fengshen/examples/deepVAE/pretrain_deep_vae.sh

#!/bin/bash

#SBATCH --job-name=deep_vae_pretrain
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=32 #
#SBATCH --gres=gpu:1                 # number of gpus
#SBATCH -o xxx/outputs/deep_vae/logs/slurm/%x-%j.log
#SBATCH -e xxx/outputs/deep_vae/logs/slurm/%x-%j.err
# SBATCH --requeue
# SBATCH --qos=preemptive

set -x -e

ulimit -s unlimited
echo "START TIME: $(date)"

MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# export MASTER_ADDR=127.0.0.1
export MASTER_PORT=$[RANDOM%10000+50000]

MICRO_BATCH_SIZE=64
ZERO_STAGE=0

ROOT_PATH=xxxx
config_json=${ROOT_PATH}/job_out/ds_config.json

# Deepspeed figures out GAS dynamically from dynamic GBS via set_train_batch_size()
cat <<EOT > $config_json
{
  "train_micro_batch_size_per_gpu": ${MICRO_BATCH_SIZE},
  "steps_per_print": 100,
  "gradient_clipping": 1.0,
  "zero_optimization": {
    "stage": $ZERO_STAGE,
    "contiguous_gradients": false,
    "overlap_comm": true,
    "reduce_scatter": true,
    "reduce_bucket_size": 50000000,
    "allgather_bucket_size": 500000000
  },
  "optimizer": {
    "type": "Adam",
    "params": {
      "lr": 1e-5,
      "betas": [
        0.9,
        0.95
      ],
      "eps": 1e-8,
      "weight_decay": 1e-2
    }
  },
  "scheduler": {
    "type": "WarmupLR",
    "params":{
      "warmup_min_lr": 5e-6,
      "warmup_max_lr": 1e-5
    }
  },
  "zero_allow_untested_optimizer": false,
  "fp16": {
    "enabled": false,
    "loss_scale": 0,
    "loss_scale_window": 1000,
    "hysteresis": 2,
    "min_loss_scale": 1
  },
  "activation_checkpointing": {
    "partition_activations": false,
    "contiguous_memory_optimization": false
  },
  "wall_clock_breakdown": false
}
EOT
export PL_DEEPSPEED_CONFIG_PATH=$config_json
export TORCH_EXTENSIONS_DIR=~/tmp

# NOTE both encoder and decoder use the same model
GPT2_MODEL_PATH=xxx
VAE_ARGS="
    --gpt2_model_path $GPT2_MODEL_PATH \
    --latent_dim 32 \
    --beta_kl_constraints_start 1e-5 \
    --beta_kl_constraints_stop 1. \
    --beta_n_cycles 40 \
"


CHECKPOINT_SAVE_PATH=${ROOT_PATH}/checkpoints
MODEL_CHECKPOINT_ARGS="\
        --monitor val_recon_loss \
        --save_top_k 1 \
        --mode min \
        --every_n_train_steps 1000 \
        --save_weights_only True \
        --dirpath $CHECKPOINT_SAVE_PATH \
        --filename checkpoint-{epoch}-{step}-filenum_20_dim_32_beta_1e-5_1_zh_finance \
        "

TRAINER_ARGS="
    --max_epochs 40 \
    --gpus 1 \
    --num_nodes 1 \
    --precision 16 \
    --val_check_interval 1000 \
    --learning_rate 5e-5 \
    --warmup_steps 10000 \
    --weight_decay 0.01 \
    --default_root_dir ${ROOT_PATH} \
    --log_every_n_steps 50 \
    --strategy deepspeed_stage_2 \
"
# --strategy deepspeed_stage_2 \

# note we use wudao optimus instead of recreating a deepVAE dataset
DATA_ARGS="
    --train_batchsize $MICRO_BATCH_SIZE \
    --eval_batchsize $MICRO_BATCH_SIZE \
    --test_batchsize $MICRO_BATCH_SIZE \
    --num_workers 32 \
    --ds_name zh_finance 
"
# --ds_name wudao_tdvae, ner_re_data, zh_finance
# --CVAE
SCRIPTS_PATH=xxx/fengshen/examples/pretrain_vae

export CMD=" \
    $SCRIPTS_PATH/pretrain_deep_vae.py \
    $TRAINER_ARGS \
    $MODEL_CHECKPOINT_ARGS \
    $VAE_ARGS \
    $DATA_ARGS \
    "
# srun python $CMD
# python -m debugpy --listen 5678 --wait-for-client $CMD
python $CMD