423A35C7/LLaMA-Factory
1
0
Fork 0
mirror of https://github.com/hiyouga/LLaMA-Factory.git synced 2025-09-12 16:12:48 +08:00
Code Issues Packages Projects Releases Wiki Activity

Merge branch 'main' into main

Browse Source 
Former-commit-id: 5f14910910154ba569435e7e68acbd6c30f79e80
This commit is contained in:
hoshi-hiyouga 2024-11-02 21:20:27 +08:00 committed by GitHub
commit d99e164cad
147 changed files with 3087 additions and 1833 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
.dockerignore
View File

@ -7,6 +7,8 @@ data
docker
saves
hf_cache
ms_cache
om_cache
output
.dockerignore
.gitattributes

17
.env.local
View File

@ -1,32 +1,35 @@
# Note: actually we do not support .env, just for reference
# api
API_HOST=0.0.0.0
API_PORT=8000
API_HOST=
API_PORT=
API_KEY=
API_MODEL_NAME=gpt-3.5-turbo
API_MODEL_NAME=
FASTAPI_ROOT_PATH=
MAX_CONCURRENT=
# general
DISABLE_VERSION_CHECK=
FORCE_CHECK_IMPORTS=
LLAMAFACTORY_VERBOSITY=
USE_MODELSCOPE_HUB=
USE_OPENMIND_HUB=
RECORD_VRAM=
# torchrun
FORCE_TORCHRUN=
MASTER_ADDR=
MASTER_PORT=
NNODES=
RANK=
NODE_RANK=
NPROC_PER_NODE=
# wandb
WANDB_DISABLED=
WANDB_PROJECT=huggingface
WANDB_PROJECT=
WANDB_API_KEY=
# gradio ui
GRADIO_SHARE=False
GRADIO_SERVER_NAME=0.0.0.0
GRADIO_SHARE=
GRADIO_SERVER_NAME=
GRADIO_SERVER_PORT=
GRADIO_ROOT_PATH=
GRADIO_IPV6=
# setup
ENABLE_SHORT_CONSOLE=1
# reserved (do not use)

46
.github/CONTRIBUTING.md vendored
View File

@ -19,3 +19,49 @@ There are several ways you can contribute to LLaMA Factory:
### Style guide
LLaMA Factory follows the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html), check it for details.
### Create a Pull Request
1. Fork the [repository](https://github.com/hiyouga/LLaMA-Factory) by clicking on the [Fork](https://github.com/hiyouga/LLaMA-Factory/fork) button on the repository's page. This creates a copy of the code under your GitHub user account.
2. Clone your fork to your local disk, and add the base repository as a remote:
```bash
git clone git@github.com:[username]/LLaMA-Factory.git
cd LLaMA-Factory
git remote add upstream https://github.com/hiyouga/LLaMA-Factory.git
```
3. Create a new branch to hold your development changes:
```bash
git checkout -b dev_your_branch
```
4. Set up a development environment by running the following command in a virtual environment:
```bash
pip install -e ".[dev]"
```
If LLaMA Factory was already installed in the virtual environment, remove it with `pip uninstall llamafactory` before reinstalling it in editable mode with the -e flag.
5. Check code before commit:
```bash
make commit
make style && make quality
make test
```
6. Submit changes:
```bash
git add .
git commit -m "commit message"
git fetch upstream
git rebase upstream/main
git push -u origin dev_your_branch
```
7. Create a merge request from your branch `dev_your_branch` at [origin repo](https://github.com/hiyouga/LLaMA-Factory).

3
.github/workflows/tests.yml vendored
View File

@ -22,7 +22,7 @@ jobs:
fail-fast: false
matrix:
python-version:
- "3.8"
- "3.8" # TODO: remove py38 in next transformers release
- "3.9"
- "3.10"
- "3.11"
@ -54,7 +54,6 @@ jobs:
- name: Install dependencies
run: |
python -m pip install --upgrade pip
python -m pip install git+https://github.com/huggingface/transformers.git
python -m pip install ".[torch,dev]"
- name: Check quality

1
.gitignore vendored
View File

@ -162,6 +162,7 @@ cython_debug/
# custom .gitignore
ms_cache/
hf_cache/
om_cache/
cache/
config/
saves/

28
.pre-commit-config.yaml Normal file
View File

@ -0,0 +1,28 @@
repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v5.0.0
hooks:
- id: check-ast
- id: check-added-large-files
args: ['--maxkb=25000']
- id: check-merge-conflict
- id: check-yaml
- id: debug-statements
- id: end-of-file-fixer
- id: trailing-whitespace
args: [--markdown-linebreak-ext=md]
- id: no-commit-to-branch
args: ['--branch', 'main']
- repo: https://github.com/asottile/pyupgrade
rev: v3.17.0
hooks:
- id: pyupgrade
args: [--py38-plus]
- repo: https://github.com/astral-sh/ruff-pre-commit
rev: v0.6.9
hooks:
- id: ruff
args: [--fix]
- id: ruff-format

11
Makefile
View File

@ -1,7 +1,14 @@
.PHONY: quality style test
.PHONY: build commit quality style test
check_dirs := scripts src tests setup.py
build:
pip install build && python -m build
commit:
pre-commit install
pre-commit run --all-files
quality:
ruff check $(check_dirs)
ruff format --check $(check_dirs)
@ -11,4 +18,4 @@ style:
ruff format $(check_dirs)
test:
CUDA_VISIBLE_DEVICES= pytest tests/
CUDA_VISIBLE_DEVICES= WANDB_DISABLED=true pytest -vv tests/

104
README.md
View File

@ -4,7 +4,7 @@
[![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/llamafactory)](https://pypi.org/project/llamafactory/)
[![Citation](https://img.shields.io/badge/citation-91-green)](#projects-using-llama-factory)
[![Citation](https://img.shields.io/badge/citation-92-green)](#projects-using-llama-factory)
[![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/rKfvV9r9FK?compact=true&style=flat)](https://discord.gg/rKfvV9r9FK)
[![Twitter](https://img.shields.io/twitter/follow/llamafactory_ai)](https://twitter.com/llamafactory_ai)
@ -26,10 +26,17 @@ https://github.com/user-attachments/assets/7c96b465-9df7-45f4-8053-bf03e58386d3
Choose your path:
- **Colab**: https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing
- **PAI-DSW**: https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory
- **PAI-DSW**: [Llama3 Example](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory) | [Qwen2-VL Example](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_qwen2vl)
- **Local machine**: Please refer to [usage](#getting-started)
- **Documentation (WIP)**: https://llamafactory.readthedocs.io/zh-cn/latest/
Recent activities:
- **2024/10/18-2024/11/30**: Build a personal tour guide bot using PAI+LLaMA Factory. [[website]](https://developer.aliyun.com/topic/llamafactory2)
> [!NOTE]
> Except for the above links, all other websites are unauthorized third-party websites. Please carefully use them.
## Table of Contents
- [Features](#features)
@ -72,6 +79,8 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
## Changelog
[24/10/09] We supported downloading pre-trained models and datasets from the **[Modelers Hub](https://modelers.cn/models)**. See [this tutorial](#download-from-modelers-hub) for usage.
[24/09/19] We support fine-tuning the **[Qwen2.5](https://qwenlm.github.io/blog/qwen2.5/)** models.
[24/08/30] We support fine-tuning the **[Qwen2-VL](https://qwenlm.github.io/blog/qwen2-vl/)** models. Thank [@simonJJJ](https://github.com/simonJJJ)'s PR.
@ -130,7 +139,7 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
[23/12/12] We supported fine-tuning the latest MoE model **[Mixtral 8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)** in our framework. See hardware requirement [here](#hardware-requirement).
[23/12/01] We supported downloading pre-trained models and datasets from the **[ModelScope Hub](https://modelscope.cn/models)** for Chinese mainland users. See [this tutorial](#download-from-modelscope-hub) for usage.
[23/12/01] We supported downloading pre-trained models and datasets from the **[ModelScope Hub](https://modelscope.cn/models)**. See [this tutorial](#download-from-modelscope-hub) for usage.
[23/10/21] We supported **[NEFTune](https://arxiv.org/abs/2310.05914)** trick for fine-tuning. Try `neftune_noise_alpha: 5` argument to activate NEFTune.
@ -162,36 +171,39 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
## Supported Models
| Model | Model size | Template |
| ----------------------------------------------------------------- | -------------------------------- | --------- |
| [Baichuan 2](https://huggingface.co/baichuan-inc) | 7B/13B | baichuan2 |
| [BLOOM/BLOOMZ](https://huggingface.co/bigscience) | 560M/1.1B/1.7B/3B/7.1B/176B | - |
| [ChatGLM3](https://huggingface.co/THUDM) | 6B | chatglm3 |
| [Command R](https://huggingface.co/CohereForAI) | 35B/104B | cohere |
| [DeepSeek (Code/MoE)](https://huggingface.co/deepseek-ai) | 7B/16B/67B/236B | deepseek |
| [Falcon](https://huggingface.co/tiiuae) | 7B/11B/40B/180B | falcon |
| [Gemma/Gemma 2/CodeGemma](https://huggingface.co/google) | 2B/7B/9B/27B | gemma |
| [GLM-4](https://huggingface.co/THUDM) | 9B | glm4 |
| [InternLM2/InternLM2.5](https://huggingface.co/internlm) | 7B/20B | intern2 |
| [Llama](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | - |
| [Llama 2](https://huggingface.co/meta-llama) | 7B/13B/70B | llama2 |
| [Llama 3/Llama 3.1](https://huggingface.co/meta-llama) | 8B/70B | llama3 |
| [LLaVA-1.5](https://huggingface.co/llava-hf) | 7B/13B | llava |
| [MiniCPM](https://huggingface.co/openbmb) | 1B/2B/4B | cpm/cpm3 |
| [Mistral/Mixtral](https://huggingface.co/mistralai) | 7B/8x7B/8x22B | mistral |
| [OLMo](https://huggingface.co/allenai) | 1B/7B | - |
| [PaliGemma](https://huggingface.co/google) | 3B | paligemma |
| [Phi-1.5/Phi-2](https://huggingface.co/microsoft) | 1.3B/2.7B | - |
| [Phi-3](https://huggingface.co/microsoft) | 4B/14B | phi |
| [Phi-3-small](https://huggingface.co/microsoft) | 7B | phi-small |
| [Qwen/Qwen1.5/Qwen2 (Code/Math/MoE)](https://huggingface.co/Qwen) | 0.5B/1.5B/4B/7B/14B/32B/72B/110B | qwen |
| [Qwen2.5 (Code/Math)](https://huggingface.co/Qwen) | 0.5B/1.5B/3B/7B/14B/32B/72B | qwen |
| [Qwen2-VL](https://huggingface.co/Qwen) | 2B/7B/72B | qwen2_vl |
| [StarCoder 2](https://huggingface.co/bigcode) | 3B/7B/15B | - |
| [XVERSE](https://huggingface.co/xverse) | 7B/13B/65B | xverse |
| [Yi/Yi-1.5 (Code)](https://huggingface.co/01-ai) | 1.5B/6B/9B/34B | yi |
| [Yi-VL](https://huggingface.co/01-ai) | 6B/34B | yi_vl |
| [Yuan 2](https://huggingface.co/IEITYuan) | 2B/51B/102B | yuan |
| Model | Model size | Template |
| ----------------------------------------------------------------- | -------------------------------- | ---------------- |
| [Baichuan 2](https://huggingface.co/baichuan-inc) | 7B/13B | baichuan2 |
| [BLOOM/BLOOMZ](https://huggingface.co/bigscience) | 560M/1.1B/1.7B/3B/7.1B/176B | - |
| [ChatGLM3](https://huggingface.co/THUDM) | 6B | chatglm3 |
| [Command R](https://huggingface.co/CohereForAI) | 35B/104B | cohere |
| [DeepSeek (Code/MoE)](https://huggingface.co/deepseek-ai) | 7B/16B/67B/236B | deepseek |
| [Falcon](https://huggingface.co/tiiuae) | 7B/11B/40B/180B | falcon |
| [Gemma/Gemma 2/CodeGemma](https://huggingface.co/google) | 2B/7B/9B/27B | gemma |
| [GLM-4](https://huggingface.co/THUDM) | 9B | glm4 |
| [Index](https://huggingface.co/IndexTeam) | 1.9B | index |
| [InternLM2/InternLM2.5](https://huggingface.co/internlm) | 7B/20B | intern2 |
| [Llama](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | - |
| [Llama 2](https://huggingface.co/meta-llama) | 7B/13B/70B | llama2 |
| [Llama 3-3.2](https://huggingface.co/meta-llama) | 1B/3B/8B/70B | llama3 |
| [LLaVA-1.5](https://huggingface.co/llava-hf) | 7B/13B | llava |
| [LLaVA-NeXT](https://huggingface.co/llava-hf) | 7B/8B/13B/34B/72B/110B | llava_next |
| [LLaVA-NeXT-Video](https://huggingface.co/llava-hf) | 7B/34B | llava_next_video |
| [MiniCPM](https://huggingface.co/openbmb) | 1B/2B/4B | cpm/cpm3 |
| [Mistral/Mixtral](https://huggingface.co/mistralai) | 7B/8x7B/8x22B | mistral |
| [OLMo](https://huggingface.co/allenai) | 1B/7B | - |
| [PaliGemma](https://huggingface.co/google) | 3B | paligemma |
| [Phi-1.5/Phi-2](https://huggingface.co/microsoft) | 1.3B/2.7B | - |
| [Phi-3](https://huggingface.co/microsoft) | 4B/14B | phi |
| [Phi-3-small](https://huggingface.co/microsoft) | 7B | phi_small |
| [Pixtral](https://huggingface.co/mistralai) | 12B | pixtral |
| [Qwen (1-2.5) (Code/Math/MoE)](https://huggingface.co/Qwen) | 0.5B/1.5B/3B/7B/14B/32B/72B/110B | qwen |
| [Qwen2-VL](https://huggingface.co/Qwen) | 2B/7B/72B | qwen2_vl |
| [StarCoder 2](https://huggingface.co/bigcode) | 3B/7B/15B | - |
| [XVERSE](https://huggingface.co/xverse) | 7B/13B/65B | xverse |
| [Yi/Yi-1.5 (Code)](https://huggingface.co/01-ai) | 1.5B/6B/9B/34B | yi |
| [Yi-VL](https://huggingface.co/01-ai) | 6B/34B | yi_vl |
| [Yuan 2](https://huggingface.co/IEITYuan) | 2B/51B/102B | yuan |
> [!NOTE]
> 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 "instruct/chat" models.
@ -360,7 +372,7 @@ cd LLaMA-Factory
pip install -e ".[torch,metrics]"
```
Extra dependencies available: torch, torch-npu, metrics, deepspeed, liger-kernel, bitsandbytes, hqq, eetq, gptq, awq, aqlm, vllm, galore, badam, adam-mini, qwen, modelscope, quality
Extra dependencies available: torch, torch-npu, metrics, deepspeed, liger-kernel, bitsandbytes, hqq, eetq, gptq, awq, aqlm, vllm, galore, badam, adam-mini, qwen, modelscope, openmind, quality
> [!TIP]
> Use `pip install --no-deps -e .` to resolve package conflicts.
@ -412,7 +424,7 @@ Download the pre-built Docker images: [32GB](http://mirrors.cn-central-221.ovaij
### Data Preparation
Please refer to [data/README.md](data/README.md) for checking the details about the format of dataset files. You can either use datasets on HuggingFace / ModelScope hub or load the dataset in local disk.
Please refer to [data/README.md](data/README.md) for checking the details about the format of dataset files. You can either use datasets on HuggingFace / ModelScope / Modelers hub or load the dataset in local disk.
> [!NOTE]
> Please update `data/dataset_info.json` to use your custom dataset.
@ -480,6 +492,7 @@ docker build -f ./docker/docker-cuda/Dockerfile \
docker run -dit --gpus=all \
-v ./hf_cache:/root/.cache/huggingface \
-v ./ms_cache:/root/.cache/modelscope \
-v ./om_cache:/root/.cache/openmind \
-v ./data:/app/data \
-v ./output:/app/output \
-p 7860:7860 \
@ -504,6 +517,7 @@ docker build -f ./docker/docker-npu/Dockerfile \
docker run -dit \
-v ./hf_cache:/root/.cache/huggingface \
-v ./ms_cache:/root/.cache/modelscope \
-v ./om_cache:/root/.cache/openmind \
-v ./data:/app/data \
-v ./output:/app/output \
-v /usr/local/dcmi:/usr/local/dcmi \
@ -537,6 +551,7 @@ docker build -f ./docker/docker-rocm/Dockerfile \
docker run -dit \
-v ./hf_cache:/root/.cache/huggingface \
-v ./ms_cache:/root/.cache/modelscope \
-v ./om_cache:/root/.cache/openmind \
-v ./data:/app/data \
-v ./output:/app/output \
-v ./saves:/app/saves \
@ -557,6 +572,7 @@ docker exec -it llamafactory bash
- `hf_cache`: Utilize Hugging Face cache on the host machine. Reassignable if a cache already exists in a different directory.
- `ms_cache`: Similar to Hugging Face cache but for ModelScope users.
- `om_cache`: Similar to Hugging Face cache but for Modelers users.
- `data`: Place datasets on this dir of the host machine so that they can be selected on LLaMA Board GUI.
- `output`: Set export dir to this location so that the merged result can be accessed directly on the host machine.
@ -570,6 +586,8 @@ API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml
> [!TIP]
> Visit [this page](https://platform.openai.com/docs/api-reference/chat/create) for API document.
>
> Examples: [Image understanding](scripts/test_image.py) | [Function calling](scripts/test_toolcall.py)
### Download from ModelScope Hub
@ -581,6 +599,16 @@ export USE_MODELSCOPE_HUB=1 # `set USE_MODELSCOPE_HUB=1` for Windows
Train the model by specifying a model ID of the ModelScope Hub as the `model_name_or_path`. You can find a full list of model IDs at [ModelScope Hub](https://modelscope.cn/models), e.g., `LLM-Research/Meta-Llama-3-8B-Instruct`.
### Download from Modelers Hub
You can also use Modelers Hub to download models and datasets.
```bash
export USE_OPENMIND_HUB=1 # `set USE_OPENMIND_HUB=1` for Windows
```
Train the model by specifying a model ID of the Modelers Hub as the `model_name_or_path`. You can find a full list of model IDs at [Modelers Hub](https://modelers.cn/models), e.g., `TeleAI/TeleChat-7B-pt`.
### Use W&B Logger
To use [Weights & Biases](https://wandb.ai) for logging experimental results, you need to add the following arguments to yaml files.
@ -684,11 +712,13 @@ If you have a project that should be incorporated, please contact via email or c
1. **[Sunsimiao](https://github.com/X-D-Lab/Sunsimiao)**: A large language model specialized in Chinese medical domain, based on Baichuan-7B and ChatGLM-6B.
1. **[CareGPT](https://github.com/WangRongsheng/CareGPT)**: A series of large language models for Chinese medical domain, based on LLaMA2-7B and Baichuan-13B.
1. **[MachineMindset](https://github.com/PKU-YuanGroup/Machine-Mindset/)**: A series of MBTI Personality large language models, capable of giving any LLM 16 different personality types based on different datasets and training methods.
1. **[Luminia-13B-v3](https://huggingface.co/Nekochu/Luminia-13B-v3)**: A large language model specialized in generate metadata for stable diffusion. [[🤗Demo]](https://huggingface.co/spaces/Nekochu/Luminia-13B_SD_Prompt)
1. **[Luminia-13B-v3](https://huggingface.co/Nekochu/Luminia-13B-v3)**: A large language model specialized in generate metadata for stable diffusion. [[demo]](https://huggingface.co/spaces/Nekochu/Luminia-13B_SD_Prompt)
1. **[Chinese-LLaVA-Med](https://github.com/BUAADreamer/Chinese-LLaVA-Med)**: A multimodal large language model specialized in Chinese medical domain, based on LLaVA-1.5-7B.
1. **[AutoRE](https://github.com/THUDM/AutoRE)**: A document-level relation extraction system based on large language models.
1. **[NVIDIA RTX AI Toolkit](https://github.com/NVIDIA/RTX-AI-Toolkit)**: SDKs for fine-tuning LLMs on Windows PC for NVIDIA RTX.
1. **[LazyLLM](https://github.com/LazyAGI/LazyLLM)**: An easy and lazy way for building multi-agent LLMs applications and supports model fine-tuning via LLaMA Factory.
1. **[RAG-Retrieval](https://github.com/NLPJCL/RAG-Retrieval)**: A full pipeline for RAG retrieval model fine-tuning, inference, and distillation. [[blog]](https://zhuanlan.zhihu.com/p/987727357)
</details>
@ -696,7 +726,7 @@ If you have a project that should be incorporated, please contact via email or c
This repository is licensed under the [Apache-2.0 License](LICENSE).
Please follow the model licenses to use the corresponding model weights: [Baichuan 2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Command R](https://cohere.com/c4ai-cc-by-nc-license) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [GLM-4](https://huggingface.co/THUDM/glm-4-9b/blob/main/LICENSE) / [InternLM2](https://github.com/InternLM/InternLM#license) / [Llama](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [Llama 2 (LLaVA-1.5)](https://ai.meta.com/llama/license/) / [Llama 3](https://llama.meta.com/llama3/license/) / [MiniCPM](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md) / [Mistral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/Phi-2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Phi-3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [StarCoder 2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yi-1.5](LICENSE) / [Yuan 2](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
Please follow the model licenses to use the corresponding model weights: [Baichuan 2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Command R](https://cohere.com/c4ai-cc-by-nc-license) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [GLM-4](https://huggingface.co/THUDM/glm-4-9b/blob/main/LICENSE) / [Index](https://huggingface.co/IndexTeam/Index-1.9B/blob/main/LICENSE) / [InternLM2](https://github.com/InternLM/InternLM#license) / [Llama](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [Llama 2 (LLaVA-1.5)](https://ai.meta.com/llama/license/) / [Llama 3](https://llama.meta.com/llama3/license/) / [MiniCPM](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md) / [Mistral/Mixtral/Pixtral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/Phi-2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Phi-3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [StarCoder 2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yi-1.5](LICENSE) / [Yuan 2](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
## Citation

99
README_zh.md
View File

@ -4,7 +4,7 @@
[![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/llamafactory)](https://pypi.org/project/llamafactory/)
[![Citation](https://img.shields.io/badge/citation-91-green)](#使用了-llama-factory-的项目)
[![Citation](https://img.shields.io/badge/citation-92-green)](#使用了-llama-factory-的项目)
[![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/rKfvV9r9FK?compact=true&style=flat)](https://discord.gg/rKfvV9r9FK)
[![Twitter](https://img.shields.io/twitter/follow/llamafactory_ai)](https://twitter.com/llamafactory_ai)
@ -26,11 +26,18 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
选择你的打开方式：
- **Colab**：https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing
- **PAI-DSW**：https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory
- **PAI-DSW**：[Llama3 案例](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory) | [Qwen2-VL 案例](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_qwen2vl)
- **本地机器**：请见[如何使用](#如何使用)
- **入门教程**：https://zhuanlan.zhihu.com/p/695287607
- **框架文档**：https://llamafactory.readthedocs.io/zh-cn/latest/
近期活动：
- **2024/10/18-2024/11/30**：使用 PAI+LLaMA Factory 构建个性化导游机器人。[[活动页面]](https://developer.aliyun.com/topic/llamafactory2)
> [!NOTE]
> 除上述链接以外的其他网站均为未经许可的第三方网站，请小心甄别。
## 目录
- [项目特色](#项目特色)
@ -73,6 +80,8 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
## 更新日志
[24/10/09] 我们支持了从 **[魔乐社区](https://modelers.cn/models)** 下载预训练模型和数据集。详细用法请参照 [此教程](#从魔乐社区下载)。
[24/09/19] 我们支持了 **[Qwen2.5](https://qwenlm.github.io/blog/qwen2.5/)** 模型的微调。
[24/08/30] 我们支持了 **[Qwen2-VL](https://qwenlm.github.io/blog/qwen2-vl/)** 模型的微调。感谢 [@simonJJJ](https://github.com/simonJJJ) 的 PR。
@ -163,35 +172,38 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
## 模型
| 模型名 | 模型大小 | Template |
| ----------------------------------------------------------------- | -------------------------------- | --------- |
| [Baichuan 2](https://huggingface.co/baichuan-inc) | 7B/13B | baichuan2 |
| [BLOOM/BLOOMZ](https://huggingface.co/bigscience) | 560M/1.1B/1.7B/3B/7.1B/176B | - |
| [ChatGLM3](https://huggingface.co/THUDM) | 6B | chatglm3 |
| [Command R](https://huggingface.co/CohereForAI) | 35B/104B | cohere |
| [DeepSeek (Code/MoE)](https://huggingface.co/deepseek-ai) | 7B/16B/67B/236B | deepseek |
| [Falcon](https://huggingface.co/tiiuae) | 7B/11B/40B/180B | falcon |
| [Gemma/Gemma 2/CodeGemma](https://huggingface.co/google) | 2B/7B/9B/27B | gemma |
| [GLM-4](https://huggingface.co/THUDM) | 9B | glm4 |
| [InternLM2/InternLM2.5](https://huggingface.co/internlm) | 7B/20B | intern2 |
| [Llama](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | - |
| [Llama 2](https://huggingface.co/meta-llama) | 7B/13B/70B | llama2 |
| [Llama 3/Llama 3.1](https://huggingface.co/meta-llama) | 8B/70B | llama3 |
| [LLaVA-1.5](https://huggingface.co/llava-hf) | 7B/13B | llava |
| [MiniCPM](https://huggingface.co/openbmb) | 1B/2B/4B | cpm/cpm3 |
| [Mistral/Mixtral](https://huggingface.co/mistralai) | 7B/8x7B/8x22B | mistral |
| [OLMo](https://huggingface.co/allenai) | 1B/7B | - |
| [PaliGemma](https://huggingface.co/google) | 3B | paligemma |
| [Phi-1.5/Phi-2](https://huggingface.co/microsoft) | 1.3B/2.7B | - |
| [Phi-3](https://huggingface.co/microsoft) | 4B/7B/14B | phi |
| [Qwen/Qwen1.5/Qwen2 (Code/Math/MoE)](https://huggingface.co/Qwen) | 0.5B/1.5B/4B/7B/14B/32B/72B/110B | qwen |
| [Qwen2.5 (Code/Math)](https://huggingface.co/Qwen) | 0.5B/1.5B/3B/7B/14B/32B/72B | qwen |
| [Qwen2-VL](https://huggingface.co/Qwen) | 2B/7B/72B | qwen2_vl |
| [StarCoder 2](https://huggingface.co/bigcode) | 3B/7B/15B | - |
| [XVERSE](https://huggingface.co/xverse) | 7B/13B/65B | xverse |
| [Yi/Yi-1.5 (Code)](https://huggingface.co/01-ai) | 1.5B/6B/9B/34B | yi |
| [Yi-VL](https://huggingface.co/01-ai) | 6B/34B | yi_vl |
| [Yuan 2](https://huggingface.co/IEITYuan) | 2B/51B/102B | yuan |
| 模型名 | 模型大小 | Template |
| ----------------------------------------------------------------- | -------------------------------- | ---------------- |
| [Baichuan 2](https://huggingface.co/baichuan-inc) | 7B/13B | baichuan2 |
| [BLOOM/BLOOMZ](https://huggingface.co/bigscience) | 560M/1.1B/1.7B/3B/7.1B/176B | - |
| [ChatGLM3](https://huggingface.co/THUDM) | 6B | chatglm3 |
| [Command R](https://huggingface.co/CohereForAI) | 35B/104B | cohere |
| [DeepSeek (Code/MoE)](https://huggingface.co/deepseek-ai) | 7B/16B/67B/236B | deepseek |
| [Falcon](https://huggingface.co/tiiuae) | 7B/11B/40B/180B | falcon |
| [Gemma/Gemma 2/CodeGemma](https://huggingface.co/google) | 2B/7B/9B/27B | gemma |
| [GLM-4](https://huggingface.co/THUDM) | 9B | glm4 |
| [Index](https://huggingface.co/IndexTeam) | 1.9B | index |
| [InternLM2/InternLM2.5](https://huggingface.co/internlm) | 7B/20B | intern2 |
| [Llama](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | - |
| [Llama 2](https://huggingface.co/meta-llama) | 7B/13B/70B | llama2 |
| [Llama 3-3.2](https://huggingface.co/meta-llama) | 1B/3B/8B/70B | llama3 |
| [LLaVA-1.5](https://huggingface.co/llava-hf) | 7B/13B | llava |
| [LLaVA-NeXT](https://huggingface.co/llava-hf) | 7B/8B/13B/34B/72B/110B | llava_next |
| [LLaVA-NeXT-Video](https://huggingface.co/llava-hf) | 7B/34B | llava_next_video |
| [MiniCPM](https://huggingface.co/openbmb) | 1B/2B/4B | cpm/cpm3 |
| [Mistral/Mixtral](https://huggingface.co/mistralai) | 7B/8x7B/8x22B | mistral |
| [OLMo](https://huggingface.co/allenai) | 1B/7B | - |
| [PaliGemma](https://huggingface.co/google) | 3B | paligemma |
| [Phi-1.5/Phi-2](https://huggingface.co/microsoft) | 1.3B/2.7B | - |
| [Phi-3](https://huggingface.co/microsoft) | 4B/7B/14B | phi |
| [Pixtral](https://huggingface.co/mistralai) | 12B | pixtral |
| [Qwen (1-2.5) (Code/Math/MoE)](https://huggingface.co/Qwen) | 0.5B/1.5B/3B/7B/14B/32B/72B/110B | qwen |
| [Qwen2-VL](https://huggingface.co/Qwen) | 2B/7B/72B | qwen2_vl |
| [StarCoder 2](https://huggingface.co/bigcode) | 3B/7B/15B | - |
| [XVERSE](https://huggingface.co/xverse) | 7B/13B/65B | xverse |
| [Yi/Yi-1.5 (Code)](https://huggingface.co/01-ai) | 1.5B/6B/9B/34B | yi |
| [Yi-VL](https://huggingface.co/01-ai) | 6B/34B | yi_vl |
| [Yuan 2](https://huggingface.co/IEITYuan) | 2B/51B/102B | yuan |
> [!NOTE]
> 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”（Instruct/Chat）模型请务必使用**对应的模板**。
@ -360,7 +372,7 @@ cd LLaMA-Factory
pip install -e ".[torch,metrics]"
```
可选的额外依赖项：torch、torch-npu、metrics、deepspeed、liger-kernel、bitsandbytes、hqq、eetq、gptq、awq、aqlm、vllm、galore、badam、adam-mini、qwen、modelscope、quality
可选的额外依赖项：torch、torch-npu、metrics、deepspeed、liger-kernel、bitsandbytes、hqq、eetq、gptq、awq、aqlm、vllm、galore、badam、adam-mini、qwen、modelscope、openmind、quality
> [!TIP]
> 遇到包冲突时，可使用 `pip install --no-deps -e .` 解决。
@ -412,7 +424,7 @@ source /usr/local/Ascend/ascend-toolkit/set_env.sh
### 数据准备
关于数据集文件的格式，请参考 [data/README_zh.md](data/README_zh.md) 的内容。你可以使用 HuggingFace / ModelScope 上的数据集或加载本地数据集。
关于数据集文件的格式，请参考 [data/README_zh.md](data/README_zh.md) 的内容。你可以使用 HuggingFace / ModelScope / Modelers 上的数据集或加载本地数据集。
> [!NOTE]
> 使用自定义数据集时，请更新 `data/dataset_info.json` 文件。
@ -480,6 +492,7 @@ docker build -f ./docker/docker-cuda/Dockerfile \
docker run -dit --gpus=all \
-v ./hf_cache:/root/.cache/huggingface \
-v ./ms_cache:/root/.cache/modelscope \
-v ./om_cache:/root/.cache/openmind \
-v ./data:/app/data \
-v ./output:/app/output \
-p 7860:7860 \
@ -504,6 +517,7 @@ docker build -f ./docker/docker-npu/Dockerfile \
docker run -dit \
-v ./hf_cache:/root/.cache/huggingface \
-v ./ms_cache:/root/.cache/modelscope \
-v ./om_cache:/root/.cache/openmind \
-v ./data:/app/data \
-v ./output:/app/output \
-v /usr/local/dcmi:/usr/local/dcmi \
@ -537,6 +551,7 @@ docker build -f ./docker/docker-rocm/Dockerfile \
docker run -dit \
-v ./hf_cache:/root/.cache/huggingface \
-v ./ms_cache:/root/.cache/modelscope \
-v ./om_cache:/root/.cache/openmind \
-v ./data:/app/data \
-v ./output:/app/output \
-v ./saves:/app/saves \
@ -557,6 +572,7 @@ docker exec -it llamafactory bash
- `hf_cache`：使用宿主机的 Hugging Face 缓存文件夹，允许更改为新的目录。
- `ms_cache`：类似 Hugging Face 缓存文件夹，为 ModelScope 用户提供。
- `om_cache`：类似 Hugging Face 缓存文件夹，为 Modelers 用户提供。
- `data`：宿主机中存放数据集的文件夹路径。
- `output`：将导出目录设置为该路径后，即可在宿主机中访问导出后的模型。
@ -570,6 +586,8 @@ API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml
> [!TIP]
> API 文档请查阅[这里](https://platform.openai.com/docs/api-reference/chat/create)。
>
> 示例：[图像理解](scripts/test_image.py) | [工具调用](scripts/test_toolcall.py)
### 从魔搭社区下载
@ -581,6 +599,16 @@ export USE_MODELSCOPE_HUB=1 # Windows 使用 `set USE_MODELSCOPE_HUB=1`
将 `model_name_or_path` 设置为模型 ID 来加载对应的模型。在[魔搭社区](https://modelscope.cn/models)查看所有可用的模型，例如 `LLM-Research/Meta-Llama-3-8B-Instruct`。
### 从魔乐社区下载
您也可以通过下述方法，使用魔乐社区下载数据集和模型。
```bash
export USE_OPENMIND_HUB=1 # Windows 使用 `set USE_OPENMIND_HUB=1`
```
将 `model_name_or_path` 设置为模型 ID 来加载对应的模型。在[魔乐社区](https://modelers.cn/models)查看所有可用的模型，例如 `TeleAI/TeleChat-7B-pt`。
### 使用 W&B 面板
若要使用 [Weights & Biases](https://wandb.ai) 记录实验数据，请在 yaml 文件中添加下面的参数。
@ -684,11 +712,12 @@ run_name: test_run # 可选
1. **[Sunsimiao](https://github.com/X-D-Lab/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。
1. **[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。
1. **[MachineMindset](https://github.com/PKU-YuanGroup/Machine-Mindset/)**：MBTI性格大模型项目，根据数据集与训练方式让任意 LLM 拥有 16 个不同的性格类型。
1. **[Luminia-13B-v3](https://huggingface.co/Nekochu/Luminia-13B-v3)**：一个用于生成 Stable Diffusion 提示词的大型语言模型。[[🤗Demo]](https://huggingface.co/spaces/Nekochu/Luminia-13B_SD_Prompt)
1. **[Luminia-13B-v3](https://huggingface.co/Nekochu/Luminia-13B-v3)**：一个用于生成 Stable Diffusion 提示词的大型语言模型。[[demo]](https://huggingface.co/spaces/Nekochu/Luminia-13B_SD_Prompt)
1. **[Chinese-LLaVA-Med](https://github.com/BUAADreamer/Chinese-LLaVA-Med)**：中文多模态医学大模型，基于 LLaVA-1.5-7B 在中文多模态医疗数据上微调而得。
1. **[AutoRE](https://github.com/THUDM/AutoRE)**：基于大语言模型的文档级关系抽取系统。
1. **[NVIDIA RTX AI Toolkit](https://github.com/NVIDIA/RTX-AI-Toolkit)**：在 Windows 主机上利用英伟达 RTX 设备进行大型语言模型微调的开发包。
1. **[LazyLLM](https://github.com/LazyAGI/LazyLLM)**：一个低代码构建多 Agent 大模型应用的开发工具，支持基于 LLaMA Factory 的模型微调.
1. **[RAG-Retrieval](https://github.com/NLPJCL/RAG-Retrieval)**：一个全链路 RAG 检索模型微调、推理和蒸馏代码库。[[blog]](https://zhuanlan.zhihu.com/p/987727357)
</details>
@ -696,7 +725,7 @@ run_name: test_run # 可选
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 2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Command R](https://cohere.com/c4ai-cc-by-nc-license) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [GLM-4](https://huggingface.co/THUDM/glm-4-9b/blob/main/LICENSE) / [InternLM2](https://github.com/InternLM/InternLM#license) / [Llama](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [Llama 2 (LLaVA-1.5)](https://ai.meta.com/llama/license/) / [Llama 3](https://llama.meta.com/llama3/license/) / [MiniCPM](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md) / [Mistral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/Phi-2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Phi-3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [StarCoder 2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yi-1.5](LICENSE) / [Yuan 2](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
使用模型权重时，请遵循对应的模型协议：[Baichuan 2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Command R](https://cohere.com/c4ai-cc-by-nc-license) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [GLM-4](https://huggingface.co/THUDM/glm-4-9b/blob/main/LICENSE) / [Index](https://huggingface.co/IndexTeam/Index-1.9B/blob/main/LICENSE) / [InternLM2](https://github.com/InternLM/InternLM#license) / [Llama](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [Llama 2 (LLaVA-1.5)](https://ai.meta.com/llama/license/) / [Llama 3](https://llama.meta.com/llama3/license/) / [MiniCPM](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md) / [Mistral/Mixtral/Pixtral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/Phi-2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Phi-3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [StarCoder 2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yi-1.5](LICENSE) / [Yuan 2](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
## 引用

BIN
assets/wechat.jpg
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 145 KiB

After

Width:  |  Height:  |  Size: 132 KiB

BIN
assets/wechat_npu.jpg
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 149 KiB

After

Width:  |  Height:  |  Size: 132 KiB

6
data/belle_multiturn/belle_multiturn.py
View File

@ -17,9 +17,9 @@ _CITATION = """\
}
"""
_HOMEPAGE = "{}/datasets/BelleGroup/multiturn_chat_0.8M".format(_HF_ENDPOINT)
_HOMEPAGE = f"{_HF_ENDPOINT}/datasets/BelleGroup/multiturn_chat_0.8M"
_LICENSE = "gpl-3.0"
_URL = "{}/datasets/BelleGroup/multiturn_chat_0.8M/resolve/main/multiturn_chat_0.8M.json".format(_HF_ENDPOINT)
_URL = f"{_HF_ENDPOINT}/datasets/BelleGroup/multiturn_chat_0.8M/resolve/main/multiturn_chat_0.8M.json"
class BelleMultiturn(datasets.GeneratorBasedBuilder):
@ -38,7 +38,7 @@ class BelleMultiturn(datasets.GeneratorBasedBuilder):
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": file_path})]
def _generate_examples(self, filepath: str):
with open(filepath, "r", encoding="utf-8") as f:
with open(filepath, encoding="utf-8") as f:
for key, row in enumerate(f):
data = json.loads(row)
conversations = []

6
data/dataset_info.json
View File

@ -54,7 +54,8 @@
},
"alpaca_en": {
"hf_hub_url": "llamafactory/alpaca_en",
"ms_hub_url": "llamafactory/alpaca_en"
"ms_hub_url": "llamafactory/alpaca_en",
"om_hub_url": "HaM/alpaca_en"
},
"alpaca_zh": {
"hf_hub_url": "llamafactory/alpaca_zh",
@ -66,7 +67,8 @@
},
"alpaca_gpt4_zh": {
"hf_hub_url": "llamafactory/alpaca_gpt4_zh",
"ms_hub_url": "llamafactory/alpaca_gpt4_zh"
"ms_hub_url": "llamafactory/alpaca_gpt4_zh",
"om_hub_url": "State_Cloud/alpaca-gpt4-data-zh"
},
"glaive_toolcall_en": {
"hf_hub_url": "llamafactory/glaive_toolcall_en",

6
data/hh_rlhf_en/hh_rlhf_en.py
View File

@ -8,9 +8,9 @@ import datasets
_HF_ENDPOINT = os.getenv("HF_ENDPOINT", "https://huggingface.co")
_DESCRIPTION = "Human preference data about helpfulness and harmlessness."
_CITATION = ""
_HOMEPAGE = "{}/datasets/Anthropic/hh-rlhf".format(_HF_ENDPOINT)
_HOMEPAGE = f"{_HF_ENDPOINT}/datasets/Anthropic/hh-rlhf"
_LICENSE = "mit"
_URL = "{}/datasets/Anthropic/hh-rlhf/resolve/main/".format(_HF_ENDPOINT)
_URL = f"{_HF_ENDPOINT}/datasets/Anthropic/hh-rlhf/resolve/main/"
_URLS = {
"train": [
_URL + "harmless-base/train.jsonl.gz",
@ -53,7 +53,7 @@ class HhRlhfEn(datasets.GeneratorBasedBuilder):
def _generate_examples(self, filepaths: List[str]):
key = 0
for filepath in filepaths:
with open(filepath, "r", encoding="utf-8") as f:
with open(filepath, encoding="utf-8") as f:
for row in f:
data = json.loads(row)
chosen = data["chosen"]

6
data/ultra_chat/ultra_chat.py
View File

@ -20,9 +20,9 @@ _CITATION = """\
}
"""
_HOMEPAGE = "{}/datasets/stingning/ultrachat".format(_HF_ENDPOINT)
_HOMEPAGE = f"{_HF_ENDPOINT}/datasets/stingning/ultrachat"
_LICENSE = "cc-by-nc-4.0"
_BASE_DATA_URL = "{}/datasets/stingning/ultrachat/resolve/main/train_{{idx}}.jsonl".format(_HF_ENDPOINT)
_BASE_DATA_URL = f"{_HF_ENDPOINT}/datasets/stingning/ultrachat/resolve/main/train_{{idx}}.jsonl"
class UltraChat(datasets.GeneratorBasedBuilder):
@ -42,7 +42,7 @@ class UltraChat(datasets.GeneratorBasedBuilder):
def _generate_examples(self, filepaths: List[str]):
for filepath in filepaths:
with open(filepath, "r", encoding="utf-8") as f:
with open(filepath, encoding="utf-8") as f:
for row in f:
try:
data = json.loads(row)

19
docker/docker-cuda/Dockerfile
View File

@ -1,6 +1,7 @@
# Use the NVIDIA official image with PyTorch 2.3.0
# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-24-02.html
FROM nvcr.io/nvidia/pytorch:24.02-py3
# Default use the NVIDIA official image with PyTorch 2.3.0
# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/index.html
ARG BASE_IMAGE=nvcr.io/nvidia/pytorch:24.02-py3
FROM ${BASE_IMAGE}
# Define environments
ENV MAX_JOBS=4
@ -12,6 +13,9 @@ ARG INSTALL_BNB=false
ARG INSTALL_VLLM=false
ARG INSTALL_DEEPSPEED=false
ARG INSTALL_FLASHATTN=false
ARG INSTALL_LIGER_KERNEL=false
ARG INSTALL_HQQ=false
ARG INSTALL_EETQ=false
ARG PIP_INDEX=https://pypi.org/simple
# Set the working directory
@ -38,6 +42,15 @@ RUN EXTRA_PACKAGES="metrics"; \
if [ "$INSTALL_DEEPSPEED" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},deepspeed"; \
fi; \
if [ "$INSTALL_LIGER_KERNEL" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},liger-kernel"; \
fi; \
if [ "$INSTALL_HQQ" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},hqq"; \
fi; \
if [ "$INSTALL_EETQ" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},eetq"; \
fi; \
pip install -e ".[$EXTRA_PACKAGES]"
# Rebuild flash attention

4
docker/docker-cuda/docker-compose.yml
View File

@ -8,11 +8,15 @@ services:
INSTALL_VLLM: false
INSTALL_DEEPSPEED: false
INSTALL_FLASHATTN: false
INSTALL_LIGER_KERNEL: false
INSTALL_HQQ: false
INSTALL_EETQ: false
PIP_INDEX: https://pypi.org/simple
container_name: llamafactory
volumes:
- ../../hf_cache:/root/.cache/huggingface
- ../../ms_cache:/root/.cache/modelscope
- ../../om_cache:/root/.cache/openmind
- ../../data:/app/data
- ../../output:/app/output
ports:

1
docker/docker-npu/docker-compose.yml
View File

@ -10,6 +10,7 @@ services:
volumes:
- ../../hf_cache:/root/.cache/huggingface
- ../../ms_cache:/root/.cache/modelscope
- ../../om_cache:/root/.cache/openmind
- ../../data:/app/data
- ../../output:/app/output
- /usr/local/dcmi:/usr/local/dcmi

8
docker/docker-rocm/Dockerfile
View File

@ -10,6 +10,8 @@ ARG INSTALL_BNB=false
ARG INSTALL_VLLM=false
ARG INSTALL_DEEPSPEED=false
ARG INSTALL_FLASHATTN=false
ARG INSTALL_LIGER_KERNEL=false
ARG INSTALL_HQQ=false
ARG PIP_INDEX=https://pypi.org/simple
# Set the working directory
@ -36,6 +38,12 @@ RUN EXTRA_PACKAGES="metrics"; \
if [ "$INSTALL_DEEPSPEED" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},deepspeed"; \
fi; \
if [ "$INSTALL_LIGER_KERNEL" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},liger-kernel"; \
fi; \
if [ "$INSTALL_HQQ" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},hqq"; \
fi; \
pip install -e ".[$EXTRA_PACKAGES]"
# Rebuild flash attention

3
docker/docker-rocm/docker-compose.yml
View File

@ -8,11 +8,14 @@ services:
INSTALL_VLLM: false
INSTALL_DEEPSPEED: false
INSTALL_FLASHATTN: false
INSTALL_LIGER_KERNEL: false
INSTALL_HQQ: false
PIP_INDEX: https://pypi.org/simple
container_name: llamafactory
volumes:
- ../../hf_cache:/root/.cache/huggingface
- ../../ms_cache:/root/.cache/modelscope
- ../../om_cache:/root/.cache/openmind
- ../../data:/app/data
- ../../output:/app/output
- ../../saves:/app/saves

3
evaluation/mmlu/mmlu.py
View File

@ -158,5 +158,4 @@ class MMLU(datasets.GeneratorBasedBuilder):
df = pd.read_csv(filepath, header=None)
df.columns = ["question", "A", "B", "C", "D", "answer"]
for i, instance in enumerate(df.to_dict(orient="records")):
yield i, instance
yield from enumerate(df.to_dict(orient="records"))

4
examples/README.md
View File

@ -89,8 +89,8 @@ llamafactory-cli train examples/train_lora/llama3_lora_predict.yaml
#### Supervised Fine-Tuning on Multiple Nodes
```bash
FORCE_TORCHRUN=1 NNODES=2 RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
FORCE_TORCHRUN=1 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
FORCE_TORCHRUN=1 NNODES=2 NODE_RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
FORCE_TORCHRUN=1 NNODES=2 NODE_RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
```
#### Supervised Fine-Tuning with DeepSpeed ZeRO-3 (Weight Sharding)

4
examples/README_zh.md
View File

@ -89,8 +89,8 @@ llamafactory-cli train examples/train_lora/llama3_lora_predict.yaml
#### 多机指令监督微调
```bash
FORCE_TORCHRUN=1 NNODES=2 RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
FORCE_TORCHRUN=1 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
FORCE_TORCHRUN=1 NNODES=2 NODE_RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
FORCE_TORCHRUN=1 NNODES=2 NODE_RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
```
#### 使用 DeepSpeed ZeRO-3 平均分配显存

9
requirements.txt
View File

@ -1,9 +1,9 @@
transformers>=4.41.2,<=4.45.0
datasets>=2.16.0,<=2.21.0
accelerate>=0.30.1,<=0.34.2
transformers>=4.41.2,<=4.46.1
datasets>=2.16.0,<=3.0.2
accelerate>=0.34.0,<=1.0.1
peft>=0.11.1,<=0.12.0
trl>=0.8.6,<=0.9.6
gradio>=4.0.0
gradio>=4.0.0,<5.0.0
pandas>=2.0.0
scipy
einops
@ -19,3 +19,4 @@ fire
packaging
pyyaml
numpy<2.0.0
av

1
scripts/cal_flops.py
View File

@ -1,4 +1,3 @@
# coding=utf-8
# Copyright 2024 Microsoft Corporation and the LlamaFactory team.
#
# This code is inspired by the Microsoft's DeepSpeed library.

3
scripts/cal_lr.py
View File

@ -1,4 +1,3 @@
# coding=utf-8
# Copyright 2024 imoneoi and the LlamaFactory team.
#
# This code is inspired by the imoneoi's OpenChat library.
@ -74,7 +73,7 @@ def calculate_lr(
elif stage == "sft":
data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, label_pad_token_id=IGNORE_INDEX)
else:
raise NotImplementedError("Stage does not supported: {}.".format(stage))
raise NotImplementedError(f"Stage does not supported: {stage}.")
dataloader = DataLoader(trainset, batch_size, shuffle=False, collate_fn=data_collator, pin_memory=True)
valid_tokens, total_tokens = 0, 0

9
scripts/cal_mfu.py
View File

@ -1,4 +1,3 @@
# coding=utf-8
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
@ -100,7 +99,7 @@ def compute_device_flops(world_size: int) -> float:
elif "4090" in device_name:
return 98 * 1e12 * world_size
else:
raise NotImplementedError("Device not supported: {}.".format(device_name))
raise NotImplementedError(f"Device not supported: {device_name}.")
def calculate_mfu(
@ -140,10 +139,10 @@ def calculate_mfu(
"bf16": True,
}
if deepspeed_stage in [2, 3]:
args["deepspeed"] = "examples/deepspeed/ds_z{}_config.json".format(deepspeed_stage)
args["deepspeed"] = f"examples/deepspeed/ds_z{deepspeed_stage}_config.json"
run_exp(args)
with open(os.path.join("saves", "test_mfu", "all_results.json"), "r", encoding="utf-8") as f:
with open(os.path.join("saves", "test_mfu", "all_results.json"), encoding="utf-8") as f:
result = json.load(f)
if dist.is_initialized():
@ -157,7 +156,7 @@ def calculate_mfu(
* compute_model_flops(model_name_or_path, total_batch_size, seq_length)
/ compute_device_flops(world_size)
)
print("MFU: {:.2f}%".format(mfu_value * 100))
print(f"MFU: {mfu_value * 100:.2f}%")
if __name__ == "__main__":

7
scripts/cal_ppl.py
View File

@ -1,4 +1,3 @@
# coding=utf-8
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
@ -100,7 +99,7 @@ def calculate_ppl(
tokenizer=tokenizer, label_pad_token_id=IGNORE_INDEX, train_on_prompt=train_on_prompt
)
else:
raise NotImplementedError("Stage does not supported: {}.".format(stage))
raise NotImplementedError(f"Stage does not supported: {stage}.")
dataloader = DataLoader(trainset, batch_size, shuffle=False, collate_fn=data_collator, pin_memory=True)
criterion = torch.nn.CrossEntropyLoss(reduction="none")
@ -125,8 +124,8 @@ def calculate_ppl(
with open(save_name, "w", encoding="utf-8") as f:
json.dump(perplexities, f, indent=2)
print("Average perplexity is {:.2f}".format(total_ppl / len(perplexities)))
print("Perplexities have been saved at {}.".format(save_name))
print(f"Average perplexity is {total_ppl / len(perplexities):.2f}")
print(f"Perplexities have been saved at {save_name}.")
if __name__ == "__main__":

3
scripts/length_cdf.py
View File

@ -1,4 +1,3 @@
# coding=utf-8
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
@ -61,7 +60,7 @@ def length_cdf(
for length, count in length_tuples:
count_accu += count
prob_accu += count / total_num * 100
print("{:d} ({:.2f}%) samples have length < {}.".format(count_accu, prob_accu, length + interval))
print(f"{count_accu:d} ({prob_accu:.2f}%) samples have length < {length + interval}.")
if __name__ == "__main__":

21
scripts/llama_pro.py
View File

@ -1,4 +1,3 @@
# coding=utf-8
# Copyright 2024 Tencent Inc. and the LlamaFactory team.
#
# This code is inspired by the Tencent's LLaMA-Pro library.
@ -40,7 +39,7 @@ if TYPE_CHECKING:
def change_name(name: str, old_index: int, new_index: int) -> str:
return name.replace(".{:d}.".format(old_index), ".{:d}.".format(new_index))
return name.replace(f".{old_index:d}.", f".{new_index:d}.")
def block_expansion(
@ -76,27 +75,27 @@ def block_expansion(
state_dict = model.state_dict()
if num_layers % num_expand != 0:
raise ValueError("`num_layers` {} should be divisible by `num_expand` {}.".format(num_layers, num_expand))
raise ValueError(f"`num_layers` {num_layers} should be divisible by `num_expand` {num_expand}.")
split = num_layers // num_expand
layer_cnt = 0
output_state_dict = OrderedDict()
for i in range(num_layers):
for key, value in state_dict.items():
if ".{:d}.".format(i) in key:
if f".{i:d}." in key:
output_state_dict[change_name(key, i, layer_cnt)] = value
print("Add layer {} copied from layer {}".format(layer_cnt, i))
print(f"Add layer {layer_cnt} copied from layer {i}")
layer_cnt += 1
if (i + 1) % split == 0:
for key, value in state_dict.items():
if ".{:d}.".format(i) in key:
if f".{i:d}." in key:
if "down_proj" in key or "o_proj" in key:
output_state_dict[change_name(key, i, layer_cnt)] = torch.zeros_like(value)
else:
output_state_dict[change_name(key, i, layer_cnt)] = torch.clone(value)
print("Add layer {} expanded from layer {}".format(layer_cnt, i))
print(f"Add layer {layer_cnt} expanded from layer {i}")
layer_cnt += 1
for key, value in state_dict.items():
@ -113,17 +112,17 @@ def block_expansion(
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)))
print(f"Model weights saved in {os.path.join(output_dir, weights_name)}")
else:
index_name = SAFE_WEIGHTS_INDEX_NAME if save_safetensors else WEIGHTS_INDEX_NAME
with open(os.path.join(output_dir, 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))
print(f"Model weights saved in {output_dir}")
print("- Fine-tune this model with:")
print("model_name_or_path: {}".format(output_dir))
print(f"model_name_or_path: {output_dir}")
print("finetuning_type: freeze")
print("freeze_trainable_layers: {}".format(num_expand))
print(f"freeze_trainable_layers: {num_expand}")
print("use_llama_pro: true")

9
scripts/llamafy_baichuan2.py
View File

@ -1,4 +1,3 @@
# coding=utf-8
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
@ -63,16 +62,16 @@ def save_weight(input_dir: str, output_dir: str, shard_size: str, save_safetenso
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)))
print(f"Model weights saved in {os.path.join(output_dir, WEIGHTS_NAME)}")
else:
index_name = SAFE_WEIGHTS_INDEX_NAME if save_safetensors else WEIGHTS_INDEX_NAME
with open(os.path.join(output_dir, 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))
print(f"Model weights saved in {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:
with open(os.path.join(input_dir, CONFIG_NAME), encoding="utf-8") as f:
llama2_config_dict: Dict[str, Any] = json.load(f)
llama2_config_dict["architectures"] = ["LlamaForCausalLM"]
@ -82,7 +81,7 @@ def save_config(input_dir: str, output_dir: str):
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)))
print(f"Model config saved in {os.path.join(output_dir, CONFIG_NAME)}")
def llamafy_baichuan2(

11
scripts/llamafy_qwen.py
View File

@ -1,4 +1,3 @@
# coding=utf-8
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
@ -86,7 +85,7 @@ def save_weight(input_dir: str, output_dir: str, shard_size: str, save_safetenso
elif "lm_head" in key:
llama2_state_dict[key] = value
else:
raise KeyError("Unable to process key {}".format(key))
raise KeyError(f"Unable to process key {key}")
weights_name = SAFE_WEIGHTS_NAME if save_safetensors else WEIGHTS_NAME
shards, index = shard_checkpoint(llama2_state_dict, max_shard_size=shard_size, weights_name=weights_name)
@ -98,18 +97,18 @@ def save_weight(input_dir: str, output_dir: str, shard_size: str, save_safetenso
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)))
print(f"Model weights saved in {os.path.join(output_dir, weights_name)}")
else:
index_name = SAFE_WEIGHTS_INDEX_NAME if save_safetensors else WEIGHTS_INDEX_NAME
with open(os.path.join(output_dir, 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))
print(f"Model weights saved in {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:
with open(os.path.join(input_dir, CONFIG_NAME), encoding="utf-8") as f:
qwen_config_dict: Dict[str, Any] = json.load(f)
llama2_config_dict: Dict[str, Any] = OrderedDict()
@ -135,7 +134,7 @@ def save_config(input_dir: str, output_dir: str, torch_dtype: str):
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)))
print(f"Model config saved in {os.path.join(output_dir, CONFIG_NAME)}")
def llamafy_qwen(

11
scripts/loftq_init.py
View File

@ -1,4 +1,3 @@
# coding=utf-8
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
#
# This code is based on the HuggingFace's PEFT library.
@ -70,19 +69,19 @@ def quantize_loftq(
setattr(peft_model.peft_config["default"], "base_model_name_or_path", os.path.abspath(output_dir))
setattr(peft_model.peft_config["default"], "init_lora_weights", True) # don't apply loftq again
peft_model.save_pretrained(loftq_dir, safe_serialization=save_safetensors)
print("Adapter weights saved in {}".format(loftq_dir))
print(f"Adapter weights saved in {loftq_dir}")
# Save base model
base_model: "PreTrainedModel" = peft_model.unload()
base_model.save_pretrained(output_dir, safe_serialization=save_safetensors)
tokenizer.save_pretrained(output_dir)
print("Model weights saved in {}".format(output_dir))
print(f"Model weights saved in {output_dir}")
print("- Fine-tune this model with:")
print("model_name_or_path: {}".format(output_dir))
print("adapter_name_or_path: {}".format(loftq_dir))
print(f"model_name_or_path: {output_dir}")
print(f"adapter_name_or_path: {loftq_dir}")
print("finetuning_type: lora")
print("quantization_bit: {}".format(loftq_bits))
print(f"quantization_bit: {loftq_bits}")
if __name__ == "__main__":

11
scripts/pissa_init.py
View File

@ -1,4 +1,3 @@
# coding=utf-8
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
#
# This code is based on the HuggingFace's PEFT library.
@ -54,7 +53,7 @@ def quantize_pissa(
lora_alpha=lora_alpha if lora_alpha is not None else lora_rank * 2,
lora_dropout=lora_dropout,
target_modules=lora_target,
init_lora_weights="pissa" if pissa_iter == -1 else "pissa_niter_{}".format(pissa_iter),
init_lora_weights="pissa" if pissa_iter == -1 else f"pissa_niter_{pissa_iter}",
)
# Init PiSSA model
@ -65,17 +64,17 @@ def quantize_pissa(
setattr(peft_model.peft_config["default"], "base_model_name_or_path", os.path.abspath(output_dir))
setattr(peft_model.peft_config["default"], "init_lora_weights", True) # don't apply pissa again
peft_model.save_pretrained(pissa_dir, safe_serialization=save_safetensors)
print("Adapter weights saved in {}".format(pissa_dir))
print(f"Adapter weights saved in {pissa_dir}")
# Save base model
base_model: "PreTrainedModel" = peft_model.unload()
base_model.save_pretrained(output_dir, safe_serialization=save_safetensors)
tokenizer.save_pretrained(output_dir)
print("Model weights saved in {}".format(output_dir))
print(f"Model weights saved in {output_dir}")
print("- Fine-tune this model with:")
print("model_name_or_path: {}".format(output_dir))
print("adapter_name_or_path: {}".format(pissa_dir))
print(f"model_name_or_path: {output_dir}")
print(f"adapter_name_or_path: {pissa_dir}")
print("finetuning_type: lora")
print("pissa_init: false")
print("pissa_convert: true")

65
scripts/test_image.py Normal file
View File

@ -0,0 +1,65 @@
# Copyright 2024 the LlamaFactory team.
#
# 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
from openai import OpenAI
from transformers.utils.versions import require_version
require_version("openai>=1.5.0", "To fix: pip install openai>=1.5.0")
def main():
client = OpenAI(
api_key="{}".format(os.environ.get("API_KEY", "0")),
base_url="http://localhost:{}/v1".format(os.environ.get("API_PORT", 8000)),
)
messages = []
messages.append(
{
"role": "user",
"content": [
{"type": "text", "text": "Output the color and number of each box."},
{
"type": "image_url",
"image_url": {"url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-VL/boxes.png"},
},
],
}
)
result = client.chat.completions.create(messages=messages, model="test")
messages.append(result.choices[0].message)
print("Round 1:", result.choices[0].message.content)
# The image shows a pyramid of colored blocks with numbers on them. Here are the colors and numbers of ...
messages.append(
{
"role": "user",
"content": [
{"type": "text", "text": "What kind of flower is this?"},
{
"type": "image_url",
"image_url": {"url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-VL/flowers.jpg"},
},
],
}
)
result = client.chat.completions.create(messages=messages, model="test")
messages.append(result.choices[0].message)
print("Round 2:", result.choices[0].message.content)
# The image shows a cluster of forget-me-not flowers. Forget-me-nots are small ...
if __name__ == "__main__":
main()

1
scripts/test_toolcall.py
View File

@ -1,4 +1,3 @@
# coding=utf-8
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");

11
setup.py
View File

@ -20,7 +20,7 @@ from setuptools import find_packages, setup
def get_version() -> str:
with open(os.path.join("src", "llamafactory", "extras", "env.py"), "r", encoding="utf-8") as f:
with open(os.path.join("src", "llamafactory", "extras", "env.py"), encoding="utf-8") as f:
file_content = f.read()
pattern = r"{}\W*=\W*\"([^\"]+)\"".format("VERSION")
(version,) = re.findall(pattern, file_content)
@ -28,7 +28,7 @@ def get_version() -> str:
def get_requires() -> List[str]:
with open("requirements.txt", "r", encoding="utf-8") as f:
with open("requirements.txt", 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
@ -54,13 +54,14 @@ extra_require = {
"gptq": ["optimum>=1.17.0", "auto-gptq>=0.5.0"],
"awq": ["autoawq"],
"aqlm": ["aqlm[gpu]>=1.1.0"],
"vllm": ["vllm>=0.4.3,<=0.6.0"],
"vllm": ["vllm>=0.4.3,<=0.6.3"],
"galore": ["galore-torch"],
"badam": ["badam>=1.2.1"],
"adam-mini": ["adam-mini"],
"qwen": ["transformers_stream_generator"],
"modelscope": ["modelscope"],
"dev": ["ruff", "pytest"],
"openmind": ["openmind"],
"dev": ["pre-commit", "ruff", "pytest"],
}
@ -71,7 +72,7 @@ def main():
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=open("README.md", 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",

6
src/api.py
View File

@ -23,9 +23,9 @@ from llamafactory.chat import ChatModel
def main():
chat_model = ChatModel()
app = create_app(chat_model)
api_host = os.environ.get("API_HOST", "0.0.0.0")
api_port = int(os.environ.get("API_PORT", "8000"))
print("Visit http://localhost:{}/docs for API document.".format(api_port))
api_host = os.getenv("API_HOST", "0.0.0.0")
api_port = int(os.getenv("API_PORT", "8000"))
print(f"Visit http://localhost:{api_port}/docs for API document.")
uvicorn.run(app, host=api_host, port=api_port)

11
src/llamafactory/__init__.py
View File

@ -20,17 +20,17 @@ Level:
Dependency graph:
main:
transformers>=4.41.2,<=4.45.0
datasets>=2.16.0,<=2.21.0
accelerate>=0.30.1,<=0.34.2
transformers>=4.41.2,<=4.46.1
datasets>=2.16.0,<=3.0.2
accelerate>=0.34.0,<=1.0.1
peft>=0.11.1,<=0.12.0
trl>=0.8.6,<=0.9.6
attention:
transformers>=4.42.4 (gemma+fa2)
longlora:
transformers>=4.41.2,<=4.45.0
transformers>=4.41.2,<=4.46.1
packing:
transformers>=4.41.2,<=4.45.0
transformers>=4.41.2,<=4.46.1
Disable version checking: DISABLE_VERSION_CHECK=1
Enable VRAM recording: RECORD_VRAM=1
@ -38,6 +38,7 @@ Force check imports: FORCE_CHECK_IMPORTS=1
Force using torchrun: FORCE_TORCHRUN=1
Set logging verbosity: LLAMAFACTORY_VERBOSITY=WARN
Use modelscope: USE_MODELSCOPE_HUB=1
Use openmind: USE_OPENMIND_HUB=1
"""
from .extras.env import VERSION

12
src/llamafactory/api/app.py
View File

@ -68,7 +68,7 @@ async def lifespan(app: "FastAPI", chat_model: "ChatModel"): # collects GPU mem
def create_app(chat_model: "ChatModel") -> "FastAPI":
root_path = os.environ.get("FASTAPI_ROOT_PATH", "")
root_path = os.getenv("FASTAPI_ROOT_PATH", "")
app = FastAPI(lifespan=partial(lifespan, chat_model=chat_model), root_path=root_path)
app.add_middleware(
CORSMiddleware,
@ -77,7 +77,7 @@ def create_app(chat_model: "ChatModel") -> "FastAPI":
allow_methods=["*"],
allow_headers=["*"],
)
api_key = os.environ.get("API_KEY", None)
api_key = os.getenv("API_KEY")
security = HTTPBearer(auto_error=False)
async def verify_api_key(auth: Annotated[Optional[HTTPAuthorizationCredentials], Depends(security)]):
@ -91,7 +91,7 @@ def create_app(chat_model: "ChatModel") -> "FastAPI":
dependencies=[Depends(verify_api_key)],
)
async def list_models():
model_card = ModelCard(id=os.environ.get("API_MODEL_NAME", "gpt-3.5-turbo"))
model_card = ModelCard(id=os.getenv("API_MODEL_NAME", "gpt-3.5-turbo"))
return ModelList(data=[model_card])
@app.post(
@ -128,7 +128,7 @@ def create_app(chat_model: "ChatModel") -> "FastAPI":
def run_api() -> None:
chat_model = ChatModel()
app = create_app(chat_model)
api_host = os.environ.get("API_HOST", "0.0.0.0")
api_port = int(os.environ.get("API_PORT", "8000"))
print("Visit http://localhost:{}/docs for API document.".format(api_port))
api_host = os.getenv("API_HOST", "0.0.0.0")
api_port = int(os.getenv("API_PORT", "8000"))
print(f"Visit http://localhost:{api_port}/docs for API document.")
uvicorn.run(app, host=api_host, port=api_port)

30
src/llamafactory/api/chat.py
View File

@ -21,7 +21,7 @@ import uuid
from typing import TYPE_CHECKING, AsyncGenerator, Dict, List, Optional, Tuple
from ..data import Role as DataRole
from ..extras.logging import get_logger
from ..extras import logging
from ..extras.packages import is_fastapi_available, is_pillow_available, is_requests_available
from .common import dictify, jsonify
from .protocol import (
@ -57,7 +57,7 @@ if TYPE_CHECKING:
from .protocol import ChatCompletionRequest, ScoreEvaluationRequest
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
ROLE_MAPPING = {
Role.USER: DataRole.USER.value,
Role.ASSISTANT: DataRole.ASSISTANT.value,
@ -69,8 +69,8 @@ ROLE_MAPPING = {
def _process_request(
request: "ChatCompletionRequest",
) -> Tuple[List[Dict[str, str]], Optional[str], Optional[str], Optional["ImageInput"]]:
logger.info("==== request ====\n{}".format(json.dumps(dictify(request), indent=2, ensure_ascii=False)))
) -> Tuple[List[Dict[str, str]], Optional[str], Optional[str], Optional[List["ImageInput"]]]:
logger.info_rank0(f"==== request ====\n{json.dumps(dictify(request), indent=2, ensure_ascii=False)}")
if len(request.messages) == 0:
raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid length")
@ -84,7 +84,7 @@ def _process_request(
raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Only supports u/a/u/a/u...")
input_messages = []
image = None
images = []
for i, message in enumerate(request.messages):
if i % 2 == 0 and message.role not in [Role.USER, Role.TOOL]:
raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid role")
@ -111,7 +111,7 @@ def _process_request(
else: # web uri
image_stream = requests.get(image_url, stream=True).raw
image = Image.open(image_stream).convert("RGB")
images.append(Image.open(image_stream).convert("RGB"))
else:
input_messages.append({"role": ROLE_MAPPING[message.role], "content": message.content})
@ -124,7 +124,7 @@ def _process_request(
else:
tools = None
return input_messages, system, tools, image
return input_messages, system, tools, images or None
def _create_stream_chat_completion_chunk(
@ -142,13 +142,13 @@ def _create_stream_chat_completion_chunk(
async def create_chat_completion_response(
request: "ChatCompletionRequest", chat_model: "ChatModel"
) -> "ChatCompletionResponse":
completion_id = "chatcmpl-{}".format(uuid.uuid4().hex)
input_messages, system, tools, image = _process_request(request)
completion_id = f"chatcmpl-{uuid.uuid4().hex}"
input_messages, system, tools, images = _process_request(request)
responses = await chat_model.achat(
input_messages,
system,
tools,
image,
images,
do_sample=request.do_sample,
temperature=request.temperature,
top_p=request.top_p,
@ -169,7 +169,7 @@ async def create_chat_completion_response(
tool_calls = []
for tool in result:
function = Function(name=tool[0], arguments=tool[1])
tool_calls.append(FunctionCall(id="call_{}".format(uuid.uuid4().hex), function=function))
tool_calls.append(FunctionCall(id=f"call_{uuid.uuid4().hex}", function=function))
response_message = ChatCompletionMessage(role=Role.ASSISTANT, tool_calls=tool_calls)
finish_reason = Finish.TOOL
@ -193,8 +193,8 @@ async def create_chat_completion_response(
async def create_stream_chat_completion_response(
request: "ChatCompletionRequest", chat_model: "ChatModel"
) -> AsyncGenerator[str, None]:
completion_id = "chatcmpl-{}".format(uuid.uuid4().hex)
input_messages, system, tools, image = _process_request(request)
completion_id = f"chatcmpl-{uuid.uuid4().hex}"
input_messages, system, tools, images = _process_request(request)
if tools:
raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Cannot stream function calls.")
@ -208,7 +208,7 @@ async def create_stream_chat_completion_response(
input_messages,
system,
tools,
image,
images,
do_sample=request.do_sample,
temperature=request.temperature,
top_p=request.top_p,
@ -229,7 +229,7 @@ async def create_stream_chat_completion_response(
async def create_score_evaluation_response(
request: "ScoreEvaluationRequest", chat_model: "ChatModel"
) -> "ScoreEvaluationResponse":
score_id = "scoreval-{}".format(uuid.uuid4().hex)
score_id = f"scoreval-{uuid.uuid4().hex}"
if len(request.messages) == 0:
raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid request")

8
src/llamafactory/chat/base_engine.py
View File

@ -66,8 +66,8 @@ class BaseEngine(ABC):
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
**input_kwargs,
) -> List["Response"]:
r"""
@ -81,8 +81,8 @@ class BaseEngine(ABC):
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
**input_kwargs,
) -> AsyncGenerator[str, None]:
r"""

26
src/llamafactory/chat/chat_model.py
View File

@ -53,7 +53,7 @@ class ChatModel:
elif model_args.infer_backend == "vllm":
self.engine: "BaseEngine" = VllmEngine(model_args, data_args, finetuning_args, generating_args)
else:
raise NotImplementedError("Unknown backend: {}".format(model_args.infer_backend))
raise NotImplementedError(f"Unknown backend: {model_args.infer_backend}")
self._loop = asyncio.new_event_loop()
self._thread = Thread(target=_start_background_loop, args=(self._loop,), daemon=True)
@ -64,15 +64,15 @@ class ChatModel:
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
**input_kwargs,
) -> List["Response"]:
r"""
Gets a list of responses of the chat model.
"""
task = asyncio.run_coroutine_threadsafe(
self.achat(messages, system, tools, image, video, **input_kwargs), self._loop
self.achat(messages, system, tools, images, videos, **input_kwargs), self._loop
)
return task.result()
@ -81,28 +81,28 @@ class ChatModel:
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
**input_kwargs,
) -> List["Response"]:
r"""
Asynchronously gets a list of responses of the chat model.
"""
return await self.engine.chat(messages, system, tools, image, video, **input_kwargs)
return await self.engine.chat(messages, system, tools, images, videos, **input_kwargs)
def stream_chat(
self,
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
**input_kwargs,
) -> Generator[str, None, None]:
r"""
Gets the response token-by-token of the chat model.
"""
generator = self.astream_chat(messages, system, tools, image, video, **input_kwargs)
generator = self.astream_chat(messages, system, tools, images, videos, **input_kwargs)
while True:
try:
task = asyncio.run_coroutine_threadsafe(generator.__anext__(), self._loop)
@ -115,14 +115,14 @@ class ChatModel:
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
**input_kwargs,
) -> AsyncGenerator[str, None]:
r"""
Asynchronously gets the response token-by-token of the chat model.
"""
async for new_token in self.engine.stream_chat(messages, system, tools, image, video, **input_kwargs):
async for new_token in self.engine.stream_chat(messages, system, tools, images, videos, **input_kwargs):
yield new_token
def get_scores(

84
src/llamafactory/chat/hf_engine.py
View File

@ -23,8 +23,8 @@ from transformers import GenerationConfig, TextIteratorStreamer
from typing_extensions import override
from ..data import get_template_and_fix_tokenizer
from ..extras import logging
from ..extras.constants import IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER
from ..extras.logging import get_logger
from ..extras.misc import get_logits_processor
from ..model import load_model, load_tokenizer
from .base_engine import BaseEngine, Response
@ -39,7 +39,7 @@ if TYPE_CHECKING:
from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
class HuggingfaceEngine(BaseEngine):
@ -63,11 +63,11 @@ class HuggingfaceEngine(BaseEngine):
try:
asyncio.get_event_loop()
except RuntimeError:
logger.warning("There is no current event loop, creating a new one.")
logger.warning_once("There is no current event loop, creating a new one.")
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
self.semaphore = asyncio.Semaphore(int(os.environ.get("MAX_CONCURRENT", "1")))
self.semaphore = asyncio.Semaphore(int(os.getenv("MAX_CONCURRENT", "1")))
@staticmethod
def _process_args(
@ -79,20 +79,20 @@ class HuggingfaceEngine(BaseEngine):
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
input_kwargs: Optional[Dict[str, Any]] = {},
) -> Tuple[Dict[str, Any], int]:
mm_input_dict = {"images": [], "videos": [], "imglens": [0], "vidlens": [0]}
if image is not None:
mm_input_dict.update({"images": [image], "imglens": [1]})
if IMAGE_PLACEHOLDER not in messages[0]["content"]:
messages[0]["content"] = IMAGE_PLACEHOLDER + messages[0]["content"]
if images is not None:
mm_input_dict.update({"images": images, "imglens": [len(images)]})
if not any(IMAGE_PLACEHOLDER not in message["content"] for message in messages):
messages[0]["content"] = IMAGE_PLACEHOLDER * len(images) + messages[0]["content"]
if video is not None:
mm_input_dict.update({"videos": [video], "vidlens": [1]})
if VIDEO_PLACEHOLDER not in messages[0]["content"]:
messages[0]["content"] = VIDEO_PLACEHOLDER + messages[0]["content"]
if videos is not None:
mm_input_dict.update({"videos": videos, "vidlens": [len(videos)]})
if not any(VIDEO_PLACEHOLDER not in message["content"] for message in messages):
messages[0]["content"] = VIDEO_PLACEHOLDER * len(videos) + messages[0]["content"]
messages = template.mm_plugin.process_messages(
messages, mm_input_dict["images"], mm_input_dict["videos"], processor
@ -119,7 +119,7 @@ class HuggingfaceEngine(BaseEngine):
stop: Optional[Union[str, List[str]]] = input_kwargs.pop("stop", None)
if stop is not None:
logger.warning("Stop parameter is not supported by the huggingface engine yet.")
logger.warning_rank0("Stop parameter is not supported by the huggingface engine yet.")
generating_args = generating_args.copy()
generating_args.update(
@ -166,7 +166,11 @@ class HuggingfaceEngine(BaseEngine):
mm_inputs = template.mm_plugin.get_mm_inputs(**mm_input_dict, seqlens=[prompt_length], processor=processor)
for key, value in mm_inputs.items():
value = value if isinstance(value, torch.Tensor) else torch.tensor(value)
if isinstance(value, list) and all(isinstance(v, torch.Tensor) for v in value): # for pixtral inputs
value = torch.stack(value) # assume they have same sizes
elif not isinstance(value, torch.Tensor):
value = torch.tensor(value)
gen_kwargs[key] = value.to(model.device)
return gen_kwargs, prompt_length
@ -182,12 +186,22 @@ class HuggingfaceEngine(BaseEngine):
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
input_kwargs: Optional[Dict[str, Any]] = {},
) -> List["Response"]:
gen_kwargs, prompt_length = HuggingfaceEngine._process_args(
model, tokenizer, processor, template, generating_args, messages, system, tools, image, video, input_kwargs
model,
tokenizer,
processor,
template,
generating_args,
messages,
system,
tools,
images,
videos,
input_kwargs,
)
generate_output = model.generate(**gen_kwargs)
response_ids = generate_output[:, prompt_length:]
@ -218,12 +232,22 @@ class HuggingfaceEngine(BaseEngine):
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
input_kwargs: Optional[Dict[str, Any]] = {},
) -> Callable[[], str]:
gen_kwargs, _ = HuggingfaceEngine._process_args(
model, tokenizer, processor, template, generating_args, messages, system, tools, image, video, input_kwargs
model,
tokenizer,
processor,
template,
generating_args,
messages,
system,
tools,
images,
videos,
input_kwargs,
)
streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
gen_kwargs["streamer"] = streamer
@ -266,8 +290,8 @@ class HuggingfaceEngine(BaseEngine):
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
**input_kwargs,
) -> List["Response"]:
if not self.can_generate:
@ -283,8 +307,8 @@ class HuggingfaceEngine(BaseEngine):
messages,
system,
tools,
image,
video,
images,
videos,
input_kwargs,
)
async with self.semaphore:
@ -297,8 +321,8 @@ class HuggingfaceEngine(BaseEngine):
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
**input_kwargs,
) -> AsyncGenerator[str, None]:
if not self.can_generate:
@ -314,8 +338,8 @@ class HuggingfaceEngine(BaseEngine):
messages,
system,
tools,
image,
video,
images,
videos,
input_kwargs,
)
async with self.semaphore:

46
src/llamafactory/chat/vllm_engine.py
View File

@ -18,8 +18,8 @@ from typing import TYPE_CHECKING, Any, AsyncGenerator, AsyncIterator, Dict, List
from typing_extensions import override
from ..data import get_template_and_fix_tokenizer
from ..extras import logging
from ..extras.constants import IMAGE_PLACEHOLDER
from ..extras.logging import get_logger
from ..extras.misc import get_device_count
from ..extras.packages import is_pillow_available, is_vllm_available
from ..model import load_config, load_tokenizer
@ -43,7 +43,7 @@ if TYPE_CHECKING:
from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
class VllmEngine(BaseEngine):
@ -87,7 +87,7 @@ class VllmEngine(BaseEngine):
if getattr(config, "is_yi_vl_derived_model", None):
import vllm.model_executor.models.llava
logger.info("Detected Yi-VL model, applying projector patch.")
logger.info_rank0("Detected Yi-VL model, applying projector patch.")
vllm.model_executor.models.llava.LlavaMultiModalProjector = LlavaMultiModalProjectorForYiVLForVLLM
self.model = AsyncLLMEngine.from_engine_args(AsyncEngineArgs(**engine_args))
@ -101,14 +101,14 @@ class VllmEngine(BaseEngine):
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
**input_kwargs,
) -> AsyncIterator["RequestOutput"]:
request_id = "chatcmpl-{}".format(uuid.uuid4().hex)
if image is not None:
if IMAGE_PLACEHOLDER not in messages[0]["content"]:
messages[0]["content"] = IMAGE_PLACEHOLDER + messages[0]["content"]
request_id = f"chatcmpl-{uuid.uuid4().hex}"
if images is not None:
if not any(IMAGE_PLACEHOLDER not in message["content"] for message in messages):
messages[0]["content"] = IMAGE_PLACEHOLDER * len(images) + messages[0]["content"]
paired_messages = messages + [{"role": "assistant", "content": ""}]
system = system or self.generating_args["default_system"]
@ -157,14 +157,18 @@ class VllmEngine(BaseEngine):
skip_special_tokens=True,
)
if image is not None: # add image features
if not isinstance(image, (str, ImageObject)):
raise ValueError("Expected image input is a path or PIL.Image, but got {}.".format(type(image)))
if images is not None: # add image features
image_data = []
for image in images:
if not isinstance(image, (str, ImageObject)):
raise ValueError(f"Expected image input is a path or PIL.Image, but got {type(image)}.")
if isinstance(image, str):
image = Image.open(image).convert("RGB")
if isinstance(image, str):
image = Image.open(image).convert("RGB")
multi_modal_data = {"image": image}
image_data.append(image)
multi_modal_data = {"image": image_data}
else:
multi_modal_data = None
@ -182,12 +186,12 @@ class VllmEngine(BaseEngine):
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
**input_kwargs,
) -> List["Response"]:
final_output = None
generator = await self._generate(messages, system, tools, image, video, **input_kwargs)
generator = await self._generate(messages, system, tools, images, videos, **input_kwargs)
async for request_output in generator:
final_output = request_output
@ -210,12 +214,12 @@ class VllmEngine(BaseEngine):
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
**input_kwargs,
) -> AsyncGenerator[str, None]:
generated_text = ""
generator = await self._generate(messages, system, tools, image, video, **input_kwargs)
generator = await self._generate(messages, system, tools, images, videos, **input_kwargs)
async for result in generator:
delta_text = result.outputs[0].text[len(generated_text) :]
generated_text = result.outputs[0].text

22
src/llamafactory/cli.py
View File

@ -22,8 +22,8 @@ from . import launcher
from .api.app import run_api
from .chat.chat_model import run_chat
from .eval.evaluator import run_eval
from .extras import logging
from .extras.env import VERSION, print_env
from .extras.logging import get_logger
from .extras.misc import get_device_count
from .train.tuner import export_model, run_exp
from .webui.interface import run_web_demo, run_web_ui
@ -47,7 +47,7 @@ USAGE = (
WELCOME = (
"-" * 58
+ "\n"
+ "| Welcome to LLaMA Factory, version {}".format(VERSION)
+ f"| Welcome to LLaMA Factory, version {VERSION}"
+ " " * (21 - len(VERSION))
+ "|\n|"
+ " " * 56
@ -56,7 +56,7 @@ WELCOME = (
+ "-" * 58
)
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
@unique
@ -86,19 +86,19 @@ def main():
elif command == Command.EXPORT:
export_model()
elif command == Command.TRAIN:
force_torchrun = os.environ.get("FORCE_TORCHRUN", "0").lower() in ["true", "1"]
force_torchrun = os.getenv("FORCE_TORCHRUN", "0").lower() in ["true", "1"]
if force_torchrun or get_device_count() > 1:
master_addr = os.environ.get("MASTER_ADDR", "127.0.0.1")
master_port = os.environ.get("MASTER_PORT", str(random.randint(20001, 29999)))
logger.info("Initializing distributed tasks at: {}:{}".format(master_addr, master_port))
master_addr = os.getenv("MASTER_ADDR", "127.0.0.1")
master_port = os.getenv("MASTER_PORT", str(random.randint(20001, 29999)))
logger.info_rank0(f"Initializing distributed tasks at: {master_addr}:{master_port}")
process = subprocess.run(
(
"torchrun --nnodes {nnodes} --node_rank {node_rank} --nproc_per_node {nproc_per_node} "
"--master_addr {master_addr} --master_port {master_port} {file_name} {args}"
).format(
nnodes=os.environ.get("NNODES", "1"),
node_rank=os.environ.get("RANK", "0"),
nproc_per_node=os.environ.get("NPROC_PER_NODE", str(get_device_count())),
nnodes=os.getenv("NNODES", "1"),
node_rank=os.getenv("NODE_RANK", "0"),
nproc_per_node=os.getenv("NPROC_PER_NODE", str(get_device_count())),
master_addr=master_addr,
master_port=master_port,
file_name=launcher.__file__,
@ -118,4 +118,4 @@ def main():
elif command == Command.HELP:
print(USAGE)
else:
raise NotImplementedError("Unknown command: {}.".format(command))
raise NotImplementedError(f"Unknown command: {command}.")

38
src/llamafactory/data/aligner.py
View File

@ -16,7 +16,7 @@ import os
from functools import partial
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Union
from ..extras.logging import get_logger
from ..extras import logging
from .data_utils import Role
@ -29,45 +29,51 @@ if TYPE_CHECKING:
from .parser import DatasetAttr
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def _convert_images(
images: Sequence["ImageInput"],
images: Union["ImageInput", Sequence["ImageInput"]],
dataset_attr: "DatasetAttr",
data_args: "DataArguments",
) -> Optional[List["ImageInput"]]:
r"""
Optionally concatenates image path to dataset dir when loading from local disk.
"""
if len(images) == 0:
if not isinstance(images, list):
images = [images]
elif len(images) == 0:
return None
else:
images = images[:]
images = images[:]
if dataset_attr.load_from in ["script", "file"]:
for i in range(len(images)):
if isinstance(images[i], str) and os.path.isfile(os.path.join(data_args.dataset_dir, images[i])):
images[i] = os.path.join(data_args.dataset_dir, images[i])
if isinstance(images[i], str) and os.path.isfile(os.path.join(data_args.image_dir, images[i])):
images[i] = os.path.join(data_args.image_dir, images[i])
return images
def _convert_videos(
videos: Sequence["VideoInput"],
videos: Union["VideoInput", Sequence["VideoInput"]],
dataset_attr: "DatasetAttr",
data_args: "DataArguments",
) -> Optional[List["VideoInput"]]:
r"""
Optionally concatenates video path to dataset dir when loading from local disk.
"""
if len(videos) == 0:
if not isinstance(videos, list):
videos = [videos]
elif len(videos) == 0:
return None
else:
videos = videos[:]
videos = videos[:]
if dataset_attr.load_from in ["script", "file"]:
for i in range(len(videos)):
if isinstance(videos[i], str) and os.path.isfile(os.path.join(data_args.dataset_dir, videos[i])):
videos[i] = os.path.join(data_args.dataset_dir, videos[i])
if isinstance(videos[i], str) and os.path.isfile(os.path.join(data_args.image_dir, videos[i])):
videos[i] = os.path.join(data_args.image_dir, videos[i])
return videos
@ -161,7 +167,7 @@ def convert_sharegpt(
broken_data = False
for turn_idx, message in enumerate(messages):
if message[dataset_attr.role_tag] not in accept_tags[turn_idx % 2]:
logger.warning("Invalid role tag in {}.".format(messages))
logger.warning_rank0(f"Invalid role tag in {messages}.")
broken_data = True
aligned_messages.append(
@ -171,7 +177,7 @@ def convert_sharegpt(
if (not dataset_attr.ranking and len(aligned_messages) % 2 != 0) or (
dataset_attr.ranking and len(aligned_messages) % 2 == 0
):
logger.warning("Invalid message count in {}.".format(messages))
logger.warning_rank0(f"Invalid message count in {messages}.")
broken_data = True
if dataset_attr.kto_tag and isinstance(example[dataset_attr.kto_tag], bool): # kto example
@ -192,7 +198,7 @@ def convert_sharegpt(
chosen[dataset_attr.role_tag] not in accept_tags[-1]
or rejected[dataset_attr.role_tag] not in accept_tags[-1]
):
logger.warning("Invalid role tag in {}.".format([chosen, rejected]))
logger.warning_rank0(f"Invalid role tag in {[chosen, rejected]}.")
broken_data = True
prompt = aligned_messages
@ -205,7 +211,7 @@ def convert_sharegpt(
response = aligned_messages[-1:]
if broken_data:
logger.warning("Skipping this abnormal example.")
logger.warning_rank0("Skipping this abnormal example.")
prompt, response = [], []
convert_images = partial(_convert_images, dataset_attr=dataset_attr, data_args=data_args)

9
src/llamafactory/data/collator.py
View File

@ -99,6 +99,9 @@ class MultiModalDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
features: Dict[str, "torch.Tensor"] = super().__call__(features)
features.update(mm_inputs)
if isinstance(features.get("pixel_values"), list): # for pixtral inputs
features = features.data # use default_collate() instead of BatchEncoding.to()
return features
@ -137,9 +140,9 @@ class PairwiseDataCollatorWithPadding(MultiModalDataCollatorForSeq2Seq):
for key in ("chosen", "rejected"):
for feature in features:
target_feature = {
"input_ids": feature["{}_input_ids".format(key)],
"attention_mask": feature["{}_attention_mask".format(key)],
"labels": feature["{}_labels".format(key)],
"input_ids": feature[f"{key}_input_ids"],
"attention_mask": feature[f"{key}_attention_mask"],
"labels": feature[f"{key}_labels"],
"images": feature["images"],
"videos": feature["videos"],
}

10
src/llamafactory/data/data_utils.py
View File

@ -17,7 +17,7 @@ from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Set, TypedDict
from datasets import DatasetDict, concatenate_datasets, interleave_datasets
from ..extras.logging import get_logger
from ..extras import logging
if TYPE_CHECKING:
@ -26,7 +26,7 @@ if TYPE_CHECKING:
from ..hparams import DataArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
SLOTS = Sequence[Union[str, Set[str], Dict[str, str]]]
@ -56,12 +56,12 @@ def merge_dataset(
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.")
logger.warning_once("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.")
logger.warning_once("We recommend using `mix_strategy=concat` in non-streaming mode.")
return interleave_datasets(
datasets=all_datasets,
@ -70,7 +70,7 @@ def merge_dataset(
stopping_strategy="first_exhausted" if data_args.mix_strategy.endswith("under") else "all_exhausted",
)
else:
raise ValueError("Unknown mixing strategy: {}.".format(data_args.mix_strategy))
raise ValueError(f"Unknown mixing strategy: {data_args.mix_strategy}.")
def split_dataset(

10
src/llamafactory/data/formatter.py
View File

@ -83,14 +83,14 @@ class StringFormatter(Formatter):
if isinstance(slot, str):
for name, value in kwargs.items():
if not isinstance(value, str):
raise RuntimeError("Expected a string, got {}".format(value))
raise RuntimeError(f"Expected a string, got {value}")
slot = slot.replace("{{" + name + "}}", value, 1)
elements.append(slot)
elif isinstance(slot, (dict, set)):
elements.append(slot)
else:
raise RuntimeError("Input must be string, set[str] or dict[str, str], got {}".format(type(slot)))
raise RuntimeError(f"Input must be string, set[str] or dict[str, str], got {type(slot)}")
return elements
@ -113,7 +113,7 @@ class FunctionFormatter(Formatter):
functions.append((tool_call["name"], json.dumps(tool_call["arguments"], ensure_ascii=False)))
except json.JSONDecodeError:
functions = []
raise RuntimeError(f"Invalid JSON format in function message: {str([content])}") # flat string
elements = []
for name, arguments in functions:
@ -124,7 +124,7 @@ class FunctionFormatter(Formatter):
elif isinstance(slot, (dict, set)):
elements.append(slot)
else:
raise RuntimeError("Input must be string, set[str] or dict[str, str], got {}".format(type(slot)))
raise RuntimeError(f"Input must be string, set[str] or dict[str, str], got {type(slot)}")
return elements
@ -141,7 +141,7 @@ class ToolFormatter(Formatter):
tools = json.loads(content)
return [self.tool_utils.tool_formatter(tools) if len(tools) != 0 else ""]
except json.JSONDecodeError:
return [""]
raise RuntimeError(f"Invalid JSON format in tool description: {str([content])}") # flat string
@override
def extract(self, content: str) -> Union[str, List["FunctionCall"]]:

59
src/llamafactory/data/loader.py
View File

@ -20,8 +20,8 @@ import numpy as np
from datasets import DatasetDict, load_dataset, load_from_disk
from transformers.utils.versions import require_version
from ..extras import logging
from ..extras.constants import FILEEXT2TYPE
from ..extras.logging import get_logger
from ..extras.misc import has_tokenized_data
from .aligner import align_dataset
from .data_utils import merge_dataset, split_dataset
@ -39,7 +39,7 @@ if TYPE_CHECKING:
from .template import Template
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def _load_single_dataset(
@ -51,9 +51,9 @@ def _load_single_dataset(
r"""
Loads a single dataset and aligns it to the standard format.
"""
logger.info("Loading dataset {}...".format(dataset_attr))
logger.info_rank0(f"Loading dataset {dataset_attr}...")
data_path, data_name, data_dir, data_files = None, None, None, None
if dataset_attr.load_from in ["hf_hub", "ms_hub"]:
if dataset_attr.load_from in ["hf_hub", "ms_hub", "om_hub"]:
data_path = dataset_attr.dataset_name
data_name = dataset_attr.subset
data_dir = dataset_attr.folder
@ -69,25 +69,24 @@ def _load_single_dataset(
if os.path.isdir(local_path): # is directory
for file_name in os.listdir(local_path):
data_files.append(os.path.join(local_path, file_name))
if data_path is None:
data_path = FILEEXT2TYPE.get(file_name.split(".")[-1], None)
elif data_path != FILEEXT2TYPE.get(file_name.split(".")[-1], None):
raise ValueError("File types should be identical.")
elif os.path.isfile(local_path): # is file
data_files.append(local_path)
data_path = FILEEXT2TYPE.get(local_path.split(".")[-1], None)
else:
raise ValueError("File {} not found.".format(local_path))
raise ValueError(f"File {local_path} not found.")
data_path = FILEEXT2TYPE.get(os.path.splitext(data_files[0])[-1][1:], None)
if data_path is None:
raise ValueError("Allowed file types: {}.".format(",".join(FILEEXT2TYPE.keys())))
if any(data_path != FILEEXT2TYPE.get(os.path.splitext(data_file)[-1][1:], None) for data_file in data_files):
raise ValueError("File types should be identical.")
else:
raise NotImplementedError("Unknown load type: {}.".format(dataset_attr.load_from))
raise NotImplementedError(f"Unknown load type: {dataset_attr.load_from}.")
if dataset_attr.load_from == "ms_hub":
require_version("modelscope>=1.11.0", "To fix: pip install modelscope>=1.11.0")
from modelscope import MsDataset
from modelscope.utils.config_ds import MS_DATASETS_CACHE
from modelscope import MsDataset # type: ignore
from modelscope.utils.config_ds import MS_DATASETS_CACHE # type: ignore
cache_dir = model_args.cache_dir or MS_DATASETS_CACHE
dataset = MsDataset.load(
@ -98,10 +97,27 @@ def _load_single_dataset(
split=dataset_attr.split,
cache_dir=cache_dir,
token=model_args.ms_hub_token,
use_streaming=(data_args.streaming and (dataset_attr.load_from != "file")),
use_streaming=data_args.streaming,
)
if isinstance(dataset, MsDataset):
dataset = dataset.to_hf_dataset()
elif dataset_attr.load_from == "om_hub":
require_version("openmind>=0.8.0", "To fix: pip install openmind>=0.8.0")
from openmind import OmDataset # type: ignore
from openmind.utils.hub import OM_DATASETS_CACHE # type: ignore
cache_dir = model_args.cache_dir or OM_DATASETS_CACHE
dataset = OmDataset.load_dataset(
path=data_path,
name=data_name,
data_dir=data_dir,
data_files=data_files,
split=dataset_attr.split,
cache_dir=cache_dir,
token=model_args.om_hub_token,
streaming=data_args.streaming,
)
else:
dataset = load_dataset(
path=data_path,
@ -111,13 +127,10 @@ def _load_single_dataset(
split=dataset_attr.split,
cache_dir=model_args.cache_dir,
token=model_args.hf_hub_token,
streaming=(data_args.streaming and (dataset_attr.load_from != "file")),
streaming=data_args.streaming,
trust_remote_code=True,
)
if data_args.streaming and (dataset_attr.load_from == "file"): # faster than specifying streaming=True
dataset = dataset.to_iterable_dataset() # TODO: add num shards parameter
if dataset_attr.num_samples is not None and not data_args.streaming:
target_num = dataset_attr.num_samples
indexes = np.random.permutation(len(dataset))[:target_num] # all samples should be included
@ -128,7 +141,7 @@ def _load_single_dataset(
assert len(indexes) == dataset_attr.num_samples, "Sample num mismatched."
dataset = dataset.select(indexes)
logger.info("Sampled {} examples from dataset {}.".format(dataset_attr.num_samples, dataset_attr))
logger.info_rank0(f"Sampled {dataset_attr.num_samples} examples from dataset {dataset_attr}.")
if data_args.max_samples is not None: # truncate dataset
max_samples = min(data_args.max_samples, len(dataset))
@ -224,9 +237,9 @@ def get_dataset(
# Load tokenized dataset
if data_args.tokenized_path is not None:
if has_tokenized_data(data_args.tokenized_path):
logger.warning("Loading dataset from disk will ignore other data arguments.")
logger.warning_rank0("Loading dataset from disk will ignore other data arguments.")
dataset_dict: "DatasetDict" = load_from_disk(data_args.tokenized_path)
logger.info("Loaded tokenized dataset from {}.".format(data_args.tokenized_path))
logger.info_rank0(f"Loaded tokenized dataset from {data_args.tokenized_path}.")
dataset_module: Dict[str, "Dataset"] = {}
if "train" in dataset_dict:
@ -277,8 +290,8 @@ def get_dataset(
if data_args.tokenized_path is not None:
if training_args.should_save:
dataset_dict.save_to_disk(data_args.tokenized_path)
logger.info("Tokenized dataset saved at {}.".format(data_args.tokenized_path))
logger.info("Please restart the training with `tokenized_path: {}`.".format(data_args.tokenized_path))
logger.info_rank0(f"Tokenized dataset saved at {data_args.tokenized_path}.")
logger.info_rank0(f"Please restart the training with `tokenized_path: {data_args.tokenized_path}`.")
sys.exit(0)

285
src/llamafactory/data/mm_plugin.py
View File

@ -4,6 +4,7 @@ from io import BytesIO
from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Tuple, TypedDict, Union
import numpy as np
from transformers.image_utils import get_image_size, to_numpy_array
from typing_extensions import override
from ..extras.constants import IGNORE_INDEX, IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER
@ -110,7 +111,7 @@ class BasePlugin:
image = Image.open(image["path"])
if not isinstance(image, ImageObject):
raise ValueError("Expect input is a list of Images, but got {}.".format(type(image)))
raise ValueError(f"Expect input is a list of Images, but got {type(image)}.")
results.append(self._preprocess_image(image, **kwargs))
@ -157,6 +158,7 @@ class BasePlugin:
It holds num_patches == torch.prod(image_grid_thw)
"""
image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
video_processor: "BaseImageProcessor" = getattr(processor, "video_processor", image_processor)
input_dict = {"images": None} # default key
if len(images) != 0:
images = self._regularize_images(
@ -174,10 +176,16 @@ class BasePlugin:
)
input_dict["videos"] = videos
if input_dict.get("images", None) is not None or input_dict.get("videos", None) is not None:
return image_processor(**input_dict, return_tensors="pt")
else:
return {}
mm_inputs = {}
if image_processor != video_processor:
if input_dict.get("images") is not None:
mm_inputs.update(image_processor(input_dict["images"], return_tensors="pt"))
if input_dict.get("videos") is not None:
mm_inputs.update(video_processor(input_dict["videos"], return_tensors="pt"))
elif input_dict.get("images") is not None or input_dict.get("videos") is not None: # same processor (qwen2-vl)
mm_inputs.update(image_processor(**input_dict, return_tensors="pt"))
return mm_inputs
def process_messages(
self,
@ -218,6 +226,14 @@ class BasePlugin:
) -> Dict[str, Union[List[int], "torch.Tensor"]]:
r"""
Builds batched multimodal inputs for VLMs.
Arguments:
images: a list of image inputs, shape (num_images,)
videos: a list of video inputs, shape (num_videos,)
imglens: number of images in each sample, shape (batch_size,)
vidlens: number of videos in each sample, shape (batch_size,)
seqlens: number of tokens in each sample, shape (batch_size,)
processor: a processor for pre-processing images and videos
"""
self._validate_input(images, videos)
return {}
@ -245,7 +261,123 @@ class LlavaPlugin(BasePlugin):
message["content"] = content.replace("{{image}}", self.image_token * image_seqlen)
if len(images) != num_image_tokens:
raise ValueError("The number of images does not match the number of {} tokens".format(IMAGE_PLACEHOLDER))
raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens")
return messages
@override
def get_mm_inputs(
self,
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
imglens: Sequence[int],
vidlens: Sequence[int],
seqlens: Sequence[int],
processor: Optional["ProcessorMixin"],
) -> Dict[str, Union[List[int], "torch.Tensor"]]:
self._validate_input(images, videos)
return self._get_mm_inputs(images, videos, processor)
class LlavaNextPlugin(BasePlugin):
@override
def process_messages(
self,
messages: Sequence[Dict[str, str]],
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
processor: Optional["ProcessorMixin"],
) -> List[Dict[str, str]]:
self._validate_input(images, videos)
num_image_tokens = 0
messages = deepcopy(messages)
mm_inputs = self._get_mm_inputs(images, videos, processor)
if "image_sizes" in mm_inputs:
image_sizes = iter(mm_inputs["image_sizes"])
if "pixel_values" in mm_inputs:
height, width = get_image_size(to_numpy_array(mm_inputs["pixel_values"][0][0]))
for message in messages:
content = message["content"]
while self.image_token in content:
image_size = next(image_sizes)
orig_height, orig_width = image_size
image_seqlen = processor._get_number_of_features(orig_height, orig_width, height, width)
if processor.vision_feature_select_strategy == "default":
image_seqlen -= 1
num_image_tokens += 1
content = content.replace(self.image_token, "{{image}}" * image_seqlen, 1)
message["content"] = content.replace("{{image}}", self.image_token)
if len(images) != num_image_tokens:
raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens")
return messages
@override
def get_mm_inputs(
self,
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
imglens: Sequence[int],
vidlens: Sequence[int],
seqlens: Sequence[int],
processor: Optional["ProcessorMixin"],
) -> Dict[str, Union[List[int], "torch.Tensor"]]:
self._validate_input(images, videos)
res = self._get_mm_inputs(images, videos, processor)
return res
class LlavaNextVideoPlugin(BasePlugin):
@override
def process_messages(
self,
messages: Sequence[Dict[str, str]],
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
processor: Optional["ProcessorMixin"],
) -> List[Dict[str, str]]:
self._validate_input(images, videos)
num_image_tokens = 0
num_video_tokens = 0
messages = deepcopy(messages)
mm_inputs = self._get_mm_inputs(images, videos, processor)
if "pixel_values" in mm_inputs:
image_sizes = iter(mm_inputs["image_sizes"])
height, width = get_image_size(to_numpy_array(mm_inputs["pixel_values"][0][0]))
for message in messages:
content = message["content"]
while self.image_token in content:
image_size = next(image_sizes)
orig_height, orig_width = image_size
image_seqlen = processor._get_number_of_features(orig_height, orig_width, height, width)
if processor.vision_feature_select_strategy == "default":
image_seqlen -= 1
num_image_tokens += 1
content = content.replace(self.image_token, "{{image}}" * image_seqlen, 1)
message["content"] = content.replace("{{image}}", self.image_token)
if "pixel_values_videos" in mm_inputs:
pixel_values_video = to_numpy_array(mm_inputs.get("pixel_values_videos")[0])
height, width = get_image_size(pixel_values_video[0])
num_frames = pixel_values_video.shape[0] # frame dim is always after batch dim
image_seqlen = (height // processor.patch_size) * (width // processor.patch_size)
video_seqlen = image_seqlen // 4 * num_frames # divide by 4 needed for avg pooling layer
for message in messages:
content = message["content"]
while self.video_token in content:
num_video_tokens += 1
content = content.replace(self.video_token, "{{video}}", 1)
message["content"] = content.replace("{{video}}", self.video_token * video_seqlen)
if len(images) != num_image_tokens:
raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens")
if len(videos) != num_video_tokens:
raise ValueError(f"The number of videos does not match the number of {IMAGE_PLACEHOLDER} tokens")
return messages
@ -284,7 +416,7 @@ class PaliGemmaPlugin(BasePlugin):
message["content"] = content.replace("{{image}}", "")
if len(images) != num_image_tokens:
raise ValueError("The number of images does not match the number of {} tokens".format(IMAGE_PLACEHOLDER))
raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens")
return messages
@ -324,6 +456,68 @@ class PaliGemmaPlugin(BasePlugin):
return mm_inputs
class PixtralPlugin(BasePlugin):
@override
def process_messages(
self,
messages: Sequence[Dict[str, str]],
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
processor: Optional["ProcessorMixin"],
) -> List[Dict[str, str]]:
self._validate_input(images, videos)
patch_size = getattr(processor, "patch_size")
image_token = getattr(processor, "image_token")
image_break_token = getattr(processor, "image_break_token")
image_end_token = getattr(processor, "image_end_token")
num_image_tokens = 0
messages = deepcopy(messages)
mm_inputs = self._get_mm_inputs(images, videos, processor)
image_input_sizes = mm_inputs.get("image_sizes", None)
for message in messages:
content = message["content"]
while IMAGE_PLACEHOLDER in content:
if image_input_sizes is None:
raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens")
image_size = image_input_sizes[0][num_image_tokens]
height, width = image_size
num_height_tokens = height // patch_size
num_width_tokens = width // patch_size
replace_tokens = [[image_token] * num_width_tokens + [image_break_token]] * num_height_tokens
replace_tokens = [item for sublist in replace_tokens for item in sublist] # flatten list
replace_tokens[-1] = image_end_token
replace_str = "".join(replace_tokens)
content = content.replace(IMAGE_PLACEHOLDER, replace_str, 1)
num_image_tokens += 1
message["content"] = content
if len(images) != num_image_tokens:
raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens")
return messages
@override
def get_mm_inputs(
self,
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
imglens: Sequence[int],
vidlens: Sequence[int],
seqlens: Sequence[int],
processor: Optional["ProcessorMixin"],
) -> Dict[str, Union[List[int], "torch.Tensor"]]:
self._validate_input(images, videos)
mm_inputs = self._get_mm_inputs(images, videos, processor)
if mm_inputs.get("pixel_values"):
mm_inputs["pixel_values"] = mm_inputs["pixel_values"][0]
mm_inputs.pop("image_sizes", None)
return mm_inputs
class Qwen2vlPlugin(BasePlugin):
@override
def _preprocess_image(self, image: "ImageObject", **kwargs) -> "ImageObject":
@ -369,7 +563,7 @@ class Qwen2vlPlugin(BasePlugin):
content = message["content"]
while IMAGE_PLACEHOLDER in content:
if num_image_tokens >= len(image_grid_thw):
raise ValueError("`len(images)` is less than the number of {} tokens.".format(IMAGE_PLACEHOLDER))
raise ValueError(f"`len(images)` is less than the number of {IMAGE_PLACEHOLDER} tokens.")
content = content.replace(
IMAGE_PLACEHOLDER,
@ -382,7 +576,7 @@ class Qwen2vlPlugin(BasePlugin):
while VIDEO_PLACEHOLDER in content:
if num_video_tokens >= len(video_grid_thw):
raise ValueError("`len(videos)` is less than the number of {} tokens.".format(VIDEO_PLACEHOLDER))
raise ValueError(f"`len(videos)` is less than the number of {VIDEO_PLACEHOLDER} tokens.")
content = content.replace(
VIDEO_PLACEHOLDER,
@ -396,10 +590,73 @@ class Qwen2vlPlugin(BasePlugin):
message["content"] = content
if len(images) != num_image_tokens:
raise ValueError("The number of images does not match the number of {} tokens".format(IMAGE_PLACEHOLDER))
raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens")
if len(videos) != num_video_tokens:
raise ValueError("The number of videos does not match the number of {} tokens".format(VIDEO_PLACEHOLDER))
raise ValueError(f"The number of videos does not match the number of {VIDEO_PLACEHOLDER} tokens")
return messages
@override
def get_mm_inputs(
self,
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
imglens: Sequence[int],
vidlens: Sequence[int],
seqlens: Sequence[int],
processor: Optional["ProcessorMixin"],
) -> Dict[str, Union[List[int], "torch.Tensor"]]:
self._validate_input(images, videos)
return self._get_mm_inputs(images, videos, processor)
class VideoLlavaPlugin(BasePlugin):
@override
def process_messages(
self,
messages: Sequence[Dict[str, str]],
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
processor: Optional["ProcessorMixin"],
) -> List[Dict[str, str]]:
self._validate_input(images, videos)
num_image_tokens = 0
num_video_tokens = 0
messages = deepcopy(messages)
mm_inputs = self._get_mm_inputs(images, videos, processor)
num_frames = 0
exist_images = "pixel_values_images" in mm_inputs
exist_videos = "pixel_values_videos" in mm_inputs
if exist_videos or exist_images:
if exist_images:
height, width = get_image_size(to_numpy_array(mm_inputs.get("pixel_values_images")[0]))
num_frames = 1
if exist_videos:
pixel_values_video = to_numpy_array(mm_inputs.get("pixel_values_videos")[0])
height, width = get_image_size(pixel_values_video[0])
num_frames = pixel_values_video.shape[0] # frame dim is always after batch dim
image_seqlen = (height // processor.patch_size) * (width // processor.patch_size) + 1
video_seqlen = image_seqlen * num_frames
if processor.vision_feature_select_strategy == "default":
image_seqlen -= 1
for message in messages:
content = message["content"]
while self.image_token in content:
num_image_tokens += 1
content = content.replace(self.image_token, "{{image}}", 1)
while self.video_token in content:
num_video_tokens += 1
content = content.replace(self.video_token, "{{video}}", 1)
content = content.replace("{{image}}", self.image_token * image_seqlen)
message["content"] = content.replace("{{video}}", self.video_token * video_seqlen)
if len(images) != num_image_tokens:
raise ValueError(f"The number of images does not match the number of {self.image_token} tokens")
if len(videos) != num_video_tokens:
raise ValueError(f"The number of videos does not match the number of {self.video_token} tokens")
return messages
@ -420,8 +677,12 @@ class Qwen2vlPlugin(BasePlugin):
PLUGINS = {
"base": BasePlugin,
"llava": LlavaPlugin,
"llava_next": LlavaNextPlugin,
"llava_next_video": LlavaNextVideoPlugin,
"paligemma": PaliGemmaPlugin,
"pixtral": PixtralPlugin,
"qwen2_vl": Qwen2vlPlugin,
"video_llava": VideoLlavaPlugin,
}
@ -432,6 +693,6 @@ def get_mm_plugin(
) -> "BasePlugin":
plugin_class = PLUGINS.get(name, None)
if plugin_class is None:
raise ValueError("Multimodal plugin `{}` not found.".format(name))
raise ValueError(f"Multimodal plugin `{name}` not found.")
return plugin_class(image_token, video_token)

24
src/llamafactory/data/parser.py
View File

@ -20,7 +20,7 @@ from typing import Any, Dict, List, Literal, Optional, Sequence
from transformers.utils import cached_file
from ..extras.constants import DATA_CONFIG
from ..extras.misc import use_modelscope
from ..extras.misc import use_modelscope, use_openmind
@dataclass
@ -30,7 +30,7 @@ class DatasetAttr:
"""
# basic configs
load_from: Literal["hf_hub", "ms_hub", "script", "file"]
load_from: Literal["hf_hub", "ms_hub", "om_hub", "script", "file"]
dataset_name: str
formatting: Literal["alpaca", "sharegpt"] = "alpaca"
ranking: bool = False
@ -87,31 +87,39 @@ def get_dataset_list(dataset_names: Optional[Sequence[str]], dataset_dir: str) -
config_path = os.path.join(dataset_dir, DATA_CONFIG)
try:
with open(config_path, "r") as f:
with open(config_path) as f:
dataset_info = json.load(f)
except Exception as err:
if len(dataset_names) != 0:
raise ValueError("Cannot open {} due to {}.".format(config_path, str(err)))
raise ValueError(f"Cannot open {config_path} due to {str(err)}.")
dataset_info = None
dataset_list: List["DatasetAttr"] = []
for name in dataset_names:
if dataset_info is None: # dataset_dir is ONLINE
load_from = "ms_hub" if use_modelscope() else "hf_hub"
if use_modelscope():
load_from = "ms_hub"
elif use_openmind():
load_from = "om_hub"
else:
load_from = "hf_hub"
dataset_attr = DatasetAttr(load_from, dataset_name=name)
dataset_list.append(dataset_attr)
continue
if name not in dataset_info:
raise ValueError("Undefined dataset {} in {}.".format(name, DATA_CONFIG))
raise ValueError(f"Undefined dataset {name} in {DATA_CONFIG}.")
has_hf_url = "hf_hub_url" in dataset_info[name]
has_ms_url = "ms_hub_url" in dataset_info[name]
has_om_url = "om_hub_url" in dataset_info[name]
if has_hf_url or has_ms_url:
if (use_modelscope() and has_ms_url) or (not has_hf_url):
if has_hf_url or has_ms_url or has_om_url:
if has_ms_url and (use_modelscope() or not has_hf_url):
dataset_attr = DatasetAttr("ms_hub", dataset_name=dataset_info[name]["ms_hub_url"])
elif has_om_url and (use_openmind() or not has_hf_url):
dataset_attr = DatasetAttr("om_hub", dataset_name=dataset_info[name]["om_hub_url"])
else:
dataset_attr = DatasetAttr("hf_hub", dataset_name=dataset_info[name]["hf_hub_url"])
elif "script_url" in dataset_info[name]:

10
src/llamafactory/data/processors/feedback.py
View File

@ -15,8 +15,8 @@
from collections import defaultdict
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple
from ...extras import logging
from ...extras.constants import IGNORE_INDEX
from ...extras.logging import get_logger
from .processor_utils import infer_seqlen
@ -28,7 +28,7 @@ if TYPE_CHECKING:
from ..template import Template
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def _encode_feedback_example(
@ -94,7 +94,9 @@ def preprocess_feedback_dataset(
model_inputs = defaultdict(list)
for i in range(len(examples["_prompt"])):
if len(examples["_prompt"][i]) % 2 != 1 or len(examples["_response"][i]) < 2:
logger.warning("Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i]))
logger.warning_rank0(
"Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i])
)
continue
input_ids, labels, kl_input_ids, kl_labels, kto_tag = _encode_feedback_example(
@ -123,6 +125,6 @@ def preprocess_feedback_dataset(
desirable_num = sum([1 for tag in model_inputs["kto_tags"] if tag])
undesirable_num = len(model_inputs["kto_tags"]) - desirable_num
if desirable_num == 0 or undesirable_num == 0:
logger.warning("Your dataset only has one preference type.")
logger.warning_rank0("Your dataset only has one preference type.")
return model_inputs

12
src/llamafactory/data/processors/pairwise.py
View File

@ -15,8 +15,8 @@
from collections import defaultdict
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple
from ...extras import logging
from ...extras.constants import IGNORE_INDEX
from ...extras.logging import get_logger
from .processor_utils import infer_seqlen
@ -28,7 +28,7 @@ if TYPE_CHECKING:
from ..template import Template
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def _encode_pairwise_example(
@ -77,7 +77,9 @@ def preprocess_pairwise_dataset(
model_inputs = defaultdict(list)
for i in range(len(examples["_prompt"])):
if len(examples["_prompt"][i]) % 2 != 1 or len(examples["_response"][i]) < 2:
logger.warning("Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i]))
logger.warning_rank0(
"Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i])
)
continue
chosen_input_ids, chosen_labels, rejected_input_ids, rejected_labels = _encode_pairwise_example(
@ -110,8 +112,8 @@ def print_pairwise_dataset_example(example: Dict[str, List[int]], tokenizer: "Pr
print("chosen_input_ids:\n{}".format(example["chosen_input_ids"]))
print("chosen_inputs:\n{}".format(tokenizer.decode(example["chosen_input_ids"], skip_special_tokens=False)))
print("chosen_label_ids:\n{}".format(example["chosen_labels"]))
print("chosen_labels:\n{}".format(tokenizer.decode(valid_chosen_labels, skip_special_tokens=False)))
print(f"chosen_labels:\n{tokenizer.decode(valid_chosen_labels, skip_special_tokens=False)}")
print("rejected_input_ids:\n{}".format(example["rejected_input_ids"]))
print("rejected_inputs:\n{}".format(tokenizer.decode(example["rejected_input_ids"], skip_special_tokens=False)))
print("rejected_label_ids:\n{}".format(example["rejected_labels"]))
print("rejected_labels:\n{}".format(tokenizer.decode(valid_rejected_labels, skip_special_tokens=False)))
print(f"rejected_labels:\n{tokenizer.decode(valid_rejected_labels, skip_special_tokens=False)}")

16
src/llamafactory/data/processors/supervised.py
View File

@ -15,8 +15,8 @@
from collections import defaultdict
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple
from ...extras import logging
from ...extras.constants import IGNORE_INDEX
from ...extras.logging import get_logger
from .processor_utils import greedy_knapsack, infer_seqlen
@ -28,7 +28,7 @@ if TYPE_CHECKING:
from ..template import Template
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def _encode_supervised_example(
@ -99,7 +99,9 @@ def preprocess_supervised_dataset(
model_inputs = defaultdict(list)
for i in range(len(examples["_prompt"])):
if len(examples["_prompt"][i]) % 2 != 1 or len(examples["_response"][i]) != 1:
logger.warning("Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i]))
logger.warning_rank0(
"Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i])
)
continue
input_ids, labels = _encode_supervised_example(
@ -141,7 +143,9 @@ def preprocess_packed_supervised_dataset(
length2indexes = defaultdict(list)
for i in range(len(examples["_prompt"])):
if len(examples["_prompt"][i]) % 2 != 1 or len(examples["_response"][i]) != 1:
logger.warning("Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i]))
logger.warning_rank0(
"Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i])
)
continue
input_ids, labels = _encode_supervised_example(
@ -160,7 +164,7 @@ def preprocess_packed_supervised_dataset(
)
length = len(input_ids)
if length > data_args.cutoff_len:
logger.warning("Dropped lengthy example with length {} > {}.".format(length, data_args.cutoff_len))
logger.warning_rank0(f"Dropped lengthy example with length {length} > {data_args.cutoff_len}.")
else:
lengths.append(length)
length2indexes[length].append(valid_num)
@ -212,4 +216,4 @@ def print_supervised_dataset_example(example: Dict[str, List[int]], tokenizer: "
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(valid_labels, skip_special_tokens=False)))
print(f"labels:\n{tokenizer.decode(valid_labels, skip_special_tokens=False)}")

8
src/llamafactory/data/processors/unsupervised.py
View File

@ -15,7 +15,7 @@
from collections import defaultdict
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple
from ...extras.logging import get_logger
from ...extras import logging
from ..data_utils import Role
from .processor_utils import infer_seqlen
@ -28,7 +28,7 @@ if TYPE_CHECKING:
from ..template import Template
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def _encode_unsupervised_example(
@ -71,7 +71,9 @@ def preprocess_unsupervised_dataset(
model_inputs = defaultdict(list)
for i in range(len(examples["_prompt"])):
if len(examples["_prompt"][i]) % 2 != 1:
logger.warning("Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i]))
logger.warning_rank0(
"Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i])
)
continue
input_ids, labels = _encode_unsupervised_example(

183
src/llamafactory/data/template.py
View File

@ -18,7 +18,7 @@ from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Tuple, Union
from transformers.utils.versions import require_version
from typing_extensions import override
from ..extras.logging import get_logger
from ..extras import logging
from .data_utils import Role
from .formatter import EmptyFormatter, FunctionFormatter, StringFormatter, ToolFormatter
from .mm_plugin import get_mm_plugin
@ -32,7 +32,7 @@ if TYPE_CHECKING:
from .mm_plugin import BasePlugin
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
@dataclass
@ -49,6 +49,7 @@ class Template:
stop_words: List[str]
efficient_eos: bool
replace_eos: bool
replace_jinja_template: bool
mm_plugin: "BasePlugin"
def encode_oneturn(
@ -146,7 +147,7 @@ class Template:
elif "eos_token" in elem and tokenizer.eos_token_id is not None:
token_ids += [tokenizer.eos_token_id]
else:
raise ValueError("Input must be string, set[str] or dict[str, str], got {}".format(type(elem)))
raise ValueError(f"Input must be string, set[str] or dict[str, str], got {type(elem)}")
return token_ids
@ -214,6 +215,7 @@ def _register_template(
stop_words: Sequence[str] = [],
efficient_eos: bool = False,
replace_eos: bool = False,
replace_jinja_template: bool = True,
mm_plugin: "BasePlugin" = get_mm_plugin(name="base"),
) -> None:
r"""
@ -263,6 +265,7 @@ def _register_template(
stop_words=stop_words,
efficient_eos=efficient_eos,
replace_eos=replace_eos,
replace_jinja_template=replace_jinja_template,
mm_plugin=mm_plugin,
)
@ -272,12 +275,12 @@ def _add_or_replace_eos_token(tokenizer: "PreTrainedTokenizer", eos_token: str)
num_added_tokens = tokenizer.add_special_tokens({"eos_token": eos_token})
if is_added:
logger.info("Add eos token: {}".format(tokenizer.eos_token))
logger.info_rank0(f"Add eos token: {tokenizer.eos_token}")
else:
logger.info("Replace eos token: {}".format(tokenizer.eos_token))
logger.info_rank0(f"Replace eos token: {tokenizer.eos_token}")
if num_added_tokens > 0:
logger.warning("New tokens have been added, make sure `resize_vocab` is True.")
logger.warning_rank0("New tokens have been added, make sure `resize_vocab` is True.")
def _jinja_escape(content: str) -> str:
@ -353,24 +356,21 @@ def get_template_and_fix_tokenizer(tokenizer: "PreTrainedTokenizer", data_args:
r"""
Gets chat template and fixes the tokenizer.
"""
if data_args.template in ["llava", "paligemma", "qwen2_vl"]:
require_version(
"transformers>=4.45.0.dev0", "To fix: pip install git+https://github.com/huggingface/transformers.git"
)
require_version("accelerate>=0.34.0", "To fix: pip install accelerate>=0.34.0")
if data_args.template is None:
template = TEMPLATES["empty"] # placeholder
else:
template = TEMPLATES.get(data_args.template, None)
if template is None:
raise ValueError("Template {} does not exist.".format(data_args.template))
raise ValueError(f"Template {data_args.template} does not exist.")
if template.mm_plugin.__class__.__name__ != "BasePlugin":
require_version("transformers>=4.45.0", "To fix: pip install transformers>=4.45.0")
if data_args.train_on_prompt and template.efficient_eos:
raise ValueError("Current template does not support `train_on_prompt`.")
if data_args.tool_format is not None:
logger.info("Using tool format: {}.".format(data_args.tool_format))
logger.info_rank0(f"Using tool format: {data_args.tool_format}.")
eos_slots = [] if template.efficient_eos else [{"eos_token"}]
template.format_function = FunctionFormatter(slots=eos_slots, tool_format=data_args.tool_format)
template.format_tools = ToolFormatter(tool_format=data_args.tool_format)
@ -388,20 +388,21 @@ def get_template_and_fix_tokenizer(tokenizer: "PreTrainedTokenizer", data_args:
if tokenizer.pad_token_id is None:
tokenizer.pad_token = tokenizer.eos_token
logger.info("Add pad token: {}".format(tokenizer.pad_token))
logger.info_rank0(f"Add pad token: {tokenizer.pad_token}")
if stop_words:
num_added_tokens = tokenizer.add_special_tokens(
dict(additional_special_tokens=stop_words), replace_additional_special_tokens=False
)
logger.info("Add {} to stop words.".format(",".join(stop_words)))
logger.info_rank0("Add {} to stop words.".format(",".join(stop_words)))
if num_added_tokens > 0:
logger.warning("New tokens have been added, make sure `resize_vocab` is True.")
logger.warning_rank0("New tokens have been added, make sure `resize_vocab` is True.")
try:
tokenizer.chat_template = _get_jinja_template(template, tokenizer)
except ValueError:
logger.info("Cannot add this chat template to tokenizer.")
if tokenizer.chat_template is None or template.replace_jinja_template:
try:
tokenizer.chat_template = _get_jinja_template(template, tokenizer)
except ValueError as e:
logger.info_rank0(f"Cannot add this chat template to tokenizer: {e}.")
return template
@ -640,6 +641,14 @@ _register_template(
)
_register_template(
name="exaone",
format_user=StringFormatter(slots=["[|user|]{{content}}\n[|assistant|]"]),
format_system=StringFormatter(slots=["[|system|]{{content}}[|endofturn|]\n"]),
format_separator=EmptyFormatter(slots=["\n"]),
)
_register_template(
name="falcon",
format_user=StringFormatter(slots=["User: {{content}}\nFalcon:"]),
@ -664,6 +673,7 @@ _register_template(
format_separator=EmptyFormatter(slots=["<end_of_turn>\n"]),
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
efficient_eos=True,
replace_jinja_template=False,
)
@ -681,6 +691,14 @@ _register_template(
)
_register_template(
name="index",
format_user=StringFormatter(slots=["reserved_0{{content}}reserved_1"]),
format_system=StringFormatter(slots=["<unk>{{content}}"]),
efficient_eos=True,
)
_register_template(
name="intern",
format_user=StringFormatter(slots=["<|User|>:{{content}}\n<|Bot|>:"]),
@ -740,6 +758,7 @@ _register_template(
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
stop_words=["<|eot_id|>"],
replace_eos=True,
replace_jinja_template=False,
)
@ -754,6 +773,107 @@ _register_template(
)
_register_template(
name="llava_next",
format_user=StringFormatter(slots=["USER: {{content}} ASSISTANT:"]),
default_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."
),
mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
)
_register_template(
name="llava_next_llama3",
format_user=StringFormatter(
slots=[
(
"<|start_header_id|>user<|end_header_id|>\n\n{{content}}<|eot_id|>"
"<|start_header_id|>assistant<|end_header_id|>\n\n"
)
]
),
format_system=StringFormatter(slots=["<|start_header_id|>system<|end_header_id|>\n\n{{content}}<|eot_id|>"]),
format_observation=StringFormatter(
slots=[
(
"<|start_header_id|>tool<|end_header_id|>\n\n{{content}}<|eot_id|>"
"<|start_header_id|>assistant<|end_header_id|>\n\n"
)
]
),
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
stop_words=["<|eot_id|>"],
replace_eos=True,
replace_jinja_template=False,
mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
)
_register_template(
name="llava_next_mistral",
format_user=StringFormatter(slots=["[INST] {{content}} [/INST]"]),
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
)
_register_template(
name="llava_next_qwen",
format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
format_observation=StringFormatter(slots=["<|im_start|>tool\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
format_separator=EmptyFormatter(slots=["\n"]),
default_system="You are a helpful assistant.",
stop_words=["<|im_end|>"],
replace_eos=True,
replace_jinja_template=False,
mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
)
_register_template(
name="llava_next_yi",
format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
format_separator=EmptyFormatter(slots=["\n"]),
stop_words=["<|im_end|>"],
replace_eos=True,
mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
)
_register_template(
name="llava_next_video",
format_user=StringFormatter(slots=["USER: {{content}} ASSISTANT:"]),
default_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."
),
mm_plugin=get_mm_plugin(name="llava_next_video", image_token="<image>", video_token="<video>"),
)
_register_template(
name="llava_next_video_mistral",
format_user=StringFormatter(slots=["[INST] {{content}} [/INST]"]),
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
mm_plugin=get_mm_plugin(name="llava_next_video", image_token="<image>", video_token="<video>"),
)
_register_template(
name="llava_next_video_yi",
format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
format_separator=EmptyFormatter(slots=["\n"]),
stop_words=["<|im_end|>"],
replace_eos=True,
mm_plugin=get_mm_plugin(name="llava_next_video", image_token="<image>", video_token="<video>"),
)
_register_template(
name="mistral",
format_user=StringFormatter(slots=["[INST] {{content}} [/INST]"]),
@ -831,6 +951,14 @@ _register_template(
replace_eos=True,
)
_register_template(
name="pixtral",
format_user=StringFormatter(slots=["[INST] {{content}} [/INST]"]),
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
mm_plugin=get_mm_plugin(name="pixtral", image_token="[IMG]"),
)
_register_template(
name="qwen",
format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
@ -840,6 +968,7 @@ _register_template(
default_system="You are a helpful assistant.",
stop_words=["<|im_end|>"],
replace_eos=True,
replace_jinja_template=False,
)
@ -852,6 +981,7 @@ _register_template(
default_system="You are a helpful assistant.",
stop_words=["<|im_end|>"],
replace_eos=True,
replace_jinja_template=False,
mm_plugin=get_mm_plugin(name="qwen2_vl", image_token="<|image_pad|>", video_token="<|video_pad|>"),
)
@ -907,6 +1037,17 @@ _register_template(
)
_register_template(
name="video_llava",
format_user=StringFormatter(slots=["USER: {{content}} ASSISTANT:"]),
default_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."
),
mm_plugin=get_mm_plugin(name="video_llava", image_token="<image>", video_token="<video>"),
)
_register_template(
name="xuanyuan",
format_user=StringFormatter(slots=["Human: {{content}} Assistant:"]),

2
src/llamafactory/data/tool_utils.py
View File

@ -177,6 +177,6 @@ TOOLS = {
def get_tool_utils(name: str) -> "ToolUtils":
tool_utils = TOOLS.get(name, None)
if tool_utils is None:
raise ValueError("Tool utils `{}` not found.".format(name))
raise ValueError(f"Tool utils `{name}` not found.")
return tool_utils

4
src/llamafactory/eval/evaluator.py
View File

@ -87,7 +87,7 @@ class Evaluator:
token=self.model_args.hf_hub_token,
)
with open(mapping, "r", encoding="utf-8") as f:
with open(mapping, encoding="utf-8") as f:
categorys: Dict[str, Dict[str, str]] = json.load(f)
category_corrects = {subj: np.array([], dtype="bool") for subj in SUBJECTS}
@ -139,7 +139,7 @@ class Evaluator:
def _save_results(self, category_corrects: Dict[str, "NDArray"], results: Dict[str, Dict[int, str]]) -> None:
score_info = "\n".join(
[
"{:>15}: {:.2f}".format(category_name, 100 * np.mean(category_correct))
f"{category_name:>15}: {100 * np.mean(category_correct):.2f}"
for category_name, category_correct in category_corrects.items()
if len(category_correct)
]

2
src/llamafactory/eval/template.py
View File

@ -61,7 +61,7 @@ def _register_eval_template(name: str, system: str, choice: str, answer: str) ->
def get_eval_template(name: str) -> "EvalTemplate":
eval_template = eval_templates.get(name, None)
assert eval_template is not None, "Template {} does not exist.".format(name)
assert eval_template is not None, f"Template {name} does not exist."
return eval_template

444
src/llamafactory/extras/constants.py
View File

@ -12,6 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from collections import OrderedDict, defaultdict
from enum import Enum
from typing import Dict, Optional
@ -47,7 +48,7 @@ FILEEXT2TYPE = {
IGNORE_INDEX = -100
IMAGE_PLACEHOLDER = "<image>"
IMAGE_PLACEHOLDER = os.environ.get("IMAGE_PLACEHOLDER", "<image>")
LAYERNORM_NAMES = {"norm", "ln"}
@ -95,7 +96,7 @@ SUPPORTED_CLASS_FOR_BLOCK_DIAG_ATTN = {
SUPPORTED_CLASS_FOR_S2ATTN = {"llama"}
VIDEO_PLACEHOLDER = "<video>"
VIDEO_PLACEHOLDER = os.environ.get("VIDEO_PLACEHOLDER", "<video>")
V_HEAD_WEIGHTS_NAME = "value_head.bin"
@ -107,6 +108,7 @@ VISION_MODELS = set()
class DownloadSource(str, Enum):
DEFAULT = "hf"
MODELSCOPE = "ms"
OPENMIND = "om"
def register_model_group(
@ -114,17 +116,12 @@ def register_model_group(
template: Optional[str] = None,
vision: bool = False,
) -> None:
prefix = None
for name, path in models.items():
if prefix is None:
prefix = name.split("-")[0]
else:
assert prefix == name.split("-")[0], "prefix should be identical."
SUPPORTED_MODELS[name] = path
if template is not None:
DEFAULT_TEMPLATE[prefix] = template
if vision:
VISION_MODELS.add(prefix)
if template is not None and any(suffix in name for suffix in ("-Chat", "-Instruct")):
DEFAULT_TEMPLATE[name] = template
if vision:
VISION_MODELS.add(name)
register_model_group(
@ -168,14 +165,17 @@ register_model_group(
"Baichuan2-13B-Base": {
DownloadSource.DEFAULT: "baichuan-inc/Baichuan2-13B-Base",
DownloadSource.MODELSCOPE: "baichuan-inc/Baichuan2-13B-Base",
DownloadSource.OPENMIND: "Baichuan/Baichuan2_13b_base_pt",
},
"Baichuan2-7B-Chat": {
DownloadSource.DEFAULT: "baichuan-inc/Baichuan2-7B-Chat",
DownloadSource.MODELSCOPE: "baichuan-inc/Baichuan2-7B-Chat",
DownloadSource.OPENMIND: "Baichuan/Baichuan2_7b_chat_pt",
},
"Baichuan2-13B-Chat": {
DownloadSource.DEFAULT: "baichuan-inc/Baichuan2-13B-Chat",
DownloadSource.MODELSCOPE: "baichuan-inc/Baichuan2-13B-Chat",
DownloadSource.OPENMIND: "Baichuan/Baichuan2_13b_chat_pt",
},
},
template="baichuan2",
@ -274,27 +274,27 @@ register_model_group(
register_model_group(
models={
"ChineseLLaMA2-1.3B": {
"Chinese-Llama-2-1.3B": {
DownloadSource.DEFAULT: "hfl/chinese-llama-2-1.3b",
DownloadSource.MODELSCOPE: "AI-ModelScope/chinese-llama-2-1.3b",
},
"ChineseLLaMA2-7B": {
"Chinese-Llama-2-7B": {
DownloadSource.DEFAULT: "hfl/chinese-llama-2-7b",
DownloadSource.MODELSCOPE: "AI-ModelScope/chinese-llama-2-7b",
},
"ChineseLLaMA2-13B": {
"Chinese-Llama-2-13B": {
DownloadSource.DEFAULT: "hfl/chinese-llama-2-13b",
DownloadSource.MODELSCOPE: "AI-ModelScope/chinese-llama-2-13b",
},
"ChineseLLaMA2-1.3B-Chat": {
"Chinese-Alpaca-2-1.3B-Chat": {
DownloadSource.DEFAULT: "hfl/chinese-alpaca-2-1.3b",
DownloadSource.MODELSCOPE: "AI-ModelScope/chinese-alpaca-2-1.3b",
},
"ChineseLLaMA2-7B-Chat": {
"Chinese-Alpaca-2-7B-Chat": {
DownloadSource.DEFAULT: "hfl/chinese-alpaca-2-7b",
DownloadSource.MODELSCOPE: "AI-ModelScope/chinese-alpaca-2-7b",
},
"ChineseLLaMA2-13B-Chat": {
"Chinese-Alpaca-2-13B-Chat": {
DownloadSource.DEFAULT: "hfl/chinese-alpaca-2-13b",
DownloadSource.MODELSCOPE: "AI-ModelScope/chinese-alpaca-2-13b",
},
@ -450,25 +450,25 @@ register_model_group(
register_model_group(
models={
"DeepSeekCoder-6.7B-Base": {
"DeepSeek-Coder-6.7B-Base": {
DownloadSource.DEFAULT: "deepseek-ai/deepseek-coder-6.7b-base",
DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-coder-6.7b-base",
},
"DeepSeekCoder-7B-Base": {
"DeepSeek-Coder-7B-Base": {
DownloadSource.DEFAULT: "deepseek-ai/deepseek-coder-7b-base-v1.5",
},
"DeepSeekCoder-33B-Base": {
"DeepSeek-Coder-33B-Base": {
DownloadSource.DEFAULT: "deepseek-ai/deepseek-coder-33b-base",
DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-coder-33b-base",
},
"DeepSeekCoder-6.7B-Instruct": {
"DeepSeek-Coder-6.7B-Instruct": {
DownloadSource.DEFAULT: "deepseek-ai/deepseek-coder-6.7b-instruct",
DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-coder-6.7b-instruct",
},
"DeepSeekCoder-7B-Instruct": {
"DeepSeek-Coder-7B-Instruct": {
DownloadSource.DEFAULT: "deepseek-ai/deepseek-coder-7b-instruct-v1.5",
},
"DeepSeekCoder-33B-Instruct": {
"DeepSeek-Coder-33B-Instruct": {
DownloadSource.DEFAULT: "deepseek-ai/deepseek-coder-33b-instruct",
DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-coder-33b-instruct",
},
@ -477,6 +477,16 @@ register_model_group(
)
register_model_group(
models={
"EXAONE-3.0-7.8B-Instruct": {
DownloadSource.DEFAULT: "LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct",
},
},
template="exaone",
)
register_model_group(
models={
"Falcon-7B": {
@ -550,10 +560,12 @@ register_model_group(
"Gemma-2-2B-Instruct": {
DownloadSource.DEFAULT: "google/gemma-2-2b-it",
DownloadSource.MODELSCOPE: "LLM-Research/gemma-2-2b-it",
DownloadSource.OPENMIND: "LlamaFactory/gemma-2-2b-it",
},
"Gemma-2-9B-Instruct": {
DownloadSource.DEFAULT: "google/gemma-2-9b-it",
DownloadSource.MODELSCOPE: "LLM-Research/gemma-2-9b-it",
DownloadSource.OPENMIND: "LlamaFactory/gemma-2-9b-it",
},
"Gemma-2-27B-Instruct": {
DownloadSource.DEFAULT: "google/gemma-2-27b-it",
@ -573,6 +585,7 @@ register_model_group(
"GLM-4-9B-Chat": {
DownloadSource.DEFAULT: "THUDM/glm-4-9b-chat",
DownloadSource.MODELSCOPE: "ZhipuAI/glm-4-9b-chat",
DownloadSource.OPENMIND: "LlamaFactory/glm-4-9b-chat",
},
"GLM-4-9B-1M-Chat": {
DownloadSource.DEFAULT: "THUDM/glm-4-9b-chat-1m",
@ -583,6 +596,33 @@ register_model_group(
)
register_model_group(
models={
"Index-1.9B-Chat": {
DownloadSource.DEFAULT: "IndexTeam/Index-1.9B-Chat",
DownloadSource.MODELSCOPE: "IndexTeam/Index-1.9B-Chat",
},
"Index-1.9B-Character-Chat": {
DownloadSource.DEFAULT: "IndexTeam/Index-1.9B-Character",
DownloadSource.MODELSCOPE: "IndexTeam/Index-1.9B-Character",
},
"Index-1.9B-Base": {
DownloadSource.DEFAULT: "IndexTeam/Index-1.9B",
DownloadSource.MODELSCOPE: "IndexTeam/Index-1.9B",
},
"Index-1.9B-Base-Pure": {
DownloadSource.DEFAULT: "IndexTeam/Index-1.9B-Pure",
DownloadSource.MODELSCOPE: "IndexTeam/Index-1.9B-Pure",
},
"Index-1.9B-Chat-32K": {
DownloadSource.DEFAULT: "IndexTeam/Index-1.9B-32K",
DownloadSource.MODELSCOPE: "IndexTeam/Index-1.9B-32K",
},
},
template="index",
)
register_model_group(
models={
"InternLM-7B": {
@ -624,16 +664,10 @@ register_model_group(
DownloadSource.DEFAULT: "internlm/internlm2-chat-20b",
DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2-chat-20b",
},
},
template="intern2",
)
register_model_group(
models={
"InternLM2.5-1.8B": {
DownloadSource.DEFAULT: "internlm/internlm2_5-1_8b",
DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-1_8b",
DownloadSource.OPENMIND: "Intern/internlm2_5-1_8b",
},
"InternLM2.5-7B": {
DownloadSource.DEFAULT: "internlm/internlm2_5-7b",
@ -642,22 +676,27 @@ register_model_group(
"InternLM2.5-20B": {
DownloadSource.DEFAULT: "internlm/internlm2_5-20b",
DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-20b",
DownloadSource.OPENMIND: "Intern/internlm2_5-20b",
},
"InternLM2.5-1.8B-Chat": {
DownloadSource.DEFAULT: "internlm/internlm2_5-1_8b-chat",
DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-1_8b-chat",
DownloadSource.OPENMIND: "Intern/internlm2_5-1_8b-chat",
},
"InternLM2.5-7B-Chat": {
DownloadSource.DEFAULT: "internlm/internlm2_5-7b-chat",
DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-7b-chat",
DownloadSource.OPENMIND: "Intern/internlm2_5-7b-chat",
},
"InternLM2.5-7B-1M-Chat": {
DownloadSource.DEFAULT: "internlm/internlm2_5-7b-chat-1m",
DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-7b-chat-1m",
DownloadSource.OPENMIND: "Intern/internlm2_5-7b-chat-1m",
},
"InternLM2.5-20B-Chat": {
DownloadSource.DEFAULT: "internlm/internlm2_5-20b-chat",
DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-20b-chat",
DownloadSource.OPENMIND: "Intern/internlm2_5-20b-chat",
},
},
template="intern2",
@ -686,19 +725,19 @@ register_model_group(
register_model_group(
models={
"LLaMA-7B": {
"Llama-7B": {
DownloadSource.DEFAULT: "huggyllama/llama-7b",
DownloadSource.MODELSCOPE: "skyline2006/llama-7b",
},
"LLaMA-13B": {
"Llama-13B": {
DownloadSource.DEFAULT: "huggyllama/llama-13b",
DownloadSource.MODELSCOPE: "skyline2006/llama-13b",
},
"LLaMA-30B": {
"Llama-30B": {
DownloadSource.DEFAULT: "huggyllama/llama-30b",
DownloadSource.MODELSCOPE: "skyline2006/llama-30b",
},
"LLaMA-65B": {
"Llama-65B": {
DownloadSource.DEFAULT: "huggyllama/llama-65b",
DownloadSource.MODELSCOPE: "skyline2006/llama-65b",
},
@ -708,27 +747,27 @@ register_model_group(
register_model_group(
models={
"LLaMA2-7B": {
"Llama-2-7B": {
DownloadSource.DEFAULT: "meta-llama/Llama-2-7b-hf",
DownloadSource.MODELSCOPE: "modelscope/Llama-2-7b-ms",
},
"LLaMA2-13B": {
"Llama-2-13B": {
DownloadSource.DEFAULT: "meta-llama/Llama-2-13b-hf",
DownloadSource.MODELSCOPE: "modelscope/Llama-2-13b-ms",
},
"LLaMA2-70B": {
"Llama-2-70B": {
DownloadSource.DEFAULT: "meta-llama/Llama-2-70b-hf",
DownloadSource.MODELSCOPE: "modelscope/Llama-2-70b-ms",
},
"LLaMA2-7B-Chat": {
"Llama-2-7B-Chat": {
DownloadSource.DEFAULT: "meta-llama/Llama-2-7b-chat-hf",
DownloadSource.MODELSCOPE: "modelscope/Llama-2-7b-chat-ms",
},
"LLaMA2-13B-Chat": {
"Llama-2-13B-Chat": {
DownloadSource.DEFAULT: "meta-llama/Llama-2-13b-chat-hf",
DownloadSource.MODELSCOPE: "modelscope/Llama-2-13b-chat-ms",
},
"LLaMA2-70B-Chat": {
"Llama-2-70B-Chat": {
DownloadSource.DEFAULT: "meta-llama/Llama-2-70b-chat-hf",
DownloadSource.MODELSCOPE: "modelscope/Llama-2-70b-chat-ms",
},
@ -739,60 +778,78 @@ register_model_group(
register_model_group(
models={
"LLaMA3-8B": {
"Llama-3-8B": {
DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3-8B",
DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3-8B",
},
"LLaMA3-70B": {
"Llama-3-70B": {
DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3-70B",
DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3-70B",
},
"LLaMA3-8B-Instruct": {
"Llama-3-8B-Instruct": {
DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3-8B-Instruct",
DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3-8B-Instruct",
},
"LLaMA3-70B-Instruct": {
"Llama-3-70B-Instruct": {
DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3-70B-Instruct",
DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3-70B-Instruct",
},
"LLaMA3-8B-Chinese-Chat": {
"Llama-3-8B-Chinese-Chat": {
DownloadSource.DEFAULT: "shenzhi-wang/Llama3-8B-Chinese-Chat",
DownloadSource.MODELSCOPE: "LLM-Research/Llama3-8B-Chinese-Chat",
DownloadSource.OPENMIND: "LlamaFactory/Llama3-Chinese-8B-Instruct",
},
"LLaMA3-70B-Chinese-Chat": {
"Llama-3-70B-Chinese-Chat": {
DownloadSource.DEFAULT: "shenzhi-wang/Llama3-70B-Chinese-Chat",
},
},
template="llama3",
)
register_model_group(
models={
"LLaMA3.1-8B": {
"Llama-3.1-8B": {
DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3.1-8B",
DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3.1-8B",
},
"LLaMA3.1-70B": {
"Llama-3.1-70B": {
DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3.1-70B",
DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3.1-70B",
},
"LLaMA3.1-405B": {
"Llama-3.1-405B": {
DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3.1-405B",
DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3.1-405B",
},
"LLaMA3.1-8B-Instruct": {
"Llama-3.1-8B-Instruct": {
DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3.1-8B-Instruct",
DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3.1-8B-Instruct",
},
"LLaMA3.1-70B-Instruct": {
"Llama-3.1-70B-Instruct": {
DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3.1-70B-Instruct",
DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3.1-70B-Instruct",
},
"LLaMA3.1-405B-Instruct": {
"Llama-3.1-405B-Instruct": {
DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3.1-405B-Instruct",
DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3.1-405B-Instruct",
},
"Llama-3.1-8B-Chinese-Chat": {
DownloadSource.DEFAULT: "shenzhi-wang/Llama3.1-8B-Chinese-Chat",
DownloadSource.MODELSCOPE: "XD_AI/Llama3.1-8B-Chinese-Chat",
},
"Llama-3.1-70B-Chinese-Chat": {
DownloadSource.DEFAULT: "shenzhi-wang/Llama3.1-70B-Chinese-Chat",
DownloadSource.MODELSCOPE: "XD_AI/Llama3.1-70B-Chinese-Chat",
},
"Llama-3.2-1B": {
DownloadSource.DEFAULT: "meta-llama/Llama-3.2-1B",
DownloadSource.MODELSCOPE: "LLM-Research/Llama-3.2-1B",
},
"Llama-3.2-3B": {
DownloadSource.DEFAULT: "meta-llama/Llama-3.2-3B",
DownloadSource.MODELSCOPE: "LLM-Research/Llama-3.2-3B",
},
"Llama-3.2-1B-Instruct": {
DownloadSource.DEFAULT: "meta-llama/Llama-3.2-1B-Instruct",
DownloadSource.MODELSCOPE: "LLM-Research/Llama-3.2-1B-Instruct",
},
"Llama-3.2-3B-Instruct": {
DownloadSource.DEFAULT: "meta-llama/Llama-3.2-3B-Instruct",
DownloadSource.MODELSCOPE: "LLM-Research/Llama-3.2-3B-Instruct",
},
},
template="llama3",
)
@ -800,11 +857,13 @@ register_model_group(
register_model_group(
models={
"LLaVA1.5-7B-Chat": {
"LLaVA-1.5-7B-Chat": {
DownloadSource.DEFAULT: "llava-hf/llava-1.5-7b-hf",
DownloadSource.MODELSCOPE: "swift/llava-1.5-7b-hf",
},
"LLaVA1.5-13B-Chat": {
"LLaVA-1.5-13B-Chat": {
DownloadSource.DEFAULT: "llava-hf/llava-1.5-13b-hf",
DownloadSource.MODELSCOPE: "swift/llava-1.5-13b-hf",
},
},
template="llava",
@ -812,6 +871,117 @@ register_model_group(
)
register_model_group(
models={
"LLaVA-NeXT-7B-Chat": {
DownloadSource.DEFAULT: "llava-hf/llava-v1.6-vicuna-7b-hf",
DownloadSource.MODELSCOPE: "swift/llava-v1.6-vicuna-7b-hf",
},
"LLaVA-NeXT-13B-Chat": {
DownloadSource.DEFAULT: "llava-hf/llava-v1.6-vicuna-13b-hf",
DownloadSource.MODELSCOPE: "swift/llava-v1.6-vicuna-13b-hf",
},
},
template="llava_next",
vision=True,
)
register_model_group(
models={
"LLaVA-NeXT-Mistral-7B-Chat": {
DownloadSource.DEFAULT: "llava-hf/llava-v1.6-mistral-7b-hf",
DownloadSource.MODELSCOPE: "swift/llava-v1.6-mistral-7b-hf",
},
},
template="llava_next_mistral",
vision=True,
)
register_model_group(
models={
"LLaVA-NeXT-Llama3-8B-Chat": {
DownloadSource.DEFAULT: "llava-hf/llama3-llava-next-8b-hf",
DownloadSource.MODELSCOPE: "swift/llama3-llava-next-8b-hf",
},
},
template="llava_next_llama3",
vision=True,
)
register_model_group(
models={
"LLaVA-NeXT-34B-Chat": {
DownloadSource.DEFAULT: "llava-hf/llava-v1.6-34b-hf",
DownloadSource.MODELSCOPE: "LLM-Research/llava-v1.6-34b-hf",
},
},
template="llava_next_yi",
vision=True,
)
register_model_group(
models={
"LLaVA-NeXT-72B-Chat": {
DownloadSource.DEFAULT: "llava-hf/llava-next-72b-hf",
DownloadSource.MODELSCOPE: "AI-ModelScope/llava-next-72b-hf",
},
"LLaVA-NeXT-110B-Chat": {
DownloadSource.DEFAULT: "llava-hf/llava-next-110b-hf",
DownloadSource.MODELSCOPE: "AI-ModelScope/llava-next-110b-hf",
},
},
template="llava_next_qwen",
vision=True,
)
register_model_group(
models={
"LLaVA-NeXT-Video-7B-Chat": {
DownloadSource.DEFAULT: "llava-hf/LLaVA-NeXT-Video-7B-hf",
DownloadSource.MODELSCOPE: "swift/LLaVA-NeXT-Video-7B-hf",
},
"LLaVA-NeXT-Video-7B-DPO-Chat": {
DownloadSource.DEFAULT: "llava-hf/LLaVA-NeXT-Video-7B-DPO-hf",
DownloadSource.MODELSCOPE: "swift/LLaVA-NeXT-Video-7B-DPO-hf",
},
},
template="llava_next_video",
vision=True,
)
register_model_group(
models={
"LLaVA-NeXT-Video-7B-32k-Chat": {
DownloadSource.DEFAULT: "llava-hf/LLaVA-NeXT-Video-7B-32K-hf",
DownloadSource.MODELSCOPE: "swift/LLaVA-NeXT-Video-7B-32K-hf",
},
},
template="llava_next_video_mistral",
vision=True,
)
register_model_group(
models={
"LLaVA-NeXT-Video-34B-Chat": {
DownloadSource.DEFAULT: "llava-hf/LLaVA-NeXT-Video-34B-hf",
DownloadSource.MODELSCOPE: "swift/LLaVA-NeXT-Video-34B-hf",
},
"LLaVA-NeXT-Video-34B-DPO-Chat": {
DownloadSource.DEFAULT: "llava-hf/LLaVA-NeXT-Video-34B-DPO-hf",
},
},
template="llava_next_video_yi",
vision=True,
)
register_model_group(
models={
"MiniCPM-2B-SFT-Chat": {
@ -832,6 +1002,7 @@ register_model_group(
"MiniCPM3-4B-Chat": {
DownloadSource.DEFAULT: "openbmb/MiniCPM3-4B",
DownloadSource.MODELSCOPE: "OpenBMB/MiniCPM3-4B",
DownloadSource.OPENMIND: "LlamaFactory/MiniCPM3-4B",
},
},
template="cpm3",
@ -1005,27 +1176,27 @@ register_model_group(
register_model_group(
models={
"Phi3-4B-4k-Instruct": {
"Phi-3-4B-4k-Instruct": {
DownloadSource.DEFAULT: "microsoft/Phi-3-mini-4k-instruct",
DownloadSource.MODELSCOPE: "LLM-Research/Phi-3-mini-4k-instruct",
},
"Phi3-4B-128k-Instruct": {
"Phi-3-4B-128k-Instruct": {
DownloadSource.DEFAULT: "microsoft/Phi-3-mini-128k-instruct",
DownloadSource.MODELSCOPE: "LLM-Research/Phi-3-mini-128k-instruct",
},
"Phi3-7B-8k-Instruct": {
"Phi-3-7B-8k-Instruct": {
DownloadSource.DEFAULT: "microsoft/Phi-3-small-8k-instruct",
DownloadSource.MODELSCOPE: "LLM-Research/Phi-3-small-8k-instruct",
},
"Phi3-7B-128k-Instruct": {
"Phi-3-7B-128k-Instruct": {
DownloadSource.DEFAULT: "microsoft/Phi-3-small-128k-instruct",
DownloadSource.MODELSCOPE: "LLM-Research/Phi-3-small-128k-instruct",
},
"Phi3-14B-8k-Instruct": {
"Phi-3-14B-8k-Instruct": {
DownloadSource.DEFAULT: "microsoft/Phi-3-medium-4k-instruct",
DownloadSource.MODELSCOPE: "LLM-Research/Phi-3-medium-4k-instruct",
},
"Phi3-14B-128k-Instruct": {
"Phi-3-14B-128k-Instruct": {
DownloadSource.DEFAULT: "microsoft/Phi-3-medium-128k-instruct",
DownloadSource.MODELSCOPE: "LLM-Research/Phi-3-medium-128k-instruct",
},
@ -1034,6 +1205,18 @@ register_model_group(
)
register_model_group(
models={
"Pixtral-12B-Chat": {
DownloadSource.DEFAULT: "mistral-community/pixtral-12b",
DownloadSource.MODELSCOPE: "AI-ModelScope/pixtral-12b",
}
},
template="pixtral",
vision=True,
)
register_model_group(
models={
"Qwen-1.8B": {
@ -1068,35 +1251,35 @@ register_model_group(
DownloadSource.DEFAULT: "Qwen/Qwen-72B-Chat",
DownloadSource.MODELSCOPE: "qwen/Qwen-72B-Chat",
},
"Qwen-1.8B-int8-Chat": {
"Qwen-1.8B-Chat-Int8": {
DownloadSource.DEFAULT: "Qwen/Qwen-1_8B-Chat-Int8",
DownloadSource.MODELSCOPE: "qwen/Qwen-1_8B-Chat-Int8",
},
"Qwen-1.8B-int4-Chat": {
"Qwen-1.8B-Chat-Int4": {
DownloadSource.DEFAULT: "Qwen/Qwen-1_8B-Chat-Int4",
DownloadSource.MODELSCOPE: "qwen/Qwen-1_8B-Chat-Int4",
},
"Qwen-7B-int8-Chat": {
"Qwen-7B-Chat-Int8": {
DownloadSource.DEFAULT: "Qwen/Qwen-7B-Chat-Int8",
DownloadSource.MODELSCOPE: "qwen/Qwen-7B-Chat-Int8",
},
"Qwen-7B-int4-Chat": {
"Qwen-7B-Chat-Int4": {
DownloadSource.DEFAULT: "Qwen/Qwen-7B-Chat-Int4",
DownloadSource.MODELSCOPE: "qwen/Qwen-7B-Chat-Int4",
},
"Qwen-14B-int8-Chat": {
"Qwen-14B-Chat-Int8": {
DownloadSource.DEFAULT: "Qwen/Qwen-14B-Chat-Int8",
DownloadSource.MODELSCOPE: "qwen/Qwen-14B-Chat-Int8",
},
"Qwen-14B-int4-Chat": {
"Qwen-14B-Chat-Int4": {
DownloadSource.DEFAULT: "Qwen/Qwen-14B-Chat-Int4",
DownloadSource.MODELSCOPE: "qwen/Qwen-14B-Chat-Int4",
},
"Qwen-72B-int8-Chat": {
"Qwen-72B-Chat-Int8": {
DownloadSource.DEFAULT: "Qwen/Qwen-72B-Chat-Int8",
DownloadSource.MODELSCOPE: "qwen/Qwen-72B-Chat-Int8",
},
"Qwen-72B-int4-Chat": {
"Qwen-72B-Chat-Int4": {
DownloadSource.DEFAULT: "Qwen/Qwen-72B-Chat-Int4",
DownloadSource.MODELSCOPE: "qwen/Qwen-72B-Chat-Int4",
},
@ -1179,75 +1362,75 @@ register_model_group(
DownloadSource.DEFAULT: "Qwen/Qwen1.5-MoE-A2.7B-Chat",
DownloadSource.MODELSCOPE: "qwen/Qwen1.5-MoE-A2.7B-Chat",
},
"Qwen1.5-0.5B-int8-Chat": {
"Qwen1.5-0.5B-Chat-GPTQ-Int8": {
DownloadSource.DEFAULT: "Qwen/Qwen1.5-0.5B-Chat-GPTQ-Int8",
DownloadSource.MODELSCOPE: "qwen/Qwen1.5-0.5B-Chat-GPTQ-Int8",
},
"Qwen1.5-0.5B-int4-Chat": {
"Qwen1.5-0.5B-Chat-AWQ": {
DownloadSource.DEFAULT: "Qwen/Qwen1.5-0.5B-Chat-AWQ",
DownloadSource.MODELSCOPE: "qwen/Qwen1.5-0.5B-Chat-AWQ",
},
"Qwen1.5-1.8B-int8-Chat": {
"Qwen1.5-1.8B-Chat-GPTQ-Int8": {
DownloadSource.DEFAULT: "Qwen/Qwen1.5-1.8B-Chat-GPTQ-Int8",
DownloadSource.MODELSCOPE: "qwen/Qwen1.5-1.8B-Chat-GPTQ-Int8",
},
"Qwen1.5-1.8B-int4-Chat": {
"Qwen1.5-1.8B-Chat-AWQ": {
DownloadSource.DEFAULT: "Qwen/Qwen1.5-1.8B-Chat-AWQ",
DownloadSource.MODELSCOPE: "qwen/Qwen1.5-1.8B-Chat-AWQ",
},
"Qwen1.5-4B-int8-Chat": {
"Qwen1.5-4B-Chat-GPTQ-Int8": {
DownloadSource.DEFAULT: "Qwen/Qwen1.5-4B-Chat-GPTQ-Int8",
DownloadSource.MODELSCOPE: "qwen/Qwen1.5-4B-Chat-GPTQ-Int8",
},
"Qwen1.5-4B-int4-Chat": {
"Qwen1.5-4B-Chat-AWQ": {
DownloadSource.DEFAULT: "Qwen/Qwen1.5-4B-Chat-AWQ",
DownloadSource.MODELSCOPE: "qwen/Qwen1.5-4B-Chat-AWQ",
},
"Qwen1.5-7B-int8-Chat": {
"Qwen1.5-7B-Chat-GPTQ-Int8": {
DownloadSource.DEFAULT: "Qwen/Qwen1.5-7B-Chat-GPTQ-Int8",
DownloadSource.MODELSCOPE: "qwen/Qwen1.5-7B-Chat-GPTQ-Int8",
},
"Qwen1.5-7B-int4-Chat": {
"Qwen1.5-7B-Chat-AWQ": {
DownloadSource.DEFAULT: "Qwen/Qwen1.5-7B-Chat-AWQ",
DownloadSource.MODELSCOPE: "qwen/Qwen1.5-7B-Chat-AWQ",
},
"Qwen1.5-14B-int8-Chat": {
"Qwen1.5-14B-Chat-GPTQ-Int8": {
DownloadSource.DEFAULT: "Qwen/Qwen1.5-14B-Chat-GPTQ-Int8",
DownloadSource.MODELSCOPE: "qwen/Qwen1.5-14B-Chat-GPTQ-Int8",
},
"Qwen1.5-14B-int4-Chat": {
"Qwen1.5-14B-Chat-AWQ": {
DownloadSource.DEFAULT: "Qwen/Qwen1.5-14B-Chat-AWQ",
DownloadSource.MODELSCOPE: "qwen/Qwen1.5-14B-Chat-AWQ",
},
"Qwen1.5-32B-int4-Chat": {
"Qwen1.5-32B-Chat-AWQ": {
DownloadSource.DEFAULT: "Qwen/Qwen1.5-32B-Chat-AWQ",
DownloadSource.MODELSCOPE: "qwen/Qwen1.5-32B-Chat-AWQ",
},
"Qwen1.5-72B-int8-Chat": {
"Qwen1.5-72B-Chat-GPTQ-Int8": {
DownloadSource.DEFAULT: "Qwen/Qwen1.5-72B-Chat-GPTQ-Int8",
DownloadSource.MODELSCOPE: "qwen/Qwen1.5-72B-Chat-GPTQ-Int8",
},
"Qwen1.5-72B-int4-Chat": {
"Qwen1.5-72B-Chat-AWQ": {
DownloadSource.DEFAULT: "Qwen/Qwen1.5-72B-Chat-AWQ",
DownloadSource.MODELSCOPE: "qwen/Qwen1.5-72B-Chat-AWQ",
},
"Qwen1.5-110B-int4-Chat": {
"Qwen1.5-110B-Chat-AWQ": {
DownloadSource.DEFAULT: "Qwen/Qwen1.5-110B-Chat-AWQ",
DownloadSource.MODELSCOPE: "qwen/Qwen1.5-110B-Chat-AWQ",
},
"Qwen1.5-MoE-A2.7B-int4-Chat": {
"Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4": {
DownloadSource.DEFAULT: "Qwen/Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4",
DownloadSource.MODELSCOPE: "qwen/Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4",
},
"Qwen1.5-Code-7B": {
"CodeQwen1.5-7B": {
DownloadSource.DEFAULT: "Qwen/CodeQwen1.5-7B",
DownloadSource.MODELSCOPE: "qwen/CodeQwen1.5-7B",
},
"Qwen1.5-Code-7B-Chat": {
"CodeQwen1.5-7B-Chat": {
DownloadSource.DEFAULT: "Qwen/CodeQwen1.5-7B-Chat",
DownloadSource.MODELSCOPE: "qwen/CodeQwen1.5-7B-Chat",
},
"Qwen1.5-Code-7B-int4-Chat": {
"CodeQwen1.5-7B-Chat-AWQ": {
DownloadSource.DEFAULT: "Qwen/CodeQwen1.5-7B-Chat-AWQ",
DownloadSource.MODELSCOPE: "qwen/CodeQwen1.5-7B-Chat-AWQ",
},
@ -1281,14 +1464,17 @@ register_model_group(
"Qwen2-0.5B-Instruct": {
DownloadSource.DEFAULT: "Qwen/Qwen2-0.5B-Instruct",
DownloadSource.MODELSCOPE: "qwen/Qwen2-0.5B-Instruct",
DownloadSource.OPENMIND: "LlamaFactory/Qwen2-0.5B-Instruct",
},
"Qwen2-1.5B-Instruct": {
DownloadSource.DEFAULT: "Qwen/Qwen2-1.5B-Instruct",
DownloadSource.MODELSCOPE: "qwen/Qwen2-1.5B-Instruct",
DownloadSource.OPENMIND: "LlamaFactory/Qwen2-1.5B-Instruct",
},
"Qwen2-7B-Instruct": {
DownloadSource.DEFAULT: "Qwen/Qwen2-7B-Instruct",
DownloadSource.MODELSCOPE: "qwen/Qwen2-7B-Instruct",
DownloadSource.OPENMIND: "LlamaFactory/Qwen2-7B-Instruct",
},
"Qwen2-72B-Instruct": {
DownloadSource.DEFAULT: "Qwen/Qwen2-72B-Instruct",
@ -1568,51 +1754,53 @@ register_model_group(
register_model_group(
models={
"Qwen2VL-2B-Instruct": {
"Qwen2-VL-2B-Instruct": {
DownloadSource.DEFAULT: "Qwen/Qwen2-VL-2B-Instruct",
DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-2B-Instruct",
DownloadSource.OPENMIND: "LlamaFactory/Qwen2-VL-2B-Instruct",
},
"Qwen2VL-7B-Instruct": {
"Qwen2-VL-7B-Instruct": {
DownloadSource.DEFAULT: "Qwen/Qwen2-VL-7B-Instruct",
DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-7B-Instruct",
DownloadSource.OPENMIND: "LlamaFactory/Qwen2-VL-7B-Instruct",
},
"Qwen2VL-72B-Instruct": {
"Qwen2-VL-72B-Instruct": {
DownloadSource.DEFAULT: "Qwen/Qwen2-VL-72B-Instruct",
DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-72B-Instruct",
},
"Qwen2VL-2B-Instruct-GPTQ-Int8": {
"Qwen2-VL-2B-Instruct-GPTQ-Int8": {
DownloadSource.DEFAULT: "Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int8",
DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-2B-Instruct-GPTQ-Int8",
},
"Qwen2VL-2B-Instruct-GPTQ-Int4": {
"Qwen2-VL-2B-Instruct-GPTQ-Int4": {
DownloadSource.DEFAULT: "Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int4",
DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-2B-Instruct-GPTQ-Int4",
},
"Qwen2VL-2B-Instruct-AWQ": {
"Qwen2-VL-2B-Instruct-AWQ": {
DownloadSource.DEFAULT: "Qwen/Qwen2-VL-2B-Instruct-AWQ",
DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-2B-Instruct-AWQ",
},
"Qwen2VL-7B-Instruct-GPTQ-Int8": {
"Qwen2-VL-7B-Instruct-GPTQ-Int8": {
DownloadSource.DEFAULT: "Qwen/Qwen2-VL-7B-Instruct-GPTQ-Int8",
DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-7B-Instruct-GPTQ-Int8",
},
"Qwen2VL-7B-Instruct-GPTQ-Int4": {
"Qwen2-VL-7B-Instruct-GPTQ-Int4": {
DownloadSource.DEFAULT: "Qwen/Qwen2-VL-7B-Instruct-GPTQ-Int4",
DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-7B-Instruct-GPTQ-Int4",
},
"Qwen2VL-7B-Instruct-AWQ": {
"Qwen2-VL-7B-Instruct-AWQ": {
DownloadSource.DEFAULT: "Qwen/Qwen2-VL-7B-Instruct-AWQ",
DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-7B-Instruct-AWQ",
},
"Qwen2VL-72B-Instruct-GPTQ-Int8": {
"Qwen2-VL-72B-Instruct-GPTQ-Int8": {
DownloadSource.DEFAULT: "Qwen/Qwen2-VL-72B-Instruct-GPTQ-Int8",
DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-72B-Instruct-GPTQ-Int8",
},
"Qwen2VL-72B-Instruct-GPTQ-Int4": {
"Qwen2-VL-72B-Instruct-GPTQ-Int4": {
DownloadSource.DEFAULT: "Qwen/Qwen2-VL-72B-Instruct-GPTQ-Int4",
DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-72B-Instruct-GPTQ-Int4",
},
"Qwen2VL-72B-Instruct-AWQ": {
"Qwen2-VL-72B-Instruct-AWQ": {
DownloadSource.DEFAULT: "Qwen/Qwen2-VL-72B-Instruct-AWQ",
DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-72B-Instruct-AWQ",
},
@ -1673,10 +1861,12 @@ register_model_group(
"TeleChat-7B-Chat": {
DownloadSource.DEFAULT: "Tele-AI/telechat-7B",
DownloadSource.MODELSCOPE: "TeleAI/telechat-7B",
DownloadSource.OPENMIND: "TeleAI/TeleChat-7B-pt",
},
"TeleChat-12B-Chat": {
DownloadSource.DEFAULT: "Tele-AI/TeleChat-12B",
DownloadSource.MODELSCOPE: "TeleAI/TeleChat-12B",
DownloadSource.OPENMIND: "TeleAI/TeleChat-12B-pt",
},
"TeleChat-12B-v2-Chat": {
DownloadSource.DEFAULT: "Tele-AI/TeleChat-12B-v2",
@ -1689,11 +1879,11 @@ register_model_group(
register_model_group(
models={
"Vicuna1.5-7B-Chat": {
"Vicuna-v1.5-7B-Chat": {
DownloadSource.DEFAULT: "lmsys/vicuna-7b-v1.5",
DownloadSource.MODELSCOPE: "Xorbits/vicuna-7b-v1.5",
},
"Vicuna1.5-13B-Chat": {
"Vicuna-v1.5-13B-Chat": {
DownloadSource.DEFAULT: "lmsys/vicuna-13b-v1.5",
DownloadSource.MODELSCOPE: "Xorbits/vicuna-13b-v1.5",
},
@ -1702,6 +1892,17 @@ register_model_group(
)
register_model_group(
models={
"Video-LLaVA-7B-Chat": {
DownloadSource.DEFAULT: "LanguageBind/Video-LLaVA-7B-hf",
},
},
template="video_llava",
vision=True,
)
register_model_group(
models={
"XuanYuan-6B": {
@ -1712,7 +1913,7 @@ register_model_group(
DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-70B",
DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-70B",
},
"XuanYuan-2-70B": {
"XuanYuan2-70B": {
DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan2-70B",
DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan2-70B",
},
@ -1724,31 +1925,31 @@ register_model_group(
DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-70B-Chat",
DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-70B-Chat",
},
"XuanYuan-2-70B-Chat": {
"XuanYuan2-70B-Chat": {
DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan2-70B-Chat",
DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan2-70B-Chat",
},
"XuanYuan-6B-int8-Chat": {
"XuanYuan-6B-Chat-8bit": {
DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-6B-Chat-8bit",
DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-6B-Chat-8bit",
},
"XuanYuan-6B-int4-Chat": {
"XuanYuan-6B-Chat-4bit": {
DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-6B-Chat-4bit",
DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-6B-Chat-4bit",
},
"XuanYuan-70B-int8-Chat": {
"XuanYuan-70B-Chat-8bit": {
DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-70B-Chat-8bit",
DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-70B-Chat-8bit",
},
"XuanYuan-70B-int4-Chat": {
"XuanYuan-70B-Chat-4bit": {
DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-70B-Chat-4bit",
DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-70B-Chat-4bit",
},
"XuanYuan-2-70B-int8-Chat": {
"XuanYuan2-70B-Chat-8bit": {
DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan2-70B-Chat-8bit",
DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan2-70B-Chat-8bit",
},
"XuanYuan-2-70B-int4-Chat": {
"XuanYuan2-70B-Chat-4bit": {
DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan2-70B-Chat-4bit",
DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan2-70B-Chat-4bit",
},
@ -1853,19 +2054,19 @@ register_model_group(
DownloadSource.DEFAULT: "01-ai/Yi-34B-Chat",
DownloadSource.MODELSCOPE: "01ai/Yi-34B-Chat",
},
"Yi-6B-int8-Chat": {
"Yi-6B-Chat-8bits": {
DownloadSource.DEFAULT: "01-ai/Yi-6B-Chat-8bits",
DownloadSource.MODELSCOPE: "01ai/Yi-6B-Chat-8bits",
},
"Yi-6B-int4-Chat": {
"Yi-6B-Chat-4bits": {
DownloadSource.DEFAULT: "01-ai/Yi-6B-Chat-4bits",
DownloadSource.MODELSCOPE: "01ai/Yi-6B-Chat-4bits",
},
"Yi-34B-int8-Chat": {
"Yi-34B-Chat-8bits": {
DownloadSource.DEFAULT: "01-ai/Yi-34B-Chat-8bits",
DownloadSource.MODELSCOPE: "01ai/Yi-34B-Chat-8bits",
},
"Yi-34B-int4-Chat": {
"Yi-34B-Chat-4bits": {
DownloadSource.DEFAULT: "01-ai/Yi-34B-Chat-4bits",
DownloadSource.MODELSCOPE: "01ai/Yi-34B-Chat-4bits",
},
@ -1884,6 +2085,7 @@ register_model_group(
"Yi-1.5-6B-Chat": {
DownloadSource.DEFAULT: "01-ai/Yi-1.5-6B-Chat",
DownloadSource.MODELSCOPE: "01ai/Yi-1.5-6B-Chat",
DownloadSource.OPENMIND: "LlamaFactory/Yi-1.5-6B-Chat",
},
"Yi-1.5-9B-Chat": {
DownloadSource.DEFAULT: "01-ai/Yi-1.5-9B-Chat",
@ -1916,10 +2118,10 @@ register_model_group(
register_model_group(
models={
"YiVL-6B-Chat": {
"Yi-VL-6B-Chat": {
DownloadSource.DEFAULT: "BUAADreamer/Yi-VL-6B-hf",
},
"YiVL-34B-Chat": {
"Yi-VL-34B-Chat": {
DownloadSource.DEFAULT: "BUAADreamer/Yi-VL-34B-hf",
},
},

2
src/llamafactory/extras/env.py
View File

@ -72,4 +72,4 @@ def print_env() -> None:
except Exception:
pass
print("\n" + "\n".join(["- {}: {}".format(key, value) for key, value in info.items()]) + "\n")
print("\n" + "\n".join([f"- {key}: {value}" for key, value in info.items()]) + "\n")

74
src/llamafactory/extras/logging.py
View File

@ -20,6 +20,7 @@ import os
import sys
import threading
from concurrent.futures import ThreadPoolExecutor
from functools import lru_cache
from typing import Optional
from .constants import RUNNING_LOG
@ -37,12 +38,11 @@ class LoggerHandler(logging.Handler):
def __init__(self, output_dir: str) -> None:
super().__init__()
formatter = logging.Formatter(
fmt="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S"
self._formatter = logging.Formatter(
fmt="[%(levelname)s|%(asctime)s] %(filename)s:%(lineno)s >> %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
self.setLevel(logging.INFO)
self.setFormatter(formatter)
os.makedirs(output_dir, exist_ok=True)
self.running_log = os.path.join(output_dir, RUNNING_LOG)
if os.path.exists(self.running_log):
@ -58,7 +58,7 @@ class LoggerHandler(logging.Handler):
if record.name == "httpx":
return
log_entry = self.format(record)
log_entry = self._formatter.format(record)
self.thread_pool.submit(self._write_log, log_entry)
def close(self) -> None:
@ -66,6 +66,21 @@ class LoggerHandler(logging.Handler):
return super().close()
class _Logger(logging.Logger):
r"""
A logger that supports info_rank0 and warning_once.
"""
def info_rank0(self, *args, **kwargs) -> None:
self.info(*args, **kwargs)
def warning_rank0(self, *args, **kwargs) -> None:
self.warning(*args, **kwargs)
def warning_once(self, *args, **kwargs) -> None:
self.warning(*args, **kwargs)
def _get_default_logging_level() -> "logging._Level":
r"""
Returns the default logging level.
@ -75,7 +90,7 @@ def _get_default_logging_level() -> "logging._Level":
if env_level_str.upper() in logging._nameToLevel:
return logging._nameToLevel[env_level_str.upper()]
else:
raise ValueError("Unknown logging level: {}.".format(env_level_str))
raise ValueError(f"Unknown logging level: {env_level_str}.")
return _default_log_level
@ -84,7 +99,7 @@ def _get_library_name() -> str:
return __name__.split(".")[0]
def _get_library_root_logger() -> "logging.Logger":
def _get_library_root_logger() -> "_Logger":
return logging.getLogger(_get_library_name())
@ -95,12 +110,12 @@ def _configure_library_root_logger() -> None:
global _default_handler
with _thread_lock:
if _default_handler:
if _default_handler: # already configured
return
formatter = logging.Formatter(
fmt="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
fmt="[%(levelname)s|%(asctime)s] %(name)s:%(lineno)s >> %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
_default_handler = logging.StreamHandler(sys.stdout)
_default_handler.setFormatter(formatter)
@ -110,7 +125,7 @@ def _configure_library_root_logger() -> None:
library_root_logger.propagate = False
def get_logger(name: Optional[str] = None) -> "logging.Logger":
def get_logger(name: Optional[str] = None) -> "_Logger":
r"""
Returns a logger with the specified name. It it not supposed to be accessed externally.
"""
@ -119,3 +134,40 @@ def get_logger(name: Optional[str] = None) -> "logging.Logger":
_configure_library_root_logger()
return logging.getLogger(name)
def add_handler(handler: "logging.Handler") -> None:
r"""
Adds a handler to the root logger.
"""
_configure_library_root_logger()
_get_library_root_logger().addHandler(handler)
def remove_handler(handler: logging.Handler) -> None:
r"""
Removes a handler to the root logger.
"""
_configure_library_root_logger()
_get_library_root_logger().removeHandler(handler)
def info_rank0(self: "logging.Logger", *args, **kwargs) -> None:
if int(os.getenv("LOCAL_RANK", "0")) == 0:
self.info(*args, **kwargs)
def warning_rank0(self: "logging.Logger", *args, **kwargs) -> None:
if int(os.getenv("LOCAL_RANK", "0")) == 0:
self.warning(*args, **kwargs)
@lru_cache(None)
def warning_once(self: "logging.Logger", *args, **kwargs) -> None:
if int(os.getenv("LOCAL_RANK", "0")) == 0:
self.warning(*args, **kwargs)
logging.Logger.info_rank0 = info_rank0
logging.Logger.warning_rank0 = warning_rank0
logging.Logger.warning_once = warning_once

45
src/llamafactory/extras/misc.py
View File

@ -32,7 +32,7 @@ from transformers.utils import (
)
from transformers.utils.versions import require_version
from .logging import get_logger
from . import logging
_is_fp16_available = is_torch_npu_available() or is_torch_cuda_available()
@ -48,7 +48,7 @@ if TYPE_CHECKING:
from ..hparams import ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
class AverageMeter:
@ -76,12 +76,12 @@ def check_dependencies() -> None:
r"""
Checks the version of the required packages.
"""
if os.environ.get("DISABLE_VERSION_CHECK", "0").lower() in ["true", "1"]:
logger.warning("Version checking has been disabled, may lead to unexpected behaviors.")
if os.getenv("DISABLE_VERSION_CHECK", "0").lower() in ["true", "1"]:
logger.warning_once("Version checking has been disabled, may lead to unexpected behaviors.")
else:
require_version("transformers>=4.41.2,<=4.45.0", "To fix: pip install transformers>=4.41.2,<=4.45.0")
require_version("datasets>=2.16.0,<=2.21.0", "To fix: pip install datasets>=2.16.0,<=2.21.0")
require_version("accelerate>=0.30.1,<=0.34.2", "To fix: pip install accelerate>=0.30.1,<=0.34.2")
require_version("transformers>=4.41.2,<=4.46.1", "To fix: pip install transformers>=4.41.2,<=4.46.1")
require_version("datasets>=2.16.0,<=3.0.2", "To fix: pip install datasets>=2.16.0,<=3.0.2")
require_version("accelerate>=0.34.0,<=1.0.1", "To fix: pip install accelerate>=0.34.0,<=1.0.1")
require_version("peft>=0.11.1,<=0.12.0", "To fix: pip install peft>=0.11.1,<=0.12.0")
require_version("trl>=0.8.6,<=0.9.6", "To fix: pip install trl>=0.8.6,<=0.9.6")
@ -231,18 +231,35 @@ def torch_gc() -> None:
torch.cuda.empty_cache()
def try_download_model_from_ms(model_args: "ModelArguments") -> str:
if not use_modelscope() or os.path.exists(model_args.model_name_or_path):
def try_download_model_from_other_hub(model_args: "ModelArguments") -> str:
if (not use_modelscope() and not use_openmind()) or os.path.exists(model_args.model_name_or_path):
return model_args.model_name_or_path
try:
from modelscope import snapshot_download
if use_modelscope():
require_version("modelscope>=1.11.0", "To fix: pip install modelscope>=1.11.0")
from modelscope import snapshot_download # type: ignore
revision = "master" if model_args.model_revision == "main" else model_args.model_revision
return snapshot_download(model_args.model_name_or_path, revision=revision, cache_dir=model_args.cache_dir)
except ImportError:
raise ImportError("Please install modelscope via `pip install modelscope -U`")
return snapshot_download(
model_args.model_name_or_path,
revision=revision,
cache_dir=model_args.cache_dir,
)
if use_openmind():
require_version("openmind>=0.8.0", "To fix: pip install openmind>=0.8.0")
from openmind.utils.hub import snapshot_download # type: ignore
return snapshot_download(
model_args.model_name_or_path,
revision=model_args.model_revision,
cache_dir=model_args.cache_dir,
)
def use_modelscope() -> bool:
return os.environ.get("USE_MODELSCOPE_HUB", "0").lower() in ["true", "1"]
def use_openmind() -> bool:
return os.environ.get("USE_OPENMIND_HUB", "0").lower() in ["true", "1"]

5
src/llamafactory/extras/packages.py
View File

@ -79,6 +79,11 @@ def is_transformers_version_greater_than_4_43():
return _get_package_version("transformers") >= version.parse("4.43.0")
@lru_cache
def is_transformers_version_equal_to_4_46():
return version.parse("4.46.0") <= _get_package_version("transformers") <= version.parse("4.46.1")
def is_uvicorn_available():
return _is_package_available("uvicorn")

10
src/llamafactory/extras/ploting.py
View File

@ -19,7 +19,7 @@ from typing import Any, Dict, List
from transformers.trainer import TRAINER_STATE_NAME
from .logging import get_logger
from . import logging
from .packages import is_matplotlib_available
@ -28,7 +28,7 @@ if is_matplotlib_available():
import matplotlib.pyplot as plt
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def smooth(scalars: List[float]) -> List[float]:
@ -75,7 +75,7 @@ def plot_loss(save_dictionary: str, keys: List[str] = ["loss"]) -> None:
Plots loss curves and saves the image.
"""
plt.switch_backend("agg")
with open(os.path.join(save_dictionary, TRAINER_STATE_NAME), "r", encoding="utf-8") as f:
with open(os.path.join(save_dictionary, TRAINER_STATE_NAME), encoding="utf-8") as f:
data = json.load(f)
for key in keys:
@ -86,13 +86,13 @@ def plot_loss(save_dictionary: str, keys: List[str] = ["loss"]) -> None:
metrics.append(data["log_history"][i][key])
if len(metrics) == 0:
logger.warning(f"No metric {key} to plot.")
logger.warning_rank0(f"No metric {key} to plot.")
continue
plt.figure()
plt.plot(steps, metrics, color="#1f77b4", alpha=0.4, label="original")
plt.plot(steps, smooth(metrics), color="#1f77b4", label="smoothed")
plt.title("training {} of {}".format(key, save_dictionary))
plt.title(f"training {key} of {save_dictionary}")
plt.xlabel("step")
plt.ylabel(key)
plt.legend()

15
src/llamafactory/hparams/data_args.py
View File

@ -41,6 +41,10 @@ class DataArguments:
default="data",
metadata={"help": "Path to the folder containing the datasets."},
)
image_dir: Optional[str] = field(
default=None,
metadata={"help": "Path to the folder containing the images or videos. Defaults to `dataset_dir`."},
)
cutoff_len: int = field(
default=1024,
metadata={"help": "The cutoff length of the tokenized inputs in the dataset."},
@ -111,7 +115,13 @@ class DataArguments:
)
tokenized_path: Optional[str] = field(
default=None,
metadata={"help": "Path to save or load the tokenized datasets."},
metadata={
"help": (
"Path to save or load the tokenized datasets. "
"If tokenized_path not exists, it will save the tokenized datasets. "
"If tokenized_path exists, it will load the tokenized datasets."
)
},
)
def __post_init__(self):
@ -123,6 +133,9 @@ class DataArguments:
self.dataset = split_arg(self.dataset)
self.eval_dataset = split_arg(self.eval_dataset)
if self.image_dir is None:
self.image_dir = self.dataset_dir
if self.dataset is None and self.val_size > 1e-6:
raise ValueError("Cannot specify `val_size` if `dataset` is None.")

34
src/llamafactory/hparams/model_args.py
View File

@ -15,7 +15,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import asdict, dataclass, field, fields
from dataclasses import dataclass, field, fields
from typing import Any, Dict, Literal, Optional, Union
import torch
@ -267,6 +267,10 @@ class ModelArguments(QuantizationArguments, ProcessorArguments, ExportArguments,
default=None,
metadata={"help": "Auth token to log in with ModelScope Hub."},
)
om_hub_token: Optional[str] = field(
default=None,
metadata={"help": "Auth token to log in with Modelers Hub."},
)
print_param_status: bool = field(
default=False,
metadata={"help": "For debugging purposes, print the status of the parameters in the model."},
@ -308,20 +312,18 @@ class ModelArguments(QuantizationArguments, ProcessorArguments, ExportArguments,
if self.export_quantization_bit is not None and self.export_quantization_dataset is None:
raise ValueError("Quantization dataset is necessary for exporting.")
def to_dict(self) -> Dict[str, Any]:
return asdict(self)
@classmethod
def copyfrom(cls, old_arg: "Self", **kwargs) -> "Self":
arg_dict = old_arg.to_dict()
arg_dict.update(**kwargs)
for attr in fields(cls):
if not attr.init:
arg_dict.pop(attr.name)
def copyfrom(cls, source: "Self", **kwargs) -> "Self":
init_args, lazy_args = {}, {}
for attr in fields(source):
if attr.init:
init_args[attr.name] = getattr(source, attr.name)
else:
lazy_args[attr.name] = getattr(source, attr.name)
new_arg = cls(**arg_dict)
new_arg.compute_dtype = old_arg.compute_dtype
new_arg.device_map = old_arg.device_map
new_arg.model_max_length = old_arg.model_max_length
new_arg.block_diag_attn = old_arg.block_diag_attn
return new_arg
init_args.update(kwargs)
result = cls(**init_args)
for name, value in lazy_args.items():
setattr(result, name, value)
return result

47
src/llamafactory/hparams/parser.py
View File

@ -15,7 +15,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import os
import sys
from typing import Any, Dict, Optional, Tuple
@ -29,8 +28,8 @@ from transformers.training_args import ParallelMode
from transformers.utils import is_torch_bf16_gpu_available, is_torch_npu_available
from transformers.utils.versions import require_version
from ..extras import logging
from ..extras.constants import CHECKPOINT_NAMES
from ..extras.logging import get_logger
from ..extras.misc import check_dependencies, get_current_device
from .data_args import DataArguments
from .evaluation_args import EvaluationArguments
@ -39,7 +38,7 @@ from .generating_args import GeneratingArguments
from .model_args import ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
check_dependencies()
@ -57,7 +56,7 @@ def _parse_args(parser: "HfArgumentParser", args: Optional[Dict[str, Any]] = Non
if args is not None:
return parser.parse_dict(args)
if len(sys.argv) == 2 and sys.argv[1].endswith(".yaml"):
if len(sys.argv) == 2 and (sys.argv[1].endswith(".yaml") or sys.argv[1].endswith(".yml")):
return parser.parse_yaml_file(os.path.abspath(sys.argv[1]))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
@ -67,14 +66,14 @@ def _parse_args(parser: "HfArgumentParser", args: Optional[Dict[str, Any]] = Non
if unknown_args:
print(parser.format_help())
print("Got unknown args, potentially deprecated arguments: {}".format(unknown_args))
raise ValueError("Some specified arguments are not used by the HfArgumentParser: {}".format(unknown_args))
print(f"Got unknown args, potentially deprecated arguments: {unknown_args}")
raise ValueError(f"Some specified arguments are not used by the HfArgumentParser: {unknown_args}")
return (*parsed_args,)
def _set_transformers_logging(log_level: Optional[int] = logging.INFO) -> None:
transformers.utils.logging.set_verbosity(log_level)
def _set_transformers_logging() -> None:
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
@ -104,7 +103,7 @@ def _verify_model_args(
raise ValueError("Quantized model only accepts a single adapter. Merge them first.")
if data_args.template == "yi" and model_args.use_fast_tokenizer:
logger.warning("We should use slow tokenizer for the Yi models. Change `use_fast_tokenizer` to False.")
logger.warning_rank0("We should use slow tokenizer for the Yi models. Change `use_fast_tokenizer` to False.")
model_args.use_fast_tokenizer = False
@ -123,7 +122,7 @@ def _check_extra_dependencies(
require_version("mixture-of-depth>=1.1.6", "To fix: pip install mixture-of-depth>=1.1.6")
if model_args.infer_backend == "vllm":
require_version("vllm>=0.4.3,<=0.6.0", "To fix: pip install vllm>=0.4.3,<=0.6.0")
require_version("vllm>=0.4.3,<=0.6.3", "To fix: pip install vllm>=0.4.3,<=0.6.3")
if finetuning_args.use_galore:
require_version("galore_torch", "To fix: pip install galore_torch")
@ -261,7 +260,7 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
raise ValueError("Unsloth is incompatible with DeepSpeed ZeRO-3.")
if data_args.neat_packing and not data_args.packing:
logger.warning("`neat_packing` requires `packing` is True. Change `packing` to True.")
logger.warning_rank0("`neat_packing` requires `packing` is True. Change `packing` to True.")
data_args.packing = True
_verify_model_args(model_args, data_args, finetuning_args)
@ -274,22 +273,26 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
and model_args.resize_vocab
and finetuning_args.additional_target is None
):
logger.warning("Remember to add embedding layers to `additional_target` to make the added tokens trainable.")
logger.warning_rank0(
"Remember to add embedding layers to `additional_target` to make the added tokens trainable."
)
if training_args.do_train and model_args.quantization_bit is not None and (not model_args.upcast_layernorm):
logger.warning("We recommend enable `upcast_layernorm` in quantized training.")
logger.warning_rank0("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.")
logger.warning_rank0("We recommend enable mixed precision training.")
if training_args.do_train and finetuning_args.use_galore and not finetuning_args.pure_bf16:
logger.warning("Using GaLore with mixed precision training may significantly increases GPU memory usage.")
logger.warning_rank0(
"Using GaLore with mixed precision training may significantly increases GPU memory usage."
)
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.")
logger.warning_rank0("Evaluating model in 4/8-bit mode may cause lower scores.")
if (not training_args.do_train) and finetuning_args.stage == "dpo" and finetuning_args.ref_model is None:
logger.warning("Specify `ref_model` for computing rewards at evaluation.")
logger.warning_rank0("Specify `ref_model` for computing rewards at evaluation.")
# Post-process training arguments
if (
@ -297,13 +300,13 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
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.")
logger.warning_rank0("`ddp_find_unused_parameters` needs to be set as False for LoRA in DDP training.")
training_args.ddp_find_unused_parameters = False
if finetuning_args.stage in ["rm", "ppo"] and finetuning_args.finetuning_type in ["full", "freeze"]:
can_resume_from_checkpoint = False
if training_args.resume_from_checkpoint is not None:
logger.warning("Cannot resume from checkpoint in current stage.")
logger.warning_rank0("Cannot resume from checkpoint in current stage.")
training_args.resume_from_checkpoint = None
else:
can_resume_from_checkpoint = True
@ -323,15 +326,15 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
if last_checkpoint is not None:
training_args.resume_from_checkpoint = last_checkpoint
logger.info("Resuming training from {}.".format(training_args.resume_from_checkpoint))
logger.info("Change `output_dir` or use `overwrite_output_dir` to avoid.")
logger.info_rank0(f"Resuming training from {training_args.resume_from_checkpoint}.")
logger.info_rank0("Change `output_dir` or use `overwrite_output_dir` to avoid.")
if (
finetuning_args.stage in ["rm", "ppo"]
and finetuning_args.finetuning_type == "lora"
and training_args.resume_from_checkpoint is not None
):
logger.warning(
logger.warning_rank0(
"Add {} to `adapter_name_or_path` to resume training from checkpoint.".format(
training_args.resume_from_checkpoint
)

32
src/llamafactory/model/adapter.py
View File

@ -20,7 +20,7 @@ from peft import LoraConfig, LoraModel, PeftModel, TaskType, get_peft_model
from transformers.integrations import is_deepspeed_zero3_enabled
from transformers.modeling_utils import is_fsdp_enabled
from ..extras.logging import get_logger
from ..extras import logging
from .model_utils.misc import find_all_linear_modules, find_expanded_modules
from .model_utils.quantization import QuantizationMethod
from .model_utils.unsloth import get_unsloth_peft_model, load_unsloth_peft_model
@ -33,7 +33,7 @@ if TYPE_CHECKING:
from ..hparams import FinetuningArguments, ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def _setup_full_tuning(
@ -45,7 +45,7 @@ def _setup_full_tuning(
if not is_trainable:
return
logger.info("Fine-tuning method: Full")
logger.info_rank0("Fine-tuning method: Full")
forbidden_modules = get_forbidden_modules(model.config, finetuning_args)
for name, param in model.named_parameters():
if not any(forbidden_module in name for forbidden_module in forbidden_modules):
@ -64,7 +64,7 @@ def _setup_freeze_tuning(
if not is_trainable:
return
logger.info("Fine-tuning method: Freeze")
logger.info_rank0("Fine-tuning method: Freeze")
if hasattr(model.config, "text_config"): # composite models
config = getattr(model.config, "text_config")
else:
@ -133,7 +133,7 @@ def _setup_freeze_tuning(
else:
param.requires_grad_(False)
logger.info("Set trainable layers: {}".format(",".join(trainable_layers)))
logger.info_rank0("Set trainable layers: {}".format(",".join(trainable_layers)))
def _setup_lora_tuning(
@ -145,7 +145,7 @@ def _setup_lora_tuning(
cast_trainable_params_to_fp32: bool,
) -> "PeftModel":
if is_trainable:
logger.info("Fine-tuning method: {}".format("DoRA" if finetuning_args.use_dora else "LoRA"))
logger.info_rank0("Fine-tuning method: {}".format("DoRA" if finetuning_args.use_dora else "LoRA"))
adapter_to_resume = None
@ -182,7 +182,7 @@ def _setup_lora_tuning(
model = model.merge_and_unload()
if len(adapter_to_merge) > 0:
logger.info("Merged {} adapter(s).".format(len(adapter_to_merge)))
logger.info_rank0(f"Merged {len(adapter_to_merge)} adapter(s).")
if adapter_to_resume is not None: # resume lora training
if model_args.use_unsloth:
@ -190,7 +190,7 @@ def _setup_lora_tuning(
else:
model = PeftModel.from_pretrained(model, adapter_to_resume, is_trainable=is_trainable, **init_kwargs)
logger.info("Loaded adapter(s): {}".format(",".join(model_args.adapter_name_or_path)))
logger.info_rank0("Loaded adapter(s): {}".format(",".join(model_args.adapter_name_or_path)))
if is_trainable and adapter_to_resume is None: # create new lora weights while training
if len(finetuning_args.lora_target) == 1 and finetuning_args.lora_target[0] == "all":
@ -219,7 +219,7 @@ def _setup_lora_tuning(
module_names.add(name.split(".")[-1])
finetuning_args.additional_target = module_names
logger.warning("Vocab has been resized, add {} to trainable params.".format(",".join(module_names)))
logger.warning_rank0("Vocab has been resized, add {} to trainable params.".format(",".join(module_names)))
peft_kwargs = {
"r": finetuning_args.lora_rank,
@ -236,11 +236,11 @@ def _setup_lora_tuning(
else:
if finetuning_args.pissa_init:
if finetuning_args.pissa_iter == -1:
logger.info("Using PiSSA initialization.")
logger.info_rank0("Using PiSSA initialization.")
peft_kwargs["init_lora_weights"] = "pissa"
else:
logger.info("Using PiSSA initialization with FSVD steps {}.".format(finetuning_args.pissa_iter))
peft_kwargs["init_lora_weights"] = "pissa_niter_{}".format(finetuning_args.pissa_iter)
logger.info_rank0(f"Using PiSSA initialization with FSVD steps {finetuning_args.pissa_iter}.")
peft_kwargs["init_lora_weights"] = f"pissa_niter_{finetuning_args.pissa_iter}"
lora_config = LoraConfig(
task_type=TaskType.CAUSAL_LM,
@ -284,11 +284,11 @@ def init_adapter(
if not is_trainable:
pass
elif finetuning_args.pure_bf16 or finetuning_args.use_badam:
logger.info("Pure bf16 / BAdam detected, remaining trainable params in half precision.")
logger.info_rank0("Pure bf16 / BAdam detected, remaining trainable params in half precision.")
elif model_args.quantization_bit is None and (is_deepspeed_zero3_enabled() or is_fsdp_enabled()):
logger.info("ZeRO3 / FSDP detected, remaining trainable params in float32.")
logger.info_rank0("ZeRO3 / FSDP detected, remaining trainable params in float32.")
else:
logger.info("Upcasting trainable params to float32.")
logger.info_rank0("Upcasting trainable params to float32.")
cast_trainable_params_to_fp32 = True
if finetuning_args.finetuning_type == "full":
@ -300,6 +300,6 @@ def init_adapter(
config, model, model_args, finetuning_args, is_trainable, cast_trainable_params_to_fp32
)
else:
raise NotImplementedError("Unknown finetuning type: {}.".format(finetuning_args.finetuning_type))
raise NotImplementedError(f"Unknown finetuning type: {finetuning_args.finetuning_type}.")
return model

42
src/llamafactory/model/loader.py
View File

@ -18,15 +18,15 @@ import torch
from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForVision2Seq, AutoProcessor, AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
from ..extras.logging import get_logger
from ..extras.misc import count_parameters, skip_check_imports, try_download_model_from_ms
from ..extras import logging
from ..extras.misc import count_parameters, skip_check_imports, try_download_model_from_other_hub
from .adapter import init_adapter
from .model_utils.liger_kernel import apply_liger_kernel
from .model_utils.misc import register_autoclass
from .model_utils.mod import convert_pretrained_model_to_mod, load_mod_pretrained_model
from .model_utils.unsloth import load_unsloth_pretrained_model
from .model_utils.valuehead import load_valuehead_params
from .model_utils.visual import get_image_seqlen
from .patcher import patch_config, patch_model, patch_tokenizer, patch_valuehead_model
from .patcher import patch_config, patch_model, patch_processor, patch_tokenizer, patch_valuehead_model
if TYPE_CHECKING:
@ -35,7 +35,7 @@ if TYPE_CHECKING:
from ..hparams import FinetuningArguments, ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
class TokenizerModule(TypedDict):
@ -50,7 +50,7 @@ def _get_init_kwargs(model_args: "ModelArguments") -> Dict[str, Any]:
Note: including inplace operation of model_args.
"""
skip_check_imports()
model_args.model_name_or_path = try_download_model_from_ms(model_args)
model_args.model_name_or_path = try_download_model_from_other_hub(model_args)
return {
"trust_remote_code": True,
"cache_dir": model_args.cache_dir,
@ -61,7 +61,7 @@ def _get_init_kwargs(model_args: "ModelArguments") -> Dict[str, Any]:
def load_tokenizer(model_args: "ModelArguments") -> "TokenizerModule":
r"""
Loads pretrained tokenizer.
Loads pretrained tokenizer and optionally loads processor.
Note: including inplace operation of model_args.
"""
@ -82,33 +82,30 @@ def load_tokenizer(model_args: "ModelArguments") -> "TokenizerModule":
padding_side="right",
**init_kwargs,
)
except Exception as e:
raise OSError("Failed to load tokenizer.") from e
if model_args.new_special_tokens is not None:
num_added_tokens = tokenizer.add_special_tokens(
dict(additional_special_tokens=model_args.new_special_tokens),
replace_additional_special_tokens=False,
)
logger.info("Add {} to special tokens.".format(",".join(model_args.new_special_tokens)))
logger.info_rank0("Add {} to special tokens.".format(",".join(model_args.new_special_tokens)))
if num_added_tokens > 0 and not model_args.resize_vocab:
model_args.resize_vocab = True
logger.warning("New tokens have been added, changed `resize_vocab` to True.")
logger.warning_rank0("New tokens have been added, changed `resize_vocab` to True.")
patch_tokenizer(tokenizer)
try:
processor = AutoProcessor.from_pretrained(model_args.model_name_or_path, **init_kwargs)
setattr(processor, "tokenizer", tokenizer)
setattr(processor, "image_seqlen", get_image_seqlen(config))
setattr(processor, "image_resolution", model_args.image_resolution)
setattr(processor, "video_resolution", model_args.video_resolution)
setattr(processor, "video_fps", model_args.video_fps)
setattr(processor, "video_maxlen", model_args.video_maxlen)
except Exception:
patch_processor(processor, config, tokenizer, model_args)
except Exception as e:
logger.debug(f"Processor was not found: {e}.")
processor = None
# Avoid load tokenizer, see:
# https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/models/auto/processing_auto.py#L324
if "Processor" not in processor.__class__.__name__:
if processor is not None and "Processor" not in processor.__class__.__name__:
processor = None
return {"tokenizer": tokenizer, "processor": processor}
@ -135,6 +132,7 @@ def load_model(
init_kwargs = _get_init_kwargs(model_args)
config = load_config(model_args)
patch_config(config, tokenizer, model_args, init_kwargs, is_trainable)
apply_liger_kernel(config, model_args, is_trainable, require_logits=(finetuning_args.stage not in ["pt", "sft"]))
model = None
lazy_load = False
@ -157,7 +155,7 @@ def load_model(
load_class = AutoModelForCausalLM
if model_args.train_from_scratch:
model = load_class.from_config(config)
model = load_class.from_config(config, trust_remote_code=True)
else:
model = load_class.from_pretrained(**init_kwargs)
@ -182,7 +180,7 @@ def load_model(
vhead_params = load_valuehead_params(vhead_path, model_args)
if vhead_params is not None:
model.load_state_dict(vhead_params, strict=False)
logger.info("Loaded valuehead from checkpoint: {}".format(vhead_path))
logger.info_rank0(f"Loaded valuehead from checkpoint: {vhead_path}")
if not is_trainable:
model.requires_grad_(False)
@ -200,9 +198,9 @@ def load_model(
trainable_params, all_param, 100 * trainable_params / all_param
)
else:
param_stats = "all params: {:,}".format(all_param)
param_stats = f"all params: {all_param:,}"
logger.info(param_stats)
logger.info_rank0(param_stats)
if model_args.print_param_status:
for name, param in model.named_parameters():

27
src/llamafactory/model/model_utils/attention.py
View File

@ -17,7 +17,7 @@ from typing import TYPE_CHECKING
from transformers.utils import is_flash_attn_2_available, is_torch_sdpa_available
from transformers.utils.versions import require_version
from ...extras.logging import get_logger
from ...extras import logging
if TYPE_CHECKING:
@ -26,7 +26,7 @@ if TYPE_CHECKING:
from ...hparams import ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def configure_attn_implementation(
@ -37,13 +37,16 @@ def configure_attn_implementation(
if is_flash_attn_2_available():
require_version("transformers>=4.42.4", "To fix: pip install transformers>=4.42.4")
require_version("flash_attn>=2.6.3", "To fix: pip install flash_attn>=2.6.3")
logger.warning("Gemma-2 should use flash attention 2, change `flash_attn` to fa2.")
model_args.flash_attn = "fa2"
if model_args.flash_attn != "fa2":
logger.warning_rank0("Gemma-2 should use flash attention 2, change `flash_attn` to fa2.")
model_args.flash_attn = "fa2"
else:
logger.warning("Gemma-2 should use eager attention, change `flash_attn` to disabled.")
logger.warning_rank0("FlashAttention-2 is not installed, use eager attention.")
model_args.flash_attn = "disabled"
elif model_args.flash_attn == "sdpa":
logger.warning("Gemma-2 should use soft-capping attention, while the SDPA attention does not support it.")
logger.warning_rank0(
"Gemma-2 should use soft-capping attention, while the SDPA attention does not support it."
)
if model_args.flash_attn == "auto":
return
@ -53,18 +56,18 @@ def configure_attn_implementation(
elif model_args.flash_attn == "sdpa":
if not is_torch_sdpa_available():
logger.warning("torch>=2.1.1 is required for SDPA attention.")
logger.warning_rank0("torch>=2.1.1 is required for SDPA attention.")
return
requested_attn_implementation = "sdpa"
elif model_args.flash_attn == "fa2":
if not is_flash_attn_2_available():
logger.warning("FlashAttention-2 is not installed.")
logger.warning_rank0("FlashAttention-2 is not installed.")
return
requested_attn_implementation = "flash_attention_2"
else:
raise NotImplementedError("Unknown attention type: {}".format(model_args.flash_attn))
raise NotImplementedError(f"Unknown attention type: {model_args.flash_attn}")
if getattr(config, "model_type", None) == "internlm2": # special case for custom models
setattr(config, "attn_implementation", requested_attn_implementation)
@ -79,8 +82,8 @@ def print_attn_implementation(config: "PretrainedConfig") -> None:
attn_implementation = getattr(config, "_attn_implementation", None)
if attn_implementation == "flash_attention_2":
logger.info("Using FlashAttention-2 for faster training and inference.")
logger.info_rank0("Using FlashAttention-2 for faster training and inference.")
elif attn_implementation == "sdpa":
logger.info("Using torch SDPA for faster training and inference.")
logger.info_rank0("Using torch SDPA for faster training and inference.")
else:
logger.info("Using vanilla attention implementation.")
logger.info_rank0("Using vanilla attention implementation.")

23
src/llamafactory/model/model_utils/checkpointing.py
View File

@ -19,14 +19,14 @@
# limitations under the License.
import inspect
from functools import partial, wraps
from functools import WRAPPER_ASSIGNMENTS, partial, wraps
from types import MethodType
from typing import TYPE_CHECKING, Any, Callable, Dict, Optional, Tuple, Union
import torch
from ...extras import logging
from ...extras.constants import LAYERNORM_NAMES
from ...extras.logging import get_logger
if TYPE_CHECKING:
@ -35,7 +35,7 @@ if TYPE_CHECKING:
from ...hparams import ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def get_unsloth_gradient_checkpointing_func() -> Callable:
@ -81,7 +81,7 @@ def get_custom_gradient_checkpointing_func(gradient_checkpointing_func: Callable
Only applies gradient checkpointing to trainable layers.
"""
@wraps(gradient_checkpointing_func)
@wraps(gradient_checkpointing_func, assigned=WRAPPER_ASSIGNMENTS + ("__self__",))
def custom_gradient_checkpointing_func(func: Callable, *args: Union["torch.Tensor", Any], **kwargs):
module: "torch.nn.Module" = func.__self__
@ -92,9 +92,6 @@ def get_custom_gradient_checkpointing_func(gradient_checkpointing_func: Callable
return gradient_checkpointing_func(func, *args, **kwargs)
if hasattr(gradient_checkpointing_func, "__self__"): # fix unsloth gc test case
custom_gradient_checkpointing_func.__self__ = gradient_checkpointing_func.__self__
return custom_gradient_checkpointing_func
@ -111,7 +108,7 @@ def _gradient_checkpointing_enable(
from torch.utils.checkpoint import checkpoint
if not self.supports_gradient_checkpointing:
raise ValueError("{} does not support gradient checkpointing.".format(self.__class__.__name__))
raise ValueError(f"{self.__class__.__name__} does not support gradient checkpointing.")
if gradient_checkpointing_kwargs is None:
gradient_checkpointing_kwargs = {"use_reentrant": True}
@ -125,7 +122,7 @@ def _gradient_checkpointing_enable(
if "value" in inspect.signature(self._set_gradient_checkpointing).parameters: # old GC format
self.apply(partial(self._set_gradient_checkpointing, value=True))
self.enable_input_require_grads()
logger.warning("You are using the old GC format, some features (e.g. BAdam) will be invalid.")
logger.warning_once("You are using the old GC format, some features (e.g. BAdam) will be invalid.")
else: # have already enabled input require gradients
self._set_gradient_checkpointing(enable=True, gradient_checkpointing_func=gradient_checkpointing_func)
@ -144,14 +141,14 @@ def prepare_model_for_training(model: "PreTrainedModel", model_args: "ModelArgum
(3) add the upcasting of the lm_head in fp32
"""
if model_args.upcast_layernorm:
logger.info("Upcasting layernorm weights in float32.")
logger.info_rank0("Upcasting layernorm weights in float32.")
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)
if not model_args.disable_gradient_checkpointing:
if not getattr(model, "supports_gradient_checkpointing", False):
logger.warning("Current model does not support gradient checkpointing.")
logger.warning_rank0("Current model does not support gradient checkpointing.")
else:
# use_reentrant=False might increase VRAM usage (have not been empirically verified yet)
# According to: https://github.com/huggingface/transformers/issues/28339
@ -161,10 +158,10 @@ def prepare_model_for_training(model: "PreTrainedModel", model_args: "ModelArgum
model.gradient_checkpointing_enable = MethodType(gradient_checkpointing_enable, model)
model.gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": True})
setattr(model.config, "use_cache", False) # turn off when gradient checkpointing is enabled
logger.info("Gradient checkpointing enabled.")
logger.info_rank0("Gradient checkpointing enabled.")
if model_args.upcast_lmhead_output:
output_layer = model.get_output_embeddings()
if isinstance(output_layer, torch.nn.Linear) and output_layer.weight.dtype != torch.float32:
logger.info("Upcasting lm_head outputs in float32.")
logger.info_rank0("Upcasting lm_head outputs in float32.")
output_layer.register_forward_hook(_fp32_forward_post_hook)

6
src/llamafactory/model/model_utils/embedding.py
View File

@ -19,14 +19,14 @@ from typing import TYPE_CHECKING
import torch
from transformers.integrations import is_deepspeed_zero3_enabled
from ...extras.logging import get_logger
from ...extras import logging
if TYPE_CHECKING:
from transformers import PreTrainedModel, PreTrainedTokenizer
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def _noisy_mean_initialization(embed_weight: "torch.Tensor", num_new_tokens: int) -> None:
@ -69,4 +69,4 @@ def resize_embedding_layer(model: "PreTrainedModel", tokenizer: "PreTrainedToken
_noisy_mean_initialization(model.get_input_embeddings().weight.data, num_new_tokens)
_noisy_mean_initialization(model.get_output_embeddings().weight.data, num_new_tokens)
logger.info("Resized token embeddings from {} to {}.".format(current_embedding_size, new_embedding_size))
logger.info_rank0(f"Resized token embeddings from {current_embedding_size} to {new_embedding_size}.")

24
src/llamafactory/model/model_utils/liger_kernel.py
View File

@ -12,9 +12,10 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
from typing import TYPE_CHECKING
from ...extras.logging import get_logger
from ...extras import logging
if TYPE_CHECKING:
@ -23,10 +24,15 @@ if TYPE_CHECKING:
from ...hparams import ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def configure_liger_kernel(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None:
def apply_liger_kernel(
config: "PretrainedConfig",
model_args: "ModelArguments",
is_trainable: bool,
require_logits: bool,
) -> None:
if not is_trainable or not model_args.enable_liger_kernel:
return
@ -48,8 +54,14 @@ def configure_liger_kernel(config: "PretrainedConfig", model_args: "ModelArgumen
elif model_type == "qwen2_vl":
from liger_kernel.transformers import apply_liger_kernel_to_qwen2_vl as apply_liger_kernel
else:
logger.warning("Current model does not support liger kernel.")
logger.warning_rank0("Current model does not support liger kernel.")
return
apply_liger_kernel()
logger.info("Liger kernel has been applied to the model.")
if require_logits and "fused_linear_cross_entropy" in inspect.signature(apply_liger_kernel).parameters:
logger.info_rank0("Current training stage does not support chunked cross entropy.")
kwargs = {"fused_linear_cross_entropy": False}
else:
kwargs = {}
apply_liger_kernel(**kwargs)
logger.info_rank0("Liger kernel has been applied to the model.")

20
src/llamafactory/model/model_utils/longlora.py
View File

@ -22,6 +22,7 @@ from typing import TYPE_CHECKING, Optional, Tuple
import torch
import torch.nn as nn
import transformers
from transformers.models.llama.modeling_llama import (
Cache,
LlamaAttention,
@ -30,11 +31,10 @@ from transformers.models.llama.modeling_llama import (
apply_rotary_pos_emb,
repeat_kv,
)
from transformers.utils import logging
from transformers.utils.versions import require_version
from ...extras import logging
from ...extras.constants import SUPPORTED_CLASS_FOR_S2ATTN
from ...extras.logging import get_logger
from ...extras.packages import is_transformers_version_greater_than_4_43
@ -44,7 +44,7 @@ if TYPE_CHECKING:
from ...hparams import ModelArguments
transformers_logger = logging.get_logger(__name__)
transformers_logger = transformers.utils.logging.get_logger(__name__)
# Modified from:
@ -86,7 +86,7 @@ def llama_attention_forward(
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)
assert q_len % groupsz == 0, f"q_len {q_len} should be divisible by group size {groupsz}."
num_groups = q_len // groupsz
def shift(state: "torch.Tensor") -> "torch.Tensor":
@ -195,7 +195,7 @@ def llama_flash_attention_2_forward(
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)
assert q_len % groupsz == 0, f"q_len {q_len} should be divisible by group size {groupsz}."
num_groups = q_len // groupsz
def shift(state: "torch.Tensor") -> "torch.Tensor":
@ -301,7 +301,7 @@ def llama_sdpa_attention_forward(
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)
assert q_len % groupsz == 0, f"q_len {q_len} should be divisible by group size {groupsz}."
num_groups = q_len // groupsz
def shift(state: "torch.Tensor") -> "torch.Tensor":
@ -353,7 +353,7 @@ def llama_sdpa_attention_forward(
def _apply_llama_patch() -> None:
require_version("transformers>=4.41.2,<=4.45.0", "To fix: pip install transformers>=4.41.2,<=4.45.0")
require_version("transformers>=4.41.2,<=4.46.1", "To fix: pip install transformers>=4.41.2,<=4.46.1")
LlamaAttention.forward = llama_attention_forward
LlamaFlashAttention2.forward = llama_flash_attention_2_forward
LlamaSdpaAttention.forward = llama_sdpa_attention_forward
@ -363,11 +363,11 @@ def configure_longlora(config: "PretrainedConfig", model_args: "ModelArguments",
if not is_trainable or not model_args.shift_attn:
return
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
if getattr(config, "model_type", None) in SUPPORTED_CLASS_FOR_S2ATTN:
setattr(config, "group_size_ratio", 0.25)
_apply_llama_patch()
logger.info("Using shift short attention with group_size_ratio=1/4.")
logger.info_rank0("Using shift short attention with group_size_ratio=1/4.")
else:
logger.warning("Current model does not support shift short attention.")
logger.warning_rank0("Current model does not support shift short attention.")

14
src/llamafactory/model/model_utils/misc.py
View File

@ -14,14 +14,14 @@
from typing import TYPE_CHECKING, List
from ...extras.logging import get_logger
from ...extras import logging
if TYPE_CHECKING:
from transformers import PretrainedConfig, PreTrainedModel, PreTrainedTokenizer
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def find_all_linear_modules(model: "PreTrainedModel", freeze_vision_tower: bool) -> List[str]:
@ -34,7 +34,7 @@ def find_all_linear_modules(model: "PreTrainedModel", freeze_vision_tower: bool)
forbidden_modules.add("output_layer")
elif model_type == "internlm2":
forbidden_modules.add("output")
elif model_type in ["llava", "paligemma"]:
elif model_type in ["llava", "llava_next", "llava_next_video", "paligemma", "video_llava"]:
forbidden_modules.add("multi_modal_projector")
elif model_type == "qwen2_vl":
forbidden_modules.add("merger")
@ -53,7 +53,7 @@ def find_all_linear_modules(model: "PreTrainedModel", freeze_vision_tower: bool)
if "Linear" in module.__class__.__name__ and "Embedding" not in module.__class__.__name__:
module_names.add(name.split(".")[-1])
logger.info("Found linear modules: {}".format(",".join(module_names)))
logger.info_rank0("Found linear modules: {}".format(",".join(module_names)))
return list(module_names)
@ -67,12 +67,12 @@ def find_expanded_modules(model: "PreTrainedModel", target_modules: List[str], n
if num_layers % num_layer_trainable != 0:
raise ValueError(
"`num_layers` {} should be divisible by `num_layer_trainable` {}.".format(num_layers, num_layer_trainable)
f"`num_layers` {num_layers} should be divisible by `num_layer_trainable` {num_layer_trainable}."
)
stride = num_layers // num_layer_trainable
trainable_layer_ids = range(stride - 1, num_layers + stride - 1, stride)
trainable_layers = [".{:d}.".format(idx) for idx in trainable_layer_ids]
trainable_layers = [f".{idx:d}." for idx in trainable_layer_ids]
module_names = []
for name, _ in model.named_modules():
if any(target_module in name for target_module in target_modules) and any(
@ -80,7 +80,7 @@ def find_expanded_modules(model: "PreTrainedModel", target_modules: List[str], n
):
module_names.append(name)
logger.info("Apply lora to layers: {}".format(",".join(map(str, trainable_layer_ids))))
logger.info_rank0("Apply lora to layers: {}".format(",".join(map(str, trainable_layer_ids))))
return module_names

8
src/llamafactory/model/model_utils/packing.py
View File

@ -43,8 +43,8 @@ import torch
import torch.nn.functional as F
from transformers.utils.versions import require_version
from ...extras import logging
from ...extras.constants import SUPPORTED_CLASS_FOR_BLOCK_DIAG_ATTN
from ...extras.logging import get_logger
from ...extras.packages import is_transformers_version_greater_than_4_43
@ -54,7 +54,7 @@ if TYPE_CHECKING:
from ...hparams import ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def get_seqlens_in_batch(attention_mask: "torch.Tensor") -> "torch.Tensor":
@ -114,7 +114,7 @@ def get_unpad_data(attention_mask: "torch.Tensor") -> Tuple["torch.Tensor", "tor
def _patch_for_block_diag_attn(model_type: str) -> None:
require_version("transformers>=4.41.2,<=4.45.0", "To fix: pip install transformers>=4.41.2,<=4.45.0")
require_version("transformers>=4.41.2,<=4.46.1", "To fix: pip install transformers>=4.41.2,<=4.46.1")
if is_transformers_version_greater_than_4_43():
import transformers.modeling_flash_attention_utils
@ -152,6 +152,6 @@ def configure_packing(config: "PretrainedConfig", model_args: "ModelArguments",
model_type = getattr(config, "model_type", None)
if model_type in SUPPORTED_CLASS_FOR_BLOCK_DIAG_ATTN:
_patch_for_block_diag_attn(model_type)
logger.info("Using block diagonal attention for sequence packing without cross-attention.")
logger.info_rank0("Using block diagonal attention for sequence packing without cross-attention.")
else:
raise ValueError("Current model does not support block diagonal attention.")

16
src/llamafactory/model/model_utils/quantization.py
View File

@ -28,8 +28,8 @@ from transformers.integrations import is_deepspeed_zero3_enabled
from transformers.modeling_utils import is_fsdp_enabled
from transformers.utils.versions import require_version
from ...extras import logging
from ...extras.constants import FILEEXT2TYPE
from ...extras.logging import get_logger
from ...extras.misc import get_current_device
@ -39,7 +39,7 @@ if TYPE_CHECKING:
from ...hparams import ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
@unique
@ -109,7 +109,7 @@ def configure_quantization(
"""
if getattr(config, "quantization_config", None): # ptq
if model_args.quantization_bit is not None:
logger.warning("`quantization_bit` will not affect on the PTQ-quantized models.")
logger.warning_rank0("`quantization_bit` will not affect on the PTQ-quantized models.")
if is_deepspeed_zero3_enabled() or is_fsdp_enabled():
raise ValueError("DeepSpeed ZeRO-3 or FSDP is incompatible with PTQ-quantized models.")
@ -130,7 +130,7 @@ def configure_quantization(
quantization_config["bits"] = 2
quant_bits = quantization_config.get("bits", "?")
logger.info("Loading {}-bit {}-quantized model.".format(quant_bits, quant_method.upper()))
logger.info_rank0(f"Loading {quant_bits}-bit {quant_method.upper()}-quantized model.")
elif model_args.export_quantization_bit is not None: # auto-gptq
if model_args.export_quantization_bit not in [8, 4, 3, 2]:
@ -149,7 +149,7 @@ def configure_quantization(
)
init_kwargs["device_map"] = "auto"
init_kwargs["max_memory"] = get_max_memory()
logger.info("Quantizing model to {} bit with AutoGPTQ.".format(model_args.export_quantization_bit))
logger.info_rank0(f"Quantizing model to {model_args.export_quantization_bit} bit with AutoGPTQ.")
elif model_args.quantization_bit is not None: # on-the-fly
if model_args.quantization_method == QuantizationMethod.BITS_AND_BYTES.value:
@ -179,7 +179,7 @@ def configure_quantization(
else:
init_kwargs["device_map"] = {"": get_current_device()} # change auto device map for inference
logger.info("Quantizing model to {} bit with bitsandbytes.".format(model_args.quantization_bit))
logger.info_rank0(f"Quantizing model to {model_args.quantization_bit} bit with bitsandbytes.")
elif model_args.quantization_method == QuantizationMethod.HQQ.value:
if model_args.quantization_bit not in [8, 6, 5, 4, 3, 2, 1]:
raise ValueError("HQQ only accepts 1/2/3/4/5/6/8-bit quantization.")
@ -191,7 +191,7 @@ def configure_quantization(
init_kwargs["quantization_config"] = HqqConfig(
nbits=model_args.quantization_bit, quant_zero=False, quant_scale=False, axis=0
) # use ATEN kernel (axis=0) for performance
logger.info("Quantizing model to {} bit with HQQ.".format(model_args.quantization_bit))
logger.info_rank0(f"Quantizing model to {model_args.quantization_bit} bit with HQQ.")
elif model_args.quantization_method == QuantizationMethod.EETQ.value:
if model_args.quantization_bit != 8:
raise ValueError("EETQ only accepts 8-bit quantization.")
@ -201,4 +201,4 @@ def configure_quantization(
require_version("eetq", "To fix: pip install eetq")
init_kwargs["quantization_config"] = EetqConfig()
logger.info("Quantizing model to {} bit with EETQ.".format(model_args.quantization_bit))
logger.info_rank0(f"Quantizing model to {model_args.quantization_bit} bit with EETQ.")

18
src/llamafactory/model/model_utils/rope.py
View File

@ -19,7 +19,7 @@
import math
from typing import TYPE_CHECKING
from ...extras.logging import get_logger
from ...extras import logging
if TYPE_CHECKING:
@ -28,7 +28,7 @@ if TYPE_CHECKING:
from ...hparams import ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def configure_rope(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None:
@ -36,30 +36,28 @@ def configure_rope(config: "PretrainedConfig", model_args: "ModelArguments", is_
return
if not hasattr(config, "rope_scaling"):
logger.warning("Current model does not support RoPE scaling.")
logger.warning_rank0("Current model does not support RoPE scaling.")
return
if model_args.model_max_length is not None:
if is_trainable and model_args.rope_scaling == "dynamic":
logger.warning(
logger.warning_rank0(
"Dynamic NTK scaling 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:
logger.info(
"Enlarge max model length from {} to {}.".format(current_max_length, model_args.model_max_length)
)
logger.info_rank0(f"Enlarge max model length from {current_max_length} to {model_args.model_max_length}.")
setattr(config, "max_position_embeddings", model_args.model_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.")
logger.warning_rank0("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)
logger.info_rank0(
f"Using {model_args.rope_scaling} scaling strategy and setting scaling factor to {scaling_factor}"
)

6
src/llamafactory/model/model_utils/unsloth.py
View File

@ -14,7 +14,7 @@
from typing import TYPE_CHECKING, Any, Dict, Optional
from ...extras.logging import get_logger
from ...extras import logging
from ...extras.misc import get_current_device
@ -24,7 +24,7 @@ if TYPE_CHECKING:
from ...hparams import ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def _get_unsloth_kwargs(
@ -56,7 +56,7 @@ def load_unsloth_pretrained_model(
try:
model, _ = FastLanguageModel.from_pretrained(**unsloth_kwargs)
except NotImplementedError:
logger.warning("Unsloth does not support model type {}.".format(getattr(config, "model_type", None)))
logger.warning_rank0("Unsloth does not support model type {}.".format(getattr(config, "model_type", None)))
model = None
model_args.use_unsloth = False

8
src/llamafactory/model/model_utils/valuehead.py
View File

@ -17,8 +17,8 @@ from typing import TYPE_CHECKING, Dict
import torch
from transformers.utils import cached_file
from ...extras import logging
from ...extras.constants import V_HEAD_SAFE_WEIGHTS_NAME, V_HEAD_WEIGHTS_NAME
from ...extras.logging import get_logger
if TYPE_CHECKING:
@ -27,7 +27,7 @@ if TYPE_CHECKING:
from ...hparams import ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def load_valuehead_params(path_or_repo_id: str, model_args: "ModelArguments") -> Dict[str, torch.Tensor]:
@ -54,8 +54,8 @@ def load_valuehead_params(path_or_repo_id: str, model_args: "ModelArguments") ->
except Exception as err:
err_text = str(err)
logger.info("Provided path ({}) does not contain value head weights: {}.".format(path_or_repo_id, err_text))
logger.info("Ignore the above message if you are not resuming the training of a value head model.")
logger.info_rank0(f"Provided path ({path_or_repo_id}) does not contain value head weights: {err_text}.")
logger.info_rank0("Ignore the above message if you are not resuming the training of a value head model.")
return None

47
src/llamafactory/model/model_utils/visual.py
View File

@ -18,11 +18,11 @@
from typing import TYPE_CHECKING, List, Sequence, Set, Tuple, Union
import torch
import transformers
import transformers.models
from transformers.activations import ACT2FN
from transformers.utils import logging
from ...extras.logging import get_logger
from ...extras import logging
if TYPE_CHECKING:
@ -31,8 +31,8 @@ if TYPE_CHECKING:
from ...hparams import FinetuningArguments, ModelArguments
logger = get_logger(__name__)
transformers_logger = logging.get_logger(__name__)
logger = logging.get_logger(__name__)
transformers_logger = transformers.utils.logging.get_logger(__name__)
class LlavaMultiModalProjectorForYiVL(torch.nn.Module):
@ -92,14 +92,14 @@ def autocast_projector_dtype(model: "PreTrainedModel", model_args: "ModelArgumen
if getattr(model, "quantization_method", None):
model_type = getattr(model.config, "model_type", None)
if model_type in ["llava", "paligemma"]:
if model_type in ["llava", "llava_next", "llava_next_video", "paligemma", "pixtral", "video_llava"]:
mm_projector: "torch.nn.Module" = getattr(model, "multi_modal_projector")
elif model_type == "qwen2_vl":
mm_projector: "torch.nn.Module" = getattr(getattr(model, "visual"), "merger")
else:
return
logger.info("Casting multimodal projector outputs in {}.".format(model_args.compute_dtype))
logger.info_rank0(f"Casting multimodal projector outputs in {model_args.compute_dtype}.")
mm_projector.register_forward_hook(_mm_projector_forward_post_hook)
@ -108,11 +108,18 @@ def configure_visual_model(config: "PretrainedConfig") -> None:
Patches VLMs before loading them.
"""
model_type = getattr(config, "model_type", None)
if model_type == "llava": # required for ds zero3 and valuehead models
if model_type in [
"llava",
"llava_next",
"llava_next_video",
"paligemma",
"pixtral",
"video_llava",
]: # required for ds zero3 and valuehead models
setattr(config, "hidden_size", getattr(config.text_config, "hidden_size", None))
if getattr(config, "is_yi_vl_derived_model", None):
logger.info("Detected Yi-VL model, applying projector patch.")
logger.info_rank0("Detected Yi-VL model, applying projector patch.")
transformers.models.llava.modeling_llava.LlavaMultiModalProjector = LlavaMultiModalProjectorForYiVL
@ -122,7 +129,7 @@ def get_forbidden_modules(config: "PretrainedConfig", finetuning_args: "Finetuni
"""
model_type = getattr(config, "model_type", None)
forbidden_modules = set()
if model_type in ["llava", "paligemma"]:
if model_type in ["llava", "llava_next", "llava_next_video", "paligemma", "pixtral", "video_llava"]:
if finetuning_args.freeze_vision_tower:
forbidden_modules.add("vision_tower")
@ -150,12 +157,28 @@ def get_image_seqlen(config: "PretrainedConfig") -> int:
image_seqlen += 1
elif model_type == "paligemma":
image_seqlen = config.vision_config.num_image_tokens
elif model_type == "qwen2_vl": # variable length
else:
image_seqlen = -1
return image_seqlen
def get_patch_size(config: "PretrainedConfig") -> int:
r"""
Computes the patch size of the vit.
"""
patch_size = getattr(config.vision_config, "patch_size", -1)
return patch_size
def get_vision_feature_select_strategy(config: "PretrainedConfig") -> int:
r"""
Get the vision_feature_select_strategy.
"""
vision_feature_select_strategy = getattr(config, "vision_feature_select_strategy", "default")
return vision_feature_select_strategy
def patch_target_modules(
config: "PretrainedConfig", finetuning_args: "FinetuningArguments", target_modules: Sequence[str]
) -> Union[str, List[str]]:
@ -164,7 +187,7 @@ def patch_target_modules(
"""
model_type = getattr(config, "model_type", None)
if finetuning_args.freeze_vision_tower:
if model_type in ["llava", "paligemma"]:
if model_type in ["llava", "llava_next", "llava_next_video", "paligemma", "pixtral", "video_llava"]:
return "^(?!.*vision_tower).*(?:{}).*".format("|".join(target_modules))
elif model_type == "qwen2_vl":
return "^(?!.*visual).*(?:{}).*".format("|".join(target_modules))
@ -173,5 +196,7 @@ def patch_target_modules(
else:
if model_type == "qwen2_vl":
return "^(?!.*patch_embed).*(?:{}).*".format("|".join(target_modules))
elif model_type == "pixtral":
return "^(?!.*patch_conv).*(?:{}).*".format("|".join(target_modules))
else:
return target_modules

39
src/llamafactory/model/patcher.py
View File

@ -22,29 +22,34 @@ from transformers import PreTrainedModel, PreTrainedTokenizerBase, is_torch_npu_
from transformers.integrations import is_deepspeed_zero3_enabled
from transformers.modeling_utils import is_fsdp_enabled
from ..extras.logging import get_logger
from ..extras import logging
from ..extras.misc import infer_optim_dtype
from .model_utils.attention import configure_attn_implementation, print_attn_implementation
from .model_utils.checkpointing import prepare_model_for_training
from .model_utils.embedding import resize_embedding_layer
from .model_utils.liger_kernel import configure_liger_kernel
from .model_utils.longlora import configure_longlora
from .model_utils.moe import add_z3_leaf_module, configure_moe
from .model_utils.packing import configure_packing
from .model_utils.quantization import configure_quantization
from .model_utils.rope import configure_rope
from .model_utils.valuehead import prepare_valuehead_model
from .model_utils.visual import autocast_projector_dtype, configure_visual_model
from .model_utils.visual import (
autocast_projector_dtype,
configure_visual_model,
get_image_seqlen,
get_patch_size,
get_vision_feature_select_strategy,
)
if TYPE_CHECKING:
from transformers import PretrainedConfig, PreTrainedTokenizer
from transformers import PretrainedConfig, PreTrainedTokenizer, ProcessorMixin
from trl import AutoModelForCausalLMWithValueHead
from ..hparams import ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def patch_tokenizer(tokenizer: "PreTrainedTokenizer") -> None:
@ -52,6 +57,22 @@ def patch_tokenizer(tokenizer: "PreTrainedTokenizer") -> None:
tokenizer._pad = MethodType(PreTrainedTokenizerBase._pad, tokenizer)
def patch_processor(
processor: "ProcessorMixin",
config: "PretrainedConfig",
tokenizer: "PreTrainedTokenizer",
model_args: "ModelArguments",
) -> None:
setattr(processor, "tokenizer", tokenizer)
setattr(processor, "image_seqlen", get_image_seqlen(config))
setattr(processor, "image_resolution", model_args.image_resolution)
setattr(processor, "patch_size", get_patch_size(config))
setattr(processor, "video_resolution", model_args.video_resolution)
setattr(processor, "video_fps", model_args.video_fps)
setattr(processor, "video_maxlen", model_args.video_maxlen)
setattr(processor, "vision_feature_select_strategy", get_vision_feature_select_strategy(config))
def patch_config(
config: "PretrainedConfig",
tokenizer: "PreTrainedTokenizer",
@ -71,7 +92,6 @@ def patch_config(
configure_attn_implementation(config, model_args, is_trainable)
configure_rope(config, model_args, is_trainable)
configure_liger_kernel(config, model_args, is_trainable)
configure_longlora(config, model_args, is_trainable)
configure_quantization(config, tokenizer, model_args, init_kwargs)
configure_moe(config, model_args, is_trainable)
@ -80,7 +100,7 @@ def patch_config(
if model_args.use_cache and not is_trainable:
setattr(config, "use_cache", True)
logger.info("Using KV cache for faster generation.")
logger.info_rank0("Using KV cache for faster generation.")
if getattr(config, "model_type", None) == "qwen":
setattr(config, "use_flash_attn", model_args.flash_attn == "fa2")
@ -90,6 +110,9 @@ def patch_config(
if getattr(config, "model_type", None) == "qwen2" and is_trainable and model_args.flash_attn == "fa2":
setattr(config, "use_cache", False) # qwen2 does not support use_cache when using flash attn
if "LlavaLlamaForCausalLM" in getattr(config, "architectures", []):
raise ValueError("Please download llava models with hf-compatible format: https://huggingface.co/llava-hf")
# deepspeed zero3 is not compatible with low_cpu_mem_usage
init_kwargs["low_cpu_mem_usage"] = model_args.low_cpu_mem_usage and (not is_deepspeed_zero3_enabled())
@ -142,7 +165,7 @@ def patch_model(
try:
model.add_model_tags(["llama-factory"])
except Exception:
logger.warning("Cannot properly tag the model.")
logger.warning_rank0("Cannot properly tag the model.")
def patch_valuehead_model(model: "AutoModelForCausalLMWithValueHead") -> None:

56
src/llamafactory/train/callbacks.py
View File

@ -13,7 +13,6 @@
# limitations under the License.
import json
import logging
import os
import signal
import sys
@ -34,8 +33,8 @@ from transformers.utils import (
)
from typing_extensions import override
from ..extras import logging
from ..extras.constants import TRAINER_LOG, V_HEAD_SAFE_WEIGHTS_NAME, V_HEAD_WEIGHTS_NAME
from ..extras.logging import LoggerHandler, get_logger
from ..extras.misc import get_peak_memory
@ -48,7 +47,7 @@ if TYPE_CHECKING:
from trl import AutoModelForCausalLMWithValueHead
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def fix_valuehead_checkpoint(
@ -92,7 +91,7 @@ def fix_valuehead_checkpoint(
else:
torch.save(v_head_state_dict, os.path.join(output_dir, V_HEAD_WEIGHTS_NAME))
logger.info("Value head model saved at: {}".format(output_dir))
logger.info_rank0(f"Value head model saved at: {output_dir}")
class FixValueHeadModelCallback(TrainerCallback):
@ -106,7 +105,7 @@ class FixValueHeadModelCallback(TrainerCallback):
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))
output_dir = os.path.join(args.output_dir, f"{PREFIX_CHECKPOINT_DIR}-{state.global_step}")
fix_valuehead_checkpoint(
model=kwargs.pop("model"), output_dir=output_dir, safe_serialization=args.save_safetensors
)
@ -123,13 +122,13 @@ class SaveProcessorCallback(TrainerCallback):
@override
def on_save(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
if args.should_save:
output_dir = os.path.join(args.output_dir, "{}-{}".format(PREFIX_CHECKPOINT_DIR, state.global_step))
getattr(self.processor, "image_processor").save_pretrained(output_dir)
output_dir = os.path.join(args.output_dir, f"{PREFIX_CHECKPOINT_DIR}-{state.global_step}")
self.processor.save_pretrained(output_dir)
@override
def on_train_end(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
if args.should_save:
getattr(self.processor, "image_processor").save_pretrained(args.output_dir)
self.processor.save_pretrained(args.output_dir)
class PissaConvertCallback(TrainerCallback):
@ -145,7 +144,7 @@ class PissaConvertCallback(TrainerCallback):
if args.should_save:
model = kwargs.pop("model")
pissa_init_dir = os.path.join(args.output_dir, "pissa_init")
logger.info("Initial PiSSA adapter will be saved at: {}.".format(pissa_init_dir))
logger.info_rank0(f"Initial PiSSA adapter will be saved at: {pissa_init_dir}.")
if isinstance(model, PeftModel):
init_lora_weights = getattr(model.peft_config["default"], "init_lora_weights")
setattr(model.peft_config["default"], "init_lora_weights", True)
@ -159,7 +158,7 @@ class PissaConvertCallback(TrainerCallback):
pissa_init_dir = os.path.join(args.output_dir, "pissa_init")
pissa_backup_dir = os.path.join(args.output_dir, "pissa_backup")
pissa_convert_dir = os.path.join(args.output_dir, "pissa_converted")
logger.info("Converted PiSSA adapter will be saved at: {}.".format(pissa_convert_dir))
logger.info_rank0(f"Converted PiSSA adapter will be saved at: {pissa_convert_dir}.")
# 1. save a pissa backup with init_lora_weights: True
# 2. save a converted lora with init_lora_weights: pissa
# 3. load the pissa backup with init_lora_weights: True
@ -200,8 +199,8 @@ class LogCallback(TrainerCallback):
self.webui_mode = os.environ.get("LLAMABOARD_ENABLED", "0").lower() in ["true", "1"]
if self.webui_mode:
signal.signal(signal.SIGABRT, self._set_abort)
self.logger_handler = LoggerHandler(os.environ.get("LLAMABOARD_WORKDIR"))
logging.root.addHandler(self.logger_handler)
self.logger_handler = logging.LoggerHandler(os.environ.get("LLAMABOARD_WORKDIR"))
logging.add_handler(self.logger_handler)
transformers.logging.add_handler(self.logger_handler)
def _set_abort(self, signum, frame) -> None:
@ -243,7 +242,7 @@ class LogCallback(TrainerCallback):
and os.path.exists(os.path.join(args.output_dir, TRAINER_LOG))
and args.overwrite_output_dir
):
logger.warning("Previous trainer log in this folder will be deleted.")
logger.warning_once("Previous trainer log in this folder will be deleted.")
os.remove(os.path.join(args.output_dir, TRAINER_LOG))
@override
@ -288,13 +287,13 @@ class LogCallback(TrainerCallback):
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),
accuracy=state.log_history[-1].get("rewards/accuracies", None),
learning_rate=state.log_history[-1].get("learning_rate", None),
epoch=state.log_history[-1].get("epoch", None),
loss=state.log_history[-1].get("loss"),
eval_loss=state.log_history[-1].get("eval_loss"),
predict_loss=state.log_history[-1].get("predict_loss"),
reward=state.log_history[-1].get("reward"),
accuracy=state.log_history[-1].get("rewards/accuracies"),
lr=state.log_history[-1].get("learning_rate"),
epoch=state.log_history[-1].get("epoch"),
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,
@ -305,16 +304,17 @@ class LogCallback(TrainerCallback):
if os.environ.get("RECORD_VRAM", "0").lower() in ["true", "1"]:
vram_allocated, vram_reserved = get_peak_memory()
logs["vram_allocated"] = round(vram_allocated / 1024 / 1024 / 1024, 2)
logs["vram_reserved"] = round(vram_reserved / 1024 / 1024 / 1024, 2)
logs["vram_allocated"] = round(vram_allocated / (1024**3), 2)
logs["vram_reserved"] = round(vram_reserved / (1024**3), 2)
logs = {k: v for k, v in logs.items() if v is not None}
if self.webui_mode and all(key in logs for key in ["loss", "learning_rate", "epoch"]):
logger.info(
"{{'loss': {:.4f}, 'learning_rate': {:2.4e}, 'epoch': {:.2f}, 'throughput': {}}}".format(
logs["loss"], logs["learning_rate"], logs["epoch"], logs.get("throughput", "N/A")
)
)
if self.webui_mode and all(key in logs for key in ("loss", "lr", "epoch")):
log_str = f"'loss': {logs['loss']:.4f}, 'learning_rate': {logs['lr']:2.4e}, 'epoch': {logs['epoch']:.2f}"
for extra_key in ("reward", "accuracy", "throughput"):
if logs.get(extra_key):
log_str += f", '{extra_key}': {logs[extra_key]:.2f}"
logger.info_rank0("{" + log_str + "}")
if self.thread_pool is not None:
self.thread_pool.submit(self._write_log, args.output_dir, logs)

76
src/llamafactory/train/dpo/trainer.py
View File

@ -29,6 +29,7 @@ from trl.trainer import disable_dropout_in_model
from typing_extensions import override
from ...extras.constants import IGNORE_INDEX
from ...extras.packages import is_transformers_version_equal_to_4_46
from ..callbacks import PissaConvertCallback, SaveProcessorCallback
from ..trainer_utils import create_custom_optimizer, create_custom_scheduler, get_batch_logps
@ -100,7 +101,7 @@ class CustomDPOTrainer(DPOTrainer):
self.callback_handler.add_callback(PissaConvertCallback)
if finetuning_args.use_badam:
from badam import BAdamCallback, clip_grad_norm_old_version
from badam import BAdamCallback, clip_grad_norm_old_version # type: ignore
self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
self.add_callback(BAdamCallback)
@ -118,6 +119,13 @@ class CustomDPOTrainer(DPOTrainer):
create_custom_scheduler(self.args, num_training_steps, optimizer)
return super().create_scheduler(num_training_steps, optimizer)
@override
def get_batch_samples(self, epoch_iterator, num_batches):
r"""
Replaces the method of KTO Trainer with the one of the standard Trainer.
"""
return Trainer.get_batch_samples(self, epoch_iterator, num_batches)
def odds_ratio_loss(self, chosen_logps: "torch.Tensor", rejected_logps: "torch.Tensor") -> "torch.Tensor":
r"""
Computes ORPO's odds ratio (OR) loss for batched log probabilities of the policy model.
@ -156,7 +164,7 @@ class CustomDPOTrainer(DPOTrainer):
elif self.loss_type == "simpo":
losses = self.simpo_loss(policy_chosen_logps, policy_rejected_logps)
else:
raise NotImplementedError("Unknown loss type: {}.".format(self.loss_type))
raise NotImplementedError(f"Unknown loss type: {self.loss_type}.")
chosen_rewards = self.beta * policy_chosen_logps.to(self.accelerator.device).detach()
rejected_rewards = self.beta * policy_rejected_logps.to(self.accelerator.device).detach()
@ -242,19 +250,59 @@ class CustomDPOTrainer(DPOTrainer):
if self.ftx_gamma > 1e-6:
losses += self.ftx_gamma * sft_loss
reward_accuracies = (chosen_rewards > rejected_rewards).float()
prefix = "eval_" if train_eval == "eval" else ""
metrics["{}rewards/chosen".format(prefix)] = chosen_rewards.mean().cpu()
metrics["{}rewards/rejected".format(prefix)] = rejected_rewards.mean().cpu()
metrics["{}rewards/accuracies".format(prefix)] = reward_accuracies.mean().cpu()
metrics["{}rewards/margins".format(prefix)] = (chosen_rewards - rejected_rewards).mean().cpu()
metrics["{}logps/rejected".format(prefix)] = policy_rejected_logps.detach().mean().cpu()
metrics["{}logps/chosen".format(prefix)] = policy_chosen_logps.detach().mean().cpu()
metrics["{}logits/rejected".format(prefix)] = policy_rejected_logits.detach().mean().cpu()
metrics["{}logits/chosen".format(prefix)] = policy_chosen_logits.detach().mean().cpu()
metrics[f"{prefix}rewards/chosen"] = chosen_rewards.mean().item()
metrics[f"{prefix}rewards/rejected"] = rejected_rewards.mean().item()
metrics[f"{prefix}rewards/accuracies"] = (chosen_rewards > rejected_rewards).float().mean().item()
metrics[f"{prefix}rewards/margins"] = (chosen_rewards - rejected_rewards).mean().item()
metrics[f"{prefix}logps/rejected"] = policy_chosen_logps.mean().item()
metrics[f"{prefix}logps/chosen"] = policy_rejected_logps.mean().item()
metrics[f"{prefix}logits/rejected"] = policy_chosen_logits.mean().item()
metrics[f"{prefix}logits/chosen"] = policy_rejected_logits.mean().item()
if self.loss_type == "orpo":
metrics["{}sft_loss".format(prefix)] = sft_loss.detach().mean().cpu()
metrics["{}odds_ratio_loss".format(prefix)] = ((losses - sft_loss) / self.beta).detach().mean().cpu()
metrics[f"{prefix}sft_loss"] = sft_loss.mean().item()
metrics[f"{prefix}odds_ratio_loss"] = ((losses - sft_loss) / self.beta).mean().item()
return losses.mean(), metrics
@override
def compute_loss(self, model, inputs, return_outputs=False, **kwargs):
r"""
Fixes the loss value for transformers 4.46.0.
https://github.com/huggingface/transformers/blob/v4.46.0/src/transformers/trainer.py#L3605
"""
loss = super().compute_loss(model, inputs, return_outputs)
if is_transformers_version_equal_to_4_46() and kwargs.pop("num_items_in_batch", False):
if return_outputs:
return (loss[0] / self.args.gradient_accumulation_steps, *loss[1:])
else:
return loss / self.args.gradient_accumulation_steps
return loss
@override
def log(self, logs: Dict[str, float]) -> None:
r"""
Log `logs` on the various objects watching training, including stored metrics.
"""
# logs either has "loss" or "eval_loss"
train_eval = "train" if "loss" in logs else "eval"
# Add averaged stored metrics to logs
key_list, metric_list = [], []
for key, metrics in self._stored_metrics[train_eval].items():
key_list.append(key)
metric_list.append(torch.tensor(metrics, dtype=torch.float).to(self.accelerator.device).mean().item())
del self._stored_metrics[train_eval]
if len(metric_list) < 10: # pad to for all reduce
for i in range(10 - len(metric_list)):
key_list.append(f"dummy_{i}")
metric_list.append(0.0)
metric_list = torch.tensor(metric_list, dtype=torch.float).to(self.accelerator.device)
metric_list = self.accelerator.reduce(metric_list, "mean").tolist()
for key, metric in zip(key_list, metric_list): # add remaining items
if not key.startswith("dummy_"):
logs[key] = metric
return Trainer.log(self, logs)

128
src/llamafactory/train/kto/trainer.py
View File

@ -28,6 +28,7 @@ from trl.trainer import disable_dropout_in_model
from typing_extensions import override
from ...extras.constants import IGNORE_INDEX
from ...extras.packages import is_transformers_version_equal_to_4_46
from ..callbacks import SaveProcessorCallback
from ..trainer_utils import create_custom_optimizer, create_custom_scheduler, get_batch_logps
@ -95,7 +96,7 @@ class CustomKTOTrainer(KTOTrainer):
self.add_callback(SaveProcessorCallback(processor))
if finetuning_args.use_badam:
from badam import BAdamCallback, clip_grad_norm_old_version
from badam import BAdamCallback, clip_grad_norm_old_version # type: ignore
self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
self.add_callback(BAdamCallback)
@ -120,20 +121,27 @@ class CustomKTOTrainer(KTOTrainer):
"""
return Trainer._get_train_sampler(self)
@override
def get_batch_samples(self, epoch_iterator, num_batches):
r"""
Replaces the method of KTO Trainer with the one of the standard Trainer.
"""
return Trainer.get_batch_samples(self, epoch_iterator, num_batches)
@override
def forward(
self, model: "PreTrainedModel", batch: Dict[str, "torch.Tensor"], prefix: Literal["", "kl_"] = ""
) -> Tuple["torch.Tensor", "torch.Tensor"]:
) -> Tuple["torch.Tensor", "torch.Tensor", "torch.Tensor"]:
r"""
Runs forward pass and computes the log probabilities.
"""
batch = {k: v.detach().clone() for k, v in batch.items()} # avoid error
model_inputs = {
"input_ids": batch["{}input_ids".format(prefix)],
"attention_mask": batch["{}attention_mask".format(prefix)],
"input_ids": batch[f"{prefix}input_ids"],
"attention_mask": batch[f"{prefix}attention_mask"],
}
if "{}token_type_ids".format(prefix) in batch:
model_inputs["token_type_ids"] = batch["{}token_type_ids".format(prefix)]
if f"{prefix}token_type_ids" in batch:
model_inputs["token_type_ids"] = batch[f"{prefix}token_type_ids"]
if "pixel_values" in batch:
model_inputs["pixel_values"] = batch["pixel_values"]
@ -142,24 +150,26 @@ class CustomKTOTrainer(KTOTrainer):
model_inputs["image_grid_thw"] = batch["image_grid_thw"]
logits = model(**model_inputs, return_dict=True, use_cache=False).logits.to(torch.float32)
logps, valid_length = get_batch_logps(logits=logits, labels=batch["{}labels".format(prefix)])
return logps, logps / valid_length
logps, valid_length = get_batch_logps(logits=logits, labels=batch[f"{prefix}labels"])
return logits, logps, logps / valid_length
@override
def concatenated_forward(
self, model: "PreTrainedModel", batch: Dict[str, "torch.Tensor"]
) -> Tuple["torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor"]:
target_logps, target_logps_avg = self.forward(model, batch)
) -> Tuple["torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor"]:
target_logits, target_logps, target_logps_avg = self.forward(model, batch)
with torch.no_grad():
kl_logps, _ = self.forward(model, batch, prefix="kl_")
_, kl_logps, _ = self.forward(model, batch, prefix="kl_")
if len(target_logps) != len(batch["kto_tags"]):
raise ValueError("Mismatched shape of inputs and labels.")
chosen_logits = target_logits[batch["kto_tags"]]
chosen_logps = target_logps[batch["kto_tags"]]
rejected_logits = target_logits[~batch["kto_tags"]]
rejected_logps = target_logps[~batch["kto_tags"]]
chosen_logps_avg = target_logps_avg[batch["kto_tags"]]
return chosen_logps, rejected_logps, kl_logps, chosen_logps_avg
return chosen_logps, rejected_logps, chosen_logits, rejected_logits, kl_logps, chosen_logps_avg
@override
def compute_reference_log_probs(
@ -176,7 +186,7 @@ class CustomKTOTrainer(KTOTrainer):
ref_context = nullcontext()
with torch.no_grad(), ref_context:
reference_chosen_logps, reference_rejected_logps, reference_kl_logps, _ = self.concatenated_forward(
reference_chosen_logps, reference_rejected_logps, _, _, reference_kl_logps, _ = self.concatenated_forward(
ref_model, batch
)
@ -192,9 +202,14 @@ class CustomKTOTrainer(KTOTrainer):
Computes the DPO loss and other metrics for the given batch of inputs for train or test.
"""
metrics = {}
policy_chosen_logps, policy_rejected_logps, policy_kl_logps, policy_chosen_logps_avg = (
self.concatenated_forward(model, batch)
)
(
policy_chosen_logps,
policy_rejected_logps,
policy_chosen_logits,
policy_rejected_logits,
policy_kl_logps,
policy_chosen_logps_avg,
) = self.concatenated_forward(model, batch)
reference_chosen_logps, reference_rejected_logps, reference_kl_logps = self.compute_reference_log_probs(
model, batch
)
@ -212,22 +227,73 @@ class CustomKTOTrainer(KTOTrainer):
sft_loss = -policy_chosen_logps_avg
losses += self.ftx_gamma * sft_loss.nanmean() / len(policy_chosen_logps) * len(batch["labels"])
num_chosen = torch.Tensor([len(chosen_rewards)]).to(self.accelerator.device)
num_rejected = torch.Tensor([len(rejected_rewards)]).to(self.accelerator.device)
num_chosen = len(chosen_rewards)
num_rejected = len(rejected_rewards)
if num_chosen > 0:
metrics["rewards/chosen_sum"] = chosen_rewards.nansum().item()
metrics["logps/chosen_sum"] = policy_chosen_logps.nansum().item()
metrics["logits/chosen_sum"] = policy_chosen_logits.nansum().item()
metrics["count/chosen"] = float(num_chosen)
all_num_chosen = self.accelerator.gather(num_chosen).sum().item()
all_num_rejected = self.accelerator.gather(num_rejected).sum().item()
if all_num_chosen > 0:
metrics["rewards/chosen_sum"] = self.accelerator.gather(chosen_rewards.nansum()).nansum().item()
metrics["logps/chosen_sum"] = self.accelerator.gather(policy_chosen_logps.nansum()).nansum().item()
metrics["count/chosen"] = all_num_chosen
if all_num_rejected > 0:
metrics["rewards/rejected_sum"] = self.accelerator.gather(rejected_rewards.nansum()).nansum().item()
metrics["logps/rejected_sum"] = self.accelerator.gather(policy_rejected_logps.nansum()).nansum().item()
metrics["count/rejected"] = all_num_rejected
if num_rejected > 0:
metrics["rewards/rejected_sum"] = rejected_rewards.nansum().item()
metrics["logps/rejected_sum"] = policy_rejected_logps.nansum().item()
metrics["logits/rejected_sum"] = policy_rejected_logits.nansum().item()
metrics["count/rejected"] = float(num_rejected)
metrics["kl"] = kl.item()
return losses, metrics
@override
def compute_loss(self, model, inputs, return_outputs=False, **kwargs):
r"""
Fixes the loss value for transformers 4.46.0.
https://github.com/huggingface/transformers/blob/v4.46.0/src/transformers/trainer.py#L3605
"""
loss = super().compute_loss(model, inputs, return_outputs)
if is_transformers_version_equal_to_4_46() and kwargs.pop("num_items_in_batch", False):
if return_outputs:
return (loss[0] / self.args.gradient_accumulation_steps, *loss[1:])
else:
return loss / self.args.gradient_accumulation_steps
return loss
@override
def log(self, logs: Dict[str, float]) -> None:
r"""
Log `logs` on the various objects watching training, including stored metrics.
"""
# logs either has "loss" or "eval_loss"
train_eval = "train" if "loss" in logs else "eval"
prefix = "eval_" if train_eval == "eval" else ""
# Add averaged stored metrics to logs
key_list, metric_list = [], []
for key, metrics in self._stored_metrics[train_eval].items():
key_list.append(key)
metric_list.append(torch.tensor(metrics, dtype=torch.float).to(self.accelerator.device).sum().item())
del self._stored_metrics[train_eval]
if len(metric_list) < 9: # pad to for all reduce
for i in range(9 - len(metric_list)):
key_list.append(f"dummy_{i}")
metric_list.append(0.0)
metric_list = torch.tensor(metric_list, dtype=torch.float).to(self.accelerator.device)
metric_list = self.accelerator.reduce(metric_list, "sum").tolist()
metric_dict: Dict[str, float] = dict(zip(key_list, metric_list))
for split in ["chosen", "rejected"]: # accumulate average metrics from sums and lengths
if f"count/{split}" in metric_dict:
for key in ("rewards", "logps", "logits"):
logs[f"{prefix}{key}/{split}"] = metric_dict[f"{key}/{split}_sum"] / metric_dict[f"count/{split}"]
del metric_dict[f"{key}/{split}_sum"]
del metric_dict[f"count/{split}"]
if f"{prefix}rewards/chosen" in logs and f"{prefix}rewards/rejected" in logs: # calculate reward margin
logs[f"{prefix}rewards/margins"] = logs[f"{prefix}rewards/chosen"] - logs[f"{prefix}rewards/rejected"]
for key, metric in metric_dict.items(): # add remaining items
if not key.startswith("dummy_"):
logs[key] = metric
return Trainer.log(self, logs)

2
src/llamafactory/train/kto/workflow.py
View File

@ -81,7 +81,7 @@ def run_kto(
trainer.save_metrics("train", train_result.metrics)
trainer.save_state()
if trainer.is_world_process_zero() and finetuning_args.plot_loss:
plot_loss(training_args.output_dir, keys=["loss", "eval_loss", "train/rewards/chosen"])
plot_loss(training_args.output_dir, keys=["loss", "eval_loss", "rewards/chosen"])
# Evaluation
if training_args.do_eval:

4
src/llamafactory/train/ppo/ppo_utils.py
View File

@ -62,8 +62,8 @@ def replace_model(model: "AutoModelForCausalLMWithValueHead", target: Literal["d
setattr(model, "default_head_bias", v_head_layer.bias.data.detach().clone())
device = v_head_layer.weight.device
v_head_layer.weight.data = model.get_buffer("{}_head_weight".format(target)).detach().clone().to(device)
v_head_layer.bias.data = model.get_buffer("{}_head_bias".format(target)).detach().clone().to(device)
v_head_layer.weight.data = model.get_buffer(f"{target}_head_weight").detach().clone().to(device)
v_head_layer.bias.data = model.get_buffer(f"{target}_head_bias").detach().clone().to(device)
def dump_layernorm(model: "PreTrainedModel") -> Dict[str, "torch.Tensor"]:

41
src/llamafactory/train/ppo/trainer.py
View File

@ -37,7 +37,7 @@ from trl.core import PPODecorators, logprobs_from_logits
from trl.models.utils import unwrap_model_for_generation
from typing_extensions import override
from ...extras.logging import get_logger
from ...extras import logging
from ...extras.misc import AverageMeter, count_parameters, get_current_device, get_logits_processor
from ..callbacks import FixValueHeadModelCallback, SaveProcessorCallback
from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
@ -58,7 +58,7 @@ if TYPE_CHECKING:
from ...hparams import FinetuningArguments, GeneratingArguments, ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
class CustomPPOTrainer(PPOTrainer, Trainer):
@ -112,7 +112,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
]
ppo_config.accelerator_kwargs["deepspeed_plugin"] = training_args.deepspeed_plugin
if ppo_config.log_with is not None:
logger.warning("PPOTrainer cannot use external logger when DeepSpeed is enabled.")
logger.warning_rank0("PPOTrainer cannot use external logger when DeepSpeed is enabled.")
ppo_config.log_with = None
# Create optimizer and scheduler
@ -160,7 +160,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
callbacks, self.accelerator.unwrap_model(self.model), self.tokenizer, self.optimizer, self.lr_scheduler
)
if self.args.max_steps > 0:
logger.info("max_steps is given, it will override any value given in num_train_epochs")
logger.info_rank0("max_steps is given, it will override any value given in num_train_epochs")
self.amp_context = torch.autocast(self.current_device.type)
warnings.simplefilter("ignore") # remove gc warnings on ref model
@ -181,7 +181,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
self.add_callback(SaveProcessorCallback(processor))
if finetuning_args.use_badam:
from badam import BAdamCallback, clip_grad_norm_old_version
from badam import BAdamCallback, clip_grad_norm_old_version # type: ignore
self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
self.add_callback(BAdamCallback)
@ -216,20 +216,19 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
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(" Num examples = {:,}".format(num_examples))
logger.info(" Num Epochs = {:,}".format(num_train_epochs))
logger.info(" Instantaneous batch size per device = {:,}".format(self.args.per_device_train_batch_size))
logger.info(
" Total train batch size (w. parallel, buffer, distributed & accumulation) = {:,}".format(
total_train_batch_size
)
logger.info_rank0("***** Running training *****")
logger.info_rank0(f" Num examples = {num_examples:,}")
logger.info_rank0(f" Num Epochs = {num_train_epochs:,}")
logger.info_rank0(f" Instantaneous batch size per device = {self.args.per_device_train_batch_size:,}")
logger.info_rank0(
" Total train batch size (w. parallel, buffer, distributed & accumulation) = {:,}".format(
total_train_batch_size
)
logger.info(" Gradient Accumulation steps = {:,}".format(self.args.gradient_accumulation_steps))
logger.info(" Num optimization epochs per batch = {:,}".format(self.finetuning_args.ppo_epochs))
logger.info(" Total training steps = {:,}".format(max_steps))
logger.info(" Number of trainable parameters = {:,}".format(count_parameters(self.model)[0]))
)
logger.info_rank0(f" Gradient Accumulation steps = {self.args.gradient_accumulation_steps:,}")
logger.info_rank0(f" Num optimization epochs per batch = {self.finetuning_args.ppo_epochs:,}")
logger.info_rank0(f" Total training steps = {max_steps:,}")
logger.info_rank0(f" Number of trainable parameters = {count_parameters(self.model)[0]:,}")
dataiter = iter(self.dataloader)
loss_meter = AverageMeter()
@ -269,7 +268,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
batch["response"] = self.tokenizer.batch_decode(responses, skip_special_tokens=True)
self.log_stats(stats, batch, rewards)
except Exception:
logger.warning("Failed to save stats due to unknown errors.")
logger.warning_rank0("Failed to save stats due to unknown errors.")
self.state.global_step += 1
self.callback_handler.on_step_end(self.args, self.state, self.control)
@ -290,7 +289,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
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))
os.path.join(self.args.output_dir, f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}")
)
self.callback_handler.on_save(self.args, self.state, self.control)
@ -498,7 +497,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
if self.args.should_save:
self._save(output_dir, state_dict=state_dict)
except ValueError:
logger.warning(
logger.warning_rank0(
" stage3_gather_16bit_weights_on_model_save=false. Saving the full checkpoint instead,"
" use zero_to_fp32.py to recover weights"
)

23
src/llamafactory/train/pt/trainer.py
View File

@ -18,7 +18,7 @@ from typing import TYPE_CHECKING, Optional
from transformers import Trainer
from typing_extensions import override
from ...extras.logging import get_logger
from ...extras.packages import is_transformers_version_equal_to_4_46
from ..callbacks import PissaConvertCallback, SaveProcessorCallback
from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
@ -30,9 +30,6 @@ if TYPE_CHECKING:
from ...hparams import FinetuningArguments
logger = get_logger(__name__)
class CustomTrainer(Trainer):
r"""
Inherits Trainer for custom optimizer.
@ -51,7 +48,7 @@ class CustomTrainer(Trainer):
self.add_callback(PissaConvertCallback)
if finetuning_args.use_badam:
from badam import BAdamCallback, clip_grad_norm_old_version
from badam import BAdamCallback, clip_grad_norm_old_version # type: ignore
self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
self.add_callback(BAdamCallback)
@ -68,3 +65,19 @@ class CustomTrainer(Trainer):
) -> "torch.optim.lr_scheduler.LRScheduler":
create_custom_scheduler(self.args, num_training_steps, optimizer)
return super().create_scheduler(num_training_steps, optimizer)
@override
def compute_loss(self, model, inputs, return_outputs=False, **kwargs):
r"""
Fixes the loss value for transformers 4.46.0.
https://github.com/huggingface/transformers/blob/v4.46.0/src/transformers/trainer.py#L3605
"""
loss = super().compute_loss(model, inputs, return_outputs, **kwargs)
if is_transformers_version_equal_to_4_46() and not getattr(self, "model_accepts_loss_kwargs", False):
# other model should not scale the loss
if return_outputs:
return (loss[0] / self.args.gradient_accumulation_steps, *loss[1:])
else:
return loss / self.args.gradient_accumulation_steps
return loss

15
src/llamafactory/train/rm/trainer.py
View File

@ -24,7 +24,8 @@ import torch
from transformers import Trainer
from typing_extensions import override
from ...extras.logging import get_logger
from ...extras import logging
from ...extras.packages import is_transformers_version_equal_to_4_46
from ..callbacks import FixValueHeadModelCallback, PissaConvertCallback, SaveProcessorCallback
from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
@ -36,7 +37,7 @@ if TYPE_CHECKING:
from ...hparams import FinetuningArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
class PairwiseTrainer(Trainer):
@ -59,7 +60,7 @@ class PairwiseTrainer(Trainer):
self.add_callback(PissaConvertCallback)
if finetuning_args.use_badam:
from badam import BAdamCallback, clip_grad_norm_old_version
from badam import BAdamCallback, clip_grad_norm_old_version # type: ignore
self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
self.add_callback(BAdamCallback)
@ -79,7 +80,7 @@ class PairwiseTrainer(Trainer):
@override
def compute_loss(
self, model: "PreTrainedModel", inputs: Dict[str, "torch.Tensor"], return_outputs: bool = False
self, model: "PreTrainedModel", inputs: Dict[str, "torch.Tensor"], return_outputs: bool = False, **kwargs
) -> 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.
@ -98,6 +99,10 @@ class PairwiseTrainer(Trainer):
chosen_scores, rejected_scores = chosen_scores.squeeze(), rejected_scores.squeeze()
loss = -torch.nn.functional.logsigmoid(chosen_scores.float() - rejected_scores.float()).mean()
if is_transformers_version_equal_to_4_46() and kwargs.pop("num_items_in_batch", False):
loss /= self.args.gradient_accumulation_steps # fixes the loss value for transformers 4.46.0
if return_outputs:
return loss, (loss, chosen_scores, rejected_scores)
else:
@ -113,7 +118,7 @@ class PairwiseTrainer(Trainer):
return
output_prediction_file = os.path.join(self.args.output_dir, "generated_predictions.jsonl")
logger.info(f"Saving prediction results to {output_prediction_file}")
logger.info_rank0(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:

25
src/llamafactory/train/sft/trainer.py
View File

@ -25,8 +25,9 @@ import torch
from transformers import Seq2SeqTrainer
from typing_extensions import override
from ...extras import logging
from ...extras.constants import IGNORE_INDEX
from ...extras.logging import get_logger
from ...extras.packages import is_transformers_version_equal_to_4_46
from ..callbacks import PissaConvertCallback, SaveProcessorCallback
from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
@ -39,7 +40,7 @@ if TYPE_CHECKING:
from ...hparams import FinetuningArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
class CustomSeq2SeqTrainer(Seq2SeqTrainer):
@ -60,7 +61,7 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
self.add_callback(PissaConvertCallback)
if finetuning_args.use_badam:
from badam import BAdamCallback, clip_grad_norm_old_version
from badam import BAdamCallback, clip_grad_norm_old_version # type: ignore
self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
self.add_callback(BAdamCallback)
@ -78,6 +79,22 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
create_custom_scheduler(self.args, num_training_steps, optimizer)
return super().create_scheduler(num_training_steps, optimizer)
@override
def compute_loss(self, model, inputs, return_outputs=False, **kwargs):
r"""
Fixes the loss value for transformers 4.46.0.
https://github.com/huggingface/transformers/blob/v4.46.0/src/transformers/trainer.py#L3605
"""
loss = super().compute_loss(model, inputs, return_outputs, **kwargs)
if is_transformers_version_equal_to_4_46() and not getattr(self, "model_accepts_loss_kwargs", False):
# other model should not scale the loss
if return_outputs:
return (loss[0] / self.args.gradient_accumulation_steps, *loss[1:])
else:
return loss / self.args.gradient_accumulation_steps
return loss
@override
def prediction_step(
self,
@ -129,7 +146,7 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
return
output_prediction_file = os.path.join(self.args.output_dir, "generated_predictions.jsonl")
logger.info(f"Saving prediction results to {output_prediction_file}")
logger.info_rank0(f"Saving prediction results to {output_prediction_file}")
labels = np.where(
predict_results.label_ids != IGNORE_INDEX, predict_results.label_ids, self.tokenizer.pad_token_id

13
src/llamafactory/train/test_utils.py
View File

@ -37,9 +37,9 @@ def compare_model(model_a: "torch.nn.Module", model_b: "torch.nn.Module", diff_k
assert set(state_dict_a.keys()) == set(state_dict_b.keys())
for name in state_dict_a.keys():
if any(key in name for key in diff_keys):
assert torch.allclose(state_dict_a[name], state_dict_b[name], rtol=1e-3, atol=1e-4) is False
assert torch.allclose(state_dict_a[name], state_dict_b[name], rtol=1e-4, atol=1e-5) is False
else:
assert torch.allclose(state_dict_a[name], state_dict_b[name], rtol=1e-3, atol=1e-4) is True
assert torch.allclose(state_dict_a[name], state_dict_b[name], rtol=1e-4, atol=1e-5) is True
def check_lora_model(model: "LoraModel") -> Tuple[Set[str], Set[str]]:
@ -80,18 +80,17 @@ def load_reference_model(
is_trainable: bool = False,
add_valuehead: bool = False,
) -> Union["PreTrainedModel", "LoraModel"]:
current_device = get_current_device()
if add_valuehead:
model: "AutoModelForCausalLMWithValueHead" = AutoModelForCausalLMWithValueHead.from_pretrained(
model_path, torch_dtype=torch.float16, device_map=get_current_device()
model_path, torch_dtype=torch.float16, device_map=current_device
)
if not is_trainable:
model.v_head = model.v_head.to(torch.float16)
return model
model = AutoModelForCausalLM.from_pretrained(
model_path, torch_dtype=torch.float16, device_map=get_current_device()
)
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16, device_map=current_device)
if use_lora or use_pissa:
model = PeftModel.from_pretrained(
model, lora_path, subfolder="pissa_init" if use_pissa else None, is_trainable=is_trainable
@ -110,7 +109,7 @@ def load_train_dataset(**kwargs) -> "Dataset":
return dataset_module["train_dataset"]
def patch_valuehead_model():
def patch_valuehead_model() -> None:
def post_init(self: "AutoModelForCausalLMWithValueHead", state_dict: Dict[str, "torch.Tensor"]) -> None:
state_dict = {k[7:]: state_dict[k] for k in state_dict.keys() if k.startswith("v_head.")}
self.v_head.load_state_dict(state_dict, strict=False)

28
src/llamafactory/train/trainer_utils.py
View File

@ -28,8 +28,8 @@ from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
from transformers.trainer_pt_utils import get_parameter_names
from typing_extensions import override
from ..extras import logging
from ..extras.constants import IGNORE_INDEX
from ..extras.logging import get_logger
from ..extras.packages import is_galore_available
from ..hparams import FinetuningArguments, ModelArguments
from ..model import find_all_linear_modules, load_model, load_tokenizer, load_valuehead_params
@ -46,7 +46,7 @@ if TYPE_CHECKING:
from ..hparams import DataArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
class DummyOptimizer(torch.optim.Optimizer):
@ -116,7 +116,7 @@ def create_ref_model(
ref_model = load_model(
tokenizer, ref_model_args, ref_finetuning_args, is_trainable=False, add_valuehead=add_valuehead
)
logger.info("Created reference model from {}".format(finetuning_args.ref_model))
logger.info_rank0(f"Created reference model from {finetuning_args.ref_model}")
else:
if finetuning_args.finetuning_type == "lora":
ref_model = None
@ -127,7 +127,7 @@ def create_ref_model(
ref_model = load_model(
tokenizer, ref_model_args, ref_finetuning_args, is_trainable=False, add_valuehead=add_valuehead
)
logger.info("Created reference model from the model itself.")
logger.info_rank0("Created reference model from the model itself.")
return ref_model
@ -140,7 +140,7 @@ def create_reward_model(
"""
if finetuning_args.reward_model_type == "api":
assert finetuning_args.reward_model.startswith("http"), "Please provide full url."
logger.info("Use reward server {}".format(finetuning_args.reward_model))
logger.info_rank0(f"Use reward server {finetuning_args.reward_model}")
return finetuning_args.reward_model
elif finetuning_args.reward_model_type == "lora":
model.pretrained_model.load_adapter(finetuning_args.reward_model, "reward")
@ -157,7 +157,7 @@ def create_reward_model(
model.register_buffer(
"default_head_bias", torch.zeros_like(vhead_params["v_head.summary.bias"]), persistent=False
)
logger.info("Loaded adapter weights of reward model from {}".format(finetuning_args.reward_model))
logger.info_rank0(f"Loaded adapter weights of reward model from {finetuning_args.reward_model}")
return None
else:
reward_model_args = ModelArguments.copyfrom(
@ -171,8 +171,8 @@ def create_reward_model(
reward_model = load_model(
tokenizer, reward_model_args, reward_finetuning_args, is_trainable=False, add_valuehead=True
)
logger.info("Loaded full weights of reward model from {}".format(finetuning_args.reward_model))
logger.warning("Please ensure the ppo model and reward model share SAME tokenizer and vocabulary.")
logger.info_rank0(f"Loaded full weights of reward model from {finetuning_args.reward_model}")
logger.warning_rank0("Please ensure the ppo model and reward model share SAME tokenizer and vocabulary.")
return reward_model
@ -231,7 +231,7 @@ def _create_galore_optimizer(
elif training_args.optim == "adafactor":
optim_class = GaLoreAdafactor
else:
raise NotImplementedError("Unknow optim: {}".format(training_args.optim))
raise NotImplementedError(f"Unknow optim: {training_args.optim}")
if finetuning_args.galore_layerwise:
if training_args.gradient_accumulation_steps != 1:
@ -265,7 +265,7 @@ def _create_galore_optimizer(
]
optimizer = optim_class(param_groups, **optim_kwargs)
logger.info("Using GaLore optimizer, may cause hanging at the start of training, wait patiently.")
logger.info_rank0("Using GaLore optimizer, may cause hanging at the start of training, wait patiently.")
return optimizer
@ -305,7 +305,7 @@ def _create_loraplus_optimizer(
dict(params=param_dict["embedding"], lr=embedding_lr, weight_decay=training_args.weight_decay),
]
optimizer = optim_class(param_groups, **optim_kwargs)
logger.info("Using LoRA+ optimizer with loraplus lr ratio {:.2f}.".format(finetuning_args.loraplus_lr_ratio))
logger.info_rank0(f"Using LoRA+ optimizer with loraplus lr ratio {finetuning_args.loraplus_lr_ratio:.2f}.")
return optimizer
@ -343,7 +343,7 @@ def _create_badam_optimizer(
verbose=finetuning_args.badam_verbose,
ds_zero3_enabled=is_deepspeed_zero3_enabled(),
)
logger.info(
logger.info_rank0(
f"Using BAdam optimizer with layer-wise update, switch mode is {finetuning_args.badam_switch_mode}, "
f"switch block every {finetuning_args.badam_switch_interval} steps, "
f"default start block is {finetuning_args.badam_start_block}"
@ -362,7 +362,7 @@ def _create_badam_optimizer(
include_embedding=False,
**optim_kwargs,
)
logger.info(
logger.info_rank0(
f"Using BAdam optimizer with ratio-based update, update ratio is {finetuning_args.badam_update_ratio}, "
f"mask mode is {finetuning_args.badam_mask_mode}"
)
@ -391,7 +391,7 @@ def _create_adam_mini_optimizer(
n_heads=num_q_head,
n_kv_heads=num_kv_head,
)
logger.info("Using Adam-mini optimizer.")
logger.info_rank0("Using Adam-mini optimizer.")
return optimizer

26
src/llamafactory/train/tuner.py
View File

@ -20,8 +20,8 @@ import torch
from transformers import PreTrainedModel
from ..data import get_template_and_fix_tokenizer
from ..extras import logging
from ..extras.constants import V_HEAD_SAFE_WEIGHTS_NAME, V_HEAD_WEIGHTS_NAME
from ..extras.logging import get_logger
from ..hparams import get_infer_args, get_train_args
from ..model import load_model, load_tokenizer
from .callbacks import LogCallback
@ -37,7 +37,7 @@ if TYPE_CHECKING:
from transformers import TrainerCallback
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def run_exp(args: Optional[Dict[str, Any]] = None, callbacks: List["TrainerCallback"] = []) -> None:
@ -57,7 +57,7 @@ def run_exp(args: Optional[Dict[str, Any]] = None, callbacks: List["TrainerCallb
elif finetuning_args.stage == "kto":
run_kto(model_args, data_args, training_args, finetuning_args, callbacks)
else:
raise ValueError("Unknown task: {}.".format(finetuning_args.stage))
raise ValueError(f"Unknown task: {finetuning_args.stage}.")
def export_model(args: Optional[Dict[str, Any]] = None) -> None:
@ -91,18 +91,18 @@ def export_model(args: Optional[Dict[str, Any]] = None) -> None:
setattr(model.config, "torch_dtype", output_dtype)
model = model.to(output_dtype)
logger.info("Convert model dtype to: {}.".format(output_dtype))
logger.info_rank0(f"Convert model dtype to: {output_dtype}.")
model.save_pretrained(
save_directory=model_args.export_dir,
max_shard_size="{}GB".format(model_args.export_size),
max_shard_size=f"{model_args.export_size}GB",
safe_serialization=(not model_args.export_legacy_format),
)
if model_args.export_hub_model_id is not None:
model.push_to_hub(
model_args.export_hub_model_id,
token=model_args.hf_hub_token,
max_shard_size="{}GB".format(model_args.export_size),
max_shard_size=f"{model_args.export_size}GB",
safe_serialization=(not model_args.export_legacy_format),
)
@ -117,13 +117,13 @@ def export_model(args: Optional[Dict[str, Any]] = None) -> None:
os.path.join(vhead_path, V_HEAD_SAFE_WEIGHTS_NAME),
os.path.join(model_args.export_dir, V_HEAD_SAFE_WEIGHTS_NAME),
)
logger.info("Copied valuehead to {}.".format(model_args.export_dir))
logger.info_rank0(f"Copied valuehead to {model_args.export_dir}.")
elif os.path.exists(os.path.join(vhead_path, V_HEAD_WEIGHTS_NAME)):
shutil.copy(
os.path.join(vhead_path, V_HEAD_WEIGHTS_NAME),
os.path.join(model_args.export_dir, V_HEAD_WEIGHTS_NAME),
)
logger.info("Copied valuehead to {}.".format(model_args.export_dir))
logger.info_rank0(f"Copied valuehead to {model_args.export_dir}.")
try:
tokenizer.padding_side = "left" # restore padding side
@ -133,11 +133,9 @@ def export_model(args: Optional[Dict[str, Any]] = None) -> None:
tokenizer.push_to_hub(model_args.export_hub_model_id, token=model_args.hf_hub_token)
if processor is not None:
getattr(processor, "image_processor").save_pretrained(model_args.export_dir)
processor.save_pretrained(model_args.export_dir)
if model_args.export_hub_model_id is not None:
getattr(processor, "image_processor").push_to_hub(
model_args.export_hub_model_id, token=model_args.hf_hub_token
)
processor.push_to_hub(model_args.export_hub_model_id, token=model_args.hf_hub_token)
except Exception:
logger.warning("Cannot save tokenizer, please copy the files manually.")
except Exception as e:
logger.warning_rank0(f"Cannot save tokenizer, please copy the files manually: {e}.")

9
src/llamafactory/webui/chatter.py
View File

@ -141,7 +141,14 @@ class WebChatModel(ChatModel):
chatbot[-1][1] = ""
response = ""
for new_text in self.stream_chat(
messages, system, tools, image, video, max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature
messages,
system,
tools,
images=[image] if image else None,
videos=[video] if video else None,
max_new_tokens=max_new_tokens,
top_p=top_p,
temperature=temperature,
):
response += new_text
if tools:

43
src/llamafactory/webui/common.py
View File

@ -19,6 +19,7 @@ from typing import Any, Dict, Optional, Tuple
from yaml import safe_dump, safe_load
from ..extras import logging
from ..extras.constants import (
CHECKPOINT_NAMES,
DATA_CONFIG,
@ -30,8 +31,7 @@ from ..extras.constants import (
VISION_MODELS,
DownloadSource,
)
from ..extras.logging import get_logger
from ..extras.misc import use_modelscope
from ..extras.misc import use_modelscope, use_openmind
from ..extras.packages import is_gradio_available
@ -39,7 +39,7 @@ if is_gradio_available():
import gradio as gr
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
DEFAULT_CACHE_DIR = "cache"
@ -56,7 +56,7 @@ def get_save_dir(*paths: str) -> os.PathLike:
Gets the path to saved model checkpoints.
"""
if os.path.sep in paths[-1]:
logger.warning("Found complex path, some features may be not available.")
logger.warning_rank0("Found complex path, some features may be not available.")
return paths[-1]
paths = (path.replace(" ", "").strip() for path in paths)
@ -75,7 +75,7 @@ def load_config() -> Dict[str, Any]:
Loads user config if exists.
"""
try:
with open(get_config_path(), "r", encoding="utf-8") as f:
with open(get_config_path(), encoding="utf-8") as f:
return safe_load(f)
except Exception:
return {"lang": None, "last_model": None, "path_dict": {}, "cache_dir": None}
@ -109,19 +109,19 @@ def get_model_path(model_name: str) -> str:
use_modelscope()
and path_dict.get(DownloadSource.MODELSCOPE)
and model_path == path_dict.get(DownloadSource.DEFAULT)
): # replace path
): # replace hf path with ms path
model_path = path_dict.get(DownloadSource.MODELSCOPE)
if (
use_openmind()
and path_dict.get(DownloadSource.OPENMIND)
and model_path == path_dict.get(DownloadSource.DEFAULT)
): # replace hf path with om path
model_path = path_dict.get(DownloadSource.OPENMIND)
return model_path
def get_prefix(model_name: str) -> str:
r"""
Gets the prefix of the model name to obtain the model family.
"""
return model_name.split("-")[0]
def get_model_info(model_name: str) -> Tuple[str, str]:
r"""
Gets the necessary information of this model.
@ -137,21 +137,14 @@ def get_template(model_name: str) -> str:
r"""
Gets the template name if the model is a chat model.
"""
if (
model_name
and any(suffix in model_name for suffix in ("-Chat", "-Instruct"))
and get_prefix(model_name) in DEFAULT_TEMPLATE
):
return DEFAULT_TEMPLATE[get_prefix(model_name)]
return "default"
return DEFAULT_TEMPLATE.get(model_name, "default")
def get_visual(model_name: str) -> bool:
r"""
Judges if the model is a vision language model.
"""
return get_prefix(model_name) in VISION_MODELS
return model_name in VISION_MODELS
def list_checkpoints(model_name: str, finetuning_type: str) -> "gr.Dropdown":
@ -179,14 +172,14 @@ def load_dataset_info(dataset_dir: str) -> Dict[str, Dict[str, Any]]:
Loads dataset_info.json.
"""
if dataset_dir == "ONLINE" or dataset_dir.startswith("REMOTE:"):
logger.info("dataset_dir is {}, using online dataset.".format(dataset_dir))
logger.info_rank0(f"dataset_dir is {dataset_dir}, using online dataset.")
return {}
try:
with open(os.path.join(dataset_dir, DATA_CONFIG), "r", encoding="utf-8") as f:
with open(os.path.join(dataset_dir, DATA_CONFIG), encoding="utf-8") as f:
return json.load(f)
except Exception as err:
logger.warning("Cannot open {} due to {}.".format(os.path.join(dataset_dir, DATA_CONFIG), str(err)))
logger.warning_rank0(f"Cannot open {os.path.join(dataset_dir, DATA_CONFIG)} due to {str(err)}.")
return {}

6
src/llamafactory/webui/components/data.py
View File

@ -41,7 +41,7 @@ def next_page(page_index: int, total_num: int) -> int:
def can_preview(dataset_dir: str, dataset: list) -> "gr.Button":
try:
with open(os.path.join(dataset_dir, DATA_CONFIG), "r", encoding="utf-8") as f:
with open(os.path.join(dataset_dir, DATA_CONFIG), encoding="utf-8") as f:
dataset_info = json.load(f)
except Exception:
return gr.Button(interactive=False)
@ -57,7 +57,7 @@ def can_preview(dataset_dir: str, dataset: list) -> "gr.Button":
def _load_data_file(file_path: str) -> List[Any]:
with open(file_path, "r", encoding="utf-8") as f:
with open(file_path, encoding="utf-8") as f:
if file_path.endswith(".json"):
return json.load(f)
elif file_path.endswith(".jsonl"):
@ -67,7 +67,7 @@ def _load_data_file(file_path: str) -> List[Any]:
def get_preview(dataset_dir: str, dataset: list, page_index: int) -> Tuple[int, list, "gr.Column"]:
with open(os.path.join(dataset_dir, DATA_CONFIG), "r", encoding="utf-8") as f:
with open(os.path.join(dataset_dir, DATA_CONFIG), encoding="utf-8") as f:
dataset_info = json.load(f)
data_path = os.path.join(dataset_dir, dataset_info[dataset[0]]["file_name"])

Some files were not shown because too many files have changed in this diff Show More