Fix Splinter test (#17854)

* fix
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

tests/models/splinter/test_modeling_splinter.py

@@ -18,7 +18,7 @@ import copy
 import unittest
 
 from transformers import is_torch_available
-from transformers.testing_utils import require_torch, slow, torch_device
+from transformers.testing_utils import require_torch, require_torch_multi_gpu, slow, torch_device
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
@@ -316,6 +316,42 @@ class SplinterModelTest(ModelTesterMixin, unittest.TestCase):
             model = SplinterModel.from_pretrained(model_name)
             self.assertIsNotNone(model)
 
+    # overwrite from common since `SplinterForPreTraining` could contain different number of question tokens in inputs.
+    # When the batch is distributed to multiple devices, each replica could get different values for the maximal number
+    # of question tokens (see `SplinterForPreTraining._prepare_question_positions()`), and the model returns different
+    # shape along dimension 1 (i.e. `num_questions`) that could not be combined into a single tensor as an output.
+    @require_torch_multi_gpu
+    def test_multi_gpu_data_parallel_forward(self):
+        from torch import nn
+
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        # some params shouldn't be scattered by nn.DataParallel
+        # so just remove them if they are present.
+        blacklist_non_batched_params = ["head_mask", "decoder_head_mask", "cross_attn_head_mask"]
+        for k in blacklist_non_batched_params:
+            inputs_dict.pop(k, None)
+
+        # move input tensors to cuda:O
+        for k, v in inputs_dict.items():
+            if torch.is_tensor(v):
+                inputs_dict[k] = v.to(0)
+
+        for model_class in self.all_model_classes:
+
+            # Skip this case since it will fail sometimes, as described above.
+            if model_class == SplinterForPreTraining:
+                continue
+
+            model = model_class(config=config)
+            model.to(0)
+            model.eval()
+
+            # Wrap model in nn.DataParallel
+            model = nn.DataParallel(model)
+            with torch.no_grad():
+                _ = model(**self._prepare_for_class(inputs_dict, model_class))
+
 
 @require_torch
 class SplinterModelIntegrationTest(unittest.TestCase):