/*******************************************************************************
* Copyright 2022 Intel Corporation
*
* 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 GPU_OCL_MULTI_CONCAT_HPP
#define GPU_OCL_MULTI_CONCAT_HPP

#include "common/concat.hpp"
#include "common/engine.hpp"
#include "common/primitive.hpp"
#include "common/primitive_desc.hpp"
#include "common/stream.hpp"
#include "gpu/gpu_concat_pd.hpp"
#include "gpu/gpu_primitive.hpp"
#include "gpu/ocl/ocl_utils.hpp"

namespace dnnl {
namespace impl {
namespace gpu {
namespace ocl {

struct multi_concat_t : public gpu_primitive_t {
    using gpu_primitive_t::gpu_primitive_t;
    struct pd_t : public gpu_concat_pd_t {
        using gpu_concat_pd_t::gpu_concat_pd_t;

        pd_t(const pd_t &rhs) = default;
        ~pd_t() = default;

        DECLARE_CONCAT_PD_T("multi:any", multi_concat_t);

        int max_batch_size() const {
            if (n_inputs() > 64) return 64;
            if (n_inputs() > 16) return 16;
            return 0;
        }

        status_t init(engine_t *engine) {
            bool ok = max_batch_size() != 0 && attr()->has_default_values()
                    && set_default_params() == status::success;
            if (!ok) return status::unimplemented;

            auto n_batches = utils::div_up(n_inputs(), max_batch_size());
            concat_pds_.resize(n_batches);
            dst_chunk_mds_.resize(n_batches);

            dim_t concat_dim_offset = 0;
            const auto ndims = dst_md()->ndims;
            status_t status = status::success;
            for (int i = 0; i < n_batches; ++i) {
                const auto src_offset = max_batch_size() * i;
                const auto remaining = n_inputs() - src_offset;
                const auto batch_size = std::min(max_batch_size(), remaining);
                dim_t batch_width = 0;
                dims_t dims, offsets = {0};
                utils::array_copy(dims, dst_md()->dims, ndims);
                for (int j = 0; j < batch_size; ++j) {
                    const auto &src = src_md(src_offset + j);
                    batch_width += src->dims[concat_dim_];
                }
                dims[concat_dim_] = batch_width;
                offsets[concat_dim_] = concat_dim_offset;
                status = memory_desc_init_submemory(
                        dst_chunk_mds_[i], *dst_md(), dims, offsets);
                if (status != status::success) {
                    concat_pds_.clear();
                    dst_chunk_mds_.clear();
                    return status;
                }
                status = concat_primitive_desc_create(concat_pds_[i], engine,
                        &dst_chunk_mds_[i], batch_size, concat_dim_,
                        src_md(src_offset), attr());
                if (status != status::success) {
                    concat_pds_.clear();
                    dst_chunk_mds_.clear();
                    return status;
                }
                concat_dim_offset += batch_width;
            }
            return status;
        }

        std::vector<std::shared_ptr<primitive_desc_t>> concat_pds_;
        std::vector<memory_desc_t> dst_chunk_mds_;
    };

    status_t init(engine_t *engine) override {
        const auto &pds = pd()->concat_pds_;
        const size_t n = pds.size();
        concats_.resize(n);
        for (size_t i = 0; i < n; ++i)
            CHECK(create_nested_primitive(concats_[i], pds[i], engine));
        return status::success;
    }

    status_t execute(const exec_ctx_t &ctx) const override {
        using namespace memory_tracking::names;
        const auto n = pd()->n_inputs();
        const auto max_batch_size = pd()->max_batch_size();

        auto execute_concat = [&](const std::shared_ptr<primitive_t> &concat,
                                      int c_num, int n_inputs) {
            exec_args_t r_args;
            const auto arg_offset = DNNL_ARG_MULTIPLE_SRC;
            for (int i = 0; i < n_inputs; ++i)
                r_args[arg_offset + i] = ctx.args().at(
                        arg_offset + max_batch_size * c_num + i);
            r_args[DNNL_ARG_DST] = ctx.args().at(DNNL_ARG_DST);
            exec_ctx_t r_ctx(ctx, std::move(r_args));

            r_ctx.set_scratchpad_grantor(ctx.grantor_handle());
            return concat->execute(r_ctx);
        };

        const auto n_batches = utils::div_up(n, max_batch_size);
        for (int i = 0; i < n_batches; ++i) {
            const auto remaining = n - max_batch_size * i;
            const auto batch_size = std::min(max_batch_size, remaining);
            CHECK(execute_concat(concats_[i], i, batch_size));
        }
        return status::success;
    }

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

} // namespace ocl
} // namespace gpu
} // namespace impl
} // namespace dnnl

#endif
