[GLM-OCR] Support GLM-OCR Model (#17582)

Signed-off-by: Xinyuan Tong <xinyuantong.cs@gmail.com> Co-authored-by: Xinyuan Tong <115166877+JustinTong0323@users.noreply.github.com> Co-authored-by: Xinyuan Tong <xinyuantong.cs@gmail.com>
2026-01-27 14:24:00 +08:00
parent 81c0f5c5ad
commit 7106f6c8e1
9 changed files with 679 additions and 29 deletions
--- a/python/sglang/srt/configs/model_config.py
+++ b/python/sglang/srt/configs/model_config.py
@@ -278,6 +278,11 @@ class ModelConfig:
        ]:
            self.hf_config.architectures[0] = "Glm4MoeForCausalLMNextN"

+        if is_draft_model and self.hf_config.architectures[0] in [
+            "GlmOcrForConditionalGeneration",
+        ]:
+            self.hf_config.architectures[0] = "GlmOcrForConditionalGenerationNextN"
+
        if (
            is_draft_model
            and self.hf_config.architectures[0] == "LongcatFlashForCausalLM"
@@ -935,7 +940,7 @@ class ModelConfig:
        needs_tf_v5 = is_glm_46vmoe

        tf_version = version.parse(tf_version_str)
-        required_version = version.parse("5.0.0")
+        required_version = version.parse("5.0.0dev0")

        if tf_version < required_version:
            if needs_tf_v5:
@@ -1132,6 +1137,7 @@ multimodal_model_archs = [
    "Gemma3nForConditionalGeneration",
    "Glm4vForConditionalGeneration",
    "Glm4vMoeForConditionalGeneration",
+    "GlmOcrForConditionalGeneration",
    "GlmAsrForConditionalGeneration",
    "Grok1VForCausalLM",
    "Grok1AForCausalLM",
--- a/python/sglang/srt/layers/attention/vision.py
+++ b/python/sglang/srt/layers/attention/vision.py
@@ -590,6 +590,7 @@ class VisionAttention(nn.Module):
        num_dummy_heads: int = 0,
        qkv_bias: bool = True,
        qk_normalization: bool = False,
+        qk_normalization_by_head_size: bool = False,
        layer_norm_eps: float = 1e-06,
        customized_position_embedding_applier: Callable[
            [torch.Tensor, torch.Tensor, Any, Any], Tuple[torch.Tensor, torch.Tensor]
@@ -617,30 +618,19 @@ class VisionAttention(nn.Module):
        self.kv_size = self.num_attention_kv_heads_per_partition * self.head_size

        self.qk_normalization = qk_normalization
+        self.qk_normalization_by_head_size = qk_normalization_by_head_size

        # Additional dummy heads are used to enable TP for common GPU counts.
        self.dummy_dim = (num_dummy_heads + num_heads) * self.head_size

        if self.qk_normalization:
-            norm_kwargs = (
-                dict(
-                    weight_dtype=torch.float32,
-                    cast_x_before_out_mul=True,
-                )
-                if get_global_server_args().rl_on_policy_target is not None
-                else {}
+            self.q_norm, self.k_norm = self._init_qk_norm(
+                self.dummy_dim, layer_norm_eps, embed_dim
            )
-            self.q_norm = RMSNorm(
-                self.dummy_dim,
-                eps=layer_norm_eps,
-                var_hidden_size=embed_dim,
-                **norm_kwargs,
-            )
-            self.k_norm = RMSNorm(
-                self.dummy_dim,
-                eps=layer_norm_eps,
-                var_hidden_size=embed_dim,
-                **norm_kwargs,
+
+        elif self.qk_normalization_by_head_size:
+            self.q_norm, self.k_norm = self._init_qk_norm(
+                self.head_size, layer_norm_eps
            )

        # Select attention backend via a unified method
@@ -702,6 +692,31 @@ class VisionAttention(nn.Module):
        self.aux_stream = aux_stream
        self.ln_events = [torch.cuda.Event(), torch.cuda.Event()] if aux_stream else []

+    def _init_qk_norm(
+        self, norm_dim: int, eps: float, var_hidden_size: Optional[int] = None
+    ):
+        norm_kwargs = (
+            dict(
+                weight_dtype=torch.float32,
+                cast_x_before_out_mul=True,
+            )
+            if get_global_server_args().rl_on_policy_target is not None
+            else {}
+        )
+        q_norm = RMSNorm(
+            norm_dim,
+            eps=eps,
+            var_hidden_size=var_hidden_size,
+            **norm_kwargs,
+        )
+        k_norm = RMSNorm(
+            norm_dim,
+            eps=eps,
+            var_hidden_size=var_hidden_size,
+            **norm_kwargs,
+        )
+        return q_norm, k_norm
+
    def _determine_attention_backend(self, passed_backend: Optional[str]) -> str:
        """Decide the multimodal attention backend string.

@@ -734,6 +749,16 @@ class VisionAttention(nn.Module):

        return backend

+    def _apply_qk_norm_head_size(self, q: torch.Tensor, k: torch.Tensor):
+        """apply qk norm for GLM-OCR vit attn"""
+        q_by_head = q.reshape(-1, self.head_size)
+        q_by_head = self.q_norm(q_by_head)
+        k_by_head = k.reshape(-1, self.head_size)
+        k_by_head = self.k_norm(k_by_head)
+        q = q_by_head.view(q.shape)
+        k = k_by_head.view(k.shape)
+        return q, k
+
    def _apply_qk_norm(self, q: torch.Tensor, k: torch.Tensor):
        """apply qk norm for internvl vit attn"""

@@ -816,6 +841,8 @@ class VisionAttention(nn.Module):
            q = q.reshape(bsz * s, head, -1).contiguous()
            k = k.reshape(bsz * s, kv_head, -1).contiguous()
            v = v.reshape(bsz * s, kv_head, -1).contiguous()
+            if self.qk_normalization_by_head_size:
+                q, k = self._apply_qk_norm_head_size(q, k)
        else:
            # [b, s, embed_dim] --> [s, b, embed_dim]
            x = rearrange(x, "b s ... -> s b ...")
@@ -837,6 +864,9 @@ class VisionAttention(nn.Module):
                rearrange(x, "s b ... -> b s ...").contiguous() for x in (q, k, v)
            ]

+            if self.qk_normalization_by_head_size:
+                q, k = self._apply_qk_norm_head_size(q, k)
+
        cos = None
        sin = None

@@ -881,7 +911,7 @@ class VisionAttention(nn.Module):
        assert v.dim() == 3, v.dim()

        # internvl
-        if self.qk_normalization:
+        if self.qk_normalization and not self.qk_normalization_by_head_size:
            # jit kernel
            if can_use_jit_qk_norm(self.head_size, q.dtype):

--- a/python/sglang/srt/models/glm4.py
+++ b/python/sglang/srt/models/glm4.py
@@ -119,6 +119,7 @@ class Glm4Attention(nn.Module):
        quant_config: Optional[QuantizationConfig] = None,
        dual_chunk_attention_config: Optional[dict[str, Any]] = None,
        partial_rotary_factor: float = 0.5,
+        bias: bool = True,
        prefix: str = "",
    ) -> None:
        super().__init__()
@@ -153,7 +154,7 @@ class Glm4Attention(nn.Module):
            self.head_dim,
            self.total_num_heads,
            self.total_num_kv_heads,
-            bias=True,
+            bias=bias,
            quant_config=quant_config,
            prefix=add_prefix("qkv_proj", prefix),
        )
@@ -216,13 +217,23 @@ class Glm4DecoderLayer(nn.Module):
    ) -> None:
        super().__init__()
        self.hidden_size = config.hidden_size
-        rope_theta = getattr(config, "rope_theta", 1000000)
-        rope_scaling = getattr(config, "rope_scaling", None)
+
+        rp = getattr(config, "rope_parameters", None)
+        if isinstance(rp, dict):
+            rope_theta = rp.get("rope_theta", getattr(config, "rope_theta", 1000000))
+            partial_rotary_factor = rp.get(
+                "partial_rotary_factor",
+                getattr(config, "partial_rotary_factor", 0.5),
+            )
+            rope_scaling = getattr(config, "rope_scaling", None)
+        else:
+            rope_theta = getattr(config, "rope_theta", 1000000)
+            rope_scaling = getattr(config, "rope_scaling", None)
+            partial_rotary_factor = getattr(config, "partial_rotary_factor", 0.5)
+
+        bias = getattr(config, "attention_bias", True)
        max_position_embeddings = getattr(config, "max_position_embeddings", 32768)
        head_dim = getattr(config, "head_dim", None)
-        partial_rotary_factor = getattr(
-            getattr(config, "rope_parameters", None), "partial_rotary_factor", None
-        ) or getattr(config, "partial_rotary_factor", 0.5)
        dual_chunk_attention_config = getattr(
            config, "dual_chunk_attention_config", None
        )
@@ -238,6 +249,7 @@ class Glm4DecoderLayer(nn.Module):
            quant_config=quant_config,
            dual_chunk_attention_config=dual_chunk_attention_config,
            partial_rotary_factor=partial_rotary_factor,
+            bias=bias,
            prefix=add_prefix("self_attn", prefix),
        )

--- a/python/sglang/srt/models/glm4v.py
+++ b/python/sglang/srt/models/glm4v.py
@@ -758,8 +758,6 @@ class Glm4vForConditionalGeneration(nn.Module):
                name = name.replace(r"model.language_model.", r"model.")
            if "model.visual." in name:
                name = name.replace("model.visual.", "visual.")
-            if name.startswith("lm_head.") and not self.pp_group.is_last_rank:
-                continue

            for param_name, weight_name, shard_id in stacked_params_mapping:
                if weight_name not in name:
--- a/python/sglang/srt/models/glm_ocr.py
+++ b/python/sglang/srt/models/glm_ocr.py
@@ -0,0 +1,435 @@
+# Copyright 2023-2024 SGLang Team
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+# Modeling from:
+# ./llama.py and
+# https://github.com/huggingface/transformers/blob/main/src/transformers/models/GlmOcr/modular_GlmOcr.py
+"""Inference-only GLM-OCR model compatible with HuggingFace weights."""
+
+import logging
+from functools import lru_cache
+from typing import Iterable, Optional, Tuple
+
+import torch
+import torch.nn as nn
+from einops import rearrange
+from transformers.models.glm_ocr.configuration_glm_ocr import (
+    GlmOcrConfig,
+    GlmOcrVisionConfig,
+)
+
+from sglang.srt.distributed.parallel_state import get_pp_group
+from sglang.srt.layers.attention import vision_utils
+from sglang.srt.layers.attention.vision import VisionAttention
+from sglang.srt.layers.layernorm import RMSNorm
+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.rotary_embedding import get_rope
+from sglang.srt.layers.utils import PPMissingLayer
+from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
+from sglang.srt.model_loader.weight_utils import default_weight_loader
+from sglang.srt.models.glm4 import Glm4Model
+from sglang.srt.models.glm4v import (
+    Glm4vForConditionalGeneration,
+    Glm4vPatchMerger,
+    Glm4vRMSNorm,
+    Glm4vVisionMLP,
+    Glm4vVisionModel,
+    Glm4vVisionPatchEmbed,
+)
+from sglang.srt.server_args import get_global_server_args
+from sglang.srt.utils import add_prefix
+from sglang.srt.utils.hf_transformers_utils import get_processor
+
+logger = logging.getLogger(__name__)
+
+cached_get_processor = lru_cache(get_processor)
+
+
+class GlmOcrRMSNorm(Glm4vRMSNorm):
+    pass
+
+
+class GlmOcrVisionMLP(Glm4vVisionMLP):
+    pass
+
+
+class GlmOcrVisionBlock(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        intermediate_dim: int,
+        num_heads: int,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+        attn_qkv_bias: bool = True,
+        num_dummy_heads: int = 0,
+        rms_norm_eps: float = 1e-5,
+        use_data_parallel: bool = False,
+    ) -> None:
+        super().__init__()
+        self.norm1 = RMSNorm(dim, eps=rms_norm_eps)
+        self.norm2 = RMSNorm(dim, eps=rms_norm_eps)
+        self.attn = VisionAttention(
+            embed_dim=dim,
+            num_heads=num_heads,
+            projection_size=dim,
+            use_qkv_parallel=True,
+            qkv_bias=attn_qkv_bias,
+            proj_bias=True,
+            qk_normalization_by_head_size=True,
+            flatten_batch=True,
+            quant_config=quant_config,
+            prefix=add_prefix("attn", prefix),
+            num_dummy_heads=num_dummy_heads,
+            use_data_parallel=use_data_parallel,
+        )
+        self.mlp = GlmOcrVisionMLP(
+            dim,
+            intermediate_dim,
+            bias=True,
+            quant_config=quant_config,
+            prefix=add_prefix("mlp", prefix),
+            use_data_parallel=use_data_parallel,
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        rotary_pos_emb_cos: torch.Tensor,
+        rotary_pos_emb_sin: torch.Tensor,
+    ) -> torch.Tensor:
+        S, B, H = x.shape
+        # norm1: flatten to 2D -> [S*B, H], then reshape back
+        x2d = x.reshape(-1, H)
+        hidden_states = self.norm1(x2d).reshape(S, B, H)
+
+        # Attention expects [B, S, H]
+        hidden_states = rearrange(hidden_states, "s b h -> b s h")
+        attn = self.attn(
+            hidden_states,
+            cu_seqlens=cu_seqlens,
+            rotary_pos_emb_cos=rotary_pos_emb_cos,
+            rotary_pos_emb_sin=rotary_pos_emb_sin,
+        )
+        attn = rearrange(attn, "b s h -> s b h")
+
+        # norm2 with fused residual-add: also 2D
+        attn2d = attn.reshape(-1, H)
+        x_norm_2d, x_after_add_2d = self.norm2(x2d, residual=attn2d)
+        x_norm = x_norm_2d.reshape(S, B, H)
+        x_after_add = x_after_add_2d.reshape(S, B, H)
+
+        # MLP and final residual
+        mlp_out = self.mlp(x_norm)
+        x = x_after_add + mlp_out
+        return x
+
+
+class GlmOcrVisionPatchEmbed(Glm4vVisionPatchEmbed):
+    pass
+
+
+class GlmOcrVisionPatchMerger(Glm4vPatchMerger):
+    pass
+
+
+class GlmOcrVisionModel(Glm4vVisionModel):
+    def __init__(
+        self,
+        vision_config: GlmOcrVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+        use_data_parallel: bool = False,
+    ) -> None:
+        super().__init__(vision_config, quant_config, prefix, use_data_parallel)
+
+        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.intermediate_size = vision_config.intermediate_size
+        self.use_data_parallel = use_data_parallel
+
+        self.patch_embed = GlmOcrVisionPatchEmbed(
+            patch_size=patch_size,
+            temporal_patch_size=temporal_patch_size,
+            in_channels=in_channels,
+            hidden_size=self.hidden_size,
+        )
+
+        head_dim = self.hidden_size // self.num_heads
+        self.rotary_pos_emb = get_rope(
+            head_size=head_dim,
+            rotary_dim=head_dim // 2,
+            max_position=8192,
+            base=10000.0,
+            is_neox_style=True,
+        )
+
+        self.blocks = nn.ModuleList(
+            [
+                GlmOcrVisionBlock(
+                    dim=self.hidden_size,
+                    intermediate_dim=self.intermediate_size,
+                    num_heads=self.num_heads,
+                    quant_config=quant_config,
+                    prefix=add_prefix(f"blocks.{layer_idx}", prefix),
+                    rms_norm_eps=vision_config.rms_norm_eps,
+                    attn_qkv_bias=vision_config.attention_bias,
+                    use_data_parallel=use_data_parallel,
+                )
+                for layer_idx in range(depth)
+            ]
+        )
+        self.merger = GlmOcrVisionPatchMerger(
+            d_model=vision_config.out_hidden_size,
+            context_dim=vision_config.out_hidden_size * vision_config.in_channels,
+            quant_config=quant_config,
+            bias=False,
+            prefix=add_prefix("merger", prefix),
+            use_data_parallel=use_data_parallel,
+        )
+
+        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 = GlmOcrRMSNorm(
+            vision_config.hidden_size, eps=vision_config.rms_norm_eps
+        )
+
+    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)
+
+        # compute position embedding
+        rotary_pos_emb_cos, rotary_pos_emb_sin, 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 = torch.cat([cu_seqlens.new_zeros(1), cu_seqlens])
+
+        rotary_pos_emb_cos = torch.cat([rotary_pos_emb_cos, rotary_pos_emb_cos], dim=-1)
+        rotary_pos_emb_sin = torch.cat([rotary_pos_emb_sin, rotary_pos_emb_sin], dim=-1)
+
+        # 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,
+                rotary_pos_emb_cos=rotary_pos_emb_cos,
+                rotary_pos_emb_sin=rotary_pos_emb_sin,
+            )
+
+        # 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 GlmOcrForConditionalGeneration(Glm4vForConditionalGeneration):
+    def __init__(
+        self,
+        config: GlmOcrConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__(config, quant_config, prefix)
+
+        self.pp_group = get_pp_group()
+        self.config = config
+        self.use_data_parallel = get_global_server_args().mm_enable_dp_encoder
+        self.visual = GlmOcrVisionModel(
+            vision_config=config.vision_config,
+            quant_config=quant_config,
+            prefix=add_prefix("visual", prefix),
+            use_data_parallel=self.use_data_parallel,
+        )
+
+        vision_utils.update_vit_attn_dummy_heads_config(self.config)
+
+        self.model = Glm4Model(
+            config,
+            quant_config=quant_config,
+            prefix=add_prefix("model", prefix),
+        )
+
+        if self.pp_group.is_last_rank:
+            if self.pp_group.world_size == 1 and self.config.tie_word_embeddings:
+                self.lm_head = self.model.embed_tokens
+            else:
+                self.lm_head = ParallelLMHead(
+                    self.config.vocab_size,
+                    self.config.hidden_size,
+                    quant_config=quant_config,
+                    prefix=add_prefix("lm_head", prefix),
+                )
+        else:
+            # ranks other than the last rank will have a placeholder layer
+            self.lm_head = PPMissingLayer()
+
+        self.is_mrope_enabled = "mrope_section" in self.config.rope_scaling
+
+        self.logits_processor = LogitsProcessor(config)
+        self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
+
+        # For EAGLE3 support
+        self.capture_aux_hidden_states = False
+
+    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", ".up_proj", 1),
+            (".gate_up_proj", ".gate_proj", 0),
+        ]
+
+        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(remove_duplicate=False))
+
+        # For the PP case, we add special handling for lm_head.weight,
+        # - On non–last ranks: we continue, because this stage is supposed to
+        #   be just an empty PPMissingLayer shell.
+        # - On the last rank: params_dict is expected to contain lm_head.weight,
+        #   so it will never hit the branch "if name not in params_dict".
+        #
+        # For all other parameters, such like
+        # "model.visual.blocks.20.mlp.gate_proj.weight", the unified rule is:
+        # If this name does not exist in the current rank’s params_dict,
+        # it does not belong to this pipeline stage, thus we simply continue.
+
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+            if "language_model" in name:
+                name = name.replace(r"model.language_model.", r"model.")
+            if "model.visual." in name:
+                name = name.replace("model.visual.", "visual.")
+
+            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")
+
+            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
+
+                if 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
+
+                    if 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)
+                if "visual" in name:
+                    loaded_weight = vision_utils.pad_vit_attn_dummy_heads(
+                        self.config, name, loaded_weight
+                    )
+                weight_loader(param, loaded_weight)
+
+
+EntryClass = [GlmOcrForConditionalGeneration]
--- a/python/sglang/srt/models/glm_ocr_nextn.py
+++ b/python/sglang/srt/models/glm_ocr_nextn.py
@@ -0,0 +1,162 @@
+# Copyright 2023-2024 SGLang Team
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Inference-only GLM-OCR Speculative Decoding."""
+
+import logging
+from typing import Iterable, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import PretrainedConfig
+
+from sglang.srt.distributed import get_tensor_model_parallel_world_size
+from sglang.srt.eplb.expert_distribution import get_global_expert_distribution_recorder
+from sglang.srt.layers.dp_attention import is_dp_attention_enabled
+from sglang.srt.layers.layernorm import RMSNorm
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.models.glm4 import Glm4DecoderLayer
+from sglang.srt.models.glm_ocr import GlmOcrForConditionalGeneration
+from sglang.srt.server_args import get_global_server_args
+from sglang.srt.utils import add_prefix
+
+logger = logging.getLogger(__name__)
+
+
+class GlmOcrModelNextN(nn.Module):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        if quant_config is not None and quant_config.get_name() == "modelopt_fp4":
+            logger.warning(
+                "Overriding GlmOcrModelNextN quant config for modelopt_fp4 GLM-OCR model."
+            )
+            quant_config = None
+
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+            enable_tp=not is_dp_attention_enabled(),
+            prefix=add_prefix("embed_tokens", prefix),
+        )
+
+        self.enorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.hnorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.eh_proj = nn.Linear(2 * config.hidden_size, config.hidden_size, bias=False)
+
+        self.decoder = Glm4DecoderLayer(
+            config,
+            0,
+            quant_config=quant_config,
+            prefix=add_prefix("decoder", prefix),
+        )
+
+        self.shared_head = nn.Module()
+        self.shared_head.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: torch.Tensor = None,
+    ) -> torch.Tensor:
+        if input_embeds is None:
+            hidden_states = self.embed_tokens(input_ids)
+        else:
+            hidden_states = input_embeds
+
+        if hidden_states.shape[0] > 0:
+            hidden_states = self.eh_proj(
+                torch.cat(
+                    (
+                        self.enorm(hidden_states),
+                        self.hnorm(forward_batch.spec_info.hidden_states),
+                    ),
+                    dim=-1,
+                )
+            )
+
+        residual = None
+        with get_global_expert_distribution_recorder().disable_this_region():
+            hidden_states, residual = self.decoder(
+                positions, hidden_states, forward_batch, residual
+            )
+
+        if not forward_batch.forward_mode.is_idle():
+            if residual is not None:
+                hidden_states, _ = self.shared_head.norm(hidden_states, residual)
+            else:
+                hidden_states = self.shared_head.norm(hidden_states)
+
+        return hidden_states
+
+
+class GlmOcrForConditionalGenerationNextN(GlmOcrForConditionalGeneration):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        nn.Module.__init__(self)
+        self.config = config
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.quant_config = quant_config
+        self.model = GlmOcrModelNextN(
+            config, quant_config, prefix=add_prefix("model", prefix)
+        )
+        self.lm_head = ParallelLMHead(
+            config.vocab_size,
+            config.hidden_size,
+            quant_config=quant_config,
+            prefix=add_prefix("model.shared_head.head", prefix),
+            use_attn_tp_group=get_global_server_args().enable_dp_lm_head,
+        )
+        self.logits_processor = LogitsProcessor(config)
+
+        self.num_fused_shared_experts = (
+            0 if get_global_server_args().disable_shared_experts_fusion else 1
+        )
+
+    @torch.no_grad()
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+    ) -> torch.Tensor:
+        hidden_states = self.model(input_ids, positions, forward_batch)
+        return self.logits_processor(
+            input_ids, hidden_states, self.lm_head, forward_batch
+        )
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        super().load_weights(weights, is_nextn=True)
+
+
+EntryClass = [GlmOcrForConditionalGenerationNextN]
--- a/python/sglang/srt/multimodal/processors/glm4v.py
+++ b/python/sglang/srt/multimodal/processors/glm4v.py
@@ -3,6 +3,7 @@ from typing import List, Union
 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.models.glm_ocr import GlmOcrForConditionalGeneration
 from sglang.srt.multimodal.processors.base_processor import (
    BaseMultimodalProcessor as SGLangBaseProcessor,
 )
@@ -10,7 +11,11 @@ from sglang.srt.multimodal.processors.base_processor import MultimodalSpecialTok


 class Glm4vImageProcessor(SGLangBaseProcessor):
-    models = [Glm4vForConditionalGeneration, Glm4vMoeForConditionalGeneration]
+    models = [
+        Glm4vForConditionalGeneration,
+        Glm4vMoeForConditionalGeneration,
+        GlmOcrForConditionalGeneration,
+    ]

    def __init__(self, hf_config, server_args, _processor, *args, **kwargs):
        super().__init__(hf_config, server_args, _processor, *args, **kwargs)
--- a/python/sglang/srt/utils/common.py
+++ b/python/sglang/srt/utils/common.py
@@ -2906,6 +2906,7 @@ def is_fa3_default_architecture(hf_config):
        "Glm4MoeForCausalLM",
        "Glm4vForConditionalGeneration",
        "Glm4vMoeForConditionalGeneration",
+        "GlmOcrForConditionalGeneration",
        "Step3VLForConditionalGeneration",
        "StepVLForConditionalGeneration",
        "MiMoV2FlashForCausalLM",