multilinguality_megatron/continue_pretraining.sh

# This script will try to run a task *outside* any specified submitter
# Note: This script is for archival; it is not actually run by ducttape
# unset CUDA_VISIBLE_DEVICES
echo $CUDA_VISIBLE_DEVICES

data_path="1 spgi_vox_mls_text_1b/data/data_text_document"
megatron_model="spgi_vox_mls_text_1b/shards"
model_dir="spgi_vox_mls_text_1b/ckpt"
tokenizer_path="spgi_vox_mls_text_1b/new_extended_tokenizer/tokenizer.model"
tp="2"
pp="1"

# --wandb_logger \
# --wandb_id "hajmola" \
# --wandb_project "Megatron" \
# --wandb_entity "hajmola" \
# --wandb_api_key "c4a95af43e910d14b0eca23fbb8165f94944d5af" \

# optimization arguments; self-explanatory. Intervals and steps are in terms of training optimizer steps
grad_accum_steps="12"
micro_batch_size="12"
warmup_steps="13"
eval_interval="500"
lr="3e-5" #lr="3e-5"
log_interval="10"
lr_min="3e-6" #lr_min="3e-6"
lr_scheduler="cosine"

# infra arguments 
save_interval="250"
n_gpus="2"
repo="multilinguality_megatron"
gpu_ids="4,5"
train_steps="1000"


# Parse command-line arguments
for arg in "$@"
do
    case $arg in
        --help)
        echo "Usage: ./script.sh [OPTIONS]"
        echo "Options:"
        echo "  --data_path=PATH       Path to dataset. Should have the form of <integer_0> <PATH_TO_DATA_TEXT_DOCUMENT_0> <integer_1> <PATH_TO_DATA_TEXT_DOCUMENT_1> ..., where the integers determine the data's relative weight in the training set. If every integer is equal, then the data is uniformly sampled."
        echo "  --megatron_model=PATH  Path to sharded megatron model"
        echo "  --model_dir=PATH       folder to save model checkpoints; if this has a checkpoint, it will be used to continue training"
        echo "  --tokenizer_path=PATH  Path to tokenizer.model of original HF model"
        echo "  --tp=NUMBER            Number of shards model is divided in"
        echo "  --pp=NUMBER            Pipeline parallel (default is 1)"
        echo "  --grad_accum_steps=NUMBER"
        echo "                         Number of gradient accumulation steps"
        echo "  --micro_batch_size=NUMBER"
        echo "                         Micro batch size"
        echo "  --warmup_steps=NUMBER  Number of warmup steps"
        echo "  --eval_interval=NUMBER Number of steps between validations"
        echo "  --lr=NUMBER            Learning rate"
        echo "  --log_interval=NUMBER  Number of steps between logging"
        echo "  --lr_min=NUMBER        Minimum learning rate of scheduler"
        echo "  --lr_scheduler=STRING  Learning rate scheduler"
        echo "  --save_interval=NUMBER Number of steps between saves"
        echo "  --n_gpus=NUMBER        Number of GPUs to use"
        echo "  --repo=PATH            Path to repo"
        echo "  --gpu_ids=STRING       GPU IDs to use"
        echo "  --train_steps=NUMBER   Number of training steps"
        exit 0
        ;;
        --data_path=*)
        data_path="${arg#*=}"
        shift
        ;;
        --megatron_model=*)
        megatron_model="${arg#*=}"
        shift
        ;;
        --model_dir=*)
        model_dir="${arg#*=}"
        shift
        ;;
        --tokenizer_path=*)
        tokenizer_path="${arg#*=}"
        shift
        ;;
        --tp=*)
        tp="${arg#*=}"
        shift
        ;;
        --pp=*)
        pp="${arg#*=}"
        shift
        ;;
        --grad_accum_steps=*)
        grad_accum_steps="${arg#*=}"
        shift
        ;;
        --micro_batch_size=*)
        micro_batch_size="${arg#*=}"
        shift
        ;;
        --warmup_steps=*)
        warmup_steps="${arg#*=}"
        shift
        ;;
        --eval_interval=*)
        eval_interval="${arg#*=}"
        shift
        ;;
        --lr=*)
        lr="${arg#*=}"
        shift
        ;;
        --log_interval=*)
        log_interval="${arg#*=}"
        shift
        ;;
        --lr_min=*)
        lr_min="${arg#*=}"
        shift
        ;;
        --lr_scheduler=*)
        lr_scheduler="${arg#*=}"
        shift
        ;;
        --save_interval=*)
        save_interval="${arg#*=}"
        shift
        ;;
        --n_gpus=*)
        n_gpus="${arg#*=}"
        shift
        ;;
        --repo=*)
        repo="${arg#*=}"
        shift
        ;;
        --gpu_ids=*)
        gpu_ids="${arg#*=}"
        shift
        ;;
        --train_steps=*)
        train_steps="${arg#*=}"
        shift
        ;;
    esac
done

# CUDA_VISIBLE_DEVICES=$gpu_ids

if [ "$model_dir" != "" ]; then
    mkdir -p $model_dir
    mkdir -p $model_dir/runs
fi

ckpt_flag=$model_dir/latest_checkpointed_iteration.txt
if [ -f $ckpt_flag ]; then
    megatron_model=$model_dir
    echo Loading from previously saved checkpoint.
fi

global_batch_size=$(($micro_batch_size * $n_gpus * $grad_accum_steps))

LOG_ARGS="--log_interval $log_interval --save_interval $save_interval --eval_interval $eval_interval"
TRAIN_ARGS="--train_iters $train_steps --lr_decay_style $lr_scheduler --lr_warmup_iters $warmup_steps --lr $lr --min_lr $lr_min"
DISTRIBUTED_ARGS="--nproc_per_node $n_gpus --nnodes 1 --node_rank 0 --master_addr localhost --master_port 50000"
COMMON_ARGS="--hidden_dropout 0.0 --attention_dropout 0.0 --no_bias_gelu_fusion"
LLAMA_ARGS="--use_rms_norm --glu_activation swiglu --no_tie_embed_logits --no_new_tokens --layernorm_epsilon 1e-5"
CUDA_DEVICE_MAX_CONNECTIONS=1 torchrun $DISTRIBUTED_ARGS $repo/finetune.py \
    --tensor_model_parallel_size $tp \
    --pipeline_model_parallel_size $pp \
    --load $megatron_model \
    --save $model_dir \
    --tensorboard_dir $model_dir/runs \
    --data_path $data_path \
    --model_name llama \
    --tokenizer_type SentencePieceTokenizer \
    --vocab_file=$tokenizer_path \
    --bf16 \
    --use_flash_attn \
    --micro_batch_size $micro_batch_size \
    --global_batch_size $global_batch_size \
    --sequence_parallel \
    --recompute_granularity selective \
    --use_checkpoint_args \
    --seq_length 2048 \
    --split 99,1,1 \
    $COMMON_ARGS $LOG_ARGS $TRAIN_ARGS $LLAMA_ARGS