File size: 3,496 Bytes
8ebda9e |
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=pretrain_bart # create a short name for your job
#SBATCH --nodes=1 # node count
#SBATCH --ntasks-per-node=8 # number of tasks to run per node
#SBATCH --cpus-per-task=30 # cpu-cores per task (>1 if multi-threaded tasks)
#SBATCH --gres=gpu:8 # number of gpus per node
#SBATCH -o %x-%j.log # output and error log file names (%x for job id)
#SBATCH -x dgx050
MODEL_NAME=hubert-base-ls960
config_json="./$MODEL_NAME.ds_config.json"
export MASTER_PORT=29503
MICRO_BATCH_SIZE=8
ZERO_STAGE=1
# Deepspeed figures out GAS dynamically from dynamic GBS via set_train_batch_size()
cat <<EOT > $config_json
{
"zero_optimization": {
"stage": ${ZERO_STAGE}
},
"fp16": {
"enabled": true,
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},
"tensorboard": {
"enabled": true,
"output_path": "/data/training_model/fengshen-${MODEL_NAME}/ds-tb-logs",
"job_name": "${MODEL_NAME}"
},
"#flops_profiler": {
"enabled": true,
"profile_step": 200,
"detailed": true,
"output_file": null
},
"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=/home/gaoxinyu/torch_extendsions
DATA_DIR=/data/common_data/librispeech_tsv/datas
LABELS_DIR=/data/common_data/librispeech_tsv/labels
DATA_ARGS="\
--dataloader_workers 2 \
--train_batchsize $MICRO_BATCH_SIZE \
--val_batchsize 32 \
--test_batchsize 8 \
--val_datasets_field valid \
--test_datasets_field valid \
--sampler_type random \
--data ${DATA_DIR} \
--label_dir ${LABELS_DIR} \
--labels km \
--label_rate 100 \
--max_sample_size 250000 \
--min_sample_size 32000 \
--pad_audio False \
--random_crop True \
--normalize False \
"
MODEL_ARGS="\
--model_path /data/pretrained_model/$MODEL_NAME/ \
--learning_rate 1e-4 \
--weight_decay 1e-2 \
--warmup_ratio 0.01 \
--pred_masked_weight 1.0 \
--loss_weights 10 \
"
MODEL_CHECKPOINT_ARGS="\
--monitor train_loss \
--save_top_k 0 \
--mode min \
--every_n_train_steps 10000 \
--dirpath /data/training_model/ckpt/fengshen-$MODEL_NAME \
--filename model-{step:02d}-{train_loss:.4f} \
--every_n_epochs 0 \
--save_last \
--not_save_on_train_epoch_end \
"
# deepspeed_stage_${ZERO_STAGE} \
TRAINER_ARGS="\
--gradient_clip_val 1.0 \
--max_epochs 10 \
--gpus 2 \
--num_nodes 1 \
--strategy deepspeed_stage_${ZERO_STAGE} \
--log_every_n_steps 100 \
--val_check_interval 500 \
--limit_val_batches 10 \
--accumulate_grad_batches 1 \
--precision 16 \
--ckpt_path /data/training_model/ckpt/fengshen-${MODEL_NAME}/last.ckpt \
--default_root_dir /data/training_model/fengshen-$MODEL_NAME \
"
export options=" \
$DATA_ARGS \
$MODEL_ARGS \
$MODEL_CHECKPOINT_ARGS \
$TRAINER_ARGS \
"
export SCRIPT_PATH=pretrain_hubert.py
eval python3 -m debugpy --listen localhost:53005 --wait-for-client $SCRIPT_PATH $options
|