/* * Copyright (c) 2021-2022 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 "src/cpu/kernels/softmax/generic/sve2/impl.h" #include "arm_compute/core/Types.h" #include "src/core/NEON/wrapper/wrapper.h" namespace arm_compute { namespace cpu { template void sve2_softmax_logits_1d_quantized(const ITensor *in, const ITensor *max, void *const tmp, ITensor *out, float beta, bool is_log, const Window &window) { const int start_x = in->info()->valid_region().anchor.x(); const int input_width = in->info()->valid_region().shape.x(); const float scale_beta = -beta * in->info()->quantization_info().uniform().scale; const auto scale_beta_vec = svdup_n_f32(scale_beta); Iterator in_it(in, window); Iterator max_it(max, window); Iterator out_it(out, window); const auto all_true_pg = wrapper::svptrue(); using SVEType = typename wrapper::traits::sve_vector::type; const int inc_1 = static_cast(svcntw()); const int inc_2 = static_cast(2 * svcntw()); const int inc_3 = static_cast(3 * svcntw()); execute_window_loop(window, [&](const Coordinates &) { /* Get pointers */ const auto in_ptr = reinterpret_cast(in_it.ptr()) + start_x; const auto out_ptr = reinterpret_cast(out_it.ptr()) + start_x; const auto tmp_ptr = reinterpret_cast(tmp); float sum{}; /* Compute exponentials and sum */ { /* Get max value */ const auto max_val = *reinterpret_cast(max_it.ptr()); const auto vec_max = wrapper::svdup_n(max_val); /* Init sum to zero */ auto vec_sum_0 = svdup_n_f32(0.f); auto vec_sum_1 = svdup_n_f32(0.f); auto vec_sum_2 = svdup_n_f32(0.f); auto vec_sum_3 = svdup_n_f32(0.f); /* Loop over row and compute exponentials and sum */ int x = 0; svbool_t pg = wrapper::svwhilelt(x, input_width); svbool_t pg_0 = svunpklo(svunpklo(pg)); svbool_t pg_1 = svunpkhi(svunpklo(pg)); svbool_t pg_2 = svunpklo(svunpkhi(pg)); svbool_t pg_3 = svunpkhi(svunpkhi(pg)); do { const auto vec_elements = svld1(pg, in_ptr + x); const auto vec_elements_sub = svreinterpret_u8(svsub_z(pg, vec_max, vec_elements)); auto vec_elements_flt_0 = svcvt_f32_z(pg_0, svunpklo(svunpklo(vec_elements_sub))); auto vec_elements_flt_1 = svcvt_f32_z(pg_1, svunpkhi(svunpklo(vec_elements_sub))); auto vec_elements_flt_2 = svcvt_f32_z(pg_2, svunpklo(svunpkhi(vec_elements_sub))); auto vec_elements_flt_3 = svcvt_f32_z(pg_3, svunpkhi(svunpkhi(vec_elements_sub))); if(is_log) { vec_elements_flt_0 = svmul_f32_z(pg_0, vec_elements_flt_0, scale_beta_vec); vec_elements_flt_1 = svmul_f32_z(pg_1, vec_elements_flt_1, scale_beta_vec); vec_elements_flt_2 = svmul_f32_z(pg_2, vec_elements_flt_2, scale_beta_vec); vec_elements_flt_3 = svmul_f32_z(pg_3, vec_elements_flt_3, scale_beta_vec); vec_sum_0 = svadd_f32_m(pg_0, vec_sum_0, svexp_f32_z(pg_0, vec_elements_flt_0)); vec_sum_1 = svadd_f32_m(pg_1, vec_sum_1, svexp_f32_z(pg_1, vec_elements_flt_1)); vec_sum_2 = svadd_f32_m(pg_2, vec_sum_2, svexp_f32_z(pg_2, vec_elements_flt_2)); vec_sum_3 = svadd_f32_m(pg_3, vec_sum_3, svexp_f32_z(pg_3, vec_elements_flt_3)); } else { vec_elements_flt_0 = svexp_f32_z(pg_0, svmul_f32_z(pg_0, vec_elements_flt_0, scale_beta_vec)); vec_elements_flt_1 = svexp_f32_z(pg_1, svmul_f32_z(pg_1, vec_elements_flt_1, scale_beta_vec)); vec_elements_flt_2 = svexp_f32_z(pg_2, svmul_f32_z(pg_2, vec_elements_flt_2, scale_beta_vec)); vec_elements_flt_3 = svexp_f32_z(pg_3, svmul_f32_z(pg_3, vec_elements_flt_3, scale_beta_vec)); vec_sum_0 = svadd_f32_m(pg_0, vec_sum_0, vec_elements_flt_0); vec_sum_1 = svadd_f32_m(pg_1, vec_sum_1, vec_elements_flt_1); vec_sum_2 = svadd_f32_m(pg_2, vec_sum_2, vec_elements_flt_2); vec_sum_3 = svadd_f32_m(pg_3, vec_sum_3, vec_elements_flt_3); } svst1_f32(pg_0, tmp_ptr + x, vec_elements_flt_0); svst1_f32(pg_1, tmp_ptr + x + inc_1, vec_elements_flt_1); svst1_f32(pg_2, tmp_ptr + x + inc_2, vec_elements_flt_2); svst1_f32(pg_3, tmp_ptr + x + inc_3, vec_elements_flt_3); x += wrapper::svcnt(); pg = wrapper::svwhilelt(x, input_width); pg_0 = svunpklo(svunpklo(pg)); pg_1 = svunpkhi(svunpklo(pg)); pg_2 = svunpklo(svunpkhi(pg)); pg_3 = svunpkhi(svunpkhi(pg)); } while(svptest_any(all_true_pg, pg)); /* Reduce sum */ const auto vec_sum = svadd_f32_z(all_true_pg, svadd_f32_z(all_true_pg, vec_sum_0, vec_sum_1), svadd_f32_z(all_true_pg, vec_sum_2, vec_sum_3)); sum = svaddv_f32(all_true_pg, vec_sum); /* Run remaining elements */ x = 0; if(is_log) { sum = std::log(sum); } else { sum = 256.f / sum; } } /* Normalize exponentials */ { constexpr bool is_qasymm8_signed = std::is_same::value; /* Loop over row and compute softmax */ int x = 0; svbool_t pg = wrapper::svwhilelt(x, input_width); svbool_t pg_0 = svunpklo(svunpklo(pg)); svbool_t pg_1 = svunpkhi(svunpklo(pg)); svbool_t pg_2 = svunpklo(svunpkhi(pg)); svbool_t pg_3 = svunpkhi(svunpkhi(pg)); do { auto vec_in_0 = svld1_f32(pg_0, tmp_ptr + x); auto vec_in_1 = svld1_f32(pg_1, tmp_ptr + x + inc_1); auto vec_in_2 = svld1_f32(pg_2, tmp_ptr + x + inc_2); auto vec_in_3 = svld1_f32(pg_3, tmp_ptr + x + inc_3); svfloat32_t res_0{}; svfloat32_t res_1{}; svfloat32_t res_2{}; svfloat32_t res_3{}; if(is_log) { res_0 = svsub_f32_z(pg_0, vec_in_0, svdup_n_f32(sum)); res_1 = svsub_f32_z(pg_1, vec_in_1, svdup_n_f32(sum)); res_2 = svsub_f32_z(pg_2, vec_in_2, svdup_n_f32(sum)); res_3 = svsub_f32_z(pg_3, vec_in_3, svdup_n_f32(sum)); } else { res_0 = svmul_f32_z(pg_0, vec_in_0, svdup_n_f32(sum)); res_1 = svmul_f32_z(pg_1, vec_in_1, svdup_n_f32(sum)); res_2 = svmul_f32_z(pg_2, vec_in_2, svdup_n_f32(sum)); res_3 = svmul_f32_z(pg_3, vec_in_3, svdup_n_f32(sum)); if(is_qasymm8_signed) { const auto offset_vec = svdup_n_f32(128.f); res_0 = svsub_z(pg_0, res_0, offset_vec); res_1 = svsub_z(pg_1, res_1, offset_vec); res_2 = svsub_z(pg_2, res_2, offset_vec); res_3 = svsub_z(pg_3, res_3, offset_vec); } } // Store value const auto out = convert_float_to_int(res_0, res_1, res_2, res_3); svst1(pg, out_ptr + x, out); x += wrapper::svcnt(); pg = wrapper::svwhilelt(x, input_width); pg_0 = svunpklo(svunpklo(pg)); pg_1 = svunpkhi(svunpklo(pg)); pg_2 = svunpklo(svunpkhi(pg)); pg_3 = svunpkhi(svunpkhi(pg)); } while(svptest_any(all_true_pg, pg)); } }, in_it, max_it, out_it); } template void sve2_softmax_logits_1d_quantized(const ITensor *in, const ITensor *max, void *const tmp, ITensor *out, float beta, bool is_log, const Window &window); template void sve2_softmax_logits_1d_quantized(const ITensor *in, const ITensor *max, void *const tmp, ITensor *out, float beta, bool is_log, const Window &window); } // namespace cpu } // namespace arm_compute