eager: Rename in_eager_mode to executing_eagerly and get rid of in_graph_mode.

This is in preparation to introduce one public, stable symbol: tf.executing_eagerly()
(i.e., part of moving APIs related to eager execution from "contrib" to a namespace
where we provide API stability guarantees)

PiperOrigin-RevId: 188212646

1 files changed, 4 insertions, 4 deletions

diff --git a/tensorflow/python/ops/math_grad.py b/tensorflow/python/ops/math_grad.py
index 55dd0c0e0d..e2ee9e4fe4 100644
--- a/tensorflow/python/ops/math_grad.py
+++ b/tensorflow/python/ops/math_grad.py
@@ -52,14 +52,14 @@ def _SumGrad(op, grad):
     if axes is not None:
       rank = len(input_0_shape)
       if np.array_equal(axes, np.arange(rank)):  # Reduce all dims.
-        if context.in_graph_mode():
-          new_shape = [1] * rank
-        else:
+        if context.executing_eagerly():
           ctx = context.context()
           new_shape = ctx.ones_rank_cache().get(rank)
           if new_shape is None:
             new_shape = constant_op.constant([1] * rank, dtype=dtypes.int32)
             ctx.ones_rank_cache().put(rank, new_shape)
+        else:
+          new_shape = [1] * rank
         grad = array_ops.reshape(grad, new_shape)
         # If shape is not fully defined (but rank is), we use Shape.
         if None not in input_0_shape:
@@ -997,7 +997,7 @@ def _SparseMatMulGrad(op, grad):
       op.inputs[0]: op.get_attr("a_is_sparse"),
       op.inputs[1]: op.get_attr("b_is_sparse"),
       # Use heuristic to figure out if grad might be sparse
-      grad: context.in_graph_mode() and (grad.op.type == "ReluGrad")
+      grad: not context.executing_eagerly() and (grad.op.type == "ReluGrad")
   }
 
   def _SparseMatMul(t1, t2, out_dtype, transpose_a=False, transpose_b=False):