#!/bin/bash
export HSA_FORCE_FINE_GRAIN_PCIE=1

lrank=$OMPI_COMM_WORLD_LOCAL_RANK
comm_rank=$OMPI_COMM_WORLD_RANK
comm_size=$OMPI_COMM_WORLD_SIZE

export PATH_PHRASE1=/public/opendas/DL_DATA/wikicorpus_en/lower_case_1_seq_len_128_max_pred_20_masked_lm_prob_0.15_random_seed_12345_dupe_factor_5_shard_1472_test_split_10/wikicorpus_en/training

APP="python3 run_pretraining_v4.py  \
    --input_dir=${PATH_PHRASE1}    \
    --output_dir=/bert-pytorch/output/phrase1/fp32 \
    --config_file=/bert-pytorch/bert_config.json \
    --bert_model=bert-large-uncased \
    --train_batch_size=16 \
    --max_seq_length=128 \
    --max_predictions_per_seq=20 \
    --max_steps=100000 \
    --warmup_proportion=0.0 \
    --num_steps_per_checkpoint=20000 \
    --learning_rate=4.0e-4 \
    --seed=12439 \
    --gradient_accumulation_steps=1 \
    --allreduce_post_accumulation \
    --do_train \
    --use_env \
    --local_rank ${comm_rank} \
    --world_size 4 \
    --gpus_per_node  1 \
    --dist_url tcp://localhost:34567 \
    --json-summary  /bert-pytorch/output/phrase1/fp32/dllogger.json
 "
case ${lrank} in
[0])
  export HIP_VISIBLE_DEVICES=0,1,2,3
  echo numactl --cpunodebind=0 --membind=0 ${APP}
  numactl --cpunodebind=0 --membind=0 ${APP}
  ;;
[1])
  export HIP_VISIBLE_DEVICES=0,1,2,3
  echo numactl --cpunodebind=1 --membind=1 ${APP}
  numactl --cpunodebind=1 --membind=1 ${APP}
  ;;
[2])
  export HIP_VISIBLE_DEVICES=0,1,2,3
  echo numactl --cpunodebind=2 --membind=2 ${APP} 
  numactl --cpunodebind=2 --membind=2 ${APP}
  ;;
[3])
  export HIP_VISIBLE_DEVICES=0,1,2,3
  echo numactl --cpunodebind=3 --membind=3 ${APP}
  numactl --cpunodebind=3 --membind=3 ${APP}
  ;;
esac

