| /* |
| * Copyright (c) 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. |
| */ |
| #ifndef SRC_CORE_SVE_KERNELS_SOFTMAX_LIST_H |
| #define SRC_CORE_SVE_KERNELS_SOFTMAX_LIST_H |
| |
| #if defined(ARM_COMPUTE_ENABLE_SVE) |
| #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 <arm_sve.h> |
| |
| namespace arm_compute |
| { |
| namespace cpu |
| { |
| template <typename ScalarType> |
| void sve_logits_1d_max(const ITensor *in, ITensor *out, const Window &window); |
| |
| template <typename ScalarType> |
| void sve_softmax_logits_1d_float(const ITensor *in, const ITensor *max, void *const tmp, |
| ITensor *out, const float beta, bool is_log, const Window &window); |
| |
| #if defined(ARM_COMPUTE_ENABLE_SVE2) |
| template <typename ScalarType> |
| void sve_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<ScalarType>(); |
| using SVEType = typename wrapper::traits::sve_vector<ScalarType>::type; |
| |
| const int inc_1 = static_cast<int>(svcntw()); |
| const int inc_2 = static_cast<int>(2 * svcntw()); |
| const int inc_3 = static_cast<int>(3 * svcntw()); |
| |
| execute_window_loop(window, [&](const Coordinates &) |
| { |
| /* Get pointers */ |
| const auto in_ptr = reinterpret_cast<const ScalarType *>(in_it.ptr()) + start_x; |
| const auto out_ptr = reinterpret_cast<ScalarType *>(out_it.ptr()) + start_x; |
| const auto tmp_ptr = reinterpret_cast<float *>(tmp); |
| |
| float sum{}; |
| |
| /* Compute exponentials and sum */ |
| { |
| /* Get max value */ |
| const auto max_val = *reinterpret_cast<const ScalarType *>(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<ScalarType>(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_elements = svld1(pg, in_ptr + x); |
| vec_elements = svsub_z(pg, vec_max, vec_elements); |
| |
| auto vec_elements_flt_0 = svcvt_f32_z(pg_0, svunpklo(svunpklo(vec_elements))); |
| auto vec_elements_flt_1 = svcvt_f32_z(pg_1, svunpkhi(svunpklo(vec_elements))); |
| auto vec_elements_flt_2 = svcvt_f32_z(pg_2, svunpklo(svunpkhi(vec_elements))); |
| auto vec_elements_flt_3 = svcvt_f32_z(pg_3, svunpkhi(svunpkhi(vec_elements))); |
| |
| 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<ScalarType>(); |
| pg = wrapper::svwhilelt<ScalarType>(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<ScalarType, qasymm8_signed_t>::value; |
| /* Loop over row and compute softmax */ |
| int x = 0; |
| svbool_t pg = wrapper::svwhilelt<ScalarType>(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, vec_in_0, offset_vec); |
| res_1 = svsub_z(pg_1, vec_in_1, offset_vec); |
| res_2 = svsub_z(pg_2, vec_in_2, offset_vec); |
| res_3 = svsub_z(pg_3, vec_in_3, offset_vec); |
| } |
| } |
| |
| // Store value |
| const auto out = convert_float_to_int<SVEType>(res_0, res_1, res_2, res_3); |
| svst1(pg, out_ptr + x, out); |
| x += wrapper::svcnt<ScalarType>(); |
| pg = wrapper::svwhilelt<ScalarType>(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); |
| } |
| #endif /* defined(ARM_COMPUTE_ENABLE_SVE2) */ |
| } // namespace cpu |
| } // namespace arm_compute |
| #endif /* defined(ARM_COMPUTE_ENABLE_SVE) */ |
| |
| #endif /* SRC_CORE_SVE_KERNELS_SOFTMAX_LIST_H */ |