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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 128 \
--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 \
--vocab_extra_ids 100
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