update

Author: openvino-dev-samples

optimum/exporters/openvino/model_configs.py

@@ -3819,7 +3819,7 @@ class Qwen3_VLOpenVINOConfig(BaseVLMOpenVINOConfig):
     SUPPORTED_BEHAVIORS = [model_type.value for model_type in Qwen3VLConfigBehavior]
     NORMALIZED_CONFIG_CLASS = NormalizedVisionConfig
     DUMMY_INPUT_GENERATOR_CLASSES = (DummyQwen3VLVisionEmbedInputGenerator,)
-    MIN_TRANSFORMERS_VERSION = version.parse("4.45.0")
+    MIN_TRANSFORMERS_VERSION = version.parse("4.56.0")
 
     def __init__(
         self,
@@ -3966,7 +3966,6 @@ def inputs(self) -> Dict[str, Dict[int, str]]:
             }
 
 
-            
     @property
     def outputs(self) -> Dict[str, Dict[int, str]]:
         if self._behavior == Qwen3VLConfigBehavior.VISION_EMBEDDINGS:

optimum/exporters/openvino/model_patcher.py

@@ -4362,9 +4362,8 @@ def lm_forward(self, attention_mask, position_ids, past_key_values, inputs_embed
                 deepstack_visual_embeds=deepstack_visual_embeds,
             )
             hidden_states = outputs[0]
-            logits_to_keep = 1
             # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-            slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+            slice_indices = slice(-1, None)
             logits = self.lm_head(hidden_states[:, slice_indices, :])
             return (logits, outputs.past_key_values.to_legacy_cache())
 

optimum/intel/openvino/modeling_visual_language.py

@@ -198,15 +198,13 @@ def prepare_inputs(
             if visual_pos_masks is not None:
                 inputs["visual_pos_masks"] = visual_pos_masks
             else:
-                inputs["visual_pos_masks"] = torch.ones(1, 1, dtype=torch.bool)
+                inputs["visual_pos_masks"] = torch.zeros(1, 1, dtype=torch.bool)
 
         if "deepstack_visual_embeds" in self.input_names:
             if isinstance(deepstack_visual_embeds, list):
                 inputs["deepstack_visual_embeds"] = torch.Tensor(deepstack_visual_embeds)
             else:
-                inputs["deepstack_visual_embeds"] = torch.ones((3, 1, 1), dtype=torch.float32)
-            print(inputs["deepstack_visual_embeds"].shape)
-
+                inputs["deepstack_visual_embeds"] = torch.zeros((3, 1, 1), dtype=torch.float32)
         if "token_type_ids" in self.input_names:
             if token_type_ids is None:
                 token_type_ids = np.zeros(inputs_embeds.shape[:2], dtype=int)
@@ -216,11 +214,6 @@ def prepare_inputs(
             inputs["beam_idx"] = (
                 self.next_beam_idx if self.next_beam_idx is not None else np.arange(batch_size, dtype=int)
             )
-        for key, value in inputs.items():
-            if hasattr(value, 'dtype'):
-                print(f"{key}: {value.dtype}")
-            else:
-                print(f"{key}: {type(value)}")
         return inputs
 
     def forward(
@@ -2549,27 +2542,6 @@ class QWen2VLModelOutputWithPast(ModelOutput):
     rope_deltas: Optional[torch.FloatTensor] = None
     second_per_grid_ts: Optional[torch.FloatTensor] = None
 
-
-# @dataclass
-# class QWen3VLModelOutputWithPast(ModelOutput):
-#     r"""
-#     past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
-#         Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
-#         `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
-
-#         Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
-#         `past_key_values` input) to speed up sequential decoding.
-#     rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*):
-#         The rope index difference between sequence length and multimodal rope.
-#     """
-
-#     last_hidden_state: Optional[torch.FloatTensor] = None
-#     past_key_values: Optional[list[torch.FloatTensor]] = None
-#     hidden_states: Optional[tuple[torch.FloatTensor]] = None
-#     attentions: Optional[tuple[torch.FloatTensor]] = None
-#     rope_deltas: Optional[torch.LongTensor] = None
-
-
 class _OVQwen2VLForCausalLM(OVModelForVisualCausalLM):
     additional_parts = ["vision_embeddings_merger"]
 
@@ -3855,9 +3827,7 @@ def get_image_features(self, pixel_values: torch.FloatTensor, image_grid_thw: Op
         image_embeds, deepstack_image_embeds = self.get_vision_embeddings(pixel_values, image_grid_thw)
         image_embeds, deepstack_image_embeds = torch.from_numpy(image_embeds), torch.from_numpy(deepstack_image_embeds)
         deepstack_image_embeds = deepstack_image_embeds.tolist()
-        print(image_grid_thw.prod(-1))
         split_sizes = (image_grid_thw.prod(-1) // self.spatial_merge_size**2).tolist()
-        print(image_embeds.shape)
         image_embeds = torch.split(image_embeds, split_sizes)
         return image_embeds, deepstack_image_embeds