[model] support MiniCPM-V-4.6 (#10472)

src/llamafactory/chat/hf_engine.py

@@ -205,6 +205,9 @@ class HuggingfaceEngine(BaseEngine):
 
             gen_kwargs.pop("image_sizes", None)
 
+        if getattr(model.config, "model_type", None) == "minicpmv4_6":
+            gen_kwargs["downsample_mode"] = os.getenv("DOWNSAMPLE_MODE", "16x")
+
         return gen_kwargs, prompt_length
 
     @staticmethod

src/llamafactory/data/collator.py

@@ -17,6 +17,7 @@
 
 import copy
 import inspect
+import os
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Any, Literal, Optional
 
@@ -474,6 +475,13 @@ class MultiModalDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
             features["position_ids"] = torch.arange(seq_length).long().repeat(bsz, 1)
             return {"data": features, "input_ids": features["input_ids"], "labels": features["labels"]}
 
+        if (
+            self.model is not None
+            and getattr(self.model.config, "model_type", None) == "minicpmv4_6"
+            and "target_sizes" in features
+        ):  # for minicpmv4_6 with new transformers (NaViT API, no image_bound)
+            features["downsample_mode"] = os.getenv("DOWNSAMPLE_MODE", "16x")
+
         return features
 
 

src/llamafactory/data/mm_plugin.py

@@ -1209,6 +1209,23 @@ class LlavaNextVideoPlugin(BasePlugin):
 
 @dataclass
 class MiniCPMVPlugin(BasePlugin):
+    def _resolve_token_id(self, tokenizer: Any, attr_name: str, token_text: str | None = None) -> int | None:
+        token_id = getattr(tokenizer, attr_name, None)
+        if isinstance(token_id, int) and token_id >= 0:
+            return token_id
+
+        if token_text is None or not hasattr(tokenizer, "convert_tokens_to_ids"):
+            return None
+
+        converted_id = tokenizer.convert_tokens_to_ids(token_text)
+        if isinstance(converted_id, list):
+            converted_id = converted_id[0] if len(converted_id) else None
+
+        if isinstance(converted_id, int) and converted_id >= 0:
+            return converted_id
+
+        return None
+
     @override
     def _get_mm_inputs(
         self,
@@ -1220,6 +1237,8 @@ class MiniCPMVPlugin(BasePlugin):
     ) -> dict[str, "torch.Tensor"]:
         image_processor: BaseImageProcessor = getattr(processor, "image_processor")
         mm_inputs = {}
+        preprocess_params = inspect.signature(image_processor.preprocess).parameters
+        downsample_mode = os.getenv("DOWNSAMPLE_MODE", "16x") if "downsample_mode" in preprocess_params else None
         if len(images) != 0:
             images = self._regularize_images(
                 images,
@@ -1236,9 +1255,15 @@ class MiniCPMVPlugin(BasePlugin):
 
                 images = new_images
 
-            image_inputs = image_processor(
-                images, do_pad=True, max_slice_nums=image_processor.max_slice_nums, return_tensors="pt"
-            )
+            image_processor_kwargs = {
+                "do_pad": True,
+                "max_slice_nums": image_processor.max_slice_nums,
+                "return_tensors": "pt",
+            }
+            if downsample_mode is not None:
+                image_processor_kwargs["downsample_mode"] = downsample_mode
+
+            image_inputs = image_processor(images, **image_processor_kwargs)
             mm_inputs.update(image_inputs)
 
         if len(videos) != 0:
@@ -1249,7 +1274,15 @@ class MiniCPMVPlugin(BasePlugin):
                 video_fps=getattr(processor, "video_fps", 2.0),
                 video_maxlen=getattr(processor, "video_maxlen", 128),
             )["videos"]
-            video_inputs = image_processor(videos, do_pad=True, max_slice_nums=2, return_tensors="pt")
+            video_processor_kwargs = {
+                "do_pad": True,
+                "max_slice_nums": 2,
+                "return_tensors": "pt",
+            }
+            if downsample_mode is not None:
+                video_processor_kwargs["downsample_mode"] = downsample_mode
+
+            video_inputs = image_processor(videos, **video_processor_kwargs)
             mm_inputs.update(video_inputs)
 
         if len(audios) != 0:
@@ -1334,7 +1367,8 @@ class MiniCPMVPlugin(BasePlugin):
 
         if self.expand_mm_tokens and mm_inputs:
             pattern = "(<image>./</image>)"
-            image_sizes = mm_inputs["image_sizes"]
+            image_sizes = mm_inputs.get("image_sizes")
+            image_grids = mm_inputs.get("grids")
             idx = 0
             for index, message in enumerate(messages):
                 text = message["content"]
@@ -1342,13 +1376,21 @@ class MiniCPMVPlugin(BasePlugin):
                 text_chunks = text.split(pattern)
                 final_text = ""
                 for i in range(len(image_tags)):
-                    final_text = (
-                        final_text
-                        + text_chunks[i]
-                        + image_processor.get_slice_image_placeholder(
-                            image_sizes[0][idx], idx, max_slice_nums, use_image_id
+                    grid = image_grids[0][idx] if image_grids and len(image_grids[0]) > idx else [1, 1]
+                    image_size = image_sizes[0][idx] if image_sizes and len(image_sizes[0]) > idx else None
+
+                    placeholder_fn = image_processor.get_slice_image_placeholder
+                    if image_size is not None:
+                        image_placeholder = placeholder_fn(
+                            image_size,
+                            image_idx=idx,
+                            max_slice_nums=max_slice_nums,
+                            use_image_id=use_image_id,
                         )
-                    )
+                    else:
+                        image_placeholder = placeholder_fn(grid)
+
+                    final_text = final_text + text_chunks[i] + image_placeholder
                     idx += 1
 
                 final_text += text_chunks[-1]
@@ -1385,15 +1427,25 @@ class MiniCPMVPlugin(BasePlugin):
         processor: Optional["MMProcessor"],
     ) -> dict[str, Union[list[int], "torch.Tensor"]]:
         self._validate_input(processor, images, videos, audios)
+        tokenizer = processor.tokenizer
+        im_start_id = self._resolve_token_id(tokenizer, "im_start_id", "<image>")
+        slice_start_id = self._resolve_token_id(tokenizer, "slice_start_id", "<slice>")
+        im_end_id = self._resolve_token_id(tokenizer, "im_end_id", "</image>")
+        slice_end_id = self._resolve_token_id(tokenizer, "slice_end_id", "</slice>")
+        if None in (im_start_id, slice_start_id, im_end_id, slice_end_id):
+            raise AttributeError(
+                "Cannot resolve MiniCPM image boundary token ids from tokenizer. "
+                "Expected attributes (im_start_id/slice_start_id/im_end_id/slice_end_id) "
+                "or corresponding special tokens (<image>, <slice>, </image>, </slice>)."
+            )
+
         # image bound
         image_bounds_list = []
         valid_image_nums_ls = []
         for i, input_ids in enumerate(batch_ids):
             input_ids_ = torch.tensor(input_ids)
-            start_cond = (input_ids_ == processor.tokenizer.im_start_id) | (
-                input_ids_ == processor.tokenizer.slice_start_id
-            )
-            end_cond = (input_ids_ == processor.tokenizer.im_end_id) | (input_ids_ == processor.tokenizer.slice_end_id)
+            start_cond = (input_ids_ == im_start_id) | (input_ids_ == slice_start_id)
+            end_cond = (input_ids_ == im_end_id) | (input_ids_ == slice_end_id)
             image_start_tokens = torch.where(start_cond)[0]
             image_start_tokens += 1
             image_end_tokens = torch.where(end_cond)[0]
@@ -1414,6 +1466,16 @@ class MiniCPMVPlugin(BasePlugin):
         mm_inputs.update({"image_bound": image_bounds_list})
 
         if len(audios) > 0:
+            audio_start_id = self._resolve_token_id(tokenizer, "audio_start_id", "<audio>")
+            audio_end_id = self._resolve_token_id(tokenizer, "audio_end_id", "</audio>")
+            spk_start_id = self._resolve_token_id(tokenizer, "spk_start_id", "<spk>")
+            spk_end_id = self._resolve_token_id(tokenizer, "spk_end_id", "</spk>")
+            if None in (audio_start_id, audio_end_id, spk_start_id, spk_end_id):
+                raise AttributeError(
+                    "Cannot resolve MiniCPM audio/speaker boundary token ids from tokenizer. "
+                    "Expected *_id attributes or corresponding special tokens."
+                )
+
             # audio bound
             audio_bounds_ls = []
             spk_bounds_ls = []
@@ -1421,15 +1483,15 @@ class MiniCPMVPlugin(BasePlugin):
 
             for input_ids, audiolen in zip(batch_ids, audlens):
                 input_ids_ = torch.tensor(input_ids)
-                audio_start_idx = torch.where(input_ids_ == processor.tokenizer.audio_start_id)[0]
-                audio_end_idx = torch.where(input_ids_ == processor.tokenizer.audio_end_id)[0]
+                audio_start_idx = torch.where(input_ids_ == audio_start_id)[0]
+                audio_end_idx = torch.where(input_ids_ == audio_end_id)[0]
                 assert len(audio_start_idx) == len(audio_end_idx)
                 audio_bounds = torch.hstack([(audio_start_idx + 1).unsqueeze(-1), audio_end_idx.unsqueeze(-1)])
                 audio_bounds_ls.append(audio_bounds)
                 valid_audio_nums_ls.append(audiolen)
 
-                spk_start_idx = torch.where(input_ids_ == processor.tokenizer.spk_start_id)[0]
-                spk_end_idx = torch.where(input_ids_ == processor.tokenizer.spk_end_id)[0]
+                spk_start_idx = torch.where(input_ids_ == spk_start_id)[0]
+                spk_end_idx = torch.where(input_ids_ == spk_end_id)[0]
                 assert len(spk_start_idx) == len(spk_end_idx)
                 spk_bounds = torch.hstack([(spk_start_idx + 1).unsqueeze(-1), spk_end_idx.unsqueeze(-1)])
                 spk_bounds_ls.append(spk_bounds)
@@ -1441,6 +1503,255 @@ class MiniCPMVPlugin(BasePlugin):
         return mm_inputs
 
 
+@dataclass
+class MiniCPMV4_6Plugin(BasePlugin):
+    """Plugin for MiniCPM-V-4.6 with new transformers (NaViT vision + get_placeholder_mask API)."""
+
+    def _get_mm_inputs(
+        self,
+        images: list["ImageInput"],
+        videos: list["VideoInput"],
+        audios: list["AudioInput"],
+        processor: "MMProcessor",
+        **kwargs,
+    ) -> dict[str, "torch.Tensor"]:
+        image_processor = getattr(processor, "image_processor")
+        video_processor = getattr(processor, "video_processor", None)
+        mm_inputs = {}
+        preprocess_params = inspect.signature(image_processor.preprocess).parameters
+        downsample_mode = os.getenv("DOWNSAMPLE_MODE", "16x") if "downsample_mode" in preprocess_params else None
+
+        if len(images) != 0:
+            images = self._regularize_images(
+                images,
+                image_max_pixels=getattr(processor, "image_max_pixels", 768 * 768),
+                image_min_pixels=getattr(processor, "image_min_pixels", 32 * 32),
+            )["images"]
+            image_processor_kwargs = {
+                "max_slice_nums": getattr(image_processor, "max_slice_nums", 9),
+                "return_tensors": "pt",
+            }
+            if downsample_mode is not None:
+                image_processor_kwargs["downsample_mode"] = downsample_mode
+            image_inputs = image_processor(images, **image_processor_kwargs)
+            mm_inputs.update(image_inputs)
+
+        if len(videos) != 0:
+            videos = self._regularize_videos(
+                videos,
+                image_max_pixels=getattr(processor, "video_max_pixels", 256 * 256),
+                image_min_pixels=getattr(processor, "video_min_pixels", 16 * 16),
+                video_fps=getattr(processor, "video_fps", 2.0),
+                video_maxlen=getattr(processor, "video_maxlen", 128),
+            )["videos"]
+            if video_processor is not None:
+                video_processor_kwargs = {
+                    "max_slice_nums": 2,
+                    "return_tensors": "pt",
+                }
+                if downsample_mode is not None:
+                    video_processor_kwargs["downsample_mode"] = downsample_mode
+                video_inputs = video_processor(videos, **video_processor_kwargs)
+                mm_inputs["pixel_values_videos"] = video_inputs["pixel_values_videos"]
+                mm_inputs["target_sizes_videos"] = video_inputs["target_sizes_videos"]
+            else:
+                # Fallback to image processor for video
+                video_processor_kwargs = {
+                    "max_slice_nums": 2,
+                    "return_tensors": "pt",
+                }
+                if downsample_mode is not None:
+                    video_processor_kwargs["downsample_mode"] = downsample_mode
+                video_inputs = image_processor(videos, **video_processor_kwargs)
+                mm_inputs["pixel_values_videos"] = video_inputs["pixel_values"]
+                mm_inputs["target_sizes_videos"] = video_inputs["target_sizes"]
+
+        if len(audios) != 0:
+            audios = self._regularize_audios(
+                audios,
+                sampling_rate=getattr(processor, "audio_sampling_rate", 16000),
+            )["audios"]
+            audio_features, audio_feature_lens, audio_phs = processor.audio_feature_extract(
+                [audios],
+                chunk_input=True,
+                sampling_rate=getattr(processor, "audio_sampling_rate", 16000),
+            )
+            audio_feature_lens = [
+                x.clone().detach() if isinstance(x, torch.Tensor) else torch.tensor(x) for x in audio_feature_lens
+            ]
+            mm_inputs.update({"audio_features": audio_features, "audio_feature_lens": audio_feature_lens})
+            if kwargs.get("ret_phs", False):
+                mm_inputs.update({"audio_phs": audio_phs})
+
+        return mm_inputs
+
+    def _build_v4_6_placeholder(
+        self,
+        image_inputs: dict[str, Any],
+        image_idx: int,
+        use_image_id: bool,
+        processor: "MMProcessor",
+    ) -> str:
+        """Build image placeholder for MiniCPM-V-4.6 using NaViT token count computation."""
+        grids = image_inputs.get("grids", [[0, 0]])
+        num_patches_per_image = image_inputs.get("num_patches_per_image", [1])
+        target_sizes = image_inputs.get("target_sizes")
+
+        downsample_mode = os.getenv("DOWNSAMPLE_MODE")
+        if downsample_mode is None:
+            image_processor = getattr(processor, "image_processor")
+            downsample_mode = getattr(image_processor, "downsample_mode", "16x")
+        token_divisor = 4 if downsample_mode == "4x" else 16
+
+        flat_index = 0
+        for idx in range(image_idx):
+            flat_index += num_patches_per_image[idx]
+        n_patches = num_patches_per_image[image_idx]
+
+        img_target_sizes = target_sizes[flat_index : flat_index + n_patches]
+        num_tokens_per_patch = img_target_sizes.prod(-1) // token_divisor
+        num_rows, num_cols = grids[image_idx]
+
+        image_start = getattr(processor, "image_start_token", "<image>")
+        image_end = getattr(processor, "image_end_token", "</image>")
+        slice_start = getattr(processor, "slice_start_token", "<slice>")
+        slice_end = getattr(processor, "slice_end_token", "</slice>")
+        image_id_start = getattr(processor, "image_id_start_token", "<image_id>")
+        image_id_end = getattr(processor, "image_id_end_token", "</image_id>")
+        image_token = (
+            getattr(processor, "image_token", None)
+            or getattr(getattr(processor, "tokenizer", None), "image_token", None)
+            or "<image>"
+        )
+
+        image_placeholder = image_start + "<|ph|>" * int(num_tokens_per_patch[0]) + image_end
+        if use_image_id:
+            image_placeholder = f"{image_id_start}{image_idx}{image_id_end}" + image_placeholder
+
+        slice_mode = getattr(processor, "slice_mode", True)
+        if slice_mode and num_rows > 0 and num_cols > 0:
+            per_slice_tokens = int(num_tokens_per_patch[1]) if len(num_tokens_per_patch) > 1 else 0
+            slice_placeholder = slice_start + "<|ph|>" * per_slice_tokens + slice_end
+            slices = [slice_placeholder * num_cols for _ in range(num_rows)]
+            image_placeholder += "\n".join(slices)
+
+        return image_placeholder.replace("<|ph|>", image_token)
+
+    @override
+    def process_messages(
+        self,
+        messages: list[dict[str, str]],
+        images: list["ImageInput"],
+        videos: list["VideoInput"],
+        audios: list["AudioInput"],
+        processor: Optional["MMProcessor"],
+    ) -> list[dict[str, str]]:
+        self._validate_input(processor, images, videos, audios)
+        self._validate_messages(messages, images, videos, audios)
+        num_image_tokens, num_video_tokens, num_audio_tokens = 0, 0, 0
+        messages = deepcopy(messages)
+        mm_inputs, audio_inputs = {}, {}
+        if len(images) != 0 and len(videos) != 0:
+            raise ValueError("MiniCPM-V model does not support input images and videos at the same time.")
+
+        use_image_id = getattr(processor, "default_use_image_id", True)
+
+        if len(videos) != 0:
+            use_image_id = False
+            mm_inputs = self._get_mm_inputs([], videos, [], processor)
+
+        for i, message in enumerate(messages):
+            content = message["content"]
+            while IMAGE_PLACEHOLDER in content:
+                content = content.replace(IMAGE_PLACEHOLDER, "{{image}}", 1)
+                num_image_tokens += 1
+
+            while VIDEO_PLACEHOLDER in content:
+                num_frames = 1
+                if "num_frames_per_video" in mm_inputs:
+                    num_frames = sum(mm_inputs["num_frames_per_video"])
+                content = content.replace(VIDEO_PLACEHOLDER, "{{image}}" * num_frames, 1)
+                num_video_tokens += 1
+
+            while AUDIO_PLACEHOLDER in content:
+                content = content.replace(AUDIO_PLACEHOLDER, "{{audio}}", 1)
+                num_audio_tokens += 1
+
+            message["content"] = content.replace("{{image}}", "(<image>./</image>)").replace(
+                "{{audio}}", "(<audio>./</audio>)"
+            )
+
+        if len(images):
+            mm_inputs = self._get_mm_inputs(images, [], [], processor)
+
+        if len(audios):
+            audio_inputs = self._get_mm_inputs([], [], audios, processor, ret_phs=True)
+
+        if self.expand_mm_tokens and mm_inputs:
+            pattern = "(<image>./</image>)"
+            idx = 0
+            for index, message in enumerate(messages):
+                text = message["content"]
+                image_tags = re.findall(pattern, text)
+                text_chunks = text.split(pattern)
+                final_text = ""
+                for i in range(len(image_tags)):
+                    image_placeholder = self._build_v4_6_placeholder(mm_inputs, idx, use_image_id, processor)
+                    final_text = final_text + text_chunks[i] + image_placeholder
+                    idx += 1
+                final_text += text_chunks[-1]
+                messages[index]["content"] = final_text
+
+        if self.expand_mm_tokens and audio_inputs:
+            pattern = "(<audio>./</audio>)"
+            idx = 0
+            for index, message in enumerate(messages):
+                text = message["content"]
+                audio_tags = re.findall(pattern, text)
+                text_chunks = text.split(pattern)
+                final_text = ""
+                for i in range(len(audio_tags)):
+                    audio_placeholder = audio_inputs["audio_phs"][0][idx]
+                    final_text = final_text + text_chunks[i] + audio_placeholder
+                    idx += 1
+                final_text += text_chunks[-1]
+                messages[index]["content"] = final_text
+
+        return messages
+
+    @override
+    def get_mm_inputs(
+        self,
+        images: list["ImageInput"],
+        videos: list["VideoInput"],
+        audios: list["AudioInput"],
+        imglens: list[int],
+        vidlens: list[int],
+        audlens: list[int],
+        batch_ids: list[list[int]],
+        processor: Optional["MMProcessor"],
+    ) -> dict[str, Union[list[int], "torch.Tensor"]]:
+        self._validate_input(processor, images, videos, audios)
+        mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
+
+        # v4.6 does NOT use image_bound — the model finds image tokens via get_placeholder_mask
+        # Ensure target_sizes key name matches the model's expected input
+        if "target_sizes" not in mm_inputs and "tgt_sizes" in mm_inputs:
+            mm_inputs["target_sizes"] = mm_inputs.pop("tgt_sizes")
+
+        if "target_sizes" not in mm_inputs:
+            mm_inputs["target_sizes"] = torch.empty(0, 2, dtype=torch.int32)
+
+        if "pixel_values" not in mm_inputs:
+            mm_inputs["pixel_values"] = torch.empty(1, 3, 14, 0)
+
+        if len(audios) > 0:
+            audio_inputs = self._get_mm_inputs([], [], audios, processor)
+            mm_inputs.update(audio_inputs)
+
+        return mm_inputs
+
+
 @dataclass
 class MllamaPlugin(BasePlugin):
     @override
@@ -2695,6 +3006,7 @@ PLUGINS = {
     "llava_next_video": LlavaNextVideoPlugin,
     "lfm2_vl": LFMVLPlugin,
     "minicpm_v": MiniCPMVPlugin,
+    "minicpm_v_4_6": MiniCPMV4_6Plugin,
     "mllama": MllamaPlugin,
     "paligemma": PaliGemmaPlugin,
     "pixtral": PixtralPlugin,

src/llamafactory/data/template.py

@@ -1704,6 +1704,17 @@ register_template(
 )
 
 
+register_template(
+    name="minicpm_v_4_6",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    stop_words=["<|im_end|>"],
+    default_system="You are a helpful assistant.",
+    mm_plugin=get_mm_plugin(name="minicpm_v_4_6", image_token="<image>", video_token="<video>"),
+)
+
+
 # copied from minicpm_v template
 register_template(
     name="minicpm_o",

src/llamafactory/extras/constants.py

@@ -1948,6 +1948,18 @@ register_model_group(
 )
 
 
+register_model_group(
+    models={
+        "MiniCPM-V-4.6": {
+            DownloadSource.DEFAULT: "openbmb/MiniCPM-V-4_6",
+            DownloadSource.MODELSCOPE: "OpenBMB/MiniCPM-V-4_6",
+        },
+    },
+    template="minicpm_v_4_6",
+    multimodal=True,
+)
+
+
 register_model_group(
     models={
         "Ministral-8B-Instruct-2410": {

src/llamafactory/model/model_utils/visual.py

@@ -321,6 +321,14 @@ _register_composite_model(
 )
 
 
+_register_composite_model(
+    model_type="minicpmv4_6",
+    projector_keys=["model.merger"],
+    vision_model_keys=["model.vision_tower"],
+    language_model_keys=["model.language_model", "lm_head"],
+)
+
+
 _register_composite_model(
     model_type="minicpmo",
     projector_keys=["resampler"],

src/llamafactory/v1/plugins/model_plugins/parallelization/ulysses.py

@@ -70,7 +70,6 @@ class UlyssesAttention(torch.nn.Module):
         gather_idx: int = 1,
         attn_fn: Optional[callable] = None,
     ) -> None:
-
         super().__init__()
         self.spg = sequence_process_group
         self.scatter_idx = scatter_idx