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MoEfication

Source code for "MoEfication: Transformer Feed-forward Layers are Mixtures of Experts" and "Exploring the Benefit of Activation Sparsity in Pre-training"

Important Update (2024/07/21): In addition to the original work, this repository now contains subsequent research that provides a faster and better parameter clustering method by incorporating GPUs and initialization from previous results. We encourage users to use the new contributions when considering MoEfication during training. This method is detailed in the faster_moefication.

Update (2022/11/30): We provide a simple example of using fastmoe for efficient MoE implementation in the branch fastmoe. We will provide how to transform a MoEfied checkpoint to a fastmoe checkpoint soon. Keep tuned!

Reqirements:

  • Python3.8
  • torch==1.6.0
  • transformers==4.20.1
  • tqdm
  • scikit-learn
  • k_means_constrained
  • datasets
  • numpy
  • scipy

Expert Construction

For parameter clustering split, we use balanced K-Means. The details of the implementation can be found in param_cluster_example.py.

For co-activation graph split, we first construct a co-activation graph by adj.py. For T5, the output graphs are named as encoder.blocks.0.ff.dense_relu_dense.wi.weight, encoder.blocks.1.ff.dense_relu_dense.wi.weight, ..., decoder.blocks.11.ff.dense_relu_dense.wi.weight, which are the weight names.

Then, we use METIS to split the graph into subgraphs.

gpmetis encoder.blocks.0.ff.dense_relu_dense.wi.weight num_expert

where num_expert is the number of experts.

Finally, we balance the neurons in each expert.

# num_expert=128
python trans_gp.py encoder.blocks.0.ff.dense_relu_dense.wi.weight.part.128

Expert Selection

For similarity selection, we average the corresponding weight columns as the expert representation. The details of the implementation can be found in similarity_select_example.py.

For MLP selection, We train a multi-layer perceptron (MLP), which takes the $\vx$ as input and predicts the sum of positive values in each expert. The details of the implementation can be found in mlp_select_example.py.

T5 Examples

We provide an example of T5-base on SST-2 in examples, including groundtruth selection and MLP selection based on parameter clustering.

First, you need to construct expert by

python examples/t5_cluster_example.py

Then, you can directly evaluate groundtruth selection by

python examples/t5-sst2-gt.py

To use MLP selection, you need to train the MLP by

python examples/t5-sst2-inf.py
python examples/t5_select_example.py

And, you can evaluate the performance of MLP selection by

python examples/t5-sst2-mlp.py

BERT Examples

We also provide an example of BERT-large on SST-2 in examples. The checkpoint of ReLU-based BERT is available here.

You need to first download it and fine-tune it on SST-2 by

python examples/bert-sst2-training.py

Then, you need to construct expert by

python moefication/param_cluster_example.py --model_path bert-sst2-bsz32/epoch_1.bin --res_path results/bert-sst2 --num-layer 24 --num-expert 128 --templates bert.encoder.layer.{}.intermediate.dense.weight

you can evaluate groundtruth selection by

python examples/bert-sst2-gt.py

Tips for Training ReLU-based BERT

We use the pre-training script from NVIDIA. The only difference is that we replace the activation function with ReLU and set the bias of the intermediate layer to None. We initialize the model with the checkpoint of BERT-Large-Uncased provided by NVIDIA. In the experiments, we found that training around 200 steps is enough to get a good performance.

Cite

If you use the code, please cite this paper:

@inproceedings{
  zhang2024exploring,
  title={Exploring the Benefit of Activation Sparsity in Pre-training},
  author={Zhengyan Zhang and Chaojun Xiao and Qiujieli Qin and Yankai Lin and Zhiyuan Zeng and Xu Han and Zhiyuan Liu and Ruobing Xie and Maosong Sun and Jie Zhou},
  booktitle={Proceedings of ICML},
  year={2024},
}

@inproceedings{zhang2022moefication,
  title={{MoEfication}: Transformer Feed-forward Layers are Mixtures of Experts},
  author={Zhang, Zhengyan and Lin, Yankai and Liu, Zhiyuan and Li, Peng and Sun, Maosong and Zhou, Jie},
  booktitle={Findings of ACL 2022},
  year={2022}
}