megenginelite-sys 1.8.2

/**
 * \file dnn/src/fallback/convolution/algos.cpp
 * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
 *
 * Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
 *
 * Unless required by applicable law or agreed to in writing,
 * software distributed under the License is distributed on an
 * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
 * implied.
 */

#include "src/fallback/convolution/algos.h"
#include "src/common/opr_delegate.h"
#include "src/fallback/convolution/col2img_helper.h"
#include "src/fallback/convolution/run_conv.h"

#include "midout.h"

using namespace megdnn;
using namespace fallback;

MIDOUT_DECL(megdnn_fallback_conv)
MIDOUT_DECL(megdnn_fallback_deconv)

namespace {

template <typename T>
void incr_ptr(T*& dst, ptrdiff_t delta) {
    dst = reinterpret_cast<T*>(reinterpret_cast<uintptr_t>(dst) + delta);
}

using NCBKernSizeParam = ConvolutionBackwardDataImpl::NCBKernSizeParam;
using NCBKernParam = ConvolutionBackwardDataImpl::NCBKernParam;

Relayout* get_relayout_opr() {
    static CpuOprDelegationStorage<> storage;
    return storage.get<Relayout>();
}

MatrixMul* get_matmul_opr(const NCBKernSizeParam& param) {
    using ConvCM = param::Convolution::ComputeMode;
    using MmCM = param::MatrixMul::ComputeMode;
    static CpuOprDelegationStorage<2> storage;
    switch (param.compute_mode) {
        default:
            return storage.get<MatrixMul, 0>({});
        case ConvCM::FLOAT32: {
            MatrixMul::Param p;
            p.compute_mode = MmCM::FLOAT32;
            return storage.get<MatrixMul, 1>(p);
        }
    }
}

WorkspaceBundle get_bundle(const NCBKernSizeParam& param) {
    UNPACK_CONV_F32_NCB_KERN_SIZES(param);
    MEGDNN_MARK_USED_VAR(N);
    MEGDNN_MARK_USED_VAR(OH);
    MEGDNN_MARK_USED_VAR(OW);
    bool can_matrix_mul_direct =
            (FH == 1 && FW == 1 && SH == 1 && SW == 1 && PH == 0 && PW == 0);
    // temp space to store unrolled matrix
    // workspace for matrix mul opr
    // workspace for relayout opr
    size_t part0, part1, part2;
    if (can_matrix_mul_direct) {
        part0 = 0;
    } else {
        part0 = (IC * FH * FW * IH * IW) * param.grad_type.size();
    }
    part2 = (OC * IC * FH * FW) * param.filter_type.size();
    {
        TensorLayout A_, B_, C_;
        A_ = TensorLayout({IC * FH * FW, OC}, param.filter_type);
        B_ = TensorLayout({OC, IH * IW}, param.diff_type);
        C_ = TensorLayout({IC * FH * FW, IH * IW}, param.grad_type);
        part1 = get_matmul_opr(param)->get_workspace_in_bytes(A_, B_, C_);
    }
    return {nullptr, {part0, part1, part2}};
}

template <typename ftype, typename dtype, typename gtype>
void kern_matmul(const NCBKernParam& param) {
    bool is_xcorr = !param.filter_meta.should_flip;
    UNPACK_CONV_F32_NCB_KERN_SIZES(param);
    auto bundle = get_bundle(param);
    bundle.set(param.workspace_ptr);
    bool is1X1 = (FH == 1 && FW == 1 && SH == 1 && SW == 1 && PH == 0 && PW == 0);

    typedef void (*Func1)(const gtype*, gtype*, int, int, int, int, int, int, int);
    typedef void (*Func2)(
            const gtype*, gtype*, int, int, int, int, int, int, int, int, int, int,
            int);
    Func1 f1 = nullptr;
    Func2 f2 = nullptr;
    if (is_xcorr) {
        f1 = col2img<true>;
        f2 = col2img_stride_padding<true>;
    } else {
        f1 = col2img<false>;
        f2 = col2img_stride_padding<false>;
    }
    ftype* filter = const_cast<ftype*>(param.filter<ftype>());
    TensorND A_src, A_dst;
    {
        A_src.layout = TensorLayout(
                {IC * FH * FW, OC},
                {static_cast<std::ptrdiff_t>(1),
                 static_cast<std::ptrdiff_t>(IC * FH * FW)},
                param.filter_type);
        A_src.reset_ptr(static_cast<void*>(filter));
        A_dst.layout = TensorLayout({IC * FH * FW, OC}, param.filter_type);
        A_dst.reset_ptr(static_cast<void*>(bundle.get(2)));
        // TODO Should be removed once armv8 convolution support transpose.
        get_relayout_opr()->exec(A_src, A_dst, inplace_cpu_handle().get());
    }
    TensorND B_, C_;
    for (size_t n = 0; n < N; ++n) {
        gtype *C_src, *C_dst;
        dtype* diff = const_cast<dtype*>(param.diff<dtype>() + n * param.inp_bs);
        gtype* grad = param.grad<gtype>() + n * param.out_bs;
        if (is1X1) {
            C_src = grad;
        } else {
            C_src = static_cast<gtype*>(bundle.get(0));
        }
        {
            B_.layout = TensorLayout({OC, IH * IW}, param.diff_type);
            B_.reset_ptr(static_cast<void*>(diff));
            C_.layout = TensorLayout({IC * FH * FW, IH * IW}, param.grad_type);
            C_.reset_ptr(C_src);
            Workspace workspace(
                    static_cast<dt_byte*>(bundle.get(1)), bundle.get_size(1));
            get_matmul_opr(param)->exec(A_dst, B_, C_, workspace);
        }

        if (!is1X1) {
            C_dst = grad;
            std::memset(C_dst, 0, param.grad_type.size() * IC * OH * OW);
            if (PH == 0 && PW == 0 && SH == 1 && SW == 1) {
                f1(C_src, C_dst, OH, OW, IC, IH, IW, FH, FW);
            } else {
                f2(C_src, C_dst, OH, OW, IC, IH, IW, FH, FW, SH, SW, PH, PW);
            }
        }
    }
}

void kern_direct(const NCBKernParam& param) {
    UNPACK_CONV_F32_NCB_KERN_SIZES(param);
    auto diff = param.diff<float>(), filter = param.filter<float>();
    auto grad = param.grad<float>();
    for (size_t n = 0; n < N; ++n) {
        convolution::run_conv_backward_data(
                diff + n * param.inp_bs, filter, grad + n * param.out_bs,
                param.workspace_ptr, IH, IW, IC, FH, FW, OH, OW, OC, PH, PW, SH, SW,
                !param.filter_meta.should_flip);
    }
}

}  // namespace

/* ===================== fallback algo ===================== */

bool ConvolutionImpl::AlgoFallback::usable(
        const NCBKernSizeParam& param,
        AlgoSelectionStrategy /*algo_selection_strategy*/) const {
    auto&& fm = param.filter_meta;
    return fm.format == param::Convolution::Format::NCHW &&
           param.src_type.enumv() == DTypeEnum::Float32 &&
           param.filter_type.enumv() == DTypeEnum::Float32 &&
           param.dst_type.enumv() == DTypeEnum::Float32 && fm.spatial_ndim == 2 &&
           fm.dilation[0] == 1 && fm.dilation[1] == 1;
}

size_t ConvolutionImpl::AlgoFallback::get_workspace(
        const NCBKernSizeParam& param) const {
    MIDOUT_BEGIN(megdnn_fallback_conv, midout_iv("AlgoFallback::get_workspace"_hash)) {
        auto FH = param.filter_meta.spatial[0], FW = param.filter_meta.spatial[1];
        size_t nr_threads = param.nr_threads;
        if (param.filter_meta.should_flip) {
            // need transpose filter
            return WorkspaceBundle{nullptr, {FH * FW * sizeof(float)}}
                           .total_size_in_bytes() *
                   nr_threads;
        } else {
            return 0;
        }
    }
    MIDOUT_END();
    return 0;
}

SmallVector<ConvolutionImpl::NCBKern> ConvolutionImpl::AlgoFallback::dispatch_kern(
        const NCBKernSizeParam& param) const {
    MIDOUT_BEGIN(megdnn_fallback_conv, midout_iv("AlgoFallback::dispatch_kern"_hash)) {
        size_t group = param.filter_meta.group;
        size_t N = param.n;
        size_t nr_threads = param.nr_threads;
        size_t workspace_per_thread = get_workspace(param) / nr_threads;
        auto kern_fallback = [workspace_per_thread](
                                     const NCBKernParam& p,
                                     const NCBKernIndex& ncb_index) {
            UNPACK_CONV_F32_NCB_KERN_SIZES(p);
            size_t batch_id = ncb_index.ndrange_id[1];
            size_t group_id = ncb_index.ndrange_id[0];
            MEGDNN_MARK_USED_VAR(N);
            auto src = p.src<float>(batch_id, group_id),
                 filter = p.filter<float>(group_id);
            auto dst = p.dst<float>(batch_id, group_id);
            size_t thread_id = ncb_index.thread_id;
            void* workspace_ptr = reinterpret_cast<void*>(
                    reinterpret_cast<ptrdiff_t>(p.workspace_ptr) +
                    workspace_per_thread * thread_id);
            convolution::run_conv(
                    src, filter, dst, workspace_ptr, IH, IW, IC, FH, FW, OH, OW, OC, PH,
                    PW, SH, SW, !p.filter_meta.should_flip);
        };
        return {{kern_fallback, {group, N, 1_z}}};
    }
    MIDOUT_END();
}

/* ===================== naive algo ===================== */

bool ConvolutionImpl::AlgoNaive::usable(
        const NCBKernSizeParam& param,
        AlgoSelectionStrategy /*algo_selection_strategy*/) const {
    bool ret = false;

#define cb(dt) ret |= (param.src_type.enumv() == DTypeTrait<dt>::enumv);
    MEGDNN_FOREACH_COMPUTING_DTYPE_FLOAT(cb);
#undef cb
#define cb(dt_src, dt_dst)                                            \
    ret |= (param.src_type.enumv() == DTypeTrait<dt_src>::enumv &&    \
            param.filter_type.enumv() == DTypeTrait<dt_src>::enumv && \
            param.dst_type.enumv() == DTypeTrait<dt_dst>::enumv)
    cb(dtype::Int8, dtype::Int16);
    cb(dtype::Int8, dtype::Int32);
    cb(dtype::Quantized8Asymm, dtype::QuantizedS32);
    cb(dtype::QuantizedS8, dtype::QuantizedS32);
#undef cb
    ret = ret && (param.filter_meta.format == param::Convolution::Format::NCHW ||
                  param.filter_meta.format == param::Convolution::Format::NHWC);
    return ret;
}

SmallVector<ConvolutionImpl::NCBKern> ConvolutionImpl::AlgoNaive::dispatch_kern(
        const NCBKernSizeParam& param) const {
    size_t N = param.n;
    size_t group = param.filter_meta.group;
#define cb(dt, cmode, compute_type)                                      \
    do {                                                                 \
        if (param.src_type.enumv() == DTypeTrait<dt>::enumv &&           \
            param.compute_mode == param::ConvBias::ComputeMode::cmode) { \
            using ctype = DTypeTrait<dt>::ctype;                         \
            using comp_type = DTypeTrait<compute_type>::ctype;           \
            MIDOUT_BEGIN(megdnn_fallback_conv, midout_iv(1)) {           \
                return {                                                 \
                        {kern_naive_forward<ctype, ctype, comp_type>,    \
                         {group, N, 1_z}}};                              \
            }                                                            \
            MIDOUT_END();                                                \
        }                                                                \
    } while (0)

    cb(dtype::Float32, DEFAULT, dtype::Float32);
#if !MEGDNN_DISABLE_FLOAT16
    cb(dtype::Float16, DEFAULT, dtype::Float16);
    cb(dtype::Float16, FLOAT32, dtype::Float32);
#endif
#undef cb

#define cb(dt_src, dt_dst)                                                             \
    do {                                                                               \
        if (param.src_type.enumv() == DTypeTrait<dt_src>::enumv &&                     \
            param.filter_type.enumv() == DTypeTrait<dt_src>::enumv &&                  \
            param.dst_type.enumv() == DTypeTrait<dt_dst>::enumv) {                     \
            MIDOUT_BEGIN(megdnn_fallback_conv, midout_iv(2)) {                         \
                return {                                                               \
                        {kern_naive_forward<                                           \
                                 DTypeTrait<dt_src>::ctype, DTypeTrait<dt_dst>::ctype, \
                                 DTypeTrait<dt_dst>::ctype>,                           \
                         {group, N, 1_z}}};                                            \
            }                                                                          \
            MIDOUT_END();                                                              \
        }                                                                              \
    } while (0)
    cb(dtype::Int8, dtype::Int16);
    cb(dtype::Int8, dtype::Int32);
    cb(dtype::Quantized8Asymm, dtype::QuantizedS32);
    cb(dtype::QuantizedS8, dtype::QuantizedS32);
    megdnn_throw("unknown convolution data type");
#undef cb
}

/* ===================== default algo ===================== */

ConvolutionImpl::AlgoDefault::AlgoDefault(ConvBiasImpl::AlgoBase* algorithm)
        : m_algorithm(algorithm) {
    megdnn_assert_internal(algorithm);
    m_name = ssprintf("CONVOLUTION_DEFAULT_%s", m_algorithm->name());
}

ConvBiasImpl::NCBKernSizeParam ConvolutionImpl::AlgoDefault::init_conv_bias_param(
        const NCBKernSizeParam& param) {
    DType bias_type = param.dst_type;
    if (bias_type.category() == DTypeCategory::QUANTIZED) {
        bias_type = dtype::QuantizedS32(mul_scale(param.src_type, param.filter_type));
    }
    return {param, bias_type, 0, BiasMode::NO_BIAS,
            param::ConvBias::NonlineMode::IDENTITY};
}

bool ConvolutionImpl::AlgoDefault::is_preferred(const NCBKernSizeParam& param) const {
    ::ConvBiasImpl::NCBKernSizeParam conv_bias_param = init_conv_bias_param(param);
    return m_algorithm->is_preferred(conv_bias_param);
}

bool ConvolutionImpl::AlgoDefault::usable(
        const NCBKernSizeParam& param,
        AlgoSelectionStrategy algo_selection_strategy) const {
    ::ConvBiasImpl::NCBKernSizeParam conv_bias_param = init_conv_bias_param(param);
    return m_algorithm->usable(
            conv_bias_param,
            static_cast<ConvBiasImpl::AlgoSelectionStrategy>(algo_selection_strategy));
}

WorkspaceBundle ConvolutionImpl::AlgoDefault::get_bundle(
        const NCBKernSizeParam& param) const {
    ::ConvBiasImpl::NCBKernSizeParam conv_bias_param = init_conv_bias_param(param);
    return WorkspaceBundle(nullptr, {m_algorithm->get_workspace(conv_bias_param)});
}

size_t ConvolutionImpl::AlgoDefault::get_workspace(
        const NCBKernSizeParam& param) const {
    MIDOUT_BEGIN(megdnn_fallback_conv, midout_iv("AlgoDefault::get_workspace"_hash)) {
        return get_bundle(param).total_size_in_bytes();
    }
    MIDOUT_END();
    return 0;
}

size_t ConvolutionImpl::AlgoDefault::get_preprocess_workspace(
        const NCBKernSizeParam& param) const {
    MIDOUT_BEGIN(
            megdnn_fallback_conv,
            midout_iv("AlgoDefault::get_preprocess_workspace"_hash)) {
        ::ConvBiasImpl::NCBKernSizeParam conv_bias_param = init_conv_bias_param(param);
        return m_algorithm->get_preprocess_workspace(conv_bias_param);
    }
    MIDOUT_END();
}

SmallVector<TensorLayout> ConvolutionImpl::AlgoDefault::
        deduce_preprocessed_filter_layout(const NCBKernSizeParam& param) const {
    MIDOUT_BEGIN(
            megdnn_fallback_conv,
            midout_iv("AlgoDefault::deduce_preprocessed_filter_layout"_hash)) {
        ::ConvBiasImpl::NCBKernSizeParam conv_bias_param = init_conv_bias_param(param);
        return m_algorithm->deduce_preprocessed_filter_layout(conv_bias_param);
    }
    MIDOUT_END();
}

//! Return the implement preprocess kernel
SmallVector<ConvolutionImpl::NCBKern> ConvolutionImpl::AlgoDefault::
        get_preprocess_kimpl(
                ConvBiasImpl::AlgoBase* algo, const NCBKernSizeParam& param) {
    MIDOUT_BEGIN(megdnn_fallback_conv, midout_iv("get_preprocess_kimpl"_hash)) {
        // construct the conv_bias kern param
        ::ConvBiasImpl::NCBKernParam conv_bias_param;
        static_cast<::ConvBiasImpl::NCBKernSizeParam&>(conv_bias_param) =
                init_conv_bias_param(param);
        auto conv_bias_preprocess_kerns =
                algo->dispatch_preprocess_kerns(conv_bias_param);
        SmallVector<ConvolutionImpl::NCBKern> convolution_preprocess_kerns;

        for (size_t i = 0; i < conv_bias_preprocess_kerns.size(); i++) {
            auto kernel = conv_bias_preprocess_kerns[i];
            //! If the kerenl batch parallel
            auto run = [conv_bias_param, kernel](
                               const NCBKernParam& p, const NCBKernIndex& ncb_index) {
                auto param = conv_bias_param;
                param.filter_ptr = p.filter_ptr;
                param.workspace_ptr = p.workspace_ptr;
                param.workspace_size = p.workspace_size;
                kernel.kern(param, {ncb_index.thread_id, ncb_index.ndrange_id});
            };
            convolution_preprocess_kerns.push_back({run, kernel.global_size});
        }
        return convolution_preprocess_kerns;
    }
    MIDOUT_END();
}

//! Return the implement kernel
SmallVector<ConvolutionImpl::NCBKern> ConvolutionImpl::AlgoDefault::get_kimpl(
        ConvBiasImpl::AlgoBase* algo, const NCBKernSizeParam& param) {
    MIDOUT_BEGIN(megdnn_fallback_conv, midout_iv(0)) {
        // construct the conv_bias kern param
        ::ConvBiasImpl::NCBKernParam conv_bias_param;
        static_cast<::ConvBiasImpl::NCBKernSizeParam&>(conv_bias_param) =
                init_conv_bias_param(param);
        auto&& conv_bias_kerns = algo->dispatch_kerns(conv_bias_param);
        SmallVector<ConvolutionImpl::NCBKern> convolution_kerns;

        for (size_t i = 0; i < conv_bias_kerns.size(); i++) {
            auto&& kernel = conv_bias_kerns[i];
            //! If the kerenl batch parallel
            auto run = [conv_bias_param, kernel](
                               const NCBKernParam& p, const NCBKernIndex& ncb_index) {
                auto param = conv_bias_param;
                param.src_ptr = p.src_ptr;
                param.filter_ptr = p.filter_ptr;
                param.dst_ptr = p.dst_ptr;
                param.workspace_ptr = p.workspace_ptr;
                param.workspace_size = p.workspace_size;
                kernel.kern(param, {ncb_index.thread_id, ncb_index.ndrange_id});
            };
            convolution_kerns.push_back({run, kernel.global_size});
        }
        return convolution_kerns;
    }
    MIDOUT_END();
}

/////////////////////////// ConvolutionBackwardData /////////////////////
/* ===================== naive algo ===================== */

bool ConvolutionBackwardDataImpl::AlgoNaive::usable(
        ConvolutionBackwardDataImpl*, const NCBKernSizeParam& param) const {
    bool ret = false;

#define cb(dt) ret |= (param.diff_type.enumv() == DTypeTrait<dt>::enumv);
    MEGDNN_FOREACH_COMPUTING_DTYPE_FLOAT(cb);
#undef cb
#define cb(dt_src, dt_dst)                                            \
    ret |= (param.diff_type.enumv() == DTypeTrait<dt_src>::enumv &&   \
            param.filter_type.enumv() == DTypeTrait<dt_src>::enumv && \
            param.grad_type.enumv() == DTypeTrait<dt_dst>::enumv)
    cb(dtype::Int8, dtype::Int32);
    cb(dtype::Quantized8Asymm, dtype::QuantizedS32);
    cb(dtype::QuantizedS8, dtype::QuantizedS32);
#undef cb
    return ret;
}

size_t ConvolutionBackwardDataImpl::AlgoNaive::get_workspace(
        ConvolutionBackwardDataImpl*, const NCBKernSizeParam&) const {
    return 0;
}

ConvolutionBackwardDataImpl::ncb_kern_t ConvolutionBackwardDataImpl::AlgoNaive::
        dispatch_kern(
                ConvolutionBackwardDataImpl*, const NCBKernSizeParam& param) const {
#define cb(_dt)                                                                       \
    do {                                                                              \
        if (param.filter_type.enumv() == DTypeTrait<_dt>::enumv) {                    \
            MIDOUT_BEGIN(megdnn_fallback_deconv, midout_iv(DTypeTrait<_dt>::enumv)) { \
                using ctype = DTypeTrait<_dt>::ctype;                                 \
                return kern_naive<ctype, ctype, ctype>;                               \
            }                                                                         \
            MIDOUT_END();                                                             \
        }                                                                             \
    } while (0);
    MEGDNN_FOREACH_COMPUTING_DTYPE_FLOAT(cb);
#undef cb
#define cb(dt_src, dt_dst)                                                          \
    do {                                                                            \
        if (param.diff_type.enumv() == DTypeTrait<dt_src>::enumv &&                 \
            param.filter_type.enumv() == DTypeTrait<dt_src>::enumv &&               \
            param.grad_type.enumv() == DTypeTrait<dt_dst>::enumv) {                 \
            MIDOUT_BEGIN(                                                           \
                    megdnn_fallback_deconv, midout_iv(DTypeTrait<dt_src>::enumv)) { \
                return kern_naive<                                                  \
                        DTypeTrait<dt_src>::ctype, DTypeTrait<dt_src>::ctype,       \
                        DTypeTrait<dt_dst>::ctype>;                                 \
            }                                                                       \
            MIDOUT_END();                                                           \
        }                                                                           \
    } while (0)
    cb(dtype::Int8, dtype::Int32);
    cb(dtype::Quantized8Asymm, dtype::QuantizedS32);
    cb(dtype::QuantizedS8, dtype::QuantizedS32);
    megdnn_throw("unsupported data type on ConvolutionBackwardData");
#undef cb
}

/* ===================== direct algo ===================== */

bool ConvolutionBackwardDataImpl::AlgoDirect::usable(
        ConvolutionBackwardDataImpl*, const NCBKernSizeParam& param) const {
    auto&& fm = param.filter_meta;
    return fm.format == param::Convolution::Format::NCHW &&
           param.diff_type.enumv() == DTypeEnum::Float32 &&
           param.filter_type.enumv() == DTypeEnum::Float32 &&
           param.grad_type.enumv() == DTypeEnum::Float32 && fm.spatial_ndim == 2 &&
           fm.group == 1 && fm.dilation[0] == 1 && fm.dilation[1] == 1;
}

size_t ConvolutionBackwardDataImpl::AlgoDirect::get_workspace(
        ConvolutionBackwardDataImpl*, const NCBKernSizeParam& param) const {
    MIDOUT_BEGIN(megdnn_fallback_deconv, midout_iv("AlgoDirect::get_workspace"_hash)) {
        auto FH = param.filter_meta.spatial[0], FW = param.filter_meta.spatial[1];
        if (param.filter_meta.should_flip) {
            // need transpose filter
            return FH * FW * sizeof(float);
        } else {
            return 0;
        }
    }
    MIDOUT_END();
    return 0;
}

ConvolutionBackwardDataImpl::ncb_kern_t ConvolutionBackwardDataImpl::AlgoDirect::
        dispatch_kern(ConvolutionBackwardDataImpl*, const NCBKernSizeParam&) const {
    MIDOUT_BEGIN(megdnn_fallback_conv, midout_iv("AlgoDirect::dispatch_kern"_hash)) {
        return kern_direct;
    }
    MIDOUT_END();
}

/* ===================== Matrix mul algo ===================== */

bool ConvolutionBackwardDataImpl::AlgoMatrixMul::usable(
        ConvolutionBackwardDataImpl*, const NCBKernSizeParam& param) const {
    auto&& fm = param.filter_meta;
    return fm.format == param::Convolution::Format::NCHW && fm.spatial_ndim == 2 &&
           fm.group == 1 && fm.dilation[0] == 1 && fm.dilation[1] == 1;
}

size_t ConvolutionBackwardDataImpl::AlgoMatrixMul::get_workspace(
        ConvolutionBackwardDataImpl*, const NCBKernSizeParam& param) const {
    MIDOUT_BEGIN(
            megdnn_fallback_deconv, midout_iv("AlgoMatrixMul::get_workspace"_hash)) {
        return get_bundle(param).total_size_in_bytes();
    }
    MIDOUT_END();
    return 0;
}

ConvolutionBackwardDataImpl::ncb_kern_t ConvolutionBackwardDataImpl::AlgoMatrixMul::
        dispatch_kern(
                ConvolutionBackwardDataImpl*, const NCBKernSizeParam& param) const {
#define cb(dt, midout_tag)                                                \
    do {                                                                  \
        if (param.filter_type.enumv() == DTypeTrait<dt>::enumv) {         \
            MIDOUT_BEGIN(megdnn_fallback_deconv, midout_iv(midout_tag)) { \
                using ctype = DTypeTrait<dt>::ctype;                      \
                return kern_matmul<ctype, ctype, ctype>;                  \
            }                                                             \
            MIDOUT_END();                                                 \
        }                                                                 \
    } while (0);
    cb(dtype::Float32, "FLOAT"_hash);
    DNN_INC_FLOAT16(cb(dtype::Float16, "FLOAT16"_hash));
    DNN_INC_FLOAT16(cb(dtype::BFloat16, "BFLOAT16"_hash));
#undef cb

#define cb(dt_src, dt_dst, midout_tag)                                        \
    do {                                                                      \
        if (param.diff_type.enumv() == DTypeTrait<dt_src>::enumv &&           \
            param.filter_type.enumv() == DTypeTrait<dt_src>::enumv &&         \
            param.grad_type.enumv() == DTypeTrait<dt_dst>::enumv) {           \
            MIDOUT_BEGIN(megdnn_fallback_deconv, midout_iv(midout_tag)) {     \
                return kern_matmul<                                           \
                        DTypeTrait<dt_src>::ctype, DTypeTrait<dt_src>::ctype, \
                        DTypeTrait<dt_dst>::ctype>;                           \
            }                                                                 \
            MIDOUT_END();                                                     \
        }                                                                     \
    } while (0)
    cb(dtype::Int8, dtype::Int32, "INT8x8x32"_hash);
    cb(dtype::QuantizedS8, dtype::QuantizedS32, "QINT8x8x32"_hash);
    cb(dtype::Quantized8Asymm, dtype::QuantizedS32, "QUINT8x8x32"_hash);
    megdnn_throw("unsupported data type on matrix mul");
#undef cb
}

bool ConvolutionBackwardDataImpl::AlgoMatrixMul::is_preferred(
        const NCBKernSizeParam& param) const {
    return is_matrix_mul_preferred(param);
}

// vim: syntax=cpp.doxygen