diff --git "a/docs/source/Instruction/\345\221\275\344\273\244\350\241\214\345\217\202\346\225\260.md" "b/docs/source/Instruction/\345\221\275\344\273\244\350\241\214\345\217\202\346\225\260.md" index 886c2dbe6..7bcc30bff 100644 --- "a/docs/source/Instruction/\345\221\275\344\273\244\350\241\214\345\217\202\346\225\260.md" +++ "b/docs/source/Instruction/\345\221\275\344\273\244\350\241\214\345\217\202\346\225\260.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。 diff --git a/docs/source_en/Instruction/Command-line-parameters.md b/docs/source_en/Instruction/Command-line-parameters.md index 94314ccf3..286e3c682 100644 --- a/docs/source_en/Instruction/Command-line-parameters.md +++ b/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. diff --git a/swift/llm/dataset/preprocessor/core.py b/swift/llm/dataset/preprocessor/core.py index f259857ef..59625a0df 100644 --- a/swift/llm/dataset/preprocessor/core.py +++ b/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 diff --git a/swift/llm/template/base.py b/swift/llm/template/base.py index 1f1c10f33..4671474b8 100644 --- a/swift/llm/template/base.py +++ b/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) diff --git a/swift/llm/template/template_inputs.py b/swift/llm/template/template_inputs.py index b72223004..f523beefd 100644 --- a/swift/llm/template/template_inputs.py +++ b/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'] diff --git a/swift/plugin/loss.py b/swift/plugin/loss.py index 3c7d3d1b7..4cf3efc60 100644 --- a/swift/plugin/loss.py +++ b/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 diff --git a/swift/trainers/trainers.py b/swift/trainers/trainers.py index 6ff2a4292..dc52a83c6 100644 --- a/swift/trainers/trainers.py +++ b/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', {})) diff --git a/tests/train/test_channel.py b/tests/train/test_channel.py new file mode 100644 index 000000000..584007a67 --- /dev/null +++ b/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()