| /* |
| * Copyright (c) 2019-2022 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/instancenorm/generic/neon/impl.h" |
| #include "src/core/NEON/wrapper/wrapper.h" |
| |
| namespace arm_compute |
| { |
| class ITensor; |
| class Window; |
| namespace cpu |
| { |
| template <typename InputType, typename AccType> |
| void vector_float_sum(AccType &result, AccType &result_square, const InputType &inputs) |
| { |
| result = wrapper::vadd(result, inputs); |
| result_square = wrapper::vadd(result_square, wrapper::vmul(inputs, inputs)); |
| } |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| template <> |
| inline void vector_float_sum(float32x4_t &result, float32x4_t &result_square, const float16x8_t &inputs) |
| { |
| vector_float_sum(result, result_square, wrapper::vcvt<float>(wrapper::vgetlow(inputs))); |
| vector_float_sum(result, result_square, wrapper::vcvt<float>(wrapper::vgethigh(inputs))); |
| } |
| template <> |
| inline float16x8_t vector_float_norm(const float16x8_t &inputs, const float32x4_t &vec_mean, const float32x4_t &vec_multip, const float32x4_t &vec_beta) |
| { |
| const auto input_low = wrapper::vcvt<float>(wrapper::vgetlow(inputs)); |
| const auto input_high = wrapper::vcvt<float>(wrapper::vgethigh(inputs)); |
| const auto result_low = wrapper::vcvt<float16_t>(vector_float_norm(input_low, vec_mean, vec_multip, vec_beta)); |
| const auto result_high = wrapper::vcvt<float16_t>(vector_float_norm(input_high, vec_mean, vec_multip, vec_beta)); |
| float16x8_t result = wrapper::vcombine(result_low, result_high); |
| |
| return result; |
| } |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| |
| template <typename InputType, typename AccType> |
| InputType vector_float_norm(const InputType &inputs, const AccType &vec_mean, const AccType &vec_multip, const AccType &vec_beta) |
| { |
| return wrapper::vadd(wrapper::vmul(wrapper::vsub(inputs, vec_mean), vec_multip), vec_beta); |
| } |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| template <typename T, typename AccType> |
| void instance_normalization_nchw(ITensor *input, ITensor *output, float gamma, float beta, float epsilon, const Window &window) |
| { |
| /** SIMD vector tag type. */ |
| using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>; |
| |
| // Clear X/Y dimensions on execution window as we handle the planes manually |
| Window win = window; |
| win.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| win.set(Window::DimY, Window::Dimension(0, 1, 1)); |
| |
| constexpr int window_step_x = 16 / sizeof(T); |
| const unsigned int elements_plane = input->info()->dimension(0) * output->info()->dimension(1); |
| |
| Iterator input_it(input, win); |
| execute_window_loop(win, [&](const Coordinates & id) |
| { |
| Window win_plane = window; |
| win_plane.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| win_plane.set(Window::DimZ, Window::Dimension(id[2], id[2] + 1, 1)); |
| win_plane.set(3, Window::Dimension(id[3], id[3] + 1, 1)); |
| |
| Iterator input_plane_it(input, win_plane); |
| Iterator output_plane_it(output, win_plane); |
| |
| auto sum_h_w = static_cast<AccType>(0.f); |
| auto sum_squares_h_w = static_cast<AccType>(0.f); |
| |
| execute_window_loop(win_plane, [&](const Coordinates &) |
| { |
| const auto input_ptr = reinterpret_cast<const T *>(input_plane_it.ptr()); |
| |
| auto vec_sum_h_w = wrapper::vdup_n(static_cast<AccType>(0.f), ExactTagType{}); |
| auto vec_sum_squares_h_w = wrapper::vdup_n(static_cast<AccType>(0.f), ExactTagType{}); |
| |
| // Compute S elements per iteration |
| int x = window.x().start(); |
| for(; x <= (window.x().end() - window_step_x); x += window_step_x) |
| { |
| auto vec_input_val = wrapper::vloadq(input_ptr + x); |
| vector_float_sum(vec_sum_h_w, vec_sum_squares_h_w, vec_input_val); |
| } |
| |
| auto vec2_sum_h_w = wrapper::vpadd(wrapper::vgethigh(vec_sum_h_w), wrapper::vgetlow(vec_sum_h_w)); |
| auto vec2_sum_squares_h_w = wrapper::vpadd(wrapper::vgethigh(vec_sum_squares_h_w), wrapper::vgetlow(vec_sum_squares_h_w)); |
| |
| vec2_sum_h_w = wrapper::vpadd(vec2_sum_h_w, vec2_sum_h_w); |
| vec2_sum_squares_h_w = wrapper::vpadd(vec2_sum_squares_h_w, vec2_sum_squares_h_w); |
| |
| sum_h_w += wrapper::vgetlane(vec2_sum_h_w, 0); |
| sum_squares_h_w += wrapper::vgetlane(vec2_sum_squares_h_w, 0); |
| |
| // Compute left-over elements |
| for(; x < window.x().end(); ++x) |
| { |
| const auto value = static_cast<AccType>(*(input_ptr + x)); |
| sum_h_w += value; |
| sum_squares_h_w += value * value; |
| } |
| }, |
| input_plane_it, output_plane_it); |
| |
| const auto mean_h_w = sum_h_w / elements_plane; |
| const auto var_h_w = sum_squares_h_w / elements_plane - mean_h_w * mean_h_w; |
| |
| const auto multip_h_w = gamma / std::sqrt(var_h_w + epsilon); |
| const auto vec_mean_h_w = wrapper::vdup_n(static_cast<AccType>(mean_h_w), ExactTagType{}); |
| const auto vec_multip_h_w = wrapper::vdup_n(static_cast<AccType>(multip_h_w), ExactTagType{}); |
| const auto vec_beta = wrapper::vdup_n(static_cast<AccType>(beta), ExactTagType{}); |
| |
| execute_window_loop(win_plane, [&](const Coordinates &) |
| { |
| auto input_ptr = reinterpret_cast<T *>(input_plane_it.ptr()); |
| auto output_ptr = reinterpret_cast<T *>(output_plane_it.ptr()); |
| |
| // Compute S elements per iteration |
| int x = window.x().start(); |
| //auto vec_val = wrapper::vdup_n(static_cast<T>(0.0f), ExactTagType{}); |
| for(; x <= (window.x().end() - window_step_x); x += window_step_x) |
| { |
| const auto vec_val = wrapper::vloadq(input_ptr + x); |
| const auto normalized_vec = vector_float_norm(vec_val, vec_mean_h_w, vec_multip_h_w, vec_beta); |
| wrapper::vstore(output_ptr + x, normalized_vec); |
| } |
| |
| // Compute left-over elements |
| for(; x < window.x().end(); ++x) |
| { |
| const auto val = static_cast<AccType>(*(input_ptr + x)); |
| *(output_ptr + x) = static_cast<T>((val - mean_h_w) * multip_h_w + beta); |
| } |
| }, |
| input_plane_it, output_plane_it); |
| }, |
| input_it); |
| } |
| |
| template void instance_normalization_nchw<float>(ITensor *input, ITensor *output, float gamma, float beta, float epsilon, const Window &window); |
| #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS) |
| template void instance_normalization_nchw<float16_t, float>(ITensor *input, ITensor *output, float gamma, float beta, float epsilon, const Window &window); |
| template void instance_normalization_nchw<float16_t>(ITensor *input, ITensor *output, float gamma, float beta, float epsilon, const Window &window); |
| #endif //defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS) |
| } // namespace cpu |
| } // namespace arm_compute |