disable flash_attn during export internvl2

optimum/exporters/openvino/model_configs.py

@@ -87,6 +87,7 @@
     InputEmbeddingPatcher,
     InternLM2Patcher,
     InternLMModelPatcher,
+    InternVL2ChatLangModelPatcher,
     InternVLChatImageEmbeddingModelPatcher,
     JaisModelPatcher,
     LlamaModelPatcher,
@@ -1642,7 +1643,11 @@ def with_behavior(
         if behavior == InternVLChatConfigBehavior.LANGUAGE:
             model_type = self._orig_config.llm_config.model_type
             return get_vlm_text_generation_config(
-                model_type, self._orig_config.llm_config, self.int_dtype, self.float_dtype
+                model_type,
+                self._orig_config.llm_config,
+                self.int_dtype,
+                self.float_dtype,
+                InternVL2ChatLangModelPatcher,
             )
 
         if behavior == InternVLChatConfigBehavior.VISION_EMBEDDINGS:

optimum/exporters/openvino/model_patcher.py

@@ -21,6 +21,7 @@
 
 import torch
 import torch.nn.functional as F
+from transformers import PreTrainedModel, TFPreTrainedModel
 from transformers.modeling_outputs import BaseModelOutputWithPast, BaseModelOutputWithPooling
 from transformers.utils import is_tf_available
 
@@ -2992,11 +2993,91 @@ def __init__(
         model.__orig_forward = model.forward
         model.forward = model.extract_feature
 
+        if model.vision_model.encoder.layers[0].attn.use_flash_attn:
+            for layer in model.vision_model.encoder.layers:
+                layer.attn._orig_use_flash_attn = layer.attn.use_flash_attn
+                layer.attn.use_flash_attn = False
+
         super().__init__(config, model, model_kwargs)
 
     def __exit__(self, exc_type, exc_value, traceback):
         super().__exit__(exc_type, exc_value, traceback)
         self._model.forward = self._model.__orig_forward
+        if hasattr(self._model.vision_model.encoder.layers[0].attn, "_orig_use_flash_attn"):
+            for layer in self._model.vision_model.encoder.layers:
+                layer.attn.use_flash_attn = layer.attn._orig_use_flash_attn
+
+
+class InternVL2ChatLangModelPatcher(DecoderModelPatcher):
+    def __init__(
+        self, config: "OnnxConfig", model: Union["PreTrainedModel", "TFPreTrainedModel"], model_kwargs: Dict[str, Any]
+    ):
+        model_type = model.config.model_type
+        patcher_for_model_type = {
+            "llama": LlamaModelPatcher,
+            "qwen2": UpdateCausalMaskModelPatcher,
+            "phi3": Phi3ModelPatcher,
+            "internlm2": InternLM2Patcher,
+        }
+        self._internal_patcher = None
+        self._patched_forward = None
+        internal_patcher_cls = patcher_for_model_type.get(model_type)
+        if internal_patcher_cls is not None:
+            self._internal_patcher = internal_patcher_cls(config, model, model_kwargs)
+            self._patched_forward = self._internal_patcher.patched_forward
+        super().__init__(config, model, model_kwargs)
+
+    @property
+    def patched_forward(self):
+        if self._internal_patcher is not None:
+            return self._internal_patcher.patched_forward
+        return self._patched_forward
+
+    @patched_forward.setter
+    def patched_forward(self, fn):
+        self._patched_forward = fn
+        if self._internal_patcher is not None:
+            self._internal_patcher.patched_forward = fn
+
+    def __enter__(self):
+        if is_torch_version(">=", "2.1.0"):
+            if self._model.config.model_type == "qwen2" and self._model.config._attn_implementation != "sdpa":
+                from transformers.models.qwen2.modeling_qwen2 import QWEN2_ATTENTION_CLASSES
+
+                sdpa_attn = QWEN2_ATTENTION_CLASSES["sdpa"]
+                self._model.config._orig_attn_implementation = self._model.config._attn_implementation
+                self._model.config._attn_implementation = "sdpa"
+
+                for layer in self._model.model.layers:
+                    layer.self_attn._orig_forward = layer.self_attn.forward
+                    layer.self_attn.forward = types.MethodType(sdpa_attn.forward, layer.self_attn)
+
+            if self._model.config.model_type == "llama" and self._model.config._attn_implementation != "sdpa":
+                self._model.config._orig_attn_implementation = self._model.config._attn_implementation
+                self._model.config._attn_implementation = "sdpa"
+                if is_transformers_version("<", "4.47"):
+                    from transformers.models.llama.modeling_llama import LLAMA_ATTENTION_CLASSES
+
+                    sdpa_attn = LLAMA_ATTENTION_CLASSES["sdpa"]
+                    for layer in self._model.model.layers:
+                        layer.self_attn._orig_forward = layer.self_attn.forward
+                        layer.self_attn.forward = types.MethodType(sdpa_attn.forward, layer.self_attn)
+
+        if self._internal_patcher is not None:
+            return self._internal_patcher.__enter__()
+        return super().__enter__()
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        if self._internal_patcher:
+            self._internal_patcher.__exit__(exc_type, exc_value, traceback)
+        else:
+            super().__exit__(exc_type, exc_value, traceback)
+
+        if hasattr(self._model.config, "_orig_attn_implementation"):
+            self._model.config._attn_implementation = self._model.config._orig_attn_implementation
+            for layer in self._model.model.layers:
+                if hasattr(layer.self_attn, "_orig_forward"):
+                    layer.self_attn.forward = layer.self_attn._orig_forward
 
 
 def llava_vision_embed_forward(self, pixel_values):