megenginelite-sys 1.8.2

/**
 * \file src/gopt/impl/padding_channel.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 "megbrain/gopt/inference.h"
#include "megbrain/opr/basic_arith.h"
#include "megbrain/opr/dnn/convolution.h"
#include "megbrain/opr/dnn/pooling.h"
#include "megbrain/opr/imgproc.h"
#include "megbrain/opr/misc.h"
#include "megbrain/opr/nn_int.h"
#include "megbrain/opr/tensor_manip.h"
#include "megbrain/opr/utility.h"
#include "megbrain/serialization/opr_shallow_copy.h"

#include "megdnn/opr_param_defs.h"
#include "megdnn/tensor_format.h"

#include "megbrain/opr/internal/megdnn_opr_wrapper.h"

#include "megbrain/gopt/misc.h"
#include "megbrain/utils/hash_ct.h"

#include "midout.h"

#include "megbrain/gopt/reformat_manager.h"

MIDOUT_DECL(megbrain_padding_channel)
#define MIDOUT_B(tag) \
    MIDOUT_BEGIN(megbrain_padding_channel, midout_iv(MGB_HASH_STR(tag))) {
#define MIDOUT_E \
    }            \
    MIDOUT_END();

using namespace mgb;
using namespace gopt;
using ReformatKey = ReformatManager::ReformatKey;

/* ==================== PaddingChannelPass ================= */
const char* PaddingChannelPass::name() const {
    return mgb_cstr_log("padding output channel to multiple of 4/32");
}

void PaddingChannelPass::apply(OptState& opt) const {
    MIDOUT_B("PaddingChannelPass::apply");
    // do not check shape
    opt.set_var_replace_check_flag(
            VarReplaceCheckFlag::CHECK_ALL ^ VarReplaceCheckFlag::CHECK_SHAPE);

    ThinHashSet<OperatorNodeBase*> padding_oprs;
    ThinHashMap<
            Typeinfo*,
            thin_function<OperatorNodeBase*(OperatorNodeBase*, const VarNodeArray&)>>
            opr_replace_funcs;

    auto rewriter = opt.graph().make_rewriter();
    auto pad_in_channels = [](VarNode* inp, size_t pad_channels) -> VarNode* {
        mgb_assert(inp->shape().ndim == 4);
        mgb_assert(
                inp->dtype().enumv() == DTypeEnum::QuantizedS4 ||
                inp->dtype().enumv() == DTypeEnum::Quantized4Asymm ||
                inp->dtype().enumv() == DTypeEnum::QuantizedS8 ||
                inp->dtype().enumv() == DTypeEnum::QuantizedS32);
        TensorShape shape{
                inp->shape()[0], pad_channels, inp->shape()[2], inp->shape()[3]};
        std::shared_ptr<HostTensorND> host_val =
                std::make_shared<HostTensorND>(inp->comp_node(), inp->dtype());
        host_val->resize(shape);
        auto ptr = host_val->raw_ptr();
        size_t size_bytes = TensorLayout{shape, inp->dtype()}.span().dist_byte();
        std::memset(ptr, 0, size_bytes);
        auto padding = opr::ImmutableTensor::make(*inp->owner_graph(), *host_val);
        auto out = opr::Concat::make({inp, padding}, 1);
        return out.node();
    };

    auto pad_out_channels = [](VarNode* inp, size_t pad_channels) -> VarNode* {
        mgb_assert(inp->shape().ndim == 4);
        mgb_assert(
                inp->dtype().enumv() == DTypeEnum::QuantizedS4 ||
                inp->dtype().enumv() == DTypeEnum::Quantized4Asymm ||
                inp->dtype().enumv() == DTypeEnum::QuantizedS8 ||
                inp->dtype().enumv() == DTypeEnum::QuantizedS32);
        TensorShape shape{
                pad_channels, inp->shape()[1], inp->shape()[2], inp->shape()[3]};
        std::shared_ptr<HostTensorND> host_val =
                std::make_shared<HostTensorND>(inp->comp_node(), inp->dtype());
        host_val->resize(shape);
        auto ptr = host_val->raw_ptr();
        size_t size_bytes = TensorLayout{shape, inp->dtype()}.span().dist_byte();
        std::memset(ptr, 0, size_bytes);
        auto padding = opr::ImmutableTensor::make(*inp->owner_graph(), *host_val);
        auto out = opr::Concat::make({inp, padding}, 0);
        return out.node();
    };

    auto extract_subtensor = [](VarNode* inp,
                                const TensorShape& orig_shape) -> VarNode* {
        mgb_assert(inp->shape().ndim == 4);
        mgb_assert(inp->shape()[0] == orig_shape[0]);
        mgb_assert(inp->shape()[2] == orig_shape[2]);
        mgb_assert(inp->shape()[3] == orig_shape[3]);
        size_t orig_channels = orig_shape[1];
        auto x = SymbolVar(inp);
        auto cv = [&x](int v) { return x.make_scalar(v); };
        using AIdx = opr::Subtensor::AxisIndexer;
        auto sub = opr::Subtensor::make(
                x, {AIdx::make_interval(0, None, None, cv(1)),
                    AIdx::make_interval(1, None, cv(orig_channels), None),
                    AIdx::make_interval(2, None, None, cv(1)),
                    AIdx::make_interval(3, None, None, cv(1))});
        return sub.node();
    };

    // padding policy for conv bias with data type qint8
    auto padding_policy_qint8 = [&padding_oprs, &pad_in_channels, &pad_out_channels](
                                        OperatorNodeBase* opr,
                                        const VarNodeArray& new_inp) {
        mgb_assert(opr->input().size() == new_inp.size());
        mgb_assert(new_inp.size() == 3);
        mgb_assert(opr->input(1)->shape().eq_shape(new_inp[1]->shape()));
        auto inps = new_inp;
        size_t out_channels = opr->input(1)->shape()[0];
        size_t in_channels = opr->input(1)->shape()[1];
        size_t new_in_channels = new_inp[0]->shape()[1];
        // pad input channels
        if (padding_oprs.count(opr->input(0)->owner_opr())) {
            size_t pad_channels = new_in_channels - in_channels;
            inps[1] = pad_in_channels(new_inp[1], pad_channels);
        } else {
            size_t pad_channels = 0;
            mgb_assert(new_in_channels == in_channels);
            if (in_channels <= 16) {
                if (in_channels % 4)
                    pad_channels = 4 - (in_channels % 4);  // pad to use dp4a
            } else {
                if (in_channels % 32)
                    pad_channels = 32 - (in_channels % 32);  // pad to use tensorcore
            }
            if (pad_channels > 0) {
                inps[0] = pad_in_channels(new_inp[0], pad_channels);
                inps[1] = pad_in_channels(new_inp[1], pad_channels);
            }
        }
        out_channels = inps[1]->shape()[0];
        in_channels = inps[1]->shape()[1];
        size_t pad_channels = 0;
        if (out_channels <= 16) {
            if (out_channels % 4)
                pad_channels = 4 - (out_channels % 4);
        } else {
            if (out_channels % 32)
                pad_channels = 32 - (out_channels % 32);
        }
        if (pad_channels > 0) {
            inps[1] = pad_out_channels(inps[1], pad_channels);
            inps[2] = pad_in_channels(inps[2], pad_channels);
            padding_oprs.insert(opr);
        }
        return serialization::copy_opr_shallow(*opr, inps, opr->config());
    };

    // padding policy for conv bias with data type qint4 and quint4
    auto padding_policy_int4 = [&padding_oprs, &pad_in_channels, &pad_out_channels](
                                       OperatorNodeBase* opr,
                                       const VarNodeArray& new_inp) {
        mgb_assert(opr->input().size() == new_inp.size());
        mgb_assert(new_inp.size() == 3);
        mgb_assert(opr->input(1)->shape().eq_shape(new_inp[1]->shape()));
        auto inps = new_inp;
        size_t out_channels = opr->input(1)->shape()[0];
        size_t in_channels = opr->input(1)->shape()[1];
        size_t new_in_channels = new_inp[0]->shape()[1];
        // pad input channels
        if (padding_oprs.count(opr->input(0)->owner_opr())) {
            if (new_in_channels <= 32) {
                if (new_in_channels % 8 == 0) {
                    size_t pad_channels = new_in_channels - in_channels;
                    inps[1] = pad_in_channels(new_inp[1], pad_channels);
                } else {
                    size_t pad_channels_0 = 8 - (new_in_channels % 8);
                    size_t pad_channels_1 = 8 - (in_channels % 8);
                    inps[0] = pad_in_channels(new_inp[0], pad_channels_0);
                    inps[1] = pad_in_channels(new_inp[1], pad_channels_1);
                }
            } else {
                if (new_in_channels % 64 == 0) {
                    size_t pad_channels = new_in_channels - in_channels;
                    inps[1] = pad_in_channels(new_inp[1], pad_channels);
                } else {
                    size_t pad_channels_0 = 64 - (new_in_channels % 64);
                    size_t pad_channels_1 = 64 - (in_channels % 64);
                    inps[0] = pad_in_channels(new_inp[0], pad_channels_0);
                    inps[1] = pad_in_channels(new_inp[1], pad_channels_1);
                }
            }
        } else {
            size_t pad_channels = 0;
            mgb_assert(new_in_channels == in_channels);
            if (in_channels <= 32) {
                if (in_channels % 8)
                    pad_channels = 8 - (in_channels % 8);
            } else {
                if (in_channels % 64)
                    pad_channels = 64 - (in_channels % 64);
            }
            if (pad_channels > 0) {
                inps[0] = pad_in_channels(new_inp[0], pad_channels);
                inps[1] = pad_in_channels(new_inp[1], pad_channels);
            }
        }
        out_channels = inps[1]->shape()[0];
        in_channels = inps[1]->shape()[1];
        size_t pad_channels = 0;
        if (out_channels <= 32) {
            if (out_channels % 8)
                pad_channels = 8 - (out_channels % 8);
        } else {
            if (out_channels % 64)
                pad_channels = 64 - (out_channels % 64);
        }
        if (pad_channels > 0) {
            inps[1] = pad_out_channels(inps[1], pad_channels);
            inps[2] = pad_in_channels(inps[2], pad_channels);
            padding_oprs.insert(opr);
        }
        return serialization::copy_opr_shallow(*opr, inps, opr->config());
    };

    opr_replace_funcs[opr::ConvBiasForward::typeinfo()] =
            [&padding_oprs, &padding_policy_qint8, &padding_policy_int4](
                    OperatorNodeBase* opr, const VarNodeArray& new_inp) {
                if (opr->input(0)->dtype().enumv() == DTypeEnum::QuantizedS8) {
                    return padding_policy_qint8(opr, new_inp);
                } else if (
                        opr->input(0)->dtype().enumv() == DTypeEnum::QuantizedS4 ||
                        opr->input(0)->dtype().enumv() == DTypeEnum::Quantized4Asymm) {
                    return padding_policy_int4(opr, new_inp);
                } else {
                    mgb_assert(
                            padding_oprs.count(opr->input(0)->owner_opr()) == 0,
                            "conv bias operator for data type(%s) cannot be "
                            "padded channel. "
                            "consumer(%s), producer(%s)",
                            opr->input(0)->dtype().name(), opr->cname(),
                            opr->input(0)->owner_opr()->cname());
                    return serialization::copy_opr_shallow(
                            *opr, new_inp, opr->config());
                }
            };
    opr_replace_funcs[opr::ConvolutionBackwardData::typeinfo()] =
            [&padding_oprs, &pad_in_channels, &pad_out_channels](
                    OperatorNodeBase* opr, const VarNodeArray& new_inp) {
                if (opr->input(1)->dtype().enumv() != DTypeEnum::QuantizedS8) {
                    mgb_assert(
                            padding_oprs.count(opr->input(0)->owner_opr()) == 0,
                            "conv bwd data operator for data type(%s) cannot "
                            "be "
                            "padded channel. "
                            "consumer(%s), producer(%s)",
                            opr->input(0)->dtype().name(), opr->cname(),
                            opr->input(0)->owner_opr()->cname());
                    return serialization::copy_opr_shallow(
                            *opr, new_inp, opr->config());
                }
                mgb_assert(opr->input().size() == new_inp.size());
                mgb_assert(
                        new_inp.size() == 2,
                        "deconv (conv bwd data) operator for inference can "
                        "only have 2 input vars(got:%zu)",
                        new_inp.size());
                mgb_assert(opr->input(0)->shape().eq_shape(new_inp[0]->shape()));
                auto inps = new_inp;
                size_t out_channels = opr->input(0)->shape()[0];
                size_t in_channels = opr->input(0)->shape()[1];
                size_t new_out_channels = new_inp[1]->shape()[1];
                // pad output channels
                if (padding_oprs.count(opr->input(1)->owner_opr())) {
                    size_t pad_channels = new_out_channels - out_channels;
                    inps[0] = pad_out_channels(new_inp[0], pad_channels);
                } else {
                    size_t pad_channels = 0;
                    if (out_channels % 4)
                        pad_channels = 4 - (out_channels % 4);
                    if (pad_channels > 0) {
                        inps[0] = pad_out_channels(new_inp[0], pad_channels);
                        inps[1] = pad_in_channels(new_inp[1], pad_channels);
                    }
                }
                out_channels = inps[0]->shape()[0];
                in_channels = inps[0]->shape()[1];
                // pad input channels
                size_t pad_channels = 0;
                if (in_channels % 4)
                    pad_channels = 4 - (in_channels % 4);
                if (pad_channels > 0) {
                    inps[0] = pad_in_channels(inps[0], pad_channels);
                    padding_oprs.insert(opr);
                }
                return serialization::copy_opr_shallow(*opr, inps, opr->config());
            };
    auto replace_format_aware_opr = [&padding_oprs](
                                            OperatorNodeBase* opr,
                                            const VarNodeArray& new_inp) {
        if (opr->input(0)->dtype().enumv() != DTypeEnum::QuantizedS8 &&
            opr->input(0)->dtype().enumv() != DTypeEnum::QuantizedS4 &&
            opr->input(0)->dtype().enumv() != DTypeEnum::Quantized4Asymm) {
            mgb_assert(
                    padding_oprs.count(opr->input(0)->owner_opr()) == 0,
                    "operator(type:%s,name:%s) for data type(%s) cannot be "
                    "padded channel. extra info:"
                    "consumer(%s), producer(%s)",
                    opr->dyn_typeinfo()->name, opr->cname(),
                    opr->input(0)->dtype().name(), opr->cname(),
                    opr->input(0)->owner_opr()->cname());
            return serialization::copy_opr_shallow(*opr, new_inp, opr->config());
        }
        mgb_assert(opr->input().size() == new_inp.size());
        if (padding_oprs.count(opr->input(0)->owner_opr())) {
            padding_oprs.insert(opr);
        }
        return serialization::copy_opr_shallow(*opr, new_inp, opr->config());
    };
    opr_replace_funcs[opr::PoolingForward::typeinfo()] = replace_format_aware_opr;
    opr_replace_funcs[opr::WarpPerspectiveForward::typeinfo()] =
            replace_format_aware_opr;

    auto replace_elemwise_like_opr = [&padding_oprs, &extract_subtensor](
                                             OperatorNodeBase* opr,
                                             const VarNodeArray& new_inp) {
        mgb_assert(opr->input().size() == new_inp.size());
        bool have_padding_inp = false;
        bool padding_all_inps = true;
        bool same_padding = true;
        size_t channels_after_padding = 0;
        size_t i = 0;
        for (auto&& cur_inp : opr->input()) {
            bool padding_cur_inp = padding_oprs.count(cur_inp->owner_opr()) > 0;
            if (padding_cur_inp) {
                if (!have_padding_inp)
                    have_padding_inp = true;
                if (channels_after_padding == 0) {
                    channels_after_padding = new_inp[i]->shape()[1];
                } else {
                    same_padding = channels_after_padding == new_inp[i]->shape()[1];
                }
            }
            if (padding_all_inps && (!padding_cur_inp || !same_padding))
                padding_all_inps = false;
            ++i;
        }
        if (have_padding_inp && !padding_all_inps) {
            auto inps = new_inp;
            for (size_t i = 0; i < new_inp.size(); ++i) {
                auto cur_inp = opr->input(i);
                bool padding_cur_inp = padding_oprs.count(cur_inp->owner_opr()) > 0;
                if (padding_cur_inp) {
                    inps[i] = extract_subtensor(inps[i], cur_inp->shape());
                }
            }
            return serialization::copy_opr_shallow(*opr, inps, opr->config());
        }
        if (padding_all_inps) {
            padding_oprs.insert(opr);
        }
        return serialization::copy_opr_shallow(*opr, new_inp, opr->config());
    };
    opr_replace_funcs[opr::ElemwiseMultiType::typeinfo()] = replace_elemwise_like_opr;
    opr_replace_funcs[opr::Elemwise::typeinfo()] = replace_elemwise_like_opr;
    opr_replace_funcs[opr::TypeCvt::typeinfo()] = replace_elemwise_like_opr;

    auto replace_nonpadding_oprs = [&padding_oprs, &extract_subtensor](
                                           OperatorNodeBase* opr,
                                           const VarNodeArray& new_inp) {
        mgb_assert(opr->input().size() == new_inp.size());
        auto inps = new_inp;
        for (size_t i = 0; i < new_inp.size(); ++i) {
            auto cur_inp = opr->input(i);
            bool padding_cur_inp = padding_oprs.count(cur_inp->owner_opr()) > 0;
            if (padding_cur_inp) {
                inps[i] = extract_subtensor(inps[i], cur_inp->shape());
            }
        }
        return serialization::copy_opr_shallow(*opr, inps, opr->config());
    };
    opr_replace_funcs[opr::Reshape::typeinfo()] = replace_nonpadding_oprs;
    opr_replace_funcs[opr::GetVarShape::typeinfo()] = replace_nonpadding_oprs;
    opr_replace_funcs[opr::Concat::typeinfo()] = replace_nonpadding_oprs;
    opr_replace_funcs[opr::Reduce::typeinfo()] = replace_nonpadding_oprs;
    opr_replace_funcs[opr::Subtensor::typeinfo()] = replace_nonpadding_oprs;

    auto on_opr = [&opt, &rewriter, &opr_replace_funcs,
                   &extract_subtensor](OperatorNodeBase* opr) {
        auto it = opr_replace_funcs.find(opr->dyn_typeinfo());
        if (it != opr_replace_funcs.end()) {
            VarNodeArray new_inp;
            new_inp.reserve(opr->input().size());
            for (auto&& inp : opr->input()) {
                new_inp.push_back(rewriter.get_var(inp));
            }
            auto new_opr = (it->second)(opr, new_inp);
            auto &&out0 = opr->output(), &&out1 = new_opr->output();
            mgb_assert(
                    out0.size() == out1.size(),
                    "bad opr replace: src=%s{%s} dst=%s{%s}, "
                    "src.size=%zu "
                    "dst.size=%zu",
                    opr->cname(), opr->dyn_typeinfo()->name, new_opr->cname(),
                    new_opr->dyn_typeinfo()->name, out0.size(), out1.size());
            for (size_t i = 0; i < out0.size(); ++i) {
                if (!out0[i]->contain_flag(VarNode::Flag::VOLATILE_CONTENT)) {
                    mgb_assert(!out1[i]->contain_flag(VarNode::Flag::VOLATILE_CONTENT));
                    auto src = out0[i];
                    auto dst = out1[i];
                    if (opt.graph().endpoint_contain(src) &&
                        !src->shape().eq_shape(dst->shape())) {
                        dst = extract_subtensor(dst, src->shape());
                    }
                    rewriter.replace_var(src, dst, nullptr);
                }
            }
        } else {
            rewriter.auto_replace_outputs(opr);
        }
    };
    opt.graph().iter(on_opr);
    rewriter.apply_inplace();

    MIDOUT_E
}

// vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}}