| /* |
| * Copyright (c) 2020-2021 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/Helpers.h" |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/utils/misc/Traits.h" |
| #include "src/core/NEON/wrapper/intrinsics/intrinsics.h" |
| #include "src/core/helpers/WindowHelpers.h" |
| |
| namespace arm_compute |
| { |
| namespace cpu |
| { |
| void add_qsymm16_neon(const ITensor *src0, const ITensor *src1, ITensor *dst, const ConvertPolicy &policy, const Window &window) |
| { |
| ARM_COMPUTE_UNUSED(policy); |
| |
| // Create input windows |
| Window input1_win = window.broadcast_if_dimension_le_one(src0->info()->tensor_shape()); |
| Window input2_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape()); |
| |
| // Clear X Dimension on execution window as we handle manually |
| Window win = window; |
| win.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| const int window_step_x = 8; |
| const auto window_start_x = static_cast<int>(window.x().start()); |
| const auto window_end_x = static_cast<int>(window.x().end()); |
| const bool is_broadcast_across_x = src0->info()->tensor_shape().x() != src1->info()->tensor_shape().x(); |
| |
| const UniformQuantizationInfo iq1_info = src0->info()->quantization_info().uniform(); |
| const UniformQuantizationInfo iq2_info = src1->info()->quantization_info().uniform(); |
| const UniformQuantizationInfo oq_info = dst->info()->quantization_info().uniform(); |
| |
| const float32x4_t vscale1 = vdupq_n_f32(iq1_info.scale); |
| const float32x4_t vscale2 = vdupq_n_f32(iq2_info.scale); |
| const float32x4_t invvscaleo = vdupq_n_f32(1.f / oq_info.scale); |
| |
| if(is_broadcast_across_x) |
| { |
| const bool is_broadcast_input_2 = input2_win.x().step() == 0; |
| Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win; |
| Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win; |
| const ITensor *broadcast_tensor = is_broadcast_input_2 ? src1 : src0; |
| const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? src1 : src0; |
| const UniformQuantizationInfo broadcast_qinfo = broadcast_tensor->info()->quantization_info().uniform(); |
| const UniformQuantizationInfo non_broadcast_qinfo = non_broadcast_tensor->info()->quantization_info().uniform(); |
| |
| // Clear X Dimension on execution window as we handle manually |
| non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| Iterator broadcast_input(broadcast_tensor, broadcast_win); |
| Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win); |
| Iterator output(dst, win); |
| |
| execute_window_loop(win, [&](const Coordinates &) |
| { |
| const auto non_broadcast_input_ptr = reinterpret_cast<const int16_t *>(non_broadcast_input.ptr()); |
| const auto output_ptr = reinterpret_cast<int16_t *>(output.ptr()); |
| |
| const int16_t broadcast_value = *reinterpret_cast<const int16_t *>(broadcast_input.ptr()); |
| const int16x8_t broadcast_value_vec = vdupq_n_s16(broadcast_value); |
| |
| const auto bf_0 = vmulq_f32(vcvtq_f32_s32(vmovl_s16(vget_low_s16(broadcast_value_vec))), vscale2); |
| const auto bf_1 = vmulq_f32(vcvtq_f32_s32(vmovl_s16(vget_high_s16(broadcast_value_vec))), vscale2); |
| const float bfs = static_cast<int32_t>(broadcast_value) * broadcast_qinfo.scale; |
| |
| // Compute S elements per iteration |
| int x = window_start_x; |
| for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| const int16x8_t a = vld1q_s16(non_broadcast_input_ptr + x); |
| const auto af_0 = vmulq_f32(vcvtq_f32_s32(vmovl_s16(vget_low_s16(a))), vscale1); |
| const auto af_1 = vmulq_f32(vcvtq_f32_s32(vmovl_s16(vget_high_s16(a))), vscale1); |
| |
| int32x4_t rf_0{}; |
| int32x4_t rf_1{}; |
| #ifdef __aarch64__ |
| rf_0 = vcvtnq_s32_f32(vmulq_f32(vaddq_f32(af_0, bf_0), invvscaleo)); |
| rf_1 = vcvtnq_s32_f32(vmulq_f32(vaddq_f32(af_1, bf_1), invvscaleo)); |
| #else //__aarch64__ |
| rf_0 = vcvtq_s32_f32(vmulq_f32(vaddq_f32(af_0, bf_0), invvscaleo)); |
| rf_1 = vcvtq_s32_f32(vmulq_f32(vaddq_f32(af_1, bf_1), invvscaleo)); |
| #endif //__aarch64__ |
| |
| const int16x8_t pa = vcombine_s16(vqmovn_s32(rf_0), vqmovn_s32(rf_1)); |
| vst1q_s16(output_ptr + x, pa); |
| } |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| const float afs = static_cast<int32_t>(*(non_broadcast_input_ptr + x)) * non_broadcast_qinfo.scale; |
| *(output_ptr + x) = quantize_qsymm16((afs + bfs), oq_info); |
| } |
| }, |
| broadcast_input, non_broadcast_input, output); |
| } |
| else |
| { |
| // Clear X Dimension on execution window as we handle manually |
| input1_win.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| input2_win.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| Iterator input1(src0, input1_win); |
| Iterator input2(src1, input2_win); |
| Iterator output(dst, win); |
| |
| execute_window_loop(win, [&](const Coordinates &) |
| { |
| const auto input1_ptr = reinterpret_cast<const int16_t *>(input1.ptr()); |
| const auto input2_ptr = reinterpret_cast<const int16_t *>(input2.ptr()); |
| const auto output_ptr = reinterpret_cast<int16_t *>(output.ptr()); |
| |
| // Compute S elements per iteration |
| int x = window_start_x; |
| for(; x <= (window_end_x - window_step_x); x += window_step_x) |
| { |
| const int16x8_t a = vld1q_s16(input1_ptr + x); |
| const int16x8_t b = vld1q_s16(input2_ptr + x); |
| |
| const auto af_0 = vmulq_f32(vcvtq_f32_s32(vmovl_s16(vget_low_s16(a))), vscale1); |
| const auto af_1 = vmulq_f32(vcvtq_f32_s32(vmovl_s16(vget_high_s16(a))), vscale1); |
| const auto bf_0 = vmulq_f32(vcvtq_f32_s32(vmovl_s16(vget_low_s16(b))), vscale2); |
| const auto bf_1 = vmulq_f32(vcvtq_f32_s32(vmovl_s16(vget_high_s16(b))), vscale2); |
| |
| int32x4_t rf_0{}; |
| int32x4_t rf_1{}; |
| #ifdef __aarch64__ |
| rf_0 = vcvtnq_s32_f32(vmulq_f32(vaddq_f32(af_0, bf_0), invvscaleo)); |
| rf_1 = vcvtnq_s32_f32(vmulq_f32(vaddq_f32(af_1, bf_1), invvscaleo)); |
| #else //__aarch64__ |
| rf_0 = vcvtq_s32_f32(vmulq_f32(vaddq_f32(af_0, bf_0), invvscaleo)); |
| rf_1 = vcvtq_s32_f32(vmulq_f32(vaddq_f32(af_1, bf_1), invvscaleo)); |
| #endif //__aarch64__ |
| |
| const int16x8_t pa = vcombine_s16(vqmovn_s32(rf_0), vqmovn_s32(rf_1)); |
| vst1q_s16(output_ptr + x, pa); |
| } |
| |
| // Compute left-over elements |
| for(; x < window_end_x; ++x) |
| { |
| const float afs = static_cast<int32_t>((*(input1_ptr + x))) * iq1_info.scale; |
| const float bfs = static_cast<int32_t>((*(input2_ptr + x))) * iq2_info.scale; |
| *(output_ptr + x) = quantize_qsymm16((afs + bfs), dst->info()->quantization_info()); |
| } |
| }, |
| input1, input2, output); |
| } |
| } |
| } // namespace cpu |
| } // namespace arm_compute |
