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Transformer for Machine Translation

This is an implementation of the Transformer model described in Vaswani, Ashish, et al. "Attention is all you need.".

Quick Start: Prerequisites & use on machine translation datasets.

Run Your Customized Experiments: Hands-on tutorial of data preparation, configuration, and model training/testing.

Quick Start

Prerequisites

Run the following command to install necessary packages for the example:

pip install -r requirements.txt

Datasets

Two example datasets are provided:

  • IWSLT'15 EN-VI for English-Vietnamese translation
  • WMT'14 EN-DE for English-German translation

Download and pre-process the IWSLT'15 EN-VI data with the following commands:

sh scripts/iwslt15_en_vi.sh
sh preprocess_data.sh spm en vi

By default, the downloaded dataset is in ./data/en_vi. As with the official implementation, spm (sentencepiece) encoding is used to encode the raw text as data pre-processing. The encoded data is by default in ./temp/run_en_vi_spm.

For the WMT'14 EN-DE data, download and pre-process with:

sh scripts/wmt14_en_de.sh
sh preprocess_data.sh bpe en de

Note that this is a large dataset and preprocessing requires large amounts of memory.

By default, the downloaded dataset is in ./data/en_de. Note that for this dataset, bpe encoding (Byte pair encoding) is used instead. The encoded data is by default in ./temp/run_en_de_bpe.

Train and evaluate the model

Train the model with the command:

python transformer_main.py \
    --run-mode=train_and_evaluate \
    --config-model=config_model \
    --config-data=config_iwslt15
  • Specify --output-dir to dump model checkpoints and training logs to a desired directory. By default it is set to ./outputs.
  • Specifying --output-dir will also restore the latest model checkpoint under the directory, if any checkpoint exists.
  • Specify --config-data=config_wmt14 to train on the WMT'14 data.
  • Additionally, you can also specify --load-checkpoint to load a previously trained checkpoint from output_dir.

Test a trained model

To only evaluate a model checkpoint without training, first load the checkpoint and generate samples:

python transformer_main.py \
    --run-mode=test \
    --config-data=config_iwslt15 \
    --output-dir=./outputs

The latest checkpoint in ./outputs is used. Generated samples are in the file ./outputs/test.output.hyp, and reference sentences are in the file ./outputs/test.output.ref. The script shows the cased BLEU score as provided by the tx.evals.file_bleu function.

Alternatively, you can also compute the BLEU score with the raw sentences using the bleu_main script:

python bleu_main.py --reference=data/en_vi/test.vi --translation=temp/test.output.hyp

Results

  • On IWSLT'15, the implementation achieves around BLEU_cased=29.05 and BLEU_uncased=29.94 (reported by bleu_main.py), which are comparable to the base_single_gpu results by the official implementation (28.12 and 28.97, respectively, as reported here).

  • On WMT'14, the implementation achieves around BLEU_cased=25.02 following the setting in config_wmt14.py (setting: base_single_gpu, batch_size=3072). It takes more than 18 hours to finish training 250k steps. You can modify max_train_epoch in config_wmt14.py to adjust the training time.

Example training log

INFO 2019-08-15 22:04:15 : Begin running with train_and_evaluate mode
WARNING 2019-08-15 22:04:15 : Specified checkpoint directory 'outputs' exists, previous checkpoints might be erased
INFO 2019-08-15 22:04:15 : Training started
INFO 2019-08-15 22:04:15 : Model architecture:
ModelWrapper(
  (model): Transformer(
    ...
  )
)
2019-08-15 22:05:51 : Epoch  1 @    500it (13.0%, 172.63ex/s), lr = 2.184e-05, loss = 7.497
2019-08-15 22:07:27 : Epoch  1 @   1000it (26.0%, 172.91ex/s), lr = 4.367e-05, loss = 6.784
2019-08-15 22:09:03 : Epoch  1 @   1500it (39.0%, 172.52ex/s), lr = 6.551e-05, loss = 6.365
2019-08-15 22:10:40 : Epoch  1 @   2000it (51.9%, 172.03ex/s), lr = 8.735e-05, loss = 5.847
2019-08-15 22:15:50 : Epoch 1, valid BLEU = 2.075
INFO 2019-08-15 22:15:54 : Current checkpoint saved to outputs/1565921750.7879117.pt

Using an NVIDIA GTX 1080Ti, the model usually converges within 5 hours (~15 epochs) on IWSLT'15.

Run Your Customized Experiments

Here is a hands-on tutorial on running Transformer with your own customized dataset.

1. Prepare raw data

Create a data directory and put the raw data in the directory. To be compatible with the data preprocessing in the next step, you may follow the convention below:

  • The data directory should be named as data/${src}_${tgt}/. Take the data downloaded with scripts/iwslt15_en_vi.sh for example, the data directory is data/en_vi.
  • The raw data should have 6 files, which contain source and target sentences of training/dev/test sets, respectively. In the iwslt15_en_vi example, data/en_vi/train.en contains the source sentences of the training set, where each line is a sentence. Other files are train.vi, dev.en, dev.vi, test.en, test.vi.

2. Preprocess the data

To obtain the processed dataset, run

preprocess_data.sh ${encoder} ${src} ${tgt} ${vocab_size} ${max_seq_length}

where

  • The encoder parameter can be bpe(byte pairwise encoding), spm (sentence piece encoding), or raw(no subword encoding).
  • vocab_size is optional. The default is 32000.
    • At this point, this parameter is used only when encoder is set to bpe or spm. For raw encoding, you'd have to truncate the vocabulary by yourself.
    • For spm encoding, the preprocessing may fail (due to the Python sentencepiece module) if vocab_size is too large. So you may want to try smaller vocab_size if it happens.
  • max_seq_length is optional. The default is 70.

In the iwslt15_en_vi example, the command is sh preprocess_data.sh spm en vi.

By default, the preprocessed data are dumped under temp/run_${src}_${tgt}_${encoder}. In the iwslt15_en_vi example, the directory is temp/run_en_vi_spm.

If you choose to use raw encoding method, notice that:

  • By default, the word embedding layer is built with the combination of source vocabulary and target vocabulary. For example, if the source vocabulary is of size 3K and the target vocabulary of size 3K and there is no overlap between the two vocabularies, then the final vocabulary used in the model is of size 6K.
  • By default, the final output layer of transformer decoder (hidden_state -> logits) shares the parameters with the word embedding layer.

3. Specify data and model configuration

Customize the Python configuration files to config the model and data.

Please refer to the example configuration files config_model.py for model configuration and config_iwslt15.py for data configuration.

4. Train the model

Train the model with the following command:

python transformer_main.py \
    --run-mode=train_and_evaluate \
    --config-model=<custom_config_model> \
    --config-data=<custom_config_data>

where the model and data configuration files are custom_config_model.py and custom_config_data.py, respectively.

Outputs such as model checkpoints are by default under outputs/.

5. Test the model

Test with the following command:

python transformer_main.py \
    --run-mode=test \
    --config-data=<custom_config_data> \
    --output-dir=./outputs

Generated samples on the test set are in outputs/test.output.hyp, and reference sentences are in outputs/test.output.ref. If you've used bpe or spm encoding in the data preprocessing step, make sure to set encoding in the data configuration to the appropriate encoding type. The generated output will be decoded using the specified encoding.

Finally, to evaluate the BLEU score against the ground truth on the test set:

python bleu_main.py --reference=<your_reference_file> --translation=temp/test.output.hyp.final

For the iwslt15_en_vi example, use --reference=data/en_vi/test.vi.