| /* |
| * Copyright (c) 2017-2018 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/NEGEMMLowpOffsetContributionKernel.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/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> |
| |
| using namespace arm_compute; |
| |
| namespace arm_compute |
| { |
| class Coordinates; |
| } // namespace arm_compute |
| |
| namespace |
| { |
| Status validate_arguments(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, |
| int32_t a_offset, int32_t b_offset) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(mm_result, 1, DataType::S32); |
| |
| // If a_offset == 0, vector_sum_col can be a nullptr |
| if(a_offset != 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_col, 1, DataType::S32); |
| ARM_COMPUTE_RETURN_ERROR_ON(vector_sum_col->dimension(0) != mm_result->dimension(0)); |
| } |
| |
| // If b_offset == 0, vector_sum_row can be a nullptr |
| if(b_offset != 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_row, 1, DataType::S32); |
| |
| // Check if input is a 3D reinterpretation |
| const bool reinterpret_as_3d = mm_result->num_dimensions() > 1 && mm_result->tensor_shape().y() != vector_sum_row->tensor_shape().x(); |
| |
| // Validate input |
| ARM_COMPUTE_RETURN_ERROR_ON(reinterpret_as_3d && vector_sum_row->dimension(0) != (mm_result->dimension(1) * mm_result->dimension(2))); |
| ARM_COMPUTE_RETURN_ERROR_ON(!reinterpret_as_3d && vector_sum_row->dimension(0) != mm_result->dimension(1)); |
| |
| TensorShape output_shape = mm_result->tensor_shape(); |
| if(output_shape.num_dimensions() > 1) |
| { |
| const unsigned int output_batch_idx = reinterpret_as_3d ? 3 : 2; |
| |
| TensorShape vector_sum_row_shape = vector_sum_row->tensor_shape(); |
| vector_sum_row_shape.collapse_from(1); |
| output_shape.collapse_from(output_batch_idx); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_row_shape[1] != output_shape[output_batch_idx], |
| "mm_result tensor must have the same number of batches of output tensor"); |
| |
| if(a_offset != 0) |
| { |
| TensorShape vector_sum_col_shape = vector_sum_col->tensor_shape(); |
| vector_sum_col_shape.collapse_from(1); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_col_shape[1] != 1 && vector_sum_col_shape[1] != vector_sum_row_shape[1], |
| "vector_sum_col tensor must have the same number of batches of vector_sum_row_shape or the number of batches must be set to 1"); |
| } |
| } |
| } |
| |
| return Status{}; |
| } |
| |
| std::pair<Status, Window> validate_and_configure_window(ITensorInfo *mm_result, ITensorInfo *vector_sum_col, ITensorInfo *vector_sum_row, |
| int32_t a_offset, int32_t b_offset) |
| { |
| constexpr unsigned int num_elems_processed_per_iteration = 16; |
| bool window_changed = false; |
| |
| // Configure kernel window |
| Window win = calculate_max_window(*mm_result, Steps(num_elems_processed_per_iteration)); |
| |
| AccessWindowHorizontal mm_result_access(mm_result, 0, num_elems_processed_per_iteration); |
| window_changed = window_changed || update_window_and_padding(win, |
| mm_result_access); |
| |
| if(a_offset != 0) |
| { |
| AccessWindowHorizontal vector_sum_col_access(vector_sum_col, 0, num_elems_processed_per_iteration); |
| window_changed = window_changed || update_window_and_padding(win, |
| vector_sum_col_access); |
| } |
| if(b_offset != 0) |
| { |
| AccessWindowStatic vector_sum_row_access(vector_sum_row, 0, 0, vector_sum_row->dimension(0), 0); // NOLINT |
| window_changed = window_changed || update_window_and_padding(win, |
| vector_sum_row_access); |
| } |
| |
| Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; |
| return std::make_pair(err, win); |
| } |
| |
| template <bool is_gemm3d> |
| void run_offset_contribution(const Window &window, |
| ITensor *mm_result, const ITensor *vector_sum_col, const ITensor *vector_sum_row, |
| int32_t a_offset, int32_t b_offset, int32_t k_offset, bool slide_vector_sum_col) |
| { |
| Window collapsed_window = window.collapse_if_possible(window, Window::DimZ); |
| |
| const int height_input = is_gemm3d ? mm_result->info()->dimension(1) : 0; |
| const int depth_input = is_gemm3d ? mm_result->info()->dimension(2) : 1; |
| |
| if((a_offset != 0) && (b_offset != 0) && (vector_sum_col != nullptr) && (vector_sum_row != nullptr)) // true, true |
| { |
| // Set window for vector_sum_col |
| Window win_vector_sum_col(collapsed_window); |
| win_vector_sum_col.set(Window::DimY, Window::Dimension(0, 0, 0)); |
| win_vector_sum_col.set(Window::DimZ, Window::Dimension(0, 0, 0)); |
| |
| // Set window for vector_sum_row |
| Window win_vector_sum_row(collapsed_window); |
| win_vector_sum_row.set(Window::DimX, Window::Dimension(0, 0, 0)); |
| win_vector_sum_row.set(Window::DimY, Window::Dimension(0, 0, 0)); |
| win_vector_sum_row.set(Window::DimZ, Window::Dimension(0, 0, 0)); |
| |
| Iterator vector_sum_col_it(vector_sum_col, win_vector_sum_col); |
| Iterator vector_sum_row_it(vector_sum_row, win_vector_sum_row); |
| Iterator mm_result_it(mm_result, window); |
| |
| const size_t sum_row_stride_y = vector_sum_row->info()->strides_in_bytes().y(); |
| |
| // Offset in case vector_sum_col is batched |
| const int vector_sum_col_batch_offset = slide_vector_sum_col ? vector_sum_col->info()->strides_in_bytes().z() : 0; |
| |
| execute_window_loop(collapsed_window, [&](const Coordinates & id) |
| { |
| const int batch_id = id.z() / depth_input; |
| const auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_id * vector_sum_col_batch_offset); |
| |
| // Compute the leftover term due to a_offset. |
| int32x4x4_t a_offset_term_s32 = |
| { |
| { |
| vld1q_s32(vector_sum_col_ptr + 0), |
| vld1q_s32(vector_sum_col_ptr + 4), |
| vld1q_s32(vector_sum_col_ptr + 8), |
| vld1q_s32(vector_sum_col_ptr + 12) |
| } |
| }; |
| |
| a_offset_term_s32.val[0] = vmulq_n_s32(a_offset_term_s32.val[0], a_offset); |
| a_offset_term_s32.val[1] = vmulq_n_s32(a_offset_term_s32.val[1], a_offset); |
| a_offset_term_s32.val[2] = vmulq_n_s32(a_offset_term_s32.val[2], a_offset); |
| a_offset_term_s32.val[3] = vmulq_n_s32(a_offset_term_s32.val[3], a_offset); |
| |
| // Compute the leftover term due to b_offset. |
| int32x4_t b_offset_term_s32 = vld1q_dup_s32(reinterpret_cast<const int32_t *>(vector_sum_row_it.ptr() + batch_id * sum_row_stride_y) + id.y() |
| + (id.z() % depth_input) * height_input); |
| b_offset_term_s32 = vmulq_n_s32(b_offset_term_s32, b_offset); |
| |
| // Add a_offset_term_s32 and b_offset_term_s32 |
| int32x4x4_t offset_term_s32 = |
| { |
| { |
| vdupq_n_s32(k_offset), |
| vdupq_n_s32(k_offset), |
| vdupq_n_s32(k_offset), |
| vdupq_n_s32(k_offset) |
| } |
| }; |
| |
| offset_term_s32.val[0] = vaddq_s32(offset_term_s32.val[0], vaddq_s32(a_offset_term_s32.val[0], b_offset_term_s32)); |
| offset_term_s32.val[1] = vaddq_s32(offset_term_s32.val[1], vaddq_s32(a_offset_term_s32.val[1], b_offset_term_s32)); |
| offset_term_s32.val[2] = vaddq_s32(offset_term_s32.val[2], vaddq_s32(a_offset_term_s32.val[2], b_offset_term_s32)); |
| offset_term_s32.val[3] = vaddq_s32(offset_term_s32.val[3], vaddq_s32(a_offset_term_s32.val[3], b_offset_term_s32)); |
| |
| int32x4x4_t in_s32 = |
| { |
| { |
| vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + 0), |
| vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + 4), |
| vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + 8), |
| vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + 12) |
| } |
| }; |
| |
| // Add the offset terms to GEMM's result |
| in_s32.val[0] = vaddq_s32(in_s32.val[0], offset_term_s32.val[0]); |
| in_s32.val[1] = vaddq_s32(in_s32.val[1], offset_term_s32.val[1]); |
| in_s32.val[2] = vaddq_s32(in_s32.val[2], offset_term_s32.val[2]); |
| in_s32.val[3] = vaddq_s32(in_s32.val[3], offset_term_s32.val[3]); |
| |
| // Store the result with the offset contribution |
| vst1q_s32(reinterpret_cast<int32_t *>(mm_result_it.ptr()) + 0, in_s32.val[0]); |
| vst1q_s32(reinterpret_cast<int32_t *>(mm_result_it.ptr()) + 4, in_s32.val[1]); |
| vst1q_s32(reinterpret_cast<int32_t *>(mm_result_it.ptr()) + 8, in_s32.val[2]); |
| vst1q_s32(reinterpret_cast<int32_t *>(mm_result_it.ptr()) + 12, in_s32.val[3]); |
| }, |
| vector_sum_col_it, vector_sum_row_it, mm_result_it); |
| } |
| else if((a_offset == 0) && (b_offset != 0) && (vector_sum_row != nullptr)) // false, true |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(vector_sum_row); |
| |
| // Set window for vector_sum_row |
| Window win_vector_sum_row(collapsed_window); |
| win_vector_sum_row.set(Window::DimX, Window::Dimension(0, 0, 0)); |
| win_vector_sum_row.set(Window::DimY, Window::Dimension(0, 0, 0)); |
| win_vector_sum_row.set(Window::DimZ, Window::Dimension(0, 0, 0)); |
| |
| Iterator vector_sum_row_it(vector_sum_row, win_vector_sum_row); |
| Iterator mm_result_it(mm_result, window); |
| |
| const size_t sum_row_stride_y = vector_sum_row->info()->strides_in_bytes().y(); |
| |
| execute_window_loop(window, [&](const Coordinates & id) |
| { |
| const int batch_id = id.z() / depth_input; |
| |
| // Compute the leftover term due to b_offset. |
| int32x4_t b_offset_term_s32 = vld1q_dup_s32(reinterpret_cast<const int32_t *>(vector_sum_row_it.ptr() + batch_id * sum_row_stride_y) + id.y() |
| + (id.z() % depth_input) * height_input); |
| b_offset_term_s32 = vmulq_n_s32(b_offset_term_s32, b_offset); |
| |
| int32x4x4_t in_s32 = |
| { |
| { |
| vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + 0), |
| vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + 4), |
| vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + 8), |
| vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + 12) |
| } |
| }; |
| |
| // Add the offset terms to GEMM's result |
| in_s32.val[0] = vaddq_s32(in_s32.val[0], b_offset_term_s32); |
| in_s32.val[1] = vaddq_s32(in_s32.val[1], b_offset_term_s32); |
| in_s32.val[2] = vaddq_s32(in_s32.val[2], b_offset_term_s32); |
| in_s32.val[3] = vaddq_s32(in_s32.val[3], b_offset_term_s32); |
| |
| // Store the result with the offset contribution |
| vst1q_s32(reinterpret_cast<int32_t *>(mm_result_it.ptr()) + 0, in_s32.val[0]); |
| vst1q_s32(reinterpret_cast<int32_t *>(mm_result_it.ptr()) + 4, in_s32.val[1]); |
| vst1q_s32(reinterpret_cast<int32_t *>(mm_result_it.ptr()) + 8, in_s32.val[2]); |
| vst1q_s32(reinterpret_cast<int32_t *>(mm_result_it.ptr()) + 12, in_s32.val[3]); |
| }, |
| vector_sum_row_it, mm_result_it); |
| } |
| else if((a_offset != 0) && (b_offset == 0) && (vector_sum_col != nullptr)) // true, false |
| { |
| // Set window for vector_sum_col |
| Window win_vector_sum_col(collapsed_window); |
| win_vector_sum_col.set(Window::DimY, Window::Dimension(0, 0, 0)); |
| win_vector_sum_col.set(Window::DimZ, Window::Dimension(0, 0, 0)); |
| |
| Iterator vector_sum_col_it(vector_sum_col, win_vector_sum_col); |
| Iterator mm_result_it(mm_result, window); |
| |
| // Offset in case vector_sum_col is batched |
| const int vector_sum_col_batch_offset = slide_vector_sum_col ? vector_sum_col->info()->strides_in_bytes().z() : 0; |
| |
| execute_window_loop(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); |
| |
| // Compute the leftover term due to a_offset. |
| int32x4x4_t a_offset_term_s32 = |
| { |
| { |
| vld1q_s32(vector_sum_col_ptr + 0), |
| vld1q_s32(vector_sum_col_ptr + 4), |
| vld1q_s32(vector_sum_col_ptr + 8), |
| vld1q_s32(vector_sum_col_ptr + 12) |
| } |
| }; |
| |
| a_offset_term_s32.val[0] = vmulq_n_s32(a_offset_term_s32.val[0], a_offset); |
| a_offset_term_s32.val[1] = vmulq_n_s32(a_offset_term_s32.val[1], a_offset); |
| a_offset_term_s32.val[2] = vmulq_n_s32(a_offset_term_s32.val[2], a_offset); |
| a_offset_term_s32.val[3] = vmulq_n_s32(a_offset_term_s32.val[3], a_offset); |
| |
| int32x4x4_t in_s32 = |
| { |
| { |
| vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + 0), |
| vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + 4), |
| vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + 8), |
| vld1q_s32(reinterpret_cast<const int32_t *>(mm_result_it.ptr()) + 12) |
| } |
| }; |
| |
| // Add the offset terms to GEMM's result |
| in_s32.val[0] = vaddq_s32(in_s32.val[0], a_offset_term_s32.val[0]); |
| in_s32.val[1] = vaddq_s32(in_s32.val[1], a_offset_term_s32.val[1]); |
| in_s32.val[2] = vaddq_s32(in_s32.val[2], a_offset_term_s32.val[2]); |
| in_s32.val[3] = vaddq_s32(in_s32.val[3], a_offset_term_s32.val[3]); |
| |
| // Store the result with the offset contribution |
| vst1q_s32(reinterpret_cast<int32_t *>(mm_result_it.ptr()) + 0, in_s32.val[0]); |
| vst1q_s32(reinterpret_cast<int32_t *>(mm_result_it.ptr()) + 4, in_s32.val[1]); |
| vst1q_s32(reinterpret_cast<int32_t *>(mm_result_it.ptr()) + 8, in_s32.val[2]); |
| vst1q_s32(reinterpret_cast<int32_t *>(mm_result_it.ptr()) + 12, in_s32.val[3]); |
| }, |
| vector_sum_col_it, mm_result_it); |
| } |
| else // false, false |
| { |
| // No offset contribution from matrix A and matrix B |
| return; |
| } |
| } |
| } // namespace |
| |
| NEGEMMLowpOffsetContributionKernel::NEGEMMLowpOffsetContributionKernel() |
| : _vector_sum_col(nullptr), _vector_sum_row(nullptr), _mm_result(nullptr), _a_offset(0), _b_offset(0), _k_offset(0), _slide_vector_sum_col(true) |
| { |
| } |
| |
| void NEGEMMLowpOffsetContributionKernel::configure(ITensor *mm_result, const ITensor *vector_sum_col, const ITensor *vector_sum_row, int32_t k, int32_t a_offset, int32_t b_offset) |
| { |
| // Perform validate step |
| ARM_COMPUTE_ERROR_ON_NULLPTR(mm_result); |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(mm_result->info(), |
| vector_sum_col != nullptr ? vector_sum_col->info() : nullptr, // NOLINT |
| vector_sum_row != nullptr ? vector_sum_row->info() : nullptr, // NOLINT |
| a_offset, b_offset)); // NOLINT |
| |
| _vector_sum_col = vector_sum_col; |
| _vector_sum_row = vector_sum_row; |
| _mm_result = mm_result; |
| _a_offset = a_offset; |
| _b_offset = b_offset; |
| _k_offset = a_offset * b_offset * k; |
| |
| // If a_offset == 0, vector_sum_col can be a nullptr |
| if(a_offset != 0) |
| { |
| // Check if vector_sum_col_shape should be slidden or not |
| // Don't slide vector_sum_col_shape along the y dimension if vector_sum_col_shape has just 1 dimension and vector_sum_row_shape more than 1 |
| // This scenario can happen when the the matrix multiplication is used to perform a convolution operation |
| _slide_vector_sum_col = vector_sum_col->info()->tensor_shape().num_dimensions() > 1; |
| } |
| |
| // Configure kernel window |
| auto win_config = validate_and_configure_window(mm_result->info(), |
| vector_sum_col != nullptr ? vector_sum_col->info() : nullptr, // NOLINT |
| vector_sum_row != nullptr ? vector_sum_row->info() : nullptr, // NOLINT |
| a_offset, b_offset); |
| ARM_COMPUTE_ERROR_THROW_ON(win_config.first); |
| INEKernel::configure(win_config.second); |
| } |
| |
| Status NEGEMMLowpOffsetContributionKernel::validate(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, |
| int32_t a_offset, int32_t b_offset) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(mm_result, vector_sum_col, vector_sum_row, a_offset, b_offset)); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(mm_result->clone().get(), |
| vector_sum_col != nullptr ? vector_sum_col->clone().get() : nullptr, |
| vector_sum_row != nullptr ? vector_sum_row->clone().get() : nullptr, |
| a_offset, b_offset) |
| .first); // NOLINT |
| |
| return Status{}; |
| } |
| |
| void NEGEMMLowpOffsetContributionKernel::run(const Window &window, const ThreadInfo &info) |
| { |
| ARM_COMPUTE_UNUSED(info); |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); |
| |
| // Check if input is a 3D reinterpretation |
| const bool reinterpret_as_3d = _vector_sum_row != nullptr |
| && _mm_result->info()->num_dimensions() > 1 |
| && _mm_result->info()->tensor_shape().y() != _vector_sum_row->info()->tensor_shape().x(); |
| |
| if(reinterpret_as_3d) |
| { |
| run_offset_contribution<true>(window, _mm_result, _vector_sum_col, _vector_sum_row, _a_offset, _b_offset, _k_offset, _slide_vector_sum_col); |
| } |
| else |
| { |
| run_offset_contribution<false>(window, _mm_result, _vector_sum_col, _vector_sum_row, _a_offset, _b_offset, _k_offset, _slide_vector_sum_col); |
| } |
| } |