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 // SPDX-License-Identifier: MIT
 // Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
  
 #pragma once
  
 #include "ck_tile/core.hpp"
 #include "ck_tile/host/host_tensor.hpp"
 #include <thread>
  
 namespace ck_tile {
  
 template <typename InputType, typename ComputeType, typename OutputType = ComputeType>
 CK_TILE_HOST void
 reference_softmax(const HostTensor<InputType>& x, HostTensor<OutputType>& y, index_t dim = -1)
 {
     index_t rank = x.get_num_of_dimension();
     assert(static_cast<std::size_t>(rank) == y.get_num_of_dimension());
     assert(dim == -1 || dim < rank);
  
     index_t target_dim  = dim == -1 ? (rank - 1) : dim;
     index_t softmax_len = x.get_length(target_dim);
     index_t n_parallel  = x.get_element_size() / softmax_len;
     auto x_len          = x.get_lengths();
  
     auto f = [&](auto i_element) {
         std::vector<size_t> coord = [&]() {
             std::vector<size_t> t_(rank, 0);
             size_t r = i_element;
             for(index_t i = rank - 1; i >= 0; i--)
             {
                 if(i == target_dim)
                     continue;
                 t_[i] = r % x_len[i];
                 r     = r / x_len[i];
             }
             return t_;
         }();
  
         ComputeType v_max = -ck_tile::numeric<ComputeType>::infinity();
  
         // compute max
         for(auto idx = 0; idx < softmax_len; idx++)
         {
             auto c_               = coord;
             c_[target_dim]        = idx;
             const ComputeType v_x = ck_tile::type_convert<ComputeType>(x(c_));
             v_max                 = v_max < v_x ? v_x : v_max;
         }
  
         ComputeType v_exp_sum = static_cast<ComputeType>(0);
  
         // sum
         for(auto idx = 0; idx < softmax_len; idx++)
         {
             auto c_        = coord;
             c_[target_dim] = idx;
  
             const ComputeType v_x = ck_tile::type_convert<ComputeType>(x(c_));
  
             v_exp_sum += ck_tile::exp(v_x - v_max);
         }
  
         // elementwise
         for(auto idx = 0; idx < softmax_len; idx++)
         {
             auto c_        = coord;
             c_[target_dim] = idx;
  
             const ComputeType v_x = ck_tile::type_convert<ComputeType>(x(c_));
  
             auto out = ck_tile::exp(v_x - v_max) / v_exp_sum;
  
             y(c_) = ck_tile::type_convert<OutputType>(out);
         }
     };
  
     make_ParallelTensorFunctor(f, n_parallel)(std::thread::hardware_concurrency());
 }
  
 template <typename InputType, typename ComputeType, typename OutputType = ComputeType>
 CK_TILE_HOST auto reference_softmax(const HostTensor<InputType>& x, index_t dim = -1)
 {
     HostTensor<OutputType> y(x.get_lengths(), x.get_strides());
  
     reference_softmax<InputType, ComputeType, OutputType>(x, y, dim);
  
     return y;
 }
 } // namespace ck_tile