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Diffstat (limited to 'reference_model/src/ops/control_flow.cc')
| -rw-r--r-- | reference_model/src/ops/control_flow.cc | 353 |
1 files changed, 353 insertions, 0 deletions
diff --git a/reference_model/src/ops/control_flow.cc b/reference_model/src/ops/control_flow.cc new file mode 100644 index 0000000..9d5db40 --- /dev/null +++ b/reference_model/src/ops/control_flow.cc @@ -0,0 +1,353 @@ + +// 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(); +} |