Fix import of meta graphs with partitioned variables into a scope.

Saver inspects SliceInfo to decide the variable name when creating a
checkpoint. Before this fix even if a partitioned variable ("weights")
was imported into a scope "a" it would still be checkpointed as ("weights")
instead of ("a/weights") since import_scoped_meta_graph was not adjusting
the SliceInfo.

WARNING: if you use import_meta_graph on graphs with partitioned_variables WITH an import_scope argument AND then create a Saver to write/read checkpoints this change
may break your checkpoint loading.
PiperOrigin-RevId: 173105796

tensorflow/python/framework/meta_graph_test.py

@@ -36,8 +36,10 @@ from tensorflow.python.ops import data_flow_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import metrics
 from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops import partitioned_variables
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops import resource_variable_ops
+from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import gfile
 from tensorflow.python.platform import test
@@ -657,5 +659,42 @@ class MetaGraphWithVariableScopeTest(test.TestCase):
         initializer = variables.local_variables_initializer()
 
 
+class ExportImportAcrossScopesTest(test.TestCase):
+
+  def testPartionedVariables(self):
+    def make_graph_with_partitioned_variables():
+      variable_scope.get_variable(
+          name="weights",
+          partitioner=partitioned_variables.fixed_size_partitioner(3, axis=0),
+          initializer=random_ops.truncated_normal([100, 10]))
+    self._testExportImportAcrossScopes(make_graph_with_partitioned_variables)
+
+  def _testExportImportAcrossScopes(self, graph_fn):
+    """Tests export and importing a graph across scopes.
+
+    Args:
+      graph_fn: A closure that creates a graph on the current scope.
+    """
+    with ops.Graph().as_default() as original_graph:
+      with variable_scope.variable_scope("dropA/dropB/keepA"):
+        graph_fn()
+    exported_meta_graph_def = meta_graph.export_scoped_meta_graph(
+        graph=original_graph,
+        export_scope="dropA/dropB")[0]
+
+    with ops.Graph().as_default() as imported_graph:
+      meta_graph.import_scoped_meta_graph(
+          exported_meta_graph_def,
+          import_scope="importA")
+
+    with ops.Graph().as_default() as expected_graph:
+      with variable_scope.variable_scope("importA/keepA"):
+        graph_fn()
+
+    result = meta_graph.export_scoped_meta_graph(graph=imported_graph)[0]
+    expected = meta_graph.export_scoped_meta_graph(graph=expected_graph)[0]
+    self.assertProtoEquals(expected, result)
+
+
 if __name__ == "__main__":
   test.main()

tensorflow/python/ops/variables.py

@@ -394,7 +394,8 @@ class Variable(object):
                                import_scope=import_scope))
     if variable_def.HasField("save_slice_info_def"):
       self._save_slice_info = Variable.SaveSliceInfo(
-          save_slice_info_def=variable_def.save_slice_info_def)
+          save_slice_info_def=variable_def.save_slice_info_def,
+          import_scope=import_scope)
     else:
       self._save_slice_info = None
     self._caching_device = None