diff options
Diffstat (limited to 'src/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.cpp')
| -rw-r--r-- | src/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.cpp | 959 |
1 files changed, 0 insertions, 959 deletions
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 |