Do not compute grad with newer modules

torch_modules/src/NumericColumnModule.cpp

@@ -43,7 +43,7 @@ torch::Tensor NumericColumnModuleImpl::forward(torch::Tensor input)
 {
   auto context = input.narrow(1, firstInputIndex, getInputSize());
   void * dataPtr = context.flatten().data_ptr();
-  auto values = torch::from_blob(dataPtr, {(long)(context.size(0)*getInputSize())}, torch::kDouble).clone().to(torch::kFloat).to(NeuralNetworkImpl::device).view({(long)context.size(0), (long)context.size(1), 1});
+  auto values = torch::from_blob(dataPtr, {(long)(context.size(0)*getInputSize())}, torch::TensorOptions(torch::kDouble).requires_grad(false)).clone().to(torch::kFloat).to(NeuralNetworkImpl::device).view({(long)context.size(0), (long)context.size(1), 1});
   return myModule->forward(values);
 }
 

torch_modules/src/UppercaseRateModule.cpp

@@ -41,7 +41,7 @@ torch::Tensor UppercaseRateModuleImpl::forward(torch::Tensor input)
 {
   auto context = input.narrow(1, firstInputIndex, getInputSize());
   void * dataPtr = context.flatten().data_ptr();
-  auto values = torch::from_blob(dataPtr, {(long)(context.size(0)*getInputSize())}, torch::kDouble).clone().to(torch::kFloat).to(NeuralNetworkImpl::device).view({(long)context.size(0), (long)context.size(1), 1});
+  auto values = torch::from_blob(dataPtr, {(long)(context.size(0)*getInputSize())}, torch::TensorOptions(torch::kDouble).requires_grad(false)).clone().to(torch::kFloat).to(NeuralNetworkImpl::device).view({(long)context.size(0), (long)context.size(1), 1});
   return myModule->forward(values);
 }