Enabling custom TF signature draft (#19249)

* Custom TF signature draft

* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* Adding tf signature tests

* Fixing signature check and adding asserts

* fixing model load path

* Adjusting signature tests

* Formatting file
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
Co-authored-by: Dimitre Oliveira <dimitreoliveira@Dimitres-MacBook-Air.local>

src/transformers/modeling_tf_utils.py

@@ -2097,6 +2097,7 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin, Pu
         saved_model=False,
         version=1,
         push_to_hub=False,
+        signatures=None,
         max_shard_size: Union[int, str] = "10GB",
         create_pr: bool = False,
         **kwargs
@@ -2118,6 +2119,8 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin, Pu
                 Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the
                 repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
                 namespace).
+            signatures (`dict` or `tf.function`, *optional*):
+                Model's signature used for serving. This will be passed to the `signatures` argument of model.save().
             max_shard_size (`int` or `str`, *optional*, defaults to `"10GB"`):
                 The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size
                 lower than this size. If expressed as a string, needs to be digits followed by a unit (like `"5MB"`).
@@ -2148,8 +2151,10 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin, Pu
             files_timestamps = self._get_files_timestamps(save_directory)
 
         if saved_model:
+            if signatures is None:
+                signatures = self.serving
             saved_model_dir = os.path.join(save_directory, "saved_model", str(version))
-            self.save(saved_model_dir, include_optimizer=False, signatures=self.serving)
+            self.save(saved_model_dir, include_optimizer=False, signatures=signatures)
             logger.info(f"Saved model created in {saved_model_dir}")
 
         # Save configuration file

tests/test_modeling_tf_common.py

@@ -2216,6 +2216,46 @@ class UtilsFunctionsTest(unittest.TestCase):
                 for p1, p2 in zip(model.weights, new_model.weights):
                     self.assertTrue(np.allclose(p1.numpy(), p2.numpy()))
 
+    def test_save_pretrained_signatures(self):
+        model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
+
+        # Short custom TF signature function.
+        # `input_signature` is specific to BERT.
+        @tf.function(
+            input_signature=[
+                [
+                    tf.TensorSpec([None, None], tf.int32, name="input_ids"),
+                    tf.TensorSpec([None, None], tf.int32, name="token_type_ids"),
+                    tf.TensorSpec([None, None], tf.int32, name="attention_mask"),
+                ]
+            ]
+        )
+        def serving_fn(input):
+            return model(input)
+
+        # Using default signature (default behavior) overrides 'serving_default'
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model.save_pretrained(tmp_dir, saved_model=True, signatures=None)
+            model_loaded = tf.keras.models.load_model(f"{tmp_dir}/saved_model/1")
+            self.assertTrue("serving_default" in list(model_loaded.signatures.keys()))
+
+        # Providing custom signature function
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model.save_pretrained(tmp_dir, saved_model=True, signatures={"custom_signature": serving_fn})
+            model_loaded = tf.keras.models.load_model(f"{tmp_dir}/saved_model/1")
+            self.assertTrue("custom_signature" in list(model_loaded.signatures.keys()))
+
+        # Providing multiple custom signature function
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model.save_pretrained(
+                tmp_dir,
+                saved_model=True,
+                signatures={"custom_signature_1": serving_fn, "custom_signature_2": serving_fn},
+            )
+            model_loaded = tf.keras.models.load_model(f"{tmp_dir}/saved_model/1")
+            self.assertTrue("custom_signature_1" in list(model_loaded.signatures.keys()))
+            self.assertTrue("custom_signature_2" in list(model_loaded.signatures.keys()))
+
 
 @require_tf
 @is_staging_test