[docs] update rejected_tools

docs/source/Customization/自定义数据集.md

@@ -88,7 +88,7 @@ alpaca格式:
 
 > 注: RM 额外支持 margin 列，参考[RM文档](../Instruction/人类对齐.md#rm)
 
-当然，你也可以直接使用`rejected_messages`，而不是只提供`rejected_response`/`rejected_images`（需ms-swift>=3.8），这将提供更大的灵活度（例如多模态/agent场景）。在多模态场景下，若使用rejected_messages，你需要额外传入"rejected_images"，"rejected_audios"，"rejected_videos"等内容。数据格式例子如下：
+当然，你也可以直接使用`rejected_messages`，而不是只提供`rejected_response`/`rejected_images`（需ms-swift>=3.8），这将提供更大的灵活度（例如多模态/agent场景）。若使用rejected_messages，在多模态场景下，你需要额外传入"rejected_images"，"rejected_audios"，"rejected_videos"等内容；在Agent场景下，你需要额外传入"rejected_tools"等内容。多模态数据格式例子如下：
 
 ```jsonl
 {"messages": [{"role": "user", "content": "<image>这是什么"}, {"role": "assistant", "content": "这是一只小猫咪。"}], "images": ["cat.png"], "rejected_messages": [{"role": "user", "content": "<image>这是什么"}, {"role": "assistant", "content": "这是一只小狗。"}], "rejected_images": ["cat.png"]}

docs/source_en/Customization/Custom-dataset.md

@@ -88,7 +88,7 @@ The format of multimodal data should follow the specifications in [Multimodal Da
 
 > Note: RM additionally supports the margin column. For details, refer to the [RM documentation](../Instruction/RLHF.md#rm).
 
-Sure, you can also directly use `rejected_messages` instead of only providing `rejected_response` / `rejected_images` (requires ms-swift>=3.8), which offers greater flexibility (e.g., for multimodal or agent scenarios). In multimodal cases, if you use `rejected_messages`, you need to additionally provide fields such as `"rejected_images"`, `"rejected_audios"`, `"rejected_videos"`, etc. An example of the data format is as follows:
+Sure, you can also directly use `rejected_messages` instead of only providing `rejected_response` / `rejected_images` (requires ms-swift>=3.8), which offers greater flexibility (e.g., for multimodal or agent scenarios). If you use "rejected_messages", then in multimodal scenarios you must also provide "rejected_images", "rejected_audios", "rejected_videos", etc.; in Agent scenarios you must also provide "rejected_tools", etc. An example of the multimodal data format is as follows:
 
 ```jsonl
 {"messages": [{"role": "user", "content": "<image>What is this?"}, {"role": "assistant", "content": "This is a kitten."}], "images": ["kitten.png"], "rejected_messages": [{"role": "user", "content": "<image>What is this?"}, {"role": "assistant", "content": "This is a puppy."}], "rejected_images": ["kitten.png"]}

swift/llm/model/model/qwen.py

@@ -6,6 +6,7 @@
 from transformers import AutoConfig, AutoTokenizer, BitsAndBytesConfig, PreTrainedTokenizerBase
 from transformers.dynamic_module_utils import get_class_from_dynamic_module
 from transformers.models.auto.tokenization_auto import get_tokenizer_config
+from transformers.utils.versions import require_version
 
 from swift.llm import TemplateType
 from swift.utils import get_device_count, get_dist_setting, get_env_args, get_logger
@@ -714,6 +715,7 @@ def get_model_tokenizer_qwen2_vl(*args, **kwargs):
         patch_get_input_embeddings(base_model.visual, 'patch_embed')
 
     from qwen_vl_utils import vision_process
+    require_version('qwen_vl_utils<0.0.12')
     global_vars = patch_qwen_vl_utils(vision_process)
     tokenizer.global_vars = global_vars  # In order to have different hashes for the template.
     return model, tokenizer

swift/megatron/trainers/dpo_trainer.py

@@ -57,6 +57,7 @@ def setup_model_and_optimizer(self, model_provider_func, model_type, *_args, **k
     def _forward_step_helper(model, inputs):
         args = get_args()
         if mpu.is_pipeline_first_stage():
+            assert args.padding_free, 'Currently `rlhf_type="dpo"` only supports padding_free.'
             micro_batch_size = 1  # use qkv_format 'thd'
             seq_length = inputs['input_ids'].shape[1]
             if args.sequence_parallel: