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CONDENSE, DON’T JUST PRUNE: ENHANCING EFFICIENCY AND PERFORMANCE IN MOE LAYER PRUNING

Official PyTorch implementation of CD-MOE, as presented in our paper: CONDENSE, DON’T JUST PRUNE: ENHANCING EFFICIENCY AND PERFORMANCE IN MOE LAYER PRUNING

Mingyu Cao, Gen Li, Jie Ji, Jiaqi Zhang, Xiaolong Ma, Shiwei Liu, Lu Yin

Shopee, Clemson University, Meituan, University of Oxford, University of Surrey

Abstract

Mixture-of-Experts (MoE) has garnered significant attention for their ability to scale up neural networks while utilizing the same or even fewer active parameters. However, MoE does not relieve the massive memory requirements of networks, which limits their practicality in real-world applications, especially in the era of large language models (LLMs). While recent work explores the possibility of removing entire layers of MoE to reduce memory, the performance degradation is still notable. In this paper, we propose ConDense-MoE (CD-MoE) that, instead of dropping the entire MoE layer, condenses the large, sparse MoE layer into a smaller, denser layer with only a few experts activated for all tokens, while maintaining hardware friendliness. Our approach is specifically designed for fine-grained MoE with shared experts, where Feed-Forward Networks are split into many small experts, with certain experts isolated to serve as shared experts that are always activated, such as DeepSeekMoE and QwenMoE. We demonstrate the effectiveness of our method. Specifically, for the \textit{DeepSeekMoE-16B} model, our approach maintains 90% of the average accuracy while reducing memory usage by 27.5% and increasing inference speed to 1.26 times. Moreover, we show that by applying lightweight expert fine-tuning---only to the condensed layers---and using 5 hours on a single 80G A100 GPU, we can successfully recover 98% of the original performance.

Results

main result1

CD-MoE against baselines on zero-shot tasks w/o fine-tuning. Left: Average accuracy with varying Memory Ratio against the original model. Right: Average accuracy with varying SpeedUp against the original model. The Gray dotted line is the original model result. CD-MoE-S represents the shared experts and no routing experts, and CD-MoE-SR represents shared with routing experts. Baseline indicated performance of the dense model.

main result2

CD-MoE with lightweight fine-tuning. Left: SFT results on CD-MoE-S with increasing number of condensed layers. Right: SFT results on CD-MoE-SR with increasing number of condensed layers. Baseline indicated performance of the dense model.

Setup

cd CD-MoE
pip install -e .
pip install -r requirements.txt

Usage

The process mainly consists of three steps: (1) obtaining the average weights of the experts, (2) selecting experts and layers through greedy search, and (3) fine-tuning the experts. The first two steps are mandatory, while the last step is optional. First, you need to download the official deepseek16B-MOE model to the local directory $model_path.

1. Expert Weight

cp cd-moe/get_weight/modeling_deepseek.py $model_path
python cd-moe/get_weight/get_weight.py \
    --input $calibration_data_file \
    --output $expert_weight_file \
    --model $model_path

2. Greedy Search

The greedy search expert must be done before the greedy search layer.

Greedy Search Expert

cp cd-moe/greedy_search/modeling_deepseek.py $model_path
python cd-moe/greedy_search/greedy_search_expert.py \
    --input $calibration_data_file \
    --model $model_path \
    --dynamic-weight-file $expert_weight_file \
    --output $greedy_search_expert_result_file

Greedy Search Layer

prune_num_expert=6
prune_num_layer=9
python cd-moe/greedy_search/greedy_search_layer.py \
    --input $calibration_data_file \
    --model $model_path \
    --dynamic-weight-file $expert_weight_file \
    --greedy-expert-file $greedy_search_expert_result_file \
    --output $greedy_search_layer_result_file \
    --prune-num-expert $prune_num_expert \
    --prune-num-layer $prune_num_layer

3. Fine-tune (Optional)

The prune experts and layers in cd-moe/exp_hyper.py need to match the options in cd-moe/finetune/finetune.py, and replace the file paths in cd-moe/modeling_deepseek.py with real paths. You can use the --no-c4 option to skip lm fine-tuning and directly fine-tune for downstream tasks.

echo "num_route_experts=${prune_num_expert};prune_layer_num=${prune_num_layer}" > cd-moe/exp_hyper.py
cp cd-moe/modeling_deepseek.py cd-moe/exp_hyper.py $model_path
python cd-moe/finetune/finetune.py \
    --input $sft_data \
    --c4-input $lm_data \
    --model $model_path \
    --dynamic-weight-file $expert_weight_file \
    --greedy-expert-file $greedy_search_expert_result_file \
    --greedy-layer-file $greedy_search_layer_result_file \
    --output-dir $sft_model_path \
    --prune-num-expert $prune_num_expert \
    --prune-num-layer $prune_num_layer

For some intermediate variables, we provide some already generated results:

  • calibration_data_file(100 c4 texts): cd-moe/data/calibration_data.json
  • expert_weight_file: cd-moe/data/dynamic_weight.json
  • greedy_search_expert_result_file: cd-moe/data/layer_idx_to_expert_idx.greedy_jl.json
  • greedy_search_layer_result_file: cd-moe/data/layer_idx_order.e6.json and cd-moe/data/layer_idx_order.e0.json

The open-source model and C4 training data need to be downloaded locally:

Evaluation

Install lm-evaluation-harness
Evaluate the pruned model or the finetuned model:

cp $expert_weight_file $greedy_search_expert_result_file $greedy_search_layer_result_file $model_path
lm_eval --model hf \
    --model_args pretrained=$model_path,dtype="bfloat16",trust_remote_code=True \
    --tasks arc_challenge,boolq,piqa,rte,openbookqa,winogrande,mmlu,hellaswag \
    --device cuda:0 \
    --batch_size 32
# batch_size cannot be set to `auto` because of the model implement

Acknowledgement

This repository is build upon the Transformers repositories.