[Bugfix]: distinguish processors for deepseek_vl2 and deepseek_ocr to p… (#12384)

python/sglang/srt/configs/deepseek_ocr.py

@@ -1,8 +1,19 @@
-from typing import Tuple
+import math
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple
 
-import torchvision.transforms as T
+import torch
-from PIL import Image
+from PIL import Image, ImageOps
-from transformers import PretrainedConfig
+from transformers import (
+    AutoProcessor,
+    LlamaTokenizerFast,
+    PretrainedConfig,
+    ProcessorMixin,
+)
+
+from sglang.srt.multimodal.customized_mm_processor_utils import (
+    register_customized_processor,
+)
 
 BASE_SIZE = 1024
 IMAGE_SIZE = 640
@@ -18,18 +29,59 @@ MODEL_PATH = "deepseek-ai/DeepSeek-OCR"  # change to your model path
 PROMPT = "<image>\n<|grounding|>Convert the document to markdown."
 
 
-class ImageTransform:
+class DictOutput(object):
+    def items(self):
+        return self.__dict__.items()
 
+    def keys(self):
+        return self.__dict__.keys()
+
+    def __getitem__(self, item):
+        return self.__dict__[item]
+
+    def __contains__(self, key):
+        return key in self.__dict__
+
+    def __setitem__(self, key, value):
+        self.__dict__[key] = value
+
+
+@dataclass
+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
+    images_seq_mask: torch.BoolTensor
+    images_spatial_crop: torch.LongTensor
+
+    def __len__(self):
+        return len(self.input_ids)
+
+
+class ImageTransform(object):
     def __init__(
         self,
-        mean: Tuple[float, float, float] = (0.5, 0.5, 0.5),
+        mean: Optional[Tuple[float, float, float]] = (0.5, 0.5, 0.5),
-        std: Tuple[float, float, float] = (0.5, 0.5, 0.5),
+        std: Optional[Tuple[float, float, float]] = (0.5, 0.5, 0.5),
         normalize: bool = True,
     ):
         self.mean = mean
         self.std = std
         self.normalize = normalize
 
+        # only load torchvision.transforms when needed
+        try:
+            import torchvision.transforms as T
+
+            # FIXME: add version check for gguf
+        except ImportError as err:
+            raise ImportError(
+                "Please install torchvision via `pip install torchvision` to use Deepseek-VL2."
+            ) from err
+
         transform_pipelines = [T.ToTensor()]
 
         if normalize:
@@ -42,6 +94,464 @@ class ImageTransform:
         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 DeepseekOCRProcessor(ProcessorMixin):
+    tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast")
+    attributes = ["tokenizer"]
+
+    def __init__(
+        self,
+        tokenizer: LlamaTokenizerFast,
+        candidate_resolutions: Tuple[Tuple[int, int]],
+        patch_size: int,
+        downsample_ratio: int,
+        image_mean: Tuple[float, float, float] = (0.5, 0.5, 0.5),
+        image_std: Tuple[float, float, float] = (0.5, 0.5, 0.5),
+        normalize: bool = True,
+        image_token: str = "<image>",
+        pad_token: str = "<｜▁pad▁｜>",
+        add_special_token: bool = False,
+        sft_format: str = "deepseek",
+        mask_prompt: bool = True,
+        ignore_id: int = -100,
+        **kwargs,
+    ):
+
+        self.candidate_resolutions = candidate_resolutions
+        self.image_size = candidate_resolutions[0][0]
+        self.patch_size = patch_size
+        self.image_mean = image_mean
+        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
+        )
+        self.tokenizer = tokenizer
+        # must set this，padding side with make a difference in batch inference
+        self.tokenizer.padding_side = "left"
+
+        # add the pad_token as special token to use 'tokenizer.pad_token' and 'tokenizer.pad_token_id'
+        if tokenizer.pad_token is None:
+            self.tokenizer.add_special_tokens({"pad_token": pad_token})
+
+        # add image token
+        image_token_id = self.tokenizer.vocab.get(image_token)
+        if image_token_id is None:
+            special_tokens = [image_token]
+            special_tokens_dict = {"additional_special_tokens": special_tokens}
+            self.tokenizer.add_special_tokens(special_tokens_dict)
+        self.image_token_id = self.tokenizer.vocab.get(image_token)
+
+        # add five special tokens for grounding-related tasks
+        # <|ref|>, <|/ref|>, <|det|>, <|/det|>, <|grounding|>
+        special_tokens = ["<|ref|>", "<|/ref|>", "<|det|>", "<|/det|>", "<|grounding|>"]
+        special_tokens_dict = {"additional_special_tokens": special_tokens}
+        self.tokenizer.add_special_tokens(special_tokens_dict)
+
+        # add special tokens for SFT data
+        special_tokens = ["<|User|>", "<|Assistant|>"]
+        special_tokens_dict = {"additional_special_tokens": special_tokens}
+        self.tokenizer.add_special_tokens(special_tokens_dict)
+
+        self.image_token = image_token
+        self.pad_token = pad_token
+        self.add_special_token = add_special_token
+        self.sft_format = sft_format
+        self.mask_prompt = mask_prompt
+        self.ignore_id = ignore_id
+
+        super().__init__(
+            tokenizer,
+            **kwargs,
+        )
+
+    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
+        images_list = []
+        images_seq_mask = []
+        images_spatial_crop = []
+
+        image_index = 0
+        image_token_cnt = messages.count(self.image_token)
+        (
+            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,
+        )
+
+        image_index = image_token_cnt
+        images_list += images
+        images_seq_mask += seq_mask
+        images_spatial_crop = spatial_crop
+
+        return (
+            input_ids,
+            masked_tokenized_data,
+            images_list,
+            images_seq_mask,
+            images_spatial_crop,
+            images_crop,
+        )
+
+    @property
+    def bos_id(self):
+        return self.tokenizer.bos_token_id
+
+    @property
+    def eos_id(self):
+        return self.tokenizer.eos_token_id
+
+    @property
+    def pad_id(self):
+        return self.tokenizer.pad_token_id
+
+    def encode(self, text: str, bos: bool = True, eos: bool = False):
+        t = self.tokenizer.encode(text, add_special_tokens=False)
+
+        if bos:
+            t = [self.bos_id] + t
+        if eos:
+            t = t + [self.eos_id]
+
+        return t
+
+    def decode(self, t: List[int], **kwargs) -> str:
+        return self.tokenizer.decode(t, **kwargs)
+
+    def process_one(
+        self,
+        prompt: str = None,
+        conversations: List[Dict[str, str]] = None,
+        images: List[Image.Image] = None,
+        apply_sft_format: bool = False,
+        inference_mode: bool = True,
+        system_prompt: str = "",
+        max_req_input_len: int = -1,
+        cropping: bool = True,
+        **kwargs,
+    ):
+        """
+
+        Args:
+            prompt (str): the formatted prompt;
+            conversations (List[Dict]): conversations with a list of messages;
+            images (List[ImageType]): the list of images;
+            apply_sft_format (bool): if prompt is not None, then apply the SFT format to prompt;
+                if conversations is not None, then it will always apply the SFT format to conversations;
+            inference_mode (bool): if True, then remove the last eos token;
+            system_prompt (str): the system prompt;
+            **kwargs:
+
+        Returns:
+            outputs (BaseProcessorOutput): the output of the processor,
+                - input_ids (torch.LongTensor): [N + image tokens]
+                - target_ids (torch.LongTensor): [N + image tokens]
+                - images (torch.FloatTensor): [n_images, 3, H, W]
+                - image_id (int): the id of the image token
+                - num_image_tokens (List[int]): the number of image tokens
+        """
+
+        prompt = conversations or prompt
+        (
+            input_ids,
+            masked_tokenized_str,
+            images_list,
+            images_seq_mask,
+            images_spatial_crop,
+            images_crop,
+        ) = self.format_messages_v2(prompt, images, max_req_input_len)
+
+        target_ids = torch.LongTensor(masked_tokenized_str)
+
+        if len(images_list) == 0:
+            images = torch.zeros((1, 3, self.image_size, self.image_size))
+        else:
+            images = torch.stack(images_list, dim=0)
+
+        images_spatial_crop = torch.stack(
+            [images_spatial_crop], dim=0
+        )  # stack the tensor to make it a batch of 1
+
+        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,
+        )
+
+        return prepare
+
+    def __call__(
+        self,
+        *,
+        prompt: str = None,
+        conversations: List[Dict[str, str]] = None,
+        images: List[Image.Image] = None,
+        apply_sft_format: bool = False,
+        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 or text,
+            conversations=conversations,
+            images=images,
+            apply_sft_format=apply_sft_format,
+            inference_mode=inference_mode,
+            system_prompt=system_prompt,
+            max_req_input_len=max_req_input_len,
+        )
+
+        return prepare
+
+    def find_all_indices(self, messages, target_value):
+        indices = []
+        for index, item in enumerate(messages):
+            if item == target_value:
+                indices.append(index)
+        return indices
+
+    def tokenize_with_images(
+        self,
+        conversation: str,
+        images: List[Image.Image],
+        bos: bool = True,
+        eos: bool = True,
+        cropping: bool = True,
+    ):
+        """Tokenize text with <image> tags."""
+
+        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)
+
+            image_shapes.append(image.size)
+
+            if image.size[0] <= 640 and image.size[1] <= 640:
+                crop_ratio = [1, 1]
+            else:
+                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.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))
+
+            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"""
+            num_queries = math.ceil(
+                (self.image_size // self.patch_size) / self.downsample_ratio
+            )
+            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)
+            num_image_tokens.append(len(tokenized_image))
+
+        """process the last text split"""
+        tokenized_sep = self.encode(text_splits[-1], bos=False, eos=False)
+
+        tokenized_str += tokenized_sep
+        images_seq_mask += [False] * len(tokenized_sep)
+
+        """add the bos and eos tokens"""
+        if bos:
+            tokenized_str = [self.bos_id] + tokenized_str
+            images_seq_mask = [False] + images_seq_mask
+        if eos:
+            tokenized_str = tokenized_str + [self.eos_id]
+            images_seq_mask = images_seq_mask + [False]
+
+        assert len(tokenized_str) == len(
+            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)}"
+
+        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 VisionEncoderConfig(PretrainedConfig):
     model_type: str = "vision"
 
@@ -223,6 +733,7 @@ class DeepseekV2Config(PretrainedConfig):
         )
 
 
+@register_customized_processor(processor_class=DeepseekOCRProcessor)
 class DeepseekVLV2Config(PretrainedConfig):
     # model_type = "deepseek_vl_v2"
     model_type = "deepseek-ocr"
@@ -232,6 +743,7 @@ class DeepseekVLV2Config(PretrainedConfig):
     tile_tag: str = "2D"
     global_view_pos: str = "head"
     candidate_resolutions: tuple[tuple[int, int]] = ((384, 384),)
+    customized_processor_type: type[Any] = DeepseekOCRProcessor
 
     def __init__(
         self,
@@ -258,5 +770,4 @@ class DeepseekVLV2Config(PretrainedConfig):
         self.hidden_size = self.text_config.hidden_size
 
 
-class DeepseekOCRConfig(DeepseekV2Config):
+AutoProcessor.register(DeepseekVLV2Config, DeepseekOCRProcessor)
-    model_type = "DeepseekOCR"

python/sglang/srt/configs/deepseekvl2.py

@@ -11,8 +11,6 @@ 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
@@ -63,7 +61,6 @@ 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
@@ -107,68 +104,6 @@ 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"]
@@ -198,7 +133,7 @@ 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
         )
@@ -241,7 +176,7 @@ class DeepseekVLV2Processor(ProcessorMixin):
             **kwargs,
         )
 
-    def format_messages_v2(self, messages: str, pil_images, max_req_input_len=-1):
+    def format_messages_v2(self, messages, 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
@@ -251,34 +186,35 @@ 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
+        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)}"
 
         return (
-            input_ids,
+            tokenized_data,
             masked_tokenized_data,
             images_list,
             images_seq_mask,
             images_spatial_crop,
-            images_crop,
         )
 
     @property
@@ -315,7 +251,6 @@ class DeepseekVLV2Processor(ProcessorMixin):
         inference_mode: bool = True,
         system_prompt: str = "",
         max_req_input_len: int = -1,
-        cropping: bool = True,
         **kwargs,
     ):
         """
@@ -339,22 +274,47 @@ class DeepseekVLV2Processor(ProcessorMixin):
                 - num_image_tokens (List[int]): the number of image tokens
         """
 
-        prompt = conversations or prompt
+        assert (
+            prompt is None or conversations is None
+        ), "prompt and conversations cannot be used at the same time."
+
         (
-            input_ids,
+            tokenized_str,
             masked_tokenized_str,
             images_list,
             images_seq_mask,
             images_spatial_crop,
-            images_crop,
+        ) = self.format_messages_v2(conversations, images, max_req_input_len)
-        ) = 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
@@ -363,7 +323,6 @@ 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,
@@ -381,14 +340,10 @@ 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 or text,
+            prompt=prompt,
             conversations=conversations,
             images=images,
             apply_sft_format=apply_sft_format,
@@ -413,83 +368,85 @@ 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)
 
-            image_shapes.append(image.size)
+            """select best resolution for anyres"""
-
-            if image.size[0] <= 640 and image.size[1] <= 640:
-                crop_ratio = [1, 1]
-            else:
             if cropping:
-                    images_crop_raw, crop_ratio = dynamic_preprocess(
+                best_width, best_height = select_best_resolution(
-                        image, image_size=IMAGE_SIZE
+                    image.size, self.candidate_resolutions
                 )
             else:
-                    crop_ratio = [1, 1]
+                best_width, best_height = self.image_size, self.image_size
+            # print(image.size, (best_width, best_height)) # check the select_best_resolutions func
 
             """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.base_size, self.base_size),
+                (self.image_size, self.image_size),
                 color=tuple(int(x * 255) for x in self.image_transform.mean),
             )
             images_list.append(self.image_transform(global_view))
 
-            num_width_tiles, num_height_tiles = crop_ratio
+            """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,
+            )
             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"""
-            num_queries = math.ceil(
+            h = w = math.ceil(
                 (self.image_size // self.patch_size) / self.downsample_ratio
             )
-            num_queries_base = math.ceil(
+            # global views tokens h * (w + 1), 1 is for line separator
-                (self.base_size // self.patch_size) / self.downsample_ratio
+            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)
             )
 
-            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)
-            num_image_tokens.append(len(tokenized_image))
+            # print(width_crop_num, height_crop_num, len(tokenized_image)) # test the correctness of the number of image-related tokens
 
         """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)
 
@@ -505,64 +462,7 @@ 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)}"
 
-        masked_tokenized_str = []
+        return tokenized_str, images_list, images_seq_mask, images_spatial_crop
-        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):
@@ -647,6 +547,7 @@ class DeepseekVL2MlpProjectorConfig(PretrainedConfig):
 
 
 class DeepseekV2Config(PretrainedConfig):
+
     model_type = "deepseek_v2"
     keys_to_ignore_at_inference = ["past_key_values"]
 

python/sglang/srt/multimodal/customized_mm_processor_utils.py

@@ -0,0 +1,35 @@
+from typing import Dict, Type
+
+from transformers import PretrainedConfig, ProcessorMixin
+
+# Useful for registering a custom processor different from Hugging Face's default.
+_CUSTOMIZED_MM_PROCESSOR: Dict[str, Type[ProcessorMixin]] = dict()
+
+
+def register_customized_processor(
+    processor_class: Type[ProcessorMixin],
+):
+    """Class decorator that maps a config class's model_type field to a customized processor class.
+
+    Args:
+        processor_class: A processor class that inherits from ProcessorMixin
+
+    Example:
+        ```python
+        @register_customized_processor(MyCustomProcessor)
+        class MyModelConfig(PretrainedConfig):
+            model_type = "my_model"
+
+        ```
+    """
+
+    def decorator(config_class: PretrainedConfig):
+        if not hasattr(config_class, "model_type"):
+            raise ValueError(
+                f"Class {config_class.__name__} with register_customized_processor should "
+                f"have a 'model_type' class attribute."
+            )
+        _CUSTOMIZED_MM_PROCESSOR[config_class.model_type] = processor_class
+        return config_class
+
+    return decorator

python/sglang/srt/utils/hf_transformers_utils.py

@@ -54,6 +54,7 @@ from sglang.srt.configs import (
 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.multimodal.customized_mm_processor_utils import _CUSTOMIZED_MM_PROCESSOR
 from sglang.srt.utils import is_remote_url, logger, lru_cache_frozenset
 
 _CONFIG_REGISTRY: List[Type[PretrainedConfig]] = [
@@ -172,6 +173,16 @@ def _load_deepseek_v32_model(
     )
 
 
+def _is_deepseek_ocr_model(config: PretrainedConfig) -> bool:
+    # TODO: Remove this workaround related when AutoConfig correctly identifies deepseek-ocr.
+    # Hugging Face's AutoConfig currently misidentifies it as deepseekvl2.
+    return (
+        getattr(config, "auto_map", None) is not None
+        and config.auto_map.get("AutoModel")
+        == "modeling_deepseekocr.DeepseekOCRForCausalLM"
+    )
+
+
 @lru_cache_frozenset(maxsize=32)
 def get_config(
     model: str,
@@ -235,11 +246,7 @@ def get_config(
     if config.model_type in _CONFIG_REGISTRY:
         model_type = config.model_type
         if model_type == "deepseek_vl_v2":
-            if (
+            if _is_deepseek_ocr_model(config):
-                getattr(config, "auto_map", None) is not None
-                and config.auto_map.get("AutoModel")
-                == "modeling_deepseekocr.DeepseekOCRForCausalLM"
-            ):
                 model_type = "deepseek-ocr"
         config_class = _CONFIG_REGISTRY[model_type]
         config = config_class.from_pretrained(model, revision=revision)
@@ -445,6 +452,10 @@ def get_processor(
         **kwargs,
     )
 
+    if _is_deepseek_ocr_model(config):
+        # Temporary hack for load deepseek-ocr
+        config.model_type = "deepseek-ocr"
+
     # fix: for Qwen2-VL and Sarashina2Vision models, inject default 'size' if not provided.
     if config.model_type in {"qwen2_vl", "sarashina2_vision"}:
         if "size" not in kwargs:
@@ -461,6 +472,15 @@ def get_processor(
                 revision=revision,
                 **kwargs,
             )
+        else:
+            if config.model_type in _CUSTOMIZED_MM_PROCESSOR:
+                processor = _CUSTOMIZED_MM_PROCESSOR[config.model_type].from_pretrained(
+                    tokenizer_name,
+                    *args,
+                    trust_remote_code=trust_remote_code,
+                    revision=revision,
+                    **kwargs,
+                )
             else:
                 processor = AutoProcessor.from_pretrained(
                     tokenizer_name,