[AINode] Integrate MOMENT as a builtin forecasting model (#17386)

Author: Eliaaazzz

LICENSE

@@ -360,3 +360,13 @@ Project page: https://github.com/SalesforceAIResearch/uni2ts
 License: https://github.com/SalesforceAIResearch/uni2ts/blob/main/LICENSE.txt
 
 --------------------------------------------------------------------------------
+
+The following files include code modified from MOMENT project.
+
+./iotdb-core/ainode/iotdb/ainode/core/model/moment/*
+
+The MOMENT is open source software licensed under the MIT License
+Project page: https://github.com/moment-timeseries-foundation-model/moment
+License: https://github.com/moment-timeseries-foundation-model/moment/blob/main/LICENSE
+
+--------------------------------------------------------------------------------

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

@@ -60,7 +60,9 @@ public class AINodeTestUtils {
               new AbstractMap.SimpleEntry<>(
                   "moirai2", new FakeModelInfo("moirai2", "moirai", "builtin", "active")),
               new AbstractMap.SimpleEntry<>(
-                  "toto", new FakeModelInfo("toto", "toto", "builtin", "active")))
+                  "toto", new FakeModelInfo("toto", "toto", "builtin", "active")),
+              new AbstractMap.SimpleEntry<>(
+                  "moment", new FakeModelInfo("moment", "moment", "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

@@ -173,4 +173,17 @@ def __repr__(self):
         },
         transformers_registered=True,
     ),
+    "moment": ModelInfo(
+        model_id="moment",
+        category=ModelCategory.BUILTIN,
+        state=ModelStates.INACTIVE,
+        model_type="moment",
+        pipeline_cls="pipeline_moment.MomentPipeline",
+        repo_id="AutonLab/MOMENT-1-large",
+        auto_map={
+            "AutoConfig": "configuration_moment.MomentConfig",
+            "AutoModelForCausalLM": "modeling_moment.MomentForPrediction",
+        },
+        transformers_registered=True,
+    ),
 }

iotdb-core/ainode/iotdb/ainode/core/model/moment/__init__.py

@@ -0,0 +1,17 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#

iotdb-core/ainode/iotdb/ainode/core/model/moment/configuration_moment.py

@@ -0,0 +1,71 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+
+# This file contains code adapted from the MOMENT project
+# (https://github.com/moment-timeseries-foundation-model/moment),
+# originally licensed under the MIT License.
+
+from typing import Optional
+
+from transformers import PretrainedConfig
+
+
+class MomentConfig(PretrainedConfig):
+    """
+    Configuration class for the MOMENT time series foundation model.
+
+    MOMENT (A Family of Open Time-series Foundation Models) is developed by
+    Auton Lab, Carnegie Mellon University. It uses a T5 encoder-only backbone
+    with patch-based input embedding and RevIN normalization for multi-task
+    time series analysis including forecasting, classification, anomaly
+    detection and imputation.
+
+    Reference: https://arxiv.org/abs/2402.03885
+    """
+
+    model_type = "moment"
+
+    def __init__(
+        self,
+        seq_len: int = 512,
+        patch_len: int = 8,
+        patch_stride_len: int = 8,
+        d_model: Optional[int] = None,
+        transformer_backbone: str = "google/flan-t5-large",
+        forecast_horizon: int = 96,
+        revin_affine: bool = False,
+        t5_config: Optional[dict] = None,
+        **kwargs,
+    ):
+        self.seq_len = seq_len
+        self.patch_len = patch_len
+        self.patch_stride_len = patch_stride_len
+        self.transformer_backbone = transformer_backbone
+        self.forecast_horizon = forecast_horizon
+        self.revin_affine = revin_affine
+        self.t5_config = t5_config
+
+        # Infer d_model: prefer explicit value, then t5_config, then default
+        if d_model is not None:
+            self.d_model = d_model
+        elif t5_config is not None and "d_model" in t5_config:
+            self.d_model = t5_config["d_model"]
+        else:
+            self.d_model = 1024  # Default for MOMENT-1-large
+
+        super().__init__(**kwargs)