diff --git a/paddle/api/Matrix.cpp b/paddle/api/Matrix.cpp
index c40a47f3accf9eff9f939753b039a662996e8943..9ae3716fa862c549065ebf530d127d168af2fb57 100644
--- a/paddle/api/Matrix.cpp
+++ b/paddle/api/Matrix.cpp
@@ -44,7 +44,7 @@ Matrix* Matrix::createZero(size_t height, size_t width, bool useGpu) {
   return m;
 }
 
-Matrix* Matrix::createDense(const std::vector<real>& data, size_t height,
+Matrix* Matrix::createDense(const std::vector<float>& data, size_t height,
                             size_t width, bool useGpu) {
   auto m = new Matrix();
   m->m->mat = paddle::Matrix::create(height, width, useGpu);
@@ -52,7 +52,7 @@ Matrix* Matrix::createDense(const std::vector<real>& data, size_t height,
   return m;
 }
 
-Matrix* Matrix::createCpuDenseFromNumpy(real* data, int dim1, int dim2,
+Matrix* Matrix::createCpuDenseFromNumpy(float* data, int dim1, int dim2,
                                         bool copy) {
   auto m = new Matrix();
   if (copy) {
@@ -64,7 +64,7 @@ Matrix* Matrix::createCpuDenseFromNumpy(real* data, int dim1, int dim2,
   return m;
 }
 
-Matrix* Matrix::createGpuDenseFromNumpy(real* data, int dim1, int dim2) {
+Matrix* Matrix::createGpuDenseFromNumpy(float* data, int dim1, int dim2) {
   auto m = new Matrix();
   m->m->mat = paddle::Matrix::create(dim1, dim2, false, true);
   m->m->mat->copyFrom(data, dim1 * dim2);
@@ -86,7 +86,7 @@ size_t Matrix::getHeight() const { return m->mat->getHeight(); }
 
 size_t Matrix::getWidth() const { return m->mat->getWidth(); }
 
-real Matrix::get(size_t x, size_t y) const throw(RangeError) {
+float Matrix::get(size_t x, size_t y) const throw(RangeError) {
   if (x > this->getWidth() || y > this->getHeight()) {
     RangeError e;
     throw e;
@@ -94,7 +94,7 @@ real Matrix::get(size_t x, size_t y) const throw(RangeError) {
   return m->mat->getElement(x, y);
 }
 
-void Matrix::set(size_t x, size_t y, real val) throw(RangeError,
+void Matrix::set(size_t x, size_t y, float val) throw(RangeError,
                                                      UnsupportError) {
   if (x > this->getWidth() || y > this->getHeight()) {
     RangeError e;
@@ -193,10 +193,10 @@ FloatArray Matrix::getData() const {
   auto rawMat = m->mat.get();
   if (dynamic_cast<paddle::GpuMemoryHandle*>(rawMat->getMemoryHandle().get())) {
     // is gpu. then copy data
-    real* data = rawMat->getData();
+    float* data = rawMat->getData();
     size_t len = rawMat->getElementCnt();
-    real* cpuData = new real[len];
-    hl_memcpy_device2host(cpuData, data, len * sizeof(real));
+    float* cpuData = new float[len];
+    hl_memcpy_device2host(cpuData, data, len * sizeof(float));
     FloatArray ret_val(cpuData, len);
     ret_val.needFree = true;
     return ret_val;
@@ -208,7 +208,7 @@ FloatArray Matrix::getData() const {
 
 void Matrix::sparseCopyFrom(
     const std::vector<int>& rows, const std::vector<int>& cols,
-    const std::vector<real>& vals) throw(UnsupportError) {
+    const std::vector<float>& vals) throw(UnsupportError) {
   auto cpuSparseMat =
       std::dynamic_pointer_cast<paddle::CpuSparseMatrix>(m->mat);
   if (cpuSparseMat != nullptr) {
@@ -217,7 +217,7 @@ void Matrix::sparseCopyFrom(
     //  <<" ValSize = "<<vals.size();
     cpuSparseMat->copyFrom(const_cast<std::vector<int>&>(rows),
                            const_cast<std::vector<int>&>(cols),
-                           const_cast<std::vector<real>&>(vals));
+                           const_cast<std::vector<float>&>(vals));
   } else {
     UnsupportError e;
     throw e;
@@ -226,7 +226,7 @@ void Matrix::sparseCopyFrom(
 
 void* Matrix::getSharedPtr() const { return &m->mat; }
 
-void Matrix::toNumpyMatInplace(real** view_data, int* dim1,
+void Matrix::toNumpyMatInplace(float** view_data, int* dim1,
                                int* dim2) throw(UnsupportError) {
   auto cpuMat = std::dynamic_pointer_cast<paddle::CpuMatrix>(m->mat);
   if (cpuMat) {
@@ -237,9 +237,9 @@ void Matrix::toNumpyMatInplace(real** view_data, int* dim1,
     throw UnsupportError();
   }
 }
-void Matrix::copyToNumpyMat(real** view_m_data, int* dim1,
+void Matrix::copyToNumpyMat(float** view_m_data, int* dim1,
                             int* dim2) throw(UnsupportError) {
-  static_assert(sizeof(paddle::real) == sizeof(real),
+  static_assert(sizeof(float) == sizeof(float),
                 "Currently PaddleAPI only support for single "
                 "precision version of paddle.");
   if (this->isSparse()) {
@@ -247,16 +247,16 @@ void Matrix::copyToNumpyMat(real** view_m_data, int* dim1,
   } else {
     *dim1 = m->mat->getHeight();
     *dim2 = m->mat->getWidth();
-    *view_m_data = new real[(*dim1) * (*dim2)];
+    *view_m_data = new float[(*dim1) * (*dim2)];
     if (auto cpuMat = dynamic_cast<paddle::CpuMatrix*>(m->mat.get())) {
       auto src = cpuMat->getData();
       auto dest = *view_m_data;
-      std::memcpy(dest, src, sizeof(paddle::real) * (*dim1) * (*dim2));
+      std::memcpy(dest, src, sizeof(float) * (*dim1) * (*dim2));
     } else if (auto gpuMat = dynamic_cast<paddle::GpuMatrix*>(m->mat.get())) {
       auto src = gpuMat->getData();
       auto dest = *view_m_data;
       hl_memcpy_device2host(dest, src,
-                            sizeof(paddle::real) * (*dim1) * (*dim2));
+                            sizeof(float) * (*dim1) * (*dim2));
     } else {
       LOG(WARNING) << "Unexpected Situation";
       throw UnsupportError();
@@ -264,7 +264,7 @@ void Matrix::copyToNumpyMat(real** view_m_data, int* dim1,
   }
 }
 
-void Matrix::copyFromNumpyMat(real* data, int dim1,
+void Matrix::copyFromNumpyMat(float* data, int dim1,
                               int dim2) throw(UnsupportError, RangeError) {
   if (isSparse()) {
     throw UnsupportError();
diff --git a/paddle/api/PaddleAPI.h b/paddle/api/PaddleAPI.h
index 69f3240a7797481b57a61f21f62f75306b5b963d..b3140617af188b6a80067d9dbd312bd9e9155adf 100644
--- a/paddle/api/PaddleAPI.h
+++ b/paddle/api/PaddleAPI.h
@@ -56,10 +56,10 @@ class UnsupportError {};
 
 /// This type will map to python's list of float.
 struct FloatArray {
-  const real* buf;
+  const float* buf;
   const size_t length;
   bool needFree;  // true if the buf is dynamic alloced.
-  FloatArray(const real* b, const size_t l);
+  FloatArray(const float* b, const size_t l);
 };
 
 /// This type will map to python's list of int
@@ -72,11 +72,11 @@ struct IntArray {
 
 /// This type will map to python's list of (int, float)
 struct IntWithFloatArray {
-  const real* valBuf;
+  const float* valBuf;
   const int* idxBuf;
   const size_t length;
   bool needFree;
-  IntWithFloatArray(const real* v, const int* i, size_t l, bool f = false);
+  IntWithFloatArray(const float* v, const int* i, size_t l, bool f = false);
 };
 
 enum SparseValueType { SPARSE_NON_VALUE = 0, SPARSE_VALUE = 1 };
@@ -122,7 +122,7 @@ public:
    * @param data  list of float should be passed in python.
    * @note        the value will be copy into a new matrix.
    */
-  static Matrix* createDense(const std::vector<real>& data, size_t height,
+  static Matrix* createDense(const std::vector<float>& data, size_t height,
                              size_t width, bool useGpu = false);
 
   /**
@@ -134,11 +134,11 @@ public:
    *  @param copy  true if copy into a new matrix, false will create
    *               matrix inplace.
    */
-  static Matrix* createCpuDenseFromNumpy(real* data, int dim1, int dim2,
+  static Matrix* createCpuDenseFromNumpy(float* data, int dim1, int dim2,
                                          bool copy = false);
 
   /// Create Gpu Dense Matrix from numpy matrix, dtype=float32
-  static Matrix* createGpuDenseFromNumpy(real* data, int dim1, int dim2);
+  static Matrix* createGpuDenseFromNumpy(float* data, int dim1, int dim2);
 
   /**
    * Cast to numpy matrix.
@@ -154,15 +154,15 @@ public:
    * numpy_mat = m.toNumpyMat()
    * @endcode
    */
-  void toNumpyMatInplace(real** view_data, int* dim1,
+  void toNumpyMatInplace(float** view_data, int* dim1,
                          int* dim2) throw(UnsupportError);
 
   /// Copy To numpy mat.
-  void copyToNumpyMat(real** view_m_data, int* dim1,
+  void copyToNumpyMat(float** view_m_data, int* dim1,
                       int* dim2) throw(UnsupportError);
 
   /// Copy From Numpy Mat
-  void copyFromNumpyMat(real* data, int dim1, int dim2) throw(UnsupportError,
+  void copyFromNumpyMat(float* data, int dim1, int dim2) throw(UnsupportError,
                                                                RangeError);
 
   /// return true if this matrix is sparse.
@@ -181,9 +181,9 @@ public:
 
   size_t getWidth() const;
 
-  real get(size_t x, size_t y) const throw(RangeError);
+  float get(size_t x, size_t y) const throw(RangeError);
 
-  void set(size_t x, size_t y, real val) throw(RangeError, UnsupportError);
+  void set(size_t x, size_t y, float val) throw(RangeError, UnsupportError);
 
   /// return type is list of float
   FloatArray getData() const;
@@ -195,8 +195,8 @@ public:
    */
   void sparseCopyFrom(const std::vector<int>& rows,
                       const std::vector<int>& cols,
-                      const std::vector<real>& values =
-                          std::vector<real>()) throw(UnsupportError);
+                      const std::vector<float>& values =
+                          std::vector<float>()) throw(UnsupportError);
 
   bool isGpu() const;
 
@@ -228,33 +228,33 @@ public:
    *
    * It will create a new vector, and copy data into it.
    */
-  static Vector* create(const std::vector<real>& data, bool useGpu = false);
+  static Vector* create(const std::vector<float>& data, bool useGpu = false);
 
   /**
    * Create Cpu Vector from numpy array, which dtype=float32
    *
    * If copy is false, it will create vector inplace.
    */
-  static Vector* createCpuVectorFromNumpy(real* data, int dim,
+  static Vector* createCpuVectorFromNumpy(float* data, int dim,
                                           bool copy = false);
 
   /// Create Gpu Vector from numpy array, which dtype=float32
-  static Vector* createGpuVectorFromNumpy(real* data, int dim);
+  static Vector* createGpuVectorFromNumpy(float* data, int dim);
 
   /// Cast to numpy array inplace.
-  void toNumpyArrayInplace(real** view_data, int* dim1) throw(UnsupportError);
+  void toNumpyArrayInplace(float** view_data, int* dim1) throw(UnsupportError);
 
   /// Copy to numpy array.
-  void copyToNumpyArray(real** view_m_data, int* dim1);
+  void copyToNumpyArray(float** view_m_data, int* dim1);
 
   /// Copy from numpy array.
-  void copyFromNumpyArray(real* data, int dim);
+  void copyFromNumpyArray(float* data, int dim);
 
   /// __getitem__ in python
-  real get(const size_t idx) const throw(RangeError, UnsupportError);
+  float get(const size_t idx) const throw(RangeError, UnsupportError);
 
   /// __setitem__ in python
-  void set(const size_t idx, real val) throw(RangeError, UnsupportError);
+  void set(const size_t idx, float val) throw(RangeError, UnsupportError);
 
   /// Return is GPU vector or not.
   bool isGpu() const;
diff --git a/paddle/api/Util.cpp b/paddle/api/Util.cpp
index fe89a62cd3908f1857d1160d156355e5fccefcea..8a6741078f2f19d8c3cb081f129447d6fc5801c9 100644
--- a/paddle/api/Util.cpp
+++ b/paddle/api/Util.cpp
@@ -31,13 +31,13 @@ void initPaddle(int argc, char** argv) {
   feenableexcept(FE_INVALID | FE_DIVBYZERO | FE_OVERFLOW);
 }
 
-FloatArray::FloatArray(const real* b, const size_t l)
+FloatArray::FloatArray(const float* b, const size_t l)
     : buf(b), length(l), needFree(false) {}
 
 IntArray::IntArray(const int* b, const size_t l, bool f)
     : buf(b), length(l), needFree(f) {}
 
-IntWithFloatArray::IntWithFloatArray(const real* v, const int* i, size_t l,
+IntWithFloatArray::IntWithFloatArray(const float* v, const int* i, size_t l,
                                      bool f)
     : valBuf(v), idxBuf(i), length(l), needFree(f) {}
 
diff --git a/paddle/api/Vector.cpp b/paddle/api/Vector.cpp
index b61eb7934b78120b660885899825ba4e749673be..1affc1a5fefb8a1109d2a442db10b7d7641cd9ee 100644
--- a/paddle/api/Vector.cpp
+++ b/paddle/api/Vector.cpp
@@ -140,7 +140,7 @@ struct VectorPrivate {
   paddle::VectorPtr vec;
 
   void safeAccessData(const size_t idx,
-                      const std::function<void(real&)>& func) const
+                      const std::function<void(float&)>& func) const
       throw(RangeError, UnsupportError) {
     auto cpuVec = std::dynamic_pointer_cast<const paddle::CpuVector>(vec);
     if (cpuVec != nullptr) {
@@ -170,7 +170,7 @@ Vector* Vector::createZero(size_t sz, bool useGpu) {
   return retVec;
 }
 
-Vector* Vector::create(const std::vector<real>& data, bool useGpu) {
+Vector* Vector::create(const std::vector<float>& data, bool useGpu) {
   auto retVec = new Vector();
   retVec->m->vec = paddle::Vector::create(data.size(), useGpu);
   retVec->m->vec->copyFrom(data.data(), data.size());
@@ -188,7 +188,7 @@ Vector* Vector::createByPaddleVectorPtr(void* ptr) {
   }
 }
 
-Vector* Vector::createCpuVectorFromNumpy(real* data, int dim, bool copy) {
+Vector* Vector::createCpuVectorFromNumpy(float* data, int dim, bool copy) {
   CHECK_GT(dim, 0);
   auto retVec = new Vector();
   if (copy) {
@@ -200,7 +200,7 @@ Vector* Vector::createCpuVectorFromNumpy(real* data, int dim, bool copy) {
   return retVec;
 }
 
-Vector* Vector::createGpuVectorFromNumpy(real* data, int dim) {
+Vector* Vector::createGpuVectorFromNumpy(float* data, int dim) {
   CHECK_GT(dim, 0);
   auto retVec = new Vector();
   retVec->m->vec = paddle::Vector::create((size_t)dim, true);
@@ -208,7 +208,7 @@ Vector* Vector::createGpuVectorFromNumpy(real* data, int dim) {
   return retVec;
 }
 
-void Vector::toNumpyArrayInplace(real** view_data,
+void Vector::toNumpyArrayInplace(float** view_data,
                                  int* dim1) throw(UnsupportError) {
   auto v = std::dynamic_pointer_cast<paddle::CpuVector>(m->vec);
   if (v != nullptr) {
@@ -219,20 +219,20 @@ void Vector::toNumpyArrayInplace(real** view_data,
   }
 }
 
-void Vector::copyToNumpyArray(real** view_m_data, int* dim1) {
+void Vector::copyToNumpyArray(float** view_m_data, int* dim1) {
   *dim1 = m->vec->getSize();
-  *view_m_data = new real[*dim1];
+  *view_m_data = new float[*dim1];
   if (auto cpuVec = dynamic_cast<paddle::CpuVector*>(m->vec.get())) {
-    std::memcpy(*view_m_data, cpuVec->getData(), sizeof(real) * (*dim1));
+    std::memcpy(*view_m_data, cpuVec->getData(), sizeof(float) * (*dim1));
   } else if (auto gpuVec = dynamic_cast<paddle::CpuVector*>(m->vec.get())) {
     hl_memcpy_device2host(*view_m_data, gpuVec->getData(),
-                          sizeof(real) * (*dim1));
+                          sizeof(float) * (*dim1));
   } else {
     LOG(INFO) << "Unexpected situation";
   }
 }
 
-void Vector::copyFromNumpyArray(real* data, int dim) {
+void Vector::copyFromNumpyArray(float* data, int dim) {
   m->vec->resize(dim);
   m->vec->copyFrom(data, dim);
 }
@@ -241,15 +241,15 @@ bool Vector::isGpu() const {
   return std::dynamic_pointer_cast<paddle::GpuVector>(m->vec) != nullptr;
 }
 
-real Vector::get(const size_t idx) const throw(RangeError, UnsupportError) {
-  real r;
-  m->safeAccessData(idx, [&](real& o) { r = o; });
+float Vector::get(const size_t idx) const throw(RangeError, UnsupportError) {
+  float r;
+  m->safeAccessData(idx, [&](float& o) { r = o; });
   return r;
 }
 
-void Vector::set(const size_t idx, real val) throw(RangeError,
+void Vector::set(const size_t idx, float val) throw(RangeError,
                                                     UnsupportError) {
-  m->safeAccessData(idx, [&](real& o) { o = val; });
+  m->safeAccessData(idx, [&](float& o) { o = val; });
 }
 
 size_t Vector::getSize() const { return m->vec->getSize(); }