Support InternVL3 (#5350)

Co-authored-by: Mick <mickjagger19@icloud.com>
Co-authored-by: Chayenne <zhaochen20@outlook.com>

python/sglang/lang/chat_template.py

@@ -270,6 +270,29 @@ register_chat_template(
     )
 )
 
+register_chat_template(
+    ChatTemplate(
+        name="janus",
+        default_system_prompt=None,
+        role_prefix_and_suffix={
+            "system": (
+                "",
+                "",
+            ),
+            "user": (
+                "<｜User｜>",
+                "",
+            ),
+            "assistant": (
+                "<｜Assistant｜>",
+                "<｜end▁of▁sentence｜>",
+            ),
+        },
+        stop_str=("<｜end▁of▁sentence｜>",),
+        image_token="<image_placeholder>\n",
+    )
+)
+
 # The difference between "llama-3-instruct-llava" and "llama-3-instruct" is that llava uses a different image_token.
 register_chat_template(
     ChatTemplate(
@@ -395,6 +418,20 @@ register_chat_template(
     )
 )
 
+# Adapted from https://huggingface.co/OpenGVLab/InternVL2-4B/blob/main/modeling_intern_vit.py
+register_chat_template(
+    ChatTemplate(
+        name="internvl-2-5",
+        default_system_prompt="你是书生·万象，英文名是InternVL，是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。",
+        role_prefix_and_suffix={
+            "system": ("<|im_start|>system\n", "<|im_end|>\n"),
+            "user": ("<|im_start|>user\n", "<|im_end|>\n"),
+            "assistant": ("<|im_start|>assistant\n", "<|im_end|>\n"),
+        },
+        stop_str=["<|im_end|>", "<|action_end|>"],
+    )
+)
+
 register_chat_template(
     ChatTemplate(
         name="granite-3-instruct",
@@ -565,6 +602,13 @@ def match_gemma3_instruct(model_path: str):
         return get_chat_template("gemma-it")
 
 
+@register_chat_template_matching_function
+def match_internvl_chat(model_path: str):
+    model_path = model_path.lower()
+    if "internvl" in model_path:
+        return get_chat_template("internvl-2-5")
+
+
 if __name__ == "__main__":
     messages = [
         {"role": "system", "content": None},  # None means default

python/sglang/srt/configs/internvl.py

+import copy
+import os
+from shutil import copyfile
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import sentencepiece as spm
+from transformers import (
+    TOKENIZER_MAPPING,
+    LlamaConfig,
+    Phi3Config,
+    PretrainedConfig,
+    PreTrainedTokenizer,
+    PreTrainedTokenizerFast,
+    Qwen2Config,
+)
+
+from sglang.utils import logger
+
+# Copied from: https://github.com/OpenGVLab/InternVL/blob/34a81000402bf8f716bab8c9b57aff1f6b436bd0/internvl_chat/internvl/model/internvl_chat/configuration_internvl_chat.py#L21
+
+
+VOCAB_FILES_NAMES = {"vocab_file": "./tokenizer.model"}
+
+PRETRAINED_VOCAB_FILES_MAP = {}
+
+
+# Modified from transformers.model.llama.configuration_llama.LlamaConfig
+class InternLM2Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`InternLM2Model`]. It is used to instantiate
+    an InternLM2 model according to the specified arguments, defining the model architecture. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the InternLM2-7B.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the InternLM2 model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`InternLM2Model`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 2048).
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-12):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        tie_word_embeddings(`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        Example:
+
+    """
+
+    model_type = "internlm2"
+    _auto_class = "AutoConfig"
+
+    def __init__(  # pylint: disable=W0102
+        self,
+        vocab_size=103168,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        bias=True,
+        rope_theta=10000,
+        rope_scaling=None,
+        attn_implementation="eager",
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.bias = bias
+
+        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 = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+
+        self.attn_implementation = attn_implementation
+        if self.attn_implementation is None:
+            self.attn_implementation = "eager"
+        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,
+        )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if (
+            rope_scaling_factor is None
+            or not isinstance(rope_scaling_factor, float)
+            or rope_scaling_factor < 1.0
+        ):
+            raise ValueError(
+                f"`rope_scaling`'s factor field must be a float >= 1, got {rope_scaling_factor}"
+            )
+
+
+class InternVisionConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`InternVisionModel`]. It is used to
+    instantiate a vision encoder according to the specified arguments, defining the model architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        num_channels (`int`, *optional*, defaults to 3):
+            Number of color channels in the input images (e.g., 3 for RGB).
+        patch_size (`int`, *optional*, defaults to 14):
+            The size (resolution) of each patch.
+        image_size (`int`, *optional*, defaults to 224):
+            The size (resolution) of each image.
+        qkv_bias (`bool`, *optional*, defaults to `False`):
+            Whether to add a bias to the queries and values in the self-attention layers.
+        hidden_size (`int`, *optional*, defaults to 3200):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_attention_heads (`int`, *optional*, defaults to 25):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (`int`, *optional*, defaults to 12800):
+            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+        qk_normalization (`bool`, *optional*, defaults to `True`):
+            Whether to normalize the queries and keys in the self-attention layers.
+        num_hidden_layers (`int`, *optional*, defaults to 48):
+            Number of hidden layers in the Transformer encoder.
+        use_flash_attn (`bool`, *optional*, defaults to `True`):
+            Whether to use flash attention mechanism.
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"selu"` and `"gelu_new"` ``"gelu"` are supported.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-6):
+            The epsilon used by the layer normalization layers.
+        dropout (`float`, *optional*, defaults to 0.0):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        drop_path_rate (`float`, *optional*, defaults to 0.0):
+            Dropout rate for stochastic depth.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        initializer_factor (`float`, *optional*, defaults to 0.1):
+            A factor for layer scale.
+    """
+
+    model_type = "intern_vit_6b"
+
+    def __init__(
+        self,
+        num_channels=3,
+        patch_size=14,
+        image_size=224,
+        qkv_bias=False,
+        hidden_size=3200,
+        num_attention_heads=25,
+        intermediate_size=12800,
+        qk_normalization=True,
+        num_hidden_layers=48,
+        use_flash_attn=True,
+        hidden_act="gelu",
+        layer_norm_eps=1e-6,
+        dropout=0.0,
+        drop_path_rate=0.0,
+        attention_dropout=0.0,
+        initializer_range=0.02,
+        initializer_factor=0.1,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.dropout = dropout
+        self.drop_path_rate = drop_path_rate
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_channels = num_channels
+        self.patch_size = patch_size
+        self.image_size = image_size
+        self.initializer_range = initializer_range
+        self.initializer_factor = initializer_factor
+        self.attention_dropout = attention_dropout
+        self.layer_norm_eps = layer_norm_eps
+        self.hidden_act = hidden_act
+        self.qkv_bias = qkv_bias
+        self.qk_normalization = qk_normalization
+        self.use_flash_attn = use_flash_attn
+
+    @classmethod
+    def from_pretrained(
+        cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs
+    ) -> "PretrainedConfig":
+        config_dict, kwargs = cls.get_config_dict(
+            pretrained_model_name_or_path, **kwargs
+        )
+
+        if "vision_config" in config_dict:
+            config_dict = config_dict["vision_config"]
+
+        if (
+            "model_type" in config_dict
+            and hasattr(cls, "model_type")
+            and config_dict["model_type"] != cls.model_type
+        ):
+            logger.warning(
+                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
+                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
+            )
+
+        return cls.from_dict(config_dict, **kwargs)
+
+
+class InternVLChatConfig(PretrainedConfig):
+    model_type = "internvl_chat"
+    is_composition = True
+
+    def __init__(
+        self,
+        vision_config=None,
+        llm_config=None,
+        use_backbone_lora=0,
+        use_llm_lora=0,
+        pad2square=False,
+        select_layer=-1,
+        force_image_size=None,
+        downsample_ratio=0.5,
+        template=None,
+        dynamic_image_size=False,
+        use_thumbnail=False,
+        ps_version="v1",
+        min_dynamic_patch=1,
+        max_dynamic_patch=6,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        if vision_config is None:
+            vision_config = {"architectures": ["InternVisionModel"]}
+            logger.info(
+                "vision_config is None. Initializing the InternVisionConfig with default values."
+            )
+
+        if llm_config is None:
+            # TODO: There might still be a bug in transformers version 4.44 and above.
+            llm_config = {"architectures": [""]}
+            logger.info(
+                "llm_config is None. Initializing the LlamaConfig config with default values (`LlamaConfig`)."
+            )
+        self.vision_config = InternVisionConfig(**vision_config)
+        if llm_config["architectures"][0] == "LlamaForCausalLM":
+            self.llm_config = LlamaConfig(**llm_config)
+        elif llm_config["architectures"][0] == "InternLM2ForCausalLM":
+            self.llm_config = InternLM2Config(**llm_config)
+        elif llm_config["architectures"][0] == "Phi3ForCausalLM":
+            self.llm_config = Phi3Config(**llm_config)
+        elif llm_config["architectures"][0] == "Qwen2ForCausalLM":
+            self.llm_config = Qwen2Config(**llm_config)
+        else:
+            raise ValueError(
+                "Unsupported architecture: {}".format(llm_config["architectures"][0])
+            )
+        self.use_backbone_lora = use_backbone_lora
+        self.use_llm_lora = use_llm_lora
+        self.pad2square = pad2square
+        self.select_layer = select_layer
+        self.force_image_size = force_image_size
+        self.downsample_ratio = downsample_ratio
+        self.template = template
+        self.dynamic_image_size = dynamic_image_size
+        self.use_thumbnail = use_thumbnail
+        self.ps_version = ps_version  # pixel shuffle version
+        self.min_dynamic_patch = min_dynamic_patch
+        self.max_dynamic_patch = max_dynamic_patch
+
+        self.hidden_size = self.llm_config.hidden_size
+        # By default, we use tie_word_embeddings=False for models of all sizes.
+        self.tie_word_embeddings = False
+        self.llm_config.tie_word_embeddings = self.tie_word_embeddings
+
+    def to_dict(self):
+        """
+        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
+
+        Returns:
+            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
+        """
+        output = copy.deepcopy(self.__dict__)
+        output["vision_config"] = self.vision_config.to_dict()
+        output["llm_config"] = self.llm_config.to_dict()
+        output["model_type"] = self.__class__.model_type
+        output["use_backbone_lora"] = self.use_backbone_lora
+        output["use_llm_lora"] = self.use_llm_lora
+        output["select_layer"] = self.select_layer
+        output["force_image_size"] = self.force_image_size
+        output["downsample_ratio"] = self.downsample_ratio
+        output["template"] = self.template
+        output["dynamic_image_size"] = self.dynamic_image_size
+        output["use_thumbnail"] = self.use_thumbnail
+        output["ps_version"] = self.ps_version
+        output["min_dynamic_patch"] = self.min_dynamic_patch
+        output["max_dynamic_patch"] = self.max_dynamic_patch
+
+        return output
+
+
+# # Modified from transformers.model.llama.tokenization_llama_fast.LlamaTokenizerFast -> InternLM2TokenizerFast
+# class InternLM2TokenizerFast(PreTrainedTokenizerFast):
+#     vocab_files_names = VOCAB_FILES_NAMES
+#     slow_tokenizer_class = InternLM2Tokenizer
+#     padding_side = 'left'
+#     model_input_names = ['input_ids', 'attention_mask']
+#     _auto_class = 'AutoTokenizer'
+#
+#     def __init__(
+#         self,
+#         vocab_file,
+#         unk_token='<unk>',
+#         bos_token='<s>',
+#         eos_token='</s>',
+#         pad_token='</s>',
+#         sp_model_kwargs: Optional[Dict[str, Any]] = None,
+#         add_bos_token=True,
+#         add_eos_token=False,
+#         decode_with_prefix_space=False,
+#         clean_up_tokenization_spaces=False,
+#         **kwargs,
+#     ):
+#         super().__init__(
+#             vocab_file=vocab_file,
+#             unk_token=unk_token,
+#             bos_token=bos_token,
+#             eos_token=eos_token,
+#             pad_token=pad_token,
+#             sp_model_kwargs=sp_model_kwargs,
+#             add_bos_token=add_bos_token,
+#             add_eos_token=add_eos_token,
+#             decode_with_prefix_space=decode_with_prefix_space,
+#             clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+#             **kwargs,
+#         )
+#         self._add_bos_token = add_bos_token
+#         self._add_eos_token = add_eos_token
+#         self.update_post_processor()
+#         self.vocab_file = vocab_file
+#
+#     @property
+#     def can_save_slow_tokenizer(self) -> bool:
+#         return os.path.isfile(self.vocab_file) if self.vocab_file else False
+#
+#     def update_post_processor(self):
+#         """
+#         Updates the underlying post processor with the current `bos_token` and `eos_token`.
+#         """
+#         bos = self.bos_token
+#         bos_token_id = self.bos_token_id
+#         if bos is None and self.add_bos_token:
+#             raise ValueError('add_bos_token = True but bos_token = None')
+#
+#         eos = self.eos_token
+#         eos_token_id = self.eos_token_id
+#         if eos is None and self.add_eos_token:
+#             raise ValueError('add_eos_token = True but eos_token = None')
+#
+#         single = f"{(bos + ':0 ') if self.add_bos_token else ''}$A:0{(' ' + eos + ':0') if self.add_eos_token else ''}"
+#         pair = f"{single}{(' ' + bos + ':1') if self.add_bos_token else ''} $B:1{(' ' + eos + ':1') if self.add_eos_token else ''}"
+#
+#         special_tokens = []
+#         if self.add_bos_token:
+#             special_tokens.append((bos, bos_token_id))
+#         if self.add_eos_token:
+#             special_tokens.append((eos, eos_token_id))
+#         self._tokenizer.post_processor = processors.TemplateProcessing(
+#             single=single, pair=pair, special_tokens=special_tokens
+#         )
+#
+#     @property
+#     def add_eos_token(self):
+#         return self._add_eos_token
+#
+#     @property
+#     def add_bos_token(self):
+#         return self._add_bos_token
+#
+#     @add_eos_token.setter
+#     def add_eos_token(self, value):
+#         self._add_eos_token = value
+#         self.update_post_processor()
+#
+#     @add_bos_token.setter
+#     def add_bos_token(self, value):
+#         self._add_bos_token = value
+#         self.update_post_processor()
+#
+#     def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
+#         if not self.can_save_slow_tokenizer:
+#             raise ValueError(
+#                 'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
+#                 'tokenizer.'
+#             )
+#
+#         if not os.path.isdir(save_directory):
+#             logger.error(f'Vocabulary path ({save_directory}) should be a directory')
+#             return
+#         out_vocab_file = os.path.join(
+#             save_directory, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']
+#         )
+#
+#         if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
+#             copyfile(self.vocab_file, out_vocab_file)
+#
+#         return (out_vocab_file,)
+
+
+# Modified from transformers.model.llama.tokenization_llama.LlamaTokenizer
+class InternLM2Tokenizer(PreTrainedTokenizer):
+    """
+    Construct a InternLM2 tokenizer. Based on byte-level Byte-Pair-Encoding.
+
+    Args:
+        vocab_file (`str`):
+            Path to the vocabulary file.
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    model_input_names = ["input_ids", "attention_mask"]
+    _auto_class = "AutoTokenizer"
+
+    def __init__(
+        self,
+        vocab_file,
+        unk_token="<unk>",
+        bos_token="<s>",
+        eos_token="</s>",
+        pad_token="</s>",
+        sp_model_kwargs: Optional[Dict[str, Any]] = None,
+        add_bos_token=True,
+        add_eos_token=False,
+        decode_with_prefix_space=False,
+        clean_up_tokenization_spaces=False,
+        **kwargs,
+    ):
+        print("register succeed")
+        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+        self.vocab_file = vocab_file
+        self.add_bos_token = add_bos_token
+        self.add_eos_token = add_eos_token
+        self.decode_with_prefix_space = decode_with_prefix_space
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.Load(vocab_file)
+        self._no_prefix_space_tokens = None
+        super().__init__(
+            bos_token=bos_token,
+            eos_token=eos_token,
+            unk_token=unk_token,
+            pad_token=pad_token,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs,
+        )
+
+    @property
+    def no_prefix_space_tokens(self):
+        if self._no_prefix_space_tokens is None:
+            vocab = self.convert_ids_to_tokens(list(range(self.vocab_size)))
+            self._no_prefix_space_tokens = {
+                i for i, tok in enumerate(vocab) if not tok.startswith("▁")
+            }
+        return self._no_prefix_space_tokens
+
+    @property
+    def vocab_size(self):
+        """Returns vocab size"""
+        return self.sp_model.get_piece_size()
+
+    @property
+    def bos_token_id(self) -> Optional[int]:
+        return self.sp_model.bos_id()
+
+    @property
+    def eos_token_id(self) -> Optional[int]:
+        return self.sp_model.eos_id()
+
+    def get_vocab(self):
+        """Returns vocab as a dict"""
+        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    def _tokenize(self, text):
+        """Returns a tokenized string."""
+        return self.sp_model.encode(text, out_type=str)
+
+    def _convert_token_to_id(self, token):
+        """Converts a token (str) in an id using the vocab."""
+        return self.sp_model.piece_to_id(token)
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        token = self.sp_model.IdToPiece(index)
+        return token
+
+    def _maybe_add_prefix_space(self, tokens, decoded):
+        if tokens and tokens[0] not in self.no_prefix_space_tokens:
+            return " " + decoded
+        else:
+            return decoded
+
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (string) in a single string."""
+        current_sub_tokens = []
+        out_string = ""
+        prev_is_special = False
+        for token in tokens:
+            # make sure that special tokens are not decoded using sentencepiece model
+            if token in self.all_special_tokens:
+                if not prev_is_special:
+                    out_string += " "
+                out_string += self.sp_model.decode(current_sub_tokens) + token
+                prev_is_special = True
+                current_sub_tokens = []
+            else:
+                current_sub_tokens.append(token)
+                prev_is_special = False
+        out_string += self.sp_model.decode(current_sub_tokens)
+        out_string = self.clean_up_tokenization(out_string)
+        out_string = self._maybe_add_prefix_space(tokens=tokens, decoded=out_string)
+        return out_string[1:]
+
+    def save_vocabulary(
+        self, save_directory, filename_prefix: Optional[str] = None
+    ) -> Tuple[str]:
+        """
+        Save the vocabulary and special tokens file to a directory.
+
+        Args:
+            save_directory (`str`):
+                The directory in which to save the vocabulary.
+
+        Returns:
+            `Tuple(str)`: Paths to the files saved.
+        """
+        if not os.path.isdir(save_directory):
+            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+            return
+        out_vocab_file = os.path.join(
+            save_directory,
+            (filename_prefix + "-" if filename_prefix else "")
+            + VOCAB_FILES_NAMES["vocab_file"],
+        )
+
+        if os.path.abspath(self.vocab_file) != os.path.abspath(
+            out_vocab_file
+        ) and os.path.isfile(self.vocab_file):
+            copyfile(self.vocab_file, out_vocab_file)
+        elif not os.path.isfile(self.vocab_file):
+            with open(out_vocab_file, "wb") as fi:
+                content_spiece_model = self.sp_model.serialized_model_proto()
+                fi.write(content_spiece_model)
+
+        return (out_vocab_file,)
+
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        if self.add_bos_token:
+            bos_token_ids = [self.bos_token_id]
+        else:
+            bos_token_ids = []
+
+        output = bos_token_ids + token_ids_0
+
+        if token_ids_1 is not None:
+            output = output + token_ids_1
+
+        if self.add_eos_token:
+            output = output + [self.eos_token_id]
+
+        return output
+
+    def get_special_tokens_mask(
+        self,
+        token_ids_0: List[int],
+        token_ids_1: Optional[List[int]] = None,
+        already_has_special_tokens: bool = False,
+    ) -> List[int]:
+        """
+        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer `prepare_for_model` method.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+
+        Returns:
+            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0,
+                token_ids_1=token_ids_1,
+                already_has_special_tokens=True,
+            )
+
+        if token_ids_1 is None:
+            return [1] + ([0] * len(token_ids_0)) + [1]
+        return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
+
+    def create_token_type_ids_from_sequences(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
+        use of token type ids, therefore a list of zeros is returned.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `List[int]`: List of zeros.
+        """
+        eos = [self.eos_token_id]
+
+        if token_ids_1 is None:
+            return len(token_ids_0 + eos) * [0]
+        return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
+
+
+TOKENIZER_MAPPING.register(
+    InternVLChatConfig, (InternLM2Tokenizer, None), exist_ok=True
+)

python/sglang/srt/configs/model_config.py

@@ -538,6 +538,7 @@ multimodal_model_archs = [
     "Qwen2_5_VLForConditionalGeneration",
     "CLIPModel",
     "KimiVLForConditionalGeneration",
+    "InternVLChatModel",
 ]
 
 

python/sglang/srt/conversation.py

@@ -48,6 +48,7 @@ class SeparatorStyle(IntEnum):
     DeepSeekVL2 = auto()
     QWEN2_VL_EMBED = auto()
     GEMMA3 = auto()
+    MPT = auto()
 
 
 @dataclasses.dataclass
@@ -327,6 +328,16 @@ class Conversation:
                     ret += role
             return ret
 
+        elif self.sep_style == SeparatorStyle.MPT:
+            ret = system_prompt + self.sep
+            for role, message in self.messages:
+                if message:
+                    if type(message) is tuple:
+                        message, _, _ = message
+                    ret += role + message + self.sep
+                else:
+                    ret += role
+            return ret
         else:
             raise ValueError(f"Invalid style: {self.sep_style}")
 
@@ -570,8 +581,11 @@ def generate_chat_conv(
                             real_content += "\n"  # for video
                         real_content += content.text
                     elif content.type == "image_url":
-                        # NOTE: Only works for llava
-                        real_content += image_token
+                        # NOTE: works for llava and intervl2_5
+                        if conv.name == "internvl-2-5":
+                            real_content = image_token + real_content
+                        else:
+                            real_content += image_token
                         conv.append_image(content.image_url.url)
                     elif content.type == "audio_url":
                         real_content += audio_token
@@ -703,6 +717,19 @@ register_conv_template(
     )
 )
 
+register_conv_template(
+    Conversation(
+        name="internvl-2-5",
+        system_template="<|im_start|>system\n{system_message}",
+        system_message="你是书生·万象，英文名是InternVL，是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。",
+        roles=("<|im_start|>user\n", "<|im_start|>assistant\n"),
+        sep_style=SeparatorStyle.MPT,
+        sep="<|im_end|>\n",
+        stop_str=["<|im_end|>", "<|action_end|>"],
+        image_token="<image>",
+    )
+)
+
 # Reference: https://huggingface.co/docs/transformers/main/model_doc/qwen2_vl#usage-example
 register_conv_template(
     Conversation(

python/sglang/srt/hf_transformers_utils.py

@@ -19,6 +19,7 @@ import warnings
 from pathlib import Path
 from typing import Dict, Optional, Type, Union
 
+import transformers
 from huggingface_hub import snapshot_download
 from transformers import (
     AutoConfig,
@@ -26,6 +27,7 @@ from transformers import (
     AutoTokenizer,
     PretrainedConfig,
     PreTrainedTokenizer,
+    PreTrainedTokenizerBase,
     PreTrainedTokenizerFast,
 )
 from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
@@ -38,6 +40,7 @@ from sglang.srt.configs import (
     KimiVLConfig,
     MultiModalityConfig,
 )
+from sglang.srt.configs.internvl import InternVLChatConfig
 from sglang.srt.connector import create_remote_connector
 from sglang.srt.utils import is_remote_url
 
@@ -48,6 +51,7 @@ _CONFIG_REGISTRY: Dict[str, Type[PretrainedConfig]] = {
     DeepseekVL2Config.model_type: DeepseekVL2Config,
     MultiModalityConfig.model_type: MultiModalityConfig,
     KimiVLConfig.model_type: KimiVLConfig,
+    InternVLChatConfig.model_type: InternVLChatConfig,
 }
 
 for name, cls in _CONFIG_REGISTRY.items():
@@ -90,6 +94,12 @@ def get_config(
         config = config_class.from_pretrained(model, revision=revision)
         # NOTE(HandH1998): Qwen2VL requires `_name_or_path` attribute in `config`.
         setattr(config, "_name_or_path", model)
+
+    if isinstance(model, str) and config.model_type == "internvl_chat":
+        for key, val in config.llm_config.__dict__.items():
+            if not hasattr(config, key):
+                setattr(config, key, val)
+
     if model_override_args:
         config.update(model_override_args)
 
@@ -211,6 +221,13 @@ def get_tokenizer(
     return tokenizer
 
 
+# Some models doesn't have an available processor, e.g.: InternVL
+def get_tokenizer_from_processor(processor):
+    if isinstance(processor, PreTrainedTokenizerBase):
+        return processor
+    return processor.tokenizer
+
+
 def get_processor(
     tokenizer_name: str,
     *args,
@@ -246,7 +263,9 @@ def get_processor(
         **kwargs,
     )
 
-    attach_additional_stop_token_ids(processor.tokenizer)
+    tokenizer = get_tokenizer_from_processor(processor)
+
+    attach_additional_stop_token_ids(tokenizer)
     return processor
 
 

python/sglang/srt/managers/multimodal_processors/internvl.py

+# Adapted from https://huggingface.co/OpenGVLab/InternVL2-4B/blob/main/modeling_intern_vit.py
+
+import numpy as np
+import torch
+from decord import VideoReader, cpu
+from numpy.distutils.cpuinfo import cpu
+from PIL import Image
+
+from sglang.srt.managers.multimodal_processors.base_processor import (
+    BaseMultimodalProcessor,
+    MultimodalSpecialTokens,
+)
+from sglang.srt.managers.schedule_batch import Modality, MultimodalDataItem
+from sglang.srt.models.internvl import InternVLChatModel
+
+
+class InternVLImageProcessor(BaseMultimodalProcessor):
+    models = [InternVLChatModel]
+
+    def __init__(self, hf_config, server_args, _image_processor):
+        super().__init__(hf_config, server_args, _image_processor)
+        image_size = hf_config.force_image_size or hf_config.vision_config.image_size
+        patch_size = hf_config.vision_config.patch_size
+
+        self.IMG_CONTEXT_TOKEN = "<IMG_CONTEXT>"
+        self.IMG_START_TOKEN = "<img>"
+        self.IMG_END_TOKEN = "</img>"
+        self.IMG_TOKEN = "<image>"
+        self.num_image_token = int(
+            (image_size // patch_size) ** 2 * (hf_config.downsample_ratio**2)
+        )
+
+        tokenizer = self._processor
+        self.img_start_token_id = tokenizer.convert_tokens_to_ids(self.IMG_START_TOKEN)
+        self.img_end_token_id = tokenizer.convert_tokens_to_ids(self.IMG_END_TOKEN)
+        self.img_context_token_id = tokenizer.convert_tokens_to_ids(
+            self.IMG_CONTEXT_TOKEN
+        )
+
+    @staticmethod
+    def build_transform(input_size):
+        IMAGENET_MEAN = (0.485, 0.456, 0.406)
+        IMAGENET_STD = (0.229, 0.224, 0.225)
+
+        def resize_image(img, size):
+            return img.resize((size, size), Image.Resampling.BICUBIC)
+
+        def to_tensor(img):
+            # Convert PIL Image to numpy array
+            img_array = np.array(img).astype(np.float32) / 255.0
+            # Convert HWC to CHW format
+            img_array = img_array.transpose(2, 0, 1)
+            return torch.from_numpy(img_array)
+
+        def normalize(tensor, mean, std):
+            mean = torch.tensor(mean).view(-1, 1, 1)
+            std = torch.tensor(std).view(-1, 1, 1)
+            return (tensor - mean) / std
+
+        def transform(img):
+            img = img.convert("RGB") if img.mode != "RGB" else img
+            img = resize_image(img, input_size)
+            tensor = to_tensor(img)
+            tensor = normalize(tensor, IMAGENET_MEAN, IMAGENET_STD)
+            return tensor
+
+        return transform
+
+    @staticmethod
+    def dynamic_preprocess(
+        image, min_num=1, max_num=12, image_size=448, use_thumbnail=False
+    ):
+
+        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
+
+        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
+
+    @staticmethod
+    def get_index(bound, fps, max_frame, first_idx=0, num_segments=32):
+        if bound:
+            start, end = bound[0], bound[1]
+        else:
+            start, end = -100000, 100000
+        start_idx = max(first_idx, round(start * fps))
+        end_idx = min(round(end * fps), max_frame)
+        seg_size = float(end_idx - start_idx) / num_segments
+        frame_indices = np.array(
+            [
+                int(start_idx + (seg_size / 2) + np.round(seg_size * idx))
+                for idx in range(num_segments)
+            ]
+        )
+        return frame_indices
+
+    @staticmethod
+    def load_video(video_path, bound=None, input_size=448, max_num=1, num_segments=32):
+        vr = VideoReader(video_path, ctx=cpu(0), num_threads=1)
+        max_frame = len(vr) - 1
+        fps = float(vr.get_avg_fps())
+
+        pixel_values_list, num_patches_list = [], []
+        transform = InternVLImageProcessor.build_transform(input_size=input_size)
+        frame_indices = InternVLImageProcessor.get_index(
+            bound, fps, max_frame, first_idx=0, num_segments=num_segments
+        )
+        for frame_index in frame_indices:
+            img = Image.fromarray(vr[frame_index].asnumpy()).convert("RGB")
+            img = InternVLImageProcessor.dynamic_preprocess(
+                img, image_size=input_size, use_thumbnail=True, max_num=max_num
+            )
+            pixel_values = [transform(tile) for tile in img]
+            pixel_values = torch.stack(pixel_values)
+            num_patches_list.append(pixel_values.shape[0])
+            pixel_values_list.append(pixel_values)
+        pixel_values = torch.cat(pixel_values_list)
+        return pixel_values, num_patches_list
+
+    async def process_mm_data_async(
+        self, image_data, input_text, request_obj, max_req_input_len, **kwargs
+    ):
+        if not image_data:
+            return None
+
+        base_output = self.load_mm_data(
+            prompt=input_text,
+            image_data=image_data,
+            multimodal_tokens=MultimodalSpecialTokens(image_token=self.IMG_TOKEN),
+            max_req_input_len=max_req_input_len,
+            discard_alpha_channel=True,
+        )
+
+        def process_image_internvl(image, input_size=448, max_num=12):
+            transform = InternVLImageProcessor.build_transform(input_size=input_size)
+            images = InternVLImageProcessor.dynamic_preprocess(
+                image, image_size=input_size, use_thumbnail=True, max_num=max_num
+            )
+            pixel_values = [transform(image) for image in images]
+            pixel_values = torch.stack(pixel_values)
+            return pixel_values
+
+        num_patches_list = []
+        pixel_values = []
+        # Process each input with allocated frames
+        for image_index, (image) in enumerate(base_output.images):
+            try:
+                # TODO: video input
+                raw_image = process_image_internvl(image)
+                pixel_value = [raw_image.to(torch.bfloat16).cuda()]
+                pixel_values += pixel_value
+                num_patches = raw_image.shape[0]
+                num_patches_list += [num_patches]
+
+            except FileNotFoundError as e:
+                print(e)
+                return None
+
+        pixel_values = torch.cat(pixel_values, dim=0)
+        items = [MultimodalDataItem(pixel_values=pixel_values, modality=Modality.IMAGE)]
+
+        for idx, num_patches in enumerate(num_patches_list):
+            image_tokens = (
+                self.IMG_START_TOKEN
+                + self.IMG_CONTEXT_TOKEN * self.num_image_token * num_patches
+                + self.IMG_END_TOKEN
+            )
+            input_text = input_text.replace("<image>", image_tokens, 1)
+
+        tokenizer = self._processor
+        return {
+            "input_ids": tokenizer(input_text, return_tensors="pt")["input_ids"]
+            .flatten()
+            .tolist(),
+            "mm_items": items,
+            "im_start_id": self.img_start_token_id,
+            "im_end_id": self.img_end_token_id,
+            "im_token_id": self.img_context_token_id,
+        }

python/sglang/srt/managers/scheduler.py

@@ -52,7 +52,11 @@ from sglang.srt.disaggregation.utils import (
     TransferBackend,
 )
 from sglang.srt.distributed import get_pp_group, get_world_group
-from sglang.srt.hf_transformers_utils import get_processor, get_tokenizer
+from sglang.srt.hf_transformers_utils import (
+    get_processor,
+    get_tokenizer,
+    get_tokenizer_from_processor,
+)
 from sglang.srt.layers.dp_attention import compute_dp_attention_world_info
 from sglang.srt.layers.logits_processor import LogitsProcessorOutput
 from sglang.srt.managers.expert_distribution import ExpertDistributionRecorder
@@ -475,7 +479,7 @@ class Scheduler(
                     revision=server_args.revision,
                     use_fast=not server_args.disable_fast_image_processor,
                 )
-                self.tokenizer = self.processor.tokenizer
+                self.tokenizer = get_tokenizer_from_processor(self.processor)
             else:
                 self.tokenizer = get_tokenizer(
                     server_args.tokenizer_path,

python/sglang/srt/managers/tokenizer_manager.py

@@ -54,7 +54,11 @@ from sglang.srt.disaggregation.utils import (
     TransferBackend,
     get_kv_class,
 )
-from sglang.srt.hf_transformers_utils import get_processor, get_tokenizer
+from sglang.srt.hf_transformers_utils import (
+    get_processor,
+    get_tokenizer,
+    get_tokenizer_from_processor,
+)
 from sglang.srt.managers.io_struct import (
     AbortReq,
     BatchEmbeddingOut,
@@ -199,7 +203,7 @@ class TokenizerManager:
                 self.tokenizer = self.processor = None
             else:
                 self.processor = _processor
-                self.tokenizer = self.processor.tokenizer
+                self.tokenizer = get_tokenizer_from_processor(self.processor)
                 os.environ["TOKENIZERS_PARALLELISM"] = "false"
         else:
             self.mm_processor = get_dummy_processor()

python/sglang/srt/managers/tp_worker.py

@@ -21,7 +21,11 @@ import torch
 
 from sglang.srt.configs.model_config import ModelConfig
 from sglang.srt.distributed import get_pp_group, get_tp_group, get_world_group
-from sglang.srt.hf_transformers_utils import get_processor, get_tokenizer
+from sglang.srt.hf_transformers_utils import (
+    get_processor,
+    get_tokenizer,
+    get_tokenizer_from_processor,
+)
 from sglang.srt.layers.logits_processor import LogitsProcessorOutput
 from sglang.srt.managers.io_struct import (
     GetWeightsByNameReqInput,
@@ -102,7 +106,7 @@ class TpModelWorker:
                     trust_remote_code=server_args.trust_remote_code,
                     revision=server_args.revision,
                 )
-                self.tokenizer = self.processor.tokenizer
+                self.tokenizer = get_tokenizer_from_processor(self.processor)
             else:
                 self.tokenizer = get_tokenizer(
                     server_args.tokenizer_path,

python/sglang/srt/models/internlm2.py

@@ -290,6 +290,9 @@ class InternLM2ForCausalLM(nn.Module):
         )
         self.logits_processor = LogitsProcessor(config)
 
+    def get_input_embeddings(self) -> nn.Embedding:
+        return self.model.tok_embeddings
+
     @torch.no_grad()
     def forward(
         self,

python/sglang/srt/models/internvl.py

+# 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.
+# ==========================582====================================================
+
+from typing import Iterable, List, Optional, Tuple, Union
+
+import torch
+
+# Adapted from https://raw.githubusercontent.com/vllm-project/vllm/7f62077af5159c625fe3ad1c812e6c1a2b93ba3b/vllm/model_executor/models/internlm2.py
+# Adapted from https://raw.githubusercontent.com/hehesangsj/sglang/refs/heads/internvl/python/sglang/srt/models/internvl.py
+import torch.nn.functional as F
+from einops import rearrange, repeat
+from sgl_kernel.flash_attn import flash_attn_varlen_func
+from torch import nn
+from transformers import PretrainedConfig, PreTrainedModel
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
+
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.managers.mm_utils import (
+    MultiModalityDataPaddingPatternTokenPairs,
+    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_janus_pro import DropPath
+from sglang.srt.models.internlm2 import InternLM2ForCausalLM
+from sglang.srt.models.qwen2 import Qwen2ForCausalLM
+from sglang.utils import logger
+
+
+class FlashAttention(nn.Module):
+    """Implement the scaled dot product attention with softmax.
+    Arguments
+    ---------
+        softmax_scale: The temperature to use for the softmax attention.
+                      (default: 1/sqrt(d_keys) where d_keys is computed at
+                      runtime)
+        attention_dropout: The dropout rate to apply to the attention
+                           (default: 0.0)
+    """
+
+    def __init__(
+        self, softmax_scale=None, attention_dropout=0.0, device=None, dtype=None
+    ):
+        super().__init__()
+        self.softmax_scale = softmax_scale
+        self.dropout_p = attention_dropout
+
+    def forward(
+        self,
+        qkv,
+        causal=False,
+        max_s=None,
+    ):
+        """Implements the multihead softmax attention.
+        Arguments
+        ---------
+            qkv: The tensor containing the query, key, and value. (B, S, 3, H, D) if key_padding_mask is None
+                if unpadded: (nnz, 3, h, d)
+        """
+        assert qkv.dtype in [torch.float16, torch.bfloat16]
+        assert qkv.is_cuda
+
+        batch_size, seqlen, _, nheads, d = qkv.shape
+        if batch_size == 0 or seqlen == 0:
+            output_shape = (batch_size, seqlen, nheads, d)
+            return (
+                torch.zeros(output_shape, dtype=qkv.dtype, device=qkv.device),
+                None,
+            )
+
+        qkv_reshaped = rearrange(qkv, "b s three h d -> (b s) three h d", three=3)
+        q, k, v = qkv_reshaped.unbind(1)
+
+        max_s = seqlen
+        cu_seqlens = torch.arange(
+            0,
+            (batch_size + 1) * seqlen,
+            step=seqlen,
+            dtype=torch.int32,
+            device=qkv.device,
+        )
+        output_reshaped = flash_attn_varlen_func(
+            q,
+            k,
+            v,
+            cu_seqlens,
+            cu_seqlens,
+            max_s,
+            max_s,
+            softmax_scale=self.softmax_scale,
+            causal=causal,
+        )
+        output = rearrange(output_reshaped, "(b s) h d -> b s h d", b=batch_size)
+        return output, None
+
+
+class InternAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.embed_dim // self.num_heads
+
+        self.scale = self.head_dim**-0.5
+        self.qkv = nn.Linear(self.embed_dim, 3 * self.embed_dim, bias=config.qkv_bias)
+        self.proj_drop = nn.Dropout(config.dropout)
+
+        self.qk_normalization = config.qk_normalization
+
+        if self.qk_normalization:
+            self.q_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
+            self.k_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
+
+        self.inner_attn = FlashAttention(softmax_scale=self.scale)
+
+        self.proj = nn.Linear(self.embed_dim, self.embed_dim)
+
+    def _flash_attn(
+        self,
+        x,
+    ):
+        qkv = self.qkv(x)
+        qkv = rearrange(
+            qkv, "b s (three h d) -> b s three h d", three=3, h=self.num_heads
+        )
+
+        if self.qk_normalization:
+            q, k, v = qkv.unbind(2)
+            q = self.q_norm(q.flatten(-2, -1)).view(q.shape)
+            k = self.k_norm(k.flatten(-2, -1)).view(k.shape)
+            qkv = torch.stack([q, k, v], dim=2)
+
+        context, _ = self.inner_attn(
+            qkv,
+        )
+        outs = self.proj(rearrange(context, "b s h d -> b s (h d)"))
+        outs = self.proj_drop(outs)
+        return outs
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        x = self._flash_attn(hidden_states)
+        return x
+
+
+class InternVisionEmbeddings(nn.Module):
+    def __init__(self, config: PretrainedConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.image_size = config.image_size
+        self.patch_size = config.patch_size
+
+        self.class_embedding = nn.Parameter(
+            torch.randn(1, 1, self.embed_dim),
+        )
+
+        self.patch_embedding = nn.Conv2d(
+            in_channels=3,
+            out_channels=self.embed_dim,
+            kernel_size=self.patch_size,
+            stride=self.patch_size,
+        )
+
+        self.num_patches = (self.image_size // self.patch_size) ** 2
+        self.num_positions = self.num_patches + 1
+
+        self.position_embedding = nn.Parameter(
+            torch.randn(1, self.num_positions, self.embed_dim)
+        )
+
+    def _get_pos_embed(self, pos_embed, H, W):
+        target_dtype = pos_embed.dtype
+        pos_embed = (
+            pos_embed.float()
+            .reshape(
+                1,
+                self.image_size // self.patch_size,
+                self.image_size // self.patch_size,
+                -1,
+            )
+            .permute(0, 3, 1, 2)
+        )
+        pos_embed = (
+            F.interpolate(pos_embed, size=(H, W), mode="bicubic", align_corners=False)
+            .reshape(1, -1, H * W)
+            .permute(0, 2, 1)
+            .to(target_dtype)
+        )
+        return pos_embed
+
+    def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
+        target_dtype = self.patch_embedding.weight.dtype
+        patch_embeds = self.patch_embedding(
+            pixel_values
+        )  # shape = [*, channel, width, height]
+        batch_size, _, height, width = patch_embeds.shape
+        patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
+        class_embeds = self.class_embedding.expand(batch_size, 1, -1).to(target_dtype)
+        embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
+        position_embedding = torch.cat(
+            [
+                self.position_embedding[:, :1, :],
+                self._get_pos_embed(self.position_embedding[:, 1:, :], height, width),
+            ],
+            dim=1,
+        )
+        embeddings = embeddings + position_embedding.to(target_dtype)
+        return embeddings
+
+
+class InternRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+class InternMLP(nn.Module):
+    def __init__(self, config: PretrainedConfig):
+        super().__init__()
+        self.config = config
+        self.act = ACT2FN[config.hidden_act]
+        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
+        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.fc1(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.fc2(hidden_states)
+        return hidden_states
+
+
+NORM2FN = {
+    "rms_norm": InternRMSNorm,
+    "layer_norm": nn.LayerNorm,
+}
+
+
+class InternVisionEncoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        drop_path_rate: float,
+        quant_config: QuantizationConfig = None,
+    ):
+        super().__init__()
+        self.embed_dim = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.norm_type = config.norm_type
+        self.attn = InternAttention(config)
+        self.mlp = InternMLP(config)
+        self.norm1 = NORM2FN[self.norm_type](self.embed_dim, eps=config.layer_norm_eps)
+        self.norm2 = NORM2FN[self.norm_type](self.embed_dim, eps=config.layer_norm_eps)
+
+        self.ls1 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
+        self.ls2 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
+        self.drop_path1 = (
+            DropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
+        )
+        self.drop_path2 = (
+            DropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> Tuple[
+        torch.FloatTensor,
+        Optional[torch.FloatTensor],
+        Optional[Tuple[torch.FloatTensor]],
+    ]:
+        """
+        Args:
+            hidden_states (`Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]`): input to the layer of shape `(batch, seq_len, embed_dim)`
+        """
+        hidden_states = hidden_states + self.drop_path1(
+            self.attn(self.norm1(hidden_states).to(hidden_states.dtype)) * self.ls1
+        )
+
+        hidden_states = hidden_states + self.drop_path2(
+            self.mlp(self.norm2(hidden_states).to(hidden_states.dtype)) * self.ls2
+        )
+
+        return hidden_states
+
+
+class InternVisionEncoder(nn.Module):
+    """
+    Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
+    [`InternEncoderLayer`].
+
+    Args:
+        config (`InternConfig`):
+            The corresponding vision configuration for the `InternEncoder`.
+    """
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ):
+        super().__init__()
+        self.config = config
+        # stochastic depth decay rule
+        dpr = [
+            x.item()
+            for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers)
+        ]
+        self.layers = nn.ModuleList(
+            [
+                InternVisionEncoderLayer(config, dpr[idx], quant_config)
+                for idx in range(config.num_hidden_layers)
+            ]
+        )
+
+    def forward(
+        self,
+        inputs_embeds,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutput]:
+        r"""
+        Args:
+            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+                Embedded representation of the inputs. Should be float, not int tokens.
+            output_hidden_states (`bool`, *optional*):
+                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+                for more detail.
+            return_dict (`bool`, *optional*):
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+        """
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        encoder_states = () if output_hidden_states else None
+        hidden_states = inputs_embeds
+
+        for idx, encoder_layer in enumerate(self.layers):
+            if output_hidden_states:
+                encoder_states = encoder_states + (hidden_states,)
+            layer_outputs = encoder_layer(
+                hidden_states,
+            )
+            hidden_states = layer_outputs
+
+        if output_hidden_states:
+            encoder_states = encoder_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, encoder_states] if v is not None)
+        return BaseModelOutput(
+            last_hidden_state=hidden_states, hidden_states=encoder_states
+        )
+
+
+class InternVisionModel(PreTrainedModel):
+    main_input_name = "pixel_values"
+    _supports_flash_attn_2 = True
+    config_class = PretrainedConfig
+    _no_split_modules = ["InternVisionEncoderLayer"]
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ):
+        super().__init__(config)
+        self.config = config
+
+        self.embeddings = InternVisionEmbeddings(
+            config,
+        )
+        self.encoder = InternVisionEncoder(config, quant_config)
+
+    def resize_pos_embeddings(self, old_size, new_size, patch_size):
+        pos_emb = self.embeddings.position_embedding
+        _, num_positions, embed_dim = pos_emb.shape
+        cls_emb = pos_emb[:, :1, :]
+        pos_emb = (
+            pos_emb[:, 1:, :]
+            .reshape(1, old_size // patch_size, old_size // patch_size, -1)
+            .permute(0, 3, 1, 2)
+        )
+        pos_emb = F.interpolate(
+            pos_emb.float(),
+            size=new_size // patch_size,
+            mode="bicubic",
+            align_corners=False,
+        )
+        pos_emb = pos_emb.to(cls_emb.dtype).reshape(1, embed_dim, -1).permute(0, 2, 1)
+        pos_emb = torch.cat([cls_emb, pos_emb], dim=1)
+        self.embeddings.position_embedding = nn.Parameter(pos_emb)
+        self.embeddings.image_size = new_size
+        logger.info(
+            "Resized position embeddings from {} to {}".format(old_size, new_size)
+        )
+
+    def get_input_embeddings(self):
+        return self.embeddings
+
+    def forward(
+        self,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        pixel_embeds: Optional[torch.FloatTensor] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPooling]:
+        pixel_values = pixel_values.to(device=self.device, dtype=self.dtype)
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        if pixel_values is None and pixel_embeds is None:
+            raise ValueError("You have to specify pixel_values or pixel_embeds")
+
+        if pixel_embeds is not None:
+            hidden_states = pixel_embeds
+        else:
+            if len(pixel_values.shape) == 4:
+                hidden_states = self.embeddings(pixel_values)
+            else:
+                raise ValueError(f"wrong pixel_values size: {pixel_values.shape}")
+        encoder_outputs = self.encoder(
+            inputs_embeds=hidden_states,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        last_hidden_state = encoder_outputs.last_hidden_state
+        pooled_output = last_hidden_state[:, 0, :]
+
+        if not return_dict:
+            return (last_hidden_state, pooled_output) + encoder_outputs[1:]
+
+        return BaseModelOutputWithPooling(
+            last_hidden_state=last_hidden_state,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )
+
+
+class InternVLChatModel(nn.Module):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        use_flash_attn=True,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.quant_config = quant_config
+
+        image_size = config.force_image_size or config.vision_config.image_size
+        patch_size = config.vision_config.patch_size
+        self.patch_size = patch_size
+        self.select_layer = config.select_layer
+        self.template = config.template
+        self.num_image_token = int(
+            (image_size // patch_size) ** 2 * (config.downsample_ratio**2)
+        )
+        self.downsample_ratio = config.downsample_ratio
+        self.ps_version = config.ps_version
+
+        config.vision_config.use_flash_attn = True if use_flash_attn else False
+        config.llm_config._attn_implementation = (
+            "flash_attention_2" if use_flash_attn else "eager"
+        )
+
+        logger.info(f"num_image_token: {self.num_image_token}")
+        logger.info(f"ps_version: {self.ps_version}")
+
+        self.vision_model = InternVisionModel(config.vision_config)
+        if config.llm_config.architectures[0] == "Qwen2ForCausalLM":
+            self.language_model = Qwen2ForCausalLM(
+                config=config.llm_config, quant_config=quant_config
+            )
+        elif config.llm_config.architectures[0] == "InternLM2ForCausalLM":
+            self.language_model = InternLM2ForCausalLM(
+                config=config.llm_config, quant_config=quant_config
+            )
+        else:
+            raise NotImplementedError(
+                f"{config.llm_config.architectures[0]} is not implemented."
+            )
+
+        vit_hidden_size = config.vision_config.hidden_size
+        llm_hidden_size = config.llm_config.hidden_size
+
+        self.mlp1 = nn.Sequential(
+            nn.LayerNorm(vit_hidden_size * int(1 / self.downsample_ratio) ** 2),
+            nn.Linear(
+                vit_hidden_size * int(1 / self.downsample_ratio) ** 2, llm_hidden_size
+            ),
+            nn.GELU(),
+            nn.Linear(llm_hidden_size, llm_hidden_size),
+        )
+
+    def pixel_shuffle(self, x, scale_factor=0.5):
+        n, w, h, c = x.size()
+        # N, W, H, C --> N, W, H * scale, C // scale
+        x = x.view(n, w, int(h * scale_factor), int(c / scale_factor))
+        # N, W, H * scale, C // scale --> N, H * scale, W, C // scale
+        x = x.permute(0, 2, 1, 3).contiguous()
+        # N, H * scale, W, C // scale --> N, H * scale, W * scale, C // (scale ** 2)
+        x = x.view(
+            n,
+            int(h * scale_factor),
+            int(w * scale_factor),
+            int(c / (scale_factor * scale_factor)),
+        )
+        if self.ps_version == "v1":
+            logger.warn(
+                "In ps_version 'v1', the height and width have not been swapped back, "
+                "which results in a transposed image."
+            )
+        else:
+            x = x.permute(0, 2, 1, 3).contiguous()
+        return x
+
+    def extract_feature(self, pixel_values):
+        if self.select_layer == -1:
+            vit_embeds = self.vision_model(
+                pixel_values=pixel_values, output_hidden_states=False, return_dict=True
+            ).last_hidden_state
+        else:
+            vit_embeds = self.vision_model(
+                pixel_values=pixel_values, output_hidden_states=True, return_dict=True
+            ).hidden_states[self.select_layer]
+        vit_embeds = vit_embeds[:, 1:, :]
+
+        h = w = int(vit_embeds.shape[1] ** 0.5)
+        vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
+        vit_embeds = self.pixel_shuffle(vit_embeds, scale_factor=self.downsample_ratio)
+        vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], -1, vit_embeds.shape[-1])
+        vit_embeds = self.mlp1(vit_embeds)
+        return vit_embeds
+
+    def get_image_feature(self, items: List[MultimodalDataItem]):
+        """
+        Projects the last hidden state from the vision model into language model space.
+
+        Returns:
+            image_features (`torch.Tensor`): Image feature tensor of shape `(num_images, image_length, embed_dim)`).
+        """
+        pixel_values = torch.cat([item.pixel_values for item in items])
+        image_features = self.extract_feature(pixel_values)
+        return image_features
+
+    @torch.no_grad()
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: torch.Tensor = None,
+    ) -> torch.Tensor:
+
+        hs = general_mm_embed_routine(
+            input_ids=input_ids,
+            forward_batch=forward_batch,
+            language_model=self.language_model,
+            image_data_embedding_func=self.get_image_feature,
+            positions=positions,
+        )
+
+        return hs
+
+    def pad_input_ids(self, input_ids: List[int], mm_inputs: MultimodalInputs):
+        # Get all special token IDs
+        im_start_id: int = mm_inputs.im_start_id
+        im_end_id: int = mm_inputs.im_end_id
+
+        media_token_pairs = [(im_start_id, im_end_id)]
+        helper = MultiModalityDataPaddingPatternTokenPairs(media_token_pairs)
+
+        return helper.pad_input_tokens(input_ids, mm_inputs)
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        if "InternLM2ForCausalLM" in self.config.llm_config.architectures:
+            stacked_params_mapping = [
+                # (param_name, shard_name, shard_id)
+                ("gate_up_proj", "w1", 0),
+                ("gate_up_proj", "w3", 1),
+            ]
+        elif "Qwen2ForCausalLM" in self.config.llm_config.architectures:
+            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())
+
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+            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
+                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
+                param = params_dict[name]
+                if "wqkv" in name:
+                    config = self.config
+                    kv_groups = config.num_attention_heads // config.num_key_value_heads
+                    head_dim = config.hidden_size // config.num_attention_heads
+                    loaded_weight = loaded_weight.view(
+                        -1, 2 + kv_groups, head_dim, loaded_weight.shape[-1]
+                    )
+                    wq, wk, wv = torch.split(loaded_weight, [kv_groups, 1, 1], dim=1)
+                    wq = wq.reshape(-1, wq.shape[-1])
+                    wk = wk.reshape(-1, wk.shape[-1])
+                    wv = wv.reshape(-1, wv.shape[-1])
+                    weight_loader = param.weight_loader
+                    weight_loader(param, wq, "q")
+                    weight_loader(param, wk, "k")
+                    weight_loader(param, wv, "v")
+                else:
+                    weight_loader = getattr(
+                        param, "weight_loader", default_weight_loader
+                    )
+                    weight_loader(param, loaded_weight)
+
+
+EntryClass = InternVLChatModel

test/srt/test_vision_openai_server.py

@@ -604,6 +604,21 @@ class TestMinicpmvServer(TestOpenAIVisionServer):
         cls.base_url += "/v1"
 
 
+class TestInternVL2_5Server(TestOpenAIVisionServer):
+    @classmethod
+    def setUpClass(cls):
+        cls.model = "OpenGVLab/InternVL2_5-2B"
+        cls.base_url = DEFAULT_URL_FOR_TEST
+        cls.api_key = "sk-123456"
+        cls.process = popen_launch_server(
+            cls.model,
+            cls.base_url,
+            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+            other_args=["--trust-remote-code", "--chat-template", "internvl-2-5"],
+        )
+        cls.base_url += "/v1"
+
+
 class TestMinicpmoServer(TestOpenAIVisionServer):
     @classmethod
     def setUpClass(cls):