Fix AINode inference output type

Author: CRZbulabula

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

@@ -42,6 +42,7 @@
 import java.sql.ResultSetMetaData;
 import java.sql.SQLException;
 import java.sql.Statement;
+import java.sql.Types;
 import java.util.Arrays;
 import java.util.HashSet;
 import java.util.LinkedList;
@@ -271,8 +272,10 @@ public static void callInferenceTest(Statement statement, AINodeTestUtils.FakeMo
       try (ResultSet resultSet = statement.executeQuery(callInferenceSQL)) {
         ResultSetMetaData resultSetMetaData = resultSet.getMetaData();
         checkHeader(resultSetMetaData, "Time,output");
+        Assert.assertEquals(Types.DOUBLE, resultSetMetaData.getColumnType(2));
         int count = 0;
         while (resultSet.next()) {
+          resultSet.getDouble("output");
           count++;
         }
         Assert.assertEquals(DEFAULT_OUTPUT_LENGTH, count);
@@ -288,8 +291,10 @@ public static void callInferenceByDefaultTest(
       try (ResultSet resultSet = statement.executeQuery(callInferenceSQL)) {
         ResultSetMetaData resultSetMetaData = resultSet.getMetaData();
         checkHeader(resultSetMetaData, "output");
+        Assert.assertEquals(Types.DOUBLE, resultSetMetaData.getColumnType(1));
         int count = 0;
         while (resultSet.next()) {
+          resultSet.getDouble("output");
           count++;
         }
         Assert.assertTrue(count > 0);

iotdb-core/ainode/iotdb/ainode/core/manager/inference_manager.py

@@ -209,10 +209,11 @@ def _do_inference_and_construct_resp(
                 logger.error("[Inference] Unsupported pipeline type.")
             outputs = inference_pipeline.postprocess(outputs, **inference_attrs)
 
-        # convert tensor into tsblock for the output in each batch
+        # DataNode currently exposes inference outputs as DOUBLE, so serialize the
+        # physical TsBlock column as double even when model tensors are float32.
         resp_list = []
-        for batch_idx, output in enumerate(outputs):
-            resp = convert_tensor_to_tsblock(output)
+        for output in outputs:
+            resp = convert_tensor_to_tsblock(output.to(dtype=torch.float64))
             resp_list.append(resp)
         return resp_list