support DoRA, AWQ, AQLM #2512

README.md

@@ -56,6 +56,8 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
 
 ## Changelog
 
+[24/02/28] We supported weight-decomposed LoRA (**[DoRA](https://arxiv.org/abs/2402.09353)**). Try `--use_dora` to activate DoRA training.
+
 [24/02/15] We supported **block expansion** proposed by [LLaMA Pro](https://github.com/TencentARC/LLaMA-Pro). See `tests/llama_pro.py` for usage.
 
 [24/02/05] Qwen1.5 (Qwen2 beta version) series models are supported in LLaMA-Factory. Check this [blog post](https://qwenlm.github.io/blog/qwen1.5/) for details.

README_zh.md

@@ -56,6 +56,8 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/6ba60acc-e2e2-4bec-b846
 
 ## 更新日志
 
+[24/02/28] 我们支持了 **[DoRA](https://arxiv.org/abs/2402.09353)** 微调。请使用 `--use_dora` 参数进行 DoRA 微调。
+
 [24/02/15] 我们支持了 [LLaMA Pro](https://github.com/TencentARC/LLaMA-Pro) 提出的**块扩展**方法。详细用法请参照 `tests/llama_pro.py`。
 
 [24/02/05] Qwen1.5（Qwen2 测试版）系列模型已在 LLaMA-Factory 中实现微调支持。详情请查阅该[博客页面](https://qwenlm.github.io/zh/blog/qwen1.5/)。

requirements.txt

@@ -2,8 +2,8 @@ torch>=1.13.1
 transformers>=4.37.2
 datasets>=2.14.3
 accelerate>=0.27.2
-peft>=0.8.2
-trl>=0.7.6
+peft>=0.9.0
+trl>=0.7.11
 gradio>=3.38.0,<4.0.0
 scipy
 einops

src/llmtuner/extras/constants.py

@@ -842,10 +842,18 @@ register_model_group(
             DownloadSource.DEFAULT: "01-ai/Yi-6B-Chat-8bits",
             DownloadSource.MODELSCOPE: "01ai/Yi-6B-Chat-8bits",
         },
+        "Yi-6B-int4-Chat": {
+            DownloadSource.DEFAULT: "01-ai/Yi-6B-Chat-4bits",
+            DownloadSource.MODELSCOPE: "01ai/Yi-6B-Chat-4bits",
+        },
         "Yi-34B-int8-Chat": {
             DownloadSource.DEFAULT: "01-ai/Yi-34B-Chat-8bits",
             DownloadSource.MODELSCOPE: "01ai/Yi-34B-Chat-8bits",
         },
+        "Yi-34B-int4-Chat": {
+            DownloadSource.DEFAULT: "01-ai/Yi-34B-Chat-4bits",
+            DownloadSource.MODELSCOPE: "01ai/Yi-34B-Chat-4bits",
+        },
     },
     template="yi",
 )

src/llmtuner/hparams/finetuning_args.py

@@ -74,6 +74,9 @@ class LoraArguments:
         default=False,
         metadata={"help": "Whether or not to use the rank stabilization scaling factor for LoRA layer."},
     )
+    use_dora: Optional[bool] = field(
+        default=False, metadata={"help": "Whether or not to use the weight-decomposed lora method (DoRA)."}
+    )
     create_new_adapter: Optional[bool] = field(
         default=False,
         metadata={"help": "Whether or not to create a new adapter with randomly initialized weight."},

src/llmtuner/hparams/parser.py

@@ -36,9 +36,9 @@ def _check_dependencies(disabled: bool) -> None:
     else:
         require_version("transformers>=4.37.2", "To fix: pip install transformers>=4.37.2")
         require_version("datasets>=2.14.3", "To fix: pip install datasets>=2.14.3")
-        require_version("accelerate>=0.21.0", "To fix: pip install accelerate>=0.21.0")
-        require_version("peft>=0.8.2", "To fix: pip install peft>=0.8.2")
-        require_version("trl>=0.7.6", "To fix: pip install trl>=0.7.6")
+        require_version("accelerate>=0.27.2", "To fix: pip install accelerate>=0.27.2")
+        require_version("peft>=0.9.0", "To fix: pip install peft>=0.9.0")
+        require_version("trl>=0.7.11", "To fix: pip install trl>=0.7.11")
 
 
 def _parse_args(parser: "HfArgumentParser", args: Optional[Dict[str, Any]] = None) -> Tuple[Any]:
@@ -144,7 +144,14 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
         raise ValueError("Please specify `lora_target` in LoRA training.")
 
     if training_args.do_train and model_args.use_unsloth and not is_unsloth_available:
-        raise ValueError("Install Unsloth: https://github.com/unslothai/unsloth")
+        raise ValueError("Unsloth was not installed: https://github.com/unslothai/unsloth")
+
+    if finetuning_args.use_dora:
+        if model_args.quantization_bit is not None:
+            raise ValueError("DoRA does not support quantization.")
+
+        if model_args.use_unsloth:
+            raise ValueError("Unsloth does not support DoRA.")
 
     _verify_model_args(model_args, finetuning_args)
     _check_dependencies(disabled=finetuning_args.disable_version_checking)

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