[Model] Initialize support for Deepseek-VL2 models (#11578)

Signed-off-by: Isotr0py <2037008807@qq.com>
Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>

.buildkite/test-pipeline.yaml

@@ -52,6 +52,7 @@ steps:
   - tests/worker
   - tests/standalone_tests/lazy_torch_compile.py
   commands:
+  - pip install git+https://github.com/Isotr0py/DeepSeek-VL2.git  # Used by multimoda processing test
   - python3 standalone_tests/lazy_torch_compile.py
   - pytest -v -s mq_llm_engine # MQLLMEngine
   - pytest -v -s async_engine # AsyncLLMEngine

docs/source/models/supported_models.md

@@ -610,6 +610,13 @@ See [this page](#generative-models) for more information on how to use generativ
   -
   - ✅︎
   - ✅︎
+* - `DeepseekVLV2ForCausalLM`
+  - DeepSeek-VL2
+  - T + I<sup>+</sup>
+  - `deepseek-ai/deepseek-vl2-tiny`(WIP), `deepseek-ai/deepseek-vl2-small`, `deepseek-ai/deepseek-vl2` etc. (see note)
+  -
+  - ✅︎
+  - ✅︎
 * - `FuyuForCausalLM`
   - Fuyu
   - T + I
@@ -755,8 +762,19 @@ See [this page](#generative-models) for more information on how to use generativ
 <sup>E</sup> Pre-computed embeddings can be inputted for this modality.  
 <sup>+</sup> Multiple items can be inputted per text prompt for this modality.
 
+````{note}
+The `deepseek-ai/deepseek-vl2-tiny` is not supported yet.
+
+To use `DeepSeek-VL2` series models, you need to install a fork version `deepseek_vl2` package:
+```shell
+pip install git+https://github.com/Isotr0py/DeepSeek-VL2.git
+```
+
+Besides, to run `DeepSeek-VL2` series models, you have to pass `--hf_overrides '{"architectures": ["DeepseekVLV2ForCausalLM"]}'` when running vLLM.
+````
+
 ```{note}
-To use `TIGER-Lab/Mantis-8B-siglip-llama3`, you have pass `--hf_overrides '{"architectures": ["MantisForConditionalGeneration"]}'` when running vLLM.
+To use `TIGER-Lab/Mantis-8B-siglip-llama3`, you have to pass `--hf_overrides '{"architectures": ["MantisForConditionalGeneration"]}'` when running vLLM.
 ```
 
 ```{note}

examples/offline_inference/vision_language.py

@@ -66,6 +66,23 @@ def run_chameleon(question: str, modality: str):
     return llm, prompt, stop_token_ids
 
 
+# Deepseek-VL2
+def run_deepseek_vl2(question: str, modality: str):
+    assert modality == "image"
+
+    model_name = "deepseek-ai/deepseek-vl2-small"
+
+    llm = LLM(model=model_name,
+              max_model_len=4096,
+              max_num_seqs=2,
+              disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
+              hf_overrides={"architectures": ["DeepseekVLV2ForCausalLM"]})
+
+    prompt = f"<|User|>: <image>\n{question}\n\n<|Assistant|>:"
+    stop_token_ids = None
+    return llm, prompt, stop_token_ids
+
+
 # Fuyu
 def run_fuyu(question: str, modality: str):
     assert modality == "image"
@@ -498,6 +515,7 @@ model_example_map = {
     "aria": run_aria,
     "blip-2": run_blip2,
     "chameleon": run_chameleon,
+    "deepseek_vl_v2": run_deepseek_vl2,
     "fuyu": run_fuyu,
     "glm4v": run_glm4v,
     "h2ovl_chat": run_h2ovl,

examples/offline_inference/vision_language_multi_image.py

@@ -54,6 +54,28 @@ def load_aria(question, image_urls: List[str]) -> ModelRequestData:
     )
 
 
+def load_deepseek_vl2(question: str, image_urls: List[str]):
+    model_name = "deepseek-ai/deepseek-vl2-small"
+
+    llm = LLM(model=model_name,
+              max_model_len=4096,
+              max_num_seqs=2,
+              hf_overrides={"architectures": ["DeepseekVLV2ForCausalLM"]},
+              limit_mm_per_prompt={"image": len(image_urls)})
+
+    placeholder = "".join(f"image_{i}:<image>\n"
+                          for i, _ in enumerate(image_urls, start=1))
+    prompt = f"<|User|>: {placeholder}{question}\n\n<|Assistant|>:"
+
+    return ModelRequestData(
+        llm=llm,
+        prompt=prompt,
+        stop_token_ids=None,
+        image_data=[fetch_image(url) for url in image_urls],
+        chat_template=None,
+    )
+
+
 def load_h2onvl(question: str, image_urls: List[str]) -> ModelRequestData:
     model_name = "h2oai/h2ovl-mississippi-2b"
 
@@ -372,6 +394,7 @@ def load_qwen2_vl(question, image_urls: List[str]) -> ModelRequestData:
 
 model_example_map = {
     "aria": load_aria,
+    "deepseek_vl2": load_deepseek_vl2,
     "h2ovl_chat": load_h2onvl,
     "idefics3": load_idefics3,
     "internvl_chat": load_internvl,

tests/models/decoder_only/vision_language/test_models.py

@@ -188,6 +188,33 @@ VLM_TEST_SETTINGS = {
         max_tokens=8,
         dtype="bfloat16",
     ),
+    "deepseek_vl_v2": VLMTestInfo(
+        models=["deepseek-ai/deepseek-vl2-small"],
+        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
+        dtype="bfloat16",
+        prompt_formatter=lambda img_prompt: f"<|User|>: {img_prompt}\n\n<|Assistant|>: ", # noqa: E501
+        max_model_len=4096,
+        max_num_seqs=2,
+        single_image_prompts=IMAGE_ASSETS.prompts({
+            "stop_sign": "<image>\nWhat's the color of the stop sign and car?",
+            "cherry_blossom": "<image>\nWhat's the color of the tower?",
+        }),
+        multi_image_prompt="image_1:<image>\nimage_2:<image>\nDescribe the two images shortly.",    # noqa: E501
+        vllm_runner_kwargs={"hf_overrides": {"architectures": ["DeepseekVLV2ForCausalLM"]}},  # noqa: E501
+        image_size_factors=[(0.10, 0.15)],
+        patch_hf_runner=model_utils.deepseekvl2_patch_hf_runner,
+        postprocess_inputs=model_utils.cast_dtype_post_processor("images"),
+        hf_output_post_proc=model_utils.deepseekvl2_trunc_hf_output,
+        stop_str=["<｜end▁of▁sentence｜>", "<｜begin▁of▁sentence｜>"],  # noqa: E501
+        num_logprobs=5,
+        marks=[
+            pytest.mark.skipif(
+                not is_flash_attn_2_available(),
+                reason="Model needs flash-attn for numeric convergence.",
+            ),
+            large_gpu_mark(min_gb=48),
+        ],
+    ),
     "fuyu": VLMTestInfo(
         models=["adept/fuyu-8b"],
         test_type=VLMTestType.IMAGE,

tests/models/decoder_only/vision_language/vlm_utils/model_utils.py

@@ -183,6 +183,14 @@ def paligemma_vllm_to_hf_output(vllm_output: RunnerOutput,
 
 
 ####### Post-processors for HF outputs
+def deepseekvl2_trunc_hf_output(hf_output: RunnerOutput,
+                                model: str) -> RunnerOutput:
+    output_ids, output_str, out_logprobs = hf_output
+    if output_str.endswith("<｜end▁of▁sentence｜>"):
+        output_str = output_str.split("<｜end▁of▁sentence｜>")[0]
+    return output_ids, output_str, out_logprobs
+
+
 def minicpmv_trunc_hf_output(hf_output: RunnerOutput,
                              model: str) -> RunnerOutput:
     output_ids, output_str, out_logprobs = hf_output
@@ -261,6 +269,34 @@ def qwen_prompt_path_encoder(
 
 
 ####### Model-specific HuggingFace runner patchers
+def deepseekvl2_patch_hf_runner(hf_model: HfRunner) -> HfRunner:
+    """Patches and returns an instance of the HfRunner to use for GLM4."""
+    hf_processor = hf_model.processor
+
+    def processor(*args, text="", images=None, **kwargs):
+        if isinstance(images, Image):
+            images = [images]
+        # inputs is a custom class instead of dict or BatchFeature
+        inputs = hf_processor(
+            *args,
+            prompt=text,
+            images=images,
+            **kwargs,
+        )
+        inputs = {
+            k: inputs[k]
+            for k in inputs.keys()  # noqa
+            if k not in ("seq_lens", "sft_format")
+        }
+        inputs = BatchEncoding(data=inputs, tensor_type="pt")
+        return inputs
+
+    hf_model.processor = processor
+    hf_model.model.get_output_embeddings = lambda: \
+        hf_model.model.language.model.embed_tokens
+    return hf_model
+
+
 def glm_patch_hf_runner(hf_model: HfRunner) -> HfRunner:
     """Patches and returns an instance of the HfRunner to use for GLM4."""
     hf_processor = hf_model.processor

tests/models/registry.py

@@ -179,6 +179,8 @@ _MULTIMODAL_EXAMPLE_MODELS = {
                                     trust_remote_code=True),
     "ChatGLMForConditionalGeneration": _HfExamplesInfo("chatglm2-6b",
                                                        is_available_online=False),
+    # TODO(Isotr0py): Use deepseek-vl2-tiny for test after it's supported
+    "DeepseekVLV2ForCausalLM": _HfExamplesInfo("deepseek-ai/deepseek-vl2-small"),   # noqa: E501
     "FuyuForCausalLM": _HfExamplesInfo("adept/fuyu-8b"),
     "H2OVLChatModel": _HfExamplesInfo("h2oai/h2ovl-mississippi-800m"),
     "InternVLChatModel": _HfExamplesInfo("OpenGVLab/InternVL2-1B",

tests/models/test_initialization.py

@@ -26,6 +26,9 @@ def test_can_initialize(model_arch):
 
     # Avoid OOM
     def hf_overrides(hf_config: PretrainedConfig) -> PretrainedConfig:
+        if hf_config.model_type == "deepseek_vl_v2":
+            hf_config.update({"architectures": ["DeepseekVLV2ForCausalLM"]})
+
         if hasattr(hf_config, "text_config"):
             text_config: PretrainedConfig = hf_config.text_config
         else:

vllm/entrypoints/chat_utils.py

@@ -403,8 +403,8 @@ class BaseMultiModalItemTracker(ABC, Generic[_T]):
             if model_type.startswith("llava"):
                 return self._cached_token_str(self._tokenizer,
                                               hf_config.image_token_index)
-            if model_type in ("chameleon", "internvl_chat", "NVLM_D",
-                              "h2ovl_chat"):
+            if model_type in ("chameleon", "deepseek_vl_v2", "internvl_chat",
+                              "NVLM_D", "h2ovl_chat"):
                 return "<image>"
             if model_type == "mllama":
                 return "<|image|>"

vllm/model_executor/models/deepseek_v2.py

@@ -243,7 +243,11 @@ class DeepseekV2Attention(nn.Module):
                                         bias=False,
                                         quant_config=quant_config,
                                         prefix=f"{prefix}.o_proj")
-        rope_scaling["rope_type"] = 'deepseek_yarn'
+        if rope_scaling:
+            rope_scaling["rope_type"] = 'deepseek_yarn'
+            self.use_normal_rope = False
+        else:
+            self.use_normal_rope = True
         self.rotary_emb = get_rope(qk_rope_head_dim,
                                    rotary_dim=qk_rope_head_dim,
                                    max_position=max_position_embeddings,
@@ -298,7 +302,18 @@ class DeepseekV2Attention(nn.Module):
                      self.qk_nope_head_dim + self.v_head_dim)
         k_nope, v = kv.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
         k_pe = latent_cache[:, :, self.kv_lora_rank:]
+
+        if self.use_normal_rope:
+            seq_len = positions.size(0)
+            ori_q_pe_shape, ori_k_pe_shape = q_pe.shape, k_pe.shape
+            q_pe = q_pe.reshape(seq_len, -1)
+            k_pe = k_pe.reshape(seq_len, -1)
+
         q_pe, k_pe = self.rotary_emb(positions, q_pe, k_pe)
+
+        if self.use_normal_rope:
+            q_pe, k_pe = q_pe.view(ori_q_pe_shape), k_pe.view(ori_k_pe_shape)
+
         q[..., self.qk_nope_head_dim:] = q_pe
         k = torch.empty_like(q)
         k[..., :self.qk_nope_head_dim] = k_nope
@@ -355,6 +370,7 @@ class DeepseekV2DecoderLayer(nn.Module):
             quant_config=quant_config,
             prefix=f"{prefix}.self_attn",
         )
+
         if (config.n_routed_experts is not None
                 and layer_idx >= config.first_k_dense_replace
                 and layer_idx % config.moe_layer_freq == 0):

vllm/model_executor/models/deepseek_v3.py

@@ -251,7 +251,11 @@ class DeepseekV3Attention(nn.Module):
                                         bias=False,
                                         quant_config=quant_config,
                                         prefix=f"{prefix}.o_proj")
-        rope_scaling["rope_type"] = 'deepseek_yarn'
+        if rope_scaling:
+            rope_scaling["rope_type"] = 'deepseek_yarn'
+            self.use_normal_rope = False
+        else:
+            self.use_normal_rope = True
         self.rotary_emb = get_rope(qk_rope_head_dim,
                                    rotary_dim=qk_rope_head_dim,
                                    max_position=max_position_embeddings,
@@ -306,7 +310,18 @@ class DeepseekV3Attention(nn.Module):
                      self.qk_nope_head_dim + self.v_head_dim)
         k_nope, v = kv.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
         k_pe = latent_cache[:, :, self.kv_lora_rank:]
+
+        if self.use_normal_rope:
+            seq_len = positions.size(0)
+            ori_q_pe_shape, ori_k_pe_shape = q_pe.shape, k_pe.shape
+            q_pe = q_pe.reshape(seq_len, -1)
+            k_pe = k_pe.reshape(seq_len, -1)
+
         q_pe, k_pe = self.rotary_emb(positions, q_pe, k_pe)
+
+        if self.use_normal_rope:
+            q_pe, k_pe = q_pe.view(ori_q_pe_shape), k_pe.view(ori_k_pe_shape)
+
         q[..., self.qk_nope_head_dim:] = q_pe
         k = torch.empty_like(q)
         k[..., :self.qk_nope_head_dim] = k_nope
@@ -583,7 +598,8 @@ class DeepseekV3ForCausalLM(nn.Module, SupportsPP):
                 continue
 
             # TODO(simon): support nextn predict layers
-            if self.config.num_nextn_predict_layers > 0:
+            if hasattr(self.config, "num_nextn_predict_layers"
+                       ) and self.config.num_nextn_predict_layers > 0:
                 assert self.config.num_nextn_predict_layers == 1
                 layer_idx = self.config.num_hidden_layers
                 if name.startswith(f"model.layers.{layer_idx}"):

vllm/model_executor/models/deepseek_vl2.py

@@ -0,0 +1,662 @@
+# adapted from https://github.com/deepseek-ai/DeepSeek-VL2/blob/faf18023f24b962b32d9f0a2d89e402a8d383a78/deepseek_vl2/models/modeling_deepseek_vl_v2.py
+"""Inference-only Deepseek-VL2 model compatible with HuggingFace weights."""
+import math
+from functools import cached_property, partial
+from typing import (Iterable, List, Literal, Mapping, Optional, Set, Tuple,
+                    TypedDict, Union)
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange, repeat
+from transformers import AutoProcessor, BatchFeature, ProcessorMixin
+
+from vllm.attention import AttentionMetadata
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+from vllm.model_executor import SamplingMetadata
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
+from vllm.model_executor.model_loader.utils import set_default_torch_dtype
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (MultiModalFieldConfig, MultiModalKwargs,
+                                    NestedTensors)
+from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
+                                   ImageSize, MultiModalDataItems)
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo, PromptReplacement)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
+from vllm.multimodal.utils import cached_get_tokenizer
+from vllm.sequence import IntermediateTensors
+from vllm.transformers_utils.configs.deepseek_vl2 import (DeepseekVLV2Config,
+                                                          MlpProjectorConfig,
+                                                          VisionEncoderConfig)
+from vllm.utils import is_list_of
+
+from .interfaces import SupportsMultiModal, SupportsPP
+from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn,
+                    init_vllm_registered_model, maybe_prefix,
+                    merge_multimodal_embeddings)
+
+logger = init_logger(__name__)
+
+# The image token id may be various
+_IMAGE_TOKEN = "<image>"
+
+
+class DeepseekVL2ImagePixelInputs(TypedDict):
+    type: Literal["pixel_values"]
+    data: Union[torch.Tensor, List[torch.Tensor]]
+    """
+    Shape: `(batch_size * num_images, num_channels, height, width)`
+    """
+    images_spatial_crop: torch.Tensor
+    """
+    Shape: `(batch_size * num_images, 2)`
+    """
+
+
+class DeepseekVL2VImageEmbeddingInputs(TypedDict):
+    type: Literal["image_embeds"]
+    data: Union[torch.Tensor, List[torch.Tensor]]
+    """Shape: `(batch_size * num_images, image_feature_size, hidden_size)`
+
+    `hidden_size` must match the hidden size of language model backbone.
+    """
+
+
+DeepseekVL2ImageInputs = Union[DeepseekVL2ImagePixelInputs,
+                               DeepseekVL2VImageEmbeddingInputs]
+
+
+class MlpProjector(nn.Module):
+
+    def __init__(self, cfg: MlpProjectorConfig):
+
+        super().__init__()
+
+        self.cfg = cfg
+        assert not cfg.token_pooling, (
+            "Token pooling is not supported currently.")
+
+        if cfg.projector_type == "downsample_mlp_gelu":
+            mlp_depth = cfg.depth
+            mlp_ratio = cfg.mlp_ratio
+            modules = [
+                nn.Linear(
+                    cfg.input_dim * cfg.downsample_ratio *
+                    cfg.downsample_ratio, cfg.n_embed * mlp_ratio)
+            ]
+            for _ in range(1, mlp_depth - 1):
+                modules.append(nn.GELU())
+                modules.append(
+                    nn.Linear(cfg.n_embed * mlp_ratio,
+                              cfg.n_embed * mlp_ratio))
+            modules.append(nn.GELU())
+            modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed))
+            modules = nn.Sequential(*modules)
+
+        else:
+            raise NotImplementedError(
+                f"Unsupported projector type: {cfg.projector_type}")
+
+        self.layers = modules
+
+    def forward(self, x):
+        bs, hw, input_dim = x.shape
+        h = w = int((hw)**0.5)
+        """compute padding"""
+        if h % self.cfg.downsample_ratio:
+            pad = self.cfg.downsample_ratio - h % self.cfg.downsample_ratio
+        else:
+            pad = 0
+        x = x.reshape(bs, h, w, input_dim)
+        if pad > 0:
+            x = F.pad(x, (0, 0, 0, pad, 0, pad), "constant", 0)
+        """4 to 1 concat"""
+        x = x.permute(0, 3, 1, 2)  # B, C, H, W
+        x = F.unfold(x,
+                     kernel_size=self.cfg.downsample_ratio,
+                     stride=self.cfg.downsample_ratio,
+                     padding=0)  # B, C*4, HW // 4
+        x = x.permute(0, 2, 1)
+
+        return self.layers(x)
+
+
+class DeepseekVL2ProcessingInfo(BaseProcessingInfo):
+
+    def get_hf_config(self):
+        return self.ctx.get_hf_config(DeepseekVLV2Config)
+
+    def get_hf_processor(self) -> ProcessorMixin:
+        # TODO(Isotr0py): we should get rid of dependency on deepseek_vl2
+        # in the future, because it's flasky and lack of maintenance.
+        try:
+            from deepseek_vl2.models.processing_deepseek_vl_v2 import (
+                DeepseekVLV2Processor, select_best_resolution)
+            AutoProcessor.register("DeepseekVLV2Processor",
+                                   DeepseekVLV2Processor)
+        except ModuleNotFoundError as exc:
+            raise ModuleNotFoundError(
+                "You need to `pip install "
+                "git+https://github.com/deepseek-ai/DeepSeek-VL2.git` "
+                "to use this model") from exc
+
+        processor = self.ctx.get_hf_processor(DeepseekVLV2Processor)
+        processor.select_best_resolution = partial(
+            select_best_resolution,
+            candidate_resolutions=processor.candidate_resolutions)
+        return processor
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"image": None}
+
+    def get_num_image_tokens(self, *, image_width: int,
+                             image_height: int) -> int:
+        hf_processor = self.get_hf_processor()
+        image_size = hf_processor.image_size
+        patch_size = hf_processor.patch_size
+        downsample_ratio = hf_processor.downsample_ratio
+
+        best_width, best_height = hf_processor.select_best_resolution(
+            (image_width, image_height))
+
+        num_width_tiles, num_height_tiles = (best_width // image_size,
+                                             best_height // image_size)
+        h = w = math.ceil((image_size // patch_size) / downsample_ratio)
+
+        global_views_tokens = h * (w + 1)
+        local_views_tokens = (num_height_tiles * h) * (num_width_tiles * w + 1)
+        return global_views_tokens + local_views_tokens + 1
+
+    def get_image_size_with_most_features(self) -> ImageSize:
+        hf_config = self.get_hf_config()
+        candidate_resolutions = hf_config.candidate_resolutions
+        height, width = max(candidate_resolutions,
+                            key=lambda x: self.get_num_image_tokens(
+                                image_width=x[1], image_height=x[0]))
+        return ImageSize(width=width, height=height)
+
+    def get_mm_max_tokens_per_item(self, seq_len: int) -> Mapping[str, int]:
+        max_image_size = self.get_image_size_with_most_features()
+        max_image_tokens = self.get_num_image_tokens(
+            image_height=max_image_size.height,
+            image_width=max_image_size.width)
+
+        return {"image": max_image_tokens}
+
+
+class DeepseekVL2DummyInputsBuilder(
+        BaseDummyInputsBuilder[DeepseekVL2ProcessingInfo]):
+
+    def get_dummy_processor_inputs(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> ProcessorInputs:
+        num_images = mm_counts.get("image", 0)
+        hf_processor = self.info.get_hf_processor()
+        image_token: str = hf_processor.image_token
+
+        max_image_size = self.info.get_image_size_with_most_features()
+
+        mm_data = {
+            "image":
+            self._get_dummy_images(width=max_image_size.width,
+                                   height=max_image_size.height,
+                                   num_images=num_images)
+        }
+
+        return ProcessorInputs(
+            prompt_text=image_token * num_images,
+            mm_data=mm_data,
+        )
+
+
+class DeepseekVL2MultiModalProcessor(
+        BaseMultiModalProcessor[DeepseekVL2ProcessingInfo]):
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        if mm_data:
+            outputs = self.info.ctx.call_hf_processor(
+                self.info.get_hf_processor(**mm_kwargs),
+                dict(prompt=prompt, **mm_data),
+                mm_kwargs,
+            )
+
+            # Deepseek-vl2 processor don't return BatchFeature,
+            # we need to manually create it
+            processed_outputs = dict(input_ids=outputs["input_ids"])
+            processed_outputs = BatchFeature(data=dict(processed_outputs),
+                                             tensor_type="pt")
+
+            # Remove batch dimension from processor outputs,
+            # because we will try batch to create NestedTensors
+            target_dtype = self.info.ctx.model_config.dtype
+            pixel_values = outputs["images"].to(target_dtype).squeeze(0)
+            images_spatial_crop = outputs["images_spatial_crop"].squeeze(0)
+            patches_per_image = [
+                x.prod().item() + 1 for x in images_spatial_crop
+            ]
+
+            # Rename `images` -> `pixel_values` to avoid confusion
+            processed_outputs["pixel_values"] = list(
+                pixel_values.split(patches_per_image))
+            processed_outputs["images_spatial_crop"] = images_spatial_crop
+        else:
+            tokenizer = self.info.get_tokenizer()
+            processed_outputs = tokenizer(prompt,
+                                          add_special_tokens=True,
+                                          return_tensors="pt")
+
+        return processed_outputs
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        return dict(
+            pixel_values=MultiModalFieldConfig.batched("image"),
+            images_spatial_crop=MultiModalFieldConfig.batched("image"),
+            image_embeds=MultiModalFieldConfig.batched("image"),
+        )
+
+    def _get_prompt_replacements(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> list[PromptReplacement]:
+        hf_processor = self.info.get_hf_processor()
+        image_token_id: int = hf_processor.image_token_id
+
+        def get_replacement_deepseek_vl2(item_idx: int):
+            images = mm_items.get_items(
+                "image", (ImageEmbeddingItems, ImageProcessorItems))
+
+            if isinstance(images, ImageEmbeddingItems):
+                num_image_tokens = images.get_feature_size(item_idx)
+            else:
+                image_size = images.get_image_size(item_idx)
+
+                num_image_tokens = self.info.get_num_image_tokens(
+                    image_width=image_size.width,
+                    image_height=image_size.height,
+                )
+            return [image_token_id] * num_image_tokens
+
+        return [
+            PromptReplacement(
+                modality="image",
+                target=[image_token_id],
+                replacement=get_replacement_deepseek_vl2,
+            )
+        ]
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    DeepseekVL2MultiModalProcessor,
+    info=DeepseekVL2ProcessingInfo,
+    dummy_inputs=DeepseekVL2DummyInputsBuilder)
+class DeepseekVLV2ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
+
+    hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={
+        "language.": "language_model.",
+    })
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config: DeepseekVLV2Config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+
+        self.config = config
+        self.multimodal_config = multimodal_config
+
+        self.vision_config = config.vision_config
+        self.projector_config = config.projector_config
+        self.text_config = config.text_config
+
+        model_config = vllm_config.model_config
+        tokenizer = cached_get_tokenizer(
+            model_config.tokenizer,
+            tokenizer_mode=model_config.tokenizer_mode,
+            tokenizer_revision=model_config.tokenizer_revision,
+            trust_remote_code=model_config.trust_remote_code,
+        )
+        self.image_token_id = tokenizer.vocab.get(_IMAGE_TOKEN)
+
+        self.vision = self._init_vision_module(self.vision_config,
+                                               quant_config,
+                                               maybe_prefix(prefix, "vision"))
+
+        self.projector = MlpProjector(self.projector_config)
+        self.tile_tag = config.tile_tag
+        self.global_view_pos = config.global_view_pos
+
+        # special token for image token sequence format
+        embed_std = 1 / torch.sqrt(
+            torch.tensor(self.projector_config.n_embed, dtype=torch.float32))
+        if self.tile_tag == "2D":
+            # <|view_separator|>, <|\n|>
+            self.image_newline = nn.Parameter(
+                torch.randn(self.projector_config.n_embed) * embed_std)
+            # This is a typo in original implementation
+            self.view_seperator = nn.Parameter(
+                torch.randn(self.projector_config.n_embed) * embed_std)
+        else:
+            raise ValueError(
+                f"Only 2D tile_tag is supported currently, got: {self.tile_tag}"
+            )
+
+        self.language_model = init_vllm_registered_model(
+            vllm_config=vllm_config,
+            hf_config=self.text_config,
+            prefix=maybe_prefix(prefix, "language"),
+            architectures=["DeepseekV3ForCausalLM"]
+            if self.text_config.topk_method == "noaux_tc" else
+            ["DeepseekV2ForCausalLM"],
+        )
+
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors)
+
+    def _init_vision_module(
+        self,
+        vision_config: VisionEncoderConfig,
+        quant_config: Optional[QuantizationConfig],
+        prefix: str = "",
+    ) -> nn.Module:
+        # TODO: refactor vision model through timm wrapper from transformers
+        try:
+            import timm
+        except ImportError:
+            raise ImportError("Please install timm") from ImportError
+
+        with set_default_torch_dtype(torch.float16):
+            model = timm.create_model(
+                "vit_so400m_patch14_siglip_384.webli",
+                pretrained=False,
+                num_classes=0,
+                dynamic_img_size=True,
+                dynamic_img_pad=True,
+            )
+
+        model = model.to(dtype=torch.get_default_dtype())
+        return model
+
+    @cached_property
+    def sampler(self):
+        if hasattr(self.language_model, "sampler"):
+            return self.language_model.sampler
+
+        return get_sampler()
+
+    def _validate_pixel_values(
+        self, data: Union[torch.Tensor, List[torch.Tensor]]
+    ) -> Union[torch.Tensor, List[torch.Tensor]]:
+
+        h = w = self.vision_config.image_size
+        expected_dims = (3, h, w)
+
+        def _validate_shape(d: torch.Tensor):
+            actual_dims = tuple(d.shape[1:])
+
+            if actual_dims != expected_dims:
+                expected_expr = ("num_patches", *map(str, expected_dims))
+                raise ValueError(
+                    "The expected shape of pixel values per image per batch "
+                    f"is {expected_expr}. You supplied {tuple(d.shape)}.")
+
+        for d in data:
+            _validate_shape(d)
+
+        return data
+
+    def _validate_images_spatial_crop(
+        self, data: Union[torch.Tensor, List[torch.Tensor]]
+    ) -> Union[torch.Tensor, List[torch.Tensor]]:
+        expected_dims = 2
+
+        def _validate_shape(d: torch.Tensor):
+            actual_dims = d.size(-1)
+
+            if actual_dims != expected_dims:
+                expected_expr = str(expected_dims)
+                raise ValueError(
+                    f"The expected shape of image sizes per image per batch "
+                    f"is {expected_expr}. You supplied {tuple(d.shape)}.")
+
+        for d in data:
+            _validate_shape(d)
+
+        return data
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[DeepseekVL2ImageInputs]:
+        pixel_values = kwargs.pop("pixel_values", None)
+        images_spatial_crop = kwargs.pop("images_spatial_crop", None)
+        image_embeds = kwargs.pop("image_embeds", None)
+
+        if pixel_values is None and image_embeds is None:
+            return None
+
+        if pixel_values is not None:
+            if not isinstance(pixel_values, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of pixel values. "
+                                 f"Got type: {type(pixel_values)}")
+
+            if not isinstance(images_spatial_crop, (torch.Tensor, list)):
+                raise ValueError("Incorrect type of image sizes. "
+                                 f"Got type: {type(images_spatial_crop)}")
+
+            return DeepseekVL2ImagePixelInputs(
+                type="pixel_values",
+                data=self._validate_pixel_values(flatten_bn(pixel_values)),
+                images_spatial_crop=self._validate_images_spatial_crop(
+                    flatten_bn(images_spatial_crop, concat=True)))
+
+        if image_embeds is not None:
+            if not isinstance(image_embeds, torch.Tensor):
+                raise ValueError("Incorrect type of image embeddings. "
+                                 f"Got type: {type(image_embeds)}")
+
+            return DeepseekVL2VImageEmbeddingInputs(
+                type="image_embeds",
+                data=flatten_bn(image_embeds),
+            )
+
+        raise AssertionError("This line should be unreachable.")
+
+    def _pixel_values_to_embedding(
+        self,
+        pixel_values: NestedTensors,
+        images_spatial_crop: torch.Tensor,
+    ) -> NestedTensors:
+        # Pixel_values: n_image * batch_size * [patch_per_img, 3, height, width]
+        total_tiles = [x for x in pixel_values]
+
+        # [batch_all_tiles, 3, height, width]
+        total_tiles = torch.cat(total_tiles, dim=0)
+
+        # [batch_all_tiles, vit_seq_len, c]
+        images_feature = self.vision.forward_features(total_tiles)
+
+        # [batch_all_tiles, hw, D]
+        images_embeds = self.projector(images_feature)
+
+        _, hw, n_dim = images_embeds.shape
+        h = w = int(hw**0.5)
+
+        # 根据self.tile_tag & self.global_view_pos填充image token sequence
+        tile_index = 0
+        vision_embeddings = []
+        for jdx in range(images_spatial_crop.size(0)):
+            # extra global & local features
+            num_width_tiles, num_height_tiles = images_spatial_crop[jdx]
+            if num_width_tiles == 0 or num_height_tiles == 0:
+                break
+            num_tiles_in_image = num_width_tiles * num_height_tiles
+
+            # [hw, D]
+            global_features = images_embeds[tile_index]
+
+            # [num_height_tiles * num_width_tiles, hw, D]
+            local_features = images_embeds[tile_index + 1:tile_index + 1 +
+                                           num_tiles_in_image]
+            tile_index += num_tiles_in_image + 1
+
+            # format global and local features
+            # ----------------- global view add newline -----------------
+            # [hw, D] -> [h, w, D]
+            global_features = global_features.view(h, w, n_dim)
+
+            # [D]     -> [h, 1, D]
+            new_lines_in_global = repeat(self.image_newline, "d -> h 1 d", h=h)
+
+            # cat([h, w, D], [h, 1, D], dim=1) -> [h, w + 1, D]
+            global_features = torch.cat([global_features, new_lines_in_global],
+                                        dim=1)
+
+            # [h, w + 1, D] -> [h * (w + 1), D]
+            global_features = global_features.view(-1, n_dim)
+
+            # ----------------- local view add newline -----------------
+            # [num_height_tiles * num_width_tiles, h * w, D] ->
+            # [num_height_tiles * h, num_width_tiles * w, D]
+            local_features = rearrange(local_features,
+                                       "(th tw) (h w) d -> (th h) (tw w) d",
+                                       th=num_height_tiles,
+                                       tw=num_width_tiles,
+                                       h=h,
+                                       w=w)
+
+            # [D] -> [num_height_tiles * h, 1, D]
+            new_lines_in_local = repeat(self.image_newline,
+                                        "d -> (th h) 1 d",
+                                        th=num_height_tiles,
+                                        h=h)
+
+            # [num_height_tiles * h, num_width_tiles * w + 1, D]
+            local_features = torch.cat([local_features, new_lines_in_local],
+                                       dim=1)
+
+            # [num_height_tiles * h, num_width_tiles * w + 1, D]
+            #   --> [(num_height_tiles * h) * (num_width_tiles * w + 1), D]
+            local_features = local_features.view(-1, n_dim)
+
+            # merge global and local tiles
+            if self.global_view_pos == "head":
+                global_local_features = torch.cat([
+                    global_features,
+                    self.view_seperator[None, :],
+                    local_features,
+                ])
+            else:
+                global_local_features = torch.cat([
+                    local_features,
+                    self.view_seperator[None, :],
+                    global_features,
+                ])
+
+            vision_embeddings.append(global_local_features)
+        return vision_embeddings
+
+    def _process_image_input(
+            self, image_input: DeepseekVL2ImageInputs) -> torch.Tensor:
+        if image_input["type"] == "image_embeds":
+            image_data = image_input["data"]
+            if is_list_of(image_data, torch.Tensor):
+                # it's already a list of tensors
+                return image_data
+            if len(image_data.shape) == 3:
+                # 3D tensor
+                return list(torch.unbind(image_data, dim=0))
+            raise ValueError(
+                "We expect batched 2D tensors;"
+                "this can be either a list of 2D tensors or a single 3D tensor."
+            )
+
+        pixel_values = image_input["data"]
+        images_spatial_crop = image_input["images_spatial_crop"]
+
+        return self._pixel_values_to_embedding(
+            pixel_values=pixel_values, images_spatial_crop=images_spatial_crop)
+
+    def get_multimodal_embeddings(self, **kwargs: object) -> torch.Tensor:
+        image_input = self._parse_and_validate_image_input(**kwargs)
+        if image_input is None:
+            return None
+        vision_embeddings = self._process_image_input(image_input)
+        return vision_embeddings
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[NestedTensors] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        if multimodal_embeddings is not None:
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids, inputs_embeds, multimodal_embeddings,
+                self.image_token_id)
+        return inputs_embeds
+
+    def forward(self,
+                input_ids: torch.Tensor,
+                positions: torch.Tensor,
+                kv_caches: List[torch.Tensor],
+                attn_metadata: AttentionMetadata,
+                intermediate_tensors: Optional[IntermediateTensors] = None,
+                inputs_embeds: Optional[torch.Tensor] = None,
+                **kwargs: object):
+
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        # NOTE: In v1, inputs_embeds is always generated at model runner, this
+        # condition is for v0 compatibility
+        elif inputs_embeds is None:
+            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
+            inputs_embeds = self.get_input_embeddings(input_ids,
+                                                      vision_embeddings)
+            input_ids = None
+
+        hidden_states = self.language_model(input_ids,
+                                            positions,
+                                            kv_caches,
+                                            attn_metadata,
+                                            intermediate_tensors,
+                                            inputs_embeds=inputs_embeds)
+
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        return self.language_model.compute_logits(hidden_states,
+                                                  sampling_metadata)
+
+    def sample(
+        self,
+        logits: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        return self.language_model.sample(logits, sampling_metadata)
+
+    def load_weights(self, weights: Iterable[Tuple[str,
+                                                   torch.Tensor]]) -> Set[str]:
+
+        loader = AutoWeightsLoader(self)
+        autoloaded_weights = loader.load_weights(weights,
+                                                 mapper=self.hf_to_vllm_mapper)
+        return autoloaded_weights

vllm/model_executor/models/minicpmv.py

@@ -657,7 +657,7 @@ class MiniCPMV2_0(MiniCPMVBaseModel):
         quant_config: Optional[QuantizationConfig],
         prefix: str = "",
     ) -> nn.Module:
-        # TODO: refactor this vision model
+        # TODO: refactor vision model through timm wrapper from transformers
         try:
             import timm
         except ImportError:

vllm/model_executor/models/registry.py

@@ -149,6 +149,7 @@ _MULTIMODAL_MODELS = {
     "ChameleonForConditionalGeneration": ("chameleon", "ChameleonForConditionalGeneration"),  # noqa: E501
     "ChatGLMModel": ("chatglm", "ChatGLMForCausalLM"),
     "ChatGLMForConditionalGeneration": ("chatglm", "ChatGLMForCausalLM"),
+    "DeepseekVLV2ForCausalLM": ("deepseek_vl2", "DeepseekVLV2ForCausalLM"),
     "FuyuForCausalLM": ("fuyu", "FuyuForCausalLM"),
     "H2OVLChatModel": ("h2ovl", "H2OVLChatModel"),
     "InternVLChatModel": ("internvl", "InternVLChatModel"),

vllm/transformers_utils/config.py

@@ -23,8 +23,9 @@ from vllm.logger import init_logger
 # yapf conflicts with isort for this block
 # yapf: disable
 from vllm.transformers_utils.configs import (ChatGLMConfig, Cohere2Config,
-                                             DbrxConfig, EAGLEConfig,
-                                             ExaoneConfig, H2OVLChatConfig,
+                                             DbrxConfig, DeepseekVLV2Config,
+                                             EAGLEConfig, ExaoneConfig,
+                                             H2OVLChatConfig,
                                              InternVLChatConfig, JAISConfig,
                                              MedusaConfig, MllamaConfig,
                                              MLPSpeculatorConfig, MPTConfig,
@@ -54,6 +55,7 @@ _CONFIG_REGISTRY: Dict[str, Type[PretrainedConfig]] = {
     "chatglm": ChatGLMConfig,
     "cohere2": Cohere2Config,
     "dbrx": DbrxConfig,
+    "deepseek_vl_v2": DeepseekVLV2Config,
     "mpt": MPTConfig,
     "RefinedWeb": RWConfig,  # For tiiuae/falcon-40b(-instruct)
     "RefinedWebModel": RWConfig,  # For tiiuae/falcon-7b(-instruct)

vllm/transformers_utils/configs/__init__.py

@@ -1,6 +1,7 @@
 from vllm.transformers_utils.configs.chatglm import ChatGLMConfig
 from vllm.transformers_utils.configs.cohere2 import Cohere2Config
 from vllm.transformers_utils.configs.dbrx import DbrxConfig
+from vllm.transformers_utils.configs.deepseek_vl2 import DeepseekVLV2Config
 from vllm.transformers_utils.configs.eagle import EAGLEConfig
 from vllm.transformers_utils.configs.exaone import ExaoneConfig
 # RWConfig is for the original tiiuae/falcon-40b(-instruct) and
@@ -25,6 +26,7 @@ __all__ = [
     "ChatGLMConfig",
     "Cohere2Config",
     "DbrxConfig",
+    "DeepseekVLV2Config",
     "MPTConfig",
     "RWConfig",
     "H2OVLChatConfig",

vllm/transformers_utils/configs/deepseek_vl2.py

@@ -0,0 +1,214 @@
+# adapted from https://github.com/deepseek-ai/DeepSeek-VL2/blob/faf18023f24b962b32d9f0a2d89e402a8d383a78/deepseek_vl2/models/modeling_deepseek_vl_v2.py#L115-L268
+from typing import Tuple
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class VisionEncoderConfig(PretrainedConfig):
+    model_type: str = "vision"
+
+    model_name: str = "vit_so400m_patch14_siglip_384.webli"
+    image_size: int = 384
+    patch_size: int = 16
+    width: int = 1024
+    layers: int = 24
+    heads: int = 16
+    mlp_ratio: int = 4
+    global_pool: str = "map"
+    ignore_head: bool = True
+    class_token: bool = False
+    num_classes: int = 0
+    use_checkpoint: bool = False
+    weight_init: str = "skip"
+    deterministic: bool = False
+    num_recomputing_layers: int = 0
+
+    def __init__(self,
+                 model_name: str = "vit_so400m_patch14_siglip_384.webli",
+                 image_size: int = 384,
+                 patch_size: int = 16,
+                 width: int = 1024,
+                 layers: int = 24,
+                 heads: int = 16,
+                 mlp_ratio: int = 4,
+                 global_pool: str = "map",
+                 ignore_head: bool = True,
+                 class_token: bool = False,
+                 num_classes: int = 0,
+                 use_checkpoint: bool = False,
+                 **kwargs):
+        self.model_name = model_name
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.width = width
+        self.layers = layers
+        self.heads = heads
+        self.mlp_ratio = mlp_ratio
+        self.global_pool = global_pool
+        self.ignore_head = ignore_head
+        self.class_token = class_token
+        self.num_classes = num_classes
+        self.use_checkpoint = use_checkpoint
+
+        super().__init__(**kwargs)
+
+
+class MlpProjectorConfig(PretrainedConfig):
+    model_type = "mlp_projector"
+    projector_type: str = "downsample_mlp_gelu"
+    input_dim: int = 1152
+    n_embed: int = 2048
+    depth: int = 2
+    mlp_ratio: int = 1
+    downsample_ratio: int = 2
+    token_pooling: bool = False
+
+    def __init__(self,
+                 projector_type: str = "downsample_mlp_gelu",
+                 input_dim: int = 1152,
+                 n_embed: int = 2048,
+                 depth: int = 2,
+                 mlp_ratio: int = 1,
+                 downsample_ratio: int = 2,
+                 **kwargs):
+        self.projector_type = projector_type
+        self.input_dim = input_dim
+        self.n_embed = n_embed
+        self.depth = depth
+        self.mlp_ratio = mlp_ratio
+        self.downsample_ratio = downsample_ratio
+
+        super().__init__(**kwargs)
+
+
+class DeepseekV2Config(PretrainedConfig):
+
+    model_type = "deepseek_v2"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=102400,
+        hidden_size=4096,
+        intermediate_size=11008,
+        moe_intermediate_size=1407,
+        num_hidden_layers=30,
+        num_attention_heads=32,
+        num_key_value_heads=32,
+        n_shared_experts=None,
+        n_routed_experts=None,
+        ep_size=1,
+        routed_scaling_factor=1.0,
+        kv_lora_rank=512,
+        q_lora_rank=1536,
+        qk_rope_head_dim=64,
+        v_head_dim=128,
+        qk_nope_head_dim=128,
+        topk_method='gready',
+        n_group=None,
+        topk_group=None,
+        num_experts_per_tok=None,
+        moe_layer_freq=1,
+        first_k_dense_replace=0,
+        norm_topk_prob=False,
+        scoring_func='softmax',
+        aux_loss_alpha=0.001,
+        seq_aux=True,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=100000,
+        eos_token_id=100001,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        use_mla=True,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.moe_intermediate_size = moe_intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.n_shared_experts = n_shared_experts
+        self.n_routed_experts = n_routed_experts
+        self.ep_size = ep_size
+        self.routed_scaling_factor = routed_scaling_factor
+        self.kv_lora_rank = kv_lora_rank
+        self.q_lora_rank = q_lora_rank
+        self.qk_rope_head_dim = qk_rope_head_dim
+        self.v_head_dim = v_head_dim
+        self.qk_nope_head_dim = qk_nope_head_dim
+        self.topk_method = topk_method
+        self.n_group = n_group
+        self.topk_group = topk_group
+        self.num_experts_per_tok = num_experts_per_tok
+        self.moe_layer_freq = moe_layer_freq
+        self.first_k_dense_replace = first_k_dense_replace
+        self.norm_topk_prob = norm_topk_prob
+        self.scoring_func = scoring_func
+        self.aux_loss_alpha = aux_loss_alpha
+        self.seq_aux = seq_aux
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = float(rms_norm_eps)
+        self.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.use_mla = use_mla
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+
+class DeepseekVLV2Config(PretrainedConfig):
+    model_type = "deepseek_vl_v2"
+    vision_config: VisionEncoderConfig
+    projector_config: MlpProjectorConfig
+
+    tile_tag: str = "2D"
+    global_view_pos: str = "head"
+    candidate_resolutions: Tuple[Tuple[int, int]] = ((384, 384), )
+
+    def __init__(self,
+                 tile_tag: str = "tile_tag",
+                 global_view_pos: str = "head",
+                 candidate_resolutions: Tuple[Tuple[int,
+                                                    int]] = ((384, 384), ),
+                 **kwargs):
+        super().__init__(**kwargs)
+
+        vision_config = kwargs.get("vision_config", {})
+        self.vision_config = VisionEncoderConfig(**vision_config)
+
+        projector_config = kwargs.get("projector_config", {})
+        self.projector_config = MlpProjectorConfig(**projector_config)
+
+        language_config = kwargs.get("language_config", {})
+        self.text_config = DeepseekV2Config(**language_config)
+
+        self.tile_tag = tile_tag
+        self.global_view_pos = global_view_pos
+        self.candidate_resolutions = candidate_resolutions
+        self.vocab_size = self.text_config.vocab_size