| /* |
| * Copyright (c) 2019-2021, 2023 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/cpu/kernels/CpuGemmLowpOffsetContributionOutputStageKernel.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 "src/core/NEON/NEAsymm.h" |
| #include "src/core/NEON/wrapper/wrapper.h" |
| |
| #include <arm_neon.h> |
| |
| namespace arm_compute |
| { |
| namespace cpu |
| { |
| namespace kernels |
| { |
| 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 is_vector_sum_col_batched, |
| GEMMLowpOutputStageInfo output_stage, |
| bool is_gemm3d, |
| bool is_bounded_relu, |
| bool is_fixed_point) |
| { |
| // Semantics of XYZW Explained for each tensor |
| // |
| // | Tensor | XYZW when is_gemm3d == false | XYZW when is_gemm3d == true | |
| // ------------------------------------------------------------------------------------------------------------------- |
| // | mm_result | x -> width, y -> height, z -> batch | x -> width, y -> height, z -> depth, w -> batch | |
| // | collapsed window | x -> width, y -> height, z -> batch | x -> width, y -> height, z -> depth * batch | |
| // | vector_sum_row | x -> height, y -> batch | x -> height * depth, y -> batch | |
| // | Vector_sum_col | x -> width, y -> batch | x -> width, y -> batch | |
| |
| 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 in y dimension |
| const int vector_sum_col_stride_batch = |
| is_vector_sum_col_batched ? vector_sum_col->info()->strides_in_bytes().y() : 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_stride_batch); |
| 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_stride_batch); |
| 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 in y dimension |
| const int vector_sum_col_stride_batch = |
| is_vector_sum_col_batched ? vector_sum_col->info()->strides_in_bytes().y() : 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_stride_batch); |
| 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_stride_batch); |
| 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 is_vector_sum_col_batched, |
| 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 in y dimension |
| const int vector_sum_col_stride_batch = |
| is_vector_sum_col_batched ? vector_sum_col->info()->strides_in_bytes().y() : 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_stride_batch); |
| 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_stride_batch); |
| 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)); |
| ARM_COMPUTE_RETURN_ERROR_ON(vector_sum_col->num_dimensions() > 2); |
| } |
| |
| // 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"); |
| } |
| } |
| |
| // Check Tensor Rank of vector_sum_row |
| ARM_COMPUTE_RETURN_ERROR_ON(vector_sum_row->num_dimensions() > 3); |
| } |
| |
| 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{}; |
| } |
| } // namespace |
| |
| void CpuGemmLowpOffsetContributionOutputStageKernel::configure(const ITensorInfo *mm_result, |
| const ITensorInfo *vector_sum_col, |
| const ITensorInfo *vector_sum_row, |
| const ITensorInfo *bias, |
| ITensorInfo *dst, |
| int32_t k, |
| int32_t a_offset, |
| int32_t b_offset, |
| GEMMLowpOutputStageInfo output_stage) |
| { |
| ARM_COMPUTE_UNUSED(vector_sum_row, bias); |
| // Perform validate step |
| ARM_COMPUTE_ERROR_ON_NULLPTR(mm_result, dst); |
| ARM_COMPUTE_ERROR_THROW_ON( |
| validate_arguments(mm_result, vector_sum_col, vector_sum_row, bias, dst, a_offset, b_offset, output_stage)); |
| |
| _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 |
| _is_vector_sum_col_batched = vector_sum_col->tensor_shape().num_dimensions() > 1; |
| } |
| |
| // Output auto inizialitation if not yet initialized |
| auto_init_if_empty(*dst, 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 |
| ICpuKernel::configure(win); |
| } |
| |
| Status CpuGemmLowpOffsetContributionOutputStageKernel::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)); |
| return Status{}; |
| } |
| |
| void CpuGemmLowpOffsetContributionOutputStageKernel::run_op(ITensorPack &tensors, |
| const Window &window, |
| const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICpuKernel::window(), window); |
| |
| auto mm_result = tensors.get_const_tensor(TensorType::ACL_SRC_0); |
| auto vector_sum_col = tensors.get_const_tensor(TensorType::ACL_SRC_1); |
| auto vector_sum_row = tensors.get_const_tensor(TensorType::ACL_SRC_2); |
| auto bias = tensors.get_const_tensor(TensorType::ACL_SRC_3); |
| auto dst = tensors.get_tensor(TensorType::ACL_DST); |
| |
| PixelValue type_min{}; |
| PixelValue type_max{}; |
| std::tie(type_min, type_max) = get_min_max(dst->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 = dst->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, dst, |
| _a_offset, _b_offset, _k_offset, _is_vector_sum_col_batched, |
| _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, dst, _a_offset, _b_offset, _k_offset, |
| _is_vector_sum_col_batched, _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, dst, _a_offset, _b_offset, _k_offset, |
| _is_vector_sum_col_batched, _output_stage, reinterpret_as_3d, is_bounded_relu, is_fixed_point); |
| } |
| } |
| } |
| |
| const char *CpuGemmLowpOffsetContributionOutputStageKernel::name() const |
| { |
| return "CpuGemmLowpOffsetContributionOutputStageKernel"; |
| } |
| } // namespace kernels |
| } // namespace cpu |
| } // namespace arm_compute |