conv_nchwqs8.cpp 7.2 KB
Newer Older
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
/**
 * \file dnn/src/cuda/conv_bias/conv_nchwqs8.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/common/conv_bias.h"
#include "src/cuda/conv_bias/algo.h"
#include "src/cuda/cudnn_wrapper.h"
#include "src/cuda/relayout_format/opr_impl.h"
#include "src/cuda/utils.h"

using namespace megdnn;
using namespace cuda;
using namespace conv_bias;

namespace {
inline void deduce_reformat_layout(std::unique_ptr<RelayoutFormat>& relayout,
                                   const TensorLayout& src_layout,
                                   TensorLayout& dst_layout,
                                   RelayoutFormat::Param::Mode mode,
                                   const int oc = 0, const int group = 1) {
    if (src_layout.ndim > 0) {
        RelayoutFormat::Param trans_param;
        trans_param.mode = mode;
        trans_param.oc = oc;
        trans_param.group = group;
        relayout->param() = trans_param;
        relayout->deduce_layout(src_layout, dst_layout);
    } else {
        dst_layout = src_layout;
    }
}
}  // namespace

void ConvBiasForwardImpl::AlgoFallbackNCHWQS8::make_inner_layout(
        const SizeArgs& args, TensorLayout& inner_src_layout,
        TensorLayout& inner_weight_layout, TensorLayout& inner_dst_layout,
        TensorLayout& inner_bias_layout, TensorLayout& inner_z_layout) const {
    auto relayout_src = args.handle->create_operator<RelayoutFormat>();
    deduce_reformat_layout(relayout_src, *args.src_layout, inner_src_layout,
                           RelayoutFormat::Param::Mode::NCHW_NCHW4, 0,
                           args.filter_meta.group);
    deduce_reformat_layout(relayout_src, *args.filter_layout,
                           inner_weight_layout,
                           RelayoutFormat::Param::Mode::NCHW_NCHW4_WEIGHT);
    deduce_reformat_layout(relayout_src, *args.dst_layout, inner_dst_layout,
                           RelayoutFormat::Param::Mode::NCHW_NCHW4, 0,
                           args.filter_meta.group);
    deduce_reformat_layout(relayout_src, *args.bias_layout, inner_bias_layout,
                           RelayoutFormat::Param::Mode::NCHW_NCHW4, 0,
                           args.filter_meta.group);
    deduce_reformat_layout(relayout_src, *args.z_layout, inner_z_layout,
                           RelayoutFormat::Param::Mode::NCHW_NCHW4, 0,
                           args.filter_meta.group);
};

bool ConvBiasForwardImpl::AlgoFallbackNCHWQS8::is_available(
        const SizeArgs& args) const {
    auto&& param = args.opr->param();
    bool is_format_ok = param.format == param::ConvBias::Format::NCHW;
    bool is_version_ok = CUDNN_VERSION >= 7500;
    bool is_dtype_ok =
            args.src_layout->dtype.enumv() == DTypeEnum::QuantizedS8;
    bool is_bias_ok =
            args.bias_layout->ndim == 0 ||
            (args.bias_layout->ndim == 4 && args.bias_layout->shape[0] == 1 &&
             args.bias_layout->shape[2] == 1 &&
             args.bias_layout->shape[3] == 1);
    bool is_ok = is_format_ok && is_version_ok && is_dtype_ok && is_bias_ok;
    return is_ok;
}

WorkspaceBundle ConvBiasForwardImpl::AlgoFallbackNCHWQS8::get_workspace_bundle(
        void* ptr, const SizeArgs& args) const {
    TensorLayout inner_src_layout;
    TensorLayout inner_weight_layout;
    TensorLayout inner_dst_layout;
    TensorLayout inner_bias_layout;
    TensorLayout inner_z_layout;
    make_inner_layout(args, inner_src_layout, inner_weight_layout,
                      inner_dst_layout, inner_bias_layout, inner_z_layout);
    auto opr = args.handle->create_operator<ConvBiasForward>();
    Param inner_conv_param = args.opr->param();
    inner_conv_param.format = Param::Format::NCHW4;
    opr->param() = inner_conv_param;
    return WorkspaceBundle(ptr, {inner_src_layout.span().dist_byte(),
                                 inner_weight_layout.span().dist_byte(),
                                 inner_dst_layout.span().dist_byte(),
                                 inner_bias_layout.span().dist_byte(),
                                 inner_z_layout.span().dist_byte(),
                                 opr->get_workspace_in_bytes(
                                         inner_src_layout, inner_weight_layout,
                                         inner_bias_layout, inner_z_layout,
                                         inner_dst_layout, nullptr)});
}

size_t ConvBiasForwardImpl::AlgoFallbackNCHWQS8::get_workspace_in_bytes(
        const SizeArgs& args) const {
    auto trans_bundle = get_workspace_bundle(nullptr, args);
    return trans_bundle.total_size_in_bytes();
}

void ConvBiasForwardImpl::AlgoFallbackNCHWQS8::exec(
        const ExecArgs& args) const {
    auto relayout_nchw_nchw4 = args.handle->create_operator<RelayoutFormat>();
    RelayoutFormat::Param in_trans;
    in_trans.mode = RelayoutFormat::Param::Mode::NCHW_NCHW4;
    in_trans.group = args.filter_meta.group;
    relayout_nchw_nchw4->param() = in_trans;

    auto relayout_weight = args.handle->create_operator<RelayoutFormat>();
    RelayoutFormat::Param weight_trans;
    weight_trans.mode = RelayoutFormat::Param::Mode::NCHW_NCHW4_WEIGHT;
    relayout_weight->param() = weight_trans;

    auto relayout_nchw4_nchw = args.handle->create_operator<RelayoutFormat>();
    RelayoutFormat::Param nchw4_nchw_trans;
    nchw4_nchw_trans.mode = RelayoutFormat::Param::Mode::NCHW4_NCHW;
    nchw4_nchw_trans.oc = args.dst_layout->shape[1];
    nchw4_nchw_trans.group = args.filter_meta.group;
    relayout_nchw4_nchw->param() = nchw4_nchw_trans;

    auto bundle = get_workspace_bundle(args.workspace.raw_ptr, args);
    TensorLayout inner_src_layout;
    TensorLayout inner_weight_layout;
    TensorLayout inner_dst_layout;
    TensorLayout inner_bias_layout;
    TensorLayout inner_z_layout;
    make_inner_layout(args, inner_src_layout, inner_weight_layout,
                      inner_dst_layout, inner_bias_layout, inner_z_layout);
    TensorND inner_src(bundle.get(0), inner_src_layout);
    TensorND inner_weight(bundle.get(1), inner_weight_layout);
    TensorND inner_dst(bundle.get(2), inner_dst_layout);
    TensorND inner_bias(bundle.get(3), inner_bias_layout);
    TensorND inner_z(bundle.get(4), inner_z_layout);

    Param inner_conv_param = args.opr->param();
    inner_conv_param.format = Param::Format::NCHW4;
    auto inner_opr = args.handle->create_operator<ConvBiasForward>();
    inner_opr->param() = inner_conv_param;

    relayout_nchw_nchw4->exec(*args.src_tensor, inner_src, {});
    relayout_weight->exec(*args.filter_tensor, inner_weight, {});
    if (inner_bias_layout.ndim > 0) {
        relayout_nchw_nchw4->exec(*args.bias_tensor, inner_bias, {});
    }
    if (inner_z_layout.ndim > 0) {
        relayout_nchw_nchw4->exec(*args.z_tensor, inner_z, {});
    }
    inner_opr->exec(inner_src, inner_weight, inner_bias, inner_z, inner_dst,
                    nullptr, Workspace((dt_byte*)bundle.get(5), bundle.get_size(5)));
    relayout_nchw4_nchw->exec(inner_dst, *args.dst_tensor, {});
}

// vim: syntax=cpp.doxygen