Automated rollback of change 141675118

Change: 142160746

2 files changed, 5 insertions, 129 deletions

diff --git a/tensorflow/core/graph/graph_partition.cc b/tensorflow/core/graph/graph_partition.cc
index 5f5e6a4b53..8e9eceb699 100644
--- a/tensorflow/core/graph/graph_partition.cc
+++ b/tensorflow/core/graph/graph_partition.cc
@@ -74,28 +74,6 @@ struct RecvInfo {
 typedef std::unordered_map<DupRecvKey, RecvInfo, DupRecvKeyHash, DupRecvKeyEq>
     DupRecvTable;
 
-struct DupControlKey {
-  int dst_node_id;      // Edge's dst node id
-  GraphDef* src_graph;  // Edge's src node is in this subgraph
-};
-
-struct DupControlKeyHash {
-  size_t operator()(const DupControlKey& k) const {
-    return Hash64(reinterpret_cast<const char*>(&k.src_graph),
-                  sizeof(k.src_graph), k.dst_node_id);
-  }
-};
-
-struct DupControlKeyEq {
-  bool operator()(const DupControlKey& x, const DupControlKey& y) const {
-    return (x.dst_node_id == y.dst_node_id) && (x.src_graph == y.src_graph);
-  }
-};
-
-typedef std::unordered_map<DupControlKey, NodeDef*, DupControlKeyHash,
-                           DupControlKeyEq>
-    DupControlTable;
-
 struct PairIntHash {
  public:
   std::size_t operator()(const std::pair<int, int>& x) const {
@@ -114,7 +92,6 @@ struct GraphInfo {
   MemoryTypeMap input_types;
   MemoryTypeMap output_types;
   std::vector<ControlFlowInfo> cf_info;
-  std::vector<int32> num_outgoing_control_edges;
 };
 
 DataType EdgeType(const Edge* e) {
@@ -540,12 +517,11 @@ Status AddControlLoop(const PartitionOptions& opts, Graph* g, const Node* src,
 // Build memory and device type info for every node in the graph.
 // TODO(yuanbyu): It might be simpler if we convert MemoryType to
 // DeviceType for the inputs/outputs of each node.
-Status BuildGraphInfo(const Graph& g, GraphInfo* info) {
-  info->device_types.resize(g.num_node_ids(), DEVICE_CPU);
-  info->num_outgoing_control_edges.resize(g.num_node_ids());
-
+Status BuildMemoryDeviceInfo(const Graph& g, GraphInfo* info) {
   MemoryTypeVector input_memory_types;
   MemoryTypeVector output_memory_types;
+
+  info->device_types.resize(g.num_node_ids(), DEVICE_CPU);
   for (const Node* node : g.nodes()) {
     if (!node->IsOp()) continue;  // Skip Sink/Source nodes.
 
@@ -568,14 +544,6 @@ Status BuildGraphInfo(const Graph& g, GraphInfo* info) {
     for (size_t i = 0; i < output_memory_types.size(); ++i) {
       info->output_types[{node_id, i}] = output_memory_types[i];
     }
-
-    int32 num_control_edges = 0;
-    for (const Edge* edge : node->out_edges()) {
-      if (edge->IsControlEdge()) {
-        ++num_control_edges;
-      }
-    }
-    info->num_outgoing_control_edges[node_id] = num_control_edges;
   }
   return Status::OK();
 }
@@ -851,13 +819,12 @@ Status Partition(const PartitionOptions& opts, Graph* g,
 
   // At this point, all the graph mutations have been done. Build memory
   // and device type info for every node and edge in the graph.
-  status = BuildGraphInfo(*g, &g_info);
+  status = BuildMemoryDeviceInfo(*g, &g_info);
   if (!status.ok()) return status;
 
   string dstp;
   std::vector<const Edge*> inputs;
   DupRecvTable dup_recv(3);
-  DupControlTable dup_control(3);
   // For a node dst, 'ref_recvs' remembers the recvs introduced by a ref
   // edge to dst. 'ref_control_inputs' remembers the inputs by a non-ref
   // edge to dst. We will add a control edge for every pair in
@@ -951,9 +918,7 @@ Status Partition(const PartitionOptions& opts, Graph* g,
       }
 
       // Check whether there is already a send/recv pair transferring
-      // the same tensor/control from src to the dst partition. This
-      // handles the dedup case when a single source in one partition
-      // going to multiple destinations in another partition.
+      // the same tensor/control from the src to dst partition.
       const bool on_host = IsDstInputOnHost(edge, g_info);
       DupRecvKey key{src->id(), edge->src_output(), dst_graph, on_host};
       auto iter = dup_recv.find(key);
@@ -978,22 +943,6 @@ Status Partition(const PartitionOptions& opts, Graph* g,
 
       NodeDefBuilder::NodeOut send_from;
       if (edge->IsControlEdge()) {
-        DupControlKey key{dst->id(), src_graph};
-        int32 num_control_edges = g_info.num_outgoing_control_edges[src->id()];
-        if (num_control_edges == 1) {
-          // Handle dedup of multiple control edges going from one partition
-          // to a single destination in another partition.
-          // Note: We require that src has only one outgoing control edge.
-          // This is to avoid non-equivalent changes to the graph when
-          // combinded with dedup of single-source-multi-destination.
-          auto iter = dup_control.find(key);
-          if (iter != dup_control.end()) {
-            // Note: This may cause start_time(src) > start_time(iter->second).
-            AddInput(iter->second, src->name(), Graph::kControlSlot);
-            continue;
-          }
-        }
-
         // Insert a dummy const node that will generate a tiny
         // data element to be sent from send to recv.
         VLOG(1) << "Send/Recv control: " << src->assigned_device_name() << "["
@@ -1007,9 +956,6 @@ Status Partition(const PartitionOptions& opts, Graph* g,
         }
         AddInput(dummy, src->name(), Graph::kControlSlot);
         send_from.Reset(dummy->name(), 0, DT_FLOAT);
-        if (num_control_edges == 1) {
-          dup_control[key] = dummy;
-        }
       } else {
         send_from.Reset(src->name(), edge->src_output(), EdgeType(edge));
       }
diff --git a/tensorflow/core/graph/graph_partition_test.cc b/tensorflow/core/graph/graph_partition_test.cc
index 3ab6b0ae36..d8322e6077 100644
--- a/tensorflow/core/graph/graph_partition_test.cc
+++ b/tensorflow/core/graph/graph_partition_test.cc
@@ -398,75 +398,5 @@ TEST_F(GraphPartitionTest, PartitionIncompleteGraph) {
   EXPECT_EQ(error::INVALID_ARGUMENT, status.code()) << status;
 }
 
-TEST_F(GraphPartitionTest, CrossDevice_MultiControl) {
-  using namespace ::tensorflow::ops;  // NOLINT(build/namespaces)
-  auto a1 = Input(in_.WithOpName("A1"));
-  auto a2 = Input(in_.WithOpName("A2"));
-  auto b1 = Input(in_.WithOpName("B1"));
-  Combine(
-      in_.WithOpName("B2").WithControlDependencies(a1).WithControlDependencies(
-          a2),
-      b1, b1);
-
-  Partition(ToGraphDef(), &partitions_);
-  EXPECT_EQ(2, partitions_.size());
-
-  string a = "/job:a/replica:0/task:0/cpu:0";
-  string b = "/job:a/replica:0/task:0/cpu:1";
-  a1 = Input(scope_a_.WithOpName("A1"));
-  a2 = Input(scope_a_.WithOpName("A2"));
-  auto c = Const(scope_a_.WithOpName("A1/_0")
-                     .WithControlDependencies(a1)
-                     .WithControlDependencies(a2),
-                 {});
-  _Send(scope_a_.WithOpName("A1/_1"), c, "edge_3_A1", a, 82, b);
-  ExpectMatchA();
-
-  auto recv =
-      _Recv(scope_b_.WithOpName("A1/_2"), DT_FLOAT, "edge_3_A1", a, 82, b);
-  auto id = Identity(scope_b_.WithOpName("A1/_3"), recv);
-  b1 = Input(scope_b_.WithOpName("B1"));
-  Combine(scope_b_.WithOpName("B2").WithControlDependencies(id), b1, b1);
-  ExpectMatchB();
-}
-
-TEST_F(GraphPartitionTest, CrossDevice_MultiControl_NoCombine) {
-  using namespace ::tensorflow::ops;  // NOLINT(build/namespaces)
-  auto a1 = Input(in_.WithOpName("A1"));
-  auto a2 = Input(in_.WithOpName("A2"));
-  auto b1 = Input(in_.WithOpName("B1"));
-  Combine(
-      in_.WithOpName("B2").WithControlDependencies(a1).WithControlDependencies(
-          a2),
-      b1, b1);
-  Combine(in_.WithOpName("B3").WithControlDependencies(a1), b1, b1);
-
-  Partition(ToGraphDef(), &partitions_);
-  EXPECT_EQ(2, partitions_.size());
-
-  string a = "/job:a/replica:0/task:0/cpu:0";
-  string b = "/job:a/replica:0/task:0/cpu:1";
-  a1 = Input(scope_a_.WithOpName("A1"));
-  a2 = Input(scope_a_.WithOpName("A2"));
-  auto c1 = Const(scope_a_.WithOpName("A1/_0").WithControlDependencies(a1), {});
-  _Send(scope_a_.WithOpName("A1/_1"), c1, "edge_3_A1", a, 82, b);
-  auto c2 = Const(scope_a_.WithOpName("A2/_4").WithControlDependencies(a2), {});
-  _Send(scope_a_.WithOpName("A2/_5"), c2, "edge_7_A2", a, 82, b);
-  ExpectMatchA();
-
-  auto recv =
-      _Recv(scope_b_.WithOpName("A1/_2"), DT_FLOAT, "edge_3_A1", a, 82, b);
-  auto id = Identity(scope_b_.WithOpName("A1/_3"), recv);
-  auto recv1 =
-      _Recv(scope_b_.WithOpName("A2/_6"), DT_FLOAT, "edge_7_A2", a, 82, b);
-  auto id1 = Identity(scope_b_.WithOpName("A2/_7"), recv1);
-  b1 = Input(scope_b_.WithOpName("B1"));
-  Combine(scope_b_.WithOpName("B2")
-              .WithControlDependencies(id)
-              .WithControlDependencies(id1),
-          b1, b1);
-  Combine(scope_b_.WithOpName("B3").WithControlDependencies(id), b1, b1);
-  ExpectMatchB();
-}
 }  // namespace
 }  // namespace tensorflow