fix attr missing in conv cudnn kernel (#38832)

paddle/fluid/operators/conv_cudnn_op.cu

@@ -65,7 +65,8 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
     int groups = ctx.Attr<int>("groups");
 
     bool exhaustive_search =
-        FLAGS_cudnn_exhaustive_search || ctx.Attr<bool>("exhaustive_search");
+        FLAGS_cudnn_exhaustive_search || (ctx.HasAttr("exhaustive_search") &&
+                                          ctx.Attr<bool>("exhaustive_search"));
     bool deterministic = FLAGS_cudnn_deterministic;
     auto exhaustive_deterministic = exhaustive_search && deterministic;
     PADDLE_ENFORCE_EQ(exhaustive_deterministic, false,
@@ -386,7 +387,8 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
     int groups = ctx.Attr<int>("groups");
 
     bool exhaustive_search =
-        FLAGS_cudnn_exhaustive_search || ctx.Attr<bool>("exhaustive_search");
+        FLAGS_cudnn_exhaustive_search || (ctx.HasAttr("exhaustive_search") &&
+                                          ctx.Attr<bool>("exhaustive_search"));
     bool deterministic = FLAGS_cudnn_deterministic;
     auto exhaustive_deterministic = exhaustive_search && deterministic;
     PADDLE_ENFORCE_EQ(exhaustive_deterministic, false,
@@ -437,7 +439,7 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
             ctx, input_grad, &transformed_input_grad_channel);
         // NOTE(zhiqiu): If inplace_addto strategy is enabled, we need to copy
         // the data of input_grad to transformed_input_grad_channel.
-        if (ctx.Attr<bool>("use_addto")) {
+        if (ctx.HasAttr("use_addto") && ctx.Attr<bool>("use_addto")) {
           TransToChannelFirst<platform::CUDADeviceContext, T>(
               ctx, input_grad, &transformed_input_grad_channel);
         }
@@ -703,15 +705,17 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
     // MIOPEN ONLY support beta to be 0.0f
     ScalingParamType<T> beta = 0.0f;
 #else
-    ScalingParamType<T> beta = ctx.Attr<bool>("use_addto") ? 1.0f : 0.0f;
+    ScalingParamType<T> beta =
+        (ctx.HasAttr("use_addto") && ctx.Attr<bool>("use_addto")) ? 1.0f : 0.0f;
 #endif
-    VLOG(4) << "Conv_grad: use_addto = " << ctx.Attr<bool>("use_addto");
+    VLOG(4) << "Conv_grad: use_addto = "
+            << (ctx.HasAttr("use_addto") && ctx.Attr<bool>("use_addto"));
 
     if (input_grad) {
 // When beta is 0, it is unnecessary to reset input_grad.
 // When beta is 1, the output cannot be reset since addt strategy used.
 #ifdef PADDLE_WITH_HIP
-      if (ctx.Attr<bool>("use_addto")) {
+      if (ctx.HasAttr("use_addto") && ctx.Attr<bool>("use_addto")) {
         Tensor temp_tensor(transformed_input_grad.type());
         temp_tensor.Resize(transformed_input_grad.dims());
         T* temp_tensor_data = temp_tensor.mutable_data<T>(ctx.GetPlace());
@@ -878,7 +882,8 @@ class CUDNNConvDoubleGradOpKernel : public framework::OpKernel<T> {
     int groups = ctx.Attr<int>("groups");
 
     bool exhaustive_search =
-        FLAGS_cudnn_exhaustive_search || ctx.Attr<bool>("exhaustive_search");
+        FLAGS_cudnn_exhaustive_search || (ctx.HasAttr("exhaustive_search") &&
+                                          ctx.Attr<bool>("exhaustive_search"));
     bool deterministic = FLAGS_cudnn_deterministic;
     auto exhaustive_deterministic = exhaustive_search && deterministic;
     PADDLE_ENFORCE_EQ(exhaustive_deterministic, false,