/******************************************************************************
 * Copyright (c) 2011, Duane Merrill.  All rights reserved.
 * Copyright (c) 2011-2018, NVIDIA CORPORATION.  All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *     * Redistributions of source code must retain the above copyright
 *       notice, this list of conditions and the following disclaimer.
 *     * Redistributions in binary form must reproduce the above copyright
 *       notice, this list of conditions and the following disclaimer in the
 *       documentation and/or other materials provided with the distribution.
 *     * Neither the name of the NVIDIA CORPORATION nor the
 *       names of its contributors may be used to endorse or promote products
 *       derived from this software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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/**
 * @file
 * cub::WarpReduceShfl provides SHFL-based variants of parallel reduction of items partitioned across a CUDA thread warp.
 */

#pragma once

#include <cub/config.cuh>

#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
#  pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
#  pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
#  pragma system_header
#endif // no system header

#include <cub/thread/thread_operators.cuh>
#include <cub/util_ptx.cuh>
#include <cub/util_type.cuh>

#include <cuda/std/type_traits>

#include <nv/target>
#include <stdint.h>

CUB_NAMESPACE_BEGIN


namespace detail
{

template <class A = int, class = A>
struct reduce_add_exists : ::cuda::std::false_type
{};

template <class T>
struct reduce_add_exists<T, decltype(__reduce_add_sync(0xFFFFFFFF, T{}))> : ::cuda::std::true_type
{};

template <class T = int, class = T>
struct reduce_min_exists : ::cuda::std::false_type
{};

template <class T>
struct reduce_min_exists<T, decltype(__reduce_min_sync(0xFFFFFFFF, T{}))> : ::cuda::std::true_type
{};

template <class T = int, class = T>
struct reduce_max_exists : ::cuda::std::false_type
{};

template <class T>
struct reduce_max_exists<T, decltype(__reduce_max_sync(0xFFFFFFFF, T{}))> : ::cuda::std::true_type
{};

}

/**
 * @brief WarpReduceShfl provides SHFL-based variants of parallel reduction of items partitioned
 *        across a CUDA thread warp.
 *
 * @tparam T
 *   Data type being reduced
 *
 * @tparam LOGICAL_WARP_THREADS
 *   Number of threads per logical warp (must be a power-of-two)
 *
 * @tparam LEGACY_PTX_ARCH
 *   The PTX compute capability for which to to specialize this collective
 */
template <typename T, int LOGICAL_WARP_THREADS, int LEGACY_PTX_ARCH = 0>
struct WarpReduceShfl
{
    static_assert(PowerOfTwo<LOGICAL_WARP_THREADS>::VALUE,
                  "LOGICAL_WARP_THREADS must be a power of two");

    //---------------------------------------------------------------------
    // Constants and type definitions
    //---------------------------------------------------------------------

    enum
    {
        /// Whether the logical warp size and the PTX warp size coincide
        IS_ARCH_WARP = (LOGICAL_WARP_THREADS == CUB_WARP_THREADS(0)),

        /// The number of warp reduction steps
        STEPS = Log2<LOGICAL_WARP_THREADS>::VALUE,

        /// Number of logical warps in a PTX warp
        LOGICAL_WARPS = CUB_WARP_THREADS(0) / LOGICAL_WARP_THREADS,

        /// The 5-bit SHFL mask for logically splitting warps into sub-segments starts 8-bits up
        SHFL_C = (CUB_WARP_THREADS(0) - LOGICAL_WARP_THREADS) << 8

    };

    template <typename S>
    struct IsInteger
    {
        enum {
            ///Whether the data type is a small (32b or less) integer for which we can use a single SFHL instruction per exchange
            IS_SMALL_UNSIGNED = (Traits<S>::CATEGORY == UNSIGNED_INTEGER) && (sizeof(S) <= sizeof(unsigned int))
        };
    };


    /// Shared memory storage layout type
    typedef NullType TempStorage;


    //---------------------------------------------------------------------
    // Thread fields
    //---------------------------------------------------------------------

    /// Lane index in logical warp
    int lane_id;

    /// Logical warp index in 32-thread physical warp
    int warp_id;

    /// 32-thread physical warp member mask of logical warp
    uint32_t member_mask;


    //---------------------------------------------------------------------
    // Construction
    //---------------------------------------------------------------------

    /// Constructor
    __device__ __forceinline__ WarpReduceShfl(
        TempStorage &/*temp_storage*/)
        : lane_id(static_cast<int>(LaneId()))
        , warp_id(IS_ARCH_WARP ? 0 : (lane_id / LOGICAL_WARP_THREADS))
        , member_mask(WarpMask<LOGICAL_WARP_THREADS>(warp_id))
    {
        if (!IS_ARCH_WARP)
        {
            lane_id = lane_id % LOGICAL_WARP_THREADS;
        }
    }


    //---------------------------------------------------------------------
    // Reduction steps
    //---------------------------------------------------------------------

    /**
     * @brief Reduction (specialized for summation across uint32 types)
     *
     * @param[in] input
     *   Calling thread's input item.
     *
     * @param[in] reduction_op
     *   Binary reduction operator
     *
     * @param[in] last_lane
     *   Index of last lane in segment
     *
     * @param[in] offset
     *   Up-offset to pull from
     */
    __device__ __forceinline__ unsigned int
    ReduceStep(unsigned int input, cub::Sum /*reduction_op*/, int last_lane, int offset)
    {
        unsigned int output;
        int shfl_c = last_lane | SHFL_C;   // Shuffle control (mask and last_lane)

        // Use predicate set from SHFL to guard against invalid peers
        asm volatile(
            "{"
            "  .reg .u32 r0;"
            "  .reg .pred p;"
            "  shfl.sync.down.b32 r0|p, %1, %2, %3, %5;"
            "  @p add.u32 r0, r0, %4;"
            "  mov.u32 %0, r0;"
            "}"
            : "=r"(output) : "r"(input), "r"(offset), "r"(shfl_c), "r"(input), "r"(member_mask));

        return output;
    }

    /**
     * @brief Reduction (specialized for summation across fp32 types)
     *
     * @param[in] input
     *   Calling thread's input item.
     *
     * @param[in] reduction_op
     *   Binary reduction operator
     *
     * @param[in] last_lane
     *   Index of last lane in segment
     *
     * @param[in] offset
     *   Up-offset to pull from
     */
    __device__ __forceinline__ float
    ReduceStep(float input, cub::Sum /*reduction_op*/, int last_lane, int offset)
    {
        float output;
        int shfl_c = last_lane | SHFL_C;   // Shuffle control (mask and last_lane)

        // Use predicate set from SHFL to guard against invalid peers
        asm volatile(
            "{"
            "  .reg .f32 r0;"
            "  .reg .pred p;"
            "  shfl.sync.down.b32 r0|p, %1, %2, %3, %5;"
            "  @p add.f32 r0, r0, %4;"
            "  mov.f32 %0, r0;"
            "}"
            : "=f"(output) : "f"(input), "r"(offset), "r"(shfl_c), "f"(input), "r"(member_mask));

        return output;
    }

    /**
     * @brief Reduction (specialized for summation across unsigned long long types)
     *
     * @param[in] input
     *   Calling thread's input item
     *
     * @param[in] reduction_op
     *   Binary reduction operator
     *
     * @param[in] last_lane
     *   Index of last lane in segment
     *
     * @param[in] offset
     *   Up-offset to pull from
     */
    __device__ __forceinline__ unsigned long long
    ReduceStep(unsigned long long input, cub::Sum /*reduction_op*/, int last_lane, int offset)
    {
        unsigned long long output;
        int shfl_c = last_lane | SHFL_C;   // Shuffle control (mask and last_lane)

        asm volatile(
            "{"
            "  .reg .u32 lo;"
            "  .reg .u32 hi;"
            "  .reg .pred p;"
            "  mov.b64 {lo, hi}, %1;"
            "  shfl.sync.down.b32 lo|p, lo, %2, %3, %4;"
            "  shfl.sync.down.b32 hi|p, hi, %2, %3, %4;"
            "  mov.b64 %0, {lo, hi};"
            "  @p add.u64 %0, %0, %1;"
            "}"
            : "=l"(output) : "l"(input), "r"(offset), "r"(shfl_c), "r"(member_mask));

        return output;
    }

    /**
     * @brief Reduction (specialized for summation across long long types)
     *
     * @param[in] input
     *   Calling thread's input item
     *
     * @param[in] reduction_op
     *   Binary reduction operator
     *
     * @param[in] last_lane
     *   Index of last lane in segment
     *
     * @param[in] offset
     *   Up-offset to pull from
     */
    __device__ __forceinline__ long long
    ReduceStep(long long input, cub::Sum /*reduction_op*/, int last_lane, int offset)
    {
        long long output;
        int shfl_c = last_lane | SHFL_C;   // Shuffle control (mask and last_lane)

        // Use predicate set from SHFL to guard against invalid peers
        asm volatile(
            "{"
            "  .reg .u32 lo;"
            "  .reg .u32 hi;"
            "  .reg .pred p;"
            "  mov.b64 {lo, hi}, %1;"
            "  shfl.sync.down.b32 lo|p, lo, %2, %3, %4;"
            "  shfl.sync.down.b32 hi|p, hi, %2, %3, %4;"
            "  mov.b64 %0, {lo, hi};"
            "  @p add.s64 %0, %0, %1;"
            "}"
            : "=l"(output) : "l"(input), "r"(offset), "r"(shfl_c), "r"(member_mask));

        return output;
    }

    /**
     * @brief Reduction (specialized for summation across double types)
     *
     * @param[in] input
     *   Calling thread's input item.
     *
     * @param[in] reduction_op
     *   Binary reduction operator
     *
     * @param[in] last_lane
     *   Index of last lane in segment
     *
     * @param[in] offset
     *   Up-offset to pull from
     */
    __device__ __forceinline__ double
    ReduceStep(double input, cub::Sum /*reduction_op*/, int last_lane, int offset)
    {
        double output;
        int shfl_c = last_lane | SHFL_C;   // Shuffle control (mask and last_lane)

        // Use predicate set from SHFL to guard against invalid peers
        asm volatile(
            "{"
            "  .reg .u32 lo;"
            "  .reg .u32 hi;"
            "  .reg .pred p;"
            "  .reg .f64 r0;"
            "  mov.b64 %0, %1;"
            "  mov.b64 {lo, hi}, %1;"
            "  shfl.sync.down.b32 lo|p, lo, %2, %3, %4;"
            "  shfl.sync.down.b32 hi|p, hi, %2, %3, %4;"
            "  mov.b64 r0, {lo, hi};"
            "  @p add.f64 %0, %0, r0;"
            "}"
            : "=d"(output) : "d"(input), "r"(offset), "r"(shfl_c), "r"(member_mask));

        return output;
    }

    /**
     * @brief Reduction (specialized for swizzled ReduceByKeyOp<cub::Sum> across
     *        KeyValuePair<KeyT, ValueT> types)
     *
     * @param[in] input
     *   Calling thread's input item
     *
     * @param[in] reduction_op
     *   Binary reduction operator
     *
     * @param[in] last_lane
     *   Index of last lane in segment
     *
     * @param[in] offset
     *   Up-offset to pull from
     */
    template <typename ValueT, typename KeyT>
    __device__ __forceinline__ KeyValuePair<KeyT, ValueT>
    ReduceStep(KeyValuePair<KeyT, ValueT> input,
               SwizzleScanOp<ReduceByKeyOp<cub::Sum>> /*reduction_op*/,
               int last_lane,
               int offset)
    {
        KeyValuePair<KeyT, ValueT> output;

        KeyT other_key = ShuffleDown<LOGICAL_WARP_THREADS>(input.key, offset, last_lane, member_mask);

        output.key = input.key;
        output.value = ReduceStep(
            input.value,
            cub::Sum(),
            last_lane,
            offset,
            Int2Type<IsInteger<ValueT>::IS_SMALL_UNSIGNED>());

        if (input.key != other_key)
            output.value = input.value;

        return output;
    }

    /**
     * @brief Reduction (specialized for swizzled ReduceBySegmentOp<cub::Sum> across
     *        KeyValuePair<OffsetT, ValueT> types)
     *
     * @param[in] input
     *   Calling thread's input item.
     *
     * @param[in] reduction_op
     *   Binary reduction operator
     *
     * @param[in] last_lane
     *   Index of last lane in segment
     *
     * @param[in] offset
     *   Up-offset to pull from
     */
    template <typename ValueT, typename OffsetT>
    __device__ __forceinline__ KeyValuePair<OffsetT, ValueT>
    ReduceStep(KeyValuePair<OffsetT, ValueT> input,
               SwizzleScanOp<ReduceBySegmentOp<cub::Sum>> /*reduction_op*/,
               int last_lane,
               int offset)
    {
        KeyValuePair<OffsetT, ValueT> output;

        output.value = ReduceStep(input.value, cub::Sum(), last_lane, offset, Int2Type<IsInteger<ValueT>::IS_SMALL_UNSIGNED>());
        output.key = ReduceStep(input.key, cub::Sum(), last_lane, offset, Int2Type<IsInteger<OffsetT>::IS_SMALL_UNSIGNED>());

        if (input.key > 0)
            output.value = input.value;

        return output;
    }

    /**
     * @brief Reduction step (generic)
     *
     * @param[in] input
     *   Calling thread's input item
     *
     * @param[in] reduction_op
     *   Binary reduction operator
     *
     * @param[in] last_lane
     *   Index of last lane in segment
     *
     * @param[in] offset
     *   Up-offset to pull from
     */
    template <typename _T, typename ReductionOp>
    __device__ __forceinline__ _T
    ReduceStep(_T input, ReductionOp reduction_op, int last_lane, int offset)
    {
        _T output = input;

        _T temp = ShuffleDown<LOGICAL_WARP_THREADS>(output, offset, last_lane, member_mask);

        // Perform reduction op if valid
        if (offset + lane_id <= last_lane)
            output = reduction_op(input, temp);

        return output;
    }

    /**
     * @brief Reduction step (specialized for small unsigned integers size 32b or less)
     *
     * @param[in] input
     *   Calling thread's input item.
     *
     * @param[in] reduction_op
     *   Binary reduction operator
     *
     * @param[in] last_lane
     *   Index of last lane in segment
     *
     * @param[in] offset
     *   Up-offset to pull from
     *
     * @param[in] is_small_unsigned
     *   Marker type indicating whether T is a small unsigned integer
     */
    template <typename _T, typename ReductionOp>
    __device__ __forceinline__ _T ReduceStep(_T input,
                                             ReductionOp reduction_op,
                                             int last_lane,
                                             int offset,
                                             Int2Type<true> /*is_small_unsigned*/)
    {
        return ReduceStep(input, reduction_op, last_lane, offset);
    }

    /**
     * @brief Reduction step (specialized for types other than small unsigned integers size
     *        32b or less)
     *
     * @param[in] input
     *   Calling thread's input item.
     *
     * @param[in] reduction_op
     *   Binary reduction operator
     *
     * @param[in] last_lane
     *   Index of last lane in segment
     *
     * @param[in] offset
     *   Up-offset to pull from
     *
     * @param[in] is_small_unsigned
     *   Marker type indicating whether T is a small unsigned integer
     */
    template <typename _T, typename ReductionOp>
    __device__ __forceinline__ _T ReduceStep(_T input,
                                             ReductionOp reduction_op,
                                             int last_lane,
                                             int offset,
                                             Int2Type<false> /*is_small_unsigned*/)
    {
        return ReduceStep(input, reduction_op, last_lane, offset);
    }


    //---------------------------------------------------------------------
    // Templated reduction iteration
    //---------------------------------------------------------------------

    /**
     * @param[in] input
     *   Calling thread's input item.
     *
     * @param[in] reduction_op
     *   Binary reduction operator
     *
     * @param[in] last_lane
     *   Index of last lane in segment
     */
    template <typename ReductionOp, int STEP>
    __device__ __forceinline__ void
    ReduceStep(T &input, ReductionOp reduction_op, int last_lane, Int2Type<STEP> /*step*/)
    {
        input = ReduceStep(input, reduction_op, last_lane, 1 << STEP, Int2Type<IsInteger<T>::IS_SMALL_UNSIGNED>());

        ReduceStep(input, reduction_op, last_lane, Int2Type<STEP + 1>());
    }

    /**
     * @param[in] input
     *   Calling thread's input item.
     *
     * @param[in] reduction_op
     *   Binary reduction operator
     *
     * @param[in] last_lane
     *   Index of last lane in segment
     */
    template <typename ReductionOp>
    __device__ __forceinline__ void ReduceStep(T & /*input*/,
                                               ReductionOp /*reduction_op*/,
                                               int /*last_lane*/,
                                               Int2Type<STEPS> /*step*/)
    {}


    //---------------------------------------------------------------------
    // Reduction operations
    //---------------------------------------------------------------------

    /**
     * @param[in] input
     *   Calling thread's input
     *
     * @param[in] valid_items
     *   Total number of valid items across the logical warp
     *
     * @param[in] reduction_op
     *   Binary reduction operator
     */
    template <typename ReductionOp>
    __device__ __forceinline__ T ReduceImpl(Int2Type<0> /* all_lanes_valid */,
                                            T input,
                                            int valid_items,
                                            ReductionOp reduction_op)
    {
        int last_lane = valid_items - 1;

        T output = input;

        // Template-iterate reduction steps
        ReduceStep(output, reduction_op, last_lane, Int2Type<0>());

        return output;
    }

    /**
     * @param[in] input
     *   Calling thread's input
     *
     * @param[in] valid_items
     *   Total number of valid items across the logical warp
     *
     * @param[in] reduction_op
     *   Binary reduction operator
     */
    template <typename ReductionOp>
    __device__ __forceinline__ T ReduceImpl(Int2Type<1> /* all_lanes_valid */,
                                            T input,
                                            int /* valid_items */,
                                            ReductionOp reduction_op)
    {
        int last_lane = LOGICAL_WARP_THREADS - 1;

        T output = input;

        // Template-iterate reduction steps
        ReduceStep(output, reduction_op, last_lane, Int2Type<0>());

        return output;
    }

    template <class U = T>
    __device__ __forceinline__
    typename std::enable_if<
               (std::is_same<int, U>::value || std::is_same<unsigned int, U>::value)
            && detail::reduce_add_exists<>::value, T>::type
    ReduceImpl(Int2Type<1> /* all_lanes_valid */,
               T input,
               int /* valid_items */,
               cub::Sum /* reduction_op */)
    {
      T output = input;

      NV_IF_TARGET(NV_PROVIDES_SM_80,
                   (output = __reduce_add_sync(member_mask, input);),
                   (output = ReduceImpl<cub::Sum>(Int2Type<1>{},
                                                  input,
                                                  LOGICAL_WARP_THREADS,
                                                  cub::Sum{});));

      return output;
    }

    template <class U = T>
    __device__ __forceinline__
    typename std::enable_if<
               (std::is_same<int, U>::value || std::is_same<unsigned int, U>::value)
            && detail::reduce_min_exists<>::value, T>::type
    ReduceImpl(Int2Type<1> /* all_lanes_valid */,
               T input,
               int /* valid_items */,
               cub::Min /* reduction_op */)
    {
      T output = input;

      NV_IF_TARGET(NV_PROVIDES_SM_80,
                   (output = __reduce_min_sync(member_mask, input);),
                   (output = ReduceImpl<cub::Min>(Int2Type<1>{},
                                                  input,
                                                  LOGICAL_WARP_THREADS,
                                                  cub::Min{});));

      return output;
    }

    template <class U = T>
    __device__ __forceinline__
    typename std::enable_if<
               (std::is_same<int, U>::value || std::is_same<unsigned int, U>::value)
            && detail::reduce_max_exists<>::value, T>::type
    ReduceImpl(Int2Type<1> /* all_lanes_valid */,
               T input,
               int /* valid_items */,
               cub::Max /* reduction_op */)
    {
      T output = input;

      NV_IF_TARGET(NV_PROVIDES_SM_80,
                   (output = __reduce_max_sync(member_mask, input);),
                   (output = ReduceImpl<cub::Max>(Int2Type<1>{},
                                                  input,
                                                  LOGICAL_WARP_THREADS,
                                                  cub::Max{});));

      return output;
    }

    /**
     * @brief Reduction
     *
     * @tparam ALL_LANES_VALID
     *   Whether all lanes in each warp are contributing a valid fold of items
     *
     * @param[in] input
     *   Calling thread's input
     *
     * @param[in] valid_items
     *   Total number of valid items across the logical warp
     *
     * @param[in] reduction_op
     *   Binary reduction operator
     */
    template <bool ALL_LANES_VALID, typename ReductionOp>
    __device__ __forceinline__ T Reduce(T input, int valid_items, ReductionOp reduction_op)
    {
        return ReduceImpl(
            Int2Type<ALL_LANES_VALID>{}, input, valid_items, reduction_op);
    }

    /**
     * @brief Segmented reduction
     *
     * @tparam HEAD_SEGMENTED
     *   Whether flags indicate a segment-head or a segment-tail
     *
     * @param[in] input
     *   Calling thread's input
     *
     * @param[in] flag
     *   Whether or not the current lane is a segment head/tail
     *
     * @param[in] reduction_op
     *   Binary reduction operator
     */
    template <bool HEAD_SEGMENTED, typename FlagT, typename ReductionOp>
    __device__ __forceinline__ T SegmentedReduce(T input, FlagT flag, ReductionOp reduction_op)
    {
        // Get the start flags for each thread in the warp.
        int warp_flags = WARP_BALLOT(flag, member_mask);

        // Convert to tail-segmented
        if (HEAD_SEGMENTED)
            warp_flags >>= 1;

        // Mask out the bits below the current thread
        warp_flags &= LaneMaskGe();

        // Mask of physical lanes outside the logical warp and convert to logical lanemask
        if (!IS_ARCH_WARP)
        {
            warp_flags = (warp_flags & member_mask) >> (warp_id * LOGICAL_WARP_THREADS);
        }

        // Mask in the last lane of logical warp
        warp_flags |= 1u << (LOGICAL_WARP_THREADS - 1);

        // Find the next set flag
        int last_lane = __clz(__brev(warp_flags));

        T output = input;

//        // Iterate reduction steps
//        #pragma unroll
//        for (int STEP = 0; STEP < STEPS; STEP++)
//        {
//            output = ReduceStep(output, reduction_op, last_lane, 1 << STEP, Int2Type<IsInteger<T>::IS_SMALL_UNSIGNED>());
//        }

        // Template-iterate reduction steps
        ReduceStep(output, reduction_op, last_lane, Int2Type<0>());

        return output;
    }
};


CUB_NAMESPACE_END
