/*
 * Copyright (c) 2017-2021, 2023-2024 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/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h"

#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/utils/DataTypeUtils.h"
#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/runtime/IWeightsManager.h"
#include "arm_compute/runtime/MemoryGroup.h"
#include "arm_compute/runtime/Tensor.h"

#include "src/core/helpers/MemoryHelpers.h"
#include "src/core/utils/quantization/AsymmHelpers.h"
#include "src/cpu/operators/CpuGemmLowpMatrixMultiplyCore.h"

#include <set>

using namespace arm_compute::experimental;

namespace arm_compute
{
struct NEGEMMLowpMatrixMultiplyCore::Impl
{
    const ITensor                                      *b{nullptr};
    std::unique_ptr<cpu::CpuGemmLowpMatrixMultiplyCore> op{nullptr};
    ITensorPack                                         run_pack{};
    ITensorPack                                         prep_pack{};
    MemoryGroup                                         memory_group{};
    IWeightsManager                                    *weights_manager{nullptr};
    MemoryRequirements                                  aux_mem_req{};
    WorkspaceData<Tensor>                               workspace_tensors{};
    ActivationLayerInfo                                 act_info{};
    bool                                                is_prepared{false};
};

NEGEMMLowpMatrixMultiplyCore::NEGEMMLowpMatrixMultiplyCore(std::shared_ptr<IMemoryManager> memory_manager,
                                                           IWeightsManager                *weights_manager)
    : _impl(std::make_unique<Impl>())
{
    _impl->weights_manager = weights_manager;
    _impl->memory_group    = MemoryGroup(memory_manager);
}
NEGEMMLowpMatrixMultiplyCore::~NEGEMMLowpMatrixMultiplyCore() = default;

void NEGEMMLowpMatrixMultiplyCore::configure(
    const ITensor *a, const ITensor *b, const ITensor *c, ITensor *output, const GEMMInfo &gemm_info)
{
    ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output);

    // Make the B matrix dynamic values.
    auto b_info_to_use = b->info()->clone();
    if (!gemm_info.reshape_b_only_on_first_run())
    {
        b_info_to_use->set_are_values_constant(false);
    }

    _impl->is_prepared = false;
    _impl->memory_group.mappings().clear();
    _impl->b  = b;
    _impl->op = std::make_unique<cpu::CpuGemmLowpMatrixMultiplyCore>();
    _impl->op->configure(a->info(), b_info_to_use.get(), (c != nullptr ? c->info() : nullptr), output->info(),
                         gemm_info);
    _impl->run_pack          = {{TensorType::ACL_SRC_0, a},
                                {TensorType::ACL_SRC_1, b},
                                {TensorType::ACL_SRC_2, c},
                                {TensorType::ACL_DST, output}};
    _impl->prep_pack         = {{TensorType::ACL_SRC_1, b}, {TensorType::ACL_SRC_2, c}};
    _impl->aux_mem_req       = _impl->op->workspace();
    _impl->act_info          = gemm_info.activation_info();
    _impl->workspace_tensors = manage_workspace<Tensor>(_impl->aux_mem_req, _impl->memory_group, _impl->run_pack,
                                                        _impl->prep_pack, /* allocate_now */ false);
}

Status NEGEMMLowpMatrixMultiplyCore::validate(const ITensorInfo *a,
                                              const ITensorInfo *b,
                                              const ITensorInfo *c,
                                              const ITensorInfo *output,
                                              const GEMMInfo    &gemm_info)
{
    // Make the B matrix dynamic values.
    auto b_info_to_use = b->clone();
    if (!gemm_info.reshape_b_only_on_first_run())
    {
        b_info_to_use->set_are_values_constant(false);
    }

    return cpu::CpuGemmLowpMatrixMultiplyCore::validate(a, b_info_to_use.get(), c, output, gemm_info);
}

void NEGEMMLowpMatrixMultiplyCore::update_quantization_parameters()
{
    // Supported activations in GEMM
    const std::set<ActivationLayerInfo::ActivationFunction> supported_acts = {
        ActivationLayerInfo::ActivationFunction::RELU, ActivationLayerInfo::ActivationFunction::BOUNDED_RELU,
        ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU};

    auto src = _impl->run_pack.get_const_tensor(ACL_SRC_0);
    auto wei = _impl->run_pack.get_const_tensor(ACL_SRC_1);
    auto dst = _impl->run_pack.get_tensor(ACL_DST);

    const QuantizationInfo iqinfo = src->info()->quantization_info();
    const QuantizationInfo wqinfo = wei->info()->quantization_info();
    const QuantizationInfo oqinfo = (dst->info()->total_size() == 0) ? iqinfo : dst->info()->quantization_info();

    PixelValue     type_min{};
    PixelValue     type_max{};
    const DataType data_type     = src->info()->data_type();
    std::tie(type_min, type_max) = get_min_max(data_type);
    int32_t min_activation       = type_min.get<int32_t>();
    int32_t max_activation       = type_max.get<int32_t>();

    const UniformQuantizationInfo uoqinfo = oqinfo.uniform();
    if (supported_acts.find(_impl->act_info.activation()) != supported_acts.end())
    {
        std::tie(min_activation, max_activation) =
            get_quantized_activation_min_max(_impl->act_info, data_type, uoqinfo);
    }

    GEMMLowpOutputStageInfo output_info;
    output_info.type                     = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;
    output_info.gemmlowp_offset          = uoqinfo.offset;
    output_info.gemmlowp_min_bound       = min_activation;
    output_info.gemmlowp_max_bound       = max_activation;
    output_info.is_quantized_per_channel = false;
    output_info.output_data_type         = dst->info()->data_type();
    quantization::calculate_quantized_multipliers(iqinfo, wqinfo, oqinfo, output_info);

    _impl->op->update_quantization_parameters(output_info, src->info()->quantization_info(),
                                              wei->info()->quantization_info(), true, true);
}

void NEGEMMLowpMatrixMultiplyCore::run()
{
    prepare();
    MemoryGroupResourceScope scope_mg(_impl->memory_group);
    _impl->op->run(_impl->run_pack);
}

void NEGEMMLowpMatrixMultiplyCore::prepare()
{
    if (!_impl->is_prepared)
    {
        allocate_tensors(_impl->aux_mem_req, _impl->workspace_tensors);
        _impl->op->prepare(_impl->prep_pack);

        auto has_reshape =
            std::find_if(_impl->aux_mem_req.begin(), _impl->aux_mem_req.end(),
                         [](const MemoryInfo &m) -> bool { return m.lifetime == MemoryLifetime::Persistent; });

        if (has_reshape != std::end(_impl->aux_mem_req))
        {
            _impl->b->mark_as_unused();
        }

        // Release temporary tensors that are only used in prepare stage
        release_temporaries<Tensor>(_impl->aux_mem_req, _impl->workspace_tensors);
        _impl->is_prepared = true;
    }
}
} // namespace arm_compute
