feat: add swanlab for experiment tracking and visualization.

src/llamafactory/hparams/finetuning_args.py

@@ -305,7 +305,37 @@ class BAdamArgument:
 
 
 @dataclass
-class FinetuningArguments(FreezeArguments, LoraArguments, RLHFArguments, GaloreArguments, BAdamArgument):
+class SwanLabArguments:
+    use_swanlab: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to use the SwanLab (an experiment tracking and visualization tool)."},
+    )
+    swanlab_project: str = field(
+        default="llamafactory",
+        metadata={"help": "The project name in SwanLab."},
+    )
+    swanlab_workspace: str = field(
+        default=None,
+        metadata={"help": "The workspace name in SwanLab."},
+    )
+    swanlab_experiment_name: str = field(
+        default=None,
+        metadata={"help": "The experiment name in SwanLab."},
+    )
+    swanlab_mode: Literal["cloud", "local"] = field(
+        default="cloud",
+        metadata={"help": "The mode of SwanLab."},
+    )
+    swanlab_api_key: str = field(
+        default=None,
+        metadata={"help": "The API key for SwanLab."},
+    )
+
+
+@dataclass
+class FinetuningArguments(
+    FreezeArguments, LoraArguments, RLHFArguments, GaloreArguments, BAdamArgument, SwanLabArguments
+):
     r"""
     Arguments pertaining to which techniques we are going to fine-tuning with.
     """

src/llamafactory/train/dpo/trainer.py

@@ -31,7 +31,7 @@
 from ...extras.constants import IGNORE_INDEX
 from ...extras.packages import is_transformers_version_equal_to_4_46
 from ..callbacks import PissaConvertCallback, SaveProcessorCallback
-from ..trainer_utils import create_custom_optimizer, create_custom_scheduler, get_batch_logps
+from ..trainer_utils import create_custom_optimizer, create_custom_scheduler, get_batch_logps, get_swanlab_callback
 
 
 if TYPE_CHECKING:
@@ -106,6 +106,9 @@ def __init__(
             self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
             self.add_callback(BAdamCallback)
 
+        if finetuning_args.use_swanlab:
+            self.add_callback(get_swanlab_callback(finetuning_args))
+
     @override
     def create_optimizer(self) -> "torch.optim.Optimizer":
         if self.optimizer is None:

src/llamafactory/train/kto/trainer.py

@@ -30,7 +30,7 @@
 from ...extras.constants import IGNORE_INDEX
 from ...extras.packages import is_transformers_version_equal_to_4_46
 from ..callbacks import SaveProcessorCallback
-from ..trainer_utils import create_custom_optimizer, create_custom_scheduler, get_batch_logps
+from ..trainer_utils import create_custom_optimizer, create_custom_scheduler, get_batch_logps, get_swanlab_callback
 
 
 if TYPE_CHECKING:
@@ -101,6 +101,9 @@ def __init__(
             self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
             self.add_callback(BAdamCallback)
 
+        if finetuning_args.use_swanlab:
+            self.add_callback(get_swanlab_callback(finetuning_args))
+
     @override
     def create_optimizer(self) -> "torch.optim.Optimizer":
         if self.optimizer is None:

src/llamafactory/train/ppo/trainer.py

@@ -40,7 +40,7 @@
 from ...extras import logging
 from ...extras.misc import AverageMeter, count_parameters, get_current_device, get_logits_processor
 from ..callbacks import FixValueHeadModelCallback, SaveProcessorCallback
-from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
+from ..trainer_utils import create_custom_optimizer, create_custom_scheduler, get_swanlab_callback
 from .ppo_utils import dump_layernorm, get_rewards_from_server, replace_model, restore_layernorm
 
 
@@ -186,6 +186,9 @@ def __init__(
             self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
             self.add_callback(BAdamCallback)
 
+        if finetuning_args.use_swanlab:
+            self.add_callback(get_swanlab_callback(finetuning_args))
+
     def ppo_train(self, resume_from_checkpoint: Optional[str] = None) -> None:
         r"""
         Implements training loop for the PPO stage, like _inner_training_loop() in Huggingface's Trainer.

src/llamafactory/train/pt/trainer.py

@@ -20,7 +20,7 @@
 
 from ...extras.packages import is_transformers_version_equal_to_4_46, is_transformers_version_greater_than
 from ..callbacks import PissaConvertCallback, SaveProcessorCallback
-from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
+from ..trainer_utils import create_custom_optimizer, create_custom_scheduler, get_swanlab_callback
 
 
 if TYPE_CHECKING:
@@ -56,6 +56,9 @@ def __init__(
             self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
             self.add_callback(BAdamCallback)
 
+        if finetuning_args.use_swanlab:
+            self.add_callback(get_swanlab_callback(finetuning_args))
+
     @override
     def create_optimizer(self) -> "torch.optim.Optimizer":
         if self.optimizer is None:

src/llamafactory/train/rm/trainer.py

@@ -27,7 +27,7 @@
 from ...extras import logging
 from ...extras.packages import is_transformers_version_equal_to_4_46, is_transformers_version_greater_than
 from ..callbacks import FixValueHeadModelCallback, PissaConvertCallback, SaveProcessorCallback
-from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
+from ..trainer_utils import create_custom_optimizer, create_custom_scheduler, get_swanlab_callback
 
 
 if TYPE_CHECKING:
@@ -68,6 +68,9 @@ def __init__(
             self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
             self.add_callback(BAdamCallback)
 
+        if finetuning_args.use_swanlab:
+            self.add_callback(get_swanlab_callback(finetuning_args))
+
     @override
     def create_optimizer(self) -> "torch.optim.Optimizer":
         if self.optimizer is None:

src/llamafactory/train/sft/trainer.py

@@ -29,7 +29,7 @@
 from ...extras.constants import IGNORE_INDEX
 from ...extras.packages import is_transformers_version_equal_to_4_46, is_transformers_version_greater_than
 from ..callbacks import PissaConvertCallback, SaveProcessorCallback
-from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
+from ..trainer_utils import create_custom_optimizer, create_custom_scheduler, get_swanlab_callback
 
 
 if TYPE_CHECKING:
@@ -71,6 +71,9 @@ def __init__(
             self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
             self.add_callback(BAdamCallback)
 
+        if finetuning_args.use_swanlab:
+            self.add_callback(get_swanlab_callback(finetuning_args))
+
     @override
     def create_optimizer(self) -> "torch.optim.Optimizer":
         if self.optimizer is None:

src/llamafactory/train/trainer_utils.py

@@ -40,7 +40,7 @@
 
 
 if TYPE_CHECKING:
-    from transformers import PreTrainedModel, Seq2SeqTrainingArguments
+    from transformers import PreTrainedModel, Seq2SeqTrainingArguments, TrainerCallback
     from trl import AutoModelForCausalLMWithValueHead
 
     from ..hparams import DataArguments
@@ -457,3 +457,24 @@ def get_batch_logps(
     labels[labels == label_pad_token_id] = 0  # dummy token
     per_token_logps = torch.gather(logits.log_softmax(-1), dim=2, index=labels.unsqueeze(2)).squeeze(2)
     return (per_token_logps * loss_mask).sum(-1), loss_mask.sum(-1)
+
+
+def get_swanlab_callback(finetuning_args: "FinetuningArguments") -> "TrainerCallback":
+    r"""
+    Gets the callback for logging to SwanLab.
+    """
+    import swanlab
+    from swanlab.integration.transformers import SwanLabCallback
+
+    if finetuning_args.swanlab_api_key is not None:
+        swanlab.login(api_key=finetuning_args.swanlab_api_key)
+
+    swanlab_callback = SwanLabCallback(
+        project=finetuning_args.swanlab_project,
+        workspace=finetuning_args.swanlab_workspace,
+        experiment_name=finetuning_args.swanlab_experiment_name,
+        mode=finetuning_args.swanlab_mode,
+        config={"Framework": "🦙LLaMA Factory"},
+    )
+
+    return swanlab_callback

src/llamafactory/webui/components/train.py

@@ -270,6 +270,28 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
         )
     )
 
+    with gr.Accordion(open=False) as swanlab_tab:
+        with gr.Row():
+            use_swanlab = gr.Checkbox()
+            swanlab_project = gr.Textbox(value="llamafactory", placeholder="Project name", interactive=True)
+            swanlab_experiment_name = gr.Textbox(value="", placeholder="Experiment name", interactive=True)
+            swanlab_workspace = gr.Textbox(value="", placeholder="Workspace name", interactive=True)
+            swanlab_api_key = gr.Textbox(value="", placeholder="API key", interactive=True)
+            swanlab_mode = gr.Dropdown(choices=["cloud", "local", "disabled"], value="cloud", interactive=True)
+
+    input_elems.update({use_swanlab, swanlab_api_key, swanlab_project, swanlab_workspace, swanlab_experiment_name, swanlab_mode})
+    elem_dict.update(
+        dict(
+            swanlab_tab=swanlab_tab,
+            use_swanlab=use_swanlab,
+            swanlab_api_key=swanlab_api_key,
+            swanlab_project=swanlab_project,
+            swanlab_workspace=swanlab_workspace,
+            swanlab_experiment_name=swanlab_experiment_name,
+            swanlab_mode=swanlab_mode,
+        )
+    )
+
     with gr.Row():
         cmd_preview_btn = gr.Button()
         arg_save_btn = gr.Button()

src/llamafactory/webui/locales.py

@@ -1353,6 +1353,121 @@
             "info": "비율-BAdam의 업데이트 비율.",
         },
     },
+    "swanlab_tab": {
+        "en": {
+            "label": "SwanLab configurations",
+        },
+        "ru": {
+            "label": "Конфигурации SwanLab",
+        },
+        "zh": {
+            "label": "SwanLab 参数设置",
+        },
+        "ko": {
+            "label": "SwanLab 설정",
+        },
+    },
+    "use_swanlab": {
+        "en": {
+            "label": "Use SwanLab",
+            "info": "Enable SwanLab for experiment tracking and visualization.",
+        },
+        "ru": {
+            "label": "Использовать SwanLab",
+            "info": "Включить SwanLab для отслеживания и визуализации экспериментов.",
+        },
+        "zh": {
+            "label": "使用 SwanLab",
+            "info": "启用 SwanLab 进行实验跟踪和可视化。",
+        },
+        "ko": {
+            "label": "SwanLab 사용",
+            "info": "SwanLab를 사용하여 실험을 추적하고 시각화합니다.",
+        },
+    },
+    "swanlab_api_key": {
+        "en": {
+            "label": "API Key(optional)",
+            "info": "API key for SwanLab. Once logged in, no need to login again in the programming environment.",
+        },
+        "ru": {
+            "label": "API ключ(Необязательный)",
+            "info": "API ключ для SwanLab. После входа в программное окружение, нет необходимости входить снова.",
+        },
+        "zh": {
+            "label": "API密钥(选填)",
+            "info": "用于在编程环境登录SwanLab，已登录则无需填写。",
+        },
+        "ko": {
+            "label": "API 키(선택 사항)",
+            "info": "SwanLab의 API 키. 프로그래밍 환경에 로그인한 후 다시 로그인할 필요가 없습니다.",
+        },
+    },
+    "swanlab_project": {
+        "en": {
+            "label": "Project(optional)",
+        },
+        "ru": {
+            "label": "Проект(Необязательный)",
+        },
+        "zh": {
+            "label": "项目(选填)",
+        },
+        "ko": {
+            "label": "프로젝트(선택 사항)",
+        },
+    },
+    "swanlab_workspace": {
+        "en": {
+            "label": "Workspace(optional)",
+            "info": "Workspace for SwanLab. If not filled, it defaults to the personal workspace.",
+
+        },
+        "ru": {
+            "label": "Рабочая область(Необязательный)",
+            "info": "Рабочая область SwanLab, если не заполнено, то по умолчанию в личной рабочей области.",
+        },
+        "zh": {
+            "label": "Workspace(选填)",
+            "info": "SwanLab组织的工作区，如不填写则默认在个人工作区下",
+        },
+        "ko": {
+            "label": "작업 영역(선택 사항)",
+            "info": "SwanLab 조직의 작업 영역, 비어 있으면 기본적으로 개인 작업 영역에 있습니다.",
+        },
+    },
+    "swanlab_experiment_name": {
+        "en": {
+            "label": "Experiment name (optional)",
+        },
+        "ru": {
+            "label": "Имя эксперимента(Необязательный)",
+        },
+        "zh": {
+            "label": "实验名(选填)  ",
+        },
+        "ko": {
+            "label": "실험 이름(선택 사항)",
+        },
+    },
+    "swanlab_mode": {
+        "en": {
+            "label": "Mode",
+            "info": "Cloud or offline version.",    
+        },
+        "ru": {
+            "label": "Режим",
+            "info": "Версия в облаке или локальная версия.",
+        },
+        "zh": {
+            "label": "模式",
+            "info": "云端版或离线版",
+        },
+        "ko": {
+            "label": "모드",
+            "info": "클라우드 버전 또는 오프라인 버전.",
+        },
+    },
     "cmd_preview_btn": {
         "en": {
             "value": "Preview command",

src/llamafactory/webui/runner.py

@@ -147,6 +147,7 @@ def _parse_train_args(self, data: Dict["Component", Any]) -> Dict[str, Any]:
             report_to="all" if get("train.report_to") else "none",
             use_galore=get("train.use_galore"),
             use_badam=get("train.use_badam"),
+            use_swanlab=get("train.use_swanlab"),
             output_dir=get_save_dir(model_name, finetuning_type, get("train.output_dir")),
             fp16=(get("train.compute_type") == "fp16"),
             bf16=(get("train.compute_type") == "bf16"),
@@ -228,6 +229,15 @@ def _parse_train_args(self, data: Dict["Component", Any]) -> Dict[str, Any]:
             args["badam_switch_interval"] = get("train.badam_switch_interval")
             args["badam_update_ratio"] = get("train.badam_update_ratio")
 
+        # swanlab config
+        if get("train.use_swanlab"):
+            args["swanlab_api_key"] = get("train.swanlab_api_key")
+            args["swanlab_project"] = get("train.swanlab_project")
+            args["swanlab_workspace"] = get("train.swanlab_workspace")
+            args["swanlab_experiment_name"] = get("train.swanlab_experiment_name")
+            args["swanlab_mode"] = get("train.swanlab_mode")
+
+
         # eval config
         if get("train.val_size") > 1e-6 and args["stage"] != "ppo":
             args["val_size"] = get("train.val_size")