Template for framework-agnostic tests (#21348)

tests/generation/test_utils.py

@@ -23,6 +23,7 @@ from transformers import is_torch_available, pipeline
 from transformers.testing_utils import require_torch, slow, torch_device
 
 from ..test_modeling_common import floats_tensor, ids_tensor
+from .test_framework_agnostic import GenerationIntegrationTestsMixin
 
 
 if is_torch_available():
@@ -1790,7 +1791,16 @@ class UtilsFunctionsTest(unittest.TestCase):
 
 
 @require_torch
-class GenerationIntegrationTests(unittest.TestCase):
+class GenerationIntegrationTests(unittest.TestCase, GenerationIntegrationTestsMixin):
+
+    # setting framework_dependent_parameters needs to be gated, just like its contents' imports
+    if is_torch_available():
+        framework_dependent_parameters = {
+            "AutoModelForSeq2SeqLM": AutoModelForSeq2SeqLM,
+            "create_tensor_fn": torch.tensor,
+            "return_tensors": "pt",
+        }
+
     @slow
     def test_diverse_beam_search(self):
         article = """Justin Timberlake and Jessica Biel, welcome to parenthood.
@@ -3022,26 +3032,6 @@ class GenerationIntegrationTests(unittest.TestCase):
         max_score_diff = (output_sequences_batched.scores[0][1] - output_sequences.scores[0][0]).abs().max()
         self.assertTrue(max_score_diff < 1e-5)
 
-    def test_validate_generation_inputs(self):
-        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-roberta")
-        model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-roberta")
-
-        encoder_input_str = "Hello world"
-        input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids
-
-        # typos are quickly detected (the correct argument is `do_sample`)
-        with self.assertRaisesRegex(ValueError, "do_samples"):
-            model.generate(input_ids, do_samples=True)
-
-        # arbitrary arguments that will not be used anywhere are also not accepted
-        with self.assertRaisesRegex(ValueError, "foo"):
-            fake_model_kwargs = {"foo": "bar"}
-            model.generate(input_ids, **fake_model_kwargs)
-
-        # However, valid model_kwargs are accepted
-        valid_model_kwargs = {"attention_mask": torch.zeros_like(input_ids)}
-        model.generate(input_ids, **valid_model_kwargs)
-
     def test_eos_token_id_int_and_list_greedy_search(self):
         generation_kwargs = {
             "do_sample": False,