Layoutlm onnx support (Issue #13300)

src/transformers/models/layoutlm/__init__.py

@@ -24,7 +24,7 @@
 
 
 _import_structure = {
-    "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig"],
+    "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
     "tokenization_layoutlm": ["LayoutLMTokenizer"],
 }
 
@@ -54,7 +54,7 @@
 
 
 if TYPE_CHECKING:
-    from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig
+    from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
     from .tokenization_layoutlm import LayoutLMTokenizer
 
     if is_tokenizers_available():

src/transformers/models/layoutlm/configuration_layoutlm.py

@@ -13,8 +13,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """ LayoutLM model configuration """
+from collections import OrderedDict
+from typing import Any, List, Mapping, Optional
 
+from transformers import PretrainedConfig, PreTrainedTokenizer, TensorType
 
+from ... import is_torch_available
+from ...onnx import OnnxConfig, PatchingSpec
 from ...utils import logging
 from ..bert.configuration_bert import BertConfig
 
@@ -125,3 +130,68 @@ def __init__(
             **kwargs,
         )
         self.max_2d_position_embeddings = max_2d_position_embeddings
+
+
+class LayoutLMOnnxConfig(OnnxConfig):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        task: str = "default",
+        patching_specs: List[PatchingSpec] = None,
+    ):
+        super().__init__(config, task=task, patching_specs=patching_specs)
+        self.max_2d_positions = config.max_2d_position_embeddings - 1
+
+    @property
+    def inputs(self) -> Mapping[str, Mapping[int, str]]:
+        return OrderedDict(
+            [
+                ("input_ids", {0: "batch", 1: "sequence"}),
+                ("bbox", {0: "batch", 1: "sequence"}),
+                ("attention_mask", {0: "batch", 1: "sequence"}),
+                ("token_type_ids", {0: "batch", 1: "sequence"}),
+            ]
+        )
+
+    def generate_dummy_inputs(
+        self,
+        tokenizer: PreTrainedTokenizer,
+        batch_size: int = -1,
+        seq_length: int = -1,
+        is_pair: bool = False,
+        framework: Optional[TensorType] = None,
+    ) -> Mapping[str, Any]:
+        """
+        Generate inputs to provide to the ONNX exporter for the specific framework
+
+        Args:
+            tokenizer: The tokenizer associated with this model configuration
+            batch_size: The batch size (int) to export the model for (-1 means dynamic axis)
+            seq_length: The sequence length (int) to export the model for (-1 means dynamic axis)
+            is_pair: Indicate if the input is a pair (sentence 1, sentence 2)
+            framework: The framework (optional) the tokenizer will generate tensor for
+
+        Returns:
+            Mapping[str, Tensor] holding the kwargs to provide to the model's forward function
+        """
+
+        input_dict = super().generate_dummy_inputs(tokenizer, batch_size, seq_length, is_pair, framework)
+
+        # Generate a dummy bbox
+        box = [48, 84, 73, 128]
+
+        if not framework == TensorType.PYTORCH:
+            raise NotImplementedError("Exporting LayoutLM to ONNX is currently only supported for PyTorch.")
+
+        if not is_torch_available():
+            raise ValueError("Cannot generate dummy inputs without PyTorch installed.")
+        import torch
+
+        input_dict["bbox"] = torch.tensor(
+            [
+                [0] * 4,
+                *[box] * seq_length,
+                [self.max_2d_positions] * 4,
+            ]
+        ).tile(batch_size, 1, 1)
+        return input_dict

src/transformers/onnx/features.py

@@ -8,6 +8,7 @@
 from ..models.distilbert import DistilBertOnnxConfig
 from ..models.gpt2 import GPT2OnnxConfig
 from ..models.gpt_neo import GPTNeoOnnxConfig
+from ..models.layoutlm import LayoutLMOnnxConfig
 from ..models.longformer import LongformerOnnxConfig
 from ..models.mbart import MBartOnnxConfig
 from ..models.roberta import RobertaOnnxConfig
@@ -78,6 +79,13 @@ class FeaturesManager:
             "sequence-classification-with-past",
             onnx_config_cls=GPTNeoOnnxConfig,
         ),
+        "layoutlm": supported_features_mapping(
+            "default",
+            "masked-lm",
+            "sequence-classification",
+            "token-classification",
+            onnx_config_cls=LayoutLMOnnxConfig,
+        ),
     }
 
     AVAILABLE_FEATURES = sorted(reduce(lambda s1, s2: s1 | s2, (v.keys() for v in _SUPPORTED_MODEL_KIND.values())))

tests/test_onnx_v2.py

@@ -10,6 +10,7 @@
     DistilBertConfig,
     GPT2Config,
     GPTNeoConfig,
+    LayoutLMConfig,
     MBartConfig,
     RobertaConfig,
     XLMRobertaConfig,
@@ -23,6 +24,7 @@
 # from transformers.models.longformer import LongformerOnnxConfig
 from transformers.models.gpt2 import GPT2OnnxConfig
 from transformers.models.gpt_neo import GPTNeoOnnxConfig
+from transformers.models.layoutlm import LayoutLMOnnxConfig
 from transformers.models.mbart import MBartOnnxConfig
 from transformers.models.roberta import RobertaOnnxConfig
 
@@ -193,6 +195,7 @@ def test_values_override(self):
         DistilBertModel,
         GPT2Model,
         GPTNeoModel,
+        LayoutLMModel,
         MBartModel,
         RobertaModel,
         XLMRobertaModel,
@@ -208,6 +211,7 @@ def test_values_override(self):
         # ("LongFormer", "longformer-base-4096", LongformerModel, LongformerConfig, LongformerOnnxConfig),
         ("Roberta", "roberta-base", RobertaModel, RobertaConfig, RobertaOnnxConfig),
         ("XLM-Roberta", "roberta-base", XLMRobertaModel, XLMRobertaConfig, XLMRobertaOnnxConfig),
+        ("LayoutLM", "microsoft/layoutlm-base-uncased", LayoutLMModel, LayoutLMConfig, LayoutLMOnnxConfig),
         ("MBart", "sshleifer/tiny-mbart", MBartModel, MBartConfig, MBartOnnxConfig),
         # ("T5", "t5-small", T5Model, T5Config, T5OnnxConfig),
     }