onednn-src 0.1.13

Source of oneAPI Deep Neural Network Library (oneDNN)
Documentation
/*******************************************************************************
 * Copyright 2020 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 GRAPH_BACKEND_DNNL_DNNL_BACKEND_HPP
#define GRAPH_BACKEND_DNNL_DNNL_BACKEND_HPP

#include <algorithm>
#include <limits>
#include <memory>
#include <string>
#include <vector>
#include <unordered_map>
#include <unordered_set>

#include "graph/interface/backend.hpp"
#include "graph/interface/c_types_map.hpp"
#include "graph/interface/logical_tensor.hpp"

#include "graph/utils/any.hpp"
#include "graph/utils/pm/pass_manager.hpp"
#include "graph/utils/utils.hpp"

#include "graph/backend/dnnl/common.hpp"
#include "graph/backend/dnnl/layout_id_mgr.hpp"
#include "graph/backend/dnnl/utils.hpp"

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

// gcc4.8.5 can 't support enum class as key
struct enum_hash_t {
    template <typename T>
    size_t operator()(const T &t) const {
        return static_cast<size_t>(t);
    }
};

class dnnl_backend_t : public backend_t {
    friend class dnnl_partition_impl_t;

public:
    static dnnl_backend_t &get_singleton() {
        static dnnl_backend_t ins("dnnl_backend", /*priority*/ 1.f);
        return ins;
    }

    // Used by DNNL backend to cache memory descriptor and get layout id
    graph::utils::optional_t<size_t> set_mem_desc(const memory::desc &md);

    graph::utils::optional_t<memory::desc> get_mem_desc(
            const size_t &layout_id) const;

    graph::pass::pass_registry_t &get_pass_registry() { return pass_registry_; }

    dnnl_layout_id_manager_t &get_layout_id_manager() {
        return layout_id_manager_;
    }

    size_t get_mem_size(const logical_tensor_t &lt) const override;

    bool compare_logical_tensor(const logical_tensor_t &lhs,
            const logical_tensor_t &rhs) const override;

    bool support_engine_kind(engine_kind_t kind) const override {
        static const std::unordered_set<engine_kind_t, enum_hash_t>
                supported_kind = {
#if DNNL_CPU_RUNTIME != DNNL_RUNTIME_NONE
                        engine_kind::cpu,
#endif

#if DNNL_GPU_RUNTIME == DNNL_RUNTIME_SYCL \
        || DNNL_GPU_RUNTIME == DNNL_RUNTIME_OCL
                        engine_kind::gpu,
#endif
                };
        return supported_kind.count(kind);
    }

    status_t get_partitions(
            graph_t &agraph, partition_policy_t policy) override {
        // - priority == 50.f: data type check pass (fixed highest priority)
        // - 50.f > priority > 20.f: large fusion pattern
        // - 20.f >= priority > 8.f: normal fusion pattern
        // - priority <= 8.f: debug fusion pattern (single op fusion)
        const float priority_ths
                = (policy == graph::partition_policy::fusion) ? 100.f : 8.0f;

        const auto &dnnl_pass_filter
                = [priority_ths](const graph::pass::pass_base_ptr &pass,
                          partition_policy_t policy) -> bool {
            UNUSED(policy);
            return pass->get_priority() <= priority_ths;
        };

        auto &pass_registry = get_pass_registry();
        graph::pass::pass_manager_t pm(pass_registry);

        if (graph::utils::get_graph_dump_mode(
                    graph::graph_dump_mode_t::pattern)) {
            std::string pass_config_json = "dnnl_graph_passes.json";
            std::ifstream fs(pass_config_json.c_str());
            if (fs) {
                verbose_printf("graph,info,pattern,load,%s\n",
                        pass_config_json.c_str());
            } else {
                verbose_printf("graph,info,pattern,dump,%s\n",
                        pass_config_json.c_str());
                pm.print_passes(pass_config_json);
            }
            pm.run_passes(agraph, &fs, policy, dnnl_pass_filter);
        } else {
            pm.run_passes(agraph, "", policy, dnnl_pass_filter);
        }

        return status::success;
    }

private:
    dnnl_backend_t(const std::string &name, float priority);
    static graph::pass::pass_registry_t register_passes();

    dnnl_layout_id_manager_t layout_id_manager_;
    static graph::pass::pass_registry_t pass_registry_;
};

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

#endif