support unsloth generate

src/llmtuner/model/adapter.py

@@ -7,10 +7,11 @@ from transformers.integrations import is_deepspeed_zero3_enabled
 from ..extras.logging import get_logger
 from .utils.misc import find_all_linear_modules, find_expanded_modules
 from .utils.quantization import QuantizationMethod
+from .utils.unsloth import get_unsloth_peft_model, load_unsloth_peft_model
 
 
 if TYPE_CHECKING:
-    from transformers.modeling_utils import PreTrainedModel
+    from transformers import PretrainedConfig, PreTrainedModel
 
     from ..hparams import FinetuningArguments, ModelArguments
 
@@ -19,7 +20,11 @@ logger = get_logger(__name__)
 
 
 def init_adapter(
-    model: "PreTrainedModel", model_args: "ModelArguments", finetuning_args: "FinetuningArguments", is_trainable: bool
+    config: "PretrainedConfig",
+    model: "PreTrainedModel",
+    model_args: "ModelArguments",
+    finetuning_args: "FinetuningArguments",
+    is_trainable: bool,
 ) -> "PreTrainedModel":
     r"""
     Initializes the adapters.
@@ -106,6 +111,10 @@ def init_adapter(
                 assert len(model_args.adapter_name_or_path) == 1, "Cannot use multiple adapters in DeepSpeed ZeRO-3."
                 is_mergeable = False
 
+            if model_args.use_unsloth:
+                assert len(model_args.adapter_name_or_path) == 1, "Unsloth model only accepts a single adapter."
+                is_mergeable = False
+
             if (is_trainable and not finetuning_args.create_new_adapter) or (not is_mergeable):
                 adapter_to_merge = model_args.adapter_name_or_path[:-1]
                 adapter_to_resume = model_args.adapter_name_or_path[-1]
@@ -122,9 +131,15 @@ def init_adapter(
                 logger.info("Merged {} adapter(s).".format(len(adapter_to_merge)))
 
             if adapter_to_resume is not None:  # resume lora training
-                model = PeftModel.from_pretrained(
-                    model, adapter_to_resume, is_trainable=is_trainable, offload_folder=model_args.offload_folder
-                )
+                if model_args.use_unsloth:
+                    model = load_unsloth_peft_model(config, model_args, is_trainable=is_trainable)
+                else:
+                    model = PeftModel.from_pretrained(
+                        model,
+                        adapter_to_resume,
+                        is_trainable=is_trainable,
+                        offload_folder=model_args.offload_folder,
+                    )
 
         if is_trainable and adapter_to_resume is None:  # create new lora weights while training
             if len(finetuning_args.lora_target) == 1 and finetuning_args.lora_target[0] == "all":
@@ -152,14 +167,8 @@ def init_adapter(
             }
 
             if model_args.use_unsloth:
-                from unsloth import FastLanguageModel  # type: ignore
-
-                unsloth_peft_kwargs = {
-                    "model": model,
-                    "max_seq_length": model_args.model_max_length,
-                    "use_gradient_checkpointing": "unsloth",
-                }
-                model = FastLanguageModel.get_peft_model(**peft_kwargs, **unsloth_peft_kwargs)
+                print(model)
+                model = get_unsloth_peft_model(model, model_args, peft_kwargs)
             else:
                 lora_config = LoraConfig(
                     task_type=TaskType.CAUSAL_LM,