[AINode]: Integrate toto as a builtin forecasting model

integration-test/src/test/java/org/apache/iotdb/ainode/utils/AINodeTestUtils.java

@@ -58,7 +58,9 @@ public class AINodeTestUtils {
               new AbstractMap.SimpleEntry<>(
                   "chronos2", new FakeModelInfo("chronos2", "t5", "builtin", "active")),
               new AbstractMap.SimpleEntry<>(
-                  "moirai2", new FakeModelInfo("moirai2", "moirai", "builtin", "active")))
+                  "moirai2", new FakeModelInfo("moirai2", "moirai", "builtin", "active")),
+              new AbstractMap.SimpleEntry<>(
+                  "toto", new FakeModelInfo("toto", "toto", "builtin", "active")))
           .collect(Collectors.toMap(Map.Entry::getKey, Map.Entry::getValue));
 
   public static final Map<String, FakeModelInfo> BUILTIN_MODEL_MAP;

iotdb-core/ainode/iotdb/ainode/core/model/model_info.py

@@ -158,4 +158,14 @@ def __repr__(self):
         },
         transformers_registered=True,
     ),
+    "toto": ModelInfo(
+        model_id="toto",
+        category=ModelCategory.BUILTIN,
+        state=ModelStates.INACTIVE,
+        model_type="toto",
+        pipeline_cls="pipeline_toto.TotoPipeline",
+        repo_id="Datadog/Toto-Open-Base-1.0",
+        auto_map=None,
+        transformers_registered=False,
+    ),
 }

iotdb-core/ainode/iotdb/ainode/core/model/model_loader.py

@@ -39,26 +39,33 @@
 from iotdb.ainode.core.model.model_info import ModelInfo
 from iotdb.ainode.core.model.sktime.modeling_sktime import create_sktime_model
 from iotdb.ainode.core.model.utils import import_class_from_path, temporary_sys_path
+from iotdb.ainode.core.model.toto.inference.forecaster import TotoForecaster
+from iotdb.ainode.core.model.toto.model.toto import Toto
+
 
 logger = Logger()
 BACKEND = DeviceManager()
 
 
 def load_model(model_info: ModelInfo, **model_kwargs) -> Any:
-    if model_info.auto_map is not None:
+    if model_info.model_type == "toto":
+        model = load_toto_model(model_info, **model_kwargs)
+    elif model_info.auto_map is not None:
         model = load_model_from_transformers(model_info, **model_kwargs)
     else:
         if model_info.model_type == "sktime":
             model = create_sktime_model(model_info.model_id)
         else:
             model = load_model_from_pt(model_info, **model_kwargs)
-
+
+    model_device = getattr(model, "device", "cpu")
     logger.info(
-        f"Model {model_info.model_id} loaded to device {model.device if model_info.model_type != 'sktime' else 'cpu'} successfully."
+        f"Model {model_info.model_id} loaded to device {model_device if model_info.model_type != 'sktime' else 'cpu'} successfully."
     )
     return model
 
 
+
 def load_model_from_transformers(model_info: ModelInfo, **model_kwargs):
     device_map = model_kwargs.get("device_map", "cpu")
     train_from_scratch = model_kwargs.get("train_from_scratch", False)
@@ -135,6 +142,19 @@ def load_model_from_pt(model_info: ModelInfo, **kwargs):
         logger.warning(f"acceleration failed, fallback to normal mode: {str(e)}")
     return BACKEND.move_model(model, device_map)
 
+def load_toto_model(model_info: ModelInfo, **model_kwargs):
+    device_map = model_kwargs.get("device_map", "cpu")
+    model_path = os.path.join(
+        os.getcwd(),
+        AINodeDescriptor().get_config().get_ain_models_dir(),
+        model_info.category.value,
+        model_info.model_id,
+    )
+
+    model = Toto.from_pretrained(model_path)
+    model = BACKEND.move_model(model, device_map)
+    return TotoForecaster(model.model)
+
 
 def load_model_for_efficient_inference():
     # TODO: An efficient model loading method for inference based on model_arguments
@@ -146,5 +166,6 @@ def load_model_for_powerful_finetune():
     pass
 
 
+
 def unload_model():
     pass