Adds CLIP to models exportable with ONNX

docs/source/en/serialization.mdx

@@ -55,6 +55,7 @@ Ready-made configurations include the following architectures:
 - BlenderbotSmall
 - BLOOM
 - CamemBERT
+- CLIP
 - CodeGen
 - ConvBERT
 - ConvNeXT

src/transformers/models/clip/__init__.py

@@ -29,7 +29,13 @@
 
 
 _import_structure = {
-    "configuration_clip": ["CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP", "CLIPConfig", "CLIPTextConfig", "CLIPVisionConfig"],
+    "configuration_clip": [
+        "CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
+        "CLIPConfig",
+        "CLIPOnnxConfig",
+        "CLIPTextConfig",
+        "CLIPVisionConfig",
+    ],
     "tokenization_clip": ["CLIPTokenizer"],
 }
 
@@ -95,7 +101,13 @@
 
 
 if TYPE_CHECKING:
-    from .configuration_clip import CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, CLIPConfig, CLIPTextConfig, CLIPVisionConfig
+    from .configuration_clip import (
+        CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
+        CLIPConfig,
+        CLIPOnnxConfig,
+        CLIPTextConfig,
+        CLIPVisionConfig,
+    )
     from .tokenization_clip import CLIPTokenizer
 
     try:

src/transformers/models/clip/configuration_clip.py

@@ -16,9 +16,16 @@
 
 import copy
 import os
-from typing import Union
+from collections import OrderedDict
+from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
+
+
+if TYPE_CHECKING:
+    from ...processing_utils import ProcessorMixin
+    from ...utils import TensorType
 
 from ...configuration_utils import PretrainedConfig
+from ...onnx import OnnxConfig
 from ...utils import logging
 
 
@@ -317,3 +324,44 @@ def to_dict(self):
         output["vision_config"] = self.vision_config.to_dict()
         output["model_type"] = self.__class__.model_type
         return output
+
+
+class CLIPOnnxConfig(OnnxConfig):
+    @property
+    def inputs(self) -> Mapping[str, Mapping[int, str]]:
+        return OrderedDict(
+            [
+                ("input_ids", {0: "batch", 1: "sequence"}),
+                ("pixel_values", {0: "batch"}),
+                ("attention_mask", {0: "batch", 1: "sequence"}),
+            ]
+        )
+
+    @property
+    def outputs(self) -> Mapping[str, Mapping[int, str]]:
+        return OrderedDict(
+            [
+                ("logits_per_image", {0: "batch"}),
+                ("logits_per_text", {0: "batch"}),
+                ("text_embeds", {0: "batch"}),
+                ("image_embeds", {0: "batch"}),
+            ]
+        )
+
+    @property
+    def atol_for_validation(self) -> float:
+        return 1e-4
+
+    def generate_dummy_inputs(
+        self,
+        processor: "ProcessorMixin",
+        framework: Optional["TensorType"] = None,
+    ) -> Mapping[str, Any]:
+
+        text_input_dict = super().generate_dummy_inputs(processor.tokenizer, framework=framework)
+        image_input_dict = super().generate_dummy_inputs(processor.feature_extractor, framework=framework)
+        return {**text_input_dict, **image_input_dict}
+
+    @property
+    def default_onnx_opset(self) -> int:
+        return 14

src/transformers/models/clip/modeling_clip.py

@@ -68,7 +68,8 @@ def contrastive_loss(logits: torch.Tensor) -> torch.Tensor:
 
 def clip_loss(similarity: torch.Tensor) -> torch.Tensor:
     caption_loss = contrastive_loss(similarity)
-    image_loss = contrastive_loss(similarity.T)
+    # .T doesn't work while converting to onnx, aten::numpy_T operator is not supported yet
+    image_loss = contrastive_loss(similarity.t())
     return (caption_loss + image_loss) / 2.0
 
 
@@ -660,7 +661,10 @@ def forward(
 
         # text_embeds.shape = [batch_size, sequence_length, transformer.width]
         # take features from the eot embedding (eot_token is the highest number in each sequence)
-        pooled_output = last_hidden_state[torch.arange(last_hidden_state.shape[0]), input_ids.argmax(dim=-1)]
+        # casting to torch.int for onnx compatibility: argmax doesn't support int64 inputs with opset 14
+        pooled_output = last_hidden_state[
+            torch.arange(last_hidden_state.shape[0]), input_ids.to(torch.int).argmax(dim=-1)
+        ]
 
         if not return_dict:
             return (last_hidden_state, pooled_output) + encoder_outputs[1:]
@@ -1050,7 +1054,8 @@ def forward(
         # cosine similarity as logits
         logit_scale = self.logit_scale.exp()
         logits_per_text = torch.matmul(text_embeds, image_embeds.t()) * logit_scale
-        logits_per_image = logits_per_text.T
+        # .T doesn't work while converting to onnx, aten::numpy_T operator is not supported yet
+        logits_per_image = logits_per_text.t()
 
         loss = None
         if return_loss:

src/transformers/models/groupvit/modeling_groupvit.py

@@ -72,7 +72,8 @@ def contrastive_loss(logits: torch.Tensor) -> torch.Tensor:
 # Copied from transformers.models.clip.modeling_clip.clip_loss with clip->groupvit
 def groupvit_loss(similarity: torch.Tensor) -> torch.Tensor:
     caption_loss = contrastive_loss(similarity)
-    image_loss = contrastive_loss(similarity.T)
+    # .T doesn't work while converting to onnx, aten::numpy_T operator is not supported yet
+    image_loss = contrastive_loss(similarity.t())
     return (caption_loss + image_loss) / 2.0
 
 
@@ -1132,7 +1133,10 @@ def forward(
 
         # text_embeds.shape = [batch_size, sequence_length, transformer.width]
         # take features from the eot embedding (eot_token is the highest number in each sequence)
-        pooled_output = last_hidden_state[torch.arange(last_hidden_state.shape[0]), input_ids.argmax(dim=-1)]
+        # casting to torch.int for onnx compatibility: argmax doesn't support int64 inputs with opset 14
+        pooled_output = last_hidden_state[
+            torch.arange(last_hidden_state.shape[0]), input_ids.to(torch.int).argmax(dim=-1)
+        ]
 
         if not return_dict:
             return (last_hidden_state, pooled_output) + encoder_outputs[1:]

src/transformers/models/owlvit/modeling_owlvit.py

@@ -71,7 +71,8 @@ def contrastive_loss(logits: torch.Tensor) -> torch.Tensor:
 # Copied from transformers.models.clip.modeling_clip.clip_loss with clip->owlvit
 def owlvit_loss(similarity: torch.Tensor) -> torch.Tensor:
     caption_loss = contrastive_loss(similarity)
-    image_loss = contrastive_loss(similarity.T)
+    # .T doesn't work while converting to onnx, aten::numpy_T operator is not supported yet
+    image_loss = contrastive_loss(similarity.t())
     return (caption_loss + image_loss) / 2.0
 
 

src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py

@@ -154,7 +154,8 @@ def contrastive_loss(logits: torch.Tensor) -> torch.Tensor:
 # Copied from transformers.models.clip.modeling_clip.clip_loss
 def clip_loss(similarity: torch.Tensor) -> torch.Tensor:
     caption_loss = contrastive_loss(similarity)
-    image_loss = contrastive_loss(similarity.T)
+    # .T doesn't work while converting to onnx, aten::numpy_T operator is not supported yet
+    image_loss = contrastive_loss(similarity.t())
     return (caption_loss + image_loss) / 2.0
 
 

src/transformers/onnx/features.py

@@ -201,6 +201,10 @@ class FeaturesManager:
             "question-answering",
             onnx_config_cls="models.camembert.CamembertOnnxConfig",
         ),
+        "clip": supported_features_mapping(
+            "default",
+            onnx_config_cls="models.clip.CLIPOnnxConfig",
+        ),
         "codegen": supported_features_mapping(
             "default",
             "causal-lm",

tests/onnx/test_onnx_v2.py

@@ -185,6 +185,7 @@ def test_values_override(self):
     ("big-bird", "google/bigbird-roberta-base"),
     ("ibert", "kssteven/ibert-roberta-base"),
     ("camembert", "camembert-base"),
+    ("clip", "openai/clip-vit-base-patch32"),
     ("convbert", "YituTech/conv-bert-base"),
     ("codegen", "Salesforce/codegen-350M-multi"),
     ("deberta", "microsoft/deberta-base"),