[AINode]Fix the parameter "predict_length" (#15900)

Author: yunbow30944

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

@@ -84,15 +84,15 @@ def infer(self, full_data, predict_length=96, **_):
 
 
 class BuiltInStrategy(InferenceStrategy):
-    def infer(self, full_data):
+    def infer(self, full_data, **_):
         data = pd.DataFrame(full_data[1]).T
         output = self.model.inference(data)
         df = pd.DataFrame(output)
         return convert_to_binary(df)
 
 
 class RegisteredStrategy(InferenceStrategy):
-    def infer(self, full_data, window_interval=None, window_step=None, **kwargs):
+    def infer(self, full_data, window_interval=None, window_step=None, **_):
         _, dataset, _, length = full_data
         if window_interval is None or window_step is None:
             window_interval = length
@@ -159,7 +159,7 @@ def _run(
 
             # inference by strategy
             strategy = self._get_strategy(model_id, model)
-            outputs = strategy.infer(full_data)
+            outputs = strategy.infer(full_data, **inference_attrs)
 
             # construct response
             status = get_status(TSStatusCode.SUCCESS_STATUS)

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

@@ -54,6 +54,11 @@ def generate(
     ) -> Union[GenerateOutput, torch.LongTensor]:
         if len(inputs.shape) != 2:
             raise ValueError("Input shape must be: [batch_size, seq_len]")
+        batch_size, cur_len = inputs.shape
+        if cur_len < self.config.input_token_len:
+            raise ValueError(
+                f"Input length must be at least {self.config.input_token_len}"
+            )
         if revin:
             means = inputs.mean(dim=-1, keepdim=True)
             stdev = inputs.std(dim=-1, keepdim=True, unbiased=False) + 1e-5