Forward-merge branch-23.10 to branch-23.12

python/cugraph-dgl/tests/nn/test_gatconv.py

@@ -35,6 +35,7 @@ def test_gatconv_equality(
 ):
     from dgl.nn.pytorch import GATConv
 
+    torch.manual_seed(12345)
     g = create_graph1().to("cuda")
 
     if idtype_int:
@@ -121,6 +122,7 @@ def test_gatconv_equality(
 def test_gatconv_edge_feats(
     bias, bipartite, concat, max_in_degree, num_heads, to_block, use_edge_feats
 ):
+    torch.manual_seed(12345)
     g = create_graph1().to("cuda")
 
     if to_block:

python/cugraph-dgl/tests/nn/test_gatv2conv.py

@@ -35,6 +35,7 @@ def test_gatv2conv_equality(
 ):
     from dgl.nn.pytorch import GATv2Conv
 
+    torch.manual_seed(12345)
     g = create_graph1().to("cuda")
 
     if idtype_int:
@@ -109,6 +110,7 @@ def test_gatv2conv_equality(
 def test_gatv2conv_edge_feats(
     bias, bipartite, concat, max_in_degree, num_heads, to_block, use_edge_feats
 ):
+    torch.manual_seed(12345)
     g = create_graph1().to("cuda")
 
     if to_block:

python/cugraph-dgl/tests/nn/test_relgraphconv.py

@@ -41,6 +41,7 @@ def test_relgraphconv_equality(
 ):
     from dgl.nn.pytorch import RelGraphConv
 
+    torch.manual_seed(12345)
     in_feat, out_feat, num_rels = 10, 2, 3
     args = (in_feat, out_feat, num_rels)
     kwargs = {
@@ -75,12 +76,18 @@ def test_relgraphconv_equality(
             size=size, src_ids=indices, cdst_ids=offsets, values=etypes, formats="csc"
         )
 
-    torch.manual_seed(0)
     conv1 = RelGraphConv(*args, **kwargs).cuda()
+    conv2 = CuGraphRelGraphConv(*args, **kwargs, apply_norm=False).cuda()
 
-    torch.manual_seed(0)
-    kwargs["apply_norm"] = False
-    conv2 = CuGraphRelGraphConv(*args, **kwargs).cuda()
+    with torch.no_grad():
+        if self_loop:
+            conv2.W.data[:-1] = conv1.linear_r.W.data
+            conv2.W.data[-1] = conv1.loop_weight.data
+        else:
+            conv2.W.data = conv1.linear_r.W.data.detach().clone()
+
+        if regularizer is not None:
+            conv2.coeff.data = conv1.linear_r.coeff.data.detach().clone()
 
     out1 = conv1(g, feat, g.edata[dgl.ETYPE])
 

python/cugraph-dgl/tests/nn/test_sageconv.py

@@ -35,6 +35,7 @@ def test_sageconv_equality(
 ):
     from dgl.nn.pytorch import SAGEConv
 
+    torch.manual_seed(12345)
     kwargs = {"aggregator_type": aggr, "bias": bias}
     g = create_graph1().to("cuda")
 

python/cugraph-dgl/tests/nn/test_transformerconv.py

@@ -41,6 +41,7 @@ def test_transformerconv(
     use_edge_feats,
     sparse_format,
 ):
+    torch.manual_seed(12345)
     device = "cuda"
     g = create_graph1().to(device)
 

python/cugraph-pyg/cugraph_pyg/tests/nn/test_gat_conv.py

@@ -32,6 +32,7 @@ def test_gat_conv_equality(
     import torch
     from torch_geometric.nn import GATConv
 
+    torch.manual_seed(12345)
     edge_index, size = request.getfixturevalue(graph)
     edge_index = edge_index.cuda()
 

python/cugraph-pyg/cugraph_pyg/tests/nn/test_gatv2_conv.py

@@ -28,6 +28,7 @@ def test_gatv2_conv_equality(bipartite, concat, heads, use_edge_attr, graph, req
     import torch
     from torch_geometric.nn import GATv2Conv
 
+    torch.manual_seed(12345)
     edge_index, size = request.getfixturevalue(graph)
     edge_index = edge_index.cuda()
 

python/cugraph-pyg/cugraph_pyg/tests/nn/test_rgcn_conv.py

@@ -31,6 +31,7 @@ def test_rgcn_conv_equality(
     import torch
     from torch_geometric.nn import FastRGCNConv as RGCNConv
 
+    torch.manual_seed(12345)
     in_channels, out_channels, num_relations = (4, 2, 3)
     kwargs = dict(aggr=aggr, bias=bias, num_bases=num_bases, root_weight=root_weight)
 

python/cugraph-pyg/cugraph_pyg/tests/nn/test_sage_conv.py

@@ -32,6 +32,7 @@ def test_sage_conv_equality(
     import torch
     from torch_geometric.nn import SAGEConv
 
+    torch.manual_seed(12345)
     edge_index, size = request.getfixturevalue(graph)
     edge_index = edge_index.cuda()
     csc = CuGraphSAGEConv.to_csc(edge_index, size)

python/cugraph-pyg/cugraph_pyg/tests/nn/test_transformer_conv.py

@@ -27,6 +27,7 @@ def test_transformer_conv_equality(bipartite, concat, heads, graph, request):
     import torch
     from torch_geometric.nn import TransformerConv
 
+    torch.manual_seed(12345)
     edge_index, size = request.getfixturevalue(graph)
     edge_index = edge_index.cuda()
     csc = CuGraphTransformerConv.to_csc(edge_index, size)