Bert is ExecuTorch compatible (#34424)

Co-authored-by: Guang Yang <[email protected]>

tests/models/bert/test_modeling_bert.py

@@ -16,6 +16,8 @@
 import tempfile
 import unittest
 
+from packaging import version
+
 from transformers import AutoTokenizer, BertConfig, is_torch_available
 from transformers.models.auto import get_values
 from transformers.testing_utils import (
@@ -749,3 +751,43 @@ def test_sdpa_ignored_mask(self):
             self.assertTrue(
                 torch.allclose(res_eager.last_hidden_state, res_sdpa.last_hidden_state, atol=1e-5, rtol=1e-4)
             )
+
+    @slow
+    def test_export(self):
+        if version.parse(torch.__version__) < version.parse("2.4.0"):
+            self.skipTest(reason="This test requires torch >= 2.4 to run.")
+
+        bert_model = "google-bert/bert-base-uncased"
+        device = "cpu"
+        attn_implementation = "sdpa"
+        max_length = 512
+
+        tokenizer = AutoTokenizer.from_pretrained(bert_model)
+        inputs = tokenizer(
+            "the man worked as a [MASK].",
+            return_tensors="pt",
+            padding="max_length",
+            max_length=max_length,
+        )
+
+        model = BertForMaskedLM.from_pretrained(
+            bert_model,
+            device_map=device,
+            attn_implementation=attn_implementation,
+            use_cache=True,
+        )
+
+        logits = model(**inputs).logits
+        eg_predicted_mask = tokenizer.decode(logits[0, 6].topk(5).indices)
+        self.assertEqual(eg_predicted_mask.split(), ["carpenter", "waiter", "barber", "mechanic", "salesman"])
+
+        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"])
+        ep_predicted_mask = tokenizer.decode(result.logits[0, 6].topk(5).indices)
+        self.assertEqual(eg_predicted_mask, ep_predicted_mask)