Emit StatelessWhile for gradient while op if the cond and body functions are stateless.

We already emit the StatelessWhile op for the forward pass.

PiperOrigin-RevId: 290967707
Change-Id: I6d22ce36874f4191131e55902fb4404ebb3402a2

tensorflow/python/kernel_tests/while_v2_test.py

@@ -305,6 +305,34 @@ class WhileV2Test(test.TestCase, parameterized.TestCase):
     self.assertEmpty(while_1.control_inputs)
     self.assertEmpty(while_2.control_inputs)
 
+  def testMultipleWhileLoopsGradStateless(self):
+
+    @def_function.function
+    def Fn():
+      x = constant_op.constant(2.)
+      with backprop.GradientTape() as tape:
+        tape.watch(x)
+        ret1 = while_loop_v2(
+            lambda v: v < 4.,
+            lambda v: v * v, [x],
+            return_same_structure=False,
+            name="while_1")  # x**2
+        ret2 = while_loop_v2(
+            lambda v: v < 16.,
+            lambda v: v * v, [x],
+            return_same_structure=False,
+            name="while_2")  # x**4
+        loss = ret1 + ret2
+      return tape.gradient(loss, x)
+
+    graph = Fn.get_concrete_function().graph
+    while_ops = [op for op in graph.get_operations() if "While" in op.type]
+    self.assertAllEqual([op.type for op in while_ops], ["StatelessWhile"] * 4,
+                        "Must have exactly 4 StatelessWhile ops.")
+    for op in while_ops:
+      self.assertEmpty(op.control_inputs,
+                       "{} should not have any control inputs".format(op.name))
+
   def testMultipleWhileLoopsWithDeps(self):
     x = variables.Variable(2.)
     c = constant_op.constant(2.)

tensorflow/python/ops/while_v2.py

@@ -207,8 +207,6 @@ def while_loop(cond,
       num_cond_captures = len(cond_graph.external_captures)
       assert (cond_graph.external_captures ==
               body_graph.external_captures[:num_cond_captures])
-      cond_graph_captures = object_identity.ObjectIdentitySet(
-          cond_graph.external_captures)
       _duplicate_body_captures_in_cond(
           cond_graph, body_graph.external_captures[num_cond_captures:])
 
@@ -266,21 +264,10 @@ def while_loop(cond,
       output_shapes[orig_loop_vars_range] = nest.flatten(
           shape_invariants, expand_composites=True)[orig_loop_vars_range]
 
-      cond_stateful_ops = [
-          op for op in cond_graph.get_operations() if op._is_stateful
-      ]
-      body_stateful_ops = [
-          op for op in body_graph.get_operations() if op._is_stateful
-      ]
-      if (cond_stateful_ops or body_stateful_ops):
-        op_fn = gen_functional_ops._while
-      else:
-        op_fn = gen_functional_ops.stateless_while
-
-      outputs = op_fn(
+      outputs = _build_while_op(
           flattened_loop_vars,
-          util.create_new_tf_function(cond_graph),
-          util.create_new_tf_function(body_graph),
+          cond_graph,
+          body_graph,
           output_shapes=output_shapes,
           parallel_iterations=parallel_iterations,
           name=scope)
@@ -406,10 +393,10 @@ def _WhileGrad(op, *grads):  # pylint: disable=invalid-name
 
   _check_num_inputs_outputs(cond_grad_graph, body_grad_graph, len(loop_vars))
 
-  outputs = gen_functional_ops._while(
+  outputs = _build_while_op(
       loop_vars,
-      util.create_new_tf_function(cond_grad_graph),
-      util.create_new_tf_function(body_grad_graph),
+      cond_grad_graph,
+      body_grad_graph,
       output_shapes=[t.shape for t in body_grad_graph.outputs],
       parallel_iterations=parallel_iterations,
       name="%s_grad" % while_op.name)
@@ -424,6 +411,29 @@ def _WhileGrad(op, *grads):  # pylint: disable=invalid-name
   return _get_structured_grad_output(outputs, grads, body_grad_graph)
 
 
+def _build_while_op(loop_vars, cond_graph, body_graph, output_shapes,
+                    parallel_iterations, name):
+  """Builds the functional StatelessWhile/While op."""
+  cond_stateful_ops = [
+      op for op in cond_graph.get_operations() if op._is_stateful
+  ]
+  body_stateful_ops = [
+      op for op in body_graph.get_operations() if op._is_stateful
+  ]
+  if (cond_stateful_ops or body_stateful_ops):
+    op_fn = gen_functional_ops._while
+  else:
+    op_fn = gen_functional_ops.stateless_while
+
+  return op_fn(
+      loop_vars,
+      util.create_new_tf_function(cond_graph),
+      util.create_new_tf_function(body_graph),
+      output_shapes=output_shapes,
+      parallel_iterations=parallel_iterations,
+      name=name)
+
+
 def _get_intermediates(func_graph):
   """Returns all tensors in `func_graph` that should be accumulated."""
   # We currently accumulate output tensors of most ops in the function and rely