From 4b869532f8b2aa7f02aa55c4f4813e994ea2df68 Mon Sep 17 00:00:00 2001 From: Luca Foschiani Date: Thu, 13 Feb 2020 15:07:36 +0000 Subject: COMPMID-2966 Add support for QASYMM8_SIGNED in NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel Signed-off-by: Luca Foschiani Change-Id: Ia8692f8fda16fa3b73f343e4b5b1b55e14403225 Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/2750 Reviewed-by: Michele Di Giorgio Tested-by: Arm Jenkins Comments-Addressed: Arm Jenkins --- .../NEGEMMLowpQuantizeDownInt32ScaleKernel.cpp | 321 +++++++++++++++++++ ...GEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp | 349 --------------------- 2 files changed, 321 insertions(+), 349 deletions(-) create mode 100644 src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ScaleKernel.cpp delete mode 100644 src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp (limited to 'src/core/NEON/kernels') diff --git a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ScaleKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ScaleKernel.cpp new file mode 100644 index 0000000000..80ba2aff93 --- /dev/null +++ b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ScaleKernel.cpp @@ -0,0 +1,321 @@ +/* + * Copyright (c) 2020 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ScaleKernel.h" + +#include "arm_compute/core/AccessWindowStatic.h" +#include "arm_compute/core/Error.h" +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/ITensor.h" +#include "arm_compute/core/NEON/wrapper/wrapper.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/core/Utils.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/core/Window.h" +#include "arm_compute/core/utils/quantization/AsymmHelpers.h" + +#include +#include +#include + +namespace arm_compute +{ +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const GEMMLowpOutputStageInfo *output_stage) +{ + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32); + + ARM_COMPUTE_RETURN_ERROR_ON(output_stage->gemmlowp_max_bound > std::get<1>(quantization::get_min_max_values_from_quantized_data_type(output_stage->output_data_type))); + ARM_COMPUTE_RETURN_ERROR_ON(output_stage->gemmlowp_min_bound < std::get<0>(quantization::get_min_max_values_from_quantized_data_type(output_stage->output_data_type)) + || output_stage->gemmlowp_min_bound > output_stage->gemmlowp_max_bound); + + // Check biases if exist + if(bias != nullptr) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias); + ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1); + ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != bias->dimension(0)); + } + + if(output->total_size() != 0) + { + if(output->data_type() != output_stage->output_data_type && (output_stage->output_data_type == DataType::QASYMM8 || output_stage->output_data_type == DataType::QASYMM8_SIGNED)) + { + ARM_COMPUTE_RETURN_ERROR_MSG("Mismatching data types"); + } + + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); + } + + return Status{}; +} + +inline void scale_input(int32x4x4_t &in_s32, int32x4_t result_offset_s32, int32_t result_mult_int) +{ + // Add the offset terms to GEMM's result + in_s32.val[0] = vaddq_s32(in_s32.val[0], result_offset_s32); + in_s32.val[1] = vaddq_s32(in_s32.val[1], result_offset_s32); + in_s32.val[2] = vaddq_s32(in_s32.val[2], result_offset_s32); + in_s32.val[3] = vaddq_s32(in_s32.val[3], result_offset_s32); + + // Multiply by result_mult_int + in_s32.val[0] = vmulq_n_s32(in_s32.val[0], result_mult_int); + in_s32.val[1] = vmulq_n_s32(in_s32.val[1], result_mult_int); + in_s32.val[2] = vmulq_n_s32(in_s32.val[2], result_mult_int); + in_s32.val[3] = vmulq_n_s32(in_s32.val[3], result_mult_int); +} + +template +inline typename std::enable_if::value, + typename wrapper::traits::neon_vector::type>::type + convert_to_8bit(const int16x8x2_t in_s16) +{ + return wrapper::vcombine(wrapper::vqmovun(in_s16.val[0]), wrapper::vqmovun(in_s16.val[1])); +} + +template +inline typename std::enable_if::value, + typename wrapper::traits::neon_vector::type>::type + convert_to_8bit(const int16x8x2_t in_s16) +{ + return wrapper::vcombine(wrapper::vqmovn(in_s16.val[0]), wrapper::vqmovn(in_s16.val[1])); +} + +template +inline typename wrapper::traits::neon_vector::type finalize_quantization(int32x4x4_t &in_s32, int32x4_t result_shift_s32, typename wrapper::traits::neon_vector::type min, + typename wrapper::traits::neon_vector::type max) +{ + // Shift final result (negative value shift right) + in_s32.val[0] = vshlq_s32(in_s32.val[0], result_shift_s32); + in_s32.val[1] = vshlq_s32(in_s32.val[1], result_shift_s32); + in_s32.val[2] = vshlq_s32(in_s32.val[2], result_shift_s32); + in_s32.val[3] = vshlq_s32(in_s32.val[3], result_shift_s32); + + // Convert S32 to S16 + const int16x8x2_t in_s16 = + { + { + vcombine_s16(vqmovn_s32(in_s32.val[0]), vqmovn_s32(in_s32.val[1])), + vcombine_s16(vqmovn_s32(in_s32.val[2]), vqmovn_s32(in_s32.val[3])) + } + }; + + // Convert S16 to S8 or U8 + typename wrapper::traits::neon_vector::type out = convert_to_8bit(in_s16); + + out = wrapper::vmax(out, min); + out = wrapper::vmin(out, max); + + return out; +} + +class Coordinates; + +template +void NEGEMMLowpQuantizeDownInt32ScaleKernel::run(const Window &window) +{ + using VectorType = typename wrapper::traits::neon_vector::type; + + const int32x4_t result_offset_s32 = vdupq_n_s32(_output_stage->gemmlowp_offset); + const int32x4_t result_shift_s32 = vdupq_n_s32(-_output_stage->gemmlowp_shift); + const int window_step_x = 16; + const auto window_start_x = static_cast(window.x().start()); + const auto window_end_x = static_cast(window.x().end()); + + const int clamp_min = (_is_bounded_relu) ? _output_stage->gemmlowp_min_bound : std::numeric_limits::lowest(); + const int clamp_max = (_is_bounded_relu) ? _output_stage->gemmlowp_max_bound : std::numeric_limits::max(); + + VectorType min = wrapper::vdup_n(static_cast(clamp_min), wrapper::traits::vector_128_tag{}); + VectorType max = wrapper::vdup_n(static_cast(clamp_max), wrapper::traits::vector_128_tag{}); + + Window win(window); + win.set(Window::DimX, Window::Dimension(0, 1, 1)); + + Iterator in(_input, win); + Iterator out(_output, win); + + if(_bias != nullptr) + { + Window win_biases; + win_biases.set(Window::DimX, Window::Dimension(0, 1, 1)); + win_biases.set(Window::DimY, Window::Dimension(0, 1, 1)); + + Iterator bias(_bias, win_biases); + execute_window_loop(win, [&](const Coordinates &) + { + // Compute 16 elements per iteration + int x = window_start_x; + for(; x <= (window_end_x - window_step_x); x += window_step_x) + { + int32x4x4_t in_s32 = + { + { + vld1q_s32(reinterpret_cast(in.ptr()) + x + 0), + vld1q_s32(reinterpret_cast(in.ptr()) + x + 4), + vld1q_s32(reinterpret_cast(in.ptr()) + x + 8), + vld1q_s32(reinterpret_cast(in.ptr()) + x + 12) + } + }; + + const int32x4x4_t bias_s32 = + { + { + vld1q_s32(reinterpret_cast(bias.ptr()) + x + 0), + vld1q_s32(reinterpret_cast(bias.ptr()) + x + 4), + vld1q_s32(reinterpret_cast(bias.ptr()) + x + 8), + vld1q_s32(reinterpret_cast(bias.ptr()) + x + 12) + } + }; + + // Add the bias to GEMM's result + in_s32.val[0] = vaddq_s32(in_s32.val[0], bias_s32.val[0]); + in_s32.val[1] = vaddq_s32(in_s32.val[1], bias_s32.val[1]); + in_s32.val[2] = vaddq_s32(in_s32.val[2], bias_s32.val[2]); + in_s32.val[3] = vaddq_s32(in_s32.val[3], bias_s32.val[3]); + + // Add the offset terms to GEMM's result and multiply by result_mult_int + scale_input(in_s32, result_offset_s32, _output_stage->gemmlowp_multiplier); + + wrapper::vstore(reinterpret_cast(out.ptr() + x), finalize_quantization(in_s32, result_shift_s32, min, max)); + } + + // Compute left-over elements + for(; x < window_end_x; ++x) + { + const int bias_value = *(reinterpret_cast(bias.ptr()) + x); + int in_value = *(reinterpret_cast(in.ptr()) + x); + + // Quantize + in_value = ((in_value + bias_value + _output_stage->gemmlowp_offset) * _output_stage->gemmlowp_multiplier) >> _output_stage->gemmlowp_shift; + + // Store the result + *(out.ptr() + x) = static_cast(utility::clamp(in_value, clamp_min, clamp_max)); + } + }, + in, bias, out); + } + else + { + execute_window_loop(win, [&](const Coordinates &) + { + // Compute 16 elements per iteration + int x = window_start_x; + for(; x <= (window_end_x - window_step_x); x += window_step_x) + { + int32x4x4_t in_s32 = + { + { + vld1q_s32(reinterpret_cast(in.ptr()) + x + 0), + vld1q_s32(reinterpret_cast(in.ptr()) + x + 4), + vld1q_s32(reinterpret_cast(in.ptr()) + x + 8), + vld1q_s32(reinterpret_cast(in.ptr()) + x + 12) + } + }; + + // Add the offset terms to GEMM's result and multiply by result_mult_int + scale_input(in_s32, result_offset_s32, _output_stage->gemmlowp_multiplier); + + wrapper::vstore(reinterpret_cast(out.ptr() + x), finalize_quantization(in_s32, result_shift_s32, min, max)); + } + + // Compute left-over elements + for(; x < window_end_x; ++x) + { + int in_value = *(reinterpret_cast(in.ptr()) + x); + + // Quantize + in_value = ((in_value + _output_stage->gemmlowp_offset) * _output_stage->gemmlowp_multiplier) >> _output_stage->gemmlowp_shift; + + // Store the result + *(out.ptr() + x) = static_cast(utility::clamp(in_value, clamp_min, clamp_max)); + } + }, + in, out); + } +} + +NEGEMMLowpQuantizeDownInt32ScaleKernel::NEGEMMLowpQuantizeDownInt32ScaleKernel() + : _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr), _output_stage(nullptr), _is_bounded_relu(false) +{ +} + +void NEGEMMLowpQuantizeDownInt32ScaleKernel::configure(const ITensor *input, const ITensor *bias, ITensor *output, const GEMMLowpOutputStageInfo *output_stage) +{ + // Perform validate step + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, output_stage); + + // Output auto inizialitation if not yet initialized + auto_init_if_empty(*output->info(), input->info()->clone()->set_data_type(output_stage->output_data_type)); + + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), + (bias != nullptr) ? bias->info() : nullptr, + output->info(), + output_stage)); + + _input = input; + _bias = bias; + _output = output; + _output_stage = output_stage; + + // Configure kernel window + Window win = calculate_max_window(*input->info(), Steps()); + Coordinates coord; + coord.set_num_dimensions(output->info()->num_dimensions()); + output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape())); + + INEKernel::configure(win); + + // Check if we need to clamp the result using min and max + _is_bounded_relu = ((_output_stage->gemmlowp_min_bound != _output_stage->gemmlowp_max_bound) + && !(_output_stage->gemmlowp_min_bound == std::get<0>(quantization::get_min_max_values_from_quantized_data_type(output_stage->output_data_type)) + && _output_stage->gemmlowp_max_bound == std::get<1>(quantization::get_min_max_values_from_quantized_data_type(output_stage->output_data_type)))); + if(_output_stage->output_data_type == DataType::QASYMM8) + { + _func = &NEGEMMLowpQuantizeDownInt32ScaleKernel::run; + } + else if(_output_stage->output_data_type == DataType::QASYMM8_SIGNED) + { + _func = &NEGEMMLowpQuantizeDownInt32ScaleKernel::run; + } + else + { + ARM_COMPUTE_ERROR("Data type not supported"); + } +} + +Status NEGEMMLowpQuantizeDownInt32ScaleKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const GEMMLowpOutputStageInfo *output_stage) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, output_stage)); + + return Status{}; +} + +void NEGEMMLowpQuantizeDownInt32ScaleKernel::run(const Window &window, const ThreadInfo &info) +{ + ARM_COMPUTE_UNUSED(info); + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); + + (this->*_func)(window); +} +} // namespace arm_compute \ No newline at end of file diff --git a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp deleted file mode 100644 index a68e4e7efb..0000000000 --- a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp +++ /dev/null @@ -1,349 +0,0 @@ -/* - * Copyright (c) 2017-2020 ARM Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#include "arm_compute/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.h" - -#include "arm_compute/core/AccessWindowStatic.h" -#include "arm_compute/core/Error.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/ITensor.h" -#include "arm_compute/core/Types.h" -#include "arm_compute/core/Utils.h" -#include "arm_compute/core/Validate.h" -#include "arm_compute/core/Window.h" - -#include -#include -#include - -using namespace arm_compute; - -namespace -{ -Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max) -{ - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32); - ARM_COMPUTE_RETURN_ERROR_ON(min > max); - - // Check biases if exist - if(bias != nullptr) - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias); - ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1); - ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != bias->dimension(0)); - } - - if(output->total_size() != 0) - { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); - } - - return Status{}; -} - -std::pair validate_and_configure_window(ITensorInfo *input, ITensorInfo *bias, ITensorInfo *output) -{ - // Note: This kernel performs 16 elements per iteration. - // However, since we use a left-over for loop, we cannot have any read or write out of memory - // For this reason num_elems_processed_per_iteration is set to 1 - constexpr unsigned int num_elems_processed_per_iteration = 1; - - // Configure kernel window - Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration)); - - AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration); - - bool window_changed = update_window_and_padding(win, - input_access); - - if(output->total_size() != 0) - { - AccessWindowHorizontal output_result_access(output, 0, num_elems_processed_per_iteration); - window_changed = window_changed || update_window_and_padding(win, output_result_access); - - output_result_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); - } - - if(bias != nullptr) - { - AccessWindowStatic bias_access(bias, 0, 0, bias->dimension(0), bias->dimension(1)); - window_changed = window_changed || update_window_and_padding(win, bias_access); - } - - Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; - return std::make_pair(err, win); -} - -inline void scale_input(int32x4x4_t &in_s32, int32x4_t result_offset_s32, int32_t result_mult_int) -{ - // Add the offset terms to GEMM's result - in_s32.val[0] = vaddq_s32(in_s32.val[0], result_offset_s32); - in_s32.val[1] = vaddq_s32(in_s32.val[1], result_offset_s32); - in_s32.val[2] = vaddq_s32(in_s32.val[2], result_offset_s32); - in_s32.val[3] = vaddq_s32(in_s32.val[3], result_offset_s32); - - // Multiply by result_mult_int - in_s32.val[0] = vmulq_n_s32(in_s32.val[0], result_mult_int); - in_s32.val[1] = vmulq_n_s32(in_s32.val[1], result_mult_int); - in_s32.val[2] = vmulq_n_s32(in_s32.val[2], result_mult_int); - in_s32.val[3] = vmulq_n_s32(in_s32.val[3], result_mult_int); -} - -template -inline uint8x16_t finalize_quantization(int32x4x4_t &in_s32, int32x4_t result_shift_s32, uint8x16_t min_u8, uint8x16_t max_u8) -{ - const static int32x4_t zero_s32 = vdupq_n_s32(0); - - // Shift final result (negative value shift right) - in_s32.val[0] = vshlq_s32(in_s32.val[0], result_shift_s32); - in_s32.val[1] = vshlq_s32(in_s32.val[1], result_shift_s32); - in_s32.val[2] = vshlq_s32(in_s32.val[2], result_shift_s32); - in_s32.val[3] = vshlq_s32(in_s32.val[3], result_shift_s32); - - // Saturate negative values - in_s32.val[0] = vmaxq_s32(in_s32.val[0], zero_s32); - in_s32.val[1] = vmaxq_s32(in_s32.val[1], zero_s32); - in_s32.val[2] = vmaxq_s32(in_s32.val[2], zero_s32); - in_s32.val[3] = vmaxq_s32(in_s32.val[3], zero_s32); - - // Convert S32 to S16 - const int16x8x2_t in_s16 = - { - { - vcombine_s16(vqmovn_s32(in_s32.val[0]), vqmovn_s32(in_s32.val[1])), - vcombine_s16(vqmovn_s32(in_s32.val[2]), vqmovn_s32(in_s32.val[3])) - } - }; - - // Convert S16 to U8 - uint8x16_t out_u8 = vcombine_u8(vqmovun_s16(in_s16.val[0]), vqmovun_s16(in_s16.val[1])); - - if(is_bounded_relu) - { - out_u8 = vmaxq_u8(out_u8, min_u8); - out_u8 = vminq_u8(out_u8, max_u8); - } - - return out_u8; -} -} // namespace - -namespace arm_compute -{ -class Coordinates; -} // namespace arm_compute - -template -void NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::run(const Window &window) -{ - const int32x4_t result_offset_s32 = vdupq_n_s32(_result_offset); - const int32x4_t result_shift_s32 = vdupq_n_s32(-_result_shift); - const uint8x16_t min_u8 = vdupq_n_u8(static_cast(_min)); - const uint8x16_t max_u8 = vdupq_n_u8(static_cast(_max)); - - ARM_COMPUTE_UNUSED(min_u8); - ARM_COMPUTE_UNUSED(max_u8); - - const int window_step_x = 16; - const auto window_start_x = static_cast(window.x().start()); - const auto window_end_x = static_cast(window.x().end()); - - Window win(window); - win.set(Window::DimX, Window::Dimension(0, 1, 1)); - - Iterator in(_input, win); - Iterator out(_output, win); - - if(_bias != nullptr) - { - Window win_biases; - win_biases.set(Window::DimX, Window::Dimension(0, 1, 1)); - win_biases.set(Window::DimY, Window::Dimension(0, 1, 1)); - - Iterator bias(_bias, win_biases); - execute_window_loop(win, [&](const Coordinates &) - { - // Compute 16 elements per iteration - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - int32x4x4_t in_s32 = - { - { - vld1q_s32(reinterpret_cast(in.ptr()) + x + 0), - vld1q_s32(reinterpret_cast(in.ptr()) + x + 4), - vld1q_s32(reinterpret_cast(in.ptr()) + x + 8), - vld1q_s32(reinterpret_cast(in.ptr()) + x + 12) - } - }; - - const int32x4x4_t bias_s32 = - { - { - vld1q_s32(reinterpret_cast(bias.ptr()) + x + 0), - vld1q_s32(reinterpret_cast(bias.ptr()) + x + 4), - vld1q_s32(reinterpret_cast(bias.ptr()) + x + 8), - vld1q_s32(reinterpret_cast(bias.ptr()) + x + 12) - } - }; - - // Add the bias to GEMM's result - in_s32.val[0] = vaddq_s32(in_s32.val[0], bias_s32.val[0]); - in_s32.val[1] = vaddq_s32(in_s32.val[1], bias_s32.val[1]); - in_s32.val[2] = vaddq_s32(in_s32.val[2], bias_s32.val[2]); - in_s32.val[3] = vaddq_s32(in_s32.val[3], bias_s32.val[3]); - - // Add the offset terms to GEMM's result and multiply by result_mult_int - scale_input(in_s32, result_offset_s32, _result_mult_int); - - vst1q_u8(out.ptr() + x, finalize_quantization(in_s32, result_shift_s32, min_u8, max_u8)); - } - - // Compute left-over elements - for(; x < window_end_x; ++x) - { - const int bias_value = *(reinterpret_cast(bias.ptr()) + x); - int in_value = *(reinterpret_cast(in.ptr()) + x); - - // Quantize - in_value = ((in_value + bias_value + _result_offset) * _result_mult_int) >> _result_shift; - - // Finalize and store the result - if(is_bounded_relu) - { - *(out.ptr() + x) = static_cast(std::max(_min, std::min(_max, in_value))); - } - else - { - *(out.ptr() + x) = static_cast(std::max(0, std::min(255, in_value))); - } - } - }, - in, bias, out); - } - else - { - execute_window_loop(win, [&](const Coordinates &) - { - // Compute 16 elements per iteration - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - int32x4x4_t in_s32 = - { - { - vld1q_s32(reinterpret_cast(in.ptr()) + x + 0), - vld1q_s32(reinterpret_cast(in.ptr()) + x + 4), - vld1q_s32(reinterpret_cast(in.ptr()) + x + 8), - vld1q_s32(reinterpret_cast(in.ptr()) + x + 12) - } - }; - - // Add the offset terms to GEMM's result and multiply by result_mult_int - scale_input(in_s32, result_offset_s32, _result_mult_int); - - vst1q_u8(out.ptr() + x, finalize_quantization(in_s32, result_shift_s32, min_u8, max_u8)); - } - - // Compute left-over elements - for(; x < window_end_x; ++x) - { - int in_value = *(reinterpret_cast(in.ptr()) + x); - - // Quantize - in_value = ((in_value + _result_offset) * _result_mult_int) >> _result_shift; - - // Finalize and store the result - if(is_bounded_relu) - { - *(out.ptr() + x) = static_cast(std::max(_min, std::min(_max, in_value))); - } - else - { - *(out.ptr() + x) = static_cast(std::max(0, std::min(255, in_value))); - } - } - }, - in, out); - } -} - -NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel() - : _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr), _result_offset(0), _result_mult_int(0), _result_shift(0), _min(0), _max(0) -{ -} - -void NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_offset, int result_mult_int, int result_shift, int min, int max) -{ - // Perform validate step - ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); - - // Output auto inizialitation if not yet initialized - auto_init_if_empty(*output->info(), input->info()->clone()->set_data_type(DataType::QASYMM8)); - - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), - (bias != nullptr) ? bias->info() : nullptr, - output->info(), - min, - max)); - - _input = input; - _bias = bias; - _output = output; - _result_offset = result_offset; - _result_mult_int = result_mult_int; - _result_shift = result_shift; - _min = min; - _max = max; - - // Configure kernel window - auto win_config = validate_and_configure_window(input->info(), (bias != nullptr) ? bias->info() : nullptr, output->info()); - ARM_COMPUTE_ERROR_THROW_ON(win_config.first); - INEKernel::configure(win_config.second); - - // Check if we need to clamp the result using min and max - const bool is_bounded_relu = !(min <= 0 && max >= 255); - _func = is_bounded_relu ? &NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::run : &NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::run; -} - -Status NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), - (bias != nullptr) ? bias->clone().get() : nullptr, - output->clone().get()) - .first); - - return Status{}; -} - -void NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::run(const Window &window, const ThreadInfo &info) -{ - ARM_COMPUTE_UNUSED(info); - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); - - (this->*_func)(window); -} -- cgit v1.2.1