support DoRA, AWQ, AQLM #2512

Former-commit-id: 6614cc1f08aa944db083e27e451bbdd733f7dd97

src/llmtuner/model/adapter.py

@@ -85,7 +85,7 @@ def init_adapter(
         logger.info("Set trainable layers: {}".format(",".join(map(str, trainable_layer_ids))))
 
     if finetuning_args.finetuning_type == "lora":
-        logger.info("Fine-tuning method: LoRA")
+        logger.info("Fine-tuning method: {}".format("DoRA" if finetuning_args.use_dora else "LoRA"))
         adapter_to_resume = None
 
         if model_args.adapter_name_or_path is not None:
@@ -123,6 +123,10 @@ def init_adapter(
             if finetuning_args.use_llama_pro:
                 target_modules = find_expanded_modules(model, target_modules, finetuning_args.num_layer_trainable)
 
+            if finetuning_args.use_dora:
+                if getattr(model, "quantization_method", None):
+                    raise ValueError("DoRA is currently not compatible with quantized models.")
+
             peft_kwargs = {
                 "r": finetuning_args.lora_rank,
                 "target_modules": target_modules,
@@ -141,6 +145,7 @@ def init_adapter(
                     task_type=TaskType.CAUSAL_LM,
                     inference_mode=False,
                     modules_to_save=finetuning_args.additional_target,
+                    use_dora=finetuning_args.use_dora,
                     **peft_kwargs,
                 )
                 model = get_peft_model(model, lora_config)

src/llmtuner/model/loader.py

@@ -51,7 +51,7 @@ def load_model_and_tokenizer(
     patch_tokenizer(tokenizer)
 
     config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
-    patch_config(config, tokenizer, model_args, config_kwargs, is_trainable)
+    patch_config(config, tokenizer, model_args, finetuning_args, config_kwargs, is_trainable)
 
     model = None
     if is_trainable and model_args.use_unsloth:

src/llmtuner/model/patcher.py

@@ -24,7 +24,7 @@ if TYPE_CHECKING:
     from transformers import PretrainedConfig, PreTrainedTokenizer
     from trl import AutoModelForCausalLMWithValueHead
 
-    from ..hparams import ModelArguments
+    from ..hparams import FinetuningArguments, ModelArguments
 
 
 logger = get_logger(__name__)
@@ -253,6 +253,7 @@ def patch_config(
     config: "PretrainedConfig",
     tokenizer: "PreTrainedTokenizer",
     model_args: "ModelArguments",
+    finetuning_args: "FinetuningArguments",
     config_kwargs: Dict[str, Any],
     is_trainable: bool,
 ) -> None:
@@ -273,6 +274,9 @@ def patch_config(
 
     _configure_quantization(config, tokenizer, model_args, config_kwargs)
 
+    if finetuning_args.use_dora:
+        config_kwargs["device_map"] = {"": get_current_device()}
+
 
 def patch_model(
     model: "PreTrainedModel", tokenizer: "PreTrainedTokenizer", model_args: "ModelArguments", is_trainable: bool