Refine the GemmConvGrad2DKernel.

paddle/operators/gemm_conv2d_op.h

@@ -109,18 +109,13 @@ class GemmConvGrad2DKernel : public framework::OpKernel {
         context.Input<Tensor>(framework::GradVarName("Output"));
     Tensor* input_grad =
         context.Output<Tensor>(framework::GradVarName("Input"));
-    Tensor* filter_grad_ =
+    Tensor* filter_grad =
         context.Output<Tensor>(framework::GradVarName("Filter"));
 
     // The filter and filter_grad will be reshaped in the calculations,
     // so here use an assignment operation,
     // that avoids modifying the variable in the Scope.
     Tensor filter = *context.Input<Tensor>("Filter");
-    Tensor filter_grad;
-    if (filter_grad_) {
-      filter_grad_->mutable_data<T>(context.GetPlace());
-      filter_grad = *filter_grad_;
-    }
 
     std::vector<int> strides = context.Attr<std::vector<int>>("strides");
     std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
@@ -165,20 +160,6 @@ class GemmConvGrad2DKernel : public framework::OpKernel {
                                            filter.numel() / filter.dims()[0]};
     filter.Resize(filter_matrix_shape);
 
-    if (filter_grad_) {
-      filter_grad.Resize(filter_matrix_shape);
-      auto t1 = framework::EigenVector<T>::Flatten(filter_grad);
-      t1.device(context.GetEigenDevice<Place>()) =
-          t1.constant(static_cast<T>(0));
-    }
-
-    if (input_grad) {
-      input_grad->mutable_data<T>(context.GetPlace());
-      auto t2 = framework::EigenVector<T>::Flatten(*input_grad);
-      t2.device(context.GetEigenDevice<Place>()) =
-          t2.constant(static_cast<T>(0));
-    }
-
     auto* device_context =
         const_cast<platform::DeviceContext*>(context.device_context_);
 
@@ -186,22 +167,21 @@ class GemmConvGrad2DKernel : public framework::OpKernel {
     // convolution backward weight operator: im2col + gemm
     int in_step = input_channels / groups;
     int out_step = output_channels / groups;
-    Tensor in_grad_batch;
-    Tensor in_batch;
-    for (int i = 0; i < batch_size; i++) {
-      Tensor out_grad_batch =
-          output_grad->Slice<T>(i, i + 1).Resize(output_matrix_shape);
-      if (input_grad) {
-        in_grad_batch = input_grad->Slice<T>(i, i + 1).Resize(input_shape);
-      }
-      if (filter_grad_) {
-        in_batch = input->Slice<T>(i, i + 1).Resize(input_shape);
-      }
-      for (int g = 0; g < groups; g++) {
-        Tensor out_grad_slice =
-            out_grad_batch.Slice<T>(g * out_step, (g + 1) * out_step);
-        if (input_grad) {
+
+    if (input_grad) {
+      input_grad->mutable_data<T>(context.GetPlace());
+      auto t = framework::EigenVector<T>::Flatten(*input_grad);
+      t.device(context.GetEigenDevice<Place>()) = t.constant(static_cast<T>(0));
+
+      for (int i = 0; i < batch_size; i++) {
+        Tensor out_grad_batch =
+            output_grad->Slice<T>(i, i + 1).Resize(output_matrix_shape);
+        Tensor in_grad_batch =
+            input_grad->Slice<T>(i, i + 1).Resize(input_shape);
+        for (int g = 0; g < groups; g++) {
           // gemm
+          Tensor out_grad_slice =
+              out_grad_batch.Slice<T>(g * out_step, (g + 1) * out_step);
           Tensor filter_slice =
               filter.Slice<T>(g * out_step, (g + 1) * out_step);
           math::matmul<Place, T>(filter_slice, true, out_grad_slice, false,
@@ -213,16 +193,31 @@ class GemmConvGrad2DKernel : public framework::OpKernel {
           col2im(in_grad_slice, col, strides[0], strides[1], paddings[0],
                  paddings[1], device_context);
         }
+      }
+    }
 
-        if (filter_grad_) {
+    if (filter_grad) {
+      filter_grad->mutable_data<T>(context.GetPlace());
+      Tensor filter_grad_ = *filter_grad;
+      filter_grad_.Resize(filter_matrix_shape);
+      auto t = framework::EigenVector<T>::Flatten(filter_grad_);
+      t.device(context.GetEigenDevice<Place>()) = t.constant(static_cast<T>(0));
+
+      for (int i = 0; i < batch_size; i++) {
+        Tensor out_grad_batch =
+            output_grad->Slice<T>(i, i + 1).Resize(output_matrix_shape);
+        Tensor in_batch = input->Slice<T>(i, i + 1).Resize(input_shape);
+        for (int g = 0; g < groups; g++) {
           // im2col
+          Tensor out_grad_slice =
+              out_grad_batch.Slice<T>(g * out_step, (g + 1) * out_step);
           Tensor in_slice = in_batch.Slice<T>(g * in_step, (g + 1) * in_step);
           im2col(in_slice, col, strides[0], strides[1], paddings[0],
                  paddings[1], device_context);
 
           // gemm
           Tensor filter_grad_slice =
-              filter_grad.Slice<T>(g * out_step, (g + 1) * out_step);
+              filter_grad_.Slice<T>(g * out_step, (g + 1) * out_step);
           math::matmul<Place, T>(out_grad_slice, false, col_matrix, true,
                                  T(1.0), &filter_grad_slice, T(1.0),
                                  device_context);