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#!/bin/bash |
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GPUS_PER_NODE=8 |
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MASTER_ADDR=localhost |
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MASTER_PORT=6000 |
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NNODES=1 |
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NODE_RANK=0 |
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WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES)) |
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DATA_PATH=<Specify path and file prefix> |
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CHECKPOINT_PATH=<Specify path> |
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DISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE --nnodes $NNODES --node_rank $NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT" |
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python -m torch.distributed.launch $DISTRIBUTED_ARGS \ |
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pretrain_t5.py \ |
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--tensor-model-parallel-size 2 \ |
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--num_layers 12 \ |
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--hidden_size 768 \ |
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--num_attention_heads 12 \ |
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--kv_channels 64 \ |
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--ffn_hidden_size 3072 \ |
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--encoder_seq_length 512 \ |
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--decoder_seq_length 128 \ |
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--micro_batch_size 16 \ |
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--global_batch_size 128 \ |
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--max_position_embeddings 512 \ |
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--train_iters 1000000 \ |
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--lr_decay_iters 1000000 \ |
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--save $CHECKPOINT_PATH \ |
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--load $CHECKPOINT_PATH \ |
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--data_path $DATA_PATH \ |
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--vocab_file t5-vocab.txt \ |
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--data_impl mmap \ |
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--split 949,50,1 \ |
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--lr 0.0001 \ |
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--min_lr 0.00001 \ |
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--lr_decay_style linear \ |
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--lr_warmup_fraction .01 \ |
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--weight_decay 1e-2 \ |
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--clip_grad 1.0 \ |
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--log_interval 100 \ |
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--save_interval 10000 \ |
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--eval_interval 1000 \ |
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--eval_iters 10 \ |
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--fp16 \ |
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--vocab_extra_ids 100 |
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