do break down without pipeline

examples/test_all.sh

@@ -2,24 +2,26 @@
 #跑了4卡的TaoBao
 # 定义数组变量
 seed=$1
-addr="192.168.1.107"
+addr="192.168.1.105"
 partition_params=("ours")
 #"metis" "ldg" "random")
 #("ours" "metis" "ldg" "random")
-partitions="16"
+partitions="8"
 node_per="4"
-nnodes="4"
-node_rank="3"
-probability_params=("0.01")
+nnodes="2"
+node_rank="0"
+probability_params=("0.1" )
 sample_type_params=("boundery_recent_decay")
 #sample_type_params=("recent" "boundery_recent_decay") #"boundery_recent_uniform")
 #memory_type=("all_update" "p2p" "all_reduce" "historical" "local")
-memory_type=("historical")
+memory_type=("all_update"  "historical" "local")
+#"historical")
+#memory_type=("local" "all_update" "historical" "all_reduce")
+shared_memory_ssim=("0.3" "0.7")
 #"historical")
 #memory_type=("local" "all_update" "historical" "all_reduce")
-shared_memory_ssim=("0.3")
 #data_param=("WIKI" "REDDIT" "LASTFM" "WikiTalk")
-data_param=("WikiTalk" "GDELT" "StackOverflow") 
+data_param=("GDELT") 
 #"GDELT")
 #data_param=("WIKI" "REDDIT" "LASTFM" "DGraphFin" "WikiTalk" "StackOverflow")
 #data_param=("WIKI" "REDDIT" "LASTFM" "WikiTalk" "StackOverflow")
@@ -32,9 +34,9 @@ data_param=("WikiTalk" "GDELT" "StackOverflow")
 #seed=(( RANDOM % 1000000 + 1 ))
 mkdir -p all_"$seed"
 for data in "${data_param[@]}"; do
-    model="APAN_large"
+    model="TGN_large"
     if [ "$data" = "WIKI" ] || [ "$data" = "REDDIT" ] || [ "$data" = "LASTFM" ]; then
-        model="APAN"
+        model="TGN"
     fi
     #model="APAN"
     mkdir all_"$seed"/"$data"
@@ -59,7 +61,7 @@ for data in "${data_param[@]}"; do
                             wait
                         fi
                     else
-                        torchrun --nnodes "$nnodes" --node_rank "$node_rank" --nproc-per-node "$node_per" --master-addr "$addr" --master-port 9445 train_boundery.py --dataname "$data" --mode "$model" --partition "$partition" --topk 0 --sample_type "$sample" --memory_type "$mem" --seed "$seed" > all_"$seed"/"$data"/"$model"/"$partitions"-"$partition"-0-"$mem"-"$sample".out &
+                        #torchrun --nnodes "$nnodes" --node_rank "$node_rank" --nproc-per-node "$node_per" --master-addr "$addr" --master-port 9445 train_boundery.py --dataname "$data" --mode "$model" --partition "$partition" --topk 0 --sample_type "$sample" --memory_type "$mem" --seed "$seed" > all_"$seed"/"$data"/"$model"/"$partitions"-"$partition"-0-"$mem"-"$sample".out &
                         wait
                         if [ "$partition" = "ours" ] && [ "$mem" != "all_local" ]; then
                             torchrun --nnodes "$nnodes" --node_rank "$node_rank" --nproc-per-node "$node_per" --master-addr "$addr" --master-port 9445 train_boundery.py --dataname "$data" --mode "$model" --partition "$partition" --topk 0.1 --sample_type "$sample" --memory_type "$mem" --seed "$seed" > all_"$seed"/"$data"/"$model"/"$partitions"-"$partition"-0.01-"$mem"-"$sample".out &

examples/train_boundery.py

@@ -202,7 +202,7 @@ def main():
     else:
         graph,full_sampler_graph,train_mask,val_mask,test_mask,full_train_mask,cache_route = load_from_speed(args.dataname,seed=123457,top=args.topk,sampler_graph_add_rev=True, feature_device=torch.device('cuda:{}'.format(ctx.local_rank)),partition=args.partition)#torch.device('cpu'))
     if(args.dataname=='GDELT'):
-        train_param['epoch'] = 10
+        train_param['epoch'] = 1
     #torch.autograd.set_detect_anomaly(True)
 # 确保 CUDA 可用
     if torch.cuda.is_available():
@@ -569,7 +569,7 @@ def main():
             #torch.cuda.synchronize()
             loss.backward()
             optimizer.step()
-            time_count.time_forward.elapsed_event(t1)
+            time_count.time_forward+=time_count.elapsed_event(t1)
             #torch.cuda.synchronize()
             ## train aps
             #y_pred = torch.cat([pred_pos, pred_neg], dim=0).sigmoid().cpu()

starrygl/sample/count_static.py

@@ -32,12 +32,12 @@ class time_count:
     @staticmethod
     def start_gpu():
         # Uncomment for better breakdown timings
-        #torch.cuda.synchronize()
-        #start_event = torch.cuda.Event(enable_timing=True)
-        #end_event = torch.cuda.Event(enable_timing=True)
-        #start_event.record()
-        #return start_event,end_event
-        return 0,0
+        torch.cuda.synchronize()
+        start_event = torch.cuda.Event(enable_timing=True)
+        end_event = torch.cuda.Event(enable_timing=True)
+        start_event.record()
+        return start_event,end_event
+        #return 0,0
     @staticmethod
     def start():
        # return time.perf_counter(),0 
@@ -56,6 +56,7 @@ class time_count:
     @staticmethod
     def print():
         print('time_count.time_forward={} time_count.time_backward={} time_count.time_memory_updater={} time_count.time_embedding={} time_count.time_local_update={} time_count.time_memory_sync={} time_count.time_sample_and_build={} time_count.time_memory_fetch={}\n'.format(
+            time_count.time_forward,
             time_count.time_backward,
             time_count.time_memory_updater,
             time_count.time_embedding,