/* 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. */

#pragma once
#include <functional>
#include <memory>  // for shared_ptr
#include <string>
#include <unordered_map>
#include "paddle/fluid/platform/cpu_info.h"
#include "paddle/fluid/platform/macros.h"

// Note: Only support on CPU yet.
namespace paddle {
namespace operators {
namespace math {
namespace jitkernel {

#define SIGMOID_THRESHOLD_MIN -40.0
#define SIGMOID_THRESHOLD_MAX 13.0
#define EXP_MAX_INPUT 40.0
#define AVX_FLOAT_BLOCK 8
#define AVX2_FLOAT_BLOCK 8
#define AVX512_FLOAT_BLOCK 16

typedef enum { kLT8, kEQ8, kGT8LT16, kEQ16, kGT16 } jit_block;

class Kernel {
 public:
  Kernel() = default;
  virtual ~Kernel() = default;
  int num_{0};
  int end_{0};
  int rest_{0};
  DISABLE_COPY_AND_ASSIGN(Kernel);
};

class KernelPool {
 public:
  static KernelPool &Instance();

  template <typename Ker, typename... ARGS>
  std::shared_ptr<const Ker> Get(ARGS... args);

  std::shared_ptr<const Kernel> Get(const std::string &key) const;

 private:
  KernelPool() = default;
  std::unordered_map<std::string, std::shared_ptr<const Kernel>> kers_;

  DISABLE_COPY_AND_ASSIGN(KernelPool);
};

template <typename T>
class VMulKernel : public Kernel {
 public:
  virtual void Compute(const T *x, const T *y, T *z) const = 0;
};

template <typename T>
class VAddKernel : public Kernel {
 public:
  virtual void Compute(const T *x, const T *y, T *z) const = 0;
};

template <typename T>
class VScalKernel : public Kernel {
 public:
  virtual void Compute(const T a, const T *x, T *y) const = 0;
  virtual void Compute(const T a, T *x) const = 0;
};

template <typename T>
class VAddBiasKernel : public Kernel {
 public:
  virtual void Compute(const T a, const T *x, T *y) const = 0;
};

template <typename T>
class VActKernel : public Kernel {
 public:
  virtual void Compute(const T *x, T *y) const = 0;
};

template <typename T>
class VReluKernel : public VActKernel<T> {
 public:
  virtual void Compute(const T *x, T *y) const = 0;
};

template <typename T>
class VIdentityKernel : public VActKernel<T> {
 public:
  virtual void Compute(const T *x, T *y) const = 0;
};

template <typename T>
class VExpKernel : public VActKernel<T> {
 public:
  virtual void Compute(const T *x, T *y) const = 0;
};

template <typename T>
class VSigmoidKernel : public VActKernel<T> {
 public:
  virtual void Compute(const T *x, T *y) const = 0;
};

template <typename T>
class VTanhKernel : public VActKernel<T> {
 public:
  virtual void Compute(const T *x, T *y) const = 0;
};

template <typename T>
class LSTMKernel : public Kernel {
 public:
  virtual void ComputeCtHt(T *gates, const T *ct_1, T *ct, T *ht,
                           /* below only used in peephole*/
                           const T *wp_data = nullptr,
                           T *checked = nullptr) const = 0;

  // compute c1 and h1 without c0 or h0
  virtual void ComputeC1H1(T *gates, T *ct, T *ht,
                           /* below only used in peephole*/
                           const T *wp_data = nullptr) const = 0;
};

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