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Adam-mini

【Important notice on Oct 18th 2024】 We are happy to anounce that we have updated Adam-mini to version 1.1.0 in PyPI (see here). This is a major update: based on a more careful investigation into the Hessian of Transformers, we change the partition strategies for Values, attn_proj, MLPs, embedding, and the output layer. In particular, our new partition strategy for the embedding & output layer eliminates the need to treat these these layers as special cases. As a result, Adam-mini now saves 50% memory over Adam for all models of any size (previously is 45% to 50% reduction for >1B models). The updated form of Adam-mini is shown in Algorithm 1 and the paper is updated accordingly: Adam-mini: Use Fewer Learning Rates To Gain More.

This repository contains the official PyTorch implementation of Adam-mini optimizer, a mini-version of Adam that achieves on-par or better performance than AdamW with 50% less memory footprint.

Adam-mini reduces memory by cutting down the learning rate (lr) resources in Adam (i.e., $1/\sqrt{v}$): we argue that >99.9% of these lr in $v$ could be harmlessly removed if we:

(1) carefully partition the parameters into blocks following our proposed principle related to Hessian structure.
(2) assign a single but good lr to each parameter block.

We find a simple and effective way to reach these requirements. The resulting algorithm is shown below in Algorithm 1. Check out more detailed descriptions in our paper: Adam-mini: Use Fewer Learning Rates To Gain More.

How to use

Install torch (>=1.8.0) and run the following commands.

pip install adam-mini

or if you prefer to import from source

git clone https://github.com/zyushun/Adam-mini
cd Adam-mini
pip install -e .

Then use Adam-mini optimizer as follows.

from adam_mini import Adam_mini

optimizer = Adam_mini(
            named_parameters = model.named_parameters(),
            lr = lr,
            betas = (beta1,beta2),
            eps = eps,
            weight_decay = weight_decay,
            dim = model_config.dim,
            n_heads = model_config.n_heads,
            n_kv_heads = model_config.n_kv_heads, #default to be none
            )

Hyperparameter choices: Regarding learning rate (lr), weight_decay, beta1, beta2, eps, we recommend using the same values as those used for AdamW.

If you are training Transformers, please also pass the following info to Adam-mini:

• dim: dimension for hidden feature. Could be unspecified if you are training non-transformer models.

• n_heads: number of attention heads. Could be unspecified if you are training non-transformer models.

• n_kv_heads: number of head for Key and Value. Or equivalently, number of query groups in Group query Attention. Also known as "n_query_groups". If is None, it will be the same value as n_head. Could be unspecified if you are training non-transformer models.

Remark: If your total training step is small (<10k or 20k), we recommend adding the following line. This will apply a single lr for Value and we find it can speed up the initial convergence of Adam-mini. See Appendix C.3 in the paper for more discussion.

optimizer.wv_names = {} 

Support

Our current implementation of Adam-mini supports popular distributed frameworks and codebase including:

1. DDP distributed framework
2. FSDP distributed framework
3. DeepSpeed
4. Hugginface Trainer
5. Torchtitan
6. LLaMA-Factory. Detailed usage instruction can be seen in examples
7. More is coming! Do not hesitate to contact us if Adam-mini does not support your codebase!

Examples

We here provide sample code on pre-training, SFT, and RLHF. You need 2xA800-80GB or 2xA100-80GB GPUs to run the experiments below.

Example 1: GPT2 series Pre-training

We pre-train GPT2 series (125M, 330M, 1.5B) using NanoGPT codebase under DDP framework. Install dependencies from pip:

conda env create -f gpt2/environment.yml
conda activate gpt2
cd examples/gpt2

Run the code for GPT2 pre-training:

bash run_gpt2.sh

You will get the following curves.

Example 2: Llama series Pre-training

We here provide a sample code for pre-training Llama series (from 39M to 13B) using Torchtitan code base under FSDP framework. We recommend using Torchtitan codebase as it will be much faster than NanoGPT codebase for processing the same amount of tokens.

Install dependence from pip (or please see the instructions from Torchtitan):

cd examples/llama
pip install -r requirements.txt
pip3 install --pre torch==2.5.0.dev20240617  --index-url https://download.pytorch.org/whl/nightly/cu121 #or cu118
pip3 install --pre torchdata --index-url https://download.pytorch.org/whl/nightly

Download a tokenizer.model. Follow the instructions on the official meta-llama repository to ensure you have access to the Llama model weights. Once you have confirmed access, you can run the following command to download the Llama 3 / Llama 2 tokenizer to your local machine.

# Get your HF token from https://huggingface.co/settings/tokens

# llama3 tokenizer.model
python torchtitan/datasets/download_tokenizer.py --repo_id meta-llama/Meta-Llama-3-8B --tokenizer_path "original" --hf_token=...

# llama2 tokenizer.model
python torchtitan/datasets/download_tokenizer.py --repo_id meta-llama/Llama-2-13b-hf --hf_token=...

Change your data path in the configuration file (for instance, ./train_configs/llama3_8b_mini.toml). To debug, you can download the small dataset "c4_mini" via GoogleDrive and put it under the path "./torchtitan/datasets/c4_mini/".

dataset = "c4" #for debug can use "c4_mini"
dataset_path = "your_path/c4" #for debug can use "./torchtitan/datasets/c4_mini/"

Then we can kick off the training. For instance, you can train Llama models from 39M to 1B and reproduce our scaling-law experiments. You can train all models for a complete pre-training run by Chinchilla's law. The total running time would be about 300 GPU hours (we tested on 4*A800-80GB GPUs).

bash run_llama_2_scaling_law.sh

You can get the following curves (after changing x-axis into FLOPs and taking log)

After a complete pre-training run by Chinchilla's law, you will get the following the final validation perplexity.

In particular, the training curves of 1B model will look like the following.

You can also pre-train Llama3-8B and Llama2-13B using the folloiwng code.

bash run_llama_3_8b.sh
bash run_llama_2_13b.sh

#after creating the optimize
optimizer.wv_names = {} # For experiments with relatively small total steps  (like the 8B and 13B experiments here, we only run for 10k steps), we apply a single lr for Value and find it performs a bit better

You will get the following curves.

Example 2: Llama2-7B Supervised Fine-tuning and RLHF

We fine-tune Llama2-7B using ReMax codebase under DeepSpeed framework. Install dependencies from pip:

conda env create -f RLHF/environment.yml
conda activate rlhf
cd examples/RLHF

Run the code for SFT with LoRA :

bash training_scripts/sft/run_sft_lora.sh

Run the code for full-parameter SFT :

bash training_scripts/sft/run_sft_full.sh

Run the code for reward model training in RLHF

bash training_scripts/reward/run_reward.sh

Run the code for reward optimization in RLHF using ReMax:

bash training_scripts/po/remax/run_remax.sh

You will get the following curves.

Remarks

How to use Adam-mini in Huggingface Trainer. If you are using Huggingface Trainer, please overwrite "create_optimizer" as follows to change optimizer:

 def create_optimizer(self) -> "torch.optim.Optimizer":
        if self.optimizer is None:
            if (self.finetuning_args.use_adammini):
                self.optimizer = Adam_mini(
            named_parameters = model.named_parameters(),
            lr = lr,
            betas = (beta1,beta2),
            eps = eps,
            weight_decay = weight_decay,
            model_sharding = True,
            dim = model_config.dim,
            n_heads = model_config.n_heads,
            n_kv_heads = model_config.n_kv_heads,
            )
        return super().create_optimizer()

About checkpoint saving under FSDP: If you are using FSDP distributed framework, we apologize that we still have unexpected error for saving checkpoints. We are working on it and will update soon.

About CPU offload: Our current implementation of Adam-mini supports CPU offload in FSDP, while it does not support CPU offload in DeepSpeed. Please turn off offload when using DeepSpeed. We will resolve this issue soon.

Changelog

[24/06/26] We are online!

[24/07/21] We now support the Adam-mini by pip install

[24/08/09] We now support the Adam-mini in LLaMA-Factory.

[24/09/04] We update Adam-mini to version 1.0.3 in PyPI (see here). We deprecate the argument "model_sharding". We will assume that model parallelism is always used and "model_sharding" is always set True. We will remove this argument in the future version.

[24/09/18] We update Adam-mini to version 1.0.4 in PyPI (see here). We add the argument "verbose" to allow manually mute the logs by Adam-mini. We support CPU-offload in FSDP.

[24/10/18] We update Adam-mini to version 1.1.0 in PyPI (see here). This is a major update: we change the partition rules for attn_proj, MLPs, embedding, and the output layer. In particular, we design a new partition strategy for the embedding & output layer, and now Adam-mini no longer need to treat these two layers as special cases. As a result, Adam-mini now saves 50% memory over Adam for all models of any size (previously is 45% to 50% reduction for >1B models).

[24/11/29] We update Adam-mini to version 1.1.1 in PyPI (see here). We fixed the log issue in Issue #30.

Acknowledgements

1. The above code is heavily based on the codebase of NanoGPT, Torchtitan, ReMax, and DeepSpeed.
2. We'd like to express our gratitude to @lessw2020 and @awgu for the support on Torchtitan and the great suggestions for refactoring the code of Adam-mini!
3. We'd like to express our gratitude to @Mrw33554432 for the pull request to pip install!
4. We'd like to express our gratitude to @relic-yuexi for the pull request to LLaMA-Factory!
5. We'd like to express our gratitude to @Ashwanth369 for the pull request to Huggingface Transformers!
6. We'd like to express our gratitude to @minienglish1 for the suggestions on CPU-offload (Issue #28)!

Citation

If you find this code helpful, please cite our paper in the following format.

@article{zhang2024adam,
  title     = {Adam-mini: Use Fewer Learning Rates To Gain More},
  author    = {Zhang, Yushun and Chen, Congliang  and Li, Ziniu and Ding, Tian and Wu, Chenwei and Kingma, Diederik P and Ye, Yinyu and Luo, Zhi-Quan and Sun, Ruoyu},
  booktitle = {arXiv preprint arXiv:2406.16793},
  year      = {2024},
}