can not reproduce PCQM4M-LSC results

Hi there,

I tried to reproduce the results of Graphormer_SMALL on the PCQM4M-LSC dataset using the v1.0 branch.

[ -z "${exp_name}" ] && exp_name="pcq"
[ -z "${seed}" ] && seed="1"
[ -z "${arch}" ] && arch="--ffn_dim 512 --hidden_dim 512 --weight_decay 0.0 --intput_dropout_rate 0.0 --dropout_rate 0.1 --n_layers 6 --peak_lr 3e-4 --edge_type multi_hop --multi_hop_max_dist 5"
[ -z "${batch_size}" ] && batch_size="256"

echo -e "\n\n"
echo "=====================================ARGS======================================"
echo "arg0: $0"
echo "exp_name: ${exp_name}"
echo "arch: ${arch}"
echo "seed: ${seed}"
echo "batch_size: ${batch_size}"
echo "==============================================================================="

default_root_dir="../../exps/pcq/$exp_name/$seed"
mkdir -p $default_root_dir
n_gpu=$(nvidia-smi -L | wc -l)

python ../../graphormer/entry.py --num_workers 8 --seed $seed --batch_size $batch_size \
      --dataset_name PCQM4M-LSC \
      --gpus $n_gpu --accelerator ddp --precision 16 --gradient_clip_val 5.0 \
      $arch \
      --default_root_dir $default_root_dir

My machine also has 8 GPUs. In this case, is the equivalent batch size 256 * 8 = 2048? After training, according to Tensorboard curves, the valid_mae is about 0.135, while it is 0.1264 for Graphormer_SMALL as reported in the paper. Do you have any ideas about the performance gap? It is suggested to set batch size 1024, so should I change the batch size to 128 (1024/8) for an 8-GPU machine?

Do you run baseline methods in Table 1 to report their performance?

I will be very appreciated if you can answer these questions!

Start with the v1.0 branch and inspect graphormer/entry.py together with the posted training command, focusing on batch-size and multi-GPU arguments. Reproduce the PCQM4M-LSC Graphormer_SMALL run and compare its TensorBoard valid_mae with the reported 0.1264 result. Done means documenting the effective configuration and explaining the performance gap or identifying the missing reproduction step.