Update finetuning_args.py

src/llmtuner/hparams/finetuning_args.py

@@ -163,47 +163,6 @@ class RLHFArguments:
         metadata={"help": "The type of the reward model in PPO training. Lora model only supports lora training."},
     )
 
-@dataclass
-class BAdamArgument:
-    r"""
-    Arguments for BAdam optimizer.
-    """
-    use_badam: bool = field(
-        default=False,
-        metadata={"help": "Whether or not to use BAdam optimizer."},
-    )
-    badam_mode: Literal["layer", "ratio"] = field(
-        default="layer",
-        metadata={"help": "The mode of BAdam optimizer. 'layer' for layer-wise, 'ratio' for ratio-wise."},
-    )
-    
-    # ======== Arguments for layer-wise update ========
-    start_block: Optional[int] = field(
-        default=None,
-        metadata={"help": "The starting block index for block-wise fine-tuning."}
-    )
-    switch_block_every: Optional[int] = field(
-        default=50,
-        metadata={"help": "how often to switch model's block update. Set to -1 to disable the block update."}
-    )
-    switch_mode: Optional[Literal["ascending", "descending", "random", "fixed"]] = field(
-        default="ascending",
-        metadata={"help": "the strategy of picking block to update."}
-    )
-    
-    # ======== Arguments for ratio-wise update ========
-    badam_update_ratio: float = field(
-        default=0.,
-        metadata={"help": "The ratio of the update for the BAdam optimizer."}
-    )
-    badam_mask_mode: Literal["adjacent", "scatter"] = field(
-        default="adjacent",
-        metadata={"help": "The mode of the mask for BAdam optimizer. `adjacent` means that the trainable parameters are adjacent to each other; `scatter` means that trainable parameters are randomly choosed from the weight."}
-    )
-    badam_verbose: int = field(
-        default=0,
-        metadata={"help": "The verbosity level of BAdam optimizer. 0 for no print, 1 for print the block prefix, 2 for print trainable parameters"}
-    )
 
 @dataclass
 class GaloreArguments:
@@ -213,7 +172,7 @@ class GaloreArguments:
 
     use_galore: bool = field(
         default=False,
-        metadata={"help": "Whether or not to use gradient low-Rank projection."},
+        metadata={"help": "Whether or not to use the gradient low-Rank projection (GaLore)."},
     )
     galore_target: str = field(
         default="all",
@@ -244,6 +203,53 @@ class GaloreArguments:
     )
 
 
+@dataclass
+class BAdamArgument:
+    r"""
+    Arguments pertaining to the BAdam optimizer.
+    """
+
+    use_badam: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to use the BAdam optimizer."},
+    )
+    badam_mode: Literal["layer", "ratio"] = field(
+        default="layer",
+        metadata={"help": "Whether to use layer-wise or ratio-wise BAdam optimizer."},
+    )
+    badam_start_block: Optional[int] = field(
+        default=None,
+        metadata={"help": "The starting block index for layer-wise BAdam."},
+    )
+    badam_switch_block_every: Optional[int] = field(
+        default=50,
+        metadata={"help": "How often to switch model's block update. Set to -1 to disable the block update."},
+    )
+    badam_switch_mode: Optional[Literal["ascending", "descending", "random", "fixed"]] = field(
+        default="ascending",
+        metadata={"help": "the strategy of picking block to update for layer-wise BAdam."},
+    )
+    badam_update_ratio: float = field(
+        default=0.0,
+        metadata={"help": "The ratio of the update for ratio-wise BAdam."},
+    )
+    badam_mask_mode: Literal["adjacent", "scatter"] = field(
+        default="adjacent",
+        metadata={
+            "help": """The mode of the mask for BAdam optimizer. \
+                    `adjacent` means that the trainable parameters are adjacent to each other, \
+                    `scatter` means that trainable parameters are randomly choosed from the weight."""
+        },
+    )
+    badam_verbose: int = field(
+        default=0,
+        metadata={
+            "help": """The verbosity level of BAdam optimizer. \
+                    0 for no print, 1 for print the block prefix, 2 for print trainable parameters"""
+        },
+    )
+
+
 @dataclass
 class FinetuningArguments(FreezeArguments, LoraArguments, RLHFArguments, GaloreArguments, BAdamArgument):
     r"""