diff --git a/.gitignore b/.gitignore index 69e8068..b293180 100644 --- a/.gitignore +++ b/.gitignore @@ -100,12 +100,15 @@ target/ # Jupyter Notebook .ipynb_checkpoints # vscode ignore -# .vscode/ +.vscode/ # IPython profile_default/ ipython_config.py +# Poetry +.dist/ + # pyenv # For a library or package, you might want to ignore these files since the code is # intended to run in multiple environments; otherwise, check them in: diff --git a/.vscode/launch.json b/.vscode/launch.json deleted file mode 100644 index 17d02ab..0000000 --- a/.vscode/launch.json +++ /dev/null @@ -1,60 +0,0 @@ -{ - // 使用 IntelliSense 了解相关属性。 - // 悬停以查看现有属性的描述。 - // 欲了解更多信息，请访问: https://go.microsoft.com/fwlink/?linkid=830387 - "version": "0.2.0", - "configurations": [ - { - "name": "Python: Current File", - "type": "python", - "request": "launch", - "program": "${file}", - "console": "integratedTerminal", - "justMyCode": false, - // "cwd": "${fileDirname}", - "cwd": "/home/zw/explained/DB-GPT-Hub/", - "args": [ - "--quantization_bit", - "4", - "--model_name_or_path", - "/home/model/Baichuan2-13B-Chat", - "--do_train", - "--dataset", - "example_text2sql", - "--max_source_length", - "2048", - "--max_target_length", - "512", - "--template", - "baichuan2", - "--finetuning_type", - "lora", - "--lora_rank", - "32", - "--lora_alpha", - "64", - "--lora_target", - "W_pack", - "--output_dir", - "sft_ckpt", - "--overwrite_cache", - "--overwrite_output_dir", - "--per_device_train_batch_size", - "1", - "--gradient_accumulation_steps", - "2", - "--lr_scheduler_type", - "cosine_with_restarts", - "--logging_steps", - "10", - "--save_steps", - "50", - "--learning_rate", - "5e-5", - "--num_train_epochs", - "0.2", - "--plot_loss" - ] - } - ] -} \ No newline at end of file diff --git a/.vscode/settings.json b/.vscode/settings.json deleted file mode 100644 index 318b689..0000000 --- a/.vscode/settings.json +++ /dev/null @@ -1,3 +0,0 @@ -{ - "Codegeex.Explanation.LanguagePreference": "zh-CN" -} \ No newline at end of file diff --git a/README.md b/README.md index 3c8097a..3fcc5af 100644 --- a/README.md +++ b/README.md @@ -9,9 +9,6 @@ forks - - License: MIT - License: MIT @@ -53,12 +50,11 @@ ## 1. What is DB-GPT-Hub -DB-GPT-Hub is an experimental project utilizing LLMs (Large Language Models) to achieve Text-to-SQL parsing. The project primarily encompasses data collection, data preprocessing, model selection and building, and fine-tuning of weights. Through this series of processes, we aim to enhance Text-to-SQL capabilities while reducing the model training costs, allowing more developers to contribute to the improvement of Text-to-SQL accuracy. Our ultimate goal is to realize automated question-answering capabilities based on databases, enabling users to execute complex database queries through natural language descriptions. +DB-GPT-Hub is an experimental project that leverages Large Language Models (LLMs) to achieve Text-to-SQL parsing. The project encompasses various stages, including data collection, data preprocessing, model selection and construction, and fine-tuning of model weights. Through these processes, our aim is to enhance Text-to-SQL capabilities while reducing model training costs, thus enabling more developers to contribute to improving Text-to-SQL accuracy. Our ultimate goal is to realize automated question-answering capabilities based on databases, allowing users to execute complex database queries using natural language descriptions. -So far, we have successfully integrated multiple large models and established a complete workflow, including data processing, model SFT (Supervised Fine-Tuning) training, prediction output, and evaluation. The code is readily reusable within this project. +To date, we have successfully integrated multiple large models and established a comprehensive workflow that includes data processing, Supervised Fine-Tuning (SFT) model training, prediction output, and evaluation. The code developed for this project is easily reusable within the project itself. - -As of 20231010, we used this project to fine-tune the open source 13B size model, combined with more relevant data, and under the zero-shot prompt, Spider-based [test-suite](https://github.com/taoyds/test-suite -sql-eval), the execution accuracy of the database (size-1.27G) can reach **0.764**, and the execution accuracy of the database (size-95M) pointed to by the Spider official [website](https://yale-lily.github.io/spider) is 0.825. +As of October 10, 2023, we have used this project to fine-tune the open-source 13B-sized model, incorporating more relevant data. Under zero-shot prompts and utilizing [the Spider-based test-suite](https://github.com/taoyds/test-suite -sql-eval), we have achieved an execution accuracy rate of 0.764 for a database with a size of 1.27G. Additionally, the execution accuracy for the database pointed to by [the Spider official website](https://yale-lily.github.io/spider), with a size of 95M, stands at 0.825. ## 2. Fine-tuning Text-to-SQL @@ -78,7 +74,7 @@ Other text2sql datasets available: - [BIRD-SQL:](https://bird-bench.github.io/) A large-scale cross-domain text-to-SQL benchmark in English, with a particular focus on large database content. The dataset contains 12,751 text-to-SQL data pairs and 95 databases with a total size of 33.4 GB across 37 occupational domains. The BIRD-SQL dataset bridges the gap between text-to-SQL research and real-world applications by exploring three additional challenges, namely dealing with large and messy database values, external knowledge inference and optimising SQL execution efficiency. - [CoSQL:](https://yale-lily.github.io/cosql) A corpus for building cross-domain conversational text-to-SQL systems. It is a conversational version of the Spider and SParC tasks. CoSQL consists of 30k+ rounds and 10k+ annotated SQL queries from Wizard-of-Oz's collection of 3k conversations querying 200 complex databases across 138 domains. Each conversation simulates a realistic DB query scenario in which a staff member explores the database as a user and a SQL expert uses SQL to retrieve answers, clarify ambiguous questions, or otherwise inform. -- Following the processing template of [NSQL](https://github.com/NumbersStationAI/NSQL), the dataset underwent basic processing, yielding approximately [20K dataset](https://huggingface.co/datasets/Healthy13/Text2SQL/tree/main) +- Following the processing template of [NSQL](https://github.com/NumbersStationAI/NSQL), the dataset underwent basic processing, yielding approximately [20W dataset](https://huggingface.co/datasets/Healthy13/Text2SQL/tree/main) @@ -115,8 +111,7 @@ git clone https://github.com/eosphoros-ai/DB-GPT-Hub.git cd DB-GPT-Hub conda create -n dbgpt_hub python=3.10 conda activate dbgpt_hub -pip install -r requirements.txt -mkdir model +poetry install ``` ### 3.2. Data preparation @@ -128,7 +123,7 @@ Download the [Spider dataset]((https://drive.google.com/uc?export=download&id=1T For the data preprocessing part, simply **run the following script** : ```bash ## generate train and dev(eval) data -sh dbgpt_hub/scripts/gen_train_eval_data.sh +poetry run sh dbgpt_hub/scripts/gen_train_eval_data.sh ``` In the directory `dbgpt_hub/data/`, you will find the newly generated training file example_text2sql_train.json and testing file example_text2sql_dev.json, containing 8659 and 1034 entries respectively. For the data used in subsequent fine-tuning, set the parameter `file_name` value to the file name of the training set in dbgpt_hub/data/dataset_info.json, such as example_text2sql_train.json @@ -152,7 +147,7 @@ The model fine-tuning supports both LoRA and QLoRA methods. We can run the follo Run the command: ```bash -sh dbgpt_hub/scripts/train_sft.sh +poetry run sh dbgpt_hub/scripts/train_sft.sh ``` After fine-tuning, the model weights will be saved by default in the adapter folder, specifically in the dbgpt_hub/output/adapter directory. @@ -210,7 +205,7 @@ In the script, during fine-tuning, different models correspond to key parameters Under the project directory ./dbgpt_hub/output/pred/, this folder is the default output location for model predictions(if not exist, just mkdir). ```bash -sh ./dbgpt_hub/scripts/predict_sft.sh +poetry run sh ./dbgpt_hub/scripts/predict_sft.sh ``` In the script, by default with the parameter `--quantization_bit`, it predicts using QLoRA. Removing it switches to the LoRA prediction method. @@ -225,7 +220,7 @@ You can find the second corresponding model weights from Huggingface [hg-eospho If you need to merge the weights of the trained base model and the fine-tuned Peft module to export a complete model, execute the following model export script: ```bash -sh ./dbgpt_hub/scripts/export_merge.sh +poetry run sh ./dbgpt_hub/scripts/export_merge.sh ``` Be sure to replace the parameter path values in the script with the paths corresponding to your project. @@ -234,7 +229,7 @@ Be sure to replace the parameter path values in the script with the paths corres To evaluate model performance on the dataset, default is spider dev dataset. Run the following command: ```bash -python dbgpt_hub/eval/evaluation.py --plug_value --input Your_model_pred_file +poetry run python dbgpt_hub/eval/evaluation.py --plug_value --input Your_model_pred_file ``` You can find the results of our latest review and part of experiment results [here](docs/eval_llm_result.md) **Note**: The database pointed to by the default code is a 95M database downloaded from [Spider official website] (https://yale-lily.github.io/spider). If you need to use Spider database (size 1.27G) in [test-suite](https://github.com/taoyds/test-suite-sql-eval), please download the database in the link to the custom directory first, and run the above evaluation command which add parameters and values ​​like `--db Your_download_db_path`. @@ -244,8 +239,9 @@ You can find the results of our latest review and part of experiment results [he The whole process we will divide into three phases: * Stage 1: - * Set up the basic framework, enabling end-to-end workflow from data processing, model SFT training, prediction output to evaluation based on multiple large models. As of 20230804, the entire pipeline has been established. - now,we has supported as follows: + * Set up the foundational framework, enabling an end-to-end workflow that encompasses data processing, model SFT (Single Fine-Tuning) training, prediction output, and evaluation using multiple large language models (LLMs). As of August 4th, 2023, the entire pipeline has been successfully established. + + Currently, we offer support for the following features: - [x] CodeLlama - [x] Baichuan2 - [x] LLaMa/LLaMa2 @@ -264,11 +260,24 @@ The whole process we will divide into three phases: - [ ] Targeted optimization and improvement of business scenarios and Chinese effects - [ ] Optimized based on more papers, such as RESDSQL and others. Combined with our community's sibling project[Awesome-Text2SQL](https://github.com/eosphoros-ai/Awesome-Text2SQL)for further enhancements.. -**If our work is even a little help to you, please give us a star to let us know ,which would be more motivation for us to release more related work.** +**If our work has provided even a small measure of assistance to you, please consider giving us a star. Your feedback and support serve as motivation for us to continue releasing more related work and improving our efforts. Thank you!** ## 5. Contributions -We welcome more folks to participate and provide feedback in areas like datasets, model fine-tuning, performance evaluation, paper recommendations, code reproduction, etc. Feel free to open issues or PRs and we'll actively respond.Before submitting the code, please format it using the black style in command `black .`. +We warmly invite more individuals to join us and actively engage in various aspects of our project, such as datasets, model fine-tuning, performance evaluation, paper recommendations, and code reproduction. Please don't hesitate to open issues or pull requests (PRs), and we will be proactive in responding to your contributions. + +Before submitting your code, please ensure that it is formatted according to the black style by using the following command: +``` +poetry run black dbgpt_hub +``` + +If you have more time to execute more detailed type checking and style checking of your code, please use the following commond: +``` +poetry run pyright dbgpt_hub +poetry run pylint dbgpt_hub +``` + +If you have any questions or need further assistance, don't hesitate to reach out. We appreciate your involvement! ## 6. Acknowledgements @@ -296,7 +305,7 @@ Thanks for all contributors ! The MIT License (MIT) ## 8、Contact Information -We are working together as a community, if you have any ideas about our community work , feel free to contact us. And you're interested in an in-depth experiment and optimization of the DB-GPT-Hub subproject, you can reach out to 'wangzai' in the WeChat group, we are welcome to make it better togther. +We are collaborating as a community, and if you have any ideas regarding our community work, please don't hesitate to get in touch with us. If you're interested in delving into an in-depth experiment and optimizing the DB-GPT-Hub subproject, you can reach out to 'wangzai' within the WeChat group. We wholeheartedly welcome your contributions to making it even better together! [![](https://dcbadge.vercel.app/api/server/nASQyBjvY?compact=true&style=flat)](https://discord.gg/nASQyBjvY)

diff --git a/dbgpt_hub/data_process/sql_data_process.py b/dbgpt_hub/data_process/sql_data_process.py index 25a3b5f..a3e30c7 100644 --- a/dbgpt_hub/data_process/sql_data_process.py +++ b/dbgpt_hub/data_process/sql_data_process.py @@ -21,7 +21,9 @@ def __init__(self, train_file=None, dev_file=None) -> None: self.train_file = train_file self.dev_file = dev_file - def decode_json_file(self, data_file_list, table_file, db_id_name, is_multiple_turn=False): + def decode_json_file( + self, data_file_list, table_file, db_id_name, is_multiple_turn=False + ): """ TO DO: 1.将相关prompt放入config中 @@ -87,22 +89,31 @@ def decode_json_file(self, data_file_list, table_file, db_id_name, is_multiple_t res = [] for data in tqdm(datas): if data[db_id_name] in db_dict.keys(): - if is_multiple_turn: #多轮 + if is_multiple_turn: # 多轮 history = [] for interaction in data["interaction"]: input = { "db_id": data[db_id_name], - "instruction": INSTRUCTION_PROMPT.format(db_dict[data[db_id_name]]), + "instruction": INSTRUCTION_PROMPT.format( + db_dict[data[db_id_name]] + ), "input": INPUT_PROMPT.format(interaction["utterance"]), "output": interaction["query"], "history": history, } res.append(input) - history.append((INPUT_PROMPT.format(interaction["utterance"]), interaction["query"])) - else: # 单轮 + history.append( + ( + INPUT_PROMPT.format(interaction["utterance"]), + interaction["query"], + ) + ) + else: # 单轮 input = { "db_id": data[db_id_name], - "instruction": INSTRUCTION_PROMPT.format(db_dict[data[db_id_name]]), + "instruction": INSTRUCTION_PROMPT.format( + db_dict[data[db_id_name]] + ), "input": INPUT_PROMPT.format(data["question"]), "output": data["query"], "history": [], @@ -125,7 +136,7 @@ def create_sft_raw_data(self): DATA_PATH, data_info["data_source"], data_info["tables_file"] ), db_id_name=data_info["db_id_name"], - is_multiple_turn=data_info['is_multiple_turn'] + is_multiple_turn=data_info["is_multiple_turn"], ) ) @@ -140,7 +151,7 @@ def create_sft_raw_data(self): DATA_PATH, data_info["data_source"], data_info["tables_file"] ), db_id_name=data_info["db_id_name"], - is_multiple_turn=data_info['is_multiple_turn'] + is_multiple_turn=data_info["is_multiple_turn"], ) ) with open(self.train_file, "w", encoding="utf-8") as s: @@ -152,5 +163,7 @@ def create_sft_raw_data(self): if __name__ == "__main__": all_in_one_train_file = os.path.join(DATA_PATH, "example_text2sql_train.json") all_in_one_dev_file = os.path.join(DATA_PATH, "example_text2sql_dev.json") - precess = ProcessSqlData(train_file=all_in_one_train_file, dev_file=all_in_one_dev_file) + precess = ProcessSqlData( + train_file=all_in_one_train_file, dev_file=all_in_one_dev_file + ) precess.create_sft_raw_data() diff --git a/dbgpt_hub/llm_base/config_parser.py b/dbgpt_hub/llm_base/config_parser.py index 0129b69..337596f 100644 --- a/dbgpt_hub/llm_base/config_parser.py +++ b/dbgpt_hub/llm_base/config_parser.py @@ -93,8 +93,7 @@ def parse_infer_args( def get_train_args( - args: Optional[Dict[str, Any]] = None, - data_args_init: bool = True + args: Optional[Dict[str, Any]] = None, data_args_init: bool = True ) -> Tuple[ ModelArguments, DataArguments, diff --git a/dbgpt_hub/llm_base/model_trainer.py b/dbgpt_hub/llm_base/model_trainer.py index 228be35..1bc08cf 100644 --- a/dbgpt_hub/llm_base/model_trainer.py +++ b/dbgpt_hub/llm_base/model_trainer.py @@ -401,7 +401,9 @@ def plot_loss( def export_model( args: Optional[Dict[str, Any]] = None, max_shard_size: Optional[str] = "10GB" ): - model_args, _, training_args, finetuning_args, _ = get_train_args(args, data_args_init=False) + model_args, _, training_args, finetuning_args, _ = get_train_args( + args, data_args_init=False + ) model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args) model.save_pretrained(training_args.output_dir, max_shard_size=max_shard_size) try: diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..3124866 --- /dev/null +++ b/poetry.lock @@ -0,0 +1,4084 @@ +# This file is automatically 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