diff --git a/.gitignore b/.gitignore index 3e563d1d..83339037 100644 --- a/.gitignore +++ b/.gitignore @@ -5,6 +5,13 @@ __pycache__ dist .venv +# Byte-compiled / optimized / DLL files +*.py[cod] +*$py.class + +# C extensions +*.so + # Log *.log *.log.* @@ -33,4 +40,157 @@ tests/state_of_the_union.txt # Build build -!dummy_file \ No newline at end of file +!dummy_file + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# 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: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. 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(multiple GPUs are not supported yet) + +Here is an example of altering the self-cognition of an instruction-tuned language model within 10 minutes on a single GPU. + +https://github.com/hiyouga/LLaMA-Factory/assets/16256802/6ba60acc-e2e2-4bec-b846-2d88920d5ba1 + +## Changelog + +[23/10/21] We supported **[NEFTune](https://arxiv.org/abs/2310.05914)** trick for fine-tuning. Try `--neft_alpha` argument to activate NEFTune, e.g., `--neft_alpha 5`. + +[23/09/27] We supported **$S^2$-Attn** proposed by [LongLoRA](https://github.com/dvlab-research/LongLoRA) for the LLaMA models. Try `--shift_attn` argument to enable shift short attention. + +[23/09/23] We integrated MMLU, C-Eval and CMMLU benchmarks in this repo. See [this example](#evaluation) to evaluate your models. + +[23/09/10] We supported using **[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)** for the LLaMA models. Try `--flash_attn` argument to enable FlashAttention-2 if you are using RTX4090, A100 or H100 GPUs. + +[23/08/12] We supported **RoPE scaling** to extend the context length of the LLaMA models. Try `--rope_scaling linear` argument in training and `--rope_scaling dynamic` argument at inference to extrapolate the position embeddings. + +[23/08/11] We supported **[DPO training](https://arxiv.org/abs/2305.18290)** for instruction-tuned models. See [this example](#dpo-training) to train your models. + +[23/07/31] We supported **dataset streaming**. Try `--streaming` and `--max_steps 10000` arguments to load your dataset in streaming mode. + +[23/07/29] We released two instruction-tuned 13B models at Hugging Face. See these Hugging Face Repos ([LLaMA-2](https://huggingface.co/hiyouga/Llama-2-Chinese-13b-chat) / [Baichuan](https://huggingface.co/hiyouga/Baichuan-13B-sft)) for details. + +[23/07/18] We developed an **all-in-one Web UI** for training, evaluation and inference. Try `train_web.py` to fine-tune models in your Web browser. Thank [@KanadeSiina](https://github.com/KanadeSiina) and [@codemayq](https://github.com/codemayq) for their efforts in the development. + +[23/07/09] We released **[FastEdit](https://github.com/hiyouga/FastEdit)** ⚡🩹, an easy-to-use package for editing the factual knowledge of large language models efficiently. Please follow [FastEdit](https://github.com/hiyouga/FastEdit) if you are interested. + +[23/06/29] We provided a **reproducible example** of training a chat model using instruction-following datasets, see [Baichuan-7B-sft](https://huggingface.co/hiyouga/Baichuan-7B-sft) for details. + +[23/06/22] We aligned the [demo API](src/api_demo.py) with the [OpenAI's](https://platform.openai.com/docs/api-reference/chat) format where you can insert the fine-tuned model in **arbitrary ChatGPT-based applications**. + +[23/06/03] We supported quantized training and inference (aka **[QLoRA](https://github.com/artidoro/qlora)**). Try `--quantization_bit 4/8` argument to work with quantized models. + +## Supported Models + +| Model | Model size | Default module | Template | +| -------------------------------------------------------- | --------------------------- | ----------------- | --------- | +| [Baichuan](https://github.com/baichuan-inc/Baichuan-13B) | 7B/13B | W_pack | baichuan | +| [Baichuan2](https://github.com/baichuan-inc/Baichuan2) | 7B/13B | W_pack | baichuan2 | +| [BLOOM](https://huggingface.co/bigscience/bloom) | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - | +| [BLOOMZ](https://huggingface.co/bigscience/bloomz) | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - | +| [ChatGLM3](https://github.com/THUDM/ChatGLM3) | 6B | query_key_value | chatglm3 | +| [Falcon](https://huggingface.co/tiiuae/falcon-7b) | 7B/40B/180B | query_key_value | - | +| [InternLM](https://github.com/InternLM/InternLM) | 7B/20B | q_proj,v_proj | intern | +| [LLaMA](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | q_proj,v_proj | - | +| [LLaMA-2](https://huggingface.co/meta-llama) | 7B/13B/70B | q_proj,v_proj | llama2 | +| [Mistral](https://huggingface.co/mistralai) | 7B | q_proj,v_proj | mistral | +| [Phi-1.5](https://huggingface.co/microsoft/phi-1_5) | 1.3B | Wqkv | - | +| [Qwen](https://github.com/QwenLM/Qwen-7B) | 7B/14B | c_attn | qwen | +| [XVERSE](https://github.com/xverse-ai) | 7B/13B/65B | q_proj,v_proj | xverse | + +> [!NOTE] +> **Default module** is used for the `--lora_target` argument, you can use `--lora_target all` to specify all the available modules. +> +> For the "base" models, the `--template` argument can be chosen from `default`, `alpaca`, `vicuna` etc. But make sure to use the **corresponding template** for the "chat" models. + +Please refer to [template.py](src/llmtuner/extras/template.py) for a full list of models we supported. + +## Supported Training Approaches + +| Approach | Full-parameter | Partial-parameter | LoRA | QLoRA | +| ---------------------- | ------------------ | ------------------ | ------------------ | ------------------ | +| Pre-Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | +| Supervised Fine-Tuning | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | +| Reward Modeling | | | :white_check_mark: | :white_check_mark: | +| PPO Training | | | :white_check_mark: | :white_check_mark: | +| DPO Training | :white_check_mark: | | :white_check_mark: | :white_check_mark: | + +> [!NOTE] +> Use `--quantization_bit 4/8` argument to enable QLoRA. + +## Provided Datasets + +
Pre-training datasets + +- [Wiki Demo (en)](data/wiki_demo.txt) +- [RefinedWeb (en)](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) +- [RedPajama V2 (en)](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2) +- [Wikipedia (en)](https://huggingface.co/datasets/olm/olm-wikipedia-20221220) +- [Wikipedia (zh)](https://huggingface.co/datasets/pleisto/wikipedia-cn-20230720-filtered) +- [Pile (en)](https://huggingface.co/datasets/EleutherAI/pile) +- [SkyPile (zh)](https://huggingface.co/datasets/Skywork/SkyPile-150B) +- [The Stack (en)](https://huggingface.co/datasets/bigcode/the-stack) +- [StarCoder (en)](https://huggingface.co/datasets/bigcode/starcoderdata) + +
+ +
Supervised fine-tuning datasets + +- [Stanford Alpaca (en)](https://github.com/tatsu-lab/stanford_alpaca) +- [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca) +- [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM) +- [Self-cognition (zh)](data/self_cognition.json) +- [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1) +- [ShareGPT (zh)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/tree/main/Chinese-instruction-collection) +- [Guanaco Dataset (multilingual)](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset) +- [BELLE 2M (zh)](https://huggingface.co/datasets/BelleGroup/train_2M_CN) +- [BELLE 1M (zh)](https://huggingface.co/datasets/BelleGroup/train_1M_CN) +- [BELLE 0.5M (zh)](https://huggingface.co/datasets/BelleGroup/train_0.5M_CN) +- [BELLE Dialogue 0.4M (zh)](https://huggingface.co/datasets/BelleGroup/generated_chat_0.4M) +- [BELLE School Math 0.25M (zh)](https://huggingface.co/datasets/BelleGroup/school_math_0.25M) +- [BELLE Multiturn Chat 0.8M (zh)](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M) +- [UltraChat (en)](https://github.com/thunlp/UltraChat) +- [LIMA (en)](https://huggingface.co/datasets/GAIR/lima) +- [OpenPlatypus (en)](https://huggingface.co/datasets/garage-bAInd/Open-Platypus) +- [CodeAlpaca 20k (en)](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k) +- [Alpaca CoT (multilingual)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT) +- [MathInstruct (en)](https://huggingface.co/datasets/TIGER-Lab/MathInstruct) +- [Firefly 1.1M (zh)](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M) +- [Web QA (zh)](https://huggingface.co/datasets/suolyer/webqa) +- [WebNovel (zh)](https://huggingface.co/datasets/zxbsmk/webnovel_cn) +- [Ad Gen (zh)](https://huggingface.co/datasets/HasturOfficial/adgen) +- [ShareGPT Hyperfiltered (en)](https://huggingface.co/datasets/totally-not-an-llm/sharegpt-hyperfiltered-3k) +- [ShareGPT4 (en&zh)](https://huggingface.co/datasets/shibing624/sharegpt_gpt4) +- [UltraChat 200k (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) +- [AgentInstruct (en)](https://huggingface.co/datasets/THUDM/AgentInstruct) +- [LMSYS Chat 1M (en)](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) +- [Evol Instruct V2 (en)](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k) + +
+ +
Preference datasets + +- [HH-RLHF (en)](https://huggingface.co/datasets/Anthropic/hh-rlhf) +- [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1) +- [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM) + +
+ +Please refer to [data/README.md](data/README.md) for details. + +Some datasets require confirmation before using them, so we recommend logging in with your Hugging Face account using these commands. + +```bash +pip install --upgrade huggingface_hub +huggingface-cli login +``` + +## Requirement + +- Python 3.8+ and PyTorch 1.13.1+ +- 🤗Transformers, Datasets, Accelerate, PEFT and TRL +- sentencepiece, protobuf and tiktoken +- fire, jieba, rouge-chinese and nltk (used at evaluation and predict) +- gradio and matplotlib (used in web UI) +- uvicorn, fastapi and sse-starlette (used in API) + +And **powerful GPUs**! + +## Getting Started + +### Data Preparation (optional) + +Please refer to [data/README.md](data/README.md) for checking the details about the format of dataset files. You can either use a single `.json` file or a [dataset loading script](https://huggingface.co/docs/datasets/dataset_script) with multiple files to create a custom dataset. + +> [!NOTE] +> Please update `data/dataset_info.json` to use your custom dataset. About the format of this file, please refer to `data/README.md`. + +### Dependence Installation (optional) + +```bash +git clone https://github.com/hiyouga/LLaMA-Factory.git +conda create -n llama_factory python=3.10 +conda activate llama_factory +cd LLaMA-Factory +pip install -r requirements.txt +``` + +If you want to enable the quantized LoRA (QLoRA) on the Windows platform, you will be required to install a pre-built version of `bitsandbytes` library, which supports CUDA 11.1 to 12.1. + +```bash +pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.39.1-py3-none-win_amd64.whl +``` + +### Train on a single GPU + +> [!IMPORTANT] +> If you want to train models on multiple GPUs, please refer to [Distributed Training](#distributed-training). + +#### Pre-Training + +```bash +CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ + --stage pt \ + --model_name_or_path path_to_llama_model \ + --do_train \ + --dataset wiki_demo \ + --finetuning_type lora \ + --lora_target q_proj,v_proj \ + --output_dir path_to_pt_checkpoint \ + --overwrite_cache \ + --per_device_train_batch_size 4 \ + --gradient_accumulation_steps 4 \ + --lr_scheduler_type cosine \ + --logging_steps 10 \ + --save_steps 1000 \ + --learning_rate 5e-5 \ + --num_train_epochs 3.0 \ + --plot_loss \ + --fp16 +``` + +#### Supervised Fine-Tuning + +```bash +CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ + --stage sft \ + --model_name_or_path path_to_llama_model \ + --do_train \ + --dataset alpaca_gpt4_en \ + --template default \ + --finetuning_type lora \ + --lora_target q_proj,v_proj \ + --output_dir path_to_sft_checkpoint \ + --overwrite_cache \ + --per_device_train_batch_size 4 \ + --gradient_accumulation_steps 4 \ + --lr_scheduler_type cosine \ + --logging_steps 10 \ + --save_steps 1000 \ + --learning_rate 5e-5 \ + --num_train_epochs 3.0 \ + --plot_loss \ + --fp16 +``` + +#### Reward Modeling + +```bash +CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ + --stage rm \ + --model_name_or_path path_to_llama_model \ + --do_train \ + --dataset comparison_gpt4_en \ + --template default \ + --finetuning_type lora \ + --lora_target q_proj,v_proj \ + --resume_lora_training False \ + --checkpoint_dir path_to_sft_checkpoint \ + --output_dir path_to_rm_checkpoint \ + --per_device_train_batch_size 2 \ + --gradient_accumulation_steps 4 \ + --lr_scheduler_type cosine \ + --logging_steps 10 \ + --save_steps 1000 \ + --learning_rate 1e-6 \ + --num_train_epochs 1.0 \ + --plot_loss \ + --fp16 +``` + +#### PPO Training + +```bash +CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ + --stage ppo \ + --model_name_or_path path_to_llama_model \ + --do_train \ + --dataset alpaca_gpt4_en \ + --template default \ + --finetuning_type lora \ + --lora_target q_proj,v_proj \ + --resume_lora_training False \ + --checkpoint_dir path_to_sft_checkpoint \ + --reward_model path_to_rm_checkpoint \ + --output_dir path_to_ppo_checkpoint \ + --per_device_train_batch_size 2 \ + --gradient_accumulation_steps 4 \ + --lr_scheduler_type cosine \ + --logging_steps 10 \ + --save_steps 1000 \ + --learning_rate 1e-5 \ + --num_train_epochs 1.0 \ + --plot_loss \ + --fp16 +``` + +#### DPO Training + +```bash +CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ + --stage dpo \ + --model_name_or_path path_to_llama_model \ + --do_train \ + --dataset comparison_gpt4_en \ + --template default \ + --finetuning_type lora \ + --lora_target q_proj,v_proj \ + --resume_lora_training False \ + --checkpoint_dir path_to_sft_checkpoint \ + --output_dir path_to_dpo_checkpoint \ + --per_device_train_batch_size 2 \ + --gradient_accumulation_steps 4 \ + --lr_scheduler_type cosine \ + --logging_steps 10 \ + --save_steps 1000 \ + --learning_rate 1e-5 \ + --num_train_epochs 1.0 \ + --plot_loss \ + --fp16 +``` + +### Distributed Training + +#### Use Huggingface Accelerate + +```bash +accelerate config # configure the environment +accelerate launch src/train_bash.py # arguments (same as above) +``` + +
Example config for LoRA training + +```yaml +compute_environment: LOCAL_MACHINE +distributed_type: MULTI_GPU +downcast_bf16: 'no' +gpu_ids: all +machine_rank: 0 +main_training_function: main +mixed_precision: fp16 +num_machines: 1 +num_processes: 4 +rdzv_backend: static +same_network: true +tpu_env: [] +tpu_use_cluster: false +tpu_use_sudo: false +use_cpu: false +``` + +
+ +#### Use DeepSpeed + +```bash +deepspeed --num_gpus 8 --master_port=9901 src/train_bash.py \ + --deepspeed ds_config.json \ + ... # arguments (same as above) +``` + +
Example config for full-parameter training with DeepSpeed ZeRO-2 + +```json +{ + "train_batch_size": "auto", + "train_micro_batch_size_per_gpu": "auto", + "gradient_accumulation_steps": "auto", + "gradient_clipping": "auto", + "zero_allow_untested_optimizer": true, + "fp16": { + "enabled": "auto", + "loss_scale": 0, + "initial_scale_power": 16, + "loss_scale_window": 1000, + "hysteresis": 2, + "min_loss_scale": 1 + }, + "zero_optimization": { + "stage": 2, + "allgather_partitions": true, + "allgather_bucket_size": 5e8, + "reduce_scatter": true, + "reduce_bucket_size": 5e8, + "overlap_comm": false, + "contiguous_gradients": true + } +} +``` + +
+ +### Export model + +```bash +python src/export_model.py \ + --model_name_or_path path_to_llama_model \ + --template default \ + --finetuning_type lora \ + --checkpoint_dir path_to_checkpoint \ + --export_dir path_to_export +``` + +### API Demo + +```bash +python src/api_demo.py \ + --model_name_or_path path_to_llama_model \ + --template default \ + --finetuning_type lora \ + --checkpoint_dir path_to_checkpoint +``` + +> [!NOTE] +> Visit `http://localhost:8000/docs` for API documentation. + +### CLI Demo + +```bash +python src/cli_demo.py \ + --model_name_or_path path_to_llama_model \ + --template default \ + --finetuning_type lora \ + --checkpoint_dir path_to_checkpoint +``` + +### Web Demo + +```bash +python src/web_demo.py \ + --model_name_or_path path_to_llama_model \ + --template default \ + --finetuning_type lora \ + --checkpoint_dir path_to_checkpoint +``` + +### Evaluation + +```bash +CUDA_VISIBLE_DEVICES=0 python src/evaluate.py \ + --model_name_or_path path_to_llama_model \ + --finetuning_type lora \ + --checkpoint_dir path_to_checkpoint \ + --template vanilla \ + --task mmlu \ + --split test \ + --lang en \ + --n_shot 5 \ + --batch_size 4 +``` + +### Predict + +```bash +CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ + --stage sft \ + --model_name_or_path path_to_llama_model \ + --do_predict \ + --dataset alpaca_gpt4_en \ + --template default \ + --finetuning_type lora \ + --checkpoint_dir path_to_checkpoint \ + --output_dir path_to_predict_result \ + --per_device_eval_batch_size 8 \ + --max_samples 100 \ + --predict_with_generate +``` + +> [!NOTE] +> We recommend using `--per_device_eval_batch_size=1` and `--max_target_length 128` at 4/8-bit predict. + +## Projects using LLaMA Factory + +- **[StarWhisper](https://github.com/Yu-Yang-Li/StarWhisper)**: A large language model for Astronomy, based on ChatGLM2-6B and Qwen-14B. +- **[DISC-LawLLM](https://github.com/FudanDISC/DISC-LawLLM)**: A large language model specialized in Chinese legal domain, based on Baichuan-13B, is capable of retrieving and reasoning on legal knowledge. +- **[Sunsimiao](https://github.com/thomas-yanxin/Sunsimiao)**: A large language model specialized in Chinese medical domain, based on Baichuan-7B and ChatGLM-6B. +- **[CareGPT](https://github.com/WangRongsheng/CareGPT)**: A series of large language models for Chinese medical domain, based on LLaMA2-7B and Baichuan-13B. + +## License + +This repository is licensed under the [Apache-2.0 License](LICENSE). + +Please follow the model licenses to use the corresponding model weights: [Baichuan](https://huggingface.co/baichuan-inc/Baichuan-13B-Base/resolve/main/Community%20License%20for%20Baichuan-13B%20Model.pdf) / [Baichuan2](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat/resolve/main/Community%20License%20for%20Baichuan2%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [InternLM](https://github.com/InternLM/InternLM#license) / [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [LLaMA-2](https://ai.meta.com/llama/license/) / [Mistral](LICENSE) / [Phi-1.5](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Qwen](https://huggingface.co/Qwen/Qwen-7B-Chat/blob/main/LICENSE) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) + +## Citation + +If this work is helpful, please kindly cite as: + +```bibtex +@Misc{llama-factory, + title = {LLaMA Factory}, + author = {hiyouga}, + howpublished = {\url{https://github.com/hiyouga/LLaMA-Factory}}, + year = {2023} +} +``` + +## Acknowledgement + +This repo benefits from [PEFT](https://github.com/huggingface/peft), [QLoRA](https://github.com/artidoro/qlora) and [FastChat](https://github.com/lm-sys/FastChat). Thanks for their wonderful works. + +## Star History + +![Star History Chart](https://api.star-history.com/svg?repos=hiyouga/LLaMA-Factory&type=Date) diff --git a/llm_rl/README_zh.md b/llm_rl/README_zh.md new file mode 100644 index 00000000..c69e3983 --- /dev/null +++ b/llm_rl/README_zh.md @@ -0,0 +1,500 @@ +# LLaMA Factory: 轻松的大模型训练与评估 + +[![GitHub Repo stars](https://img.shields.io/github/stars/hiyouga/LLaMA-Factory?style=social)](https://github.com/hiyouga/LLaMA-Factory/stargazers) +[![GitHub Code License](https://img.shields.io/github/license/hiyouga/LLaMA-Factory)](LICENSE) +[![GitHub last commit](https://img.shields.io/github/last-commit/hiyouga/LLaMA-Factory)](https://github.com/hiyouga/LLaMA-Factory/commits/main) +[![PyPI](https://img.shields.io/pypi/v/llmtuner)](https://pypi.org/project/llmtuner/) +[![Downloads](https://static.pepy.tech/badge/llmtuner)](https://pypi.org/project/llmtuner/) +[![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue)](https://github.com/hiyouga/LLaMA-Factory/pulls) +[![Discord](https://dcbadge.vercel.app/api/server/e73gccsSd?compact=true&style=flat)](https://discord.gg/e73gccsSd) + +👋 加入我们的[微信群](assets/wechat.jpg)。 + +\[ [English](README.md) | 中文 \] + +## LLaMA Board: 通过一站式网页界面快速上手 LLaMA Factory + +使用 `CUDA_VISIBLE_DEVICES=0 python src/train_web.py` 启动 **LLaMA Board**。（该界面目前仅支持单卡训练） + +下面是使用单张 GPU 在 10 分钟内更改对话式大型语言模型自我认知的示例。 + +https://github.com/hiyouga/LLaMA-Factory/assets/16256802/6ba60acc-e2e2-4bec-b846-2d88920d5ba1 + +## 更新日志 + +[23/10/21] 我们支持了 **[NEFTune](https://arxiv.org/abs/2310.05914)** 训练技巧。请使用 `--neft_alpha` 参数启用 NEFTune，例如 `--neft_alpha 5`。 + +[23/09/27] 我们针对 LLaMA 模型支持了 [LongLoRA](https://github.com/dvlab-research/LongLoRA) 提出的 **$S^2$-Attn**。请使用 `--shift_attn` 参数以启用该功能。 + +[23/09/23] 我们在项目中集成了 MMLU、C-Eval 和 CMMLU 评估集。使用方法请参阅[此示例](#模型评估)。 + +[23/09/10] 我们针对 LLaMA 模型支持了 **[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)**。如果您使用的是 RTX4090、A100 或 H100 GPU，请使用 `--flash_attn` 参数以启用 FlashAttention-2。 + +[23/08/12] 我们支持了 **RoPE 插值**来扩展 LLaMA 模型的上下文长度。请使用 `--rope_scaling linear` 参数训练模型或使用 `--rope_scaling dynamic` 参数评估模型。 + +[23/08/11] 我们支持了指令模型的 **[DPO 训练](https://arxiv.org/abs/2305.18290)**。使用方法请参阅[此示例](#dpo-训练)。 + +[23/07/31] 我们支持了**数据流式加载**。请尝试使用 `--streaming` 和 `--max_steps 10000` 参数来流式加载数据集。 + +[23/07/29] 我们在 Hugging Face 发布了两个 13B 指令微调模型。详细内容请查阅我们的 Hugging Face 项目（[LLaMA-2](https://huggingface.co/hiyouga/Llama-2-Chinese-13b-chat) / [Baichuan](https://huggingface.co/hiyouga/Baichuan-13B-sft)）。 + +[23/07/18] 我们开发了支持训练和测试的**浏览器一体化界面**。请使用 `train_web.py` 在您的浏览器中微调模型。感谢 [@KanadeSiina](https://github.com/KanadeSiina) 和 [@codemayq](https://github.com/codemayq) 在该功能开发中付出的努力。 + +[23/07/09] 我们开源了 **[FastEdit](https://github.com/hiyouga/FastEdit)** ⚡🩹，一个简单易用的、能迅速编辑大模型事实记忆的工具包。如果您感兴趣请关注我们的 [FastEdit](https://github.com/hiyouga/FastEdit) 项目。 + +[23/06/29] 我们提供了一个**可复现的**指令模型微调示例，详细内容请查阅 [Baichuan-7B-sft](https://huggingface.co/hiyouga/Baichuan-7B-sft)。 + +[23/06/22] 我们对齐了[示例 API](src/api_demo.py) 与 [OpenAI API](https://platform.openai.com/docs/api-reference/chat) 的格式，您可以将微调模型接入**任意基于 ChatGPT 的应用**中。 + +[23/06/03] 我们实现了 4 比特的 LoRA 训练（也称 **[QLoRA](https://github.com/artidoro/qlora)**）。请使用 `--quantization_bit 4` 参数进行 4 比特量化微调。 + +## 模型 + +| 模型名 | 模型大小 | 默认模块 | Template | +| -------------------------------------------------------- | --------------------------- | ----------------- | --------- | +| [Baichuan](https://github.com/baichuan-inc/Baichuan-13B) | 7B/13B | W_pack | baichuan | +| [Baichuan2](https://github.com/baichuan-inc/Baichuan2) | 7B/13B | W_pack | baichuan2 | +| [BLOOM](https://huggingface.co/bigscience/bloom) | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - | +| [BLOOMZ](https://huggingface.co/bigscience/bloomz) | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - | +| [ChatGLM3](https://github.com/THUDM/ChatGLM3) | 6B | query_key_value | chatglm3 | +| [Falcon](https://huggingface.co/tiiuae/falcon-7b) | 7B/40B/180B | query_key_value | - | +| [InternLM](https://github.com/InternLM/InternLM) | 7B/20B | q_proj,v_proj | intern | +| [LLaMA](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | q_proj,v_proj | - | +| [LLaMA-2](https://huggingface.co/meta-llama) | 7B/13B/70B | q_proj,v_proj | llama2 | +| [Mistral](https://huggingface.co/mistralai) | 7B | q_proj,v_proj | mistral | +| [Phi-1.5](https://huggingface.co/microsoft/phi-1_5) | 1.3B | Wqkv | - | +| [Qwen](https://github.com/QwenLM/Qwen-7B) | 7B/14B | c_attn | qwen | +| [XVERSE](https://github.com/xverse-ai) | 7B/13B/65B | q_proj,v_proj | xverse | + +> [!NOTE] +> **默认模块**应作为 `--lora_target` 参数的默认值，可使用 `--lora_target all` 参数指定全部模块。 +> +> 对于所有“基座”（Base）模型，`--template` 参数可以是 `default`, `alpaca`, `vicuna` 等任意值。但“对话”（Chat）模型请务必使用**对应的模板**。 + +项目所支持模型的完整列表请参阅 [template.py](src/llmtuner/extras/template.py)。 + +## 训练方法 + +| 方法 | 全参数训练 | 部分参数训练 | LoRA | QLoRA | +| ---------------------- | ------------------ | ------------------ | ------------------ | ------------------ | +| 预训练 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | +| 指令监督微调 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | +| 奖励模型训练 | | | :white_check_mark: | :white_check_mark: | +| PPO 训练 | | | :white_check_mark: | :white_check_mark: | +| DPO 训练 | :white_check_mark: | | :white_check_mark: | :white_check_mark: | + +> [!NOTE] +> 请使用 `--quantization_bit 4/8` 参数来启用 QLoRA 训练。 + +## 数据集 + +
预训练数据集 + +- [Wiki Demo (en)](data/wiki_demo.txt) +- [RefinedWeb (en)](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) +- [RedPajama V2 (en)](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2) +- [Wikipedia (en)](https://huggingface.co/datasets/olm/olm-wikipedia-20221220) +- [Wikipedia (zh)](https://huggingface.co/datasets/pleisto/wikipedia-cn-20230720-filtered) +- [Pile (en)](https://huggingface.co/datasets/EleutherAI/pile) +- [SkyPile (zh)](https://huggingface.co/datasets/Skywork/SkyPile-150B) +- [The Stack (en)](https://huggingface.co/datasets/bigcode/the-stack) +- [StarCoder (en)](https://huggingface.co/datasets/bigcode/starcoderdata) + +
+ +
指令微调数据集 + +- [Stanford Alpaca (en)](https://github.com/tatsu-lab/stanford_alpaca) +- [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca) +- [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM) +- [Self-cognition (zh)](data/self_cognition.json) +- [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1) +- [ShareGPT (zh)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/tree/main/Chinese-instruction-collection) +- [Guanaco Dataset (multilingual)](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset) +- [BELLE 2M (zh)](https://huggingface.co/datasets/BelleGroup/train_2M_CN) +- [BELLE 1M (zh)](https://huggingface.co/datasets/BelleGroup/train_1M_CN) +- [BELLE 0.5M (zh)](https://huggingface.co/datasets/BelleGroup/train_0.5M_CN) +- [BELLE Dialogue 0.4M (zh)](https://huggingface.co/datasets/BelleGroup/generated_chat_0.4M) +- [BELLE School Math 0.25M (zh)](https://huggingface.co/datasets/BelleGroup/school_math_0.25M) +- [BELLE Multiturn Chat 0.8M (zh)](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M) +- [UltraChat (en)](https://github.com/thunlp/UltraChat) +- [LIMA (en)](https://huggingface.co/datasets/GAIR/lima) +- [OpenPlatypus (en)](https://huggingface.co/datasets/garage-bAInd/Open-Platypus) +- [CodeAlpaca 20k (en)](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k) +- [Alpaca CoT (multilingual)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT) +- [MathInstruct (en)](https://huggingface.co/datasets/TIGER-Lab/MathInstruct) +- [Firefly 1.1M (zh)](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M) +- [Web QA (zh)](https://huggingface.co/datasets/suolyer/webqa) +- [WebNovel (zh)](https://huggingface.co/datasets/zxbsmk/webnovel_cn) +- [Ad Gen (zh)](https://huggingface.co/datasets/HasturOfficial/adgen) +- [ShareGPT Hyperfiltered (en)](https://huggingface.co/datasets/totally-not-an-llm/sharegpt-hyperfiltered-3k) +- [ShareGPT4 (en&zh)](https://huggingface.co/datasets/shibing624/sharegpt_gpt4) +- [UltraChat 200k (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) +- [AgentInstruct (en)](https://huggingface.co/datasets/THUDM/AgentInstruct) +- [LMSYS Chat 1M (en)](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) +- [Evol Instruct V2 (en)](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k) + +
+ +
偏好数据集 + +- [HH-RLHF (en)](https://huggingface.co/datasets/Anthropic/hh-rlhf) +- [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1) +- [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM) + +
+ +使用方法请参考 [data/README_zh.md](data/README_zh.md) 文件。 + +部分数据集的使用需要确认，我们推荐使用下述命令登录您的 Hugging Face 账户。 + +```bash +pip install --upgrade huggingface_hub +huggingface-cli login +``` + +## 软件依赖 + +- Python 3.8+ 和 PyTorch 1.13.1+ +- 🤗Transformers, Datasets, Accelerate, PEFT 和 TRL +- sentencepiece, protobuf 和 tiktoken +- fire, jieba, rouge-chinese 和 nltk (用于评估及预测) +- gradio 和 matplotlib (用于网页端交互) +- uvicorn, fastapi 和 sse-starlette (用于 API) + +以及 **强而有力的 GPU**！ + +## 如何使用 + +### 数据准备（可跳过） + +关于数据集文件的格式，请参考 [data/README_zh.md](data/README_zh.md) 的内容。构建自定义数据集时，既可以使用单个 `.json` 文件，也可以使用一个[数据加载脚本](https://huggingface.co/docs/datasets/dataset_script)和多个文件。 + +> [!NOTE] +> 使用自定义数据集时，请更新 `data/dataset_info.json` 文件，该文件的格式请参考 `data/README_zh.md`。 + +### 环境搭建（可跳过） + +```bash +git clone https://github.com/hiyouga/LLaMA-Factory.git +conda create -n llama_factory python=3.10 +conda activate llama_factory +cd LLaMA-Factory +pip install -r requirements.txt +``` + +如果要在 Windows 平台上开启量化 LoRA（QLoRA），需要安装预编译的 `bitsandbytes` 库, 支持 CUDA 11.1 到 12.1. + +```bash +pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.39.1-py3-none-win_amd64.whl +``` + +### 单 GPU 训练 + +> [!IMPORTANT] +> 如果您使用多张 GPU 训练模型，请移步[多 GPU 分布式训练](#多-gpu-分布式训练)部分。 + +#### 预训练 + +```bash +CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ + --stage pt \ + --model_name_or_path path_to_llama_model \ + --do_train \ + --dataset wiki_demo \ + --finetuning_type lora \ + --lora_target q_proj,v_proj \ + --output_dir path_to_pt_checkpoint \ + --overwrite_cache \ + --per_device_train_batch_size 4 \ + --gradient_accumulation_steps 4 \ + --lr_scheduler_type cosine \ + --logging_steps 10 \ + --save_steps 1000 \ + --learning_rate 5e-5 \ + --num_train_epochs 3.0 \ + --plot_loss \ + --fp16 +``` + +#### 指令监督微调 + +```bash +CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ + --stage sft \ + --model_name_or_path path_to_llama_model \ + --do_train \ + --dataset alpaca_gpt4_zh \ + --template default \ + --finetuning_type lora \ + --lora_target q_proj,v_proj \ + --output_dir path_to_sft_checkpoint \ + --overwrite_cache \ + --per_device_train_batch_size 4 \ + --gradient_accumulation_steps 4 \ + --lr_scheduler_type cosine \ + --logging_steps 10 \ + --save_steps 1000 \ + --learning_rate 5e-5 \ + --num_train_epochs 3.0 \ + --plot_loss \ + --fp16 +``` + +#### 奖励模型训练 + +```bash +CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ + --stage rm \ + --model_name_or_path path_to_llama_model \ + --do_train \ + --dataset comparison_gpt4_zh \ + --template default \ + --finetuning_type lora \ + --lora_target q_proj,v_proj \ + --resume_lora_training False \ + --checkpoint_dir path_to_sft_checkpoint \ + --output_dir path_to_rm_checkpoint \ + --per_device_train_batch_size 2 \ + --gradient_accumulation_steps 4 \ + --lr_scheduler_type cosine \ + --logging_steps 10 \ + --save_steps 1000 \ + --learning_rate 1e-6 \ + --num_train_epochs 1.0 \ + --plot_loss \ + --fp16 +``` + +#### PPO 训练 + +```bash +CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ + --stage ppo \ + --model_name_or_path path_to_llama_model \ + --do_train \ + --dataset alpaca_gpt4_zh \ + --template default \ + --finetuning_type lora \ + --lora_target q_proj,v_proj \ + --resume_lora_training False \ + --checkpoint_dir path_to_sft_checkpoint \ + --reward_model path_to_rm_checkpoint \ + --output_dir path_to_ppo_checkpoint \ + --per_device_train_batch_size 2 \ + --gradient_accumulation_steps 4 \ + --lr_scheduler_type cosine \ + --logging_steps 10 \ + --save_steps 1000 \ + --learning_rate 1e-5 \ + --num_train_epochs 1.0 \ + --plot_loss +``` + +#### DPO 训练 + +```bash +CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ + --stage dpo \ + --model_name_or_path path_to_llama_model \ + --do_train \ + --dataset comparison_gpt4_zh \ + --template default \ + --finetuning_type lora \ + --lora_target q_proj,v_proj \ + --resume_lora_training False \ + --checkpoint_dir path_to_sft_checkpoint \ + --output_dir path_to_dpo_checkpoint \ + --per_device_train_batch_size 2 \ + --gradient_accumulation_steps 4 \ + --lr_scheduler_type cosine \ + --logging_steps 10 \ + --save_steps 1000 \ + --learning_rate 1e-5 \ + --num_train_epochs 1.0 \ + --plot_loss \ + --fp16 +``` + +### 多 GPU 分布式训练 + +#### 使用 Huggingface Accelerate + +```bash +accelerate config # 首先配置分布式环境 +accelerate launch src/train_bash.py # 参数同上 +``` + +
LoRA 训练的 Accelerate 配置示例 + +```yaml +compute_environment: LOCAL_MACHINE +distributed_type: MULTI_GPU +downcast_bf16: 'no' +gpu_ids: all +machine_rank: 0 +main_training_function: main +mixed_precision: fp16 +num_machines: 1 +num_processes: 4 +rdzv_backend: static +same_network: true +tpu_env: [] +tpu_use_cluster: false +tpu_use_sudo: false +use_cpu: false +``` + +
+ +#### 使用 DeepSpeed + +```bash +deepspeed --num_gpus 8 --master_port=9901 src/train_bash.py \ + --deepspeed ds_config.json \ + ... # 参数同上 +``` + +
使用 DeepSpeed ZeRO-2 进行全参数训练的 DeepSpeed 配置示例 + +```json +{ + "train_batch_size": "auto", + "train_micro_batch_size_per_gpu": "auto", + "gradient_accumulation_steps": "auto", + "gradient_clipping": "auto", + "zero_allow_untested_optimizer": true, + "fp16": { + "enabled": "auto", + "loss_scale": 0, + "initial_scale_power": 16, + "loss_scale_window": 1000, + "hysteresis": 2, + "min_loss_scale": 1 + }, + "zero_optimization": { + "stage": 2, + "allgather_partitions": true, + "allgather_bucket_size": 5e8, + "reduce_scatter": true, + "reduce_bucket_size": 5e8, + "overlap_comm": false, + "contiguous_gradients": true + } +} +``` + +
+ +### 导出微调后的完整模型 + +```bash +python src/export_model.py \ + --model_name_or_path path_to_llama_model \ + --template default \ + --finetuning_type lora \ + --checkpoint_dir path_to_checkpoint \ + --export_dir path_to_export +``` + +### API 服务 + +```bash +python src/api_demo.py \ + --model_name_or_path path_to_llama_model \ + --template default \ + --finetuning_type lora \ + --checkpoint_dir path_to_checkpoint +``` + +> [!NOTE] +> 关于 API 文档请见 `http://localhost:8000/docs`。 + +### 命令行测试 + +```bash +python src/cli_demo.py \ + --model_name_or_path path_to_llama_model \ + --template default \ + --finetuning_type lora \ + --checkpoint_dir path_to_checkpoint +``` + +### 浏览器测试 + +```bash +python src/web_demo.py \ + --model_name_or_path path_to_llama_model \ + --template default \ + --finetuning_type lora \ + --checkpoint_dir path_to_checkpoint +``` + +### 模型评估 + +```bash +CUDA_VISIBLE_DEVICES=0 python src/evaluate.py \ + --model_name_or_path path_to_llama_model \ + --finetuning_type lora \ + --checkpoint_dir path_to_checkpoint \ + --template vanilla \ + --task ceval \ + --split validation \ + --lang zh \ + --n_shot 5 \ + --batch_size 4 +``` + +### 模型预测 + +```bash +CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ + --stage sft \ + --model_name_or_path path_to_llama_model \ + --do_predict \ + --dataset alpaca_gpt4_zh \ + --template default \ + --finetuning_type lora \ + --checkpoint_dir path_to_checkpoint \ + --output_dir path_to_predict_result \ + --per_device_eval_batch_size 8 \ + --max_samples 100 \ + --predict_with_generate +``` + +> [!NOTE] +> 我们建议在量化模型的预测中使用 `--per_device_eval_batch_size=1` 和 `--max_target_length 128`。 + +## 使用了 LLaMA Factory 的项目 + +- **[StarWhisper](https://github.com/Yu-Yang-Li/StarWhisper)**: 天文大模型 StarWhisper，基于 ChatGLM2-6B 和 Qwen-14B 在天文数据上微调而得。 +- **[DISC-LawLLM](https://github.com/FudanDISC/DISC-LawLLM)**: 中文法律领域大模型 DISC-LawLLM，基于 Baichuan-13B 微调而得，具有法律推理和知识检索能力。 +- **[Sunsimiao](https://github.com/thomas-yanxin/Sunsimiao)**: 孙思邈中文医疗大模型 Sumsimiao，基于 Baichuan-7B 和 ChatGLM-6B 在中文医疗数据上微调而得。 +- **[CareGPT](https://github.com/WangRongsheng/CareGPT)**: 医疗大模型项目 CareGPT，基于 LLaMA2-7B 和 Baichuan-13B 在中文医疗数据上微调而得。 + +## 协议 + +本仓库的代码依照 [Apache-2.0](LICENSE) 协议开源。 + +使用模型权重时，请遵循对应的模型协议：[Baichuan](https://huggingface.co/baichuan-inc/Baichuan-13B-Base/resolve/main/Community%20License%20for%20Baichuan-13B%20Model.pdf) / [Baichuan2](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat/resolve/main/Community%20License%20for%20Baichuan2%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [InternLM](https://github.com/InternLM/InternLM#license) / [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [LLaMA-2](https://ai.meta.com/llama/license/) / [Mistral](LICENSE) / [Phi-1.5](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Qwen](https://huggingface.co/Qwen/Qwen-7B-Chat/blob/main/LICENSE) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) + +## 引用 + +如果您觉得此项目有帮助，请考虑以下列格式引用 + +```bibtex +@Misc{llama-factory, + title = {LLaMA Factory}, + author = {hiyouga}, + howpublished = {\url{https://github.com/hiyouga/LLaMA-Factory}}, + year = {2023} +} +``` + +## 致谢 + +本项目受益于 [PEFT](https://github.com/huggingface/peft)、[QLoRA](https://github.com/artidoro/qlora) 和 [FastChat](https://github.com/lm-sys/FastChat)，感谢以上诸位作者的付出。 + +## Star History + +![Star History Chart](https://api.star-history.com/svg?repos=hiyouga/LLaMA-Factory&type=Date) diff --git a/llm_rl/assets/wechat.jpg b/llm_rl/assets/wechat.jpg new file mode 100644 index 00000000..8df68f9c Binary files /dev/null and b/llm_rl/assets/wechat.jpg differ diff --git a/llm_rl/data/README.md b/llm_rl/data/README.md new file mode 100644 index 00000000..9010fb64 --- /dev/null +++ b/llm_rl/data/README.md @@ -0,0 +1,107 @@ +If you are using a custom dataset, please provide your dataset definition in the following format in `dataset_info.json`. + +```json +"dataset_name": { + "hf_hub_url": "the name of the dataset repository on the Hugging Face hub. (if specified, ignore below 3 arguments)", + "script_url": "the name of the directory containing a dataset loading script. (if specified, ignore below 2 arguments)", + "file_name": "the name of the dataset file in the this directory. (required if above are not specified)", + "file_sha1": "the SHA-1 hash value of the dataset file. (optional, does not affect training)", + "subset": "the name of the subset. (optional, default: None)", + "ranking": "whether the dataset is a preference dataset or not. (default: false)", + "formatting": "the format of the dataset. (optional, default: alpaca, can be chosen from {alpaca, sharegpt})", + "columns": { + "prompt": "the column name in the dataset containing the prompts. (default: instruction, for alpaca)", + "query": "the column name in the dataset containing the queries. (default: input, for alpaca)", + "response": "the column name in the dataset containing the responses. (default: output, for alpaca)", + "history": "the column name in the dataset containing the histories. (default: None, for alpaca)", + "messages": "the column name in the dataset containing the messages. (default: conversations, for sharegpt)", + "role": "the key in the message represents the identity. (default: from, for sharegpt)", + "content": "the key in the message represents the content. (default: value, for sharegpt)" + } +} +``` + +Given above, you can use the custom dataset via specifying `--dataset dataset_name`. + +Currently we support dataset in **alpaca** or **sharegpt** format, the dataset in alpaca format should follow the below format: + +```json +[ + { + "instruction": "user instruction (required)", + "input": "user input (optional)", + "output": "model response (required)", + "history": [ + ["user instruction in the first round (optional)", "model response in the first round (optional)"], + ["user instruction in the second round (optional)", "model response in the second round (optional)"] + ] + } +] +``` + +Regarding the above dataset, the `columns` in `dataset_info.json` should be: + +```json +"dataset_name": { + "columns": { + "prompt": "instruction", + "query": "input", + "response": "output", + "history": "history" + } +} +``` + +where the `prompt` and `response` columns should contain non-empty values, represent instruction and response respectively. The `query` column will be concatenated with the `prompt` column and used as input for the model. + +The `history` column is a list consisting string tuples representing query-response pairs in history. Note that the responses **in each round will be used for training**. + +For the pre-training datasets, only the `prompt` column will be used for training. + +For the preference datasets, the `response` column should be a string list whose length is 2, with the preferred answers appearing first, for example: + +```json +{ + "instruction": "user instruction", + "input": "user input", + "output": [ + "chosen answer", + "rejected answer" + ] +} +``` + +The dataset in sharegpt format should follow the below format: + +```json +[ + { + "conversations": [ + { + "from": "human", + "value": "user instruction" + }, + { + "from": "gpt", + "value": "model response" + } + ] + } +] +``` + +Regarding the above dataset, the `columns` in `dataset_info.json` should be: + +```json +"dataset_name": { + "columns": { + "messages": "conversations", + "role": "from", + "content": "value" + } +} +``` + +where the `messages` column should be a list whose length is even, and follow the `u/a/u/a/u/a` order. + +Pre-training datasets and preference datasets are incompatible with the sharegpt format yet. diff --git a/llm_rl/data/README_zh.md b/llm_rl/data/README_zh.md new file mode 100644 index 00000000..740e27db --- /dev/null +++ b/llm_rl/data/README_zh.md @@ -0,0 +1,107 @@ +如果您使用自定义数据集，请务必在 `dataset_info.json` 文件中按照以下格式提供数据集定义。 + +```json +"数据集名称": { + "hf_hub_url": "Hugging Face 上的项目地址（若指定，则忽略下列三个参数）", + "script_url": "包含数据加载脚本的本地文件夹名称（若指定，则忽略下列两个参数）", + "file_name": "该目录下数据集文件的名称（若上述参数未指定，则此项必需）", + "file_sha1": "数据集文件的SHA-1哈希值（可选，留空不影响训练）", + "subset": "数据集子集的名称（可选，默认：None）", + "ranking": "是否为偏好数据集（可选，默认：False）", + "formatting": "数据集格式（可选，默认：alpaca，可以为 alpaca 或 sharegpt）", + "columns": { + "prompt": "数据集代表提示词的表头名称（默认：instruction，用于 alpaca 格式）", + "query": "数据集代表请求的表头名称（默认：input，用于 alpaca 格式）", + "response": "数据集代表回答的表头名称（默认：output，用于 alpaca 格式）", + "history": "数据集代表历史对话的表头名称（默认：None，用于 alpaca 格式）", + "messages": "数据集代表消息列表的表头名称（默认：conversations，用于 sharegpt 格式）", + "role": "消息中代表发送者身份的键名（默认：from，用于 sharegpt 格式）", + "content": "消息中代表文本内容的键名（默认：value，用于 sharegpt 格式）" + } +} +``` + +添加后可通过指定 `--dataset 数据集名称` 参数使用自定义数据集。 + +该项目目前支持两种格式的数据集：**alpaca** 和 **sharegpt**，其中 alpaca 格式的数据集按照以下方式组织： + +```json +[ + { + "instruction": "用户指令（必填）", + "input": "用户输入（选填）", + "output": "模型回答（必填）", + "history": [ + ["第一轮指令（选填）", "第一轮回答（选填）"], + ["第二轮指令（选填）", "第二轮回答（选填）"] + ] + } +] +``` + +对于上述格式的数据，`dataset_info.json` 中的 `columns` 应为： + +```json +"数据集名称": { + "columns": { + "prompt": "instruction", + "query": "input", + "response": "output", + "history": "history" + } +} +``` + +其中 `prompt` 和 `response` 列应当是非空的字符串，分别代表用户指令和模型回答。`query` 列的内容将会和 `prompt` 列拼接作为模型输入。 + +`history` 列是由多个字符串二元组构成的列表，分别代表历史消息中每轮的指令和回答。注意每轮的模型回答**均会被用于训练**。 + +对于预训练数据集，仅 `prompt` 列中的内容会用于模型训练。 + +对于偏好数据集，`response` 列应当是一个长度为 2 的字符串列表，排在前面的代表更优的回答，例如： + +```json +{ + "instruction": "用户指令", + "input": "用户输入", + "output": [ + "优质回答", + "劣质回答" + ] +} +``` + +而 sharegpt 格式的数据集按照以下方式组织： + +```json +[ + { + "conversations": [ + { + "from": "human", + "value": "用户指令" + }, + { + "from": "gpt", + "value": "模型回答" + } + ] + } +] +``` + +对于上述格式的数据，`dataset_info.json` 中的 `columns` 应为： + +```json +"数据集名称": { + "columns": { + "messages": "conversations", + "role": "from", + "content": "value" + } +} +``` + +其中 `messages` 列必须为偶数长度的列表，且符合 `用户/模型/用户/模型/用户/模型` 的顺序。 + +预训练数据集和偏好数据集尚不支持 sharegpt 格式。 diff --git a/llm_rl/data/belle_multiturn/belle_multiturn.py b/llm_rl/data/belle_multiturn/belle_multiturn.py new file mode 100644 index 00000000..4426b480 --- /dev/null +++ b/llm_rl/data/belle_multiturn/belle_multiturn.py @@ -0,0 +1,79 @@ +import json +import datasets +from typing import Any, Dict, List + + +_DESCRIPTION = "BELLE multiturn chat dataset." + +_CITATION = """\ +@article{belle2023exploring, + title={Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use Cases}, + author={Yunjie Ji, Yong Deng, Yan Gong, Yiping Peng, Qiang Niu, Lei Zhang, Baochang Ma, Xiangang Li}, + journal={arXiv preprint arXiv:2303.14742}, + year={2023} +} +""" + +_HOMEPAGE = "https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M" +_LICENSE = "gpl-3.0" +_URL = "https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M/resolve/main/multiturn_chat_0.8M.json" + + +class BelleMultiturn(datasets.GeneratorBasedBuilder): + + VERSION = datasets.Version("0.0.0") + + def _info(self) -> datasets.DatasetInfo: + features = datasets.Features({ + "instruction": datasets.Value("string"), + "output": datasets.Value("string"), + "history": datasets.Sequence(datasets.Sequence(datasets.Value("string"))) + }) + return datasets.DatasetInfo( + description=_DESCRIPTION, + features=features, + homepage=_HOMEPAGE, + license=_LICENSE, + citation=_CITATION + ) + + def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: + file_path = dl_manager.download(_URL) + return [ + datasets.SplitGenerator( + name=datasets.Split.TRAIN, + gen_kwargs={ + "filepath": file_path + } + ) + ] + + def _generate_examples(self, filepath: str) -> Dict[int, Dict[str, Any]]: # generate multi-turn chat with history + with open(filepath, "r", encoding="utf-8") as f: + for key, row in enumerate(f): + data = json.loads(row) + prompt = data["instruction"].strip() + response = data["output"].strip() + + assist_idx = prompt.rfind("Assistant:") + human_idx = prompt.rfind("Human:") + query = prompt[human_idx+6:assist_idx].strip() + prompt = prompt[:human_idx].strip() + history = [] + + while prompt.rfind("Assistant:") != -1: + assist_idx = prompt.rfind("Assistant:") + human_idx = prompt.rfind("Human:") + if human_idx != -1: + old_query = prompt[human_idx+6:assist_idx].strip() + old_resp = prompt[assist_idx+10:].strip() + history.insert(0, (old_query, old_resp)) + else: + break + prompt = prompt[:human_idx].strip() + + yield key, { + "instruction": query, + "output": response, + "history": history + } diff --git a/llm_rl/data/example_dataset/example_dataset.py b/llm_rl/data/example_dataset/example_dataset.py new file mode 100644 index 00000000..db3e9ffb --- /dev/null +++ b/llm_rl/data/example_dataset/example_dataset.py @@ -0,0 +1,46 @@ +import json +import datasets +from typing import Any, Dict, List + + +_DESCRIPTION = "An example of dataset for LLaMA." +_CITATION = "" +_HOMEPAGE = "" +_LICENSE = "" +_URL = "examples.json" + + +class ExampleDataset(datasets.GeneratorBasedBuilder): + + VERSION = datasets.Version("0.0.0") + + def _info(self) -> datasets.DatasetInfo: + features = datasets.Features({ + "instruction": datasets.Value("string"), + "input": datasets.Value("string"), + "output": datasets.Value("string"), + "history": datasets.Sequence(datasets.Sequence(datasets.Value("string"))) + }) + return datasets.DatasetInfo( + description=_DESCRIPTION, + features=features, + homepage=_HOMEPAGE, + license=_LICENSE, + citation=_CITATION + ) + + def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: + file_path = dl_manager.download(_URL) + return [ + datasets.SplitGenerator( + name=datasets.Split.TRAIN, + gen_kwargs={ + "filepath": file_path + } + ) + ] + + def _generate_examples(self, filepath: str) -> Dict[int, Dict[str, Any]]: + example_dataset = json.load(open(filepath, "r", encoding="utf-8")) + for key, example in enumerate(example_dataset): + yield key, example diff --git a/llm_rl/data/hh_rlhf_en/hh_rlhf_en.py b/llm_rl/data/hh_rlhf_en/hh_rlhf_en.py new file mode 100644 index 00000000..8d51e4c4 --- /dev/null +++ b/llm_rl/data/hh_rlhf_en/hh_rlhf_en.py @@ -0,0 +1,97 @@ +import json +import datasets +from typing import Any, Dict, List + + +_DESCRIPTION = "Human preference data about helpfulness and harmlessness for ChatGLM." +_CITATION = "" +_HOMEPAGE = "https://huggingface.co/datasets/Anthropic/hh-rlhf" +_LICENSE = "mit" +_URL = "https://huggingface.co/datasets/Anthropic/hh-rlhf/resolve/main/" +_URLS = { + "train": [ + _URL + "harmless-base/train.jsonl.gz", + _URL + "helpful-base/train.jsonl.gz", + _URL + "helpful-online/train.jsonl.gz", + _URL + "helpful-rejection-sampled/train.jsonl.gz" + ], + "test": [ + _URL + "harmless-base/test.jsonl.gz", + _URL + "helpful-base/test.jsonl.gz", + _URL + "helpful-online/test.jsonl.gz", + _URL + "helpful-rejection-sampled/test.jsonl.gz" + ] +} + + +class HhRlhfEn(datasets.GeneratorBasedBuilder): + + VERSION = datasets.Version("0.0.0") + + def _info(self) -> datasets.DatasetInfo: + features = datasets.Features({ + "instruction": datasets.Value("string"), + "output": datasets.Sequence(datasets.Value("string")), + "history": datasets.Sequence(datasets.Sequence(datasets.Value("string"))) + }) + return datasets.DatasetInfo( + description=_DESCRIPTION, + features=features, + homepage=_HOMEPAGE, + license=_LICENSE, + citation=_CITATION + ) + + def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: + file_path = dl_manager.download_and_extract(_URLS) + return [ + datasets.SplitGenerator( + name=datasets.Split.TRAIN, + gen_kwargs={ + "filepaths": file_path["train"] + } + ), + datasets.SplitGenerator( + name=datasets.Split.TEST, + gen_kwargs={ + "filepaths": file_path["test"] + } + ) + ] + + def _generate_examples(self, filepaths: List[str]) -> Dict[int, Dict[str, Any]]: # generate multi-turn chat for ChatGLM + key = 0 + for filepath in filepaths: + with open(filepath, "r", encoding="utf-8") as f: + for row in f: + data = json.loads(row) + chosen = data["chosen"] + rejected = data["rejected"] + + assist_idx = rejected.rfind("\n\nAssistant: ") + r_reject = rejected[assist_idx+13:].strip() + assist_idx = chosen.rfind("\n\nAssistant: ") + r_accept = chosen[assist_idx+13:].strip() + + human_idx = chosen.rfind("\n\nHuman: ") + query = chosen[human_idx+9:assist_idx].strip() + prompt = chosen[:human_idx] + history = [] + + while prompt.rfind("\n\nAssistant: ") != -1: + assist_idx = prompt.rfind("\n\nAssistant: ") + human_idx = prompt.rfind("\n\nHuman: ") + if human_idx != -1: + old_query = prompt[human_idx+9:assist_idx].strip() + old_resp = prompt[assist_idx+13:].strip() + history.insert(0, (old_query, old_resp)) + else: + break + prompt = prompt[:human_idx] + + yield key, { + "instruction": query, + "output": [r_accept, r_reject], + "history": history + } + key += 1 diff --git a/llm_rl/data/ultra_chat/ultra_chat.py b/llm_rl/data/ultra_chat/ultra_chat.py new file mode 100644 index 00000000..dd29311c --- /dev/null +++ b/llm_rl/data/ultra_chat/ultra_chat.py @@ -0,0 +1,76 @@ +import json +import datasets +from typing import Any, Dict, List + + +_DESCRIPTION = "UltraChat: Large-scale, Informative, and Diverse Multi-round Dialogue Data." + +_CITATION = """\ +@misc{UltraChat, + author = {Ding, Ning and Chen, Yulin and Xu, Bokai and Hu, Shengding and Qin, Yujia and Liu, Zhiyuan and Sun, Maosong and Zhou, Bowen}, + title = {UltraChat: A Large-scale Auto-generated Multi-round Dialogue Data}, + year = {2023}, + publisher = {GitHub}, + journal = {GitHub repository}, + howpublished = {\\url{https://github.com/thunlp/ultrachat}}, +} +""" + +_HOMEPAGE = "https://huggingface.co/datasets/stingning/ultrachat" +_LICENSE = "cc-by-nc-4.0" +_BASE_DATA_URL = "https://huggingface.co/datasets/stingning/ultrachat/resolve/main/train_{idx}.jsonl" + + +class BelleMultiturn(datasets.GeneratorBasedBuilder): + + VERSION = datasets.Version("0.0.0") + + def _info(self) -> datasets.DatasetInfo: + features = datasets.Features({ + "instruction": datasets.Value("string"), + "output": datasets.Value("string"), + "history": datasets.Sequence(datasets.Sequence(datasets.Value("string"))) + }) + return datasets.DatasetInfo( + description=_DESCRIPTION, + features=features, + homepage=_HOMEPAGE, + license=_LICENSE, + citation=_CITATION + ) + + def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: + file_paths = [dl_manager.download(_BASE_DATA_URL.format(idx=idx)) for idx in range(9)] # multiple shards + return [ + datasets.SplitGenerator( + name=datasets.Split.TRAIN, + gen_kwargs={ + "filepaths": file_paths + } + ) + ] + + def _generate_examples(self, filepaths: List[str]) -> Dict[int, Dict[str, Any]]: # generate multi-turn chat for ChatGLM + for filepath in filepaths: + with open(filepath, "r", encoding="utf-8") as f: + for row in f: + try: + data = json.loads(row) + except: + continue + key = data["id"] + content = data["data"] + if len(content) % 2 == 1: + content.pop(-1) + if len(content) < 2: + continue + + query = content[-2] + response = content[-1] + history = [[content[2*i], content[2*i+1]] for i in range(len(content) // 2 - 1)] + + yield key, { + "instruction": query, + "output": response, + "history": history + } diff --git a/llm_rl/data/wiki_demo.txt b/llm_rl/data/wiki_demo.txt new file mode 100644 index 00000000..4b6fd2b0 --- /dev/null +++ b/llm_rl/data/wiki_demo.txt @@ -0,0 +1,50 @@ +Machine learning (ML) is a field devoted to understanding and building methods that let machines "learn" – that is, methods that leverage data to improve computer performance on some set of tasks. +Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks. +A subset of machine learning is closely related to computational statistics, which focuses on making predictions using computers, but not all machine learning is statistical learning. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning. +Some implementations of machine learning use data and neural networks in a way that mimics the working of a biological brain. +In its application across business problems, machine learning is also referred to as predictive analytics. +Learning algorithms work on the basis that strategies, algorithms, and inferences that worked well in the past are likely to continue working well in the future. These inferences can sometimes be obvious, such as "since the sun rose every morning for the last 10,000 days, it will probably rise tomorrow morning as well". Other times, they can be more nuanced, such as "X% of families have geographically separate species with color variants, so there is a Y% chance that undiscovered black swans exist". +Machine learning programs can perform tasks without being explicitly programmed to do so. It involves computers learning from data provided so that they carry out certain tasks. For simple tasks assigned to computers, it is possible to program algorithms telling the machine how to execute all steps required to solve the problem at hand; on the computer's part, no learning is needed. For more advanced tasks, it can be challenging for a human to manually create the needed algorithms. In practice, it can turn out to be more effective to help the machine develop its own algorithm, rather than having human programmers specify every needed step. +The discipline of machine learning employs various approaches to teach computers to accomplish tasks where no fully satisfactory algorithm is available. In cases where vast numbers of potential answers exist, one approach is to label some of the correct answers as valid. This can then be used as training data for the computer to improve the algorithm(s) it uses to determine correct answers. For example, to train a system for the task of digital character recognition, the MNIST dataset of handwritten digits has often been used. +The term machine learning was coined in 1959 by Arthur Samuel, an IBM employee and pioneer in the field of computer gaming and artificial intelligence. The synonym self-teaching computers was also used in this time period. +By the early 1960s an experimental "learning machine" with punched tape memory, called Cybertron, had been developed by Raytheon Company to analyze sonar signals, electrocardiograms, and speech patterns using rudimentary reinforcement learning. It was repetitively "trained" by a human operator/teacher to recognize patterns and equipped with a "goof" button to cause it to re-evaluate incorrect decisions. A representative book on research into machine learning during the 1960s was Nilsson's book on Learning Machines, dealing mostly with machine learning for pattern classification. Interest related to pattern recognition continued into the 1970s, as described by Duda and Hart in 1973. In 1981 a report was given on using teaching strategies so that a neural network learns to recognize 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal. +Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E." This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turing's proposal in his paper "Computing Machinery and Intelligence", in which the question "Can machines think?" is replaced with the question "Can machines do what we (as thinking entities) can do?". +Modern-day machine learning has two objectives, one is to classify data based on models which have been developed, the other purpose is to make predictions for future outcomes based on these models. A hypothetical algorithm specific to classifying data may use computer vision of moles coupled with supervised learning in order to train it to classify the cancerous moles. A machine learning algorithm for stock trading may inform the trader of future potential predictions. +As a scientific endeavor, machine learning grew out of the quest for artificial intelligence (AI). In the early days of AI as an academic discipline, some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what were then termed "neural networks"; these were mostly perceptrons and other models that were later found to be reinventions of the generalized linear models of statistics. Probabilistic reasoning was also employed, especially in automated medical diagnosis.: 488  +However, an increasing emphasis on the logical, knowledge-based approach caused a rift between AI and machine learning. Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation.: 488  By 1980, expert systems had come to dominate AI, and statistics was out of favor. Work on symbolic/knowledge-based learning did continue within AI, leading to inductive logic programming, but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval.: 708–710, 755  Neural networks research had been abandoned by AI and computer science around the same time. This line, too, was continued outside the AI/CS field, as "connectionism", by researchers from other disciplines including Hopfield, Rumelhart, and Hinton. Their main success came in the mid-1980s with the reinvention of backpropagation.: 25  +Machine learning (ML), reorganized and recognized as its own field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics, fuzzy logic, and probability theory. +Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases). Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously unknown knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data. +Machine learning also has intimate ties to optimization: many learning problems are formulated as minimization of some loss function on a training set of examples. Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the pre-assigned labels of a set of examples). +The difference between optimization and machine learning arises from the goal of generalization: while optimization algorithms can minimize the loss on a training set, machine learning is concerned with minimizing the loss on unseen samples. Characterizing the generalization of various learning algorithms is an active topic of current research, especially for deep learning algorithms. +Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population inferences from a sample, while machine learning finds generalizable predictive patterns. According to Michael I. Jordan, the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics. He also suggested the term data science as a placeholder to call the overall field. +Leo Breiman distinguished two statistical modeling paradigms: data model and algorithmic model, wherein "algorithmic model" means more or less the machine learning algorithms like Random Forest. +Some statisticians have adopted methods from machine learning, leading to a combined field that they call statistical learning. +Analytical and computational techniques derived from deep-rooted physics of disordered systems can be extended to large-scale problems, including machine learning, e.g., to analyze the weight space of deep neural networks. Statistical physics is thus finding applications in the area of medical diagnostics. +A core objective of a learner is to generalize from its experience. Generalization in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set. The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases. +The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory via the Probably Approximately Correct Learning (PAC) model. Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms. Instead, probabilistic bounds on the performance are quite common. The bias–variance decomposition is one way to quantify generalization error. +For the best performance in the context of generalization, the complexity of the hypothesis should match the complexity of the function underlying the data. If the hypothesis is less complex than the function, then the model has under fitted the data. If the complexity of the model is increased in response, then the training error decreases. But if the hypothesis is too complex, then the model is subject to overfitting and generalization will be poorer. +In addition to performance bounds, learning theorists study the time complexity and feasibility of learning. In computational learning theory, a computation is considered feasible if it can be done in polynomial time. There are two kinds of time complexity results: Positive results show that a certain class of functions can be learned in polynomial time. Negative results show that certain classes cannot be learned in polynomial time. +Machine learning approaches are traditionally divided into three broad categories, which correspond to learning paradigms, depending on the nature of the "signal" or "feedback" available to the learning system: +Supervised learning: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs. +Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning). +Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). As it navigates its problem space, the program is provided feedback that's analogous to rewards, which it tries to maximize. Although each algorithm has advantages and limitations, no single algorithm works for all problems. +Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs. The data is known as training data, and consists of a set of training examples. Each training example has one or more inputs and the desired output, also known as a supervisory signal. In the mathematical model, each training example is represented by an array or vector, sometimes called a feature vector, and the training data is represented by a matrix. Through iterative optimization of an objective function, supervised learning algorithms learn a function that can be used to predict the output associated with new inputs. An optimal function will allow the algorithm to correctly determine the output for inputs that were not a part of the training data. An algorithm that improves the accuracy of its outputs or predictions over time is said to have learned to perform that task. +Types of supervised-learning algorithms include active learning, classification and regression. Classification algorithms are used when the outputs are restricted to a limited set of values, and regression algorithms are used when the outputs may have any numerical value within a range. As an example, for a classification algorithm that filters emails, the input would be an incoming email, and the output would be the name of the folder in which to file the email. +Similarity learning is an area of supervised machine learning closely related to regression and classification, but the goal is to learn from examples using a similarity function that measures how similar or related two objects are. It has applications in ranking, recommendation systems, visual identity tracking, face verification, and speaker verification. +Unsupervised learning algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. The algorithms, therefore, learn from test data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. A central application of unsupervised learning is in the field of density estimation in statistics, such as finding the probability density function. Though unsupervised learning encompasses other domains involving summarizing and explaining data features. Unsupervised learning algorithms streamlined the process of survey and graph large indel based haplotypes of a gene of interest from pan-genome. +Cluster analysis is the assignment of a set of observations into subsets (called clusters) so that observations within the same cluster are similar according to one or more predesignated criteria, while observations drawn from different clusters are dissimilar. Different clustering techniques make different assumptions on the structure of the data, often defined by some similarity metric and evaluated, for example, by internal compactness, or the similarity between members of the same cluster, and separation, the difference between clusters. Other methods are based on estimated density and graph connectivity. +Semi-supervised learning falls between unsupervised learning (without any labeled training data) and supervised learning (with completely labeled training data). Some of the training examples are missing training labels, yet many machine-learning researchers have found that unlabeled data, when used in conjunction with a small amount of labeled data, can produce a considerable improvement in learning accuracy. +In weakly supervised learning, the training labels are noisy, limited, or imprecise; however, these labels are often cheaper to obtain, resulting in larger effective training sets. +Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. Due to its generality, the field is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In machine learning, the environment is typically represented as a Markov decision process (MDP). Many reinforcements learning algorithms use dynamic programming techniques. Reinforcement learning algorithms do not assume knowledge of an exact mathematical model of the MDP and are used when exact models are infeasible. Reinforcement learning algorithms are used in autonomous vehicles or in learning to play a game against a human opponent. +Dimensionality reduction is a process of reducing the number of random variables under consideration by obtaining a set of principal variables. In other words, it is a process of reducing the dimension of the feature set, also called the "number of features". Most of the dimensionality reduction techniques can be considered as either feature elimination or extraction. One of the popular methods of dimensionality reduction is principal component analysis (PCA). PCA involves changing higher-dimensional data (e.g., 3D) to a smaller space (e.g., 2D). This results in a smaller dimension of data (2D instead of 3D), while keeping all original variables in the model without changing the data. The manifold hypothesis proposes that high-dimensional data sets lie along low-dimensional manifolds, and many dimensionality reduction techniques make this assumption, leading to the area of manifold learning and manifold regularization. +Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results. Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems. +In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision. Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested. +Machine learning has been used as a strategy to update the evidence related to a systematic review and increased reviewer burden related to the growth of biomedical literature. While it has improved with training sets, it has not yet developed sufficiently to reduce the workload burden without limiting the necessary sensitivity for the findings research themselves. +Machine learning approaches in particular can suffer from different data biases. A machine learning system trained specifically on current customers may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on human-made data, machine learning is likely to pick up the constitutional and unconscious biases already present in society. Language models learned from data have been shown to contain human-like biases. Machine learning systems used for criminal risk assessment have been found to be biased against black people. In 2015, Google photos would often tag black people as gorillas, and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all. Similar issues with recognizing non-white people have been found in many other systems. In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language. Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains. Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There's nothing artificial about AI...It's inspired by people, it's created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility." +Learners can also disappoint by "learning the wrong lesson". A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses. A real-world example is that, unlike humans, current image classifiers often do not primarily make judgments from the spatial relationship between components of the picture, and they learn relationships between pixels that humans are oblivious to, but that still correlate with images of certain types of real objects. Modifying these patterns on a legitimate image can result in "adversarial" images that the system misclassifies. +Adversarial vulnerabilities can also result in nonlinear systems, or from non-pattern perturbations. Some systems are so brittle that changing a single adversarial pixel predictably induces misclassification.[citation needed] Machine learning models are often vulnerable to manipulation and/or evasion via adversarial machine learning. +Researchers have demonstrated how backdoors can be placed undetectably into classifying (e.g., for categories "spam" and well-visible "not spam" of posts) machine learning models which are often developed and/or trained by third parties. Parties can change the classification of any input, including in cases for which a type of data/software transparency is provided, possibly including white-box access. +Machine learning poses a host of ethical questions. Systems that are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices. For example, in 1988, the UK's Commission for Racial Equality found that St. George's Medical School had been using a computer program trained from data of previous admissions staff and this program had denied nearly 60 candidates who were found to be either women or had non-European sounding names. Using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants by similarity to previous successful applicants. Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. +AI can be well-equipped to make decisions in technical fields, which rely heavily on data and historical information. These decisions rely on the objectivity and logical reasoning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases. +Other forms of ethical challenges, not related to personal biases, are seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increase profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is potential for machine learning in health care to provide professionals an additional tool to diagnose, medicate, and plan recovery paths for patients, but this requires these biases to be mitigated. +Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units. By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI. OpenAI estimated the hardware computing used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months. diff --git a/llm_rl/evaluation/ceval/ceval.py b/llm_rl/evaluation/ceval/ceval.py new file mode 100644 index 00000000..33005de3 --- /dev/null +++ b/llm_rl/evaluation/ceval/ceval.py @@ -0,0 +1,166 @@ +# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os + +import datasets +import pandas as pd + + +_CITATION = """\ +@article{huang2023ceval, + title={C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models}, + author={Huang, Yuzhen and Bai, Yuzhuo and Zhu, Zhihao and Zhang, Junlei and Zhang, Jinghan and Su, Tangjun and Liu, Junteng and Lv, Chuancheng and Zhang, Yikai and Lei, Jiayi and Fu, Yao and Sun, Maosong and He, Junxian}, + journal={arXiv preprint arXiv:2305.08322}, + year={2023} +} +""" + +_DESCRIPTION = """\ +C-Eval is a comprehensive Chinese evaluation suite for foundation models. It consists of 13948 multi-choice questions spanning 52 diverse disciplines and four difficulty levels. +""" + +_HOMEPAGE = "https://cevalbenchmark.com" + +_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License" + +_URL = "ceval.zip" + +task_list = [ + "computer_network", + "operating_system", + "computer_architecture", + "college_programming", + "college_physics", + "college_chemistry", + "advanced_mathematics", + "probability_and_statistics", + "discrete_mathematics", + "electrical_engineer", + "metrology_engineer", + "high_school_mathematics", + "high_school_physics", + "high_school_chemistry", + "high_school_biology", + "middle_school_mathematics", + "middle_school_biology", + "middle_school_physics", + "middle_school_chemistry", + "veterinary_medicine", + "college_economics", + "business_administration", + "marxism", + "mao_zedong_thought", + "education_science", + "teacher_qualification", + "high_school_politics", + "high_school_geography", + "middle_school_politics", + "middle_school_geography", + "modern_chinese_history", + "ideological_and_moral_cultivation", + "logic", + "law", + "chinese_language_and_literature", + "art_studies", + "professional_tour_guide", + "legal_professional", + "high_school_chinese", + "high_school_history", + "middle_school_history", + "civil_servant", + "sports_science", + "plant_protection", + "basic_medicine", + "clinical_medicine", + "urban_and_rural_planner", + "accountant", + "fire_engineer", + "environmental_impact_assessment_engineer", + "tax_accountant", + "physician", +] + + +class CevalConfig(datasets.BuilderConfig): + def __init__(self, **kwargs): + super().__init__(version=datasets.Version("1.0.0"), **kwargs) + + +class Ceval(datasets.GeneratorBasedBuilder): + BUILDER_CONFIGS = [ + CevalConfig( + name=task_name, + ) + for task_name in task_list + ] + + def _info(self): + features = datasets.Features( + { + "id": datasets.Value("int32"), + "question": datasets.Value("string"), + "A": datasets.Value("string"), + "B": datasets.Value("string"), + "C": datasets.Value("string"), + "D": datasets.Value("string"), + "answer": datasets.Value("string"), + "explanation": datasets.Value("string"), + } + ) + return datasets.DatasetInfo( + description=_DESCRIPTION, + features=features, + homepage=_HOMEPAGE, + license=_LICENSE, + citation=_CITATION, + ) + + def _split_generators(self, dl_manager): + data_dir = dl_manager.download_and_extract(_URL) + task_name = self.config.name + return [ + datasets.SplitGenerator( + name=datasets.Split.TEST, + gen_kwargs={ + "filepath": os.path.join( + data_dir, "test", f"{task_name}_test.csv" + ), + }, + ), + datasets.SplitGenerator( + name=datasets.Split.VALIDATION, + gen_kwargs={ + "filepath": os.path.join( + data_dir, "val", f"{task_name}_val.csv" + ), + }, + ), + datasets.SplitGenerator( + name=datasets.Split.TRAIN, + gen_kwargs={ + "filepath": os.path.join( + data_dir, "dev", f"{task_name}_dev.csv" + ), + }, + ), + ] + + def _generate_examples(self, filepath): + df = pd.read_csv(filepath, encoding="utf-8") + for i, instance in enumerate(df.to_dict(orient="records")): + if "answer" not in instance.keys(): + instance["answer"] = "" + if "explanation" not in instance.keys(): + instance["explanation"] = "" + yield i, instance diff --git a/llm_rl/evaluation/ceval/ceval.zip b/llm_rl/evaluation/ceval/ceval.zip new file mode 100644 index 00000000..d39274a3 Binary files /dev/null and b/llm_rl/evaluation/ceval/ceval.zip differ diff --git a/llm_rl/evaluation/cmmlu/cmmlu.py b/llm_rl/evaluation/cmmlu/cmmlu.py new file mode 100644 index 00000000..62096203 --- /dev/null +++ b/llm_rl/evaluation/cmmlu/cmmlu.py @@ -0,0 +1,167 @@ +# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os + +import datasets +import pandas as pd + + +_CITATION = """\ +@article{li2023cmmlu, + title={CMMLU: Measuring massive multitask language understanding in Chinese}, + author={Haonan Li and Yixuan Zhang and Fajri Koto and Yifei Yang and Hai Zhao and Yeyun Gong and Nan Duan and Timothy Baldwin}, + journal={arXiv preprint arXiv:2306.09212}, + year={2023} +} +""" + +_DESCRIPTION = """\ +CMMLU is a comprehensive Chinese assessment suite specifically designed to evaluate the advanced knowledge and reasoning abilities of LLMs within the Chinese language and cultural context. +""" + +_HOMEPAGE = "https://github.com/haonan-li/CMMLU" + +_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License" + +_URL = "cmmlu.zip" + +task_list = [ + 'agronomy', + 'anatomy', + 'ancient_chinese', + 'arts', + 'astronomy', + 'business_ethics', + 'chinese_civil_service_exam', + 'chinese_driving_rule', + 'chinese_food_culture', + 'chinese_foreign_policy', + 'chinese_history', + 'chinese_literature', + 'chinese_teacher_qualification', + 'clinical_knowledge', + 'college_actuarial_science', + 'college_education', + 'college_engineering_hydrology', + 'college_law', + 'college_mathematics', + 'college_medical_statistics', + 'college_medicine', + 'computer_science', + 'computer_security', + 'conceptual_physics', + 'construction_project_management', + 'economics', + 'education', + 'electrical_engineering', + 'elementary_chinese', + 'elementary_commonsense', + 'elementary_information_and_technology', + 'elementary_mathematics', + 'ethnology', + 'food_science', + 'genetics', + 'global_facts', + 'high_school_biology', + 'high_school_chemistry', + 'high_school_geography', + 'high_school_mathematics', + 'high_school_physics', + 'high_school_politics', + 'human_sexuality', + 'international_law', + 'journalism', + 'jurisprudence', + 'legal_and_moral_basis', + 'logical', + 'machine_learning', + 'management', + 'marketing', + 'marxist_theory', + 'modern_chinese', + 'nutrition', + 'philosophy', + 'professional_accounting', + 'professional_law', + 'professional_medicine', + 'professional_psychology', + 'public_relations', + 'security_study', + 'sociology', + 'sports_science', + 'traditional_chinese_medicine', + 'virology', + 'world_history', + 'world_religions', +] + + +class CMMLUConfig(datasets.BuilderConfig): + def __init__(self, **kwargs): + super().__init__(version=datasets.Version("1.0.1"), **kwargs) + + +class CMMLU(datasets.GeneratorBasedBuilder): + BUILDER_CONFIGS = [ + CMMLUConfig( + name=task_name, + ) + for task_name in task_list + ] + + def _info(self): + features = datasets.Features( + { + "question": datasets.Value("string"), + "A": datasets.Value("string"), + "B": datasets.Value("string"), + "C": datasets.Value("string"), + "D": datasets.Value("string"), + "answer": datasets.Value("string"), + } + ) + return datasets.DatasetInfo( + description=_DESCRIPTION, + features=features, + homepage=_HOMEPAGE, + license=_LICENSE, + citation=_CITATION, + ) + + def _split_generators(self, dl_manager): + data_dir = dl_manager.download_and_extract(_URL) + task_name = self.config.name + return [ + datasets.SplitGenerator( + name=datasets.Split.TEST, + gen_kwargs={ + "filepath": os.path.join(data_dir, f"test/{task_name}.csv"), + }, + ), + datasets.SplitGenerator( + name=datasets.Split.TRAIN, + gen_kwargs={ + "filepath": os.path.join(data_dir, f"dev/{task_name}.csv"), + }, + ), + ] + + def _generate_examples(self, filepath): + df = pd.read_csv(filepath, header=0, index_col=0, encoding="utf-8") + for i, instance in enumerate(df.to_dict(orient="records")): + question = instance.pop("Question", "") + answer = instance.pop("Answer", "") + instance["question"] = question + instance["answer"] = answer + yield i, instance diff --git a/llm_rl/evaluation/cmmlu/cmmlu.zip b/llm_rl/evaluation/cmmlu/cmmlu.zip new file mode 100644 index 00000000..c6bede1d Binary files /dev/null and b/llm_rl/evaluation/cmmlu/cmmlu.zip differ diff --git a/llm_rl/evaluation/mmlu/mmlu.py b/llm_rl/evaluation/mmlu/mmlu.py new file mode 100644 index 00000000..9f1bd101 --- /dev/null +++ b/llm_rl/evaluation/mmlu/mmlu.py @@ -0,0 +1,167 @@ +# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os + +import datasets +import pandas as pd + + +_CITATION = """\ +@article{hendryckstest2021, + title={Measuring Massive Multitask Language Understanding}, + author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt}, + journal={Proceedings of the International Conference on Learning Representations (ICLR)}, + year={2021} +} +""" + +_DESCRIPTION = """\ +Measuring Massive Multitask Language Understanding by Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt (ICLR 2021). +""" + +_HOMEPAGE = "https://github.com/hendrycks/test" + +_LICENSE = "MIT" + +_URL = "mmlu.zip" + +task_list = [ + "high_school_european_history", + "business_ethics", + "clinical_knowledge", + "medical_genetics", + "high_school_us_history", + "high_school_physics", + "high_school_world_history", + "virology", + "high_school_microeconomics", + "econometrics", + "college_computer_science", + "high_school_biology", + "abstract_algebra", + "professional_accounting", + "philosophy", + "professional_medicine", + "nutrition", + "global_facts", + "machine_learning", + "security_studies", + "public_relations", + "professional_psychology", + "prehistory", + "anatomy", + "human_sexuality", + "college_medicine", + "high_school_government_and_politics", + "college_chemistry", + "logical_fallacies", + "high_school_geography", + "elementary_mathematics", + "human_aging", + "college_mathematics", + "high_school_psychology", + "formal_logic", + "high_school_statistics", + "international_law", + "high_school_mathematics", + "high_school_computer_science", + "conceptual_physics", + "miscellaneous", + "high_school_chemistry", + "marketing", + "professional_law", + "management", + "college_physics", + "jurisprudence", + "world_religions", + "sociology", + "us_foreign_policy", + "high_school_macroeconomics", + "computer_security", + "moral_scenarios", + "moral_disputes", + "electrical_engineering", + "astronomy", + "college_biology", +] + + +class MMLUConfig(datasets.BuilderConfig): + def __init__(self, **kwargs): + super().__init__(version=datasets.Version("1.0.0"), **kwargs) + + +class MMLU(datasets.GeneratorBasedBuilder): + BUILDER_CONFIGS = [ + MMLUConfig( + name=task_name, + ) + for task_name in task_list + ] + + def _info(self): + features = datasets.Features( + { + "question": datasets.Value("string"), + "A": datasets.Value("string"), + "B": datasets.Value("string"), + "C": datasets.Value("string"), + "D": datasets.Value("string"), + "answer": datasets.Value("string"), + } + ) + return datasets.DatasetInfo( + description=_DESCRIPTION, + features=features, + homepage=_HOMEPAGE, + license=_LICENSE, + citation=_CITATION, + ) + + def _split_generators(self, dl_manager): + data_dir = dl_manager.download_and_extract(_URL) + task_name = self.config.name + return [ + datasets.SplitGenerator( + name=datasets.Split.TEST, + gen_kwargs={ + "filepath": os.path.join( + data_dir, "data", "test", f"{task_name}_test.csv" + ), + }, + ), + datasets.SplitGenerator( + name=datasets.Split.VALIDATION, + gen_kwargs={ + "filepath": os.path.join( + data_dir, "data", "val", f"{task_name}_val.csv" + ), + }, + ), + datasets.SplitGenerator( + name=datasets.Split.TRAIN, + gen_kwargs={ + "filepath": os.path.join( + data_dir, "data", "dev", f"{task_name}_dev.csv" + ), + }, + ), + ] + + def _generate_examples(self, filepath): + df = pd.read_csv(filepath) + df.columns = ["question", "A", "B", "C", "D", "answer"] + + for i, instance in enumerate(df.to_dict(orient="records")): + yield i, instance diff --git a/llm_rl/evaluation/mmlu/mmlu.zip b/llm_rl/evaluation/mmlu/mmlu.zip new file mode 100644 index 00000000..1aaee65f Binary files /dev/null and b/llm_rl/evaluation/mmlu/mmlu.zip differ diff --git a/llm_rl/pyproject.toml b/llm_rl/pyproject.toml new file mode 100644 index 00000000..638dd9c5 --- /dev/null +++ b/llm_rl/pyproject.toml @@ -0,0 +1,3 @@ +[build-system] +requires = ["setuptools>=61.0"] +build-backend = "setuptools.build_meta" diff --git a/llm_rl/requirements.txt b/llm_rl/requirements.txt new file mode 100644 index 00000000..840d2f2d --- /dev/null +++ b/llm_rl/requirements.txt @@ -0,0 +1,20 @@ +torch>=1.13.1 +transformers>=4.31.0,<4.35.0 +datasets>=2.12.0 +accelerate>=0.21.0 +peft>=0.4.0 +trl>=0.7.2 +gradio>=3.38.0,<4.0.0 +scipy +sentencepiece +protobuf +tiktoken +fire +jieba +rouge-chinese +nltk +uvicorn +pydantic +fastapi +sse-starlette +matplotlib diff --git a/llm_rl/reward_model.sh b/llm_rl/reward_model.sh new file mode 100644 index 00000000..3068fb43 --- /dev/null +++ b/llm_rl/reward_model.sh @@ -0,0 +1,21 @@ +python src/train_bash.py \ + --stage rm \ + --model_name_or_path meta-llama/Llama-2-13b \ + --do_train \ + --dataset comparison_gpt4_en \ + --template default \ + --finetuning_type lora \ + --lora_target q_proj,v_proj \ + --resume_lora_training False \ + --checkpoint_dir ./llama-2-13b-rm \ + --output_dir ./llama-2-13b-rm \ + --per_device_train_batch_size 2 \ + --gradient_accumulation_steps 4 \ + --lr_scheduler_type cosine \ + --logging_steps 10 \ + --save_steps 1000 \ + --learning_rate 1e-6 \ + --num_train_epochs 1.0 \ + --plot_loss \ + --fp16 \ + --hf_auth_token "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" \ No newline at end of file diff --git a/llm_rl/setup.py b/llm_rl/setup.py new file mode 100644 index 00000000..7638eaab --- /dev/null +++ b/llm_rl/setup.py @@ -0,0 +1,55 @@ +import os +import re +from setuptools import setup, find_packages + + +def get_version(): + with open(os.path.join("src", "llmtuner", "__init__.py"), "r", encoding="utf-8") as f: + file_content = f.read() + pattern = r"{0}\W*=\W*\"([^\"]+)\"".format("__version__") + version, = re.findall(pattern, file_content) + return version + + +def get_requires(): + with open("requirements.txt", "r", encoding="utf-8") as f: + file_content = f.read() + lines = [line.strip() for line in file_content.strip().split("\n") if not line.startswith("#")] + return lines + + +def main(): + + setup( + name="llmtuner", + version=get_version(), + author="hiyouga", + author_email="hiyouga" "@" "buaa.edu.cn", + description="Easy-to-use LLM fine-tuning framework", + long_description=open("README.md", "r", encoding="utf-8").read(), + long_description_content_type="text/markdown", + keywords=["LLaMA", "BLOOM", "Falcon", "LLM", "ChatGPT", "transformer", "pytorch", "deep learning"], + license="Apache 2.0 License", + url="https://github.com/hiyouga/LLaMA-Factory", + package_dir={"": "src"}, + packages=find_packages("src"), + python_requires=">=3.8.0", + install_requires=get_requires(), + classifiers=[ + "Development Status :: 3 - Alpha", + "Intended Audience :: Developers", + "Intended Audience :: Education", + "Intended Audience :: Science/Research", + "License :: OSI Approved :: Apache Software License", + "Operating System :: OS Independent", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + ] + ) + + +if __name__ == "__main__": + main() diff --git a/llm_rl/src/api_demo.py b/llm_rl/src/api_demo.py new file mode 100644 index 00000000..720089fd --- /dev/null +++ b/llm_rl/src/api_demo.py @@ -0,0 +1,14 @@ +import uvicorn + +from llmtuner import ChatModel, create_app + + +def main(): + chat_model = ChatModel() + app = create_app(chat_model) + print("Visit http://localhost:8000/docs for API document.") + uvicorn.run(app, host="0.0.0.0", port=8000, workers=1) + + +if __name__ == "__main__": + main() diff --git a/llm_rl/src/cli_demo.py b/llm_rl/src/cli_demo.py new file mode 100644 index 00000000..fe6a0bc4 --- /dev/null +++ b/llm_rl/src/cli_demo.py @@ -0,0 +1,39 @@ +import readline +from llmtuner import ChatModel + + +def main(): + chat_model = ChatModel() + history = [] + print("Welcome to the CLI application, use `clear` to remove the history, use `exit` to exit the application.") + + while True: + try: + query = input("\nUser: ") + except UnicodeDecodeError: + print("Detected decoding error at the inputs, please set the terminal encoding to utf-8.") + continue + except Exception: + raise + + if query.strip() == "exit": + break + + if query.strip() == "clear": + history = [] + print("History has been removed.") + continue + + print("Assistant: ", end="", flush=True) + + response = "" + for new_text in chat_model.stream_chat(query, history): + print(new_text, end="", flush=True) + response += new_text + print() + + history = history + [(query, response)] + + +if __name__ == "__main__": + main() diff --git a/llm_rl/src/evaluate.py b/llm_rl/src/evaluate.py new file mode 100644 index 00000000..8af8c12c --- /dev/null +++ b/llm_rl/src/evaluate.py @@ -0,0 +1,190 @@ +# coding=utf-8 +# Evaluates the performance of pre-trained models. +# Usage: python evaluate.py --model_name_or_path path_to_model --checkpoint_dir path_to_ckpt --template vanilla +# --task ceval --split validation --lang zh --n_shot 5 --batch_size 4 --save_name result +# Inspired by: https://github.com/hendrycks/test/blob/master/evaluate_flan.py + +import os +import fire +import json +import torch +import numpy as np +import transformers +from collections import Counter +from datasets import load_dataset +from dataclasses import dataclass +from tqdm import tqdm, trange +from typing import TYPE_CHECKING, Dict, List, Literal, Optional, Tuple + +from llmtuner import ChatModel + +if TYPE_CHECKING: + from datasets import Dataset + + +choices = ["A", "B", "C", "D"] + + +@dataclass +class EvalTemplate: + + system: str + choice: str + answer: str + prefix: str + + def parse_example( + self, + example: Dict[str, str] + ) -> Tuple[str, str]: + candidates = [self.choice.format(choice=ch, content=example[ch]) for ch in choices if ch in example] + return "".join([example["question"]] + candidates + [self.answer]), example["answer"] + + def format_example( + self, + target_data: Dict[str, str], + support_set: "Dataset", + subject_name: str, + use_history: bool + ) -> Tuple[str, str, List[Tuple[str, str]]]: + query, resp = self.parse_example(target_data) + history = [self.parse_example(support_set[k]) for k in range(len(support_set))] + + if len(history): + temp = history.pop(0) + history.insert(0, (self.system.format(subject=subject_name) + temp[0], temp[1])) + else: + query = self.system.format(subject=subject_name) + query + + if not use_history: + query = "\n\n".join(["".join(item) for item in history] + [query]) + history = [] + return query.strip(), resp, history + + +eval_templates = { + "en": EvalTemplate( + system="The following are multiple choice questions (with answers) about {subject}.\n\n", + choice="\n{choice}. {content}", + answer="\nAnswer: ", + prefix=" " + ), + "zh": EvalTemplate( + system="以下是中国关于{subject}考试的单项选择题，请选出其中的正确答案。\n\n", + choice="\n{choice}. {content}", + answer="\n答案：", + prefix="\n" + ) +} + + +@torch.inference_mode() +def batch_inference( + chat_model: ChatModel, + batch_input: Dict[str, torch.Tensor], + prefix_char: str +) -> List[str]: + logits = chat_model.model(**batch_input).logits + lengths = torch.sum(batch_input["attention_mask"], dim=-1) + nextword_logits = torch.stack([logits[i, lengths[i] - 1] for i in range(len(lengths))], dim=0) + probs = torch.nn.functional.softmax( + torch.stack( + [ + nextword_logits[:, chat_model.tokenizer.encode(prefix_char + choice, add_special_tokens=False)[-1]] + for choice in choices + ], + dim=-1 + ), + dim=-1 + ).detach() + return [chr(ord("A") + offset.item()) for offset in torch.argmax(probs, dim=-1)] + + +def evaluate( + model_name_or_path: str, + finetuning_type: Optional[str] = "lora", + checkpoint_dir: Optional[str] = None, + template: Optional[str] = "vanilla", + task: Optional[str] = "ceval", + dataset_dir: Optional[str] = "evaluation", + split: Optional[Literal["validation", "test"]] = "validation", + lang: Optional[Literal["zh", "en"]] = "zh", + n_shot: Optional[int] = 5, + n_avg: Optional[int] = 1, + batch_size: Optional[int] = 4, + save_name: Optional[str] = None, + seed: Optional[int] = 42 +): + with open(os.path.join(dataset_dir, task, "mapping.json"), "r", encoding="utf-8") as f: + categorys: Dict[str, Dict[str, str]] = json.load(f) + + transformers.set_seed(seed) + chat_model = ChatModel(dict( + model_name_or_path=model_name_or_path, + finetuning_type=finetuning_type, + checkpoint_dir=checkpoint_dir, + template=template + )) + chat_model.tokenizer.padding_side = "right" # avoid overflow issue in batched inference for llama2 + eval_template = eval_templates[lang] + + category_corrects: Dict[str, np.ndarray] = { + subj: np.array([], dtype="bool") for subj in ["Average", "STEM", "Social Sciences", "Humanities", "Other"] + } + pbar = tqdm(categorys.keys(), desc="Processing subjects", position=0) + results = {} + for subject in pbar: + dataset = load_dataset(os.path.join(dataset_dir, task), subject) + labels, answers, all_outputs = [], [], [] + for epoch in range(n_avg): + pbar.set_postfix_str("{} Trial: {}".format(categorys[subject]["name"], epoch)) + inputs, outputs = [], [] + for i in trange(len(dataset[split]), desc="Formatting batches", position=1, leave=False): + support_set = dataset["train"].shuffle().select(range(min(n_shot, len(dataset["train"])))) + query, resp, history = eval_template.format_example( + target_data=dataset[split][i], + support_set=support_set, + subject_name=categorys[subject]["name"], + use_history=chat_model.template.use_history + ) + input_ids, _ = chat_model.template.encode_oneturn( + tokenizer=chat_model.tokenizer, query=query, resp=resp, history=history + ) + inputs.append({"input_ids": input_ids, "attention_mask": [1] * len(input_ids)}) + if epoch == 0: + labels.append(resp) + + for i in trange(0, len(inputs), batch_size, desc="Predicting batches", position=1, leave=False): + batch_input = chat_model.tokenizer.pad( + inputs[i : i + batch_size], return_attention_mask=True, return_tensors="pt" + ).to(chat_model.model.device) + preds = batch_inference(chat_model, batch_input, eval_template.prefix) + outputs += preds + all_outputs.append(outputs) + + for i in range(len(all_outputs[0])): + count = Counter([all_outputs[epoch][i] for epoch in range(n_avg)]) + answers.append(count.most_common(1)[0][0]) + + corrects = (np.array(answers) == np.array(labels)) + category_name = categorys[subject]["category"] + category_corrects[category_name] = np.concatenate([category_corrects[category_name], corrects], axis=0) + category_corrects["Average"] = np.concatenate([category_corrects["Average"], corrects], axis=0) + results[subject] = {str(i): answers[i] for i in range(len(answers))} + + score_info = "\n".join([ + "{:>15}: {:.2f}".format(category_name, 100 * np.mean(category_correct)) + for category_name, category_correct in category_corrects.items() if len(category_correct) + ]) + + print(score_info) + if save_name is not None: + with open(save_name + ".json", "w", encoding="utf-8", newline="\n") as f: + json.dump(results, f, indent=2) + + with open(save_name + ".log", "w", encoding="utf-8", newline="\n") as f: + f.write(score_info) + + +if __name__ == "__main__": + fire.Fire(evaluate) diff --git a/llm_rl/src/export_model.py b/llm_rl/src/export_model.py new file mode 100644 index 00000000..4baeb2c3 --- /dev/null +++ b/llm_rl/src/export_model.py @@ -0,0 +1,9 @@ +from llmtuner import export_model + + +def main(): + export_model() + + +if __name__ == "__main__": + main() diff --git a/llm_rl/src/llmtuner/__init__.py b/llm_rl/src/llmtuner/__init__.py new file mode 100644 index 00000000..37eb9535 --- /dev/null +++ b/llm_rl/src/llmtuner/__init__.py @@ -0,0 +1,9 @@ +# Level: api, webui > chat > tuner > dsets > extras, hparams + +from llmtuner.api import create_app +from llmtuner.chat import ChatModel +from llmtuner.tuner import export_model, run_exp +from llmtuner.webui import create_ui, create_web_demo + + +__version__ = "0.2.0" diff --git a/llm_rl/src/llmtuner/api/__init__.py b/llm_rl/src/llmtuner/api/__init__.py new file mode 100644 index 00000000..b3ce183a --- /dev/null +++ b/llm_rl/src/llmtuner/api/__init__.py @@ -0,0 +1 @@ +from llmtuner.api.app import create_app diff --git a/llm_rl/src/llmtuner/api/app.py b/llm_rl/src/llmtuner/api/app.py new file mode 100644 index 00000000..27fb19e0 --- /dev/null +++ b/llm_rl/src/llmtuner/api/app.py @@ -0,0 +1,146 @@ +import json +import uvicorn +from fastapi import FastAPI, HTTPException, status +from fastapi.middleware.cors import CORSMiddleware +from contextlib import asynccontextmanager +from sse_starlette import EventSourceResponse +from typing import List, Tuple +from pydantic import BaseModel + +from llmtuner.extras.misc import torch_gc +from llmtuner.chat import ChatModel +from llmtuner.api.protocol import ( + Role, + Finish, + ModelCard, + ModelList, + ChatMessage, + DeltaMessage, + ChatCompletionRequest, + ChatCompletionResponse, + ChatCompletionStreamResponse, + ChatCompletionResponseChoice, + ChatCompletionResponseStreamChoice, + ChatCompletionResponseUsage +) + + +@asynccontextmanager +async def lifespan(app: FastAPI): # collects GPU memory + yield + torch_gc() + + +def to_json(data: BaseModel) -> str: + try: # pydantic v2 + return json.dumps(data.model_dump(exclude_unset=True), ensure_ascii=False) + except: # pydantic v1 + return data.json(exclude_unset=True, ensure_ascii=False) + + +def create_app(chat_model: ChatModel) -> FastAPI: + app = FastAPI(lifespan=lifespan) + + app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], + ) + + @app.get("/v1/models", response_model=ModelList) + async def list_models(): + model_card = ModelCard(id="gpt-3.5-turbo") + return ModelList(data=[model_card]) + + @app.post("/v1/chat/completions", response_model=ChatCompletionResponse, status_code=status.HTTP_200_OK) + async def create_chat_completion(request: ChatCompletionRequest): + if len(request.messages) < 1 or request.messages[-1].role != Role.USER: + raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid request") + + query = request.messages[-1].content + prev_messages = request.messages[:-1] + if len(prev_messages) > 0 and prev_messages[0].role == Role.SYSTEM: + system = prev_messages.pop(0).content + else: + system = None + + history = [] + if len(prev_messages) % 2 == 0: + for i in range(0, len(prev_messages), 2): + if prev_messages[i].role == Role.USER and prev_messages[i+1].role == Role.ASSISTANT: + history.append([prev_messages[i].content, prev_messages[i+1].content]) + else: + raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Only supports u/a/u/a/u...") + + if request.stream: + generate = predict(query, history, system, request) + return EventSourceResponse(generate, media_type="text/event-stream") + + response, (prompt_length, response_length) = chat_model.chat( + query, history, system, + do_sample=request.do_sample, + temperature=request.temperature, + top_p=request.top_p, + max_new_tokens=request.max_tokens, + num_return_sequences=request.n + ) + + usage = ChatCompletionResponseUsage( + prompt_tokens=prompt_length, + completion_tokens=response_length, + total_tokens=prompt_length+response_length + ) + + choices = [ChatCompletionResponseChoice( + index=i, + message=ChatMessage(role=Role.ASSISTANT, content=choice), + finish_reason=Finish.STOP + ) for i, choice in enumerate(response)] + + return ChatCompletionResponse(model=request.model, choices=choices, usage=usage) + + async def predict(query: str, history: List[Tuple[str, str]], system: str, request: ChatCompletionRequest): + choice_data = ChatCompletionResponseStreamChoice( + index=0, + delta=DeltaMessage(role=Role.ASSISTANT), + finish_reason=None + ) + chunk = ChatCompletionStreamResponse(model=request.model, choices=[choice_data]) + yield to_json(chunk) + + for new_text in chat_model.stream_chat( + query, history, system, + do_sample=request.do_sample, + temperature=request.temperature, + top_p=request.top_p, + max_new_tokens=request.max_tokens + ): + if len(new_text) == 0: + continue + + choice_data = ChatCompletionResponseStreamChoice( + index=0, + delta=DeltaMessage(content=new_text), + finish_reason=None + ) + chunk = ChatCompletionStreamResponse(model=request.model, choices=[choice_data]) + yield to_json(chunk) + + choice_data = ChatCompletionResponseStreamChoice( + index=0, + delta=DeltaMessage(), + finish_reason=Finish.STOP + ) + chunk = ChatCompletionStreamResponse(model=request.model, choices=[choice_data]) + yield to_json(chunk) + yield "[DONE]" + + return app + + +if __name__ == "__main__": + chat_model = ChatModel() + app = create_app(chat_model) + uvicorn.run(app, host="0.0.0.0", port=8000, workers=1) diff --git a/llm_rl/src/llmtuner/api/protocol.py b/llm_rl/src/llmtuner/api/protocol.py new file mode 100644 index 00000000..6b99da40 --- /dev/null +++ b/llm_rl/src/llmtuner/api/protocol.py @@ -0,0 +1,83 @@ +import time +from enum import Enum +from pydantic import BaseModel, Field +from typing import List, Optional + + +class Role(str, Enum): + USER = "user" + ASSISTANT = "assistant" + SYSTEM = "system" + + +class Finish(str, Enum): + STOP = "stop" + LENGTH = "length" + + +class ModelCard(BaseModel): + id: str + object: Optional[str] = "model" + created: Optional[int] = Field(default_factory=lambda: int(time.time())) + owned_by: Optional[str] = "owner" + + +class ModelList(BaseModel): + object: Optional[str] = "list" + data: Optional[List[ModelCard]] = [] + + +class ChatMessage(BaseModel): + role: Role + content: str + + +class DeltaMessage(BaseModel): + role: Optional[Role] = None + content: Optional[str] = None + + +class ChatCompletionRequest(BaseModel): + model: str + messages: List[ChatMessage] + do_sample: Optional[bool] = True + temperature: Optional[float] = None + top_p: Optional[float] = None + n: Optional[int] = 1 + max_tokens: Optional[int] = None + stream: Optional[bool] = False + + +class ChatCompletionResponseChoice(BaseModel): + index: int + message: ChatMessage + finish_reason: Finish + + +class ChatCompletionResponseStreamChoice(BaseModel): + index: int + delta: DeltaMessage + finish_reason: Optional[Finish] = None + + +class ChatCompletionResponseUsage(BaseModel): + prompt_tokens: int + completion_tokens: int + total_tokens: int + + +class ChatCompletionResponse(BaseModel): + id: Optional[str] = "chatcmpl-default" + object: Optional[str] = "chat.completion" + created: Optional[int] = Field(default_factory=lambda: int(time.time())) + model: str + choices: List[ChatCompletionResponseChoice] + usage: ChatCompletionResponseUsage + + +class ChatCompletionStreamResponse(BaseModel): + id: Optional[str] = "chatcmpl-default" + object: Optional[str] = "chat.completion.chunk" + created: Optional[int] = Field(default_factory=lambda: int(time.time())) + model: str + choices: List[ChatCompletionResponseStreamChoice] diff --git a/llm_rl/src/llmtuner/chat/__init__.py b/llm_rl/src/llmtuner/chat/__init__.py new file mode 100644 index 00000000..ba240d05 --- /dev/null +++ b/llm_rl/src/llmtuner/chat/__init__.py @@ -0,0 +1 @@ +from llmtuner.chat.stream_chat import ChatModel diff --git a/llm_rl/src/llmtuner/chat/stream_chat.py b/llm_rl/src/llmtuner/chat/stream_chat.py new file mode 100644 index 00000000..cc815d1b --- /dev/null +++ b/llm_rl/src/llmtuner/chat/stream_chat.py @@ -0,0 +1,109 @@ +import torch +from typing import Any, Dict, Generator, List, Optional, Tuple +from threading import Thread +from transformers import GenerationConfig, TextIteratorStreamer + +from llmtuner.extras.misc import dispatch_model, get_logits_processor +from llmtuner.extras.template import get_template_and_fix_tokenizer +from llmtuner.tuner.core import get_infer_args, load_model_and_tokenizer + + +class ChatModel: + + def __init__(self, args: Optional[Dict[str, Any]] = None) -> None: + model_args, data_args, finetuning_args, self.generating_args = get_infer_args(args) + self.model, self.tokenizer = load_model_and_tokenizer(model_args, finetuning_args) + self.tokenizer.padding_side = "left" + self.model = dispatch_model(self.model) + self.template = get_template_and_fix_tokenizer(data_args.template, self.tokenizer) + self.system_prompt = data_args.system_prompt + + def process_args( + self, + query: str, + history: Optional[List[Tuple[str, str]]] = None, + system: Optional[str] = None, + **input_kwargs + ) -> Tuple[Dict[str, Any], int]: + system = system or self.system_prompt + prompt, _ = self.template.encode_oneturn( + tokenizer=self.tokenizer, query=query, resp="", history=history, system=system + ) + prompt_length = len(prompt) + input_ids = torch.tensor([prompt], device=self.model.device) + + do_sample = input_kwargs.pop("do_sample", None) + temperature = input_kwargs.pop("temperature", None) + top_p = input_kwargs.pop("top_p", None) + top_k = input_kwargs.pop("top_k", None) + num_return_sequences = input_kwargs.pop("num_return_sequences", None) + repetition_penalty = input_kwargs.pop("repetition_penalty", None) + max_length = input_kwargs.pop("max_length", None) + max_new_tokens = input_kwargs.pop("max_new_tokens", None) + + generating_args = self.generating_args.to_dict() + generating_args.update(dict( + do_sample=do_sample if do_sample is not None else generating_args["do_sample"], + temperature=temperature or generating_args["temperature"], + top_p=top_p or generating_args["top_p"], + top_k=top_k or generating_args["top_k"], + num_return_sequences=num_return_sequences or 1, + repetition_penalty=repetition_penalty or generating_args["repetition_penalty"], + eos_token_id=[self.tokenizer.eos_token_id] + self.tokenizer.additional_special_tokens_ids, + pad_token_id=self.tokenizer.pad_token_id + )) + + if isinstance(num_return_sequences, int) and num_return_sequences > 1: + generating_args["do_sample"] = True + + if max_length: + generating_args.pop("max_new_tokens", None) + generating_args["max_length"] = max_length + + if max_new_tokens: + generating_args.pop("max_length", None) + generating_args["max_new_tokens"] = max_new_tokens + + gen_kwargs = dict( + inputs=input_ids, + generation_config=GenerationConfig(**generating_args), + logits_processor=get_logits_processor() + ) + + return gen_kwargs, prompt_length + + @torch.inference_mode() + def chat( + self, + query: str, + history: Optional[List[Tuple[str, str]]] = None, + system: Optional[str] = None, + **input_kwargs + ) -> Tuple[List[str], Tuple[int, int]]: + gen_kwargs, prompt_length = self.process_args(query, history, system, **input_kwargs) + generate_output = self.model.generate(**gen_kwargs) + response_ids = generate_output[:, prompt_length:] + response = self.tokenizer.batch_decode(response_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True) + response_length = 0 + for i in range(len(response_ids)): + eos_index = (response_ids[i] == self.tokenizer.eos_token_id).nonzero() + response_length += eos_index[0].item() if len(eos_index) else len(response_ids[i]) + + return response, (prompt_length, response_length) + + @torch.inference_mode() + def stream_chat( + self, + query: str, + history: Optional[List[Tuple[str, str]]] = None, + system: Optional[str] = None, + **input_kwargs + ) -> Generator[str, None, None]: + gen_kwargs, _ = self.process_args(query, history, system, **input_kwargs) + streamer = TextIteratorStreamer(self.tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True) + gen_kwargs["streamer"] = streamer + + thread = Thread(target=self.model.generate, kwargs=gen_kwargs) + thread.start() + + yield from streamer diff --git a/llm_rl/src/llmtuner/dsets/__init__.py b/llm_rl/src/llmtuner/dsets/__init__.py new file mode 100644 index 00000000..cccbd745 --- /dev/null +++ b/llm_rl/src/llmtuner/dsets/__init__.py @@ -0,0 +1,3 @@ +from llmtuner.dsets.loader import get_dataset +from llmtuner.dsets.preprocess import preprocess_dataset +from llmtuner.dsets.utils import split_dataset diff --git a/llm_rl/src/llmtuner/dsets/loader.py b/llm_rl/src/llmtuner/dsets/loader.py new file mode 100644 index 00000000..834ef733 --- /dev/null +++ b/llm_rl/src/llmtuner/dsets/loader.py @@ -0,0 +1,145 @@ +import os +from typing import TYPE_CHECKING, Any, Dict, List, Union + +from datasets import concatenate_datasets, interleave_datasets, load_dataset + +from llmtuner.dsets.utils import checksum, EXT2TYPE +from llmtuner.extras.logging import get_logger + +if TYPE_CHECKING: + from datasets import Dataset, IterableDataset + from llmtuner.hparams import ModelArguments, DataArguments + + +logger = get_logger(__name__) + + +def get_dataset( + model_args: "ModelArguments", + data_args: "DataArguments" +) -> Union["Dataset", "IterableDataset"]: + max_samples = data_args.max_samples + all_datasets: List[Union["Dataset", "IterableDataset"]] = [] # support multiple datasets + + for dataset_attr in data_args.dataset_list: + logger.info("Loading dataset {}...".format(dataset_attr)) + + if dataset_attr.load_from == "hf_hub": + data_path = dataset_attr.dataset_name + data_name = dataset_attr.subset + data_files = None + elif dataset_attr.load_from == "script": + data_path = os.path.join(data_args.dataset_dir, dataset_attr.dataset_name) + data_name = dataset_attr.subset + data_files = None + elif dataset_attr.load_from == "file": + data_path, data_name = None, None + data_files: List[str] = [] + if os.path.isdir(os.path.join(data_args.dataset_dir, dataset_attr.dataset_name)): # is directory + for file_name in os.listdir(os.path.join(data_args.dataset_dir, dataset_attr.dataset_name)): + data_files.append(os.path.join(data_args.dataset_dir, dataset_attr.dataset_name, file_name)) + if data_path is None: + data_path = EXT2TYPE.get(file_name.split(".")[-1], None) + else: + assert data_path == EXT2TYPE.get(file_name.split(".")[-1], None), "file types are not identical." + elif os.path.isfile(os.path.join(data_args.dataset_dir, dataset_attr.dataset_name)): # is file + data_files.append(os.path.join(data_args.dataset_dir, dataset_attr.dataset_name)) + data_path = EXT2TYPE.get(dataset_attr.dataset_name.split(".")[-1], None) + else: + raise ValueError("File not found.") + + assert data_path, "File extension must be txt, csv, json or jsonl." + checksum(data_files, dataset_attr.dataset_sha1) + else: + raise NotImplementedError + + dataset = load_dataset( + path=data_path, + name=data_name, + data_files=data_files, + split=data_args.split, + cache_dir=model_args.cache_dir, + streaming=data_args.streaming, + use_auth_token=True if model_args.use_auth_token else None + ) + + if max_samples is not None: # truncate dataset + dataset = dataset.select(range(min(len(dataset), max_samples))) + + def convert_format(examples: Dict[str, List[Any]]) -> Dict[str, List[Any]]: + # convert dataset from sharegpt format to alpaca format + outputs = {"prompt": [], "query": [], "response": [], "history": []} + for msg_list in examples[dataset_attr.messages]: + msg_list = msg_list[:len(msg_list) // 2 * 2] # should be multiples of 2 + if len(msg_list) == 0: + continue + + msg_pairs = [] + user_role, assistant_role = None, None + for idx in range(0, len(msg_list), 2): + if user_role is None and assistant_role is None: + user_role = msg_list[idx][dataset_attr.role] + assistant_role = msg_list[idx + 1][dataset_attr.role] + else: + if ( + msg_list[idx][dataset_attr.role] != user_role + or msg_list[idx+1][dataset_attr.role] != assistant_role + ): + raise ValueError("Only accepts conversation in u/a/u/a/u/a order.") + msg_pairs.append((msg_list[idx][dataset_attr.content], msg_list[idx + 1][dataset_attr.content])) + + if len(msg_pairs) != 0: + outputs["prompt"].append(msg_pairs[-1][0]) + outputs["query"].append("") + outputs["response"].append(msg_pairs[-1][1]) + outputs["history"].append(msg_pairs[:-1]) + + return outputs + + if dataset_attr.formatting == "sharegpt": # convert format + column_names = list(next(iter(dataset)).keys()) + kwargs = {} + if not data_args.streaming: + kwargs = dict( + num_proc=data_args.preprocessing_num_workers, + load_from_cache_file=(not data_args.overwrite_cache), + desc="Converting format of dataset" + ) + + dataset = dataset.map( + convert_format, + batched=True, + remove_columns=column_names, + **kwargs + ) + else: + for column_name in ["prompt", "query", "response", "history"]: # align dataset + if getattr(dataset_attr, column_name) and getattr(dataset_attr, column_name) != column_name: + dataset = dataset.rename_column(getattr(dataset_attr, column_name), column_name) + + if dataset_attr.system_prompt: # add system prompt + system_prompt = dataset_attr.system_prompt + if data_args.streaming: + dataset = dataset.map(lambda _: {"system": system_prompt}) + else: + dataset = dataset.add_column("system", [system_prompt] * len(dataset)) + + all_datasets.append(dataset) + + if len(data_args.dataset_list) == 1: + return all_datasets[0] + elif data_args.mix_strategy == "concat": + if data_args.streaming: + logger.warning("The samples between different datasets will not be mixed in streaming mode.") + return concatenate_datasets(all_datasets) + elif data_args.mix_strategy.startswith("interleave"): + if not data_args.streaming: + logger.warning("We recommend using `mix_strategy=concat` in non-streaming mode.") + return interleave_datasets( + datasets=all_datasets, + probabilities=data_args.interleave_probs, + seed=data_args.seed, + stopping_strategy="first_exhausted" if data_args.mix_strategy.endswith("under") else "all_exhausted" + ) + else: + raise ValueError("Unknown mixing strategy.") diff --git a/llm_rl/src/llmtuner/dsets/preprocess.py b/llm_rl/src/llmtuner/dsets/preprocess.py new file mode 100644 index 00000000..0484b78e --- /dev/null +++ b/llm_rl/src/llmtuner/dsets/preprocess.py @@ -0,0 +1,268 @@ +import os +import tiktoken +from itertools import chain +from typing import TYPE_CHECKING, Any, Dict, Generator, List, Literal, Union + +from datasets import load_from_disk + +from llmtuner.extras.constants import IGNORE_INDEX +from llmtuner.extras.logging import get_logger +from llmtuner.extras.template import get_template_and_fix_tokenizer + +if TYPE_CHECKING: + from datasets import Dataset, IterableDataset + from transformers import Seq2SeqTrainingArguments + from transformers.tokenization_utils import PreTrainedTokenizer + from llmtuner.hparams import DataArguments + + +logger = get_logger(__name__) + + +def preprocess_dataset( + dataset: Union["Dataset", "IterableDataset"], + tokenizer: "PreTrainedTokenizer", + data_args: "DataArguments", + training_args: "Seq2SeqTrainingArguments", + stage: Literal["pt", "sft", "rm", "ppo"] +) -> Union["Dataset", "IterableDataset"]: + template = get_template_and_fix_tokenizer(data_args.template, tokenizer) + + if data_args.train_on_prompt and template.efficient_eos: + raise ValueError("Current template does not support `train_on_prompt`.") + + def construct_example(examples: Dict[str, List[Any]]) -> Generator[Any, None, None]: + for i in range(len(examples["prompt"])): + query, response = examples["prompt"][i], examples["response"][i] + query = query + "\n" + examples["query"][i] if "query" in examples and examples["query"][i] else query + history = examples["history"][i] if "history" in examples else None + system = examples["system"][i] if "system" in examples else None + yield query, response, history, system + + def preprocess_pretrain_dataset(examples: Dict[str, List[Any]]) -> Dict[str, List[List[int]]]: + # build grouped texts with format `X1 X2 X3 ...` + if isinstance(getattr(tokenizer, "tokenizer", None), tiktoken.Encoding): # for tiktoken tokenizer (Qwen) + kwargs = dict(allowed_special="all") + else: + kwargs = dict(add_special_tokens=True) + + if hasattr(tokenizer, "add_eos_token"): # for LLaMA tokenizer + setattr(tokenizer, "add_eos_token", True) + + tokenized_examples = tokenizer(examples["prompt"], **kwargs) + concatenated_examples = {k: list(chain(*tokenized_examples[k])) for k in tokenized_examples.keys()} + total_length = len(concatenated_examples[list(concatenated_examples.keys())[0]]) + block_size = data_args.cutoff_len + # we drop the small remainder, and if the total_length < block_size, we exclude this batch + total_length = (total_length // block_size) * block_size + # split by chunks of cutoff_len + result = { + k: [t[i: i + block_size] for i in range(0, total_length, block_size)] + for k, t in concatenated_examples.items() + } + return result + + def preprocess_supervised_dataset(examples: Dict[str, List[Any]]) -> Dict[str, List[List[int]]]: + # build inputs with format ` X Y ` and labels with format ` ... Y ` + # for multiturn examples, we only mask the prompt part in each prompt-response pair. + model_inputs = {"input_ids": [], "attention_mask": [], "labels": []} + + for query, response, history, system in construct_example(examples): + if not (isinstance(query, str) and isinstance(response, str) and query != "" and response != ""): + continue + + input_ids, labels = [], [] + for turn_idx, (source_ids, target_ids) in enumerate(template.encode_multiturn( + tokenizer, query, response, history, system + )): + total_len = len(source_ids) + len(target_ids) + max_source_len = int(data_args.cutoff_len * (len(source_ids) / total_len)) + max_target_len = int(data_args.cutoff_len * (len(target_ids) / total_len)) + + if len(source_ids) > max_source_len: + source_ids = source_ids[:max_source_len] + if len(target_ids) > max_target_len: + target_ids = target_ids[:max_target_len] + + if data_args.train_on_prompt: + source_mask = source_ids + elif turn_idx != 0 and template.efficient_eos: + source_mask = [tokenizer.eos_token_id] + [IGNORE_INDEX] * (len(source_ids) - 1) + else: + source_mask = [IGNORE_INDEX] * len(source_ids) + + input_ids += source_ids + target_ids + labels += source_mask + target_ids + + if template.efficient_eos: + input_ids += [tokenizer.eos_token_id] + labels += [tokenizer.eos_token_id] + + if len(input_ids) > data_args.cutoff_len: + input_ids = input_ids[:data_args.cutoff_len] + labels = labels[:data_args.cutoff_len] + + model_inputs["input_ids"].append(input_ids) + model_inputs["attention_mask"].append([1] * len(input_ids)) + model_inputs["labels"].append(labels) + + return model_inputs + + def preprocess_packed_supervised_dataset(examples: Dict[str, List[Any]]) -> Dict[str, List[List[int]]]: + # build inputs with format ` X1 Y1 X2 Y2 ` + # and labels with format ` ... Y1 ... Y2 ` + model_inputs = {"input_ids": [], "attention_mask": [], "labels": []} + input_ids, labels = [], [] + for query, response, history, system in construct_example(examples): + if not (isinstance(query, str) and isinstance(response, str) and query != "" and response != ""): + continue + + for turn_idx, (source_ids, target_ids) in enumerate(template.encode_multiturn( + tokenizer, query, response, history, system + )): + if data_args.train_on_prompt: + source_mask = source_ids + elif turn_idx != 0 and template.efficient_eos: + source_mask = [tokenizer.eos_token_id] + [IGNORE_INDEX] * (len(source_ids) - 1) + else: + source_mask = [IGNORE_INDEX] * len(source_ids) + input_ids += source_ids + target_ids + labels += source_mask + target_ids + + if template.efficient_eos: + input_ids += [tokenizer.eos_token_id] + labels += [tokenizer.eos_token_id] + + total_length = len(input_ids) + block_size = data_args.cutoff_len + # we drop the small remainder, and if the total_length < block_size, we exclude this batch + total_length = (total_length // block_size) * block_size + # split by chunks of cutoff_len + for i in range(0, total_length, block_size): + model_inputs["input_ids"].append(input_ids[i: i + block_size]) + model_inputs["attention_mask"].append([1] * block_size) + model_inputs["labels"].append(labels[i: i + block_size]) + + return model_inputs + + def preprocess_unsupervised_dataset(examples: Dict[str, List[Any]]) -> Dict[str, List[List[int]]]: + # build inputs with format ` X` and labels with format `Y ` + model_inputs = {"input_ids": [], "attention_mask": [], "labels": []} + + for query, response, history, system in construct_example(examples): + if not (isinstance(query, str) and query != ""): + continue + + input_ids, labels = template.encode_oneturn(tokenizer, query, response, history, system) + + if template.efficient_eos: + labels += [tokenizer.eos_token_id] + + if len(input_ids) > data_args.cutoff_len: + input_ids = input_ids[:data_args.cutoff_len] + if len(labels) > data_args.cutoff_len: + labels = labels[:data_args.cutoff_len] + + model_inputs["input_ids"].append(input_ids) + model_inputs["attention_mask"].append([1] * len(input_ids)) + model_inputs["labels"].append(labels) + + return model_inputs + + def preprocess_pairwise_dataset(examples: Dict[str, List[Any]]) -> Dict[str, List[List[int]]]: + # build input pairs with format ` X`, `Y1 ` and `Y2 ` + model_inputs = {"prompt_ids": [], "chosen_ids": [], "rejected_ids": []} + for query, response, history, system in construct_example(examples): + if not (isinstance(query, str) and isinstance(response, list) and query != "" and len(response) > 1): + continue + + prompt_ids, chosen_ids = template.encode_oneturn(tokenizer, query, response[0], history, system) + _, rejected_ids = template.encode_oneturn(tokenizer, query, response[1], history, system) + + if template.efficient_eos: + chosen_ids += [tokenizer.eos_token_id] + rejected_ids += [tokenizer.eos_token_id] + + total_len = len(prompt_ids) + max(len(chosen_ids), len(rejected_ids)) + max_source_len = int(data_args.cutoff_len * (len(prompt_ids) / total_len)) + max_target_len = int(data_args.cutoff_len * (max(len(chosen_ids), len(rejected_ids)) / total_len)) + + if len(prompt_ids) > max_source_len: + prompt_ids = prompt_ids[:max_source_len] + if len(chosen_ids) > max_target_len: + chosen_ids = chosen_ids[:max_target_len] + if len(rejected_ids) > max_target_len: + rejected_ids = rejected_ids[:max_target_len] + + model_inputs["prompt_ids"].append(prompt_ids) + model_inputs["chosen_ids"].append(chosen_ids) + model_inputs["rejected_ids"].append(rejected_ids) + + return model_inputs + + def print_supervised_dataset_example(example: Dict[str, List[int]]) -> None: + print("input_ids:\n{}".format(example["input_ids"])) + print("inputs:\n{}".format(tokenizer.decode(example["input_ids"], skip_special_tokens=False))) + print("label_ids:\n{}".format(example["labels"])) + print("labels:\n{}".format( + tokenizer.decode(list(filter(lambda x: x != IGNORE_INDEX, example["labels"])), skip_special_tokens=False) + )) + + def print_pairwise_dataset_example(example: Dict[str, List[int]]) -> None: + print("prompt_ids:\n{}".format(example["prompt_ids"])) + print("prompt:\n{}".format(tokenizer.decode(example["prompt_ids"], skip_special_tokens=False))) + print("chosen_ids:\n{}".format(example["chosen_ids"])) + print("chosen:\n{}".format(tokenizer.decode(example["chosen_ids"], skip_special_tokens=False))) + print("rejected_ids:\n{}".format(example["rejected_ids"])) + print("rejected:\n{}".format(tokenizer.decode(example["rejected_ids"], skip_special_tokens=False))) + + def print_unsupervised_dataset_example(example: Dict[str, List[int]]) -> None: + print("input_ids:\n{}".format(example["input_ids"])) + print("inputs:\n{}".format(tokenizer.decode(example["input_ids"], skip_special_tokens=False))) + + if stage == "pt": + preprocess_func = preprocess_pretrain_dataset + print_function = print_unsupervised_dataset_example + elif stage == "sft" and not training_args.predict_with_generate: + preprocess_func = preprocess_packed_supervised_dataset if data_args.sft_packing else preprocess_supervised_dataset + print_function = print_supervised_dataset_example + elif stage == "rm": + preprocess_func = preprocess_pairwise_dataset + print_function = print_pairwise_dataset_example + else: + preprocess_func = preprocess_unsupervised_dataset + print_function = print_unsupervised_dataset_example + + if data_args.cache_path is not None and os.path.exists(data_args.cache_path): + logger.warning("Loading dataset from disk will ignore other data arguments.") + return load_from_disk(data_args.cache_path) + + with training_args.main_process_first(desc="dataset map pre-processing"): + column_names = list(next(iter(dataset)).keys()) + kwargs = {} + if not data_args.streaming: + kwargs = dict( + num_proc=data_args.preprocessing_num_workers, + load_from_cache_file=(not data_args.overwrite_cache), + desc="Running tokenizer on dataset" + ) + + dataset = dataset.map( + preprocess_func, + batched=True, + remove_columns=column_names, + **kwargs + ) + + if data_args.cache_path is not None and not os.path.exists(data_args.cache_path): + if training_args.should_save: + dataset.save_to_disk(data_args.cache_path) + raise SystemExit("Dataset saved, rerun this script with the same `--cache_file`.") + + if training_args.should_log: + try: + print_function(next(iter(dataset))) + except StopIteration: + raise RuntimeError("Empty dataset!") + + return dataset diff --git a/llm_rl/src/llmtuner/dsets/utils.py b/llm_rl/src/llmtuner/dsets/utils.py new file mode 100644 index 00000000..bf337014 --- /dev/null +++ b/llm_rl/src/llmtuner/dsets/utils.py @@ -0,0 +1,59 @@ +import hashlib +from typing import TYPE_CHECKING, Dict, List, Optional, Union + +from llmtuner.extras.logging import get_logger + +if TYPE_CHECKING: + from datasets import Dataset, IterableDataset + from transformers import TrainingArguments + from llmtuner.hparams import DataArguments + + +logger = get_logger(__name__) + + +EXT2TYPE = { + "csv": "csv", + "json": "json", + "jsonl": "json", + "txt": "text" +} + + +def checksum(data_files: List[str], file_sha1: Optional[str] = None) -> None: + if file_sha1 is None: + logger.warning("Checksum failed: missing SHA-1 hash value in dataset_info.json.") + return + + if len(data_files) != 1: + logger.warning("Checksum failed: too many files.") + return + + with open(data_files[0], "rb") as f: + sha1 = hashlib.sha1(f.read()).hexdigest() + if sha1 != file_sha1: + logger.warning("Checksum failed: mismatched SHA-1 hash value at {}.".format(data_files[0])) + + +def split_dataset( + dataset: Union["Dataset", "IterableDataset"], + data_args: "DataArguments", + training_args: "TrainingArguments" +) -> Dict[str, "Dataset"]: + if training_args.do_train: + if data_args.val_size > 1e-6: # Split the dataset + if data_args.streaming: + val_set = dataset.take(int(data_args.val_size)) + train_set = dataset.skip(int(data_args.val_size)) + dataset = dataset.shuffle(buffer_size=data_args.buffer_size, seed=training_args.seed) + return {"train_dataset": train_set, "eval_dataset": val_set} + else: + val_size = int(data_args.val_size) if data_args.val_size > 1 else data_args.val_size + dataset = dataset.train_test_split(test_size=val_size, seed=training_args.seed) + return {"train_dataset": dataset["train"], "eval_dataset": dataset["test"]} + else: + if data_args.streaming: + dataset = dataset.shuffle(buffer_size=data_args.buffer_size, seed=training_args.seed) + return {"train_dataset": dataset} + else: # do_eval or do_predict + return {"eval_dataset": dataset} diff --git a/llm_rl/src/llmtuner/extras/__init__.py b/llm_rl/src/llmtuner/extras/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/llm_rl/src/llmtuner/extras/callbacks.py b/llm_rl/src/llmtuner/extras/callbacks.py new file mode 100644 index 00000000..7398d424 --- /dev/null +++ b/llm_rl/src/llmtuner/extras/callbacks.py @@ -0,0 +1,155 @@ +import os +import json +import time +from typing import TYPE_CHECKING +from datetime import timedelta + +from transformers import TrainerCallback +from transformers.trainer_utils import has_length, PREFIX_CHECKPOINT_DIR + +from llmtuner.extras.constants import LOG_FILE_NAME +from llmtuner.extras.logging import get_logger + +if TYPE_CHECKING: + from transformers import TrainingArguments, TrainerState, TrainerControl + + +logger = get_logger(__name__) + + +class SavePeftModelCallback(TrainerCallback): + + def on_save(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs): + r""" + Event called after a checkpoint save. + """ + if args.should_save: + output_dir = os.path.join(args.output_dir, "{}-{}".format(PREFIX_CHECKPOINT_DIR, state.global_step)) + model = kwargs.pop("model") + if getattr(model, "is_peft_model", False): + getattr(model, "pretrained_model").save_pretrained(output_dir) + + def on_train_end(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs): + r""" + Event called at the end of training. + """ + if args.should_save: + model = kwargs.pop("model") + if getattr(model, "is_peft_model", False): + getattr(model, "pretrained_model").save_pretrained(args.output_dir) + + +class LogCallback(TrainerCallback): + + def __init__(self, runner=None): + self.runner = runner + self.in_training = False + self.start_time = time.time() + self.cur_steps = 0 + self.max_steps = 0 + self.elapsed_time = "" + self.remaining_time = "" + + def timing(self): + cur_time = time.time() + elapsed_time = cur_time - self.start_time + avg_time_per_step = elapsed_time / self.cur_steps if self.cur_steps != 0 else 0 + remaining_time = (self.max_steps - self.cur_steps) * avg_time_per_step + self.elapsed_time = str(timedelta(seconds=int(elapsed_time))) + self.remaining_time = str(timedelta(seconds=int(remaining_time))) + + def on_train_begin(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs): + r""" + Event called at the beginning of training. + """ + if state.is_local_process_zero: + self.in_training = True + self.start_time = time.time() + self.max_steps = state.max_steps + if os.path.exists(os.path.join(args.output_dir, LOG_FILE_NAME)) and args.overwrite_output_dir: + logger.warning("Previous log file in this folder will be deleted.") + os.remove(os.path.join(args.output_dir, LOG_FILE_NAME)) + + def on_train_end(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs): + r""" + Event called at the end of training. + """ + if state.is_local_process_zero: + self.in_training = False + self.cur_steps = 0 + self.max_steps = 0 + + def on_substep_end(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs): + r""" + Event called at the end of an substep during gradient accumulation. + """ + if state.is_local_process_zero and self.runner is not None and self.runner.aborted: + control.should_epoch_stop = True + control.should_training_stop = True + + def on_step_end(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs): + r""" + Event called at the end of a training step. + """ + if state.is_local_process_zero: + self.cur_steps = state.global_step + self.timing() + if self.runner is not None and self.runner.aborted: + control.should_epoch_stop = True + control.should_training_stop = True + + def on_evaluate(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs): + r""" + Event called after an evaluation phase. + """ + if state.is_local_process_zero and not self.in_training: + self.cur_steps = 0 + self.max_steps = 0 + + def on_predict(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", *other, **kwargs): + r""" + Event called after a successful prediction. + """ + if state.is_local_process_zero and not self.in_training: + self.cur_steps = 0 + self.max_steps = 0 + + def on_log(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs) -> None: + r""" + Event called after logging the last logs. + """ + if not state.is_local_process_zero: + return + + logs = dict( + current_steps=self.cur_steps, + total_steps=self.max_steps, + loss=state.log_history[-1].get("loss", None), + eval_loss=state.log_history[-1].get("eval_loss", None), + predict_loss=state.log_history[-1].get("predict_loss", None), + reward=state.log_history[-1].get("reward", None), + learning_rate=state.log_history[-1].get("learning_rate", None), + epoch=state.log_history[-1].get("epoch", None), + percentage=round(self.cur_steps / self.max_steps * 100, 2) if self.max_steps != 0 else 100, + elapsed_time=self.elapsed_time, + remaining_time=self.remaining_time + ) + if self.runner is not None: + logger.info("{{'loss': {:.4f}, 'learning_rate': {:2.4e}, 'epoch': {:.2f}}}".format( + logs["loss"] or 0, logs["learning_rate"] or 0, logs["epoch"] or 0 + )) + + os.makedirs(args.output_dir, exist_ok=True) + with open(os.path.join(args.output_dir, "trainer_log.jsonl"), "a", encoding="utf-8") as f: + f.write(json.dumps(logs) + "\n") + + def on_prediction_step(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs): + r""" + Event called after a prediction step. + """ + eval_dataloader = kwargs.pop("eval_dataloader", None) + if state.is_local_process_zero and has_length(eval_dataloader) and not self.in_training: + if self.max_steps == 0: + self.max_steps = len(eval_dataloader) + self.cur_steps += 1 + self.timing() diff --git a/llm_rl/src/llmtuner/extras/constants.py b/llm_rl/src/llmtuner/extras/constants.py new file mode 100644 index 00000000..dc55a080 --- /dev/null +++ b/llm_rl/src/llmtuner/extras/constants.py @@ -0,0 +1,92 @@ +IGNORE_INDEX = -100 + +LOG_FILE_NAME = "trainer_log.jsonl" + +LAYERNORM_NAMES = ["norm", "ln_f", "ln_attn", "ln_mlp", "ln_1", "ln_2"] + +METHODS = ["full", "freeze", "lora"] + +TRAINING_STAGES = { + "Supervised Fine-Tuning": "sft", + "Reward Modeling": "rm", + "PPO": "ppo", + "DPO": "dpo", + "Pre-Training": "pt" +} + +SUPPORTED_MODELS = { + "LLaMA-7B": "huggyllama/llama-7b", + "LLaMA-13B": "huggyllama/llama-13b", + "LLaMA-30B": "huggyllama/llama-30b", + "LLaMA-65B": "huggyllama/llama-65b", + "LLaMA2-7B": "meta-llama/Llama-2-7b-hf", + "LLaMA2-13B": "meta-llama/Llama-2-13b-hf", + "LLaMA2-70B": "meta-llama/Llama-2-70b-hf", + "LLaMA2-7B-Chat": "meta-llama/Llama-2-7b-chat-hf", + "LLaMA2-13B-Chat": "meta-llama/Llama-2-13b-chat-hf", + "LLaMA2-70B-Chat": "meta-llama/Llama-2-70b-chat-hf", + "ChineseLLaMA2-7B": "ziqingyang/chinese-llama-2-7b", + "ChineseLLaMA2-13B": "ziqingyang/chinese-llama-2-13b", + "ChineseLLaMA2-7B-Chat": "ziqingyang/chinese-alpaca-2-7b", + "ChineseLLaMA2-13B-Chat": "ziqingyang/chinese-alpaca-2-13b", + "BLOOM-560M": "bigscience/bloom-560m", + "BLOOM-3B": "bigscience/bloom-3b", + "BLOOM-7B1": "bigscience/bloom-7b1", + "BLOOMZ-560M": "bigscience/bloomz-560m", + "BLOOMZ-3B": "bigscience/bloomz-3b", + "BLOOMZ-7B1-mt": "bigscience/bloomz-7b1-mt", + "Falcon-7B": "tiiuae/falcon-7b", + "Falcon-40B": "tiiuae/falcon-40b", + "Falcon-7B-Chat": "tiiuae/falcon-7b-instruct", + "Falcon-40B-Chat": "tiiuae/falcon-40b-instruct", + "Baichuan-7B": "baichuan-inc/Baichuan-7B", + "Baichuan-13B": "baichuan-inc/Baichuan-13B-Base", + "Baichuan-13B-Chat": "baichuan-inc/Baichuan-13B-Chat", + "Baichuan2-7B": "baichuan-inc/Baichuan2-7B-Base", + "Baichuan2-13B": "baichuan-inc/Baichuan2-13B-Base", + "Baichuan2-7B-Chat": "baichuan-inc/Baichuan2-7B-Chat", + "Baichuan2-13B-Chat": "baichuan-inc/Baichuan2-13B-Chat", + "InternLM-7B": "internlm/internlm-7b", + "InternLM-20B": "internlm/internlm-20b", + "InternLM-7B-Chat": "internlm/internlm-chat-7b", + "InternLM-20B-Chat": "internlm/internlm-chat-20b", + "Qwen-7B": "Qwen/Qwen-7B", + "Qwen-14B": "Qwen/Qwen-14B", + "Qwen-7B-Chat": "Qwen/Qwen-7B-Chat", + "Qwen-14B-Chat": "Qwen/Qwen-14B-Chat", + "XVERSE-13B": "xverse/XVERSE-13B", + "XVERSE-13B-Chat": "xverse/XVERSE-13B-Chat", + "ChatGLM2-6B-Chat": "THUDM/chatglm2-6b", + "ChatGLM3-6B-Base": "THUDM/chatglm3-6b-base", + "ChatGLM3-6B-Chat": "THUDM/chatglm3-6b", + "Phi1.5-1.3B": "microsoft/phi-1_5" +} + +DEFAULT_MODULE = { + "LLaMA": "q_proj,v_proj", + "LLaMA2": "q_proj,v_proj", + "ChineseLLaMA2": "q_proj,v_proj", + "BLOOM": "query_key_value", + "BLOOMZ": "query_key_value", + "Falcon": "query_key_value", + "Baichuan": "W_pack", + "Baichuan2": "W_pack", + "InternLM": "q_proj,v_proj", + "Qwen": "c_attn", + "XVERSE": "q_proj,v_proj", + "ChatGLM2": "query_key_value", + "ChatGLM3": "query_key_value", + "Phi1.5": "Wqkv" +} + +DEFAULT_TEMPLATE = { + "LLaMA2": "llama2", + "ChineseLLaMA2": "llama2_zh", + "Baichuan": "baichuan", + "Baichuan2": "baichuan2", + "InternLM": "intern", + "Qwen": "chatml", + "XVERSE": "xverse", + "ChatGLM2": "chatglm2", + "ChatGLM3": "chatglm3" +} diff --git a/llm_rl/src/llmtuner/extras/logging.py b/llm_rl/src/llmtuner/extras/logging.py new file mode 100644 index 00000000..d6f185e6 --- /dev/null +++ b/llm_rl/src/llmtuner/extras/logging.py @@ -0,0 +1,43 @@ +import sys +import logging + + +class LoggerHandler(logging.Handler): + + def __init__(self): + super().__init__() + self.log = "" + + def reset(self): + self.log = "" + + def emit(self, record): + if record.name == "httpx": + return + log_entry = self.format(record) + self.log += log_entry + self.log += "\n\n" + + +def reset_logging(): + r""" + Removes basic config of root logger + """ + root = logging.getLogger() + list(map(root.removeHandler, root.handlers)) + list(map(root.removeFilter, root.filters)) + + +def get_logger(name: str) -> logging.Logger: + formatter = logging.Formatter( + fmt="%(asctime)s - %(levelname)s - %(name)s - %(message)s", + datefmt="%m/%d/%Y %H:%M:%S" + ) + handler = logging.StreamHandler(sys.stdout) + handler.setFormatter(formatter) + + logger = logging.getLogger(name) + logger.setLevel(logging.INFO) + logger.addHandler(handler) + + return logger diff --git a/llm_rl/src/llmtuner/extras/misc.py b/llm_rl/src/llmtuner/extras/misc.py new file mode 100644 index 00000000..960d43ee --- /dev/null +++ b/llm_rl/src/llmtuner/extras/misc.py @@ -0,0 +1,118 @@ +import gc +import torch +from typing import TYPE_CHECKING, Tuple +from transformers import InfNanRemoveLogitsProcessor, LogitsProcessorList + +try: + from transformers.utils import ( + is_torch_bf16_cpu_available, + is_torch_bf16_gpu_available, + is_torch_cuda_available, + is_torch_npu_available + ) + _is_fp16_available = is_torch_npu_available() or is_torch_cuda_available() + _is_bf16_available = is_torch_bf16_gpu_available() or is_torch_bf16_cpu_available +except ImportError: + _is_fp16_available = torch.cuda.is_available() + _is_bf16_available = torch.cuda.is_bf16_supported() + +if TYPE_CHECKING: + from transformers.modeling_utils import PreTrainedModel + + +class AverageMeter: + r""" + Computes and stores the average and current value. + """ + def __init__(self): + self.reset() + + def reset(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + + +def count_parameters(model: torch.nn.Module) -> Tuple[int, int]: + r""" + Returns the number of trainable parameters and number of all parameters in the model. + """ + trainable_params, all_param = 0, 0 + for param in model.parameters(): + num_params = param.numel() + # if using DS Zero 3 and the weights are initialized empty + if num_params == 0 and hasattr(param, "ds_numel"): + num_params = param.ds_numel + + # Due to the design of 4bit linear layers from bitsandbytes, multiply the number of parameters by 2 + if param.__class__.__name__ == "Params4bit": + num_params = num_params * 2 + + all_param += num_params + if param.requires_grad: + trainable_params += num_params + + return trainable_params, all_param + + +def infer_optim_dtype(model_dtype: torch.dtype) -> torch.dtype: + r""" + Infers the optimal dtype according to the model_dtype and device compatibility. + """ + if _is_bf16_available and model_dtype == torch.bfloat16: + return torch.bfloat16 + elif _is_fp16_available: + return torch.float16 + else: + return torch.float32 + + +def get_logits_processor() -> LogitsProcessorList: + r""" + Gets logits processor that removes NaN and Inf logits. + """ + logits_processor = LogitsProcessorList() + logits_processor.append(InfNanRemoveLogitsProcessor()) + return logits_processor + + +def torch_gc() -> None: + r""" + Collects GPU memory. + """ + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.ipc_collect() + + +def dispatch_model(model: "PreTrainedModel") -> "PreTrainedModel": + r""" + Dispatches a pre-trained model to GPUs with balanced memory. + Borrowed from: https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/modeling_utils.py#L2803 + """ + if getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False): # do nothing + return model + + if torch.cuda.device_count() > 1: + from accelerate import dispatch_model + from accelerate.utils import infer_auto_device_map, get_balanced_memory + + if model._no_split_modules is None: + raise ValueError("The model class needs to implement the `_no_split_modules` attribute.") + + kwargs = {"dtype": model.dtype, "no_split_module_classes": model._no_split_modules} + max_memory = get_balanced_memory(model, **kwargs) + # Make sure tied weights are tied before creating the device map. + model.tie_weights() + device_map = infer_auto_device_map(model, max_memory=max_memory, **kwargs) + return dispatch_model(model, device_map) + else: + return model.cuda() diff --git a/llm_rl/src/llmtuner/extras/patches/__init__.py b/llm_rl/src/llmtuner/extras/patches/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/llm_rl/src/llmtuner/extras/patches/llama_patch.py b/llm_rl/src/llmtuner/extras/patches/llama_patch.py new file mode 100644 index 00000000..a8473311 --- /dev/null +++ b/llm_rl/src/llmtuner/extras/patches/llama_patch.py @@ -0,0 +1,218 @@ +import math +import torch +import torch.nn as nn +from typing import Optional, Tuple +from transformers.utils import logging +from transformers.models.llama.modeling_llama import LlamaAttention, apply_rotary_pos_emb, repeat_kv + +try: + from flash_attn import flash_attn_func, flash_attn_varlen_func # type: ignore + from flash_attn.bert_padding import pad_input, unpad_input # type: ignore +except ImportError: + print("FlashAttention-2 is not installed, ignore this if you are not using FlashAttention.") + + +logger = logging.get_logger(__name__) + + +# Modified from: https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py +class LlamaShiftShortAttention(LlamaAttention): + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: bool = False, + use_cache: bool = False, + **kwargs + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value[0].shape[-2] + + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + + if past_key_value is not None: # reuse k, v, self_attention + key_states = torch.cat([past_key_value[0], key_states], dim=2) + value_states = torch.cat([past_key_value[1], value_states], dim=2) + + past_key_value = (key_states, value_states) if use_cache else None + + if getattr(self, "num_key_value_groups"): + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + if getattr(self.config, "group_size_ratio", None) and self.training: # shift + groupsz = int(q_len * getattr(self.config, "group_size_ratio")) + assert q_len % groupsz == 0, "q_len {} should be divisible by group size {}.".format(q_len, groupsz) + num_groups = q_len // groupsz + def shift(state: torch.Tensor) -> torch.Tensor: + state = state.transpose(1, 2) # output: (bsz, seq_len, n_heads, head_dim) + state = torch.cat(( + state[:, :, :self.num_heads//2], state[:, :, self.num_heads//2:].roll(-groupsz//2, dims=1) + ), dim=2) + return state.reshape(bsz * num_groups, groupsz, self.num_heads, self.head_dim).transpose(1, 2) + + query_states, key_states, value_states = shift(query_states), shift(key_states), shift(value_states) + if attention_mask is not None: + attention_mask = attention_mask[:, :, :groupsz, :groupsz].repeat(num_groups, 1, 1, 1) + + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) + + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) + attn_output = torch.matmul(attn_weights, value_states) # (bsz, :, seq_len, :) or (bsz*n_group, :, groupsz, :) + attn_output = attn_output.transpose(1, 2).contiguous() + + if getattr(self.config, "group_size_ratio", None) and self.training: # shift back + attn_output.reshape(bsz, q_len, self.num_heads, self.head_dim) + attn_output = torch.cat(( + attn_output[:, :, :self.num_heads//2], attn_output[:, :, self.num_heads//2:].roll(groupsz//2, dims=1) + )) + + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +class LlamaFlashAttention2(LlamaAttention): + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: bool = False, + use_cache: bool = False, + **kwargs + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + # LlamaFlashAttention2 attention does not support output_attentions + output_attentions = False + + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + # FlashAttention requires the input to have the shape (bsz, seq_len, n_heads, head_dim) + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value[0].shape[-2] + + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + + if past_key_value is not None: # reuse k, v, self_attention + key_states = torch.cat([past_key_value[0], key_states], dim=2) + value_states = torch.cat([past_key_value[1], value_states], dim=2) + + past_key_value = (key_states, value_states) if use_cache else None + + # cast to half precision + input_dtype = query_states.dtype + if input_dtype == torch.float32: + logger.warning_once("The input hidden states seems to be silently casted in float32.") + query_states = query_states.to(self.config.torch_dtype) + key_states = key_states.to(self.config.torch_dtype) + value_states = value_states.to(self.config.torch_dtype) + + if getattr(self, "num_key_value_groups", None): + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + query_states = query_states.transpose(1, 2) # (bsz, seq_len, n_heads, head_dim) + key_states = key_states.transpose(1, 2) # (bsz, seq_len, n_heads, head_dim) + value_states = value_states.transpose(1, 2) # (bsz, seq_len, n_heads, head_dim) + + if getattr(self.config, "group_size_ratio", None) and self.training: # shift + groupsz = int(q_len * getattr(self.config, "group_size_ratio")) + assert q_len % groupsz == 0, "q_len {} should be divisible by group size {}.".format(q_len, groupsz) + num_groups = q_len // groupsz + def shift(state: torch.Tensor) -> torch.Tensor: + state = torch.cat(( + state[:, :, :self.num_heads//2], state[:, :, self.num_heads//2:].roll(-groupsz//2, dims=1) + ), dim=2) + return state.reshape(bsz * num_groups, groupsz, self.num_heads, self.head_dim) + + query_states, key_states, value_states = shift(query_states), shift(key_states), shift(value_states) + if attention_mask is not None: + attention_mask = attention_mask.reshape(bsz * num_groups, groupsz) + + if attention_mask is not None: + logger.warning_once("Padded sequences are less efficient in FlashAttention.") + # -q_len: assumes left padding when q_len != kv_len + unpadded_q, indices_q, cu_seqlens_q, max_seqlen_q = unpad_input(query_states, attention_mask[:, -q_len:]) + unpadded_k, _, cu_seqlens_k, max_seqlen_k = unpad_input(key_states, attention_mask) + unpadded_v, _, _, _ = unpad_input(value_states, attention_mask) + attn_output_unpad = flash_attn_varlen_func( + unpadded_q, + unpadded_k, + unpadded_v, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_q, + max_seqlen_k=max_seqlen_k, + dropout_p=0.0, + softmax_scale=None, + causal=True, + ) + attn_output = pad_input(attn_output_unpad, indices_q, bsz, q_len) + else: + attn_output = flash_attn_func( + query_states, key_states, value_states, 0.0, softmax_scale=None, causal=True + ) + + if getattr(self.config, "group_size_ratio", None) and self.training: # shift back + attn_output.reshape(bsz, q_len, self.num_heads, self.head_dim) + attn_output = torch.cat(( + attn_output[:, :, :self.num_heads//2], attn_output[:, :, self.num_heads//2:].roll(groupsz//2, dims=1) + )) + + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous() + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +# Disable the transformation of the attention mask in LlamaModel as flash attention +# takes a boolean padding_mask. Fills in the past kv length for use in forward. +def _prepare_decoder_attention_mask( + self, + attention_mask: torch.Tensor, + input_shape: torch.Tensor, + inputs_embeds: torch.Tensor, + past_key_values_length: int +) -> torch.Tensor: + if attention_mask is not None and torch.all(attention_mask): + return None # This uses the faster call when training with full samples + + return attention_mask diff --git a/llm_rl/src/llmtuner/extras/ploting.py b/llm_rl/src/llmtuner/extras/ploting.py new file mode 100644 index 00000000..82530e45 --- /dev/null +++ b/llm_rl/src/llmtuner/extras/ploting.py @@ -0,0 +1,52 @@ +import os +import math +import json +import matplotlib.pyplot as plt +from typing import List, Optional +from transformers.trainer import TRAINER_STATE_NAME + +from llmtuner.extras.logging import get_logger + + +logger = get_logger(__name__) + + +def smooth(scalars: List[float]) -> List[float]: + r""" + EMA implementation according to TensorBoard. + """ + last = scalars[0] + smoothed = list() + weight = 1.8 * (1 / (1 + math.exp(-0.05 * len(scalars))) - 0.5) # a sigmoid function + for next_val in scalars: + smoothed_val = last * weight + (1 - weight) * next_val + smoothed.append(smoothed_val) + last = smoothed_val + return smoothed + + +def plot_loss(save_dictionary: os.PathLike, keys: Optional[List[str]] = ["loss"]) -> None: + + with open(os.path.join(save_dictionary, TRAINER_STATE_NAME), "r", encoding="utf-8") as f: + data = json.load(f) + + for key in keys: + steps, metrics = [], [] + for i in range(len(data["log_history"])): + if key in data["log_history"][i]: + steps.append(data["log_history"][i]["step"]) + metrics.append(data["log_history"][i][key]) + + if len(metrics) == 0: + logger.warning(f"No metric {key} to plot.") + continue + + plt.figure() + plt.plot(steps, metrics, alpha=0.4, label="original") + plt.plot(steps, smooth(metrics), label="smoothed") + plt.title("training {} of {}".format(key, save_dictionary)) + plt.xlabel("step") + plt.ylabel(key) + plt.legend() + plt.savefig(os.path.join(save_dictionary, "training_{}.png".format(key)), format="png", dpi=100) + print("Figure saved:", os.path.join(save_dictionary, "training_{}.png".format(key))) diff --git a/llm_rl/src/llmtuner/extras/save_and_load.py b/llm_rl/src/llmtuner/extras/save_and_load.py new file mode 100644 index 00000000..6d819ce6 --- /dev/null +++ b/llm_rl/src/llmtuner/extras/save_and_load.py @@ -0,0 +1,21 @@ +import os +import torch +from transformers.trainer import WEIGHTS_NAME + +from llmtuner.extras.logging import get_logger + + +logger = get_logger(__name__) + + +def load_valuehead_params(model: torch.nn.Module, checkpoint_dir: os.PathLike) -> bool: + vhead_file = os.path.join(checkpoint_dir, WEIGHTS_NAME) + if not os.path.exists(vhead_file): + logger.warning("Provided path ({}) does not contain valuehead weights.".format(checkpoint_dir)) + return False + vhead_params = torch.load(vhead_file, map_location="cpu") + model.register_buffer("reward_head_weight", vhead_params["v_head.summary.weight"], persistent=False) + model.register_buffer("reward_head_bias", vhead_params["v_head.summary.bias"], persistent=False) + model.register_buffer("default_head_weight", torch.zeros_like(vhead_params["v_head.summary.weight"]), persistent=False) + model.register_buffer("default_head_bias", torch.zeros_like(vhead_params["v_head.summary.bias"]), persistent=False) + return True diff --git a/llm_rl/src/llmtuner/extras/template.py b/llm_rl/src/llmtuner/extras/template.py new file mode 100644 index 00000000..401750ce --- /dev/null +++ b/llm_rl/src/llmtuner/extras/template.py @@ -0,0 +1,713 @@ +import tiktoken +from dataclasses import dataclass +from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union + +from llmtuner.extras.logging import get_logger + +if TYPE_CHECKING: + from transformers import PreTrainedTokenizer + + +logger = get_logger(__name__) + + +@dataclass +class Template: + + prefix: List[Union[str, Dict[str, str]]] + prompt: List[Union[str, Dict[str, str]]] + system: str + sep: List[Union[str, Dict[str, str]]] + stop_words: List[str] + use_history: bool + efficient_eos: bool + + def encode_oneturn( + self, + tokenizer: "PreTrainedTokenizer", + query: str, + resp: str, + history: Optional[List[Tuple[str, str]]] = None, + system: Optional[str] = None + ) -> Tuple[List[int], List[int]]: + r""" + Returns a single pair of token ids representing prompt and response respectively. + """ + system, history = self._format(query, resp, history, system) + encoded_pairs = self._encode(tokenizer, system, history) + prompt_ids = [] + for query_ids, resp_ids in encoded_pairs[:-1]: + prompt_ids = prompt_ids + query_ids + resp_ids + prompt_ids, answer_ids = prompt_ids + encoded_pairs[-1][0], encoded_pairs[-1][1] + return prompt_ids, answer_ids + + def encode_multiturn( + self, + tokenizer: "PreTrainedTokenizer", + query: str, + resp: str, + history: Optional[List[Tuple[str, str]]] = None, + system: Optional[str] = None + ) -> List[Tuple[List[int], List[int]]]: + r""" + Returns multiple pairs of token ids representing prompts and responses respectively. + """ + system, history = self._format(query, resp, history, system) + encoded_pairs = self._encode(tokenizer, system, history) + return encoded_pairs + + def _format( + self, + query: str, + resp: str, + history: Optional[List[Tuple[str, str]]] = None, + system: Optional[str] = None + ) -> Tuple[str, List[Tuple[str, str]]]: + r""" + Aligns inputs to the standard format. + """ + system = system or self.system # use system if provided + history = history if (history and self.use_history) else [] + history = history + [(query, resp)] + return system, history + + def _get_special_ids( + self, + tokenizer: "PreTrainedTokenizer" + ) -> Tuple[List[int], List[int]]: + if tokenizer.bos_token_id is not None and getattr(tokenizer, "add_bos_token", True): + bos_ids = [tokenizer.bos_token_id] + else: # baichuan, qwen and gpt2 models have no bos token + bos_ids = [] + + if tokenizer.eos_token_id is None: + raise ValueError("EOS token is required.") + + if self.efficient_eos: # used in baichuan, qwen, chatglm, etc. + eos_ids = [] + else: + eos_ids = [tokenizer.eos_token_id] + + return bos_ids, eos_ids + + def _encode( + self, + tokenizer: "PreTrainedTokenizer", + system: str, + history: List[Tuple[str, str]] + ) -> List[Tuple[List[int], List[int]]]: + r""" + Encodes formatted inputs to pairs of token ids. + Turn 0: bos + prefix + sep + query resp + eos + Turn t: sep + bos + query resp + eos + """ + bos_ids, eos_ids = self._get_special_ids(tokenizer) + sep_ids = self._convert_inputs_to_ids(tokenizer, context=self.sep) + encoded_pairs = [] + for turn_idx, (query, resp) in enumerate(history): + if turn_idx == 0: + prefix_ids = self._convert_inputs_to_ids(tokenizer, context=self.prefix, system=system) + if len(prefix_ids) != 0: # has prefix + prefix_ids = bos_ids + prefix_ids + sep_ids + else: + prefix_ids = bos_ids + else: + prefix_ids = sep_ids + bos_ids + + query_ids = self._convert_inputs_to_ids(tokenizer, context=self.prompt, query=query, idx=str(turn_idx)) + resp_ids = self._convert_inputs_to_ids(tokenizer, context=[resp]) + encoded_pairs.append((prefix_ids + query_ids, resp_ids + eos_ids)) + return encoded_pairs + + def _convert_inputs_to_ids( + self, + tokenizer: "PreTrainedTokenizer", + context: List[Union[str, Dict[str, str]]], + system: Optional[str] = None, + query: Optional[str] = None, + idx: Optional[str] = None + ) -> List[int]: + r""" + Converts context to token ids. + """ + if isinstance(getattr(tokenizer, "tokenizer", None), tiktoken.Encoding): # for tiktoken tokenizer (Qwen) + kwargs = dict(allowed_special="all") + else: + kwargs = dict(add_special_tokens=False) + + token_ids = [] + for elem in context: + if isinstance(elem, str): + elem = elem.replace("{{system}}", system, 1) if system is not None else elem + elem = elem.replace("{{query}}", query, 1) if query is not None else elem + elem = elem.replace("{{idx}}", idx, 1) if idx is not None else elem + if len(elem) != 0: + token_ids = token_ids + tokenizer.encode(elem, **kwargs) + elif isinstance(elem, dict): + token_ids = token_ids + [tokenizer.convert_tokens_to_ids(elem.get("token"))] + else: + raise ValueError("Input must be string or dict[str, str], got {}".format(type(elem))) + + return token_ids + + +@dataclass +class Llama2Template(Template): + + def _encode( + self, + tokenizer: "PreTrainedTokenizer", + system: str, + history: List[Tuple[str, str]] + ) -> List[Tuple[List[int], List[int]]]: + r""" + Encodes formatted inputs to pairs of token ids. + Turn 0: bos + prefix + query resp + eos + Turn t: bos + query resp + eos + """ + bos_ids, eos_ids = self._get_special_ids(tokenizer) + encoded_pairs = [] + for turn_idx, (query, resp) in enumerate(history): + if turn_idx == 0: # llama2 template has no sep_ids + query = self.prefix[0].replace("{{system}}", system) + query + query_ids = self._convert_inputs_to_ids(tokenizer, context=self.prompt, query=query) + resp_ids = self._convert_inputs_to_ids(tokenizer, context=[resp]) + encoded_pairs.append((bos_ids + query_ids, resp_ids + eos_ids)) + return encoded_pairs + + +templates: Dict[str, Template] = {} + + +def register_template( + name: str, + prefix: List[Union[str, Dict[str, str]]], + prompt: List[Union[str, Dict[str, str]]], + system: str, + sep: List[Union[str, Dict[str, str]]], + stop_words: Optional[List[str]] = [], + use_history: Optional[bool] = True, + efficient_eos: Optional[bool] = False +) -> None: + template_class = Llama2Template if "llama2" in name else Template + templates[name] = template_class( + prefix=prefix, + prompt=prompt, + system=system, + sep=sep, + stop_words=stop_words, + use_history=use_history, + efficient_eos=efficient_eos + ) + + +def get_template_and_fix_tokenizer( + name: str, + tokenizer: "PreTrainedTokenizer" +) -> Template: + if tokenizer.eos_token_id is None: + tokenizer.eos_token = "<|endoftext|>" + logger.info("Add eos token: {}".format(tokenizer.eos_token)) + + if tokenizer.pad_token_id is None: + tokenizer.pad_token = tokenizer.eos_token + logger.info("Add pad token: {}".format(tokenizer.pad_token)) + + if name is None: + return None + + template = templates.get(name, None) + assert template is not None, "Template {} does not exist.".format(name) + tokenizer.add_special_tokens( + dict(additional_special_tokens=template.stop_words), + replace_additional_special_tokens=False + ) + return template + + +r""" +Supports: https://huggingface.co/tatsu-lab/alpaca-7b-wdiff +""" +register_template( + name="alpaca", + prefix=[ + "{{system}}" + ], + prompt=[ + "### Instruction:\n{{query}}\n\n### Response:\n" + ], + system=( + "Below is an instruction that describes a task. " + "Write a response that appropriately completes the request." + ), + sep=[ + "\n\n" + ] +) + + +r""" +Supports: https://huggingface.co/BAAI/AquilaChat-7B + https://huggingface.co/BAAI/AquilaChat2-7B + https://huggingface.co/BAAI/AquilaChat2-34B +""" +register_template( + name="aquila", + prefix=[ + "{{system}}" + ], + prompt=[ + "Human: {{query}}###Assistant:" + ], + system=( + "A chat between a curious human and an artificial intelligence assistant. " + "The assistant gives helpful, detailed, and polite answers to the human's questions." + ), + sep=[ + "###" + ], + stop_words=[ + "" + ], + efficient_eos=True +) + + +r""" +Supports: https://huggingface.co/baichuan-inc/Baichuan-13B-Chat +""" +register_template( + name="baichuan", + prefix=[ + "{{system}}" + ], + prompt=[ + {"token": ""}, # user token + "{{query}}", + {"token": ""} # assistant token + ], + system="", + sep=[], + efficient_eos=True +) + + +r""" +Supports: https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat + https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat +""" +register_template( + name="baichuan2", + prefix=[ + "{{system}}" + ], + prompt=[ + {"token": ""}, # user token + "{{query}}", + {"token": ""} # assistant token + ], + system="", + sep=[], + efficient_eos=True +) + + +r""" +Supports: https://huggingface.co/BelleGroup/BELLE-LLaMA-EXT-13B +""" +register_template( + name="belle", + prefix=[ + "{{system}}" + ], + prompt=[ + "Human: {{query}}\n\nBelle: " + ], + system="", + sep=[ + "\n\n" + ] +) + + +r""" +Supports: https://huggingface.co/vivo-ai/BlueLM-7B-Chat +""" +register_template( + name="bluelm", + prefix=[ + "{{system}}" + ], + prompt=[ + {"token": "[|Human|]:"}, + "{{query}}", + {"token": "[|AI|]:"} + ], + system="", + sep=[] +) + + +r""" +Supports: https://huggingface.co/THUDM/chatglm2-6b +""" +register_template( + name="chatglm2", + prefix=[ + {"token": "[gMASK]"}, + {"token": "sop"}, + "{{system}}" + ], + prompt=[ + "[Round {{idx}}]\n\n问：{{query}}\n\n答：" + ], + system="", + sep=[ + "\n\n" + ], + efficient_eos=True +) + + +r""" +Supports: https://huggingface.co/THUDM/chatglm3-6b +""" +register_template( + name="chatglm3", + prefix=[ + {"token": "[gMASK]"}, + {"token": "sop"}, + "{{system}}" + ], + prompt=[ + {"token": "<|user|>"}, + "\n", + "{{query}}", + {"token": "<|assistant|>"} + ], + system="", + sep=[], + stop_words=[ + "<|user|>", + "<|observation|>" + ], + efficient_eos=True +) + + +r""" +Supports: https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-instruct + https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct + https://huggingface.co/deepseek-ai/deepseek-coder-33b-instruct +""" +register_template( + name="deepseek", + prefix=[ + "{{system}}" + ], + prompt=[ + "### Instruction:\n{{query}}\n\n### Response:\n" + ], + system=( + "You are an AI programming assistant, utilizing the Deepseek Coder model, " + "developed by Deepseek Company, and you only answer questions related to computer science. " + "For politically sensitive questions, security and privacy issues, " + "and other non-computer science questions, you will refuse to answer." + ), + sep=[ + "\n", + {"token": "<|EOT|>"}, + "\n\n" + ], + stop_words=[ + "<|EOT|>" + ], + efficient_eos=True +) + + +r""" +Default template. +""" +register_template( + name="default", + prefix=[ + "{{system}}" + ], + prompt=[ + "Human: {{query}}\nAssistant:" + ], + system=( + "A chat between a curious user and an artificial intelligence assistant. " + "The assistant gives helpful, detailed, and polite answers to the user's questions." + ), + sep=[ + "\n" + ] +) + + +r""" +Supports: https://huggingface.co/internlm/internlm-chat-7b + https://huggingface.co/internlm/internlm-chat-20b +""" +register_template( + name="intern", + prefix=[ + "{{system}}" + ], + prompt=[ + "<|User|>:{{query}}", + {"token": ""}, + "\n<|Bot|>:" + ], + system="", + sep=[ + {"token": ""}, + "\n" + ], + stop_words=[ + "" + ], + efficient_eos=True +) + + +r""" +Supports: https://huggingface.co/meta-llama/Llama-2-7b-chat-hf + https://huggingface.co/meta-llama/Llama-2-13b-chat-hf + https://huggingface.co/meta-llama/Llama-2-70b-chat-hf +""" +register_template( + name="llama2", + prefix=[ + "<>\n{{system}}\n<>\n\n" + ], + prompt=[ + "[INST] {{query}} [/INST]" + ], + system=( + "You are a helpful, respectful and honest assistant. " + "Always answer as helpfully as possible, while being safe. " + "Your answers should not include any harmful, unethical, " + "racist, sexist, toxic, dangerous, or illegal content. " + "Please ensure that your responses are socially unbiased and positive in nature.\n\n" + "If a question does not make any sense, or is not factually coherent, " + "explain why instead of answering something not correct. " + "If you don't know the answer to a question, please don't share false information." + ), + sep=[] +) + + +r""" +Supports: https://huggingface.co/ziqingyang/chinese-alpaca-2-7b + https://huggingface.co/ziqingyang/chinese-alpaca-2-13b +""" +register_template( + name="llama2_zh", + prefix=[ + "<>\n{{system}}\n<>\n\n" + ], + prompt=[ + "[INST] {{query}} [/INST]" + ], + system="You are a helpful assistant. 你是一个乐于助人的助手。", + sep=[] +) + + +r""" +Supports: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1 +""" +register_template( + name="mistral", + prefix=[ + "{{system}}" + ], + prompt=[ + "[INST] {{query}} [/INST]" + ], + system="", + sep=[] +) + + +r""" +Supports: https://huggingface.co/openchat/openchat_3.5 +""" +register_template( + name="openchat", + prefix=[ + "{{system}}" + ], + prompt=[ + "GPT4 Correct User: {{query}}", + {"token": "<|end_of_turn|>"}, + "GPT4 Correct Assistant:" + ], + system="You are a helpful assistant.", + sep=[ + {"token": "<|end_of_turn|>"} + ], + stop_words=[ + "<|end_of_turn|>" + ], + efficient_eos=True +) + + +r""" +Supports: https://huggingface.co/Qwen/Qwen-7B-Chat + https://huggingface.co/Qwen/Qwen-14B-Chat +""" +register_template( + name="qwen", + prefix=[ + {"token": "<|im_start|>"}, + "system\n{{system}}" + ], + prompt=[ + {"token": "<|im_start|>"}, + "user\n{{query}}", + {"token": "<|im_end|>"}, + "\n", + {"token": "<|im_start|>"}, + "assistant\n" + ], + system="You are a helpful assistant.", + sep=[ + {"token": "<|im_end|>"}, + "\n" + ], + stop_words=[ + "<|im_end|>" + ], + efficient_eos=True +) + + +r""" +Supports: https://huggingface.co/HuggingFaceH4/starchat-alpha + https://huggingface.co/HuggingFaceH4/starchat-beta +""" +register_template( + name="starchat", + prefix=[ + {"token": "<|system|>"}, + "\n{{system}}", + ], + prompt=[ + {"token": "<|user|>"}, + "\n{{query}}", + {"token": "<|end|>"}, + "\n", + {"token": "<|assistant|>"} + ], + system="", + sep=[ + {"token": "<|end|>"}, + "\n" + ], + stop_words=[ + "<|end|>" + ], + efficient_eos=True +) + + +r""" +Supports language model inference without histories. +""" +register_template( + name="vanilla", + prefix=[], + prompt=[ + "{{query}}" + ], + system="", + sep=[], + use_history=False +) + + +r""" +Supports: https://huggingface.co/lmsys/vicuna-7b-v1.5 + https://huggingface.co/lmsys/vicuna-13b-v1.5 +""" +register_template( + name="vicuna", + prefix=[ + "{{system}}" + ], + prompt=[ + "USER: {{query}} ASSISTANT:" + ], + system=( + "A chat between a curious user and an artificial intelligence assistant. " + "The assistant gives helpful, detailed, and polite answers to the user's questions." + ), + sep=[] +) + + +r""" +Supports: https://huggingface.co/xverse/XVERSE-7B-Chat + https://huggingface.co/xverse/XVERSE-13B-Chat +""" +register_template( + name="xverse", + prefix=[ + "{{system}}" + ], + prompt=[ + "Human: {{query}}\n\nAssistant: " + ], + system="", + sep=[] +) + + +r""" +Supports: https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha + https://huggingface.co/HuggingFaceH4/zephyr-7b-beta +""" +register_template( + name="zephyr", + prefix=[ + {"token": "<|system|>"}, + "\n{{system}}", + {"token": ""} + ], + prompt=[ + {"token": "<|user|>"}, + "\n{{query}}", + {"token": ""}, + {"token": "<|assistant|>"} + ], + system="You are a friendly chatbot who always responds in the style of a pirate", + sep=[] +) + + +r""" +Supports: https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1 + https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1.1 + https://huggingface.co/IDEA-CCNL/Ziya2-13B-Chat +""" +register_template( + name="ziya", + prefix=[ + "{{system}}" + ], + prompt=[ + {"token": ""}, + ":{{query}}\n", + {"token": ""}, + ":" + ], + system="", + sep=[ + "\n" + ] +) diff --git a/llm_rl/src/llmtuner/hparams/__init__.py b/llm_rl/src/llmtuner/hparams/__init__.py new file mode 100644 index 00000000..f0547cc5 --- /dev/null +++ b/llm_rl/src/llmtuner/hparams/__init__.py @@ -0,0 +1,4 @@ +from .data_args import DataArguments +from .finetuning_args import FinetuningArguments +from .generating_args import GeneratingArguments +from .model_args import ModelArguments diff --git a/llm_rl/src/llmtuner/hparams/data_args.py b/llm_rl/src/llmtuner/hparams/data_args.py new file mode 100644 index 00000000..4c67dd65 --- /dev/null +++ b/llm_rl/src/llmtuner/hparams/data_args.py @@ -0,0 +1,169 @@ +import os +import json +from typing import List, Literal, Optional +from dataclasses import dataclass, field + + +@dataclass +class DatasetAttr: + + load_from: str + dataset_name: Optional[str] = None + dataset_sha1: Optional[str] = None + system_prompt: Optional[str] = None + subset: Optional[str] = None + ranking: Optional[bool] = False + formatting: Optional[Literal["alpaca", "sharegpt"]] = "alpaca" + + prompt: Optional[str] = "instruction" + query: Optional[str] = "input" + response: Optional[str] = "output" + history: Optional[str] = None + messages: Optional[str] = "conversations" + role: Optional[str] = "from" + content: Optional[str] = "value" + + def __repr__(self) -> str: + return self.dataset_name + + +@dataclass +class DataArguments: + r""" + Arguments pertaining to what data we are going to input our model for training and evaluation. + """ + template: Optional[str] = field( + default=None, + metadata={"help": "Which template to use for constructing prompts in training and inference."} + ) + dataset: Optional[str] = field( + default=None, + metadata={"help": "The name of provided dataset(s) to use. Use commas to separate multiple datasets."} + ) + dataset_dir: Optional[str] = field( + default="data", + metadata={"help": "The name of the folder containing datasets."} + ) + split: Optional[str] = field( + default="train", + metadata={"help": "Which dataset split to use for training and evaluation."} + ) + cutoff_len: Optional[int] = field( + default=1024, + metadata={"help": "The maximum length of the model inputs after tokenization."} + ) + train_on_prompt: Optional[bool] = field( + default=False, + metadata={"help": "Whether to disable the mask on the prompt or not."} + ) + streaming: Optional[bool] = field( + default=False, + metadata={"help": "Enable dataset streaming."} + ) + buffer_size: Optional[int] = field( + default=16384, + metadata={"help": "Size of the buffer to randomly sample examples from in dataset streaming."} + ) + mix_strategy: Optional[Literal["concat", "interleave_under", "interleave_over"]] = field( + default="concat", + metadata={"help": "Strategy to use in dataset mixing (concat/interleave) (undersampling/oversampling)."} + ) + interleave_probs: Optional[str] = field( + default=None, + metadata={"help": "Probabilities to sample data from datasets. Use commas to separate multiple datasets."} + ) + overwrite_cache: Optional[bool] = field( + default=False, + metadata={"help": "Overwrite the cached training and evaluation sets."} + ) + preprocessing_num_workers: Optional[int] = field( + default=None, + metadata={"help": "The number of processes to use for the preprocessing."} + ) + max_samples: Optional[int] = field( + default=None, + metadata={"help": "For debugging purposes, truncate the number of examples for each dataset."} + ) + eval_num_beams: Optional[int] = field( + default=None, + metadata={"help": "Number of beams to use for evaluation. This argument will be passed to `model.generate`"} + ) + ignore_pad_token_for_loss: Optional[bool] = field( + default=True, + metadata={"help": "Whether to ignore the tokens corresponding to padded labels in the loss computation or not."} + ) + system_prompt: Optional[str] = field( + default=None, + metadata={"help": "System prompt to add before the user query. Use `|` to separate multiple prompts in training."} + ) + val_size: Optional[float] = field( + default=0, + metadata={"help": "Size of the development set, should be an integer or a float in range `[0,1)`."} + ) + sft_packing: Optional[bool] = field( + default=False, + metadata={"help": "Packing the questions and answers in the supervised fine-tuning stage."} + ) + cache_path: Optional[str] = field( + default=None, + metadata={"help": "Path to save or load the preprocessed datasets."} + ) + + def __post_init__(self): + if self.streaming and self.val_size > 1e-6 and self.val_size < 1: + raise ValueError("Streaming mode should have an integer val size.") + + if self.streaming and self.max_samples is not None: + raise ValueError("`max_samples` is incompatible with `streaming`.") + + if self.streaming and self.cache_path: + raise ValueError("`cache_path` is incompatible with `streaming`.") + + def init_for_training(self, seed: int): # support mixing multiple datasets + self.seed = seed + dataset_names = [ds.strip() for ds in self.dataset.split(",")] if self.dataset is not None else [] + try: + with open(os.path.join(self.dataset_dir, "dataset_info.json"), "r") as f: + dataset_info = json.load(f) + except Exception: + if self.dataset is not None: + raise ValueError("Cannot find dataset_info.json in `dataset_dir`.") + dataset_info = None + + prompt_list = self.system_prompt.split("|") if self.system_prompt else [None] + prompt_list = prompt_list * (len(dataset_names) // len(prompt_list)) + assert len(prompt_list) == len(dataset_names), "Number of system prompts should be equal to datasets or 1." + + if self.interleave_probs is not None: + self.interleave_probs = [float(prob.strip()) for prob in self.interleave_probs.split(",")] + + self.dataset_list: List[DatasetAttr] = [] + for i, name in enumerate(dataset_names): + if name not in dataset_info: + 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"]) + 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) + ) + + if "columns" in dataset_info[name]: + dataset_attr.prompt = dataset_info[name]["columns"].get("prompt", None) + dataset_attr.query = dataset_info[name]["columns"].get("query", None) + dataset_attr.response = dataset_info[name]["columns"].get("response", None) + dataset_attr.history = dataset_info[name]["columns"].get("history", None) + dataset_attr.messages = dataset_info[name]["columns"].get("messages", None) + dataset_attr.role = dataset_info[name]["columns"].get("role", None) + dataset_attr.content = dataset_info[name]["columns"].get("content", None) + + dataset_attr.subset = dataset_info[name].get("subset", None) + dataset_attr.ranking = dataset_info[name].get("ranking", False) + dataset_attr.formatting = dataset_info[name].get("formatting", "alpaca") + dataset_attr.system_prompt = prompt_list[i] + self.dataset_list.append(dataset_attr) diff --git a/llm_rl/src/llmtuner/hparams/finetuning_args.py b/llm_rl/src/llmtuner/hparams/finetuning_args.py new file mode 100644 index 00000000..d5ef323d --- /dev/null +++ b/llm_rl/src/llmtuner/hparams/finetuning_args.py @@ -0,0 +1,107 @@ +import json +from typing import Literal, Optional +from dataclasses import asdict, dataclass, field + + +@dataclass +class FinetuningArguments: + r""" + Arguments pertaining to which techniques we are going to fine-tuning with. + """ + stage: Optional[Literal["pt", "sft", "rm", "ppo", "dpo"]] = field( + default="sft", + metadata={"help": "Which stage will be performed in training."} + ) + finetuning_type: Optional[Literal["lora", "freeze", "full", "none"]] = field( + default="lora", + metadata={"help": "Which fine-tuning method to use."} + ) + num_layer_trainable: Optional[int] = field( + default=3, + metadata={"help": "Number of trainable layers for partial-parameter (freeze) fine-tuning."} + ) + name_module_trainable: Optional[Literal["mlp", "self_attn", "self_attention"]] = field( + default="mlp", + metadata={"help": "Name of trainable modules for partial-parameter (freeze) fine-tuning. \ + LLaMA choices: [\"mlp\", \"self_attn\"], \ + BLOOM & Falcon & ChatGLM2 choices: [\"mlp\", \"self_attention\"], \ + Qwen choices: [\"mlp\", \"attn\"], \ + Phi-1.5 choices: [\"mlp\", \"mixer\"], \ + LLaMA-2, Baichuan, InternLM, XVERSE choices: the same as LLaMA."} + ) + lora_rank: Optional[int] = field( + default=8, + metadata={"help": "The intrinsic dimension for LoRA fine-tuning."} + ) + lora_alpha: Optional[float] = field( + default=32.0, + metadata={"help": "The scale factor for LoRA fine-tuning (similar with the learning rate)."} + ) + lora_dropout: Optional[float] = field( + default=0.1, + metadata={"help": "Dropout rate for the LoRA fine-tuning."} + ) + lora_target: Optional[str] = field( + default=None, + metadata={"help": "Name(s) of target modules to apply LoRA. Use commas to separate multiple modules. \ + LLaMA choices: [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"], \ + BLOOM & Falcon & ChatGLM2 choices: [\"query_key_value\", \"self_attention.dense\", \"mlp.dense\"], \ + Baichuan choices: [\"W_pack\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"], \ + Qwen choices: [\"c_attn\", \"attn.c_proj\", \"w1\", \"w2\", \"mlp.c_proj\"], \ + Phi-1.5 choices: [\"Wqkv\", \"out_proj\", \"fc1\", \"fc2\"], \ + LLaMA-2, InternLM, XVERSE choices: the same as LLaMA."} + ) + additional_target: Optional[str] = field( + default=None, + metadata={"help": "Name(s) of modules apart from LoRA layers to be set as trainable and saved in the final checkpoint."} + ) + 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."} + ) + ppo_score_norm: Optional[bool] = field( + default=False, + metadata={"help": "Use score normalization in PPO training."} + ) + ppo_logger: Optional[str] = field( + default=None, + metadata={"help": "Log with either 'wandb' or 'tensorboard' in PPO training."} + ) + ppo_target: Optional[float] = field( + default=6.0, + metadata={"help": "Target KL value for adaptive KL control in PPO training."} + ) + dpo_beta: Optional[float] = field( + default=0.1, + metadata={"help": "The beta parameter for the DPO loss."} + ) + upcast_layernorm: Optional[bool] = field( + default=False, + metadata={"help": "Whether to upcast the layernorm weights in fp32."} + ) + neft_alpha: Optional[float] = field( + default=0, + metadata={"help": "The alpha parameter to control the noise magnitude in NEFTune."} + ) + + def __post_init__(self): + if isinstance(self.lora_target, str): # support custom target modules/layers of LoRA + self.lora_target = [target.strip() for target in self.lora_target.split(",")] + + if isinstance(self.additional_target, str): + self.additional_target = [target.strip() for target in self.additional_target.split(",")] + + assert self.finetuning_type in ["lora", "freeze", "full", "none"], "Invalid fine-tuning method." + + def save_to_json(self, json_path: str): + r"""Saves the content of this instance in JSON format inside `json_path`.""" + json_string = json.dumps(asdict(self), indent=2, sort_keys=True) + "\n" + with open(json_path, "w", encoding="utf-8") as f: + f.write(json_string) + + @classmethod + def load_from_json(cls, json_path: str): + r"""Creates an instance from the content of `json_path`.""" + with open(json_path, "r", encoding="utf-8") as f: + text = f.read() + return cls(**json.loads(text)) diff --git a/llm_rl/src/llmtuner/hparams/general_args.py b/llm_rl/src/llmtuner/hparams/general_args.py new file mode 100644 index 00000000..c0c1a0de --- /dev/null +++ b/llm_rl/src/llmtuner/hparams/general_args.py @@ -0,0 +1,13 @@ +from typing import Literal, Optional +from dataclasses import dataclass, field + + +@dataclass +class GeneralArguments: + r""" + Arguments pertaining to which stage we are going to perform. + """ + stage: Optional[Literal["pt", "sft", "rm", "ppo", "dpo"]] = field( + default="sft", + metadata={"help": "Which stage will be performed in training."} + ) diff --git a/llm_rl/src/llmtuner/hparams/generating_args.py b/llm_rl/src/llmtuner/hparams/generating_args.py new file mode 100644 index 00000000..c04a5c36 --- /dev/null +++ b/llm_rl/src/llmtuner/hparams/generating_args.py @@ -0,0 +1,53 @@ +from typing import Any, Dict, Optional +from dataclasses import asdict, dataclass, field + + +@dataclass +class GeneratingArguments: + r""" + Arguments pertaining to specify the decoding parameters. + """ + do_sample: Optional[bool] = field( + default=True, + metadata={"help": "Whether or not to use sampling, use greedy decoding otherwise."} + ) + temperature: Optional[float] = field( + default=0.95, + metadata={"help": "The value used to modulate the next token probabilities."} + ) + top_p: Optional[float] = field( + default=0.7, + metadata={"help": "The smallest set of most probable tokens with probabilities that add up to top_p or higher are kept."} + ) + top_k: Optional[int] = field( + default=50, + metadata={"help": "The number of highest probability vocabulary tokens to keep for top-k filtering."} + ) + num_beams: Optional[int] = field( + default=1, + metadata={"help": "Number of beams for beam search. 1 means no beam search."} + ) + max_length: Optional[int] = field( + default=512, + metadata={"help": "The maximum length the generated tokens can have. It can be overridden by max_new_tokens."} + ) + max_new_tokens: Optional[int] = field( + default=512, + metadata={"help": "The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt."} + ) + repetition_penalty: Optional[float] = field( + default=1.0, + metadata={"help": "The parameter for repetition penalty. 1.0 means no penalty."} + ) + length_penalty: Optional[float] = field( + default=1.0, + metadata={"help": "Exponential penalty to the length that is used with beam-based generation."} + ) + + def to_dict(self) -> Dict[str, Any]: + args = asdict(self) + if args.get("max_new_tokens", -1) > 0: + args.pop("max_length", None) + else: + args.pop("max_new_tokens", None) + return args diff --git a/llm_rl/src/llmtuner/hparams/model_args.py b/llm_rl/src/llmtuner/hparams/model_args.py new file mode 100644 index 00000000..7c25fad1 --- /dev/null +++ b/llm_rl/src/llmtuner/hparams/model_args.py @@ -0,0 +1,93 @@ +from typing import Literal, Optional +from dataclasses import dataclass, field + + +@dataclass +class ModelArguments: + r""" + Arguments pertaining to which model/config/tokenizer we are going to fine-tune. + """ + model_name_or_path: str = field( + metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."} + ) + cache_dir: Optional[str] = field( + default=None, + metadata={"help": "Where to store the pretrained models downloaded from huggingface.co."} + ) + use_fast_tokenizer: Optional[bool] = field( + default=True, + metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."} + ) + split_special_tokens: Optional[bool] = field( + default=False, + metadata={"help": "Whether or not the special tokens should be split during the tokenization process."} + ) + use_auth_token: Optional[bool] = field( + default=False, + metadata={"help": "Will use the token generated when running `huggingface-cli login`."} + ) + model_revision: Optional[str] = field( + default="main", + metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} + ) + quantization_bit: Optional[int] = field( + default=None, + metadata={"help": "The number of bits to quantize the model."} + ) + quantization_type: Optional[Literal["fp4", "nf4"]] = field( + default="nf4", + metadata={"help": "Quantization data type to use in int4 training."} + ) + double_quantization: Optional[bool] = field( + default=True, + metadata={"help": "Whether to use double quantization in int4 training or not."} + ) + rope_scaling: Optional[Literal["linear", "dynamic"]] = field( + default=None, + metadata={"help": "Adopt scaled rotary positional embeddings."} + ) + checkpoint_dir: Optional[str] = field( + default=None, + metadata={"help": "Path to the directory(s) containing the delta model checkpoints as well as the configurations."} + ) + flash_attn: Optional[bool] = field( + default=False, + metadata={"help": "Enable FlashAttention-2 for faster training."} + ) + shift_attn: Optional[bool] = field( + default=False, + metadata={"help": "Enable shift short attention (S^2-Attn) proposed by LongLoRA."} + ) + reward_model: Optional[str] = field( + default=None, + metadata={"help": "Path to the directory containing the checkpoints of the reward model."} + ) + plot_loss: Optional[bool] = field( + default=False, + metadata={"help": "Whether to plot the training loss after fine-tuning or not."} + ) + hf_auth_token: Optional[str] = field( + default=None, + metadata={"help": "Auth token to log in with Hugging Face Hub."} + ) + export_dir: Optional[str] = field( + default=None, + metadata={"help": "Path to the directory to save the exported model."} + ) + + def __post_init__(self): + self.compute_dtype = None + self.model_max_length = None + + if self.split_special_tokens and self.use_fast_tokenizer: + raise ValueError("`split_special_tokens` is only supported for slow tokenizers.") + + if self.checkpoint_dir is not None: # support merging multiple lora weights + self.checkpoint_dir = [cd.strip() for cd in self.checkpoint_dir.split(",")] + + if self.quantization_bit is not None: + assert self.quantization_bit in [4, 8], "We only accept 4-bit or 8-bit quantization." + + if self.use_auth_token == True and self.hf_auth_token is not None: + from huggingface_hub.hf_api import HfFolder # lazy load + HfFolder.save_token(self.hf_auth_token) diff --git a/llm_rl/src/llmtuner/tuner/__init__.py b/llm_rl/src/llmtuner/tuner/__init__.py new file mode 100644 index 00000000..4d5a83e4 --- /dev/null +++ b/llm_rl/src/llmtuner/tuner/__init__.py @@ -0,0 +1 @@ +from llmtuner.tuner.tune import export_model, run_exp diff --git a/llm_rl/src/llmtuner/tuner/core/__init__.py b/llm_rl/src/llmtuner/tuner/core/__init__.py new file mode 100644 index 00000000..bd1c5cf0 --- /dev/null +++ b/llm_rl/src/llmtuner/tuner/core/__init__.py @@ -0,0 +1,2 @@ +from llmtuner.tuner.core.parser import get_train_args, get_infer_args +from llmtuner.tuner.core.loader import load_model_and_tokenizer diff --git a/llm_rl/src/llmtuner/tuner/core/adapter.py b/llm_rl/src/llmtuner/tuner/core/adapter.py new file mode 100644 index 00000000..4fcc6e62 --- /dev/null +++ b/llm_rl/src/llmtuner/tuner/core/adapter.py @@ -0,0 +1,101 @@ +import torch +from typing import TYPE_CHECKING + +from peft import ( + PeftModel, + TaskType, + LoraConfig, + get_peft_model +) + +from llmtuner.extras.logging import get_logger +from llmtuner.tuner.core.utils import find_all_linear_modules + +if TYPE_CHECKING: + from transformers.modeling_utils import PreTrainedModel + from llmtuner.hparams import ModelArguments, FinetuningArguments + + +logger = get_logger(__name__) + + +def init_adapter( + model: "PreTrainedModel", + model_args: "ModelArguments", + finetuning_args: "FinetuningArguments", + is_trainable: bool, + is_mergeable: bool +) -> "PreTrainedModel": + r""" + Initializes the adapters. + + Support full-parameter, freeze and LoRA training. + + Note that the trainable parameters must be cast to float32. + """ + + if finetuning_args.finetuning_type == "none" and is_trainable: + raise ValueError("You cannot use finetuning_type=none while training.") + + if finetuning_args.finetuning_type == "full" and is_trainable: + logger.info("Fine-tuning method: Full") + model = model.float() + + if finetuning_args.finetuning_type == "freeze": + logger.info("Fine-tuning method: Freeze") + num_layers = getattr(model.config, "num_layers") + if finetuning_args.num_layer_trainable > 0: # fine-tuning the last n layers if num_layer_trainable > 0 + trainable_layer_ids = [num_layers - k - 1 for k in range(finetuning_args.num_layer_trainable)] + else: # fine-tuning the first n layers if num_layer_trainable < 0 + trainable_layer_ids = [k for k in range(-finetuning_args.num_layer_trainable)] + + trainable_layers = ["{:d}.{}".format(idx, finetuning_args.name_module_trainable) for idx in trainable_layer_ids] + for name, param in model.named_parameters(): + if not any(trainable_layer in name for trainable_layer in trainable_layers): + param.requires_grad_(False) + else: + param.data = param.data.to(torch.float32) + + if finetuning_args.finetuning_type == "lora": + logger.info("Fine-tuning method: LoRA") + latest_checkpoint = None + + if model_args.checkpoint_dir is not None: + if (is_trainable and finetuning_args.resume_lora_training) or (not is_mergeable): # continually fine-tuning + checkpoints_to_merge, latest_checkpoint = model_args.checkpoint_dir[:-1], model_args.checkpoint_dir[-1] + else: + checkpoints_to_merge = model_args.checkpoint_dir + + for checkpoint in checkpoints_to_merge: + model = PeftModel.from_pretrained(model, checkpoint) + model = model.merge_and_unload() + + if len(checkpoints_to_merge) > 0: + logger.info("Merged {} model checkpoint(s).".format(len(checkpoints_to_merge))) + + if latest_checkpoint is not None: # resume lora training or quantized inference + model = PeftModel.from_pretrained(model, latest_checkpoint, is_trainable=is_trainable) + + if is_trainable and latest_checkpoint is None: # create new lora weights while training + if len(finetuning_args.lora_target) == 1 and finetuning_args.lora_target[0] == "all": + target_modules = find_all_linear_modules(model, model_args.quantization_bit) + else: + target_modules = finetuning_args.lora_target + + lora_config = LoraConfig( + task_type=TaskType.CAUSAL_LM, + inference_mode=False, + r=finetuning_args.lora_rank, + lora_alpha=finetuning_args.lora_alpha, + lora_dropout=finetuning_args.lora_dropout, + target_modules=target_modules, + modules_to_save=finetuning_args.additional_target + ) + model = get_peft_model(model, lora_config) + if id(model.peft_config) != id(model.base_model.peft_config): # https://github.com/huggingface/peft/issues/923 + model.base_model.peft_config = model.peft_config + + if model_args.checkpoint_dir is not None: + logger.info("Loaded fine-tuned model from checkpoint(s): {}".format(",".join(model_args.checkpoint_dir))) + + return model diff --git a/llm_rl/src/llmtuner/tuner/core/loader.py b/llm_rl/src/llmtuner/tuner/core/loader.py new file mode 100644 index 00000000..e77c4945 --- /dev/null +++ b/llm_rl/src/llmtuner/tuner/core/loader.py @@ -0,0 +1,244 @@ +import os +import math +import torch +from types import MethodType +from typing import TYPE_CHECKING, Literal, Optional, Tuple + +from transformers import ( + AutoConfig, + AutoModelForCausalLM, + AutoTokenizer, + BitsAndBytesConfig, + PretrainedConfig, + PreTrainedModel, + PreTrainedTokenizerBase +) +from transformers.models.llama import modeling_llama as LlamaModule +from transformers.utils.versions import require_version +from trl import AutoModelForCausalLMWithValueHead + +try: + from transformers.integrations import is_deepspeed_zero3_enabled +except ImportError: # https://github.com/huggingface/transformers/releases/tag/v4.33.1 + from transformers.deepspeed import is_deepspeed_zero3_enabled + +from llmtuner.extras.logging import reset_logging, get_logger +from llmtuner.extras.misc import count_parameters, infer_optim_dtype +from llmtuner.extras.patches import llama_patch as LlamaPatches +from llmtuner.extras.save_and_load import load_valuehead_params +from llmtuner.hparams import FinetuningArguments +from llmtuner.tuner.core.adapter import init_adapter +from llmtuner.tuner.core.utils import prepare_model_for_training + +if TYPE_CHECKING: + from transformers import PreTrainedTokenizer + from llmtuner.hparams import ModelArguments + + +logger = get_logger(__name__) + + +require_version("transformers>=4.31.0,<4.35.0", "To fix: pip install \"transformers>=4.31.0,<4.35.0\"") +require_version("datasets>=2.12.0", "To fix: pip install datasets>=2.12.0") +require_version("accelerate>=0.21.0", "To fix: pip install accelerate>=0.21.0") +require_version("peft>=0.4.0", "To fix: pip install peft>=0.4.0") +require_version("trl>=0.7.2", "To fix: pip install trl>=0.7.2") + + +def load_model_and_tokenizer( + model_args: "ModelArguments", + finetuning_args: "FinetuningArguments", + is_trainable: Optional[bool] = False, + stage: Optional[Literal["pt", "sft", "rm", "ppo"]] = "sft" +) -> Tuple[PreTrainedModel, "PreTrainedTokenizer"]: + r""" + Loads pretrained model and tokenizer. + + Support both training and inference. + """ + if (not is_trainable) and model_args.checkpoint_dir is None: + logger.warning("Checkpoint is not found at evaluation, load the original model.") + finetuning_args = FinetuningArguments(finetuning_type="none") + + config_kwargs = { + "trust_remote_code": True, + "cache_dir": model_args.cache_dir, + "revision": model_args.model_revision, + "use_auth_token": True if model_args.use_auth_token else None, + } + + tokenizer = AutoTokenizer.from_pretrained( + model_args.model_name_or_path, + use_fast=model_args.use_fast_tokenizer, + split_special_tokens=model_args.split_special_tokens, + padding_side="right", # training with left-padded tensors in fp16 precision may cause overflow + **config_kwargs + ) + + if finetuning_args.finetuning_type != "lora" and model_args.checkpoint_dir is not None: + model_to_load = model_args.checkpoint_dir[0] + else: + model_to_load = model_args.model_name_or_path + + config = AutoConfig.from_pretrained(model_to_load, **config_kwargs) + + # Fix tokenizer (for ChatGLM2 and ChatGLM3) + if getattr(config, "model_type", None) == "chatglm": + tokenizer._pad = MethodType(PreTrainedTokenizerBase._pad, tokenizer) + + # Set model dtype + if model_args.compute_dtype is not None: # for training + setattr(config, "torch_dtype", model_args.compute_dtype) + else: # for evaluation, priority: bf16 > fp16 > fp32 + model_args.compute_dtype = infer_optim_dtype(model_dtype=getattr(config, "torch_dtype", None)) + + # Fix config (for Qwen) + if getattr(config, "model_type", None) == "qwen": + for dtype_name, dtype in [("fp16", torch.float16), ("bf16", torch.bfloat16), ("fp32", torch.float32)]: + setattr(config, dtype_name, getattr(config, "torch_dtype", None) == dtype) + + # Set RoPE scaling + if model_args.rope_scaling is not None: + if hasattr(config, "use_dynamic_ntk"): # for Qwen models + if is_trainable: + logger.warning("Qwen model does not support RoPE scaling in training.") + else: + setattr(config, "use_dynamic_ntk", True) + setattr(config, "use_logn_attn", True) + logger.info("Using dynamic NTK scaling.") + + elif hasattr(config, "rope_scaling"): # for LLaMA and Falcon models + if is_trainable: + if model_args.rope_scaling == "dynamic": + logger.warning( + "Dynamic NTK may not work well with fine-tuning. " + "See: https://github.com/huggingface/transformers/pull/24653" + ) + + current_max_length = getattr(config, "max_position_embeddings", None) + if current_max_length and model_args.model_max_length > current_max_length: + scaling_factor = float(math.ceil(model_args.model_max_length / current_max_length)) + else: + logger.warning("Input length is smaller than max length. Consider increase input length.") + scaling_factor = 1.0 + else: + scaling_factor = 2.0 + + setattr(config, "rope_scaling", {"type": model_args.rope_scaling, "factor": scaling_factor}) + logger.info("Using {} scaling strategy and setting scaling factor to {}".format( + model_args.rope_scaling, scaling_factor + )) + + else: + logger.warning("Current model does not support RoPE scaling.") + + # Set FlashAttention-2 + if model_args.flash_attn: + if getattr(config, "model_type", None) == "llama": + LlamaModule.LlamaAttention = LlamaPatches.LlamaFlashAttention2 + LlamaModule.LlamaModel._prepare_decoder_attention_mask = LlamaPatches._prepare_decoder_attention_mask + logger.info("Using FlashAttention-2 for faster training and inference.") + elif getattr(config, "model_type", None) == "qwen": + logger.info("Qwen models automatically enable FlashAttention if installed.") + else: + logger.warning("Current model does not support FlashAttention-2.") + elif is_trainable and model_args.shift_attn and getattr(config, "model_type", None) == "llama": + LlamaModule.LlamaAttention = LlamaPatches.LlamaShiftShortAttention + logger.warning("Using `--flash_attn` for faster training in large context length.") + + # Set shift short attention (S^2-Attn) + if is_trainable and model_args.shift_attn: + if getattr(config, "model_type", None) == "llama": + setattr(config, "group_size_ratio", 0.25) + logger.info("Using shift short attention with group_size_ratio=1/4.") + else: + logger.warning("Current model does not support shift short attention.") + + # Quantization configurations (using bitsandbytes library). + is_mergeable = True + if model_args.quantization_bit is not None: + if is_deepspeed_zero3_enabled(): + raise ValueError("DeepSpeed ZeRO-3 is incompatible with quantization.") + + if model_args.quantization_bit == 8: + require_version("bitsandbytes>=0.37.0", "To fix: pip install bitsandbytes>=0.37.0") + config_kwargs["load_in_8bit"] = True + config_kwargs["quantization_config"] = BitsAndBytesConfig(load_in_8bit=True) + + elif model_args.quantization_bit == 4: + require_version("bitsandbytes>=0.39.0", "To fix: pip install bitsandbytes>=0.39.0") + config_kwargs["load_in_4bit"] = True + config_kwargs["quantization_config"] = BitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_compute_dtype=model_args.compute_dtype, + bnb_4bit_use_double_quant=model_args.double_quantization, + bnb_4bit_quant_type=model_args.quantization_type + ) + + is_mergeable = False + config_kwargs["device_map"] = {"": int(os.environ.get("LOCAL_RANK", "0"))} if is_trainable else "auto" + logger.info("Quantizing model to {} bit.".format(model_args.quantization_bit)) + + # Load and prepare pre-trained models (without valuehead). + model = AutoModelForCausalLM.from_pretrained( + model_to_load, + config=config, + torch_dtype=model_args.compute_dtype, + low_cpu_mem_usage=(not is_deepspeed_zero3_enabled()), + **config_kwargs + ) + + # Disable custom generate method (for Qwen and Baichuan2) + if isinstance(model, PreTrainedModel) and "GenerationMixin" not in str(model.generate.__func__): + model.generate = MethodType(PreTrainedModel.generate, model) + + # Fix LM head (for ChatGLM2 and ChatGLM3) + if getattr(config, "model_type", None) == "chatglm": + setattr(model, "lm_head", model.transformer.output_layer) + setattr(model, "_keys_to_ignore_on_save", ["lm_head.weight"]) + + # Register auto class to save the custom code files. + if isinstance(config, PretrainedConfig) and "AutoConfig" in getattr(config, "auto_map", {}): + config.__class__.register_for_auto_class() + if isinstance(model, PreTrainedModel) and "AutoModelForCausalLM" in getattr(config, "auto_map", {}): + model.__class__.register_for_auto_class() + if isinstance(tokenizer, PreTrainedTokenizerBase) and "AutoTokenizer" in tokenizer.init_kwargs.get("auto_map", {}): + tokenizer.__class__.register_for_auto_class() + + # Initialize adapters + model = prepare_model_for_training(model=model, finetuning_args=finetuning_args) if is_trainable else model + model = init_adapter(model, model_args, finetuning_args, is_trainable, is_mergeable) + model = model.train() if is_trainable else model.eval() + + # Prepare model with valuehead for RLHF + if stage == "rm" or stage == "ppo": + model: "AutoModelForCausalLMWithValueHead" = AutoModelForCausalLMWithValueHead.from_pretrained(model) + reset_logging() + if stage == "rm" and model_args.checkpoint_dir is not None: # load valuehead weights to evaluate reward model + logger.warning("Only the last checkpoint containing valuehead will be loaded.") + if load_valuehead_params(model, model_args.checkpoint_dir[-1]): + model.v_head.load_state_dict({ + "summary.weight": getattr(model, "reward_head_weight"), + "summary.bias": getattr(model, "reward_head_bias") + }) + + if stage == "ppo": # load reward model + logger.info("Load reward model from {}".format(model_args.reward_model)) + if getattr(model, "is_peft_model", False): + model.pretrained_model.load_adapter(model_args.reward_model, "reward") + assert load_valuehead_params(model, model_args.reward_model), "Reward model is not correctly loaded." + + # Prepare model for inference + if not is_trainable: + model.requires_grad_(False) # fix all model params + model = model.to(model_args.compute_dtype) if model_args.quantization_bit is None else model + + trainable_params, all_param = count_parameters(model) + logger.info("trainable params: {:d} || all params: {:d} || trainable%: {:.4f}".format( + trainable_params, all_param, 100 * trainable_params / all_param + )) + + if not is_trainable: + logger.info("This IS expected that the trainable params is 0 if you are using model for inference only.") + + return model, tokenizer diff --git a/llm_rl/src/llmtuner/tuner/core/parser.py b/llm_rl/src/llmtuner/tuner/core/parser.py new file mode 100644 index 00000000..603fc1bc --- /dev/null +++ b/llm_rl/src/llmtuner/tuner/core/parser.py @@ -0,0 +1,226 @@ +import os +import sys +import torch +import datasets +import transformers +from typing import Any, Dict, Optional, Tuple +from transformers import HfArgumentParser, Seq2SeqTrainingArguments +from transformers.trainer_utils import get_last_checkpoint + +from llmtuner.extras.logging import get_logger +from llmtuner.hparams import ( + ModelArguments, + DataArguments, + FinetuningArguments, + GeneratingArguments +) + + +logger = get_logger(__name__) + + +def _parse_args(parser: HfArgumentParser, args: Optional[Dict[str, Any]] = None) -> Tuple[Any]: + if args is not None: + return parser.parse_dict(args) + elif len(sys.argv) == 2 and sys.argv[1].endswith(".yaml"): + return parser.parse_yaml_file(os.path.abspath(sys.argv[1])) + elif len(sys.argv) == 2 and sys.argv[1].endswith(".json"): + return parser.parse_json_file(os.path.abspath(sys.argv[1])) + else: + return parser.parse_args_into_dataclasses() + + +def parse_train_args( + args: Optional[Dict[str, Any]] = None +) -> Tuple[ + ModelArguments, + DataArguments, + Seq2SeqTrainingArguments, + FinetuningArguments, + GeneratingArguments +]: + parser = HfArgumentParser(( + ModelArguments, + DataArguments, + Seq2SeqTrainingArguments, + FinetuningArguments, + GeneratingArguments + )) + return _parse_args(parser, args) + + +def parse_infer_args( + args: Optional[Dict[str, Any]] = None +) -> Tuple[ + ModelArguments, + DataArguments, + FinetuningArguments, + GeneratingArguments +]: + parser = HfArgumentParser(( + ModelArguments, + DataArguments, + FinetuningArguments, + GeneratingArguments + )) + return _parse_args(parser, args) + + +def get_train_args( + args: Optional[Dict[str, Any]] = None +) -> Tuple[ + ModelArguments, + DataArguments, + Seq2SeqTrainingArguments, + FinetuningArguments, + GeneratingArguments +]: + model_args, data_args, training_args, finetuning_args, generating_args = parse_train_args(args) + + # Setup logging + if training_args.should_log: + # The default of training_args.log_level is passive, so we set log level at info here to have that default. + transformers.utils.logging.set_verbosity_info() + + log_level = training_args.get_process_log_level() + datasets.utils.logging.set_verbosity(log_level) + transformers.utils.logging.set_verbosity(log_level) + transformers.utils.logging.enable_default_handler() + transformers.utils.logging.enable_explicit_format() + + # Check arguments + data_args.init_for_training(training_args.seed) + + if finetuning_args.stage != "pt" and data_args.template is None: + raise ValueError("Please specify which `template` to use.") + + if finetuning_args.stage != "sft" and training_args.predict_with_generate: + raise ValueError("`predict_with_generate` cannot be set as True except SFT.") + + if finetuning_args.stage == "sft" and training_args.do_predict and not training_args.predict_with_generate: + raise ValueError("Please enable `predict_with_generate` to save model predictions.") + + if finetuning_args.stage in ["rm", "ppo"] and finetuning_args.finetuning_type != "lora": + raise ValueError("RM and PPO stages can only be performed with the LoRA method.") + + if finetuning_args.stage in ["rm", "ppo"] and training_args.resume_from_checkpoint is not None: + raise ValueError("RM and PPO stages do not support `resume_from_checkpoint`.") + + if finetuning_args.stage == "ppo" and not training_args.do_train: + raise ValueError("PPO training does not support evaluation.") + + if finetuning_args.stage in ["rm", "dpo"]: + for dataset_attr in data_args.dataset_list: + if not dataset_attr.ranking: + raise ValueError("Please use ranked datasets for reward modeling or DPO training.") + + if finetuning_args.stage == "ppo" and model_args.reward_model is None: + raise ValueError("Reward model is necessary for PPO training.") + + if finetuning_args.stage == "ppo" and model_args.shift_attn: + raise ValueError("PPO training is incompatible with S^2-Attn.") + + if training_args.max_steps == -1 and data_args.streaming: + raise ValueError("Please specify `max_steps` in streaming mode.") + + if training_args.do_train and training_args.predict_with_generate: + raise ValueError("`predict_with_generate` cannot be set as True while training.") + + if training_args.do_train and finetuning_args.finetuning_type == "lora" and finetuning_args.lora_target is None: + raise ValueError("Please specify `lora_target` in LoRA training.") + + if model_args.quantization_bit is not None and finetuning_args.finetuning_type != "lora": + raise ValueError("Quantization is only compatible with the LoRA method.") + + if model_args.checkpoint_dir is not None: + if finetuning_args.finetuning_type != "lora" and len(model_args.checkpoint_dir) != 1: + raise ValueError("Only LoRA tuning accepts multiple checkpoints.") + + if model_args.quantization_bit is not None: + if len(model_args.checkpoint_dir) != 1: + raise ValueError("Quantized model only accepts a single checkpoint. Merge them first.") + + if not finetuning_args.resume_lora_training: + raise ValueError("Quantized model cannot create new LoRA weight. Merge them first.") + + if training_args.do_train and model_args.quantization_bit is not None and (not finetuning_args.upcast_layernorm): + logger.warning("We recommend enable `upcast_layernorm` in quantized training.") + + if training_args.do_train and (not training_args.fp16) and (not training_args.bf16): + logger.warning("We recommend enable mixed precision training.") + + if (not training_args.do_train) and model_args.quantization_bit is not None: + logger.warning("Evaluating model in 4/8-bit mode may cause lower scores.") + + # postprocess training_args + if ( + training_args.local_rank != -1 + and training_args.ddp_find_unused_parameters is None + and finetuning_args.finetuning_type == "lora" + ): + logger.warning("`ddp_find_unused_parameters` needs to be set as False for LoRA in DDP training.") + training_args_dict = training_args.to_dict() + training_args_dict.update(dict(ddp_find_unused_parameters=False)) + training_args = Seq2SeqTrainingArguments(**training_args_dict) + + if ( + training_args.resume_from_checkpoint is None + and training_args.do_train + and os.path.isdir(training_args.output_dir) + and not training_args.overwrite_output_dir + ): + last_checkpoint = get_last_checkpoint(training_args.output_dir) + if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0: + raise ValueError("Output directory already exists and is not empty. Please set `overwrite_output_dir`.") + + if last_checkpoint is not None: + training_args_dict = training_args.to_dict() + training_args_dict.update(dict(resume_from_checkpoint=last_checkpoint)) + training_args = Seq2SeqTrainingArguments(**training_args_dict) + logger.info( + "Resuming from checkpoint. Change `output_dir` or use `overwrite_output_dir` to avoid." + ) + + # postprocess model_args + model_args.compute_dtype = ( + torch.bfloat16 if training_args.bf16 else (torch.float16 if training_args.fp16 else None) + ) + model_args.model_max_length = data_args.cutoff_len + + # Log on each process the small summary: + logger.info("Process rank: {}, device: {}, n_gpu: {}\n distributed training: {}, compute dtype: {}".format( + training_args.local_rank, training_args.device, training_args.n_gpu, + bool(training_args.local_rank != -1), str(model_args.compute_dtype) + )) + logger.info(f"Training/evaluation parameters {training_args}") + + # Set seed before initializing model. + transformers.set_seed(training_args.seed) + + return model_args, data_args, training_args, finetuning_args, generating_args + + +def get_infer_args( + args: Optional[Dict[str, Any]] = None +) -> Tuple[ + ModelArguments, + DataArguments, + FinetuningArguments, + GeneratingArguments +]: + model_args, data_args, finetuning_args, generating_args = parse_infer_args(args) + + if data_args.template is None: + raise ValueError("Please specify which `template` to use.") + + if model_args.quantization_bit is not None and finetuning_args.finetuning_type != "lora": + raise ValueError("Quantization is only compatible with the LoRA method.") + + if model_args.checkpoint_dir is not None: + if finetuning_args.finetuning_type != "lora" and len(model_args.checkpoint_dir) != 1: + raise ValueError("Only LoRA tuning accepts multiple checkpoints.") + + if model_args.quantization_bit is not None and len(model_args.checkpoint_dir) != 1: + raise ValueError("Quantized model only accepts a single checkpoint. Merge them first.") + + return model_args, data_args, finetuning_args, generating_args diff --git a/llm_rl/src/llmtuner/tuner/core/utils.py b/llm_rl/src/llmtuner/tuner/core/utils.py new file mode 100644 index 00000000..d9a1aac9 --- /dev/null +++ b/llm_rl/src/llmtuner/tuner/core/utils.py @@ -0,0 +1,94 @@ +import torch +from types import MethodType +from typing import TYPE_CHECKING, List, Optional + +from llmtuner.extras.constants import LAYERNORM_NAMES +from llmtuner.extras.logging import get_logger + +if TYPE_CHECKING: + from transformers.modeling_utils import PreTrainedModel + from llmtuner.hparams import FinetuningArguments + + +logger = get_logger(__name__) + + +def find_all_linear_modules( + model: "PreTrainedModel", + quantization_bit: Optional[int] = None, + output_layer_name: Optional[str] = "lm_head" +) -> List[str]: + if quantization_bit is not None: + import bitsandbytes as bnb + linear_cls = bnb.nn.Linear4bit if quantization_bit == 4 else bnb.nn.Linear8bitLt + else: + linear_cls = torch.nn.Linear + + module_names = set() + for name, module in model.named_modules(): + if output_layer_name not in name and isinstance(module, linear_cls): + module_names.add(name.split(".")[-1]) + + if output_layer_name in module_names: + module_names.pop(output_layer_name) + + return list(module_names) + + +def prepare_model_for_training( + model: "PreTrainedModel", + finetuning_args: "FinetuningArguments", + output_layer_name: Optional[str] = "lm_head", + use_gradient_checkpointing: Optional[bool] = True, + layernorm_names: Optional[List[str]] = LAYERNORM_NAMES +) -> "PreTrainedModel": + r""" + Includes: + (1) cast the layernorm in fp32 + (2) make output embedding layer require grads + (3) upcast the lm_head to fp32 + Inspired by: https://github.com/huggingface/peft/blob/v0.2.0/src/peft/utils/other.py#L33 + """ + if finetuning_args.upcast_layernorm: + for name, param in model.named_parameters(): + if param.ndim == 1 and any(ln_name in name for ln_name in layernorm_names): + param.data = param.data.to(torch.float32) + logger.info("Upcasting weights in layernorm in float32.") + + if finetuning_args.neft_alpha > 1e-6: + input_embed = model.get_input_embeddings() + if isinstance(input_embed, torch.nn.Embedding): + def noisy_forward(self: torch.nn.Embedding, x: torch.Tensor) -> torch.Tensor: + embeddings = input_embed.__class__.forward(self, x) + if self.training: + dims = self.num_embeddings * self.embedding_dim + mag_norm = finetuning_args.neft_alpha / (dims ** 0.5) + embeddings += torch.zeros_like(embeddings).uniform_(-mag_norm, mag_norm) + return embeddings + + input_embed.forward = MethodType(noisy_forward, input_embed) + logger.info("Using noisy embedding with alpha={:.2f}".format(finetuning_args.neft_alpha)) + else: + logger.warning("Input embeddings are not normal nn.Embedding, cannot transform into noisy embedding.") + + if use_gradient_checkpointing: + if hasattr(model, "enable_input_require_grads"): + model.enable_input_require_grads() + else: + def make_inputs_require_grad(module: torch.nn.Module, input: torch.Tensor, output: torch.Tensor): + output.requires_grad_(True) + model.get_input_embeddings().register_forward_hook(make_inputs_require_grad) + + model.gradient_checkpointing_enable() + model.config.use_cache = False # turn off when gradient checkpointing is enabled + logger.info("Gradient checkpointing enabled.") + + if finetuning_args.finetuning_type != "full" and hasattr(model, output_layer_name): + output_layer = getattr(model, output_layer_name) + if isinstance(output_layer, torch.nn.Linear): + def forward_in_fp32(self, x: torch.Tensor) -> torch.Tensor: + return output_layer.__class__.forward(self, x.to(output_layer.weight.dtype)).to(torch.float32) + + output_layer.forward = MethodType(forward_in_fp32, output_layer) + + return model diff --git a/llm_rl/src/llmtuner/tuner/dpo/__init__.py b/llm_rl/src/llmtuner/tuner/dpo/__init__.py new file mode 100644 index 00000000..f2b5cfb5 --- /dev/null +++ b/llm_rl/src/llmtuner/tuner/dpo/__init__.py @@ -0,0 +1 @@ +from llmtuner.tuner.dpo.workflow import run_dpo diff --git a/llm_rl/src/llmtuner/tuner/dpo/collator.py b/llm_rl/src/llmtuner/tuner/dpo/collator.py new file mode 100644 index 00000000..5c862b4f --- /dev/null +++ b/llm_rl/src/llmtuner/tuner/dpo/collator.py @@ -0,0 +1,51 @@ +import torch +from dataclasses import dataclass +from typing import Any, Dict, List, Sequence, Tuple +from transformers import DataCollatorForSeq2Seq + + +@dataclass +class DPODataCollatorWithPadding(DataCollatorForSeq2Seq): + r""" + Data collator for pairwise data. + """ + + def _pad_labels(self, batch: torch.Tensor, positions: List[Tuple[int, int]]) -> torch.Tensor: + padded_labels = [] + for feature, (prompt_len, answer_len) in zip(batch, positions): + if self.tokenizer.padding_side == "left": + start, end = feature.size(0) - answer_len, feature.size(0) + else: + start, end = prompt_len, prompt_len + answer_len + padded_tensor = self.label_pad_token_id * torch.ones_like(feature) + padded_tensor[start:end] = feature[start:end] + padded_labels.append(padded_tensor) + return torch.stack(padded_labels, dim=0).contiguous() # in contiguous memory + + def __call__(self, features: Sequence[Dict[str, Any]]) -> Dict[str, torch.Tensor]: + r""" + Pads batched data to the longest sequence in the batch. + + We generate 2 * n examples where the first n examples represent chosen examples and + the last n examples represent rejected examples. + """ + concatenated_features = [] + label_positions = [] + for key in ("chosen_ids", "rejected_ids"): + for feature in features: + prompt_len, answer_len = len(feature["prompt_ids"]), len(feature[key]) + concatenated_features.append({ + "input_ids": feature["prompt_ids"] + feature[key], + "attention_mask": [1] * (prompt_len + answer_len) + }) + label_positions.append((prompt_len, answer_len)) + + batch = self.tokenizer.pad( + concatenated_features, + padding=self.padding, + max_length=self.max_length, + pad_to_multiple_of=self.pad_to_multiple_of, + return_tensors=self.return_tensors, + ) + batch["labels"] = self._pad_labels(batch["input_ids"], label_positions) + return batch diff --git a/llm_rl/src/llmtuner/tuner/dpo/trainer.py b/llm_rl/src/llmtuner/tuner/dpo/trainer.py new file mode 100644 index 00000000..8a9f8dd6 --- /dev/null +++ b/llm_rl/src/llmtuner/tuner/dpo/trainer.py @@ -0,0 +1,104 @@ +import torch +import deepspeed # type: ignore +from copy import deepcopy +from collections import defaultdict +from typing import TYPE_CHECKING, Dict, Literal, Optional, Tuple, Union +from transformers import BatchEncoding, Trainer +from trl import DPOTrainer +from trl.trainer.utils import disable_dropout_in_model + +from llmtuner.extras.constants import IGNORE_INDEX + +if TYPE_CHECKING: + from transformers import PreTrainedModel + from trl import PreTrainedModelWrapper + + +class CustomDPOTrainer(DPOTrainer): + + def __init__( + self, + beta: float, + model: Union["PreTrainedModel", torch.nn.Module], + ref_model: Optional[Union["PreTrainedModel", torch.nn.Module]] = None, + disable_dropout: Optional[bool] = True, + loss_type: Optional[Literal["sigmoid", "hinge"]] = "sigmoid", + **kwargs + ): + if disable_dropout: + disable_dropout_in_model(model) + if ref_model is not None: + disable_dropout_in_model(ref_model) + + self.is_encoder_decoder = model.config.is_encoder_decoder + self.ref_model = ref_model + self.use_dpo_data_collator = True # hack to avoid warning + self.generate_during_eval = False # disable at evaluation + self.label_pad_token_id = IGNORE_INDEX + self.padding_value = 0 + self.beta = beta + self.loss_type = loss_type + self._stored_metrics = defaultdict(lambda: defaultdict(list)) + + Trainer.__init__(self, model=model, **kwargs) + if not hasattr(self, "accelerator"): + raise AttributeError("Please update `transformers`.") + + if ref_model is not None: + if self.is_deepspeed_enabled: + self.ref_model = self._prepare_deepspeed(self.ref_model) + else: + self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True) + + def _prepare_deepspeed(self, model: "PreTrainedModelWrapper"): + # Adapted from accelerate: https://github.com/huggingface/accelerate/blob/739b135f8367becb67ffaada12fe76e3aa60fefd/src/accelerate/accelerator.py#L1473 + deepspeed_plugin = self.accelerator.state.deepspeed_plugin + config_kwargs = deepcopy(deepspeed_plugin.deepspeed_config) + if model is not None: + if hasattr(model, "config"): + hidden_size = ( + max(model.config.hidden_sizes) + if getattr(model.config, "hidden_sizes", None) + else getattr(model.config, "hidden_size", None) + ) + if hidden_size is not None and config_kwargs["zero_optimization"]["stage"] == 3: + # Note that `stage3_prefetch_bucket_size` can produce DeepSpeed messages like: `Invalidate trace cache @ step 0: expected module 1, but got module 0` + # This is expected and is not an error, see: https://github.com/microsoft/DeepSpeed/discussions/4081 + config_kwargs.update( + { + "zero_optimization.reduce_bucket_size": hidden_size * hidden_size, + "zero_optimization.stage3_param_persistence_threshold": 10 * hidden_size, + "zero_optimization.stage3_prefetch_bucket_size": 0.9 * hidden_size * hidden_size, + } + ) + + # If ZeRO-3 is used, we shard both the active and reference model. + # Otherwise, we assume the reference model fits in memory and is initialized on each device with ZeRO disabled (stage 0) + if config_kwargs["zero_optimization"]["stage"] != 3: + config_kwargs["zero_optimization"]["stage"] = 0 + model, *_ = deepspeed.initialize(model=model, config=config_kwargs) + model.eval() + return model + + def concatenated_forward( + self, + model: Optional[torch.nn.Module] = None, + batch: Optional[Dict[str, torch.Tensor]] = None + ) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]: + batch_copied = BatchEncoding({k: v.detach().clone() for k, v in batch.items()}) # avoid error + + all_logits = model( + input_ids=batch_copied["input_ids"], + attention_mask=batch_copied["attention_mask"], + return_dict=True + ).logits.to(torch.float32) + + all_logps = self._get_batch_logps( + all_logits, + batch["labels"], + average_log_prob=False + ) + batch_size = batch["input_ids"].size(0) // 2 + chosen_logps, rejected_logps = all_logps.split(batch_size, dim=0) + chosen_logits, rejected_logits = all_logits.split(batch_size, dim=0) + return chosen_logps, rejected_logps, chosen_logits, rejected_logits diff --git a/llm_rl/src/llmtuner/tuner/dpo/workflow.py b/llm_rl/src/llmtuner/tuner/dpo/workflow.py new file mode 100644 index 00000000..6e16dd18 --- /dev/null +++ b/llm_rl/src/llmtuner/tuner/dpo/workflow.py @@ -0,0 +1,66 @@ +# Inspired by: https://github.com/huggingface/trl/blob/main/examples/research_projects/stack_llama_2/scripts/dpo_llama2.py + +from copy import deepcopy +from peft import PeftModel +from typing import TYPE_CHECKING, Optional, List +from transformers import Seq2SeqTrainingArguments + +from llmtuner.dsets import get_dataset, preprocess_dataset, split_dataset +from llmtuner.extras.constants import IGNORE_INDEX +from llmtuner.extras.ploting import plot_loss +from llmtuner.tuner.core import load_model_and_tokenizer +from llmtuner.tuner.dpo.collator import DPODataCollatorWithPadding +from llmtuner.tuner.dpo.trainer import CustomDPOTrainer + +if TYPE_CHECKING: + from transformers import TrainerCallback + from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments + + +def run_dpo( + model_args: "ModelArguments", + data_args: "DataArguments", + training_args: "Seq2SeqTrainingArguments", + finetuning_args: "FinetuningArguments", + callbacks: Optional[List["TrainerCallback"]] = None +): + dataset = get_dataset(model_args, data_args) + model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, training_args.do_train, stage="sft") + dataset = preprocess_dataset(dataset, tokenizer, data_args, training_args, stage="rm") + data_collator = DPODataCollatorWithPadding( + tokenizer=tokenizer, + pad_to_multiple_of=4, + label_pad_token_id=IGNORE_INDEX if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id + ) + + training_args_dict = training_args.to_dict() + training_args_dict.update(dict(remove_unused_columns=False)) # important for pairwise dataset + training_args = Seq2SeqTrainingArguments(**training_args_dict) + + # Initialize our Trainer + trainer = CustomDPOTrainer( + beta=finetuning_args.dpo_beta, + model=model, + ref_model=deepcopy(model) if not isinstance(model, PeftModel) else None, + args=training_args, + tokenizer=tokenizer, + data_collator=data_collator, + callbacks=callbacks, + **split_dataset(dataset, data_args, training_args) + ) + + # Training + if training_args.do_train: + train_result = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint) + trainer.log_metrics("train", train_result.metrics) + trainer.save_metrics("train", train_result.metrics) + trainer.save_state() + trainer.save_model() + if trainer.is_world_process_zero() and model_args.plot_loss: + plot_loss(training_args.output_dir, keys=["loss", "eval_loss"]) + + # Evaluation + if training_args.do_eval: + metrics = trainer.evaluate(metric_key_prefix="eval") + trainer.log_metrics("eval", metrics) + trainer.save_metrics("eval", metrics) diff --git a/llm_rl/src/llmtuner/tuner/ppo/__init__.py b/llm_rl/src/llmtuner/tuner/ppo/__init__.py new file mode 100644 index 00000000..11519bab --- /dev/null +++ b/llm_rl/src/llmtuner/tuner/ppo/__init__.py @@ -0,0 +1 @@ +from llmtuner.tuner.ppo.workflow import run_ppo diff --git a/llm_rl/src/llmtuner/tuner/ppo/trainer.py b/llm_rl/src/llmtuner/tuner/ppo/trainer.py new file mode 100644 index 00000000..372c4891 --- /dev/null +++ b/llm_rl/src/llmtuner/tuner/ppo/trainer.py @@ -0,0 +1,310 @@ +import os +import sys +import math +import torch +from tqdm import tqdm +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple + +from transformers import GenerationConfig, Trainer, TrainerState, TrainerControl +from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR + +from trl import PPOTrainer +from trl.core import PPODecorators, logprobs_from_logits + +from llmtuner.extras.callbacks import LogCallback, SavePeftModelCallback +from llmtuner.extras.logging import get_logger +from llmtuner.extras.misc import AverageMeter, count_parameters, get_logits_processor +from llmtuner.tuner.ppo.utils import dump_layernorm, restore_layernorm, replace_model + +if TYPE_CHECKING: + from transformers import Seq2SeqTrainingArguments, TrainerCallback + from trl import AutoModelForCausalLMWithValueHead + from llmtuner.hparams import ModelArguments, FinetuningArguments, GeneratingArguments + + +logger = get_logger(__name__) + + +class CustomPPOTrainer(PPOTrainer, Trainer): + r""" + Inherits PPOTrainer. + """ + + def __init__( + self, + model_args: "ModelArguments", + training_args: "Seq2SeqTrainingArguments", + finetuning_args: "FinetuningArguments", + generating_args: "GeneratingArguments", + callbacks: List["TrainerCallback"], + **kwargs + ): + PPOTrainer.__init__(self, **kwargs) + self.args = training_args + self.model_args = model_args + self.finetuning_args = finetuning_args + self.generation_config = GenerationConfig( + pad_token_id=self.tokenizer.pad_token_id, + eos_token_id=[self.tokenizer.eos_token_id] + self.tokenizer.additional_special_tokens_ids, + **generating_args.to_dict() + ) + self.state = TrainerState() + self.control = TrainerControl() + self.log_callback, self.save_callback = callbacks[0], callbacks[1] + assert isinstance(self.log_callback, LogCallback) and isinstance(self.save_callback, SavePeftModelCallback) + if self.args.max_steps > 0: + logger.info("max_steps is given, it will override any value given in num_train_epochs") + + def ppo_train(self) -> None: + r""" + Implements training loop for the PPO stage, like _inner_training_loop() in Huggingface's Trainer. + """ + total_train_batch_size = ( + self.args.per_device_train_batch_size * self.args.gradient_accumulation_steps * self.args.world_size + ) + if self.args.max_steps > 0: + num_examples = total_train_batch_size * self.args.max_steps + num_train_epochs = sys.maxsize + max_steps = self.args.max_steps + steps_in_epoch = self.args.max_steps * self.args.gradient_accumulation_steps + else: + len_dataloader = len(self.dataloader) + num_examples = len(self.dataset) + num_train_epochs = self.args.num_train_epochs + max_steps = math.ceil(num_train_epochs * len_dataloader) + steps_in_epoch = len_dataloader + + self.state.max_steps = max_steps + self.state.num_train_epochs = num_train_epochs + self.state.is_local_process_zero = self.is_local_process_zero() + self.state.is_world_process_zero = self.is_world_process_zero() + + if self.is_world_process_zero(): + logger.info("***** Running training *****") + logger.info(f" Num examples = {num_examples}") + logger.info(f" Num Epochs = {num_train_epochs}") + logger.info(f" Instantaneous batch size per device = {self.args.per_device_train_batch_size}") + logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_train_batch_size}") + logger.info(f" Gradient Accumulation steps = {self.args.gradient_accumulation_steps}") + logger.info(f" Total optimization steps = {max_steps}") + logger.info(f" Number of trainable parameters = {count_parameters(self.model)[0]}") + + unwrapped_model: "AutoModelForCausalLMWithValueHead" = self.accelerator.unwrap_model(self.model) + dataiter = iter(self.dataloader) + loss_meter = AverageMeter() + reward_meter = AverageMeter() + self.log_callback.on_train_begin(self.args, self.state, self.control) + + for step in tqdm(range(max_steps), disable=not self.is_local_process_zero()): + try: + batch = next(dataiter) + except StopIteration: + dataiter = iter(self.dataloader) + batch = next(dataiter) + + # Cast to inference mode + unwrapped_model.gradient_checkpointing_disable() + unwrapped_model.config.use_cache = True + self.model.eval() + + # Get inputs + queries, responses = self.get_inputs(batch) + self.tokenizer.padding_side = "right" # change padding side + rewards = self.get_rewards(queries, responses, unwrapped_model) + + # Cast to training mode + unwrapped_model.gradient_checkpointing_enable() + unwrapped_model.config.use_cache = False + self.model.train() + + # Run PPO step + stats = self.step(queries, responses, rewards) + self.tokenizer.padding_side = "left" # restore padding side + loss_meter.update(float(stats["ppo/loss/total"]), n=len(rewards)) + reward_meter.update(torch.stack(rewards).mean().item(), n=len(rewards)) + + if self.config.log_with is not None: + try: + batch["query"] = self.tokenizer.batch_decode(queries, skip_special_tokens=True) + batch["response"] = self.tokenizer.batch_decode(responses, skip_special_tokens=True) + self.log_stats(stats, batch, rewards) + except: + logger.warning("Failed to save stats due to unknown errors.") + + self.state.global_step += 1 + self.log_callback.on_step_end(self.args, self.state, self.control) + + if self.is_local_process_zero() and (step+1) % self.args.logging_steps == 0: + logs = dict( + loss=round(loss_meter.avg, 4), + reward=round(reward_meter.avg, 4), + learning_rate=stats["ppo/learning_rate"], + epoch=round(step / steps_in_epoch, 2) + ) + tqdm.write(str(logs)) + logs["step"] = step + self.state.log_history.append(logs) + self.log_callback.on_log(self.args, self.state, self.control) + loss_meter.reset() + reward_meter.reset() + + if (step+1) % self.args.save_steps == 0: # save checkpoint + self.save_model(os.path.join( + self.args.output_dir, "{}-{}".format(PREFIX_CHECKPOINT_DIR, self.state.global_step) + )) + self.save_callback.on_save( + self.args, self.state, self.control, model=self.accelerator.unwrap_model(self.model) + ) + + if self.control.should_epoch_stop or self.control.should_training_stop: + break + + self.log_callback.on_train_end(self.args, self.state, self.control) + self.save_callback.on_train_end( + self.args, self.state, self.control, model=self.accelerator.unwrap_model(self.model) + ) + + @torch.no_grad() + def get_inputs(self, batch: Dict[str, torch.Tensor]) -> Tuple[List[torch.Tensor], List[torch.Tensor]]: + r""" + Generates model's responses given queries. + """ + if self.finetuning_args.upcast_layernorm: + layernorm_params = dump_layernorm(self.model) + + unwrapped_model: "AutoModelForCausalLMWithValueHead" = self.accelerator.unwrap_model(self.model) + response: torch.Tensor = unwrapped_model.generate( + generation_config=self.generation_config, + logits_processor=get_logits_processor(), + **batch + ) + + if self.finetuning_args.upcast_layernorm: + restore_layernorm(self.model, layernorm_params) + + query, response = batch["input_ids"].detach().cpu(), response[:, batch["input_ids"].size(-1):].detach().cpu() + queries, responses = [], [] + for i in range(len(query)): + query_length = (query[i] != self.tokenizer.pad_token_id).nonzero()[0].item() + response_index = (response[i] != self.tokenizer.pad_token_id).nonzero() + + if len(response_index) == 0: + response_length = 1 # allow empty response + elif self.tokenizer.pad_token_id == self.tokenizer.eos_token_id: + response_length = response_index[-1].item() + 2 # save the EOS token + else: + response_length = response_index[-1].item() + 1 + + queries.append(query[i, query_length:]) # remove padding from left + responses.append(response[i, :response_length]) # remove padding from right + + return queries, responses + + @torch.no_grad() + def get_rewards( + self, + queries: List[torch.Tensor], + responses: List[torch.Tensor], + unwrapped_model: "AutoModelForCausalLMWithValueHead" + ) -> List[torch.Tensor]: + r""" + Computes scores using given reward model. + """ + replace_model(unwrapped_model, target="reward") + batch = self.prepare_model_inputs(queries, responses) + + with torch.cuda.amp.autocast(dtype=self.model_args.compute_dtype): # support bf16 + _, _, values = self.model(**batch, output_hidden_states=True, return_dict=True) + + if values.size(0) != batch["input_ids"].size(0): # adapt to chatglm2 + values = torch.transpose(values, 0, 1) + + rewards = [] + for i in range(values.size(0)): + end_index = batch["attention_mask"][i].nonzero()[-1].item() # use the score on the EOS token + rewards.append(values[i, end_index].float().detach().cpu()) # use fp32 type + + replace_model(unwrapped_model, target="default") + return rewards + + @PPODecorators.empty_cuda_cache() + def batched_forward_pass( + self, + model: "AutoModelForCausalLMWithValueHead", + queries: torch.Tensor, + responses: torch.Tensor, + model_inputs: dict, + return_logits: Optional[bool] = False, + response_masks: Optional[torch.Tensor] = None + ): + r""" + Calculates model outputs in multiple batches. + + Subclass and override to inject custom behavior. + """ + bs = len(queries) + fbs = self.config.mini_batch_size + all_logprobs = [] + all_logits = [] + all_masks = [] + all_values = [] + + for i in range(math.ceil(bs / fbs)): + input_kwargs = {key: value[i * fbs : (i + 1) * fbs] for key, value in model_inputs.items()} + query_batch = queries[i * fbs : (i + 1) * fbs] + response_batch = responses[i * fbs : (i + 1) * fbs] + if response_masks is not None: + response_masks_batch = response_masks[i * fbs : (i + 1) * fbs] + input_ids = input_kwargs["input_ids"] + attention_mask = input_kwargs["attention_mask"] + + with torch.cuda.amp.autocast(dtype=self.model_args.compute_dtype): # support bf16 + logits, _, values = model(**input_kwargs) + + if values.size(0) != input_ids.size(0): # adapt to chatglm2 + values = torch.transpose(values, 0, 1) + + logprobs = logprobs_from_logits(logits[:, :-1, :], input_ids[:, 1:]) + masks = torch.zeros_like(attention_mask) + masks[:, :-1] = attention_mask[:, 1:] + + for j in range(len(query_batch)): + start = len(query_batch[j]) - 1 + if attention_mask[j, 0] == 0: # offset left padding + start += attention_mask[j, :].nonzero()[0].item() + end = start + len(response_batch[j]) + + if response_masks is not None: + response_masks_batch = torch.cat( + (torch.zeros_like(query_batch[j]), response_masks_batch[j]) + )[1:] + + masks[j, :start] = 0 + masks[j, end:] = 0 + if response_masks is not None: + masks[j, start:end] = masks[j, start:end] * response_masks_batch[j][start:end] + + if return_logits: + all_logits.append(logits) + else: + del logits + + all_values.append(values) + all_logprobs.append(logprobs) + all_masks.append(masks) + + return ( + torch.cat(all_logprobs), + torch.cat(all_logits)[:, :-1] if return_logits else None, + torch.cat(all_values)[:, :-1], + torch.cat(all_masks)[:, :-1], + ) + + def save_model(self, output_dir: Optional[str] = None) -> None: + r""" + Saves model checkpoint. + + Subclass and override to inject custom behavior. + """ + if self.args.should_save: + self._save(output_dir) diff --git a/llm_rl/src/llmtuner/tuner/ppo/utils.py b/llm_rl/src/llmtuner/tuner/ppo/utils.py new file mode 100644 index 00000000..74453a39 --- /dev/null +++ b/llm_rl/src/llmtuner/tuner/ppo/utils.py @@ -0,0 +1,35 @@ +import torch +from typing import TYPE_CHECKING, Dict, Literal, Optional + +if TYPE_CHECKING: + from transformers import PreTrainedModel + from trl import AutoModelForCausalLMWithValueHead + + +def replace_model(model: "AutoModelForCausalLMWithValueHead", target: Literal["default", "reward"]) -> None: + if target == "reward": # save default head temporarily + valuehead_state_dict: Dict[str, torch.Tensor] = model.v_head.state_dict() + setattr(model, "default_head_weight", valuehead_state_dict["summary.weight"].detach().clone()) + setattr(model, "default_head_bias", valuehead_state_dict["summary.bias"].detach().clone()) + + model.pretrained_model.set_adapter(target) # set the LoRA adapter to be active + model.v_head.load_state_dict({ + "summary.weight": model.get_buffer("{}_head_weight".format(target)).detach().clone(), + "summary.bias": model.get_buffer("{}_head_bias".format(target)).detach().clone() + }) + + +def dump_layernorm(model: "PreTrainedModel") -> Dict[str, torch.Tensor]: + layer_norm_params = {} + for name, param in model.named_parameters(): + if param.data.dtype == torch.float32: + layer_norm_params[name] = param.data.detach().clone() + param.data = param.data.to(model.config.torch_dtype) + + return layer_norm_params + + +def restore_layernorm(model: "PreTrainedModel", layernorm_params: Optional[Dict[str, torch.Tensor]] = None) -> None: + for name, param in model.named_parameters(): + if name in layernorm_params: + param.data = layernorm_params[name] diff --git a/llm_rl/src/llmtuner/tuner/ppo/workflow.py b/llm_rl/src/llmtuner/tuner/ppo/workflow.py new file mode 100644 index 00000000..4c35f628 --- /dev/null +++ b/llm_rl/src/llmtuner/tuner/ppo/workflow.py @@ -0,0 +1,92 @@ +# Inspired by: https://github.com/lvwerra/trl/blob/main/examples/research_projects/stack_llama/scripts/rl_training.py + +import math +from trl import PPOConfig +from torch.optim import AdamW +from typing import TYPE_CHECKING, Optional, List +from transformers import DataCollatorWithPadding +from transformers.optimization import get_scheduler + +from llmtuner.dsets import get_dataset, preprocess_dataset +from llmtuner.extras.callbacks import SavePeftModelCallback +from llmtuner.extras.ploting import plot_loss +from llmtuner.tuner.core import load_model_and_tokenizer +from llmtuner.tuner.ppo.trainer import CustomPPOTrainer + +if TYPE_CHECKING: + from transformers import Seq2SeqTrainingArguments, TrainerCallback + from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments + + +def run_ppo( + model_args: "ModelArguments", + data_args: "DataArguments", + training_args: "Seq2SeqTrainingArguments", + finetuning_args: "FinetuningArguments", + generating_args: "GeneratingArguments", + callbacks: Optional[List["TrainerCallback"]] = None +): + dataset = get_dataset(model_args, data_args) + model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, training_args.do_train, stage="ppo") + dataset = preprocess_dataset(dataset, tokenizer, data_args, training_args, stage="ppo") + + tokenizer.padding_side = "left" # use left-padding in generation while using right-padding in training + data_collator = DataCollatorWithPadding(tokenizer=tokenizer) + + ppo_config = PPOConfig( + model_name=model_args.model_name_or_path, + learning_rate=training_args.learning_rate, + mini_batch_size=training_args.per_device_train_batch_size, + batch_size=training_args.per_device_train_batch_size * training_args.gradient_accumulation_steps, + gradient_accumulation_steps=training_args.gradient_accumulation_steps, + ppo_epochs=1, + max_grad_norm=training_args.max_grad_norm, + seed=training_args.seed, + optimize_cuda_cache=True, + target=finetuning_args.ppo_target, + log_with=finetuning_args.ppo_logger, + use_score_scaling=finetuning_args.ppo_score_norm, + use_score_norm=finetuning_args.ppo_score_norm, + accelerator_kwargs={"step_scheduler_with_optimizer": False} + ) + + optimizer = AdamW(filter(lambda p: p.requires_grad, model.parameters()), lr=training_args.learning_rate) + if training_args.max_steps > 0: + num_training_steps = training_args.max_steps + else: + total_train_batch_size = ( + training_args.per_device_train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size + ) + num_training_steps = training_args.num_train_epochs * math.ceil(len(dataset) / total_train_batch_size) + + lr_scheduler = get_scheduler( + training_args.lr_scheduler_type, + optimizer=optimizer, + num_warmup_steps=training_args.get_warmup_steps(num_training_steps), + num_training_steps=num_training_steps + ) + + # Initialize our Trainer + ppo_trainer = CustomPPOTrainer( + model_args=model_args, + training_args=training_args, + finetuning_args=finetuning_args, + generating_args=generating_args, + callbacks=callbacks + [SavePeftModelCallback()], + config=ppo_config, + model=model, + ref_model=None, + tokenizer=tokenizer, + dataset=dataset, + data_collator=data_collator, + optimizer=optimizer, + lr_scheduler=lr_scheduler + ) + + # Training + if training_args.do_train: + ppo_trainer.ppo_train() + ppo_trainer.save_model() + ppo_trainer.save_state() # must be called after save_model to have a folder + if ppo_trainer.is_world_process_zero() and model_args.plot_loss: + plot_loss(training_args.output_dir, keys=["loss", "reward"]) diff --git a/llm_rl/src/llmtuner/tuner/pt/__init__.py b/llm_rl/src/llmtuner/tuner/pt/__init__.py new file mode 100644 index 00000000..8ce509db --- /dev/null +++ b/llm_rl/src/llmtuner/tuner/pt/__init__.py @@ -0,0 +1 @@ +from llmtuner.tuner.pt.workflow import run_pt diff --git a/llm_rl/src/llmtuner/tuner/pt/workflow.py b/llm_rl/src/llmtuner/tuner/pt/workflow.py new file mode 100644 index 00000000..66d08de7 --- /dev/null +++ b/llm_rl/src/llmtuner/tuner/pt/workflow.py @@ -0,0 +1,58 @@ +# Inspired by: https://github.com/huggingface/transformers/blob/v4.29.2/examples/pytorch/language-modeling/run_clm.py + +import math +from typing import TYPE_CHECKING, Optional, List +from transformers import DataCollatorForLanguageModeling, Trainer + +from llmtuner.dsets import get_dataset, preprocess_dataset, split_dataset +from llmtuner.extras.ploting import plot_loss +from llmtuner.tuner.core import load_model_and_tokenizer + +if TYPE_CHECKING: + from transformers import Seq2SeqTrainingArguments, TrainerCallback + from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments + + +def run_pt( + model_args: "ModelArguments", + data_args: "DataArguments", + training_args: "Seq2SeqTrainingArguments", + finetuning_args: "FinetuningArguments", + callbacks: Optional[List["TrainerCallback"]] = None +): + dataset = get_dataset(model_args, data_args) + model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, training_args.do_train, stage="pt") + dataset = preprocess_dataset(dataset, tokenizer, data_args, training_args, stage="pt") + data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False) + + # Initialize our Trainer + trainer = Trainer( + model=model, + args=training_args, + tokenizer=tokenizer, + data_collator=data_collator, + callbacks=callbacks, + **split_dataset(dataset, data_args, training_args) + ) + + # Training + if training_args.do_train: + train_result = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint) + trainer.log_metrics("train", train_result.metrics) + trainer.save_metrics("train", train_result.metrics) + trainer.save_state() + trainer.save_model() + if trainer.is_world_process_zero() and model_args.plot_loss: + plot_loss(training_args.output_dir, keys=["loss", "eval_loss"]) + + # Evaluation + if training_args.do_eval: + metrics = trainer.evaluate(metric_key_prefix="eval") + try: + perplexity = math.exp(metrics["eval_loss"]) + except OverflowError: + perplexity = float("inf") + + metrics["perplexity"] = perplexity + trainer.log_metrics("eval", metrics) + trainer.save_metrics("eval", metrics) diff --git a/llm_rl/src/llmtuner/tuner/rm/__init__.py b/llm_rl/src/llmtuner/tuner/rm/__init__.py new file mode 100644 index 00000000..54d3d943 --- /dev/null +++ b/llm_rl/src/llmtuner/tuner/rm/__init__.py @@ -0,0 +1 @@ +from llmtuner.tuner.rm.workflow import run_rm diff --git a/llm_rl/src/llmtuner/tuner/rm/collator.py b/llm_rl/src/llmtuner/tuner/rm/collator.py new file mode 100644 index 00000000..161f003d --- /dev/null +++ b/llm_rl/src/llmtuner/tuner/rm/collator.py @@ -0,0 +1,27 @@ +import torch +from dataclasses import dataclass +from typing import Any, Dict, Sequence +from transformers import DataCollatorWithPadding + + +@dataclass +class PairwiseDataCollatorWithPadding(DataCollatorWithPadding): + r""" + Data collator for pairwise data. + """ + + def __call__(self, features: Sequence[Dict[str, Any]]) -> Dict[str, torch.Tensor]: + r""" + Pads batched data to the longest sequence in the batch. + + We generate 2 * n examples where the first n examples represent chosen examples and + the last n examples represent rejected examples. + """ + features = [ + { + "input_ids": feature["prompt_ids"] + feature[key], + "attention_mask": [1] * (len(feature["prompt_ids"]) + len(feature[key])) + } + for key in ("chosen_ids", "rejected_ids") for feature in features + ] + return super().__call__(features) diff --git a/llm_rl/src/llmtuner/tuner/rm/metric.py b/llm_rl/src/llmtuner/tuner/rm/metric.py new file mode 100644 index 00000000..db9c9243 --- /dev/null +++ b/llm_rl/src/llmtuner/tuner/rm/metric.py @@ -0,0 +1,7 @@ +import numpy as np +from typing import Dict, Sequence, Tuple, Union + + +def compute_accuracy(eval_preds: Sequence[Union[np.ndarray, Tuple[np.ndarray]]]) -> Dict[str, float]: + preds, _ = eval_preds + return {"accuracy": (preds[0] > preds[1]).sum() / len(preds[0])} diff --git a/llm_rl/src/llmtuner/tuner/rm/trainer.py b/llm_rl/src/llmtuner/tuner/rm/trainer.py new file mode 100644 index 00000000..80502937 --- /dev/null +++ b/llm_rl/src/llmtuner/tuner/rm/trainer.py @@ -0,0 +1,105 @@ +import os +import json +import torch +from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union +from transformers import Trainer + +from llmtuner.extras.logging import get_logger + +if TYPE_CHECKING: + from transformers.trainer import PredictionOutput + from transformers.modeling_utils import PreTrainedModel + + +logger = get_logger(__name__) + + +class PairwiseTrainer(Trainer): + r""" + Inherits PeftTrainer to compute pairwise loss. + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.can_return_loss = True # override property to return eval_loss + + def compute_loss( + self, + model: "PreTrainedModel", + inputs: Dict[str, torch.Tensor], + return_outputs: Optional[bool] = False + ) -> Union[torch.Tensor, Tuple[torch.Tensor, List[torch.Tensor]]]: + r""" + Computes pairwise loss. The first n examples are chosen and the last n examples are rejected. + + Subclass and override to inject custom behavior. + + Note that the first element will be removed from the output tuple. + See: https://github.com/huggingface/transformers/blob/v4.30.2/src/transformers/trainer.py#L3509 + """ + # Compute rewards + _, _, values = model(**inputs, output_hidden_states=True, return_dict=True) + if values.size(0) != inputs["input_ids"].size(0): # adapt to chatglm2 + values = torch.transpose(values, 0, 1) + + # Split the inputs and rewards into two parts, chosen and rejected + batch_size = inputs["input_ids"].size(0) // 2 + chosen_input_ids, rejected_input_ids = inputs["input_ids"][:batch_size], inputs["input_ids"][batch_size:] + chosen_attn_mask, rejected_attn_mask = ( + inputs["attention_mask"][:batch_size], inputs["attention_mask"][batch_size:] + ) + chosen_rewards, rejected_rewards = values[:batch_size], values[batch_size:] + chosen_scores, rejected_scores = [], [] + + # Compute pairwise loss. Only backprop on the different tokens before padding + # Inspired by: https://github.com/CarperAI/trlx/blob/main/examples/summarize_rlhf/reward_model/reward_model.py + loss = 0 + for i in range(batch_size): + chosen_length = chosen_attn_mask[i].nonzero()[-1] + 1 + rejected_length = rejected_attn_mask[i].nonzero()[-1] + 1 + check_divergence = (chosen_input_ids[i] != rejected_input_ids[i]).nonzero() + + if len(check_divergence) == 0: + end_index = chosen_length + div_index = end_index - 1 + else: + end_index = max(chosen_length, rejected_length) + div_index = check_divergence[0] + + assert div_index > 0 + chosen_trunc_rewards = chosen_rewards[i, div_index:end_index] + rejected_trunc_rewards = rejected_rewards[i, div_index:end_index] + if return_outputs: # use the score on the EOS token for inference + chosen_scores.append(chosen_rewards[i, chosen_length-1]) + rejected_scores.append(rejected_rewards[i, rejected_length-1]) + loss += -torch.nn.functional.logsigmoid(chosen_trunc_rewards - rejected_trunc_rewards).mean() + + loss = loss / batch_size + if return_outputs: + chosen_scores, rejected_scores = torch.stack(chosen_scores), torch.stack(rejected_scores) + return loss, [loss, chosen_scores, rejected_scores] + + return loss + + def save_predictions( + self, + predict_results: "PredictionOutput" + ) -> None: + r""" + Saves model predictions to `output_dir`. + + A custom behavior that not contained in Seq2SeqTrainer. + """ + if not self.is_world_process_zero(): + return + + output_prediction_file = os.path.join(self.args.output_dir, "generated_predictions.jsonl") + logger.info(f"Saving prediction results to {output_prediction_file}") + + chosen_scores, rejected_scores = predict_results.predictions + + with open(output_prediction_file, "w", encoding="utf-8") as writer: + res: List[str] = [] + for c_score, r_score in zip(chosen_scores, rejected_scores): + res.append(json.dumps({"chosen": round(float(c_score), 2), "rejected": round(float(r_score), 2)})) + writer.write("\n".join(res)) diff --git a/llm_rl/src/llmtuner/tuner/rm/workflow.py b/llm_rl/src/llmtuner/tuner/rm/workflow.py new file mode 100644 index 00000000..6d2c4422 --- /dev/null +++ b/llm_rl/src/llmtuner/tuner/rm/workflow.py @@ -0,0 +1,68 @@ +# Inspired by: +# https://github.com/CarperAI/trlx/blob/main/examples/summarize_rlhf/reward_model/train_reward_model_gptj.py + +from typing import TYPE_CHECKING, Optional, List +from transformers import Seq2SeqTrainingArguments + +from llmtuner.dsets import get_dataset, preprocess_dataset, split_dataset +from llmtuner.extras.callbacks import SavePeftModelCallback +from llmtuner.extras.ploting import plot_loss +from llmtuner.tuner.core import load_model_and_tokenizer +from llmtuner.tuner.rm.metric import compute_accuracy +from llmtuner.tuner.rm.collator import PairwiseDataCollatorWithPadding +from llmtuner.tuner.rm.trainer import PairwiseTrainer + +if TYPE_CHECKING: + from transformers import TrainerCallback + from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments + + +def run_rm( + model_args: "ModelArguments", + data_args: "DataArguments", + training_args: "Seq2SeqTrainingArguments", + finetuning_args: "FinetuningArguments", + callbacks: Optional[List["TrainerCallback"]] = None +): + dataset = get_dataset(model_args, data_args) + model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, training_args.do_train, stage="rm") + dataset = preprocess_dataset(dataset, tokenizer, data_args, training_args, stage="rm") + data_collator = PairwiseDataCollatorWithPadding(tokenizer, pad_to_multiple_of=4) + + training_args_dict = training_args.to_dict() + training_args_dict.update(dict(remove_unused_columns=False)) # important for pairwise dataset + training_args = Seq2SeqTrainingArguments(**training_args_dict) + + # Initialize our Trainer + trainer = PairwiseTrainer( + model=model, + args=training_args, + tokenizer=tokenizer, + data_collator=data_collator, + callbacks=callbacks + [SavePeftModelCallback()], + compute_metrics=compute_accuracy, + **split_dataset(dataset, data_args, training_args) + ) + + # Training + if training_args.do_train: + train_result = trainer.train() + trainer.log_metrics("train", train_result.metrics) + trainer.save_metrics("train", train_result.metrics) + trainer.save_state() + trainer.save_model() + if trainer.is_world_process_zero() and model_args.plot_loss: + plot_loss(training_args.output_dir, keys=["loss", "eval_loss"]) + + # Evaluation + if training_args.do_eval: + metrics = trainer.evaluate(metric_key_prefix="eval") + trainer.log_metrics("eval", metrics) + trainer.save_metrics("eval", metrics) + + # Predict + if training_args.do_predict: + predict_results = trainer.predict(dataset, metric_key_prefix="predict") + trainer.log_metrics("predict", predict_results.metrics) + trainer.save_metrics("predict", predict_results.metrics) + trainer.save_predictions(predict_results) diff --git a/llm_rl/src/llmtuner/tuner/sft/__init__.py b/llm_rl/src/llmtuner/tuner/sft/__init__.py new file mode 100644 index 00000000..493dd1a7 --- /dev/null +++ b/llm_rl/src/llmtuner/tuner/sft/__init__.py @@ -0,0 +1 @@ +from llmtuner.tuner.sft.workflow import run_sft diff --git a/llm_rl/src/llmtuner/tuner/sft/metric.py b/llm_rl/src/llmtuner/tuner/sft/metric.py new file mode 100644 index 00000000..812896ee --- /dev/null +++ b/llm_rl/src/llmtuner/tuner/sft/metric.py @@ -0,0 +1,53 @@ +import numpy as np +from dataclasses import dataclass +from typing import TYPE_CHECKING, Dict, Sequence, Tuple, Union + +import jieba +from rouge_chinese import Rouge +from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction + +from llmtuner.extras.constants import IGNORE_INDEX + +if TYPE_CHECKING: + from transformers.tokenization_utils import PreTrainedTokenizer + + +@dataclass +class ComputeMetrics: + r""" + Wraps the tokenizer into metric functions, used in Seq2SeqPeftTrainer. + """ + + tokenizer: "PreTrainedTokenizer" + + def __call__(self, eval_preds: Sequence[Union[np.ndarray, Tuple[np.ndarray]]]) -> Dict[str, float]: + r""" + Uses the model predictions to compute metrics. + """ + preds, labels = eval_preds + score_dict = {"rouge-1": [], "rouge-2": [], "rouge-l": [], "bleu-4": []} + + preds = np.where(preds != IGNORE_INDEX, preds, self.tokenizer.pad_token_id) + labels = np.where(labels != IGNORE_INDEX, labels, self.tokenizer.pad_token_id) + + decoded_preds = self.tokenizer.batch_decode(preds, skip_special_tokens=True) + decoded_labels = self.tokenizer.batch_decode(labels, skip_special_tokens=True) + + for pred, label in zip(decoded_preds, decoded_labels): + hypothesis = list(jieba.cut(pred)) + reference = list(jieba.cut(label)) + + if len(" ".join(hypothesis).split()) == 0 or len(" ".join(reference).split()) == 0: + result = {"rouge-1": {"f": 0.0}, "rouge-2": {"f": 0.0}, "rouge-l": {"f": 0.0}} + else: + rouge = Rouge() + scores = rouge.get_scores(" ".join(hypothesis), " ".join(reference)) + result = scores[0] + + for k, v in result.items(): + score_dict[k].append(round(v["f"] * 100, 4)) + + bleu_score = sentence_bleu([list(label)], list(pred), smoothing_function=SmoothingFunction().method3) + score_dict["bleu-4"].append(round(bleu_score * 100, 4)) + + return {k: float(np.mean(v)) for k, v in score_dict.items()} diff --git a/llm_rl/src/llmtuner/tuner/sft/trainer.py b/llm_rl/src/llmtuner/tuner/sft/trainer.py new file mode 100644 index 00000000..c65cd255 --- /dev/null +++ b/llm_rl/src/llmtuner/tuner/sft/trainer.py @@ -0,0 +1,92 @@ +import os +import json +import torch +import numpy as np +import torch.nn as nn +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union +from transformers import Seq2SeqTrainer + +from llmtuner.extras.constants import IGNORE_INDEX +from llmtuner.extras.logging import get_logger + +if TYPE_CHECKING: + from transformers.trainer import PredictionOutput + + +logger = get_logger(__name__) + + +class CustomSeq2SeqTrainer(Seq2SeqTrainer): + r""" + Inherits PeftTrainer to compute generative metrics such as BLEU and ROUGE. + """ + + def prediction_step( + self, + model: nn.Module, + inputs: Dict[str, Union[torch.Tensor, Any]], + prediction_loss_only: bool, + ignore_keys: Optional[List[str]] = None, + ) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]: + r""" + Removes the prompt part in the generated tokens. + + Subclass and override to inject custom behavior. + """ + labels = inputs["labels"].detach().clone() if "labels" in inputs else None # backup labels + if self.args.predict_with_generate: + assert self.tokenizer.padding_side == "left", "This method only accepts left-padded tensor." + prompt_len, label_len = inputs["input_ids"].size(-1), inputs["labels"].size(-1) + if prompt_len > label_len: + inputs["labels"] = self._pad_tensors_to_target_len(inputs["labels"], inputs["input_ids"]) + if label_len > prompt_len: + inputs["labels"] = inputs["labels"][:, :prompt_len] # truncate the labels instead of padding the inputs + + loss, generated_tokens, _ = super().prediction_step( + model, inputs, prediction_loss_only=prediction_loss_only, ignore_keys=ignore_keys + ) + if generated_tokens is not None and self.args.predict_with_generate: + generated_tokens[:, :prompt_len] = self.tokenizer.pad_token_id + generated_tokens = generated_tokens.contiguous() + + return loss, generated_tokens, labels + + def _pad_tensors_to_target_len( + self, + src_tensor: torch.Tensor, + tgt_tensor: torch.Tensor + ) -> torch.Tensor: + r""" + Pads the tensor to the same length as the target tensor. + """ + assert self.tokenizer.pad_token_id is not None, "Pad token is required." + padded_tensor = self.tokenizer.pad_token_id * torch.ones_like(tgt_tensor) + padded_tensor[:, -src_tensor.shape[-1]:] = src_tensor # adopt left-padding + return padded_tensor.contiguous() # in contiguous memory + + def save_predictions( + self, + predict_results: "PredictionOutput" + ) -> None: + r""" + Saves model predictions to `output_dir`. + + A custom behavior that not contained in Seq2SeqTrainer. + """ + if not self.is_world_process_zero(): + return + + output_prediction_file = os.path.join(self.args.output_dir, "generated_predictions.jsonl") + logger.info(f"Saving prediction results to {output_prediction_file}") + + preds = np.where(predict_results.predictions != IGNORE_INDEX, predict_results.predictions, self.tokenizer.pad_token_id) + labels = np.where(predict_results.label_ids != IGNORE_INDEX, predict_results.label_ids, self.tokenizer.pad_token_id) + + decoded_preds = self.tokenizer.batch_decode(preds, skip_special_tokens=True, clean_up_tokenization_spaces=True) + decoded_labels = self.tokenizer.batch_decode(labels, skip_special_tokens=True, clean_up_tokenization_spaces=True) + + with open(output_prediction_file, "w", encoding="utf-8") as writer: + res: List[str] = [] + for pred, label in zip(decoded_preds, decoded_labels): + res.append(json.dumps({"label": label, "predict": pred}, ensure_ascii=False)) + writer.write("\n".join(res)) diff --git a/llm_rl/src/llmtuner/tuner/sft/workflow.py b/llm_rl/src/llmtuner/tuner/sft/workflow.py new file mode 100644 index 00000000..8d53605d --- /dev/null +++ b/llm_rl/src/llmtuner/tuner/sft/workflow.py @@ -0,0 +1,90 @@ +# Inspired by: https://github.com/huggingface/transformers/blob/v4.29.2/examples/pytorch/summarization/run_summarization.py + +from typing import TYPE_CHECKING, Optional, List +from transformers import DataCollatorForSeq2Seq, Seq2SeqTrainingArguments + +from llmtuner.dsets import get_dataset, preprocess_dataset, split_dataset +from llmtuner.extras.constants import IGNORE_INDEX +from llmtuner.extras.misc import get_logits_processor +from llmtuner.extras.ploting import plot_loss +from llmtuner.tuner.core import load_model_and_tokenizer +from llmtuner.tuner.sft.metric import ComputeMetrics +from llmtuner.tuner.sft.trainer import CustomSeq2SeqTrainer + +if TYPE_CHECKING: + from transformers import TrainerCallback + from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments + + +def run_sft( + model_args: "ModelArguments", + data_args: "DataArguments", + training_args: "Seq2SeqTrainingArguments", + finetuning_args: "FinetuningArguments", + generating_args: "GeneratingArguments", + callbacks: Optional[List["TrainerCallback"]] = None +): + dataset = get_dataset(model_args, data_args) + model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, training_args.do_train, stage="sft") + dataset = preprocess_dataset(dataset, tokenizer, data_args, training_args, stage="sft") + + if training_args.predict_with_generate: + tokenizer.padding_side = "left" # use left-padding in generation + + data_collator = DataCollatorForSeq2Seq( + tokenizer=tokenizer, + pad_to_multiple_of=4 if tokenizer.padding_side == "right" else None, # for shift short attention + label_pad_token_id=IGNORE_INDEX if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id + ) + + # Override the decoding parameters of Seq2SeqTrainer + training_args_dict = training_args.to_dict() + training_args_dict.update(dict( + generation_max_length=training_args.generation_max_length or data_args.cutoff_len, + generation_num_beams=data_args.eval_num_beams or training_args.generation_num_beams + )) + training_args = Seq2SeqTrainingArguments(**training_args_dict) + + # Initialize our Trainer + trainer = CustomSeq2SeqTrainer( + model=model, + args=training_args, + tokenizer=tokenizer, + data_collator=data_collator, + callbacks=callbacks, + compute_metrics=ComputeMetrics(tokenizer) if training_args.predict_with_generate else None, + **split_dataset(dataset, data_args, training_args) + ) + + # Keyword arguments for `model.generate` + gen_kwargs = generating_args.to_dict() + gen_kwargs["eos_token_id"] = [tokenizer.eos_token_id] + tokenizer.additional_special_tokens_ids + gen_kwargs["pad_token_id"] = tokenizer.pad_token_id + gen_kwargs["logits_processor"] = get_logits_processor() + + # Training + if training_args.do_train: + train_result = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint) + trainer.log_metrics("train", train_result.metrics) + trainer.save_metrics("train", train_result.metrics) + trainer.save_state() + trainer.save_model() + if trainer.is_world_process_zero() and model_args.plot_loss: + plot_loss(training_args.output_dir, keys=["loss", "eval_loss"]) + + # Evaluation + if training_args.do_eval: + metrics = trainer.evaluate(metric_key_prefix="eval", **gen_kwargs) + if training_args.predict_with_generate: # eval_loss will be wrong if predict_with_generate is enabled + metrics.pop("eval_loss", None) + trainer.log_metrics("eval", metrics) + trainer.save_metrics("eval", metrics) + + # Predict + if training_args.do_predict: + predict_results = trainer.predict(dataset, metric_key_prefix="predict", **gen_kwargs) + if training_args.predict_with_generate: # predict_loss will be wrong if predict_with_generate is enabled + predict_results.metrics.pop("predict_loss", None) + trainer.log_metrics("predict", predict_results.metrics) + trainer.save_metrics("predict", predict_results.metrics) + trainer.save_predictions(predict_results) diff --git a/llm_rl/src/llmtuner/tuner/tune.py b/llm_rl/src/llmtuner/tuner/tune.py new file mode 100644 index 00000000..4eb7f78f --- /dev/null +++ b/llm_rl/src/llmtuner/tuner/tune.py @@ -0,0 +1,51 @@ +from typing import TYPE_CHECKING, Any, Dict, List, Optional + +from llmtuner.extras.callbacks import LogCallback +from llmtuner.extras.logging import get_logger +from llmtuner.tuner.core import get_train_args, get_infer_args, load_model_and_tokenizer +from llmtuner.tuner.pt import run_pt +from llmtuner.tuner.sft import run_sft +from llmtuner.tuner.rm import run_rm +from llmtuner.tuner.ppo import run_ppo +from llmtuner.tuner.dpo import run_dpo + +if TYPE_CHECKING: + from transformers import TrainerCallback + + +logger = get_logger(__name__) + + +def run_exp(args: Optional[Dict[str, Any]] = None, callbacks: Optional[List["TrainerCallback"]] = None): + model_args, data_args, training_args, finetuning_args, generating_args = get_train_args(args) + callbacks = [LogCallback()] if callbacks is None else callbacks + + if finetuning_args.stage == "pt": + run_pt(model_args, data_args, training_args, finetuning_args, callbacks) + elif finetuning_args.stage == "sft": + run_sft(model_args, data_args, training_args, finetuning_args, generating_args, callbacks) + elif finetuning_args.stage == "rm": + run_rm(model_args, data_args, training_args, finetuning_args, callbacks) + elif finetuning_args.stage == "ppo": + run_ppo(model_args, data_args, training_args, finetuning_args, generating_args, callbacks) + elif finetuning_args.stage == "dpo": + run_dpo(model_args, data_args, training_args, finetuning_args, callbacks) + else: + raise ValueError("Unknown task.") + + +def export_model(args: Optional[Dict[str, Any]] = None, max_shard_size: Optional[str] = "10GB"): + model_args, _, finetuning_args, _ = get_infer_args(args) + model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args) + model.config.use_cache = True + model.save_pretrained(model_args.export_dir, max_shard_size=max_shard_size) + try: + tokenizer.padding_side = "left" # restore padding side + tokenizer.init_kwargs["padding_side"] = "left" + tokenizer.save_pretrained(model_args.export_dir) + except: + logger.warning("Cannot save tokenizer, please copy the files manually.") + + +if __name__ == "__main__": + run_exp() diff --git a/llm_rl/src/llmtuner/webui/__init__.py b/llm_rl/src/llmtuner/webui/__init__.py new file mode 100644 index 00000000..a27c7f6e --- /dev/null +++ b/llm_rl/src/llmtuner/webui/__init__.py @@ -0,0 +1 @@ +from llmtuner.webui.interface import create_ui, create_web_demo diff --git a/llm_rl/src/llmtuner/webui/chatter.py b/llm_rl/src/llmtuner/webui/chatter.py new file mode 100644 index 00000000..57eadb01 --- /dev/null +++ b/llm_rl/src/llmtuner/webui/chatter.py @@ -0,0 +1,101 @@ +import gradio as gr +from gradio.components import Component # cannot use TYPE_CHECKING here +from typing import TYPE_CHECKING, Any, Dict, Generator, List, Optional, Tuple + +from llmtuner.chat.stream_chat import ChatModel +from llmtuner.extras.misc import torch_gc +from llmtuner.hparams import GeneratingArguments +from llmtuner.webui.common import get_save_dir +from llmtuner.webui.locales import ALERTS + +if TYPE_CHECKING: + from llmtuner.webui.manager import Manager + + +class WebChatModel(ChatModel): + + def __init__(self, manager: "Manager", lazy_init: Optional[bool] = True) -> None: + self.manager = manager + self.model = None + self.tokenizer = None + self.generating_args = GeneratingArguments() + if not lazy_init: + super().__init__() + + @property + def loaded(self) -> bool: + return self.model is not None + + def load_model(self, data: Dict[Component, Any]) -> Generator[str, None, None]: + get = lambda name: data[self.manager.get_elem_by_name(name)] + lang = get("top.lang") + error = "" + if self.loaded: + error = ALERTS["err_exists"][lang] + elif not get("top.model_name"): + error = ALERTS["err_no_model"][lang] + elif not get("top.model_path"): + error = ALERTS["err_no_path"][lang] + + if error: + gr.Warning(error) + yield error + return + + if get("top.checkpoints"): + checkpoint_dir = ",".join([ + get_save_dir(get("top.model_name"), get("top.finetuning_type"), ckpt) for ckpt in get("top.checkpoints") + ]) + else: + checkpoint_dir = None + + yield ALERTS["info_loading"][lang] + args = dict( + model_name_or_path=get("top.model_path"), + checkpoint_dir=checkpoint_dir, + finetuning_type=get("top.finetuning_type"), + quantization_bit=int(get("top.quantization_bit")) if get("top.quantization_bit") in ["8", "4"] else None, + template=get("top.template"), + system_prompt=get("top.system_prompt"), + flash_attn=get("top.flash_attn"), + shift_attn=get("top.shift_attn"), + rope_scaling=get("top.rope_scaling") if get("top.rope_scaling") in ["linear", "dynamic"] else None + ) + super().__init__(args) + + yield ALERTS["info_loaded"][lang] + + def unload_model(self, data: Dict[Component, Any]) -> Generator[str, None, None]: + lang = data[self.manager.get_elem_by_name("top.lang")] + yield ALERTS["info_unloading"][lang] + self.model = None + self.tokenizer = None + torch_gc() + yield ALERTS["info_unloaded"][lang] + + def predict( + self, + chatbot: List[Tuple[str, str]], + query: str, + history: List[Tuple[str, str]], + system: str, + max_new_tokens: int, + top_p: float, + temperature: float + ) -> Generator[Tuple[List[Tuple[str, str]], List[Tuple[str, str]]], None, None]: + chatbot.append([query, ""]) + response = "" + for new_text in self.stream_chat( + query, history, system, max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature + ): + response += new_text + new_history = history + [(query, response)] + chatbot[-1] = [query, self.postprocess(response)] + yield chatbot, new_history + + def postprocess(self, response: str) -> str: + blocks = response.split("```") + for i, block in enumerate(blocks): + if i % 2 == 0: + blocks[i] = block.replace("<", "<").replace(">", ">") + return "```".join(blocks) diff --git a/llm_rl/src/llmtuner/webui/common.py b/llm_rl/src/llmtuner/webui/common.py new file mode 100644 index 00000000..5a6c16d3 --- /dev/null +++ b/llm_rl/src/llmtuner/webui/common.py @@ -0,0 +1,103 @@ +import os +import json +import gradio as gr +from typing import Any, Dict, Optional +from transformers.utils import ( + WEIGHTS_NAME, + WEIGHTS_INDEX_NAME, + SAFE_WEIGHTS_NAME, + SAFE_WEIGHTS_INDEX_NAME, + ADAPTER_WEIGHTS_NAME, + ADAPTER_SAFE_WEIGHTS_NAME +) + +from llmtuner.extras.constants import DEFAULT_MODULE, DEFAULT_TEMPLATE, SUPPORTED_MODELS, TRAINING_STAGES + + +DEFAULT_CACHE_DIR = "cache" +DEFAULT_DATA_DIR = "data" +DEFAULT_SAVE_DIR = "saves" +USER_CONFIG = "user.config" +DATA_CONFIG = "dataset_info.json" +CKPT_NAMES = [ + WEIGHTS_NAME, + WEIGHTS_INDEX_NAME, + SAFE_WEIGHTS_NAME, + SAFE_WEIGHTS_INDEX_NAME, + ADAPTER_WEIGHTS_NAME, + ADAPTER_SAFE_WEIGHTS_NAME +] + + +def get_save_dir(*args) -> os.PathLike: + return os.path.join(DEFAULT_SAVE_DIR, *args) + + +def get_config_path() -> os.PathLike: + return os.path.join(DEFAULT_CACHE_DIR, USER_CONFIG) + + +def load_config() -> Dict[str, Any]: + try: + with open(get_config_path(), "r", encoding="utf-8") as f: + return json.load(f) + except: + return {"lang": None, "last_model": None, "path_dict": {}, "cache_dir": None} + + +def save_config(lang: str, model_name: Optional[str] = None, model_path: Optional[str] = None) -> None: + os.makedirs(DEFAULT_CACHE_DIR, exist_ok=True) + user_config = load_config() + user_config["lang"] = lang or user_config["lang"] + if model_name: + user_config["last_model"] = model_name + user_config["path_dict"][model_name] = model_path + with open(get_config_path(), "w", encoding="utf-8") as f: + json.dump(user_config, f, indent=2, ensure_ascii=False) + + +def get_model_path(model_name: str) -> str: + user_config = load_config() + return user_config["path_dict"].get(model_name, None) or SUPPORTED_MODELS.get(model_name, "") + + +def get_module(model_name: str) -> str: + return DEFAULT_MODULE.get(model_name.split("-")[0], "q_proj,v_proj") + + +def get_template(model_name: str) -> str: + if model_name.endswith("Chat") and model_name.split("-")[0] in DEFAULT_TEMPLATE: + return DEFAULT_TEMPLATE[model_name.split("-")[0]] + return "default" + + +def list_checkpoint(model_name: str, finetuning_type: str) -> Dict[str, Any]: + checkpoints = [] + if model_name: + save_dir = get_save_dir(model_name, finetuning_type) + if save_dir and os.path.isdir(save_dir): + for checkpoint in os.listdir(save_dir): + if ( + os.path.isdir(os.path.join(save_dir, checkpoint)) + and any([os.path.isfile(os.path.join(save_dir, checkpoint, name)) for name in CKPT_NAMES]) + ): + checkpoints.append(checkpoint) + return gr.update(value=[], choices=checkpoints) + + +def load_dataset_info(dataset_dir: str) -> Dict[str, Any]: + try: + with open(os.path.join(dataset_dir, DATA_CONFIG), "r", encoding="utf-8") as f: + return json.load(f) + except: + print("Cannot find {} in {}.".format(DATA_CONFIG, dataset_dir)) + return {} + + +def list_dataset( + dataset_dir: Optional[str] = None, training_stage: Optional[str] = list(TRAINING_STAGES.keys())[0] +) -> Dict[str, Any]: + dataset_info = load_dataset_info(dataset_dir if dataset_dir is not None else DEFAULT_DATA_DIR) + ranking = TRAINING_STAGES[training_stage] in ["rm", "dpo"] + datasets = [k for k, v in dataset_info.items() if v.get("ranking", False) == ranking] + return gr.update(value=[], choices=datasets) diff --git a/llm_rl/src/llmtuner/webui/components/__init__.py b/llm_rl/src/llmtuner/webui/components/__init__.py new file mode 100644 index 00000000..32228b8e --- /dev/null +++ b/llm_rl/src/llmtuner/webui/components/__init__.py @@ -0,0 +1,6 @@ +from llmtuner.webui.components.top import create_top +from llmtuner.webui.components.train import create_train_tab +from llmtuner.webui.components.eval import create_eval_tab +from llmtuner.webui.components.infer import create_infer_tab +from llmtuner.webui.components.export import create_export_tab +from llmtuner.webui.components.chatbot import create_chat_box diff --git a/llm_rl/src/llmtuner/webui/components/chatbot.py b/llm_rl/src/llmtuner/webui/components/chatbot.py new file mode 100644 index 00000000..13e2dd4d --- /dev/null +++ b/llm_rl/src/llmtuner/webui/components/chatbot.py @@ -0,0 +1,49 @@ +import gradio as gr +from typing import TYPE_CHECKING, Dict, Optional, Tuple + +if TYPE_CHECKING: + from gradio.blocks import Block + from gradio.components import Component + from llmtuner.webui.engine import Engine + + +def create_chat_box( + engine: "Engine", + visible: Optional[bool] = False +) -> Tuple["Block", "Component", "Component", Dict[str, "Component"]]: + with gr.Box(visible=visible) as chat_box: + chatbot = gr.Chatbot() + history = gr.State([]) + with gr.Row(): + with gr.Column(scale=4): + system = gr.Textbox(show_label=False) + query = gr.Textbox(show_label=False, lines=8) + submit_btn = gr.Button(variant="primary") + + with gr.Column(scale=1): + clear_btn = gr.Button() + gen_kwargs = engine.chatter.generating_args + max_new_tokens = gr.Slider(10, 2048, value=gen_kwargs.max_new_tokens, step=1) + top_p = gr.Slider(0.01, 1, value=gen_kwargs.top_p, step=0.01) + temperature = gr.Slider(0.01, 1.5, value=gen_kwargs.temperature, step=0.01) + + submit_btn.click( + engine.chatter.predict, + [chatbot, query, history, system, max_new_tokens, top_p, temperature], + [chatbot, history], + show_progress=True + ).then( + lambda: gr.update(value=""), outputs=[query] + ) + + clear_btn.click(lambda: ([], []), outputs=[chatbot, history], show_progress=True) + + return chat_box, chatbot, history, dict( + system=system, + query=query, + submit_btn=submit_btn, + clear_btn=clear_btn, + max_new_tokens=max_new_tokens, + top_p=top_p, + temperature=temperature + ) diff --git a/llm_rl/src/llmtuner/webui/components/data.py b/llm_rl/src/llmtuner/webui/components/data.py new file mode 100644 index 00000000..effa39da --- /dev/null +++ b/llm_rl/src/llmtuner/webui/components/data.py @@ -0,0 +1,103 @@ +import os +import json +import gradio as gr +from typing import TYPE_CHECKING, Any, Dict, Tuple + +from llmtuner.webui.common import DATA_CONFIG + +if TYPE_CHECKING: + from gradio.components import Component + + +PAGE_SIZE = 2 + + +def prev_page(page_index: int) -> int: + return page_index - 1 if page_index > 0 else page_index + + +def next_page(page_index: int, total_num: int) -> int: + return page_index + 1 if (page_index + 1) * PAGE_SIZE < total_num else page_index + + +def can_preview(dataset_dir: str, dataset: list) -> Dict[str, Any]: + with open(os.path.join(dataset_dir, DATA_CONFIG), "r", encoding="utf-8") as f: + dataset_info = json.load(f) + + if ( + len(dataset) > 0 + and "file_name" in dataset_info[dataset[0]] + and os.path.isfile(os.path.join(dataset_dir, dataset_info[dataset[0]]["file_name"])) + ): + return gr.update(interactive=True) + else: + return gr.update(interactive=False) + + +def get_preview(dataset_dir: str, dataset: list, page_index: int) -> Tuple[int, list, Dict[str, Any]]: + with open(os.path.join(dataset_dir, DATA_CONFIG), "r", encoding="utf-8") as f: + dataset_info = json.load(f) + + data_file: str = dataset_info[dataset[0]]["file_name"] + with open(os.path.join(dataset_dir, data_file), "r", encoding="utf-8") as f: + if data_file.endswith(".json"): + data = json.load(f) + elif data_file.endswith(".jsonl"): + data = [json.loads(line) for line in f] + else: + data = [line for line in f] + return len(data), data[PAGE_SIZE * page_index : PAGE_SIZE * (page_index + 1)], gr.update(visible=True) + + +def create_preview_box(dataset_dir: "gr.Textbox", dataset: "gr.Dropdown") -> Dict[str, "Component"]: + data_preview_btn = gr.Button(interactive=False, scale=1) + with gr.Column(visible=False, elem_classes="modal-box") as preview_box: + with gr.Row(): + preview_count = gr.Number(value=0, interactive=False, precision=0) + page_index = gr.Number(value=0, interactive=False, precision=0) + + with gr.Row(): + prev_btn = gr.Button() + next_btn = gr.Button() + close_btn = gr.Button() + + with gr.Row(): + preview_samples = gr.JSON(interactive=False) + + dataset.change( + can_preview, [dataset_dir, dataset], [data_preview_btn], queue=False + ).then( + lambda: 0, outputs=[page_index], queue=False + ) + data_preview_btn.click( + get_preview, + [dataset_dir, dataset, page_index], + [preview_count, preview_samples, preview_box], + queue=False + ) + prev_btn.click( + prev_page, [page_index], [page_index], queue=False + ).then( + get_preview, + [dataset_dir, dataset, page_index], + [preview_count, preview_samples, preview_box], + queue=False + ) + next_btn.click( + next_page, [page_index, preview_count], [page_index], queue=False + ).then( + get_preview, + [dataset_dir, dataset, page_index], + [preview_count, preview_samples, preview_box], + queue=False + ) + close_btn.click(lambda: gr.update(visible=False), outputs=[preview_box], queue=False) + return dict( + data_preview_btn=data_preview_btn, + preview_count=preview_count, + page_index=page_index, + prev_btn=prev_btn, + next_btn=next_btn, + close_btn=close_btn, + preview_samples=preview_samples + ) diff --git a/llm_rl/src/llmtuner/webui/components/eval.py b/llm_rl/src/llmtuner/webui/components/eval.py new file mode 100644 index 00000000..36c994a6 --- /dev/null +++ b/llm_rl/src/llmtuner/webui/components/eval.py @@ -0,0 +1,70 @@ +import gradio as gr +from typing import TYPE_CHECKING, Dict + +from llmtuner.webui.common import list_dataset, DEFAULT_DATA_DIR +from llmtuner.webui.components.data import create_preview_box + +if TYPE_CHECKING: + from gradio.components import Component + from llmtuner.webui.engine import Engine + + +def create_eval_tab(engine: "Engine") -> Dict[str, "Component"]: + input_elems = engine.manager.get_base_elems() + elem_dict = dict() + + with gr.Row(): + dataset_dir = gr.Textbox(value=DEFAULT_DATA_DIR, scale=2) + dataset = gr.Dropdown(multiselect=True, scale=4) + preview_elems = create_preview_box(dataset_dir, dataset) + + dataset_dir.change(list_dataset, [dataset_dir], [dataset], queue=False) + + input_elems.update({dataset_dir, dataset}) + elem_dict.update(dict(dataset_dir=dataset_dir, dataset=dataset, **preview_elems)) + + with gr.Row(): + cutoff_len = gr.Slider(value=1024, minimum=4, maximum=8192, step=1) + max_samples = gr.Textbox(value="100000") + batch_size = gr.Slider(value=8, minimum=1, maximum=512, step=1) + predict = gr.Checkbox(value=True) + + input_elems.update({cutoff_len, max_samples, batch_size, predict}) + elem_dict.update(dict( + cutoff_len=cutoff_len, max_samples=max_samples, batch_size=batch_size, predict=predict + )) + + with gr.Row(): + max_new_tokens = gr.Slider(10, 2048, value=128, step=1) + top_p = gr.Slider(0.01, 1, value=0.7, step=0.01) + temperature = gr.Slider(0.01, 1.5, value=0.95, step=0.01) + + input_elems.update({max_new_tokens, top_p, temperature}) + elem_dict.update(dict( + max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature + )) + + with gr.Row(): + cmd_preview_btn = gr.Button() + start_btn = gr.Button() + stop_btn = gr.Button() + + with gr.Row(): + resume_btn = gr.Checkbox(visible=False, interactive=False, value=False) + process_bar = gr.Slider(visible=False, interactive=False) + + with gr.Box(): + output_box = gr.Markdown() + + output_elems = [output_box, process_bar] + elem_dict.update(dict( + cmd_preview_btn=cmd_preview_btn, start_btn=start_btn, stop_btn=stop_btn, + resume_btn=resume_btn, process_bar=process_bar, output_box=output_box + )) + + cmd_preview_btn.click(engine.runner.preview_eval, input_elems, output_elems) + start_btn.click(engine.runner.run_eval, input_elems, output_elems) + stop_btn.click(engine.runner.set_abort, queue=False) + resume_btn.change(engine.runner.monitor, outputs=output_elems) + + return elem_dict diff --git a/llm_rl/src/llmtuner/webui/components/export.py b/llm_rl/src/llmtuner/webui/components/export.py new file mode 100644 index 00000000..d16fa3d1 --- /dev/null +++ b/llm_rl/src/llmtuner/webui/components/export.py @@ -0,0 +1,79 @@ +import gradio as gr +from typing import TYPE_CHECKING, Dict, Generator, List + +from llmtuner.tuner import export_model +from llmtuner.webui.common import get_save_dir +from llmtuner.webui.locales import ALERTS + +if TYPE_CHECKING: + from gradio.components import Component + from llmtuner.webui.engine import Engine + + +def save_model( + lang: str, + model_name: str, + model_path: str, + checkpoints: List[str], + finetuning_type: str, + template: str, + max_shard_size: int, + export_dir: str +) -> Generator[str, None, None]: + error = "" + if not model_name: + error = ALERTS["err_no_model"][lang] + elif not model_path: + error = ALERTS["err_no_path"][lang] + elif not checkpoints: + error = ALERTS["err_no_checkpoint"][lang] + elif not export_dir: + error = ALERTS["err_no_export_dir"][lang] + + if error: + gr.Warning(error) + yield error + return + + args = dict( + model_name_or_path=model_path, + checkpoint_dir=",".join([get_save_dir(model_name, finetuning_type, ckpt) for ckpt in checkpoints]), + finetuning_type=finetuning_type, + template=template, + export_dir=export_dir + ) + + yield ALERTS["info_exporting"][lang] + export_model(args, max_shard_size="{}GB".format(max_shard_size)) + yield ALERTS["info_exported"][lang] + + +def create_export_tab(engine: "Engine") -> Dict[str, "Component"]: + with gr.Row(): + export_dir = gr.Textbox() + max_shard_size = gr.Slider(value=10, minimum=1, maximum=100) + + export_btn = gr.Button() + info_box = gr.Textbox(show_label=False, interactive=False) + + export_btn.click( + save_model, + [ + engine.manager.get_elem_by_name("top.lang"), + engine.manager.get_elem_by_name("top.model_name"), + engine.manager.get_elem_by_name("top.model_path"), + engine.manager.get_elem_by_name("top.checkpoints"), + engine.manager.get_elem_by_name("top.finetuning_type"), + engine.manager.get_elem_by_name("top.template"), + max_shard_size, + export_dir + ], + [info_box] + ) + + return dict( + export_dir=export_dir, + max_shard_size=max_shard_size, + export_btn=export_btn, + info_box=info_box + ) diff --git a/llm_rl/src/llmtuner/webui/components/infer.py b/llm_rl/src/llmtuner/webui/components/infer.py new file mode 100644 index 00000000..d6dd7eed --- /dev/null +++ b/llm_rl/src/llmtuner/webui/components/infer.py @@ -0,0 +1,39 @@ +import gradio as gr +from typing import TYPE_CHECKING, Dict + +from llmtuner.webui.components.chatbot import create_chat_box + +if TYPE_CHECKING: + from gradio.components import Component + from llmtuner.webui.engine import Engine + + +def create_infer_tab(engine: "Engine") -> Dict[str, "Component"]: + input_elems = engine.manager.get_base_elems() + elem_dict = dict() + + with gr.Row(): + load_btn = gr.Button() + unload_btn = gr.Button() + + info_box = gr.Textbox(show_label=False, interactive=False) + elem_dict.update(dict(load_btn=load_btn, unload_btn=unload_btn, info_box=info_box)) + + chat_box, chatbot, history, chat_elems = create_chat_box(engine, visible=False) + elem_dict.update(dict(chat_box=chat_box, **chat_elems)) + + load_btn.click( + engine.chatter.load_model, input_elems, [info_box] + ).then( + lambda: gr.update(visible=engine.chatter.loaded), outputs=[chat_box] + ) + + unload_btn.click( + engine.chatter.unload_model, input_elems, [info_box] + ).then( + lambda: ([], []), outputs=[chatbot, history] + ).then( + lambda: gr.update(visible=engine.chatter.loaded), outputs=[chat_box] + ) + + return elem_dict diff --git a/llm_rl/src/llmtuner/webui/components/top.py b/llm_rl/src/llmtuner/webui/components/top.py new file mode 100644 index 00000000..c6299cab --- /dev/null +++ b/llm_rl/src/llmtuner/webui/components/top.py @@ -0,0 +1,74 @@ +import gradio as gr +from typing import TYPE_CHECKING, Dict + +from llmtuner.extras.constants import METHODS, SUPPORTED_MODELS +from llmtuner.extras.template import templates +from llmtuner.webui.common import get_model_path, get_template, list_checkpoint, save_config +from llmtuner.webui.utils import can_quantize + +if TYPE_CHECKING: + from gradio.components import Component + + +def create_top() -> Dict[str, "Component"]: + available_models = list(SUPPORTED_MODELS.keys()) + ["Custom"] + + with gr.Row(): + lang = gr.Dropdown(choices=["en", "zh"], scale=1) + model_name = gr.Dropdown(choices=available_models, scale=3) + model_path = gr.Textbox(scale=3) + + with gr.Row(): + finetuning_type = gr.Dropdown(choices=METHODS, value="lora", scale=1) + checkpoints = gr.Dropdown(multiselect=True, scale=5) + refresh_btn = gr.Button(scale=1) + + with gr.Accordion(label="Advanced config", open=False) as advanced_tab: + with gr.Row(): + quantization_bit = gr.Dropdown(choices=["none", "8", "4"], value="none", scale=1) + template = gr.Dropdown(choices=list(templates.keys()), value="default", scale=1) + system_prompt = gr.Textbox(scale=2) + + with gr.Accordion(label="Model config (LLaMA only)", open=False) as llama_tab: + with gr.Row(): + with gr.Column(): + flash_attn = gr.Checkbox(value=False) + shift_attn = gr.Checkbox(value=False) + rope_scaling = gr.Radio(choices=["none", "linear", "dynamic"], value="none") + + model_name.change( + list_checkpoint, [model_name, finetuning_type], [checkpoints], queue=False + ).then( + get_model_path, [model_name], [model_path], queue=False + ).then( + get_template, [model_name], [template], queue=False + ) # do not save config since the below line will save + + model_path.change(save_config, inputs=[lang, model_name, model_path], queue=False) + + finetuning_type.change( + list_checkpoint, [model_name, finetuning_type], [checkpoints], queue=False + ).then( + can_quantize, [finetuning_type], [quantization_bit], queue=False + ) + + refresh_btn.click( + list_checkpoint, [model_name, finetuning_type], [checkpoints], queue=False + ) + + return dict( + lang=lang, + model_name=model_name, + model_path=model_path, + finetuning_type=finetuning_type, + checkpoints=checkpoints, + refresh_btn=refresh_btn, + advanced_tab=advanced_tab, + quantization_bit=quantization_bit, + template=template, + system_prompt=system_prompt, + llama_tab=llama_tab, + flash_attn=flash_attn, + shift_attn=shift_attn, + rope_scaling=rope_scaling + ) diff --git a/llm_rl/src/llmtuner/webui/components/train.py b/llm_rl/src/llmtuner/webui/components/train.py new file mode 100644 index 00000000..11109c97 --- /dev/null +++ b/llm_rl/src/llmtuner/webui/components/train.py @@ -0,0 +1,154 @@ +import gradio as gr +from typing import TYPE_CHECKING, Dict +from transformers.trainer_utils import SchedulerType + +from llmtuner.extras.constants import TRAINING_STAGES +from llmtuner.webui.common import list_checkpoint, list_dataset, DEFAULT_DATA_DIR +from llmtuner.webui.components.data import create_preview_box +from llmtuner.webui.utils import gen_plot + +if TYPE_CHECKING: + from gradio.components import Component + from llmtuner.webui.engine import Engine + + +def create_train_tab(engine: "Engine") -> Dict[str, "Component"]: + input_elems = engine.manager.get_base_elems() + elem_dict = dict() + + with gr.Row(): + training_stage = gr.Dropdown( + choices=list(TRAINING_STAGES.keys()), value=list(TRAINING_STAGES.keys())[0], scale=2 + ) + dataset_dir = gr.Textbox(value=DEFAULT_DATA_DIR, scale=2) + dataset = gr.Dropdown(multiselect=True, scale=4) + preview_elems = create_preview_box(dataset_dir, dataset) + + training_stage.change(list_dataset, [dataset_dir, training_stage], [dataset], queue=False) + dataset_dir.change(list_dataset, [dataset_dir, training_stage], [dataset], queue=False) + + input_elems.update({training_stage, dataset_dir, dataset}) + elem_dict.update(dict( + training_stage=training_stage, dataset_dir=dataset_dir, dataset=dataset, **preview_elems + )) + + with gr.Row(): + cutoff_len = gr.Slider(value=1024, minimum=4, maximum=8192, step=1) + learning_rate = gr.Textbox(value="5e-5") + num_train_epochs = gr.Textbox(value="3.0") + max_samples = gr.Textbox(value="100000") + compute_type = gr.Radio(choices=["fp16", "bf16"], value="fp16") + + input_elems.update({cutoff_len, learning_rate, num_train_epochs, max_samples, compute_type}) + elem_dict.update(dict( + cutoff_len=cutoff_len, learning_rate=learning_rate, num_train_epochs=num_train_epochs, + max_samples=max_samples, compute_type=compute_type + )) + + with gr.Row(): + batch_size = gr.Slider(value=4, minimum=1, maximum=512, step=1) + gradient_accumulation_steps = gr.Slider(value=4, minimum=1, maximum=512, step=1) + lr_scheduler_type = gr.Dropdown( + choices=[scheduler.value for scheduler in SchedulerType], value="cosine" + ) + max_grad_norm = gr.Textbox(value="1.0") + val_size = gr.Slider(value=0, minimum=0, maximum=1, step=0.001) + + input_elems.update({batch_size, gradient_accumulation_steps, lr_scheduler_type, max_grad_norm, val_size}) + elem_dict.update(dict( + batch_size=batch_size, gradient_accumulation_steps=gradient_accumulation_steps, + lr_scheduler_type=lr_scheduler_type, max_grad_norm=max_grad_norm, val_size=val_size + )) + + with gr.Accordion(label="Advanced config", open=False) as advanced_tab: + with gr.Row(): + logging_steps = gr.Slider(value=5, minimum=5, maximum=1000, step=5) + save_steps = gr.Slider(value=100, minimum=10, maximum=5000, step=10) + warmup_steps = gr.Slider(value=0, minimum=0, maximum=5000, step=1) + neft_alpha = gr.Slider(value=0, minimum=0, maximum=10, step=0.1) + + with gr.Column(): + train_on_prompt = gr.Checkbox(value=False) + upcast_layernorm = gr.Checkbox(value=False) + + input_elems.update({logging_steps, save_steps, warmup_steps, neft_alpha, train_on_prompt, upcast_layernorm}) + elem_dict.update(dict( + advanced_tab=advanced_tab, logging_steps=logging_steps, save_steps=save_steps, warmup_steps=warmup_steps, + neft_alpha=neft_alpha, train_on_prompt=train_on_prompt, upcast_layernorm=upcast_layernorm + )) + + with gr.Accordion(label="LoRA config", open=False) as lora_tab: + with gr.Row(): + lora_rank = gr.Slider(value=8, minimum=1, maximum=1024, step=1, scale=1) + lora_dropout = gr.Slider(value=0.1, minimum=0, maximum=1, step=0.01, scale=1) + lora_target = gr.Textbox(scale=1) + additional_target = gr.Textbox(scale=1) + resume_lora_training = gr.Checkbox(value=True, scale=1) + + input_elems.update({lora_rank, lora_dropout, lora_target, additional_target, resume_lora_training}) + elem_dict.update(dict( + lora_tab=lora_tab, lora_rank=lora_rank, lora_dropout=lora_dropout, lora_target=lora_target, + additional_target=additional_target, resume_lora_training=resume_lora_training, + )) + + with gr.Accordion(label="RLHF config", open=False) as rlhf_tab: + with gr.Row(): + dpo_beta = gr.Slider(value=0.1, minimum=0, maximum=1, step=0.01, scale=1) + reward_model = gr.Dropdown(scale=3) + refresh_btn = gr.Button(scale=1) + + refresh_btn.click( + list_checkpoint, + [engine.manager.get_elem_by_name("top.model_name"), engine.manager.get_elem_by_name("top.finetuning_type")], + [reward_model], + queue=False + ) + + input_elems.update({dpo_beta, reward_model}) + elem_dict.update(dict(rlhf_tab=rlhf_tab, dpo_beta=dpo_beta, reward_model=reward_model, refresh_btn=refresh_btn)) + + with gr.Row(): + cmd_preview_btn = gr.Button() + start_btn = gr.Button() + stop_btn = gr.Button() + + with gr.Row(): + with gr.Column(scale=3): + with gr.Row(): + output_dir = gr.Textbox() + + with gr.Row(): + resume_btn = gr.Checkbox(visible=False, interactive=False, value=False) + process_bar = gr.Slider(visible=False, interactive=False) + + with gr.Box(): + output_box = gr.Markdown() + + with gr.Column(scale=1): + loss_viewer = gr.Plot() + + input_elems.add(output_dir) + output_elems = [output_box, process_bar] + + cmd_preview_btn.click(engine.runner.preview_train, input_elems, output_elems) + start_btn.click(engine.runner.run_train, input_elems, output_elems) + stop_btn.click(engine.runner.set_abort, queue=False) + resume_btn.change(engine.runner.monitor, outputs=output_elems) + + elem_dict.update(dict( + cmd_preview_btn=cmd_preview_btn, start_btn=start_btn, stop_btn=stop_btn, output_dir=output_dir, + resume_btn=resume_btn, process_bar=process_bar, output_box=output_box, loss_viewer=loss_viewer + )) + + output_box.change( + gen_plot, + [ + engine.manager.get_elem_by_name("top.model_name"), + engine.manager.get_elem_by_name("top.finetuning_type"), + output_dir + ], + loss_viewer, + queue=False + ) + + return elem_dict diff --git a/llm_rl/src/llmtuner/webui/css.py b/llm_rl/src/llmtuner/webui/css.py new file mode 100644 index 00000000..c86fb96b --- /dev/null +++ b/llm_rl/src/llmtuner/webui/css.py @@ -0,0 +1,20 @@ +CSS = r""" +.modal-box { + position: fixed !important; + top: 50%; + left: 50%; + transform: translate(-50%, -50%); /* center horizontally */ + max-width: 1000px; + max-height: 750px; + overflow-y: auto; + background-color: var(--input-background-fill); + flex-wrap: nowrap !important; + border: 2px solid black !important; + z-index: 1000; + padding: 10px; +} + +.dark .modal-box { + border: 2px solid white !important; +} +""" diff --git a/llm_rl/src/llmtuner/webui/engine.py b/llm_rl/src/llmtuner/webui/engine.py new file mode 100644 index 00000000..661dfb48 --- /dev/null +++ b/llm_rl/src/llmtuner/webui/engine.py @@ -0,0 +1,57 @@ +import gradio as gr +from gradio.components import Component # cannot use TYPE_CHECKING here +from typing import Any, Dict, Generator, Optional + +from llmtuner.webui.chatter import WebChatModel +from llmtuner.webui.common import get_model_path, list_dataset, load_config +from llmtuner.webui.locales import LOCALES +from llmtuner.webui.manager import Manager +from llmtuner.webui.runner import Runner +from llmtuner.webui.utils import get_time + + +class Engine: + + def __init__(self, pure_chat: Optional[bool] = False) -> None: + self.pure_chat = pure_chat + self.manager: "Manager" = Manager() + self.runner: "Runner" = Runner(self.manager) + self.chatter: "WebChatModel" = WebChatModel(manager=self.manager, lazy_init=(not pure_chat)) + + def _form_dict(self, resume_dict: Dict[str, Dict[str, Any]]): + return {self.manager.get_elem_by_name(k): gr.update(**v) for k, v in resume_dict.items()} + + def resume(self) -> Generator[Dict[Component, Dict[str, Any]], None, None]: + user_config = load_config() + lang = user_config.get("lang", None) or "en" + + init_dict = { + "top.lang": {"value": lang}, + "infer.chat_box": {"visible": self.chatter.loaded} + } + + if not self.pure_chat: + init_dict["train.dataset"] = {"choices": list_dataset()["choices"]} + init_dict["eval.dataset"] = {"choices": list_dataset()["choices"]} + + if user_config.get("last_model", None): + init_dict["top.model_name"] = {"value": user_config["last_model"]} + init_dict["top.model_path"] = {"value": get_model_path(user_config["last_model"])} + + yield self._form_dict(init_dict) + + if not self.pure_chat: + if self.runner.alive: + yield {elem: gr.update(value=value) for elem, value in self.runner.running_data.items()} + if self.runner.do_train: + yield self._form_dict({"train.resume_btn": {"value": True}}) + else: + yield self._form_dict({"eval.resume_btn": {"value": True}}) + else: + yield self._form_dict({"train.output_dir": {"value": get_time()}}) + + def change_lang(self, lang: str) -> Dict[Component, Dict[str, Any]]: + return { + component: gr.update(**LOCALES[name][lang]) + for elems in self.manager.all_elems.values() for name, component in elems.items() if name in LOCALES + } diff --git a/llm_rl/src/llmtuner/webui/interface.py b/llm_rl/src/llmtuner/webui/interface.py new file mode 100644 index 00000000..ba663f24 --- /dev/null +++ b/llm_rl/src/llmtuner/webui/interface.py @@ -0,0 +1,66 @@ +import gradio as gr +from transformers.utils.versions import require_version + +from llmtuner.webui.components import ( + create_top, + create_train_tab, + create_eval_tab, + create_infer_tab, + create_export_tab, + create_chat_box +) +from llmtuner.webui.common import save_config +from llmtuner.webui.css import CSS +from llmtuner.webui.engine import Engine + + +require_version("gradio>=3.38.0,<4.0.0", "To fix: pip install \"gradio>=3.38.0,<4.0.0\"") + + +def create_ui() -> gr.Blocks: + engine = Engine(pure_chat=False) + + with gr.Blocks(title="LLaMA Board", css=CSS) as demo: + engine.manager.all_elems["top"] = create_top() + lang: "gr.Dropdown" = engine.manager.get_elem_by_name("top.lang") + + with gr.Tab("Train"): + engine.manager.all_elems["train"] = create_train_tab(engine) + + with gr.Tab("Evaluate"): + engine.manager.all_elems["eval"] = create_eval_tab(engine) + + with gr.Tab("Chat"): + engine.manager.all_elems["infer"] = create_infer_tab(engine) + + with gr.Tab("Export"): + engine.manager.all_elems["export"] = create_export_tab(engine) + + demo.load(engine.resume, outputs=engine.manager.list_elems()) + lang.change(engine.change_lang, [lang], engine.manager.list_elems(), queue=False) + lang.input(save_config, inputs=[lang], queue=False) + + return demo + + +def create_web_demo() -> gr.Blocks: + engine = Engine(pure_chat=True) + + with gr.Blocks(title="Web Demo", css=CSS) as demo: + lang = gr.Dropdown(choices=["en", "zh"]) + engine.manager.all_elems["top"] = dict(lang=lang) + + chat_box, _, _, chat_elems = create_chat_box(engine, visible=True) + engine.manager.all_elems["infer"] = dict(chat_box=chat_box, **chat_elems) + + demo.load(engine.resume, outputs=engine.manager.list_elems()) + lang.change(engine.change_lang, [lang], engine.manager.list_elems(), queue=False) + lang.input(save_config, inputs=[lang], queue=False) + + return demo + + +if __name__ == "__main__": + demo = create_ui() + demo.queue() + demo.launch(server_name="0.0.0.0", server_port=7860, share=False, inbrowser=True) diff --git a/llm_rl/src/llmtuner/webui/locales.py b/llm_rl/src/llmtuner/webui/locales.py new file mode 100644 index 00000000..cc2a1842 --- /dev/null +++ b/llm_rl/src/llmtuner/webui/locales.py @@ -0,0 +1,698 @@ +LOCALES = { + "lang": { + "en": { + "label": "Lang" + }, + "zh": { + "label": "语言" + } + }, + "model_name": { + "en": { + "label": "Model name" + }, + "zh": { + "label": "模型名称" + } + }, + "model_path": { + "en": { + "label": "Model path", + "info": "Path to pretrained model or model identifier from Hugging Face." + }, + "zh": { + "label": "模型路径", + "info": "本地模型的文件路径或 Hugging Face 的模型标识符。" + } + }, + "finetuning_type": { + "en": { + "label": "Finetuning method" + }, + "zh": { + "label": "微调方法" + } + }, + "checkpoints": { + "en": { + "label": "Checkpoints" + }, + "zh": { + "label": "模型断点" + } + }, + "refresh_btn": { + "en": { + "value": "Refresh checkpoints" + }, + "zh": { + "value": "刷新断点" + } + }, + "advanced_tab": { + "en": { + "label": "Advanced configurations" + }, + "zh": { + "label": "高级设置" + } + }, + "quantization_bit": { + "en": { + "label": "Quantization bit", + "info": "Enable 4/8-bit model quantization (QLoRA)." + }, + "zh": { + "label": "量化等级", + "info": "启用 4/8 比特模型量化（QLoRA）。" + } + }, + "template": { + "en": { + "label": "Prompt template", + "info": "The template used in constructing prompts." + }, + "zh": { + "label": "提示模板", + "info": "构建提示词时使用的模板" + } + }, + "system_prompt": { + "en": { + "label": "System prompt (optional)", + "info": "A sequence used as the default system prompt." + }, + "zh": { + "label": "系统提示词（非必填）", + "info": "默认使用的系统提示词" + } + }, + "llama_tab": { + "en": { + "label": "Model configurations (LLaMA only)" + }, + "zh": { + "label": "模型设置（仅LLaMA）" + } + }, + "flash_attn": { + "en": { + "label": "Use FlashAttention-2" + }, + "zh": { + "label": "使用 FlashAttention-2" + } + }, + "shift_attn": { + "en": { + "label": "Use shift short attention (S^2-Attn)" + }, + "zh": { + "label": "使用 shift short attention (S^2-Attn)" + } + }, + "rope_scaling": { + "en": { + "label": "RoPE scaling" + }, + "zh": { + "label": "RoPE 插值方法" + } + }, + "training_stage": { + "en": { + "label": "Stage", + "info": "The stage to perform in training." + }, + "zh": { + "label": "训练阶段", + "info": "目前采用的训练方式。" + } + }, + "dataset_dir": { + "en": { + "label": "Data dir", + "info": "Path of the data directory." + }, + "zh": { + "label": "数据路径", + "info": "数据文件夹的路径。" + } + }, + "dataset": { + "en": { + "label": "Dataset" + }, + "zh": { + "label": "数据集" + } + }, + "data_preview_btn": { + "en": { + "value": "Preview dataset" + }, + "zh": { + "value": "预览数据集" + } + }, + "preview_count": { + "en": { + "label": "Count" + }, + "zh": { + "label": "数量" + } + }, + "page_index": { + "en": { + "label": "Page" + }, + "zh": { + "label": "页数" + } + }, + "prev_btn": { + "en": { + "value": "Prev" + }, + "zh": { + "value": "上一页" + } + }, + "next_btn": { + "en": { + "value": "Next" + }, + "zh": { + "value": "下一页" + } + }, + "close_btn": { + "en": { + "value": "Close" + }, + "zh": { + "value": "关闭" + } + }, + "preview_samples": { + "en": { + "label": "Samples" + }, + "zh": { + "label": "样例" + } + }, + "cutoff_len": { + "en": { + "label": "Cutoff length", + "info": "Max tokens in input sequence." + }, + "zh": { + "label": "截断长度", + "info": "输入序列分词后的最大长度。" + } + }, + "learning_rate": { + "en": { + "label": "Learning rate", + "info": "Initial learning rate for AdamW." + }, + "zh": { + "label": "学习率", + "info": "AdamW 优化器的初始学习率。" + } + }, + "num_train_epochs": { + "en": { + "label": "Epochs", + "info": "Total number of training epochs to perform." + }, + "zh": { + "label": "训练轮数", + "info": "需要执行的训练总轮数。" + } + }, + "max_samples": { + "en": { + "label": "Max samples", + "info": "Maximum samples per dataset." + }, + "zh": { + "label": "最大样本数", + "info": "每个数据集最多使用的样本数。" + } + }, + "compute_type": { + "en": { + "label": "Compute type", + "info": "Whether to use fp16 or bf16 mixed precision training." + }, + "zh": { + "label": "计算类型", + "info": "是否启用 FP16 或 BF16 混合精度训练。" + } + }, + "batch_size": { + "en": { + "label": "Batch size", + "info": "Number of samples to process per GPU." + }, + "zh":{ + "label": "批处理大小", + "info": "每块 GPU 上处理的样本数量。" + } + }, + "gradient_accumulation_steps": { + "en": { + "label": "Gradient accumulation", + "info": "Number of gradient accumulation steps." + }, + "zh": { + "label": "梯度累积", + "info": "梯度累积的步数。" + } + }, + "lr_scheduler_type": { + "en": { + "label": "LR Scheduler", + "info": "Name of learning rate scheduler.", + }, + "zh": { + "label": "学习率调节器", + "info": "采用的学习率调节器名称。" + } + }, + "max_grad_norm": { + "en": { + "label": "Maximum gradient norm", + "info": "Norm for gradient clipping.." + }, + "zh": { + "label": "最大梯度范数", + "info": "用于梯度裁剪的范数。" + } + }, + "val_size": { + "en": { + "label": "Val size", + "info": "Proportion of data in the dev set." + }, + "zh": { + "label": "验证集比例", + "info": "验证集占全部样本的百分比。" + } + }, + "logging_steps": { + "en": { + "label": "Logging steps", + "info": "Number of steps between two logs." + }, + "zh": { + "label": "日志间隔", + "info": "每两次日志输出间的更新步数。" + } + }, + "save_steps": { + "en": { + "label": "Save steps", + "info": "Number of steps between two checkpoints." + }, + "zh": { + "label": "保存间隔", + "info": "每两次断点保存间的更新步数。" + } + }, + "warmup_steps": { + "en": { + "label": "Warmup steps", + "info": "Number of steps used for warmup." + }, + "zh": { + "label": "预热步数", + "info": "学习率预热采用的步数。" + } + }, + "neft_alpha": { + "en": { + "label": "NEFTune Alpha", + "info": "Magnitude of noise adding to embedding vectors." + }, + "zh": { + "label": "NEFTune 噪声参数", + "info": "嵌入向量所添加的噪声大小。" + } + }, + "train_on_prompt": { + "en": { + "label": "Train on prompt", + "info": "Compute loss on the prompt tokens in supervised fine-tuning." + }, + "zh": { + "label": "计算输入损失", + "info": "在监督微调时候计算输入序列的损失。" + } + }, + "upcast_layernorm": { + "en": { + "label": "Upcast LayerNorm", + "info": "Upcast weights of layernorm in float32." + }, + "zh": { + "label": "缩放归一化层", + "info": "将归一化层权重缩放至 32 位浮点数。" + } + }, + "lora_tab": { + "en": { + "label": "LoRA configurations" + }, + "zh": { + "label": "LoRA 参数设置" + } + }, + "lora_rank": { + "en": { + "label": "LoRA rank", + "info": "The rank of LoRA matrices." + }, + "zh": { + "label": "LoRA 秩", + "info": "LoRA 矩阵的秩。" + } + }, + "lora_dropout": { + "en": { + "label": "LoRA Dropout", + "info": "Dropout ratio of LoRA weights." + }, + "zh": { + "label": "LoRA 随机丢弃", + "info": "LoRA 权重随机丢弃的概率。" + } + }, + "lora_target": { + "en": { + "label": "LoRA modules (optional)", + "info": "Name(s) of target modules to apply LoRA. Use commas to separate multiple modules." + }, + "zh": { + "label": "LoRA 作用模块（非必填）", + "info": "应用 LoRA 的目标模块名称。使用英文逗号分隔多个名称。" + } + }, + "additional_target": { + "en": { + "label": "Additional modules (optional)", + "info": "Name(s) of modules apart from LoRA layers to be set as trainable. Use commas to separate multiple modules." + }, + "zh": { + "label": "附加模块（非必填）", + "info": "除 LoRA 层以外的可训练模块名称。使用英文逗号分隔多个名称。" + } + }, + "resume_lora_training": { + "en": { + "label": "Resume LoRA training", + "info": "Whether to resume training from the last LoRA weights or create new lora weights." + }, + "zh": { + "label": "继续上次的训练", + "info": "接着上次的 LoRA 权重训练或创建一个新的 LoRA 权重。" + } + }, + "rlhf_tab": { + "en": { + "label": "RLHF configurations" + }, + "zh": { + "label": "RLHF 参数设置" + } + }, + "dpo_beta": { + "en": { + "label": "DPO beta", + "info": "Value of the beta parameter in the DPO loss." + }, + "zh": { + "label": "DPO beta 参数", + "info": "DPO 损失函数中 beta 超参数大小。" + } + }, + "reward_model": { + "en": { + "label": "Reward model", + "info": "Checkpoint of the reward model for PPO training. (Needs to refresh checkpoints)" + }, + "zh": { + "label": "奖励模型", + "info": "PPO 训练中奖励模型的断点路径。（需要刷新断点）" + } + }, + "cmd_preview_btn": { + "en": { + "value": "Preview command" + }, + "zh": { + "value": "预览命令" + } + }, + "start_btn": { + "en": { + "value": "Start" + }, + "zh": { + "value": "开始" + } + }, + "stop_btn": { + "en": { + "value": "Abort" + }, + "zh": { + "value": "中断" + } + }, + "output_dir": { + "en": { + "label": "Checkpoint name", + "info": "Directory to save checkpoint." + }, + "zh": { + "label": "断点名称", + "info": "保存模型断点的文件夹名称。" + } + }, + "output_box": { + "en": { + "value": "Ready." + }, + "zh": { + "value": "准备就绪。" + } + }, + "loss_viewer": { + "en": { + "label": "Loss" + }, + "zh": { + "label": "损失" + } + }, + "predict": { + "en": { + "label": "Save predictions" + }, + "zh": { + "label": "保存预测结果" + } + }, + "load_btn": { + "en": { + "value": "Load model" + }, + "zh": { + "value": "加载模型" + } + }, + "unload_btn": { + "en": { + "value": "Unload model" + }, + "zh": { + "value": "卸载模型" + } + }, + "info_box": { + "en": { + "value": "Model unloaded, please load a model first." + }, + "zh": { + "value": "模型未加载，请先加载模型。" + } + }, + "system": { + "en": { + "placeholder": "System prompt (optional)" + }, + "zh": { + "placeholder": "系统提示词（非必填）" + } + }, + "query": { + "en": { + "placeholder": "Input..." + }, + "zh": { + "placeholder": "输入..." + } + }, + "submit_btn": { + "en": { + "value": "Submit" + }, + "zh": { + "value": "提交" + } + }, + "clear_btn": { + "en": { + "value": "Clear history" + }, + "zh": { + "value": "清空历史" + } + }, + "max_length": { + "en": { + "label": "Maximum length" + }, + "zh": { + "label": "最大长度" + } + }, + "max_new_tokens": { + "en": { + "label": "Maximum new tokens" + }, + "zh": { + "label": "最大生成长度" + } + }, + "top_p": { + "en": { + "label": "Top-p" + }, + "zh": { + "label": "Top-p 采样值" + } + }, + "temperature": { + "en": { + "label": "Temperature" + }, + "zh": { + "label": "温度系数" + } + }, + "export_dir": { + "en": { + "label": "Export dir", + "info": "Directory to save exported model." + }, + "zh": { + "label": "导出目录", + "info": "保存导出模型的文件夹路径。" + } + }, + "max_shard_size": { + "en": { + "label": "Max shard size (GB)", + "info": "The maximum size for a model file." + }, + "zh": { + "label": "最大分块大小（GB）", + "info": "模型文件的最大大小。" + } + }, + "export_btn": { + "en": { + "value": "Export" + }, + "zh": { + "value": "开始导出" + } + } +} + + +ALERTS = { + "err_conflict": { + "en": "A process is in running, please abort it firstly.", + "zh": "任务已存在，请先中断训练。" + }, + "err_exists": { + "en": "You have loaded a model, please unload it first.", + "zh": "模型已存在，请先卸载模型。" + }, + "err_no_model": { + "en": "Please select a model.", + "zh": "请选择模型。" + }, + "err_no_path": { + "en": "Model not found.", + "zh": "模型未找到。" + }, + "err_no_dataset": { + "en": "Please choose a dataset.", + "zh": "请选择数据集。" + }, + "err_no_checkpoint": { + "en": "Please select a checkpoint.", + "zh": "请选择断点。" + }, + "err_no_export_dir": { + "en": "Please provide export dir.", + "zh": "请填写导出目录" + }, + "err_failed": { + "en": "Failed.", + "zh": "训练出错。" + }, + "info_aborting": { + "en": "Aborted, wait for terminating...", + "zh": "训练中断，正在等待线程结束……" + }, + "info_aborted": { + "en": "Ready.", + "zh": "准备就绪。" + }, + "info_finished": { + "en": "Finished.", + "zh": "训练完毕。" + }, + "info_loading": { + "en": "Loading model...", + "zh": "加载中……" + }, + "info_unloading": { + "en": "Unloading model...", + "zh": "卸载中……" + }, + "info_loaded": { + "en": "Model loaded, now you can chat with your model!", + "zh": "模型已加载，可以开始聊天了！" + }, + "info_unloaded": { + "en": "Model unloaded.", + "zh": "模型已卸载。" + }, + "info_exporting": { + "en": "Exporting model...", + "zh": "正在导出模型……" + }, + "info_exported": { + "en": "Model exported.", + "zh": "模型导出完成。" + } +} diff --git a/llm_rl/src/llmtuner/webui/manager.py b/llm_rl/src/llmtuner/webui/manager.py new file mode 100644 index 00000000..ca067aea --- /dev/null +++ b/llm_rl/src/llmtuner/webui/manager.py @@ -0,0 +1,35 @@ +from typing import TYPE_CHECKING, Dict, List, Set + +if TYPE_CHECKING: + from gradio.components import Component + + +class Manager: + + def __init__(self) -> None: + self.all_elems: Dict[str, Dict[str, "Component"]] = {} + + def get_elem_by_name(self, name: str) -> "Component": + r""" + Example: top.lang, train.dataset + """ + tab_name, elem_name = name.split(".") + return self.all_elems[tab_name][elem_name] + + def get_base_elems(self) -> Set["Component"]: + return { + self.all_elems["top"]["lang"], + self.all_elems["top"]["model_name"], + self.all_elems["top"]["model_path"], + self.all_elems["top"]["checkpoints"], + self.all_elems["top"]["finetuning_type"], + self.all_elems["top"]["quantization_bit"], + self.all_elems["top"]["template"], + self.all_elems["top"]["system_prompt"], + self.all_elems["top"]["flash_attn"], + self.all_elems["top"]["shift_attn"], + self.all_elems["top"]["rope_scaling"] + } + + def list_elems(self) -> List["Component"]: + return [elem for elems in self.all_elems.values() for elem in elems.values()] diff --git a/llm_rl/src/llmtuner/webui/runner.py b/llm_rl/src/llmtuner/webui/runner.py new file mode 100644 index 00000000..ab9e9ffc --- /dev/null +++ b/llm_rl/src/llmtuner/webui/runner.py @@ -0,0 +1,254 @@ +import os +import time +import logging +import gradio as gr +from threading import Thread +from gradio.components import Component # cannot use TYPE_CHECKING here +from typing import TYPE_CHECKING, Any, Dict, Generator, List, Tuple + +import transformers +from transformers.trainer import TRAINING_ARGS_NAME + +from llmtuner.extras.callbacks import LogCallback +from llmtuner.extras.constants import TRAINING_STAGES +from llmtuner.extras.logging import LoggerHandler +from llmtuner.extras.misc import torch_gc +from llmtuner.tuner import run_exp +from llmtuner.webui.common import get_module, get_save_dir, load_config +from llmtuner.webui.locales import ALERTS +from llmtuner.webui.utils import gen_cmd, get_eval_results, update_process_bar + +if TYPE_CHECKING: + from llmtuner.webui.manager import Manager + + +class Runner: + + def __init__(self, manager: "Manager") -> None: + self.manager = manager + """ Resume """ + self.thread: "Thread" = None + self.do_train = True + self.running_data: Dict["Component", Any] = None + self.monitor_inputs: Dict[str, str] = None + """ State """ + self.aborted = False + self.running = False + """ Handler """ + self.logger_handler = LoggerHandler() + self.logger_handler.setLevel(logging.INFO) + logging.root.addHandler(self.logger_handler) + transformers.logging.add_handler(self.logger_handler) + + @property + def alive(self) -> bool: + return self.thread is not None + + def set_abort(self) -> None: + self.aborted = True + self.running = False + + def _initialize(self, data: Dict[Component, Any], do_train: bool) -> str: + get = lambda name: data[self.manager.get_elem_by_name(name)] + lang, model_name, model_path = get("top.lang"), get("top.model_name"), get("top.model_path") + dataset = get("train.dataset") if do_train else get("eval.dataset") + + if self.running: + return ALERTS["err_conflict"][lang] + + if not model_name: + return ALERTS["err_no_model"][lang] + + if not model_path: + return ALERTS["err_no_path"][lang] + + if len(dataset) == 0: + return ALERTS["err_no_dataset"][lang] + + self.aborted = False + self.logger_handler.reset() + self.trainer_callback = LogCallback(self) + return "" + + def _finalize(self, lang: str, finish_info: str) -> str: + self.thread = None + self.running = False + torch_gc() + if self.aborted: + return ALERTS["info_aborted"][lang] + else: + return finish_info + + def _parse_train_args(self, data: Dict[Component, Any]) -> Dict[str, Any]: + get = lambda name: data[self.manager.get_elem_by_name(name)] + user_config = load_config() + + if get("top.checkpoints"): + checkpoint_dir = ",".join([ + get_save_dir(get("top.model_name"), get("top.finetuning_type"), ckpt) for ckpt in get("top.checkpoints") + ]) + else: + checkpoint_dir = None + + args = dict( + stage=TRAINING_STAGES[get("train.training_stage")], + model_name_or_path=get("top.model_path"), + do_train=True, + cache_dir=user_config.get("cache_dir", None), + checkpoint_dir=checkpoint_dir, + finetuning_type=get("top.finetuning_type"), + quantization_bit=int(get("top.quantization_bit")) if get("top.quantization_bit") in ["8", "4"] else None, + template=get("top.template"), + system_prompt=get("top.system_prompt"), + flash_attn=get("top.flash_attn"), + shift_attn=get("top.shift_attn"), + rope_scaling=get("top.rope_scaling") if get("top.rope_scaling") in ["linear", "dynamic"] else None, + dataset_dir=get("train.dataset_dir"), + dataset=",".join(get("train.dataset")), + cutoff_len=get("train.cutoff_len"), + learning_rate=float(get("train.learning_rate")), + num_train_epochs=float(get("train.num_train_epochs")), + max_samples=int(get("train.max_samples")), + per_device_train_batch_size=get("train.batch_size"), + gradient_accumulation_steps=get("train.gradient_accumulation_steps"), + lr_scheduler_type=get("train.lr_scheduler_type"), + max_grad_norm=float(get("train.max_grad_norm")), + logging_steps=get("train.logging_steps"), + save_steps=get("train.save_steps"), + warmup_steps=get("train.warmup_steps"), + neft_alpha=get("train.neft_alpha"), + train_on_prompt=get("train.train_on_prompt"), + upcast_layernorm=get("train.upcast_layernorm"), + lora_rank=get("train.lora_rank"), + lora_dropout=get("train.lora_dropout"), + lora_target=get("train.lora_target") or get_module(get("top.model_name")), + additional_target=get("train.additional_target") if get("train.additional_target") else None, + resume_lora_training=get("train.resume_lora_training"), + output_dir=get_save_dir(get("top.model_name"), get("top.finetuning_type"), get("train.output_dir")) + ) + args[get("train.compute_type")] = True + args["disable_tqdm"] = True + + if TRAINING_STAGES[get("train.training_stage")] in ["rm", "ppo", "dpo"]: + args["resume_lora_training"] = (args["quantization_bit"] is not None) + + if args["quantization_bit"] is not None: + args["upcast_layernorm"] = True + + if args["stage"] == "ppo": + args["reward_model"] = get("train.reward_model") + + if args["stage"] == "dpo": + args["dpo_beta"] = get("train.dpo_beta") + + if get("train.val_size") > 1e-6 and args["stage"] != "ppo": + args["val_size"] = get("train.val_size") + args["evaluation_strategy"] = "steps" + args["eval_steps"] = get("train.save_steps") + args["load_best_model_at_end"] = True + + return args + + def _parse_eval_args(self, data: Dict[Component, Any]) -> Dict[str, Any]: + get = lambda name: data[self.manager.get_elem_by_name(name)] + user_config = load_config() + + if get("top.checkpoints"): + checkpoint_dir = ",".join([ + get_save_dir(get("top.model_name"), get("top.finetuning_type"), ckpt) for ckpt in get("top.checkpoints") + ]) + output_dir = get_save_dir( + get("top.model_name"), get("top.finetuning_type"), "eval_" + "_".join(get("top.checkpoints")) + ) + else: + checkpoint_dir = None + output_dir = get_save_dir(get("top.model_name"), get("top.finetuning_type"), "eval_base") + + args = dict( + stage="sft", + model_name_or_path=get("top.model_path"), + do_eval=True, + predict_with_generate=True, + cache_dir=user_config.get("cache_dir", None), + checkpoint_dir=checkpoint_dir, + finetuning_type=get("top.finetuning_type"), + quantization_bit=int(get("top.quantization_bit")) if get("top.quantization_bit") in ["8", "4"] else None, + template=get("top.template"), + system_prompt=get("top.system_prompt"), + flash_attn=get("top.flash_attn"), + shift_attn=get("top.shift_attn"), + rope_scaling=get("top.rope_scaling") if get("top.rope_scaling") in ["linear", "dynamic"] else None, + dataset_dir=get("eval.dataset_dir"), + dataset=",".join(get("eval.dataset")), + cutoff_len=get("eval.cutoff_len"), + max_samples=int(get("eval.max_samples")), + per_device_eval_batch_size=get("eval.batch_size"), + max_new_tokens=get("eval.max_new_tokens"), + top_p=get("eval.top_p"), + temperature=get("eval.temperature"), + output_dir=output_dir + ) + + if get("eval.predict"): + args.pop("do_eval", None) + args["do_predict"] = True + + return args + + def _preview(self, data: Dict[Component, Any], do_train: bool) -> Generator[Tuple[str, Dict[str, Any]], None, None]: + error = self._initialize(data, do_train) + if error: + gr.Warning(error) + yield error, gr.update(visible=False) + else: + args = self._parse_train_args(data) if do_train else self._parse_eval_args(data) + yield gen_cmd(args), gr.update(visible=False) + + def _launch(self, data: Dict[Component, Any], do_train: bool) -> Generator[Tuple[str, Dict[str, Any]], None, None]: + error = self._initialize(data, do_train) + if error: + gr.Warning(error) + yield error, gr.update(visible=False) + else: + args = self._parse_train_args(data) if do_train else self._parse_eval_args(data) + run_kwargs = dict(args=args, callbacks=[self.trainer_callback]) + self.running = True + self.do_train, self.running_data = do_train, data + self.monitor_inputs = dict(lang=data[self.manager.get_elem_by_name("top.lang")], output_dir=args["output_dir"]) + self.thread = Thread(target=run_exp, kwargs=run_kwargs) + self.thread.start() + yield from self.monitor() + + def preview_train(self, data: Dict[Component, Any]) -> Generator[Tuple[str, Dict[str, Any]], None, None]: + yield from self._preview(data, do_train=True) + + def preview_eval(self, data: Dict[Component, Any]) -> Generator[Tuple[str, Dict[str, Any]], None, None]: + yield from self._preview(data, do_train=False) + + def run_train(self, data: Dict[Component, Any]) -> Generator[Tuple[str, Dict[str, Any]], None, None]: + yield from self._launch(data, do_train=True) + + def run_eval(self, data: Dict[Component, Any]) -> Generator[Tuple[str, Dict[str, Any]], None, None]: + yield from self._launch(data, do_train=False) + + def monitor(self) -> Generator[Tuple[str, Dict[str, Any]], None, None]: + lang, output_dir = self.monitor_inputs["lang"], self.monitor_inputs["output_dir"] + while self.thread.is_alive(): + time.sleep(2) + if self.aborted: + yield ALERTS["info_aborting"][lang], gr.update(visible=False) + else: + yield self.logger_handler.log, update_process_bar(self.trainer_callback) + + if self.do_train: + if os.path.exists(os.path.join(output_dir, TRAINING_ARGS_NAME)): + finish_info = ALERTS["info_finished"][lang] + else: + finish_info = ALERTS["err_failed"][lang] + else: + if os.path.exists(os.path.join(output_dir, "all_results.json")): + finish_info = get_eval_results(os.path.join(output_dir, "all_results.json")) + else: + finish_info = ALERTS["err_failed"][lang] + + yield self._finalize(lang, finish_info), gr.update(visible=False) diff --git a/llm_rl/src/llmtuner/webui/utils.py b/llm_rl/src/llmtuner/webui/utils.py new file mode 100644 index 00000000..933d951d --- /dev/null +++ b/llm_rl/src/llmtuner/webui/utils.py @@ -0,0 +1,85 @@ +import os +import json +import gradio as gr +import matplotlib.figure +import matplotlib.pyplot as plt +from typing import TYPE_CHECKING, Any, Dict +from datetime import datetime + +from llmtuner.extras.ploting import smooth +from llmtuner.webui.common import get_save_dir + +if TYPE_CHECKING: + from llmtuner.extras.callbacks import LogCallback + + +def update_process_bar(callback: "LogCallback") -> Dict[str, Any]: + if not callback.max_steps: + return gr.update(visible=False) + + percentage = round(100 * callback.cur_steps / callback.max_steps, 0) if callback.max_steps != 0 else 100.0 + label = "Running {:d}/{:d}: {} < {}".format( + callback.cur_steps, + callback.max_steps, + callback.elapsed_time, + callback.remaining_time + ) + return gr.update(label=label, value=percentage, visible=True) + + +def get_time() -> str: + return datetime.now().strftime('%Y-%m-%d-%H-%M-%S') + + +def can_quantize(finetuning_type: str) -> Dict[str, Any]: + if finetuning_type != "lora": + return gr.update(value="None", interactive=False) + else: + return gr.update(interactive=True) + + +def gen_cmd(args: Dict[str, Any]) -> str: + args.pop("disable_tqdm", None) + args["plot_loss"] = args.get("do_train", None) + cmd_lines = ["CUDA_VISIBLE_DEVICES=0 python src/train_bash.py "] + for k, v in args.items(): + if v is not None and v != "": + cmd_lines.append(" --{} {} ".format(k, str(v))) + cmd_text = "\\\n".join(cmd_lines) + cmd_text = "```bash\n{}\n```".format(cmd_text) + return cmd_text + + +def get_eval_results(path: os.PathLike) -> str: + with open(path, "r", encoding="utf-8") as f: + result = json.dumps(json.load(f), indent=4) + return "```json\n{}\n```\n".format(result) + + +def gen_plot(base_model: str, finetuning_type: str, output_dir: str) -> matplotlib.figure.Figure: + if not base_model: + return + log_file = get_save_dir(base_model, finetuning_type, output_dir, "trainer_log.jsonl") + if not os.path.isfile(log_file): + return + + plt.close("all") + fig = plt.figure() + ax = fig.add_subplot(111) + steps, losses = [], [] + with open(log_file, "r", encoding="utf-8") as f: + for line in f: + log_info = json.loads(line) + if log_info.get("loss", None): + steps.append(log_info["current_steps"]) + losses.append(log_info["loss"]) + + if len(losses) == 0: + return None + + ax.plot(steps, losses, alpha=0.4, label="original") + ax.plot(steps, smooth(losses), label="smoothed") + ax.legend() + ax.set_xlabel("step") + ax.set_ylabel("loss") + return fig diff --git a/llm_rl/src/train_bash.py b/llm_rl/src/train_bash.py new file mode 100644 index 00000000..9ddd0586 --- /dev/null +++ b/llm_rl/src/train_bash.py @@ -0,0 +1,14 @@ +from llmtuner import run_exp + + +def main(): + run_exp() + + +def _mp_fn(index): + # For xla_spawn (TPUs) + main() + + +if __name__ == "__main__": + main() diff --git a/llm_rl/src/train_web.py b/llm_rl/src/train_web.py new file mode 100644 index 00000000..38efd64d --- /dev/null +++ b/llm_rl/src/train_web.py @@ -0,0 +1,11 @@ +from llmtuner import create_ui + + +def main(): + demo = create_ui() + demo.queue() + demo.launch(server_name="0.0.0.0", server_port=7860, share=False, inbrowser=True) + + +if __name__ == "__main__": + main() diff --git a/llm_rl/src/web_demo.py b/llm_rl/src/web_demo.py new file mode 100644 index 00000000..257536ab --- /dev/null +++ b/llm_rl/src/web_demo.py @@ -0,0 +1,11 @@ +from llmtuner import create_web_demo + + +def main(): + demo = create_web_demo() + demo.queue() + demo.launch(server_name="0.0.0.0", server_port=7860, share=False, inbrowser=True) + + +if __name__ == "__main__": + main() diff --git a/llm_rl/tests/cal_flops.py b/llm_rl/tests/cal_flops.py new file mode 100644 index 00000000..01b005af --- /dev/null +++ b/llm_rl/tests/cal_flops.py @@ -0,0 +1,44 @@ +# coding=utf-8 +# Calculates the flops of pre-trained models. +# Usage: python cal_flops.py --model_name_or_path path_to_model --batch_size 1 --seq_length 512 +# Inspired by: https://www.deepspeed.ai/tutorials/flops-profiler/ + +import fire +import torch +from typing import Optional +from deepspeed.accelerator import get_accelerator # type: ignore +from deepspeed.profiling.flops_profiler import get_model_profile # type: ignore + +from llmtuner import ChatModel + + +def calculate( + model_name_or_path: str, + batch_size: Optional[int] = 1, + seq_length: Optional[int] = 256, + flash_attn: Optional[bool] = False +): + with get_accelerator().device(0): + chat_model = ChatModel(dict( + model_name_or_path=model_name_or_path, + template="vanilla", + flash_attn=flash_attn + )) + fake_input = torch.ones((batch_size, seq_length), dtype=torch.long, device=chat_model.model.device) + input_dict = { + "input_ids": fake_input, + "labels": fake_input.clone() + } + flops, macs, params = get_model_profile( + chat_model.model, + kwargs=input_dict, + print_profile=True, + detailed=True + ) + print("FLOPs:", flops) + print("MACs:", macs) + print("Params:", params) + + +if __name__ == "__main__": + fire.Fire(calculate) diff --git a/llm_rl/tests/llamafy_baichuan2.py b/llm_rl/tests/llamafy_baichuan2.py new file mode 100644 index 00000000..d08eee1c --- /dev/null +++ b/llm_rl/tests/llamafy_baichuan2.py @@ -0,0 +1,86 @@ +# coding=utf-8 +# Converts the Baichuan2-7B model in the same format as LLaMA2-7B. +# Usage: python llamafy_baichuan2.py --input_dir input --output_dir output --shard_size 10GB +# Inspired by: https://huggingface.co/fireballoon/baichuan-llama-7b/blob/main/convert_baichuan_to_llama.py +# Converted model: https://huggingface.co/hiyouga/Baichuan2-7B-Base-LLaMAfied + +import os +import fire +import json +import torch +from collections import OrderedDict +from transformers.modeling_utils import shard_checkpoint, WEIGHTS_NAME, WEIGHTS_INDEX_NAME +from typing import Any, Dict + + +CONFIG_NAME = "config.json" + + +def save_weight( + input_dir: str, + output_dir: str, + shard_size: str +): + baichuan2_state_dict: Dict[str, torch.Tensor] = OrderedDict() + for filepath in os.listdir(input_dir): + if os.path.isfile(os.path.join(input_dir, filepath)) and filepath.endswith(".bin"): + shard_weight = torch.load(os.path.join(input_dir, filepath), map_location="cpu") + baichuan2_state_dict.update(shard_weight) + + llama2_state_dict: Dict[str, torch.Tensor] = OrderedDict() + for key, value in baichuan2_state_dict.items(): + if "W_pack" in key: + proj_size = value.size(0) // 3 + llama2_state_dict[key.replace("W_pack", "q_proj")] = value[:proj_size, :] + llama2_state_dict[key.replace("W_pack", "k_proj")] = value[proj_size:2*proj_size, :] + llama2_state_dict[key.replace("W_pack", "v_proj")] = value[2*proj_size:, :] + elif "lm_head" in key: + llama2_state_dict[key] = torch.nn.functional.normalize(value) + else: + llama2_state_dict[key] = value + + shards, index = shard_checkpoint(llama2_state_dict, max_shard_size=shard_size, weights_name=WEIGHTS_NAME) + for shard_file, shard in shards.items(): + torch.save(shard, os.path.join(output_dir, shard_file)) + + if index is None: + print("Model weights saved in {}".format(os.path.join(output_dir, WEIGHTS_NAME))) + else: + with open(os.path.join(output_dir, WEIGHTS_INDEX_NAME), "w", encoding="utf-8") as f: + json.dump(index, f, indent=2, sort_keys=True) + print("Model weights saved in {}".format(output_dir)) + + +def save_config( + input_dir: str, + output_dir: str +): + with open(os.path.join(input_dir, CONFIG_NAME), "r", encoding="utf-8") as f: + llama2_config_dict: Dict[str, Any] = json.load(f) + + llama2_config_dict["architectures"] = ["LlamaForCausalLM"] + llama2_config_dict.pop("auto_map", None) + llama2_config_dict.pop("tokenizer_class", None) + llama2_config_dict["model_type"] = "llama" + + with open(os.path.join(output_dir, CONFIG_NAME), "w", encoding="utf-8") as f: + json.dump(llama2_config_dict, f, indent=2) + print("Model config saved in {}".format(os.path.join(output_dir, CONFIG_NAME))) + + +def llamafy_baichuan2( + input_dir: str, + output_dir: str, + shard_size: str +): + try: + os.makedirs(output_dir, exist_ok=False) + except Exception as e: + raise print("Output dir already exists", e) + + save_weight(input_dir, output_dir, shard_size) + save_config(input_dir, output_dir) + + +if __name__ == "__main__": + fire.Fire(llamafy_baichuan2) diff --git a/llm_rl/tests/llamafy_qwen.py b/llm_rl/tests/llamafy_qwen.py new file mode 100644 index 00000000..8b9fc395 --- /dev/null +++ b/llm_rl/tests/llamafy_qwen.py @@ -0,0 +1,135 @@ +# coding=utf-8 +# Converts the Qwen models in the same format as LLaMA2. +# Usage: python llamafy_qwen.py --input_dir input --output_dir output --shard_size 10GB + +import os +import fire +import json +import torch +from collections import OrderedDict +from safetensors import safe_open +from transformers.modeling_utils import shard_checkpoint, WEIGHTS_NAME, WEIGHTS_INDEX_NAME +from transformers.utils import check_min_version +from typing import Any, Dict + +try: + check_min_version("4.34.0") +except: + raise ValueError("Please upgrade `transformers` to 4.34.0") + + +CONFIG_NAME = "config.json" + + +def save_weight( + input_dir: str, + output_dir: str, + shard_size: str +) -> str: + qwen_state_dict: Dict[str, torch.Tensor] = OrderedDict() + for filepath in os.listdir(input_dir): + if os.path.isfile(os.path.join(input_dir, filepath)) and filepath.endswith(".safetensors"): + with safe_open(os.path.join(input_dir, filepath), framework="pt", device="cpu") as f: + for key in f.keys(): + qwen_state_dict[key] = f.get_tensor(key) + + llama2_state_dict: Dict[str, torch.Tensor] = OrderedDict() + torch_dtype = None + for key, value in qwen_state_dict.items(): + if torch_dtype is None: + torch_dtype = value.dtype + if "wte" in key: + llama2_state_dict["model.embed_tokens.weight"] = value + elif "ln_f" in key: + llama2_state_dict["model.norm.weight"] = value + else: + key = key.replace("transformer.h", "model.layers") + if "attn.c_attn" in key: + proj_size = value.size(0) // 3 + llama2_state_dict[key.replace("attn.c_attn", "self_attn.q_proj")] = value[:proj_size, ...] + llama2_state_dict[key.replace("attn.c_attn", "self_attn.k_proj")] = value[proj_size:2*proj_size, ...] + llama2_state_dict[key.replace("attn.c_attn", "self_attn.v_proj")] = value[2*proj_size:, ...] + elif "attn.c_proj" in key: + llama2_state_dict[key.replace("attn.c_proj", "self_attn.o_proj")] = value + llama2_state_dict[key.replace("attn.c_proj.weight", "self_attn.o_proj.bias")] = ( + torch.zeros_like(value[:, 0]).squeeze() + ) + elif "ln_1" in key: + llama2_state_dict[key.replace("ln_1", "input_layernorm")] = value + elif "ln_2" in key: + llama2_state_dict[key.replace("ln_2", "post_attention_layernorm")] = value + elif "mlp.w1" in key: + llama2_state_dict[key.replace("mlp.w1", "mlp.up_proj")] = value + elif "mlp.w2" in key: + llama2_state_dict[key.replace("mlp.w2", "mlp.gate_proj")] = value + elif "mlp.c_proj" in key: + llama2_state_dict[key.replace("mlp.c_proj", "mlp.down_proj")] = value + elif "lm_head" in key: + llama2_state_dict[key] = value + else: + raise KeyError("Unable to process key {}".format(key)) + + shards, index = shard_checkpoint(llama2_state_dict, max_shard_size=shard_size, weights_name=WEIGHTS_NAME) + for shard_file, shard in shards.items(): + torch.save(shard, os.path.join(output_dir, shard_file)) + + if index is None: + print("Model weights saved in {}".format(os.path.join(output_dir, WEIGHTS_NAME))) + else: + with open(os.path.join(output_dir, WEIGHTS_INDEX_NAME), "w", encoding="utf-8") as f: + json.dump(index, f, indent=2, sort_keys=True) + print("Model weights saved in {}".format(output_dir)) + + return str(torch_dtype).replace("torch.", "") + + +def save_config( + input_dir: str, + output_dir: str, + torch_dtype: str +): + with open(os.path.join(input_dir, CONFIG_NAME), "r", encoding="utf-8") as f: + qwen_config_dict: Dict[str, Any] = json.load(f) + + llama2_config_dict: Dict[str, Any] = OrderedDict() + llama2_config_dict["architectures"] = ["LlamaForCausalLM"] + llama2_config_dict["hidden_act"] = "silu" + llama2_config_dict["hidden_size"] = qwen_config_dict["hidden_size"] + llama2_config_dict["initializer_range"] = qwen_config_dict["initializer_range"] + llama2_config_dict["intermediate_size"] = qwen_config_dict["intermediate_size"] // 2 + llama2_config_dict["max_position_embeddings"] = qwen_config_dict["max_position_embeddings"] + llama2_config_dict["model_type"] = "llama" + llama2_config_dict["num_attention_heads"] = qwen_config_dict["num_attention_heads"] + llama2_config_dict["num_hidden_layers"] = qwen_config_dict["num_hidden_layers"] + llama2_config_dict["num_key_value_heads"] = qwen_config_dict["hidden_size"] // qwen_config_dict["kv_channels"] + llama2_config_dict["pretraining_tp"] = 1 + llama2_config_dict["rms_norm_eps"] = qwen_config_dict["layer_norm_epsilon"] + llama2_config_dict["rope_scaling"] = None + llama2_config_dict["tie_word_embeddings"] = qwen_config_dict["tie_word_embeddings"] + llama2_config_dict["torch_dtype"] = torch_dtype + llama2_config_dict["transformers_version"] = "4.34.0" + llama2_config_dict["use_cache"] = True + llama2_config_dict["vocab_size"] = qwen_config_dict["vocab_size"] + llama2_config_dict["attention_bias"] = True + + with open(os.path.join(output_dir, CONFIG_NAME), "w", encoding="utf-8") as f: + json.dump(llama2_config_dict, f, indent=2) + print("Model config saved in {}".format(os.path.join(output_dir, CONFIG_NAME))) + + +def llamafy_qwen( + input_dir: str, + output_dir: str, + shard_size: str +): + try: + os.makedirs(output_dir, exist_ok=False) + except Exception as e: + raise print("Output dir already exists", e) + + torch_dtype = save_weight(input_dir, output_dir, shard_size) + save_config(input_dir, output_dir, torch_dtype) + + +if __name__ == "__main__": + fire.Fire(llamafy_qwen) diff --git a/llm_rl/tests/quantize.py b/llm_rl/tests/quantize.py new file mode 100644 index 00000000..25321cf3 --- /dev/null +++ b/llm_rl/tests/quantize.py @@ -0,0 +1,50 @@ +# coding=utf-8 +# Quantizes models with AutoGPTQ (https://github.com/PanQiWei/AutoGPTQ). +# Usage: python quantize.py --input_dir path_to_llama_model --output_dir path_to_quant_model --data_file alpaca.json +# --max_length 1024 --max_samples 1024 +# dataset format: instruction (string), input (string), output (string), history (List[string]) + + +import fire +from datasets import load_dataset +from transformers import AutoTokenizer +from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig + + +def quantize(input_dir: str, output_dir: str, data_file: str, max_length: int, max_samples: int): + tokenizer = AutoTokenizer.from_pretrained(input_dir, use_fast=False, padding_side="left") + + def format_example(examples): + prefix=("A chat between a curious user and an artificial intelligence assistant. " + "The assistant gives helpful, detailed, and polite answers to the user's questions.") + texts = [] + for i in range(len(examples["instruction"])): + prompt = prefix + "\n" + if "history" in examples: + for user_query, bot_resp in examples["history"][i]: + prompt += "Human: {}\nAssistant: {}\n".format(user_query, bot_resp) + prompt += "Human: {}\nAssistant: {}".format( + examples["instruction"][i] + "\n" + examples["input"][i], examples["output"][i] + ) + texts.append(prompt) + return tokenizer(texts, truncation=True, max_length=max_length) + + dataset = load_dataset("json", data_files=data_file)["train"] + column_names = list(dataset.column_names) + dataset = dataset.select(range(min(len(dataset), max_samples))) + dataset = dataset.map(format_example, batched=True, remove_columns=column_names) + dataset = dataset.shuffle() + + quantize_config = BaseQuantizeConfig( + bits=4, + group_size=128, + desc_act=False + ) + + model = AutoGPTQForCausalLM.from_pretrained(input_dir, quantize_config, trust_remote_code=True) + model.quantize(dataset) + model.save_quantized(output_dir) + + +if __name__ == "__main__": + fire.Fire(quantize)