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/*
* Copyright (c) 2021-2023 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#ifndef SRC_CORE_NEON_KERNELS_FUSE_BATCH_NORMALIZATION_GENERIC_IMPL_H
#define SRC_CORE_NEON_KERNELS_FUSE_BATCH_NORMALIZATION_GENERIC_IMPL_H
#include "arm_compute/core/Helpers.h"
#include "src/core/NEON/wrapper/wrapper.h"
namespace arm_compute
{
namespace cpu
{
template <typename T>
void fused_batch_normalization_conv(const ITensor *conv_weights, const ITensor *conv_bias, ITensor *fused_weights, ITensor *fused_bias,
const ITensor *bn_mean, const ITensor *bn_var, const ITensor *bn_beta, const ITensor *bn_gamma, float epsilon, const Window &window)
{
using ScalarType = T;
const int size = 16 / conv_weights->info()->element_size();
using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>;
const bool run_in_place_weights = (fused_weights == nullptr) || (fused_weights == conv_weights);
const bool run_in_place_bias = (fused_bias == nullptr) || (conv_bias != nullptr && fused_bias == conv_bias);
// Set build options
Window win = window;
win.set(Window::DimX, Window::Dimension(0, 1, 1));
const int window_step_x = size;
const auto window_start_x = static_cast<int>(window.x().start());
const auto window_end_x = static_cast<int>(window.x().end());
Iterator conv_w_in(conv_weights, win);
Iterator conv_w_out(run_in_place_weights ? conv_weights : fused_weights, win);
const auto conv_bias_in = (conv_bias != nullptr ? reinterpret_cast<ScalarType *>(conv_bias->ptr_to_element(Coordinates(0, 0))) : nullptr);
auto conv_bias_out = (run_in_place_bias ? conv_bias_in : reinterpret_cast<ScalarType *>(fused_bias->ptr_to_element(Coordinates(0, 0))));
const auto input_mean = reinterpret_cast<const ScalarType *>(bn_mean->ptr_to_element(Coordinates(0, 0)));
const auto input_var = reinterpret_cast<const ScalarType *>(bn_var->ptr_to_element(Coordinates(0, 0)));
const auto input_gamma = (bn_gamma != nullptr) ? reinterpret_cast<const ScalarType *>(bn_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr;
const auto input_beta = (bn_beta != nullptr) ? reinterpret_cast<const ScalarType *>(bn_beta->ptr_to_element(Coordinates(0, 0))) : nullptr;
auto mean_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{});
auto var_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{});
auto gamma_vec = wrapper::vdup_n(ScalarType(1), ExactTagType{});
auto beta_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{});
auto rvar_vec = wrapper::vdup_n(ScalarType(0), ExactTagType{});
const auto epsilon_vec = wrapper::vdup_n(ScalarType(epsilon), ExactTagType{});
auto mean = ScalarType(0.0);
auto var = ScalarType(0.0);
auto gamma = ScalarType(1.0);
auto beta = ScalarType(0.0);
auto conv_bias_in_scalar = ScalarType(0.0);
execute_window_loop(win, [&](const Coordinates & id)
{
var = input_var[id[3]];
if(input_gamma != nullptr)
{
gamma = input_gamma[id[3]];
}
if((id[0] == 0) && (id[1] == 0) && (id[2] == 0))
{
if(input_beta != nullptr)
{
beta = input_beta[id[3]];
beta_vec = wrapper::vdup_n(beta, ExactTagType{});
}
// Construct vectors
mean = input_mean[id[3]];
mean_vec = wrapper::vdup_n(mean, ExactTagType{});
if(conv_bias_in != nullptr)
{
conv_bias_in_scalar = conv_bias_in[id[3]];
}
auto conv_bias_tmp_scalar = (conv_bias_in_scalar - mean) / std::sqrt(var + ScalarType(epsilon));
conv_bias_out[id[3]] = (conv_bias_tmp_scalar * gamma) + beta;
}
int x = window_start_x;
auto conv_w_in_ptr = reinterpret_cast<const ScalarType *>(conv_w_in.ptr());
auto conv_w_out_ptr = reinterpret_cast<ScalarType *>(conv_w_out.ptr());
var_vec = wrapper::vdup_n(var, ExactTagType{});
gamma_vec = wrapper::vdup_n(gamma, ExactTagType{});
rvar_vec = wrapper::vinvsqrt(wrapper::vadd(var_vec, epsilon_vec));
for(; x <= (window_end_x - window_step_x); x += window_step_x)
{
auto wn = wrapper::vloadq(conv_w_in_ptr + x);
wn = wrapper::vmul(wn, rvar_vec);
wn = wrapper::vmul(wn, gamma_vec);
// Store results
wrapper::vstore(conv_w_out_ptr + x, wn);
}
// Compute left-over elements
for(; x < window_end_x; ++x)
{
*(conv_w_out_ptr + x) = *(conv_w_in_ptr + x) / std::sqrt(var + ScalarType(epsilon)) * gamma;
}
},
conv_w_in, conv_w_out);
}
}
}
#endif //SRC_CORE_NEON_KERNELS_FUSE_BATCH_NORMALIZATION_GENERIC_IMPL_H
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