This PyTorch package implements MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation (NAACL 2022).

Related tags

Deep LearningMoEBERT
Overview

MoEBERT

This PyTorch package implements MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation (NAACL 2022).

Installation

  • Create and activate conda environment.
conda env create -f environment.yml
  • Install Transformers locally.
pip install -e .
  • Note: The code is adapted from this codebase. Arguments regarding LoRA and adapter can be safely ignored.

Instructions

MoEBERT targets task-specific distillation. Before running any distillation code, a pre-trained BERT model should be fine-tuned on the target task. Path to the fine-tuned model should be passed to --model_name_or_path.

Importance Score Computation

  • Use bert_base_mnli_example.sh to compute the importance scores, add a --preprocess_importance argument, remove the --do_train argument.
  • If multiple GPUs are used to compute the importance scores, a importance_[rank].pkl file will be saved for each GPU. Use merge_importance.py to merge these files.
  • To use the pre-computed importance scores, pass the file name to --moebert_load_importance.

Knowledge Distillation

  • For GLUE tasks, see examples/text-classification/run_glue.py.
  • For question answering tasks, see examples/question-answering/run_qa.py.
  • Run bash bert_base_mnli_example.sh as an example.
  • The codebase supports different routing strategies: gate-token, gate-sentence, hash-random and hash-balance. Choices should be passed to --moebert_route_method.
    • To use hash-balance, a balanced hash list needs to be pre-computed using hash_balance.py. Path to the saved hash list should be passed to --moebert_route_hash_list.
    • Add a load balancing loss by setting --moebert_load_balance when using trainable gating mechanisms.
    • The sentence-based gating mechanism (gate-sentence) is advantageous for inference because it induces significantly less communication overhead compared with token-level routing methods.
Owner
Simiao Zuo
PhD Student @ Georgia Tech
Simiao Zuo
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