Add ERROR_IF to control flow ops.
Signed-off-by: Kevin Cheng <kevin.cheng@arm.com>
Change-Id: Ifd771171904d1e5a9db3ea1cae3ac9017e971c8c
diff --git a/reference_model/src/ops/control_flow.cc b/reference_model/src/ops/control_flow.cc
index 2446457..7105caf 100644
--- a/reference_model/src/ops/control_flow.cc
+++ b/reference_model/src/ops/control_flow.cc
@@ -37,16 +37,17 @@
DEBUG_MED(OP, "Evaluating block %s", block_name.c_str());
- SubgraphTraverser gt(block, tsh);
+ SubgraphTraverser block_sgt(block, tsh);
- ERROR_IF(gt.initializeGraph(), "Unable to initialize graph traverser for block %s", block_name.c_str());
+ 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());
- ERROR_IF(gt.linkTensorsAndNodes(), "Failed to link tensors and nodes for block %s", block_name.c_str());
-
- ERROR_IF(gt.validateGraph(), "Failed to validate subgraph for block %s", block_name.c_str());
-
- int num_input_tensors = gt.getNumInputTensors();
- int num_output_tensors = gt.getNumOutputTensors();
+ int num_input_tensors = block_sgt.getNumInputTensors();
+ int num_output_tensors = block_sgt.getNumOutputTensors();
for (size_t i = 0; i < block_inputs.size(); i++)
{
@@ -67,15 +68,9 @@
// 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;
- }
+ 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]))
{
@@ -91,18 +86,45 @@
{
if (gn->hasAllInputsReady() && !gn->getOnNextNodeList())
{
- gt.addToNextNodeList(gn);
+ block_sgt.addToNextNodeList(gn);
}
}
}
- ERROR_IF(gt.evaluateAll(), "Error evaluating network. Giving up.");
+ 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 = gt.getOutputTensor(i);
+ const TosaReference::Tensor* ct = block_sgt.getOutputTensor(i);
ASSERT_MEM(ct);
if (!ct->getIsValid())
{
@@ -116,7 +138,7 @@
}
if (!all_output_valid)
{
- gt.dumpGraph(g_func_debug.func_debug_file);
+ 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());
}
@@ -124,8 +146,9 @@
// 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());
+ 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))
{
@@ -150,18 +173,11 @@
int OpCondIf::checkTensorAttributes()
{
- if (getInputs().size() < 1)
- {
- WARNING("OpCondIf: must have at least 1 operand");
- return 1;
- }
+ ERROR_IF(getInputs().size() < 1, "OpCondIf: must have at least 1 operand");
- 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;
- }
+ 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);
@@ -169,16 +185,69 @@
then_block = tsh->GetBlockByName(attribute->then_branch());
else_block = tsh->GetBlockByName(attribute->else_branch());
- if (!then_block)
+ 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++)
{
- WARNING("OpCondIf: fail to resolve then_branch %s", attribute->then_branch().c_str());
- return 1;
+ 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");
+ }
}
- if (!else_block)
+ for (int32_t i = 0; i < num_output_tensor; i++)
{
- WARNING("OpCondIf: fail to resolve else_branch %s", attribute->else_branch().c_str());
- return 1;
+ 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;
@@ -241,43 +310,62 @@
cond_block = tsh->GetBlockByName(attribute->cond_branch());
body_block = tsh->GetBlockByName(attribute->body_branch());
- if (!cond_block)
+ 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++)
{
- WARNING("OpWhileLoop: fail to resolve cond_branch %s", attribute->cond_branch().c_str());
- return 1;
+ 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");
+ }
}
- 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;
- }
+ 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;
}
diff --git a/reference_model/src/subgraph_traverser.h b/reference_model/src/subgraph_traverser.h
index d53a4c0..8c66d73 100644
--- a/reference_model/src/subgraph_traverser.h
+++ b/reference_model/src/subgraph_traverser.h
@@ -61,6 +61,10 @@
int dumpNextNodeList(FILE* out) const;
int clearAllNodeMarkings();
+ std::string getBlockName() const
+ {
+ return block->GetName();
+ }
int getNumInputTensors() const;
Tensor* getInputTensor(const unsigned int idx) const;
Tensor* getInputTensorByName(const std::string name) const;