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Composable Kernel: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-composable-kernel/checkouts/develop/include/ck_tile/host/reference/reference_batched_softmax.hpp Source File

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 // SPDX-License-Identifier: MIT
 // Copyright (c) 2018-2023, 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 ADataType,
           typename CompDataType,
           typename BDataType,
           typename CompElementOp = ck_tile::identity>
 CK_TILE_HOST void reference_batched_softmax(
     const HostTensor<ADataType>& a_b_m_n,
     HostTensor<BDataType>& b_b_m_n,
     const CompElementOp& comp_element_op                                    = {},
     std::optional<std::reference_wrapper<HostTensor<CompDataType>>> lse_b_m = std::nullopt)
 {
     const int N = a_b_m_n.mDesc.get_lengths()[2];
  
     auto f = [&](auto batch, auto m) {
         CompDataType v_max = -ck_tile::numeric<CompDataType>::infinity();
  
         // max
         for(int n = 0; n < N; ++n)
         {
             const CompDataType v_a = ck_tile::type_convert<CompDataType>(a_b_m_n(batch, m, n));
  
             v_max = v_max < v_a ? v_a : v_max;
         }
  
         CompDataType v_exp_sum = 0;
         // validate v_max if all the elements within a row are -INF
         if(std::isinf(v_max) && v_max < 0)
         {
             v_max = ck_tile::type_convert<CompDataType>(0.f);
         }
  
         // sum
         for(int n = 0; n < N; ++n)
         {
             const CompDataType v_a = ck_tile::type_convert<CompDataType>(a_b_m_n(batch, m, n));
  
             v_exp_sum += ck_tile::exp(v_a - v_max);
         }
  
         // if sum is zero(masked), or nan/inf(other computation error), don't do divide
         CompDataType inv_sum = (v_exp_sum == 0.f ? 1.f : 1.f / v_exp_sum);
  
         // elementwise
         for(int n = 0; n < N; ++n)
         {
             const CompDataType v_a = ck_tile::type_convert<CompDataType>(a_b_m_n(batch, m, n));
             const CompDataType v_b = ck_tile::exp(v_a - v_max) * inv_sum;
  
             b_b_m_n(batch, m, n) = ck_tile::type_convert<BDataType>(comp_element_op(v_b));
         }
         // lse
         if(lse_b_m)
         {
             lse_b_m->get()(batch, m) = v_max + ck_tile::log(v_exp_sum);
         }
     };
  
     make_ParallelTensorFunctor(f, b_b_m_n.mDesc.get_lengths()[0], b_b_m_n.mDesc.get_lengths()[1])(
         std::thread::hardware_concurrency());
 }
 } // namespace ck_tile