Fixes regression for issue tensorflow#5548: Avoid attempting to conve…

…rt dtypes from "mixed precision" policy types. (tensorflow#6859)

Following-up on PR tensorflow#6857, which seems to have introduced a regression
for issue tensorflow#5548.

This change just gracefully degrades the functionality to avoid crashing
on an error (as it was before the recent change in tensorflow#6857), but it might
not be a proper fix for the scenario described in that issue.

tensorboard/plugins/graph/BUILD

@@ -114,6 +114,7 @@ py_library(
     deps = [
         "//tensorboard/compat/proto:protos_all_py_pb2",
         "//tensorboard/compat/tensorflow_stub",
+        "//tensorboard/util:tb_logging",
     ],
 )
 

tensorboard/plugins/graph/keras_util.py

@@ -43,6 +43,10 @@
 """
 from tensorboard.compat.proto.graph_pb2 import GraphDef
 from tensorboard.compat.tensorflow_stub import dtypes
+from tensorboard.util import tb_logging
+
+
+logger = tb_logging.get_logger()
 
 
 def _walk_layers(keras_layer):
@@ -259,19 +263,34 @@ def keras_model_to_graph_def(keras_layer):
 
         dtype_or_policy = layer_config.get("dtype")
         dtype = None
+        has_unsupported_value = False
         # If this is a dict, try and extract the dtype string from
-        # `config.name`; keras will export like this for non-input layers. If
-        # we can't find `config.name`, we skip it as it's presumably a instance
-        # of tf/keras/mixed_precision/Policy rather than a single dtype.
-        # TODO(#5548): parse the policy dict and populate the dtype attr with the variable dtype.
-        if isinstance(dtype_or_policy, dict):
-            if "config" in dtype_or_policy:
-                dtype = dtype_or_policy.get("config").get("name")
+        # `config.name`. Keras will export like this for non-input layers and
+        # some other cases (e.g. tf/keras/mixed_precision/Policy, as described
+        # in issue #5548).
+        if isinstance(dtype_or_policy, dict) and "config" in dtype_or_policy:
+            dtype = dtype_or_policy.get("config").get("name")
         elif dtype_or_policy is not None:
             dtype = dtype_or_policy
+
         if dtype is not None:
-            tf_dtype = dtypes.as_dtype(dtype)
-            node_def.attr["dtype"].type = tf_dtype.as_datatype_enum
+            try:
+                tf_dtype = dtypes.as_dtype(dtype)
+                node_def.attr["dtype"].type = tf_dtype.as_datatype_enum
+            except TypeError:
+                has_unsupported_value = True
+        elif dtype_or_policy is not None:
+            has_unsupported_value = True
+
+        if has_unsupported_value:
+            # There's at least one known case when this happens, which is when
+            # mixed precision dtype policies are used, as described in issue
+            # #5548. (See https://keras.io/api/mixed_precision/).
+            # There might be a better way to handle this, but here we are.
+            logger.warning(
+                "Unsupported dtype value in graph model config (json):\n%s",
+                dtype_or_policy,
+            )
         if layer.get("inbound_nodes") is not None:
             for name, size, index in _get_inbound_nodes(layer):
                 inbound_name = _scoped_name(name_scope, name)

tensorboard/plugins/graph/keras_util_test.py

@@ -1043,6 +1043,20 @@ def test_keras_model_to_graph_def_functional_multiple_inbound_nodes_from_same_no
 
         self.assertGraphDefToModel(expected_proto, model)
 
+    def test__keras_model_to_graph_def__does_not_crash_with_mixed_precision_dtype_policy(
+        self,
+    ):
+        # See https://keras.io/api/mixed_precision/ for more info.
+        # Test to avoid regression on issue #5548
+        first_layer = tf.keras.layers.Dense(
+            1, input_shape=(1,), dtype="mixed_float16"
+        )
+        model = tf.keras.Sequential([first_layer])
+
+        model_config = json.loads(model.to_json())
+        # This line should not raise errors:
+        keras_util.keras_model_to_graph_def(model_config)
+
 
 class _DoublingLayer(tf.keras.layers.Layer):
     def call(self, inputs):