remove visual_inputs, fix qlora

Former-commit-id: be30c01c4f1482520ece770bd54c6a4837c26f0a

src/llamafactory/hparams/model_args.py

@@ -16,15 +16,12 @@
 # limitations under the License.
 
 from dataclasses import asdict, dataclass, field
-from typing import TYPE_CHECKING, Any, Dict, Literal, Optional, Union
+from typing import Any, Dict, Literal, Optional, Union
 
+import torch
 from typing_extensions import Self
 
 
-if TYPE_CHECKING:
-    import torch
-
-
 @dataclass
 class ModelArguments:
     r"""
@@ -121,10 +118,6 @@ class ModelArguments:
         default=False,
         metadata={"help": "Whether or not to enable liger kernel for faster training."},
     )
-    visual_inputs: bool = field(
-        default=False,
-        metadata={"help": "Whethor or not to use multimodal LLM that accepts visual inputs."},
-    )
     moe_aux_loss_coef: Optional[float] = field(
         default=None,
         metadata={"help": "Coefficient of the auxiliary router loss in mixture-of-experts model."},
@@ -225,19 +218,31 @@ class ModelArguments:
         default=False,
         metadata={"help": "For debugging purposes, print the status of the parameters in the model."},
     )
+    compute_dtype: Optional[torch.dtype] = field(
+        default=None,
+        init=False,
+        metadata={"help": "Torch data type for computing model outputs, derived from `fp/bf16`. Do not specify it."},
+    )
+    device_map: Optional[Union[str, Dict[str, Any]]] = field(
+        default=None,
+        init=False,
+        metadata={"help": "Device map for model placement, derived from training stage. Do not specify it."},
+    )
+    model_max_length: Optional[int] = field(
+        default=None,
+        init=False,
+        metadata={"help": "The maximum input length for model, derived from `cutoff_len`. Do not specify it."},
+    )
+    block_diag_attn: bool = field(
+        default=False,
+        init=False,
+        metadata={"help": "Whether use block diag attention or not, derived from `neat_packing`. Do not specify it."},
+    )
 
     def __post_init__(self):
-        self.compute_dtype: Optional["torch.dtype"] = None
-        self.device_map: Optional[Union[str, Dict[str, Any]]] = None
-        self.model_max_length: Optional[int] = None
-        self.block_diag_attn: bool = False
-
         if self.split_special_tokens and self.use_fast_tokenizer:
             raise ValueError("`split_special_tokens` is only supported for slow tokenizers.")
 
-        if self.visual_inputs and self.use_unsloth:
-            raise ValueError("Unsloth does not support MLLM yet. Stay tuned.")
-
         if self.adapter_name_or_path is not None:  # support merging multiple lora weights
             self.adapter_name_or_path = [path.strip() for path in self.adapter_name_or_path.split(",")]
 

src/llamafactory/hparams/parser.py

@@ -257,9 +257,6 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
     if model_args.infer_backend == "vllm":
         raise ValueError("vLLM backend is only available for API, CLI and Web.")
 
-    if model_args.visual_inputs and data_args.packing:
-        raise ValueError("Cannot use packing in MLLM fine-tuning.")
-
     if model_args.use_unsloth and is_deepspeed_zero3_enabled():
         raise ValueError("Unsloth is incompatible with DeepSpeed ZeRO-3.")
 
@@ -388,9 +385,6 @@ def get_infer_args(args: Optional[Dict[str, Any]] = None) -> _INFER_CLS:
         if model_args.adapter_name_or_path is not None and len(model_args.adapter_name_or_path) != 1:
             raise ValueError("vLLM only accepts a single adapter. Merge them first.")
 
-    if finetuning_args.stage == "rm" and model_args.visual_inputs:
-        raise ValueError("Reward server does not support MLLM yet. Stay tuned.")
-
     _verify_model_args(model_args, data_args, finetuning_args)
     _check_extra_dependencies(model_args, finetuning_args)