From d5e65c71261fd42d3e69478507fbfcc8cf36befc Mon Sep 17 00:00:00 2001 From: Michele Di Giorgio Date: Wed, 26 Jul 2017 17:09:17 +0100 Subject: COMPMID-456: Add support for QS16 NEON Normalization Layer. Change-Id: I1e542808cfd7774c67cc4e9a58e42449e4fb29aa Reviewed-on: http://mpd-gerrit.cambridge.arm.com/81735 Tested-by: Kaizen Reviewed-by: Anthony Barbier --- .../NEON/kernels/NENormalizationLayerKernel.cpp | 131 +++++++++++++++------ 1 file changed, 98 insertions(+), 33 deletions(-) (limited to 'src/core/NEON/kernels') diff --git a/src/core/NEON/kernels/NENormalizationLayerKernel.cpp b/src/core/NEON/kernels/NENormalizationLayerKernel.cpp index 76ace91c20..085d412558 100644 --- a/src/core/NEON/kernels/NENormalizationLayerKernel.cpp +++ b/src/core/NEON/kernels/NENormalizationLayerKernel.cpp @@ -46,7 +46,7 @@ BorderSize NENormalizationLayerKernel::border_size() const void NENormalizationLayerKernel::configure(const ITensor *input, const ITensor *input_squared, ITensor *output, NormalizationLayerInfo norm_info) { - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32, DataType::QS8); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32); ARM_COMPUTE_ERROR_ON_NULLPTR(output); // Output tensor auto initialization if not yet initialized auto_init_if_empty(*output->info(), input->info()->tensor_shape(), 1, input->info()->data_type(), input->info()->fixed_point_position()); @@ -118,14 +118,35 @@ void NENormalizationLayerKernel::configure(const ITensor *input, const ITensor * switch(norm_info.type()) { case NormType::IN_MAP_1D: - _func = &NENormalizationLayerKernel::normalize_fixed_point<0, false>; + _func = &NENormalizationLayerKernel::normalize_fixed_point; break; case NormType::IN_MAP_2D: // Normalize over X and Y - _func = &NENormalizationLayerKernel::normalize_fixed_point<0, true>; + _func = &NENormalizationLayerKernel::normalize_fixed_point; break; case NormType::CROSS_MAP: - _func = &NENormalizationLayerKernel::normalize_fixed_point<2, false>; + _func = &NENormalizationLayerKernel::normalize_fixed_point; + break; + default: + ARM_COMPUTE_ERROR("Not supported"); + break; + } + break; + } + case DataType::QS16: + { + num_elems_processed_per_iteration = 8; + switch(norm_info.type()) + { + case NormType::IN_MAP_1D: + _func = &NENormalizationLayerKernel::normalize_fixed_point; + break; + case NormType::IN_MAP_2D: + // Normalize over X and Y + _func = &NENormalizationLayerKernel::normalize_fixed_point; + break; + case NormType::CROSS_MAP: + _func = &NENormalizationLayerKernel::normalize_fixed_point; break; default: ARM_COMPUTE_ERROR("Not supported"); @@ -250,7 +271,7 @@ void NENormalizationLayerKernel::normalize_float(const Window &window) } } -template +template void NENormalizationLayerKernel::normalize_fixed_point(const Window &window) { Iterator input(_input, window); @@ -269,40 +290,84 @@ void NENormalizationLayerKernel::normalize_fixed_point(const Window &window) const int fixed_point_position = _input->info()->fixed_point_position(); - const qint8x16_t coeff_vec = vdupq_n_qs8_f32(_norm_info.scale_coeff(), fixed_point_position); - const qint8x16_t beta_vec = vdupq_n_qs8_f32(_norm_info.beta(), fixed_point_position); - const qint8x16_t kappa_vec = vdupq_n_qs8_f32(_norm_info.kappa(), fixed_point_position); + if(dt == DataType::QS8) + { + const qint8x16_t coeff_vec = vdupq_n_qs8_f32(_norm_info.scale_coeff(), fixed_point_position); + const qint8x16_t beta_vec = vdupq_n_qs8_f32(_norm_info.beta(), fixed_point_position); + const qint8x16_t kappa_vec = vdupq_n_qs8_f32(_norm_info.kappa(), fixed_point_position); - execute_window_loop(window, [&](const Coordinates & id) + execute_window_loop(window, [&](const Coordinates & id) + { + // Get range to normalize + const int current_row = do_2D_norm ? id[dim_y] : 0; + const int current_slice = id[dim]; + const int first_row = do_2D_norm ? std::max(current_row - radius, min_top) : 0; + const int last_row = do_2D_norm ? std::min(current_row + radius, max_bottom) : 0; + const int first_slice = std::max(current_slice - radius, min_left); + const int last_slice = std::min(current_slice + radius, max_right); + + // Accumulate 2D In-Map values + qint8x16_t accu = vdupq_n_qs8(0); + for(int j = first_row; j <= last_row; ++j) + { + // Compute row displacement + const int row = (j - current_row) * _input_squared->info()->strides_in_bytes()[dim_y]; + const uint8_t *const input_squared_ptr = input_squared.ptr() + row - (current_slice * input_squared_stride); + for(int i = first_slice; i <= last_slice; ++i) + { + accu = vqaddq_qs8(accu, vld1q_qs8(reinterpret_cast(input_squared_ptr + i * input_squared_stride))); + } + } + + // Normalize + const qint8x16_t accu_scale = vqmlaq_qs8(kappa_vec, coeff_vec, accu, fixed_point_position); + const qint8x16_t normalized = vqpowq_qs8(accu_scale, beta_vec, fixed_point_position); + const qint8x16_t normalized_pixel = vdivq_qs8(vld1q_qs8(reinterpret_cast(input.ptr())), normalized, fixed_point_position); + vst1q_qs8(reinterpret_cast(output.ptr()), normalized_pixel); + }, + input, input_squared, output); + } + else if(dt == DataType::QS16) { - // Get range to normalize - const int current_row = do_2D_norm ? id[dim_y] : 0; - const int current_slice = id[dim]; - const int first_row = do_2D_norm ? std::max(current_row - radius, min_top) : 0; - const int last_row = do_2D_norm ? std::min(current_row + radius, max_bottom) : 0; - const int first_slice = std::max(current_slice - radius, min_left); - const int last_slice = std::min(current_slice + radius, max_right); - - // Accumulate 2D In-Map values - qint8x16_t accu = vdupq_n_qs8(0); - for(int j = first_row; j <= last_row; ++j) + const qint16x8_t coeff_vec = vdupq_n_qs16_f32(_norm_info.scale_coeff(), fixed_point_position); + const qint16x8_t beta_vec = vdupq_n_qs16_f32(_norm_info.beta(), fixed_point_position); + const qint16x8_t kappa_vec = vdupq_n_qs16_f32(_norm_info.kappa(), fixed_point_position); + + execute_window_loop(window, [&](const Coordinates & id) { - // Compute row displacement - const int row = (j - current_row) * _input_squared->info()->strides_in_bytes()[dim_y]; - const uint8_t *const input_squared_ptr = input_squared.ptr() + row - (current_slice * input_squared_stride); - for(int i = first_slice; i <= last_slice; ++i) + // Get range to normalize + const int current_row = do_2D_norm ? id[dim_y] : 0; + const int current_slice = id[dim]; + const int first_row = do_2D_norm ? std::max(current_row - radius, min_top) : 0; + const int last_row = do_2D_norm ? std::min(current_row + radius, max_bottom) : 0; + const int first_slice = std::max(current_slice - radius, min_left); + const int last_slice = std::min(current_slice + radius, max_right); + + // Accumulate 2D In-Map values + qint16x8_t accu = vdupq_n_qs16(0); + for(int j = first_row; j <= last_row; ++j) { - accu = vqaddq_qs8(accu, vld1q_qs8(reinterpret_cast(input_squared_ptr + i * input_squared_stride))); + // Compute row displacement + const int row = (j - current_row) * _input_squared->info()->strides_in_bytes()[dim_y]; + const uint8_t *const input_squared_ptr = input_squared.ptr() + row - (current_slice * input_squared_stride); + for(int i = first_slice; i <= last_slice; ++i) + { + accu = vqaddq_qs16(accu, vld1q_qs16(reinterpret_cast(input_squared_ptr + i * input_squared_stride))); + } } - } - // Normalize - const qint8x16_t accu_scale = vqmlaq_qs8(kappa_vec, coeff_vec, accu, fixed_point_position); - const qint8x16_t normalized = vqpowq_qs8(accu_scale, beta_vec, fixed_point_position); - const qint8x16_t normalized_pixel = vdivq_qs8(vld1q_qs8(reinterpret_cast(input.ptr())), normalized, fixed_point_position); - vst1q_qs8(reinterpret_cast(output.ptr()), normalized_pixel); - }, - input, input_squared, output); + // Normalize + const qint16x8_t accu_scale = vqmlaq_qs16(kappa_vec, coeff_vec, accu, fixed_point_position); + const qint16x8_t normalized = vqpowq_qs16(accu_scale, beta_vec, fixed_point_position); + const qint16x8_t normalized_pixel = vdivq_qs16(vld1q_qs16(reinterpret_cast(input.ptr())), normalized, fixed_point_position); + vst1q_qs16(reinterpret_cast(output.ptr()), normalized_pixel); + }, + input, input_squared, output); + } + else + { + ARM_COMPUTE_ERROR("Not supported"); + } } void NENormalizationLayerKernel::run(const Window &window) -- cgit v1.2.1