diff options
author | Manuel Bottini <manuel.bottini@arm.com> | 2021-06-17 17:18:45 +0100 |
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committer | Manuel Bottini <manuel.bottini@arm.com> | 2021-06-22 17:03:54 +0000 |
commit | ae58bdf3b58739e105a24e3640d0245e81cea5ee (patch) | |
tree | e993b8768c3eff364a7c706db411c799fa86bfe0 /src/core/NEON | |
parent | 2db3a9955ef22be4be8ccd5a45bc0973ef80e42a (diff) | |
download | ComputeLibrary-ae58bdf3b58739e105a24e3640d0245e81cea5ee.tar.gz |
Port NEGEMMLowp Part 1
Details:
Port NEGEMMLowpQuantizeDownInt32ScaleKernel to CpuGemmLowpQuantizeDownInt32ScaleKernel
Port NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel to CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel
Port NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel to CpuGemmLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel
Port NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel to CpuGemmLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel
Port NEGEMMLowpOutputStage functions to CpuGemmLowpOutputStage operators
Partially Resolves: COMPMID-4403
Change-Id: I6d5f45e43f35d731d564ed3b5c0e804d2a318fb1
Signed-off-by: Manuel Bottini <manuel.bottini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5833
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Diffstat (limited to 'src/core/NEON')
9 files changed, 0 insertions, 1520 deletions
diff --git a/src/core/NEON/NEKernels.h b/src/core/NEON/NEKernels.h index ea15f4eddd..268871a4e8 100644 --- a/src/core/NEON/NEKernels.h +++ b/src/core/NEON/NEKernels.h @@ -45,10 +45,6 @@ #include "src/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.h" #include "src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.h" #include "src/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.h" -#include "src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ScaleKernel.h" -#include "src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.h" -#include "src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel.h" -#include "src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h" #include "src/core/NEON/kernels/NEGEMMLowpReductionKernel.h" #include "src/core/NEON/kernels/NEGEMMMatrixAdditionKernel.h" #include "src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h" diff --git a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ScaleKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ScaleKernel.cpp deleted file mode 100644 index 84365ba25b..0000000000 --- a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ScaleKernel.cpp +++ /dev/null @@ -1,320 +0,0 @@ -/* - * Copyright (c) 2020-2021 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 "src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ScaleKernel.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 "arm_compute/core/utils/quantization/AsymmHelpers.h" -#include "src/core/AccessWindowStatic.h" -#include "src/core/NEON/wrapper/wrapper.h" -#include "src/core/helpers/AutoConfiguration.h" -#include "src/core/helpers/WindowHelpers.h" - -#include <arm_neon.h> -#include <cstddef> -#include <cstdint> - -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 <typename T> -inline typename std::enable_if<std::is_same<T, uint8_t>::value, - typename wrapper::traits::neon_vector<T, 16>::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 <typename T> -inline typename std::enable_if<std::is_same<T, int8_t>::value, - typename wrapper::traits::neon_vector<T, 16>::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 <typename T> -inline typename wrapper::traits::neon_vector<T, 16>::type finalize_quantization(int32x4x4_t &in_s32, int32x4_t result_shift_s32, typename wrapper::traits::neon_vector<T, 16>::type min, - typename wrapper::traits::neon_vector<T, 16>::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<T, 16>::type out = convert_to_8bit<T>(in_s16); - - out = wrapper::vmax(out, min); - out = wrapper::vmin(out, max); - - return out; -} - -class Coordinates; - -template <typename T> -void NEGEMMLowpQuantizeDownInt32ScaleKernel::run(const Window &window) -{ - using VectorType = typename wrapper::traits::neon_vector<T, 16>::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<int>(window.x().start()); - const auto window_end_x = static_cast<int>(window.x().end()); - - const int clamp_min = (_is_bounded_relu) ? _output_stage->gemmlowp_min_bound : std::numeric_limits<T>::lowest(); - const int clamp_max = (_is_bounded_relu) ? _output_stage->gemmlowp_max_bound : std::numeric_limits<T>::max(); - - VectorType min = wrapper::vdup_n(static_cast<T>(clamp_min), wrapper::traits::vector_128_tag{}); - VectorType max = wrapper::vdup_n(static_cast<T>(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<const int32_t *>(in.ptr()) + x + 0), - vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4), - vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8), - vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12) - } - }; - - const int32x4x4_t bias_s32 = - { - { - vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 0), - vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 4), - vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 8), - vld1q_s32(reinterpret_cast<const int32_t *>(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<T *>(out.ptr() + x), finalize_quantization<T>(in_s32, result_shift_s32, min, max)); - } - - // Compute left-over elements - for(; x < window_end_x; ++x) - { - const int bias_value = *(reinterpret_cast<const int *>(bias.ptr()) + x); - int in_value = *(reinterpret_cast<const int *>(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<T>(utility::clamp<int>(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<const int32_t *>(in.ptr()) + x + 0), - vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4), - vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8), - vld1q_s32(reinterpret_cast<const int32_t *>(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<T *>(out.ptr() + x), finalize_quantization<T>(in_s32, result_shift_s32, min, max)); - } - - // Compute left-over elements - for(; x < window_end_x; ++x) - { - int in_value = *(reinterpret_cast<const int *>(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<T>(utility::clamp<int>(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()); - - 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<uint8_t>; - } - else if(_output_stage->output_data_type == DataType::QASYMM8_SIGNED) - { - _func = &NEGEMMLowpQuantizeDownInt32ScaleKernel::run<int8_t>; - } - 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/NEGEMMLowpQuantizeDownInt32ScaleKernel.h b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ScaleKernel.h deleted file mode 100644 index 021ff8e2e0..0000000000 --- a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ScaleKernel.h +++ /dev/null @@ -1,114 +0,0 @@ -/* - * Copyright (c) 2020-2021 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. - */ -#ifndef ARM_COMPUTE_NEGEMMLOWPQUANTIZEDOWNINT32SCALEKERNEL_H -#define ARM_COMPUTE_NEGEMMLOWPQUANTIZEDOWNINT32SCALEKERNEL_H - -#include "src/core/NEON/INEKernel.h" - -namespace arm_compute -{ -class ITensor; - -/** Kernel used to quantize down the int32 accumulator values of GEMMLowp to QASYMM8/QASYMM8_SIGNED - * - * This kernel takes a final int32 accumulator value (the output of @ref NEGEMMLowpMatrixMultiplyKernel), and processes it to obtain the final QASYMM8/QASYMM8_SIGNED value. - * The following computations will be performed by the kernel: - * - * -# Add offset terms to final result - * -# Multiply each entry of result by result_mult_int - * -# Add bias to final result if bias tensor is not a nullptr - * -# Shift the int32 accumulator by result_shift - * -# Clamp the value between the specified min and max bounds - * -# Clamp the resulting int32 values: - * -# -to the [0..255] range and cast to QASYMM8. - * -# -to the [-128..127] range and cast to QASYMM8_SIGNED. - * - */ -class NEGEMMLowpQuantizeDownInt32ScaleKernel : public INEKernel -{ -public: - const char *name() const override - { - return "NEGEMMLowpQuantizeDownInt32ScaleKernel"; - } - /** Constructor */ - NEGEMMLowpQuantizeDownInt32ScaleKernel(); - /** Prevent instances of this class from being copied (As this class contains pointers)*/ - NEGEMMLowpQuantizeDownInt32ScaleKernel(const NEGEMMLowpQuantizeDownInt32ScaleKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers)*/ - NEGEMMLowpQuantizeDownInt32ScaleKernel &operator=(const NEGEMMLowpQuantizeDownInt32ScaleKernel &) = delete; - /** Allow instances of this class to be moved */ - NEGEMMLowpQuantizeDownInt32ScaleKernel(NEGEMMLowpQuantizeDownInt32ScaleKernel &&) = default; - /** Allow instances of this class to be moved */ - NEGEMMLowpQuantizeDownInt32ScaleKernel &operator=(NEGEMMLowpQuantizeDownInt32ScaleKernel &&) = default; - /** Default destructor */ - ~NEGEMMLowpQuantizeDownInt32ScaleKernel() = default; - /** Initialise the kernel's input and output. - * - * @param[in] input Input tensor. Data type supported: S32 - * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the biases addition is not required. - * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input. - * @param[out] output Output tensor. Data type supported: Data type supported: QASYMM8/QASYMM8_SIGNED - * @param[out] output_stage GEMMLowp output stage metadata. - */ - void configure(const ITensor *input, const ITensor *bias, ITensor *output, const GEMMLowpOutputStageInfo *output_stage); - /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMLowpQuantizeDownInt32ScaleKernel - * - * @param[in] input Input tensor. Data type supported: S32 - * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the biases addition is not required. - * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input. - * @param[in] output Output tensor. Data type supported: Data type supported: QASYMM8/QASYMM8_SIGNED - * @param[out] output_stage GEMMLowp output stage metadata. - * - * @return a status - */ - static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const GEMMLowpOutputStageInfo *output_stage); - - // Inherited methods overridden: - void run(const Window &window, const ThreadInfo &info) override; - -private: - /** Template function to run the NEGEMMLowpQuantizeDownInt32ScaleKernel - * - * @param[in] window Region on which to execute the kernel. (Must be a valid region of the window returned by window()). - */ - template <typename T> - void run(const Window &window); - - /** Common signature for all the specialised NEGEMMLowpQuantizeDownInt32ScaleKernel functions - * - * @param[in] window Region on which to execute the kernel. - */ - using QuantizeDownFunctionPtr = void (NEGEMMLowpQuantizeDownInt32ScaleKernel::*)(const Window &window); - - QuantizeDownFunctionPtr _func; - const ITensor *_input; - const ITensor *_bias; - ITensor *_output; - const GEMMLowpOutputStageInfo *_output_stage; - bool _is_bounded_relu; -}; -} // namespace arm_compute - -#endif /* ARM_COMPUTE_NEGEMMLOWPQUANTIZEDOWNINT32SCALEKERNEL_H */ diff --git a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.cpp deleted file mode 100644 index aa54b80436..0000000000 --- a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.cpp +++ /dev/null @@ -1,234 +0,0 @@ -/* - * Copyright (c) 2019-2021 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 "src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.h" - -#include "arm_compute/core/Error.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/ITensor.h" -#include "arm_compute/core/TensorInfo.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/misc/ShapeCalculator.h" -#include "src/core/NEON/NESymm.h" -#include "src/core/helpers/AutoConfiguration.h" -#include "src/core/helpers/WindowHelpers.h" - -#include <arm_neon.h> -#include <cstddef> -#include <cstdint> - -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::QSYMM16); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, input); - } - - return Status{}; -} - -std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output) -{ - // Output auto inizialitation if not yet initialized - auto_init_if_empty(*output, input->clone()->set_data_type(DataType::QSYMM16)); - - // Configure kernel window - Window win = calculate_max_window(*input, Steps()); - - // NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel doesn't need padding so update_window_and_padding() can be skipped - - return std::make_pair(Status{}, win); -} -} // namespace - -class Coordinates; - -template <bool is_bounded_relu> -void NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::run(const Window &window) -{ - const int16x8_t min_s16 = vdupq_n_s16(static_cast<int16_t>(_min)); - const int16x8_t max_s16 = vdupq_n_s16(static_cast<int16_t>(_max)); - - ARM_COMPUTE_UNUSED(min_s16); - ARM_COMPUTE_UNUSED(max_s16); - - const int window_step_x = 8; - const auto window_start_x = static_cast<int>(window.x().start()); - const auto window_end_x = static_cast<int>(window.x().end()); - - Window win_collapsed = window.collapse_if_possible(window, Window::DimZ); - win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1)); - - Iterator in(_input, win_collapsed); - Iterator out(_output, win_collapsed); - 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_collapsed, [&](const Coordinates &) - { - // Compute 16 elements per iteration - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - int32x4x2_t in_s32 = - { - { - vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0), - vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4) - } - }; - - const int32x4x2_t bias_s32 = - { - { - vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 0), - vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 4) - } - }; - - // 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]); - - vst1q_s16(reinterpret_cast<int16_t *>(out.ptr()) + x, finalize_quantization_int16<is_bounded_relu>(in_s32, _result_fixedpoint_multiplier, _result_shift, min_s16, max_s16)); - } - - // Compute left-over elements - for(; x < window_end_x; ++x) - { - const int32_t bias_value = *(reinterpret_cast<const int32_t *>(bias.ptr()) + x); - int32_t in_value = *(reinterpret_cast<const int32_t *>(in.ptr()) + x); - - // Add bias - in_value += bias_value; - // Finalize and store the result - *(reinterpret_cast<int16_t *>(out.ptr()) + x) = finalize_quantization_int16<is_bounded_relu>(in_value, _result_fixedpoint_multiplier, _result_shift, static_cast<int16_t>(_min), - static_cast<int16_t>(_max)); - } - }, - in, out, bias); - } - else - { - execute_window_loop(win_collapsed, [&](const Coordinates &) - { - // Compute 16 elements per iteration - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - int32x4x2_t in_s32 = - { - { - vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0), - vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4) - } - }; - - vst1q_s16(reinterpret_cast<int16_t *>(out.ptr()) + x, finalize_quantization_int16<is_bounded_relu>(in_s32, _result_fixedpoint_multiplier, _result_shift, min_s16, max_s16)); - } - - // Compute left-over elements - for(; x < window_end_x; ++x) - { - const int32_t in_value = *(reinterpret_cast<const int32_t *>(in.ptr()) + x); - ARM_COMPUTE_UNUSED(in_value); - // Finalize and store the result - *(reinterpret_cast<int16_t *>(out.ptr()) + x) = finalize_quantization_int16<is_bounded_relu>(in_value, _result_fixedpoint_multiplier, _result_shift, static_cast<int16_t>(_min), - static_cast<int16_t>(_max)); - } - }, - in, out); - } -} - -NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel() - : _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr), _result_fixedpoint_multiplier(0), _result_shift(0), _min(0), _max(0) -{ -} - -void NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift, - int min, int max) -{ - // Perform validate step - ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); - 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_fixedpoint_multiplier = result_fixedpoint_multiplier; - _result_shift = result_shift; - _min = min; - _max = max; - - // Configure kernel window - auto win_config = validate_and_configure_window(input->info(), 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 <= -32768 && max >= 32767); - _func = is_bounded_relu ? &NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::run<true> : &NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::run<false>; -} - -Status NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::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(), output->clone().get()).first); - - return Status{}; -} - -void NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::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 diff --git a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.h b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.h deleted file mode 100644 index b01b204a6f..0000000000 --- a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.h +++ /dev/null @@ -1,118 +0,0 @@ -/* - * Copyright (c) 2019-2021 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. - */ -#ifndef ARM_COMPUTE_NEGEMMLOWPQUANTIZEDOWNINT32TOINT16SCALEBYFIXEDPOINTKERNEL_H -#define ARM_COMPUTE_NEGEMMLOWPQUANTIZEDOWNINT32TOINT16SCALEBYFIXEDPOINTKERNEL_H - -#include "src/core/NEON/INEKernel.h" - -namespace arm_compute -{ -class ITensor; - -/** Kernel used to quantize down the int32 accumulator values of GEMMLowp to QSYMM16 - * - * This kernel takes a final int32 accumulator value (the output of @ref NEGEMMLowpMatrixMultiplyKernel), and processes it to obtain the final QSYMM16 value. - * The following computations will be performed by the kernel: - * - * -# Compute fixed point multiplication between each entry of input by result_fixedpoint_multiplier - * -# Add bias to final result if bias tensor is not a nullptr - * -# Round to nearest division by a power-of-two using result_shift - * -# Clamp the value between the specified min and max bounds - * -# Clamp the resulting int32 values to the [-32768, 32767] range and cast to QSYMM16. - * - */ -class NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel : public INEKernel -{ -public: - const char *name() const override - { - return "NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel"; - } - /** Constructor */ - NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel(); - /** Prevent instances of this class from being copied (As this class contains pointers)*/ - NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel(const NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers)*/ - NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel &operator=(const NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel &) = delete; - /** Allow instances of this class to be moved */ - NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel(NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel &&) = default; - /** Allow instances of this class to be moved */ - NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel &operator=(NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel &&) = default; - /** Default destructor */ - ~NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel() = default; - /** Initialise the kernel's input and output. - * - * @param[in] input Input tensor. Data type supported: S32 - * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the biases addition is not required. - * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input. - * @param[out] output Output tensor. Data type supported: Data type supported: QSYMM16 - * @param[in] result_fixedpoint_multiplier Fixed point value to be multiplied to each element of the input matrix when once the result_offset has been add - * @param[in] result_shift Integer value used to round to nearest division by a power-of-two the result after the fixed point multiplication - * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QSYMM16. Defaults to 0. - * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QSYMM16. - * Along with @p min, this value can be used to implement "rectified linear unit" activation functions. Defaults to 0. - */ - void configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift, int min = 0, int max = 0); - /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel - * - * @param[in] input Input tensor info. Data type supported: S32 - * @param[in] bias Biases tensor info. Only shared biases supported and it can be a nullptr if the biases addition is not required. - * Biases are 1D tensor info with dimensions [OFM]. Data type supported: Same as @p input. - * @param[in] output Output tensor info. Data type supported: Data type supported: QSYMM16 - * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QSYMM16. Defaults to 0. - * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QSYMM16, - * Along with @p min, this value can be used to implement "rectified linear unit" activation functions. Defaults to 0. - * - * @return a status - */ - static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = 0, int max = 0); - - // Inherited methods overridden: - void run(const Window &window, const ThreadInfo &info) override; - -private: - /** Template function to run the NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel - * - * @param[in] window Region on which to execute the kernel. (Must be a valid region of the window returned by window()). - */ - template <bool is_bounded_relu> - void run(const Window &window); - - /** Common signature for all the specialised NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel functions - * - * @param[in] window Region on which to execute the kernel. - */ - using QuantizeDownFunctionPtr = void (NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::*)(const Window &window); - - QuantizeDownFunctionPtr _func; - const ITensor *_input; - const ITensor *_bias; - ITensor *_output; - int _result_fixedpoint_multiplier; - int _result_shift; - int _min; - int _max; -}; -} // namespace arm_compute -#endif /* ARM_COMPUTE_NEGEMMLOWPQUANTIZEDOWNINT32TOINT16SCALEBYFIXEDPOINTKERNEL_H */ diff --git a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel.cpp deleted file mode 100644 index 9ed85e62aa..0000000000 --- a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel.cpp +++ /dev/null @@ -1,243 +0,0 @@ -/* - * Copyright (c) 2019-2021 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 "src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel.h" - -#include "arm_compute/core/Error.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/ITensor.h" -#include "arm_compute/core/TensorInfo.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/misc/ShapeCalculator.h" -#include "src/core/NEON/NEAsymm.h" -#include "src/core/helpers/AutoConfiguration.h" -#include "src/core/helpers/WindowHelpers.h" - -#include <arm_neon.h> -#include <cstddef> -#include <cstdint> - -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_SIGNED); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, input); - } - - return Status{}; -} - -std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output) -{ - // Output auto initialization if not yet initialized - auto_init_if_empty(*output, input->clone()->set_data_type(DataType::QASYMM8_SIGNED)); - - // Configure kernel window - Window win = calculate_max_window(*input, Steps()); - - // NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel doesn't need padding so update_window_and_padding() can be skipped - - return std::make_pair(Status{}, win); -} -} // namespace - -template <bool is_bounded_relu> -void NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel::run(const Window &window) -{ - const int32x4_t result_offset_after_shift_s32 = vdupq_n_s32(_result_offset_after_shift); - const int8x16_t min_s8 = vdupq_n_s8(static_cast<int8_t>(_min)); - const int8x16_t max_s8 = vdupq_n_s8(static_cast<int8_t>(_max)); - - ARM_COMPUTE_UNUSED(min_s8, max_s8); - - const int window_step_x = 16; - const auto window_start_x = static_cast<int>(window.x().start()); - const auto window_end_x = static_cast<int>(window.x().end()); - - Window win_collapsed = window.collapse_if_possible(window, Window::DimZ); - win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1)); - - Iterator in(_input, win_collapsed); - Iterator out(_output, win_collapsed); - 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_collapsed, [&](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<const int32_t *>(in.ptr()) + x + 0), - vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4), - vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8), - vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12) - } - }; - - const int32x4x4_t bias_s32 = - { - { - vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 0), - vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 4), - vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 8), - vld1q_s32(reinterpret_cast<const int32_t *>(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]); - - vst1q_s8(reinterpret_cast<int8_t *>(out.ptr() + x), - finalize_quantization(in_s32, _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, min_s8, max_s8, is_bounded_relu)); - } - - // Compute left-over elements - for(; x < window_end_x; ++x) - { - const int32_t bias_value = *(reinterpret_cast<const int32_t *>(bias.ptr()) + x); - int32_t in_value = *(reinterpret_cast<const int32_t *>(in.ptr()) + x); - - // Add bias - in_value += bias_value; - // Finalize and store the result - *reinterpret_cast<int8_t *>(out.ptr() + x) = finalize_quantization(in_value, _result_fixedpoint_multiplier, _result_shift, _result_offset_after_shift, - static_cast<int8_t>(_min), static_cast<int8_t>(_max), is_bounded_relu); - } - }, - in, out, bias); - } - else - { - execute_window_loop(win_collapsed, [&](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<const int32_t *>(in.ptr()) + x + 0), - vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4), - vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8), - vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12) - } - }; - - vst1q_s8(reinterpret_cast<int8_t *>(out.ptr() + x), - finalize_quantization(in_s32, _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, min_s8, max_s8, is_bounded_relu)); - } - - // Compute left-over elements - for(; x < window_end_x; ++x) - { - const int32_t in_value = *(reinterpret_cast<const int32_t *>(in.ptr()) + x); - - // Finalize and store the result - *reinterpret_cast<int8_t *>(out.ptr() + x) = finalize_quantization(in_value, _result_fixedpoint_multiplier, _result_shift, _result_offset_after_shift, - static_cast<int8_t>(_min), static_cast<int8_t>(_max), is_bounded_relu); - } - }, - in, out); - } -} - -NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel::NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel() - : _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr), _result_fixedpoint_multiplier(0), _result_shift(0), _result_offset_after_shift(0), _min(0), _max(0) -{ -} - -void NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel::configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift, - int result_offset_after_shift, int min, int max) -{ - // Perform validate step - ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); - 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_fixedpoint_multiplier = result_fixedpoint_multiplier; - _result_shift = result_shift; - _result_offset_after_shift = result_offset_after_shift; - _min = min; - _max = max; - - // Configure kernel window - auto win_config = validate_and_configure_window(input->info(), 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 <= -128 && max >= 127); - _func = is_bounded_relu ? &NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel::run<true> : &NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel::run<false>; -} - -Status NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel::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(), output->clone().get()).first); - - return Status{}; -} - -void NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel::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 diff --git a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel.h b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel.h deleted file mode 100644 index 9e7dc2f599..0000000000 --- a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel.h +++ /dev/null @@ -1,121 +0,0 @@ -/* - * Copyright (c) 2019-2021 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. - */ -#ifndef ARM_COMPUTE_NEGEMMLOWPQUANTIZEDOWNINT32TOINT8SCALEBYFIXEDPOINTKERNEL_H -#define ARM_COMPUTE_NEGEMMLOWPQUANTIZEDOWNINT32TOINT8SCALEBYFIXEDPOINTKERNEL_H - -#include "src/core/NEON/INEKernel.h" - -namespace arm_compute -{ -class ITensor; - -/** Kernel used to quantize down the int32 accumulator values of GEMMLowp to QASYMM8_SIGNED - * - * This kernel takes a final int32 accumulator value (the output of @ref NEGEMMLowpMatrixMultiplyKernel), and processes it to obtain the final QASYMM8_SIGNED value. - * The following computations will be performed by the kernel: - * - * -# Compute fixed point multiplication between each entry of input by result_fixedpoint_multiplier - * -# Add bias to final result if bias tensor is not a nullptr - * -# Round to nearest division by a power-of-two using result_shift - * -# Add offset to each result - * -# Clamp the value between the specified min and max bounds - * -# Clamp the resulting int32 values to the [-128..127] range and cast to QASYMM8_SIGNED. - * - */ -class NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel : public INEKernel -{ -public: - const char *name() const override - { - return "NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel"; - } - /** Constructor */ - NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel(); - /** Prevent instances of this class from being copied (As this class contains pointers)*/ - NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel(const NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers)*/ - NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel &operator=(const NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel &) = delete; - /** Allow instances of this class to be moved */ - NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel(NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel &&) = default; - /** Allow instances of this class to be moved */ - NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel &operator=(NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel &&) = default; - /** Default destructor */ - ~NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel() = default; - /** Initialise the kernel's input and output. - * - * @param[in] input Input tensor. Data type supported: S32 - * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the biases addition is not required. - * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input. - * @param[out] output Output tensor. Data type supported: Data type supported: QASYMM8_SIGNED - * @param[in] result_fixedpoint_multiplier Fixed point value to be multiplied to each element of the input matrix when once the result_offset has been add - * @param[in] result_shift Integer value used to round to nearest division by a power-of-two the result after the fixed point multiplication - * @param[in] result_offset_after_shift Offset to be applied to result before converting it back to QASYMM8_SIGNED - * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8_SIGNED - * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QASYMM8_SIGNED, - * Along with @p min, this value can be used to implement "rectified linear unit" activation functions - */ - void configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift, int min = 0, int max = 0); - /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel - * - * @param[in] input Input tensor. Data type supported: S32 - * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the biases addition is not required. - * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input. - * @param[in] output Output tensor. Data type supported: Data type supported: QASYMM8_SIGNED - * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8_SIGNED - * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QASYMM8_SIGNED, - * Along with @p min, this value can be used to implement "rectified linear unit" activation functions - * - * @return a status - */ - static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = 0, int max = 0); - - // Inherited methods overridden: - void run(const Window &window, const ThreadInfo &info) override; - -private: - /** Template function to run the NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel - * - * @param[in] window Region on which to execute the kernel. (Must be a valid region of the window returned by window()). - */ - template <bool is_bounded_relu> - void run(const Window &window); - - /** Common signature for all the specialised NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel functions - * - * @param[in] window Region on which to execute the kernel. - */ - using QuantizeDownFunctionPtr = void (NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel::*)(const Window &window); - - QuantizeDownFunctionPtr _func; - const ITensor *_input; - const ITensor *_bias; - ITensor *_output; - int _result_fixedpoint_multiplier; - int _result_shift; - int _result_offset_after_shift; - int _min; - int _max; -}; -} // namespace arm_compute -#endif /* ARM_COMPUTE_NEGEMMLOWPQUANTIZEDOWNINT32TOINT8SCALEBYFIXEDPOINTKERNEL_H */ diff --git a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp deleted file mode 100644 index 83ca6f944d..0000000000 --- a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp +++ /dev/null @@ -1,245 +0,0 @@ -/* - * Copyright (c) 2017-2021 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 "src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h" - -#include "arm_compute/core/Error.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/ITensor.h" -#include "arm_compute/core/TensorInfo.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/misc/ShapeCalculator.h" -#include "src/core/NEON/NEAsymm.h" -#include "src/core/helpers/AutoConfiguration.h" -#include "src/core/helpers/WindowHelpers.h" - -#include <arm_neon.h> -#include <cstddef> -#include <cstdint> - -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(output, input); - } - - return Status{}; -} - -std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output) -{ - // Output auto inizialitation if not yet initialized - auto_init_if_empty(*output, input->clone()->set_data_type(DataType::QASYMM8)); - - // Configure kernel window - Window win = calculate_max_window(*input, Steps()); - - // NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel doesn't need padding so update_window_and_padding() can be skipped - - return std::make_pair(Status{}, win); -} -} // namespace - -namespace arm_compute -{ -class Coordinates; -} // namespace arm_compute - -template <bool is_bounded_relu> -void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run(const Window &window) -{ - const int32x4_t result_offset_after_shift_s32 = vdupq_n_s32(_result_offset_after_shift); - const uint8x16_t min_u8 = vdupq_n_u8(static_cast<uint8_t>(_min)); - const uint8x16_t max_u8 = vdupq_n_u8(static_cast<uint8_t>(_max)); - - ARM_COMPUTE_UNUSED(min_u8); - ARM_COMPUTE_UNUSED(max_u8); - - const int window_step_x = 16; - const auto window_start_x = static_cast<int>(window.x().start()); - const auto window_end_x = static_cast<int>(window.x().end()); - - Window win_collapsed = window.collapse_if_possible(window, Window::DimZ); - win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1)); - - Iterator in(_input, win_collapsed); - Iterator out(_output, win_collapsed); - 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_collapsed, [&](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<const int32_t *>(in.ptr()) + x + 0), - vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4), - vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8), - vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12) - } - }; - - const int32x4x4_t bias_s32 = - { - { - vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 0), - vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 4), - vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 8), - vld1q_s32(reinterpret_cast<const int32_t *>(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]); - - vst1q_u8(out.ptr() + x, finalize_quantization(in_s32, _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, min_u8, max_u8, is_bounded_relu)); - } - - // Compute left-over elements - for(; x < window_end_x; ++x) - { - const int32_t bias_value = *(reinterpret_cast<const int32_t *>(bias.ptr()) + x); - int32_t in_value = *(reinterpret_cast<const int32_t *>(in.ptr()) + x); - - // Add bias - in_value += bias_value; - // Finalize and store the result - *(out.ptr() + x) = finalize_quantization(in_value, _result_fixedpoint_multiplier, _result_shift, _result_offset_after_shift, static_cast<uint8_t>(_min), static_cast<uint8_t>(_max), is_bounded_relu); - } - }, - in, out, bias); - } - else - { - execute_window_loop(win_collapsed, [&](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<const int32_t *>(in.ptr()) + x + 0), - vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4), - vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8), - vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12) - } - }; - - vst1q_u8(out.ptr() + x, finalize_quantization(in_s32, _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, min_u8, max_u8, is_bounded_relu)); - } - - // Compute left-over elements - for(; x < window_end_x; ++x) - { - const int32_t in_value = *(reinterpret_cast<const int32_t *>(in.ptr()) + x); - - // Finalize and store the result - *(out.ptr() + x) = finalize_quantization(in_value, _result_fixedpoint_multiplier, _result_shift, _result_offset_after_shift, static_cast<uint8_t>(_min), static_cast<uint8_t>(_max), is_bounded_relu); - } - }, - in, out); - } -} - -NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel() - : _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr), _result_fixedpoint_multiplier(0), _result_shift(0), _result_offset_after_shift(0), _min(0), _max(0) -{ -} - -void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift, - int result_offset_after_shift, int min, int max) -{ - // Perform validate step - ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); - 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_fixedpoint_multiplier = result_fixedpoint_multiplier; - _result_shift = result_shift; - _result_offset_after_shift = result_offset_after_shift; - _min = min; - _max = max; - - // Configure kernel window - auto win_config = validate_and_configure_window(input->info(), 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 ? &NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run<true> : &NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run<false>; -} - -Status NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::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(), output->clone().get()).first); - - return Status{}; -} - -void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::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 diff --git a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h deleted file mode 100644 index def0573967..0000000000 --- a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h +++ /dev/null @@ -1,121 +0,0 @@ -/* - * Copyright (c) 2017-2021 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. - */ -#ifndef ARM_COMPUTE_NEGEMMLOWPQUANTIZEDOWNINT32TOUINT8SCALEBYFIXEDPOINTKERNEL_H -#define ARM_COMPUTE_NEGEMMLOWPQUANTIZEDOWNINT32TOUINT8SCALEBYFIXEDPOINTKERNEL_H - -#include "src/core/NEON/INEKernel.h" - -namespace arm_compute -{ -class ITensor; - -/** Kernel used to quantize down the int32 accumulator values of GEMMLowp to QASYMM8 - * - * This kernel takes a final int32 accumulator value (the output of @ref NEGEMMLowpMatrixMultiplyKernel), and processes it to obtain the final QASYMM8 value. - * The following computations will be performed by the kernel: - * - * -# Compute fixed point multiplication between each entry of input by result_fixedpoint_multiplier - * -# Add bias to final result if bias tensor is not a nullptr - * -# Round to nearest division by a power-of-two using result_shift - * -# Add offset to each result - * -# Clamp the value between the specified min and max bounds - * -# Clamp the resulting int32 values to the [0..255] range and cast to QASYMM8. - * - */ -class NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel : public INEKernel -{ -public: - const char *name() const override - { - return "NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel"; - } - /** Constructor */ - NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel(); - /** Prevent instances of this class from being copied (As this class contains pointers)*/ - NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel(const NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers)*/ - NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel &operator=(const NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel &) = delete; - /** Allow instances of this class to be moved */ - NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel(NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel &&) = default; - /** Allow instances of this class to be moved */ - NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel &operator=(NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel &&) = default; - /** Default destructor */ - ~NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel() = default; - /** Initialise the kernel's input and output. - * - * @param[in] input Input tensor. Data type supported: S32 - * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the biases addition is not required. - * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input. - * @param[out] output Output tensor. Data type supported: Data type supported: QASYMM8 - * @param[in] result_fixedpoint_multiplier Fixed point value to be multiplied to each element of the input matrix when once the result_offset has been add - * @param[in] result_shift Integer value used to round to nearest division by a power-of-two the result after the fixed point multiplication - * @param[in] result_offset_after_shift Offset to be applied to result before converting it back to QASYMM8 - * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8 - * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QASYMM8, - * Along with @p min, this value can be used to implement "rectified linear unit" activation functions - */ - void configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift, int min = 0, int max = 0); - /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel - * - * @param[in] input Input tensor. Data type supported: S32 - * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the biases addition is not required. - * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input. - * @param[in] output Output tensor. Data type supported: Data type supported: QASYMM8 - * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8 - * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QASYMM8, - * Along with @p min, this value can be used to implement "rectified linear unit" activation functions - * - * @return a status - */ - static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = 0, int max = 0); - - // Inherited methods overridden: - void run(const Window &window, const ThreadInfo &info) override; - -private: - /** Template function to run the NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel - * - * @param[in] window Region on which to execute the kernel. (Must be a valid region of the window returned by window()). - */ - template <bool is_bounded_relu> - void run(const Window &window); - - /** Common signature for all the specialised NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel functions - * - * @param[in] window Region on which to execute the kernel. - */ - using QuantizeDownFunctionPtr = void (NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::*)(const Window &window); - - QuantizeDownFunctionPtr _func; - const ITensor *_input; - const ITensor *_bias; - ITensor *_output; - int _result_fixedpoint_multiplier; - int _result_shift; - int _result_offset_after_shift; - int _min; - int _max; -}; -} // namespace arm_compute -#endif /* ARM_COMPUTE_NEGEMMLOWPQUANTIZEDOWNINT32TOUINT8SCALEBYFIXEDPOINTKERNEL_H */ |