| /* |
| * Copyright (c) 2017-2022,2024 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/CpuGemmLowpOffsetContributionKernel.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 <arm_neon.h> |
| |
| namespace arm_compute |
| { |
| namespace cpu |
| { |
| namespace kernels |
| { |
| 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, DataType::F32); |
| |
| // We run if the offset is nonzero or a sum col has been provided, we need |
| // the second option in case the QuantizationInfo is dynamic |
| if (a_offset != 0 || vector_sum_col != nullptr) |
| { |
| 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)); |
| } |
| |
| // We run if the offset is nonzero or a sum row has been provided, we need |
| // the second option in case the QuantizationInfo is dynamic |
| if (b_offset != 0 || vector_sum_row != nullptr) |
| { |
| 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 (vector_sum_col != nullptr) |
| { |
| 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{}; |
| } |
| |
| void run_offset_contribution_float(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, |
| float scale, |
| bool slide_vector_sum_col, |
| bool is_gemm3d) |
| { |
| Window collapsed_window = window.collapse_if_possible(window, Window::DimZ); |
| collapsed_window.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| 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 int window_start_x = window.x().start(); |
| const int window_end_x = window.x().end(); |
| const int window_step_x = 16; |
| |
| // if vector_sum_col is nullptr then stride_y is 0, else get stride_y |
| const size_t sum_col_stride_y = (vector_sum_col != nullptr) ? (vector_sum_col->info()->strides_in_bytes().y()) : 0; |
| Iterator mm_result_it(mm_result, collapsed_window); |
| |
| 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); |
| |
| 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 size_t batch_offset_col = batch_id * (sum_col_stride_y); |
| auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_offset_col + |
| batch_id * vector_sum_col_batch_offset); |
| auto mm_result_ptr = reinterpret_cast<float *>(mm_result_it.ptr()); |
| |
| // Compute the leftover term due to b_offset. |
| int32_t b_offset_term_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 *= b_offset; |
| |
| const int32x4_t b_offset_term_s32_vec = vdupq_n_s32(b_offset_term_s32); |
| |
| int x = window_start_x; |
| for (; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| // Compute the leftover term due to a_offset. |
| int32x4x4_t a_offset_term_s32 = { |
| {vld1q_s32(vector_sum_col_ptr + x + 0), vld1q_s32(vector_sum_col_ptr + x + 4), |
| vld1q_s32(vector_sum_col_ptr + x + 8), vld1q_s32(vector_sum_col_ptr + x + 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); |
| |
| // 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_vec)); |
| offset_term_s32.val[1] = |
| vaddq_s32(offset_term_s32.val[1], vaddq_s32(a_offset_term_s32.val[1], b_offset_term_s32_vec)); |
| offset_term_s32.val[2] = |
| vaddq_s32(offset_term_s32.val[2], vaddq_s32(a_offset_term_s32.val[2], b_offset_term_s32_vec)); |
| offset_term_s32.val[3] = |
| vaddq_s32(offset_term_s32.val[3], vaddq_s32(a_offset_term_s32.val[3], b_offset_term_s32_vec)); |
| |
| float32x4x4_t in_f32 = {{vld1q_f32(mm_result_ptr + x + 0), vld1q_f32(mm_result_ptr + x + 4), |
| vld1q_f32(mm_result_ptr + x + 8), vld1q_f32(mm_result_ptr + x + 12)}}; |
| |
| // Convert and scale the S32 offsets to match the already scaled GEMM results |
| float32x4x4_t offset_terms_scaled = {{ |
| vmulq_n_f32(vcvtq_f32_s32(offset_term_s32.val[0]), scale), |
| vmulq_n_f32(vcvtq_f32_s32(offset_term_s32.val[1]), scale), |
| vmulq_n_f32(vcvtq_f32_s32(offset_term_s32.val[2]), scale), |
| vmulq_n_f32(vcvtq_f32_s32(offset_term_s32.val[3]), scale), |
| }}; |
| |
| // Add the offset terms to the GEMM result |
| in_f32.val[0] = vaddq_f32(in_f32.val[0], offset_terms_scaled.val[0]); |
| in_f32.val[1] = vaddq_f32(in_f32.val[1], offset_terms_scaled.val[1]); |
| in_f32.val[2] = vaddq_f32(in_f32.val[2], offset_terms_scaled.val[2]); |
| in_f32.val[3] = vaddq_f32(in_f32.val[3], offset_terms_scaled.val[3]); |
| |
| // Store the result with the offset contribution |
| vst1q_f32(mm_result_ptr + x + 0, in_f32.val[0]); |
| vst1q_f32(mm_result_ptr + x + 4, in_f32.val[1]); |
| vst1q_f32(mm_result_ptr + x + 8, in_f32.val[2]); |
| vst1q_f32(mm_result_ptr + x + 12, in_f32.val[3]); |
| } |
| |
| // Left-overs loop |
| for (; x < window_end_x; ++x) |
| { |
| // Compute the leftover term due to a_offset. |
| int32_t a_offset_term_s32 = *(vector_sum_col_ptr + x); |
| |
| a_offset_term_s32 *= a_offset; |
| |
| // Add the offset terms to GEMM's result |
| // Store the result with the offset contribution |
| mm_result_ptr[x] += (k_offset + a_offset_term_s32 + b_offset_term_s32) * scale; |
| } |
| }, |
| 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); |
| |
| const size_t sum_row_stride_y = vector_sum_row->info()->strides_in_bytes().y(); |
| |
| execute_window_loop( |
| collapsed_window, |
| [&](const Coordinates &id) |
| { |
| const int batch_id = id.z() / depth_input; |
| auto mm_result_ptr = reinterpret_cast<float *>(mm_result_it.ptr()); |
| |
| // Compute the leftover term due to b_offset. |
| int32_t row_sum = |
| *(reinterpret_cast<const int32_t *>(vector_sum_row_it.ptr() + batch_id * sum_row_stride_y) + |
| id.y() + (id.z() % depth_input) * height_input); |
| float scaled_b_offset_term_f32 = row_sum * b_offset * scale; |
| |
| const float32x4_t b_offset_term_f32_vec = vdupq_n_f32(scaled_b_offset_term_f32); |
| |
| int x = window_start_x; |
| for (; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| float32x4x4_t in_f32 = {{vld1q_f32(mm_result_ptr + x + 0), vld1q_f32(mm_result_ptr + x + 4), |
| vld1q_f32(mm_result_ptr + x + 8), vld1q_f32(mm_result_ptr + x + 12)}}; |
| |
| // Add the offset terms to GEMM's result |
| in_f32.val[0] = vaddq_f32(in_f32.val[0], b_offset_term_f32_vec); |
| in_f32.val[1] = vaddq_f32(in_f32.val[1], b_offset_term_f32_vec); |
| in_f32.val[2] = vaddq_f32(in_f32.val[2], b_offset_term_f32_vec); |
| in_f32.val[3] = vaddq_f32(in_f32.val[3], b_offset_term_f32_vec); |
| |
| // Store the result with the offset contribution |
| vst1q_f32(mm_result_ptr + x + 0, in_f32.val[0]); |
| vst1q_f32(mm_result_ptr + x + 4, in_f32.val[1]); |
| vst1q_f32(mm_result_ptr + x + 8, in_f32.val[2]); |
| vst1q_f32(mm_result_ptr + x + 12, in_f32.val[3]); |
| } |
| |
| // Left-overs loop |
| for (; x < window_end_x; ++x) |
| { |
| // Add the offset terms to GEMM's result |
| // Store the result with the offset contribution |
| mm_result_ptr[x] += scaled_b_offset_term_f32; |
| } |
| }, |
| 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); |
| |
| // 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 size_t batch_offset_col = |
| batch_id * |
| (sum_col_stride_y); // Value to offset vector_sum_col_ptr to allow for iteration of y values in tensor |
| auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_offset_col + |
| batch_id * vector_sum_col_batch_offset); |
| auto mm_result_ptr = reinterpret_cast<float *>(mm_result_it.ptr()); |
| |
| int x = window_start_x; |
| for (; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| // Compute the leftover term due to a_offset. |
| int32x4x4_t a_offset_term_s32 = { |
| {vld1q_s32(vector_sum_col_ptr + x + 0), vld1q_s32(vector_sum_col_ptr + x + 4), |
| vld1q_s32(vector_sum_col_ptr + x + 8), vld1q_s32(vector_sum_col_ptr + x + 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); |
| |
| float32x4x4_t a_offset_term_scaled = {{ |
| vmulq_n_f32(vcvtq_f32_s32(a_offset_term_s32.val[0]), scale), |
| vmulq_n_f32(vcvtq_f32_s32(a_offset_term_s32.val[1]), scale), |
| vmulq_n_f32(vcvtq_f32_s32(a_offset_term_s32.val[2]), scale), |
| vmulq_n_f32(vcvtq_f32_s32(a_offset_term_s32.val[3]), scale), |
| }}; |
| |
| float32x4x4_t in_f32 = {{vld1q_f32(mm_result_ptr + x + 0), vld1q_f32(mm_result_ptr + x + 4), |
| vld1q_f32(mm_result_ptr + x + 8), vld1q_f32(mm_result_ptr + x + 12)}}; |
| |
| // Add the offset terms to GEMM's result |
| in_f32.val[0] = vaddq_f32(in_f32.val[0], a_offset_term_scaled.val[0]); |
| in_f32.val[1] = vaddq_f32(in_f32.val[1], a_offset_term_scaled.val[1]); |
| in_f32.val[2] = vaddq_f32(in_f32.val[2], a_offset_term_scaled.val[2]); |
| in_f32.val[3] = vaddq_f32(in_f32.val[3], a_offset_term_scaled.val[3]); |
| |
| // Store the result with the offset contribution |
| vst1q_f32(mm_result_ptr + x + 0, in_f32.val[0]); |
| vst1q_f32(mm_result_ptr + x + 4, in_f32.val[1]); |
| vst1q_f32(mm_result_ptr + x + 8, in_f32.val[2]); |
| vst1q_f32(mm_result_ptr + x + 12, in_f32.val[3]); |
| } |
| |
| // Left-overs loop |
| for (; x < window_end_x; ++x) |
| { |
| // Compute the leftover term due to a_offset. |
| const int32_t a_offset_term_s32 = *(vector_sum_col_ptr + x); |
| |
| // Add the offset terms to GEMM's result |
| // Store the result with the offset contribution |
| mm_result_ptr[x] += a_offset_term_s32 * a_offset * scale; |
| } |
| }, |
| vector_sum_col_it, mm_result_it); |
| } |
| else // false, false |
| { |
| // No offset contribution from matrix A and matrix B |
| return; |
| } |
| } |
| |
| 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, |
| bool is_gemm3d) |
| { |
| Window collapsed_window = window.collapse_if_possible(window, Window::DimZ); |
| collapsed_window.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| 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 int window_start_x = window.x().start(); |
| const int window_end_x = window.x().end(); |
| const int window_step_x = 16; |
| |
| // if vector_sum_col is nullptr then stride_y is 0, else get stride_y |
| const size_t sum_col_stride_y = (vector_sum_col != nullptr) ? (vector_sum_col->info()->strides_in_bytes().y()) : 0; |
| Iterator mm_result_it(mm_result, collapsed_window); |
| |
| 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); |
| |
| 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 size_t batch_offset_col = batch_id * (sum_col_stride_y); |
| auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_offset_col + |
| batch_id * vector_sum_col_batch_offset); |
| auto mm_result_ptr = reinterpret_cast<int32_t *>(mm_result_it.ptr()); |
| |
| // Compute the leftover term due to b_offset. |
| int32_t b_offset_term_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 *= b_offset; |
| |
| const int32x4_t b_offset_term_s32_vec = vdupq_n_s32(b_offset_term_s32); |
| |
| int x = window_start_x; |
| for (; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| // Compute the leftover term due to a_offset. |
| int32x4x4_t a_offset_term_s32 = { |
| {vld1q_s32(vector_sum_col_ptr + x + 0), vld1q_s32(vector_sum_col_ptr + x + 4), |
| vld1q_s32(vector_sum_col_ptr + x + 8), vld1q_s32(vector_sum_col_ptr + x + 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); |
| |
| // 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_vec)); |
| offset_term_s32.val[1] = |
| vaddq_s32(offset_term_s32.val[1], vaddq_s32(a_offset_term_s32.val[1], b_offset_term_s32_vec)); |
| offset_term_s32.val[2] = |
| vaddq_s32(offset_term_s32.val[2], vaddq_s32(a_offset_term_s32.val[2], b_offset_term_s32_vec)); |
| offset_term_s32.val[3] = |
| vaddq_s32(offset_term_s32.val[3], vaddq_s32(a_offset_term_s32.val[3], b_offset_term_s32_vec)); |
| |
| int32x4x4_t in_s32 = {{vld1q_s32(mm_result_ptr + x + 0), vld1q_s32(mm_result_ptr + x + 4), |
| vld1q_s32(mm_result_ptr + x + 8), vld1q_s32(mm_result_ptr + x + 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(mm_result_ptr + x + 0, in_s32.val[0]); |
| vst1q_s32(mm_result_ptr + x + 4, in_s32.val[1]); |
| vst1q_s32(mm_result_ptr + x + 8, in_s32.val[2]); |
| vst1q_s32(mm_result_ptr + x + 12, in_s32.val[3]); |
| } |
| |
| // Left-overs loop |
| for (; x < window_end_x; ++x) |
| { |
| // Compute the leftover term due to a_offset. |
| int32_t a_offset_term_s32 = *(vector_sum_col_ptr + x); |
| |
| a_offset_term_s32 *= a_offset; |
| |
| // Add the offset terms to GEMM's result |
| // Store the result with the offset contribution |
| mm_result_ptr[x] += k_offset + a_offset_term_s32 + b_offset_term_s32; |
| } |
| }, |
| 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); |
| |
| const size_t sum_row_stride_y = vector_sum_row->info()->strides_in_bytes().y(); |
| |
| execute_window_loop( |
| collapsed_window, |
| [&](const Coordinates &id) |
| { |
| const int batch_id = id.z() / depth_input; |
| auto mm_result_ptr = reinterpret_cast<int32_t *>(mm_result_it.ptr()); |
| |
| // Compute the leftover term due to b_offset. |
| int32_t b_offset_term_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 *= b_offset; |
| |
| const int32x4_t b_offset_term_s32_vec = vdupq_n_s32(b_offset_term_s32); |
| |
| int x = window_start_x; |
| for (; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| int32x4x4_t in_s32 = {{vld1q_s32(mm_result_ptr + x + 0), vld1q_s32(mm_result_ptr + x + 4), |
| vld1q_s32(mm_result_ptr + x + 8), vld1q_s32(mm_result_ptr + x + 12)}}; |
| |
| // Add the offset terms to GEMM's result |
| in_s32.val[0] = vaddq_s32(in_s32.val[0], b_offset_term_s32_vec); |
| in_s32.val[1] = vaddq_s32(in_s32.val[1], b_offset_term_s32_vec); |
| in_s32.val[2] = vaddq_s32(in_s32.val[2], b_offset_term_s32_vec); |
| in_s32.val[3] = vaddq_s32(in_s32.val[3], b_offset_term_s32_vec); |
| |
| // Store the result with the offset contribution |
| vst1q_s32(mm_result_ptr + x + 0, in_s32.val[0]); |
| vst1q_s32(mm_result_ptr + x + 4, in_s32.val[1]); |
| vst1q_s32(mm_result_ptr + x + 8, in_s32.val[2]); |
| vst1q_s32(mm_result_ptr + x + 12, in_s32.val[3]); |
| } |
| |
| // Left-overs loop |
| for (; x < window_end_x; ++x) |
| { |
| // Add the offset terms to GEMM's result |
| // Store the result with the offset contribution |
| mm_result_ptr[x] += b_offset_term_s32; |
| } |
| }, |
| 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); |
| |
| // 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 size_t batch_offset_col = |
| batch_id * |
| (sum_col_stride_y); // Value to offset vector_sum_col_ptr to allow for iteration of y values in tensor |
| auto vector_sum_col_ptr = reinterpret_cast<const int32_t *>(vector_sum_col_it.ptr() + batch_offset_col + |
| batch_id * vector_sum_col_batch_offset); |
| auto mm_result_ptr = reinterpret_cast<int32_t *>(mm_result_it.ptr()); |
| |
| int x = window_start_x; |
| for (; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| // Compute the leftover term due to a_offset. |
| int32x4x4_t a_offset_term_s32 = { |
| {vld1q_s32(vector_sum_col_ptr + x + 0), vld1q_s32(vector_sum_col_ptr + x + 4), |
| vld1q_s32(vector_sum_col_ptr + x + 8), vld1q_s32(vector_sum_col_ptr + x + 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(mm_result_ptr + x + 0), vld1q_s32(mm_result_ptr + x + 4), |
| vld1q_s32(mm_result_ptr + x + 8), vld1q_s32(mm_result_ptr + x + 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(mm_result_ptr + x + 0, in_s32.val[0]); |
| vst1q_s32(mm_result_ptr + x + 4, in_s32.val[1]); |
| vst1q_s32(mm_result_ptr + x + 8, in_s32.val[2]); |
| vst1q_s32(mm_result_ptr + x + 12, in_s32.val[3]); |
| } |
| |
| // Left-overs loop |
| for (; x < window_end_x; ++x) |
| { |
| // Compute the leftover term due to a_offset. |
| const int32_t a_offset_term_s32 = *(vector_sum_col_ptr + x); |
| |
| // Add the offset terms to GEMM's result |
| // Store the result with the offset contribution |
| mm_result_ptr[x] += a_offset_term_s32 * a_offset; |
| } |
| }, |
| vector_sum_col_it, mm_result_it); |
| } |
| else // false, false |
| { |
| // No offset contribution from matrix A and matrix B |
| return; |
| } |
| } |
| } // namespace |
| |
| void CpuGemmLowpOffsetContributionKernel::configure(ITensorInfo *mm_result, |
| ITensorInfo *vector_sum_col, |
| ITensorInfo *vector_sum_row, |
| int32_t k, |
| int32_t a_offset, |
| int32_t b_offset, |
| float scale) |
| { |
| // Perform validate step |
| ARM_COMPUTE_UNUSED(vector_sum_row); |
| ARM_COMPUTE_ERROR_ON_NULLPTR(mm_result); |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(mm_result, vector_sum_col, vector_sum_row, a_offset, b_offset)); |
| |
| _a_offset = a_offset; |
| _b_offset = b_offset; |
| _k = k; |
| |
| _scale = scale; |
| |
| if (vector_sum_col != nullptr) |
| { |
| // 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->tensor_shape().num_dimensions() > 1; |
| } |
| |
| // Configure kernel window |
| Window win = calculate_max_window(*mm_result, Steps()); |
| ICpuKernel::configure(win); |
| } |
| |
| void CpuGemmLowpOffsetContributionKernel::set_a_offset(int32_t a_offset) |
| { |
| _a_offset = a_offset; |
| } |
| |
| void CpuGemmLowpOffsetContributionKernel::set_b_offset(int32_t b_offset) |
| { |
| _b_offset = b_offset; |
| } |
| |
| void CpuGemmLowpOffsetContributionKernel::set_scale(float scale) |
| { |
| _scale = scale; |
| } |
| |
| Status CpuGemmLowpOffsetContributionKernel::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)); |
| return Status{}; |
| } |
| |
| void CpuGemmLowpOffsetContributionKernel::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 vector_sum_col = tensors.get_const_tensor(TensorType::ACL_SRC_0); |
| auto vector_sum_row = tensors.get_const_tensor(TensorType::ACL_SRC_1); |
| auto mm_result = tensors.get_tensor(TensorType::ACL_DST); |
| |
| // Check if input is a 3D reinterpretation |
| const bool reinterpret_as_3d = vector_sum_row != nullptr && mm_result->info()->num_dimensions() > 1 && |
| mm_result->info()->tensor_shape().y() != vector_sum_row->info()->tensor_shape().x(); |
| |
| // check to see what is the output type of result |
| auto k_offset = _a_offset * _b_offset * _k; |
| if (mm_result->info()->data_type() == DataType::F32) |
| { |
| run_offset_contribution_float(window, mm_result, vector_sum_col, vector_sum_row, _a_offset, _b_offset, k_offset, |
| _scale, _slide_vector_sum_col, reinterpret_as_3d); |
| } |
| else |
| { |
| run_offset_contribution(window, mm_result, vector_sum_col, vector_sum_row, _a_offset, _b_offset, k_offset, |
| _slide_vector_sum_col, reinterpret_as_3d); |
| } |
| } |
| |
| const char *CpuGemmLowpOffsetContributionKernel::name() const |
| { |
| return "CpuGemmLowpOffsetContributionKernel"; |
| } |
| } // namespace kernels |
| } // namespace cpu |
| } // namespace arm_compute |