onednn-src 0.1.13

/*******************************************************************************
* Copyright 2024 Intel Corporation
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
*     http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*******************************************************************************/

#ifndef GPU_GENERIC_CONVOLUTION_DECONVOLUTION_HPP
#define GPU_GENERIC_CONVOLUTION_DECONVOLUTION_HPP

#include "common/c_types_map.hpp"
#include "common/primitive.hpp"
#include "common/primitive_desc_iterator.hpp"
#include "common/type_helpers.hpp"
#include "common/utils.hpp"
#include "gpu/gpu_deconvolution_pd.hpp"
#include "gpu/gpu_primitive.hpp"

namespace dnnl {
namespace impl {
namespace gpu {
namespace generic {

static status_t weights_axes_permutation(
        memory_desc_t *o_md, const memory_desc_t *i_md, bool with_groups) {
    using namespace memory_extra_flags;
    int perm[DNNL_MAX_NDIMS] {}; // deconv to conv weight permutation
    for (int d = 0; d < DNNL_MAX_NDIMS; ++d)
        perm[d] = d;
    nstl::swap(perm[0 + with_groups], perm[1 + with_groups]);
    CHECK(memory_desc_permute_axes(*o_md, *i_md, perm));
    if (o_md->extra.flags & compensation_gpu_conv_asymmetric_src)
        o_md->extra.flags |= compensation_gpu_conv_asymmetric_src_swap;
    return status::success;
}

static status_t conv_descr_create(
        const deconvolution_desc_t *dd, convolution_desc_t *cd) {
    using namespace prop_kind;
    alg_kind_t alg_kind = alg_kind::convolution_direct;

    const memory_desc_t *src_md, *dst_md, *d_weights_d;
    prop_kind_t prop_kind;

    switch (dd->prop_kind) {
        case forward:
        case forward_inference:
            prop_kind = backward_data;
            src_md = &dd->dst_desc;
            dst_md = &dd->src_desc;
            d_weights_d = &dd->weights_desc;
            break;
        case backward_data:
            prop_kind = forward_training;
            src_md = &dd->diff_dst_desc;
            dst_md = &dd->diff_src_desc;
            d_weights_d = &dd->weights_desc;
            break;
        case backward_weights:
            prop_kind = dd->prop_kind;
            src_md = &dd->diff_dst_desc;
            dst_md = &dd->src_desc;
            d_weights_d = &dd->diff_weights_desc;
            break;
        default: assert(!"unknown prop kind"); return status::invalid_arguments;
    }

    // Create weights desc for convolution
    memory_desc_t c_weights_d;
    const bool with_groups = d_weights_d->ndims == src_md->ndims + 1;
    CHECK(weights_axes_permutation(&c_weights_d, d_weights_d, with_groups));

    return conv_desc_init(cd, prop_kind, alg_kind, src_md, &c_weights_d,
            prop_kind != backward_weights ? &dd->bias_desc : nullptr, dst_md,
            dd->strides, dd->dilates, dd->padding[0], dd->padding[1]);
}

struct convolution_deconvolution_fwd_t : public gpu::primitive_t {
    using gpu::primitive_t::primitive_t;
    struct pd_t : public gpu_deconvolution_fwd_pd_t {
        using gpu_deconvolution_fwd_pd_t::gpu_deconvolution_fwd_pd_t;

        DECLARE_COMMON_PD_T(name_.c_str(), convolution_deconvolution_fwd_t);
        status_t init_convolution(impl::engine_t *engine) {
            convolution_desc_t cd;
            CHECK(conv_descr_create(desc(), &cd));
            primitive_attr_t conv_attr(*attr());
            if (!conv_attr.is_initialized()) return status::out_of_memory;
            primitive_desc_iterator_t it(
                    engine, (op_desc_t *)&cd, &conv_attr, nullptr);
            if (!it.is_initialized()) return status::out_of_memory;
            conv_pd_ = *(++it);

            return (conv_pd_) ? status::success : status::unimplemented;
        }

        status_t init(impl::engine_t *engine) {
            using namespace format_tag;
            using sm = primitive_attr_t::skip_mask_t;

            const auto attr_skip_mask
                    = sm::post_ops | sm::zero_points | sm::scales;

            VDISPATCH_DECONVOLUTION(is_fwd(), VERBOSE_BAD_PROPKIND);
            VDISPATCH_DECONVOLUTION(
                    desc()->alg_kind == alg_kind::deconvolution_direct,
                    VERBOSE_BAD_ALGORITHM);
            VDISPATCH_DECONVOLUTION(attr()->has_default_values(attr_skip_mask),
                    VERBOSE_UNSUPPORTED_ATTR);
            VDISPATCH_DECONVOLUTION(
                    (utils::everyone_is(data_type::f32,
                             desc()->src_desc.data_type,
                             desc()->weights_desc.data_type,
                             desc()->dst_desc.data_type)
                            || (utils::everyone_is(data_type::f64,
                                    desc()->src_desc.data_type,
                                    desc()->weights_desc.data_type,
                                    desc()->dst_desc.data_type))
                            || ((utils::everyone_is(data_type::f16,
                                         desc()->src_desc.data_type,
                                         desc()->weights_desc.data_type)
                                        || utils::everyone_is(data_type::f32,
                                                desc()->src_desc.data_type,
                                                desc()->weights_desc.data_type)
                                        || utils::everyone_is(data_type::bf16,
                                                desc()->src_desc.data_type,
                                                desc()->weights_desc.data_type))
                                    && utils::one_of(desc()->dst_desc.data_type,
                                            data_type::f16, data_type::u8,
                                            data_type::s8))
                            || (utils::everyone_is(data_type::bf16,
                                        desc()->src_desc.data_type,
                                        desc()->weights_desc.data_type)
                                    && utils::one_of(desc()->dst_desc.data_type,
                                            data_type::f32, data_type::bf16))
                            || (utils::everyone_is(data_type::f16,
                                        desc()->src_desc.data_type,
                                        desc()->weights_desc.data_type)
                                    && utils::one_of(desc()->dst_desc.data_type,
                                            data_type::f32, data_type::f16))
                            || (desc()->weights_desc.data_type == data_type::s8
                                    && utils::one_of(desc()->src_desc.data_type,
                                            data_type::u8, data_type::s8)
                                    && desc()->dst_desc.data_type
                                            != data_type::f64)),
                    VERBOSE_UNSUPPORTED_DT);

            VDISPATCH_DECONVOLUTION_SC(
                    init_convolution(engine), "init_convolution()");
            if (weights_md_.format_kind == format_kind::any) {
                VDISPATCH_DECONVOLUTION_SC(
                        weights_axes_permutation(&weights_md_,
                                conv_pd_->weights_md(), with_groups()),
                        "weights_axes_permutation()");
            }
            if (src_md_.format_kind == format_kind::any)
                src_md_ = *conv_pd_->diff_dst_md();
            if (dst_md_.format_kind == format_kind::any)
                dst_md_ = *conv_pd_->diff_src_md();
            if (bias_md_.format_kind == format_kind::any) {
                VDISPATCH_DECONVOLUTION_SC(memory_desc_init_by_tag(bias_md_, x),
                        VERBOSE_UNSUPPORTED_TAG);
            }
            init_name();
            init_scratchpad();
            VDISPATCH_DECONVOLUTION_SC(attr_.set_default_formats(dst_md(0)),
                    VERBOSE_UNSUPPORTED_ATTR);

            return status::success;
        }

        std::shared_ptr<primitive_desc_t> conv_pd_;

    private:
        std::string name_ = "conv:any";

        void init_name() {
            name_.append("+");
            name_.append(conv_pd_->name());
        }

        void init_scratchpad() {
            auto scratchpad = scratchpad_registry().registrar();
            scratchpad.book(memory_tracking::names::key_nested,
                    conv_pd_->scratchpad_registry());
        }
    };

    status_t init(impl::engine_t *engine) override {
        return create_nested_primitive(conv_p_, pd()->conv_pd_, engine);
    }

    status_t execute(const exec_ctx_t &ctx) const override {
        using namespace memory_tracking::names;
        const auto &args = ctx.args();
        exec_args_t conv_args;
        conv_args[DNNL_ARG_DIFF_DST] = args.at(DNNL_ARG_SRC);
        conv_args[DNNL_ARG_WEIGHTS] = args.at(DNNL_ARG_WEIGHTS);
        conv_args[DNNL_ARG_DIFF_SRC] = args.at(DNNL_ARG_DST);
        if (pd()->with_bias())
            conv_args[DNNL_ARG_BIAS] = args.at(DNNL_ARG_BIAS);

        for (int idx = 0; idx < pd()->attr()->post_ops_.len(); ++idx) {
            if (pd()->attr()->post_ops_.entry_[idx].is_binary()) {
                conv_args[DNNL_ARG_ATTR_MULTIPLE_POST_OP(idx) | DNNL_ARG_SRC_1]
                        = args.at(DNNL_ARG_ATTR_MULTIPLE_POST_OP(idx)
                                | DNNL_ARG_SRC_1);
            } else if (pd()->attr()->post_ops_.entry_[idx].is_prelu()) {
                conv_args[DNNL_ARG_ATTR_MULTIPLE_POST_OP(idx)
                        | DNNL_ARG_WEIGHTS]
                        = args.at(DNNL_ARG_ATTR_MULTIPLE_POST_OP(idx)
                                | DNNL_ARG_WEIGHTS);
            }
        }
        const auto z_src = DNNL_ARG_ATTR_ZERO_POINTS | DNNL_ARG_SRC;
        const auto z_dst = DNNL_ARG_ATTR_ZERO_POINTS | DNNL_ARG_DST;
        if (args.find(z_src) != args.end()) conv_args[z_src] = args.at(z_src);
        if (args.find(z_dst) != args.end()) conv_args[z_dst] = args.at(z_dst);

        for (int arg : {DNNL_ARG_SRC, DNNL_ARG_WEIGHTS, DNNL_ARG_DST}) {
            int key = DNNL_ARG_ATTR_SCALES | arg;
            if (args.find(key) != args.end()) conv_args[key] = args.at(key);
        }

        exec_ctx_t conv_ctx(ctx, std::move(conv_args));

        auto *nested_grantor
                = create_nested_grantor(ctx.get_scratchpad_grantor(),
                        key_nested, conv_p_->pd()->scratchpad_registry());
        conv_ctx.set_scratchpad_grantor(nested_grantor);
        // Executing the convolution kernel
        return conv_p_->execute(conv_ctx);
    }

private:
    const pd_t *pd() const { return (const pd_t *)primitive_t::pd().get(); }
    std::shared_ptr<impl::primitive_t> conv_p_;
};

struct convolution_deconvolution_bwd_data_t : public gpu::primitive_t {
    using gpu::primitive_t::primitive_t;
    struct pd_t : public gpu_deconvolution_bwd_data_pd_t {
        using gpu_deconvolution_bwd_data_pd_t::gpu_deconvolution_bwd_data_pd_t;

        DECLARE_COMMON_PD_T(
                name_.c_str(), convolution_deconvolution_bwd_data_t);

        status_t init_convolution(impl::engine_t *engine) {
            convolution_desc_t cd;
            CHECK(conv_descr_create(desc(), &cd));
            primitive_attr_t conv_attr(*attr());
            if (!conv_attr.is_initialized()) return status::out_of_memory;
            primitive_desc_iterator_t it(
                    engine, (op_desc_t *)&cd, &conv_attr, nullptr);
            if (!it.is_initialized()) return status::out_of_memory;
            conv_pd_ = *(++it);
            return (conv_pd_) ? status::success : status::unimplemented;
        }

        status_t init(impl::engine_t *engine) {
            VDISPATCH_DECONVOLUTION(
                    desc()->prop_kind == prop_kind::backward_data,
                    VERBOSE_BAD_PROPKIND);

            VDISPATCH_DECONVOLUTION(
                    (utils::everyone_is(data_type::f32,
                             desc()->diff_src_desc.data_type,
                             desc()->weights_desc.data_type,
                             desc()->diff_dst_desc.data_type)
                            || (utils::everyone_is(data_type::f64,
                                    desc()->diff_src_desc.data_type,
                                    desc()->weights_desc.data_type,
                                    desc()->diff_dst_desc.data_type))
                            || utils::everyone_is(data_type::f16,
                                    desc()->weights_desc.data_type,
                                    desc()->diff_dst_desc.data_type)
                            || utils::everyone_is(data_type::bf16,
                                    desc()->weights_desc.data_type,
                                    desc()->diff_dst_desc.data_type)),
                    VERBOSE_UNSUPPORTED_DT);

            VDISPATCH_DECONVOLUTION(
                    utils::one_of(desc()->diff_src_desc.data_type,
                            data_type::bf16, data_type::f16, data_type::f32,
                            data_type::f64),
                    VERBOSE_UNSUPPORTED_DT);
            VDISPATCH_DECONVOLUTION(
                    desc()->alg_kind == alg_kind::deconvolution_direct,
                    VERBOSE_BAD_ALGORITHM);
            VDISPATCH_DECONVOLUTION(
                    attr()->has_default_values(), VERBOSE_UNSUPPORTED_ATTR);

            VDISPATCH_DECONVOLUTION_SC(
                    init_convolution(engine), "init_convolution()");
            if (weights_md_.format_kind == format_kind::any)
                VDISPATCH_DECONVOLUTION_SC(
                        weights_axes_permutation(&weights_md_,
                                conv_pd_->weights_md(), with_groups()),
                        "weights_axes_permutation()");
            if (diff_src_md_.format_kind == format_kind::any)
                diff_src_md_ = *conv_pd_->dst_md();
            if (diff_dst_md_.format_kind == format_kind::any)
                diff_dst_md_ = *conv_pd_->src_md();

            init_name();
            init_scratchpad();

            return status::success;
        }

        std::shared_ptr<primitive_desc_t> conv_pd_;

    private:
        std::string name_ = "conv:any";

        void init_name() {
            name_.append("+");
            name_.append(conv_pd_->name());
        }

        void init_scratchpad() {
            auto scratchpad = scratchpad_registry().registrar();
            scratchpad.book(memory_tracking::names::key_nested,
                    conv_pd_->scratchpad_registry());
        }
    };

    status_t init(impl::engine_t *engine) override {
        return create_nested_primitive(conv_p_, pd()->conv_pd_, engine);
    }

    status_t execute(const exec_ctx_t &ctx) const override {
        using namespace memory_tracking::names;
        const auto &args = ctx.args();
        exec_args_t conv_args;
        conv_args[DNNL_ARG_SRC] = args.at(DNNL_ARG_DIFF_DST);
        conv_args[DNNL_ARG_WEIGHTS] = args.at(DNNL_ARG_WEIGHTS);
        conv_args[DNNL_ARG_DST] = args.at(DNNL_ARG_DIFF_SRC);
        if (!types::is_zero_md(pd()->scratchpad_md()))
            conv_args[DNNL_ARG_SCRATCHPAD] = args.at(DNNL_ARG_SCRATCHPAD);
        exec_ctx_t conv_ctx(ctx, std::move(conv_args));

        auto *nested_grantor
                = create_nested_grantor(ctx.get_scratchpad_grantor(),
                        key_nested, conv_p_->pd()->scratchpad_registry());
        conv_ctx.set_scratchpad_grantor(nested_grantor);
        // Executing the convolution kernel
        return conv_p_->execute(conv_ctx);
    }

private:
    const pd_t *pd() const { return (const pd_t *)primitive_t::pd().get(); }
    std::shared_ptr<impl::primitive_t> conv_p_;
};

} // namespace generic
} // namespace gpu
} // namespace impl
} // namespace dnnl

#endif