Replace tff.types.tensorflow_to_type with tff.tensorflow.to_type.

This is a no-op change. PiperOrigin-RevId: 679158905
google-parfait · Sep 26, 2024 · 92df35e · 92df35e
1 parent d665301
commit 92df35e
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Showing 5 changed files with 7 additions and 7 deletions.
diff --git a/docs/tutorials/building_your_own_federated_learning_algorithm.ipynb b/docs/tutorials/building_your_own_federated_learning_algorithm.ipynb
@@ -894,7 +894,7 @@
       "outputs": [],
       "source": [
         "tf_dataset_type = tff.SequenceType(\n",
-        "    tff.types.tensorflow_to_type(tff_model.input_spec)\n",
+        "    tff.tensorflow.to_type(tff_model.input_spec)\n",
         ")"
       ]
     },

diff --git a/docs/tutorials/composing_learning_algorithms.ipynb b/docs/tutorials/composing_learning_algorithms.ipynb
@@ -289,7 +289,7 @@
         "    optimizer: tff.learning.optimizers.Optimizer,\n",
         ") -\u003e tff.learning.templates.ClientWorkProcess:\n",
         "  \"\"\"Creates a client work process that uses gradient clipping.\"\"\"\n",
-        "  data_type = tff.SequenceType(tff.types.tensorflow_to_type(model.input_spec))\n",
+        "  data_type = tff.SequenceType(tff.tensorflow.to_type(model.input_spec))\n",
         "  model_weights_type = tff.types.to_type(\n",
         "      tf.nest.map_structure(\n",
         "          lambda arr: tff.types.TensorType(shape=arr.shape, dtype=arr.dtype),\n",

diff --git a/docs/tutorials/custom_federated_algorithms_2.ipynb b/docs/tutorials/custom_federated_algorithms_2.ipynb
@@ -399,7 +399,7 @@
         "BATCH_SPEC = collections.OrderedDict(\n",
         "    x=tf.TensorSpec(shape=[None, 784], dtype=tf.float32),\n",
         "    y=tf.TensorSpec(shape=[None], dtype=tf.int32))\n",
-        "BATCH_TYPE = tff.types.tensorflow_to_type(BATCH_SPEC)\n",
+        "BATCH_TYPE = tff.tensorflow.to_type(BATCH_SPEC)\n",
         "\n",
         "str(BATCH_TYPE)"
       ]
@@ -447,7 +447,7 @@
         "MODEL_SPEC = collections.OrderedDict(\n",
         "    weights=tf.TensorSpec(shape=[784, 10], dtype=tf.float32),\n",
         "    bias=tf.TensorSpec(shape=[10], dtype=tf.float32))\n",
-        "MODEL_TYPE = tff.types.tensorflow_to_type(MODEL_SPEC)\n",
+        "MODEL_TYPE = tff.tensorflow.to_type(MODEL_SPEC)\n",
         "print(MODEL_TYPE)"
       ]
     },

diff --git a/examples/simple_fedavg/simple_fedavg_tff.py b/examples/simple_fedavg/simple_fedavg_tff.py
@@ -76,7 +76,7 @@ def client_update_fn(server_message, tf_dataset):
     # function, using tff.sequenece_reduce.
 
     client_update_type_spec = client_update_fn.type_signature.result
-    batch_type = tff.types.tensorflow_to_type(model_fn().input_spec)
+    batch_type = tff.tensorflow.to_type(model_fn().input_spec)
 
     @tff.tensorflow.computation(client_update_type_spec, batch_type)
     def client_update_batch_fn(client_data, batch):
@@ -163,7 +163,7 @@ def server_message_fn(server_state):
     return build_server_broadcast_message(server_state)
 
   server_message_type = server_message_fn.type_signature.result
-  element_type = tff.types.tensorflow_to_type(whimsy_model.input_spec)
+  element_type = tff.tensorflow.to_type(whimsy_model.input_spec)
   tf_dataset_type = tff.SequenceType(element_type)
 
   federated_server_state_type = tff.FederatedType(server_state_type, tff.SERVER)

diff --git a/examples/stateful_clients/stateful_fedavg_tff.py b/examples/stateful_clients/stateful_fedavg_tff.py
@@ -112,7 +112,7 @@ def server_message_fn(server_state):
     return stateful_fedavg_tf.build_server_broadcast_message(server_state)
 
   server_message_type = server_message_fn.type_signature.result
-  element_type = tff.types.tensorflow_to_type(whimsy_model.input_spec)
+  element_type = tff.tensorflow.to_type(whimsy_model.input_spec)
   tf_dataset_type = tff.SequenceType(element_type)
 
   @tff.tensorflow.computation(