improve aligner
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hiyouga
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@ -174,6 +174,7 @@ Please refer to [constants.py](src/llmtuner/extras/constants.py) for a full list
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- [CodeAlpaca 20k (en)](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k)
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- [Alpaca CoT (multilingual)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT)
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- [OpenOrca (en)](https://huggingface.co/datasets/Open-Orca/OpenOrca)
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- [SlimOrca (en)](https://huggingface.co/datasets/Open-Orca/SlimOrca)
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- [MathInstruct (en)](https://huggingface.co/datasets/TIGER-Lab/MathInstruct)
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- [Firefly 1.1M (zh)](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M)
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- [Wiki QA (en)](https://huggingface.co/datasets/wiki_qa)
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@ -174,6 +174,7 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/6ba60acc-e2e2-4bec-b846
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- [CodeAlpaca 20k (en)](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k)
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- [Alpaca CoT (multilingual)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT)
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- [OpenOrca (en)](https://huggingface.co/datasets/Open-Orca/OpenOrca)
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- [SlimOrca (en)](https://huggingface.co/datasets/Open-Orca/SlimOrca)
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- [MathInstruct (en)](https://huggingface.co/datasets/TIGER-Lab/MathInstruct)
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- [Firefly 1.1M (zh)](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M)
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- [Wiki QA (en)](https://huggingface.co/datasets/wiki_qa)
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@ -11,7 +11,7 @@ If you are using a custom dataset, please provide your dataset definition in the
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"folder": "the name of the folder of the dataset repository on the Hugging Face hub. (optional, default: None)",
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"ranking": "whether the dataset is a preference dataset or not. (default: false)",
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"formatting": "the format of the dataset. (optional, default: alpaca, can be chosen from {alpaca, sharegpt})",
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"columns": {
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"columns (optional)": {
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"prompt": "the column name in the dataset containing the prompts. (default: instruction)",
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"query": "the column name in the dataset containing the queries. (default: input)",
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"response": "the column name in the dataset containing the responses. (default: output)",
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@ -20,14 +20,14 @@ If you are using a custom dataset, please provide your dataset definition in the
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"system": "the column name in the dataset containing the system prompts. (default: None)",
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"tools": "the column name in the dataset containing the tool description. (default: None)"
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},
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"tags": {
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"tags (optional, used for the sharegpt format)": {
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"role_tag": "the key in the message represents the identity. (default: from)",
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"content_tag": "the key in the message represents the content. (default: value)",
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"user_tag": "the value of the role_tag represents the user. (default: human)",
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"assistant_tag": "the value of the role_tag represents the assistant. (default: gpt)",
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"observation_tag": "the value of the role_tag represents the tool results. (default: observation)",
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"function_tag": "the value of the role_tag represents the function call. (default: function_call)",
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"system_tag": "the value of the role_tag represents the system prompt. (default: None) incompatible with system column"
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"system_tag": "the value of the role_tag represents the system prompt. (default: system, can override system column)"
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}
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}
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```
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@ -11,7 +11,7 @@
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"folder": "Hugging Face 仓库的文件夹名称（可选，默认：None）",
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"ranking": "是否为偏好数据集（可选，默认：False）",
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"formatting": "数据集格式（可选，默认：alpaca，可以为 alpaca 或 sharegpt）",
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"columns": {
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"columns（可选）": {
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"prompt": "数据集代表提示词的表头名称（默认：instruction）",
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"query": "数据集代表请求的表头名称（默认：input）",
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"response": "数据集代表回答的表头名称（默认：output）",
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@ -20,13 +20,14 @@
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"system": "数据集代表系统提示的表头名称（默认：None）",
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"tools": "数据集代表工具描述的表头名称（默认：None）"
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},
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"tags": {
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"tags（可选，用于 sharegpt 格式）": {
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"role_tag": "消息中代表发送者身份的键名（默认：from）",
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"content_tag": "消息中代表文本内容的键名（默认：value）",
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"user_tag": "消息中代表用户的 role_tag（默认：human）",
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"assistant_tag": "消息中代表助手的 role_tag（默认：gpt）",
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"observation_tag": "消息中代表工具返回结果的 role_tag（默认：observation）",
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"function_tag": "消息中代表工具调用的 role_tag（默认：function_call）"
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"function_tag": "消息中代表工具调用的 role_tag（默认：function_call）",
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"system_tag": "消息中代表系统提示的 role_tag（默认：system，会覆盖 system 列）"
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}
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}
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```
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@ -15,9 +15,6 @@
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"file_name": "alpaca_gpt4_data_zh.json",
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"file_sha1": "3eaa3bda364ccdd59925d7448a698256c31ef845"
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},
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"alpaca-gpt4_de": {
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"hf_hub_url": "mayflowergmbh/alpaca-gpt4_de"
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},
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"self_cognition": {
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"file_name": "self_cognition.json",
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"file_sha1": "6287a730ada924fc5d9eadc6d8f865e01b7a6f67"
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@ -42,9 +39,6 @@
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"history": "history"
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}
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},
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"oasst_de": {
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"hf_hub_url": "mayflowergmbh/oasst_de"
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},
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"lima": {
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"file_name": "lima.json",
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"file_sha1": "9db59f6b7007dc4b17529fc63379b9cd61640f37",
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@ -126,44 +120,8 @@
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"system": "system_prompt"
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}
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},
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"slimorca": {
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"hf_hub_url": "Open-Orca/SlimOrca",
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"formatting": "sharegpt",
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"columns": {
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"messages": "conversations"
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},
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"tags": {
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"role_tag": "from",
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"content_tag": "value",
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"user_tag": "human",
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"assistant_tag": "gpt",
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"system_tag": "system"
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}
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},
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"intel_orca_dpo_pairs_de" : {
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"hf_hub_url": "mayflowergmbh/intel_orca_dpo_pairs_de",
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"ranking": true
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},
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"airoboros-3.0_de": {
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"hf_hub_url": "mayflowergmbh/airoboros-3.0_de"
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},
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"booksum_de": {
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"hf_hub_url": "mayflowergmbh/booksum_de"
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},
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"dolphin_de": {
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"hf_hub_url": "mayflowergmbh/dolphin_de"
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},
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"wiki_qa_de": {
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"hf_hub_url": "mayflowergmbh/wiki_qa_de"
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},
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"evol-instruct_de": {
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"hf_hub_url": "mayflowergmbh/evol-instruct_de"
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},
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"openschnabeltier_de": {
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"hf_hub_url": "mayflowergmbh/openschnabeltier_de"
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},
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"dolly-15k_de": {
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"hf_hub_url": "mayflowergmbh/dolly-15k_de"
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"slimorca": {
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"hf_hub_url": "Open-Orca/SlimOrca"
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},
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"mathinstruct": {
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"hf_hub_url": "TIGER-Lab/MathInstruct",
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@ -180,6 +138,13 @@
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"response": "target"
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}
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},
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"wikiqa": {
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"hf_hub_url": "wiki_qa",
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"columns": {
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"prompt": "question",
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"response": "answer"
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}
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},
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"webqa": {
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"hf_hub_url": "suolyer/webqa",
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"ms_hub_url": "AI-ModelScope/webqa",
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@ -193,7 +158,8 @@
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"ms_hub_url": "AI-ModelScope/webnovel_cn"
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},
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"nectar_sft": {
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"hf_hub_url": "mlinmg/SFT-Nectar"
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"hf_hub_url": "mlinmg/SFT-Nectar",
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"ms_hub_url": "AI-ModelScope/SFT-Nectar"
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},
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"deepctrl": {
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"ms_hub_url": "deepctrl/deepctrl-sft-data"
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@ -229,9 +195,6 @@
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},
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"formatting": "sharegpt"
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},
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"ultrachat_chat_de": {
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"hf_hub_url": "mayflowergmbh/ultra-chat_de"
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},
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"agent_instruct": {
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"hf_hub_url": "THUDM/AgentInstruct",
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"ms_hub_url": "ZhipuAI/AgentInstruct",
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@ -253,8 +216,36 @@
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},
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"evol_instruct": {
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"hf_hub_url": "WizardLM/WizardLM_evol_instruct_V2_196k",
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"ms_hub_url": "AI-ModelScope/WizardLM_evol_instruct_V2_196k",
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"formatting": "sharegpt"
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},
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"oasst_de": {
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"hf_hub_url": "mayflowergmbh/oasst_de"
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},
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"dolly_15k_de": {
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"hf_hub_url": "mayflowergmbh/dolly-15k_de"
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},
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"alpaca-gpt4_de": {
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"hf_hub_url": "mayflowergmbh/alpaca-gpt4_de"
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},
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"openschnabeltier_de": {
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"hf_hub_url": "mayflowergmbh/openschnabeltier_de"
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},
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"evol_instruct_de": {
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"hf_hub_url": "mayflowergmbh/evol-instruct_de"
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},
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"dolphin_de": {
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"hf_hub_url": "mayflowergmbh/dolphin_de"
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},
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"booksum_de": {
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"hf_hub_url": "mayflowergmbh/booksum_de"
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},
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"airoboros_de": {
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"hf_hub_url": "mayflowergmbh/airoboros-3.0_de"
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},
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"ultrachat_de": {
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"hf_hub_url": "mayflowergmbh/ultra-chat_de"
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},
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"hh_rlhf_en": {
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"script_url": "hh_rlhf_en",
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"columns": {
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@ -298,6 +289,11 @@
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},
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"nectar_rm": {
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"hf_hub_url": "mlinmg/RLAIF-Nectar",
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"ms_hub_url": "AI-ModelScope/RLAIF-Nectar",
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"ranking": true
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},
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"orca_dpo_de" : {
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"hf_hub_url": "mayflowergmbh/intel_orca_dpo_pairs_de",
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"ranking": true
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},
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"wiki_demo": {
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@ -329,6 +325,7 @@
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},
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"wikipedia_en": {
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"hf_hub_url": "olm/olm-wikipedia-20221220",
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"ms_hub_url": "AI-ModelScope/olm-wikipedia-20221220",
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"columns": {
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"prompt": "text"
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}
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@ -342,6 +339,7 @@
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},
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"pile": {
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"hf_hub_url": "EleutherAI/pile",
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"ms_hub_url": "AI-ModelScope/pile",
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"columns": {
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"prompt": "text"
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},
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@ -349,6 +347,7 @@
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},
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"skypile": {
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"hf_hub_url": "Skywork/SkyPile-150B",
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"ms_hub_url": "AI-ModelScope/SkyPile-150B",
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"columns": {
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"prompt": "text"
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}
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@ -49,40 +49,32 @@ def convert_sharegpt(examples: Dict[str, List[Any]], dataset_attr: "DatasetAttr"
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dataset_attr.function_tag: Role.FUNCTION,
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dataset_attr.system_tag: Role.SYSTEM,
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}
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odd_tags = (dataset_attr.user_tag, dataset_attr.observation_tag)
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even_tags = (dataset_attr.assistant_tag, dataset_attr.function_tag)
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accept_tags = (odd_tags, even_tags)
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for i, messages in enumerate(examples[dataset_attr.messages]):
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if len(messages) <= 1:
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if dataset_attr.system_tag and messages[0][dataset_attr.role_tag] == dataset_attr.system_tag:
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system = messages[0][dataset_attr.content_tag]
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messages = messages[1:]
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else:
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system = examples[dataset_attr.system][i] if dataset_attr.system else ""
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messages = messages[: len(messages) // 2 * 2] # should be multiples of 2
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if len(messages) == 0:
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continue
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prompt = []
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response = []
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n_sys = 0
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aligned_messages = []
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for turn_idx, message in enumerate(messages):
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if dataset_attr.system_tag and message[dataset_attr.role_tag] == dataset_attr.system_tag:
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outputs["system"].append(message[dataset_attr.content_tag])
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n_sys = 1
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continue
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if (turn_idx - n_sys) % 2 == 0:
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accept_tags = [dataset_attr.user_tag, dataset_attr.observation_tag]
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else:
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accept_tags = [dataset_attr.assistant_tag, dataset_attr.function_tag]
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if message[dataset_attr.role_tag] not in accept_tags:
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if message[dataset_attr.role_tag] not in accept_tags[turn_idx % 2]:
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raise ValueError("Invalid role tag in {}.".format(messages))
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prompt.append(
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aligned_messages.append(
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{"role": tag_mapping[message[dataset_attr.role_tag]], "content": message[dataset_attr.content_tag]}
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)
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if len(prompt) % 2 == 1:
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# Last message was neither from assistant nor function
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prompt.pop(-1)
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last_message = prompt.pop(-1)
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response.append(last_message)
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outputs["prompt"].append(prompt)
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outputs["response"].append(response)
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if n_sys == 0:
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outputs["system"].append(examples[dataset_attr.system][i] if dataset_attr.system else "")
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outputs["prompt"].append(aligned_messages[:-1])
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outputs["response"].append(aligned_messages[-1:])
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outputs["system"].append(system)
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outputs["tools"].append(examples[dataset_attr.tools][i] if dataset_attr.tools else "")
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return outputs
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@ -93,8 +85,8 @@ def align_dataset(
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) -> Union["Dataset", "IterableDataset"]:
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r"""
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Aligned dataset:
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prompt: [{"role": "user", "content": "..."}]
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response: [{"role": "assistant", "content": "..."}]
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prompt: [{"role": "user", "content": "..."}] * (2T - 1)
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response: [{"role": "assistant", "content": "..."}] * N (N > 1 for ranking dataset)
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system: "..."
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tools: "..."
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"""
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@ -30,6 +30,7 @@ def load_single_dataset(
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model_args: "ModelArguments",
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data_args: "DataArguments",
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):
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logger.info("Loading dataset {}...".format(dataset_attr))
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data_path, data_name, data_dir, data_files = None, None, None, None
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if dataset_attr.load_from in ["hf_hub", "ms_hub"]:
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data_path = dataset_attr.dataset_name
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@ -60,7 +61,7 @@ def load_single_dataset(
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if data_path is None:
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raise ValueError("File extension must be txt, csv, json or jsonl.")
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checksum(data_files, dataset_attr.dataset_sha1)
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checksum(data_files, dataset_attr.file_sha1)
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else:
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raise NotImplementedError
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@ -157,7 +158,7 @@ def get_dataset(
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with training_args.main_process_first(desc="load dataset"):
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all_datasets = []
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for dataset_attr in get_dataset_list(data_args): # TODO: add split
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for dataset_attr in get_dataset_list(data_args):
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all_datasets.append(load_single_dataset(dataset_attr, model_args, data_args))
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dataset = merge_dataset(all_datasets, data_args, training_args)
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@ -185,6 +186,6 @@ def get_dataset(
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try:
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print_function(next(iter(dataset)))
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except StopIteration:
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raise RuntimeError("Empty dataset!")
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raise RuntimeError("Cannot find valid samples, check `data/README.md` for the data format.")
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return dataset
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@ -1,7 +1,7 @@
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import json
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import os
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from dataclasses import dataclass
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from typing import TYPE_CHECKING, List, Literal, Optional
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from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional
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from ..extras.constants import DATA_CONFIG
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from ..extras.misc import use_modelscope
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@ -13,38 +13,44 @@ if TYPE_CHECKING:
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@dataclass
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class DatasetAttr:
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r"""
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Dataset attributes.
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"""
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""" basic configs """
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load_from: Literal["hf_hub", "ms_hub", "script", "file"]
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dataset_name: Optional[str] = None
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dataset_sha1: Optional[str] = None
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""" extra configs """
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file_sha1: Optional[str] = None
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subset: Optional[str] = None
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folder: Optional[str] = None
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ranking: Optional[bool] = False
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formatting: Optional[Literal["alpaca", "sharegpt"]] = "alpaca"
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""" columns """
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system: Optional[str] = None
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||||
|
||||
""" columns for the alpaca format """
|
||||
prompt: Optional[str] = "instruction"
|
||||
query: Optional[str] = "input"
|
||||
response: Optional[str] = "output"
|
||||
history: Optional[str] = None
|
||||
|
||||
""" columns for the sharegpt format """
|
||||
messages: Optional[str] = "conversations"
|
||||
tools: Optional[str] = None
|
||||
|
||||
""" tags for the sharegpt format """
|
||||
role_tag: Optional[str] = "from"
|
||||
content_tag: Optional[str] = "value"
|
||||
user_tag: Optional[str] = "human"
|
||||
assistant_tag: Optional[str] = "gpt"
|
||||
observation_tag: Optional[str] = "observation"
|
||||
function_tag: Optional[str] = "function_call"
|
||||
system_tag: Optional[str] = None
|
||||
|
||||
assert system_tag is None or system is None, f"Can not provide both system message (system_tag={system_tag}) and system column(system={system})"
|
||||
|
||||
system_tag: Optional[str] = "system"
|
||||
|
||||
def __repr__(self) -> str:
|
||||
return self.dataset_name
|
||||
|
||||
def set_attr(self, key: str, obj: Dict[str, Any], default: Optional[Any] = None) -> None:
|
||||
setattr(self, key, obj.get(key, default))
|
||||
|
||||
|
||||
def get_dataset_list(data_args: "DataArguments") -> List["DatasetAttr"]:
|
||||
dataset_names = [ds.strip() for ds in data_args.dataset.split(",")] if data_args.dataset is not None else []
|
||||
|
|
@ -77,30 +83,36 @@ def get_dataset_list(data_args: "DataArguments") -> List["DatasetAttr"]:
|
|||
elif "script_url" in dataset_info[name]:
|
||||
dataset_attr = DatasetAttr("script", dataset_name=dataset_info[name]["script_url"])
|
||||
else:
|
||||
dataset_attr = DatasetAttr(
|
||||
"file",
|
||||
dataset_name=dataset_info[name]["file_name"],
|
||||
dataset_sha1=dataset_info[name].get("file_sha1", None),
|
||||
)
|
||||
dataset_attr = DatasetAttr("file", dataset_name=dataset_info[name]["file_name"])
|
||||
|
||||
dataset_attr.subset = dataset_info[name].get("subset", None)
|
||||
dataset_attr.folder = dataset_info[name].get("folder", None)
|
||||
dataset_attr.ranking = dataset_info[name].get("ranking", False)
|
||||
dataset_attr.formatting = dataset_info[name].get("formatting", "alpaca")
|
||||
dataset_attr.set_attr("file_sha1", dataset_info[name])
|
||||
dataset_attr.set_attr("subset", dataset_info[name])
|
||||
dataset_attr.set_attr("folder", dataset_info[name])
|
||||
dataset_attr.set_attr("ranking", dataset_info[name], default=False)
|
||||
dataset_attr.set_attr("formatting", dataset_info[name], default="alpaca")
|
||||
|
||||
if "columns" in dataset_info[name]:
|
||||
column_names = ["system"]
|
||||
if dataset_attr.formatting == "alpaca":
|
||||
column_names = ["prompt", "query", "response", "history"]
|
||||
column_names.extend(["prompt", "query", "response", "history"])
|
||||
else:
|
||||
column_names = ["messages", "tools"]
|
||||
column_names.extend(["messages", "tools"])
|
||||
|
||||
column_names += ["system"]
|
||||
for column_name in column_names:
|
||||
setattr(dataset_attr, column_name, dataset_info[name]["columns"].get(column_name, None))
|
||||
dataset_attr.set_attr(column_name, dataset_info[name]["columns"])
|
||||
|
||||
if dataset_attr.formatting == "sharegpt" and "tags" in dataset_info[name]:
|
||||
for tag in ["role_tag", "content_tag", "user_tag", "assistant_tag", "observation_tag", "function_tag", "system_tag"]:
|
||||
setattr(dataset_attr, tag, dataset_info[name]["tags"].get(tag, None))
|
||||
tag_names = (
|
||||
"role_tag",
|
||||
"content_tag",
|
||||
"user_tag",
|
||||
"assistant_tag",
|
||||
"observation_tag",
|
||||
"function_tag",
|
||||
"system_tag",
|
||||
)
|
||||
for tag in tag_names:
|
||||
dataset_attr.set_attr(tag, dataset_info[name]["tags"])
|
||||
|
||||
dataset_list.append(dataset_attr)
|
||||
|
||||
|
|
|
|||
|
|
@ -247,7 +247,7 @@ def _add_or_replace_eos_token(tokenizer: "PreTrainedTokenizer", eos_token: str)
|
|||
logger.info("Replace eos token: {}".format(tokenizer.eos_token))
|
||||
|
||||
if is_oov:
|
||||
logger.warning("New token is added, you must enable `resize_vocab` to activate it.")
|
||||
logger.warning("New tokens have been added, make sure `resize_vocab` is True.")
|
||||
|
||||
|
||||
def get_template_and_fix_tokenizer(
|
||||
|
|
|
|||
|
|
@ -19,9 +19,9 @@ logger = get_logger(__name__)
|
|||
class Role(str, Enum):
|
||||
USER = "user"
|
||||
ASSISTANT = "assistant"
|
||||
SYSTEM = "system"
|
||||
OBSERVATION = "observation"
|
||||
FUNCTION = "function"
|
||||
SYSTEM = "system"
|
||||
|
||||
|
||||
def checksum(data_files: List[str], file_sha1: Optional[str] = None) -> None:
|
||||
|
|
|
|||
|
|
@ -67,7 +67,7 @@ def _verify_model_args(model_args: "ModelArguments", finetuning_args: "Finetunin
|
|||
raise ValueError("Quantized model only accepts a single adapter. Merge them first.")
|
||||
|
||||
if model_args.adapter_name_or_path is not None and finetuning_args.finetuning_type != "lora":
|
||||
raise ValueError("Only LoRA method has adapters.")
|
||||
raise ValueError("Adapter is only valid for the LoRA method.")
|
||||
|
||||
|
||||
def _parse_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
|
||||
|
|
@ -125,6 +125,14 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
|
|||
|
||||
_verify_model_args(model_args, finetuning_args)
|
||||
|
||||
if (
|
||||
training_args.do_train
|
||||
and finetuning_args.finetuning_type == "lora"
|
||||
and model_args.resize_vocab
|
||||
and finetuning_args.additional_target is None
|
||||
):
|
||||
logger.warning("Add token embeddings to `additional_target` to make the added tokens trainable.")
|
||||
|
||||
if training_args.do_train and model_args.quantization_bit is not None and (not model_args.upcast_layernorm):
|
||||
logger.warning("We recommend enable `upcast_layernorm` in quantized training.")
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue