Support glm4.1v and glm4.5v (#8798)

Signed-off-by: Xinyuan Tong <justinning0323@outlook.com>
Signed-off-by: Xinyuan Tong <xinyuantong.cs@gmail.com>
Co-authored-by: Xinyuan Tong <justinning0323@outlook.com>
Co-authored-by: Xinyuan Tong <115166877+JustinTong0323@users.noreply.github.com>
Co-authored-by: Xinyuan Tong <xinyuantong.cs@gmail.com>
Co-authored-by: zRzRzRzRzRzRzR <2448370773@qq.com>
Co-authored-by: Minglei Zhu <mingleizhu1122@gmail.com>
Co-authored-by: Chang Su <csu272@usc.edu>

python/sglang/lang/chat_template.py

@@ -505,6 +505,22 @@ register_chat_template(
     )
 )
 
+# Reference: https://huggingface.co/docs/transformers/main/model_doc/glm4_v#usage-example
+register_chat_template(
+    ChatTemplate(
+        name="glm-4v",
+        default_system_prompt=None,
+        role_prefix_and_suffix={
+            "system": ("<|system|>\n", "\n"),
+            "user": ("<|user|>\n", "\n"),
+            "assistant": ("<|assistant|>\n", "\n"),
+        },
+        style=ChatTemplateStyle.PLAIN,
+        stop_str=["<|user|>", "<|endoftext|>", "<|observation|>"],
+        image_token="<|image|>",
+    )
+)
+
 
 @register_chat_template_matching_function
 def match_deepseek(model_path: str):
@@ -562,6 +578,8 @@ def match_chat_ml(model_path: str):
         return "chatml"
     if re.search(r"qwen.*vl", model_path, re.IGNORECASE):
         return "qwen2-vl"
+    if re.search(r"glm[-_]?4(\.\d+)?v", model_path, re.IGNORECASE):
+        return "glm-4v"
     if re.search(r"qwen.*(chat|instruct)", model_path, re.IGNORECASE) and not re.search(
         r"llava", model_path, re.IGNORECASE
     ):

python/sglang/srt/configs/model_config.py

@@ -659,6 +659,8 @@ multimodal_model_archs = [
     "DeepseekVL2ForCausalLM",
     "Gemma3ForConditionalGeneration",
     "Gemma3nForConditionalGeneration",
+    "Glm4vForConditionalGeneration",
+    "Glm4vMoeForConditionalGeneration",
     "Grok1VForCausalLM",
     "Grok1AForCausalLM",
     "LlavaLlamaForCausalLM",

python/sglang/srt/function_call/ebnf_composer.py

@@ -316,6 +316,7 @@ class EBNFComposer:
 
             combined_args = "".join(rule_parts)
             arguments_rule = args_template.format(arg_rules=combined_args)
+            arguments_rule = arguments_rule or '""'
 
             # Add the function call rule and its arguments rule
             ebnf_lines.append(

python/sglang/srt/function_call/glm4_moe_detector.py

@@ -158,7 +158,7 @@ class Glm4MoeDetector(BaseFormatDetector):
             individual_call_end_token=self.eot_token,
             tool_call_separator="\\n",
             function_format="xml",
-            call_rule_fmt='"{name}" "\\n" {arguments_rule} "\\n"',
+            call_rule_fmt='"{name}" "\\n" ( {arguments_rule} "\\n" )?',
             key_value_rule_fmt='"<arg_key>{key}</arg_key>" "\\n" "<arg_value>" {valrule} "</arg_value>"',
             key_value_separator="\\n",
         )

python/sglang/srt/jinja_template_utils.py

@@ -102,6 +102,12 @@ def detect_jinja_template_content_format(chat_template: str) -> str:
             if _is_var_or_elems_access(loop_iter, "message", "content"):
                 return "openai"  # Found content iteration → openai format
 
+            # Also check for patterns like: {%- for item in msg.content -%} or {%- for item in m.content -%}
+            if _is_var_or_elems_access(
+                loop_iter, "msg", "content"
+            ) or _is_var_or_elems_access(loop_iter, "m", "content"):
+                return "openai"  # Found content iteration → openai format (glm4v)
+
         return "string"  # No content loops found → string format
     except Exception as e:
         logger.debug(f"Error when parsing AST of Jinja template: {e}")

python/sglang/srt/layers/rotary_embedding.py

@@ -1,6 +1,7 @@
 # Adapted from https://raw.githubusercontent.com/vllm-project/vllm/refs/tags/v0.6.6.post1/vllm/model_executor/layers/rotary_embedding.py
 
 """Rotary Positional Embeddings."""
+import itertools
 import math
 from typing import Any, Dict, List, Optional, Tuple, Union
 
@@ -946,7 +947,37 @@ class MRotaryEmbedding(RotaryEmbedding):
 
         self.mrope_section = mrope_section
         if self.mrope_section:
-            assert sum(self.mrope_section) == rotary_dim // 2
+            expected_sum = rotary_dim // 2
+            actual_sum = sum(self.mrope_section)
+            if actual_sum != expected_sum:
+                print(
+                    f"MRoPE section sum mismatch: expected {expected_sum}, got {actual_sum}. "
+                    f"Adjusting mrope_section to match rotary_dim // 2 = {expected_sum}"
+                )
+                # Auto-correct by scaling the mrope_section proportionally
+                if actual_sum > 0:
+                    scale_factor = expected_sum / actual_sum
+                    self.mrope_section = [
+                        max(1, int(section * scale_factor))
+                        for section in self.mrope_section
+                    ]
+                    # Ensure the sum exactly matches by adjusting the last element
+                    current_sum = sum(self.mrope_section)
+                    if current_sum != expected_sum:
+                        self.mrope_section[-1] += expected_sum - current_sum
+                else:
+                    # If all sections are 0, create a default distribution
+                    self.mrope_section = [
+                        expected_sum // len(self.mrope_section)
+                    ] * len(self.mrope_section)
+                    # Handle remainder
+                    remainder = expected_sum % len(self.mrope_section)
+                    for i in range(remainder):
+                        self.mrope_section[i] += 1
+
+                print(
+                    f"Corrected mrope_section: {self.mrope_section} (sum={sum(self.mrope_section)})"
+                )
 
     def forward(
         self,
@@ -1153,6 +1184,204 @@ class MRotaryEmbedding(RotaryEmbedding):
             mrope_position_deltas = max_position_ids + 1 - s
             return position_ids, mrope_position_deltas
 
+    # Adapted from https://github.com/vllm-project/vllm/blob/3779eb8c81449b924a23457fc77e45a0e6171178/vllm/model_executor/layers/rotary_embedding.py#L1120
+    @staticmethod
+    def get_rope_index_glm4v(
+        input_ids: torch.Tensor,
+        hf_config: Any,
+        image_grid_thw: Union[list[list[int]], torch.Tensor],
+        video_grid_thw: Union[list[list[int]], torch.Tensor],
+        attention_mask: torch.Tensor,
+        **kwargs,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """Get mrope input positions and delta value for GLM4V."""
+        image_token_id = hf_config.image_token_id
+        video_start_token_id = hf_config.video_start_token_id
+        video_end_token_id = hf_config.video_end_token_id
+        spatial_merge_size = hf_config.vision_config.spatial_merge_size
+
+        mrope_position_deltas = []
+        if input_ids is not None and (
+            image_grid_thw is not None or video_grid_thw is not None
+        ):
+            total_input_ids = input_ids
+            if attention_mask is None:
+                attention_mask = torch.ones_like(total_input_ids)
+            position_ids = torch.ones(
+                3,
+                input_ids.shape[0],
+                input_ids.shape[1],
+                dtype=input_ids.dtype,
+                device=input_ids.device,
+            )
+            image_index, video_index = 0, 0
+            video_group_index = 0
+            attention_mask = attention_mask.to(total_input_ids.device)
+            for i, input_ids in enumerate(total_input_ids):
+                input_ids = input_ids[attention_mask[i] == 1]
+                input_tokens = input_ids.tolist()
+
+                input_token_type = []
+                video_check_flg = False
+                for token in input_tokens:
+                    if token == video_start_token_id:
+                        video_check_flg = True
+                    elif token == video_end_token_id:
+                        video_check_flg = False
+
+                    if token == image_token_id and not video_check_flg:
+                        input_token_type.append("image")
+                    elif token == image_token_id and video_check_flg:
+                        input_token_type.append("video")
+                    else:
+                        input_token_type.append("text")
+
+                input_type_group = []
+                for key, group in itertools.groupby(
+                    enumerate(input_token_type), lambda x: x[1]
+                ):
+                    group = list(group)
+                    start_index = group[0][0]
+                    end_index = group[-1][0] + 1
+                    input_type_group.append((key, start_index, end_index))
+
+                llm_pos_ids_list = []
+                video_frame_num = 1
+                for modality_type, start_idx, end_idx in input_type_group:
+                    st_idx = (
+                        llm_pos_ids_list[-1].max() + 1
+                        if len(llm_pos_ids_list) > 0
+                        else 0
+                    )
+
+                    if modality_type == "image":
+                        t, h, w = (
+                            image_grid_thw[image_index][0],
+                            image_grid_thw[image_index][1],
+                            image_grid_thw[image_index][2],
+                        )
+                        llm_grid_t, llm_grid_h, llm_grid_w = (
+                            t.item(),
+                            h.item() // spatial_merge_size,
+                            w.item() // spatial_merge_size,
+                        )
+
+                        t_index = (
+                            torch.arange(llm_grid_t)
+                            .view(-1, 1)
+                            .expand(-1, llm_grid_h * llm_grid_w)
+                            .flatten()
+                        )
+                        h_index = (
+                            torch.arange(llm_grid_h)
+                            .view(1, -1, 1)
+                            .expand(llm_grid_t, -1, llm_grid_w)
+                            .flatten()
+                        )
+                        w_index = (
+                            torch.arange(llm_grid_w)
+                            .view(1, 1, -1)
+                            .expand(llm_grid_t, llm_grid_h, -1)
+                            .flatten()
+                        )
+                        llm_pos_ids_list.append(
+                            torch.stack([t_index, h_index, w_index]) + st_idx
+                        )
+
+                        image_index += 1
+                        video_frame_num = 1
+
+                    elif modality_type == "video":
+                        t, h, w = (
+                            video_frame_num,
+                            video_grid_thw[video_index][1],
+                            video_grid_thw[video_index][2],
+                        )
+
+                        llm_grid_t, llm_grid_h, llm_grid_w = (
+                            t,
+                            h.item() // spatial_merge_size,
+                            w.item() // spatial_merge_size,
+                        )
+
+                        for t_idx in range(llm_grid_t):
+                            t_index = (
+                                torch.tensor(t_idx)
+                                .view(-1, 1)
+                                .expand(-1, llm_grid_h * llm_grid_w)
+                                .flatten()
+                            )
+
+                            h_index = (
+                                torch.arange(llm_grid_h)
+                                .view(1, -1, 1)
+                                .expand(1, -1, llm_grid_w)
+                                .flatten()
+                            )
+                            w_index = (
+                                torch.arange(llm_grid_w)
+                                .view(1, 1, -1)
+                                .expand(1, llm_grid_h, -1)
+                                .flatten()
+                            )
+                            llm_pos_ids_list.append(
+                                torch.stack([t_index, h_index, w_index]) + st_idx
+                            )
+
+                        video_group_index += 1
+
+                        if video_group_index >= video_grid_thw[video_index][0]:
+                            video_index += 1
+                            video_group_index = 0
+
+                        video_frame_num += 1
+
+                    else:
+                        text_len = end_idx - start_idx
+                        llm_pos_ids_list.append(
+                            torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx
+                        )
+
+                        video_frame_num = 1
+
+                llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
+                position_ids[..., i, attention_mask[i] == 1] = llm_positions.to(
+                    position_ids.device
+                )
+                mrope_position_deltas.append(
+                    llm_positions.max() + 1 - len(total_input_ids[i])
+                )
+            mrope_position_deltas = torch.tensor(
+                mrope_position_deltas, device=input_ids.device
+            ).unsqueeze(1)
+            return position_ids, mrope_position_deltas
+        else:
+            if attention_mask is not None:
+                position_ids = attention_mask.long().cumsum(-1) - 1
+                position_ids.masked_fill_(attention_mask == 0, 1)
+                position_ids = (
+                    position_ids.unsqueeze(0)
+                    .expand(3, -1, -1)
+                    .to(attention_mask.device)
+                )
+                max_position_ids = position_ids.max(0, keepdim=False)[0].max(
+                    -1, keepdim=True
+                )[0]
+                mrope_position_deltas = max_position_ids + 1 - attention_mask.shape[-1]
+            else:
+                position_ids = (
+                    torch.arange(input_ids.shape[1], device=input_ids.device)
+                    .view(1, 1, -1)
+                    .expand(3, input_ids.shape[0], -1)
+                )
+                mrope_position_deltas = torch.zeros(
+                    [input_ids.shape[0], 1],
+                    device=input_ids.device,
+                    dtype=input_ids.dtype,
+                )
+
+            return position_ids, mrope_position_deltas
+
     @staticmethod
     def get_next_input_positions(
         mrope_position_delta: int,

python/sglang/srt/models/glm4.py

@@ -218,6 +218,12 @@ class Glm4Model(nn.Module):
 
         self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
 
+    def get_input_embeddings(self) -> nn.Embedding:
+        return self.embed_tokens
+
+    def dtype(self) -> torch.dtype:
+        return next(self.parameters()).dtype
+
     @torch.no_grad()
     def forward(
         self,

python/sglang/srt/models/glm4v.py

@@ -0,0 +1,589 @@
+import logging
+from functools import lru_cache, partial
+from typing import Iterable, List, Optional, Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers.models.glm4v.configuration_glm4v import Glm4vConfig, Glm4vVisionConfig
+
+from sglang.srt.hf_transformers_utils import get_processor
+from sglang.srt.layers.activation import SiluAndMul
+from sglang.srt.layers.layernorm import RMSNorm
+from sglang.srt.layers.linear import (
+    ColumnParallelLinear,
+    MergedColumnParallelLinear,
+    RowParallelLinear,
+)
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.pooler import Pooler, PoolingType
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
+from sglang.srt.managers.schedule_batch import MultimodalDataItem
+from sglang.srt.model_loader.weight_utils import default_weight_loader
+from sglang.srt.models.glm4 import Glm4Model
+from sglang.srt.models.qwen2_5_vl import (
+    Qwen2_5_VisionBlock,
+    Qwen2_5_VLForConditionalGeneration,
+)
+from sglang.srt.utils import add_prefix
+
+logger = logging.getLogger(__name__)
+
+cached_get_processor = lru_cache(get_processor)
+
+
+class Glm4vRMSNorm(RMSNorm):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        original_shape = x.shape
+        x_2d = x.contiguous().reshape(-1, original_shape[-1])
+        x_2d = super().forward(x_2d)
+        x = x_2d.reshape(original_shape)
+        return x
+
+
+class Glm4vVisionMLP(nn.Module):
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: int,
+        bias: bool = False,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            input_size=in_features,
+            output_sizes=[hidden_features] * 2,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=add_prefix("gate_up_proj", prefix),
+        )
+        self.down_proj = RowParallelLinear(
+            hidden_features,
+            in_features,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=add_prefix("down_proj", prefix),
+        )
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x: torch.Tensor):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class Glm4vVisionBlock(Qwen2_5_VisionBlock):
+    def __init__(
+        self,
+        config: Glm4vVisionConfig,
+        norm_layer: Optional[nn.Module] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__(
+            dim=config.hidden_size,
+            intermediate_dim=config.out_hidden_size,
+            num_heads=config.num_heads,
+            hidden_act=config.hidden_act,
+            norm_layer=norm_layer,
+            quant_config=quant_config,
+            prefix=prefix,
+        )
+        self.norm1 = Glm4vRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.norm2 = Glm4vRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.mlp = Glm4vVisionMLP(
+            config.hidden_size,
+            config.out_hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=add_prefix("mlp", prefix),
+        )
+
+
+class Glm4vVisionPatchEmbed(nn.Module):
+    def __init__(
+        self,
+        patch_size: int = 14,
+        temporal_patch_size: int = 2,
+        in_channels: int = 3,
+        hidden_size: int = 1536,
+    ) -> None:
+        super().__init__()
+        self.patch_size = patch_size
+        self.temporal_patch_size = temporal_patch_size
+        self.hidden_size = hidden_size
+        self.in_channels = in_channels
+
+        kernel_size = (temporal_patch_size, patch_size, patch_size)
+        self.proj = nn.Conv3d(
+            in_channels,
+            hidden_size,
+            kernel_size=kernel_size,
+            stride=kernel_size,
+            bias=True,
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x.view(
+            -1,
+            self.in_channels,
+            self.temporal_patch_size,
+            self.patch_size,
+            self.patch_size,
+        )
+        x = self.proj(x).view(-1, self.hidden_size)
+        return x
+
+
+class Glm4vPatchMerger(nn.Module):
+    def __init__(
+        self,
+        d_model: int,
+        context_dim: int,
+        quant_config: Optional[QuantizationConfig] = None,
+        bias: bool = False,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.hidden_size = d_model
+        self.proj = ColumnParallelLinear(
+            self.hidden_size,
+            self.hidden_size,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=add_prefix("proj", prefix),
+            gather_output=True,
+        )
+        self.post_projection_norm = nn.LayerNorm(self.hidden_size)
+        self.gate_up_proj = MergedColumnParallelLinear(
+            input_size=self.hidden_size,
+            output_sizes=[context_dim] * 2,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=add_prefix("gate_up_proj", prefix),
+        )
+        self.down_proj = RowParallelLinear(
+            context_dim,
+            self.hidden_size,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=add_prefix("down_proj", prefix),
+        )
+        self.extra_activation_func = nn.GELU()
+
+    def forward(self, x: torch.Tensor):
+        x, _ = self.proj(x)
+        x = self.extra_activation_func(self.post_projection_norm(x))
+        gate_up, _ = self.gate_up_proj(x)
+        gate, up = gate_up.chunk(2, dim=-1)
+        x = F.silu(gate) * up
+        x, _ = self.down_proj(x)
+        return x
+
+
+class Glm4vVisionEmbeddings(nn.Module):
+    def __init__(self, config: Glm4vVisionConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.image_size = config.image_size
+        self.patch_size = config.patch_size
+
+        self.num_patches = (self.image_size // self.patch_size) ** 2
+        self.num_positions = self.num_patches
+        self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
+        self.register_buffer(
+            "position_ids",
+            torch.arange(self.num_positions).expand((1, -1)),
+            persistent=False,
+        )
+
+    def forward(
+        self, embeddings, lengths, image_shapes, h_coords, w_coords
+    ) -> torch.Tensor:
+        pos_embed_weight = self.position_embedding.weight
+        hidden_size = pos_embed_weight.shape[1]
+        total_seq = h_coords.shape[0]
+        device = pos_embed_weight.device
+
+        # Move coordinates to correct device
+        h_coords, w_coords = h_coords.to(device), w_coords.to(device)
+
+        # Handle empty sequence case
+        if total_seq == 0:
+            adapted_pos_embed = torch.empty(
+                0, hidden_size, device=device, dtype=pos_embed_weight.dtype
+            )
+        else:
+            # Convert inputs to tensors if needed
+            if isinstance(lengths, list):
+                lengths = torch.tensor(lengths, device=device, dtype=torch.long)
+            if not isinstance(image_shapes, torch.Tensor):
+                image_shapes = torch.tensor(
+                    image_shapes, device=device, dtype=torch.long
+                )
+
+            # Prepare 2D position embedding
+            orig_size_sq = pos_embed_weight.shape[0]
+            orig_size = int(orig_size_sq**0.5)
+            pos_embed_2d = (
+                pos_embed_weight.view(orig_size, orig_size, hidden_size)
+                .permute(2, 0, 1)
+                .unsqueeze(0)
+                .to(device=device, dtype=torch.float32)
+            )
+
+            # Calculate target dimensions for each patch
+            target_h = torch.cat(
+                [image_shapes[i, 1].repeat(lengths[i]) for i in range(len(lengths))]
+            ).to(device=device, dtype=torch.float32)
+            target_w = torch.cat(
+                [image_shapes[i, 2].repeat(lengths[i]) for i in range(len(lengths))]
+            ).to(device=device, dtype=torch.float32)
+
+            # Normalize coordinates to [-1, 1] range for grid_sample
+            h_coords = h_coords.to(device=device, dtype=torch.float32)
+            w_coords = w_coords.to(device=device, dtype=torch.float32)
+            norm_w = ((w_coords + 0.5) / target_w) * 2 - 1
+            norm_h = ((h_coords + 0.5) / target_h) * 2 - 1
+
+            # Create sampling grid
+            grid = torch.stack((norm_w, norm_h), dim=-1).unsqueeze(0).unsqueeze(2)
+
+            # Perform bicubic interpolation
+            interpolated_embed_fp32 = F.grid_sample(
+                pos_embed_2d,
+                grid,
+                mode="bicubic",
+                align_corners=False,
+                padding_mode="border",
+            )
+
+            # Reshape and convert back to original dtype
+            adapted_pos_embed_fp32 = (
+                interpolated_embed_fp32.squeeze(0).squeeze(-1).permute(1, 0)
+            )
+            adapted_pos_embed = adapted_pos_embed_fp32.to(pos_embed_weight.dtype).to(
+                embeddings.device
+            )
+
+        # Add adapted position encoding to embeddings
+        embeddings = embeddings + adapted_pos_embed
+        return embeddings
+
+
+class Glm4vVisionRotaryEmbedding(nn.Module):
+    def __init__(self, dim: int, theta: float = 10000.0) -> None:
+        super().__init__()
+        self.dim = dim
+        self.theta = theta
+        inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self._seq_len_cached = 0
+        self._freqs_cached = None
+
+    def update_freqs_cache(self, seqlen: int) -> None:
+        if seqlen > self._seq_len_cached:
+            seqlen *= 2
+            self._seq_len_cached = seqlen
+            self.inv_freq = 1.0 / (
+                self.theta
+                ** (
+                    torch.arange(
+                        0,
+                        self.dim,
+                        2,
+                        dtype=torch.float,
+                        device=self.inv_freq.device,
+                    )
+                    / self.dim
+                )
+            )
+            seq = torch.arange(
+                seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype
+            )
+            freqs = torch.outer(seq, self.inv_freq)
+            self._freqs_cached = freqs
+
+    def forward(self, seqlen: int) -> torch.Tensor:
+        self.update_freqs_cache(seqlen)
+        return self._freqs_cached[:seqlen]
+
+
+class Glm4vVisionModel(nn.Module):
+    def __init__(
+        self,
+        vision_config: Glm4vVisionConfig,
+        norm_eps: float = 1e-6,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        patch_size = vision_config.patch_size
+        temporal_patch_size = vision_config.temporal_patch_size
+        in_channels = vision_config.in_channels
+        depth = vision_config.depth
+        self.hidden_size = vision_config.hidden_size
+        self.num_heads = vision_config.num_heads
+
+        self.patch_size = vision_config.patch_size
+        self.spatial_merge_size = vision_config.spatial_merge_size
+        self.out_hidden_size = vision_config.out_hidden_size
+
+        self.patch_embed = Glm4vVisionPatchEmbed(
+            patch_size=patch_size,
+            temporal_patch_size=temporal_patch_size,
+            in_channels=in_channels,
+            hidden_size=self.hidden_size,
+        )
+
+        norm_layer = partial(Glm4vRMSNorm, eps=norm_eps)
+        head_dim = self.hidden_size // self.num_heads
+        self.rotary_pos_emb = Glm4vVisionRotaryEmbedding(head_dim // 2)
+
+        self.blocks = nn.ModuleList(
+            [
+                Glm4vVisionBlock(
+                    config=vision_config,
+                    norm_layer=norm_layer,
+                    quant_config=quant_config,
+                    prefix=add_prefix(f"blocks.{layer_idx}", prefix),
+                )
+                for layer_idx in range(depth)
+            ]
+        )
+
+        self.merger = Glm4vPatchMerger(
+            d_model=vision_config.out_hidden_size,
+            context_dim=vision_config.intermediate_size,
+            quant_config=quant_config,
+            bias=False,
+            prefix=add_prefix("merger", prefix),
+        )
+
+        self.embeddings = Glm4vVisionEmbeddings(vision_config)
+
+        self.post_conv_layernorm = Glm4vRMSNorm(
+            vision_config.hidden_size, eps=vision_config.rms_norm_eps
+        )
+        self.downsample = nn.Conv2d(
+            in_channels=vision_config.hidden_size,
+            out_channels=vision_config.out_hidden_size,
+            kernel_size=vision_config.spatial_merge_size,
+            stride=vision_config.spatial_merge_size,
+        )
+        self.post_layernorm = Glm4vRMSNorm(
+            vision_config.hidden_size, eps=vision_config.rms_norm_eps
+        )
+
+    @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 rot_pos_emb(self, grid_thw: torch.Tensor) -> torch.Tensor:
+        pos_ids = []
+        for t, h, w in grid_thw:
+            hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
+            wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
+            hpos_ids = (
+                hpos_ids.reshape(
+                    h // self.spatial_merge_size,
+                    self.spatial_merge_size,
+                    w // self.spatial_merge_size,
+                    self.spatial_merge_size,
+                )
+                .permute(0, 2, 1, 3)
+                .flatten()
+            )
+            wpos_ids = (
+                wpos_ids.reshape(
+                    h // self.spatial_merge_size,
+                    self.spatial_merge_size,
+                    w // self.spatial_merge_size,
+                    self.spatial_merge_size,
+                )
+                .permute(0, 2, 1, 3)
+                .flatten()
+            )
+            pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))
+        pos_ids = torch.cat(pos_ids, dim=0)
+        max_grid_size = grid_thw[:, 1:].max()
+        rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
+        rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
+        return rotary_pos_emb, pos_ids
+
+    def forward(self, x: torch.Tensor, grid_thw: torch.Tensor) -> torch.Tensor:
+        # patchify
+        x = x.to(device=self.device, dtype=self.dtype)
+        x = self.patch_embed(x)
+        x = self.post_conv_layernorm(x)
+
+        # compute position embedding
+        rotary_pos_emb, image_type_ids = self.rot_pos_emb(grid_thw)
+        # compute cu_seqlens
+        cu_seqlens = torch.repeat_interleave(
+            grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]
+        ).cumsum(dim=0, dtype=torch.int32)
+        cu_seqlens = F.pad(cu_seqlens, (1, 0), "constant", 0)
+
+        seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
+        x = self.embeddings(
+            x, seqlens, grid_thw, image_type_ids[:, 0], image_type_ids[:, 1]
+        )
+
+        emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1)
+        rotary_pos_emb_tuple = (emb.cos(), emb.sin())
+
+        # x.shape: (s, b, d) where b=1 for vision processing
+        # transformers
+        x = x.unsqueeze(1)
+        for blk in self.blocks:
+            x = blk(x, cu_seqlens=cu_seqlens, position_embeddings=rotary_pos_emb_tuple)
+
+        # adapter
+        x = self.post_layernorm(x)
+        x = x.view(-1, self.spatial_merge_size, self.spatial_merge_size, x.shape[-1])
+        x = x.permute(0, 3, 1, 2)
+        x = self.downsample(x).view(-1, self.out_hidden_size)
+        x = self.merger(x)
+
+        return x
+
+
+class Glm4vForConditionalGeneration(Qwen2_5_VLForConditionalGeneration):
+    def __init__(
+        self,
+        config: Glm4vConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        nn.Module.__init__(self)
+
+        self.config = config
+
+        self.model = Glm4Model(
+            config,
+            quant_config,
+            prefix=add_prefix("model", prefix),
+        )
+        self.visual = Glm4vVisionModel(
+            config.vision_config,
+            norm_eps=getattr(config, "rms_norm_eps", 1e-5),
+            quant_config=quant_config,
+            prefix=add_prefix("visual", prefix),
+        )
+
+        if config.tie_word_embeddings:
+            self.lm_head = self.model.embed_tokens
+        else:
+            self.lm_head = ParallelLMHead(
+                config.vocab_size,
+                config.hidden_size,
+                quant_config=quant_config,
+                prefix=add_prefix("lm_head", prefix),
+            )
+
+        self.logits_processor = LogitsProcessor(config)
+        self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
+        self.is_mrope_enabled = "mrope_section" in self.config.rope_scaling
+
+    def get_image_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
+        pixel_values = torch.cat(
+            [item.feature.squeeze(0) for item in items], dim=0
+        ).type(self.visual.dtype)
+        image_grid_thw = torch.concat([item.image_grid_thw for item in items], dim=0)
+        # For multi-image, pixel_values is [num_of_images, L, C] shape
+        # assert pixel_values.dim() == 2, pixel_values.dim()
+        assert image_grid_thw.dim() == 2, image_grid_thw.dim()
+        image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw)
+        split_sizes = (
+            image_grid_thw.prod(-1) // self.visual.spatial_merge_size**2
+        ).tolist()
+        image_embeds = torch.split(image_embeds, split_sizes)
+        return torch.cat(image_embeds)
+
+    def get_video_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
+        pixel_values_videos = torch.cat(
+            [item.feature.squeeze(0) for item in items], dim=0
+        ).type(self.visual.dtype)
+        video_grid_thw = torch.concat([item.video_grid_thw for item in items], dim=0)
+        # For multi-video, pixel_values_videos is [num_of_videos, L, C] shape
+        # assert pixel_values_videos.dim() == 2, pixel_values_videos.dim()
+        assert video_grid_thw.dim() == 2, video_grid_thw.dim()
+
+        # reshape video_grid_thw -> [b, 3] -> [1, h, w] * frames
+        temp_frames_hw = []
+        for t, h, w in video_grid_thw:
+            repeated_row = (
+                torch.tensor([1, h.item(), w.item()]).unsqueeze(0).repeat(t, 1)
+            )
+            temp_frames_hw.append(repeated_row)
+        flattened_video_grid_thw = torch.cat(temp_frames_hw, dim=0)
+        video_embeds = self.visual(
+            pixel_values_videos, grid_thw=flattened_video_grid_thw
+        )
+        split_sizes = (
+            video_grid_thw.prod(-1) // self.visual.spatial_merge_size**2
+        ).tolist()
+        video_embeds = torch.split(video_embeds, split_sizes)
+        return torch.cat(video_embeds)
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            (".qkv_proj", ".q_proj", "q"),
+            (".qkv_proj", ".k_proj", "k"),
+            (".qkv_proj", ".v_proj", "v"),
+            (".gate_up_proj", ".up_proj", 1),
+            (".gate_up_proj", ".gate_proj", 0),
+        ]
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        for name, loaded_weight in weights:
+            if "language_model." in name:
+                name = name.replace("language_model.", "")
+            if "model.visual." in name:
+                name = name.replace("model.visual.", "visual.")
+
+            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:
+                if "visual" in name:
+                    # adapt to VisionAttention
+                    name = name.replace(r"attn.qkv.", r"attn.qkv_proj.")
+
+                try:
+                    # Skip loading extra bias for GPTQ models.
+                    if name.endswith(".bias") and name not in params_dict:
+                        continue
+                    param = params_dict[name]
+                except KeyError:
+                    print(params_dict.keys())
+                    raise
+
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+
+
+EntryClass = [Glm4vForConditionalGeneration]

python/sglang/srt/models/glm4v_moe.py

@@ -0,0 +1,400 @@
+import logging
+from functools import lru_cache
+from typing import Iterable, Optional, Tuple
+
+import torch
+import torch.nn as nn
+from transformers.models.glm4v_moe.configuration_glm4v_moe import Glm4vMoeConfig
+
+from sglang.srt.distributed import (
+    get_moe_expert_parallel_world_size,
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+    parallel_state,
+    tensor_model_parallel_all_reduce,
+)
+from sglang.srt.hf_transformers_utils import get_processor
+from sglang.srt.layers.dp_attention import (
+    get_attention_tp_rank,
+    get_attention_tp_size,
+    get_local_attention_dp_size,
+)
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.moe.ep_moe.layer import get_moe_impl_class
+from sglang.srt.layers.pooler import Pooler, PoolingType
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
+from sglang.srt.managers.schedule_batch import global_server_args_dict
+from sglang.srt.model_loader.weight_utils import default_weight_loader
+from sglang.srt.models.glm4_moe import Glm4MoeModel
+from sglang.srt.models.glm4v import Glm4vForConditionalGeneration, Glm4vVisionModel
+from sglang.srt.utils import add_prefix, is_cuda, log_info_on_rank0
+
+_is_cuda = is_cuda()
+
+logger = logging.getLogger(__name__)
+
+cached_get_processor = lru_cache(get_processor)
+
+
+class Glm4vMoeForConditionalGeneration(Glm4vForConditionalGeneration):
+    def __init__(
+        self,
+        config: Glm4vMoeConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        nn.Module.__init__(self)
+
+        config.moe_layer_freq = 1
+        self.config = config
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.dp_size = get_local_attention_dp_size()
+        self.quant_config = quant_config
+        self.determine_num_fused_shared_experts("Glm4MoeForCausalLM")
+        self.num_fused_shared_experts = (
+            0
+            if global_server_args_dict["disable_shared_experts_fusion"]
+            else config.n_shared_experts
+        )
+
+        self.model = Glm4MoeModel(
+            config,
+            quant_config,
+            prefix=add_prefix("language_model", prefix),
+        )
+        self.visual = Glm4vVisionModel(
+            config.vision_config,
+            norm_eps=getattr(config, "rms_norm_eps", 1e-5),
+            quant_config=quant_config,
+            prefix=add_prefix("visual", prefix),
+        )
+
+        self.lm_head = ParallelLMHead(
+            config.vocab_size,
+            config.hidden_size,
+            quant_config=quant_config,
+            prefix=add_prefix("lm_head", prefix),
+            use_attn_tp_group=global_server_args_dict["enable_dp_lm_head"],
+        )
+        self.logits_processor = LogitsProcessor(config)
+        self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
+        self.is_mrope_enabled = "mrope_section" in self.config.rope_scaling
+
+    def determine_num_fused_shared_experts(
+        self, architecture: str = "Glm4MoeForCausalLM"
+    ):
+        self.num_fused_shared_experts = 0
+        if global_server_args_dict["disable_shared_experts_fusion"]:
+            return
+
+        # Only Deepseek V3/R1 can use shared experts fusion optimization now.
+        disable_reason = None
+        if (
+            not _is_cuda
+            or torch.cuda.get_device_capability("cuda") < (8, 0)
+            or self.config.architectures[0] != architecture
+            or self.config.n_shared_experts != 1
+        ):
+            disable_reason = "Only GLM-4.5 on NV-platform with capability >= 80 can use shared experts fusion optimization."
+        elif get_moe_expert_parallel_world_size() > 1:
+            disable_reason = "Deepseek and GLM-4.5 can not use shared experts fusion optimization under expert parallelism."
+
+        if disable_reason is not None:
+            global_server_args_dict["disable_shared_experts_fusion"] = True
+            self.num_fused_shared_experts = 0
+            log_info_on_rank0(
+                logger,
+                f"{disable_reason} Shared experts fusion optimization is disabled.",
+            )
+            return
+
+        self.num_fused_shared_experts = self.config.n_shared_experts
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]], is_nextn=False):
+
+        if is_nextn:
+            if hasattr(self.config, "num_nextn_predict_layers"):
+                num_nextn_layers = self.config.num_nextn_predict_layers
+                assert num_nextn_layers == 1, "Only 1 nextn layer is supported"
+                # compatible with old design
+                nextn_layer_id = (
+                    0
+                    if self.config.num_hidden_layers == 1
+                    else self.config.num_hidden_layers
+                )
+            else:
+                raise ValueError("num_nextn_predict_layers is not in the config")
+
+        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),
+        ]
+        if self.num_fused_shared_experts > 0:
+            assert self.num_fused_shared_experts == 1
+            weights_list = list(weights)
+            weights_dict = dict(weights_list)
+            if self.quant_config is not None:
+                if self.quant_config.get_name() == "w8a8_int8":
+                    suffix_list = [
+                        "down_proj.weight",
+                        "down_proj.weight_scale",
+                        "gate_proj.weight",
+                        "gate_proj.weight_scale",
+                        "up_proj.weight",
+                        "up_proj.weight_scale",
+                    ]
+                elif (
+                    self.quant_config.get_name() == "fp8"
+                    or self.quant_config.get_name() == "blockwise_int8"
+                    or self.quant_config.get_name() == "compressed_tensors"
+                ):
+                    suffix_list = [
+                        "down_proj.weight",
+                        "down_proj.weight_scale",
+                        "gate_proj.weight",
+                        "gate_proj.weight_scale",
+                        "up_proj.weight",
+                        "up_proj.weight_scale",
+                    ]
+                elif self.quant_config.get_name() == "awq":
+                    suffix_list = [
+                        "down_proj.qweight",
+                        "down_proj.qzeros",
+                        "down_proj.scales",
+                        "gate_proj.qweight",
+                        "gate_proj.qzeros",
+                        "gate_proj.scales",
+                        "up_proj.qweight",
+                        "up_proj.qzeros",
+                        "up_proj.scales",
+                    ]
+                elif self.quant_config.get_name() == "modelopt_fp4":
+                    suffix_list = [
+                        "down_proj.weight",
+                        "down_proj.weight_scale",
+                        "down_proj.weight_scale_2",
+                        "down_proj.input_scale",
+                        "gate_proj.weight",
+                        "gate_proj.weight_scale",
+                        "gate_proj.weight_scale_2",
+                        "gate_proj.input_scale",
+                        "up_proj.weight",
+                        "up_proj.weight_scale",
+                        "up_proj.weight_scale_2",
+                        "up_proj.input_scale",
+                    ]
+                else:
+                    raise ValueError(
+                        f"Unsupported shared expert fusion for quantization: {self.quant_config.get_name()}."
+                    )
+            else:
+                suffix_list = [
+                    "down_proj.weight",
+                    "gate_proj.weight",
+                    "up_proj.weight",
+                ]
+            names_to_remove = []
+
+            moe_layers = (
+                range(
+                    self.config.first_k_dense_replace,
+                    self.config.num_hidden_layers,
+                    self.config.moe_layer_freq,
+                )
+                if not is_nextn
+                else [nextn_layer_id]
+            )
+
+            for moe_layer in moe_layers:
+                for suffix in suffix_list:
+                    shared_expert_weight_name = (
+                        f"model.layers.{moe_layer}.mlp.shared_experts.{suffix}"
+                    )
+                    # online fp8 quantization does not load weight_scale
+                    if shared_expert_weight_name not in weights_dict:
+                        continue
+                    weights_list.append(
+                        (
+                            f"model.layers.{moe_layer}."
+                            f"mlp.experts."
+                            f"{self.config.n_routed_experts + 0}"
+                            f".{suffix}",
+                            weights_dict[shared_expert_weight_name],
+                        )
+                    )
+                    names_to_remove += [shared_expert_weight_name]
+            weights = [w for w in weights_list if w[0] not in names_to_remove]
+
+        # Params for weights, fp8 weight scales, fp8 activation scales
+        # (param_name, weight_name, expert_id, shard_id)
+        expert_params_mapping = get_moe_impl_class().make_expert_params_mapping(
+            ckpt_gate_proj_name="gate_proj",
+            ckpt_down_proj_name="down_proj",
+            ckpt_up_proj_name="up_proj",
+            num_experts=self.config.n_routed_experts + self.num_fused_shared_experts,
+        )
+
+        # Fuse q_a_proj and kv_a_proj_with_mqa along output dimension when q_lora_rank is not None
+        fuse_qkv_a_proj = hasattr(self.config, "q_lora_rank") and (
+            self.config.q_lora_rank is not None
+        )
+        cached_a_proj = {} if fuse_qkv_a_proj else None
+
+        if is_nextn:
+            nextn_layer_prefix = f"model.layers.{nextn_layer_id}"
+            nextn_spec_weight_names = [
+                "shared_head.norm",
+                "eh_proj",
+                "enorm",
+                "hnorm",
+            ]
+
+        params_dict = dict(self.named_parameters())
+        weight_names = []
+        for name, loaded_weight in weights:
+            weight_names.append(name)
+
+            if not is_nextn:
+                if hasattr(self.config, "num_nextn_predict_layers"):
+                    num_nextn_layers = self.config.num_nextn_predict_layers
+                    if num_nextn_layers > 0 and name.startswith("model.layers"):
+                        name_list = name.split(".")
+                        if (
+                            len(name_list) >= 3
+                            and int(name_list[2]) >= self.config.num_hidden_layers
+                        ):
+                            continue
+            else:
+                if not name.startswith(nextn_layer_prefix):
+                    continue
+
+                # Use shared head and embed weights from target model
+                if "shared_head.head" in name or "embed_tokens" in name:
+                    continue
+
+                is_decoder = True
+                # For nextn specific weights
+                for weight_name in nextn_spec_weight_names:
+                    if weight_name in name:
+                        name = name.replace(nextn_layer_prefix, "model")
+                        is_decoder = False
+                        break
+                # For decoder layer weights
+                if is_decoder:
+                    name = name.replace(nextn_layer_prefix, "model.decoder")
+
+            if "language_model." in name:
+                name = name.replace("language_model.", "")
+            if "model.visual." in name:
+                name = name.replace("model.visual.", "visual.")
+            if "rotary_emb.inv_freq" in name:
+                continue
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                # Skip non-stacked layers and experts (experts handled below).
+                if weight_name not in name:
+                    continue
+                # We have mlp.experts[0].gate_proj in the checkpoint.
+                # Since we handle the experts below in expert_params_mapping,
+                # we need to skip here BEFORE we update the name, otherwise
+                # name will be updated to mlp.experts[0].gate_up_proj, which
+                # will then be updated below in expert_params_mapping
+                # for mlp.experts[0].gate_gate_up_proj, which breaks load.
+                if ("mlp.experts." in name) and name not in params_dict:
+                    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:
+                for mapping in expert_params_mapping:
+                    param_name, weight_name, expert_id, shard_id = mapping
+                    if weight_name not in name:
+                        continue
+                    name = name.replace(weight_name, param_name)
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    weight_loader(
+                        param,
+                        loaded_weight,
+                        name,
+                        shard_id=shard_id,
+                        expert_id=expert_id,
+                    )
+                    break
+                else:
+                    if "visual" in name:
+                        # adapt to VisionAttention
+                        name = name.replace(r"attn.qkv.", r"attn.qkv_proj.")
+
+                    # Skip loading extra bias for GPTQ models.
+                    if name.endswith(".bias") and name not in params_dict:
+                        continue
+                    if fuse_qkv_a_proj and (
+                        "q_a_proj" in name or "kv_a_proj_with_mqa" in name
+                    ):
+                        cached_a_proj[name] = loaded_weight
+                        q_a_proj_name = (
+                            name
+                            if "q_a_proj" in name
+                            else name.replace("kv_a_proj_with_mqa", "q_a_proj")
+                        )
+                        kv_a_proj_name = (
+                            name
+                            if "kv_a_proj_with_mqa" in name
+                            else name.replace("q_a_proj", "kv_a_proj_with_mqa")
+                        )
+
+                        # When both q_a_proj and kv_a_proj_with_mqa has been cached, load the fused weight to parameter
+                        if (
+                            q_a_proj_name in cached_a_proj
+                            and kv_a_proj_name in cached_a_proj
+                        ):
+                            q_a_proj_weight = cached_a_proj[q_a_proj_name]
+                            kv_a_proj_weight = cached_a_proj[kv_a_proj_name]
+                            fused_weight = torch.cat(
+                                [q_a_proj_weight, kv_a_proj_weight], dim=0
+                            )
+                            param_name = (
+                                name.replace("q_a_proj", "fused_qkv_a_proj_with_mqa")
+                                if "q_a_proj" in name
+                                else name.replace(
+                                    "kv_a_proj_with_mqa", "fused_qkv_a_proj_with_mqa"
+                                )
+                            )
+                            param = params_dict[param_name]
+
+                            weight_loader = getattr(
+                                param, "weight_loader", default_weight_loader
+                            )
+                            weight_loader(param, fused_weight)
+                            cached_a_proj.pop(q_a_proj_name)
+                            cached_a_proj.pop(kv_a_proj_name)
+                    else:
+                        if (
+                            "k_scale" in name or "v_scale" in name
+                        ) and name not in params_dict:
+                            # modelopt attn kv scale is named differently
+                            if any(scale in name for scale in ["k_scale", "v_scale"]):
+                                name = name.replace("_proj", "attn_mqa")
+                            else:
+                                logger.warning(
+                                    f"Unknown scale found in checkpoint: {name}"
+                                )
+                        param = params_dict[name]
+                        weight_loader = getattr(
+                            param, "weight_loader", default_weight_loader
+                        )
+                        weight_loader(param, loaded_weight)
+
+
+EntryClass = [Glm4vMoeForConditionalGeneration]

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

@@ -22,13 +22,19 @@ class BaseMultiModalProcessorOutput:
     input_text: str
 
     # frames loaded from image, in given order
-    images: Optional[list[Union[Image.Image, dict]]] = None
+    images: Optional[list[Union[Image.Image, dict]]] = dataclasses.field(
+        default_factory=list
+    )
 
     # videos
-    videos: Optional[list[Union[torch.Tensor, dict]]] = None
+    videos: Optional[list[Union[torch.Tensor, dict]]] = dataclasses.field(
+        default_factory=list
+    )
 
     # audios
-    audios: Optional[list[Union[np.ndarray, dict]]] = None
+    audios: Optional[list[Union[np.ndarray, dict]]] = dataclasses.field(
+        default_factory=list
+    )
 
     def organize_results(self) -> List[Tuple[Modality, Any]]:
         """

python/sglang/srt/multimodal/processors/glm4v.py

@@ -0,0 +1,132 @@
+import re
+from typing import List, Union
+
+from decord import VideoReader
+from transformers.video_utils import VideoMetadata
+
+from sglang.srt.layers.rotary_embedding import MRotaryEmbedding
+from sglang.srt.models.glm4v import Glm4vForConditionalGeneration
+from sglang.srt.models.glm4v_moe import Glm4vMoeForConditionalGeneration
+from sglang.srt.multimodal.processors.base_processor import (
+    BaseMultimodalProcessor as SGLangBaseProcessor,
+)
+from sglang.srt.multimodal.processors.base_processor import (
+    BaseMultiModalProcessorOutput,
+    MultimodalSpecialTokens,
+)
+
+
+class Glm4vImageProcessor(SGLangBaseProcessor):
+    models = [Glm4vForConditionalGeneration, Glm4vMoeForConditionalGeneration]
+
+    def __init__(self, hf_config, server_args, _processor, *args, **kwargs):
+        super().__init__(hf_config, server_args, _processor, *args, **kwargs)
+
+        # GLM-4.1V and GLM-4.5V specific tokens
+        self.IMAGE_TOKEN = "<|image|>"
+        self.VIDEO_TOKEN = "<|video|>"
+        self.IMAGE_START_TOKEN = "<|begin_of_image|>"
+        self.IMAGE_END_TOKEN = "<|end_of_image|>"
+        self.VIDEO_START_TOKEN = "<|begin_of_video|>"
+        self.VIDEO_END_TOKEN = "<|end_of_video|>"
+
+        # Token IDs
+        self.IM_TOKEN_ID = hf_config.image_token_id
+        self.VIDEO_TOKEN_ID = hf_config.video_token_id
+        self.IMAGE_START_TOKEN_ID = hf_config.image_start_token_id
+        self.IMAGE_END_TOKEN_ID = hf_config.image_end_token_id
+        self.VIDEO_START_TOKEN_ID = hf_config.video_start_token_id
+        self.VIDEO_END_TOKEN_ID = hf_config.video_end_token_id
+
+        # Vision config
+        self.IMAGE_FACTOR = 28
+        self.MIN_PIXELS = 112 * 112
+        self.MAX_PIXELS = 30000 * 28 * 28 * 2
+
+        self.mm_tokens = MultimodalSpecialTokens(
+            image_token=self.IMAGE_TOKEN,
+            image_token_id=self.IM_TOKEN_ID,
+            video_token=self.VIDEO_TOKEN,
+            # Note: For GLM4v videos, it uses the video token before tokenization but uses image token after tokenization
+            video_token_id=self.IM_TOKEN_ID,
+        ).build(_processor)
+
+    # adapted from https://github.com/huggingface/transformers/blob/369c99d0cea403b77bd0aef818527106453fd9fc/src/transformers/video_utils.py#L312
+    async def preprocess_video(self, vr: VideoReader):
+        """
+        Preprocess video using VideoReader from Decord backend.
+
+        Args:
+            vr (VideoReader): VideoReader object from decord
+
+        Returns:
+            tuple: A tuple containing processed frames and metadata
+        """
+        video_fps = vr.get_avg_fps()
+        total_num_frames = len(vr)
+        duration = total_num_frames / video_fps if video_fps else 0
+
+        metadata = VideoMetadata(
+            total_num_frames=int(total_num_frames),
+            fps=float(video_fps),
+            duration=float(duration),
+            video_backend="decord",
+        )
+
+        # Extract all frames
+        indices = list(range(total_num_frames))
+        frames = vr.get_batch(indices).asnumpy()
+        metadata.frames_indices = indices
+
+        return frames, metadata
+
+    async def process_mm_data_async(
+        self,
+        image_data: List[Union[str, bytes]],
+        input_text,
+        request_obj,
+        *args,
+        **kwargs,
+    ):
+        base_output = self.load_mm_data(
+            prompt=input_text,
+            image_data=image_data,
+            video_data=request_obj.video_data,
+            multimodal_tokens=self.mm_tokens,
+        )
+
+        video_metadata = None
+
+        if base_output.videos:
+            videos_processed = [
+                await self.preprocess_video(video) for video in base_output.videos
+            ]
+            base_output.videos, video_metadata = map(list, zip(*videos_processed))
+            # transformer requires the video inputs to be under this format
+            base_output.videos = [base_output.videos]
+            video_metadata = [video_metadata]
+
+        mm_items, input_ids, ret = self.process_and_combine_mm_data(
+            base_output, self.mm_tokens, video_metadata=video_metadata
+        )
+
+        input_ids = input_ids.flatten()
+        mrope_positions, mrope_position_delta = MRotaryEmbedding.get_rope_index_glm4v(
+            input_ids=input_ids.unsqueeze(0),
+            hf_config=self.hf_config,
+            image_grid_thw=getattr(ret, "image_grid_thw", None),
+            video_grid_thw=getattr(ret, "video_grid_thw", None),
+            attention_mask=getattr(ret, "attention_mask", None),
+        )
+        mrope_positions = mrope_positions.squeeze(1)
+
+        mm_inputs = {
+            "input_ids": input_ids.tolist(),
+            "mm_items": mm_items,
+            "im_token_id": self.mm_tokens.image_token_id,
+            "video_token_id": self.mm_tokens.video_token_id,
+            "mrope_positions": mrope_positions,
+            "mrope_position_delta": mrope_position_delta,
+        }
+
+        return mm_inputs

python/sglang/srt/utils.py

@@ -815,7 +815,7 @@ def load_video(video_file: Union[str, bytes], use_gpu: bool = True):
                 vr = VideoReader(tmp_file.name, ctx=ctx)
             elif video_file.startswith("data:"):
                 _, encoded = video_file.split(",", 1)
-                video_bytes = base64.b64decode(encoded)
+                video_bytes = pybase64.b64decode(encoded)
                 tmp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
                 tmp_file.write(video_bytes)
                 tmp_file.close()
@@ -823,7 +823,7 @@ def load_video(video_file: Union[str, bytes], use_gpu: bool = True):
             elif os.path.isfile(video_file):
                 vr = VideoReader(video_file, ctx=ctx)
             else:
-                video_bytes = base64.b64decode(video_file)
+                video_bytes = pybase64.b64decode(video_file)
                 tmp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
                 tmp_file.write(video_bytes)
                 tmp_file.close()