Merge pull request #651 from hiyouga/feature-dataset_stage

add dataset stage

Former-commit-id: 3b0ef57405cbc22ff8ce4eef2cfcb73872519db5

README.md

@@ -105,6 +105,7 @@
   - [Web QA (zh)](https://huggingface.co/datasets/suolyer/webqa)
   - [UltraChat (en)](https://github.com/thunlp/UltraChat)
   - [WebNovel (zh)](https://huggingface.co/datasets/zxbsmk/webnovel_cn)
+  - [Ad Gen (zh)](https://arxiv.org/abs/1908.06605)
 - For reward modeling or DPO training:
   - [HH-RLHF (en)](https://huggingface.co/datasets/Anthropic/hh-rlhf)
   - [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1)

README_zh.md

@@ -105,6 +105,7 @@
   - [Web QA (zh)](https://huggingface.co/datasets/suolyer/webqa)
   - [UltraChat (en)](https://github.com/thunlp/UltraChat)
   - [WebNovel (zh)](https://huggingface.co/datasets/zxbsmk/webnovel_cn)
+  - [Ad Gen (zh)](https://arxiv.org/abs/1908.06605)
 - 用于奖励模型或 DPO 训练：
   - [HH-RLHF (en)](https://huggingface.co/datasets/Anthropic/hh-rlhf)
   - [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1)

data/README.md

@@ -11,7 +11,8 @@ If you are using a custom dataset, please provide your dataset definition in the
     "query": "the name of the column in the datasets containing the queries. (default: input)",
     "response": "the name of the column in the datasets containing the responses. (default: output)",
     "history": "the name of the column in the datasets containing the history of chat. (default: None)"
-  }
+  },
+  "stage": "The stage at which the data is being used: pt, sft, and rm, which correspond to pre-training, supervised fine-tuning(PPO), and reward model (DPO) training, respectively.(default: None)"
 }
 ```
 
@@ -26,6 +27,7 @@ For datasets used in reward modeling or DPO training, the `response` column shou
   "output": [
     "Chosen answer",
     "Rejected answer"
-  ]
+  ],
+  "stage": "rm"
 }
 ```

data/README_zh.md

@@ -11,7 +11,8 @@
     "query": "数据集代表请求的表头名称（默认：input）",
     "response": "数据集代表回答的表头名称（默认：output）",
     "history": "数据集代表历史对话的表头名称（默认：None）"
-  }
+  },
+  "stage": "数据所应用的训练阶段，可选值有 pt, sft, rm 三个，对应预训练，指令监督微调(PPO)，奖励模型(DPO)训练, 默认为None，表示不限制"
 }
 ```
 
@@ -26,6 +27,7 @@
   "output": [
     "Chosen answer",
     "Rejected answer"
-  ]
+  ],
+  "stage": "rm"
 }
 ```

src/llmtuner/extras/constants.py

@@ -18,6 +18,14 @@ STAGES = [
     "Pre-Training"
 ]
 
+DATASET_STAGE_MAP = {
+    "SFT": "sft",
+    "Pre-Training": "pt",
+    "Reward Modeling": "rm",
+    "PPO": "sft",
+    "DPO": "rm"
+}
+
 SUPPORTED_MODELS = {
     "LLaMA-7B": "huggyllama/llama-7b",
     "LLaMA-13B": "huggyllama/llama-13b",

src/llmtuner/hparams/data_args.py

@@ -11,6 +11,7 @@ class DatasetAttr:
     dataset_name: Optional[str] = None
     dataset_sha1: Optional[str] = None
     system_prompt: Optional[str] = None
+    stage: Optional[str] = None
 
     def __repr__(self) -> str:
         return self.dataset_name
@@ -113,14 +114,21 @@ class DataArguments:
                 raise ValueError("Undefined dataset {} in dataset_info.json.".format(name))
 
             if "hf_hub_url" in dataset_info[name]:
-                dataset_attr = DatasetAttr("hf_hub", dataset_name=dataset_info[name]["hf_hub_url"])
+                dataset_attr = DatasetAttr(
+                    "hf_hub",
+                    dataset_name=dataset_info[name]["hf_hub_url"],
+                    stage=dataset_info[name].get("stage", None))
             elif "script_url" in dataset_info[name]:
-                dataset_attr = DatasetAttr("script", dataset_name=dataset_info[name]["script_url"])
+                dataset_attr = DatasetAttr(
+                    "script",
+                    dataset_name=dataset_info[name]["script_url"],
+                    stage=dataset_info[name].get("stage", None))
             else:
                 dataset_attr = DatasetAttr(
                     "file",
                     dataset_name=dataset_info[name]["file_name"],
-                    dataset_sha1=dataset_info[name].get("file_sha1", None)
+                    dataset_sha1=dataset_info[name].get("file_sha1", None),
+                    stage=dataset_info[name].get("stage", None)
                 )
 
             if "columns" in dataset_info[name]:

src/llmtuner/webui/common.py

@@ -6,7 +6,7 @@ import gradio as gr
 from peft.utils import WEIGHTS_NAME as PEFT_WEIGHTS_NAME
 from transformers.trainer import WEIGHTS_NAME, WEIGHTS_INDEX_NAME
 
-from llmtuner.extras.constants import DEFAULT_TEMPLATE, SUPPORTED_MODELS
+from llmtuner.extras.constants import DEFAULT_TEMPLATE, SUPPORTED_MODELS, DATASET_STAGE_MAP
 
 
 DEFAULT_CACHE_DIR = "cache"
@@ -78,6 +78,11 @@ def load_dataset_info(dataset_dir: str) -> Dict[str, Any]:
         return {}
 
 
-def list_dataset(dataset_dir: Optional[str] = None) -> Dict[str, Any]:
+def list_dataset(dataset_dir: Optional[str] = None, stage: Optional[str] = None) -> Dict[str, Any]:
     dataset_info = load_dataset_info(dataset_dir if dataset_dir is not None else DEFAULT_DATA_DIR)
+    if stage:
+        dataset_stage = DATASET_STAGE_MAP[stage]
+        dataset_info = {key: value for key, value in dataset_info.items()
+                        if ("stage" not in value) or value["stage"] == dataset_stage}
+
     return gr.update(value=[], choices=list(dataset_info.keys()))

src/llmtuner/webui/components/train.py

@@ -22,7 +22,8 @@ def create_train_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dic
 
     preview_box, preview_count, preview_samples, close_btn = create_preview_box()
 
-    dataset_dir.change(list_dataset, [dataset_dir], [dataset])
+    training_stage.change(list_dataset, [dataset_dir, training_stage], [dataset])
+    dataset_dir.change(list_dataset, [dataset_dir, training_stage], [dataset])
     dataset.change(can_preview, [dataset_dir, dataset], [data_preview_btn])
     data_preview_btn.click(
         get_preview,