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A collection of transformer's guides, implementations and variants.

A collection of transformer's guides, implementations and so on(For those who want to do some research using transformer as a baseline or simply reproduce paper's performance).

Please feel free to pull requests or report issues.

Why this project

Transformer is a powerful model applied in sequence to sequence learning. However, when we were using transformer as our baseline in NMT research we found no good & reliable guide to reproduce approximate result as reported in original paper(even official tensor2tensor implementation), which means our research would be unauthentic. We collected some implementations, obtained corresponding performance-reproducable approaches and other materials, which eventually formed this project.

Papers

NMT Basic

Transformer original paper

Implementations & How to reproduce paper's result

Indeed there are lots of transformer implementations on the Internet, in order to simplify learning curve, here we only include the most valuable projects.

[Note]: In transformer original paper, there are WMT14 English-German, WMT14 English-French two results transformer result Here we regard a implementation as performance-reproducable if there exists approaches to reproduce WMT14 English-German BLEU score. Therefore, we'll also support corresponding approach to reproduce WMT14 English-German result.

Minimal, paper-equavalent but not certainly performance-reproducable implementations(both PyTorch implementations)

  1. attention-is-all-you-need-pytorch

  2. Harvard NLP Group's annotation

Complex, performance-reproducable implementations

Because transformer's original implementation should run on 8 GPU to replicate corresponding result, where each GPU loads one batch and after forward propagation 8 batch's loss is summed to execute backward operation, so we can accumulate every 8 batch's loss to execute backward operation if we only have 1 GPU to imitate this process. You'd better assemble gpu_count, tokens_on_each_gpu and gradient_accumulation_count to satisfy gpu_count * tokens_on_each_gpu * gradient_accumulation_count = 4096 * 8. See each implementation's guide for details.

Although original paper used multi-bleu.perl to evaluate bleu score, we recommend using sacrebleu, which should be equivalent to mteval-v13a.pl but more convenient, to calculate bleu score and report the signature as BLEU+case.mixed+lang.de-en+test.wmt17 = 32.97 66.1/40.2/26.6/18.1 (BP = 0.980 ratio = 0.980 hyp_len = 63134 ref_len = 64399) for easy reproduction.

# calculate lowercase bleu on all tokenized text
cat model_prediction | sacrebleu -tok none -lc ground_truth
# calculate lowercase bleu on all tokenized text if you have 3 ground truth
cat model_prediction | sacrebleu -tok none -lc ground_truth_1 ground_truth_2 ground_truth_3 
# calculate lowercase bleu on all untokenized romance-language text using v13a tokenization
cat model_prediction | sacrebleu -tok 13a -lc ground_truth
# calculate lowercase bleu on all untokenized romance-language text using v14 tokenization
cat model_prediction | sacrebleu -tok intl -lc ground_truth

The transformer paper's original model settings can be found in tensor2tensor transformer.py. For example, You can find base model configs intransformer_base function.

As you can see, OpenNMT-tf also has a replicable instruction but we prefer tensor2tensor as a baseline to reproduce paper's result if we have to use TensorFlow since it is official.

Paper's original implementation: tensor2tensor(using TensorFlow)

Code
Code annotation
Steps to reproduce WMT14 English-German result:

(updated on v1.10.0)

# 1. Install tensor2tensor toolkit
pip install tensor2tensor

# 2. Basic config
# For BPE model use this problem
PROBLEM=translate_ende_wmt_bpe32k
MODEL=transformer
HPARAMS=transformer_base
# or use transformer_large to reproduce large model
# HPARAMS=transformer_large
DATA_DIR=$HOME/t2t_data
TMP_DIR=/tmp/t2t_datagen
TRAIN_DIR=$HOME/t2t_train/$PROBLEM/$MODEL-$HPARAMS

mkdir -p $DATA_DIR $TMP_DIR $TRAIN_DIR

# 3. Download and preprocess corpus
# Note that tensor2tensor has an inner tokenizer
t2t-datagen \
  --data_dir=$DATA_DIR \
  --tmp_dir=$TMP_DIR \
  --problem=$PROBLEM

# 4. Train on 8 GPUs. You'll get nearly expected performance after ~250k steps and certainly expected performance after ~500k steps.
t2t-trainer \
  --data_dir=$DATA_DIR \
  --problem=$PROBLEM \
  --model=$MODEL \
  --hparams_set=$HPARAMS \
  --output_dir=$TRAIN_DIR \ 
  --train_steps=600000

# 5. Translate
DECODE_FILE=$TMP_DIR/newstest2014.tok.bpe.32000.en
BEAM_SIZE=4
ALPHA=0.6

t2t-decoder \
  --data_dir=$DATA_DIR \
  --problem=$PROBLEM \
  --model=$MODEL \
  --hparams_set=$HPARAMS \
  --output_dir=$TRAIN_DIR \
  --decode_hparams="beam_size=$BEAM_SIZE,alpha=$ALPHA" \
  --decode_from_file=$DECODE_FILE \
  --decode_to_file=$TMP_DIR/newstest2014.en.tok.32kbpe.transformer_base.beam5.alpha0.6.decode

# 6. Debpe
cat $TMP_DIR/newstest2014.en.tok.32kbpe.transformer_base.beam5.alpha0.6.decode | sed 's/@@ //g' > $TMP_DIR/newstest2014.en.tok.32kbpe.transformer_base.beam5.alpha0.6.decode.debpe
# Do compound splitting on the translation
perl -ple 's{(\S)-(\S)}{$1 ##AT##-##AT## $2}g' < $TMP_DIR/newstest2014.en.tok.32kbpe.transformer_base.beam5.alpha0.6.decode.debpe > $TMP_DIR/newstest2014.en.tok.32kbpe.transformer_base.beam5.alpha0.6.decode.debpe.atat
# Do same compound splitting on the ground truth and then score bleu
# ...

Note that step 6 remains a postprocessing. For some historical reasons, Google split compound words before getting the final BLEU results which will bring moderate increase. see get_ende_bleu.sh for more details.

If you have only 1 GPU, you can use transformer_base_multistep8 hparams to imitate 8 GPU.

transformer_base_multistep8

You can also modify transformer_base_multistep8 function to accumulate gradient times you want. Here is an example using 4 GPU to run transformer big model. Note that hparams.optimizer_multistep_accumulate_steps = 2 since we only need to accumulate gradient twice for 4 GPU.

@registry.register_hparams
def transformer_base_multistep8():
 """HParams for simulating 8 GPUs with MultistepAdam optimizer."""
 hparams = transformer_big()
 hparams.optimizer = "MultistepAdam"
 hparams.optimizer_multistep_accumulate_steps = 2
 return hparams
Resources

Harvard NLP Group's implementation: OpenNMT-py(using PyTorch)

Code
Steps to reproduce WMT14 English-German result:

(updated on v0.5.0)

For command arguments meaning, see OpenNMT-py doc or OpenNMT-py opts.py

  1. Download corpus preprocessed by OpenNMT, sentencepiece model preprocessed by OpenNMT. Note that the preprocess procedure includes tokenization, bpe/word-piece operation(here using sentencepiece powered by Google which implements word-piece algorithm), see OpenNMT-tf script for more details.

  2. Preprocess. Because English and German are similar languages here we use -share_vocab to share vocabulary between source language and target language, which means you don't need to set this flag for distant language pairs such as Chinese-English. Meanwhile, we use a max sequence length of 100 to cover almostly all sentences on the basis of sentence length distribution of corpus. For example:

    python preprocess.py \
    -train_src ../wmt-en-de/train.en.shuf \
    -train_tgt ../wmt-en-de/train.de.shuf \
    -valid_src ../wmt-en-de/valid.en \
    -valid_tgt ../wmt-en-de/valid.de \
    -save_data ../wmt-en-de/processed \
    -src_seq_length 100 \
    -tgt_seq_length 100 \
    -max_shard_size 200000000 \
    -share_vocab

  3. Train. For example, if you only have 4 GPU:

    python  train.py -data /tmp/de2/data -save_model /tmp/extra \
    -layers 6 -rnn_size 512 -word_vec_size 512 -transformer_ff 2048 -heads 8  \
    -encoder_type transformer -decoder_type transformer -position_encoding \
    -train_steps 200000  -max_generator_batches 2 -dropout 0.1 \
    -batch_size 4096 -batch_type tokens -normalization tokens  -accum_count 2 \
    -optim adam -adam_beta2 0.998 -decay_method noam -warmup_steps 8000 -learning_rate 2 \
    -max_grad_norm 0 -param_init 0  -param_init_glorot \
    -label_smoothing 0.1 -valid_steps 10000 -save_checkpoint_steps 10000 \
    -world_size 4 -gpu_ranks 0 1 2 3

    Note that here -accum_count means every N batches accumulating loss to backward, so it's 2 for 4 GPUs and so on.

  4. Translate. For example:
    You can set -batch_size(default 30) larger to boost the translation.

    python translate.py -gpu 0 -replace_unk -alpha 0.6 -beta 0.0 -beam_size 5 -length_penalty wu -coverage_penalty wu \
     -share_vocab vocab_file -max_length 200 -model model_file -src newstest2014.en.32kspe -output model.pred -verbose

    Note that testset in corpus preprocessed by OpenNMT is newstest2017 while it is newstest2014 in original paper, which may be a mistake. To obtain newstest2014 testset as in paper, here we can use sentencepiece to encode newstest2014.en manually. You can find <model_file>in step 1's downloaded archive.

    spm_encode --model=<model_file> --output_format=piece < newstest2014.en > newstest2014.en.32kspe

  5. Detokenization. Since training data is processed by sentencepiece, step 4's translation should be sentencepiece-encoded style, so we need a decoding procedure to obtain a detokenized plain prediction. For example:

    spm_decode --model=<model_file> --input_format=piece < input > output

  6. Postprocess

There is also a bpe-version WMT'16 ENDE corpus preprocessed by Google. See subword-nmt for bpe encoding and decoding.

Resources

FAIR's implementation: fairseq-py(using PyTorch)

Code
Steps to reproduce WMT14 English-German result:

(updated on commit 7e60d45)

For arguments meaning, see doc. Note that we can use --update-freq when training to accumulate every N batches loss to backward, so it's 8 for 1 GPU, 2 for 4 GPUs and so on.

  1. Download the preprocessed WMT'16 EN-DE data provided by Google and extract it.

    TEXT=wmt16_en_de_bpe32k
mkdir $TEXT
tar -xzvf wmt16_en_de.tar.gz -C $TEXT

  2. Preprocess the dataset with a joined dictionary

    python preprocess.py --source-lang en --target-lang de \
        --trainpref $TEXT/train.tok.clean.bpe.32000 \
        --validpref $TEXT/newstest2013.tok.bpe.32000 \
        --testpref $TEXT/newstest2014.tok.bpe.32000 \
        --destdir data-bin/wmt16_en_de_bpe32k \
        --joined-dictionary

  3. Train. For a base model.

    # train about 180k steps
python train.py data-bin/wmt16_en_de_bpe32k \
    --arch transformer_wmt_en_de --share-all-embeddings \
    --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \
    --lr-scheduler inverse_sqrt --warmup-init-lr 1e-07 --warmup-updates 4000 \
    --lr 0.0007 --min-lr 1e-09 \
    --weight-decay 0.0 --criterion label_smoothed_cross_entropy \ 
    --label-smoothing 0.1 --max-tokens 4096 --update-freq 2 \
    --no-progress-bar --log-format json --log-interval 10 --save-interval-updates 1000 \
    --keep-interval-updates 5
# average last 5 checkpoints
modelfile=checkpoints
python scripts/average_checkpoints.py --inputs $modelfile --num-update-checkpoints 5 \
    --output $modelfile/average-model.pt

    For a big model.

    # train about 270k steps
python train.py data-bin/wmt16_en_de_bpe32k \
    --arch transformer_vaswani_wmt_en_de_big --share-all-embeddings \
    --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \
    --lr-scheduler inverse_sqrt --warmup-init-lr 1e-07 --warmup-updates 4000 \
    --lr 0.0005 --min-lr 1e-09 \
    --weight-decay 0.0 --criterion label_smoothed_cross_entropy \		
    --label-smoothing 0.1 --max-tokens 4096 --update-freq 2\
    --no-progress-bar --log-format json --log-interval 10 --save-interval-updates 1000 \
    --keep-interval-updates 20
# average last 20 checkpoints
modelfile=checkpoints
python scripts/average_checkpoints.py --inputs $modelfile --num-update-checkpoints 20 \ 
    --output $modelfile/average-model.pt

  4. Inference

    model=average-model.pt
subset=test
python generate.py data-bin/wmt16_en_de_bpe32k --path $modelfile/$model \
    --gen-subset $subset --beam 4 --batch-size 128 --remove-bpe --lenpen 0.6 > pred.de
# because fairseq's output is unordered, we need to recover its order
grep ^H pred.de | cut -f1,3- | cut -c3- | sort -k1n | cut -f2- > pred.de

  5. Postprocess

Resources

Complex, not certainly performance-reproducable implementations

  • Marian(purely c++ implementation without any deep learning framework)

Training tips

Further

Contributors

This project is developed and maintained by Natural Language Processing Group, ICT/CAS.

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