From cfefacaa37453a15c55866d019887f24e886a577 Mon Sep 17 00:00:00 2001 From: hiyouga Date: Wed, 28 Feb 2024 19:53:28 +0800 Subject: [PATCH] support DoRA, AWQ, AQLM #2512 --- README.md | 2 ++ README_zh.md | 2 ++ requirements.txt | 4 ++-- src/llmtuner/extras/constants.py | 8 ++++++++ src/llmtuner/hparams/finetuning_args.py | 3 +++ src/llmtuner/hparams/parser.py | 15 +++++++++++---- src/llmtuner/model/adapter.py | 7 ++++++- src/llmtuner/model/loader.py | 2 +- src/llmtuner/model/patcher.py | 6 +++++- 9 files changed, 40 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index c8ec1054..841f6f6d 100644 --- a/README.md +++ b/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. diff --git a/README_zh.md b/README_zh.md index 6b2e3d53..7d2dbfe7 100644 --- a/README_zh.md +++ b/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/)。 diff --git a/requirements.txt b/requirements.txt index 94e011f8..6cda27c5 100644 --- a/requirements.txt +++ b/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 diff --git a/src/llmtuner/extras/constants.py b/src/llmtuner/extras/constants.py index e8e991fe..62221346 100644 --- a/src/llmtuner/extras/constants.py +++ b/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", ) diff --git a/src/llmtuner/hparams/finetuning_args.py b/src/llmtuner/hparams/finetuning_args.py index 88e4d65c..aebcb83a 100644 --- a/src/llmtuner/hparams/finetuning_args.py +++ b/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."}, diff --git a/src/llmtuner/hparams/parser.py b/src/llmtuner/hparams/parser.py index 6685816c..16e6c2c6 100644 --- a/src/llmtuner/hparams/parser.py +++ b/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) diff --git a/src/llmtuner/model/adapter.py b/src/llmtuner/model/adapter.py index c79055e0..83affd64 100644 --- a/src/llmtuner/model/adapter.py +++ b/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) diff --git a/src/llmtuner/model/loader.py b/src/llmtuner/model/loader.py index 29d213a7..cf3fb787 100644 --- a/src/llmtuner/model/loader.py +++ b/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: diff --git a/src/llmtuner/model/patcher.py b/src/llmtuner/model/patcher.py index 1dd9f2da..2eba15b6 100644 --- a/src/llmtuner/model/patcher.py +++ b/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