Simplify the get_random_model_and_data() method (meta-pytorch#1251)

Summary:

As titled. The sample data type is fixed after construction and should be handled separately based on its type.

Differential Revision: D55153967

Author: cicichen01

tests/influence/_utils/common.py

@@ -285,21 +285,17 @@ def get_random_model_and_data(
             torch.normal(0, 1, (num_samples, in_features)).double()
             for _ in range(num_inputs)
         ]
-        all_samples = (
-            _move_sample_to_cuda(all_samples)
-            if isinstance(all_samples, list) and use_gpu
-            else (all_samples.cuda() if use_gpu else all_samples)
-        )
+        if use_gpu:
+            all_samples = _move_sample_to_cuda(all_samples)
+
         train_samples = [ts[:num_train] for ts in all_samples]
         test_samples = [ts[num_train:] for ts in all_samples]
         hessian_samples = [ts[:num_hessian] for ts in all_samples]
     else:
         all_samples = torch.normal(0, 1, (num_samples, in_features)).double()
-        all_samples = (
-            _move_sample_to_cuda(all_samples)
-            if isinstance(all_samples, list) and use_gpu
-            else (all_samples.cuda() if use_gpu else all_samples)
-        )
+
+        if use_gpu:
+            all_samples = all_samples.cuda()
         train_samples = all_samples[:num_train]
         test_samples = all_samples[num_train:]
         hessian_samples = all_samples[:num_hessian]