Fix batching tests for new models (Mamba and SegGPT) (#29633)

* fix batchinng tests for new models

* Update tests/models/seggpt/test_modeling_seggpt.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

---------

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

tests/models/seggpt/test_modeling_seggpt.py

@@ -245,6 +245,60 @@ class SegGptModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
 
             check_hidden_states_output(inputs_dict, config, model_class)
 
+    def test_batching_equivalence(self):
+        def recursive_check(batched_object, single_row_object, model_name, key):
+            if isinstance(batched_object, (list, tuple)):
+                for batched_object_value, single_row_object_value in zip(batched_object, single_row_object):
+                    recursive_check(batched_object_value, single_row_object_value, model_name, key)
+            else:
+                batched_row = batched_object[:1]
+                self.assertFalse(
+                    torch.isnan(batched_row).any(), f"Batched output has `nan` in {model_name} for key={key}"
+                )
+                self.assertFalse(
+                    torch.isinf(batched_row).any(), f"Batched output has `inf` in {model_name} for key={key}"
+                )
+                self.assertFalse(
+                    torch.isnan(single_row_object).any(), f"Single row output has `nan` in {model_name} for key={key}"
+                )
+                self.assertFalse(
+                    torch.isinf(single_row_object).any(), f"Single row output has `inf` in {model_name} for key={key}"
+                )
+                self.assertTrue(
+                    torch.max(torch.abs(batched_row - single_row_object)) <= 1e-03,
+                    msg=(
+                        f"Batched and Single row outputs are not equal in {model_name} for key={key}. "
+                        f"Difference={torch.max(torch.abs(batched_row - single_row_object))}."
+                    ),
+                )
+
+        config, batched_input = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            config.output_hidden_states = True
+
+            model_name = model_class.__name__
+            batched_input_prepared = self._prepare_for_class(batched_input, model_class)
+            model = model_class(config).to(torch_device).eval()
+
+            batch_size = self.model_tester.batch_size
+            single_row_input = {}
+            for key, value in batched_input_prepared.items():
+                if isinstance(value, torch.Tensor) and value.shape[0] % batch_size == 0:
+                    single_batch_shape = value.shape[0] // batch_size
+                    single_row_input[key] = value[:single_batch_shape]
+
+            with torch.no_grad():
+                model_batched_output = model(**batched_input_prepared)
+                model_row_output = model(**single_row_input)
+
+            for key in model_batched_output:
+                # the first hidden state in SegGPT has weird hack of adding first half of batch with second half
+                if key == "hidden_states":
+                    model_batched_output[key] = model_batched_output[key][1:]
+                    model_row_output[key] = model_row_output[key][1:]
+                recursive_check(model_batched_output[key], model_row_output[key], model_name, key)
+
     @slow
     def test_model_from_pretrained(self):
         for model_name in SEGGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:

tests/test_modeling_common.py

@@ -720,8 +720,8 @@ class ModelTesterMixin:
                     batched_object.values(), single_row_object.values()
                 ):
                     recursive_check(batched_object_value, single_row_object_value, model_name, key)
-            # do not compare returned loss (0-dim tensor) or codebook ids (int)
-            elif batched_object is None or isinstance(batched_object, int):
+            # do not compare returned loss (0-dim tensor) / codebook ids (int) / caching objects
+            elif batched_object is None or not isinstance(batched_object, torch.Tensor):
                 return
             elif batched_object.dim() == 0:
                 return