[loss] fix vlm channel loss

docs/source/Instruction/命令行参数.md

@@ -150,6 +150,7 @@
 - 🔥report_to: 默认值为`tensorboard`。你也可以指定`--report_to tensorboard wandb swanlab`、`--report_to all`。
 - logging_first_step: 是否记录第一个step的日志，默认为True。
 - logging_steps: 日志打印间隔，默认为5。
+- logging_dir: tensorboard日志路径。默认为None，即设置为`f'{self.output_dir}/runs'`。
 - predict_with_generate: 验证时使用生成式的方式，默认为False。
 - metric_for_best_model: 默认为None，即当`predict_with_generate`设置为False时，设置为'loss'，否则设置为'rouge-l'（在PPO训练时，不进行默认值设置；GRPO训练设置为'reward'）。
 - greater_is_better: 默认为None，即当`metric_for_best_model`含'loss'时，设置为False，否则设置为True。

docs/source_en/Instruction/Command-line-parameters.md

@@ -153,6 +153,7 @@ This parameter list inherits from transformers `Seq2SeqTrainingArguments`, with
 - 🔥report_to: Default value is `tensorboard`. You can also specify `--report_to tensorboard wandb swanlab` or `--report_to all`.
 - logging_first_step: Whether to log the first step, defaults to True.
 - logging_steps: Interval for logging, defaults to 5.
+- logging_dir: The path for TensorBoard logs. Defaults to None, which means it is set to `f'{self.output_dir}/runs'`.
 - predict_with_generate: Whether to use generative method during validation, default is False.
 - metric_for_best_model: Default is None, which means that when predict_with_generate is set to False, it is set to 'loss'; otherwise, it is set to 'rouge-l' (during PPO training, the default value is not set; in GRPO training, it is set to 'reward').
 - greater_is_better: Defaults to None, which sets it to False when `metric_for_best_model` contains 'loss', otherwise sets to True.

swift/llm/dataset/preprocessor/core.py

@@ -310,7 +310,6 @@ def __call__(
         dataset = self._cast_pil_image(dataset)
 
         ignore_max_length_error = True if isinstance(dataset, HfDataset) and num_proc > 1 else False
-        keep_columns = ['channel']
         with self._patch_arrow_writer(), safe_ddp_context(None, True):
             try:
                 dataset_mapped = dataset.map(
@@ -319,7 +318,7 @@ def __call__(
                         'strict': strict,
                         'ignore_max_length_error': ignore_max_length_error
                     },
-                    remove_columns=[col for col in dataset.features.keys() if col not in keep_columns],
+                    remove_columns=list(dataset.features.keys()),
                     **map_kwargs)
             except NotImplementedError:
                 pass

swift/llm/template/base.py

@@ -432,6 +432,8 @@ def encode(self,
             encoded = self._kto_encode(inputs)
         elif self.mode == 'embedding':
             encoded = self._embedding_encode(inputs)
+        if inputs.channel is not None:
+            encoded['channel'] = inputs.channel
         for key in list(encoded.keys()):
             if encoded[key] is None:
                 encoded.pop(key)

swift/llm/template/template_inputs.py

@@ -102,6 +102,7 @@ class StdTemplateInputs:
 
     rejected_response: Optional[str] = None
     label: Optional[int] = None
+    channel: Optional[str] = None
 
     images: List[Union[str, Image.Image]] = field(default_factory=list)
     audios: List[str] = field(default_factory=list)
@@ -133,7 +134,7 @@ def is_multimodal(self):
     @classmethod
     def from_dict(cls, inputs: Dict[str, Any]) -> 'StdTemplateInputs':
         kwargs = {}
-        for key in ['rejected_response', 'label']:
+        for key in ['rejected_response', 'label', 'channel']:
             if key in inputs:
                 kwargs[key] = inputs[key]
         messages = inputs['messages']

swift/plugin/loss.py

@@ -388,9 +388,9 @@ def online_contrastive_loss(outputs, labels, loss_scale=None, num_items_in_batch
 
 @register_loss_func(LossType.channel_loss)
 def channel_loss_func(outputs, labels, num_items_in_batch=None, sample_channels=None, trainer=None) -> torch.Tensor:
-    assert sample_channels is not None, 'Data does not have channel field.'
     channels = trainer.args.channels
     assert channels is not None, 'Please pass --channels as a hyperparameter.'
+    assert sample_channels is not None, 'Data does not have channel field.'
     logits = outputs.logits
 
     # compute token loss

swift/trainers/trainers.py

@@ -166,7 +166,7 @@ def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=N
                 compute_loss_func = get_loss_func('loss_scale')
 
         sample_channels = inputs.pop('channel', None)
-        if sample_channels is not None:
+        if sample_channels is not None and self.args.channels is not None:
             state = self.state
             setattr(state, 'local_step', getattr(state, 'local_step', 0))
             setattr(state, 'ch_loss_steps', getattr(state, 'ch_loss_steps', {}))

tests/train/test_channel.py

@@ -0,0 +1,17 @@
+import os
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+
+def test_channel():
+    from swift.llm import sft_main, TrainArguments
+    sft_main(
+        TrainArguments(
+            model='Qwen/Qwen2.5-VL-7B-Instruct',
+            dataset=['channel.jsonl#1000'],
+            channels=['aaa', 'abc'],
+            loss_type='channel_loss'))
+
+
+if __name__ == '__main__':
+    test_channel()