[OpenVINO] Add support for GLM-4.1V-9B-Thinkin

optimum/exporters/openvino/model_configs.py

@@ -96,6 +96,9 @@
     FluxTransfromerModelPatcher,
     Gemma2ModelPatcher,
     Gemma3LMModelPatcher,
+    Glm4vVisionEmbMergerPatcher,
+    Glm4vVisionEmbeddingsPatcher,
+    Glm4vLanguageModelPatcher,
     GptBigCodeModelPatcher,
     GptJModelPatcher,
     GptNeoModelPatcher,
@@ -154,6 +157,10 @@
 def init_model_configs():
     if "open_clip" not in TasksManager._LIBRARY_TO_SUPPORTED_MODEL_TYPES:
         TasksManager._LIBRARY_TO_SUPPORTED_MODEL_TYPES["open_clip"] = {}
+    TasksManager._CUSTOM_CLASSES[("pt", "glm4v", "image-text-to-text")] = (
+        "transformers",
+        "Glm4vForConditionalGeneration",
+    )
     TasksManager._CUSTOM_CLASSES[("pt", "llava", "image-text-to-text")] = (
         "transformers",
         "LlavaForConditionalGeneration",
@@ -4490,3 +4497,210 @@ def generate_dummy_inputs(self, framework: str = "pt", **kwargs):
                 )
 
         return dummy_inputs
+
+
+class DummyGlm4vVisionEmbedInputGenerator(DummyVisionInputGenerator):
+    SUPPORTED_INPUT_NAMES = (
+        "hidden_states",
+        "seqlens",
+        "grid_thw",
+        "attention_mask",
+        "image_type_ids",
+        "rotary_pos_emb",
+    )
+
+    def __init__(
+        self,
+        task: str,
+        normalized_config: NormalizedVisionConfig,
+        batch_size: int = 1,
+        num_channels: int = DEFAULT_DUMMY_SHAPES["num_channels"],
+        width: int = 420,
+        height: int = 420,
+        **kwargs,
+    ):
+        self.batch_size = batch_size
+        self.height = height
+        self.width = width
+        self.num_channels = num_channels
+        self.temporal_patch_size = normalized_config.config.temporal_patch_size
+        self.patch_size = normalized_config.config.patch_size
+        if normalized_config.use_embed_dim:
+            self.embed_dim = (
+                normalized_config.config.embed_dim
+                if hasattr(normalized_config.config, "embed_dim")
+                else normalized_config.hidden_size
+            )
+        else:
+            self.embed_dim = self.num_channels * self.temporal_patch_size * self.patch_size * self.patch_size
+        self.num_heads = normalized_config.config.num_heads
+        self.spatial_merge_size = None
+        if hasattr(normalized_config.config, "spatial_merge_size"):
+            self.spatial_merge_size = normalized_config.config.spatial_merge_size
+
+    def generate(self, input_name: str, framework: str = "pt", int_dtype: str = "int64", float_dtype: str = "fp32"):
+        grid_h, grid_w = self.height // self.patch_size, self.width // self.patch_size
+        grid_t = self.batch_size
+        import torch
+
+        if input_name == "hidden_states":
+            return self.random_float_tensor(
+                [grid_t * grid_h * grid_w, self.embed_dim], framework=framework, dtype=float_dtype
+            )
+
+        if input_name == "seqlens":
+            return torch.tensor([grid_t * grid_h * grid_w], dtype=torch.int64)
+
+        if input_name in ["attention_mask", "window_attention_mask"]:
+            return self.random_mask_tensor(
+                [1, grid_t * grid_h * grid_w, grid_t * grid_h * grid_w], framework=framework, dtype=float_dtype
+            )
+
+        if input_name == "rotary_pos_emb":
+            dim = self.embed_dim // self.num_heads // 2
+            return self.random_float_tensor([grid_h * grid_t * grid_w, dim], framework=framework, dtype=float_dtype)
+
+        if input_name == "image_type_ids":
+            return self.random_int_tensor(
+                [grid_t * grid_h * grid_w, 2], max_value=grid_h, framework=framework, dtype=int_dtype
+            )
+
+        if input_name == "grid_thw":
+            return torch.tensor([[grid_t, grid_h, grid_w]], dtype=torch.int64)
+
+
+@register_in_tasks_manager("glm4v", *["image-text-to-text", "video-text-to-text"], library_name="transformers")
+class Glm4vOpenVINOConfig(BaseVLMOpenVINOConfig):
+    SUPPORTED_BEHAVIORS = [model_type.value for model_type in Qwen2VLConfigBehavior]
+    NORMALIZED_CONFIG_CLASS = NormalizedVisionConfig
+    DUMMY_INPUT_GENERATOR_CLASSES = (DummyGlm4vVisionEmbedInputGenerator,)
+    MIN_TRANSFORMERS_VERSION = version.parse("4.54.0")
+
+    def __init__(
+        self,
+        config: "PretrainedConfig",
+        task: str = "feature-extraction",
+        int_dtype: str = "int64",
+        float_dtype: str = "fp32",
+        behavior: Qwen2VLConfigBehavior = Qwen2VLConfigBehavior.VISION_EMBEDDINGS,
+        preprocessors: Optional[List[Any]] = None,
+        **kwargs,
+    ):
+        super().__init__(
+            config=config,
+            task=task,
+            int_dtype=int_dtype,
+            float_dtype=float_dtype,
+            preprocessors=preprocessors,
+        )
+        self._behavior = behavior
+        self._orig_config = config
+        if self._behavior == Qwen2VLConfigBehavior.VISION_EMBEDDINGS and hasattr(config, "vision_config"):
+            self._config = config.vision_config
+            self._normalized_config = self.NORMALIZED_CONFIG_CLASS(self._config)
+            self._normalized_config.use_embed_dim = False
+        if self._behavior == Qwen2VLConfigBehavior.VISION_EMBEDDINGS_MERGER and hasattr(config, "vision_config"):
+            self._config = config.vision_config
+            self._normalized_config = self.NORMALIZED_CONFIG_CLASS(self._config)
+            self._normalized_config.use_embed_dim = True
+
+    @staticmethod
+    def get_model_for_behavior(model, behavior: Union[str, Qwen2VLConfigBehavior]):
+        if isinstance(behavior, str) and not isinstance(behavior, Qwen2VLConfigBehavior):
+            behavior = Qwen2VLConfigBehavior(behavior)
+
+        if behavior == Qwen2VLConfigBehavior.LANGUAGE:
+            return model
+
+        if behavior == Qwen2VLConfigBehavior.VISION_EMBEDDINGS:
+            vision_embeddings = model.visual
+            vision_embeddings.config = model.config.vision_config
+            return vision_embeddings
+
+        if behavior == Qwen2VLConfigBehavior.VISION_EMBEDDINGS_MERGER:
+            vision_emb_merger = model.visual
+            vision_emb_merger.config = model.config.vision_config
+            return vision_emb_merger
+
+        if behavior == Qwen2VLConfigBehavior.TEXT_EMBEDDINGS:
+            text_embedding = (
+                model.model.embed_tokens if hasattr(model.model, "embed_tokens") else model.language_model.embed_tokens
+            )
+            text_embedding.config = model.config
+            return text_embedding
+
+    def with_behavior(
+        self,
+        behavior: Union[str, Qwen2VLConfigBehavior],
+    ):
+        """
+        Creates a config for different behaviour.
+        Args:
+            behavior ([`ConfigBehavior`]):
+                The behavior to use for the new instance.
+        """
+        if isinstance(behavior, str) and not isinstance(behavior, Qwen2VLConfigBehavior):
+            behavior = Qwen2VLConfigBehavior(behavior)
+
+        if behavior == Qwen2VLConfigBehavior.TEXT_EMBEDDINGS:
+            return get_vlm_text_embeddings_config("qwen2", self._orig_config, self.int_dtype, self.float_dtype)
+
+        if behavior == Qwen2VLConfigBehavior.LANGUAGE:
+            return get_vlm_text_generation_config(
+                "qwen2",
+                self._orig_config,
+                self.int_dtype,
+                self.float_dtype,
+                model_patcher=Glm4vLanguageModelPatcher,
+                dummy_input_generator=DummyQwen2VLLMInputGenerator,
+                inputs_update={"position_ids": {1: "batch_size", 2: "sequence_length"}},
+            )
+
+        if behavior == Qwen2VLConfigBehavior.VISION_EMBEDDINGS:
+            return self.__class__(
+                self._orig_config,
+                task=self.task,
+                int_dtype=self.int_dtype,
+                float_dtype=self.float_dtype,
+                behavior=behavior,
+                preprocessors=self._preprocessors,
+            )
+        if behavior == Qwen2VLConfigBehavior.VISION_EMBEDDINGS_MERGER:
+            return self.__class__(
+                self._orig_config,
+                task=self.task,
+                int_dtype=self.int_dtype,
+                float_dtype=self.float_dtype,
+                behavior=behavior,
+                preprocessors=self._preprocessors,
+            )
+
+    def patch_model_for_export(
+        self, model: Union["PreTrainedModel", "TFPreTrainedModel"], model_kwargs: Optional[Dict[str, Any]] = None
+    ):
+        model_kwargs = model_kwargs or {}
+        if self._behavior == Qwen2VLConfigBehavior.VISION_EMBEDDINGS_MERGER:
+            return Glm4vVisionEmbMergerPatcher(self, model, model_kwargs)
+        if self._behavior == Qwen2VLConfigBehavior.VISION_EMBEDDINGS:
+            return Glm4vVisionEmbeddingsPatcher(self, model, model_kwargs=model_kwargs)
+        return super().patch_model_for_export(model, model_kwargs)
+
+    @property
+    def inputs(self) -> Dict[str, Dict[int, str]]:
+        if self._behavior == Qwen2VLConfigBehavior.VISION_EMBEDDINGS:
+            return {"hidden_states": {0: "patch_thw_grid", 1: "patch_temporal_channels"}}
+        if self._behavior == Qwen2VLConfigBehavior.VISION_EMBEDDINGS_MERGER:
+            return {
+                "hidden_states": {0: "sequence_length"},
+                "seqlens": {0: "sequence_length"},
+                "grid_thw": {0: "sequence_length"},
+                "attention_mask": {1: "sequence_length", 2: "sequence_length"},
+                "image_type_ids": {0: "sequence_length"},
+                "rotary_pos_emb": {0: "sequence_length"},
+            }
+
+    @property
+    def outputs(self) -> Dict[str, Dict[int, str]]:
+        if self._behavior in [Qwen2VLConfigBehavior.VISION_EMBEDDINGS, Qwen2VLConfigBehavior.VISION_EMBEDDINGS_MERGER]:
+            return {"last_hidden_state": {0: "seq_len"}}
+        return {}