reference_model/src/ops/control_flow.cc - tosa/reference_model - Gitiles


 // Copyright (c) 2020-2023, ARM Limited.
 //
 //    Licensed under the Apache License, Version 2.0 (the "License");
 //    you may not use this file except in compliance with the License.
 //    You may obtain a copy of the License at
 //
 //         http://www.apache.org/licenses/LICENSE-2.0
 //
 //    Unless required by applicable law or agreed to in writing, software
 //    distributed under the License is distributed on an "AS IS" BASIS,
 //    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 //    See the License for the specific language governing permissions and
 //    limitations under the License.

 #include "control_flow.h"
 #include "subgraph_traverser.h"
 using namespace TosaReference;
 using namespace Eigen;
 using namespace tosa;

 OpControlFlow::OpControlFlow(SubgraphTraverser* sgt_, TosaSerializationHandler* tsh_, Op op_, uint64_t id_)
     : GraphNode(sgt_, op_, id_)
 {
     tsh = tsh_;
 }

 OpControlFlow::~OpControlFlow()
 {}

 int OpControlFlow::evalBlock(TosaSerializationBasicBlock* block,
                              std::vector<TosaReference::Tensor*>& block_inputs,
                              std::vector<TosaReference::Tensor*>& block_outputs)
 {
     std::string block_name = block->GetName();

     DEBUG_MED(OP, "Evaluating block %s", block_name.c_str());

     SubgraphTraverser block_sgt(block, tsh, this->parent_sgt);

     ERROR_IF(block_sgt.initializeGraph(), "evalBlock(): Unable to initialize graph traverser for %s",
              block_name.c_str());
     ERROR_IF(block_sgt.linkTensorsAndNodes(), "evalBlock(): Failed to link tensors and nodes for %s",
              block_name.c_str());
     ERROR_IF(block_sgt.validateGraph(), "evalBlock(): Failed to validate subgraph for %s", block_name.c_str());
     ERROR_IF(block_sgt.allocateTensor(), "evalBlock(): Failed to allocate tensor for %s", block_name.c_str());

     int num_input_tensors  = block_sgt.getNumInputTensors();
     int num_output_tensors = block_sgt.getNumOutputTensors();

     for (size_t i = 0; i < block_inputs.size(); i++)
     {
         DEBUG_HIGH(OP, "Input[%ld]:  %s", i, block_inputs[i]->getName().c_str());
     }
     for (size_t i = 0; i < block_outputs.size(); i++)
     {
         DEBUG_HIGH(OP, "Output[%ld]: %s", i, block_outputs[i]->getName().c_str());
     }

     ASSERT_MSG((size_t)num_input_tensors == block_inputs.size(),
                "op block %s inputs[%lu] does not match with graph traverser's inputs[%d]", block_name.c_str(),
                block_inputs.size(), num_input_tensors);
     ASSERT_MSG((size_t)num_output_tensors == block_outputs.size(),
                "op block %s outputs[%lu] does not match with graph traverser's outputs[%d]", block_name.c_str(),
                block_outputs.size(), num_output_tensors);

     // set graph traverser's input = basic block's input
     for (int i = 0; i < num_input_tensors; i++)
     {
         TosaReference::Tensor* tensor = block_sgt.getInputTensor(i);
         ERROR_IF(!tensor->is_allocated(), "block %s input tensor %s are not initialized before use", block_name.c_str(),
                  tensor->getName().c_str());

         if (tensor->copyValueFrom(block_inputs[i]))
         {
             WARNING("Fail to copy tensor value %s -> %s", block_inputs[i]->getName().c_str(),
                     tensor->getName().c_str());
             return 1;
         }

         tensor->setIsValid();

         // Push ready consumers to the next node list
         for (auto gn : tensor->getConsumers())
         {
             if (gn->hasAllInputsReady() && !gn->getOnNextNodeList())
             {
                 block_sgt.addToNextNodeList(gn);
             }
         }
     }

     ERROR_IF(block_sgt.evaluateAll(), "Error evaluating network.  Giving up.");

     // pass block status back
     switch (block_sgt.getGraphStatus())
     {
         case GraphStatus::TOSA_VALID:
         {
             DEBUG_MED(OP, "Successfully evaluating block %s", block_name.c_str());
             break;
         }
         case GraphStatus::TOSA_UNPREDICTABLE:
         {
             DEBUG_MED(OP, "Finish evaluating block %s but result is UNPREDICTABLE", block_name.c_str());
             DEBUG_MED(OP, "Setting parent graph status to UNPREDICTABLE");
             parent_sgt->setGraphStatus(GraphStatus::TOSA_UNPREDICTABLE);
             break;
         }
         case GraphStatus::TOSA_ERROR:
         {
             DEBUG_MED(OP, "Fail evaluating block %s. Result is ERROR", block_name.c_str());
             if (parent_sgt->getGraphStatus() != GraphStatus::TOSA_UNPREDICTABLE)
             {
                 DEBUG_MED(OP, "Setting parent graph status to ERROR");
                 parent_sgt->setGraphStatus(GraphStatus::TOSA_ERROR);
                 return 1;
             }
         }
     }

     // make sure output tensor is evaluated and show its value
     bool all_output_valid = true;
     for (int i = 0; i < num_output_tensors; i++)
     {
         const TosaReference::Tensor* ct = block_sgt.getOutputTensor(i);
         ASSERT_MEM(ct);
         if (!ct->getIsValid())
         {
             ct->dumpTensorParams(g_func_debug.func_debug_file);
             if (DEBUG_ENABLED(DEBUG_VERB_HIGH, GT))
             {
                 ct->dumpTensor(g_func_debug.func_debug_file);
             }
             all_output_valid = false;
         }
     }
     if (!all_output_valid)
     {
         block_sgt.dumpGraph(g_func_debug.func_debug_file);
         ERROR_IF(true, "SubgraphTraverser \"%s\" error: Output tensors are not all valid at the end of evaluation.",
                  block_name.c_str());
     }

     // set basic block's output = subgraph_traverser's output
     for (int i = 0; i < num_output_tensors; i++)
     {
         TosaReference::Tensor* tensor = block_sgt.getOutputTensor(i);
         ERROR_IF(!tensor->is_allocated(), "block %s input tensor %s are not initialized before use", block_name.c_str(),
                  tensor->getName().c_str());

         if (block_outputs[i]->copyValueFrom(tensor))
         {
             WARNING("Fail to copy tensor value %s -> %s", tensor->getName().c_str(), outputs[i]->getName().c_str());
             return 1;
         }
     }
     return 0;
 }

 OpCondIf::OpCondIf(SubgraphTraverser* sgt_, TosaSerializationHandler* tsh_, TosaAttributeBase* attribute_, uint64_t id_)
     : OpControlFlow(sgt_, tsh_, Op_COND_IF, id_)
 {
     INIT_ATTRIBUTE(CondIf);
 }

 OpCondIf::~OpCondIf()
 {
     if (attribute)
         delete attribute;
 }

 int OpCondIf::checkTensorAttributes()
 {
     ERROR_IF(getInputs().size() < 1, "OpCondIf: must have at least 1 operand");

     ERROR_IF(inputs[0]->getDtype() != DType_BOOL || inputs[0]->getRank() != 0,
              "OpCondIf: invalid tensor dtype=%s, rank=%d", EnumNamesDType()[inputs[0]->getDtype()],
              inputs[0]->getRank());

     cond = dynamic_cast<TosaReference::Tensor0<bool>*>(inputs[0]);
     ASSERT_MEM(cond);

     auto region_name = getParentSGT()->getRegionName();
     auto curr_region = tsh->GetRegionByName(region_name);
     then_block = curr_region->GetBlockByName(attribute->then_branch());
     else_block = curr_region->GetBlockByName(attribute->else_branch());

     ERROR_IF(!then_block, "OpCondIf: fail to resolve then_branch %s", attribute->then_branch().c_str());

     ERROR_IF(!else_block, "OpCondIf: fail to resolve else_branch %s", attribute->else_branch().c_str());

     // Make sure operator input/output matches block input/output
     // Skip the first rank 0 bool tensor on input list
     int32_t num_input_tensor  = getInputs().size() - 1;
     int32_t num_output_tensor = getOutputs().size();

     ERROR_IF((int32_t)then_block->GetInputs().size() != num_input_tensor,
              "OpCondIf: then_block has unexpected number of input");
     ERROR_IF((int32_t)else_block->GetInputs().size() != num_input_tensor,
              "OpCondIf: else_block has unexpected number of input");
     ERROR_IF((int32_t)then_block->GetOutputs().size() != num_output_tensor,
              "OpCondIf: then_block has unexpected number of output");
     ERROR_IF((int32_t)else_block->GetOutputs().size() != num_output_tensor,
              "OpCondIf: else_block has unexpected number of output");

     for (int32_t i = 0; i < num_input_tensor; i++)
     {
         Tensor* operator_input                    = getInputs()[i + 1];
         std::string then_block_input_name         = then_block->GetInputs()[i];
         std::string else_block_input_name         = else_block->GetInputs()[i];
         TosaSerializationTensor* then_block_input = then_block->GetTensorByName(then_block_input_name);
         TosaSerializationTensor* else_block_input = else_block->GetTensorByName(else_block_input_name);
         ERROR_IF(operator_input->getDtype() != then_block_input->GetDtype(),
                  "OpCondIf: input tensor type mismatch with then_block input type");
         ERROR_IF(operator_input->getDtype() != else_block_input->GetDtype(),
                  "OpCondIf: input tensor type mismatch with else_block input type");
         ERROR_IF(operator_input->getRank() != (int32_t)then_block_input->GetShape().size(),
                  "OpCondIf: input tensor rank mismatch with then_block input rank");
         ERROR_IF(operator_input->getRank() != (int32_t)else_block_input->GetShape().size(),
                  "OpCondIf: input tensor rank mismatch with else_block input rank");
         for (int32_t d = 0; d < operator_input->getRank(); d++)
         {
             ERROR_IF(operator_input->getShape()[d] != then_block_input->GetShape()[d],
                      "OpCondIf: input tensor dimension mismatch with then_block input dimension");
             ERROR_IF(operator_input->getShape()[d] != else_block_input->GetShape()[d],
                      "OpCondIf: input tensor dimension mismatch with else_block input dimension");
         }
     }

     for (int32_t i = 0; i < num_output_tensor; i++)
     {
         Tensor* operator_output                    = getOutputs()[i];
         std::string then_block_output_name         = then_block->GetOutputs()[i];
         std::string else_block_output_name         = else_block->GetOutputs()[i];
         TosaSerializationTensor* then_block_output = then_block->GetTensorByName(then_block_output_name);
         TosaSerializationTensor* else_block_output = else_block->GetTensorByName(else_block_output_name);
         ERROR_IF(operator_output->getDtype() != then_block_output->GetDtype(),
                  "OpCondIf: output tensor type mismatch with then_block output type");
         ERROR_IF(operator_output->getDtype() != else_block_output->GetDtype(),
                  "OpCondIf: output tensor type mismatch with else_block output type");
         ERROR_IF(operator_output->getRank() != (int32_t)then_block_output->GetShape().size(),
                  "OpCondIf: output tensor rank mismatch with then_block output rank");
         ERROR_IF(operator_output->getRank() != (int32_t)else_block_output->GetShape().size(),
                  "OpCondIf: output tensor rank mismatch with else_block output rank");
         for (int32_t d = 0; d < operator_output->getRank(); d++)
         {
             ERROR_IF(operator_output->getShape()[d] != then_block_output->GetShape()[d],
                      "OpCondIf: output tensor dimension mismatch with then_block output dimension");
             ERROR_IF(operator_output->getShape()[d] != else_block_output->GetShape()[d],
                      "OpCondIf: output tensor dimension mismatch with else_block output dimension");
         }
     }

     return 0;
 }

 int OpCondIf::eval()
 {
     bool cond_val = cond->getTensor()(0);
     std::vector<TosaReference::Tensor*> block_inputs(getInputs().begin() + 1, getInputs().end());

     if (cond_val)
     {
         if (evalBlock(then_block, block_inputs, getOutputs()))
         {
             WARNING("OpCondIf: Fail to evaluate then branch block %s", attribute->then_branch().c_str());
             return 1;
         }
     }
     else
     {
         if (evalBlock(else_block, block_inputs, getOutputs()))
         {
             WARNING("OpCondIf: Fail to evaluate else branch block %s", attribute->else_branch().c_str());
             return 1;
         }
     }

     return GraphNode::eval();
 }

 OpWhileLoop::OpWhileLoop(SubgraphTraverser* sgt_,
                          TosaSerializationHandler* tsh_,
                          TosaAttributeBase* attribute_,
                          uint64_t id_)
     : OpControlFlow(sgt_, tsh_, Op_WHILE_LOOP, id_)
 {
     INIT_ATTRIBUTE(WhileLoop);
 }

 OpWhileLoop::~OpWhileLoop()
 {
     if (attribute)
         delete attribute;
 }

 int OpWhileLoop::checkTensorAttributes()
 {
     if (getInputs().size() <= 0)
     {
         WARNING("OpWhileLoop: must have at least 1 operands");
         return 1;
     }

     if (getInputs().size() != getOutputs().size())
     {
         WARNING("OpWhileLoop: inputs and outputs size must match");
         return 1;
     }

     auto region_name = getParentSGT()->getRegionName();
     auto curr_region = tsh->GetRegionByName(region_name);
     cond_block = curr_region->GetBlockByName(attribute->cond_branch());
     body_block = curr_region->GetBlockByName(attribute->body_branch());

     ERROR_IF(!cond_block, "OpWhileLoop: fail to resolve cond_branch %s", attribute->cond_branch().c_str());
     ERROR_IF(!body_block, "OpWhileLoop: fail to resolve body_branch %s", attribute->body_branch().c_str());

     // Make sure operator input/output matches block input/output
     int32_t num_block_tensor = getInputs().size();
     ERROR_IF((int32_t)getOutputs().size() != num_block_tensor,
              "OpWhileLoop: operator input tensor doesn't match output");
     ERROR_IF((int32_t)cond_block->GetInputs().size() != num_block_tensor,
              "OpWhileLoop: cond_block has unexpected number of input");
     ERROR_IF((int32_t)body_block->GetInputs().size() != num_block_tensor,
              "OpWhileLoop: body_block has unexpected number of input");
     ERROR_IF((int32_t)body_block->GetOutputs().size() != num_block_tensor,
              "OpWhileLoop: body_block has unexpected number of output");
     for (int32_t i = 0; i < num_block_tensor; i++)
     {
         Tensor* operator_input  = getInputs()[i];
         Tensor* operator_output = getOutputs()[i];
         ERROR_IF(operator_input->matchRankTypeShape(*operator_output),
                  "OpWhileLoop: operator input tensor mismatch operator output tensor");

         std::string cond_block_input_name          = cond_block->GetInputs()[i];
         std::string body_block_input_name          = body_block->GetInputs()[i];
         std::string body_block_output_name         = body_block->GetOutputs()[i];
         TosaSerializationTensor* cond_block_input  = cond_block->GetTensorByName(cond_block_input_name);
         TosaSerializationTensor* body_block_input  = body_block->GetTensorByName(body_block_input_name);
         TosaSerializationTensor* body_block_output = body_block->GetTensorByName(body_block_output_name);

         ERROR_IF(operator_input->getDtype() != cond_block_input->GetDtype(),
                  "OpWhileLoop: input tensor type mismatch with cond_block input type");
         ERROR_IF(operator_input->getDtype() != body_block_input->GetDtype(),
                  "OpWhileLoop: input tensor type mismatch with body_block input type");
         ERROR_IF(operator_input->getDtype() != body_block_output->GetDtype(),
                  "OpWhileLoop: input tensor type mismatch with body_block output type");
         ERROR_IF(operator_input->getRank() != (int32_t)cond_block_input->GetShape().size(),
                  "OpWhileLoop: input tensor rank mismatch with cond_block input rank");
         ERROR_IF(operator_input->getRank() != (int32_t)body_block_input->GetShape().size(),
                  "OpWhileLoop: input tensor rank mismatch with body_block input rank");
         ERROR_IF(operator_input->getRank() != (int32_t)body_block_output->GetShape().size(),
                  "OpWhileLoop: input tensor rank mismatch with body_block output rank");

         for (int32_t d = 0; d < operator_input->getRank(); d++)
         {
             ERROR_IF(operator_input->getShape()[d] != cond_block_input->GetShape()[d],
                      "OpWhileLoop: input tensor dimension mismatch with cond_block input dimension");
             ERROR_IF(operator_input->getShape()[d] != body_block_input->GetShape()[d],
                      "OpWhileLoop: input tensor dimension mismatch with body_block input dimension");
             ERROR_IF(operator_input->getShape()[d] != body_block_output->GetShape()[d],
                      "OpWhileLoop: input tensor dimension mismatch with body_block output dimension");
         }
     }

     ERROR_IF(cond_block->GetOutputs().size() != 1, "OpWhileLoop: cond_block can only have 1 output tensor");
     std::string cond_block_output_name         = cond_block->GetOutputs()[0];
     TosaSerializationTensor* cond_block_output = cond_block->GetTensorByName(cond_block_output_name);
     ERROR_IF(cond_block_output->GetDtype() != DType_BOOL, "OpWhileLoop: cond_block output can only be bool type");
     ERROR_IF(cond_block_output->GetShape().size() != 0, "OpWhileLoop: cond_block output can only be rank 0");

     return 0;
 }

 int OpWhileLoop::eval()
 {

     TosaReference::Tensor0<bool> cond_output_ctensor(std::string("cond_output"), DType_BOOL, std::vector<int32_t>({}));

     cond_output_ctensor.allocate();
     std::vector<TosaReference::Tensor*> cond_block_outputs;
     cond_block_outputs.push_back(&cond_output_ctensor);

     size_t num_input_output = getInputs().size();
     size_t eval_count       = 0;

     while (eval_count++ < MAX_WHILE_LOOP_ITERATION)
     {
         if (evalBlock(cond_block, getInputs(), cond_block_outputs))
         {
             WARNING("OpWhileLoop: Fail to evaluate cond block %s", attribute->cond_branch().c_str());
             return 1;
         }
         bool cond_val = cond_output_ctensor.getTensor()(0);
         DEBUG_HIGH(OP, "Conditional block value: %d", cond_val);

         if (cond_val)
         {
             if (evalBlock(body_block, getInputs(), getOutputs()))
             {
                 WARNING("OpWhileLoop: Fail to evaluate body block %s", attribute->body_branch().c_str());
                 return 1;
             }

             // assigning output tensors value back to input tensors value for next iteration
             for (size_t i = 0; i < num_input_output; i++)
             {
                 getInputs()[i] = getOutputs()[i];
             }
         }
         else
         {
             // in last iteration or the case it never evaluates body block
             // assign input tensors value to output tensors
             for (size_t i = 0; i < num_input_output; i++)
             {
                 if (getOutputs()[i]->copyValueFrom(getInputs()[i]))
                 {
                     WARNING("Fail to copy tensor value %s -> %s", getInputs()[i]->getName().c_str(),
                             getOutputs()[i]->getName().c_str());
                     return 1;
                 }
             }
             break;
         }
     }

     return GraphNode::eval();
 }

	// Copyright (c) 2020-2023, ARM Limited.
	//
	// Licensed under the Apache License, Version 2.0 (the "License");
	// you may not use this file except in compliance with the License.
	// You may obtain a copy of the License at
	//
	// http://www.apache.org/licenses/LICENSE-2.0
	//
	// Unless required by applicable law or agreed to in writing, software
	// distributed under the License is distributed on an "AS IS" BASIS,
	// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	// See the License for the specific language governing permissions and
	// limitations under the License.

	#include "control_flow.h"
	#include "subgraph_traverser.h"
	using namespace TosaReference;
	using namespace Eigen;
	using namespace tosa;

	OpControlFlow::OpControlFlow(SubgraphTraverser* sgt_, TosaSerializationHandler* tsh_, Op op_, uint64_t id_)
	: GraphNode(sgt_, op_, id_)
	{
	tsh = tsh_;
	}

	OpControlFlow::~OpControlFlow()
	{}

	int OpControlFlow::evalBlock(TosaSerializationBasicBlock* block,
	std::vector<TosaReference::Tensor*>& block_inputs,
	std::vector<TosaReference::Tensor*>& block_outputs)
	{
	std::string block_name = block->GetName();

	DEBUG_MED(OP, "Evaluating block %s", block_name.c_str());

	SubgraphTraverser block_sgt(block, tsh, this->parent_sgt);

	ERROR_IF(block_sgt.initializeGraph(), "evalBlock(): Unable to initialize graph traverser for %s",
	block_name.c_str());
	ERROR_IF(block_sgt.linkTensorsAndNodes(), "evalBlock(): Failed to link tensors and nodes for %s",
	block_name.c_str());
	ERROR_IF(block_sgt.validateGraph(), "evalBlock(): Failed to validate subgraph for %s", block_name.c_str());
	ERROR_IF(block_sgt.allocateTensor(), "evalBlock(): Failed to allocate tensor for %s", block_name.c_str());

	int num_input_tensors = block_sgt.getNumInputTensors();
	int num_output_tensors = block_sgt.getNumOutputTensors();

	for (size_t i = 0; i < block_inputs.size(); i++)
	{
	DEBUG_HIGH(OP, "Input[%ld]: %s", i, block_inputs[i]->getName().c_str());
	}
	for (size_t i = 0; i < block_outputs.size(); i++)
	{
	DEBUG_HIGH(OP, "Output[%ld]: %s", i, block_outputs[i]->getName().c_str());
	}

	ASSERT_MSG((size_t)num_input_tensors == block_inputs.size(),
	"op block %s inputs[%lu] does not match with graph traverser's inputs[%d]", block_name.c_str(),
	block_inputs.size(), num_input_tensors);
	ASSERT_MSG((size_t)num_output_tensors == block_outputs.size(),
	"op block %s outputs[%lu] does not match with graph traverser's outputs[%d]", block_name.c_str(),
	block_outputs.size(), num_output_tensors);

	// set graph traverser's input = basic block's input
	for (int i = 0; i < num_input_tensors; i++)
	{
	TosaReference::Tensor* tensor = block_sgt.getInputTensor(i);
	ERROR_IF(!tensor->is_allocated(), "block %s input tensor %s are not initialized before use", block_name.c_str(),
	tensor->getName().c_str());

	if (tensor->copyValueFrom(block_inputs[i]))
	{
	WARNING("Fail to copy tensor value %s -> %s", block_inputs[i]->getName().c_str(),
	tensor->getName().c_str());
	return 1;
	}

	tensor->setIsValid();

	// Push ready consumers to the next node list
	for (auto gn : tensor->getConsumers())
	{
	if (gn->hasAllInputsReady() && !gn->getOnNextNodeList())
	{
	block_sgt.addToNextNodeList(gn);
	}
	}
	}

	ERROR_IF(block_sgt.evaluateAll(), "Error evaluating network. Giving up.");

	// pass block status back
	switch (block_sgt.getGraphStatus())
	{
	case GraphStatus::TOSA_VALID:
	{
	DEBUG_MED(OP, "Successfully evaluating block %s", block_name.c_str());
	break;
	}
	case GraphStatus::TOSA_UNPREDICTABLE:
	{
	DEBUG_MED(OP, "Finish evaluating block %s but result is UNPREDICTABLE", block_name.c_str());
	DEBUG_MED(OP, "Setting parent graph status to UNPREDICTABLE");
	parent_sgt->setGraphStatus(GraphStatus::TOSA_UNPREDICTABLE);
	break;
	}
	case GraphStatus::TOSA_ERROR:
	{
	DEBUG_MED(OP, "Fail evaluating block %s. Result is ERROR", block_name.c_str());
	if (parent_sgt->getGraphStatus() != GraphStatus::TOSA_UNPREDICTABLE)
	{
	DEBUG_MED(OP, "Setting parent graph status to ERROR");
	parent_sgt->setGraphStatus(GraphStatus::TOSA_ERROR);
	return 1;
	}
	}
	}

	// make sure output tensor is evaluated and show its value
	bool all_output_valid = true;
	for (int i = 0; i < num_output_tensors; i++)
	{
	const TosaReference::Tensor* ct = block_sgt.getOutputTensor(i);
	ASSERT_MEM(ct);
	if (!ct->getIsValid())
	{
	ct->dumpTensorParams(g_func_debug.func_debug_file);
	if (DEBUG_ENABLED(DEBUG_VERB_HIGH, GT))
	{
	ct->dumpTensor(g_func_debug.func_debug_file);
	}
	all_output_valid = false;
	}
	}
	if (!all_output_valid)
	{
	block_sgt.dumpGraph(g_func_debug.func_debug_file);
	ERROR_IF(true, "SubgraphTraverser \"%s\" error: Output tensors are not all valid at the end of evaluation.",
	block_name.c_str());
	}

	// set basic block's output = subgraph_traverser's output
	for (int i = 0; i < num_output_tensors; i++)
	{
	TosaReference::Tensor* tensor = block_sgt.getOutputTensor(i);
	ERROR_IF(!tensor->is_allocated(), "block %s input tensor %s are not initialized before use", block_name.c_str(),
	tensor->getName().c_str());

	if (block_outputs[i]->copyValueFrom(tensor))
	{
	WARNING("Fail to copy tensor value %s -> %s", tensor->getName().c_str(), outputs[i]->getName().c_str());
	return 1;
	}
	}
	return 0;
	}

	OpCondIf::OpCondIf(SubgraphTraverser* sgt_, TosaSerializationHandler* tsh_, TosaAttributeBase* attribute_, uint64_t id_)
	: OpControlFlow(sgt_, tsh_, Op_COND_IF, id_)
	{
	INIT_ATTRIBUTE(CondIf);
	}

	OpCondIf::~OpCondIf()
	{
	if (attribute)
	delete attribute;
	}

	int OpCondIf::checkTensorAttributes()
	{
	ERROR_IF(getInputs().size() < 1, "OpCondIf: must have at least 1 operand");

	ERROR_IF(inputs[0]->getDtype() != DType_BOOL \|\| inputs[0]->getRank() != 0,
	"OpCondIf: invalid tensor dtype=%s, rank=%d", EnumNamesDType()[inputs[0]->getDtype()],
	inputs[0]->getRank());

	cond = dynamic_cast<TosaReference::Tensor0<bool>*>(inputs[0]);
	ASSERT_MEM(cond);

	auto region_name = getParentSGT()->getRegionName();
	auto curr_region = tsh->GetRegionByName(region_name);
	then_block = curr_region->GetBlockByName(attribute->then_branch());
	else_block = curr_region->GetBlockByName(attribute->else_branch());

	ERROR_IF(!then_block, "OpCondIf: fail to resolve then_branch %s", attribute->then_branch().c_str());

	ERROR_IF(!else_block, "OpCondIf: fail to resolve else_branch %s", attribute->else_branch().c_str());

	// Make sure operator input/output matches block input/output
	// Skip the first rank 0 bool tensor on input list
	int32_t num_input_tensor = getInputs().size() - 1;
	int32_t num_output_tensor = getOutputs().size();

	ERROR_IF((int32_t)then_block->GetInputs().size() != num_input_tensor,
	"OpCondIf: then_block has unexpected number of input");
	ERROR_IF((int32_t)else_block->GetInputs().size() != num_input_tensor,
	"OpCondIf: else_block has unexpected number of input");
	ERROR_IF((int32_t)then_block->GetOutputs().size() != num_output_tensor,
	"OpCondIf: then_block has unexpected number of output");
	ERROR_IF((int32_t)else_block->GetOutputs().size() != num_output_tensor,
	"OpCondIf: else_block has unexpected number of output");

	for (int32_t i = 0; i < num_input_tensor; i++)
	{
	Tensor* operator_input = getInputs()[i + 1];
	std::string then_block_input_name = then_block->GetInputs()[i];
	std::string else_block_input_name = else_block->GetInputs()[i];
	TosaSerializationTensor* then_block_input = then_block->GetTensorByName(then_block_input_name);
	TosaSerializationTensor* else_block_input = else_block->GetTensorByName(else_block_input_name);
	ERROR_IF(operator_input->getDtype() != then_block_input->GetDtype(),
	"OpCondIf: input tensor type mismatch with then_block input type");
	ERROR_IF(operator_input->getDtype() != else_block_input->GetDtype(),
	"OpCondIf: input tensor type mismatch with else_block input type");
	ERROR_IF(operator_input->getRank() != (int32_t)then_block_input->GetShape().size(),
	"OpCondIf: input tensor rank mismatch with then_block input rank");
	ERROR_IF(operator_input->getRank() != (int32_t)else_block_input->GetShape().size(),
	"OpCondIf: input tensor rank mismatch with else_block input rank");
	for (int32_t d = 0; d < operator_input->getRank(); d++)
	{
	ERROR_IF(operator_input->getShape()[d] != then_block_input->GetShape()[d],
	"OpCondIf: input tensor dimension mismatch with then_block input dimension");
	ERROR_IF(operator_input->getShape()[d] != else_block_input->GetShape()[d],
	"OpCondIf: input tensor dimension mismatch with else_block input dimension");
	}
	}

	for (int32_t i = 0; i < num_output_tensor; i++)
	{
	Tensor* operator_output = getOutputs()[i];
	std::string then_block_output_name = then_block->GetOutputs()[i];
	std::string else_block_output_name = else_block->GetOutputs()[i];
	TosaSerializationTensor* then_block_output = then_block->GetTensorByName(then_block_output_name);
	TosaSerializationTensor* else_block_output = else_block->GetTensorByName(else_block_output_name);
	ERROR_IF(operator_output->getDtype() != then_block_output->GetDtype(),
	"OpCondIf: output tensor type mismatch with then_block output type");
	ERROR_IF(operator_output->getDtype() != else_block_output->GetDtype(),
	"OpCondIf: output tensor type mismatch with else_block output type");
	ERROR_IF(operator_output->getRank() != (int32_t)then_block_output->GetShape().size(),
	"OpCondIf: output tensor rank mismatch with then_block output rank");
	ERROR_IF(operator_output->getRank() != (int32_t)else_block_output->GetShape().size(),
	"OpCondIf: output tensor rank mismatch with else_block output rank");
	for (int32_t d = 0; d < operator_output->getRank(); d++)
	{
	ERROR_IF(operator_output->getShape()[d] != then_block_output->GetShape()[d],
	"OpCondIf: output tensor dimension mismatch with then_block output dimension");
	ERROR_IF(operator_output->getShape()[d] != else_block_output->GetShape()[d],
	"OpCondIf: output tensor dimension mismatch with else_block output dimension");
	}
	}

	return 0;
	}

	int OpCondIf::eval()
	{
	bool cond_val = cond->getTensor()(0);
	std::vector<TosaReference::Tensor*> block_inputs(getInputs().begin() + 1, getInputs().end());

	if (cond_val)
	{
	if (evalBlock(then_block, block_inputs, getOutputs()))
	{
	WARNING("OpCondIf: Fail to evaluate then branch block %s", attribute->then_branch().c_str());
	return 1;
	}
	}
	else
	{
	if (evalBlock(else_block, block_inputs, getOutputs()))
	{
	WARNING("OpCondIf: Fail to evaluate else branch block %s", attribute->else_branch().c_str());
	return 1;
	}
	}

	return GraphNode::eval();
	}

	OpWhileLoop::OpWhileLoop(SubgraphTraverser* sgt_,
	TosaSerializationHandler* tsh_,
	TosaAttributeBase* attribute_,
	uint64_t id_)
	: OpControlFlow(sgt_, tsh_, Op_WHILE_LOOP, id_)
	{
	INIT_ATTRIBUTE(WhileLoop);
	}

	OpWhileLoop::~OpWhileLoop()
	{
	if (attribute)
	delete attribute;
	}

	int OpWhileLoop::checkTensorAttributes()
	{
	if (getInputs().size() <= 0)
	{
	WARNING("OpWhileLoop: must have at least 1 operands");
	return 1;
	}

	if (getInputs().size() != getOutputs().size())
	{
	WARNING("OpWhileLoop: inputs and outputs size must match");
	return 1;
	}

	auto region_name = getParentSGT()->getRegionName();
	auto curr_region = tsh->GetRegionByName(region_name);
	cond_block = curr_region->GetBlockByName(attribute->cond_branch());
	body_block = curr_region->GetBlockByName(attribute->body_branch());

	ERROR_IF(!cond_block, "OpWhileLoop: fail to resolve cond_branch %s", attribute->cond_branch().c_str());
	ERROR_IF(!body_block, "OpWhileLoop: fail to resolve body_branch %s", attribute->body_branch().c_str());

	// Make sure operator input/output matches block input/output
	int32_t num_block_tensor = getInputs().size();
	ERROR_IF((int32_t)getOutputs().size() != num_block_tensor,
	"OpWhileLoop: operator input tensor doesn't match output");
	ERROR_IF((int32_t)cond_block->GetInputs().size() != num_block_tensor,
	"OpWhileLoop: cond_block has unexpected number of input");
	ERROR_IF((int32_t)body_block->GetInputs().size() != num_block_tensor,
	"OpWhileLoop: body_block has unexpected number of input");
	ERROR_IF((int32_t)body_block->GetOutputs().size() != num_block_tensor,
	"OpWhileLoop: body_block has unexpected number of output");
	for (int32_t i = 0; i < num_block_tensor; i++)
	{
	Tensor* operator_input = getInputs()[i];
	Tensor* operator_output = getOutputs()[i];
	ERROR_IF(operator_input->matchRankTypeShape(*operator_output),
	"OpWhileLoop: operator input tensor mismatch operator output tensor");

	std::string cond_block_input_name = cond_block->GetInputs()[i];
	std::string body_block_input_name = body_block->GetInputs()[i];
	std::string body_block_output_name = body_block->GetOutputs()[i];
	TosaSerializationTensor* cond_block_input = cond_block->GetTensorByName(cond_block_input_name);
	TosaSerializationTensor* body_block_input = body_block->GetTensorByName(body_block_input_name);
	TosaSerializationTensor* body_block_output = body_block->GetTensorByName(body_block_output_name);

	ERROR_IF(operator_input->getDtype() != cond_block_input->GetDtype(),
	"OpWhileLoop: input tensor type mismatch with cond_block input type");
	ERROR_IF(operator_input->getDtype() != body_block_input->GetDtype(),
	"OpWhileLoop: input tensor type mismatch with body_block input type");
	ERROR_IF(operator_input->getDtype() != body_block_output->GetDtype(),
	"OpWhileLoop: input tensor type mismatch with body_block output type");
	ERROR_IF(operator_input->getRank() != (int32_t)cond_block_input->GetShape().size(),
	"OpWhileLoop: input tensor rank mismatch with cond_block input rank");
	ERROR_IF(operator_input->getRank() != (int32_t)body_block_input->GetShape().size(),
	"OpWhileLoop: input tensor rank mismatch with body_block input rank");
	ERROR_IF(operator_input->getRank() != (int32_t)body_block_output->GetShape().size(),
	"OpWhileLoop: input tensor rank mismatch with body_block output rank");

	for (int32_t d = 0; d < operator_input->getRank(); d++)
	{
	ERROR_IF(operator_input->getShape()[d] != cond_block_input->GetShape()[d],
	"OpWhileLoop: input tensor dimension mismatch with cond_block input dimension");
	ERROR_IF(operator_input->getShape()[d] != body_block_input->GetShape()[d],
	"OpWhileLoop: input tensor dimension mismatch with body_block input dimension");
	ERROR_IF(operator_input->getShape()[d] != body_block_output->GetShape()[d],
	"OpWhileLoop: input tensor dimension mismatch with body_block output dimension");
	}
	}

	ERROR_IF(cond_block->GetOutputs().size() != 1, "OpWhileLoop: cond_block can only have 1 output tensor");
	std::string cond_block_output_name = cond_block->GetOutputs()[0];
	TosaSerializationTensor* cond_block_output = cond_block->GetTensorByName(cond_block_output_name);
	ERROR_IF(cond_block_output->GetDtype() != DType_BOOL, "OpWhileLoop: cond_block output can only be bool type");
	ERROR_IF(cond_block_output->GetShape().size() != 0, "OpWhileLoop: cond_block output can only be rank 0");

	return 0;
	}

	int OpWhileLoop::eval()
	{

	TosaReference::Tensor0<bool> cond_output_ctensor(std::string("cond_output"), DType_BOOL, std::vector<int32_t>({}));

	cond_output_ctensor.allocate();
	std::vector<TosaReference::Tensor*> cond_block_outputs;
	cond_block_outputs.push_back(&cond_output_ctensor);

	size_t num_input_output = getInputs().size();
	size_t eval_count = 0;

	while (eval_count++ < MAX_WHILE_LOOP_ITERATION)
	{
	if (evalBlock(cond_block, getInputs(), cond_block_outputs))
	{
	WARNING("OpWhileLoop: Fail to evaluate cond block %s", attribute->cond_branch().c_str());
	return 1;
	}
	bool cond_val = cond_output_ctensor.getTensor()(0);
	DEBUG_HIGH(OP, "Conditional block value: %d", cond_val);

	if (cond_val)
	{
	if (evalBlock(body_block, getInputs(), getOutputs()))
	{
	WARNING("OpWhileLoop: Fail to evaluate body block %s", attribute->body_branch().c_str());
	return 1;
	}

	// assigning output tensors value back to input tensors value for next iteration
	for (size_t i = 0; i < num_input_output; i++)
	{
	getInputs()[i] = getOutputs()[i];
	}
	}
	else
	{
	// in last iteration or the case it never evaluates body block
	// assign input tensors value to output tensors
	for (size_t i = 0; i < num_input_output; i++)
	{
	if (getOutputs()[i]->copyValueFrom(getInputs()[i]))
	{
	WARNING("Fail to copy tensor value %s -> %s", getInputs()[i]->getName().c_str(),
	getOutputs()[i]->getName().c_str());
	return 1;
	}
	}
	break;
	}
	}

	return GraphNode::eval();
	}