eager: Rename in_eager_mode to executing_eagerly and get rid of in_graph_mode.

This is in preparation to introduce one public, stable symbol: tf.executing_eagerly()
(i.e., part of moving APIs related to eager execution from "contrib" to a namespace
where we provide API stability guarantees)

PiperOrigin-RevId: 188212646

1 files changed, 50 insertions, 46 deletions

diff --git a/tensorflow/python/training/saver.py b/tensorflow/python/training/saver.py
index df3ccce63e..2ce57c4432 100644
--- a/tensorflow/python/training/saver.py
+++ b/tensorflow/python/training/saver.py
@@ -582,7 +582,20 @@ class BaseSaverBuilder(object):
             BaseSaverBuilder.OpListToDict(
                 list(var._gather_saveables_for_checkpoint().values())))
       else:
-        if context.in_graph_mode():
+        if context.executing_eagerly():
+          if not isinstance(var, resource_variable_ops.ResourceVariable):
+            raise ValueError(
+                "Can only save/restore ResourceVariables when eager execution "
+                "is enabled, type: %s." % type(var))
+          set_var = names_to_saveables.setdefault(var._shared_name, var)
+          if set_var is not var:
+            raise ValueError(
+                ("Two different ResourceVariable objects with the same "
+                 "shared_name '%s' were passed to the Saver. This likely means "
+                 "that they were created in different Graphs or isolation "
+                 "contexts, and may not be checkpointed together.") %
+                (var._shared_name,))
+        else:
           if convert_variable_to_tensor:
             if isinstance(var, resource_variable_ops.ResourceVariable):
               var = var._graph_element  # pylint: disable=protected-access
@@ -598,18 +611,6 @@ class BaseSaverBuilder(object):
             raise ValueError("At least two variables have the same name: %s" %
                              name)
           names_to_saveables[name] = var
-        else:
-          if not isinstance(var, resource_variable_ops.ResourceVariable):
-            raise ValueError("Can only save/restore ResourceVariable eager "
-                             "mode is enabled, type: %s." % type(var))
-          set_var = names_to_saveables.setdefault(var._shared_name, var)
-          if set_var is not var:
-            raise ValueError(
-                ("Two different ResourceVariable objects with the same "
-                 "shared_name '%s' were passed to the Saver. This likely means "
-                 "that they were created in different Graphs or isolation "
-                 "contexts, and may not be checkpointed together.") % (
-                     var._shared_name,))
 
       # pylint: enable=protected-access
     return names_to_saveables
@@ -671,7 +672,7 @@ class BaseSaverBuilder(object):
         # pylint: enable=protected-access
       else:
         # A variable or tensor.
-        if context.in_eager_mode():
+        if context.executing_eagerly():
           if not isinstance(op, resource_variable_ops.ResourceVariable):
             raise ValueError("Can only save/restore ResourceVariable eager "
                              "mode is enabled, type: %s." % type(op))
@@ -778,8 +779,10 @@ class BaseSaverBuilder(object):
                       build_save=True,
                       build_restore=True):
     """build() with option to only perform save and restore."""
-    if context.in_graph_mode() and (not build_save or not build_restore):
-      raise ValueError("Graph mode needs to build save and restore together.")
+    if not context.executing_eagerly() and (not build_save or
+                                            not build_restore):
+      raise ValueError("save and restore operations need to be built together "
+                       " when eager execution is not enabled.")
 
     saveables = self._ValidateAndSliceInputs(names_to_saveables)
     if max_to_keep is None:
@@ -816,22 +819,22 @@ class BaseSaverBuilder(object):
     # such usage model makes sense.
     #
     # assert restore_op.name.endswith("restore_all"), restore_op.name
-    if context.in_graph_mode():
+    if context.executing_eagerly():
+      # Store the tensor values to the tensor_names.
+      save_tensor_name = save_tensor.numpy() if build_save else ""
       return saver_pb2.SaverDef(
-          filename_tensor_name=filename_tensor.name,
-          save_tensor_name=save_tensor.name,
-          restore_op_name=restore_op.name,
+          filename_tensor_name=filename_tensor.numpy(),
+          save_tensor_name=save_tensor_name,
+          restore_op_name="",
           max_to_keep=max_to_keep,
           sharded=sharded,
           keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours,
           version=self._write_version)
     else:
-      # Store the tensor values to the tensor_names.
-      save_tensor_name = save_tensor.numpy() if build_save else ""
       return saver_pb2.SaverDef(
-          filename_tensor_name=filename_tensor.numpy(),
-          save_tensor_name=save_tensor_name,
-          restore_op_name="",
+          filename_tensor_name=filename_tensor.name,
+          save_tensor_name=save_tensor.name,
+          restore_op_name=restore_op.name,
           max_to_keep=max_to_keep,
           sharded=sharded,
           keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours,
@@ -1280,7 +1283,7 @@ class Saver(object):
       raise ValueError(
           "If `var_list` is provided then build cannot be deferred. "
           "Either set defer_build=False or var_list=None.")
-    if context.in_eager_mode() and var_list is None:
+    if context.executing_eagerly() and var_list is None:
       raise RuntimeError(
           "When eager execution is enabled, `var_list` must specify a list or "
           "dict of variables to save")
@@ -1301,10 +1304,10 @@ class Saver(object):
     self._filename = filename
     self._last_checkpoints = []
     self._checkpoints_to_be_deleted = []
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       self._next_checkpoint_time = (
           time.time() + self._keep_checkpoint_every_n_hours * 3600)
-    if not defer_build and context.in_graph_mode():
+    elif not defer_build:
       self.build()
     if self.saver_def:
       self._check_saver_def()
@@ -1312,7 +1315,7 @@ class Saver(object):
     self._save_relative_paths = save_relative_paths
 
   def build(self):
-    if context.in_eager_mode():
+    if context.executing_eagerly():
       raise RuntimeError("Use save/restore instead of build in eager mode.")
     self._build(self._filename, build_save=True, build_restore=True)
 
@@ -1322,12 +1325,12 @@ class Saver(object):
 
   def _build(self, checkpoint_path, build_save, build_restore):
     """Builds saver_def."""
-    if context.in_graph_mode():
+    if not context.executing_eagerly():
       if self._is_built:
         return
       self._is_built = True
 
-    if not self.saver_def or context.in_eager_mode():
+    if not self.saver_def or context.executing_eagerly():
       if self._builder is None:
         self._builder = BulkSaverBuilder(self._write_version)
 
@@ -1364,8 +1367,9 @@ class Saver(object):
           self.saver_def.restore_op_name, self._name)
 
     self._check_saver_def()
-    if context.in_graph_mode():  # Set in __init__ when executing eagerly.
+    if not context.executing_eagerly():
       # Updates next checkpoint time.
+      # Set in __init__ when executing eagerly.
       self._next_checkpoint_time = (
           time.time() + self.saver_def.keep_checkpoint_every_n_hours * 3600)
 
@@ -1373,7 +1377,7 @@ class Saver(object):
     if not isinstance(self.saver_def, saver_pb2.SaverDef):
       raise ValueError("saver_def must be a saver_pb2.SaverDef: %s" %
                        self.saver_def)
-    if context.in_graph_mode():
+    if not context.executing_eagerly():
       if not self.saver_def.save_tensor_name:
         raise ValueError("saver_def must specify the save_tensor_name: %s" %
                          str(self.saver_def))
@@ -1623,7 +1627,7 @@ class Saver(object):
       RuntimeError: If save and restore ops weren't built.
     """
     # pylint: enable=line-too-long
-    if not self._is_built and context.in_graph_mode():
+    if not self._is_built and not context.executing_eagerly():
       raise RuntimeError(
           "`build()` should be called before save if defer_build==True")
     if latest_filename is None:
@@ -1655,21 +1659,21 @@ class Saver(object):
             "'latest_filename' collides with 'save_path': '%s' and '%s'" %
             (latest_filename, save_path))
 
-    if (context.in_graph_mode() and
+    if (not context.executing_eagerly() and
         not isinstance(sess, session.SessionInterface)):
       raise TypeError("'sess' must be a Session; %s" % sess)
 
     save_path_parent = os.path.dirname(save_path)
     if not self._is_empty:
       try:
-        if context.in_graph_mode():
-          model_checkpoint_path = sess.run(
-              self.saver_def.save_tensor_name,
-              {self.saver_def.filename_tensor_name: checkpoint_file})
-        else:
+        if context.executing_eagerly():
           self._build_eager(
               checkpoint_file, build_save=True, build_restore=False)
           model_checkpoint_path = self.saver_def.save_tensor_name
+        else:
+          model_checkpoint_path = sess.run(
+              self.saver_def.save_tensor_name,
+              {self.saver_def.filename_tensor_name: checkpoint_file})
 
         model_checkpoint_path = compat.as_str(model_checkpoint_path)
         if write_state:
@@ -1691,7 +1695,7 @@ class Saver(object):
     if write_meta_graph:
       meta_graph_filename = self._MetaGraphFilename(
           checkpoint_file, meta_graph_suffix=meta_graph_suffix)
-      if context.in_graph_mode():
+      if not context.executing_eagerly():
         with sess.graph.as_default():
           self.export_meta_graph(
               meta_graph_filename, strip_default_attrs=strip_default_attrs)
@@ -1764,11 +1768,11 @@ class Saver(object):
     if save_path is None:
       raise ValueError("Can't load save_path when it is None.")
     logging.info("Restoring parameters from %s", save_path)
-    if context.in_graph_mode():
+    if context.executing_eagerly():
+      self._build_eager(save_path, build_save=False, build_restore=True)
+    else:
       sess.run(self.saver_def.restore_op_name,
                {self.saver_def.filename_tensor_name: save_path})
-    else:
-      self._build_eager(save_path, build_save=False, build_restore=True)
 
   @staticmethod
   def _add_collection_def(meta_graph_def, key, export_scope=None):
@@ -1908,7 +1912,7 @@ def import_meta_graph(meta_graph_or_file, clear_devices=False,
   execution is enabled.
   @end_compatibility
   """  # pylint: disable=g-doc-exception
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError("Exporting/importing meta graphs is not supported when "
                        "eager execution is enabled. No graph exists when eager "
                        "execution is enabled.")
@@ -1991,7 +1995,7 @@ def export_meta_graph(filename=None,
   @end_compatibility
   """
   # pylint: enable=line-too-long
-  if context.in_eager_mode():
+  if context.executing_eagerly():
     raise RuntimeError("Exporting/importing meta graphs is not supported when "
                        "eager execution is enabled. No graph exists when eager "
                        "execution is enabled.")