Skip to content

Latest commit

 

History

History
126 lines (107 loc) · 5.16 KB

README.md

File metadata and controls

126 lines (107 loc) · 5.16 KB
Raw

LightSeq Training for Fairseq

Table of contents

This repo contains examples for how to use LightSeq to accerate the training of Fairseq.

First you should install these requirements.

pip install lightseq fairseq sacremoses

Accerate translation task

Train

Then you can train a translation task on wmt14 en2de dataset using LightSeq by running the following script:

sh examples/training/fairseq/ls_fairseq_wmt14en2de.sh

Or you can use LightSeq modules like --arch ls_transformer_wmt_en_de_big_t2t, by adding --user-dir=${LIGHTSEQ_DIR}/lightseq/training/cli/fs_modules to fairseq-train.

You can use --use-torch-layer to replace LightSeq layers with custom Torch layers based on native Fairseq layers.

You can use --enable-quant and --quant-mode qat to run quantization aware training for subsequent LightSeq fast int8 inference.

This script firstly download the dataset and then run native Fairseq training script using optimized model and optimizer. The lightseq-train command is just a easy-to-use wrapper of fairseq-train with adding LightSeq to --user-dir.

We also provide other training scripts to support custom Torch layers and quantization. All model files have been publicly released. Refer to examples/inference/python/README.md for more training, export and inference details.

LightSeq can achieve about 1.47x speedup using batch size 4096 on 8 V100 GPUs, compared with original Fairseq implementation. You can delete the ls prefix in parameters to switch to fairseq modules.

Evaluation

Then you can evaluate on wmt14 en2de dataset by running the following command:

lightseq-validate /tmp/wmt14_en_de/ \
    --valid-subset valid \
    --path checkpoints/checkpoint_best.pt \
    --task translation \
    --max-tokens 8192 \
    --criterion ls_label_smoothed_cross_entropy \
    --fp16 \
    --quiet

Generate

You can also generate on wmt14 en2de dataset by running the following command:

lightseq-generate /tmp/wmt14_en_de/ \
    --gen-subset test \
    --path checkpoints/checkpoint_best.pt \
    --task translation \
    --batch-size 128 \
    --beam 4 \
    --lenpen 0.6 \
    --fp16 \
    --quiet \
    --scoring sacrebleu

Sharded databin for Fairseq

To solve the situation where extremely large data cannot be loaded into memory at once, we can use Fairseq with sharded databin to train translation tasks.

Preprocess

You need to use script ls_preprocess_sharded_databin.sh to pre-process the data. --text-dir is directory path containing the train, valid, test bpe data. --n-line-per-file is the number of lines per file, default 100w lines. Other parameters are the same as fairseq-preprocess.

$ bash ls_preprocess_shared_databin.sh \
    --source-lang src --target-lang tgt \
    --text-dir /path/to/bpe_data \
    --destdir /path/to/databin_dir \
    --n-lines-per-file 1000000

Train

You need to use ls_translation task. And add a colon: to the end of the databin path. Databin path can be either the local path, or hdfs path. --npick means each epoch picks up n files for training.

$ lightseq-train hdfs://path/to/hdfs/path: \
    --task ls_translation \
    --source-lang src --target-lang tgt \
    --arch ls_transformer --share-decoder-input-output-embed \
    --optimizer ls_adam --adam-betas '(0.9, 0.98)' \
    --clip-norm 0.0 \
    --lr 5e-4 --lr-scheduler inverse_sqrt --warmup-updates 4000 --weight-decay 0.0001 \
    --criterion ls_label_smoothed_cross_entropy --label-smoothing 0.1 \
    --max-tokens 8192 \
    --eval-bleu \
    --eval-bleu-args '{"beam": 5, "max_len_a": 1.2, "max_len_b": 10}' \
    --eval-bleu-detok moses \
    --eval-bleu-remove-bpe \
    --eval-bleu-print-samples \
    --best-checkpoint-metric bleu \
    --maximize-best-checkpoint-metric \
    --npick 10 \
    --fp16

GCQ for Fairseq

You can train a translation task on wmt14 en2de dataset using LightSeq with GCQ by running the following script:

sh ls_fairseq_gcq_wmt14en2de.sh

You can use --enable_GCQ to enable GCQ in your multi-machine distributed training. You can set --GCQ_quantile to a float value between 0.0 and 1.0, which will use the quantile of gradient bucket as clip-max value when quantizing gradients. E.g., when setting --GCQ_quantile 0.99, the clip-max value is equal to the 0.99-th quantile of gradient bucket.

You can use multiple NICs in NCCL communication. E.g., if every machine has 4 NICs: eth0, eth1, eth2, eth3, you can use the following command:

export NCCL_SOCKET_IFNAME=eth0,eth1,eth2,eth3

Fine-tune BART model

You can fine-tune a BART model using the script ls_finetune_bart/ls_fairseq_summarization_cnn_dm.sh, and then convert the model to Hugging Face format using ls_finetune_bart/convert_lightseq_to_huggingface.sh.