Making large AI models cheaper, faster and more accessible
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ROOT=$(realpath $(dirname $0))
echo $ROOT
PY_SCRIPT=${ROOT}/benchmark_llama.py
GPU=$(nvidia-smi -L | head -1 | cut -d' ' -f4 | cut -d'-' -f1)
mode=$1
mkdir -p logs
CUDA_VISIBLE_DEVICES_set_n_least_memory_usage() {
local n=${1:-"9999"}
echo "GPU Memory Usage:"
local FIRST_N_GPU_IDS=$(nvidia-smi --query-gpu=memory.used --format=csv \
| tail -n +2 \
| nl -v 0 \
| tee /dev/tty \
| sort -g -k 2 \
| awk '{print $1}' \
| head -n $n)
export CUDA_VISIBLE_DEVICES=$(echo $FIRST_N_GPU_IDS | sed 's/ /,/g')
echo "Now CUDA_VISIBLE_DEVICES is set to:"
echo "CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES"
}
CUDA_VISIBLE_DEVICES_set_n_least_memory_usage 1
# benchmark llama2-7b one single GPU
for input_len in 128 512 1024; do
for output_len in 128 256; do
for bsz in 16 32 64; do
python3 ${PY_SCRIPT} -m llama2-7b --tp_size 1 -b ${bsz} -s ${input_len} --output_len ${output_len} --mode ${mode} --test_random_weight | tee logs/${bsz}_${input_len}_${output_len}_${mode}_${GPU}.txt
done
done
done