Fix tape recording of attributes of vectorized ops

nest.flatten was mangling some attribute lists

Fixes #40895.

PiperOrigin-RevId: 339965951
Change-Id: Ibdbbb5a12b2d06a077b62902764ca4849a2b8d2a

tensorflow/python/eager/backprop_test.py

@@ -1772,6 +1772,31 @@ class JacobianTest(test.TestCase):
                           array_ops.reshape(def_function.function(f)(x), [-1]),
                           rtol=1e-3)
 
+  def test_grad_jacobian_conv(self):
+    def _inner(x):
+      kernel = array_ops.ones([3, 3, 1, 9])
+      with backprop.GradientTape() as tape:
+        tape.watch(x)
+        y = nn_ops.conv2d(x, kernel, strides=(1, 1), padding='SAME',
+                          data_format='NHWC')
+        reduced = math_ops.reduce_sum(y ** 2., axis=[2, 3])
+      return math_ops.reduce_sum(tape.batch_jacobian(reduced, x))
+
+    theoretical, numerical = gradient_checker_v2.compute_gradient(
+        def_function.function(_inner), [array_ops.ones([10, 4, 4, 1])])
+    self.assertAllClose(numerical, theoretical, rtol=1e-1)
+
+    @def_function.function
+    def _outer():
+      with backprop.GradientTape() as tape:
+        x = array_ops.ones([10, 4, 4, 1])
+        tape.watch(x)
+        y = _inner(x)
+      return tape.gradient(y, x)
+
+    self.assertAllClose(array_ops.reshape(numerical, [-1]),
+                        array_ops.reshape(_outer(), [-1]), rtol=1e-1)
+
   @test_util.run_in_graph_and_eager_modes
   def test_indexed_slices(self):
     with backprop.GradientTape(persistent=True) as g:

tensorflow/python/ops/parallel_for/pfor.py

@@ -1001,7 +1001,11 @@ def _create_op(op_type, inputs, op_dtypes, attrs=None):
   """Utility to create an op."""
   op = ops.get_default_graph().create_op(
       op_type, inputs, op_dtypes, attrs=attrs, compute_device=True)
-  flat_attrs = nest.flatten([(str(a), op.get_attr(str(a))) for a in attrs])
+  flat_attrs = []
+  # The tape expects an alternating flat list of names and attribute values.
+  for a in attrs:
+    flat_attrs.append(str(a))
+    flat_attrs.append(op.get_attr(str(a)))
   execute.record_gradient(op_type, op.inputs, tuple(flat_attrs), op.outputs[:])
   return op