path: root/thirdparty/oidn/mkl-dnn/src/cpu/cpu_reducer.hpp

/*******************************************************************************
* Copyright 2017-2018 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 CPU_REDUCER_HPP
#define CPU_REDUCER_HPP

#include <assert.h>

#include "c_types_map.hpp"
#include "memory_tracking.hpp"
#include "mkldnn_thread.hpp"
#include "mkldnn_types.h"
#include "nstl.hpp"
#include "type_helpers.hpp"

#include "cpu_barrier.hpp"

namespace mkldnn {
namespace impl {
namespace cpu {

/** class to perform balancing over 3D array
 *
 * Conceptually the reduction happens according to the picture below:
 *
 *  <--job_size->
 *  +-----------+   +-----------+           +-----------+  ^
 *  |           |   |           |           |           |  |
 *  |           |   |           |           |           |  |
 *  |     1     |   |     2     |   . . .   |   njobs   |  | reduction_size
 *  |           |   |           |           |           |  |
 *  |           |   |           |           |           |  |
 *  +-----------+   +-----------+           +-----------+  v
 *
 *    |   |   |       |   |   |               |   |   |
 *    v   v   v       v   v   v               v   v   v
 *  ===================================================== vertical reduction
 *
 *  +-----------+   +-----------+   . . .   +-----------+ result
 *
 * In a simple case the result must be contiguous in memory.
 * @class cpu_reducer_t is an implementation.
 *
 * Threads are divided into groups. The groups are independent of each other.
 * Each group may work on several jobs (the distribution is not uniform, since
 * njobs might be not a multiple of groups). Threads within a group work on
 * different parts of the reduction dimension. Thread 0 in each group is called
 * master (@sa reduce_balancer_t::master()).
 *
 * If threading driver does not allow sync between sub-group of threads (e.g.
 * Intel(R) TBB) the # of thread per group is enforced to be 1.
 */
struct reduce_balancer_t {
    reduce_balancer_t() { init(1, 1, 1, 1, 0); } /* trivial balance */
    reduce_balancer_t(int nthr, int job_size, int njobs, int reduction_size,
            size_t max_buffer_size)
    { init(nthr, job_size, njobs, reduction_size, max_buffer_size); }

    reduce_balancer_t &init(int nthr, int job_size, int njobs,
            int reduction_size, size_t max_buffer_size)
    {
        syncable_ = mkldnn_thr_syncable();
        nthr_ = nthr;
        job_size_ = job_size;
        njobs_ = njobs;
        reduction_size_ = reduction_size;
        max_buffer_size_ = max_buffer_size;
        balance();
        return *this;
    }

    bool syncable_;
    int nthr_;
    int job_size_, njobs_, reduction_size_;

    int ngroups_; /** number of independent work (thread) groups */
    int nthr_per_group_; /** number of threads within a single work group */
    int njobs_per_group_ub_; /** the max # of jobs within a work group */

    bool master(int ithr) const { return id_in_group(ithr) == 0; }
    bool idle(int ithr) const { return ithr >= nthr_per_group_ * ngroups_; }

    int group_id(int ithr) const { return ithr / nthr_per_group_; }
    int id_in_group(int ithr) const { return ithr % nthr_per_group_; }

    int grp_njobs(int grp) const {
        if (grp >= ngroups_) return 0;
        return njobs_ / ngroups_ + (grp < njobs_ % ngroups_);
    }
    int grp_job_off(int grp) const {
        if (grp >= ngroups_) return njobs_;
        return njobs_ / ngroups_ * grp + nstl::min(grp, njobs_ % ngroups_);
    }

    int ithr_njobs(int ithr) const { return grp_njobs(group_id(ithr)); }
    int ithr_job_off(int ithr) const { return grp_job_off(group_id(ithr)); }

private:
    size_t max_buffer_size_;
    void balance();
};

/** forward declaration of reduce driver */
template <impl::data_type_t data_type> struct reducer_2d_driver_t;

/** class to perform a reduction over 3D array
 *
 * Balancing is based on @class reduce_balancer_t.
 * Restrictions: the result of the reduction must be contiguous in memory. *
 * The reduction happens according to the picture below (once more):
 *
 *  <--job_size->
 *  +-----------+   +-----------+           +-----------+  ^
 *  |           |   |           |           |           |  |
 *  |           |   |           |           |           |  |
 *  |     1     |   |     2     |   . . .   |   njobs   |  | reduction_size
 *  |           |   |           |           |           |  |
 *  |           |   |           |           |           |  |
 *  +-----------+   +-----------+           +-----------+  v
 *
 *    |   |   |       |   |   |               |   |   |
 *    v   v   v       v   v   v               v   v   v
 *  ===================================================== vertical reduction
 *
 *  +-----------+   +-----------+   . . .   +-----------+ (contiguous) result
 *
 * An example how work might be shared is shown below.
 *
 * In this example group 0 owns 2 (independent) jobs -- 2 big squares.
 * The number of threads per group is also 2 (thread 0 of group 0 and thread 1
 * of group 0). Master threads (i.e. threads with id 0 in corresponding group)
 * from each group put the partial result directly into destination memory,
 * while all the other threads with-in the group use workspace (on the picture
 * the only thread 1). Once intermediate results obtained each group reduces
 * corresponding part (own jobs) to the destination memory.
 *
 *  <-------   group 0   ------->
 *
 *  +-----------+   +-----------+  ^
 *  |           |   |           |  | thread 0 of  reduces to the dest-memory
 *  |           |   |           |  | group 0      +-----------+   +-----------+
 *  |- - - - - -|   |- - - - - -|  X
 *  |           |   |           |  | thread 1 of  reduces to workspace[tid=1]:
 *  |           |   |           |  | group 0      +-----------+   +-----------+
 *  +-----------+   +-----------+  v
 *                                                  |   |   |       |   |   |
 *                                                  v   v   v       v   v   v
 *                                   ((barrier))  =============================
 *
 *                                  dest-memory:  +-----------+   +-----------+
 */
template <impl::data_type_t data_type>
struct cpu_reducer_t {
    typedef typename prec_traits<data_type>::type data_t;

    struct conf_t {
        conf_t() = default;
        conf_t &init(const reduce_balancer_t &balancer)
        { balancer_ = balancer; return *this; }

        void init_scratchpad(memory_tracking::registrar_t &scratchpad) const;

        reduce_balancer_t balancer_;
    };

    cpu_reducer_t(const conf_t &conf);
    ~cpu_reducer_t();

    /** initializes reducer.
     * Must be called from a single thread prior to actual usage */
    void init(const memory_tracking::grantor_t &scratchpad) const {
        if (balancer().nthr_per_group_ == 1) return;

        auto bctx = scratchpad.template get<simple_barrier::ctx_t>(
                memory_tracking::names::key_reducer_space_bctx);
        for (int i = 0; i < balancer().ngroups_; ++i)
            simple_barrier::ctx_init(&bctx[i]);
    }

    /** for given thread returns the pointer where to put partial results.
     * Reduction destination @p dst must be provided as well (master threads
     * from each group will use it for partial result to reduce memory
     * pressure).
     *
     * @note: job offset is already applied by get_local_ptr(), which means all
     *        threads should start writing from the very beginning of returned
     *        address.
     */
    data_t *get_local_ptr(int ithr, data_t *dst,
            const memory_tracking::grantor_t &scratchpad) const;

    /** performs the reduction with built-in synchronization. */
    void reduce(int ithr, data_t *dst,
            const memory_tracking::grantor_t &scratchpad) const {
        bool redundant_reduction = balancer().nthr_per_group_ == 1
            || balancer().idle(ithr);
        if (redundant_reduction) return;

        auto bctx = scratchpad.template get<simple_barrier::ctx_t>(
                memory_tracking::names::key_reducer_space_bctx);
        simple_barrier::barrier(&bctx[balancer().group_id(ithr)],
                balancer().nthr_per_group_);

        reduce_nolock(ithr, dst, scratchpad);
    }

    const reduce_balancer_t &balancer() const { return conf_.balancer_; }

private:
    static size_t space_per_thread(const reduce_balancer_t &balancer)
    { return balancer.njobs_per_group_ub_ * balancer.job_size_; }

    /* The scratchpad is organized as follows:
     *
     * data_t space[nthr_][njobs_per_group_ub_][jobs_size_];
     * simple_barrier::ctx_t barriers[groups_]; */

    const conf_t conf_;
    reducer_2d_driver_t<data_type> *drv_;

    void reduce_nolock(int ithr, data_t *dst,
            const memory_tracking::grantor_t &scratchpad) const;
};

template <impl::data_type_t data_type>
struct cpu_reducer_2d_t {
    typedef typename prec_traits<data_type>::type data_t;

    struct conf_t {
        conf_t() = default;
        conf_t &init(const reduce_balancer_t &balancer, int job_size_x,
                int job_size_y, int x_block, int dst_x, int dst_y) {
            balancer_ = balancer;
            job_size_x_ = job_size_x;
            job_size_y_ = job_size_y;
            x_block_ = x_block;
            dst_x_ = dst_x;
            dst_y_ = dst_y;
            return *this;
        }

        void init_scratchpad(memory_tracking::registrar_t &scratchpad) const;

        reduce_balancer_t balancer_;
        int job_size_x_, job_size_y_, x_block_, dst_x_, dst_y_;
    };

    cpu_reducer_2d_t(const conf_t &conf);
    ~cpu_reducer_2d_t();

    /** initializes reducer.
     * Must be called from a single thread prior to actual usage */
    void init(const memory_tracking::grantor_t &scratchpad) const {
        if (balancer().nthr_per_group_ == 1) return;

        auto bctx = scratchpad.template get<simple_barrier::ctx_t>(
                memory_tracking::names::key_reducer_space_bctx);
        for (int i = 0; i < balancer().ngroups_; ++i)
            simple_barrier::ctx_init(&bctx[i]);
    }

    /** for given thread returns the pointer where to put partial results */
    data_t *get_local_ptr(int ithr,
            const memory_tracking::grantor_t &scratchpad) const;

    /** performs the reduction with built-in synchronization. */
    void reduce(int ithr, data_t *dst,
            const memory_tracking::grantor_t &scratchpad) const {
        bool redundant_reduction = balancer().nthr_per_group_ == 1
            || balancer().idle(ithr);
        if (redundant_reduction) return;

        auto bctx = scratchpad.template get<simple_barrier::ctx_t>(
                memory_tracking::names::key_reducer_space_bctx);
        simple_barrier::barrier(&bctx[balancer().group_id(ithr)],
                balancer().nthr_per_group_);

        reduce_nolock(ithr, dst, scratchpad);
    }

    const reduce_balancer_t &balancer() const { return conf_.balancer_; }

private:
    static size_t space_per_thread(const reduce_balancer_t &balancer)
    { return balancer.njobs_per_group_ub_ * balancer.job_size_; }

    /* The scratchpad is organized as follows:
     *
     * data_t space[nthr_][njobs_per_group_ub_][jobs_size_];
     * simple_barrier::ctx_t barriers[groups_]; */

    const conf_t conf_;
    reducer_2d_driver_t<data_type> *drv_;

    int choose_x_blocking(int nx, int ny, int nthr_per_grp) const;
    void reduce_block(const data_t* space_base, data_t *dst,
            int job, int start_y, int start_x,
            int ny_start, int nx_start, int ny_step, int nx_step) const;
    void reduce_nolock(int ithr, data_t *dst,
            const memory_tracking::grantor_t &scratchpad) const;
};

/** simple 1d accumulator: y[:] += x[:] */
template <impl::data_type_t data_type>
struct cpu_accumulator_1d_t {
    typedef typename prec_traits<data_type>::type data_t;

    cpu_accumulator_1d_t();
    ~cpu_accumulator_1d_t();
    void accumulate(data_t *dst, const data_t *src, size_t size);

    reducer_2d_driver_t<data_type> *drv_;
};

}
}
}

#endif

// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s