diff options
Diffstat (limited to 'src/core/NEON/kernels/softmax/impl/SVE/list.h')
-rw-r--r-- | src/core/NEON/kernels/softmax/impl/SVE/list.h | 429 |
1 files changed, 0 insertions, 429 deletions
diff --git a/src/core/NEON/kernels/softmax/impl/SVE/list.h b/src/core/NEON/kernels/softmax/impl/SVE/list.h deleted file mode 100644 index 0936bd5a56..0000000000 --- a/src/core/NEON/kernels/softmax/impl/SVE/list.h +++ /dev/null @@ -1,429 +0,0 @@ -/* - * 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_FEATURE_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 -{ -namespace -{ -#if defined(__ARM_FEATURE_SVE2) -template <typename int_vec_type> -int_vec_type convert_float_to_int(const svfloat32_t &in_0, const svfloat32_t &in_1, const svfloat32_t &in_2, const svfloat32_t &in_3); - -template <> -svuint8_t convert_float_to_int<svuint8_t>(const svfloat32_t &in_0, const svfloat32_t &in_1, const svfloat32_t &in_2, const svfloat32_t &in_3) -{ - svuint8_t out; - const auto all_true_pg = svptrue_b32(); - auto tmp_0 = svcvt_u32_f32_z(all_true_pg, in_0); - auto tmp_1 = svcvt_u32_f32_z(all_true_pg, in_1); - auto tmp_2 = svcvt_u32_f32_z(all_true_pg, in_2); - auto tmp_3 = svcvt_u32_f32_z(all_true_pg, in_3); - - auto tmp_16_0 = svqxtnt_u32(svqxtnb_u32(tmp_0), tmp_1); - auto tmp_16_1 = svqxtnt_u32(svqxtnb_u32(tmp_2), tmp_3); - - auto tmp_16_uzp_0 = svuzp1(tmp_16_0, tmp_16_0); - auto tmp_16_uzp_1 = svuzp2(tmp_16_0, tmp_16_0); - auto tmp_16_uzp_2 = svuzp1(tmp_16_1, tmp_16_1); - auto tmp_16_uzp_3 = svuzp2(tmp_16_1, tmp_16_1); - - auto pg = svwhilelt_b16_s32(0, svcnth() / 2); - - tmp_16_0 = svsplice(pg, tmp_16_uzp_0, tmp_16_uzp_1); - tmp_16_1 = svsplice(pg, tmp_16_uzp_2, tmp_16_uzp_3); - - out = svqxtnt_u16(svqxtnb_u16(tmp_16_0), tmp_16_1); - - auto out_uzp_0 = svuzp1(out, out); - auto out_uzp_1 = svuzp2(out, out); - - pg = svwhilelt_b8_s32(0, svcntb() / 2); - out = svsplice(pg, out_uzp_0, out_uzp_1); - - return out; -} - -template <> -svint8_t convert_float_to_int<svint8_t>(const svfloat32_t &in_0, const svfloat32_t &in_1, const svfloat32_t &in_2, const svfloat32_t &in_3) -{ - svint8_t out; - const auto all_true_pg = svptrue_b32(); - auto tmp_0 = svcvt_s32_f32_z(all_true_pg, in_0); - auto tmp_1 = svcvt_s32_f32_z(all_true_pg, in_1); - auto tmp_2 = svcvt_s32_f32_z(all_true_pg, in_2); - auto tmp_3 = svcvt_s32_f32_z(all_true_pg, in_3); - - auto tmp_16_0 = svqxtnt_s32(svqxtnb_s32(tmp_0), tmp_1); - auto tmp_16_1 = svqxtnt_s32(svqxtnb_s32(tmp_2), tmp_3); - - auto tmp_16_uzp_0 = svuzp1(tmp_16_0, tmp_16_0); - auto tmp_16_uzp_1 = svuzp2(tmp_16_0, tmp_16_0); - auto tmp_16_uzp_2 = svuzp1(tmp_16_1, tmp_16_1); - auto tmp_16_uzp_3 = svuzp2(tmp_16_1, tmp_16_1); - - auto pg = svwhilelt_b16_s32(0, svcnth() / 2); - - tmp_16_0 = svsplice(pg, tmp_16_uzp_0, tmp_16_uzp_1); - tmp_16_1 = svsplice(pg, tmp_16_uzp_2, tmp_16_uzp_3); - - out = svqxtnt_s16(svqxtnb_s16(tmp_16_0), tmp_16_1); - - auto out_uzp_0 = svuzp1(out, out); - auto out_uzp_1 = svuzp2(out, out); - - pg = svwhilelt_b8_s32(0, svcntb() / 2); - out = svsplice(pg, out_uzp_0, out_uzp_1); - - return out; -} -#endif /* defined(__ARM_FEATURE_SVE2) */ -} // namespace - -template <typename ScalarType> -void sve_logits_1d_max(const ITensor *in, ITensor *out, const Window &window) -{ - const auto all_true_pg = wrapper::svptrue<ScalarType>(); - 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)); - Iterator input(in, win); - Iterator output(out, win); - - execute_window_loop(win, [&](const Coordinates &) - { - // Get pointers - const auto in_ptr = reinterpret_cast<const ScalarType *>(input.ptr()); - const auto out_ptr = reinterpret_cast<ScalarType *>(output.ptr()); - - // Init max value - auto vec_max = wrapper::svdup_n(support::cpp11::lowest<ScalarType>()); - - int x = window_start_x; - svbool_t pg = wrapper::svwhilelt<ScalarType>(x, window_end_x); - do - { - const auto current_value = svld1(pg, in_ptr + x); - vec_max = svmax_m(pg, vec_max, current_value); - - x += wrapper::svcnt<ScalarType>(); - pg = wrapper::svwhilelt<ScalarType>(x, window_end_x); - } - while(svptest_any(all_true_pg, pg)); - - auto max_val = svmaxv(all_true_pg, vec_max); - - *out_ptr = max_val; - }, - input, output); -} - -#if defined(__ARM_FEATURE_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_FEATURE_SVE2) */ - -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) -{ - const int start_x = in->info()->valid_region().anchor.x(); - const int input_width = in->info()->valid_region().shape.x(); - - Iterator in_it(in, window); - Iterator max_it(max, window); - Iterator out_it(out, window); - - const auto all_true_pg = wrapper::svptrue<ScalarType>(); - - 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<ScalarType *>(tmp); - - ScalarType sum{ 0 }; - - /* 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 = wrapper::svdup_n(static_cast<ScalarType>(0)); - - /* Loop over row and compute exponentials and sum */ - int x = 0; - svbool_t pg = wrapper::svwhilelt<ScalarType>(x, input_width); - do - { - auto vec_elements = svld1(pg, in_ptr + x); - vec_elements = svsub_z(pg, vec_elements, vec_max); - if(is_log) - { - vec_elements = svmul_z(pg, vec_elements, wrapper::svdup_n(static_cast<ScalarType>(beta))); - vec_sum = svadd_m(pg, vec_sum, wrapper::svexp_z(pg, vec_elements)); - } - else - { - vec_elements = wrapper::svexp_z(pg, svmul_z(pg, vec_elements, wrapper::svdup_n(static_cast<ScalarType>(beta)))); - vec_sum = svadd_m(pg, vec_sum, vec_elements); - } - svst1(pg, tmp_ptr + x, vec_elements); - - x += wrapper::svcnt<ScalarType>(); - pg = wrapper::svwhilelt<ScalarType>(x, input_width); - } - while(svptest_any(all_true_pg, pg)); - - /* Reduce sum */ - sum = svaddv(all_true_pg, vec_sum); - - if(is_log) - { - sum = static_cast<ScalarType>(std::log(sum)); - } - else - { - sum = ScalarType(1) / sum; - } - } - - /* Normalize exponentials */ - { - /* Loop over row and compute softmax */ - int x = 0; - svbool_t pg = wrapper::svwhilelt<ScalarType>(x, input_width); - do - { - auto vec_in = svld1(pg, tmp_ptr + x); - auto normalized_value = wrapper::svdup_n(static_cast<ScalarType>(0)); - if(is_log) - { - normalized_value = svsub_z(pg, vec_in, wrapper::svdup_n(static_cast<ScalarType>(sum))); - } - else - { - normalized_value = svmul_z(pg, vec_in, wrapper::svdup_n(static_cast<ScalarType>(sum))); - } - svst1(pg, out_ptr + x, normalized_value); - - x += wrapper::svcnt<ScalarType>(); - pg = wrapper::svwhilelt<ScalarType>(x, input_width); - } - while(svptest_any(all_true_pg, pg)); - } - }, - in_it, max_it, out_it); -} - -} // namespace cpu -} // namespace arm_compute -#endif /* defined(__ARM_FEATURE_SVE) */ - -#endif /* SRC_CORE_SVE_KERNELS_SOFTMAX_LIST_H */ |