Create token type ids when not provided (#2081)

* create token type ids when needed

* add test

optimum/onnxruntime/modeling_ort.py

@@ -931,7 +931,6 @@ def _prepare_onnx_inputs(
         self, use_torch: bool, **inputs: Union[torch.Tensor, np.ndarray]
     ) -> Dict[str, np.ndarray]:
         onnx_inputs = {}
-
         # converts pytorch inputs into numpy inputs for onnx
         for input_name in self.input_names.keys():
             onnx_inputs[input_name] = inputs.pop(input_name)
@@ -1086,6 +1085,9 @@ def forward(
         use_torch = isinstance(input_ids, torch.Tensor)
         self.raise_on_numpy_input_io_binding(use_torch)
 
+        if token_type_ids is None and "token_type_ids" in self.input_names:
+            token_type_ids = torch.zeros_like(input_ids) if use_torch else np.zeros_like(input_ids)
+
         if self.device.type == "cuda" and self.use_io_binding:
             io_binding, output_shapes, output_buffers = self.prepare_io_binding(
                 input_ids,
@@ -1241,6 +1243,9 @@ def forward(
         use_torch = isinstance(input_ids, torch.Tensor)
         self.raise_on_numpy_input_io_binding(use_torch)
 
+        if token_type_ids is None and "token_type_ids" in self.input_names:
+            token_type_ids = torch.zeros_like(input_ids) if use_torch else np.zeros_like(input_ids)
+
         if self.device.type == "cuda" and self.use_io_binding:
             io_binding, output_shapes, output_buffers = self.prepare_io_binding(
                 input_ids,
@@ -1330,6 +1335,9 @@ def forward(
         use_torch = isinstance(input_ids, torch.Tensor)
         self.raise_on_numpy_input_io_binding(use_torch)
 
+        if token_type_ids is None and "token_type_ids" in self.input_names:
+            token_type_ids = torch.zeros_like(input_ids) if use_torch else np.zeros_like(input_ids)
+
         if self.device.type == "cuda" and self.use_io_binding:
             io_binding, output_shapes, output_buffers = self.prepare_io_binding(
                 input_ids,
@@ -1437,6 +1445,9 @@ def forward(
         use_torch = isinstance(input_ids, torch.Tensor)
         self.raise_on_numpy_input_io_binding(use_torch)
 
+        if token_type_ids is None and "token_type_ids" in self.input_names:
+            token_type_ids = torch.zeros_like(input_ids) if use_torch else np.zeros_like(input_ids)
+
         if self.device.type == "cuda" and self.use_io_binding:
             io_binding, output_shapes, output_buffers = self.prepare_io_binding(
                 input_ids,
@@ -1527,6 +1538,9 @@ def forward(
         use_torch = isinstance(input_ids, torch.Tensor)
         self.raise_on_numpy_input_io_binding(use_torch)
 
+        if token_type_ids is None and "token_type_ids" in self.input_names:
+            token_type_ids = torch.zeros_like(input_ids) if use_torch else np.zeros_like(input_ids)
+
         if self.device.type == "cuda" and self.use_io_binding:
             io_binding, output_shapes, output_buffers = self.prepare_io_binding(
                 input_ids,
@@ -1610,6 +1624,9 @@ def forward(
         use_torch = isinstance(input_ids, torch.Tensor)
         self.raise_on_numpy_input_io_binding(use_torch)
 
+        if token_type_ids is None and "token_type_ids" in self.input_names:
+            token_type_ids = torch.zeros_like(input_ids) if use_torch else np.zeros_like(input_ids)
+
         if self.device.type == "cuda" and self.use_io_binding:
             io_binding, output_shapes, output_buffers = self.prepare_io_binding(
                 input_ids,

tests/onnxruntime/test_modeling.py

@@ -2192,6 +2192,18 @@ def test_compare_to_io_binding(self, model_arch):
 
         gc.collect()
 
+    def test_default_token_type_ids(self):
+        model_id = MODEL_NAMES["bert"]
+        model = ORTModelForFeatureExtraction.from_pretrained(model_id, export=True)
+        tokenizer = AutoTokenizer.from_pretrained(model_id)
+        tokens = tokenizer("this is a simple input", return_tensors="np")
+        self.assertTrue("token_type_ids" in model.input_names)
+        token_type_ids = tokens.pop("token_type_ids")
+        outs = model(token_type_ids=token_type_ids, **tokens)
+        outs_without_token_type_ids = model(**tokens)
+        self.assertTrue(np.allclose(outs.last_hidden_state, outs_without_token_type_ids.last_hidden_state))
+        gc.collect()
+
 
 class ORTModelForMultipleChoiceIntegrationTest(ORTModelTestMixin):
     # Multiple Choice tests are conducted on different models due to mismatch size in model's classifier