Changes convert_stack's return type from list to tuple to make the value hashable.

PiperOrigin-RevId: 259392206

tensorflow/python/debug/lib/session_debug_testlib.py

@@ -1462,14 +1462,14 @@ class SessionDebugTestBase(test_util.TensorFlowTestCase):
 
       # Lookup should work with node name input.
       traceback = dump.node_traceback("traceback/w")
-      self.assertIsInstance(traceback, list)
+      self.assertIsInstance(traceback, tuple)
       self.assertGreater(len(traceback), 0)
       for trace in traceback:
         self.assertIsInstance(trace, tuple)
 
       # Lookup should also work with tensor name input.
       traceback = dump.node_traceback("traceback/w:0")
-      self.assertIsInstance(traceback, list)
+      self.assertIsInstance(traceback, tuple)
       self.assertGreater(len(traceback), 0)
       for trace in traceback:
         self.assertIsInstance(trace, tuple)

tensorflow/python/distribute/mirrored_strategy_test.py

@@ -49,6 +49,7 @@ from tensorflow.python.framework import tensor_util
 from tensorflow.python.keras.engine import training as keras_training
 from tensorflow.python.keras.layers import core as keras_core
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import gradients
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables
@@ -1203,6 +1204,24 @@ class MultiWorkerMirroredStrategyTestWithChief(
     self._test_summary_for_replica_zero_only(strategy)
 
 
+class MirroredVariableStopGradientTest(test.TestCase, parameterized.TestCase):
+
+  @combinations.generate(
+      combinations.combine(
+          distribution=[
+              strategy_combinations.mirrored_strategy_with_one_cpu,
+              strategy_combinations.mirrored_strategy_with_one_gpu,
+          ],
+          mode=["graph"]))
+  def testMirroredVariableAsStopGradient(self, distribution):
+    with distribution.scope():
+      inp = constant_op.constant(1.0)
+      x = variables.Variable(1.0)
+      y = inp*x
+      grads = gradients.gradients(x, y, stop_gradients=x)
+      self.assertIsNone(grads[0])
+
+
 def _replica_id():
   replica_id = ds_context.get_replica_context().replica_id_in_sync_group
   if not isinstance(replica_id, ops.Tensor):

tensorflow/python/util/tf_stack.py

@@ -199,21 +199,22 @@ def convert_stack(stack, include_func_start_lineno=False):
       included as the 5th entry in return tuples.
 
   Returns:
-    A list of n 4-tuples or 5-tuples
+    A tuple of n 4-tuples or 5-tuples
     (filename, lineno, name, code, [optional: func_start_lineno]), where the
     code tuple element is calculated from the corresponding elements of the
     input tuple.
   """
-  ret = []
-  for (filename, lineno, name, frame_globals, func_start_lineno) in stack:
-    linecache.checkcache(filename)
-    line = linecache.getline(filename, lineno, frame_globals)
-    if line:
-      line = line.strip()
-    else:
-      line = None
-    if include_func_start_lineno:
-      ret.append((filename, lineno, name, line, func_start_lineno))
-    else:
-      ret.append((filename, lineno, name, line))
-  return ret
+  def _tuple_generator():  # pylint: disable=missing-docstring
+    for (filename, lineno, name, frame_globals, func_start_lineno) in stack:
+      linecache.checkcache(filename)
+      line = linecache.getline(filename, lineno, frame_globals)
+      if line:
+        line = line.strip()
+      else:
+        line = None
+      if include_func_start_lineno:
+        yield (filename, lineno, name, line, func_start_lineno)
+      else:
+        yield (filename, lineno, name, line)
+
+  return tuple(_tuple_generator())