/* * Copyright (c) 2020-2022 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/SVEMath.h" #include "src/core/NEON/wrapper/intrinsics/intrinsics.h" #include namespace arm_compute { namespace cpu { void add_qsymm16_sve2( 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 auto window_start_x = static_cast(window.x().start()); const auto window_end_x = static_cast(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 auto vscale1 = svdup_n_f32(iq1_info.scale); const auto vscale2 = svdup_n_f32(iq2_info.scale); const auto invvscaleo = svdup_n_f32(1.f / oq_info.scale); const auto all_true_pg = svptrue_b16(); 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; // 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(non_broadcast_input.ptr()); const auto output_ptr = reinterpret_cast(output.ptr()); const int16_t broadcast_value = *reinterpret_cast(broadcast_input.ptr()); const auto broadcast_value_vec = svdup_n_s16(broadcast_value); int x = window_start_x; svbool_t pg = svwhilelt_b16(x, window_end_x); const auto bf_0 = svmul_f32_z(pg, svcvt_f32_s32_z(pg, svmovlb_s32(broadcast_value_vec)), vscale2); const auto bf_1 = svmul_f32_z(pg, svcvt_f32_s32_z(pg, svmovlt_s32(broadcast_value_vec)), vscale2); do { const auto a = svld1_s16(pg, non_broadcast_input_ptr + x); const auto af_0 = svmul_f32_z(pg, svcvt_f32_s32_z(pg, svmovlb_s32(a)), vscale1); const auto af_1 = svmul_f32_z(pg, svcvt_f32_s32_z(pg, svmovlt_s32(a)), vscale1); const auto rf_0 = svcvt_s32_f32_z(pg, svmul_f32_z(pg, svadd_f32_z(pg, af_0, bf_0), invvscaleo)); const auto rf_1 = svcvt_s32_f32_z(pg, svmul_f32_z(pg, svadd_f32_z(pg, af_1, bf_1), invvscaleo)); const auto res = svqxtnt_s32(svqxtnb_s32(rf_0), rf_1); svst1_s16(pg, output_ptr + x, res); x += svcnth(); pg = svwhilelt_b16(x, window_end_x); } while (svptest_any(all_true_pg, pg)); }, 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(input1.ptr()); const auto input2_ptr = reinterpret_cast(input2.ptr()); const auto output_ptr = reinterpret_cast(output.ptr()); int x = window_start_x; svbool_t pg = svwhilelt_b16(x, window_end_x); do { auto a = svld1_s16(pg, input1_ptr + x); auto b = svld1_s16(pg, input2_ptr + x); const auto af_0 = svmul_f32_z(pg, svcvt_f32_s32_z(pg, svmovlb_s32(a)), vscale1); const auto af_1 = svmul_f32_z(pg, svcvt_f32_s32_z(pg, svmovlt_s32(a)), vscale1); const auto bf_0 = svmul_f32_z(pg, svcvt_f32_s32_z(pg, svmovlb_s32(b)), vscale2); const auto bf_1 = svmul_f32_z(pg, svcvt_f32_s32_z(pg, svmovlt_s32(b)), vscale2); const auto rf_0 = svcvt_s32_f32_z(pg, svmul_f32_z(pg, svadd_f32_z(pg, af_0, bf_0), invvscaleo)); const auto rf_1 = svcvt_s32_f32_z(pg, svmul_f32_z(pg, svadd_f32_z(pg, af_1, bf_1), invvscaleo)); const auto res = svqxtnt_s32(svqxtnb_s32(rf_0), rf_1); svst1_s16(pg, output_ptr + x, res); x += svcnth(); pg = svwhilelt_b16(x, window_end_x); } while (svptest_any(all_true_pg, pg)); }, input1, input2, output); } } } // namespace cpu } // namespace arm_compute