Traceback (most recent call last):
 File "/home/jenkins0/sjw/mindformers/run_mindformer.py", line 268, in <module>
   main(config_)
 File "/home/jenkins0/sjw/mindformers/mindformers/tools/cloud_adapter/cloud_monitor.py", line 44, in wrapper
   raise exc
 File "/home/jenkins0/sjw/mindformers/mindformers/tools/cloud_adapter/cloud_monitor.py", line 34, in wrapper
   result = run_func(*args, **kwargs)
 File "/home/jenkins0/sjw/mindformers/run_mindformer.py", line 43, in main
   trainer.predict(predict_checkpoint=config.load_checkpoint, input_data=config.input_data,
 File "/home/jenkins0/.conda/envs/wxy39/lib/python3.9/site-packages/mindspore/_checkparam.py", line 1371, in wrapper
   return func(*args, **kwargs)
 File "/home/jenkins0/sjw/mindformers/mindformers/trainer/trainer.py", line 684, in predict
   output_result = self.trainer.predict(
 File "/home/jenkins0/sjw/mindformers/mindformers/trainer/causal_language_modeling/causal_language_modeling.py", line 338, in predict
   return self.predict_process(config=config,
 File "/home/jenkins0/sjw/mindformers/mindformers/trainer/base_trainer.py", line 937, in predict_process
   output_results = self.pipeline_task(input_data, top_k=top_k)
 File "/home/jenkins0/sjw/mindformers/mindformers/pipeline/base_pipeline.py", line 149, in __call__
   outputs = self.run_multi(inputs, batch_size, preprocess_params, forward_params, postprocess_params)
 File "/home/jenkins0/sjw/mindformers/mindformers/pipeline/text_generation_pipeline.py", line 187, in run_multi
   outputs.extend(self.run_single(item, preprocess_params,
 File "/home/jenkins0/sjw/mindformers/mindformers/pipeline/base_pipeline.py", line 236, in run_single
   model_inputs = self.preprocess(inputs, **preprocess_params)
 File "/home/jenkins0/sjw/mindformers/mindformers/pipeline/text_generation_pipeline.py", line 135, in preprocess
   return self.tokenizer.build_batch_input(inputs)
 File "/home/jenkins0/sjw/mindformers/mindformers/models/glm3/glm3_tokenizer.py", line 248, in build_batch_input
   return self.batch_encode_plus(batch_inputs, return_tensors="np", is_split_into_words=True)
 File "/home/jenkins0/sjw/mindformers/mindformers/models/tokenization_utils_base.py", line 3352, in batch_encode_plus
   return self._batch_encode_plus(
 File "/home/jenkins0/sjw/mindformers/mindformers/models/tokenization_utils.py", line 871, in _batch_encode_plus
   batch_outputs = self._batch_prepare_for_model(
 File "/home/jenkins0/sjw/mindformers/mindformers/models/tokenization_utils.py", line 951, in _batch_prepare_for_model
   batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors)
 File "/home/jenkins0/sjw/mindformers/mindformers/models/tokenization_utils_base.py", line 273, in __init__
   self.convert_to_tensors(tensor_type=tensor_type, prepend_batch_axis=prepend_batch_axis)
 File "/home/jenkins0/sjw/mindformers/mindformers/models/tokenization_utils_base.py", line 796, in convert_to_tensors
   raise ValueError(
ValueError: Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length. Perhaps your features (`input_ids` in this case) have excessive nesting (inputs type `list` where type `int` is expected).