[inference] fix stop token for object detection (#6624)

* fix stop token

* update minicpm data pipeline

* fix npu qlora examples

Former-commit-id: 844919fadaa8a61dfae47020971ea80730b2346f

scripts/vllm_infer.py

@@ -50,11 +50,15 @@ def vllm_infer(
     top_k: int = 50,
     max_new_tokens: int = 1024,
     repetition_penalty: float = 1.0,
+    pipeline_parallel_size: int = 1,
 ):
     r"""
     Performs batch generation using vLLM engine, which supports tensor parallelism.
     Usage: python vllm_infer.py --model_name_or_path meta-llama/Llama-2-7b-hf --template llama --dataset alpaca_en_demo
     """
+    if pipeline_parallel_size > get_device_count():
+        raise ValueError("Pipeline parallel size should be smaller than the number of gpus.")
+
     model_args, data_args, _, generating_args = get_infer_args(
         dict(
             model_name_or_path=model_name_or_path,
@@ -107,7 +111,7 @@ def vllm_infer(
         temperature=generating_args.temperature,
         top_p=generating_args.top_p or 1.0,  # top_p must > 0
         top_k=generating_args.top_k,
-        stop_token_ids=[tokenizer.eos_token_id] + tokenizer.additional_special_tokens_ids,
+        stop_token_ids=template_obj.get_stop_token_ids(tokenizer),
         max_tokens=generating_args.max_new_tokens,
         skip_special_tokens=False,
     )
@@ -120,7 +124,8 @@ def vllm_infer(
         "model": model_args.model_name_or_path,
         "trust_remote_code": True,
         "dtype": model_args.infer_dtype,
-        "tensor_parallel_size": get_device_count() or 1,
+        "tensor_parallel_size": (get_device_count() // pipeline_parallel_size) or 1,
+        "pipeline_parallel_size": pipeline_parallel_size,
         "disable_log_stats": True,
         "enable_lora": model_args.adapter_name_or_path is not None,
     }