set -eu

set -x

#bash -x ./env.sh

TASK_NAME=$1

CONF_FILE=$2

VOCAB_PATH=$3

ERNIE_VIL_CONFIG=$4

PRETRAIN_MODELS=$5

source $CONF_FILE

#configure your cuda and cudnn

#configure nccl

#export LD_LIBRARY_PATH=/home/work/cuda-9.0/lib64:/home/work/cudnn/cudnn_v7/cuda/lib64:$LD_LIBRARY_PATH

#export LD_LIBRARY_PATH=./nccl_2.3.5/lib/:$LD_LIBRARY_PATH

export FLAGS_fast_eager_deletion_mode=1

export FLAGS_eager_delete_tensor_gb=0.0

export FLAGS_fraction_of_gpu_memory_to_use=0.98

e_executor=$(echo ${use_experimental_executor-'True'} | tr '[A-Z]' '[a-z]')

use_fuse=$(echo ${use_fuse-'False'} | tr '[A-Z]' '[a-z]')

if [[ ${use_fuse} == "true" ]]; then

export FLAGS_fuse_parameter_memory_size=131072

export FLAGS_fuse_parameter_groups_size=10

fi

TASK_GROUP_JSON=./conf/$TASK_NAME/task_${TASK_NAME}.json

gpu_cnt=`echo $CUDA_VISIBLE_DEVICES | awk -F"\t" '{len=split($0,vec,",");print len}'`

echo "gpu_cnt", $gpu_cnt

python finetune.py --use_cuda "True" \

--is_distributed "False" \

--use_fast_executor ${e_executor-"True"} \

--nccl_comm_num ${nccl_comm_num:-"1"} \

--batch_size $((BATCH_SIZE/gpu_cnt)) \

--do_train "True" \

--do_test "False" \

--task_name ${TASK_NAME} \

--vocab_path ${VOCAB_PATH} \

--task_group_json ${TASK_GROUP_JSON} \

--lr_scheduler ${lr_scheduler} \

--decay_steps ${decay_steps-""} \

--lr_decay_ratio ${lr_decay_ratio-0.1} \

--layer_decay_rate ${layer_decay_rate-0.0} \

--text_init_layers ${text_init_layers-18} \

--n_layers ${n_layers-30} \

--margin ${margin-0.3} \

--num_train_steps ${num_train_steps} \

--checkpoints $output_model_path \

--save_steps ${SAVE_STEPS} \

--init_checkpoint ${PRETRAIN_MODELS} \

--ernie_config_path ${ERNIE_VIL_CONFIG} \

--learning_rate ${LR_RATE} \

--warmup_steps ${WARMUP_STEPS} \

--weight_decay ${WEIGHT_DECAY:-0} \

--max_seq_len ${MAX_LEN} \

--skip_steps 10