diff --git a/tensorflow/python/eager/forwardprop_test.py b/tensorflow/python/eager/forwardprop_test.py
index dd0bad30cb8..4a1156f534f 100644
--- a/tensorflow/python/eager/forwardprop_test.py
+++ b/tensorflow/python/eager/forwardprop_test.py
@@ -90,6 +90,26 @@ def _jacfwd(f, primals):
   return nest.pack_sequence_as(primals, jac_flat)
 
 
+def _jvp_batch(f, primal, tangents):
+  tf_function = def_function.function(f)
+
+  return control_flow_ops.vectorized_map(
+      functools.partial(_jvp, tf_function, primal), tangents)
+
+
+def _jvp_batch_matmul(f, primals, tangent_batch):
+  """Compute the jacobian of `f` at `primals` multiplied by `tangents`."""
+  jac_fwd = _jacfwd(f, primals)
+
+  def jac_mul(tangent):
+    flat_tangent = array_ops.reshape(tangent, shape=[-1])
+    tangent_vector = array_ops.expand_dims(flat_tangent, 1)
+    jvp_vector = math_ops.matmul(jac_fwd, tangent_vector)
+    return array_ops.reshape(jvp_vector, tangent.shape)
+
+  return control_flow_ops.vectorized_map(jac_mul, tangent_batch)
+
+
 def _grad(f, argnums=0):
   """Return a function which computes the gradient of `f`."""
 
@@ -941,6 +961,18 @@ class HessianTests(test.TestCase, parameterized.TestCase):
     self.assertAllClose(hess_value, hessian_pfor)
 
 
+class JacobianTests(test.TestCase, parameterized.TestCase):
+
+  @parameterized.parameters([(math_ops.sin, (2, 3), 5),
+                             (math_ops.sin, (2, 3, 4), 10)])
+  def testJVPBatchCorrectness(self, f, primal_shape, batch_size):
+    primals = [random_ops.random_uniform(primal_shape)]
+    tangent_batch = [random_ops.random_uniform([batch_size, *primal_shape])]
+    self.assertAllClose(
+        _jvp_batch(f, primals, tangent_batch)[1],
+        _jvp_batch_matmul(f, primals, *tangent_batch))
+
+
 if __name__ == "__main__":
   # TODO(allenl): Also test with 1.x-style graph mode.
   ops.enable_eager_execution()