Remove explicit tape watching in backprop tests.

PiperOrigin-RevId: 210956690

1 files changed, 2 insertions, 7 deletions

diff --git a/tensorflow/python/eager/backprop_test.py b/tensorflow/python/eager/backprop_test.py
index 3d3f54b9c4..45f2d0d6ac 100644
--- a/tensorflow/python/eager/backprop_test.py
+++ b/tensorflow/python/eager/backprop_test.py
@@ -23,7 +23,6 @@ import numpy as np
 from tensorflow.python import pywrap_tensorflow
 from tensorflow.python.eager import backprop
 from tensorflow.python.eager import context
-from tensorflow.python.eager import tape
 from tensorflow.python.eager import test
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
@@ -87,7 +86,6 @@ class BackpropTest(test.TestCase):
         initial_value=constant_op.constant(1.0), name='x')
 
     def fn():
-      tape.watch_variable(x)
       b = constant_op.constant(2.0)
       c = math_ops.add(x.value(), b)
       return math_ops.add(c, constant_op.constant(3.0))
@@ -194,7 +192,6 @@ class BackpropTest(test.TestCase):
         initial_value=random_init, dtype=dtypes.float32, name='embedding')
 
     def f():
-      tape.watch_variable(embedding)
       embedded_x = embedding_ops.embedding_lookup(embedding, x)
       return constant_op.constant(1.0, dtypes.float32) - embedded_x
 
@@ -404,7 +401,6 @@ class BackpropTest(test.TestCase):
 
     def f():
       with context.device('gpu:0'):
-        tape.watch_variable(v)
         return v.read_value()
 
     self.assertEqual(
@@ -784,7 +780,6 @@ class BackpropTest(test.TestCase):
         initial_value=array_ops.constant([1.0]), name='x')
 
     def fn():
-      tape.watch_variable(x)
       a = math_ops.add(x.value(), 1.0)
       # Make sure convert_to_tensor works correctly with list of TensorNodes.
       b = array_ops.stack([a, a], axis=0)
@@ -931,12 +926,12 @@ class BackpropTest(test.TestCase):
         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:
+          with backprop.GradientTape() as tape:
             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))
+          return self.evaluate(tape.gradient(y, x))
 
       grad1 = get_grad()
       grad2 = get_grad()