File size: 3,851 Bytes
bbc9069 |
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 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 |
#!/bin/bash
# This example script is contributed by external user https://github.com/LydiaXiaohongLi
set -ex
######################################
# Change the below configurations here
BASE_PATH=./tmp
DS_CONFIG=${BASE_PATH}/deepspeed.json
DATASET_1="./tmp/data/bookcorpus_train_1m_text_sentence"
DATASET="1 ${DATASET_1}"
CHECKPOINT_PATH=./tmp
TOKENIZER_PATH=./tmp/tokenizer.model # offical llama tokenizer.model
TP=2
PP=2
ZERO_STAGE=0
GPUS_PER_NODE=8
MASTER_ADDR=localhost
MASTER_PORT=6000
NNODES=1
NODE_RANK=0
HIDDEN_SIZE=2048 # e.g. llama-13b: 5120
FFN_HIDDEN_SIZE=5504 # e.g. llama-13b: 13824
NUM_LAYERS=24 # e.g. llama-13b: 40
NUM_HEADS=16 # e.g. llama-13b: 40
SEQ_LENGTH=2048
MICRO_BATCH_SIZE=4
GLOBAL_BATCH_SIZE=32 # e.g. llama: 4M tokens
TRAIN_STEPS=250000 # e.g. llama: 1T tokens / 4M tokens_per_batch = 250000 steps
LR=3e-4
MIN_LR=3e-5
LR_WARMUP_STEPS=2000
WEIGHT_DECAY=0.1
GRAD_CLIP=1
## Activation checkpointing saves GPU memory, but reduces training speed
# activation_checkpoint="true"
activation_checkpoint="false"
# Below configuration required for llama model as per llama paper
# --no-query-key-layer-scaling \
# --attention-dropout 0 \
# --hidden-dropout 0 \
# --use-rotary-position-embeddings \
# --untie-embeddings-and-output-weights \
# --swiglu \
# --normalization rmsnorm \
# --disable-bias-linear \
######################################
cat <<EOT > $DS_CONFIG
{
"train_batch_size" : $GLOBAL_BATCH_SIZE,
"train_micro_batch_size_per_gpu": $MICRO_BATCH_SIZE,
"steps_per_print": 1,
"zero_optimization": {
"stage": $ZERO_STAGE
},
"bf16": {
"enabled": true
}
}
EOT
ds_args=""
ds_args=" --deepspeed ${ds_args}"
ds_args=" --deepspeed_config=$DS_CONFIG ${ds_args}"
ds_args=" --zero-stage=$ZERO_STAGE ${ds_args}"
if [ "${activation_checkpoint}" = "true" ]; then
ds_args="--deepspeed-activation-checkpointing ${ds_args}"
## old argument for recomputing the transformer layer
# ds_args="--checkpoint-activations ${ds_args}"
## new argument for recomputing the transformer layer
ds_args="--recompute-granularity full --recompute-method uniform ${ds_args}"
## new argument for recomputing only the attention layer
# ds_args="--recompute-granularity selective ${ds_args}"
fi
DISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE --nnodes $NNODES --node_rank $NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT"
torchrun $DISTRIBUTED_ARGS \
pretrain_gpt.py \
--tensor-model-parallel-size $TP \
--pipeline-model-parallel-size $PP \
--num-layers $NUM_LAYERS \
--hidden-size $HIDDEN_SIZE \
--ffn-hidden-size $FFN_HIDDEN_SIZE \
--num-attention-heads $NUM_HEADS \
--micro-batch-size $MICRO_BATCH_SIZE \
--global-batch-size $GLOBAL_BATCH_SIZE \
--seq-length $SEQ_LENGTH \
--max-position-embeddings $SEQ_LENGTH \
--train-iters $TRAIN_STEPS \
--save $CHECKPOINT_PATH \
--load $CHECKPOINT_PATH \
--data-path $DATASET \
--data-impl mmap \
--tokenizer-type GPTSentencePieceTokenizer \
--tokenizer-model $TOKENIZER_PATH \
--split 949,50,1 \
--distributed-backend nccl \
--lr $LR \
--lr-decay-style cosine \
--min-lr $MIN_LR \
--weight-decay $WEIGHT_DECAY \
--clip-grad $GRAD_CLIP \
--lr-warmup-iters $LR_WARMUP_STEPS \
--optimizer adam \
--adam-beta1 0.9 \
--adam-beta2 0.95 \
--log-interval 1 \
--save-interval 10000 \
--eval-interval 1000 \
--eval-iters 10 \
--bf16 \
--no-query-key-layer-scaling \
--attention-dropout 0 \
--hidden-dropout 0 \
--use-rotary-position-embeddings \
--untie-embeddings-and-output-weights \
--swiglu \
--normalization rmsnorm \
--disable-bias-linear \
$ds_args |