diff --git a/paddle/operators/gemm_conv_op.h b/paddle/operators/gemm_conv_op.h
index cdcc0039b0fa8f87b3d24abba3a1fb9004cc96bc..3b7ba685c8da691921a4b1859e2dd2e37609ef21 100644
--- a/paddle/operators/gemm_conv_op.h
+++ b/paddle/operators/gemm_conv_op.h
@@ -58,7 +58,7 @@ class GemmConvKernel : public framework::OpKernel {
         input_channels * filter_height * filter_width,
         output_height * output_width};
     Tensor col;
-    col.mutable_data<float>(col_shape, context.GetPlace());
+    col.mutable_data<T>(col_shape, context.GetPlace());
     // col_matrix shares the same piece of data with col,
     // but will be reshaped into a two-dimensional matrix shape
     // to call the matrix multiplication interface.
@@ -67,8 +67,8 @@ class GemmConvKernel : public framework::OpKernel {
 
     framework::DDim input_shape = {input->dims()[1], input->dims()[2],
                                    input->dims()[3]};
-    framework::DDim filter_matrix_shape = {
-        filter.dims()[0], framework::product(filter.dims()) / filter.dims()[0]};
+    framework::DDim filter_matrix_shape = {filter.dims()[0],
+                                           filter.numel() / filter.dims()[0]};
     filter.Resize(filter_matrix_shape);
 
     framework::DDim output_matrix_shape = {output_channels,
@@ -80,14 +80,12 @@ class GemmConvKernel : public framework::OpKernel {
     // convolution operator: im2col + gemm
     for (int i = 0; i < batch_size; i++) {
       // im2col
-      Tensor in_slice = input->Slice<T>(i, i + 1);
-      in_slice.Resize(input_shape);
+      Tensor in_slice = input->Slice<T>(i, i + 1).Resize(input_shape);
       im2col(in_slice, col, strides[0], strides[1], paddings[0], paddings[1],
              device_context);
 
       // gemm
-      Tensor out_slice = output->Slice<T>(i, i + 1);
-      out_slice.Resize(output_matrix_shape);
+      Tensor out_slice = output->Slice<T>(i, i + 1).Resize(output_matrix_shape);
       math::matmul<Place, T>(filter, false, col_matrix, false, T(1.0),
                              &out_slice, T(0.0), device_context);
     }
@@ -138,7 +136,7 @@ class GemmConvGradKernel : public framework::OpKernel {
         input_channels * filter_height * filter_width,
         output_height * output_width};
     Tensor col;
-    col.mutable_data<float>(col_shape, context.GetPlace());
+    col.mutable_data<T>(col_shape, context.GetPlace());
     // col_matrix shares the same piece of data with col,
     // but will be reshaped into a two-dimensional matrix shape
     // to call the matrix multiplication interface.
@@ -151,8 +149,8 @@ class GemmConvGradKernel : public framework::OpKernel {
         output_grad->dims()[1],
         output_grad->dims()[2] * output_grad->dims()[3]};
 
-    framework::DDim filter_matrix_shape = {
-        filter.dims()[0], framework::product(filter.dims()) / filter.dims()[0]};
+    framework::DDim filter_matrix_shape = {filter.dims()[0],
+                                           filter.numel() / filter.dims()[0]};
     filter.Resize(filter_matrix_shape);
     filter_grad.Resize(filter_matrix_shape);
 
@@ -168,20 +166,18 @@ class GemmConvGradKernel : public framework::OpKernel {
     // convolution backward weight operator: im2col + gemm
     for (int i = 0; i < batch_size; i++) {
       // gemm
-      Tensor out_slice = output_grad->Slice<T>(i, i + 1);
-      out_slice.Resize(output_matrix_shape);
+      Tensor out_slice =
+          output_grad->Slice<T>(i, i + 1).Resize(output_matrix_shape);
       math::matmul<Place, T>(filter, true, out_slice, false, T(1.0),
                              &col_matrix, T(0.0), device_context);
 
       // col2im
-      Tensor in_grad_slice = input_grad->Slice<T>(i, i + 1);
-      in_grad_slice.Resize(input_shape);
+      Tensor in_grad_slice = input_grad->Slice<T>(i, i + 1).Resize(input_shape);
       col2im(in_grad_slice, col, strides[0], strides[1], paddings[0],
              paddings[1], device_context);
 
       // im2col
-      Tensor in_slice = input->Slice<T>(i, i + 1);
-      in_slice.Resize(input_shape);
+      Tensor in_slice = input->Slice<T>(i, i + 1).Resize(input_shape);
       im2col(in_slice, col, strides[0], strides[1], paddings[0], paddings[1],
              device_context);