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https://github.com/hiyouga/LLaMA-Factory.git
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improve aligner
Former-commit-id: cc7296b92e10c24967fc753393275b71d300683f
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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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@ -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 """
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prompt: Optional[str] = "instruction"
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query: Optional[str] = "input"
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response: Optional[str] = "output"
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history: Optional[str] = None
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""" columns for the sharegpt format """
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messages: Optional[str] = "conversations"
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tools: Optional[str] = None
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""" tags for the sharegpt format """
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role_tag: Optional[str] = "from"
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content_tag: Optional[str] = "value"
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user_tag: Optional[str] = "human"
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assistant_tag: Optional[str] = "gpt"
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observation_tag: Optional[str] = "observation"
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function_tag: Optional[str] = "function_call"
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system_tag: Optional[str] = None
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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})"
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system_tag: Optional[str] = "system"
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def __repr__(self) -> str:
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return self.dataset_name
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def set_attr(self, key: str, obj: Dict[str, Any], default: Optional[Any] = None) -> None:
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setattr(self, key, obj.get(key, default))
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def get_dataset_list(data_args: "DataArguments") -> List["DatasetAttr"]:
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dataset_names = [ds.strip() for ds in data_args.dataset.split(",")] if data_args.dataset is not None else []
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@ -77,30 +83,36 @@ def get_dataset_list(data_args: "DataArguments") -> List["DatasetAttr"]:
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elif "script_url" in dataset_info[name]:
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dataset_attr = DatasetAttr("script", dataset_name=dataset_info[name]["script_url"])
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else:
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dataset_attr = DatasetAttr(
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"file",
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dataset_name=dataset_info[name]["file_name"],
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dataset_sha1=dataset_info[name].get("file_sha1", None),
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)
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dataset_attr = DatasetAttr("file", dataset_name=dataset_info[name]["file_name"])
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dataset_attr.subset = dataset_info[name].get("subset", None)
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dataset_attr.folder = dataset_info[name].get("folder", None)
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dataset_attr.ranking = dataset_info[name].get("ranking", False)
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dataset_attr.formatting = dataset_info[name].get("formatting", "alpaca")
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dataset_attr.set_attr("file_sha1", dataset_info[name])
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dataset_attr.set_attr("subset", dataset_info[name])
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dataset_attr.set_attr("folder", dataset_info[name])
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dataset_attr.set_attr("ranking", dataset_info[name], default=False)
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dataset_attr.set_attr("formatting", dataset_info[name], default="alpaca")
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if "columns" in dataset_info[name]:
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column_names = ["system"]
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if dataset_attr.formatting == "alpaca":
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column_names = ["prompt", "query", "response", "history"]
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column_names.extend(["prompt", "query", "response", "history"])
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else:
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column_names = ["messages", "tools"]
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column_names.extend(["messages", "tools"])
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column_names += ["system"]
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for column_name in column_names:
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setattr(dataset_attr, column_name, dataset_info[name]["columns"].get(column_name, None))
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dataset_attr.set_attr(column_name, dataset_info[name]["columns"])
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if dataset_attr.formatting == "sharegpt" and "tags" in dataset_info[name]:
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for tag in ["role_tag", "content_tag", "user_tag", "assistant_tag", "observation_tag", "function_tag", "system_tag"]:
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setattr(dataset_attr, tag, dataset_info[name]["tags"].get(tag, None))
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tag_names = (
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"role_tag",
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"content_tag",
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"user_tag",
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"assistant_tag",
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"observation_tag",
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"function_tag",
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"system_tag",
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)
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for tag in tag_names:
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dataset_attr.set_attr(tag, dataset_info[name]["tags"])
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dataset_list.append(dataset_attr)
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@ -247,7 +247,7 @@ def _add_or_replace_eos_token(tokenizer: "PreTrainedTokenizer", eos_token: str)
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logger.info("Replace eos token: {}".format(tokenizer.eos_token))
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if is_oov:
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logger.warning("New token is added, you must enable `resize_vocab` to activate it.")
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logger.warning("New tokens have been added, make sure `resize_vocab` is True.")
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def get_template_and_fix_tokenizer(
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@ -19,9 +19,9 @@ logger = get_logger(__name__)
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class Role(str, Enum):
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USER = "user"
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ASSISTANT = "assistant"
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SYSTEM = "system"
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OBSERVATION = "observation"
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FUNCTION = "function"
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SYSTEM = "system"
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def checksum(data_files: List[str], file_sha1: Optional[str] = None) -> None:
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@ -67,7 +67,7 @@ def _verify_model_args(model_args: "ModelArguments", finetuning_args: "Finetunin
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raise ValueError("Quantized model only accepts a single adapter. Merge them first.")
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if model_args.adapter_name_or_path is not None and finetuning_args.finetuning_type != "lora":
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raise ValueError("Only LoRA method has adapters.")
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raise ValueError("Adapter is only valid for the LoRA method.")
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def _parse_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
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@ -125,6 +125,14 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
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_verify_model_args(model_args, finetuning_args)
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if (
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training_args.do_train
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and finetuning_args.finetuning_type == "lora"
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and model_args.resize_vocab
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and finetuning_args.additional_target is None
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):
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logger.warning("Add token embeddings to `additional_target` to make the added tokens trainable.")
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if training_args.do_train and model_args.quantization_bit is not None and (not model_args.upcast_layernorm):
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logger.warning("We recommend enable `upcast_layernorm` in quantized training.")
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