diff --git a/src/decagon_pytorch/decode/pairwise.py b/src/decagon_pytorch/decode/pairwise.py
index 910a8ce..93ff68c 100644
--- a/src/decagon_pytorch/decode/pairwise.py
+++ b/src/decagon_pytorch/decode/pairwise.py
@@ -37,7 +37,9 @@ class DEDICOMDecoder(torch.nn.Module):
             product1 = torch.mm(inputs_row, relation)
             product2 = torch.mm(product1, self.global_interaction)
             product3 = torch.mm(product2, relation)
-            rec = torch.mm(product3, torch.transpose(inputs_col, 0, 1))
+            rec = torch.bmm(product3.view(product3.shape[0], 1, product3.shape[1]),
+                inputs_col.view(inputs_col.shape[0], inputs_col.shape[1], 1))
+            rec = torch.flatten(rec)
             outputs.append(self.activation(rec))
         return outputs
 
@@ -67,7 +69,9 @@ class DistMultDecoder(torch.nn.Module):
             relation = torch.diag(self.relation[k])
 
             intermediate_product = torch.mm(inputs_row, relation)
-            rec = torch.mm(intermediate_product, torch.transpose(inputs_col, 0, 1))
+            rec = torch.bmm(intermediate_product.view(intermediate_product.shape[0], 1, intermediate_product.shape[1]),
+                inputs_col.view(inputs_col.shape[0], inputs_col.shape[1], 1))
+            rec = torch.flatten(rec)
             outputs.append(self.activation(rec))
         return outputs
 
@@ -95,7 +99,9 @@ class BilinearDecoder(torch.nn.Module):
             inputs_col = dropout(inputs_col, 1.-self.drop_prob)
 
             intermediate_product = torch.mm(inputs_row, self.relation[k])
-            rec = torch.mm(intermediate_product, torch.transpose(inputs_col, 0, 1))
+            rec = torch.bmm(intermediate_product.view(intermediate_product.shape[0], 1, intermediate_product.shape[1]),
+                inputs_col.view(inputs_col.shape[0], inputs_col.shape[1], 1))
+            rec = torch.flatten(rec)
             outputs.append(self.activation(rec))
         return outputs
 
@@ -118,6 +124,8 @@ class InnerProductDecoder(torch.nn.Module):
             inputs_row = dropout(inputs_row, 1.-self.drop_prob)
             inputs_col = dropout(inputs_col, 1.-self.drop_prob)
 
-            rec = torch.mm(inputs_row, torch.transpose(inputs_col, 0, 1))
+            rec = torch.bmm(inputs_row.view(inputs_row.shape[0], 1, inputs_row.shape[1]),
+                inputs_col.view(inputs_col.shape[0], inputs_col.shape[1], 1))
+            rec = torch.flatten(rec)
             outputs.append(self.activation(rec))
         return outputs
diff --git a/tests/decagon_pytorch/test_decode_pairwise.py b/tests/decagon_pytorch/test_decode_pairwise.py
new file mode 100644
index 0000000..9b47eef
--- /dev/null
+++ b/tests/decagon_pytorch/test_decode_pairwise.py
@@ -0,0 +1,59 @@
+import decagon_pytorch.decode.cartesian as cart
+import decagon_pytorch.decode.pairwise as pair
+import torch
+
+
+def _common(cart_class, pair_class):
+    input_dim = 10
+    n_nodes = 20
+    num_relation_types = 7
+
+    inputs_row = torch.rand((n_nodes, input_dim))
+    inputs_col = torch.rand((n_nodes, input_dim))
+
+    cart_dec = cart_class(input_dim=input_dim,
+        num_relation_types=num_relation_types)
+    pair_dec = pair_class(input_dim=input_dim,
+        num_relation_types=num_relation_types)
+
+    if isinstance(cart_dec, cart.DEDICOMDecoder):
+        pair_dec.global_interaction = cart_dec.global_interaction
+        pair_dec.local_variation = cart_dec.local_variation
+    elif isinstance(cart_dec, cart.InnerProductDecoder):
+        pass
+    else:
+        pair_dec.relation = cart_dec.relation
+
+    cart_pred = cart_dec(inputs_row, inputs_col)
+    pair_pred = pair_dec(inputs_row, inputs_col)
+
+    assert isinstance(cart_pred, list)
+    assert isinstance(pair_pred, list)
+
+    assert len(cart_pred) == len(pair_pred)
+    assert len(cart_pred) == num_relation_types
+
+    for i in range(num_relation_types):
+        assert isinstance(cart_pred[i], torch.Tensor)
+        assert isinstance(pair_pred[i], torch.Tensor)
+
+        assert cart_pred[i].shape == (n_nodes, n_nodes)
+        assert pair_pred[i].shape == (n_nodes,)
+
+        assert torch.all(torch.abs(pair_pred[i] - torch.diag(cart_pred[i])) < 0.000001)
+
+
+def test_dedicom_decoder():
+    _common(cart.DEDICOMDecoder, pair.DEDICOMDecoder)
+
+
+def test_dist_mult_decoder():
+    _common(cart.DistMultDecoder, pair.DistMultDecoder)
+
+
+def test_bilinear_decoder():
+    _common(cart.BilinearDecoder, pair.BilinearDecoder)
+
+
+def test_inner_product_decoder():
+    _common(cart.InnerProductDecoder, pair.InnerProductDecoder)