Add pickle support for Keras model (#19555)

* Implement unit tests for pickling

* Reformat model_test

* Reformat model_test

* Rename depickle to unpickle

* Rename depickle to unpickle

* Reformat

* remove a comment

keras/src/models/model.py

@@ -1,8 +1,10 @@
 import inspect
+import io
 import json
 import typing
 import warnings
 
+import keras.src.saving.saving_lib as saving_lib
 from keras.src import backend
 from keras.src import utils
 from keras.src.api_export import keras_export
@@ -348,6 +350,30 @@ def load_weights(self, filepath, skip_mismatch=False, **kwargs):
             self, filepath, skip_mismatch=skip_mismatch, **kwargs
         )
 
+    # Note: renaming this function will cause old pickles to be broken.
+    # This is probably not a huge deal, as pickle should not be a recommended
+    # saving format -- it should only be supported for use with distributed
+    # computing frameworks.
+    @classmethod
+    def _unpickle_model(cls, bytesio):
+        # pickle is not safe regardless of what you do.
+        return saving_lib._load_model_from_fileobj(
+            bytesio, custom_objects=None, compile=True, safe_mode=False
+        )
+
+    def __reduce__(self):
+        """__reduce__ is used to customize the behavior of `pickle.pickle()`.
+
+        The method returns a tuple of two elements: a function, and a list of
+        arguments to pass to that function.  In this case we just leverage the
+        keras saving library."""
+        buf = io.BytesIO()
+        saving_lib._save_model_to_fileobj(self, buf, "h5")
+        return (
+            self._unpickle_model,
+            (buf,),
+        )
+
     def quantize(self, mode):
         """Quantize the weights of the model.
 

keras/src/models/model_test.py

@@ -1,3 +1,5 @@
+import pickle
+
 import numpy as np
 import pytest
 from absl.testing import parameterized
@@ -116,6 +118,29 @@ def call(self, x):
         )
         self.assertIsInstance(new_model, Functional)
 
+    @parameterized.named_parameters(
+        ("single_output_1", _get_model_single_output),
+        ("single_output_2", _get_model_single_output),
+        ("single_output_3", _get_model_single_output),
+        ("single_output_4", _get_model_single_output),
+        ("single_list_output_1", _get_model_single_output_list),
+        ("single_list_output_2", _get_model_single_output_list),
+        ("single_list_output_3", _get_model_single_output_list),
+        ("single_list_output_4", _get_model_single_output_list),
+    )
+    def test_functional_pickling(self, model_fn):
+        model = model_fn()
+        self.assertIsInstance(model, Functional)
+        model.compile()
+        x = np.random.rand(8, 3)
+
+        reloaded_pickle = pickle.loads(pickle.dumps(model))
+
+        pred_reloaded = reloaded_pickle.predict(x)
+        pred = model.predict(x)
+
+        self.assertAllClose(np.array(pred_reloaded), np.array(pred))
+
     @parameterized.named_parameters(
         ("single_output_1", _get_model_single_output, None),
         ("single_output_2", _get_model_single_output, "list"),
@@ -138,7 +163,7 @@ def test_functional_single_output(self, model_fn, loss_type):
             loss = [loss]
         elif loss_type == "dict":
             loss = {"output_a": loss}
-        elif loss_type == "dict_lsit":
+        elif loss_type == "dict_list":
             loss = {"output_a": [loss]}
         model.compile(
             optimizer="sgd",