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pretrain_t5_distributed_with_mp.sh
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pretrain_t5_distributed_with_mp.sh
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#!/bin/bash
GPUS_PER_NODE=8
# Change for multinode config
MASTER_ADDR=localhost
MASTER_PORT=6000
NNODES=1
NODE_RANK=0
WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES))
DATA_PATH=<Specify path and file prefix>
CHECKPOINT_PATH=<Specify path>
DISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE --nnodes $NNODES --node_rank $NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT"
python -m torch.distributed.launch $DISTRIBUTED_ARGS \
pretrain_t5.py \
--tensor-model-parallel-size 2 \
--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 \
--global-batch-size 2048 \
--seq-length 512 \
--max-position-embeddings 512 \
--train-iters 1000000 \
--lr-decay-iters 1000000 \
--save $CHECKPOINT_PATH \
--load $CHECKPOINT_PATH \
--data-path $DATA_PATH \
--vocab-file t5-vocab.txt \
--data-impl mmap \
--split 949,50,1 \
--lr 0.0001 \
--min-lr 0.00001 \
--lr-decay-style linear \
--lr-warmup-fraction .01 \
--weight-decay 1e-2 \
--clip-grad 1.0 \
--log-interval 100 \
--save-interval 10000 \
--eval-interval 1000 \
--eval-iters 10 \
--fp16