[OpenVINO]add support for minicpmv4/4_5

optimum/exporters/openvino/model_configs.py

@@ -2516,7 +2516,7 @@ def generate(self, input_name: str, framework: str = "pt", int_dtype: str = "int
 
 
 class DummyMiniCPMVResampleInputGenerator(DummyVisionInputGenerator):
-    SUPPORTED_INPUT_NAMES = ("image_feature", "pos_embed", "key_padding_mask")
+    SUPPORTED_INPUT_NAMES = ("image_feature", "pos_embed", "key_padding_mask", "temporal_embed")
 
     def __init__(
         self,
@@ -2553,6 +2553,9 @@ def generate(self, input_name: str, framework: str = "pt", int_dtype: str = "int
         if input_name == "pos_embed":
             return self.random_float_tensor(shape=[self.feat_size, self.batch_size, self.hidden_size])
 
+        if input_name == "temporal_embed":
+            return self.random_float_tensor(shape=[1, self.batch_size, self.hidden_size])
+
 
 class MiniCPMVConfigBehavior(str, enum.Enum):
     RESAMPLER = "resampler"
@@ -2585,6 +2588,8 @@ def __init__(
         )
         self._behavior = behavior
         self._orig_config = config
+        model_mapping = {2.6: "llama", 4.0: "qwen2", 4.5: "qwen3"}
+        self.model_type = model_mapping[self._orig_config.version]
         if self._behavior == MiniCPMVConfigBehavior.VISION_EMBEDDINGS and hasattr(config, "vision_config"):
             self._config = config.vision_config
             self.DUMMY_INPUT_GENERATOR_CLASSES = (DummyMiniCPMVImageInputGenerator,)
@@ -2601,11 +2606,19 @@ def inputs(self) -> Dict[str, Dict[int, str]]:
                 "position_ids": {0: "batch_size", 1: "patch_size"},
             }
         if self._behavior == MiniCPMVConfigBehavior.RESAMPLER:
-            return {
-                "image_feature": {0: "batch_size", 1: "patch_height", 2: "patch_width"},
-                "pos_embed": {0: "patch_size", 1: "batch_size", 2: "num_patches"},
-                "key_padding_mask": {0: "batch_size", 1: "patch_size"},
-            }
+            if self._orig_config.version == 4.5:
+                return {
+                    "image_feature": {0: "batch_size", 1: "patch_height", 2: "patch_width"},
+                    "pos_embed": {0: "patch_size", 1: "batch_size", 2: "num_patches"},
+                    "key_padding_mask": {0: "batch_size", 1: "patch_size"},
+                    "temporal_embed": {0: "patch_size", 1: "batch_size"},
+                }
+            else:
+                return {
+                    "image_feature": {0: "batch_size", 1: "patch_height", 2: "patch_width"},
+                    "pos_embed": {0: "patch_size", 1: "batch_size", 2: "num_patches"},
+                    "key_padding_mask": {0: "batch_size", 1: "patch_size"},
+                }
         return {}
 
     @property
@@ -2631,10 +2644,20 @@ def with_behavior(
             behavior = MiniCPMVConfigBehavior(behavior)
 
         if behavior == MiniCPMVConfigBehavior.TEXT_EMBEDDINGS:
-            return get_vlm_text_embeddings_config("qwen2", self._orig_config, self.int_dtype, self.float_dtype)
+            return get_vlm_text_embeddings_config(
+                self.model_type,
+                self._orig_config,
+                self.int_dtype,
+                self.float_dtype,
+            )
 
         if behavior == MiniCPMVConfigBehavior.LANGUAGE:
-            return get_vlm_text_generation_config("qwen2", self._orig_config, self.int_dtype, self.float_dtype)
+            return get_vlm_text_generation_config(
+                self.model_type,
+                self._orig_config,
+                self.int_dtype,
+                self.float_dtype,
+            )
 
         if behavior == MiniCPMVConfigBehavior.VISION_EMBEDDINGS:
             return self.__class__(

optimum/exporters/openvino/model_patcher.py

@@ -3323,6 +3323,27 @@ def _minicpmv_resampler_forward(self, image_feature, pos_embed, key_padding_mask
 
     out = self.attn(q_bs, image_feature + pos_embed, image_feature, key_padding_mask=key_padding_mask)[
         0
+    ]  # Q * B * D  # L * B * D +  L * B * Dpos_embed
+    #  out: Q * B * D
+    x = out.permute(1, 0, 2)  # B * Q * D
+
+    x = self.ln_post(x)
+    x = x @ self.proj
+    return x
+
+
+def _minicpmv4_5_resampler_forward(self, image_feature, pos_embed, key_padding_mask, temporal_embed):
+    image_feature = self.kv_proj(image_feature)  # B * L * D
+    image_feature = self.ln_kv(image_feature).permute(1, 0, 2)  # L * B * D
+    image_feature_emb = image_feature + pos_embed
+    image_feature_temporal = image_feature_emb + temporal_embed  # [L, bs, D] + [1, bs, D]
+    bs = image_feature_temporal.shape[1]
+    q = self.ln_q(self.query)  # Q * D
+
+    q_bs = q.unsqueeze(1).repeat(1, bs, 1)
+
+    out = self.attn(q_bs, image_feature_temporal, image_feature, key_padding_mask=key_padding_mask)[
+        0
     ]  # Q * B * D  # L * B * D +  L * B * D
     #  out: Q * B * D
     x = out.permute(1, 0, 2)  # B * Q * D
@@ -3482,7 +3503,10 @@ def __init__(
         model_kwargs: Dict[str, Any],
     ):
         model.__orig_forward = model.forward
-        model.forward = types.MethodType(_minicpmv_resampler_forward, model)
+        has_temporal_ids = "temporal_ids" in inspect.signature(model.__orig_forward).parameters
+        model.forward = types.MethodType(
+            _minicpmv4_5_resampler_forward if has_temporal_ids else _minicpmv_resampler_forward, model
+        )
 
         super().__init__(config, model, model_kwargs)
 

optimum/intel/openvino/modeling_visual_language.py

@@ -285,11 +285,21 @@ def __init__(self, model: ov.Model, parent_model: OVBaseModel) -> None:
         self.output_dtypes = {key.get_any_name(): key.get_element_type().get_type_name() for key in self.model.outputs}
         self.output_names = {key.get_any_name(): idx for idx, key in enumerate(self.model.outputs)}
 
-    def forward(self, image_feature, pos_embed, key_padding_mask):
+    def forward(self, image_feature, pos_embed, key_padding_mask, temporal_embed=None):
         self.compile()
-        result = self.request(
-            {"image_feature": image_feature, "pos_embed": pos_embed, "key_padding_mask": key_padding_mask}
-        )[0]
+        if temporal_embed is not None:
+            result = self.request(
+                {
+                    "image_feature": image_feature,
+                    "pos_embed": pos_embed,
+                    "key_padding_mask": key_padding_mask,
+                    "temporal_embed": temporal_embed,
+                }
+            )[0]
+        else:
+            result = self.request(
+                {"image_feature": image_feature, "pos_embed": pos_embed, "key_padding_mask": key_padding_mask}
+            )[0]
         return result
 
 
@@ -784,6 +794,7 @@ def forward(
         audio_embed_sizes=None,
         audio_attention_mask=None,
         input_mode=None,
+        temporal_ids=None,
         **kwargs,
     ):
         if pixel_values is None:
@@ -809,6 +820,7 @@ def forward(
             audio_embed_sizes=audio_embed_sizes,
             audio_attention_mask=audio_attention_mask,
             input_mode=input_mode,
+            temporal_ids=temporal_ids,
             **kwargs,
         )
         return self.language_model.forward(
@@ -921,6 +933,7 @@ def prepare_inputs_for_generation(
                 "input_audio_embeds": kwargs.get("input_audio_embeds", kwargs.get("audio_input_features")),
                 "audio_embed_sizes": kwargs.get("audio_embed_sizes"),
                 "input_mode": kwargs.get("input_mode"),
+                "temporal_ids": kwargs.get("temporal_ids"),
             }
         )
         return model_inputs
@@ -1923,10 +1936,18 @@ def __init__(
         max_size = self.config.vision_config.image_size // self.config.vision_config.patch_size
         self._pos_embeds = torch.from_numpy(self._get_2d_sincos_pos_embed(self.embed_dim, max_size)).float()
         self.max_size = (max_size, max_size)
+        self.max_temporal_size = 72000
 
-    def get_vision_embeddings(self, pixel_values, input_ids=None, **kwargs):
+    def get_vision_embeddings(self, pixel_values, input_ids=None, temporal_ids=None, **kwargs):
         if input_ids is not None and input_ids.shape[1] == 1:
             return None
+
+        all_temporal_ids = None
+        if temporal_ids is not None:
+            all_temporal_ids = []
+            for t in temporal_ids:
+                all_temporal_ids.extend(t)
+
         tgt_sizes = kwargs["tgt_sizes"]
         pixel_values_list = pixel_values
         vision_hidden_states = []
@@ -1963,7 +1984,7 @@ def get_vision_embeddings(self, pixel_values, input_ids=None, **kwargs):
                     pixel_values=all_pixel_values, patch_attention_mask=patch_attn_mask, position_ids=position_ids
                 )[0]
             )
-            vision_embedding = self.resampling(vision_embedding, tgt_sizes)
+            vision_embedding = self.resampling(vision_embedding, tgt_sizes, all_temporal_ids)
 
             start = 0
             for pixel_value in pixel_values_list:
@@ -1979,26 +2000,57 @@ def get_vision_embeddings(self, pixel_values, input_ids=None, **kwargs):
                 vision_hidden_states.append(dummy_feature)
         return vision_hidden_states
 
-    def resampling(self, x, tgt_sizes):
+    def resampling(self, x, tgt_sizes, temporal_ids=None):
+        from itertools import chain
+
         bs = x.shape[0]
 
         patch_len = tgt_sizes[:, 0] * tgt_sizes[:, 1]
 
         self._adjust_pos_cache(tgt_sizes)
 
+        temporal_pos_emb = False
+        temporal_ids_flatten = None
+        if temporal_ids is not None:
+            # example: [[-1], [-1], [2, 6, 9]]
+            temporal_ids_flatten = list(chain.from_iterable(temporal_ids))
+            max_temporal_size = max(temporal_ids_flatten) + 1
+            if max_temporal_size > -1:
+                temporal_pos_emb = True
+            if max_temporal_size > self.max_temporal_size:
+                self._adjust_temporal_pos_cache(max_temporal_size, "cpu")
+
         max_patch_len = torch.max(patch_len)
         key_padding_mask = torch.zeros((bs, max_patch_len), dtype=torch.bool)
 
+        temporal_embed = None
         pos_embed = []
+        pos_embed_temporal = []
         for i in range(bs):
             tgt_h, tgt_w = tgt_sizes[i]
+
+            if temporal_pos_emb:
+                if temporal_ids_flatten[i] == -1:
+                    pos_embed_temporal.append(torch.zeros(self.embed_dim, dtype=torch.float32, device="cpu"))
+                else:
+                    pos_embed_temporal.append(self.temporal_pos_embed[temporal_ids_flatten[i]].to(torch.float32))  # D
+
             pos_embed.append(self._pos_embeds[:tgt_h, :tgt_w, :].reshape((tgt_h * tgt_w, -1)))  # patches * D
             key_padding_mask[i, patch_len[i] :] = True
 
         pos_embed = torch.nn.utils.rnn.pad_sequence(pos_embed, batch_first=True, padding_value=0.0).permute(
             1, 0, 2
         )  # BLD => L * B * D
-        res = torch.from_numpy(self.resampler(image_feature=x, pos_embed=pos_embed, key_padding_mask=key_padding_mask))
+        if temporal_pos_emb:
+            temporal_embed = torch.stack(pos_embed_temporal, dim=0).unsqueeze(0)
+        res = torch.from_numpy(
+            self.resampler(
+                image_feature=x,
+                pos_embed=pos_embed,
+                key_padding_mask=key_padding_mask,
+                temporal_embed=temporal_embed,
+            )
+        )
         return res
 
     def _set_2d_pos_cache(self, max_size):

tests/openvino/test_exporters_cli.py

@@ -618,6 +618,28 @@ class OVCLIExportTestCase(unittest.TestCase):
                 "resampler_model": {"int8": 6},
             },
         ),
+        (
+            "image-text-to-text",
+            "minicpmv4",
+            "int4 --group-size 4 --ratio 0.8 --trust-remote-code",
+            {
+                "lm_model": {"int8": 12, "int4": 18},
+                "text_embeddings_model": {"int8": 1},
+                "vision_embeddings_model": {"int8": 14},
+                "resampler_model": {"int8": 6},
+            },
+        ),
+        (
+            "image-text-to-text",
+            "minicpmv4_5",
+            "int4 --group-size 4 --ratio 0.8 --trust-remote-code",
+            {
+                "lm_model": {"int8": 12, "int4": 18},
+                "text_embeddings_model": {"int8": 1},
+                "vision_embeddings_model": {"int8": 14},
+                "resampler_model": {"int8": 6},
+            },
+        ),
         (
             "image-text-to-text",
             "internvl_chat",
@@ -743,13 +765,13 @@ def _openvino_export(self, model_name: str, task: str, model_kwargs: Dict = None
 
     def test_filtered_architectures(cls):
         if is_transformers_version("<", "4.49"):
-            expected = {"llama4", "qwen2_5_vl", "phi4mm"}
+            expected = {"llama4", "qwen2_5_vl", "phi4mm", "minicpmv4", "minicpmv4_5"}
         elif is_transformers_version("<", "4.51"):
             expected = {"llama4", "phi4mm"}
         elif is_transformers_version("<", "4.52"):
             expected = set()
         else:
-            expected = {"llava-qwen2", "phi3_v", "phi4mm", "minicpmo"}
+            expected = {"llava-qwen2", "phi3_v", "phi4mm", "minicpmo", "minicpmv4", "minicpmv4_5"}
 
         all_model_type = {config[1] for config in cls.TRANSFORMERS_4BIT_CONFIGURATIONS}
         filtered_model_type = {config[1] for config in cls.SUPPORTED_4BIT_CONFIGURATIONS}

tests/openvino/test_quantization.py

@@ -938,6 +938,48 @@ class OVWeightCompressionTest(unittest.TestCase):
                 "resampler_model": {"int8": 6},
             },
         ),
+        (
+            OVModelForVisualCausalLM,
+            "minicpmv4",
+            True,
+            dict(
+                bits=4,
+                group_size=16,
+                dataset="contextual",
+                ratio=0.8,
+                sensitivity_metric="mean_activation_magnitude",
+                num_samples=1,
+                processor=MODEL_NAMES["minicpmv4"],
+                trust_remote_code=True,
+            ),
+            {
+                "lm_model": {"int8": 8, "int4": 22},
+                "text_embeddings_model": {"int8": 1},
+                "vision_embeddings_model": {"int8": 26},
+                "resampler_model": {"int8": 6},
+            },
+        ),
+        (
+            OVModelForVisualCausalLM,
+            "minicpmv4_5",
+            True,
+            dict(
+                bits=4,
+                group_size=16,
+                dataset="contextual",
+                ratio=0.8,
+                sensitivity_metric="mean_activation_magnitude",
+                num_samples=1,
+                processor=MODEL_NAMES["minicpmv4_5"],
+                trust_remote_code=True,
+            ),
+            {
+                "lm_model": {"int8": 8, "int4": 22},
+                "text_embeddings_model": {"int8": 1},
+                "vision_embeddings_model": {"int8": 26},
+                "resampler_model": {"int8": 6},
+            },
+        ),
     ]
 
     # filter models type depending on min max transformers version
@@ -964,6 +1006,7 @@ class OVWeightCompressionTest(unittest.TestCase):
         (OVModelForVisualCausalLM, "llava_next_video", False),
         (OVModelForVisualCausalLM, "minicpmv", True),
         (OVModelForVisualCausalLM, "qwen2_vl", False),
+        (OVModelForVisualCausalLM, "minicpmv4", True),
     ]
 
     if is_transformers_version("<", "4.54.0"):
@@ -972,6 +1015,9 @@ class OVWeightCompressionTest(unittest.TestCase):
     if is_transformers_version("<", "4.52.0"):
         SUPPORTED_ARCHITECTURES_WITH_AUTO_COMPRESSION.append((OVModelForVisualCausalLM, "minicpmo", True))
 
+    if is_transformers_version(">=", "4.51.0"):
+        SUPPORTED_ARCHITECTURES_WITH_AUTO_COMPRESSION.append((OVModelForVisualCausalLM, "minicpmv4_5", True))
+
     SUPPORTED_ARCHITECTURES_WITH_HYBRID_QUANTIZATION = [
         (OVStableDiffusionPipeline, "stable-diffusion", 72, 195),
         (OVStableDiffusionXLPipeline, "stable-diffusion-xl", 84, 331),
@@ -987,13 +1033,13 @@ class OVWeightCompressionTest(unittest.TestCase):
 
     def test_filtered_architectures(cls):
         if is_transformers_version("<", "4.49"):
-            expected = {"llama4", "qwen2_5_vl"}
+            expected = {"llama4", "qwen2_5_vl", "minicpmv4", "minicpmv4_5"}
         elif is_transformers_version("<", "4.51"):
             expected = {"llama4"}
         elif is_transformers_version("<", "4.52"):
             expected = set()
         else:
-            expected = {"llava-qwen2", "phi3_v", "minicpmo"}
+            expected = {"llava-qwen2", "phi3_v", "minicpmo", "minicpmv4", "minicpmv4_5"}
 
         all_model_type = {config[1] for config in cls.TRANSFORMERS_4BIT_CONFIGURATIONS}
         filtered_model_type = {config[1] for config in cls.LOAD_IN_4_BITS_SCOPE}