From df11cce2e600581087f29ef0b85286f7e582572d Mon Sep 17 00:00:00 2001
From: Tamara Norman <tamaranorman@google.com>
Date: Tue, 9 Oct 2018 09:18:53 -0700
Subject: Throw error when evaluating have variable target in GradientTape.

PiperOrigin-RevId: 216368178
---
 tensorflow/python/eager/backprop.py      |  9 ++++++++-
 tensorflow/python/eager/backprop_test.py | 12 +++++++++++-
 2 files changed, 19 insertions(+), 2 deletions(-)

diff --git a/tensorflow/python/eager/backprop.py b/tensorflow/python/eager/backprop.py
index deac29111f..44ce69ee60 100644
--- a/tensorflow/python/eager/backprop.py
+++ b/tensorflow/python/eager/backprop.py
@@ -868,6 +868,7 @@ class GradientTape(object):
     Raises:
       RuntimeError: if called inside the context of the tape, or if called more
        than once on a non-persistent tape.
+      ValueError: if called on variable target.
     """
     if self._tape is None:
       raise RuntimeError("GradientTape.gradient can only be called once on "
@@ -887,6 +888,12 @@ class GradientTape(object):
                             "gradient in order to compute higher order "
                             "derrivatives.", 1)
 
+    flat_targets = nest.flatten(target)
+    for t in flat_targets:
+      if resource_variable_ops.is_resource_variable(t):
+        raise ValueError("GradientTape.gradient is not supported for variable "
+                         "targets.")
+
     flat_sources = nest.flatten(sources)
     flat_sources = [_handle_or_self(x) for x in flat_sources]
 
@@ -896,7 +903,7 @@ class GradientTape(object):
 
     flat_grad = imperative_grad.imperative_grad(
         self._tape,
-        nest.flatten(target),
+        flat_targets,
         flat_sources,
         output_gradients=output_gradients)
 
diff --git a/tensorflow/python/eager/backprop_test.py b/tensorflow/python/eager/backprop_test.py
index 32731747b7..7e5c9f3cb6 100644
--- a/tensorflow/python/eager/backprop_test.py
+++ b/tensorflow/python/eager/backprop_test.py
@@ -548,6 +548,17 @@ class BackpropTest(test.TestCase):
     grad = g.gradient(y, [x])[0]
     self.assertEqual(self.evaluate(grad), 6.0)
 
+  @test_util.assert_no_new_tensors
+  @test_util.run_in_graph_and_eager_modes
+  def testGadientTapeCalledOnConstantTarget(self):
+    with backprop.GradientTape() as g:
+      x = variables.Variable([3.0])
+      y = variables.Variable([2.0])
+    with self.assertRaisesRegexp(
+        ValueError,
+        'GradientTape.gradient is not supported for variable targets.'):
+      g.gradient(x, y)
+
   @test_util.run_in_graph_and_eager_modes
   def testGradientTapeWithCond(self):
     x = constant_op.constant(3.0)
@@ -982,7 +993,6 @@ class BackpropTest(test.TestCase):
     self.assertIsNone(dy)
     self.assertEqual(self.evaluate(dz), 3.0)
 
-
   @test_util.run_in_graph_and_eager_modes
   def testDifferentiatingScalarCache(self):
     # In the following test, if x2 = x1 (i.e the objects are the exact same),
-- 
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