Add Onnx Config for PoolFormer (#20868)

poolformer onnx

Co-authored-by: syed <syed.abdul@sandlogic.com>

docs/source/en/serialization.mdx

@@ -102,6 +102,7 @@ Ready-made configurations include the following architectures:
 - OWL-ViT
 - Perceiver
 - PLBart
+- PoolFormer
 - RemBERT
 - ResNet
 - RoBERTa

src/transformers/models/poolformer/__init__.py

@@ -21,7 +21,13 @@ from typing import TYPE_CHECKING
 from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
 
 
-_import_structure = {"configuration_poolformer": ["POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "PoolFormerConfig"]}
+_import_structure = {
+    "configuration_poolformer": [
+        "POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
+        "PoolFormerConfig",
+        "PoolFormerOnnxConfig",
+    ]
+}
 
 try:
     if not is_vision_available():
@@ -47,7 +53,11 @@ else:
 
 
 if TYPE_CHECKING:
-    from .configuration_poolformer import POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, PoolFormerConfig
+    from .configuration_poolformer import (
+        POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
+        PoolFormerConfig,
+        PoolFormerOnnxConfig,
+    )
 
     try:
         if not is_vision_available():

src/transformers/models/poolformer/configuration_poolformer.py

@@ -13,8 +13,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """ PoolFormer 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
 
 
@@ -125,3 +130,20 @@ class PoolFormerConfig(PretrainedConfig):
         self.layer_scale_init_value = layer_scale_init_value
         self.initializer_range = initializer_range
         super().__init__(**kwargs)
+
+
+class PoolFormerOnnxConfig(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: "num_channels", 2: "height", 3: "width"}),
+            ]
+        )
+
+    @property
+    def atol_for_validation(self) -> float:
+        return 2e-3

src/transformers/onnx/features.py

@@ -447,6 +447,9 @@ class FeaturesManager:
             "sequence-classification",
             onnx_config_cls="models.perceiver.PerceiverOnnxConfig",
         ),
+        "poolformer": supported_features_mapping(
+            "default", "image-classification", onnx_config_cls="models.poolformer.PoolFormerOnnxConfig"
+        ),
         "rembert": supported_features_mapping(
             "default",
             "masked-lm",

tests/onnx/test_onnx_v2.py

@@ -210,6 +210,7 @@ PYTORCH_EXPORT_MODELS = {
     ("owlvit", "google/owlvit-base-patch32"),
     ("perceiver", "hf-internal-testing/tiny-random-PerceiverModel", ("masked-lm", "sequence-classification")),
     ("perceiver", "hf-internal-testing/tiny-random-PerceiverModel", ("image-classification",)),
+    ("poolformer", "sail/poolformer_s12"),
     ("rembert", "google/rembert"),
     ("resnet", "microsoft/resnet-50"),
     ("roberta", "hf-internal-testing/tiny-random-RobertaModel"),