bigscience/experiments/gpt2-meg/meg_gpt2_base_n8.slurm

#!/bin/bash
#SBATCH --job-name=meg_gpt2_base_n8_dp1_tp4_pp8
#SBATCH --constraint=v100-32g
#SBATCH --nodes=8
#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 00:10:00              # maximum execution time (HH:MM:SS)
#SBATCH --output=%x-%j.out           # output file name
#SBATCH --error=%x-%j.out            # error file name (same to watch just one file)
#SBATCH --account=six@gpu

set -x -e

source $six_ALL_CCFRWORK/start-prod

nvidia-smi

cd $six_ALL_CCFRWORK/code/megatron-lm/

CHECKPOINT_PATH=$six_ALL_CCFRWORK/models-custom/megatron-gpt2/megatron_lm_345m_v0.0/release
VOCAB_FILE=$CHECKPOINT_PATH/gpt2-vocab.json
MERGE_FILE=$CHECKPOINT_PATH/gpt2-merges.txt
DATA_PATH=$six_ALL_CCFRWORK/datasets-custom/openwebtext-10k/meg-gpt2_text_document
SAVE_CHECKPOINT_PATH=$six_ALL_CCFRSCRATCH/checkpoints/gpt2-1-node

MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
MASTER_PORT=6000

# adjust depending on the number of the nodes

NNODES=8
PP_SIZE=8 # NLAYERS must be a multiple of PP_SIZE here
MICRO_BATCH_SIZE=1
PP_CHUNKS=4

MSIZE=30 # maximum @32


if   [[ ${MSIZE} == 7 ]];    then NHIDDEN=4096;  NLAYERS=36
elif [[ ${MSIZE} == 14 ]];   then NHIDDEN=6144;  NLAYERS=32
elif [[ ${MSIZE} == 18 ]];   then NHIDDEN=6144;  NLAYERS=40
elif [[ ${MSIZE} == 25 ]];   then NHIDDEN=7168;  NLAYERS=40
elif [[ ${MSIZE} == 30 ]];   then NHIDDEN=7168;  NLAYERS=48
elif [[ ${MSIZE} == 39 ]];   then NHIDDEN=8192;  NLAYERS=48
elif [[ ${MSIZE} == 52 ]];   then NHIDDEN=8192;  NLAYERS=64
elif [[ ${MSIZE} == 65 ]];   then NHIDDEN=9216;  NLAYERS=64
elif [[ ${MSIZE} == 81 ]];   then NHIDDEN=10240; NLAYERS=64
elif [[ ${MSIZE} == 97 ]];   then NHIDDEN=11264; NLAYERS=64
elif [[ ${MSIZE} == 116 ]];  then NHIDDEN=12288; NLAYERS=64
elif [[ ${MSIZE} == 136 ]];  then NHIDDEN=13312; NLAYERS=64
elif [[ ${MSIZE} == 158 ]];  then NHIDDEN=14336; NLAYERS=64
elif [[ ${MSIZE} == 181 ]];  then NHIDDEN=15360; NLAYERS=64
elif [[ ${MSIZE} == 206 ]];  then NHIDDEN=16384; NLAYERS=64
else echo "invalid MSIZE: $MSIZE"
fi


GPUS_PER_NODE=4

NHEADS=32
SEQ_LEN=1024
VOCAB_SIZE=50257

DP_SIZE=1
TP_SIZE=4

GLOBAL_BATCH_SIZE=$(($MICRO_BATCH_SIZE*$PP_CHUNKS*$DP_SIZE))
WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES))

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 \
    --global-batch-size $GLOBAL_BATCH_SIZE
    --lr 0.00015 \
    --lr-decay-style cosine \
    --min-lr 1.0e-5 \
    --train-iters 1000 \
    --lr-decay-iters 800 \
    --lr-warmup-fraction .01 \
    --weight-decay 1e-2 \
    --clip-grad 1.0 \
    --vocab-file $VOCAB_FILE \
    --merge-file $MERGE_FILE \
    --fp16 \
    --checkpoint-activations \
    "

OUTPUT_ARGS=" \
    --log-interval 1 \
    --save-interval 500 \
    --eval-interval 100 \
    --eval-iters 10 \
    "

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 $SAVE_CHECKPOINT_PATH \
    --load $SAVE_CHECKPOINT_PATH \
    --data-path $DATA_PATH \
    --data-impl mmap \
    --split 949,50,1 \
    --distributed-backend nccl \
    "

# clear old checkpoint as it'd mismatch while we sort things out
rm -rf $six_ALL_CCFRWORK/checkpoints/gpt2-1-node

# model size
python -c "h=$NHIDDEN; l=$NLAYERS; s=$SEQ_LEN; v=$VOCAB_SIZE; print(f'Model size: {(l * (12*h**2 + 13*h) + (v * h) + (s * h) ) / 10**9 :.0f}B')"

# to debug - add echo (it exits and prints what it would have launched)
srun --jobid $SLURM_JOBID bash -c '$LAUNCHER --node_rank $SLURM_PROCID $CMD'

# iteration 180/ 1000 | consumed samples: 720 | elapsed time per iteration (ms): 1618.3 | learning
# rate: 1.375E-04 | global batch size: 4 | lm loss: 7.811917E+00 | loss scale: 16384.0 | grad norm:
# 3.312 | number of skipped iterations: 0 | number of nan iterations: 0 | time (ms) | forward-compute:
# 144.73 | forward-recv: 236.49 | backward-compute: 398.13 | backward-send: 0.75 |
# backward-send-forward-recv: 5.22 | backward-params-all-reduce: 19.27 |
# backward-embedding-all-reduce: 721.73 | optimizer-copy-to-main-grad: 9.19 |
# optimizer-unscale-and-check-inf: 16.78 | optimizer-clip-main-grad: 16.21 |
# optimizer-copy-main-to-model-params: 9.36 | optimizer: 90.96 | batch-generator: 2.58