| /* |
| * Copyright (c) 2017-2020 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/core/NEON/kernels/NEL2NormalizeLayerKernel.h" |
| |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/Window.h" |
| #include "src/core/NEON/NEMath.h" |
| #include "src/core/helpers/AutoConfiguration.h" |
| #include "src/core/helpers/WindowHelpers.h" |
| |
| #include "src/core/NEON/wrapper/wrapper.h" |
| #include <arm_neon.h> |
| #include <cmath> |
| |
| namespace arm_compute |
| { |
| namespace |
| { |
| constexpr int max_input_tensor_dim = 3; |
| |
| 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); |
| } |
| |
| Status validate_arguments(const ITensorInfo *input, const ITensorInfo *sum, const ITensorInfo *output, int axis, float epsilon) |
| { |
| ARM_COMPUTE_UNUSED(epsilon); |
| |
| const uint32_t actual_axis = wrap_around(axis, max_input_tensor_dim); |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, sum, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, sum); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(actual_axis > 2, "Actual axis greater than 2 is not supported"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(actual_axis >= TensorShape::num_max_dimensions, "Actual normalization axis greater than max number of dimensions"); |
| |
| // Reduce shape on axis |
| TensorShape sum_shape = input->tensor_shape(); |
| sum_shape.set(actual_axis, 1); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(sum->tensor_shape(), sum_shape); |
| |
| if(output->total_size() != 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(input->tensor_shape(), output->tensor_shape()); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output); |
| } |
| |
| return Status{}; |
| } |
| |
| std::tuple<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output) |
| { |
| Window win = calculate_max_window(*input, Steps()); |
| |
| // Output auto initialization if not yet initialized |
| auto_init_if_empty(*output, input->tensor_shape(), 1, input->data_type()); |
| |
| // NEL2NormalizeLayerKernel doesn't need padding so update_window_and_padding() can be skipped |
| Coordinates coord; |
| coord.set_num_dimensions(output->num_dimensions()); |
| output->set_valid_region(ValidRegion(coord, output->tensor_shape())); |
| |
| return std::make_tuple(Status{}, win); |
| } |
| } // namespace |
| |
| NEL2NormalizeLayerKernel::NEL2NormalizeLayerKernel() |
| : _input(nullptr), _sum(nullptr), _output(nullptr), _actual_axis(0), _epsilon(1e-12) |
| { |
| } |
| |
| void NEL2NormalizeLayerKernel::configure(const ITensor *input, const ITensor *sum, ITensor *output, int axis, float epsilon) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, sum, output); |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), sum->info(), output->info(), axis, epsilon)); |
| |
| _input = input; |
| _sum = sum; |
| _output = output; |
| _actual_axis = wrap_around(axis, max_input_tensor_dim); |
| _epsilon = epsilon; |
| |
| // Configure kernel window |
| auto win_config = validate_and_configure_window(_input->info(), _output->info()); |
| ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config)); |
| |
| INEKernel::configure(std::get<1>(win_config)); |
| } |
| |
| Status NEL2NormalizeLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *sum, const ITensorInfo *output, int axis, float epsilon) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, sum, output, axis, epsilon)); |
| ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input->clone().get(), output->clone().get()))); |
| |
| return Status{}; |
| } |
| |
| void NEL2NormalizeLayerKernel::run(const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); |
| |
| if(_actual_axis > 2) |
| { |
| ARM_COMPUTE_ERROR("Unsupported normalization axis"); |
| } |
| |
| switch(_input->info()->data_type()) |
| { |
| case DataType::F32: |
| (_actual_axis == Window::DimX) ? l2_normalize_X<float, 4>(_input, _sum, _output, _epsilon, window) : l2_normalize_YZ<float, 4>(_input, _sum, _output, _epsilon, window, _actual_axis); |
| break; |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| case DataType::F16: |
| (_actual_axis == Window::DimX) ? l2_normalize_X<float16_t, 8>(_input, _sum, _output, _epsilon, window) : l2_normalize_YZ<float16_t, 8>(_input, _sum, _output, _epsilon, window, _actual_axis); |
| break; |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| default: |
| ARM_COMPUTE_ERROR("Not implemented"); |
| } |
| } |
| } // namespace arm_compute |