diff --git a/captum/influence/_core/tracincp_fast_rand_proj.py b/captum/influence/_core/tracincp_fast_rand_proj.py
index dec58914f3..5b3c309ec4 100644
--- a/captum/influence/_core/tracincp_fast_rand_proj.py
+++ b/captum/influence/_core/tracincp_fast_rand_proj.py
@@ -1380,6 +1380,7 @@ def _set_projections_tracincp_fast_rand_proj(
                 1
             ]  # this is the dimension of the input of the last fully-connected layer
             device = batch_jacobians.device
+            dtype = batch_jacobians.dtype
 
             # choose projection if needed
             # without projection, the dimension of the intermediate quantities returned
@@ -1409,8 +1410,8 @@ def _set_projections_tracincp_fast_rand_proj(
                 )
 
                 projection_quantities = jacobian_projection.to(
-                    device
-                ), layer_input_projection.to(device)
+                    device=device, dtype=dtype
+                ), layer_input_projection.to(device=device, dtype=dtype)
 
         return projection_quantities
 
diff --git a/tests/influence/_utils/common.py b/tests/influence/_utils/common.py
index dbfc0de550..26a5146785 100644
--- a/tests/influence/_utils/common.py
+++ b/tests/influence/_utils/common.py
@@ -190,35 +190,42 @@ def get_random_model_and_data(
         BasicLinearNet(in_features, hidden_nodes, out_features)
         if not unpack_inputs
         else MultLinearNet(in_features, hidden_nodes, out_features, num_inputs)
-    )
+    ).double()
 
     num_checkpoints = 5
 
     for i in range(num_checkpoints):
-        net.linear1.weight.data = torch.normal(3, 4, (hidden_nodes, in_features))
-        net.linear2.weight.data = torch.normal(5, 6, (out_features, hidden_nodes))
+        net.linear1.weight.data = torch.normal(
+            3, 4, (hidden_nodes, in_features)
+        ).double()
+        net.linear2.weight.data = torch.normal(
+            5, 6, (out_features, hidden_nodes)
+        ).double()
         if unpack_inputs:
             net.pre.weight.data = torch.normal(
                 3, 4, (in_features, in_features * num_inputs)
             )
+        if hasattr(net, "pre"):
+            net.pre.weight.data = net.pre.weight.data.double()
         checkpoint_name = "-".join(["checkpoint-reg", str(i + 1) + ".pt"])
         net_adjusted = _wrap_model_in_dataparallel(net) if use_gpu else net
         torch.save(net_adjusted.state_dict(), os.path.join(tmpdir, checkpoint_name))
 
     num_samples = 50
     num_train = 32
-    all_labels = torch.normal(1, 2, (num_samples, out_features))
+    all_labels = torch.normal(1, 2, (num_samples, out_features)).double()
     train_labels = all_labels[:num_train]
     test_labels = all_labels[num_train:]
 
     if unpack_inputs:
         all_samples = [
-            torch.normal(0, 1, (num_samples, in_features)) for _ in range(num_inputs)
+            torch.normal(0, 1, (num_samples, in_features)).double()
+            for _ in range(num_inputs)
         ]
         train_samples = [ts[:num_train] for ts in all_samples]
         test_samples = [ts[num_train:] for ts in all_samples]
     else:
-        all_samples = torch.normal(0, 1, (num_samples, in_features))
+        all_samples = torch.normal(0, 1, (num_samples, in_features)).double()
         train_samples = all_samples[:num_train]
         test_samples = all_samples[num_train:]