Add test for pruning of unused outputs in while_v2.

PiperOrigin-RevId: 255286547

tensorflow/python/kernel_tests/BUILD

@@ -3844,6 +3844,7 @@ cuda_py_test(
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:constant_op",
         "//tensorflow/python:control_flow_ops",
+        "//tensorflow/python:control_flow_util",
         "//tensorflow/python:dtypes",
         "//tensorflow/python:framework",
         "//tensorflow/python:framework_ops",

tensorflow/python/kernel_tests/while_v2_test.py

@@ -24,6 +24,8 @@ from tensorflow.core.protobuf import config_pb2
 from tensorflow.core.protobuf import rewriter_config_pb2
 from tensorflow.python.eager import backprop
 from tensorflow.python.eager import context
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import control_flow_util
 from tensorflow.python.eager import def_function
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
@@ -38,7 +40,6 @@ from tensorflow.python.ops import map_fn
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import variables
 from tensorflow.python.ops import while_v2
-from tensorflow.python.ops.control_flow_ops import while_loop as while_loop_v1
 from tensorflow.python.ops.while_v2 import while_loop as while_loop_v2
 from tensorflow.python.platform import test
 
@@ -51,7 +52,7 @@ class WhileV2Test(test.TestCase, parameterized.TestCase):
     ret = while_loop_v2(
         lambda v: v < 8., lambda v: v * v, [x], return_same_structure=False)
     grad = gradients_impl.gradients(ret, [x])
-    with self.cached_session() as sess:
+    with self.cached_session():
       self.assertEqual(self.evaluate(ret), 16.)
       self.assertSequenceEqual(self.evaluate(grad), [32.])
 
@@ -131,7 +132,7 @@ class WhileV2Test(test.TestCase, parameterized.TestCase):
 
     # Note: This is simply d_ret[0]/d_x since d_ret[1]/d_x is 0.
     grad = gradients_impl.gradients(ret, [x])  # [2*x*y]
-    with self.cached_session() as sess:
+    with self.cached_session():
       self.assertSequenceEqual(self.evaluate(ret), [45., 3.])
       self.assertSequenceEqual(self.evaluate(grad), [9.])
 
@@ -157,7 +158,7 @@ class WhileV2Test(test.TestCase, parameterized.TestCase):
     grady_0 = gradients_impl.gradients(ret[0], [y])  # [2*x*y + x**2]
     grady_1 = gradients_impl.gradients(ret[1], [y])  # [x + 1]
     grady_2 = gradients_impl.gradients(ret, [y])  # [2*x*y + x**2 + x + 1]
-    with self.cached_session() as sess:
+    with self.cached_session():
       self.assertSequenceEqual(self.evaluate(ret), [120., 23.])
       self.assertSequenceEqual(self.evaluate(gradx_0), [39.])
       self.assertSequenceEqual(self.evaluate(gradx_1), [4.])
@@ -189,7 +190,7 @@ class WhileV2Test(test.TestCase, parameterized.TestCase):
         return_same_structure=False)  # x**4
     grad = gradients_impl.gradients(ret2, [x])  # 4x**3
     grad_grad = gradients_impl.gradients(grad, [x])  # 12x**2
-    with self.cached_session() as sess:
+    with self.cached_session():
       self.assertSequenceEqual(self.evaluate(grad), [32.])
       self.assertSequenceEqual(self.evaluate(grad_grad), [48.])
 
@@ -201,13 +202,12 @@ class WhileV2Test(test.TestCase, parameterized.TestCase):
         return_same_structure=False)  # x**4
     grad = gradients_impl.gradients(ret, [x])  # 4x**3
     grad_grad = gradients_impl.gradients(grad, [x])  # 12x**2
-    with self.cached_session() as sess:
+    with self.cached_session():
       self.assertEqual(self.evaluate(ret), 16.)
       self.assertSequenceEqual(self.evaluate(grad), [32.])
       self.assertSequenceEqual(self.evaluate(grad_grad), [48.])
 
-  @test_util.run_v1_only("b/120545219")
+  def _testPruning(self):
-  def testPruning(self):
     x = constant_op.constant(1)
 
     tensor_list = list_ops.empty_tensor_list(
@@ -220,7 +220,7 @@ class WhileV2Test(test.TestCase, parameterized.TestCase):
     def Body(x, tl):
       return x + 1, list_ops.tensor_list_push_back(tl, x)
 
-    outputs = while_loop_v1(Cond, Body, [x, tensor_list])
+    outputs = control_flow_ops.while_loop(Cond, Body, [x, tensor_list])
 
     train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP)
     train_op.append(outputs[0])
@@ -235,12 +235,37 @@ class WhileV2Test(test.TestCase, parameterized.TestCase):
       return tf_optimizer.OptimizeGraph(config, mg)
 
     g = GetOptimizedGraph()
-    self.assertEqual(len([n for n in g.node if n.op == "Enter"]), 1)
+    # TODO(b/136034023): while_v2 adds an extra loop_counter which is not pruned
+    # away, causing an extra Enter node.
+    enter_count = 2 if control_flow_util.ENABLE_CONTROL_FLOW_V2 else 1
+    self.assertLen([n for n in g.node if n.op == "Enter"], enter_count)
+    # Test that the TensorList is pruned out.
+    self.assertEmpty([
+        n for n in g.node if n.op == "Enter" and
+        n.attr["T"].type == dtypes.variant.as_datatype_enum
+    ])
 
     stack = list_ops.tensor_list_stack(outputs[1], element_dtype=x.dtype)
     train_op.append(stack)
     g = GetOptimizedGraph()
-    self.assertEqual(len([n for n in g.node if n.op == "Enter"]), 2)
+    # TODO(b/136034023): while_v2 adds an extra loop_counter which is not pruned
+    # away, causing an extra Enter node.
+    enter_count = 3 if control_flow_util.ENABLE_CONTROL_FLOW_V2 else 2
+    self.assertLen([n for n in g.node if n.op == "Enter"], enter_count)
+    # Test that the TensorList is not pruned out.
+    self.assertNotEmpty([
+        n for n in g.node if n.op == "Enter" and
+        n.attr["T"].type == dtypes.variant.as_datatype_enum
+    ])
+
+  @test_util.run_deprecated_v1
+  def testPruningV1(self):
+    self._testPruning()
+
+  @test_util.enable_control_flow_v2
+  @test_util.run_deprecated_v1
+  def testPruningV2(self):
+    self._testPruning()
 
   @test_util.run_deprecated_v1
   def testCaptureExternalTensorInCond(self):