diff --git a/paddle/fluid/operators/math/blas_impl.cu.h b/paddle/fluid/operators/math/blas_impl.cu.h
index c76fc17d78cce514b5e35ce8e5ca890d7cec1e98..d84c88cb3bc1a13acb83b3444dbd1bfca3cba503 100644
--- a/paddle/fluid/operators/math/blas_impl.cu.h
+++ b/paddle/fluid/operators/math/blas_impl.cu.h
@@ -96,10 +96,22 @@ struct CUBlas<platform::float16> {
                                        reinterpret_cast<__half *>(C), ldc));
   }
 
-  template <typename... ARGS>
-  static void GEMM_BATCH(ARGS... args) {
+  static void GEMM_BATCH(cublasHandle_t handle, cublasOperation_t transa,
+                         cublasOperation_t transb, int m, int n, int k,
+                         const float16 *alpha, const float16 *A, int lda,
+                         long long int strideA, const float16 *B,  // NOLINT
+                         int ldb, long long int strideB,           // NOLINT
+                         const float16 *beta, float16 *C, int ldc,
+                         long long int strideC,  // NOLINT
+                         int batchCount) {
 #if CUDA_VERSION >= 8000
-    PADDLE_ENFORCE(platform::dynload::cublasHgemmStridedBatched(args...));
+    PADDLE_ENFORCE(platform::dynload::cublasHgemmStridedBatched(
+        handle, transa, transb, m, n, k,
+        reinterpret_cast<const __half *>(alpha),
+        reinterpret_cast<const __half *>(A), lda, strideA,
+        reinterpret_cast<const __half *>(B), ldb, strideB,
+        reinterpret_cast<const __half *>(beta), reinterpret_cast<__half *>(C),
+        ldc, strideC, batchCount));
 #else
     PADDLE_THROW("HgemmStridedBatched is not supported on cuda <= 7.5");
 #endif
diff --git a/paddle/fluid/operators/math/blas_impl.h b/paddle/fluid/operators/math/blas_impl.h
index 577cbe3beb806ffcb2f1a7d7a469402be9b69224..14b3624b420cb883b36268c0a5a9e8692dbb5b43 100644
--- a/paddle/fluid/operators/math/blas_impl.h
+++ b/paddle/fluid/operators/math/blas_impl.h
@@ -172,9 +172,9 @@ void Blas<platform::CPUDeviceContext>::BatchedGEMM(
                        c_array.data(), &ldc, 1 /* group_count */, &batchCount);
 #else
   for (int k = 0; k < batchCount; ++k) {
-    const float *Ak = &A[k * strideA];
-    const float *Bk = &B[k * strideB];
-    float *Ck = &C[k * M * N];
+    auto *Ak = &A[k * strideA];
+    auto *Bk = &B[k * strideB];
+    auto *Ck = &C[k * M * N];
     this->template GEMM<T>(transA, transB, M, N, K, alpha, Ak, Bk, beta, Ck);
   }
 #endif
diff --git a/paddle/fluid/operators/math/math_function.cu b/paddle/fluid/operators/math/math_function.cu
index b5bf84e5178c143de35ec6dcb16b1bde5577c166..d5af718723e8d44da0971ea7756b8c36e771cca2 100644
--- a/paddle/fluid/operators/math/math_function.cu
+++ b/paddle/fluid/operators/math/math_function.cu
@@ -33,9 +33,10 @@ template struct SetConstant<platform::CUDADeviceContext, int>;
 template struct SetConstant<platform::CUDADeviceContext, int64_t>;
 template struct SetConstant<platform::CUDADeviceContext, bool>;
 
-#define DEFINE_GPU_TRANS(RANK)                                         \
-  template struct Transpose<platform::CUDADeviceContext, float, RANK>; \
-  template struct Transpose<platform::CUDADeviceContext, double, RANK>;
+#define DEFINE_GPU_TRANS(RANK)                                          \
+  template struct Transpose<platform::CUDADeviceContext, float, RANK>;  \
+  template struct Transpose<platform::CUDADeviceContext, double, RANK>; \
+  template struct Transpose<platform::CUDADeviceContext, float16, RANK>;
 
 DEFINE_GPU_TRANS(1);
 DEFINE_GPU_TRANS(2);
diff --git a/paddle/fluid/operators/matmul_op.cc b/paddle/fluid/operators/matmul_op.cc
index 2d05449822e6addc42c4fea5af8422ae6dcfd37d..7182149164854038bb67a9f06cdbec8a4a0f1fb2 100644
--- a/paddle/fluid/operators/matmul_op.cc
+++ b/paddle/fluid/operators/matmul_op.cc
@@ -25,7 +25,7 @@ namespace operators {
  * Get row matrix shape from a vector shape. If the rank of x_dim > 1, the
  * original x_dim is returned.
  */
-static framework::DDim RowMatrixFromVector(const framework::DDim& x_dim) {
+static framework::DDim RowMatrixFromVector(const framework::DDim &x_dim) {
   if (x_dim.size() > 1) {
     return x_dim;
   }
@@ -36,7 +36,7 @@ static framework::DDim RowMatrixFromVector(const framework::DDim& x_dim) {
  * Get column matrix shape from a vector shape. If the ran of y_dim > 1, the
  * original y_dim is returned.
  */
-static framework::DDim ColumnMatrixFromVector(const framework::DDim& y_dim) {
+static framework::DDim ColumnMatrixFromVector(const framework::DDim &y_dim) {
   if (y_dim.size() > 1) {
     return y_dim;
   }
@@ -46,12 +46,12 @@ static framework::DDim ColumnMatrixFromVector(const framework::DDim& y_dim) {
 template <typename DeviceContext, typename T>
 class MatMulKernel : public framework::OpKernel<T> {
  public:
-  void Compute(const framework::ExecutionContext& context) const override {
-    auto& x =
+  void Compute(const framework::ExecutionContext &context) const override {
+    auto &x =
         detail::Ref(context.Input<framework::Tensor>("X"), "Cannot find X");
-    auto& y =
+    auto &y =
         detail::Ref(context.Input<framework::Tensor>("Y"), "Cannot find Y");
-    auto* out = context.Output<framework::Tensor>("Out");
+    auto *out = context.Output<framework::Tensor>("Out");
     out->mutable_data<T>(context.GetPlace());
 
     auto blas = math::GetBlas<DeviceContext, T>(context);
@@ -65,7 +65,7 @@ class MatMulKernel : public framework::OpKernel<T> {
 
 // Reshape a rank-3 tensor from P x M x N to (P * M) x N.
 // Identity op if the tensor is not of rank 3.
-static framework::Tensor FoldInitDims(const framework::Tensor& input) {
+static framework::Tensor FoldInitDims(const framework::Tensor &input) {
   auto output = input;
   auto in_dims = input.dims();
   if (in_dims.size() == 3) {
@@ -78,8 +78,8 @@ static framework::Tensor FoldInitDims(const framework::Tensor& input) {
 // (Warning: This requires transposing data and writes into new memory.)
 // Identity op if the tensor is not of rank 3.
 template <typename DeviceContext, typename T>
-static framework::Tensor FoldHeadAndLastDims(const DeviceContext& context,
-                                             const framework::Tensor& input) {
+static framework::Tensor FoldHeadAndLastDims(const DeviceContext &context,
+                                             const framework::Tensor &input) {
   auto in_dims = input.dims();
   if (in_dims.size() != 3) {
     return input;
@@ -102,7 +102,7 @@ static framework::Tensor FoldHeadAndLastDims(const DeviceContext& context,
  * If transposed, `H,W` will be swapped.
  */
 static void ReshapeTensorIntoMatrixSequence(
-    framework::Tensor* x, const math::MatDescriptor& descriptor) {
+    framework::Tensor *x, const math::MatDescriptor &descriptor) {
   int64_t h, w;
   h = descriptor.height_;
   w = descriptor.width_;
@@ -130,9 +130,9 @@ static void ReshapeTensorIntoMatrixSequence(
  * If any of `X` and `Y` has batch size BatchSize, the out will have the
  * BatchSize.
  */
-static void ReshapeXYOutIntoMatrixSequence(framework::Tensor* x,
-                                           framework::Tensor* y,
-                                           framework::Tensor* out, bool trans_x,
+static void ReshapeXYOutIntoMatrixSequence(framework::Tensor *x,
+                                           framework::Tensor *y,
+                                           framework::Tensor *out, bool trans_x,
                                            bool trans_y) {
   auto x_dim = RowMatrixFromVector(x->dims());
   auto y_dim = ColumnMatrixFromVector(y->dims());
@@ -177,10 +177,10 @@ static void ReshapeXYOutIntoMatrixSequence(framework::Tensor* x,
 template <typename DeviceContext, typename T>
 class MatMulGradKernel : public framework::OpKernel<T> {
  public:
-  void MatMul(const framework::ExecutionContext& context,
-              const framework::Tensor& a, bool trans_a,
-              const framework::Tensor& b, bool trans_b,
-              framework::Tensor* out) const {
+  void MatMul(const framework::ExecutionContext &context,
+              const framework::Tensor &a, bool trans_a,
+              const framework::Tensor &b, bool trans_b,
+              framework::Tensor *out) const {
     out->mutable_data<T>(context.GetPlace());
     auto blas = math::GetBlas<DeviceContext, T>(context);
     auto mat_dim_a = math::CreateMatrixDescriptor(a.dims(), 0, trans_a);
@@ -188,18 +188,18 @@ class MatMulGradKernel : public framework::OpKernel<T> {
     blas.MatMul(a, mat_dim_a, b, mat_dim_b, T(1), out, T(0));
   }
 
-  void CalcInputGrad(const framework::ExecutionContext& context,
-                     const framework::Tensor& a, bool trans_a,
-                     bool is_fold_init_dims_a, const framework::Tensor& b,
+  void CalcInputGrad(const framework::ExecutionContext &context,
+                     const framework::Tensor &a, bool trans_a,
+                     bool is_fold_init_dims_a, const framework::Tensor &b,
                      bool trans_b, bool is_fold_init_dims_b,
-                     framework::Tensor* out) const {
+                     framework::Tensor *out) const {
     if (out == nullptr) return;
     bool need_combine = (a.dims().size() == 3 || b.dims().size() == 3) &&
                         out->dims().size() == 2;
     if (!need_combine) {
       MatMul(context, a, trans_a, b, trans_b, out);
     } else {
-      auto& ctx = context.template device_context<DeviceContext>();
+      auto &ctx = context.template device_context<DeviceContext>();
       MatMul(context, is_fold_init_dims_a
                           ? FoldInitDims(a)
                           : FoldHeadAndLastDims<DeviceContext, T>(ctx, a),
@@ -210,13 +210,13 @@ class MatMulGradKernel : public framework::OpKernel<T> {
     }
   }
 
-  void Compute(const framework::ExecutionContext& context) const override {
+  void Compute(const framework::ExecutionContext &context) const override {
     auto x = *context.Input<framework::Tensor>("X");
     auto y = *context.Input<framework::Tensor>("Y");
     auto dout =
         *context.Input<framework::Tensor>(framework::GradVarName("Out"));
-    auto* dx = context.Output<framework::Tensor>(framework::GradVarName("X"));
-    auto* dy = context.Output<framework::Tensor>(framework::GradVarName("Y"));
+    auto *dx = context.Output<framework::Tensor>(framework::GradVarName("X"));
+    auto *dy = context.Output<framework::Tensor>(framework::GradVarName("Y"));
     bool transpose_x = context.Attr<bool>("transpose_X");
     bool transpose_y = context.Attr<bool>("transpose_Y");
 
@@ -269,7 +269,7 @@ class MatMulOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
  protected:
-  void InferShape(framework::InferShapeContext* context) const override {
+  void InferShape(framework::InferShapeContext *context) const override {
     PADDLE_ENFORCE(context->HasInput("X"),
                    "Input(X) of MatMulOp should not be null.");
     PADDLE_ENFORCE(context->HasInput("Y"),
@@ -375,7 +375,7 @@ class MatMulOpGrad : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
  protected:
-  void InferShape(framework::InferShapeContext* context) const override {
+  void InferShape(framework::InferShapeContext *context) const override {
     PADDLE_ENFORCE(context->HasInput("X"), "Input(X) should not be null");
     PADDLE_ENFORCE(context->HasInput("Y"), "Input(Y) should not be null");
     PADDLE_ENFORCE(context->HasInput(framework::GradVarName("Out")),
@@ -401,7 +401,7 @@ class MatMulOpGradMaker : public framework::SingleGradOpDescMaker {
 
  protected:
   std::unique_ptr<framework::OpDesc> Apply() const override {
-    auto* retv = new framework::OpDesc();
+    auto *retv = new framework::OpDesc();
     retv->SetType("matmul_grad");
     retv->SetInput("X", Input("X"));
     retv->SetInput("Y", Input("Y"));
@@ -420,15 +420,27 @@ REGISTER_OPERATOR(matmul, ops::MatMulOp, ops::MatMulOpMaker,
                   ops::MatMulOpGradMaker);
 REGISTER_OPERATOR(matmul_grad, ops::MatMulOpGrad);
 REGISTER_OP_CPU_KERNEL(
-    matmul, ops::MatMulKernel<paddle::platform::CPUDeviceContext, float>);
+    matmul, ops::MatMulKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::MatMulKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::MatMulKernel<paddle::platform::CPUDeviceContext,
+                      paddle::platform::float16>);
 REGISTER_OP_CPU_KERNEL(
     matmul_grad,
-    ops::MatMulGradKernel<paddle::platform::CPUDeviceContext, float>);
+    ops::MatMulGradKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::MatMulGradKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::MatMulGradKernel<paddle::platform::CPUDeviceContext,
+                          paddle::platform::float16>);
 
 #ifdef PADDLE_WITH_CUDA
 REGISTER_OP_CUDA_KERNEL(
-    matmul, ops::MatMulKernel<paddle::platform::CUDADeviceContext, float>);
+    matmul, ops::MatMulKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::MatMulKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::MatMulKernel<paddle::platform::CUDADeviceContext,
+                      paddle::platform::float16>);
 REGISTER_OP_CUDA_KERNEL(
     matmul_grad,
-    ops::MatMulGradKernel<paddle::platform::CUDADeviceContext, float>);
+    ops::MatMulGradKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::MatMulGradKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::MatMulGradKernel<paddle::platform::CUDADeviceContext,
+                          paddle::platform::float16>);
 #endif