model: support deepseek-ocr (#11891)

Co-authored-by: yhyang201 <47235274+yhyang201@users.noreply.github.com>
Co-authored-by: yhyang201 <yhyang201@gmail.com>
Co-authored-by: Shi Shuai <126407087+shuaills@users.noreply.github.com>
Co-authored-by: Xinyuan Tong <xinyuantong.cs@gmail.com>

python/sglang/srt/configs/deepseek_ocr.py

+from typing import Tuple
+
+import torchvision.transforms as T
+from PIL import Image
+from transformers import PretrainedConfig
+
+BASE_SIZE = 1024
+IMAGE_SIZE = 640
+CROP_MODE = True
+MIN_CROPS = 2
+MAX_CROPS = 6  # max:9; If your GPU memory is small, it is recommended to set it to 6.
+MAX_CONCURRENCY = 100  # If you have limited GPU memory, lower the concurrency count.
+NUM_WORKERS = 64  # image pre-process (resize/padding) workers
+PRINT_NUM_VIS_TOKENS = False
+SKIP_REPEAT = True
+MODEL_PATH = "deepseek-ai/DeepSeek-OCR"  # change to your model path
+
+PROMPT = "<image>\n<|grounding|>Convert the document to markdown."
+
+
+class ImageTransform:
+
+    def __init__(
+        self,
+        mean: Tuple[float, float, float] = (0.5, 0.5, 0.5),
+        std: Tuple[float, float, float] = (0.5, 0.5, 0.5),
+        normalize: bool = True,
+    ):
+        self.mean = mean
+        self.std = std
+        self.normalize = normalize
+
+        transform_pipelines = [T.ToTensor()]
+
+        if normalize:
+            transform_pipelines.append(T.Normalize(mean, std))
+
+        self.transform = T.Compose(transform_pipelines)
+
+    def __call__(self, pil_img: Image.Image):
+        x = self.transform(pil_img)
+        return x
+
+
+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"
+    model_type = "deepseek-ocr"
+    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
+        self.hidden_size = self.text_config.hidden_size
+
+
+class DeepseekOCRConfig(DeepseekV2Config):
+    model_type = "DeepseekOCR"

python/sglang/srt/configs/deepseekvl2.py

@@ -11,6 +11,8 @@ from transformers import (
     ProcessorMixin,
 )
 
+from sglang.srt.configs.deepseek_ocr import BASE_SIZE, IMAGE_SIZE, MAX_CROPS, MIN_CROPS
+
 
 def select_best_resolution(image_size, candidate_resolutions):
     # used for cropping
@@ -61,6 +63,7 @@ class DictOutput(object):
 class VLChatProcessorOutput(DictOutput):
     input_ids: torch.LongTensor
     target_ids: torch.LongTensor
+    images_crop: torch.LongTensor
     pixel_values: (
         torch.Tensor
     )  # rename from "images" to "pixel_values" for compatibility
@@ -104,6 +107,68 @@ class ImageTransform(object):
         return x
 
 
+def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
+    best_ratio_diff = float("inf")
+    best_ratio = (1, 1)
+    area = width * height
+    for ratio in target_ratios:
+        target_aspect_ratio = ratio[0] / ratio[1]
+        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
+        if ratio_diff < best_ratio_diff:
+            best_ratio_diff = ratio_diff
+            best_ratio = ratio
+        elif ratio_diff == best_ratio_diff:
+            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
+                best_ratio = ratio
+    return best_ratio
+
+
+def dynamic_preprocess(
+    image, min_num=MIN_CROPS, max_num=MAX_CROPS, image_size=640, use_thumbnail=False
+):
+    orig_width, orig_height = image.size
+    aspect_ratio = orig_width / orig_height
+
+    # calculate the existing image aspect ratio
+    target_ratios = set(
+        (i, j)
+        for n in range(min_num, max_num + 1)
+        for i in range(1, n + 1)
+        for j in range(1, n + 1)
+        if i * j <= max_num and i * j >= min_num
+    )
+    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
+
+    # find the closest aspect ratio to the target
+    target_aspect_ratio = find_closest_aspect_ratio(
+        aspect_ratio, target_ratios, orig_width, orig_height, image_size
+    )
+
+    # calculate the target width and height
+    target_width = image_size * target_aspect_ratio[0]
+    target_height = image_size * target_aspect_ratio[1]
+    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+
+    # resize the image
+    resized_img = image.resize((target_width, target_height))
+    processed_images = []
+    for i in range(blocks):
+        box = (
+            (i % (target_width // image_size)) * image_size,
+            (i // (target_width // image_size)) * image_size,
+            ((i % (target_width // image_size)) + 1) * image_size,
+            ((i // (target_width // image_size)) + 1) * image_size,
+        )
+        # split the image
+        split_img = resized_img.crop(box)
+        processed_images.append(split_img)
+    assert len(processed_images) == blocks
+    if use_thumbnail and len(processed_images) != 1:
+        thumbnail_img = image.resize((image_size, image_size))
+        processed_images.append(thumbnail_img)
+    return processed_images, target_aspect_ratio
+
+
 class DeepseekVLV2Processor(ProcessorMixin):
     tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast")
     attributes = ["tokenizer"]
@@ -133,7 +198,7 @@ class DeepseekVLV2Processor(ProcessorMixin):
         self.image_std = image_std
         self.normalize = normalize
         self.downsample_ratio = downsample_ratio
-
+        self.base_size = BASE_SIZE
         self.image_transform = ImageTransform(
             mean=image_mean, std=image_std, normalize=normalize
         )
@@ -176,7 +241,7 @@ class DeepseekVLV2Processor(ProcessorMixin):
             **kwargs,
         )
 
-    def format_messages_v2(self, messages, pil_images, max_req_input_len=-1):
+    def format_messages_v2(self, messages: str, pil_images, max_req_input_len=-1):
         """play the role of format_messages_v2 and get_images_info in the last version"""
         tokenized_data = []
         masked_tokenized_data = []  # labels
@@ -186,35 +251,34 @@ class DeepseekVLV2Processor(ProcessorMixin):
 
         image_index = 0
         image_token_cnt = messages.count(self.image_token)
-        tokenized_str, images, seq_mask, spatial_crop = self.tokenize_with_images(
+        (
+            input_ids,
+            images,
+            images_crop,
+            seq_mask,
+            spatial_crop,
+            num_image_tokens,
+            image_shapes,
+        ) = self.tokenize_with_images(
             messages,
             pil_images[image_index : image_index + image_token_cnt],
             bos=True,
             eos=True,
             cropping=len(pil_images) <= 2,
-            max_req_input_len=max_req_input_len,
         )
 
         image_index = image_token_cnt
-        tokenized_data += tokenized_str
-        if self.mask_prompt:
-            masked_tokenized_data += [self.ignore_id] * len(tokenized_str)
-        else:
-            masked_tokenized_data += tokenized_str
         images_list += images
         images_seq_mask += seq_mask
-        images_spatial_crop += spatial_crop
-
-        assert len(tokenized_data) == len(
-            images_seq_mask
-        ), f"format_messages_v2: tokenized_str's length {len(tokenized_str)} is not equal to imags_seq_mask's length {len(images_seq_mask)}"
+        images_spatial_crop = spatial_crop
 
         return (
-            tokenized_data,
+            input_ids,
             masked_tokenized_data,
             images_list,
             images_seq_mask,
             images_spatial_crop,
+            images_crop,
         )
 
     @property
@@ -251,6 +315,7 @@ class DeepseekVLV2Processor(ProcessorMixin):
         inference_mode: bool = True,
         system_prompt: str = "",
         max_req_input_len: int = -1,
+        cropping: bool = True,
         **kwargs,
     ):
         """
@@ -274,47 +339,22 @@ class DeepseekVLV2Processor(ProcessorMixin):
                 - num_image_tokens (List[int]): the number of image tokens
         """
 
-        assert (
-            prompt is None or conversations is None
-        ), "prompt and conversations cannot be used at the same time."
-
+        prompt = conversations or prompt
         (
-            tokenized_str,
+            input_ids,
             masked_tokenized_str,
             images_list,
             images_seq_mask,
             images_spatial_crop,
-        ) = self.format_messages_v2(conversations, images, max_req_input_len)
+            images_crop,
+        ) = self.format_messages_v2(prompt, images, max_req_input_len)
 
-        assert (
-            len(tokenized_str) == len(images_seq_mask) == len(masked_tokenized_str)
-        ), (
-            f"tokenized_str's length {len(tokenized_str)}, input_ids' length {len(masked_tokenized_str)}, "
-            f"imags_seq_mask's length {len(images_seq_mask)}, are not equal"
-        )
-
-        input_ids = torch.LongTensor(tokenized_str)
         target_ids = torch.LongTensor(masked_tokenized_str)
-        images_seq_mask = torch.tensor(images_seq_mask, dtype=torch.bool)
-
-        # set input_ids < 0 | input_ids == self.image_token_id as ignore_id
-        target_ids[(input_ids < 0) | (input_ids == self.image_token_id)] = (
-            self.ignore_id
-        )
-        input_ids[input_ids < 0] = self.pad_id
-
-        if inference_mode:
-            assert input_ids[-1] == self.eos_id
-            input_ids = input_ids[:-1]
-            target_ids = target_ids[:-1]
-            images_seq_mask = images_seq_mask[:-1]
 
         if len(images_list) == 0:
             images = torch.zeros((1, 3, self.image_size, self.image_size))
-            images_spatial_crop = torch.zeros((1, 2), dtype=torch.long)
         else:
             images = torch.stack(images_list, dim=0)
-            images_spatial_crop = torch.tensor(images_spatial_crop, dtype=torch.long)
 
         images_spatial_crop = torch.stack(
             [images_spatial_crop], dim=0
@@ -323,6 +363,7 @@ class DeepseekVLV2Processor(ProcessorMixin):
         prepare = VLChatProcessorOutput(
             input_ids=input_ids,
             target_ids=target_ids,
+            images_crop=images_crop,
             pixel_values=images,
             images_seq_mask=images_seq_mask,
             images_spatial_crop=images_spatial_crop,
@@ -340,10 +381,14 @@ class DeepseekVLV2Processor(ProcessorMixin):
         inference_mode: bool = True,
         system_prompt: str = "",
         max_req_input_len: int = -1,
+        text: list[str] = None,
         **kwargs,
     ):
+        assert text is None or isinstance(text, list)
+        if text is not None:
+            text = text[0]
         prepare = self.process_one(
-            prompt=prompt,
+            prompt=prompt or text,
             conversations=conversations,
             images=images,
             apply_sft_format=apply_sft_format,
@@ -368,85 +413,83 @@ class DeepseekVLV2Processor(ProcessorMixin):
         bos: bool = True,
         eos: bool = True,
         cropping: bool = True,
-        max_req_input_len: int = -1,
     ):
         """Tokenize text with <image> tags."""
-        images_list, images_seq_mask, images_spatial_crop = [], [], []
+
+        conversation = conversation
+        assert conversation.count(self.image_token) == len(images)
         text_splits = conversation.split(self.image_token)
+        images_list, images_crop_list, images_seq_mask, images_spatial_crop = (
+            [],
+            [],
+            [],
+            [],
+        )
+        image_shapes = []
+        num_image_tokens = []
         tokenized_str = []
         for text_sep, image in zip(text_splits, images):
             """encode text_sep"""
             tokenized_sep = self.encode(text_sep, bos=False, eos=False)
+
             tokenized_str += tokenized_sep
             images_seq_mask += [False] * len(tokenized_sep)
 
-            """select best resolution for anyres"""
-            if cropping:
-                best_width, best_height = select_best_resolution(
-                    image.size, self.candidate_resolutions
-                )
+            image_shapes.append(image.size)
+
+            if image.size[0] <= 640 and image.size[1] <= 640:
+                crop_ratio = [1, 1]
             else:
-                best_width, best_height = self.image_size, self.image_size
-            # print(image.size, (best_width, best_height)) # check the select_best_resolutions func
+                if cropping:
+                    images_crop_raw, crop_ratio = dynamic_preprocess(
+                        image, image_size=IMAGE_SIZE
+                    )
+                else:
+                    crop_ratio = [1, 1]
 
             """process the global view"""
+            if self.image_size <= 640 and not cropping:
+                image = image.resize((self.image_size, self.image_size))
+
             global_view = ImageOps.pad(
                 image,
-                (self.image_size, self.image_size),
+                (self.base_size, self.base_size),
                 color=tuple(int(x * 255) for x in self.image_transform.mean),
             )
             images_list.append(self.image_transform(global_view))
 
-            """process the local views"""
-            local_view = ImageOps.pad(
-                image,
-                (best_width, best_height),
-                color=tuple(int(x * 255) for x in self.image_transform.mean),
-            )
-            for i in range(0, best_height, self.image_size):
-                for j in range(0, best_width, self.image_size):
-                    images_list.append(
-                        self.image_transform(
-                            local_view.crop(
-                                (j, i, j + self.image_size, i + self.image_size)
-                            )
-                        )
-                    )
-
-            """record height / width crop num"""
-            num_width_tiles, num_height_tiles = (
-                best_width // self.image_size,
-                best_height // self.image_size,
-            )
+            num_width_tiles, num_height_tiles = crop_ratio
             images_spatial_crop.append([num_width_tiles, num_height_tiles])
 
+            if num_width_tiles > 1 or num_height_tiles > 1:
+                for i in range(len(images_crop_raw)):
+                    images_crop_list.append(self.image_transform(images_crop_raw[i]))
+
             """add image tokens"""
-            h = w = math.ceil(
+            num_queries = math.ceil(
                 (self.image_size // self.patch_size) / self.downsample_ratio
             )
-            # global views tokens h * (w + 1), 1 is for line separator
-            tokenized_image = [self.image_token_id] * h * (w + 1)
-            # add a separator between global and local views
-            tokenized_image += [self.image_token_id]
-            # local views tokens, (num_height_tiles * h) * (num_width_tiles * w + 1)
-            tokenized_image += (
-                [self.image_token_id]
-                * (num_height_tiles * h)
-                * (num_width_tiles * w + 1)
+            num_queries_base = math.ceil(
+                (self.base_size // self.patch_size) / self.downsample_ratio
             )
 
+            tokenized_image = (
+                [self.image_token_id] * num_queries_base + [self.image_token_id]
+            ) * num_queries_base
+            tokenized_image += [self.image_token_id]
+            if num_width_tiles > 1 or num_height_tiles > 1:
+                tokenized_image += (
+                    [self.image_token_id] * (num_queries * num_width_tiles)
+                    + [self.image_token_id]
+                ) * (num_queries * num_height_tiles)
             tokenized_str += tokenized_image
+
             images_seq_mask += [True] * len(tokenized_image)
-            # print(width_crop_num, height_crop_num, len(tokenized_image)) # test the correctness of the number of image-related tokens
+            num_image_tokens.append(len(tokenized_image))
 
         """process the last text split"""
         tokenized_sep = self.encode(text_splits[-1], bos=False, eos=False)
-        # deal with video, limit with request len
-        if max_req_input_len > -1:
-            if max_req_input_len < len(tokenized_sep) + len(tokenized_str) - 1:
-                rest = max_req_input_len - len(tokenized_sep) - 1 - 1024
-                tokenized_str = tokenized_str[:rest]
-                images_seq_mask = images_seq_mask[:rest]
+
         tokenized_str += tokenized_sep
         images_seq_mask += [False] * len(tokenized_sep)
 
@@ -462,7 +505,64 @@ class DeepseekVLV2Processor(ProcessorMixin):
             images_seq_mask
         ), f"tokenize_with_images func: tokenized_str's length {len(tokenized_str)} is not equal to imags_seq_mask's length {len(images_seq_mask)}"
 
-        return tokenized_str, images_list, images_seq_mask, images_spatial_crop
+        masked_tokenized_str = []
+        for token_index in tokenized_str:
+            if token_index != self.image_token_id:
+                masked_tokenized_str.append(token_index)
+            else:
+                masked_tokenized_str.append(self.ignore_id)
+
+        assert (
+            len(tokenized_str) == len(images_seq_mask) == len(masked_tokenized_str)
+        ), (
+            f"tokenized_str's length {len(tokenized_str)}, input_ids' length {len(masked_tokenized_str)}, "
+            f"imags_seq_mask's length {len(images_seq_mask)}, are not equal"
+        )
+        input_ids = torch.LongTensor(tokenized_str)
+        target_ids = torch.LongTensor(masked_tokenized_str)
+        images_seq_mask = torch.tensor(images_seq_mask, dtype=torch.bool)
+
+        # set input_ids < 0 | input_ids == self.image_token_id as ignore_id
+        target_ids[(input_ids < 0) | (input_ids == self.image_token_id)] = (
+            self.ignore_id
+        )
+        input_ids[input_ids < 0] = self.pad_id
+
+        inference_mode = True
+
+        if inference_mode:
+            # Remove the ending eos token
+            assert input_ids[-1] == self.eos_id
+            input_ids = input_ids[:-1]
+            target_ids = target_ids[:-1]
+            images_seq_mask = images_seq_mask[:-1]
+
+        if len(images_list) == 0:
+            pixel_values = torch.zeros((1, 3, self.base_size, self.base_size))
+            images_spatial_crop = torch.zeros((1, 1), dtype=torch.long)
+            images_crop = torch.zeros(
+                (1, 3, self.image_size, self.image_size)
+            ).unsqueeze(0)
+        else:
+            pixel_values = torch.stack(images_list, dim=0)
+            images_spatial_crop = torch.tensor(images_spatial_crop, dtype=torch.long)
+            if images_crop_list:
+                images_crop = torch.stack(images_crop_list, dim=0).unsqueeze(0)
+            else:
+                images_crop = torch.zeros(
+                    (1, 3, self.image_size, self.image_size)
+                ).unsqueeze(0)
+
+        input_ids = input_ids.unsqueeze(0)
+        return (
+            input_ids,
+            pixel_values,
+            images_crop,
+            images_seq_mask,
+            images_spatial_crop,
+            num_image_tokens,
+            image_shapes,
+        )
 
 
 class DeepseekVL2VisionEncoderConfig(PretrainedConfig):
@@ -547,7 +647,6 @@ class DeepseekVL2MlpProjectorConfig(PretrainedConfig):
 
 
 class DeepseekV2Config(PretrainedConfig):
-
     model_type = "deepseek_v2"
     keys_to_ignore_at_inference = ["past_key_values"]
 

python/sglang/srt/configs/model_config.py

@@ -921,6 +921,7 @@ multimodal_model_archs = [
     "DotsVLMForCausalLM",
     "DotsOCRForCausalLM",
     "Sarashina2VisionForCausalLM",
+    "DeepseekOCRForCausalLM",
 ]
 
 

python/sglang/srt/model_loader/utils.py

@@ -99,7 +99,6 @@ def get_model_architecture(model_config: ModelConfig) -> Tuple[Type[nn.Module],
 
     if not is_native_supported or model_config.model_impl == ModelImpl.TRANSFORMERS:
         architectures = resolve_transformers_arch(model_config, architectures)
-
     return ModelRegistry.resolve_model_cls(architectures)
 
 

python/sglang/srt/models/deepseek_ocr.py

+# Copyright 2025 The SwissAI Initiative
+# Copyright 2023-2024 SGLang Team
+# 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.
+# ==============================================================================
+
+# Adapted from
+# https://github.com/vllm-project/vllm/blob/c7f2cf2b7f67bce5842fedfdba508440fe257375/vllm/model_executor/models/llama.py#L1
+"""Inference-only Apertus model compatible with HuggingFace weights."""
+import copy
+import logging
+import math
+from functools import partial
+from typing import Iterable, List, Optional, Set, Tuple, Type, TypeAlias, Union
+
+import torch
+import torch.nn.functional as F
+from torch import Tensor, nn
+from transformers.models.vitdet.modeling_vitdet import get_rel_pos
+
+from sglang.srt.configs.deepseek_ocr import DeepseekVLV2Config
+from sglang.srt.layers.quantization import QuantizationConfig
+from sglang.srt.managers.mm_utils import (
+    MultiModalityDataPaddingPatternMultimodalTokens,
+    general_mm_embed_routine,
+)
+from sglang.srt.managers.schedule_batch import MultimodalDataItem, MultimodalInputs
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.model_loader.weight_utils import default_weight_loader
+from sglang.srt.models.deepseek import DeepseekForCausalLM
+from sglang.srt.models.deepseek_v2 import DeepseekV2ForCausalLM, DeepseekV3ForCausalLM
+from sglang.srt.models.transformers import maybe_prefix
+
+NestedTensors: TypeAlias = Union[
+    list["NestedTensors"],
+    list["torch.Tensor"],
+    "torch.Tensor",
+    tuple["torch.Tensor", ...],
+]
+
+MultiModalEmbeddings: TypeAlias = list[Tensor] | Tensor | tuple[Tensor, ...]
+
+logger = logging.getLogger(__name__)
+
+
+def _flatten_embeddings(embeddings: NestedTensors) -> torch.Tensor:
+    """
+    Recursively flattens and concatenates NestedTensors on all but the last
+    dimension.
+    """
+
+    if isinstance(embeddings, torch.Tensor):
+        # Flatten all but the last dimension.
+        return embeddings.flatten(0, -2)
+
+    return torch.cat(tuple(_flatten_embeddings(t) for t in embeddings))
+
+
+def _embedding_count_expression(embeddings: NestedTensors) -> str:
+    """
+    Constructs a debugging representation of the number of embeddings in the
+    NestedTensors.
+    """
+
+    if isinstance(embeddings, torch.Tensor):
+        return " x ".join([str(dim) for dim in embeddings.shape[:-1]])
+
+    return " + ".join(_embedding_count_expression(inner) for inner in embeddings)
+
+
+def _merge_multimodal_embeddings(
+    inputs_embeds: torch.Tensor,
+    multimodal_embeddings: NestedTensors,
+    is_multimodal: torch.Tensor,
+) -> torch.Tensor:
+    """
+    Merge `multimodal_embeddings` into `inputs_embeds` by overwriting the
+    positions in `inputs_embeds` corresponding to placeholder tokens in
+    `input_ids`.
+
+    Note:
+        This updates `inputs_embeds` in place.
+    """
+    if len(multimodal_embeddings) == 0:
+        return inputs_embeds
+
+    mm_embeds_flat = _flatten_embeddings(multimodal_embeddings)
+    input_dtype = inputs_embeds.dtype
+
+    try:
+        # NOTE: This can avoid D2H sync (#22105), but fails to
+        # raise an error if is_multimodal.sum() < len(mm_embeds_flat)
+        inputs_embeds.masked_scatter_(
+            is_multimodal.unsqueeze(-1), mm_embeds_flat.to(dtype=input_dtype)
+        )
+    except RuntimeError as e:
+        num_actual_tokens = len(mm_embeds_flat)
+        num_expected_tokens = is_multimodal.sum().item()
+
+        if num_actual_tokens != num_expected_tokens:
+            expr = _embedding_count_expression(multimodal_embeddings)
+
+            raise ValueError(
+                f"Attempted to assign {expr} = {num_actual_tokens} "
+                f"multimodal tokens to {num_expected_tokens} placeholders"
+            ) from e
+
+        raise ValueError("Error during masked scatter operation") from e
+
+    return inputs_embeds
+
+
+def isin_list(
+    elements: torch.Tensor,
+    test_elements_list: list[int],
+) -> torch.Tensor:
+    test_elements = torch.tensor(test_elements_list, pin_memory=True).to(
+        device=elements.device, non_blocking=True
+    )
+
+    return torch.isin(elements, test_elements)
+
+
+def merge_multimodal_embeddings(
+    input_ids: torch.Tensor,
+    inputs_embeds: torch.Tensor,
+    multimodal_embeddings: NestedTensors,
+    placeholder_token_id: int | list[int],
+) -> torch.Tensor:
+    """
+    Merge `multimodal_embeddings` into `inputs_embeds` by overwriting the
+    positions in `inputs_embeds` corresponding to placeholder tokens in
+    `input_ids`.
+
+    `placeholder_token_id` can be a list of token ids (e.g, token ids
+    of img_start, img_break, and img_end tokens) when needed: This means
+    the order of these tokens in the `input_ids` MUST MATCH the order of
+    their embeddings in `multimodal_embeddings` since we need to
+    slice-merge instead of individually scattering.
+
+    For example, if input_ids is "TTTTTSIIIBIIIBIIIETTT", where
+    - T is text token
+    - S is image start token
+    - I is image embedding token
+    - B is image break token
+    - E is image end token.
+
+    Then the image embeddings (that correspond to I's) from vision encoder
+    must be padded with embeddings of S, B, and E in the same order of
+    input_ids for a correct embedding merge.
+
+    Note:
+        This updates `inputs_embeds` in place.
+    """
+    if isinstance(placeholder_token_id, list):
+        is_multimodal = isin_list(input_ids, placeholder_token_id)
+    else:
+        is_multimodal = input_ids == placeholder_token_id
+
+    return _merge_multimodal_embeddings(
+        inputs_embeds,
+        multimodal_embeddings=multimodal_embeddings,
+        is_multimodal=is_multimodal,
+    )
+
+
+class MlpProjector(nn.Module):
+
+    def __init__(
+        self,
+        projector_type,
+        input_dim,
+        n_embed,
+        depth=1,
+        mlp_ratio=1,
+        downsample_ratio=4,
+    ):
+        self.projector_type = projector_type
+        self.input_dim = input_dim
+        self.n_embed = n_embed
+        self.depth = depth
+        self.token_pooling = False
+        self.conv_fusion_high_low_features = False
+
+        super().__init__()
+
+        if projector_type == "identity":
+            modules = nn.Identity()
+
+        elif projector_type == "linear":
+            modules = nn.Linear(input_dim, n_embed)
+
+        elif projector_type == "mlp_gelu":
+            mlp_depth = depth
+            modules = [nn.Linear(input_dim, n_embed)]
+            for _ in range(1, mlp_depth):
+                modules.append(nn.GELU())
+                modules.append(nn.Linear(n_embed, n_embed))
+            modules = nn.Sequential(*modules)
+
+        elif projector_type == "normlayer_downsample_mlp_gelu":
+            mlp_depth = depth
+            mlp_ratio = mlp_ratio
+            modules = [
+                nn.LayerNorm(input_dim * downsample_ratio * downsample_ratio),
+                nn.Linear(
+                    input_dim * downsample_ratio * downsample_ratio,
+                    n_embed * mlp_ratio,
+                ),
+            ]
+            for _ in range(1, mlp_depth - 1):
+                modules.append(nn.GELU())
+                modules.append(nn.Linear(n_embed * mlp_ratio, n_embed * mlp_ratio))
+            modules.append(nn.GELU())
+            modules.append(nn.Linear(n_embed * mlp_ratio, n_embed))
+            modules = nn.Sequential(*modules)
+
+        elif projector_type == "downsample_mlp_gelu":
+            mlp_depth = depth
+            mlp_ratio = mlp_ratio
+            modules = [
+                nn.Linear(
+                    input_dim * downsample_ratio * downsample_ratio,
+                    n_embed * mlp_ratio,
+                )
+            ]
+            for _ in range(1, mlp_depth - 1):
+                modules.append(nn.GELU())
+                modules.append(nn.Linear(n_embed * mlp_ratio, n_embed * mlp_ratio))
+            modules.append(nn.GELU())
+            modules.append(nn.Linear(n_embed * mlp_ratio, n_embed))
+            modules = nn.Sequential(*modules)
+
+        elif projector_type == "low_high_hybrid_split_mlp_gelu":
+            mlp_depth = depth
+            self.high_up_proj = nn.Linear(input_dim, n_embed // 2)
+            self.low_up_proj = nn.Linear(input_dim, n_embed // 2)
+
+            modules = []
+            for _ in range(1, mlp_depth):
+                modules.append(nn.GELU())
+                modules.append(nn.Linear(n_embed, n_embed))
+            modules = nn.Sequential(*modules)
+
+        elif projector_type == "hybrid_split_feature_mlp_gelu":
+            mlp_depth = depth
+            channel_div = 0.5
+            self.high_up_proj = nn.Linear(input_dim[0], int(n_embed * channel_div))
+            self.low_up_proj = nn.Linear(
+                input_dim[1], n_embed - int(n_embed * channel_div)
+            )
+
+            modules = []
+            for _ in range(1, mlp_depth):
+                modules.append(nn.GELU())
+                modules.append(nn.Linear(n_embed, n_embed))
+            modules = nn.Sequential(*modules)
+
+        elif projector_type == "low_high_split_mlp_gelu":
+            mlp_depth = depth
+            modules = []
+            for _ in range(1, mlp_depth):
+                modules.append(nn.GELU())
+                modules.append(nn.Linear(n_embed // 2, n_embed // 2))
+            modules = nn.Sequential(*modules)
+            self.high_layers = nn.Sequential(*modules)
+            self.low_layers = copy.deepcopy(modules)
+
+        else:
+            raise ValueError(f"Unknown projector type: {projector_type}")
+
+        self.layers = modules
+
+    def forward(self, x):
+        if self.token_pooling:
+            batch_size, wxh, channels = x.shape
+            w = h = int(wxh**0.5)
+            x = x.view(batch_size, w, h, channels)
+            x = x.permute(0, 3, 1, 2)
+            patches = x.unfold(2, 2, 2).unfold(3, 2, 2)
+            batch_size, channels, h_patches, w_patches, _, _ = patches.size()
+            # Concatenate on channel dimension
+            patches = patches.contiguous().view(
+                batch_size, channels, h_patches * w_patches, -1
+            )
+
+            # Pass through linear layer
+            patches = patches.permute(0, 2, 1, 3).contiguous()
+            patches = patches.view(batch_size, h_patches * w_patches, channels * 4)
+
+            x = self.token_pooling_layer(patches)
+
+        if self.conv_fusion_high_low_features:
+            x = self.fusion_layer(x[:, 0]) + x[:, 1]
+
+        if self.projector_type == "low_high_hybrid_split_mlp_gelu":
+            high_x, low_x = x[0], x[1]
+            high_x = self.high_up_proj(high_x)
+            low_x = self.low_up_proj(low_x)
+            x = torch.concat([high_x, low_x], dim=-1)
+
+        if self.projector_type == "hybrid_split_feature_mlp_gelu":
+            high_x = x[..., : self.input_dim[0]]
+            low_x = x[..., self.input_dim[0] :]
+            high_x = self.high_up_proj(high_x)
+            low_x = self.low_up_proj(low_x)
+            x = torch.concat([high_x, low_x], dim=-1)
+
+        if self.projector_type == "low_high_split_mlp_gelu":
+            high_x, low_x = x[0], x[1]
+            high_x = self.high_layers(high_x)
+            low_x = self.low_layers(low_x)
+            x = torch.concat([high_x, low_x], dim=-1)
+            return x
+
+        if (
+            self.projector_type == "downsample_mlp_gelu"
+            or self.projector_type == "normlayer_downsample_mlp_gelu"
+        ):
+            bs, hw, input_dim = x.shape
+            h = w = int((hw) ** 0.5)
+
+            """compute padding"""
+            if h % self.downsample_ratio:
+                pad = self.downsample_ratio - h % self.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.downsample_ratio,
+                stride=self.downsample_ratio,
+                padding=0,
+            )  # B, C*4, HW // 4
+            x = x.permute(0, 2, 1)
+
+        return self.layers(x)
+
+
+class LayerNorm2d(nn.Module):
+    def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(num_channels))
+        self.bias = nn.Parameter(torch.zeros(num_channels))
+        self.eps = eps
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        u = x.mean(1, keepdim=True)
+        s = (x - u).pow(2).mean(1, keepdim=True)
+        x = (x - u) / torch.sqrt(s + self.eps)
+        x = self.weight[:, None, None] * x + self.bias[:, None, None]
+        return x
+
+
+class MLPBlock(nn.Module):
+    def __init__(
+        self,
+        embedding_dim: int,
+        mlp_dim: int,
+        act: Type[nn.Module] = nn.GELU,
+    ) -> None:
+        super().__init__()
+        self.lin1 = nn.Linear(embedding_dim, mlp_dim)
+        self.lin2 = nn.Linear(mlp_dim, embedding_dim)
+        self.act = act()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.lin2(self.act(self.lin1(x)))
+
+
+def add_decomposed_rel_pos(
+    q: torch.Tensor,
+    rel_pos_h: torch.Tensor,
+    rel_pos_w: torch.Tensor,
+    q_size: Tuple[int, int],
+    k_size: Tuple[int, int],
+) -> torch.Tensor:
+    """
+    Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
+    https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py   # noqa B950
+    Args:
+        q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
+        rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
+        rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
+        q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
+        k_size (Tuple): spatial sequence size of key k with (k_h, k_w).
+    Returns:
+        attn (Tensor): attention map with added relative positional embeddings.
+    """
+    q_h, q_w = q_size
+    k_h, k_w = k_size
+    Rh = get_rel_pos(q_h, k_h, rel_pos_h)
+    Rw = get_rel_pos(q_w, k_w, rel_pos_w)
+
+    B, _, dim = q.shape
+    r_q = q.reshape(B, q_h, q_w, dim)
+    rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
+    rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
+    rel_h = rel_h.unsqueeze(-1)
+    rel_w = rel_w.unsqueeze(-2)
+    rel_h = rel_h.reshape(B, q_h * q_w, k_h, 1)
+    rel_w = rel_w.reshape(B, q_h * q_w, 1, k_w)
+
+    return rel_h, rel_w
+
+
+class Attention(nn.Module):
+    """Multi-head Attention block with relative position embeddings."""
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int = 8,
+        qkv_bias: bool = True,
+        use_rel_pos: bool = False,
+        rel_pos_zero_init: bool = True,
+        input_size: Optional[Tuple[int, int]] = None,
+    ) -> None:
+        """
+        Args:
+            dim (int): Number of input channels.
+            num_heads (int): Number of attention heads.
+            qkv_bias (bool):  If True, add a learnable bias to query, key, value.
+            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
+            input_size (tuple(int, int) or None): Input resolution for calculating the relative
+                positional parameter size.
+        """
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = head_dim**-0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.proj = nn.Linear(dim, dim)
+
+        self.use_rel_pos = use_rel_pos
+        if self.use_rel_pos:
+            assert (
+                input_size is not None
+            ), "Input size must be provided if using relative positional encoding."
+            # initialize relative positional embeddings
+            self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
+            self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        B, H, W, _ = x.shape
+        # qkv with shape (3, B, nHead, H * W, C)
+        qkv = (
+            self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        )
+        # q, k, v with shape (B * nHead, H * W, C)
+        q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)
+
+        rel_h, rel_w = None, None
+        if self.use_rel_pos:
+            rel_h, rel_w = add_decomposed_rel_pos(
+                q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W)
+            )
+
+        q = q.view(B, self.num_heads, H * W, -1)
+        k = k.view(B, self.num_heads, H * W, -1)
+        v = v.view(B, self.num_heads, H * W, -1)
+
+        if self.use_rel_pos:
+            rel_h = rel_h.view(
+                B, self.num_heads, rel_h.size(1), rel_h.size(2), rel_h.size(3)
+            )
+            rel_w = rel_w.view(
+                B, self.num_heads, rel_w.size(1), rel_w.size(2), rel_w.size(3)
+            )
+            attn_bias = (rel_h + rel_w).view(
+                B, self.num_heads, rel_h.size(2), rel_h.size(3) * rel_w.size(4)
+            )
+            x = torch.nn.functional.scaled_dot_product_attention(
+                q, k, v, attn_mask=attn_bias
+            )
+            # x = _attention_rel_h_rel_w(q, k, v, rel_h, rel_w)
+        else:
+            x = torch.nn.functional.scaled_dot_product_attention(q, k, v)
+
+        x = (
+            x.view(B, self.num_heads, H, W, -1)
+            .permute(0, 2, 3, 1, 4)
+            .reshape(B, H, W, -1)
+        )
+
+        x = self.proj(x)
+
+        return x
+
+
+def window_partition(
+    x: torch.Tensor, window_size: int
+) -> Tuple[torch.Tensor, Tuple[int, int]]:
+    """
+    Partition into non-overlapping windows with padding if needed.
+    Args:
+        x (tensor): input tokens with [B, H, W, C].
+        window_size (int): window size.
+    Returns:
+        windows: windows after partition with [B * num_windows, window_size, window_size, C].
+        (Hp, Wp): padded height and width before partition
+    """
+    B, H, W, C = x.shape
+
+    pad_h = (window_size - H % window_size) % window_size
+    pad_w = (window_size - W % window_size) % window_size
+    if pad_h > 0 or pad_w > 0:
+        x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
+    Hp, Wp = H + pad_h, W + pad_w
+
+    x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
+    windows = (
+        x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    )
+    return windows, (Hp, Wp)
+
+
+def window_unpartition(
+    windows: torch.Tensor,
+    window_size: int,
+    pad_hw: Tuple[int, int],
+    hw: Tuple[int, int],
+) -> torch.Tensor:
+    """
+    Window unpartition into original sequences and removing padding.
+    Args:
+        windows (tensor): input tokens with [B * num_windows, window_size, window_size, C].
+        window_size (int): window size.
+        pad_hw (Tuple): padded height and width (Hp, Wp).
+        hw (Tuple): original height and width (H, W) before padding.
+    Returns:
+        x: unpartitioned sequences with [B, H, W, C].
+    """
+    Hp, Wp = pad_hw
+    H, W = hw
+    B = windows.shape[0] // (Hp * Wp // window_size // window_size)
+    x = windows.view(
+        B, Hp // window_size, Wp // window_size, window_size, window_size, -1
+    )
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
+
+    if Hp > H or Wp > W:
+        x = x[:, :H, :W, :].contiguous()
+    return x
+
+
+class Block(nn.Module):
+    """Transformer blocks with support of window attention and residual propagation blocks"""
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = True,
+        norm_layer: Type[nn.Module] = nn.LayerNorm,
+        act_layer: Type[nn.Module] = nn.GELU,
+        use_rel_pos: bool = False,
+        rel_pos_zero_init: bool = True,
+        window_size: int = 0,
+        input_size: Optional[Tuple[int, int]] = None,
+    ) -> None:
+        """
+        Args:
+            dim (int): Number of input channels.
+            num_heads (int): Number of attention heads in each ViT block.
+            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+            qkv_bias (bool): If True, add a learnable bias to query, key, value.
+            norm_layer (nn.Module): Normalization layer.
+            act_layer (nn.Module): Activation layer.
+            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
+            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
+            window_size (int): Window size for window attention blocks. If it equals 0, then
+                use global attention.
+            input_size (tuple(int, int) or None): Input resolution for calculating the relative
+                positional parameter size.
+        """
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = Attention(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            use_rel_pos=use_rel_pos,
+            rel_pos_zero_init=rel_pos_zero_init,
+            input_size=input_size if window_size == 0 else (window_size, window_size),
+        )
+
+        self.norm2 = norm_layer(dim)
+        self.mlp = MLPBlock(
+            embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer
+        )
+
+        self.window_size = window_size
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        shortcut = x
+        x = self.norm1(x)
+        # Window partition
+        if self.window_size > 0:
+            H, W = x.shape[1], x.shape[2]
+            x, pad_hw = window_partition(x, self.window_size)
+
+        x = self.attn(x)
+        # Reverse window partition
+        if self.window_size > 0:
+            x = window_unpartition(x, self.window_size, pad_hw, (H, W))
+
+        x = shortcut + x
+        x = x + self.mlp(self.norm2(x))
+
+        return x
+
+
+class PatchEmbed(nn.Module):
+    """
+    Image to Patch Embedding.
+    """
+
+    def __init__(
+        self,
+        kernel_size: Tuple[int, int] = (16, 16),
+        stride: Tuple[int, int] = (16, 16),
+        padding: Tuple[int, int] = (0, 0),
+        in_chans: int = 3,
+        embed_dim: int = 768,
+    ) -> None:
+        """
+        Args:
+            kernel_size (Tuple): kernel size of the projection layer.
+            stride (Tuple): stride of the projection layer.
+            padding (Tuple): padding size of the projection layer.
+            in_chans (int): Number of input image channels.
+            embed_dim (int): Patch embedding dimension.
+        """
+        super().__init__()
+
+        self.proj = nn.Conv2d(
+            in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.proj(x)
+        # B C H W -> B H W C
+        x = x.permute(0, 2, 3, 1)
+        return x
+
+
+def get_abs_pos_sam(abs_pos, tgt_size):
+    dtype = abs_pos.dtype
+
+    src_size = abs_pos.size(1)
+
+    if src_size != tgt_size:
+        old_pos_embed = abs_pos.permute(0, 3, 1, 2)
+        old_pos_embed = old_pos_embed.to(torch.float32)
+        new_pos_embed = F.interpolate(
+            old_pos_embed,
+            size=(tgt_size, tgt_size),
+            mode="bicubic",
+            antialias=True,
+            align_corners=False,
+        ).to(dtype)
+        new_pos_embed = new_pos_embed.permute(0, 2, 3, 1)
+        return new_pos_embed
+    else:
+        return abs_pos
+
+
+# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa
+class ImageEncoderViT(nn.Module):
+    def __init__(
+        self,
+        img_size: int = 1024,
+        patch_size: int = 16,
+        in_chans: int = 3,
+        embed_dim: int = 768,
+        depth: int = 12,
+        num_heads: int = 12,
+        mlp_ratio: float = 4.0,
+        out_chans: int = 256,
+        qkv_bias: bool = True,
+        norm_layer: Type[nn.Module] = nn.LayerNorm,
+        act_layer: Type[nn.Module] = nn.GELU,
+        use_abs_pos: bool = True,
+        use_rel_pos: bool = False,
+        rel_pos_zero_init: bool = True,
+        window_size: int = 0,
+        global_attn_indexes: Tuple[int, ...] = (),
+    ) -> None:
+        """
+        Args:
+            img_size (int): Input image size.
+            patch_size (int): Patch size.
+            in_chans (int): Number of input image channels.
+            embed_dim (int): Patch embedding dimension.
+            depth (int): Depth of ViT.
+            num_heads (int): Number of attention heads in each ViT block.
+            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+            qkv_bias (bool): If True, add a learnable bias to query, key, value.
+            norm_layer (nn.Module): Normalization layer.
+            act_layer (nn.Module): Activation layer.
+            use_abs_pos (bool): If True, use absolute positional embeddings.
+            use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
+            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
+            window_size (int): Window size for window attention blocks.
+            global_attn_indexes (list): Indexes for blocks using global attention.
+        """
+        super().__init__()
+        self.img_size = img_size
+
+        self.patch_embed = PatchEmbed(
+            kernel_size=(patch_size, patch_size),
+            stride=(patch_size, patch_size),
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+        )
+
+        self.pos_embed: Optional[nn.Parameter] = None
+        if use_abs_pos:
+            # Initialize absolute positional embedding with pretrain image size.
+            self.pos_embed = nn.Parameter(
+                torch.zeros(
+                    1, img_size // patch_size, img_size // patch_size, embed_dim
+                )
+            )
+
+        self.blocks = nn.ModuleList()
+        for i in range(depth):
+            block = Block(
+                dim=embed_dim,
+                num_heads=num_heads,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                norm_layer=norm_layer,
+                act_layer=act_layer,
+                use_rel_pos=use_rel_pos,
+                rel_pos_zero_init=rel_pos_zero_init,
+                window_size=window_size if i not in global_attn_indexes else 0,
+                input_size=(img_size // patch_size, img_size // patch_size),
+            )
+            self.blocks.append(block)
+
+        self.neck = nn.Sequential(
+            nn.Conv2d(
+                embed_dim,
+                out_chans,
+                kernel_size=1,
+                bias=False,
+            ),
+            LayerNorm2d(out_chans),
+            nn.Conv2d(
+                out_chans,
+                out_chans,
+                kernel_size=3,
+                padding=1,
+                bias=False,
+            ),
+            LayerNorm2d(out_chans),
+        )
+
+        self.net_2 = nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False)
+        self.net_3 = nn.Conv2d(
+            512, 1024, kernel_size=3, stride=2, padding=1, bias=False
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.patch_embed(x)
+        if self.pos_embed is not None:
+            x = x + get_abs_pos_sam(self.pos_embed, x.size(1))
+
+        for blk in self.blocks:
+            x = blk(x)
+
+        x = self.neck(x.permute(0, 3, 1, 2))
+        x2 = self.net_2(x)
+        x3 = self.net_3(x2.clone())
+
+        return x3
+
+
+def _build_sam(
+    encoder_embed_dim,
+    encoder_depth,
+    encoder_num_heads,
+    encoder_global_attn_indexes,
+    checkpoint=None,
+):
+    prompt_embed_dim = 256
+    image_size = 1024
+    vit_patch_size = 16
+    image_encoder = ImageEncoderViT(
+        depth=encoder_depth,
+        embed_dim=encoder_embed_dim,
+        img_size=image_size,
+        mlp_ratio=4,
+        norm_layer=partial(torch.nn.LayerNorm, eps=1e-6),
+        num_heads=encoder_num_heads,
+        patch_size=vit_patch_size,
+        qkv_bias=True,
+        use_rel_pos=True,
+        global_attn_indexes=encoder_global_attn_indexes,
+        window_size=14,
+        out_chans=prompt_embed_dim,
+    )
+    image_encoder.eval()
+    if checkpoint is not None:
+        state_dict = torch.load(checkpoint)
+        image_encoder.load_state_dict(
+            {k[30:]: v for k, v in state_dict.items() if "vision_tower_high" in k},
+            strict=True,
+        )
+    return image_encoder
+
+
+def build_sam_vit_b(checkpoint=None):
+    return _build_sam(
+        encoder_embed_dim=768,
+        encoder_depth=12,
+        encoder_num_heads=12,
+        encoder_global_attn_indexes=[2, 5, 8, 11],
+        checkpoint=checkpoint,
+    )
+
+
+def get_abs_pos(abs_pos, tgt_size):
+    # abs_pos: L, C
+    # tgt_size: M
+    # return: M, C
+    dim = abs_pos.size(-1)
+    abs_pos_new = abs_pos.squeeze(0)
+    cls_token, old_pos_embed = abs_pos_new[:1], abs_pos_new[1:]
+
+    src_size = int(math.sqrt(abs_pos_new.shape[0] - 1))
+    tgt_size = int(math.sqrt(tgt_size))
+    dtype = abs_pos.dtype
+
+    if src_size != tgt_size:
+        old_pos_embed = (
+            old_pos_embed.view(1, src_size, src_size, dim)
+            .permute(0, 3, 1, 2)
+            .contiguous()
+        )
+        old_pos_embed = old_pos_embed.to(torch.float32)
+        new_pos_embed = F.interpolate(
+            old_pos_embed,
+            size=(tgt_size, tgt_size),
+            mode="bicubic",
+            antialias=True,
+            align_corners=False,
+        ).to(dtype)
+        new_pos_embed = new_pos_embed.permute(0, 2, 3, 1)
+        new_pos_embed = new_pos_embed.view(tgt_size * tgt_size, dim)
+        vision_pos_embed = torch.cat([cls_token, new_pos_embed], dim=0)
+        vision_pos_embed = vision_pos_embed.view(1, tgt_size * tgt_size + 1, dim)
+        return vision_pos_embed
+    else:
+        return abs_pos
+
+
+class CLIPVisionEmbeddings(nn.Module):
+    def __init__(self, hidden_size=1024, image_size=224, patch_size=14, num_channels=3):
+        super().__init__()
+        self.embed_dim = hidden_size
+        self.image_size = image_size
+        self.patch_size = patch_size
+
+        self.class_embedding = torch.nn.Parameter(torch.randn(self.embed_dim))
+
+        self.patch_embedding = torch.nn.Conv2d(
+            in_channels=num_channels,
+            out_channels=self.embed_dim,
+            kernel_size=self.patch_size,
+            stride=self.patch_size,
+            bias=False,
+        )
+
+        self.num_patches = (self.image_size // self.patch_size) ** 2
+        self.num_positions = self.num_patches + 1
+        self.position_embedding = torch.nn.Embedding(self.num_positions, self.embed_dim)
+        self.register_buffer(
+            "position_ids", torch.arange(self.num_positions).expand((1, -1))
+        )
+
+    def forward(self, pixel_values, patch_embeds):
+        batch_size = pixel_values.shape[0]
+
+        if patch_embeds is not None:
+            patch_embeds = patch_embeds
+        else:
+            patch_embeds = self.patch_embedding(pixel_values)
+
+        patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
+
+        class_embeds = self.class_embedding.expand(batch_size, 1, -1)
+        embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
+
+        embeddings = embeddings + get_abs_pos(
+            self.position_embedding(self.position_ids), embeddings.size(1)
+        )
+        return embeddings
+
+
+class NoTPAttention(torch.nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+        self.num_heads = cfg["num_attention_heads"]
+        self.n_local_heads = cfg["num_attention_heads"]
+        self.head_dim = cfg["hidden_size"] // cfg["num_attention_heads"]
+        self.max_seq_len = cfg["seq_length"]
+        self.use_flash_attention = cfg["use_flash_attn"]
+
+        self.qkv_proj = torch.nn.Linear(
+            cfg["hidden_size"], cfg["hidden_size"] * 3, bias=True
+        )
+        self.out_proj = torch.nn.Linear(
+            cfg["hidden_size"], cfg["hidden_size"], bias=True
+        )
+
+        # self.core_attention = CoreAttention(cfg, AttnType.self_attn)
+
+        self.attn_drop = cfg["attention_dropout"]
+
+    def forward(
+        self,
+        x: torch.Tensor,
+    ):
+        bsz, seqlen, _ = x.shape
+        xqkv = self.qkv_proj(x)
+        xqkv = xqkv.view(bsz, seqlen, 3, self.num_heads, self.head_dim)
+
+        if self.use_flash_attention:
+
+            xq, xk, xv = torch.split(xqkv, 1, dim=2)
+            xq = xq.squeeze(2)
+            xk = xk.squeeze(2)
+            xv = xv.squeeze(2)
+            # xq, xk, xv = xqkv[:, :, 0, ...], xqkv[:, :, 1, ...], xqkv[:, :, 2, ...]
+
+            # （B, num_head, S, head_size)
+            xq = xq.permute(0, 2, 1, 3)
+            xk = xk.permute(0, 2, 1, 3)
+            xv = xv.permute(0, 2, 1, 3)
+            output = torch.nn.functional.scaled_dot_product_attention(
+                xq, xk, xv, attn_mask=None
+            )
+            output = output.permute(0, 2, 1, 3).reshape(bsz, seqlen, -1)
+        else:
+            xq, xk, xv = torch.split(xqkv, 1, dim=2)
+            xq = xq.squeeze(2)
+            xk = xk.squeeze(2)
+            xv = xv.squeeze(2)
+
+            xq = xq.permute(0, 2, 1, 3)
+            xk = xk.permute(0, 2, 1, 3)
+            xv = xv.permute(0, 2, 1, 3)
+            output = torch.nn.functional.scaled_dot_product_attention(
+                xq, xk, xv, attn_mask=None
+            )
+            output = output.permute(0, 2, 1, 3).reshape(bsz, seqlen, -1)
+        output = self.out_proj(output)
+        return output
+
+
+@torch.jit.script
+def quick_gelu(x):
+    return x * torch.sigmoid(1.702 * x)
+
+
+class NoTPFeedForward(nn.Module):
+    def __init__(
+        self,
+        cfg,
+        dim: int,
+        hidden_dim: int,
+    ):
+        super().__init__()
+
+        self.fc1 = torch.nn.Linear(dim, hidden_dim, bias=True)
+        self.fc2 = torch.nn.Linear(hidden_dim, dim, bias=True)
+
+    def forward(self, x):
+        output = self.fc2(quick_gelu(self.fc1(x)))
+        return output
+
+
+class LayerNormfp32(torch.nn.LayerNorm):
+    """Subclass torch's LayerNorm to handle fp16."""
+
+    def forward(self, x: torch.Tensor):
+        orig_type = x.dtype
+        ret = super().forward(x.type(torch.float32))
+        return ret.type(orig_type)
+
+
+class NoTPTransformerBlock(nn.Module):
+    def __init__(self, cfg, layer_id: int, multiple_of=256):
+        super().__init__()
+
+        self.n_heads = cfg["num_attention_heads"]
+        self.dim = cfg["hidden_size"]
+        self.head_dim = cfg["hidden_size"] // cfg["num_attention_heads"]
+        self.self_attn = NoTPAttention(cfg)
+        self.mlp = NoTPFeedForward(
+            cfg, dim=cfg["hidden_size"], hidden_dim=cfg["ffn_hidden_size"]
+        )
+        self.layer_id = layer_id
+        self.layer_norm1 = torch.nn.LayerNorm(
+            cfg["hidden_size"], eps=cfg["layernorm_epsilon"]
+        )
+        self.layer_norm2 = torch.nn.LayerNorm(
+            cfg["hidden_size"], eps=cfg["layernorm_epsilon"]
+        )
+
+    def forward(self, x: torch.Tensor):
+        residual = self.self_attn.forward(self.layer_norm1(x))
+        h = x + residual
+        out = h + self.mlp.forward(self.layer_norm2(h))
+        return out
+
+
+class NoTPTransformer(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+
+        self.cfg = cfg
+        self.num_layers = cfg["num_layers"]
+
+        self.layers = torch.nn.ModuleList()
+        for layer_id in range(self.num_layers):
+            self.layers.append(
+                NoTPTransformerBlock(
+                    cfg,
+                    layer_id + 1,
+                )
+            )
+
+    def forward(
+        self,
+        hidden_states,
+    ):
+
+        for layer in self.layers:
+            hidden_states = layer(hidden_states)
+
+        return hidden_states
+
+
+class VitModel(nn.Module):
+    def __init__(self, cfg, freeze_embed=False, freeze_pre_norm=False) -> None:
+        super().__init__()
+
+        self.embeddings = CLIPVisionEmbeddings(
+            hidden_size=cfg["hidden_size"],
+            image_size=cfg["image_size"],
+            patch_size=cfg["patch_size"],
+        )
+
+        if freeze_embed:
+            for _, param in self.embeddings.named_parameters():
+                param.requires_grad = False
+
+        self.transformer = NoTPTransformer(cfg=cfg)
+
+        if cfg.get("fp32norm", False):
+            logger.info("Load fp32 layernorm for ViT.")
+            self.pre_layrnorm = LayerNormfp32(
+                cfg["hidden_size"],
+                eps=cfg.get("pre_layernorm_epsilon", 1e-5),
+            )
+        else:
+            self.pre_layrnorm = torch.nn.LayerNorm(
+                cfg["hidden_size"],
+                eps=cfg.get("pre_layernorm_epsilon", 1e-5),
+            )
+
+        if freeze_pre_norm:
+            for _, param in self.pre_layrnorm.named_parameters():
+                param.requires_grad = False
+
+        for p in self.parameters():
+            p.micro_dp = True
+
+    @property
+    def dtype(self):
+        return next(self.parameters()).dtype
+
+    def set_input_tensor(self, input_tensor):
+        if not isinstance(input_tensor, list):
+            input_tensor = [input_tensor]
+        self.transformer.set_input_tensor(input_tensor[0])
+
+    def __str__(self) -> str:
+        return "open_clip"
+
+    def forward(self, x, patch_embeds):
+        x = self.embeddings(x, patch_embeds)
+        hidden_states = self.pre_layrnorm(x)
+
+        output = self.transformer(hidden_states)
+
+        return output
+
+
+vit_model_cfg = dict(
+    num_layers=24,
+    hidden_size=1024,
+    num_heads=16,
+    num_attention_heads=16,
+    ffn_hidden_size=4096,
+    seq_length=256,
+    max_position_embeddings=256,
+    use_flash_attn=False,
+    understand_projector_stride=2,
+    hidden_dropout=0.0,
+    attention_dropout=0.0,
+    no_persist_layer_norm=False,
+    layernorm_epsilon=1e-5,
+    pre_layernorm_epsilon=1e-5,
+    image_size=224,
+    patch_size=14,
+    recompute_list=[],
+)
+
+
+def build_clip_l():
+    return VitModel(
+        cfg=vit_model_cfg,
+        freeze_embed=False,
+        freeze_pre_norm=False,
+    )
+
+
+class DeepseekOCRForCausalLM(nn.Module):
+    def __init__(
+        self,
+        *,
+        config: DeepseekVLV2Config,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+
+        self.config = config
+
+        self.vision_config = config.vision_config
+        self.projector_config = config.projector_config
+        self.text_config = config.text_config
+
+        n_embed = 1280
+
+        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(n_embed, dtype=torch.float32))
+        if self.tile_tag == "2D":
+            # <|view_separator|>, <|\n|>
+            self.image_newline = nn.Parameter(torch.randn(n_embed) * embed_std)
+            self.view_seperator = nn.Parameter(torch.randn(n_embed) * embed_std)
+        else:
+            raise ValueError(
+                f"Only 2D tile_tag is supported currently, got: {self.tile_tag}"
+            )
+
+        if self.text_config.topk_method == "noaux_tc":
+            self.model = DeepseekV3ForCausalLM(
+                config=config.text_config,
+                quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "language"),
+            )
+        elif not self.text_config.use_mla:
+            self.model = DeepseekForCausalLM(
+                config=config.text_config,
+                quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "language"),
+            )
+        else:
+            self.model = DeepseekV2ForCausalLM(
+                config=config.text_config,
+                quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "language"),
+            )
+
+        self.sam_model = build_sam_vit_b()
+        self.vision_model = build_clip_l()
+        n_embed = 1280
+        self.projector = MlpProjector(
+            projector_type="linear",
+            input_dim=2048,
+            n_embed=n_embed,
+        )
+
+    def _parse_and_validate_image_input(self, **kwargs: object):
+
+        pixel_values = kwargs.pop("pixel_values", None)
+        images_spatial_crop = kwargs.pop("images_spatial_crop", None)
+        images_crop = kwargs.pop("images_crop", None)
+
+        if pixel_values is None or torch.sum(pixel_values).item() == 0:
+            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)}"
+                )
+
+            if not isinstance(images_crop, (torch.Tensor, list)):
+                raise ValueError(
+                    "Incorrect type of image crop. " f"Got type: {type(images_crop)}"
+                )
+
+            return [pixel_values, images_crop, images_spatial_crop]
+
+        raise AssertionError("This line should be unreachable.")
+
+    def _pixel_values_to_embedding(
+        self,
+        pixel_values: torch.Tensor,
+        images_crop: torch.Tensor,
+        images_spatial_crop: torch.Tensor,
+    ) -> NestedTensors:
+
+        # Pixel_values (global view): [n_image, batch_size, 3, height, width]
+        # images_spatial_crop: [n_image, batch_size, [num_tiles_w, num_tiles_h]]
+        # images_crop (local view): [n_image, batch_size, num_pathes, 3, h, w]
+        # split the pixel and image_crop, all batch_size = 1
+
+        images_in_this_batch = []
+
+        with torch.no_grad():
+            for jdx in range(images_spatial_crop.size(0)):
+                patches = images_crop[jdx][0].to(torch.bfloat16)
+                image_ori = pixel_values[jdx]
+                crop_shape = images_spatial_crop[jdx][0]
+
+                if torch.sum(patches).item() != 0:
+                    local_features_1 = self.sam_model(patches)
+                    local_features_2 = self.vision_model(patches, local_features_1)
+
+                    local_features = torch.cat(
+                        (
+                            local_features_2[:, 1:],
+                            local_features_1.flatten(2).permute(0, 2, 1),
+                        ),
+                        dim=-1,
+                    )
+                    local_features = self.projector(local_features)
+
+                    global_features_1 = self.sam_model(image_ori)
+                    global_features_2 = self.vision_model(image_ori, global_features_1)
+                    global_features = torch.cat(
+                        (
+                            global_features_2[:, 1:],
+                            global_features_1.flatten(2).permute(0, 2, 1),
+                        ),
+                        dim=-1,
+                    )
+                    global_features = self.projector(global_features)
+
+                    _, hw, n_dim = global_features.shape
+                    h = w = int(hw**0.5)
+
+                    _2, hw2, n_dim2 = local_features.shape
+                    h2 = w2 = int(hw2**0.5)
+
+                    width_crop_num, height_crop_num = int(crop_shape[0]), int(
+                        crop_shape[1]
+                    )
+
+                    global_features = global_features.view(h, w, n_dim)
+
+                    global_features = torch.cat(
+                        [
+                            global_features,
+                            self.image_newline[None, None, :].expand(h, 1, n_dim),
+                        ],
+                        dim=1,
+                    )
+
+                    global_features = global_features.view(-1, n_dim)
+
+                    local_features = (
+                        local_features.view(
+                            height_crop_num, width_crop_num, h2, w2, n_dim2
+                        )
+                        .permute(0, 2, 1, 3, 4)
+                        .reshape(height_crop_num * h2, width_crop_num * w2, n_dim2)
+                    )
+                    local_features = torch.cat(
+                        [
+                            local_features,
+                            self.image_newline[None, None, :].expand(
+                                height_crop_num * h2, 1, n_dim2
+                            ),
+                        ],
+                        dim=1,
+                    )
+                    local_features = local_features.view(-1, n_dim2)
+
+                    global_local_features = torch.cat(
+                        [local_features, global_features, self.view_seperator[None, :]],
+                        dim=0,
+                    )
+
+                else:
+                    global_features_1 = self.sam_model(image_ori)
+                    global_features_2 = self.vision_model(image_ori, global_features_1)
+                    global_features = torch.cat(
+                        (
+                            global_features_2[:, 1:],
+                            global_features_1.flatten(2).permute(0, 2, 1),
+                        ),
+                        dim=-1,
+                    )
+                    global_features = self.projector(global_features)
+
+                    _, hw, n_dim = global_features.shape
+                    h = w = int(hw**0.5)
+
+                    global_features = global_features.view(h, w, n_dim)
+
+                    global_features = torch.cat(
+                        [
+                            global_features,
+                            self.image_newline[None, None, :].expand(h, 1, n_dim),
+                        ],
+                        dim=1,
+                    )
+
+                    global_features = global_features.view(-1, n_dim)
+
+                    global_local_features = torch.cat(
+                        [global_features, self.view_seperator[None, :]], dim=0
+                    )
+
+                images_in_this_batch.append(global_local_features)
+
+        return images_in_this_batch
+
+    def _process_image_input(self, mm_items: List[MultimodalDataItem]) -> torch.Tensor:
+        pixel_values = torch.stack([item.feature for item in mm_items], dim=0).type(
+            self.vision_model.dtype
+        )
+
+        images_crop = (
+            torch.stack([item.images_crop for item in mm_items], dim=0)
+            .type(torch.long)
+            .to(device=pixel_values.device)
+        )
+        images_spatial_crop = (
+            torch.cat([item.images_spatial_crop for item in mm_items], dim=0)
+            .type(torch.long)
+            .to(device=pixel_values.device)
+        )
+
+        assert images_crop.dim() == 6
+        assert images_spatial_crop.dim() == 3
+
+        vision_feature_lists = self._pixel_values_to_embedding(
+            pixel_values=pixel_values,
+            images_crop=images_crop,
+            images_spatial_crop=images_spatial_crop,
+        )
+        vision_features = torch.cat(vision_feature_lists, dim=0).type(
+            self.vision_model.dtype
+        )
+
+        return vision_features
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.model
+
+    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
+        vision_embeddings = self._process_image_input(image_input)
+        return vision_embeddings
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+
+        inputs_embeds = self.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 pad_input_ids(self, input_ids: List[int], mm_inputs: MultimodalInputs):
+        pattern = MultiModalityDataPaddingPatternMultimodalTokens()
+        return pattern.pad_input_tokens(input_ids, mm_inputs)
+
+    def get_image_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
+        vision_embeddings = self._process_image_input(items)
+        return vision_embeddings
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        **kwargs: object,
+    ):
+        hidden_states = general_mm_embed_routine(
+            input_ids=input_ids,
+            forward_batch=forward_batch,
+            language_model=self.model,
+            multimodal_model=self,
+            positions=positions,
+        )
+
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            (".qkv_proj", ".q_proj", "q"),
+            (".qkv_proj", ".k_proj", "k"),
+            (".qkv_proj", ".v_proj", "v"),
+            (".gate_up_proj", ".gate_proj", 0),
+            (".gate_up_proj", ".up_proj", 1),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: Set[str] = set()
+
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+            if name == "lm_head.weight":
+                name = "model.lm_head.weight"
+            elif name.startswith("model."):
+                if (
+                    "image_newline" in name
+                    or ".projector" in name
+                    or "vision_model" in name
+                    or "sam_model" in name
+                    or "view_seperator" in name
+                ):
+                    name = name[len("model.") :]
+                elif not (
+                    ".projector" in name
+                    or "vision_model" in name
+                    or "sam_model" in name
+                    or "image_newline" in name
+                ):
+                    name = name.replace("model.", "model.model.")
+
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                # Skip experts that are not assigned to this worker.
+                if (
+                    "mlp.experts." in name or "mlp.shared_experts." in name
+                ) and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                # Skip experts that are not assigned to this worker.
+                if (
+                    "mlp.experts." in name or "mlp.shared_experts." in name
+                ) and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        unloaded_params = params_dict.keys() - loaded_params
+        if unloaded_params:
+            raise RuntimeError(
+                f"Some weights are not initialized from checkpoints: {unloaded_params}"
+            )
+
+
+EntryClass = [DeepseekOCRForCausalLM]

python/sglang/srt/models/deepseek_v2.py

@@ -200,7 +200,6 @@ _is_flashinfer_available = is_flashinfer_available()
 _is_sm100_supported = is_cuda() and is_sm100_supported()
 _is_cublas_ge_129 = is_nvidia_cublas_cu12_version_ge_12_9()
 
-
 logger = logging.getLogger(__name__)
 
 

python/sglang/srt/multimodal/processors/base_processor.py

@@ -178,6 +178,7 @@ class BaseMultimodalProcessor(ABC):
             "image_attention_mask": Modality.IMAGE,
             "image_emb_mask": Modality.IMAGE,
             "images_spatial_crop": Modality.IMAGE,
+            "images_crop": Modality.IMAGE,
             "tgt_size": Modality.IMAGE,
             "image_grid_hws": Modality.IMAGE,
             "aspect_ratio_ids": Modality.IMAGE,

python/sglang/srt/multimodal/processors/deepseek_ocr.py

+from typing import List, Union
+
+from sglang.srt.models.deepseek_ocr import DeepseekOCRForCausalLM
+from sglang.srt.multimodal.processors.base_processor import (
+    BaseMultimodalProcessor,
+    MultimodalSpecialTokens,
+)
+
+
+class DeepseekOCRProcessor(BaseMultimodalProcessor):
+    models = [DeepseekOCRForCausalLM]
+
+    def __init__(self, hf_config, server_args, _processor, *args, **kwargs):
+        _processor.image_size = 640
+        super().__init__(hf_config, server_args, _processor, *args, **kwargs)
+        self.mm_tokens = MultimodalSpecialTokens(
+            image_token="<image>", image_token_id=self._processor.image_token_id
+        ).build(_processor)
+
+    async def process_mm_data_async(
+        self, image_data: List[Union[str, bytes]], input_text, *args, **kwargs
+    ):
+        base_output = self.load_mm_data(
+            prompt=input_text,
+            multimodal_tokens=self.mm_tokens,
+            image_data=image_data,
+        )
+
+        mm_items, input_ids, _ = self.process_and_combine_mm_data(
+            base_output, self.mm_tokens
+        )
+
+        return {
+            "input_ids": input_ids.tolist(),
+            "mm_items": mm_items,
+            "im_token_id": self.mm_tokens.image_token_id,
+        }

python/sglang/srt/parser/conversation.py

@@ -838,6 +838,19 @@ register_conv_template(
     )
 )
 
+register_conv_template(
+    Conversation(
+        name="deepseek-ocr",
+        system_message="",
+        system_template="",
+        roles=("", ""),
+        sep="",
+        sep_style=SeparatorStyle.NO_COLON_SINGLE,
+        stop_str=["<｜end▁of▁sentence｜>"],
+        image_token="<image>",
+    )
+)
+
 register_conv_template(
     Conversation(
         name="deepseek-vl2",
@@ -981,6 +994,7 @@ MODEL_TYPE_TO_TEMPLATE = {
     "phi4mm": "phi-4-mm",
     "minicpmv": "minicpmv",
     "minicpmo": "minicpmo",
+    "deepseek-ocr": "deepseek-ocr",
 }
 
 
@@ -1057,3 +1071,11 @@ def match_phi_4_mm(model_path: str):
         return "phi-4-mm"
     model_type = get_model_type(model_path)
     return MODEL_TYPE_TO_TEMPLATE.get(model_type)
+
+
+@register_conv_template_matching_function
+def match_deepseek_ocr(model_path: str):
+    if "deepseek-ocr" in model_path.lower():
+        return "deepseek-ocr"
+    model_type = get_model_type(model_path)
+    return MODEL_TYPE_TO_TEMPLATE.get(model_type)

python/sglang/srt/utils/hf_transformers_utils.py

@@ -19,7 +19,7 @@ import os
 import tempfile
 import warnings
 from pathlib import Path
-from typing import Any, Dict, Optional, Type, Union
+from typing import Any, Dict, List, Optional, Type, Union
 
 import torch
 from huggingface_hub import snapshot_download
@@ -51,26 +51,32 @@ from sglang.srt.configs import (
     Qwen3NextConfig,
     Step3VLConfig,
 )
+from sglang.srt.configs.deepseek_ocr import DeepseekVLV2Config
 from sglang.srt.configs.internvl import InternVLChatConfig
 from sglang.srt.connector import create_remote_connector
 from sglang.srt.utils import is_remote_url, logger, lru_cache_frozenset
 
-_CONFIG_REGISTRY: Dict[str, Type[PretrainedConfig]] = {
-    ChatGLMConfig.model_type: ChatGLMConfig,
-    DbrxConfig.model_type: DbrxConfig,
-    ExaoneConfig.model_type: ExaoneConfig,
-    DeepseekVL2Config.model_type: DeepseekVL2Config,
-    MultiModalityConfig.model_type: MultiModalityConfig,
-    KimiVLConfig.model_type: KimiVLConfig,
-    InternVLChatConfig.model_type: InternVLChatConfig,
-    Step3VLConfig.model_type: Step3VLConfig,
-    LongcatFlashConfig.model_type: LongcatFlashConfig,
-    Olmo3Config.model_type: Olmo3Config,
-    Qwen3NextConfig.model_type: Qwen3NextConfig,
-    FalconH1Config.model_type: FalconH1Config,
-    DotsVLMConfig.model_type: DotsVLMConfig,
-    DotsOCRConfig.model_type: DotsOCRConfig,
-    NemotronHConfig.model_type: NemotronHConfig,
+_CONFIG_REGISTRY: List[Type[PretrainedConfig]] = [
+    ChatGLMConfig,
+    DbrxConfig,
+    ExaoneConfig,
+    DeepseekVL2Config,
+    MultiModalityConfig,
+    KimiVLConfig,
+    InternVLChatConfig,
+    Step3VLConfig,
+    LongcatFlashConfig,
+    Olmo3Config,
+    Qwen3NextConfig,
+    FalconH1Config,
+    DotsVLMConfig,
+    DotsOCRConfig,
+    NemotronHConfig,
+    DeepseekVLV2Config,
+]
+
+_CONFIG_REGISTRY = {
+    config_cls.model_type: config_cls for config_cls in _CONFIG_REGISTRY
 }
 
 for name, cls in _CONFIG_REGISTRY.items():
@@ -191,6 +197,11 @@ def get_config(
         config = AutoConfig.from_pretrained(
             model, trust_remote_code=trust_remote_code, revision=revision, **kwargs
         )
+        if "deepseek-ai/DeepSeek-OCR" in model:
+            config.model_type = "deepseek-ocr"
+            # Due to an unknown reason, Hugging Face’s AutoConfig mistakenly recognizes the configuration of deepseek-ocr as deepseekvl2.
+            # This is a temporary workaround and will require further optimization.
+
     except ValueError as e:
         if not "deepseek_v32" in str(e):
             raise e
@@ -213,7 +224,8 @@ def get_config(
             "intermediate_size": 4304,
             "model_type": "siglip_vision_model",
             "num_attention_heads": 16,
-            "num_hidden_layers": 26,  # Model is originally 27-layer, we only need the first 26 layers for feature extraction.
+            "num_hidden_layers": 26,
+            # Model is originally 27-layer, we only need the first 26 layers for feature extraction.
             "patch_size": 14,
         }
         config.vision_config = SiglipVisionConfig(**vision_config)

python/sglang/test/test_utils.py

@@ -619,7 +619,6 @@ def popen_launch_server(
     start_time = time.perf_counter()
     with requests.Session() as session:
         while time.perf_counter() - start_time < timeout:
-
             return_code = process.poll()
             if return_code is not None:
                 # Server failed to start (non-zero exit code) or crashed

test/srt/test_vision_openai_server_a.py

@@ -150,6 +150,62 @@ class TestQwen2AudioServer(AudioOpenAITestMixin):
     model = "Qwen/Qwen2-Audio-7B-Instruct"
 
 
+class TestDeepseekOCRServer(TestOpenAIMLLMServerBase):
+    model = "deepseek-ai/DeepSeek-OCR"
+    trust_remote_code = False
+
+    def verify_single_image_response_for_ocr(self, response):
+        """Verify DeepSeek-OCR grounding output with coordinates"""
+        assert response.choices[0].message.role == "assistant"
+        text = response.choices[0].message.content
+        assert isinstance(text, str)
+
+        # DeepSeek-OCR uses grounding format, outputs coordinates
+        assert "text" in text.lower(), f"OCR text: {text}, should contain 'text'"
+
+        # Verify coordinate format [[x1, y1, x2, y2]]
+        import re
+
+        coord_pattern = r"\[\[[\d\s,]+\]\]"
+        assert re.search(
+            coord_pattern, text
+        ), f"OCR text: {text}, should contain coordinate format [[x1, y1, x2, y2]]"
+
+        # Verify basic response fields
+        assert response.id
+        assert response.created
+        assert response.usage.prompt_tokens > 0
+        assert response.usage.completion_tokens > 0
+        assert response.usage.total_tokens > 0
+
+    def test_single_image_chat_completion(self):
+        client = openai.Client(api_key=self.api_key, base_url=self.base_url)
+        image_url = "https://raw.githubusercontent.com/sgl-project/sgl-test-files/refs/heads/main/images/ocr-text.png"
+
+        response = client.chat.completions.create(
+            model="default",
+            messages=[
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "image_url",
+                            "image_url": {"url": image_url},
+                        },
+                        {
+                            "type": "text",
+                            "text": "<|grounding|>Convert the document to markdown.",
+                        },
+                    ],
+                },
+            ],
+            temperature=0,
+            **(self.get_vision_request_kwargs()),
+        )
+
+        self.verify_single_image_response_for_ocr(response)
+
+
 if __name__ == "__main__":
     del (
         TestOpenAIMLLMServerBase,

test/srt/test_vision_openai_server_common.py

@@ -32,6 +32,7 @@ class TestOpenAIMLLMServerBase(CustomTestCase):
     model: str
     extra_args: list = []
     fixed_args: list = ["--trust-remote-code", "--enable-multimodal"]
+    trust_remote_code: bool = True
 
     @classmethod
     def setUpClass(cls):
@@ -42,7 +43,11 @@ class TestOpenAIMLLMServerBase(CustomTestCase):
             cls.base_url,
             timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
             api_key=cls.api_key,
-            other_args=cls.extra_args + cls.fixed_args,
+            other_args=(
+                cls.extra_args + cls.fixed_args + ["--trust-remote-code"]
+                if cls.trust_remote_code
+                else []
+            ),
         )
         cls.base_url += "/v1"