[MODEL ADDITION] Ovis2 Model Addition (#15826)

Signed-off-by: Marco <121761685+mlinmg@users.noreply.github.com>
Signed-off-by: Isotr0py <2037008807@qq.com>
Signed-off-by: isotr0py <2037008807@qq.com>
Co-authored-by: Isotr0py <2037008807@qq.com>
Co-authored-by: Isotr0py <mozf@mail2.sysu.edu.cn>

docs/source/models/supported_models.md

@@ -1014,6 +1014,13 @@ See [this page](#generative-models) for more information on how to use generativ
   *
   * ✅︎
   * ✅︎
+- * `Ovis2ForConditionalGeneration`<sup>^</sup>
+  * Ovis2
+  * T + I<sup>+</sup>
+  * `AIDC-AI/Ovis2-1B`, `AIDC-AI/Ovis2-2B`, etc.
+  *
+  *
+  * ✅︎
 - * `PaliGemmaForConditionalGeneration`
   * PaliGemma, PaliGemma 2
   * T + I<sup>E</sup>

examples/offline_inference/vision_language.py

@@ -725,6 +725,36 @@ def run_nvlm_d(questions: list[str], modality: str) -> ModelRequestData:
     )
 
 
+# Ovis2
+def run_ovis2(questions: list[str], modality: str) -> ModelRequestData:
+    assert modality == "image"
+
+    model_name = "AIDC-AI/Ovis2-1B"
+    tokenizer = "Isotr0py/Ovis2-tokenizer"
+
+    engine_args = EngineArgs(
+        model=model_name,
+        tokenizer=tokenizer,
+        max_model_len=4096,
+        max_num_seqs=2,
+        trust_remote_code=True,
+        dtype="half",
+        hf_overrides={"architectures": ["Ovis2ForConditionalGeneration"]},
+        limit_mm_per_prompt={"image": 1},
+    )
+
+    placeholder = "<image>\n"
+    prompts = [("<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
+                f"<|im_start|>user\n{placeholder}"
+                f"{question}<|im_end|>\n"
+                "<|im_start|>assistant\n") for question in questions]
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompts=prompts,
+    )
+
+
 # PaliGemma
 def run_paligemma(questions: list[str], modality: str) -> ModelRequestData:
     assert modality == "image"
@@ -1041,6 +1071,7 @@ model_example_map = {
     "llama4": run_llama4,
     "molmo": run_molmo,
     "NVLM_D": run_nvlm_d,
+    "ovis2": run_ovis2,
     "paligemma": run_paligemma,
     "paligemma2": run_paligemma2,
     "phi3_v": run_phi3v,

examples/offline_inference/vision_language_multi_image.py

@@ -436,6 +436,36 @@ def load_nvlm_d(question: str, image_urls: list[str]) -> ModelRequestData:
     )
 
 
+# Ovis2
+def load_ovis2(question: str, image_urls: list[str]) -> ModelRequestData:
+    model_name = "AIDC-AI/Ovis2-1B"
+    tokenizer = "Isotr0py/Ovis2-tokenizer"
+
+    engine_args = EngineArgs(
+        model=model_name,
+        tokenizer=tokenizer,
+        max_model_len=8192,
+        max_num_seqs=2,
+        trust_remote_code=True,
+        dtype="half",
+        limit_mm_per_prompt={"image": len(image_urls)},
+        hf_overrides={"architectures": ["Ovis2ForConditionalGeneration"]},
+    )
+
+    placeholder = '\n'.join(
+        [f'Image {i+1}: <image>' for i in range(len(image_urls))]) + '\n'
+    prompt = ("<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
+              f"<|im_start|>user\n{placeholder}"
+              f"{question}<|im_end|>\n"
+              "<|im_start|>assistant\n")
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompt=prompt,
+        image_data=[fetch_image(url) for url in image_urls],
+    )
+
+
 def load_pixtral_hf(question: str, image_urls: list[str]) -> ModelRequestData:
     model_name = "mistral-community/pixtral-12b"
 
@@ -685,6 +715,7 @@ model_example_map = {
     "mistral3": load_mistral3,
     "mllama": load_mllama,
     "NVLM_D": load_nvlm_d,
+    "ovis2": load_ovis2,
     "phi3_v": load_phi3v,
     "phi4_mm": load_phi4mm,
     "pixtral_hf": load_pixtral_hf,

tests/models/decoder_only/vision_language/test_models.py

@@ -467,6 +467,18 @@ VLM_TEST_SETTINGS = {
         max_num_seqs=2,
         patch_hf_runner=model_utils.molmo_patch_hf_runner,
     ),
+    "ovis2": VLMTestInfo(
+        models=["AIDC-AI/Ovis2-1B"],
+        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
+        prompt_formatter=lambda img_prompt: f"<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n{img_prompt}<|im_end|>\n<|im_start|>assistant\n", # noqa: E501
+        img_idx_to_prompt=lambda idx: "<image>\n", # noqa: E501
+        max_model_len=4096,
+        max_num_seqs=2,
+        dtype="half",
+        # use sdpa mode for hf runner since ovis2 didn't work with flash_attn
+        hf_model_kwargs={"llm_attn_implementation": "sdpa"},
+        patch_hf_runner=model_utils.ovis2_patch_hf_runner,
+    ),
     "phi3v": VLMTestInfo(
         models=["microsoft/Phi-3.5-vision-instruct"],
         test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),

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

@@ -67,7 +67,7 @@ def run_test(
         "disable_mm_preprocessor_cache": True,
     }
     if model_info.tokenizer:
-        vllm_runner_kwargs_["tokenizer"] = model_info.tokenizer
+        vllm_runner_kwargs_["tokenizer_name"] = model_info.tokenizer
     if model_info.tokenizer_mode:
         vllm_runner_kwargs_["tokenizer_mode"] = model_info.tokenizer_mode
     if model_info.hf_overrides:

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

@@ -676,3 +676,33 @@ def molmo_patch_hf_runner(hf_model: HfRunner) -> HfRunner:
     hf_model.model.generate = types.MethodType(_generate, hf_model.model)
 
     return hf_model
+
+
+def ovis2_patch_hf_runner(hf_model: HfRunner) -> HfRunner:
+    """Patches and returns an instance of the HfRunner to use for Ovis2."""
+    hf_model.model.visual_tokenizer.to(hf_model.dtype)
+    hf_model.model.vte.to(hf_model.dtype)
+    hf_model.model.llm.to(hf_model.dtype)
+
+    hf_model.model.get_output_embeddings = lambda: \
+        hf_model.model.llm.get_output_embeddings()
+
+    def processor(*args, text="", images=None, **kwargs):
+        text_tokenizer = hf_model.model.get_text_tokenizer()
+        images = [images] if isinstance(images, Image) else images
+
+        text = text.split("<|im_start|>user\n")[1].split("<|im_end|>\n")[0]
+
+        prompt, input_ids, pixel_values = hf_model.model.preprocess_inputs(
+            text_or_conversations=text, images=images)
+        attention_mask = torch.ne(input_ids, text_tokenizer.pad_token_id)
+
+        inputs = {
+            "inputs": input_ids.unsqueeze(0),
+            "pixel_values": pixel_values.unsqueeze(0),
+            "attention_mask": attention_mask.unsqueeze(0),
+        }
+        return BatchFeature(data=inputs, tensor_type="pt")
+
+    hf_model.processor = processor
+    return hf_model

tests/models/multimodal/processing/test_common.py

@@ -274,6 +274,7 @@ def _test_processing_correctness_mistral(
     "allenai/Molmo-7B-D-0924",
     "allenai/Molmo-7B-O-0924",
     "nvidia/NVLM-D-72B",
+    "AIDC-AI/Ovis2-1B",
     "google/paligemma-3b-mix-224",
     "google/paligemma2-3b-ft-docci-448",
     "microsoft/Phi-4-multimodal-instruct",

tests/models/registry.py

@@ -348,6 +348,10 @@ _MULTIMODAL_EXAMPLE_MODELS = {
                                         max_transformers_version="4.48",
                                         transformers_version_reason="Use of deprecated imports which have been removed.",  # noqa: E501
                                         extras={"phi3.5": "microsoft/Phi-3.5-vision-instruct"}),  # noqa: E501
+    "Ovis2ForConditionalGeneration": _HfExamplesInfo("AIDC-AI/Ovis2-1B",
+                                                    tokenizer="Isotr0py/Ovis2-tokenizer",
+                                                    trust_remote_code=True,
+                                                    hf_overrides={"architectures": ["Ovis2ForConditionalGeneration"]}), # noqa: E501
     "Phi4MMForCausalLM": _HfExamplesInfo("microsoft/Phi-4-multimodal-instruct",
                                         trust_remote_code=True),
     "PixtralForConditionalGeneration": _HfExamplesInfo("mistralai/Pixtral-12B-2409",  # noqa: E501

vllm/entrypoints/chat_utils.py

@@ -496,9 +496,10 @@ 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 ("aya_vision", "chameleon", "deepseek_vl_v2",
-                              "internvl_chat", "skywork_chat", "NVLM_D",
-                              "h2ovl_chat", "idefics3", "smolvlm"):
+                              "internvl_chat", "ovis2", "skywork_chat",
+                              "NVLM_D", "h2ovl_chat", "idefics3", "smolvlm"):
                 return "<image>"
             if model_type in ("mllama", "llama4"):
                 return "<|image|>"

vllm/model_executor/models/aimv2.py

@@ -0,0 +1,322 @@
+# SPDX-License-Identifier: Apache-2.0
+
+# A modified implementation of the AIMv2 Transformer
+# inserted here also the image tokenizer used by Ovis2
+from typing import Optional
+
+import torch
+from torch import nn, softmax
+from torch.nn import functional as F
+from torch.nn.functional import gumbel_softmax, pad
+
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import ReplicatedLinear
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.transformers_utils.configs.ovis2 import (AIMv2Config,
+                                                   Aimv2VisualTokenizerConfig)
+
+IMAGE_INDICATOR_IDS = [-301, -302, -303, -304,
+                       -305]  # kept for vocab prefixed tokens
+
+
+def st_argmax(y_soft: torch.Tensor, dim: int):  # straight-through softmax
+    index = y_soft.max(dim, keepdim=True)[1]
+    y_hard = torch.zeros_like(
+        y_soft, memory_format=torch.legacy_contiguous_format).scatter_(
+            dim, index, 1.0)
+    ret = y_hard - y_soft.detach() + y_soft
+    return ret
+
+
+class Aimv2VisualTokenizer(torch.nn.Module):
+
+    def __init__(self,
+                 config: Aimv2VisualTokenizerConfig,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = "",
+                 **kwargs):
+        super().__init__()
+        self.config = config
+        self.backbone = AIMv2Model(
+            config=config.backbone_config,  # noqa
+            quant_config=quant_config,
+            prefix=f"{prefix}.visual_tokenizer")
+        # reserved tokens for IMAGE_INDICATORS
+        head_dim = config.vocab_size - len(IMAGE_INDICATOR_IDS)
+        self.head = torch.nn.Sequential(
+            ReplicatedLinear(
+                config.backbone_config.hidden_size * config.hidden_stride *
+                config.hidden_stride,
+                head_dim,
+                bias=False,
+            ), torch.nn.LayerNorm(head_dim))
+
+    @property
+    def dtype(self):
+        return self.backbone.dtype
+
+    @property
+    def device(self):
+        return self.backbone.device
+
+    def tokenize(self, logits):
+        if self.config.tokenize_function == 'softmax':
+            tokens = softmax(logits, dim=-1)
+        elif self.config.tokenize_function == 'gumbel_argmax':
+            tokens = gumbel_softmax(logits, tau=self.config.tau, hard=True)
+        elif self.config.tokenize_function == 'st_argmax':
+            tokens = st_argmax(logits, dim=-1)
+        else:
+            raise ValueError(
+                'Invalid `max_type`, expected softmax or gumbel_argmax '
+                f'or st_argmax, but got {self.config.tokenize_function}')
+        return tokens
+
+    def encode(self, pixel_values):
+        features = self.backbone(pixel_values)
+        if self.config.drop_cls_token:
+            features = features[:, 1:, :]
+
+        # merge number of `hidden_stride * hidden_stride` hidden states together
+        # to reduce token sequence length
+        # e.g., for hidden_stride=2, this leads to a token length reduction:
+        # 1024 -> 256 for aimv2
+        if self.config.hidden_stride > 1:
+            # this `d` maybe different from the above `d``
+            n, L, d = features.shape
+            sqrt_l = int(L**0.5)
+            assert sqrt_l**2 == L, (
+                "The token sequence length should be a perfect square.")
+            features = features.reshape(n, sqrt_l, sqrt_l, d)
+            pl = (self.config.hidden_stride -
+                  (sqrt_l %
+                   self.config.hidden_stride)) % self.config.hidden_stride
+            features = pad(features, (0, 0, 0, pl, 0, pl), "constant", 0)
+            sqrt_l += pl
+            features = features.reshape(n, sqrt_l // self.config.hidden_stride,
+                                        self.config.hidden_stride,
+                                        sqrt_l // self.config.hidden_stride,
+                                        self.config.hidden_stride, d)
+            # [n, sqrt_l/hs, sqrt_l/hs, hs, hs, d]
+            features = features.permute(0, 1, 3, 2, 4, 5)
+            # [n, sqrt_l/hs, sqrt_l/hs, hs*hs*d]
+            features = features.flatten(3)
+            # [n, sqrt_l/hs*sqrt_l/hs, hs*hs*d]
+            features = features.reshape(
+                n, -1,
+                self.config.hidden_stride * self.config.hidden_stride * d)
+
+        return features
+
+    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
+        """[BatchSize, ImageShape] -> [BatchSize, Token, VocabSize]"""
+        features = self.encode(pixel_values)
+        logits, _ = self.head[0](
+            features)  # we spllit the sequncial here for not throwing an error
+        logits = self.head[1](logits)
+        tokens = self.tokenize(logits)
+        # tokens' shape is [BatchSize, #Token, VocabSize-5], so padding with
+        # [BatchSize, #Token, 5], after which, tokens' shape should become
+        # [BatchSize, #Token, VocabSize]
+        batch_size, token_len, _ = tokens.shape
+        padding_tensor = torch.zeros(size=(batch_size, token_len,
+                                           len(IMAGE_INDICATOR_IDS)),
+                                     dtype=tokens.dtype,
+                                     device=tokens.device,
+                                     layout=tokens.layout,
+                                     requires_grad=False)
+        tokens = torch.cat((tokens, padding_tensor), dim=2)
+        return tokens
+
+
+class AIMv2SwiGLUFFN(nn.Module):
+
+    def __init__(self, config: AIMv2Config, quant_config: QuantizationConfig,
+                 prefix: str):
+        super().__init__()
+        hidden_features = config.intermediate_size
+        in_features = config.hidden_size
+        bias = config.use_bias
+
+        # TODO(Isotr0py): investigate if we can add TP to visual tokenizer
+        self.fc1 = ReplicatedLinear(in_features,
+                                    hidden_features,
+                                    bias=bias,
+                                    quant_config=quant_config,
+                                    prefix=f"{prefix}.fc1")
+        self.fc2 = ReplicatedLinear(hidden_features,
+                                    in_features,
+                                    bias=bias,
+                                    quant_config=quant_config,
+                                    prefix=f"{prefix}.fc2")
+        self.fc3 = ReplicatedLinear(in_features,
+                                    hidden_features,
+                                    bias=bias,
+                                    quant_config=quant_config,
+                                    prefix=f"{prefix}.fc3")
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x_parallel, _ = self.fc1(x)
+        gate, _ = self.fc3(x)
+        x_parallel = F.silu(x_parallel) * gate
+        out, _ = self.fc2(x_parallel)
+        return out
+
+
+class AIMv2PatchEmbed(nn.Module):
+
+    def __init__(self, config: AIMv2Config):
+        super().__init__()
+        self.proj = nn.Conv2d(
+            config.num_channels,
+            config.hidden_size,
+            kernel_size=(config.patch_size, config.patch_size),
+            stride=(config.patch_size, config.patch_size),
+        )
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.proj(x).flatten(2).transpose(1, 2)
+        x = self.norm.forward_native(x)
+        return x
+
+
+class AIMv2ViTPreprocessor(nn.Module):
+
+    def __init__(self, config: AIMv2Config):
+        super().__init__()
+        num_patches = (config.image_size // config.patch_size)**2
+
+        self.patchifier = AIMv2PatchEmbed(config)
+        self.pos_embed = nn.Parameter(
+            torch.zeros((1, num_patches, config.hidden_size)))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        tokens = self.patchifier(x)
+        _, N, _ = tokens.shape
+        pos_embed = self.pos_embed.to(tokens.device)
+        tokens = tokens + pos_embed[:, :N]
+        return tokens
+
+
+class AIMv2Attention(nn.Module):
+
+    def __init__(self, config: AIMv2Config, quant_config: QuantizationConfig,
+                 prefix: str):
+        super().__init__()
+        dim = config.hidden_size
+
+        # TODO(Isotr0py): investigate if we can add TP to visual tokenizer
+        self.num_heads = config.num_attention_heads
+        self.qkv = ReplicatedLinear(dim, dim * 3, bias=config.qkv_bias)
+        # self.qkv = QKVParallelLinear(
+        #               hidden_size=dim,
+        #               head_size=dim // config.num_attention_heads,
+        #               total_num_heads=config.num_attention_heads,
+        #               bias=config.qkv_bias,
+        #               quant_config=quant_config,
+        #               prefix=f"{prefix}.qkv")
+        self.proj = ReplicatedLinear(dim, dim, bias=config.use_bias)
+        # self.proj = RowParallelLinear(input_size=dim,
+        #                  output_size=dim,
+        #                  bias = config.use_bias,
+        #                  quant_config=quant_config,
+        #                  prefix=f"{prefix}.proj")
+
+    def forward(  # todo might implement multiple attn implementations
+            self,
+            x: torch.Tensor,
+            mask: Optional[torch.Tensor] = None) -> torch.Tensor:
+        B, N, C = x.shape
+        qkv, _ = self.qkv(x)
+
+        qkv = qkv.reshape(B, N, 3, self.num_heads,
+                          C // self.num_heads).permute(2, 0, 3, 1, 4)
+
+        q, k, v = qkv.unbind(0)
+
+        x = F.scaled_dot_product_attention(q, k, v, attn_mask=mask)
+        x = x.transpose(1, 2).contiguous().reshape(B, N, C)
+        x, _ = self.proj(x)
+        return x
+
+
+class AIMv2Block(nn.Module):
+
+    def __init__(self, config: AIMv2Config, quant_config: QuantizationConfig,
+                 prefix: str):
+        super().__init__()
+        self.attn = AIMv2Attention(config,
+                                   quant_config=quant_config,
+                                   prefix=f"{prefix}.attn")
+        self.norm_1 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.mlp = AIMv2SwiGLUFFN(config,
+                                  quant_config=quant_config,
+                                  prefix=f"{prefix}.mlp")
+        self.norm_2 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(self,
+                x: torch.Tensor,
+                mask: Optional[torch.Tensor] = None) -> torch.Tensor:
+        x = x + self.attn(self.norm_1.forward_native(x), mask)
+        x = x + self.mlp(self.norm_2.forward_native(x))
+        return x
+
+
+class AIMv2Transformer(nn.Module):
+
+    def __init__(self, config: AIMv2Config, quant_config: QuantizationConfig,
+                 prefix: str):
+        super().__init__()
+
+        self.blocks = nn.ModuleList([
+            AIMv2Block(config, quant_config, prefix=f"{prefix}.blocks.{i}")
+            for i in range(config.num_hidden_layers)
+        ])
+        self.post_trunk_norm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        tokens: torch.Tensor,
+        mask: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        # they take the -1 as the ref embeddings, like a clip skip
+        for block in self.blocks:
+            tokens = block(tokens, mask)
+        # NO NORM IN THE OG IMPLEMENTATION
+        # tokens = self.post_trunk_norm(tokens)
+        return tokens
+
+
+class AIMv2Model(torch.nn.Module):
+
+    def __init__(self,
+                 config: AIMv2Config,
+                 quant_config: QuantizationConfig,
+                 prefix: str = ""):
+        super().__init__()
+        self.preprocessor = AIMv2ViTPreprocessor(config)
+        self.trunk = AIMv2Transformer(config,
+                                      quant_config=quant_config,
+                                      prefix=f"{prefix}.trunk")
+
+    @property
+    def dtype(self):
+        return self.trunk.blocks[0].attn.qkv.weight.dtype
+
+    @property
+    def device(self):
+        return self.trunk.blocks[0].attn.qkv.device
+
+    def forward(
+        self,
+        pixel_values: torch.Tensor,
+        mask: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+
+        x = self.preprocessor(pixel_values)
+        x = self.trunk(x, mask)
+
+        return x

vllm/model_executor/models/ovis2.py

@@ -0,0 +1,331 @@
+# SPDX-License-Identifier: Apache-2.0
+
+# adapted from https://github.com/huggingface/transformers/blob/v4.39.3/src/transformers/models/ovis/modeling_ovis.py
+# Copyright 2023 The vLLM team.
+# Copyright 2023 HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch Ovis2 model."""
+from typing import (Iterable, List, Literal, Mapping, Optional, Set, Tuple,
+                    TypedDict, Union)
+
+import torch
+import torch.nn as nn
+from torch import Tensor
+from transformers import BatchFeature
+
+from vllm.config import VllmConfig
+from vllm.model_executor.models.aimv2 import Aimv2VisualTokenizer
+from vllm.model_executor.models.utils import (AutoWeightsLoader, flatten_bn,
+                                              init_vllm_registered_model,
+                                              maybe_prefix)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
+                                    MultiModalKwargs)
+from vllm.multimodal.parse import ImageSize, MultiModalDataItems
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo, PromptReplacement)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.sequence import IntermediateTensors
+from vllm.transformers_utils.configs.ovis2 import OvisConfig
+from vllm.transformers_utils.processors.ovis2 import OvisProcessor
+
+from .interfaces import MultiModalEmbeddings, SupportsMultiModal
+from .utils import merge_multimodal_embeddings
+
+# Cannot find the following number from hf config.
+IMAGE_TOKEN = "<image>"
+IMAGE_ATOM_TOKEN_ID = 151666
+IMAGE_PAD_TOKEN_ID = 151672
+NUMBER_OF_TOKEN_TO_RESERVE_FOR_SEGMENT = 256
+
+
+class Ovis2ImagePatchInputs(TypedDict):
+    type: Literal["image_patches"]
+    flat_data: torch.Tensor
+    """
+    Shape: 
+    `(batch_size * num_patches, patch_size_x * patch_size_y * num_channels)`
+    """
+
+    patches_per_image: List[int]
+    """
+    List of number of total patches for each image in the batch.
+    This is used to restore the first two dimensions of `flat_data`.
+    """
+
+
+class VisualEmbedding(torch.nn.Embedding):
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def forward(self, visual_tokens: Tensor) -> Tensor:
+        if visual_tokens.dtype in [
+                torch.int8, torch.int16, torch.int32, torch.int64, torch.long
+        ]:
+            return super().forward(visual_tokens)
+        return torch.matmul(visual_tokens, self.weight)
+
+    @property
+    def device(self):
+        return self.weight.device
+
+    @property
+    def dtype(self):
+        return self.weight.dtype
+
+
+class Ovis2ProcessingInfo(BaseProcessingInfo):
+
+    def get_hf_config(self):
+        return self.ctx.get_hf_config(OvisConfig)
+
+    def get_hf_processor(self, **kwargs):
+        return self.ctx.get_hf_processor(OvisProcessor)
+
+    def get_image_processor(self) -> OvisProcessor:
+        return self.get_hf_processor().image_processor  # type: ignore
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {  # 32k is model token limit at the moment
+            "image":
+            self.get_hf_config().multimodal_max_length //
+            ((9 + 1) * NUMBER_OF_TOKEN_TO_RESERVE_FOR_SEGMENT)
+        }
+
+    def get_image_size_with_most_features(self) -> ImageSize:
+        image_processor = self.get_image_processor()
+        return ImageSize(width=image_processor.size['shortest_edge'] * 9 * 2,
+                         height=image_processor.size['shortest_edge'] * 9 * 2)
+
+
+class Ovis2DummyInputsBuilder(BaseDummyInputsBuilder[Ovis2ProcessingInfo]):
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_images = mm_counts.get("image", 0)
+        return IMAGE_TOKEN * num_images
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        num_images = mm_counts.get("image", 0)
+
+        target_width, target_height = \
+            self.info.get_image_size_with_most_features()
+
+        mm_data = {
+            "image":
+            self._get_dummy_images(width=target_width,
+                                   height=target_height,
+                                   num_images=num_images),
+        }
+        return mm_data
+
+
+class Ovis2MultiModalProcessor(BaseMultiModalProcessor[Ovis2ProcessingInfo]):
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        if not mm_data:
+            #    # Avoid warning from HF logger for text-only input
+            prompt_ids = self.info.get_tokenizer().encode(prompt)
+            # prompt_ids = self._apply_hf_processor_tokens_only(prompt_ids) nope
+            return BatchFeature(dict(input_ids=[prompt_ids]), tensor_type="pt")
+
+        processed_outputs = super()._call_hf_processor(
+            prompt=prompt,
+            mm_data=mm_data,
+            mm_kwargs=mm_kwargs,
+        )
+
+        return processed_outputs
+
+    def _apply_hf_processor_tokens_only(
+        self,
+        prompt_tokens: list[int],
+    ) -> list[int]:
+
+        return prompt_tokens
+
+    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"),
+                    grids=MultiModalFieldConfig.batched("image"))
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> list[PromptReplacement]:
+
+        def get_replacement_ovis(item_idx):
+            grid = out_mm_kwargs["grids"][item_idx]
+
+            hf_processor = self.info.get_hf_processor()
+            return hf_processor.construct_image_placeholders(grid)
+
+        return [
+            PromptReplacement(
+                modality="image",
+                target=IMAGE_TOKEN,
+                replacement=get_replacement_ovis,
+            ),
+        ]
+
+
+@MULTIMODAL_REGISTRY.register_processor(Ovis2MultiModalProcessor,
+                                        info=Ovis2ProcessingInfo,
+                                        dummy_inputs=Ovis2DummyInputsBuilder)
+class Ovis2ForConditionalGeneration(nn.Module, SupportsMultiModal):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+
+        self.config: OvisConfig = config
+        self.llm = init_vllm_registered_model(
+            vllm_config=vllm_config.with_hf_config(config.get_text_config()),
+            prefix=maybe_prefix(prefix, "llm"),
+        )
+
+        self.visual_tokenizer = Aimv2VisualTokenizer(
+            config=config.visual_tokenizer_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.visual_tokenizer",
+            image_processor_name_or_path=config.visual_tokenizer_config.
+            backbone_config.name_or_path,
+        )
+
+        self.vte = VisualEmbedding(
+            self.config.visual_tokenizer_config.vocab_size,
+            self.config.hidden_size)
+
+        # TODO(Isotr0py): PP support
+        # self.make_empty_intermediate_tensors = (
+        #    self.language_model.make_empty_intermediate_tensors)
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[Ovis2ImagePatchInputs]:
+        pixel_values = kwargs.pop("pixel_values", None)
+        if pixel_values 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)}")
+
+            return Ovis2ImagePatchInputs(
+                type="image_patches",
+                flat_data=flatten_bn(flatten_bn(pixel_values), concat=True),
+                patches_per_image=[
+                    x.shape[0] for x in flatten_bn(pixel_values)
+                ],
+            )
+
+        raise AssertionError("This line should be unreachable.")
+
+    def _process_image_input(
+            self, image_input: Ovis2ImagePatchInputs) -> MultiModalEmbeddings:
+        image_patches_flat = image_input["flat_data"]
+        patches_per_image = image_input["patches_per_image"]
+
+        target_dtype = self.visual_tokenizer.dtype
+        visual_tokens = self.visual_tokenizer(
+            image_patches_flat.to(target_dtype))
+        visual_embeds = self.vte(visual_tokens)  # 1:1 numeric eq.
+
+        return tuple(
+            x.flatten(0, 1)
+            for x in visual_embeds.split(patches_per_image, dim=0))
+
+    def get_multimodal_embeddings(
+            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+        image_input = self._parse_and_validate_image_input(**kwargs)
+        if image_input is None:
+            return None
+
+        image_features = self._process_image_input(image_input)
+
+        return image_features
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.llm.get_input_embeddings(input_ids)
+        if multimodal_embeddings is not None:
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids, inputs_embeds, multimodal_embeddings,
+                [IMAGE_ATOM_TOKEN_ID, IMAGE_PAD_TOKEN_ID])
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: object,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        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
+
+        # up until here we have a inputs_embeds 100% numerical identity
+        # between the OG HF Transformers implementation and ours
+        hidden_states = self.llm(
+            input_ids=input_ids,
+            positions=positions,
+            intermediate_tensors=intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+        )
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.llm.logits_processor(self.llm.lm_head, hidden_states,
+                                           sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[Tuple[str,
+                                                   torch.Tensor]]) -> Set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.llm

vllm/model_executor/models/registry.py

@@ -195,6 +195,7 @@ _MULTIMODAL_MODELS = {
     "Mistral3ForConditionalGeneration": ("mistral3", "Mistral3ForConditionalGeneration"),  # noqa: E501
     "MolmoForCausalLM": ("molmo", "MolmoForCausalLM"),
     "NVLM_D": ("nvlm_d", "NVLM_D_Model"),
+    "Ovis2ForConditionalGeneration": ("ovis2", "Ovis2ForConditionalGeneration"),
     "PaliGemmaForConditionalGeneration": ("paligemma", "PaliGemmaForConditionalGeneration"),  # noqa: E501
     "Phi3VForCausalLM": ("phi3v", "Phi3VForCausalLM"),
     "PixtralForConditionalGeneration": ("pixtral", "PixtralForConditionalGeneration"),  # noqa: E501

vllm/transformers_utils/config.py

@@ -38,9 +38,9 @@ from vllm.transformers_utils.configs import (ChatGLMConfig, Cohere2Config,
                                              MiniMaxVL01Config, MllamaConfig,
                                              MLPSpeculatorConfig, MPTConfig,
                                              NemotronConfig, NVLM_D_Config,
-                                             RWConfig, SkyworkR1VChatConfig,
-                                             SolarConfig, Telechat2Config,
-                                             UltravoxConfig)
+                                             OvisConfig, RWConfig,
+                                             SkyworkR1VChatConfig, SolarConfig,
+                                             Telechat2Config, UltravoxConfig)
 # yapf: enable
 from vllm.transformers_utils.utils import check_gguf_file
 from vllm.utils import resolve_obj_by_qualname
@@ -79,6 +79,7 @@ _CONFIG_REGISTRY: Dict[str, Type[PretrainedConfig]] = {
     "minimax_vl_01": MiniMaxVL01Config,
     "nemotron": NemotronConfig,
     "NVLM_D": NVLM_D_Config,
+    "ovis": OvisConfig,
     "solar": SolarConfig,
     "skywork_chat": SkyworkR1VChatConfig,
     "telechat": Telechat2Config,

vllm/transformers_utils/configs/__init__.py

@@ -23,6 +23,7 @@ from vllm.transformers_utils.configs.moonvit import MoonViTConfig
 from vllm.transformers_utils.configs.mpt import MPTConfig
 from vllm.transformers_utils.configs.nemotron import NemotronConfig
 from vllm.transformers_utils.configs.nvlm_d import NVLM_D_Config
+from vllm.transformers_utils.configs.ovis2 import OvisConfig
 from vllm.transformers_utils.configs.skyworkr1v import SkyworkR1VChatConfig
 from vllm.transformers_utils.configs.solar import SolarConfig
 from vllm.transformers_utils.configs.telechat2 import Telechat2Config
@@ -49,6 +50,7 @@ __all__ = [
     "KimiVLConfig",
     "NemotronConfig",
     "NVLM_D_Config",
+    "OvisConfig",
     "SkyworkR1VChatConfig",
     "SolarConfig",
     "Telechat2Config",

vllm/transformers_utils/configs/ovis2.py

@@ -0,0 +1,170 @@
+# SPDX-License-Identifier: Apache-2.0
+
+# yapf: disable
+# ruff: noqa: E501
+# copied from https://huggingface.co/AIDC-AI/Ovis2-1B/blob/main/configuration_aimv2.py
+# and https://huggingface.co/AIDC-AI/Ovis2-1B/blob/main/configuration_ovis.py
+from typing import Any, Optional, Union
+
+from transformers import AutoConfig, PretrainedConfig
+
+
+class AIMv2Config(PretrainedConfig):
+    """This is the configuration class to store the configuration of an [`AIMv2Model`].
+
+    Instantiating a configuration with the defaults will yield a similar configuration
+    to that of the [apple/aimv2-large-patch14-224](https://huggingface.co/apple/aimv2-large-patch14-224).
+
+    Args:
+        hidden_size: Dimension of the hidden representations.
+        intermediate_size: Dimension of the SwiGLU representations.
+        num_hidden_layers: Number of hidden layers in the Transformer.
+        num_attention_heads: Number of attention heads for each attention layer
+            in the Transformer.
+        num_channels: Number of input channels.
+        image_size: Image size.
+        patch_size: Patch size.
+        rms_norm_eps: Epsilon value used for the RMS normalization layer.
+        attention_dropout: Dropout ratio for attention probabilities.
+        projection_dropout: Dropout ratio for the projection layer after the attention.
+        qkv_bias: Whether to add a bias to the queries, keys and values.
+        use_bias: Whether to add a bias in the feed-forward and projection layers.
+        kwargs: Keyword arguments for the [`PretrainedConfig`].
+    """
+
+    model_type: str = "aimv2"
+
+    def __init__(
+        self,
+        hidden_size: int = 1024,
+        intermediate_size: int = 2816,
+        num_hidden_layers: int = 24,
+        num_attention_heads: int = 8,
+        num_channels: int = 3,
+        image_size: int = 224,
+        patch_size: int = 14,
+        rms_norm_eps: float = 1e-5,
+        attention_dropout: float = 0.0,
+        projection_dropout: float = 0.0,
+        qkv_bias: bool = False,
+        use_bias: bool = False,
+        **kwargs: Any,
+    ):
+        super().__init__(**kwargs)
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_channels = num_channels
+        self.patch_size = patch_size
+        self.image_size = image_size
+        self.attention_dropout = attention_dropout
+        self.rms_norm_eps = rms_norm_eps
+
+        self.projection_dropout = projection_dropout
+        self.qkv_bias = qkv_bias
+        self.use_bias = use_bias
+
+
+IGNORE_ID = -100
+IMAGE_TOKEN_ID = -200
+IMAGE_TOKEN = "<image>"
+IMAGE_ATOM_ID = -300
+IMAGE_INDICATOR_IDS = [-301, -302, -303, -304, -305]
+
+AutoConfig.register("aimv2", AIMv2Config)
+
+
+# ----------------------------------------------------------------------
+#                     Visual Tokenizer Configuration
+# ----------------------------------------------------------------------
+class BaseVisualTokenizerConfig(PretrainedConfig):
+
+    def __init__(self,
+                 vocab_size=16384,
+                 tokenize_function="softmax",
+                 tau=1.0,
+                 depths=None,
+                 drop_cls_token=False,
+                 backbone_config: Optional[Union[PretrainedConfig,
+                                                 dict]] = None,
+                 hidden_stride: int = 1,
+                 **kwargs):
+        super().__init__(**kwargs)
+        self.vocab_size = vocab_size
+        self.tokenize_function = tokenize_function
+        self.tau = tau
+        if isinstance(depths, str):
+            depths = [int(x) for x in depths.split('|')]
+        self.depths = depths
+        self.backbone_kwargs = dict[str, Any]()
+        self.drop_cls_token = drop_cls_token
+        if backbone_config is not None:
+            assert isinstance(backbone_config, (PretrainedConfig, dict)), \
+                f"expect `backbone_config` to be instance of PretrainedConfig or dict, but got {type(backbone_config)} type"
+            if not isinstance(backbone_config, PretrainedConfig):
+                model_type = backbone_config['model_type']
+                backbone_config.pop('model_type')
+                backbone_config = AutoConfig.for_model(model_type,
+                                                       **backbone_config)
+        self.backbone_config = backbone_config
+        self.hidden_stride = hidden_stride
+
+
+class Aimv2VisualTokenizerConfig(BaseVisualTokenizerConfig):
+    model_type = "aimv2_visual_tokenizer"
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        if self.drop_cls_token:
+            self.drop_cls_token = False
+        if self.depths:
+            assert len(self.depths) == 1
+            self.backbone_kwargs['num_hidden_layers'] = self.depths[0]
+
+
+AutoConfig.register("aimv2_visual_tokenizer", Aimv2VisualTokenizerConfig)
+
+
+# ----------------------------------------------------------------------
+#                           Ovis Configuration
+# ----------------------------------------------------------------------
+class OvisConfig(PretrainedConfig):
+    model_type = "ovis"
+
+    def __init__(self,
+                 llm_config: Optional[Union[PretrainedConfig, dict]] = None,
+                 visual_tokenizer_config: Optional[Union[PretrainedConfig,
+                                                         dict]] = None,
+                 multimodal_max_length=8192,
+                 hidden_size=None,
+                 conversation_formatter_class=None,
+                 llm_attn_implementation=None,
+                 disable_tie_weight=False,
+                 **kwargs):
+        super().__init__(**kwargs)
+        if llm_config is not None:
+            assert isinstance(llm_config, (PretrainedConfig, dict)), \
+                f"expect `llm_config` to be instance of PretrainedConfig or dict, but got {type(llm_config)} type"
+            if not isinstance(llm_config, PretrainedConfig):
+                model_type = llm_config['model_type']
+                llm_config.pop('model_type')
+                llm_config = AutoConfig.for_model(model_type, **llm_config)
+
+        # map llm_config to text_config
+        self.text_config = llm_config
+        if visual_tokenizer_config is not None:
+            assert isinstance(visual_tokenizer_config, (PretrainedConfig, dict)), \
+                f"expect `visual_tokenizer_config` to be instance of PretrainedConfig or dict, but got {type(visual_tokenizer_config)} type"
+            if not isinstance(visual_tokenizer_config, PretrainedConfig):
+                model_type = visual_tokenizer_config['model_type']
+                visual_tokenizer_config.pop('model_type')
+                visual_tokenizer_config = AutoConfig.for_model(
+                    model_type, **visual_tokenizer_config)
+
+        self.visual_tokenizer_config = visual_tokenizer_config
+        self.multimodal_max_length = multimodal_max_length
+        self.hidden_size = hidden_size
+        self.conversation_formatter_class = conversation_formatter_class
+        self.llm_attn_implementation = llm_attn_implementation
+        self.disable_tie_weight = disable_tie_weight

vllm/transformers_utils/processors/__init__.py

@@ -2,5 +2,6 @@
 
 from vllm.transformers_utils.processors.deepseek_vl2 import (
     DeepseekVLV2Processor)
+from vllm.transformers_utils.processors.ovis2 import OvisProcessor
 
-__all__ = ["DeepseekVLV2Processor"]
+__all__ = ["DeepseekVLV2Processor", "OvisProcessor"]

vllm/transformers_utils/processors/ovis2.py

@@ -0,0 +1,397 @@
+# SPDX-License-Identifier: Apache-2.0
+
+# yapf: disable
+# ruff: noqa: E501
+# coding=utf-8
+# adapted from https://github.com/AIDC-AI/Ovis/blob/35ab51a1a1e3542fa6db260a1084cefbc8f164bb/ovis/vllm/processing_ovis.py
+# Copyright 2025 The Qwen Team and The HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import List, Union
+
+import PIL
+import torch
+from transformers import AutoProcessor, BatchFeature
+from transformers.image_utils import ImageInput
+from transformers.processing_utils import (ProcessingKwargs, ProcessorMixin,
+                                           Unpack)
+from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
+
+__all__ = [ 'OvisProcessor']
+IGNORE_ID = -100
+
+class OvisProcessorKwargs(ProcessingKwargs, total=False):   # type: ignore[call-arg]
+    _defaults = {
+        "text_kwargs": {
+            "padding": False,
+        },
+        "images_kwargs": {
+            'max_partition':9,
+            'covering_threshold':0.9,
+            'convert_to_rgb':True,
+        'return_tensors':'pt'},
+    }
+
+
+
+class OvisProcessor(ProcessorMixin):
+    r"""
+    Constructs a Ovis processor which wraps a Ovis image processor and a Qwen2 tokenizer into a single processor.
+    [`OvisProcessor`] offers all the functionalities of [`Qwen2VLImageProcessor`] and [`Qwen2TokenizerFast`]. See the
+    [`~OvisProcessor.__call__`] and [`~OvisProcessor.decode`] for more information.
+    Args:
+        image_processor ([`Qwen2VLImageProcessor`], *optional*):
+            The image processor is a required input.
+        tokenizer ([`Qwen2TokenizerFast`], *optional*):
+            The tokenizer is a required input.
+        chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
+            in a chat into a tokenizable string.
+    """
+
+    attributes = ["image_processor", "tokenizer"]
+    valid_kwargs = ["chat_template"]
+
+    image_processor_class = "AutoImageProcessor"
+    tokenizer_class = "Qwen2Tokenizer"
+
+    def __init__(self, image_processor=None, tokenizer=None, chat_template=None, **kwargs):
+        self.image_token = "<|image_pad|>" if not hasattr(tokenizer, "image_token") else tokenizer.image_token
+        self.video_token = "<|video_pad|>" if not hasattr(tokenizer, "video_token") else tokenizer.video_token
+        super().__init__(image_processor, tokenizer, chat_template=chat_template)
+
+        self.extra_special_tokens = {
+            "image_token": "<image>",
+            "image_atom": "<image_atom>",
+            "image_start": "<img>",
+            "image_prefix": "<pre>",
+            "image_col_sep": "<col>",
+            "image_row_sep": "<row>",
+            "image_end": "</img>",
+            'image_pad': '<image_pad>',
+        }
+
+    def __call__(
+        self,
+        images: ImageInput = None,
+        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
+        **kwargs: Unpack[OvisProcessorKwargs],
+    ) -> BatchFeature:
+        """
+        Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
+        and `kwargs` arguments to Qwen2TokenizerFast's [`~Qwen2TokenizerFast.__call__`] if `text` is not `None` to encode
+        the text. To prepare the vision inputs, this method forwards the `vision_infos` and `kwrags` arguments to
+        Qwen2VLImageProcessor's [`~Qwen2VLImageProcessor.__call__`] if `vision_infos` is not `None`.
+            Args:
+                images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
+                    The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+                    tensor. Both channels-first and channels-last formats are supported.
+                text (`str`, `List[str]`, `List[List[str]]`):
+                    The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+                    (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+                    `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
+                videos (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`):
+                    The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch
+                    tensor, or a nested list of 3D frames. Both channels-first and channels-last formats are supported.
+                return_tensors (`str` or [`~utils.TensorType`], *optional*):
+                    If set, will return tensors of a particular framework. Acceptable values are:
+                    - `'tf'`: Return TensorFlow `tf.constant` objects.
+                    - `'pt'`: Return PyTorch `torch.Tensor` objects.
+                    - `'np'`: Return NumPy `np.ndarray` objects.
+                    - `'jax'`: Return JAX `jnp.ndarray` objects.
+            Returns:
+                [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+                - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+                - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+                  `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
+                  `None`).
+                - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+                - **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`.
+                - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`.
+                - **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`.
+                - **second_per_grid_ts** -- List of video seconds per time grid. Returned when `videos` is not `None`.
+        """
+        output_kwargs = self._merge_kwargs(
+            OvisProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+
+        # Process all images first
+        image_features = {}
+        if images is not None:
+            processed_images = []
+            image_placeholders_list = []
+            grids = []
+
+            # Process each image
+            for image in images if isinstance(images, list) else [images]:
+                pixel_values, image_placeholders, grid = self.preprocess_image(
+                    image=image, **output_kwargs["images_kwargs"]
+                )
+                processed_images.append(pixel_values)
+                image_placeholders_list.append(image_placeholders)
+                grids.append(grid)
+
+            # assign all processed images
+            if processed_images:
+                image_features["image_placeholders"] = image_placeholders_list
+
+        # Process text input
+        if text is not None:
+
+            if not isinstance(text, list):
+                text = [text]
+
+            tokenized_batched_text = self.tokenizer.batch_encode_plus(
+                text,
+                **output_kwargs["text_kwargs"]
+            )
+            image_token_id = self.get_token_value("image_token")
+            replaced_ids_list = []
+            replaced_attn_mask_list = []
+            idx = 0
+            for ids_tensor, attn_mask in zip(tokenized_batched_text['input_ids'],
+                                             tokenized_batched_text['attention_mask']):
+                if image_token_id in ids_tensor and "image_placeholders" in image_features:
+                    if idx < len(image_features["image_placeholders"]):
+                        # Converts in list for ease of use
+                        ids_list = ids_tensor.tolist()
+                        attn_list = attn_mask.tolist()
+
+                        new_ids = []
+                        new_attn = []
+
+                        # replace placeholders
+                        for i, token_id in enumerate(ids_list):
+                            if token_id == image_token_id:
+                                placeholder_ids = image_features["image_placeholders"][idx]
+                                new_ids.extend(placeholder_ids)
+                                new_attn.extend([1] * len(placeholder_ids))
+                                idx += 1
+                            else:
+                                new_ids.append(token_id)
+                                new_attn.append(attn_list[i])
+
+                        # Converts back to tensors
+                        ids_tensor = torch.tensor(new_ids, dtype=torch.long)
+                        attn_mask = torch.tensor(new_attn, dtype=torch.long)
+                    else:
+                        raise RuntimeError(
+                            'Mismatch between the images you provided and the number of placeholder present in the text')
+
+                replaced_ids_list.append(ids_tensor)
+                replaced_attn_mask_list.append(attn_mask)
+
+            if replaced_ids_list:
+                replaced_and_tokenized_ids = torch.stack(replaced_ids_list)
+                replaced_and_tokenized_attn_mask = torch.stack(replaced_attn_mask_list)
+            else:
+                replaced_and_tokenized_ids = torch.tensor([], dtype=torch.long)
+                replaced_and_tokenized_attn_mask = torch.tensor([], dtype=torch.long)
+
+            # Create the output with text features
+            output = BatchFeature(
+                data={
+                    "input_ids": replaced_and_tokenized_ids,
+                    "attention_mask": replaced_and_tokenized_attn_mask,
+                }
+            )
+
+            # Add image features if present
+            if image_features:
+                output["pixel_values"] = processed_images
+                output['grids'] = grids
+
+            return output
+
+
+        # If only images were provided
+        return BatchFeature(data=image_features)
+
+
+    def get_image_size(self):
+        height = self.image_processor.crop_size["height"]
+        width = self.image_processor.crop_size["width"]
+        return height, width
+
+    def get_token_value(self, tok):
+        return self.tokenizer.get_vocab()[self.extra_special_tokens[tok]]
+
+    def construct_image_placeholders(self, grid):
+
+        image_placeholders = [self.get_token_value('image_start'),
+                              self.get_token_value('image_atom'),
+                              self.get_token_value('image_prefix')]
+        if grid[0] * grid[1] > 1:
+            for r in range(grid[0]):
+                for c in range(grid[1]):
+                    image_placeholders.append(self.get_token_value('image_atom') )
+                    if c < grid[1] - 1:
+                        image_placeholders.append(self.get_token_value('image_col_sep'))
+                if r < grid[0] - 1:
+                    image_placeholders.append(self.get_token_value('image_row_sep'))
+        image_placeholders.append(self.get_token_value('image_end'))
+        # return image_placeholders
+
+        image_atom_token_id = self.get_token_value('image_atom')
+        # Extract the padding token ID from tokenizer
+        image_padding_token_id = self.get_token_value('image_pad')
+
+        # Create a new list with padding tokens inserted
+        padded_placeholder_tokens = []
+        for token in image_placeholders:
+            padded_placeholder_tokens.append(token)
+            if token == image_atom_token_id:
+                # Add 255 padding tokens after each image atom token
+                padded_placeholder_tokens.extend([image_padding_token_id] * 255)
+        return padded_placeholder_tokens
+
+    def preprocess_image(self, image: PIL.Image.Image, max_partition, covering_threshold, convert_to_rgb, return_tensors):
+        def _preprocess(img: PIL.Image.Image, side):
+            # first resize and preprocess
+            w, h = img.size
+            if w == h:
+                new_width = new_height = side
+            elif w > h:
+                new_width = side
+                new_height = int(h / w * new_width)
+            else:
+                new_height = side
+                new_width = int(w / h * new_height)
+            new_size = dict(height=new_height, width=new_width)
+            pixel_values = self.image_processor.preprocess(img, size=new_size, return_tensors=return_tensors)['pixel_values']
+
+            # then pad to square
+            square_values = torch.zeros([1, 3, side, side], dtype=pixel_values.dtype, device=pixel_values.device)
+            new_height, new_width = pixel_values.shape[2:]
+            if new_height == new_width:
+                square_values[:, :, :, :] = pixel_values
+            elif new_height > new_width:
+                from_index = (side - new_width) // 2
+                square_values[:, :, :, from_index:from_index + new_width] = pixel_values
+            else:
+                from_index = (side - new_height) // 2
+                square_values[:, :, from_index:from_index + new_height, :] = pixel_values
+
+            return square_values
+
+        def _partition(img, grid) -> list[tuple[int, int, int, int]]:
+            w, h = img.size
+            row_height = h // grid[0]
+            col_width = w // grid[1]
+
+            partition = []
+            for row in range(grid[0]):
+                for col in range(grid[1]):
+                    left = col * col_width
+                    upper = row * row_height
+                    right = w if col == grid[1] - 1 else (col + 1) * col_width
+                    lower = h if row == grid[0] - 1 else (row + 1) * row_height
+                    partition.append((left, upper, right, lower))
+
+            return partition
+
+        def _covering_area(left, upper, right, lower, side):
+            w = right - left
+            h = lower - upper
+            w, h = max(w, h), min(w, h)
+            if w > side:
+                h = h / w * side
+                w = side
+            return w * h
+
+        def _get_best_grid(img, side):
+            img_area = img.size[0] * img.size[1]
+
+            candidate_grids = []
+            for i in range(1, max_partition + 1):
+                for j in range(1, max_partition + 1):
+                    if i * j <= max_partition:
+                        candidate_grids.append((i, j))
+
+            all_grids = []
+            good_grids = []
+            for grid in candidate_grids:
+                partition = _partition(img, grid)
+                covering_ratio = sum([_covering_area(*p, side) for p in partition]) / img_area
+                assert covering_ratio <= 1.0
+                all_grids.append((grid, covering_ratio))
+                if covering_ratio > covering_threshold:
+                    good_grids.append((grid, covering_ratio))
+
+            if len(good_grids) > 0:
+                # pick the good partition with minimum #sub_images and break the tie using covering_ratio
+                return sorted(good_grids, key=lambda x: (x[0][0] * x[0][1], -x[1]))[0][0]
+            else:
+                # pick the partition with maximum covering_ratio and break the tie using #sub_images
+                return sorted(all_grids, key=lambda x: (-x[1], x[0][0] * x[0][1]))[0][0]
+
+        if convert_to_rgb and image.mode != 'RGB':
+            image = image.convert('RGB')
+
+
+        sides = self.get_image_size()
+        if sides[0] != sides[1]:
+            raise ValueError('get_image_size() returns non-square size')
+        side = sides[0]
+        grid = _get_best_grid(image, side)
+        partition = _partition(image, grid)
+        crops = [image.crop(p) for p in partition]
+        if len(crops) > 1:
+            crops.insert(0, image)
+        pixel_values = torch.cat([_preprocess(crop, side) for crop in crops], dim=0)
+        image_placeholders = self.construct_image_placeholders(grid)
+        return pixel_values, image_placeholders, grid
+
+    def batch_decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
+        refer to the docstring of this method for more information.
+        """
+        return self.tokenizer.batch_decode(*args, **kwargs)
+
+    def decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
+        the docstring of this method for more information.
+        """
+        return self.tokenizer.decode(*args, **kwargs)
+
+    def post_process_image_text_to_text(self, generated_outputs):
+        """
+        Post-process the output of the model to decode the text.
+        Args:
+            generated_outputs (`torch.Tensor` or `np.ndarray`):
+                The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
+                or `(sequence_length,)`.
+        Returns:
+            `List[str]`: The decoded text.
+        """
+        return self.tokenizer.batch_decode(
+            generated_outputs, skip_special_tokens=True, clean_up_tokenization_spaces=False
+        )
+
+    @property
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names
+        image_processor_input_names = self.image_processor.model_input_names
+        names_from_processor = list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
+        return names_from_processor + ["second_per_grid_ts"]
+
+
+AutoProcessor.register("OvisProcessor", OvisProcessor)