From 373b407558f99eb4bba632c170d03d807941dd2a Mon Sep 17 00:00:00 2001 From: Michalis Spyrou Date: Wed, 20 Jan 2021 16:41:12 +0000 Subject: Make Softmax kernels and operator stateless COMPMID-3997 Change-Id: I3a3cc76d8247dd769d9a5e6e171d718ea909312c Signed-off-by: Michalis Spyrou Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/4986 Tested-by: Arm Jenkins Reviewed-by: Michele Di Giorgio Comments-Addressed: Arm Jenkins --- src/core/NEON/kernels/softmax/impl/NEON/list.h | 425 ------------------------ src/core/NEON/kernels/softmax/impl/SVE/list.h | 429 ------------------------- 2 files changed, 854 deletions(-) delete mode 100644 src/core/NEON/kernels/softmax/impl/NEON/list.h delete mode 100644 src/core/NEON/kernels/softmax/impl/SVE/list.h (limited to 'src/core/NEON/kernels/softmax/impl') diff --git a/src/core/NEON/kernels/softmax/impl/NEON/list.h b/src/core/NEON/kernels/softmax/impl/NEON/list.h deleted file mode 100644 index a8f781f439..0000000000 --- a/src/core/NEON/kernels/softmax/impl/NEON/list.h +++ /dev/null @@ -1,425 +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_NEON_KERNELS_SOFTMAX_LIST_H -#define SRC_CORE_NEON_KERNELS_SOFTMAX_LIST_H - -#include "src/core/NEON/wrapper/wrapper.h" -#include "support/SaturateCast.h" -#include "src/core/NEON/NEFixedPoint.h" -#include "src/core/NEON/NEMath.h" - -namespace arm_compute -{ -namespace cpu -{ -namespace -{ -template -int_vec_type convert_float_to_int(const float_vec_type &in); - -template -float_vec_type convert_int_to_float(const int_vec_type &in); - -template <> -uint8x16_t convert_float_to_int(const float32x4x4_t &in) -{ - uint8x16_t out; - convert_float32x4x4_to_uint8x16(in, out); - return out; -} - -template <> -int8x16_t convert_float_to_int(const float32x4x4_t &in) -{ - int8x16_t out; - convert_float32x4x4_to_int8x16(in, out); - return out; -} - -template <> -float32x4x4_t convert_int_to_float(const uint8x16_t &in) -{ - return convert_uint8x16_to_float32x4x4(in); -} - -template <> -float32x4x4_t convert_int_to_float(const int8x16_t &in) -{ - return convert_int8x16_to_float32x4x4(in); -} -} // namespace - -template -void neon_logits_1d_max(const ITensor *in, ITensor *out, const Window &window) -{ - /** NEON vector tag type. */ - using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t; - - constexpr int window_step_x = 16 / sizeof(T); - const auto window_start_x = static_cast(window.x().start()); - const auto window_end_x = static_cast(window.x().end()); - - Window win{ window }; - win.set(Window::DimX, Window::Dimension(0, 1, 1)); - Iterator input(in, win); - Iterator output(out, win); - - const int sum_stages = log2(window_step_x / 2); - execute_window_loop(win, [&](const Coordinates &) - { - // Get pointers - const auto in_ptr = reinterpret_cast(input.ptr()); - const auto out_ptr = reinterpret_cast(output.ptr()); - - // Init max value - auto vec_max = wrapper::vdup_n(support::cpp11::lowest(), ExactTagType{}); - int x = window_start_x; - - for(; x <= (window_end_x - window_step_x); x += window_step_x) - { - const auto current_value = wrapper::vloadq(in_ptr + x); - vec_max = wrapper::vmax(vec_max, current_value); - } - auto carry_max = wrapper::vpmax(wrapper::vgethigh(vec_max), wrapper::vgetlow(vec_max)); - - for(int i = 0; i < sum_stages; ++i) - { - carry_max = wrapper::vpmax(carry_max, carry_max); - } - T max_val = wrapper::vgetlane(carry_max, 0); - - // Compute left-over elements - for(; x < window_end_x; ++x) - { - max_val = *(in_ptr + x) > max_val ? *(in_ptr + x) : max_val; - } - - *out_ptr = max_val; - }, - input, output); -} - -template -void neon_softmax_logits_1d_quantized(const ITensor *in, const ITensor *max, void *const tmp, - ITensor *out, float beta, bool is_log, const Window &window) -{ - static_assert(std::is_same::value - || std::is_same::value, - "quantized type should be either qasymm8_t or qasymm8_signed_t."); - - 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 = vdupq_n_f32(scale_beta); - - Iterator in_it(in, window); - Iterator max_it(max, window); - Iterator out_it(out, window); - constexpr int vec_size = 16; - - 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{}; - float sum_inversed{}; - - /* Compute exponentials and sum */ - { - /* Get max value */ - const auto max_val = *reinterpret_cast(max_it.ptr()); - const auto vec_max = wrapper::vdup_n(max_val, wrapper::traits::vector_128_tag{}); - - /* Init sum to zero */ - float32x4x4_t vec_sum = - { - vdupq_n_f32(0.f), - vdupq_n_f32(0.f), - vdupq_n_f32(0.f), - vdupq_n_f32(0.f), - }; - - /* Loop over row and compute exponentials and sum */ - int x = 0; - for(; x <= (input_width - vec_size); x += vec_size) - { - auto vec_elements = wrapper::vloadq(in_ptr + x); - vec_elements = wrapper::vqsub(vec_max, vec_elements); - auto vec_elements_flt = convert_int_to_float(vec_elements); - - if(is_log) - { - vec_elements_flt.val[0] = vmulq_f32(vec_elements_flt.val[0], scale_beta_vec); - vec_elements_flt.val[1] = vmulq_f32(vec_elements_flt.val[1], scale_beta_vec); - vec_elements_flt.val[2] = vmulq_f32(vec_elements_flt.val[2], scale_beta_vec); - vec_elements_flt.val[3] = vmulq_f32(vec_elements_flt.val[3], scale_beta_vec); - vec_sum.val[0] = vaddq_f32(vec_sum.val[0], vexpq_f32(vec_elements_flt.val[0])); - vec_sum.val[1] = vaddq_f32(vec_sum.val[1], vexpq_f32(vec_elements_flt.val[1])); - vec_sum.val[2] = vaddq_f32(vec_sum.val[2], vexpq_f32(vec_elements_flt.val[2])); - vec_sum.val[3] = vaddq_f32(vec_sum.val[3], vexpq_f32(vec_elements_flt.val[3])); - } - else - { - vec_elements_flt.val[0] = vexpq_f32(vmulq_f32(vec_elements_flt.val[0], scale_beta_vec)); - vec_elements_flt.val[1] = vexpq_f32(vmulq_f32(vec_elements_flt.val[1], scale_beta_vec)); - vec_elements_flt.val[2] = vexpq_f32(vmulq_f32(vec_elements_flt.val[2], scale_beta_vec)); - vec_elements_flt.val[3] = vexpq_f32(vmulq_f32(vec_elements_flt.val[3], scale_beta_vec)); - vec_sum.val[0] = vaddq_f32(vec_sum.val[0], vec_elements_flt.val[0]); - vec_sum.val[1] = vaddq_f32(vec_sum.val[1], vec_elements_flt.val[1]); - vec_sum.val[2] = vaddq_f32(vec_sum.val[2], vec_elements_flt.val[2]); - vec_sum.val[3] = vaddq_f32(vec_sum.val[3], vec_elements_flt.val[3]); - } - - vst4q_f32(tmp_ptr + x, vec_elements_flt); - } - - /* Reduce sum */ - const auto sum_16_byte = vaddq_f32(vaddq_f32(vec_sum.val[0], vec_sum.val[1]), vaddq_f32(vec_sum.val[2], vec_sum.val[3])); - auto sum_res = vpadd_f32(vget_high_f32(sum_16_byte), vget_low_f32(sum_16_byte)); - sum_res = vpadd_f32(sum_res, sum_res); - sum = wrapper::vgetlane(sum_res, 0); - - /* Run remaining elements */ - for(; x < input_width; ++x) - { - float element{}; - if(is_log) - { - element = (max_val - in_ptr[x]) * scale_beta; - sum += std::exp(element); - } - else - { - element = std::exp((max_val - in_ptr[x]) * scale_beta); - sum += element; - } - - tmp_ptr[x] = element; - } - - if(!is_log) - { - sum_inversed = 256.f / sum; - } - else - { - sum = std::log(sum); - } - } - - /* Normalize exponentials */ - { - constexpr bool is_qasymm8_signed = std::is_same::value; - /* Loop over row and compute softmax */ - int x = 0; - for(; x <= (input_width - vec_size); x += vec_size) - { - using int_vec_type = wrapper::traits::neon_vector_t; - float32x4x4_t vec_in = vld4q_f32(tmp_ptr + x); - int_vec_type normalized_value{}; - if(is_log) - { - const float32x4x4_t sub = - { - vsubq_f32(vec_in.val[0], vdupq_n_f32(sum)), - vsubq_f32(vec_in.val[1], vdupq_n_f32(sum)), - vsubq_f32(vec_in.val[2], vdupq_n_f32(sum)), - vsubq_f32(vec_in.val[3], vdupq_n_f32(sum)), - }; - normalized_value = convert_float_to_int(sub); - } - else - { - float32x4x4_t mul = - { - vmulq_f32(vec_in.val[0], vdupq_n_f32(sum_inversed)), - vmulq_f32(vec_in.val[1], vdupq_n_f32(sum_inversed)), - vmulq_f32(vec_in.val[2], vdupq_n_f32(sum_inversed)), - vmulq_f32(vec_in.val[3], vdupq_n_f32(sum_inversed)), - }; - - if(is_qasymm8_signed) - { - const auto offset_vec = wrapper::vdup_n(128.f, wrapper::traits::vector_128_tag{}); - mul.val[0] = wrapper::vsub(mul.val[0], offset_vec); - mul.val[1] = wrapper::vsub(mul.val[1], offset_vec); - mul.val[2] = wrapper::vsub(mul.val[2], offset_vec); - mul.val[3] = wrapper::vsub(mul.val[3], offset_vec); - } - - normalized_value = convert_float_to_int(mul); - } - wrapper::vstore(out_ptr + x, normalized_value); - } - /* Run remaining elements */ - for(; x < input_width; ++x) - { - if(is_log) - { - out_ptr[x] = utils::cast::saturate_cast(tmp_ptr[x] - sum); - } - else - { - out_ptr[x] = utils::cast::saturate_cast((tmp_ptr[x] * sum_inversed) - (is_qasymm8_signed ? 128.f : 0)); - } - } - } - }, - in_it, max_it, out_it); -} - -template -void neon_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); - - /** NEON vector tag type. */ - using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t; - - constexpr int vec_size = 16 / sizeof(T); - const int sum_stages = log2(vec_size / 2); - - 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); - - T sum{}; - T sum_inversed{}; - - /* Compute exponentials and sum */ - { - /* Get max value */ - const auto max_val = *reinterpret_cast(max_it.ptr()); - const auto vec_max = wrapper::vdup_n(max_val, ExactTagType{}); - - /* Init sum to zero */ - auto vec_sum = wrapper::vdup_n(static_cast(0), ExactTagType{}); - - /* Loop over row and compute exponentials and sum */ - int x = 0; - for(; x <= (input_width - vec_size); x += vec_size) - { - auto vec_elements = wrapper::vloadq(in_ptr + x); - vec_elements = wrapper::vsub(vec_elements, vec_max); - if(is_log) - { - vec_elements = wrapper::vmul(vec_elements, wrapper::vdup_n(static_cast(beta), ExactTagType{})); - vec_sum = wrapper::vadd(vec_sum, wrapper::vexpq(vec_elements)); - } - else - { - vec_elements = wrapper::vexpq(wrapper::vmul(vec_elements, wrapper::vdup_n(static_cast(beta), ExactTagType{}))); - vec_sum = wrapper::vadd(vec_sum, vec_elements); - } - wrapper::vstore(tmp_ptr + x, vec_elements); - } - - /* Reduce sum */ - auto sum_res = wrapper::vpadd(wrapper::vgethigh(vec_sum), wrapper::vgetlow(vec_sum)); - for(int i = 0; i < sum_stages; ++i) - { - sum_res = wrapper::vpadd(sum_res, sum_res); - } - sum = wrapper::vgetlane(sum_res, 0); - - /* Run remaining elements */ - for(; x < input_width; ++x) - { - T element{}; - - if(is_log) - { - element = (in_ptr[x] - max_val) * beta; - sum += std::exp(element); - } - else - { - element = std::exp((in_ptr[x] - max_val) * beta); - sum += element; - } - tmp_ptr[x] = element; - } - - if(!is_log) - { - sum_inversed = T(1) / sum; - } - else - { - sum = static_cast(std::log(sum)); - } - } - - /* Normalize exponentials */ - { - /* Loop over row and compute softmax */ - int x = 0; - for(; x <= (input_width - vec_size); x += vec_size) - { - auto vec_in = wrapper::vloadq(tmp_ptr + x); - auto normalized_value = wrapper::vdup_n(static_cast(0), ExactTagType{}); - if(is_log) - { - normalized_value = wrapper::vsub(vec_in, wrapper::vdup_n(static_cast(sum), ExactTagType{})); - } - else - { - normalized_value = wrapper::vmul(vec_in, wrapper::vdup_n(static_cast(sum_inversed), ExactTagType{})); - } - wrapper::vstore(out_ptr + x, normalized_value); - } - /* Run remaining elements */ - for(; x < input_width; ++x) - { - if(is_log) - { - out_ptr[x] = tmp_ptr[x] - sum; - } - else - { - out_ptr[x] = tmp_ptr[x] * sum_inversed; - } - } - } - }, - in_it, max_it, out_it); -} - -} // namespace cpu -} // namespace arm_compute - -#endif /* SRC_CORE_NEON_KERNELS_SOFTMAX_LIST_H */ 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 - -namespace arm_compute -{ -namespace cpu -{ -namespace -{ -#if defined(__ARM_FEATURE_SVE2) -template -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(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(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 -void sve_logits_1d_max(const ITensor *in, ITensor *out, const Window &window) -{ - const auto all_true_pg = wrapper::svptrue(); - const auto window_start_x = static_cast(window.x().start()); - const auto window_end_x = static_cast(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(input.ptr()); - const auto out_ptr = reinterpret_cast(output.ptr()); - - // Init max value - auto vec_max = wrapper::svdup_n(support::cpp11::lowest()); - - int x = window_start_x; - svbool_t pg = wrapper::svwhilelt(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(); - pg = wrapper::svwhilelt(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 -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(); - 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 - { - 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(); - 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, 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(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); -} -#endif /* defined(__ARM_FEATURE_SVE2) */ - -template -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(); - - 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); - - ScalarType sum{ 0 }; - - /* 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 = wrapper::svdup_n(static_cast(0)); - - /* Loop over row and compute exponentials and sum */ - int x = 0; - svbool_t pg = wrapper::svwhilelt(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(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(beta)))); - vec_sum = svadd_m(pg, vec_sum, vec_elements); - } - svst1(pg, tmp_ptr + x, vec_elements); - - x += wrapper::svcnt(); - pg = wrapper::svwhilelt(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(std::log(sum)); - } - else - { - sum = ScalarType(1) / sum; - } - } - - /* Normalize exponentials */ - { - /* Loop over row and compute softmax */ - int x = 0; - svbool_t pg = wrapper::svwhilelt(x, input_width); - do - { - auto vec_in = svld1(pg, tmp_ptr + x); - auto normalized_value = wrapper::svdup_n(static_cast(0)); - if(is_log) - { - normalized_value = svsub_z(pg, vec_in, wrapper::svdup_n(static_cast(sum))); - } - else - { - normalized_value = svmul_z(pg, vec_in, wrapper::svdup_n(static_cast(sum))); - } - svst1(pg, out_ptr + x, normalized_value); - - x += wrapper::svcnt(); - pg = wrapper::svwhilelt(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 */ -- cgit v1.2.1