MobileBERT is ExecuTorch compatible

tests/models/mobilebert/test_modeling_mobilebert.py

@@ -16,7 +16,9 @@
 
 import unittest
 
-from transformers import MobileBertConfig, is_torch_available
+from packaging import version
+
+from transformers import AutoTokenizer, MobileBertConfig, MobileBertForMaskedLM, is_torch_available
 from transformers.models.auto import get_values
 from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
 
@@ -384,3 +386,42 @@ def test_inference_no_head(self):
         upper_bound = torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE)
 
         self.assertTrue(lower_bound and upper_bound)
+
+    @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.")
+
+        mobilebert_model = "google/mobilebert-uncased"
+        device = "cpu"
+        attn_implementation = "eager"
+        max_length = 512
+
+        tokenizer = AutoTokenizer.from_pretrained(mobilebert_model)
+        inputs = tokenizer(
+            f"the man worked as a {tokenizer.mask_token}.",
+            return_tensors="pt",
+            padding="max_length",
+            max_length=max_length,
+        )
+
+        model = MobileBertForMaskedLM.from_pretrained(
+            mobilebert_model,
+            device_map=device,
+            attn_implementation=attn_implementation,
+        )
+
+        logits = model(**inputs).logits
+        eg_predicted_mask = tokenizer.decode(logits[0, 6].topk(5).indices)
+        self.assertEqual(eg_predicted_mask.split(), ["carpenter", "waiter", "mechanic", "teacher", "clerk"])
+
+        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)