Add error if calling jacobian or batch_jacobian on an exhausted tape.

Currently, this is failing silently: returns None.

PiperOrigin-RevId: 324700276
Change-Id: If5b4fc76bc3bfd2280ca67395015aca5bcf62f91

tensorflow/python/eager/backprop.py

@@ -997,6 +997,9 @@ class GradientTape(object):
                unconnected_gradients=UnconnectedGradients.NONE):
     """Computes the gradient using operations recorded in context of this tape.
 
+    Note: Unless you set `persistent=True` a GradientTape can only be used to
+    compute one set of gradients (or jacobians).
+
     Args:
       target: a list or nested structure of Tensors or Variables to be
         differentiated.
@@ -1015,14 +1018,14 @@ class GradientTape(object):
       the structure of `sources`.
 
     Raises:
-      RuntimeError: if called inside the context of the tape, or if called more
-       than once on a non-persistent tape.
-      ValueError: if the target is a variable or if unconnected gradients is
+      RuntimeError: If called on a used, non-persistent tape.
+      RuntimeError: If called inside the context of the tape.
+      ValueError: If the target is a variable or if unconnected gradients is
        called with an unknown value.
     """
     if self._tape is None:
-      raise RuntimeError("GradientTape.gradient can only be called once on "
-                         "non-persistent tapes.")
+      raise RuntimeError("A non-persistent GradientTape can only be used to"
+                         "compute one set of gradients (or jacobians)")
     if self._recording:
       if not self._persistent:
         self._pop_tape()
@@ -1101,6 +1104,9 @@ class GradientTape(object):
                experimental_use_pfor=True):
     """Computes the jacobian using operations recorded in context of this tape.
 
+    Note: Unless you set `persistent=True` a GradientTape can only be used to
+    compute one set of gradients (or jacobians).
+
     See[wikipedia article](http://en.wikipedia.org/wiki/jacobian_matrix_and_determinant)
     for the definition of a Jacobian.
 
@@ -1139,10 +1145,15 @@ class GradientTape(object):
 
 
     Raises:
+      RuntimeError: If called on a used, non-persistent tape.
       RuntimeError: If called on a non-persistent tape with eager execution
         enabled and without enabling experimental_use_pfor.
       ValueError: If vectorization of jacobian computation fails.
     """
+    if self._tape is None:
+      raise RuntimeError("A non-persistent GradientTape can only be used to"
+                         "compute one set of gradients (or jacobians)")
+
     flat_sources = nest.flatten(sources)
     rewrap_as_ndarray = False
     if isinstance(target, np_arrays.ndarray):
@@ -1225,6 +1236,9 @@ class GradientTape(object):
     are lower dimensional and avoid a bunch of redundant zeros which would
     result in the jacobian computation given the independence assumption.
 
+    Note: Unless you set `persistent=True` a GradientTape can only be used to
+    compute one set of gradients (or jacobians).
+
     Example usage:
 
     ```python
@@ -1255,11 +1269,15 @@ class GradientTape(object):
       per-example jacobians.
 
     Raises:
+      RuntimeError: If called on a used, non-persistent tape.
       RuntimeError: If called on a non-persistent tape with eager execution
         enabled and without enabling experimental_use_pfor.
       ValueError: If vectorization of jacobian computation fails or if first
         dimension of `target` and `source` do not match.
     """
+    if self._tape is None:
+      raise RuntimeError("A non-persistent GradientTape can only be used to"
+                         "compute one set of gradients (or jacobians)")
     rewrap_as_ndarray = False
     if isinstance(target, np_arrays.ndarray):
       target = target.data

tensorflow/python/eager/backprop_test.py

@@ -837,9 +837,33 @@ class BackpropTest(test.TestCase, parameterized.TestCase):
       z = y * y
     g.gradient(z, [x])
     with self.assertRaisesRegex(
-        RuntimeError, 'GradientTape.gradient can only be called once'):
+        RuntimeError, 'A non-persistent GradientTape can only'):
       g.gradient(y, [x])
 
+  @test_util.assert_no_new_tensors
+  def testGradientTapeJacobianCalledMultipleTimes(self):
+    with backprop.GradientTape() as g:
+      x = constant_op.constant(3.0)
+      g.watch(x)
+      y = x * x
+      z = y * y
+    g.jacobian(z, [x])
+    with self.assertRaisesRegex(
+        RuntimeError, 'A non-persistent GradientTape can only'):
+      g.jacobian(y, [x])
+
+  @test_util.assert_no_new_tensors
+  def testGradientTapeBatchJacobianCalledMultipleTimes(self):
+    with backprop.GradientTape() as g:
+      x = constant_op.constant([[3.0]])
+      g.watch(x)
+      y = x * x
+      z = y * y
+    g.batch_jacobian(z, x)
+    with self.assertRaisesRegex(
+        RuntimeError, 'A non-persistent GradientTape can only'):
+      g.batch_jacobian(y, [x])
+
   @test_util.assert_no_new_tensors
   @test_util.run_in_graph_and_eager_modes
   @test_util.run_v1_only('b/120545219')