src/core/CL/kernels/CLPoolingLayerKernel.cpp

/*
 * 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/CLPoolingLayerKernel.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/ICLKernel.h"
#include "arm_compute/core/CL/ICLTensor.h"
#include "arm_compute/core/CL/OpenCL.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/Window.h"

#include <set>
#include <string>
#include <tuple>

using namespace arm_compute;

CLPoolingLayerKernel::CLPoolingLayerKernel()
    : _input(nullptr), _output(nullptr), _pool_info(), _border_size(0), _num_elems_processed_per_iteration(1)
{
}

BorderSize CLPoolingLayerKernel::border_size() const
{
    return _border_size;
}

void CLPoolingLayerKernel::configure(const ICLTensor *input, ICLTensor *output, const PoolingLayerInfo &pool_info)
{
    int                 pool_pad_x      = 0;
    int                 pool_pad_y      = 0;
    int                 pool_stride_x   = 0;
    int                 pool_stride_y   = 0;
    unsigned int        pooled_w        = 0;
    unsigned int        pooled_h        = 0;
    const PoolingType   pool_type       = pool_info.pool_type();
    const int           pool_size       = pool_info.pool_size();
    const PadStrideInfo pad_stride_info = pool_info.pad_stride_info();
    bool                exclude_padding = pool_info.exclude_padding();
    std::tie(pool_pad_x, pool_pad_y)       = pad_stride_info.pad();
    std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride();

    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);

    // Check output dimensions
    std::tie(pooled_w, pooled_h) = scaled_dimensions(input->info()->dimension(0),
                                                     input->info()->dimension(1),
                                                     pool_size,
                                                     pool_size,
                                                     pool_info.pad_stride_info());

    // Output auto initialization if not yet initialized
    {
        TensorShape output_shape{ input->info()->tensor_shape() };
        output_shape.set(0, pooled_w);
        output_shape.set(1, pooled_h);

        auto_init_if_empty(*output->info(),
                           output_shape,
                           1,
                           input->info()->data_type(),
                           input->info()->fixed_point_position(),
                           input->info()->quantization_info());
    }

    ARM_COMPUTE_ERROR_THROW_ON(CLPoolingLayerKernel::validate(input->info(), output->info(), pool_info));

    const int input_width  = input->info()->dimension(0);
    const int input_height = input->info()->dimension(1);

    // Set instance variables
    _input       = input;
    _output      = output;
    _pool_info   = pool_info;
    _border_size = BorderSize(pool_pad_y, pool_pad_x);

    const GPUTarget gpu_target = get_arch_from_target(get_target());
    const DataType  data_type  = input->info()->data_type();

    // Set build options
    CLBuildOptions build_opts;
    build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type));
    build_opts.add_option("-DPOOL_" + string_from_pooling_type(pool_type));
    build_opts.add_option_if(is_data_type_fixed_point(data_type),
                             "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position()));
    build_opts.add_option("-DSTRIDE_X=" + support::cpp11::to_string(pool_stride_x));
    if(pool_type != PoolingType::MAX)
    {
        build_opts.add_option_if(exclude_padding, "-DEXCLUDE_PADDING");
        build_opts.add_option("-DMAX_WIDTH=" + support::cpp11::to_string(input->info()->dimension(0) + (exclude_padding ? 0 : pool_pad_x)));
        build_opts.add_option("-DMAX_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_y)));
        build_opts.add_option("-DSTRIDE_Y=" + support::cpp11::to_string(pool_stride_y));
        build_opts.add_option("-DPAD_X=" + support::cpp11::to_string(pool_pad_x));
        build_opts.add_option("-DPAD_Y=" + support::cpp11::to_string(pool_pad_y));
    }

    // Create kernel
    if((pool_size == 3) && !is_data_type_quantized_asymmetric(data_type))
    {
        // Check if we have pool3x3 with stride_x less equal than 3. In these cases, run an optimized OpenCL kernel where
        // each thread computes 4 output elements
        const bool is_pool3x3_stride_le3 = (pool_size == 3) && (pool_stride_x <= 3) && !is_data_type_fixed_point(data_type);

        int num_elems_read_per_iteration = pool_size;
        if(is_pool3x3_stride_le3)
        {
            // Change the number of elements processed and the number of elements read per iteration
            // for pooling 3x3 with stride less equal than 3
            _num_elems_processed_per_iteration = 4;
            num_elems_read_per_iteration       = pool_size * (pool_stride_x + 1);
        }

        const int upper_bound_w = ((pooled_w - 1) * pool_stride_x - pool_pad_x + num_elems_read_per_iteration) - input_width;
        const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size) - input_height;

        _border_size.right  = std::max(upper_bound_w, pool_pad_x);
        _border_size.bottom = std::max(upper_bound_h, pool_pad_y);

        std::string kernel_name = ((is_pool3x3_stride_le3) ? "pooling_layer_optimized_" : "pooling_layer_")
                                  + support::cpp11::to_string(pool_size);
        _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
    }
    else // Run general case
    {
        const int upper_bound_w = ((pooled_w - 1) * pool_stride_x - pool_pad_x + pool_size) - input_width;
        const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size) - input_height;

        _border_size.right  = std::max(upper_bound_w, pool_pad_x);
        _border_size.bottom = std::max(upper_bound_h, pool_pad_y);

        build_opts.add_option("-DPOOL_SIZE=" + support::cpp11::to_string(pool_size));
        build_opts.add_option_if(data_type == DataType::F16, "-DFP16");

        std::string kernel_name = is_data_type_quantized_asymmetric(data_type) ? "pooling_layer_N_quantized" : "pooling_layer_N";
        _kernel                 = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
    }

    // Configure kernel window
    Window win = calculate_max_window(*output->info(), Steps(_num_elems_processed_per_iteration));

    // Configure the local work size (hint) from the first two dimensions of the global work size.
    // On Bifrost, this works for up to 35x35xC filters, for which the pooling_layer_3_optimized
    // kernel is launched with gws=(9, 33, C). In any case, the hint will be ignored if it is
    // invalid (e.g. exceeds the maximum workgroup size that the kernel can be launched with).
    if(gpu_target == GPUTarget::BIFROST)
    {
        cl::NDRange gws = ICLKernel::gws_from_window(win);
        _lws_hint       = cl::NDRange(gws[0], gws[1], 1);
    }

    AccessWindowStatic     input_access(input->info(), -pool_pad_x, -pool_pad_y, input_width + _border_size.right, input_height + _border_size.bottom);
    AccessWindowHorizontal output_access(output->info(), 0, _num_elems_processed_per_iteration);
    update_window_and_padding(win, input_access, output_access);
    output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
    ICLKernel::configure(win);
}

Error CLPoolingLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const PoolingLayerInfo &pool_info)
{
    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
    ARM_COMPUTE_RETURN_ERROR_ON_MSG((is_data_type_quantized_asymmetric(input->data_type()) && pool_info.pool_type() == PoolingType::L2),
                                    "Unsupported combination of parameters!");

    int pool_pad_x = 0;
    int pool_pad_y = 0;
    int pool_size  = pool_info.pool_size();
    std::tie(pool_pad_x, pool_pad_y) = pool_info.pad_stride_info().pad();
    ARM_COMPUTE_RETURN_ERROR_ON_MSG(((pool_pad_x >= pool_size) || (pool_pad_y >= pool_size)),
                                    "Invalid pool size and pool pad combination!");

    // Checks performed when output is configured
    if(output->total_size() != 0)
    {
        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);

        unsigned int pooled_w = 0;
        unsigned int pooled_h = 0;
        std::tie(pooled_w, pooled_h) = scaled_dimensions(input->dimension(0),
                                                         input->dimension(1),
                                                         pool_size,
                                                         pool_size,
                                                         pool_info.pad_stride_info());
        ARM_COMPUTE_RETURN_ERROR_ON_MSG((output->dimension(0) != pooled_w) != (output->dimension(1) != pooled_h),
                                        "Invalid output pooling dimensions!");
    }

    return Error{};
}

void CLPoolingLayerKernel::run(const Window &window, cl::CommandQueue &queue)
{
    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);

    unsigned int pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y = 0;
    std::tie(pool_pad_x, pool_pad_y)       = _pool_info.pad_stride_info().pad();
    std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride();

    Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ);
    Window slice            = window_collapsed.first_slice_window_3D();

    do
    {
        // Upsample input by pool size
        Window in_slice(slice);
        in_slice.set(Window::DimX, Window::Dimension(in_slice.x().start() - pool_pad_x, in_slice.x().end() * pool_stride_x, pool_stride_x * _num_elems_processed_per_iteration));
        in_slice.set(Window::DimY, Window::Dimension(in_slice.y().start() - pool_pad_y, in_slice.y().end() * pool_stride_y, pool_stride_y));

        // Set inputs
        unsigned int idx = 0;
        add_3D_tensor_argument(idx, _input, in_slice);
        add_3D_tensor_argument(idx, _output, slice);
        enqueue(queue, *this, slice, _lws_hint);
    }
    while(window_collapsed.slide_window_slice_3D(slice));
}