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Diffstat (limited to 'src/cpu/kernels/CpuGemmLowpOffsetContributionOutputStageKernel.cpp')
| -rw-r--r-- | src/cpu/kernels/CpuGemmLowpOffsetContributionOutputStageKernel.cpp | 1036 |
1 files changed, 1036 insertions, 0 deletions
diff --git a/src/cpu/kernels/CpuGemmLowpOffsetContributionOutputStageKernel.cpp b/src/cpu/kernels/CpuGemmLowpOffsetContributionOutputStageKernel.cpp new file mode 100644 index 0000000000..3c113f2828 --- /dev/null +++ b/src/cpu/kernels/CpuGemmLowpOffsetContributionOutputStageKernel.cpp @@ -0,0 +1,1036 @@ +/* + * Copyright (c) 2019-2021, 2023-2024 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 = 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::set_a_offset(int32_t a_offset) +{ + _a_offset = a_offset; +} + +void CpuGemmLowpOffsetContributionOutputStageKernel::set_b_offset(int32_t b_offset) +{ + _b_offset = b_offset; +} + +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; + + auto k_offset = _a_offset * _b_offset * _k; + 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 |