Address comments

python/mxnet/ndarray/contrib.py

@@ -364,12 +364,12 @@ def _func_wrapper(loop_vars):
     return stacked_outputs, list(loop_vars)
 
 def ifelse(cond, then_func, else_func, inputs):
-    """Run a if-then-else using user-defined condition and computation
+    """Run an if-then-else using user-defined condition and computation
 
     This operator simulates a if-like branch which chooses to do one of
     the two customized computations according to the specified condition.
 
-    `inputs` is a list of NDArrays on which the condition and computations reply on.
+    `inputs` is a list of NDArrays on which the condition and computations rely on.
 
     `cond` is a user-defined function, used as the if condition.
     It consumes `inputs`, and produces a scalar MXNet NDArray,

python/mxnet/symbol/contrib.py

@@ -558,12 +558,12 @@ def _union_inputs(*graphs):
     return outputs, final_loop_vars
 
 def ifelse(cond, then_func, else_func, inputs, name="ifelse"):
-    """Run a if-then-else using user-defined condition and computation
+    """Run an if-then-else using user-defined condition and computation
 
     This operator simulates a if-like branch which chooses to do one of
     the two customized computations according to the specified condition.
 
-    `inputs` is a list of Symbols on which the condition and computations reply on.
+    `inputs` is a list of Symbols on which the condition and computations rely on.
 
     `cond` is a user-defined function, used as the if condition.
     It consumes `inputs`, and produces a scalar MXNet symbol,

src/operator/control_flow.cc

@@ -1117,6 +1117,7 @@ static bool IfelseShape(const nnvm::NodeAttrs& attrs,
                            params.then_input_locs, true);
   bool succ_2 = infer_subg(attrs.subgraphs[2], &else_out_shape, \
                            params.else_input_locs, true);
+  sync_out_out(&then_out_shape, &else_out_shape, is_udf);
   return succ_0 && succ_1 && succ_2;
 }
 

src/operator/subgraph_op_common.h

@@ -106,6 +106,19 @@ bool sync_in_in(const nnvm::Tuple<dim_t> &input_locs,
   return true;
 }
 
+template <typename T>
+bool sync_out_out(std::vector<T> *out_1,
+                  std::vector<T> *out_2,
+                  std::function<bool(const T &)> is_empty) {
+  CHECK_EQ(out_1->size(), out_2->size());
+  for (size_t i = 0; i < out_1->size(); ++i) {
+    T &x = out_1->at(i);
+    T &y = out_2->at(i);
+    fill_value(&x, &y, is_empty(x), is_empty(y));
+  }
+  return true;
+}
+
 /*
  * This contains the states for running a loop and provides methods
  * of running the subgraph computation for an iteration.