src/core/CL/CLUtils.cpp

/*
 * Copyright (c) 2020-2023 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
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 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */
#include "arm_compute/core/CL/CLCompileContext.h"
#include "arm_compute/core/CL/CLKernelLibrary.h"
#include "arm_compute/core/CL/ICLTensor.h"
#include "arm_compute/core/Validate.h"
#include "support/StringSupport.h"

#include "src/core/CL/CLUtils.h"
#include "src/core/experimental/PostOpUtils.h"

namespace arm_compute
{
cl::Image2D create_image2d_from_tensor(const ICLTensor *tensor, CLImage2DType image_type)
{
    ARM_COMPUTE_ERROR_ON_NULLPTR(tensor);

    const cl::Context &ctx    = CLKernelLibrary::get().context();
    const cl::Buffer  &buffer = tensor->cl_buffer();
    const ITensorInfo *info   = tensor->info();
    ARM_COMPUTE_ERROR_ON_MSG(info->lock_paddings(),
                             "Tensor paddings must not be locked to allow extending paddings to satisfy cl_image pitch alignment requirement");

    const size_t image_w{ info->dimension(0) / 4 };
    const size_t image_h{ info->tensor_shape().total_size() / info->dimension(0) };
    const size_t max_image_w{ CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_IMAGE2D_MAX_WIDTH>() };
    const size_t max_image_h{ CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_IMAGE2D_MAX_HEIGHT>() };

    ARM_COMPUTE_UNUSED(max_image_w, max_image_h);
    ARM_COMPUTE_ERROR_ON_MSG(image_w > max_image_w, "Image width exceeds maximum width for exporting to cl_image");
    ARM_COMPUTE_ERROR_ON_MSG(image_h > max_image_h, "Image height exceeds maximum height for exporting to cl_image");

    const TensorShape shape2d(image_w, image_h);
    const size_t      image_row_pitch = info->strides_in_bytes()[1];

    return create_image2d_from_buffer(ctx, buffer, shape2d, info->data_type(), image_row_pitch, image_type);
}

cl::Image2D create_image2d_from_buffer(const cl::Context &ctx, const cl::Buffer &buffer, const TensorShape &shape2d, DataType data_type, size_t image_row_pitch, CLImage2DType image_type)
{
    ARM_COMPUTE_ERROR_ON_MSG(!image2d_from_buffer_supported(CLKernelLibrary::get().get_device()),
                             "The extension cl_khr_image2d_from_buffer is not supported on the target platform");
    ARM_COMPUTE_ERROR_ON_MSG(get_cl_image_pitch_alignment(CLKernelLibrary::get().get_device()) == 0,
                             "Impossible to retrieve the cl_image pitch alignment");
    ARM_COMPUTE_ERROR_ON_MSG(buffer.get() == nullptr,
                             "Cannot create cl_image from empty cl_buffer");

    cl_channel_type cl_data_type;

    switch(data_type)
    {
        case DataType::F32:
            cl_data_type = CL_FLOAT;
            break;
        case DataType::F16:
            cl_data_type = CL_HALF_FLOAT;
            break;
        default:
            ARM_COMPUTE_ERROR("Data type not support with OpenCL image2d");
    }

    cl_mem cl_image;
    cl_int err = CL_SUCCESS;

    const cl_image_format format = { CL_RGBA, cl_data_type };

    cl_image_desc desc;
    memset(&desc, 0, sizeof(desc));
    desc.image_type      = CL_MEM_OBJECT_IMAGE2D;
    desc.mem_object      = buffer();
    desc.image_row_pitch = image_row_pitch;
    desc.image_width     = shape2d[0];
    desc.image_height    = shape2d[1];

    switch(image_type)
    {
        case CLImage2DType::ReadOnly:
            cl_image = clCreateImage(ctx(), CL_MEM_READ_ONLY, &format, &desc, nullptr, &err);
            break;
        case CLImage2DType::WriteOnly:
            cl_image = clCreateImage(ctx(), CL_MEM_WRITE_ONLY, &format, &desc, nullptr, &err);
            break;
        default:
            ARM_COMPUTE_ERROR("Unsupported CLImage2DType");
    }

    ARM_COMPUTE_UNUSED(err);
    ARM_COMPUTE_ERROR_ON_MSG(err != CL_SUCCESS, "Error during the creation of CL image from buffer");

    return cl::Image2D(cl_image);
}

namespace experimental
{
PostOpCLKernelUtils::PostOpCLKernelUtils(const Config &supported_config)
    : _supported_config(supported_config)
{
    ARM_COMPUTE_ERROR_ON_MSG(supported_config.empty(), "Empty PostOp CL kernel support configuration is not allowed");
    for(auto it = _supported_config.begin(); it != _supported_config.end(); ++it)
    {
        auto post_op_sequence = it->first;
        auto post_op_slots    = std::get<1>(it->second);
        ARM_COMPUTE_ERROR_ON_MSG(post_op_sequence.size() != post_op_slots.size(), "The number of PostOps must be the same as that of the assigned slots");
    }
}

bool PostOpCLKernelUtils::are_post_op_shapes_compliant(const ITensorInfo *dst, const experimental::PostOpList<ITensorInfo *> &post_ops)
{
    for(const auto &op : post_ops.get_list())
    {
        for(const auto &tensor : op->arguments())
        {
            const TensorShape &out_shape = TensorShape::broadcast_shape(dst->tensor_shape(), (*tensor)->tensor_shape());
            // All post ops must be elementwise and must not alter the shape of the original dst tensor after broadcasting
            if(detail::have_different_dimensions(out_shape, dst->tensor_shape(), 0))
            {
                return false;
            }
            // NOTE: Kernel limitation: currently only the following broadcasting types are supported:
            //  1. Post op arg is scalar, broadcast in both first and second dims
            //  2. Post op arg is of shape: second dim=1, first dim=N, broadcast only in second dim
            //  This means this case: Post op arg is of shape: second dim=M, first dim=1, broadcast only in first dim, is NOT supported
            if(dst->dimension(0) > 1 && dst->dimension(1) > 1 && (*tensor)->dimension(0) == 1 && (*tensor)->dimension(1) > 1)
            {
                return false;
            }
        }
    }
    return true;
}

bool PostOpCLKernelUtils::is_post_op_sequence_supported(const PostOpList<ITensorInfo *> &post_ops) const
{
    if(post_ops.size() == 0)
    {
        return true; // Always support cases where no post op is specified
    }
    const auto post_op_sequence = get_post_op_sequence(post_ops);

    return _supported_config.find(post_op_sequence) != _supported_config.end();
}

void PostOpCLKernelUtils::set_post_ops_cl_build_options(CLBuildOptions &build_opts, const PostOpList<ITensorInfo *> &post_ops) const
{
    const auto post_op_sequence = get_post_op_sequence(post_ops);
    const auto slots            = std::get<1>(_supported_config.at(post_op_sequence));
    for(size_t post_op_id = 0; post_op_id < post_ops.size(); ++post_op_id)
    {
        const auto &post_op     = post_ops.get_list().at(post_op_id);
        const auto  slot_prefix = "-DP" + support::cpp11::to_string(slots[post_op_id]);
        if(post_op->type() == experimental::PostOpType::Activation)
        {
            const auto _post_op  = utils::cast::polymorphic_downcast<const experimental::PostOpAct<ITensorInfo *> *>(post_op.get());
            const auto act_type  = slot_prefix + "_ACTIVATION_TYPE=" + lower_string(string_from_activation_func(_post_op->_act_info.activation()));
            const auto act_a_val = slot_prefix + "_ACTIVATION_A_VAL=" + float_to_string_with_full_precision(_post_op->_act_info.a());
            const auto act_b_val = slot_prefix + "_ACTIVATION_B_VAL=" + float_to_string_with_full_precision(_post_op->_act_info.b());
            build_opts.add_option(act_type);
            build_opts.add_option(act_a_val);
            build_opts.add_option(act_b_val);
        }
        else if(post_op->type() == experimental::PostOpType::Eltwise_Add)
        {
            size_t     arg_id     = 1;
            const auto eltwise_op = slot_prefix + "_ELTWISE_OP=ADD" + "_X_POS_" + support::cpp11::to_string(post_op->prev_dst_pos());
            build_opts.add_option(eltwise_op);
            for(const auto &tensor : post_op->arguments())
            {
                const auto height = slot_prefix + "_ELTWISE_ARG" + support::cpp11::to_string(arg_id) + "_HEIGHT=" + support::cpp11::to_string((*tensor)->dimension(1));
                const auto width  = slot_prefix + "_ELTWISE_ARG" + support::cpp11::to_string(arg_id) + "_WIDTH=" + support::cpp11::to_string((*tensor)->dimension(0));
                build_opts.add_option(height);
                build_opts.add_option(width);
                ++arg_id;
            }
        }
        else if(post_op->type() == experimental::PostOpType::Eltwise_PRelu)
        {
            size_t     arg_id     = 1;
            const auto eltwise_op = slot_prefix + "_ELTWISE_OP=PRELU" + "_X_POS_" + support::cpp11::to_string(post_op->prev_dst_pos());
            build_opts.add_option(eltwise_op);
            for(const auto &tensor : post_op->arguments())
            {
                const auto height = slot_prefix + "_ELTWISE_ARG" + support::cpp11::to_string(arg_id) + "_HEIGHT=" + support::cpp11::to_string((*tensor)->dimension(1));
                const auto width  = slot_prefix + "_ELTWISE_ARG" + support::cpp11::to_string(arg_id) + "_WIDTH=" + support::cpp11::to_string((*tensor)->dimension(0));
                build_opts.add_option(height);
                build_opts.add_option(width);
                ++arg_id;
            }
        }
    }
}

void PostOpCLKernelUtils::set_post_ops_cl_kernel_name(std::string &kernel_name, const PostOpList<ITensorInfo *> &post_ops) const
{
    const auto post_op_sequence = get_post_op_sequence(post_ops);
    const auto postfix          = std::get<0>(_supported_config.at(post_op_sequence));
    kernel_name += postfix;
}
} // namespace experimental

} // namespace arm_compute