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[trainer] Add Muon Optimizer (#7749)

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Co-authored-by: hoshi-hiyouga <hiyouga@buaa.edu.cn>
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Juanxi Tian 2025-04-21 23:38:37 +08:00 committed by GitHub
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23
README.md
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@ -77,7 +77,7 @@ Choose your path:
- **Various models**: LLaMA, LLaVA, Mistral, Mixtral-MoE, Qwen, Qwen2-VL, DeepSeek, Yi, Gemma, ChatGLM, Phi, etc.
- **Integrated methods**: (Continuous) pre-training, (multimodal) supervised fine-tuning, reward modeling, PPO, DPO, KTO, ORPO, etc.
- **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.
- **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 and PiSSA.
- **Advanced algorithms**: [Muon](https://github.com/KellerJordan/Muon), [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 and PiSSA.
- **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.
- **Wide tasks**: Multi-turn dialogue, tool using, image understanding, visual grounding, video recognition, audio understanding, etc.
- **Experiment monitors**: LlamaBoard, TensorBoard, Wandb, MLflow, [SwanLab](https://github.com/SwanHubX/SwanLab), etc.
@ -107,7 +107,7 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
## Changelog
[25/04/16] We supported fine-tuning the **[InternVL3](https://huggingface.co/OpenGVLab/InternVL3-8B)** model. See [PR #7258](https://github.com/hiyouga/LLaMA-Factory/pull/7258) to get started.
[25/04/16] We supported **[Muon](https://github.com/KellerJordan/Muon)** optimizer. See [examples](examples/README.md) for usage. Thank [Juanxi Tian](https://tianshijing.github.io)'s PR.
[25/04/14] We supported fine-tuning the **[GLM-Z1](https://huggingface.co/THUDM/GLM-Z1-9B-0414)** and **[Kimi-VL](https://huggingface.co/moonshotai/Kimi-VL-A3B-Instruct)** models.
@ -115,14 +115,14 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
[25/03/31] We supported fine-tuning the **[Qwen2.5 Omni](https://qwenlm.github.io/blog/qwen2.5-omni/)** model. See [PR #7537](https://github.com/hiyouga/LLaMA-Factory/pull/7537) to get started.
<details><summary>Full Changelog</summary>
[25/03/15] We supported **[SGLang](https://github.com/sgl-project/sglang)** as inference backend. Try `infer_backend: sglang` to accelerate inference.
[25/03/12] We supported fine-tuning the **[Gemma 3](https://huggingface.co/blog/gemma3)** model.
[25/02/24] Announcing **[EasyR1](https://github.com/hiyouga/EasyR1)**, an efficient, scalable and multi-modality RL training framework for efficient GRPO training.
<details><summary>Full Changelog</summary>
[25/02/11] We supported saving the **[Ollama](https://github.com/ollama/ollama)** modelfile when exporting the model checkpoints. See [examples](examples/README.md) for usage.
[25/02/05] We supported fine-tuning the **[Qwen2-Audio](Qwen/Qwen2-Audio-7B-Instruct)** and **[MiniCPM-o-2.6](https://huggingface.co/openbmb/MiniCPM-o-2_6)** on audio understanding tasks.
@ -245,11 +245,11 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
| [Gemma 3](https://huggingface.co/google) | 1B/4B/12B/27B | gemma3/gemma (1B) |
| [GLM-4/GLM-4-0414/GLM-Z1](https://huggingface.co/THUDM) | 9B/32B | glm4 |
| [GPT-2](https://huggingface.co/openai-community) | 0.1B/0.4B/0.8B/1.5B | - |
| [Granite 3.0-3.3](https://huggingface.co/ibm-granite) | 1B/2B/3B/8B | granite3 |
| [Granite 3.0-3.1](https://huggingface.co/ibm-granite) | 1B/2B/3B/8B | granite3 |
| [Hunyuan](https://huggingface.co/tencent/) | 7B | hunyuan |
| [Index](https://huggingface.co/IndexTeam) | 1.9B | index |
| [InternLM 2-3](https://huggingface.co/internlm) | 7B/8B/20B | intern2 |
| [InternVL 2.5-3](https://huggingface.co/OpenGVLab)\*\* | 1B/2B/4B/8B/9B/14B/26B/38B/78B | intern_vl |
| [InternVL2_5-3](https://huggingface.co/OpenGVLab/InternVL) | 1B/2B/4B/8B/9B/14B/26B/38B/78B | intern_vl |
| [Kimi-VL](https://huggingface.co/moonshotai) | 16B | kimi_vl |
| [Llama](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | - |
| [Llama 2](https://huggingface.co/meta-llama) | 7B/13B/70B | llama2 |
@ -417,11 +417,11 @@ huggingface-cli login
| Mandatory | Minimum | Recommend |
| ------------ | ------- | --------- |
| python | 3.9 | 3.10 |
| torch | 2.0.0 | 2.6.0 |
| transformers | 4.45.0 | 4.50.0 |
| torch | 1.13.1 | 2.6.0 |
| transformers | 4.41.2 | 4.50.0 |
| datasets | 2.16.0 | 3.2.0 |
| accelerate | 0.34.0 | 1.2.1 |
| peft | 0.14.0 | 0.15.1 |
| peft | 0.14.0 | 0.15.0 |
| trl | 0.8.6 | 0.9.6 |
| Optional | Minimum | Recommend |
@ -430,7 +430,7 @@ huggingface-cli login
| deepspeed | 0.10.0 | 0.16.4 |
| bitsandbytes | 0.39.0 | 0.43.1 |
| vllm | 0.4.3 | 0.8.2 |
| flash-attn | 2.5.6 | 2.7.2 |
| flash-attn | 2.3.0 | 2.7.2 |
### Hardware Requirement
@ -458,7 +458,7 @@ 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, sglang, galore, apollo, badam, adam-mini, qwen, minicpm_v, modelscope, openmind, swanlab, quality
Extra dependencies available: torch, torch-npu, metrics, deepspeed, liger-kernel, bitsandbytes, hqq, eetq, gptq, awq, aqlm, vllm, sglang, muon, galore, apollo, badam, adam-mini, qwen, minicpm_v, modelscope, openmind, swanlab, quality
> [!TIP]
> Use `pip install --no-deps -e .` to resolve package conflicts.
@ -519,7 +519,6 @@ source /usr/local/Ascend/ascend-toolkit/set_env.sh
| torch | 2.1.0 | 2.4.0 |
| torch-npu | 2.1.0 | 2.4.0.post2 |
| deepspeed | 0.13.2 | 0.13.2 |
| vllm-ascend | - | 0.7.3 |
Remember to use `ASCEND_RT_VISIBLE_DEVICES` instead of `CUDA_VISIBLE_DEVICES` to specify the device to use.

23
README_zh.md
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@ -80,7 +80,7 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
- **多种模型**：LLaMA、LLaVA、Mistral、Mixtral-MoE、Qwen、Qwen2-VL、DeepSeek、Yi、Gemma、ChatGLM、Phi 等等。
- **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。
- **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。
- **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 和 PiSSA。
- **先进算法**：[Muon](https://github.com/KellerJordan/Muon), [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 和 PiSSA。
- **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。
- **广泛任务**：多轮对话、工具调用、图像理解、视觉定位、视频识别和语音理解等等。
- **实验监控**：LlamaBoard、TensorBoard、Wandb、MLflow、[SwanLab](https://github.com/SwanHubX/SwanLab) 等等。
@ -110,7 +110,7 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
## 更新日志
[25/04/16] 我们支持了 **[InternVL3](https://huggingface.co/OpenGVLab/InternVL3-8B)** 模型的微调。查看 [PR #7258](https://github.com/hiyouga/LLaMA-Factory/pull/7258) 以使用。
[25/04/16] 我们支持了 **[Muon](https://github.com/KellerJordan/Muon)** 优化器。详细用法请参照 [examples](examples/README_zh.md)。感谢 [@Juanxi Tian](https://tianshijing.github.io) 的 PR。
[25/04/14] 我们支持了 **[GLM-Z1](https://huggingface.co/THUDM/GLM-Z1-9B-0414)** 和 **[Kimi-VL](https://huggingface.co/moonshotai/Kimi-VL-A3B-Instruct)** 模型的微调。
@ -118,14 +118,14 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
[25/03/31] 我们支持了 **[Qwen2.5 Omni](https://qwenlm.github.io/blog/qwen2.5-omni/)** 模型的微调。查看 [PR #7537](https://github.com/hiyouga/LLaMA-Factory/pull/7537) 以使用。
<details><summary>展开日志</summary>
[25/03/15] 我们支持了 **[SGLang](https://github.com/sgl-project/sglang)** 推理后端，请使用 `infer_backend: sglang` 启用。
[25/03/12] 我们支持了 **[Gemma 3](https://huggingface.co/blog/gemma3)** 模型的微调。
[25/02/24] 我们宣布开源 **[EasyR1](https://github.com/hiyouga/EasyR1)**，一个高效可扩展的多模态强化学习框架，支持高效的 GRPO 训练。
<details><summary>展开日志</summary>
[25/02/11] 我们支持了在导出模型时保存 **[Ollama](https://github.com/ollama/ollama)** 配置文件。详细用法请参照 [examples](examples/README_zh.md)。
[25/02/05] 我们支持了在语音理解任务上微调 **[Qwen2-Audio](Qwen/Qwen2-Audio-7B-Instruct)** 和 **[MiniCPM-o-2.6](https://huggingface.co/openbmb/MiniCPM-o-2_6)** 模型。
@ -248,11 +248,11 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
| [Gemma 3](https://huggingface.co/google) | 1B/4B/12B/27B | gemma3/gemma (1B) |
| [GLM-4/GLM-4-0414/GLM-Z1](https://huggingface.co/THUDM) | 9B/32B | glm4 |
| [GPT-2](https://huggingface.co/openai-community) | 0.1B/0.4B/0.8B/1.5B | - |
| [Granite 3.0-3.3](https://huggingface.co/ibm-granite) | 1B/2B/3B/8B | granite3 |
| [Granite 3.0-3.1](https://huggingface.co/ibm-granite) | 1B/2B/3B/8B | granite3 |
| [Hunyuan](https://huggingface.co/tencent/) | 7B | hunyuan |
| [Index](https://huggingface.co/IndexTeam) | 1.9B | index |
| [InternLM 2-3](https://huggingface.co/internlm) | 7B/8B/20B | intern2 |
| [InternVL 2.5-3](https://huggingface.co/OpenGVLab)\*\* | 1B/2B/4B/8B/9B/14B/26B/38B/78B | intern_vl |
| [InternVL2_5-3](https://huggingface.co/OpenGVLab/InternVL) | 1B/2B/4B/8B/9B/14B/26B/38B/78B | intern_vl |
| [Kimi-VL](https://huggingface.co/moonshotai) | 16B | kimi_vl |
| [Llama](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | - |
| [Llama 2](https://huggingface.co/meta-llama) | 7B/13B/70B | llama2 |
@ -420,11 +420,11 @@ huggingface-cli login
| 必需项 | 至少 | 推荐 |
| ------------ | ------- | --------- |
| python | 3.9 | 3.10 |
| torch | 2.0.0 | 2.6.0 |
| transformers | 4.45.0 | 4.50.0 |
| torch | 1.13.1 | 2.6.0 |
| transformers | 4.41.2 | 4.50.0 |
| datasets | 2.16.0 | 3.2.0 |
| accelerate | 0.34.0 | 1.2.1 |
| peft | 0.14.0 | 0.15.1 |
| peft | 0.14.0 | 0.15.0 |
| trl | 0.8.6 | 0.9.6 |
| 可选项 | 至少 | 推荐 |
@ -433,7 +433,7 @@ huggingface-cli login
| deepspeed | 0.10.0 | 0.16.4 |
| bitsandbytes | 0.39.0 | 0.43.1 |
| vllm | 0.4.3 | 0.8.2 |
| flash-attn | 2.5.6 | 2.7.2 |
| flash-attn | 2.3.0 | 2.7.2 |
### 硬件依赖
@ -461,7 +461,7 @@ 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、sglang、galore、apollo、badam、adam-mini、qwen、minicpm_v、modelscope、openmind、swanlab、quality
可选的额外依赖项：torch、torch-npu、metrics、deepspeed、liger-kernel、bitsandbytes、hqq、eetq、gptq、awq、aqlm、vllm、sglang、muon, galore、apollo、badam、adam-mini、qwen、minicpm_v、modelscope、openmind、swanlab、quality
> [!TIP]
> 遇到包冲突时，可使用 `pip install --no-deps -e .` 解决。
@ -523,7 +523,6 @@ source /usr/local/Ascend/ascend-toolkit/set_env.sh
| torch | 2.1.0 | 2.4.0 |
| torch-npu | 2.1.0 | 2.4.0.post2 |
| deepspeed | 0.13.2 | 0.13.2 |
| vllm-ascend | - | 0.7.3 |
请使用 `ASCEND_RT_VISIBLE_DEVICES` 而非 `CUDA_VISIBLE_DEVICES` 来指定运算设备。

6
examples/README.md
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@ -215,6 +215,12 @@ llamafactory-cli api examples/inference/llama3_lora_sft.yaml
### Extras
#### Full-Parameter Fine-Tuning using Muon
```bash
llamafactory-cli train examples/extras/muon/qwen2_full_sft.yaml
```
#### Full-Parameter Fine-Tuning using GaLore
```bash

6
examples/README_zh.md
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@ -215,6 +215,12 @@ llamafactory-cli api examples/inference/llama3_lora_sft.yaml
### 杂项
#### 使用 Muon 进行全参数训练
```bash
llamafactory-cli train examples/extras/muon/qwen2_full_sft.yaml
```
#### 使用 GaLore 进行全参数训练
```bash

43
examples/extras/muon/qwen2_full_sft.yaml Normal file
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@ -0,0 +1,43 @@
### model
model_name_or_path: Qwen/Qwen2-1.5B-Instruct
trust_remote_code: true
### method
stage: sft
do_train: true
finetuning_type: full
use_muon: true
### dataset
dataset: identity,alpaca_en_demo
template: qwen
cutoff_len: 2048
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16
dataloader_num_workers: 4
### output
output_dir: saves/qwen2-1_5b/full/sft
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
save_only_model: false
report_to: none # choices: [none, wandb, tensorboard, swanlab, mlflow]
### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 8
learning_rate: 1.0e-5
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: true
ddp_timeout: 180000000
### eval
# val_size: 0.1
# per_device_eval_batch_size: 1
# eval_strategy: steps
# eval_steps: 500

4
src/llamafactory/hparams/finetuning_args.py
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@ -411,6 +411,10 @@ class FinetuningArguments(
default=False,
metadata={"help": "Whether or not to use the Adam-mini optimizer."},
)
use_muon: bool = field(
default=False,
metadata={"help": "Whether or not to use the Muon optimizer."},
)
freeze_vision_tower: bool = field(
default=True,
metadata={"help": "Whether ot not to freeze the vision tower in MLLM training."},

2
src/llamafactory/hparams/parser.py
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@ -153,7 +153,7 @@ def _check_extra_dependencies(
elif model_args.infer_backend == EngineName.SGLANG:
check_version("sglang>=0.4.4")
check_version("sglang", mandatory=True)
if finetuning_args.use_galore:
check_version("galore_torch", mandatory=True)

18
src/llamafactory/third_party/muon/__init__.py vendored Normal file
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@ -0,0 +1,18 @@
# Copyright 2025 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.
from .muon import Muon
__all__ = ["Muon"]

232
src/llamafactory/third_party/muon/muon.py vendored Normal file
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@ -0,0 +1,232 @@
# Copyright 2025 Moonshot AI and the LlamaFactory team.
#
# This code is based on the MoonshotAI's Moonlight library.
# https://github.com/MoonshotAI/Moonlight/blob/master/examples/toy_train.py
#
# 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.
#
# MIT License
#
# Copyright (c) 2025 Moonshot AI
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import math
import torch
# This code snippet is a modified version adapted from the following GitHub repository:
# https://github.com/KellerJordan/Muon/blob/master/muon.py
@torch.compile
def zeropower_via_newtonschulz5(G, steps):
"""Newton-Schulz iteration to compute the zeroth power / orthogonalization of G.
We opt to use a quintic iteration whose coefficients are selected to maximize the slope at zero.
For the purpose of minimizing steps, it turns out to be empirically effective to keep increasing the slope at
zero even beyond the point where the iteration no longer converges all the way to one everywhere
on the interval. This iteration therefore does not produce UV^T but rather something like US'V^T
where S' is diagonal with S_{ii}' ~ Uniform(0.5, 1.5), which turns out not to hurt model
performance at all relative to UV^T, where USV^T = G is the SVD.
"""
assert len(G.shape) == 2
a, b, c = (3.4445, -4.7750, 2.0315)
X = G.bfloat16()
if G.size(0) > G.size(1):
X = X.T
# Ensure spectral norm is at most 1
X = X / (X.norm() + 1e-7)
# Perform the NS iterations
for _ in range(steps):
A = X @ X.T
B = b * A + c * A @ A # adapted from suggestion by @jxbz, @leloykun, and @YouJiacheng
X = a * X + B @ X
if G.size(0) > G.size(1):
X = X.T
return X
class Muon(torch.optim.Optimizer):
"""Muon - MomentUm Orthogonalized by Newton-schulz.
Muon internally runs standard SGD-momentum, and then performs an orthogonalization post-
processing step, in which each 2D parameter's update is replaced with the nearest orthogonal
matrix. To efficiently orthogonalize each update, we use a Newton-Schulz iteration, which has
the advantage that it can be stably run in bfloat16 on the GPU.
Some warnings:
- We believe this optimizer is unlikely to work well for training with small batch size.
- We believe it may not work well for finetuning pretrained models, but we haven't tested this.
Arguments:
muon_params: The parameters to be optimized by Muon.
lr: The learning rate. The updates will have spectral norm of `lr`. (0.02 is a good default)
momentum: The momentum used by the internal SGD. (0.95 is a good default)
nesterov: Whether to use Nesterov-style momentum in the internal SGD. (recommended)
ns_steps: The number of Newton-Schulz iterations to run. (6 is probably always enough)
adamw_params: The parameters to be optimized by AdamW. Any parameters in `muon_params` which are
{0, 1}-D or are detected as being the embed or lm_head will be optimized by AdamW as well.
adamw_lr: The learning rate for the internal AdamW.
adamw_betas: The betas for the internal AdamW.
adamw_eps: The epsilon for the internal AdamW.
adamw_wd: The weight decay for the internal AdamW.
"""
def __init__(
self,
lr=1e-3,
wd=0.1,
muon_params=None,
momentum=0.95,
nesterov=True,
ns_steps=5,
adamw_params=None,
adamw_betas=(0.9, 0.95),
adamw_eps=1e-8,
):
defaults = dict(
lr=lr,
wd=wd,
momentum=momentum,
nesterov=nesterov,
ns_steps=ns_steps,
adamw_betas=adamw_betas,
adamw_eps=adamw_eps,
)
params = list(muon_params)
adamw_params = list(adamw_params) if adamw_params is not None else []
params.extend(adamw_params)
super().__init__(params, defaults)
# Sort parameters into those for which we will use Muon, and those for which we will not
for p in muon_params:
# Use Muon for every parameter in muon_params which is >= 2D and doesn't look like an embedding or head layer
assert p.ndim == 2, p.ndim
self.state[p]["use_muon"] = True
for p in adamw_params:
# Do not use Muon for parameters in adamw_params
self.state[p]["use_muon"] = False
def adjust_lr_for_muon(self, lr, param_shape):
A, B = param_shape[:2]
# We adjust the learning rate and weight decay based on the size of the parameter matrix
# as describted in the paper
adjusted_ratio = 0.2 * math.sqrt(max(A, B))
adjusted_lr = lr * adjusted_ratio
return adjusted_lr
def step(self, closure=None):
"""Perform a single optimization step.
Args:
closure (Callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
############################
# Muon #
############################
params = [p for p in group["params"] if self.state[p]["use_muon"]]
# import pdb; pdb.set_trace()
lr = group["lr"]
wd = group["wd"]
momentum = group["momentum"]
# generate weight updates in distributed fashion
for p in params:
# sanity check
g = p.grad
if g is None:
continue
if g.ndim > 2:
g = g.view(g.size(0), -1)
assert g is not None
# calc update
state = self.state[p]
if "momentum_buffer" not in state:
state["momentum_buffer"] = torch.zeros_like(g)
buf = state["momentum_buffer"]
buf.mul_(momentum).add_(g)
if group["nesterov"]:
g = g.add(buf, alpha=momentum)
else:
g = buf
u = zeropower_via_newtonschulz5(g, steps=group["ns_steps"])
# scale update
adjusted_lr = self.adjust_lr_for_muon(lr, p.shape)
# apply weight decay
p.data.mul_(1 - lr * wd)
# apply update
p.data.add_(u, alpha=-adjusted_lr)
############################
# AdamW backup #
############################
params = [p for p in group["params"] if not self.state[p]["use_muon"]]
lr = group["lr"]
beta1, beta2 = group["adamw_betas"]
eps = group["adamw_eps"]
weight_decay = group["wd"]
for p in params:
g = p.grad
if g is None:
continue
state = self.state[p]
if "step" not in state:
state["step"] = 0
state["moment1"] = torch.zeros_like(g)
state["moment2"] = torch.zeros_like(g)
state["step"] += 1
step = state["step"]
buf1 = state["moment1"]
buf2 = state["moment2"]
buf1.lerp_(g, 1 - beta1)
buf2.lerp_(g.square(), 1 - beta2)
g = buf1 / (eps + buf2.sqrt())
bias_correction1 = 1 - beta1**step
bias_correction2 = 1 - beta2**step
scale = bias_correction1 / bias_correction2**0.5
p.data.mul_(1 - lr * weight_decay)
p.data.add_(g, alpha=-lr / scale)
return loss

39
src/llamafactory/train/trainer_utils.py
View File

@ -489,12 +489,51 @@ def _create_adam_mini_optimizer(
logger.info_rank0("Using Adam-mini optimizer.")
return optimizer
def _create_muon_optimizer(
model: "PreTrainedModel",
training_args: "TrainingArguments",
) -> "torch.optim.Optimizer":
from llamafactory.third_party.muon import Muon # type: ignore
# Separate parameters for Muon (2D parameters) and AdamW (others)
muon_params = []
adamw_params = []
for name, param in model.named_parameters():
if param.requires_grad:
# Use Muon for 2D parameters that aren't embeddings or heads
if param.ndim == 2 and "embed" not in name and "lm_head" not in name:
muon_params.append(param)
else:
adamw_params.append(param)
# Get optimizer settings from training_args
ns_steps = getattr(training_args, "ns_steps", 5)
# Create Muon optimizer
optimizer = Muon(
lr=training_args.learning_rate,
wd=training_args.weight_decay,
muon_params=muon_params,
momentum=0.95, # default momentum for Muon
nesterov=True, # default nesterov for Muon
ns_steps=ns_steps,
adamw_params=adamw_params,
adamw_betas=(training_args.adam_beta1, training_args.adam_beta2),
adamw_eps=training_args.adam_epsilon,
)
logger.info_rank0(f"Using Muon optimizer with {len(muon_params)} Muon params and {len(adamw_params)} AdamW params.")
return optimizer
def create_custom_optimizer(
model: "PreTrainedModel",
training_args: "TrainingArguments",
finetuning_args: "FinetuningArguments",
) -> Optional["torch.optim.Optimizer"]:
if finetuning_args.use_muon:
return _create_muon_optimizer(model, training_args)
if finetuning_args.use_galore:
return _create_galore_optimizer(model, training_args, finetuning_args)