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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_rmsnorm2d_fwd.hpp Source File

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 // SPDX-License-Identifier: MIT
 // Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
  
 #pragma once
  
 #include "ck_tile/core.hpp"
 #include "ck_tile/host/host_tensor.hpp"
  
 namespace ck_tile {
  
 // Note: for simplicity, each functor only care about single M
 struct reference_rmsnorm2d_default_epilogue
 {
     template <typename OutDataType, typename AccDataType>
     void operator()(int m, HostTensor<OutDataType>& o, const HostTensor<AccDataType>& acc)
     {
         const int N = acc.mDesc.get_lengths()[1];
         for(int n = 0; n < N; ++n)
         {
             o(m, n) = ck_tile::type_convert<OutDataType>(acc(m, n));
         }
     }
  
     template <typename OutDataType, typename AccDataType>
     auto operator()(int m, const HostTensor<AccDataType>& acc)
     {
         HostTensor<OutDataType> o(acc.get_lengths(), acc.get_strides());
         operator()(m, o, acc);
         return o;
     }
 };
  
 template <typename XDataType,
           typename GammaDataType,
           typename ComputeDataType,
           typename YDataType,
           typename InvRmsDataType,
           typename UnquantYDataType,
           typename Epilogue = reference_rmsnorm2d_default_epilogue>
 void reference_rmsnorm2d_fwd(const HostTensor<XDataType>& x_m_n,
                              const HostTensor<GammaDataType>& gamma_n,
                              HostTensor<YDataType>& y_m_n,
                              HostTensor<InvRmsDataType>& invRms_m,
                              HostTensor<UnquantYDataType>& unquant_y_m_n,
                              ComputeDataType epsilon,
                              Epilogue epilogue_functor = {})
 {
     auto rmsnorm2d_fwd_func = [&](auto m) {
         const int N = x_m_n.mDesc.get_lengths()[1];
  
         ComputeDataType mean_square = 0;
         ComputeDataType divisor     = 0;
  
         for(int n = 0; n < N; ++n)
         {
             ComputeDataType x = ck_tile::type_convert<ComputeDataType>(x_m_n(m, n));
             mean_square += x * x;
         }
  
         mean_square = mean_square / N;
         divisor = ck_tile::type_convert<ComputeDataType>(1) / ck_tile::sqrt(mean_square + epsilon);
  
         if constexpr(!std::is_same_v<InvRmsDataType, ck_tile::null_type>)
             invRms_m(m) = ck_tile::type_convert<InvRmsDataType>(divisor);
  
         HostTensor<ComputeDataType> acc(x_m_n.get_lengths(), x_m_n.get_strides());
         for(int n = 0; n < N; ++n)
         {
             ComputeDataType x     = ck_tile::type_convert<ComputeDataType>(x_m_n(m, n));
             ComputeDataType gamma = ck_tile::type_convert<ComputeDataType>(gamma_n(n));
             acc(m, n)             = x * divisor * gamma;
         }
  
         if constexpr(!std::is_same_v<UnquantYDataType, ck_tile::null_type>)
         {
             epilogue_functor(m, unquant_y_m_n, y_m_n, acc);
         }
         else
         {
             epilogue_functor(m, y_m_n, acc);
         }
     };
  
     make_ParallelTensorFunctor(rmsnorm2d_fwd_func, invRms_m.mDesc.get_lengths()[0])(
         std::thread::hardware_concurrency());
 }
 } // namespace ck_tile