From 5d00c69051bef9f27b60ba136c0efc49a45bf8e6 Mon Sep 17 00:00:00 2001 From: Kevin Cheng Date: Fri, 15 Oct 2021 20:06:00 +0000 Subject: Add ERROR_IF to control flow ops. Signed-off-by: Kevin Cheng Change-Id: Ifd771171904d1e5a9db3ea1cae3ac9017e971c8c --- reference_model/src/ops/control_flow.cc | 236 +++++++++++++++++++++---------- reference_model/src/subgraph_traverser.h | 4 + 2 files changed, 166 insertions(+), 74 deletions(-) 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 @@ int OpControlFlow::evalBlock(TosaSerializationBasicBlock* block, 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 @@ int OpControlFlow::evalBlock(TosaSerializationBasicBlock* block, // 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 @@ int OpControlFlow::evalBlock(TosaSerializationBasicBlock* block, { 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 @@ int OpControlFlow::evalBlock(TosaSerializationBasicBlock* block, } 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 @@ int OpControlFlow::evalBlock(TosaSerializationBasicBlock* block, // 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 @@ OpCondIf::~OpCondIf() 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*>(inputs[0]); ASSERT_MEM(cond); @@ -169,16 +185,69 @@ int OpCondIf::checkTensorAttributes() 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 @@ int OpWhileLoop::checkTensorAttributes() 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) + 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: 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; + 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 (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 @@ public: 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; 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