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run_ner.sh
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run_ner.sh
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# batch size based on large/base/small
# arguments for best metric
# other parameters
# three different types of model runs
#!/bin/bash
# flag to run with slurm commands
USE_SLURM=0
# defaults
NUM_INSTANCES=1
DEMUXING="index_pos"
MUXING="gaussian_hadamard"
MODEL_TYPE="bert"
CONFIG_NAME="datamux_pretraining/configs/bert_base.json"
LEARNING_RATE=5e-5
TASK_NAME="ner"
LEARN_MUXING=0
DO_TRAIN=0
DO_EVAL=0
GRADIENT_ACCUMULATION=4
# commmand line arguments
#!/bin/bash
show_help() {
echo 'Usage run_glue.sh [OPTIONS]'
echo 'options:'
echo '-N --num_instances [2,5,10,20,40]'
echo '-d --demuxing [index, mlp]'
echo '-m --muxing [gaussian_hadamard, binary_hadamard, random_ortho]'
echo '-s --setting [baseline, finetuning, retrieval_pretraining]'
echo '--task [mnli, qnli, sst2, qqp]'
echo '--config_name CONFIG_NAME'
echo '--lr LR'
echo '--batch_size BATCH_SIZE'
echo '--model_path MODEL_PATH'
echo '--learn_muxing'
echo '--continue'
echo '--do_train'
echo '--do_eval'
}
die() {
printf '%s\n' "$1" >&2
exit 1
}
while :; do
case $1 in
-h|-\?|--help)
show_help # Display a usage synopsis.
exit
;;
-N|--num-instances) # Takes an option argument; ensure it has been specified.
if [ "$2" ]; then
NUM_INSTANCES=$2
shift # shift consumes $2 without treating it as another argument
else
die 'ERROR: "--num-instances" requires a non-empty option argument.'
fi
;;
-d|--demuxing)
if [ "$2" ]; then
DEMUXING=$2
shift
else
die 'ERROR: "--demuxing" requires a non-empty option argument.'
fi
;;
-m|--muxing)
if [ "$2" ]; then
MUXING=$2
shift
else
die 'ERROR: "--muxing" requires a non-empty option argument.'
fi
;;
-s|--setting)
if [ "$2" ]; then
SETTING=$2
shift
else
die 'ERROR: "--setting" requires a non-empty option argument.'
fi
;;
--config_name)
if [ "$2" ]; then
CONFIG_NAME=$2
shift
else
die 'ERROR: "--config_name" requires a non-empty option argument.'
fi
;;
--lr)
if [ "$2" ]; then
LEARNING_RATE=$2
shift
else
die 'ERROR: "--lr" requires a non-empty option argument.'
fi
;;
--batch_size)
if [ "$2" ]; then
BATCH_SIZE=$2
shift
else
die 'ERROR: "--batch_size" requires a non-empty option argument.'
fi
;;
--task)
if [ "$2" ]; then
TASK_NAME=$2
shift
else
die 'ERROR: "--task" requires a non-empty option argument.'
fi
;;
--gradient_accumulation)
if [ "$2" ]; then
GRADIENT_ACCUMULATION=$2
shift
else
die 'ERROR: "--gradient_accumulation" requires a non-empty option argument.'
fi
;;
--model_type)
if [ "$2" ]; then
MODEL_TYPE=$2
shift
else
die 'ERROR: "--model_type" requires a non-empty option argument.'
fi
;;
--model_path)
if [ "$2" ]; then
MODEL_PATH=$2
shift
else
die 'ERROR: "--model_path" requires a non-empty option argument.'
fi
;;
--learn_muxing)
LEARN_MUXING=1
;;
--do_train)
DO_TRAIN=1
;;
--do_eval)
DO_EVAL=1
;;
--) # End of all options.
shift
break
;;
-?*)
die "ERROR: Unknown option : ${1}"
;;
*) # Default case: No more options, so break out of the loop.
break
esac
shift
done
BEST_METRIC="eval_f1"
NUM_TRAIN_STEPS=20000
NUM_EVAL_STEPS=100
declare -A task2dataset
task2dataset[ner]="conll2003"
task2dataset[pos]="universal_dependencies"
declare -A task2labelcol
task2labelcol[ner]="ner_tags"
task2labelcol[pos]="upos"
DATASET_NAME=${task2dataset[$TASK_NAME]}
LABEL_COLUMN_NAME=${task2labelcol[$TASK_NAME]}
# other miscelleneous params
MAX_SEQ_LENGTH=128
if [ "$SETTING" = "finetuning" ]; then
RANDOM_ENCODING_NORM=1
RETRIEVAL_PERCENTAGE=1.0
RETRIEVAL_LOSS_COEFF=0
TASK_LOSS_COEFF=1
SHOULD_MUX=1
DATALOADER_DROP_LST=1
OUTPUT_DIR_BASE="checkpoints/finetune"
# add task name
# save steps + save strategy + num epochs
CMD_DIFF="--task_name ${TASK_NAME}\
--evaluation_strategy steps \
--eval_steps ${NUM_EVAL_STEPS} \
--max_steps ${NUM_TRAIN_STEPS} \
--save_steps ${NUM_EVAL_STEPS} \
--should_mux 1"
elif [ "$SETTING" = "baseline" ]; then
echo "Setting is baseline; sets --num-instances to 1."
RANDOM_ENCODING_NORM=1
RETRIEVAL_PERCENTAGE=1.0
RETRIEVAL_LOSS_COEFF=0
TASK_LOSS_COEFF=1
SHOULD_MUX=0
DATALOADER_DROP_LAST=0
OUTPUT_DIR_BASE="checkpoints/baselines"
NUM_INSTANCES=1
# add task name
# save steps + save strategy + num epochs
CMD_DIFF="--task_name ${TASK_NAME}\
--evaluation_strategy steps \
--eval_steps ${NUM_EVAL_STEPS} \
--max_steps ${NUM_TRAIN_STEPS} \
--save_steps ${NUM_EVAL_STEPS} \
--should_mux 0"
else
echo "setting (${SETTING}) not recognized or unset. run \"run_glue.sh -h\" for usage."
exit 0
fi
if [ "$TASK_NAME" = "pos" ]; then
CMD_DIFF="${CMD_DIFF} --dataset_config_name en_ewt"
fi
if [[ $LEARN_MUXING -ge 1 ]]; then
OUTPUT_DIR=$OUTPUT_DIR_BASE/${CONFIG_NAME}_${TASK_NAME}_${MODEL_PATH}_${MUXING}_${DEMUXING}_${NUM_INSTANCES}_norm_${RANDOM_ENCODING_NORM}_rc_${RETRIEVAL_LOSS_COEFF}_lr${LEARNING_RATE}_tc_${TASK_LOSS_COEFF}_learntmuxing
RUN_NAME=${CONFIG_NAME}_${TASK_NAME}_${MODEL_PATH}_${MUXING}_${DEMUXING}_${NUM_INSTANCES}_${RETRIEVAL_PERCENTAGE}_norm_${RANDOM_ENCODING_NORM}_rc_${RETRIEVAL_LOSS_COEFF}_lr${LEARNING_RATE}_tc_${TASK_LOSS_COEFF}_learnmuxing
else
OUTPUT_DIR=$OUTPUT_DIR_BASE/${CONFIG_NAME}_${TASK_NAME}_${MODEL_PATH}_${MUXING}_${DEMUXING}_${NUM_INSTANCES}_norm_${RANDOM_ENCODING_NORM}_rc_${RETRIEVAL_LOSS_COEFF}_lr${LEARNING_RATE}_tc_${TASK_LOSS_COEFF}
RUN_NAME=${CONFIG_NAME}_${TASK_NAME}_${MODEL_PATH}_${MUXING}_${DEMUXING}_${NUM_INSTANCES}_${RETRIEVAL_PERCENTAGE}_norm_${RANDOM_ENCODING_NORM}_rc_${RETRIEVAL_LOSS_COEFF}_lr${LEARNING_RATE}_tc_${TASK_LOSS_COEFF}
fi
BATCH_SIZE=32
BATCH_SIZE=$(($BATCH_SIZE * NUM_INSTANCES))
CMD="python run_ner.py \
--tokenizer_name bert-base-uncased \
--config_name ${CONFIG_NAME} \
--model_version ${MODEL_TYPE} \
--max_seq_length $MAX_SEQ_LENGTH \
--per_device_train_batch_size $BATCH_SIZE \
--per_device_eval_batch_size $BATCH_SIZE \
--learning_rate $LEARNING_RATE \
--output_dir $OUTPUT_DIR \
--run_name $RUN_NAME \
--logging_steps 100 \
--dataloader_drop_last $DATALOADER_DROP_LAST \
--dataloader_num_workers 4 \
--warmup_ratio 0.1 \
--lr_scheduler_type linear \
--retrieval_percentage $RETRIEVAL_PERCENTAGE \
--retrieval_loss_coeff $RETRIEVAL_LOSS_COEFF \
--task_loss_coeff $TASK_LOSS_COEFF \
--num_instances ${NUM_INSTANCES} \
--muxing_variant ${MUXING} \
--demuxing_variant ${DEMUXING} \
--should_mux ${SHOULD_MUX} \
--report_to wandb \
--gaussian_hadamard_norm ${RANDOM_ENCODING_NORM} \
--gradient_accumulation_steps ${GRADIENT_ACCUMULATION} \
--learn_muxing ${LEARN_MUXING} \
--load_best_model_at_end 1 \
--metric_for_best_model ${BEST_METRIC} \
--dataset_name ${DATASET_NAME} \
--label_column_name ${LABEL_COLUMN_NAME} \
--num_hidden_demux_layers 3 \
--save_total_limit 1"
if [ "$DO_TRAIN" -eq 1 ]; then
CMD="${CMD} --do_train"
fi
if [ "$DO_EVAL" -eq 1 ]; then
CMD="${CMD} --do_eval"
fi
if [ ! -z "$MODEL_PATH" ] # if MODEL PATH is set manually
then
CMD="${CMD} --model_name_or_path ${MODEL_PATH}"
fi
CMD=${CMD}" "${CMD_DIFF}
TIME="30:00:00"
if [[ $USE_SLURM = 1 ]]; then
sbatch --time=$TIME --mem=32G --output=logs/%x-%j.out --job-name=${TASK_NAME}_${NUM_INSTANCES}_${MUXING}_${DEMUXING} --gres=gpu:1 ./run_job.sh \
"$CMD"
else
./run_job.sh "$CMD"
fi