/*
 * Copyright (c) 2022 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 "ClTemplateDirectConv2d.h"

#include "src/dynamic_fusion/sketch/gpu/GpuKernelComponentGroup.h"
#include "src/dynamic_fusion/sketch/gpu/components/cl/ClComponentDirectConv2d.h"

#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "src/core/helpers/WindowHelpers.h"

#include "support/StringSupport.h"

namespace arm_compute
{
namespace experimental
{
namespace dynamic_fusion
{
ClTemplateDirectConv2d::ClTemplateDirectConv2d(ComponentId                      id,
                                               const ArgumentPack<ITensorInfo> &tensors,
                                               const Attributes                &attributes,
                                               const Settings                  &settings)
    : IGpuTemplateComponentWriter{ id, tensors },
      _src{},
      _weight{},
      _bias{},
      _dst{},
      _attributes{ attributes },
      _settings{ settings }
{
    _src    = this->tensors().get_const_tensor(TensorType::ACL_SRC_0);
    _weight = this->tensors().get_const_tensor(TensorType::ACL_SRC_1);
    if(this->tensors().get_const_tensor(TensorType::ACL_SRC_2))
    {
        _bias = this->tensors().get_const_tensor(TensorType::ACL_SRC_2);
    }
    _dst = this->tensors().get_const_tensor(TensorType::ACL_DST_0);
    ARM_COMPUTE_ERROR_ON_NULLPTR(_src, _weight, _dst);
}

std::string ClTemplateDirectConv2d::get_name() const
{
    return "direct_conv2d";
}

std::string ClTemplateDirectConv2d::get_component_code(const ComponentGroup &comp_group) const
{
    ARM_COMPUTE_UNUSED(comp_group);

    const auto channel_idx   = get_data_layout_dimension_index(_src->data_layout(), DataLayoutDimension::CHANNEL);
    const auto k0            = adjust_vec_size(is_data_type_quantized(_src->data_type()) ? 16u : 8u, _src->dimension(channel_idx));
    const bool leftover_loop = (_src->dimension(channel_idx) % k0) != 0;

    std::string code = R"_(
//------------------ START KERNEL {{meta_kernel_id}} ---------------------
// IN_0(src)            {{src}}
// IN_1(wei)            {{weight}}
)_";
    if(_bias && _bias->has_valid_id())
    {
        code += R"_(
// IN_1(bia)            {{bias}}
)_";
    }
    code += R"_(
// OUT(dst, accum)      {{dst}}

// Initialize the accumulators
TILE({{ACC_DATA_TYPE}}, M0, N0, {{dst}});
{
    // All the tensor dimensions are passed at compile time.
    // In case of dynamic tensor support, the following dimensions should be passed as function argument.
#define _IWEI_WIDTH {{WEI_WIDTH}}
#define _IWEI_HEIGHT {{WEI_HEIGHT}}
#define _ISRC_WIDTH {{src}}_w
#define _ISRC_HEIGHT {{src}}_h
#define _ISRC_CHANNELS {{src}}_c
#define _IDST_WIDTH {{arg_dst}}_w
#define _IDST_HEIGHT {{arg_dst}}_h
#define _IDST_CHANNELS {{arg_dst}}_c
#define _IY_MULTIPLIER (_IWEI_WIDTH * _IWEI_HEIGHT)

    // .v    = access the whole vector (OpenCL vector)
    // .s[x] = access the vector element at position x (scalar access)
    TILE(int, M0, 1, xi);
    TILE(int, M0, 1, yi);

    // Convert the linear index to coordinate
    LOOP_UNROLLING(int, i, 0, 1, M0,
    {
        xi[i].v = ((g_ind_1 + i) % _IDST_WIDTH) * {{STRIDE_X}};
        yi[i].v = ((g_ind_1 + i) / _IDST_WIDTH) * {{STRIDE_Y}};
        xi[i].v -= {{PAD_LEFT}};
        yi[i].v -= {{PAD_TOP}};
    })

    LOOP_UNROLLING(int, i, 0, 1, M0,
    {
        {{dst}}[i].v = 0;
    })

    for(int i = 0; i < (_IWEI_WIDTH * _IWEI_HEIGHT); ++i)
    {
        int ck = 0;
        int xk = i % _IWEI_WIDTH;
        int yk = i / _IWEI_WIDTH;

        int k = 0;
        for(; k <= (_ISRC_CHANNELS - K0); k += K0)
        {
            TILE({{SRC_DATA_TYPE}}, M0, K0, a);
            TILE({{WEI_DATA_TYPE}}, N0, K0, b);

            // Initialize tiles
            LOOP_UNROLLING(int, i, 0, 1, M0,
            {
                a[i].v = {{ZERO_VALUE}};
            })

            LOOP_UNROLLING(int, i, 0, 1, N0,
            {
                b[i].v = {{ZERO_VALUE}};
            })

            // Load tile from the src tensor
            T_LOAD_NHWC_INDIRECT({{SRC_DATA_TYPE}}, M0, K0, {{SRC_TENSOR_TYPE}}, {{src}}, g_ind_2, yk, xk, ck, _ISRC_WIDTH, _ISRC_HEIGHT, {{src}}_stride_y, xi, yi, a);

            // Load tile from the weights tensor
            T_LOAD({{WEI_DATA_TYPE}}, N0, K0, {{WEI_TENSOR_TYPE}}, {{weight}}, ck, g_ind_0 * _IY_MULTIPLIER + i, _IY_MULTIPLIER, {{weight}}_stride_y, b);

            // Compute the matrix multiplication between two tiles
            T_MMUL({{SRC_DATA_TYPE}}, {{WEI_DATA_TYPE}}, {{ACC_DATA_TYPE}}, M0, N0, K0, NT, T, a, b, {{dst}});

            ck += K0;
        }

        // We voluntarily use SRC_CHANNELS rather than _DSRC_CHANNELS
        // This #if directive should be removed in case of dynamic tensor support
)_";

    if(leftover_loop)
    {
        code += R"_(
        // Left-over accumulations
        for(; k < _ISRC_CHANNELS; ++k)
        {
            TILE({{SRC_DATA_TYPE}}, M0, 1, a);
            TILE({{WEI_DATA_TYPE}}, N0, 1, b);

            // Initialize tiles
            LOOP_UNROLLING(int, i, 0, 1, M0,
            {
                a[i].v = {{ZERO_VALUE}};
            })

            LOOP_UNROLLING(int, i, 0, 1, N0,
            {
                b[i].v = {{ZERO_VALUE}};
            })

            // Load tile from the src tensor
            T_LOAD_NHWC_INDIRECT({{SRC_DATA_TYPE}}, M0, 1, {{SRC_TENSOR_TYPE}}, {{src}}, g_ind_2, yk, xk, ck, _ISRC_WIDTH, _ISRC_HEIGHT, {{src}}_stride_y, xi, yi, a);

            // Load tile from the weights tensor
            // The T_LOAD for the left-over elements can only use BUFFER because we load one element per iteration
            T_LOAD({{WEI_DATA_TYPE}}, N0, 1, BUFFER, {{weight}}, ck, g_ind_0 * _IY_MULTIPLIER + i, _IY_MULTIPLIER, {{weight}}_stride_y, b);

            // Compute the matrix multiplication between two tiles
            T_MMUL({{SRC_DATA_TYPE}}, {{WEI_DATA_TYPE}}, {{ACC_DATA_TYPE}}, M0, N0, 1, NT, T, a, b, {{dst}});

            ++ck;
        }
    )_";
}

code += R"_(
#undef _I_WEI_WIDTH
#undef _I_WEI_HEIGHT
#undef _ISRC_WIDTH
#undef _ISRC_HEIGHT
#undef _ISRC_CHANNELS
#undef _IDST_WIDTH
#undef _IDST_HEIGHT
#undef _IDST_CHANNELS
#undef _IY_MULTIPLIER

    }
)_";

    if(_bias && _bias->has_valid_id())
    {
        code += R"_(
        TILE({{BIA_DATA_TYPE}}, 1, N0, bias0);

        T_LOAD({{BIA_DATA_TYPE}}, 1, N0, BUFFER, {{bias}}, g_ind_0, 0, 1, 0, bias0);

        // c = c + bias[broadcasted]
        T_ELTWISE_BROADCAST_ADD_X({{ACC_DATA_TYPE}}, M0, N0, {{dst}}, bias0, {{dst}});
    )_";
}

code += R"_(
}
//------------------ END KERNEL {{meta_kernel_id}} ---------------------
)_";
    return code;
}

void ClTemplateDirectConv2d::declare_variables(GpuKernelVariableTable &vtable, const ComponentGroup &comp_group) const
{
    vtable.declare_variable(
        _src,
        GpuKernelArgumentInfo(GpuKernelArgumentInfo::Type::Tensor_4D_t_Buffer),
        comp_group.is_intermediate_tensor(_src),
        "src");

    const GpuKernelArgumentInfo::Type weight_type = _settings.export_to_cl_image() ? GpuKernelArgumentInfo::Type::Tensor_4D_t_Image : GpuKernelArgumentInfo::Type::Tensor_4D_t_Buffer;
    vtable.declare_variable(
        _weight,
        GpuKernelArgumentInfo(weight_type),
        comp_group.is_intermediate_tensor(_weight),
        "weight");

    if(_bias && _bias->has_valid_id()) // optional bias
    {
        vtable.declare_variable(
            _bias,
            GpuKernelArgumentInfo(GpuKernelArgumentInfo::Type::Vector),
            comp_group.is_intermediate_tensor(_bias),
            "bias");
    }
    vtable.declare_variable(
        _dst,
        GpuKernelArgumentInfo(GpuKernelArgumentInfo::Type::Tensor_4D_t_Buffer),
        comp_group.is_intermediate_tensor(_dst),
        "dst");
}

TagLUT ClTemplateDirectConv2d::get_tag_lut(const GpuKernelVariableTable &vtable, const ComponentGroup &comp_group) const
{
    TagLUT lut{};
    // Arguments and global shared variables
    lut["src"]    = vtable.get_variable(_src);
    lut["weight"] = vtable.get_variable(_weight);

    if(_bias && _bias->has_valid_id()) // optional bias
    {
        lut["bias"]          = vtable.get_variable(_bias);
        lut["BIA_DATA_TYPE"] = get_cl_type_from_data_type(_bias->data_type());
    }
    lut["dst"] = vtable.get_variable(_dst);

    const auto dst_argument = vtable.get_variable(comp_group.get_dst_tensors()[0]);
    lut["arg_dst"]          = dst_argument.uniq_name;

    // Local build options
    lut["meta_kernel_id"] = id();
    lut["ACC_DATA_TYPE"]  = _src->data_type();
    lut["SRC_DATA_TYPE"]  = _src->data_type();
    lut["WEI_DATA_TYPE"]  = _weight->data_type();

    lut["SRC_TENSOR_TYPE"] = "BUFFER";
    switch(vtable.get_variable(_weight).kernel_argument_info.type)
    {
        case GpuKernelArgumentInfo::Type::Image_Export_To_ClImage2D:
        case GpuKernelArgumentInfo::Type::Image_3D_Export_To_ClImage2D:
        case GpuKernelArgumentInfo::Type::Tensor_4D_t_Image:
    {
        lut["WEI_TENSOR_TYPE"] = "IMAGE";
        break;
    }
        default:
    {
        lut["WEI_TENSOR_TYPE"] = "BUFFER";
        break;
    }
    }
    const auto width_idx  = 1;
    const auto height_idx = 2;
    lut["WEI_WIDTH"]      = _weight->dimension(width_idx);
    lut["WEI_HEIGHT"]     = _weight->dimension(height_idx);

    lut["STRIDE_X"] = _attributes.stride().x();
    lut["STRIDE_Y"] = _attributes.stride().y();

    lut["PAD_LEFT"] = _attributes.pad().left;
    lut["PAD_TOP"]  = _attributes.pad().top;

    lut["ZERO_VALUE"] = 0;

    return lut;
}

CLBuildOptions ClTemplateDirectConv2d::get_build_options(const ComponentGroup &comp_group) const
{
    const unsigned int channel_idx = get_data_layout_dimension_index(_src->data_layout(), DataLayoutDimension::CHANNEL);
    const DataType     data_type   = _src->data_type();

    /// NOTE: For now tile sizes (n0, m0, n0) are set by the execution window. This may change in the future
    const auto         root_window      = comp_group.get_root_component()->template_writer()->get_window();
    const unsigned int n0               = root_window.x().step();
    const unsigned int m0               = root_window.y().step();
    const unsigned int k0               = adjust_vec_size(is_data_type_quantized(data_type) ? 16u : 8u, _src->dimension(channel_idx));
    const unsigned int partial_store_n0 = _dst->dimension(0) % n0;

    CLBuildOptions build_opts{};
    if(_settings.fast_relaxed_math())
    {
        build_opts.add_option("-cl-fast-relaxed-math");
    }
    else
    {
        // -cl-fast-relaxed-math also sets -cl-finite-math-only and -cl-unsafe-math-optimizations
        // to disable -cl-finite-math-only, we only include -cl-unsafe-math-optimizations
        build_opts.add_option("-cl-unsafe-math-optimizations");
    }
    build_opts.add_option("-DIS_TILED");
    build_opts.add_option("-DN0=" + support::cpp11::to_string(n0));
    build_opts.add_option("-DM0=" + support::cpp11::to_string(m0));
    build_opts.add_option("-DK0=" + support::cpp11::to_string(k0));
    build_opts.add_option("-DPARTIAL_N0=" + support::cpp11::to_string(partial_store_n0));

    return build_opts;
}

std::string ClTemplateDirectConv2d::get_config_id() const
{
    const DataType   data_type   = _src->data_type();
    const DataLayout data_layout = _src->data_layout();

    const unsigned int width_idx  = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
    const unsigned int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);

    const unsigned int kernel_size = _weight->dimension(width_idx);

    std::string config_id{};
    config_id += lower_string(string_from_data_type(data_type));
    config_id += "_";
    config_id += support::cpp11::to_string(kernel_size);
    config_id += "_";
    config_id += support::cpp11::to_string(_attributes.stride().x());
    config_id += "_";
    config_id += support::cpp11::to_string(_attributes.stride().y());
    config_id += "_";
    config_id += support::cpp11::to_string(_dst->dimension(width_idx));
    config_id += "_";
    config_id += support::cpp11::to_string(_dst->dimension(height_idx));
    config_id += "_";
    config_id += lower_string(string_from_data_layout(data_layout));
    return config_id;
}

std::set<std::string> ClTemplateDirectConv2d::get_headers_list() const
{
    return std::set<std::string>{ "helpers.h", "tile_helpers.h" };
}

Window ClTemplateDirectConv2d::get_window() const
{
    ARM_COMPUTE_ERROR_ON_MSG(_dst->tensor_shape().total_size() == 0U, "Destination tensor is not initialized");

    const auto output_shape = _dst->tensor_shape();

    const unsigned int vec_size = std::min(static_cast<unsigned int>(output_shape[0]), 4u);
    const unsigned int num_rows = (_dst->tensor_shape()[0] > 16) ? ((_src->data_type() == DataType::F32) ? 2U : 4U) : 1U;

    // Create and configure kernel window
    Window win = calculate_max_window(output_shape, Steps(vec_size, num_rows));

    const size_t dim_y_collapsed = ceil_to_multiple(output_shape[1] * output_shape[2], num_rows);
    win.set(Window::DimY, Window::Dimension(0, dim_y_collapsed, num_rows));
    win.set(Window::DimZ, Window::Dimension(0, output_shape.total_size_upper(3), 1));

    return win;
}

} // namespace dynamic_fusion
} // namespace experimental
} // namespace arm_compute