/*******************************************************************************
* Copyright 2020-2022 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_GEN9_BATCH_NORMALIZATION_HPP
#define GPU_OCL_GEN9_BATCH_NORMALIZATION_HPP

#include "common/experimental.hpp"
#include "common/primitive.hpp"
#include "gpu/compute/compute.hpp"
#include "gpu/gpu_batch_normalization_pd.hpp"
#include "gpu/gpu_primitive.hpp"
#include "gpu/gpu_resource.hpp"
#include "gpu/primitive_conf.hpp"

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

struct gen9_batch_normalization_fwd_t : public gpu_primitive_t {
    using gpu_primitive_t::gpu_primitive_t;
    struct pd_t : public gpu_batch_normalization_fwd_pd_t {
        pd_t(const batch_normalization_desc_t *adesc,
                const primitive_attr_t *attr,
                const batch_normalization_fwd_pd_t *hint_fwd_pd)
            : gpu_batch_normalization_fwd_pd_t(adesc, attr, hint_fwd_pd) {}

        DECLARE_COMMON_PD_T(impl_name(), gen9_batch_normalization_fwd_t);

        const char *impl_name() const {
            return conf.use_stats_one_pass ? "ocl:gen9:blocked:onepass"
                                           : "ocl:gen9:blocked";
        }
        status_t init(engine_t *engine) {
            using namespace data_type;
            auto *compute_engine
                    = utils::downcast<compute::compute_engine_t *>(engine);
            auto src_data_t = src_md()->data_type;
            auto dst_data_t = dst_md()->data_type;

            const auto attr_skip_mask = primitive_attr_t::skip_mask_t::post_ops;

            bool ok = is_fwd()
                    && (utils::everyone_is(f16, src_data_t, dst_data_t)
                            || utils::everyone_is(bf16, src_data_t, dst_data_t)
                            || utils::everyone_is(f32, src_data_t, dst_data_t)
                            || utils::everyone_is(s8, src_data_t, dst_data_t))
                    && IMPLICATION(utils::one_of(src_data_t, s8),
                            !is_training() && stats_is_src())
                    && attr()->has_default_values(attr_skip_mask)
                    && IMPLICATION(!attr()->has_default_values(),
                            attr()->post_ops_.len() == 1 && with_relu_post_op())
                    && compute_engine->mayiuse(
                            compute::device_ext_t::intel_subgroups);
            if (!ok) return status::unimplemented;

            if (is_training() && fuse_norm_relu()) init_default_ws(8);

            status_t status = init_conf(engine);
            if (status != status::success) return status;
            init_scratchpad();

            return status::success;
        }

        status_t init_conf(engine_t *engine);
        status_t init_kernel_ctx(compute::kernel_ctx_t &kernel_ctx) const;
        void init_scratchpad();

        bnorm_conf_t conf;
        offsets_t off;
    };

    status_t init(engine_t *engine) override {
        compute::kernel_ctx_t kernel_ctx;

        status_t status = pd()->init_kernel_ctx(kernel_ctx);
        CHECK(status);

        std::vector<const char *> kernel_names
                = {"gen9_bnorm_fwd", nullptr, nullptr, nullptr, nullptr};
        if (pd()->conf.calculate_stats) {
            if (pd()->conf.use_stats_one_pass) {
                kernel_names[1] = "gen9_calc_mean_var";
                kernel_names[2] = "gen9_reduce_mean_var";
            } else {
                kernel_names[1] = "gen9_calc_mean";
                kernel_names[2] = "gen9_calc_variance";
                kernel_names[3] = "gen9_reduce_mean";
                kernel_names[4] = "gen9_reduce_variance";
            }
        }

        std::vector<compute::kernel_t> kernels;
        status = create_kernels(engine, &kernels, kernel_names, kernel_ctx);
        CHECK(status);

        kernel_ = kernels[0];
        if (pd()->conf.use_stats_one_pass) {
            calculate_mean_var_kernel_ = kernels[1];
            reduce_mean_var_kernel_ = kernels[2];
        } else {
            calculate_mean_kernel_ = kernels[1];
            calculate_variance_kernel_ = kernels[2];
            reduce_mean_kernel_ = kernels[3];
            reduce_variance_kernel_ = kernels[4];
        }

        return status::success;
    }

    status_t execute(const exec_ctx_t &ctx) const override {
        return execute_forward(ctx);
    }

private:
    status_t execute_forward(const exec_ctx_t &ctx) const;
    const pd_t *pd() const { return (const pd_t *)primitive_t::pd().get(); }
    compute::kernel_t kernel_;
    compute::kernel_t calculate_mean_kernel_;
    compute::kernel_t reduce_mean_kernel_;
    compute::kernel_t calculate_variance_kernel_;
    compute::kernel_t reduce_variance_kernel_;
    compute::kernel_t calculate_mean_var_kernel_;
    compute::kernel_t reduce_mean_var_kernel_;
};

struct gen9_batch_normalization_bwd_t : public gpu_primitive_t {
    using gpu_primitive_t::gpu_primitive_t;
    struct pd_t : public gpu_batch_normalization_bwd_pd_t {
        pd_t(const batch_normalization_desc_t *adesc,
                const primitive_attr_t *attr,
                const batch_normalization_fwd_pd_t *hint_fwd_pd)
            : gpu_batch_normalization_bwd_pd_t(adesc, attr, hint_fwd_pd) {}

        DECLARE_COMMON_PD_T("ocl:gen9:blocked", gen9_batch_normalization_bwd_t);

        status_t init(engine_t *engine) {
            auto *compute_engine
                    = utils::downcast<compute::compute_engine_t *>(engine);
            using namespace data_type;
            bool ok = is_bwd() && set_default_formats_common()
                    && (utils::everyone_is(f32, src_md()->data_type,
                                diff_src_md()->data_type)
                            || utils::everyone_is(bf16, src_md()->data_type,
                                    diff_src_md()->data_type))
                    && check_scale_shift_data_type()
                    && attr()->has_default_values()
                    && compute_engine->mayiuse(
                            compute::device_ext_t::intel_subgroups);
            if (!ok) return status::unimplemented;

            if (fuse_norm_relu()) {
                init_default_ws(8);
                if (!compare_ws(hint_fwd_pd_)) return status::unimplemented;
            }

            status_t status = init_conf(engine);
            if (status != status::success) return status;
            init_scratchpad();

            return status::success;
        }

        status_t init_conf(engine_t *engine);
        status_t init_kernel_ctx(compute::kernel_ctx_t &kernel_ctx) const;
        void init_scratchpad();

        bnorm_conf_t conf;
        offsets_t off;
    };

    status_t init(engine_t *engine) override {
        compute::kernel_ctx_t kernel_ctx;

        status_t status = pd()->init_kernel_ctx(kernel_ctx);
        CHECK(status);

        std::vector<const char *> kernel_names = {
                "gen9_bnorm_bwd", "gen9_calculate_stats", "gen9_reduce_stats"};

        std::vector<compute::kernel_t> kernels;
        status = create_kernels(engine, &kernels, kernel_names, kernel_ctx);
        CHECK(status);

        bwd_kernel_ = kernels[0];
        calculate_stats_kernel_ = kernels[1];
        reduce_stats_kernel_ = kernels[2];

        return status::success;
    }

    status_t execute(const exec_ctx_t &ctx) const override {
        return execute_backward(ctx);
    }

private:
    status_t execute_backward(const exec_ctx_t &ctx) const;
    const pd_t *pd() const { return (const pd_t *)primitive_t::pd().get(); }
    compute::kernel_t bwd_kernel_;
    compute::kernel_t calculate_stats_kernel_;
    compute::kernel_t reduce_stats_kernel_;
};

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

#endif // GPU_OCL_GEN9_BATCH_NORMALIZATION_HPP
