jit_kernel_exp.cc

/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */

#include "paddle/fluid/operators/math/jit_kernel.h"
#include <cmath>  // for exp
#include <string>
#include "paddle/fluid/operators/math/jit_kernel_macro.h"
#ifdef PADDLE_WITH_MKLML
#include "paddle/fluid/platform/dynload/mklml.h"
#endif

#ifdef __AVX__
#include <immintrin.h>
#endif

namespace paddle {
namespace operators {
namespace math {
namespace jitkernel {
namespace jit = platform::jit;

/* VExp JitKernel */
template <typename T, jit::cpu_isa_t isa, jit_block>
class VExpKernelImpl : public VExpKernel<T> {
 public:
  explicit VExpKernelImpl(int d) : VExpKernel<T>() { this->num_ = d; }
  void Compute(const T* x, T* y) const override {
    for (int i = 0; i < this->num_; ++i) {
      y[i] = std::exp(x[i]);
    }
  }
};

#ifdef PADDLE_WITH_MKLML
#define MKL_FLOAT(isa, block)                                               \
  template <>                                                               \
  void VExpKernelImpl<float, isa, block>::Compute(const float* x, float* y) \
      const {                                                               \
    platform::dynload::vsExp(this->num_, x, y);                             \
  }

#define MKL_DOUBLE(isa, block)                                                 \
  template <>                                                                  \
  void VExpKernelImpl<double, isa, block>::Compute(const double* x, double* y) \
      const {                                                                  \
    platform::dynload::vdExp(this->num_, x, y);                                \
  }
FOR_EACH_ISA(MKL_FLOAT, kLT8);
FOR_EACH_ISA(MKL_FLOAT, kGT8LT16);
FOR_EACH_ISA(MKL_FLOAT, kGT16);
FOR_EACH_ISA_BLOCK(MKL_DOUBLE);
#endif

namespace detail {

#ifdef __AVX__

#define ALIGN32 __attribute__((aligned(32)))

#define _PS256_CONST(Name, Val)                                      \
  static const float _ps256_##Name[8] ALIGN32 = {Val, Val, Val, Val, \
                                                 Val, Val, Val, Val}

#define _PI256_CONST(Name, Val)                                    \
  static const int _pi256_##Name[8] ALIGN32 = {Val, Val, Val, Val, \
                                               Val, Val, Val, Val}

_PI256_CONST(0x7f, 0x7f);
_PS256_CONST(one, 1.f);
_PS256_CONST(0p5, 0.5f);
_PS256_CONST(exp_hi, 88.3762626647949f);
_PS256_CONST(exp_lo, -88.3762626647949f);
_PS256_CONST(cephes_LOG2EF, 1.44269504088896341);
_PS256_CONST(cephes_exp_C1, 0.693359375);
_PS256_CONST(cephes_exp_C2, -2.12194440e-4);
_PS256_CONST(cephes_exp_p0, 1.9875691500E-4);
_PS256_CONST(cephes_exp_p1, 1.3981999507E-3);
_PS256_CONST(cephes_exp_p2, 8.3334519073E-3);
_PS256_CONST(cephes_exp_p3, 4.1665795894E-2);
_PS256_CONST(cephes_exp_p4, 1.6666665459E-1);
_PS256_CONST(cephes_exp_p5, 5.0000001201E-1);

typedef union imm_xmm_union {
  __m256i imm;
  __m128i xmm[2];
} imm_xmm_union;

#define COPY_IMM_TO_XMM(imm_, xmm0_, xmm1_) \
  {                                         \
    imm_xmm_union u ALIGN32;                \
    u.imm = imm_;                           \
    xmm0_ = u.xmm[0];                       \
    xmm1_ = u.xmm[1];                       \
  }

#define COPY_XMM_TO_IMM(xmm0_, xmm1_, imm_) \
  {                                         \
    imm_xmm_union u ALIGN32;                \
    u.xmm[0] = xmm0_;                       \
    u.xmm[1] = xmm1_;                       \
    imm_ = u.imm;                           \
  }

#define AVX2_BITOP_USING_SSE2(fn)                           \
  static inline __m256i avx2_mm256_##fn(__m256i x, int y) { \
    /* use SSE2 to perform the bitop AVX2 */                \
    __m128i x1, x2;                                         \
    __m256i ret;                                            \
    COPY_IMM_TO_XMM(x, x1, x2);                             \
    x1 = _mm_##fn(x1, y);                                   \
    x2 = _mm_##fn(x2, y);                                   \
    COPY_XMM_TO_IMM(x1, x2, ret);                           \
    return ret;                                             \
  }

#define AVX2_INTOP_USING_SSE2(fn)                                    \
  static inline __m256i avx2_mm256_add_epi32(__m256i x, __m256i y) { \
    /* use SSE2 to perform the AVX2 integer operation */             \
    __m128i x1, x2;                                                  \
    __m128i y1, y2;                                                  \
    __m256i ret;                                                     \
    COPY_IMM_TO_XMM(x, x1, x2);                                      \
    COPY_IMM_TO_XMM(y, y1, y2);                                      \
    x1 = _mm_##fn(x1, y1);                                           \
    x2 = _mm_##fn(x2, y2);                                           \
    COPY_XMM_TO_IMM(x1, x2, ret);                                    \
    return ret;                                                      \
  }

AVX2_BITOP_USING_SSE2(slli_epi32);
AVX2_INTOP_USING_SSE2(add_epi32);

#define AVXEXP_BASE                                                            \
  __m256 tmp = _mm256_setzero_ps(), fx;                                        \
  __m256 one = *reinterpret_cast<const __m256*>(_ps256_one);                   \
  __m256i imm0;                                                                \
  x = _mm256_min_ps(x, *reinterpret_cast<const __m256*>(_ps256_exp_hi));       \
  x = _mm256_max_ps(x, *reinterpret_cast<const __m256*>(_ps256_exp_lo));       \
  /* express exp(x) as exp(g + n*log(2)) */                                    \
  fx = _mm256_mul_ps(x,                                                        \
                     *reinterpret_cast<const __m256*>(_ps256_cephes_LOG2EF));  \
  fx = _mm256_add_ps(fx, *reinterpret_cast<const __m256*>(_ps256_0p5));        \
  tmp = _mm256_floor_ps(fx);                                                   \
  /* if greater, substract 1 */                                                \
  __m256 mask = _mm256_cmp_ps(tmp, fx, _CMP_GT_OS);                            \
  mask = _mm256_and_ps(mask, one);                                             \
  fx = _mm256_sub_ps(tmp, mask);                                               \
  tmp = _mm256_mul_ps(fx,                                                      \
                      *reinterpret_cast<const __m256*>(_ps256_cephes_exp_C1)); \
  __m256 z = _mm256_mul_ps(                                                    \
      fx, *reinterpret_cast<const __m256*>(_ps256_cephes_exp_C2));             \
  x = _mm256_sub_ps(x, tmp);                                                   \
  x = _mm256_sub_ps(x, z);                                                     \
  z = _mm256_mul_ps(x, x);                                                     \
  __m256 y = *reinterpret_cast<const __m256*>(_ps256_cephes_exp_p0);           \
  y = _mm256_mul_ps(y, x);                                                     \
  y = _mm256_add_ps(y,                                                         \
                    *reinterpret_cast<const __m256*>(_ps256_cephes_exp_p1));   \
  y = _mm256_mul_ps(y, x);                                                     \
  y = _mm256_add_ps(y,                                                         \
                    *reinterpret_cast<const __m256*>(_ps256_cephes_exp_p2));   \
  y = _mm256_mul_ps(y, x);                                                     \
  y = _mm256_add_ps(y,                                                         \
                    *reinterpret_cast<const __m256*>(_ps256_cephes_exp_p3));   \
  y = _mm256_mul_ps(y, x);                                                     \
  y = _mm256_add_ps(y,                                                         \
                    *reinterpret_cast<const __m256*>(_ps256_cephes_exp_p4));   \
  y = _mm256_mul_ps(y, x);                                                     \
  y = _mm256_add_ps(y,                                                         \
                    *reinterpret_cast<const __m256*>(_ps256_cephes_exp_p5));   \
  y = _mm256_mul_ps(y, z);                                                     \
  y = _mm256_add_ps(y, x);                                                     \
  y = _mm256_add_ps(y, one);                                                   \
  /* build 2^n */                                                              \
  imm0 = _mm256_cvttps_epi32(fx)

__m256 ExpAVX(__m256 x) {
  AVXEXP_BASE;
  // two AVX2 instructions using SSE2
  imm0 = avx2_mm256_add_epi32(imm0,
                              *reinterpret_cast<const __m256i*>(_pi256_0x7f));
  imm0 = avx2_mm256_slli_epi32(imm0, 23);
  __m256 pow2n = _mm256_castsi256_ps(imm0);
  y = _mm256_mul_ps(y, pow2n);
  return y;
}
#endif

#ifdef __AVX2__
__m256 ExpAVX2(__m256 x) {
  AVXEXP_BASE;
  // two AVX2 instructions
  imm0 = _mm256_add_epi32(imm0, *reinterpret_cast<const __m256i*>(_pi256_0x7f));
  imm0 = _mm256_slli_epi32(imm0, 23);
  __m256 pow2n = _mm256_castsi256_ps(imm0);
  y = _mm256_mul_ps(y, pow2n);
  return y;
}
#endif

}  // namespace detail

#define INTRI8_FLOAT(isa, expisa)                                          \
  template <>                                                              \
  void VExpKernelImpl<float, isa, kEQ8>::Compute(const float* x, float* y) \
      const {                                                              \
    __m256 tmp = _mm256_loadu_ps(x);                                       \
    _mm256_storeu_ps(y, expisa(tmp));                                      \
  }

#define INTRI16_FLOAT(isa, expisa)                                          \
  template <>                                                               \
  void VExpKernelImpl<float, isa, kEQ16>::Compute(const float* x, float* y) \
      const {                                                               \
    __m256 tmp0 = _mm256_loadu_ps(x);                                       \
    __m256 tmp1 = _mm256_loadu_ps(x + 8);                                   \
    tmp0 = expisa(tmp0);                                                    \
    tmp1 = expisa(tmp1);                                                    \
    _mm256_storeu_ps(y, tmp0);                                              \
    _mm256_storeu_ps(y + 8, tmp1);                                          \
  }

#ifdef __AVX__
INTRI8_FLOAT(jit::avx, detail::ExpAVX);
INTRI16_FLOAT(jit::avx, detail::ExpAVX);
#endif
#ifdef __AVX2__
INTRI8_FLOAT(jit::avx2, detail::ExpAVX2);
INTRI16_FLOAT(jit::avx2, detail::ExpAVX2);
#endif
#ifdef __AVX512F__
INTRI8_FLOAT(jit::avx512f, detail::ExpAVX2);
INTRI16_FLOAT(jit::avx512f, detail::ExpAVX2);
#endif
// TODO(TJ): eq16 test and complete avx512

#undef INTRI8_FLOAT
#undef INTRI16_FLOAT
#undef MKL_FLOAT
#undef MKL_DOUBLE

REGISTER_JITKERNEL_DEPRECATED(vexp, VExpKernel);

/* VSigmoid JitKernel */
template <typename T, jit::cpu_isa_t isa, jit_block>
class VSigmoidKernelImpl : public VSigmoidKernel<T> {
 public:
  explicit VSigmoidKernelImpl(int d) : VSigmoidKernel<T>() {
    this->num_ = d;
    vexp_ = KernelPool::Instance().template Get<VExpKernel<T>>(d);
  }
  void Compute(const T* x, T* y) const override {
    const T min = SIGMOID_THRESHOLD_MIN;
    const T max = SIGMOID_THRESHOLD_MAX;
    for (int i = 0; i < this->num_; ++i) {
      y[i] = (x[i] < min) ? min : ((x[i] > max) ? max : x[i]);
      y[i] = static_cast<T>(0) - y[i];
    }
    vexp_->Compute(y, y);
    for (int i = 0; i < this->num_; ++i) {
      y[i] = static_cast<T>(1) / (static_cast<T>(1) + y[i]);
    }
  }

 private:
  std::shared_ptr<const VExpKernel<T>> vexp_;
};

#define INTRI_SIGMOID(tmp, min, max, expisa)      \
  tmp = _mm256_max_ps(tmp, min);                  \
  tmp = _mm256_min_ps(tmp, max);                  \
  tmp = _mm256_sub_ps(_mm256_set1_ps(0.0f), tmp); \
  tmp = expisa(tmp);                              \
  tmp = _mm256_add_ps(_mm256_set1_ps(1.0f), tmp); \
  tmp = _mm256_div_ps(_mm256_set1_ps(1.0f), tmp)

#define INTRI8_FLOAT(isa, expisa)                                              \
  template <>                                                                  \
  void VSigmoidKernelImpl<float, isa, kEQ8>::Compute(const float* x, float* y) \
      const {                                                                  \
    /* TODO(TJ): try to use static const*/                                     \
    __m256 max = _mm256_set1_ps(SIGMOID_THRESHOLD_MAX);                        \
    __m256 min = _mm256_set1_ps(SIGMOID_THRESHOLD_MIN);                        \
    __m256 tmp = _mm256_loadu_ps(x);                                           \
    INTRI_SIGMOID(tmp, min, max, expisa);                                      \
    _mm256_storeu_ps(y, tmp);                                                  \
  }

#define INTRI16_FLOAT(isa, expisa)                                      \
  template <>                                                           \
  void VSigmoidKernelImpl<float, isa, kEQ16>::Compute(const float* x,   \
                                                      float* y) const { \
    __m256 max = _mm256_set1_ps(SIGMOID_THRESHOLD_MAX);                 \
    __m256 min = _mm256_set1_ps(SIGMOID_THRESHOLD_MIN);                 \
    __m256 tmp0 = _mm256_loadu_ps(x);                                   \
    __m256 tmp1 = _mm256_loadu_ps(x + 8);                               \
    INTRI_SIGMOID(tmp0, min, max, expisa);                              \
    INTRI_SIGMOID(tmp1, min, max, expisa);                              \
    _mm256_storeu_ps(y, tmp0);                                          \
    _mm256_storeu_ps(y + 8, tmp1);                                      \
  }

#define INTRI_GT8LT16_FLOAT(isa, expisa)                                     \
  template <>                                                                \
  VSigmoidKernelImpl<float, isa, kGT8LT16>::VSigmoidKernelImpl(int d)        \
      : VSigmoidKernel<float>() {                                            \
    this->num_ = d;                                                          \
    this->end_ = AVX_FLOAT_BLOCK;                                            \
    this->rest_ = d - this->end_;                                            \
    vexp_ =                                                                  \
        KernelPool::Instance().template Get<VExpKernel<float>>(this->rest_); \
  }                                                                          \
  template <>                                                                \
  void VSigmoidKernelImpl<float, isa, kGT8LT16>::Compute(const float* x,     \
                                                         float* y) const {   \
    __m256 max = _mm256_set1_ps(SIGMOID_THRESHOLD_MAX);                      \
    __m256 min = _mm256_set1_ps(SIGMOID_THRESHOLD_MIN);                      \
    __m256 tmp = _mm256_loadu_ps(x);                                         \
    INTRI_SIGMOID(tmp, min, max, expisa);                                    \
    _mm256_storeu_ps(y, tmp);                                                \
    const float min_ = SIGMOID_THRESHOLD_MIN;                                \
    const float max_ = SIGMOID_THRESHOLD_MAX;                                \
    for (int i = this->end_; i < this->num_; ++i) {                          \
      y[i] = (x[i] < min_) ? min_ : ((x[i] > max_) ? max_ : x[i]);           \
      y[i] = 0.f - y[i];                                                     \
    }                                                                        \
    vexp_->Compute(y + this->end_, y + this->end_);                          \
    for (int i = this->end_; i < this->num_; ++i) {                          \
      y[i] = 1.f / (1.f + y[i]);                                             \
    }                                                                        \
  }

#define INTRI_GT16_FLOAT(isa, expisa)                                        \
  template <>                                                                \
  VSigmoidKernelImpl<float, isa, kGT16>::VSigmoidKernelImpl(int d)           \
      : VSigmoidKernel<float>() {                                            \
    this->num_ = d;                                                          \
    this->rest_ = d % AVX_FLOAT_BLOCK;                                       \
    this->end_ = d - this->rest_;                                            \
    vexp_ =                                                                  \
        KernelPool::Instance().template Get<VExpKernel<float>>(this->rest_); \
  }                                                                          \
  template <>                                                                \
  void VSigmoidKernelImpl<float, isa, kGT16>::Compute(const float* x,        \
                                                      float* y) const {      \
    __m256 max = _mm256_set1_ps(SIGMOID_THRESHOLD_MAX);                      \
    __m256 min = _mm256_set1_ps(SIGMOID_THRESHOLD_MIN);                      \
    for (int i = 0; i < this->end_; i += AVX_FLOAT_BLOCK) {                  \
      __m256 tmp = _mm256_loadu_ps(x + i);                                   \
      INTRI_SIGMOID(tmp, min, max, expisa);                                  \
      _mm256_storeu_ps(y + i, tmp);                                          \
    }                                                                        \
    const float min_ = SIGMOID_THRESHOLD_MIN;                                \
    const float max_ = SIGMOID_THRESHOLD_MAX;                                \
    for (int i = this->end_; i < this->num_; ++i) {                          \
      y[i] = (x[i] < min_) ? min_ : ((x[i] > max_) ? max_ : x[i]);           \
      y[i] = 0.f - y[i];                                                     \
    }                                                                        \
    vexp_->Compute(y + this->end_, y + this->end_);                          \
    for (int i = this->end_; i < this->num_; ++i) {                          \
      y[i] = 1.f / (1.f + y[i]);                                             \
    }                                                                        \
  }

#ifdef __AVX__
INTRI8_FLOAT(jit::avx, detail::ExpAVX);
INTRI16_FLOAT(jit::avx, detail::ExpAVX);
INTRI_GT8LT16_FLOAT(jit::avx, detail::ExpAVX);
INTRI_GT16_FLOAT(jit::avx, detail::ExpAVX);
#endif
#ifdef __AVX2__
INTRI8_FLOAT(jit::avx2, detail::ExpAVX2);
INTRI16_FLOAT(jit::avx2, detail::ExpAVX2);
// maybe use avx at gt8lt16 and gt16
#endif
#ifdef __AVX512F__
INTRI8_FLOAT(jit::avx512f, detail::ExpAVX2);
INTRI16_FLOAT(jit::avx512f, detail::ExpAVX2);
// maybe use avx2 at gt8lt16 and gt16
#endif

#undef INTRI8_FLOAT
#undef INTRI16_FLOAT
#undef INTRI_GT8LT16_FLOAT
#undef INTRI_GT16_FLOAT
#undef INTRI_VSIGMOID

REGISTER_JITKERNEL_DEPRECATED(vsigmoid, VSigmoidKernel);

/* VTanh JitKernel */
template <typename T, jit::cpu_isa_t isa, jit_block>
class VTanhKernelImpl : public VTanhKernel<T> {
 public:
  explicit VTanhKernelImpl(int d) : VTanhKernel<T>() {
    this->num_ = d;
    vscal_ = KernelPool::Instance().template Get<VScalKernel<T>>(d);
    vsigmoid_ = KernelPool::Instance().template Get<VSigmoidKernel<T>>(d);
    vaddbias_ = KernelPool::Instance().template Get<VAddBiasKernel<T>>(d);
  }
  void Compute(const T* x, T* y) const override {
    const T a = static_cast<T>(2), b = static_cast<T>(-1);
    vscal_->Compute(&a, x, y, this->num_);
    vsigmoid_->Compute(y, y);
    vscal_->Compute(&a, y, y, this->num_);
    vaddbias_->Compute(&b, y, y, this->num_);
  }

 private:
  std::shared_ptr<const VScalKernel<T>> vscal_;
  std::shared_ptr<const VSigmoidKernel<T>> vsigmoid_;
  std::shared_ptr<const VAddBiasKernel<T>> vaddbias_;
};

#define INTRI_VTANH(tmp, expisa)                           \
  tmp = _mm256_mul_ps(_mm256_set1_ps(-2.0f), tmp);         \
  tmp = _mm256_min_ps(tmp, _mm256_set1_ps(EXP_MAX_INPUT)); \
  tmp = expisa(tmp);                                       \
  tmp = _mm256_add_ps(_mm256_set1_ps(1.0f), tmp);          \
  tmp = _mm256_div_ps(_mm256_set1_ps(2.0f), tmp);          \
  tmp = _mm256_sub_ps(tmp, _mm256_set1_ps(1.0f))

#define INTRI8_FLOAT(isa, expisa)                                           \
  template <>                                                               \
  void VTanhKernelImpl<float, isa, kEQ8>::Compute(const float* x, float* y) \
      const {                                                               \
    __m256 tmp = _mm256_loadu_ps(x);                                        \
    INTRI_VTANH(tmp, expisa);                                               \
    _mm256_storeu_ps(y, tmp);                                               \
  }

#define INTRI16_FLOAT(isa, expisa)                                           \
  template <>                                                                \
  void VTanhKernelImpl<float, isa, kEQ16>::Compute(const float* x, float* y) \
      const {                                                                \
    __m256 tmp0 = _mm256_loadu_ps(x);                                        \
    __m256 tmp1 = _mm256_loadu_ps(x + 8);                                    \
    INTRI_VTANH(tmp0, expisa);                                               \
    INTRI_VTANH(tmp1, expisa);                                               \
    _mm256_storeu_ps(y, tmp0);                                               \
    _mm256_storeu_ps(y + 8, tmp1);                                           \
  }

#define INTRI_GT8LT16_FLOAT(isa, expisa)                                      \
  template <>                                                                 \
  VTanhKernelImpl<float, isa, kGT8LT16>::VTanhKernelImpl(int d)               \
      : VTanhKernel<float>() {                                                \
    this->num_ = d;                                                           \
    this->end_ = AVX_FLOAT_BLOCK;                                             \
    this->rest_ = d - this->end_;                                             \
    vscal_ =                                                                  \
        KernelPool::Instance().template Get<VScalKernel<float>>(this->rest_); \
    vsigmoid_ = KernelPool::Instance().template Get<VSigmoidKernel<float>>(   \
        this->rest_);                                                         \
    vaddbias_ = KernelPool::Instance().template Get<VAddBiasKernel<float>>(   \
        this->rest_);                                                         \
  }                                                                           \
  template <>                                                                 \
  void VTanhKernelImpl<float, isa, kGT8LT16>::Compute(const float* x,         \
                                                      float* y) const {       \
    __m256 tmp = _mm256_loadu_ps(x);                                          \
    INTRI_VTANH(tmp, expisa);                                                 \
    _mm256_storeu_ps(y, tmp);                                                 \
    x += AVX_FLOAT_BLOCK;                                                     \
    y += AVX_FLOAT_BLOCK;                                                     \
    const float a = 2.f, b = -1.f;                                            \
    vscal_->Compute(&a, x, y, this->num_);                                    \
    vsigmoid_->Compute(y, y);                                                 \
    vscal_->Compute(&a, y, y, this->num_);                                    \
    vaddbias_->Compute(&b, y, y, this->num_);                                 \
  }

#define INTRI_GT16_FLOAT(isa, expisa)                                         \
  template <>                                                                 \
  VTanhKernelImpl<float, isa, kGT16>::VTanhKernelImpl(int d)                  \
      : VTanhKernel<float>() {                                                \
    this->num_ = d;                                                           \
    this->rest_ = d % AVX_FLOAT_BLOCK;                                        \
    this->end_ = d - this->rest_;                                             \
    vscal_ =                                                                  \
        KernelPool::Instance().template Get<VScalKernel<float>>(this->rest_); \
    vsigmoid_ = KernelPool::Instance().template Get<VSigmoidKernel<float>>(   \
        this->rest_);                                                         \
    vaddbias_ = KernelPool::Instance().template Get<VAddBiasKernel<float>>(   \
        this->rest_);                                                         \
  }                                                                           \
  template <>                                                                 \
  void VTanhKernelImpl<float, isa, kGT16>::Compute(const float* x, float* y)  \
      const {                                                                 \
    for (int i = 0; i < this->end_; i += AVX_FLOAT_BLOCK) {                   \
      __m256 tmp = _mm256_loadu_ps(x + i);                                    \
      INTRI_VTANH(tmp, expisa);                                               \
      _mm256_storeu_ps(y + i, tmp);                                           \
    }                                                                         \
    x += this->end_;                                                          \
    y += this->end_;                                                          \
    const float a = 2.f, b = -1.f;                                            \
    vscal_->Compute(&a, x, y, this->num_);                                    \
    vsigmoid_->Compute(y, y);                                                 \
    vscal_->Compute(&a, y, y, this->num_);                                    \
    vaddbias_->Compute(&b, y, y, this->num_);                                 \
  }

#ifdef __AVX__
INTRI8_FLOAT(jit::avx, detail::ExpAVX);
INTRI16_FLOAT(jit::avx, detail::ExpAVX);
INTRI_GT8LT16_FLOAT(jit::avx, detail::ExpAVX);
INTRI_GT16_FLOAT(jit::avx, detail::ExpAVX);
#endif
#ifdef __AVX2__
INTRI8_FLOAT(jit::avx2, detail::ExpAVX2);
INTRI16_FLOAT(jit::avx2, detail::ExpAVX2);
// maybe use avx at gt8lt16 and gt16
#endif
#ifdef __AVX512F__
INTRI8_FLOAT(jit::avx512f, detail::ExpAVX2);
INTRI16_FLOAT(jit::avx512f, detail::ExpAVX2);
// maybe use avx at gt8lt16 and gt16
#endif

#undef INTRI8_FLOAT
#undef INTRI16_FLOAT
#undef INTRI_GT8LT16_FLOAT
#undef INTRI_GT16_FLOAT
#undef INTRI_VTANH

REGISTER_JITKERNEL_DEPRECATED(vtanh, VTanhKernel);

#undef JITKERNEL_NEW_ACT_IMPL

}  // namespace jitkernel
}  // namespace math
}  // namespace operators
}  // namespace paddle