diff options
Diffstat (limited to 'src/core')
-rw-r--r-- | src/core/NEON/kernels/NEL2NormalizeLayerKernel.cpp | 142 |
1 files changed, 55 insertions, 87 deletions
diff --git a/src/core/NEON/kernels/NEL2NormalizeLayerKernel.cpp b/src/core/NEON/kernels/NEL2NormalizeLayerKernel.cpp index 9bda82d416..8ab0288ab1 100644 --- a/src/core/NEON/kernels/NEL2NormalizeLayerKernel.cpp +++ b/src/core/NEON/kernels/NEL2NormalizeLayerKernel.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2021 Arm Limited. + * Copyright (c) 2017-2022 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -30,11 +30,13 @@ #include "arm_compute/core/Utils.h" #include "arm_compute/core/Validate.h" #include "arm_compute/core/Window.h" +#include "src/common/cpuinfo/CpuIsaInfo.h" #include "src/core/NEON/NEMath.h" +#include "src/core/common/Registrars.h" #include "src/core/helpers/AutoConfiguration.h" #include "src/core/helpers/WindowHelpers.h" +#include "src/cpu/kernels/l2normlayer/list.h" -#include "src/core/NEON/wrapper/wrapper.h" #include <arm_neon.h> #include <cmath> @@ -44,90 +46,64 @@ 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) +struct L2NormalizeLayerSelectorData { - using ExactTagType = typename wrapper::traits::neon_vector<T, S>::tag_type; + DataType dt; + unsigned int actual_axis; + cpuinfo::CpuIsaInfo isa; +}; - 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()); +using L2NormalizeLayerKernelSelctorPtr = std::add_pointer<bool(const L2NormalizeLayerSelectorData &data)>::type; - Window win_collapsed = window.collapse_if_possible(window, Window::DimZ); - win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1)); +using L2NormalizeLayerPtr = std::add_pointer<void(const ITensor *in, const ITensor *sum, ITensor *out, float epsilon, const Window &window, size_t axis)>::type; - Iterator input_it(in, win_collapsed); - Iterator sum_it(sum, win_collapsed); - Iterator output_it(out, win_collapsed); +struct L2NormalizeLayerKernel +{ + const char *name; + const L2NormalizeLayerKernelSelctorPtr is_selected; + L2NormalizeLayerPtr ukernel; +}; - execute_window_loop(win_collapsed, [&](const Coordinates &) +static const L2NormalizeLayerKernel available_kernels[] = +{ { - 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; - } + "fp32_neon_l2normalize_x", + [](const L2NormalizeLayerSelectorData & data) { return data.dt == DataType::F32 && data.actual_axis == Window::DimX; }, + REGISTER_FP32_NEON(arm_compute::cpu::neon_fp32_l2_normalize_x) }, - input_it, sum_it, output_it); -} + { + "fp32_neon_l2normalize_yz", + [](const L2NormalizeLayerSelectorData & data) { return data.dt == DataType::F32 && data.actual_axis != Window::DimX; }, + REGISTER_FP32_NEON(arm_compute::cpu::neon_fp32_l2_normalize_yz) + }, + { + "fp16_neon_l2normalize_x", + [](const L2NormalizeLayerSelectorData & data) { return data.dt == DataType::F16 && data.isa.fp16 && data.actual_axis == Window::DimX; }, + REGISTER_FP16_NEON(arm_compute::cpu::neon_fp16_l2_normalize_x), + }, + { + "fp16_neon_l2normalize_yz", + [](const L2NormalizeLayerSelectorData & data) { return data.dt == DataType::F16 && data.isa.fp16 && data.actual_axis != Window::DimX; }, + REGISTER_FP16_NEON(arm_compute::cpu::neon_fp16_l2_normalize_yz), + }, +}; -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) +/** Micro-kernel selector + * + * @param[in] data Selection data passed to help pick the appropriate micro-kernel + * + * @return A matching micro-kernel else nullptr + */ +const L2NormalizeLayerKernel *get_implementation(const L2NormalizeLayerSelectorData &data) { - 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 &) + for(const auto &uk : available_kernels) { - 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) + if(uk.is_selected(data)) { - 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)); + return &uk; } - - // 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); + } + return nullptr; } Status validate_arguments(const ITensorInfo *input, const ITensorInfo *sum, const ITensorInfo *output, int axis, float epsilon) @@ -212,18 +188,10 @@ void NEL2NormalizeLayerKernel::run(const Window &window, const ThreadInfo &info) 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"); - } + const auto *uk = get_implementation(L2NormalizeLayerSelectorData{ _output->info()->data_type(), _actual_axis, CPUInfo::get().get_isa() }); + ARM_COMPUTE_ERROR_ON(uk == nullptr); + ARM_COMPUTE_ERROR_ON(uk->ukernel == nullptr); + + uk->ukernel(_input, _sum, _output, _epsilon, window, _actual_axis); } } // namespace arm_compute |