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Diffstat (limited to 'compute/ARMComputeEx/src/core/NEON/kernels/NEInstanceNormalizationLayerKernelEx.cpp')
-rw-r--r-- | compute/ARMComputeEx/src/core/NEON/kernels/NEInstanceNormalizationLayerKernelEx.cpp | 280 |
1 files changed, 280 insertions, 0 deletions
diff --git a/compute/ARMComputeEx/src/core/NEON/kernels/NEInstanceNormalizationLayerKernelEx.cpp b/compute/ARMComputeEx/src/core/NEON/kernels/NEInstanceNormalizationLayerKernelEx.cpp new file mode 100644 index 000000000..1ea77fb5c --- /dev/null +++ b/compute/ARMComputeEx/src/core/NEON/kernels/NEInstanceNormalizationLayerKernelEx.cpp @@ -0,0 +1,280 @@ +/* + * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2019 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 "arm_compute/core/NEON/kernels/NEInstanceNormalizationLayerKernelEx.h" + +#include "arm_compute/core/CPP/Validate.h" +#include "arm_compute/core/Error.h" +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/ITensor.h" +#include "arm_compute/core/NEON/NEMath.h" +#include "arm_compute/core/NEON/wrapper/wrapper.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 <arm_neon.h> + +namespace arm_compute +{ +namespace +{ +template <typename T> +void instance_normalization_nchw(ITensor *input, ITensor *output, ITensor *gamma, ITensor *beta, + float epsilon, const Window &window) +{ + /** NEON 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); + const auto channel_idx = + get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::CHANNEL); + + 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<T>(0.f); + auto sum_squares_h_w = static_cast<T>(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<T>(0.f), ExactTagType{}); + auto vec_sum_squares_h_w = wrapper::vdup_n(static_cast<T>(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); + vec_sum_h_w = wrapper::vadd(vec_sum_h_w, vec_input_val); + vec_sum_squares_h_w = + wrapper::vadd(vec_sum_squares_h_w, wrapper::vmul(vec_input_val, 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)); + for (int i = 0; i < window_step_x / 4; ++i) + { + 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 = *(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; + + auto gamma_val = 1.0f; + if (gamma != nullptr) + { + gamma_val = *reinterpret_cast<T *>(gamma->ptr_to_element({id[channel_idx]})); + } + const auto multip_h_w = gamma_val / std::sqrt(var_h_w + epsilon); + const auto vec_mean_h_w = wrapper::vdup_n(static_cast<T>(mean_h_w), ExactTagType{}); + const auto vec_multip_h_w = wrapper::vdup_n(static_cast<T>(multip_h_w), ExactTagType{}); + auto beta_val = 0.0f; + if (beta != nullptr) + { + beta_val = *reinterpret_cast<T *>(beta->ptr_to_element({id[channel_idx]})); + } + const auto vec_beta = wrapper::vdup_n(static_cast<T>(beta_val), 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) + { + vec_val = wrapper::vloadq(input_ptr + x); + vec_val = wrapper::vadd( + wrapper::vmul(wrapper::vsub(vec_val, vec_mean_h_w), vec_multip_h_w), vec_beta); + wrapper::vstore(output_ptr + x, vec_val); + } + + // Compute left-over elements + for (; x < window.x().end(); ++x) + { + *(output_ptr + x) = ((*(input_ptr + x)) - mean_h_w) * multip_h_w + beta_val; + } + }, + input_plane_it, output_plane_it); + }, + input_it); +} + +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, + const ITensorInfo *gamma, const ITensorInfo *beta, float epsilon) +{ + ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(epsilon == 0.f, "Epsilon must be different than 0"); + + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(input, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_layout() == DataLayout::NHWC, + "NHWC data layout is not supported by the kernel directly"); + + if (output != nullptr && 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_DATA_LAYOUT(input, output); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->num_channels() != output->num_channels(), + "Input and output have different number of channels"); + } + + if (gamma != nullptr) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, gamma); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->dimension(get_data_layout_dimension_index( + input->data_layout(), DataLayoutDimension::CHANNEL)) != + gamma->dimension(0), + "Gamma's size must be the same as size of input's channel"); + } + + if (beta != nullptr) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, beta); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->dimension(get_data_layout_dimension_index( + input->data_layout(), DataLayoutDimension::CHANNEL)) != + beta->dimension(0), + "Beta's size must be the same as size of input's channel"); + } + + return Status{}; +} + +std::tuple<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output) +{ + // We handle the planes manually + Window win = calculate_max_window(*input, Steps(1)); + + // Output auto initialization if not yet initialized + auto_init_if_empty(*output, input->tensor_shape(), 1, input->data_type()); + + // NEInstanceNormalizationLayerKernelEx 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_pair(Status{}, win); +} +} // namespace + +NEInstanceNormalizationLayerKernelEx::NEInstanceNormalizationLayerKernelEx() + : _func(nullptr), _input(nullptr), _output(nullptr), _gamma(nullptr), _beta(nullptr), + _epsilon(1e-12) +{ +} + +void NEInstanceNormalizationLayerKernelEx::configure(ITensor *input, ITensor *output, + ITensor *gamma, ITensor *beta, float epsilon) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input); + + _input = input; + _output = output == nullptr ? input : output; + _gamma = gamma; + _beta = beta; + _epsilon = epsilon; + + ARM_COMPUTE_ERROR_THROW_ON( + validate_arguments(_input->info(), _output->info(), gamma->info(), beta->info(), epsilon)); + + if (_input->info()->data_type() == DataType::F32) + { + _func = &instance_normalization_nchw<float>; + } +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + else if (_input->info()->data_type() == DataType::F16) + { + _func = &instance_normalization_nchw<float16_t>; + } +#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + else + { + ARM_COMPUTE_ERROR("Unsupported data type"); + } + + // 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 NEInstanceNormalizationLayerKernelEx::validate(const ITensorInfo *input, + const ITensorInfo *output, + const ITensorInfo *gamma, + const ITensorInfo *beta, float epsilon) +{ + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, gamma, beta, epsilon)); + ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window( + input->clone().get(), (output == nullptr ? input->clone().get() : output->clone().get())))); + return Status{}; +} + +void NEInstanceNormalizationLayerKernelEx::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); + (*_func)(_input, _output, _gamma, _beta, _epsilon, window); +} +} // namespace arm_compute |