Use single GITOnnxConfig class

optimum/exporters/onnx/base.py

@@ -150,6 +150,7 @@ class OnnxConfig(ExportConfig, ABC):
         "fill-mask": OrderedDict({"logits": {0: "batch_size", 1: "sequence_length"}}),
         "image-classification": OrderedDict({"logits": {0: "batch_size"}}),
         "image-segmentation": OrderedDict({"logits": {0: "batch_size", 1: "num_labels", 2: "height", 3: "width"}}),
+        "image-text-to-text": OrderedDict({"logits": {0: "batch_size", 1: "sequence_length"}}),
         "image-to-text": OrderedDict({"logits": {0: "batch_size", 1: "sequence_length"}}),
         "image-to-image": OrderedDict(
             {"reconstruction": {0: "batch_size", 1: "num_channels", 2: "height", 3: "width"}}

optimum/exporters/onnx/model_configs.py

@@ -2623,21 +2623,33 @@ class EncoderDecoderOnnxConfig(EncoderDecoderBaseOnnxConfig):
     DEFAULT_ONNX_OPSET = 14  # uses SDPA in Transformers, hence opset>=14.
 
 
-class GITOnnxConfig(VisionOnnxConfig):
-    NORMALIZED_CONFIG_CLASS = NormalizedVisionConfig
-    DUMMY_INPUT_GENERATOR_CLASSES = (DummyTextInputGenerator, DummyVisionInputGenerator)    
-
-    @property
-    def inputs(self) -> Dict[str, Dict[int, str]]:
-        return {
-            "input_ids": {0: "text_batch_size", 1: "sequence_length"},
-            "pixel_values": {0: "image_batch_size", 1: "num_channels", 2: "height", 3: "width"}
-        }
-
+class GITOnnxConfig(TextAndVisionOnnxConfig):
+    NORMALIZED_CONFIG_CLASS = NormalizedTextAndVisionConfig.with_args(vision_config="vision_config")
+    DUMMY_INPUT_GENERATOR_CLASSES_MAP = {
+        "feature-extraction": (DummyVisionInputGenerator,),
+        "image-text-to-text": (DummyTextInputGenerator, DummyVisionInputGenerator,),
+        "image-to-text": (DummyVisionInputGenerator,),
+    }
 
-class GITVisionModelOnnxConfig(VisionOnnxConfig):
-    NORMALIZED_CONFIG_CLASS = NormalizedVisionConfig
+    def _create_dummy_input_generator_classes(self, **kwargs) -> List["DummyInputGenerator"]:
+        dummy_inputs_generators = []
+        for dummy_input_generator in self.DUMMY_INPUT_GENERATOR_CLASSES_MAP[self.task]:
+            print(self.task, dummy_input_generator)
+            dummy_input_generator_instantiated = dummy_input_generator(
+                self.task, self._normalized_config, **kwargs
+            )
+            dummy_inputs_generators.append(dummy_input_generator_instantiated)
 
+        return dummy_inputs_generators
+
     @property
     def inputs(self) -> Dict[str, Dict[int, str]]:
-        return {"pixel_values": {0: "batch_size", 1: "num_channels", 2: "height", 3: "width"}}
+        if self.task == "image-text-to-text":
+            return {
+                "input_ids": {0: "text_batch_size", 1: "sequence_length"},
+                "pixel_values": {0: "image_batch_size", 1: "num_channels", 2: "height", 3: "width"},
+            }
+        else:
+            return {
+                "pixel_values": {0: "image_batch_size", 1: "num_channels", 2: "height", 3: "width"},
+        }

optimum/exporters/tasks.py

@@ -215,6 +215,7 @@ class TasksManager:
                 "AutoModelForInstanceSegmentation",
                 "AutoModelForUniversalSegmentation",
             ),
+            "image-text-to-text": ("AutoModelForCausalLM", "AutoModel"),
             "image-to-image": "AutoModelForImageToImage",
             "image-to-text": ("AutoModelForVision2Seq", "AutoModel"),
             "mask-generation": "AutoModel",
@@ -698,11 +699,6 @@ class TasksManager:
             "image-to-text",
             onnx="GITOnnxConfig",
         ),
-        "git-vision-model": supported_tasks_mapping(
-            "feature-extraction",
-            "image-to-text",
-            onnx="GITVisionModelOnnxConfig",
-        ),        
         "glpn": supported_tasks_mapping(
             "feature-extraction",
             "depth-estimation",