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


 // Copyright (c) 2020, 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(TosaSerializationHandler* tsh_, Op op_, uint64_t id_)
     : GraphNode(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 gt(block, tsh);

     if (gt.initializeGraph())
     {
         FATAL_ERROR("Unable to initialize graph traverser for block %s", block_name.c_str());
     }

     if (gt.linkTensorsAndNodes())
     {
         FATAL_ERROR("Failed to link tensors and nodes for block %s", block_name.c_str());
     }

     if (gt.validateGraph())
     {
         FATAL_ERROR("Failed to validate subgraph for block %s", block_name.c_str());
     }

     int num_input_tensors  = gt.getNumInputTensors();
     int num_output_tensors = gt.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 = gt.getInputTensor(i);
         ASSERT_MSG(!tensor->is_allocated(), "block %s input tensors are unexpectedly initialized before",
                    block_name.c_str());

         if (tensor->allocate())
         {
             WARNING("Fail to allocate tensor %s", tensor->getName().c_str());
             return 1;
         }

         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;
         }

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

     if (gt.evaluateAll())
     {
         FATAL_ERROR("Error evaluating network.  Giving up.");
     }

     // 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 = gt.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)
     {
         gt.dumpGraph(g_func_debug.func_debug_file);
         FATAL_ERROR("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 = gt.getOutputTensor(i);
         ASSERT_MSG(tensor->is_allocated(), "tensor %s is not allocated", 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(TosaSerializationHandler* tsh_, TosaAttributeBase* attribute_, uint64_t id_)
     : OpControlFlow(tsh_, Op_COND_IF, id_)
 {
     INIT_ATTRIBUTE(CondIf);
 }

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

 int OpCondIf::checkTensorAttributes()
 {
     if (getInputs().size() < 1)
     {
         WARNING("OpCondIf: must have at least 1 operand");
         return 1;
     }

     if (inputs[0]->getDtype() != DType_BOOL || inputs[0]->getRank() != 0)
     {
         WARNING("OpCondIf: invalid tensor dtype=%s, rank=%d", EnumNamesDType()[inputs[0]->getDtype()],
                 inputs[0]->getRank());
         return 1;
     }

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

     then_block = tsh->GetBlockByName(attribute->then_branch());
     else_block = tsh->GetBlockByName(attribute->else_branch());

     if (!then_block)
     {
         WARNING("OpCondIf: fail to resolve then_branch %s", attribute->then_branch().c_str());
         return 1;
     }

     if (!else_block)
     {
         WARNING("OpCondIf: fail to resolve else_branch %s", attribute->else_branch().c_str());
         return 1;
     }

     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(TosaSerializationHandler* tsh_, TosaAttributeBase* attribute_, uint64_t id_)
     : OpControlFlow(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;
     }

     cond_block = tsh->GetBlockByName(attribute->cond_branch());
     body_block = tsh->GetBlockByName(attribute->body_branch());

     if (!cond_block)
     {
         WARNING("OpWhileLoop: fail to resolve cond_branch %s", attribute->cond_branch().c_str());
         return 1;
     }

     if (!body_block)
     {
         WARNING("OpWhileLoop: fail to resolve body_branch %s", attribute->body_branch().c_str());
         return 1;
     }

     if (cond_block->GetOutputs().size() != 1)
     {
         WARNING("OpWhileLoop: invalid cond_block output size %lu", cond_block->GetOutputs().size());
         return 1;
     }

     TosaSerializationTensor* cond_output_tensor = cond_block->GetTensorByName(cond_block->GetOutputs()[0]);

     if (!cond_output_tensor)
     {
         WARNING("OpWhileLoop: fail to resolve cond_block's output tensor %s", cond_block->GetOutputs()[0].c_str());
         return 1;
     }

     if (cond_output_tensor->GetDtype() != DType_BOOL)
     {
         WARNING("OpWhileLoop: invalid cond_block's output tensor data type %s",
                 EnumNamesDType()[cond_output_tensor->GetDtype()]);
         return 1;
     }
     if (cond_output_tensor->GetShape().size() != 0)
     {
         WARNING("OpWhileLoop: invalid cond_block's output rank %lu", cond_output_tensor->GetShape().size());
         return 1;
     }

     return 0;
 }

 int OpWhileLoop::eval()
 {

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

     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++)
             {
                 if (getInputs()[i]->copyValueFrom(getOutputs()[i]))
                 {
                     WARNING("Fail to copy tensor value %s -> %s", getOutputs()[i]->getName().c_str(),
                             getInputs()[i]->getName().c_str());
                     return 1;
                 }
             }
         }
         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, 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(TosaSerializationHandler* tsh_, Op op_, uint64_t id_)
	: GraphNode(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 gt(block, tsh);

	if (gt.initializeGraph())
	{
	FATAL_ERROR("Unable to initialize graph traverser for block %s", block_name.c_str());
	}

	if (gt.linkTensorsAndNodes())
	{
	FATAL_ERROR("Failed to link tensors and nodes for block %s", block_name.c_str());
	}

	if (gt.validateGraph())
	{
	FATAL_ERROR("Failed to validate subgraph for block %s", block_name.c_str());
	}

	int num_input_tensors = gt.getNumInputTensors();
	int num_output_tensors = gt.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 = gt.getInputTensor(i);
	ASSERT_MSG(!tensor->is_allocated(), "block %s input tensors are unexpectedly initialized before",
	block_name.c_str());

	if (tensor->allocate())
	{
	WARNING("Fail to allocate tensor %s", tensor->getName().c_str());
	return 1;
	}

	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;
	}

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

	if (gt.evaluateAll())
	{
	FATAL_ERROR("Error evaluating network. Giving up.");
	}

	// 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 = gt.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)
	{
	gt.dumpGraph(g_func_debug.func_debug_file);
	FATAL_ERROR("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 = gt.getOutputTensor(i);
	ASSERT_MSG(tensor->is_allocated(), "tensor %s is not allocated", 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(TosaSerializationHandler* tsh_, TosaAttributeBase* attribute_, uint64_t id_)
	: OpControlFlow(tsh_, Op_COND_IF, id_)
	{
	INIT_ATTRIBUTE(CondIf);
	}

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

	int OpCondIf::checkTensorAttributes()
	{
	if (getInputs().size() < 1)
	{
	WARNING("OpCondIf: must have at least 1 operand");
	return 1;
	}

	if (inputs[0]->getDtype() != DType_BOOL \|\| inputs[0]->getRank() != 0)
	{
	WARNING("OpCondIf: invalid tensor dtype=%s, rank=%d", EnumNamesDType()[inputs[0]->getDtype()],
	inputs[0]->getRank());
	return 1;
	}

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

	then_block = tsh->GetBlockByName(attribute->then_branch());
	else_block = tsh->GetBlockByName(attribute->else_branch());

	if (!then_block)
	{
	WARNING("OpCondIf: fail to resolve then_branch %s", attribute->then_branch().c_str());
	return 1;
	}

	if (!else_block)
	{
	WARNING("OpCondIf: fail to resolve else_branch %s", attribute->else_branch().c_str());
	return 1;
	}

	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(TosaSerializationHandler* tsh_, TosaAttributeBase* attribute_, uint64_t id_)
	: OpControlFlow(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;
	}

	cond_block = tsh->GetBlockByName(attribute->cond_branch());
	body_block = tsh->GetBlockByName(attribute->body_branch());

	if (!cond_block)
	{
	WARNING("OpWhileLoop: fail to resolve cond_branch %s", attribute->cond_branch().c_str());
	return 1;
	}

	if (!body_block)
	{
	WARNING("OpWhileLoop: fail to resolve body_branch %s", attribute->body_branch().c_str());
	return 1;
	}

	if (cond_block->GetOutputs().size() != 1)
	{
	WARNING("OpWhileLoop: invalid cond_block output size %lu", cond_block->GetOutputs().size());
	return 1;
	}

	TosaSerializationTensor* cond_output_tensor = cond_block->GetTensorByName(cond_block->GetOutputs()[0]);

	if (!cond_output_tensor)
	{
	WARNING("OpWhileLoop: fail to resolve cond_block's output tensor %s", cond_block->GetOutputs()[0].c_str());
	return 1;
	}

	if (cond_output_tensor->GetDtype() != DType_BOOL)
	{
	WARNING("OpWhileLoop: invalid cond_block's output tensor data type %s",
	EnumNamesDType()[cond_output_tensor->GetDtype()]);
	return 1;
	}
	if (cond_output_tensor->GetShape().size() != 0)
	{
	WARNING("OpWhileLoop: invalid cond_block's output rank %lu", cond_output_tensor->GetShape().size());
	return 1;
	}

	return 0;
	}

	int OpWhileLoop::eval()
	{

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

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