COMPMID-1694: Fuse offset contribution with the output stage when we use NEGEMMLowpMatrixMultiplyCore
Change-Id: Ic1a681e4cc03e1eba3bf8485d9cdb17b3e926047
Signed-off-by: giuros01 <giuseppe.rossini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/561
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.cpp b/src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.cpp
index 83af0c6..8fba342 100644
--- a/src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -51,7 +51,6 @@
{
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(mm_result, 1, DataType::S32);
ARM_COMPUTE_RETURN_ERROR_ON(output_stage.type == GEMMLowpOutputStageType::NONE);
- ARM_COMPUTE_RETURN_ERROR_ON(bias == nullptr && a_offset == 0 && b_offset == 0);
ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_max_bound > 255);
ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_min_bound < 0 || output_stage.gemmlowp_min_bound > output_stage.gemmlowp_max_bound);
diff --git a/src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.cpp
index 33a5b4a..2293926 100644
--- a/src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.cpp
+++ b/src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017-2018 ARM Limited.
+ * Copyright (c) 2017-2019 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -106,20 +106,17 @@
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);
+ 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);
+ 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);
+ 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{};
diff --git a/src/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.cpp
new file mode 100644
index 0000000..ebbea08
--- /dev/null
+++ b/src/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.cpp
@@ -0,0 +1,651 @@
+/*
+ * Copyright (c) 2019 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/NEGEMMLowpOffsetContributionOutputStageKernel.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/NEON/NEAsymm.h"
+#include "arm_compute/core/NEON/wrapper/wrapper.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>
+#include <map>
+
+namespace arm_compute
+{
+class Coordinates;
+
+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 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)
+ }
+ };
+}
+
+template <bool is_bounded_relu>
+inline uint8x16_t finalize_quantization_floating_point(int32x4x4_t &in_s32, int32x4_t result_shift_s32, uint8x16_t min_u8, uint8x16_t max_u8)
+{
+ 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 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;
+}
+
+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)
+ }
+ };
+}
+
+template <bool has_a_offset, bool has_b_offset, bool has_bias, bool is_bounded_relu, bool is_fixed_point>
+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, uint8x16_t min_u8, uint8x16_t max_u8,
+ int32_t a_offset, int32_t b_offset, int32_t k_offset,
+ GEMMLowpOutputStageInfo output_stage, int window_step_x, int window_start_x, int window_end_x)
+{
+ 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, output_stage.gemmlowp_multiplier);
+ }
+
+ if(is_fixed_point)
+ {
+ vst1q_u8(out_it.ptr() + x, finalize_quantization<is_bounded_relu>(in_s32, output_stage.gemmlowp_multiplier, output_stage.gemmlowp_shift, result_offset_s32, min_u8, max_u8));
+ }
+ else
+ {
+ vst1q_u8(out_it.ptr() + x, finalize_quantization_floating_point<is_bounded_relu>(in_s32, result_shift_s32, min_u8, max_u8));
+ }
+ }
+ // 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<is_bounded_relu>(in_value, output_stage.gemmlowp_multiplier, output_stage.gemmlowp_shift,
+ output_stage.gemmlowp_offset, static_cast<uint8_t>(output_stage.gemmlowp_min_bound), static_cast<uint8_t>(output_stage.gemmlowp_max_bound));
+ }
+ else
+ {
+ // Finalize quantization
+ in_value = (in_value * output_stage.gemmlowp_multiplier) >> output_stage.gemmlowp_shift;
+
+ // Bound and store the result
+ if(is_bounded_relu)
+ {
+ in_value = static_cast<uint8_t>(std::max(output_stage.gemmlowp_min_bound, std::min(output_stage.gemmlowp_max_bound, in_value)));
+ }
+ *(out_it.ptr() + x) = static_cast<uint8_t>(std::max(0, std::min(255, in_value)));
+ }
+ }
+}
+
+template <bool is_gemm3d, bool is_bounded_relu, bool is_fixed_point>
+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 slide_vector_sum_col,
+ GEMMLowpOutputStageInfo output_stage)
+{
+ 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 int32x4_t result_offset_s32 = vdupq_n_s32(output_stage.gemmlowp_offset);
+ const int32x4_t result_shift_s32 = vdupq_n_s32(is_fixed_point ? output_stage.gemmlowp_shift : -output_stage.gemmlowp_shift);
+ const uint8x16_t min_u8 = vdupq_n_u8(static_cast<uint8_t>(output_stage.gemmlowp_min_bound));
+ const uint8x16_t max_u8 = vdupq_n_u8(static_cast<uint8_t>(output_stage.gemmlowp_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) && (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
+ const int vector_sum_col_batch_offset = slide_vector_sum_col ? vector_sum_col->info()->strides_in_bytes().z() : 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_batch_offset);
+ 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<true, true, true, is_bounded_relu, is_fixed_point>(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_u8, max_u8, a_offset, b_offset, k_offset,
+ output_stage, window_step_x, window_start_x, window_end_x);
+ },
+ 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_batch_offset);
+ 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<true, true, false, is_bounded_relu, is_fixed_point>(vector_sum_col_ptr, vector_sum_row_ptr, nullptr, mm_result_it, out_it,
+ result_offset_s32, result_shift_s32, min_u8, max_u8, a_offset, b_offset, k_offset,
+ output_stage, window_step_x, window_start_x, window_end_x);
+ },
+ 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<false, true, true, is_bounded_relu, is_fixed_point>(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_u8, max_u8, a_offset, b_offset, k_offset,
+ output_stage, window_step_x, window_start_x, window_end_x);
+ },
+ 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<false, true, false, is_bounded_relu, is_fixed_point>(nullptr, vector_sum_row_ptr, nullptr, mm_result_it, out_it,
+ result_offset_s32, result_shift_s32, min_u8, max_u8, a_offset, b_offset, k_offset,
+ output_stage, window_step_x, window_start_x, window_end_x);
+ },
+ 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
+ const int vector_sum_col_batch_offset = slide_vector_sum_col ? vector_sum_col->info()->strides_in_bytes().z() : 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_batch_offset);
+ run_offset_contribution_output_stage_window<true, false, true, is_bounded_relu, is_fixed_point>(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_u8, max_u8, a_offset, b_offset, k_offset,
+ output_stage, window_step_x, window_start_x, window_end_x);
+ },
+ 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_batch_offset);
+ run_offset_contribution_output_stage_window<true, false, false, is_bounded_relu, is_fixed_point>(vector_sum_col_ptr, nullptr, nullptr, mm_result_it, out_it,
+ result_offset_s32, result_shift_s32, min_u8, max_u8, a_offset, b_offset, k_offset,
+ output_stage, window_step_x, window_start_x, window_end_x);
+ },
+ 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 & id)
+ {
+ run_offset_contribution_output_stage_window<false, false, true, is_bounded_relu, is_fixed_point>(nullptr, nullptr, reinterpret_cast<const int32_t *>(bias_it.ptr()), mm_result_it, out_it,
+ result_offset_s32, result_shift_s32, min_u8, max_u8, a_offset, b_offset, k_offset,
+ output_stage, window_step_x, window_start_x, window_end_x);
+ },
+ bias_it, mm_result_it, out_it);
+ }
+ else
+ {
+ execute_window_loop(collapsed_window, [&](const Coordinates & id)
+ {
+ run_offset_contribution_output_stage_window<false, false, false, is_bounded_relu, is_fixed_point>(nullptr, nullptr, nullptr, mm_result_it, out_it,
+ result_offset_s32, result_shift_s32, min_u8, max_u8, a_offset, b_offset, k_offset,
+ output_stage, window_step_x, window_start_x, window_end_x);
+ },
+ 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);
+ ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_max_bound > 255);
+ ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_min_bound < 0 || 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));
+ }
+
+ // 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");
+ }
+ }
+ }
+
+ if(output->total_size() != 0)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mm_result, output);
+ }
+
+ return Status{};
+}
+
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *mm_result, ITensorInfo *output)
+{
+ // Output auto inizialitation if not yet initialized
+ auto_init_if_empty(*output, 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
+ Coordinates coord;
+ coord.set_num_dimensions(output->num_dimensions());
+ output->set_valid_region(ValidRegion(coord, output->tensor_shape()));
+
+ return std::make_pair(Status{}, win);
+}
+
+NEGEMMLowpOffsetContributionOutputStageKernel::NEGEMMLowpOffsetContributionOutputStageFunction
+get_configured_function(const ITensor *mm_result, const ITensor *vector_sum_row, GEMMLowpOutputStageInfo output_stage)
+{
+ static std::map<uint8_t, NEGEMMLowpOffsetContributionOutputStageKernel::NEGEMMLowpOffsetContributionOutputStageFunction> map_function =
+ {
+ { 0, &run_offset_contribution_output_stage<false, false, false> },
+ { 1, &run_offset_contribution_output_stage<true, false, false> },
+ { 2, &run_offset_contribution_output_stage<false, true, false> },
+ { 3, &run_offset_contribution_output_stage<true, true, false> },
+ { 4, &run_offset_contribution_output_stage<false, false, true> },
+ { 5, &run_offset_contribution_output_stage<true, false, true> },
+ { 6, &run_offset_contribution_output_stage<false, true, true> },
+ { 7, &run_offset_contribution_output_stage<true, true, true> }
+ };
+
+ // 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 if we need to clamp the result using min and max
+ const bool is_bounded_relu = ((output_stage.gemmlowp_min_bound != output_stage.gemmlowp_max_bound)
+ && !(output_stage.gemmlowp_min_bound == 0 && output_stage.gemmlowp_max_bound == 255));
+
+ const bool is_fixed_point = output_stage.type != GEMMLowpOutputStageType::QUANTIZE_DOWN;
+
+ // key acts as a bitset, setting the first bit on reinterpret_as_3d,
+ // the second on is_bounded_relu, and the third on is_fixed_point.
+ uint8_t key = (reinterpret_as_3d ? 1UL : 0UL) | ((is_bounded_relu ? 1UL : 0UL) << 1) | ((is_fixed_point ? 1UL : 0UL) << 2);
+ return map_function.find(key)->second;
+}
+} // namespace
+
+NEGEMMLowpOffsetContributionOutputStageKernel::NEGEMMLowpOffsetContributionOutputStageKernel()
+ : _function(nullptr), _vector_sum_col(nullptr), _vector_sum_row(nullptr), _bias(nullptr), _mm_result(nullptr), _output(nullptr), _a_offset(0), _b_offset(0), _k_offset(0), _slide_vector_sum_col(true),
+ _output_stage(GEMMLowpOutputStageInfo())
+
+{
+}
+
+void NEGEMMLowpOffsetContributionOutputStageKernel::configure(const ITensor *mm_result, const ITensor *vector_sum_col,
+ const ITensor *vector_sum_row, const ITensor *bias, ITensor *output, int32_t k,
+ int32_t a_offset, int32_t b_offset, GEMMLowpOutputStageInfo output_stage)
+{
+ // Perform validate step
+ ARM_COMPUTE_ERROR_ON_NULLPTR(mm_result, output);
+
+ 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
+ bias != nullptr ? bias->info() : nullptr, // NOLINT
+ output->info(), a_offset, b_offset, output_stage)); // NOLINT
+
+ _vector_sum_col = vector_sum_col;
+ _vector_sum_row = vector_sum_row;
+ _bias = bias;
+ _mm_result = mm_result;
+ _output = output;
+ _a_offset = a_offset;
+ _b_offset = b_offset;
+ _k_offset = a_offset * b_offset * k;
+ _output_stage = output_stage;
+
+ // If a_offset == 0, vector_sum_col can be a nullptr
+ if(a_offset != 0)
+ {
+ // Check if vector_sum_col_shape should be slidden or not
+ // Don't slide vector_sum_col_shape along the y dimension if vector_sum_col_shape has just 1 dimension and vector_sum_row_shape more than 1
+ // This scenario can happen when the the matrix multiplication is used to perform a convolution operation
+ _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(), output->info());
+ ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+ INEKernel::configure(win_config.second);
+
+ _function = get_configured_function(mm_result, vector_sum_row, output_stage);
+}
+
+Status NEGEMMLowpOffsetContributionOutputStageKernel::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));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(mm_result->clone().get(), output->clone().get()).first);
+ return Status{};
+}
+
+void NEGEMMLowpOffsetContributionOutputStageKernel::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);
+ _function(window, _mm_result, _vector_sum_col, _vector_sum_row, _bias, _output, _a_offset, _b_offset, _k_offset, _slide_vector_sum_col, _output_stage);
+}
+
+} // namespace arm_compute
\ No newline at end of file
diff --git a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp
index f0ac695..d3cfc7a 100644
--- a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp
+++ b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp
@@ -86,37 +86,6 @@
namespace arm_compute
{
class Coordinates;
-
-/* Function used by the left-over for loop to perform the quantization */
-template <bool is_bounded_relu>
-inline uint8_t finalize_quantization(int32x4_t in_s32, int result_fixedpoint_multiplier, int32_t result_shift, int32x4_t result_offset_after_shift_s32, uint8_t min_u8, uint8_t max_u8)
-{
- const static int32x4_t zero_s32 = vdupq_n_s32(0);
- const static int32x4_t sat_value_s32 = vdupq_n_s32(255);
-
- // Fixed point multiplication with vector saturating rounding doubling multiply high with scalar
- in_s32 = vqrdmulhq_n_s32(in_s32, result_fixedpoint_multiplier);
-
- // Round to the nearest division by a power-of-two using result_shift_s32
- in_s32 = rounding_divide_by_pow2(in_s32, result_shift);
-
- // Add the offset terms
- in_s32 = vaddq_s32(in_s32, result_offset_after_shift_s32);
-
- // Saturate negative values
- in_s32 = vmaxq_s32(in_s32, zero_s32);
- in_s32 = vminq_s32(in_s32, sat_value_s32);
-
- auto out_u8 = static_cast<uint8_t>(vgetq_lane_s32(in_s32, 0));
-
- if(is_bounded_relu)
- {
- out_u8 = std::max(out_u8, min_u8);
- out_u8 = std::min(out_u8, max_u8);
- }
-
- return out_u8;
-}
} // namespace arm_compute
template <bool is_bounded_relu>
@@ -188,10 +157,8 @@
// Add bias
in_value += bias_value;
-
// Finalize and store the result
- *(out.ptr() + x) = finalize_quantization<is_bounded_relu>(vdupq_n_s32(in_value), _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, static_cast<uint8_t>(_min),
- static_cast<uint8_t>(_max));
+ *(out.ptr() + x) = finalize_quantization<is_bounded_relu>(in_value, _result_fixedpoint_multiplier, _result_shift, _result_offset_after_shift, static_cast<uint8_t>(_min), static_cast<uint8_t>(_max));
}
},
in, out, bias);
@@ -220,10 +187,10 @@
// Compute left-over elements
for(; x < window_end_x; ++x)
{
- const int32x4_t in_s32 = vld1q_dup_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x);
+ const int32_t in_value = *(reinterpret_cast<const int32_t *>(in.ptr()) + x);
// Finalize and store the result
- *(out.ptr() + x) = finalize_quantization<is_bounded_relu>(in_s32, _result_fixedpoint_multiplier, _result_shift, result_offset_after_shift_s32, static_cast<uint8_t>(_min), static_cast<uint8_t>(_max));
+ *(out.ptr() + x) = finalize_quantization<is_bounded_relu>(in_value, _result_fixedpoint_multiplier, _result_shift, _result_offset_after_shift, static_cast<uint8_t>(_min), static_cast<uint8_t>(_max));
}
},
in, out);
diff --git a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
index be7cc2d..b6c3734 100644
--- a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017-2018 ARM Limited.
+ * Copyright (c) 2017-2019 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -90,16 +90,17 @@
}
NEGEMMConvolutionLayer::NEGEMMConvolutionLayer(const std::shared_ptr<IMemoryManager> &memory_manager)
- : _memory_group(memory_manager), _reshape_weights(), _im2col_kernel(), _mm_gemm(memory_manager), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(), _col2im_kernel(), _activationlayer_function(),
- _add_bias_kernel(), _reshape_layer(), _original_weights(nullptr), _im2col_output(), _weights_reshaped(), _gemm_output(), _tmp_output(), _data_layout(DataLayout::NCHW), _append_bias(false),
- _skip_im2col(false), _skip_col2im(false), _is_quantized(false), _is_activationlayer_enabled(false), _is_prepared(false)
+ : _memory_group(memory_manager), _reshape_weights(), _im2col_kernel(), _mm_gemm(memory_manager), _mm_gemmlowp(memory_manager), _col2im_kernel(), _activationlayer_function(), _add_bias_kernel(),
+ _reshape_layer(), _original_weights(nullptr), _im2col_output(), _weights_reshaped(), _gemm_output(), _tmp_output(), _data_layout(DataLayout::NCHW), _append_bias(false), _skip_im2col(false),
+ _skip_col2im(false), _is_quantized(false), _is_activationlayer_enabled(false), _is_prepared(false)
{
}
-void NEGEMMConvolutionLayer::configure_mm(const ITensor *input, const ITensor *weights, ITensor *output, int gemm_3d_depth)
+void NEGEMMConvolutionLayer::configure_mm(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const ActivationLayerInfo &act_info, int gemm_3d_depth)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights);
- ARM_COMPUTE_ERROR_THROW_ON(validate_mm(input->info(), weights->info(), output->info(), gemm_3d_depth, _skip_im2col));
+ ARM_COMPUTE_ERROR_THROW_ON(validate_mm(input->info(), weights->info(), biases == nullptr ? nullptr : biases->info(), output == nullptr ? nullptr : output->info(), act_info, gemm_3d_depth,
+ _skip_im2col));
const GEMMInfo &gemm_info = GEMMInfo(false, false, true /* Reshape weights only for the first run */,
gemm_3d_depth, _skip_im2col /* Reinterpret the input as 3D if im2col is skipped */);
@@ -114,7 +115,40 @@
input->info()->set_quantization_info(QuantizationInfo(input_quantization_info.scale, -input_quantization_info.offset));
weights->info()->set_quantization_info(QuantizationInfo(weights_quantization_info.scale, -weights_quantization_info.offset));
- _mm_gemmlowp.configure(input, weights, nullptr, output, gemm_info);
+ const QuantizationInfo output_quant_info = (output->info()->total_size() == 0) ? input_quantization_info : output->info()->quantization_info();
+
+ float multiplier = input_quantization_info.scale * weights->info()->quantization_info().scale / output_quant_info.scale;
+ int output_multiplier, output_shift;
+ quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
+
+ // Merge activation with output stage
+ int min_activation = 0;
+ int max_activation = 0;
+
+ const std::set<ActivationLayerInfo::ActivationFunction> supported_acts = { ActivationLayerInfo::ActivationFunction::RELU,
+ ActivationLayerInfo::ActivationFunction::BOUNDED_RELU,
+ ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU
+ };
+ if(_is_activationlayer_enabled && supported_acts.count(act_info.activation()) != 0)
+ {
+ const int a_const_int = output_quant_info.quantize(act_info.a(), RoundingPolicy::TO_NEAREST_UP);
+ const int b_const_int = output_quant_info.quantize(act_info.b(), RoundingPolicy::TO_NEAREST_UP);
+
+ min_activation = act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU ? output_quant_info.offset : b_const_int;
+ max_activation = act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU ? 255 : a_const_int;
+
+ _is_activationlayer_enabled = false;
+ }
+
+ GEMMLowpOutputStageInfo output_info;
+ output_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;
+ output_info.gemmlowp_offset = output_quant_info.offset;
+ output_info.gemmlowp_multiplier = output_multiplier;
+ output_info.gemmlowp_shift = output_shift;
+ output_info.gemmlowp_min_bound = min_activation;
+ output_info.gemmlowp_max_bound = max_activation;
+
+ _mm_gemmlowp.configure(input, weights, biases, output, GEMMInfo(false, false, true, gemm_3d_depth, _skip_im2col, false, output_info));
// Revert back QuantizatioInfo as input and weights could be used in other convolution layers
input->info()->set_quantization_info(input_quantization_info);
@@ -127,9 +161,11 @@
}
}
-Status NEGEMMConvolutionLayer::validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, int gemm_3d_depth, bool skip_im2col)
+Status NEGEMMConvolutionLayer::validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const ActivationLayerInfo &act_info,
+ int gemm_3d_depth, bool skip_im2col)
{
- const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());
+ const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());
+ const bool is_activation_enabled = act_info.enabled();
const GEMMInfo &gemm_info = GEMMInfo(false, false, true /* Reshape weights only for the first run */,
gemm_3d_depth, skip_im2col /* Reinterpret the input as 3D if im2col is skipped */);
@@ -145,8 +181,39 @@
input_qa->set_quantization_info(QuantizationInfo(input_quantization_info.scale, -input_quantization_info.offset));
weights_qa->set_quantization_info(QuantizationInfo(weights_quantization_info.scale, -weights_quantization_info.offset));
+ const QuantizationInfo output_quant_info = (output->total_size() == 0) ? input_quantization_info : output->quantization_info();
+
+ float multiplier = input_quantization_info.scale * weights->quantization_info().scale / output_quant_info.scale;
+ int output_multiplier, output_shift;
+ quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
+
+ // Merge activation with output stage
+ int min_activation = 0;
+ int max_activation = 0;
+
+ const std::set<ActivationLayerInfo::ActivationFunction> supported_acts = { ActivationLayerInfo::ActivationFunction::RELU,
+ ActivationLayerInfo::ActivationFunction::BOUNDED_RELU,
+ ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU
+ };
+ if(is_activation_enabled && supported_acts.count(act_info.activation()) != 0)
+ {
+ const int a_const_int = output_quant_info.quantize(act_info.a(), RoundingPolicy::TO_NEAREST_UP);
+ const int b_const_int = output_quant_info.quantize(act_info.b(), RoundingPolicy::TO_NEAREST_UP);
+
+ min_activation = act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU ? output_quant_info.offset : b_const_int;
+ max_activation = act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU ? 255 : a_const_int;
+ }
+
+ GEMMLowpOutputStageInfo output_info;
+ output_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;
+ output_info.gemmlowp_offset = output_quant_info.offset;
+ output_info.gemmlowp_multiplier = output_multiplier;
+ output_info.gemmlowp_shift = output_shift;
+ output_info.gemmlowp_min_bound = min_activation;
+ output_info.gemmlowp_max_bound = max_activation;
+
// Perform validation step on GEMMLowp
- return NEGEMMLowpMatrixMultiplyCore::validate(input_qa.get(), weights_qa.get(), nullptr, output, gemm_info);
+ return NEGEMMLowpMatrixMultiplyCore::validate(input_qa.get(), weights_qa.get(), biases, output, GEMMInfo(false, false, true, gemm_3d_depth, skip_im2col, false, output_info));
}
else
{
@@ -155,19 +222,18 @@
}
}
-Status NEGEMMConvolutionLayer::validate_gemm3d(DataType data_type, int gemm_3d_depth, bool skip_im2col)
+Status NEGEMMConvolutionLayer::validate_gemm3d(const ITensorInfo *input_info, const ActivationLayerInfo &act_info, int gemm_3d_depth, bool skip_im2col)
{
- const bool is_quantized = is_data_type_quantized_asymmetric(data_type);
- const DataType output_gemm_data_type = is_quantized ? DataType::S32 : data_type;
- const unsigned int mult_y = skip_im2col ? 1U : gemm_3d_depth;
- const unsigned int mult_z = skip_im2col ? gemm_3d_depth : 1U;
+ const DataType data_type = input_info->data_type();
+ const unsigned int mult_y = skip_im2col ? 1U : gemm_3d_depth;
+ const unsigned int mult_z = skip_im2col ? gemm_3d_depth : 1U;
// Set dummy tensor shapes for the validation
- const TensorInfo dummy_input_info(TensorShape(4U, 4U * mult_y, 1U * mult_z), 1, data_type);
+ const TensorInfo dummy_input_info(TensorShape(4U, 4U * mult_y, 1U * mult_z), 1, data_type, input_info->quantization_info());
const TensorInfo dummy_weights_info(TensorShape(4U, 4U), 1, data_type);
- const TensorInfo dummy_output_info(TensorShape(4U, 4U, gemm_3d_depth), 1, output_gemm_data_type);
+ const TensorInfo dummy_output_info(TensorShape(4U, 4U, gemm_3d_depth), 1, data_type, input_info->quantization_info());
- return validate_mm(&dummy_input_info, &dummy_weights_info, &dummy_output_info, gemm_3d_depth, skip_im2col);
+ return validate_mm(&dummy_input_info, &dummy_weights_info, nullptr, &dummy_output_info, act_info, gemm_3d_depth, skip_im2col);
}
void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info,
@@ -202,9 +268,8 @@
_append_bias = (biases != nullptr) && (!_is_quantized);
_is_activationlayer_enabled = act_info.enabled();
- const ITensor *gemm_input_to_use = input;
- ITensor *gemm_output_to_use = output;
- ITensor *gemm_output_staged_to_use = output;
+ const ITensor *gemm_input_to_use = input;
+ ITensor *gemm_output_to_use = output;
// Get convolved dimensions
unsigned int conv_w = 0;
@@ -219,7 +284,7 @@
// Check if GEMM3D is supported
if(data_layout == DataLayout::NHWC)
{
- _skip_col2im = bool(validate_gemm3d(input->info()->data_type(), conv_h, true));
+ _skip_col2im = bool(validate_gemm3d(input->info(), act_info, conv_h, true));
// If not supported, we need to perform im2col and col2im (or reshape layer)
if(!_skip_col2im)
{
@@ -262,26 +327,17 @@
}
// Create temporary GEMM output tensor in case we cannot skip col2im
- if(!_skip_col2im || _is_quantized)
+ if(!_skip_col2im)
{
- // GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input.
- const DataType gemm_data_type = _is_quantized ? DataType::S32 : data_type;
- TensorShape shape_gemm;
+ TensorShape shape_gemm;
- if(_is_quantized && _skip_col2im)
- {
- shape_gemm = output->info()->tensor_shape();
- }
- else
- {
- // Calculate GEMM output shape
- shape_gemm = _im2col_output.info()->tensor_shape();
- shape_gemm.set(0, mat_weights_cols);
- shape_gemm.set(1, conv_w * conv_h);
- }
+ // Calculate GEMM output shape
+ shape_gemm = _im2col_output.info()->tensor_shape();
+ shape_gemm.set(0, mat_weights_cols);
+ shape_gemm.set(1, conv_w * conv_h);
// FIXME: input->clone() doesn't work with subtensors for grouped convolutions.
- TensorInfo info_gemm(shape_gemm, 1, gemm_data_type);
+ TensorInfo info_gemm(shape_gemm, 1, data_type);
info_gemm.set_quantization_info(output->info()->quantization_info()).set_data_layout(input->info()->data_layout());
_gemm_output.allocator()->init(info_gemm);
_memory_group.manage(&_gemm_output);
@@ -293,62 +349,24 @@
// Configure GEMM
// In case we need to skip col2im, GEMM3D (gemm_3d_depth != 0) must be called in order to avoid reshaping the output matrix
const unsigned int gemm_3d_depth = _skip_col2im ? conv_h : 0;
- configure_mm(gemm_input_to_use, &_weights_reshaped, gemm_output_to_use, gemm_3d_depth);
+ configure_mm(gemm_input_to_use, &_weights_reshaped, biases, gemm_output_to_use, act_info, gemm_3d_depth);
if(!_skip_im2col)
{
_im2col_output.allocator()->allocate();
}
- // Configure output stage for quantized case
- if(_is_quantized)
- {
- const QuantizationInfo input_quant_info = input->info()->quantization_info();
- const QuantizationInfo output_quant_info = (output->info()->total_size() == 0) ? input_quant_info : output->info()->quantization_info();
-
- float multiplier = input_quant_info.scale * weights->info()->quantization_info().scale / output_quant_info.scale;
- int output_multiplier, output_shift;
- quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
-
- if(!_skip_col2im)
- {
- _memory_group.manage(&_tmp_output);
- gemm_output_staged_to_use = &_tmp_output;
- }
-
- // Merge activation with output stage
- int min_activation = 0;
- int max_activation = 0;
-
- const std::set<ActivationLayerInfo::ActivationFunction> supported_acts = { ActivationLayerInfo::ActivationFunction::RELU,
- ActivationLayerInfo::ActivationFunction::BOUNDED_RELU,
- ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU
- };
- if(_is_activationlayer_enabled && supported_acts.count(act_info.activation()) != 0)
- {
- const int a_const_int = output_quant_info.quantize(act_info.a(), RoundingPolicy::TO_NEAREST_UP);
- const int b_const_int = output_quant_info.quantize(act_info.b(), RoundingPolicy::TO_NEAREST_UP);
-
- min_activation = act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU ? output_quant_info.offset : b_const_int;
- max_activation = act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU ? 255 : a_const_int;
-
- _is_activationlayer_enabled = false;
- }
-
- _gemmlowp_output_stage.configure(gemm_output_to_use, biases, gemm_output_staged_to_use, output_multiplier, output_shift, output_quant_info.offset, min_activation, max_activation);
- }
-
if(!_skip_col2im)
{
if(_data_layout == DataLayout::NCHW)
{
// Configure col2im
- _col2im_kernel.configure(_is_quantized ? gemm_output_staged_to_use : gemm_output_to_use, output, Size2D(conv_w, conv_h));
+ _col2im_kernel.configure(gemm_output_to_use, output, Size2D(conv_w, conv_h));
}
else
{
// Configure reshape layer
- _reshape_layer.configure(_is_quantized ? gemm_output_staged_to_use : gemm_output_to_use, output);
+ _reshape_layer.configure(gemm_output_to_use, output);
}
}
@@ -395,10 +413,9 @@
const unsigned int kernel_height = weights->dimension(idx_height);
TensorInfo im2col_reshaped_info, info_gemm, tmp_info, weights_reshaped_info;
- const ITensorInfo *gemm_input_to_use = input;
- const ITensorInfo *gemm_output_to_use = output;
- const ITensorInfo *gemm_output_staged_to_use = output;
- const ITensorInfo *weights_to_use = weights;
+ const ITensorInfo *gemm_input_to_use = input;
+ const ITensorInfo *gemm_output_to_use = output;
+ const ITensorInfo *weights_to_use = weights;
const bool is_quantized = is_data_type_quantized_asymmetric(data_type);
const bool append_bias = (biases != nullptr) && (!is_quantized);
@@ -420,7 +437,7 @@
bool skip_col2im = false;
if(data_layout == DataLayout::NHWC)
{
- skip_col2im = bool(validate_gemm3d(input->data_type(), conv_h, true));
+ skip_col2im = bool(validate_gemm3d(input, act_info, conv_h, true));
// If not supported, we need to perform im2col and col2im (or reshape layer)
if(!skip_col2im)
{
@@ -431,7 +448,7 @@
if(skip_col2im)
{
// If not supported, we need to perform im2col and col2im (or reshape layer)
- if(!bool(validate_gemm3d(input->data_type(), conv_h, skip_im2col)))
+ if(!bool(validate_gemm3d(input, act_info, conv_h, skip_im2col)))
{
skip_im2col = false;
skip_col2im = false;
@@ -495,68 +512,25 @@
}
// Create temporary GEMM output tensor in case we cannot skip col2im
- const DataType gemm_data_type = is_quantized ? DataType::S32 : data_type;
if(!skip_col2im)
{
TensorShape shape_gemm = gemm_input_to_use->tensor_shape();
shape_gemm.set(0, mat_weights_cols);
shape_gemm.set(1, conv_w * conv_h);
- info_gemm = TensorInfo(shape_gemm, 1, gemm_data_type);
+ info_gemm = TensorInfo(shape_gemm, 1, data_type);
}
else
{
- info_gemm = TensorInfo(output->tensor_shape(), 1, gemm_data_type);
+ info_gemm = TensorInfo(output->tensor_shape(), 1, data_type);
}
info_gemm.set_quantization_info(output->quantization_info()).set_data_layout(input->data_layout());
gemm_output_to_use = &info_gemm;
-
- ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemm_input_to_use, weights_to_use, gemm_output_to_use, skip_col2im ? conv_h : 0, skip_im2col));
-
- if(is_quantized)
- {
- const QuantizationInfo input_quant_info = input->quantization_info();
- const QuantizationInfo output_quant_info = (output->total_size() == 0) ? input_quant_info : output->quantization_info();
- const float multiplier = input_quant_info.scale * weights_to_use->quantization_info().scale / output_quant_info.scale;
- int output_multiplier, output_shift;
- quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
-
- if(!skip_col2im)
- {
- tmp_info = TensorInfo(gemm_output_to_use->tensor_shape(), 1, DataType::QASYMM8);
- tmp_info.set_quantization_info(output->quantization_info()).set_data_layout(data_layout);
- gemm_output_staged_to_use = &tmp_info;
- }
-
- // Merge activation with output stage
- int min_activation = 0;
- int max_activation = 0;
-
- const std::set<ActivationLayerInfo::ActivationFunction> supported_acts = { ActivationLayerInfo::ActivationFunction::RELU,
- ActivationLayerInfo::ActivationFunction::BOUNDED_RELU,
- ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU
- };
-
- if(is_activation_enabled && supported_acts.count(act_info.activation()) != 0)
- {
- const int a_const_int = output_quant_info.quantize(act_info.a(), RoundingPolicy::TO_NEAREST_UP);
- const int b_const_int = output_quant_info.quantize(act_info.b(), RoundingPolicy::TO_NEAREST_UP);
-
- min_activation = act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU ? output_quant_info.offset : b_const_int;
- max_activation = act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU ? 255 : a_const_int;
-
- is_activation_enabled = false;
- }
-
- // Validate output stage for quantized case
- NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(gemm_output_to_use, biases, gemm_output_staged_to_use, min_activation, max_activation);
- }
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemm_input_to_use, weights_to_use, biases, gemm_output_to_use, act_info, skip_col2im ? conv_h : 0, skip_im2col));
// Validate Col2Im/ReshapeLayer
if(!skip_col2im && (data_layout == DataLayout::NCHW))
{
- ARM_COMPUTE_RETURN_ON_ERROR(NECol2ImKernel::validate(is_quantized ? gemm_output_staged_to_use : gemm_output_to_use,
- output,
- Size2D(conv_w, conv_h)));
+ ARM_COMPUTE_RETURN_ON_ERROR(NECol2ImKernel::validate(gemm_output_to_use, output, Size2D(conv_w, conv_h)));
}
//Validate Activation Layer
@@ -586,9 +560,6 @@
{
// Run gemmlowp
_mm_gemmlowp.run();
-
- // Run output stage
- _gemmlowp_output_stage.run();
}
else
{
diff --git a/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp b/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp
index 5286f11..85e49fd 100644
--- a/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp
+++ b/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp
@@ -42,8 +42,8 @@
NEGEMMLowpMatrixMultiplyCore::NEGEMMLowpMatrixMultiplyCore(std::shared_ptr<IMemoryManager> memory_manager)
: _memory_group(memory_manager), _asm_glue(memory_manager), _mm_kernel(nullptr), _mtx_a_reshape_kernel(nullptr), _mtx_b_reshape_kernel(nullptr), _mtx_a_reduction_kernel(), _mtx_b_reduction_kernel(),
- _offset_contribution_kernel(), _vector_sum_col(), _vector_sum_row(), _tmp_a(), _tmp_b(), _original_b(nullptr), _a_offset(0), _b_offset(0), _run_vector_matrix_multiplication(false),
- _dot_product_path(false), _reshape_b_only_on_first_run(false), _is_prepared(false)
+ _offset_contribution_kernel(), _offset_contribution_output_stage_kernel(), _vector_sum_col(), _vector_sum_row(), _tmp_a(), _tmp_b(), _mm_result_s32(), _original_b(nullptr), _a_offset(0), _b_offset(0),
+ _run_vector_matrix_multiplication(false), _dot_product_path(false), _reshape_b_only_on_first_run(false), _is_prepared(false), _fuse_output_stage(false)
{
}
@@ -53,6 +53,9 @@
ARM_COMPUTE_UNUSED(c);
ARM_COMPUTE_ERROR_THROW_ON(NEGEMMLowpMatrixMultiplyCore::validate(a->info(), b->info(), c != nullptr ? c->info() : nullptr, output->info(), gemm_info));
+ const ITensor *matrix_a = a;
+ const ITensor *matrix_b = b;
+
// Clear state
_mtx_a_reshape_kernel = nullptr;
_mtx_b_reshape_kernel = nullptr;
@@ -65,6 +68,18 @@
_is_prepared = false;
_original_b = b;
+ // If GEMMLowpOutputStage != NONE, fuse the offset contribution with the output stage
+ if(gemm_info.gemmlowp_output_stage().type != GEMMLowpOutputStageType::NONE)
+ {
+ _fuse_output_stage = true;
+
+ _memory_group.manage(&_mm_result_s32);
+
+ TensorInfo info_mm_result_s32(output->info()->tensor_shape(), 1, DataType::S32);
+
+ _mm_result_s32.allocator()->init(info_mm_result_s32);
+ }
+
#ifdef __aarch64__
switch(a->info()->data_type())
{
@@ -72,7 +87,7 @@
case DataType::U8:
case DataType::S8:
{
- _asm_glue.configure(a, b, output, 1.f, 0.f, _reshape_b_only_on_first_run);
+ _asm_glue.configure(a, b, _fuse_output_stage ? &_mm_result_s32 : output, 1.f, 0.f, _reshape_b_only_on_first_run);
_dot_product_path = _asm_glue.is_configured();
break;
}
@@ -83,51 +98,35 @@
}
}
#endif /* __aarch64__ */
- if(!_dot_product_path)
+ if(!(_dot_product_path || _run_vector_matrix_multiplication))
{
- if(_run_vector_matrix_multiplication)
+ matrix_a = &_tmp_a;
+ matrix_b = &_tmp_b;
+
+ // The interleaved output matrix will have the following shape: [ a_height * 4, ceil(a_width / 4.0f) ]
+ TensorInfo a_info(compute_interleaved_shape(*a->info()), 1, a->info()->data_type());
+ // The transpose1xW output matrix will have the following shape: [ b_height * 16, ceil(b_width / 16.0f) ]
+ TensorInfo b_info(compute_transpose1xW_shape(*b->info()), 1, b->info()->data_type());
+ _tmp_a.allocator()->init(a_info);
+ _tmp_b.allocator()->init(b_info);
+ _memory_group.manage(&_tmp_a);
+ if(!_reshape_b_only_on_first_run)
{
- // Configure matrix multiply kernel
- {
- auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpMatrixMultiplyKernel>();
- k->configure(a, b, output);
- _mm_kernel = std::move(k);
- }
+ _memory_group.manage(&_tmp_b);
}
- else
+
+ // Configure interleave kernel
{
- // The interleaved output matrix will have the following shape: [ a_height * 4, ceil(a_width / 4.0f) ]
- TensorInfo info_a = a->info()->clone()->set_tensor_shape(compute_interleaved_shape(*a->info())).set_is_resizable(true);
- // The transpose1xW output matrix will have the following shape: [ b_height * 16, ceil(b_width / 16.0f) ]
- TensorInfo info_b = b->info()->clone()->set_tensor_shape(compute_transpose1xW_shape(*b->info())).set_is_resizable(true);
- _tmp_a.allocator()->init(info_a);
- _tmp_b.allocator()->init(info_b);
- _memory_group.manage(&_tmp_a);
- if(!_reshape_b_only_on_first_run)
- {
- _memory_group.manage(&_tmp_b);
- }
+ auto k = arm_compute::support::cpp14::make_unique<NEGEMMInterleave4x4Kernel>();
+ k->configure(a, &_tmp_a);
+ _mtx_a_reshape_kernel = std::move(k);
+ }
- // Configure interleave kernel
- {
- auto k = arm_compute::support::cpp14::make_unique<NEGEMMInterleave4x4Kernel>();
- k->configure(a, &_tmp_a);
- _mtx_a_reshape_kernel = std::move(k);
- }
-
- // Configure transpose kernel
- {
- auto k = arm_compute::support::cpp14::make_unique<NEGEMMTranspose1xWKernel>();
- k->configure(b, &_tmp_b);
- _mtx_b_reshape_kernel = std::move(k);
- }
-
- // Configure matrix multiply kernel
- {
- auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpMatrixMultiplyKernel>();
- k->configure(&_tmp_a, &_tmp_b, output);
- _mm_kernel = std::move(k);
- }
+ // Configure transpose kernel
+ {
+ auto k = arm_compute::support::cpp14::make_unique<NEGEMMTranspose1xWKernel>();
+ k->configure(b, &_tmp_b);
+ _mtx_b_reshape_kernel = std::move(k);
}
}
@@ -158,8 +157,33 @@
_mtx_a_reduction_kernel.configure(a, &_vector_sum_row, a->info()->dimension(0), false);
}
- // Configure offset contribution kernel
- _offset_contribution_kernel.configure(output, _a_offset == 0 ? nullptr : &_vector_sum_col, _b_offset == 0 ? nullptr : &_vector_sum_row, a->info()->dimension(0), _a_offset, _b_offset);
+ if(_fuse_output_stage)
+ {
+ // Configure matrix multiply kernel
+ if(!_dot_product_path)
+ {
+ auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpMatrixMultiplyKernel>();
+ k->configure(matrix_a, matrix_b, &_mm_result_s32);
+ _mm_kernel = std::move(k);
+ }
+
+ _offset_contribution_output_stage_kernel.configure(&_mm_result_s32, _a_offset == 0 ? nullptr : &_vector_sum_col, _b_offset == 0 ? nullptr : &_vector_sum_row, c, output, a->info()->dimension(0),
+ _a_offset, _b_offset, gemm_info.gemmlowp_output_stage());
+
+ _mm_result_s32.allocator()->allocate();
+ }
+ else
+ {
+ // Configure matrix multiply kernel
+ if(!_dot_product_path)
+ {
+ auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpMatrixMultiplyKernel>();
+ k->configure(matrix_a, matrix_b, output);
+ _mm_kernel = std::move(k);
+ }
+ // Configure offset contribution kernel
+ _offset_contribution_kernel.configure(output, _a_offset == 0 ? nullptr : &_vector_sum_col, _b_offset == 0 ? nullptr : &_vector_sum_row, a->info()->dimension(0), _a_offset, _b_offset);
+ }
// Allocate tensors
if(!_dot_product_path && !_run_vector_matrix_multiplication)
@@ -185,43 +209,53 @@
Status NEGEMMLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, const GEMMInfo &gemm_info)
{
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::QASYMM8);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32, DataType::QASYMM8);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(a, b);
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(c != nullptr, "Bias addition not supported in NEGEMMLowpMatrixMultiplyCore");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(c != nullptr && gemm_info.gemmlowp_output_stage().type == GEMMLowpOutputStageType::NONE, "Bias addition not supported in NEGEMMLowpMatrixMultiplyCore for output S32");
ARM_COMPUTE_RETURN_ERROR_ON_MSG((a)->dimension(0) != (b)->dimension(1),
"The product AB is defined only if the number of columns in A is equal to the number of rows in B");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_a_reshaped(), "Matrix A already reshaped is not supported");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_b_reshaped(), "Matrix B already reshaped is not supported");
+ const ITensorInfo *matrix_a_info = a;
+ const ITensorInfo *matrix_b_info = b;
+
+ TensorInfo tmp_a_info{};
+ TensorInfo tmp_b_info{};
+ TensorInfo mm_result_s32_info{};
+
int32_t a_offset = a->quantization_info().offset;
int32_t b_offset = b->quantization_info().offset;
const bool reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run();
+ bool fuse_output_stage = gemm_info.gemmlowp_output_stage().type != GEMMLowpOutputStageType::NONE;
+ if(fuse_output_stage)
+ {
+ auto_init_if_empty(mm_result_s32_info, a->clone()->set_tensor_shape(output->tensor_shape()).set_data_type(DataType::S32));
+ }
+
// Check if we need to run the optimized assembly kernel
- const bool run_optimised = bool(NEGEMMAssemblyDispatch::validate(a, b, output, 1.f, 0.f, reshape_b_only_on_first_run));
+ const bool run_optimised = bool(NEGEMMAssemblyDispatch::validate(a, b, fuse_output_stage ? &mm_result_s32_info : output, 1.f, 0.f, reshape_b_only_on_first_run));
if(run_optimised)
{
- if(output->total_size() != 0)
+ ARM_COMPUTE_RETURN_ERROR_ON(b->dimension(0) != output->dimension(0));
+ if(gemm_info.depth_output_gemm3d() != 0)
{
- ARM_COMPUTE_RETURN_ERROR_ON(b->dimension(0) != output->dimension(0));
- if(gemm_info.depth_output_gemm3d() != 0)
+ if(gemm_info.reinterpret_input_as_3d())
{
- if(gemm_info.reinterpret_input_as_3d())
- {
- ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(1) != output->dimension(1));
- ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(2) != output->dimension(2));
- }
- else
- {
- ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(1) != output->dimension(1) * output->dimension(2));
- }
+ ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(1) != output->dimension(1));
+ ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(2) != output->dimension(2));
}
else
{
- ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(1) != output->dimension(1));
+ ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(1) != output->dimension(1) * output->dimension(2));
}
}
+ else
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(1) != output->dimension(1));
+ }
}
else
{
@@ -231,6 +265,9 @@
const bool run_vector_matrix_multiplication = a->dimension(1) < 2;
if(!run_vector_matrix_multiplication)
{
+ matrix_a_info = &tmp_a_info;
+ matrix_b_info = &tmp_b_info;
+
// The interleaved output matrix will have the following shape: [ a_height * 4, ceil(a_width / 4.0f) ]
TensorShape shape_tmp_a = a->tensor_shape();
shape_tmp_a.set(0, a->dimension(0) * 4);
@@ -241,16 +278,12 @@
shape_tmp_b.set(0, b->dimension(1) * 16);
shape_tmp_b.set(1, std::ceil(b->dimension(0) / 16.f));
- TensorInfo info_a = a->clone()->set_tensor_shape(shape_tmp_a).set_is_resizable(true);
- TensorInfo info_b = b->clone()->set_tensor_shape(shape_tmp_b).set_is_resizable(true);
+ // Validate interleave kernel
+ auto_init_if_empty(tmp_a_info, a->clone()->set_tensor_shape(shape_tmp_a));
+ auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(shape_tmp_b));
- ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMInterleave4x4Kernel::validate(a, &info_a));
- ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMTranspose1xWKernel::validate(b, &info_b));
- ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixMultiplyKernel::validate(&info_a, &info_b, output));
- }
- else
- {
- ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixMultiplyKernel::validate(a, b, output));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMInterleave4x4Kernel::validate(a, &tmp_a_info));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMTranspose1xWKernel::validate(b, &tmp_b_info));
}
}
@@ -274,12 +307,32 @@
ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixAReductionKernel::validate(a, &info_vector_sum_row, a->dimension(0), false));
}
- // Validate offset contribution kernel
- ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpOffsetContributionKernel::validate(output,
- a_offset == 0 ? nullptr : &info_vector_sum_col,
- b_offset == 0 ? nullptr : &info_vector_sum_row,
- a_offset, b_offset));
+ if(fuse_output_stage)
+ {
+ if(!run_optimised)
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixMultiplyKernel::validate(matrix_a_info, matrix_b_info, &mm_result_s32_info));
+ }
+ // Validate offset contribution kernel
+ ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpOffsetContributionOutputStageKernel::validate(&mm_result_s32_info,
+ a_offset == 0 ? nullptr : &info_vector_sum_col,
+ b_offset == 0 ? nullptr : &info_vector_sum_row,
+ c, output, a_offset, b_offset,
+ gemm_info.gemmlowp_output_stage()));
+ }
+ else
+ {
+ if(!run_optimised)
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixMultiplyKernel::validate(matrix_a_info, matrix_b_info, output));
+ }
+ // Validate offset contribution kernel
+ ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpOffsetContributionKernel::validate(output,
+ a_offset == 0 ? nullptr : &info_vector_sum_col,
+ b_offset == 0 ? nullptr : &info_vector_sum_row,
+ a_offset, b_offset));
+ }
return Status{};
}
@@ -321,8 +374,16 @@
NEScheduler::get().schedule(&_mtx_b_reduction_kernel, Window::DimX);
}
- // Run offset contribution kernel
- NEScheduler::get().schedule(&_offset_contribution_kernel, Window::DimY);
+ if(_fuse_output_stage)
+ {
+ // Run offset contribution kernel
+ NEScheduler::get().schedule(&_offset_contribution_output_stage_kernel, Window::DimY);
+ }
+ else
+ {
+ // Run offset contribution kernel
+ NEScheduler::get().schedule(&_offset_contribution_kernel, Window::DimY);
+ }
_memory_group.release();
}