/* Copyright (c) 2022 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/phi/kernels/activation_kernel.h"

#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/full_kernel.h"
#include "paddle/phi/kernels/funcs/activation_functor.h"
#include "paddle/phi/kernels/impl/activation_impl.h"

namespace phi {

#define DEFINE_CPU_ACTIVATION_KERNEL(name, functor_class)               \
  template <typename T, typename Context>                               \
  void name##Kernel(                                                    \
      const Context& dev_ctx, const DenseTensor& x, DenseTensor* out) { \
    funcs::functor_class<T> functor;                                    \
    ActivationImpl<T, T, Context, funcs::functor_class<T>>(             \
        dev_ctx, x, out, functor);                                      \
  }

#define DEFINE_CPU_ACTIVATION_KERNEL_WITH_INT_IN_FLOAT_OUT(name,           \
                                                           functor_class)  \
  template <typename T, typename Context>                                  \
  void name##Kernel(                                                       \
      const Context& dev_ctx, const DenseTensor& x, DenseTensor* out) {    \
    funcs::functor_class<T> functor;                                       \
    using U =                                                              \
        typename std::conditional_t<std::is_integral<T>::value, float, T>; \
    ActivationImpl<T, U, Context, funcs::functor_class<T>>(                \
        dev_ctx, x, out, functor);                                         \
  }

#define DEFINE_CPU_ACT_KERNEL_WITH_ONE_ATTRS(name, functor_class, attr) \
  template <typename T, typename Context>                               \
  void name##Kernel(const Context& dev_ctx,                             \
                    const DenseTensor& x,                               \
                    float attr,                                         \
                    DenseTensor* out) {                                 \
    funcs::functor_class<T> functor;                                    \
    auto attrs = functor.GetAttrs();                                    \
    *(attrs[0].second) = attr;                                          \
    ActivationImpl<T, T, Context, funcs::functor_class<T>>(             \
        dev_ctx, x, out, functor);                                      \
  }

#define DEFINE_CPU_ACT_KERNEL_WITH_ONE_DOUBLE_ATTRS(name, functor_class, attr) \
  template <typename T, typename Context>                                      \
  void name##Kernel(const Context& dev_ctx,                                    \
                    const DenseTensor& x,                                      \
                    double attr,                                               \
                    DenseTensor* out) {                                        \
    funcs::functor_class<T> functor;                                           \
    auto attrs = functor.GetAttrs();                                           \
    *(attrs[0].second) = attr;                                                 \
    ActivationImpl<T, T, Context, funcs::functor_class<T>>(                    \
        dev_ctx, x, out, functor);                                             \
  }

#define DEFINE_CPU_ACT_KERNEL_WITH_TWO_ATTRS(               \
    name, functor_class, attr1, attr2)                      \
  template <typename T, typename Context>                   \
  void name##Kernel(const Context& dev_ctx,                 \
                    const DenseTensor& x,                   \
                    float attr1,                            \
                    float attr2,                            \
                    DenseTensor* out) {                     \
    funcs::functor_class<T> functor;                        \
    auto attrs = functor.GetAttrs();                        \
    *(attrs[0].second) = attr1;                             \
    *(attrs[1].second) = attr2;                             \
    ActivationImpl<T, T, Context, funcs::functor_class<T>>( \
        dev_ctx, x, out, functor);                          \
  }

#define DEFINE_CPU_ACT_KERNEL_WITH_TWO_DOUBLE_ATTRS(        \
    name, functor_class, attr1, attr2)                      \
  template <typename T, typename Context>                   \
  void name##Kernel(const Context& dev_ctx,                 \
                    const DenseTensor& x,                   \
                    double attr1,                           \
                    double attr2,                           \
                    DenseTensor* out) {                     \
    funcs::functor_class<T> functor;                        \
    auto attrs = functor.GetAttrs();                        \
    *(attrs[0].second) = attr1;                             \
    *(attrs[1].second) = attr2;                             \
    ActivationImpl<T, T, Context, funcs::functor_class<T>>( \
        dev_ctx, x, out, functor);                          \
  }

// Specialized Sin kernel with vectorized Sleef implementation for float/double
// This ensures high precision computation
template <typename T, typename Context>
void SinKernel(const Context& dev_ctx, const DenseTensor& x, DenseTensor* out) {
  if constexpr (std::is_same<T, float>::value ||
                std::is_same<T, double>::value) {
    // Use vectorized Sleef path for float/double for high precision
    VectorizedSinImpl<T, Context>(dev_ctx, x, out);
  } else {
    // Use generic path for other types (float16, bfloat16, complex)
    funcs::SinFunctor<T> functor;
    ActivationImpl<T, T, Context, funcs::SinFunctor<T>>(
        dev_ctx, x, out, functor);
  }
}

// Specialized Cos kernel with vectorized Sleef implementation for float/double
template <typename T, typename Context>
void CosKernel(const Context& dev_ctx, const DenseTensor& x, DenseTensor* out) {
  if constexpr (std::is_same<T, float>::value ||
                std::is_same<T, double>::value) {
    // Use vectorized Sleef path for float/double for high precision
    VectorizedCosImpl<T, Context>(dev_ctx, x, out);
  } else {
    // Use generic path for other types (float16, bfloat16, complex)
    funcs::CosFunctor<T> functor;
    ActivationImpl<T, T, Context, funcs::CosFunctor<T>>(
        dev_ctx, x, out, functor);
  }
}
DEFINE_CPU_ACTIVATION_KERNEL(Tan, TanFunctor)
DEFINE_CPU_ACTIVATION_KERNEL(Asin, AsinFunctor)
DEFINE_CPU_ACTIVATION_KERNEL(Atan, AtanFunctor)
DEFINE_CPU_ACTIVATION_KERNEL(Acos, AcosFunctor)
DEFINE_CPU_ACTIVATION_KERNEL(Sinh, SinhFunctor)
DEFINE_CPU_ACTIVATION_KERNEL(Cosh, CoshFunctor)
DEFINE_CPU_ACTIVATION_KERNEL(Asinh, AsinhFunctor)
DEFINE_CPU_ACTIVATION_KERNEL(Acosh, AcoshFunctor)
DEFINE_CPU_ACTIVATION_KERNEL(Atanh, AtanhFunctor)
DEFINE_CPU_ACTIVATION_KERNEL(Relu, ReluCPUFunctor)
DEFINE_CPU_ACTIVATION_KERNEL(Tanh, TanhFunctor)
DEFINE_CPU_ACTIVATION_KERNEL(TanhShrink, TanhShrinkFunctor)
DEFINE_CPU_ACTIVATION_KERNEL(Silu, SiluFunctor)
DEFINE_CPU_ACTIVATION_KERNEL(Reciprocal, ReciprocalFunctor)
DEFINE_CPU_ACTIVATION_KERNEL(Square, SquareFunctor)
DEFINE_CPU_ACTIVATION_KERNEL(Sqrt, SqrtFunctor)
DEFINE_CPU_ACTIVATION_KERNEL(Rsqrt, RsqrtFunctor)
DEFINE_CPU_ACTIVATION_KERNEL(Softsign, SoftsignFunctor)
DEFINE_CPU_ACTIVATION_KERNEL(Sigmoid, SigmoidFunctor)
DEFINE_CPU_ACTIVATION_KERNEL(LogSigmoid, LogSigmoidFunctor)
DEFINE_CPU_ACTIVATION_KERNEL(Floor, FloorFunctor)
DEFINE_CPU_ACTIVATION_KERNEL(Ceil, CeilFunctor)
DEFINE_CPU_ACTIVATION_KERNEL(Negative, NegativeFunctor)
DEFINE_CPU_ACTIVATION_KERNEL(Rint, RintFunctor)

DEFINE_CPU_ACTIVATION_KERNEL_WITH_INT_IN_FLOAT_OUT(Log, LogFunctor)
DEFINE_CPU_ACTIVATION_KERNEL_WITH_INT_IN_FLOAT_OUT(Log2, Log2Functor)
DEFINE_CPU_ACTIVATION_KERNEL_WITH_INT_IN_FLOAT_OUT(Log10, Log10Functor)
DEFINE_CPU_ACTIVATION_KERNEL_WITH_INT_IN_FLOAT_OUT(Log1p, Log1pFunctor)
DEFINE_CPU_ACTIVATION_KERNEL_WITH_INT_IN_FLOAT_OUT(Expm1, Expm1Functor)

// Specialized Exp kernel with vectorized MKL VML/Sleef implementation
// for float/double. This ensures high precision computation.
template <typename T, typename Context>
void ExpKernel(const Context& dev_ctx, const DenseTensor& x, DenseTensor* out) {
  if constexpr (std::is_same<T, float>::value ||
                std::is_same<T, double>::value) {
    // Use vectorized MKL VML/Sleef path for float/double for high precision
    VectorizedExpImpl<T, Context>(dev_ctx, x, out);
  } else {
    // Use generic path for other types (int, int64, float16, complex)
    funcs::ExpFunctor<T> functor;
    using U = typename std::conditional_t<std::is_integral<T>::value, float, T>;
    ActivationImpl<T, U, Context, funcs::ExpFunctor<T>>(
        dev_ctx, x, out, functor);
  }
}

DEFINE_CPU_ACT_KERNEL_WITH_ONE_DOUBLE_ATTRS(LeakyRelu, LeakyReluFunctor, alpha)
DEFINE_CPU_ACT_KERNEL_WITH_ONE_ATTRS(Mish, MishFunctor, threshold)
DEFINE_CPU_ACT_KERNEL_WITH_ONE_ATTRS(HardShrink, HardShrinkFunctor, threshold)
DEFINE_CPU_ACT_KERNEL_WITH_ONE_ATTRS(SoftShrink, SoftShrinkFunctor, lambda)
DEFINE_CPU_ACT_KERNEL_WITH_ONE_ATTRS(Elu, ELUFunctor, alpha)
DEFINE_CPU_ACT_KERNEL_WITH_ONE_ATTRS(Celu, CELUFunctor, alpha)

DEFINE_CPU_ACT_KERNEL_WITH_TWO_ATTRS(HardTanh, HardTanhFunctor, t_min, t_max)
DEFINE_CPU_ACT_KERNEL_WITH_TWO_ATTRS(STanh, STanhFunctor, scale_a, scale_b)
DEFINE_CPU_ACT_KERNEL_WITH_TWO_DOUBLE_ATTRS(Softplus,
                                            SoftplusFunctor,
                                            beta,
                                            threshold)
DEFINE_CPU_ACT_KERNEL_WITH_TWO_ATTRS(HardSigmoid,
                                     HardSigmoidFunctor,
                                     slope,
                                     offset)
DEFINE_CPU_ACT_KERNEL_WITH_TWO_ATTRS(ThresholdedRelu,
                                     ThresholdedReluFunctor,
                                     threshold,
                                     value)

template <typename T, typename Context>
void HardSwishKernel(const Context& dev_ctx,
                     const DenseTensor& x,
                     DenseTensor* out) {
  funcs::HardSwishFunctor<T> functor;
  float threshold = 6;
  float scale = 6;
  float offset = 3;
  auto attrs = functor.GetAttrs();
  *(attrs[0].second) = threshold;
  *(attrs[1].second) = scale;
  *(attrs[2].second) = offset;
  ActivationImpl<T, T, Context, funcs::HardSwishFunctor<T>>(
      dev_ctx, x, out, functor);
}

template <typename T, typename Context>
void SwishKernel(const Context& dev_ctx,
                 const DenseTensor& x,
                 DenseTensor* out) {
  funcs::SwishFunctor<T> functor;
  auto attrs = functor.GetAttrs();
  *(attrs[0].second) = 1.0;
  ActivationImpl<T, T, Context, funcs::SwishFunctor<T>>(
      dev_ctx, x, out, functor);
}

template <typename T, typename Context>
void Relu6Kernel(const Context& dev_ctx,
                 const DenseTensor& x,
                 DenseTensor* out) {
  funcs::Relu6Functor<T> functor;
  auto attrs = functor.GetAttrs();
  *(attrs[0].second) = 6.0;
  ActivationImpl<T, T, Context, funcs::Relu6Functor<T>>(
      dev_ctx, x, out, functor);
}

template <typename T, typename Context>
void RoundKernel(const Context& dev_ctx,
                 const DenseTensor& x,
                 const int decimals,
                 DenseTensor* out) {
  funcs::RoundFunctor<T> functor;
  auto attrs = functor.GetAttrs();
  *(attrs[0].second) = decimals;
  ActivationImpl<T, T, Context, funcs::RoundFunctor<T>>(
      dev_ctx, x, out, functor);
}

template <typename T, typename Context>
void PowKernel(const Context& dev_ctx,
               const DenseTensor& x,
               const Scalar& factor,
               DenseTensor* out) {
  PADDLE_ENFORCE_NOT_NULL(
      out, errors::InvalidArgument("Output Out should not be nullptr"));
  dev_ctx.template Alloc<T>(out);
  if (factor.to<float>() == 0) {
    std::vector<int64_t> vec_dims = vectorize(out->dims());
    Full<T, Context>(dev_ctx, vec_dims, static_cast<T>(1), out);
    return;
  }
  if (factor.to<float>() == 1) {
    Copy<Context>(dev_ctx, x, dev_ctx.GetPlace(), false, out);
    return;
  }
  auto x_flatten =
      EigenVector<T>::Flatten(GET_DATA_SAFELY(&x, "Input", "X", "Activation"));
  auto out_flatten = EigenVector<T>::Flatten(
      GET_DATA_SAFELY(out, "Output", "Out", "Activation"));
  auto* place = dev_ctx.eigen_device();
  funcs::PowFunctor<T> functor;
  auto attrs = functor.GetAttrs();
  *(attrs[0].second) = factor.to<float>();
  functor(*place, x_flatten, out_flatten);
}

}  // namespace phi
PD_REGISTER_KERNEL(relu, CPU, ALL_LAYOUT, phi::ReluKernel, float, double) {}

#define PD_REGISTER_ACTIVATION_KERNEL(name, func) \
  PD_REGISTER_KERNEL(name, CPU, ALL_LAYOUT, phi::func, float, double) {}

#define PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(name, func) \
  PD_REGISTER_KERNEL(name,                                     \
                     CPU,                                      \
                     ALL_LAYOUT,                               \
                     phi::func,                                \
                     float,                                    \
                     double,                                   \
                     phi::complex64,                           \
                     phi::complex128) {}

PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(sin, SinKernel)
PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(cos, CosKernel)
PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(tan, TanKernel)
PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(acos, AcosKernel)
PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(asin, AsinKernel)
PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(atan, AtanKernel)
PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(sinh, SinhKernel)
PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(cosh, CoshKernel)
PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(asinh, AsinhKernel)
PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(acosh, AcoshKernel)
PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(atanh, AtanhKernel)
PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(tanh, TanhKernel)
PD_REGISTER_ACTIVATION_KERNEL(hardtanh, HardTanhKernel)
PD_REGISTER_ACTIVATION_KERNEL(leaky_relu, LeakyReluKernel)
PD_REGISTER_ACTIVATION_KERNEL(thresholded_relu, ThresholdedReluKernel)
PD_REGISTER_ACTIVATION_KERNEL(hard_shrink, HardShrinkKernel)
PD_REGISTER_ACTIVATION_KERNEL(softshrink, SoftShrinkKernel)
PD_REGISTER_ACTIVATION_KERNEL(tanh_shrink, TanhShrinkKernel)
PD_REGISTER_ACTIVATION_KERNEL(elu, EluKernel)
PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(silu, SiluKernel)
PD_REGISTER_ACTIVATION_KERNEL(mish, MishKernel)
PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(stanh, STanhKernel)
PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(reciprocal, ReciprocalKernel)
PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(sqrt, SqrtKernel)
PD_REGISTER_ACTIVATION_KERNEL(rsqrt, RsqrtKernel)
PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(softplus, SoftplusKernel)
PD_REGISTER_ACTIVATION_KERNEL(logit, LogitKernel)
PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(softsign, SoftsignKernel)
PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(sigmoid, SigmoidKernel)
PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(logsigmoid, LogSigmoidKernel)
PD_REGISTER_ACTIVATION_KERNEL(hardsigmoid, HardSigmoidKernel)
PD_REGISTER_ACTIVATION_KERNEL(swish, SwishKernel)
PD_REGISTER_ACTIVATION_KERNEL(relu6, Relu6Kernel)
PD_REGISTER_ACTIVATION_KERNEL_WITH_COMPLEX(hardswish, HardSwishKernel)
PD_REGISTER_ACTIVATION_KERNEL(celu, CeluKernel)

PD_REGISTER_KERNEL(
    rint, CPU, ALL_LAYOUT, phi::RintKernel, int, int64_t, float, double) {}

PD_REGISTER_KERNEL(round,
                   CPU,
                   ALL_LAYOUT,
                   phi::RoundKernel,
                   int,
                   int64_t,
                   float,
                   double,
                   phi::complex64,
                   phi::complex128) {}

PD_REGISTER_KERNEL(exp,
                   CPU,
                   ALL_LAYOUT,
                   phi::ExpKernel,
                   float,
                   double,
                   int,
                   int64_t,
                   phi::float16,
                   phi::complex64,
                   phi::complex128) {}

PD_REGISTER_KERNEL(expm1,
                   CPU,
                   ALL_LAYOUT,
                   phi::Expm1Kernel,
                   float,
                   double,
                   int,
                   int64_t,
                   phi::float16,
                   phi::complex64,
                   phi::complex128) {}

PD_REGISTER_KERNEL(square,
                   CPU,
                   ALL_LAYOUT,
                   phi::SquareKernel,
                   float,
                   double,
                   int,
                   int64_t,
                   phi::complex64,
                   phi::complex128) {}

PD_REGISTER_KERNEL(log,
                   CPU,
                   ALL_LAYOUT,
                   phi::LogKernel,
                   float,
                   double,
                   int,
                   int64_t,
                   phi::float16,
                   phi::bfloat16,
                   phi::complex64,
                   phi::complex128) {}

PD_REGISTER_KERNEL(log2,
                   CPU,
                   ALL_LAYOUT,
                   phi::Log2Kernel,
                   float,
                   double,
                   int,
                   int64_t,
                   phi::float16,
                   phi::bfloat16,
                   phi::complex64,
                   phi::complex128) {}

PD_REGISTER_KERNEL(log10,
                   CPU,
                   ALL_LAYOUT,
                   phi::Log10Kernel,
                   float,
                   double,
                   int,
                   int64_t,
                   phi::float16,
                   phi::bfloat16,
                   phi::complex64,
                   phi::complex128) {}

PD_REGISTER_KERNEL(log1p,
                   CPU,
                   ALL_LAYOUT,
                   phi::Log1pKernel,
                   float,
                   double,
                   int,
                   int64_t,
                   phi::float16,
                   phi::bfloat16,
                   phi::complex64,
                   phi::complex128) {}

PD_REGISTER_KERNEL(negative,
                   CPU,
                   ALL_LAYOUT,
                   phi::NegativeKernel,
                   float,
                   double,
                   int16_t,
                   int,
                   int64_t,
                   phi::complex64,
                   phi::complex128) {}

PD_REGISTER_KERNEL(pow,
                   CPU,
                   ALL_LAYOUT,
                   phi::PowKernel,
                   float,
                   double,
                   int,
                   int64_t,
                   phi::complex64,
                   phi::complex128) {}

PD_REGISTER_KERNEL(ceil,
                   CPU,
                   ALL_LAYOUT,
                   phi::CeilKernel,
                   float,
                   double,
                   uint8_t,
                   int8_t,
                   int16_t,
                   int,
                   int64_t,
                   phi::float16,
                   phi::bfloat16) {}

PD_REGISTER_KERNEL(floor,
                   CPU,
                   ALL_LAYOUT,
                   phi::FloorKernel,
                   float,
                   double,
                   uint8_t,
                   int8_t,
                   int16_t,
                   int,
                   int64_t,
                   phi::float16,
                   phi::bfloat16) {}