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 --- arm_compute/core/NEON/NEFixedPoint.h | 25 +++- arm_compute/core/NEON/NEFixedPoint.inl | 17 +++ .../core/NEON/kernels/NENormalizationLayerKernel.h | 4 +- .../NEON/kernels/NENormalizationLayerKernel.cpp | 131 +++++++++++++++------ tests/benchmark_new/NEON/NormalizationLayer.cpp | 4 +- tests/validation_new/CPP/NormalizationLayer.cpp | 1 + tests/validation_new/NEON/NormalizationLayer.cpp | 21 +++- 7 files changed, 163 insertions(+), 40 deletions(-) diff --git a/arm_compute/core/NEON/NEFixedPoint.h b/arm_compute/core/NEON/NEFixedPoint.h index 50463b5efe..08f680801d 100644 --- a/arm_compute/core/NEON/NEFixedPoint.h +++ b/arm_compute/core/NEON/NEFixedPoint.h @@ -235,13 +235,22 @@ qint8x16_t vdupq_n_qs8(qint8_t a); /** Duplicate a float and convert it to 8 bit fixed point vector (16 elements) * - * @param[in] a 8 bit fixed point to duplicate + * @param[in] a floating point value to convert and duplicate * @param[in] fixed_point_position Fixed point position that expresses the number of bits for the fractional part of the number * * @return The result of the vector duplication */ qint8x16_t vdupq_n_qs8_f32(float a, int fixed_point_position); +/** Duplicate a float and convert it to 16 bit fixed point vector (8 elements) + * + * @param[in] a floating point value to convert and duplicate + * @param[in] fixed_point_position Fixed point position that expresses the number of bits for the fractional part of the number + * + * @return The result of the vector duplication + */ +qint16x8_t vdupq_n_qs16_f32(float a, int fixed_point_position); + /** 16 bit fixed point vector duplicate (8 elements) * * @param[in] a 16 bit fixed point to duplicate @@ -1178,7 +1187,19 @@ qint16x8_t vqtanhq_qs16(qint16x8_t a, int fixed_point_position); * * @return The result of the 8bit power. */ -qint8x8_t vqpowq_qs8(qint8x8_t a, qint8x16_t b, int fixed_point_position); +qint8x16_t vqpowq_qs8(qint8x16_t a, qint8x16_t b, int fixed_point_position); + +/** Calculate saturating n power for fixed point 16bit (8 elements). + * + * pow(a,b) = e^(b*log(a)) + * + * @param[in] a 16bit fixed point input vector + * @param[in] b 16bit fixed point power vector + * @param[in] fixed_point_position Fixed point position that expresses the number of bits for the fractional part of the number + * + * @return The result of the 16bit power. + */ +qint16x8_t vqpowq_qs16(qint16x8_t a, qint16x8_t b, int fixed_point_position); /** Compute lane-by-lane maximum between elements of a float vector with 4x2 elements * diff --git a/arm_compute/core/NEON/NEFixedPoint.inl b/arm_compute/core/NEON/NEFixedPoint.inl index 7cebfad924..c879d3e275 100644 --- a/arm_compute/core/NEON/NEFixedPoint.inl +++ b/arm_compute/core/NEON/NEFixedPoint.inl @@ -250,6 +250,18 @@ inline qint8x16_t vdupq_n_qs8_f32(float a, int fixed_point_position) return vqcvtq_qs8_f32(res, fixed_point_position); } +inline qint16x8_t vdupq_n_qs16_f32(float a, int fixed_point_position) +{ + float32x4x2_t res = + { + { + vdupq_n_f32(a), + vdupq_n_f32(a), + } + }; + return vqcvtq_qs16_f32(res, fixed_point_position); +} + inline qint16x8_t vdupq_n_qs16(qint16_t a) { return vdupq_n_s16(a); @@ -1941,6 +1953,11 @@ inline qint8x16_t vqpowq_qs8(qint8x16_t a, qint8x16_t b, int fixed_point_positio return vqexpq_qs8(vqmulq_qs8(b, vlogq_qs8(a, fixed_point_position), fixed_point_position), fixed_point_position); } +inline qint16x8_t vqpowq_qs16(qint16x8_t a, qint16x8_t b, int fixed_point_position) +{ + return vqexpq_qs16(vqmulq_qs16(b, vlogq_qs16(a, fixed_point_position), fixed_point_position), fixed_point_position); +} + inline float32x4x2_t vmax2q_f32(float32x4x2_t a, float32x4x2_t b) { float32x4x2_t res = diff --git a/arm_compute/core/NEON/kernels/NENormalizationLayerKernel.h b/arm_compute/core/NEON/kernels/NENormalizationLayerKernel.h index b1bc594e4c..e24e481f46 100644 --- a/arm_compute/core/NEON/kernels/NENormalizationLayerKernel.h +++ b/arm_compute/core/NEON/kernels/NENormalizationLayerKernel.h @@ -50,7 +50,7 @@ public: /** Set the input and output tensors. * * @param[in] input Source tensor. 3 lower dims represent a single input with dimensions [width, height, IFM], - * and an optional 4th dimension for batch of inputs. Data types supported: QS8/F32. + * and an optional 4th dimension for batch of inputs. Data types supported: QS8/QS16/FP16/F32. * @param[in] input_squared Source with each element has been squared. 3 lower dims represent a single input with dimensions [width, height, IFM], * Data type supported: same as @p input * @param[out] output Destination tensor. Output will have the same number of dimensions as input. Data type supported: same as @p input @@ -86,7 +86,7 @@ private: * * @param[in] window Region on which to execute the kernel. */ - template + template void normalize_fixed_point(const Window &window); /** Common signature for all the specialised normalization functions * 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) diff --git a/tests/benchmark_new/NEON/NormalizationLayer.cpp b/tests/benchmark_new/NEON/NormalizationLayer.cpp index 71dd9c354c..de7183d2ec 100644 --- a/tests/benchmark_new/NEON/NormalizationLayer.cpp +++ b/tests/benchmark_new/NEON/NormalizationLayer.cpp @@ -41,9 +41,9 @@ namespace test namespace { #ifdef ARM_COMPUTE_ENABLE_FP16 -const auto normalization_layer_data_types = framework::dataset::make("DataType", { DataType::F16, DataType::F32, DataType::QS8 }); +const auto normalization_layer_data_types = framework::dataset::make("DataType", { DataType::QS8, DataType::QS16, DataType::F16, DataType::F32 }); #else /* ARM_COMPUTE_ENABLE_FP16 */ -const auto normalization_layer_data_types = framework::dataset::make("DataType", { DataType::F32, DataType::QS8 }); +const auto normalization_layer_data_types = framework::dataset::make("DataType", { DataType::QS8, DataType::QS16, DataType::F32 }); #endif /* ARM_COMPUTE_ENABLE_FP16 */ } // namespace using NENormalizationLayerFixture = NormalizationLayerFixture; diff --git a/tests/validation_new/CPP/NormalizationLayer.cpp b/tests/validation_new/CPP/NormalizationLayer.cpp index 72f49007cc..a8818d8b5c 100644 --- a/tests/validation_new/CPP/NormalizationLayer.cpp +++ b/tests/validation_new/CPP/NormalizationLayer.cpp @@ -268,6 +268,7 @@ SimpleTensor normalization_layer(const SimpleTensor &src, NormalizationLay template SimpleTensor normalization_layer(const SimpleTensor &src, NormalizationLayerInfo info); template SimpleTensor normalization_layer(const SimpleTensor &src, NormalizationLayerInfo info); template SimpleTensor normalization_layer(const SimpleTensor &src, NormalizationLayerInfo info); +template SimpleTensor normalization_layer(const SimpleTensor &src, NormalizationLayerInfo info); } // namespace reference } // namespace validation } // namespace test diff --git a/tests/validation_new/NEON/NormalizationLayer.cpp b/tests/validation_new/NEON/NormalizationLayer.cpp index f364975332..dfe793131a 100644 --- a/tests/validation_new/NEON/NormalizationLayer.cpp +++ b/tests/validation_new/NEON/NormalizationLayer.cpp @@ -50,7 +50,8 @@ constexpr float tolerance_f16 = 0.001f; #endif /* ARM_COMPUTE_ENABLE_FP16 */ constexpr float tolerance_f32 = 0.00001f; /** Tolerance for fixed point operations */ -constexpr int8_t tolerance_qs8 = 2; +constexpr int8_t tolerance_qs8 = 2; +constexpr int16_t tolerance_qs16 = 3; /** Input data set. */ const auto NormalizationDataset = combine(combine(combine(datasets::SmallShapes(), datasets::NormalizationTypes()), framework::dataset::make("NormalizationSize", 3, 9, 2)), @@ -116,6 +117,24 @@ FIXTURE_DATA_TEST_CASE(RunLarge, NENormalizationLayerFixedPointFixture, validate(Accessor(_target), _reference, tolerance_qs8); } TEST_SUITE_END() + +TEST_SUITE(QS16) +// Testing for fixed point position [1,14) as reciprocal limits the maximum fixed point position to 14 +FIXTURE_DATA_TEST_CASE(RunSmall, NENormalizationLayerFixedPointFixture, framework::DatasetMode::PRECOMMIT, combine(combine(NormalizationDataset, framework::dataset::make("DataType", + DataType::QS16)), + framework::dataset::make("FractionalBits", 1, 14))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_qs16); +} +FIXTURE_DATA_TEST_CASE(RunLarge, NENormalizationLayerFixedPointFixture, framework::DatasetMode::NIGHTLY, combine(combine(NormalizationDataset, framework::dataset::make("DataType", + DataType::QS16)), + framework::dataset::make("FractionalBits", 1, 14))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_qs16); +} +TEST_SUITE_END() TEST_SUITE_END() TEST_SUITE_END() -- cgit v1.2.1