[AINode] Refactor forecast tvf

integration-test/src/test/java/org/apache/iotdb/ainode/it/AINodeConcurrentForecastIT.java

@@ -49,7 +49,7 @@ public class AINodeConcurrentForecastIT {
   private static final Logger LOGGER = LoggerFactory.getLogger(AINodeConcurrentForecastIT.class);
 
   private static final String FORECAST_TABLE_FUNCTION_SQL_TEMPLATE =
-      "SELECT * FROM FORECAST(model_id=>'%s', input=>(SELECT time,s FROM root.AI) ORDER BY time, output_length=>%d)";
+      "SELECT * FROM FORECAST(model_id=>'%s', targets=>(SELECT time,s FROM root.AI) ORDER BY time, output_length=>%d)";
 
   @BeforeClass
   public static void setUp() throws Exception {

integration-test/src/test/java/org/apache/iotdb/ainode/it/AINodeForecastIT.java

@@ -38,13 +38,21 @@
 import java.sql.Statement;
 
 import static org.apache.iotdb.ainode.utils.AINodeTestUtils.BUILTIN_MODEL_MAP;
+import static org.apache.iotdb.ainode.utils.AINodeTestUtils.errorTest;
 
 @RunWith(IoTDBTestRunner.class)
 @Category({AIClusterIT.class})
 public class AINodeForecastIT {
 
   private static final String FORECAST_TABLE_FUNCTION_SQL_TEMPLATE =
-      "SELECT * FROM FORECAST(model_id=>'%s', input=>(SELECT time, s%d FROM db.AI) ORDER BY time)";
+      "SELECT * FROM FORECAST("
+          + "model_id=>'%s', "
+          + "targets=>(SELECT time, s%d FROM db.AI WHERE time<%d ORDER BY time DESC LIMIT %d) ORDER BY time, "
+          + "output_start_time=>%d, "
+          + "output_length=>%d, "
+          + "output_interval=>%d, "
+          + "timecol=>'%s'"
+          + ")";
 
   @BeforeClass
   public static void setUp() throws Exception {
@@ -55,7 +63,7 @@ public static void setUp() throws Exception {
       statement.execute("CREATE DATABASE db");
       statement.execute(
           "CREATE TABLE db.AI (s0 FLOAT FIELD, s1 DOUBLE FIELD, s2 INT32 FIELD, s3 INT64 FIELD)");
-      for (int i = 0; i < 2880; i++) {
+      for (int i = 0; i < 5760; i++) {
         statement.execute(
             String.format(
                 "INSERT INTO db.AI(time,s0,s1,s2,s3) VALUES(%d,%f,%f,%d,%d)",
@@ -81,18 +89,100 @@ public void forecastTableFunctionTest() throws SQLException {
 
   public void forecastTableFunctionTest(
       Statement statement, AINodeTestUtils.FakeModelInfo modelInfo) throws SQLException {
-    // Invoke call inference for specified models, there should exist result.
+    // Invoke forecast table function for specified models, there should exist result.
     for (int i = 0; i < 4; i++) {
       String forecastTableFunctionSQL =
-          String.format(FORECAST_TABLE_FUNCTION_SQL_TEMPLATE, modelInfo.getModelId(), i);
+          String.format(
+              FORECAST_TABLE_FUNCTION_SQL_TEMPLATE,
+              modelInfo.getModelId(),
+              i,
+              5760,
+              2880,
+              5760,
+              96,
+              1,
+              "time");
       try (ResultSet resultSet = statement.executeQuery(forecastTableFunctionSQL)) {
         int count = 0;
         while (resultSet.next()) {
           count++;
         }
-        // Ensure the call inference return results
+        // Ensure the forecast sentence return results
         Assert.assertTrue(count > 0);
       }
     }
   }
+
+  @Test
+  public void forecastTableFunctionErrorTest() throws SQLException {
+    for (AINodeTestUtils.FakeModelInfo modelInfo : BUILTIN_MODEL_MAP.values()) {
+      try (Connection connection = EnvFactory.getEnv().getConnection(BaseEnv.TABLE_SQL_DIALECT);
+          Statement statement = connection.createStatement()) {
+        forecastTableFunctionErrorTest(statement, modelInfo);
+      }
+    }
+  }
+
+  public void forecastTableFunctionErrorTest(
+      Statement statement, AINodeTestUtils.FakeModelInfo modelInfo) throws SQLException {
+    // OUTPUT_START_TIME error
+    String invalidOutputStartTimeSQL =
+        String.format(
+            FORECAST_TABLE_FUNCTION_SQL_TEMPLATE,
+            modelInfo.getModelId(),
+            0,
+            5760,
+            2880,
+            5759,
+            96,
+            1,
+            "time");
+    errorTest(
+        statement,
+        invalidOutputStartTimeSQL,
+        "701: The OUTPUT_START_TIME should be greater than the maximum timestamp of target time series. Expected greater than [5759] but found [5759].");
+
+    // OUTPUT_LENGTH error
+    String invalidOutputLengthSQL =
+        String.format(
+            FORECAST_TABLE_FUNCTION_SQL_TEMPLATE,
+            modelInfo.getModelId(),
+            0,
+            5760,
+            2880,
+            5760,
+            0,
+            1,
+            "time");
+    errorTest(statement, invalidOutputLengthSQL, "701: OUTPUT_LENGTH should be greater than 0");
+
+    // OUTPUT_INTERVAL error
+    String invalidOutputIntervalSQL =
+        String.format(
+            FORECAST_TABLE_FUNCTION_SQL_TEMPLATE,
+            modelInfo.getModelId(),
+            0,
+            5760,
+            2880,
+            5760,
+            96,
+            -1,
+            "time");
+    errorTest(statement, invalidOutputIntervalSQL, "701: OUTPUT_INTERVAL should be greater than 0");
+
+    // TIMECOL error
+    String invalidTimecolSQL2 =
+        String.format(
+            FORECAST_TABLE_FUNCTION_SQL_TEMPLATE,
+            modelInfo.getModelId(),
+            0,
+            5760,
+            2880,
+            5760,
+            96,
+            1,
+            "s0");
+    errorTest(
+        statement, invalidTimecolSQL2, "701: The type of the column [s0] is not as expected.");
+  }
 }

iotdb-core/ainode/iotdb/ainode/core/model/sundial/modeling_sundial.py

@@ -610,7 +610,11 @@ def prepare_inputs_for_generation(
             if attention_mask is not None and attention_mask.shape[1] > (
                 input_ids.shape[1] // self.config.input_token_len
             ):
-                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+                input_ids = input_ids[
+                    :,
+                    -(attention_mask.shape[1] - past_length)
+                    * self.config.input_token_len :,
+                ]
             # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
             # input_ids based on the past_length.
             elif past_length < (input_ids.shape[1] // self.config.input_token_len):
@@ -623,9 +627,10 @@ def prepare_inputs_for_generation(
             position_ids = attention_mask.long().cumsum(-1) - 1
             position_ids.masked_fill_(attention_mask == 0, 1)
             if past_key_values:
-                position_ids = position_ids[
-                    :, -(input_ids.shape[1] // self.config.input_token_len) :
-                ]
+                token_num = (
+                    input_ids.shape[1] + self.config.input_token_len - 1
+                ) // self.config.input_token_len
+                position_ids = position_ids[:, -token_num:]
 
         # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
         if inputs_embeds is not None and past_key_values is None:

iotdb-core/ainode/iotdb/ainode/core/model/timer_xl/modeling_timer.py

@@ -603,7 +603,11 @@ def prepare_inputs_for_generation(
             if attention_mask is not None and attention_mask.shape[1] > (
                 input_ids.shape[1] // self.config.input_token_len
             ):
-                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+                input_ids = input_ids[
+                    :,
+                    -(attention_mask.shape[1] - past_length)
+                    * self.config.input_token_len :,
+                ]
             # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
             # input_ids based on the past_length.
             elif past_length < (input_ids.shape[1] // self.config.input_token_len):

iotdb-core/datanode/src/main/java/org/apache/iotdb/db/queryengine/plan/relational/function/TableBuiltinTableFunction.java

@@ -25,6 +25,7 @@
 import org.apache.iotdb.commons.udf.builtin.relational.tvf.SessionTableFunction;
 import org.apache.iotdb.commons.udf.builtin.relational.tvf.TumbleTableFunction;
 import org.apache.iotdb.commons.udf.builtin.relational.tvf.VariationTableFunction;
+import org.apache.iotdb.db.queryengine.plan.relational.function.tvf.ClassifyTableFunction;
 import org.apache.iotdb.db.queryengine.plan.relational.function.tvf.ForecastTableFunction;
 import org.apache.iotdb.db.queryengine.plan.relational.function.tvf.PatternMatchTableFunction;
 import org.apache.iotdb.udf.api.relational.TableFunction;
@@ -42,7 +43,8 @@ public enum TableBuiltinTableFunction {
   VARIATION("variation"),
   CAPACITY("capacity"),
   FORECAST("forecast"),
-  PATTERN_MATCH("pattern_match");
+  PATTERN_MATCH("pattern_match"),
+  CLASSIFY("classify");
 
   private final String functionName;
 
@@ -86,6 +88,8 @@ public static TableFunction getBuiltinTableFunction(String functionName) {
         return new CapacityTableFunction();
       case "forecast":
         return new ForecastTableFunction();
+      case "classify":
+        return new ClassifyTableFunction();
       default:
         throw new UnsupportedOperationException("Unsupported table function: " + functionName);
     }