Generate: Add GPTNeoX integration test (#22346)

tests/models/gpt_neox/test_modeling_gpt_neox.py

@@ -17,8 +17,8 @@
 
 import unittest
 
-from transformers import GPTNeoXConfig, is_torch_available
+from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available
-from transformers.testing_utils import require_torch, torch_device
+from transformers.testing_utils import require_torch, slow, torch_device
 
 from ...generation.test_utils import GenerationTesterMixin
 from ...test_configuration_common import ConfigTester
@@ -232,3 +232,28 @@ class GPTNeoXModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMi
     @unittest.skip(reason="Feed forward chunking is not implemented")
     def test_feed_forward_chunking(self):
         pass
+
+
+@require_torch
+class GPTNeoXLanguageGenerationTest(unittest.TestCase):
+    @slow
+    def test_lm_generate_codegen(self):
+        tokenizer = AutoTokenizer.from_pretrained("EleutherAI/pythia-410m-deduped")
+        for checkpointing in [True, False]:
+            model = GPTNeoXForCausalLM.from_pretrained("EleutherAI/pythia-410m-deduped")
+
+            if checkpointing:
+                model.gradient_checkpointing_enable()
+            else:
+                model.gradient_checkpointing_disable()
+            model.to(torch_device)
+
+            inputs = tokenizer("My favorite food is", return_tensors="pt").to(torch_device)
+            expected_output = (
+                "My favorite food is the chicken and rice.\n\nI love to cook and bake. I love to cook and bake"
+            )
+
+            output_ids = model.generate(**inputs, do_sample=False, max_new_tokens=20)
+            output_str = tokenizer.batch_decode(output_ids)[0]
+
+            self.assertEqual(output_str, expected_output)