| /* |
| * Copyright (c) 2017-2022 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/l2normlayer/generic/neon/impl.h" |
| |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "src/core/NEON/wrapper/wrapper.h" |
| #include "src/core/common/Registrars.h" |
| |
| #include <cstddef> |
| |
| namespace arm_compute |
| { |
| namespace cpu |
| { |
| template <typename T, int S> |
| void l2_normalize_x(const ITensor *in, const ITensor *sum, ITensor *out, float epsilon, const Window &window) |
| { |
| using ExactTagType = typename wrapper::traits::neon_vector<T, S>::tag_type; |
| |
| const int window_step_x = 16 / data_size_from_type(in->info()->data_type()); |
| const auto window_start_x = static_cast<int>(window.x().start()); |
| const auto window_end_x = static_cast<int>(window.x().end()); |
| |
| Window win_collapsed = window.collapse_if_possible(window, Window::DimZ); |
| win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| Iterator input_it(in, win_collapsed); |
| Iterator sum_it(sum, win_collapsed); |
| Iterator output_it(out, win_collapsed); |
| |
| execute_window_loop(win_collapsed, [&](const Coordinates &) |
| { |
| const auto in_ptr = reinterpret_cast<const T *>(input_it.ptr()); |
| const auto out_ptr = reinterpret_cast<T *>(output_it.ptr()); |
| |
| const T sum_value = *reinterpret_cast<const T *>(sum_it.ptr()); |
| const T norm_value = static_cast<T>(1.f) / std::sqrt(std::max(sum_value, static_cast<T>(epsilon))); |
| const auto vec_norm_value = wrapper::vdup_n(norm_value, ExactTagType{}); |
| |
| // Compute elements over vector steps |
| int x = window_start_x; |
| for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| wrapper::vstore(out_ptr + x, wrapper::vmul(wrapper::vloadq(in_ptr + x), vec_norm_value)); |
| } |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| out_ptr[x] = in_ptr[x] * norm_value; |
| } |
| }, |
| input_it, sum_it, output_it); |
| } |
| |
| template <typename T, int S> |
| void l2_normalize_yz(const ITensor *in, const ITensor *sum, ITensor *out, float epsilon, const Window &window, size_t axis) |
| { |
| using ExactTagType = typename wrapper::traits::neon_vector<T, S>::tag_type; |
| |
| const int window_step_x = 16 / data_size_from_type(in->info()->data_type()); |
| const auto window_start_x = static_cast<int>(window.x().start()); |
| const auto window_end_x = static_cast<int>(window.x().end()); |
| |
| Window win = window; |
| win.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| Window window_sum(win); |
| window_sum.set(axis, Window::Dimension(0, 0, 0)); |
| |
| Iterator input_it(in, win); |
| Iterator sum_it(sum, window_sum); |
| Iterator output_it(out, win); |
| |
| const auto vec_eps = wrapper::vdup_n(static_cast<T>(epsilon), ExactTagType{}); |
| |
| execute_window_loop(win, [&](const Coordinates &) |
| { |
| const auto in_ptr = reinterpret_cast<const T *>(input_it.ptr()); |
| const auto sum_ptr = reinterpret_cast<const T *>(sum_it.ptr()); |
| const auto out_ptr = reinterpret_cast<T *>(output_it.ptr()); |
| |
| // Compute elements over vector steps |
| int x = window_start_x; |
| for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| const auto vec_norm_value = wrapper::vinvsqrt(wrapper::vmax(wrapper::vloadq(sum_ptr + x), vec_eps)); |
| wrapper::vstore(out_ptr + x, wrapper::vmul(wrapper::vloadq(in_ptr + x), vec_norm_value)); |
| } |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| const T norm_value = static_cast<T>(1.f) / std::sqrt(std::max(sum_ptr[x], static_cast<T>(epsilon))); |
| out_ptr[x] = in_ptr[x] * norm_value; |
| } |
| }, |
| input_it, sum_it, output_it); |
| } |
| |
| template void l2_normalize_yz<float, 4>(const ITensor *in, const ITensor *sum, ITensor *out, float epsilon, const Window &window, size_t axis); |
| template void l2_normalize_x<float, 4>(const ITensor *in, const ITensor *sum, ITensor *out, float epsilon, const Window &window); |
| |
| #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS) |
| template void l2_normalize_yz<float16_t, 8>(const ITensor *in, const ITensor *sum, ITensor *out, float epsilon, const Window &window, size_t axis); |
| template void l2_normalize_x<float16_t, 8>(const ITensor *in, const ITensor *sum, ITensor *out, float epsilon, const Window &window); |
| #endif //defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS) |
| } // namespace cpu |
| } // namespace arm_compute |