/*******************************************************************************
* Copyright 2020-2025 Intel Corporation
* Copyright 2022 Arm Ltd. and affiliates
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
*     http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*******************************************************************************/

#ifndef CPU_REF_FUSED_CONVOLUTION_HPP
#define CPU_REF_FUSED_CONVOLUTION_HPP

#include "common/primitive.hpp"
#include "common/primitive_desc_iterator.hpp"
#include "common/reorder.hpp"
#include "common/stream.hpp"

#include "cpu/cpu_convolution_pd.hpp"
#include "cpu/dw_convolution_utils.hpp"

namespace dnnl {
namespace impl {
namespace cpu {

struct ref_fused_convolution_fwd_t : public primitive_t {

    struct arg_cache_t {
        struct arg_info_t {
            int op_arg;
            bool is_ctx_arg;
            bool is_const;
            union {
                size_t offset;
                int ctx_arg;
            };
            memory_desc_t md;
        };

        void append_ctx_arg(int op_arg, int ctx_arg) {
            arg_info_t arg_info;
            arg_info.op_arg = op_arg;
            arg_info.is_ctx_arg = true;
            arg_info.is_const = false; // unused
            arg_info.ctx_arg = ctx_arg;
            arg_info.md = glob_zero_md;
            info_.push_back(arg_info);
        }

        void append_inout_arg(int arg, size_t offset, const memory_desc_t *md,
                bool is_const) {
            arg_info_t arg_info;
            arg_info.op_arg = arg;
            arg_info.is_ctx_arg = false;
            arg_info.is_const = is_const;
            arg_info.offset = offset;
            arg_info.md = *md;
            info_.push_back(arg_info);
        }

        void append_ctx_arg(int arg) { append_ctx_arg(arg, arg); }

        const std::vector<arg_info_t> &info() const { return info_; }

    private:
        std::vector<arg_info_t> info_;
    };

    struct pd_t : public cpu_convolution_fwd_pd_t {
        using cpu_convolution_fwd_pd_t::cpu_convolution_fwd_pd_t;

        DECLARE_COMMON_PD_T(name_.c_str(), ref_fused_convolution_fwd_t);

        virtual status_t init(engine_t *engine) {
            using namespace primitive_kind;

            VDISPATCH_CONV(is_fwd(), VERBOSE_BAD_PROPKIND);
            VDISPATCH_CONV(attr()->post_ops_.has_default_values(
                                   {binary, eltwise, convolution}),
                    VERBOSE_UNSUPPORTED_ATTR);

            CHECK(init_ops(engine));
            init_name();
            return status::success;
        }

        // NOLINTBEGIN(google-default-arguments)
        const memory_desc_t *src_md(
                int index = 0, bool user_input = false) const override {
            if (op_pds_.empty())
                return cpu_convolution_fwd_pd_t::src_md(index, user_input);
            return op_pds_.front()->src_md(index, user_input);
        }

        const memory_desc_t *dst_md(
                int index = 0, bool user_input = false) const override {
            if (op_pds_.empty())
                return cpu_convolution_fwd_pd_t::dst_md(index, user_input);
            return op_pds_.back()->dst_md(index, user_input);
        }

        const memory_desc_t *weights_md(
                int index = 0, bool user_input = false) const override {
            if (op_pds_.empty())
                return cpu_convolution_fwd_pd_t::weights_md(index, user_input);
            return op_pds_.front()->weights_md(index, user_input); // for now
        }

        const memory_desc_t *arg_md(
                int arg, bool user_input = false) const override {
            if (op_pds_.empty())
                return cpu_convolution_fwd_pd_t::arg_md(arg, user_input);
            // Binary post-op:
            // format_tag::any should be supported here since output dst_md
            // may be different from the intermediate one and they should be
            // initialized and queried separately.
            if (arg >= DNNL_ARG_ATTR_MULTIPLE_POST_OP(0)
                    && arg < DNNL_ARG_ATTR_MULTIPLE_POST_OP(
                               post_ops_t::post_ops_limit)) {
                const auto &po = attr()->post_ops_;
                auto dw_idx = po.find(primitive_kind::convolution);
                for (int idx = 0; idx < po.len(); ++idx) {
                    if (arg
                            != (DNNL_ARG_ATTR_MULTIPLE_POST_OP(idx)
                                    | DNNL_ARG_SRC_1))
                        continue;

                    if (dw_idx > idx)
                        return &op_pds_.front()
                                        ->attr()
                                        ->post_ops_.entry_[idx]
                                        .binary.src1_desc;
                    else
                        return &op_pds_.back()
                                        ->attr()
                                        ->post_ops_.entry_[idx - (dw_idx + 1)]
                                        .binary.src1_desc;
                }
            }

            switch (arg) { // for now
                case DNNL_ARG_ATTR_POST_OP_DW | DNNL_ARG_SRC:
                    return op_pds_.front()->dst_md(0, user_input);
                case DNNL_ARG_ATTR_POST_OP_DW | DNNL_ARG_WEIGHTS:
                    return op_pds_.back()->weights_md(0);
                case DNNL_ARG_ATTR_POST_OP_DW | DNNL_ARG_BIAS:
                    return op_pds_.back()->weights_md(1);
                default: return convolution_fwd_pd_t::arg_md(arg, user_input);
            }
        }
        // NOLINTEND(google-default-arguments)

        arg_usage_t arg_usage(int arg) const override {
            if (arg == (DNNL_ARG_ATTR_POST_OP_DW | DNNL_ARG_WEIGHTS))
                return arg_usage_t::input;

            if (arg == (DNNL_ARG_ATTR_POST_OP_DW | DNNL_ARG_BIAS))
                return attr_post_op_dw_inputs() > 1 ? arg_usage_t::input
                                                    : arg_usage_t::unused;

            if (arg == (DNNL_ARG_ATTR_POST_OP_DW | DNNL_ARG_SRC))
                return arg_usage_t::input;

            if (arg == (DNNL_ARG_ATTR_POST_OP_DW | DNNL_ARG_DST))
                return arg_usage_t::input;

            return convolution_fwd_pd_t::arg_usage(arg);
        }

        size_t user_scratchpad_size_;
        std::vector<std::shared_ptr<primitive_desc_t>> op_pds_;
        std::vector<arg_cache_t> args_;

    private:
        std::string name_ = "ref_fused_convolution:any";
        const unsigned int max_fusions_ = 1;

        status_t append_op(std::shared_ptr<primitive_desc_t> &op_pd,
                size_t &sp_begin, size_t &sp_end, engine_t *engine) {
            auto from_md = op_pds_.back()->dst_md();
            auto to_md = op_pd->src_md();

            if (*from_md != *to_md) {
                //TODO: Find a test-case for this
                std::shared_ptr<primitive_desc_t> pd;
                CHECK(reorder_primitive_desc_create(
                        pd, engine, from_md, to_md));
                op_pds_.emplace_back(std::move(pd));

                arg_cache_t arg_cache;
                arg_cache.append_inout_arg(
                        DNNL_ARG_FROM, sp_begin, from_md, true);
                arg_cache.append_inout_arg(DNNL_ARG_TO, sp_end, to_md, false);
                args_.push_back(arg_cache);

                // Increment scratchpad offsets
                sp_begin = sp_end;
                sp_end += memory_desc_wrapper(to_md).size();

                user_scratchpad_size_ = nstl::max<size_t>(user_scratchpad_size_,
                        op_pds_.back()->scratchpad_size(
                                attr()->scratchpad_mode_));
            }

            op_pds_.emplace_back(std::move(op_pd));
            user_scratchpad_size_ = nstl::max<size_t>(user_scratchpad_size_,
                    op_pds_.back()->scratchpad_size(attr()->scratchpad_mode_));
            return status::success;
        }

        status_t init_ops(engine_t *engine) {
            using namespace data_type;
            primitive_attr_t root_attr(*attr());
            if (!root_attr.is_initialized()) return status::out_of_memory;
            auto po_op_iter
                    = attr()->post_ops_.find(primitive_kind::convolution);
            if (po_op_iter == -1) return status::unimplemented;

            primitive_attr_t attr_1x1(*attr());
            // erase dw_conv post-op scales
            for (auto arg : {DNNL_ARG_SRC, DNNL_ARG_WEIGHTS, DNNL_ARG_DST}) {
                if (!attr_1x1.scales_.has_default_values(
                            DNNL_ARG_ATTR_POST_OP_DW | arg))
                    CHECK(attr_1x1.scales_.set(DNNL_ARG_ATTR_POST_OP_DW | arg,
                            default_quant_entry()));
            }
            // erase post-ops after fusion as they will be handled separately
            auto &e = attr_1x1.post_ops_.entry_;
            e.erase(e.begin() + po_op_iter, e.end());

            primitive_desc_iterator_t it(engine, op_desc(), &attr_1x1, nullptr);
            if (!it.is_initialized()) return status::out_of_memory;
            std::shared_ptr<primitive_desc_t> root_pd = *(++it);
            if (!root_pd) return status::unimplemented;
            op_pds_.emplace_back(root_pd);
            // Scratchpad offsets. Simulate offset computation so that offset
            // computation can be avoided during execution.
            size_t inout_sp_offset_begin = 0;
            size_t inout_sp_offset_end = 0;
            user_scratchpad_size_
                    = root_pd->scratchpad_size(attr()->scratchpad_mode_);

            // Create arg cache for the root pd
            arg_cache_t arg_cache;
            arg_cache.append_ctx_arg(DNNL_ARG_SRC);
            arg_cache.append_ctx_arg(DNNL_ARG_WEIGHTS);
            for (auto arg : {DNNL_ARG_SRC, DNNL_ARG_WEIGHTS, DNNL_ARG_DST})
                if (!attr_1x1.scales_.has_default_values(arg))
                    arg_cache.append_ctx_arg(DNNL_ARG_ATTR_SCALES | arg);
            if (desc()->bias_desc.data_type != data_type::undef)
                arg_cache.append_ctx_arg(DNNL_ARG_BIAS);
            arg_cache.append_inout_arg(DNNL_ARG_DST, inout_sp_offset_end,
                    root_pd->dst_md(), false);
            // Initialize binary post_op.
            CHECK(attr_1x1.set_default_formats(root_pd->dst_md()));
            for (int idx = 0; idx < attr_1x1.post_ops_.len(); ++idx) {
                if (attr_1x1.post_ops_.contain(primitive_kind::binary, idx))
                    arg_cache.append_ctx_arg(DNNL_ARG_ATTR_MULTIPLE_POST_OP(idx)
                            | DNNL_ARG_SRC_1);
            }
            args_.push_back(arg_cache);

            // Increment scratchpad offsets
            inout_sp_offset_begin = inout_sp_offset_end;
            inout_sp_offset_end
                    += memory_desc_wrapper(root_pd->dst_md()).size();

            const auto &po = attr()->post_ops_;
            const auto &end = po.len();

            unsigned int fusion_ops = 0;
            // Loop through the post-ops until we reach the end
            // (if we have more than one op to fuse later)
            while (po_op_iter < end) {
                if (fusion_ops++ > max_fusions_) return status::unimplemented;

                const auto &prev_op_pd = op_pds_.back();

                if (po.entry_[po_op_iter].kind != primitive_kind::convolution)
                    return status::unimplemented;

                if (prev_op_pd->kind() != primitive_kind::convolution)
                    return status::unimplemented;

                auto conv_pd = reinterpret_cast<convolution_pd_t *>(
                        prev_op_pd.get());
                bool ok = true && is_fwd()
                        && utils::everyone_is(
                                1, conv_pd->KD(), conv_pd->KH(), conv_pd->KW());
                if (!ok) return status::unimplemented;

                convolution_desc_t cd_dw;
                primitive_attr_t attr_dw;
                CHECK(get_depthwise_conv_desc(cd_dw, *(conv_pd->dst_md()),
                        root_attr, attr_dw, po_op_iter));
                primitive_desc_iterator_t it(
                        engine, (op_desc_t *)&cd_dw, &attr_dw, nullptr);
                if (!it.is_initialized()) return status::out_of_memory;

                std::shared_ptr<primitive_desc_t> append_conv_pd = *(++it);
                if (!append_conv_pd) return status::unimplemented;

                CHECK(append_op(append_conv_pd, inout_sp_offset_begin,
                        inout_sp_offset_end, engine));

                const auto &op = op_pds_.back();
                arg_cache_t arg_cache;
                arg_cache.append_inout_arg(DNNL_ARG_SRC, inout_sp_offset_begin,
                        op->src_md(), true);
                arg_cache.append_ctx_arg(DNNL_ARG_DST);
                arg_cache.append_ctx_arg(DNNL_ARG_WEIGHTS,
                        DNNL_ARG_ATTR_POST_OP_DW | DNNL_ARG_WEIGHTS);
                for (auto arg : {DNNL_ARG_WEIGHTS, DNNL_ARG_DST})
                    if (!attr_dw.scales_.has_default_values(arg))
                        arg_cache.append_ctx_arg(DNNL_ARG_ATTR_SCALES | arg,
                                DNNL_ARG_ATTR_POST_OP_DW | DNNL_ARG_ATTR_SCALES
                                        | arg);
                // dw_conv src_scale = 1x1_conv dst_scale
                if (!attr_1x1.scales_.has_default_values(DNNL_ARG_DST))
                    arg_cache.append_ctx_arg(
                            DNNL_ARG_ATTR_SCALES | DNNL_ARG_SRC,
                            DNNL_ARG_ATTR_SCALES | DNNL_ARG_DST);
                if (op->weights_md(1)->data_type != data_type::undef)
                    arg_cache.append_ctx_arg(DNNL_ARG_BIAS,
                            DNNL_ARG_ATTR_POST_OP_DW | DNNL_ARG_BIAS);
                // Initialize binary post_op.
                CHECK(attr_dw.set_default_formats(op->dst_md()));
                for (int idx = 0; idx < attr_dw.post_ops_.len(); ++idx) {
                    if (attr_dw.post_ops_.contain(primitive_kind::binary, idx))
                        arg_cache.append_ctx_arg(
                                (DNNL_ARG_ATTR_MULTIPLE_POST_OP(idx)
                                        | DNNL_ARG_SRC_1),
                                (DNNL_ARG_ATTR_MULTIPLE_POST_OP(
                                         idx + po_op_iter + 1)
                                        | DNNL_ARG_SRC_1));
                }

                args_.push_back(arg_cache);

                while (++po_op_iter < end) {
                    if (utils::one_of(po.entry_[po_op_iter].kind,
                                primitive_kind::convolution))
                        break;
                }
            }

            assert(!op_pds_.empty());

            CHECK(init_scratchpad_memory(inout_sp_offset_end));

            return status::success;
        }

        status_t init_scratchpad_memory(size_t inout_buffer_size) {

            auto scratchpad = scratchpad_registry().registrar();

            scratchpad.book(memory_tracking::names::key_fusion_inout_buffer,
                    inout_buffer_size, 1, 16);
            scratchpad.book(
                    memory_tracking::names::key_fusion_forward_scratchpad,
                    user_scratchpad_size_, 1, 16);
            return status::success;
        }

        void init_name() {
            for (const auto &op_pd : op_pds_) {
                name_.append(":");
                name_.append(op_pd->name());
            }
        }
    };

    ref_fused_convolution_fwd_t(const pd_t *apd) : primitive_t(apd) {}

    status_t init(engine_t *engine) override {
        const auto &op_pds = pd()->op_pds_;
        for (auto &op_pd : op_pds) {
            std::shared_ptr<primitive_t> p;
            op_pd->create_primitive(p, engine);
            primitives_.emplace_back(p);
        }
        return status::success;
    }

#if DNNL_AARCH64 && defined(DNNL_AARCH64_USE_ACL)
    status_t create_resource(
            engine_t *engine, resource_mapper_t &mapper) const override {
        for (auto &p : primitives_) {
            CHECK(p->create_resource(engine, mapper));
        }
        return status::success;
    }
#endif

    status_t execute(const exec_ctx_t &ctx) const override {
        engine_t *engine = ctx.stream()->engine();
        const auto scratchpad = ctx.get_scratchpad_grantor();

        const auto inout_buffer = scratchpad.get_memory_storage(
                memory_tracking::names::key_fusion_inout_buffer);

        const auto &ctx_args = ctx.args();
        const auto op_count = primitives_.size();
        std::vector<std::unique_ptr<memory_t, memory_deleter_t>> inout_memory;

        for (size_t i = 0; i < op_count; ++i) {
            const auto &op = primitives_[i];
            const auto &arg_cache = pd()->args_[i];

            exec_args_t exec_args;

            for (const auto &arg_info : arg_cache.info()) {
                if (arg_info.is_ctx_arg) {
                    exec_args[arg_info.op_arg] = ctx_args.at(arg_info.ctx_arg);
                } else {
                    inout_memory.emplace_back(new memory_t(engine, &arg_info.md,
                            inout_buffer->get_sub_storage(arg_info.offset,
                                    memory_desc_wrapper(arg_info.md).size())));
                    exec_args[arg_info.op_arg].mem = inout_memory.back().get();
                    exec_args[arg_info.op_arg].is_const = arg_info.is_const;
                }
            }

            exec_ctx_t op_ctx(ctx, std::move(exec_args));

            nested_scratchpad_t ns(ctx,
                    memory_tracking::names::key_fusion_forward_scratchpad, op);
            op_ctx.set_scratchpad_grantor(ns.grantor());
            CHECK(op->execute(op_ctx));
        }

        return status::success;
    }

private:
    const pd_t *pd() const { return (const pd_t *)primitive_t::pd().get(); }
    std::vector<std::shared_ptr<primitive_t>> primitives_;
};

} // namespace cpu
} // namespace impl
} // namespace dnnl

#endif

// vim: et ts=4 sw=4 cindent cino+=l0,\:4,N-s
