Move saving logic to the base classes.

keras_nlp/models/backbone.py

@@ -16,13 +16,17 @@
 from keras_nlp.backend import config
 from keras_nlp.backend import keras
 from keras_nlp.utils.preset_utils import CONFIG_FILE
+from keras_nlp.utils.preset_utils import MODEL_WEIGHTS_FILE
 from keras_nlp.utils.preset_utils import check_config_class
 from keras_nlp.utils.preset_utils import get_file
 from keras_nlp.utils.preset_utils import jax_memory_cleanup
 from keras_nlp.utils.preset_utils import list_presets
 from keras_nlp.utils.preset_utils import list_subclasses
 from keras_nlp.utils.preset_utils import load_serialized_object
-from keras_nlp.utils.preset_utils import save_to_preset
+from keras_nlp.utils.preset_utils import make_preset_dir
+from keras_nlp.utils.preset_utils import save_metadata
+from keras_nlp.utils.preset_utils import save_serialized_object
+from keras_nlp.utils.preset_utils import save_weights
 from keras_nlp.utils.python_utils import classproperty
 
 
@@ -214,7 +218,11 @@ def save_to_preset(self, preset):
         Args:
             preset: The path to the local model preset directory.
         """
-        save_to_preset(self, preset)
+        make_preset_dir(preset)
+        save_serialized_object(self, preset, config_file=CONFIG_FILE)
+        save_weights(self, preset, MODEL_WEIGHTS_FILE)
+        save_metadata(self, preset)
+        # save_to_preset(self, preset)
 
     def enable_lora(self, rank):
         """Enable Lora on the backbone.

keras_nlp/models/preprocessor.py

@@ -24,8 +24,10 @@
 from keras_nlp.utils.preset_utils import get_file
 from keras_nlp.utils.preset_utils import list_presets
 from keras_nlp.utils.preset_utils import list_subclasses
+from keras_nlp.utils.preset_utils import load_config
 from keras_nlp.utils.preset_utils import load_serialized_object
 from keras_nlp.utils.preset_utils import load_tokenizer
+from keras_nlp.utils.preset_utils import make_preset_dir
 from keras_nlp.utils.preset_utils import save_serialized_object
 from keras_nlp.utils.python_utils import classproperty
 
@@ -167,8 +169,14 @@ def from_preset(
                     f"Found multiple possible subclasses {names}. "
                     "Please call `from_preset` on a subclass directly."
                 )
-
-        preprocessor = load_serialized_object(preset, PREPROCESSOR_CONFIG_FILE)
+        tokenizer_config = load_config(preset, TOKENIZER_CONFIG_FILE)
+        # TODO: this is not really an override! It's an addition! Should I rename this?
+        config_overrides = {"tokenizer": tokenizer_config}
+        preprocessor = load_serialized_object(
+            preset,
+            PREPROCESSOR_CONFIG_FILE,
+            config_overrides=config_overrides,
+        )
         preprocessor.tokenizer = load_tokenizer(
             preset,
             config_file=TOKENIZER_CONFIG_FILE,
@@ -183,6 +191,7 @@ def save_to_preset(self, preset):
         Args:
             preset: The path to the local model preset directory.
         """
+        make_preset_dir(preset)
         self.tokenizer.save_to_preset(preset)
         save_serialized_object(
             self,

keras_nlp/models/task.py

@@ -22,6 +22,7 @@
 from keras_nlp.models.preprocessor import Preprocessor
 from keras_nlp.utils.keras_utils import print_msg
 from keras_nlp.utils.pipeline_model import PipelineModel
+from keras_nlp.utils.preset_utils import CONFIG_FILE
 from keras_nlp.utils.preset_utils import MODEL_WEIGHTS_FILE
 from keras_nlp.utils.preset_utils import PREPROCESSOR_CONFIG_FILE
 from keras_nlp.utils.preset_utils import TASK_CONFIG_FILE
@@ -30,8 +31,11 @@
 from keras_nlp.utils.preset_utils import get_file
 from keras_nlp.utils.preset_utils import list_presets
 from keras_nlp.utils.preset_utils import list_subclasses
+from keras_nlp.utils.preset_utils import load_config
 from keras_nlp.utils.preset_utils import load_serialized_object
-from keras_nlp.utils.preset_utils import save_to_preset
+from keras_nlp.utils.preset_utils import make_preset_dir
+from keras_nlp.utils.preset_utils import save_serialized_object
+from keras_nlp.utils.preset_utils import save_weights
 from keras_nlp.utils.python_utils import classproperty
 
 
@@ -284,8 +288,14 @@ def from_preset(
                 f"which is not a subclass of calling class `{cls.__name__}`. Call "
                 f"`from_preset` directly on `{task_preset_cls.__name__}` instead."
             )
-
-        task = load_serialized_object(preset, TASK_CONFIG_FILE)
+        backbone_config = load_config(preset, CONFIG_FILE)
+        # TODO: this is not really an override! It's an addition! Should I rename this?
+        config_overrides = {"backbone": backbone_config}
+        task = load_serialized_object(
+            preset,
+            TASK_CONFIG_FILE,
+            config_overrides=config_overrides,
+        )
         if load_weights:
             task.load_weights(get_file(preset, TASK_WEIGHTS_FILE))
             task.backbone.load_weights(get_file(preset, MODEL_WEIGHTS_FILE))
@@ -334,19 +344,22 @@ def save_to_preset(self, preset):
         Args:
             preset: The path to the local model preset directory.
         """
+        make_preset_dir(preset)
         if self.preprocessor is None:
             raise ValueError(
                 "Cannot save `task` to preset: `Preprocessor` is not initialized."
             )
 
         self.preprocessor.save_to_preset(preset)
         self.backbone.save_to_preset(preset)
-        save_to_preset(
+
+        save_serialized_object(
             self,
             preset,
-            config_filename=TASK_CONFIG_FILE,
-            weights_filename=TASK_WEIGHTS_FILE,
+            config_file=TASK_CONFIG_FILE,
+            config_to_skip=["preprocessor", "backbone"],
         )
+        save_weights(self, preset, TASK_WEIGHTS_FILE)
 
     @property
     def layers(self):

keras_nlp/tokenizers/tokenizer.py

@@ -22,7 +22,9 @@
 from keras_nlp.utils.preset_utils import list_presets
 from keras_nlp.utils.preset_utils import list_subclasses
 from keras_nlp.utils.preset_utils import load_tokenizer
-from keras_nlp.utils.preset_utils import save_to_preset
+from keras_nlp.utils.preset_utils import make_preset_dir
+from keras_nlp.utils.preset_utils import save_serialized_object
+from keras_nlp.utils.preset_utils import save_tokenizer_assets
 from keras_nlp.utils.python_utils import classproperty
 
 
@@ -139,7 +141,10 @@ def save_to_preset(self, preset):
         Args:
             preset: The path to the local model preset directory.
         """
-        save_to_preset(self, preset, config_filename=TOKENIZER_CONFIG_FILE)
+        make_preset_dir(preset)
+        save_tokenizer_assets(self, preset)
+        save_serialized_object(self, preset, config_file=TOKENIZER_CONFIG_FILE)
+        # save_to_preset(self, preset, config_filename=TOKENIZER_CONFIG_FILE)
 
     def call(self, inputs, *args, training=None, **kwargs):
         return self.tokenize(inputs, *args, **kwargs)

keras_nlp/utils/preset_utils.py

@@ -201,68 +201,52 @@ def recursive_pop(config, key):
             recursive_pop(value, key)
 
 
-def save_to_preset(
-    layer,
-    preset,
-    save_weights=True,
-    config_filename=CONFIG_FILE,
-    weights_filename=MODEL_WEIGHTS_FILE,
-):
-    """Save a KerasNLP layer to a preset directory."""
-    if not backend_config.keras_3():
-        raise ValueError(
-            "`save_to_preset` requires Keras 3. Run `pip install -U keras` "
-            "upgrade your Keras version, or see https://keras.io/getting_started/ "
-            "for more info on Keras versions and installation."
-        )
+def make_preset_dir(preset):
     os.makedirs(preset, exist_ok=True)
 
-    # Save tokenizers assets.
-    tokenizer = get_tokenizer(layer)
-    assets = []
+
+def save_tokenizer_assets(tokenizer, preset):
     if tokenizer:
         asset_dir = os.path.join(preset, TOKENIZER_ASSET_DIR)
         os.makedirs(asset_dir, exist_ok=True)
         tokenizer.save_assets(asset_dir)
-        for asset_path in os.listdir(asset_dir):
-            assets.append(os.path.join(TOKENIZER_ASSET_DIR, asset_path))
 
-    # Optionally save weights.
-    save_weights = save_weights and hasattr(layer, "save_weights")
-    if save_weights:
-        weights_path = os.path.join(preset, weights_filename)
-        layer.save_weights(weights_path)
 
-    # Save a serialized Keras object.
-    config_path = os.path.join(preset, config_filename)
+def save_serialized_object(
+    layer,
+    preset,
+    config_file=CONFIG_FILE,
+    config_to_skip=[],
+):
+    config_path = os.path.join(preset, config_file)
     config = keras.saving.serialize_keras_object(layer)
-    # Include references to weights and assets.
-    config["assets"] = assets
-    config["weights"] = weights_filename if save_weights else None
-    recursive_pop(config, "compile_config")
-    recursive_pop(config, "build_config")
-    # Remove preprocessor and backbone from task.json to prevent redundancy in config files.
-    if config_filename == TASK_CONFIG_FILE:
-        recursive_pop(config, "preprocessor")
-        recursive_pop(config, "backbone")
+    config_to_skip += ["compile_config", "build_config"]
+    for c in config_to_skip:
+        recursive_pop(config, c)
     with open(config_path, "w") as config_file:
         config_file.write(json.dumps(config, indent=4))
 
+
+def save_metadata(layer, preset):
     from keras_nlp import __version__ as keras_nlp_version
 
     keras_version = keras.version() if hasattr(keras, "version") else None
+    metadata = {
+        "keras_version": keras_version,
+        "keras_nlp_version": keras_nlp_version,
+        "parameter_count": layer.count_params(),
+        "date_saved": datetime.datetime.now().strftime("%Y-%m-%d@%H:%M:%S"),
+    }
+    metadata_path = os.path.join(preset, "metadata.json")
+    with open(metadata_path, "w") as metadata_file:
+        metadata_file.write(json.dumps(metadata, indent=4))
+
 
-    # Save any associated metadata.
-    if config_filename == CONFIG_FILE:
-        metadata = {
-            "keras_version": keras_version,
-            "keras_nlp_version": keras_nlp_version,
-            "parameter_count": layer.count_params(),
-            "date_saved": datetime.datetime.now().strftime("%Y-%m-%d@%H:%M:%S"),
-        }
-        metadata_path = os.path.join(preset, "metadata.json")
-        with open(metadata_path, "w") as metadata_file:
-            metadata_file.write(json.dumps(metadata, indent=4))
+def save_weights(layer, preset, weights_file):
+    if not hasattr(layer, "save_weights"):
+        raise ValueError(f"`save_weights` hasn't been defined for `{layer}`.")
+    weights_path = os.path.join(preset, weights_file)
+    layer.save_weights(weights_path)
 
 
 def _validate_tokenizer(preset, allow_incomplete=False):
@@ -399,23 +383,21 @@ def upload_preset(
         )
 
 
-def load_serialized_object(preset, config_file, config_overrides={}):
+def load_config(preset, config_file=CONFIG_FILE):
     config_path = get_file(preset, config_file)
     with open(config_path) as config_file:
         config = json.load(config_file)
-    config["config"] = {**config["config"], **config_overrides}
-    return keras.saving.deserialize_keras_object(config)
+    return config
 
 
-def save_serialized_object(
-    layer, preset, config_file=CONFIG_FILE, config_to_skip=[]
+def load_serialized_object(
+    preset,
+    config_file=CONFIG_FILE,
+    config_overrides={},
 ):
-    config_path = os.path.join(preset, config_file)
-    config = keras.saving.serialize_keras_object(layer)
-    for c in config_to_skip:
-        recursive_pop(config, c)
-    with open(config_path, "w") as config_file:
-        config_file.write(json.dumps(config, indent=4))
+    config = load_config(preset, config_file)
+    config["config"] = {**config["config"], **config_overrides}
+    return keras.saving.deserialize_keras_object(config)
 
 
 def check_config_class(