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_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"