Commit c190582a authored by Dmitry I. Lyakh's avatar Dmitry I. Lyakh
Browse files

Fixed inconsistency in initTensorsRnd to make sure unmutable tensors are not initialized. 



Signed-off-by: default avatarDmitry I. Lyakh <quant4me@gmail.com>
parent ce6c2ce8

src/exatn/num_server.cpp

+5 −33
Original line number Diff line number Diff line 
/** ExaTN::Numerics: Numerical server

REVISION: 2021/10/30

REVISION: 2021/12/10



Copyright (C) 2018-2021 Dmitry I. Lyakh (Liakh)

Copyright (C) 2018-2021 Oak Ridge National Laboratory (UT-Battelle) **/
@@ -1565,14 +1565,12 @@ bool NumServer::initTensorRndSync(const std::string & name) 
bool NumServer::initTensorsRnd(TensorNetwork & tensor_network)

{

 bool success = true;

 std::unordered_set<std::string> tensor_names;

 for(auto tens = tensor_network.cbegin(); tens != tensor_network.cend(); ++tens){

  auto tensor = tens->second.getTensor();

  const auto & tens_name = tensor->getName();

  if(tens->first != 0){ //input tensor

   if(tensorAllocated(tens_name)){

    //auto res = tensor_names.emplace(tens_name);

    success = initTensorRnd(tens_name);

    if(tens->second.isOptimizable()) success = initTensorRnd(tens_name);

   }else{

    success = false;

   }
@@ -1581,25 +1579,18 @@ bool NumServer::initTensorsRnd(TensorNetwork & tensor_network) 
  }

  if(!success) break;

 }

 if(success){

  for(const auto & tens_name: tensor_names){

   success = initTensorRnd(tens_name); if(!success) break;

  }

 }

 return success;

}



bool NumServer::initTensorsRndSync(TensorNetwork & tensor_network)

{

 bool success = true;

 std::unordered_set<std::string> tensor_names;

 for(auto tens = tensor_network.cbegin(); tens != tensor_network.cend(); ++tens){

  auto tensor = tens->second.getTensor();

  const auto & tens_name = tensor->getName();

  if(tens->first != 0){ //input tensor

   if(tensorAllocated(tens_name)){

    //auto res = tensor_names.emplace(tens_name);

    success = initTensorRndSync(tens_name);

    if(tens->second.isOptimizable()) success = initTensorRndSync(tens_name);

   }else{

    success = false;

   }
@@ -1608,26 +1599,19 @@ bool NumServer::initTensorsRndSync(TensorNetwork & tensor_network) 
  }

  if(!success) break;

 }

 if(success){

  for(const auto & tens_name: tensor_names){

   success = initTensorRndSync(tens_name); if(!success) break;

  }

 }

 return success;

}



bool NumServer::initTensorsRnd(TensorExpansion & tensor_expansion)

{

 bool success = true;

 std::unordered_set<std::string> tensor_names;

 for(auto tensor_network = tensor_expansion.cbegin(); tensor_network != tensor_expansion.cend(); ++tensor_network){

  for(auto tens = tensor_network->network->cbegin(); tens != tensor_network->network->cend(); ++tens){

   auto tensor = tens->second.getTensor();

   const auto & tens_name = tensor->getName();

   if(tens->first != 0){ //input tensor

    if(tensorAllocated(tens_name)){

     //auto res = tensor_names.emplace(tens_name);

     success = initTensorRnd(tens_name);

     if(tens->second.isOptimizable()) success = initTensorRnd(tens_name);

    }else{

     success = false;

    }
@@ -1637,26 +1621,19 @@ bool NumServer::initTensorsRnd(TensorExpansion & tensor_expansion) 
   if(!success) break;

  }

 }

 if(success){

  for(const auto & tens_name: tensor_names){

   success = initTensorRnd(tens_name); if(!success) break;

  }

 }

 return success;

}



bool NumServer::initTensorsRndSync(TensorExpansion & tensor_expansion)

{

 bool success = true;

 std::unordered_set<std::string> tensor_names;

 for(auto tensor_network = tensor_expansion.cbegin(); tensor_network != tensor_expansion.cend(); ++tensor_network){

  for(auto tens = tensor_network->network->cbegin(); tens != tensor_network->network->cend(); ++tens){

   auto tensor = tens->second.getTensor();

   const auto & tens_name = tensor->getName();

   if(tens->first != 0){ //input tensor

    if(tensorAllocated(tens_name)){

     //auto res = tensor_names.emplace(tens_name);

     success = initTensorRndSync(tens_name);

     if(tens->second.isOptimizable()) success = initTensorRndSync(tens_name);

    }else{

     success = false;

    }
@@ -1666,11 +1643,6 @@ bool NumServer::initTensorsRndSync(TensorExpansion & tensor_expansion) 
   if(!success) break;

  }

 }

 if(success){

  for(const auto & tens_name: tensor_names){

   success = initTensorRndSync(tens_name); if(!success) break;

  }

 }

 return success;

}