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Diffstat (limited to 'src/cpu/kernels/l2normlayer/generic/neon/impl.h')
| -rw-r--r-- | src/cpu/kernels/l2normlayer/generic/neon/impl.h | 131 |
1 files changed, 131 insertions, 0 deletions
diff --git a/src/cpu/kernels/l2normlayer/generic/neon/impl.h b/src/cpu/kernels/l2normlayer/generic/neon/impl.h new file mode 100644 index 0000000000..6bd19299b7 --- /dev/null +++ b/src/cpu/kernels/l2normlayer/generic/neon/impl.h @@ -0,0 +1,131 @@ +/* + * Copyright (c) 2017-2023 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_L2NORMLAYER_LIST_H +#define SRC_CORE_NEON_KERNELS_L2NORMLAYER_LIST_H + +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/TensorInfo.h" + +#include "src/core/common/Registrars.h" +#include "src/core/NEON/wrapper/wrapper.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); +} +} // namespace cpu +} // namespace arm_compute +#endif //SRC_CORE_NEON_KERNELS_L2NORMLAYER_LIST_H |