Support limited forward compatibility when importing a MetaGraphDef.

Change: 121880284

1 files changed, 37 insertions, 4 deletions

diff --git a/tensorflow/python/framework/importer.py b/tensorflow/python/framework/importer.py
index 3d8b4e460e..afb3b55d83 100644
--- a/tensorflow/python/framework/importer.py
+++ b/tensorflow/python/framework/importer.py
@@ -23,8 +23,8 @@ import contextlib
 from tensorflow.core.framework import attr_value_pb2
 from tensorflow.core.framework import graph_pb2
 from tensorflow.core.framework import types_pb2
-from tensorflow.python.framework import op_def_registry
 from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import op_def_registry
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.util import compat
@@ -142,8 +142,15 @@ def _MaybeDevice(device):
     yield
 
 
+def _FindAttrInOpDef(attr_name, op_def):
+  for attr_def in op_def.attr:
+    if attr_name == attr_def.name:
+      return attr_def
+  return None
+
+
 def import_graph_def(graph_def, input_map=None, return_elements=None,
-                     name=None, op_dict=None):
+                     name=None, op_dict=None, producer_op_list=None):
   """Imports the TensorFlow graph in `graph_def` into the Python `Graph`.
 
   This function provides a way to import a serialized TensorFlow
@@ -167,6 +174,12 @@ def import_graph_def(graph_def, input_map=None, return_elements=None,
     op_dict: (Optional.) A dictionary mapping op type names to `OpDef` protos.
       Must contain an `OpDef` proto for each op type named in `graph_def`.
       If omitted, uses the `OpDef` protos registered in the global registry.
+    producer_op_list: (Optional.) An `OpList` proto with the (possibly stripped)
+      list of `OpDef`s used by the producer of the graph. If provided, attrs
+      for ops in `graph_def` that are not in `op_dict` that have their default
+      value according to `producer_op_list` will be removed. This will allow
+      some more `GraphDef`s produced by later binaries to be accepted by
+      earlier binaries.
 
   Returns:
     A list of `Operation` and/or `Tensor` objects from the imported graph,
@@ -213,6 +226,11 @@ def import_graph_def(graph_def, input_map=None, return_elements=None,
   if op_dict is None:
     op_dict = op_def_registry.get_registered_ops()
 
+  if producer_op_list is None:
+    producer_op_dict = None
+  else:
+    producer_op_dict = {op.name: op for op in producer_op_list.op}
+
   with ops.op_scope(input_map.values(), name, 'import'):
     g = ops.get_default_graph()
     g.graph_def_versions.CopyFrom(graph_def.versions)
@@ -233,6 +251,21 @@ def import_graph_def(graph_def, input_map=None, return_elements=None,
           value = node.attr[key]
           if value is None or value.WhichOneof('value') is None:
             node.attr[key].CopyFrom(attr_def.default_value)
+      if producer_op_dict:
+        # Remove any default attr values that aren't in op_def.
+        if node.op in producer_op_dict:
+          producer_op_def = producer_op_dict[node.op]
+          # We make a copy of node.attr to iterate through since we
+          # may modify node.attr inside the loop.
+          for key in list(node.attr):
+            if _FindAttrInOpDef(key, op_def) is None:
+              # No attr_def in consumer, look in producer.
+              attr_def = _FindAttrInOpDef(key, producer_op_def)
+              if (attr_def and attr_def.HasField('default_value') and
+                  node.attr[key] == attr_def.default_value):
+                # Unknown attr had default value in producer, delete it
+                # so it can be understood by consumer.
+                del node.attr[key]
 
       output_types = _OutputTypes(node, op_dict)
       name_to_op[node.name] = g.create_op(
@@ -326,8 +359,8 @@ def import_graph_def(graph_def, input_map=None, return_elements=None,
             _InvalidNodeMessage(
                 node,
                 'Input types mismatch (expected %r but got %r)'
-                % (", ".join(dtypes.as_dtype(x).name for x in input_types),
-                   ", ".join(x.name for x in op._input_dtypes))))
+                % (', '.join(dtypes.as_dtype(x).name for x in input_types),
+                   ', '.join(x.name for x in op._input_dtypes))))
       # pylint: enable=protected_access
 
       # Execute shape inference for this op.