Change a logic in pipeline test regarding TF (#20710)

* Fix the pipeline test regarding TF

* Fix the pipeline test regarding TF

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

tests/pipelines/test_pipelines_summarization.py

@@ -18,9 +18,10 @@ from transformers import (
     MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
     TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
     SummarizationPipeline,
+    TFPreTrainedModel,
     pipeline,
 )
-from transformers.testing_utils import require_tf, require_torch, slow, torch_device
+from transformers.testing_utils import get_gpu_count, require_tf, require_torch, slow, torch_device
 from transformers.tokenization_utils import TruncationStrategy
 
 from .test_pipelines_common import ANY, PipelineTestCaseMeta
@@ -51,6 +52,7 @@ class SummarizationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMe
         )
         self.assertEqual(outputs, [{"summary_text": ANY(str)}])
 
+        # Some models (Switch Transformers, LED, T5, LongT5, etc) can handle long sequences.
         model_can_handle_longer_seq = [
             "SwitchTransformersConfig",
             "T5Config",
@@ -62,8 +64,14 @@ class SummarizationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMe
             "ProphetNetConfig",  # positional embeddings up to a fixed maximum size (otherwise clamping the values)
         ]
         if model.config.__class__.__name__ not in model_can_handle_longer_seq:
-            # Switch Transformers, LED, T5, LongT5 can handle it.
-            # Too long.
+            # Too long and exception is expected.
+            # For TF models, if the weights are initialized in GPU context, we won't get expected index error from
+            # the embedding layer.
+            if not (
+                isinstance(model, TFPreTrainedModel)
+                and get_gpu_count() > 0
+                and len(summarizer.model.trainable_weights) > 0
+            ):
                 with self.assertRaises(Exception):
                     outputs = summarizer("This " * 1000)
         outputs = summarizer("This " * 1000, truncation=TruncationStrategy.ONLY_FIRST)