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#!/bin/bash
#SBATCH --job-name=t5_cn_small_pretrain
#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 /cognitive_comp/ganruyi/fengshen/t5_cn_small_pretrain/%x-%j.log
#SBATCH -e /cognitive_comp/ganruyi/fengshen/t5_cn_small_pretrain/%x-%j.err
set -x -e
echo "START TIME: $(date)"
MICRO_BATCH_SIZE=128
ROOT_DIR=/cognitive_comp/ganruyi/fengshen/t5_cn_small_pretrain/
ZERO_STAGE=2
config_json="$ROOT_DIR/ds_config.t5_cn_small_pretrain.json"
export MASTER_PORT=$[RANDOM%10000+30000]
# Deepspeed figures out GAS dynamically from dynamic GBS via set_train_batch_size()
cat <<EOT > $config_json
{
"train_micro_batch_size_per_gpu": 128,
"steps_per_print": 100,
"gradient_clipping": 1.0,
"zero_optimization": {
"stage": $ZERO_STAGE,
"contiguous_gradients": false,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 50000000,
"allgather_bucket_size": 500000000
},
"optimizer": {
"type": "AdamW",
"params": {
"lr": 1e-4,
"betas": [
0.9,
0.95
],
"eps": 1e-8,
"weight_decay": 1e-2
}
},
"scheduler": {
"type": "WarmupLR",
"params":{
"warmup_min_lr": 0,
"warmup_max_lr": 1e-4,
"warmup_num_steps": 10000
}
},
"zero_allow_untested_optimizer": false,
"fp16": {
"enabled": true,
"loss_scale": 0,
"loss_scale_window": 1000,
"hysteresis": 2,
"min_loss_scale": 1
},
"activation_checkpointing": {
"partition_activations": false,
"contiguous_memory_optimization": false
},
"wall_clock_breakdown": false
}
EOT
export PL_DEEPSPEED_CONFIG_PATH=$config_json
export TORCH_EXTENSIONS_DIR=/cognitive_comp/ganruyi/tmp/torch_extendsions
# strategy=ddp
strategy=deepspeed_stage_2
TRAINER_ARGS="
--max_epochs 1 \
--gpus 1 \
--num_nodes 1 \
--strategy ${strategy} \
--default_root_dir $ROOT_DIR \
--dirpath $ROOT_DIR/ckpt \
--save_top_k 10 \
--monitor train_loss \
--mode min \
--save_last \
--val_check_interval 0.01 \
--accumulate_grad_batches 8 \
--resume_from_checkpoint /cognitive_comp/ganruyi/fengshen/t5_cn_small_pretrain/old-ckpt/last.ckpt \
--do_eval_only \
"
# --accumulate_grad_batches 8 \
DATA_DIR=wudao_180g_mt5_tokenized
DATA_ARGS="
--train_batchsize $MICRO_BATCH_SIZE \
--valid_batchsize $MICRO_BATCH_SIZE \
--train_data wudao_180g_mt5_tokenized\
--train_split_size 0.999 \
--max_seq_length 1024 \
"
MODEL_ARGS="
--pretrained_model_path /cognitive_comp/ganruyi/hf_models/google/mt5-small \
--new_vocab_path /cognitive_comp/ganruyi/hf_models/t5_cn_small/sentencepiece_cn.model \
--learning_rate 1e-4 \
--weight_decay 0.1 \
--keep_tokens_path /cognitive_comp/ganruyi/hf_models/t5_cn_small/sentencepiece_cn_keep_tokens.json \
"
SCRIPTS_PATH=/cognitive_comp/ganruyi/fengshen/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"
# clear; srun singularity exec --nv -B /cognitive_comp/:/cognitive_comp/ $SINGULARITY_PATH bash -c '/home/ganruyi/anaconda3/bin/python $CMD'
/home/ganruyi/anaconda3/bin/python $CMD |