Don't cache zero tensors in graph at all

PiperOrigin-RevId: 205885372

1 files changed, 17 insertions, 26 deletions

diff --git a/tensorflow/python/eager/backprop_test.py b/tensorflow/python/eager/backprop_test.py
index 95a3a8b629..3d3f54b9c4 100644
--- a/tensorflow/python/eager/backprop_test.py
+++ b/tensorflow/python/eager/backprop_test.py
@@ -925,32 +925,23 @@ class BackpropTest(test.TestCase):
         'did you forget to return a value from fn?'):
       val_and_grads_fn(x, y)
 
-  def testZerosCacheDoesntLeakAcrossModes(self):
-    with ops.Graph().as_default():
-      t = random_ops.random_normal(shape=[100, 2])
-      x = random_ops.random_normal(shape=[100, 4])
-      dy = random_ops.random_normal(shape=[100, 4])
-      with backprop.GradientTape() as gradient_tape:
-        gradient_tape.watch(x)
-        x1, _ = array_ops.split(x, num_or_size_splits=2, axis=1)
-        y1 = x1 ** 2.
-        y = array_ops.concat([y1, t], axis=1)
-
-      dx = gradient_tape.gradient(y, x, output_gradients=dy)
-      with self.test_session() as sess:
-        sess.run(variables.global_variables_initializer())
-        sess.run(dx)
-
-    t = random_ops.random_normal(shape=[100, 2])
-    x = random_ops.random_normal(shape=[100, 4])
-    dy = random_ops.random_normal(shape=[100, 4])
-    with backprop.GradientTape() as gradient_tape:
-      gradient_tape.watch(x)
-      x1, _ = array_ops.split(x, num_or_size_splits=2, axis=1)
-      y1 = x1 ** 2.
-      y = array_ops.concat([y1, t], axis=1)
-
-    dx = gradient_tape.gradient(y, x, output_gradients=dy)
+  def testZerosCacheDoesntLeakAcrossGraphs(self):
+    with context.graph_mode():
+      def get_grad():
+        with ops.Graph().as_default(), self.test_session():
+          t = constant_op.constant(1, dtype=dtypes.float32, shape=(10, 4))
+          x = constant_op.constant(2, dtype=dtypes.float32, shape=(10, 4))
+          with backprop.GradientTape() as gt:
+            tape.watch(x)
+            x1, _ = array_ops.split(x, num_or_size_splits=2, axis=1)
+            y1 = x1**2
+            y = array_ops.concat([y1, t], axis=1)
+          return self.evaluate(gt.gradient(y, x))
+
+      grad1 = get_grad()
+      grad2 = get_grad()
+
+      self.assertAllEqual(grad1, grad2)
 
 
 if __name__ == '__main__':