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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#pragma once
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#include <cmath>
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#include <string>
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#include "paddle/fluid/platform/cpu_info.h"
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#ifdef __AVX__
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#include <immintrin.h>
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#endif
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#ifdef PADDLE_WITH_MKLML
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#include "paddle/fluid/platform/dynload/mklml.h"
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#endif
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namespace paddle {
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namespace operators {
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namespace math {
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#define SIGMOID_THRESHOLD_MIN -40.0
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#define SIGMOID_THRESHOLD_MAX 13.0
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#define AVX_FLOAT_BLOCK 8
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#define AVX_DOUBLE_BLOCK 4
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#define AVX2_FLOAT_BLOCK 8
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#define AVX2_DOUBLE_BLOCK 4
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#define AVX512_FLOAT_BLOCK 16
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#define AVX512_DOUBLE_BLOCK 8
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template <typename T>
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inline void vec_exp(const int n, const T* x, T* y) {
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for (int i = 0; i < n; ++i) {
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y[i] = std::exp(x[i]);
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}
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}
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#ifdef PADDLE_WITH_MKLML
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template <>
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inline void vec_exp<float>(const int n, const float* x, float* y) {
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platform::dynload::vsExp(n, x, y);
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}
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template <>
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inline void vec_exp<double>(const int n, const double* x, double* y) {
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platform::dynload::vdExp(n, x, y);
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}
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#endif
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template <typename T, platform::jit::cpu_isa_t isa = platform::jit::isa_any>
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inline void vec_identity(const int n, const T* x, T* y) {
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// do nothing
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return;
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}
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template <typename T, platform::jit::cpu_isa_t isa = platform::jit::isa_any>
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inline void vec_sigmoid(const int n, const T* x, T* y) {
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const T min = SIGMOID_THRESHOLD_MIN;
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const T max = SIGMOID_THRESHOLD_MAX;
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for (int i = 0; i < n; ++i) {
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y[i] = (x[i] < min) ? min : ((x[i] > max) ? max : x[i]);
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y[i] = static_cast<T>(0) - y[i];
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}
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vec_exp<T>(n, y, y);
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for (int i = 0; i < n; ++i) {
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y[i] = static_cast<T>(1) / (static_cast<T>(1) + y[i]);
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}
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}
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template <>
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inline void vec_sigmoid<float, platform::jit::avx>(const int n, const float* x,
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float* y) {
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#ifdef __AVX__
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constexpr int block = AVX_FLOAT_BLOCK;
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if (n < block) { // can use larger threshold if necessary
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vec_sigmoid<float, platform::jit::isa_any>(n, x, y);
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return;
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}
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const int rest = n % block;
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const int end = n - rest;
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int i = 0;
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__m256 max = _mm256_set1_ps(SIGMOID_THRESHOLD_MAX);
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__m256 min = _mm256_set1_ps(SIGMOID_THRESHOLD_MIN);
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__m256 zeros = _mm256_setzero_ps();
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__m256 tmp;
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#define MOVE_ONE_STEP \
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tmp = _mm256_loadu_ps(x + i); \
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tmp = _mm256_max_ps(tmp, min); \
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tmp = _mm256_min_ps(tmp, max); \
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tmp = _mm256_sub_ps(zeros, tmp); \
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_mm256_storeu_ps(y + i, tmp)
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for (i = 0; i < end; i += block) {
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MOVE_ONE_STEP;
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}
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if (rest != 0) {
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i = n - block;
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MOVE_ONE_STEP;
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}
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#undef MOVE_ONE_STEP
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vec_exp<float>(n, y, y);
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__m256 ones = _mm256_set1_ps(1.0f);
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#define MOVE_ONE_STEP \
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tmp = _mm256_loadu_ps(y + i); \
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tmp = _mm256_add_ps(ones, tmp); \
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tmp = _mm256_div_ps(ones, tmp); \
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_mm256_storeu_ps(y + i, tmp)
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for (i = 0; i < end; i += block) {
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MOVE_ONE_STEP;
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}
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#undef MOVE_ONE_STEP
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if (rest == 0) {
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return;
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}
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// can not continue move step
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for (i = n - rest; i < n; ++i) {
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y[i] = 1.f / (1.f + y[i]);
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}
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#else
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vec_sigmoid<float, platform::jit::isa_any>(n, x, y);
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#endif
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}
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template <>
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inline void vec_sigmoid<float, platform::jit::avx2>(const int n, const float* x,
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float* y) {
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vec_sigmoid<float, platform::jit::avx>(n, x, y);
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}
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template <>
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inline void vec_sigmoid<float, platform::jit::avx512_common>(const int n,
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const float* x,
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float* y) {
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#ifdef __AVX512F__
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constexpr int block = AVX512_FLOAT_BLOCK;
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if (n < block) {
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vec_sigmoid<float, platform::jit::isa_any>(n, x, y);
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return;
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}
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const int rest = n % block;
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const int end = n - rest;
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int i = 0;
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__m512 max = _mm512_set1_ps(SIGMOID_THRESHOLD_MAX);
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__m512 min = _mm512_set1_ps(SIGMOID_THRESHOLD_MIN);
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__m512 zeros = _mm512_setzero_ps();
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__m512 tmp;
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#define MOVE_ONE_STEP \
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tmp = _mm512_loadu_ps(x + i); \
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tmp = _mm512_max_ps(tmp, min); \
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tmp = _mm512_min_ps(tmp, max); \
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tmp = _mm512_sub_ps(zeros, tmp); \
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_mm512_storeu_ps(y + i, tmp)
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for (i = 0; i < end; i += block) {
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MOVE_ONE_STEP;
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}
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if (rest != 0) {
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i = n - block;
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MOVE_ONE_STEP;
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}
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#undef MOVE_ONE_STEP
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vec_exp<float>(n, y, y);
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__m512 ones = _mm512_set1_ps(1.0f);
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#define MOVE_ONE_STEP \
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tmp = _mm512_loadu_ps(y + i); \
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tmp = _mm512_add_ps(ones, tmp); \
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tmp = _mm512_div_ps(ones, tmp); \
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_mm512_storeu_ps(y + i, tmp)
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for (i = 0; i < end; i += block) {
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MOVE_ONE_STEP;
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}
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#undef MOVE_ONE_STEP
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if (rest == 0) {
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return;
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}
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for (i = n - rest; i < n; ++i) {
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y[i] = 1.f / (1.f + y[i]);
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}
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#else
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vec_sigmoid<float, platform::jit::isa_any>(n, x, y);
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#endif
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}
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template <typename T, platform::jit::cpu_isa_t isa = platform::jit::isa_any>
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inline void vec_tanh(const int n, const T* x, T* y) {
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for (int i = 0; i < n; ++i) {
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y[i] = static_cast<T>(2) * x[i];
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}
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vec_sigmoid<T>(n, y, y);
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for (int i = 0; i < n; ++i) {
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y[i] = static_cast<T>(2) * y[i] - static_cast<T>(1);
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}
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}
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template <typename T, platform::jit::cpu_isa_t isa = platform::jit::isa_any>
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inline void vec_relu(const int n, const T* x, T* y) {
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for (int i = 0; i < n; ++i) {
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y[i] = x[i] > 0 ? x[i] : 0;
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}
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}
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template <>
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inline void vec_relu<float, platform::jit::avx>(const int n, const float* x,
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float* y) {
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#ifdef __AVX__
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constexpr int block = AVX_FLOAT_BLOCK;
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if (n < block) {
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vec_relu<float, platform::jit::isa_any>(n, x, y);
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return;
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}
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const int rest = n % block;
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const int end = n - rest;
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int i = 0;
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__m256 zeros = _mm256_setzero_ps();
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__m256 tmp;
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#define MOVE_ONE_STEP \
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tmp = _mm256_loadu_ps(x + i); \
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tmp = _mm256_max_ps(tmp, zeros); \
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_mm256_storeu_ps(y + i, tmp)
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for (i = 0; i < end; i += block) {
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MOVE_ONE_STEP;
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}
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if (rest == 0) {
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return;
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}
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i = n - block;
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MOVE_ONE_STEP;
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#undef MOVE_ONE_STEP
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#else
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vec_relu<float, platform::jit::isa_any>(n, x, y);
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#endif
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}
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template <>
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inline void vec_relu<float, platform::jit::avx2>(const int n, const float* x,
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float* y) {
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vec_relu<float, platform::jit::avx>(n, x, y);
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}
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template <>
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inline void vec_relu<float, platform::jit::avx512_common>(const int n,
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const float* x,
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float* y) {
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#ifdef __AVX512F__
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// test me
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constexpr int block = AVX512_FLOAT_BLOCK;
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if (n < block) {
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vec_relu<float, platform::jit::avx2>(n, x, y);
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return;
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}
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const int rest = n % block;
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const int end = n - rest;
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int i = 0;
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__m512 zeros = _mm512_setzero_ps();
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__m512 tmp;
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#define MOVE_ONE_STEP \
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tmp = _mm512_loadu_ps(x + i); \
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tmp = _mm512_max_ps(tmp, zeros); \
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_mm512_storeu_ps(y + i, tmp)
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for (i = 0; i < end; i += block) {
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MOVE_ONE_STEP;
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}
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if (rest == 0) {
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return;
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}
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i = n - block;
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MOVE_ONE_STEP;
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#undef MOVE_ONE_STEP
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#else
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vec_relu<float, platform::jit::avx2>(n, x, y);
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#endif
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}
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// TODO(TJ): optimize double of sigmoid, tanh and relu if necessary
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template <typename T, platform::jit::cpu_isa_t isa = platform::jit::isa_any>
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class VecActivations {
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public:
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std::function<void(const int, const T*, T*)> operator()(
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const std::string& type) {
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if (type == "sigmoid") {
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return vec_sigmoid<T, isa>;
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} else if (type == "relu") {
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return vec_relu<T, isa>;
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} else if (type == "tanh") {
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return vec_tanh<T, isa>;
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} else if (type == "identity" || type == "") {
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return vec_identity<T, isa>;
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}
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LOG(FATAL) << "Not support type: " << type;
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}
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};
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} // namespace math
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} // namespace operators
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} // namespace paddle
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