Fix bugs.

keras_nlp/models/task.py

@@ -275,29 +275,6 @@ def from_preset(
                 f"Received: backbone={kwargs['backbone']}."
             )
 
-        # Q: Should I move task loading logic to preset_utils.py?
-        # Load Task from preset.
-        # TODO: Do we expect remote paths here? os.path doesn't work for remote paths. replacements: https://github.com/keras-team/keras/blob/master/keras/utils/file_utils.py
-        task_config_path = os.path.join(preset, TASK_CONFIG_FILE)
-        if issubclass(cls, Task) and os.path.exists(task_config_path):
-            task_config_class = check_config_class(
-                preset, config_file=TASK_CONFIG_FILE
-            )
-            if not issubclass(task_config_class, cls):
-                raise ValueError(
-                    f"`{TASK_CONFIG_FILE}` has type `{task_config_class.__name__}` "
-                    f"which is not a subclass of calling class `{cls.__name__}`. Call "
-                    f"`from_preset` directly on `{task_config_class.__name__}` instead."
-                )
-
-            task_config_file = os.path.join(preset, TASK_CONFIG_FILE)
-            with open(task_config_file, "r") as config_file:
-                task_config = json.load(config_file)
-            task = keras.saving.deserialize_keras_object(task_config)
-            load_weights = load_weights and task_config["weights"]
-            task_weights_path = os.path.join(preset, task_config["weights"])
-            task.load_task_weights(task_weights_path)
-
         # Load backbone from preset.
         config_path = os.path.join(preset, CONFIG_FILE)
         if not os.path.exists(config_path):
@@ -343,11 +320,39 @@ def from_preset(
                 config_file=TOKENIZER_CONFIG_FILE,
             )
             preprocessor = cls.preprocessor_cls(tokenizer=tokenizer)
-        if task:
-            task.backbone = backbone
-            task.preprocessor = preprocessor
-            return task
-        return cls(backbone=backbone, preprocessor=preprocessor, **kwargs)
+
+        # TODO: Do we expect remote paths here? os.path doesn't work for remote paths. replacements: https://github.com/keras-team/keras/blob/master/keras/utils/file_utils.py
+        task_config_path = os.path.join(preset, TASK_CONFIG_FILE)
+        if not os.path.exists(task_config_path):
+            return cls(backbone=backbone, preprocessor=preprocessor, **kwargs)
+
+        # TODO: I should probably move task loading logic to preset_utils.py?
+        # Load Task from preset.
+        if not issubclass(cls, Task):
+            raise ValueError(
+                "`{cls.__name__}` should be subclass of Task!"
+            )  # TODO: update error message
+        task_config_class = check_config_class(
+            preset, config_file=TASK_CONFIG_FILE
+        )
+        if not issubclass(task_config_class, cls):
+            raise ValueError(
+                f"`{TASK_CONFIG_FILE}` has type `{task_config_class.__name__}` "
+                f"which is not a subclass of calling class `{cls.__name__}`. Call "
+                f"`from_preset` directly on `{task_config_class.__name__}` instead."
+            )
+
+        task_config_file = os.path.join(preset, TASK_CONFIG_FILE)
+        with open(task_config_file, "r") as config_file:
+            task_config = json.load(config_file)
+        # TODO: add back backbone and preprocessor config when save_to_preset removes them (rn, save_to_preset, doesn't remove them!).
+        # task_config.update(backbone_config)
+        # task_config.update(preprocessor_config)
+        task = keras.saving.deserialize_keras_object(task_config)
+        load_weights = load_weights and task_config["weights"]
+        task_weights_path = os.path.join(preset, task_config["weights"])
+        task.load_task_weights(task_weights_path)
+        return task
 
     def load_task_weights(self, filepath, skip_mismatch=False):
         """Load only the tasks specific weights not in the backbone."""
@@ -400,6 +405,7 @@ def save_task_weights(self, filepath):
         )
         weights_store.close()
 
+    # TODO: do we want to have a `save_weights` flag in this public save_to_preset? probably yes!
     def save_to_preset(self, preset):
         """TODO: add docstring"""
         if self.preprocessor is None:
@@ -409,17 +415,20 @@ def save_to_preset(self, preset):
 
         self.preprocessor.save_to_preset(preset)
         self.backbone.save_to_preset(preset)
+        weights_filename = "task.weights.h5"
 
         # TODO: the serialization and saving logic should probably be moved to preset_utils.py
         task_config_path = os.path.join(preset, TASK_CONFIG_FILE)
         task_config = keras.saving.serialize_keras_object(self)
         recursive_pop(task_config, "compile_config")
         recursive_pop(task_config, "build_config")
-        recursive_pop(task_config, "preprocessor")
-        recursive_pop(task_config, "backbone")
+        # TODO: remove preprocessor and backbone from task.json to prevent redundancy in config files.
+        # recursive_pop(task_config, "preprocessor")
+        # recursive_pop(task_config, "backbone")
+        task_config["weights"] = weights_filename
         with open(task_config_path, "w") as config_file:
             config_file.write(json.dumps(task_config, indent=4))
-        task_weights_path = os.path.join(preset, "task.weights.h5")
+        task_weights_path = os.path.join(preset, weights_filename)
         self.save_task_weights(task_weights_path)
 
     @property