Roberta is ExecuTorch compatible (#34425)

* Roberta is ExecuTorch compatible

* [run_slow] roberta

---------

Co-authored-by: Guang Yang <guangyang@fb.com>

tests/models/roberta/test_modeling_roberta.py

@@ -16,7 +16,7 @@
 
 import unittest
 
-from transformers import RobertaConfig, is_torch_available
+from transformers import AutoTokenizer, RobertaConfig, is_torch_available
 from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
 
 from ...generation.test_utils import GenerationTesterMixin
@@ -41,6 +41,7 @@ if is_torch_available():
         RobertaEmbeddings,
         create_position_ids_from_input_ids,
     )
+    from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_4
 
 ROBERTA_TINY = "sshleifer/tiny-distilroberta-base"
 
@@ -576,3 +577,43 @@ class RobertaModelIntegrationTest(TestCasePlus):
         # expected_tensor = roberta.predict("mnli", input_ids, return_logits=True).detach()
 
         self.assertTrue(torch.allclose(output, expected_tensor, atol=1e-4))
+
+    @slow
+    def test_export(self):
+        if not is_torch_greater_or_equal_than_2_4:
+            self.skipTest(reason="This test requires torch >= 2.4 to run.")
+
+        roberta_model = "FacebookAI/roberta-base"
+        device = "cpu"
+        attn_implementation = "sdpa"
+        max_length = 512
+
+        tokenizer = AutoTokenizer.from_pretrained(roberta_model)
+        inputs = tokenizer(
+            "The goal of life is <mask>.",
+            return_tensors="pt",
+            padding="max_length",
+            max_length=max_length,
+        )
+
+        model = RobertaForMaskedLM.from_pretrained(
+            roberta_model,
+            device_map=device,
+            attn_implementation=attn_implementation,
+            use_cache=True,
+        )
+
+        logits = model(**inputs).logits
+        eager_predicted_mask = tokenizer.decode(logits[0, 6].topk(5).indices)
+        self.assertEqual(eager_predicted_mask.split(), ["happiness", "love", "peace", "freedom", "simplicity"])
+
+        exported_program = torch.export.export(
+            model,
+            args=(inputs["input_ids"],),
+            kwargs={"attention_mask": inputs["attention_mask"]},
+            strict=True,
+        )
+
+        result = exported_program.module().forward(inputs["input_ids"], inputs["attention_mask"])
+        exported_predicted_mask = tokenizer.decode(result.logits[0, 6].topk(5).indices)
+        self.assertEqual(eager_predicted_mask, exported_predicted_mask)