delete weight in c scourcee

csrc/sampler/include/sampler.h

@@ -308,14 +308,14 @@ void ParallelSampler :: neighbor_sample_from_nodes_with_before_layer(
                         double p0 = (double)rand_r(&loc_seeds[tid]) / (RAND_MAX + 1.0);
                         double ep = boundery_probility*pr[cal_cnt-1]/sum_p*sum_1;
                         if(p0 > ep)continue;
-                        tgb_i[tid].sample_weight.emplace_back((float)ep);
+                        //tgb_i[tid].sample_weight.emplace_back((float)ep);
                     }
                     else continue;
                     //cout<<"in"<<endl;
                     
                 }
                 else{
-                    tgb_i[tid].sample_weight.emplace_back((float)1.0);
+                    //tgb_i[tid].sample_weight.emplace_back((float)1.0);
                 }
                 tgb_i[tid].src_index.emplace_back(i);
                 tgb_i[tid].sample_nodes.emplace_back(tnb.neighbors[node][cid]);
@@ -373,8 +373,8 @@ void ParallelSampler :: neighbor_sample_from_nodes_with_before_layer(
 
 #pragma omp parallel for schedule(static, 1)
     for(int i = 0; i<threads; i++){
-        if(policy == "boundery_recent_decay")
-            copy(tgb_i[i].sample_weight.begin(), tgb_i[i].sample_weight.end(), ret[cur_layer].sample_weight.begin()+each_begin[i]);
+        //if(policy == "boundery_recent_decay")
+        //    copy(tgb_i[i].sample_weight.begin(), tgb_i[i].sample_weight.end(), ret[cur_layer].sample_weight.begin()+each_begin[i]);
         copy(tgb_i[i].eid.begin(), tgb_i[i].eid.end(), ret[cur_layer].eid.begin()+each_begin[i]);
         copy(tgb_i[i].src_index.begin(), tgb_i[i].src_index.end(), ret[cur_layer].src_index.begin()+each_begin[i]);
         copy(tgb_i[i].delta_ts.begin(), tgb_i[i].delta_ts.end(), ret[cur_layer].delta_ts.begin()+each_begin[i]);

starrygl/module/memorys.py

@@ -496,7 +496,7 @@ class AsyncMemeoryUpdater(torch.nn.Module):
             self.last_updated_ts = b.srcdata['ts'].detach().clone()
             self.last_updated_memory = updated_memory.detach().clone()
             self.last_updated_nid = b.srcdata['ID'].detach().clone()
-
+            """
             with torch.no_grad():
                 if param is not None:
                     _,src,dst,ts,edge_feats,nxt_fetch_func = param
@@ -523,7 +523,7 @@ class AsyncMemeoryUpdater(torch.nn.Module):
                     self.mailbox.set_memory_local(DistIndex(index[local_mask]).loc,memory[local_mask],memory_ts[local_mask], Reduce_Op = 'max')
                     is_deliver=(self.mailbox.deliver_to == 'neighbors')
                     self.update_hunk(index,memory,memory_ts,index0,mail,mail_ts,nxt_fetch_func,spread_mail= is_deliver)
-
+            """
             if self.memory_param['combine_node_feature'] and self.dim_node_feat > 0:
                 if self.dim_node_feat == self.dim_hid:
                     b.srcdata['h'] += updated_memory