fist

examples/train_boundery.py

@@ -67,7 +67,7 @@ parser.add_argument('--probability', default=1, type=float, metavar='W',
                     help='name of model')
                     help='name of model')
 parser.add_argument('--sample_type', default='recent', type=str, metavar='W',
 parser.add_argument('--sample_type', default='recent', type=str, metavar='W',
                     help='name of model')
                     help='name of model')
-parser.add_argument('--local_neg_sample', default=False, type=bool, metavar='W',
+parser.add_argument('--local_neg_sample', default='local', type=str, metavar='W',
                     help='name of model')
                     help='name of model')
 parser.add_argument('--shared_memory_ssim', default=2, type=float, metavar='W',
 parser.add_argument('--shared_memory_ssim', default=2, type=float, metavar='W',
                     help='name of model')
                     help='name of model')
@@ -275,10 +275,13 @@ def main():
     neg_samples = args.eval_neg_samples
     neg_samples = args.eval_neg_samples
     mask = DistIndex(graph.nids_mapper[graph.edge_index[1,:]].to('cpu')).part == dist.get_rank()
     mask = DistIndex(graph.nids_mapper[graph.edge_index[1,:]].to('cpu')).part == dist.get_rank()
     
     
-    if args.local_neg_sample:
+    if args.local_neg_sample == 'local':
         print('dst len {} origin len {}'.format(graph.edge_index[1,mask].unique().shape[0],full_dst.unique().shape[0]))
         print('dst len {} origin len {}'.format(graph.edge_index[1,mask].unique().shape[0],full_dst.unique().shape[0]))
         train_neg_sampler = LocalNegativeSampling('triplet',amount = args.neg_samples,dst_node_list = graph.edge_index[1,mask].unique())
         train_neg_sampler = LocalNegativeSampling('triplet',amount = args.neg_samples,dst_node_list = graph.edge_index[1,mask].unique())
-        
+    elif args.local_neg_sample == 'all':
+        train_neg_sampler = LocalNegativeSampling('triplet',amount = args.neg_samples,dst_node_list = full_dst.unique(),local_mask=(DistIndex(graph.nids_mapper[full_dst.unique()].to('cpu')).part == dist.get_rank()),prob=1)
+        train_ratio_pos = 1
+        train_ratio_neg = 1
     else:
     else:
         #train_neg_sampler = LocalNegativeSampling('triplet',amount = args.neg_samples,dst_node_list = full_dst.unique())
         #train_neg_sampler = LocalNegativeSampling('triplet',amount = args.neg_samples,dst_node_list = full_dst.unique())
         train_neg_sampler = LocalNegativeSampling('triplet',amount = args.neg_samples,dst_node_list = full_dst.unique(),local_mask=(DistIndex(graph.nids_mapper[full_dst.unique()].to('cpu')).part == dist.get_rank()),prob=args.probability)
         train_neg_sampler = LocalNegativeSampling('triplet',amount = args.neg_samples,dst_node_list = full_dst.unique(),local_mask=(DistIndex(graph.nids_mapper[full_dst.unique()].to('cpu')).part == dist.get_rank()),prob=args.probability)
@@ -300,7 +303,7 @@ def main():
                                         mode='train',
                                         mode='train',
                                         queue_size = 200,
                                         queue_size = 200,
                                         mailbox = mailbox,
                                         mailbox = mailbox,
-                                        is_pipeline=True,
+                                        is_pipeline=False,
                                         use_local_feature = False,
                                         use_local_feature = False,
                                         device = torch.device('cuda:{}'.format(local_rank)),
                                         device = torch.device('cuda:{}'.format(local_rank)),
                                         probability=args.probability,
                                         probability=args.probability,
@@ -561,7 +564,7 @@ def main():
             
             
             #print(time_count.elapsed_event(t2))
             #print(time_count.elapsed_event(t2))
             loss = creterion(pred_pos, torch.ones_like(pred_pos)) 
             loss = creterion(pred_pos, torch.ones_like(pred_pos)) 
-            if args.local_neg_sample is False:
+            if args.local_neg_sample != 'local':
                 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)
                 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)
                 neg_creterion = torch.nn.BCEWithLogitsLoss(weight)
                 neg_creterion = torch.nn.BCEWithLogitsLoss(weight)
                 loss += neg_creterion(pred_neg, torch.zeros_like(pred_neg))
                 loss += neg_creterion(pred_neg, torch.zeros_like(pred_neg))