEPTH=" + support::cpp11::to_string(input->info()->dimension(2)));
         build_opts.emplace("-DCONVOLVED_WIDTH=" + support::cpp11::to_string(_convolved_dims.first));
         build_opts.emplace("-DCONVOLVED_HEIGHT=" + support::cpp11::to_string(_convolved_dims.second));
         build_opts.emplace("-DSTRIDE_X=" + support::cpp11::to_string(conv_info.stride().first));
         build_opts.emplace("-DSTRIDE_Y=" + support::cpp11::to_string(conv_info.stride().second));
         build_opts.emplace("-DPAD_X=" + support::cpp11::to_string(conv_info.pad().first));
         build_opts.emplace("-DPAD_Y=" + support::cpp11::to_string(conv_info.pad().second));
         build_opts.emplace("-DSRC_WIDTH=" + support::cpp11::to_string(input->info()->dimension(0)));
         build_opts.emplace("-DSRC_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(1)));

         if(kernel_dims.width == 3 && kernel_dims.height == 3 && conv_info.pad().first == 0 && conv_info.pad().second == 0)
         {
             _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("im2col_kernel3x3_padx0_pady0", build_opts));
         }
         else
         {
             _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("im2col_generic", build_opts));
         }
         _run_func = &CLIm2ColKernel::run_generic;
     }
     else
     {
         _num_elems_processed_per_iteration = 1;
         _kernel                            = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("im2col_reduced", build_opts));
         _run_func                          = &CLIm2ColKernel::run_reduced;
     }

     // Configure  kernel window
     Window win = calculate_max_window(*input->info(), Steps());
     // The CLIm2ColKernel doesn't need padding so update_window_and_padding() can be skipped
     output->info()->set_valid_region(ValidRegion(Coordinates(), output->info()->tensor_shape()));
     if(!run_img2col_reduced)
     {
         // set the Z dimension's step same size as the whole dimension so that one can't split across the Z dimension
         win.set_dimension_step(Window::DimZ, win[Window::DimZ].end() - win[Window::DimZ].start());
     }

     ICLKernel::configure(win);

     // Set config_id for enabling LWS tuning
     _config_id = "im2col_";
     _config_id += (run_img2col_reduced ? "reduced_" : "");
     _config_id += lower_string(string_from_data_type(input->info()->data_type()));
     _config_id += "_";
     _config_id += support::cpp11::to_string(output->info()->dimension(0));
     _config_id += "_";
     _config_id += support::cpp11::to_string(output->info()->dimension(1));
 }

 void CLIm2ColKernel::run(const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON(_run_func == nullptr);
     (this->*_run_func)(window, queue);
 }

 void CLIm2ColKernel::run_generic(const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(ICLKernel::window(), window);

     // Get initial windows
     Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ);
     // Change the Z dimension's step back to 1
     window_collapsed.set_dimension_step(Window::DimZ, 1);

     Window slice     = window_collapsed.first_slice_window_3D();
     Window slice_in  = window_collapsed.first_slice_window_3D();
     Window slice_out = window_collapsed.first_slice_window_3D();

     // Setup slice
     slice.set(Window::DimX, Window::Dimension(0, static_cast<int>(_convolved_dims.first), 1));
     slice.set(Window::DimY, Window::Dimension(0, static_cast<int>(_convolved_dims.second), 1));

     // Setup input slice
     // The first three dimensions of the input are increased by the inner loops
     slice_in.set(Window::DimX, Window::Dimension(0, 0, 0));
     slice_in.set(Window::DimY, Window::Dimension(0, 0, 0));
     slice_in.set(Window::DimZ, Window::Dimension(0, 0, 0));

     // Setup output slice
     slice_out.set(Window::DimX, Window::Dimension(0, _output->info()->dimension(0), _num_elems_processed_per_iteration));
     slice_out.set(Window::DimY, Window::Dimension(0, _output->info()->dimension(1), 1));
     slice_out.set(Window::DimZ, Window::Dimension(0, 1, 1));

     do
     {
         unsigned int idx = 0;
         add_3D_tensor_argument(idx, _input, slice_in);
         add_2D_tensor_argument(idx, _output, slice_out);
         _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input->info()->dimension(2)));
         _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input->info()->strides_in_bytes()[3]));
         _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_output->info()->strides_in_bytes()[3]));
         enqueue(queue, *this, slice, _lws_hint);
     }
     while(window_collapsed.slide_window_slice_3D(slice) && window_collapsed.slide_window_slice_3D(slice_out) && window_collapsed.slide_window_slice_3D(slice_in));
 }

 void CLIm2ColKernel::run_reduced(const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(ICLKernel::window(), window);

     Window out_window;
     out_window.use_tensor_dimensions(_output->info()->tensor_shape());

     Window out_slice = out_window.first_slice_window_1D();
     Window in_slice  = window.first_slice_window_3D();

     // Run kernel
     do
     {
         // Set arguments
         unsigned int idx = 0;
         add_3D_tensor_argument(idx, _input, in_slice);
         add_1D_tensor_argument(idx, _output, out_slice);

         _kernel.setArg<cl_uint>(idx++, _input->info()->dimension(0));
         _kernel.setArg<cl_uint>(idx++, _input->info()->dimension(1));
         enqueue(queue, *this, in_slice);
     }
     while(window.slide_window_slice_3D(in_slice) && out_window.slide_window_slice_1D(out_slice));
 }
	/*
	* 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/CLIm2ColKernel.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/CL/OpenCL.h"
	#include "arm_compute/core/Error.h"
	#include "arm_compute/core/Helpers.h"
	#include "arm_compute/core/Size2D.h"
	#include "arm_compute/core/Types.h"
	#include "arm_compute/core/Validate.h"
	#include "support/ToolchainSupport.h"

	#include <cmath>
	#include <tuple>

	using namespace arm_compute;

	CLIm2ColKernel::CLIm2ColKernel()
	: _input(nullptr), _output(nullptr), _convolved_dims(), _num_elems_processed_per_iteration(1), _run_func(nullptr)
	{
	}

	void CLIm2ColKernel::configure(const ICLTensor input, ICLTensor output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias)
	{
	ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
	ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
	ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);

	_input = input;
	_output = output;

	// Create kernel
	std::set<std::string> build_opts;
	build_opts.emplace(("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())));
	build_opts.emplace((has_bias ? "-DHAS_BIAS" : ""));

	if(is_data_type_fixed_point(input->info()->data_type()))
	{
	build_opts.emplace("-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position()));
	}

	int pad_x = 0;
	int pad_y = 0;
	int stride_x = 0;
	int stride_y = 0;
	std::tie(pad_x, pad_y) = conv_info.pad();
	std::tie(stride_x, stride_y) = conv_info.stride();

	const bool run_img2col_reduced = (output->info()->dimension(0) == (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2))) && (TensorShape::num_max_dimensions >= 4)
	&& (std::equal(input->info()->tensor_shape().cbegin() + 3,
	input->info()->tensor_shape().cend(),
	output->info()->tensor_shape().cbegin() + 1))
	&& ((stride_x == 1) && (stride_y == 1) && (pad_x == 0) && (pad_y == 0));

	if(!run_img2col_reduced)
	{
	_convolved_dims = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1),
	kernel_dims.width, kernel_dims.height,
	conv_info);
	_num_elems_processed_per_iteration = output->info()->dimension(0);

	build_opts.emplace("-DKERNEL_WIDTH=" + support::cpp11::to_string(kernel_dims.width));
	build_opts.emplace("-DKERNEL_HEIGHT=" + support::cpp11::to_string(kernel_dims.height));
	build_opts.emplace("-DKERNEL_DEPTH=" + support::cpp11::to_string(input->info()->dimension(2)));
	build_opts.emplace("-DCONVOLVED_WIDTH=" + support::cpp11::to_string(_convolved_dims.first));
	build_opts.emplace("-DCONVOLVED_HEIGHT=" + support::cpp11::to_string(_convolved_dims.second));
	build_opts.emplace("-DSTRIDE_X=" + support::cpp11::to_string(conv_info.stride().first));
	build_opts.emplace("-DSTRIDE_Y=" + support::cpp11::to_string(conv_info.stride().second));
	build_opts.emplace("-DPAD_X=" + support::cpp11::to_string(conv_info.pad().first));
	build_opts.emplace("-DPAD_Y=" + support::cpp11::to_string(conv_info.pad().second));
	build_opts.emplace("-DSRC_WIDTH=" + support::cpp11::to_string(input->info()->dimension(0)));
	build_opts.emplace("-DSRC_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(1)));

	if(kernel_dims.width == 3 && kernel_dims.height == 3 && conv_info.pad().first == 0 && conv_info.pad().second == 0)
	{
	_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("im2col_kernel3x3_padx0_pady0", build_opts));
	}
	else
	{
	_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("im2col_generic", build_opts));
	}
	_run_func = &CLIm2ColKernel::run_generic;
	}
	else
	{
	_num_elems_processed_per_iteration = 1;
	_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("im2col_reduced", build_opts));
	_run_func = &CLIm2ColKernel::run_reduced;
	}

	// Configure kernel window
	Window win = calculate_max_window(*input->info(), Steps());
	// The CLIm2ColKernel doesn't need padding so update_window_and_padding() can be skipped
	output->info()->set_valid_region(ValidRegion(Coordinates(), output->info()->tensor_shape()));
	if(!run_img2col_reduced)
	{
	// set the Z dimension's step same size as the whole dimension so that one can't split across the Z dimension
	win.set_dimension_step(Window::DimZ, win[Window::DimZ].end() - win[Window::DimZ].start());
	}

	ICLKernel::configure(win);

	// Set config_id for enabling LWS tuning
	_config_id = "im2col_";
	_config_id += (run_img2col_reduced ? "reduced_" : "");
	_config_id += lower_string(string_from_data_type(input->info()->data_type()));
	_config_id += "_";
	_config_id += support::cpp11::to_string(output->info()->dimension(0));
	_config_id += "_";
	_config_id += support::cpp11::to_string(output->info()->dimension(1));
	}

	void CLIm2ColKernel::run(const Window &window, cl::CommandQueue &queue)
	{
	ARM_COMPUTE_ERROR_ON(_run_func == nullptr);
	(this->*_run_func)(window, queue);
	}

	void CLIm2ColKernel::run_generic(const Window &window, cl::CommandQueue &queue)
	{
	ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
	ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(ICLKernel::window(), window);

	// Get initial windows
	Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ);
	// Change the Z dimension's step back to 1
	window_collapsed.set_dimension_step(Window::DimZ, 1);

	Window slice = window_collapsed.first_slice_window_3D();
	Window slice_in = window_collapsed.first_slice_window_3D();
	Window slice_out = window_collapsed.first_slice_window_3D();

	// Setup slice
	slice.set(Window::DimX, Window::Dimension(0, static_cast<int>(_convolved_dims.first), 1));
	slice.set(Window::DimY, Window::Dimension(0, static_cast<int>(_convolved_dims.second), 1));

	// Setup input slice
	// The first three dimensions of the input are increased by the inner loops
	slice_in.set(Window::DimX, Window::Dimension(0, 0, 0));
	slice_in.set(Window::DimY, Window::Dimension(0, 0, 0));
	slice_in.set(Window::DimZ, Window::Dimension(0, 0, 0));

	// Setup output slice
	slice_out.set(Window::DimX, Window::Dimension(0, _output->info()->dimension(0), _num_elems_processed_per_iteration));
	slice_out.set(Window::DimY, Window::Dimension(0, _output->info()->dimension(1), 1));
	slice_out.set(Window::DimZ, Window::Dimension(0, 1, 1));

	do
	{
	unsigned int idx = 0;
	add_3D_tensor_argument(idx, _input, slice_in);
	add_2D_tensor_argument(idx, _output, slice_out);
	_kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input->info()->dimension(2)));
	_kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input->info()->strides_in_bytes()[3]));
	_kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_output->info()->strides_in_bytes()[3]));
	enqueue(queue, *this, slice, _lws_hint);
	}
	while(window_collapsed.slide_window_slice_3D(slice) && window_collapsed.slide_window_slice_3D(slice_out) && window_collapsed.slide_window_slice_3D(slice_in));
	}

	void CLIm2ColKernel::run_reduced(const Window &window, cl::CommandQueue &queue)
	{
	ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
	ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(ICLKernel::window(), window);

	Window out_window;
	out_window.use_tensor_dimensions(_output->info()->tensor_shape());

	Window out_slice = out_window.first_slice_window_1D();
	Window in_slice = window.first_slice_window_3D();

	// Run kernel
	do
	{
	// Set arguments
	unsigned int idx = 0;
	add_3D_tensor_argument(idx, _input, in_slice);
	add_1D_tensor_argument(idx, _output, out_slice);

	_kernel.setArg<cl_uint>(idx++, _input->info()->dimension(0));
	_kernel.setArg<cl_uint>(idx++, _input->info()->dimension(1));
	enqueue(queue, *this, in_slice);
	}
	while(window.slide_window_slice_3D(in_slice) && out_window.slide_window_slice_1D(out_slice));
	}