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| author | Manuel Bottini <manuel.bottini@arm.com> | 2021-06-18 15:47:28 +0100 |
|---|---|---|
| committer | Manuel Bottini <manuel.bottini@arm.com> | 2021-07-08 14:47:38 +0000 |
| commit | cfac51c779f9bf05e8b2d386fbfb4022767d1d30 (patch) | |
| tree | 6ded148068c32bb1b2926946f59d0262d928b9ab /src/core/NEON/kernels | |
| parent | 06ac6e438fc95aa7f8228be8217e0776d692b8e7 (diff) | |
| download | ComputeLibrary-cfac51c779f9bf05e8b2d386fbfb4022767d1d30.tar.gz | |
Port NEGEMMLowp Part 2
Details:
Extend NEConvertQuantizedSignednessKernel
Port NEGEMMInterleave4x4Kernel to CpuGemmInterleave4x4Kernel
Port NEGEMMTranspose1xWKernel to CpuGemmTranspose1xWKernel
Port NEGEMMLowpMatrixAReductionKernel to CpuGemmLowpMatrixAReductionKernel
Port NEGEMMLowpMatrixBReductionKernel to CpuGemmLowpMatrixBReductionKernel
Port NEGEMMLowpOffsetContributionOutputStageKernel to CpuGemmLowpOffsetContributionOutputStageKernel
Port NEGEMMLowpOffsetContributionKernel to CpuGemmLowpOffsetContributionKernel
Resolves: COMPMID-4403
Change-Id: I3227f052f25e7b41d073bbea1da8a881fcd78b8e
Signed-off-by: Manuel Bottini <manuel.bottini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5875
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Diffstat (limited to 'src/core/NEON/kernels')
10 files changed, 0 insertions, 3550 deletions
diff --git a/src/core/NEON/kernels/NEConvertQuantizedSignednessKernel.cpp b/src/core/NEON/kernels/NEConvertQuantizedSignednessKernel.cpp deleted file mode 100644 index 1f2170f42a..0000000000 --- a/src/core/NEON/kernels/NEConvertQuantizedSignednessKernel.cpp +++ /dev/null @@ -1,138 +0,0 @@ -/* - * Copyright (c) 2019-2020 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 "src/core/NEON/kernels/NEConvertQuantizedSignednessKernel.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/Validate.h" -#include "arm_compute/core/Window.h" -#include "src/core/NEON/wrapper/wrapper.h" -#include "src/core/helpers/AutoConfiguration.h" -#include "src/core/helpers/WindowHelpers.h" - -namespace arm_compute -{ -namespace -{ -Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output) -{ - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED); - - // Validate output if initialized - if(output->total_size() != 0) - { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(input->tensor_shape(), output->tensor_shape()); - } - - return Status{}; -} - -std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output) -{ - // Output auto inizialitation if not yet initialized - { - const bool is_input_signed = input->data_type() == DataType::QASYMM8_SIGNED; - const DataType dt = is_input_signed ? DataType::QASYMM8 : DataType::QASYMM8_SIGNED; - const UniformQuantizationInfo qinfo = input->quantization_info().uniform(); - const int offset_correction = is_input_signed ? -128 : 128; - const QuantizationInfo corrected_qinfo = QuantizationInfo(qinfo.scale, qinfo.offset + offset_correction); - - auto_init_if_empty(*output, input->clone()->set_data_type(dt).set_quantization_info(corrected_qinfo)); - } - - return std::make_pair(Status{}, calculate_max_window(*output)); -} -} // namespace - -NEConvertQuantizedSignednessKernel::NEConvertQuantizedSignednessKernel() - : _input(nullptr), _output(nullptr) -{ -} - -void NEConvertQuantizedSignednessKernel::configure(const ITensor *input, ITensor *output) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info())); - - _input = input; - _output = output; - - std::pair<Status, Window> win_config = validate_and_configure_window(input->info(), output->info()); - ARM_COMPUTE_ERROR_THROW_ON(win_config.first); - INEKernel::configure(win_config.second); -} - -Status NEConvertQuantizedSignednessKernel::validate(const arm_compute::ITensorInfo *input, const arm_compute::ITensorInfo *output) -{ - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get()).first); - return Status{}; -} - -void NEConvertQuantizedSignednessKernel::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); - - Window win_collapsed = window.collapse_if_possible(window, Window::DimZ); - win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1)); - - Iterator input(_input, win_collapsed); - Iterator output(_output, win_collapsed); - - 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 uint8_t mask = 128; - const auto vmask = wrapper::vdup_n(mask, wrapper::traits::vector_128_tag{}); - - execute_window_loop(win_collapsed, [&](const Coordinates &) - { - const auto input_ptr = reinterpret_cast<const uint8_t *>(input.ptr()); - const auto output_ptr = reinterpret_cast<uint8_t *>(output.ptr()); - - // Compute S elements per iteration - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - const auto vin = wrapper::vloadq(input_ptr + x); - wrapper::vstore(output_ptr + x, wrapper::veor(vin, vmask)); - } - - // Compute left-over elements - for(; x < window_end_x; ++x) - { - const uint8_t in = *(reinterpret_cast<const uint8_t *>(input_ptr + x)); - *(output_ptr + x) = in ^ mask; - } - }, - input, output); -} -} // namespace arm_compute diff --git a/src/core/NEON/kernels/NEConvertQuantizedSignednessKernel.h b/src/core/NEON/kernels/NEConvertQuantizedSignednessKernel.h deleted file mode 100644 index 67d5ca246e..0000000000 --- a/src/core/NEON/kernels/NEConvertQuantizedSignednessKernel.h +++ /dev/null @@ -1,78 +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_NECONVERTQUANTIZEDSIGNEDNESSKERNEL_H -#define ARM_COMPUTE_NECONVERTQUANTIZEDSIGNEDNESSKERNEL_H - -#include "arm_compute/core/Types.h" -#include "src/core/NEON/INEKernel.h" - -namespace arm_compute -{ -// Forward declarations -class ITensor; - -/** Kernel to convert asymmetric signed to asymmetric signed and vice-versa */ -class NEConvertQuantizedSignednessKernel : public INEKernel -{ -public: - const char *name() const override - { - return "NEConvertQuantizedSignednessKernel"; - } - /** Default constructor */ - NEConvertQuantizedSignednessKernel(); - /** Prevent instances of this class from being copied (As this class contains pointers). */ - NEConvertQuantizedSignednessKernel(const NEConvertQuantizedSignednessKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers). */ - NEConvertQuantizedSignednessKernel &operator=(const NEConvertQuantizedSignednessKernel &) = delete; - /** Allow instances of this class to be moved */ - NEConvertQuantizedSignednessKernel(NEConvertQuantizedSignednessKernel &&) = default; - /** Allow instances of this class to be moved */ - NEConvertQuantizedSignednessKernel &operator=(NEConvertQuantizedSignednessKernel &&) = default; - /** Default destructor */ - ~NEConvertQuantizedSignednessKernel() = default; - /** Initialize the kernel's input, output. - * - * @param[in] input Source tensor. Data types supported: QASYMM8/QASYMM8_SIGNED. - * @param[out] output Destination tensor. Data types supported: opposite of @p input. - */ - void configure(const ITensor *input, ITensor *output); - /** Static function to check if given info will lead to a valid configuration of @ref NEConvertQuantizedSignednessKernel - * - * @param[in] input Source tensor. Data types supported: QASYMM8/QASYMM8_SIGNED. - * @param[in] output Destination tensor. Data types supported: opposite of @p input. - * - * @return a status - */ - static Status validate(const ITensorInfo *input, const ITensorInfo *output); - - // Inherited methods overridden: - void run(const Window &window, const ThreadInfo &info) override; - -private: - const ITensor *_input; - ITensor *_output; -}; -} // namespace arm_compute -#endif /*ARM_COMPUTE_NECONVERTQUANTIZEDSIGNEDNESSKERNEL_H */ diff --git a/src/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.cpp deleted file mode 100644 index 6bcf59ee96..0000000000 --- a/src/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.cpp +++ /dev/null @@ -1,1052 +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/NEGEMMLowpMatrixMultiplyKernel.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 "src/core/helpers/AutoConfiguration.h" -#include "src/core/helpers/WindowHelpers.h" - -#include <arm_neon.h> - -using namespace arm_compute; - -namespace arm_compute -{ -namespace -{ -void inline vector_matrix_multiply_u8(Iterator &ina, Iterator &inb, Iterator &out, int width_a, int width_b, int width_out, size_t stride_b, const Window &window) -{ - execute_window_loop(window, [&](const Coordinates & id) - { - if(id.x() > width_b) - { - return; - } - - // Note: Since the input are all positives, we can use uint32_t - // Accumulators for the block 0 - uint32x4x4_t c0 = - { - { - vdupq_n_u32(0), - vdupq_n_u32(0), - vdupq_n_u32(0), - vdupq_n_u32(0) - } - }; - - auto vec_a = reinterpret_cast<const uint8_t *>(ina.ptr()); - auto matrix_b = reinterpret_cast<const uint8_t *>(inb.ptr()); - auto vec_a_end_addr = vec_a + width_a; - - // This for loop performs 8 accumulations - for(; vec_a <= (vec_a_end_addr - 8);) - { - const uint8x8_t a00_u8 = vld1_u8(vec_a); - const uint8x16_t b00_u8 = vld1q_u8(matrix_b + 0 * stride_b); - const uint8x16_t b10_u8 = vld1q_u8(matrix_b + 1 * stride_b); - const uint8x16_t b20_u8 = vld1q_u8(matrix_b + 2 * stride_b); - const uint8x16_t b30_u8 = vld1q_u8(matrix_b + 3 * stride_b); - const uint8x16_t b40_u8 = vld1q_u8(matrix_b + 4 * stride_b); - const uint8x16_t b50_u8 = vld1q_u8(matrix_b + 5 * stride_b); - const uint8x16_t b60_u8 = vld1q_u8(matrix_b + 6 * stride_b); - const uint8x16_t b70_u8 = vld1q_u8(matrix_b + 7 * stride_b); - - // Convert a00_u8 to uint16_t and get the lower part - const uint16x4x2_t a00_u16 = - { - { - vget_low_u16(vmovl_u8(a00_u8)), - vget_high_u16(vmovl_u8(a00_u8)) - } - }; - - const uint16x4x4_t b00_u16 = - { - { - vget_low_u16(vmovl_u8(vget_low_u8(b00_u8))), - vget_high_u16(vmovl_u8(vget_low_u8(b00_u8))), - vget_low_u16(vmovl_u8(vget_high_u8(b00_u8))), - vget_high_u16(vmovl_u8(vget_high_u8(b00_u8))) - } - }; - - const uint16x4x4_t b10_u16 = - { - { - vget_low_u16(vmovl_u8(vget_low_u8(b10_u8))), - vget_high_u16(vmovl_u8(vget_low_u8(b10_u8))), - vget_low_u16(vmovl_u8(vget_high_u8(b10_u8))), - vget_high_u16(vmovl_u8(vget_high_u8(b10_u8))) - } - }; - - const uint16x4x4_t b20_u16 = - { - { - vget_low_u16(vmovl_u8(vget_low_u8(b20_u8))), - vget_high_u16(vmovl_u8(vget_low_u8(b20_u8))), - vget_low_u16(vmovl_u8(vget_high_u8(b20_u8))), - vget_high_u16(vmovl_u8(vget_high_u8(b20_u8))) - } - }; - - const uint16x4x4_t b30_u16 = - { - { - vget_low_u16(vmovl_u8(vget_low_u8(b30_u8))), - vget_high_u16(vmovl_u8(vget_low_u8(b30_u8))), - vget_low_u16(vmovl_u8(vget_high_u8(b30_u8))), - vget_high_u16(vmovl_u8(vget_high_u8(b30_u8))) - } - }; - - const uint16x4x4_t b40_u16 = - { - { - vget_low_u16(vmovl_u8(vget_low_u8(b40_u8))), - vget_high_u16(vmovl_u8(vget_low_u8(b40_u8))), - vget_low_u16(vmovl_u8(vget_high_u8(b40_u8))), - vget_high_u16(vmovl_u8(vget_high_u8(b40_u8))) - } - }; - - const uint16x4x4_t b50_u16 = - { - { - vget_low_u16(vmovl_u8(vget_low_u8(b50_u8))), - vget_high_u16(vmovl_u8(vget_low_u8(b50_u8))), - vget_low_u16(vmovl_u8(vget_high_u8(b50_u8))), - vget_high_u16(vmovl_u8(vget_high_u8(b50_u8))) - } - }; - - const uint16x4x4_t b60_u16 = - { - { - vget_low_u16(vmovl_u8(vget_low_u8(b60_u8))), - vget_high_u16(vmovl_u8(vget_low_u8(b60_u8))), - vget_low_u16(vmovl_u8(vget_high_u8(b60_u8))), - vget_high_u16(vmovl_u8(vget_high_u8(b60_u8))) - } - }; - - const uint16x4x4_t b70_u16 = - { - { - vget_low_u16(vmovl_u8(vget_low_u8(b70_u8))), - vget_high_u16(vmovl_u8(vget_low_u8(b70_u8))), - vget_low_u16(vmovl_u8(vget_high_u8(b70_u8))), - vget_high_u16(vmovl_u8(vget_high_u8(b70_u8))) - } - }; - - // Accumulate 0: - c0.val[0] = vmlal_lane_u16(c0.val[0], b00_u16.val[0], a00_u16.val[0], 0); - c0.val[1] = vmlal_lane_u16(c0.val[1], b00_u16.val[1], a00_u16.val[0], 0); - c0.val[2] = vmlal_lane_u16(c0.val[2], b00_u16.val[2], a00_u16.val[0], 0); - c0.val[3] = vmlal_lane_u16(c0.val[3], b00_u16.val[3], a00_u16.val[0], 0); - - // Accumulate 1: - c0.val[0] = vmlal_lane_u16(c0.val[0], b10_u16.val[0], a00_u16.val[0], 1); - c0.val[1] = vmlal_lane_u16(c0.val[1], b10_u16.val[1], a00_u16.val[0], 1); - c0.val[2] = vmlal_lane_u16(c0.val[2], b10_u16.val[2], a00_u16.val[0], 1); - c0.val[3] = vmlal_lane_u16(c0.val[3], b10_u16.val[3], a00_u16.val[0], 1); - - // Accumulate 2: - c0.val[0] = vmlal_lane_u16(c0.val[0], b20_u16.val[0], a00_u16.val[0], 2); - c0.val[1] = vmlal_lane_u16(c0.val[1], b20_u16.val[1], a00_u16.val[0], 2); - c0.val[2] = vmlal_lane_u16(c0.val[2], b20_u16.val[2], a00_u16.val[0], 2); - c0.val[3] = vmlal_lane_u16(c0.val[3], b20_u16.val[3], a00_u16.val[0], 2); - - // Accumulate 3: - c0.val[0] = vmlal_lane_u16(c0.val[0], b30_u16.val[0], a00_u16.val[0], 3); - c0.val[1] = vmlal_lane_u16(c0.val[1], b30_u16.val[1], a00_u16.val[0], 3); - c0.val[2] = vmlal_lane_u16(c0.val[2], b30_u16.val[2], a00_u16.val[0], 3); - c0.val[3] = vmlal_lane_u16(c0.val[3], b30_u16.val[3], a00_u16.val[0], 3); - - // Accumulate 4: - c0.val[0] = vmlal_lane_u16(c0.val[0], b40_u16.val[0], a00_u16.val[1], 0); - c0.val[1] = vmlal_lane_u16(c0.val[1], b40_u16.val[1], a00_u16.val[1], 0); - c0.val[2] = vmlal_lane_u16(c0.val[2], b40_u16.val[2], a00_u16.val[1], 0); - c0.val[3] = vmlal_lane_u16(c0.val[3], b40_u16.val[3], a00_u16.val[1], 0); - - // Accumulate 5: - c0.val[0] = vmlal_lane_u16(c0.val[0], b50_u16.val[0], a00_u16.val[1], 1); - c0.val[1] = vmlal_lane_u16(c0.val[1], b50_u16.val[1], a00_u16.val[1], 1); - c0.val[2] = vmlal_lane_u16(c0.val[2], b50_u16.val[2], a00_u16.val[1], 1); - c0.val[3] = vmlal_lane_u16(c0.val[3], b50_u16.val[3], a00_u16.val[1], 1); - - // Accumulate 6: - c0.val[0] = vmlal_lane_u16(c0.val[0], b60_u16.val[0], a00_u16.val[1], 2); - c0.val[1] = vmlal_lane_u16(c0.val[1], b60_u16.val[1], a00_u16.val[1], 2); - c0.val[2] = vmlal_lane_u16(c0.val[2], b60_u16.val[2], a00_u16.val[1], 2); - c0.val[3] = vmlal_lane_u16(c0.val[3], b60_u16.val[3], a00_u16.val[1], 2); - - // Accumulate 7: - c0.val[0] = vmlal_lane_u16(c0.val[0], b70_u16.val[0], a00_u16.val[1], 3); - c0.val[1] = vmlal_lane_u16(c0.val[1], b70_u16.val[1], a00_u16.val[1], 3); - c0.val[2] = vmlal_lane_u16(c0.val[2], b70_u16.val[2], a00_u16.val[1], 3); - c0.val[3] = vmlal_lane_u16(c0.val[3], b70_u16.val[3], a00_u16.val[1], 3); - - vec_a += 8; - matrix_b += 8 * stride_b; - } - - // This for loop performs the left-over accumulations - for(; vec_a < vec_a_end_addr;) - { - const uint8x8_t a00_u8 = vld1_dup_u8(vec_a); - const uint8x16_t b00_u8 = vld1q_u8(matrix_b); - - const uint16x4x4_t b00_u16 = - { - { - vget_low_u16(vmovl_u8(vget_low_u8(b00_u8))), - vget_high_u16(vmovl_u8(vget_low_u8(b00_u8))), - vget_low_u16(vmovl_u8(vget_high_u8(b00_u8))), - vget_high_u16(vmovl_u8(vget_high_u8(b00_u8))) - } - }; - - // Convert a00_u8 to uint16_t and get the lower part - const uint16x4_t a00_u16 = vget_low_u16(vmovl_u8(a00_u8)); - - // Accumulate 0: - c0.val[0] = vmlal_lane_u16(c0.val[0], b00_u16.val[0], a00_u16, 0); - c0.val[1] = vmlal_lane_u16(c0.val[1], b00_u16.val[1], a00_u16, 0); - c0.val[2] = vmlal_lane_u16(c0.val[2], b00_u16.val[2], a00_u16, 0); - c0.val[3] = vmlal_lane_u16(c0.val[3], b00_u16.val[3], a00_u16, 0); - - vec_a += 1; - matrix_b += stride_b; - } - - auto vec_out = reinterpret_cast<int32_t *>(out.ptr()); - if(id.x() < (width_out - 16)) - { - vst1q_s32(vec_out + 0, vreinterpretq_s32_u32(c0.val[0])); - vst1q_s32(vec_out + 4, vreinterpretq_s32_u32(c0.val[1])); - vst1q_s32(vec_out + 8, vreinterpretq_s32_u32(c0.val[2])); - vst1q_s32(vec_out + 12, vreinterpretq_s32_u32(c0.val[3])); - } - else - { - auto left_over = width_out - id.x(); - for(auto k = 0; k < 4 && left_over; ++k) - { - for(auto j = 0; j < 4 && left_over; ++j, --left_over) - { - *(vec_out + k * 4 + j) = c0.val[k][j]; - } - } - } - }, - ina, inb, out); -} - -void inline vector_matrix_multiply_s8(Iterator &ina, Iterator &inb, Iterator &out, int width_a, int width_b, int width_out, size_t stride_b, const Window &window) -{ - execute_window_loop(window, [&](const Coordinates & id) - { - if(id.x() > width_b) - { - return; - } - - // Accumulators for the block 0 - int32x4x4_t c0 = - { - { - vdupq_n_s32(0), - vdupq_n_s32(0), - vdupq_n_s32(0), - vdupq_n_s32(0) - } - }; - - auto vec_a = reinterpret_cast<const int8_t *>(ina.ptr()); - auto matrix_b = reinterpret_cast<const int8_t *>(inb.ptr()); - auto vec_a_end_addr = vec_a + width_a; - - // This for loop performs 8 accumulations - for(; vec_a <= (vec_a_end_addr - 8);) - { - const int8x8_t a00_s8 = vld1_s8(vec_a); - const int8x16_t b00_s8 = vld1q_s8(matrix_b + 0 * stride_b); - const int8x16_t b10_s8 = vld1q_s8(matrix_b + 1 * stride_b); - const int8x16_t b20_s8 = vld1q_s8(matrix_b + 2 * stride_b); - const int8x16_t b30_s8 = vld1q_s8(matrix_b + 3 * stride_b); - const int8x16_t b40_s8 = vld1q_s8(matrix_b + 4 * stride_b); - const int8x16_t b50_s8 = vld1q_s8(matrix_b + 5 * stride_b); - const int8x16_t b60_s8 = vld1q_s8(matrix_b + 6 * stride_b); - const int8x16_t b70_s8 = vld1q_s8(matrix_b + 7 * stride_b); - - // Convert a00_s8 to int16_t and get the lower part - const int16x4x2_t a00_s16 = - { - { - vget_low_s16(vmovl_s8(a00_s8)), - vget_high_s16(vmovl_s8(a00_s8)) - } - }; - - const int16x4x4_t b00_s16 = - { - { - vget_low_s16(vmovl_s8(vget_low_s8(b00_s8))), - vget_high_s16(vmovl_s8(vget_low_s8(b00_s8))), - vget_low_s16(vmovl_s8(vget_high_s8(b00_s8))), - vget_high_s16(vmovl_s8(vget_high_s8(b00_s8))) - } - }; - - const int16x4x4_t b10_s16 = - { - { - vget_low_s16(vmovl_s8(vget_low_s8(b10_s8))), - vget_high_s16(vmovl_s8(vget_low_s8(b10_s8))), - vget_low_s16(vmovl_s8(vget_high_s8(b10_s8))), - vget_high_s16(vmovl_s8(vget_high_s8(b10_s8))) - } - }; - - const int16x4x4_t b20_s16 = - { - { - vget_low_s16(vmovl_s8(vget_low_s8(b20_s8))), - vget_high_s16(vmovl_s8(vget_low_s8(b20_s8))), - vget_low_s16(vmovl_s8(vget_high_s8(b20_s8))), - vget_high_s16(vmovl_s8(vget_high_s8(b20_s8))) - } - }; - - const int16x4x4_t b30_s16 = - { - { - vget_low_s16(vmovl_s8(vget_low_s8(b30_s8))), - vget_high_s16(vmovl_s8(vget_low_s8(b30_s8))), - vget_low_s16(vmovl_s8(vget_high_s8(b30_s8))), - vget_high_s16(vmovl_s8(vget_high_s8(b30_s8))) - } - }; - - const int16x4x4_t b40_s16 = - { - { - vget_low_s16(vmovl_s8(vget_low_s8(b40_s8))), - vget_high_s16(vmovl_s8(vget_low_s8(b40_s8))), - vget_low_s16(vmovl_s8(vget_high_s8(b40_s8))), - vget_high_s16(vmovl_s8(vget_high_s8(b40_s8))) - } - }; - - const int16x4x4_t b50_s16 = - { - { - vget_low_s16(vmovl_s8(vget_low_s8(b50_s8))), - vget_high_s16(vmovl_s8(vget_low_s8(b50_s8))), - vget_low_s16(vmovl_s8(vget_high_s8(b50_s8))), - vget_high_s16(vmovl_s8(vget_high_s8(b50_s8))) - } - }; - - const int16x4x4_t b60_s16 = - { - { - vget_low_s16(vmovl_s8(vget_low_s8(b60_s8))), - vget_high_s16(vmovl_s8(vget_low_s8(b60_s8))), - vget_low_s16(vmovl_s8(vget_high_s8(b60_s8))), - vget_high_s16(vmovl_s8(vget_high_s8(b60_s8))) - } - }; - - const int16x4x4_t b70_s16 = - { - { - vget_low_s16(vmovl_s8(vget_low_s8(b70_s8))), - vget_high_s16(vmovl_s8(vget_low_s8(b70_s8))), - vget_low_s16(vmovl_s8(vget_high_s8(b70_s8))), - vget_high_s16(vmovl_s8(vget_high_s8(b70_s8))) - } - }; - - // Accumulate 0: - c0.val[0] = vmlal_lane_s16(c0.val[0], b00_s16.val[0], a00_s16.val[0], 0); - c0.val[1] = vmlal_lane_s16(c0.val[1], b00_s16.val[1], a00_s16.val[0], 0); - c0.val[2] = vmlal_lane_s16(c0.val[2], b00_s16.val[2], a00_s16.val[0], 0); - c0.val[3] = vmlal_lane_s16(c0.val[3], b00_s16.val[3], a00_s16.val[0], 0); - - // Accumulate 1: - c0.val[0] = vmlal_lane_s16(c0.val[0], b10_s16.val[0], a00_s16.val[0], 1); - c0.val[1] = vmlal_lane_s16(c0.val[1], b10_s16.val[1], a00_s16.val[0], 1); - c0.val[2] = vmlal_lane_s16(c0.val[2], b10_s16.val[2], a00_s16.val[0], 1); - c0.val[3] = vmlal_lane_s16(c0.val[3], b10_s16.val[3], a00_s16.val[0], 1); - - // Accumulate 2: - c0.val[0] = vmlal_lane_s16(c0.val[0], b20_s16.val[0], a00_s16.val[0], 2); - c0.val[1] = vmlal_lane_s16(c0.val[1], b20_s16.val[1], a00_s16.val[0], 2); - c0.val[2] = vmlal_lane_s16(c0.val[2], b20_s16.val[2], a00_s16.val[0], 2); - c0.val[3] = vmlal_lane_s16(c0.val[3], b20_s16.val[3], a00_s16.val[0], 2); - - // Accumulate 3: - c0.val[0] = vmlal_lane_s16(c0.val[0], b30_s16.val[0], a00_s16.val[0], 3); - c0.val[1] = vmlal_lane_s16(c0.val[1], b30_s16.val[1], a00_s16.val[0], 3); - c0.val[2] = vmlal_lane_s16(c0.val[2], b30_s16.val[2], a00_s16.val[0], 3); - c0.val[3] = vmlal_lane_s16(c0.val[3], b30_s16.val[3], a00_s16.val[0], 3); - - // Accumulate 4: - c0.val[0] = vmlal_lane_s16(c0.val[0], b40_s16.val[0], a00_s16.val[1], 0); - c0.val[1] = vmlal_lane_s16(c0.val[1], b40_s16.val[1], a00_s16.val[1], 0); - c0.val[2] = vmlal_lane_s16(c0.val[2], b40_s16.val[2], a00_s16.val[1], 0); - c0.val[3] = vmlal_lane_s16(c0.val[3], b40_s16.val[3], a00_s16.val[1], 0); - - // Accumulate 5: - c0.val[0] = vmlal_lane_s16(c0.val[0], b50_s16.val[0], a00_s16.val[1], 1); - c0.val[1] = vmlal_lane_s16(c0.val[1], b50_s16.val[1], a00_s16.val[1], 1); - c0.val[2] = vmlal_lane_s16(c0.val[2], b50_s16.val[2], a00_s16.val[1], 1); - c0.val[3] = vmlal_lane_s16(c0.val[3], b50_s16.val[3], a00_s16.val[1], 1); - - // Accumulate 6: - c0.val[0] = vmlal_lane_s16(c0.val[0], b60_s16.val[0], a00_s16.val[1], 2); - c0.val[1] = vmlal_lane_s16(c0.val[1], b60_s16.val[1], a00_s16.val[1], 2); - c0.val[2] = vmlal_lane_s16(c0.val[2], b60_s16.val[2], a00_s16.val[1], 2); - c0.val[3] = vmlal_lane_s16(c0.val[3], b60_s16.val[3], a00_s16.val[1], 2); - - // Accumulate 7: - c0.val[0] = vmlal_lane_s16(c0.val[0], b70_s16.val[0], a00_s16.val[1], 3); - c0.val[1] = vmlal_lane_s16(c0.val[1], b70_s16.val[1], a00_s16.val[1], 3); - c0.val[2] = vmlal_lane_s16(c0.val[2], b70_s16.val[2], a00_s16.val[1], 3); - c0.val[3] = vmlal_lane_s16(c0.val[3], b70_s16.val[3], a00_s16.val[1], 3); - - vec_a += 8; - matrix_b += 8 * stride_b; - } - - // This for loop performs the left-over accumulations - for(; vec_a < vec_a_end_addr;) - { - const int8x8_t a00_s8 = vld1_dup_s8(vec_a); - const int8x16_t b00_s8 = vld1q_s8(matrix_b); - - const int16x4x4_t b00_s16 = - { - { - vget_low_s16(vmovl_s8(vget_low_s8(b00_s8))), - vget_high_s16(vmovl_s8(vget_low_s8(b00_s8))), - vget_low_s16(vmovl_s8(vget_high_s8(b00_s8))), - vget_high_s16(vmovl_s8(vget_high_s8(b00_s8))) - } - }; - - // Convert a00_s8 to uint16_t and get the lower part - const int16x4_t a00_s16 = vget_low_s16(vmovl_s8(a00_s8)); - - // Accumulate 0: - c0.val[0] = vmlal_lane_s16(c0.val[0], b00_s16.val[0], a00_s16, 0); - c0.val[1] = vmlal_lane_s16(c0.val[1], b00_s16.val[1], a00_s16, 0); - c0.val[2] = vmlal_lane_s16(c0.val[2], b00_s16.val[2], a00_s16, 0); - c0.val[3] = vmlal_lane_s16(c0.val[3], b00_s16.val[3], a00_s16, 0); - - vec_a += 1; - matrix_b += stride_b; - } - - auto vec_out = reinterpret_cast<int32_t *>(out.ptr()); - if(id.x() < (width_out - 16)) - { - vst1q_s32(vec_out + 0, c0.val[0]); - vst1q_s32(vec_out + 4, c0.val[1]); - vst1q_s32(vec_out + 8, c0.val[2]); - vst1q_s32(vec_out + 12, c0.val[3]); - } - else - { - auto left_over = width_out - id.x(); - for(auto k = 0; k < 4 && left_over; ++k) - { - for(auto j = 0; j < 4 && left_over; ++j, --left_over) - { - *(vec_out + k * 4 + j) = c0.val[k][j]; - } - } - } - }, - ina, inb, out); -} - -void inline matrix_multiply_u8(Iterator &ina, Iterator &inb, Iterator &out, int width_b, const TensorInfo &out_info, const Window &window) -{ - const auto width_out = static_cast<int>(out_info.dimension(0)); - const auto height_out = static_cast<int>(out_info.dimension(1)); - const size_t out_stride = out_info.strides_in_bytes()[1] / out_info.element_size(); - execute_window_loop(window, [&](const Coordinates & id) - { - const uint8_t *mtx_a0 = ina.ptr(); - const uint8_t *mtx_b0 = inb.ptr(); - - // Note: Since the input are all positives, we can use uint32_t - // Accumulators for the block 0 - uint32x4x4_t c0 = - { - { - vdupq_n_u32(0), - vdupq_n_u32(0), - vdupq_n_u32(0), - vdupq_n_u32(0) - } - }; - - // Accumulators for the block 1 - uint32x4x4_t c1 = - { - { - vdupq_n_u32(0), - vdupq_n_u32(0), - vdupq_n_u32(0), - vdupq_n_u32(0) - } - }; - - // Accumulators for the block 2 - uint32x4x4_t c2 = - { - { - vdupq_n_u32(0), - vdupq_n_u32(0), - vdupq_n_u32(0), - vdupq_n_u32(0) - } - }; - - // Accumulators for the block 3 - uint32x4x4_t c3 = - { - { - vdupq_n_u32(0), - vdupq_n_u32(0), - vdupq_n_u32(0), - vdupq_n_u32(0) - } - }; - - for(int k = 0; k < width_b; k += 16, mtx_a0 += 4, mtx_b0 += 16) - { - const uint8x8_t a00_u8 = vld1_u8(mtx_a0); - const uint8x16_t b00_u8 = vld1q_u8(mtx_b0); - - // Convert a00_u8 to uint16_t and get the lower part - const uint16x4_t a00_u16 = vget_low_u16(vmovl_u8(a00_u8)); - - // Convert b00_s8 to uint16_t - const uint16x4x4_t b00_u16 = - { - { - vget_low_u16(vmovl_u8(vget_low_u8(b00_u8))), - vget_high_u16(vmovl_u8(vget_low_u8(b00_u8))), - vget_low_u16(vmovl_u8(vget_high_u8(b00_u8))), - vget_high_u16(vmovl_u8(vget_high_u8(b00_u8))) - } - }; - - // 4x4 block 0 - c0.val[0] = vmlal_lane_u16(c0.val[0], b00_u16.val[0], a00_u16, 0); - c0.val[1] = vmlal_lane_u16(c0.val[1], b00_u16.val[1], a00_u16, 0); - c0.val[2] = vmlal_lane_u16(c0.val[2], b00_u16.val[2], a00_u16, 0); - c0.val[3] = vmlal_lane_u16(c0.val[3], b00_u16.val[3], a00_u16, 0); - - // 4x4 block 1 - c1.val[0] = vmlal_lane_u16(c1.val[0], b00_u16.val[0], a00_u16, 1); - c1.val[1] = vmlal_lane_u16(c1.val[1], b00_u16.val[1], a00_u16, 1); - c1.val[2] = vmlal_lane_u16(c1.val[2], b00_u16.val[2], a00_u16, 1); - c1.val[3] = vmlal_lane_u16(c1.val[3], b00_u16.val[3], a00_u16, 1); - - // 4x4 block 2 - c2.val[0] = vmlal_lane_u16(c2.val[0], b00_u16.val[0], a00_u16, 2); - c2.val[1] = vmlal_lane_u16(c2.val[1], b00_u16.val[1], a00_u16, 2); - c2.val[2] = vmlal_lane_u16(c2.val[2], b00_u16.val[2], a00_u16, 2); - c2.val[3] = vmlal_lane_u16(c2.val[3], b00_u16.val[3], a00_u16, 2); - - // 4x4 block 3 - c3.val[0] = vmlal_lane_u16(c3.val[0], b00_u16.val[0], a00_u16, 3); - c3.val[1] = vmlal_lane_u16(c3.val[1], b00_u16.val[1], a00_u16, 3); - c3.val[2] = vmlal_lane_u16(c3.val[2], b00_u16.val[2], a00_u16, 3); - c3.val[3] = vmlal_lane_u16(c3.val[3], b00_u16.val[3], a00_u16, 3); - } - - auto mtx_out = reinterpret_cast<int32_t *>(out.ptr()); - - if(id.y() < height_out && id.x() < (width_out - 16)) - { - vst1q_s32(mtx_out + 0 * out_stride + 0, vreinterpretq_s32_u32(c0.val[0])); - vst1q_s32(mtx_out + 0 * out_stride + 4, vreinterpretq_s32_u32(c0.val[1])); - vst1q_s32(mtx_out + 0 * out_stride + 8, vreinterpretq_s32_u32(c0.val[2])); - vst1q_s32(mtx_out + 0 * out_stride + 12, vreinterpretq_s32_u32(c0.val[3])); - if(id.y() + 1 < height_out) - { - vst1q_s32(mtx_out + 1 * out_stride + 0, vreinterpretq_s32_u32(c1.val[0])); - vst1q_s32(mtx_out + 1 * out_stride + 4, vreinterpretq_s32_u32(c1.val[1])); - vst1q_s32(mtx_out + 1 * out_stride + 8, vreinterpretq_s32_u32(c1.val[2])); - vst1q_s32(mtx_out + 1 * out_stride + 12, vreinterpretq_s32_u32(c1.val[3])); - if(id.y() + 2 < height_out) - { - vst1q_s32(mtx_out + 2 * out_stride + 0, vreinterpretq_s32_u32(c2.val[0])); - vst1q_s32(mtx_out + 2 * out_stride + 4, vreinterpretq_s32_u32(c2.val[1])); - vst1q_s32(mtx_out + 2 * out_stride + 8, vreinterpretq_s32_u32(c2.val[2])); - vst1q_s32(mtx_out + 2 * out_stride + 12, vreinterpretq_s32_u32(c2.val[3])); - if(id.y() + 3 < height_out) - { - vst1q_s32(mtx_out + 3 * out_stride + 0, vreinterpretq_s32_u32(c3.val[0])); - vst1q_s32(mtx_out + 3 * out_stride + 4, vreinterpretq_s32_u32(c3.val[1])); - vst1q_s32(mtx_out + 3 * out_stride + 8, vreinterpretq_s32_u32(c3.val[2])); - vst1q_s32(mtx_out + 3 * out_stride + 12, vreinterpretq_s32_u32(c3.val[3])); - } - } - } - } - else - { - const auto left_over_value = width_out - id.x(); - auto left_over = left_over_value; - for(auto k = 0; k < 4 && left_over; ++k) - { - for(auto j = 0; j < 4 && left_over; ++j, --left_over) - { - *(mtx_out + k * 4 + j) = c0.val[k][j]; - } - } - if(id.y() + 1 < height_out) - { - left_over = left_over_value; - for(auto k = 0; k < 4 && left_over; ++k) - { - for(auto j = 0; j < 4 && left_over; ++j, --left_over) - { - *(mtx_out + out_stride + k * 4 + j) = c1.val[k][j]; - } - } - if(id.y() + 2 < height_out) - { - left_over = left_over_value; - for(auto k = 0; k < 4 && left_over; ++k) - { - for(auto j = 0; j < 4 && left_over; ++j, --left_over) - { - *(mtx_out + out_stride * 2 + k * 4 + j) = c2.val[k][j]; - } - } - if(id.y() + 3 < height_out) - { - left_over = left_over_value; - for(auto k = 0; k < 4 && left_over; ++k) - { - for(auto j = 0; j < 4 && left_over; ++j, --left_over) - { - *(mtx_out + out_stride * 3 + k * 4 + j) = c3.val[k][j]; - } - } - } - } - } - } - }, - ina, inb, out); -} - -void inline matrix_multiply_s8(Iterator &ina, Iterator &inb, Iterator &out, int width_b, const TensorInfo &out_info, const Window &window) -{ - const auto width_out = static_cast<int>(out_info.dimension(0)); - const auto height_out = static_cast<int>(out_info.dimension(1)); - const size_t out_stride = out_info.strides_in_bytes()[1] / out_info.element_size(); - // The implementation assumes that the matrix A and Matrix B have been reshaped respectively with CpuGemmInterleave4x4 and CpuGemmTranspose1xW - // The reshaping of the matrices helps to have a cache friendly implementation and helps to avoid the data re-arrangements needed for computing 16x4 elements per iteration - // All the values needed for computing a single 4x4 block will be read from consecutive memory positions - execute_window_loop(window, [&](const Coordinates & id) - { - auto *mtx_a0 = reinterpret_cast<const int8_t *>(ina.ptr()); - auto *mtx_b0 = reinterpret_cast<const int8_t *>(inb.ptr()); - - // Note: Since the input are all positives, we can use uint32_t - // Accumulators for the block 0 - int32x4x4_t c0 = - { - { - vdupq_n_s32(0), - vdupq_n_s32(0), - vdupq_n_s32(0), - vdupq_n_s32(0) - } - }; - - // Accumulators for the block 1 - int32x4x4_t c1 = - { - { - vdupq_n_s32(0), - vdupq_n_s32(0), - vdupq_n_s32(0), - vdupq_n_s32(0) - } - }; - - // Accumulators for the block 2 - int32x4x4_t c2 = - { - { - vdupq_n_s32(0), - vdupq_n_s32(0), - vdupq_n_s32(0), - vdupq_n_s32(0) - } - }; - - // Accumulators for the block 3 - int32x4x4_t c3 = - { - { - vdupq_n_s32(0), - vdupq_n_s32(0), - vdupq_n_s32(0), - vdupq_n_s32(0) - } - }; - - for(int k = 0; k < width_b; k += 16, mtx_a0 += 4, mtx_b0 += 16) - { - const int8x8_t a00_s8 = vld1_s8(mtx_a0); - const int8x16_t b00_s8 = vld1q_s8(mtx_b0); - - // Convert a00_s8 to uint16_t and get the lower part - const int16x4_t a00_s16 = vget_low_s16(vmovl_s8(a00_s8)); - - // Convert b00_s8 to int16_t - const int16x4x4_t b00_s16 = - { - { - vget_low_s16(vmovl_s8(vget_low_s8(b00_s8))), - vget_high_s16(vmovl_s8(vget_low_s8(b00_s8))), - vget_low_s16(vmovl_s8(vget_high_s8(b00_s8))), - vget_high_s16(vmovl_s8(vget_high_s8(b00_s8))) - } - }; - - // 4x4 block 0 - c0.val[0] = vmlal_lane_s16(c0.val[0], b00_s16.val[0], a00_s16, 0); - c0.val[1] = vmlal_lane_s16(c0.val[1], b00_s16.val[1], a00_s16, 0); - c0.val[2] = vmlal_lane_s16(c0.val[2], b00_s16.val[2], a00_s16, 0); - c0.val[3] = vmlal_lane_s16(c0.val[3], b00_s16.val[3], a00_s16, 0); - - // 4x4 block 1 - c1.val[0] = vmlal_lane_s16(c1.val[0], b00_s16.val[0], a00_s16, 1); - c1.val[1] = vmlal_lane_s16(c1.val[1], b00_s16.val[1], a00_s16, 1); - c1.val[2] = vmlal_lane_s16(c1.val[2], b00_s16.val[2], a00_s16, 1); - c1.val[3] = vmlal_lane_s16(c1.val[3], b00_s16.val[3], a00_s16, 1); - - // 4x4 block 2 - c2.val[0] = vmlal_lane_s16(c2.val[0], b00_s16.val[0], a00_s16, 2); - c2.val[1] = vmlal_lane_s16(c2.val[1], b00_s16.val[1], a00_s16, 2); - c2.val[2] = vmlal_lane_s16(c2.val[2], b00_s16.val[2], a00_s16, 2); - c2.val[3] = vmlal_lane_s16(c2.val[3], b00_s16.val[3], a00_s16, 2); - - // 4x4 block 3 - c3.val[0] = vmlal_lane_s16(c3.val[0], b00_s16.val[0], a00_s16, 3); - c3.val[1] = vmlal_lane_s16(c3.val[1], b00_s16.val[1], a00_s16, 3); - c3.val[2] = vmlal_lane_s16(c3.val[2], b00_s16.val[2], a00_s16, 3); - c3.val[3] = vmlal_lane_s16(c3.val[3], b00_s16.val[3], a00_s16, 3); - } - auto mtx_out = reinterpret_cast<int32_t *>(out.ptr()); - if(id.y() < height_out && id.x() < (width_out - 16)) - { - vst1q_s32(mtx_out + 0 * out_stride + 0, c0.val[0]); - vst1q_s32(mtx_out + 0 * out_stride + 4, c0.val[1]); - vst1q_s32(mtx_out + 0 * out_stride + 8, c0.val[2]); - vst1q_s32(mtx_out + 0 * out_stride + 12, c0.val[3]); - if(id.y() + 1 < height_out) - { - vst1q_s32(mtx_out + 1 * out_stride + 0, c1.val[0]); - vst1q_s32(mtx_out + 1 * out_stride + 4, c1.val[1]); - vst1q_s32(mtx_out + 1 * out_stride + 8, c1.val[2]); - vst1q_s32(mtx_out + 1 * out_stride + 12, c1.val[3]); - if(id.y() + 2 < height_out) - { - vst1q_s32(mtx_out + 2 * out_stride + 0, c2.val[0]); - vst1q_s32(mtx_out + 2 * out_stride + 4, c2.val[1]); - vst1q_s32(mtx_out + 2 * out_stride + 8, c2.val[2]); - vst1q_s32(mtx_out + 2 * out_stride + 12, c2.val[3]); - if(id.y() + 3 < height_out) - { - vst1q_s32(mtx_out + 3 * out_stride + 0, c3.val[0]); - vst1q_s32(mtx_out + 3 * out_stride + 4, c3.val[1]); - vst1q_s32(mtx_out + 3 * out_stride + 8, c3.val[2]); - vst1q_s32(mtx_out + 3 * out_stride + 12, c3.val[3]); - } - } - } - } - else if(id.y() < height_out) - { - const auto left_over_value = width_out - id.x(); - auto left_over = left_over_value; - for(auto k = 0; k < 4 && left_over; ++k) - { - for(auto j = 0; j < 4 && left_over; ++j, --left_over) - { - *(mtx_out + k * 4 + j) = c0.val[k][j]; - } - } - if(id.y() + 1 < height_out) - { - left_over = left_over_value; - for(auto k = 0; k < 4 && left_over; ++k) - { - for(auto j = 0; j < 4 && left_over; ++j, --left_over) - { - *(mtx_out + out_stride + k * 4 + j) = c1.val[k][j]; - } - } - if(id.y() + 2 < height_out) - { - left_over = left_over_value; - for(auto k = 0; k < 4 && left_over; ++k) - { - for(auto j = 0; j < 4 && left_over; ++j, --left_over) - { - *(mtx_out + out_stride * 2 + k * 4 + j) = c2.val[k][j]; - } - } - if(id.y() + 3 < height_out) - { - left_over = left_over_value; - for(auto k = 0; k < 4 && left_over; ++k) - { - for(auto j = 0; j < 4 && left_over; ++j, --left_over) - { - *(mtx_out + out_stride * 3 + k * 4 + j) = c3.val[k][j]; - } - } - } - } - } - } - - }, - ina, inb, out); -} -} // namespace - -namespace -{ -Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output) -{ - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::S8, DataType::U8); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8, DataType::QSYMM8_PER_CHANNEL, DataType::S8, DataType::U8); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32); - - TensorShape in0_shape = input0->tensor_shape(); - TensorShape in1_shape = input1->tensor_shape(); - TensorShape out_shape = output->tensor_shape(); - - // Check vector-by-matrix case - if(out_shape[1] == 1) - { - ARM_COMPUTE_RETURN_ERROR_ON_MSG(in0_shape[0] != in1_shape[1], "The number of input0's columns must be equal to input1's rows"); - } - else - { - in0_shape.collapse(2); - in1_shape.collapse(2); - out_shape.collapse(2); - - ARM_COMPUTE_RETURN_ERROR_ON_MSG(in0_shape[2] != out_shape[2], "Output tensor must have the same number of batches of input0 tensor"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(in1_shape[2] != 1 && in0_shape[2] != in1_shape[2], "Input1 tensor must have the same number of batches of input0 or the number of batches must be set to 1"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(in1_shape[0] % 16, "Input1's width must be a multiple of 16"); - } - - return Status{}; -} -} // namespace - -NEGEMMLowpMatrixMultiplyKernel::NEGEMMLowpMatrixMultiplyKernel() - : _input0(nullptr), _input1(nullptr), _output(nullptr), _slide_matrix_b(true) -{ -} - -void NEGEMMLowpMatrixMultiplyKernel::configure(const ITensor *input0, const ITensor *input1, ITensor *output) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info())); - - TensorShape in1_shape = input1->info()->tensor_shape(); - in1_shape.collapse(2); - - _input0 = input0; - _input1 = input1; - _output = output; - _slide_matrix_b = in1_shape[2] != 1; - - constexpr unsigned int num_elems_processed_per_iteration_x = 16; - constexpr unsigned int num_elems_processed_per_iteration_y = 4; - - Window win; - - // Check if the output tensor is a vector. If so,the kernel runs the vector-matrix multiplication - if((output->info()->dimension(1) == 1)) - { - // Configure kernel window - win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration_x)); - } - else - { - win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); - } - - INEKernel::configure(win); -} - -Status NEGEMMLowpMatrixMultiplyKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output) -{ - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output)); - - return Status{}; -} - -void NEGEMMLowpMatrixMultiplyKernel::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); - - // Check if the output tensor is a vector. If so,the kernel runs the vector-matrix multiplication path - if((_output->info()->dimension(1) == 1)) - { - const auto width_matrix_a = static_cast<int>(_input0->info()->dimension(0)); - const auto width_matrix_b = static_cast<int>(_input1->info()->dimension(0)); - const auto width_out = static_cast<int>(_output->info()->dimension(0)); - const auto in_b_stride = static_cast<int>(_input1->info()->strides_in_bytes()[1] / data_size_from_type(_input1->info()->data_type())); - - // The implementation computes 16 elements per iteration - const int window_start_x = 16 * info.thread_id; - const int window_step_x = 16 * info.num_threads; - // Make sure (window_end_x - window_start_x) is a multiple of window_step_x - const int window_end_x = ceil_to_multiple(width_matrix_b - window_start_x, window_step_x) + window_start_x; - - Window win_out(window); - win_out.set(Window::DimX, Window::Dimension(window_start_x, window_end_x, window_step_x)); - win_out.set(Window::DimY, Window::Dimension(0, 1, 1)); - - Window win_a(window); - win_a.set(Window::DimX, Window::Dimension(0, 0, 0)); - win_a.set(Window::DimY, Window::Dimension(0, 0, 0)); - - Window win_b; - // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2 - // This scenario can happen when the the matrix multiplication is used to perform a convolution operation - if(_input1->info()->num_dimensions() >= 3) - { - win_b = window; - } - win_b.set(Window::DimX, Window::Dimension(window_start_x, window_end_x, window_step_x)); - win_b.set(Window::DimY, Window::Dimension(0, 1, 1)); - - Iterator ina(_input0, win_a); - Iterator inb(_input1, win_b); - Iterator out(_output, win_out); - - switch(_input0->info()->data_type()) - { - case DataType::S8: - case DataType::QASYMM8_SIGNED: - { - vector_matrix_multiply_s8(ina, inb, out, width_matrix_a, width_matrix_b, width_out, in_b_stride, window); - break; - } - case DataType::U8: - case DataType::QASYMM8: - { - vector_matrix_multiply_u8(ina, inb, out, width_matrix_a, width_matrix_b, width_out, in_b_stride, window); - break; - } - default: - { - ARM_COMPUTE_ERROR("Not supported"); - break; - } - } - } - else - { - const size_t in_b_stride = _input1->info()->strides_in_bytes()[1]; - const int width_b = _input1->info()->dimension(0); - - // Set step_x and step_y for matrix A. Scale by a factor of 4 the Y range as the input interleaved matrix A has 4 times less the rows of the output matrix - Window win_a(window); - win_a.set(Window::DimX, Window::Dimension(0, 0, 0)); - win_a.set(Window::DimY, Window::Dimension(window.y().start() / 4, window.y().end() / 4, 1)); - - // Set step_x and step_y for matrix B. Scale by a factor of 16 the X range as the input transposed matrix A has 16 times less the columns of the output matrix - Window win_b; - // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2 - // This scenario can happen when the the matrix multiplication is used to perform a convolution operation - if(_slide_matrix_b) - { - win_b = window; - } - win_b.set(Window::DimX, Window::Dimension(window.x().start() / 16, window.x().end() / 16, in_b_stride)); - win_b.set(Window::DimY, Window::Dimension(0, 0, 0)); - - // The step x and step y for the output matrix has been already set using in configure() - Iterator ina(_input0, win_a); - Iterator inb(_input1, win_b); - Iterator out(_output, window); - - switch(_input0->info()->data_type()) - { - case DataType::S8: - case DataType::QASYMM8_SIGNED: - { - matrix_multiply_s8(ina, inb, out, width_b, *_output->info(), window); - break; - } - case DataType::U8: - case DataType::QASYMM8: - { - matrix_multiply_u8(ina, inb, out, width_b, *_output->info(), window); - break; - } - default: - { - ARM_COMPUTE_ERROR("Not supported"); - break; - } - } - } -} -} // namespace arm_compute diff --git a/src/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.h b/src/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.h deleted file mode 100644 index b9a1b5e840..0000000000 --- a/src/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.h +++ /dev/null @@ -1,92 +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_NEGEMMLOWPMATRIXMULTIPLYKERNEL_H -#define ARM_COMPUTE_NEGEMMLOWPMATRIXMULTIPLYKERNEL_H - -#include "src/core/NEON/INEKernel.h" - -namespace arm_compute -{ -class ITensor; - -/** Kernel to multiply matrices - * - * @note @ref NEGEMMLowpMatrixMultiplyKernel low precision matrix product kernel - * This kernel performs the following computation: - * - * -# Convert a values from int8 to int32 - * -# Convert b values from int8 to int32 - * -# Compute the int32 matrix product of the resulting a * b and store the result as int32 - * - */ -class NEGEMMLowpMatrixMultiplyKernel : public INEKernel -{ -public: - const char *name() const override - { - return "NEGEMMLowpMatrixMultiplyKernel"; - } - /** Constructor */ - NEGEMMLowpMatrixMultiplyKernel(); - /** Prevent instances of this class from being copied (As this class contains pointers)*/ - NEGEMMLowpMatrixMultiplyKernel(const NEGEMMLowpMatrixMultiplyKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers)*/ - NEGEMMLowpMatrixMultiplyKernel &operator=(const NEGEMMLowpMatrixMultiplyKernel &) = delete; - /** Allow instances of this class to be moved */ - NEGEMMLowpMatrixMultiplyKernel(NEGEMMLowpMatrixMultiplyKernel &&) = default; - /** Allow instances of this class to be moved */ - NEGEMMLowpMatrixMultiplyKernel &operator=(NEGEMMLowpMatrixMultiplyKernel &&) = default; - /** Default destructor */ - ~NEGEMMLowpMatrixMultiplyKernel() = default; - /** Initialise the kernel's input and output. - * - * The input matrices @p input0 and @p input1 must be the output of the kernels: cpu::kernels::CpuGemmInterleave4x4Kernel and @ref cpu::kernels::CpuGemmTranspose1xWKernel. These two - * kernels change the layout of the original matrices to be more cache-friendly. - * - * @param[in] input0 Input tensor containing the interleaved Matrix A. Data type supported: U8/QASYMM8/S8/QASYMM8_SIGNED - * @param[in] input1 Input tensor containing the transposed1xW Matrix B. Data type supported: U8/QASYMM8/S8/QASYMM8_SIGNED/QSYMM8/QSYMM8_PER_CHANNEL - * @param[out] output Output tensor to store the result of matrix multiplication. Data type supported: S32 - */ - void configure(const ITensor *input0, const ITensor *input1, ITensor *output); - /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMLowpMatrixMultiplyKernel - * - * @param[in] input0 Input tensor info containing the interleaved Matrix A. Data type supported: U8/QASYMM8/S8/QASYMM8_SIGNED - * @param[in] input1 Input tensor info containing the transposed Matrix B. Data type supported: U8/QASYMM8/S8/QASYMM8_SIGNED/QSYMM8/QSYMM8_PER_CHANNEL - * @param[in] output Output tensor info to store the result of matrix multiplication. Data type supported: S32 - * - * @return a status - */ - static Status validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output); - - // Inherited methods overridden: - void run(const Window &window, const ThreadInfo &info) override; - -private: - const ITensor *_input0; - const ITensor *_input1; - ITensor *_output; - bool _slide_matrix_b; -}; -} // namespace arm_compute -#endif /*ARM_COMPUTE_NEGEMMLOWPMATRIXMULTIPLYKERNEL_H*/ diff --git a/src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.cpp deleted file mode 100644 index 867beca0ac..0000000000 --- a/src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.cpp +++ /dev/null @@ -1,413 +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/NEGEMMLowpOffsetContributionKernel.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 "src/core/helpers/AutoConfiguration.h" -#include "src/core/helpers/WindowHelpers.h" - -#include <arm_neon.h> - -namespace arm_compute -{ -namespace -{ -Status validate_arguments(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, - int32_t a_offset, int32_t b_offset) -{ - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(mm_result, 1, DataType::S32); - - // If a_offset == 0, vector_sum_col can be a nullptr - if(a_offset != 0) - { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_col, 1, DataType::S32); - ARM_COMPUTE_RETURN_ERROR_ON(vector_sum_col->dimension(0) != mm_result->dimension(0)); - } - - // If b_offset == 0, vector_sum_row can be a nullptr - if(b_offset != 0) - { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_row, 1, DataType::S32); - - // Check if input is a 3D reinterpretation - const bool reinterpret_as_3d = mm_result->num_dimensions() > 1 && mm_result->tensor_shape().y() != vector_sum_row->tensor_shape().x(); - - // Validate input - ARM_COMPUTE_RETURN_ERROR_ON(reinterpret_as_3d && vector_sum_row->dimension(0) != (mm_result->dimension(1) * mm_result->dimension(2))); - ARM_COMPUTE_RETURN_ERROR_ON(!reinterpret_as_3d && vector_sum_row->dimension(0) != mm_result->dimension(1)); - - TensorShape output_shape = mm_result->tensor_shape(); - if(output_shape.num_dimensions() > 1) - { - const unsigned int output_batch_idx = reinterpret_as_3d ? 3 : 2; - - TensorShape vector_sum_row_shape = vector_sum_row->tensor_shape(); - vector_sum_row_shape.collapse_from(1); - output_shape.collapse_from(output_batch_idx); - - ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_row_shape[1] != output_shape[output_batch_idx], - "mm_result tensor must have the same number of batches of output tensor"); - - if(a_offset != 0) - { - TensorShape vector_sum_col_shape = vector_sum_col->tensor_shape(); - vector_sum_col_shape.collapse_from(1); - - ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_col_shape[1] != 1 && vector_sum_col_shape[1] != vector_sum_row_shape[1], - "vector_sum_col tensor must have the same number of batches of vector_sum_row_shape or the number of batches must be set to 1"); - } - } - } - - return Status{}; -} - -void run_offset_contribution(const Window &window, - ITensor *mm_result, const ITensor *vector_sum_col, const ITensor *vector_sum_row, - int32_t a_offset, int32_t b_offset, int32_t k_offset, bool slide_vector_sum_col, bool is_gemm3d) -{ - Window collapsed_window = window.collapse_if_possible(window, Window::DimZ); - collapsed_window.set(Window::DimX, Window::Dimension(0, 1, 1)); - - const int height_input = is_gemm3d ? mm_result->info()->dimension(1) : 0; - const int depth_input = is_gemm3d ? mm_result->info()->dimension(2) : 1; - - const int window_start_x = window.x().start(); - const int window_end_x = window.x().end(); - const int window_step_x = 16; - - Iterator mm_result_it(mm_result, collapsed_window); - - if((a_offset != 0) && (b_offset != 0) && (vector_sum_col != nullptr) && (vector_sum_row != nullptr)) // true, true - { - // Set window for vector_sum_col - Window win_vector_sum_col(collapsed_window); - win_vector_sum_col.set(Window::DimY, Window::Dimension(0, 0, 0)); - win_vector_sum_col.set(Window::DimZ, Window::Dimension(0, 0, 0)); - - // Set window for vector_sum_row - Window win_vector_sum_row(collapsed_window); - win_vector_sum_row.set(Window::DimX, Window::Dimension(0, 0, 0)); - win_vector_sum_row.set(Window::DimY, Window::Dimension(0, 0, 0)); - win_vector_sum_row.set(Window::DimZ, Window::Dimension(0, 0, 0)); - - Iterator vector_sum_col_it(vector_sum_col, win_vector_sum_col); - Iterator vector_sum_row_it(vector_sum_row, win_vector_sum_row); - - const size_t sum_row_stride_y = vector_sum_row->info()->strides_in_bytes().y(); - - // Offset in case vector_sum_col is batched - const int vector_sum_col_batch_offset = slide_vector_sum_col ? vector_sum_col->info()->strides_in_bytes().z() : 0; - - execute_window_loop(collapsed_window, [&](const Coordinates & id) - { - const int batch_id = id.z() / depth_input; - auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_id * vector_sum_col_batch_offset); - auto mm_result_ptr = reinterpret_cast<int32_t *>(mm_result_it.ptr()); - - // Compute the leftover term due to b_offset. - int32_t b_offset_term_s32 = *(reinterpret_cast<const int32_t *>(vector_sum_row_it.ptr() + batch_id * sum_row_stride_y) + id.y() + (id.z() % depth_input) * height_input); - b_offset_term_s32 *= b_offset; - - const int32x4_t b_offset_term_s32_vec = vdupq_n_s32(b_offset_term_s32); - - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - // Compute the leftover term due to a_offset. - int32x4x4_t a_offset_term_s32 = - { - { - vld1q_s32(vector_sum_col_ptr + x + 0), - vld1q_s32(vector_sum_col_ptr + x + 4), - vld1q_s32(vector_sum_col_ptr + x + 8), - vld1q_s32(vector_sum_col_ptr + x + 12) - } - }; - - a_offset_term_s32.val[0] = vmulq_n_s32(a_offset_term_s32.val[0], a_offset); - a_offset_term_s32.val[1] = vmulq_n_s32(a_offset_term_s32.val[1], a_offset); - a_offset_term_s32.val[2] = vmulq_n_s32(a_offset_term_s32.val[2], a_offset); - a_offset_term_s32.val[3] = vmulq_n_s32(a_offset_term_s32.val[3], a_offset); - - // Add a_offset_term_s32 and b_offset_term_s32 - int32x4x4_t offset_term_s32 = - { - { - vdupq_n_s32(k_offset), - vdupq_n_s32(k_offset), - vdupq_n_s32(k_offset), - vdupq_n_s32(k_offset) - } - }; - - offset_term_s32.val[0] = vaddq_s32(offset_term_s32.val[0], vaddq_s32(a_offset_term_s32.val[0], b_offset_term_s32_vec)); - offset_term_s32.val[1] = vaddq_s32(offset_term_s32.val[1], vaddq_s32(a_offset_term_s32.val[1], b_offset_term_s32_vec)); - offset_term_s32.val[2] = vaddq_s32(offset_term_s32.val[2], vaddq_s32(a_offset_term_s32.val[2], b_offset_term_s32_vec)); - offset_term_s32.val[3] = vaddq_s32(offset_term_s32.val[3], vaddq_s32(a_offset_term_s32.val[3], b_offset_term_s32_vec)); - - int32x4x4_t in_s32 = - { - { - vld1q_s32(mm_result_ptr + x + 0), - vld1q_s32(mm_result_ptr + x + 4), - vld1q_s32(mm_result_ptr + x + 8), - vld1q_s32(mm_result_ptr + x + 12) - } - }; - - // Add the offset terms to GEMM's result - in_s32.val[0] = vaddq_s32(in_s32.val[0], offset_term_s32.val[0]); - in_s32.val[1] = vaddq_s32(in_s32.val[1], offset_term_s32.val[1]); - in_s32.val[2] = vaddq_s32(in_s32.val[2], offset_term_s32.val[2]); - in_s32.val[3] = vaddq_s32(in_s32.val[3], offset_term_s32.val[3]); - - // Store the result with the offset contribution - vst1q_s32(mm_result_ptr + x + 0, in_s32.val[0]); - vst1q_s32(mm_result_ptr + x + 4, in_s32.val[1]); - vst1q_s32(mm_result_ptr + x + 8, in_s32.val[2]); - vst1q_s32(mm_result_ptr + x + 12, in_s32.val[3]); - } - - // Left-overs loop - for(; x < window_end_x; ++x) - { - // Compute the leftover term due to a_offset. - int32_t a_offset_term_s32 = *(vector_sum_col_ptr + x); - - a_offset_term_s32 *= a_offset; - - // Add the offset terms to GEMM's result - // Store the result with the offset contribution - mm_result_ptr[x] += k_offset + a_offset_term_s32 + b_offset_term_s32; - } - }, - vector_sum_col_it, vector_sum_row_it, mm_result_it); - } - else if((a_offset == 0) && (b_offset != 0) && (vector_sum_row != nullptr)) // false, true - { - ARM_COMPUTE_ERROR_ON_NULLPTR(vector_sum_row); - - // Set window for vector_sum_row - Window win_vector_sum_row(collapsed_window); - win_vector_sum_row.set(Window::DimX, Window::Dimension(0, 0, 0)); - win_vector_sum_row.set(Window::DimY, Window::Dimension(0, 0, 0)); - win_vector_sum_row.set(Window::DimZ, Window::Dimension(0, 0, 0)); - - Iterator vector_sum_row_it(vector_sum_row, win_vector_sum_row); - - const size_t sum_row_stride_y = vector_sum_row->info()->strides_in_bytes().y(); - - execute_window_loop(collapsed_window, [&](const Coordinates & id) - { - const int batch_id = id.z() / depth_input; - auto mm_result_ptr = reinterpret_cast<int32_t *>(mm_result_it.ptr()); - - // Compute the leftover term due to b_offset. - int32_t b_offset_term_s32 = *(reinterpret_cast<const int32_t *>(vector_sum_row_it.ptr() + batch_id * sum_row_stride_y) + id.y() + (id.z() % depth_input) * height_input); - b_offset_term_s32 *= b_offset; - - const int32x4_t b_offset_term_s32_vec = vdupq_n_s32(b_offset_term_s32); - - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - int32x4x4_t in_s32 = - { - { - vld1q_s32(mm_result_ptr + x + 0), - vld1q_s32(mm_result_ptr + x + 4), - vld1q_s32(mm_result_ptr + x + 8), - vld1q_s32(mm_result_ptr + x + 12) - } - }; - - // Add the offset terms to GEMM's result - in_s32.val[0] = vaddq_s32(in_s32.val[0], b_offset_term_s32_vec); - in_s32.val[1] = vaddq_s32(in_s32.val[1], b_offset_term_s32_vec); - in_s32.val[2] = vaddq_s32(in_s32.val[2], b_offset_term_s32_vec); - in_s32.val[3] = vaddq_s32(in_s32.val[3], b_offset_term_s32_vec); - - // Store the result with the offset contribution - vst1q_s32(mm_result_ptr + x + 0, in_s32.val[0]); - vst1q_s32(mm_result_ptr + x + 4, in_s32.val[1]); - vst1q_s32(mm_result_ptr + x + 8, in_s32.val[2]); - vst1q_s32(mm_result_ptr + x + 12, in_s32.val[3]); - } - - // Left-overs loop - for(; x < window_end_x; ++x) - { - // Add the offset terms to GEMM's result - // Store the result with the offset contribution - mm_result_ptr[x] += b_offset_term_s32; - } - }, - vector_sum_row_it, mm_result_it); - } - else if((a_offset != 0) && (b_offset == 0) && (vector_sum_col != nullptr)) // true, false - { - // Set window for vector_sum_col - Window win_vector_sum_col(collapsed_window); - win_vector_sum_col.set(Window::DimY, Window::Dimension(0, 0, 0)); - win_vector_sum_col.set(Window::DimZ, Window::Dimension(0, 0, 0)); - - Iterator vector_sum_col_it(vector_sum_col, win_vector_sum_col); - - // Offset in case vector_sum_col is batched - const int vector_sum_col_batch_offset = slide_vector_sum_col ? vector_sum_col->info()->strides_in_bytes().z() : 0; - - execute_window_loop(collapsed_window, [&](const Coordinates & id) - { - const int batch_id = id.z() / depth_input; - auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_id * vector_sum_col_batch_offset); - auto mm_result_ptr = reinterpret_cast<int32_t *>(mm_result_it.ptr()); - - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - // Compute the leftover term due to a_offset. - int32x4x4_t a_offset_term_s32 = - { - { - vld1q_s32(vector_sum_col_ptr + x + 0), - vld1q_s32(vector_sum_col_ptr + x + 4), - vld1q_s32(vector_sum_col_ptr + x + 8), - vld1q_s32(vector_sum_col_ptr + x + 12) - } - }; - - a_offset_term_s32.val[0] = vmulq_n_s32(a_offset_term_s32.val[0], a_offset); - a_offset_term_s32.val[1] = vmulq_n_s32(a_offset_term_s32.val[1], a_offset); - a_offset_term_s32.val[2] = vmulq_n_s32(a_offset_term_s32.val[2], a_offset); - a_offset_term_s32.val[3] = vmulq_n_s32(a_offset_term_s32.val[3], a_offset); - - int32x4x4_t in_s32 = - { - { - vld1q_s32(mm_result_ptr + x + 0), - vld1q_s32(mm_result_ptr + x + 4), - vld1q_s32(mm_result_ptr + x + 8), - vld1q_s32(mm_result_ptr + x + 12) - } - }; - - // Add the offset terms to GEMM's result - in_s32.val[0] = vaddq_s32(in_s32.val[0], a_offset_term_s32.val[0]); - in_s32.val[1] = vaddq_s32(in_s32.val[1], a_offset_term_s32.val[1]); - in_s32.val[2] = vaddq_s32(in_s32.val[2], a_offset_term_s32.val[2]); - in_s32.val[3] = vaddq_s32(in_s32.val[3], a_offset_term_s32.val[3]); - - // Store the result with the offset contribution - vst1q_s32(mm_result_ptr + x + 0, in_s32.val[0]); - vst1q_s32(mm_result_ptr + x + 4, in_s32.val[1]); - vst1q_s32(mm_result_ptr + x + 8, in_s32.val[2]); - vst1q_s32(mm_result_ptr + x + 12, in_s32.val[3]); - } - - // Left-overs loop - for(; x < window_end_x; ++x) - { - // Compute the leftover term due to a_offset. - const int32_t a_offset_term_s32 = *(vector_sum_col_ptr + x); - - // Add the offset terms to GEMM's result - // Store the result with the offset contribution - mm_result_ptr[x] += a_offset_term_s32 * a_offset; - } - }, - vector_sum_col_it, mm_result_it); - } - else // false, false - { - // No offset contribution from matrix A and matrix B - return; - } -} -} // namespace - -NEGEMMLowpOffsetContributionKernel::NEGEMMLowpOffsetContributionKernel() - : _vector_sum_col(nullptr), _vector_sum_row(nullptr), _mm_result(nullptr), _a_offset(0), _b_offset(0), _k_offset(0), _slide_vector_sum_col(true) -{ -} - -void NEGEMMLowpOffsetContributionKernel::configure(ITensor *mm_result, const ITensor *vector_sum_col, const ITensor *vector_sum_row, int32_t k, int32_t a_offset, int32_t b_offset) -{ - // Perform validate step - ARM_COMPUTE_ERROR_ON_NULLPTR(mm_result); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(mm_result->info(), - vector_sum_col != nullptr ? vector_sum_col->info() : nullptr, // NOLINT - vector_sum_row != nullptr ? vector_sum_row->info() : nullptr, // NOLINT - a_offset, b_offset)); // NOLINT - - _vector_sum_col = vector_sum_col; - _vector_sum_row = vector_sum_row; - _mm_result = mm_result; - _a_offset = a_offset; - _b_offset = b_offset; - _k_offset = a_offset * b_offset * k; - - // If a_offset == 0, vector_sum_col can be a nullptr - if(a_offset != 0) - { - // Check if vector_sum_col_shape should be slidden or not - // Don't slide vector_sum_col_shape along the y dimension if vector_sum_col_shape has just 1 dimension and vector_sum_row_shape more than 1 - // This scenario can happen when the the matrix multiplication is used to perform a convolution operation - _slide_vector_sum_col = vector_sum_col->info()->tensor_shape().num_dimensions() > 1; - } - - // Configure kernel window - Window win = calculate_max_window(*mm_result->info(), Steps()); - INEKernel::configure(win); -} - -Status NEGEMMLowpOffsetContributionKernel::validate(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, - int32_t a_offset, int32_t b_offset) -{ - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(mm_result, vector_sum_col, vector_sum_row, a_offset, b_offset)); - - return Status{}; -} - -void NEGEMMLowpOffsetContributionKernel::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); - - // Check if input is a 3D reinterpretation - const bool reinterpret_as_3d = _vector_sum_row != nullptr - && _mm_result->info()->num_dimensions() > 1 - && _mm_result->info()->tensor_shape().y() != _vector_sum_row->info()->tensor_shape().x(); - - run_offset_contribution(window, _mm_result, _vector_sum_col, _vector_sum_row, _a_offset, _b_offset, _k_offset, _slide_vector_sum_col, reinterpret_as_3d); -} -} // namespace arm_compute diff --git a/src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.h b/src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.h deleted file mode 100644 index f71929fe9e..0000000000 --- a/src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.h +++ /dev/null @@ -1,105 +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_NEGEMMLOWPOFFSETCONTRIBUTIONKERNEL_H -#define ARM_COMPUTE_NEGEMMLOWPOFFSETCONTRIBUTIONKERNEL_H - -#include "src/core/NEON/INEKernel.h" - -namespace arm_compute -{ -class ITensor; - -/** Kernel used to add the offset contribution after @ref NEGEMMLowpMatrixMultiplyKernel. The computation is performed in-place - * - * This kernel takes a final int32 accumulator value (the output of @ref NEGEMMLowpMatrixMultiplyKernel), - * and adds to it the offset contribution of matrix A and matrix B in-place. - * - * The final result is: - * - * mm_result[i][k] = mm_result[i][k] + - * (vector_sum_col[k] * a_offset) + - * (vector_sum_row[i] * b_offset) + - * (a_offset * b_offset * k) - * - */ -class NEGEMMLowpOffsetContributionKernel : public INEKernel -{ -public: - const char *name() const override - { - return "NEGEMMLowpOffsetContributionKernel"; - } - /** Constructor */ - NEGEMMLowpOffsetContributionKernel(); - /** Prevent instances of this class from being copied (As this class contains pointers)*/ - NEGEMMLowpOffsetContributionKernel(const NEGEMMLowpOffsetContributionKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers)*/ - NEGEMMLowpOffsetContributionKernel &operator=(const NEGEMMLowpOffsetContributionKernel &) = delete; - /** Allow instances of this class to be moved */ - NEGEMMLowpOffsetContributionKernel(NEGEMMLowpOffsetContributionKernel &&) = default; - /** Allow instances of this class to be moved */ - NEGEMMLowpOffsetContributionKernel &operator=(NEGEMMLowpOffsetContributionKernel &&) = default; - /** Default destructor */ - ~NEGEMMLowpOffsetContributionKernel() = default; - /** Initialise the kernel's input and output. - * - * @param[in, out] mm_result Input tensor containing the result of @ref NEGEMMLowpMatrixMultiplyKernel. Data type supported: S32 - * @param[in] vector_sum_col Input row-vector of sums of all the entries in each column of matrix B. - * Note: vector_sum_col can be a nullptr in case a_offset = 0. Data type supported: same as @p mm_result - * @param[in] vector_sum_row Input row-vector of sums of all the entries in each row of matrix A. - * Note: vector_sum_row can be a nullptr in case b_offset = 0. Data type supported: same as @p mm_result - * @param[in] k Number of matrix A columns or Matrix B rows - * @param[in] a_offset Offset to be added to each element of the matrix A. - * @param[in] b_offset Offset to be added to each element of the matrix B. - */ - void configure(ITensor *mm_result, const ITensor *vector_sum_col, const ITensor *vector_sum_row, int32_t k, int32_t a_offset, int32_t b_offset); - /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMLowpOffsetContributionKernel - * - * @param[in] mm_result Input tensor containing the result of @ref NEGEMMLowpMatrixMultiplyKernel. Data type supported: S32 - * @param[in] vector_sum_col Input row-vector of sums of all the entries in each column of matrix B. - * Note: vector_sum_col can be a nullptr in case a_offset = 0. Data type supported: same as @p mm_result - * @param[in] vector_sum_row Input row-vector of sums of all the entries in each row of matrix A. - * Note: vector_sum_row can be a nullptr in case b_offset = 0. Data type supported: same as @p mm_result - * @param[in] a_offset Offset to be added to each element of the matrix A. - * @param[in] b_offset Offset to be added to each element of the matrix B. - * - * @return a status - */ - static Status validate(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, int32_t a_offset, int32_t b_offset); - - // Inherited methods overridden: - void run(const Window &window, const ThreadInfo &info) override; - -private: - const ITensor *_vector_sum_col; - const ITensor *_vector_sum_row; - ITensor *_mm_result; - int32_t _a_offset; - int32_t _b_offset; - int32_t _k_offset; - bool _slide_vector_sum_col; -}; -} // namespace arm_compute - -#endif /* ARM_COMPUTE_NEGEMMLOWPOFFSETCONTRIBUTIONKERNEL_H */ diff --git a/src/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.cpp deleted file mode 100644 index dfed7f0bb8..0000000000 --- a/src/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.cpp +++ /dev/null @@ -1,959 +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/NEGEMMLowpOffsetContributionOutputStageKernel.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 "src/core/NEON/NEAsymm.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> -#include <map> - -namespace arm_compute -{ -namespace -{ -inline int32x4x4_t load_results_input(const Iterator &mm_result_it, int32_t x) -{ - return - { - { - vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + x + 0), - vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + x + 4), - vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + x + 8), - vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + x + 12) - } - }; -} - -inline int32x4x4_t load(const int32_t *ptr, int32_t x) -{ - return - { - { - vld1q_s32(ptr + x + 0), - vld1q_s32(ptr + x + 4), - vld1q_s32(ptr + x + 8), - vld1q_s32(ptr + x + 12) - } - }; -} - -inline int32x4x4_t add_s32(int32x4x4_t a, int32x4_t b) -{ - return - { - { - vaddq_s32(a.val[0], b), - vaddq_s32(a.val[1], b), - vaddq_s32(a.val[2], b), - vaddq_s32(a.val[3], b) - } - }; -} - -inline int32x4x4_t add_s32(int32x4x4_t a, int32x4x4_t b) -{ - return - { - { - vaddq_s32(a.val[0], b.val[0]), - vaddq_s32(a.val[1], b.val[1]), - vaddq_s32(a.val[2], b.val[2]), - vaddq_s32(a.val[3], b.val[3]) - } - }; -} - -inline int32x4x4_t mul_s32(int32x4x4_t &a, int32_t mul_scalar) -{ - return - { - { - vmulq_n_s32(a.val[0], mul_scalar), - vmulq_n_s32(a.val[1], mul_scalar), - vmulq_n_s32(a.val[2], mul_scalar), - vmulq_n_s32(a.val[3], mul_scalar) - } - }; -} - -inline int32x4x4_t mul_s32(int32x4x4_t &a, const int32_t *multilpier) -{ - return - { - { - vmulq_s32(a.val[0], vld1q_s32(multilpier)), - vmulq_s32(a.val[1], vld1q_s32(multilpier + 4)), - vmulq_s32(a.val[2], vld1q_s32(multilpier + 8)), - vmulq_s32(a.val[3], vld1q_s32(multilpier + 12)) - } - }; -} - -inline int32x4x4_t get_a_offset(const int32_t *vector_sum_col_ptr, int32_t a_offset, int32_t x) -{ - int32x4x4_t a_offset_term_s32 = load(vector_sum_col_ptr, x); - - a_offset_term_s32.val[0] = vmulq_n_s32(a_offset_term_s32.val[0], a_offset); - a_offset_term_s32.val[1] = vmulq_n_s32(a_offset_term_s32.val[1], a_offset); - a_offset_term_s32.val[2] = vmulq_n_s32(a_offset_term_s32.val[2], a_offset); - a_offset_term_s32.val[3] = vmulq_n_s32(a_offset_term_s32.val[3], a_offset); - return a_offset_term_s32; -} - -inline int32x4_t get_b_offset(const int32_t *vector_sum_row_ptr, int32_t b_offset) -{ - int32x4_t b_offset_term_s32 = vld1q_dup_s32(vector_sum_row_ptr); - b_offset_term_s32 = vmulq_n_s32(b_offset_term_s32, b_offset); - return b_offset_term_s32; -} - -inline int32x4x4_t get_k_offset(int32_t k_offset) -{ - return - { - { - vdupq_n_s32(k_offset), - vdupq_n_s32(k_offset), - vdupq_n_s32(k_offset), - vdupq_n_s32(k_offset) - } - }; -} - -inline uint8x16_t finalize_quantization_floating_point(int32x4x4_t &in_s32, int32x4_t result_shift_s32, uint8x16_t min_u8, uint8x16_t max_u8, bool is_bounded_relu) -{ - 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; -} - -inline int8x16_t finalize_quantization_floating_point(int32x4x4_t &in_s32, int32x4_t result_shift_s32, int8x16_t min_s8, int8x16_t max_s8, bool is_bounded_relu) -{ - 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 S8 - int8x16_t out_s8 = vcombine_s8(vqmovn_s16(in_s16.val[0]), vqmovn_s16(in_s16.val[1])); - - if(is_bounded_relu) - { - out_s8 = vmaxq_s8(out_s8, min_s8); - out_s8 = vminq_s8(out_s8, max_s8); - } - - return out_s8; -} - -inline int8x16_t finalize_quantization_floating_point(int32x4x4_t &in_s32, int32x4x4_t result_shift_s32, int8x16_t min_s8, int8x16_t max_s8, bool is_bounded_relu) -{ - 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], vnegq_s32(result_shift_s32.val[0])); - in_s32.val[1] = vshlq_s32(in_s32.val[1], vnegq_s32(result_shift_s32.val[1])); - in_s32.val[2] = vshlq_s32(in_s32.val[2], vnegq_s32(result_shift_s32.val[2])); - in_s32.val[3] = vshlq_s32(in_s32.val[3], vnegq_s32(result_shift_s32.val[3])); - - // 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 S8 - int8x16_t out_s8 = vcombine_s8(vqmovn_s16(in_s16.val[0]), vqmovn_s16(in_s16.val[1])); - - if(is_bounded_relu) - { - out_s8 = vmaxq_s8(out_s8, min_s8); - out_s8 = vminq_s8(out_s8, max_s8); - } - - return out_s8; -} - -template <typename T> -struct VectorTyper -{ - using stype = T; - using vtype = typename wrapper::traits::neon_bitvector_t<T, wrapper::traits::BitWidth::W128>; -}; - -inline Window get_win_vector_sum(const Window &window) -{ - Window win_vector_sum(window); - win_vector_sum.set(Window::DimY, Window::Dimension(0, 0, 0)); - win_vector_sum.set(Window::DimZ, Window::Dimension(0, 0, 0)); - return win_vector_sum; -} - -inline Iterator get_vector_sum_col_it(const Window &window, const ITensor *vector_sum_col) -{ - Iterator vector_sum_col_it(vector_sum_col, get_win_vector_sum(window)); - return vector_sum_col_it; -} - -inline Iterator get_vector_sum_row_it(const Window &window, const ITensor *vector_sum_row) -{ - Window win_vector_sum_row = get_win_vector_sum(window); - win_vector_sum_row.set(Window::DimX, Window::Dimension(0, 0, 0)); - Iterator vector_sum_row_it(vector_sum_row, win_vector_sum_row); - return vector_sum_row_it; -} - -inline Iterator get_bias_it(const Window &window, const ITensor *bias) -{ - Window win_bias(window); - win_bias.set(Window::DimY, Window::Dimension(0, 1, 1)); - win_bias.set(Window::DimZ, Window::Dimension(0, 1, 1)); - Iterator bias_it(bias, win_bias); - return bias_it; -} - -template <typename VT> -inline void run_offset_contribution_output_stage_window(const int32_t *vector_sum_col_ptr, const int32_t *vector_sum_row_ptr, const int32_t *bias_ptr, Iterator mm_result_it, Iterator out_it, - const int32x4_t result_offset_s32, const int32x4_t result_shift_s32, - typename VT::vtype min_vec, typename VT::vtype max_vec, - int32_t a_offset, int32_t b_offset, int32_t k_offset, - int32_t multiplier, int32_t shift, int32_t offset, int32_t min_bound, int32_t max_bound, - int window_step_x, int window_start_x, int window_end_x, bool has_a_offset, bool has_b_offset, bool has_bias, bool is_bounded_relu, bool is_fixed_point) -{ - int32x4x4_t offset_term_s32 = { 0, 0, 0, 0 }; - if(!is_fixed_point) - { - // Combine quantization offset with other offsets. - offset_term_s32 = add_s32(offset_term_s32, result_offset_s32); - } - if(has_a_offset && has_b_offset) - { - offset_term_s32 = add_s32(offset_term_s32, get_k_offset(k_offset)); - } - if(has_b_offset) - { - offset_term_s32 = add_s32(offset_term_s32, get_b_offset(vector_sum_row_ptr, b_offset)); - } - - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - int32x4x4_t in_s32 = load_results_input(mm_result_it, x); - - if(has_a_offset) - { - in_s32 = add_s32(in_s32, get_a_offset(vector_sum_col_ptr, a_offset, x)); - } - if(has_bias) - { - in_s32 = add_s32(in_s32, load(bias_ptr, x)); - } - if(!is_fixed_point || has_b_offset) - { - in_s32 = add_s32(in_s32, offset_term_s32); - } - if(!is_fixed_point) - { - in_s32 = mul_s32(in_s32, multiplier); - } - - if(is_fixed_point) - { - wrapper::vstore(reinterpret_cast<typename VT::stype *>(out_it.ptr() + x), - finalize_quantization(in_s32, multiplier, shift, result_offset_s32, min_vec, max_vec, is_bounded_relu)); - } - else - { - wrapper::vstore(reinterpret_cast<typename VT::stype *>(out_it.ptr() + x), - finalize_quantization_floating_point(in_s32, result_shift_s32, min_vec, max_vec, is_bounded_relu)); - } - } - // Compute left-over elements - for(; x < window_end_x; ++x) - { - int32_t in_value = *(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + x) + wrapper::vgetlane(offset_term_s32.val[0], 0); - - if(has_a_offset) - { - in_value += (*(vector_sum_col_ptr + x) * a_offset); - } - if(has_bias) - { - in_value += *(bias_ptr + x); - } - - if(is_fixed_point) - { - // Finalize and store the result - *reinterpret_cast<typename VT::stype *>(out_it.ptr() + x) = finalize_quantization(in_value, multiplier, shift, offset, - static_cast<typename VT::stype>(min_bound), - static_cast<typename VT::stype>(max_bound), is_bounded_relu); - } - else - { - // Finalize quantization - in_value = (in_value * multiplier) >> shift; - - // Bound and store the result - if(is_bounded_relu) - { - in_value = static_cast<typename VT::stype>(std::max<int32_t>(min_bound, std::min<int32_t>(max_bound, in_value))); - } - *reinterpret_cast<typename VT::stype *>(out_it.ptr() + x) = static_cast<typename VT::stype>(std::max<int32_t>(static_cast<int32_t>(std::numeric_limits<typename VT::stype>::lowest()), - std::min<int32_t>(static_cast<int32_t>(std::numeric_limits<typename VT::stype>::max()), in_value))); - } - } -} - -inline void run_offset_contribution_output_stage_window_symm(const int32_t *vector_sum_col_ptr, const int32_t *bias_ptr, Iterator mm_result_it, Iterator out_it, - const int32_t *result_multipliers, const int32_t *result_shifts, - const int32x4_t result_offset, int8x16_t min_s8, int8x16_t max_s8, - int32_t a_offset, int32_t offset, int32_t min_bound, int32_t max_bound, - int window_step_x, int window_start_x, int window_end_x, bool has_a_offset, bool has_bias, bool is_bounded_relu, bool is_fixed_point) -{ - int32x4x4_t offset_term_s32 = { 0, 0, 0, 0 }; - if(!is_fixed_point) - { - // Combine quantization offset with other offsets. - offset_term_s32 = add_s32(offset_term_s32, result_offset); - } - - int x = window_start_x; - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - int32x4x4_t in_s32 = load_results_input(mm_result_it, x); - - if(has_a_offset) - { - in_s32 = add_s32(in_s32, get_a_offset(vector_sum_col_ptr, a_offset, x)); - } - if(has_bias) - { - in_s32 = add_s32(in_s32, load(bias_ptr, x)); - } - if(!is_fixed_point) - { - in_s32 = add_s32(in_s32, offset_term_s32); - in_s32 = mul_s32(in_s32, result_multipliers + x); - } - - if(is_fixed_point) - { - vst1q_s8(reinterpret_cast<int8_t *>(out_it.ptr() + x), finalize_quantization_symm(in_s32, load(result_multipliers, x), load(result_shifts, x), result_offset, min_s8, max_s8, is_bounded_relu)); - } - else - { - vst1q_s8(reinterpret_cast<int8_t *>(out_it.ptr() + x), finalize_quantization_floating_point(in_s32, load(result_shifts, x), min_s8, max_s8, is_bounded_relu)); - } - } - // Compute left-over elements - for(; x < window_end_x; ++x) - { - int32_t in_value = *(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + x) + wrapper::vgetlane(offset_term_s32.val[0], 0); - - if(has_a_offset) - { - in_value += (*(vector_sum_col_ptr + x) * a_offset); - } - if(has_bias) - { - in_value += *(bias_ptr + x); - } - - if(is_fixed_point) - { - // Finalize and store the result - *(out_it.ptr() + x) = finalize_quantization(in_value, result_multipliers[x], result_shifts[x], offset, static_cast<int8_t>(min_bound), static_cast<int8_t>(max_bound), is_bounded_relu); - } - else - { - // Finalize quantization - in_value = (in_value * result_multipliers[x]) >> (-result_shifts[x]); - - // Bound and store the result - if(is_bounded_relu) - { - in_value = static_cast<int8_t>(std::max<int32_t>(min_bound, std::min<int32_t>(max_bound, in_value))); - } - *(out_it.ptr() + x) = static_cast<int8_t>(std::max<int32_t>(-128, std::min<int32_t>(127, in_value))); - } - } -} - -template <typename T> -void run_offset_contribution_output_stage(const Window &window, - const ITensor *mm_result, const ITensor *vector_sum_col, const ITensor *vector_sum_row, const ITensor *bias, ITensor *output, - int32_t a_offset, int32_t b_offset, int32_t k_offset, bool slide_vector_sum_col, - GEMMLowpOutputStageInfo output_stage, bool is_gemm3d, bool is_bounded_relu, bool is_fixed_point) -{ - using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>; - using Typer = VectorTyper<T>; - - const int height_input = is_gemm3d ? mm_result->info()->dimension(1) : 0; - const int depth_input = is_gemm3d ? mm_result->info()->dimension(2) : 1; - - const int32_t multiplier = output_stage.gemmlowp_multiplier; - const int32_t shift = output_stage.gemmlowp_shift; - const int32_t offset = output_stage.gemmlowp_offset; - const int32_t min_bound = output_stage.gemmlowp_min_bound; - const int32_t max_bound = output_stage.gemmlowp_max_bound; - - const int32x4_t result_offset_s32 = vdupq_n_s32(offset); - const int32x4_t result_shift_s32 = vdupq_n_s32(is_fixed_point ? shift : -shift); - const auto min_vec = wrapper::vdup_n(static_cast<T>(min_bound), ExactTagType{}); - const auto max_vec = wrapper::vdup_n(static_cast<T>(max_bound), ExactTagType{}); - - 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(window); - win.set(Window::DimX, Window::Dimension(0, 1, 1)); - - Window collapsed_window = win.collapse_if_possible(win, Window::DimZ); - - Iterator mm_result_it(mm_result, win); - Iterator out_it(output, win); - - if((a_offset != 0) && (b_offset != 0)) - { - ARM_COMPUTE_ERROR_ON_NULLPTR(vector_sum_col); - ARM_COMPUTE_ERROR_ON_NULLPTR(vector_sum_row); - - Iterator vector_sum_col_it = get_vector_sum_col_it(collapsed_window, vector_sum_col); - Iterator vector_sum_row_it = get_vector_sum_row_it(collapsed_window, vector_sum_row); - - const size_t sum_row_stride_y = vector_sum_row->info()->strides_in_bytes().y(); - - // Offset in case vector_sum_col is batched - const int vector_sum_col_batch_offset = slide_vector_sum_col ? vector_sum_col->info()->strides_in_bytes().z() : 0; - - if(bias != nullptr) - { - Iterator bias_it = get_bias_it(collapsed_window, bias); - execute_window_loop(collapsed_window, [&](const Coordinates & id) - { - const int batch_id = id.z() / depth_input; - const auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_id * vector_sum_col_batch_offset); - const auto vector_sum_row_ptr = reinterpret_cast<const int32_t *>(vector_sum_row_it.ptr() + batch_id * sum_row_stride_y) - + id.y() + (id.z() % depth_input) * height_input; - run_offset_contribution_output_stage_window<Typer>(vector_sum_col_ptr, vector_sum_row_ptr, reinterpret_cast<const int32_t *>(bias_it.ptr()), - mm_result_it, - out_it, - result_offset_s32, result_shift_s32, - min_vec, max_vec, a_offset, b_offset, k_offset, - multiplier, shift, offset, min_bound, max_bound, - window_step_x, window_start_x, window_end_x, true, true, true, is_bounded_relu, is_fixed_point); - }, - vector_sum_col_it, vector_sum_row_it, bias_it, mm_result_it, out_it); - } - else - { - execute_window_loop(collapsed_window, [&](const Coordinates & id) - { - const int batch_id = id.z() / depth_input; - const auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_id * vector_sum_col_batch_offset); - const auto vector_sum_row_ptr = reinterpret_cast<const int32_t *>(vector_sum_row_it.ptr() + batch_id * sum_row_stride_y) - + id.y() + (id.z() % depth_input) * height_input; - run_offset_contribution_output_stage_window<Typer>(vector_sum_col_ptr, vector_sum_row_ptr, nullptr, mm_result_it, out_it, - result_offset_s32, result_shift_s32, - min_vec, max_vec, a_offset, b_offset, k_offset, - multiplier, shift, offset, min_bound, max_bound, - window_step_x, window_start_x, window_end_x, true, true, false, is_bounded_relu, is_fixed_point); - }, - vector_sum_col_it, vector_sum_row_it, mm_result_it, out_it); - } - } - else if((a_offset == 0) && (b_offset != 0)) - { - ARM_COMPUTE_ERROR_ON_NULLPTR(vector_sum_row); - - Iterator vector_sum_row_it = get_vector_sum_row_it(collapsed_window, vector_sum_row); - - const size_t sum_row_stride_y = vector_sum_row->info()->strides_in_bytes().y(); - - if(bias != nullptr) - { - Iterator bias_it = get_bias_it(collapsed_window, bias); - execute_window_loop(collapsed_window, [&](const Coordinates & id) - { - const int batch_id = id.z() / depth_input; - const auto vector_sum_row_ptr = reinterpret_cast<const int32_t *>(vector_sum_row_it.ptr() + batch_id * sum_row_stride_y) - + id.y() + (id.z() % depth_input) * height_input; - run_offset_contribution_output_stage_window<Typer>(nullptr, vector_sum_row_ptr, reinterpret_cast<const int32_t *>(bias_it.ptr()), mm_result_it, - out_it, - result_offset_s32, result_shift_s32, - min_vec, max_vec, a_offset, b_offset, k_offset, - multiplier, shift, offset, min_bound, max_bound, - window_step_x, window_start_x, window_end_x, false, true, true, is_bounded_relu, is_fixed_point); - }, - vector_sum_row_it, bias_it, mm_result_it, out_it); - } - else - { - execute_window_loop(collapsed_window, [&](const Coordinates & id) - { - const int batch_id = id.z() / depth_input; - const auto vector_sum_row_ptr = reinterpret_cast<const int32_t *>(vector_sum_row_it.ptr() + batch_id * sum_row_stride_y) - + id.y() + (id.z() % depth_input) * height_input; - run_offset_contribution_output_stage_window<Typer>(nullptr, vector_sum_row_ptr, nullptr, mm_result_it, out_it, - result_offset_s32, result_shift_s32, - min_vec, max_vec, a_offset, b_offset, k_offset, - multiplier, shift, offset, min_bound, max_bound, - window_step_x, window_start_x, window_end_x, false, true, false, is_bounded_relu, is_fixed_point); - }, - vector_sum_row_it, mm_result_it, out_it); - } - } - else if((a_offset != 0) && (b_offset == 0)) - { - ARM_COMPUTE_ERROR_ON_NULLPTR(vector_sum_col); - - Iterator vector_sum_col_it = get_vector_sum_col_it(collapsed_window, vector_sum_col); - - // Offset in case vector_sum_col is batched - const int vector_sum_col_batch_offset = slide_vector_sum_col ? vector_sum_col->info()->strides_in_bytes().z() : 0; - - if(bias != nullptr) - { - Iterator bias_it = get_bias_it(collapsed_window, bias); - execute_window_loop(collapsed_window, [&](const Coordinates & id) - { - const int batch_id = id.z() / depth_input; - const auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_id * vector_sum_col_batch_offset); - run_offset_contribution_output_stage_window<Typer>(vector_sum_col_ptr, nullptr, reinterpret_cast<const int32_t *>(bias_it.ptr()), mm_result_it, - out_it, - result_offset_s32, result_shift_s32, - min_vec, max_vec, a_offset, b_offset, k_offset, - multiplier, shift, offset, min_bound, max_bound, - window_step_x, window_start_x, window_end_x, true, false, true, is_bounded_relu, is_fixed_point); - }, - vector_sum_col_it, bias_it, mm_result_it, out_it); - } - else - { - execute_window_loop(collapsed_window, [&](const Coordinates & id) - { - const int batch_id = id.z() / depth_input; - const auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_id * vector_sum_col_batch_offset); - run_offset_contribution_output_stage_window<Typer>(vector_sum_col_ptr, nullptr, nullptr, mm_result_it, out_it, - result_offset_s32, result_shift_s32, - min_vec, max_vec, a_offset, b_offset, k_offset, - multiplier, shift, offset, min_bound, max_bound, - window_step_x, window_start_x, window_end_x, true, false, false, is_bounded_relu, is_fixed_point); - }, - vector_sum_col_it, mm_result_it, out_it); - } - } - else - { - if(bias != nullptr) - { - Iterator bias_it = get_bias_it(collapsed_window, bias); - execute_window_loop(collapsed_window, [&](const Coordinates &) - { - run_offset_contribution_output_stage_window<Typer>(nullptr, nullptr, reinterpret_cast<const int32_t *>(bias_it.ptr()), mm_result_it, out_it, - result_offset_s32, result_shift_s32, - min_vec, max_vec, a_offset, b_offset, k_offset, - multiplier, shift, offset, min_bound, max_bound, - window_step_x, window_start_x, window_end_x, false, false, true, is_bounded_relu, is_fixed_point); - }, - bias_it, mm_result_it, out_it); - } - else - { - execute_window_loop(collapsed_window, [&](const Coordinates &) - { - run_offset_contribution_output_stage_window<Typer>(nullptr, nullptr, nullptr, mm_result_it, out_it, - result_offset_s32, result_shift_s32, - min_vec, max_vec, a_offset, b_offset, k_offset, - multiplier, shift, offset, min_bound, max_bound, - window_step_x, window_start_x, window_end_x, false, false, false, is_bounded_relu, is_fixed_point); - }, - mm_result_it, out_it); - } - return; - } -} - -void run_offset_contribution_output_stage_symm(const Window &window, - const ITensor *mm_result, const ITensor *vector_sum_col, const ITensor *vector_sum_row, const ITensor *bias, ITensor *output, - int32_t a_offset, int32_t b_offset, int32_t k_offset, bool slide_vector_sum_col, - GEMMLowpOutputStageInfo output_stage, bool is_gemm3d, bool is_bounded_relu, bool is_fixed_point) -{ - ARM_COMPUTE_UNUSED(vector_sum_row, b_offset, k_offset); - - const int depth_input = is_gemm3d ? mm_result->info()->dimension(2) : 1; - - const int32_t offset = output_stage.gemmlowp_offset; - const int32_t min_bound = output_stage.gemmlowp_min_bound; - const int32_t max_bound = output_stage.gemmlowp_max_bound; - - const int32_t *result_multipliers = output_stage.gemmlowp_multipliers.data(); - const int32_t *result_shifts = output_stage.gemmlowp_shifts.data(); - const int32x4_t result_offset_s32 = vdupq_n_s32(offset); - const int8x16_t min_s8 = vdupq_n_s8(static_cast<int8_t>(min_bound)); - const int8x16_t max_s8 = vdupq_n_s8(static_cast<int8_t>(max_bound)); - - 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(window); - win.set(Window::DimX, Window::Dimension(0, 1, 1)); - - Window collapsed_window = win.collapse_if_possible(win, Window::DimZ); - - Iterator mm_result_it(mm_result, win); - Iterator out_it(output, win); - - if(a_offset != 0) - { - ARM_COMPUTE_ERROR_ON_NULLPTR(vector_sum_col); - - Iterator vector_sum_col_it = get_vector_sum_col_it(collapsed_window, vector_sum_col); - - // Offset in case vector_sum_col is batched - const int vector_sum_col_batch_offset = slide_vector_sum_col ? vector_sum_col->info()->strides_in_bytes().z() : 0; - - if(bias != nullptr) - { - Iterator bias_it = get_bias_it(collapsed_window, bias); - execute_window_loop(collapsed_window, [&](const Coordinates & id) - { - const int batch_id = id.z() / depth_input; - const auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_id * vector_sum_col_batch_offset); - run_offset_contribution_output_stage_window_symm(vector_sum_col_ptr, reinterpret_cast<const int32_t *>(bias_it.ptr()), mm_result_it, out_it, - result_multipliers, result_shifts, - result_offset_s32, min_s8, max_s8, - a_offset, offset, min_bound, max_bound, - window_step_x, window_start_x, window_end_x, true, true, is_bounded_relu, is_fixed_point); - }, - vector_sum_col_it, bias_it, mm_result_it, out_it); - } - else - { - execute_window_loop(collapsed_window, [&](const Coordinates & id) - { - const int batch_id = id.z() / depth_input; - const auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_id * vector_sum_col_batch_offset); - run_offset_contribution_output_stage_window_symm(vector_sum_col_ptr, nullptr, mm_result_it, out_it, - result_multipliers, result_shifts, - result_offset_s32, min_s8, max_s8, - a_offset, offset, min_bound, max_bound, - window_step_x, window_start_x, window_end_x, true, false, is_bounded_relu, is_fixed_point); - }, - vector_sum_col_it, mm_result_it, out_it); - } - } - else - { - if(bias != nullptr) - { - Iterator bias_it = get_bias_it(collapsed_window, bias); - execute_window_loop(collapsed_window, [&](const Coordinates &) - { - run_offset_contribution_output_stage_window_symm(nullptr, reinterpret_cast<const int32_t *>(bias_it.ptr()), mm_result_it, out_it, - result_multipliers, result_shifts, - result_offset_s32, min_s8, max_s8, - a_offset, offset, min_bound, max_bound, - window_step_x, window_start_x, window_end_x, false, true, is_bounded_relu, is_fixed_point); - }, - bias_it, mm_result_it, out_it); - } - else - { - execute_window_loop(collapsed_window, [&](const Coordinates &) - { - run_offset_contribution_output_stage_window_symm(nullptr, nullptr, mm_result_it, out_it, - result_multipliers, result_shifts, - result_offset_s32, min_s8, max_s8, - a_offset, offset, min_bound, max_bound, - window_step_x, window_start_x, window_end_x, false, false, is_bounded_relu, is_fixed_point); - }, - mm_result_it, out_it); - } - return; - } -} - -Status validate_arguments(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, const ITensorInfo *bias, const ITensorInfo *output, - int32_t a_offset, int32_t b_offset, GEMMLowpOutputStageInfo output_stage) -{ - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(mm_result, 1, DataType::S32); - if(output->data_type() != DataType::QASYMM8) - { - ARM_COMPUTE_RETURN_ERROR_ON(mm_result->dimension(0) > 1 && output_stage.gemmlowp_multipliers.size() > 1 && b_offset != 0); - } - ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_min_bound > output_stage.gemmlowp_max_bound); - ARM_COMPUTE_RETURN_ERROR_ON(output_stage.type != GEMMLowpOutputStageType::QUANTIZE_DOWN && output_stage.type != GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT); - - if(bias != nullptr) - { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32); - ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1); - ARM_COMPUTE_RETURN_ERROR_ON(mm_result->dimension(0) != bias->dimension(0)); - } - - // If a_offset == 0, vector_sum_col can be a nullptr - if(a_offset != 0) - { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_col, 1, DataType::S32); - ARM_COMPUTE_RETURN_ERROR_ON(vector_sum_col->dimension(0) != mm_result->dimension(0)); - } - - // If b_offset == 0, vector_sum_row can be a nullptr - if(b_offset != 0) - { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_row, 1, DataType::S32); - - // Check if input is a 3D reinterpretation - const bool reinterpret_as_3d = mm_result->num_dimensions() > 1 && mm_result->tensor_shape().y() != vector_sum_row->tensor_shape().x(); - - // Validate input - ARM_COMPUTE_RETURN_ERROR_ON(reinterpret_as_3d && vector_sum_row->dimension(0) != (mm_result->dimension(1) * mm_result->dimension(2))); - ARM_COMPUTE_RETURN_ERROR_ON(!reinterpret_as_3d && vector_sum_row->dimension(0) != mm_result->dimension(1)); - - TensorShape output_shape = output->tensor_shape(); - if(output_shape.num_dimensions() > 1) - { - const unsigned int output_batch_idx = reinterpret_as_3d ? 3 : 2; - - TensorShape vector_sum_row_shape = vector_sum_row->tensor_shape(); - vector_sum_row_shape.collapse_from(1); - output_shape.collapse_from(output_batch_idx); - - ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_row_shape[1] != output_shape[output_batch_idx], - "mm_result tensor must have the same number of batches of output tensor"); - - if(a_offset != 0) - { - TensorShape vector_sum_col_shape = vector_sum_col->tensor_shape(); - vector_sum_col_shape.collapse_from(1); - - ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_col_shape[1] != 1 && vector_sum_col_shape[1] != vector_sum_row_shape[1], - "vector_sum_col tensor must have the same number of batches of vector_sum_row_shape or the number of batches must be set to 1"); - } - } - } - - if(output->total_size() != 0) - { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mm_result, output); - } - - return Status{}; -} - -std::pair<Status, Window> validate_and_configure_window(ITensorInfo *mm_result, ITensorInfo *output) -{ - // Output auto inizialitation if not yet initialized - auto_init_if_empty(*output, mm_result->clone()->set_data_type(DataType::QASYMM8)); - - // Configure kernel window - Window win = calculate_max_window(*mm_result, Steps()); - - // 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 1 and so update_window_and_padding() can be skipped - - return std::make_pair(Status{}, win); -} -} // namespace - -NEGEMMLowpOffsetContributionOutputStageKernel::NEGEMMLowpOffsetContributionOutputStageKernel() - : _vector_sum_col(nullptr), _vector_sum_row(nullptr), _bias(nullptr), _mm_result(nullptr), _output(nullptr), _a_offset(0), _b_offset(0), _k_offset(0), _slide_vector_sum_col(true), - _output_stage(GEMMLowpOutputStageInfo()) - -{ -} - -void NEGEMMLowpOffsetContributionOutputStageKernel::configure(const ITensor *mm_result, const ITensor *vector_sum_col, - const ITensor *vector_sum_row, const ITensor *bias, ITensor *output, - int32_t k, int32_t a_offset, int32_t b_offset, - GEMMLowpOutputStageInfo output_stage) -{ - // Perform validate step - ARM_COMPUTE_ERROR_ON_NULLPTR(mm_result, output); - - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(mm_result->info(), - vector_sum_col != nullptr ? vector_sum_col->info() : nullptr, // NOLINT - vector_sum_row != nullptr ? vector_sum_row->info() : nullptr, // NOLINT - bias != nullptr ? bias->info() : nullptr, // NOLINT - output->info(), a_offset, b_offset, output_stage)); // NOLINT - - _vector_sum_col = vector_sum_col; - _vector_sum_row = vector_sum_row; - _bias = bias; - _mm_result = mm_result; - _output = output; - _a_offset = a_offset; - _b_offset = b_offset; - _k_offset = a_offset * b_offset * k; - _output_stage = output_stage; - - // If a_offset == 0, vector_sum_col can be a nullptr - if(a_offset != 0) - { - // Check if vector_sum_col_shape should be slidden or not - // Don't slide vector_sum_col_shape along the y dimension if vector_sum_col_shape has just 1 dimension and vector_sum_row_shape more than 1 - // This scenario can happen when the the matrix multiplication is used to perform a convolution operation - _slide_vector_sum_col = vector_sum_col->info()->tensor_shape().num_dimensions() > 1; - } - - // Configure kernel window - auto win_config = validate_and_configure_window(mm_result->info(), output->info()); - ARM_COMPUTE_ERROR_THROW_ON(win_config.first); - INEKernel::configure(win_config.second); -} - -Status NEGEMMLowpOffsetContributionOutputStageKernel::validate(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, - const ITensorInfo *vector_sum_row, const ITensorInfo *bias, const ITensorInfo *output, - int32_t a_offset, int32_t b_offset, GEMMLowpOutputStageInfo output_stage) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(mm_result, output); - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(mm_result, vector_sum_col, vector_sum_row, bias, output, a_offset, b_offset, output_stage)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(mm_result->clone().get(), output->clone().get()).first); - return Status{}; -} - -void NEGEMMLowpOffsetContributionOutputStageKernel::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); - - PixelValue type_min{}; - PixelValue type_max{}; - std::tie(type_min, type_max) = get_min_max(_output->info()->data_type()); - int32_t type_min_int = type_min.get<int32_t>(); - int32_t type_max_int = type_max.get<int32_t>(); - - const bool reinterpret_as_3d = _vector_sum_row != nullptr - && _mm_result->info()->num_dimensions() > 1 - && _mm_result->info()->tensor_shape().y() != _vector_sum_row->info()->tensor_shape().x(); - - const bool is_bounded_relu = !(_output_stage.gemmlowp_min_bound <= type_min_int && _output_stage.gemmlowp_max_bound >= type_max_int); - - // Check if we need to perform fixed point requantization - const bool is_fixed_point = _output_stage.type != GEMMLowpOutputStageType::QUANTIZE_DOWN; - - // Check if symmetric per-channel execution - const bool is_signed = _output->info()->data_type() == DataType::QASYMM8_SIGNED; - - // Check if symmetric per-channel execution - const bool is_symm = _output_stage.is_quantized_per_channel; - - if(is_symm) - { - run_offset_contribution_output_stage_symm(window, _mm_result, _vector_sum_col, _vector_sum_row, _bias, _output, _a_offset, _b_offset, _k_offset, _slide_vector_sum_col, _output_stage, - reinterpret_as_3d, is_bounded_relu, is_fixed_point); - } - else - { - if(is_signed) - { - run_offset_contribution_output_stage<int8_t>(window, _mm_result, _vector_sum_col, _vector_sum_row, _bias, _output, _a_offset, _b_offset, _k_offset, _slide_vector_sum_col, _output_stage, - reinterpret_as_3d, is_bounded_relu, is_fixed_point); - } - else - { - run_offset_contribution_output_stage<uint8_t>(window, _mm_result, _vector_sum_col, _vector_sum_row, _bias, _output, _a_offset, _b_offset, _k_offset, _slide_vector_sum_col, _output_stage, - reinterpret_as_3d, is_bounded_relu, is_fixed_point); - } - } -} - -} // namespace arm_compute diff --git a/src/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.h b/src/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.h deleted file mode 100644 index 6908f37aad..0000000000 --- a/src/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.h +++ /dev/null @@ -1,135 +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_NEGEMMLOWPOFFSETCONTRIBUTIONOUTPUTSTAGEKERNEL_H -#define ARM_COMPUTE_NEGEMMLOWPOFFSETCONTRIBUTIONOUTPUTSTAGEKERNEL_H - -#include "src/core/NEON/INEKernel.h" - -namespace arm_compute -{ -class ITensor; - -/** Kernel used to add the offset contribution and perform the output stage after @ref NEGEMMLowpMatrixMultiplyKernel. - * - * The computation is performed in-place - * - * This kernel takes a final int32 accumulator value (the output of @ref NEGEMMLowpMatrixMultiplyKernel), - * and adds to it the offset contribution of matrix A and matrix B in-place. - * - * The output stage can perform either QuantizeDownInt32ToUint8Scale or QuantizeDownInt32ToUint8ScaleByFixedPoint for Uint8. - * The output stage can perform either QuantizeDownInt32ToInt8Scale or QuantizeDownInt32ToInt8ScaleByFixedPoint for Int8. - * - * For QuantizeDownInt32ToUint8Scale/QuantizeDownInt32ToInt8Scale the final result is: - * - * ((mm_result'[i][k] + result_offset) * result_mult_int) >> result_shift - * - * For QuantizeDownInt32ToUint8ScaleByFixedPoint/QuantizeDownInt32ToInt8ScaleByFixedPoint the final result is: - * - * (FixedPointMul(mm_result'[i][k], result_fixedpoint_multiplier) >> result_shift) + result_offset_after_shift - * - * where FixedPointMul(x, y) is the nearest integer to the following - * mathematical expression, evaluated without overflow or intermediate rounding: - * - * (x * y) / 2^31 - * - * and mm_result'[i][k] = mm_result[i][k] + - * (vector_sum_col[k] * a_offset) + - * (vector_sum_row[i] * b_offset) + - * (a_offset * b_offset * k) - */ - -class NEGEMMLowpOffsetContributionOutputStageKernel : public INEKernel -{ -public: - const char *name() const override - { - return "NEGEMMLowpOffsetContributionOutputStageKernel"; - } - /** Constructor */ - NEGEMMLowpOffsetContributionOutputStageKernel(); - /** Prevent instances of this class from being copied (As this class contains pointers)*/ - NEGEMMLowpOffsetContributionOutputStageKernel(const NEGEMMLowpOffsetContributionOutputStageKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers)*/ - NEGEMMLowpOffsetContributionOutputStageKernel &operator=(const NEGEMMLowpOffsetContributionOutputStageKernel &) = delete; - /** Allow instances of this class to be moved */ - NEGEMMLowpOffsetContributionOutputStageKernel(NEGEMMLowpOffsetContributionOutputStageKernel &&) = default; - /** Allow instances of this class to be moved */ - NEGEMMLowpOffsetContributionOutputStageKernel &operator=(NEGEMMLowpOffsetContributionOutputStageKernel &&) = default; - /** Default destructor */ - ~NEGEMMLowpOffsetContributionOutputStageKernel() = default; - /** Initialise the kernel's input and output. - * - * @param[in] mm_result Input tensor containing the result of @ref NEGEMMLowpMatrixMultiplyKernel. Data type supported: S32 - * @param[in] vector_sum_col Input row-vector of sums of all the entries in each column of matrix B. - * Note: vector_sum_col can be a nullptr in case a_offset = 0. Data type supported: same as @p mm_result - * @param[in] vector_sum_row Input row-vector of sums of all the entries in each row of matrix A. - * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the addition of biases is not required. - * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p mm_result. - * @param[out] output Output tensor containing the final quantized result. Data type supported: QASYMM8/QASYMM8_SIGNED - * @param[in] k Number of matrix A columns or Matrix B rows - * @param[in] a_offset Offset to be added to each element of the matrix A. - * @param[in] b_offset Offset to be added to each element of the matrix B. - * @param[in] output_stage GEMMLowp output stage info, providing the type of quantization and the necessary parameters. - */ - void configure(const ITensor *mm_result, const ITensor *vector_sum_col, const ITensor *vector_sum_row, const ITensor *bias, ITensor *output, int32_t k, int32_t a_offset, int32_t b_offset, - GEMMLowpOutputStageInfo output_stage); - /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMLowpOffsetContributionOutputStageKernel - * - * @param[in] mm_result Input tensor info containing the result of @ref NEGEMMLowpMatrixMultiplyKernel. Data type supported: S32 - * @param[in] vector_sum_col Tensor info for the input row-vector of sums of all the entries in each column of matrix B. - * Note: vector_sum_col can be a nullptr in case a_offset = 0. Data type supported: same as @p mm_result - * @param[in] vector_sum_row Tensor info for the input row-vector of sums of all the entries in each row of matrix A. - * Note: vector_sum_row can be a nullptr in case b_offset = 0. Data type supported: same as @p mm_result - * @param[in] bias Biases tensor info. Only shared biases supported and it can be a nullptr if the addition of biases is not required. - * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p mm_result. - * @param[in] output Output tensor info containing the final quantized result. Data type supported: QASYMM8/QASYMM8_SIGNED - * @param[in] a_offset Offset to be added to each element of the matrix A. - * @param[in] b_offset Offset to be added to each element of the matrix B. - * @param[in] output_stage GEMMLowp output stage info, providing the type of quantization and the necessary parameters. - * - * @return a status - */ - static Status validate(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, const ITensorInfo *bias, const ITensorInfo *output, int32_t a_offset, - int32_t b_offset, - GEMMLowpOutputStageInfo output_stage); - - // Inherited methods overridden: - void run(const Window &window, const ThreadInfo &info) override; - -private: - /** Function to use for the particular tensors passed to configure() */ - const ITensor *_vector_sum_col; - const ITensor *_vector_sum_row; - const ITensor *_bias; - const ITensor *_mm_result; - ITensor *_output; - int32_t _a_offset; - int32_t _b_offset; - int32_t _k_offset; - bool _slide_vector_sum_col; - GEMMLowpOutputStageInfo _output_stage; -}; -} // namespace arm_compute - -#endif /* ARM_COMPUTE_NEGEMMLOWPOFFSETCONTRIBUTIONOUTPUTSTAGEKERNEL_H */ diff --git a/src/core/NEON/kernels/NEGEMMLowpReductionKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpReductionKernel.cpp deleted file mode 100644 index dfbfbd6fab..0000000000 --- a/src/core/NEON/kernels/NEGEMMLowpReductionKernel.cpp +++ /dev/null @@ -1,382 +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/NEGEMMLowpReductionKernel.h" - -#include "arm_compute/core/ITensor.h" -#include "arm_compute/core/KernelDescriptors.h" -#include "arm_compute/core/TensorInfo.h" -#include "src/core/NEON/wrapper/wrapper.h" -#include "src/core/helpers/AutoConfiguration.h" -#include "src/core/helpers/WindowHelpers.h" - -namespace arm_compute -{ -namespace -{ -Status validate_arguments_matrix_a_reduction(const ITensorInfo *input, const ITensorInfo *output) -{ - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8, DataType::QSYMM8_PER_CHANNEL); - - if(output->total_size() > 0) - { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(0) != input->dimension(1), "Output vector must have length equal to the number of rows of the input matrix"); - } - return Status{}; -} -Status validate_arguments_matrix_b_reduction(const ITensorInfo *input, const ITensorInfo *output) -{ - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8, DataType::QSYMM8_PER_CHANNEL); - - if(output->total_size() > 0) - { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(0) != input->dimension(0), "Output vector must have length equal to the number of columns of the input matrix"); - } - return Status{}; -} -} // namespace - -INEGEMMLowpReductionKernel::INEGEMMLowpReductionKernel() - : _input(), _output(), _k(0), _scalar(0), _mul_by_scalar(false) -{ -} - -void NEGEMMLowpMatrixAReductionKernel::configure(const ITensor *mtx_a, ITensor *vector_sum_row, const GEMMLowpReductionKernelInfo &info) -{ - // Perform validate step - ARM_COMPUTE_ERROR_ON_NULLPTR(mtx_a, vector_sum_row); - ARM_COMPUTE_ERROR_ON_MSG(info.is_reshaped == true, "Not supported"); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_matrix_a_reduction(mtx_a->info(), vector_sum_row->info())); - _input = mtx_a; - _output = vector_sum_row; - _k = info.k; - _scalar = info.scalar; - _mul_by_scalar = info.mul_by_scalar; - - // Output auto initialization if not yet initialized - auto_init_if_empty(*_output->info(), TensorShape(_input->info()->dimension(1)), 1, DataType::S32); - - Window win = calculate_max_window(*_output->info(), Steps(1)); - - INEKernel::configure(win); -} - -Status NEGEMMLowpMatrixAReductionKernel::validate(const ITensorInfo *mtx_a, const ITensorInfo *vector_sum_row, const GEMMLowpReductionKernelInfo &info) -{ - ARM_COMPUTE_UNUSED(info); - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_matrix_a_reduction(mtx_a, vector_sum_row)); - return Status{}; -} - -template <typename T> -void NEGEMMLowpMatrixAReductionKernel::run_internal(const arm_compute::Window &window) -{ - // Intermediate and final accumulator types - using TIAcc = wrapper::traits::promote_t<T>; - using TAcc = wrapper::traits::promote_t<TIAcc>; - - Window collapsed_window = window.collapse_if_possible(IKernel::window(), Window::DimY); - - Window win_input(collapsed_window); - win_input.set(Window::DimX, Window::Dimension(0, 0, 0)); - win_input.set(Window::DimY, Window::Dimension(0, 0, 0)); - win_input.set(Window::DimZ, Window::Dimension(0, 0, 0)); - - Iterator in(_input, win_input); - Iterator out(_output, collapsed_window); - - execute_window_loop(collapsed_window, [&](const Coordinates & id) - { - auto vsum_row = wrapper::vdup_n(static_cast<TAcc>(0), wrapper::traits::vector_128_tag{}); - TAcc sum_row = 0; - - const T *matrix_a = reinterpret_cast<const T *>((in.ptr() + id.x() * _input->info()->strides_in_bytes()[1] + id.y() * _input->info()->strides_in_bytes()[2])); - -#if __arm__ - asm volatile("PLD [%0, #128*4]" ::"r"(matrix_a)); -#endif /* __arm__ */ - - int i = 0; - // This for loop performs 16 accumulations - for(; i <= (_k - 16); i += 16) - { - const auto a0_d8 = wrapper::vloadq(matrix_a + i); - - // Partial accumulations in U16 - const auto tmp_sum0 = wrapper::vaddl(wrapper::vgetlow(a0_d8), wrapper::vgethigh(a0_d8)); - - // Accumulate to U32 - vsum_row = wrapper::vadd(vsum_row, wrapper::vpaddl(tmp_sum0)); - } - - // This for loop performs the leftover accumulations - for(; i < _k; ++i) - { - sum_row += static_cast<TAcc>(matrix_a[i]); - } - -#if defined(__aarch64__) - // Reduction operation available on 64 bit architectures only - sum_row += wrapper::vaddv(vsum_row); -#else // __aarch64__ - auto tmp = wrapper::vpadd(wrapper::vgethigh(vsum_row), wrapper::vgetlow(vsum_row)); - tmp = wrapper::vpadd(tmp, tmp); - - sum_row += wrapper::vgetlane(tmp, 0); -#endif // __aarch64__ - - // Multiply by scalar if necessary - if(_mul_by_scalar) - { - sum_row *= _scalar; - } - - *(reinterpret_cast<int *>(out.ptr())) = static_cast<int32_t>(sum_row); - }, - in, out); -} - -void NEGEMMLowpMatrixAReductionKernel::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); - - switch(_input->info()->data_type()) - { - case DataType::QASYMM8: - run_internal<uint8_t>(window); - break; - case DataType::QASYMM8_SIGNED: - case DataType::QSYMM8: - case DataType::QSYMM8_PER_CHANNEL: - run_internal<int8_t>(window); - break; - default: - ARM_COMPUTE_ERROR("Unsupported data type"); - } -} - -void NEGEMMLowpMatrixBReductionKernel::configure(const ITensor *mtx_b, ITensor *vector_sum_col, const GEMMLowpReductionKernelInfo &info) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(mtx_b, vector_sum_col); - ARM_COMPUTE_ERROR_ON_MSG(info.is_reshaped == true, "Not supported"); - - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_matrix_b_reduction(mtx_b->info(), vector_sum_col->info())); - - _input = mtx_b; - _output = vector_sum_col; - _k = info.k; - _scalar = info.scalar; - _mul_by_scalar = info.mul_by_scalar; - - // Configure kernel window - constexpr unsigned int num_elems_processed_per_iteration = 16; - - // Output auto initialization if not yet initialized - auto_init_if_empty(*_output->info(), TensorShape(_input->info()->dimension(0)), 1, DataType::S32); - - // Configure kernel window - Window win = calculate_max_window_horizontal(*_output->info(), Steps(num_elems_processed_per_iteration)); - INEKernel::configure(win); -} - -Status NEGEMMLowpMatrixBReductionKernel::validate(const ITensorInfo *mtx_b, const ITensorInfo *vector_sum_col, const GEMMLowpReductionKernelInfo &info) -{ - ARM_COMPUTE_UNUSED(info); - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_matrix_b_reduction(mtx_b, vector_sum_col)); - - return Status{}; -} - -template <typename T> -void NEGEMMLowpMatrixBReductionKernel::run_internal(const Window &window, const ThreadInfo &info) -{ - // Intermediate and final accumulator types - using TIAcc = wrapper::traits::promote_t<T>; - using TAcc = wrapper::traits::promote_t<TIAcc>; - - Window collapsed_window = window.collapse_if_possible(IKernel::window(), Window::DimY); - const auto vec_scalar = wrapper::vdup_n(static_cast<TAcc>(_scalar), wrapper::traits::vector_128_tag{}); - - const auto width_matrix_b = static_cast<int>(_input->info()->dimension(0)); - const auto in_b_stride = static_cast<int>(_input->info()->strides_in_bytes()[1]); - - // The implementation computes 16 elements per iteration - const int window_start_x = 16 * info.thread_id; - const int window_step_x = 16 * info.num_threads; - // Make sure (window_end_x - window_start_x) is a multiple of window_step_x - const int window_end_x = ceil_to_multiple(width_matrix_b - window_start_x, window_step_x) + window_start_x; - - Window win_out(collapsed_window); - win_out.set(Window::DimX, Window::Dimension(window_start_x, window_end_x, window_step_x)); - - Window win_in(win_out); - win_in.set(Window::DimY, Window::Dimension(0, 0, 0)); - win_in.set(Window::DimZ, Window::Dimension(0, 0, 0)); - - Iterator inb(_input, win_in); - Iterator out(_output, win_out); - - execute_window_loop(win_out, [&](const Coordinates & id) - { - if(id.x() > width_matrix_b) - { - return; - } - - // Note: Since the input is unsigned char, we can safely use unsigned int for the accumulation - typename wrapper::traits::neon_bitvector<TAcc, wrapper::traits::BitWidth::W128>::type sum_col[4] = - { - wrapper::vdup_n(static_cast<TAcc>(0), wrapper::traits::vector_128_tag{}), - wrapper::vdup_n(static_cast<TAcc>(0), wrapper::traits::vector_128_tag{}), - wrapper::vdup_n(static_cast<TAcc>(0), wrapper::traits::vector_128_tag{}), - wrapper::vdup_n(static_cast<TAcc>(0), wrapper::traits::vector_128_tag{}) - }; - - const auto *matrix_b = reinterpret_cast<const T *>(inb.ptr() + id.y() * _input->info()->strides_in_bytes()[2]); - -#if __arm__ - asm volatile("PLD [%0, #128*4]" ::"r"(matrix_b)); - asm volatile("PLD [%0, #128*4]" ::"r"(matrix_b + in_b_stride)); -#endif /* __arm__ */ - - int i = 0; - // This for loop performs 4 accumulations - for(; i <= (_k - 4); i += 4) - { - const auto b0_u8 = wrapper::vloadq(matrix_b + 0 * in_b_stride); - const auto b1_u8 = wrapper::vloadq(matrix_b + 1 * in_b_stride); - const auto b2_u8 = wrapper::vloadq(matrix_b + 2 * in_b_stride); - const auto b3_u8 = wrapper::vloadq(matrix_b + 3 * in_b_stride); - -#if __arm__ - asm volatile("PLD [%0, #128*1]" ::"r"(matrix_b + 1 * in_b_stride)); - asm volatile("PLD [%0, #128*1]" ::"r"(matrix_b + 2 * in_b_stride)); - asm volatile("PLD [%0, #128*1]" ::"r"(matrix_b + 3 * in_b_stride)); - asm volatile("PLD [%0, #128*1]" ::"r"(matrix_b + 4 * in_b_stride)); -#endif /* __arm__ */ - - // Partial accumulation in 16bit - typename wrapper::traits::neon_bitvector<TIAcc, wrapper::traits::BitWidth::W128>::type tmp_sum[2] = - { - wrapper::vdup_n(static_cast<TIAcc>(0), wrapper::traits::vector_128_tag{}), - wrapper::vdup_n(static_cast<TIAcc>(0), wrapper::traits::vector_128_tag{}) - }; - - tmp_sum[0] = wrapper::vaddw(tmp_sum[0], wrapper::vgetlow(b1_u8)); - tmp_sum[0] = wrapper::vaddw(tmp_sum[0], wrapper::vgetlow(b0_u8)); - tmp_sum[0] = wrapper::vaddw(tmp_sum[0], wrapper::vgetlow(b2_u8)); - tmp_sum[0] = wrapper::vaddw(tmp_sum[0], wrapper::vgetlow(b3_u8)); - tmp_sum[1] = wrapper::vaddw(tmp_sum[1], wrapper::vgethigh(b0_u8)); - tmp_sum[1] = wrapper::vaddw(tmp_sum[1], wrapper::vgethigh(b1_u8)); - tmp_sum[1] = wrapper::vaddw(tmp_sum[1], wrapper::vgethigh(b2_u8)); - tmp_sum[1] = wrapper::vaddw(tmp_sum[1], wrapper::vgethigh(b3_u8)); - - // Accumulate to 32bit - sum_col[0] = wrapper::vaddw(sum_col[0], wrapper::vgetlow(tmp_sum[0])); - sum_col[1] = wrapper::vaddw(sum_col[1], wrapper::vgethigh(tmp_sum[0])); - sum_col[2] = wrapper::vaddw(sum_col[2], wrapper::vgetlow(tmp_sum[1])); - sum_col[3] = wrapper::vaddw(sum_col[3], wrapper::vgethigh(tmp_sum[1])); - - matrix_b += 4 * in_b_stride; - } - - // This for loop perfoms the leftover accumulations - for(; i < _k; ++i) - { - const auto b0_b8 = wrapper::vloadq(matrix_b + 0 * in_b_stride); - - // Convert S8 to S16 - const typename wrapper::traits::neon_bitvector<TIAcc, wrapper::traits::BitWidth::W128>::type b0_b16[2] - { - wrapper::vmovl(wrapper::vgetlow(b0_b8)), - wrapper::vmovl(wrapper::vgethigh(b0_b8)) - }; - - // Accumulate to 32bit - sum_col[0] = wrapper::vaddw(sum_col[0], wrapper::vgetlow(b0_b16[0])); - sum_col[1] = wrapper::vaddw(sum_col[1], wrapper::vgethigh(b0_b16[0])); - sum_col[2] = wrapper::vaddw(sum_col[2], wrapper::vgetlow(b0_b16[1])); - sum_col[3] = wrapper::vaddw(sum_col[3], wrapper::vgethigh(b0_b16[1])); - - matrix_b += in_b_stride; - } - - // Multiply by scalar if necessary - if(_mul_by_scalar) - { - sum_col[0] = wrapper::vmul(sum_col[0], vec_scalar); - sum_col[1] = wrapper::vmul(sum_col[1], vec_scalar); - sum_col[2] = wrapper::vmul(sum_col[2], vec_scalar); - sum_col[3] = wrapper::vmul(sum_col[3], vec_scalar); - } - - auto vector_sum_col = reinterpret_cast<int32_t *>(out.ptr()); - if(id.x() + 16 < width_matrix_b) - { - wrapper::vstore(vector_sum_col + 0, wrapper::vreinterpret(sum_col[0])); - wrapper::vstore(vector_sum_col + 4, wrapper::vreinterpret(sum_col[1])); - wrapper::vstore(vector_sum_col + 8, wrapper::vreinterpret(sum_col[2])); - wrapper::vstore(vector_sum_col + 12, wrapper::vreinterpret(sum_col[3])); - } - else - { - auto left_over = width_matrix_b - id.x(); - for(auto k = 0; k < 4 && left_over; ++k) - { - for(auto j = 0; j < 4 && left_over; ++j, --left_over) - { - *(vector_sum_col + k * 4 + j) = sum_col[k][j]; - } - } - } - }, - inb, out); -} - -void NEGEMMLowpMatrixBReductionKernel::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); - - switch(_input->info()->data_type()) - { - case DataType::QASYMM8: - run_internal<uint8_t>(window, info); - break; - case DataType::QASYMM8_SIGNED: - case DataType::QSYMM8: - case DataType::QSYMM8_PER_CHANNEL: - run_internal<int8_t>(window, info); - break; - default: - ARM_COMPUTE_ERROR("Unsupported data type"); - } -} -} // namespace arm_compute diff --git a/src/core/NEON/kernels/NEGEMMLowpReductionKernel.h b/src/core/NEON/kernels/NEGEMMLowpReductionKernel.h deleted file mode 100644 index 9be618d656..0000000000 --- a/src/core/NEON/kernels/NEGEMMLowpReductionKernel.h +++ /dev/null @@ -1,196 +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_NEGEMMLOWREDUCTIONKERNEL_H -#define ARM_COMPUTE_NEGEMMLOWREDUCTIONKERNEL_H - -#include "src/core/NEON/INEKernel.h" - -namespace arm_compute -{ -// Forward declarations -class ITensor; -struct GEMMLowpReductionKernelInfo; - -/** Common interface for all reduction kernels */ -class INEGEMMLowpReductionKernel : public INEKernel -{ -public: - /** Constructor */ - INEGEMMLowpReductionKernel(); - /** Prevent instances of this class from being copied (As this class contains pointers)*/ - INEGEMMLowpReductionKernel(const INEGEMMLowpReductionKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers)*/ - INEGEMMLowpReductionKernel &operator=(const INEGEMMLowpReductionKernel &) = delete; - /** Allow instances of this class to be moved */ - INEGEMMLowpReductionKernel(INEGEMMLowpReductionKernel &&) = default; - /** Allow instances of this class to be moved */ - INEGEMMLowpReductionKernel &operator=(INEGEMMLowpReductionKernel &&) = default; - /** Default destructor */ - virtual ~INEGEMMLowpReductionKernel() = default; - - /** Initialise the kernel's input and output. - * - * @param[in] input Input tensor. Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8/QSYMM8_PER_CHANNEL - * @param[out] output Output row-vector of sums of all the entries in each row/col of input tensor. Data type supported: S32 - * @param[in] info Kernel metadata: - * - k Number of matrix columns/rows depending on the type of reduction. - * - is_reshaped True if the matrix has been reshaped. - * - scalar Scalar value to multiply each reduced column/row by. - * - mul_byscalar True if each reduced column/row must be multiplied by a scalar value. - */ - virtual void configure(const ITensor *input, ITensor *output, const GEMMLowpReductionKernelInfo &info) = 0; - -protected: - const ITensor *_input; - ITensor *_output; - int32_t _k; - int32_t _scalar; - bool _mul_by_scalar; -}; - -/** Kernel used to compute the row-vectors of sums of all the entries in each row of Matrix A. - * - * @note This stage is needed to handle the offset of matrix product - * https://github.com/google/gemmlowp/blob/master/doc/low-precision.md - */ -class NEGEMMLowpMatrixAReductionKernel : public INEGEMMLowpReductionKernel -{ -public: - const char *name() const override - { - return "NEGEMMLowpMatrixAReductionKernel"; - } - /** Default constructor */ - NEGEMMLowpMatrixAReductionKernel() = default; - /** Prevent instances of this class from being copied */ - NEGEMMLowpMatrixAReductionKernel(const NEGEMMLowpMatrixAReductionKernel &) = delete; - /** Prevent instances of this class from being copied */ - NEGEMMLowpMatrixAReductionKernel &operator=(const NEGEMMLowpMatrixAReductionKernel &) = delete; - /** Allow instances of this class to be moved */ - NEGEMMLowpMatrixAReductionKernel(NEGEMMLowpMatrixAReductionKernel &&) = default; - /** Allow instances of this class to be moved */ - NEGEMMLowpMatrixAReductionKernel &operator=(NEGEMMLowpMatrixAReductionKernel &&) = default; - /** Default destructor */ - ~NEGEMMLowpMatrixAReductionKernel() = default; - /** Initialise the kernel's input and output. - * - * @param[in] mtx_a Input tensor. Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8/QSYMM8_PER_CHANNEL - * @param[out] vector_sum_row Output row-vector of sums of all the entries in each row of mtx_a. Data type supported: S32 - * @param[in] info Kernel metadata: - * - k (num_mtx_a_cols) Number of matrix A columns - * - is_reshaped (is_interleaved4x4) True if the matrix A has been interleaved4x4 - * - scalar Scalar value to multiply each reduced row by. - * - mul_byscalar True if each reduced column must be multiplied by a scalar value. - */ - void configure(const ITensor *mtx_a, ITensor *vector_sum_row, const GEMMLowpReductionKernelInfo &info) override; - /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMLowpMatrixAReductionKernel - * - * @param[in] mtx_a Input tensor. Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8/QSYMM8_PER_CHANNEL - * @param[in] vector_sum_row Output row-vector of sums of all the entries in each row of mtx_a. Data type supported: S32 - * @param[in] info Kernel metadata: - * - k (num_mtx_a_cols) Number of matrix A columns - * - is_reshaped (is_interleaved4x4) True if the matrix A has been interleaved4x4 - * - scalar Scalar value to multiply each reduced row by. - * - mul_byscalar True if each reduced column must be multiplied by a scalar value. - * - * @return a status - */ - static Status validate(const ITensorInfo *mtx_a, const ITensorInfo *vector_sum_row, const GEMMLowpReductionKernelInfo &info); - - // Inherited methods overridden: - void run(const Window &window, const ThreadInfo &info) override; - -private: - /** Execution of the reduction kernel specialized on the input type - * - * @param[in] window Execution window - */ - template <typename T> - void run_internal(const Window &window); -}; - -/** Kernel used to compute the row-vectors of sums of all the entries in each column of Matrix B. - * - * @note This stage is needed to handle the offset of matrix product - * https://github.com/google/gemmlowp/blob/master/doc/low-precision.md - */ -class NEGEMMLowpMatrixBReductionKernel : public INEGEMMLowpReductionKernel -{ -public: - const char *name() const override - { - return "NEGEMMLowpMatrixBReductionKernel"; - } - /** Default constructor */ - NEGEMMLowpMatrixBReductionKernel() = default; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - NEGEMMLowpMatrixBReductionKernel(const NEGEMMLowpMatrixBReductionKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - NEGEMMLowpMatrixBReductionKernel &operator=(const NEGEMMLowpMatrixBReductionKernel &) = delete; - /** Allow instances of this class to be moved */ - NEGEMMLowpMatrixBReductionKernel(NEGEMMLowpMatrixBReductionKernel &&) = default; - /** Allow instances of this class to be moved */ - NEGEMMLowpMatrixBReductionKernel &operator=(NEGEMMLowpMatrixBReductionKernel &&) = default; - /** Default destructor */ - ~NEGEMMLowpMatrixBReductionKernel() = default; - /** Initialise the kernel's input and output. - * - * @param[in] mtx_b Input tensor. Data type supported: Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8/QSYMM8_PER_CHANNEL - * @param[out] vector_sum_col Output row-vector of sums of all the entries in each column of mtx_b. Data type supported: S32 - * @param[in] info Kernel metadata: - * - k (num_mtx_b_rows) Number of matrix B rows. - * - is_reshaped (is_transposed1xW) True if the input tensor is transposed 1xW. - * - scalar Scalar value to multiply each reduced row by. - * - mul_byscalar True if each reduced row must be multiplied by a scalar value. - */ - void configure(const ITensor *mtx_b, ITensor *vector_sum_col, const GEMMLowpReductionKernelInfo &info) override; - /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMLowpMatrixBReductionKernel - * - * @param[in] mtx_b Input tensor. Data type supported: Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8/QSYMM8_PER_CHANNEL - * @param[in] vector_sum_col Output row-vector of sums of all the entries in each column of mtx_b. Data type supported: S32 - * @param[in] info Kernel metadata: - * - k (num_mtx_b_rows) Number of matrix B rows. - * - is_reshaped (is_transposed1xW) True if the input tensor is transposed 1xW. - * - scalar Scalar value to multiply each reduced row by. - * - mul_byscalar True if each reduced row must be multiplied by a scalar value. - * - * @return a status - */ - static Status validate(const ITensorInfo *mtx_b, const ITensorInfo *vector_sum_col, const GEMMLowpReductionKernelInfo &info); - - // Inherited methods overridden: - void run(const Window &window, const ThreadInfo &info) override; - -private: - /** Execution of the reduction kernel specialized on the input type - * - * @param[in] window Execution window - * @param[in] info Thread-related information - */ - template <typename T> - void run_internal(const Window &window, const ThreadInfo &info); -}; -} // namespace arm_compute - -#endif /* ARM_COMPUTE_NEGEMMLOWREDUCTIONKERNEL_H */ |