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[optim] clean apollo (#6645)
* clean apollo code * update readme Former-commit-id: 7a04021d0461caea2c7b82169839340b7f51f463
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README.md
32
README.md
@ -66,7 +66,7 @@ Choose your path:
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- **Various models**: LLaMA, LLaVA, Mistral, Mixtral-MoE, Qwen, Qwen2-VL, Yi, Gemma, Baichuan, ChatGLM, Phi, etc.
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- **Integrated methods**: (Continuous) pre-training, (multimodal) supervised fine-tuning, reward modeling, PPO, DPO, KTO, ORPO, etc.
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- **Scalable resources**: 16-bit full-tuning, freeze-tuning, LoRA and 2/3/4/5/6/8-bit QLoRA via AQLM/AWQ/GPTQ/LLM.int8/HQQ/EETQ.
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- **Advanced algorithms**: [GaLore](https://github.com/jiaweizzhao/GaLore), [BAdam](https://github.com/Ledzy/BAdam), [Adam-mini](https://github.com/zyushun/Adam-mini), DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ, PiSSA and Agent tuning.
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- **Advanced algorithms**: [GaLore](https://github.com/jiaweizzhao/GaLore), [BAdam](https://github.com/Ledzy/BAdam), [APOLLO](https://github.com/zhuhanqing/APOLLO), [Adam-mini](https://github.com/zyushun/Adam-mini), DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ, PiSSA and Agent tuning.
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- **Practical tricks**: [FlashAttention-2](https://github.com/Dao-AILab/flash-attention), [Unsloth](https://github.com/unslothai/unsloth), [Liger Kernel](https://github.com/linkedin/Liger-Kernel), RoPE scaling, NEFTune and rsLoRA.
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- **Experiment monitors**: LlamaBoard, TensorBoard, Wandb, MLflow, SwanLab, etc.
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- **Faster inference**: OpenAI-style API, Gradio UI and CLI with vLLM worker.
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@ -88,18 +88,20 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
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## Changelog
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[25/01/15] We supported fine-tuning the **[MiniCPM-o-2.6](https://huggingface.co/openbmb/MiniCPM-o-2_6)** and **[MiniCPM-V-2.6](https://huggingface.co/openbmb/MiniCPM-V-2_6)** models. Thank [@BUAADreamer](https://github.com/BUAADreamer)'s PR.
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[25/01/15] We supported **[APOLLO](https://arxiv.org/abs/2412.05270)** optimizer. See [examples](examples/README.md) for usage.
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[25/01/15] We supported fine-tuning the **[InternLM3](https://huggingface.co/collections/internlm/)** models. Thank [@hhaAndroid](https://github.com/hhaAndroid)'s PR.
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[25/01/14] We supported fine-tuning the **[MiniCPM-o-2.6](https://huggingface.co/openbmb/MiniCPM-o-2_6)** and **[MiniCPM-V-2.6](https://huggingface.co/openbmb/MiniCPM-V-2_6)** models. Thank [@BUAADreamer](https://github.com/BUAADreamer)'s PR.
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[25/01/14] We supported fine-tuning the **[InternLM3](https://huggingface.co/collections/internlm/)** models. Thank [@hhaAndroid](https://github.com/hhaAndroid)'s PR.
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[25/01/10] We supported fine-tuning the **[Phi-4](https://huggingface.co/microsoft/phi-4)** model.
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<details><summary>Full Changelog</summary>
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[24/12/21] We supported using **[SwanLab](https://github.com/SwanHubX/SwanLab)** for experiment tracking and visualization. See [this section](#use-swanlab-logger) for details.
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[24/11/27] We supported fine-tuning the **[Skywork-o1](https://huggingface.co/Skywork/Skywork-o1-Open-Llama-3.1-8B)** model and the **[OpenO1](https://huggingface.co/datasets/O1-OPEN/OpenO1-SFT)** dataset.
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<details><summary>Full Changelog</summary>
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[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.
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[24/09/19] We supported fine-tuning the **[Qwen2.5](https://qwenlm.github.io/blog/qwen2.5/)** models.
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@ -378,15 +380,15 @@ huggingface-cli login
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\* *estimated*
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| Method | Bits | 7B | 13B | 30B | 70B | 110B | 8x7B | 8x22B |
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| ----------------- | ---- | ----- | ----- | ----- | ------ | ------ | ----- | ------ |
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| Full | AMP | 120GB | 240GB | 600GB | 1200GB | 2000GB | 900GB | 2400GB |
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| Full | 16 | 60GB | 120GB | 300GB | 600GB | 900GB | 400GB | 1200GB |
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| Freeze | 16 | 20GB | 40GB | 80GB | 200GB | 360GB | 160GB | 400GB |
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| LoRA/GaLore/BAdam | 16 | 16GB | 32GB | 64GB | 160GB | 240GB | 120GB | 320GB |
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| QLoRA | 8 | 10GB | 20GB | 40GB | 80GB | 140GB | 60GB | 160GB |
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| QLoRA | 4 | 6GB | 12GB | 24GB | 48GB | 72GB | 30GB | 96GB |
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| QLoRA | 2 | 4GB | 8GB | 16GB | 24GB | 48GB | 18GB | 48GB |
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| Method | Bits | 7B | 13B | 30B | 70B | 110B | 8x7B | 8x22B |
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| ------------------------ | ---- | ----- | ----- | ----- | ------ | ------ | ----- | ------ |
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| Full | 32 | 120GB | 240GB | 600GB | 1200GB | 2000GB | 900GB | 2400GB |
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| Full | 16 | 60GB | 120GB | 300GB | 600GB | 900GB | 400GB | 1200GB |
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| Freeze | 16 | 20GB | 40GB | 80GB | 200GB | 360GB | 160GB | 400GB |
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| LoRA/GaLore/APOLLO/BAdam | 16 | 16GB | 32GB | 64GB | 160GB | 240GB | 120GB | 320GB |
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| QLoRA | 8 | 10GB | 20GB | 40GB | 80GB | 140GB | 60GB | 160GB |
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| QLoRA | 4 | 6GB | 12GB | 24GB | 48GB | 72GB | 30GB | 96GB |
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| QLoRA | 2 | 4GB | 8GB | 16GB | 24GB | 48GB | 18GB | 48GB |
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## Getting Started
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@ -401,7 +403,7 @@ cd LLaMA-Factory
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pip install -e ".[torch,metrics]"
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```
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Extra dependencies available: torch, torch-npu, metrics, deepspeed, liger-kernel, bitsandbytes, hqq, eetq, gptq, awq, aqlm, vllm, galore, badam, adam-mini, qwen, modelscope, openmind, swanlab, quality
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Extra dependencies available: torch, torch-npu, metrics, deepspeed, liger-kernel, bitsandbytes, hqq, eetq, gptq, awq, aqlm, vllm, galore, apollo, badam, adam-mini, qwen, minicpm_v, modelscope, openmind, swanlab, quality
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> [!TIP]
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> Use `pip install --no-deps -e .` to resolve package conflicts.
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34
README_zh.md
34
README_zh.md
@ -67,7 +67,7 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
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- **多种模型**：LLaMA、LLaVA、Mistral、Mixtral-MoE、Qwen、Qwen2-VL、Yi、Gemma、Baichuan、ChatGLM、Phi 等等。
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- **集成方法**：（增量）预训练、（多模态）指令监督微调、奖励模型训练、PPO 训练、DPO 训练、KTO 训练、ORPO 训练等等。
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- **多种精度**：16 比特全参数微调、冻结微调、LoRA 微调和基于 AQLM/AWQ/GPTQ/LLM.int8/HQQ/EETQ 的 2/3/4/5/6/8 比特 QLoRA 微调。
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- **先进算法**：[GaLore](https://github.com/jiaweizzhao/GaLore)、[BAdam](https://github.com/Ledzy/BAdam)、[Adam-mini](https://github.com/zyushun/Adam-mini)、DoRA、LongLoRA、LLaMA Pro、Mixture-of-Depths、LoRA+、LoftQ、PiSSA 和 Agent 微调。
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- **先进算法**：[GaLore](https://github.com/jiaweizzhao/GaLore)、[BAdam](https://github.com/Ledzy/BAdam)、[APOLLO](https://github.com/zhuhanqing/APOLLO)、[Adam-mini](https://github.com/zyushun/Adam-mini)、DoRA、LongLoRA、LLaMA Pro、Mixture-of-Depths、LoRA+、LoftQ、PiSSA 和 Agent 微调。
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- **实用技巧**：[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)、[Unsloth](https://github.com/unslothai/unsloth)、[Liger Kernel](https://github.com/linkedin/Liger-Kernel)、RoPE scaling、NEFTune 和 rsLoRA。
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- **实验监控**：LlamaBoard、TensorBoard、Wandb、MLflow、SwanLab 等等。
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- **极速推理**：基于 vLLM 的 OpenAI 风格 API、浏览器界面和命令行接口。
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@ -89,18 +89,20 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
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## 更新日志
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[25/01/15] 我们支持了 **[MiniCPM-o-2.6](https://huggingface.co/openbmb/MiniCPM-o-2_6)** 和 **[MiniCPM-V-2.6](https://huggingface.co/openbmb/MiniCPM-V-2_6)** 模型的微调。 感谢 [@BUAADreamer](https://github.com/BUAADreamer) 的 PR.
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[25/01/15] 我们支持了 **[APOLLO](https://arxiv.org/abs/2412.05270)** 优化器。详细用法请参照 [examples](examples/README_zh.md)。
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[25/01/15] 我们支持了 **[InternLM3](https://huggingface.co/collections/internlm/)** 模型的微调。感谢 [@hhaAndroid](https://github.com/hhaAndroid) 的 PR。
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[25/01/14] 我们支持了 **[MiniCPM-o-2.6](https://huggingface.co/openbmb/MiniCPM-o-2_6)** 和 **[MiniCPM-V-2.6](https://huggingface.co/openbmb/MiniCPM-V-2_6)** 模型的微调。 感谢 [@BUAADreamer](https://github.com/BUAADreamer) 的 PR.
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[25/01/14] 我们支持了 **[InternLM3](https://huggingface.co/collections/internlm/)** 模型的微调。感谢 [@hhaAndroid](https://github.com/hhaAndroid) 的 PR。
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[25/01/10] 我们支持了 **[Phi-4](https://huggingface.co/microsoft/phi-4)** 模型的微调。
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<details><summary>展开日志</summary>
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[24/12/21] 我们支持了使用 **[SwanLab](https://github.com/SwanHubX/SwanLab)** 跟踪与可视化实验。详细用法请参考 [此部分](#使用-swanlab-面板)。
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[24/11/27] 我们支持了 **[Skywork-o1](https://huggingface.co/Skywork/Skywork-o1-Open-Llama-3.1-8B)** 模型的微调和 **[OpenO1](https://huggingface.co/datasets/O1-OPEN/OpenO1-SFT)** 数据集。
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<details><summary>展开日志</summary>
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[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)。
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[24/09/19] 我们支持了 **[Qwen2.5](https://qwenlm.github.io/blog/qwen2.5/)** 模型的微调。
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@ -246,7 +248,7 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
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## 训练方法
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| 方法 | 全参数训练 | 部分参数训练 | LoRA | QLoRA |
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| ---------------------- | ------------------ | ------------------ | ------------------ | ------------------ |
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| --------------------- | ------------------ | ------------------ | ------------------ | ------------------ |
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| 预训练 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
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| 指令监督微调 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
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| 奖励模型训练 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
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@ -379,15 +381,15 @@ huggingface-cli login
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\* *估算值*
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| 方法 | 精度 | 7B | 13B | 30B | 70B | 110B | 8x7B | 8x22B |
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| ----------------- | ---- | ----- | ----- | ----- | ------ | ------ | ----- | ------ |
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| Full | AMP | 120GB | 240GB | 600GB | 1200GB | 2000GB | 900GB | 2400GB |
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| Full | 16 | 60GB | 120GB | 300GB | 600GB | 900GB | 400GB | 1200GB |
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| Freeze | 16 | 20GB | 40GB | 80GB | 200GB | 360GB | 160GB | 400GB |
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| LoRA/GaLore/BAdam | 16 | 16GB | 32GB | 64GB | 160GB | 240GB | 120GB | 320GB |
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| QLoRA | 8 | 10GB | 20GB | 40GB | 80GB | 140GB | 60GB | 160GB |
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| QLoRA | 4 | 6GB | 12GB | 24GB | 48GB | 72GB | 30GB | 96GB |
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| QLoRA | 2 | 4GB | 8GB | 16GB | 24GB | 48GB | 18GB | 48GB |
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| 方法 | 精度 | 7B | 13B | 30B | 70B | 110B | 8x7B | 8x22B |
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| ------------------------ | ---- | ----- | ----- | ----- | ------ | ------ | ----- | ------ |
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| Full | 32 | 120GB | 240GB | 600GB | 1200GB | 2000GB | 900GB | 2400GB |
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| Full | 16 | 60GB | 120GB | 300GB | 600GB | 900GB | 400GB | 1200GB |
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| Freeze | 16 | 20GB | 40GB | 80GB | 200GB | 360GB | 160GB | 400GB |
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| LoRA/GaLore/APOLLO/BAdam | 16 | 16GB | 32GB | 64GB | 160GB | 240GB | 120GB | 320GB |
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| QLoRA | 8 | 10GB | 20GB | 40GB | 80GB | 140GB | 60GB | 160GB |
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| QLoRA | 4 | 6GB | 12GB | 24GB | 48GB | 72GB | 30GB | 96GB |
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| QLoRA | 2 | 4GB | 8GB | 16GB | 24GB | 48GB | 18GB | 48GB |
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## 如何使用
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@ -402,7 +404,7 @@ cd LLaMA-Factory
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pip install -e ".[torch,metrics]"
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```
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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、swanlab、quality
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可选的额外依赖项：torch、torch-npu、metrics、deepspeed、liger-kernel、bitsandbytes、hqq、eetq、gptq、awq、aqlm、vllm、galore、apollo、badam、adam-mini、qwen、minicpm_v、modelscope、openmind、swanlab、quality
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> [!TIP]
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> 遇到包冲突时，可使用 `pip install --no-deps -e .` 解决。
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@ -204,6 +204,12 @@ llamafactory-cli api examples/inference/llama3_lora_sft.yaml
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llamafactory-cli train examples/extras/galore/llama3_full_sft.yaml
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```
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#### Full-Parameter Fine-Tuning using APOLLO
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```bash
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llamafactory-cli train examples/extras/apollo/llama3_full_sft.yaml
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```
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#### Full-Parameter Fine-Tuning using BAdam
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```bash
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@ -204,6 +204,12 @@ llamafactory-cli api examples/inference/llama3_lora_sft.yaml
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llamafactory-cli train examples/extras/galore/llama3_full_sft.yaml
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```
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#### 使用 APOLLO 进行全参数训练
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```bash
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llamafactory-cli train examples/extras/apollo/llama3_full_sft.yaml
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```
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#### 使用 BAdam 进行全参数训练
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```bash
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@ -7,8 +7,8 @@ stage: sft
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do_train: true
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finetuning_type: full
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use_apollo: true
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apollo_layerwise: true
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apollo_target: mlp,self_attn
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apollo_layerwise: true # choices: [true, false], use false for DDP training
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apollo_target: all
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apollo_rank: 128
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apollo_scale: 32.0
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apollo_scale_type: channel
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@ -22,7 +22,7 @@ overwrite_cache: true
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preprocessing_num_workers: 16
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### output
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output_dir: saves/llama3-8b/apollo_full-scale32/sft
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output_dir: saves/llama3-8b/full/sft
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logging_steps: 10
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save_steps: 500
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plot_loss: true
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@ -30,7 +30,7 @@ overwrite_output_dir: true
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### train
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per_device_train_batch_size: 1
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gradient_accumulation_steps: 1
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gradient_accumulation_steps: 1 # use 1 for layerwise apollo
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learning_rate: 1.0e-5
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num_train_epochs: 3.0
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lr_scheduler_type: cosine
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@ -7,8 +7,8 @@ stage: sft
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do_train: true
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finetuning_type: full
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use_galore: true
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galore_layerwise: true
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galore_target: mlp,self_attn
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galore_layerwise: true # choices: [true, false], use false for DDP training
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galore_target: all
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galore_rank: 128
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galore_scale: 2.0
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@ -29,7 +29,7 @@ overwrite_output_dir: true
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### train
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per_device_train_batch_size: 1
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gradient_accumulation_steps: 1
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gradient_accumulation_steps: 1 # use 1 for layerwise galore
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learning_rate: 1.0e-5
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num_train_epochs: 3.0
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lr_scheduler_type: cosine
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@ -49,9 +49,11 @@ def is_fastapi_available():
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def is_galore_available():
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return _is_package_available("galore_torch")
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def is_apollo_available():
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return _is_package_available("apollo_torch")
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def is_gradio_available():
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return _is_package_available("gradio")
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@ -286,7 +286,7 @@ class ApolloArguments:
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default="random",
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metadata={"help": "Type of APOLLO low-rank projection algorithm (svd or random)."},
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)
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apollo_proj_type: Literal["std", "right", "left",] = field(
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apollo_proj_type: Literal["std", "right", "left"] = field(
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default="std",
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metadata={"help": "Type of APOLLO projection."},
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)
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@ -475,17 +475,11 @@ class FinetuningArguments(
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if self.use_llama_pro and self.finetuning_type == "full":
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raise ValueError("`use_llama_pro` is only valid for Freeze or LoRA training.")
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if self.finetuning_type == "lora" and (self.use_galore or self.use_badam or self.use_apollo):
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raise ValueError("Cannot use LoRA with GaLore or BAdam together.")
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if self.finetuning_type == "lora" and (self.use_galore or self.use_apollo or self.use_badam):
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raise ValueError("Cannot use LoRA with GaLore, APOLLO or BAdam together.")
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if self.use_galore and self.use_badam:
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raise ValueError("Cannot use GaLore with BAdam together.")
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if self.use_galore and self.use_apollo:
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raise ValueError("Cannot use GaLore with APOLLO together.")
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if self.use_badam and self.use_apollo:
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raise ValueError("Cannot use BAdam with APOLLO together.")
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if int(self.use_galore) + int(self.use_apollo) + (self.use_badam) > 1:
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raise ValueError("Cannot use GaLore, APOLLO or BAdam together.")
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if self.pissa_init and (self.stage in ["ppo", "kto"] or self.use_ref_model):
|
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raise ValueError("Cannot use PiSSA for current training stage.")
|
||||
|
||||
@ -258,31 +258,21 @@ def get_train_args(args: Optional[Union[Dict[str, Any], List[str]]] = None) -> _
|
||||
if is_deepspeed_zero3_enabled():
|
||||
raise ValueError("`pure_bf16` is incompatible with DeepSpeed ZeRO-3.")
|
||||
|
||||
if (
|
||||
finetuning_args.use_galore
|
||||
and finetuning_args.galore_layerwise
|
||||
and training_args.parallel_mode == ParallelMode.DISTRIBUTED
|
||||
):
|
||||
raise ValueError("Distributed training does not support layer-wise GaLore.")
|
||||
if training_args.parallel_mode == ParallelMode.DISTRIBUTED:
|
||||
if finetuning_args.use_galore and finetuning_args.galore_layerwise:
|
||||
raise ValueError("Distributed training does not support layer-wise GaLore.")
|
||||
|
||||
if (
|
||||
finetuning_args.use_apollo
|
||||
and finetuning_args.apollo_layerwise
|
||||
and training_args.parallel_mode == ParallelMode.DISTRIBUTED
|
||||
):
|
||||
raise ValueError("Distributed training does not support layer-wise APOLLO.")
|
||||
if finetuning_args.use_apollo and finetuning_args.apollo_layerwise:
|
||||
raise ValueError("Distributed training does not support layer-wise APOLLO.")
|
||||
|
||||
if finetuning_args.use_badam and training_args.parallel_mode == ParallelMode.DISTRIBUTED:
|
||||
if finetuning_args.badam_mode == "ratio":
|
||||
raise ValueError("Radio-based BAdam does not yet support distributed training, use layer-wise BAdam.")
|
||||
elif not is_deepspeed_zero3_enabled():
|
||||
raise ValueError("Layer-wise BAdam only supports DeepSpeed ZeRO-3 training.")
|
||||
if finetuning_args.use_badam:
|
||||
if finetuning_args.badam_mode == "ratio":
|
||||
raise ValueError("Radio-based BAdam does not yet support distributed training, use layer-wise BAdam.")
|
||||
elif not is_deepspeed_zero3_enabled():
|
||||
raise ValueError("Layer-wise BAdam only supports DeepSpeed ZeRO-3 training.")
|
||||
|
||||
if finetuning_args.use_galore and training_args.deepspeed is not None:
|
||||
raise ValueError("GaLore is incompatible with DeepSpeed yet.")
|
||||
|
||||
if finetuning_args.use_apollo and training_args.deepspeed is not None:
|
||||
raise ValueError("APOLLO is incompatible with DeepSpeed yet.")
|
||||
if training_args.deepspeed is not None and (finetuning_args.use_galore or finetuning_args.use_apollo):
|
||||
raise ValueError("GaLore and APOLLO are incompatible with DeepSpeed yet.")
|
||||
|
||||
if model_args.infer_backend == "vllm":
|
||||
raise ValueError("vLLM backend is only available for API, CLI and Web.")
|
||||
@ -314,14 +304,13 @@ def get_train_args(args: Optional[Union[Dict[str, Any], List[str]]] = None) -> _
|
||||
if training_args.do_train and (not training_args.fp16) and (not training_args.bf16):
|
||||
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:
|
||||
if (
|
||||
training_args.do_train
|
||||
and (finetuning_args.use_galore or finetuning_args.use_apollo)
|
||||
and not finetuning_args.pure_bf16
|
||||
):
|
||||
logger.warning_rank0(
|
||||
"Using GaLore with mixed precision training may significantly increases GPU memory usage."
|
||||
)
|
||||
|
||||
if training_args.do_train and finetuning_args.use_apollo and not finetuning_args.pure_bf16:
|
||||
logger.warning_rank0(
|
||||
"Using APOLLO with mixed precision training may significantly increases GPU memory usage."
|
||||
"Using GaLore or APOLLO with mixed precision training may significantly increases GPU memory usage."
|
||||
)
|
||||
|
||||
if (not training_args.do_train) and model_args.quantization_bit is not None:
|
||||
@ -397,7 +386,6 @@ def get_train_args(args: Optional[Union[Dict[str, Any], List[str]]] = None) -> _
|
||||
str(model_args.compute_dtype),
|
||||
)
|
||||
)
|
||||
|
||||
transformers.set_seed(training_args.seed)
|
||||
|
||||
return model_args, data_args, training_args, finetuning_args, generating_args
|
||||
|
||||
@ -27,7 +27,7 @@ logger = logging.get_logger(__name__)
|
||||
|
||||
def find_all_linear_modules(model: "PreTrainedModel", freeze_vision_tower: bool) -> List[str]:
|
||||
r"""
|
||||
Finds all available modules to apply lora or galore or apollo.
|
||||
Finds all available modules to apply LoRA, GaLore or APOLLO.
|
||||
"""
|
||||
model_type = getattr(model.config, "model_type", None)
|
||||
forbidden_modules = {"lm_head"}
|
||||
|
||||
@ -32,7 +32,7 @@ from typing_extensions import override
|
||||
|
||||
from ..extras import logging
|
||||
from ..extras.constants import IGNORE_INDEX
|
||||
from ..extras.packages import is_galore_available, is_ray_available, is_apollo_available
|
||||
from ..extras.packages import is_apollo_available, is_galore_available, is_ray_available
|
||||
from ..hparams import FinetuningArguments, ModelArguments
|
||||
from ..model import find_all_linear_modules, load_model, load_tokenizer, load_valuehead_params
|
||||
|
||||
@ -40,9 +40,11 @@ from ..model import find_all_linear_modules, load_model, load_tokenizer, load_va
|
||||
if is_galore_available():
|
||||
from galore_torch import GaLoreAdafactor, GaLoreAdamW, GaLoreAdamW8bit # type: ignore
|
||||
|
||||
|
||||
if is_apollo_available():
|
||||
from apollo_torch import APOLLOAdamW # type: ignore
|
||||
|
||||
|
||||
if is_ray_available():
|
||||
from ray.train import RunConfig, ScalingConfig
|
||||
from ray.train.torch import TorchTrainer
|
||||
@ -240,9 +242,10 @@ def _create_galore_optimizer(
|
||||
elif training_args.optim == "adafactor":
|
||||
optim_class = GaLoreAdafactor
|
||||
else:
|
||||
raise NotImplementedError(f"Unknow optim: {training_args.optim}")
|
||||
raise NotImplementedError(f"Unknown optim: {training_args.optim}.")
|
||||
|
||||
if finetuning_args.galore_layerwise:
|
||||
logger.warning_rank0("The displayed gradient norm will be all zeros in layerwise GaLore.")
|
||||
if training_args.gradient_accumulation_steps != 1:
|
||||
raise ValueError("Per-layer GaLore does not support gradient accumulation.")
|
||||
|
||||
@ -274,9 +277,13 @@ def _create_galore_optimizer(
|
||||
]
|
||||
optimizer = optim_class(param_groups, **optim_kwargs)
|
||||
|
||||
logger.info_rank0("Using GaLore optimizer, may cause hanging at the start of training, wait patiently.")
|
||||
logger.info_rank0(
|
||||
f"Using GaLore optimizer with args: {galore_kwargs}. "
|
||||
"It may cause hanging at the start of training, wait patiently."
|
||||
)
|
||||
return optimizer
|
||||
|
||||
|
||||
def _create_apollo_optimizer(
|
||||
model: "PreTrainedModel",
|
||||
training_args: "TrainingArguments",
|
||||
@ -304,11 +311,9 @@ def _create_apollo_optimizer(
|
||||
"scale_front": finetuning_args.apollo_scale_front,
|
||||
}
|
||||
|
||||
print(apollo_kwargs)
|
||||
|
||||
id_apollo_params = {id(param) for param in apollo_params}
|
||||
decay_params, nodecay_params = [], [] # they are non-galore parameters
|
||||
trainable_params: List["torch.nn.Parameter"] = [] # galore_params + decay_params + nodecay_params
|
||||
decay_params, nodecay_params = [], [] # they are non-apollo parameters
|
||||
trainable_params: List["torch.nn.Parameter"] = [] # apollo_params + decay_params + nodecay_params
|
||||
decay_param_names = _get_decay_parameter_names(model)
|
||||
for name, param in model.named_parameters():
|
||||
if param.requires_grad:
|
||||
@ -324,9 +329,10 @@ def _create_apollo_optimizer(
|
||||
if training_args.optim == "adamw_torch":
|
||||
optim_class = APOLLOAdamW
|
||||
else:
|
||||
raise NotImplementedError(f"Unknow optim: {training_args.optim}")
|
||||
raise NotImplementedError(f"Unknown optim: {training_args.optim}.")
|
||||
|
||||
if finetuning_args.apollo_layerwise:
|
||||
logger.warning_rank0("The displayed gradient norm will be all zeros in layerwise APOLLO.")
|
||||
if training_args.gradient_accumulation_steps != 1:
|
||||
raise ValueError("Per-layer APOLLO does not support gradient accumulation.")
|
||||
|
||||
@ -337,7 +343,7 @@ def _create_apollo_optimizer(
|
||||
for param in decay_params:
|
||||
param_groups = [dict(params=[param], weight_decay=training_args.weight_decay)]
|
||||
optimizer_dict[param] = optim_class(param_groups, **optim_kwargs)
|
||||
for param in apollo_params: # galore params have weight decay
|
||||
for param in apollo_params: # apollo params have weight decay
|
||||
param_groups = [dict(params=[param], weight_decay=training_args.weight_decay, **apollo_kwargs)]
|
||||
optimizer_dict[param] = optim_class(param_groups, **optim_kwargs)
|
||||
|
||||
@ -358,7 +364,7 @@ def _create_apollo_optimizer(
|
||||
]
|
||||
optimizer = optim_class(param_groups, **optim_kwargs)
|
||||
|
||||
logger.info_rank0("Using APOLLO optimizer.")
|
||||
logger.info_rank0(f"Using APOLLO optimizer with args: {apollo_kwargs}.")
|
||||
return optimizer
|
||||
|
||||
|
||||
|
||||
@ -234,8 +234,8 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
|
||||
with gr.Row():
|
||||
use_galore = gr.Checkbox()
|
||||
galore_rank = gr.Slider(minimum=1, maximum=1024, value=16, step=1)
|
||||
galore_update_interval = gr.Slider(minimum=1, maximum=1024, value=200, step=1)
|
||||
galore_scale = gr.Slider(minimum=0, maximum=1, value=0.25, step=0.01)
|
||||
galore_update_interval = gr.Slider(minimum=1, maximum=2048, value=200, step=1)
|
||||
galore_scale = gr.Slider(minimum=0, maximum=100, value=2.0, step=0.1)
|
||||
galore_target = gr.Textbox(value="all")
|
||||
|
||||
input_elems.update({use_galore, galore_rank, galore_update_interval, galore_scale, galore_target})
|
||||
@ -254,9 +254,10 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
|
||||
with gr.Row():
|
||||
use_apollo = gr.Checkbox()
|
||||
apollo_rank = gr.Slider(minimum=1, maximum=1024, value=16, step=1)
|
||||
apollo_update_interval = gr.Slider(minimum=1, maximum=1024, value=200, step=1)
|
||||
apollo_scale = gr.Slider(minimum=0, maximum=1, value=0.25, step=0.01)
|
||||
apollo_update_interval = gr.Slider(minimum=1, maximum=2048, value=200, step=1)
|
||||
apollo_scale = gr.Slider(minimum=0, maximum=100, value=32.0, step=0.1)
|
||||
apollo_target = gr.Textbox(value="all")
|
||||
|
||||
input_elems.update({use_apollo, apollo_rank, apollo_update_interval, apollo_scale, apollo_target})
|
||||
elem_dict.update(
|
||||
dict(
|
||||
|
||||
@ -1162,19 +1162,19 @@ LOCALES = {
|
||||
"use_galore": {
|
||||
"en": {
|
||||
"label": "Use GaLore",
|
||||
"info": "Enable gradient low-Rank projection.",
|
||||
"info": "Use GaLore optimizer.",
|
||||
},
|
||||
"ru": {
|
||||
"label": "Использовать GaLore",
|
||||
"info": "Включить проекцию градиента на низкоранговое пространство.",
|
||||
"info": "Используйте оптимизатор GaLore.",
|
||||
},
|
||||
"zh": {
|
||||
"label": "使用 GaLore",
|
||||
"info": "使用梯度低秩投影。",
|
||||
"info": "使用 GaLore 优化器。",
|
||||
},
|
||||
"ko": {
|
||||
"label": "GaLore 사용",
|
||||
"info": "그레디언트 로우 랭크 프로젝션을 활성화합니다.",
|
||||
"info": "GaLore 최적화를 사용하세요.",
|
||||
},
|
||||
},
|
||||
"galore_rank": {
|
||||
@ -1266,19 +1266,19 @@ LOCALES = {
|
||||
"use_apollo": {
|
||||
"en": {
|
||||
"label": "Use APOLLO",
|
||||
"info": "Enable gradient low-Rank projection.",
|
||||
"info": "Use APOLLO optimizer.",
|
||||
},
|
||||
"ru": {
|
||||
"label": "Использовать APOLLO",
|
||||
"info": "Включить проекцию градиента на низкоранговое пространство.",
|
||||
"info": "Используйте оптимизатор APOLLO.",
|
||||
},
|
||||
"zh": {
|
||||
"label": "使用 APOLLO",
|
||||
"info": "使用梯度低秩投影。",
|
||||
"info": "使用 APOLLO 优化器。",
|
||||
},
|
||||
"ko": {
|
||||
"label": "APOLLO 사용",
|
||||
"info": "그레디언트 로우 랭크 프로젝션을 활성화합니다.",
|
||||
"info": "APOLLO 최적화를 사용하세요.",
|
||||
},
|
||||
},
|
||||
"apollo_rank": {
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user