Add scripts for running gcn with dp & fix tensor under different device

benchmark/FedHPOB/scripts/gcn/cora_dp.yaml

@@ -0,0 +1,41 @@
+use_gpu: True
+device: 0
+early_stop:
+  patience: 100
+seed: 12345
+federate:
+  mode: standalone
+  make_global_eval: True
+  client_num: 5
+  total_round_num: 500
+  share_local_model: True
+  online_aggr: True
+data:
+  root: data/
+  type: cora
+  splitter: 'louvain'
+  batch_size: 1
+model:
+  type: gcn
+  hidden: 64
+  dropout: 0.5
+  out_channels: 7
+criterion:
+  type: CrossEntropyLoss
+train:
+  local_update_steps: 1
+  optimizer:
+    lr: 0.25
+    weight_decay: 0.0005
+trainer:
+  type: nodefullbatch_trainer
+eval:
+  freq: 1
+  metrics: ['acc', 'correct', 'f1']
+  split: ['test', 'val', 'train']
+nbafl:
+  use: True
+  mu: 0.0
+  w_clip: 0.1
+  epsilon: 2000
+  constant: 1

benchmark/FedHPOB/scripts/gcn/run_hpo_cora_dp.sh

@@ -0,0 +1,34 @@
+set -e
+
+cudaid=$1
+sample_num=$2
+eps=$3
+
+# eps = (5000, 500, 50)
+
+cd ../../../..
+
+dataset=cora
+
+out_dir=out_${dataset}_dp${eps}
+
+echo "HPO starts..."
+
+lrs=(0.01 0.01668 0.02783 0.04642 0.07743 0.12915 0.21544 0.35938 0.59948 1.0)
+wds=(0.0 0.001 0.01 0.1)
+dps=(0.0 0.5)
+steps=(1 2 3 4 5 6 7 8)
+
+for ((l = 0; l < ${#lrs[@]}; l++)); do
+  for ((w = 0; w < ${#wds[@]}; w++)); do
+    for ((d = 0; d < ${#dps[@]}; d++)); do
+      for ((s = 0; s < ${#steps[@]}; s++)); do
+        for k in {1..3}; do
+          python federatedscope/main.py --cfg benchmark/FedHPOB/scripts/gcn/cora_dp.yaml device $cudaid train.optimizer.lr ${lrs[$l]} train.optimizer.weight_decay ${wds[$w]} model.dropout ${dps[$d]} train.local_update_steps ${steps[$s]} federate.sample_client_num $sample_num seed $k outdir ${out_dir}/${sample_num} expname lr${lrs[$l]}_wd${wds[$w]}_dropout${dps[$d]}_step${steps[$s]}_seed${k} >/dev/null 2>&1
+        done
+      done
+    done
+  done
+done
+
+echo "HPO ends."

federatedscope/core/trainers/trainer_nbafl.py

@@ -109,8 +109,9 @@ def inject_noise_in_broadcast(cfg, sample_client_num, model):
 
     # Clip weight
     for p in model.parameters():
-        p.data = p.data / torch.max(torch.ones(size=p.shape),
-                                    torch.abs(p.data) / cfg.nbafl.w_clip)
+        p.data = p.data / torch.max(
+            torch.ones(size=p.shape, device=p.data.device),
+            torch.abs(p.data) / cfg.nbafl.w_clip)
 
     if len(sample_client_num) > 0:
         # Inject noise