| |
| // 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(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); |
| |
| 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); |
| |
| then_block = tsh->GetBlockByName(attribute->then_branch()); |
| else_block = tsh->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; |
| } |
| |
| cond_block = tsh->GetBlockByName(attribute->cond_branch()); |
| body_block = tsh->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++) |
| { |
| 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(); |
| } |