Merge pull request #26 from BUAADreamer/main

add code for reading from multi files in one directory
2 years ago · e3f380c1be
6 changed files with 209 additions and 159 deletions
--- a/data/dataset_info.json
+++ b/data/dataset_info.json
@ -1,107 +1,116 @@
 {
-    "alpaca_en": {
-        "hf_hub_url": "tatsu-lab/alpaca"
-    },
-    "alpaca_zh": {
-        "file_name": "alpaca_data_zh_51k.json",
-        "file_sha1": "e655af3db557a4197f7b0cf92e1986b08fae6311"
-    },
-    "alpaca_gpt4_en": {
-        "file_name": "alpaca_gpt4_data_en.json",
-        "file_sha1": "647f4ad447bd993e4b6b6223d1be15208bab694a"
-    },
-    "alpaca_gpt4_zh": {
-        "file_name": "alpaca_gpt4_data_zh.json",
-        "file_sha1": "3eaa3bda364ccdd59925d7448a698256c31ef845"
-    },
-    "belle_0.5m": {
-        "hf_hub_url": "BelleGroup/train_0.5M_CN"
-    },
-    "belle_1m": {
-        "hf_hub_url": "BelleGroup/train_1M_CN"
-    },
-    "belle_2m": {
-        "hf_hub_url": "BelleGroup/train_2M_CN"
-    },
-    "belle_dialog": {
-        "hf_hub_url": "BelleGroup/generated_chat_0.4M"
-    },
-    "belle_math": {
-        "hf_hub_url": "BelleGroup/school_math_0.25M"
-    },
-    "belle_multiturn": {
-        "hf_hub_url": "BelleGroup/multiturn_chat_0.8M"
-    },
-    "guanaco": {
-        "hf_hub_url": "JosephusCheung/GuanacoDataset"
-    },
-    "firefly": {
-        "hf_hub_url": "YeungNLP/firefly-train-1.1M",
-        "columns": {
-            "prompt": "input",
-            "query": "",
-            "response": "target",
-            "history": ""
-        }
-    },
-    "codealpaca": {
-        "hf_hub_url": "sahil2801/CodeAlpaca-20k"
-    },
-    "alpaca_cot": {
-        "hf_hub_url": "QingyiSi/Alpaca-CoT"
-    },
-    "webqa": {
-        "hf_hub_url": "suolyer/webqa",
-        "columns": {
-            "prompt": "input",
-            "query": "",
-            "response": "output",
-            "history": ""
-        }
-    },
-    "ultra_chat": {
-        "script_url": "ultra_chat",
-        "columns": {
-            "prompt": "instruction",
-            "query": "",
-            "response": "output",
-            "history": "history"
-        }
-    },
-    "example": {
-        "script_url": "example_dataset",
-        "columns": {
-            "prompt": "instruction",
-            "query": "input",
-            "response": "output",
-            "history": "history"
-        }
-    },
-    "comparison_gpt4_en": {
-        "file_name": "comparison_gpt4_data_en.json",
-        "file_sha1": "eeb295ce0ab011c37af52596460c8a57d07ad19f"
-    },
-    "comparison_gpt4_zh": {
-        "file_name": "comparison_gpt4_data_zh.json",
-        "file_sha1": "b99a41c1c864019d9b0c07dbcd5df0560cf33ce0"
-    },
-    "hh_rlhf_en": {
-        "script_url": "hh_rlhf_en",
-        "columns": {
-            "prompt": "instruction",
-            "query": "",
-            "response": "output",
-            "history": "history"
-        }
-    },
-    "wiki_demo": {
-        "file_name": "wiki_demo.txt",
-        "file_sha1": "b2288edb05b233e5b35250fd4b308a5fa21fa66d",
-        "columns": {
-            "prompt": "text",
-            "query": "",
-            "response": "",
-            "history": ""
-        }
+  "alpaca_en": {
+    "hf_hub_url": "tatsu-lab/alpaca"
+  },
+  "alpaca_zh": {
+    "file_name": "alpaca_data_zh_51k.json",
+    "file_sha1": "e655af3db557a4197f7b0cf92e1986b08fae6311"
+  },
+  "alpaca_gpt4_en": {
+    "file_name": "alpaca_gpt4_data_en.json",
+    "file_sha1": "647f4ad447bd993e4b6b6223d1be15208bab694a"
+  },
+  "alpaca_gpt4_zh": {
+    "file_name": "alpaca_gpt4_data_zh.json",
+    "file_sha1": "3eaa3bda364ccdd59925d7448a698256c31ef845"
+  },
+  "belle_0.5m": {
+    "hf_hub_url": "BelleGroup/train_0.5M_CN"
+  },
+  "belle_1m": {
+    "hf_hub_url": "BelleGroup/train_1M_CN"
+  },
+  "belle_2m": {
+    "hf_hub_url": "BelleGroup/train_2M_CN"
+  },
+  "belle_dialog": {
+    "hf_hub_url": "BelleGroup/generated_chat_0.4M"
+  },
+  "belle_math": {
+    "hf_hub_url": "BelleGroup/school_math_0.25M"
+  },
+  "belle_multiturn": {
+    "hf_hub_url": "BelleGroup/multiturn_chat_0.8M"
+  },
+  "guanaco": {
+    "hf_hub_url": "JosephusCheung/GuanacoDataset"
+  },
+  "firefly": {
+    "hf_hub_url": "YeungNLP/firefly-train-1.1M",
+    "columns": {
+      "prompt": "input",
+      "query": "",
+      "response": "target",
+      "history": ""
    }
+  },
+  "codealpaca": {
+    "hf_hub_url": "sahil2801/CodeAlpaca-20k"
+  },
+  "alpaca_cot": {
+    "hf_hub_url": "QingyiSi/Alpaca-CoT"
+  },
+  "webqa": {
+    "hf_hub_url": "suolyer/webqa",
+    "columns": {
+      "prompt": "input",
+      "query": "",
+      "response": "output",
+      "history": ""
+    }
+  },
+  "ultra_chat": {
+    "script_url": "ultra_chat",
+    "columns": {
+      "prompt": "instruction",
+      "query": "",
+      "response": "output",
+      "history": "history"
+    }
+  },
+  "example": {
+    "script_url": "example_dataset",
+    "columns": {
+      "prompt": "instruction",
+      "query": "input",
+      "response": "output",
+      "history": "history"
+    }
+  },
+  "comparison_gpt4_en": {
+    "file_name": "comparison_gpt4_data_en.json",
+    "file_sha1": "eeb295ce0ab011c37af52596460c8a57d07ad19f"
+  },
+  "comparison_gpt4_zh": {
+    "file_name": "comparison_gpt4_data_zh.json",
+    "file_sha1": "b99a41c1c864019d9b0c07dbcd5df0560cf33ce0"
+  },
+  "hh_rlhf_en": {
+    "script_url": "hh_rlhf_en",
+    "columns": {
+      "prompt": "instruction",
+      "query": "",
+      "response": "output",
+      "history": "history"
+    }
+  },
+  "wiki_demo": {
+    "file_name": "wiki_demo.txt",
+    "file_sha1": "b2288edb05b233e5b35250fd4b308a5fa21fa66d",
+    "columns": {
+      "prompt": "text",
+      "query": "",
+      "response": "",
+      "history": ""
+    }
+  },
+  "pretrain_data": {
+    "file_name": "pretrain_data",
+    "columns": {
+      "prompt": "content",
+      "query": "",
+      "response": "",
+      "history": ""
+    }
+  }
 }
--- a/data/pretrain_data/blog.json
+++ b/data/pretrain_data/blog.json
@ -0,0 +1,7 @@
+[
+  {
+    "id": 0,
+    "title": "拥有自己的航空器",
+    "content": "想自己驾驶飞机或从事通用航空事业的人，大都想拥有自己的航空器。\"自己的\"意思包括自己购买、自己制造、可供自己使用（租用或借用）等等。\n花自己的钱买一架飞机来开一开，国内有些人或企业已实现了这个愿望。现在一架国产超轻型的“蜜蜂”飞机售价在l0万元以下，进口的一些单发的双座飞机售价在100万元之内。据估计，全国大约有几十万人具有这种购买能力。\n自己造一架飞机来开也是一个好创意。美国的通航飞机中有l／5是自制的。有的自制飞机甚至还创造了世界飞行纪录。今天自己造飞机比当年莱特兄弟容易多了。飞机的基本构造已无秘密可言，各种飞机部件和材料都不难买到。尤其主要的是，技术进步大大改进了配件的性能，与此同时，配件的重量也下降了很多。莱特兄弟当年使用的12马力汽油发动机比现在30马力的同类产品还重。如果有人有志于此而且具备造飞机的种种条件，应该说这个目标也是可以实现的。有两点值得注意，一是在莱特兄弟造飞机时没有前人经验，全靠自己摸索。现在不同了，航空制造已有了上百年的知识和经验可供后人学习和利用。现在如果谁想自己造飞机就不用闭门造车了。制造者本人首先应该去学习和掌握一些必要知识和经验才行。其次，在莱特兄弟时代，没有国家民航当局，他们的航空活动不受法规约束。今天就不一样了，所有要升空的航空器必须先接受民航当局的鉴定，以保证飞行安全。绝不允许以生命为赌注的任何冒险行为。\n租用飞机也是实现自驾飞机的方式之一。国内也还有另一种形式，即参加飞行驾驶学校接受培训，当然所交的学费价格是不菲的。预计未来在我国必将出现出各类飞行俱乐部。到那时，飞行爱好者可以租用飞机去上天过一把瘾了。"
+  }
+]
--- a/data/pretrain_data/wiki.json
+++ b/data/pretrain_data/wiki.json
@ -0,0 +1,12 @@
+[
+  {
+    "id": 0,
+    "title": "大卫·亨利",
+    "content": "大卫·亨利\n\n大卫·克莱顿·亨利（David Clayton Henrie，），美国演员。近来在迪士尼频道原创电视影集《少年魔法师》（Wizards of Waverly Place）当中演出贾斯汀·鲁索（Justin Russo）一角。\n\n大卫·亨利出生在加州Mission Viejo，在凤凰城长大。他的胞弟劳伦斯·亨利（Lorenzo Henrie）也是演员。大卫·亨利就读夏安传统学校。家中是信奉罗马天主教。 \n\n大卫在2007年拍摄少年魔法师期间认识女演员露西·海尔（Lucy Hale），之后与其交往，于2009年分手。\n\n10岁时，大卫·亨利和SAG在凤凰城签订了合约，并开始走出去试镜。 9岁的时候，在沙加缅度进行商业拍摄，SAG董事建议大卫·亨利搬到洛杉矶。在10岁那年夏天，他和他的家人搬到了好莱坞。他预定他的前2支商业试镜，扮演主要角色为汉堡王和桂格燕麦。他初演电视节目为Providence。 \n\n到了13岁，大卫有了他的第一次重大突破，在福克斯公司的喜剧The Pitts饰演 Petey Pitt一角。大卫下出作品为的Hallmark movie为Monster Maker，和琳达布莱儿、乔治甘迺迪共同演出，并要求回来Hallmark movie公司。 \n\n在18岁时，大卫得到了迪士尼频道原创系列演出机会，该节目2007年10月12日首播。大卫2008年参加了迪士尼频道的游戏节目。他是绿色团队的队长，隔年，为旋风队队长。他在迪士尼原创电影《少年魔法师》之后在《酷爸的疯狂假期》中有饰演一角。\n"
+  },
+  {
+    "id": 1,
+    "title": "大卫·亨利",
+    "content": "大卫·亨利\n\n大卫·克莱顿·亨利（David Clayton Henrie，），美国演员。近来在迪士尼频道原创电视影集《少年魔法师》（Wizards of Waverly Place）当中演出贾斯汀·鲁索（Justin Russo）一角。\n\n大卫·亨利出生在加州Mission Viejo，在凤凰城长大。他的胞弟劳伦斯·亨利（Lorenzo Henrie）也是演员。大卫·亨利就读夏安传统学校。家中是信奉罗马天主教。 \n\n大卫在2007年拍摄少年魔法师期间认识女演员露西·海尔（Lucy Hale），之后与其交往，于2009年分手。\n\n10岁时，大卫·亨利和SAG在凤凰城签订了合约，并开始走出去试镜。 9岁的时候，在沙加缅度进行商业拍摄，SAG董事建议大卫·亨利搬到洛杉矶。在10岁那年夏天，他和他的家人搬到了好莱坞。他预定他的前2支商业试镜，扮演主要角色为汉堡王和桂格燕麦。他初演电视节目为Providence。 \n\n到了13岁，大卫有了他的第一次重大突破，在福克斯公司的喜剧The Pitts饰演 Petey Pitt一角。大卫下出作品为的Hallmark movie为Monster Maker，和琳达布莱儿、乔治甘迺迪共同演出，并要求回来Hallmark movie公司。 \n\n在18岁时，大卫得到了迪士尼频道原创系列演出机会，该节目2007年10月12日首播。大卫2008年参加了迪士尼频道的游戏节目。他是绿色团队的队长，隔年，为旋风队队长。他在迪士尼原创电影《少年魔法师》之后在《酷爸的疯狂假期》中有饰演一角。\n"
+  }
+]
--- a/data/pretrain_data/wiki_long.jsonl
+++ b/data/pretrain_data/wiki_long.jsonl
@ -0,0 +1,2 @@
+{"id": 0,"title": "大卫·亨利","content": "大卫·亨利\n\n大卫·克莱顿·亨利（David Clayton Henrie，），美国演员。近来在迪士尼频道原创电视影集《少年魔法师》（Wizards of Waverly Place）当中演出贾斯汀·鲁索（Justin Russo）一角。\n\n大卫·亨利出生在加州Mission Viejo，在凤凰城长大。他的胞弟劳伦斯·亨利（Lorenzo Henrie）也是演员。大卫·亨利就读夏安传统学校。家中是信奉罗马天主教。 \n\n大卫在2007年拍摄少年魔法师期间认识女演员露西·海尔（Lucy Hale），之后与其交往，于2009年分手。\n\n10岁时，大卫·亨利和SAG在凤凰城签订了合约，并开始走出去试镜。 9岁的时候，在沙加缅度进行商业拍摄，SAG董事建议大卫·亨利搬到洛杉矶。在10岁那年夏天，他和他的家人搬到了好莱坞。他预定他的前2支商业试镜，扮演主要角色为汉堡王和桂格燕麦。他初演电视节目为Providence。 \n\n到了13岁，大卫有了他的第一次重大突破，在福克斯公司的喜剧The Pitts饰演 Petey Pitt一角。大卫下出作品为的Hallmark movie为Monster Maker，和琳达布莱儿、乔治甘迺迪共同演出，并要求回来Hallmark movie公司。 \n\n在18岁时，大卫得到了迪士尼频道原创系列演出机会，该节目2007年10月12日首播。大卫2008年参加了迪士尼频道的游戏节目。他是绿色团队的队长，隔年，为旋风队队长。他在迪士尼原创电影《少年魔法师》之后在《酷爸的疯狂假期》中有饰演一角。\n"}
+{"id": 1,"title": "大卫·亨利","content": "大卫·亨利\n\n大卫·克莱顿·亨利（David Clayton Henrie，），美国演员。近来在迪士尼频道原创电视影集《少年魔法师》（Wizards of Waverly Place）当中演出贾斯汀·鲁索（Justin Russo）一角。\n\n大卫·亨利出生在加州Mission Viejo，在凤凰城长大。他的胞弟劳伦斯·亨利（Lorenzo Henrie）也是演员。大卫·亨利就读夏安传统学校。家中是信奉罗马天主教。 \n\n大卫在2007年拍摄少年魔法师期间认识女演员露西·海尔（Lucy Hale），之后与其交往，于2009年分手。\n\n10岁时，大卫·亨利和SAG在凤凰城签订了合约，并开始走出去试镜。 9岁的时候，在沙加缅度进行商业拍摄，SAG董事建议大卫·亨利搬到洛杉矶。在10岁那年夏天，他和他的家人搬到了好莱坞。他预定他的前2支商业试镜，扮演主要角色为汉堡王和桂格燕麦。他初演电视节目为Providence。 \n\n到了13岁，大卫有了他的第一次重大突破，在福克斯公司的喜剧The Pitts饰演 Petey Pitt一角。大卫下出作品为的Hallmark movie为Monster Maker，和琳达布莱儿、乔治甘迺迪共同演出，并要求回来Hallmark movie公司。 \n\n在18岁时，大卫得到了迪士尼频道原创系列演出机会，该节目2007年10月12日首播。大卫2008年参加了迪士尼频道的游戏节目。他是绿色团队的队长，隔年，为旋风队队长。他在迪士尼原创电影《少年魔法师》之后在《酷爸的疯狂假期》中有饰演一角。\n"}
--- a/src/utils/common.py
+++ b/src/utils/common.py
@ -56,7 +56,6 @@ require_version("accelerate>=0.19.0", "To fix: pip install accelerate>=0.19.0")
 require_version("peft>=0.3.0", "To fix: pip install peft>=0.3.0")
 require_version("trl>=0.4.1", "To fix: pip install trl>=0.4.1")

-
 logger = get_logger(__name__)


@ -92,10 +91,12 @@ def _init_adapter(

    if model_args.checkpoint_dir is not None:
        if finetuning_args.finetuning_type != "lora":
-            assert is_mergeable and len(model_args.checkpoint_dir) == 1, "Only LoRA tuning accepts multiple checkpoints."
-            load_trainable_params(model, model_args.checkpoint_dir[0]) # load model checkpoints for non-peft methods
+            assert is_mergeable and len(
+                model_args.checkpoint_dir) == 1, "Only LoRA tuning accepts multiple checkpoints."
+            load_trainable_params(model, model_args.checkpoint_dir[0])  # load model checkpoints for non-peft methods
        else:
-            assert is_mergeable or len(model_args.checkpoint_dir) == 1, "Quantized model only accepts a single checkpoint."
+            assert is_mergeable or len(
+                model_args.checkpoint_dir) == 1, "Quantized model only accepts a single checkpoint."

    if finetuning_args.finetuning_type == "lora":
        logger.info("Fine-tuning method: LoRA")
@ -105,7 +106,8 @@ def _init_adapter(
            assert os.path.exists(os.path.join(model_args.checkpoint_dir[0], CONFIG_NAME)), \
                "The given checkpoint is not a LoRA checkpoint, please specify `--finetuning_type full/freeze` instead."

-            if (is_trainable and model_args.resume_lora_training) or (not is_mergeable): # continually train on the lora weights
+            if (is_trainable and model_args.resume_lora_training) or (
+                    not is_mergeable):  # continually train on the lora weights
                checkpoints_to_merge, lastest_checkpoint = model_args.checkpoint_dir[:-1], model_args.checkpoint_dir[-1]
            else:
                checkpoints_to_merge = model_args.checkpoint_dir
@ -117,10 +119,10 @@ def _init_adapter(
            if len(checkpoints_to_merge) > 0:
                logger.info("Merged {} model checkpoint(s).".format(len(checkpoints_to_merge)))

-            if lastest_checkpoint is not None: # resume lora training or quantized inference
+            if lastest_checkpoint is not None:  # resume lora training or quantized inference
                model = PeftModel.from_pretrained(model, lastest_checkpoint, is_trainable=is_trainable)

-        if is_trainable and lastest_checkpoint is None: # create new lora weights while training
+        if is_trainable and lastest_checkpoint is None:  # create new lora weights while training
            lora_config = LoraConfig(
                task_type=TaskType.CAUSAL_LM,
                inference_mode=False,
@ -168,7 +170,7 @@ def load_pretrained(
        padding_side="left",
        **config_kwargs
    )
-    tokenizer.pad_token_id = 0 if tokenizer.pad_token_id is None else tokenizer.pad_token_id # set as the <unk> token
+    tokenizer.pad_token_id = 0 if tokenizer.pad_token_id is None else tokenizer.pad_token_id  # set as the <unk> token

    config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
    is_mergeable = True
@ -184,9 +186,11 @@ def load_pretrained(
            )
        elif model_args.quantization_bit == 4:
            require_version("bitsandbytes>=0.39.0", "To fix: pip install bitsandbytes>=0.39.0")
-            require_version("transformers>=4.30.0.dev0", "To fix: pip install git+https://github.com/huggingface/transformers.git")
+            require_version("transformers>=4.30.0.dev0",
+                            "To fix: pip install git+https://github.com/huggingface/transformers.git")
            require_version("peft>=0.4.0.dev0", "To fix: pip install git+https://github.com/huggingface/peft.git")
-            require_version("accelerate>=0.20.0.dev0", "To fix: pip install git+https://github.com/huggingface/accelerate.git")
+            require_version("accelerate>=0.20.0.dev0",
+                            "To fix: pip install git+https://github.com/huggingface/accelerate.git")
            config_kwargs["load_in_4bit"] = True
            config_kwargs["quantization_config"] = BitsAndBytesConfig(
                load_in_4bit=True,
@ -214,10 +218,10 @@ def load_pretrained(
    model = prepare_model_for_training(model) if is_trainable else model
    model = _init_adapter(model, model_args, finetuning_args, is_trainable, is_mergeable)

-    if stage == "rm" or stage == "ppo": # add value head
+    if stage == "rm" or stage == "ppo":  # add value head
        model = AutoModelForCausalLMWithValueHead.from_pretrained(model)

-        if stage == "ppo": # load reward model
+        if stage == "ppo":  # load reward model
            assert is_trainable, "PPO stage cannot be performed at evaluation."
            assert model_args.reward_model is not None, "Reward model is necessary for PPO training."
            logger.info("Load reward model from {}".format(model_args.reward_model))
@ -230,8 +234,8 @@ def load_pretrained(
            model._is_int8_training_enabled = True

    if not is_trainable:
-        model.requires_grad_(False) # fix all model params
-        model = model.half() if model_args.quantization_bit is None else model # cast from fp32 to fp16
+        model.requires_grad_(False)  # fix all model params
+        model = model.half() if model_args.quantization_bit is None else model  # cast from fp32 to fp16

    print_trainable_params(model)

@ -241,11 +245,11 @@ def load_pretrained(
 def prepare_args(
        stage: Literal["pt", "sft", "rm", "ppo"]
 ) -> Tuple[ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments, FinetuningArguments]:
-
    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments, FinetuningArguments))

-    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # Provide arguments with a json file.
-        model_args, data_args, training_args, finetuning_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):  # Provide arguments with a json file.
+        model_args, data_args, training_args, finetuning_args = parser.parse_json_file(
+            json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args, finetuning_args = parser.parse_args_into_dataclasses()

@ -286,7 +290,7 @@ def prepare_args(
        logger.warning("`ddp_find_unused_parameters` needs to be set as False in DDP training.")
        training_args.ddp_find_unused_parameters = False

-    training_args.optim = "adamw_torch" if training_args.optim == "adamw_hf" else training_args.optim # suppress warning
+    training_args.optim = "adamw_torch" if training_args.optim == "adamw_hf" else training_args.optim  # suppress warning

    if model_args.quantization_bit is not None:
        if training_args.fp16:
@ -310,10 +314,9 @@ def prepare_args(


 def prepare_infer_args() -> Tuple[ModelArguments, DataTrainingArguments, FinetuningArguments]:
-
    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, FinetuningArguments))

-    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # Provide arguments with a json file.
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):  # Provide arguments with a json file.
        model_args, data_args, finetuning_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, finetuning_args = parser.parse_args_into_dataclasses()
@ -331,7 +334,6 @@ def prepare_data(
        model_args: ModelArguments,
        data_args: DataTrainingArguments
 ) -> Dataset:
-
    def checksum(file_path, hash):
        with open(file_path, "rb") as datafile:
            binary_data = datafile.read()
@ -340,7 +342,7 @@ def prepare_data(
            logger.warning("Checksum failed for {}. It may vary depending on the platform.".format(file_path))

    max_samples = data_args.max_samples
-    all_datasets: List[Dataset] = [] # support multiple datasets
+    all_datasets: List[Dataset] = []  # support multiple datasets

    for dataset_attr in data_args.dataset_list:

@ -356,12 +358,10 @@ def prepare_data(
        elif dataset_attr.load_from == "file":
            data_file = os.path.join(data_args.dataset_dir, dataset_attr.file_name)
            extension = dataset_attr.file_name.split(".")[-1]
-
            if dataset_attr.file_sha1 is not None:
                checksum(data_file, dataset_attr.file_sha1)
            else:
                logger.warning("Checksum failed: missing SHA-1 hash value in dataset_info.json.")
-
            raw_datasets = load_dataset(
                extension if extension in ["csv", "json"] else "text",
                data_files=data_file,
@ -383,11 +383,11 @@ def prepare_data(
            ("query_column", "query"),
            ("response_column", "response"),
            ("history_column", "history")
-        ]: # every dataset will have 4 columns same as each other
+        ]:  # every dataset will have 4 columns same as each other
            if getattr(dataset_attr, column_name) != target_name:
                if getattr(dataset_attr, column_name):
                    dataset = dataset.rename_column(getattr(dataset_attr, column_name), target_name)
-                else: # None or empty string
+                else:  # None or empty string
                    dataset = dataset.add_column(target_name, dummy_data)
        all_datasets.append(dataset)

@ -406,7 +406,6 @@ def preprocess_data(
        training_args: Seq2SeqTrainingArguments,
        stage: Literal["pt", "sft", "rm", "ppo"]
 ) -> Dataset:
-
    column_names = list(dataset.column_names)
    prefix = data_args.source_prefix if data_args.source_prefix is not None else ""
    prompt_template = Template(data_args.prompt_template)
@ -429,7 +428,8 @@ def preprocess_data(
        # we drop the small remainder, and if the total_length < block_size, we exclude this batch
        total_length = (total_length // data_args.max_source_length) * data_args.max_source_length
        # split by chunks of max_source_length
-        result = [concatenated_ids[i: i+data_args.max_source_length] for i in range(0, total_length, data_args.max_source_length)]
+        result = [concatenated_ids[i: i + data_args.max_source_length] for i in
+                  range(0, total_length, data_args.max_source_length)]
        return {
            "input_ids": result,
            "labels": result.copy()
@ -442,9 +442,9 @@ def preprocess_data(
            source_ids = tokenizer.encode(text=prompt, add_special_tokens=False)
            target_ids = tokenizer.encode(text=answer, add_special_tokens=False)

-            if len(source_ids) > data_args.max_source_length - 1: # bos token
+            if len(source_ids) > data_args.max_source_length - 1:  # bos token
                source_ids = source_ids[:data_args.max_source_length - 1]
-            if len(target_ids) > data_args.max_target_length - 1: # eos token
+            if len(target_ids) > data_args.max_target_length - 1:  # eos token
                target_ids = target_ids[:data_args.max_target_length - 1]

            input_ids = source_ids + [tokenizer.bos_token_id] + target_ids + [tokenizer.eos_token_id]
@ -461,9 +461,9 @@ def preprocess_data(
            source_ids = tokenizer.encode(text=prompt, add_special_tokens=False)
            target_ids = tokenizer.encode(text=answer, add_special_tokens=False)

-            if len(source_ids) > data_args.max_source_length - 1: # bos token
+            if len(source_ids) > data_args.max_source_length - 1:  # bos token
                source_ids = source_ids[:data_args.max_source_length - 1]
-            if len(target_ids) > data_args.max_target_length - 1: # bos token
+            if len(target_ids) > data_args.max_target_length - 1:  # bos token
                target_ids = target_ids[:data_args.max_target_length - 1]

            input_ids = source_ids + [tokenizer.bos_token_id]
@ -481,11 +481,11 @@ def preprocess_data(
            accept_ids = tokenizer.encode(text=answer[0], add_special_tokens=False)
            reject_ids = tokenizer.encode(text=answer[1], add_special_tokens=False)

-            if len(source_ids) > data_args.max_source_length - 1: # bos token
+            if len(source_ids) > data_args.max_source_length - 1:  # bos token
                source_ids = source_ids[:data_args.max_source_length - 1]
-            if len(accept_ids) > data_args.max_target_length - 1: # eos token
+            if len(accept_ids) > data_args.max_target_length - 1:  # eos token
                accept_ids = accept_ids[:data_args.max_target_length - 1]
-            if len(reject_ids) > data_args.max_target_length - 1: # eos token
+            if len(reject_ids) > data_args.max_target_length - 1:  # eos token
                reject_ids = reject_ids[:data_args.max_target_length - 1]

            accept_ids = source_ids + [tokenizer.bos_token_id] + accept_ids + [tokenizer.eos_token_id]
--- a/src/utils/config.py
+++ b/src/utils/config.py
@ -7,7 +7,6 @@ from dataclasses import asdict, dataclass, field

@dataclass
 class DatasetAttr:
-
    load_from: str
    dataset_name: Optional[str] = None
    file_name: Optional[str] = None
@ -68,7 +67,8 @@ class ModelArguments:
    )
    checkpoint_dir: Optional[str] = field(
        default=None,
-        metadata={"help": "Path to the directory(s) containing the delta model checkpoints as well as the configurations."}
+        metadata={
+            "help": "Path to the directory(s) containing the delta model checkpoints as well as the configurations."}
    )
    reward_model: Optional[str] = field(
        default=None,
@ -76,7 +76,8 @@ class ModelArguments:
    )
    resume_lora_training: Optional[bool] = field(
        default=True,
-        metadata={"help": "Whether to resume training from the last LoRA weights or create new weights after merging them."}
+        metadata={
+            "help": "Whether to resume training from the last LoRA weights or create new weights after merging them."}
    )
    plot_loss: Optional[bool] = field(
        default=False,
@ -84,7 +85,7 @@ class ModelArguments:
    )

    def __post_init__(self):
-        if self.checkpoint_dir is not None: # support merging multiple lora weights
+        if self.checkpoint_dir is not None:  # support merging multiple lora weights
            self.checkpoint_dir = [cd.strip() for cd in self.checkpoint_dir.split(",")]


@ -146,7 +147,7 @@ class DataTrainingArguments:
        metadata={"help": "Which template to use for constructing prompts in training and inference."}
    )

-    def __post_init__(self): # support mixing multiple datasets
+    def __post_init__(self):  # support mixing multiple datasets
        dataset_names = [ds.strip() for ds in self.dataset.split(",")]
        with open(os.path.join(self.dataset_dir, "dataset_info.json"), "r") as f:
            dataset_info = json.load(f)
@ -155,25 +156,42 @@ class DataTrainingArguments:
        for name in dataset_names:
            if name not in dataset_info:
                raise ValueError("Undefined dataset {} in dataset_info.json.".format(name))
-
+            dataset_attrs = []
+            dataset_attr = None
            if "hf_hub_url" in dataset_info[name]:
                dataset_attr = DatasetAttr("hf_hub", dataset_name=dataset_info[name]["hf_hub_url"])
            elif "script_url" in dataset_info[name]:
                dataset_attr = DatasetAttr("script", dataset_name=dataset_info[name]["script_url"])
-            else:
+            elif os.path.isfile(os.path.join(self.dataset_dir, dataset_info[name]["file_name"])):
                dataset_attr = DatasetAttr(
                    "file",
                    file_name=dataset_info[name]["file_name"],
                    file_sha1=dataset_info[name]["file_sha1"] if "file_sha1" in dataset_info[name] else None
                )
-
-            if "columns" in dataset_info[name]:
-                dataset_attr.prompt_column = dataset_info[name]["columns"].get("prompt", None)
-                dataset_attr.query_column = dataset_info[name]["columns"].get("query", None)
-                dataset_attr.response_column = dataset_info[name]["columns"].get("response", None)
-                dataset_attr.history_column = dataset_info[name]["columns"].get("history", None)
-
-            self.dataset_list.append(dataset_attr)
+            else:
+                # Support Directory
+                for file_name in os.listdir(os.path.join(self.dataset_dir, dataset_info[name]["file_name"])):
+                    path = os.path.join(dataset_info[name]["file_name"], file_name)
+                    dataset_attrs.append(DatasetAttr(
+                        "file",
+                        file_name=path,
+                        file_sha1=dataset_info[name]["file_sha1"] if "file_sha1" in dataset_info[name] else None
+                    ))
+            if dataset_attr is not None:
+                if "columns" in dataset_info[name]:
+                    dataset_attr.prompt_column = dataset_info[name]["columns"].get("prompt", None)
+                    dataset_attr.query_column = dataset_info[name]["columns"].get("query", None)
+                    dataset_attr.response_column = dataset_info[name]["columns"].get("response", None)
+                    dataset_attr.history_column = dataset_info[name]["columns"].get("history", None)
+                self.dataset_list.append(dataset_attr)
+            else:
+                for i, dataset_attr in enumerate(dataset_attrs):
+                    if "columns" in dataset_info[name]:
+                        dataset_attr.prompt_column = dataset_info[name]["columns"].get("prompt", None)
+                        dataset_attr.query_column = dataset_info[name]["columns"].get("query", None)
+                        dataset_attr.response_column = dataset_info[name]["columns"].get("response", None)
+                        dataset_attr.history_column = dataset_info[name]["columns"].get("history", None)
+                    self.dataset_list.append(dataset_attr)


@dataclass
@ -216,14 +234,16 @@ class FinetuningArguments:

    def __post_init__(self):
        if isinstance(self.lora_target, str):
-            self.lora_target = [target.strip() for target in self.lora_target.split(",")] # support custom target modules of LoRA
+            self.lora_target = [target.strip() for target in
+                                self.lora_target.split(",")]  # support custom target modules of LoRA

-        if self.num_layer_trainable > 0: # fine-tuning the last n layers if num_layer_trainable > 0
-            trainable_layer_ids = [27-k for k in range(self.num_layer_trainable)]
-        else: # fine-tuning the first n layers if num_layer_trainable < 0
+        if self.num_layer_trainable > 0:  # fine-tuning the last n layers if num_layer_trainable > 0
+            trainable_layer_ids = [27 - k for k in range(self.num_layer_trainable)]
+        else:  # fine-tuning the first n layers if num_layer_trainable < 0
            trainable_layer_ids = [k for k in range(-self.num_layer_trainable)]

-        self.trainable_layers = ["layers.{:d}.{}".format(idx, self.name_module_trainable) for idx in trainable_layer_ids]
+        self.trainable_layers = ["layers.{:d}.{}".format(idx, self.name_module_trainable) for idx in
+                                 trainable_layer_ids]

        assert self.finetuning_type in ["none", "freeze", "lora", "full"], "Invalid fine-tuning method."