diff --git a/python/cugraph-dgl/tests/nn/test_gatconv.py b/python/cugraph-dgl/tests/nn/test_gatconv.py
index ef3047dc2cd..ce145b2bc87 100644
--- a/python/cugraph-dgl/tests/nn/test_gatconv.py
+++ b/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:
diff --git a/python/cugraph-dgl/tests/nn/test_gatv2conv.py b/python/cugraph-dgl/tests/nn/test_gatv2conv.py
index cc46a6e4b39..52003edacca 100644
--- a/python/cugraph-dgl/tests/nn/test_gatv2conv.py
+++ b/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:
diff --git a/python/cugraph-dgl/tests/nn/test_relgraphconv.py b/python/cugraph-dgl/tests/nn/test_relgraphconv.py
index 901f9ba1433..bdaa89e57f2 100644
--- a/python/cugraph-dgl/tests/nn/test_relgraphconv.py
+++ b/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])
 
diff --git a/python/cugraph-dgl/tests/nn/test_sageconv.py b/python/cugraph-dgl/tests/nn/test_sageconv.py
index e2acf9e6596..b5d0a44b868 100644
--- a/python/cugraph-dgl/tests/nn/test_sageconv.py
+++ b/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")
 
diff --git a/python/cugraph-dgl/tests/nn/test_transformerconv.py b/python/cugraph-dgl/tests/nn/test_transformerconv.py
index b2b69cb35ab..5ac4fd7bea7 100644
--- a/python/cugraph-dgl/tests/nn/test_transformerconv.py
+++ b/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)