| /* |
| * Copyright (c) 2019 ARM Limited. |
| * |
| * SPDX-License-Identifier: MIT |
| * |
| * Permission is hereby granted, free of charge, to any person obtaining a copy |
| * of this software and associated documentation files (the "Software"), to |
| * deal in the Software without restriction, including without limitation the |
| * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| * sell copies of the Software, and to permit persons to whom the Software is |
| * furnished to do so, subject to the following conditions: |
| * |
| * The above copyright notice and this permission notice shall be included in all |
| * copies or substantial portions of the Software. |
| * |
| * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| * SOFTWARE. |
| */ |
| #include "arm_compute/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.h" |
| |
| #include "arm_compute/core/AccessWindowStatic.h" |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/NEON/NEAsymm.h" |
| #include "arm_compute/core/NEON/wrapper/wrapper.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/Window.h" |
| |
| #include <arm_neon.h> |
| #include <cstddef> |
| #include <cstdint> |
| #include <map> |
| |
| namespace arm_compute |
| { |
| class Coordinates; |
| |
| 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 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) |
| } |
| }; |
| } |
| |
| template <bool is_bounded_relu> |
| inline uint8x16_t finalize_quantization_floating_point(int32x4x4_t &in_s32, int32x4_t result_shift_s32, uint8x16_t min_u8, uint8x16_t max_u8) |
| { |
| const static int32x4_t zero_s32 = vdupq_n_s32(0); |
| |
| // Shift final result (negative value shift right) |
| in_s32.val[0] = vshlq_s32(in_s32.val[0], result_shift_s32); |
| in_s32.val[1] = vshlq_s32(in_s32.val[1], result_shift_s32); |
| in_s32.val[2] = vshlq_s32(in_s32.val[2], result_shift_s32); |
| in_s32.val[3] = vshlq_s32(in_s32.val[3], result_shift_s32); |
| |
| // Saturate negative values |
| in_s32.val[0] = vmaxq_s32(in_s32.val[0], zero_s32); |
| in_s32.val[1] = vmaxq_s32(in_s32.val[1], zero_s32); |
| in_s32.val[2] = vmaxq_s32(in_s32.val[2], zero_s32); |
| in_s32.val[3] = vmaxq_s32(in_s32.val[3], zero_s32); |
| |
| // Convert S32 to S16 |
| const int16x8x2_t in_s16 = |
| { |
| { |
| vcombine_s16(vqmovn_s32(in_s32.val[0]), vqmovn_s32(in_s32.val[1])), |
| vcombine_s16(vqmovn_s32(in_s32.val[2]), vqmovn_s32(in_s32.val[3])) |
| } |
| }; |
| |
| // Convert S16 to U8 |
| uint8x16_t out_u8 = vcombine_u8(vqmovun_s16(in_s16.val[0]), vqmovun_s16(in_s16.val[1])); |
| |
| if(is_bounded_relu) |
| { |
| out_u8 = vmaxq_u8(out_u8, min_u8); |
| out_u8 = vminq_u8(out_u8, max_u8); |
| } |
| |
| return out_u8; |
| } |
| |
| 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; |
| } |
| |
| 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) |
| } |
| }; |
| } |
| |
| template <bool has_a_offset, bool has_b_offset, bool has_bias, bool is_bounded_relu, bool is_fixed_point> |
| 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, uint8x16_t min_u8, uint8x16_t max_u8, |
| int32_t a_offset, int32_t b_offset, int32_t k_offset, |
| GEMMLowpOutputStageInfo output_stage, int window_step_x, int window_start_x, int window_end_x) |
| { |
| 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, output_stage.gemmlowp_multiplier); |
| } |
| |
| if(is_fixed_point) |
| { |
| vst1q_u8(out_it.ptr() + x, finalize_quantization<is_bounded_relu>(in_s32, output_stage.gemmlowp_multiplier, output_stage.gemmlowp_shift, result_offset_s32, min_u8, max_u8)); |
| } |
| else |
| { |
| vst1q_u8(out_it.ptr() + x, finalize_quantization_floating_point<is_bounded_relu>(in_s32, result_shift_s32, min_u8, max_u8)); |
| } |
| } |
| // 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<is_bounded_relu>(in_value, output_stage.gemmlowp_multiplier, output_stage.gemmlowp_shift, |
| output_stage.gemmlowp_offset, static_cast<uint8_t>(output_stage.gemmlowp_min_bound), static_cast<uint8_t>(output_stage.gemmlowp_max_bound)); |
| } |
| else |
| { |
| // Finalize quantization |
| in_value = (in_value * output_stage.gemmlowp_multiplier) >> output_stage.gemmlowp_shift; |
| |
| // Bound and store the result |
| if(is_bounded_relu) |
| { |
| in_value = static_cast<uint8_t>(std::max<int32_t>(output_stage.gemmlowp_min_bound, std::min<int32_t>(output_stage.gemmlowp_max_bound, in_value))); |
| } |
| *(out_it.ptr() + x) = static_cast<uint8_t>(std::max<int32_t>(0, std::min<int32_t>(255, in_value))); |
| } |
| } |
| } |
| |
| template <bool is_gemm3d, bool is_bounded_relu, bool is_fixed_point> |
| 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) |
| { |
| 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 int32x4_t result_offset_s32 = vdupq_n_s32(output_stage.gemmlowp_offset); |
| const int32x4_t result_shift_s32 = vdupq_n_s32(is_fixed_point ? output_stage.gemmlowp_shift : -output_stage.gemmlowp_shift); |
| const uint8x16_t min_u8 = vdupq_n_u8(static_cast<uint8_t>(output_stage.gemmlowp_min_bound)); |
| const uint8x16_t max_u8 = vdupq_n_u8(static_cast<uint8_t>(output_stage.gemmlowp_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) && (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<true, true, true, is_bounded_relu, is_fixed_point>(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_u8, max_u8, a_offset, b_offset, k_offset, |
| output_stage, window_step_x, window_start_x, window_end_x); |
| }, |
| 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<true, true, false, is_bounded_relu, is_fixed_point>(vector_sum_col_ptr, vector_sum_row_ptr, nullptr, mm_result_it, out_it, |
| result_offset_s32, result_shift_s32, min_u8, max_u8, a_offset, b_offset, k_offset, |
| output_stage, window_step_x, window_start_x, window_end_x); |
| }, |
| 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<false, true, true, is_bounded_relu, is_fixed_point>(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_u8, max_u8, a_offset, b_offset, k_offset, |
| output_stage, window_step_x, window_start_x, window_end_x); |
| }, |
| 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<false, true, false, is_bounded_relu, is_fixed_point>(nullptr, vector_sum_row_ptr, nullptr, mm_result_it, out_it, |
| result_offset_s32, result_shift_s32, min_u8, max_u8, a_offset, b_offset, k_offset, |
| output_stage, window_step_x, window_start_x, window_end_x); |
| }, |
| 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<true, false, true, is_bounded_relu, is_fixed_point>(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_u8, max_u8, a_offset, b_offset, k_offset, |
| output_stage, window_step_x, window_start_x, window_end_x); |
| }, |
| 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<true, false, false, is_bounded_relu, is_fixed_point>(vector_sum_col_ptr, nullptr, nullptr, mm_result_it, out_it, |
| result_offset_s32, result_shift_s32, min_u8, max_u8, a_offset, b_offset, k_offset, |
| output_stage, window_step_x, window_start_x, window_end_x); |
| }, |
| 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<false, false, true, is_bounded_relu, is_fixed_point>(nullptr, nullptr, reinterpret_cast<const int32_t *>(bias_it.ptr()), mm_result_it, out_it, |
| result_offset_s32, result_shift_s32, min_u8, max_u8, a_offset, b_offset, k_offset, |
| output_stage, window_step_x, window_start_x, window_end_x); |
| }, |
| bias_it, mm_result_it, out_it); |
| } |
| else |
| { |
| execute_window_loop(collapsed_window, [&](const Coordinates &) |
| { |
| run_offset_contribution_output_stage_window<false, false, false, is_bounded_relu, is_fixed_point>(nullptr, nullptr, nullptr, mm_result_it, out_it, |
| result_offset_s32, result_shift_s32, min_u8, max_u8, a_offset, b_offset, k_offset, |
| output_stage, window_step_x, window_start_x, window_end_x); |
| }, |
| 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); |
| ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_max_bound > 255); |
| ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_min_bound < 0 || 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); |
| 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 |
| Coordinates coord; |
| coord.set_num_dimensions(output->num_dimensions()); |
| output->set_valid_region(ValidRegion(coord, output->tensor_shape())); |
| |
| return std::make_pair(Status{}, win); |
| } |
| |
| NEGEMMLowpOffsetContributionOutputStageKernel::NEGEMMLowpOffsetContributionOutputStageFunction |
| get_configured_function(const ITensor *mm_result, const ITensor *vector_sum_row, GEMMLowpOutputStageInfo output_stage) |
| { |
| static std::map<uint8_t, NEGEMMLowpOffsetContributionOutputStageKernel::NEGEMMLowpOffsetContributionOutputStageFunction> map_function = |
| { |
| { 0, &run_offset_contribution_output_stage<false, false, false> }, |
| { 1, &run_offset_contribution_output_stage<true, false, false> }, |
| { 2, &run_offset_contribution_output_stage<false, true, false> }, |
| { 3, &run_offset_contribution_output_stage<true, true, false> }, |
| { 4, &run_offset_contribution_output_stage<false, false, true> }, |
| { 5, &run_offset_contribution_output_stage<true, false, true> }, |
| { 6, &run_offset_contribution_output_stage<false, true, true> }, |
| { 7, &run_offset_contribution_output_stage<true, true, true> } |
| }; |
| |
| // 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(); |
| |
| // Check if we need to clamp the result using min and max |
| const bool is_bounded_relu = ((output_stage.gemmlowp_min_bound != output_stage.gemmlowp_max_bound) |
| && !(output_stage.gemmlowp_min_bound == 0 && output_stage.gemmlowp_max_bound == 255)); |
| |
| const bool is_fixed_point = output_stage.type != GEMMLowpOutputStageType::QUANTIZE_DOWN; |
| |
| // key acts as a bitset, setting the first bit on reinterpret_as_3d, |
| // the second on is_bounded_relu, and the third on is_fixed_point. |
| uint8_t key = (reinterpret_as_3d ? 1UL : 0UL) | ((is_bounded_relu ? 1UL : 0UL) << 1) | ((is_fixed_point ? 1UL : 0UL) << 2); |
| return map_function.find(key)->second; |
| } |
| } // namespace |
| |
| NEGEMMLowpOffsetContributionOutputStageKernel::NEGEMMLowpOffsetContributionOutputStageKernel() |
| : _function(nullptr), _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); |
| |
| _function = get_configured_function(mm_result, vector_sum_row, output_stage); |
| } |
| |
| 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); |
| _function(window, _mm_result, _vector_sum_col, _vector_sum_row, _bias, _output, _a_offset, _b_offset, _k_offset, _slide_vector_sum_col, _output_stage); |
| } |
| |
| } // namespace arm_compute |