diff options
Diffstat (limited to 'src/core/NEON/kernels/NEInstanceNormalizationLayerKernel.cpp')
| -rw-r--r-- | src/core/NEON/kernels/NEInstanceNormalizationLayerKernel.cpp | 219 |
1 files changed, 63 insertions, 156 deletions
diff --git a/src/core/NEON/kernels/NEInstanceNormalizationLayerKernel.cpp b/src/core/NEON/kernels/NEInstanceNormalizationLayerKernel.cpp index d33431a8d2..0a1780f6ee 100644 --- a/src/core/NEON/kernels/NEInstanceNormalizationLayerKernel.cpp +++ b/src/core/NEON/kernels/NEInstanceNormalizationLayerKernel.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2019-2021 Arm Limited. + * Copyright (c) 2019-2022 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -31,11 +31,14 @@ #include "arm_compute/core/Utils.h" #include "arm_compute/core/Validate.h" #include "arm_compute/core/Window.h" + +#include "src/core/common/Registrars.h" #include "src/core/CPP/Validate.h" -#include "src/core/NEON/NEMath.h" -#include "src/core/NEON/wrapper/wrapper.h" #include "src/core/helpers/AutoConfiguration.h" #include "src/core/helpers/WindowHelpers.h" +#include "src/core/NEON/NEMath.h" +#include "src/core/NEON/wrapper/wrapper.h" +#include "src/cpu/kernels/instancenorm/list.h" #include <arm_neon.h> @@ -43,137 +46,52 @@ namespace arm_compute { namespace { -template <typename InputType, typename AccType = InputType> -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) +struct InstanceNormSelectorData { - vector_float_sum(result, result_square, wrapper::vcvt<float>(wrapper::vgetlow(inputs))); - vector_float_sum(result, result_square, wrapper::vcvt<float>(wrapper::vgethigh(inputs))); -} -#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - -template <typename InputType, typename AccType = InputType> -InputType vector_float_norm(const InputType &inputs, const AccType &vec_mean, const AccType &vec_multip, const AccType &vec_beta) + DataType dt; +}; + +using InstanceNormSelctorPtr = std::add_pointer<bool(const InstanceNormSelectorData &data)>::type; +using InstanceNormUKernelPtr = std::add_pointer<void(ITensor *input, + ITensor *output, + float gamma, + float beta, + float epsilon, + bool use_mixed_precision, + const Window &window)>::type; + +struct InstanceNormKernel { - return wrapper::vadd(wrapper::vmul(wrapper::vsub(inputs, vec_mean), vec_multip), vec_beta); -} - + const char *name; + const InstanceNormSelctorPtr is_selected; + InstanceNormUKernelPtr ukernel; +}; + +static const InstanceNormKernel available_kernels[] = { + {"fp32_neon_instancenorm", [](const InstanceNormSelectorData &data) { return data.dt == DataType::F32; }, + REGISTER_FP32_NEON(arm_compute::cpu::neon_fp32_instancenorm)}, #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC -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; -} + {"fp16_neon_instancenorm", [](const InstanceNormSelectorData &data) { return data.dt == DataType::F16; }, + REGISTER_FP16_NEON(arm_compute::cpu::neon_fp16_instancenorm)}, #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +}; -template <typename T, typename AccType = T> -void instance_normalization_nchw(ITensor *input, ITensor *output, float gamma, float beta, float epsilon, const Window &window) +/** Micro-kernel selector + * + * @param[in] data Selection data passed to help pick the appropriate micro-kernel + * + * @return A matching micro-kernel else nullptr + */ +const InstanceNormKernel *get_implementation(const InstanceNormSelectorData &data) { - /** 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) + for (const auto &uk : available_kernels) { - 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 &) + if (uk.is_selected(data)) { - 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); + return &uk; + } + } + return nullptr; } Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, float gamma, float beta, float epsilon) @@ -184,14 +102,16 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, f 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"); + 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) + 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"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->num_channels() != output->num_channels(), + "Input and output have different number of channels"); } return Status{}; } @@ -210,11 +130,13 @@ std::tuple<Status, Window> validate_and_configure_window(ITensorInfo *input, ITe } // namespace NEInstanceNormalizationLayerKernel::NEInstanceNormalizationLayerKernel() - : _func(nullptr), _input(nullptr), _output(nullptr), _gamma(1), _beta(0), _epsilon(1e-12) + : _input(nullptr), _output(nullptr), _gamma(1), _beta(0), _epsilon(1e-12) { } -void NEInstanceNormalizationLayerKernel::configure(ITensor *input, ITensor *output, const InstanceNormalizationLayerKernelInfo &info) +void NEInstanceNormalizationLayerKernel::configure(ITensor *input, + ITensor *output, + const InstanceNormalizationLayerKernelInfo &info) { ARM_COMPUTE_ERROR_ON_NULLPTR(input); @@ -227,28 +149,6 @@ void NEInstanceNormalizationLayerKernel::configure(ITensor *input, ITensor *outp ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(_input->info(), _output->info(), _gamma, _beta, _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) - { - if(_use_mixed_precision) - { - _func = &instance_normalization_nchw<float16_t, float>; - } - else - { - _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)); @@ -256,10 +156,13 @@ void NEInstanceNormalizationLayerKernel::configure(ITensor *input, ITensor *outp INEKernel::configure(std::get<1>(win_config)); } -Status NEInstanceNormalizationLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const InstanceNormalizationLayerKernelInfo &info) +Status NEInstanceNormalizationLayerKernel::validate(const ITensorInfo *input, + const ITensorInfo *output, + const InstanceNormalizationLayerKernelInfo &info) { ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, info.gamma, info.beta, info.epsilon)); - ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input->clone().get(), (output == nullptr ? input->clone().get() : output->clone().get())))); + 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{}; } @@ -268,6 +171,10 @@ void NEInstanceNormalizationLayerKernel::run(const Window &window, const ThreadI 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); + + const auto *uk = get_implementation(InstanceNormSelectorData{_input->info()->data_type()}); + ARM_COMPUTE_ERROR_ON(uk == nullptr || uk->ukernel == nullptr); + + uk->ukernel(_input, _output, _gamma, _beta, _epsilon, _use_mixed_precision, window); } } // namespace arm_compute |