/* 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 <string>
#include "paddle/fluid/operators/math/jit_kernel_macro.h"
#include "paddle/fluid/platform/enforce.h"

#ifdef PADDLE_WITH_XBYAK
#include "paddle/fluid/operators/math/jit_code.h"
#endif

#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;

template <typename T>
void VMulRefer(const T* x, const T* y, T* z, int n) {
  for (int i = 0; i < n; ++i) {
    z[i] = x[i] * y[i];
  }
}

template <typename T>
void VAddRefer(const T* x, const T* y, T* z, int n) {
  for (int i = 0; i < n; ++i) {
    z[i] = x[i] + y[i];
  }
}

template <typename T>
void VAddReluRefer(const T* x, const T* y, T* z, int n) {
  for (int i = 0; i < n; ++i) {
    z[i] = x[i] + y[i];
    z[i] = z[i] > 0 ? z[i] : 0;
  }
}

#ifdef PADDLE_WITH_MKLML
template <typename T>
void VMulMKL(const T* x, const T* y, T* z, int n);

template <>
void VMulMKL<float>(const float* x, const float* y, float* z, int n) {
  platform::dynload::vsMul(n, x, y, z);
}

template <>
void VMulMKL<double>(const double* x, const double* y, double* z, int n) {
  platform::dynload::vdMul(n, x, y, z);
}

template <typename T>
void VAddMKL(const T* x, const T* y, T* z, int n);

template <>
void VAddMKL<float>(const float* x, const float* y, float* z, int n) {
  platform::dynload::vsAdd(n, x, y, z);
}

template <>
void VAddMKL<double>(const double* x, const double* y, double* z, int n) {
  platform::dynload::vdAdd(n, x, y, z);
}
#endif

#define DECLARE_STATIC_FUNC                                 \
  static inline std::string name(int d) {                   \
    PADDLE_THROW("DType should be either float or double"); \
  }                                                         \
  static inline bool useJIT(int d) { return false; }        \
  static inline bool useMKL(int d) { return false; }

/* VMUL JitKernel */
template <typename T>
class VMulKernelImpl : public VMulKernel<T> {
 public:
  DECLARE_STATIC_FUNC;
  explicit VMulKernelImpl(int d) : VMulKernel<T>() {
#ifdef PADDLE_WITH_XBYAK
    if (useJIT(d)) {
      // roughly estimate the size of code
      size_t sz = 96 + d / AVX_FLOAT_BLOCK * 4 * 8;
      jitcode_.reset(new gen::VVVJitCode(d, gen::operand_type::mul, false,
                                         sz > 4096 ? sz : 4096));
      this->Compute =
          jitcode_->getCode<void (*)(const T*, const T*, T*, int)>();
      return;
    }
#endif
#ifdef PADDLE_WITH_MKLML
    if (useMKL(d)) {
      this->Compute = VMulMKL<T>;
      return;
    }
#endif
    this->Compute = VMulRefer<T>;
  }

#ifdef PADDLE_WITH_XBYAK

 private:
  std::unique_ptr<gen::VVVJitCode> jitcode_{nullptr};
#endif
};

#ifdef PADDLE_WITH_XBYAK
template <>
bool VMulKernelImpl<float>::useJIT(int d) {
  return gen::VVVJitCode::init(d);
}
#endif

#ifdef PADDLE_WITH_MKLML
template <>
bool VMulKernelImpl<float>::useMKL(int d) {
  return jit::MayIUse(jit::avx512f) && d > 512;
}

template <>
bool VMulKernelImpl<double>::useMKL(int d) {
  return true;
}
#endif

/* VAdd JitKernel */
template <typename T>
class VAddKernelImpl : public VAddKernel<T> {
 public:
  DECLARE_STATIC_FUNC;
  explicit VAddKernelImpl(int d) : VAddKernel<T>() {
#ifdef PADDLE_WITH_XBYAK
    if (useJIT(d)) {
      size_t sz = 96 + d / AVX_FLOAT_BLOCK * 4 * 8;
      jitcode_.reset(new gen::VVVJitCode(d, gen::operand_type::add, false,
                                         sz > 4096 ? sz : 4096));
      this->Compute =
          jitcode_->getCode<void (*)(const T*, const T*, T*, int)>();
      return;
    }
#endif
#ifdef PADDLE_WITH_MKLML
    if (useMKL(d)) {
      this->Compute = VAddMKL<T>;
      return;
    }
#endif
    this->Compute = VAddRefer<T>;
  }

 private:
  std::unique_ptr<gen::VVVJitCode> jitcode_{nullptr};
};

#ifdef PADDLE_WITH_XBYAK
template <>
bool VAddKernelImpl<float>::useJIT(int d) {
  return gen::VVVJitCode::init(d);
}
#endif

#ifdef PADDLE_WITH_MKLML
template <>
bool VAddKernelImpl<float>::useMKL(int d) {
  return d > 512;
}

template <>
bool VAddKernelImpl<double>::useMKL(int d) {
  return true;
}
#endif

/* VAddRelu JitKernel */
template <typename T>
class VAddReluKernelImpl : public VAddReluKernel<T> {
 public:
  DECLARE_STATIC_FUNC;
  explicit VAddReluKernelImpl(int d) : VAddReluKernel<T>() {
#ifdef PADDLE_WITH_XBYAK
    if (useJIT(d)) {
      size_t sz = 96 + d / AVX_FLOAT_BLOCK * 4 * 8;
      jitcode_.reset(new gen::VVVJitCode(d, gen::operand_type::add, true,
                                         sz > 4096 ? sz : 4096));
      this->Compute =
          jitcode_->getCode<void (*)(const T*, const T*, T*, int)>();
      return;
    }
#endif
    this->Compute = VAddReluRefer<T>;
  }

 private:
  std::unique_ptr<gen::VVVJitCode> jitcode_{nullptr};
};

#ifdef PADDLE_WITH_XBYAK
template <>
bool VAddReluKernelImpl<float>::useJIT(int d) {
  return gen::VVVJitCode::init(d);
}
#endif

#undef DECLARE_STATIC_FUNC

REGISTER_JITKERNEL(vmul, VMulKernel);
REGISTER_JITKERNEL(vadd, VAddKernel);
REGISTER_JITKERNEL(vaddrelu, VAddReluKernel);

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

#ifdef PADDLE_WITH_MKLML
#define MKL_FLOAT(isa, block)                                               \
  template <>                                                               \
  void VScalKernelImpl<float, isa, block>::Compute(const float a, float* x) \
      const {                                                               \
    platform::dynload::cblas_sscal(this->num_, a, x, 1);                    \
  }

#define MKL_DOUBLE(isa, block)                                                 \
  template <>                                                                  \
  void VScalKernelImpl<double, isa, block>::Compute(const double a, double* x) \
      const {                                                                  \
    platform::dynload::cblas_dscal(this->num_, a, x, 1);                       \
  }

FOR_EACH_ISA(MKL_FLOAT, kGT16);
FOR_EACH_ISA_BLOCK(MKL_DOUBLE);
#endif

#define INTRI8_FLOAT(isa)                              \
  template <>                                          \
  void VScalKernelImpl<float, isa, kEQ8>::Compute(     \
      const float a, const float* x, float* y) const { \
    __m256 tmp;                                        \
    __m256 scalar = _mm256_set1_ps(a);                 \
    tmp = _mm256_loadu_ps(x);                          \
    tmp = _mm256_mul_ps(tmp, scalar);                  \
    _mm256_storeu_ps(y, tmp);                          \
  }
#define INTRI8_INPLACE_FLOAT(isa)                                          \
  template <>                                                              \
  void VScalKernelImpl<float, isa, kEQ8>::Compute(const float a, float* x) \
      const {                                                              \
    __m256 tmp;                                                            \
    __m256 scalar = _mm256_set1_ps(a);                                     \
    tmp = _mm256_loadu_ps(x);                                              \
    tmp = _mm256_mul_ps(tmp, scalar);                                      \
    _mm256_storeu_ps(x, tmp);                                              \
  }

#ifdef __AVX__
INTRI8_FLOAT(jit::avx);
INTRI8_INPLACE_FLOAT(jit::avx);
#endif
#ifdef __AVX2__
INTRI8_FLOAT(jit::avx2);
INTRI8_INPLACE_FLOAT(jit::avx2);
#endif
#ifdef __AVX512F__
INTRI8_FLOAT(jit::avx512f);
INTRI8_INPLACE_FLOAT(jit::avx512f);
#endif
// TODO(TJ): eq16 test and complete avx512

#undef INTRI8_FLOAT
#undef INTRI8_INPLACE_FLOAT
#undef MKL_FLOAT
#undef MKL_DOUBLE

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

#define INTRI8_FLOAT(isa)                              \
  template <>                                          \
  void VAddBiasKernelImpl<float, isa, kEQ8>::Compute(  \
      const float a, const float* x, float* y) const { \
    __m256 tmp = _mm256_loadu_ps(x);                   \
    tmp = _mm256_add_ps(tmp, _mm256_set1_ps(a));       \
    _mm256_storeu_ps(y, tmp);                          \
  }

#define INTRI16_FLOAT(isa)                             \
  template <>                                          \
  void VAddBiasKernelImpl<float, isa, kEQ16>::Compute( \
      const float a, const float* x, float* y) const { \
    __m256 tmp0 = _mm256_loadu_ps(x);                  \
    __m256 tmp1 = _mm256_loadu_ps(x + 8);              \
    tmp0 = _mm256_add_ps(tmp0, _mm256_set1_ps(a));     \
    tmp1 = _mm256_add_ps(tmp1, _mm256_set1_ps(a));     \
    _mm256_storeu_ps(y, tmp0);                         \
    _mm256_storeu_ps(y + 8, tmp1);                     \
  }

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

#undef INTRI8_FLOAT
#undef INTRI16_FLOAT

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

#define INTRI8_FLOAT(isa)                                                   \
  template <>                                                               \
  void VReluKernelImpl<float, isa, kEQ8>::Compute(const float* x, float* y) \
      const {                                                               \
    __m256 tmp = _mm256_loadu_ps(x);                                        \
    tmp = _mm256_max_ps(tmp, _mm256_setzero_ps());                          \
    _mm256_storeu_ps(y, tmp);                                               \
  }

#define INTRI16_FLOAT(isa)                                                   \
  template <>                                                                \
  void VReluKernelImpl<float, isa, kEQ16>::Compute(const float* x, float* y) \
      const {                                                                \
    __m256 zeros = _mm256_setzero_ps();                                      \
    __m256 tmp0 = _mm256_loadu_ps(x);                                        \
    __m256 tmp1 = _mm256_loadu_ps(x + 8);                                    \
    tmp0 = _mm256_max_ps(tmp0, zeros);                                       \
    tmp1 = _mm256_max_ps(tmp1, zeros);                                       \
    _mm256_storeu_ps(y, tmp0);                                               \
    _mm256_storeu_ps(y + 8, tmp1);                                           \
  }

#define INTRI_GT8LT16_FLOAT(isa)                                        \
  template <>                                                           \
  VReluKernelImpl<float, isa, kGT8LT16>::VReluKernelImpl(int d)         \
      : VReluKernel<float>() {                                          \
    this->num_ = d;                                                     \
    this->end_ = AVX_FLOAT_BLOCK;                                       \
    this->rest_ = d - AVX_FLOAT_BLOCK;                                  \
  }                                                                     \
  template <>                                                           \
  void VReluKernelImpl<float, isa, kGT8LT16>::Compute(const float* x,   \
                                                      float* y) const { \
    __m256 zeros = _mm256_setzero_ps();                                 \
    __m256 tmp0 = _mm256_loadu_ps(x);                                   \
    __m256 tmp1 = _mm256_loadu_ps(x + this->rest_);                     \
    tmp0 = _mm256_max_ps(tmp0, zeros);                                  \
    tmp1 = _mm256_max_ps(tmp1, zeros);                                  \
    _mm256_storeu_ps(y, tmp0);                                          \
    _mm256_storeu_ps(y + this->rest_, tmp1);                            \
  }

#define INTRI_GT16_FLOAT(isa)                                                \
  template <>                                                                \
  VReluKernelImpl<float, isa, kGT16>::VReluKernelImpl(int d)                 \
      : VReluKernel<float>() {                                               \
    this->num_ = d;                                                          \
    this->end_ = d - d % AVX_FLOAT_BLOCK;                                    \
    this->rest_ = d - AVX_FLOAT_BLOCK;                                       \
  }                                                                          \
  template <>                                                                \
  void VReluKernelImpl<float, isa, kGT16>::Compute(const float* x, float* y) \
      const {                                                                \
    __m256 zeros = _mm256_setzero_ps();                                      \
    for (int i = 0; i < this->end_; i += AVX_FLOAT_BLOCK) {                  \
      __m256 tmp = _mm256_loadu_ps(x + i);                                   \
      tmp = _mm256_max_ps(tmp, zeros);                                       \
      _mm256_storeu_ps(y + i, tmp);                                          \
    }                                                                        \
    __m256 tmp = _mm256_loadu_ps(x + this->rest_);                           \
    tmp = _mm256_max_ps(tmp, zeros);                                         \
    _mm256_storeu_ps(y + this->rest_, tmp);                                  \
  }

#ifdef __AVX__
INTRI8_FLOAT(jit::avx);
INTRI16_FLOAT(jit::avx);
INTRI_GT8LT16_FLOAT(jit::avx);
INTRI_GT16_FLOAT(jit::avx);
#endif
#ifdef __AVX2__
INTRI8_FLOAT(jit::avx2);
INTRI16_FLOAT(jit::avx2);
INTRI_GT8LT16_FLOAT(jit::avx2);
INTRI_GT16_FLOAT(jit::avx2);
#endif
#ifdef __AVX512F__
// TODO(TJ): refine avx512
INTRI8_FLOAT(jit::avx512f);
INTRI16_FLOAT(jit::avx512f);
INTRI_GT8LT16_FLOAT(jit::avx512f);
INTRI_GT16_FLOAT(jit::avx512f);
#endif

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

/* An empty JitKernel */
template <typename T, platform::jit::cpu_isa_t isa, jit_block>
class VIdentityKernelImpl : public VIdentityKernel<T> {
 public:
  explicit VIdentityKernelImpl(int d) : VIdentityKernel<T>() { this->num_ = d; }
  void Compute(const T* x, T* y) const override {}
};

REGISTER_JITKERNEL_DEPRECATED(vscal, VScalKernel);
REGISTER_JITKERNEL_DEPRECATED(vaddb, VAddBiasKernel);
REGISTER_JITKERNEL_DEPRECATED(vrelu, VReluKernel);
REGISTER_JITKERNEL_DEPRECATED(videntity, VIdentityKernel);

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