Awesome

Subformer

This repository contains the code for the Subformer. To help overcome this we propose the Subformer, allowing us to retain performance while reducing parameters in generative Transformers from 25% ~ 70%. The Subformer consists of the following two techniques:

1. Sandwich-style parameter sharing, in which we share all the layers in a block except the first and last. This allows us the use the central shared layers --"sandwich module" -- as a large representation learner (similar to BERT vs ALBERT) while the input and output model layers are able to focus on more specific representations for token prediction/generation while maintaining performance.
2. For our sequence to sequence tasks, we also introduce SAFE (self-attentive factorized embeddings), which help us reduce embedding parameters significantly, while still retaining performance.

If you used this code or found our work useful, please cite:

@inproceedings{reid2021subformer,
    title = {{S}ubformer: {E}xploring {W}eight {S}haring for {P}arameter {E}fficiency in {G}enerative {T}ransformers},
    author = {Machel Reid and Edison Marrese-Taylor and Yutaka Matsuo},
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
}

Requirements and Installation

(As this code is based on fairseq, some installation instructions are taken straight from their README)

• PyTorch version >= 1.5.0
• Python version >= 3.6
• For training new models, you'll also need an NVIDIA GPU and NCCL
• To install and develop locally:

git clone https://github.com/machelreid/subformer
cd subformer
pip install --e ./

# on MacOS:
# CFLAGS="-stdlib=libc++" pip install --editable ./

• For faster training install NVIDIA's apex library:

git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" \
  --global-option="--deprecated_fused_adam" --global-option="--xentropy" \
  --global-option="--fast_multihead_attn" ./

• For large datasets install PyArrow: pip install pyarrow
• If you use Docker make sure to increase the shared memory size either with --ipc=host or --shm-size as command line options to nvidia-docker run .

Training

Machine Translation

python train.py $DATA_BIN --arch transformer_wmt_en_de \
    --criterion label_smoothed_cross_entropy --label-smoothing 0.1 --lr 5e-4 \
    --warmup-init-lr 1e-7 --stop-min-lr 1e-9 --lr-scheduler inverse_sqrt --warmup-updates 10000 \
    --optimizer adam --adam-betas '(0.9, 0.999)' --adam-eps 1e-6 --task translation \
    --max-tokens 8192 --weight-decay 0.01 --dropout 0.2 --encoder-layers 6 --encoder-embed-dim 512 \
    --decoder-layers 6 --decoder-embed-dim 512 --fp16 --max-source-positions 10000 \
    --max-target-positions 10000 --max-update 200000 --seed 1 \
    --save-dir $CHECKPOINT_DIR --share-all-embeddings \
    --share-encoder-parameters-sandwich --share-decoder-parameters-sandwich \ #for sandwich-style parameter sharing
    --reduction-dim 320 #for SAFE embeddings

Generation

python generate.py --path $CHECKPOINT --gen-subset $SPLIT --beam 5 --lenpen $LENPEN --batch-size 400 --remove-bpe

CNN-DM Summarization

fairseq-train $DATA_BIN \
   --share-decoder-input-output-embed \
   --max-update 30000 \
   --optimizer adam --adam-betas '(0.9, 0.98)' --skip-invalid-size-inputs-valid-test \
   --lr-scheduler inverse_sqrt --warmup-init-lr 1e-07 --warmup-updates 10000 --lr 0.0005 \
   --stop-min-lr 1e-09 --clip-norm 0.1 --dropout 0.3 --weight-decay 0.0 \
   --criterion label_smoothed_cross_entropy --label-smoothing 0.1 --update-freq 7 --attention-dropout 0.2 \
   --max-tokens 8192 --arch transformer_wmt_en_de --seed 1 --warmup-init-lr 1e-7 \
   --source-lang source_bpe --target-lang target_bpe --save-dir $CHECKPOINT_DIR --no-epoch-checkpoints --keep-best-checkpoints 10 --truncate-source --max-source-positions 512 --share-encoder-parameters-sandwich --share-decoder-parameters-sandwich --sandwich-embed-dim 1024 --sandwich-ffn-embed-dim 3072 --reduction-dim 256

Generation

fairseq-generate $DATA_BIN --task translation --gen-subset $SPLIT --batch-size 32 --path $CHECKPOINT --remove-bpe  --min-len 55 --beam 5 --max-len-b 140 --no-repeat-ngram-size 3 --lenpen $LENPEN -s source_bpe -t target_bpe --truncate-source --max-source-positions 512

Note that the min,max len parameters can be tuned for better performance

For post processing and ROUGE calculation feel free to take a look at this.

Citation

Please cite as:

@inproceedings{reid2021subformer,
    title = {{S}ubformer: {E}xploring {W}eight {S}haring for {P}arameter {E}fficiency in {G}enerative {T}ransformers},
    author = {Machel Reid and Edison Marrese-Taylor and Yutaka Matsuo},
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
}