/*******************************************************************************
* 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.
*******************************************************************************/

#include <assert.h>
#include <float.h>

#include "c_types_map.hpp"
#include "dnnl_thread.hpp"
#include "memory_tracking.hpp"
#include "nstl.hpp"
#include "type_helpers.hpp"
#include "utils.hpp"

#include "cpu_barrier.hpp"
#include "memory.hpp"

#include "jit_avx512_common_1x1_conv_kernel.hpp"
#include "jit_uni_1x1_conv_utils.hpp"

#define GET_OFF(field) offsetof(jit_1x1_conv_call_s, field)

namespace dnnl {
namespace impl {
namespace cpu {

using namespace dnnl::impl::format_tag;
using namespace dnnl::impl::prop_kind;
using namespace dnnl::impl::utils;

using namespace Xbyak;

void jit_avx512_common_1x1_conv_kernel::bcast_loop(int load_loop_blk) {
    mov(aux1_reg_bcast_data, reg_bcast_data);
    mov(aux_reg_bcast_data, reg_bcast_data);

    mov(aux_reg_output_data, reg_output_data);
    mov(bcast_loop_iter, EVEX_compress_addr(rsp, bcast_loop_work_offt));

    if (jcp.ver == ver_4fma) {
        Label bcast_loop;
        Label bcast_loop_wraparound;
        Label bcast_loop_out;
        Label bcast_loop_ur_full;

        cmp(bcast_loop_iter, jcp.ur);
        jle(bcast_loop_wraparound, T_NEAR);

        L(bcast_loop);
        {
            assert(jcp.bcast_block % jcp.ur == 0);
            int num_substeps = jcp.bcast_block / jcp.ur;
            assert(num_substeps > 0 && num_substeps < 10);
            for (int i = 0; i < num_substeps; i++) {
                reduce_loop(load_loop_blk, jcp.ur, i, false);
                if (i < num_substeps - 1) {
                    add(aux1_reg_bcast_data, jcp.bcast_loop_bcast_substep);
                    add(aux_reg_output_data, jcp.bcast_loop_output_substep);
                } else {
                    add(aux1_reg_bcast_data,
                            jcp.bcast_loop_bcast_step
                                    - (num_substeps - 1)
                                            * jcp.bcast_loop_bcast_substep);
                    add(aux_reg_output_data,
                            jcp.bcast_loop_output_step
                                    - (num_substeps - 1)
                                            * jcp.bcast_loop_output_substep);
                }
            }
            sub(bcast_loop_iter, jcp.bcast_block);
            cmp(bcast_loop_iter, jcp.bcast_block);
            jg(bcast_loop, T_NEAR);
        }

        L(bcast_loop_wraparound);
        if (jcp.ur_tail) {
            je(bcast_loop_ur_full, T_NEAR);
            reduce_loop(load_loop_blk, jcp.ur_tail, 0, true);
            jmp(bcast_loop_out, T_NEAR);
        }
        L(bcast_loop_ur_full);
        reduce_loop(load_loop_blk, jcp.ur, 0, true);
        L(bcast_loop_out);
    } else {
        Label bcast_loop;
        Label bcast_loop_tail;

        cmp(bcast_loop_iter, jcp.ur);
        jl(bcast_loop_tail, T_NEAR);

        L(bcast_loop);
        {
            assert(jcp.bcast_block % jcp.ur == 0);
            int num_substeps = jcp.bcast_block / jcp.ur;
            assert(num_substeps > 0 && num_substeps < 10);
            for (int i = 0; i < num_substeps; i++) {
                reduce_loop(load_loop_blk, jcp.ur, i, false);
                if (i < num_substeps - 1) {
                    add(aux1_reg_bcast_data, jcp.bcast_loop_bcast_substep);
                    add(aux_reg_output_data, jcp.bcast_loop_output_substep);
                } else {
                    add(aux1_reg_bcast_data,
                            jcp.bcast_loop_bcast_step
                                    - (num_substeps - 1)
                                            * jcp.bcast_loop_bcast_substep);
                    add(aux_reg_output_data,
                            jcp.bcast_loop_output_step
                                    - (num_substeps - 1)
                                            * jcp.bcast_loop_output_substep);
                }
            }
            sub(bcast_loop_iter, jcp.bcast_block);
            cmp(bcast_loop_iter, jcp.bcast_block);
            jge(bcast_loop, T_NEAR);
        }

        L(bcast_loop_tail);
        if (jcp.ur_tail) {
            Label bcast_loop_tail_out;
            cmp(bcast_loop_iter, 0);
            jz(bcast_loop_tail_out, T_NEAR);
            reduce_loop(load_loop_blk, jcp.ur_tail, 0, true);
            L(bcast_loop_tail_out);
        }
    }
}

void jit_avx512_common_1x1_conv_kernel::reduce_loop(
        int load_loop_blk, int ur, int substep, bool wraparound) {
    auto vreg_load = [=](int i_load, int i_fma) {
        return Zmm(utils::rnd_up(ur * load_loop_blk, jcp.fma_step)
                + jcp.fma_step * i_load + i_fma);
    };

    auto vreg_accum = [=](int i_load, int i_ur) {
        return Zmm(i_ur * load_loop_blk + i_load);
    };

    auto bias_ptr = [=](int i_load) {
        return EVEX_compress_addr(
                reg_bias_data, jcp.typesize_out * jcp.oc_block * i_load);
    };

    auto bcast_ptr = [=](int i_reduce, int i_ur, bool bcast) {
        assert(i_ur < jcp.ur);
        assert(i_reduce <= jcp.reduce_loop_unroll);
        int offt;
        if (one_of(jcp.prop_kind, forward_training, forward_inference,
                    backward_data)) {
            assert(jcp.reduce_loop_unroll == jcp.reduce_block);
            offt = (i_reduce == jcp.reduce_loop_unroll)
                    ? (jcp.bcast_dim + i_ur) * jcp.reduce_loop_unroll
                    : i_ur * jcp.reduce_loop_unroll + i_reduce;
        } else {
            if (jcp.transpose_src) {
                const int reduce_group = i_reduce / 4;
                const int reduce_shift = i_reduce % 4;
                offt = 4 * (reduce_group * jcp.ic_block + i_ur) + reduce_shift;
            } else
                offt = i_reduce * jcp.ic_block + i_ur;
        }
        return EVEX_compress_addr(
                aux_reg_bcast_data, jcp.typesize_in * offt, bcast);
    };

    auto load_ptr = [=](int i_reduce, int i_load) {
        int offt;
        int u0 = i_reduce % jcp.reduce_loop_unroll;
        int u1 = i_reduce / jcp.reduce_loop_unroll;
        offt = (i_load * jcp.reduce_dim + u0) * jcp.load_block;
        return EVEX_compress_addr(aux_reg_load_data,
                u1 * jcp.reduce_loop_load_step + jcp.typesize_in * offt);
    };

    auto output_ptr = [=](int i_load, int i_ur) {
        if (one_of(jcp.prop_kind, forward_training, forward_inference,
                    backward_data))
            return EVEX_compress_addr(aux_reg_output_data,
                    (i_load * jcp.bcast_dim + i_ur) * jcp.load_block
                            * jcp.typesize_out);
        else
            return ptr[aux_reg_output_data
                    + (i_load ? reg_output_stride * i_load
                              : 0) // TODO: Xbyak should allow 0 scale
                    + jcp.typesize_out * jcp.load_block * i_ur];
    };

    auto init = [=]() {
        Label init_done;
        Label init_zero;

        if (jcp.with_sum) {
            for (int i_load = 0; i_load < load_loop_blk; ++i_load) {
                for (int i_ur = 0; i_ur < ur; ++i_ur) {
                    mic_prefetcht1(output_ptr(i_load, i_ur));
                }
            }
        }

        if (jcp.with_bias
                && one_of(jcp.prop_kind, forward_training, forward_inference)) {
            test(reg_reduce_pos_flag, FLAG_REDUCE_FIRST);
            jz(init_zero, T_NEAR);

            for (int i_load = 0; i_load < load_loop_blk; i_load++)
                for (int i_ur = 0; i_ur < ur; ++i_ur)
                    vmovups(vreg_accum(i_load, i_ur), bias_ptr(i_load));
            jmp(init_done, T_NEAR);
        }

        L(init_zero);
        for (int i_load = 0; i_load < load_loop_blk; ++i_load)
            for (int i_ur = 0; i_ur < ur; ++i_ur) {
                auto r = vreg_accum(i_load, i_ur);
                vpxord(r, r, r);
            }
        L(init_done);
    };

    auto store = [=]() {
        Label store_noadd;
        if (!jcp.with_sum) {
            test(reg_reduce_pos_flag, FLAG_REDUCE_FIRST);
            jnz(store_noadd, T_NEAR);
        }

        for (int i_ur = 0; i_ur < ur; ++i_ur)
            for (int i_load = 0; i_load < load_loop_blk; ++i_load) {
                auto r = vreg_accum(i_load, i_ur);
                vaddps(r, r, output_ptr(i_load, i_ur));
            }

        L(store_noadd);
        if (jcp.with_eltwise) {
            Label store_noeltwise;
            test(reg_reduce_pos_flag, FLAG_REDUCE_LAST);
            jz(store_noeltwise, T_NEAR);

            eltwise_injector_->compute_vector_range(0, ur * load_loop_blk);

            L(store_noeltwise);
        }

        auto store_output = [=](bool output_is_aligned) {
            for (int i_ur = 0; i_ur < ur; ++i_ur)
                for (int i_load = 0; i_load < load_loop_blk; ++i_load)
                    if (output_is_aligned && jcp.use_vmovntps)
                        vmovntps(output_ptr(i_load, i_ur),
                                vreg_accum(i_load, i_ur));
                    else
                        vmovups(output_ptr(i_load, i_ur),
                                vreg_accum(i_load, i_ur));
        };

        Label unaligned_store, end_store;
        test(aux_reg_output_data, cpu_isa_traits<avx512_common>::vlen - 1);
        jnz(unaligned_store, T_NEAR);
        store_output(true);
        jmp(end_store, T_NEAR);
        L(unaligned_store);
        { store_output(false); }
        L(end_store);
    };

    auto prefetch_callback = [=](int ur, int i_reduce, int i_ur, int i_load,
                                     bool last_block, bool wraparound,
                                     int reduce_step) {
        bool pf_ker_l1 = true;
        bool pf_ker_l2 = wraparound;
        int n_ops = (jcp.reduce_loop_unroll / reduce_step) * ur * load_loop_blk;
        int i_op = (i_reduce / reduce_step) * ur * load_loop_blk
                + i_ur * load_loop_blk + i_load;

        int n_pf_ker_l1 = pf_ker_l1 ? jcp.reduce_block : 0;
        int n_pf_ker_l2 = pf_ker_l2 && wraparound ? jcp.reduce_block : 0;
        int n_pf_out_l1 = jcp.use_vmovntps ? 0 : ur;

        int pf_inp_ops = n_ops / 2; // # of operations during which to pf input
        int pf_inp_trigger;
        if (jcp.prop_kind == backward_weights)
            pf_inp_trigger = nstl::max(1, pf_inp_ops / jcp.reduce_block);
        else
            pf_inp_trigger = nstl::max(1, pf_inp_ops / ur);

        int n_other_pf
                = load_loop_blk * (n_pf_ker_l1 + n_pf_ker_l2 + n_pf_out_l1);
        int n_other_pf_ops = n_ops - pf_inp_ops;
        int other_pf_trigger
                = n_other_pf ? nstl::max(1, n_other_pf_ops / n_other_pf) : 0;

        if (i_op < pf_inp_ops && i_op % pf_inp_trigger == 0) {
            // input prefetches have the highest priority b/c the
            // first iteration of the kernel block touches all the
            // cache lines
            int i_pf = i_op / pf_inp_trigger;
            auto pf_reg = wraparound && last_block
                    ? reg_bcast_data
                    : (last_block ? aux1_reg_bcast_data : aux_reg_bcast_data);
            int offt = i_pf;
            if (jcp.prop_kind == backward_weights) {
                offt += wraparound && last_block
                        ? 0
                        : (last_block ? jcp.is : jcp.reduce_block);
                offt *= jcp.bcast_block;
            } else {
                offt += wraparound && last_block
                        ? 0
                        : (last_block ? jcp.ur : jcp.bcast_dim);
                offt *= jcp.reduce_block;
            }
            mic_prefetcht0(ptr[pf_reg + offt * jcp.typesize_in]);
        } else if (i_op >= pf_inp_ops && n_other_pf) {
            // remaining prefetches are spread among the rest of the
            // operations; prefetches for output take priority
            // TODO: spread L2 prefetches among L1 prefetches
            i_op -= pf_inp_ops;
            if (i_op % other_pf_trigger == 0) {
                int i_pf = i_op / (load_loop_blk * other_pf_trigger);
                if (i_pf < n_pf_ker_l2) {
                    int offt = (i_pf + (i_load + 1) * jcp.reduce_dim)
                            * jcp.load_block;
                    mic_prefetcht1(
                            ptr[aux_reg_load_data + offt * jcp.typesize_in]);
                } else if (i_pf < n_pf_ker_l2 + n_pf_ker_l1) {
                    i_pf -= n_pf_ker_l2;
                    auto pf_reg
                            = last_block ? reg_load_data : aux_reg_load_data;
                    int offt = (i_pf + i_load * jcp.reduce_dim
                                       + (last_block ? (wraparound
                                                          ? jcp.reduce_dim
                                                          : 0)
                                                     : jcp.reduce_block))
                            * jcp.load_block;
                    mic_prefetcht0(ptr[pf_reg + offt * jcp.typesize_in]);
                } else if (i_pf < n_pf_ker_l1 + n_pf_ker_l2 + n_pf_out_l1) {
                    i_pf -= n_pf_ker_l1 + n_pf_ker_l2;
                    int offt = i_pf * jcp.load_block;
                    mic_prefetcht0(
                            ptr[aux_reg_output_data + offt * jcp.typesize_out]);
                }
            }
        }
    };

    auto fma_block = [=](bool last_block) {
        assert(jcp.reduce_loop_unroll % jcp.fma_step == 0);

        int reduce_step = jcp.fma_step;

        for (int i_reduce = 0; i_reduce < jcp.reduce_loop_unroll;
                i_reduce += reduce_step) {
            for (int i_load = 0; i_load < load_loop_blk; ++i_load) {
                // if transposed input data used and if spatial size is
                // not divided by transpose step (4) then for last reduce step
                // we should load only needed load_registers data
                // and clear remaining
                if (jcp.transpose_src && jcp.is % jcp.fma_step && last_block
                        && i_reduce == jcp.reduce_loop_unroll - reduce_step) {
                    Label load_all;
                    Label load_finish;
                    test(reg_reduce_pos_flag, FLAG_SP_LAST);
                    jz(load_all, T_NEAR);

                    const int n_loads = jcp.is % jcp.fma_step;
                    for (int i_fma = 0; i_fma < jcp.fma_step; i_fma++) {
                        if (i_fma < n_loads)
                            vmovups(vreg_load(i_load, i_fma),
                                    load_ptr(i_reduce + i_fma, i_load));
                        else
                            vpxord(vreg_load(i_load, i_fma),
                                    vreg_load(i_load, i_fma),
                                    vreg_load(i_load, i_fma));
                    }
                    jmp(load_finish);

                    L(load_all);
                    for (int i_fma = 0; i_fma < jcp.fma_step; i_fma++) {
                        vmovups(vreg_load(i_load, i_fma),
                                load_ptr(i_reduce + i_fma, i_load));
                    }
                    L(load_finish);
                } else {
                    for (int i_fma = 0; i_fma < jcp.fma_step; i_fma++) {
                        vmovups(vreg_load(i_load, i_fma),
                                load_ptr(i_reduce + i_fma, i_load));
                    }
                }
            }

            for (int i_ur = 0; i_ur < ur; ++i_ur) {
                if (jcp.ver == ver_avx512_core && jcp.expl_bcast
                        && load_loop_blk > 1)
                    vbroadcastss(vreg_bcast, bcast_ptr(i_reduce, i_ur, false));
                for (int i_load = 0; i_load < load_loop_blk; ++i_load) {
                    if (jcp.ver == ver_4fma)
                        v4fmaddps(vreg_accum(i_load, i_ur),
                                vreg_load(i_load, 0),
                                bcast_ptr(i_reduce, i_ur, false));
                    else if (jcp.ver == ver_avx512_core && jcp.expl_bcast
                            && load_loop_blk > 1)
                        vfmadd231ps(vreg_accum(i_load, i_ur),
                                vreg_load(i_load, 0), vreg_bcast);
                    else
                        vfmadd231ps(vreg_accum(i_load, i_ur),
                                vreg_load(i_load, 0),
                                bcast_ptr(i_reduce, i_ur, true));
                    prefetch_callback(ur, i_reduce, i_ur, i_load, last_block,
                            wraparound, reduce_step);
                }
            }
        }
    };
    Label reduce_loop;
    Label reduce_loop_tail;

    mov(aux_reg_load_data, reg_load_data);

    mov(aux_reg_bcast_data, aux1_reg_bcast_data);
    init();

    mov(reduce_loop_iter, reg_reduce_loop_work);
    sub(reduce_loop_iter, jcp.reduce_loop_unroll);
    jle(reduce_loop_tail, T_NEAR);

    L(reduce_loop);
    {
        fma_block(false);
        add(aux_reg_bcast_data, jcp.reduce_loop_bcast_step);
        add(aux_reg_load_data, jcp.reduce_loop_load_step);
        sub(reduce_loop_iter, jcp.reduce_loop_unroll);
        jg(reduce_loop, T_NEAR);
    }

    L(reduce_loop_tail);
    fma_block(true);

    store();
}

void jit_avx512_common_1x1_conv_kernel::generate() {
    preamble();

    mov(reg_bcast_data, ptr[param1 + GET_OFF(bcast_data)]);
    mov(reg_load_data, ptr[param1 + GET_OFF(load_data)]);
    mov(reg_output_data, ptr[param1 + GET_OFF(output_data)]);

    sub(rsp, stack_space_needed);

    if (jcp.with_bias) mov(reg_bias_data, ptr[param1 + GET_OFF(bias_data)]);

    mov(reg_load_loop_work, ptr[param1 + GET_OFF(load_dim)]);
    mov(reg_bcast_loop_work, ptr[param1 + GET_OFF(bcast_dim)]);
    mov(EVEX_compress_addr(rsp, bcast_loop_work_offt), reg_bcast_loop_work);
    mov(reg_reduce_loop_work, ptr[param1 + GET_OFF(reduce_dim)]);
    mov(reg_reduce_pos_flag, ptr[param1 + GET_OFF(first_last_flag)]);
    if (one_of(jcp.prop_kind, forward_training, forward_inference))
        mov(reg_relu_ns, reinterpret_cast<size_t>(&jcp.eltwise.alpha));
    if (jcp.prop_kind == backward_weights)
        mov(reg_output_stride, ptr[param1 + GET_OFF(output_stride)]);

    auto load_loop_body = [=](int load_loop_blk) {
        bcast_loop(load_loop_blk);
        add(reg_load_data, load_loop_blk * jcp.load_loop_load_step);
        switch (jcp.prop_kind) {
            case forward_training:
            case forward_inference:
                add(reg_bias_data,
                        load_loop_blk * jcp.load_block * jcp.typesize_out);
                add(reg_output_data,
                        load_loop_blk * jcp.bcast_dim * jcp.load_block
                                * jcp.typesize_out);
                break;
            case backward_data:
                add(reg_output_data,
                        load_loop_blk * jcp.bcast_dim * jcp.load_block
                                * jcp.typesize_out);
                break;
            case backward_weights:
                for (int i_load = 0; i_load < load_loop_blk; i_load++)
                    add(reg_output_data, reg_output_stride);
                break;
            default: assert(!"invalid prop_kind");
        }
        sub(reg_load_loop_work, load_loop_blk * jcp.load_loop_iter_step);
    };

    const int simd_w = 16;

    Label load_loop_blk[7];

    static const int ur_cases_fma_embd_bcast[] = {2, 4, 5, 8, 14, 32};
    static const int ur_cases_fma_expl_bcast[] = {2, 5, 6, 9, 14, 32};
    static const int ur_cases_4fma[] = {2, 4, 6, 12, 32};

    const int size_ur_cases_fma = (jcp.ver == ver_avx512_core && jcp.expl_bcast)
            ? sizeof(ur_cases_fma_expl_bcast)
            : sizeof(ur_cases_fma_embd_bcast);
    const int size_ur_cases_4fma = sizeof(ur_cases_4fma);

    const int *ur_cases_fma = (jcp.ver == ver_avx512_core && jcp.expl_bcast)
            ? ur_cases_fma_expl_bcast
            : ur_cases_fma_embd_bcast;
    const int *ur_cases = jcp.ver == ver_4fma ? ur_cases_4fma : ur_cases_fma;
    const int num_ur_cases
            = (jcp.ver == ver_4fma ? size_ur_cases_4fma : size_ur_cases_fma)
            / sizeof(*ur_cases);

    for (int ur_idx = num_ur_cases - 1; ur_idx > 0; ur_idx--) {
        int label_idx = num_ur_cases - ur_idx - 1;
        if (jcp.ur <= ur_cases[ur_idx]) {
            cmp(reg_load_loop_work, simd_w * (label_idx + 1));
            jle(load_loop_blk[label_idx], T_NEAR);
        }
    }

    for (int ur_idx = 0; ur_idx < num_ur_cases; ur_idx++) {
        if (jcp.ur <= ur_cases[ur_idx]) {
            int label_idx = num_ur_cases - ur_idx - 1;
            L(load_loop_blk[label_idx]);
            {
                if (label_idx == 0) {
                    cmp(reg_load_loop_work, 0);
                    je(load_loop_blk[num_ur_cases], T_NEAR);
                }
                load_loop_body(label_idx + 1);
                if (label_idx - 1 > 0) {
                    cmp(reg_load_loop_work, 2 * label_idx * simd_w);
                    je(load_loop_blk[label_idx - 1], T_NEAR);
                }
                cmp(reg_load_loop_work, (label_idx + 1) * simd_w);
                jge(load_loop_blk[label_idx]);
            }
            for (int idx = label_idx - 1; idx > 0; --idx) {
                cmp(reg_load_loop_work, simd_w * (idx + 1));
                je(load_loop_blk[idx], T_NEAR);
            }
            if (ur_idx < num_ur_cases - 2) {
                cmp(reg_load_loop_work, simd_w);
                jle(load_loop_blk[0], T_NEAR);
            }
        }
    }
    L(load_loop_blk[num_ur_cases]);

    add(rsp, stack_space_needed);

    postamble();

    if (jcp.with_eltwise) eltwise_injector_->prepare_table();
}

bool jit_avx512_common_1x1_conv_kernel::post_ops_ok(
        jit_1x1_conv_conf_t &jcp, const primitive_attr_t &attr) {
    const auto &p = attr.post_ops_;

    auto is_eltwise = [&](int idx) { return p.entry_[idx].is_eltwise(); };
    auto is_sum = [&](int idx) { return p.entry_[idx].is_sum(); };

    switch (p.len_) {
        case 0: return true; // no post_ops
        case 1: return is_eltwise(0) || is_sum(0); // sum OR eltwise
        case 2: return is_sum(0) && is_eltwise(1); // sum -> eltwise
        default: return false;
    }

    return false;
}

status_t jit_avx512_common_1x1_conv_kernel::init_conf(jit_1x1_conv_conf_t &jcp,
        const convolution_desc_t &cd, const memory_desc_wrapper &src_d,
        const memory_desc_wrapper &weights_d, const memory_desc_wrapper &dst_d,
        const primitive_attr_t &attr, int nthreads, bool reduce_src) {
    if (!mayiuse(avx512_common)) return status::unimplemented;

    const bool with_groups = weights_d.ndims() == src_d.ndims() + 1;
    const int simd_w = cpu_isa_traits<avx512_common>::vlen / sizeof(float);
    const int ndims = src_d.ndims();

    jcp.prop_kind = cd.prop_kind;

    jcp.ngroups = with_groups ? weights_d.dims()[0] : 1;
    jcp.mb = src_d.dims()[0];

    jcp.oc_without_padding = dst_d.dims()[1] / jcp.ngroups;
    jcp.oc = dst_d.dims()[1] / jcp.ngroups;
    jcp.ic = src_d.dims()[1] / jcp.ngroups;

    bool ok_to_pad_channels
            = true && jcp.ngroups == 1 && src_d.data_type() == data_type::f32;
    if (ok_to_pad_channels) {
        jcp.oc = rnd_up(jcp.oc, simd_w);
        jcp.ic = rnd_up(jcp.ic, simd_w);
    }

    jcp.ih = (ndims == 3) ? 1 : src_d.dims()[2];
    jcp.iw = src_d.dims()[ndims - 1];
    jcp.oh = (ndims == 3) ? 1 : dst_d.dims()[2];
    jcp.ow = dst_d.dims()[ndims - 1];

    jcp.kh = (ndims == 3) ? 1 : weights_d.dims()[with_groups + 2];
    jcp.kw = weights_d.dims()[with_groups + ndims - 1];

    jcp.t_pad = (ndims == 3) ? 0 : cd.padding[0][0];
    jcp.l_pad = cd.padding[0][ndims - 3];

    jcp.stride_h = (ndims == 3) ? 1 : cd.strides[0];
    jcp.stride_w = cd.strides[ndims - 3];

    jcp.with_bias = pick_by_prop_kind(jcp.prop_kind, cd.bias_desc.format_kind,
                            format_kind::undef, cd.diff_bias_desc.format_kind)
            != format_kind::undef;

    jcp.os = jcp.oh * jcp.ow;
    jcp.is = jcp.ih * jcp.iw;
    jcp.tr_is = rnd_up(jcp.is, 4);

    if (!post_ops_ok(jcp, attr)) return status::unimplemented;

    const auto &p = attr.post_ops_;
    jcp.with_sum = p.find(primitive_kind::sum) != -1;
    const int eltwise_ind = p.find(primitive_kind::eltwise);
    jcp.with_eltwise = eltwise_ind != -1;
    if (jcp.with_eltwise) {
        jcp.eltwise = p.entry_[eltwise_ind].eltwise;
        if (dst_d.data_type() == data_type::s32) return status::unimplemented;
    }

    auto dat_tag = pick(ndims - 3, nCw16c, nChw16c);
    jcp.src_tag = src_d.matches_one_of_tag(dat_tag);
    jcp.dst_tag = dst_d.matches_one_of_tag(dat_tag);

    bool args_ok = true && jcp.ngroups == 1 && jcp.src_tag == dat_tag
            && jcp.dst_tag == dat_tag;
    if (!args_ok) return status::unimplemented;

    args_ok = true && jcp.oc % simd_w == 0 && jcp.ic % simd_w == 0
            && jcp.t_pad == 0 && jcp.l_pad == 0 && jcp.stride_w == 1
            && jcp.stride_h == 1 // TODO: support some strides
            && jcp.ow == jcp.iw && jcp.oh == jcp.ih // enforce rpad=0
            && jcp.kh == 1 && jcp.kw == 1;
    if (!args_ok) return status::unimplemented;

    jcp.ic_block = jcp.oc_block = simd_w;
    jcp.transpose_src = false;
    jcp.use_vmovntps = false;

    if (everyone_is(data_type::f32, src_d.data_type(), weights_d.data_type(),
                dst_d.data_type())) {
        const int is_bwd_d = jcp.prop_kind == backward_data;
        format_tag_t wei_tag = with_groups
                ? pick(2 * ndims - 6 + is_bwd_d, gOIw16i16o, gIOw16o16i,
                        gOIhw16i16o, gIOhw16o16i)
                : pick(2 * ndims - 6 + is_bwd_d, OIw16i16o, IOw16o16i,
                        OIhw16i16o, IOhw16o16i);

        jcp.wei_tag = weights_d.matches_one_of_tag(wei_tag);
        if (jcp.wei_tag != wei_tag) return status::unimplemented;

        if (jcp.prop_kind != backward_weights && mayiuse(avx512_mic_4ops)
                && ((jcp.prop_kind == backward_data) ? jcp.oc_block
                                                     : jcp.ic_block)
                                % 4
                        == 0) {
            jcp.ver = ver_4fma;
            jcp.fma_step = 4;
        } else if (jcp.prop_kind == backward_weights && mayiuse(avx512_mic_4ops)
                && !reduce_src
                /* Heuristic condition for relation of src size to oc. Otherwise
                   the src transposition overhead exceed the benefit from 4fma
                */
                && ((jcp.is * jcp.ic) / jcp.oc <= 2048)
                && dnnl_thr_syncable()) {
            jcp.transpose_src = true;
            jcp.ver = ver_4fma;
            jcp.fma_step = 4;
        } else {
            jcp.ver = (mayiuse(avx512_core)) ? ver_avx512_core : ver_fma;
            jcp.fma_step = 1;
        }
        jcp.typesize_in = sizeof(prec_traits<data_type::f32>::type);
        jcp.typesize_out = sizeof(prec_traits<data_type::f32>::type);
    } else {
        return status::unimplemented;
    }

    /* once all the formats are set, check the padding consistency */
    args_ok = true && jcp.ic <= src_d.padded_dims()[1]
            && jcp.oc <= dst_d.padded_dims()[1]
            && jcp.ic <= weights_d.padded_dims()[with_groups + 1]
            && jcp.oc <= weights_d.padded_dims()[with_groups + 0];
    if (!args_ok) return status::unimplemented;

    const int SMALL_SPATIAL = 10;
    const int BIG_SPATIAL = 28;
    const int BIG_REDUCE_DIM = 1024;
    const int BIG_LOAD_DIM = 256;

    int load_blocking {0};
    int load_blocking_max {0};
    int bcast_blocking {0};
    int bcast_blocking_max {0};
    int reduce_blocking {0};
    int reduce_blocking_max {0};

    jcp.load_grp_count = 1;

    const int L1_capacity = get_cache_size(1, true) / sizeof(float);
    const int L2_size = get_cache_size(2, true) / sizeof(float);
    const int L2_capacity = (L2_size * 3) / 4;

    if (one_of(jcp.prop_kind, forward_training, forward_inference,
                backward_data)) {
        if (one_of(jcp.prop_kind, forward_training, forward_inference)) {
            jcp.reduce_dim = jcp.ic;
            jcp.reduce_block = jcp.ic_block;

            jcp.load_dim = jcp.oc;
            jcp.load_block = jcp.oc_block;

            jcp.bcast_dim = jcp.is;
        } else {
            jcp.reduce_dim = jcp.oc;
            jcp.reduce_block = jcp.oc_block;

            jcp.load_dim = jcp.ic;
            jcp.load_block = jcp.ic_block;

            jcp.bcast_dim = jcp.os;
        }
        jcp.reduce_loop_unroll = jcp.reduce_block;
        jcp.reduce_loop_bcast_step
                = jcp.reduce_loop_unroll * jcp.bcast_dim * jcp.typesize_in;

        jcp.reduce_loop_load_step
                = jcp.reduce_loop_unroll * jcp.load_block * jcp.typesize_in;
        jcp.load_loop_load_step
                = jcp.reduce_dim * jcp.load_block * jcp.typesize_in;

        // adjusting registry blocking
        int max_regs, min_regs, size_treshold, ur_step;
        const int spatial
                = (one_of(jcp.prop_kind, forward_training, forward_inference))
                ? jcp.oh
                : jcp.ih;
        if (jcp.ver == ver_avx512_core && (8 * jcp.mb) / nthreads >= 1) {
            max_regs = 9;
            min_regs = 6;
            size_treshold = 14;
            ur_step = 1;
            jcp.expl_bcast = true;

            if (jcp.load_dim > 128 && jcp.load_dim < BIG_LOAD_DIM
                    && spatial > SMALL_SPATIAL && spatial < BIG_SPATIAL) {
                max_regs = 6;
                min_regs = 5;
            }
        } else {
            max_regs = jcp.ver == ver_4fma ? 28 : 30;
            min_regs = 9;
            size_treshold = jcp.ver == ver_4fma ? 28 : 14;
            ur_step = jcp.ver == ver_4fma ? 4 : 1;
            jcp.expl_bcast = false;
            jcp.use_vmovntps = true;
        }
        jcp.ur = 1;
        for (int ur_w = max_regs; ur_w >= min_regs; ur_w -= ur_step) {
            if ((spatial >= size_treshold && spatial % ur_w == 0)
                    || (spatial < size_treshold && jcp.os % ur_w == 0)) {
                jcp.ur = ur_w;
                break;
            }
        }
        if (jcp.ur == 1) {
            jcp.ur = nstl::min(max_regs, jcp.os);
            int os_tail = jcp.os % max_regs;
            for (int i = max_regs; i >= min_regs; i -= ur_step) {
                int i_tail = jcp.os % i;
                if (i_tail > os_tail || i_tail == 0) {
                    jcp.ur = i;
                    os_tail = i_tail;
                    if (i_tail == 0) break;
                }
            }
        }

        jcp.reduce_loop_unroll = jcp.reduce_block;
        jcp.reduce_loop_bcast_step
                = jcp.reduce_loop_unroll * jcp.bcast_dim * jcp.typesize_in;

        jcp.bcast_block = jcp.ur;

        jcp.bcast_loop_output_step = jcp.ur * jcp.load_block * jcp.typesize_out;
        jcp.bcast_loop_output_substep = -1; // unused
        jcp.bcast_loop_bcast_step = jcp.ur * jcp.reduce_block * jcp.typesize_in;
        jcp.bcast_loop_bcast_substep = -1; // unused

        jcp.load_loop_iter_step = jcp.load_block;

        if (jcp.prop_kind == backward_data)
            jcp.loop_order = loop_lbr;
        else
            jcp.loop_order = reduce_src ? loop_blr : loop_lbr;

        int nb_bcast = div_up(jcp.bcast_dim, jcp.bcast_block);
        int nb_reduce = div_up(jcp.reduce_dim, jcp.reduce_block);
        int nb_load = div_up(jcp.load_dim, jcp.load_block);

        if (jcp.ver == ver_avx512_core && jcp.expl_bcast) {
            if (jcp.load_dim <= BIG_LOAD_DIM && spatial > SMALL_SPATIAL
                    && spatial < BIG_SPATIAL)
                reduce_blocking = nstl::min(jcp.reduce_dim, 80);
            else if (spatial > SMALL_SPATIAL)
                reduce_blocking = nstl::min(jcp.reduce_dim, 512);
            else
                reduce_blocking = nstl::min(jcp.reduce_dim, 256);
        } else {
            reduce_blocking = nb_reduce;
            if (spatial <= SMALL_SPATIAL && jcp.reduce_dim >= BIG_REDUCE_DIM)
                reduce_blocking = 16;
            else if (spatial > SMALL_SPATIAL
                    && jcp.reduce_dim >= BIG_REDUCE_DIM)
                reduce_blocking = 8;
            reduce_blocking = best_divider(nb_reduce, 1, reduce_blocking, true);
            reduce_blocking *= jcp.reduce_block;
        }

        // Check input data cache aliasing.
        // For other ISA constants may be updated.
        // 64 * 1024 is chosen due to 1MB L2 16-way cache.
        // 7 is empirical value. It is about half of 16.
        // So we leave about half of the set for other data - weights, dst
        int way_size = (64 * 1024) / jcp.typesize_in;
        int max_hits = 7;
        if (jcp.bcast_dim * reduce_blocking > way_size * max_hits) {
            int nrb = reduce_blocking / simd_w;
            int sp = jcp.bcast_dim;
            int wl = way_size / simd_w;
            for (int start_off = 0; start_off < jcp.ur; start_off++) {
                for (int off = start_off, hits = 0; off < sp * nrb; off += wl) {
                    if (off % sp >= jcp.ur || ++hits < max_hits) continue;
                    int max_r_blocking = simd_w * nstl::max(1, (off + wl) / sp);
                    reduce_blocking
                            = nstl::min(reduce_blocking, max_r_blocking);
                    break;
                }
            }
        }

        if (reduce_blocking < jcp.reduce_dim) {
            if (jcp.prop_kind == backward_data)
                jcp.loop_order = reduce_src ? loop_lbr : loop_rlb;
            else
                jcp.loop_order = reduce_src ? loop_rbl : loop_rlb;
        }
        load_blocking = jcp.load_dim;

        int load_size = jcp.load_dim * jcp.reduce_dim;
        auto bcast_size
                = (dim_t)jcp.mb * jcp.ngroups * jcp.bcast_dim * jcp.reduce_dim;

        if (jcp.ver == ver_avx512_core && nthreads <= 28 && jcp.mb < nthreads
                && nb_load * nb_bcast > nthreads) {
            // Some heuristic here
            float calc_koef = 0.01, best_cost = FLT_MAX;
            int n_lgc = nthreads;
            float ratio = (float)load_size / (float)bcast_size;
            int best_lgc = ratio > 1 ? n_lgc : 1;
            auto calc_job_cost = [&](int lb, int tg, float mem_k) {
                int bb_size = jcp.mb * div_up(nb_bcast, tg);
                float calc_size = (float)(bb_size * jcp.ur)
                        * (lb * jcp.load_block) * jcp.reduce_dim;
                float mem_size = (float)(bb_size * jcp.ur + lb * jcp.load_block)
                        * jcp.reduce_dim;
                return calc_koef * calc_size + mem_k * mem_size;
            };
            for (int lgc, ilgc = 0; ilgc < n_lgc; ilgc++) {
                lgc = ratio > 1 ? n_lgc - ilgc : ilgc + 1;
                int min_lb = nb_load / lgc;
                int max_lb = div_up(nb_load, lgc);
                int min_tg = nthreads / lgc;
                int max_tg = div_up(nthreads, lgc);
                // Some heuristic here
                float mem_koef = (max_tg == 1) ? 1.f : 1.3f;
                float job_cost = 0.;
                if (nthreads % lgc < nb_load % lgc) {
                    job_cost = calc_job_cost(max_lb, min_tg, mem_koef);
                } else {
                    auto job_cost1 = calc_job_cost(max_lb, max_tg, mem_koef);
                    auto job_cost2 = calc_job_cost(min_lb, min_tg, mem_koef);
                    job_cost = nstl::max(job_cost1, job_cost2);
                }

                if (job_cost < best_cost) {
                    best_lgc = lgc;
                    best_cost = job_cost;
                }
            }
            jcp.load_grp_count = best_lgc;
            load_blocking
                    = div_up(nb_load, jcp.load_grp_count) * jcp.load_block;
        } else {
            jcp.load_grp_count
                    = div_up(nthreads, jcp.mb * jcp.ngroups * nb_bcast);
            jcp.load_grp_count = best_divider(nthreads, jcp.load_grp_count,
                    2 * jcp.load_grp_count, false);
        }

        if (jcp.ver == ver_avx512_core && jcp.expl_bcast && jcp.bcast_dim <= 64
                && load_size >= L2_size) {
            jcp.load_grp_count = nstl::max(jcp.load_grp_count, 4);
        } else if (jcp.bcast_dim <= 49 && jcp.mb <= nthreads
                && jcp.load_dim > 512 && jcp.load_dim / jcp.reduce_dim >= 4) {
            jcp.load_grp_count = nstl::max(jcp.load_grp_count, 2);
            load_blocking = jcp.load_block;
        }

        if (jcp.ver == ver_4fma && jcp.bcast_dim * jcp.mb < jcp.load_dim
                && jcp.oh * jcp.ow > 64
                && IMPLICATION(reduce_src, jcp.load_dim < 1024)) {
            /* Looking for best loading dimension blocking
            * to get the best thread and data read/write efficiency
            * by finding the optimal 'load_chunk' value
            * Example:
            * for 72 threads and convolution with mb=1, ih=iw=7, oc = 512
            * the 'best' load_chunk value should be 1
            * TODO: remove heuristic constants in above condition
            * TODO: check this blocking for other ISA
            */
            float best_eff = -1.f;
            int best_lgc = 1;

            for (int load_chunk = 1; load_chunk <= nb_load; load_chunk++) {
                int lgc = div_up(nb_load, load_chunk);
                if (lgc > nthreads) continue;
                int thr_per_grp = div_up(nthreads, lgc);
                int bcast_per_thr = div_up(jcp.mb * nb_bcast, thr_per_grp)
                        * jcp.bcast_block;
                int load_per_thr = load_chunk * simd_w;
                float data_norm = (bcast_per_thr + load_per_thr) / 2.f;
                float data_eff = (bcast_per_thr * load_per_thr)
                        / (data_norm * data_norm);
                float thr_eff_over_grp = (float)nstl::max(1, nthreads / lgc)
                        / div_up(nthreads, lgc);
                float thr_eff_in_grp = ((float)jcp.mb * nb_bcast)
                        / rnd_up(jcp.mb * nb_bcast, thr_per_grp);
                float thr_eff = thr_eff_over_grp * thr_eff_in_grp;
                float load_eff = (float)nb_load / rnd_up(nb_load, lgc);
                float overall_eff = data_eff + thr_eff + load_eff;
                if (overall_eff > best_eff) {
                    best_eff = overall_eff;
                    best_lgc = lgc;
                }
            }
            jcp.load_grp_count = best_lgc;
            load_blocking
                    = div_up(nb_load, jcp.load_grp_count) * jcp.load_block;
        }
        bcast_blocking = div_up(jcp.mb * jcp.ngroups * nb_bcast,
                                 div_up(nthreads, jcp.load_grp_count))
                * jcp.bcast_block;
        bcast_blocking = nstl::min(jcp.bcast_dim, bcast_blocking);
        bcast_blocking = rnd_up(bcast_blocking, jcp.bcast_block);

        int space_for_bcast = (L2_capacity - /* kernel_size - */
                2 * jcp.load_block * reduce_blocking - jcp.ur * reduce_blocking
                - 3 * 1024);
        if (jcp.reduce_dim * jcp.bcast_dim > L2_capacity) space_for_bcast /= 2;

        int bcast_in_cache
                = nstl::max(jcp.bcast_block, space_for_bcast / reduce_blocking);
        bcast_blocking = nstl::min(
                bcast_blocking, rnd_dn(bcast_in_cache, jcp.bcast_block));

        load_blocking_max = load_blocking;
        bcast_blocking_max = bcast_blocking * 3 / 2;
        reduce_blocking_max = reduce_blocking;

    } else if (jcp.prop_kind == backward_weights) {

        if (jcp.is > SMALL_SPATIAL * SMALL_SPATIAL && jcp.ver == ver_4fma)
            jcp.use_vmovntps = true;

        if (jcp.transpose_src)
            jcp.reduce_dim = jcp.tr_is;
        else
            jcp.reduce_dim = jcp.is;

        if (jcp.ver == ver_4fma) {
            // reduce_block should be divided by fma_step
            jcp.reduce_block = best_divider(jcp.reduce_dim, 4, 16, true, 4);
        } else {
            jcp.reduce_block = best_divider(jcp.reduce_dim, 7, 16, true);
            if (jcp.reduce_dim % jcp.reduce_block != 0)
                jcp.reduce_block = best_divider(jcp.iw, 4, jcp.iw, false);
            if (jcp.reduce_block > 256) { jcp.reduce_block = 1; }
        }

        jcp.load_dim = jcp.oc;
        jcp.load_block = jcp.oc_block;

        jcp.bcast_dim = jcp.ic;
        jcp.bcast_block = jcp.ic_block;

        if (jcp.ver == ver_avx512_core && jcp.reduce_block <= 19) {
            // if reduce_block is big then generated JIT code may be big
            // for small values of ur because reduce_loop_unroll = reduce_block
            jcp.ur = jcp.bcast_block / 2;
            jcp.expl_bcast = true;
        } else {
            jcp.ur = jcp.bcast_block;
            jcp.expl_bcast = false;
        }

        jcp.reduce_loop_unroll = jcp.reduce_block;
        jcp.reduce_loop_bcast_step
                = jcp.reduce_loop_unroll * jcp.ic_block * jcp.typesize_in;
        jcp.reduce_loop_load_step
                = jcp.reduce_loop_unroll * jcp.oc_block * jcp.typesize_in;

        jcp.bcast_loop_output_step
                = jcp.oc_block * jcp.ic_block * jcp.typesize_out;
        jcp.bcast_loop_output_substep
                = jcp.oc_block * jcp.ur * jcp.typesize_out;
        jcp.bcast_loop_bcast_step
                = jcp.ic_block * jcp.reduce_dim * jcp.typesize_in;
        jcp.bcast_loop_bcast_substep = jcp.ur * jcp.typesize_in;

        jcp.load_loop_load_step = jcp.oc_block * jcp.os * jcp.typesize_in;
        jcp.load_loop_iter_step = jcp.oc_block;

        /* --- */
        balance(jcp, nthreads);

        load_blocking = div_up(jcp.load_dim, jcp.load_block);
        load_blocking = best_divider(load_blocking, 16, load_blocking, false);
        load_blocking *= jcp.load_block;

        load_blocking_max = load_blocking;
        assert(jcp.load_dim % load_blocking == 0);

        int max_bcast_blocking = div_up(jcp.bcast_dim, jcp.bcast_block);
        int min_bcast_blocking = 5;

        bcast_blocking = div_up(jcp.bcast_dim, jcp.bcast_block);
        bcast_blocking = best_divider(
                bcast_blocking, min_bcast_blocking, max_bcast_blocking, false);
        bcast_blocking *= jcp.bcast_block;
        bcast_blocking_max = bcast_blocking;
        assert(jcp.bcast_dim % bcast_blocking == 0);

        // for reduction balance
        if (jcp.ver == ver_avx512_core) {
            int max_reduce_blocking
                    = nstl::min(L1_capacity / jcp.ur, jcp.reduce_dim);
            int min_reduce_blocking = nstl::min(
                    L1_capacity / jcp.ur, nstl::max(jcp.iw, jcp.ih));
            reduce_blocking = best_divider(jcp.reduce_dim, min_reduce_blocking,
                    max_reduce_blocking, true);
            reduce_blocking
                    = nstl::max(rnd_dn(reduce_blocking, jcp.reduce_block),
                            jcp.reduce_block);
        } else {
            int max_reduce_blocking = L2_capacity
                    / ((bcast_blocking + load_blocking) * jcp.reduce_block);
            max_reduce_blocking = nstl::min(max_reduce_blocking,
                    (L1_capacity / (jcp.bcast_block)) / jcp.reduce_block);

            int num_jobs = div_up(jcp.load_dim, load_blocking)
                    * div_up(jcp.bcast_dim, bcast_blocking);
            int threads_per_job = nstl::max(1, nthreads / num_jobs);
            reduce_blocking = div_up(jcp.mb * jcp.reduce_dim, jcp.reduce_block);
            reduce_blocking = div_up(reduce_blocking, threads_per_job);

            reduce_blocking = best_divider(reduce_blocking,
                    max_reduce_blocking - 2, max_reduce_blocking, true);
            reduce_blocking *= jcp.reduce_block;
        }

        reduce_blocking_max = rnd_dn(reduce_blocking * 3 / 2, jcp.reduce_block);
    } else
        return status::unimplemented;

    assert(load_blocking);
    assert(load_blocking_max);
    assert(bcast_blocking);
    assert(bcast_blocking_max);
    assert(reduce_blocking);
    assert(reduce_blocking_max);
    assert(load_blocking % jcp.load_block == 0);
    assert(reduce_blocking % jcp.reduce_block == 0);
    assert(load_blocking_max % jcp.load_block == 0);
    assert(reduce_blocking_max % jcp.reduce_block == 0);
    if (jcp.ver == ver_4fma) {
        assert(jcp.reduce_loop_unroll % jcp.fma_step == 0);
        assert(jcp.reduce_dim % jcp.reduce_loop_unroll == 0);
    }

    assert(jcp.bcast_block % jcp.ur == 0);
    assert(jcp.reduce_dim % jcp.reduce_block == 0);

    jcp.ur_tail = jcp.bcast_dim % jcp.ur;

    jcp.nb_bcast_blocking = bcast_blocking / jcp.bcast_block;
    jcp.nb_bcast_blocking_max = bcast_blocking_max / jcp.bcast_block;
    jcp.nb_load_blocking = load_blocking / jcp.load_block;
    jcp.nb_load_blocking_max = load_blocking_max / jcp.load_block;
    jcp.nb_reduce_blocking = reduce_blocking / jcp.reduce_block;
    jcp.nb_reduce_blocking_max = reduce_blocking_max / jcp.reduce_block;

    jcp.nb_bcast = div_up(jcp.bcast_dim, jcp.bcast_block);
    jcp.nb_load = div_up(jcp.load_dim, jcp.load_block);
    jcp.nb_reduce = div_up(jcp.reduce_dim, jcp.reduce_block);

    return status::success;
}

void jit_avx512_common_1x1_conv_kernel::init_scratchpad(
        memory_tracking::registrar_t &scratchpad,
        const jit_1x1_conv_conf_t &jcp) {
    using namespace dnnl::impl::memory_tracking::names;

    if (jcp.prop_kind != backward_data && jcp.with_bias
            && jcp.oc != jcp.oc_without_padding)
        scratchpad.book(key_conv_padded_bias, jcp.typesize_out * jcp.oc);

    if (jcp.prop_kind == backward_weights) {
        const size_t wei_size = (size_t)jcp.ngroups * jcp.oc * jcp.ic;
        scratchpad.book(key_conv_wei_reduction,
                jcp.typesize_out * wei_size * (jcp.nthr_mb - 1));
    }

    if (jcp.transpose_src) {
        const size_t tr_src_size
                = (size_t)jcp.nthr_mb * jcp.ngroups * jcp.ic * jcp.tr_is;
        scratchpad.book(key_conv_tr_src, jcp.typesize_out * tr_src_size);
        scratchpad.book(
                key_conv_tr_src_bctx, sizeof(simple_barrier::ctx_t) * jcp.nthr);
    }
}

void jit_avx512_common_1x1_conv_kernel::balance(
        jit_1x1_conv_conf_t &jcp, int nthreads) {
    // initialize jcp reduction threading properties
    jcp.nthr = jcp.nthr_mb = jcp.nthr_g = jcp.nthr_oc_b = jcp.nthr_ic_b = 1;
    if (nthreads < jcp.ngroups) {
        /* simplification... fortunately it doesn't hurt much */
        return;
    }
    const int nb_bcast = div_up(jcp.bcast_dim, jcp.bcast_block);
    const int nb_load = div_up(jcp.load_dim, jcp.load_block);
    const int nb_reduce = div_up(jcp.reduce_dim, jcp.reduce_block);

    jcp.nthr_g = jcp.ngroups;
    const int nthr = nthreads / jcp.nthr_g;

    auto calc_mem_cost = [=](int nthr_mb, int nthr_oc_b, int nthr_ic_b) {
        /* calculate per thread memory cost (read/write). high level
        * optimizer tries to minimize memory consumption. few notes: (n1)
        * unclear why, but that essentially helps first convolution...
        *  (n2) assuming the reduction over minibatch is always there:
        *    - instead of 8 it should be 5 here (write ~= 2 read):
        *      kernel: temporal workspace 1 write
        *      reduction: 1 read from workspace and 1 write to the diff_wei
        *    - but experiments showed 8 works better than 5 or 6... */
        int bcast_koeff = 1;
        int load_koeff = 1;
        int output_koeff = 12;
        if (jcp.transpose_src) {
            bcast_koeff = 5;
            load_koeff = 1;
            output_koeff = 8;
        }
        return 0
                + (size_t)bcast_koeff * div_up(jcp.mb * nb_reduce, nthr_mb)
                * div_up(jcp.ngroups, jcp.nthr_g) * div_up(nb_bcast, nthr_ic_b)
                * jcp.ic_block * jcp.reduce_block / jcp.stride_h
                / jcp.stride_w /* (n1) */
                + (size_t)load_koeff * div_up(jcp.mb * nb_reduce, nthr_mb)
                * div_up(jcp.ngroups, jcp.nthr_g) * div_up(nb_load, nthr_oc_b)
                * jcp.oc_block * jcp.reduce_block
                + (size_t)output_koeff /* (n2) */
                * div_up(jcp.ngroups, jcp.nthr_g) * div_up(nb_load, nthr_oc_b)
                * div_up(nb_bcast, nthr_ic_b) * jcp.ic_block * jcp.oc_block;
    };

    int nthr_mb = 1, nthr_oc_b = 1, nthr_ic_b = 1;
    auto best_mem_cost = calc_mem_cost(nthr_mb, nthr_oc_b, nthr_ic_b);

    /* step 1: find the best thread distribution with lowest memory cost */
    const int nthr_mb_max = nstl::min(nthr, jcp.mb * nb_reduce);
    for (nthr_mb = 1; nthr_mb <= nthr_mb_max; ++nthr_mb) {
        const int nthr_par = nthr / nthr_mb;
        const int nthr_oc_b_max = nstl::min(nthr_par, nb_load);
        for (nthr_oc_b = 1; nthr_oc_b <= nthr_oc_b_max; ++nthr_oc_b) {
            nthr_ic_b = nstl::min(nthr_par / nthr_oc_b, nb_bcast);
            auto mem_cost = calc_mem_cost(nthr_mb, nthr_oc_b, nthr_ic_b);
            if (mem_cost <= best_mem_cost) {
                best_mem_cost = mem_cost;
                jcp.nthr_mb = nthr_mb;
                jcp.nthr_oc_b = nthr_oc_b;
                jcp.nthr_ic_b = nthr_ic_b;
            }
        }

        const bool ready_for_async
                = utils::one_of(jcp.ver, ver_fma, ver_avx512_core);
        if (!ready_for_async && !dnnl_thr_syncable()) {
            assert(nthr_mb == 1);
            break;
        }
    }
    if (jcp.nthr_mb > nthreads / 2 && jcp.nthr_mb < nthreads)
        jcp.nthr_mb = nstl::min(jcp.mb, nthreads);

    jcp.nthr = jcp.nthr_mb * jcp.nthr_g * jcp.nthr_oc_b * jcp.nthr_ic_b;
    assert(jcp.nthr <= nthreads);
}

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