COMPMID-616 - Optimizing GEMMLowp on NEON intrinsics
Change-Id: Ibbeff5d37249b6e8fc34ad496035a1511c9da5a3
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/94072
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Pablo Tello <pablo.tello@arm.com>
diff --git a/src/core/NEON/kernels/NEGEMMLowpFinalizeKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpFinalizeKernel.cpp
new file mode 100644
index 0000000..400c6d9
--- /dev/null
+++ b/src/core/NEON/kernels/NEGEMMLowpFinalizeKernel.cpp
@@ -0,0 +1,509 @@
+/*
+ * Copyright (c) 2017 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/NEGEMMLowpFinalizeKernel.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
+
+template <bool add_a_offset, bool add_b_offset>
+void NEGEMMLowpFinalizeKernel::finalize(const Window &window)
+{
+ const int32x4_t c_offset_s32 = vdupq_n_s32(_c_offset);
+ const int32x4_t shift_s32 = vdupq_n_s32(-_shift);
+
+ Window collapsed_window = window.collapse_if_possible(IKernel::window(), Window::DimZ);
+
+ if(add_a_offset && add_b_offset) // 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));
+ if(!_slide_vector_sum_col)
+ {
+ 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));
+
+ Iterator vector_sum_col(_vector_sum_col, win_vector_sum_col);
+ Iterator vector_sum_row(_vector_sum_row, win_vector_sum_row);
+ Iterator mm_result(_mm_result, window);
+ Iterator out(_output, window);
+
+ execute_window_loop(window, [&](const Coordinates & id)
+ {
+ // Compute the leftover term due to a_offset.
+ int32x4x4_t a_offset_term_s32 =
+ {
+ {
+ vld1q_s32(reinterpret_cast<const int32_t *>(vector_sum_col.ptr()) + 0),
+ vld1q_s32(reinterpret_cast<const int32_t *>(vector_sum_col.ptr()) + 4),
+ vld1q_s32(reinterpret_cast<const int32_t *>(vector_sum_col.ptr()) + 8),
+ vld1q_s32(reinterpret_cast<const int32_t *>(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.ptr()) + id.y());
+ 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));
+
+ // Add c_offset
+ offset_term_s32.val[0] = vaddq_s32(offset_term_s32.val[0], c_offset_s32);
+ offset_term_s32.val[1] = vaddq_s32(offset_term_s32.val[1], c_offset_s32);
+ offset_term_s32.val[2] = vaddq_s32(offset_term_s32.val[2], c_offset_s32);
+ offset_term_s32.val[3] = vaddq_s32(offset_term_s32.val[3], c_offset_s32);
+
+ int32x4x4_t in_s32 =
+ {
+ {
+ vld1q_s32(reinterpret_cast<const int32_t *>(mm_result.ptr()) + 0),
+ vld1q_s32(reinterpret_cast<const int32_t *>(mm_result.ptr()) + 4),
+ vld1q_s32(reinterpret_cast<const int32_t *>(mm_result.ptr()) + 8),
+ vld1q_s32(reinterpret_cast<const int32_t *>(mm_result.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]);
+
+ // Multiply by c_mult_int
+ in_s32.val[0] = vmulq_n_s32(in_s32.val[0], _c_mult_int);
+ in_s32.val[1] = vmulq_n_s32(in_s32.val[1], _c_mult_int);
+ in_s32.val[2] = vmulq_n_s32(in_s32.val[2], _c_mult_int);
+ in_s32.val[3] = vmulq_n_s32(in_s32.val[3], _c_mult_int);
+
+ // Shift final result (negative value shift right)
+ in_s32.val[0] = vshlq_s32(in_s32.val[0], shift_s32);
+ in_s32.val[1] = vshlq_s32(in_s32.val[1], shift_s32);
+ in_s32.val[2] = vshlq_s32(in_s32.val[2], shift_s32);
+ in_s32.val[3] = vshlq_s32(in_s32.val[3], shift_s32);
+
+ // Convert S32 to U16
+ const int16x8x2_t in_u16 =
+ {
+ {
+ 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 U16 to U8
+ const uint8x16_t out_u8 = vcombine_u8(vqmovun_s16(in_u16.val[0]), vqmovun_s16(in_u16.val[1]));
+
+ vst1q_u8(out.ptr(), out_u8);
+ },
+ vector_sum_col, vector_sum_row, mm_result, out);
+ }
+ else if(!add_a_offset && add_b_offset) // false, true
+ {
+ // 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));
+
+ Iterator vector_sum_row(_vector_sum_row, win_vector_sum_row);
+ Iterator mm_result(_mm_result, window);
+ Iterator out(_output, window);
+
+ execute_window_loop(window, [&](const Coordinates & id)
+ {
+ // 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.ptr()) + id.y());
+ b_offset_term_s32 = vmulq_n_s32(b_offset_term_s32, _b_offset);
+
+ // Add b_offset_term_s32 and c_offset_term_s32
+ int32x4_t offset_term_s32 = vaddq_s32(b_offset_term_s32, c_offset_s32);
+
+ int32x4x4_t in_s32 =
+ {
+ {
+ vld1q_s32(reinterpret_cast<const int32_t *>(mm_result.ptr()) + 0),
+ vld1q_s32(reinterpret_cast<const int32_t *>(mm_result.ptr()) + 4),
+ vld1q_s32(reinterpret_cast<const int32_t *>(mm_result.ptr()) + 8),
+ vld1q_s32(reinterpret_cast<const int32_t *>(mm_result.ptr()) + 12)
+ }
+ };
+
+ // Add the offset terms to GEMM's result
+ in_s32.val[0] = vaddq_s32(in_s32.val[0], offset_term_s32);
+ in_s32.val[1] = vaddq_s32(in_s32.val[1], offset_term_s32);
+ in_s32.val[2] = vaddq_s32(in_s32.val[2], offset_term_s32);
+ in_s32.val[3] = vaddq_s32(in_s32.val[3], offset_term_s32);
+
+ // Multiply by c_mult_int
+ in_s32.val[0] = vmulq_n_s32(in_s32.val[0], _c_mult_int);
+ in_s32.val[1] = vmulq_n_s32(in_s32.val[1], _c_mult_int);
+ in_s32.val[2] = vmulq_n_s32(in_s32.val[2], _c_mult_int);
+ in_s32.val[3] = vmulq_n_s32(in_s32.val[3], _c_mult_int);
+
+ // Shift final result (negative value shift right)
+ in_s32.val[0] = vshlq_s32(in_s32.val[0], shift_s32);
+ in_s32.val[1] = vshlq_s32(in_s32.val[1], shift_s32);
+ in_s32.val[2] = vshlq_s32(in_s32.val[2], shift_s32);
+ in_s32.val[3] = vshlq_s32(in_s32.val[3], shift_s32);
+
+ // Convert S32 to U16
+ const int16x8x2_t in_u16 =
+ {
+ {
+ 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 U16 to U8
+ const uint8x16_t out_u8 = vcombine_u8(vqmovun_s16(in_u16.val[0]), vqmovun_s16(in_u16.val[1]));
+
+ vst1q_u8(out.ptr(), out_u8);
+ },
+ vector_sum_row, mm_result, out);
+ }
+ else if(add_a_offset && !add_b_offset) // 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));
+ if(!_slide_vector_sum_col)
+ {
+ win_vector_sum_col.set(Window::DimZ, Window::Dimension(0, 0, 0));
+ }
+
+ Iterator vector_sum_col(_vector_sum_col, win_vector_sum_col);
+ Iterator mm_result(_mm_result, window);
+ Iterator out(_output, window);
+
+ execute_window_loop(window, [&](const Coordinates & id)
+ {
+ // Compute the leftover term due to a_offset.
+ int32x4x4_t a_offset_term_s32 =
+ {
+ {
+ vld1q_s32(reinterpret_cast<const int32_t *>(vector_sum_col.ptr()) + 0),
+ vld1q_s32(reinterpret_cast<const int32_t *>(vector_sum_col.ptr()) + 4),
+ vld1q_s32(reinterpret_cast<const int32_t *>(vector_sum_col.ptr()) + 8),
+ vld1q_s32(reinterpret_cast<const int32_t *>(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);
+
+ // Add a_offset_term_s32 and b_offset_term_s32
+ int32x4x4_t offset_term_s32 =
+ {
+ {
+ vaddq_s32(c_offset_s32, a_offset_term_s32.val[0]),
+ vaddq_s32(c_offset_s32, a_offset_term_s32.val[1]),
+ vaddq_s32(c_offset_s32, a_offset_term_s32.val[2]),
+ vaddq_s32(c_offset_s32, a_offset_term_s32.val[3])
+ }
+ };
+
+ int32x4x4_t in_s32 =
+ {
+ {
+ vld1q_s32(reinterpret_cast<const int32_t *>(mm_result.ptr()) + 0),
+ vld1q_s32(reinterpret_cast<const int32_t *>(mm_result.ptr()) + 4),
+ vld1q_s32(reinterpret_cast<const int32_t *>(mm_result.ptr()) + 8),
+ vld1q_s32(reinterpret_cast<const int32_t *>(mm_result.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]);
+
+ // Multiply by c_mult_int
+ in_s32.val[0] = vmulq_n_s32(in_s32.val[0], _c_mult_int);
+ in_s32.val[1] = vmulq_n_s32(in_s32.val[1], _c_mult_int);
+ in_s32.val[2] = vmulq_n_s32(in_s32.val[2], _c_mult_int);
+ in_s32.val[3] = vmulq_n_s32(in_s32.val[3], _c_mult_int);
+
+ // Shift final result (negative value shift right)
+ in_s32.val[0] = vshlq_s32(in_s32.val[0], shift_s32);
+ in_s32.val[1] = vshlq_s32(in_s32.val[1], shift_s32);
+ in_s32.val[2] = vshlq_s32(in_s32.val[2], shift_s32);
+ in_s32.val[3] = vshlq_s32(in_s32.val[3], shift_s32);
+
+ // Convert S32 to U16
+ const int16x8x2_t in_u16 =
+ {
+ {
+ 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 U16 to U8
+ const uint8x16_t out_u8 = vcombine_u8(vqmovun_s16(in_u16.val[0]), vqmovun_s16(in_u16.val[1]));
+
+ vst1q_u8(out.ptr(), out_u8);
+ },
+ vector_sum_col, mm_result, out);
+ }
+ else // false, false
+ {
+ Iterator mm_result(_mm_result, window);
+ Iterator out(_output, window);
+
+ execute_window_loop(window, [&](const Coordinates & id)
+ {
+ int32x4x4_t in_s32 =
+ {
+ {
+ vld1q_s32(reinterpret_cast<const int32_t *>(mm_result.ptr()) + 0),
+ vld1q_s32(reinterpret_cast<const int32_t *>(mm_result.ptr()) + 4),
+ vld1q_s32(reinterpret_cast<const int32_t *>(mm_result.ptr()) + 8),
+ vld1q_s32(reinterpret_cast<const int32_t *>(mm_result.ptr()) + 12)
+ }
+ };
+
+ // Add the offset terms to GEMM's result
+ in_s32.val[0] = vaddq_s32(in_s32.val[0], c_offset_s32);
+ in_s32.val[1] = vaddq_s32(in_s32.val[1], c_offset_s32);
+ in_s32.val[2] = vaddq_s32(in_s32.val[2], c_offset_s32);
+ in_s32.val[3] = vaddq_s32(in_s32.val[3], c_offset_s32);
+
+ // Multiply by c_mult_int
+ in_s32.val[0] = vmulq_n_s32(in_s32.val[0], _c_mult_int);
+ in_s32.val[1] = vmulq_n_s32(in_s32.val[1], _c_mult_int);
+ in_s32.val[2] = vmulq_n_s32(in_s32.val[2], _c_mult_int);
+ in_s32.val[3] = vmulq_n_s32(in_s32.val[3], _c_mult_int);
+
+ // Shift final result (negative value shift right)
+ in_s32.val[0] = vshlq_s32(in_s32.val[0], shift_s32);
+ in_s32.val[1] = vshlq_s32(in_s32.val[1], shift_s32);
+ in_s32.val[2] = vshlq_s32(in_s32.val[2], shift_s32);
+ in_s32.val[3] = vshlq_s32(in_s32.val[3], shift_s32);
+
+ // Convert S32 to U16
+ const int16x8x2_t in_u16 =
+ {
+ {
+ 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 U16 to U8
+ const uint8x16_t out_u8 = vcombine_u8(vqmovun_s16(in_u16.val[0]), vqmovun_s16(in_u16.val[1]));
+
+ vst1q_u8(out.ptr(), out_u8);
+ },
+ mm_result, out);
+ }
+}
+
+NEGEMMLowpFinalizeKernel::NEGEMMLowpFinalizeKernel()
+ : _func(nullptr), _vector_sum_col(nullptr), _vector_sum_row(nullptr), _mm_result(nullptr), _output(nullptr), _a_offset(0), _b_offset(0), _c_offset(0), _k_offset(0), _c_mult_int(0), _shift(0),
+ _slide_vector_sum_col(true)
+{
+}
+
+void NEGEMMLowpFinalizeKernel::configure(const ITensor *vector_sum_col, const ITensor *vector_sum_row, const ITensor *mm_result, ITensor *output, int32_t num_mtx_a_cols, int32_t a_offset,
+ int32_t b_offset,
+ int32_t c_offset, int32_t c_mult_int, int32_t shift)
+{
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(mm_result, 1, DataType::S32);
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8);
+
+ TensorShape mm_result_shape = mm_result->info()->tensor_shape();
+ TensorShape output_shape = output->info()->tensor_shape();
+
+ mm_result_shape.collapse(2);
+ output_shape.collapse(2);
+
+ ARM_COMPUTE_ERROR_ON_MSG(mm_result_shape[2] != output_shape[2], "mm_result tensor must have the same number of batches of output tensor");
+
+ // If a_offset == 0, vector_sum_col can be a nullptr
+ if(a_offset != 0)
+ {
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_col, 1, DataType::S32);
+ ARM_COMPUTE_ERROR_ON(vector_sum_col->info()->dimension(0) != mm_result->info()->dimension(0));
+
+ TensorShape vector_sum_col_shape = vector_sum_col->info()->tensor_shape();
+ vector_sum_col_shape.collapse(1);
+
+ // 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_shape[1] != 1;
+ }
+
+ // If b_offset == 0, vector_sum_row can be a nullptr
+ if(b_offset != 0)
+ {
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_row, 1, DataType::S32);
+ ARM_COMPUTE_ERROR_ON(vector_sum_row->info()->dimension(0) != mm_result->info()->dimension(1));
+
+ TensorShape vector_sum_row_shape = vector_sum_row->info()->tensor_shape();
+ vector_sum_row_shape.collapse(1);
+
+ ARM_COMPUTE_ERROR_ON_MSG(vector_sum_row_shape[1] != output_shape[2], "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->info()->tensor_shape();
+ vector_sum_col_shape.collapse(1);
+
+ ARM_COMPUTE_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");
+ }
+ }
+
+ _vector_sum_col = vector_sum_col;
+ _vector_sum_row = vector_sum_row;
+ _mm_result = mm_result;
+ _output = output;
+ _a_offset = a_offset;
+ _b_offset = b_offset;
+ _k_offset = a_offset * b_offset * num_mtx_a_cols;
+ _c_offset = c_offset;
+ _c_mult_int = c_mult_int;
+ _shift = shift;
+
+ constexpr unsigned int num_elems_processed_per_iteration = 16;
+
+ // Configure kernel window
+ Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration));
+
+ AccessWindowHorizontal mm_result_access(mm_result->info(), 0, num_elems_processed_per_iteration);
+ AccessWindowHorizontal output_result_access(output->info(), 0, num_elems_processed_per_iteration);
+
+ // Accordingly with a_offset and b_offset, we can have 4 cases:
+ // a_offset != 0 && b_offset != 0
+ // a_offset = 0 && b_offset != 0
+ // a_offset != 0 && b_offset = 0
+ // a_offset = 0 && b_offset = 0
+ if(a_offset != 0 && b_offset != 0)
+ {
+ // Set the function to use
+ _func = &NEGEMMLowpFinalizeKernel::finalize<true, true>;
+
+ AccessWindowStatic vector_sum_row_access(vector_sum_row->info(), 0, 0, vector_sum_row->info()->dimension(0), 0);
+ AccessWindowHorizontal vector_sum_col_access(vector_sum_col->info(), 0, num_elems_processed_per_iteration);
+
+ update_window_and_padding(win,
+ vector_sum_col_access,
+ vector_sum_row_access,
+ mm_result_access,
+ output_result_access);
+ }
+ else if(a_offset == 0 && b_offset != 0)
+ {
+ // Set the function to use
+ _func = &NEGEMMLowpFinalizeKernel::finalize<false, true>;
+
+ AccessWindowStatic vector_sum_row_access(vector_sum_row->info(), 0, 0, vector_sum_row->info()->dimension(0), 0);
+
+ update_window_and_padding(win,
+ vector_sum_row_access,
+ mm_result_access,
+ output_result_access);
+ }
+ else if(a_offset != 0 && b_offset == 0)
+ {
+ // Set the function to use
+ _func = &NEGEMMLowpFinalizeKernel::finalize<true, false>;
+
+ AccessWindowHorizontal vector_sum_col_access(vector_sum_col->info(), 0, num_elems_processed_per_iteration);
+
+ update_window_and_padding(win,
+ vector_sum_col_access,
+ mm_result_access,
+ output_result_access);
+ }
+ else
+ {
+ // Set the function to use
+ _func = &NEGEMMLowpFinalizeKernel::finalize<false, false>;
+
+ update_window_and_padding(win,
+ mm_result_access,
+ output_result_access);
+ }
+
+ output_result_access.set_valid_region(win, ValidRegion(Coordinates(0, 0), output->info()->tensor_shape()));
+
+ INEKernel::configure(win);
+}
+
+void NEGEMMLowpFinalizeKernel::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);
+
+ (this->*_func)(window);
+}