Support transformers 4.47 (#1088)

* test 4.47

* update optimum

* patch gemma attn functions

* style

* force attn model

* latest qwen2 vl position_ids formula

* latest qwen2 vl position_ids formula

* revert

optimum/exporters/openvino/__main__.py

@@ -49,7 +49,7 @@
 )
 
 
-FORCE_ATTN_MODEL_CLASSES = {"phi3-v": "eager"}
+FORCE_ATTN_MODEL_CLASSES = {"phi3-v": "eager", "gemma2": "sdpa"}
 
 if TYPE_CHECKING:
     from optimum.intel.openvino.configuration import OVConfig

optimum/exporters/openvino/model_patcher.py

@@ -2827,26 +2827,6 @@ def patched_forward(*args, **kwargs):
 
         self.patched_forward = patched_forward
 
-    def __enter__(self):
-        super().__enter__()
-        if is_transformers_version(">=", "4.45.0"):
-            from transformers.models.gemma2.modeling_gemma2 import GEMMA2_ATTENTION_CLASSES
-
-            sdpa_attn = GEMMA2_ATTENTION_CLASSES["sdpa"]
-            eager_attn = GEMMA2_ATTENTION_CLASSES["eager"]
-
-            for layer in self._model.model.layers:
-                if isinstance(layer.self_attn, eager_attn):
-                    layer.self_attn._orig_forward = layer.self_attn.forward
-                    layer.self_attn.forward = types.MethodType(sdpa_attn.forward, layer.self_attn)
-
-    def __exit__(self, exc_type, exc_value, traceback):
-        super().__exit__(exc_type, exc_value, traceback)
-        if is_transformers_version(">=", "4.45.0"):
-            for layer in self._model.model.layers:
-                if hasattr(layer.self_attn, "_orig_forward"):
-                    layer.self_attn.forward = layer.self_attn._orig_forward
-
 
 def _decilm_attn_forward(
     self,

optimum/intel/openvino/modeling_visual_language.py

@@ -53,7 +53,7 @@
 
 
 if TYPE_CHECKING:
-    from PIL import Image
+    from PIL.Image import Image
 
 
 logger = logging.getLogger(__name__)
@@ -2100,6 +2100,8 @@ def __init__(
             quantization_config=quantization_config,
             **kwargs,
         )
+        self.rope_deltas = None  # cache rope_deltas here
+
         if is_transformers_version(">=", "4.45.0"):
             from transformers.models.qwen2_vl.modeling_qwen2_vl import (
                 Qwen2VLForConditionalGeneration,
@@ -2197,6 +2199,7 @@ def get_multimodal_embeddings(
         pixel_values_videos=None,
         image_grid_thw=None,
         video_grid_thw=None,
+        cache_position=None,
         **kwargs,
     ):
         inputs_embeds = torch.from_numpy(self.get_text_embeddings(input_ids))
@@ -2209,6 +2212,26 @@ def get_multimodal_embeddings(
             video_embeds = torch.from_numpy(self.get_vision_embeddings(pixel_values_videos, video_grid_thw))
             video_mask = input_ids == self.config.video_token_id
             inputs_embeds[video_mask] = video_embeds
+
+        # if we get 4D attention mask we cannot calculate rope deltas anymore.
+        if position_ids is None and input_ids is not None and (attention_mask is None or attention_mask.ndim == 2):
+            # calculate RoPE index once per generation in the pre-fill stage only
+            if (cache_position is not None and cache_position[0] == 0) or self.rope_deltas is None:
+                position_ids, rope_deltas = self.get_rope_index(
+                    input_ids, image_grid_thw, video_grid_thw, attention_mask
+                )
+                self.rope_deltas = rope_deltas
+            # then use the prev pre-calculated rope-deltas to get the correct position ids
+            else:
+                batch_size, seq_length, _ = inputs_embeds.shape
+                delta = cache_position[0] + self.rope_deltas if cache_position is not None else 0
+                position_ids = torch.arange(seq_length, device=inputs_embeds.device)
+                position_ids = position_ids.view(1, -1).expand(batch_size, -1)
+                if cache_position is not None:  # otherwise `deltas` is an int `0`
+                    delta = delta.repeat_interleave(batch_size // delta.shape[0], dim=0)
+                position_ids = position_ids.add(delta)
+                position_ids = position_ids.unsqueeze(0).expand(3, -1, -1)
+
         return inputs_embeds, attention_mask, position_ids
 
     def forward(

setup.py

@@ -29,7 +29,7 @@
 INSTALL_REQUIRE = [
     "torch>=1.11",
     "optimum@git+https://github.com/huggingface/optimum.git",
-    "transformers>=4.36,<4.47",
+    "transformers>=4.36,<4.48",
     "datasets>=1.4.0",
     "sentencepiece",
     "setuptools",