Generate: general TF XLA constrastive search are now slow tests (#20277)

* move contrastive search test to slow

tests/test_modeling_tf_common.py

@@ -1800,7 +1800,7 @@ class TFModelTesterMixin:
                 model.compile(optimizer="sgd", run_eagerly=True)
                 model.train_on_batch(test_batch, test_batch_labels)
 
-    def _test_xla_generate(self, num_beams, num_return_sequences, max_length, **generate_kwargs):
+    def _test_xla_generate(self, **generate_kwargs):
         def _generate_and_check_results(model, config, inputs_dict):
             if "input_ids" in inputs_dict:
                 inputs = inputs_dict["input_ids"]
@@ -1826,20 +1826,7 @@ class TFModelTesterMixin:
         for model_class in self.all_generative_model_classes:
             config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
             config.eos_token_id = None  # Generate until max length
-            config.max_length = max_length
             config.do_sample = False
-            config.num_beams = num_beams
-            config.num_return_sequences = num_return_sequences
-
-            # fix config for models with additional sequence-length limiting settings
-            for var_name in ["max_position_embeddings", "max_target_positions"]:
-                if hasattr(config, var_name):
-                    try:
-                        setattr(config, var_name, max_length)
-                    except NotImplementedError:
-                        # xlnet will raise an exception when trying to set
-                        # max_position_embeddings.
-                        pass
 
             model = model_class(config)
 
@@ -1856,23 +1843,18 @@ class TFModelTesterMixin:
 
         Either the model supports XLA generation and passes the inner test, or it raises an appropriate exception
         """
-        num_beams = 1
+        self._test_xla_generate(num_beams=1, num_return_sequences=1, max_new_tokens=3)
-        num_return_sequences = 1
-        max_length = 10
-        self._test_xla_generate(num_beams, num_return_sequences, max_length)
 
+    @slow
     def test_xla_generate_contrastive(self):
         """
-        Similar to `test_xla_generate_fast`, but for contrastive search -- contrastive search directly manipulates the
+        Slow and challenging version of `test_xla_generate_fast` for contrastive search -- contrastive search directly
-        model cache and other outputs, and this test ensures that they are in a valid format that is also supported
+        manipulates the model cache and other outputs, and this test ensures that they are in a valid format that is
-        by XLA.
+        also supported by XLA.
 
         Either the model supports XLA generation and passes the inner test, or it raises an appropriate exception
         """
-        num_beams = 1
+        self._test_xla_generate(num_beams=1, num_return_sequences=1, max_new_tokens=64, penalty_alpha=0.5, top_k=4)
-        num_return_sequences = 1
-        max_length = 10
-        self._test_xla_generate(num_beams, num_return_sequences, max_length, penalty_alpha=0.5, top_k=5)
 
     @slow
     def test_xla_generate_slow(self):
@@ -1883,10 +1865,7 @@ class TFModelTesterMixin:
 
         Either the model supports XLA generation and passes the inner test, or it raises an appropriate exception
         """
-        num_beams = 8
+        self._test_xla_generate(num_beams=8, num_return_sequences=2, max_new_tokens=128)
-        num_return_sequences = 2
-        max_length = 128
-        self._test_xla_generate(num_beams, num_return_sequences, max_length)
 
     def _generate_random_bad_tokens(self, num_bad_tokens, model):
         # special tokens cannot be bad tokens