[v1] add sft (#9752)

src/llamafactory/v1/config/arg_parser.py

@@ -30,9 +30,9 @@ from .training_args import TrainingArguments
 InputArgument = dict[str, Any] | list[str] | None
 
 
-def get_args(args: InputArgument = None) -> tuple[DataArguments, ModelArguments, TrainingArguments, SampleArguments]:
+def get_args(args: InputArgument = None) -> tuple[ModelArguments, DataArguments, TrainingArguments, SampleArguments]:
     """Parse arguments from command line or config file."""
-    parser = HfArgumentParser([DataArguments, ModelArguments, TrainingArguments, SampleArguments])
+    parser = HfArgumentParser([ModelArguments, DataArguments, TrainingArguments, SampleArguments])
     allow_extra_keys = is_env_enabled("ALLOW_EXTRA_KEYS")
 
     if args is None:

src/llamafactory/v1/config/data_args.py

@@ -18,7 +18,11 @@ from dataclasses import dataclass, field
 
 @dataclass
 class DataArguments:
-    dataset: str | None = field(
+    train_dataset: str | None = field(
         default=None,
-        metadata={"help": "Path to the dataset."},
+        metadata={"help": "Path to the training dataset."},
+    )
+    eval_dataset: str | None = field(
+        default=None,
+        metadata={"help": "Path to the evaluation dataset."},
     )

src/llamafactory/v1/config/training_args.py

@@ -33,13 +33,21 @@ class TrainingArguments:
         default=None,
         metadata={"help": "Global batch size for training, default to DP size * micro batch size."},
     )
+    cutoff_len: int = field(
+        default=2048,
+        metadata={"help": "Maximum sequence length for training."},
+    )
     learning_rate: float = field(
         default=1e-4,
         metadata={"help": "Learning rate for training."},
     )
-    cutoff_len: int = field(
-        default=2048,
-        metadata={"help": "Maximum sequence length for training."},
+    num_train_epochs: int = field(
+        default=3,
+        metadata={"help": "Number of training epochs."},
+    )
+    max_grad_norm: float = field(
+        default=1.0,
+        metadata={"help": "Maximum gradient norm for training."},
     )
     bf16: bool = field(
         default=False,
@@ -53,10 +61,24 @@ class TrainingArguments:
         default=16,
         metadata={"help": "Number of workers for batching."},
     )
+    enable_activation_checkpointing: bool = field(
+        default=True,
+        metadata={"help": "Enable activation checkpointing for training."},
+    )
     dist_config: PluginConfig | None = field(
         default=None,
         metadata={"help": "Distribution configuration for training."},
     )
+    optim_config: PluginConfig | None = field(
+        default=None,
+        metadata={"help": "Optimizer configuration for training."},
+    )
+    lr_scheduler_config: PluginConfig | None = field(
+        default=None,
+        metadata={"help": "Learning rate scheduler configuration for training."},
+    )
 
     def __post_init__(self) -> None:
         self.dist_config = get_plugin_config(self.dist_config)
+        self.optim_config = get_plugin_config(self.optim_config)
+        self.lr_scheduler_config = get_plugin_config(self.lr_scheduler_config)