/**
 * test_Tensor.cpp
 *
 * Author: hedaoyuan (hedaoyuan@baidu.com)
 * Created on: 2016-06-06
 *
 * Copyright (c) Baidu.com, Inc. All Rights Reserved
 */

#include <gtest/gtest.h>
#include "paddle/math/Matrix.h"
using namespace paddle;  // NOLINT
using namespace std;     // NOLINT

template<typename Tensor>
extern void TensorCheckEqual(const Tensor& tensor1, const Tensor& tensor2);

void TensorCheckEqual(const CpuMatrix& matrix1, const CpuMatrix& matrix2) {
  CHECK(matrix1.getHeight() == matrix2.getHeight());
  CHECK(matrix1.getWidth() == matrix2.getWidth());

  int height = matrix1.getHeight();
  int width = matrix1.getWidth();
  const real* data1 = matrix1.getData();
  const real* data2 = matrix2.getData();
  int count = 0;
  for (int i = 0; i < height; i++) {
    for (int j = 0; j < width; j++) {
      if (data1[i * width + j] != data2[i * width + j]) {
        count++;
      }
    }
  }
  EXPECT_EQ(count, 0) << "There are " << count << " different element.";
}

void TensorCheckEqual(const GpuMatrix& matrix1, const GpuMatrix& matrix2) {
  CpuMatrix cpu1(matrix1.getHeight(), matrix1.getWidth());
  CpuMatrix cpu2(matrix2.getHeight(), matrix2.getWidth());
  cpu1.copyFrom(matrix1);
  cpu2.copyFrom(matrix2);
  TensorCheckEqual(cpu1, cpu2);
}

void TensorCheckErr(const CpuMatrix& matrix1, const CpuMatrix& matrix2) {
  CHECK(matrix1.getHeight() == matrix2.getHeight());
  CHECK(matrix1.getWidth() == matrix2.getWidth());
#ifndef PADDLE_TYPE_DOUBLE
  real err = 1e-5;
#else
  real err = 1e-10;
#endif

  int height = matrix1.getHeight();
  int width = matrix1.getWidth();
  const real* data1 = matrix1.getData();
  const real* data2 = matrix2.getData();
  int count = 0;
  for (int i = 0; i < height; i++) {
    for (int j = 0; j < width; j++) {
      real a = data1[i * width + j];
      real b = data2[i * width + j];
      if (fabs(a - b) > err) {
        if ((fabsf(a - b) / fabsf(a)) > (err / 10.0f)) {
          count++;
        }
      }
    }
  }
  EXPECT_EQ(count, 0) << "There are " << count << " different element.";
}

void TensorCheckErr(const GpuMatrix& matrix1, const GpuMatrix& matrix2) {
  CpuMatrix cpu1(matrix1.getHeight(), matrix1.getWidth());
  CpuMatrix cpu2(matrix2.getHeight(), matrix2.getWidth());
  cpu1.copyFrom(matrix1);
  cpu2.copyFrom(matrix2);
  TensorCheckErr(cpu1, cpu2);
}

template<class T>
void TensorCheckEqual(const CpuVectorT<T>& vector1,
                      const CpuVectorT<T>& vector2) {
  CHECK(vector1.getSize() == vector2.getSize());

  const T* data1 = vector1.getData();
  const T* data2 = vector2.getData();
  size_t size = vector1.getSize();
  int count = 0;
  for (size_t i = 0; i < size; i++) {
    if (data1[i] != data2[i]) {
      count++;
    }
  }
  EXPECT_EQ(count, 0) << "There are " << count << " different element.";
}

template<class T>
void TensorCheckEqual(const GpuVectorT<T>& vector1,
                      const GpuVectorT<T>& vector2) {
  CpuVectorT<T> cpu1(vector1.getSize());
  CpuVectorT<T> cpu2(vector2.getSize());
  cpu1.copyFrom(vector1);
  cpu2.copyFrom(vector2);
  TensorCheckEqual(cpu1, cpu2);
}

#define INIT_UNARY(A1, A2)                  \
    Tensor A1(height, width);               \
    Tensor A2(height, width);               \
    A1.randomizeUniform();                  \
    A2.copyFrom(A1)
#define INIT_BINARY(A1, A2, B)              \
    INIT_UNARY(A1, A2);                     \
    Tensor B(height, width);                \
    B.randomizeUniform()
#define INIT_TERNARY(A1, A2, B, C)          \
    INIT_BINARY(A1, A2, B);                 \
    Tensor C(height, width);                \
    C.randomizeUniform()
#define INIT_QUATERNARY(A1, A2, B, C, D)    \
    INIT_TERNARY(A1, A2, B, C);             \
    Tensor D(height, width);                \
    D.randomizeUniform()

template<typename Tensor>
struct TestUnaryMatrix {
  typedef std::function<void(Tensor& A1, Tensor& A2)> UnaryFunc;

  explicit TestUnaryMatrix(UnaryFunc testUnaryFunc) {
    for (auto height : {1, 11, 73, 128, 200, 330}) {
      for (auto width : {1, 32, 100, 512, 1000, 3210}) {
        LOG(INFO) << " height=" << height << " width=" << width;
        INIT_UNARY(A1, A2);
        testUnaryFunc(A1, A2);
      }
    }
  }
};

template<typename Tensor>
struct TestBinaryMatrix {
  typedef std::function<void(Tensor& A1, Tensor& A2, Tensor& B)> BinaryFunc;

  explicit TestBinaryMatrix(BinaryFunc testBinaryFunc) {
    for (auto height : {1, 11, 73, 128, 200, 330}) {
      for (auto width : {1, 32, 100, 512, 1000, 3210}) {
        LOG(INFO) << " height=" << height << " width=" << width;
        INIT_BINARY(A1, A2, B);
        testBinaryFunc(A1, A2, B);
      }
    }
  }
};

template<typename Tensor>
struct TestTernaryMatrix {
  typedef std::function<void(
    Tensor& A1, Tensor& A2, Tensor& B, Tensor& C)> TernaryFunc;

  explicit TestTernaryMatrix(TernaryFunc testTernaryFunc) {
    for (auto height : {1, 11, 73, 128, 200, 330}) {
      for (auto width : {1, 32, 100, 512, 1000, 3210}) {
        LOG(INFO) << " height=" << height << " width=" << width;
        INIT_TERNARY(A1, A2, B, C);
        testTernaryFunc(A1, A2, B, C);
      }
    }
  }
};

template<typename Tensor>
struct TestQuaternaryMatrix {
  typedef std::function<void(
    Tensor& A1, Tensor& A2, Tensor& B, Tensor& C, Tensor& D)> QuaternaryFunc;

  explicit TestQuaternaryMatrix(QuaternaryFunc testQuaternaryFunc) {
    for (auto height : {1, 11, 73, 128, 200, 330}) {
      for (auto width : {1, 32, 100, 512, 1000, 3210}) {
        LOG(INFO) << " height=" << height << " width=" << width;
        INIT_QUATERNARY(A1, A2, B, C, D);
        testQuaternaryFunc(A1, A2, B, C, D);
      }
    }
  }
};

template<typename Tensor, class T>
struct TestUnaryVectorT {
  typedef std::function<void(Tensor& A1, Tensor& A2)> UnaryFunc;

  explicit TestUnaryVectorT(UnaryFunc testUnaryFunc) {
    for (auto size : {1, 11, 73, 128, 200, 330, 512, 1000, 4210}) {
      LOG(INFO) << " size=" << size;
      Tensor A1(size);
      Tensor A2(size);
      if (typeid(T) == typeid(real)) {
        A1.rand();
      } else {
        A1.rand(1000);
      }
      A2.copyFrom(A1);
      testUnaryFunc(A1, A2);
    }
  }
};

void SetTensorValue(Matrix& matrix, real value) {
  int height = matrix.getHeight();
  int width = matrix.getWidth();
  int stride = matrix.getStride();
  real* data = matrix.getData();
  for (int i = 0; i < height; i++) {
    int j = rand() % width;  // NOLINT
    if (typeid(matrix) == typeid(CpuMatrix)) {
      data[i * stride + j] = value;
    } else if (typeid(matrix) == typeid(GpuMatrix)) {
      hl_memcpy(&data[i * stride + j], &value, sizeof(real));
    } else {
    }
  }
}

template<typename Tensor>
void testTensorAddScalar(Tensor& A1, Tensor& A2) {
  real p1 = 2.5;
  real p2 = 3.0;
  A1.add(p1);   // a += p
  A2 += p1;
  TensorCheckEqual(A1, A2);

  A1.add(p1, p2);  // a = a * p1 + p2
  A2 = A2 * p1 + p2;
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorSubScalar(Tensor& A1, Tensor& A2) {
  real p = 2.5;
  A1.subScalar(p);  // a -= p
  A2 -= p;
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorMulScalar(Tensor& A1, Tensor& A2) {
  real p = 2.5;
  A1.mulScalar(p);  // a *= p
  A2 *= p;
  TensorCheckEqual(A1, A2);

  real learningRate = 0.7f;
  real decayRate = 1.2f;
  A1.applyL2(learningRate, decayRate);
  A2 = A2 * (1.0f / (1.0f + learningRate * decayRate));
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorDivScalar(Tensor& A1, Tensor& A2) {
  real p = 2.5;
  A1.divScalar(p);  // a /= p
  A2 /= p;
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorNeg(Tensor& A1, Tensor& A2) {
  A1.neg();  // a = -a
  A2 = -A2;
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorAbs(Tensor& A1, Tensor& A2) {
  A1.abs2();  // a = a > 0 ? a : -a
  A2 = A2.abs();
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorSquare(Tensor& A1, Tensor& A2) {
  A1.square2();  // a = a * a
  A2 = A2.square();
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorReciprocal(Tensor& A1, Tensor& A2) {
  A1.reciprocal2();  // a = 1.0f / a
  A2 = A2.reciprocal();
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorSign(Tensor& A1, Tensor& A2) {
  A1.sign2();  // a = (a > 0) - (a < 0)
  A2 = A2.sign();
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorAssign(Tensor& A1, Tensor& A2) {
  A1.assign(1.5);   // a = p
  A2 = A2.constant(1.5);
  TensorCheckEqual(A1, A2);

  A1.one();  // a = 1
  A2 = A2.constant(1.0);
  TensorCheckEqual(A1, A2);

  A1.zero();  // a = 0
  A2 = A2.constant(0.0);
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testUnaryBaseOp(Tensor& A1, Tensor& A2) {
  testTensorAddScalar(A1, A2);
  testTensorSubScalar(A1, A2);
  testTensorMulScalar(A1, A2);
  testTensorDivScalar(A1, A2);
  testTensorNeg(A1, A2);
  testTensorAbs(A1, A2);
  testTensorSquare(A1, A2);
  testTensorReciprocal(A1, A2);
  testTensorSign(A1, A2);
  testTensorAssign(A1, A2);
}

template<typename Tensor>
void testUnaryBaseOpInt(Tensor& A1, Tensor& A2) {
  A1.add(2);   // a += p
  A2 += 2;
  TensorCheckEqual(A1, A2);

  A1.add(3, 2);  // a = a * p1 + p2
  A2 = A2 * 3 + 2;
  TensorCheckEqual(A1, A2);

  testTensorNeg(A1, A2);
  testTensorAbs(A1, A2);
}

TEST(Unary, BaseOp) {
  TestUnaryMatrix<CpuMatrix> testCpuMatrix(testUnaryBaseOp<CpuMatrix>);
  TestUnaryVectorT<CpuVector, real> testCpuVector(testUnaryBaseOp<CpuVector>);
  TestUnaryVectorT<CpuIVector, int>
    testCpuIVector(testUnaryBaseOpInt<CpuIVector>);

#ifndef PADDLE_ONLY_CPU
  TestUnaryMatrix<GpuMatrix> testGpuMatrix(testUnaryBaseOp<GpuMatrix>);
  TestUnaryVectorT<GpuVector, real> testGpuVector(testUnaryBaseOp<GpuVector>);
  TestUnaryVectorT<GpuIVector, int>
    testGpuIVector(testUnaryBaseOpInt<GpuIVector>);
#endif
}

template<typename Tensor>
void testTensorExp(Tensor& A1, Tensor& A2) {
  A1.exp2();  // a = exp(a)
  A2 = A2.exp();
  TensorCheckErr(A1, A2);
}

template<typename Tensor>
void testTensorLog(Tensor& A1, Tensor& A2) {
  A1.log2();  // a = log(a)
  A2 = A2.log();
  TensorCheckErr(A1, A2);
}

template<typename Tensor>
void testTensorSqrt(Tensor& A1, Tensor& A2) {
  A1.sqrt2();  // a = sqrt(a)
  A2 = A2.sqrt();
  TensorCheckErr(A1, A2);
}

template<typename Tensor>
void testTensorPow(Tensor& A1, Tensor& A2) {
  A1.pow2(3.2);  // a = pow(a, p)
  A2 = A2.pow(3.2);
  TensorCheckErr(A1, A2);
}

template<typename Tensor>
void testUnayrMathOp(Tensor& A1, Tensor& A2) {
  testTensorExp(A1, A2);
  testTensorLog(A1, A2);
  testTensorSqrt(A1, A2);
  testTensorPow(A1, A2);
}

TEST(Unary, MathOp) {
  TestUnaryMatrix<CpuMatrix> testCpu(testUnayrMathOp<CpuMatrix>);

#ifndef PADDLE_ONLY_CPU
  TestUnaryMatrix<GpuMatrix> testGpu(testUnayrMathOp<GpuMatrix>);
#endif
}

template<typename Tensor>
void testTensorClip(Tensor& A1, Tensor& A2) {
  real p1 = 0.003f;
  real p2 = 0.877f;
  A1.clip(p1, p2);  // a = a < p1 ? p1 : (a > p2 ? p2 : a)
  // A2 = A2.min(0.877f).max(0.003f);
  A2 = (A2 < p1).condition(p1, (A2 > p2).condition(p2, A2));
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorBiggerThanScalar(Tensor& A1, Tensor& A2) {
  real p = 0.5f;
  A1.biggerThanScalar(p);  // a = a > p ? 1.0f : 0.0f
  A2 = (A2 > p).condition((real)1.0, (real)0.0);
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorapplyL1(Tensor& A1, Tensor& A2) {
  /**
   * T lambda = p;
   * a = (a > lambda) ? (a - lambda)
   *                  : (a < -lambda) ? (a + lambda) : 0
   *
   * p = learningRate * decayRate;
   */
  real learningRate = 0.7f;
  real decayRate = 0.6f;
  A1.applyL1(learningRate, decayRate);
  A2 = (A2 > (learningRate * decayRate)).condition(
    (A2 - (learningRate * decayRate)),
    (A2 < -(learningRate * decayRate)).condition(
      (A2 + (learningRate * decayRate)), (real)0.0));
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testUnayrCompareOp(Tensor& A1, Tensor& A2) {
  testTensorClip(A1, A2);
  testTensorBiggerThanScalar(A1, A2);

  A1.randomizeUniform();
  A1.subScalar(0.5f);
  A2.copyFrom(A1);
  testTensorapplyL1(A1, A2);
}

TEST(Unary, CompareOp) {
  TestUnaryMatrix<CpuMatrix> testCpu(testUnayrCompareOp<CpuMatrix>);

#ifndef PADDLE_ONLY_CPU
  TestUnaryMatrix<GpuMatrix> testGpu(testUnayrCompareOp<GpuMatrix>);
#endif
}

template<typename Tensor>
void testTensorAdd(Tensor& A1, Tensor& A2, Tensor& B) {
  real p1 = 2.5;
  real p2 = 3.2;
  A1.add(B);  // a += b
  A2 += B;
  TensorCheckEqual(A1, A2);

  A1.add(B, p1);  // a += b * p
  A2 += B * p1;
  TensorCheckEqual(A1, A2);

  A1.add(B, p1, p2);  // a = p1 * a + p2 * b
  A2 = A2 * p1 + B * p2;
  TensorCheckEqual(A1, A2);

  A1.addScalar(B, p1);  // a = b + p
  A2 = B + p1;
  TensorCheckEqual(A1, A2);

  A1.addSquare(B, p1);  // a += p * b * b
  A2 += B.constant(p1) * B * B;
  TensorCheckEqual(A1, A2);

  A1.decayAddSquare(B, p1, p2);  // a = p1 * a + p2 * b * b
  A2 = A2 * p1 + B.constant(p2) * B * B;
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorSub(Tensor& A1, Tensor& A2, Tensor& B) {
  real p = 2.5;
  A1.sub(B);  // a -= b
  A2 -= B;
  TensorCheckEqual(A1, A2);

  A1.sub(B, p);  // a -= b * p
  A2 -= B * p;
  TensorCheckEqual(A1, A2);

  A1.subScalar(B, p);  // a = b - p
  A2 = B - p;
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorMul(Tensor& A1, Tensor& A2, Tensor& B) {
  real p = 2.5;
  A1.mulScalar(B, p);  // a = b * p
  A2 = B * p;
  TensorCheckEqual(A1, A2);

  A1.dotMulSquare(B);  // a *= b * b
  A2 *= B * B;
  TensorCheckEqual(A1, A2);

  A1.dotSquareMul(B);  // a = a * a * b
  A2 = A2 * A2 * B;
  TensorCheckEqual(A1, A2);

  A1.dotMul(B);  // a *= b
  A2 *= B;
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorDiv(Tensor& A1, Tensor& A2, Tensor& B) {
  real p = 2.5;
  A1.divScalar(B, p);  // a = b / p
  A2 = B / p;
  TensorCheckEqual(A1, A2);

  A1.scalarDiv(B, p);  // a = p / b
  A2 = B.constant(p) / B;
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorAssign(Tensor& A1, Tensor& A2, Tensor& B) {
  A1.assign(B);  // a = b
  A2 = B;
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorSquare(Tensor& A1, Tensor& A2, Tensor& B) {
  B.square2(A1);   // b = a * a
  A2 = B.square();
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorSquareDerivative(Tensor& A1, Tensor& A2, Tensor& B) {
  A1.squareDerivative(B);  // a *= 2.0 * b
  A2 = A2 * (real)2.0 * B;
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorReciprocal(Tensor& A1, Tensor& A2, Tensor& B) {
  B.reciprocal2(A1);  // b = 1.0f / a
  A2 = B.reciprocal();
  TensorCheckEqual(A1, A2);

  real p1 = 0.58;
  real p2 = 0.32;
  A1.reciprocal2(B, p1, p2);  // a = 1 / (p1 * b + p2)
  A2 = (B * p1 + p2).reciprocal();
  TensorCheckEqual(A1, A2);

  real learningRate = 0.7f;
  real decayRate = 1.2f;
  A1.applyL2(B, learningRate, decayRate);  // a *= (1.0f / (1.0f + p * b))
  A2 *= (B.constant(1.0f) +
    B.constant(learningRate * decayRate) * B).reciprocal();
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorReciprocalDerivative(Tensor& A1, Tensor& A2, Tensor& B) {
  A1.reciprocalDerivative(B);  // a *= -b * b
  A2 *= (-B) * B;
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorSign(Tensor& A1, Tensor& A2, Tensor& B) {
  B.sign2(A1);  // b = a > 0.0f ? 1.0f : -1.0f
  A2 = B.sign();
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorAbs(Tensor& A1, Tensor& A2, Tensor& B) {
  B.abs2(A1);  // b = a > 0.0f ? a : -a
  A2 = B.abs();
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testBinaryBaseOp(Tensor& A1, Tensor& A2, Tensor& B) {
  testTensorAdd(A1, A2, B);
  testTensorSub(A1, A2, B);
  testTensorMul(A1, A2, B);
  testTensorDiv(A1, A2, B);
  testTensorSquare(A1, A2, B);
  testTensorSquareDerivative(A1, A2, B);
  testTensorReciprocal(A1, A2, B);
  testTensorReciprocalDerivative(A1, A2, B);
  testTensorAbs(A1, A2, B);
  testTensorSign(A1, A2, B);
  testTensorAssign(A1, A2, B);
}

TEST(Binary, BaseOp) {
  TestBinaryMatrix<CpuMatrix> testCpu(testBinaryBaseOp<CpuMatrix>);

#ifndef PADDLE_ONLY_CPU
  TestBinaryMatrix<GpuMatrix> testGpu(testBinaryBaseOp<GpuMatrix>);
#endif
}

template<typename Tensor>
void testTensorExp(Tensor& A1, Tensor& A2, Tensor& B) {
  // a = exp(b)
  A1.exp2(B);
  A2 = B.exp();
  TensorCheckErr(A1, A2);
}

template<typename Tensor>
void testTensorExpDerivative(Tensor& A1, Tensor& A2, Tensor& B) {
  A1.expDerivative(B);  // a *= b
  A2 *= B;
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorLog(Tensor& A1, Tensor& A2, Tensor& B) {
  // a = log(b)
  A1.log2(B);
  A2 = B.log();
  TensorCheckErr(A1, A2);
}

template<typename Tensor>
void testTensorSqrt(Tensor& A1, Tensor& A2, Tensor& B) {
  // a = sqrt(b)
  A1.sqrt2(B);
  A2 = B.sqrt();
  TensorCheckErr(A1, A2);
}

template<typename Tensor>
void testTensorInvSqrt(Tensor& A1, Tensor& A2, Tensor& B) {
  // a = 1.0f / sqrt(b)
  A1.invSqrt(B);
  A2 = B.sqrt().reciprocal();
  TensorCheckErr(A1, A2);
}

template<typename Tensor>
void testTensorPow(Tensor& A1, Tensor& A2, Tensor& B) {
  A1.pow2(B, 2.5f);  // a = pow(b, p)
  A2 = B.pow(2.5f);
  TensorCheckErr(A1, A2);
}

template<typename Tensor>
void testTensorSoftrelu(Tensor& A1, Tensor& A2, Tensor& B) {
  /*
   * const T THRESHOLD = 40.0;
   * b = log(1.0 +
   *         exp((a > THRESHOLD) ? THRESHOLD
   *             : ((a < -THRESHOLD) ? (-THRESHOLD) : a)))
   */
  B.softrelu(A1);

  real THRESHOLD = 40.0;
  A2 = (B.constant(1.0f) +
        (B > THRESHOLD).condition(
          THRESHOLD, (B < -THRESHOLD).condition(-THRESHOLD, B)).exp()).log();
  TensorCheckErr(A1, A2);
}

template<typename Tensor>
void testTensorSoftreluDerivative(Tensor& A1, Tensor& A2, Tensor& B) {
  /*
   * const T THRESHOLD = 40.0;
   * a *= (1.0 - exp(-1.0 * ((b > THRESHOLD)
   *                             ? THRESHOLD
   *                             : ((b < -THRESHOLD) ? (-THRESHOLD) : b)))));
   */
  A1.softreluDerivative(B);
  real THRESHOLD = 40.0;
  A2 = A2 * (B.constant(1.0f) -
             (B.constant(-1.0f) *
              (B > THRESHOLD).condition(
                THRESHOLD, (B < -THRESHOLD).condition(-THRESHOLD, B))).exp());
  TensorCheckErr(A1, A2);
}

template<typename Tensor>
void testTensorSigmoid(Tensor& A1, Tensor& A2, Tensor& B) {
  /*
    const T THRESHOLD_MIN = -40.0;
    const T THRESHOLD_MAX = 13.0;
    T tmp = (a < THRESHOLD_MIN) ? THRESHOLD_MIN
            : ((a > THRESHOLD_MAX) ? THRESHOLD_MAX : a);
    b = 1.0f / (1.0f + exp(-tmp)))
   */
  B.sigmoid(A1);

  const real THRESHOLD_MIN = -40.0;
  const real THRESHOLD_MAX = 13.0;
  auto tmp = (B < THRESHOLD_MIN).condition(
    THRESHOLD_MIN, (B > THRESHOLD_MAX).condition(THRESHOLD_MAX, B));
  A2 = (B.constant(1.0f) + (-tmp).exp()).reciprocal();
  TensorCheckErr(A1, A2);
}

template<typename Tensor>
void testTensorSigmoidDerivative(Tensor& A1, Tensor& A2, Tensor& B) {
  A1.sigmoidDerivative(B);  // a *= b * (1 - b)
  A2 *= B * (B.constant(1.0f) - B);
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorTanh(Tensor& A1, Tensor& A2, Tensor& B) {
  B.tanh(A1);  // b = 2.0 / (1.0 + exp(-2 * a)) - 1.0
  A2 = B.constant(2.0f) / ((B * ((real)-2.0f)).exp() + (real)1.0f) - (real)1.0f;
  TensorCheckErr(A1, A2);
}

template<typename Tensor>
void testTensorTanhDerivative(Tensor& A1, Tensor& A2, Tensor& B) {
  A1.tanhDerivative(B);  // a *= 1 - b * b
  A2 *= B.constant(1.0f) - B * B;
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorScaledTanh(Tensor& A1, Tensor& A2, Tensor& B) {
  real p1 = 2.5;
  real p2 = 3.1;
  // b = p1 * (2.0 / (1.0 + exp(-2 * p2 * a)) - 1.0)
  B.scaledTanh(A1, p1, p2);
  A2 = B.constant(p1) *
      (B.constant(2.0f) / ((B.constant(-2.0f) * p2 * B).exp() + (real)1.0)
       - (real)1.0);
  TensorCheckErr(A1, A2);
}

template<typename Tensor>
void testTensorScaledTanhDerivative(Tensor& A1, Tensor& A2, Tensor& B) {
  real p1 = 2.5;
  real p2 = 3.1;
  // a *= (p2 / p1) * (p1 * p1 - b * b));
  A1.scaledTanhDerivative(B, p1, p2);
  A2 = A2 * (B.constant(p2 / p1) * (B.constant(p1 * p1) - B * B));
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testBinaryMathOp(Tensor& A1, Tensor& A2, Tensor& B) {
  testTensorTanhDerivative(A1, A2, B);
  testTensorScaledTanhDerivative(A1, A2, B);
  testTensorSigmoidDerivative(A1, A2, B);
  testTensorExpDerivative(A1, A2, B);
  testTensorScaledTanh(A1, A2, B);
  testTensorTanh(A1, A2, B);
  testTensorExp(A1, A2, B);
  testTensorLog(A1, A2, B);
  testTensorSqrt(A1, A2, B);
  testTensorInvSqrt(A1, A2, B);
  testTensorPow(A1, A2, B);

  testTensorSoftrelu(A1, A2, B);
  testTensorSoftreluDerivative(A1, A2, B);
  testTensorSigmoid(A1, A2, B);
}

TEST(Binary, MathOp) {
  TestBinaryMatrix<CpuMatrix> testCpu(testBinaryMathOp<CpuMatrix>);

#ifndef PADDLE_ONLY_CPU
  TestBinaryMatrix<GpuMatrix> testGpu(testBinaryMathOp<GpuMatrix>);
#endif
}

template<typename Tensor>
void testTensorRelu(Tensor& A1, Tensor& A2, Tensor& B) {
  B.relu(A1);  // b = a > 0.0f ? a : 0.0f
  A2 = (B > (real)0.0f).condition(B, (real)0.0f);
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorReluDerivative(Tensor& A1, Tensor& A2, Tensor& B) {
  A1.reluDerivative(B);  // a *= (b > 0.0f ? 1.0f : 0.0f)
  A2 *= (B > (real)0.0).condition((real)1.0, (real)0.0);
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorBrelu(Tensor& A1, Tensor& A2, Tensor& B) {
  /*
   * b = a > p1 ? a : p1
   * b = b < p2 ? b : p2
   * int p1 = 0, p2 = 24;
   */
  SetTensorValue(B, 32.0f);
  B.brelu(A1);
  auto tmp = (B > (real)0.0f).condition(B, (real)0.0f);
  A2 = (tmp < (real)24.0f).condition(tmp, (real)24.0f);
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorBreluDerivative(Tensor& A1, Tensor& A2, Tensor& B) {
  SetTensorValue(B, 32.0f);
  /*
   * a *= (b > p1 && b < p2) ? 1.0 : 0.0
   * int p1 = 0, p2 = 24;
   */
  A1.breluDerivative(B);
  A2 *= (B > (real)0.0f && B < (real)24.0f).condition((real)1.0f, (real)0.0f);
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorAbsDerivative(Tensor& A1, Tensor& A2, Tensor& B) {
  A1.absDerivative(B);  // a = (b > 0) ? a : (b < 0) ? -a : 0
  A2 = (B > (real)0.0f).condition(A2,
    (B < (real)0.0f).condition(-A2, (real)0.0f));
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorIsEqualTo(Tensor& A1, Tensor& A2, Tensor& B) {
  real p = 0.613;
  SetTensorValue(B, p);
  A1.isEqualTo(B, p);  // a = (b == p)
  A2 = (B == p);
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorapplyL1(Tensor& A1, Tensor& A2, Tensor& B) {
  /**
   * T lambda = p * b;
   * a = (a > lambda) ? (a - lambda)
   *                  : (a < -lambda) ? (a + lambda) : 0
   *
   * p = learningRate * decayRate;
   */
  real learningRate = 0.7f;
  real decayRate = 0.6f;
  A1.applyL1(B, learningRate, decayRate);
  auto lambda = B.constant(learningRate * decayRate) * B;
  A2 = (A2 > lambda).condition(
    (A2 - lambda), (A2 < -lambda).condition((A2 + lambda), (real)0.0f));
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testBinaryCompareOp(Tensor& A1, Tensor& A2, Tensor& B) {
  B.subScalar(0.5f);
  SetTensorValue(B, 0.0f);
  testTensorReluDerivative(A1, A2, B);

  A1.randomizeUniform();
  A2.copyFrom(A1);
  testTensorBreluDerivative(A1, A2, B);

  testTensorAbsDerivative(A1, A2, B);
  testTensorRelu(A1, A2, B);
  testTensorBrelu(A1, A2, B);
  testTensorIsEqualTo(A1, A2, B);
}

TEST(Binary, CompareOp) {
  TestBinaryMatrix<CpuMatrix> testCpu(testBinaryCompareOp<CpuMatrix>);

#ifndef PADDLE_ONLY_CPU
  TestBinaryMatrix<GpuMatrix> testGpu(testBinaryCompareOp<GpuMatrix>);
#endif
}

template<typename Tensor>
void testTensorAdd(Tensor& A1, Tensor& A2, Tensor& B, Tensor& C) {
  A1.add(B, C);  // a = b + c
  A2 = B + C;
  TensorCheckEqual(A1, A2);

  real p1 = 1.5;
  real p2 = 2.5;
  real p3 = 3.8;
  A1.add(B, p1, C, p2);  // a = p1 * b + p2 * c
  A2 = B * p1 + C * p2;
  TensorCheckEqual(A1, A2);

  A1.add2(B, C);  // a = a + b + c
  A2 = A2 + B + C;
  TensorCheckEqual(A1, A2);

  A1.add2(B, C, p1, p2, p3);  // a = p1 * a + p2 * b + p3 * c
  A2 = A2 * p1 + B * p2 + C * p3;
  TensorCheckEqual(A1, A2);

  A1.decayAddSquareMul(B, C, p1, p2);  // a = p1 * a + p2 * b * b * c * c
  A2 = A2 * p1 + B.constant(p2) * B * B * C * C;
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorSub(Tensor& A1, Tensor& A2, Tensor& B, Tensor& C) {
  A1.sub(B, C);  // a = b - c
  A2 = B - C;
  TensorCheckEqual(A1, A2);

  real p1 = 1.5;
  real p2 = 2.5;
  A1.sub(B, p1, C, p2);  // a = p1 * b - p2 * c
  A2 = B * p1 - C * p2;
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorMul(Tensor& A1, Tensor& A2, Tensor& B, Tensor& C) {
  A1.dotMul(B, C);  // a = b * c
  A2 = B * C;
  TensorCheckEqual(A1, A2);

  A1.dotMulSquare(B, C);  // a = b * c * c
  A2 = B * C * C;
  TensorCheckEqual(A1, A2);

  A1.dotSquareSquare(B, C);  // a = b * b * c * c
  A2 = B * B * C * C;
  TensorCheckEqual(A1, A2);

  real p1 = 1.5;
  real p2 = 2.5;

  /*
   * T tmp = p1 * b + p2 * c;
   * a *= tmp * tmp
   */
  A1.dotMulSquareSum(B, C, p1, p2);
  auto tmp = B * p1 + C * p2;
  A2 *= tmp * tmp;
  TensorCheckEqual(A1, A2);

  /*
   * T tmp = p1 * b + p2 * c;
   * a = tmp * tmp
   */
  A1.dotSquareSum(B, C, p1, p2);
  auto tmp2 = B * p1 + C * p2;
  A2 = tmp2 * tmp2;
  TensorCheckEqual(A1, A2);

  // a *= p1 * b + p2 * c
  A1.dotMulSum(B, C, p1, p2);
  A2 *= B * p1 + C * p2;
  TensorCheckEqual(A1, A2);

  // a = p1 * a + p2 * b * c
  A1.addDotMul(B, C, p1, p2);
  A2 = A2 * p1 + B.constant(p2) * B * C;
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorDiv(Tensor& A1, Tensor& A2, Tensor& B, Tensor& C) {
  A1.dotDiv(B, C);  // a = (b == 0.0) ? 0.0 : b / c
  A2 = (B == (real)0.0).condition((real)0.0, B / C);
  TensorCheckEqual(A1, A2);

  real p1 = 1.5;
  real p2 = 2.5;
  A1.dotDiv(B, C, p1, p2);  // a = (b + p1) / (c + p2)
  A2 = (B + p1) / (C + p2);
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorReciprocal(Tensor& A1, Tensor& A2, Tensor& B, Tensor& C) {
  real p1 = 1.5;
  real p2 = 2.5;
  real p3 = 3.5;
  A1.reciprocalSum(B, C, p1, p2, p3);  // a = 1 / (p1 * b + p2 * c + p3)
  A2 = (B * p1 + C * p2 + p3).reciprocal();
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorSoftCrossEntropy(Tensor& A1, Tensor& A2, Tensor& B, Tensor& C) {
  A1.softCrossEntropy(B, C);  // a = -c * log(b) - (1 - c) * log(1 - b)
  A2 = -C * B.log() - (C.constant(1.0f) - C) * (B.constant(1.0f) - B).log();
  TensorCheckErr(A1, A2);
}

template<typename Tensor>
void testTensorSoftCrossEntropyBp(Tensor& A1,
                                  Tensor& A2,
                                  Tensor& B,
                                  Tensor& C) {
  A1.softCrossEntropyBp(B, C);  // a += (b - c) / (b * (1 - b))
  A2 += (B - C) / (B * (B.constant(1.0f) - B));
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTernaryBaseOp(Tensor& A1, Tensor& A2, Tensor& B, Tensor& C) {
  testTensorAdd(A1, A2, B, C);
  testTensorSub(A1, A2, B, C);
  testTensorMul(A1, A2, B, C);
  testTensorDiv(A1, A2, B, C);
  testTensorReciprocal(A1, A2, B, C);
  testTensorSoftCrossEntropyBp(A1, A2, B, C);

  testTensorSoftCrossEntropy(A1, A2, B, C);
}

TEST(Ternary, BaseOp) {
  TestTernaryMatrix<CpuMatrix> testCpu(testTernaryBaseOp<CpuMatrix>);

#ifndef PADDLE_ONLY_CPU
  TestTernaryMatrix<GpuMatrix> testGpu(testTernaryBaseOp<GpuMatrix>);
#endif
}

template<typename Tensor>
void testTensorBinaryLabelCrossEntropy(Tensor& A1,
                                       Tensor& A2,
                                       Tensor& B,
                                       Tensor& C) {
  A1.binaryLabelCrossEntropy(B, C);  // a = c > 0.5 ? -log(b) : -log(1.0 - b)
  A2 = (C > (real)0.5).condition(
    -(B.log()), -((B.constant(1.0f) - B).log()));
  TensorCheckErr(A1, A2);
}

template<typename Tensor>
void testTensorBinaryLabelCrossEntropyBp(Tensor& A1,
                                         Tensor& A2,
                                         Tensor& B,
                                         Tensor& C) {
  // a += c > 0.5 ? -1.0 / b : 1.0 / (1.0 - b)
  A1.binaryLabelCrossEntropyBp(B, C);
  A2 += (C > (real)0.5).condition(
    (B.constant(-1.0f) / B), (B.constant(1.0f) - B).reciprocal());
  TensorCheckErr(A1, A2);
}

template<typename Tensor>
void testTensorLogisticRegressionLoss(Tensor& A1,
                                      Tensor& A2,
                                      Tensor& B,
                                      Tensor& C) {
  SetTensorValue(B, 50.0f);
  SetTensorValue(B, -50.0f);
  /**
   * const T THRESHOLD = 40.0;
   * T x = (b > THRESHOLD) ? THRESHOLD : (b < -THRESHOLD)
   *                                        ? -THRESHOLD
   *                                        : b;
   * a = log(1 + exp(x)) - c * x
   */
  A1.logisticRegressionLoss(B, C);
  real THRESHOLD = 40.0;
  auto tmp = (B > THRESHOLD).condition(
    THRESHOLD, (B < -THRESHOLD).condition(-THRESHOLD, B));
  A2 = (C.constant(1.0f) + tmp.exp()).log() - C * tmp;
  TensorCheckErr(A1, A2);
}

template<typename Tensor>
void testTensorLogisticRegressionLossBp(Tensor& A1,
                                        Tensor& A2,
                                        Tensor& B,
                                        Tensor& C) {
  SetTensorValue(B, 50.0f);
  SetTensorValue(B, -50.0f);
  /**
   * const T THRESHOLD = 40.0;
   * T x = (b > THRESHOLD) ? THRESHOLD : (b < -THRESHOLD)
   *                                        ? -THRESHOLD
   *                                        : b;
   * x = exp(x); a = x / (1 + x) - c
   */
  A1.logisticRegressionLossBp(B, C);
  real THRESHOLD = 40.0;
  auto tmp = (B > THRESHOLD).condition(
    THRESHOLD, (B < -THRESHOLD).condition(-THRESHOLD, B));
  auto tmp2 = tmp.exp();
  A2 = tmp2 / (C.constant(1.0) + tmp2) - C;
  TensorCheckErr(A1, A2);
}

template<typename Tensor>
void testTensorBiggerThan(Tensor& A1, Tensor& A2, Tensor& B, Tensor& C) {
  A1.biggerThan(B, C);  // a = (b > c) ? 1.0f : 0.0f
  A2 = (B > C).condition((real)1.0f, (real)0.0f);
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorMax(Tensor& A1, Tensor& A2, Tensor& B, Tensor& C) {
  A1.max2(B, C);  // a = (b > c) ? b : c
  A2 = (B > C).condition(B, C);
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTernaryCompareOp(Tensor& A1, Tensor& A2, Tensor& B, Tensor& C) {
  testTensorBinaryLabelCrossEntropyBp(A1, A2, B, C);
  testTensorBinaryLabelCrossEntropy(A1, A2, B, C);
  testTensorBiggerThan(A1, A2, B, C);
  testTensorMax(A1, A2, B, C);

  testTensorLogisticRegressionLoss(A1, A2, B, C);
  testTensorLogisticRegressionLossBp(A1, A2, B, C);
}

TEST(Ternary, CompareOp) {
  TestTernaryMatrix<CpuMatrix> testCpu(testTernaryCompareOp<CpuMatrix>);

#ifndef PADDLE_ONLY_CPU
  TestTernaryMatrix<GpuMatrix> testGpu(testTernaryCompareOp<GpuMatrix>);
#endif
}

template<typename Tensor>
void testQuaternaryAdd(Tensor& A1,
                       Tensor& A2,
                       Tensor& B,
                       Tensor& C,
                       Tensor& D) {
  // A1.add3(B, C, D, 1.5f, 2.5f, 3.5f);  // a = p1 * b + p2 * c + p3 * d
  // A2 = B * 1.5f + C * 2.5f + D * 3.5f;
  // TensorCheckEqual(A1, A2);

  /*
   * T tmp = p1 * b + p2 * c + p3 * d;
   * a += tmp * tmp
   */
  real p1 = 1.5f;
  real p2 = 2.5f;
  real p3 = 3.5f;
  A1.addSquareSum(B, C, D, p1, p2, p3);
  auto tmp = B * p1 + C * p2 + D * p3;
  A2 += tmp * tmp;
  TensorCheckEqual(A1, A2);
}

TEST(Quaternary, BaseOp) {
  TestQuaternaryMatrix<CpuMatrix> testCpu(testQuaternaryAdd<CpuMatrix>);

#ifndef PADDLE_ONLY_CPU
  TestQuaternaryMatrix<GpuMatrix> testGpu(testQuaternaryAdd<GpuMatrix>);
#endif
}

template<typename Tensor>
void testTensorBiggerThan(Tensor& A1,
                          Tensor& A2,
                          Tensor& B,
                          Tensor& C,
                          Tensor& D) {
  // a = ((b > c && d > 0.5f) || (b < c && d < 0.5f)) ? 1.0f : 0.0f);
  A1.biggerThan(B, C, D);
  A2 = ((B > C && D > (real)0.5)
        || (B < C && D < (real)0.5)).condition((real)1.0, (real)0.0);
  TensorCheckEqual(A1, A2);
}

template<typename Tensor>
void testTensorRankLoss(Tensor& A1,
                        Tensor& A2,
                        Tensor& B,
                        Tensor& C,
                        Tensor& D) {
  /**
   * const T THRESHOLD = 40.0; a = b - c;
   * a = (a > THRESHOLD)
   *         ? THRESHOLD
   *         : ((a < -THRESHOLD) ? (-THRESHOLD) : a);
   * a = log(1 + exp(a)) - a * d
   */
  A1.rankLoss(B, C, D);

  real THRESHOLD = 40.0;
  auto tmp = B - C;
  auto tmp2 = (tmp > THRESHOLD).condition(
    THRESHOLD, (tmp < -THRESHOLD).condition(-THRESHOLD, tmp));
  A2 = (D.constant(1.0f) + tmp2.exp()).log() - tmp2 * D;

  TensorCheckErr(A1, A2);
}

template<typename Tensor>
void testTensorRankLossBp(Tensor& A1,
                          Tensor& A2,
                          Tensor& B,
                          Tensor& C,
                          Tensor& D) {
  /**
   * const T THRESHOLD = 40.0; a = b - c;
   * a = (a > THRESHOLD)
   *         ? THRESHOLD
   *         : ((a < -THRESHOLD) ? (-THRESHOLD) : a);
   * a = exp(a); a = (a / (1 + a) - d)
   */
  A1.rankLossBp(B, C, D);
  real THRESHOLD = 40.0;
  auto tmp = B - C;
  auto tmp2 = (tmp > THRESHOLD).condition(
    THRESHOLD, (tmp < -THRESHOLD).condition(-THRESHOLD, tmp));
  auto tmp3 = tmp2.exp();
  A2 = tmp3 / (D.constant(1.0f) + tmp3) - D;

  TensorCheckErr(A1, A2);
}

template<typename Tensor>
void testQuaternaryCompareOp(Tensor& A1,
                             Tensor& A2,
                             Tensor& B,
                             Tensor& C,
                             Tensor& D) {
  testTensorBiggerThan(A1, A2, B, C, D);
  testTensorRankLoss(A1, A2, B, C, D);
  testTensorRankLossBp(A1, A2, B, C, D);
}

TEST(Quaternary, CompareOp) {
  TestQuaternaryMatrix<CpuMatrix> testCpu(testQuaternaryCompareOp<CpuMatrix>);

#ifndef PADDLE_ONLY_CPU
  TestQuaternaryMatrix<GpuMatrix> testGpu(testQuaternaryCompareOp<GpuMatrix>);
#endif
}

int main(int argc, char** argv) {
  testing::InitGoogleTest(&argc, argv);
  hl_start();
  hl_init(0);
  return RUN_ALL_TESTS();
}