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tr8-104B.slurm
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tr8-104B.slurm
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#!/bin/bash
#SBATCH --job-name=tr8-104B
#SBATCH --constraint=v100-32g
#SBATCH --nodes=128
#SBATCH --ntasks-per-node=1 # crucial - only 1 task per dist per node!
#SBATCH --cpus-per-task=40 # number of cores per tasks
#SBATCH --hint=nomultithread # we get physical cores not logical
#SBATCH --gres=gpu:4 # number of gpus
#SBATCH --time 20:00:00 # maximum execution time (HH:MM:SS)
#SBATCH --output=%x-%j.out # output file name
#SBATCH --account=six@v100
set -x -e
source $six_ALL_CCFRWORK/code/tr8-104B/bigscience/train/tr8-104B-wide/start-tr8-104B
echo "START TIME: $(date)"
DATA_OUTPUT_PATH=$six_ALL_CCFRSCRATCH/checkpoints/tr8-104B
CHECKPOINT_PATH=$DATA_OUTPUT_PATH/checkpoints
REPO_PATH=$DATA_OUTPUT_PATH/tr8-104B-logs
TENSORBOARD_PATH=$REPO_PATH/tensorboard
LOGS_PATH=$REPO_PATH/logs
mkdir -p $LOGS_PATH
MEGATRON_DEEPSPEED_REPO=$six_ALL_CCFRWORK/code/tr8-104B/Megatron-DeepSpeed-tr8-104B
VOCAB_FILE=$MEGATRON_DEEPSPEED_REPO/data/gpt2-vocab.json
MERGE_FILE=$MEGATRON_DEEPSPEED_REPO/data/gpt2-merges.txt
DATA_PATH=$six_ALL_CCFRWORK/datasets-custom/oscar-en/meg-gpt2_text_document
cd $MEGATRON_DEEPSPEED_REPO
MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
MASTER_PORT=6000
GPUS_PER_NODE=4
NNODES=128 # switch to 128
TP_SIZE=4 # always fixed to the size of a single node
PP_SIZE=32 # NLAYERS must be a multiple of PP_SIZE here
#DP_SIZE=$NNODES*$GPUS_PER_NODE/($PP_SIZE*$TP_SIZE) # will get derived automatically by trainer
# GLOBAL_BATCH_SIZE has to be divisible by MICRO_BATCH_SIZE*DP_size
# GLOBAL_BATCH_SIZE=$(($MICRO_BATCH_SIZE*$GAS*$DP_SIZE)) - GAS is auto-derived by deepspeed
MICRO_BATCH_SIZE=1
GLOBAL_BATCH_SIZE=2048
NLAYERS=64
NHIDDEN=11600
NHEADS=80
SEQ_LEN=2048
VOCAB_SIZE=50257
SAVE_INTERVAL=300
OPTIMIZER_ARGS=" \
--optimizer adam \
--adam-beta1 0.9 \
--adam-beta2 0.95 \
--adam-eps 1e-8 \
--lr 6e-5 \
--min-lr 6e-6 \
--lr-decay-style cosine \
--lr-decay-samples 126_953_125 \
--lr-warmup-samples 216_320 \
--clip-grad 1.0 \
--weight-decay 1e-1 \
"
EXIT_OPTS=" \
--exit-duration-in-mins 1190 \
"
GPT_ARGS=" \
--num-layers $NLAYERS \
--hidden-size $NHIDDEN \
--num-attention-heads $NHEADS \
--seq-length $SEQ_LEN \
--max-position-embeddings $SEQ_LEN \
--micro-batch-size $MICRO_BATCH_SIZE \
--rampup-batch-size 16 16 6_000_000 \
--global-batch-size $GLOBAL_BATCH_SIZE \
--train-samples 300_000_000 \
--vocab-file $VOCAB_FILE \
--merge-file $MERGE_FILE \
--loss-scale 12 \
--init-method-std 0.006 \
--embed-layernorm \
--fp16 \
--checkpoint-activations \
--seed 43 \
$OPTIMIZER_ARGS \
$EXIT_OPTS \
"
OUTPUT_ARGS=" \
--log-interval 1 \
--save-interval $SAVE_INTERVAL \
--eval-interval 1000 \
--eval-iters 5 \
--tensorboard-dir $TENSORBOARD_PATH \
--tensorboard-queue-size 5 \
--log-timers-to-tensorboard \
--log-batch-size-to-tensorboard \
--log-validation-ppl-to-tensorboard \
"
ZERO_STAGE=1
config_json="./ds_config.$SLURM_JOBID.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,
"train_batch_size": $GLOBAL_BATCH_SIZE,
"gradient_clipping": 1.0,
"zero_optimization": {
"stage": $ZERO_STAGE
},
"fp16": {
"enabled": true,
"loss_scale": 0,
"loss_scale_window": 500,
"hysteresis": 2,
"min_loss_scale": 1,
"initial_scale_power": 12
},
"steps_per_print": 2000,
"wall_clock_breakdown": false
}
EOT
DEEPSPEED_ARGS=" \
--deepspeed \
--deepspeed_config ${config_json} \
--zero-stage ${ZERO_STAGE} \
--deepspeed-activation-checkpointing \
"
export LAUNCHER="python -u -m torch.distributed.launch \
--nproc_per_node $GPUS_PER_NODE \
--nnodes $NNODES \
--master_addr $MASTER_ADDR \
--master_port $MASTER_PORT \
"
export CMD=" \
`pwd`/pretrain_gpt.py \
--tensor-model-parallel-size $TP_SIZE \
--pipeline-model-parallel-size $PP_SIZE \
$GPT_ARGS \
$OUTPUT_ARGS \
--save $CHECKPOINT_PATH \
--load $CHECKPOINT_PATH \
--data-path $DATA_PATH \
--data-impl mmap \
--split 949,50,1 \
--distributed-backend nccl \
$DEEPSPEED_ARGS \
"
echo $CMD
# to debug - add echo (it exits and prints what it would have launched)
clear; srun --jobid $SLURM_JOBID bash -c '$LAUNCHER --node_rank $SLURM_PROCID $CMD' 2>&1 | tee -a $LOGS_PATH/main_log.txt
echo "END TIME: $(date)"
#