Llama_pretraining.sh

#!/bin/bash
set -eux

#export FLASH_ATTENTION_PRINT_PARAM=1
# Runs the "7B" parameter model
export HSA_FORCE_FINE_GRAIN_PCIE=1
export OMP_NUM_THREADS=1
export NCCL_P2P_LEVEL=PXB # SYS

#export HIP_ALLOC_INITIALIZE=0
#export GPU_MAX_HW_QUEUES=20

export NCCL_ALGO=Ring
export NCCL_NCHANNELS_PER_PEER=16
export NCCL_MIN_NCHANNELS=20
export NCCL_IB_TIMEOUT=22
export CUDA_DEVICE_MAX_CONNECTIONS=1

export NCCL_IB_HCA=mlx5_1,mlx5_2
export NCCL_NET_GDR_LEVEL=SYS
export NCCL_NET_GDR_READ=0
source /opt/dtk/env.sh
# te调用gemm需要导入hipblaslt库
# export LD_LIBRARY_PATH=/data/hipblaslt-install-0904/lib:$LD_LIBRARY_PATH 

CHECKPOINT_PATH=./tmp_7b #$1 #<Specify path>
TENSORBOARD_LOGS_PATH=./tmp_7b  #$2 #<Specify path>
DATA_PATH="/datasets/oscar-1GB-llama_text_document" #<Specify path and file prefix>_text_document

GPT_MODEL_ARGS=(
    --num-layers 36
    --hidden-size 4096
    --ffn-hidden-size 11008 
    --num-attention-heads 32
    --seq-length 4096 #4096
    --max-position-embeddings 4096
)

# export NVTE_FLASH_ATTN=1 # 走autlass
# export NVTE_FLASH_ATTN_TRITON=1 # 走triton_fa
# --transformer-impl transformer_engine
    # --use-mcore-models
    
TRAINING_ARGS=(
    --transformer-impl local
    --use-legacy-models 
    --micro-batch-size 1 
    --global-batch-size 60 #240 #512 #64
    --train-iters 100
    --weight-decay 0.1 
    --adam-beta1 0.9 
    --adam-beta2 0.95 
    --init-method-std 0.006 
    --clip-grad 1.0 
    --bf16
    --use-distributed-optimizer 
    --ckpt-format torch
    --disable-bias-linear
    --overlap-grad-reduce
    --attention-dropout 0
    --hidden-dropout 0
    --ddp-average-in-collective
    --recompute-granularity full
    --recompute-num-layers 5
    --recompute-method block
    --no-gradient-accumulation-fusion
    --swiglu
    --lr 3.0e-5 
    --lr-decay-style cosine 
    --min-lr 3.0e-6
    --lr-warmup-iters 1
    --use-flash-attn-triton
)
# --use-flash-attn-ck
# --use-flash-attn-triton

MODEL_PARALLEL_ARGS=(
    --sequence-parallel
	--tensor-model-parallel-size 2
	--pipeline-model-parallel-size 2
)

DATA_ARGS=(
    --data-path $DATA_PATH 
    --split 949,50,1
    --untie-embeddings-and-output-weights
    --use-rotary-position-embeddings 
    --normalization RMSNorm 
    --no-position-embedding 
    --tokenizer-type Llama2Tokenizer
    --tokenizer-model /path/to/llama2_7b_hf/tokenizer.model
)

EVAL_AND_LOGGING_ARGS=(
    --log-interval 1
    --log-throughput
    --save-interval 1000 
    --eval-interval 1000 
    --save $CHECKPOINT_PATH 
    --load $CHECKPOINT_PATH 
    --eval-iters 10
    --tensorboard-dir $TENSORBOARD_LOGS_PATH 
)

RANK=$OMPI_COMM_WORLD_RANK
LOCAL_RANK=$OMPI_COMM_WORLD_LOCAL_RANK
WORLD_SIZE=$OMPI_COMM_WORLD_SIZE
DIST_URL=${1}
DIST_PORT=34566

DISTRIBUTED_ARGS=(
    --rank ${RANK}
    --world-size ${WORLD_SIZE}
    --local-rank ${LOCAL_RANK}
    --dist-url tcp://${DIST_URL}:${DIST_PORT}
)

APP="python -u pretrain_gpt.py \
        ${GPT_MODEL_ARGS[@]} \
        ${TRAINING_ARGS[@]} \
        ${MODEL_PARALLEL_ARGS[@]} \
        ${DATA_ARGS[@]} \
        ${EVAL_AND_LOGGING_ARGS[@]} \
        ${DISTRIBUTED_ARGS[@]} \
"

case ${LOCAL_RANK} in
[0])
  export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
#   ${APP}
  numactl --cpunodebind=0 --membind=0 ${APP}
  ;;
[1])
  export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
#   ${APP}
  numactl --cpunodebind=0 --membind=0 ${APP}
  ;;
[2])
  export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
#   ${APP}
  numactl --cpunodebind=0 --membind=0 ${APP}
  ;;
[3])
  export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
#   ${APP}
  numactl --cpunodebind=0 --membind=0 ${APP}
  ;;
[4])
  export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
#   ${APP}
  numactl --cpunodebind=0 --membind=0 ${APP}
  ;;
[5])
  export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
#   ${APP}
  numactl --cpunodebind=0 --membind=0 ${APP}
  ;;
[6])
  export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
#   ${APP}
  numactl --cpunodebind=0 --membind=0 ${APP}
  ;;
[7])
  export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
#   ${APP}
  numactl --cpunodebind=0 --membind=0 ${APP}
  ;;
esac