Prevent unitialized variable use in grappler.

PiperOrigin-RevId: 399702928
Change-Id: Id7e75451fbff297692dfb687f60ea04b25c96b24

Author: mihaimaruseac

tensorflow/core/grappler/optimizers/auto_parallel.cc

@@ -152,7 +152,7 @@ Status AutoParallel::Initialize(const GrapplerItem& item) {
   TF_RETURN_IF_ERROR(ComputeTransitiveFanin(graph_, item.fetch, &train_nodes));
   LOG(INFO) << "Number of training nodes: " << train_nodes.size();
 
-  const NodeDef* dequeue_node;
+  const NodeDef* dequeue_node = nullptr;
   for (const auto& train_node : train_nodes) {
     if (IsDequeueOp(*train_node)) {
       dequeue_node = train_node;

tensorflow/core/grappler/optimizers/auto_parallel_test.cc

@@ -126,6 +126,30 @@ TEST_F(AutoParallelTest, SimpleParallel) {
   EXPECT_EQ("^AutoParallel-Control-Fetch", node_gradient.input(0));
 }
 
+TEST_F(AutoParallelTest, SimpleParallelNoDequeue) {
+  tensorflow::Scope s = tensorflow::Scope::DisabledShapeInferenceScope();
+  Output constant_a = ops::Const(s.WithOpName("constant_a"), 1.0f, {1});
+  Output constant_c = ops::Const(s.WithOpName("constant_c"), 1.0f, {1});
+  Output constant_b = ops::Const(s.WithOpName("constant_b"), 1, {1});
+  Output var = ops::Variable(s.WithOpName("var"), {1}, DT_FLOAT);
+  Output assign = ops::Assign(s.WithOpName("assign"), {var}, {constant_a});
+  Output add = ops::AddN(s.WithOpName("add"), {constant_a, constant_c});
+  Output learning_rate = ops::Const(s.WithOpName("learning_rate"), 0.01f, {1});
+  Output apply_gradient = ops::ApplyGradientDescent(
+      s.WithOpName("apply_gradient"), {var}, {learning_rate}, {add});
+
+  GrapplerItem item;
+  item.init_ops.push_back("assign");
+  item.fetch.push_back("apply_gradient");
+  item.init_ops.push_back("assign");
+  TF_CHECK_OK(s.ToGraphDef(&item.graph));
+
+  AutoParallel parallel(2);
+  GraphDef output;
+  Status status = parallel.Optimize(nullptr, item, &output);
+  TF_EXPECT_OK(status);
+}
+
 }  // namespace
 }  // namespace grappler
 }  // namespace tensorflow