/*******************************************************************************
* Copyright 2016 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 "oneapi/dnnl/dnnl.h"
#include "opdesc.hpp"
#include "primitive_desc_iface.hpp"

#include "c_types_map.hpp"
#include "type_helpers.hpp"
#include "utils.hpp"

using namespace dnnl::impl;
using namespace dnnl::impl::utils;
using namespace dnnl::impl::status;
using namespace dnnl::impl::prop_kind;
using namespace dnnl::impl::alg_kind;
using namespace dnnl::impl::types;

#define VCHECK_CONV(cond, msg, ...) \
    VCONDCHECK(primitive, create, check, convolution, (cond), \
            status::invalid_arguments, msg, ##__VA_ARGS__)

#define VCHECK_CONV_UNIMPL(cond, msg, ...) \
    VCONDCHECK(primitive, create, check, convolution, (cond), \
            status::unimplemented, msg, ##__VA_ARGS__)

namespace dnnl {
namespace impl {
status_t conv_desc_init(convolution_desc_t *conv_desc, prop_kind_t prop_kind,
        alg_kind_t alg_kind, const memory_desc_t *src_desc,
        const memory_desc_t *weights_desc, const memory_desc_t *bias_desc,
        const memory_desc_t *dst_desc, const dims_t strides,
        const dims_t dilates, const dims_t padding_l, const dims_t padding_r) {
    VCHECK_CONV(!any_null(conv_desc, src_desc, weights_desc, dst_desc, strides,
                        padding_l),
            VERBOSE_NULL_ARG);
    VCHECK_CONV(one_of(alg_kind, convolution_auto, convolution_direct,
                        convolution_winograd),
            VERBOSE_BAD_ALGORITHM);
    VCHECK_CONV(!any_memory_desc_host_scalar(
                        src_desc, weights_desc, bias_desc, dst_desc),
            VERBOSE_UNSUPPORTED_FORMAT_KIND);

    if (padding_r == nullptr) padding_r = padding_l;

    auto cd = convolution_desc_t();
    cd.primitive_kind = primitive_kind::convolution;
    cd.prop_kind = prop_kind;
    cd.alg_kind = alg_kind;
    cd.use_inversion = false; // Not to be specified by user, only for internal.

    cd.diff_src_desc = cd.src_desc = zero_md();
    cd.diff_dst_desc = cd.dst_desc = zero_md();
    cd.diff_weights_desc = cd.weights_desc = zero_md();
    cd.diff_bias_desc = cd.bias_desc = zero_md();

    const bool is_fwd = one_of(prop_kind, forward_training, forward_inference);
    const bool with_bias
            = bias_desc && bias_desc->format_kind != format_kind::undef;
    const bool with_groups = weights_desc->ndims == src_desc->ndims + 1;

    bool runtime_dims_or_strides
            = memory_desc_wrapper(src_desc).has_runtime_dims_or_strides()
            || memory_desc_wrapper(weights_desc).has_runtime_dims_or_strides()
            || memory_desc_wrapper(dst_desc).has_runtime_dims_or_strides();
    if (with_bias)
        runtime_dims_or_strides = runtime_dims_or_strides
                || memory_desc_wrapper(bias_desc).has_runtime_dims_or_strides();
    VCHECK_CONV_UNIMPL(
            !runtime_dims_or_strides, VERBOSE_RUNTIMEDIM_UNSUPPORTED);

    (prop_kind == backward_data ? cd.diff_src_desc : cd.src_desc) = *src_desc;
    (is_fwd ? cd.dst_desc : cd.diff_dst_desc) = *dst_desc;
    (prop_kind == backward_weights ? cd.diff_weights_desc : cd.weights_desc)
            = *weights_desc;
    if (with_bias)
        (prop_kind == backward_weights ? cd.diff_bias_desc : cd.bias_desc)
                = *bias_desc;

    cd.accum_data_type = types::default_accum_data_type(src_desc->data_type,
            weights_desc->data_type, dst_desc->data_type, prop_kind);
    VCHECK_CONV(cd.accum_data_type != data_type::undef,
            VERBOSE_INVALID_DATATYPE, "accumulation");

    VCHECK_CONV(memory_desc_wrapper(weights_desc).nelems(),
            VERBOSE_EMPTY_TENSOR, "weights");
    VCHECK_CONV(src_desc->ndims == dst_desc->ndims,
            VERBOSE_INCONSISTENT_NDIMS_WITH_VALS, "src", "dst", src_desc->ndims,
            dst_desc->ndims);
    VCHECK_CONV(utils::one_of(src_desc->ndims, 3, 4, 5), VERBOSE_BAD_NDIMS,
            "src", src_desc->ndims);
    VCHECK_CONV(utils::one_of(weights_desc->ndims, src_desc->ndims,
                        src_desc->ndims + 1),
            VERBOSE_INCONSISTENT_NDIMS_WITH_VALS, "src", "weights",
            weights_desc->ndims, src_desc->ndims);

    const dim_t g = with_groups ? weights_desc->dims[0] : 1;
    const dim_t bias_dim = prop_kind == backward_data ? src_desc->dims[1]
                                                      : dst_desc->dims[1];
    VCHECK_CONV(
            IMPLICATION(with_bias,
                    bias_desc->ndims == 1 && bias_desc->dims[0] == bias_dim),
            VERBOSE_BAD_DIM, "bias", 0);
    VCHECK_CONV(src_desc->dims[0] == dst_desc->dims[0],
            VERBOSE_INCONSISTENT_DIM, "src", 0, "dst", 0);
    VCHECK_CONV(src_desc->dims[1] == g * weights_desc->dims[with_groups + 1],
            VERBOSE_INCONSISTENT_DIM, "src", 1, "weights", with_groups + 1);
    VCHECK_CONV(dst_desc->dims[1] == g * weights_desc->dims[with_groups + 0],
            VERBOSE_INCONSISTENT_DIM, "dst", 1, "weights", with_groups + 0);
    // s4/u4/f4 weights requires channels to be multiple of 2 to be byte aligned
    VCHECK_CONV(IMPLICATION(utils::one_of(weights_desc->data_type,
                                    data_type::s4, data_type::u4,
                                    data_type::f4_e2m1, data_type::f4_e3m0),
                        weights_desc->dims[with_groups + 1] % 2 == 0),
            VERBOSE_BAD_DIM, "weights", with_groups + 1);
    // s4/u4/f4 src requires channels to be multiple of 2 to be byte aligned
    VCHECK_CONV(IMPLICATION(utils::one_of(src_desc->data_type, data_type::s4,
                                    data_type::u4, data_type::f4_e2m1,
                                    data_type::f4_e3m0),
                        src_desc->dims[1] % 2 == 0),
            VERBOSE_BAD_DIM, "src", 1);

    int sp_dims = src_desc->ndims - 2;
    utils::array_copy(cd.strides, strides, sp_dims);
    utils::array_copy(cd.padding[0], padding_l, sp_dims);
    utils::array_copy(cd.padding[1], padding_r, sp_dims);
    if (dilates)
        utils::array_copy(cd.dilates, dilates, sp_dims);
    else
        utils::array_set(cd.dilates, 0, sp_dims);

    for (int i = 2; i < src_desc->ndims; ++i) {
        dim_t src = src_desc->dims[i];
        dim_t ker = weights_desc->dims[with_groups + i];
        dim_t dil = cd.dilates[i - 2];
        dim_t pad_l = padding_l[i - 2];
        dim_t pad_r = padding_r[i - 2];
        dim_t str = strides[i - 2];
        dim_t dst = dst_desc->dims[i];
        dim_t ker_range = 1 + (ker - 1) * (dil + 1);
        VCHECK_CONV(str > 0, VERBOSE_BAD_DIM, "strides", i - 2);
        VCHECK_CONV(dil >= 0, "%s: dilation (%d) must be non-negative",
                VERBOSE_INCONSISTENT_PRB, static_cast<int>(dil));
        VCHECK_CONV(pad_l >= 0,
                "%s: left padding value (%d) must be non-negative",
                VERBOSE_INCONSISTENT_PRB, static_cast<int>(pad_l));
        VCHECK_CONV(pad_r + str > 0,
                "%s: right padding (%d) and stride (%d) must sum up to a "
                "positive value",
                VERBOSE_INCONSISTENT_PRB, static_cast<int>(pad_r),
                static_cast<int>(str));
        VCHECK_CONV((src - ker_range + pad_l + pad_r) / str + 1 == dst,
                "%s: mismatch between actual and computed dst dims, dst (%d) "
                "!= (src(%d) - ker(%d) + pad_l(%d) + pad_r(%d))/ str(%d) + 1",
                VERBOSE_INCONSISTENT_PRB, static_cast<int>(dst),
                static_cast<int>(src), static_cast<int>(ker_range),
                static_cast<int>(pad_l), static_cast<int>(pad_r),
                static_cast<int>(str));
    }

    *conv_desc = cd;
    return success;
}

status_t conv_attr_check(const convolution_desc_t &desc, const engine_t *engine,
        const primitive_attr_t *attr) {
    using smask_t = primitive_attr_t::skip_mask_t;

    if (attr == nullptr) return status::success;
    if (attr->has_default_values()) return status::success;

    // Check attributes
    if (utils::one_of(desc.prop_kind, prop_kind::forward_inference,
                prop_kind::forward_training)) {
        const data_type_t src_dt = desc.src_desc.data_type;
        const data_type_t dst_dt = desc.dst_desc.data_type;

        auto fwd_attr_mask = smask_t::post_ops | smask_t::sum_dt
                | smask_t::fpmath_mode | smask_t::rounding_mode;
        const bool is_gpu = engine->kind() == engine_kind::gpu;

        const bool is_int8 = utils::one_of(src_dt, data_type::s8, data_type::u8)
                || (is_gpu
                        && utils::one_of(dst_dt, data_type::s8, data_type::u8,
                                data_type::s32));
        const bool is_fp8
                = utils::one_of(src_dt, data_type::f8_e5m2, data_type::f8_e4m3)
                || (is_gpu
                        && utils::one_of(dst_dt, data_type::f8_e5m2,
                                data_type::f8_e4m3));
        const bool enable_quantization = is_int8 || is_fp8;
        if (enable_quantization)
            fwd_attr_mask |= smask_t::zero_points_data_type
                    | smask_t::scales_data_type;

        VCHECK_CONV_UNIMPL(attr->has_default_values(fwd_attr_mask, dst_dt),
                VERBOSE_UNSUPPORTED_ATTR);

        // Check scales
        if (!attr->scales_.has_default_values()) {
            const auto &sc = attr->scales_;
            const bool with_groups
                    = desc.src_desc.ndims != desc.weights_desc.ndims;
            VCHECK_CONV_UNIMPL(IMPLICATION(!sc.has_default_values(DNNL_ARG_SRC),
                                       sc.get_mask(DNNL_ARG_SRC) == 0),
                    VERBOSE_UNSUPPORTED_SCALES_CFG);
            VCHECK_CONV_UNIMPL(
                    IMPLICATION(!sc.has_default_values(DNNL_ARG_WEIGHTS),
                            utils::one_of(sc.get_mask(DNNL_ARG_WEIGHTS), 0,
                                    with_groups ? 3 : 1)),
                    VERBOSE_UNSUPPORTED_SCALES_CFG);
            VCHECK_CONV_UNIMPL(
                    IMPLICATION(!sc.has_default_values(DNNL_ARG_DST),
                            utils::one_of(sc.get_mask(DNNL_ARG_DST), 0, 2)),
                    VERBOSE_UNSUPPORTED_SCALES_CFG);
        }

        // Check zero points
        if (!attr->zero_points_.has_default_values()) {
            const auto &zp = attr->zero_points_;

            VCHECK_CONV_UNIMPL(IMPLICATION(!zp.has_default_values(DNNL_ARG_SRC),
                                       utils::one_of(zp.get_mask(DNNL_ARG_SRC),
                                               0, 1 << 1)),
                    VERBOSE_UNSUPPORTED_ZP_CFG);
            VCHECK_CONV_UNIMPL(
                    IMPLICATION(!zp.has_default_values(DNNL_ARG_WEIGHTS),
                            zp.get_mask(DNNL_ARG_WEIGHTS) == 0),
                    VERBOSE_UNSUPPORTED_ZP_CFG);
            VCHECK_CONV_UNIMPL(IMPLICATION(!zp.has_default_values(DNNL_ARG_DST),
                                       utils::one_of(zp.get_mask(DNNL_ARG_DST),
                                               0, 1 << 1)),
                    VERBOSE_UNSUPPORTED_ZP_CFG);
        }

        // Check post-ops
        if (!attr->post_ops_.has_default_values()) {
            const auto &po = attr->post_ops_;
            using namespace primitive_kind;
            VCHECK_CONV_UNIMPL(po.has_default_values({binary, eltwise, prelu,
                                       sum, convolution}),
                    VERBOSE_UNSUPPORTED_POSTOP);

            // Check sum
            VCHECK_CONV_UNIMPL(po.check_sum_consistency(dst_dt, is_int8, true),
                    VERBOSE_UNSUPPORTED_POSTOP);

            // Note: verbose support is inside the call.
            CHECK(po.validate_binary(engine->kind(), &desc.dst_desc));
        }
    } else {
        auto bwd_attr_mask = smask_t::fpmath_mode | smask_t::accumulation_mode;
        VCHECK_CONV_UNIMPL(attr->has_default_values(bwd_attr_mask),
                VERBOSE_UNSUPPORTED_ATTR);
    }

    return status::success;
}

} // namespace impl
} // namespace dnnl

status_t dnnl_convolution_forward_primitive_desc_create(
        primitive_desc_iface_t **primitive_desc_iface, engine_t *engine,
        prop_kind_t prop_kind, alg_kind_t alg_kind,
        const memory_desc_t *src_desc, const memory_desc_t *weights_desc,
        const memory_desc_t *bias_desc, const memory_desc_t *dst_desc,
        const dims_t strides, const dims_t dilates, const dims_t padding_l,
        const dims_t padding_r, const primitive_attr_t *attr) {
    if (!one_of(prop_kind, forward_training, forward_inference))
        return invalid_arguments;

    auto conv_desc = convolution_desc_t();
    CHECK(dnnl::impl::conv_desc_init(&conv_desc, prop_kind, alg_kind, src_desc,
            weights_desc, bias_desc, dst_desc, strides, dilates, padding_l,
            padding_r));
    CHECK(dnnl::impl::conv_attr_check(conv_desc, engine, attr));
    return primitive_desc_create(primitive_desc_iface, engine,
            (const op_desc_t *)&conv_desc, nullptr, attr);
}

status_t dnnl_convolution_backward_data_primitive_desc_create(
        primitive_desc_iface_t **primitive_desc_iface, engine_t *engine,
        alg_kind_t alg_kind, const memory_desc_t *diff_src_desc,
        const memory_desc_t *weights_desc, const memory_desc_t *diff_dst_desc,
        const dims_t strides, const dims_t dilates, const dims_t padding_l,
        const dims_t padding_r, const primitive_desc_iface_t *hint_fwd_pd,
        const primitive_attr_t *attr) {

    auto conv_desc = convolution_desc_t();
    CHECK(dnnl::impl::conv_desc_init(&conv_desc, backward_data, alg_kind,
            diff_src_desc, weights_desc, nullptr, diff_dst_desc, strides,
            dilates, padding_l, padding_r));
    CHECK(dnnl::impl::conv_attr_check(conv_desc, engine, attr));
    return primitive_desc_create(primitive_desc_iface, engine,
            (const op_desc_t *)&conv_desc, hint_fwd_pd, attr);
}

status_t dnnl_convolution_backward_weights_primitive_desc_create(
        primitive_desc_iface_t **primitive_desc_iface, engine_t *engine,
        alg_kind_t alg_kind, const memory_desc_t *src_desc,
        const memory_desc_t *diff_weights_desc,
        const memory_desc_t *diff_bias_desc, const memory_desc_t *diff_dst_desc,
        const dims_t strides, const dims_t dilates, const dims_t padding_l,
        const dims_t padding_r, const primitive_desc_iface_t *hint_fwd_pd,
        const primitive_attr_t *attr) {

    auto conv_desc = convolution_desc_t();
    CHECK(dnnl::impl::conv_desc_init(&conv_desc, backward_weights, alg_kind,
            src_desc, diff_weights_desc, diff_bias_desc, diff_dst_desc, strides,
            dilates, padding_l, padding_r));
    CHECK(dnnl::impl::conv_attr_check(conv_desc, engine, attr));
    return primitive_desc_create(primitive_desc_iface, engine,
            (const op_desc_t *)&conv_desc, hint_fwd_pd, attr);
}

// vim: et ts=4 sw=4 cindent cino+=l0,\:4,N-s