fix config

config/JODIE_large.yml

@@ -21,6 +21,6 @@ gnn:
 train:
 train:
   - epoch: 50
   - epoch: 50
     batch_size: 3000
     batch_size: 3000
-    lr: 0.0004
+    lr: 0.0016
     dropout: 0.1
     dropout: 0.1
     all_on_gpu: True
     all_on_gpu: True
\ No newline at end of file

examples/test_all.sh

@@ -19,7 +19,7 @@ memory_type=("historical")
 #memory_type=("local" "all_update" "historical" "all_reduce")
 #memory_type=("local" "all_update" "historical" "all_reduce")
 shared_memory_ssim=("0.3")
 shared_memory_ssim=("0.3")
 #data_param=("WIKI" "REDDIT" "LASTFM" "WikiTalk")
 #data_param=("WIKI" "REDDIT" "LASTFM" "WikiTalk")
-data_param=("LASTFM" "WikiTalk" "StackOverflow" "GDELT")
+data_param=("StackOverflow" "GDELT")
 # "StackOverflow" "GDELT") 
 # "StackOverflow" "GDELT") 
 #"GDELT")
 #"GDELT")
 #data_param=("WIKI" "REDDIT" "LASTFM" "DGraphFin" "WikiTalk" "StackOverflow")
 #data_param=("WIKI" "REDDIT" "LASTFM" "DGraphFin" "WikiTalk" "StackOverflow")

starrygl/module/utils.py

@@ -167,12 +167,13 @@ class AdaParameter:
     #print(self.alpha)                                                                               
     #print(self.alpha)                                                                               
     self.beta = max(min(self.beta, self.max_beta),self.min_beta)
     self.beta = max(min(self.beta, self.max_beta),self.min_beta)
     self.alpha = max(min(self.alpha, self.max_alpha),self.min_alpha)
     self.alpha = max(min(self.alpha, self.max_alpha),self.min_alpha)
+    #print(self.count_fetch,self.count_memory_update,self.count_gnn_aggregate,self.count_memory_sync)
     #print(self.beta,self.alpha)
     #print(self.beta,self.alpha)
     ctx = DistributedContext.get_default_context()
     ctx = DistributedContext.get_default_context()
-    beta_comm=torch.tensor([self.beta])
+    beta_comm=torch.tensor([self.beta],dtype=torch.float)
     torch.distributed.all_reduce(beta_comm,group=ctx.gloo_group)
     torch.distributed.all_reduce(beta_comm,group=ctx.gloo_group)
     self.beta = beta_comm[0].item()/ctx.world_size
     self.beta = beta_comm[0].item()/ctx.world_size
-    alpha_comm=torch.tensor([self.alpha])
+    alpha_comm=torch.tensor([self.alpha],dtype=torch.float)
     torch.distributed.all_reduce(alpha_comm,group=ctx.gloo_group)
     torch.distributed.all_reduce(alpha_comm,group=ctx.gloo_group)
     self.alpha = alpha_comm[0].item()/ctx.world_size
     self.alpha = alpha_comm[0].item()/ctx.world_size
     #print('gnn aggregate {} fetch {} memory sync {} memory update {}'.format(average_gnn_aggregate,average_fetch,average_memory_sync_time,average_memory_update_time))
     #print('gnn aggregate {} fetch {} memory sync {} memory update {}'.format(average_gnn_aggregate,average_fetch,average_memory_sync_time,average_memory_update_time))