/*******************************************************************************
 * Copyright 2022 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 <algorithm>
#include <memory>
#include <set>
#include <string>
#include <vector>

#include "graph/interface/value.hpp"

#include "graph/backend/dnnl/internal_attrs.hpp"
#include "graph/backend/dnnl/passes/utils.hpp"
#include "graph/backend/dnnl/utils.hpp"

namespace dnnl {
namespace impl {
namespace graph {
namespace dnnl_impl {

namespace {
bool has_scratchpad(const op_t *op) {
    // the following ops do not have scratchpad output by definition
    const static std::set<op_kind_t> no_scratchpad_ops {
            op_kind::dnnl_constant_scales, op_kind::dnnl_constant_zps,
            op_kind::dnnl_add_zps, op_kind::dnnl_sub_zps,
            op_kind::dnnl_to_group, op_kind::dnnl_from_group,
            op_kind::dnnl_permute, op_kind::dnnl_squeeze,
            op_kind::dnnl_unsqueeze, op_kind::dnnl_transpose,
            op_kind::dnnl_reshape, op_kind::dnnl_gen_index, op_kind::dnnl_mask};

    // the following ops may have scratchpad output if output size > 1
    const static std::set<op_kind_t> may_have_scratchpad_ops {
            op_kind::dnnl_mul_scales,
            op_kind::dnnl_reorder,
    };

    const op_kind_t kind = op->get_kind();
    const bool no_scratchpad = no_scratchpad_ops.count(kind)
            || (may_have_scratchpad_ops.count(kind) && op->num_outputs() == 1);

    return !no_scratchpad;
}
}; // namespace

status_t constant_propagation(std::shared_ptr<subgraph_t> &sg) {
    using op_t = op_t;
    using ltw = logical_tensor_wrapper_t;

    // Because we don't know which logical tensors (may be partition's ins/outs
    // edges, or edges inside partition) will be set to constant by FWK, so we
    // have to do constant propagation bidirectionally
    bool changed;
    do {
        changed = false;
        auto ret = topo_order_visit(sg->get_output_ops(), [&](op_t *op) {
            // FIXME(xx): Because constant cache is only for inference
            // optimization, so we don't consider the workspace output
            // currently. But this may be a potential issue if we also use
            // constant cache in training scenarios
            size_t scpad_num = has_scratchpad(op) ? 1 : 0;

            bool all_inputs_are_constant = true;
            for (const auto &in : op->get_input_values()) {
                if (ltw(in->get_logical_tensor()).property_type()
                        != property_type::constant) {
                    all_inputs_are_constant = false;
                    break;
                }
            }

            bool all_outputs_are_constant = true;
            for (size_t i = 0; i < op->num_outputs() - scpad_num; i++) {
                auto out = op->get_output_value(i);
                if (ltw(out->get_logical_tensor()).property_type()
                        != property_type::constant) {
                    all_outputs_are_constant = false;
                    break;
                }
            }

            const bool is_constant
                    = all_inputs_are_constant || all_outputs_are_constant;
            op->set_attr<bool>(op_attr::is_constant, is_constant);

            // FIXME(xx): Currently, we consider that if the inputs of an op are
            // constant, then its outputs should also be constant, vice versa.
            // But this assumption may be broken if we have more ops in the
            // future. For example, if we have an Shape op, who returns the
            // shape of input tensor. In static shape scenarios, its output will
            // be constant, but its input can be variable. Such ops should have
            // special constant propagation rules
            if (all_inputs_are_constant && !all_outputs_are_constant) {
                // propagate from in to out
                for (size_t i = 0; i < op->num_outputs() - scpad_num; i++) {
                    auto out = op->get_output_value(i);
                    out->set_property(property_type::constant);
                }
                changed = changed || true;
            } else if (!all_inputs_are_constant && all_outputs_are_constant) {
                // propagate from out to in
                for (auto &in : op->get_input_values()) {
                    in->set_property(property_type::constant);
                }
                changed = changed || true;
            } else {
                changed = changed || false;
            }
            return status::success;
        });

        if (ret != status::success) return ret;
    } while (changed);

    // Special handle for constant external output. we will always re-compute
    // the partition when the external output is constant. This fix is mainly
    // for functionality purpose. For best performance, we suggest two ways:
    // - Do constant folding in FWK side
    // - Fuse the constant output op to subsequent partition to make the
    //   constant lt internal.
    auto sg_outs = sg->get_output_values();
    for (auto &out : sg_outs) {
        if (ltw(out->get_logical_tensor()).property_type()
                != property_type::constant)
            continue;
        // ignore the constant property
        out->set_property(property_type::variable);
        out->get_producer().set_attr<bool>(op_attr::is_constant, false);
    }

    return status::success;
}

} // namespace dnnl_impl
} // namespace graph
} // namespace impl
} // namespace dnnl
