1

config/APAN_large.yml

+sampling:
+  - layer: 1
+    neighbor: 
+      - 20
+    strategy: 'recent'
+    history: 1
+    no_neg: True
+memory: 
+  - type: 'node'
+    dim_time: 100
+    deliver_to: 'neighbors'
+    mail_combine: 'last'
+    memory_update: 'transformer'
+    historical_fix: False
+    async: True
+    attention_head: 2
+    mailbox_size: 10
+    combine_node_feature: False
+    dim_out: 100
+gnn:
+  - arch: 'identity'
+train:
+  - epoch: 50
+    batch_size: 3000
+    lr: 0.0002
+    dropout: 0.1
+    att_dropout: 0.1
+    # all_on_gpu: True
\ No newline at end of file

config/JODIE_large.yml

+sampling:
+  - strategy: 'identity'
+    history: 1
+
+memory: 
+  - type: 'node'
+    dim_time: 100
+    deliver_to: 'self'
+    mail_combine: 'last'
+    memory_update: 'rnn'
+    historical_fix: False
+    async: True
+    mailbox_size: 1
+    combine_node_feature: True
+    dim_out: 100
+gnn:
+  - arch: 'identity'
+    use_src_emb: False
+    use_dst_emb: False
+    time_transform: 'JODIE'
+train:
+  - epoch: 50
+    batch_size: 3000
+    lr: 0.0002
+    dropout: 0.1
+    all_on_gpu: True
\ No newline at end of file

examples-probability-sample/train_boundery.py

@@ -504,7 +504,9 @@ def main():
         local_edge_comm = []
         remote_edge_comm = []
         b_cnt = 0
+        start = time_count.start_gpu()
         for roots,mfgs,metadata in trainloader:
+            t1 = time_count.elapsed_event(start)
             #print('rank is {} batch max ts is {} batch min ts is {}'.format(dist.get_rank(),roots.ts.min(),roots.ts.max()))
             b_cnt = b_cnt + 1
             #local_access.append(trainloader.local_node)
@@ -557,6 +559,7 @@ def main():
             #torch.cuda.synchronize()
             mailbox.update_shared()
             mailbox.update_p2p()
+            start = time_count.start_gpu()
             #torch.cuda.empty_cache()
 
             """

examples/result.py

+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+# 读取文件内容
+
+probability_values = [0.1]#[0.1,0.05,0.01,0]
+data_values = ['WikiTalk']  # 存储从文件中读取的数据
+seed = ['12357']#,'12347','63377','53473','54763']
+partition = 'ours_shared'
+
+# 从文件中读取数据，假设数据存储在文件 data.txt 中
+#all/"$data"/"$partitions"-ours_shared-0.01-"$mem"-"$ssim"-"$sample".out
+partitions=4
+topk=0.01
+mem='historical-0.3'#'historical'
+model0='APAN'
+def average(l):
+    return sum(l)/len(l)
+for data in data_values:
+    ap_list = []
+    comm_list = []
+    for sd in seed :
+        for p in probability_values:
+            if data == 'WIKI' or data =='LASTFM':
+                model = model0
+            else:
+                model = model0+'_large'
+            if p == 1:
+                file = 'all_{}/{}/{}/{}-{}-{}-{}-recent.out'.format(sd,data,model,partitions,partition,topk,mem)
+            else:
+                file = 'all_{}/{}/{}/{}-{}-{}-{}-boundery_recent_decay-{}.out'.format(sd,data,model,partitions,partition,topk,mem,p)
+            prefix = "val ap:"
+            max_val_ap = 0
+            test_ap = 0
+            
+            with open(file, 'r') as file:
+                for line in file:
+                    if line.find('Epoch 50:')!=-1:
+                        break
+                    if line.find(prefix)!=-1:
+                        pos = line.find(prefix)+len(prefix)
+                        posr = line.find(' ',pos)
+                        #print(line[pos:posr])
+                        val_ap = float(line[pos:posr])
+                    
+                        pos = line.find("test ap ")+len("test ap ")
+                        posr = line.find(' ',pos)
+                        #print(line[pos:posr])
+                        _test_ap = float(line[pos:posr])
+                        if(val_ap>max_val_ap):
+                            max_val_ap = val_ap
+                            test_ap = _test_ap
+                ap_list.append(test_ap)
+        print('data {} model {} ap: {}'.format(data,model,ap_list))

examples/train_boundery.py

@@ -472,7 +472,7 @@ def main():
         cos = torch.nn.CosineSimilarity(dim=0)
         return cos(normalize(x1),normalize(x2)).sum()/x1.size(dim=0)
     creterion = torch.nn.BCEWithLogitsLoss()
-    optimizer = torch.optim.Adam(model.parameters(), lr=train_param['lr'],weight_decay=1e-4)
+    optimizer = torch.optim.Adam(model.parameters(), lr=train_param['lr'])#,weight_decay=1e-4)
     early_stopper = EarlyStopMonitor(max_round=args.patience)
     MODEL_SAVE_PATH = f'../saved_models/{args.model}-{args.dataname}-{dist.get_world_size()}.pth'
     total_test_time = 0
@@ -481,7 +481,7 @@ def main():
     val_list = []
     loss_list = []
     for e in range(train_param['epoch']):
-#        model.module.memory_updater.empty_cache()
+        model.module.memory_updater.empty_cache()
         tt._zero()
         torch.cuda.synchronize()
         epoch_start_time = time.time()
@@ -509,7 +509,10 @@ def main():
         local_edge_comm = []
         remote_edge_comm = []
         b_cnt = 0
+        start = time_count.start_gpu()
         for roots,mfgs,metadata in trainloader:
+            end = time_count.elapsed_event(start)
+            #print('time {}'.format(end))
             #print('rank is {} batch max ts is {} batch min ts is {}'.format(dist.get_rank(),roots.ts.min(),roots.ts.max()))
             b_cnt = b_cnt + 1
             #local_access.append(trainloader.local_node)
@@ -525,7 +528,7 @@ def main():
             #    sum_remote_edge_comm +=remote_edge_comm[b_cnt-1]
             #sum_local_comm +=local_comm[b_cnt-1]
             #sum_remote_comm +=remote_comm[b_cnt-1]
-            
+            t1 = time_count.start_gpu()
             if mailbox is not None:
                 if(graph.efeat.device.type != 'cpu'):
                     edge_feats = graph.get_local_efeat(graph.eids_mapper[roots.eids.to('cpu')]).to('cuda')
@@ -539,12 +542,14 @@ def main():
                 param = (update_mail,src,dst,ts,edge_feats,trainloader.async_feature)
             else:
                 param = None
+            #print(time_count.elapsed_event(t1))
             model.train()
-            
+            t2 = time_count.start_gpu()
             optimizer.zero_grad()
             ones = torch.ones(metadata['dst_neg_index'].shape[0],device = model.device,dtype=torch.float)
             weight = torch.where(DistIndex(mfgs[0][0].srcdata['ID'][metadata['dst_neg_index']]).part == torch.distributed.get_rank(),ones*train_ratio_pos,ones*train_ratio_neg).reshape(-1,1)
             pred_pos, pred_neg = model(mfgs,metadata,neg_samples=args.neg_samples,async_param = param)
+            #print(time_count.elapsed_event(t2))
             loss = creterion(pred_pos, torch.ones_like(pred_pos)) 
             neg_creterion = torch.nn.BCEWithLogitsLoss(weight)
             loss += neg_creterion(pred_neg, torch.zeros_like(pred_neg))
@@ -563,6 +568,7 @@ def main():
             mailbox.update_shared()
             mailbox.update_p2p_mem()
             mailbox.update_p2p_mail()
+            start = time_count.start_gpu()
             #torch.cuda.empty_cache()
 
             """

starrygl/sample/count_static.py

@@ -33,21 +33,23 @@ class time_count:
     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
+        #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 
+       # return time.perf_counter(),0 
+        return 0,0
     @staticmethod
     def elapsed_event(start_event):
-        #if isinstance(start_event,tuple):
+        # if isinstance(start_event,tuple):
         #    start_event,end_event = start_event
         #    end_event.record()
         #    end_event.synchronize()
         #    return start_event.elapsed_time(end_event)
-        #else:
+        # else:
         #    torch.cuda.synchronize()
         #    return time.perf_counter() - start_event
         return 0

starrygl/sample/memory/shared_mailbox.py

@@ -187,13 +187,16 @@ class SharedMailBox():
             #print('root is {} {} {} {}\n'.format(root,root.shape,root.max(),block.edges()[0].shape))
             #pos_index = torch.arange(root.shape[0],device=root.device,dtype=root.dtype)
             pos,idx = torch_scatter.scatter_max(mail_ts,root,0)
-            mail = torch.cat([mail, mail[idx]],dim=0)
-            mail_ts = torch.cat([mail_ts, mail_ts[idx]], dim=0)
+            #print(block.number_of_edges())
+            mail = torch.cat([mail, mail[idx[block.edges()[0].long()]]],dim=0)
+            mail_ts = torch.cat([mail_ts, mail_ts[idx[block.edges()[0].long()]]], dim=0)
+            
             #print('pos is {} {}\n'.format(pos,block.edges()[0].long()))
             #mail = torch.cat([mail, mail[pos[block.edges()[0].long()]]],dim=0)
             #mail_ts = torch.cat([mail_ts, mail_ts[pos[block.edges()[0].long()]]], dim=0)
             #print(root,block.edges()[1].long())
             index = torch.cat([index,block.dstdata['ID'][block.edges()[1].long()]],dim=0)
+            #print(index)
             #mail = torch.cat([mail, mail[block.edges()[0].long()]], dim=0)
             #mail_ts = torch.cat([mail_ts, mail_ts[block.edges()[0].long()]], dim=0)
             #index = torch.cat([index,block.dstdata['ID'][block.edges()[1].long()]],dim=0)