diff options
Diffstat (limited to 'src/core/NEON/kernels/NEL2NormalizeLayerKernel.cpp')
-rw-r--r-- | src/core/NEON/kernels/NEL2NormalizeLayerKernel.cpp | 170 |
1 files changed, 71 insertions, 99 deletions
diff --git a/src/core/NEON/kernels/NEL2NormalizeLayerKernel.cpp b/src/core/NEON/kernels/NEL2NormalizeLayerKernel.cpp index dae5b57fec..eea57a17d3 100644 --- a/src/core/NEON/kernels/NEL2NormalizeLayerKernel.cpp +++ b/src/core/NEON/kernels/NEL2NormalizeLayerKernel.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2020 Arm Limited. + * Copyright (c) 2017-2022 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -30,11 +30,14 @@ #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/common/cpuinfo/CpuIsaInfo.h" +#include "src/core/common/Registrars.h" #include "src/core/helpers/AutoConfiguration.h" #include "src/core/helpers/WindowHelpers.h" +#include "src/core/NEON/NEMath.h" +#include "src/cpu/kernels/l2normlayer/list.h" -#include "src/core/NEON/wrapper/wrapper.h" #include <arm_neon.h> #include <cmath> @@ -44,93 +47,68 @@ 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); - - execute_window_loop(win_collapsed, [&](const Coordinates &) +struct L2NormalizeLayerKernel +{ + const char *name; + const L2NormalizeLayerKernelSelctorPtr is_selected; + L2NormalizeLayerPtr ukernel; +}; + +static const L2NormalizeLayerKernel available_kernels[] = { + {"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)}, + {"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)}, { - 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; - } + "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), }, - input_it, sum_it, output_it); -} + { + "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) +Status +validate_arguments(const ITensorInfo *input, const ITensorInfo *sum, const ITensorInfo *output, int axis, float epsilon) { ARM_COMPUTE_UNUSED(epsilon); @@ -139,14 +117,15 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *sum, cons 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"); + 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) + if (output->total_size() != 0) { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); @@ -165,9 +144,6 @@ std::tuple<Status, Window> validate_and_configure_window(ITensorInfo *input, ITe 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); } @@ -178,7 +154,8 @@ NEL2NormalizeLayerKernel::NEL2NormalizeLayerKernel() { } -void NEL2NormalizeLayerKernel::configure(const ITensor *input, const ITensor *sum, ITensor *output, int axis, float epsilon) +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)); @@ -196,10 +173,12 @@ void NEL2NormalizeLayerKernel::configure(const ITensor *input, const ITensor *su INEKernel::configure(std::get<1>(win_config)); } -Status NEL2NormalizeLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *sum, const ITensorInfo *output, int axis, float epsilon) +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()))); + ARM_COMPUTE_RETURN_ON_ERROR( + std::get<0>(validate_and_configure_window(input->clone().get(), output->clone().get()))); return Status{}; } @@ -210,23 +189,16 @@ void NEL2NormalizeLayerKernel::run(const Window &window, const ThreadInfo &info) ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); - if(_actual_axis > 2) + 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"); - } + 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 |