Remove While op stuff (pytorch#10102)

Summary:
Pull Request resolved: pytorch#10102

these codepaths are unused, deleting them

Reviewed By: yinghai

Differential Revision: D9109764

fbshipit-source-id: 8ace42a399806632bfbcada96b383268f0a8ae89

Author: bwasti

caffe2/opt/converter.cc

@@ -270,145 +270,6 @@ std::unique_ptr<repr::NeuralNetOperator> convertToNeuralNetOperator(
   return nnOp;
 }
 
-void handleWhileOp(
-    repr::NNGraph& dfg,
-    repr::NNCFGraph& cfg,
-    repr::NNGraph::NodeRef& opNode,
-    repr::NNCFGraph::NodeRef& bbNode,
-    OperatorDef& op,
-    std::unordered_map<std::string, repr::NNGraph::NodeRef>& blobMap
-) {
-  opNode->resetData(util::make_unique<repr::While>());
-  auto argMap = Converter::getArgumentsFromOperator(op);
-  std::string bodyNetSerialized = argMap["body"].s();
-  auto bodyNet = caffe2::NetDef();
-  bodyNet.ParseFromString(bodyNetSerialized);
-
-  std::unordered_map<std::string, repr::NNGraph::NodeRef> bodyBlobMap;
-  auto bodyNN = convertToNNModule(bodyNet, &bodyBlobMap);
-  repr::NNGraph bodyGraph = std::move(bodyNN.dataFlow);
-  repr::NNCFGraph bodyCFGraph = std::move(bodyNN.controlFlow);
-
-  auto rev_sorted = algorithm::tarjans(&bodyGraph);
-
-  for (auto& k : bodyBlobMap) {
-    auto name = k.first;
-    if (blobMap.count(name)) {
-      auto oldNode = blobMap[name];
-      printf("Exit tensor %s is in the parent scope, inserting Phi node...\n", k.first.c_str());
-      auto phiNode = dfg.createNode(util::make_unique<repr::NNPhi>()); // NN variant of a Phi node
-      // Clone the operator.
-      auto tensor = dyn_cast<repr::NeuralNetData>(blobMap[name]->data().get());
-      auto* clonedTensor = tensor->clone();
-      auto phiOut = dfg.createNode(std::unique_ptr<repr::NeuralNetData>(clonedTensor));
-      dfg.createEdge(phiNode, phiOut);
-      dfg.createEdge(oldNode, phiNode);
-      dfg.createEdge(bodyBlobMap[name], phiNode);
-      blobMap[name] = phiOut;
-      for (auto& inEdge : opNode->getInEdges()) {
-        if (inEdge->tail() == oldNode) {
-          dfg.deleteEdge(inEdge);
-          dfg.createEdge(phiOut, opNode);
-        }
-      }
-    }
-  }
-
-  // Dependencies simply have no producers
-  std::unordered_map<repr::NNGraph::NodeRef, repr::NNGraph::NodeRef> inNodeMap;
-  for (auto& n : bodyGraph.getMutableNodes()) {
-    if (!isa<repr::NeuralNetData>(n->data())) { continue; }
-    if (n->getInEdges().size() == 0) {
-      auto name = dyn_cast<repr::NeuralNetData>(n->data().get())->getName();
-      // TODO(bwasti): this may be needed, depending on constraints
-      //assert(blobMap.count(name) != 0 && "Loop body takes undefined dependency.");
-      if (blobMap.count(name)) {
-        inNodeMap[n] = blobMap[name];
-      }
-    }
-  }
-
-  CAFFE_ENFORCE(rev_sorted.front().getNodes().size() == 1,
-      "More than one exit node.");
-  CAFFE_ENFORCE(rev_sorted.back().getNodes().size() == 1,
-      "More than one entry node.");
-
-  auto exit_tensor = *(rev_sorted.front().getNodes().begin());
-  CAFFE_ENFORCE(isa<repr::NeuralNetData>(exit_tensor->data()),
-      "Exit node is not a tensor.");
-
-  auto bodyNodes = bodyGraph.getMutableNodes();
-  auto bodyEdges = bodyGraph.getMutableEdges();
-
-  for (auto node : bodyNodes) {
-    bodyGraph.importNode(node, dfg);
-  }
-
-  for (auto edge : bodyEdges) {
-    bodyGraph.importEdge(edge, dfg);
-  }
-
-  // Merge all dependencies
-  for (auto node : dfg.getMutableNodes()) {
-    if (inNodeMap.count(node)) {
-      dfg.replaceNode(node, inNodeMap[node]);
-      dfg.deleteNode(node);
-    }
-  }
-
-  for (const auto& inEdge : opNode->getInEdges()) {
-    auto* inputData = dyn_cast<repr::NeuralNetData>(inEdge->tail()->data().get());
-    auto* exitData = dyn_cast<repr::NeuralNetData>(exit_tensor->data().get());
-    if (inputData->getName() == exitData->getName()) {
-      dfg.replaceNode(exit_tensor, inEdge->tail());
-      dfg.deleteNode(exit_tensor);
-    }
-  }
-
-  // CFG Handling
-  auto bodyCFNodes = bodyCFGraph.getMutableNodes();
-  auto bodyCFEdges = bodyCFGraph.getMutableEdges();
-
-  // Create a while loop CFG node.
-  auto whileBasicBlock = util::make_unique<repr::BasicBlockType<repr::NNGraph>>();
-  for (auto& inEdge : opNode->getInEdges()) {
-    auto node = inEdge->tail();
-    for (auto& parentInEdge : node->getInEdges()) {
-      auto parentNode = parentInEdge->tail();
-      if (isa<repr::Phi>(parentNode->data().get())) {
-        whileBasicBlock->pushInstructionNode(parentNode);
-      }
-    }
-  }
-  whileBasicBlock->pushInstructionNode(opNode);
-
-  auto whileCFNode = cfg.createNode(std::move(whileBasicBlock));
-  cfg.createEdge(bbNode, whileCFNode, 0);
-
-  // The true path executes the body of the loop, so we
-  // take that BB and point to it.
-  for (auto cfNode : bodyCFNodes) {
-    bodyCFGraph.importNode(cfNode, cfg);
-    // If the CFG node has no children, we loop back to the top of the
-    // while loop.
-    if (cfNode->getOutEdges().size() == 0) {
-      cfg.createEdge(cfNode, whileCFNode, 0);
-    }
-    // TODO check for a single entry point
-    if (cfNode->getInEdges().size() == 0) {
-      cfg.createEdge(whileCFNode, cfNode, 1);
-    }
-  }
-  for (auto cfEdge : bodyCFEdges) {
-    bodyCFGraph.importEdge(cfEdge, cfg);
-  }
-
-  // Now create the false case.
-  bbNode =
-    cfg.createNode(util::make_unique<repr::BasicBlockType<repr::NNGraph>>());
-  cfg.createEdge(whileCFNode, bbNode, -1);
-}
-
 
 /// \brief Ingest a caffe2 protobuf model and output an NNModule.
 /// \param net The caffe2 protobuf NetDef
@@ -455,13 +316,9 @@ repr::NNModule convertToNNModule(caffe2::NetDef &net, std::unordered_map<std::st
       blobMap[output] = tensorNode;
     }
 
-    if (op.type() == "While") {
-      handleWhileOp(dfg, cfg, opNode, bbNode, op, blobMap);
-    } else {
-      opNode->resetData(convertToNeuralNetOperator(op));
-      auto currentBasicBlock = bbNode->mutableData()->get();
-      currentBasicBlock->pushInstructionNode(opNode);
-    }
+    opNode->resetData(convertToNeuralNetOperator(op));
+    auto currentBasicBlock = bbNode->mutableData()->get();
+    currentBasicBlock->pushInstructionNode(opNode);
   }
 
   repr::NNModule module;

caffe2/opt/converter_nomigraph_test.cc

@@ -48,65 +48,3 @@ TEST(Converter, UnknownType) {
   auto new_netdef = caffe2::convertToCaffe2Proto(nn);
 }
 
-/* Temporarily disabled While conversion tests
-TEST(Converter, While) {
-  caffe2::NetDef net;
-
-  caffe2::OperatorDef *def = net.add_op();
-  def->set_type("While");
-  def->add_input("X");
-
-  caffe2::NetDef body_net;
-  {
-    caffe2::OperatorDef *rdef = body_net.add_op();
-    rdef->set_type("Relu");
-    rdef->add_input("X");
-    rdef->add_output("X");
-  }
-  std::string body_net_serialized;
-  assert(body_net.SerializeToString(&body_net_serialized));
-  ADD_ARG(def, "body", s, body_net_serialized);
-
-  auto nn = caffe2::convertToNNModule(net);
-}
-
-TEST(Converter, ComplexWhile) {
-  caffe2::NetDef net;
-
-  {
-    caffe2::OperatorDef *rdef = net.add_op();
-    rdef->set_type("Relu");
-    rdef->add_input("X");
-    rdef->add_output("X");
-  }
-
-  caffe2::OperatorDef *def = net.add_op();
-  def->set_type("While");
-  def->add_input("X");
-
-  caffe2::NetDef body_net;
-  {
-    caffe2::OperatorDef *rdef = body_net.add_op();
-    rdef->set_type("Instr1");
-    rdef->add_input("X");
-    rdef->add_output("X");
-  }
-  {
-    caffe2::OperatorDef *rdef = body_net.add_op();
-    rdef->set_type("Instr2");
-    rdef->add_input("X");
-    rdef->add_output("X");
-  }
-  {
-    caffe2::OperatorDef *rdef = body_net.add_op();
-    rdef->set_type("Instr3");
-    rdef->add_input("X");
-    rdef->add_output("X");
-  }
-  std::string body_net_serialized;
-  assert(body_net.SerializeToString(&body_net_serialized));
-  ADD_ARG(def, "body", s, body_net_serialized);
-
-  auto nn = caffe2::convertToNNModule(net);
-}
-*/