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train.sh
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train.sh
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while getopts ":p:t:d:" opt
do
case $opt in
p)
PRETRAINED_MODEL_PATH="$OPTARG"
;;
t)
PRETRAINED_MODEL_TYPE="$OPTARG"
;;
d)
DATASET="$OPTARG"
;;
?)
echo "未知参数"
exit 1;;
esac
done
PROJECT_PATH='.'
if [ "$PRETRAINED_MODEL_TYPE" == "dialogved_standard" ]; then
echo '-------- model type: dialogved standard --------'
ARCH=ngram_transformer_prophet_vae_standard
elif [ "$PRETRAINED_MODEL_TYPE" == "dialogved_large" ]; then
echo '-------- model type: dialogved large --------'
ARCH=ngram_transformer_prophet_vae_large
elif [ "$PRETRAINED_MODEL_TYPE" == "dialogved_seq2seq" ]; then
echo '-------- model type: dialogved seq2seq --------'
ARCH=ngram_transformer_prophet_seq2seq
else
echo 'model type '"$PRETRAINED_MODEL_TYPE"' not found!'
exit 1
fi
if [ "$DATASET" == "dailydialog" ]; then
echo '-------- fine-tune on dataset: dailydialog --------'
NUM_WORKERS=10
CRITERION=ved_loss
TASK=ved_translate
USER_DIR=${PROJECT_PATH}/src
DATA_DIR=${PROJECT_PATH}/data/finetune/dailydialog
SAVE_DIR=${DATA_DIR}/checkpoints
TB_LOGDIR=${DATA_DIR}/tensorboard
fairseq-train \
${DATA_DIR}/binary \
--fp16 \
--user-dir ${USER_DIR} --task ${TASK} --arch ${ARCH} \
--optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 1.0 \
--lr 0.0003 \
--lr-scheduler inverse_sqrt --warmup-init-lr 1e-07 --warmup-updates 2000 \
--criterion $CRITERION --label-smoothing 0.1 \
--update-freq 4 --max-tokens 4500 --max-sentences 16 \
--num-workers ${NUM_WORKERS} \
--dropout 0.1 --attention-dropout 0.1 --activation-dropout 0.0 --weight-decay 0.01 \
--encoder-layer-drop 0.0 \
--save-dir ${SAVE_DIR} \
--max-epoch 10 \
--keep-last-epochs 10 \
--max-source-positions 512 \
--max-target-positions 128 \
--kl-loss-weight 1.0 \
--target-kl 5.0 \
--cls-bow-loss-weight 0.0 \
--latent-bow-loss-weight 1.0 \
--masked-lm-loss-weight 0.0 \
--tensorboard-logdir ${TB_LOGDIR} \
--dataset-impl mmap \
--empty-cache-freq 64 \
--seed 1 \
--skip-invalid-size-inputs-valid-test \
--distributed-no-spawn \
--ddp-backend no_c10d \
--load-from-pretrained-model "${PRETRAINED_MODEL_PATH}"
elif [ "$DATASET" == "dstc7avsd" ]; then
echo '-------- fine-tune on dataset: dstc7avsd --------'
NUM_WORKERS=10
CRITERION=ved_loss
TASK=ved_translate
USER_DIR=${PROJECT_PATH}/src
DATA_DIR=${PROJECT_PATH}/data/finetune/dstc7avsd
SAVE_DIR=${DATA_DIR}/checkpoints
TB_LOGDIR=${DATA_DIR}/tensorboard
fairseq-train \
${DATA_DIR}/binary \
--fp16 \
--user-dir ${USER_DIR} --task ${TASK} --arch ${ARCH} \
--optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 1.0 \
--lr 0.0003 \
--lr-scheduler inverse_sqrt --warmup-init-lr 1e-07 --warmup-updates 2000 \
--criterion $CRITERION --label-smoothing 0.1 \
--update-freq 4 --max-tokens 4500 --max-sentences 16 \
--num-workers ${NUM_WORKERS} \
--dropout 0.1 --attention-dropout 0.1 --activation-dropout 0.0 --weight-decay 0.01 \
--encoder-layer-drop 0.0 \
--save-dir ${SAVE_DIR} \
--max-epoch 10 \
--keep-last-epochs 10 \
--max-source-positions 512 \
--max-target-positions 128 \
--kl-loss-weight 1.0 \
--target-kl 5.0 \
--cls-bow-loss-weight 0.0 \
--latent-bow-loss-weight 1.0 \
--masked-lm-loss-weight 0.0 \
--tensorboard-logdir ${TB_LOGDIR} \
--dataset-impl mmap \
--empty-cache-freq 64 \
--seed 1 \
--skip-invalid-size-inputs-valid-test \
--distributed-no-spawn \
--ddp-backend no_c10d \
--load-from-pretrained-model "${PRETRAINED_MODEL_PATH}"
elif [ "$DATASET" == "personachat" ]; then
echo '-------- fine-tune on dataset: personachat --------'
NUM_WORKERS=10
CRITERION=ved_loss
TASK=ved_translate
USER_DIR=${PROJECT_PATH}/src
DATA_DIR=${PROJECT_PATH}/data/finetune/personachat
SAVE_DIR=${DATA_DIR}/checkpoints
TB_LOGDIR=${DATA_DIR}/tensorboard
fairseq-train \
${DATA_DIR}/binary \
--fp16 \
--user-dir ${USER_DIR} --task ${TASK} --arch ${ARCH} \
--optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 1.0 \
--lr 0.0003 \
--lr-scheduler inverse_sqrt --warmup-init-lr 1e-07 --warmup-updates 2000 \
--criterion $CRITERION --label-smoothing 0.1 \
--update-freq 4 --max-tokens 4500 --max-sentences 16 \
--num-workers ${NUM_WORKERS} \
--dropout 0.1 --attention-dropout 0.1 --activation-dropout 0.0 --weight-decay 0.01 \
--encoder-layer-drop 0.0 \
--save-dir ${SAVE_DIR} \
--max-epoch 10 \
--keep-last-epochs 10 \
--max-source-positions 512 \
--max-target-positions 128 \
--kl-loss-weight 1.0 \
--target-kl 5.0 \
--cls-bow-loss-weight 0.0 \
--latent-bow-loss-weight 1.0 \
--masked-lm-loss-weight 0.0 \
--tensorboard-logdir ${TB_LOGDIR} \
--dataset-impl mmap \
--empty-cache-freq 64 \
--seed 1 \
--skip-invalid-size-inputs-valid-test \
--distributed-no-spawn \
--ddp-backend no_c10d \
--load-from-pretrained-model "${PRETRAINED_MODEL_PATH}"
else
# echo 'dataset not found!'
echo 'dataset '"$DATASET"' not found!'
exit 1
fi