/*******************************************************************************
* Copyright 2017-2019 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 GEMM_X8S8S32X_CONVOLUTION_HPP
#define GEMM_X8S8S32X_CONVOLUTION_HPP

#include "c_types_map.hpp"
#include "memory_tracking.hpp"

#include "cpu_convolution_pd.hpp"

#include "gemm_convolution_utils.hpp"
#include "jit_generator.hpp"
#include "jit_primitive_conf.hpp"
#include "jit_uni_eltwise_injector.hpp"
#include "ref_eltwise.hpp"

#include "gemm/gemm.hpp"

namespace dnnl {
namespace impl {
namespace cpu {

template <data_type_t src_type, data_type_t dst_type>
struct _gemm_x8s8s32x_convolution_fwd_t : public primitive_impl_t {
    struct pd_t : public cpu_convolution_fwd_pd_t {
        pd_t(engine_t *engine, const convolution_desc_t *adesc,
                const primitive_attr_t *attr,
                const typename pd_t::base_class *hint_fwd_pd)
            : cpu_convolution_fwd_pd_t(engine, adesc, attr, hint_fwd_pd)
            , jcp_() {}

        DECLARE_COMMON_PD_T(IGEMM_S8U8S32_ISA_STR,
                _gemm_x8s8s32x_convolution_fwd_t, USE_GLOBAL_SCRATCHPAD);

        status_t init() {
            using namespace data_type;

            bool ok = true && is_fwd()
                    && set_default_alg_kind(alg_kind::convolution_direct)
                    && expect_data_types(
                            src_type, s8, data_type::undef, dst_type, s32)
                    && IMPLICATION(with_bias(),
                            utils::one_of(desc()->bias_desc.data_type, f32, s32,
                                    s8, u8))
                    && !has_zero_dim_memory()
                    && set_default_formats_common(
                            dat_tag(), format_tag::any, dat_tag())
                    && attr()->has_default_values(
                            primitive_attr_t::skip_mask_t::oscale
                            | primitive_attr_t::skip_mask_t::post_ops)
                    && output_scales_mask_ok() && post_ops_ok()
                    && memory_desc_matches_tag(*src_md(), dat_tag())
                    && memory_desc_matches_tag(*dst_md(), dat_tag())
                    && set_or_check_wei_format();
            if (!ok) return status::unimplemented;

            auto scratchpad = scratchpad_registry().registrar();
            return jit_gemm_convolution_utils::init_conf(jcp_, scratchpad,
                    *desc(), src_md(), weights_md(0), dst_md(),
                    dnnl_get_max_threads());
        }

        jit_gemm_conv_conf_t jcp_;

    protected:
        format_tag_t dat_tag() const {
            int ndims = src_md()->ndims;
            return utils::pick(ndims - 4, format_tag::nhwc, format_tag::ndhwc);
        }

        bool set_or_check_wei_format() {
            using namespace format_tag;
            int ndims = src_md()->ndims;

            const bool is_src_s8 = src_md_.data_type == data_type::s8;

            memory_desc_t want_wei_md = weights_md_;
            memory_desc_init_by_tag(want_wei_md,
                    with_groups() ? utils::pick(ndims - 4, hwigo, dhwigo)
                                  : utils::pick(ndims - 4, hwio, dhwio));

            if (is_src_s8) {
                want_wei_md.extra.flags = 0
                        | memory_extra_flags::compensation_conv_s8s8
                        | memory_extra_flags::scale_adjust;
                want_wei_md.extra.compensation_mask
                        = (1 << 0) + (with_groups() ? (1 << 1) : 0);
                want_wei_md.extra.scale_adjust
                        = mayiuse(avx512_core_vnni) ? 1.f : 0.5f;
            }

            if (weights_md_.format_kind == format_kind::any) {
                weights_md_ = want_wei_md;
                return true;
            }

            return weights_md_ == want_wei_md;
        }

        bool output_scales_mask_ok() const {
            const auto &mask = attr()->output_scales_.mask_;
            return mask == 0 || mask == 1 << 1;
        }

        bool post_ops_ok() const {
            using namespace dnnl::impl::primitive_kind;
            auto const &po = attr()->post_ops_;
            auto is_eltwise
                    = [&](int idx) { return po.entry_[idx].is_eltwise(); };

            switch (po.len_) {
                case 0: return true;
                case 1: return is_eltwise(0) || po.contain(sum, 0);
                case 2:
                    return (po.contain(sum, 0) && is_eltwise(1))
                            || (po.contain(sum, 1) && is_eltwise(0));
                default: return false;
            }
            return false;
        }
    };

    _gemm_x8s8s32x_convolution_fwd_t(const pd_t *apd)
        : primitive_impl_t(apd), pp_ker_(nullptr) {
        pp_ker_ = new pp_ker_t(pd());
    }
    ~_gemm_x8s8s32x_convolution_fwd_t() { delete pp_ker_; }

    typedef typename prec_traits<src_type>::type src_data_t;
    typedef typename prec_traits<data_type::s8>::type wei_data_t;
    typedef typename prec_traits<dst_type>::type dst_data_t;
    typedef typename prec_traits<data_type::s32>::type acc_data_t;

    virtual status_t execute(const exec_ctx_t &ctx) const override {
        execute_forward(ctx);
        return status::success;
    }

private:
    // XXX: this is throwaway code that will become unnecessary when we have a
    // sufficiently advanced igemm jit generator that supports quantization,
    // relu, and whatnot
    class pp_ker_t : jit_generator {
    public:
        DECLARE_CPU_JIT_AUX_FUNCTIONS(
                _gemm_x8s8s32x_convolution_fwd_t::pp_kernel);
        pp_ker_t(const pd_t *pd);
        ~pp_ker_t() {
            if (eltwise_injector_) delete eltwise_injector_;
            if (eltwise_) delete eltwise_;
        }

        void operator()(dst_data_t *dst, const acc_data_t *acc,
                const char *bias, const float *scales, float nslope,
                float sum_scale, float signed_scale, int g, size_t start,
                size_t end);

        size_t dst_os_stride_;

    private:
        void generate();

        struct ker_args {
            dst_data_t *dst;
            const acc_data_t *acc;
            const char *bias;
            const float *scales;
            float nslope;
            float sum_scale;
            float signed_scale;
            size_t len;
            size_t oc_offset;
        };
        void (*ker_)(const ker_args *args);

        const jit_gemm_conv_conf_t &jcp_;
        size_t OC_;
        size_t OS_;
        data_type_t bias_data_type_;
        size_t bias_data_type_size_;
        size_t scale_idx_mult_;
        bool do_bias_;
        bool do_eltwise_;
        bool do_sum_;
        bool do_signed_scaling_;
        size_t vlen_;
        jit_uni_eltwise_injector_f32<avx512_common> *eltwise_injector_;
        ref_eltwise_scalar_fwd_t *eltwise_;
    };

    const pd_t *pd() const { return (const pd_t *)primitive_impl_t::pd(); }
    void execute_forward(const exec_ctx_t &ctx) const;
    void execute_forward_thr(const int ithr, const int nthr,
            const src_data_t *src_base, const wei_data_t *wei_base,
            const char *bia_base, dst_data_t *dst_base,
            const memory_tracking::grantor_t &scratchpad) const;

    int nthr_ = 0;
    pp_ker_t *pp_ker_;
};

template <data_type_t dst_type>
struct _gemm_u8s8s32x_convolution_bwd_data_t : public primitive_impl_t {
    struct pd_t : public cpu_convolution_bwd_data_pd_t {
        pd_t(engine_t *engine, const convolution_desc_t *adesc,
                const primitive_attr_t *attr,
                const convolution_fwd_pd_t *hint_fwd_pd)
            : cpu_convolution_bwd_data_pd_t(engine, adesc, attr, hint_fwd_pd)
            , jcp_() {}

        DECLARE_COMMON_PD_T(IGEMM_S8U8S32_ISA_STR,
                _gemm_u8s8s32x_convolution_bwd_data_t, USE_GLOBAL_SCRATCHPAD);

        status_t init() {
            using namespace data_type;

            bool ok = true && desc()->prop_kind == prop_kind::backward_data
                    && set_default_alg_kind(alg_kind::convolution_direct)
                    && expect_data_types(
                            dst_type, s8, data_type::undef, u8, s32)
                    && IMPLICATION(with_bias(),
                            utils::one_of(desc()->bias_desc.data_type, f32, s32,
                                    s8, u8))
                    && !has_zero_dim_memory()
                    && set_default_formats_common(
                            dat_tag(), wei_tag(), dat_tag())
                    && attr()->has_default_values(
                            primitive_attr_t::skip_mask_t::oscale)
                    && output_scales_mask_ok()
                    && memory_desc_matches_tag(*diff_src_md(), dat_tag())
                    && memory_desc_matches_tag(*diff_dst_md(), dat_tag())
                    && memory_desc_matches_tag(*weights_md(), wei_tag());
            if (!ok) return status::unimplemented;

            auto scratchpad = scratchpad_registry().registrar();
            return jit_gemm_convolution_utils::init_conf(jcp_, scratchpad,
                    *desc(), diff_src_md(), weights_md(), diff_dst_md(),
                    dnnl_get_max_threads());
        }

        virtual bool support_bias() const override { return true; }

        jit_gemm_conv_conf_t jcp_;

    protected:
        format_tag_t dat_tag() const {
            int ndims = diff_src_md()->ndims;
            return utils::pick(ndims - 4, format_tag::nhwc, format_tag::ndhwc);
        }

        format_tag_t wei_tag() const {
            using namespace format_tag;
            int ndims = diff_src_md()->ndims;
            return with_groups() ? utils::pick(ndims - 4, hwigo, dhwigo)
                                 : utils::pick(ndims - 4, hwio, dhwio);
        }
        bool output_scales_mask_ok() const {
            const auto &mask = attr()->output_scales_.mask_;
            return mask == 0 || mask == 1 << 1;
        }
    };

    _gemm_u8s8s32x_convolution_bwd_data_t(const pd_t *apd)
        : primitive_impl_t(apd) {}

    typedef typename prec_traits<data_type::u8>::type diff_dst_data_t;
    typedef typename prec_traits<data_type::s8>::type wei_data_t;
    typedef typename prec_traits<dst_type>::type diff_src_data_t;
    typedef typename prec_traits<data_type::s32>::type acc_data_t;

    virtual status_t execute(const exec_ctx_t &ctx) const override {
        execute_backward_data(ctx);
        return status::success;
    }

private:
    void execute_backward_data(const exec_ctx_t &ctx) const;
    void execute_backward_data_thr(const int ithr, const int nthr,
            const diff_dst_data_t *diff_dst_base, const wei_data_t *wei_base,
            const char *bia_base, diff_src_data_t *diff_src_base,
            const memory_tracking::grantor_t &scratchpad) const;
    const pd_t *pd() const { return (const pd_t *)primitive_impl_t::pd(); }
};

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

#endif
