| /* |
| * Copyright (c) 2018-2022 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. |
| */ |
| |
| #include "src/cpu/kernels/fuse_batch_normalization/generic/impl.h" |
| |
| namespace arm_compute |
| { |
| namespace cpu |
| { |
| template <typename T> |
| void fused_batch_normalization_dwc_nchw(const ITensor *dwc_weights, const ITensor *dwc_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 / dwc_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 == dwc_weights); |
| const bool run_in_place_bias = (fused_bias == nullptr) || (dwc_bias != nullptr && fused_bias == dwc_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 dwc_w_in(dwc_weights, win); |
| Iterator dwc_w_out(run_in_place_weights ? dwc_weights : fused_weights, win); |
| |
| const auto dwc_bias_in = (dwc_bias != nullptr ? reinterpret_cast<ScalarType *>(dwc_bias->ptr_to_element(Coordinates(0, 0))) : nullptr); |
| auto dwc_bias_out = (run_in_place_bias ? dwc_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 dwc_bias_in_scalar = ScalarType(0.0); |
| execute_window_loop(win, [&](const Coordinates & id) |
| { |
| var = input_var[id[2]]; |
| if(input_gamma != nullptr) |
| { |
| gamma = input_gamma[id[2]]; |
| } |
| |
| if(id[1] == 0) |
| { |
| mean = input_mean[id[2]]; |
| |
| // Construct vectors |
| mean_vec = wrapper::vdup_n(mean, ExactTagType{}); |
| if(input_beta != nullptr) |
| { |
| beta = input_beta[id[2]]; |
| beta_vec = wrapper::vdup_n(beta, ExactTagType{}); |
| } |
| |
| if(dwc_bias_in != nullptr) |
| { |
| dwc_bias_in_scalar = dwc_bias_in[id[2]]; |
| } |
| |
| auto dwc_bias_tmp_scalar = (dwc_bias_in_scalar - mean) / std::sqrt(var + ScalarType(epsilon)); |
| dwc_bias_out[id[2]] = (dwc_bias_tmp_scalar * gamma) + beta; |
| } |
| |
| int x = window_start_x; |
| auto dwc_w_in_ptr = reinterpret_cast<const ScalarType *>(dwc_w_in.ptr()); |
| auto dwc_w_out_ptr = reinterpret_cast<ScalarType *>(dwc_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(dwc_w_in_ptr + x); |
| wn = wrapper::vmul(wn, rvar_vec); |
| wn = wrapper::vmul(wn, gamma_vec); |
| |
| // Store results |
| wrapper::vstore(dwc_w_out_ptr + x, wn); |
| } |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| *(dwc_w_out_ptr + x) = *(dwc_w_in_ptr + x) / std::sqrt(var + ScalarType(epsilon)) * gamma; |
| } |
| }, |
| dwc_w_in, dwc_w_out); |
| } |
| |
| void fused_batch_normalization_dwc_nchw_f32(const ITensor *dwc_weights, const ITensor *dwc_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) |
| { |
| return fused_batch_normalization_dwc_nchw<float32_t>(dwc_weights, dwc_bias, fused_weights, fused_bias, |
| bn_mean, bn_var, bn_beta, bn_gamma, epsilon, window); |
| } |
| |
| #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS) |
| void fused_batch_normalization_dwc_nchw_f16(const ITensor *dwc_weights, const ITensor *dwc_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) |
| { |
| return fused_batch_normalization_dwc_nchw<float16_t>(dwc_weights, dwc_bias, fused_weights, fused_bias, |
| bn_mean, bn_var, bn_beta, bn_gamma, epsilon, window); |
| } |
| #endif /* defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS) */ |
| |
| } // namespace cpu |
| } // namespace arm_compute |