Spaces:
Runtime error
Runtime error
File size: 3,535 Bytes
50f0fbb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 |
#!/bin/bash
#SBATCH --job-name=t5_cn_small_pretrain_v2
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=8
#SBATCH --gres=gpu:8 # number of gpus
#SBATCH --cpus-per-task=30 # cpu-cores per task (>1 if multi-threaded tasks)
#SBATCH -o %x-%j.log
#SBATCH -e %x-%j.err
#SBATCH -x dgx050
set -x -e
source activate base
echo "START TIME: $(date)"
MICRO_BATCH_SIZE=32
ROOT_DIR=/cognitive_comp/ganruyi/experiments/t5_cn_small_pretrain_v2/
ZERO_STAGE=1
config_json="$ROOT_DIR/ds_config.t5_cn_small_pretrain_v2.$SLURM_JOBID.json"
export MASTER_PORT=$[RANDOM%10000+30000]
# Deepspeed figures out GAS dynamically from dynamic GBS via set_train_batch_size()
cat <<EOT > $config_json
{
"zero_optimization": {
"stage": 1
},
"fp16": {
"enabled": true,
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},
"optimizer": {
"params": {
"betas": [
0.9,
0.95
],
"eps": 1e-08,
"lr": 1e-04,
"weight_decay": 0.01
},
"type": "AdamW"
},
"scheduler": {
"type": "WarmupLR",
"params":{
"warmup_min_lr": 0,
"warmup_max_lr": 1e-4,
"warmup_num_steps": 10000
}
},
"steps_per_print": 100,
"gradient_clipping": 1,
"train_micro_batch_size_per_gpu": $MICRO_BATCH_SIZE,
"zero_allow_untested_optimizer": false
}
EOT
export PL_DEEPSPEED_CONFIG_PATH=$config_json
export TORCH_EXTENSIONS_DIR=/cognitive_comp/ganruyi/tmp/torch_extendsions
# strategy=ddp
strategy=deepspeed_stage_1
TRAINER_ARGS="
--max_epochs 1 \
--gpus 8 \
--num_nodes 1 \
--strategy ${strategy} \
--default_root_dir $ROOT_DIR \
--dirpath $ROOT_DIR/ckpt \
--save_top_k 3 \
--every_n_train_steps 0 \
--monitor train_loss \
--mode min \
--save_last \
--val_check_interval 0.01 \
--preprocessing_num_workers 20 \
"
# --accumulate_grad_batches 8 \
DATA_DIR=wudao_180g_mt5_tokenized
DATA_ARGS="
--train_batchsize $MICRO_BATCH_SIZE \
--valid_batchsize $MICRO_BATCH_SIZE \
--train_data ${DATA_DIR} \
--train_split_size 0.999 \
--max_seq_length 1024 \
"
MODEL_ARGS="
--pretrained_model_path /cognitive_comp/ganruyi/experiments/t5_cn_small_pretrain/Randeng-T5-77M \
--learning_rate 1e-4 \
--weight_decay 0.1 \
--keep_tokens_path /cognitive_comp/ganruyi/hf_models/t5_cn_small/sentencepiece_cn_keep_tokens.json \
"
# --resume_from_checkpoint /cognitive_comp/ganruyi/fengshen/t5_cn_small_pretrain/ckpt/last.ckpt \
SCRIPTS_PATH=/cognitive_comp/ganruyi/Fengshenbang-LM/fengshen/examples/pretrain_t5/pretrain_t5.py
export CMD=" \
$SCRIPTS_PATH \
$TRAINER_ARGS \
$MODEL_ARGS \
$DATA_ARGS \
"
echo $CMD
SINGULARITY_PATH=/cognitive_comp/ganruyi/pytorch21_06_py3_docker_image_v2.sif
# to debug - add echo (it exits and prints what it would have launched)
#run_cmd="$PY_LAUNCHER $CMD"
# salloc --nodes=1 --gres=gpu:2 --cpus-per-gpu=20 -t 24:00:00
clear; srun singularity exec --nv -B /cognitive_comp/:/cognitive_comp/ $SINGULARITY_PATH bash -c '/home/ganruyi/anaconda3/bin/python $CMD'
# clear; srun --job-name=t5_cn_small_pretrain_v2 --jobid=153124 --nodes=1 --ntasks-per-node=8 --gres=gpu:8 --cpus-per-task=30 -o %x-%j.log -e %x-%j.err singularity exec --nv -B /cognitive_comp/:/cognitive_comp/ $SINGULARITY_PATH bash -c '/home/ganruyi/anaconda3/bin/python $CMD'
|