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 // SPDX-License-Identifier: MIT
 // Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
  
 #pragma once
  
 #include "ck_tile/core.hpp"
 #include <tuple>
  
 // This file is not support cross warp reduce
 namespace ck_tile {
  
 /*
  * TODO: block_tile_reduce_sync() currently has a limitation
  * Y dim must have at least one dim not been reduced
  */
 // synchronize reduce result (cross lane reduction and broadcast on replicated dimension)
 template <typename AccDistributedTensor_,
           typename ReduceFunc,
           bool WithBroadcast = true,
           bool CrossWarp     = true>
 CK_TILE_DEVICE void block_tile_reduce_sync(AccDistributedTensor_& acc_tensor,
                                            const ReduceFunc& reduce_func,
                                            bool_constant<WithBroadcast> = {},
                                            bool_constant<CrossWarp>     = {})
 {
     using Dstr             = typename AccDistributedTensor_::StaticTileDistribution;
     using DstrEncode       = typename Dstr::DstrEncode;
     using DstrEncodeDetail = typename DstrEncode::detail;
  
     constexpr index_t NDimP = Dstr::get_num_of_dimension_p();
     constexpr index_t NDimR = Dstr::get_num_of_dimension_r();
  
     constexpr index_t idim_p_lane = NDimP - 1;
  
     const auto ps_idx = detail::get_partition_index(acc_tensor.get_tile_distribution());
     const auto rs_idx = acc_tensor.get_tile_distribution().calculate_rs_index_from_ps_index(ps_idx);
  
     constexpr index_t thread_buf_size = AccDistributedTensor_::get_thread_buffer_size();
  
     // loop over thread data
     static_for<0, thread_buf_size, 1>{}([&](auto i) {
         auto v_local = acc_tensor.get_thread_buffer()[i];
  
         // cross-lane reduce for replication
         // only reduce on R dimension correspond to lane
         // (lane id maps to this R dimension)
         static_for<0, NDimR, 1>{}([&](auto idim_r) {
             // FIXME: nasty to use does_p_own_r_
             if constexpr(DstrEncodeDetail::does_p_own_r_[idim_p_lane][idim_r])
             {
                 constexpr index_t r_length = DstrEncode::rs_lengths_[idim_r];
  
                 constexpr index_t lid_over_rid_derivative =
                     DstrEncodeDetail::ps_over_rs_derivative_[idim_p_lane][idim_r];
  
                 static_assert(is_power_of_two_integer(r_length),
                               "wrong! only support power of 2 reduction");
  
                 constexpr index_t nstage = integer_log2_floor(r_length);
  
                 // reduction sweep forward
                 static_for<0, nstage, 1>{}([&](auto istage) {
                     if constexpr(CrossWarp)
                     {
                         constexpr index_t lid_delta =
                             lid_over_rid_derivative * (1 << (nstage - istage - 1));
  
                         // pull data from remote lane
                         const auto v_remote = warp_shuffle_down(v_local, lid_delta);
  
                         // reduce
                         v_local = reduce_func(v_local, v_remote);
                     }
                     else
                     {
                         // pull data from remote lane
                         const auto v_swapped_regs = warp_shuffle_down_pair(v_local);
                         // reduce
                         v_local = reduce_func(v_swapped_regs.at(0), v_swapped_regs.at(1));
                     }
                 });
             }
         });
  
         if constexpr(WithBroadcast)
         {
             // cross-lane broadcast for replication
             // only broadcast on R dimension correspond to lane
             // (lane id maps to this R dimension)
             static_for<0, NDimR, 1>{}([&](auto idim_r) {
                 // FIXME: nasty to use does_p_own_r_
                 if constexpr(DstrEncodeDetail::does_p_own_r_[idim_p_lane][idim_r])
                 {
                     const index_t r_id = rs_idx[idim_r];
  
                     constexpr index_t r_length = DstrEncode::rs_lengths_[idim_r];
  
                     constexpr index_t lid_over_rid_derivative =
                         DstrEncodeDetail::ps_over_rs_derivative_[NDimP - 1][idim_r];
  
                     static_assert(is_power_of_two_integer(r_length),
                                   "wrong! only support power of 2 reduction");
  
                     constexpr index_t nstage = integer_log2_floor(r_length);
  
                     // broadcast sweep backward
                     static_for<0, nstage, 1>{}([&](auto istage) {
                         // do I hold reduced data?
                         const bool do_i_hold_reduced_data = r_id < (1 << istage);
  
                         constexpr index_t lid_delta = lid_over_rid_derivative * (1 << istage);
  
                         // pull data from remote lane
                         const auto v_remote = warp_shuffle_up(v_local, lid_delta);
  
                         // decide whether to update local data with remote data
                         v_local = do_i_hold_reduced_data ? v_local : v_remote;
                     });
                 }
             });
         }
  
         acc_tensor.get_thread_buffer()(i) = v_local;
     });
 }
  
 /*
  * this version is faster, using xor to do reduce, no need broadcast anymore
  * TODO: the limitation is to-be-reduced P dim can only mapping to one R dim?
  */
 template <typename AccDistributedTensor_, typename ReduceFunc>
 CK_TILE_DEVICE void block_tile_reduce_xor_sync(AccDistributedTensor_& acc_tensor,
                                                const ReduceFunc& reduce_func)
 {
     using Dstr             = typename AccDistributedTensor_::StaticTileDistribution;
     using DstrEncode       = typename Dstr::DstrEncode;
     using DstrEncodeDetail = typename DstrEncode::detail;
  
     constexpr index_t NDimP = Dstr::get_num_of_dimension_p();
     constexpr index_t NDimR = Dstr::get_num_of_dimension_r();
  
     constexpr index_t idim_p_lane = NDimP - 1;
  
     constexpr index_t thread_buf_size = AccDistributedTensor_::get_thread_buffer_size();
  
     // loop over thread data
     static_for<0, thread_buf_size, 1>{}([&](auto i) {
         auto v_local = acc_tensor.get_thread_buffer()[i];
  
         // cross-lane reduce for replication
         // only reduce on R dimension correspond to lane
         // (lane id maps to this R dimension)
         static_for<0, NDimR, 1>{}([&](auto idim_r) {
             // FIXME: nasty to use does_p_own_r_
             if constexpr(DstrEncodeDetail::does_p_own_r_[idim_p_lane][idim_r])
             {
                 constexpr index_t r_length = DstrEncode::rs_lengths_[idim_r];
  
                 constexpr index_t lid_over_rid_derivative =
                     DstrEncodeDetail::ps_over_rs_derivative_[idim_p_lane][idim_r];
  
                 static_assert(is_power_of_two_integer(r_length),
                               "wrong! only support power of 2 reduction");
  
                 constexpr index_t nstage = integer_log2_floor(r_length);
  
                 // reduction sweep forward
                 static_for<0, nstage, 1>{}([&](auto istage) {
                     // xor
                     index_t src_lane =
                         __lane_id() ^ (number<lid_over_rid_derivative << istage.value>{}.value);
  
                     // pull data from remote lane
                     const auto v_remote = warp_shuffle(v_local, src_lane);
  
                     // reduce
                     v_local = reduce_func(v_local, v_remote);
                 });
             }
         });
  
         acc_tensor.get_thread_buffer()(i) = v_local;
     });
 }
  
 // FIXME: this is for 2D to 1D reduce only, need to support n-D
 template <typename AccDistributedTensor_,
           typename InDistributedTensor_,
           index_t... InReduceDims,
           typename ReduceFunc>
 CK_TILE_DEVICE void block_tile_reduce(AccDistributedTensor_& acc_tensor,
                                       const InDistributedTensor_& in_tensor,
                                       sequence<InReduceDims...>,
                                       const ReduceFunc& reduce_func)
 {
     constexpr auto I0 = number<0>{};
     constexpr auto I1 = number<1>{};
  
 #if 0
     constexpr auto in_reduce_dims = sequence<InReduceDims...>{};
  
     constexpr index_t ndim_in        = InDistributedTensor_::get_num_of_dimension();
     constexpr index_t ndim_in_reduce = in_reduce_dims.size();
     constexpr index_t ndim_in_free   = ndim_in - ndim_in_reduce;
  
     constexpr auto in_free_dims_arr = [&] {
         array<bool, ndim_free> is_free_dims{true};
  
         for(index_t i = 0; i < ndim_reduce; i++)
         {
             is_free_dims(in_reduce_dims[i]) = false;
         }
  
         array<index_t, ndim_free> in_free_dims{-1};
  
         index_t cnt = 0;
  
         for(index_t i = 0; i < ndim_in; i++)
         {
             if(is_free_dims[i])
             {
                 in_free_dims(cnt) = i;
  
                 cnt++
             }
         }
  
         return is_free_dims;
     }();
  
     constexpr auto in_free_dims = TO_SEQUENCE(is_free_dims_arr, ndim_in_free);
 #else
  
     constexpr auto spans = InDistributedTensor_::get_distributed_spans();
  
     // in-thread reduction
     // FIXME: hard coded to be 2D to 1D reduction
     sweep_tile_span(spans[I0], [&](auto dstr_idx_i0) {
         constexpr auto acc_dstr_idx = make_tuple(dstr_idx_i0);
  
         auto acc = acc_tensor[acc_dstr_idx];
  
         // FIXME
         sweep_tile_span(spans[I1], [&](auto dstr_idx_i1) {
             constexpr auto in_dstr_idx = make_tuple(dstr_idx_i0, dstr_idx_i1);
  
             const auto in = in_tensor[in_dstr_idx];
  
             acc = reduce_func(acc, in);
         });
  
         acc_tensor(acc_dstr_idx) = acc;
     });
 #endif
 }
  
 /*
  * TODO: block_tile_reduce() currently has a limitation
  * Y dim must have at least one dim not been reduced
  */
 template <typename AccDataType_,
           typename InDistributedTensor_,
           index_t... InReduceDims,
           typename ReduceFunc,
           typename InDataType_>
 CK_TILE_DEVICE auto block_tile_reduce(const InDistributedTensor_& in_tensor,
                                       sequence<InReduceDims...> in_reduce_dims,
                                       const ReduceFunc& reduce_func,
                                       const InDataType_& reduce_init)
 {
     using InDataType  = typename InDistributedTensor_::DataType;
     using AccDataType = remove_cvref_t<AccDataType_>;
  
     static_assert(std::is_same_v<InDataType, remove_cvref_t<InDataType_>>, "wrong!");
  
     // declare acc_tensor
     constexpr auto acc_dstr =
         make_static_tile_distribution(ck_tile::detail::make_reduce_tile_distribution_encoding(
             InDistributedTensor_::get_tile_distribution().get_static_tile_distribution_encoding(),
             sequence<InReduceDims...>{}));
  
     auto acc_tensor = make_static_distributed_tensor<AccDataType>(acc_dstr);
  
     // init acc_tensor
     tile_elementwise_inout([&](auto& acc) { acc = type_convert<AccDataType>(reduce_init); },
                            acc_tensor);
  
     // warp reduce
     block_tile_reduce(acc_tensor, in_tensor, in_reduce_dims, reduce_func);
  
     return acc_tensor;
 }
  
 // this version only support 2D->1D reduce (reduce-dim=seq<0, 1>)
 // this version only support in/acc/out datatypes are the same
 // this version will call thread/warp+sync in one function call
 //
 template <typename InDistributedTensor_>
 struct BlockReduce2D
 {
     using InDistributedTensor = remove_cvref_t<InDistributedTensor_>;
     using InDataType          = typename InDistributedTensor::DataType;
  
     CK_TILE_HOST_DEVICE BlockReduce2D(const InDistributedTensor& t_, const InDataType& reduce_init_)
         : t(t_), reduce_init(reduce_init_)
     {
     }
  
     CK_TILE_HOST_DEVICE constexpr auto MakeDstBlockTile() const
     {
         using ReduceDim = sequence<1>; // hard coded
         constexpr auto acc_dstr =
             make_static_tile_distribution(ck_tile::detail::make_reduce_tile_distribution_encoding(
                 InDistributedTensor::get_tile_distribution()
                     .get_static_tile_distribution_encoding(),
                 ReduceDim{}));
  
         auto dst_ = make_static_distributed_tensor<InDataType>(acc_dstr);
         // init acc_tensor
         tile_elementwise_inout([&](auto& x_) { x_ = type_convert<InDataType>(reduce_init); }, dst_);
         return dst_;
     }
  
     // return number of pixels each lane need to reduce
     CK_TILE_HOST_DEVICE constexpr auto get_reduce_length_y() const
     {
         constexpr auto spans = InDistributedTensor::get_distributed_spans();
     }
  
     // Here ReducePacksPerXDim is not the same meaning as that in static_uford/sweep_tile_uspan
     // this is number of packs along the X-dim. We need to compute the Unpacks along the Y dim
     // internally
     // For simplicity, we just support along the row dimension, ReducePacksPerXDim is always 2
     // element , and the first element is always ignored For simplicity, will always try from
     // right-to-left to find alone which Y dim to split
     template <typename ReduceFunc,
               typename ReduceSyncFunc,
               typename ReducePacksPerXDim = uniform_sequence_gen_t<2, 1>>
     CK_TILE_HOST_DEVICE auto operator()(const ReduceFunc& reduce_func,
                                         const ReduceSyncFunc& reduce_sync_func,
                                         ReducePacksPerXDim = {}) const
     {
         constexpr auto spans = InDistributedTensor::get_distributed_spans();
  
         constexpr auto row_y_unpacks = [&]() {
             constexpr auto row_y_lengths = typename decltype(spans[number<1>{}])::Impl{};
             constexpr auto row_y_size =
                 reduce_on_sequence(row_y_lengths, multiplies{}, number<1>{});
             constexpr auto row_y_packs = ReducePacksPerXDim{}.at(number<1>{});
  
             static_assert(row_y_size % row_y_packs == 0);
  
             constexpr auto row_y_slice_size = row_y_size / row_y_packs;
  
             constexpr auto slice_info = slice_sequence(row_y_lengths, number<row_y_slice_size>{});
             constexpr auto unpacks    = slice_info[number<1>{}];
             return unpacks;
         }();
  
         auto acc_tensor = MakeDstBlockTile();
  
         // in-thread reduction
         // FIXME: hard coded to be 2D to 1D reduction
         sweep_tile_span(spans[number<0>{}], [&](auto dstr_idx_i0) {
             constexpr auto acc_dstr_idx = make_tuple(dstr_idx_i0);
  
             auto acc = acc_tensor[acc_dstr_idx];
  
             sweep_tile_uspan(
                 spans[number<1>{}],
                 [&](auto... dstr_idx_i1) {
                     acc = reduce_func(acc, t[make_tuple(dstr_idx_i0, dstr_idx_i1)]...);
                 },
                 row_y_unpacks);
  
             acc_tensor(acc_dstr_idx) = acc;
         });
  
         // TODO: always use xor to do cross-lane reduce
         block_tile_reduce_xor_sync(acc_tensor, reduce_sync_func);
  
         return acc_tensor;
     }
  
     template <typename ReduceFunc>
     CK_TILE_HOST_DEVICE auto operator()(const ReduceFunc& reduce_func) const
     {
         return operator()(reduce_func, reduce_func);
     }
  
     InDistributedTensor t;
     InDataType reduce_init;
 };
  
 // deduction guide
 template <typename T>
 CK_TILE_HOST_DEVICE_EXTERN BlockReduce2D(const T&, const typename T::DataType&) -> BlockReduce2D<T>;
  
 } // namespace ck_tile