model: support Kimi-K2.5 (#17789)

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

python/sglang/srt/configs/__init__.py

@@ -9,6 +9,7 @@ from sglang.srt.configs.falcon_h1 import FalconH1Config
 from sglang.srt.configs.janus_pro import MultiModalityConfig
 from sglang.srt.configs.jet_nemotron import JetNemotronConfig
 from sglang.srt.configs.jet_vlm import JetVLMConfig
+from sglang.srt.configs.kimi_k25 import KimiK25Config
 from sglang.srt.configs.kimi_linear import KimiLinearConfig
 from sglang.srt.configs.kimi_vl import KimiVLConfig
 from sglang.srt.configs.kimi_vl_moonvit import MoonViTConfig
@@ -39,6 +40,7 @@ __all__ = [
     "Step3VisionEncoderConfig",
     "Olmo3Config",
     "KimiLinearConfig",
+    "KimiK25Config",
     "Qwen3NextConfig",
     "DotsVLMConfig",
     "DotsOCRConfig",

python/sglang/srt/configs/kimi_k25.py

@@ -0,0 +1,171 @@
+"""
+Kimi K25 Model Configuration.
+"""
+
+from transformers import DeepseekV3Config
+from transformers.configuration_utils import PretrainedConfig
+
+
+class KimiK25VisionConfig(PretrainedConfig):
+    """Vision configuration for K2-VL (vision tower + mm projector).
+
+    Args:
+        Vision Tower Parameters:
+            patch_size: Patch size for vision tower.
+            init_pos_emb_height: Initial position embedding height.
+            init_pos_emb_width: Initial position embedding width.
+            init_pos_emb_time: Initial position embedding time dimension.
+            pos_emb_type: Type of position embedding.
+            num_attention_heads: Number of attention heads in vision tower.
+            num_hidden_layers: Number of hidden layers in vision tower.
+            hidden_size: Hidden size of vision tower.
+            intermediate_size: Intermediate size in vision tower FFN.
+            merge_kernel_size: Kernel size for spatial patch merging.
+            video_attn_type: Type of video attention.
+            merge_type: Type of merge operation.
+
+        MM Projector Parameters:
+            mm_projector_type: Type of multimodal projector.
+            mm_hidden_size: Hidden size for projector (defaults to hidden_size).
+            projector_hidden_act: Activation function for projector.
+            projector_ln_eps: Layer norm epsilon for projector.
+    """
+
+    model_type = "kimi_k25"
+
+    def __init__(
+        self,
+        # Vision Tower
+        patch_size: int = 14,
+        init_pos_emb_height: int = 64,
+        init_pos_emb_width: int = 64,
+        init_pos_emb_time: int = 4,
+        pos_emb_type: str = "divided_fixed",
+        num_attention_heads: int = 16,
+        num_hidden_layers: int = 27,
+        hidden_size: int = 1152,
+        intermediate_size: int = 4304,
+        merge_kernel_size: tuple[int, int] = (2, 2),
+        video_attn_type: str = "spatial_temporal",
+        merge_type: str = "sd2_tpool",
+        # MM Projector
+        mm_projector_type: str = "patchmerger",
+        mm_hidden_size: int | None = None,
+        projector_hidden_act: str = "gelu",
+        projector_ln_eps: float = 1e-5,
+        text_hidden_size: int = 7168,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        # Vision Tower
+        self.patch_size = patch_size
+        self.init_pos_emb_height = init_pos_emb_height
+        self.init_pos_emb_width = init_pos_emb_width
+        self.init_pos_emb_time = init_pos_emb_time
+        self.pos_emb_type = pos_emb_type
+        self.num_attention_heads = num_attention_heads
+        self.num_hidden_layers = num_hidden_layers
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.merge_kernel_size = merge_kernel_size
+        self.video_attn_type = video_attn_type
+        self.merge_type = merge_type
+        # MM Projector
+        self.mm_projector_type = mm_projector_type
+        if mm_hidden_size is not None:
+            self.mm_hidden_size = mm_hidden_size
+        else:
+            self.mm_hidden_size = hidden_size
+        self.projector_hidden_act = projector_hidden_act
+        self.projector_ln_eps = projector_ln_eps
+        self.text_hidden_size = text_hidden_size
+
+
+class KimiK25Config(PretrainedConfig):
+    """K2-VL model configuration.
+
+    K2-VL extends Kimi-VL with video support using video-chunks.
+    A video-chunk consists of multiple consecutive frames (default: 4)
+    that are processed together with temporal pooling.
+
+    Args:
+        text_config: Configuration for the text model (DeepseekV3).
+
+        Vision Tower Parameters:
+            patch_size: Patch size for vision tower.
+            init_pos_emb_height: Initial position embedding height.
+            init_pos_emb_width: Initial position embedding width.
+            init_pos_emb_time: Initial position embedding time dimension.
+            pos_emb_type: Type of position embedding.
+            vt_num_attention_heads: Number of attention heads in vision tower.
+            vt_num_hidden_layers: Number of hidden layers in vision tower.
+            vt_hidden_size: Hidden size of vision tower.
+            vt_intermediate_size: Intermediate size in vision tower FFN.
+            merge_kernel_size: Kernel size for spatial patch merging.
+            video_attn_type: Type of video attention.
+            merge_type: Type of merge operation.
+
+        Video-Chunk Parameters:
+            temporal_merge_kernel_size: Number of frames per video chunk.
+                Default is 4, meaning 4 frames are merged into 1 chunk.
+            sample_fps: Video sampling frame rate.
+            timestamp_mode: Format for chunk timestamps.
+
+        MM Projector Parameters:
+            mm_projector_type: Type of multimodal projector.
+            mm_hidden_size: Hidden size from vision tower.
+            projector_hidden_act: Activation function for projector.
+            projector_ln_eps: Layer norm epsilon for projector.
+
+        Other Parameters:
+            ignore_index: The ignore index for the loss function.
+            media_placeholder_token_id: The token ID for media placeholders.
+            pad_token_id: The token ID for padding.
+    """
+
+    model_type = "kimi_k25"
+
+    def __init__(
+        self,
+        text_config: dict | DeepseekV3Config | None = None,
+        vision_config: dict | KimiK25VisionConfig | None = None,
+        # Other parameters
+        ignore_index: int = -100,
+        media_placeholder_token_id: int = 163605,
+        pad_token_id: int = 0,
+        use_unified_vision_chunk: bool = False,
+        video_placeholder: str = "<|kimi_k25_video_placeholder|>",
+        **kwargs,
+    ):
+        if text_config is None:
+            text_config = DeepseekV3Config()
+        elif isinstance(text_config, dict):
+            text_config = DeepseekV3Config(**text_config)
+
+        if vision_config is None:
+            vision_config = KimiK25VisionConfig()
+        elif isinstance(vision_config, dict):
+            vision_config = KimiK25VisionConfig(**vision_config)
+        self.vision_config = vision_config
+        self.text_config = text_config
+        # Other config
+        self.ignore_index = ignore_index
+        self.media_placeholder_token_id = media_placeholder_token_id
+        self.use_unified_vision_chunk = use_unified_vision_chunk
+        self.video_placeholder = video_placeholder
+
+        # Propagate quantization config from text model
+        if getattr(self.text_config, "quantization_config", None) is not None:
+            self.quantization_config = self.text_config.quantization_config
+
+        super().__init__(pad_token_id=pad_token_id, **kwargs)
+
+    @property
+    def hidden_size(self) -> int:
+        """Get hidden size from text config for compatibility."""
+        return self.text_config.hidden_size
+
+    @property
+    def vocab_size(self) -> int:
+        """Get vocab size from text config for compatibility."""
+        return self.text_config.vocab_size

python/sglang/srt/configs/model_config.py

@@ -391,16 +391,17 @@ class ModelConfig:
             or "MistralLarge3ForCausalLM" in self.hf_config.architectures
             or "PixtralForConditionalGeneration" in self.hf_config.architectures
             or "MistralLarge3ForCausalLMEagle" in self.hf_config.architectures
+            or "KimiK25ForConditionalGeneration" in self.hf_config.architectures
         ):
             self.head_dim = 256
             self.attention_arch = AttentionArch.MLA
-            self.kv_lora_rank = self.hf_config.kv_lora_rank
-            self.qk_nope_head_dim = self.hf_config.qk_nope_head_dim
-            self.qk_rope_head_dim = self.hf_config.qk_rope_head_dim
-            self.v_head_dim = self.hf_config.v_head_dim
+            self.kv_lora_rank = self.hf_text_config.kv_lora_rank
+            self.qk_nope_head_dim = self.hf_text_config.qk_nope_head_dim
+            self.qk_rope_head_dim = self.hf_text_config.qk_rope_head_dim
+            self.v_head_dim = self.hf_text_config.v_head_dim
             self.index_head_dim = (
-                get_nsa_index_head_dim(self.hf_config)
-                if is_deepseek_nsa(self.hf_config)
+                get_nsa_index_head_dim(self.hf_text_config)
+                if is_deepseek_nsa(self.hf_text_config)
                 else None
             )
 
@@ -412,11 +413,11 @@ class ModelConfig:
                 self.scaling = 1 / math.sqrt(
                     self.qk_nope_head_dim + self.qk_rope_head_dim
                 )
-                if self.hf_config.rope_scaling:
-                    mscale_all_dim = self.hf_config.rope_scaling.get(
+                if self.hf_text_config.rope_scaling:
+                    mscale_all_dim = self.hf_text_config.rope_scaling.get(
                         "mscale_all_dim", False
                     )
-                    scaling_factor = self.hf_config.rope_scaling["factor"]
+                    scaling_factor = self.hf_text_config.rope_scaling["factor"]
                     mscale = yarn_get_mscale(scaling_factor, float(mscale_all_dim))
                     self.scaling = self.scaling * mscale * mscale
 
@@ -1169,6 +1170,7 @@ multimodal_model_archs = [
     "PaddleOCRVLForConditionalGeneration",
     "MiDashengLMModel",
     "StepVLForConditionalGeneration",
+    "KimiK25ForConditionalGeneration",
 ]
 
 if external_mm_model_arch := envs.SGLANG_EXTERNAL_MM_MODEL_ARCH.get():

python/sglang/srt/entrypoints/openai/serving_chat.py

@@ -1194,7 +1194,7 @@ class OpenAIServingChat(OpenAIServingBase):
         """Judge whether the request needs reasoning"""
         if not self.reasoning_parser:
             return False
-        if self.reasoning_parser in ["deepseek-v3"]:
+        if self.reasoning_parser in ["deepseek-v3", "kimi_k2"]:
             return (
                 request.chat_template_kwargs is not None
                 and request.chat_template_kwargs.get("thinking") is True

python/sglang/srt/layers/attention/vision.py

@@ -13,7 +13,11 @@ from einops import rearrange
 
 from sglang.jit_kernel.norm import can_use_fused_inplace_qknorm as can_use_jit_qk_norm
 from sglang.srt.environ import envs
-from sglang.srt.layers.dp_attention import get_attention_tp_rank, get_attention_tp_size
+from sglang.srt.layers.dp_attention import (
+    get_attention_tp_group,
+    get_attention_tp_rank,
+    get_attention_tp_size,
+)
 from sglang.srt.models.utils import apply_qk_norm
 from sglang.srt.utils import (
     get_bool_env_var,
@@ -692,6 +696,7 @@ class VisionAttention(nn.Module):
             quant_config=quant_config,
             tp_rank=self.tp_rank,
             tp_size=self.tp_size,
+            reduce_results=False,
             prefix=add_prefix("proj", prefix),
         )
         self.aux_stream = aux_stream
@@ -914,6 +919,8 @@ class VisionAttention(nn.Module):
 
             # [b, s, h * head_size] --> [b, s, h * head_size]
             output, _ = self.proj(output)
+            if self.tp_size > 1:
+                output = get_attention_tp_group().all_reduce(output)
         else:
             # [b * s, h, head_size] --> [s, b, h * head_size]
             context_layer = rearrange(
@@ -922,6 +929,8 @@ class VisionAttention(nn.Module):
 
             # [s, b, h * head_size] --> [s, b, h * head_size]
             output, _ = self.proj(context_layer)
+            if self.tp_size > 1:
+                output = get_attention_tp_group().all_reduce(output)
 
             # [s, b, h * head_size] --> [b, s, h * head_size]
             output = output.view(bsz, s, -1)

python/sglang/srt/models/kimi_k25.py

@@ -0,0 +1,744 @@
+import logging
+from copy import deepcopy
+from typing import Iterable, List, Optional, Sequence, Tuple
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import nn
+from transformers import activations
+
+from sglang.srt.configs.kimi_k25 import KimiK25Config, KimiK25VisionConfig
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.managers.mm_utils import (
+    MultiModalityDataPaddingPatternMultimodalTokens,
+    general_mm_embed_routine,
+)
+
+try:
+    from transformers.activations import PytorchGELUTanh
+except ImportError:
+    from transformers.activations import GELUTanh
+
+    activations.PytorchGELUTanh = GELUTanh
+    PytorchGELUTanh = GELUTanh
+
+from sglang.srt.layers.attention.vision import VisionAttention
+from sglang.srt.layers.linear import ReplicatedLinear
+from sglang.srt.managers.schedule_batch import (
+    Modality,
+    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_v2 import DeepseekV3ForCausalLM
+from sglang.srt.models.kimi_vl_moonvit import MLP2
+from sglang.srt.utils import add_prefix
+
+KIMIV_VT_INFER_MAX_PATCH_NUM = 16328
+logger = logging.getLogger(__name__)
+
+
+def apply_rope(
+    xq: torch.Tensor, xk: torch.Tensor, freqs_cis: torch.Tensor, x_shape=None
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """
+    Args: (The leading dimensions of all inputs should be the same)
+        xq: query, tensor of shape (..., num_heads, head_dim)
+        xk: key, tensor of shape (..., num_heads, head_dim)
+        freqs_cis: tensor of shape (..., head_dim/2), dtype=torch.complex64. It contains the precomputed cis(freqs) for each position in the 2D grid.
+    Returns:
+        xq_out, xk_out: tensors of shape (..., num_heads, head_dim)
+    """
+
+    freqs_cis = freqs_cis.unsqueeze(-2)  # ..., 1, head_dim/2
+    # ..., num_heads, head_dim/2
+    xq_ = torch.view_as_complex(xq.float().view(*xq.shape[:-1], -1, 2))
+    xk_ = torch.view_as_complex(xk.float().view(*xq.shape[:-1], -1, 2))
+    xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(-2)  # ..., num_heads, head_dim
+    xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(-2)  # ..., num_heads, head_dim
+    return xq_out.type_as(xq), xk_out.type_as(xk)
+
+
+def tpool_patch_merger(
+    x: torch.Tensor,
+    grid_thws: torch.Tensor,
+    merge_kernel_size: tuple[int, int] = (2, 2),
+) -> list[torch.Tensor]:
+    d_model = x.size(-1)
+
+    outputs = []
+    pre_sum = 0
+    for t, h, w in grid_thws.tolist():
+        # Get the current sequence
+        seq = x[pre_sum : pre_sum + t * h * w]
+        # Reshape along self.merge_kernel_size and concat to the last dimension
+        kernel_height, kernel_width = merge_kernel_size
+        new_height, new_width = h // kernel_height, w // kernel_width
+        reshaped_seq = seq.view(
+            t, new_height, kernel_height, new_width, kernel_width, d_model
+        )
+        reshaped_seq = (
+            reshaped_seq.permute(0, 1, 3, 2, 4, 5).contiguous().mean(dim=0)
+        )  # temporal pooling
+        padded_seq = reshaped_seq.view(
+            new_height * new_width, kernel_height * kernel_width, -1
+        )
+        outputs.append(padded_seq)
+        pre_sum += t * h * w
+
+    return outputs
+
+
+class MoonViTEncoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        num_heads: int,
+        hidden_dim: int,
+        mlp_dim: int,
+        *,
+        activation=F.gelu,
+        attn_bias: bool = False,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+        use_data_parallel: bool = False,
+    ):
+        super().__init__()
+        self.num_heads = num_heads
+        self.hidden_dim = hidden_dim
+        self.hidden_size_per_attention_head = self.hidden_dim // self.num_heads
+
+        self.norm0 = nn.LayerNorm(hidden_dim)
+        self.norm1 = nn.LayerNorm(hidden_dim)
+        self.mlp = MLP2([hidden_dim, mlp_dim, hidden_dim], activation)
+
+        self.attn = VisionAttention(
+            embed_dim=hidden_dim,
+            num_heads=num_heads,
+            projection_size=hidden_dim,
+            use_qkv_parallel=True,
+            qkv_bias=attn_bias,
+            proj_bias=attn_bias,
+            flatten_batch=True,
+            quant_config=quant_config,
+            prefix=add_prefix("attn", prefix),
+            use_data_parallel=use_data_parallel,
+            customized_position_embedding_applier=apply_rope,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        max_seqlen: int,
+        rope_freqs_cis: torch.Tensor | None = None,
+    ):
+        residual = hidden_states
+        hidden_states = self.norm0(hidden_states)
+
+        hidden_states = self.attn(
+            hidden_states,
+            cu_seqlens=cu_seqlens,
+            position_embeddings=rope_freqs_cis,
+        )
+
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.norm1(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+
+
+def get_rope_shape_decorate(func):
+    _get_rope_shape_first_call_flag = set()
+
+    def wrapper(org, interpolation_mode, shape):
+        key = (org.requires_grad, torch.is_grad_enabled(), interpolation_mode)
+        if key not in _get_rope_shape_first_call_flag:
+            _get_rope_shape_first_call_flag.add(key)
+            _ = func(org, interpolation_mode, shape=(64, 64))
+        return func(org, interpolation_mode, shape)
+
+    return wrapper
+
+
+def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
+    """
+    From:
+    https://github.com/OpenGVLab/InternVideo/blob/421f6d2361fc8f61a3394244571f2601a4e99e29/InternVideo2/multi_modality/models/backbones/internvideo2/pos_embed.py#L86
+    embed_dim: output dimension for each position
+    pos: a list of positions to be encoded: size (M,)
+    out: (M, D)
+    """
+    assert embed_dim % 2 == 0
+    omega = np.arange(embed_dim // 2, dtype=np.float32)
+    omega /= embed_dim / 2.0
+    omega = 1.0 / 10000**omega  # (D/2,)
+
+    pos = pos.reshape(-1)  # (M,)
+    out = np.einsum("m,d->md", pos, omega)  # (M, D/2), outer product
+
+    emb_sin = np.sin(out)  # (M, D/2)
+    emb_cos = np.cos(out)  # (M, D/2)
+
+    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
+    return emb
+
+
+@get_rope_shape_decorate
+@torch.compile(dynamic=True)
+def get_rope_shape(org, interpolation_mode, shape):
+    return (
+        F.interpolate(
+            org.permute((2, 0, 1)).unsqueeze(0),
+            size=shape,
+            mode=interpolation_mode,
+        )
+        .squeeze(0)
+        .permute((1, 2, 0))
+        .flatten(end_dim=1)
+    )
+
+
+def get_1d_sincos_pos_embed(embed_dim, t_size, cls_token=False):
+    """
+    t_size: int of the temporal size
+    return:
+    pos_embed: [t_size, embed_dim] or [1+t_size, embed_dim] (w/ or w/o cls_token)
+    """
+    grid_t = np.arange(t_size, dtype=np.float32)
+    pos_embed = get_1d_sincos_pos_embed_from_grid(embed_dim, grid_t)
+    if cls_token:
+        pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
+    return pos_embed
+
+
+class Learnable2DInterpPosEmbDivided_fixed(nn.Module):
+
+    def __init__(
+        self,
+        height: int,
+        width: int,
+        num_frames: int,
+        dim: int,
+        interpolation_mode: str = "bicubic",
+    ) -> None:
+        super().__init__()
+        self.height = height
+        self.width = width
+        self.num_frames = num_frames
+        self.dim = dim
+        self.interpolation_mode = interpolation_mode
+        self.weight = nn.Parameter(torch.empty(height, width, dim))
+        self.register_buffer(
+            "time_weight",
+            torch.from_numpy(get_1d_sincos_pos_embed(self.dim, self.num_frames))
+            .float()
+            .unsqueeze(1),
+            persistent=False,
+        )
+
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        nn.init.normal_(self.weight)
+
+    def forward(self, x: torch.Tensor, grid_thws: torch.Tensor) -> torch.Tensor:
+        pos_embs = []
+        for t, h, w in grid_thws.tolist():
+            assert t <= self.num_frames, f"t:{t} > self.num_frames:{self.num_frames}"
+            if (h, w) == self.weight.shape[:-1]:
+                pos_emb_2d = self.weight.flatten(end_dim=1)
+            else:
+                pos_emb_2d = get_rope_shape(
+                    self.weight,
+                    interpolation_mode=self.interpolation_mode,
+                    shape=(h, w),
+                )
+
+            if t == 1:
+                pos_emb_3d = pos_emb_2d
+            else:
+                pos_emb_3d = (
+                    pos_emb_2d.unsqueeze(0).repeat(t, 1, 1) + self.time_weight[0:t]
+                )
+
+            pos_embs.append(pos_emb_3d.reshape(-1, pos_emb_3d.shape[-1]))
+
+        out = x + torch.cat(pos_embs)
+        return out
+
+
+class Rope2DPosEmbRepeated(nn.Module):
+    """2D rotary position embedding with multi-resolution support.
+    This class is intended to be used in the following way:
+    1. Before training, create an instance of Rope2DPosEmb. This instance will hold the precomputed cis.
+    2. Before each forward pass, call `get_freqs_cis_by_*` to get the `freqs_cis` tensor for this iteration.
+    3. During the forward pass, pass the `freqs_cis` tensor to each attention layer, and call `apply` just before each attention operation.
+        The rope is shared across all attention layers and all heads.
+    Refs:
+    - RoFormer: https://arxiv.org/abs/2104.09864
+    - VisionLLaMA: https://arxiv.org/abs/2403.00522
+    - https://github.com/Meituan-AutoML/VisionLLaMA/blob/main/dit/models.py
+    Args:
+        dim (int): usually the multi-head attention dimension, should be divisible by 4 (TODO: relax this constraint if needed)
+        max_height (int): the maximum height of the 2D grid
+        max_width (int): the maximum width of the 2D grid
+        theta_base (float): the base of the theta
+    """
+
+    def __init__(self, dim: int, max_height: int, max_width: int, theta_base=10000):
+        super().__init__()
+        self.dim = dim
+        assert self.dim % 4 == 0, "dim must be divisible by 4"
+        self.max_height = max_height
+        self.max_width = max_width
+        self.theta_base = theta_base
+
+    def extra_repr(self):
+        return f"dim={self.dim}, max_height={self.max_height}, max_width={self.max_width}, theta_base={self.theta_base}"
+
+    def _precompute_freqs_cis(self, device: torch.device) -> torch.Tensor:
+        """Calculate the cis(freqs) for each position in the 2D grid.
+        Return: complex tensor of shape (max_height, max_width, dim//2) and value:
+            height axis: ret[h, w, 2*i] = cis(h * theta_base**(-4*i/dim))
+            weight axis: ret[h, w, 2*i+1] = cis(w * theta_base**(-4*i/dim))   with (i in [0, dim//4))
+            note: `cis` is a mathematical notation defined by cis x = cos x + i sin x,
+        """
+        N = self.max_height * self.max_width
+        flat_pos = torch.arange(0, N).float().to(device)
+        x_pos = flat_pos % self.max_width
+        y_pos = flat_pos // self.max_width
+        dim_range = (
+            torch.arange(0, self.dim, 4)[: (self.dim // 4)].float().to(device)
+        )  # C/4
+        freqs = 1.0 / (self.theta_base ** (dim_range / self.dim))
+        x_freqs = torch.outer(x_pos, freqs).float()  # N, C/4
+        y_freqs = torch.outer(y_pos, freqs).float()  # N, C/4
+        x_cis = torch.polar(torch.ones_like(x_freqs), x_freqs)  # N, C/4
+        y_cis = torch.polar(torch.ones_like(y_freqs), y_freqs)  # N, C/4
+        # N, C/4, 2
+        freqs_cis = torch.cat(
+            [x_cis.unsqueeze(dim=-1), y_cis.unsqueeze(dim=-1)], dim=-1
+        )
+        # max_height, max_width, C/2
+        freqs_cis = freqs_cis.reshape(self.max_height, self.max_width, -1)
+        return freqs_cis
+
+    def get_freqs_cis(
+        self, grid_thws: torch.Tensor, device: torch.device
+    ) -> torch.Tensor:
+        """
+        Args:
+            grid_thws (torch.Tensor): grid time, height and width
+        Returns:
+            freqs_cis: tensor of shape (sum(t * height * width), dim//2)
+        """
+        if not hasattr(self, "freqs_cis"):
+            self.register_buffer(
+                "freqs_cis", self._precompute_freqs_cis(device), persistent=False
+            )
+
+        shapes = grid_thws.tolist()
+        assert all(
+            1 <= h <= self.max_height and 1 <= w <= self.max_width for t, h, w in shapes
+        ), (
+            shapes,
+            self.max_height,
+            self.max_width,
+        )
+        freqs_cis = torch.cat(
+            [
+                self.freqs_cis[:h, :w].reshape(-1, self.dim // 2).repeat(t, 1)
+                for t, h, w in shapes
+            ],
+            dim=0,
+        )
+        return freqs_cis
+
+
+class MoonVision3dPatchEmbed(nn.Module):
+
+    def __init__(
+        self,
+        out_dim: int,
+        in_dim: int = 3,
+        patch_size: int | tuple[int, int] = (14, 14),
+        pos_emb_height: int = 14,
+        pos_emb_width: int = 14,
+        pos_emb_time: int = 4,
+        pos_emb_type: str = "divided_fixed",
+    ):
+        super().__init__()
+        assert isinstance(
+            patch_size, int | Sequence
+        ), f"Invalid patch_size type: {type(patch_size)}"
+        if isinstance(patch_size, int):
+            patch_size = (patch_size, patch_size)
+        assert (
+            len(patch_size) == 2
+        ), f"Expected patch_size to be a tuple of 2, got {patch_size}"
+        self.patch_size = patch_size
+
+        self.proj = nn.Conv2d(
+            in_dim, out_dim, kernel_size=patch_size, stride=patch_size
+        )
+
+        if pos_emb_type == "divided_fixed":
+            self.pos_emb = Learnable2DInterpPosEmbDivided_fixed(
+                height=pos_emb_height,
+                width=pos_emb_width,
+                num_frames=pos_emb_time,
+                dim=out_dim,
+            )
+        else:
+            raise NotImplementedError(f"Not support pos_emb_type: {pos_emb_type}")
+
+    def forward(self, x: torch.Tensor, grid_thws: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            x (L, Channels): input tensor
+            grid_hws (N, 3): temporal, height and width
+        Returns:
+            (L, Cout) tensor
+        """
+        x = self.proj(x).view(x.size(0), -1)
+        # apply positional embedding
+        x = self.pos_emb(x, grid_thws)
+        return x
+
+
+class MoonViT3dEncoder(nn.Module):
+
+    def __init__(
+        self,
+        hidden_dim: int,
+        num_layers: int,
+        block_cfg: dict,
+        video_attn_type: str = "spatial_temporal",
+    ) -> None:
+        super().__init__()
+
+        assert (
+            video_attn_type == "spatial_temporal"
+        ), f'video_attn_type must be "spatial_temporal", got {video_attn_type}'
+        self.video_attn_type = video_attn_type
+        self.rope_2d = Rope2DPosEmbRepeated(
+            block_cfg["hidden_dim"] // block_cfg["num_heads"], 512, 512
+        )
+        self.blocks = nn.ModuleList(
+            [MoonViTEncoderLayer(**block_cfg) for _ in range(num_layers)]
+        )
+        self.final_layernorm = nn.LayerNorm(hidden_dim)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        grid_thws: torch.Tensor,
+    ) -> torch.Tensor:
+        rope_freqs_cis = self.rope_2d.get_freqs_cis(
+            grid_thws=grid_thws, device=hidden_states.device
+        )
+
+        lengths = torch.cat(
+            (
+                torch.zeros(1, dtype=grid_thws.dtype, device=grid_thws.device),
+                grid_thws[:, 0] * grid_thws[:, 1] * grid_thws[:, 2],
+            )
+        )
+
+        max_seqlen = lengths.max()
+        cu_seqlens = lengths.to(hidden_states.device).cumsum(dim=0, dtype=torch.int32)
+
+        for block in self.blocks:
+            hidden_states = block(
+                hidden_states, cu_seqlens, max_seqlen, rope_freqs_cis=rope_freqs_cis
+            )
+
+        hidden_states = self.final_layernorm(hidden_states)
+
+        return hidden_states
+
+
+class MoonViT3dPretrainedModel(nn.Module):
+    model_type = "moonvit3d"
+    _no_split_modules = ["PackingTransformer"]
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+
+    def __init__(self, config, *inputs, **kwargs):
+        super().__init__()
+        config = deepcopy(config)
+        self.merge_kernel_size = config.merge_kernel_size
+        self.patch_size = config.patch_size
+        self.merge_type = config.merge_type
+
+        self.patch_embed = MoonVision3dPatchEmbed(
+            out_dim=config.hidden_size,
+            patch_size=config.patch_size,
+            pos_emb_height=config.init_pos_emb_height,
+            pos_emb_width=config.init_pos_emb_width,
+            pos_emb_time=config.init_pos_emb_time,
+            pos_emb_type=config.pos_emb_type,
+        )
+
+        self.encoder = MoonViT3dEncoder(
+            hidden_dim=config.hidden_size,
+            num_layers=config.num_hidden_layers,
+            block_cfg={
+                "num_heads": config.num_attention_heads,
+                "hidden_dim": config.hidden_size,
+                "mlp_dim": config.intermediate_size,
+                "activation": PytorchGELUTanh(),
+                "attn_bias": True,
+            },
+            video_attn_type=config.video_attn_type,
+        )
+
+    @property
+    def dtype(self) -> torch.dtype:
+        return self.patch_embed.proj.weight.dtype
+
+    @property
+    def device(self) -> torch.device:
+        return self.patch_embed.proj.weight.device
+
+    def forward(
+        self, pixel_values: torch.Tensor, grid_thws: torch.Tensor
+    ) -> torch.Tensor:
+        """
+        Args:
+            pixel_values (torch.Tensor): The input pixel values.
+            grid_thws (torch.Tensor): Temporal, height and width.
+        Returns:
+            torch.Tensor: The output tokens.
+        """
+        assert grid_thws.ndim == 2, f"grid_thws should be 2D, got {grid_thws.ndim}"
+        assert grid_thws.size(1) == 3, f"No support for _thw: {grid_thws}"
+        hidden_states = self.patch_embed(pixel_values, grid_thws)
+        hidden_states = self.encoder(hidden_states, grid_thws)
+        hidden_states = hidden_states.squeeze(0)
+        # spatial downsampling 2x with temporal pooling all
+        hidden_states = tpool_patch_merger(
+            hidden_states, grid_thws, merge_kernel_size=self.merge_kernel_size
+        )
+
+        return hidden_states
+
+
+class K2VLMultiModalProjector(nn.Module):
+    """Multi-modal projector with patch merging for K2-VL."""
+
+    def __init__(
+        self,
+        config: KimiK25VisionConfig,
+        use_data_parallel: bool = False,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.use_data_parallel = use_data_parallel
+
+        # Hidden size after patch merging
+        merge_h, merge_w = config.merge_kernel_size
+        self.hidden_size = config.vt_hidden_size * merge_h * merge_w
+
+        self.pre_norm = torch.nn.LayerNorm(config.vt_hidden_size, eps=1e-5)
+        self.linear_1 = ReplicatedLinear(
+            self.hidden_size,
+            self.hidden_size,
+            bias=True,
+            prefix=add_prefix(prefix, "linear_1"),
+        )
+        self.linear_2 = ReplicatedLinear(
+            self.hidden_size,
+            config.text_hidden_size,
+            bias=True,
+            prefix=add_prefix(prefix, "linear_2"),
+        )
+        self.act = nn.GELU()
+
+    def forward(self, image_features: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.pre_norm(image_features).view(-1, self.hidden_size)
+        hidden_states, _ = self.linear_1(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states, _ = self.linear_2(hidden_states)
+        return hidden_states
+
+
+@torch.inference_mode()
+def mm_projection_auto(
+    mm_projector: torch.nn.Module | None, vt_output: list[torch.Tensor]
+):
+    """Apply MM projector to vision tower outputs."""
+    if mm_projector is None:
+        return vt_output
+
+    num_embedding_list = [x.shape[0] for x in vt_output]
+    batched = torch.cat(vt_output, dim=0)
+    proj_out = mm_projector(batched) if mm_projector else batched
+    proj_out = proj_out.reshape(-1, proj_out.shape[-1])
+    proj_out = torch.split(proj_out, num_embedding_list)
+    return proj_out
+
+
+@torch.inference_mode()
+def vision_tower_forward_auto(
+    vision_tower: torch.nn.Module,
+    pixel_values: torch.Tensor,
+    grid_thw: torch.Tensor,
+    mm_projector: torch.nn.Module | None = None,
+) -> list[torch.Tensor]:
+    """Auto-batched vision tower forward."""
+    assert isinstance(
+        pixel_values, torch.Tensor
+    ), "expect pixel_values to be a tensor, get {}".format(type(pixel_values))
+    n = grid_thw.shape[0]
+    n_patches_each_media = grid_thw.prod(-1)
+    max_infer_batch = max(n_patches_each_media.max(), KIMIV_VT_INFER_MAX_PATCH_NUM)
+    logger.debug(
+        "vt max_infer_batch: %s, KIMIV_VT_INFER_MAX_PATCH_NUM: %s",
+        max_infer_batch,
+        KIMIV_VT_INFER_MAX_PATCH_NUM,
+    )
+    tensors = []
+    pre_sum = 0
+    current_group_start = 0
+    current_group_patches = 0
+
+    for i in range(n):
+        current_media_patches = n_patches_each_media[i].item()
+        if current_group_patches + current_media_patches <= max_infer_batch:
+            current_group_patches += current_media_patches
+        else:
+            if current_group_start < i:
+                group_grid_thw = grid_thw[current_group_start:i]
+                group_n_patches = n_patches_each_media[current_group_start:i].sum()
+                group_input = pixel_values[pre_sum : pre_sum + group_n_patches]
+                group_output = vision_tower(group_input, group_grid_thw)
+                proj_out = mm_projection_auto(mm_projector, group_output)
+                tensors.extend(proj_out)
+                pre_sum += group_n_patches
+
+            current_group_start = i
+            current_group_patches = current_media_patches
+
+    # Process the last group
+    if current_group_start < n:
+        group_grid_thw = grid_thw[current_group_start:n]
+        group_n_patches = n_patches_each_media[current_group_start:n].sum()
+        group_input = pixel_values[pre_sum : pre_sum + group_n_patches]
+        group_output = vision_tower(group_input, group_grid_thw)
+        proj_out = mm_projection_auto(mm_projector, group_output)
+        tensors.extend(proj_out)
+
+    return tensors
+
+
+class KimiK25ForConditionalGeneration(nn.Module):
+    def __init__(
+        self,
+        config: KimiK25Config,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+        **kwargs,  # fix init_tts argument error
+    ) -> None:
+        super().__init__()
+        self.config = config
+        # Create vision tower
+        self.vision_tower = MoonViT3dPretrainedModel(config.vision_config)
+        # Create mm projector
+        self.mm_projector = K2VLMultiModalProjector(config.vision_config)
+
+        self.language_model = DeepseekV3ForCausalLM(config.text_config, quant_config)
+
+        # Ensure that the dtype of the vision_tower and mm_projector matches that of the language_model.
+        # This solves the dtype mismatch issue when using device_map="auto" and torch_dtype.
+        if hasattr(self.language_model, "dtype"):
+            target_dtype = self.language_model.dtype
+            self.vision_tower = self.vision_tower.to(dtype=target_dtype)
+            self.mm_projector = self.mm_projector.to(dtype=target_dtype)
+
+    def get_image_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
+        pixel_values = torch.cat([item.feature for item in items], dim=0).type(
+            self.vision_tower.dtype
+        )
+        grid_thws = torch.concat([item.grid_thws for item in items], dim=0).to(
+            self.vision_tower.device
+        )
+
+        target_dtype = self.vision_tower.patch_embed.proj.weight.dtype
+        pixel_values = pixel_values.to(target_dtype)
+        image_features = vision_tower_forward_auto(
+            self.vision_tower,
+            pixel_values,
+            grid_thws,
+            mm_projector=self.mm_projector,
+        )
+        image_features = torch.cat(image_features, dim=0)
+        return image_features
+
+    def pad_input_ids(self, input_ids: List[int], mm_inputs: MultimodalInputs):
+        pattern = MultiModalityDataPaddingPatternMultimodalTokens()
+        return pattern.pad_input_tokens(input_ids, mm_inputs)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        get_embedding: bool = False,
+    ):
+        hidden_states = general_mm_embed_routine(
+            input_ids=input_ids,
+            forward_batch=forward_batch,
+            language_model=self.language_model,
+            data_embedding_funcs={
+                Modality.IMAGE: self.get_image_feature,
+            },
+            positions=positions,
+        )
+
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        """Load weights for the model, separating vision and language weights"""
+        weights = list(weights)
+
+        # Separate vision tower weights and language model weights
+        vision_weights = []
+        language_weights = []
+
+        for name, loaded_weight in weights:
+            if "vision_tower" in name or "mm_projector" in name:
+                name = name.replace(r"wqkv.", r"attn.qkv_proj.")
+                name = name.replace(r"wo.", r"attn.proj.")
+                name = name.replace("mm_projector.proj.0", "mm_projector.linear_1")
+                name = name.replace("mm_projector.proj.2", "mm_projector.linear_2")
+                vision_weights.append((name, loaded_weight))
+            else:
+                name = name.replace("language_model.", "")
+                # All other weights go to language model
+                language_weights.append((name, loaded_weight))
+
+        # Load vision tower weights
+        vision_state_dict = dict(vision_weights)
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        for name, loaded_weight in vision_state_dict.items():
+            if name not in params_dict:
+                raise ValueError(f"Weight {name} not found in params_dict")
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            # loaded_weight = self._pad_vit_attn_dummy_heads(name, loaded_weight)
+            weight_loader(param, loaded_weight)
+
+        # Load language model weights
+        if language_weights:
+            self.language_model.load_weights(language_weights)
+
+
+EntryClass = [KimiK25ForConditionalGeneration]

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

@@ -210,6 +210,7 @@ class BaseMultimodalProcessor(ABC):
             "num_patches": Modality.IMAGE,
             "patch_pixel_values": Modality.IMAGE,
             "block_sizes": Modality.IMAGE,
+            "grid_thws": Modality.IMAGE,  # for kimi k2.5
             # Audio-related attributes
             "audio_features": Modality.AUDIO,
             "audio_feature_lens": Modality.AUDIO,

python/sglang/srt/multimodal/processors/kimi_k25.py

@@ -0,0 +1,88 @@
+import re
+from typing import Dict, List, Tuple, Union
+
+import torch
+
+from sglang.srt.managers.schedule_batch import MultimodalDataItem
+from sglang.srt.models.kimi_k25 import KimiK25ForConditionalGeneration
+from sglang.srt.multimodal.processors.base_processor import (
+    BaseMultimodalProcessor as SGLangBaseProcessor,
+)
+from sglang.srt.multimodal.processors.base_processor import MultimodalSpecialTokens
+
+
+# Compatible with KimiVLForConditionalGeneration
+class KimiK2_5VLImageProcessor(SGLangBaseProcessor):
+    models = [KimiK25ForConditionalGeneration]
+
+    def __init__(self, hf_config, server_args, _processor, *args, **kwargs):
+        super().__init__(hf_config, server_args, _processor, *args, **kwargs)
+        self.mm_tokens = MultimodalSpecialTokens(
+            image_token="<|media_pad|>",
+            # TODO: could we convert in MultimodalSpecialTokens?
+            image_token_id=hf_config.media_placeholder_token_id,
+            image_token_regex=re.compile(r"(?:<\|media_pad\|>)+"),
+        ).build(_processor)
+
+    async def process_mm_data_async(
+        self,
+        image_data: List[Union[str, bytes, Dict]],
+        input_text,
+        request_obj,
+        *args,
+        **kwargs,
+    ):
+        base_output = self.load_mm_data(
+            prompt=input_text,
+            image_data=image_data,
+            multimodal_tokens=self.mm_tokens,
+        )
+        prompt = base_output.input_text
+
+        mm_items, input_ids, _ = self.process_and_combine_mm_data(
+            base_output, self.mm_tokens
+        )
+
+        return {
+            "input_ids": input_ids.tolist(),
+            "mm_items": mm_items,
+            "im_token_id": self.mm_tokens.image_token_id,
+        }
+
+    def _process_and_collect_mm_items(
+        self, input_text: str, images=None, audios=None, videos=None, **kwargs
+    ) -> Tuple[List[MultimodalDataItem], torch.Tensor, dict]:
+        """
+        Helper method to process multimodal data and create mm_items in one step.
+
+        Returns:
+            Tuple of (created mm_items, input_ids)
+        """
+
+        parts = input_text.split(self.mm_tokens.image_token)
+
+        result = [parts[0]]
+        for image, part in zip(images, parts[1:]):
+            num_tokens = self._processor.media_processor.media_tokens_calculator(
+                {"type": "image", "image": image}
+            )
+            result.append(self.mm_tokens.image_token * num_tokens + part)
+
+        input_text = "".join(result)
+
+        if images:  # for kimi k2 vl
+            mediums = []
+            for image in images:
+                mediums.append({"type": "image", "image": image})
+            key = "_medias"[1:]  # bypass lint
+            kwargs[key] = mediums
+            images = None
+
+        ret = self.process_mm_data(
+            input_text=input_text, images=images, audios=audios, videos=videos, **kwargs
+        )
+
+        input_ids = ret["input_ids"].flatten()
+        collected_items = self.collect_mm_items_from_processor_output(ret)
+
+        return collected_items, input_ids, ret

python/sglang/srt/parser/reasoning_parser.py

@@ -148,6 +148,26 @@ class DeepSeekR1Detector(BaseReasoningFormatDetector):
         # https://github.com/sgl-project/sglang/pull/3202#discussion_r1950153599
 
 
+class KimiK2Detector(BaseReasoningFormatDetector):
+    """
+    Detector for Kimi K2 model.
+
+    It uses the DeepSeek-R1 reasoning format: (<think>)*(.*)</think>.
+    Defaults to thinking mode (force_reasoning=True), but allows disabling it
+    if the model is configured to not think.
+    """
+
+    def __init__(self, stream_reasoning: bool = True, force_reasoning: bool = True):
+        super().__init__(
+            "<think>",
+            "</think>",
+            # Allow force_reasoning to be controlled by arguments, defaulting to True
+            # to match vLLM's default `thinking=True` behavior.
+            force_reasoning=force_reasoning,
+            stream_reasoning=stream_reasoning,
+        )
+
+
 class Qwen3Detector(BaseReasoningFormatDetector):
     """
     Detector for Qwen3 models (e.g., Qwen/Qwen3-235B-A22B).
@@ -307,7 +327,7 @@ class ReasoningParser:
         "glm45": Qwen3Detector,
         "gpt-oss": GptOssDetector,
         "kimi": KimiDetector,
-        "kimi_k2": DeepSeekR1Detector,
+        "kimi_k2": KimiK2Detector,
         "qwen3": Qwen3Detector,
         "qwen3-thinking": Qwen3Detector,
         "minimax": Qwen3Detector,

python/sglang/srt/server_args.py

@@ -1174,6 +1174,7 @@ class ServerArgs:
 
         if model_arch in [
             "DeepseekV3ForCausalLM",
+            "KimiK25ForConditionalGeneration",
             "MistralLarge3ForCausalLM",
             "PixtralForConditionalGeneration",
         ]:
@@ -1592,6 +1593,7 @@ class ServerArgs:
                 "Glm4MoeForCausalLM",
                 "Glm4MoeLiteForCausalLM",
                 "Qwen3MoeForCausalLM",
+                "KimiK25ForConditionalGeneration",
             ]
             and (is_sm90_supported() or is_sm100_supported())
             and not self.enable_dp_attention

python/sglang/srt/utils/hf_transformers_utils.py

@@ -54,6 +54,7 @@ from sglang.srt.configs import (
     FalconH1Config,
     JetNemotronConfig,
     JetVLMConfig,
+    KimiK25Config,
     KimiLinearConfig,
     KimiVLConfig,
     LongcatFlashConfig,
@@ -93,6 +94,7 @@ _CONFIG_REGISTRY: List[Type[PretrainedConfig]] = [
     DeepseekVLV2Config,
     JetNemotronConfig,
     JetVLMConfig,
+    KimiK25Config,
 ]
 
 _CONFIG_REGISTRY = {