[model] Support MiniCPM-V 4.0 (#22166)

Co-authored-by: imning3 <hbning@pku.edu.cn>

docs/models/supported_models.md

@@ -622,7 +622,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
 | `LlavaNextVideoForConditionalGeneration` | LLaVA-NeXT-Video | T + V | `llava-hf/LLaVA-NeXT-Video-7B-hf`, etc. | | ✅︎ | ✅︎ |
 | `LlavaOnevisionForConditionalGeneration` | LLaVA-Onevision | T + I<sup>+</sup> + V<sup>+</sup> | `llava-hf/llava-onevision-qwen2-7b-ov-hf`, `llava-hf/llava-onevision-qwen2-0.5b-ov-hf`, etc. | | ✅︎ | ✅︎ |
 | `MiniCPMO` | MiniCPM-O | T + I<sup>E+</sup> + V<sup>E+</sup> + A<sup>E+</sup> | `openbmb/MiniCPM-o-2_6`, etc. | ✅︎ | ✅︎ | ✅︎ |
-| `MiniCPMV` | MiniCPM-V | T + I<sup>E+</sup> + V<sup>E+</sup> | `openbmb/MiniCPM-V-2` (see note), `openbmb/MiniCPM-Llama3-V-2_5`, `openbmb/MiniCPM-V-2_6`, etc. | ✅︎ | | ✅︎ |
+| `MiniCPMV` | MiniCPM-V | T + I<sup>E+</sup> + V<sup>E+</sup> | `openbmb/MiniCPM-V-2` (see note), `openbmb/MiniCPM-Llama3-V-2_5`, `openbmb/MiniCPM-V-2_6`, `openbmb/MiniCPM-V-4`, etc. | ✅︎ | | ✅︎ |
 | `MiniMaxVL01ForConditionalGeneration` | MiniMax-VL | T + I<sup>E+</sup> | `MiniMaxAI/MiniMax-VL-01`, etc. | | ✅︎ | ✅︎ |
 | `Mistral3ForConditionalGeneration` | Mistral3 (HF Transformers) | T + I<sup>+</sup> | `mistralai/Mistral-Small-3.1-24B-Instruct-2503`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `MllamaForConditionalGeneration` | Llama 3.2 | T + I<sup>+</sup> | `meta-llama/Llama-3.2-90B-Vision-Instruct`, `meta-llama/Llama-3.2-11B-Vision`, etc. | | | |

tests/models/registry.py

@@ -427,7 +427,7 @@ _MULTIMODAL_EXAMPLE_MODELS = {
     "MiniCPMO": _HfExamplesInfo("openbmb/MiniCPM-o-2_6",
                                 trust_remote_code=True),
     "MiniCPMV": _HfExamplesInfo("openbmb/MiniCPM-Llama3-V-2_5",
-                                extras={"2.6": "openbmb/MiniCPM-V-2_6"},  # noqa: E501
+                                extras={"2.6": "openbmb/MiniCPM-V-2_6", "4.0": "openbmb/MiniCPM-V-4"},  # noqa: E501
                                 trust_remote_code=True),
     "MiniMaxVL01ForConditionalGeneration": _HfExamplesInfo("MiniMaxAI/MiniMax-VL-01", # noqa: E501
                                               trust_remote_code=True,

vllm/model_executor/models/minicpmv.py

@@ -38,6 +38,8 @@ from typing_extensions import TypeVar
 
 from vllm.config import VllmConfig
 from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.quantization.awq import AWQConfig
+from vllm.model_executor.layers.quantization.awq_marlin import AWQMarlinConfig
 from vllm.model_executor.layers.resampler import (BaseResampler, Resampler2,
                                                   get_2d_sincos_pos_embed)
 from vllm.model_executor.model_loader.utils import set_default_torch_dtype
@@ -339,7 +341,9 @@ class MiniCPMVProcessingInfo(BaseProcessingInfo):
 
     def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
         mm_limits = {"image": None}
-        if self.get_model_version() == (2, 6):
+        if self.get_model_version() == (2,
+                                        6) or self.get_model_version() == (4,
+                                                                           0):
             mm_limits["video"] = None
 
         return mm_limits
@@ -620,7 +624,8 @@ class MiniCPMVMultiModalProcessor(BaseMultiModalProcessor[_I]):
         out_keys: set[str],
     ) -> dict[str, NestedTensors]:
         # This processor supports zipping prompt and mm_data together
-        if self.info.get_model_version() == (2, 6):
+        if self.info.get_model_version() == (
+                2, 6) or self.info.get_model_version() == (4, 0):
             inputs = super()._call_hf_processor(
                 prompt=prompts,  # type: ignore
                 mm_data=mm_data,
@@ -679,10 +684,18 @@ class MiniCPMVMultiModalProcessor(BaseMultiModalProcessor[_I]):
         hf_processor_mm_kwargs: Mapping[str, object],
         out_mm_kwargs: MultiModalKwargs,
     ) -> Sequence[PromptUpdate]:
-        placeholder = {
-            "image": self.info.image_pattern,
-            "video": self.info.video_pattern,
-        }
+        placeholders = [("image", self.info.image_pattern),
+                        ("video", self.info.video_pattern)]
+
+        # hard code for inconsistency of encode-decode image_pattern
+        additional_placeholders = []
+        tokenizer = self.info.get_tokenizer()
+        for modality, pattern in placeholders:
+            sub_pattern = tokenizer.decode(
+                tokenizer.encode(pattern, add_special_tokens=False))
+            if sub_pattern != pattern:
+                additional_placeholders.append((modality, sub_pattern))
+        placeholders += additional_placeholders
 
         def get_image_replacement(item_idx: int):
             images = mm_items.get_items(
@@ -714,9 +727,9 @@ class MiniCPMVMultiModalProcessor(BaseMultiModalProcessor[_I]):
 
         return [
             PromptReplacement(modality=modality,
-                              target=placeholder[modality],
+                              target=pattern,
                               replacement=get_replacement[modality])
-            for modality in ("image", "video")
+            for modality, pattern in placeholders
         ]
 
     def _get_mm_fields_config(
@@ -1262,11 +1275,124 @@ class MiniCPMV2_6(MiniCPMVBaseModel, SupportsLoRA):
 
         return self.resampler(vision_embedding, tgt_sizes)
 
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self,
+                                   skip_prefixes=["apm.", "audio", "tts"])
+        return loader.load_weights(weights)
+
+
+class MiniCPMV4_0(MiniCPMVBaseModel, SupportsLoRA):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+        assert self.version == (4, 0)
+
+    def _maybe_ignore_quant_config(self, quant_config: QuantizationConfig):
+        if isinstance(quant_config, (AWQConfig, AWQMarlinConfig)):
+            return None
+        return quant_config
+
+    def init_llm(
+        self,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+    ) -> nn.Module:
+        return LlamaForCausalLM(vllm_config=vllm_config, prefix=prefix)
+
+    def init_vision_module(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> nn.Module:
+        quant_config = self._maybe_ignore_quant_config(quant_config)
+        model = Idefics2VisionTransformer(config.vision_config,
+                                          quant_config=quant_config,
+                                          prefix=prefix)
+        if self.config.drop_vision_last_layer:
+            model.encoder.layers = model.encoder.layers[:-1]
+        return model
+
+    def init_resampler(
+        self,
+        embed_dim: int,
+        vision_dim: int,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> nn.Module:
+        quant_config = self._maybe_ignore_quant_config(quant_config)
+        with set_default_torch_dtype(torch.float16):
+            # The resampler in 4.0 remains consistent with the one in 2.5/2.6.
+            resampler = Resampler2_5(num_queries=self.config.query_num,
+                                     embed_dim=embed_dim,
+                                     num_heads=embed_dim // 128,
+                                     kv_dim=vision_dim,
+                                     quant_config=quant_config,
+                                     prefix=prefix)
+
+        return resampler.to(device=current_platform.device_type,
+                            dtype=torch.get_default_dtype())
+
+    def get_vision_hidden_states(
+            self, data: MiniCPMVImagePixelInputs) -> torch.Tensor:
+        pixel_values = data["pixel_values"]
+        tgt_sizes = data["tgt_sizes"]
+
+        B = len(pixel_values)
+        P = pixel_values[0].shape[-2]
+        L = max(item.shape[-1] for item in pixel_values)
+        device = pixel_values[0].device
+        dtype = pixel_values[0].dtype
+
+        all_pixel_values = torch.zeros((B, 3, P, L),
+                                       dtype=dtype,
+                                       device=device)
+        for i, pixel_values_item in enumerate(pixel_values):
+            L_item = pixel_values_item.shape[-1]
+            all_pixel_values[i, ..., :L_item] = pixel_values_item
+
+        num_patches = tgt_sizes.prod(-1)
+        max_patches = num_patches.max().item()
+        assert isinstance(max_patches, int)
+
+        patch_attn_mask = torch.zeros((B, max_patches),
+                                      dtype=torch.bool,
+                                      device=device)
+        for i, num_patches_item in enumerate(num_patches):
+            patch_attn_mask[i, :num_patches_item] = True
+
+        vision_embedding = self.vpm(
+            all_pixel_values,
+            patch_attention_mask=patch_attn_mask.unsqueeze(1),
+            tgt_sizes=tgt_sizes,
+        )
+
+        return self.resampler(vision_embedding, tgt_sizes)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self,
+                                   skip_prefixes=["apm.", "audio", "tts"])
+        return loader.load_weights(weights)
+
 
 _SUPPORT_VERSION = {
     (2, 0): MiniCPMV2_0,
     (2, 5): MiniCPMV2_5,
     (2, 6): MiniCPMV2_6,
+    (4, 0): MiniCPMV4_0,
 }
 
 
@@ -1294,8 +1420,10 @@ class MiniCPMV(MiniCPMVBaseModel, SupportsMultiModal, SupportsLoRA):
         # Dispatch class based on version
         instance_cls = _SUPPORT_VERSION.get(version)
         if instance_cls is None:
-            raise ValueError(
-                "Currently, MiniCPMV only supports versions 2.0, 2.5, and 2.6")
+            supported_versions = ", ".join(
+                [f"{v[0]}.{v[1]}" for v in sorted(_SUPPORT_VERSION.keys())])
+            raise ValueError(f"Currently, MiniCPMV only supports versions "
+                             f"{supported_versions}. Got version: {version}")
 
         # quant_config references base class members,
         # so update values before init is called