File size: 2,424 Bytes
cf5659c |
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 |
#!/bin/bash
#SBATCH --job-name=meg_t5_multi_node
#SBATCH --nodes=2
#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 --error=%x-%j.out # error file name (same to watch just one file)
#SBATCH --account=six@gpu
GPUS_PER_NODE=4
NNODES=$SLURM_JOB_NUM_NODES
WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES))
set -x -e
source $six_ALL_CCFRWORK/start-prod
cd $six_ALL_CCFRWORK/code/megatron-lm
VOCAB_FILE=$six_ALL_CCFRWORK/datasets-custom/vocabs/bert-large-uncased-vocab.txt
DATA_PATH=$six_ALL_CCFRWORK/datasets-custom/openwebtext-10k/meg-t5_text_sentence
SAVE_CHECKPOINT_PATH=$six_ALL_CCFRWORK/checkpoints/t5
MASTER_ADDR=`hostname`
MASTER_PORT=13370
# --train-iters 100000 \
# --lr-decay-iters 320000 \
# from t5 training:
# --global-batch-size 2048 \
GPT_ARGS=" \
--num-layers 12 \
--hidden-size 768 \
--num-attention-heads 12 \
--kv-channels 64 \
--ffn-hidden-size 3072 \
--encoder-seq-length 512 \
--decoder-seq-length 128 \
--micro-batch-size 16 \
--max-position-embeddings 512 \
--train-iters 1000000 \
--lr-decay-iters 1000000 \
--vocab-file $VOCAB_FILE \
--vocab-extra-ids 100 \
--lr 0.0001 \
--min-lr 0.00001 \
--lr-decay-style linear \
--lr-warmup-fraction .01 \
--weight-decay 1e-2 \
--clip-grad 1.0 \
--fp16 \
--checkpoint-activations \
"
OUTPUT_ARGS=" \
--log-interval 10 \
--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_t5.py \
--tensor-model-parallel-size 2 \
$GPT_ARGS \
$OUTPUT_ARGS \
--save $SAVE_CHECKPOINT_PATH \
--load $SAVE_CHECKPOINT_PATH \
--data-path $DATA_PATH \
--data-impl mmap \
--split 949,50,1 \
--distributed-backend nccl \
"
# to debug - add echo (it exits and prints what it would have launched)
srun bash -c '$LAUNCHER --node_rank $SLURM_PROCID $CMD'
|