Add ONNX export for BeiT (#16498)

* Add beit onnx conversion support

* Updated docs

* Added cross reference to ViT ONNX config

docs/source/serialization.mdx

@@ -47,6 +47,7 @@ Ready-made configurations include the following architectures:
 
 - ALBERT
 - BART
+- BEiT
 - BERT
 - Blenderbot
 - BlenderbotSmall

src/transformers/models/beit/__init__.py

@@ -22,7 +22,7 @@ from ...utils import _LazyModule, is_flax_available, is_torch_available, is_visi
 
 
 _import_structure = {
-    "configuration_beit": ["BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BeitConfig"],
+    "configuration_beit": ["BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BeitConfig", "BeitOnnxConfig"],
 }
 
 if is_vision_available():
@@ -48,7 +48,7 @@ if is_flax_available():
     ]
 
 if TYPE_CHECKING:
-    from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig
+    from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig
 
     if is_vision_available():
         from .feature_extraction_beit import BeitFeatureExtractor

src/transformers/models/beit/configuration_beit.py

@@ -13,8 +13,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """ BEiT model configuration"""
+from collections import OrderedDict
+from typing import Mapping
+
+from packaging import version
 
 from ...configuration_utils import PretrainedConfig
+from ...onnx import OnnxConfig
 from ...utils import logging
 
 
@@ -176,3 +181,21 @@ class BeitConfig(PretrainedConfig):
         self.auxiliary_num_convs = auxiliary_num_convs
         self.auxiliary_concat_input = auxiliary_concat_input
         self.semantic_loss_ignore_index = semantic_loss_ignore_index
+
+
+# Copied from transformers.models.vit.configuration_vit.ViTOnnxConfig
+class BeitOnnxConfig(OnnxConfig):
+
+    torch_onnx_minimum_version = version.parse("1.11")
+
+    @property
+    def inputs(self) -> Mapping[str, Mapping[int, str]]:
+        return OrderedDict(
+            [
+                ("pixel_values", {0: "batch", 1: "sequence"}),
+            ]
+        )
+
+    @property
+    def atol_for_validation(self) -> float:
+        return 1e-4

src/transformers/onnx/features.py

@@ -4,6 +4,7 @@ from typing import Callable, Dict, Optional, Tuple, Type, Union
 from .. import PretrainedConfig, PreTrainedModel, TFPreTrainedModel, is_tf_available, is_torch_available
 from ..models.albert import AlbertOnnxConfig
 from ..models.bart import BartOnnxConfig
+from ..models.beit import BeitOnnxConfig
 from ..models.bert import BertOnnxConfig
 from ..models.blenderbot import BlenderbotOnnxConfig
 from ..models.blenderbot_small import BlenderbotSmallOnnxConfig
@@ -270,6 +271,7 @@ class FeaturesManager:
             onnx_config_cls=ElectraOnnxConfig,
         ),
         "vit": supported_features_mapping("default", "image-classification", onnx_config_cls=ViTOnnxConfig),
+        "beit": supported_features_mapping("default", "image-classification", onnx_config_cls=BeitOnnxConfig),
         "blenderbot": supported_features_mapping(
             "default",
             "default-with-past",

tests/onnx/test_onnx_v2.py

@@ -15,14 +15,13 @@ from transformers.onnx import (
     export,
     validate_model_outputs,
 )
+from transformers.onnx.utils import compute_effective_axis_dimension, compute_serialized_parameters_size
+from transformers.testing_utils import require_onnx, require_tf, require_torch, require_vision, slow
 
 
 if is_torch_available() or is_tf_available():
     from transformers.onnx.features import FeaturesManager
 
-from transformers.onnx.utils import compute_effective_axis_dimension, compute_serialized_parameters_size
-from transformers.testing_utils import require_onnx, require_tf, require_torch, require_vision, slow
-
 
 @require_onnx
 class OnnxUtilsTestCaseV2(TestCase):
@@ -181,6 +180,7 @@ PYTORCH_EXPORT_MODELS = {
     ("xlm-roberta", "xlm-roberta-base"),
     ("layoutlm", "microsoft/layoutlm-base-uncased"),
     ("vit", "google/vit-base-patch16-224"),
+    ("beit", "microsoft/beit-base-patch16-224"),
 }
 
 PYTORCH_EXPORT_WITH_PAST_MODELS = {